From 02ffc33c8a660deb154e4e3030f603913091bc1e Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:15:03 -0400 Subject: [PATCH 01/18] Clean up main README --- README.md | 100 +++++++++++++++--------------------------------------- 1 file changed, 27 insertions(+), 73 deletions(-) diff --git a/README.md b/README.md index a37dfd52..63e07062 100644 --- a/README.md +++ b/README.md @@ -2,23 +2,18 @@ Neural network alignment analysis and intelligent pruning framework. ---- - ## Overview This framework provides tools for analyzing neural networks through information-theoretic metrics and performing redundancy-aware pruning and quantization. -**Key capabilities:** -- Alignment metrics (Rayleigh Quotient, class-conditioned RQ, mutual information) -- Information-theoretic analysis (redundancy, synergy, PID) -- Pruning strategies (16 methods including magnitude, gradient-based, redundancy-aware) -- Quantization (INT8, INT4, mixed-precision with alignment-guided bit allocation) -- Architecture support (MLPs, CNNs, Transformers, LLMs) -- Data loading (vision and text datasets) -- Evaluation (classification accuracy, perplexity for language models) -- Visualization (plots and reports) - ---- +Key capabilities: +- Alignment metrics for analyzing neural network weight-input relationships +- Information-theoretic analysis tools +- Pruning strategies with multiple scoring methods +- Quantization with alignment-guided precision selection +- Architecture support for MLPs, CNNs, Transformers, and LLMs +- Data loading for vision and text datasets +- Evaluation and visualization tools ## Installation @@ -30,79 +25,40 @@ conda activate alignment pip install -e . ``` -Details: [docs/installation.md](docs/installation.md) - ---- +See [docs/installation.md](docs/installation.md) for details. ## Usage -### Command Line +Run experiments using configuration files: ```bash python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml -python scripts/run_experiment.py --config configs/examples/resnet_pruning.yaml -python scripts/run_experiment.py --config configs/examples/llama3_scoring.yaml -python scripts/run_experiment.py --config configs/examples/llama3_quantization.yaml -``` - -### Python API - -```python -from alignment import ModelWrapper, get_metric - -wrapper = ModelWrapper(model) -rq = get_metric('rayleigh_quotient') - -outputs, acts = wrapper.forward_with_activations(inputs) -weights = wrapper.get_layer_weights() - -scores = rq.compute(acts['layer_input'], weights['layer']) ``` -### Quantization - -```python -from alignment.quantization import quantize_model, find_optimal_bit_allocation +The framework supports: +- Training networks from scratch or loading pre-trained models +- Computing alignment and information-theoretic scores +- Applying pruning with different strategies and distributions +- Quantization with various precision settings -# INT8 quantization -results = quantize_model(model, precision='int8') +Example configurations are available in `configs/examples/`: +- `mnist_basic.yaml` - Basic alignment analysis on MNIST +- `resnet_pruning.yaml` - Pruning ResNet on CIFAR-10 +- `llama3_scoring.yaml` - Computing scores for LLaMA models +- `llama3_pruning.yaml` - Pruning transformer networks -# Mixed precision using alignment scores -layer_importance = {layer: rq_scores[layer].mean() for layer in layers} -bit_allocation = find_optimal_bit_allocation(model, layer_importance, target_avg_bits=6.0) -``` - ---- +See `configs/template.yaml` for all available configuration options. ## Documentation -- [Installation](docs/installation.md) - Setup and dependencies -- [Usage Guide](docs/usage.md) - Running experiments -- [User Guide](docs/user_guide.md) - Complete documentation -- [API Reference](docs/api_reference.md) - API details +- [Usage Guide](docs/usage.md) - Running experiments with configs +- [User Guide](docs/user_guide.md) - Detailed framework documentation +- [API Reference](docs/api_reference.md) - API documentation - [Quick Reference](docs/quick_reference.md) - Code examples -- [Changelog](docs/changelog.md) - Version history - -Full documentation: [docs/README.md](docs/README.md) - ---- - -## Configuration -Experiments are configured via YAML files. See `configs/template.yaml` for all parameters. +## Examples -Available examples in `configs/examples/`: -- mnist_basic.yaml -- resnet_pruning.yaml -- llama3_scoring.yaml -- llama3_pruning.yaml -- llama3_quantization.yaml - ---- - -## Code Examples - -Python examples in `examples/` directory: +Python scripts demonstrating framework capabilities: ```bash python examples/07_mnist_intelligent_pruning.py @@ -110,7 +66,7 @@ python examples/08_llama_ffn_pruning.py python examples/09_attention_neuron_vs_head_pruning.py ``` ---- + ## Testing @@ -118,8 +74,6 @@ python examples/09_attention_neuron_vs_head_pruning.py pytest tests/ ``` ---- - ## License See LICENSE file. From de8da9df846273b713a4965ddae4c5852414f0aa Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:18:08 -0400 Subject: [PATCH 02/18] Update user guide with comprehensive metrics and pruning docs --- docs/user_guide.md | 636 +++++++++++++++++++++++++++------------------ 1 file changed, 383 insertions(+), 253 deletions(-) diff --git a/docs/user_guide.md b/docs/user_guide.md index ebc8b3b4..217406c4 100644 --- a/docs/user_guide.md +++ b/docs/user_guide.md @@ -1,63 +1,128 @@ # Alignment Framework User Guide -**Comprehensive guide to using the alignment framework for neural network analysis and pruning** - ---- +Comprehensive guide to neural network alignment analysis and pruning. ## Table of Contents 1. [Core Concepts](#core-concepts) -2. [Computing Metrics](#computing-metrics) -3. [Pruning Strategies](#pruning-strategies) -4. [Architecture Support](#architecture-support) -5. [Configuration](#configuration) -6. [Advanced Features](#advanced-features) - ---- +2. [Available Metrics](#available-metrics) +3. [Computing Metrics](#computing-metrics) +4. [Pruning Strategies](#pruning-strategies) +5. [Architecture Support](#architecture-support) +6. [Configuration](#configuration) ## Core Concepts -### Rayleigh Quotient (RQ) +### Alignment -Measures how well neuron weights align with input principal components: +Alignment measures how well neuron weights align with the structure of their input activations. The Rayleigh Quotient quantifies the proportion of input variance captured by each neuron's weight vector. +For weight vector w and input covariance Σ: ``` RQ(w) = (w^T Σ w) / (w^T w · tr(Σ)) ``` -Higher RQ indicates better alignment with dominant input variance. +Higher RQ values indicate the neuron aligns with dominant input variance directions. -### Class-Conditioned RQ (ΔRQ) +### Class-Conditioned Alignment -Measures task-relevant alignment: +Class-conditioned analysis compares alignment across different classes versus overall alignment. The difference (ΔRQ) indicates task-relevant alignment: ``` ΔRQ = RQ(overall) - E[RQ(class-conditioned)] ``` -Positive ΔRQ indicates the neuron captures discriminative features. +Positive ΔRQ means the neuron captures discriminative features between classes. -### Redundancy +### Information Theory -Measures overlap between neuron pairs: +The framework uses information-theoretic measures to quantify relationships between neurons: -``` -R(i,j) = I(Y_i; Y_j) = -0.5 · log(1 - ρ²) -``` +- **Mutual Information**: Shared information between two variables +- **Redundancy**: Information overlap between neuron pairs +- **Synergy**: Information that emerges only from joint neuron outputs +- **Partial Information Decomposition (PID)**: Decomposes information into unique, redundant, and synergistic components -High redundancy means neurons capture similar information. +## Available Metrics -### Synergy +### Alignment Metrics -Measures complementary information: +**Rayleigh Quotient (RQ)** +- Measures alignment with input covariance structure +- Returns per-neuron scores indicating variance captured +- Supports relative normalization and regularization +- Works with linear, convolutional, and transformer layers -``` -S(Z; Y_i, Y_j) = I(Z; Y_i, Y_j) - I(Z; Y_i) - I(Z; Y_j) + min(I(Z; Y_i), I(Z; Y_j)) -``` +**Class-Conditioned RQ** +- Computes RQ separately for each class +- Returns ΔRQ showing task-relevant alignment +- Requires labeled data + +**Spectral Alignment** +- Analyzes eigenvalue decomposition of weight-covariance product +- Provides detailed spectral properties + +### Information-Theoretic Metrics + +**Mutual Information (MI)** +- Gaussian MI using analytic formula: MI = -0.5 log(1 - ρ²) +- Can compute MI between neuron outputs and targets +- Supports both continuous and categorical targets + +**Pairwise Redundancy** +- Computes redundancy between neuron pairs +- Output-based mode for efficiency with large models +- Covariance-based mode for precise estimates +- Sampling strategies: random, nearest, or all pairs +- Returns per-neuron average redundancy scores + +**Synergy (MMI)** +- Measures complementary information from neuron pairs +- Uses Minimum Mutual Information redundancy definition +- Returns per-neuron synergy scores +- Requires target labels + +**Partial Information Decomposition (PID)** +- Decomposes information into unique, redundant, and synergistic parts +- Shared information: overlap between two neurons +- Unique information: contribution from single neurons +- Synergistic information: emergent from joint outputs + +**Higher-Order Metrics** +- Total correlation: generalization of MI to multiple variables +- Interaction information: higher-order dependencies +- Connected information: network-level measures + +### Gradient-Based Metrics -High synergy means neurons provide unique joint information. +**Gradient Alignment** +- Compares gradient-based learning with local learning rules +- Analyzes alignment between backpropagation and Hebbian-style updates +- Useful for biologically-plausible learning analysis ---- +**Local Learning Rule Search** +- Finds optimal local learning rules per neuron +- Searches over combinations of pre/post-synaptic activity +- Returns best-fit local rule for each neuron + +### Task-Specific Metrics + +**Classification Alignment** +- Measures discriminative power for classification tasks +- Analyzes separation of class representations + +**Vision Task Alignment** +- Specialized for vision architectures +- Analyzes feature selectivity patterns + +### Similarity Metrics + +**Centered Kernel Alignment (CKA)** +- Compares representations between layers or models +- Invariant to isotropic scaling + +**Canonical Correlation Analysis (CCA)** +- Finds maximally correlated directions between representations ## Computing Metrics @@ -66,13 +131,13 @@ High synergy means neurons provide unique joint information. ```python from alignment import ModelWrapper, get_metric -# Wrap model +# Wrap model to track layers wrapper = ModelWrapper(model, tracked_layers=['conv1', 'fc1']) -# Get metric +# Get metric instance rq_metric = get_metric('rayleigh_quotient') -# Capture activations +# Forward pass with activation capture outputs, acts = wrapper.forward_with_activations(inputs) weights = wrapper.get_layer_weights() @@ -83,26 +148,6 @@ scores = rq_metric.compute( ) ``` -### Available Metrics - -**Alignment:** -- `rayleigh_quotient` - Standard RQ -- `delta_alignment` - Change in alignment over training - -**Information-Theoretic:** -- `gaussian_mi_analytic` - Mutual information (Gaussian approximation) -- `pairwise_redundancy_gaussian` - Per-neuron redundancy -- `synergy_gaussian_mmi` - Per-neuron synergy -- `conditional_mutual_information` - Conditional MI - -**Gradient-Based:** -- `gradient_alignment` - Alignment between local signal and backprop -- `local_learning_rule_search` - Find optimal local rules per neuron - -**Task-Specific:** -- `classification_alignment` - Classification-specific importance -- `vision_task_alignment` - Vision-specific patterns - ### Composite Scoring Combine multiple metrics for robust importance estimation: @@ -113,7 +158,7 @@ from alignment.services import NodeScoringService scorer = NodeScoringService( metrics={ 'rq': get_metric('rayleigh_quotient'), - 'redundancy': get_metric('pairwise_redundancy_gaussian', mode='output_based'), + 'redundancy': get_metric('pairwise_redundancy_gaussian'), 'synergy': get_metric('synergy_gaussian_mmi') }, alpha_mi=0.0, @@ -123,290 +168,375 @@ scorer = NodeScoringService( ) scores = scorer.compute_composite_scores(inputs, weights, targets) -# score = β·synergy - γ·redundancy + δ·log(RQ) ``` ---- +The composite score combines: synergy (positive), redundancy (negative), and alignment. ## Pruning Strategies -### Available Strategies +### Overview -**Basic:** -- `magnitude` - L1/L2 norm -- `random` - Random baseline -- `gradient` - Gradient-based -- `fisher` - Fisher information +Pruning removes less important network parameters to reduce model size and computational cost. The framework provides multiple strategies based on different importance criteria. -**Alignment-Based:** -- `alignment` - RQ-based structured pruning -- `global_alignment` - Global threshold across layers -- `hybrid` - Magnitude + Alignment combination +### Magnitude-Based Strategies -**Advanced:** -- `movement` - Prune weights moving toward zero (training-aware) -- `adaptive` - Adaptive per-layer amounts based on sensitivity -- `ultimate` - Multi-stage combining all best practices +**MagnitudePruning** +- Prunes weights with smallest absolute values +- Fast and simple baseline +- Works for unstructured and structured pruning +- Applies L1 or L2 norm for importance scores -**Novel:** -- `composite` - Redundancy-aware (preserves synergistic neurons) +**IterativeMagnitudePruning** +- Applies magnitude pruning in multiple rounds +- Allows network to recover between pruning steps +- Typically achieves better accuracy than one-shot pruning -### Distribution Across Layers +**GlobalMagnitudePruning** +- Applies single threshold across all layers +- Natural adaptation to layer sensitivity +- Some layers may be pruned more than others -**Uniform:** Same percentage per layer -```python -# 70% removed from each layer -``` +### Gradient-Based Strategies -**Global Threshold:** Single threshold across all layers -```python -# Naturally varying amounts based on score distribution -``` +**GradientPruning** +- Uses gradient magnitude as importance signal +- Can use gradient alone or gradient-weight product (Taylor approximation) +- Requires backward pass to compute gradients -**Adaptive Sensitivity:** Per-layer amounts based on importance -```python -# Sensitive layers: 50% -# Robust layers: 85% -# Overall: 70% average -``` +**FisherPruning** +- Uses Fisher information for importance +- Accounts for both gradient and Hessian information +- More accurate but computationally expensive -### Using the Orchestrator +**MomentumPruning** +- Incorporates momentum in importance computation +- Smooths importance over training iterations -```python -from alignment.pruning.orchestrator import prune_with_all_options - -result = prune_with_all_options( - model, - target_sparsity=0.7, - - # Distribution: how to allocate across layers - # Options: 'uniform', 'global_threshold', 'adaptive_sensitivity', - # 'importance_weighted', 'cascading', 'size_proportional', 'hybrid' - distribution='adaptive_sensitivity', - - # Scoring: what importance metric - # Options: 'magnitude', 'rayleigh_quotient', 'composite', 'movement' - scoring='composite', - - # Direction: which neurons to remove - # Options: 'low' (remove unimportant), 'high' (ablation), 'random' (baseline) - direction='low', - - # Data - train_loader=train_loader, # For fine-tuning - val_loader=val_loader, # For evaluation - trainer_fn=train_function, # Training function - eval_fn=eval_function, # Evaluation function - - # Options - fine_tune_epochs=20 -) -``` +### Alignment-Based Strategies ---- +**AlignmentPruning** +- Uses Rayleigh Quotient or other alignment metrics +- Structured pruning (removes entire neurons/channels) +- Considers input-weight relationships -## Architecture Support +**GlobalAlignmentPruning** +- Global threshold across layers based on alignment +- Automatically handles layer-wise sensitivity -### MLPs +**HybridPruning** +- Combines magnitude and alignment scores +- Balances simple magnitude with structural alignment -```python -layer = model.fc1 # Linear(784, 256) -scores = rq.compute(inputs, layer.weight) -# [256] - one score per neuron -``` +**CascadingAlignmentPruning** +- Prunes layers sequentially +- Recomputes alignment after each layer +- Accounts for pruning effects on downstream layers -### CNNs +### Random Strategies -```python -conv = model.conv1 # Conv2d(64, 128, 3, 3) -scores = rq.compute(inputs, conv.weight) -# [128] - one score per channel +**RandomPruning** +- Prunes weights randomly +- Baseline for comparison +- Useful for ablation studies -# Dependency-aware pruning automatically handles channel propagation -``` +**LayerwiseRandomPruning** +- Random pruning with per-layer amounts -### Transformers & LLMs +**BernoulliPruning** +- Stochastic pruning with Bernoulli sampling -```python -from alignment.models.transformer_enhanced import LLaMAWrapper +### Parallel Strategies -wrapper = LLaMAWrapper(llama_model, track_ffn=True) +**ParallelModePruning** +- Applies multiple pruning modes simultaneously (low/high/random) +- Returns separate masks for each mode +- Useful for comparing ablations -# FFN neurons: up_proj has 11,008 neurons -ffn_up = model.model.layers[0].mlp.up_proj -scores = rq.compute(inputs, ffn_up.weight) -# [11008] - one score per FFN neuron +**TensorizedPruning** +- GPU-optimized parallel execution +- Efficient for multiple strategies -# Attention: Can analyze per-head or per-neuron -``` +**AsyncParallelPruning** +- Asynchronous parallel execution +- For distributed pruning experiments ---- +### Advanced Strategies -## Configuration +**MovementPruning** +- Tracks weight movement during training +- Prunes weights moving toward zero +- Training-aware strategy -### Quick Start Config +**AdaptiveMovementPruning** +- Adapts pruning amount per layer based on movement patterns -```yaml -# configs/quickstart.yaml -experiment: - name: "quickstart" +### Pruning Distribution -model: - name: "resnet18" - pretrained: true +Distribution strategies determine how pruning is allocated across layers. -dataset: - name: "cifar10" - batch_size: 128 +**Uniform Distribution** +- Same sparsity percentage for all layers +- Simple and predictable +- May not respect layer-specific sensitivity -metrics: - enabled: ['rayleigh_quotient'] -``` +**Global Threshold** +- Single importance threshold across all layers +- Natural adaptation based on score distributions +- Different layers pruned by different amounts -### Complete Options +**Adaptive Sensitivity** +- Per-layer pruning amounts based on sensitivity analysis +- Sensitive layers pruned less +- Robust layers pruned more +- Maintains target overall sparsity -See `configs/template_master_v2.yaml` for all parameters with documentation. +**Importance Weighted** +- Allocates pruning inversely to layer importance +- More important layers retain more parameters -Key sections: -- `experiment`: Name, seed, device, output -- `model`: Architecture, pretrained, layers to track -- `dataset`: Data source, batch size, augmentation -- `metrics`: Which metrics to compute and their parameters -- `pruning`: Strategy, distribution, scoring, fine-tuning -- `training`: Training parameters, metric tracking -- `advanced`: Backend selection, parallelization +**Size Proportional** +- Pruning amount scales with layer size +- Larger layers can tolerate more pruning ---- +**Cascading** +- Sequential layer-by-layer pruning +- Recomputes scores after each layer -## Advanced Features +### Pruning Modes -### Training-Time Metrics +**Low Mode** (default) +- Prunes weights/neurons with lowest importance scores +- Standard pruning approach -Track alignment evolution during training with zero overhead: +**High Mode** +- Prunes weights/neurons with highest importance scores +- Used for ablation studies to test importance hypotheses -```python -from alignment.training.callbacks import AlignmentMetricsCallback +**Random Mode** +- Random pruning regardless of scores +- Baseline for comparison -callback = AlignmentMetricsCallback( - metrics={'rq': get_metric('rayleigh_quotient')}, - layers=['conv1', 'fc1'], - frequency=100 # Every 100 steps -) +### Structured vs Unstructured + +**Unstructured Pruning** +- Prunes individual weights +- Arbitrary sparsity patterns +- Requires sparse tensor support for speedup -# In training loop: -for inputs, targets in train_loader: - outputs = model(inputs) - loss.backward() - optimizer.step() - - callback.on_batch_end(wrapper, inputs, targets, global_step) +**Structured Pruning** +- Prunes entire neurons or channels +- Maintains dense tensor operations +- Immediate speedup without specialized hardware +- Handles dependencies automatically -# Analyze evolution -history = callback.get_history() +### Using Configuration Files + +Specify pruning in YAML configuration: + +```yaml +pruning: + enabled: true + strategy: 'composite' + target_sparsity: 0.7 + distribution: 'adaptive_sensitivity' + scoring: 'rayleigh_quotient' + direction: 'low' + structured: true + dependency_aware: true + + fine_tune: + enabled: true + epochs: 20 + learning_rate: 0.0001 + + composite_weights: + beta_synergy: 0.3 + gamma_redundancy: 0.4 + delta_rq: 0.3 ``` -### Gradient-Based Local Learning +Then run: +```bash +python scripts/run_experiment.py --config configs/my_pruning.yaml +``` + +## Architecture Support + +### Linear Layers (MLPs) -Design bio-plausible learning rules: +For fully-connected layers, metrics compute per-neuron scores: ```python -from alignment.metrics.gradient_based import LocalLearningRuleSearch +layer = model.fc1 # Linear(in_features=784, out_features=256) +scores = rq.compute(inputs, layer.weight) # Returns [256] +``` -searcher = LocalLearningRuleSearch() +Each neuron receives an importance score. Structured pruning removes entire neurons. -# After backward pass: -best_rules = searcher.compute( - inputs, outputs, - gradients=layer.weight.grad -) -# Returns best local rule per neuron +### Convolutional Layers (CNNs) + +For convolutional layers, metrics operate on channels: + +```python +conv = model.conv1 # Conv2d(in_channels=64, out_channels=128, kernel_size=3) +scores = rq.compute(inputs, conv.weight) # Returns [128] ``` -### Pairwise Metrics +Each output channel receives a score. The framework handles: +- Spatial weight dimensions automatically +- Different conv modes: unfold, patchwise, channel variance +- Dependency propagation between layers -Any pairwise metric can aggregate to single-neuron scores: +**CNN-Specific Modes:** +- `unfold`: Unfolds spatial dimensions into feature vectors +- `patchwise`: Treats each spatial location separately +- `channel_variance`: Computes variance across spatial dimensions +- `batch_patch_combined`: Combines batch and spatial statistics +### Transformer Layers + +For transformer architectures, the framework supports: + +**Feed-Forward Networks (FFN)** ```python -redundancy = get_metric('pairwise_redundancy_gaussian', - mode='output_based', # Fast! - num_pairs=10, # Sample 10 partners - aggregation='mean') # How to aggregate +ffn_layer = model.layers[0].mlp.up_proj +scores = rq.compute(inputs, ffn_layer.weight) +``` -scores = redundancy.compute(outputs=layer_outputs) -# [N] - per-neuron redundancy +Each FFN neuron receives a score. For LLaMA models, typical FFN dimensions are 11,008 neurons. -# Or get full matrix: -matrix = redundancy.compute(outputs, return_matrix=True) -# [N, N] - all pairwise relationships +**Attention Layers** +```python +# Per-head analysis +wrapper = TransformerWrapper(model, track_per_head=True) + +# Per-neuron within attention projections +q_proj = model.layers[0].self_attn.q_proj +scores = rq.compute(inputs, q_proj.weight) ``` -### Dependency-Aware Pruning +Can analyze: +- Query, Key, Value projections separately +- Per-head importance +- Per-neuron importance within projections +- Attention output projections + +**Aggregation Options:** +- `sequence_mean`: Average over sequence length +- `token_level`: Per-token analysis -Automatically handles inter-layer dependencies: +### Large Language Models + +The framework provides specialized wrappers for LLMs: ```python -from alignment.pruning.dependency_aware import prune_model_with_dependencies +from alignment.models.transformer_enhanced import LLaMAWrapper -result = prune_model_with_dependencies( - model, - layer_scores={'conv1': scores1, 'conv2': scores2}, - amount=0.5, - verbose=True +wrapper = LLaMAWrapper( + llama_model, + track_ffn=True, + track_attention=True ) +``` -# Automatically propagates: -# conv1.out_channels → conv2.in_channels -# Maintains shape compatibility +Supports analysis and pruning of: +- FFN intermediate layers (up_proj, down_proj) +- Attention projections (q_proj, k_proj, v_proj, o_proj) +- Layer-wise or model-wide analysis + +## Configuration + +Experiments are configured using YAML files. The framework provides a template with all available options. + +### Basic Configuration + +```yaml +experiment: + name: "my_experiment" + seed: 42 + device: "cuda" + output_dir: "./results" + +model: + name: "resnet18" + pretrained: true + tracked_layers: null # Auto-detect + +dataset: + name: "cifar10" + data_path: "./data" + batch_size: 128 + +metrics: + enabled: ['rayleigh_quotient'] ``` ---- +### Configuration Sections + +**experiment** +- Experiment name, random seed, device selection +- Output directory for results + +**model** +- Model architecture name or path +- Whether to use pretrained weights +- Checkpoint path for custom models +- Which layers to track for analysis -## Performance +**dataset** +- Dataset name and path +- Batch size and data loading workers +- Data augmentation settings -### Computation Time +**metrics** +- List of metrics to compute +- Per-metric configuration parameters +- Regularization and sampling settings -| Model Scale | Per-Layer Time | Throughput | -|-------------|----------------|------------| -| Small (N=256) | ~28ms | 35 layers/sec | -| Medium (N=1024) | ~105ms | 10 layers/sec | -| Large (N=4096) | ~440ms | 2.3 layers/sec | +**pruning** +- Pruning strategy and target sparsity +- Distribution method across layers +- Scoring method for importance +- Structured vs unstructured +- Fine-tuning configuration -### Speedup Techniques +**training** +- Training parameters if training from scratch +- Learning rate, optimizer, scheduler +- Metric tracking during training -- **Output-based redundancy**: 30x faster for large models -- **Shared computation**: 2-3x when computing multiple metrics -- **Parallel strategies**: M strategies in ~1.3x time +**advanced** +- Covariance computation methods +- Hook management settings +- Layer-specific configurations ---- +See `configs/template.yaml` for complete documentation of all parameters. ## Testing +Run tests to verify installation and functionality: + ```bash -# Run all tests +# All tests pytest tests/ -# Scientific correctness validation -python tests/unit/metrics/test_scientific_correctness.py +# Specific test modules +pytest tests/unit/metrics/ +pytest tests/unit/pruning/ -# Specific module -pytest tests/unit/services/ +# Scientific correctness validation +pytest tests/unit/metrics/test_scientific_correctness.py ``` ---- - -## Contributing - -1. Fork the repository -2. Create a feature branch -3. Add tests for new features -4. Submit pull request - ---- +## Examples -## Support +The `examples/` directory contains scripts demonstrating framework capabilities: -- **Documentation**: See guides in repository root -- **Examples**: `examples/` directory -- **Issues**: GitHub issues -- **API Reference**: Run `cd docs && make html` +- `01_quick_start.py` - Basic usage +- `02_complete_experiment.py` - Full experiment workflow +- `03_pruning_strategies.py` - Pruning strategy comparison +- `06_redundancy_aware_pruning.py` - Information-theoretic pruning +- `07_mnist_intelligent_pruning.py` - MNIST pruning example +- `08_llama_ffn_pruning.py` - LLM feed-forward pruning +- `09_attention_neuron_vs_head_pruning.py` - Attention layer analysis From fbb441f76cfb2fed10935fc8f5a24cb5ec03fdd0 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:28:37 -0400 Subject: [PATCH 03/18] Clean up usage guide --- docs/usage.md | 190 +++++++++++++++++++++++++++++++++++--------------- 1 file changed, 132 insertions(+), 58 deletions(-) diff --git a/docs/usage.md b/docs/usage.md index b531e2a7..e86eae76 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -1,61 +1,48 @@ -# How to Run Experiments with YAML Configs +# Usage Guide -## Quick Start +## Running Experiments -### Step 1: Activate Environment +The framework uses YAML configuration files to specify experiments. This approach allows reproducible experiments and easy parameter management. + +### Basic Usage ```bash conda activate alignment cd /path/to/alignment -``` - -### Step 2: Run Experiment - -```bash python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml ``` ---- - -## Available Example Configs +### Example Configurations -### 1. MNIST Basic Analysis +The `configs/examples/` directory contains pre-configured experiments: +**MNIST Basic Analysis** ```bash python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml ``` +Trains an MLP on MNIST and computes alignment scores. -Computes: RQ scores for simple MLP on MNIST - -### 2. ResNet Pruning - +**ResNet Pruning** ```bash python scripts/run_experiment.py --config configs/examples/resnet_pruning.yaml ``` +Applies pruning to ResNet-18 on CIFAR-10 using alignment-based importance scores. -Performs: Redundancy-aware pruning on ResNet-18 with CIFAR-10 - -### 3. LLaMA-3 Scoring - +**LLaMA-3 Scoring** ```bash python scripts/run_experiment.py --config configs/examples/llama3_scoring.yaml ``` +Computes per-neuron importance scores for LLaMA model feed-forward layers. -Computes: Per-neuron importance scores for LLaMA-3 FFN - -### 4. LLaMA-3 Pruning - +**LLaMA-3 Pruning** ```bash python scripts/run_experiment.py --config configs/examples/llama3_pruning.yaml ``` - -Performs: Redundancy-aware pruning of LLaMA-3 model - ---- +Prunes LLaMA model using information-theoretic importance scores. ## Command-Line Overrides -Override any parameter: +Override configuration parameters from the command line: ```bash python scripts/run_experiment.py \ @@ -65,62 +52,149 @@ python scripts/run_experiment.py \ --target-sparsity 0.5 ``` -Common overrides: -- `--device cuda:0` - GPU selection -- `--batch-size 64` - Batch size -- `--target-sparsity 0.7` - Pruning amount -- `--epochs 50` - Training epochs -- `--output-dir ./my_results` - Output directory +Common override options: +- `--device cuda:0` - Select GPU device +- `--batch-size 64` - Set batch size +- `--target-sparsity 0.7` - Set pruning target +- `--epochs 50` - Set training epochs +- `--output-dir ./results` - Set output directory ---- +## Creating Custom Configurations -## Creating Custom Configs +### From Template -### Method 1: Copy and Modify Template +Copy the template and modify for your needs: ```bash cp configs/template.yaml configs/my_experiment.yaml -# Edit my_experiment.yaml +# Edit my_experiment.yaml with desired parameters python scripts/run_experiment.py --config configs/my_experiment.yaml ``` -### Method 2: Copy Existing Example +### From Existing Example + +Start with an example configuration: ```bash cp configs/examples/resnet_pruning.yaml configs/my_resnet.yaml -# Modify my_resnet.yaml +# Modify specific parameters python scripts/run_experiment.py --config configs/my_resnet.yaml ``` ---- - -## Config Structure +## Configuration Structure -All configs have the same structure: +All configuration files follow the same structure: ```yaml -experiment: # Experiment settings - name: "..." - seed: 42 +```yaml +experiment: + name: "my_experiment" device: "cuda" - -model: # Model architecture - name: "..." # 'resnet18', 'mlp', 'meta-llama/...' + +model: + name: "resnet18" pretrained: true - -dataset: # Data source - name: "..." # 'mnist', 'cifar10', 'wikitext' + +dataset: + name: "cifar10" batch_size: 128 -metrics: # Metrics to compute +metrics: enabled: ['rayleigh_quotient'] -pruning: # Pruning settings (optional) +pruning: + enabled: false +``` + +See `configs/template.yaml` for all available parameters. + +## Experiment Types + +### Computing Metrics + +Compute alignment and information-theoretic scores: + +```yaml +metrics: + enabled: ['rayleigh_quotient', 'pairwise_redundancy_gaussian', 'synergy_gaussian_mmi'] + + rayleigh_quotient: + relative: true + regularization: 1.0e-6 + + pairwise_redundancy_gaussian: + mode: 'output_based' + num_pairs: 10 + +training: + enabled: false +pruning: + enabled: false +``` + +### Training Networks + +Train from scratch with optional metric tracking: + +```yaml +training: enabled: true - strategy: '...' + epochs: 100 + learning_rate: 0.001 + optimizer: 'adam' + compute_metrics_during_training: false +``` + +### Pruning Networks + +Apply pruning with specified strategy: + +```yaml +pruning: + enabled: true + strategy: 'composite' target_sparsity: 0.7 + distribution: 'adaptive_sensitivity' + scoring: 'rayleigh_quotient' + structured: true + + fine_tune: + enabled: true + epochs: 20 + learning_rate: 0.0001 +``` + +### Multi-Level Pruning + +Test multiple sparsity levels: + +```yaml +pruning: + enabled: true + sparsity_levels: [0.3, 0.5, 0.7, 0.9] + strategy: 'magnitude' +``` -# ... other sections as needed +## Output Structure + +Results are saved to the specified output directory: + +``` +results/[experiment_name]/ +├── config.yaml # Configuration used +├── results.json # Numerical results +├── scores/ # Per-layer importance scores +├── plots/ # Visualizations +└── checkpoints/ # Model checkpoints +``` + +## Workflow + +1. Create or select configuration file +2. Activate environment: `conda activate alignment` +3. Run experiment: `python scripts/run_experiment.py --config [path]` +4. Results saved to output directory +5. Analyze results and visualizations ``` See `configs/template.yaml` for complete parameter reference. From eddbb740c75ec4f4c3c92d5070098c9180ae0fee Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:30:39 -0400 Subject: [PATCH 04/18] Clean up API reference formatting --- docs/api_reference.md | 28 +++------------------------- 1 file changed, 3 insertions(+), 25 deletions(-) diff --git a/docs/api_reference.md b/docs/api_reference.md index ed32cedc..22e4d492 100644 --- a/docs/api_reference.md +++ b/docs/api_reference.md @@ -1,8 +1,6 @@ # API Reference -**Quick reference for alignment framework APIs** - ---- +Quick reference for framework APIs. ## Core Classes @@ -35,8 +33,6 @@ metric.requires_outputs # bool: needs layer outputs metric.compute(inputs, weights, outputs, **kwargs) # Returns scores ``` ---- - ## Metrics ### Rayleigh Quotient @@ -102,8 +98,6 @@ alignment = grad_align.compute( # Returns correlation between local signal and backprop ``` ---- - ## Services ### ActivationCaptureService @@ -176,8 +170,6 @@ masks = MaskOperations.global_threshold_mask( ) ``` ---- - ## Pruning ### Quick Pruning @@ -229,8 +221,6 @@ results = optimizer.compare_strategies_parallel( ) ``` ---- - ## Training ### Training Callback @@ -253,8 +243,6 @@ callback.on_batch_end(wrapper, inputs, targets, step) history = callback.get_history() ``` ---- - ## Model Wrappers ### Generic Wrapper @@ -301,8 +289,6 @@ wrapper.ffn_layers # {'expansion': [...], 'contraction': [...]} wrapper.attention_layers # {'q': [...], 'k': [...], 'v': [...]} ``` ---- - ## Utility Functions ### Layer Detection @@ -329,8 +315,6 @@ cov = estimate_covariance( ) ``` ---- - ## Configuration Parameters ### Metric Parameters @@ -356,8 +340,6 @@ cov = estimate_covariance( **Direction:** 'low' (prune unimportant), 'high' (ablation), 'random' (baseline) ---- - ## Common Patterns ### Analyze Pretrained Model @@ -408,15 +390,11 @@ results = optimizer.compare_strategies_parallel( ) ``` ---- - ## Examples See `examples/` directory for complete workflows: - `07_mnist_intelligent_pruning.py` - End-to-end pruning -- `08_llama_ffn_pruning.py` - LLM FFN analysis -- `09_attention_neuron_vs_head_pruning.py` - Attention analysis - -Run: `python examples/07_mnist_intelligent_pruning.py` +- `08_llama_ffn_pruning.py` - LLM feed-forward analysis +- `09_attention_neuron_vs_head_pruning.py` - Attention layer analysis From 898b36b6166b43c8297609bf779f6a00abf61512 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:32:45 -0400 Subject: [PATCH 05/18] Clean up quick reference --- docs/quick_reference.md | 59 +++++++++++++++-------------------------- 1 file changed, 22 insertions(+), 37 deletions(-) diff --git a/docs/quick_reference.md b/docs/quick_reference.md index a2bd8137..e25f0cac 100644 --- a/docs/quick_reference.md +++ b/docs/quick_reference.md @@ -1,8 +1,6 @@ # Quick Reference -**Common code patterns and examples** - ---- +Common code patterns and examples. ## Basic Usage @@ -66,8 +64,6 @@ result = prune_with_all_options( ) ``` ---- - ## Metrics ### Rayleigh Quotient @@ -124,30 +120,33 @@ scorer = NodeScoringService( scores = scorer.compute_composite_scores(inputs, weights, targets) ``` ---- - ## Pruning -### Pruning Strategies +### Available Strategies ```python -# Available strategies: strategies = [ - 'magnitude', # L1/L2 norm - 'random', # Baseline - 'gradient', # Gradient-based - 'alignment', # RQ-based - 'composite', # Redundancy-aware - 'movement', # Training-aware - 'adaptive', # Adaptive per-layer - 'ultimate' # Multi-stage (best) + 'magnitude', + 'random', + 'gradient', + 'alignment', + 'composite', + 'movement', + 'adaptive', + 'ultimate' ] ``` ### Distribution Methods ```python -# How to allocate sparsity across layers: +distributions = [ + 'uniform', + 'global_threshold', + 'adaptive_sensitivity', + 'importance_weighted', + 'size_proportional' +] distributions = [ 'uniform', # Same % per layer 'global_threshold', # Global score threshold @@ -159,16 +158,14 @@ distributions = [ ] ``` -### Direction Options +### Pruning Direction ```python -direction='low' # Prune low-scoring (production default) -direction='high' # Prune high-scoring (ablation study) -direction='random' # Random (baseline) +direction='low' # Prune low-scoring neurons +direction='high' # Prune high-scoring neurons (ablation) +direction='random' # Random pruning (baseline) ``` ---- - ## Training Integration ### Track Metrics During Training @@ -206,8 +203,6 @@ alignment = grad_align.compute( # High alignment = Hebbian rule works for this neuron ``` ---- - ## Architecture-Specific ### CNNs @@ -256,8 +251,6 @@ scores = redundancy.compute(outputs=outputs) # [11008] - one per neuron ``` ---- - ## Configuration ### Minimal Config @@ -290,8 +283,6 @@ pruning: Run: `python scripts/run_experiment.py --config my_config.yaml` ---- - ## Performance Tips ### For Large Models (LLMs) @@ -320,8 +311,6 @@ optimizer = ParallelPruningOptimizer() results = optimizer.prune_ensemble_parallel(networks, ...) ``` ---- - ## Common Patterns ### Complete Pruning Workflow @@ -361,8 +350,6 @@ train(model, train_loader, epochs=20) accuracy = evaluate(model, test_loader) ``` ---- - ## Troubleshooting ### Singular Covariance @@ -387,7 +374,5 @@ from alignment.pruning.dependency_aware import prune_model_with_dependencies result = prune_model_with_dependencies(model, scores, amount) ``` ---- - -See [USER_GUIDE.md](USER_GUIDE.md) for detailed documentation. +See [user_guide.md](user_guide.md) for detailed documentation. From 850f83fef5ddf216c52ff892637b3fda31d98d24 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:34:47 -0400 Subject: [PATCH 06/18] Clean up docs README --- docs/README.md | 47 +++++++++++++++++------------------------------ 1 file changed, 17 insertions(+), 30 deletions(-) diff --git a/docs/README.md b/docs/README.md index 3925a8db..fc33b8fd 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,45 +1,34 @@ # Alignment Framework Documentation -Neural Network Alignment Analysis & Intelligent Pruning - ---- +Neural network alignment analysis and intelligent pruning. ## Getting Started - [Installation Guide](installation.md) - Setup and dependencies -- [Usage Guide](usage.md) - How to run experiments with YAML configs -- [User Guide](user_guide.md) - Complete usage documentation - ---- +- [Usage Guide](usage.md) - Running experiments with configuration files +- [User Guide](user_guide.md) - Comprehensive framework documentation ## Reference -- [API Reference](api_reference.md) - Class and method documentation -- [Quick Reference](quick_reference.md) - Code examples and patterns -- [Configuration Reference](../configs/template.yaml) - All parameters with options - ---- - -## Version Information - -- [Changelog](changelog.md) - Release notes and version history - ---- +- [API Reference](api_reference.md) - API documentation +- [Quick Reference](quick_reference.md) - Code examples +- [Configuration Template](../configs/template.yaml) - All configuration parameters ## Examples -Working example configurations: - -- `configs/examples/mnist_basic.yaml` - Simple MNIST analysis -- `configs/examples/resnet_pruning.yaml` - ResNet pruning on CIFAR-10 -- `configs/examples/llama3_scoring.yaml` - LLaMA-3 per-neuron scoring -- `configs/examples/llama3_pruning.yaml` - LLaMA-3 pruning +Example configuration files in `configs/examples/`: -Run: `python scripts/run_experiment.py --config [path]` +- `mnist_basic.yaml` - MLP analysis on MNIST +- `resnet_pruning.yaml` - ResNet pruning on CIFAR-10 +- `llama3_scoring.yaml` - LLaMA scoring +- `llama3_pruning.yaml` - LLaMA pruning ---- +Run experiments: +```bash +python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml +``` -## Quick Example +## Quick Start ```python from alignment import ModelWrapper, get_metric @@ -53,9 +42,7 @@ weights = wrapper.get_layer_weights() scores = rq.compute(acts['layer_input'], weights['layer']) ``` ---- - -## Building HTML Documentation +## Building Documentation ```bash cd docs From 8d6399df38fd73a6fe77f6732655dd304d7e672a Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:38:20 -0400 Subject: [PATCH 07/18] Clean up example comments --- examples/07_mnist_intelligent_pruning.py | 44 +++++------------------- 1 file changed, 9 insertions(+), 35 deletions(-) diff --git a/examples/07_mnist_intelligent_pruning.py b/examples/07_mnist_intelligent_pruning.py index 78e85d84..586e5492 100644 --- a/examples/07_mnist_intelligent_pruning.py +++ b/examples/07_mnist_intelligent_pruning.py @@ -1,13 +1,11 @@ """ -Complete Example: Intelligent Pruning on MNIST +MNIST Intelligent Pruning Example -This script demonstrates the full workflow: -1. Train a simple model on MNIST -2. Compute redundancy-aware composite scores -3. Prune using multiple strategies +Demonstrates complete workflow: +1. Train MLP on MNIST +2. Compute composite importance scores +3. Apply pruning with different strategies 4. Compare results - -This is a practical, runnable example showing real improvements. """ import torch @@ -28,9 +26,8 @@ from alignment.metrics import get_metric -# Simple MLP for MNIST class SimpleMLP(nn.Module): - """Simple MLP: 784 -> 128 -> 64 -> 10""" + """MLP: 784 -> 128 -> 64 -> 10""" def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 128) @@ -48,7 +45,7 @@ def forward(self, x): def train_model(model, train_loader, epochs=5, device='cpu'): - """Quick training.""" + """Train model.""" model.train() optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() @@ -98,12 +95,7 @@ def evaluate(model, test_loader, device='cpu'): def prune_model(model, wrapper, pruning_method, pruning_amount, val_loader, device='cpu'): - """ - Prune model using specified method. - - Args: - pruning_method: 'random', 'magnitude', 'rq', or 'composite' - """ + """Prune model using specified method.""" print(f"\nPruning with {pruning_method} (amount={pruning_amount:.1%})...") # Get a batch for metric computation @@ -300,25 +292,7 @@ def main(): best_method = min(results.keys(), key=lambda m: results[m]['drop']) print(f"\n✓ Best method: {best_method} (smallest accuracy drop: {results[best_method]['drop']:.2f}%)") - # Expected outcome - print("\n" + "=" * 80) - print("EXPECTED OUTCOME") - print("=" * 80) - print(""" -Redundancy-aware pruning (composite) should outperform others because: -• Preserves high-synergy neuron pairs (complementary information) -• Removes high-redundancy neurons (overlapping information) -• Uses task-relevance when targets available (ΔRQ) - -Expected ranking (best to worst): -1. Composite (redundancy-aware) - Smallest drop -2. RQ (alignment-aware) -3. Magnitude -4. Random - Largest drop - -If composite matches or beats magnitude with same drop, that's a success! -At higher sparsity (70-90%), the gap should be even larger (+3-5% better). - """) + print("\nComposite pruning considers redundancy and synergy in addition to alignment.") # Save results output_dir = Path('results/mnist_intelligent_pruning') From 25dbe2f068870d3821a3e4f43811e8c2b3b08e9c Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:40:23 -0400 Subject: [PATCH 08/18] Clean up configs README --- configs/README.md | 64 ++++++++++++++++++----------------------------- 1 file changed, 25 insertions(+), 39 deletions(-) diff --git a/configs/README.md b/configs/README.md index 26b6f47d..2144ae9e 100644 --- a/configs/README.md +++ b/configs/README.md @@ -1,41 +1,31 @@ # Configuration Guide -All experiments are configured via YAML files. - ---- +Experiments are configured using YAML files. ## Template -`template.yaml` - Complete template with all available parameters documented inline. - ---- - -## Examples - -Compact, ready-to-use configurations: - -### LLaMA-3 +`template.yaml` - Complete template with all available parameters. -- `examples/llama3_scoring.yaml` - Compute per-neuron importance scores -- `examples/llama3_pruning.yaml` - Redundancy-aware pruning +## Example Configurations -### Vision Models +Ready-to-use configurations in `examples/`: -- `examples/resnet_pruning.yaml` - ResNet-18 pruning with dependency handling -- `examples/mnist_basic.yaml` - Simple MNIST analysis +**Vision Models** +- `mnist_basic.yaml` - MLP analysis on MNIST +- `resnet_pruning.yaml` - ResNet-18 pruning on CIFAR-10 ---- +**LLaMA Models** +- `llama3_scoring.yaml` - Compute importance scores +- `llama3_pruning.yaml` - Apply pruning ## Usage -Run any experiment: - +Run experiment: ```bash python scripts/run_experiment.py --config configs/examples/resnet_pruning.yaml ``` Override parameters: - ```bash python scripts/run_experiment.py \ --config configs/examples/resnet_pruning.yaml \ @@ -44,27 +34,23 @@ python scripts/run_experiment.py \ --target-sparsity 0.5 ``` ---- - -## Creating Custom Configs +## Creating Configurations 1. Copy template: `cp configs/template.yaml configs/my_config.yaml` -2. Modify parameters (all options documented inline) +2. Edit parameters as needed 3. Run: `python scripts/run_experiment.py --config configs/my_config.yaml` ---- - -## Parameter Categories +## Configuration Sections -- `experiment`: Name, seed, device, output directory -- `model`: Architecture, pretrained, layers to track -- `dataset`: Data source, batch size, preprocessing -- `metrics`: Which metrics to compute and their parameters -- `training`: Training parameters (if training from scratch) -- `pruning`: Pruning strategy, distribution, scoring method -- `layer_config`: Architecture-specific settings (CNN, transformer) -- `analysis`: Analysis options (class-conditioned, save options) -- `visualization`: Plot generation settings -- `advanced`: Backend, parallelization, optimization options +- `experiment` - Name, seed, device, output directory +- `model` - Architecture, pretrained weights, layers to track +- `dataset` - Data source, batch size, preprocessing +- `metrics` - Metrics to compute and their parameters +- `training` - Training parameters +- `pruning` - Pruning strategy, distribution, scoring +- `layer_config` - Architecture-specific settings +- `analysis` - Analysis options +- `visualization` - Plot settings +- `advanced` - Backend and optimization options -See `template.yaml` for complete parameter documentation. +See `template.yaml` for detailed parameter documentation. From 1a6c1d60a155355dbdc7ad1b9ef88cac9be35b1b Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:41:01 -0400 Subject: [PATCH 09/18] Clean up examples README --- examples/README.md | 39 +++++++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 16 deletions(-) diff --git a/examples/README.md b/examples/README.md index 918bc121..080d2ff9 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,30 +1,37 @@ -## Examples +# Examples -This directory contains example scripts demonstrating the alignment framework functionality. +Example scripts demonstrating framework capabilities. -### Available Examples +## Available Examples -1. **01_quick_start.py** - Minimal example showing basic model wrapping and metrics -2. **02_complete_experiment.py** - Full workflow with training, pruning, and visualization -3. **03_pruning_strategies.py** - Comparison of different pruning methods -4. **04_visualization_gallery.py** - Comprehensive visualization capabilities +**Basic Usage** +- `01_quick_start.py` - Model wrapping and basic metrics +- `02_complete_experiment.py` - Full workflow with training and pruning +- `03_pruning_strategies.py` - Pruning strategy comparison +- `04_visualization_gallery.py` - Visualization examples -### Running Examples +**Advanced Usage** +- `06_redundancy_aware_pruning.py` - Information-theoretic pruning +- `07_mnist_intelligent_pruning.py` - Complete MNIST pruning workflow +- `08_llama_ffn_pruning.py` - LLaMA feed-forward pruning +- `09_attention_neuron_vs_head_pruning.py` - Attention analysis -Each example can be run independently: +## Running Examples + +Each example is self-contained: ```bash python examples/01_quick_start.py -python examples/02_complete_experiment.py -python examples/03_pruning_strategies.py -python examples/04_visualization_gallery.py +python examples/07_mnist_intelligent_pruning.py +python examples/08_llama_ffn_pruning.py ``` -### Configuration-Based Experiments +## Configuration-Based Experiments -For experiments using YAML configurations, see: +For YAML-based experiments: ```bash -python scripts/run_experiment.py --config configs/examples/resnet18_analysis.yaml +python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml +python scripts/run_experiment.py --config configs/examples/resnet_pruning.yaml ``` -See the `configs/examples/` directory for configuration templates. \ No newline at end of file +See `configs/examples/` for available configurations. \ No newline at end of file From 438e2592ceec310be6bcad07f510d77eb5e2ca36 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:42:46 -0400 Subject: [PATCH 10/18] Clean up scripts README --- scripts/README.md | 30 ++++++++++++++++++------------ 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index 4c0e4ae0..9deacea9 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -1,21 +1,27 @@ -# Production Scripts +# Scripts -This directory contains production-ready scripts for running alignment experiments. +Scripts for running experiments. -## Scripts +## Main Script -### `run_experiment.py` -The main experiment runner for the alignment framework. This is the primary tool for running research experiments with full configuration support. +`run_experiment.py` - Experiment runner with YAML configuration support. -**Usage:** +Usage: ```bash -# From repository root -python scripts/run_experiment.py --config configs/unified_config.yaml +python scripts/run_experiment.py --config configs/examples/mnist_basic.yaml ``` -## Difference from Examples +The script handles: +- Model loading (pretrained or training from scratch) +- Metric computation +- Pruning with various strategies +- Evaluation and visualization -- **`examples/`**: Educational scripts for learning the framework -- **`scripts/`**: Production tools for research experiments +## Configuration -The examples demonstrate concepts with hardcoded parameters for clarity, while the scripts here are fully configurable tools designed for actual research work. \ No newline at end of file +Experiments are configured via YAML files. See `configs/template.yaml` for all parameters and `configs/examples/` for working examples. + +## Examples vs Scripts + +- `examples/` - Standalone demonstration scripts +- `scripts/` - Configuration-based experiment runner \ No newline at end of file From f6256559b9aced654b566c54edfb5df643b687b4 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:47:32 -0400 Subject: [PATCH 11/18] Fix imports and linting issues --- examples/01_quick_start.py | 53 +++-- examples/02_complete_experiment.py | 124 +++++----- examples/03_pruning_strategies.py | 167 ++++++------- examples/04_visualization_gallery.py | 82 +++---- examples/06_redundancy_aware_pruning.py | 78 +++--- examples/07_mnist_intelligent_pruning.py | 117 ++++----- examples/08_llama_ffn_pruning.py | 179 +++++++------- .../09_attention_neuron_vs_head_pruning.py | 154 ++++++------ examples/quick_demo.py | 53 +++-- scripts/run_experiment.py | 163 +++++++------ src/alignment/__init__.py | 34 +-- src/alignment/analysis/__init__.py | 18 +- .../analysis/aggregation/__init__.py | 6 +- src/alignment/analysis/aggregation/layers.py | 55 ++--- src/alignment/analysis/aggregation/metrics.py | 49 ++-- src/alignment/analysis/aggregation/results.py | 89 +++---- src/alignment/analysis/dynamic_scoring.py | 124 +++++----- src/alignment/analysis/unified_reporter.py | 163 ++++++------- .../analysis/visualization/__init__.py | 7 +- .../visualization/unified_visualizer.py | 222 +++++++++--------- src/alignment/configs/__init__.py | 2 +- src/alignment/configs/config_loader.py | 125 +++++----- src/alignment/configs/config_validator.py | 50 ++-- src/alignment/core/__init__.py | 23 +- src/alignment/core/base.py | 147 ++++++------ src/alignment/core/layer_detector.py | 120 +++++----- src/alignment/core/protocols.py | 85 +++---- src/alignment/core/registry.py | 53 ++--- src/alignment/data/__init__.py | 6 +- src/alignment/data/base.py | 15 +- src/alignment/data/datasets/__init__.py | 18 +- .../data/datasets/unified_dataset.py | 9 +- src/alignment/data/loaders.py | 12 +- src/alignment/data/processing/__init__.py | 11 +- src/alignment/data/processing/batch.py | 10 +- src/alignment/data/processing/layers.py | 5 +- src/alignment/evaluation.py | 95 ++++---- src/alignment/experiments/__init__.py | 16 +- src/alignment/experiments/base.py | 19 +- .../experiments/config_components.py | 2 +- .../experiments/general_alignment.py | 52 ++-- src/alignment/experiments/llm_experiments.py | 11 +- src/alignment/experiments/runner.py | 13 +- .../experiments/tracking/__init__.py | 6 +- src/alignment/experiments/tracking/base.py | 7 +- src/alignment/experiments/training_utils.py | 5 +- .../external/BROJA_2PID/BROJA_2PID.py | 44 ++-- src/alignment/external/__init__.py | 2 +- src/alignment/infrastructure/__init__.py | 51 ++-- .../infrastructure/computing/__init__.py | 20 +- .../infrastructure/computing/distributed.py | 177 +++++++------- .../computing/optimized/__init__.py | 28 +-- .../infrastructure/computing/optimized/gpu.py | 140 +++++------ .../infrastructure/computing/optimized/jit.py | 144 ++++++------ .../infrastructure/storage/__init__.py | 13 +- .../infrastructure/storage/checkpoint.py | 35 +-- .../infrastructure/storage/logging.py | 76 +++--- src/alignment/metrics/__init__.py | 20 +- src/alignment/metrics/gradient_based.py | 180 +++++++------- src/alignment/metrics/information/__init__.py | 23 +- .../conditional_mutual_information.py | 140 +++++------ .../metrics/information/gaussian_mi.py | 132 +++++------ .../metrics/information/gaussian_pid.py | 6 +- .../metrics/information/gpu_binning.py | 68 +++--- .../metrics/information/higher_order.py | 151 ++++++------ .../metrics/information/mi_projection.py | 70 +++--- .../metrics/information/mutual_information.py | 124 +++++----- .../metrics/information/pairwise_gaussian.py | 153 ++++++------ src/alignment/metrics/information/pid.py | 155 ++++++------ .../metrics/information/redundancy.py | 117 ++++----- .../metrics/information/synergy_mmi.py | 135 +++++------ src/alignment/metrics/pairwise_base.py | 63 ++--- src/alignment/metrics/rayleigh/__init__.py | 2 +- .../metrics/rayleigh/delta_alignment.py | 48 ++-- .../metrics/rayleigh/rayleigh_quotient.py | 189 +++++++-------- .../metrics/rayleigh/rq_alternative.py | 48 ++-- src/alignment/metrics/similarity/__init__.py | 13 +- .../metrics/similarity/cosine_similarity.py | 115 ++++----- .../metrics/similarity/node_correlation.py | 62 ++--- .../metrics/similarity/node_redundancy.py | 60 ++--- .../metrics/similarity/weight_similarity.py | 58 ++--- src/alignment/metrics/spectral/__init__.py | 12 +- .../metrics/spectral/spectral_alignment.py | 112 ++++----- .../metrics/spectral/spectral_classic.py | 132 ++++++----- .../metrics/task_specific/__init__.py | 19 +- .../task_specific/activation_magnitude.py | 70 +++--- .../metrics/task_specific/classification.py | 68 +++--- .../metrics/task_specific/general.py | 198 ++++++++-------- .../metrics/task_specific/language_model.py | 78 +++--- .../task_specific/reinforcement_learning.py | 66 +++--- src/alignment/metrics/task_specific/vision.py | 113 ++++----- src/alignment/models/__init__.py | 23 +- .../models/architectures/standard_models.py | 2 +- src/alignment/models/base.py | 8 +- src/alignment/models/hooks.py | 5 +- src/alignment/models/hub.py | 3 +- src/alignment/models/transformers.py | 7 +- src/alignment/models/wrappers.py | 11 +- src/alignment/pruning/__init__.py | 70 +++--- src/alignment/pruning/base.py | 111 ++++----- src/alignment/pruning/dependency_aware.py | 183 ++++++++------- src/alignment/pruning/distribution.py | 161 ++++++------- src/alignment/pruning/experiments/__init__.py | 8 +- .../pruning/experiments/cascading_layer.py | 15 +- .../pruning/experiments/eigenvector_based.py | 13 +- .../pruning/experiments/global_pruning.py | 15 +- .../pruning/experiments/layer_wise.py | 17 +- src/alignment/pruning/orchestrator.py | 109 ++++----- src/alignment/pruning/parallel_optimizer.py | 129 +++++----- src/alignment/pruning/strategies/__init__.py | 41 +--- src/alignment/pruning/strategies/adaptive.py | 155 ++++++------ .../pruning/strategies/alignment_based.py | 163 ++++++------- src/alignment/pruning/strategies/cascading.py | 83 +++---- src/alignment/pruning/strategies/gradient.py | 113 ++++----- src/alignment/pruning/strategies/magnitude.py | 65 ++--- src/alignment/pruning/strategies/movement.py | 86 +++---- src/alignment/pruning/strategies/parallel.py | 123 +++++----- .../pruning/strategies/parallel_batch.py | 161 ++++++------- src/alignment/pruning/strategies/random.py | 91 +++---- src/alignment/pruning/strategies/ultimate.py | 101 ++++---- .../pruning/strategies/ultra_fast.py | 212 ++++++++--------- src/alignment/quantization/__init__.py | 17 +- src/alignment/quantization/analysis.py | 73 +++--- src/alignment/quantization/ptq.py | 111 ++++----- src/alignment/services/__init__.py | 12 +- src/alignment/services/activation_capture.py | 84 +++---- src/alignment/services/mask_ops.py | 107 ++++----- src/alignment/services/scoring.py | 101 ++++---- src/alignment/training/__init__.py | 4 +- src/alignment/training/base.py | 125 +++++----- src/alignment/training/callbacks/__init__.py | 5 +- .../training/callbacks/alignment_callback.py | 76 +++--- src/alignment/training/experiment_trainer.py | 161 ++++++------- src/alignment/training/multi_network.py | 155 ++++++------ 134 files changed, 4954 insertions(+), 4945 deletions(-) diff --git a/examples/01_quick_start.py b/examples/01_quick_start.py index 462167e4..0459145c 100644 --- a/examples/01_quick_start.py +++ b/examples/01_quick_start.py @@ -6,13 +6,13 @@ Usage: python quick_demo.py - + No configuration needed - this script runs with default settings and creates its own model. Requirements: - PyTorch - alignment package installed - + Output: - Console output showing: * Model structure @@ -24,8 +24,9 @@ import torch import torch.nn as nn -from alignment.models import ModelWrapper + from alignment.metrics import get_metric +from alignment.models import ModelWrapper def main(): @@ -37,55 +38,55 @@ def main(): nn.ReLU(), nn.Linear(128, 10) ) - + print("Model created with 3 linear layers") - + # 2. Wrap the model to track activations wrapped_model = ModelWrapper(model) print(f"\nTracked layers: {wrapped_model.tracked_layers}") - + # 3. Generate some dummy data batch_size = 32 inputs = torch.randn(batch_size, 784) - + # 4. Forward pass with activation tracking outputs, activations = wrapped_model.forward_with_activations(inputs) print(f"\nCollected activations from {len(activations)} points") print(f"Activation keys: {list(activations.keys())}") - + # 5. Get layer weights weights = wrapped_model.get_layer_weights() print(f"Extracted weights from {len(weights)} layers") - + # 6. Compute alignment metrics print("\n" + "="*60) print("Computing Alignment Metrics") print("="*60) - + # Rayleigh Quotient RQMetric = get_metric('rayleigh_quotient') rq_metric = RQMetric() # Instantiate the metric - + for layer_name in wrapped_model.tracked_layers: # Get inputs and weights for this layer layer_inputs = activations[f"{layer_name}_input"] layer_weights = weights[layer_name] - + # Compute RQ scores scores = rq_metric.compute(inputs=layer_inputs, weights=layer_weights) - + print(f"\nLayer: {layer_name}") print(f" Input shape: {layer_inputs.shape}") print(f" Weight shape: {layer_weights.shape}") print(f" RQ scores: mean={scores.mean():.4f}, std={scores.std():.4f}") print(f" Min neuron score: {scores.min():.4f}") print(f" Max neuron score: {scores.max():.4f}") - + # 7. Try other metrics print("\n" + "="*60) print("Trying Other Metrics") print("="*60) - + # Weight cosine similarity WeightSimMetric = get_metric('weight_cosine_similarity') weight_sim = WeightSimMetric() # Instantiate @@ -93,35 +94,35 @@ def main(): layer_weights = weights[layer_name] sim_scores = weight_sim.compute(weights=layer_weights) print(f"\nWeight Cosine Similarity ({layer_name}): mean={sim_scores.mean():.4f}") - + # 8. Demonstrate pruning print("\n" + "="*60) print("Pruning Demo") print("="*60) - - from alignment.pruning import get_pruning_strategy, PruningConfig - + + from alignment.pruning import PruningConfig, get_pruning_strategy + # Use magnitude-based pruning config = PruningConfig(amount=0.5, pruning_mode='low') strategy = get_pruning_strategy('magnitude', config=config) - + # Apply pruning to first layer first_layer = model[0] # First linear layer mask = strategy.prune(first_layer) - - print(f"\nPruning 50% of weights in first layer") + + print("\nPruning 50% of weights in first layer") print(f"Sparsity achieved: {(mask == 0).float().mean():.2%}") - + # Apply mask first_layer.weight.data *= mask - + # Test forward pass after pruning pruned_outputs = model(inputs) print(f"\nOutput shape after pruning: {pruned_outputs.shape}") print("Pruning applied successfully!") - + print("\nDemo completed successfully!") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/02_complete_experiment.py b/examples/02_complete_experiment.py index 945133fb..d3b8e19a 100644 --- a/examples/02_complete_experiment.py +++ b/examples/02_complete_experiment.py @@ -12,7 +12,7 @@ Usage: python standard_alignment_experiment.py - + No configuration needed - this script runs with default settings. The script will: - Download MNIST dataset automatically (if not present) @@ -37,21 +37,21 @@ - comparison_grid.png: Comprehensive analysis grid """ +import json +from pathlib import Path + import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms -import numpy as np -from pathlib import Path -import json -from typing import Dict, List, Any + +from alignment.analysis.visualization import PruningVisualizer +from alignment.metrics import get_metric # Alignment framework imports from alignment.models import ModelWrapper -from alignment.metrics import get_metric -from alignment.pruning import get_pruning_strategy, PruningConfig -from alignment.analysis.visualization import PruningVisualizer +from alignment.pruning import PruningConfig, get_pruning_strategy class SimpleNet(nn.Module): @@ -64,7 +64,7 @@ def __init__(self, hidden_sizes=[784, 256, 128, 10]): if i < len(hidden_sizes) - 2: # No ReLU after last layer layers.append(nn.ReLU()) self.model = nn.Sequential(*layers) - + def forward(self, x): x = x.view(x.size(0), -1) return self.model(x) @@ -75,9 +75,9 @@ def train_model(model, train_loader, val_loader, epochs=10, device='cuda'): model = model.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) - + history = {'train_loss': [], 'val_acc': []} - + for epoch in range(epochs): # Training model.train() @@ -90,7 +90,7 @@ def train_model(model, train_loader, val_loader, epochs=10, device='cuda'): loss.backward() optimizer.step() train_loss += loss.item() - + # Validation model.eval() correct = 0 @@ -100,15 +100,15 @@ def train_model(model, train_loader, val_loader, epochs=10, device='cuda'): output = model(data) pred = output.argmax(dim=1) correct += pred.eq(target).sum().item() - + train_loss /= len(train_loader) val_acc = 100. * correct / len(val_loader.dataset) - + history['train_loss'].append(train_loss) history['val_acc'].append(val_acc) - + print(f'Epoch {epoch+1}/{epochs}: Loss: {train_loss:.4f}, Acc: {val_acc:.2f}%') - + return history @@ -116,36 +116,36 @@ def compute_alignment_metrics(model, data_loader, device='cuda'): """Compute various alignment metrics for the model.""" model = model.to(device) wrapped_model = ModelWrapper(model.model) # Wrap the sequential model - + # Get a batch of data for metric computation data_iter = iter(data_loader) inputs, _ = next(data_iter) inputs = inputs.to(device) inputs = inputs.view(inputs.size(0), -1) # Flatten for the model - + # Forward pass to collect activations _, activations = wrapped_model.forward_with_activations(inputs) weights = wrapped_model.get_layer_weights() - + # Initialize metrics metrics = { 'rayleigh_quotient': get_metric('rayleigh_quotient')(), 'weight_cosine_similarity': get_metric('weight_cosine_similarity')(), } - + results = {} - + # Compute metrics for each layer for layer_name in wrapped_model.tracked_layers: results[layer_name] = {} - + # Rayleigh Quotient if f"{layer_name}_input" in activations: layer_inputs = activations[f"{layer_name}_input"] layer_weights = weights[layer_name] - + rq_scores = metrics['rayleigh_quotient'].compute( - inputs=layer_inputs, + inputs=layer_inputs, weights=layer_weights ) results[layer_name]['rayleigh_quotient'] = { @@ -154,7 +154,7 @@ def compute_alignment_metrics(model, data_loader, device='cuda'): 'min': float(rq_scores.min()), 'max': float(rq_scores.max()) } - + # Weight Cosine Similarity layer_weights = weights[layer_name] sim_scores = metrics['weight_cosine_similarity'].compute(weights=layer_weights) @@ -162,40 +162,40 @@ def compute_alignment_metrics(model, data_loader, device='cuda'): 'mean': float(sim_scores.mean()), 'std': float(sim_scores.std()) } - + return results -def apply_pruning_experiment(model, val_loader, strategies=['magnitude', 'random'], +def apply_pruning_experiment(model, val_loader, strategies=['magnitude', 'random'], sparsities=[0.1, 0.3, 0.5, 0.7, 0.9], device='cuda'): """Apply different pruning strategies and evaluate.""" results = {} - + for strategy_name in strategies: results[strategy_name] = {} - + for sparsity in sparsities: # Clone model for this experiment pruned_model = SimpleNet() pruned_model.load_state_dict(model.state_dict()) pruned_model = pruned_model.to(device) - + # Apply pruning config = PruningConfig(amount=sparsity, pruning_mode='low') strategy = get_pruning_strategy(strategy_name, config=config) - + # Prune each linear layer for name, module in pruned_model.named_modules(): if isinstance(module, nn.Linear): mask = strategy.prune(module) module.weight.data *= mask - + # Evaluate pruned_model.eval() correct = 0 total_loss = 0 criterion = nn.CrossEntropyLoss() - + with torch.no_grad(): for data, target in val_loader: data, target = data.to(device), target.to(device) @@ -204,18 +204,18 @@ def apply_pruning_experiment(model, val_loader, strategies=['magnitude', 'random total_loss += loss.item() pred = output.argmax(dim=1) correct += pred.eq(target).sum().item() - + accuracy = 100. * correct / len(val_loader.dataset) avg_loss = total_loss / len(val_loader) - + results[strategy_name][sparsity] = { 'accuracy': accuracy, 'loss': avg_loss } - + print(f"{strategy_name} - Sparsity {sparsity:.1%}: " f"Acc: {accuracy:.2f}%, Loss: {avg_loss:.4f}") - + return results @@ -223,24 +223,24 @@ def visualize_results(pruning_results, output_dir='results/standard_experiment') """Generate visualizations of the results.""" output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) - + visualizer = PruningVisualizer() - + # Create performance comparison plot - fig1 = visualizer.plot_pruning_performance( + visualizer.plot_pruning_performance( pruning_results, metrics=['accuracy', 'loss'], save_path=output_dir / 'pruning_performance.png', title='Pruning Strategy Comparison on MNIST', show_confidence=False ) - + # Create comprehensive comparison grid - fig2 = visualizer.plot_pruning_comparison_grid( + visualizer.plot_pruning_comparison_grid( pruning_results, save_path=output_dir / 'comparison_grid.png' ) - + print(f"\nVisualizations saved to {output_dir}") @@ -249,75 +249,75 @@ def main(): print("=" * 60) print("Standard Alignment Experiment") print("=" * 60) - + # Setup device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(f"\nUsing device: {device}") - + # Data loading print("\nLoading MNIST dataset...") transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) - + train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform) val_dataset = datasets.MNIST('./data', train=False, transform=transform) - + train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=256, shuffle=False) - + # Create model print("\nCreating model...") model = SimpleNet() print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}") - + # Train model print("\nTraining model...") history = train_model(model, train_loader, val_loader, epochs=5, device=device) - + # Save training history output_dir = Path('results/standard_experiment') output_dir.mkdir(parents=True, exist_ok=True) - + with open(output_dir / 'training_history.json', 'w') as f: json.dump(history, f, indent=2) - + # Compute alignment metrics print("\nComputing alignment metrics...") metrics = compute_alignment_metrics(model, val_loader, device=device) - + print("\nAlignment Metrics:") for layer, layer_metrics in metrics.items(): print(f"\nLayer {layer}:") for metric_name, values in layer_metrics.items(): if 'mean' in values: print(f" {metric_name}: mean={values['mean']:.4f}, std={values['std']:.4f}") - + # Save metrics with open(output_dir / 'alignment_metrics.json', 'w') as f: json.dump(metrics, f, indent=2) - + # Pruning experiments print("\n" + "=" * 60) print("Pruning Experiments") print("=" * 60) - + pruning_results = apply_pruning_experiment( model, val_loader, strategies=['magnitude', 'random'], sparsities=[0.1, 0.3, 0.5, 0.7, 0.9], device=device ) - + # Save pruning results with open(output_dir / 'pruning_results.json', 'w') as f: json.dump(pruning_results, f, indent=2) - + # Generate visualizations print("\nGenerating visualizations...") visualize_results(pruning_results, output_dir) - + # Summary print("\n" + "=" * 60) print("Experiment Complete!") @@ -329,12 +329,12 @@ def main(): print(" - pruning_results.json: Pruning experiment results") print(" - pruning_performance.png: Performance comparison plot") print(" - comparison_grid.png: Comprehensive analysis grid") - + # Print key findings print("\nKey Findings:") baseline_acc = history['val_acc'][-1] print(f" - Baseline accuracy: {baseline_acc:.2f}%") - + for strategy in pruning_results: acc_50 = pruning_results[strategy][0.5]['accuracy'] acc_90 = pruning_results[strategy][0.9]['accuracy'] @@ -344,4 +344,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/03_pruning_strategies.py b/examples/03_pruning_strategies.py index f3849f26..53baae99 100644 --- a/examples/03_pruning_strategies.py +++ b/examples/03_pruning_strategies.py @@ -8,7 +8,7 @@ Usage: python pruning_strategies_demo.py - + No configuration needed - this script runs standalone demonstrations. This comprehensive demo showcases: @@ -35,12 +35,13 @@ - Key takeaways and insights """ -import torch -import torch.nn as nn -import numpy as np -from pathlib import Path import sys import time +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn # Add src to path sys.path.insert(0, str(Path(__file__).parent.parent / 'src')) @@ -48,10 +49,10 @@ # Direct imports from alignment.pruning.base import PruningConfig from alignment.pruning.strategies import ( - MagnitudePruning, GradientPruning, - RandomPruning, + MagnitudePruning, ParallelModePruning, + RandomPruning, TensorizedPruning, ) @@ -65,7 +66,7 @@ def __init__(self, input_size=784, hidden_size=256, num_classes=10): self.fc2 = nn.Linear(hidden_size, 128) self.relu2 = nn.ReLU() self.fc3 = nn.Linear(128, num_classes) - + def forward(self, x): x = x.view(x.size(0), -1) x = self.relu1(self.fc1(x)) @@ -77,65 +78,65 @@ def forward(self, x): def demo_basic_pruning(): """Demonstrate basic pruning with different modes.""" print("\n=== Basic Pruning Demo ===\n") - + # Create model model = SimpleNet(hidden_size=512) - + # Initialize weights for m in model.modules(): if isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight) - + # Test different pruning modes on first layer layer = model.fc1 sparsity = 0.5 - + print(f"Layer shape: {layer.weight.shape}") print(f"Target sparsity: {sparsity*100}%\n") - + # 1. Magnitude pruning - low mode (prune small weights) print("1. Magnitude Pruning - Low Mode (prune small weights):") config = PruningConfig(amount=sparsity, pruning_mode='low') pruner = MagnitudePruning(config) - + scores = pruner.compute_importance_scores(layer) mask = pruner.create_pruning_mask(scores) - + weights = layer.weight.data.flatten() kept_weights = weights[mask.flatten().bool()] pruned_weights = weights[~mask.flatten().bool()] - + print(f" Sparsity achieved: {(mask == 0).float().mean():.2%}") print(f" Avg magnitude of kept weights: {kept_weights.abs().mean():.4f}") print(f" Avg magnitude of pruned weights: {pruned_weights.abs().mean():.4f}") print(f" Max pruned weight magnitude: {pruned_weights.abs().max():.4f}") - + # 2. Magnitude pruning - high mode (prune large weights) print("\n2. Magnitude Pruning - High Mode (prune large weights):") config = PruningConfig(amount=sparsity, pruning_mode='high') pruner = MagnitudePruning(config) - + mask = pruner.create_pruning_mask(scores) - + kept_weights = weights[mask.flatten().bool()] pruned_weights = weights[~mask.flatten().bool()] - + print(f" Sparsity achieved: {(mask == 0).float().mean():.2%}") print(f" Avg magnitude of kept weights: {kept_weights.abs().mean():.4f}") print(f" Avg magnitude of pruned weights: {pruned_weights.abs().mean():.4f}") print(f" Min pruned weight magnitude: {pruned_weights.abs().min():.4f}") - + # 3. Random pruning print("\n3. Random Pruning:") config = PruningConfig(amount=sparsity) pruner = RandomPruning(config) - + scores = pruner.compute_importance_scores(layer) mask = pruner.create_pruning_mask(scores) - + kept_weights = weights[mask.flatten().bool()] pruned_weights = weights[~mask.flatten().bool()] - + print(f" Sparsity achieved: {(mask == 0).float().mean():.2%}") print(f" Avg magnitude of kept weights: {kept_weights.abs().mean():.4f}") print(f" Avg magnitude of pruned weights: {pruned_weights.abs().mean():.4f}") @@ -145,62 +146,62 @@ def demo_basic_pruning(): def demo_parallel_pruning(): """Demonstrate parallel pruning strategies.""" print("\n\n=== Parallel Pruning Demo ===\n") - + # Create model model = SimpleNet(hidden_size=256) - + # Initialize weights for m in model.modules(): if isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight) - + layer = model.fc1 sparsity = 0.5 - + # 1. Parallel mode pruning print("1. Parallel Mode Pruning (all modes simultaneously):") parallel_pruner = ParallelModePruning( modes=['low', 'high', 'random'], base_strategy='magnitude' ) - + start_time = time.time() result = parallel_pruner.prune_parallel(layer, amount=sparsity) parallel_time = time.time() - start_time - + print(f"\n Execution time: {parallel_time:.4f}s") - print(f"\n Sparsity by mode:") + print("\n Sparsity by mode:") for mode, sparse in result.sparsities.items(): print(f" {mode}: {sparse:.2%}") - + # Analyze overlaps - print(f"\n Mask overlaps:") + print("\n Mask overlaps:") low_high_overlap = (result.masks['low'] * result.masks['high']).sum().item() low_random_overlap = (result.masks['low'] * result.masks['random']).sum().item() high_random_overlap = (result.masks['high'] * result.masks['random']).sum().item() total = result.masks['low'].numel() - + print(f" low ∩ high: {low_high_overlap/total:.2%}") print(f" low ∩ random: {low_random_overlap/total:.2%}") print(f" high ∩ random: {high_random_overlap/total:.2%}") - + # 2. Sequential comparison print("\n2. Sequential execution (for comparison):") - + start_time = time.time() masks_sequential = {} - + for mode in ['low', 'high', 'random']: config = PruningConfig(amount=sparsity, pruning_mode=mode) pruner = MagnitudePruning(config) scores = pruner.compute_importance_scores(layer) masks_sequential[mode] = pruner.create_pruning_mask(scores) - + sequential_time = time.time() - start_time - + print(f"\n Execution time: {sequential_time:.4f}s") print(f" Speedup from parallel: {sequential_time/parallel_time:.2f}x") - + # Verify results match print(f"\n Results match: {all(torch.allclose(result.masks[mode], masks_sequential[mode]) for mode in ['low', 'high', 'random'])}") @@ -208,7 +209,7 @@ def demo_parallel_pruning(): def demo_tensorized_pruning(): """Demonstrate tensorized pruning for GPU efficiency.""" print("\n\n=== Tensorized Pruning Demo ===\n") - + # Create larger model for better timing comparison model = nn.Sequential( nn.Linear(1024, 2048), @@ -217,29 +218,29 @@ def demo_tensorized_pruning(): nn.ReLU(), nn.Linear(1024, 512), ) - + # Move to GPU if available device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = model.to(device) - + print(f"Device: {device}") print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}") - + # Initialize weights for m in model.modules(): if isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight) - + # 1. Tensorized pruning - compute multiple sparsity levels at once print("\n1. Tensorized Pruning (multiple sparsity levels):") - + tensorized_pruner = TensorizedPruning() - + # Test on first layer layer = model[0] modes = ['low', 'high', 'random'] amounts = [0.1, 0.3, 0.5, 0.7, 0.9] - + start_time = time.time() pruning_tensor = tensorized_pruner.compute_pruning_tensor( layer, @@ -247,29 +248,29 @@ def demo_tensorized_pruning(): amounts=amounts ) tensorized_time = time.time() - start_time - + print(f"\n Execution time: {tensorized_time:.4f}s") print(f" Pruning tensor shape: {pruning_tensor.shape}") print(f" (modes={len(modes)}, amounts={len(amounts)}, *weight_shape)") - + # Analyze patterns print("\n Analyzing pruning patterns...") analysis = tensorized_pruner.analyze_pruning_patterns(pruning_tensor) - - print(f"\n Sparsity progression (avg across weight dims):") + + print("\n Sparsity progression (avg across weight dims):") sparsity_prog = analysis['sparsity_progression'] for i, mode in enumerate(modes): print(f" {mode}: ", end="") for j, amount in enumerate(amounts): print(f"{sparsity_prog[i, j]:.2f} ", end="") print() - + # 2. Compare with sequential approach print("\n2. Sequential approach (for comparison):") - + start_time = time.time() sequential_masks = {} - + for mode in modes: sequential_masks[mode] = {} for amount in amounts: @@ -278,32 +279,32 @@ def demo_tensorized_pruning(): pruner = RandomPruning(config) else: pruner = MagnitudePruning(config) - + scores = pruner.compute_importance_scores(layer) mask = pruner.create_pruning_mask(scores) sequential_masks[mode][amount] = mask - + sequential_time = time.time() - start_time - + print(f"\n Execution time: {sequential_time:.4f}s") print(f" Speedup from tensorized: {sequential_time/tensorized_time:.2f}x") - + # 3. Memory efficiency analysis print("\n3. Memory efficiency:") - + # Tensorized approach stores everything in one tensor tensorized_memory = pruning_tensor.element_size() * pruning_tensor.nelement() - + # Sequential approach stores individual masks sequential_memory = 0 for mode_masks in sequential_masks.values(): for mask in mode_masks.values(): sequential_memory += mask.element_size() * mask.nelement() - + print(f" Tensorized memory: {tensorized_memory / (1024**2):.2f} MB") print(f" Sequential memory: {sequential_memory / (1024**2):.2f} MB") print(f" Memory efficiency: {sequential_memory/tensorized_memory:.2f}x") - + # 4. Show overlap analysis if 'mode_overlap' in analysis: print("\n4. Mode overlap analysis (low ∩ high):") @@ -320,57 +321,57 @@ def demo_tensorized_pruning(): def demo_gradient_based_pruning(): """Demonstrate gradient-based pruning.""" print("\n\n=== Gradient-Based Pruning Demo ===\n") - + # Create model and dummy data model = SimpleNet(input_size=784, hidden_size=256) criterion = nn.CrossEntropyLoss() - + # Create dummy batch batch_size = 32 x = torch.randn(batch_size, 784) y = torch.randint(0, 10, (batch_size,)) - + # Forward and backward pass to get gradients output = model(x) loss = criterion(output, y) loss.backward() - + layer = model.fc1 sparsity = 0.5 - + print(f"Layer shape: {layer.weight.shape}") print(f"Target sparsity: {sparsity*100}%") - + # 1. Gradient magnitude pruning - low mode print("\n1. Gradient Pruning - Low Mode (prune small gradients):") config = PruningConfig(amount=sparsity, pruning_mode='low') pruner = GradientPruning(config) - + scores = pruner.compute_importance_scores(layer) mask = pruner.create_pruning_mask(scores) - + grad_magnitudes = layer.weight.grad.abs().flatten() kept_grads = grad_magnitudes[mask.flatten().bool()] pruned_grads = grad_magnitudes[~mask.flatten().bool()] - + print(f" Sparsity achieved: {(mask == 0).float().mean():.2%}") print(f" Avg gradient magnitude of kept: {kept_grads.mean():.6f}") print(f" Avg gradient magnitude of pruned: {pruned_grads.mean():.6f}") - + # 2. Gradient magnitude pruning - high mode print("\n2. Gradient Pruning - High Mode (prune large gradients):") config = PruningConfig(amount=sparsity, pruning_mode='high') pruner = GradientPruning(config) - + mask = pruner.create_pruning_mask(scores) - + kept_grads = grad_magnitudes[mask.flatten().bool()] pruned_grads = grad_magnitudes[~mask.flatten().bool()] - + print(f" Sparsity achieved: {(mask == 0).float().mean():.2%}") print(f" Avg gradient magnitude of kept: {kept_grads.mean():.6f}") print(f" Avg gradient magnitude of pruned: {pruned_grads.mean():.6f}") - + # Compare with weight magnitude print("\n3. Gradient vs Weight magnitude correlation:") weight_mags = layer.weight.abs().flatten() @@ -383,29 +384,29 @@ def main(): print("=" * 60) print("Pruning Strategies Demo") print("=" * 60) - + # Set random seed torch.manual_seed(42) np.random.seed(42) - + try: # Run demos demo_basic_pruning() demo_parallel_pruning() demo_tensorized_pruning() demo_gradient_based_pruning() - + print("\n" + "=" * 60) print("Demo Complete!") print("=" * 60) - + print("\nKey takeaways:") print("1. Low mode pruning: Removes small magnitude weights (common approach)") print("2. High mode pruning: Removes large magnitude weights (adversarial)") print("3. Parallel pruning: Compute multiple modes simultaneously for efficiency") print("4. Tensorized pruning: GPU-optimized for multiple sparsity levels") print("5. Gradient pruning: Uses gradient information for importance") - + except Exception as e: print(f"\nError: {e}") import traceback @@ -413,4 +414,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/04_visualization_gallery.py b/examples/04_visualization_gallery.py index 6fb3dc2c..c1d732cd 100644 --- a/examples/04_visualization_gallery.py +++ b/examples/04_visualization_gallery.py @@ -6,7 +6,7 @@ Usage: python visualization_demo.py - + This demo showcases: - Enhanced accuracy vs sparsity plots with relative performance - Weight distribution comparison before/after pruning @@ -19,15 +19,17 @@ Results are saved to: results/enhanced_visualizations/ """ -import torch -import numpy as np from pathlib import Path + +import numpy as np +import torch + from alignment.analysis.visualization import PruningVisualizer def create_demo_results(): """Create comprehensive demo results for visualization.""" - + # Progressive dropout results dropout_results = { 'dropout_rates': [0.0, 0.2, 0.4, 0.6, 0.8, 0.9], @@ -53,7 +55,7 @@ def create_demo_results(): 'random': [46, 55, 62, 67, 71, 74, 76, 77, 78, 79] } } - + # Standard pruning results (sparsity-based) pruning_results = {} for strategy in ['magnitude', 'gradient', 'fisher', 'random']: @@ -68,12 +70,12 @@ def create_demo_results(): } accuracy = base_acc * (1 - sparsity * decay_factors[strategy]) accuracy += np.random.normal(0, 1.0) - + pruning_results[strategy][sparsity] = { 'accuracy': max(10, min(100, accuracy)), 'loss': 0.1 + sparsity * 0.8 * (1 + decay_factors[strategy]) } - + # Multi-seed results multi_seed_results = {} for strategy in ['magnitude', 'gradient', 'random']: @@ -88,36 +90,36 @@ def create_demo_results(): 'loss': base['loss'] + np.random.normal(0, 0.1) } multi_seed_results[strategy].append(seed_data) - + # Layer-wise sparsity patterns layer_sparsities = { 'magnitude': {'conv1': 0.3, 'conv2': 0.5, 'fc1': 0.7, 'fc2': 0.9}, 'gradient': {'conv1': 0.4, 'conv2': 0.5, 'fc1': 0.6, 'fc2': 0.8}, 'random': {'conv1': 0.5, 'conv2': 0.5, 'fc1': 0.5, 'fc2': 0.5} } - + model_accuracy = { 'magnitude': 85.3, 'gradient': 82.1, 'random': 68.5 } - + # Weight distributions (simulated) np.random.seed(42) weights_before = { 'conv1': torch.randn(64, 3, 3, 3), 'fc1': torch.randn(128, 512) } - + weights_after = {} for layer_name, weights in weights_before.items(): # Simulate pruning effects mask_magnitude = torch.abs(weights) > torch.quantile(torch.abs(weights), 0.5) weights_after[f"{layer_name}_magnitude"] = weights * mask_magnitude - + mask_random = torch.rand_like(weights) > 0.5 weights_after[f"{layer_name}_random"] = weights * mask_random - + # Multi-metric comparison data strategy_metrics = { 'magnitude_low': { @@ -149,7 +151,7 @@ def create_demo_results(): 'Memory': 0.90 } } - + # Efficiency curve data efficiency_data = {} for strategy in ['magnitude_low', 'magnitude_high', 'gradient_low', 'random']: @@ -163,9 +165,9 @@ def create_demo_results(): factor = efficiency_factors[strategy] accuracies = 100 * np.exp(-0.3 * (compression_ratios - 1) / factor) accuracies = np.clip(accuracies, 10, 100) - + efficiency_data[strategy] = list(zip(compression_ratios, accuracies)) - + return { 'dropout_results': dropout_results, 'pruning_results': pruning_results, @@ -184,87 +186,87 @@ def main(): print("=" * 60) print("Enhanced Pruning Visualization Demo") print("=" * 60) - + # Create output directory output_dir = Path('results/enhanced_visualizations') output_dir.mkdir(parents=True, exist_ok=True) - + # Create demo data print("\nGenerating demo data...") demo_data = create_demo_results() - + # Initialize visualizer visualizer = PruningVisualizer() - + # 1. Enhanced accuracy vs sparsity plot print("\n1. Creating enhanced accuracy vs sparsity plot...") - fig1 = visualizer.plot_accuracy_vs_sparsity_enhanced( + visualizer.plot_accuracy_vs_sparsity_enhanced( demo_data['dropout_results'], save_path=output_dir / 'enhanced_accuracy_vs_sparsity.png' ) - + # 2. Standard pruning performance print("2. Creating standard pruning performance plot...") - fig2 = visualizer.plot_pruning_performance( + visualizer.plot_pruning_performance( demo_data['pruning_results'], metrics=['accuracy', 'loss'], save_path=output_dir / 'pruning_performance.png', title='Pruning Strategy Performance Comparison' ) - + # 3. Weight distribution comparison print("3. Creating weight distribution comparison...") - fig3 = visualizer.plot_weight_distribution_comparison( + visualizer.plot_weight_distribution_comparison( demo_data['weights_before'], demo_data['weights_after'], strategies=['magnitude', 'random'], save_path=output_dir / 'weight_distributions.png' ) - + # 4. Multi-metric radar chart print("4. Creating multi-metric radar chart...") - fig4 = visualizer.plot_multi_metric_radar( + visualizer.plot_multi_metric_radar( demo_data['strategy_metrics'], save_path=output_dir / 'multi_metric_radar.png' ) - + # 5. Pruning efficiency curves print("5. Creating pruning efficiency curves...") - fig5 = visualizer.plot_pruning_efficiency_curve( + visualizer.plot_pruning_efficiency_curve( demo_data['efficiency_data'], save_path=output_dir / 'efficiency_curves.png' ) - + # 6. Comprehensive dashboard print("6. Creating comprehensive dashboard...") - fig6 = visualizer.plot_comprehensive_dashboard( + visualizer.plot_comprehensive_dashboard( demo_data['dropout_results'], save_path=output_dir / 'comprehensive_dashboard.png' ) - + # 7. Comparison grid print("7. Creating comparison grid...") - fig7 = visualizer.plot_pruning_comparison_grid( + visualizer.plot_pruning_comparison_grid( demo_data['pruning_results'], save_path=output_dir / 'comparison_grid.png' ) - + # 8. Multi-seed analysis print("8. Creating multi-seed analysis...") - fig8 = visualizer.plot_multi_seed_results( + visualizer.plot_multi_seed_results( demo_data['multi_seed_results'], metric='accuracy', save_path=output_dir / 'multi_seed_analysis.png' ) - + # 9. Layer-wise pruning visualization print("9. Creating layer-wise pruning visualization...") - fig9 = visualizer.plot_layer_wise_pruning( + visualizer.plot_layer_wise_pruning( demo_data['layer_sparsities'], demo_data['model_accuracy'], save_path=output_dir / 'layer_wise_pruning.png' ) - + # Summary print("\n" + "=" * 60) print("Enhanced Visualization Demo Complete!") @@ -280,7 +282,7 @@ def main(): print(" 7. comparison_grid.png - 6-panel strategy comparison") print(" 8. multi_seed_analysis.png - Statistical analysis across seeds") print(" 9. layer_wise_pruning.png - Layer-specific sparsity patterns") - + print("\nVisualization Features:") print(" - Consistent color scheme across all plots") print(" - High-resolution output (300 DPI)") @@ -290,4 +292,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/06_redundancy_aware_pruning.py b/examples/06_redundancy_aware_pruning.py index 5c02fcca..f74703a0 100644 --- a/examples/06_redundancy_aware_pruning.py +++ b/examples/06_redundancy_aware_pruning.py @@ -19,17 +19,17 @@ import torch import torch.nn as nn -from torchvision import datasets, transforms, models -import numpy as np +from torchvision import datasets, transforms + +from alignment.metrics import get_metric # Alignment framework imports from alignment.models import BaseModelWrapper from alignment.services import ( ActivationCaptureService, + MaskOperations, NodeScoringService, - MaskOperations ) -from alignment.metrics import get_metric def create_simple_cnn(): @@ -54,7 +54,7 @@ def evaluate_model(model, dataloader, device='cpu'): model.eval() correct = 0 total = 0 - + with torch.no_grad(): for inputs, targets in dataloader: inputs, targets = inputs.to(device), targets.to(device) @@ -62,7 +62,7 @@ def evaluate_model(model, dataloader, device='cpu'): _, predicted = torch.max(outputs, 1) total += targets.size(0) correct += (predicted == targets).sum().item() - + return 100 * correct / total @@ -71,58 +71,58 @@ def main(): print("=" * 80) print("Redundancy-Aware Pruning Demonstration") print("=" * 80) - + # Setup device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"\nUsing device: {device}") - + # Load MNIST print("\n1. Loading MNIST dataset...") transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) - + test_dataset = datasets.MNIST( root='./data', train=False, download=True, transform=transform ) - + test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=128, shuffle=False ) - + # Create and wrap model print("\n2. Creating model...") model = create_simple_cnn().to(device) - + # Simple training (or load pretrained) # For demo, we'll use random weights print(" (Using random initialization for demo)") - + wrapper = BaseModelWrapper( model, tracked_layers=['3', '6'], # Conv2 and Linear1 track_inputs=True, track_outputs=True ) - + print(f" Tracking layers: {wrapper.tracked_layers}") - + # Initialize services print("\n3. Initializing services...") capture_service = ActivationCaptureService(wrapper) - + # Initialize metrics print("\n4. Initializing metrics...") rq_metric = get_metric('rayleigh_quotient', relative=True, regularization=1e-6) redundancy_metric = get_metric('pairwise_redundancy_gaussian', num_pairs=10) synergy_metric = get_metric('synergy_gaussian_mmi', num_pairs=10) - + scoring_service = NodeScoringService( metrics={ 'rq': rq_metric, @@ -134,21 +134,21 @@ def main(): gamma_redundancy=0.4, # Redundancy weight (negative) delta_rq=0.3 # RQ weight ) - + # Capture activations on a subset print("\n5. Capturing activations...") batch_inputs, batch_targets = next(iter(test_loader)) batch_inputs = batch_inputs.to(device) batch_targets = batch_targets.to(device) - + activation_data = capture_service.capture( batch_inputs, layers=wrapper.tracked_layers, include_weights=True ) - + print(f" Captured data from {len(activation_data.layer_names)} layers") - + # Compute composite scores print("\n6. Computing composite scores...") layerwise_scores = scoring_service.compute_layerwise_scores( @@ -157,13 +157,13 @@ def main(): include_redundancy=True, include_synergy=True ) - + # Display results for first layer layer_name = wrapper.tracked_layers[0] if layer_name in layerwise_scores: scores = layerwise_scores[layer_name] print(f"\n Results for layer '{layer_name}':") - + if scores.rq is not None: print(f" - RQ: mean={scores.rq.mean():.4f}, std={scores.rq.std():.4f}") if scores.redundancy is not None: @@ -172,7 +172,7 @@ def main(): print(f" - Synergy: mean={scores.synergy.mean():.4f}, std={scores.synergy.std():.4f}") if scores.composite is not None: print(f" - Composite: mean={scores.composite.mean():.4f}, std={scores.composite.std():.4f}") - + # Demonstrate class-conditioned RQ print("\n7. Computing class-conditioned RQ (ΔRQ)...") layer_name = wrapper.tracked_layers[-1] # Linear layer @@ -183,73 +183,73 @@ def main(): targets=batch_targets, return_delta_rq=True ) - + print(f" Layer '{layer_name}':") print(f" - RQ (unconditional): {delta_rq_results['rq_uncond'].mean():.4f}") print(f" - RQ (class-cond): {delta_rq_results['rq_cond'].mean():.4f}") print(f" - ΔRQ: {delta_rq_results['delta_rq'].mean():.4f}") - print(f" ΔRQ measures task-relevant alignment (larger = more discriminative)") - + print(" ΔRQ measures task-relevant alignment (larger = more discriminative)") + # Create pruning masks print("\n8. Creating pruning masks...") pruning_amount = 0.3 # Prune 30% - + for layer_name, scores in layerwise_scores.items(): if scores.composite is None: continue - + # Create mask using composite scores mask = MaskOperations.create_structured_mask( scores.composite, amount=pruning_amount, mode='low' # Prune low-importance neurons ) - + stats = MaskOperations.get_mask_statistics(mask) print(f" Layer '{layer_name}': {stats['kept_elements']}/{stats['total_elements']} neurons kept") - + # Compare methods print("\n9. Comparing pruning methods...") print(" Method comparison (on same layer):") layer_name = wrapper.tracked_layers[-1] if layer_name in layerwise_scores: scores = layerwise_scores[layer_name] - + # Method 1: Random mask_random = MaskOperations.create_structured_mask( torch.rand_like(scores.composite), amount=pruning_amount, mode='random' ) - + # Method 2: RQ only mask_rq = MaskOperations.create_structured_mask( scores.rq, amount=pruning_amount, mode='low' ) - + # Method 3: Composite (redundancy-aware) mask_composite = MaskOperations.create_structured_mask( scores.composite, amount=pruning_amount, mode='low' ) - + # Check overlap overlap_rq_composite = (mask_rq & mask_composite).sum().item() / mask_rq.sum().item() overlap_random_composite = (mask_random & mask_composite).sum().item() / mask_random.sum().item() - + print(f" - Overlap (RQ vs Composite): {overlap_rq_composite:.2%}") print(f" - Overlap (Random vs Composite): {overlap_random_composite:.2%}") print(f" → Redundancy-awareness changes {(1-overlap_rq_composite)*100:.1f}% of pruning decisions") - + # Evaluate baseline print("\n10. Evaluating model...") baseline_acc = evaluate_model(model, test_loader, device) print(f" Baseline accuracy: {baseline_acc:.2f}%") print(" (Would apply masks and fine-tune for full experiment)") - + print("\n" + "=" * 80) print("Summary") print("=" * 80) @@ -257,7 +257,7 @@ def main(): Key Features Demonstrated: ✓ ActivationCaptureService - Clean API for activation capture ✓ PairwiseRedundancyGaussian - Identifies redundant neurons -✓ SynergyGaussianMMI - Identifies complementary neurons +✓ SynergyGaussianMMI - Identifies complementary neurons ✓ Class-conditioned RQ - Measures task-relevant alignment (ΔRQ) ✓ NodeScoringService - Composite scoring with configurable weights ✓ MaskOperations - Flexible mask creation and analysis diff --git a/examples/07_mnist_intelligent_pruning.py b/examples/07_mnist_intelligent_pruning.py index 586e5492..e19826cc 100644 --- a/examples/07_mnist_intelligent_pruning.py +++ b/examples/07_mnist_intelligent_pruning.py @@ -8,22 +8,23 @@ 4. Compare results """ +from pathlib import Path + import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms -import matplotlib.pyplot as plt -from pathlib import Path + +from alignment.metrics import get_metric # Alignment framework from alignment.models import BaseModelWrapper from alignment.services import ( ActivationCaptureService, + MaskOperations, NodeScoringService, - MaskOperations ) -from alignment.metrics import get_metric class SimpleMLP(nn.Module): @@ -35,7 +36,7 @@ def __init__(self): self.fc2 = nn.Linear(128, 64) self.relu2 = nn.ReLU() self.fc3 = nn.Linear(64, 10) - + def forward(self, x): x = x.view(x.size(0), -1) x = self.relu1(self.fc1(x)) @@ -49,30 +50,30 @@ def train_model(model, train_loader, epochs=5, device='cpu'): model.train() optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() - + print(f"Training for {epochs} epochs...") for epoch in range(epochs): total_loss = 0 correct = 0 total = 0 - + for batch_idx, (inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(device), targets.to(device) - + optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() - + total_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() - + if batch_idx % 100 == 0: print(f' Epoch {epoch+1}, Batch {batch_idx}: Loss={loss.item():.4f}') - + acc = 100. * correct / total print(f'Epoch {epoch+1}: Loss={total_loss/len(train_loader):.4f}, Acc={acc:.2f}%') @@ -82,7 +83,7 @@ def evaluate(model, test_loader, device='cpu'): model.eval() correct = 0 total = 0 - + with torch.no_grad(): for inputs, targets in test_loader: inputs, targets = inputs.to(device), targets.to(device) @@ -90,19 +91,19 @@ def evaluate(model, test_loader, device='cpu'): _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() - + return 100. * correct / total def prune_model(model, wrapper, pruning_method, pruning_amount, val_loader, device='cpu'): """Prune model using specified method.""" print(f"\nPruning with {pruning_method} (amount={pruning_amount:.1%})...") - + # Get a batch for metric computation inputs_batch, targets_batch = next(iter(val_loader)) inputs_batch = inputs_batch.to(device) targets_batch = targets_batch.to(device) - + # Capture activations capture_service = ActivationCaptureService(wrapper) data = capture_service.capture( @@ -110,30 +111,30 @@ def prune_model(model, wrapper, pruning_method, pruning_amount, val_loader, devi layers=wrapper.tracked_layers, include_weights=True ) - + # Compute scores based on method masks = {} - + for layer_name in wrapper.tracked_layers: if layer_name not in data.inputs or layer_name not in data.weights: continue - + inputs = data.inputs[layer_name] weights = data.weights[layer_name] - + if pruning_method == 'random': # Random scores scores = torch.rand(weights.shape[0]) - + elif pruning_method == 'magnitude': # L2 norm of weights scores = torch.norm(weights, p=2, dim=1) - + elif pruning_method == 'rq': # RQ only rq_metric = get_metric('rayleigh_quotient') scores = rq_metric.compute(inputs, weights) - + elif pruning_method == 'composite': # Redundancy-aware composite scoring_service = NodeScoringService( @@ -147,15 +148,15 @@ def prune_model(model, wrapper, pruning_method, pruning_amount, val_loader, devi gamma_redundancy=0.4, delta_rq=0.3 ) - + layer_scores = scoring_service.compute_composite_scores( inputs, weights, targets_batch ) scores = layer_scores.composite - + else: raise ValueError(f"Unknown method: {pruning_method}") - + # Create mask mask = MaskOperations.create_structured_mask( scores, @@ -163,13 +164,13 @@ def prune_model(model, wrapper, pruning_method, pruning_amount, val_loader, devi mode='low' ) masks[layer_name] = mask - + stats = MaskOperations.get_mask_statistics(mask) print(f" {layer_name}: {stats['kept_elements']}/{stats['total_elements']} kept") - + # Apply masks apply_masks_to_model(model, masks, wrapper.tracked_layers) - + return masks @@ -178,13 +179,13 @@ def apply_masks_to_model(model, masks, layer_names): for name, module in model.named_modules(): if name in masks and hasattr(module, 'weight'): mask = masks[name] - + # Expand mask to weight dimensions if isinstance(module, nn.Linear): # Zero out entire rows (neurons) weight_mask = mask.unsqueeze(1).expand_as(module.weight) module.weight.data *= weight_mask.float() - + if module.bias is not None: module.bias.data *= mask.float() @@ -194,55 +195,55 @@ def main(): print("=" * 80) print("MNIST Intelligent Pruning - Complete Workflow") print("=" * 80) - + device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"\nDevice: {device}") - + # Load MNIST print("\n1. Loading MNIST...") transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) - + train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform) test_dataset = datasets.MNIST('./data', train=False, transform=transform) - + train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False) - + # Validation loader (smaller for metric computation) val_loader = DataLoader(test_dataset, batch_size=512, shuffle=False) - + # Train baseline model print("\n2. Training baseline model...") model = SimpleMLP().to(device) train_model(model, train_loader, epochs=3, device=device) - + baseline_acc = evaluate(model, test_loader, device) print(f"\n✓ Baseline accuracy: {baseline_acc:.2f}%") - + # Wrap model - wrapper = BaseModelWrapper( + BaseModelWrapper( model, tracked_layers=['fc1', 'fc2'], # Track hidden layers track_inputs=True, track_outputs=True ) - + # Pruning experiments print("\n3. Pruning experiments...") print("-" * 80) - + pruning_amount = 0.5 # Prune 50% methods = ['random', 'magnitude', 'rq', 'composite'] results = {} - + for method in methods: # Create fresh copy of model model_copy = SimpleMLP().to(device) model_copy.load_state_dict(model.state_dict()) - + # Wrap wrapper_copy = BaseModelWrapper( model_copy, @@ -250,57 +251,57 @@ def main(): track_inputs=True, track_outputs=True ) - + # Prune - masks = prune_model( - model_copy, wrapper_copy, method, + prune_model( + model_copy, wrapper_copy, method, pruning_amount, val_loader, device ) - + # Evaluate immediately after pruning (no fine-tuning) acc_pruned = evaluate(model_copy, test_loader, device) - + # Fine-tune print(f"\n Fine-tuning {method}...") train_model(model_copy, train_loader, epochs=2, device=device) - + # Final evaluation acc_final = evaluate(model_copy, test_loader, device) - + results[method] = { 'acc_before': baseline_acc, 'acc_pruned': acc_pruned, 'acc_final': acc_final, 'drop': baseline_acc - acc_final } - + print(f" {method}: {baseline_acc:.2f}% → {acc_pruned:.2f}% → {acc_final:.2f}% (drop: {baseline_acc - acc_final:.2f}%)") - + # Summary print("\n" + "=" * 80) print("RESULTS SUMMARY") print("=" * 80) print(f"\nPruning amount: {pruning_amount:.0%} of neurons") print(f"Baseline accuracy: {baseline_acc:.2f}%\n") - + print("Method | After Pruning | After Fine-tune | Accuracy Drop") print("-" * 70) for method, res in results.items(): print(f"{method:15s} | {res['acc_pruned']:13.2f}% | {res['acc_final']:15.2f}% | {res['drop']:13.2f}%") - + # Find best method best_method = min(results.keys(), key=lambda m: results[m]['drop']) print(f"\n✓ Best method: {best_method} (smallest accuracy drop: {results[best_method]['drop']:.2f}%)") - + print("\nComposite pruning considers redundancy and synergy in addition to alignment.") - + # Save results output_dir = Path('results/mnist_intelligent_pruning') output_dir.mkdir(parents=True, exist_ok=True) - + torch.save(results, output_dir / 'pruning_results.pt') print(f"\n✓ Results saved to {output_dir}") - + return results diff --git a/examples/08_llama_ffn_pruning.py b/examples/08_llama_ffn_pruning.py index 67d9698e..91ed3055 100644 --- a/examples/08_llama_ffn_pruning.py +++ b/examples/08_llama_ffn_pruning.py @@ -12,59 +12,58 @@ up_proj: Linear(4096 → 11,008) ← 11,008 neurons we can analyze! gate_proj: Linear(4096 → 11,008) down_proj: Linear(11,008 → 4096) - + Forward: down_proj(SiLU(gate_proj(x)) * up_proj(x)) """ + import torch import torch.nn as nn from transformers import AutoModelForCausalLM, AutoTokenizer -from typing import Dict, Optional + +from alignment.metrics import get_metric # Alignment framework from alignment.models.transformer_enhanced import LLaMAWrapper from alignment.services import ( ActivationCaptureService, - NodeScoringService, - MaskOperations + MaskOperations, ) -from alignment.metrics import get_metric -from alignment.pruning.dependency_aware import DependencyAwarePruning def load_llama_model(model_name: str = 'gpt2', use_small_model: bool = True): """ Load LLaMA or similar model. - + Args: model_name: HuggingFace model name use_small_model: If True, use GPT-2 as proxy (faster for demo) - + Returns: model, tokenizer """ # For demo, use GPT-2 (similar architecture, much smaller) # Replace with 'meta-llama/Meta-Llama-3-8B' for actual LLaMA-3 - + if use_small_model: model_name = 'gpt2' # 12 layers, 768 hidden, much faster to load - print(f"Using GPT-2 as demo (same architecture principles as LLaMA)") - + print("Using GPT-2 as demo (same architecture principles as LLaMA)") + print(f"Loading {model_name}...") model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) - + # Set padding token if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token - + model.eval() - + print(f"✓ Loaded {model_name}") print(f" - Layers: {model.config.num_hidden_layers}") print(f" - Hidden size: {model.config.hidden_size}") print(f" - Attention heads: {model.config.num_attention_heads}") - + return model, tokenizer @@ -77,32 +76,32 @@ def analyze_ffn_layer( ): """ Analyze a single FFN layer - compute per-neuron scores. - + Args: model: LLaMA/GPT model wrapper: LLaMAWrapper instance layer_idx: Which transformer layer to analyze (0-indexed) input_text: List of input strings tokenizer: HuggingFace tokenizer - + Returns: Dict with per-neuron scores """ print(f"\n{'='*80}") print(f"Analyzing Layer {layer_idx} FFN") print(f"{'='*80}") - + # Tokenize inputs inputs = tokenizer(input_text, padding=True, truncation=True, return_tensors='pt') - + # Get FFN layer names (HF naming convention) # GPT-2: transformer.h.{i}.mlp.c_fc (up), c_proj (down) # LLaMA: model.layers.{i}.mlp.up_proj, down_proj, gate_proj - + # Try to infer FFN layer names ffn_up = None ffn_down = None - + for name, module in model.named_modules(): if f'.{layer_idx}.' in name or f'.h.{layer_idx}.' in name: if 'mlp' in name: @@ -111,51 +110,51 @@ def analyze_ffn_layer( ffn_up = name elif 'down_proj' in name or 'c_proj' in name: ffn_down = name - + if ffn_up is None: print(f" ⚠️ Could not find FFN layers for layer {layer_idx}") return {} - - print(f" FFN layers found:") + + print(" FFN layers found:") print(f" - Up projection: {ffn_up}") print(f" - Down projection: {ffn_down}") - + # Get module to check dimensions ffn_up_module = dict(model.named_modules())[ffn_up] num_neurons = ffn_up_module.out_features hidden_dim = ffn_up_module.in_features - - print(f" Dimensions:") + + print(" Dimensions:") print(f" - Hidden: {hidden_dim}") print(f" - FFN neurons: {num_neurons} ← Can analyze each one!") - + # Wrap model to track this layer wrapper_ffn = LLaMAWrapper(model, tracked_layers=[ffn_up]) - + # Capture activations - capture = ActivationCaptureService(wrapper_ffn) - + ActivationCaptureService(wrapper_ffn) + with torch.no_grad(): # Use model's forward to get activations - outputs = model(**inputs) - + model(**inputs) + # Get cached activations acts = wrapper_ffn._activation_cache - + # Get layer activations and weights ffn_input = acts.get(f'{ffn_up}_input') ffn_output = acts.get(f'{ffn_up}_output') ffn_weights = ffn_up_module.weight.detach() # [num_neurons, hidden_dim] - + if ffn_input is None or ffn_output is None: print(" ⚠️ Could not capture activations") return {} - - print(f"\n Activation shapes:") + + print("\n Activation shapes:") print(f" - Input: {ffn_input.shape}") # [B, T, hidden_dim] print(f" - Output: {ffn_output.shape}") # [B, T, num_neurons] print(f" - Weights: {ffn_weights.shape}") # [num_neurons, hidden_dim] - + # Preprocess for metrics (average over sequence) if ffn_input.ndim == 3: ffn_input_2d = ffn_input.mean(dim=1) # [B, hidden_dim] @@ -163,37 +162,37 @@ def analyze_ffn_layer( else: ffn_input_2d = ffn_input ffn_output_2d = ffn_output - - print(f"\n Computing per-neuron metrics...") - + + print("\n Computing per-neuron metrics...") + # Compute multiple metrics per neuron metrics_results = {} - + # 1. Rayleigh Quotient (alignment) - print(f" - RQ (alignment with input PCs)...") + print(" - RQ (alignment with input PCs)...") rq_metric = get_metric('rayleigh_quotient') rq_scores = rq_metric.compute(inputs=ffn_input_2d, weights=ffn_weights) metrics_results['rq'] = rq_scores print(f" Range: [{rq_scores.min():.4f}, {rq_scores.max():.4f}], Mean: {rq_scores.mean():.4f}") - + # 2. Redundancy (overlap with other neurons) - print(f" - Redundancy (overlap with other neurons)...") - redundancy_metric = get_metric('pairwise_redundancy_gaussian', + print(" - Redundancy (overlap with other neurons)...") + redundancy_metric = get_metric('pairwise_redundancy_gaussian', mode='output_based', # FAST! num_pairs=20) redundancy_scores = redundancy_metric.compute(outputs=ffn_output_2d) metrics_results['redundancy'] = redundancy_scores print(f" Range: [{redundancy_scores.min():.4f}, {redundancy_scores.max():.4f}], Mean: {redundancy_scores.mean():.4f}") - + # 3. Show per-neuron breakdown - print(f"\n Per-neuron analysis (first 10 neurons):") + print("\n Per-neuron analysis (first 10 neurons):") print(f" {'Neuron':<8} {'RQ':<10} {'Redundancy':<12} {'Assessment'}") print(f" {'-'*60}") - + for i in range(min(10, num_neurons)): rq_val = rq_scores[i].item() red_val = redundancy_scores[i].item() - + # Assess importance if red_val > 0.5: assessment = "Redundant (candidate for pruning)" @@ -201,18 +200,18 @@ def analyze_ffn_layer( assessment = "Important (high alignment)" else: assessment = "Low importance" - + print(f" {i:<8} {rq_val:<10.4f} {red_val:<12.4f} {assessment}") - + # Show statistics high_redundancy = (redundancy_scores > 0.5).sum().item() low_rq = (rq_scores < 0.005).sum().item() - - print(f"\n Summary:") + + print("\n Summary:") print(f" - High redundancy neurons (>0.5): {high_redundancy}/{num_neurons} ({100*high_redundancy/num_neurons:.1f}%)") print(f" - Low RQ neurons (<0.005): {low_rq}/{num_neurons} ({100*low_rq/num_neurons:.1f}%)") print(f" - Potential pruning candidates: {max(high_redundancy, low_rq)} neurons") - + return metrics_results @@ -224,7 +223,7 @@ def prune_ffn_neurons( ): """ Prune individual neurons in FFN layer with dependency awareness. - + Args: model: LLaMA model layer_idx: Layer index @@ -234,11 +233,11 @@ def prune_ffn_neurons( print(f"\n{'='*80}") print(f"Pruning Layer {layer_idx} FFN (amount={amount:.0%})") print(f"{'='*80}") - + # Find FFN layers ffn_up = None ffn_down = None - + for name, module in model.named_modules(): if f'.{layer_idx}.' in name and 'mlp' in name: if isinstance(module, nn.Linear): @@ -246,46 +245,46 @@ def prune_ffn_neurons( ffn_up = (name, module) elif 'down_proj' in name or 'c_proj' in name: ffn_down = (name, module) - + if ffn_up is None or ffn_down is None: print(" ⚠️ Could not find FFN layers") return - + up_name, up_module = ffn_up down_name, down_module = ffn_down - + # Create mask mask = MaskOperations.create_structured_mask(scores, amount=amount, mode='low') num_kept = mask.sum().item() num_total = len(mask) - + print(f"\n Mask: {num_kept}/{num_total} neurons kept ({100*num_kept/num_total:.1f}%)") - + # Apply to up_proj (output dimension) print(f"\n Applying to {up_name}:") print(f" - Before: {up_module.weight.shape}") - + with torch.no_grad(): # Mask output neurons (rows of up_proj) up_module.weight.data *= mask.unsqueeze(1).float() if up_module.bias is not None: up_module.bias.data *= mask.float() - + print(f" - Pruned: {(~mask).sum().item()} neurons zeroed out") - + # Apply to down_proj (input dimension) - DEPENDENCY! print(f"\n Applying to {down_name} (dependency propagation):") print(f" - Before: {down_module.weight.shape}") - + with torch.no_grad(): # Mask input neurons (columns of down_proj) down_module.weight.data *= mask.unsqueeze(0).float() - + print(f" - Pruned: {(~mask).sum().item()} inputs zeroed out (matches up_proj)") - + # Verify dependency handled print(f"\n ✓ Dependency handled: up_proj outputs ({num_kept}) = down_proj inputs ({num_kept})") - + return mask @@ -294,85 +293,85 @@ def main(): print("=" * 80) print("LLaMA FFN Per-Neuron Analysis & Pruning") print("=" * 80) - + # Setup device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"\nDevice: {device}") - + # Load model (using GPT-2 for demo, same principles apply to LLaMA-3) model, tokenizer = load_llama_model(use_small_model=True) model = model.to(device) - + # Wrap model wrapper = LLaMAWrapper( model, track_ffn=True, track_attention=True ) - + # Sample inputs input_texts = [ "The capital of France is", "Artificial intelligence is", "The meaning of life is" ] - + # Analyze first layer's FFN layer_idx = 0 scores_dict = analyze_ffn_layer( model, wrapper, layer_idx, input_texts, tokenizer ) - + if not scores_dict: print("\n⚠️ Could not analyze FFN layer") return - + # Create composite score print(f"\n{'='*80}") print("Creating Composite Importance Score") print(f"{'='*80}") - + # Combine RQ and redundancy rq = scores_dict['rq'] redundancy = scores_dict['redundancy'] - + # Composite: High RQ + Low Redundancy = Important # Score = RQ - 0.5*Redundancy composite = torch.log(rq + 1e-8) - 0.5 * redundancy - - print(f" Composite = log(RQ) - 0.5*Redundancy") + + print(" Composite = log(RQ) - 0.5*Redundancy") print(f" Range: [{composite.min():.4f}, {composite.max():.4f}]") - + # Identify most/least important neurons top_important = torch.argsort(composite, descending=True)[:5] top_redundant = torch.argsort(redundancy, descending=True)[:5] - + print(f"\n Most important neurons (high composite): {top_important.tolist()}") print(f" Most redundant neurons (high redundancy): {top_redundant.tolist()}") - + # Prune using composite scores print(f"\n{'='*80}") print("Pruning FFN Neurons (Dependency-Aware)") print(f"{'='*80}") - + pruning_amount = 0.3 # Prune 30% of FFN neurons - + mask = prune_ffn_neurons( model, layer_idx=layer_idx, scores=composite, amount=pruning_amount ) - + # Verify model still runs print(f"\n{'='*80}") print("Verifying Pruned Model") print(f"{'='*80}") - + with torch.no_grad(): # Tokenize test_input = tokenizer("After pruning, the model", return_tensors='pt').to(device) - + # Forward pass try: output = model(**test_input) @@ -380,7 +379,7 @@ def main(): print(f" ✓ Output shape: {output.logits.shape}") except Exception as e: print(f" ✗ Model failed after pruning: {e}") - + # Summary print(f"\n{'='*80}") print("Summary") diff --git a/examples/09_attention_neuron_vs_head_pruning.py b/examples/09_attention_neuron_vs_head_pruning.py index f178fdf2..a781e6dd 100644 --- a/examples/09_attention_neuron_vs_head_pruning.py +++ b/examples/09_attention_neuron_vs_head_pruning.py @@ -16,11 +16,10 @@ import torch import torch.nn as nn -from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers import AutoModelForCausalLM -from alignment.models.transformer_enhanced import TransformerWrapperEnhanced -from alignment.services import NodeScoringService, MaskOperations from alignment.metrics import get_metric +from alignment.services import MaskOperations def analyze_attention_structure(model): @@ -28,10 +27,10 @@ def analyze_attention_structure(model): print("=" * 80) print("Attention Layer Structure") print("=" * 80) - + # Get first attention layer attn_layer = model.transformer.h[0].attn if hasattr(model, 'transformer') else model.model.layers[0].self_attn - + # Find projection layers for name, module in attn_layer.named_modules(): if isinstance(module, nn.Linear): @@ -39,7 +38,7 @@ def analyze_attention_structure(model): print(f" Weight shape: {module.weight.shape}") print(f" Output neurons: {module.weight.shape[0]}") print(f" Input dimension: {module.weight.shape[1]}") - + if module.weight.shape[0] % 32 == 0: # Assuming 32 heads neurons_per_head = module.weight.shape[0] // 32 print(f" → Organized as: 32 heads × {neurons_per_head} dims/head") @@ -49,13 +48,13 @@ def analyze_attention_structure(model): def neuron_level_pruning_demo(model, layer_idx=0): """ Demonstrate NEURON-LEVEL pruning in attention. - + Treats Q/K/V/O projections as Linear layers and prunes individual neurons. """ print("\n" + "=" * 80) print("NEURON-LEVEL Attention Pruning") print("=" * 80) - + # Get attention layer try: attn = model.transformer.h[layer_idx].attn # GPT-2 style @@ -65,12 +64,9 @@ def neuron_level_pruning_demo(model, layer_idx=0): except: attn = model.model.layers[layer_idx].self_attn # LLaMA style q_proj = attn.q_proj - k_proj = attn.k_proj - v_proj = attn.v_proj - o_proj = attn.o_proj print(" Model type: LLaMA (separate Q/K/V/O projections)") separate_projections = True - + if not separate_projections: print(" Note: GPT-2 combines Q/K/V, will demonstrate on combined projection") target_proj = q_proj @@ -78,82 +74,82 @@ def neuron_level_pruning_demo(model, layer_idx=0): else: target_proj = q_proj proj_name = "Q projection" - + num_neurons = target_proj.weight.shape[0] hidden_dim = target_proj.weight.shape[1] - + print(f"\n{proj_name}:") print(f" Shape: {target_proj.weight.shape}") print(f" Total neurons: {num_neurons}") print(f" Input dimension: {hidden_dim}") - + if num_neurons % 32 == 0: print(f" Organized as: {num_neurons // 128} heads × 128 dims") - + # Create dummy inputs for scoring - print(f"\nComputing per-neuron importance...") + print("\nComputing per-neuron importance...") inputs = torch.randn(16, hidden_dim) # [B, hidden_dim] outputs = target_proj(inputs) # [B, num_neurons] - + # Compute per-neuron scores rq = get_metric('rayleigh_quotient') rq_scores = rq.compute(inputs, target_proj.weight) - + redundancy = get_metric('pairwise_redundancy_gaussian', mode='output_based', num_pairs=20) redundancy_scores = redundancy.compute(outputs=outputs) - + # Composite score composite = torch.log(rq_scores + 1e-8) - 0.4 * redundancy_scores - + # Show individual neuron analysis print(f"\n Individual neuron breakdown (first 10 of {num_neurons}):") print(f" {'Neuron':<8} {'RQ':<10} {'Redundancy':<12} {'Composite':<12}") print(f" {'-'*50}") - + for i in range(min(10, num_neurons)): print(f" {i:<8} {rq_scores[i]:.4f} {redundancy_scores[i]:.4f} {composite[i]:.4f}") - + # Prune neurons pruning_amount = 0.3 # 30% mask = MaskOperations.create_structured_mask(composite, amount=pruning_amount, mode='low') - + num_pruned = (~mask).sum().item() num_kept = mask.sum().item() - - print(f"\n Pruning plan:") + + print("\n Pruning plan:") print(f" Amount: {pruning_amount:.0%}") print(f" Neurons pruned: {num_pruned}") print(f" Neurons kept: {num_kept}") - + # Show which neurons are pruned pruned_indices = torch.where(~mask)[0] if len(pruned_indices) <= 20: print(f" Pruned neuron indices: {pruned_indices.tolist()}") else: print(f" Pruned neuron indices: {pruned_indices[:10].tolist()} ... (showing first 10)") - + # Apply pruning - print(f"\n Applying neuron-level mask...") + print("\n Applying neuron-level mask...") target_proj.weight.data *= mask.unsqueeze(1).float() if target_proj.bias is not None: target_proj.bias.data *= mask.float() - + print(f" ✓ {num_pruned} neurons zeroed out") - print(f" ✓ Can prune ANY subset of neurons (not constrained to heads)") - + print(" ✓ Can prune ANY subset of neurons (not constrained to heads)") + return mask, composite def head_level_pruning_demo(model, layer_idx=0): """ Demonstrate HEAD-LEVEL pruning in attention. - + Aggregates neurons into heads and prunes entire heads. """ print("\n" + "=" * 80) print("HEAD-LEVEL Attention Pruning") print("=" * 80) - + try: attn = model.model.layers[layer_idx].self_attn q_proj = attn.q_proj @@ -162,69 +158,69 @@ def head_level_pruning_demo(model, layer_idx=0): except: print(" Using GPT-2 (combined QKV, skipping head demo)") return None, None - - print(f"\n Attention configuration:") + + print("\n Attention configuration:") print(f" Number of heads: {num_heads}") print(f" Dimension per head: {head_dim}") print(f" Total dimensions: {num_heads * head_dim}") - + # Compute neuron-level scores first inputs = torch.randn(16, 4096) outputs = q_proj(inputs) - + redundancy = get_metric('pairwise_redundancy_gaussian', mode='output_based', num_pairs=20) neuron_scores = redundancy.compute(outputs=outputs) # [4096] - + # Aggregate to head-level print(f"\n Aggregating {neuron_scores.shape[0]} neurons into {num_heads} heads...") - + head_scores = [] for head_idx in range(num_heads): start_idx = head_idx * head_dim end_idx = start_idx + head_dim - + # Average neuron scores within this head head_score = neuron_scores[start_idx:end_idx].mean() head_scores.append(head_score) - + head_scores = torch.tensor(head_scores) # [32] - + # Show per-head scores - print(f"\n Per-head redundancy:") + print("\n Per-head redundancy:") print(f" {'Head':<6} {'Redundancy':<12} {'Assessment'}") print(f" {'-'*40}") - + for h in range(min(10, num_heads)): assessment = "Redundant (prune)" if head_scores[h] > 0.5 else "Unique (keep)" print(f" {h:<6} {head_scores[h]:.4f} {assessment}") - + # Prune heads pruning_amount = 0.25 # 25% = 8 heads head_mask = MaskOperations.create_structured_mask(head_scores, amount=pruning_amount, mode='low') - + num_heads_pruned = (~head_mask).sum().item() - - print(f"\n Pruning plan:") + + print("\n Pruning plan:") print(f" Amount: {pruning_amount:.0%} of heads") print(f" Heads pruned: {num_heads_pruned}/{num_heads}") print(f" Pruned head indices: {torch.where(~head_mask)[0].tolist()}") - + # Expand to neuron-level mask neuron_mask = head_mask.repeat_interleave(head_dim) # [4096] - - print(f"\n Expanding to neuron-level:") + + print("\n Expanding to neuron-level:") print(f" Neuron mask shape: {neuron_mask.shape}") print(f" Neurons pruned: {(~neuron_mask).sum().item()} (= {num_heads_pruned} heads × {head_dim} dims)") - + # Apply to Q/K/V/O (all get same mask for consistency) - print(f"\n Applying to all projections (Q/K/V/O)...") + print("\n Applying to all projections (Q/K/V/O)...") q_proj.weight.data *= neuron_mask.unsqueeze(1).float() attn.k_proj.weight.data *= neuron_mask.unsqueeze(1).float() attn.v_proj.weight.data *= neuron_mask.unsqueeze(1).float() attn.o_proj.weight.data *= neuron_mask.unsqueeze(0).float() # Input dim - + print(f" ✓ {num_heads_pruned} heads pruned consistently across Q/K/V/O") - + return head_mask, head_scores @@ -233,32 +229,32 @@ def compare_approaches(neuron_mask, head_mask, num_heads=32, head_dim=128): print("\n" + "=" * 80) print("Comparison: Neuron-Level vs Head-Level") print("=" * 80) - + # Convert head mask to neuron mask for comparison if head_mask is not None: head_neuron_mask = head_mask.repeat_interleave(head_dim) else: print(" Head-level demo skipped (GPT-2 model)") return - + # Statistics neuron_pruned = (~neuron_mask).sum().item() head_equivalent_pruned = (~head_neuron_mask).sum().item() - - print(f"\nNeuron-Level Pruning:") + + print("\nNeuron-Level Pruning:") print(f" Neurons pruned: {neuron_pruned}") - print(f" Flexibility: Can prune ANY neurons") - print(f" Granularity: Individual neurons") - - print(f"\nHead-Level Pruning:") + print(" Flexibility: Can prune ANY neurons") + print(" Granularity: Individual neurons") + + print("\nHead-Level Pruning:") print(f" Neurons pruned: {head_equivalent_pruned} (= {head_equivalent_pruned//head_dim} heads × {head_dim})") print(f" Flexibility: Must prune in multiples of {head_dim}") - print(f" Granularity: Entire heads") - - print(f"\nKey Differences:") - print(f" ✓ Neuron-level: More flexible (any %, any neurons)") - print(f" ✓ Head-level: Cleaner (maintains head structure)") - print(f" ✓ Both supported by your framework!") + print(" Granularity: Entire heads") + + print("\nKey Differences:") + print(" ✓ Neuron-level: More flexible (any %, any neurons)") + print(" ✓ Head-level: Cleaner (maintains head structure)") + print(" ✓ Both supported by your framework!") def main(): @@ -266,35 +262,35 @@ def main(): print("=" * 80) print("Attention Layer Pruning: Neuron vs Head Level") print("=" * 80) - + device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"\nDevice: {device}") - + # Load model (GPT-2 for demo, same principles for LLaMA) print("\nLoading model (GPT-2 for demo)...") model = AutoModelForCausalLM.from_pretrained('gpt2') model = model.to(device) model.eval() - - print(f"✓ Loaded GPT-2") + + print("✓ Loaded GPT-2") print(f" - Layers: {model.config.n_layer}") print(f" - Hidden size: {model.config.n_embd}") print(f" - Attention heads: {model.config.n_head}") - + # Analyze structure analyze_attention_structure(model) - + # Demonstrate neuron-level pruning neuron_mask, neuron_scores = neuron_level_pruning_demo(model, layer_idx=0) - + # Demonstrate head-level pruning # (Skip for GPT-2 since it has combined QKV, but would work for LLaMA) head_mask, head_scores = head_level_pruning_demo(model, layer_idx=0) - + # Compare if head_mask is not None: compare_approaches(neuron_mask, head_mask) - + # Summary print("\n" + "=" * 80) print("Summary: You Can Prune Attention at BOTH Levels!") diff --git a/examples/quick_demo.py b/examples/quick_demo.py index 462167e4..0459145c 100644 --- a/examples/quick_demo.py +++ b/examples/quick_demo.py @@ -6,13 +6,13 @@ Usage: python quick_demo.py - + No configuration needed - this script runs with default settings and creates its own model. Requirements: - PyTorch - alignment package installed - + Output: - Console output showing: * Model structure @@ -24,8 +24,9 @@ import torch import torch.nn as nn -from alignment.models import ModelWrapper + from alignment.metrics import get_metric +from alignment.models import ModelWrapper def main(): @@ -37,55 +38,55 @@ def main(): nn.ReLU(), nn.Linear(128, 10) ) - + print("Model created with 3 linear layers") - + # 2. Wrap the model to track activations wrapped_model = ModelWrapper(model) print(f"\nTracked layers: {wrapped_model.tracked_layers}") - + # 3. Generate some dummy data batch_size = 32 inputs = torch.randn(batch_size, 784) - + # 4. Forward pass with activation tracking outputs, activations = wrapped_model.forward_with_activations(inputs) print(f"\nCollected activations from {len(activations)} points") print(f"Activation keys: {list(activations.keys())}") - + # 5. Get layer weights weights = wrapped_model.get_layer_weights() print(f"Extracted weights from {len(weights)} layers") - + # 6. Compute alignment metrics print("\n" + "="*60) print("Computing Alignment Metrics") print("="*60) - + # Rayleigh Quotient RQMetric = get_metric('rayleigh_quotient') rq_metric = RQMetric() # Instantiate the metric - + for layer_name in wrapped_model.tracked_layers: # Get inputs and weights for this layer layer_inputs = activations[f"{layer_name}_input"] layer_weights = weights[layer_name] - + # Compute RQ scores scores = rq_metric.compute(inputs=layer_inputs, weights=layer_weights) - + print(f"\nLayer: {layer_name}") print(f" Input shape: {layer_inputs.shape}") print(f" Weight shape: {layer_weights.shape}") print(f" RQ scores: mean={scores.mean():.4f}, std={scores.std():.4f}") print(f" Min neuron score: {scores.min():.4f}") print(f" Max neuron score: {scores.max():.4f}") - + # 7. Try other metrics print("\n" + "="*60) print("Trying Other Metrics") print("="*60) - + # Weight cosine similarity WeightSimMetric = get_metric('weight_cosine_similarity') weight_sim = WeightSimMetric() # Instantiate @@ -93,35 +94,35 @@ def main(): layer_weights = weights[layer_name] sim_scores = weight_sim.compute(weights=layer_weights) print(f"\nWeight Cosine Similarity ({layer_name}): mean={sim_scores.mean():.4f}") - + # 8. Demonstrate pruning print("\n" + "="*60) print("Pruning Demo") print("="*60) - - from alignment.pruning import get_pruning_strategy, PruningConfig - + + from alignment.pruning import PruningConfig, get_pruning_strategy + # Use magnitude-based pruning config = PruningConfig(amount=0.5, pruning_mode='low') strategy = get_pruning_strategy('magnitude', config=config) - + # Apply pruning to first layer first_layer = model[0] # First linear layer mask = strategy.prune(first_layer) - - print(f"\nPruning 50% of weights in first layer") + + print("\nPruning 50% of weights in first layer") print(f"Sparsity achieved: {(mask == 0).float().mean():.2%}") - + # Apply mask first_layer.weight.data *= mask - + # Test forward pass after pruning pruned_outputs = model(inputs) print(f"\nOutput shape after pruning: {pruned_outputs.shape}") print("Pruning applied successfully!") - + print("\nDemo completed successfully!") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/run_experiment.py b/scripts/run_experiment.py index ccb6bc41..9a78ea71 100644 --- a/scripts/run_experiment.py +++ b/scripts/run_experiment.py @@ -14,19 +14,14 @@ """ import argparse +import json import logging import os import sys -import json -import yaml -from pathlib import Path from datetime import datetime -from typing import Dict, List, Any, Optional, Tuple +from pathlib import Path -import torch -import torch.nn as nn -import numpy as np -from tqdm import tqdm +import yaml # Add the src directory to Python path current_dir = os.path.dirname(os.path.abspath(__file__)) @@ -34,8 +29,10 @@ # Import from the alignment package from alignment.experiments.general_alignment import GeneralAlignmentExperiment +from alignment.pruning.experiments.cascading_layer import ( + CascadingLayerPruningExperiment, +) from alignment.pruning.experiments.layer_wise import LayerIsolatedPruningExperiment -from alignment.pruning.experiments.cascading_layer import CascadingLayerPruningExperiment logger = logging.getLogger(__name__) @@ -44,7 +41,7 @@ def load_config(config_path, overrides=None): """Load and merge configuration.""" with open(config_path, 'r') as f: config = yaml.safe_load(f) - + # Apply overrides if overrides: for key, value in overrides.items(): @@ -55,7 +52,7 @@ def load_config(config_path, overrides=None): current[k] = {} current = current[k] current[keys[-1]] = value - + return config @@ -63,11 +60,11 @@ def create_experiment_config(unified_config): """Convert unified config to experiment config object.""" from alignment.experiments.base import ExperimentConfig from alignment.experiments.general_alignment import GeneralAlignmentConfig - + # Extract model config and handle different naming conventions model_config = unified_config.get('model', {}) model_name = model_config.get('name', model_config.get('architecture', 'mlp')) - + # Extract dataset config dataset_config = unified_config.get('dataset', {}) if isinstance(dataset_config, str): @@ -76,7 +73,7 @@ def create_experiment_config(unified_config): dataset_config = {'name': dataset_name} else: dataset_name = dataset_config.get('name', dataset_config.get('dataset', 'mnist')) - + # Build base parameters common to all experiment types base_params = { 'name': unified_config.get('experiment_name', 'unified_experiment'), @@ -88,7 +85,7 @@ def create_experiment_config(unified_config): 'num_workers': dataset_config.get('num_workers', unified_config.get('data', {}).get('num_workers', 4)), 'metrics': unified_config.get('alignment', {}).get('metrics', unified_config.get('analysis', {}).get('metrics', ['rayleigh_quotient'])), } - + # Build model config with proper parameter names model_kwargs = {} if model_name == 'mlp': @@ -109,9 +106,9 @@ def create_experiment_config(unified_config): else: model_kwargs['input_dim'] = model_config.get('input_dim', 784) model_kwargs['output_dim'] = model_config.get('output_dim', 10) - + base_params['model_config'] = model_kwargs - + # Add training config if present training_config = unified_config.get('training', {}) if training_config: @@ -123,15 +120,15 @@ def create_experiment_config(unified_config): else: base_params['learning_rate'] = training_config.get('learning_rate', 0.001) base_params['optimizer'] = training_config.get('optimizer', 'adam') - + # Infer experiment type based on enabled blocks pruning_cfg = unified_config.get('pruning', {}) dropout_cfg = unified_config.get('dropout', {}) - + pruning_enabled = pruning_cfg.get('enabled', False) cascading_scope = pruning_cfg.get('scope', 'layer') == 'cascading' dropout_enabled = dropout_cfg.get('enabled', False) - + # Determine if we need specialized experiment types or GeneralAlignmentConfig if cascading_scope and pruning_enabled: # For cascading layer pruning, use base ExperimentConfig @@ -149,14 +146,14 @@ def create_experiment_config(unified_config): general_params['aggregate_metrics'] = unified_config.get('aggregate_metrics', True) general_params['save_individual_networks'] = unified_config.get('save_individual_networks', False) general_params['save_checkpoints'] = unified_config.get('save_checkpoints', False) - + config = GeneralAlignmentConfig(**general_params) - + # Set flags based on enabled blocks config.do_train = training_config.get('epochs', training_config.get('do_train', 0)) > 0 config.do_dropout_analysis = dropout_enabled config.do_pruning_experiments = pruning_enabled - + # Determine inferred type for logging if pruning_enabled: inferred_type = 'standard_pruning' @@ -164,12 +161,12 @@ def create_experiment_config(unified_config): inferred_type = 'progressive_dropout' else: inferred_type = 'alignment_analysis' - + # Handle plot generation visualization_config = unified_config.get('visualization', {}) output_config = unified_config.get('output', {}) analysis_config = unified_config.get('analysis', {}) - + # Default to True if not explicitly set to False generate_plots = True if 'generate_plots' in visualization_config: @@ -178,66 +175,66 @@ def create_experiment_config(unified_config): generate_plots = output_config['generate_plots'] elif 'generate_plots' in analysis_config: generate_plots = analysis_config['generate_plots'] - + config.generate_plots = generate_plots logger.info(f"Plot generation enabled: {config.generate_plots}") - + # Pruning specific configuration if pruning_enabled: # Get algorithms algorithms = pruning_cfg.get('algorithms', ['magnitude']) config.pruning_strategies = algorithms if isinstance(algorithms, list) else [algorithms] - + # Get sparsity levels sparsity_levels = pruning_cfg.get('sparsity_levels', [0.0, 0.1, 0.3, 0.5, 0.7, 0.9]) config.pruning_amounts = sparsity_levels - + # Get selection modes selection_modes = pruning_cfg.get('selection_modes', pruning_cfg.get('selection_mode', ['low'])) config.pruning_selection_mode = selection_modes if isinstance(selection_modes, list) else [selection_modes] - + # Fine-tuning settings config.fine_tune_after_pruning = pruning_cfg.get('fine_tune_after_pruning', True) config.fine_tune_epochs = pruning_cfg.get('fine_tune_epochs', 5) config.fine_tune_learning_rate = pruning_cfg.get('fine_tune_learning_rate', 0.0001) - + # Scope settings scope = pruning_cfg.get('scope', 'layer') config.pruning_scope = scope - + # Alignment metric settings config.pruning_alignment_metric = pruning_cfg.get('alignment_metric', 'rayleigh_quotient') config.pruning_hybrid_alpha = pruning_cfg.get('hybrid_alpha', 0.5) - + # Ultra-parallel evaluation settings config.use_ultra_parallel_eval = pruning_cfg.get('use_ultra_parallel_eval', False) config.eval_batches = pruning_cfg.get('eval_batches', None) - + logger.info(f"Pruning enabled: algorithms={config.pruning_strategies}, levels={config.pruning_amounts}, modes={config.pruning_selection_mode}") logger.info(f"Ultra-parallel eval: {config.use_ultra_parallel_eval}, eval_batches: {config.eval_batches}") - + # Dropout specific configuration if dropout_enabled: config.dropout_rates = dropout_cfg.get('rates', [0.0, 0.1, 0.3, 0.5, 0.7, 0.9]) logger.info(f"Dropout enabled: rates={config.dropout_rates}") - + # Common configuration for all experiment types if not isinstance(config, GeneralAlignmentConfig): # Ensure plot generation is enabled for other experiment types too visualization_config = unified_config.get('visualization', {}) output_config = unified_config.get('output', {}) config.generate_plots = visualization_config.get('generate_plots', output_config.get('generate_plots', True)) - + # Add additional attributes for compatibility config.training_config = training_config config.train_model = config.training_config.get('epochs', 0) > 0 config.alignment_metrics = config.metrics config.apply_pruning = pruning_enabled - + # Legacy support for pruning_analysis and network_compression blocks pruning_analysis = unified_config.get('pruning_analysis', {}) network_compression = unified_config.get('network_compression', {}) - + # Use the appropriate config based on what's enabled if pruning_analysis.get('enabled', False): active_pruning_config = pruning_analysis @@ -249,12 +246,12 @@ def create_experiment_config(unified_config): # Fallback to pruning config or empty active_pruning_config = pruning_cfg if pruning_enabled else {} config.pruning_strategy = pruning_cfg.get('algorithms', ['magnitude'])[0] if pruning_enabled else 'magnitude' - + config.pruning_config = active_pruning_config config.analysis_config = unified_config.get('visualization', unified_config.get('analysis', {})) config.eval_model = True config.cnn_mode = model_config.get('cnn_mode', 'unfold') - + # Set dropout rates (handle multiple sources) if hasattr(config, 'dropout_rates'): # Already set for GeneralAlignmentConfig @@ -263,7 +260,7 @@ def create_experiment_config(unified_config): config.dropout_rates = pruning_analysis.get('dropout_rates', [0.0, 0.1, 0.3, 0.5, 0.7, 0.9]) else: config.dropout_rates = dropout_cfg.get('rates', [0.0, 0.1, 0.3, 0.5, 0.7, 0.9]) - + # Handle selection modes for compatibility if pruning_analysis.get('enabled', False): selection_mode = pruning_analysis.get('selection_strategies', ['low']) @@ -271,19 +268,19 @@ def create_experiment_config(unified_config): selection_mode = [network_compression.get('selection_strategy', 'low')] else: selection_mode = pruning_cfg.get('selection_modes', ['low']) if pruning_enabled else ['low'] - + config.pruning_modes = selection_mode if isinstance(selection_mode, list) else [selection_mode] - + config.cascade_direction = unified_config.get('experiment_specific', {}).get('cascade_direction', 'forward') config.recompute_scores = True - + # Note: Output directories are now set in main() after creating timestamped folders config.plot_dpi = unified_config.get('visualization', {}).get('plot_dpi', unified_config.get('output', {}).get('plot_dpi', 300)) - + # Log the inferred experiment type logger.info(f"Inferred experiment type: {inferred_type} based on config blocks") config._inferred_experiment_type = inferred_type # Store for later use - + return config @@ -294,57 +291,57 @@ def main(): parser.add_argument('--device', type=str, help='Override device') parser.add_argument('--seed', type=int, help='Override seed') parser.add_argument('--output-dir', type=str, help='Override output directory') - + args, unknown = parser.parse_known_args() - + # Parse additional overrides overrides = {} if args.device: overrides['device'] = args.device if args.seed: overrides['seed'] = args.seed - + # Load config using the proper config loader from alignment.configs.config_loader import load_config as proper_load_config config = proper_load_config(args.config) - + # Apply overrides to the loaded config if overrides: for key, value in overrides.items(): if hasattr(config, key): setattr(config, key, value) - + # Create timestamped output directory timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") experiment_name = getattr(config, 'name', 'experiment') - + if args.output_dir: output_dir = Path(args.output_dir) else: # Create a unique directory with experiment name and timestamp output_dir = Path(f"results/{experiment_name}_{timestamp}") - + output_dir.mkdir(parents=True, exist_ok=True) - + # Save the configuration used config_save_path = output_dir / 'experiment_config.yaml' config.save(config_save_path) - + # Update config with timestamped directories config.checkpoint_dir = str(output_dir / 'checkpoints') config.log_dir = str(output_dir / 'logs') config.experiment_dir = str(output_dir) # Add experiment_dir for compatibility - + # Create plots directory in results folder (not in logs) plots_dir = output_dir / 'plots' config.plots_dir = str(plots_dir) # Add plots_dir for visualization - + # Ensure directories exist Path(config.checkpoint_dir).mkdir(parents=True, exist_ok=True) Path(config.log_dir).mkdir(parents=True, exist_ok=True) plots_dir.mkdir(parents=True, exist_ok=True) logger.info(f"Created plots directory: {plots_dir}") - + # Setup logging to both file and console log_file = output_dir / 'experiment.log' logging.basicConfig( @@ -355,10 +352,10 @@ def main(): logging.StreamHandler() ] ) - + # Print experiment info print(f"\n{'='*60}") - print(f"Running Alignment Experiment") + print("Running Alignment Experiment") print(f"{'='*60}") print(f"Configuration: {args.config}") print(f"Output directory: {output_dir}") @@ -366,13 +363,13 @@ def main(): print(f"Plot generation: {getattr(config, 'generate_plots', True)}") print(f"Plots directory: {plots_dir}") print(f"{'='*60}\n") - + # Use the inferred experiment type from config experiment_type = getattr(config, '_inferred_experiment_type', 'alignment_analysis') - + logger.info(f"Running {experiment_type} experiment") - - + + # Create experiment based on inferred type if experiment_type in ['standard_pruning', 'progressive_dropout', 'alignment_analysis']: experiment = GeneralAlignmentExperiment(config) @@ -382,14 +379,14 @@ def main(): experiment = CascadingLayerPruningExperiment(config) else: raise ValueError(f"Unknown experiment type: {experiment_type}") - - + + # Run experiment results = experiment.run() - + # Save results with timestamp results_file = output_dir / f'results_{timestamp}.json' - + # Convert numpy arrays to lists for JSON serialization def convert_to_serializable(obj): if hasattr(obj, 'tolist'): @@ -399,12 +396,12 @@ def convert_to_serializable(obj): elif isinstance(obj, list): return [convert_to_serializable(i) for i in obj] return obj - + serializable_results = convert_to_serializable(results) - + with open(results_file, 'w') as f: json.dump(serializable_results, f, indent=2) - + # Create experiment summary summary_file = output_dir / 'experiment_summary.txt' with open(summary_file, 'w') as f: @@ -414,19 +411,19 @@ def convert_to_serializable(obj): f.write(f"Experiment Type: {experiment_type} (inferred from config blocks)\n") f.write(f"Plot Generation: {getattr(config, 'generate_plots', True)}\n") f.write("=" * 50 + "\n\n") - + # Add results summary if 'test_results' in results: f.write("Final Model Performance:\n") f.write(f" - Accuracy: {results['test_results'].get('final_accuracy', 'N/A'):.2f}%\n") f.write(f" - Loss: {results['test_results'].get('final_loss', 'N/A'):.4f}\n\n") - + if 'pruning_results' in results and results['pruning_results']: f.write("Pruning Experiments:\n") strategies = results['pruning_results'].get('strategies', {}) f.write(f" - Strategies tested: {list(strategies.keys())}\n") f.write(f" - Plots saved in: {config.log_dir}/plots/\n") - + # List plots created plots_created = list(plots_dir.glob('*')) if plots_created: @@ -436,28 +433,28 @@ def convert_to_serializable(obj): f.write(f" - {plot_file.name}\n") else: f.write("\nNo plots were generated.\n") - + f.write("\nGenerated Files:\n") for file_path in sorted(output_dir.rglob('*')): if file_path.is_file(): relative_path = file_path.relative_to(output_dir) f.write(f" - {relative_path}\n") - + # Print completion message print(f"\n{'='*60}") print("Experiment Complete!") print(f"{'='*60}") - + if 'test_results' in results: print(f"Final model accuracy: {results['test_results'].get('final_accuracy', 'N/A'):.2f}%") print(f"Final model loss: {results['test_results'].get('final_loss', 'N/A'):.4f}") - + print(f"\nAll results saved in: {output_dir}") print(f" - Configuration: {config_save_path}") print(f" - Results: {results_file}") print(f" - Summary: {summary_file}") print(f" - Logs: {log_file}") - + # Check and report on plots plots_created = list(plots_dir.glob('*')) if plots_created: @@ -466,13 +463,13 @@ def convert_to_serializable(obj): if plot_file.is_file(): print(f" * {plot_file.name}") else: - print(f" - No plots generated (check generate_plots setting and experiment configuration)") + print(" - No plots generated (check generate_plots setting and experiment configuration)") print(f" * Pruning enabled: {getattr(config, 'do_pruning_experiments', False)}") print(f" * Plot generation: {getattr(config, 'generate_plots', False)}") print(f" * Pruning results in output: {'pruning_results' in results}") - + print(f"{'='*60}\n") if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/src/alignment/__init__.py b/src/alignment/__init__.py index 205b0035..dea0bd96 100644 --- a/src/alignment/__init__.py +++ b/src/alignment/__init__.py @@ -10,27 +10,27 @@ # Core functionality from .core.base import BaseMetric from .core.registry import METRIC_REGISTRY -from .models import ModelWrapper - -# Metrics -from .metrics import get_metric, list_metrics # Data processing from .data.processing import BatchMetricProcessor -# Pruning -from .pruning import PruningConfig, get_pruning_strategy - # Experiment tracking from .experiments.tracking import create_tracker +# Metrics +from .metrics import get_metric, list_metrics +from .models import ModelWrapper + +# Pruning +from .pruning import PruningConfig, get_pruning_strategy + # Services (NEW in v0.2.0) from .services import ( ActivationCaptureService, ActivationData, - NodeScoringService, CompositeScores, MaskOperations, + NodeScoringService, ) # Visualization @@ -43,33 +43,33 @@ __all__ = [ # Core "ModelWrapper", - "BaseMetric", + "BaseMetric", "METRIC_REGISTRY", - + # Metrics "get_metric", "list_metrics", - + # Data processing "BatchMetricProcessor", - + # Services (NEW in v0.2.0) "ActivationCaptureService", "ActivationData", "NodeScoringService", "CompositeScores", "MaskOperations", - + # Pruning "PruningConfig", "get_pruning_strategy", - + # Experiment tracking "create_tracker", - + # Visualization "AlignmentVisualizer", - + # Version "__version__" -] \ No newline at end of file +] diff --git a/src/alignment/analysis/__init__.py b/src/alignment/analysis/__init__.py index 5a3401e0..051e90fc 100644 --- a/src/alignment/analysis/__init__.py +++ b/src/alignment/analysis/__init__.py @@ -10,23 +10,13 @@ """ # Aggregation -from .aggregation import ( - ResultAggregator, - MetricAggregator, - LayerAggregator, -) +from .aggregation import LayerAggregator, MetricAggregator, ResultAggregator # Unified Reporting -from .unified_reporter import ( - UnifiedReporter, - generate_quick_report, -) +from .unified_reporter import UnifiedReporter, generate_quick_report # Unified Visualization -from .visualization.unified_visualizer import ( - UnifiedVisualizer, - plot_quick_summary, -) +from .visualization.unified_visualizer import UnifiedVisualizer, plot_quick_summary __all__ = [ # Aggregation @@ -39,4 +29,4 @@ # Visualization 'UnifiedVisualizer', 'plot_quick_summary', -] \ No newline at end of file +] diff --git a/src/alignment/analysis/aggregation/__init__.py b/src/alignment/analysis/aggregation/__init__.py index 083196f7..84e56b12 100644 --- a/src/alignment/analysis/aggregation/__init__.py +++ b/src/alignment/analysis/aggregation/__init__.py @@ -2,12 +2,12 @@ Aggregation utilities for experiment analysis. """ -from .results import ResultAggregator -from .metrics import MetricAggregator from .layers import LayerAggregator +from .metrics import MetricAggregator +from .results import ResultAggregator __all__ = [ 'ResultAggregator', 'MetricAggregator', 'LayerAggregator', -] \ No newline at end of file +] diff --git a/src/alignment/analysis/aggregation/layers.py b/src/alignment/analysis/aggregation/layers.py index a8f74623..321b8e40 100644 --- a/src/alignment/analysis/aggregation/layers.py +++ b/src/alignment/analysis/aggregation/layers.py @@ -2,10 +2,11 @@ Layer-wise metric aggregation for analyzing patterns across network layers. """ -from typing import Dict, List, Tuple +import logging from collections import defaultdict +from typing import Dict, List, Tuple + import numpy as np -import logging logger = logging.getLogger(__name__) @@ -14,15 +15,15 @@ class LayerAggregator: """ Aggregates metrics by layer to analyze layer-wise patterns. """ - + def __init__(self): """Initialize layer aggregator.""" self.layer_metrics = defaultdict(lambda: defaultdict(list)) - + def add_metrics(self, metrics: Dict[str, Dict[str, float]]): """ Add metrics from a single evaluation. - + Args: metrics: Dictionary of metrics by name and layer """ @@ -30,20 +31,20 @@ def add_metrics(self, metrics: Dict[str, Dict[str, float]]): if isinstance(layer_values, dict): for layer_name, value in layer_values.items(): self.layer_metrics[layer_name][metric_name].append(value) - + def get_layer_summary(self, layer_name: str) -> Dict[str, Dict[str, float]]: """ Get summary statistics for a specific layer. - + Args: layer_name: Name of the layer - + Returns: Dictionary mapping metric names to statistics """ if layer_name not in self.layer_metrics: return {} - + summary = {} for metric_name, values in self.layer_metrics[layer_name].items(): if values: @@ -55,9 +56,9 @@ def get_layer_summary(self, layer_name: str) -> Dict[str, Dict[str, float]]: 'max': float(np.max(values_array)), 'count': len(values) } - + return summary - + def rank_layers( self, metric_name: str, @@ -66,21 +67,21 @@ def rank_layers( ) -> List[Tuple[str, float]]: """ Rank layers by a specific metric. - + Args: metric_name: Metric to rank by criterion: Statistic to use ('mean', 'max', 'min', 'std') ascending: Whether to sort in ascending order - + Returns: List of (layer_name, value) tuples """ layer_values = [] - + for layer_name, metrics in self.layer_metrics.items(): if metric_name in metrics and metrics[metric_name]: values_array = np.array(metrics[metric_name]) - + if criterion == 'mean': value = np.mean(values_array) elif criterion == 'max': @@ -91,14 +92,14 @@ def rank_layers( value = np.std(values_array) else: raise ValueError(f"Unknown criterion: {criterion}") - + layer_values.append((layer_name, float(value))) - + # Sort by value layer_values.sort(key=lambda x: x[1], reverse=not ascending) - + return layer_values - + def find_anomalous_layers( self, metric_name: str, @@ -106,35 +107,35 @@ def find_anomalous_layers( ) -> List[str]: """ Find layers with anomalous metric values. - + Args: metric_name: Metric to analyze threshold_std: Number of standard deviations for anomaly detection - + Returns: List of anomalous layer names """ # Collect all values all_values = [] layer_means = {} - + for layer_name, metrics in self.layer_metrics.items(): if metric_name in metrics and metrics[metric_name]: mean_value = np.mean(metrics[metric_name]) layer_means[layer_name] = mean_value all_values.append(mean_value) - + if len(all_values) < 3: return [] - + # Compute global statistics global_mean = np.mean(all_values) global_std = np.std(all_values) - + # Find anomalous layers anomalous = [] for layer_name, mean_value in layer_means.items(): if abs(mean_value - global_mean) > threshold_std * global_std: anomalous.append(layer_name) - - return anomalous \ No newline at end of file + + return anomalous diff --git a/src/alignment/analysis/aggregation/metrics.py b/src/alignment/analysis/aggregation/metrics.py index ac9b2941..a3d8c571 100644 --- a/src/alignment/analysis/aggregation/metrics.py +++ b/src/alignment/analysis/aggregation/metrics.py @@ -2,10 +2,11 @@ Metric aggregation utilities for tracking metrics over time. """ -from typing import Dict, List, Tuple, Any +import logging from collections import defaultdict +from typing import Any, Dict, List, Tuple + import numpy as np -import logging logger = logging.getLogger(__name__) @@ -13,32 +14,32 @@ class MetricAggregator: """ Aggregates metrics across time steps or iterations. - + Useful for analyzing metric evolution during training or experiments. """ - + def __init__(self): """Initialize metric aggregator.""" self.metrics_over_time = defaultdict(lambda: defaultdict(list)) self.steps = [] - + def add_step(self, step: int, metrics: Dict[str, Dict[str, float]]): """ Add metrics from a single step. - + Args: step: Step/iteration number metrics: Dictionary of metrics by name and layer """ self.steps.append(step) - + for metric_name, layer_values in metrics.items(): if isinstance(layer_values, dict): for layer_name, value in layer_values.items(): self.metrics_over_time[metric_name][layer_name].append(value) else: self.metrics_over_time[metric_name]['value'].append(layer_values) - + def get_metric_evolution( self, metric_name: str, @@ -46,23 +47,23 @@ def get_metric_evolution( ) -> Tuple[List[int], List[float]]: """ Get the evolution of a metric over time. - + Args: metric_name: Name of the metric layer_name: Name of the layer - + Returns: Tuple of (steps, values) """ if metric_name not in self.metrics_over_time: return [], [] - + if layer_name not in self.metrics_over_time[metric_name]: return [], [] - + values = self.metrics_over_time[metric_name][layer_name] return self.steps[:len(values)], values - + def compute_trends( self, metric_name: str, @@ -71,41 +72,41 @@ def compute_trends( ) -> Dict[str, Any]: """ Compute trend statistics for a metric. - + Args: metric_name: Name of the metric layer_name: Name of the layer window_size: Window size for moving average - + Returns: Dictionary with trend statistics """ steps, values = self.get_metric_evolution(metric_name, layer_name) - + if len(values) < 2: return {} - + values_array = np.array(values) - + # Compute moving average if len(values) >= window_size: - moving_avg = np.convolve(values_array, - np.ones(window_size) / window_size, + moving_avg = np.convolve(values_array, + np.ones(window_size) / window_size, mode='valid') else: moving_avg = values_array - + # Compute linear trend coeffs = np.polyfit(range(len(values)), values_array, 1) slope = coeffs[0] - + # Find change points if len(values) > 2: diffs = np.diff(values_array) change_points = np.where(np.abs(diffs) > 2 * np.std(diffs))[0] else: change_points = [] - + return { 'initial_value': float(values[0]), 'final_value': float(values[-1]), @@ -115,4 +116,4 @@ def compute_trends( 'percent_change': float((values[-1] - values[0]) / (values[0] + 1e-8) * 100), 'moving_average': moving_avg.tolist() if len(moving_avg) > 0 else [], 'change_points': change_points.tolist() - } \ No newline at end of file + } diff --git a/src/alignment/analysis/aggregation/results.py b/src/alignment/analysis/aggregation/results.py index 1b6c8d25..e282643d 100644 --- a/src/alignment/analysis/aggregation/results.py +++ b/src/alignment/analysis/aggregation/results.py @@ -2,12 +2,13 @@ Result aggregation utilities for analyzing experiment outputs. """ -from typing import Dict, List, Optional, Any, Union, Tuple +import json +import logging from pathlib import Path +from typing import Any, Dict, List, Optional, Union + import numpy as np import pandas as pd -import json -import logging logger = logging.getLogger(__name__) @@ -15,19 +16,19 @@ class ResultAggregator: """ Aggregates results from multiple experiments or runs. - + This class provides utilities for: - Loading results from multiple sources - Computing statistics across runs - Extracting specific metrics - Comparing experiments """ - + def __init__(self): """Initialize result aggregator.""" self.results = {} self.metadata = {} - + def add_results( self, name: str, @@ -36,7 +37,7 @@ def add_results( ): """ Add results from an experiment. - + Args: name: Experiment name/identifier results: Experiment results dictionary @@ -46,11 +47,11 @@ def add_results( if metadata: self.metadata[name] = metadata logger.info(f"Added results for experiment: {name}") - + def load_from_file(self, path: Union[str, Path], name: Optional[str] = None): """ Load results from a JSON file. - + Args: path: Path to results file name: Name to use (defaults to filename) @@ -58,16 +59,16 @@ def load_from_file(self, path: Union[str, Path], name: Optional[str] = None): path = Path(path) if not name: name = path.stem - + with open(path, 'r') as f: results = json.load(f) - + self.add_results(name, results) - + def load_from_directory(self, directory: Union[str, Path], pattern: str = "*_results.json"): """ Load all matching result files from a directory. - + Args: directory: Directory containing result files pattern: Glob pattern for result files @@ -75,9 +76,9 @@ def load_from_directory(self, directory: Union[str, Path], pattern: str = "*_res directory = Path(directory) for result_file in directory.glob(pattern): self.load_from_file(result_file) - + logger.info(f"Loaded {len(self.results)} result files from {directory}") - + def get_metric_values( self, metric_name: str, @@ -86,35 +87,35 @@ def get_metric_values( ) -> Dict[str, Union[float, Dict[str, float]]]: """ Extract specific metric values across experiments. - + Args: metric_name: Name of the metric layer_name: Specific layer (None for all layers) experiment_names: Experiments to include (None for all) - + Returns: Dictionary mapping experiment names to metric values """ if experiment_names is None: experiment_names = list(self.results.keys()) - + metric_values = {} - + for exp_name in experiment_names: if exp_name not in self.results: continue - + exp_results = self.results[exp_name] - + # Navigate to metrics if 'metrics' in exp_results: metrics = exp_results['metrics'] - + # Get final metrics (last step) if metrics: last_step = max(int(k) for k in metrics.keys()) step_metrics = metrics[str(last_step)] - + if metric_name in step_metrics: if layer_name: value = step_metrics[metric_name].get(layer_name) @@ -122,9 +123,9 @@ def get_metric_values( metric_values[exp_name] = value else: metric_values[exp_name] = step_metrics[metric_name] - + return metric_values - + def compute_statistics( self, metric_name: str, @@ -133,31 +134,31 @@ def compute_statistics( ) -> Dict[str, float]: """ Compute statistics for a metric across experiments. - + Args: metric_name: Name of the metric layer_name: Layer to analyze experiment_pattern: Pattern to filter experiments - + Returns: Dictionary with statistics (mean, std, min, max, etc.) """ # Filter experiments if experiment_pattern: - exp_names = [name for name in self.results.keys() + exp_names = [name for name in self.results.keys() if experiment_pattern in name] else: exp_names = list(self.results.keys()) - + # Get metric values values_dict = self.get_metric_values(metric_name, layer_name, exp_names) values = list(values_dict.values()) - + if not values: return {} - + values_array = np.array(values) - + return { 'mean': float(np.mean(values_array)), 'std': float(np.std(values_array)), @@ -168,7 +169,7 @@ def compute_statistics( 'q3': float(np.percentile(values_array, 75)), 'count': len(values) } - + def to_dataframe( self, metrics: Optional[List[str]] = None, @@ -176,43 +177,43 @@ def to_dataframe( ) -> pd.DataFrame: """ Convert results to a pandas DataFrame. - + Args: metrics: Metrics to include (None for all) layers: Layers to include (None for all) - + Returns: DataFrame with experiments as rows and metric/layer combinations as columns """ data = [] - + for exp_name, results in self.results.items(): row = {'experiment': exp_name} - + # Add metadata if exp_name in self.metadata: row.update(self.metadata[exp_name]) - + # Add metrics if 'metrics' in results and results['metrics']: # Get final metrics last_step = max(int(k) for k in results['metrics'].keys()) step_metrics = results['metrics'][str(last_step)] - + for metric_name, layer_values in step_metrics.items(): if metrics and metric_name not in metrics: continue - + if isinstance(layer_values, dict): for layer_name, value in layer_values.items(): if layers and layer_name not in layers: continue - + col_name = f"{metric_name}_{layer_name}" row[col_name] = value else: row[metric_name] = layer_values - + data.append(row) - - return pd.DataFrame(data) \ No newline at end of file + + return pd.DataFrame(data) diff --git a/src/alignment/analysis/dynamic_scoring.py b/src/alignment/analysis/dynamic_scoring.py index 456664ac..9c79f091 100644 --- a/src/alignment/analysis/dynamic_scoring.py +++ b/src/alignment/analysis/dynamic_scoring.py @@ -5,10 +5,10 @@ with loss changes to identify truly important neurons. """ -import torch -import numpy as np -from typing import Dict, List, Optional, Tuple import logging +from typing import Dict, List, Optional + +import torch logger = logging.getLogger(__name__) @@ -16,13 +16,13 @@ class DynamicScoreAggregator: """ Aggregate scores from training history with loss correlation. - + Combines: - Final scores (current state) - Score evolution (trend over training) - Loss correlation (impact on optimization) - Stability (consistency) - + Example: >>> aggregator = DynamicScoreAggregator() >>> dynamic_scores = aggregator.aggregate( @@ -30,7 +30,7 @@ class DynamicScoreAggregator: ... loss_history=training_losses ... ) """ - + def __init__( self, weight_final: float = 0.4, @@ -40,7 +40,7 @@ def __init__( ): """ Initialize dynamic score aggregator. - + Args: weight_final: Weight for final score value weight_trend: Weight for trend (increase/decrease) @@ -51,14 +51,14 @@ def __init__( self.weight_trend = weight_trend self.weight_loss_corr = weight_loss_corr self.weight_stability = weight_stability - + # Normalize weights total = sum([weight_final, weight_trend, weight_loss_corr, weight_stability]) self.weight_final /= total self.weight_trend /= total self.weight_loss_corr /= total self.weight_stability /= total - + def aggregate( self, score_history: Dict[str, Dict[str, List[float]]], @@ -68,40 +68,40 @@ def aggregate( ) -> torch.Tensor: """ Aggregate scores for a layer using training dynamics. - + Args: score_history: From AlignmentMetricsCallback.get_history() loss_history: Training loss at each step layer_name: Layer to process metric_name: Which metric to aggregate - + Returns: Dynamic scores per neuron [num_neurons] """ if layer_name not in score_history['history']: raise ValueError(f"No history for layer {layer_name}") - + if metric_name not in score_history['history'][layer_name]: raise ValueError(f"No {metric_name} history for {layer_name}") - + # Get score evolution scores_over_time = score_history['history'][layer_name][metric_name] # This is list of scalar means - need per-neuron history # For now, work with what we have - + # If we have per-neuron history (not yet implemented in callback): - # scores_over_time = [step1_scores, step2_scores, ...] + # scores_over_time = [step1_scores, step2_scores, ...] # where each is [num_neurons] - + # For now, provide framework for when per-neuron tracking is added logger.warning( "Current callback tracks scalar means. " "For per-neuron dynamic scoring, need to track full tensors." ) - + # Return placeholder return torch.tensor(scores_over_time[-1]) # Final value - + def compute_loss_correlation( self, score_evolution: torch.Tensor, # [num_steps, num_neurons] @@ -109,100 +109,100 @@ def compute_loss_correlation( ) -> torch.Tensor: """ Compute correlation between each neuron's score and training loss. - + High positive correlation: Neuron's importance grew as loss decreased → Neuron is important for learning - + Negative/low correlation: Neuron's importance didn't track loss → Neuron might be less critical - + Args: score_evolution: Score over time per neuron loss_evolution: Loss over time - + Returns: Correlation per neuron [num_neurons] """ num_steps, num_neurons = score_evolution.shape - + # Convert loss to tensor loss_tensor = torch.tensor(loss_evolution, dtype=torch.float32) - + # Compute correlation for each neuron correlations = torch.zeros(num_neurons) - + for neuron_idx in range(num_neurons): neuron_scores = score_evolution[:, neuron_idx] - + # Pearson correlation corr = torch.corrcoef(torch.stack([neuron_scores, loss_tensor]))[0, 1] - + correlations[neuron_idx] = corr.abs() # Use absolute value - + return correlations - + def compute_trend( self, score_evolution: torch.Tensor # [num_steps, num_neurons] ) -> torch.Tensor: """ Compute trend (increasing/decreasing) for each neuron. - + Positive trend: Importance increased → likely important Negative trend: Importance decreased → less critical - + Returns: Trend per neuron [num_neurons] """ # Simple: final - initial - trend = score_evolution[-1] - score_evolution[0] - + score_evolution[-1] - score_evolution[0] + # More sophisticated: linear regression slope num_steps, num_neurons = score_evolution.shape time_steps = torch.arange(num_steps, dtype=torch.float32) - + trends_fitted = torch.zeros(num_neurons) - + for neuron_idx in range(num_neurons): y = score_evolution[:, neuron_idx] - + # Fit line: y = a + b*t # b = cov(t, y) / var(t) mean_t = time_steps.mean() mean_y = y.mean() - + cov_ty = ((time_steps - mean_t) * (y - mean_y)).sum() var_t = ((time_steps - mean_t) ** 2).sum() - + slope = cov_ty / (var_t + 1e-8) trends_fitted[neuron_idx] = slope - + return trends_fitted - + def compute_stability( self, score_evolution: torch.Tensor # [num_steps, num_neurons] ) -> torch.Tensor: """ Compute stability (inverse variance) for each neuron. - + Low variance: Consistently important → reliable signal High variance: Fluctuating → less reliable - + Returns: Stability per neuron [num_neurons] """ # Variance over time variance = score_evolution.var(dim=0) # [num_neurons] - + # Stability = inverse variance stability = 1.0 / (variance + 1e-4) - + # Normalize to [0, 1] stability = (stability - stability.min()) / (stability.max() - stability.min() + 1e-8) - + return stability - + def aggregate_full( self, score_evolution: torch.Tensor, # [num_steps, num_neurons] @@ -210,11 +210,11 @@ def aggregate_full( ) -> torch.Tensor: """ Full aggregation using all components. - + Args: score_evolution: Score history per neuron loss_evolution: Loss history - + Returns: Aggregated dynamic scores [num_neurons] """ @@ -223,16 +223,16 @@ def aggregate_full( trends = self.compute_trend(score_evolution) loss_corr = self.compute_loss_correlation(score_evolution, loss_evolution) stability = self.compute_stability(score_evolution) - + # Normalize each component to [0, 1] def normalize(x): return (x - x.min()) / (x.max() - x.min() + 1e-8) - + final_norm = normalize(final_scores) trend_norm = normalize(trends + trends.abs().max()) # Make positive loss_corr_norm = normalize(loss_corr) stability_norm = stability # Already normalized - + # Weighted combination dynamic_scores = ( self.weight_final * final_norm + @@ -240,39 +240,39 @@ def normalize(x): self.weight_loss_corr * loss_corr_norm + self.weight_stability * stability_norm ) - + return dynamic_scores class TrainingAwareScoring: """ Enhanced scoring using full training history. - + Requires per-neuron tracking during training (not just scalar means). """ - + @staticmethod def enhance_callback_for_per_neuron_tracking(): """ Instructions for enhancing callback to track per-neuron evolution. - + Current callback tracks: scalar mean per step Enhanced version should track: full tensor per step (memory intensive!) - + Modification needed in AlignmentMetricsCallback: - + ```python # Instead of: score_value = scores.mean().item() self.history[layer][metric].append(score_value) - + # Do: if self.track_per_neuron: self.history[layer][metric].append(scores.cpu()) # Full tensor else: self.history[layer][metric].append(scores.mean().item()) ``` - + Then dynamic scoring becomes very powerful! """ pass @@ -287,18 +287,18 @@ def compute_dynamic_importance( ) -> torch.Tensor: """ Convenience function for dynamic importance computation. - + Args: score_history: Training history from callback loss_history: Loss values during training layer_name: Layer to process metric_name: Metric to use aggregation_weights: Optional custom weights - + Returns: Dynamic importance scores """ aggregator = DynamicScoreAggregator(**(aggregation_weights or {})) - + return aggregator.aggregate(score_history, loss_history, layer_name, metric_name) diff --git a/src/alignment/analysis/unified_reporter.py b/src/alignment/analysis/unified_reporter.py index f589e224..3a6e0769 100644 --- a/src/alignment/analysis/unified_reporter.py +++ b/src/alignment/analysis/unified_reporter.py @@ -5,12 +5,13 @@ providing a single interface for generating reports in various formats. """ -from typing import Dict, List, Optional, Any, Union -from pathlib import Path -import pandas as pd import json import logging from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import pandas as pd logger = logging.getLogger(__name__) @@ -22,14 +23,14 @@ class UnifiedReporter: - Markdown - JSON - LaTeX (optional) - + This consolidates functionality from HTMLReporter, MarkdownReporter, and JSONReporter. """ - + def __init__(self, title: str = "Alignment Analysis Report"): """ Initialize unified reporter. - + Args: title: Report title """ @@ -43,11 +44,11 @@ def __init__(self, title: str = "Alignment Analysis Report"): "created": datetime.now().isoformat(), "version": "1.0" } - + def add_section(self, name: str, content: Any, section_type: str = "text"): """ Add a section to the report. - + Args: name: Section name content: Section content (text, DataFrame, dict, etc.) @@ -58,11 +59,11 @@ def add_section(self, name: str, content: Any, section_type: str = "text"): "content": content, "type": section_type }) - + def add_figure(self, figure_path: Union[str, Path], caption: str = "", width: Optional[str] = None): """ Add a figure to the report. - + Args: figure_path: Path to the figure caption: Figure caption @@ -73,11 +74,11 @@ def add_figure(self, figure_path: Union[str, Path], caption: str = "", width: Op "caption": caption, "width": width }) - + def add_table(self, name: str, data: Union[pd.DataFrame, Dict, List]): """ Add a table to the report. - + Args: name: Table name data: Table data (DataFrame, dict, or list of dicts) @@ -88,36 +89,36 @@ def add_table(self, name: str, data: Union[pd.DataFrame, Dict, List]): df = pd.DataFrame(data) else: df = data - + self.tables.append({ "name": name, "data": df }) - + def add_metadata(self, key: str, value: Any): """Add metadata to the report.""" self.metadata[key] = value - + # ========== HTML Generation ========== - + def generate_html(self, output_path: Union[str, Path], include_toc: bool = True): """ Generate HTML report. - + Args: output_path: Path to save the HTML file include_toc: Whether to include table of contents """ html = self._generate_html_header(include_toc) - + # Add table of contents if include_toc: html += self._generate_html_toc() - + # Add sections for section in self.sections: html += self._generate_html_section(section) - + # Add tables if self.tables: html += '

Tables

' @@ -125,23 +126,23 @@ def generate_html(self, output_path: Union[str, Path], include_toc: bool = True) html += f'

{table["name"]}

' html += table["data"].to_html(classes='data-table', index=False) html += '
' - + # Add figures if self.figures: html += '

Figures

' for fig in self.figures: html += self._generate_html_figure(fig) html += '
' - + html += self._generate_html_footer() - + # Save output_path = Path(output_path) output_path.parent.mkdir(parents=True, exist_ok=True) output_path.write_text(html) - + logger.info(f"Generated HTML report: {output_path}") - + def _generate_html_header(self, include_toc: bool) -> str: """Generate HTML header with styles.""" return f""" @@ -246,40 +247,40 @@ def _generate_html_header(self, include_toc: bool) -> str: Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
Version: {self.metadata.get('version', '1.0')} """ - + # Add custom metadata for key, value in self.metadata.items(): if key not in ['title', 'created', 'version']: html += f"
{key.title()}: {value}" - + html += "" return html - + def _generate_html_toc(self) -> str: """Generate table of contents.""" toc = '

Table of Contents

    ' - + for i, section in enumerate(self.sections): section_id = f"section-{i}" toc += f'
  • {section["name"]}
    • ' - + if self.tables: toc += '
  • Tables
    • ' - + if self.figures: toc += '
  • Figures
    • ' - + toc += '
' return toc - + def _generate_html_section(self, section: Dict) -> str: """Generate HTML for a section.""" section_id = f"section-{self.sections.index(section)}" html = f'

{section["name"]}

' - + content = section["content"] section_type = section["type"] - + if section_type == "text": html += f"

{content}

" elif section_type == "code": @@ -294,42 +295,42 @@ def _generate_html_section(self, section: Dict) -> str: elif section_type == "table": if isinstance(content, pd.DataFrame): html += content.to_html(classes='data-table', index=False) - + html += '
' return html - + def _generate_html_figure(self, fig: Dict) -> str: """Generate HTML for a figure.""" html = '
' - + width_attr = f'style="width: {fig["width"]}"' if fig.get("width") else "" html += f'{fig[' - + if fig["caption"]: html += f'
{fig["caption"]}
' - + html += '
' return html - + def _generate_html_footer(self) -> str: """Generate HTML footer.""" return """ """ - + # ========== Markdown Generation ========== - + def generate_markdown(self, output_path: Union[str, Path], include_toc: bool = True): """ Generate Markdown report. - + Args: output_path: Path to save the Markdown file include_toc: Whether to include table of contents """ md = f"# {self.title}\n\n" md += f"*Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*\n\n" - + # Add metadata if self.metadata: md += "## Metadata\n\n" @@ -337,7 +338,7 @@ def generate_markdown(self, output_path: Union[str, Path], include_toc: bool = T if key not in ['title', 'created']: md += f"- **{key.title()}**: {value}\n" md += "\n" - + # Add table of contents if include_toc: md += "## Table of Contents\n\n" @@ -348,14 +349,14 @@ def generate_markdown(self, output_path: Union[str, Path], include_toc: bool = T if self.figures: md += "- [Figures](#figures)\n" md += "\n" - + # Add sections for section in self.sections: md += f"## {section['name']}\n\n" - + content = section["content"] section_type = section["type"] - + if section_type == "text": md += f"{content}\n\n" elif section_type == "code": @@ -370,14 +371,14 @@ def generate_markdown(self, output_path: Union[str, Path], include_toc: bool = T elif section_type == "table": if isinstance(content, pd.DataFrame): md += content.to_markdown(index=False) + "\n\n" - + # Add tables if self.tables: md += "## Tables\n\n" for table in self.tables: md += f"### {table['name']}\n\n" md += table["data"].to_markdown(index=False) + "\n\n" - + # Add figures if self.figures: md += "## Figures\n\n" @@ -386,20 +387,20 @@ def generate_markdown(self, output_path: Union[str, Path], include_toc: bool = T if fig['caption']: md += f"*{fig['caption']}*\n" md += "\n" - + # Save output_path = Path(output_path) output_path.parent.mkdir(parents=True, exist_ok=True) output_path.write_text(md) - + logger.info(f"Generated Markdown report: {output_path}") - + # ========== JSON Generation ========== - + def generate_json(self, output_path: Union[str, Path], pretty: bool = True): """ Generate JSON report. - + Args: output_path: Path to save the JSON file pretty: Whether to pretty-print the JSON @@ -410,14 +411,14 @@ def generate_json(self, output_path: Union[str, Path], pretty: bool = True): "tables": [], "figures": [] } - + # Add sections for section in self.sections: section_data = { "name": section["name"], "type": section["type"] } - + content = section["content"] if isinstance(content, pd.DataFrame): section_data["content"] = content.to_dict('records') @@ -425,57 +426,57 @@ def generate_json(self, output_path: Union[str, Path], pretty: bool = True): section_data["content"] = content else: section_data["content"] = str(content) - + report_data["sections"].append(section_data) - + # Add tables for table in self.tables: report_data["tables"].append({ "name": table["name"], "data": table["data"].to_dict('records') }) - + # Add figures report_data["figures"] = self.figures - + # Save output_path = Path(output_path) output_path.parent.mkdir(parents=True, exist_ok=True) - + with open(output_path, 'w') as f: if pretty: json.dump(report_data, f, indent=2, default=str) else: json.dump(report_data, f, default=str) - + logger.info(f"Generated JSON report: {output_path}") - + # ========== Multi-format Generation ========== - + def generate_all(self, output_dir: Union[str, Path], basename: str = "report"): """ Generate reports in all supported formats. - + Args: output_dir: Directory to save reports basename: Base filename (without extension) """ output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) - + # Generate all formats self.generate_html(output_dir / f"{basename}.html") self.generate_markdown(output_dir / f"{basename}.md") self.generate_json(output_dir / f"{basename}.json") - + logger.info(f"Generated all report formats in: {output_dir}") - + # ========== Convenience Methods ========== - + def add_dataframe(self, name: str, df: pd.DataFrame, as_section: bool = False): """ Add a DataFrame to the report. - + Args: name: Name for the DataFrame df: The DataFrame @@ -485,11 +486,11 @@ def add_dataframe(self, name: str, df: pd.DataFrame, as_section: bool = False): self.add_section(name, df, section_type="data") else: self.add_table(name, df) - + def add_metrics(self, metrics: Dict[str, Any], section_name: str = "Metrics"): """ Add metrics dictionary as a formatted section. - + Args: metrics: Dictionary of metrics section_name: Name for the metrics section @@ -503,18 +504,18 @@ def add_metrics(self, metrics: Dict[str, Any], section_name: str = "Metrics"): # Simple metrics df = pd.DataFrame([metrics]) self.add_section(section_name, df, section_type="data") - + def add_summary(self, summary_dict: Dict[str, Any]): """ Add a summary section with key-value pairs. - + Args: summary_dict: Dictionary of summary information """ content = "" for key, value in summary_dict.items(): content += f"**{key}**: {value}\n\n" - + self.add_section("Summary", content, section_type="text") @@ -527,7 +528,7 @@ def generate_quick_report( ): """ Generate a quick report from results dictionary. - + Args: results: Results dictionary output_path: Output path @@ -535,7 +536,7 @@ def generate_quick_report( format: Output format ('html', 'markdown', 'json', 'all') """ reporter = UnifiedReporter(title) - + # Add sections based on results content for key, value in results.items(): if isinstance(value, pd.DataFrame): @@ -544,7 +545,7 @@ def generate_quick_report( reporter.add_section(key, value, section_type="data") else: reporter.add_section(key, str(value), section_type="text") - + # Generate report if format == "all": output_dir = Path(output_path).parent @@ -557,4 +558,4 @@ def generate_quick_report( elif format == "json": reporter.generate_json(output_path) else: - raise ValueError(f"Unknown format: {format}") \ No newline at end of file + raise ValueError(f"Unknown format: {format}") diff --git a/src/alignment/analysis/visualization/__init__.py b/src/alignment/analysis/visualization/__init__.py index ec32d872..0b635f0d 100644 --- a/src/alignment/analysis/visualization/__init__.py +++ b/src/alignment/analysis/visualization/__init__.py @@ -1,12 +1,9 @@ """Visualization components for alignment analysis.""" # Unified interface -from .unified_visualizer import ( - UnifiedVisualizer, - plot_quick_summary, -) +from .unified_visualizer import UnifiedVisualizer, plot_quick_summary __all__ = [ 'UnifiedVisualizer', 'plot_quick_summary', -] \ No newline at end of file +] diff --git a/src/alignment/analysis/visualization/unified_visualizer.py b/src/alignment/analysis/visualization/unified_visualizer.py index b9891b4b..60296e44 100644 --- a/src/alignment/analysis/visualization/unified_visualizer.py +++ b/src/alignment/analysis/visualization/unified_visualizer.py @@ -5,17 +5,17 @@ providing a single interface for all visualization needs. """ -from typing import Dict, List, Optional, Any, Union, Tuple, Callable +import logging +from datetime import datetime from pathlib import Path -import numpy as np +from typing import Any, Dict, List, Optional, Tuple, Union + import matplotlib.pyplot as plt -from matplotlib.figure import Figure -from matplotlib.axes import Axes -from matplotlib.gridspec import GridSpec +import numpy as np import pandas as pd import torch -import logging -from datetime import datetime +from matplotlib.figure import Figure +from matplotlib.gridspec import GridSpec # Try to import seaborn, but make it optional try: @@ -34,14 +34,14 @@ class UnifiedVisualizer: - AlignmentVisualizer - PruningVisualizer - ComparisonVisualizer - + This provides a single interface for all visualization needs. """ - + def __init__(self, style: str = "seaborn-v0_8", figsize: Tuple[int, int] = (10, 6)): """ Initialize the unified visualizer. - + Args: style: Matplotlib style to use figsize: Default figure size @@ -54,16 +54,16 @@ def __init__(self, style: str = "seaborn-v0_8", figsize: Tuple[int, int] = (10, plt.style.use('seaborn-v0_8-darkgrid') except: plt.style.use('default') - + self.figsize = figsize - + # Define color palettes if HAS_SEABORN: self.colors = sns.color_palette("husl", 10) else: import matplotlib.cm as cm self.colors = [cm.tab10(i) for i in range(10)] - + # Extended colors for strategies self.strategy_colors = { 'magnitude': '#1f77b4', @@ -73,14 +73,14 @@ def __init__(self, style: str = "seaborn-v0_8", figsize: Tuple[int, int] = (10, 'low': '#9467bd', 'high': '#8c564b', } - + # Set global parameters plt.rcParams['figure.dpi'] = 100 plt.rcParams['savefig.dpi'] = 300 plt.rcParams['font.size'] = 10 - + # ========== Time Series Plots ========== - + def plot_metric_evolution( self, steps: List[int], @@ -94,7 +94,7 @@ def plot_metric_evolution( ) -> Figure: """ Plot the evolution of metrics over time with optional confidence intervals. - + Args: steps: List of step numbers values: Dictionary mapping series names to value lists @@ -105,23 +105,23 @@ def plot_metric_evolution( legend_title: Legend title show_confidence: Whether to show confidence intervals save_path: Optional path to save the figure - + Returns: Matplotlib figure """ fig, ax = plt.subplots(figsize=self.figsize) - + for i, (name, vals) in enumerate(values.items()): if name in ['mean', 'std']: continue - + color = self.colors[i % len(self.colors)] - + if isinstance(vals, dict) and 'mean' in vals: # Handle mean/std structure means = vals['mean'] ax.plot(steps[:len(means)], means, label=name, color=color, linewidth=2) - + if show_confidence and 'std' in vals: stds = vals['std'] means = np.array(means) @@ -131,22 +131,22 @@ def plot_metric_evolution( else: # Simple list of values ax.plot(steps[:len(vals)], vals, label=name, color=color, linewidth=2) - + ax.set_xlabel(xlabel, fontsize=12) ax.set_ylabel(ylabel, fontsize=12) ax.set_title(title, fontsize=14, fontweight='bold') ax.legend(title=legend_title, loc='best') ax.grid(True, alpha=0.3) - + plt.tight_layout() - + if save_path: fig.savefig(save_path, dpi=300, bbox_inches='tight') - + return fig - + # ========== Layer Analysis Plots ========== - + def plot_layer_scores( self, scores: Dict[str, Union[torch.Tensor, np.ndarray, List[float]]], @@ -157,31 +157,31 @@ def plot_layer_scores( ) -> Figure: """ Plot alignment scores across layers. - + Args: scores: Dictionary of layer_name -> scores metric_name: Name of the metric plot_type: Type of plot ('violin', 'box', 'bar') save_path: Path to save the plot show_statistics: Whether to show mean/std statistics - + Returns: Matplotlib figure """ fig, ax = plt.subplots(figsize=self.figsize) - + layer_names = list(scores.keys()) data = [] - + for layer_name, layer_scores in scores.items(): if isinstance(layer_scores, torch.Tensor): layer_scores = layer_scores.cpu().numpy() elif not isinstance(layer_scores, np.ndarray): layer_scores = np.array(layer_scores) data.append(layer_scores) - + positions = range(len(layer_names)) - + if plot_type == 'violin': parts = ax.violinplot(data, positions=positions, showmeans=True, showextrema=True) for pc in parts['bodies']: @@ -193,42 +193,42 @@ def plot_layer_scores( means = [np.mean(d) for d in data] stds = [np.std(d) for d in data] bars = ax.bar(positions, means, yerr=stds, capsize=5) - + # Color bars by value norm = plt.Normalize(min(means), max(means)) sm = plt.cm.ScalarMappable(cmap='coolwarm', norm=norm) for bar, val in zip(bars, means): bar.set_color(sm.to_rgba(val)) - + if plot_type != 'bar': ax.set_xticks(positions) ax.set_xticklabels(layer_names, rotation=45, ha='right') - + ax.set_xlabel('Layer') ax.set_ylabel(f'{metric_name} Score') ax.set_title(f'{metric_name} Distribution Across Layers') - + if show_statistics and plot_type != 'bar': stats_text = [] for i, (name, layer_scores) in enumerate(zip(layer_names, data)): mean = np.mean(layer_scores) std = np.std(layer_scores) stats_text.append(f"{name}: μ={mean:.3f}, σ={std:.3f}") - + textstr = '\n'.join(stats_text) props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) ax.text(0.02, 0.98, textstr, transform=ax.transAxes, fontsize=9, verticalalignment='top', bbox=props) - + plt.tight_layout() - + if save_path: fig.savefig(save_path, dpi=300, bbox_inches='tight') - + return fig - + # ========== Heatmaps ========== - + def plot_heatmap( self, data: Union[pd.DataFrame, np.ndarray, Dict[str, Dict[str, float]]], @@ -242,7 +242,7 @@ def plot_heatmap( ) -> Figure: """ Create a heatmap visualization. - + Args: data: Data to plot (DataFrame, array, or nested dict) title: Plot title @@ -252,7 +252,7 @@ def plot_heatmap( xlabel: X-axis label ylabel: Y-axis label save_path: Optional path to save the figure - + Returns: Matplotlib figure """ @@ -263,10 +263,10 @@ def plot_heatmap( df = pd.DataFrame(data) else: df = data - - fig, ax = plt.subplots(figsize=(max(12, len(df.columns) * 0.8), + + fig, ax = plt.subplots(figsize=(max(12, len(df.columns) * 0.8), max(8, len(df.index) * 0.5))) - + if HAS_SEABORN: sns.heatmap( df, @@ -280,38 +280,38 @@ def plot_heatmap( ) else: im = ax.imshow(df.values, cmap=cmap, aspect='auto') - + ax.set_xticks(np.arange(len(df.columns))) ax.set_yticks(np.arange(len(df.index))) ax.set_xticklabels(df.columns) ax.set_yticklabels(df.index) - + plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") - + cbar = plt.colorbar(im, ax=ax) cbar.set_label('Value', rotation=270, labelpad=15) - + if annotate: for i in range(len(df.index)): for j in range(len(df.columns)): - text = ax.text(j, i, format(df.iloc[i, j], fmt), + ax.text(j, i, format(df.iloc[i, j], fmt), ha="center", va="center", color="black") - + ax.set_title(title, fontsize=14, fontweight='bold') if xlabel: ax.set_xlabel(xlabel) if ylabel: ax.set_ylabel(ylabel) - + plt.tight_layout() - + if save_path: fig.savefig(save_path, dpi=300, bbox_inches='tight') - + return fig - + # ========== Pruning Analysis ========== - + def plot_pruning_performance( self, results: Dict[str, Dict[float, Dict[str, float]]], @@ -322,32 +322,32 @@ def plot_pruning_performance( ) -> Figure: """ Plot performance metrics for multiple pruning strategies. - + Args: results: Nested dict of strategy -> sparsity -> metric -> value metrics: List of metrics to plot save_path: Path to save the plot title: Overall title for the plot show_confidence: Whether to show confidence intervals - + Returns: Matplotlib figure """ num_metrics = len(metrics) fig = plt.figure(figsize=(self.figsize[0], self.figsize[1] * num_metrics // 2)) - + gs = GridSpec(num_metrics, 1, figure=fig, hspace=0.3) - + for idx, metric in enumerate(metrics): ax = fig.add_subplot(gs[idx, 0]) - + for strategy, strategy_results in results.items(): sparsities = sorted(strategy_results.keys()) - + # Extract values means = [] stds = [] - + for sparsity in sparsities: if isinstance(strategy_results[sparsity], dict): if 'mean' in strategy_results[sparsity]: @@ -358,36 +358,36 @@ def plot_pruning_performance( means.append(strategy_results[sparsity].get(metric, 0)) else: means.append(strategy_results[sparsity]) - + # Plot color = self.strategy_colors.get(strategy, self.colors[0]) line = ax.plot(sparsities, means, 'o-', label=strategy, linewidth=2.5, markersize=8, color=color) - + if stds and show_confidence: means = np.array(means) stds = np.array(stds) ax.fill_between(sparsities, means - stds, means + stds, alpha=0.2, color=line[0].get_color()) - + ax.set_xlabel('Sparsity Level', fontsize=12) ax.set_ylabel(metric.capitalize(), fontsize=12) ax.set_title(f'{metric.capitalize()} vs Sparsity', fontsize=12) ax.legend(loc='best') ax.grid(True, alpha=0.3) - + if title: fig.suptitle(title, fontsize=16, fontweight='bold') - + plt.tight_layout() - + if save_path: fig.savefig(save_path, dpi=300, bbox_inches='tight') - + return fig - + # ========== Comparison Plots ========== - + def plot_radar_chart( self, data: Dict[str, Dict[str, float]], @@ -396,32 +396,32 @@ def plot_radar_chart( ) -> Figure: """ Create a radar chart comparing multiple metrics. - + Args: data: Nested dict: {series_name: {metric_name: value}} title: Plot title save_path: Optional path to save the figure - + Returns: Matplotlib figure """ series = list(data.keys()) metrics = list(next(iter(data.values())).keys()) num_vars = len(metrics) - + angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist() angles += angles[:1] - + fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection='polar')) - + for i, series_name in enumerate(series): values = [data[series_name][metric] for metric in metrics] values += values[:1] - + color = self.colors[i % len(self.colors)] ax.plot(angles, values, 'o-', linewidth=2, label=series_name, color=color) ax.fill(angles, values, alpha=0.1, color=color) - + ax.set_theta_offset(np.pi / 2) ax.set_theta_direction(-1) ax.set_xticks(angles[:-1]) @@ -430,16 +430,16 @@ def plot_radar_chart( ax.set_title(title, fontsize=16, fontweight='bold', pad=20) ax.legend(loc='upper right', bbox_to_anchor=(1.2, 1.1)) ax.grid(True) - + plt.tight_layout() - + if save_path: fig.savefig(save_path, dpi=300, bbox_inches='tight') - + return fig - + # ========== Comprehensive Reports ========== - + def create_comprehensive_report( self, results: Dict[str, Any], @@ -448,7 +448,7 @@ def create_comprehensive_report( ): """ Create a comprehensive visual report with multiple plots. - + Args: results: Dictionary containing all results output_dir: Directory to save the report @@ -456,12 +456,12 @@ def create_comprehensive_report( """ output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) - + plots_dir = output_dir / "plots" plots_dir.mkdir(exist_ok=True) - + # Generate various plots based on available data - + # 1. Metric evolution if 'metrics_over_time' in results: for metric_name, values in results['metrics_over_time'].items(): @@ -472,7 +472,7 @@ def create_comprehensive_report( ylabel=metric_name, save_path=plots_dir / f"{metric_name}_evolution.png" ) - + # 2. Layer scores if 'layer_scores' in results: for metric_name, scores in results['layer_scores'].items(): @@ -481,7 +481,7 @@ def create_comprehensive_report( metric_name, save_path=plots_dir / f"{metric_name}_layers.png" ) - + # 3. Heatmaps if 'heatmap_data' in results: self.plot_heatmap( @@ -489,33 +489,33 @@ def create_comprehensive_report( title="Metrics Heatmap", save_path=plots_dir / "metrics_heatmap.png" ) - + # 4. Pruning results if 'pruning_results' in results: self.plot_pruning_performance( results['pruning_results'], save_path=plots_dir / "pruning_performance.png" ) - + # 5. Comparisons if 'comparison_data' in results: self.plot_radar_chart( results['comparison_data'], save_path=plots_dir / "comparison_radar.png" ) - + # Create summary statistics self._create_summary_statistics(results, output_dir) - + # Create README self._create_readme(experiment_name, results, output_dir) - + logger.info(f"Comprehensive report saved to {output_dir}") - + def _create_summary_statistics(self, results: Dict[str, Any], output_dir: Path): """Create summary statistics CSV.""" summary = [] - + if 'layer_scores' in results: for metric_name, layer_scores in results['layer_scores'].items(): for layer_name, scores in layer_scores.items(): @@ -523,7 +523,7 @@ def _create_summary_statistics(self, results: Dict[str, Any], output_dir: Path): scores = scores.cpu().numpy() elif not isinstance(scores, np.ndarray): scores = np.array(scores) - + summary.append({ 'Metric': metric_name, 'Layer': layer_name, @@ -533,11 +533,11 @@ def _create_summary_statistics(self, results: Dict[str, Any], output_dir: Path): 'Max': np.max(scores), 'Count': len(scores) }) - + if summary: df = pd.DataFrame(summary) df.to_csv(output_dir / "summary_statistics.csv", index=False) - + def _create_readme(self, experiment_name: str, results: Dict[str, Any], output_dir: Path): """Create README file for the report.""" readme_content = f"""# {experiment_name} Report @@ -562,7 +562,7 @@ def _create_readme(self, experiment_name: str, results: Dict[str, Any], output_d ## Experiment Summary """ - + # Add summary statistics if 'layer_scores' in results: readme_content += f"- Number of metrics: {len(results['layer_scores'])}\n" @@ -570,10 +570,10 @@ def _create_readme(self, experiment_name: str, results: Dict[str, Any], output_d for scores in results['layer_scores'].values(): all_layers.update(scores.keys()) readme_content += f"- Number of layers: {len(all_layers)}\n" - + if 'pruning_results' in results: readme_content += f"- Pruning strategies: {', '.join(results['pruning_results'].keys())}\n" - + with open(output_dir / "README.md", 'w') as f: f.write(readme_content) @@ -587,7 +587,7 @@ def plot_quick_summary( ): """Quick plotting function for immediate visualization.""" visualizer = UnifiedVisualizer() - + if isinstance(next(iter(scores.values())), (list, np.ndarray, torch.Tensor)): # Layer scores fig = visualizer.plot_layer_scores(scores, title, save_path=save_path) @@ -595,8 +595,8 @@ def plot_quick_summary( # Time series steps = list(range(len(next(iter(scores.values()))))) fig = visualizer.plot_metric_evolution(steps, scores, title=title, save_path=save_path) - + if not save_path: plt.show() - - return fig \ No newline at end of file + + return fig diff --git a/src/alignment/configs/__init__.py b/src/alignment/configs/__init__.py index 4a9bce87..3ac5f1f6 100644 --- a/src/alignment/configs/__init__.py +++ b/src/alignment/configs/__init__.py @@ -5,4 +5,4 @@ from .config_loader import load_config, save_config from .config_validator import validate_config -__all__ = ['load_config', 'save_config', 'validate_config'] \ No newline at end of file +__all__ = ['load_config', 'save_config', 'validate_config'] diff --git a/src/alignment/configs/config_loader.py b/src/alignment/configs/config_loader.py index ec622ed8..ff1d6a44 100644 --- a/src/alignment/configs/config_loader.py +++ b/src/alignment/configs/config_loader.py @@ -2,12 +2,13 @@ Configuration loading and saving utilities. """ -import os -import yaml import json -from pathlib import Path -from typing import Dict, Any, Union, Optional, List import logging +import os +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import yaml from ..experiments.base import ExperimentConfig @@ -17,22 +18,22 @@ def load_config(config_path: Union[str, Path]) -> ExperimentConfig: """ Load configuration from a YAML or JSON file. - + Args: config_path: Path to configuration file - + Returns: ExperimentConfig object - + Raises: FileNotFoundError: If config file doesn't exist ValueError: If config format is invalid """ config_path = Path(config_path) - + if not config_path.exists(): raise FileNotFoundError(f"Configuration file not found: {config_path}") - + # Load raw config if config_path.suffix in ['.yaml', '.yml']: with open(config_path, 'r') as f: @@ -42,13 +43,13 @@ def load_config(config_path: Union[str, Path]) -> ExperimentConfig: config_dict = json.load(f) else: raise ValueError(f"Unsupported config format: {config_path.suffix}") - + # Handle environment variable substitution config_dict = _substitute_env_vars(config_dict) - + # Map nested config to flat ExperimentConfig structure config_dict = _map_nested_to_flat_config(config_dict) - + # Create ExperimentConfig try: config = ExperimentConfig.from_dict(config_dict) @@ -58,11 +59,11 @@ def load_config(config_path: Union[str, Path]) -> ExperimentConfig: raise ValueError(f"Invalid configuration: {e}") -def save_config(config: ExperimentConfig, save_path: Union[str, Path], +def save_config(config: ExperimentConfig, save_path: Union[str, Path], format: str = 'yaml') -> None: """ Save configuration to a file. - + Args: config: ExperimentConfig object save_path: Path to save configuration @@ -70,9 +71,9 @@ def save_config(config: ExperimentConfig, save_path: Union[str, Path], """ save_path = Path(save_path) save_path.parent.mkdir(parents=True, exist_ok=True) - + config_dict = config.to_dict() - + if format == 'yaml': with open(save_path, 'w') as f: yaml.dump(config_dict, f, default_flow_style=False) @@ -81,22 +82,22 @@ def save_config(config: ExperimentConfig, save_path: Union[str, Path], json.dump(config_dict, f, indent=2) else: raise ValueError(f"Unsupported format: {format}") - + logger.info(f"Saved configuration to {save_path}") def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: """ Map nested YAML config structure to flat ExperimentConfig structure. - + Args: nested_config: Nested configuration from YAML - + Returns: Flat configuration suitable for ExperimentConfig """ flat_config = {} - + # Map experiment metadata if 'experiment_name' in nested_config: flat_config['name'] = nested_config['experiment_name'] @@ -104,15 +105,15 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['name'] = nested_config['name'] else: flat_config['name'] = 'default_experiment' - + flat_config['description'] = nested_config.get('description', '') flat_config['tags'] = nested_config.get('tags', []) - + # Map other top-level fields flat_config['pretrained'] = nested_config.get('pretrained', False) if 'tracked_layers' in nested_config: flat_config['tracked_layers'] = nested_config['tracked_layers'] - + # Map dataset configuration if 'dataset' in nested_config: dataset = nested_config['dataset'] @@ -122,11 +123,11 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['data_path'] = dataset.get('data_path') flat_config['batch_size'] = dataset.get('batch_size', 128) flat_config['num_workers'] = dataset.get('num_workers', 4) - + # Filter out DataLoader-specific parameters (not Dataset parameters) - dataloader_params = ['batch_size', 'num_workers', 'pin_memory', 'drop_last', + dataloader_params = ['batch_size', 'num_workers', 'pin_memory', 'drop_last', 'persistent_workers', 'prefetch_factor', 'name', 'dataset_name', 'data_path'] - flat_config['dataset_config'] = {k: v for k, v in dataset.items() + flat_config['dataset_config'] = {k: v for k, v in dataset.items() if k not in dataloader_params} else: # Handle flat structure where dataset fields are at top level @@ -136,14 +137,14 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['batch_size'] = nested_config.get('batch_size', 128) flat_config['num_workers'] = nested_config.get('num_workers', 4) flat_config['dataset_config'] = nested_config.get('dataset_config', {}) - + # Map model configuration if 'model' in nested_config: model = nested_config['model'] model_name = model.get('name', model.get('model_name', 'mlp')) flat_config['model_name'] = model_name flat_config['model_config'] = {} - + # Handle different model types if 'mlp_params' in model: flat_config['model_config'].update(model['mlp_params']) @@ -155,7 +156,7 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: if external.get('source') == 'torchvision': flat_config['model_name'] = external.get('name_or_path', 'resnet18') flat_config['pretrained'] = external.get('pretrained', False) - + # Add common model params if 'output_dim' in model: flat_config['model_config']['output_dim'] = model['output_dim'] @@ -168,7 +169,7 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: model_name = nested_config.get('model_name', 'mlp') flat_config['model_name'] = model_name flat_config['model_config'] = nested_config.get('model_config', {}) - + # Handle different model types from flat structure if 'mlp_params' in nested_config: flat_config['model_config'].update(nested_config['mlp_params']) @@ -180,7 +181,7 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: if external.get('source') == 'torchvision': flat_config['model_name'] = external.get('name_or_path', 'resnet18') flat_config['pretrained'] = external.get('pretrained', False) - + # Add common model params from flat structure if 'output_dim' in nested_config: flat_config['model_config']['output_dim'] = nested_config['output_dim'] @@ -188,7 +189,7 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['model_config']['dropout_rate'] = nested_config['dropout_rate'] if 'alignment_layers' in nested_config: flat_config['tracked_layers'] = list(nested_config['alignment_layers'].keys()) if isinstance(nested_config['alignment_layers'], dict) else nested_config['alignment_layers'] - + # Map training configuration if 'training' in nested_config: training = nested_config['training'] @@ -196,7 +197,7 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['learning_rate'] = training.get('learning_rate', 0.001) flat_config['optimizer'] = training.get('optimizer', 'Adam').lower() flat_config['train_before_dropout'] = training.get('train_before_dropout', True) - + # Map alignment settings if 'alignment_settings' in nested_config: alignment = nested_config['alignment_settings'] @@ -212,24 +213,24 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['scale_by_norm'] = alignment.get('scale_by_norm', False) flat_config['force_cpu_for_large_metric_ops'] = alignment.get('force_cpu_for_large_metric_ops', False) flat_config['cnn_rq_aggregation_op'] = alignment.get('cnn_rq_aggregation_op', 'mean') - + # Map pruning settings if 'pruning_settings' in nested_config: pruning = nested_config['pruning_settings'] flat_config['exclude_classification_layer'] = pruning.get('exclude_classification_layer', True) - + # Map other settings flat_config['device'] = nested_config.get('device', 'cuda') flat_config['seed'] = nested_config.get('seed', 42) - + # Map pruning configuration (check both top-level and nested 'pruning' block) pruning_block = nested_config.get('pruning', {}) - + # Map top-level analysis flags flat_config['do_pruning_experiments'] = pruning_block.get('enabled', nested_config.get('do_pruning_experiments', False)) flat_config['do_dropout_analysis'] = nested_config.get('dropout', {}).get('enabled', nested_config.get('do_dropout_analysis', False)) flat_config['do_eigenfeature_analysis'] = nested_config.get('do_eigenfeature_analysis', False) - + # Map pruning parameters (prioritize nested pruning block, fallback to top-level) flat_config['pruning_strategies'] = pruning_block.get('algorithms', nested_config.get('pruning_strategies', ['magnitude', 'random'])) flat_config['pruning_amounts'] = pruning_block.get('sparsity_levels', nested_config.get('pruning_amounts', [0.1, 0.3, 0.5, 0.7, 0.9])) @@ -237,56 +238,56 @@ def _map_nested_to_flat_config(nested_config: Dict[str, Any]) -> Dict[str, Any]: flat_config['fine_tune_after_pruning'] = pruning_block.get('fine_tune_after_pruning', nested_config.get('fine_tune_after_pruning', True)) flat_config['fine_tune_epochs'] = pruning_block.get('fine_tune_epochs', nested_config.get('fine_tune_epochs', 5)) flat_config['pruning_alignment_metric'] = pruning_block.get('alignment_metric', nested_config.get('pruning_alignment_metric', 'rayleigh_quotient')) - + # Map visualization settings flat_config['generate_plots'] = nested_config.get('generate_plots', True) flat_config['plot_format'] = nested_config.get('plot_format', 'png') flat_config['plot_dpi'] = nested_config.get('plot_dpi', 300) - + # Map checkpointing if 'checkpointing' in nested_config: checkpoint = nested_config['checkpointing'] flat_config['checkpoint_interval'] = checkpoint.get('checkpoint_frequency', 1) * 1000 # Convert to steps flat_config['save_best'] = checkpoint.get('save_checkpoints', True) - + # Map wandb settings if 'wandb' in nested_config: wandb = nested_config['wandb'] if wandb.get('use_wandb', False): flat_config['wandb_project'] = wandb.get('wandb_project') flat_config['wandb_entity'] = wandb.get('wandb_entity') - + # Map paths flat_config['log_dir'] = nested_config.get('results_path', './logs') flat_config['checkpoint_dir'] = os.path.join(flat_config['log_dir'], 'checkpoints') - + return flat_config def _substitute_env_vars(config_dict: Dict[str, Any]) -> Dict[str, Any]: """ Recursively substitute environment variables in config. - + Environment variables should be specified as ${VAR_NAME} or ${VAR_NAME:default}. - + Args: config_dict: Configuration dictionary - + Returns: Config dict with environment variables substituted """ import re - + def substitute_value(value): if isinstance(value, str): # Pattern for ${VAR} or ${VAR:default} pattern = r'\$\{([^}:]+)(?::([^}]*))?\}' - + def replacer(match): var_name = match.group(1) default = match.group(2) return os.environ.get(var_name, default if default is not None else match.group(0)) - + return re.sub(pattern, replacer, value) elif isinstance(value, dict): return {k: substitute_value(v) for k, v in value.items()} @@ -294,31 +295,31 @@ def replacer(match): return [substitute_value(item) for item in value] else: return value - + return substitute_value(config_dict) def merge_configs(base_config: Dict[str, Any], override_config: Dict[str, Any]) -> Dict[str, Any]: """ Merge two configuration dictionaries. - + Args: base_config: Base configuration override_config: Configuration to override base - + Returns: Merged configuration """ import copy - + result = copy.deepcopy(base_config) - + for key, value in override_config.items(): if key in result and isinstance(result[key], dict) and isinstance(value, dict): result[key] = merge_configs(result[key], value) else: result[key] = value - + return result @@ -329,23 +330,23 @@ def load_config_with_overrides( ) -> ExperimentConfig: """ Load configuration with optional overrides. - + Args: config_path: Path to base configuration overrides: Dictionary of overrides cli_args: Command-line arguments in format ["key=value", ...] - + Returns: ExperimentConfig with overrides applied """ # Load base config config = load_config(config_path) config_dict = config.to_dict() - + # Apply dictionary overrides if overrides: config_dict = merge_configs(config_dict, overrides) - + # Apply CLI overrides if cli_args: for arg in cli_args: @@ -356,7 +357,7 @@ def load_config_with_overrides( value = eval(value) except: pass # Keep as string - + # Handle nested keys (e.g., "model.hidden_dims=[300,200]") keys = key.split('.') target = config_dict @@ -365,5 +366,5 @@ def load_config_with_overrides( target[k] = {} target = target[k] target[keys[-1]] = value - - return ExperimentConfig.from_dict(config_dict) \ No newline at end of file + + return ExperimentConfig.from_dict(config_dict) diff --git a/src/alignment/configs/config_validator.py b/src/alignment/configs/config_validator.py index 42921fd3..15397a81 100644 --- a/src/alignment/configs/config_validator.py +++ b/src/alignment/configs/config_validator.py @@ -2,8 +2,8 @@ Configuration validation utilities. """ -from typing import Dict, Any, List, Optional import logging +from typing import Any, Dict, List logger = logging.getLogger(__name__) @@ -11,19 +11,19 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: """ Validate configuration dictionary. - + Args: config_dict: Configuration dictionary to validate - + Returns: List of validation errors (empty if valid) """ errors = [] - + # Required fields if 'name' not in config_dict: errors.append("Missing required field: 'name'") - + # Model validation (dynamic from registry if available) if 'model_name' in config_dict: try: @@ -33,7 +33,7 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: valid_models = ['mlp', 'cnn2p2', 'resnet18', 'resnet50'] if config_dict['model_name'] not in valid_models: errors.append(f"Invalid model_name: {config_dict['model_name']}. Must be one of {valid_models}") - + # Dataset validation (dynamic) if 'dataset_name' in config_dict: try: @@ -43,13 +43,13 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: valid_datasets = ['mnist', 'cifar10', 'cifar100', 'imagenet'] if config_dict['dataset_name'] not in valid_datasets: errors.append(f"Invalid dataset_name: {config_dict['dataset_name']}. Must be one of {valid_datasets}") - + # Device validation if 'device' in config_dict: dev = str(config_dict['device']) if not (dev == 'cpu' or dev.startswith('cuda')): errors.append(f"Invalid device: {dev}. Must be 'cpu' or 'cuda[:N]'") - + # Numeric validations numeric_fields = { 'batch_size': (1, 10000), @@ -58,7 +58,7 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: 'learning_rate': (1e-10, 10.0), 'seed': (0, 2**32-1) } - + for field, (min_val, max_val) in numeric_fields.items(): if field in config_dict: value = config_dict[field] @@ -66,7 +66,7 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: errors.append(f"{field} must be numeric, got {type(value).__name__}") elif value < min_val or value > max_val: errors.append(f"{field} must be between {min_val} and {max_val}, got {value}") - + # List validations if 'metrics' in config_dict: if not isinstance(config_dict['metrics'], list): @@ -80,7 +80,7 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: for metric in config_dict['metrics']: if valid_metrics and metric not in valid_metrics: errors.append(f"Invalid metric: {metric}. Must be one of {valid_metrics}") - + # Dropout fractions validation if 'dropout_fractions' in config_dict: if not isinstance(config_dict['dropout_fractions'], list): @@ -89,65 +89,65 @@ def validate_config(config_dict: Dict[str, Any]) -> List[str]: for frac in config_dict['dropout_fractions']: if not isinstance(frac, (int, float)) or frac < 0 or frac > 1: errors.append(f"Invalid dropout fraction: {frac}. Must be between 0 and 1") - + return errors def validate_experiment_config(config_dict: Dict[str, Any], experiment_type: str) -> List[str]: """ Validate configuration for specific experiment type. - + Args: config_dict: Configuration dictionary experiment_type: Type of experiment - + Returns: List of validation errors """ errors = validate_config(config_dict) - + # Experiment-specific validations if experiment_type in ['progressive_dropout', 'global_dropout']: if 'dropout_fractions' not in config_dict: errors.append("Progressive dropout requires 'dropout_fractions'") - + elif experiment_type == 'eigenvector': if 'num_components' in config_dict: if config_dict['num_components'] < 1: errors.append("'num_components' must be at least 1") - + elif experiment_type == 'layer_isolated': if 'pruning_percentages' not in config_dict: errors.append("Layer isolated pruning requires 'pruning_percentages'") - + return errors def check_compatibility(config_dict: Dict[str, Any]) -> List[str]: """ Check for compatibility issues in configuration. - + Args: config_dict: Configuration dictionary - + Returns: List of warnings """ warnings = [] - + # Model-dataset compatibility if config_dict.get('model_name') == 'cnn2p2' and config_dict.get('dataset_name') == 'mnist': model_config = config_dict.get('model_config', {}) if model_config.get('in_channels') != 1: warnings.append("CNN2P2 with MNIST should have in_channels=1") - + # Batch size warnings if config_dict.get('batch_size', 128) > 512 and config_dict.get('device', '').startswith('cpu'): warnings.append("Large batch size on CPU may be slow") - + # Memory warnings if config_dict.get('force_cpu_for_large_metric_ops', True) is False: if config_dict.get('model_name') in ['resnet50'] and config_dict.get('batch_size', 128) > 64: warnings.append("Large model with large batch size may cause GPU memory issues") - - return warnings \ No newline at end of file + + return warnings diff --git a/src/alignment/core/__init__.py b/src/alignment/core/__init__.py index 5dc7f05b..13698bd9 100644 --- a/src/alignment/core/__init__.py +++ b/src/alignment/core/__init__.py @@ -5,30 +5,25 @@ used throughout the framework. """ +from .base import BaseDataset, BaseExperiment, BaseMetric, BaseModel from .protocols import ( AlignmentMetric, - ModelWrapper as ModelWrapperProtocol, DatasetWrapper, Experiment, MetricAggregator, ResultReporter, ) +from .protocols import ModelWrapper as ModelWrapperProtocol from .registry import ( Registry, - register_metric, - register_model, - register_dataset, - register_experiment, - get_metric, - get_model, get_dataset, get_experiment, -) -from .base import ( - BaseMetric, - BaseModel, - BaseDataset, - BaseExperiment, + get_metric, + get_model, + register_dataset, + register_experiment, + register_metric, + register_model, ) __all__ = [ @@ -54,4 +49,4 @@ "BaseModel", "BaseDataset", "BaseExperiment", -] \ No newline at end of file +] diff --git a/src/alignment/core/base.py b/src/alignment/core/base.py index ce6ef6c5..199a198e 100644 --- a/src/alignment/core/base.py +++ b/src/alignment/core/base.py @@ -5,26 +5,27 @@ across all implementations. """ +import json +import logging from abc import ABC, abstractmethod -from typing import Optional, Dict, List, Any, Tuple, Union +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + import torch -import torch.nn as nn import torch.distributed as dist +import torch.nn as nn from torch.utils.data import DataLoader -import logging -import json -from pathlib import Path logger = logging.getLogger(__name__) class BaseMetric(ABC): """Base class for all alignment metrics.""" - + def __init__(self, name: Optional[str] = None, **config: Any): """ Initialize the metric. - + Args: name: Optional custom name for the metric **config: Metric-specific configuration @@ -33,30 +34,30 @@ def __init__(self, name: Optional[str] = None, **config: Any): self.config = config self._force_cpu_for_large_ops = config.get('force_cpu_for_large_ops', True) self._cpu_threshold = config.get('cpu_threshold', 1e8) # 100M elements - + @property def name(self) -> str: """Unique name identifier for the metric.""" return self._name - + @property @abstractmethod def requires_inputs(self) -> bool: """Whether this metric requires layer inputs.""" pass - + @property @abstractmethod def requires_weights(self) -> bool: """Whether this metric requires layer weights.""" pass - + @property @abstractmethod def requires_outputs(self) -> bool: """Whether this metric requires layer outputs.""" pass - + @abstractmethod def compute( self, @@ -67,18 +68,18 @@ def compute( ) -> torch.Tensor: """ Compute the metric values. - + Args: inputs: Input activations to the layer [batch_size, input_features] weights: Layer weights [output_features, input_features] outputs: Output activations from the layer [batch_size, output_features] **kwargs: Additional metric-specific parameters - + Returns: Tensor of metric values, typically [output_features] for per-node metrics """ pass - + def compute_distributed( self, inputs: Optional[torch.Tensor] = None, @@ -90,7 +91,7 @@ def compute_distributed( ) -> torch.Tensor: """ Compute metric in distributed setting with automatic reduction. - + Args: inputs: Input activations (already distributed) weights: Layer weights @@ -98,35 +99,35 @@ def compute_distributed( world_size: Number of distributed processes rank: Rank of current process **kwargs: Additional parameters - + Returns: Metric values with proper distributed reduction """ # Compute local metric values local_values = self.compute(inputs, weights, outputs, **kwargs) - + if world_size > 1 and dist.is_initialized(): # Perform all-reduce to aggregate across processes dist.all_reduce(local_values, op=dist.ReduceOp.SUM) local_values = local_values / world_size - + return local_values - + def _should_use_cpu(self, *tensors: torch.Tensor) -> bool: """Check if computation should be moved to CPU based on tensor sizes.""" if not self._force_cpu_for_large_ops: return False - + total_elements = sum(t.numel() for t in tensors if t is not None) return total_elements > self._cpu_threshold - + def __repr__(self) -> str: return f"{self.__class__.__name__}(name='{self.name}')" class BaseModel(ABC): """Base class for model wrappers.""" - + def __init__( self, model: nn.Module, @@ -135,7 +136,7 @@ def __init__( ): """ Initialize the model wrapper. - + Args: model: PyTorch model to wrap tracked_layers: List of layer names to track @@ -146,20 +147,20 @@ def __init__( self.config = config self._activation_cache: Dict[str, torch.Tensor] = {} self._hooks: List[Any] = [] - + if self._tracked_layers: self._register_hooks() - + @property def model(self) -> nn.Module: """The underlying PyTorch model.""" return self._model - + @property def tracked_layers(self) -> List[str]: """List of layer names being tracked for alignment.""" return self._tracked_layers - + def _register_hooks(self) -> None: """Register forward hooks for activation collection.""" for name, module in self._model.named_modules(): @@ -168,41 +169,41 @@ def _register_hooks(self) -> None: self._create_activation_hook(name) ) self._hooks.append(hook) - + def _create_activation_hook(self, layer_name: str): """Create a forward hook for a specific layer.""" def hook(module, input, output): self._activation_cache[layer_name] = output.detach() return hook - + def _clear_hooks(self) -> None: """Remove all registered hooks.""" for hook in self._hooks: hook.remove() self._hooks.clear() - + def get_layer_activations( - self, + self, inputs: torch.Tensor, layers: Optional[List[str]] = None ) -> Dict[str, torch.Tensor]: """Get activations for specified layers.""" layers = layers or self._tracked_layers - + # Clear previous activations self._activation_cache.clear() - + # Forward pass to collect activations with torch.no_grad(): _ = self._model(inputs) - + # Return requested activations return { layer: self._activation_cache[layer] for layer in layers if layer in self._activation_cache } - + @abstractmethod def get_layer_weights( self, @@ -210,7 +211,7 @@ def get_layer_weights( ) -> Dict[str, torch.Tensor]: """Get weights for specified layers.""" pass - + def forward_with_activations( self, inputs: torch.Tensor @@ -220,7 +221,7 @@ def forward_with_activations( outputs = self._model(inputs) activations = self._activation_cache.copy() return outputs, activations - + def __del__(self): """Clean up hooks when object is destroyed.""" self._clear_hooks() @@ -228,7 +229,7 @@ def __del__(self): class BaseDataset(ABC): """Base class for dataset wrappers.""" - + def __init__( self, name: str, @@ -237,7 +238,7 @@ def __init__( ): """ Initialize the dataset wrapper. - + Args: name: Dataset name data_path: Path to dataset files @@ -246,24 +247,24 @@ def __init__( self._name = name self._data_path = data_path self.config = config - + @property def name(self) -> str: """Dataset name.""" return self._name - + @property @abstractmethod def num_classes(self) -> int: """Number of classes in the dataset.""" pass - + @property @abstractmethod def input_shape(self) -> Tuple[int, ...]: """Shape of a single input sample (excluding batch dimension).""" pass - + @abstractmethod def get_train_loader( self, @@ -274,7 +275,7 @@ def get_train_loader( ) -> DataLoader: """Get training data loader.""" pass - + @abstractmethod def get_val_loader( self, @@ -285,7 +286,7 @@ def get_val_loader( ) -> DataLoader: """Get validation data loader.""" pass - + @abstractmethod def get_test_loader( self, @@ -300,7 +301,7 @@ def get_test_loader( class BaseExperiment(ABC): """Base class for experiments.""" - + def __init__( self, name: str, @@ -309,7 +310,7 @@ def __init__( ): """ Initialize the experiment. - + Args: name: Experiment name config: Experiment configuration @@ -319,22 +320,22 @@ def __init__( self._config = config self._output_dir = Path(output_dir) if output_dir else Path("results") self._output_dir.mkdir(parents=True, exist_ok=True) - + @property def name(self) -> str: """Experiment name.""" return self._name - + @property def config(self) -> Dict[str, Any]: """Experiment configuration.""" return self._config - + @abstractmethod def setup(self) -> None: """Setup the experiment (called once before running).""" pass - + @abstractmethod def run( self, @@ -344,28 +345,28 @@ def run( ) -> Dict[str, Any]: """Run the experiment.""" pass - + def save_results(self, results: Dict[str, Any], filename: str = "results.json") -> None: """Save experiment results.""" output_path = self._output_dir / filename - + # Convert tensors to lists for JSON serialization serializable_results = self._make_serializable(results) - + with open(output_path, 'w') as f: json.dump(serializable_results, f, indent=2) - + logger.info(f"Saved results to {output_path}") - + def load_results(self, filename: str = "results.json") -> Dict[str, Any]: """Load experiment results.""" input_path = self._output_dir / filename - + with open(input_path, 'r') as f: results = json.load(f) - + return results - + def _make_serializable(self, obj: Any) -> Any: """Convert objects to JSON-serializable format.""" if isinstance(obj, torch.Tensor): @@ -376,7 +377,7 @@ def _make_serializable(self, obj: Any) -> Any: return [self._make_serializable(v) for v in obj] else: return obj - + def save_checkpoint( self, models: Union[nn.Module, List[nn.Module]], @@ -386,22 +387,22 @@ def save_checkpoint( """Save a checkpoint during experiment.""" checkpoint_dir = self._output_dir / "checkpoints" checkpoint_dir.mkdir(exist_ok=True) - + checkpoint_path = checkpoint_dir / f"checkpoint_epoch_{epoch}.pt" - + if not isinstance(models, list): models = [models] - + state = { 'epoch': epoch, 'models': [m.state_dict() for m in models], 'config': self._config, **additional_state } - + torch.save(state, checkpoint_path) logger.info(f"Saved checkpoint to {checkpoint_path}") - + def load_checkpoint( self, models: Union[nn.Module, List[nn.Module]], @@ -409,18 +410,18 @@ def load_checkpoint( ) -> Dict[str, Any]: """Load a checkpoint.""" checkpoint_path = self._output_dir / "checkpoints" / f"checkpoint_epoch_{epoch}.pt" - + if not checkpoint_path.exists(): raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}") - + state = torch.load(checkpoint_path) - + if not isinstance(models, list): models = [models] - + for model, state_dict in zip(models, state['models']): model.load_state_dict(state_dict) - + logger.info(f"Loaded checkpoint from {checkpoint_path}") - - return state \ No newline at end of file + + return state diff --git a/src/alignment/core/layer_detector.py b/src/alignment/core/layer_detector.py index f4fd6f80..0c1bfbd3 100644 --- a/src/alignment/core/layer_detector.py +++ b/src/alignment/core/layer_detector.py @@ -5,11 +5,11 @@ making the framework truly model-agnostic. """ -from typing import Dict, List, Optional, Tuple, Any -import torch -import torch.nn as nn import logging from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple + +import torch.nn as nn logger = logging.getLogger(__name__) @@ -29,20 +29,20 @@ class LayerInfo: class LayerDetector: """ Generic layer detection using structural analysis. - + Works for any model architecture without hard-coded naming patterns. Detects layer roles based on: - Layer type (Conv, Linear, etc.) - Dimension ratios - Position in network - Local graph structure - + Example: >>> detector = LayerDetector() >>> layers = detector.detect_all_layers(model) >>> ffn_layers = [l for l in layers if 'ffn' in l.role] """ - + def __init__( self, min_neurons: int = 1, @@ -51,7 +51,7 @@ def __init__( ): """ Initialize layer detector. - + Args: min_neurons: Minimum neurons/channels to track max_neurons: Maximum neurons/channels to track (None = no limit) @@ -60,7 +60,7 @@ def __init__( self.min_neurons = min_neurons self.max_neurons = max_neurons self.track_normalization = track_normalization - + def detect_all_layers( self, model: nn.Module, @@ -68,45 +68,45 @@ def detect_all_layers( ) -> List[LayerInfo]: """ Detect all trackable layers in a model. - + Args: model: PyTorch model include_roles: Filter by roles (None = all) - + Returns: List of LayerInfo objects """ all_layers = [] - + # Build module parent map parent_map = self._build_parent_map(model) - + # Analyze each module for name, module in model.named_modules(): # Detect layer type and role layer_type = self._classify_layer_type(module) - + if layer_type == 'skip': continue - + # Get dimensions in_dim, out_dim = self._get_dimensions(module) - + if in_dim is None or out_dim is None: continue - + # Filter by size if out_dim < self.min_neurons: continue if self.max_neurons and out_dim > self.max_neurons: continue - + # Infer role from structure role = self._infer_role(module, name, parent_map, layer_type) - + # Determine if trackable is_trackable = self._is_trackable(module, role) - + # Create LayerInfo layer_info = LayerInfo( name=name, @@ -117,20 +117,20 @@ def detect_all_layers( is_trackable=is_trackable, parent_block=parent_map.get(name) ) - + all_layers.append(layer_info) - + # Filter by requested roles if include_roles: all_layers = [l for l in all_layers if l.role in include_roles] - + # Filter by trackable all_layers = [l for l in all_layers if l.is_trackable] - + logger.info(f"Detected {len(all_layers)} trackable layers") - + return all_layers - + def _classify_layer_type(self, module: nn.Module) -> str: """Classify layer into basic categories.""" if isinstance(module, nn.Linear): @@ -147,28 +147,28 @@ def _classify_layer_type(self, module: nn.Module) -> str: return 'dropout' else: return 'skip' - + def _get_dimensions(self, module: nn.Module) -> Tuple[Optional[int], Optional[int]]: """Get input and output dimensions of a module.""" if isinstance(module, nn.Linear): return module.in_features, module.out_features - + elif isinstance(module, (nn.Conv2d, nn.Conv1d)): return module.in_channels, module.out_channels - + elif isinstance(module, nn.MultiheadAttention): return module.embed_dim, module.embed_dim - + elif hasattr(module, 'normalized_shape'): # LayerNorm if isinstance(module.normalized_shape, tuple): dim = module.normalized_shape[0] else: dim = module.normalized_shape return dim, dim - + else: return None, None - + def _infer_role( self, module: nn.Module, @@ -178,52 +178,52 @@ def _infer_role( ) -> str: """ Infer semantic role using structural cues (not names!). - + Returns: Role string like 'linear_general', 'ffn_expansion', 'attention_proj', etc. """ if layer_type == 'conv': return 'conv' - + elif layer_type == 'attention': return 'attention' - + elif layer_type == 'normalization': return 'normalization' - + elif layer_type == 'linear': # Infer Linear role from dimensions in_dim, out_dim = self._get_dimensions(module) - + # Check dimension ratios (model-agnostic!) ratio = out_dim / in_dim if in_dim > 0 else 1.0 - + if ratio > 2.5: # Expansion (likely FFN up_proj or gate_proj) return 'ffn_expansion' - + elif ratio < 0.4: # Contraction (likely FFN down_proj) return 'ffn_contraction' - + elif 0.9 <= ratio <= 1.1: # Same dimensions (likely attention Q/K/V/O or residual) # Check if part of attention block (heuristic) - parent = parent_map.get(name, '') - + parent_map.get(name, '') + # Look for attention-related siblings if self._has_attention_siblings(name, parent_map): return 'attention_projection' else: return 'linear_residual' - + else: # General linear layer return 'linear_general' - + else: return layer_type - + def _has_attention_siblings( self, layer_name: str, @@ -231,7 +231,7 @@ def _has_attention_siblings( ) -> bool: """ Check if layer has siblings that look like Q/K/V projections. - + Heuristic: If there are 3-4 Linear layers with same dimensions in the same parent block, likely attention. """ @@ -239,10 +239,10 @@ def _has_attention_siblings( parent = parent_map.get(layer_name) if not parent: return False - + # Find siblings (other layers with same parent) siblings = [name for name, p in parent_map.items() if p == parent] - + # Count Linear siblings with similar dimensions linear_siblings = 0 for sibling in siblings: @@ -250,46 +250,46 @@ def _has_attention_siblings( # In practice, would check actual modules if 'linear' in sibling.lower() or 'proj' in sibling.lower(): linear_siblings += 1 - + # If 3-4 Linear siblings, likely Q/K/V (+ maybe O) return linear_siblings >= 3 - + def _build_parent_map(self, model: nn.Module) -> Dict[str, str]: """Build map of module names to parent names.""" parent_map = {} - + for name, module in model.named_modules(): # Get parent name if '.' in name: parent = '.'.join(name.split('.')[:-1]) parent_map[name] = parent - + return parent_map - + def _is_trackable(self, module: nn.Module, role: str) -> bool: """Determine if layer should be tracked for metrics.""" # Skip non-parametric layers if not hasattr(module, 'weight') or module.weight is None: return False - + # Skip normalization unless requested if role == 'normalization' and not self.track_normalization: return False - + # Track everything else return True - + def group_by_role(self, layers: List[LayerInfo]) -> Dict[str, List[LayerInfo]]: """Group layers by their role.""" grouped = {} - + for layer in layers: if layer.role not in grouped: grouped[layer.role] = [] grouped[layer.role].append(layer) - + return grouped - + def get_layers_by_role( self, model: nn.Module, @@ -307,12 +307,12 @@ def detect_trackable_layers( ) -> List[str]: """ Convenience function to get trackable layer names. - + Args: model: PyTorch model min_neurons: Minimum size to track roles: Filter by roles (None = all) - + Returns: List of layer names suitable for tracking """ diff --git a/src/alignment/core/protocols.py b/src/alignment/core/protocols.py index 16132303..8e3e44f4 100644 --- a/src/alignment/core/protocols.py +++ b/src/alignment/core/protocols.py @@ -5,35 +5,36 @@ ensuring consistency and enabling easy extension of the framework. """ -from typing import Protocol, Optional, Dict, List, Any, Tuple, Union +from typing import Any, Dict, List, Optional, Protocol, Tuple, Union + import torch import torch.nn as nn -from torch.utils.data import DataLoader, Dataset +from torch.utils.data import DataLoader class AlignmentMetric(Protocol): """Protocol for all alignment metrics.""" - + @property def name(self) -> str: """Unique name identifier for the metric.""" ... - + @property def requires_inputs(self) -> bool: """Whether this metric requires layer inputs.""" ... - + @property def requires_weights(self) -> bool: """Whether this metric requires layer weights.""" ... - + @property def requires_outputs(self) -> bool: """Whether this metric requires layer outputs.""" ... - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -43,18 +44,18 @@ def compute( ) -> torch.Tensor: """ Compute the metric values. - + Args: inputs: Input activations to the layer [batch_size, input_features] weights: Layer weights [output_features, input_features] outputs: Output activations from the layer [batch_size, output_features] **kwargs: Additional metric-specific parameters - + Returns: Tensor of metric values, typically [output_features] for per-node metrics """ ... - + def compute_distributed( self, inputs: Optional[torch.Tensor] = None, @@ -70,48 +71,48 @@ def compute_distributed( class ModelWrapper(Protocol): """Protocol for model wrappers that support alignment analysis.""" - + @property def model(self) -> nn.Module: """The underlying PyTorch model.""" ... - + @property def tracked_layers(self) -> List[str]: """List of layer names being tracked for alignment.""" ... - + def get_layer_activations( - self, + self, inputs: torch.Tensor, layers: Optional[List[str]] = None ) -> Dict[str, torch.Tensor]: """ Get activations for specified layers. - + Args: inputs: Input tensor to the model layers: Specific layers to get activations for (None = all tracked) - + Returns: Dictionary mapping layer names to activation tensors """ ... - + def get_layer_weights( self, layers: Optional[List[str]] = None ) -> Dict[str, torch.Tensor]: """Get weights for specified layers.""" ... - + def forward_with_activations( self, inputs: torch.Tensor ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: """Forward pass that also returns intermediate activations.""" ... - + def apply_dropout_mask( self, dropout_masks: Dict[str, torch.Tensor], @@ -123,22 +124,22 @@ def apply_dropout_mask( class DatasetWrapper(Protocol): """Protocol for dataset wrappers.""" - + @property def name(self) -> str: """Dataset name.""" ... - + @property def num_classes(self) -> int: """Number of classes in the dataset.""" ... - + @property def input_shape(self) -> Tuple[int, ...]: """Shape of a single input sample (excluding batch dimension).""" ... - + def get_train_loader( self, batch_size: int, @@ -148,7 +149,7 @@ def get_train_loader( ) -> DataLoader: """Get training data loader.""" ... - + def get_val_loader( self, batch_size: int, @@ -158,7 +159,7 @@ def get_val_loader( ) -> DataLoader: """Get validation data loader.""" ... - + def get_test_loader( self, batch_size: int, @@ -172,21 +173,21 @@ def get_test_loader( class Experiment(Protocol): """Protocol for experiments.""" - + @property def name(self) -> str: """Experiment name.""" ... - + @property def config(self) -> Dict[str, Any]: """Experiment configuration.""" ... - + def setup(self) -> None: """Setup the experiment (called once before running).""" ... - + def run( self, models: Union[nn.Module, List[nn.Module]], @@ -195,21 +196,21 @@ def run( ) -> Dict[str, Any]: """ Run the experiment. - + Args: models: Model or list of models to experiment on dataset: Dataset to use **kwargs: Additional experiment-specific parameters - + Returns: Dictionary of experiment results """ ... - + def save_results(self, results: Dict[str, Any], path: str) -> None: """Save experiment results.""" ... - + def load_results(self, path: str) -> Dict[str, Any]: """Load experiment results.""" ... @@ -217,12 +218,12 @@ def load_results(self, path: str) -> Dict[str, Any]: class MetricAggregator(Protocol): """Protocol for metric aggregation strategies.""" - + @property def name(self) -> str: """Aggregator name.""" ... - + def aggregate( self, metrics: Dict[str, torch.Tensor], @@ -231,12 +232,12 @@ def aggregate( ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: """ Aggregate metrics across layers or other dimensions. - + Args: metrics: Dictionary of metric values by layer mode: Aggregation mode ("layer", "global", "network", etc.) **kwargs: Additional aggregation parameters - + Returns: Aggregated metrics """ @@ -245,12 +246,12 @@ def aggregate( class ResultReporter(Protocol): """Protocol for result reporting and visualization.""" - + @property def name(self) -> str: """Reporter name.""" ... - + def report( self, results: Dict[str, Any], @@ -259,14 +260,14 @@ def report( ) -> None: """ Generate report from results. - + Args: results: Experiment results to report output_path: Optional path to save report **kwargs: Additional reporting parameters """ ... - + def visualize( self, results: Dict[str, Any], @@ -274,4 +275,4 @@ def visualize( **kwargs: Any ) -> Any: """Generate visualizations from results.""" - ... \ No newline at end of file + ... diff --git a/src/alignment/core/registry.py b/src/alignment/core/registry.py index ee5056e7..713b7aaa 100644 --- a/src/alignment/core/registry.py +++ b/src/alignment/core/registry.py @@ -5,9 +5,8 @@ datasets, and experiments, making them easily discoverable and instantiable. """ -from typing import Dict, Type, Any, Optional, Callable, TypeVar, Union, List import logging -from functools import wraps +from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union logger = logging.getLogger(__name__) @@ -16,18 +15,18 @@ class Registry: """Generic registry for framework components.""" - + def __init__(self, name: str): """ Initialize a registry. - + Args: name: Name of the registry (e.g., "metrics", "models") """ self.name = name self._registry: Dict[str, Type[Any]] = {} self._metadata: Dict[str, Dict[str, Any]] = {} - + def register( self, name: str, @@ -36,14 +35,14 @@ def register( ) -> Union[Callable[[Type[T]], Type[T]], Type[T]]: """ Register a class in the registry. - + Can be used as a decorator or called directly. - + Args: name: Name to register the class under cls: Class to register (if not using as decorator) **metadata: Additional metadata to store with the registration - + Returns: Registered class or decorator function """ @@ -54,31 +53,31 @@ def decorator(cls_to_register: Type[T]) -> Type[T]: ) self._registry[name] = cls_to_register self._metadata[name] = metadata - + # Add registry info to the class setattr(cls_to_register, '_registry_name', name) setattr(cls_to_register, '_registry', self.name) - + logger.debug(f"Registered '{name}' in {self.name} registry") return cls_to_register - + if cls is None: # Used as decorator return decorator else: # Direct registration return decorator(cls) - + def get(self, name: str) -> Type[Any]: """ Get a registered class by name. - + Args: name: Name of the registered class - + Returns: The registered class - + Raises: KeyError: If name is not registered """ @@ -89,33 +88,33 @@ def get(self, name: str) -> Type[Any]: f"Available: {available}" ) return self._registry[name] - + def get_metadata(self, name: str) -> Dict[str, Any]: """Get metadata for a registered class.""" return self._metadata.get(name, {}) - + def list(self) -> List[str]: """List all registered names.""" return list(self._registry.keys()) - + def create(self, name: str, **kwargs: Any) -> Any: """ Create an instance of a registered class. - + Args: name: Name of the registered class **kwargs: Arguments to pass to the class constructor - + Returns: Instance of the registered class """ cls = self.get(name) return cls(**kwargs) - + def __contains__(self, name: str) -> bool: """Check if a name is registered.""" return name in self._registry - + def __len__(self) -> int: """Get number of registered items.""" return len(self._registry) @@ -196,17 +195,17 @@ def get_reporter(name: str, **kwargs: Any) -> Any: def discover_and_register(module_path: str, registry_type: str = "all") -> None: """ Auto-discover and register components from a module. - + Args: module_path: Python module path to scan registry_type: Type of components to register ("all", "metrics", etc.) """ import importlib import pkgutil - + try: module = importlib.import_module(module_path) - + # Recursively walk through submodules for importer, modname, ispkg in pkgutil.walk_packages( path=module.__path__, @@ -218,6 +217,6 @@ def discover_and_register(module_path: str, registry_type: str = "all") -> None: logger.debug(f"Imported module: {modname}") except Exception as e: logger.warning(f"Failed to import {modname}: {e}") - + except Exception as e: - logger.error(f"Failed to discover components from {module_path}: {e}") \ No newline at end of file + logger.error(f"Failed to discover components from {module_path}: {e}") diff --git a/src/alignment/data/__init__.py b/src/alignment/data/__init__.py index 1affc42e..8a813650 100644 --- a/src/alignment/data/__init__.py +++ b/src/alignment/data/__init__.py @@ -6,18 +6,18 @@ """ from alignment.data.base import BaseDataset, DatasetWrapper +from alignment.data.datasets import get_dataset from alignment.data.loaders import ( + DataLoaderConfig, create_data_loader, create_distributed_loader, - DataLoaderConfig, ) -from alignment.data.datasets import get_dataset # Import dataset implementations when they're created try: - from alignment.data.datasets.mnist import MNISTDataset from alignment.data.datasets.cifar import CIFAR10Dataset, CIFAR100Dataset from alignment.data.datasets.imagenet import ImageNetDataset + from alignment.data.datasets.mnist import MNISTDataset except ImportError: pass # Datasets will be implemented next diff --git a/src/alignment/data/base.py b/src/alignment/data/base.py index 1731291e..1b2f11f8 100644 --- a/src/alignment/data/base.py +++ b/src/alignment/data/base.py @@ -6,14 +6,15 @@ ensuring consistency across different datasets. """ -from typing import Dict, Tuple, Optional, Any, List, Union, Callable -import torch -from torch.utils.data import Dataset, DataLoader, Sampler -import torch.distributed as dist -from abc import ABC, abstractmethod import logging -import numpy as np +from abc import ABC, abstractmethod from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.distributed as dist +from torch.utils.data import DataLoader, Dataset, Sampler from alignment.core.base import BaseDataset as CoreBaseDataset @@ -227,7 +228,7 @@ def get_class_balanced_sampler(self) -> Sampler: Sampler for class-balanced training """ from torch.utils.data import WeightedRandomSampler - + # Get class counts if hasattr(self._dataset, 'targets'): targets = torch.tensor(self._dataset.targets) diff --git a/src/alignment/data/datasets/__init__.py b/src/alignment/data/datasets/__init__.py index a253d1b2..aa9fa2ff 100644 --- a/src/alignment/data/datasets/__init__.py +++ b/src/alignment/data/datasets/__init__.py @@ -5,24 +5,20 @@ various dataset types without code duplication. """ -from typing import Tuple, Optional -import torch.utils.data +from typing import Optional, Tuple -from alignment.data.datasets.unified_dataset import ( - UnifiedDataset, - DATASET_CONFIGS, -) +import torch.utils.data +# Import for backward compatibility - these are now created dynamically +# but we import them to make them available at module level +from alignment.core.registry import DATASET_REGISTRY from alignment.data.datasets.text_datasets import ( + C4Dataset, TextDataset, WikiTextDataset, - C4Dataset, load_text_dataset, ) - -# Import for backward compatibility - these are now created dynamically -# but we import them to make them available at module level -from alignment.core.registry import DATASET_REGISTRY +from alignment.data.datasets.unified_dataset import DATASET_CONFIGS, UnifiedDataset # Get dynamically created dataset classes MNISTDataset = DATASET_REGISTRY.get("mnist") diff --git a/src/alignment/data/datasets/unified_dataset.py b/src/alignment/data/datasets/unified_dataset.py index 417346ce..1ab23b15 100644 --- a/src/alignment/data/datasets/unified_dataset.py +++ b/src/alignment/data/datasets/unified_dataset.py @@ -5,15 +5,16 @@ various dataset types without code duplication. """ -from typing import Optional, List, Callable, Tuple, Dict, Any, Union +import logging +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + import torch from torch.utils.data import Dataset from torchvision import datasets, transforms -from pathlib import Path -import logging -from alignment.data.base import BaseDataset from alignment.core.registry import register_dataset +from alignment.data.base import BaseDataset logger = logging.getLogger(__name__) diff --git a/src/alignment/data/loaders.py b/src/alignment/data/loaders.py index e6bd3dc5..004b89ae 100644 --- a/src/alignment/data/loaders.py +++ b/src/alignment/data/loaders.py @@ -5,12 +5,18 @@ proper configuration for distributed training and memory efficiency. """ -from typing import Optional, Any, Dict, Union +import logging from dataclasses import dataclass +from typing import Any, Dict, Optional, Union + import torch import torch.distributed as dist -from torch.utils.data import DataLoader, DistributedSampler, RandomSampler, SequentialSampler -import logging +from torch.utils.data import ( + DataLoader, + DistributedSampler, + RandomSampler, + SequentialSampler, +) logger = logging.getLogger(__name__) diff --git a/src/alignment/data/processing/__init__.py b/src/alignment/data/processing/__init__.py index 1828fefd..f10144bf 100644 --- a/src/alignment/data/processing/__init__.py +++ b/src/alignment/data/processing/__init__.py @@ -7,17 +7,14 @@ """ from .batch import BatchMetricProcessor +from .covariance import CovarianceEstimator, estimate_covariance from .layers import ( + AttentionPreprocessor, + CNNPreprocessor, LayerPreprocessor, LinearPreprocessor, - CNNPreprocessor, - AttentionPreprocessor, - preprocess_layer_activations, get_preprocessor, -) -from .covariance import ( - CovarianceEstimator, - estimate_covariance, + preprocess_layer_activations, ) __all__ = [ diff --git a/src/alignment/data/processing/batch.py b/src/alignment/data/processing/batch.py index d9143c52..37827bb2 100644 --- a/src/alignment/data/processing/batch.py +++ b/src/alignment/data/processing/batch.py @@ -2,10 +2,11 @@ Batch processing utilities for efficient metric computation on large datasets. """ +import logging +from typing import Any, Callable, Dict, List, Optional, Tuple + import torch -from typing import Dict, List, Optional, Callable, Tuple, Any from torch.utils.data import DataLoader -import logging from tqdm import tqdm logger = logging.getLogger(__name__) @@ -264,9 +265,10 @@ def compute_metrics_parallel( Returns: Results dictionary """ - import torch.multiprocessing as mp from concurrent.futures import ProcessPoolExecutor, as_completed - + + import torch.multiprocessing as mp + # Determine devices if devices is None: if torch.cuda.is_available(): diff --git a/src/alignment/data/processing/layers.py b/src/alignment/data/processing/layers.py index 76eb1225..bb1efa80 100644 --- a/src/alignment/data/processing/layers.py +++ b/src/alignment/data/processing/layers.py @@ -5,11 +5,12 @@ including convolutional, linear, and other specialized layers. """ -from typing import Dict, Any, Optional, Union, Tuple, List +import logging from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional, Tuple, Union + import torch import torch.nn as nn -import logging logger = logging.getLogger(__name__) diff --git a/src/alignment/evaluation.py b/src/alignment/evaluation.py index 76b8b16c..27d6fd06 100644 --- a/src/alignment/evaluation.py +++ b/src/alignment/evaluation.py @@ -4,10 +4,11 @@ Provides standard evaluation functions for different model types and tasks. """ +import logging +from typing import Dict, Optional + import torch import torch.nn as nn -from typing import Optional, Dict, Any, Callable -import logging logger = logging.getLogger(__name__) @@ -20,40 +21,40 @@ def evaluate_classification( ) -> Dict[str, float]: """ Evaluate classification model. - + Args: model: Model to evaluate data_loader: Validation/test dataloader device: Device to use criterion: Loss function (default: CrossEntropyLoss) - + Returns: Dict with 'loss' and 'accuracy' """ if criterion is None: criterion = nn.CrossEntropyLoss() - + model.eval() total_loss = 0.0 correct = 0 total = 0 - + with torch.no_grad(): for inputs, targets in data_loader: inputs, targets = inputs.to(device), targets.to(device) - + outputs = model(inputs) loss = criterion(outputs, targets) - + total_loss += loss.item() - + _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() - + avg_loss = total_loss / len(data_loader) accuracy = 100. * correct / total - + return { 'loss': avg_loss, 'accuracy': accuracy @@ -68,25 +69,25 @@ def evaluate_perplexity( ) -> Dict[str, float]: """ Evaluate language model perplexity. - + Args: model: Language model data_loader: Text dataloader with 'input_ids' and 'labels' device: Device to use max_batches: Maximum batches to evaluate (None = all) - + Returns: Dict with 'perplexity' and 'loss' """ model.eval() total_loss = 0.0 total_tokens = 0 - + with torch.no_grad(): for batch_idx, batch in enumerate(data_loader): if max_batches and batch_idx >= max_batches: break - + # Handle different batch formats if isinstance(batch, dict): input_ids = batch['input_ids'].to(device) @@ -95,10 +96,10 @@ def evaluate_perplexity( input_ids, labels = batch input_ids = input_ids.to(device) labels = labels.to(device) - + # Forward pass outputs = model(input_ids, labels=labels) - + # Get loss if hasattr(outputs, 'loss'): loss = outputs.loss @@ -109,15 +110,15 @@ def evaluate_perplexity( logits.view(-1, logits.size(-1)), labels.view(-1) ) - + # Accumulate batch_tokens = (labels != -100).sum().item() # Count non-padding tokens total_loss += loss.item() * batch_tokens total_tokens += batch_tokens - + avg_loss = total_loss / total_tokens perplexity = torch.exp(torch.tensor(avg_loss)).item() - + return { 'perplexity': perplexity, 'loss': avg_loss @@ -133,26 +134,26 @@ def evaluate_model( ) -> Dict[str, float]: """ General evaluation function that dispatches to task-specific evaluators. - + Args: model: Model to evaluate data_loader: Dataloader task: Task type ('classification', 'language_modeling', 'regression') device: Device **kwargs: Additional arguments for specific evaluators - + Returns: Evaluation metrics dictionary """ if task == 'classification': return evaluate_classification(model, data_loader, device, **kwargs) - + elif task in ['language_modeling', 'lm', 'causal_lm']: return evaluate_perplexity(model, data_loader, device, **kwargs) - + elif task == 'regression': return evaluate_regression(model, data_loader, device, **kwargs) - + else: raise ValueError(f"Unknown task: {task}") @@ -165,38 +166,38 @@ def evaluate_regression( ) -> Dict[str, float]: """ Evaluate regression model. - + Args: model: Regression model data_loader: Dataloader device: Device criterion: Loss function (default: MSELoss) - + Returns: Dict with 'mse' and 'mae' """ if criterion is None: criterion = nn.MSELoss() - + model.eval() total_mse = 0.0 total_mae = 0.0 total = 0 - + with torch.no_grad(): for inputs, targets in data_loader: inputs, targets = inputs.to(device), targets.to(device) - + outputs = model(inputs) - + mse = nn.MSELoss()(outputs, targets).item() mae = nn.L1Loss()(outputs, targets).item() - + batch_size = inputs.size(0) total_mse += mse * batch_size total_mae += mae * batch_size total += batch_size - + return { 'mse': total_mse / total, 'mae': total_mae / total @@ -206,20 +207,20 @@ def evaluate_regression( class EvaluationManager: """ Manager for handling multiple evaluation metrics. - + Useful for tracking multiple metrics during experiments. """ - + def __init__(self, task: str = 'classification'): """ Initialize evaluation manager. - + Args: task: Primary task type """ self.task = task self.history = [] - + def evaluate( self, model: nn.Module, @@ -230,43 +231,43 @@ def evaluate( ) -> Dict[str, float]: """ Evaluate and record results. - + Args: model: Model to evaluate data_loader: Dataloader device: Device step: Training step number (for tracking) **kwargs: Additional arguments - + Returns: Evaluation metrics """ results = evaluate_model(model, data_loader, self.task, device, **kwargs) - + if step is not None: results['step'] = step - + self.history.append(results) - + return results - + def get_history(self) -> list: """Get evaluation history.""" return self.history - + def get_best(self, metric: str = 'accuracy') -> Dict[str, float]: """ Get best result based on metric. - + Args: metric: Metric to optimize ('accuracy', 'perplexity', 'loss') - + Returns: Best evaluation result """ if not self.history: return {} - + if metric in ['perplexity', 'loss', 'mse', 'mae']: # Lower is better return min(self.history, key=lambda x: x.get(metric, float('inf'))) diff --git a/src/alignment/experiments/__init__.py b/src/alignment/experiments/__init__.py index afd24683..05a8b0a5 100644 --- a/src/alignment/experiments/__init__.py +++ b/src/alignment/experiments/__init__.py @@ -6,26 +6,26 @@ """ from .base import BaseExperiment, ExperimentConfig -from .general_alignment import GeneralAlignmentExperiment, GeneralAlignmentConfig -from .llm_experiments import LLMAlignmentExperiment # Configuration components from .config_components import ( - TrainingConfig, - PruningConfig, - EvaluationConfig, CNNConfig, + EvaluationConfig, MultiNetworkConfig, + PruningConfig, + TrainingConfig, + create_backward_compatible_config, create_config_from_dict, - create_backward_compatible_config ) +from .general_alignment import GeneralAlignmentConfig, GeneralAlignmentExperiment +from .llm_experiments import LLMAlignmentExperiment # Training utilities from .training_utils import ( + convert_training_history, create_experiment_trainer, - train_with_metrics, evaluate_with_metrics, - convert_training_history + train_with_metrics, ) __all__ = [ diff --git a/src/alignment/experiments/base.py b/src/alignment/experiments/base.py index e862c9b4..18817f51 100644 --- a/src/alignment/experiments/base.py +++ b/src/alignment/experiments/base.py @@ -5,20 +5,21 @@ handling common functionality like checkpointing, logging, and metrics. """ -from typing import Dict, List, Optional, Any, Union, Callable -from dataclasses import dataclass, field -from pathlib import Path -import torch -import logging -from abc import ABC, abstractmethod import json +import logging import time +from abc import ABC, abstractmethod +from dataclasses import dataclass, field from datetime import datetime +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Union + +import torch from alignment.core.base import BaseExperiment as CoreBaseExperiment -from alignment.core.registry import get_metric, get_model, get_dataset, DATASET_REGISTRY -from alignment.models import ModelWrapper +from alignment.core.registry import DATASET_REGISTRY, get_dataset, get_metric, get_model from alignment.data.loaders import create_distributed_loader +from alignment.models import ModelWrapper logger = logging.getLogger(__name__) @@ -236,7 +237,7 @@ def _initialize_model(self): # Try to get model from registry first try: from alignment.core.registry import MODEL_REGISTRY - + # Handle parameter mapping for specific models model_kwargs = self.config.model_config.copy() diff --git a/src/alignment/experiments/config_components.py b/src/alignment/experiments/config_components.py index 14c3e6ec..9b0fd2ea 100644 --- a/src/alignment/experiments/config_components.py +++ b/src/alignment/experiments/config_components.py @@ -6,7 +6,7 @@ """ from dataclasses import dataclass, field -from typing import List, Optional, Dict, Any +from typing import Any, Dict, List, Optional @dataclass diff --git a/src/alignment/experiments/general_alignment.py b/src/alignment/experiments/general_alignment.py index e84015b6..ac3f4785 100644 --- a/src/alignment/experiments/general_alignment.py +++ b/src/alignment/experiments/general_alignment.py @@ -9,25 +9,30 @@ - Generate comprehensive visualizations """ -from typing import Dict, List, Optional, Any, Tuple -import torch -import torch.nn as nn -import numpy as np +import copy import logging -from dataclasses import dataclass, field -from pathlib import Path import multiprocessing as mp -import copy import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn -from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.core.registry import register_experiment +from alignment.data.processing import preprocess_layer_activations +from alignment.experiments.base import BaseExperiment, ExperimentConfig +from alignment.metrics.rayleigh.rayleigh_quotient import RayleighQuotient from alignment.models import ModelWrapper from alignment.pruning.base import PruningConfig +from alignment.pruning.strategies import ( + MagnitudePruning, + ParallelBatchPruning, + RandomPruning, +) from alignment.services import ActivationCaptureService, MaskOperations -from alignment.pruning.strategies import MagnitudePruning, RandomPruning, ParallelBatchPruning -from alignment.metrics.rayleigh.rayleigh_quotient import RayleighQuotient -from alignment.data.processing import preprocess_layer_activations logger = logging.getLogger(__name__) @@ -1010,12 +1015,19 @@ def _pruning_experiments_single(self) -> Dict[str, Any]: if strategy_name == "magnitude": if pruning_config.global_pruning: - from alignment.pruning.strategies import GlobalMagnitudePruning + from alignment.pruning.strategies import ( + GlobalMagnitudePruning, + ) strategy = GlobalMagnitudePruning(config=pruning_config) else: strategy = MagnitudePruning(config=pruning_config) elif strategy_name == "alignment": - from alignment.pruning.strategies import AlignmentPruning, GlobalAlignmentPruning, CascadingAlignmentPruning + from alignment.pruning.strategies import ( + AlignmentPruning, + CascadingAlignmentPruning, + GlobalAlignmentPruning, + ) + # Get the alignment metric from config (default to rayleigh_quotient) alignment_metric = getattr(self.config, 'pruning_alignment_metric', 'rayleigh_quotient') @@ -1040,7 +1052,9 @@ def _pruning_experiments_single(self) -> Dict[str, Any]: elif strategy_name == "cascading_alignment": # Legacy cascading_alignment handling logger.warning("'cascading_alignment' algorithm is deprecated. Use algorithms=['alignment'] with scope='cascading'") - from alignment.pruning.strategies import CascadingAlignmentPruning + from alignment.pruning.strategies import ( + CascadingAlignmentPruning, + ) alignment_metric = getattr(self.config, 'pruning_alignment_metric', 'rayleigh_quotient') pruning_config.structured = True strategy = CascadingAlignmentPruning( @@ -2284,9 +2298,10 @@ def _generate_visualizations(self): # Pruning experiments - now enhanced with before/after comparisons if self.pruning_results and "strategies" in self.pruning_results: - from alignment.analysis.visualization import UnifiedVisualizer import matplotlib.pyplot as plt - + + from alignment.analysis.visualization import UnifiedVisualizer + # Group results by algorithm (for multi-selection mode comparison) algorithm_results = {} @@ -2502,7 +2517,10 @@ def _create_pruning_strategy(self, strategy_name: str, pruning_config: PruningCo else: return MagnitudePruning(config=pruning_config) elif strategy_name == "alignment": - from alignment.pruning.strategies import AlignmentPruning, GlobalAlignmentPruning + from alignment.pruning.strategies import ( + AlignmentPruning, + GlobalAlignmentPruning, + ) alignment_metric = getattr(self.config, 'pruning_alignment_metric', 'rayleigh_quotient') if self.config.pruning_scope == 'global': diff --git a/src/alignment/experiments/llm_experiments.py b/src/alignment/experiments/llm_experiments.py index a113c0d4..3d8225f2 100644 --- a/src/alignment/experiments/llm_experiments.py +++ b/src/alignment/experiments/llm_experiments.py @@ -9,17 +9,18 @@ - Multi-metric comparison """ -from typing import Dict, List, Optional, Any, Tuple +import logging +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + import torch import torch.nn as nn -from pathlib import Path -import logging -from .base import BaseExperiment -from ..models.transformers import TransformerWrapperEnhanced as TransformerWrapper from ..metrics import get_metric +from ..models.transformers import TransformerWrapperEnhanced as TransformerWrapper from ..pruning import AlignmentPruning, PruningConfig from ..training.base import BaseTrainer +from .base import BaseExperiment logger = logging.getLogger(__name__) diff --git a/src/alignment/experiments/runner.py b/src/alignment/experiments/runner.py index 6e709721..55be3b1b 100644 --- a/src/alignment/experiments/runner.py +++ b/src/alignment/experiments/runner.py @@ -2,17 +2,18 @@ Experiment runner for managing and executing experiments. """ -from typing import Dict, List, Optional, Any, Union, Type -from pathlib import Path -import torch +import json import logging -from datetime import datetime import multiprocessing as mp from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor -import json +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional, Type, Union + +import torch -from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.core.registry import get_experiment +from alignment.experiments.base import BaseExperiment, ExperimentConfig logger = logging.getLogger(__name__) diff --git a/src/alignment/experiments/tracking/__init__.py b/src/alignment/experiments/tracking/__init__.py index ef7d9b51..a1ceb609 100644 --- a/src/alignment/experiments/tracking/__init__.py +++ b/src/alignment/experiments/tracking/__init__.py @@ -1,11 +1,11 @@ """Experiment tracking utilities for the alignment framework.""" from .base import ( + DummyTracker, ExperimentTracker, - WandBTracker, - TensorBoardTracker, MultiTracker, - DummyTracker, + TensorBoardTracker, + WandBTracker, create_tracker, ) diff --git a/src/alignment/experiments/tracking/base.py b/src/alignment/experiments/tracking/base.py index c8030237..e409f6fd 100644 --- a/src/alignment/experiments/tracking/base.py +++ b/src/alignment/experiments/tracking/base.py @@ -2,12 +2,13 @@ Experiment tracking integration for alignment metrics. """ -import torch -from typing import Dict, Any, Optional, List, Union +import json import logging from pathlib import Path -import json +from typing import Any, Dict, List, Optional, Union + import numpy as np +import torch logger = logging.getLogger(__name__) diff --git a/src/alignment/experiments/training_utils.py b/src/alignment/experiments/training_utils.py index 6133400b..af4ca06c 100644 --- a/src/alignment/experiments/training_utils.py +++ b/src/alignment/experiments/training_utils.py @@ -2,10 +2,11 @@ Utility functions for integrating ExperimentTrainer into experiments. """ -from typing import Dict, Any, Optional, Union, List +from dataclasses import asdict +from typing import Any, Dict, List, Optional, Union + import torch import torch.nn as nn -from dataclasses import asdict from alignment.training import ExperimentTrainer, ExperimentTrainingConfig diff --git a/src/alignment/external/BROJA_2PID/BROJA_2PID.py b/src/alignment/external/BROJA_2PID/BROJA_2PID.py index 72097b9c..eff37017 100644 --- a/src/alignment/external/BROJA_2PID/BROJA_2PID.py +++ b/src/alignment/external/BROJA_2PID/BROJA_2PID.py @@ -20,14 +20,14 @@ # Please cite this paper when you use this software (cf. README.md) ############################################################################################################## -import ecos -from scipy import sparse -import numpy as np -from numpy import linalg as LA +import logging import math +import time from collections import defaultdict -import time -import logging + +import ecos +import numpy as np +from scipy import sparse log = math.log2 ln = math.log @@ -104,7 +104,7 @@ def create_model(self): m = len(self.b_xy) + len(self.b_xz) n_vars = 3*n n_cons = n+m - + # # Create the equations: Ax = b # @@ -258,7 +258,7 @@ def condYmutinf(self): mysum = 0. for x in self.X: for z in self.Z: - if not (x,z) in self.b_xz.keys(): continue + if (x,z) not in self.b_xz.keys(): continue for y in self.Y: if (x,y,z) in self.idx_of_trip.keys(): i = q_vidx(self.idx_of_trip[ (x,y,z) ]) @@ -277,7 +277,7 @@ def condZmutinf(self): mysum = 0. for x in self.X: for y in self.Y: - if not (x,y) in self.b_xy.keys(): continue + if (x,y) not in self.b_xy.keys(): continue for z in self.Z: if (x,y,z) in self.idx_of_trip.keys(): i = q_vidx(self.idx_of_trip[ (x,y,z) ]) @@ -295,7 +295,7 @@ def entropy_X(self,pdf): for x in self.X: psum = 0. for y in self.Y: - if not (x,y) in self.b_xy: continue + if (x,y) not in self.b_xy: continue for z in self.Z: if (x,y,z) in pdf.keys(): psum += pdf[(x,y,z)] @@ -344,7 +344,7 @@ def condentropy__orig(self,pdf): def dual_value(self): return -np.dot(self.sol_lambda, self.b) #^ dual_value() - + def check_feasibility(self): # returns pair (p,d) of primal/dual infeasibility (maxima) # Primal infeasiblility # --------------------- @@ -381,7 +381,7 @@ def check_feasibility(self): # returns pair (p,d) of primal/dual infeasibility ( #^ fox xz primal_infeasability = max(max_violation_of_eqn,max_q_negativity) - + # Dual infeasiblility # ------------------- idx_of_xy = dict() @@ -403,14 +403,14 @@ def check_feasibility(self): # returns pair (p,d) of primal/dual infeasibility ( #^ for dual_infeasability = 0. - + # Compute mu_*yz # mu_xyz: dual variable of the coupling constraints mu_yz = defaultdict(lambda: 0.) for j,xyz in enumerate(self.trip_of_idx): x,y,z = xyz mu_yz[(y,z)] += self.sol_lambda[j] - + for i,xyz in enumerate(self.trip_of_idx): x,y,z = xyz @@ -427,7 +427,7 @@ def check_feasibility(self): # returns pair (p,d) of primal/dual infeasibility ( ) #^ for - + # for i,xyz in enumerate(self.trip_of_idx): # x,y,z = xyz # mu_yz = 0. @@ -441,7 +441,7 @@ def check_feasibility(self): # returns pair (p,d) of primal/dual infeasibility ( # u,v,w = uvw # if v == y and w == z: # mu_yz += self.sol_lambda[j] - + # # Find the most violated dual ieq # dual_infeasability = max( dual_infeasability, -self.sol_lambda[xy_idx] # - self.sol_lambda[xz_idx] @@ -483,7 +483,7 @@ def I_Y(p): if r > 0 : marg_x[x] += r marg_y[y] += r - + for xy,t in b_xy.items(): x,y = xy if t > 0: mysum += t * log( t / ( marg_x[x]*marg_y[y] ) ) @@ -501,7 +501,7 @@ def I_Z(p): if r > 0 : marg_x[x] += r marg_z[z] += r - + for xz,t in b_xz.items(): x,z = xz if t > 0: mysum += t * log( t / ( marg_x[x]*marg_z[z] ) ) @@ -518,7 +518,7 @@ def I_YZ(p): if r > 0 : marg_x[x] += r marg_yz[(y,z)] += r - + for xyz,t in p.items(): x,y,z = xyz if t > 0: mysum += t * log( t / ( marg_x[x]*marg_yz[(y,z)] ) ) @@ -572,7 +572,7 @@ def pid(pdf_dirty, cone_solver="ECOS", output=0, **solver_args): logger.info("BROJA_2PID: Starting solver...") if output > 1: # print("BROJA_2PID: Starting solver.") logger.debug("BROJA_2PID: Starting solver (verbose). Details to follow.") # Using debug for output > 1 - + retval = solver.solve() if retval != "success": # print("\\nCone Programming solver failed to find (near) optimal solution.\\nPlease report the input probability density function to abdullah.makkeh@gmail.com\\n") @@ -611,10 +611,10 @@ def pid(pdf_dirty, cone_solver="ECOS", output=0, **solver_args): return_data["UIZ"] = ( condYmutinf ) * bits return_data["CI"] = ( condent - condent__orig ) * bits - itic = time.process_time() + itic = time.process_time() primal_infeas,dual_infeas = solver.check_feasibility() itoc = time.process_time() - if output > 0: # print("Time to check optimiality conditions: ",itoc - itic,"secs") + if output > 0: # print("Time to check optimiality conditions: ",itoc - itic,"secs") logger.info(f"BROJA_2PID: Time to check optimiality conditions: {itoc - itic:.4f} secs") return_data["Num_err"] = (primal_infeas, dual_infeas, max(-condent*ln(2) - dual_val, 0.0)) return_data["Solver"] = "ECOS http://www.embotech.com/ECOS" diff --git a/src/alignment/external/__init__.py b/src/alignment/external/__init__.py index b7bc34be..ea195470 100644 --- a/src/alignment/external/__init__.py +++ b/src/alignment/external/__init__.py @@ -7,4 +7,4 @@ from .BROJA_2PID import BROJA_2PID __all__ = ['BROJA_2PID'] except ImportError: - __all__ = [] \ No newline at end of file + __all__ = [] diff --git a/src/alignment/infrastructure/__init__.py b/src/alignment/infrastructure/__init__.py index 754286e3..e846e6ba 100644 --- a/src/alignment/infrastructure/__init__.py +++ b/src/alignment/infrastructure/__init__.py @@ -6,52 +6,47 @@ """ # Computing infrastructure -from .computing import ( - # Distributed +from .computing import ( # Distributed; GPU optimization; JIT compilation DistributedConfig, DistributedTrainer, - setup_distributed, - cleanup_distributed, - is_main_process, - get_world_size, - get_rank, - # GPU optimization GPUOptimizer, - optimize_gpu_memory, - get_gpu_memory_stats, - # JIT compilation JITCompiler, + cleanup_distributed, compile_model, + get_gpu_memory_stats, + get_rank, + get_world_size, + is_main_process, + optimize_gpu_memory, optimize_trace, -) - -# Storage infrastructure -from .storage import ( - # Checkpointing - CheckpointManager, - save_checkpoint, - load_checkpoint, - cleanup_old_checkpoints, - # Logging - setup_logging, - get_logger, - log_metrics, - log_experiment_config, + setup_distributed, ) # Configuration infrastructure from .configuration import ( Config, + DataConfig, ExperimentConfig, MetricConfig, ModelConfig, - DataConfig, load_config, - save_config, merge_configs, + save_config, validate_config, ) +# Storage infrastructure +from .storage import ( # Checkpointing; Logging + CheckpointManager, + cleanup_old_checkpoints, + get_logger, + load_checkpoint, + log_experiment_config, + log_metrics, + save_checkpoint, + setup_logging, +) + __all__ = [ # Computing 'DistributedConfig', @@ -86,4 +81,4 @@ 'save_config', 'merge_configs', 'validate_config', -] \ No newline at end of file +] diff --git a/src/alignment/infrastructure/computing/__init__.py b/src/alignment/infrastructure/computing/__init__.py index f8e943be..426d0716 100644 --- a/src/alignment/infrastructure/computing/__init__.py +++ b/src/alignment/infrastructure/computing/__init__.py @@ -3,24 +3,16 @@ from .distributed import ( DistributedConfig, DistributedTrainer, - setup_distributed, cleanup_distributed, - is_main_process, - get_world_size, get_rank, + get_world_size, + is_main_process, + setup_distributed, ) # Import from optimized submodule -from .optimized.gpu import ( - GPUOptimizer, - optimize_gpu_memory, - get_gpu_memory_stats, -) -from .optimized.jit import ( - JITCompiler, - compile_model, - optimize_trace, -) +from .optimized.gpu import GPUOptimizer, get_gpu_memory_stats, optimize_gpu_memory +from .optimized.jit import JITCompiler, compile_model, optimize_trace __all__ = [ # Distributed computing @@ -39,4 +31,4 @@ 'JITCompiler', 'compile_model', 'optimize_trace', -] \ No newline at end of file +] diff --git a/src/alignment/infrastructure/computing/distributed.py b/src/alignment/infrastructure/computing/distributed.py index 874bd57a..5c2d04b7 100644 --- a/src/alignment/infrastructure/computing/distributed.py +++ b/src/alignment/infrastructure/computing/distributed.py @@ -2,13 +2,14 @@ Distributed computing utilities for multi-GPU training. """ +import logging import os +from contextlib import contextmanager +from typing import Any, Callable, Dict, List, Optional, Tuple + import torch import torch.distributed as dist -from typing import Optional, List, Union, Dict, Any, Callable, Tuple -import logging from torch.nn.parallel import DistributedDataParallel as DDP -from contextlib import contextmanager logger = logging.getLogger(__name__) @@ -46,42 +47,42 @@ def setup_distributed( init_method: URL specifying how to initialize the process group world_size: Number of processes rank: Rank of the current process - + Returns: True if distributed setup successful, False otherwise """ if not torch.cuda.is_available() and backend == "nccl": logger.warning("CUDA not available, falling back to gloo backend") backend = "gloo" - + # Try to get from environment if world_size is None: world_size = int(os.environ.get("WORLD_SIZE", 1)) if rank is None: rank = int(os.environ.get("RANK", 0)) - + if world_size > 1: try: if init_method is None: init_method = os.environ.get("INIT_METHOD", "env://") - + dist.init_process_group( backend=backend, init_method=init_method, world_size=world_size, rank=rank ) - + if torch.cuda.is_available(): torch.cuda.set_device(rank % torch.cuda.device_count()) - + logger.info(f"Initialized distributed training: rank {rank}/{world_size}") return True - + except Exception as e: logger.error(f"Failed to initialize distributed training: {e}") return False - + return False @@ -112,24 +113,24 @@ def reduce_tensor( ) -> torch.Tensor: """ Reduce tensor across all processes. - + Args: tensor: Tensor to reduce op: Reduction operation dst: Destination rank - + Returns: Reduced tensor """ if not dist.is_initialized(): return tensor - + tensor = tensor.clone() dist.reduce(tensor, dst=dst, op=op) - + if op == dist.ReduceOp.SUM and is_main_process(): tensor = tensor / dist.get_world_size() - + return tensor @@ -139,20 +140,20 @@ def gather_tensor( ) -> Optional[List[torch.Tensor]]: """ Gather tensors from all processes. - + Args: tensor: Local tensor dst: Destination rank - + Returns: List of tensors if on destination rank, None otherwise """ if not dist.is_initialized(): return [tensor] - + world_size = dist.get_world_size() rank = dist.get_rank() - + if rank == dst: gathered = [torch.empty_like(tensor) for _ in range(world_size)] dist.gather(tensor, gathered, dst=dst) @@ -168,23 +169,23 @@ def all_reduce( ) -> torch.Tensor: """ All-reduce tensor across all processes. - + Args: tensor: Tensor to reduce op: Reduction operation - + Returns: Reduced tensor """ if not dist.is_initialized(): return tensor - + tensor = tensor.clone() dist.all_reduce(tensor, op=op) - + if op == dist.ReduceOp.SUM: tensor = tensor / dist.get_world_size() - + return tensor @@ -194,17 +195,17 @@ def broadcast( ) -> torch.Tensor: """ Broadcast tensor from source to all processes. - + Args: tensor: Tensor to broadcast src: Source rank - + Returns: Broadcasted tensor """ if not dist.is_initialized(): return tensor - + tensor = tensor.clone() dist.broadcast(tensor, src=src) return tensor @@ -213,15 +214,15 @@ def broadcast( class DistributedMetricComputer: """ Compute metrics in a distributed manner across multiple GPUs/nodes. - + This class provides high-level functionality for distributed metric computation, building on the basic distributed utilities. """ - + def __init__(self, backend: str = 'nccl'): """ Initialize distributed computing. - + Args: backend: Distributed backend ('nccl' for GPU, 'gloo' for CPU) """ @@ -229,27 +230,27 @@ def __init__(self, backend: str = 'nccl'): self.initialized = False self.rank = 0 self.world_size = 1 - + def setup(self, rank: Optional[int] = None, world_size: Optional[int] = None): """ Setup distributed computing environment. - + Args: rank: Process rank (auto-detected if None) world_size: Total number of processes (auto-detected if None) """ if self.initialized: return - + # Auto-detect from environment if rank is None: rank = int(os.environ.get('RANK', 0)) if world_size is None: world_size = int(os.environ.get('WORLD_SIZE', 1)) - + self.rank = rank self.world_size = world_size - + if world_size > 1: # Initialize process group dist.init_process_group( @@ -258,13 +259,13 @@ def setup(self, rank: Optional[int] = None, world_size: Optional[int] = None): world_size=world_size ) self.initialized = True - + def cleanup(self): """Cleanup distributed environment.""" if self.initialized and dist.is_initialized(): dist.destroy_process_group() self.initialized = False - + @contextmanager def distributed_context(self): """Context manager for distributed operations.""" @@ -273,52 +274,52 @@ def distributed_context(self): finally: if self.initialized: dist.barrier() - + def all_gather_metrics(self, local_metric: torch.Tensor) -> List[torch.Tensor]: """ Gather metrics from all processes. - + Args: local_metric: Local metric value - + Returns: List of metrics from all processes """ if not self.initialized or self.world_size == 1: return [local_metric] - + # Ensure tensor is on correct device if not local_metric.is_cuda and self.backend == 'nccl': local_metric = local_metric.cuda() - + # Gather from all processes gathered = [torch.zeros_like(local_metric) for _ in range(self.world_size)] dist.all_gather(gathered, local_metric) - + return gathered - - def reduce_metrics(self, + + def reduce_metrics(self, local_metric: torch.Tensor, reduction: str = 'mean') -> torch.Tensor: """ Reduce metrics across all processes. - + Args: local_metric: Local metric value reduction: Reduction operation ('mean', 'sum', 'max', 'min') - + Returns: Reduced metric """ if not self.initialized or self.world_size == 1: return local_metric - + # Clone to avoid modifying original metric = local_metric.clone() - + if not metric.is_cuda and self.backend == 'nccl': metric = metric.cuda() - + # Reduce across processes if reduction == 'sum': dist.all_reduce(metric, op=dist.ReduceOp.SUM) @@ -331,32 +332,32 @@ def reduce_metrics(self, dist.all_reduce(metric, op=dist.ReduceOp.MIN) else: raise ValueError(f"Unknown reduction: {reduction}") - + return metric - + def distributed_metric_computation(self, metric_fn: Callable, data_loader: torch.utils.data.DataLoader, **metric_kwargs) -> Dict[str, float]: """ Compute metrics in distributed fashion. - + Args: metric_fn: Metric computation function data_loader: Distributed data loader **metric_kwargs: Additional arguments for metric function - + Returns: Dictionary of computed metrics """ local_results = [] - + with self.distributed_context(): for batch in data_loader: # Compute metric on local batch result = metric_fn(batch, **metric_kwargs) local_results.append(result) - + # Aggregate local results if local_results: local_metric = torch.tensor( @@ -365,20 +366,20 @@ def distributed_metric_computation(self, ) else: local_metric = torch.tensor(0.0) - + # Reduce across all processes global_metric = self.reduce_metrics(local_metric, reduction='mean') - + return {'metric': global_metric.item()} class DistributedModelWrapper: """Wrapper for distributed model evaluation.""" - + def __init__(self, model: torch.nn.Module, device_ids: Optional[List[int]] = None): """ Initialize distributed model wrapper. - + Args: model: Model to wrap device_ids: GPU device IDs to use @@ -386,23 +387,23 @@ def __init__(self, model: torch.nn.Module, device_ids: Optional[List[int]] = Non self.model = model self.device_ids = device_ids self.ddp_model = None - + def setup_ddp(self, rank: int): """Setup DistributedDataParallel.""" if self.device_ids: device = self.device_ids[rank % len(self.device_ids)] else: device = rank - + torch.cuda.set_device(device) self.model = self.model.cuda(device) - + self.ddp_model = DDP( self.model, device_ids=[device], output_device=device ) - + def get_model(self) -> torch.nn.Module: """Get the wrapped model.""" return self.ddp_model if self.ddp_model is not None else self.model @@ -416,19 +417,19 @@ def distributed_metric_aggregation( ) -> float: """ Aggregate metric computation across data partitions. - + Args: metric_computer: Metric computation object data_partitions: List of data partitions (inputs, weights, outputs) metric_name: Name of metric to compute **compute_kwargs: Additional arguments for compute - + Returns: Aggregated metric value """ dist_computer = DistributedMetricComputer() dist_computer.setup() - + try: # Compute local metrics local_scores = [] @@ -441,33 +442,33 @@ def distributed_metric_aggregation( **compute_kwargs ) local_scores.append(score) - + # Average local scores if local_scores: local_avg = sum(local_scores) / len(local_scores) local_tensor = torch.tensor(local_avg, dtype=torch.float32) else: local_tensor = torch.tensor(0.0, dtype=torch.float32) - + # Reduce across processes global_avg = dist_computer.reduce_metrics(local_tensor, reduction='mean') - + return global_avg.item() - + finally: dist_computer.cleanup() class DistributedBatchProcessor: """Process batches in distributed fashion with automatic load balancing.""" - - def __init__(self, + + def __init__(self, world_size: int, rank: int, device: Optional[torch.device] = None): """ Initialize distributed batch processor. - + Args: world_size: Total number of processes rank: Current process rank @@ -476,36 +477,36 @@ def __init__(self, self.world_size = world_size self.rank = rank self.device = device or torch.device(f'cuda:{rank}') - + def split_batch(self, batch: torch.Tensor) -> torch.Tensor: """ Split batch across processes. - + Args: batch: Input batch - + Returns: Local portion of batch """ batch_size = batch.size(0) chunk_size = (batch_size + self.world_size - 1) // self.world_size - + start_idx = self.rank * chunk_size end_idx = min(start_idx + chunk_size, batch_size) - + if start_idx < batch_size: return batch[start_idx:end_idx].to(self.device) else: # Return empty tensor if this rank has no data return torch.empty(0, *batch.shape[1:], device=self.device) - + def gather_results(self, local_result: torch.Tensor) -> torch.Tensor: """ Gather results from all processes. - + Args: local_result: Local computation result - + Returns: Concatenated results from all processes """ @@ -513,20 +514,20 @@ def gather_results(self, local_result: torch.Tensor) -> torch.Tensor: local_size = torch.tensor(local_result.size(0), device=self.device) sizes = [torch.zeros_like(local_size) for _ in range(self.world_size)] dist.all_gather(sizes, local_size) - + # Gather tensors with variable sizes max_size = max(s.item() for s in sizes) padded_result = torch.zeros(max_size, *local_result.shape[1:], device=self.device) if local_result.size(0) > 0: padded_result[:local_result.size(0)] = local_result - + gathered = [torch.zeros_like(padded_result) for _ in range(self.world_size)] dist.all_gather(gathered, padded_result) - + # Concatenate non-padded portions results = [] for i, size in enumerate(sizes): if size > 0: results.append(gathered[i][:size]) - - return torch.cat(results, dim=0) if results else torch.empty(0, *local_result.shape[1:]) \ No newline at end of file + + return torch.cat(results, dim=0) if results else torch.empty(0, *local_result.shape[1:]) diff --git a/src/alignment/infrastructure/computing/optimized/__init__.py b/src/alignment/infrastructure/computing/optimized/__init__.py index a5507b12..5a2292a0 100644 --- a/src/alignment/infrastructure/computing/optimized/__init__.py +++ b/src/alignment/infrastructure/computing/optimized/__init__.py @@ -6,34 +6,34 @@ # GPU-accelerated functions from .gpu import ( + GPUAcceleratedMetrics, + gpu_conditional_entropy, + gpu_entropy, gpu_histogram1d, gpu_histogram2d, gpu_mutual_information, - gpu_entropy, - gpu_conditional_entropy, - GPUAcceleratedMetrics, ) -# JIT-compiled functions +# JIT-compiled functions from .jit import ( - compute_rayleigh_quotient_jit, + JITMutualInformation, + JITNodeCorrelation, + JITRayleighQuotient, + benchmark_jit_vs_regular, + compute_batch_histogram_jit, compute_cosine_similarity_matrix_jit, - compute_mutual_information_gaussian_jit, compute_eigenvalue_entropy_jit, + compute_mutual_information_gaussian_jit, compute_node_correlation_jit, + compute_rayleigh_quotient_jit, compute_spectral_norm_jit, - compute_batch_histogram_jit, - JITRayleighQuotient, - JITMutualInformation, - JITNodeCorrelation, create_jit_metric, - benchmark_jit_vs_regular, ) __all__ = [ # GPU functions 'gpu_histogram1d', - 'gpu_histogram2d', + 'gpu_histogram2d', 'gpu_mutual_information', 'gpu_entropy', 'gpu_conditional_entropy', @@ -47,8 +47,8 @@ 'compute_spectral_norm_jit', 'compute_batch_histogram_jit', 'JITRayleighQuotient', - 'JITMutualInformation', + 'JITMutualInformation', 'JITNodeCorrelation', 'create_jit_metric', 'benchmark_jit_vs_regular', -] \ No newline at end of file +] diff --git a/src/alignment/infrastructure/computing/optimized/gpu.py b/src/alignment/infrastructure/computing/optimized/gpu.py index a193070e..4f0a2999 100644 --- a/src/alignment/infrastructure/computing/optimized/gpu.py +++ b/src/alignment/infrastructure/computing/optimized/gpu.py @@ -2,10 +2,10 @@ GPU-accelerated utilities for fast computation of alignment metrics. """ -import torch -import torch.nn.functional as F -from typing import Tuple, Optional, Union import math +from typing import Optional, Tuple, Union + +import torch def gpu_histogram1d( @@ -17,14 +17,14 @@ def gpu_histogram1d( ) -> Tuple[torch.Tensor, torch.Tensor]: """ GPU-accelerated 1D histogram computation. - + Args: data: Input tensor to bin bins: Number of bins min_val: Minimum value for binning range max_val: Maximum value for binning range density: If True, normalize to density - + Returns: hist: Histogram counts bin_edges: Bin edge values @@ -33,24 +33,24 @@ def gpu_histogram1d( min_val = data.min().item() if max_val is None: max_val = data.max().item() - + # Create bin edges bin_edges = torch.linspace(min_val, max_val, bins + 1, device=data.device) - + # Compute bin indices for each data point bin_width = (max_val - min_val) / bins indices = ((data - min_val) / bin_width).long() indices = indices.clamp(0, bins - 1) - + # Count occurrences using scatter_add hist = torch.zeros(bins, dtype=torch.float32, device=data.device) ones = torch.ones_like(data, dtype=torch.float32) hist.scatter_add_(0, indices.flatten(), ones.flatten()) - + if density: # Normalize to density hist = hist / (hist.sum() * bin_width) - + return hist, bin_edges @@ -63,14 +63,14 @@ def gpu_histogram2d( ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ GPU-accelerated 2D histogram computation. - + Args: x: First variable y: Second variable bins: Number of bins (can be different for x and y) range: Range for binning ((xmin, xmax), (ymin, ymax)) density: If True, normalize to density - + Returns: hist: 2D histogram x_edges: Bin edges for x @@ -80,38 +80,38 @@ def gpu_histogram2d( x_bins = y_bins = bins else: x_bins, y_bins = bins - + if range is None: x_min, x_max = x.min().item(), x.max().item() y_min, y_max = y.min().item(), y.max().item() else: (x_min, x_max), (y_min, y_max) = range - + # Create bin edges x_edges = torch.linspace(x_min, x_max, x_bins + 1, device=x.device) y_edges = torch.linspace(y_min, y_max, y_bins + 1, device=y.device) - + # Compute bin indices x_width = (x_max - x_min) / x_bins y_width = (y_max - y_min) / y_bins - + x_indices = ((x - x_min) / x_width).long().clamp(0, x_bins - 1) y_indices = ((y - y_min) / y_width).long().clamp(0, y_bins - 1) - + # Convert to linear indices linear_indices = y_indices * x_bins + x_indices - + # Count occurrences hist = torch.zeros(y_bins * x_bins, dtype=torch.float32, device=x.device) ones = torch.ones_like(x, dtype=torch.float32) hist.scatter_add_(0, linear_indices.flatten(), ones.flatten()) - + # Reshape to 2D hist = hist.reshape(y_bins, x_bins) - + if density: hist = hist / (hist.sum() * x_width * y_width) - + return hist, x_edges, y_edges @@ -123,13 +123,13 @@ def gpu_mutual_information( ) -> torch.Tensor: """ GPU-accelerated mutual information computation. - + Args: x: First variable y: Second variable bins: Number of bins for discretization method: Method to use ('histogram' or 'kraskov') - + Returns: Mutual information value """ @@ -137,67 +137,67 @@ def gpu_mutual_information( # Compute 2D histogram hist_xy, _, _ = gpu_histogram2d(x, y, bins=bins) hist_xy = hist_xy + 1e-10 # Add small constant - + # Normalize to joint probability p_xy = hist_xy / hist_xy.sum() - + # Compute marginals p_x = p_xy.sum(dim=0) p_y = p_xy.sum(dim=1) - + # Compute MI: sum(p_xy * log(p_xy / (p_x * p_y))) p_x_p_y = p_x.unsqueeze(0) * p_y.unsqueeze(1) - + # Use log trick to avoid log(0) log_term = torch.where( p_xy > 1e-10, torch.log(p_xy / (p_x_p_y + 1e-10)), torch.tensor(0.0, device=x.device) ) - + mi = (p_xy * log_term).sum() - + return mi - + elif method == "kraskov": # Simplified Kraskov estimator # This is a basic version - full implementation would be more complex - + n = x.shape[0] k = min(3, n // 10) # Number of neighbors - + # Standardize x_std = (x - x.mean()) / (x.std() + 1e-8) y_std = (y - y.mean()) / (y.std() + 1e-8) - + # Stack for joint space xy = torch.stack([x_std, y_std], dim=1) - + # Compute distances in joint space distances = torch.cdist(xy, xy) - + # Get k-th nearest neighbor distances kth_distances, _ = distances.topk(k + 1, largest=False, dim=1) epsilon = kth_distances[:, -1] # k-th neighbor distance - + # Count neighbors in marginal spaces x_distances = torch.abs(x_std.unsqueeze(1) - x_std.unsqueeze(0)) y_distances = torch.abs(y_std.unsqueeze(1) - y_std.unsqueeze(0)) - + n_x = (x_distances < epsilon.unsqueeze(1)).sum(dim=1).float() n_y = (y_distances < epsilon.unsqueeze(1)).sum(dim=1).float() - + # Digamma function approximation def digamma_approx(x): return x.log() - 1.0 / (2.0 * x) - + # MI estimation mi = digamma_approx(torch.tensor(k, dtype=torch.float32)) + \ digamma_approx(torch.tensor(n, dtype=torch.float32)) - \ digamma_approx(n_x).mean() - digamma_approx(n_y).mean() - + return mi.clamp(min=0) - + else: raise ValueError(f"Unknown method: {method}") @@ -209,34 +209,34 @@ def gpu_entropy( ) -> torch.Tensor: """ GPU-accelerated entropy computation. - + Args: data: Input data bins: Number of bins method: Method to use ('histogram' or 'gaussian') - + Returns: Entropy value """ if method == "histogram": hist, _ = gpu_histogram1d(data, bins=bins) - + # Normalize to probability p = hist / hist.sum() p = p + 1e-10 # Add small constant - + # Compute entropy entropy = -(p * p.log()).sum() - + return entropy - + elif method == "gaussian": # Entropy of Gaussian: 0.5 * log(2 * pi * e * var) var = data.var() entropy = 0.5 * torch.log(2 * math.pi * math.e * var) - + return entropy - + else: raise ValueError(f"Unknown method: {method}") @@ -249,52 +249,52 @@ def gpu_conditional_entropy( ) -> torch.Tensor: """ GPU-accelerated conditional entropy H(X,Y|Z). - + Args: x: First variable y: Second variable condition: Conditioning variable bins: Number of bins - + Returns: Conditional entropy """ # Create 3D histogram n = x.shape[0] - + # Compute ranges x_min, x_max = x.min().item(), x.max().item() y_min, y_max = y.min().item(), y.max().item() z_min, z_max = condition.min().item(), condition.max().item() - + # Compute bin indices x_idx = ((x - x_min) / ((x_max - x_min) / bins)).long().clamp(0, bins - 1) y_idx = ((y - y_min) / ((y_max - y_min) / bins)).long().clamp(0, bins - 1) z_idx = ((condition - z_min) / ((z_max - z_min) / bins)).long().clamp(0, bins - 1) - + # Linear indices for 3D histogram indices = z_idx * bins * bins + y_idx * bins + x_idx - + # Count occurrences hist_xyz = torch.zeros(bins ** 3, device=x.device) ones = torch.ones(n, device=x.device) hist_xyz.scatter_add_(0, indices, ones) hist_xyz = hist_xyz.reshape(bins, bins, bins) + 1e-10 - + # Normalize to probability p_xyz = hist_xyz / hist_xyz.sum() - + # Marginal p(z) p_z = p_xyz.sum(dim=(1, 2)) - + # Conditional entropy h_xy_given_z = 0 - + for z in range(bins): if p_z[z] > 1e-10: # p(x,y|z) = p(x,y,z) / p(z) p_xy_given_z = p_xyz[z] / p_z[z] - + # H(X,Y|Z=z) log_term = torch.where( p_xy_given_z > 1e-10, @@ -302,10 +302,10 @@ def gpu_conditional_entropy( torch.tensor(0.0, device=x.device) ) h_xy_z = -(p_xy_given_z * log_term).sum() - + # Weight by p(z) h_xy_given_z += p_z[z] * h_xy_z - + return h_xy_given_z @@ -313,36 +313,36 @@ class GPUAcceleratedMetrics: """ Collection of GPU-accelerated metric computations. """ - + @staticmethod @torch.jit.script def fast_covariance(X: torch.Tensor) -> torch.Tensor: """ JIT-compiled fast covariance computation. - + Args: X: Input tensor (n_samples, n_features) - + Returns: Covariance matrix """ X_centered = X - X.mean(dim=0, keepdim=True) cov = X_centered.T @ X_centered / (X.shape[0] - 1) return cov - - @staticmethod + + @staticmethod @torch.jit.script def fast_correlation(X: torch.Tensor) -> torch.Tensor: """ JIT-compiled fast correlation computation. - + Args: X: Input tensor (n_samples, n_features) - + Returns: Correlation matrix """ # Standardize X_std = (X - X.mean(dim=0, keepdim=True)) / (X.std(dim=0, keepdim=True) + 1e-8) corr = X_std.T @ X_std / (X.shape[0] - 1) - return corr \ No newline at end of file + return corr diff --git a/src/alignment/infrastructure/computing/optimized/jit.py b/src/alignment/infrastructure/computing/optimized/jit.py index 47b0c822..d264f7da 100644 --- a/src/alignment/infrastructure/computing/optimized/jit.py +++ b/src/alignment/infrastructure/computing/optimized/jit.py @@ -2,11 +2,9 @@ JIT-optimized implementations of alignment metrics. """ -import torch -import torch.nn.functional as F from typing import Tuple -import math +import torch # JIT-compiled helper functions @@ -18,34 +16,34 @@ def compute_rayleigh_quotient_jit( ) -> torch.Tensor: """ JIT-compiled Rayleigh Quotient computation. - + Args: inputs: Input activations (batch_size, input_dim) weights: Weight matrix (output_dim, input_dim) epsilon: Small constant for stability - + Returns: RQ scores for each neuron """ # Center inputs inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) - + # Compute covariance C = (inputs_centered.T @ inputs_centered) / (inputs.shape[0] - 1) - + # Add small diagonal for stability C = C + epsilon * torch.eye(C.shape[0], device=C.device) - + # Compute RQ for each weight vector num_neurons = weights.shape[0] rq_scores = torch.zeros(num_neurons, device=weights.device) - + for i in range(num_neurons): w = weights[i] numerator = w @ C @ w denominator = w @ w rq_scores[i] = numerator / (denominator + epsilon) - + return rq_scores @@ -53,19 +51,19 @@ def compute_rayleigh_quotient_jit( def compute_cosine_similarity_matrix_jit(weights: torch.Tensor) -> torch.Tensor: """ JIT-compiled cosine similarity matrix computation. - + Args: weights: Weight matrix (n_neurons, input_dim) - + Returns: Cosine similarity matrix (n_neurons, n_neurons) """ # Normalize weight vectors weights_norm = weights / (torch.norm(weights, p=2, dim=1, keepdim=True) + 1e-8) - + # Compute similarity matrix similarity = weights_norm @ weights_norm.T - + return similarity @@ -77,38 +75,38 @@ def compute_mutual_information_gaussian_jit( ) -> torch.Tensor: """ JIT-compiled Gaussian mutual information estimation. - + Args: x: First variable (n_samples,) y: Second variable (n_samples,) epsilon: Small constant for stability - + Returns: Mutual information estimate """ n = x.shape[0] - + # Standardize x_std = (x - x.mean()) / (x.std() + epsilon) y_std = (y - y.mean()) / (y.std() + epsilon) - + # Stack variables xy = torch.stack([x_std, y_std], dim=1) - + # Compute covariance matrix cov = (xy.T @ xy) / (n - 1) - + # Add small diagonal cov = cov + epsilon * torch.eye(2, device=cov.device) - + # Compute determinants det_joint = torch.det(cov) var_x = cov[0, 0] var_y = cov[1, 1] - + # MI = 0.5 * log(var_x * var_y / det_joint) mi = 0.5 * torch.log(var_x * var_y / (det_joint + epsilon)) - + return mi.clamp(min=0) @@ -120,30 +118,30 @@ def compute_eigenvalue_entropy_jit( ) -> torch.Tensor: """ JIT-compiled eigenvalue entropy computation. - + Args: matrix: Square matrix temperature: Temperature parameter epsilon: Small constant for stability - + Returns: Eigenvalue entropy """ # Compute eigenvalues eigenvalues = torch.linalg.eigvalsh(matrix) - + # Filter small eigenvalues eigenvalues = eigenvalues[eigenvalues > epsilon] - + # Normalize eigenvalues = eigenvalues / eigenvalues.sum() - + # Apply temperature eigenvalues = eigenvalues / temperature - + # Compute entropy entropy = -(eigenvalues * eigenvalues.log()).sum() - + return entropy @@ -154,11 +152,11 @@ def compute_node_correlation_jit( ) -> torch.Tensor: """ JIT-compiled node correlation computation. - + Args: activations: Neuron activations (batch_size, n_neurons) epsilon: Small constant for stability - + Returns: Average correlation for each neuron """ @@ -166,18 +164,18 @@ def compute_node_correlation_jit( mean = activations.mean(dim=0, keepdim=True) std = activations.std(dim=0, keepdim=True) activations_std = (activations - mean) / (std + epsilon) - + # Compute correlation matrix corr_matrix = (activations_std.T @ activations_std) / (activations.shape[0] - 1) - + # Zero out diagonal n = corr_matrix.shape[0] mask = torch.ones_like(corr_matrix) - torch.eye(n, device=corr_matrix.device) corr_matrix = corr_matrix * mask - + # Average correlation per neuron avg_corr = corr_matrix.abs().sum(dim=1) / (n - 1) - + return avg_corr @@ -185,76 +183,76 @@ def compute_node_correlation_jit( def compute_spectral_norm_jit(weights: torch.Tensor) -> torch.Tensor: """ JIT-compiled spectral norm computation. - + Args: weights: Weight matrix - + Returns: Spectral norm (largest singular value) """ # Use power iteration for efficiency n_iter = 5 - + # Random initialization u = torch.randn(weights.shape[0], device=weights.device) v = torch.randn(weights.shape[1], device=weights.device) - + for _ in range(n_iter): # v = W^T u / ||W^T u|| v = weights.T @ u v = v / (v.norm() + 1e-8) - + # u = W v / ||W v|| u = weights @ v u = u / (u.norm() + 1e-8) - + # Spectral norm = u^T W v spectral_norm = u @ weights @ v - + return spectral_norm -@torch.jit.script +@torch.jit.script def compute_batch_histogram_jit( data: torch.Tensor, bins: int = 10 ) -> Tuple[torch.Tensor, torch.Tensor]: """ JIT-compiled batch histogram computation. - + Args: data: Input data (batch_size, n_features) bins: Number of bins - + Returns: histograms: Histogram for each feature (n_features, bins) bin_edges: Bin edges (bins + 1,) """ batch_size, n_features = data.shape - + # Get global min/max data_min = data.min() data_max = data.max() - + # Create bin edges bin_edges = torch.linspace(data_min, data_max, bins + 1, device=data.device) bin_width = (data_max - data_min) / bins - + # Initialize histograms histograms = torch.zeros(n_features, bins, device=data.device) - + # Compute histograms for each feature for i in range(n_features): feature_data = data[:, i] - + # Compute bin indices indices = ((feature_data - data_min) / bin_width).long() indices = indices.clamp(0, bins - 1) - + # Count occurrences for j in range(batch_size): histograms[i, indices[j]] += 1 - + return histograms, bin_edges @@ -262,33 +260,33 @@ def compute_batch_histogram_jit( class JITRayleighQuotient: """JIT-optimized Rayleigh Quotient metric.""" - + def __init__(self, epsilon: float = 1e-8): self.epsilon = epsilon self.compute = torch.jit.script(compute_rayleigh_quotient_jit) - + def __call__(self, inputs: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: return self.compute(inputs, weights, self.epsilon) class JITMutualInformation: """JIT-optimized Mutual Information metric.""" - + def __init__(self, epsilon: float = 1e-8): self.epsilon = epsilon self.compute = torch.jit.script(compute_mutual_information_gaussian_jit) - + def __call__(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: return self.compute(x, y, self.epsilon) class JITNodeCorrelation: """JIT-optimized Node Correlation metric.""" - + def __init__(self, epsilon: float = 1e-8): self.epsilon = epsilon self.compute = torch.jit.script(compute_node_correlation_jit) - + def __call__(self, activations: torch.Tensor) -> torch.Tensor: return self.compute(activations, self.epsilon) @@ -298,11 +296,11 @@ def __call__(self, activations: torch.Tensor) -> torch.Tensor: def create_jit_metric(metric_name: str, **kwargs): """ Create a JIT-optimized version of a metric. - + Args: metric_name: Name of the metric **kwargs: Metric-specific parameters - + Returns: JIT-optimized metric instance """ @@ -311,10 +309,10 @@ def create_jit_metric(metric_name: str, **kwargs): 'mutual_information': JITMutualInformation, 'node_correlation': JITNodeCorrelation, } - + if metric_name not in jit_metrics: raise ValueError(f"No JIT implementation for metric: {metric_name}") - + return jit_metrics[metric_name](**kwargs) @@ -328,31 +326,31 @@ def benchmark_jit_vs_regular( ) -> Tuple[float, float]: """ Benchmark JIT vs regular implementation. - + Args: metric_name: Metric to benchmark input_shape: Shape of input data n_iterations: Number of iterations device: Device to use - + Returns: jit_time: Time for JIT version regular_time: Time for regular version """ import time - + # Create dummy data if metric_name == 'rayleigh_quotient': inputs = torch.randn(input_shape[0], input_shape[1], device=device) weights = torch.randn(input_shape[2], input_shape[1], device=device) - + # JIT version jit_metric = JITRayleighQuotient() - + # Warmup for _ in range(10): _ = jit_metric(inputs, weights) - + # Time JIT torch.cuda.synchronize() start = time.time() @@ -360,12 +358,12 @@ def benchmark_jit_vs_regular( _ = jit_metric(inputs, weights) torch.cuda.synchronize() jit_time = time.time() - start - + # Regular version would go here # For now, we'll use the same time as placeholder regular_time = jit_time * 1.5 # Assume 50% slower - + else: raise ValueError(f"Benchmark not implemented for: {metric_name}") - - return jit_time, regular_time \ No newline at end of file + + return jit_time, regular_time diff --git a/src/alignment/infrastructure/storage/__init__.py b/src/alignment/infrastructure/storage/__init__.py index 54eb6e8b..938ed393 100644 --- a/src/alignment/infrastructure/storage/__init__.py +++ b/src/alignment/infrastructure/storage/__init__.py @@ -2,16 +2,11 @@ from .checkpoint import ( CheckpointManager, - save_checkpoint, - load_checkpoint, cleanup_old_checkpoints, + load_checkpoint, + save_checkpoint, ) -from .logging import ( - setup_logging, - get_logger, - log_metrics, - log_experiment_config, -) +from .logging import get_logger, log_experiment_config, log_metrics, setup_logging __all__ = [ # Checkpointing @@ -24,4 +19,4 @@ 'get_logger', 'log_metrics', 'log_experiment_config', -] \ No newline at end of file +] diff --git a/src/alignment/infrastructure/storage/checkpoint.py b/src/alignment/infrastructure/storage/checkpoint.py index e79eda64..23517df1 100644 --- a/src/alignment/infrastructure/storage/checkpoint.py +++ b/src/alignment/infrastructure/storage/checkpoint.py @@ -2,10 +2,11 @@ Checkpoint utilities for saving models with hooks. """ -import torch import logging -from typing import Dict, Any, Optional from pathlib import Path +from typing import Any, Dict, Optional + +import torch logger = logging.getLogger(__name__) @@ -20,7 +21,7 @@ def save_checkpoint( ) -> None: """ Save a checkpoint, handling models with hooks gracefully. - + Args: model: The model to save optimizer: Optimizer state to save (optional) @@ -33,13 +34,13 @@ def save_checkpoint( 'epoch': epoch, 'model_state_dict': model.state_dict(), } - + if optimizer is not None: checkpoint['optimizer_state_dict'] = optimizer.state_dict() - + if additional_state is not None: checkpoint.update(additional_state) - + if save_hooks: # Try to save the full model, but warn about hooks try: @@ -47,7 +48,7 @@ def save_checkpoint( logger.warning("Saving full model with hooks. This may not be loadable.") except Exception as e: logger.warning(f"Failed to save full model: {e}. Saving state_dict only.") - + try: torch.save(checkpoint, filepath) logger.info(f"Checkpoint saved to {filepath}") @@ -64,21 +65,21 @@ def load_checkpoint( ) -> Dict[str, Any]: """ Load a checkpoint, handling different checkpoint formats. - + Args: filepath: Path to the checkpoint file model: Model to load state into (optional) optimizer: Optimizer to load state into (optional) map_location: Device mapping location - + Returns: Dictionary containing the loaded checkpoint data """ if not Path(filepath).exists(): raise FileNotFoundError(f"Checkpoint not found: {filepath}") - + checkpoint = torch.load(filepath, map_location=map_location) - + # Load model state if model is provided if model is not None and 'model_state_dict' in checkpoint: try: @@ -92,7 +93,7 @@ def load_checkpoint( logger.warning("Model state loaded with strict=False") except Exception as e2: logger.error(f"Failed to load model state even with strict=False: {e2}") - + # Load optimizer state if optimizer is provided if optimizer is not None and 'optimizer_state_dict' in checkpoint: try: @@ -100,7 +101,7 @@ def load_checkpoint( logger.info("Optimizer state loaded successfully") except Exception as e: logger.error(f"Failed to load optimizer state: {e}") - + return checkpoint @@ -111,7 +112,7 @@ def save_model_for_inference( ) -> None: """ Save a model for inference, optionally removing hooks. - + Args: model: The model to save filepath: Path to save the model @@ -124,11 +125,11 @@ def save_model_for_inference( if hasattr(module, '_forward_hooks') and len(module._forward_hooks) > 0: hooks_backup[name] = dict(module._forward_hooks) module._forward_hooks.clear() - + # Save model without hooks torch.save(model.state_dict(), filepath) logger.info(f"Model saved for inference (hooks removed): {filepath}") - + # Restore hooks for name, module in model.named_modules(): if name in hooks_backup: @@ -136,4 +137,4 @@ def save_model_for_inference( else: # Save model as is torch.save(model.state_dict(), filepath) - logger.info(f"Model saved for inference: {filepath}") \ No newline at end of file + logger.info(f"Model saved for inference: {filepath}") diff --git a/src/alignment/infrastructure/storage/logging.py b/src/alignment/infrastructure/storage/logging.py index 0f9ac23f..d586a883 100644 --- a/src/alignment/infrastructure/storage/logging.py +++ b/src/alignment/infrastructure/storage/logging.py @@ -2,12 +2,12 @@ Logging utilities for the alignment framework. """ +import json import logging import sys -from typing import Dict, Any, Optional, Union -from pathlib import Path from datetime import datetime -import json +from pathlib import Path +from typing import Any, Dict, Optional, Union def setup_logging( @@ -18,22 +18,22 @@ def setup_logging( ) -> logging.Logger: """ Setup logging configuration. - + Args: log_level: Logging level log_file: Optional file to log to format_string: Custom format string disable_existing: Whether to disable existing loggers - + Returns: Root logger """ if isinstance(log_level, str): log_level = getattr(logging, log_level.upper()) - + if format_string is None: format_string = "[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s" - + # Configure logging logging_config = { 'version': 1, @@ -57,12 +57,12 @@ def setup_logging( 'handlers': ['console'] } } - + # Add file handler if specified if log_file: log_file = Path(log_file) log_file.parent.mkdir(parents=True, exist_ok=True) - + logging_config['handlers']['file'] = { 'class': 'logging.FileHandler', 'level': log_level, @@ -71,22 +71,22 @@ def setup_logging( 'mode': 'a' } logging_config['root']['handlers'].append('file') - + logging.config.dictConfig(logging_config) - + logger = logging.getLogger() logger.info(f"Logging configured with level {logging.getLevelName(log_level)}") - + return logger def get_logger(name: str) -> logging.Logger: """ Get a logger with the specified name. - + Args: name: Logger name - + Returns: Logger instance """ @@ -101,7 +101,7 @@ def log_metrics( ): """ Log metrics in a structured format. - + Args: metrics: Dictionary of metrics step: Optional step/iteration number @@ -110,7 +110,7 @@ def log_metrics( """ if logger is None: logger = logging.getLogger() - + # Format metrics formatted = [] for key, value in metrics.items(): @@ -119,13 +119,13 @@ def log_metrics( formatted.append(f"{metric_name}: {value:.4f}") else: formatted.append(f"{metric_name}: {value}") - + # Create log message if step is not None: message = f"[Step {step}] " + " | ".join(formatted) else: message = " | ".join(formatted) - + logger.info(message) @@ -133,7 +133,7 @@ class MetricLogger: """ Logger specifically for tracking metrics over time. """ - + def __init__( self, log_dir: Union[str, Path], @@ -141,7 +141,7 @@ def __init__( ): """ Initialize metric logger. - + Args: log_dir: Directory to save logs experiment_name: Name of the experiment @@ -149,19 +149,19 @@ def __init__( self.log_dir = Path(log_dir) self.log_dir.mkdir(parents=True, exist_ok=True) self.experiment_name = experiment_name - + # Create log files timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") self.metrics_file = self.log_dir / f"{experiment_name}_metrics_{timestamp}.jsonl" self.summary_file = self.log_dir / f"{experiment_name}_summary_{timestamp}.json" - + self.metrics_history = [] self.start_time = datetime.now() - + def log(self, metrics: Dict[str, Any], step: int): """ Log metrics for a specific step. - + Args: metrics: Metrics to log step: Current step @@ -171,18 +171,18 @@ def log(self, metrics: Dict[str, Any], step: int): 'timestamp': datetime.now().isoformat(), 'metrics': metrics } - + # Append to history self.metrics_history.append(entry) - + # Write to file with open(self.metrics_file, 'a') as f: f.write(json.dumps(entry) + '\n') - + def write_summary(self, final_metrics: Optional[Dict[str, Any]] = None): """ Write a summary of the experiment. - + Args: final_metrics: Optional final metrics """ @@ -194,24 +194,24 @@ def write_summary(self, final_metrics: Optional[Dict[str, Any]] = None): 'num_steps': len(self.metrics_history), 'final_metrics': final_metrics or {} } - + # Compute metric statistics if self.metrics_history: metric_stats = {} all_metrics = set() - + # Collect all metric names for entry in self.metrics_history: all_metrics.update(entry['metrics'].keys()) - + # Compute stats for each metric for metric_name in all_metrics: values = [ - entry['metrics'][metric_name] - for entry in self.metrics_history + entry['metrics'][metric_name] + for entry in self.metrics_history if metric_name in entry['metrics'] ] - + if values and all(isinstance(v, (int, float)) for v in values): metric_stats[metric_name] = { 'min': min(values), @@ -219,11 +219,11 @@ def write_summary(self, final_metrics: Optional[Dict[str, Any]] = None): 'mean': sum(values) / len(values), 'final': values[-1] } - + summary['metric_statistics'] = metric_stats - + # Write summary with open(self.summary_file, 'w') as f: json.dump(summary, f, indent=2) - - get_logger(__name__).info(f"Wrote experiment summary to {self.summary_file}") \ No newline at end of file + + get_logger(__name__).info(f"Wrote experiment summary to {self.summary_file}") diff --git a/src/alignment/metrics/__init__.py b/src/alignment/metrics/__init__.py index bfadebfd..8fb6dfd5 100644 --- a/src/alignment/metrics/__init__.py +++ b/src/alignment/metrics/__init__.py @@ -5,23 +5,21 @@ from ..core.registry import METRIC_REGISTRY # Import all metric modules to register them -from . import rayleigh -from . import information -from .information import pairwise_gaussian # Ensure registration side-effects -from . import similarity -from . import spectral -from . import task_specific -from .information import gaussian_pid # Register gaussian PID synergy +from . import information, rayleigh, similarity, spectral, task_specific +from .information import ( + gaussian_pid, # Register gaussian PID synergy + pairwise_gaussian, # Ensure registration side-effects +) def get_metric(name: str, **kwargs): """ Get a metric instance by name. - + Args: name: Name of the metric **kwargs: Parameters to pass to metric constructor - + Returns: Instantiated metric object """ @@ -31,7 +29,7 @@ def get_metric(name: str, **kwargs): def list_metrics(): """ List all available metrics. - + Returns: List of metric names """ @@ -39,4 +37,4 @@ def list_metrics(): # For convenience, expose the registry and functions -__all__ = ['METRIC_REGISTRY', 'get_metric', 'list_metrics'] \ No newline at end of file +__all__ = ['METRIC_REGISTRY', 'get_metric', 'list_metrics'] diff --git a/src/alignment/metrics/gradient_based.py b/src/alignment/metrics/gradient_based.py index a8eda883..f96119ed 100644 --- a/src/alignment/metrics/gradient_based.py +++ b/src/alignment/metrics/gradient_based.py @@ -10,10 +10,10 @@ can be used to design efficient local learning algorithms. """ -from typing import Optional, Any, Dict, List -import torch -import torch.nn as nn import logging +from typing import Any, Dict, List, Optional + +import torch from ..core.base import BaseMetric from ..core.registry import register_metric @@ -25,15 +25,15 @@ class GradientAlignment(BaseMetric): """ Measures alignment between local signals and backprop gradient. - + Computes correlation between various local signals (activations, weights, etc.) and the true backprop gradient to identify optimal local learning rules. - + Use during training to: - Find local rules that approximate backprop - Design biologically-plausible learning - Identify credit assignment strategies - + Example: >>> metric = GradientAlignment() >>> # During training (after backward()): @@ -45,7 +45,7 @@ class GradientAlignment(BaseMetric): ... ) >>> # High alignment = this local signal correlates with gradient """ - + def __init__( self, local_signal: str = 'hebbian', # 'hebbian', 'anti_hebbian', 'output', 'input' @@ -55,7 +55,7 @@ def __init__( ): """ Initialize gradient alignment metric. - + Args: local_signal: Which local signal to test - 'hebbian': x_i * y_j (Hebbian rule) @@ -71,24 +71,24 @@ def __init__( self.local_signal = local_signal self.normalize = normalize self.accumulate_over_batches = accumulate_over_batches - + # Accumulation for multi-batch correlation if accumulate_over_batches: self.accumulated_gradients = {} self.accumulated_signals = {} - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return False - + @property def requires_outputs(self) -> bool: return True - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -100,24 +100,24 @@ def compute( ) -> torch.Tensor: """ Compute alignment between local signal and backprop gradient. - + Args: inputs: Layer inputs [B, D_in] outputs: Layer outputs [B, N] gradients: Weight gradients [N, D_in] (from backprop) targets: Target labels (for anti-Hebbian) **kwargs: Additional parameters - + Returns: Alignment scores per neuron [N] """ if inputs is None or outputs is None: raise ValueError("GradientAlignment requires inputs and outputs") - + if gradients is None: logger.warning("No gradients provided, cannot compute gradient alignment") return torch.zeros(outputs.shape[1], device=outputs.device) - + # Ensure shapes if inputs.ndim > 2: inputs = inputs.reshape(inputs.shape[0], -1) @@ -125,21 +125,21 @@ def compute( outputs = outputs.reshape(outputs.shape[0], -1) if gradients.ndim > 2: gradients = gradients.reshape(gradients.shape[0], -1) - + B, D_in = inputs.shape B, N = outputs.shape - + # Compute local signal local_signal = self._compute_local_signal( inputs, outputs, targets, gradients.shape ) # [N, D_in] - same shape as gradients - + # Compute correlation with gradients alignment = self._compute_correlation(local_signal, gradients) # [N] - alignment per neuron - + return alignment - + def _compute_local_signal( self, inputs: torch.Tensor, # [B, D_in] @@ -149,18 +149,18 @@ def _compute_local_signal( ) -> torch.Tensor: """ Compute local learning signal. - + Returns: Local signal [N, D_in] (same shape as weight gradient) """ B, D_in = inputs.shape B, N = outputs.shape - + if self.local_signal == 'hebbian': # Hebbian: Δw_ij ∝ x_i * y_j # Averaged over batch: [N, D_in] signal = outputs.T @ inputs / B # Outer product averaged - + elif self.local_signal == 'anti_hebbian': # Anti-Hebbian: Δw_ij ∝ x_i * (target - y_j) if targets is None: @@ -172,10 +172,10 @@ def _compute_local_signal( target_onehot = torch.nn.functional.one_hot(targets, num_classes=N).float() else: target_onehot = targets - + error = target_onehot - outputs # [B, N] signal = error.T @ inputs / B - + elif self.local_signal == 'oja': # Oja's rule: Hebbian with weight decay # Δw = η * y * (x - y*w) @@ -187,22 +187,22 @@ def _compute_local_signal( hebbian = outputs.T @ inputs / B # This is simplified; full Oja needs weight norm signal = hebbian - + elif self.local_signal == 'output': # Just output: Δw ∝ y # Broadcast to weight shape signal = outputs.mean(dim=0).unsqueeze(1).expand(N, D_in) - + elif self.local_signal == 'input': # Just input: Δw ∝ x # Broadcast to weight shape signal = inputs.mean(dim=0).unsqueeze(0).expand(N, D_in) - + else: raise ValueError(f"Unknown local signal: {self.local_signal}") - + return signal - + def _compute_correlation( self, signal: torch.Tensor, # [N, D_in] @@ -210,38 +210,38 @@ def _compute_correlation( ) -> torch.Tensor: """ Compute correlation between local signal and backprop gradient. - + High correlation → local signal approximates gradient well → Can be used as local learning rule! - + Returns: Correlation per neuron [N] """ N, D_in = signal.shape - + # Compute correlation for each neuron correlations = torch.zeros(N, device=signal.device) - + for i in range(N): signal_i = signal[i] # [D_in] grad_i = gradients[i] # [D_in] - + if self.normalize: # Pearson correlation signal_centered = signal_i - signal_i.mean() grad_centered = grad_i - grad_i.mean() - + cov = (signal_centered * grad_centered).sum() std_signal = signal_centered.std() std_grad = grad_centered.std() - + corr = cov / (std_signal * std_grad + 1e-8) else: # Dot product (unnormalized) corr = (signal_i * grad_i).sum() / (signal_i.norm() * grad_i.norm() + 1e-8) - + correlations[i] = corr.abs() # Use absolute correlation - + return correlations @@ -249,19 +249,19 @@ def _compute_correlation( class LocalLearningRuleSearch(BaseMetric): """ Search for optimal local learning rules that approximate backprop. - + Tests multiple candidate local rules and identifies which best correlate with backprop gradients. - + Candidate rules: - Hebbian: Δw ∝ x * y - Anti-Hebbian: Δw ∝ x * (target - y) - Oja: Δw ∝ y * (x - y*w) - Contrastive: Δw ∝ x_pos * y_pos - x_neg * y_neg - Random feedback: Δw ∝ x * (B @ error) where B is random - + Returns the best rule per neuron. - + Example: >>> searcher = LocalLearningRuleSearch() >>> best_rules = searcher.compute( @@ -270,7 +270,7 @@ class LocalLearningRuleSearch(BaseMetric): >>> # best_rules[i] = index of best rule for neuron i >>> # Can then use this to train with local rules! """ - + def __init__( self, candidate_rules: Optional[List[str]] = None, @@ -278,29 +278,29 @@ def __init__( ): """ Initialize local learning rule search. - + Args: candidate_rules: List of rules to test (None = all) **config: Additional configuration """ super().__init__(**config) - + self.candidate_rules = candidate_rules or [ 'hebbian', 'anti_hebbian', 'oja', 'output', 'input' ] - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return False - + @property def requires_outputs(self) -> bool: return True - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -313,23 +313,23 @@ def compute( ) -> torch.Tensor: """ Find best local learning rule per neuron. - + Args: inputs, outputs: Layer activations gradients: Backprop gradients targets: For anti-Hebbian rules return_correlations: If True, return correlation matrix [N, num_rules] **kwargs: Additional args - + Returns: Best rule index per neuron [N] or correlations [N, num_rules] """ if gradients is None: raise ValueError("LocalLearningRuleSearch requires gradients") - + # Test all candidate rules correlations = {} - + for rule in self.candidate_rules: metric = GradientAlignment(local_signal=rule) corr = metric.compute( @@ -340,18 +340,18 @@ def compute( targets=targets ) correlations[rule] = corr - + # Stack into matrix [N, num_rules] corr_matrix = torch.stack([correlations[rule] for rule in self.candidate_rules], dim=1) - + if return_correlations: return corr_matrix - + # Find best rule per neuron best_rule_indices = corr_matrix.argmax(dim=1) # [N] - + return best_rule_indices - + def get_learning_rule_for_neuron( self, neuron_idx: int, @@ -364,27 +364,27 @@ def get_learning_rule_for_neuron( class GradientStatisticsTracker: """ Track gradient statistics during training for analysis. - + Collects: - Gradient magnitude evolution - Gradient direction consistency - Correlation with various local signals - + Use to design local learning rules. """ - + def __init__(self): """Initialize tracker.""" self.gradient_history: Dict[str, List[torch.Tensor]] = {} self.signal_history: Dict[str, List[torch.Tensor]] = {} self.correlation_history: Dict[str, List[float]] = {} - + def register_layer(self, layer_name: str): """Register a layer for tracking.""" self.gradient_history[layer_name] = [] self.signal_history[layer_name] = [] self.correlation_history[layer_name] = [] - + def update( self, layer_name: str, @@ -393,9 +393,9 @@ def update( ): """ Update statistics after a training step. - + Call this after loss.backward() but before optimizer.step() - + Args: layer_name: Name of layer gradient: Backprop gradient (layer.weight.grad) @@ -403,25 +403,25 @@ def update( """ if layer_name not in self.gradient_history: self.register_layer(layer_name) - + # Store self.gradient_history[layer_name].append(gradient.detach().cpu().clone()) self.signal_history[layer_name].append(local_signal.detach().cpu().clone()) - + # Compute correlation grad_flat = gradient.flatten() signal_flat = local_signal.flatten() - + corr = torch.corrcoef(torch.stack([grad_flat, signal_flat]))[0, 1] self.correlation_history[layer_name].append(corr.item()) - + def get_average_correlation(self, layer_name: str) -> float: """Get average correlation over training.""" if layer_name not in self.correlation_history: return 0.0 - + return sum(self.correlation_history[layer_name]) / len(self.correlation_history[layer_name]) - + def get_best_local_rule( self, layer_name: str, @@ -429,14 +429,14 @@ def get_best_local_rule( ) -> Tuple[str, float]: """ Determine which local rule best approximates backprop for this layer. - + Returns: (best_rule_name, correlation) """ # This would require testing multiple rules # For now, return based on accumulated correlation avg_corr = self.get_average_correlation(layer_name) - + return (self.local_signal if hasattr(self, 'local_signal') else 'hebbian', avg_corr) @@ -446,40 +446,40 @@ def design_local_learning_rule( ) -> Dict[str, Any]: """ Design optimal local learning rule for a layer based on gradient analysis. - + Args: gradient_tracker: Tracker that monitored training layer_name: Layer to design rule for - + Returns: Local learning rule specification """ # Analyze gradient statistics grad_history = gradient_tracker.gradient_history[layer_name] - + if not grad_history: return {'rule': 'hebbian', 'params': {}} - + # Compute gradient statistics avg_grad_magnitude = torch.stack([g.abs().mean() for g in grad_history]).mean() grad_direction_consistency = compute_direction_consistency(grad_history) - + # Design rule based on statistics if grad_direction_consistency > 0.8: # Consistent gradient → simple Hebbian works rule = 'hebbian' learning_rate = avg_grad_magnitude.item() * 0.1 - + elif grad_direction_consistency < 0.3: # Inconsistent → need more sophisticated rule rule = 'oja' learning_rate = avg_grad_magnitude.item() * 0.05 - + else: # Moderate → anti-Hebbian rule = 'anti_hebbian' learning_rate = avg_grad_magnitude.item() * 0.08 - + return { 'rule': rule, 'params': { @@ -492,27 +492,27 @@ def design_local_learning_rule( def compute_direction_consistency(gradient_history: List[torch.Tensor]) -> float: """ Compute how consistent gradient direction is across training. - + High consistency → gradient always points similar direction Low consistency → gradient changes direction frequently - + Returns: Consistency score [0, 1] """ if len(gradient_history) < 2: return 1.0 - + # Normalize gradients grads_normalized = [g / (g.norm() + 1e-8) for g in gradient_history] - + # Compute pairwise cosine similarities similarities = [] for i in range(len(grads_normalized) - 1): cos_sim = (grads_normalized[i] * grads_normalized[i+1]).sum() similarities.append(cos_sim.abs().item()) - + # Average similarity consistency = sum(similarities) / len(similarities) - + return consistency diff --git a/src/alignment/metrics/information/__init__.py b/src/alignment/metrics/information/__init__.py index 001b3d6f..d79acbd1 100644 --- a/src/alignment/metrics/information/__init__.py +++ b/src/alignment/metrics/information/__init__.py @@ -2,30 +2,23 @@ Information-theoretic metrics for neural network alignment. """ -from .mutual_information import ( - MutualInformationGaussian, - MutualInformationBinning, -) -from .redundancy import AverageRedundancy -from .pid import ( - SharedInformation, - UniqueInformationX, - UniqueInformationY, - SynergisticInformation as PIDSynergisticInformation, -) from .conditional_mutual_information import ConditionalMutualInformation -from .mi_projection import MIProjectionVsMeanInput from .gaussian_mi import GaussianMIAnalytic +from .mi_projection import MIProjectionVsMeanInput +from .mutual_information import MutualInformationBinning, MutualInformationGaussian from .pairwise_gaussian import PairwiseRedundancyGaussian +from .pid import SharedInformation, UniqueInformationX, UniqueInformationY +from .pid import SynergisticInformation as PIDSynergisticInformation +from .redundancy import AverageRedundancy from .synergy_mmi import SynergyGaussianMMI # Import higher-order metrics if available try: from .higher_order import ( - TotalCorrelation, - InteractionInformation, ConnectedInformation, + InteractionInformation, SynergisticInformation, + TotalCorrelation, ) _has_higher_order = True except ImportError: @@ -58,4 +51,4 @@ 'InteractionInformation', 'ConnectedInformation', 'SynergisticInformation', - ]) \ No newline at end of file + ]) diff --git a/src/alignment/metrics/information/conditional_mutual_information.py b/src/alignment/metrics/information/conditional_mutual_information.py index 28ca3a30..4b099261 100644 --- a/src/alignment/metrics/information/conditional_mutual_information.py +++ b/src/alignment/metrics/information/conditional_mutual_information.py @@ -2,10 +2,12 @@ Conditional mutual information metric for neural network analysis. """ -import torch -import numpy as np import logging from typing import Optional + +import numpy as np +import torch + from ...core.base import BaseMetric logger = logging.getLogger(__name__) @@ -17,26 +19,26 @@ class ConditionalMutualInformation(BaseMetric): - Y is the output of a neuron - Z is a reference signal (e.g., target outputs or mean of other neurons) - X is the input - + This measures how much information Y provides about Z beyond what X already provides. """ - + name = "conditional_mutual_information" requires_weights = False requires_inputs = True requires_outputs = True - + def __init__(self, bins: int = 10, use_gaussian: bool = False): """ Initialize the conditional MI metric. - + Args: bins: Number of bins for discretization (if not using Gaussian approximation) use_gaussian: Whether to use Gaussian approximation instead of binning """ self.bins = bins self.use_gaussian = use_gaussian - + @torch.no_grad() def compute( self, @@ -48,20 +50,20 @@ def compute( ) -> torch.Tensor: """ Compute conditional MI scores for each neuron. - + Args: inputs: Input activations [batch_size, num_input_features] weights: Not used outputs: Output activations [batch_size, num_neurons] target_outputs: Reference signal [batch_size, num_targets] (optional) **kwargs: Additional arguments - + Returns: CMI scores per neuron [num_neurons] """ if inputs is None or outputs is None: raise ValueError("Conditional MI requires both inputs and outputs") - + # Handle dimensions if inputs.ndim != 2: if inputs.ndim > 2: @@ -69,17 +71,17 @@ def compute( else: logger.warning(f"Inputs have unexpected shape: {inputs.shape}") return torch.zeros(1, device=outputs.device) - + if outputs.ndim != 2: logger.warning(f"Outputs have unexpected shape: {outputs.shape}") return torch.zeros(1, device=outputs.device) - + batch_size, num_neurons = outputs.shape - + if batch_size < 10: # Need enough samples for estimation logger.warning(f"Too few samples for CMI estimation: {batch_size}") return torch.zeros(num_neurons, device=outputs.device) - + # Use first principal component of inputs as conditioning variable if inputs.shape[1] > 1: # Simple PCA approximation using SVD @@ -88,7 +90,7 @@ def compute( X = torch.matmul(inputs_centered, V[0:1, :].T) # First PC else: X = inputs - + # Determine reference signal Z if target_outputs is not None: Z = target_outputs @@ -97,12 +99,12 @@ def compute( else: # Use mean of other neurons as reference Z = None - + if self.use_gaussian: return self._compute_gaussian(X, outputs, Z) else: return self._compute_binning(X, outputs, Z) - + def _compute_gaussian( self, X: torch.Tensor, @@ -112,11 +114,11 @@ def _compute_gaussian( """Compute CMI using Gaussian approximation.""" num_neurons = Y.shape[1] cmi_scores = torch.zeros(num_neurons, device=Y.device) - + for i in range(num_neurons): # Current neuron output y_i = Y[:, i:i+1] - + # Reference signal if Z is not None: z = Z @@ -128,50 +130,50 @@ def _compute_gaussian( z = Y[:, mask].mean(dim=1, keepdim=True) else: continue - + try: # Compute I(Y;Z|X) = H(Y|X) + H(Z|X) - H(Y,Z|X) # Under Gaussian assumption, we can compute this from covariances - + # Stack variables x_flat = X.reshape(X.shape[0], -1) xyz = torch.cat([x_flat, y_i, z], dim=1) - + # Compute covariance matrix cov = self._covariance(xyz) - + # Indices n_x = x_flat.shape[1] idx_x = slice(0, n_x) idx_y = n_x - idx_z = slice(n_x + 1, cov.shape[0]) - + slice(n_x + 1, cov.shape[0]) + # Compute conditional entropies using Schur complement # H(Y|X) cov_x = cov[idx_x, idx_x] cov_y = cov[idx_y, idx_y] cov_xy = cov[idx_x, idx_y] - + if torch.linalg.matrix_rank(cov_x) == cov_x.shape[0]: cov_y_given_x = cov_y - cov_xy.T @ torch.linalg.inv(cov_x) @ cov_xy - h_y_given_x = 0.5 * torch.log(2 * np.pi * np.e * torch.clamp(cov_y_given_x, min=1e-10)) + 0.5 * torch.log(2 * np.pi * np.e * torch.clamp(cov_y_given_x, min=1e-10)) else: - h_y_given_x = 0.5 * torch.log(2 * np.pi * np.e * torch.clamp(cov_y, min=1e-10)) - + 0.5 * torch.log(2 * np.pi * np.e * torch.clamp(cov_y, min=1e-10)) + # Similar for H(Z|X) and H(Y,Z|X) # Simplified: just use correlation-based approximation corr_yz_given_x = self._partial_correlation(y_i, z, x_flat) - + # Approximate CMI if torch.abs(corr_yz_given_x) < 0.999: cmi_scores[i] = -0.5 * torch.log(1 - corr_yz_given_x**2) - + except Exception as e: logger.debug(f"Error computing Gaussian CMI for neuron {i}: {e}") continue - + return torch.nan_to_num(cmi_scores, nan=0.0) - + def _compute_binning( self, X: torch.Tensor, @@ -181,18 +183,18 @@ def _compute_binning( """Compute CMI using binning/discretization.""" num_neurons = Y.shape[1] cmi_scores = torch.zeros(num_neurons, device=Y.device) - + # Convert to numpy for binning X_np = X.cpu().numpy() Y_np = Y.cpu().numpy() - + # Discretize X X_discrete = self._discretize(X_np) - + for i in range(num_neurons): # Current neuron output y_i_np = Y_np[:, i] - + # Reference signal if Z is not None: z_np = Z.cpu().numpy() @@ -206,31 +208,31 @@ def _compute_binning( z_np = Y_np[:, mask].mean(axis=1) else: continue - + # Discretize Y and Z y_discrete = self._discretize(y_i_np) z_discrete = self._discretize(z_np) - + try: # Compute CMI using chain rule: # I(Y;Z|X) = I(Y;Z,X) - I(Y;X) mi_yzx = self._mutual_information_3way(y_discrete, z_discrete, X_discrete) mi_yx = self._mutual_information(y_discrete, X_discrete) - + cmi = mi_yzx - mi_yx cmi_scores[i] = max(0, cmi) # CMI should be non-negative - + except Exception as e: logger.debug(f"Error computing binned CMI for neuron {i}: {e}") continue - + return cmi_scores.to(Y.device) - + def _covariance(self, X: torch.Tensor) -> torch.Tensor: """Compute covariance matrix.""" X_centered = X - X.mean(dim=0, keepdim=True) return torch.matmul(X_centered.T, X_centered) / (X.shape[0] - 1) - + def _partial_correlation( self, Y: torch.Tensor, @@ -240,17 +242,17 @@ def _partial_correlation( """Compute partial correlation between Y and Z given X.""" # Stack all variables all_vars = torch.cat([Y, Z, X], dim=1) - + # Compute correlation matrix corr = self._correlation_matrix(all_vars) - + # Extract submatrices # corr = [[r_yy, r_yz, r_yx], # [r_zy, r_zz, r_zx], # [r_xy, r_xz, r_xx]] - + n_x = X.shape[1] - + # Partial correlation formula try: # Get relevant correlations @@ -258,16 +260,16 @@ def _partial_correlation( r_yx = corr[0, 2:2+n_x] r_zx = corr[1, 2:2+n_x] R_xx = corr[2:2+n_x, 2:2+n_x] - + # Compute partial correlation if torch.linalg.matrix_rank(R_xx) == R_xx.shape[0]: R_xx_inv = torch.linalg.inv(R_xx) partial_corr = r_yz - r_yx @ R_xx_inv @ r_zx - + # Normalize var_y_given_x = 1 - r_yx @ R_xx_inv @ r_yx var_z_given_x = 1 - r_zx @ R_xx_inv @ r_zx - + if var_y_given_x > 1e-10 and var_z_given_x > 1e-10: partial_corr = partial_corr / torch.sqrt(var_y_given_x * var_z_given_x) else: @@ -275,53 +277,53 @@ def _partial_correlation( else: # Fallback to simple correlation partial_corr = r_yz - + except Exception: # Fallback to simple correlation partial_corr = corr[0, 1] - + return torch.clamp(partial_corr, -1.0, 1.0) - + def _correlation_matrix(self, X: torch.Tensor) -> torch.Tensor: """Compute correlation matrix.""" cov = self._covariance(X) std = torch.sqrt(torch.diag(cov) + 1e-10) outer_std = torch.outer(std, std) return torch.where(outer_std > 1e-10, cov / outer_std, torch.zeros_like(cov)) - + def _discretize(self, x: np.ndarray) -> np.ndarray: """Discretize continuous values into bins.""" min_val, max_val = np.min(x), np.max(x) - + if max_val - min_val < 1e-10: return np.zeros_like(x, dtype=int) - + bins = np.linspace(min_val, max_val, self.bins + 1) digitized = np.digitize(x, bins[:-1]) - 1 return np.clip(digitized, 0, self.bins - 1) - + def _mutual_information(self, x: np.ndarray, y: np.ndarray) -> float: """Compute mutual information between two discrete variables.""" # Joint probability joint_hist = np.zeros((self.bins, self.bins)) for i in range(len(x)): joint_hist[x[i], y[i]] += 1 - + joint_prob = joint_hist / len(x) - + # Marginal probabilities p_x = joint_prob.sum(axis=1) p_y = joint_prob.sum(axis=0) - + # Compute MI mi = 0.0 for i in range(self.bins): for j in range(self.bins): if joint_prob[i, j] > 1e-10 and p_x[i] > 1e-10 and p_y[j] > 1e-10: mi += joint_prob[i, j] * np.log(joint_prob[i, j] / (p_x[i] * p_y[j])) - + return mi / np.log(2) # Convert to bits - + def _mutual_information_3way( self, y: np.ndarray, @@ -331,19 +333,19 @@ def _mutual_information_3way( """Compute I(Y;Z,X) = I(Y;(Z,X)) treating (Z,X) as a joint variable.""" # Create joint variable for (Z,X) zx_joint = z * self.bins + x # Simple encoding - + # Ensure we don't exceed the range max_val = self.bins * self.bins - 1 zx_joint = np.clip(zx_joint, 0, max_val) - + # Compute MI between Y and the joint variable # Need to adjust bins for the joint variable original_bins = self.bins self.bins = self.bins * self.bins - + mi = self._mutual_information(y, zx_joint) - + # Restore original bins self.bins = original_bins - - return mi \ No newline at end of file + + return mi diff --git a/src/alignment/metrics/information/gaussian_mi.py b/src/alignment/metrics/information/gaussian_mi.py index fab1538d..1b2e812f 100644 --- a/src/alignment/metrics/information/gaussian_mi.py +++ b/src/alignment/metrics/information/gaussian_mi.py @@ -5,32 +5,32 @@ Gaussian distributions, with Edgeworth expansions for higher-order corrections. """ +from typing import Dict, Optional + import torch -import torch.nn as nn -from typing import Optional, Dict, Any, Tuple -import numpy as np -from ...core.registry import register_metric + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("gaussian_mi_analytic") class GaussianMIAnalytic(BaseMetric): """ Compute mutual information between input and output assuming Gaussian distributions. - + For linear transformations Y = WX + ε where X and ε are Gaussian: - I(X;Y) = 1/2 * log(det(Σ_Y) / det(Σ_Y|X)) - + With Edgeworth expansion for non-Gaussian corrections up to specified order. """ - + name = "gaussian_mi_analytic" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( - self, + self, expansion_order: int = 2, noise_std: float = 0.1, regularization: float = 1e-6, @@ -39,7 +39,7 @@ def __init__( ): """ Initialize Gaussian MI metric with expansion. - + Args: expansion_order: Order of Edgeworth expansion (0=pure Gaussian, 1-3 for corrections) noise_std: Assumed noise standard deviation @@ -52,11 +52,11 @@ def __init__( self.regularization = regularization self.per_neuron = per_neuron self.use_entropy_edgeworth = use_entropy_edgeworth - + def _compute_cumulants(self, data: torch.Tensor, max_order: int = 4) -> Dict[int, torch.Tensor]: """ Compute cumulants up to specified order. - + For centered data: - κ₁ = 0 (mean) - κ₂ = variance @@ -65,22 +65,22 @@ def _compute_cumulants(self, data: torch.Tensor, max_order: int = 4) -> Dict[int """ # Center the data data_centered = data - data.mean(dim=0, keepdim=True) - n_samples = data.shape[0] - + data.shape[0] + cumulants = {} - + # Second cumulant (variance) cumulants[2] = torch.var(data_centered, dim=0, unbiased=True) - + if max_order >= 3: # Third cumulant (related to skewness) cumulants[3] = torch.mean(data_centered ** 3, dim=0) - + if max_order >= 4: # Fourth cumulant (related to kurtosis) fourth_moment = torch.mean(data_centered ** 4, dim=0) cumulants[4] = fourth_moment - 3 * cumulants[2] ** 2 - + return cumulants def _univariate_entropy_edgeworth(self, data: torch.Tensor, eps: float = 1e-12) -> torch.Tensor: @@ -103,48 +103,48 @@ def _univariate_entropy_edgeworth(self, data: torch.Tensor, eps: float = 1e-12) h_gauss = 0.5 * torch.log(2 * torch.pi * torch.e * var) corr = - (gamma1 ** 2) / 12.0 - (gamma2 ** 2) / 48.0 return h_gauss + corr - + def _gaussian_mi( - self, - cov_x: torch.Tensor, - cov_y: torch.Tensor, + self, + cov_x: torch.Tensor, + cov_y: torch.Tensor, cov_xy: torch.Tensor ) -> torch.Tensor: """ Compute Gaussian mutual information. - + I(X;Y) = 1/2 * log(det(Σ_X) * det(Σ_Y) / det(Σ)) where Σ is the joint covariance matrix. """ # Construct joint covariance matrix n_x = cov_x.shape[0] n_y = cov_y.shape[0] - + joint_cov = torch.zeros(n_x + n_y, n_x + n_y, device=cov_x.device) joint_cov[:n_x, :n_x] = cov_x joint_cov[n_x:, n_x:] = cov_y joint_cov[:n_x, n_x:] = cov_xy joint_cov[n_x:, :n_x] = cov_xy.T - + # Add regularization for numerical stability reg_eye_x = self.regularization * torch.eye(n_x, device=cov_x.device) reg_eye_y = self.regularization * torch.eye(n_y, device=cov_y.device) reg_eye_joint = self.regularization * torch.eye(n_x + n_y, device=joint_cov.device) - + cov_x = cov_x + reg_eye_x cov_y = cov_y + reg_eye_y joint_cov = joint_cov + reg_eye_joint - + # Compute determinants det_x = torch.linalg.det(cov_x) det_y = torch.linalg.det(cov_y) det_joint = torch.linalg.det(joint_cov) - + # Mutual information mi = 0.5 * torch.log(det_x * det_y / det_joint) - + return mi - + def _edgeworth_correction( self, cumulants_x: Dict[int, torch.Tensor], @@ -154,12 +154,12 @@ def _edgeworth_correction( ) -> torch.Tensor: """ Compute Edgeworth expansion corrections to mutual information. - + The corrections involve higher-order cumulants and capture deviations from Gaussianity. """ correction = 0.0 - + if order >= 1 and 3 in cumulants_x and 3 in cumulants_y: # First-order correction involves third cumulants (skewness) # This is a simplified approximation @@ -167,34 +167,34 @@ def _edgeworth_correction( kappa3_y = cumulants_y[3].item() if hasattr(cumulants_y[3], 'item') else cumulants_y[3] var_x = cumulants_x[2].mean() if cumulants_x[2].numel() > 1 else cumulants_x[2].item() var_y = cumulants_y[2].item() if hasattr(cumulants_y[2], 'item') else cumulants_y[2] - + # Normalized third cumulants gamma1_x = kappa3_x / (var_x ** 1.5 + 1e-8) gamma1_y = kappa3_y / (var_y ** 1.5 + 1e-8) - + # Correlation coefficient (cov_xy is already scalar) corr = cov_xy / (torch.sqrt(torch.tensor(var_x * var_y)) + 1e-8) - + # First-order correction (simplified form) correction += (1/6) * corr * gamma1_x * gamma1_y - + if order >= 2 and 4 in cumulants_x and 4 in cumulants_y: # Second-order correction involves fourth cumulants (kurtosis) kappa4_x = cumulants_x[4].mean() if cumulants_x[4].numel() > 1 else cumulants_x[4].item() kappa4_y = cumulants_y[4].item() if hasattr(cumulants_y[4], 'item') else cumulants_y[4] var_x = cumulants_x[2].mean() if cumulants_x[2].numel() > 1 else cumulants_x[2].item() var_y = cumulants_y[2].item() if hasattr(cumulants_y[2], 'item') else cumulants_y[2] - + # Normalized fourth cumulants (excess kurtosis) gamma2_x = kappa4_x / (var_x ** 2 + 1e-8) gamma2_y = kappa4_y / (var_y ** 2 + 1e-8) - + # Correlation coefficient corr = cov_xy / (torch.sqrt(torch.tensor(var_x * var_y)) + 1e-8) - + # Second-order correction (simplified form) correction += (1/24) * (corr ** 2) * (gamma2_x + gamma2_y) - + if 3 in cumulants_x and 3 in cumulants_y: # Mixed term involving third cumulants kappa3_x = cumulants_x[3].mean() if cumulants_x[3].numel() > 1 else cumulants_x[3].item() @@ -202,7 +202,7 @@ def _edgeworth_correction( gamma1_x = kappa3_x / (var_x ** 1.5 + 1e-8) gamma1_y = kappa3_y / (var_y ** 1.5 + 1e-8) correction += (1/72) * (gamma1_x ** 2 + gamma1_y ** 2) - + if order >= 3: # Third-order corrections become quite complex # Here we provide a simplified version @@ -210,26 +210,26 @@ def _edgeworth_correction( var_x = cumulants_x[2].mean() if cumulants_x[2].numel() > 1 else cumulants_x[2].item() var_y = cumulants_y[2].item() if hasattr(cumulants_y[2], 'item') else cumulants_y[2] corr = cov_xy / (torch.sqrt(torch.tensor(var_x * var_y)) + 1e-8) - + kappa3_x = cumulants_x[3].mean() if cumulants_x[3].numel() > 1 else cumulants_x[3].item() kappa3_y = cumulants_y[3].item() if hasattr(cumulants_y[3], 'item') else cumulants_y[3] kappa4_x = cumulants_x[4].mean() if cumulants_x[4].numel() > 1 else cumulants_x[4].item() kappa4_y = cumulants_y[4].item() if hasattr(cumulants_y[4], 'item') else cumulants_y[4] - + gamma1_x = kappa3_x / (var_x ** 1.5 + 1e-8) gamma1_y = kappa3_y / (var_y ** 1.5 + 1e-8) gamma2_x = kappa4_x / (var_x ** 2 + 1e-8) gamma2_y = kappa4_y / (var_y ** 2 + 1e-8) - + # Third-order correction (highly simplified) correction += (1/144) * corr * (gamma1_x * gamma2_y + gamma1_y * gamma2_x) - + # Convert to scalar if needed if hasattr(correction, 'item'): correction = correction.item() - + return correction - + def compute( self, inputs: torch.Tensor, @@ -239,18 +239,18 @@ def compute( ) -> torch.Tensor: """ Compute Gaussian MI with non-Gaussian corrections. - + Args: inputs: Input activations [batch_size, input_dim] weights: Weight matrix [output_dim, input_dim] outputs: Output activations (computed if not provided) - + Returns: MI scores for each neuron [output_dim] or single score """ batch_size, input_dim = inputs.shape output_dim = weights.shape[0] - + # Compute outputs if not provided if outputs is None: outputs = inputs @ weights.T @@ -258,25 +258,25 @@ def compute( if self.noise_std > 0: noise = torch.randn_like(outputs) * self.noise_std outputs = outputs + noise - + if self.per_neuron: # Compute MI for each output neuron separately mi_scores = torch.zeros(output_dim, device=inputs.device) - + # Compute input statistics once inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) cov_x = (inputs_centered.T @ inputs_centered) / (batch_size - 1) cumulants_x = self._compute_cumulants(inputs, self.expansion_order + 2) - + for i in range(output_dim): # Get single output y = outputs[:, i].unsqueeze(1) y_centered = y - y.mean(dim=0, keepdim=True) - + # Compute covariances cov_y = (y_centered.T @ y_centered) / (batch_size - 1) cov_xy = (inputs_centered.T @ y_centered) / (batch_size - 1) - + # Gaussian MI baseline mi_gaussian = self._gaussian_mi(cov_x, cov_y, cov_xy) @@ -299,27 +299,27 @@ def compute( mi_scores[i] = mi_edge else: mi_scores[i] = mi_gaussian - + return mi_scores - + else: # Compute joint MI between all inputs and all outputs inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) outputs_centered = outputs - outputs.mean(dim=0, keepdim=True) - + # Compute covariances cov_x = (inputs_centered.T @ inputs_centered) / (batch_size - 1) cov_y = (outputs_centered.T @ outputs_centered) / (batch_size - 1) cov_xy = (inputs_centered.T @ outputs_centered) / (batch_size - 1) - + # Gaussian MI mi_gaussian = self._gaussian_mi(cov_x, cov_y, cov_xy) - + # Add corrections if requested if self.expansion_order > 0: cumulants_x = self._compute_cumulants(inputs, self.expansion_order + 2) - cumulants_y = self._compute_cumulants(outputs, self.expansion_order + 2) - + self._compute_cumulants(outputs, self.expansion_order + 2) + # For joint MI, we need to handle the corrections differently # Here we provide a simplified scalar correction avg_correction = 0.0 @@ -327,16 +327,16 @@ def compute( y_single = outputs[:, i].unsqueeze(1) cumulants_y_single = self._compute_cumulants(y_single, self.expansion_order + 2) cov_xy_single = cov_xy[:, i].unsqueeze(1) - + correction = self._edgeworth_correction( cumulants_x, cumulants_y_single, cov_xy_single.squeeze(), self.expansion_order ) avg_correction += correction - + avg_correction = avg_correction / output_dim total_mi = mi_gaussian + avg_correction else: total_mi = mi_gaussian - + # Return same value for all neurons - return torch.full((output_dim,), total_mi.item(), device=inputs.device) \ No newline at end of file + return torch.full((output_dim,), total_mi.item(), device=inputs.device) diff --git a/src/alignment/metrics/information/gaussian_pid.py b/src/alignment/metrics/information/gaussian_pid.py index 7a262f92..0b8bd89e 100644 --- a/src/alignment/metrics/information/gaussian_pid.py +++ b/src/alignment/metrics/information/gaussian_pid.py @@ -8,9 +8,10 @@ Returns average synergy per neuron over sampled partners j. """ -from typing import Optional, Any -import torch import logging +from typing import Any, Optional + +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -139,7 +140,6 @@ def partner_indices(i: int) -> torch.Tensor: # Compute synergy per i averaged over j in partners synergy = torch.zeros(N, device=device) - eps = 1e-12 if d == 1: z = Z.view(B) for i in range(N): diff --git a/src/alignment/metrics/information/gpu_binning.py b/src/alignment/metrics/information/gpu_binning.py index 48aea878..9c28a356 100644 --- a/src/alignment/metrics/information/gpu_binning.py +++ b/src/alignment/metrics/information/gpu_binning.py @@ -1,27 +1,26 @@ +from typing import Optional, Tuple, Union + import torch -import torch.nn as nn -from typing import Tuple, Optional, Union, List -import math class GPUBinning: """GPU-accelerated binning operations using PyTorch operations.""" - + def __init__(self, device: str = 'cuda'): """ Initialize GPU binning. - + Args: device: Device to use ('cuda' or 'cpu') """ self.device = torch.device(device if torch.cuda.is_available() else 'cpu') - + @staticmethod @torch.jit.script - def _compute_bin_indices_1d(data: torch.Tensor, - min_val: float, - max_val: float, + def _compute_bin_indices_1d(data: torch.Tensor, + min_val: float, + max_val: float, n_bins: int) -> torch.Tensor: """JIT-compiled function to compute 1D bin indices.""" # Normalize to [0, 1] @@ -31,112 +30,111 @@ def _compute_bin_indices_1d(data: torch.Tensor, # Convert to bin indices indices = (normalized * n_bins).long() return indices - - def fast_histogram_1d(self, - data: torch.Tensor, + + def fast_histogram_1d(self, + data: torch.Tensor, n_bins: int = 256, range_min: Optional[float] = None, range_max: Optional[float] = None) -> torch.Tensor: """ Compute 1D histogram on GPU. - + Args: data: Input tensor (will be flattened) n_bins: Number of bins range_min: Minimum value (computed from data if None) range_max: Maximum value (computed from data if None) - + Returns: Histogram counts """ data = data.to(self.device).flatten() - + # Determine range if range_min is None: range_min = data.min().item() if range_max is None: range_max = data.max().item() - + # Compute bin indices indices = self._compute_bin_indices_1d(data, range_min, range_max, n_bins) - + # Count using scatter_add hist = torch.zeros(n_bins, dtype=torch.float32, device=self.device) ones = torch.ones_like(indices, dtype=torch.float32) hist.scatter_add_(0, indices, ones) - + return hist - + def fast_histogram_2d(self, data_x: torch.Tensor, data_y: torch.Tensor, n_bins: Union[int, Tuple[int, int]] = 64) -> torch.Tensor: """ Compute 2D histogram on GPU. - + Args: data_x: First dimension data data_y: Second dimension data n_bins: Number of bins (single value or tuple for each dimension) - + Returns: 2D histogram """ data_x = data_x.to(self.device).flatten() data_y = data_y.to(self.device).flatten() - + if isinstance(n_bins, int): n_bins_x = n_bins_y = n_bins else: n_bins_x, n_bins_y = n_bins - + # Compute ranges x_min, x_max = data_x.min().item(), data_x.max().item() y_min, y_max = data_y.min().item(), data_y.max().item() - + # Compute bin indices x_indices = self._compute_bin_indices_1d(data_x, x_min, x_max, n_bins_x) y_indices = self._compute_bin_indices_1d(data_y, y_min, y_max, n_bins_y) - + # Convert to linear indices linear_indices = y_indices * n_bins_x + x_indices - + # Count hist = torch.zeros(n_bins_x * n_bins_y, dtype=torch.float32, device=self.device) ones = torch.ones_like(linear_indices, dtype=torch.float32) hist.scatter_add_(0, linear_indices, ones) - + return hist.view(n_bins_y, n_bins_x) - + def mutual_information_gpu(self, x: torch.Tensor, y: torch.Tensor, n_bins: int = 64) -> float: """ Compute mutual information using GPU-accelerated binning. - + Args: x: First variable y: Second variable n_bins: Number of bins - + Returns: Mutual information estimate """ # Compute 2D histogram joint_hist = self.fast_histogram_2d(x, y, n_bins) joint_hist = joint_hist + 1e-10 # Avoid log(0) - + # Normalize to get joint probability joint_prob = joint_hist / joint_hist.sum() - + # Marginal probabilities p_x = joint_prob.sum(dim=0) p_y = joint_prob.sum(dim=1) - + # Compute MI log_ratio = torch.log(joint_prob / (p_x.unsqueeze(0) * p_y.unsqueeze(1))) mi = (joint_prob * log_ratio).sum().item() - + return float(mi) - \ No newline at end of file diff --git a/src/alignment/metrics/information/higher_order.py b/src/alignment/metrics/information/higher_order.py index 4b0ed818..ac2038a0 100644 --- a/src/alignment/metrics/information/higher_order.py +++ b/src/alignment/metrics/information/higher_order.py @@ -4,28 +4,29 @@ These metrics capture complex multivariate dependencies beyond pairwise relationships. """ -import torch -import torch.nn as nn -from typing import Optional, Dict, Any, List, Tuple +from typing import List, Optional + import numpy as np -from ...core.registry import register_metric +import torch + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("total_correlation") class TotalCorrelation(BaseMetric): """ Measures total correlation (multi-information) among variables. - + Total correlation quantifies the amount of dependency among a set of random variables. It's the KL divergence between the joint distribution and the product of marginal distributions. - + TC(X1, ..., Xn) = sum(H(Xi)) - H(X1, ..., Xn) """ - + name = "total_correlation" - + def __init__(self, n_bins: int = 30, normalize: bool = True): """ Args: @@ -35,12 +36,12 @@ def __init__(self, n_bins: int = 30, normalize: bool = True): super().__init__() self.n_bins = n_bins self.normalize = normalize - + def _estimate_entropy(self, data: torch.Tensor) -> float: """Estimate entropy using histogram method.""" # Discretize continuous data data_np = data.detach().cpu().numpy() - + if data.dim() == 1: # Single variable entropy hist, _ = np.histogram(data_np, bins=self.n_bins) @@ -50,13 +51,13 @@ def _estimate_entropy(self, data: torch.Tensor) -> float: else: # Joint entropy for multiple variables # Use multi-dimensional histogram - ranges = [(data_np[:, i].min(), data_np[:, i].max()) + ranges = [(data_np[:, i].min(), data_np[:, i].max()) for i in range(data_np.shape[1])] hist, _ = np.histogramdd(data_np, bins=self.n_bins, range=ranges) hist = hist.flatten() + 1e-10 hist = hist / hist.sum() return -np.sum(hist * np.log(hist)) - + def compute(self, inputs: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, @@ -64,29 +65,29 @@ def compute(self, """Compute total correlation.""" if outputs is None: raise ValueError("Outputs required for total correlation") - + if outputs.dim() == 1: outputs = outputs.unsqueeze(1) - + n_vars = outputs.size(1) if n_vars < 2: return 0.0 # No correlation for single variable - + # Compute marginal entropies marginal_entropies = [] for i in range(n_vars): H_i = self._estimate_entropy(outputs[:, i]) marginal_entropies.append(H_i) - + # Compute joint entropy joint_entropy = self._estimate_entropy(outputs) - + # Total correlation tc = sum(marginal_entropies) - joint_entropy - + if self.normalize: tc = tc / n_vars - + return float(tc) @@ -94,17 +95,17 @@ def compute(self, class InteractionInformation(BaseMetric): """ Compute interaction information (co-information) among triplets of variables. - + This measures the amount of information that is present only when all three variables are considered together. """ - + name = "interaction_information" - + def __init__(self, n_samples: int = 100, bins: int = 10): """ Initialize interaction information metric. - + Args: n_samples: Number of triplet samples to evaluate bins: Number of bins for discretization @@ -112,7 +113,7 @@ def __init__(self, n_samples: int = 100, bins: int = 10): super().__init__() self.n_samples = n_samples self.bins = bins - + def compute( self, inputs: torch.Tensor, @@ -122,34 +123,34 @@ def compute( ) -> torch.Tensor: """ Compute interaction information scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations - + Returns: Interaction information scores for each neuron """ if outputs is None: outputs = inputs @ weights.T - + n_neurons = outputs.shape[1] interaction_scores = torch.zeros(n_neurons, device=outputs.device) - + # Sample triplets of neurons n_triplets = min(self.n_samples, n_neurons * (n_neurons - 1) * (n_neurons - 2) // 6) - + for _ in range(n_triplets): # Randomly select 3 different neurons idx = torch.randperm(n_neurons)[:3] i, j, k = idx[0], idx[1], idx[2] - + # Get activations for these neurons X = outputs[:, i] Y = outputs[:, j] Z = outputs[:, k] - + # Compute pairwise mutual information using histogram method MI_XY = self._estimate_mi_binning( X.unsqueeze(1), Y.unsqueeze(1) @@ -160,60 +161,60 @@ def compute( MI_YZ = self._estimate_mi_binning( Y.unsqueeze(1), Z.unsqueeze(1) ) - + # Compute conditional mutual information I(X;Y|Z) # Using approximation: I(X;Y|Z) ≈ I(X;Y) - I(X;Y;Z) # Where I(X;Y;Z) is the interaction information - + # For simplicity, we'll use the difference of mutual informations # as an approximation of interaction information interaction = MI_XY + MI_XZ + MI_YZ - + # Distribute score to participating neurons interaction_scores[i] += interaction / 3 interaction_scores[j] += interaction / 3 interaction_scores[k] += interaction / 3 - + # Normalize by number of samples interaction_scores = interaction_scores / n_triplets - + return interaction_scores - + def _estimate_mi_binning(self, x: torch.Tensor, y: torch.Tensor) -> float: """Estimate mutual information using binning method.""" x_np = x.detach().cpu().numpy() y_np = y.detach().cpu().numpy() - + # Create 2D histogram hist, _, _ = np.histogram2d(x_np.flatten(), y_np.flatten(), bins=self.bins) hist = hist + 1e-10 # Avoid log(0) - + # Normalize to get probabilities pxy = hist / hist.sum() px = pxy.sum(axis=1) py = pxy.sum(axis=0) - + # Compute MI mi = 0.0 for i in range(len(px)): for j in range(len(py)): if pxy[i, j] > 0: mi += pxy[i, j] * np.log(pxy[i, j] / (px[i] * py[j])) - + return mi -@register_metric("connected_information") +@register_metric("connected_information") class ConnectedInformation(BaseMetric): """ Measures connected information (interaction information of order n). - + This captures pure n-way interactions that cannot be reduced to lower-order interactions, useful for understanding complex dependencies in neural networks. """ - + name = "connected_information" - + def __init__(self, n_bins: int = 20, max_order: int = 4): """ Args: @@ -224,19 +225,19 @@ def __init__(self, n_bins: int = 20, max_order: int = 4): self.n_bins = n_bins self.max_order = max_order self._entropy_est = TotalCorrelation(n_bins=n_bins) - + def _compute_interaction_info(self, data: torch.Tensor, indices: List[int]) -> float: """Compute interaction information for a subset of variables.""" if len(indices) < 2: return 0.0 - + # Use inclusion-exclusion principle total = 0.0 n = len(indices) - + # Generate all non-empty subsets from itertools import combinations - + for k in range(1, n + 1): sign = (-1) ** (n - k) for subset in combinations(indices, k): @@ -246,9 +247,9 @@ def _compute_interaction_info(self, data: torch.Tensor, indices: List[int]) -> f else: entropy = self._entropy_est._estimate_entropy(subset_data) total += sign * entropy - + return total - + def compute(self, inputs: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, @@ -256,30 +257,30 @@ def compute(self, """Compute connected information up to max_order.""" if outputs is None: raise ValueError("Outputs required for connected information") - + if outputs.dim() == 1: outputs = outputs.unsqueeze(1) - + n_vars = outputs.size(1) if n_vars < 2: return 0.0 - + # Compute interaction information for different orders total_connected = 0.0 - + from itertools import combinations for order in range(2, min(n_vars + 1, self.max_order + 1)): for var_subset in combinations(range(n_vars), order): interaction = self._compute_interaction_info(outputs, list(var_subset)) total_connected += abs(interaction) # Use absolute value - + # Normalize by number of possible interactions n_interactions = sum(1 for order in range(2, min(n_vars + 1, self.max_order + 1)) for _ in combinations(range(n_vars), order)) - + if n_interactions > 0: total_connected /= n_interactions - + return float(total_connected) @@ -289,13 +290,13 @@ class SynergisticInformation(BaseMetric): Compute synergistic information - information that can only be obtained from the joint state of multiple neurons. """ - + name = "synergistic_information" - + def __init__(self, group_size: int = 3, n_groups: int = 50): """ Initialize synergistic information metric. - + Args: group_size: Size of neuron groups to analyze n_groups: Number of random groups to sample @@ -303,7 +304,7 @@ def __init__(self, group_size: int = 3, n_groups: int = 50): super().__init__() self.group_size = group_size self.n_groups = n_groups - + def compute( self, inputs: torch.Tensor, @@ -313,55 +314,55 @@ def compute( ) -> torch.Tensor: """ Compute synergistic information scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations - + Returns: Synergistic information scores for each neuron """ if outputs is None: outputs = inputs @ weights.T - + batch_size, n_neurons = outputs.shape synergy_scores = torch.zeros(n_neurons, device=outputs.device) - + # Sample groups of neurons n_groups_actual = min(self.n_groups, n_neurons // self.group_size) - + for _ in range(n_groups_actual): # Select a random group idx = torch.randperm(n_neurons)[:self.group_size] group_outputs = outputs[:, idx] - + # Compute joint entropy of the group # Using Gaussian assumption for efficiency group_centered = group_outputs - group_outputs.mean(dim=0, keepdim=True) cov_group = (group_centered.T @ group_centered) / (batch_size - 1) cov_group = cov_group + 1e-8 * torch.eye(self.group_size, device=outputs.device) - + # Joint entropy under Gaussian assumption # H = 0.5 * log(det(2πe * Σ)) det_cov = torch.linalg.det(cov_group) joint_entropy = 0.5 * torch.log(2 * np.pi * np.e * det_cov) - + # Compute sum of individual entropies individual_entropies = 0 for i in range(self.group_size): var = cov_group[i, i] individual_entropies += 0.5 * torch.log(2 * np.pi * np.e * var) - + # Synergy approximation: joint entropy - sum of individual entropies # (negative of this gives redundancy, positive gives synergy) synergy = joint_entropy - individual_entropies - + # Distribute score to participating neurons for i in idx: synergy_scores[i] += synergy / self.group_size - + # Normalize by number of groups synergy_scores = synergy_scores / n_groups_actual - - return synergy_scores \ No newline at end of file + + return synergy_scores diff --git a/src/alignment/metrics/information/mi_projection.py b/src/alignment/metrics/information/mi_projection.py index 4b08a281..bea997c5 100644 --- a/src/alignment/metrics/information/mi_projection.py +++ b/src/alignment/metrics/information/mi_projection.py @@ -2,10 +2,12 @@ Mutual information between neuron projections and mean input. """ -import torch -import numpy as np import logging from typing import Optional + +import numpy as np +import torch + from ...core.base import BaseMetric logger = logging.getLogger(__name__) @@ -14,21 +16,21 @@ class MIProjectionVsMeanInput(BaseMetric): """ Compute mutual information between each neuron's projection and the mean input. - + For each neuron with weights w, computes MI(w^T x, mean(x)) where x is the input. This measures how much information the neuron's projection preserves about the average input pattern. """ - + name = "mi_projection_vs_mean_input" requires_weights = True requires_inputs = True requires_outputs = False - + def __init__(self, bins: int = 30, eps: float = 1e-9, force_cpu: bool = False): """ Initialize the MI projection metric. - + Args: bins: Number of bins for discretization eps: Small value for numerical stability @@ -37,7 +39,7 @@ def __init__(self, bins: int = 30, eps: float = 1e-9, force_cpu: bool = False): self.bins = bins self.eps = eps self.force_cpu = force_cpu - + @torch.no_grad() def compute( self, @@ -48,19 +50,19 @@ def compute( ) -> torch.Tensor: """ Compute MI scores for each neuron. - + Args: inputs: Input activations [batch_size, num_features] weights: Weight matrix [num_neurons, num_features] outputs: Not used **kwargs: Additional arguments - + Returns: MI scores per neuron [num_neurons] """ if inputs is None or weights is None: raise ValueError("MI projection requires both inputs and weights") - + # Handle dimensions if inputs.ndim != 2: if inputs.ndim > 2: @@ -68,35 +70,35 @@ def compute( else: logger.warning(f"Inputs have unexpected shape: {inputs.shape}") return torch.zeros(1, device=weights.device) - + if weights.ndim != 2: if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) else: logger.warning(f"Weights have unexpected shape: {weights.shape}") return torch.zeros(1, device=weights.device) - + batch_size, num_features = inputs.shape num_neurons = weights.shape[0] - + # Check dimension compatibility if weights.shape[1] != num_features: min_dim = min(weights.shape[1], num_features) weights = weights[:, :min_dim] inputs = inputs[:, :min_dim] logger.warning(f"Dimension mismatch, truncating to {min_dim} features") - + # Need enough samples for MI estimation if batch_size < 10: logger.warning(f"Too few samples for MI estimation: {batch_size}") return torch.zeros(num_neurons, device=weights.device) - + # Compute mean input across features mean_input = inputs.mean(dim=1) # [batch_size] - + # Initialize scores mi_scores = torch.zeros(num_neurons, device=weights.device) - + # Move to CPU if requested and tensors are large if self.force_cpu and inputs.is_cuda and inputs.numel() > 1e6: inputs_cpu = inputs.cpu() @@ -106,67 +108,67 @@ def compute( inputs_cpu = inputs weights_cpu = weights mean_input_cpu = mean_input - + # Convert to numpy for binning mean_input_np = mean_input_cpu.numpy() - + # Discretize mean input mean_min, mean_max = np.min(mean_input_np), np.max(mean_input_np) if mean_max - mean_min < self.eps: logger.warning("Mean input has no variation") return torch.zeros(num_neurons, device=weights.device) - + mean_bins = np.linspace(mean_min, mean_max, self.bins + 1) mean_digitized = np.digitize(mean_input_np, mean_bins[:-1]) - 1 mean_digitized = np.clip(mean_digitized, 0, self.bins - 1) - + # Compute MI for each neuron for i in range(num_neurons): try: # Compute projection: w^T x projection = torch.matmul(inputs_cpu, weights_cpu[i]) # [batch_size] projection_np = projection.numpy() - + # Discretize projection proj_min, proj_max = np.min(projection_np), np.max(projection_np) if proj_max - proj_min < self.eps: continue - + proj_bins = np.linspace(proj_min, proj_max, self.bins + 1) proj_digitized = np.digitize(projection_np, proj_bins[:-1]) - 1 proj_digitized = np.clip(proj_digitized, 0, self.bins - 1) - + # Compute joint histogram joint_hist = np.zeros((self.bins, self.bins)) for j in range(batch_size): joint_hist[proj_digitized[j], mean_digitized[j]] += 1 - + # Convert to probabilities joint_prob = joint_hist / batch_size - + # Marginal probabilities p_proj = joint_prob.sum(axis=1) p_mean = joint_prob.sum(axis=0) - + # Compute MI mi = 0.0 for pi in range(self.bins): for mi_idx in range(self.bins): - if (joint_prob[pi, mi_idx] > self.eps and - p_proj[pi] > self.eps and + if (joint_prob[pi, mi_idx] > self.eps and + p_proj[pi] > self.eps and p_mean[mi_idx] > self.eps): mi += joint_prob[pi, mi_idx] * np.log2( joint_prob[pi, mi_idx] / (p_proj[pi] * p_mean[mi_idx]) ) - + mi_scores[i] = mi - + except Exception as e: logger.debug(f"Error computing MI for neuron {i}: {e}") continue - + # Convert back to torch tensor on original device if mi_scores.device != weights.device: mi_scores = mi_scores.to(weights.device) - - return torch.nan_to_num(mi_scores, nan=0.0) \ No newline at end of file + + return torch.nan_to_num(mi_scores, nan=0.0) diff --git a/src/alignment/metrics/information/mutual_information.py b/src/alignment/metrics/information/mutual_information.py index b50b06ce..bbee30ad 100644 --- a/src/alignment/metrics/information/mutual_information.py +++ b/src/alignment/metrics/information/mutual_information.py @@ -2,12 +2,12 @@ Mutual Information metrics for measuring dependencies between neural representations. """ -import torch -import torch.nn.functional as F -from typing import Optional, Dict, Any, Tuple -import numpy as np import logging -import warnings +from typing import Any, Optional + +import numpy as np +import torch + from ...core.base import BaseMetric from ...core.registry import register_metric @@ -18,14 +18,14 @@ class MutualInformationGaussian(BaseMetric): """ Mutual Information metric assuming Gaussian distributions. - + Computes MI between layer outputs and a reference signal (target outputs or first principal component of inputs) using the Gaussian approximation: MI = -0.5 * log(1 - ρ²) - + where ρ is the correlation coefficient. """ - + def __init__( self, use_pc_reference: bool = True, @@ -34,7 +34,7 @@ def __init__( ): """ Initialize the Gaussian MI metric. - + Args: use_pc_reference: If True and no target provided, use PC1 of inputs as reference min_samples: Minimum samples required for computation @@ -43,19 +43,19 @@ def __init__( super().__init__(**config) self.use_pc_reference = use_pc_reference self.min_samples = min_samples - + @property def requires_inputs(self) -> bool: return self.use_pc_reference - + @property def requires_weights(self) -> bool: return False - + @property def requires_outputs(self) -> bool: return True - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -66,35 +66,35 @@ def compute( ) -> torch.Tensor: """ Compute Gaussian MI for each output neuron. - + Args: inputs: Input activations (used for PC reference if needed) weights: Not used outputs: Layer output activations [batch_size, num_neurons] target_outputs: Target reference signal [batch_size, num_targets] - + Returns: MI values for each neuron [num_neurons] """ if outputs is None: raise ValueError("MutualInformationGaussian requires outputs") - + if outputs.ndim != 2: outputs = outputs.reshape(outputs.shape[0], -1) - + batch_size, num_neurons = outputs.shape - + if batch_size < self.min_samples: logger.warning(f"MI_gaussian: Only {batch_size} samples, returning zeros") return torch.zeros(num_neurons, device=outputs.device, dtype=outputs.dtype) - + # Determine reference signal if target_outputs is None: if self.use_pc_reference and inputs is not None: # Use first PC of inputs as reference if inputs.ndim != 2: inputs = inputs.reshape(inputs.shape[0], -1) - + try: # Compute covariance and get first PC inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) @@ -111,12 +111,12 @@ def compute( ref_data = target_outputs if ref_data.ndim == 1: ref_data = ref_data.unsqueeze(1) - + # Ensure ref_data has correct batch size if ref_data.shape[0] != batch_size: - logger.warning(f"MI_gaussian: Reference batch size mismatch") + logger.warning("MI_gaussian: Reference batch size mismatch") return torch.zeros(num_neurons, device=outputs.device, dtype=outputs.dtype) - + # Vectorized MI computation across neurons and reference dims # Standardize outputs and references along batch dimension eps = 1e-12 @@ -145,7 +145,7 @@ def compute( # Average over reference dims mi_scores = mi_per_ref.mean(dim=1) mi_scores = mi_scores.to(outputs.device) - + return torch.nan_to_num(mi_scores) @@ -153,11 +153,11 @@ def compute( class MutualInformationBinning(BaseMetric): """ Mutual Information using histogram binning method. - + Computes MI using discrete probability distributions estimated through histogram binning of continuous values. """ - + def __init__( self, bins: int = 10, @@ -166,7 +166,7 @@ def __init__( ): """ Initialize the binning MI metric. - + Args: bins: Number of bins for histogram min_samples: Minimum samples (need more for binning) @@ -175,19 +175,19 @@ def __init__( super().__init__(**config) self.bins = bins self.min_samples = min_samples - + @property def requires_inputs(self) -> bool: return False - + @property def requires_weights(self) -> bool: return False - + @property def requires_outputs(self) -> bool: return True - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -198,31 +198,31 @@ def compute( ) -> torch.Tensor: """ Compute binning-based MI for each output neuron. - + Args: inputs: Not used weights: Not used outputs: Layer output activations [batch_size, num_neurons] target_outputs: Target reference signal [batch_size, num_targets] - + Returns: MI values for each neuron [num_neurons] """ if outputs is None: raise ValueError("MutualInformationBinning requires outputs") - + if outputs.ndim != 2: outputs = outputs.reshape(outputs.shape[0], -1) - + batch_size, num_neurons = outputs.shape - + if batch_size < self.min_samples: logger.warning(f"MI_binning: Only {batch_size} samples, need {self.min_samples}") return torch.zeros(num_neurons, device=outputs.device, dtype=outputs.dtype) - + # Convert to numpy for binning operations outputs_np = outputs.cpu().numpy() - + # Determine reference if target_outputs is not None: ref_np = target_outputs.cpu().numpy() @@ -231,12 +231,12 @@ def compute( else: # Use mean of other neurons as reference ref_np = None - + mi_scores = np.zeros(num_neurons) - + for i in range(num_neurons): neuron_i_np = outputs_np[:, i] - + # Get reference for this neuron if ref_np is None and num_neurons > 1: # Use mean of other neurons @@ -247,27 +247,27 @@ def compute( continue else: current_ref_np = ref_np - + if current_ref_np is None: continue - + # Compute MI with each reference dimension mi_sum = 0.0 valid_refs = 0 - + for k in range(current_ref_np.shape[1]): ref_k_np = current_ref_np[:, k] - + # Bin the data hist_2d, x_edges, y_edges = np.histogram2d( neuron_i_np, ref_k_np, bins=self.bins ) - + # Convert to probabilities joint_p = hist_2d / batch_size p_x = np.sum(joint_p, axis=1) p_y = np.sum(joint_p, axis=0) - + # Compute MI (in nats; use natural logarithm) mi_val = 0.0 for xi in range(self.bins): @@ -276,13 +276,13 @@ def compute( mi_val += joint_p[xi, yi] * np.log( joint_p[xi, yi] / (p_x[xi] * p_y[yi]) ) - + mi_sum += mi_val valid_refs += 1 - + if valid_refs > 0: mi_scores[i] = mi_sum / valid_refs - + return torch.tensor(mi_scores, device=outputs.device, dtype=outputs.dtype) @@ -290,11 +290,11 @@ def compute( class ConditionalMutualInformation(MutualInformationGaussian): """ Conditional Mutual Information I(X;Y|Z). - + Measures information between X and Y that is not explained by Z. Uses Gaussian approximation. """ - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -306,45 +306,45 @@ def compute( ) -> torch.Tensor: """ Compute conditional MI: I(outputs; target | condition). - + Args: outputs: X variable [batch_size, num_neurons] target_outputs: Y variable [batch_size, num_targets] condition_on: Z variable to condition on [batch_size, num_conditions] - + Returns: Conditional MI values [num_neurons] """ if outputs is None or target_outputs is None or condition_on is None: raise ValueError("ConditionalMI requires outputs, target_outputs, and condition_on") - + # Compute I(X;Y) - I(X;Y;Z) using Gaussian approximation # This is a simplified implementation - + # Regular MI between outputs and targets mi_xy = super().compute( outputs=outputs, target_outputs=target_outputs, **kwargs ) - + # MI between outputs and condition mi_xz = super().compute( outputs=outputs, target_outputs=condition_on, **kwargs ) - - # MI between targets and condition + + # MI between targets and condition mi_yz = super().compute( outputs=target_outputs, target_outputs=condition_on, **kwargs ) - + # Approximate CMI (this is simplified - proper implementation would use # partial correlations or multivariate Gaussian formulas) cmi = mi_xy - torch.min(mi_xz, mi_yz.mean() * torch.ones_like(mi_xz)) cmi = torch.clamp(cmi, min=0.0) # CMI is non-negative - - return cmi \ No newline at end of file + + return cmi diff --git a/src/alignment/metrics/information/pairwise_gaussian.py b/src/alignment/metrics/information/pairwise_gaussian.py index ea808094..97e1f35a 100644 --- a/src/alignment/metrics/information/pairwise_gaussian.py +++ b/src/alignment/metrics/information/pairwise_gaussian.py @@ -7,9 +7,10 @@ where ρ is correlation in the Σ_X space. """ -from typing import Optional, Any -import torch import logging +from typing import Any, Optional + +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -21,19 +22,19 @@ class PairwiseRedundancyGaussian(BaseMetric): """ Compute per-neuron redundancy via pairwise Gaussian mutual information. - + For each neuron, computes average redundancy with K sampled partner neurons. Redundancy proxy: I(Y_i; Y_j) = -0.5 * log(1 - ρ²) - + This is a target-free redundancy measure based on correlation in the input covariance space. - + Example: >>> redundancy_metric = PairwiseRedundancyGaussian(num_pairs=10) >>> redundancy = redundancy_metric.compute(inputs, weights) >>> print(redundancy.shape) # [num_neurons] """ - + def __init__( self, num_pairs: int = 10, @@ -44,7 +45,7 @@ def __init__( ): """ Initialize pairwise redundancy metric. - + Args: num_pairs: Number of partner neurons to sample per neuron sampling_strategy: How to sample pairs ('random', 'nearest', 'all') @@ -59,19 +60,19 @@ def __init__( self.sampling_strategy = sampling_strategy self.mode = mode self.regularization = regularization - + @property def requires_inputs(self) -> bool: return self.mode == 'covariance_based' - + @property def requires_weights(self) -> bool: return self.mode == 'covariance_based' - + @property def requires_outputs(self) -> bool: return self.mode == 'output_based' - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -81,13 +82,13 @@ def compute( ) -> torch.Tensor: """ Compute per-neuron redundancy scores. - + Args: inputs: Input activations [batch_size, input_features] (for covariance_based) weights: Layer weights [num_neurons, input_features] (for covariance_based) outputs: Layer outputs [batch_size, num_neurons] (for output_based) **kwargs: Additional parameters - + Returns: Per-neuron redundancy scores [num_neurons] """ @@ -103,67 +104,67 @@ def compute( if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) outputs = inputs @ weights.T - + return self._compute_output_based(outputs) - + elif self.mode == 'covariance_based': if inputs is None or weights is None: raise ValueError("covariance_based mode requires inputs and weights") return self._compute_covariance_based(inputs, weights) - + else: raise ValueError(f"Unknown mode: {self.mode}") - + def _compute_output_based(self, outputs: torch.Tensor) -> torch.Tensor: """ Compute redundancy from outputs directly (FAST!). - + This is 3-30x faster than covariance-based, especially for high-dimensional inputs. - + Complexity: O(B·N² + N·K) instead of O(B·D²·N·K) For LLMs with D=4096, N=4096: ~30x speedup! - + Args: outputs: Layer outputs [batch_size, num_neurons] - + Returns: Per-neuron redundancy [num_neurons] """ if outputs.ndim > 2: outputs = outputs.reshape(outputs.shape[0], -1) - + B, N = outputs.shape - + if B < 2: logger.warning("Output-based redundancy needs B >= 2, returning zeros") return torch.zeros(N, device=outputs.device) - + # Center outputs Y_centered = outputs - outputs.mean(dim=0, keepdim=True) - + # Compute all pairwise covariances at once: [N, N] cov_Y = (Y_centered.T @ Y_centered) / max(1, B - 1) - + # Variances (diagonal) var_Y = torch.diag(cov_Y) # [N] - + # Correlation matrix std_matrix = torch.sqrt(var_Y.unsqueeze(1) * var_Y.unsqueeze(0) + 1e-12) corr_matrix = cov_Y / (std_matrix + 1e-12) - + # Clip to valid range corr_matrix = torch.clamp(corr_matrix, -0.9999, 0.9999) - + # Redundancy: I(Yi; Yj) = -0.5·log(1 - ρ²) rho_sq = corr_matrix ** 2 R_matrix = -0.5 * torch.log(1 - rho_sq + 1e-8) # [N, N] - + # Zero out diagonal (neuron with itself) R_matrix.fill_diagonal_(0) - + # Per-neuron redundancy: average over sampled partners redundancy = torch.zeros(N, device=outputs.device) - + if self.sampling_strategy == 'all': # Average over all partners row_sums = R_matrix.sum(dim=1) @@ -174,9 +175,9 @@ def _compute_output_based(self, outputs: torch.Tensor) -> torch.Tensor: partners = self._sample_partners_fast(i, N) if len(partners) > 0: redundancy[i] = R_matrix[i, partners].mean() - + return redundancy - + def _compute_covariance_based( self, inputs: torch.Tensor, @@ -184,53 +185,53 @@ def _compute_covariance_based( ) -> torch.Tensor: """ Compute redundancy via input covariance (SLOWER but works without outputs). - + Use when outputs not available. For large D, this is expensive. - + Args: inputs: Input activations [batch_size, input_features] weights: Layer weights [num_neurons, input_features] - + Returns: Per-neuron redundancy [num_neurons] """ - + # Flatten if needed if inputs.ndim > 2: inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) - + batch_size, input_features = inputs.shape num_neurons, weight_features = weights.shape - + # Check compatibility if input_features != weight_features: min_dim = min(input_features, weight_features) inputs = inputs[:, :min_dim] weights = weights[:, :min_dim] input_features = min_dim - + # Compute input covariance inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) cov = (inputs_centered.T @ inputs_centered) / max(1, batch_size - 1) - + # Add regularization if self.regularization > 0: cov = cov + self.regularization * torch.eye( input_features, device=cov.device, dtype=cov.dtype ) - + # Compute redundancy for each neuron redundancy = torch.zeros(num_neurons, device=weights.device, dtype=weights.dtype) - + for i in range(num_neurons): # Sample partner neurons partner_indices = self._sample_partners_fast(i, num_neurons) - + if len(partner_indices) == 0: continue - + # Compute correlation with each partner correlations = [] for j in partner_indices: @@ -238,7 +239,7 @@ def _compute_covariance_based( weights[i], weights[j], cov ) correlations.append(rho_sq) - + # Average redundancy with partners if correlations: # I(Y_i; Y_j) = -0.5 * log(1 - ρ²) @@ -247,9 +248,9 @@ def _compute_covariance_based( correlations_tensor = torch.clamp(correlations_tensor, max=0.9999) redundancy_values = -0.5 * torch.log(1 - correlations_tensor + 1e-8) redundancy[i] = redundancy_values.mean() - + return redundancy - + def _sample_partners_fast( self, neuron_idx: int, @@ -257,45 +258,45 @@ def _sample_partners_fast( ) -> torch.Tensor: """ Sample partner neurons for redundancy computation. - + Args: neuron_idx: Index of current neuron num_neurons: Total number of neurons - + Returns: Indices of partner neurons """ # Exclude self available = list(range(num_neurons)) available.remove(neuron_idx) - + if self.sampling_strategy == 'all': # Use all other neurons return torch.tensor(available, dtype=torch.long) - + elif self.sampling_strategy == 'random': # Random sample num_to_sample = min(self.num_pairs, len(available)) if num_to_sample == 0: return torch.tensor([], dtype=torch.long) - + indices = torch.randperm(len(available))[:num_to_sample] return torch.tensor([available[i] for i in indices], dtype=torch.long) - + elif self.sampling_strategy == 'nearest': # Sample nearby indices (assumes some ordering) num_to_sample = min(self.num_pairs, len(available)) if num_to_sample == 0: return torch.tensor([], dtype=torch.long) - + # Get closest indices distances = torch.abs(torch.tensor(available) - neuron_idx) _, nearest_indices = torch.topk(distances, num_to_sample, largest=False) return torch.tensor([available[i] for i in nearest_indices], dtype=torch.long) - + else: raise ValueError(f"Unknown sampling strategy: {self.sampling_strategy}") - + def _compute_correlation_squared( self, w_i: torch.Tensor, @@ -304,35 +305,35 @@ def _compute_correlation_squared( ) -> torch.Tensor: """ Compute squared correlation between two neurons in covariance space. - + Args: w_i: Weight vector of neuron i [features] w_j: Weight vector of neuron j [features] cov: Input covariance matrix [features, features] - + Returns: ρ² = (w_i^T Σ w_j)² / [(w_i^T Σ w_i)(w_j^T Σ w_j)] """ # Compute variances var_i = w_i @ cov @ w_i # scalar var_j = w_j @ cov @ w_j # scalar - + # Compute covariance cov_ij = w_i @ cov @ w_j # scalar - + # Compute squared correlation denominator = var_i * var_j - + if denominator < 1e-12: return torch.tensor(0.0, device=w_i.device, dtype=w_i.dtype) - + rho_sq = (cov_ij ** 2) / denominator - + # Clip to [0, 1] rho_sq = torch.clamp(rho_sq, min=0.0, max=1.0) - + return rho_sq - + def compute_pairwise_matrix( self, inputs: torch.Tensor, @@ -340,11 +341,11 @@ def compute_pairwise_matrix( ) -> torch.Tensor: """ Compute full pairwise redundancy matrix. - + Args: inputs: Input activations [batch_size, features] weights: Layer weights [num_neurons, features] - + Returns: Redundancy matrix [num_neurons, num_neurons] where R[i,j] = redundancy between neurons i and j @@ -353,25 +354,25 @@ def compute_pairwise_matrix( inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) - + batch_size = inputs.shape[0] num_neurons = weights.shape[0] - + # Compute covariance inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) cov = (inputs_centered.T @ inputs_centered) / max(1, batch_size - 1) - + if self.regularization > 0: cov = cov + self.regularization * torch.eye( cov.shape[0], device=cov.device, dtype=cov.dtype ) - + # Compute pairwise redundancy redundancy_matrix = torch.zeros( num_neurons, num_neurons, device=weights.device, dtype=weights.dtype ) - + for i in range(num_neurons): for j in range(i + 1, num_neurons): rho_sq = self._compute_correlation_squared( @@ -380,9 +381,9 @@ def compute_pairwise_matrix( # Clip correlation rho_sq = torch.clamp(rho_sq, max=0.9999) redundancy = -0.5 * torch.log(1 - rho_sq + 1e-8) - + # Symmetric redundancy_matrix[i, j] = redundancy redundancy_matrix[j, i] = redundancy - + return redundancy_matrix diff --git a/src/alignment/metrics/information/pid.py b/src/alignment/metrics/information/pid.py index 1c84d4a9..a9f6a794 100644 --- a/src/alignment/metrics/information/pid.py +++ b/src/alignment/metrics/information/pid.py @@ -5,11 +5,11 @@ about the output into unique, redundant, and synergistic components. """ -from typing import Optional, Dict, Any, Tuple -import torch -import numpy as np import logging -from pathlib import Path +from typing import Dict, Optional, Tuple + +import numpy as np +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -18,8 +18,9 @@ # Try to import the BROJA 2PID module try: - import sys import os + import sys + # Add the external module to path if needed from ...external.BROJA_2PID import BROJA_2PID HAS_BROJA = True @@ -33,12 +34,12 @@ class BasePIDMetric(BaseMetric): """Base class for PID-based metrics.""" - + # PID metrics require inputs and outputs requires_inputs = True requires_weights = False requires_outputs = True - + def __init__( self, bins: int = 10, @@ -48,7 +49,7 @@ def __init__( ): """ Initialize PID metric. - + Args: bins: Number of bins for discretization normalize: Whether to normalize the result @@ -59,7 +60,7 @@ def __init__( self.bins = bins self.normalize = normalize self.use_pca_inputs = use_pca_inputs - + def _prepare_pid_input( self, x1: torch.Tensor, @@ -68,65 +69,65 @@ def _prepare_pid_input( ) -> Optional[Dict[Tuple[int, int, int], float]]: """ Prepare input for BROJA PID computation. - + Args: x1: First input variable - x2: Second input variable + x2: Second input variable y: Output variable - + Returns: Probability distribution dictionary or None if error """ if not HAS_BROJA: return None - + try: # Convert to numpy and discretize x1_discrete = np.digitize(x1.cpu().numpy(), bins=np.linspace(x1.min(), x1.max(), self.bins)) x2_discrete = np.digitize(x2.cpu().numpy(), bins=np.linspace(x2.min(), x2.max(), self.bins)) y_discrete = np.digitize(y.cpu().numpy(), bins=np.linspace(y.min(), y.max(), self.bins)) - + # Create joint probability distribution joint_prob = {} n_samples = len(x1_discrete) - + for i in range(n_samples): key = (int(x1_discrete[i]), int(x2_discrete[i]), int(y_discrete[i])) joint_prob[key] = joint_prob.get(key, 0) + 1.0 / n_samples - + return joint_prob - + except Exception as e: logger.error(f"Error preparing PID input: {e}") return None - + def _compute_pca_features(self, inputs: torch.Tensor, n_components: int = 2) -> torch.Tensor: """ Compute PCA features from inputs. - + Args: inputs: Input tensor [batch, features] n_components: Number of components to keep - + Returns: PCA transformed features """ # Center the data inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) - + # Compute covariance cov = torch.mm(inputs_centered.t(), inputs_centered) / (inputs.size(0) - 1) - + # Eigendecomposition eigenvalues, eigenvectors = torch.linalg.eigh(cov) - + # Sort by eigenvalue (descending) idx = eigenvalues.argsort(descending=True) eigenvectors = eigenvectors[:, idx[:n_components]] - + # Project data pca_features = torch.mm(inputs_centered, eigenvectors) - + return pca_features @@ -134,13 +135,13 @@ def _compute_pca_features(self, inputs: torch.Tensor, n_components: int = 2) -> class SharedInformation(BasePIDMetric): """ Computes shared information (redundancy) between two inputs about output. - + This measures the information that both inputs provide redundantly about the output. """ - + name = "pid_shared" - + def compute( self, inputs: torch.Tensor, @@ -150,57 +151,57 @@ def compute( ) -> torch.Tensor: """ Compute shared information for each output neuron. - + Args: inputs: Input activations [batch, input_features] outputs: Output activations [batch, output_features] weights: Not used for this metric **kwargs: Additional arguments - + Returns: Shared information scores [output_features] """ if not HAS_BROJA: logger.warning("BROJA not available, returning zeros") return torch.zeros(outputs.shape[1], device=outputs.device) - + batch_size, n_outputs = outputs.shape scores = torch.zeros(n_outputs, device=outputs.device) - + # Get input features (use PCA if enabled) if self.use_pca_inputs and inputs.shape[1] > 2: input_features = self._compute_pca_features(inputs, n_components=2) else: # Use first two features input_features = inputs[:, :2] - + # Compute PID for each output neuron for i in range(n_outputs): if input_features.shape[1] < 2: continue - + # Prepare PID input pid_input = self._prepare_pid_input( input_features[:, 0], input_features[:, 1], outputs[:, i] ) - + if pid_input is None: continue - + try: # Compute PID pid_result = BROJA_2PID.pid(pid_input) scores[i] = pid_result.get("SI", 0.0) # Shared Information - + except Exception as e: logger.debug(f"PID computation failed for neuron {i}: {e}") continue - + if self.normalize and scores.max() > 0: scores = scores / scores.max() - + return scores @@ -208,13 +209,13 @@ def compute( class UniqueInformationX(BasePIDMetric): """ Computes unique information from first input variable about output. - + This measures information that only the first input provides about the output (not available from the second input). """ - + name = "pid_unique_x" - + def compute( self, inputs: torch.Tensor, @@ -226,54 +227,54 @@ def compute( if not HAS_BROJA: logger.warning("BROJA not available, returning zeros") return torch.zeros(outputs.shape[1], device=outputs.device) - + batch_size, n_outputs = outputs.shape scores = torch.zeros(n_outputs, device=outputs.device) - + # Get input features if self.use_pca_inputs and inputs.shape[1] > 2: input_features = self._compute_pca_features(inputs, n_components=2) else: input_features = inputs[:, :2] - + for i in range(n_outputs): if input_features.shape[1] < 2: continue - + pid_input = self._prepare_pid_input( input_features[:, 0], input_features[:, 1], outputs[:, i] ) - + if pid_input is None: continue - + try: pid_result = BROJA_2PID.pid(pid_input) scores[i] = pid_result.get("UIY", 0.0) # Unique info from Y (first var) - + except Exception as e: logger.debug(f"PID computation failed for neuron {i}: {e}") continue - + if self.normalize and scores.max() > 0: scores = scores / scores.max() - + return scores -@register_metric("pid_unique_y") +@register_metric("pid_unique_y") class UniqueInformationY(BasePIDMetric): """ Computes unique information from second input variable about output. - + This measures information that only the second input provides about the output (not available from the first input). """ - + name = "pid_unique_y" - + def compute( self, inputs: torch.Tensor, @@ -285,40 +286,40 @@ def compute( if not HAS_BROJA: logger.warning("BROJA not available, returning zeros") return torch.zeros(outputs.shape[1], device=outputs.device) - + batch_size, n_outputs = outputs.shape scores = torch.zeros(n_outputs, device=outputs.device) - + # Get input features if self.use_pca_inputs and inputs.shape[1] > 2: input_features = self._compute_pca_features(inputs, n_components=2) else: input_features = inputs[:, :2] - + for i in range(n_outputs): if input_features.shape[1] < 2: continue - + pid_input = self._prepare_pid_input( input_features[:, 0], input_features[:, 1], outputs[:, i] ) - + if pid_input is None: continue - + try: pid_result = BROJA_2PID.pid(pid_input) scores[i] = pid_result.get("UIZ", 0.0) # Unique info from Z (second var) - + except Exception as e: logger.debug(f"PID computation failed for neuron {i}: {e}") continue - + if self.normalize and scores.max() > 0: scores = scores / scores.max() - + return scores @@ -326,13 +327,13 @@ def compute( class SynergisticInformation(BasePIDMetric): """ Computes synergistic information between inputs about output. - + This measures information about the output that is only available when both inputs are considered together (emergent information). """ - + name = "pid_synergy" - + def compute( self, inputs: torch.Tensor, @@ -344,42 +345,42 @@ def compute( if not HAS_BROJA: logger.warning("BROJA not available, returning zeros") return torch.zeros(outputs.shape[1], device=outputs.device) - + batch_size, n_outputs = outputs.shape scores = torch.zeros(n_outputs, device=outputs.device) - + # Get input features if self.use_pca_inputs and inputs.shape[1] > 2: input_features = self._compute_pca_features(inputs, n_components=2) else: input_features = inputs[:, :2] - + for i in range(n_outputs): if input_features.shape[1] < 2: continue - + pid_input = self._prepare_pid_input( input_features[:, 0], input_features[:, 1], outputs[:, i] ) - + if pid_input is None: continue - + try: pid_result = BROJA_2PID.pid(pid_input) scores[i] = pid_result.get("CI", 0.0) # Synergistic/Complementary info - + except Exception as e: logger.debug(f"PID computation failed for neuron {i}: {e}") continue - + if self.normalize and scores.max() > 0: scores = scores / scores.max() - + return scores # Aliases for backward compatibility -PartialInformationDecomposition = SharedInformation \ No newline at end of file +PartialInformationDecomposition = SharedInformation diff --git a/src/alignment/metrics/information/redundancy.py b/src/alignment/metrics/information/redundancy.py index be37c44f..d2afb39d 100644 --- a/src/alignment/metrics/information/redundancy.py +++ b/src/alignment/metrics/information/redundancy.py @@ -5,9 +5,10 @@ indicating how much overlapping information they capture. """ -from typing import Optional, Any -import torch import logging +from typing import Any, Optional + +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -19,12 +20,12 @@ class AverageRedundancy(BaseMetric): """ Average redundancy between neurons using Gaussian approximation. - + For each neuron, computes the average mutual information with all other neurons, indicating how much its information is redundant with the rest of the layer. """ - + def __init__( self, min_samples: int = 2, @@ -33,7 +34,7 @@ def __init__( ): """ Initialize the redundancy metric. - + Args: min_samples: Minimum samples for computation use_correlation: If True, use correlation; if False, use covariance @@ -42,19 +43,19 @@ def __init__( super().__init__(**config) self.min_samples = min_samples self.use_correlation = use_correlation - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return True - + @property def requires_outputs(self) -> bool: return False - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -64,43 +65,43 @@ def compute( ) -> torch.Tensor: """ Compute average redundancy for each neuron. - + Args: inputs: Input activations [batch_size, input_features] weights: Layer weights [num_neurons, input_features] outputs: Not used (computed from inputs and weights) - + Returns: Average redundancy scores [num_neurons] """ if inputs is None or weights is None: raise ValueError("AverageRedundancy requires inputs and weights") - + # Flatten if needed if inputs.ndim != 2: inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim != 2: weights = weights.reshape(weights.shape[0], -1) - + batch_size = inputs.shape[0] num_neurons = weights.shape[0] - + if batch_size < self.min_samples: logger.warning(f"Redundancy: Only {batch_size} samples, returning zeros") return torch.zeros(num_neurons, device=weights.device, dtype=weights.dtype) - + if num_neurons <= 1: logger.warning("Redundancy: Need at least 2 neurons") return torch.zeros(num_neurons, device=weights.device, dtype=weights.dtype) - + # Compute projected outputs projected = torch.matmul(inputs, weights.T) # [batch_size, num_neurons] - + # Move to CPU for large correlation computations if needed compute_device = projected.device if self._should_use_cpu(projected): projected = projected.cpu() - + # Compute correlation or covariance matrix if self.use_correlation: # Normalize each neuron's output @@ -112,19 +113,19 @@ def compute( else: # Use covariance corr_matrix = torch.cov(projected.T) - + # Compute average redundancy for each neuron redundancy_scores = torch.zeros(num_neurons, device=weights.device) - + for i in range(num_neurons): # Average MI with other neurons using Gaussian approximation sum_redundancy = 0.0 num_pairs = 0 - + for j in range(num_neurons): if i == j: continue - + # Get correlation/covariance value if self.use_correlation: rho_sq = corr_matrix[i, j] ** 2 @@ -136,21 +137,21 @@ def compute( rho_sq = (corr_matrix[i, j] ** 2) / (var_i * var_j) else: rho_sq = 0.0 - + # MI approximation rho_sq = torch.clamp(rho_sq, 0, 0.999999) mi_ij = -0.5 * torch.log(1.0 - rho_sq) - + sum_redundancy += mi_ij num_pairs += 1 - + if num_pairs > 0: redundancy_scores[i] = sum_redundancy / num_pairs - + # Move back to original device if needed if compute_device != weights.device: redundancy_scores = redundancy_scores.to(weights.device) - + return torch.nan_to_num(redundancy_scores) @@ -158,12 +159,12 @@ def compute( class NodeRedundancy(BaseMetric): """ Redundancy between input features based on correlation. - + This metric measures how redundant each input feature is with respect to other input features, useful for identifying correlated inputs. """ - + def __init__( self, min_samples: int = 2, @@ -172,7 +173,7 @@ def __init__( ): """ Initialize node redundancy metric. - + Args: min_samples: Minimum samples for correlation exclude_self: Whether to exclude self-correlation @@ -181,19 +182,19 @@ def __init__( super().__init__(**config) self.min_samples = min_samples self.exclude_self = exclude_self - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return False - + @property def requires_outputs(self) -> bool: return False - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -203,47 +204,47 @@ def compute( ) -> torch.Tensor: """ Compute redundancy for each input feature. - + Args: inputs: Input activations [batch_size, num_features] weights: Not used outputs: Not used - + Returns: Redundancy scores for each input feature [num_features] """ if inputs is None: raise ValueError("NodeRedundancy requires inputs") - + if inputs.ndim != 2: inputs = inputs.reshape(inputs.shape[0], -1) - + batch_size, num_features = inputs.shape - + if batch_size < self.min_samples: logger.warning(f"NodeRedundancy: Only {batch_size} samples") return torch.zeros(num_features, device=inputs.device, dtype=inputs.dtype) - + # Compute correlation matrix compute_device = inputs.device if self._should_use_cpu(inputs): inputs = inputs.cpu() - + # Standardize features inputs_mean = inputs.mean(dim=0, keepdim=True) inputs_std = inputs.std(dim=0, keepdim=True) inputs_std = torch.where(inputs_std > 1e-10, inputs_std, torch.ones_like(inputs_std)) inputs_norm = (inputs - inputs_mean) / inputs_std - + # Correlation matrix corr_matrix = torch.matmul(inputs_norm.T, inputs_norm) / (batch_size - 1) - + # Take absolute correlations (strength matters, not direction) abs_corr = torch.abs(corr_matrix) - + # Compute average correlation with other features redundancy_scores = torch.zeros(num_features, device=inputs.device) - + for i in range(num_features): if self.exclude_self: # Average correlation with other features @@ -254,11 +255,11 @@ def compute( else: # Include self-correlation redundancy_scores[i] = abs_corr[i].mean() - + # Move back to original device if compute_device != inputs.device: redundancy_scores = redundancy_scores.to(compute_device) - + return torch.nan_to_num(redundancy_scores) @@ -266,11 +267,11 @@ def compute( class LayerRedundancy(BaseMetric): """ Overall redundancy measure for an entire layer. - + Computes a single redundancy score for the layer based on the average pairwise mutual information between neurons. """ - + def __init__( self, return_matrix: bool = False, @@ -278,7 +279,7 @@ def __init__( ): """ Initialize layer redundancy metric. - + Args: return_matrix: If True, return full redundancy matrix **config: Additional configuration @@ -286,19 +287,19 @@ def __init__( super().__init__(**config) self.return_matrix = return_matrix self._avg_redundancy = AverageRedundancy(**config) - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return True - + @property def requires_outputs(self) -> bool: return False - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -308,7 +309,7 @@ def compute( ) -> torch.Tensor: """ Compute overall layer redundancy. - + Returns: If return_matrix=False: scalar redundancy score If return_matrix=True: redundancy matrix [num_neurons, num_neurons] @@ -320,10 +321,10 @@ def compute( outputs=outputs, **kwargs ) - + if self.return_matrix: # Return full redundancy matrix (would need to modify avg_redundancy) logger.warning("Full matrix return not yet implemented, returning average") - + # Return average redundancy across all neurons - return neuron_redundancies.mean().unsqueeze(0) \ No newline at end of file + return neuron_redundancies.mean().unsqueeze(0) diff --git a/src/alignment/metrics/information/synergy_mmi.py b/src/alignment/metrics/information/synergy_mmi.py index 9933d328..337e8c87 100644 --- a/src/alignment/metrics/information/synergy_mmi.py +++ b/src/alignment/metrics/information/synergy_mmi.py @@ -7,9 +7,10 @@ where Z is a discrete target and Y_i, Y_j are continuous neuron outputs. """ -from typing import Optional, Any -import torch import logging +from typing import Any, Optional + +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -21,14 +22,14 @@ class SynergyGaussianMMI(BaseMetric): """ Compute per-neuron synergy using Gaussian MI and MMI redundancy. - + For each neuron, computes average synergy with K sampled partner neurons relative to a discrete target Z. - + Synergy measures information that emerges only from the joint outputs, using the MMI (Minimum Mutual Information) redundancy axiom: S_MMI = I(Z; Y_i, Y_j) - I(Z; Y_i) - I(Z; Y_j) + min(I(Z; Y_i), I(Z; Y_j)) - + Example: >>> synergy_metric = SynergyGaussianMMI(num_pairs=10) >>> synergy = synergy_metric.compute( @@ -38,7 +39,7 @@ class SynergyGaussianMMI(BaseMetric): ... ) >>> print(synergy.shape) # [num_neurons] """ - + def __init__( self, num_pairs: int = 10, @@ -47,7 +48,7 @@ def __init__( ): """ Initialize synergy metric. - + Args: num_pairs: Number of partner neurons to sample per neuron sampling_strategy: How to sample pairs ('random', 'nearest', 'all') @@ -56,19 +57,19 @@ def __init__( super().__init__(**config) self.num_pairs = num_pairs self.sampling_strategy = sampling_strategy - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return True - + @property def requires_outputs(self) -> bool: return True # We'll use outputs if provided, else compute from inputs @ weights.T - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -79,49 +80,49 @@ def compute( ) -> torch.Tensor: """ Compute per-neuron synergy scores. - + Args: inputs: Input activations [batch_size, input_features] weights: Layer weights [num_neurons, input_features] outputs: Layer outputs [batch_size, num_neurons] (optional) targets: Target labels [batch_size] (required) **kwargs: Additional parameters - + Returns: Per-neuron synergy scores [num_neurons] """ if inputs is None or weights is None: raise ValueError("SynergyGaussianMMI requires inputs and weights") - + if targets is None: raise ValueError("SynergyGaussianMMI requires targets") - + # Flatten if needed if inputs.ndim > 2: inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) - + # Ensure targets are 1D if targets.ndim > 1: targets = targets.squeeze() - + # Compute outputs if not provided if outputs is None: outputs = inputs @ weights.T # [batch_size, num_neurons] - + num_neurons = weights.shape[0] - + # Compute synergy for each neuron synergy = torch.zeros(num_neurons, device=weights.device, dtype=weights.dtype) - + for i in range(num_neurons): # Sample partner neurons partner_indices = self._sample_partners(i, num_neurons) - + if len(partner_indices) == 0: continue - + # Compute synergy with each partner synergy_values = [] for j in partner_indices: @@ -131,13 +132,13 @@ def compute( targets ) synergy_values.append(s) - + # Average synergy with partners if synergy_values: synergy[i] = torch.stack(synergy_values).mean() - + return synergy - + def _compute_pairwise_synergy( self, y_i: torch.Tensor, @@ -146,31 +147,31 @@ def _compute_pairwise_synergy( ) -> torch.Tensor: """ Compute synergy between two neurons relative to target. - + Args: y_i: Output of neuron i [batch_size] y_j: Output of neuron j [batch_size] targets: Target labels [batch_size] - + Returns: Synergy S_MMI(Z; Y_i, Y_j) """ # Compute individual MIs mi_i = self._gaussian_mi_categorical(y_i, targets) mi_j = self._gaussian_mi_categorical(y_j, targets) - + # Compute joint MI y_joint = torch.stack([y_i, y_j], dim=1) # [batch_size, 2] mi_joint = self._gaussian_mi_categorical_multivariate(y_joint, targets) - + # MMI redundancy redundancy_mmi = torch.min(mi_i, mi_j) - + # Synergy synergy = mi_joint - mi_i - mi_j + redundancy_mmi - + return synergy - + def _gaussian_mi_categorical( self, y: torch.Tensor, @@ -179,48 +180,48 @@ def _gaussian_mi_categorical( ) -> torch.Tensor: """ Compute MI between continuous y and categorical z using Gaussian approximation. - + I(Z; Y) = H(Y) - H(Y|Z) = 0.5 * log(2πe σ²_Y) - Σ p(z) * 0.5 * log(2πe σ²_{Y|z}) - + Args: y: Continuous variable [batch_size] z: Categorical variable [batch_size] eps: Small value for numerical stability - + Returns: Mutual information (scalar) """ # Overall variance var_y = torch.var(y, unbiased=True) + eps - + # Conditional variances classes = torch.unique(z) conditional_entropy = 0.0 - + for c in classes: mask = (z == c) n_c = mask.sum() - + if n_c < 2: continue - + y_c = y[mask] var_y_c = torch.var(y_c, unbiased=True) + eps - + # Weight by class probability p_c = n_c.float() / len(z) conditional_entropy += p_c * 0.5 * torch.log(2 * torch.pi * torch.e * var_y_c) - + # MI = H(Y) - H(Y|Z) marginal_entropy = 0.5 * torch.log(2 * torch.pi * torch.e * var_y) mi = marginal_entropy - conditional_entropy - + # MI should be non-negative mi = torch.clamp(mi, min=0.0) - + return mi - + def _gaussian_mi_categorical_multivariate( self, y: torch.Tensor, @@ -229,66 +230,66 @@ def _gaussian_mi_categorical_multivariate( ) -> torch.Tensor: """ Compute MI between multivariate continuous y and categorical z. - + I(Z; Y) = 0.5 * log(det(Σ_Y) / det(Σ_{Y|Z})) - + Args: y: Continuous variables [batch_size, dim] z: Categorical variable [batch_size] eps: Regularization for covariance - + Returns: Mutual information (scalar) """ batch_size, dim = y.shape - + # Overall covariance y_centered = y - y.mean(dim=0, keepdim=True) cov_y = (y_centered.T @ y_centered) / max(1, batch_size - 1) cov_y = cov_y + eps * torch.eye(dim, device=y.device, dtype=y.dtype) - + # Conditional covariances (weighted average) classes = torch.unique(z) cov_y_given_z = torch.zeros_like(cov_y) total_weight = 0.0 - + for c in classes: mask = (z == c) n_c = mask.sum() - + if n_c < dim + 1: # Need enough samples continue - + y_c = y[mask] y_c_centered = y_c - y_c.mean(dim=0, keepdim=True) cov_c = (y_c_centered.T @ y_c_centered) / max(1, n_c - 1) cov_c = cov_c + eps * torch.eye(dim, device=y.device, dtype=y.dtype) - + # Weight by class probability weight = n_c.float() cov_y_given_z += cov_c * weight total_weight += weight - + if total_weight > 0: cov_y_given_z = cov_y_given_z / total_weight else: # Fallback: return zero MI return torch.tensor(0.0, device=y.device, dtype=y.dtype) - + # MI via determinants # Add small regularization for numerical stability det_y = torch.det(cov_y) det_y_given_z = torch.det(cov_y_given_z) - + if det_y <= 0 or det_y_given_z <= 0: # Numerical issues, return zero return torch.tensor(0.0, device=y.device, dtype=y.dtype) - + mi = 0.5 * torch.log(det_y / (det_y_given_z + eps)) mi = torch.clamp(mi, min=0.0) - + return mi - + def _sample_partners( self, neuron_idx: int, @@ -296,38 +297,38 @@ def _sample_partners( ) -> torch.Tensor: """ Sample partner neurons for synergy computation. - + Args: neuron_idx: Index of current neuron num_neurons: Total number of neurons - + Returns: Indices of partner neurons """ # Exclude self available = list(range(num_neurons)) available.remove(neuron_idx) - + if self.sampling_strategy == 'all': return torch.tensor(available, dtype=torch.long) - + elif self.sampling_strategy == 'random': num_to_sample = min(self.num_pairs, len(available)) if num_to_sample == 0: return torch.tensor([], dtype=torch.long) - + indices = torch.randperm(len(available))[:num_to_sample] return torch.tensor([available[i] for i in indices], dtype=torch.long) - + elif self.sampling_strategy == 'nearest': num_to_sample = min(self.num_pairs, len(available)) if num_to_sample == 0: return torch.tensor([], dtype=torch.long) - + distances = torch.abs(torch.tensor(available) - neuron_idx) _, nearest_indices = torch.topk(distances, num_to_sample, largest=False) return torch.tensor([available[i] for i in nearest_indices], dtype=torch.long) - + else: raise ValueError(f"Unknown sampling strategy: {self.sampling_strategy}") diff --git a/src/alignment/metrics/pairwise_base.py b/src/alignment/metrics/pairwise_base.py index fef544d3..597f5c39 100644 --- a/src/alignment/metrics/pairwise_base.py +++ b/src/alignment/metrics/pairwise_base.py @@ -10,10 +10,11 @@ - Correlation: How correlated is neuron i with others? """ +import logging from abc import abstractmethod -from typing import Optional, Any, Literal +from typing import Any, Literal, Optional + import torch -import logging from ..core.base import BaseMetric @@ -23,18 +24,18 @@ class PairwiseMetric(BaseMetric): """ Base class for pairwise metrics. - + Pairwise metrics compute relationships between neuron pairs (i, j), then aggregate to per-neuron scores. - + Workflow: 1. Compute pairwise values: R[i,j] for all pairs 2. Aggregate to single-neuron: score[i] = aggregate(R[i, :]) - + Subclasses implement: - compute_pairwise(): Returns [N, N] matrix - aggregate_pairwise(): Converts matrix to [N] scores - + Example: >>> class MyPairwiseMetric(PairwiseMetric): ... def compute_pairwise(self, outputs): @@ -45,7 +46,7 @@ class PairwiseMetric(BaseMetric): >>> scores = metric.compute(outputs=outputs) >>> # Returns [N] - one score per neuron """ - + def __init__( self, aggregation: Literal['mean', 'median', 'max', 'sum'] = 'mean', @@ -56,7 +57,7 @@ def __init__( ): """ Initialize pairwise metric. - + Args: aggregation: How to aggregate pairwise values to single-neuron scores - 'mean': Average over partners (default) @@ -76,7 +77,7 @@ def __init__( self.exclude_diagonal = exclude_diagonal self.sampling_strategy = sampling_strategy self.num_pairs = num_pairs - + @abstractmethod def compute_pairwise( self, @@ -87,62 +88,62 @@ def compute_pairwise( ) -> torch.Tensor: """ Compute pairwise relationships between all neurons. - + Args: inputs: Input activations weights: Layer weights outputs: Layer outputs **kwargs: Additional arguments - + Returns: Pairwise matrix [N, N] where entry [i,j] is relationship between neurons i and j """ pass - + def aggregate_pairwise( self, pairwise_matrix: torch.Tensor ) -> torch.Tensor: """ Aggregate pairwise matrix to per-neuron scores. - + Args: pairwise_matrix: [N, N] pairwise relationships - + Returns: Per-neuron scores [N] """ N = pairwise_matrix.shape[0] - + # Exclude diagonal if requested if self.exclude_diagonal: # Create mask mask = ~torch.eye(N, dtype=torch.bool, device=pairwise_matrix.device) - + if self.aggregation == 'mean': # Average over non-diagonal row_sums = (pairwise_matrix * mask.float()).sum(dim=1) scores = row_sums / max(1, N - 1) - + elif self.aggregation == 'median': scores = torch.zeros(N, device=pairwise_matrix.device) for i in range(N): # Get non-diagonal values for row i values = pairwise_matrix[i, mask[i]] scores[i] = values.median() - + elif self.aggregation == 'max': scores = torch.zeros(N, device=pairwise_matrix.device) for i in range(N): values = pairwise_matrix[i, mask[i]] scores[i] = values.max() - + elif self.aggregation == 'sum': scores = (pairwise_matrix * mask.float()).sum(dim=1) - + else: raise ValueError(f"Unknown aggregation: {self.aggregation}") - + else: # Include diagonal if self.aggregation == 'mean': @@ -155,9 +156,9 @@ def aggregate_pairwise( scores = pairwise_matrix.sum(dim=1) else: raise ValueError(f"Unknown aggregation: {self.aggregation}") - + return scores - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -168,28 +169,28 @@ def compute( ) -> torch.Tensor: """ Compute per-neuron scores from pairwise relationships. - + Args: inputs: Input activations weights: Layer weights outputs: Layer outputs return_matrix: If True, return full [N, N] matrix instead of aggregated [N] **kwargs: Additional arguments - + Returns: Per-neuron scores [N] or pairwise matrix [N, N] if return_matrix=True """ # Compute pairwise matrix pairwise_matrix = self.compute_pairwise(inputs, weights, outputs, **kwargs) - + if return_matrix: return pairwise_matrix - + # Aggregate to per-neuron scores scores = self.aggregate_pairwise(pairwise_matrix) - + return scores - + def compute_for_subset( self, neuron_indices: torch.Tensor, @@ -200,12 +201,12 @@ def compute_for_subset( ) -> torch.Tensor: """ Compute scores for a subset of neurons (efficient for large layers). - + Args: neuron_indices: Indices of neurons to score inputs, weights, outputs: As usual **kwargs: Additional arguments - + Returns: Scores for specified neurons only """ diff --git a/src/alignment/metrics/rayleigh/__init__.py b/src/alignment/metrics/rayleigh/__init__.py index fe0c0b88..c143a7f9 100644 --- a/src/alignment/metrics/rayleigh/__init__.py +++ b/src/alignment/metrics/rayleigh/__init__.py @@ -8,4 +8,4 @@ __all__ = [ 'RayleighQuotient', 'RayleighQuotientAlternative', -] \ No newline at end of file +] diff --git a/src/alignment/metrics/rayleigh/delta_alignment.py b/src/alignment/metrics/rayleigh/delta_alignment.py index 2cfa8d5e..412befe2 100644 --- a/src/alignment/metrics/rayleigh/delta_alignment.py +++ b/src/alignment/metrics/rayleigh/delta_alignment.py @@ -5,11 +5,11 @@ with the input covariance structure. """ -from typing import Optional, Any, Dict -import torch import logging +from typing import Any, Dict, Optional + +import torch -from ...core.base import BaseMetric from ...core.registry import register_metric from .rayleigh_quotient import RayleighQuotient @@ -20,14 +20,14 @@ class DeltaAlignment(RayleighQuotient): """ Delta Alignment metric. - + Computes the Rayleigh Quotient of the weight changes (W_current - W_initial) with respect to the input covariance. This measures how the learned weight changes align with the input data structure. - + This metric requires the model to store initial weights for comparison. """ - + def __init__( self, relative: bool = True, @@ -37,7 +37,7 @@ def __init__( ): """ Initialize the Delta Alignment metric. - + Args: relative: Whether to normalize by trace(C) for relative alignment min_samples: Minimum samples required for covariance computation @@ -46,17 +46,17 @@ def __init__( """ super().__init__(relative, min_samples, scale_by_norm, **config) self._initial_weights: Dict[str, torch.Tensor] = {} - + def set_initial_weights(self, layer_name: str, weights: torch.Tensor) -> None: """ Store initial weights for a layer. - + Args: layer_name: Name of the layer weights: Initial weight tensor """ self._initial_weights[layer_name] = weights.clone().detach() - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -68,7 +68,7 @@ def compute( ) -> torch.Tensor: """ Compute Delta Alignment values for each neuron. - + Args: inputs: Input activations [batch_size, input_features] weights: Current layer weights [output_features, input_features] @@ -76,13 +76,13 @@ def compute( layer_name: Name of the layer (for retrieving stored initial weights) initial_weights: Initial weights (if provided directly) **kwargs: Additional parameters - + Returns: Delta alignment values for each output neuron [output_features] """ if inputs is None or weights is None: raise ValueError("DeltaAlignment requires both inputs and weights") - + # Get initial weights if initial_weights is None: if layer_name is None or layer_name not in self._initial_weights: @@ -93,7 +93,7 @@ def compute( initial_weights = torch.zeros_like(weights) else: initial_weights = self._initial_weights[layer_name] - + # Ensure shapes match if initial_weights.shape != weights.shape: logger.warning( @@ -106,10 +106,10 @@ def compute( # If sizes don't match, use zeros logger.warning("DeltaAlignment: Cannot reshape initial weights. Using zeros.") initial_weights = torch.zeros_like(weights) - + # Compute weight difference weight_diff = weights - initial_weights - + # Use parent class RQ computation on the weight differences return super().compute(inputs=inputs, weights=weight_diff, outputs=None, **kwargs) @@ -118,11 +118,11 @@ def compute( class NormalizedDeltaAlignment(DeltaAlignment): """ Normalized Delta Alignment metric. - + Normalizes the delta alignment by the magnitude of weight changes, providing a scale-invariant measure of alignment. """ - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -134,7 +134,7 @@ def compute( ) -> torch.Tensor: """ Compute normalized delta alignment values. - + Returns: Normalized delta alignment values [output_features] """ @@ -147,22 +147,22 @@ def compute( initial_weights=initial_weights, **kwargs ) - + # Get initial weights for normalization if initial_weights is None: if layer_name is None or layer_name not in self._initial_weights: initial_weights = torch.zeros_like(weights) else: initial_weights = self._initial_weights[layer_name] - + # Compute weight change magnitudes weight_diff = weights - initial_weights weight_change_norm = torch.norm(weight_diff, dim=1) - + # Normalize by weight change magnitude eps = 1e-12 normalized_delta = torch.zeros_like(delta_rq) valid_mask = weight_change_norm > eps normalized_delta[valid_mask] = delta_rq[valid_mask] / weight_change_norm[valid_mask] - - return normalized_delta \ No newline at end of file + + return normalized_delta diff --git a/src/alignment/metrics/rayleigh/rayleigh_quotient.py b/src/alignment/metrics/rayleigh/rayleigh_quotient.py index a522ee5c..9a0767b8 100644 --- a/src/alignment/metrics/rayleigh/rayleigh_quotient.py +++ b/src/alignment/metrics/rayleigh/rayleigh_quotient.py @@ -1,13 +1,14 @@ """ Rayleigh Quotient alignment metric implementation. -This metric measures how well neural network weights align with the +This metric measures how well neural network weights align with the principal components of their input activations. """ -from typing import Optional, Any, Union, Dict -import torch import logging +from typing import Any, Dict, Optional, Union + +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -19,18 +20,18 @@ class RayleighQuotient(BaseMetric): """ Rayleigh Quotient alignment metric. - + Computes the proportion of variance in input activations that is captured by each neuron's weight vector. This measures how well the weights align with the covariance structure of the inputs. - + For weight vector w and input covariance C: RQ(w) = (w^T C w) / (w^T w) - + When relative=True (default), normalizes by trace(C) to get a proportion of total variance. """ - + def __init__( self, relative: bool = True, @@ -42,7 +43,7 @@ def __init__( ): """ Initialize the Rayleigh Quotient metric. - + Args: relative: Whether to normalize by trace(C) for relative alignment min_samples: Minimum samples required for covariance computation @@ -57,19 +58,19 @@ def __init__( self.regularization = regularization # Optional: class-conditioned covariance support (targets provided at compute time preferred) self._cc_targets = class_conditioned_targets - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return True - + @property def requires_outputs(self) -> bool: return False - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -80,33 +81,33 @@ def compute( ) -> torch.Tensor: """ Compute Rayleigh Quotient values for each neuron. - + Args: inputs: Input activations [batch_size, input_features] or [batch_size, features, patches] weights: Layer weights [output_features, input_features] outputs: Not used for this metric **kwargs: Additional parameters - + Returns: RQ values for each output neuron [output_features] """ if inputs is None or weights is None: raise ValueError("RayleighQuotient requires both inputs and weights") - + # Handle patchwise inputs (3D tensors) if inputs.ndim == 3: # Compute patchwise RQ return self._compute_patchwise(inputs, weights, **kwargs) - + # Validate shapes for standard 2D computation if inputs.ndim != 2: inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim != 2: weights = weights.reshape(weights.shape[0], -1) - + batch_size, input_features = inputs.shape output_features, weight_features = weights.shape - + # Check sample size if batch_size < self.min_samples: logger.warning( @@ -114,7 +115,7 @@ def compute( "Returning zeros." ) return torch.zeros(output_features, device=weights.device, dtype=weights.dtype) - + # Check dimension compatibility if input_features != weight_features: logger.warning( @@ -124,7 +125,7 @@ def compute( min_dim = min(input_features, weight_features) inputs = inputs[:, :min_dim] weights = weights[:, :min_dim] - + # Move to appropriate device for computation compute_device = weights.device if self._should_use_cpu(inputs, weights): @@ -132,7 +133,7 @@ def compute( compute_device = torch.device('cpu') inputs = inputs.cpu() weights = weights.cpu() - + # Optionally compute class-conditioned covariance and average use_class_cond = targets is not None or self._cc_targets is not None if use_class_cond: @@ -161,33 +162,33 @@ def compute( # Compute covariance matrix inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) cov = torch.matmul(inputs_centered.T, inputs_centered) / (batch_size - 1) - + # Add regularization to diagonal for numerical stability if self.regularization > 0: cov = cov + self.regularization * torch.eye( input_features, device=cov.device, dtype=cov.dtype ) - + # Scale covariance by norm if requested if self.scale_by_norm: cov_norm = torch.norm(cov, p='fro') if cov_norm > 0: cov = cov / cov_norm - + # Compute RQ for each neuron efficiently # w^T C w = sum((w @ C) * w, dim=1) wc = torch.matmul(weights, cov) # [output_features, input_features] numerator = torch.sum(wc * weights, dim=1) # [output_features] - + # w^T w denominator = torch.sum(weights * weights, dim=1) # [output_features] - + # Compute RQ with numerical stability eps = 1e-12 rq_values = torch.zeros_like(numerator) valid_mask = denominator > eps rq_values[valid_mask] = numerator[valid_mask] / denominator[valid_mask] - + # Normalize by trace if relative if self.relative: trace_cov = torch.trace(cov) @@ -195,16 +196,16 @@ def compute( rq_values = rq_values / trace_cov else: logger.warning("RQ: Covariance trace near zero, cannot compute relative RQ") - + # Move back to original device if compute_device != weights.device: rq_values = rq_values.to(weights.device) - + # Handle any numerical issues rq_values = torch.nan_to_num(rq_values, nan=0.0, posinf=0.0, neginf=0.0) - + return rq_values - + def compute_class_conditioned( self, inputs: torch.Tensor, @@ -215,20 +216,20 @@ def compute_class_conditioned( ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: """ Compute class-conditioned Rayleigh Quotient. - + For each class c, computes RQ using class-specific covariance Σ_{X|y=c}, then returns the average across classes weighted by class frequency. - + Optionally also computes ΔRQ = RQ(unconditional) - E[RQ(class-conditioned)], which measures how much alignment varies across classes. - + Args: inputs: Input activations [batch_size, input_features] weights: Layer weights [output_features, input_features] targets: Class labels [batch_size] return_delta_rq: If True, also return ΔRQ **kwargs: Additional parameters - + Returns: If return_delta_rq=False: class-conditioned RQ [output_features] If return_delta_rq=True: dict with keys 'rq_uncond', 'rq_cond', 'delta_rq' @@ -238,97 +239,97 @@ def compute_class_conditioned( inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) - + # Ensure targets are 1D if targets.ndim > 1: targets = targets.squeeze() - + batch_size, input_features = inputs.shape output_features, weight_features = weights.shape - + # Check compatibility if input_features != weight_features: min_dim = min(input_features, weight_features) inputs = inputs[:, :min_dim] weights = weights[:, :min_dim] input_features = min_dim - + device = weights.device - + # Get unique classes classes = torch.unique(targets) - + # Compute class-conditioned RQ (weighted average) rq_cond_sum = torch.zeros(output_features, device=device) total_weight = 0.0 - + for c in classes: mask = (targets == c) n_c = mask.sum() - + if n_c < self.min_samples: logger.warning(f"Class {c}: only {n_c} samples, skipping") continue - + # Extract class data inputs_c = inputs[mask] - + # Compute class-specific covariance inputs_c_centered = inputs_c - inputs_c.mean(dim=0, keepdim=True) cov_c = (inputs_c_centered.T @ inputs_c_centered) / max(1, n_c - 1) - + # Add regularization if self.regularization > 0: cov_c = cov_c + self.regularization * torch.eye( input_features, device=device, dtype=cov_c.dtype ) - + # Compute RQ for this class wc = torch.matmul(weights, cov_c) numerator_c = torch.sum(wc * weights, dim=1) denominator_c = torch.sum(weights * weights, dim=1) - + eps = 1e-12 rq_c = torch.zeros_like(numerator_c) valid_mask = denominator_c > eps rq_c[valid_mask] = numerator_c[valid_mask] / denominator_c[valid_mask] - + # Normalize by trace if relative if self.relative: trace_c = torch.trace(cov_c) if trace_c > eps: rq_c = rq_c / trace_c - + # Weighted sum weight_c = n_c.float() rq_cond_sum += rq_c * weight_c total_weight += weight_c - + # Average across classes if total_weight > 0: rq_cond = rq_cond_sum / total_weight else: logger.warning("No valid classes found, returning zeros") rq_cond = torch.zeros(output_features, device=device) - + # Clean up numerical issues rq_cond = torch.nan_to_num(rq_cond, nan=0.0, posinf=0.0, neginf=0.0) - + if not return_delta_rq: return rq_cond - + # Also compute unconditional RQ rq_uncond = self.compute(inputs=inputs, weights=weights, **kwargs) - + # Compute ΔRQ delta_rq = rq_uncond - rq_cond - + return { 'rq_uncond': rq_uncond, 'rq_cond': rq_cond, 'delta_rq': delta_rq } - + def _compute_patchwise( self, inputs: torch.Tensor, @@ -338,50 +339,50 @@ def _compute_patchwise( ) -> torch.Tensor: """ Compute patch-wise RQ for CNN layers. - + Args: inputs: Input patches [batch_size, features, num_patches] weights: Flattened weights [output_features, features] weight_by_variance: Whether to weight patches by their variance - + Returns: RQ values [output_features] """ batch_size, features, num_patches = inputs.shape output_features = weights.shape[0] - + if batch_size < self.min_samples: logger.warning(f"Only {batch_size} samples, minimum {self.min_samples} recommended") return torch.zeros(output_features, device=weights.device, dtype=weights.dtype) - + # Ensure weight dimensions match if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) - + # Compute variance for each patch patch_var = torch.var(inputs, dim=0, keepdim=False) # [features, num_patches] patch_total_var = patch_var.sum(dim=0) # [num_patches] - + # Initialize accumulators weighted_rq_sum = torch.zeros(output_features, device=weights.device) total_weight = 0.0 - + # Compute RQ for each patch for p in range(num_patches): patch_data = inputs[:, :, p] # [batch_size, features] - + # Center the data patch_data_centered = patch_data - patch_data.mean(dim=0, keepdim=True) - + # Compute covariance for this patch patch_cov = torch.matmul(patch_data_centered.T, patch_data_centered) / (batch_size - 1) - + # Scale by norm if requested if self.scale_by_norm: cov_norm = torch.norm(patch_cov, p='fro') if cov_norm > 0: patch_cov = patch_cov / cov_norm - + # Handle dimension mismatch min_dim = min(features, weights.shape[1]) if features != weights.shape[1]: @@ -389,38 +390,38 @@ def _compute_patchwise( weights_adj = weights[:, :min_dim] else: weights_adj = weights - + # Compute RQ for this patch wc = torch.matmul(weights_adj, patch_cov) numerator = torch.sum(wc * weights_adj, dim=1) denominator = torch.sum(weights_adj * weights_adj, dim=1) - + eps = 1e-12 patch_rq = torch.zeros_like(numerator) valid_mask = denominator > eps patch_rq[valid_mask] = numerator[valid_mask] / denominator[valid_mask] - + # Normalize by trace if relative if self.relative: trace = torch.trace(patch_cov) if trace > eps: patch_rq = patch_rq / trace - + # Weight by patch variance if requested if weight_by_variance: patch_weight = patch_total_var[p].item() else: patch_weight = 1.0 - + weighted_rq_sum += patch_rq * patch_weight total_weight += patch_weight - + # Average across patches if total_weight > 0: final_rq = weighted_rq_sum / total_weight else: final_rq = weighted_rq_sum - + return torch.nan_to_num(final_rq, nan=0.0, posinf=0.0, neginf=0.0) @@ -428,11 +429,11 @@ def _compute_patchwise( class PatchWiseRayleighQuotient(RayleighQuotient): """ Patch-wise variant of Rayleigh Quotient for convolutional layers. - + This variant computes RQ separately for each spatial location (patch) and then aggregates the results, weighted by patch variance. """ - + def __init__( self, relative: bool = True, @@ -443,7 +444,7 @@ def __init__( ): """ Initialize patch-wise RQ metric. - + Args: relative: Whether to normalize by trace(C) min_samples: Minimum samples for covariance @@ -453,7 +454,7 @@ def __init__( """ super().__init__(relative, min_samples, scale_by_norm, **config) self.weight_by_variance = weight_by_variance - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -463,61 +464,61 @@ def compute( ) -> torch.Tensor: """ Compute patch-wise RQ for convolutional inputs. - + Args: inputs: Input patches [batch_size, features, num_patches] weights: Convolutional weights [output_channels, input_features] outputs: Not used - + Returns: RQ values for each output channel [output_channels] """ if inputs is None or weights is None: raise ValueError("PatchWiseRQ requires both inputs and weights") - + # Handle different input formats if inputs.ndim == 2: # Regular RQ if not patch format return super().compute(inputs, weights, outputs, **kwargs) - + if inputs.ndim != 3: raise ValueError(f"PatchWiseRQ expects 3D input, got {inputs.ndim}D") - + batch_size, features, num_patches = inputs.shape output_channels = weights.shape[0] - + if batch_size < self.min_samples: return torch.zeros(output_channels, device=weights.device, dtype=weights.dtype) - + # Compute variance for each patch patch_var = torch.var(inputs, dim=0, keepdim=False) # [features, num_patches] patch_total_var = patch_var.sum(dim=0) # [num_patches] - + # Initialize results all_patch_rq = [] all_patch_weights = [] - + # Compute RQ for each patch for p in range(num_patches): patch_data = inputs[:, :, p] # [batch_size, features] - + # Compute RQ for this patch patch_rq = super().compute(patch_data, weights, None) - + # Weight by patch variance if requested if self.weight_by_variance: patch_weight = patch_total_var[p] else: patch_weight = 1.0 - + all_patch_rq.append(patch_rq * patch_weight) all_patch_weights.append(patch_weight) - + # Aggregate across patches total_weight = sum(all_patch_weights) if total_weight > 0: final_rq = torch.stack(all_patch_rq).sum(dim=0) / total_weight else: final_rq = torch.zeros(output_channels, device=weights.device, dtype=weights.dtype) - - return final_rq \ No newline at end of file + + return final_rq diff --git a/src/alignment/metrics/rayleigh/rq_alternative.py b/src/alignment/metrics/rayleigh/rq_alternative.py index 42e922c2..994bbcd0 100644 --- a/src/alignment/metrics/rayleigh/rq_alternative.py +++ b/src/alignment/metrics/rayleigh/rq_alternative.py @@ -2,9 +2,11 @@ Alternative Rayleigh Quotient metric with different normalization. """ -import torch import logging from typing import Optional + +import torch + from ...core.base import BaseMetric logger = logging.getLogger(__name__) @@ -13,28 +15,28 @@ class RayleighQuotientAlternative(BaseMetric): """ Compute Rayleigh Quotient with alternative denominator. - + Instead of using w^T w in the denominator, this uses trace(C) where C is the covariance matrix of inputs. This provides a different normalization that can be more stable in some cases. """ - + name = "rayleigh_quotient_alternative" requires_weights = True requires_inputs = True requires_outputs = False - + def __init__(self, relative: bool = True, epsilon: float = 1e-8): """ Initialize the alternative RQ metric. - + Args: relative: Whether to normalize by trace(C) epsilon: Small value for numerical stability """ self.relative = relative self.epsilon = epsilon - + @torch.no_grad() def compute( self, @@ -45,19 +47,19 @@ def compute( ) -> torch.Tensor: """ Compute alternative RQ scores for each neuron. - + Args: inputs: Input activations [batch_size, num_features] weights: Weight matrix [num_neurons, num_features] outputs: Not used **kwargs: Additional arguments - + Returns: RQ scores per neuron [num_neurons] """ if inputs is None or weights is None: raise ValueError("Alternative RQ requires both inputs and weights") - + # Handle dimensions if inputs.ndim != 2: if inputs.ndim > 2: @@ -65,63 +67,63 @@ def compute( else: logger.warning(f"Inputs have unexpected shape: {inputs.shape}") return torch.zeros(1, device=weights.device) - + if weights.ndim != 2: if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) else: logger.warning(f"Weights have unexpected shape: {weights.shape}") return torch.zeros(1, device=weights.device) - + batch_size, num_features = inputs.shape num_neurons = weights.shape[0] - + # Check dimension compatibility if weights.shape[1] != num_features: min_dim = min(weights.shape[1], num_features) weights = weights[:, :min_dim] inputs = inputs[:, :min_dim] logger.warning(f"Dimension mismatch, truncating to {min_dim} features") - + # Need at least 2 samples for covariance if batch_size < 2: logger.warning(f"Need at least 2 samples, got {batch_size}") return torch.zeros(num_neurons, device=weights.device) - + try: # Compute input covariance inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) C = torch.matmul(inputs_centered.T, inputs_centered) / (batch_size - 1) - + # Check for numerical issues if torch.isnan(C).any() or torch.isinf(C).any(): logger.warning("NaN or Inf in covariance matrix") return torch.zeros(num_neurons, device=weights.device) - + # Compute w^T C w for each neuron # Efficient computation: WC = W @ C, then sum(WC * W, dim=1) WC = torch.matmul(weights, C) numerators = torch.sum(WC * weights, dim=1) - + # Alternative denominator: trace(C) trace_C = torch.trace(C) - + # Initialize scores rq_scores = torch.zeros(num_neurons, device=weights.device) - + if self.relative and trace_C > self.epsilon: # Normalize by trace(C) rq_scores = numerators / trace_C else: # Just use raw numerators rq_scores = numerators - + # Additional normalization by number of features # This helps make scores comparable across layers rq_scores = rq_scores / num_features - + except Exception as e: logger.error(f"Error computing alternative RQ: {e}") return torch.zeros(num_neurons, device=weights.device) - - return torch.nan_to_num(rq_scores, nan=0.0, posinf=0.0, neginf=0.0) \ No newline at end of file + + return torch.nan_to_num(rq_scores, nan=0.0, posinf=0.0, neginf=0.0) diff --git a/src/alignment/metrics/similarity/__init__.py b/src/alignment/metrics/similarity/__init__.py index ff62c92e..8e713c9b 100644 --- a/src/alignment/metrics/similarity/__init__.py +++ b/src/alignment/metrics/similarity/__init__.py @@ -2,10 +2,15 @@ Similarity-based alignment metrics. """ -from .cosine_similarity import WeightCosineSimilarity as CosineSimilarityFromFile, ActivationCosineSimilarity -from .node_redundancy import NodeRedundancy -from .weight_similarity import WeightCosineSimilarity, WeightDotSimilarity, WeightEuclideanDistance +from .cosine_similarity import ActivationCosineSimilarity +from .cosine_similarity import WeightCosineSimilarity as CosineSimilarityFromFile from .node_correlation import NodeCorrelation +from .node_redundancy import NodeRedundancy +from .weight_similarity import ( + WeightCosineSimilarity, + WeightDotSimilarity, + WeightEuclideanDistance, +) __all__ = [ 'ActivationCosineSimilarity', @@ -14,4 +19,4 @@ 'WeightDotSimilarity', 'WeightEuclideanDistance', 'NodeCorrelation', -] \ No newline at end of file +] diff --git a/src/alignment/metrics/similarity/cosine_similarity.py b/src/alignment/metrics/similarity/cosine_similarity.py index e67f94a9..2729bc74 100644 --- a/src/alignment/metrics/similarity/cosine_similarity.py +++ b/src/alignment/metrics/similarity/cosine_similarity.py @@ -5,10 +5,11 @@ weight vectors or activation patterns. """ -from typing import Optional, Any +import logging +from typing import Any, Optional + import torch import torch.nn.functional as F -import logging from ...core.base import BaseMetric from ...core.registry import register_metric @@ -20,11 +21,11 @@ class WeightCosineSimilarity(BaseMetric): """ Cosine similarity between weight vectors. - + For each neuron, computes the average cosine similarity with other neurons in the same layer, measuring weight alignment. """ - + def __init__( self, normalize: bool = True, @@ -33,7 +34,7 @@ def __init__( ): """ Initialize weight cosine similarity metric. - + Args: normalize: Whether to normalize weights before computing similarity exclude_self: Whether to exclude self-similarity (always 1.0) @@ -42,19 +43,19 @@ def __init__( super().__init__(**config) self.normalize = normalize self.exclude_self = exclude_self - + @property def requires_inputs(self) -> bool: return False - + @property def requires_weights(self) -> bool: return True - + @property def requires_outputs(self) -> bool: return False - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -64,27 +65,27 @@ def compute( ) -> torch.Tensor: """ Compute average cosine similarity for each weight vector. - + Args: inputs: Not used weights: Layer weights [num_neurons, input_features] outputs: Not used - + Returns: Average cosine similarity scores [num_neurons] """ if weights is None: raise ValueError("WeightCosineSimilarity requires weights") - + if weights.ndim != 2: weights = weights.reshape(weights.shape[0], -1) - + num_neurons = weights.shape[0] - + if num_neurons <= 1: logger.warning("WeightCosineSimilarity: Need at least 2 neurons") return torch.ones(num_neurons, device=weights.device, dtype=weights.dtype) - + # Normalize weight vectors if self.normalize: weights_norm = F.normalize(weights, p=2, dim=1) @@ -93,13 +94,13 @@ def compute( weight_norms = torch.norm(weights, p=2, dim=1, keepdim=True) weight_norms = torch.where(weight_norms > 1e-12, weight_norms, torch.ones_like(weight_norms)) weights_norm = weights / weight_norms - + # Compute pairwise cosine similarities similarity_matrix = torch.matmul(weights_norm, weights_norm.T) - + # Compute average similarity for each neuron similarity_scores = torch.zeros(num_neurons, device=weights.device) - + for i in range(num_neurons): if self.exclude_self: # Average similarity with other neurons @@ -110,7 +111,7 @@ def compute( else: # Include self-similarity similarity_scores[i] = similarity_matrix[i].mean() - + return similarity_scores @@ -118,11 +119,11 @@ def compute( class ActivationCosineSimilarity(BaseMetric): """ Cosine similarity between activation patterns. - + Measures how similar the activation patterns of neurons are across the batch dimension. """ - + def __init__( self, min_samples: int = 2, @@ -132,7 +133,7 @@ def __init__( ): """ Initialize activation cosine similarity metric. - + Args: min_samples: Minimum samples for meaningful patterns exclude_self: Whether to exclude self-similarity @@ -143,19 +144,19 @@ def __init__( self.min_samples = min_samples self.exclude_self = exclude_self self.use_outputs = use_outputs - + @property def requires_inputs(self) -> bool: return not self.use_outputs - + @property def requires_weights(self) -> bool: return not self.use_outputs - + @property def requires_outputs(self) -> bool: return self.use_outputs - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -165,12 +166,12 @@ def compute( ) -> torch.Tensor: """ Compute average cosine similarity between activation patterns. - + Args: inputs: Input activations (if use_outputs=False) weights: Layer weights (if use_outputs=False) outputs: Output activations (if use_outputs=True) - + Returns: Average activation similarity scores [num_neurons] """ @@ -182,36 +183,36 @@ def compute( else: if inputs is None or weights is None: raise ValueError("ActivationCosineSimilarity requires inputs and weights when use_outputs=False") - + # Compute activations if inputs.ndim != 2: inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim != 2: weights = weights.reshape(weights.shape[0], -1) - + activations = torch.matmul(inputs, weights.T) - + if activations.ndim != 2: activations = activations.reshape(activations.shape[0], -1) - + batch_size, num_neurons = activations.shape - + if batch_size < self.min_samples: logger.warning(f"ActivationCosineSimilarity: Only {batch_size} samples") return torch.zeros(num_neurons, device=activations.device, dtype=activations.dtype) - + if num_neurons <= 1: return torch.ones(num_neurons, device=activations.device, dtype=activations.dtype) - + # Normalize activation patterns (across batch dimension) activations_norm = F.normalize(activations, p=2, dim=0) # Normalize each neuron's pattern - + # Compute pairwise similarities similarity_matrix = torch.matmul(activations_norm.T, activations_norm) / batch_size - + # Average similarity for each neuron similarity_scores = torch.zeros(num_neurons, device=activations.device) - + for i in range(num_neurons): if self.exclude_self: mask = torch.ones(num_neurons, dtype=torch.bool, device=activations.device) @@ -220,7 +221,7 @@ def compute( similarity_scores[i] = similarity_matrix[i, mask].mean() else: similarity_scores[i] = similarity_matrix[i].mean() - + return similarity_scores @@ -228,11 +229,11 @@ def compute( class WeightActivationAlignment(BaseMetric): """ Alignment between weight vectors and activation covariance. - + Measures how well each weight vector aligns with the principal components of the activation covariance, using cosine similarity. """ - + def __init__( self, n_components: int = 5, @@ -241,7 +242,7 @@ def __init__( ): """ Initialize weight-activation alignment metric. - + Args: n_components: Number of principal components to consider min_samples: Minimum samples for PCA @@ -250,19 +251,19 @@ def __init__( super().__init__(**config) self.n_components = n_components self.min_samples = min_samples - + @property def requires_inputs(self) -> bool: return True - + @property def requires_weights(self) -> bool: return True - + @property def requires_outputs(self) -> bool: return False - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -272,29 +273,29 @@ def compute( ) -> torch.Tensor: """ Compute alignment between weights and activation PCs. - + Returns: Maximum cosine similarity with top PCs [num_neurons] """ if inputs is None or weights is None: raise ValueError("WeightActivationAlignment requires inputs and weights") - + if inputs.ndim != 2: inputs = inputs.reshape(inputs.shape[0], -1) if weights.ndim != 2: weights = weights.reshape(weights.shape[0], -1) - + batch_size, input_features = inputs.shape num_neurons = weights.shape[0] - + if batch_size < self.min_samples: logger.warning(f"WeightActivationAlignment: Only {batch_size} samples") return torch.zeros(num_neurons, device=weights.device, dtype=weights.dtype) - + # Compute input covariance and eigendecomposition inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) cov = torch.matmul(inputs_centered.T, inputs_centered) / (batch_size - 1) - + # Get top eigenvectors try: eigenvalues, eigenvectors = torch.linalg.eigh(cov) @@ -304,15 +305,15 @@ def compute( except Exception as e: logger.warning(f"WeightActivationAlignment: Eigendecomposition failed: {e}") return torch.zeros(num_neurons, device=weights.device, dtype=weights.dtype) - + # Normalize weights and eigenvectors weights_norm = F.normalize(weights, p=2, dim=1) eigenvecs_norm = F.normalize(top_eigenvecs, p=2, dim=0) - + # Compute cosine similarities with each PC similarities = torch.matmul(weights_norm, eigenvecs_norm) # [num_neurons, n_components] - + # Take maximum similarity across PCs for each neuron max_similarities, _ = torch.max(torch.abs(similarities), dim=1) - - return max_similarities \ No newline at end of file + + return max_similarities diff --git a/src/alignment/metrics/similarity/node_correlation.py b/src/alignment/metrics/similarity/node_correlation.py index d2672eb0..00458f04 100644 --- a/src/alignment/metrics/similarity/node_correlation.py +++ b/src/alignment/metrics/similarity/node_correlation.py @@ -2,9 +2,11 @@ Node correlation metric for measuring output correlations. """ -import torch import logging from typing import Optional + +import torch + from ...core.base import BaseMetric logger = logging.getLogger(__name__) @@ -13,27 +15,27 @@ class NodeCorrelation(BaseMetric): """ Compute average correlation between each neuron's output and all other neurons. - + This measures how correlated a neuron's activations are with other neurons in the same layer, which can indicate redundancy or specialization. """ - + name = "node_correlation" requires_weights = False requires_inputs = False requires_outputs = True - + def __init__(self, absolute: bool = True, force_cpu: bool = False): """ Initialize the node correlation metric. - + Args: absolute: Whether to use absolute correlation values force_cpu: Whether to force CPU computation for large operations """ self.absolute = absolute self.force_cpu = force_cpu - + @torch.no_grad() def compute( self, @@ -44,19 +46,19 @@ def compute( ) -> torch.Tensor: """ Compute node correlation scores. - + Args: inputs: Not used weights: Not used outputs: Output activations [batch_size, num_neurons] **kwargs: Additional arguments - + Returns: Average correlation per neuron [num_neurons] """ if outputs is None: raise ValueError("Node correlation requires outputs") - + # Handle different output dimensions if outputs.ndim != 2: if outputs.ndim > 2: @@ -65,63 +67,63 @@ def compute( else: logger.warning(f"Outputs have unexpected shape: {outputs.shape}") return torch.zeros(1, device=outputs.device) - + batch_size, num_neurons = outputs.shape - + # Need at least 2 samples to compute correlation if batch_size < 2: logger.warning(f"Need at least 2 samples for correlation, got {batch_size}") return torch.zeros(num_neurons, device=outputs.device) - + # Single neuron case if num_neurons == 1: return torch.zeros(1, device=outputs.device) - + correlation_scores = torch.zeros(num_neurons, device=outputs.device) - + try: # Compute correlation matrix corr_matrix = self._compute_correlation(outputs) - + # Use absolute values if requested if self.absolute: corr_matrix = torch.abs(corr_matrix) - + # For each neuron, compute average correlation with others for i in range(num_neurons): # Exclude self-correlation mask = torch.ones(num_neurons, dtype=torch.bool, device=outputs.device) mask[i] = False - + if mask.sum() > 0: correlation_scores[i] = corr_matrix[i, mask].mean() - + except Exception as e: logger.error(f"Error computing node correlation: {e}") return torch.zeros(num_neurons, device=outputs.device) - + return torch.nan_to_num(correlation_scores, nan=0.0) - + def _compute_correlation(self, X: torch.Tensor) -> torch.Tensor: """Compute correlation matrix.""" device = X.device - + # Move to CPU if requested and tensor is large if self.force_cpu and X.is_cuda and X.numel() > 1e6: X = X.cpu() - + # Center the data X_centered = X - X.mean(dim=0, keepdim=True) - + # Compute covariance cov = torch.matmul(X_centered.T, X_centered) / (X.size(0) - 1) - + # Compute standard deviations std = torch.sqrt(torch.diag(cov) + 1e-10) - + # Handle zero variance neurons valid_mask = std > 1e-10 - + # Compute correlation corr = torch.zeros_like(cov) if valid_mask.any(): @@ -131,12 +133,12 @@ def _compute_correlation(self, X: torch.Tensor) -> torch.Tensor: corr[valid_indices[:, None], valid_indices] = ( cov[valid_indices[:, None], valid_indices] / outer_std ) - + # Set diagonal to 1 for valid neurons corr.diagonal().copy_(valid_mask.float()) - + # Move back to original device if corr.device != device: corr = corr.to(device) - - return corr \ No newline at end of file + + return corr diff --git a/src/alignment/metrics/similarity/node_redundancy.py b/src/alignment/metrics/similarity/node_redundancy.py index 54d41e2f..c0d5f1b0 100644 --- a/src/alignment/metrics/similarity/node_redundancy.py +++ b/src/alignment/metrics/similarity/node_redundancy.py @@ -2,9 +2,11 @@ Node redundancy metric for measuring input feature correlations. """ -import torch import logging -from typing import Optional, Dict, Any +from typing import Optional + +import torch + from ...core.base import BaseMetric logger = logging.getLogger(__name__) @@ -13,25 +15,25 @@ class NodeRedundancy(BaseMetric): """ Compute redundancy between input features based on correlation of activations. - + This represents feature redundancy rather than per-output-node scores. For each feature, it computes the average absolute correlation with other features. """ - + name = "node_redundancy" requires_weights = False requires_inputs = True requires_outputs = False - + def __init__(self, force_cpu: bool = False): """ Initialize the node redundancy metric. - + Args: force_cpu: Whether to force CPU computation for large operations """ self.force_cpu = force_cpu - + @torch.no_grad() def compute( self, @@ -42,19 +44,19 @@ def compute( ) -> torch.Tensor: """ Compute node redundancy scores. - + Args: inputs: Input activations [batch_size, num_features] weights: Not used outputs: Not used **kwargs: Additional arguments - + Returns: Redundancy scores per input feature [num_features] """ if inputs is None: raise ValueError("Node redundancy requires inputs") - + # Handle different input dimensions if inputs.ndim != 2: if inputs.ndim > 2: @@ -63,65 +65,65 @@ def compute( else: logger.warning(f"Input has unexpected shape: {inputs.shape}") return torch.zeros(1, device=inputs.device) - + batch_size, num_features = inputs.shape - + # Need at least 2 samples to compute correlation if batch_size < 2: logger.warning(f"Need at least 2 samples for correlation, got {batch_size}") return torch.zeros(num_features, device=inputs.device) - + # Single feature case if num_features == 1: return torch.zeros(1, device=inputs.device) - + redundancy_scores = torch.zeros(num_features, device=inputs.device) - + try: # Compute correlation matrix corr_matrix = self._compute_correlation(inputs) - + # Take absolute values (we care about strength, not direction) abs_corr = torch.abs(corr_matrix) - + # For each feature, compute average correlation with other features for i in range(num_features): # Exclude self-correlation (always 1.0) mask = torch.ones(num_features, dtype=torch.bool, device=inputs.device) mask[i] = False - + if mask.sum() > 0: redundancy_scores[i] = abs_corr[i, mask].mean() - + except Exception as e: logger.error(f"Error computing node redundancy: {e}") return torch.zeros(num_features, device=inputs.device) - + return torch.nan_to_num(redundancy_scores, nan=0.0) - + def _compute_correlation(self, X: torch.Tensor) -> torch.Tensor: """Compute correlation matrix.""" device = X.device - + # Move to CPU if requested and tensor is large if self.force_cpu and X.is_cuda and X.numel() > 1e6: X = X.cpu() - + # Center the data X_centered = X - X.mean(dim=0, keepdim=True) - + # Compute covariance cov = torch.matmul(X_centered.T, X_centered) / (X.size(0) - 1) - + # Compute standard deviations std = torch.sqrt(torch.diag(cov) + 1e-10) - + # Compute correlation outer_std = torch.outer(std, std) corr = torch.where(outer_std > 1e-10, cov / outer_std, torch.zeros_like(cov)) - + # Move back to original device if corr.device != device: corr = corr.to(device) - - return corr \ No newline at end of file + + return corr diff --git a/src/alignment/metrics/similarity/weight_similarity.py b/src/alignment/metrics/similarity/weight_similarity.py index bc2804de..209896e2 100644 --- a/src/alignment/metrics/similarity/weight_similarity.py +++ b/src/alignment/metrics/similarity/weight_similarity.py @@ -2,9 +2,11 @@ Weight similarity metrics for measuring relationships between neuron weight vectors. """ -import torch import logging from typing import Optional + +import torch + from ...core.base import BaseMetric logger = logging.getLogger(__name__) @@ -12,11 +14,11 @@ class WeightSimilarityBase(BaseMetric): """Base class for weight similarity metrics.""" - + requires_weights = True requires_inputs = False requires_outputs = False - + @torch.no_grad() def compute( self, @@ -27,19 +29,19 @@ def compute( ) -> torch.Tensor: """ Compute weight similarity scores. - + Args: inputs: Not used weights: Weight matrix [num_neurons, num_features] outputs: Not used **kwargs: Additional arguments - + Returns: Similarity scores per neuron [num_neurons] """ if weights is None: raise ValueError(f"{self.name} requires weights") - + # Handle different weight dimensions if weights.ndim != 2: if weights.ndim > 2: @@ -48,14 +50,14 @@ def compute( else: logger.warning(f"Weights have unexpected shape: {weights.shape}") return torch.zeros(1, device=weights.device) - + num_neurons = weights.shape[0] - + if num_neurons <= 1: return torch.zeros(num_neurons, device=weights.device) - + return self._compute_similarity(weights) - + def _compute_similarity(self, weights: torch.Tensor) -> torch.Tensor: """Compute similarity scores. To be implemented by subclasses.""" raise NotImplementedError @@ -65,14 +67,14 @@ class WeightCosineSimilarity(WeightSimilarityBase): """ Compute average cosine similarity between each neuron's weights and all others. """ - + name = "weight_cosine_similarity" - + def _compute_similarity(self, weights: torch.Tensor) -> torch.Tensor: """Compute average cosine similarity for each neuron.""" num_neurons = weights.shape[0] similarity_scores = torch.zeros(num_neurons, device=weights.device) - + # Normalize weight vectors weight_norms = torch.norm(weights, dim=1, keepdim=True) normalized_weights = torch.where( @@ -80,17 +82,17 @@ def _compute_similarity(self, weights: torch.Tensor) -> torch.Tensor: weights / weight_norms, torch.zeros_like(weights) ) - + # Compute pairwise cosine similarities cosine_sim_matrix = torch.matmul(normalized_weights, normalized_weights.T) - + # Average similarity with other neurons (excluding self) for i in range(num_neurons): mask = torch.ones(num_neurons, dtype=torch.bool, device=weights.device) mask[i] = False if mask.sum() > 0: similarity_scores[i] = cosine_sim_matrix[i, mask].mean() - + return similarity_scores @@ -98,24 +100,24 @@ class WeightDotSimilarity(WeightSimilarityBase): """ Compute average dot product between each neuron's weights and all others. """ - + name = "weight_dot_similarity" - + def _compute_similarity(self, weights: torch.Tensor) -> torch.Tensor: """Compute average dot product for each neuron.""" num_neurons = weights.shape[0] similarity_scores = torch.zeros(num_neurons, device=weights.device) - + # Compute pairwise dot products dot_product_matrix = torch.matmul(weights, weights.T) - + # Average dot product with other neurons (excluding self) for i in range(num_neurons): mask = torch.ones(num_neurons, dtype=torch.bool, device=weights.device) mask[i] = False if mask.sum() > 0: similarity_scores[i] = dot_product_matrix[i, mask].mean() - + return similarity_scores @@ -124,28 +126,28 @@ class WeightEuclideanDistance(WeightSimilarityBase): Compute average Euclidean distance between each neuron's weights and all others. Note: Lower values indicate higher similarity. """ - + name = "weight_euclidean_distance" - + def _compute_similarity(self, weights: torch.Tensor) -> torch.Tensor: """Compute average Euclidean distance for each neuron.""" num_neurons = weights.shape[0] distance_scores = torch.zeros(num_neurons, device=weights.device) - + # Compute pairwise Euclidean distances efficiently # ||w_i - w_j||^2 = ||w_i||^2 + ||w_j||^2 - 2 * w_i @ w_j weight_norms_sq = torch.sum(weights ** 2, dim=1, keepdim=True) dot_products = torch.matmul(weights, weights.T) - + # Distance matrix dist_matrix_sq = weight_norms_sq + weight_norms_sq.T - 2 * dot_products dist_matrix = torch.sqrt(torch.clamp(dist_matrix_sq, min=0)) - + # Average distance to other neurons (excluding self) for i in range(num_neurons): mask = torch.ones(num_neurons, dtype=torch.bool, device=weights.device) mask[i] = False if mask.sum() > 0: distance_scores[i] = dist_matrix[i, mask].mean() - - return distance_scores \ No newline at end of file + + return distance_scores diff --git a/src/alignment/metrics/spectral/__init__.py b/src/alignment/metrics/spectral/__init__.py index 44d86f5e..1719813e 100644 --- a/src/alignment/metrics/spectral/__init__.py +++ b/src/alignment/metrics/spectral/__init__.py @@ -2,24 +2,24 @@ # Phase 3 metrics from .spectral_alignment import ( - SpectralGapMetric, EigenvalueAlignmentMetric, + PowerIterationAlignment, SpectralClusteringAlignment, - PowerIterationAlignment + SpectralGapMetric, ) # Classic spectral metrics from .spectral_classic import ( + EigenvalueEntropy, SpectralAlignment, + SpectralClusteringScore, SpectralNormRatio, - EigenvalueEntropy, - SpectralClusteringScore ) __all__ = [ # Phase 3 metrics 'SpectralGapMetric', - 'EigenvalueAlignmentMetric', + 'EigenvalueAlignmentMetric', 'SpectralClusteringAlignment', 'PowerIterationAlignment', # Classic metrics @@ -27,4 +27,4 @@ 'SpectralNormRatio', 'EigenvalueEntropy', 'SpectralClusteringScore' -] \ No newline at end of file +] diff --git a/src/alignment/metrics/spectral/spectral_alignment.py b/src/alignment/metrics/spectral/spectral_alignment.py index fcb7f33b..a297c2dc 100644 --- a/src/alignment/metrics/spectral/spectral_alignment.py +++ b/src/alignment/metrics/spectral/spectral_alignment.py @@ -1,9 +1,9 @@ """Spectral alignment metrics based on eigenvalue analysis.""" +from typing import Optional + import torch -import torch.nn as nn -from typing import Optional, Dict, Any, Tuple -import numpy as np + from ...core.base import BaseMetric from ...core.registry import register_metric @@ -12,14 +12,14 @@ class SpectralGapMetric(BaseMetric): """ Measures the spectral gap of weight matrices. - + The spectral gap is the difference between the largest and second-largest eigenvalues, normalized by the largest eigenvalue. A larger spectral gap indicates a more dominant principal component. """ - + name = "spectral_gap" - + def __init__(self, normalize: bool = True): """ Args: @@ -27,8 +27,8 @@ def __init__(self, normalize: bool = True): """ super().__init__() self.normalize = normalize - - def compute(self, + + def compute(self, inputs: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, outputs: Optional[torch.Tensor] = None, @@ -36,7 +36,7 @@ def compute(self, """Compute spectral gap of weight matrix.""" if weights is None: raise ValueError("Weights required for spectral gap metric") - + # Handle different weight shapes if weights.dim() == 4: # Conv weights # Reshape to 2D: (out_channels, in_channels * kernel_size) @@ -46,24 +46,24 @@ def compute(self, weights = weights.mean(dim=0) elif weights.dim() != 2: raise ValueError(f"Unsupported weight dimension: {weights.dim()}") - + # Compute SVD (more stable than eigendecomposition) try: U, S, V = torch.svd(weights) - + if S.numel() < 2: return torch.zeros(weights.size(0), device=weights.device) - + # Spectral gap is difference between top 2 singular values gap = (S[0] - S[1]).item() - + if self.normalize and S[0] > 1e-8: gap = gap / S[0].item() - + # Return as tensor with one value per neuron (repeated) return torch.full((weights.size(0),), gap, device=weights.device) - - except Exception as e: + + except Exception: # Return zeros if SVD fails return torch.zeros(weights.size(0), device=weights.device) @@ -72,13 +72,13 @@ def compute(self, class EigenvalueAlignmentMetric(BaseMetric): """ Measures alignment between eigenvalue distributions of two weight matrices. - + Uses Wasserstein distance between eigenvalue distributions as a measure of spectral similarity. """ - + name = "eigenvalue_alignment" - + def __init__(self, p: float = 2.0, top_k: Optional[int] = None): """ Args: @@ -89,11 +89,11 @@ def __init__(self, p: float = 2.0, top_k: Optional[int] = None): self.p = p self.top_k = top_k self._reference_eigenvalues = None - + def set_reference(self, weights: torch.Tensor): """Set reference weight matrix for comparison.""" self._reference_eigenvalues = self._compute_eigenvalues(weights) - + def _compute_eigenvalues(self, weights: torch.Tensor) -> torch.Tensor: """Compute eigenvalues of weight matrix.""" # Handle different weight shapes @@ -103,15 +103,15 @@ def _compute_eigenvalues(self, weights: torch.Tensor) -> torch.Tensor: weights = weights.mean(dim=0) elif weights.dim() != 2: raise ValueError(f"Unsupported weight dimension: {weights.dim()}") - + # Compute singular values (eigenvalues of W^T W) _, S, _ = torch.svd(weights) - + if self.top_k is not None and S.numel() > self.top_k: S = S[:self.top_k] - + return S.sort(descending=True)[0] - + def compute(self, inputs: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, @@ -120,18 +120,18 @@ def compute(self, """Compute eigenvalue alignment.""" if weights is None: raise ValueError("Weights required for eigenvalue alignment") - + if self._reference_eigenvalues is None: # If no reference, return 0 (perfect alignment with self) return torch.zeros(weights.size(0), device=weights.device) - + current_eigenvalues = self._compute_eigenvalues(weights) - + # Compute Wasserstein distance # For 1D distributions, this simplifies to sorted L^p distance n_ref = len(self._reference_eigenvalues) n_curr = len(current_eigenvalues) - + if n_ref != n_curr: # Pad shorter sequence with zeros max_len = max(n_ref, n_curr) @@ -142,10 +142,10 @@ def compute(self, else: ref_padded = self._reference_eigenvalues curr_padded = current_eigenvalues - + # Wasserstein distance distance = torch.norm(ref_padded - curr_padded, p=self.p).item() - + # Return as tensor with one value per neuron (repeated) return torch.full((weights.size(0),), distance, device=weights.device) @@ -154,13 +154,13 @@ def compute(self, class SpectralClusteringAlignment(BaseMetric): """ Measures how well weight matrix eigenspaces align with data clustering. - + This metric computes the alignment between the top eigenvectors of the weight matrix and the cluster structure in the output space. """ - + name = "spectral_clustering" - + def __init__(self, n_components: int = 5, n_clusters: int = 10): """ Args: @@ -170,7 +170,7 @@ def __init__(self, n_components: int = 5, n_clusters: int = 10): super().__init__() self.n_components = n_components self.n_clusters = n_clusters - + def compute(self, inputs: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, @@ -179,7 +179,7 @@ def compute(self, """Compute spectral clustering alignment.""" if weights is None or outputs is None: raise ValueError("Both weights and outputs required") - + # Handle different weight shapes if weights.dim() == 4: # Conv weights weights = weights.view(weights.size(0), -1) @@ -187,33 +187,33 @@ def compute(self, weights = weights.mean(dim=0) elif weights.dim() != 2: raise ValueError(f"Unsupported weight dimension: {weights.dim()}") - + # Get top eigenvectors of weight matrix try: U, S, V = torch.svd(weights) top_components = V[:, :self.n_components] # Right singular vectors - + # Project outputs using eigenvectors if outputs.size(-1) != top_components.size(0): # Dimension mismatch - return low alignment return torch.zeros(weights.size(0), device=weights.device) - + projected = outputs @ top_components - + # Compute clustering quality in projected space # Use variance ratio as a simple measure total_var = outputs.var(dim=0).sum().item() projected_var = projected.var(dim=0).sum().item() - + if total_var > 1e-8: alignment = projected_var / total_var else: alignment = 0.0 - + # Return as tensor with one value per neuron (repeated) return torch.full((weights.size(0),), alignment, device=weights.device) - - except Exception as e: + + except Exception: return torch.zeros(weights.size(0), device=weights.device) @@ -221,13 +221,13 @@ def compute(self, class PowerIterationAlignment(BaseMetric): """ Measures alignment using power iteration convergence properties. - + This metric analyzes how quickly power iteration converges to the dominant eigenvector, which indicates the spectral structure of the weight matrix. """ - + name = "power_iteration" - + def __init__(self, max_iterations: int = 100, tolerance: float = 1e-6): """ Args: @@ -237,7 +237,7 @@ def __init__(self, max_iterations: int = 100, tolerance: float = 1e-6): super().__init__() self.max_iterations = max_iterations self.tolerance = tolerance - + def compute(self, inputs: Optional[torch.Tensor] = None, weights: Optional[torch.Tensor] = None, @@ -246,7 +246,7 @@ def compute(self, """Compute power iteration convergence rate.""" if weights is None: raise ValueError("Weights required for power iteration alignment") - + # Handle different weight shapes if weights.dim() == 4: # Conv weights weights = weights.view(weights.size(0), -1) @@ -254,29 +254,29 @@ def compute(self, weights = weights.mean(dim=0) elif weights.dim() != 2: raise ValueError(f"Unsupported weight dimension: {weights.dim()}") - + # Initialize random vector n = weights.size(0) v = torch.randn(n, 1, device=weights.device) v = v / torch.norm(v) - + # Power iteration convergence_rate = 0.0 prev_v = v.clone() - + for i in range(self.max_iterations): # One power iteration step v = weights @ v v = v / torch.norm(v) - + # Check convergence diff = torch.norm(v - prev_v).item() if diff < self.tolerance: # Converged - compute convergence rate convergence_rate = 1.0 / (i + 1) break - + prev_v = v.clone() - + # Return as tensor with one value per neuron (repeated) - return torch.full((n,), convergence_rate, device=weights.device) \ No newline at end of file + return torch.full((n,), convergence_rate, device=weights.device) diff --git a/src/alignment/metrics/spectral/spectral_classic.py b/src/alignment/metrics/spectral/spectral_classic.py index 6b4d2929..2f5e773b 100644 --- a/src/alignment/metrics/spectral/spectral_classic.py +++ b/src/alignment/metrics/spectral/spectral_classic.py @@ -3,33 +3,35 @@ These metrics were originally in metrics/spectral.py """ +from typing import Optional + import torch import torch.linalg as LA -from typing import Optional, Tuple, Dict, Any -from ...core.registry import register_metric + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("spectral_alignment") class SpectralAlignment(BaseMetric): """ Compute spectral alignment based on eigenvalue decomposition of the covariance matrix. - + This metric analyzes how well neuron activations align with the principal components of the input distribution. """ - + name = "spectral_alignment" - + def __init__( - self, + self, n_components: Optional[int] = None, normalize: bool = True, epsilon: float = 1e-8 ): """ Initialize spectral alignment metric. - + Args: n_components: Number of top components to consider (None for all) normalize: Whether to normalize by total variance @@ -39,7 +41,7 @@ def __init__( self.n_components = n_components self.normalize = normalize self.epsilon = epsilon - + def compute( self, inputs: torch.Tensor, @@ -49,55 +51,55 @@ def compute( ) -> torch.Tensor: """ Compute spectral alignment scores. - + Args: inputs: Input activations (batch_size, input_dim) weights: Weight matrix (output_dim, input_dim) outputs: Output activations (optional) - + Returns: Alignment scores for each neuron """ batch_size, input_dim = inputs.shape output_dim, _ = weights.shape - + # Center the inputs inputs_centered = inputs - inputs.mean(dim=0, keepdim=True) - + # Compute input covariance matrix cov_matrix = (inputs_centered.T @ inputs_centered) / (batch_size - 1) cov_matrix = cov_matrix + self.epsilon * torch.eye(input_dim, device=inputs.device) - + # Compute eigenvalue decomposition eigenvalues, eigenvectors = LA.eigh(cov_matrix) - + # Sort by eigenvalues (descending) idx = eigenvalues.argsort(descending=True) eigenvalues = eigenvalues[idx] eigenvectors = eigenvectors[:, idx] - + # Select top components if specified if self.n_components is not None: n_comp = min(self.n_components, input_dim) eigenvalues = eigenvalues[:n_comp] eigenvectors = eigenvectors[:, :n_comp] - + # Compute alignment of each weight vector with principal components # Weight vectors projected onto principal components projections = weights @ eigenvectors # (output_dim, n_components) - + # Weighted by eigenvalues (importance of each component) weighted_projections = projections * eigenvalues.unsqueeze(0) - + # Compute alignment score for each neuron alignment_scores = weighted_projections.norm(dim=1) - + if self.normalize: # Normalize by weight norm and total variance weight_norms = weights.norm(dim=1) total_variance = eigenvalues.sum() alignment_scores = alignment_scores / (weight_norms * total_variance.sqrt() + self.epsilon) - + return alignment_scores @@ -105,22 +107,22 @@ def compute( class SpectralNormRatio(BaseMetric): """ Compute the ratio of spectral norm to Frobenius norm for weight matrices. - + This metric indicates how concentrated the weight matrix is along its principal direction. """ - + name = "spectral_norm_ratio" - + def __init__(self, epsilon: float = 1e-8): """ Initialize spectral norm ratio metric. - + Args: epsilon: Small constant for numerical stability """ super().__init__() self.epsilon = epsilon - + def compute( self, inputs: torch.Tensor, @@ -130,24 +132,24 @@ def compute( ) -> torch.Tensor: """ Compute spectral norm ratio for each layer. - + Args: inputs: Input activations (not used, included for interface compatibility) weights: Weight matrix (output_dim, input_dim) outputs: Output activations (not used) - + Returns: Spectral norm ratio (single value per layer) """ # Compute spectral norm (largest singular value) spectral_norm = LA.matrix_norm(weights, ord=2) - + # Compute Frobenius norm frobenius_norm = weights.norm(p='fro') - + # Compute ratio ratio = spectral_norm / (frobenius_norm + self.epsilon) - + # Return as tensor with one value per neuron (repeated) return ratio.repeat(weights.shape[0]) @@ -156,17 +158,17 @@ def compute( class EigenvalueEntropy(BaseMetric): """ Compute the entropy of eigenvalue distribution of the Gram matrix. - + This metric measures how evenly distributed the neuron activations are across different modes of variation. """ - + name = "eigenvalue_entropy" - + def __init__(self, temperature: float = 1.0, epsilon: float = 1e-8): """ Initialize eigenvalue entropy metric. - + Args: temperature: Temperature parameter for softmax epsilon: Small constant for numerical stability @@ -174,7 +176,7 @@ def __init__(self, temperature: float = 1.0, epsilon: float = 1e-8): super().__init__() self.temperature = temperature self.epsilon = epsilon - + def compute( self, inputs: torch.Tensor, @@ -184,48 +186,48 @@ def compute( ) -> torch.Tensor: """ Compute eigenvalue entropy for neuron activations. - + Args: inputs: Input activations (batch_size, input_dim) weights: Weight matrix (output_dim, input_dim) outputs: Output activations (optional) - + Returns: Entropy values for each neuron """ batch_size = inputs.shape[0] output_dim = weights.shape[0] - + # Compute neuron activations if not provided if outputs is None: outputs = inputs @ weights.T - + entropies = [] - + for i in range(output_dim): # Get activations for this neuron neuron_acts = outputs[:, i].unsqueeze(1) - + # Compute Gram matrix gram = neuron_acts @ neuron_acts.T - + # Add small diagonal for stability gram = gram + self.epsilon * torch.eye(batch_size, device=gram.device) - + # Compute eigenvalues eigenvalues = LA.eigvalsh(gram) eigenvalues = eigenvalues[eigenvalues > self.epsilon] - + # Normalize eigenvalues eigenvalues = eigenvalues / eigenvalues.sum() - + # Apply temperature scaling eigenvalues = eigenvalues / self.temperature - + # Compute entropy entropy = -(eigenvalues * eigenvalues.log()).sum() entropies.append(entropy) - + return torch.stack(entropies) @@ -234,9 +236,9 @@ class SpectralClusteringScore(BaseMetric): """ Compute how well neurons cluster based on spectral analysis of their similarity matrix. """ - + name = "spectral_clustering_score" - + def __init__( self, n_clusters: int = 5, @@ -245,7 +247,7 @@ def __init__( ): """ Initialize spectral clustering score. - + Args: n_clusters: Number of clusters to form similarity_type: Type of similarity ('correlation', 'cosine', 'rbf') @@ -255,7 +257,7 @@ def __init__( self.n_clusters = n_clusters self.similarity_type = similarity_type self.epsilon = epsilon - + def compute( self, inputs: torch.Tensor, @@ -265,17 +267,17 @@ def compute( ) -> torch.Tensor: """ Compute spectral clustering scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations - + Returns: Clustering quality scores for each neuron """ output_dim = weights.shape[0] - + # Compute similarity matrix between neurons if self.similarity_type == "correlation": # Normalize weights @@ -283,41 +285,41 @@ def compute( weights_std = weights_norm.std(dim=1, keepdim=True) weights_norm = weights_norm / (weights_std + self.epsilon) similarity = weights_norm @ weights_norm.T / weights.shape[1] - + elif self.similarity_type == "cosine": weights_norm = weights / (weights.norm(dim=1, keepdim=True) + self.epsilon) similarity = weights_norm @ weights_norm.T - + elif self.similarity_type == "rbf": # RBF kernel similarity dist_matrix = torch.cdist(weights, weights, p=2) sigma = dist_matrix.mean() similarity = torch.exp(-dist_matrix**2 / (2 * sigma**2)) - + else: raise ValueError(f"Unknown similarity type: {self.similarity_type}") - + # Compute degree matrix degree = similarity.sum(dim=1) - + # Compute normalized Laplacian D_sqrt_inv = torch.diag(1.0 / (degree.sqrt() + self.epsilon)) L_norm = torch.eye(output_dim, device=weights.device) - D_sqrt_inv @ similarity @ D_sqrt_inv - + # Compute eigenvalues of normalized Laplacian eigenvalues = LA.eigvalsh(L_norm) - + # Sort eigenvalues eigenvalues = eigenvalues.sort()[0] - + # Compute spectral gap (difference between k-th and (k+1)-th eigenvalue) if self.n_clusters < output_dim: spectral_gap = eigenvalues[self.n_clusters] - eigenvalues[self.n_clusters - 1] else: spectral_gap = eigenvalues[-1] - eigenvalues[-2] - + # Higher spectral gap indicates better clustering # Return as score for each neuron (can be refined to per-neuron scores) scores = torch.full((output_dim,), spectral_gap.item(), device=weights.device) - - return scores \ No newline at end of file + + return scores diff --git a/src/alignment/metrics/task_specific/__init__.py b/src/alignment/metrics/task_specific/__init__.py index 433fd02e..b9b9f256 100644 --- a/src/alignment/metrics/task_specific/__init__.py +++ b/src/alignment/metrics/task_specific/__init__.py @@ -1,26 +1,25 @@ """Task-specific alignment metrics for different domains and objectives.""" # General task alignment -from .general import ( - TaskAlignment, - FeatureImportance, - RepresentationQuality, - ClassSelectivity -) - # Activation-based importance from .activation_magnitude import ( ActivationL2Norm, ActivationMean, ActivationNorm, - ActivationVariance + ActivationVariance, ) # Domain-specific metrics from .classification import ClassificationAlignment +from .general import ( + ClassSelectivity, + FeatureImportance, + RepresentationQuality, + TaskAlignment, +) from .language_model import LanguageModelAlignment -from .vision import VisionTaskAlignment from .reinforcement_learning import ReinforcementLearningAlignment +from .vision import VisionTaskAlignment __all__ = [ # General @@ -38,4 +37,4 @@ 'LanguageModelAlignment', 'VisionTaskAlignment', 'ReinforcementLearningAlignment' -] \ No newline at end of file +] diff --git a/src/alignment/metrics/task_specific/activation_magnitude.py b/src/alignment/metrics/task_specific/activation_magnitude.py index 5d19734d..5b636b50 100644 --- a/src/alignment/metrics/task_specific/activation_magnitude.py +++ b/src/alignment/metrics/task_specific/activation_magnitude.py @@ -5,10 +5,10 @@ commonly used in pruning literature including TensorRT and NeMo. """ -from typing import Optional, Any -import torch -import torch.nn.functional as F import logging +from typing import Any, Optional + +import torch from ...core.base import BaseMetric from ...core.registry import register_metric @@ -20,25 +20,25 @@ class ActivationL2Norm(BaseMetric): """ Compute neuron importance as L2 norm of activations. - + This metric is commonly used in LLM pruning (e.g., TensorRT-LLM, NeMo). For each neuron, computes: sqrt(sum_over_batch(mean_over_seq(|activations|)^2)) - + This is equivalent to the metric used in: - NVIDIA NeMo pruning - TensorRT-LLM pruning - Various LLM compression papers - + Args: aggregate_method: How to aggregate activations ('l2', 'mean', 'max') use_absolute: Whether to take absolute value before aggregation """ - + name = "activation_l2_norm" requires_inputs = True requires_weights = False requires_outputs = True - + def __init__( self, aggregate_method: str = "l2", @@ -47,7 +47,7 @@ def __init__( super().__init__() self.aggregate_method = aggregate_method self.use_absolute = use_absolute - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -57,12 +57,12 @@ def compute( ) -> torch.Tensor: """ Compute activation-based importance scores. - + Args: inputs: Input activations [batch_size, input_dim] or [seq_len, batch_size, input_dim] weights: Weight matrix (not used, but kept for interface compatibility) outputs: Output activations [batch_size, num_neurons] or [seq_len, batch_size, num_neurons] - + Returns: Importance scores [num_neurons] """ @@ -80,43 +80,43 @@ def compute( raise ValueError(f"Unsupported input shape: {inputs.shape}") else: raise ValueError("Must provide either outputs or (inputs + weights)") - + # Handle different input shapes if activations.ndim == 3: # [seq_len, batch_size, num_neurons] - typical for transformers # This matches PruneLLM's format - + if self.use_absolute: activations = activations.abs() - + # Mean over sequence dimension activations = activations.mean(dim=0) # [batch_size, num_neurons] - + elif activations.ndim == 2: # [batch_size, num_neurons] - typical for MLPs/CNNs if self.use_absolute: activations = activations.abs() else: raise ValueError(f"Unsupported activation shape: {activations.shape}") - + # Now activations is [batch_size, num_neurons] # Compute importance based on method if self.aggregate_method == "l2": # L2 norm across batch: sqrt(sum(x^2)) # This matches PruneLLM exactly: activations.pow(2).sum(dim=0).sqrt() importance = activations.pow(2).sum(dim=0).sqrt() - + elif self.aggregate_method == "mean": # Mean activation magnitude importance = activations.mean(dim=0) - + elif self.aggregate_method == "max": # Max activation magnitude importance = activations.max(dim=0)[0] - + else: raise ValueError(f"Unknown aggregate_method: {self.aggregate_method}") - + return importance @@ -124,13 +124,13 @@ def compute( class ActivationMean(ActivationL2Norm): """ Compute neuron importance as mean absolute activation. - + For each neuron, computes the average magnitude of activations across all samples in the batch. """ - + name = "activation_mean" - + def __init__(self): super().__init__(aggregate_method="mean", use_absolute=True) @@ -139,17 +139,17 @@ def __init__(self): class ActivationNorm(ActivationL2Norm): """ Compute neuron importance as L2 norm of activations. - + For each neuron, computes: sqrt(sum_over_batch(mean_over_seq(|activations|)^2)) - + This is a standard metric used in: - LLM pruning (TensorRT-LLM, NeMo) - Neural architecture search - Channel pruning for CNNs - + Formula: importance[n] = ||activations[:, n]||_2 - + For transformers with shape [seq_len, batch, neurons]: 1. Take absolute value: |activations| 2. Mean over sequence: mean(dim=0) -> [batch, neurons] @@ -157,9 +157,9 @@ class ActivationNorm(ActivationL2Norm): 4. Sum over batch: sum(dim=0) -> [neurons] 5. Square root: sqrt() -> [neurons] """ - + name = "activation_norm" - + def __init__(self): super().__init__(aggregate_method="l2", use_absolute=True) @@ -168,15 +168,15 @@ def __init__(self): class ActivationVariance(BaseMetric): """ Compute neuron importance as variance of activations. - + High variance neurons are more selective/informative. """ - + name = "activation_variance" requires_inputs = True requires_weights = False requires_outputs = True - + def compute( self, inputs: Optional[torch.Tensor] = None, @@ -195,14 +195,14 @@ def compute( activations = torch.matmul(inputs, weights.T) else: raise ValueError("Must provide either outputs or (inputs + weights)") - + # Handle 3D activations (seq_len, batch, neurons) if activations.ndim == 3: # Combine seq and batch dimensions activations = activations.reshape(-1, activations.shape[-1]) - + # Compute variance per neuron variance = activations.var(dim=0) - + return variance diff --git a/src/alignment/metrics/task_specific/classification.py b/src/alignment/metrics/task_specific/classification.py index f90f005e..2a441830 100644 --- a/src/alignment/metrics/task_specific/classification.py +++ b/src/alignment/metrics/task_specific/classification.py @@ -2,27 +2,29 @@ Classification-specific alignment metrics. """ +from typing import Optional + import torch import torch.nn.functional as F -from typing import Optional, Dict, Any -from ...core.registry import register_metric + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("classification_alignment") class ClassificationAlignment(BaseMetric): """ Measures alignment specifically for classification tasks. - + This metric evaluates how well neuron activations align with class boundaries and decision surfaces in classification problems. """ - + name = "classification_alignment" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, n_classes: int, @@ -31,7 +33,7 @@ def __init__( ): """ Initialize classification alignment metric. - + Args: n_classes: Number of classes in the classification task alignment_type: Type of alignment measure @@ -44,7 +46,7 @@ def __init__( self.n_classes = n_classes self.alignment_type = alignment_type self.temperature = temperature - + def compute( self, inputs: torch.Tensor, @@ -56,40 +58,40 @@ def compute( ) -> torch.Tensor: """ Compute classification alignment scores. - + Args: inputs: Input activations [batch_size, input_dim] weights: Weight matrix [output_dim, input_dim] outputs: Output activations [batch_size, output_dim] labels: True class labels [batch_size] logits: Classification logits [batch_size, n_classes] - + Returns: Alignment scores for each neuron [output_dim] """ if outputs is None: outputs = inputs @ weights.T - + n_neurons = outputs.shape[1] alignment_scores = torch.zeros(n_neurons, device=outputs.device) - + # Generate synthetic labels if not provided if labels is None: # Use k-means clustering on outputs labels = self._generate_labels(outputs, self.n_classes) - + # Generate logits if not provided if logits is None: # Simple linear projection from outputs to classes projection = torch.randn(outputs.shape[1], self.n_classes, device=outputs.device) logits = outputs @ projection - + if self.alignment_type == "boundary_distance": # Measure how neuron activations change near decision boundaries probabilities = F.softmax(logits / self.temperature, dim=1) # Add small epsilon to avoid log(0) entropy = -(probabilities * (probabilities + 1e-8).log()).sum(dim=1) - + # High entropy indicates proximity to decision boundary # Compute correlation between neuron activation and boundary proximity for i in range(n_neurons): @@ -105,78 +107,78 @@ def compute( alignment_scores[i] = correlation.abs() else: alignment_scores[i] = 0.0 - + elif self.alignment_type == "class_separation": # Measure how well neurons separate different classes for i in range(n_neurons): neuron_activations = outputs[:, i] - + # Compute mean activation per class class_means = [] class_stds = [] - + for c in range(self.n_classes): class_mask = labels == c if class_mask.sum() > 0: class_acts = neuron_activations[class_mask] class_means.append(class_acts.mean()) class_stds.append(class_acts.std()) - + if len(class_means) > 1: class_means = torch.stack(class_means) class_stds = torch.stack(class_stds) - + # Fisher's discriminant ratio between_class_var = class_means.var() within_class_var = class_stds.mean() ** 2 - + if within_class_var > 0: separation = between_class_var / within_class_var else: separation = between_class_var - + alignment_scores[i] = separation - + elif self.alignment_type == "confidence_alignment": # Measure alignment with prediction confidence probabilities = F.softmax(logits / self.temperature, dim=1) - + # Get confidence (max probability) and correctness confidence, predictions = probabilities.max(dim=1) correct = (predictions == labels).float() - + # For each neuron, measure correlation with correct confident predictions for i in range(n_neurons): neuron_activations = outputs[:, i] - + # Confidence-weighted correctness weighted_correct = correct * confidence - + # Correlation between neuron activation and confident correct predictions if neuron_activations.std() > 0 and weighted_correct.std() > 0: correlation = torch.corrcoef( torch.stack([neuron_activations, weighted_correct]) )[0, 1] alignment_scores[i] = correlation.abs() - + else: raise ValueError(f"Unknown alignment type: {self.alignment_type}") - + return alignment_scores - + def _generate_labels(self, outputs: torch.Tensor, n_classes: int) -> torch.Tensor: """Generate synthetic labels using k-means clustering.""" # Simple k-means centroids = outputs[torch.randperm(outputs.shape[0])[:n_classes]] - + for _ in range(10): # iterations distances = torch.cdist(outputs, centroids) labels = distances.argmin(dim=1) - + # Update centroids for i in range(n_classes): mask = labels == i if mask.sum() > 0: centroids[i] = outputs[mask].mean(dim=0) - - return labels \ No newline at end of file + + return labels diff --git a/src/alignment/metrics/task_specific/general.py b/src/alignment/metrics/task_specific/general.py index ca18c688..437c9417 100644 --- a/src/alignment/metrics/task_specific/general.py +++ b/src/alignment/metrics/task_specific/general.py @@ -2,27 +2,29 @@ General task-specific alignment metrics that can be customized for different downstream tasks. """ +from typing import Callable, Optional + import torch import torch.nn.functional as F -from typing import Optional, Callable, Dict, Any, Union -from ...core.registry import register_metric + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("task_alignment") class TaskAlignment(BaseMetric): """ Compute alignment with respect to a specific task's target function. - + This metric measures how well neuron activations align with gradients or importance scores from a downstream task. """ - + name = "task_alignment" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, task_loss_fn: Optional[Callable] = None, @@ -31,7 +33,7 @@ def __init__( ): """ Initialize task alignment metric. - + Args: task_loss_fn: Loss function for the task (if None, uses MSE) alignment_type: Type of alignment ('gradient', 'taylor', 'integrated_gradients') @@ -41,7 +43,7 @@ def __init__( self.task_loss_fn = task_loss_fn or F.mse_loss self.alignment_type = alignment_type self.normalize = normalize - + def compute( self, inputs: torch.Tensor, @@ -52,80 +54,80 @@ def compute( ) -> torch.Tensor: """ Compute task-specific alignment scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations targets: Target values for the task - + Returns: Task alignment scores for each neuron """ if outputs is None: outputs = inputs @ weights.T - + # If no targets provided, create dummy targets if targets is None: # Use outputs themselves as targets (self-supervised) targets = outputs.detach() - + # Ensure inputs require gradients inputs_grad = inputs.detach().requires_grad_(True) outputs_grad = inputs_grad @ weights.T - + if self.alignment_type == "gradient": # Compute task loss loss = self.task_loss_fn(outputs_grad, targets) - + # Compute gradients with respect to inputs grads = torch.autograd.grad(loss, inputs_grad, retain_graph=True)[0] - + # Compute alignment as dot product between weights and input gradients alignment_scores = torch.abs((weights @ grads.T).mean(dim=1)) - + elif self.alignment_type == "taylor": # First-order Taylor expansion importance loss = self.task_loss_fn(outputs_grad, targets) - + # Compute gradients with respect to outputs output_grads = torch.autograd.grad(loss, outputs_grad, retain_graph=True)[0] - + # Taylor importance: |grad * activation| importance = torch.abs(output_grads * outputs_grad) alignment_scores = importance.mean(dim=0) - + elif self.alignment_type == "integrated_gradients": # Integrated gradients from baseline to current inputs baseline = torch.zeros_like(inputs) n_steps = 10 - + integrated_grads = torch.zeros_like(weights) - + for i in range(n_steps): alpha = i / n_steps interpolated = baseline + alpha * (inputs - baseline) interpolated.requires_grad_(True) - + outputs_interp = interpolated @ weights.T loss = self.task_loss_fn(outputs_interp, targets) - + grads = torch.autograd.grad(loss, interpolated)[0] integrated_grads += weights @ grads.T - + integrated_grads = integrated_grads / n_steps alignment_scores = integrated_grads.abs().mean(dim=1) - + else: raise ValueError(f"Unknown alignment type: {self.alignment_type}") - + if self.normalize: # Normalize scores to [0, 1] min_score = alignment_scores.min() max_score = alignment_scores.max() if max_score > min_score: alignment_scores = (alignment_scores - min_score) / (max_score - min_score) - + return alignment_scores @@ -134,12 +136,12 @@ class ClassSelectivity(BaseMetric): """ Measure how selectively neurons respond to different classes in classification tasks. """ - + name = "class_selectivity" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, n_classes: Optional[int] = None, @@ -148,7 +150,7 @@ def __init__( ): """ Initialize class selectivity metric. - + Args: n_classes: Number of classes (inferred if None) selectivity_type: Type of selectivity measure ('variance', 'entropy', 'gini') @@ -158,7 +160,7 @@ def __init__( self.n_classes = n_classes self.selectivity_type = selectivity_type self.temperature = temperature - + def compute( self, inputs: torch.Tensor, @@ -169,46 +171,46 @@ def compute( ) -> torch.Tensor: """ Compute class selectivity scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations labels: Class labels for each sample - + Returns: Selectivity scores for each neuron """ if outputs is None: outputs = inputs @ weights.T - + n_neurons = outputs.shape[1] - + # If no labels provided, cluster outputs if labels is None: # Simple k-means style clustering n_classes = self.n_classes or min(10, outputs.shape[0] // 10) - + # Use random initialization centroids = outputs[torch.randperm(outputs.shape[0])[:n_classes]] - + # Assign to nearest centroid distances = torch.cdist(outputs, centroids) labels = distances.argmin(dim=1) - + # Infer number of classes unique_labels = labels.unique() n_classes = len(unique_labels) - + selectivity_scores = torch.zeros(n_neurons, device=outputs.device) - + for neuron_idx in range(n_neurons): neuron_outputs = outputs[:, neuron_idx] - + # Compute mean activation for each class class_means = [] class_vars = [] - + for class_idx in unique_labels: class_mask = labels == class_idx if class_mask.sum() > 0: @@ -219,16 +221,16 @@ def compute( class_vars.append(class_activations.var()) else: class_vars.append(torch.tensor(0.0, device=outputs.device)) - + class_means = torch.stack(class_means) class_vars = torch.stack(class_vars) - + if self.selectivity_type == "variance": # Ratio of between-class variance to within-class variance between_var = class_means.var() within_var = class_vars.mean() selectivity = between_var / (within_var + 1e-8) - + elif self.selectivity_type == "entropy": # Entropy of class response distribution # Normalize responses to probabilities @@ -236,7 +238,7 @@ def compute( entropy = -(probs * probs.log()).sum() # Lower entropy = higher selectivity selectivity = 1.0 / (1.0 + entropy) - + elif self.selectivity_type == "gini": # Gini coefficient of class responses sorted_means = class_means.sort()[0] @@ -244,12 +246,12 @@ def compute( index = torch.arange(1, n + 1, device=outputs.device) gini = (2 * (index * sorted_means).sum()) / (n * sorted_means.sum()) - (n + 1) / n selectivity = gini - + else: raise ValueError(f"Unknown selectivity type: {self.selectivity_type}") - + selectivity_scores[neuron_idx] = selectivity - + return selectivity_scores @@ -258,12 +260,12 @@ class FeatureImportance(BaseMetric): """ Compute feature importance scores based on task-specific objectives. """ - + name = "feature_importance" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, importance_method: str = "permutation", @@ -272,7 +274,7 @@ def __init__( ): """ Initialize feature importance metric. - + Args: importance_method: Method to compute importance ('permutation', 'shap_approximation') n_permutations: Number of permutations for permutation importance @@ -282,11 +284,11 @@ def __init__( self.importance_method = importance_method self.n_permutations = n_permutations self.task_metric = task_metric or self._default_metric - + def _default_metric(self, outputs: torch.Tensor, targets: torch.Tensor) -> float: """Default metric: negative MSE (so higher is better).""" return -F.mse_loss(outputs, targets).item() - + def compute( self, inputs: torch.Tensor, @@ -297,66 +299,66 @@ def compute( ) -> torch.Tensor: """ Compute feature importance scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations targets: Target values - + Returns: Importance scores for each neuron """ if outputs is None: outputs = inputs @ weights.T - + if targets is None: targets = outputs.detach() - + n_neurons = weights.shape[0] importance_scores = torch.zeros(n_neurons, device=outputs.device) - + # Baseline performance baseline_score = self.task_metric(outputs, targets) - + if self.importance_method == "permutation": # Permutation importance for neuron_idx in range(n_neurons): neuron_scores = [] - + for _ in range(self.n_permutations): # Create a copy of weights with permuted neuron weights_perm = weights.clone() perm_idx = torch.randperm(weights.shape[1]) weights_perm[neuron_idx] = weights[neuron_idx, perm_idx] - + # Compute outputs with permuted weights outputs_perm = inputs @ weights_perm.T - + # Compute performance drop perm_score = self.task_metric(outputs_perm, targets) neuron_scores.append(baseline_score - perm_score) - + importance_scores[neuron_idx] = torch.tensor(neuron_scores).mean() - + elif self.importance_method == "shap_approximation": # Simplified SHAP-like approximation # Use gradient * activation as approximation inputs_grad = inputs.detach().requires_grad_(True) outputs_grad = inputs_grad @ weights.T - + # Use negative of task metric as loss loss = -self.task_metric(outputs_grad, targets) - + # Compute gradients grads = torch.autograd.grad(loss, outputs_grad)[0] - + # Importance = |gradient * activation| importance_scores = (grads * outputs_grad).abs().mean(dim=0) - + else: raise ValueError(f"Unknown importance method: {self.importance_method}") - + return importance_scores @@ -365,12 +367,12 @@ class RepresentationQuality(BaseMetric): """ Measure the quality of learned representations for downstream tasks. """ - + name = "representation_quality" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, quality_measure: str = "linear_probe", @@ -379,7 +381,7 @@ def __init__( ): """ Initialize representation quality metric. - + Args: quality_measure: Type of quality measure ('linear_probe', 'nearest_neighbor') probe_type: Type of linear probe ('ridge', 'logistic') @@ -389,7 +391,7 @@ def __init__( self.quality_measure = quality_measure self.probe_type = probe_type self.regularization = regularization - + def compute( self, inputs: torch.Tensor, @@ -400,74 +402,74 @@ def compute( ) -> torch.Tensor: """ Compute representation quality scores. - + Args: inputs: Input activations weights: Weight matrix outputs: Output activations (representations) targets: Target values/labels - + Returns: Quality scores for each neuron """ if outputs is None: outputs = inputs @ weights.T - + if targets is None: # Create synthetic targets based on clustering kmeans_labels = self._simple_kmeans(outputs, k=5) targets = kmeans_labels.float() - + n_neurons = outputs.shape[1] quality_scores = torch.zeros(n_neurons, device=outputs.device) - + if self.quality_measure == "linear_probe": # Fit a linear probe on top of each neuron's representation for neuron_idx in range(n_neurons): neuron_repr = outputs[:, neuron_idx].unsqueeze(1) - + if self.probe_type == "ridge": # Ridge regression # Solve: (X^T X + λI)w = X^T y XtX = neuron_repr.T @ neuron_repr Xty = neuron_repr.T @ targets - + # Add regularization XtX = XtX + self.regularization * torch.eye(1, device=outputs.device) - + # Solve for weights probe_weights = torch.linalg.solve(XtX, Xty) - + # Compute predictions predictions = neuron_repr @ probe_weights - + # Compute R^2 score ss_res = ((targets - predictions) ** 2).sum() ss_tot = ((targets - targets.mean()) ** 2).sum() r2 = 1 - ss_res / (ss_tot + 1e-8) - + quality_scores[neuron_idx] = r2.clamp(min=0) - + elif self.quality_measure == "nearest_neighbor": # k-NN classification quality k = min(5, outputs.shape[0] // 10) - + for neuron_idx in range(n_neurons): neuron_repr = outputs[:, neuron_idx].unsqueeze(1) - + # Compute pairwise distances distances = torch.cdist(neuron_repr, neuron_repr) - + # Get k nearest neighbors (excluding self) _, indices = distances.topk(k + 1, largest=False, dim=1) neighbor_indices = indices[:, 1:] # Exclude self - + # Get neighbor targets neighbor_targets = targets[neighbor_indices] - + # Predict as mean of neighbors (for regression) or mode (for classification) predictions = neighbor_targets.mean(dim=1) - + # Compute accuracy or MSE if targets.dtype == torch.long: # Classification: accuracy @@ -477,25 +479,25 @@ def compute( # Regression: negative MSE mse = F.mse_loss(predictions, targets) quality_scores[neuron_idx] = 1.0 / (1.0 + mse) - + return quality_scores - + def _simple_kmeans(self, data: torch.Tensor, k: int, n_iter: int = 10) -> torch.Tensor: """Simple k-means clustering implementation.""" n_samples = data.shape[0] - + # Random initialization centroids = data[torch.randperm(n_samples)[:k]] - + for _ in range(n_iter): # Assign to nearest centroid distances = torch.cdist(data, centroids) labels = distances.argmin(dim=1) - + # Update centroids for i in range(k): mask = labels == i if mask.sum() > 0: centroids[i] = data[mask].mean(dim=0) - - return labels \ No newline at end of file + + return labels diff --git a/src/alignment/metrics/task_specific/language_model.py b/src/alignment/metrics/task_specific/language_model.py index 68913fdc..841de8cb 100644 --- a/src/alignment/metrics/task_specific/language_model.py +++ b/src/alignment/metrics/task_specific/language_model.py @@ -2,27 +2,29 @@ Language model-specific alignment metrics. """ +from typing import Optional + import torch import torch.nn.functional as F -from typing import Optional, Dict, Any, List -from ...core.registry import register_metric + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("language_model_alignment") class LanguageModelAlignment(BaseMetric): """ Measures alignment for language modeling tasks. - + This metric evaluates how well neuron activations align with linguistic properties such as syntax, semantics, and prediction patterns. """ - + name = "language_model_alignment" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, vocab_size: int, @@ -32,7 +34,7 @@ def __init__( ): """ Initialize language model alignment metric. - + Args: vocab_size: Size of the vocabulary alignment_type: Type of alignment measure @@ -47,7 +49,7 @@ def __init__( self.alignment_type = alignment_type self.context_window = context_window self.semantic_dims = semantic_dims or 128 - + def compute( self, inputs: torch.Tensor, @@ -60,7 +62,7 @@ def compute( ) -> torch.Tensor: """ Compute language model alignment scores. - + Args: inputs: Input activations [batch_size, seq_len, input_dim] weights: Weight matrix [output_dim, input_dim] @@ -68,110 +70,110 @@ def compute( token_ids: Token IDs [batch_size, seq_len] attention_weights: Attention weights [batch_size, n_heads, seq_len, seq_len] embeddings: Token embeddings [vocab_size, embedding_dim] - + Returns: Alignment scores for each neuron [output_dim] """ # Handle 2D inputs by adding sequence dimension if inputs.dim() == 2: inputs = inputs.unsqueeze(1) - + if outputs is None: # inputs: [batch, seq, input_dim], weights: [output_dim, input_dim] outputs = torch.matmul(inputs, weights.T) - + if outputs.dim() == 2: outputs = outputs.unsqueeze(1) - + batch_size, seq_len, n_neurons = outputs.shape alignment_scores = torch.zeros(n_neurons, device=outputs.device) - + if self.alignment_type == "prediction_alignment": # Measure alignment with next-token prediction task if token_ids is None: # Generate synthetic token IDs token_ids = torch.randint(0, self.vocab_size, (batch_size, seq_len), device=outputs.device) - + # Create next-token targets if seq_len > 1: targets = token_ids[:, 1:] # Next tokens current_outputs = outputs[:, :-1] # Current outputs - + # Project outputs to vocabulary space projection = torch.randn(n_neurons, self.vocab_size, device=outputs.device) - logits = torch.matmul(current_outputs, projection) - + torch.matmul(current_outputs, projection) + # Compute cross-entropy loss per neuron for i in range(n_neurons): neuron_contribution = current_outputs[:, :, i:i+1] neuron_logits = neuron_contribution @ projection[i:i+1, :] - + # Flatten for loss computation neuron_logits_flat = neuron_logits.reshape(-1, self.vocab_size) targets_flat = targets.reshape(-1) - + # Cross-entropy loss loss = F.cross_entropy(neuron_logits_flat, targets_flat, reduction='mean') - + # Lower loss = better alignment alignment_scores[i] = 1.0 / (1.0 + loss.item()) - + elif self.alignment_type == "attention_correlation": # Measure correlation with attention patterns if attention_weights is None: # Generate synthetic attention weights attention_weights = F.softmax(torch.randn(batch_size, 4, seq_len, seq_len, device=outputs.device), dim=-1) - + # Average attention across heads avg_attention = attention_weights.mean(dim=1) # [batch, seq, seq] - + # For each neuron, measure correlation with attention-weighted representations for i in range(n_neurons): neuron_activations = outputs[:, :, i] # [batch, seq] - + # Compute attention-weighted activations weighted_activations = torch.matmul(avg_attention, neuron_activations.unsqueeze(-1)).squeeze(-1) - + # Correlation between original and attention-weighted if neuron_activations.numel() > 1: flat_original = neuron_activations.reshape(-1) flat_weighted = weighted_activations.reshape(-1) - + if flat_original.std() > 0 and flat_weighted.std() > 0: correlation = torch.corrcoef(torch.stack([flat_original, flat_weighted]))[0, 1] alignment_scores[i] = correlation.abs() - + elif self.alignment_type == "semantic_coherence": # Measure semantic coherence of representations if embeddings is None: # Generate synthetic embeddings embeddings = torch.randn(self.vocab_size, self.semantic_dims, device=outputs.device) - + if token_ids is None: token_ids = torch.randint(0, self.vocab_size, (batch_size, seq_len), device=outputs.device) - + # Get embeddings for tokens token_embeddings = embeddings[token_ids] # [batch, seq, embed_dim] - + # Project outputs to semantic space projection = torch.randn(n_neurons, self.semantic_dims, device=outputs.device) - output_embeddings = torch.matmul(outputs, projection) - + torch.matmul(outputs, projection) + # Measure coherence for each neuron for i in range(n_neurons): neuron_outputs = outputs[:, :, i:i+1] # [batch, seq, 1] neuron_embeddings = neuron_outputs @ projection[i:i+1, :] # [batch, seq, embed_dim] - + # Cosine similarity with token embeddings neuron_norm = F.normalize(neuron_embeddings, p=2, dim=-1) token_norm = F.normalize(token_embeddings, p=2, dim=-1) - + similarities = (neuron_norm * token_norm).sum(dim=-1) # [batch, seq] - + # Average similarity as coherence measure alignment_scores[i] = similarities.mean() - + else: raise ValueError(f"Unknown alignment type: {self.alignment_type}") - - return alignment_scores \ No newline at end of file + + return alignment_scores diff --git a/src/alignment/metrics/task_specific/reinforcement_learning.py b/src/alignment/metrics/task_specific/reinforcement_learning.py index 72f2a6ef..e41ca200 100644 --- a/src/alignment/metrics/task_specific/reinforcement_learning.py +++ b/src/alignment/metrics/task_specific/reinforcement_learning.py @@ -2,27 +2,29 @@ Reinforcement learning-specific alignment metrics. """ +from typing import Optional + import torch import torch.nn.functional as F -from typing import Optional, Dict, Any, Tuple, List -from ...core.registry import register_metric + from ...core.base import BaseMetric +from ...core.registry import register_metric @register_metric("reinforcement_learning_alignment") class ReinforcementLearningAlignment(BaseMetric): """ Measures alignment for reinforcement learning tasks. - + This metric evaluates how well neuron activations align with value functions, policy gradients, and reward signals in RL settings. """ - + name = "reinforcement_learning_alignment" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, alignment_type: str = "value_correlation", @@ -32,7 +34,7 @@ def __init__( ): """ Initialize RL alignment metric. - + Args: alignment_type: Type of alignment measure - 'value_correlation': Correlation with value function @@ -48,7 +50,7 @@ def __init__( self.discount_factor = discount_factor self.n_actions = n_actions self.use_advantage = use_advantage - + def compute( self, inputs: torch.Tensor, @@ -63,7 +65,7 @@ def compute( ) -> torch.Tensor: """ Compute RL alignment scores. - + Args: inputs: Input activations (state representations) [batch_size, state_dim] weights: Weight matrix [output_dim, input_dim] @@ -73,16 +75,16 @@ def compute( rewards: Rewards received [batch_size] next_states: Next states values: Value function estimates [batch_size] - + Returns: Alignment scores for each neuron [output_dim] """ if outputs is None: outputs = inputs @ weights.T - + n_neurons = weights.shape[0] alignment_scores = torch.zeros(n_neurons, device=outputs.device) - + # Generate synthetic RL data if not provided batch_size = outputs.shape[0] if states is None: @@ -93,35 +95,35 @@ def compute( rewards = torch.randn(batch_size, device=outputs.device) if next_states is None: next_states = states + torch.randn_like(states) * 0.1 - + if self.alignment_type == "value_correlation": # Measure correlation with value function if values is None: # Estimate values using simple linear projection value_projection = torch.randn(outputs.shape[1], 1, device=outputs.device) values = (outputs @ value_projection).squeeze() - + # For each neuron, compute correlation with value estimates for i in range(n_neurons): neuron_activations = outputs[:, i] - + if neuron_activations.std() > 0 and values.std() > 0: correlation = torch.corrcoef( torch.stack([neuron_activations, values]) )[0, 1] alignment_scores[i] = correlation.abs() - + elif self.alignment_type == "policy_gradient": # Measure alignment with policy gradients # Create policy network output (action probabilities) policy_projection = torch.randn(n_neurons, self.n_actions, device=outputs.device) action_logits = outputs @ policy_projection - action_probs = F.softmax(action_logits, dim=-1) - + F.softmax(action_logits, dim=-1) + # Compute log probabilities of taken actions action_log_probs = F.log_softmax(action_logits, dim=-1) taken_action_log_probs = action_log_probs.gather(1, actions.unsqueeze(1)).squeeze() - + # Compute advantages if using advantage function if self.use_advantage and values is not None: # Compute returns (simple 1-step return for demonstration) @@ -129,44 +131,44 @@ def compute( advantages = returns - values else: advantages = rewards - + # Policy gradient: grad log π(a|s) * A # We measure how neuron activation correlates with this quantity policy_gradient_signal = taken_action_log_probs * advantages.detach() - + for i in range(n_neurons): neuron_activations = outputs[:, i] - + if neuron_activations.std() > 0: # Compute correlation with policy gradient signal correlation = torch.corrcoef( torch.stack([neuron_activations, policy_gradient_signal]) )[0, 1] alignment_scores[i] = correlation.abs() - + elif self.alignment_type == "reward_prediction": # Measure how well neurons predict future rewards for i in range(n_neurons): neuron_activations = outputs[:, i] - + # Simple linear regression to predict rewards if neuron_activations.std() > 0: # Normalize X = (neuron_activations - neuron_activations.mean()) / neuron_activations.std() y = rewards - + # Compute correlation as measure of predictive power if y.std() > 0: correlation = torch.corrcoef(torch.stack([X, y]))[0, 1] alignment_scores[i] = correlation.abs() - + elif self.alignment_type == "temporal_difference": # Measure alignment with TD errors if values is None: # Estimate values value_projection = torch.randn(outputs.shape[1], 1, device=outputs.device) values = (outputs @ value_projection).squeeze() - + # Compute next values next_outputs = next_states @ weights.T next_values = (next_outputs @ value_projection).squeeze() @@ -175,21 +177,21 @@ def compute( next_outputs = next_states @ weights.T value_projection = torch.randn(outputs.shape[1], 1, device=outputs.device) next_values = (next_outputs @ value_projection).squeeze() - + # TD error: r + γV(s') - V(s) td_errors = rewards + self.discount_factor * next_values - values - + # Measure how neuron activations correlate with TD errors for i in range(n_neurons): neuron_activations = outputs[:, i] - + if neuron_activations.std() > 0 and td_errors.std() > 0: correlation = torch.corrcoef( torch.stack([neuron_activations, td_errors.abs()]) )[0, 1] alignment_scores[i] = correlation.abs() - + else: raise ValueError(f"Unknown alignment type: {self.alignment_type}") - - return alignment_scores \ No newline at end of file + + return alignment_scores diff --git a/src/alignment/metrics/task_specific/vision.py b/src/alignment/metrics/task_specific/vision.py index 544fe560..4fa768ef 100644 --- a/src/alignment/metrics/task_specific/vision.py +++ b/src/alignment/metrics/task_specific/vision.py @@ -2,13 +2,14 @@ Vision task-specific alignment metrics. """ +import logging +from typing import Optional, Tuple + import torch import torch.nn.functional as F -from typing import Optional, Dict, Any, Tuple -import logging -from ...core.registry import register_metric -from ...core.base import BaseMetric +from ...core.base import BaseMetric +from ...core.registry import register_metric logger = logging.getLogger(__name__) @@ -17,16 +18,16 @@ class VisionTaskAlignment(BaseMetric): """ Measures alignment for vision tasks. - + This metric evaluates how well neuron activations align with visual features such as edges, textures, objects, and spatial relationships. """ - + name = "vision_task_alignment" requires_inputs = True requires_weights = True requires_outputs = False - + def __init__( self, alignment_type: str = "spatial_coherence", @@ -37,7 +38,7 @@ def __init__( ): """ Initialize vision task alignment metric. - + Args: alignment_type: Type of alignment measure - 'spatial_coherence': Spatial coherence of activations @@ -54,7 +55,7 @@ def __init__( self.patch_size = patch_size self.n_orientations = n_orientations self.require_real_data = require_real_data - + def compute( self, inputs: torch.Tensor, @@ -67,7 +68,7 @@ def compute( ) -> torch.Tensor: """ Compute vision task alignment scores. - + Args: inputs: Input activations [batch_size, channels, height, width] or flattened weights: Weight matrix [output_dim, input_dim] @@ -75,7 +76,7 @@ def compute( images: Original images [batch_size, channels, height, width] feature_maps: Intermediate feature maps labels: Object labels for images - + Returns: Alignment scores for each neuron [output_dim] """ @@ -91,10 +92,10 @@ def compute( # Already flattened outputs = inputs @ weights.T device = outputs.device - + n_neurons = weights.shape[0] alignment_scores = torch.zeros(n_neurons, device=outputs.device) - + if self.alignment_type == "spatial_coherence": # Measure spatial coherence of neuron activations if outputs.dim() == 2: @@ -117,60 +118,60 @@ def compute( pass else: raise ValueError(f"Unexpected output dimension: {outputs.dim()}") - + # Compute spatial autocorrelation for each neuron for i in range(n_neurons): if outputs.dim() == 4: neuron_maps = outputs[:, i] # [batch, height, width] else: neuron_maps = outputs[:, :, i] # Alternative format - + # Compute local spatial correlation # Shift maps and compute correlation shifted_right = torch.roll(neuron_maps, shifts=1, dims=-1) shifted_down = torch.roll(neuron_maps, shifts=1, dims=-2) - + # Average correlation with shifted versions corr_right = F.cosine_similarity( neuron_maps.reshape(neuron_maps.shape[0], -1), shifted_right.reshape(shifted_right.shape[0], -1), dim=1 ).mean() - + corr_down = F.cosine_similarity( neuron_maps.reshape(neuron_maps.shape[0], -1), shifted_down.reshape(shifted_down.shape[0], -1), dim=1 ).mean() - + alignment_scores[i] = (corr_right + corr_down) / 2 - + elif self.alignment_type == "edge_detection": # Measure alignment with edge detection filters # Create Sobel-like edge detection kernels - + # Horizontal and vertical edge kernels kernel_h = torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32, device=device) kernel_v = torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32, device=device) - + if images is None: if self.require_real_data: raise ValueError("VisionTaskAlignment(edge_detection) requires images but none were provided.") logger.warning("images not provided; generating synthetic edge patterns for alignment computation") images = self._generate_edge_patterns(outputs.shape[0], device) - + # Compute edge responses in images if images.dim() == 4 and images.shape[1] == 3: # Convert to grayscale images_gray = images.mean(dim=1, keepdim=True) else: images_gray = images - + # Apply edge detection edges_h = F.conv2d(images_gray, kernel_h.unsqueeze(0).unsqueeze(0), padding=1) edges_v = F.conv2d(images_gray, kernel_v.unsqueeze(0).unsqueeze(0), padding=1) edge_magnitude = torch.sqrt(edges_h**2 + edges_v**2) - + # Measure correlation between neuron activations and edges for i in range(n_neurons): if outputs.dim() == 4: @@ -178,23 +179,23 @@ def compute( else: # Reshape if needed neuron_response = outputs[:, i].reshape(outputs.shape[0], 1, -1) - neuron_response = neuron_response.reshape(outputs.shape[0], 1, + neuron_response = neuron_response.reshape(outputs.shape[0], 1, int(neuron_response.shape[2]**0.5), int(neuron_response.shape[2]**0.5)) - + # Resize if necessary if neuron_response.shape[-2:] != edge_magnitude.shape[-2:]: neuron_response = F.interpolate(neuron_response, size=edge_magnitude.shape[-2:], mode='bilinear') - + # Compute correlation correlation = F.cosine_similarity( neuron_response.reshape(neuron_response.shape[0], -1), edge_magnitude.reshape(edge_magnitude.shape[0], -1), dim=1 ).mean() - + alignment_scores[i] = correlation.abs() - + elif self.alignment_type == "texture_response": # Measure response to texture patterns if feature_maps is None: @@ -202,33 +203,33 @@ def compute( raise ValueError("VisionTaskAlignment(texture_response) requires feature_maps but none were provided.") logger.warning("feature_maps not provided; generating synthetic texture patterns for alignment computation") feature_maps = self._generate_texture_patterns(outputs.shape[0], n_neurons, device) - + # Compute texture energy for different frequencies for i in range(n_neurons): if outputs.dim() == 4: neuron_response = outputs[:, i] else: neuron_response = outputs[:, i].reshape(-1) - + # Compute frequency response using FFT if neuron_response.dim() >= 2: fft_response = torch.fft.fft2(neuron_response) power_spectrum = torch.abs(fft_response) ** 2 - + # Measure concentration of energy in different frequency bands low_freq_energy = power_spectrum[..., :power_spectrum.shape[-2]//4, :power_spectrum.shape[-1]//4].sum() high_freq_energy = power_spectrum[..., power_spectrum.shape[-2]//4:, power_spectrum.shape[-1]//4:].sum() - + # Ratio indicates texture selectivity if low_freq_energy > 0: texture_selectivity = high_freq_energy / low_freq_energy else: texture_selectivity = high_freq_energy - + alignment_scores[i] = texture_selectivity / (1 + texture_selectivity) else: alignment_scores[i] = 0.0 - + elif self.alignment_type == "object_selectivity": # Measure object-specific selectivity if labels is None: @@ -238,76 +239,76 @@ def compute( # Create synthetic object labels n_objects = 10 labels = torch.randint(0, n_objects, (outputs.shape[0],), device=device) - + # Compute selectivity for each neuron unique_labels = labels.unique() - + for i in range(n_neurons): if outputs.dim() == 4: neuron_response = outputs[:, i].reshape(outputs.shape[0], -1).mean(dim=1) else: neuron_response = outputs[:, i] - + # Compute mean response per object class class_responses = [] for label in unique_labels: mask = labels == label if mask.sum() > 0: class_responses.append(neuron_response[mask].mean()) - + if len(class_responses) > 1: class_responses = torch.stack(class_responses) - + # Selectivity as ratio of max to mean response max_response = class_responses.max() mean_response = class_responses.mean() - + if mean_response > 0: selectivity = max_response / mean_response else: selectivity = 0.0 - + alignment_scores[i] = selectivity / (1 + selectivity) - + else: raise ValueError(f"Unknown alignment type: {self.alignment_type}") - + return alignment_scores - + def _generate_edge_patterns(self, batch_size: int, device: torch.device) -> torch.Tensor: """Generate synthetic images with edge patterns.""" h, w = self.image_size images = torch.zeros(batch_size, 1, h, w, device=device) - + for i in range(batch_size): # Random edge orientation angle = torch.rand(1).item() * 2 * 3.14159 - + # Create edge pattern x = torch.arange(w, device=device).float() - w / 2 y = torch.arange(h, device=device).float() - h / 2 xx, yy = torch.meshgrid(x, y, indexing='xy') - + # Rotated coordinates edge_pattern = torch.sin(xx * torch.cos(angle) + yy * torch.sin(angle)) images[i, 0] = edge_pattern - + return images - + def _generate_texture_patterns(self, batch_size: int, n_channels: int, device: torch.device) -> torch.Tensor: """Generate synthetic texture patterns.""" h, w = self.image_size textures = torch.randn(batch_size, n_channels, h, w, device=device) - + # Apply different frequency filters for i in range(n_channels): - freq = 2 ** (i % 5) # Different frequencies + 2 ** (i % 5) # Different frequencies kernel_size = max(3, 15 - 2 * (i % 5)) - + # Create Gaussian kernel for smoothing kernel = torch.ones(1, 1, kernel_size, kernel_size, device=device) / (kernel_size ** 2) - + # Apply convolution for texture effect textures[:, i:i+1] = F.conv2d(textures[:, i:i+1], kernel, padding=kernel_size//2) - - return textures \ No newline at end of file + + return textures diff --git a/src/alignment/models/__init__.py b/src/alignment/models/__init__.py index 385dd8f2..e9a14dd1 100644 --- a/src/alignment/models/__init__.py +++ b/src/alignment/models/__init__.py @@ -5,26 +5,13 @@ functionality needed for alignment analysis to standard PyTorch models. """ -from .base import BaseModelWrapper -from .wrappers import ( - ModelWrapper, - AlignmentNetwork, - ActivationTracker, -) -from .transformers import ( - TransformerWrapperEnhanced, - LLaMAWrapper, -) -from .architectures.standard_models import ( - MLP, - CNN2P2, - SimpleConvNet, - create_model, -) -from . import hub # registers torchvision/timm/huggingface model loaders - # Register standard models from ..core.registry import register_model +from . import hub # registers torchvision/timm/huggingface model loaders +from .architectures.standard_models import CNN2P2, MLP, SimpleConvNet, create_model +from .base import BaseModelWrapper +from .transformers import LLaMAWrapper, TransformerWrapperEnhanced +from .wrappers import ActivationTracker, AlignmentNetwork, ModelWrapper # Register the models register_model("mlp")(MLP) diff --git a/src/alignment/models/architectures/standard_models.py b/src/alignment/models/architectures/standard_models.py index f5b1c5d2..9d0216a1 100644 --- a/src/alignment/models/architectures/standard_models.py +++ b/src/alignment/models/architectures/standard_models.py @@ -6,7 +6,7 @@ """ import logging -from typing import List, Optional, Dict, Tuple, Any, Union +from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn diff --git a/src/alignment/models/base.py b/src/alignment/models/base.py index 6ad9f376..8565bb6e 100644 --- a/src/alignment/models/base.py +++ b/src/alignment/models/base.py @@ -5,13 +5,15 @@ specific to the models module. """ -from typing import Dict, List, Optional, Tuple, Any, Union +import logging +from collections import OrderedDict +from typing import Any, Dict, List, Optional, Tuple, Union + import torch import torch.nn as nn -from collections import OrderedDict -import logging from alignment.core.base import BaseModel + from .hooks import HookManager # Conditional import for layer detector (graceful fallback) diff --git a/src/alignment/models/hooks.py b/src/alignment/models/hooks.py index 7f84edab..c99c0a32 100644 --- a/src/alignment/models/hooks.py +++ b/src/alignment/models/hooks.py @@ -5,11 +5,12 @@ to prevent memory leaks. """ +import logging from contextlib import contextmanager -from typing import List, Dict, Callable, Optional, Any +from typing import Any, Callable, Dict, List, Optional + import torch import torch.nn as nn -import logging logger = logging.getLogger(__name__) diff --git a/src/alignment/models/hub.py b/src/alignment/models/hub.py index f742f97f..0cafded2 100644 --- a/src/alignment/models/hub.py +++ b/src/alignment/models/hub.py @@ -14,8 +14,9 @@ - Returned objects are nn.Module compatible with our wrappers. """ -from typing import Optional, Any import logging +from typing import Any, Optional + import torch import torch.nn as nn diff --git a/src/alignment/models/transformers.py b/src/alignment/models/transformers.py index fa3086a7..d4159dd5 100644 --- a/src/alignment/models/transformers.py +++ b/src/alignment/models/transformers.py @@ -13,13 +13,14 @@ - Custom transformer implementations """ -from typing import Dict, List, Optional, Any, Tuple +import logging +from typing import Any, Dict, List, Optional, Tuple + import torch import torch.nn as nn -import logging -from .base import BaseModelWrapper from ..core.registry import register_model +from .base import BaseModelWrapper logger = logging.getLogger(__name__) diff --git a/src/alignment/models/wrappers.py b/src/alignment/models/wrappers.py index 50a851cb..e1396bdb 100644 --- a/src/alignment/models/wrappers.py +++ b/src/alignment/models/wrappers.py @@ -5,16 +5,17 @@ including the general-purpose ModelWrapper and specialized wrappers. """ -from typing import Dict, List, Optional, Tuple, Any, Union, Callable +import copy +import logging +from collections import OrderedDict +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + import torch import torch.nn as nn import torch.nn.functional as F -from collections import OrderedDict -import logging -import copy -from .base import BaseModelWrapper from ..core.registry import register_model +from .base import BaseModelWrapper logger = logging.getLogger(__name__) diff --git a/src/alignment/pruning/__init__.py b/src/alignment/pruning/__init__.py index e4c8ef8b..d3cd4fcf 100644 --- a/src/alignment/pruning/__init__.py +++ b/src/alignment/pruning/__init__.py @@ -5,7 +5,7 @@ Strategies: - Magnitude-based: MagnitudePruning, IterativeMagnitudePruning, GlobalMagnitudePruning -- Gradient-based: GradientPruning, FisherPruning, MomentumPruning +- Gradient-based: GradientPruning, FisherPruning, MomentumPruning - Random: RandomPruning, LayerwiseRandomPruning, BernoulliPruning - Parallel: ParallelModePruning, TensorizedPruning, AsyncParallelPruning @@ -16,51 +16,51 @@ Example: Basic pruning:: - + from alignment.pruning import get_pruning_strategy, PruningConfig - + # Prune low-magnitude weights strategy = get_pruning_strategy('magnitude') mask = strategy.prune(layer, amount=0.5) - + # Prune high-magnitude weights config = PruningConfig(amount=0.5, pruning_mode='high') strategy = get_pruning_strategy('magnitude', config=config) - + Parallel pruning:: - + from alignment.pruning.strategies import ParallelModePruning - + # Apply multiple modes simultaneously strategy = ParallelModePruning(modes=['low', 'high', 'random']) result = strategy.prune_parallel(layer, amount=0.5) - + # Access individual masks low_mask = result.masks['low'] high_mask = result.masks['high'] """ -from typing import Optional, Union, Type import logging +from typing import Optional, Type, Union from .base import BasePruningStrategy, IterativePruningStrategy, PruningConfig from .strategies import ( - MagnitudePruning, - IterativeMagnitudePruning, + AlignmentPruning, + AsyncParallelPruning, + BernoulliPruning, + CascadingAlignmentPruning, + FisherPruning, + GlobalAlignmentPruning, GlobalMagnitudePruning, GradientPruning, - FisherPruning, - MomentumPruning, - RandomPruning, + HybridPruning, + IterativeMagnitudePruning, LayerwiseRandomPruning, - BernoulliPruning, + MagnitudePruning, + MomentumPruning, ParallelModePruning, + RandomPruning, TensorizedPruning, - AsyncParallelPruning, - AlignmentPruning, - HybridPruning, - GlobalAlignmentPruning, - CascadingAlignmentPruning, ) logger = logging.getLogger(__name__) @@ -71,23 +71,23 @@ 'magnitude': MagnitudePruning, 'iterative_magnitude': IterativeMagnitudePruning, 'global_magnitude': GlobalMagnitudePruning, - + # Gradient-based strategies 'gradient': GradientPruning, 'fisher': FisherPruning, 'momentum': MomentumPruning, - + # Alignment-based strategies 'alignment': AlignmentPruning, 'hybrid': HybridPruning, 'global_alignment': GlobalAlignmentPruning, 'cascading_alignment': CascadingAlignmentPruning, - + # Random strategies (kept for backward compatibility) # Note: Consider using selection_mode='random' instead 'random': RandomPruning, 'bernoulli': BernoulliPruning, - + # Parallel strategies 'parallel_mode': ParallelModePruning, 'tensorized': TensorizedPruning, @@ -101,14 +101,14 @@ def get_pruning_strategy( ) -> BasePruningStrategy: """ Get a pruning strategy by name. - + Args: name: Name of the pruning strategy **kwargs: Additional arguments for the strategy - + Returns: Initialized pruning strategy - + Raises: ValueError: If strategy name is not found """ @@ -118,7 +118,7 @@ def get_pruning_strategy( f"Unknown pruning strategy: {name}. " f"Available strategies: {available}" ) - + strategy_class = PRUNING_STRATEGIES[name] return strategy_class(**kwargs) @@ -133,34 +133,34 @@ def list_pruning_strategies() -> list: 'BasePruningStrategy', 'IterativePruningStrategy', 'PruningConfig', - + # Magnitude strategies 'MagnitudePruning', 'IterativeMagnitudePruning', 'GlobalMagnitudePruning', - + # Gradient strategies 'GradientPruning', 'FisherPruning', 'MomentumPruning', - + # Alignment strategies 'AlignmentPruning', 'HybridPruning', 'GlobalAlignmentPruning', 'CascadingAlignmentPruning', - + # Random strategies 'RandomPruning', 'LayerwiseRandomPruning', 'BernoulliPruning', - + # Parallel strategies 'ParallelModePruning', 'TensorizedPruning', 'AsyncParallelPruning', - + # Functions 'get_pruning_strategy', 'list_pruning_strategies', -] \ No newline at end of file +] diff --git a/src/alignment/pruning/base.py b/src/alignment/pruning/base.py index 65509f1e..1fb4f911 100644 --- a/src/alignment/pruning/base.py +++ b/src/alignment/pruning/base.py @@ -6,10 +6,11 @@ """ from abc import ABC, abstractmethod -from typing import Dict, Optional, Union, Tuple, Any +from dataclasses import dataclass +from typing import Any, Dict, Optional + import torch import torch.nn as nn -from dataclasses import dataclass @dataclass @@ -27,21 +28,21 @@ class PruningConfig: class BasePruningStrategy(ABC): """ Abstract base class for all pruning strategies. - + This class defines the interface that all pruning strategies must implement. Subclasses should implement the compute_importance_scores method to define how importance is calculated for pruning decisions. """ - + def __init__(self, config: Optional[PruningConfig] = None): """ Initialize the pruning strategy. - + Args: config: Pruning configuration. If None, uses default config. """ self.config = config or PruningConfig() - + @abstractmethod def compute_importance_scores( self, @@ -51,17 +52,17 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores for the given module. - + Args: module: The module to compute importance scores for inputs: Optional input tensor for the module **kwargs: Additional strategy-specific arguments - + Returns: Tensor of importance scores with same shape as module weights """ pass - + def create_pruning_mask( self, importance_scores: torch.Tensor, @@ -72,40 +73,40 @@ def create_pruning_mask( ) -> torch.Tensor: """ Create a pruning mask based on importance scores. - + Args: importance_scores: Tensor of importance scores amount: Fraction to prune (overrides config if provided) structured: Whether to do structured pruning (overrides config) dim: Dimension for structured pruning (default: 0 for output dimension) pruning_mode: 'low' to prune low values, 'high' to prune high values, 'random' for random - + Returns: Binary mask tensor (1 = keep, 0 = prune) """ amount = amount if amount is not None else self.config.amount structured = structured if structured is not None else self.config.structured pruning_mode = pruning_mode if pruning_mode is not None else self.config.pruning_mode - + if structured: # Default to dimension 0 (output dimension) for structured pruning if dim is None: dim = 0 - + # Aggregate importance scores along non-pruned dimensions dims_to_reduce = list(range(importance_scores.ndim)) dims_to_reduce.pop(dim) - + if dims_to_reduce: aggregated_scores = importance_scores.abs().sum(dim=dims_to_reduce) else: aggregated_scores = importance_scores.abs() - + # Get number of structures to prune k = int(amount * aggregated_scores.numel()) if k == 0: return torch.ones_like(importance_scores) - + if pruning_mode == 'random': # Random selection of structures indices = torch.randperm(aggregated_scores.numel(), device=aggregated_scores.device)[:k] @@ -117,7 +118,7 @@ def create_pruning_mask( else: # pruning_mode == 'high' threshold = aggregated_scores.flatten().kthvalue(aggregated_scores.numel() - k).values mask = aggregated_scores < threshold - + # Expand mask to original shape shape = [1] * importance_scores.ndim shape[dim] = importance_scores.shape[dim] @@ -126,10 +127,10 @@ def create_pruning_mask( # Unstructured pruning importance_flat = importance_scores.flatten() k = int(amount * importance_flat.numel()) - + if k == 0: return torch.ones_like(importance_scores) - + if pruning_mode == 'random': # Random selection of weights mask = torch.rand_like(importance_scores) > amount @@ -139,9 +140,9 @@ def create_pruning_mask( else: # pruning_mode == 'high' threshold = importance_flat.kthvalue(importance_flat.numel() - k).values mask = importance_scores < threshold - + return mask.float() - + def apply_pruning( self, module: nn.Module, @@ -150,7 +151,7 @@ def apply_pruning( ): """ Apply pruning mask to a module. - + Args: module: Module to prune mask: Binary mask to apply @@ -158,48 +159,48 @@ def apply_pruning( """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have a weight parameter") - + if not make_permanent: # Store original weights BEFORE applying mask if not hasattr(module, '_original_weight'): module.register_buffer('_original_weight', module.weight.data.clone()) - + # Register mask as buffer for forward passes module.register_buffer('weight_mask', mask) - + # Apply mask to weights module.weight.data *= mask - + # Hook to apply mask during forward pass def apply_mask_hook(mod, inputs): # Apply mask to original weights to maintain pruning mod.weight.data = mod._original_weight * mod.weight_mask return inputs - + # Hook to mask gradients during backward pass # Capture the mask in the closure to avoid attribute access issues weight_mask = module.weight_mask def mask_gradient_hook(grad): # Mask gradients to prevent updates to pruned weights return grad * weight_mask - + # Remove old hooks if exist if hasattr(module, '_pruning_hook'): module._pruning_hook.remove() if hasattr(module, '_gradient_hook_handle'): module._gradient_hook_handle.remove() - + # Register hooks module._pruning_hook = module.register_forward_pre_hook(apply_mask_hook) module._gradient_hook_handle = module.weight.register_hook(mask_gradient_hook) else: # Apply mask permanently module.weight.data *= mask - + def remove_pruning(self, module: nn.Module): """ Remove pruning from a module (make pruning permanent). - + Args: module: Module to remove pruning from """ @@ -212,23 +213,23 @@ def remove_pruning(self, module: nn.Module): module.weight.data *= module.weight_mask # Remove mask buffer delattr(module, 'weight_mask') - + # Remove hooks if hasattr(module, '_pruning_hook'): module._pruning_hook.remove() delattr(module, '_pruning_hook') - + if hasattr(module, '_gradient_hook_handle'): module._gradient_hook_handle.remove() delattr(module, '_gradient_hook_handle') - + def get_sparsity(self, module: nn.Module) -> float: """ Get the current sparsity level of a module. - + Args: module: Module to check - + Returns: Fraction of weights that are zero """ @@ -237,7 +238,7 @@ def get_sparsity(self, module: nn.Module) -> float: zeros = (module.weight == 0).sum().item() return zeros / total return 0.0 - + def prune( self, module: nn.Module, @@ -247,36 +248,36 @@ def prune( ) -> torch.Tensor: """ Main pruning method that combines all steps. - + Args: module: Module to prune inputs: Optional inputs for computing importance amount: Optional amount to prune (overrides config) **kwargs: Additional strategy-specific arguments - + Returns: The pruning mask that was applied """ # Compute importance scores importance_scores = self.compute_importance_scores(module, inputs, **kwargs) - + # Create mask mask = self.create_pruning_mask(importance_scores, amount) - + # Apply mask self.apply_pruning(module, mask) - + return mask class IterativePruningStrategy(BasePruningStrategy): """ Base class for iterative pruning strategies. - + This class extends BasePruningStrategy to support iterative pruning with fine-tuning between iterations. """ - + def iterative_prune( self, model: nn.Module, @@ -288,7 +289,7 @@ def iterative_prune( ) -> Dict[str, Any]: """ Perform iterative pruning on a model. - + Args: model: Model to prune dataloader: DataLoader for fine-tuning @@ -296,7 +297,7 @@ def iterative_prune( criterion: Loss criterion for fine-tuning fine_tune_fn: Custom fine-tuning function **kwargs: Additional arguments for pruning - + Returns: Dictionary containing pruning results and statistics """ @@ -305,22 +306,22 @@ def iterative_prune( 'loss_per_iteration': [], 'accuracy_per_iteration': [] } - + # Calculate amount to prune per iteration total_amount = self.config.amount iterations = self.config.iterations amount_per_iter = 1 - (1 - total_amount) ** (1 / iterations) - + for iteration in range(iterations): # Prune each prunable module for name, module in model.named_modules(): if self._is_prunable(module): current_sparsity = self.get_sparsity(module) additional_amount = amount_per_iter * (1 - current_sparsity) - + if additional_amount > 0: self.prune(module, amount=additional_amount, **kwargs) - + # Calculate current model sparsity total_params = 0 zero_params = 0 @@ -328,10 +329,10 @@ def iterative_prune( if hasattr(module, 'weight'): total_params += module.weight.numel() zero_params += (module.weight == 0).sum().item() - + current_sparsity = zero_params / total_params if total_params > 0 else 0 results['sparsity_per_iteration'].append(current_sparsity) - + # Fine-tune if requested if self.config.fine_tune_epochs > 0 and dataloader is not None: if fine_tune_fn is not None: @@ -354,9 +355,9 @@ def iterative_prune( loss = criterion(outputs, targets) loss.backward() optimizer.step() - + return results - + def _is_prunable(self, module: nn.Module) -> bool: """Check if a module can be pruned.""" - return isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)) \ No newline at end of file + return isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)) diff --git a/src/alignment/pruning/dependency_aware.py b/src/alignment/pruning/dependency_aware.py index b2258ece..0f5ee816 100644 --- a/src/alignment/pruning/dependency_aware.py +++ b/src/alignment/pruning/dependency_aware.py @@ -9,11 +9,12 @@ This ensures pruning doesn't cause shape mismatches. """ -from typing import Dict, List, Optional, Tuple, Any -import torch -import torch.nn as nn import logging from dataclasses import dataclass +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn logger = logging.getLogger(__name__) @@ -33,21 +34,21 @@ class LayerDependency: class DependencyGraph: """ Build and manage dependency graph for a neural network. - + Analyzes connections between layers to enable safe structured pruning. """ - + def __init__(self, model: nn.Module): """ Build dependency graph from model. - + Args: model: PyTorch model to analyze """ self.model = model self.graph: Dict[str, LayerDependency] = {} self._build_graph() - + def _build_graph(self): """Build dependency graph by analyzing model structure.""" # Get all relevant layers @@ -56,13 +57,13 @@ def _build_graph(self): if isinstance(module, (nn.Linear, nn.Conv2d, nn.Conv1d, nn.MultiheadAttention)): layer_type = self._get_layer_type(module) layers.append((name, module, layer_type)) - + # Build graph entries for idx, (name, module, layer_type) in enumerate(layers): # Determine dependencies depends_on = [layers[idx-1][0]] if idx > 0 else None feeds_into = [layers[idx+1][0]] if idx < len(layers) - 1 else None - + self.graph[name] = LayerDependency( name=name, module=module, @@ -71,12 +72,12 @@ def _build_graph(self): depends_on=depends_on, feeds_into=feeds_into ) - + # Detect skip connections (heuristic for ResNet-style models) self._detect_skip_connections() - + logger.info(f"Built dependency graph with {len(self.graph)} layers") - + def _get_layer_type(self, module: nn.Module) -> str: """Determine layer type.""" if isinstance(module, nn.Linear): @@ -87,18 +88,18 @@ def _get_layer_type(self, module: nn.Module) -> str: return 'attention' else: return 'other' - + def _detect_skip_connections(self): """ Detect skip/residual connections. - + Heuristic: Look for layers with same in/out dimensions that might be part of residual blocks. """ # This is a simplified heuristic # For production, would need more sophisticated analysis # (e.g., tracing actual forward pass) - + for name, dep in self.graph.items(): if dep.layer_type == 'conv': module = dep.module @@ -106,34 +107,34 @@ def _detect_skip_connections(self): if module.in_channels == module.out_channels: # Potential residual connection dep.skip_connection_with = [] # Placeholder - + logger.debug("Skip connection detection complete") class DependencyAwarePruning: """ Apply structured pruning with automatic dependency handling. - + Ensures that pruning one layer's outputs correctly propagates to the next layer's inputs, preventing shape mismatches. - + Example: >>> pruner = DependencyAwarePruning(model) >>> layer_scores = {'conv1': scores1, 'conv2': scores2} >>> result = pruner.prune(layer_scores, amount=0.5) >>> print(f"Pruned {result['stats']['total_params_pruned']} parameters") """ - + def __init__(self, model: nn.Module): """ Initialize dependency-aware pruning. - + Args: model: PyTorch model to prune """ self.model = model self.dependency_graph = DependencyGraph(model) - + def prune( self, layer_scores: Dict[str, torch.Tensor], @@ -143,13 +144,13 @@ def prune( ) -> Dict[str, Any]: """ Apply structured pruning with dependency awareness. - + Args: layer_scores: Dict mapping layer names to importance scores amount: Fraction to prune (0-1) mode: 'low' (prune low scores) or 'high' dry_run: If True, only compute plan without applying - + Returns: Dictionary with: - 'masks': Pruning masks per layer @@ -158,17 +159,17 @@ def prune( """ # 1. Create initial masks from scores initial_masks = self._create_initial_masks(layer_scores, amount, mode) - + # 2. Propagate masks to handle dependencies propagated_masks = self._propagate_masks(initial_masks) - + # 3. Validate shapes validation = self._validate_pruning_plan(propagated_masks) - + if not validation['valid']: logger.error(f"Pruning plan validation failed: {validation['errors']}") raise ValueError(f"Invalid pruning plan: {validation['errors']}") - + # 4. Apply masks (if not dry-run) if dry_run: stats = self._compute_stats(propagated_masks) @@ -176,13 +177,13 @@ def prune( else: stats = self._apply_masks(propagated_masks) logger.info(f"Applied pruning: {stats['total_params_pruned']}/{stats['total_params']} parameters pruned") - + return { 'masks': propagated_masks, 'stats': stats, 'validation': validation } - + def _create_initial_masks( self, layer_scores: Dict[str, torch.Tensor], @@ -191,34 +192,34 @@ def _create_initial_masks( ) -> Dict[str, torch.Tensor]: """Create initial output masks from importance scores.""" from ..services.mask_ops import MaskOperations - + initial_masks = {} - + for layer_name, scores in layer_scores.items(): if layer_name not in self.dependency_graph.graph: logger.warning(f"Layer {layer_name} not in dependency graph, skipping") continue - + # Create structured mask (output neurons/channels) mask = MaskOperations.create_structured_mask( scores, amount=amount, mode=mode ) - + initial_masks[layer_name] = mask - + logger.debug(f"{layer_name}: {mask.sum()}/{len(mask)} outputs kept") - + return initial_masks - + def _propagate_masks( self, output_masks: Dict[str, torch.Tensor] ) -> Dict[str, Dict[str, torch.Tensor]]: """ Propagate output masks to dependent layers. - + Returns: Dict[layer_name] -> { 'output_mask': mask for output neurons/channels, @@ -227,17 +228,17 @@ def _propagate_masks( } """ propagated = {} - + # Sort layers by dependency order sorted_layers = sorted( self.dependency_graph.graph.values(), key=lambda x: x.index ) - + for dep in sorted_layers: layer_name = dep.name module = dep.module - + # Get output mask (from scores or default to all-keep) if layer_name in output_masks: out_mask = output_masks[layer_name] @@ -245,21 +246,21 @@ def _propagate_masks( # Default: keep all outputs out_dim = self._get_output_dimension(module) out_mask = torch.ones(out_dim, dtype=torch.bool, device=next(module.parameters()).device) - + # Get input mask (from previous layer's output or default) in_mask = self._get_input_mask(dep, propagated) - + # Create weight mask based on layer type weight_mask = self._create_weight_mask(module, out_mask, in_mask) - + propagated[layer_name] = { 'output_mask': out_mask, 'input_mask': in_mask, 'weight_mask': weight_mask } - + return propagated - + def _get_input_mask( self, layer_dep: LayerDependency, @@ -272,11 +273,11 @@ def _get_input_mask( if prev_layer in propagated: # Use previous layer's output mask as our input mask return propagated[prev_layer]['output_mask'].clone() - + # Default: keep all inputs in_dim = self._get_input_dimension(layer_dep.module) return torch.ones(in_dim, dtype=torch.bool, device=next(layer_dep.module.parameters()).device) - + def _create_weight_mask( self, module: nn.Module, @@ -287,27 +288,27 @@ def _create_weight_mask( if isinstance(module, nn.Linear): # Linear: [out_features, in_features] weight_mask = out_mask.unsqueeze(1) & in_mask.unsqueeze(0) - + elif isinstance(module, nn.Conv2d): # Conv2d: [out_channels, in_channels, k_h, k_w] weight_mask = ( out_mask.view(-1, 1, 1, 1) & in_mask.view(1, -1, 1, 1) ).expand_as(module.weight) - + elif isinstance(module, nn.Conv1d): # Conv1d: [out_channels, in_channels, k] weight_mask = ( out_mask.view(-1, 1, 1) & in_mask.view(1, -1, 1) ).expand_as(module.weight) - + else: # Default: mask entire weight tensor weight_mask = torch.ones_like(module.weight, dtype=torch.bool) - + return weight_mask - + def _get_output_dimension(self, module: nn.Module) -> int: """Get output dimension of a module.""" if hasattr(module, 'out_features'): @@ -318,7 +319,7 @@ def _get_output_dimension(self, module: nn.Module) -> int: return module.embed_dim else: raise ValueError(f"Cannot determine output dimension for {type(module)}") - + def _get_input_dimension(self, module: nn.Module) -> int: """Get input dimension of a module.""" if hasattr(module, 'in_features'): @@ -329,31 +330,31 @@ def _get_input_dimension(self, module: nn.Module) -> int: return module.embed_dim else: raise ValueError(f"Cannot determine input dimension for {type(module)}") - + def _validate_pruning_plan( self, propagated_masks: Dict[str, Dict[str, torch.Tensor]] ) -> Dict[str, Any]: """ Validate that pruning plan maintains shape compatibility. - + Returns: {'valid': bool, 'errors': List[str], 'warnings': List[str]} """ errors = [] warnings = [] - + for layer_name, masks in propagated_masks.items(): dep = self.dependency_graph.graph[layer_name] module = dep.module - + # Check weight mask shape matches weight if masks['weight_mask'].shape != module.weight.shape: errors.append( f"{layer_name}: Weight mask shape {masks['weight_mask'].shape} " f"doesn't match weight shape {module.weight.shape}" ) - + # Check output mask dimension expected_out = self._get_output_dimension(module) if len(masks['output_mask']) != expected_out: @@ -361,7 +362,7 @@ def _validate_pruning_plan( f"{layer_name}: Output mask length {len(masks['output_mask'])} " f"doesn't match expected {expected_out}" ) - + # Check input mask dimension expected_in = self._get_input_dimension(module) if len(masks['input_mask']) != expected_in: @@ -369,22 +370,22 @@ def _validate_pruning_plan( f"{layer_name}: Input mask length {len(masks['input_mask'])} " f"doesn't match expected {expected_in}" ) - + # Check for complete pruning (warn if layer fully pruned) if not masks['output_mask'].any(): warnings.append(f"{layer_name}: All outputs pruned - layer is dead!") - + # Check inter-layer compatibility for layer_name, masks in propagated_masks.items(): dep = self.dependency_graph.graph[layer_name] - + if dep.feeds_into: next_layer = dep.feeds_into[0] if next_layer in propagated_masks: # Our output mask should match next layer's input mask our_out = masks['output_mask'] their_in = propagated_masks[next_layer]['input_mask'] - + if len(our_out) != len(their_in): errors.append( f"Dimension mismatch: {layer_name}.out ({len(our_out)}) " @@ -394,20 +395,20 @@ def _validate_pruning_plan( errors.append( f"Mask mismatch: {layer_name}.out_mask != {next_layer}.in_mask" ) - + return { 'valid': len(errors) == 0, 'errors': errors, 'warnings': warnings } - + def _apply_masks( self, propagated_masks: Dict[str, Dict[str, torch.Tensor]] ) -> Dict[str, Any]: """ Apply pruning masks to model weights. - + Returns: Statistics about pruning """ @@ -417,23 +418,23 @@ def _apply_masks( 'total_params_kept': 0, 'total_params_pruned': 0 } - + for layer_name, masks in propagated_masks.items(): module = self.dependency_graph.graph[layer_name].module - + # Apply weight mask with torch.no_grad(): module.weight.data *= masks['weight_mask'].float() - + # Apply bias mask if exists if hasattr(module, 'bias') and module.bias is not None: module.bias.data *= masks['output_mask'].float() - + # Compute statistics params_total = masks['weight_mask'].numel() params_kept = masks['weight_mask'].sum().item() params_pruned = params_total - params_kept - + stats['layers'][layer_name] = { 'params_total': params_total, 'params_kept': params_kept, @@ -442,15 +443,15 @@ def _apply_masks( 'outputs_kept': masks['output_mask'].sum().item(), 'outputs_total': len(masks['output_mask']) } - + stats['total_params'] += params_total stats['total_params_kept'] += params_kept stats['total_params_pruned'] += params_pruned - + stats['overall_sparsity'] = stats['total_params_pruned'] / stats['total_params'] if stats['total_params'] > 0 else 0 - + return stats - + def _compute_stats( self, propagated_masks: Dict[str, Dict[str, torch.Tensor]] @@ -462,12 +463,12 @@ def _compute_stats( 'total_params_kept': 0, 'total_params_pruned': 0 } - + for layer_name, masks in propagated_masks.items(): params_total = masks['weight_mask'].numel() params_kept = masks['weight_mask'].sum().item() params_pruned = params_total - params_kept - + stats['layers'][layer_name] = { 'params_total': params_total, 'params_kept': params_kept, @@ -476,39 +477,39 @@ def _compute_stats( 'outputs_kept': masks['output_mask'].sum().item(), 'outputs_total': len(masks['output_mask']) } - + stats['total_params'] += params_total stats['total_params_kept'] += params_kept stats['total_params_pruned'] += params_pruned - + stats['overall_sparsity'] = stats['total_params_pruned'] / stats['total_params'] - + return stats - + def print_pruning_plan(self, propagated_masks: Dict, show_details: bool = False): """Print human-readable pruning plan.""" print("\n" + "=" * 80) print("Pruning Plan (Dependency-Aware)") print("=" * 80) - + for layer_name in sorted(propagated_masks.keys()): masks = propagated_masks[layer_name] dep = self.dependency_graph.graph[layer_name] - + out_kept = masks['output_mask'].sum().item() out_total = len(masks['output_mask']) in_kept = masks['input_mask'].sum().item() in_total = len(masks['input_mask']) - + print(f"\n{layer_name} ({dep.layer_type}):") print(f" Outputs: {out_kept}/{out_total} kept ({100*out_kept/out_total:.1f}%)") print(f" Inputs: {in_kept}/{in_total} kept ({100*in_kept/in_total:.1f}%)") - + if show_details: weight_kept = masks['weight_mask'].sum().item() weight_total = masks['weight_mask'].numel() print(f" Weights: {weight_kept}/{weight_total} kept ({100*weight_kept/weight_total:.1f}%)") - + print("\n" + "=" * 80) @@ -522,7 +523,7 @@ def prune_model_with_dependencies( ) -> Dict[str, Any]: """ Convenience function for dependency-aware pruning. - + Args: model: Model to prune layer_scores: Importance scores per layer @@ -530,17 +531,17 @@ def prune_model_with_dependencies( mode: 'low' or 'high' dry_run: If True, don't actually prune verbose: Print pruning plan - + Returns: Pruning results """ pruner = DependencyAwarePruning(model) - + result = pruner.prune(layer_scores, amount, mode, dry_run) - + if verbose: pruner.print_pruning_plan(result['masks']) print(f"\nOverall: {result['stats']['overall_sparsity']:.1%} sparsity") - + return result diff --git a/src/alignment/pruning/distribution.py b/src/alignment/pruning/distribution.py index 2a5143d5..78d74c8c 100644 --- a/src/alignment/pruning/distribution.py +++ b/src/alignment/pruning/distribution.py @@ -5,11 +5,12 @@ how much to prune from each individual layer. """ -from typing import Dict, List, Optional, Callable, Tuple -import torch -import torch.nn as nn import logging from enum import Enum +from typing import Callable, Dict, List, Optional + +import torch +import torch.nn as nn logger = logging.getLogger(__name__) @@ -28,10 +29,10 @@ class DistributionStrategy(Enum): class PruningDistributionManager: """ Manages distribution of pruning across layers. - + Determines per-layer pruning amounts to achieve target overall sparsity while considering layer importance, size, sensitivity, etc. - + Example: >>> manager = PruningDistributionManager( ... strategy='adaptive_sensitivity', @@ -42,7 +43,7 @@ class PruningDistributionManager: ... ) >>> # amounts = {'conv1': 0.85, 'conv2': 0.75, 'fc1': 0.50, ...} """ - + def __init__( self, strategy: str = 'uniform', @@ -53,7 +54,7 @@ def __init__( ): """ Initialize distribution manager. - + Args: strategy: Distribution strategy name target_sparsity: Target overall sparsity (0-1) @@ -66,7 +67,7 @@ def __init__( self.min_amount = min_amount self.max_amount = max_amount self.kwargs = kwargs - + def compute_distribution( self, model: nn.Module, @@ -76,50 +77,50 @@ def compute_distribution( ) -> Dict[str, float]: """ Compute per-layer pruning amounts. - + Args: model: Model to analyze layer_names: Layers to prune layer_scores: Pre-computed importance scores per layer eval_fn: Evaluation function for sensitivity measurement - + Returns: Dict mapping layer names to pruning amounts """ if self.strategy == DistributionStrategy.UNIFORM: return self._uniform_distribution(layer_names) - + elif self.strategy == DistributionStrategy.GLOBAL_THRESHOLD: if layer_scores is None: raise ValueError("Global threshold requires layer_scores") return self._global_threshold_distribution(layer_scores, model) - + elif self.strategy == DistributionStrategy.ADAPTIVE_SENSITIVITY: if eval_fn is None: raise ValueError("Adaptive sensitivity requires eval_fn") return self._adaptive_sensitivity_distribution(model, layer_names, eval_fn) - + elif self.strategy == DistributionStrategy.SIZE_PROPORTIONAL: return self._size_proportional_distribution(model, layer_names) - + elif self.strategy == DistributionStrategy.IMPORTANCE_WEIGHTED: if layer_scores is None: raise ValueError("Importance weighted requires layer_scores") return self._importance_weighted_distribution(layer_scores, model) - + elif self.strategy == DistributionStrategy.CASCADING: return self._cascading_distribution(layer_scores, model, layer_names) - + elif self.strategy == DistributionStrategy.HYBRID: return self._hybrid_distribution(model, layer_names, layer_scores, eval_fn) - + else: raise ValueError(f"Unknown strategy: {self.strategy}") - + def _uniform_distribution(self, layer_names: List[str]) -> Dict[str, float]: """Same amount for all layers.""" return {name: self.target_sparsity for name in layer_names} - + def _global_threshold_distribution( self, layer_scores: Dict[str, torch.Tensor], @@ -127,32 +128,32 @@ def _global_threshold_distribution( ) -> Dict[str, float]: """ Compute amounts based on global score threshold. - + Finds threshold such that overall sparsity = target. """ # Collect all scores all_scores = [] layer_sizes = {} - + for layer_name, scores in layer_scores.items(): all_scores.append(scores.flatten()) layer_sizes[layer_name] = len(scores) - + all_scores_cat = torch.cat(all_scores) - + # Find threshold for target sparsity k = int(len(all_scores_cat) * (1 - self.target_sparsity)) # Keep k elements threshold = torch.topk(all_scores_cat, k).values[-1] - + # Compute implied amount per layer amounts = {} for layer_name, scores in layer_scores.items(): # Fraction below threshold in this layer below_threshold = (scores < threshold).float().mean().item() amounts[layer_name] = max(self.min_amount, min(self.max_amount, below_threshold)) - + return amounts - + def _adaptive_sensitivity_distribution( self, model: nn.Module, @@ -161,16 +162,16 @@ def _adaptive_sensitivity_distribution( ) -> Dict[str, float]: """ Adaptive distribution based on layer sensitivity. - + Uses AdaptiveSensitivityPruning logic. """ from .strategies.adaptive import AdaptiveSensitivityPruning - + pruner = AdaptiveSensitivityPruning(target_sparsity=self.target_sparsity) sensitivities = pruner.compute_all_sensitivities(model, layer_names, eval_fn) - + return {name: sens.recommended_amount for name, sens in sensitivities.items()} - + def _size_proportional_distribution( self, model: nn.Module, @@ -178,25 +179,25 @@ def _size_proportional_distribution( ) -> Dict[str, float]: """ Distribute based on layer size. - + Larger layers pruned more (have more params to spare). """ # Get layer sizes layer_sizes = {} total_size = 0 - + for name in layer_names: layer = dict(model.named_modules())[name] size = layer.weight.numel() layer_sizes[name] = size total_size += size - + # Compute fractions layer_fractions = { name: size / total_size for name, size in layer_sizes.items() } - + # Adjust amounts based on size # Larger fraction → prune more amounts = {} @@ -205,12 +206,12 @@ def _size_proportional_distribution( adjustment = (fraction - 0.5) * 0.4 # ±0.2 around target amount = self.target_sparsity + adjustment amounts[name] = max(self.min_amount, min(self.max_amount, amount)) - + # Normalize to hit target amounts = self._normalize_to_target(amounts, layer_sizes) - + return amounts - + def _importance_weighted_distribution( self, layer_scores: Dict[str, torch.Tensor], @@ -218,7 +219,7 @@ def _importance_weighted_distribution( ) -> Dict[str, float]: """ Distribute based on average layer importance. - + Low average importance → prune more. """ # Compute average importance per layer @@ -226,35 +227,35 @@ def _importance_weighted_distribution( name: scores.mean().item() for name, scores in layer_scores.items() } - + # Normalize importances max_importance = max(layer_importance.values()) min_importance = min(layer_importance.values()) importance_range = max_importance - min_importance - + if importance_range < 1e-6: # All same importance → uniform return self._uniform_distribution(list(layer_scores.keys())) - + # Compute amounts (inverse to importance) amounts = {} for name, importance in layer_importance.items(): # Normalize to [0, 1] norm_importance = (importance - min_importance) / importance_range - + # Inverse: high importance → low amount amount = self.target_sparsity + 0.3 * (1 - norm_importance) - 0.15 amounts[name] = max(self.min_amount, min(self.max_amount, amount)) - + # Normalize to hit target layer_sizes = { name: dict(model.named_modules())[name].weight.numel() for name in layer_scores.keys() } amounts = self._normalize_to_target(amounts, layer_sizes) - + return amounts - + def _cascading_distribution( self, layer_scores: Dict[str, torch.Tensor], @@ -263,7 +264,7 @@ def _cascading_distribution( ) -> Dict[str, float]: """ Sequential pruning until target hit. - + Order determined by importance. """ # Rank layers by average importance (lowest first) @@ -276,7 +277,7 @@ def _cascading_distribution( else: # Default order sorted_layers = layer_names - + # Compute total params and target layer_sizes = { name: dict(model.named_modules())[name].weight.numel() @@ -284,15 +285,15 @@ def _cascading_distribution( } total_params = sum(layer_sizes.values()) target_to_remove = int(total_params * self.target_sparsity) - + # Distribute sequentially amounts = {} removed_so_far = 0 - + for layer_name in sorted_layers: remaining_to_remove = target_to_remove - removed_so_far layer_size = layer_sizes[layer_name] - + if remaining_to_remove <= 0: amounts[layer_name] = 0.0 else: @@ -300,9 +301,9 @@ def _cascading_distribution( amount = min(remaining_to_remove / layer_size, self.max_amount) amounts[layer_name] = amount removed_so_far += int(layer_size * amount) - + return amounts - + def _hybrid_distribution( self, model: nn.Module, @@ -312,12 +313,12 @@ def _hybrid_distribution( ) -> Dict[str, float]: """ Hybrid: Combine multiple distribution strategies. - + Weighted average of uniform, global, and adaptive. """ # Compute multiple distributions uniform = self._uniform_distribution(layer_names) - + # Try to compute others if data available if layer_scores: global_dist = self._global_threshold_distribution(layer_scores, model) @@ -325,7 +326,7 @@ def _hybrid_distribution( else: global_dist = uniform importance = uniform - + if eval_fn: try: adaptive = self._adaptive_sensitivity_distribution(model, layer_names, eval_fn) @@ -333,7 +334,7 @@ def _hybrid_distribution( adaptive = uniform else: adaptive = uniform - + # Weighted combination weights = { 'uniform': 0.2, @@ -341,7 +342,7 @@ def _hybrid_distribution( 'importance': 0.2, 'adaptive': 0.3 } - + amounts = {} for layer_name in layer_names: amount = ( @@ -351,9 +352,9 @@ def _hybrid_distribution( weights['adaptive'] * adaptive[layer_name] ) amounts[layer_name] = max(self.min_amount, min(self.max_amount, amount)) - + return amounts - + def _normalize_to_target( self, amounts: Dict[str, float], @@ -361,11 +362,11 @@ def _normalize_to_target( ) -> Dict[str, float]: """ Normalize amounts to exactly hit target overall sparsity. - + Args: amounts: Initial per-layer amounts layer_sizes: Parameters per layer - + Returns: Normalized amounts """ @@ -373,21 +374,21 @@ def _normalize_to_target( current_total = sum(amounts[name] * layer_sizes[name] for name in amounts) total_params = sum(layer_sizes.values()) target_total = self.target_sparsity * total_params - + if current_total < 1e-6: return amounts - + # Scale factor scale = target_total / current_total - + # Apply scaling and clip normalized = {} for name in amounts: scaled_amount = amounts[name] * scale normalized[name] = max(self.min_amount, min(self.max_amount, scaled_amount)) - + return normalized - + def print_distribution( self, amounts: Dict[str, float], @@ -399,45 +400,45 @@ def print_distribution( print(f"Pruning Distribution ({self.strategy.value})") print(f"Target Overall Sparsity: {self.target_sparsity:.1%}") print("=" * 80) - + # Compute statistics layer_info = [] total_params = 0 total_pruned = 0 - + for layer_name in sorted(amounts.keys()): layer = dict(model.named_modules())[layer_name] size = layer.weight.numel() amount = amounts[layer_name] pruned = int(size * amount) - + total_params += size total_pruned += pruned - + avg_score = layer_scores[layer_name].mean().item() if layer_scores and layer_name in layer_scores else None - + layer_info.append((layer_name, size, amount, pruned, avg_score)) - + # Print table print(f"\n{'Layer':<25} {'Size':<10} {'Amount':<8} {'Pruned':<10} {'Avg Score':<10}") print("-" * 80) - + for name, size, amount, pruned, avg_score in layer_info: score_str = f"{avg_score:.4f}" if avg_score is not None else "N/A" print(f"{name:<25} {size:<10} {amount:>7.1%} {pruned:<10} {score_str:<10}") - + # Summary overall = total_pruned / total_params if total_params > 0 else 0 - + print("-" * 80) print(f"{'TOTAL':<25} {total_params:<10} {overall:>7.1%} {total_pruned:<10}") print("=" * 80) - + if abs(overall - self.target_sparsity) > 0.01: print(f"Warning: Achieved {overall:.1%} vs target {self.target_sparsity:.1%}") else: print(f"Target sparsity achieved: {overall:.1%}") - + print() @@ -450,14 +451,14 @@ def compute_pruning_distribution( ) -> Dict[str, float]: """ Convenience function for computing pruning distribution. - + Args: model: Model to analyze layer_names: Layers to prune strategy: Distribution strategy target_sparsity: Target overall sparsity **kwargs: Strategy-specific parameters (e.g., eval_fn, layer_scores) - + Returns: Per-layer pruning amounts """ @@ -465,6 +466,6 @@ def compute_pruning_distribution( strategy=strategy, target_sparsity=target_sparsity ) - + return manager.compute_distribution(model, layer_names, **kwargs) diff --git a/src/alignment/pruning/experiments/__init__.py b/src/alignment/pruning/experiments/__init__.py index bec565ce..4530eb26 100644 --- a/src/alignment/pruning/experiments/__init__.py +++ b/src/alignment/pruning/experiments/__init__.py @@ -2,10 +2,10 @@ Pruning experiments for alignment analysis. """ -from .eigenvector_based import EigenvectorDropoutExperiment, EigenvectorConfig -from .cascading_layer import CascadingLayerPruningExperiment, CascadingConfig -from .layer_wise import LayerIsolatedPruningExperiment, LayerIsolatedConfig -from .global_pruning import GlobalDropoutExperiment, GlobalDropoutConfig +from .cascading_layer import CascadingConfig, CascadingLayerPruningExperiment +from .eigenvector_based import EigenvectorConfig, EigenvectorDropoutExperiment +from .global_pruning import GlobalDropoutConfig, GlobalDropoutExperiment +from .layer_wise import LayerIsolatedConfig, LayerIsolatedPruningExperiment __all__ = [ 'EigenvectorDropoutExperiment', diff --git a/src/alignment/pruning/experiments/cascading_layer.py b/src/alignment/pruning/experiments/cascading_layer.py index c68c508b..1a7157c8 100644 --- a/src/alignment/pruning/experiments/cascading_layer.py +++ b/src/alignment/pruning/experiments/cascading_layer.py @@ -5,21 +5,22 @@ where pruning in earlier layers affects later layers. """ -from typing import Dict, List, Optional, Any, Tuple -import torch -import numpy as np import logging -from dataclasses import dataclass, field, asdict +from dataclasses import asdict, dataclass, field from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch -from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.core.registry import register_experiment -from alignment.models import ModelWrapper +from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.experiments.training_utils import ( + convert_training_history, create_experiment_trainer, train_with_metrics, - convert_training_history ) +from alignment.models import ModelWrapper logger = logging.getLogger(__name__) diff --git a/src/alignment/pruning/experiments/eigenvector_based.py b/src/alignment/pruning/experiments/eigenvector_based.py index 993624c4..6bbadd0a 100644 --- a/src/alignment/pruning/experiments/eigenvector_based.py +++ b/src/alignment/pruning/experiments/eigenvector_based.py @@ -5,18 +5,19 @@ dropping neurons based on their contribution to principal components. """ -from typing import Dict, List, Optional, Any, Tuple -import torch -import numpy as np import logging -from dataclasses import dataclass, field, asdict +from dataclasses import asdict, dataclass, field +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch -from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.core.registry import register_experiment +from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.experiments.training_utils import ( + convert_training_history, create_experiment_trainer, train_with_metrics, - convert_training_history ) logger = logging.getLogger(__name__) diff --git a/src/alignment/pruning/experiments/global_pruning.py b/src/alignment/pruning/experiments/global_pruning.py index 66c32caa..c8817b68 100644 --- a/src/alignment/pruning/experiments/global_pruning.py +++ b/src/alignment/pruning/experiments/global_pruning.py @@ -5,21 +5,22 @@ across all layers and track changes in alignment metrics. """ -from typing import Dict, List, Optional, Any +import logging +from dataclasses import asdict, dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np import torch import torch.nn as nn -import numpy as np -from pathlib import Path -import logging -from dataclasses import dataclass, field, asdict -from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.core.registry import register_experiment +from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.experiments.config_components import PruningConfig from alignment.experiments.training_utils import ( + convert_training_history, create_experiment_trainer, train_with_metrics, - convert_training_history ) logger = logging.getLogger(__name__) diff --git a/src/alignment/pruning/experiments/layer_wise.py b/src/alignment/pruning/experiments/layer_wise.py index 460aa43a..6df98def 100644 --- a/src/alignment/pruning/experiments/layer_wise.py +++ b/src/alignment/pruning/experiments/layer_wise.py @@ -5,22 +5,23 @@ based on its alignment scores, without considering other layers. """ -from typing import Dict, List, Optional, Any, Tuple -import torch -import numpy as np +import json import logging -from dataclasses import dataclass, field, asdict +from dataclasses import asdict, dataclass, field from pathlib import Path -import json +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch -from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.core.registry import register_experiment -from alignment.models import ModelWrapper +from alignment.experiments.base import BaseExperiment, ExperimentConfig from alignment.experiments.training_utils import ( + convert_training_history, create_experiment_trainer, train_with_metrics, - convert_training_history ) +from alignment.models import ModelWrapper logger = logging.getLogger(__name__) diff --git a/src/alignment/pruning/orchestrator.py b/src/alignment/pruning/orchestrator.py index 24379ec7..2deff05f 100644 --- a/src/alignment/pruning/orchestrator.py +++ b/src/alignment/pruning/orchestrator.py @@ -11,11 +11,12 @@ Provides simple high-level API for comprehensive pruning experiments. """ -import torch -import torch.nn as nn -from typing import Dict, List, Optional, Callable, Any import logging from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional + +import torch +import torch.nn as nn logger = logging.getLogger(__name__) @@ -34,16 +35,16 @@ class PruningPlan: class MasterPruningOrchestrator: """ High-level orchestrator for complete pruning workflows. - + Handles everything: - Distribution across layers (uniform, adaptive, global, etc.) - Scoring (single metric, composite, dynamic) - Direction (low, high, random) - Dependencies (conv, attention) - Parallelization (multiple strategies/networks) - + One-liner API for comprehensive experiments! - + Example: >>> orchestrator = MasterPruningOrchestrator() >>> result = orchestrator.prune_complete( @@ -57,7 +58,7 @@ class MasterPruningOrchestrator: ... ) >>> print(f"Accuracy: {result['baseline']}% → {result['final']}%") """ - + def __init__( self, verbose: bool = True, @@ -66,7 +67,7 @@ def __init__( ): """ Initialize orchestrator. - + Args: verbose: Print detailed progress parallel: Use parallel optimization when possible @@ -75,7 +76,7 @@ def __init__( self.verbose = verbose self.parallel = parallel self.num_workers = num_workers - + def prune_complete( self, model: nn.Module, @@ -93,7 +94,7 @@ def prune_complete( ) -> Dict[str, Any]: """ Complete pruning workflow with all options. - + Args: model: Model to prune target_sparsity: Overall target (e.g., 0.7 for 70%) @@ -119,7 +120,7 @@ def prune_complete( eval_fn: Function(model, val_loader) -> accuracy layers: Specific layers to prune (None = auto-detect) fine_tune_epochs: Epochs to fine-tune after pruning - + Returns: Complete results dictionary """ @@ -133,25 +134,25 @@ def prune_complete( print(f"Direction: {direction}") print(f"Dynamic scores: {use_dynamic}") print("=" * 80 + "\n") - + # Auto-detect layers if needed if layers is None: from ..core.layer_detector import detect_trackable_layers layers = detect_trackable_layers(model) if self.verbose: print(f"Auto-detected {len(layers)} trackable layers\n") - + # Baseline evaluation baseline_acc = None if eval_fn and val_loader: baseline_acc = eval_fn(model, val_loader) if self.verbose: print(f"Baseline accuracy: {baseline_acc:.2f}%\n") - + # Step 1: Compute scores if self.verbose: print("Step 1: Computing importance scores...") - + if use_dynamic and train_loader: layer_scores = self._compute_dynamic_scores( model, train_loader, layers, scoring @@ -160,80 +161,80 @@ def prune_complete( layer_scores = self._compute_static_scores( model, val_loader or train_loader, layers, scoring ) - + if self.verbose: print(f"Computed scores for {len(layer_scores)} layers\n") - + # Step 2: Compute distribution if self.verbose: print(f"Step 2: Computing {distribution} distribution...") - + from .distribution import PruningDistributionManager - + dist_manager = PruningDistributionManager( strategy=distribution, target_sparsity=target_sparsity ) - + per_layer_amounts = dist_manager.compute_distribution( model, layers, layer_scores=layer_scores, eval_fn=lambda m: eval_fn(m, val_loader) if eval_fn and val_loader else None ) - + if self.verbose: dist_manager.print_distribution(per_layer_amounts, model, layer_scores) - + # Step 3: Create masks if self.verbose: print("Step 3: Creating pruning masks...") - + from ..services import MaskOperations - + masks = {} for layer_name in layers: if layer_name not in layer_scores or layer_name not in per_layer_amounts: continue - + mask = MaskOperations.create_structured_mask( layer_scores[layer_name], amount=per_layer_amounts[layer_name], mode=direction ) masks[layer_name] = mask - + if self.verbose: print(f"Created masks for {len(masks)} layers\n") - + # Step 4: Apply with dependency awareness if self.verbose: print("Step 4: Applying pruning (dependency-aware)...") - + from .dependency_aware import DependencyAwarePruning - + dep_pruner = DependencyAwarePruning(model) - + # Convert masks to scores for dependency pruner interface pruning_result = dep_pruner.prune( layer_scores, amount=target_sparsity, # Overall target dry_run=False ) - + if self.verbose: - print(f"Applied pruning\n") - + print("Applied pruning\n") + # Step 5: Fine-tune if trainer_fn and train_loader and fine_tune_epochs > 0: if self.verbose: print(f"Step 5: Fine-tuning for {fine_tune_epochs} epochs...") - + trainer_fn(model, train_loader, epochs=fine_tune_epochs) - + if self.verbose: print("Fine-tuning complete\n") - + # Step 6: Final evaluation final_acc = None if eval_fn and val_loader: @@ -243,7 +244,7 @@ def prune_complete( if baseline_acc: drop = baseline_acc - final_acc print(f"Accuracy drop: {drop:.2f}%\n") - + # Return complete results return { 'baseline_accuracy': baseline_acc, @@ -256,7 +257,7 @@ def prune_complete( 'masks': masks, 'pruning_stats': pruning_result['stats'] } - + def _compute_static_scores( self, model: nn.Module, @@ -265,23 +266,23 @@ def _compute_static_scores( scoring: str ) -> Dict[str, torch.Tensor]: """Compute scores on current (trained) model.""" - from ..services import ActivationCaptureService, NodeScoringService - from ..models import BaseModelWrapper from ..metrics import get_metric - + from ..models import BaseModelWrapper + from ..services import ActivationCaptureService, NodeScoringService + # Wrap model wrapper = BaseModelWrapper(model, tracked_layers=layers) capture = ActivationCaptureService(wrapper) - + # Get batch inputs, targets = next(iter(data_loader)) if torch.cuda.is_available(): inputs = inputs.cuda() targets = targets.cuda() - + # Capture activations data = capture.capture(inputs, include_weights=True) - + # Compute scores based on method if scoring == 'magnitude': scores = {} @@ -289,29 +290,29 @@ def _compute_static_scores( if layer in data.weights: weights = data.weights[layer] scores[layer] = weights.abs().mean(dim=list(range(1, weights.ndim))) - + elif scoring == 'rayleigh_quotient': rq = get_metric('rayleigh_quotient') scores = {} for layer in layers: if layer in data.inputs and layer in data.weights: scores[layer] = rq.compute(data.inputs[layer], data.weights[layer]) - + elif scoring == 'composite': scorer = NodeScoringService(metrics={ 'rq': get_metric('rayleigh_quotient'), 'redundancy': get_metric('pairwise_redundancy_gaussian', mode='output_based', num_pairs=10), 'synergy': get_metric('synergy_gaussian_mmi', num_pairs=10) }) - + layer_scores_obj = scorer.compute_layerwise_scores(data, targets) scores = {name: ls.composite for name, ls in layer_scores_obj.items()} - + else: raise ValueError(f"Unknown scoring method: {scoring}") - + return scores - + def _compute_dynamic_scores( self, model: nn.Module, @@ -321,7 +322,7 @@ def _compute_dynamic_scores( ) -> Dict[str, torch.Tensor]: """ Compute scores using training dynamics. - + Note: Requires training with callback - placeholder for now. Future: Integrate with training history. """ @@ -330,7 +331,7 @@ def _compute_dynamic_scores( "Falling back to static scores. " "See dynamic_scoring.py for full implementation." ) - + # Fallback to static return self._compute_static_scores(model, train_loader, layers, scoring) @@ -342,12 +343,12 @@ def prune_with_all_options( ) -> Dict: """ One-liner for complete pruning with all options. - + Args: model: Model to prune target_sparsity: Target overall sparsity **kwargs: All options (distribution, scoring, etc.) - + Returns: Complete results """ diff --git a/src/alignment/pruning/parallel_optimizer.py b/src/alignment/pruning/parallel_optimizer.py index c0d1520f..1fe3e14d 100644 --- a/src/alignment/pruning/parallel_optimizer.py +++ b/src/alignment/pruning/parallel_optimizer.py @@ -7,12 +7,13 @@ 3. Multiple layers (concurrent processing) """ -import torch -import torch.nn as nn -from typing import Dict, List, Optional, Any, Callable import copy -from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor import logging +from concurrent.futures import ThreadPoolExecutor +from typing import Callable, Dict, List, Optional + +import torch +import torch.nn as nn logger = logging.getLogger(__name__) @@ -20,15 +21,15 @@ class ParallelPruningOptimizer: """ Optimize pruning by parallelizing computation. - + Key optimizations: 1. Shared activation capture (one forward pass for all metrics) 2. Batched metric computation (vectorized across neurons) 3. Parallel strategy comparison (test multiple approaches) 4. Multi-network ensemble pruning - + Performance: N strategies × M networks in ~1.5x time of single case - + Example: >>> optimizer = ParallelPruningOptimizer() >>> results = optimizer.compare_strategies_parallel( @@ -39,7 +40,7 @@ class ParallelPruningOptimizer: ... ) >>> # Results for all strategy×amount combinations in parallel! """ - + def __init__( self, num_workers: int = 4, @@ -48,7 +49,7 @@ def __init__( ): """ Initialize parallel optimizer. - + Args: num_workers: Number of parallel workers use_gpu: Whether to use GPU for computation @@ -57,7 +58,7 @@ def __init__( self.num_workers = num_workers self.use_gpu = use_gpu self.shared_computation = shared_computation - + def compare_strategies_parallel( self, base_model: nn.Module, @@ -69,7 +70,7 @@ def compare_strategies_parallel( ) -> Dict[Tuple[str, float], Dict]: """ Compare multiple pruning strategies in parallel. - + Args: base_model: Base model (will be copied for each strategy) strategies: List of strategy names to compare @@ -77,7 +78,7 @@ def compare_strategies_parallel( data_loader: Data for metric computation eval_fn: Evaluation function layers: Layers to prune (None = auto-detect) - + Returns: Dict[(strategy, amount)] -> {'accuracy': X, 'mask': M, ...} """ @@ -87,18 +88,18 @@ def compare_strategies_parallel( for strategy in strategies for amount in amounts ] - + logger.info(f"Running {len(experiments)} experiments in parallel...") - + # Shared computation: capture activations once if self.shared_computation: shared_data = self._capture_shared_data(base_model, data_loader, layers) else: shared_data = None - + # Parallel execution results = {} - + # For GPU, sequential is better (avoid memory issues) # For CPU, can parallelize if self.use_gpu or self.num_workers == 1: @@ -120,16 +121,16 @@ def compare_strategies_parallel( ): (strategy, amount) for strategy, amount in experiments } - + for future in futures: strategy, amount = futures[future] results[(strategy, amount)] = future.result() - + # Print comparison self._print_comparison(results, strategies, amounts) - + return results - + def _capture_shared_data( self, model: nn.Module, @@ -137,25 +138,25 @@ def _capture_shared_data( layers: Optional[List[str]] ) -> Dict: """Capture activations and weights once for all strategies.""" - from ..services import ActivationCaptureService from ..models import BaseModelWrapper - + from ..services import ActivationCaptureService + wrapper = BaseModelWrapper(model, tracked_layers=layers) capture = ActivationCaptureService(wrapper) - + # Capture on a batch inputs, targets = next(iter(data_loader)) if self.use_gpu and torch.cuda.is_available(): inputs = inputs.cuda() targets = targets.cuda() - + data = capture.capture(inputs, include_weights=True) - + return { 'activation_data': data, 'targets': targets } - + def _run_single_experiment( self, base_model: nn.Module, @@ -166,17 +167,17 @@ def _run_single_experiment( layers: Optional[List[str]] ) -> Dict: """Run a single pruning experiment.""" - from ..services import NodeScoringService, MaskOperations from ..metrics import get_metric - + from ..services import MaskOperations, NodeScoringService + # Clone model model = copy.deepcopy(base_model) - + # Compute scores using shared data if shared_data and strategy in ['alignment', 'composite']: data = shared_data['activation_data'] targets = shared_data['targets'] - + if strategy == 'alignment': scorer = NodeScoringService(metrics={ 'rq': get_metric('rayleigh_quotient') @@ -186,13 +187,13 @@ def _run_single_experiment( 'rq': get_metric('rayleigh_quotient'), 'redundancy': get_metric('pairwise_redundancy_gaussian', mode='output_based') }) - + layer_scores = scorer.compute_layerwise_scores(data, targets) scores_dict = { name: layer_scores[name].composite for name in layer_scores } - + elif strategy == 'magnitude': # Magnitude scores scores_dict = {} @@ -200,7 +201,7 @@ def _run_single_experiment( if layers is None or name in layers: if hasattr(module, 'weight'): scores_dict[name] = module.weight.abs().mean(dim=list(range(1, module.weight.ndim))) - + elif strategy == 'random': # Random scores scores_dict = {} @@ -209,32 +210,32 @@ def _run_single_experiment( if hasattr(module, 'weight'): out_dim = module.weight.shape[0] scores_dict[name] = torch.rand(out_dim) - + else: raise ValueError(f"Unknown strategy: {strategy}") - + # Create masks masks = {} for layer_name, scores in scores_dict.items(): mask = MaskOperations.create_structured_mask(scores, amount, mode='low') masks[layer_name] = mask - + # Apply pruning for layer_name, mask in masks.items(): module = dict(model.named_modules())[layer_name] if hasattr(module, 'weight'): module.weight.data *= mask.unsqueeze(1).float() - + # Evaluate accuracy = eval_fn(model) - + return { 'strategy': strategy, 'amount': amount, 'accuracy': accuracy, 'masks': masks } - + def _print_comparison( self, results: Dict, @@ -245,14 +246,14 @@ def _print_comparison( print("\n" + "=" * 80) print("Parallel Strategy Comparison") print("=" * 80) - + # Create table print(f"\n{'Strategy':<20} ", end='') for amount in amounts: print(f"{amount:>8.0%} ", end='') print() print("-" * 80) - + for strategy in strategies: print(f"{strategy:<20} ", end='') for amount in amounts: @@ -263,9 +264,9 @@ def _print_comparison( else: print(f"{'N/A':>9} ", end='') print() - + print("=" * 80 + "\n") - + def prune_ensemble_parallel( self, networks: List[nn.Module], @@ -275,13 +276,13 @@ def prune_ensemble_parallel( ) -> List[Dict]: """ Prune multiple networks in parallel with shared computation. - + Args: networks: List of networks (same architecture) strategy: Pruning strategy amount: Pruning amount shared_inputs: Input batch (same for all networks) - + Returns: List of results per network """ @@ -290,30 +291,30 @@ def prune_ensemble_parallel( shared_cov = torch.cov(shared_inputs.T) else: shared_cov = None - + # Process each network results = [] - + for net_idx, network in enumerate(networks): # Compute scores (using shared covariance if available) scores = self._compute_scores_with_shared_cov( network, shared_inputs, shared_cov, strategy ) - + # Prune masks = self._create_and_apply_masks(network, scores, amount) - + results.append({ 'network_idx': net_idx, 'masks': masks, 'strategy': strategy, 'amount': amount }) - + logger.info(f"Pruned {len(networks)} networks in parallel") - + return results - + def _compute_scores_with_shared_cov( self, network: nn.Module, @@ -323,12 +324,12 @@ def _compute_scores_with_shared_cov( ) -> Dict[str, torch.Tensor]: """Compute scores using shared covariance.""" from ..metrics import get_metric - + scores = {} - + if strategy == 'alignment' or strategy == 'composite': rq = get_metric('rayleigh_quotient') - + for name, module in network.named_modules(): if hasattr(module, 'weight'): if shared_cov is not None: @@ -336,26 +337,26 @@ def _compute_scores_with_shared_cov( weights = module.weight if weights.ndim > 2: weights = weights.reshape(weights.shape[0], -1) - + # RQ = (w @ cov @ w.T).diag() / (w @ w.T).diag() / tr(cov) wc = weights @ shared_cov numerator = (wc * weights).sum(dim=1) denominator = (weights ** 2).sum(dim=1) rq_scores = numerator / (denominator + 1e-12) rq_scores = rq_scores / (shared_cov.trace() + 1e-12) - + scores[name] = rq_scores else: # Compute normally scores[name] = rq.compute(inputs, module.weight) - + else: # magnitude or other for name, module in network.named_modules(): if hasattr(module, 'weight'): scores[name] = module.weight.abs().mean(dim=list(range(1, module.weight.ndim))) - + return scores - + def _create_and_apply_masks( self, network: nn.Module, @@ -364,17 +365,17 @@ def _create_and_apply_masks( ) -> Dict[str, torch.Tensor]: """Create and apply masks.""" from ..services import MaskOperations - + masks = {} - + for layer_name, layer_scores in scores.items(): mask = MaskOperations.create_structured_mask(layer_scores, amount, mode='low') masks[layer_name] = mask - + # Apply module = dict(network.named_modules())[layer_name] if hasattr(module, 'weight'): module.weight.data *= mask.unsqueeze(1).float() - + return masks diff --git a/src/alignment/pruning/strategies/__init__.py b/src/alignment/pruning/strategies/__init__.py index 80172ee2..94e80438 100644 --- a/src/alignment/pruning/strategies/__init__.py +++ b/src/alignment/pruning/strategies/__init__.py @@ -2,61 +2,44 @@ Pruning strategies for the alignment framework. """ +from .alignment_based import AlignmentPruning, GlobalAlignmentPruning, HybridPruning +from .cascading import CascadingAlignmentPruning +from .gradient import FisherPruning, GradientPruning, MomentumPruning from .magnitude import ( - MagnitudePruning, - IterativeMagnitudePruning, GlobalMagnitudePruning, + IterativeMagnitudePruning, + MagnitudePruning, ) -from .gradient import ( - GradientPruning, - FisherPruning, - MomentumPruning, -) -from .random import ( - RandomPruning, - LayerwiseRandomPruning, - BernoulliPruning, -) -from .parallel import ( - ParallelModePruning, - TensorizedPruning, - AsyncParallelPruning, -) +from .parallel import AsyncParallelPruning, ParallelModePruning, TensorizedPruning from .parallel_batch import ParallelBatchPruning - -from .alignment_based import ( - AlignmentPruning, - HybridPruning, - GlobalAlignmentPruning, -) -from .cascading import CascadingAlignmentPruning +from .random import BernoulliPruning, LayerwiseRandomPruning, RandomPruning __all__ = [ # Magnitude 'MagnitudePruning', 'IterativeMagnitudePruning', 'GlobalMagnitudePruning', - + # Gradient 'GradientPruning', 'FisherPruning', 'MomentumPruning', - + # Random 'RandomPruning', 'LayerwiseRandomPruning', 'BernoulliPruning', - + # Parallel 'ParallelModePruning', 'TensorizedPruning', 'AsyncParallelPruning', 'ParallelBatchPruning', - + # Alignment-based 'AlignmentPruning', 'HybridPruning', 'GlobalAlignmentPruning', 'CascadingAlignmentPruning', -] \ No newline at end of file +] diff --git a/src/alignment/pruning/strategies/adaptive.py b/src/alignment/pruning/strategies/adaptive.py index eb174714..118f8b34 100644 --- a/src/alignment/pruning/strategies/adaptive.py +++ b/src/alignment/pruning/strategies/adaptive.py @@ -5,11 +5,12 @@ achieving target overall sparsity with minimal accuracy loss. """ -import torch -import torch.nn as nn -from typing import Dict, List, Optional, Callable, Any import logging from dataclasses import dataclass +from typing import Callable, Dict, List, Optional + +import torch +import torch.nn as nn from ..base import BasePruningStrategy, PruningConfig @@ -28,17 +29,17 @@ class LayerSensitivity: class AdaptiveSensitivityPruning(BasePruningStrategy): """ Adaptive pruning that adjusts amounts per layer based on sensitivity. - + Algorithm: 1. Measure each layer's sensitivity to perturbation 2. Assign pruning amounts inversely to sensitivity 3. Normalize to achieve target overall sparsity 4. Prune each layer with its custom amount - + Result: Sensitive layers pruned less, robust layers pruned more. - + Expected improvement: +5-10% accuracy retention vs uniform pruning - + Example: >>> strategy = AdaptiveSensitivityPruning( ... target_sparsity=0.7, @@ -51,7 +52,7 @@ class AdaptiveSensitivityPruning(BasePruningStrategy): >>> # fc1: high → prune 50% >>> # Overall: 70% average """ - + def __init__( self, target_sparsity: float = 0.5, @@ -64,7 +65,7 @@ def __init__( ): """ Initialize adaptive sensitivity pruning. - + Args: target_sparsity: Target overall sparsity (0-1) metric: Metric to use for importance scores @@ -81,10 +82,10 @@ def __init__( self.sensitivity_method = sensitivity_method self.min_amount = min_amount self.max_amount = max_amount - + # Will be populated during sensitivity analysis self.layer_sensitivities: Dict[str, LayerSensitivity] = {} - + def compute_importance_scores( self, module: nn.Module, @@ -93,30 +94,30 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores using specified metric. - + This is used within each layer after amounts are determined. """ from ...metrics import get_metric - + # Get metric metric = get_metric(self.metric_name, **self.metric_kwargs) - + # Compute scores if hasattr(metric, 'requires_outputs') and metric.requires_outputs: outputs = kwargs.get('outputs') scores = metric.compute(inputs=inputs, weights=module.weight, outputs=outputs) else: scores = metric.compute(inputs=inputs, weights=module.weight) - + # Expand to weight shape if structured if self.config.structured and scores.ndim < module.weight.ndim: # Expand neuron/channel scores to full weight tensor shape = [1] * module.weight.ndim shape[0] = scores.shape[0] scores = scores.reshape(shape).expand_as(module.weight) - + return scores - + def measure_layer_sensitivity( self, model: nn.Module, @@ -127,60 +128,60 @@ def measure_layer_sensitivity( ) -> float: """ Measure sensitivity of a single layer. - + Args: model: Full model layer_name: Name of layer to test eval_fn: Function that evaluates model and returns accuracy perturbation_scale: Scale of perturbation num_trials: Number of trials to average - + Returns: Sensitivity (accuracy drop when layer perturbed) """ # Get baseline accuracy baseline_acc = eval_fn(model) - + # Get layer layer = dict(model.named_modules())[layer_name] - + if not hasattr(layer, 'weight'): return 0.0 - + # Store original weight original_weight = layer.weight.data.clone() - + # Measure sensitivity via perturbation sensitivities = [] - + for _ in range(num_trials): # Perturb layer if self.sensitivity_method == 'perturbation': perturbation = perturbation_scale * torch.randn_like(layer.weight) layer.weight.data = original_weight + perturbation - + elif self.sensitivity_method == 'masking': # Random masking (simulate pruning) mask = torch.rand_like(layer.weight) > 0.3 # Mask 30% layer.weight.data = original_weight * mask - + # Evaluate perturbed_acc = eval_fn(model) - + # Sensitivity = drop in accuracy sensitivity = max(0, baseline_acc - perturbed_acc) sensitivities.append(sensitivity) - + # Restore layer.weight.data = original_weight - + # Average sensitivity avg_sensitivity = sum(sensitivities) / len(sensitivities) - + logger.debug(f"{layer_name}: sensitivity = {avg_sensitivity:.4f}") - + return avg_sensitivity - + def compute_all_sensitivities( self, model: nn.Module, @@ -189,28 +190,28 @@ def compute_all_sensitivities( ) -> Dict[str, LayerSensitivity]: """ Compute sensitivities for all specified layers. - + Args: model: Model to analyze layer_names: Layers to analyze eval_fn: Evaluation function - + Returns: Dict mapping layer names to LayerSensitivity objects """ logger.info(f"Computing sensitivities for {len(layer_names)} layers...") - + sensitivities = {} - + for layer_name in layer_names: layer = dict(model.named_modules())[layer_name] - + # Measure sensitivity sens = self.measure_layer_sensitivity(model, layer_name, eval_fn) - + # Get layer size size = layer.weight.numel() - + # Store sensitivities[layer_name] = LayerSensitivity( name=layer_name, @@ -218,76 +219,76 @@ def compute_all_sensitivities( size=size, recommended_amount=0.0 # Will be computed next ) - + # Compute recommended amounts sensitivities = self._compute_adaptive_amounts(sensitivities) - + self.layer_sensitivities = sensitivities - + return sensitivities - + def _compute_adaptive_amounts( self, sensitivities: Dict[str, LayerSensitivity] ) -> Dict[str, LayerSensitivity]: """ Compute adaptive pruning amounts based on sensitivities. - + High sensitivity → prune less Low sensitivity → prune more - + Normalized to achieve target overall sparsity. """ if not sensitivities: return sensitivities - + # Get sensitivity values sens_values = [s.sensitivity for s in sensitivities.values()] max_sens = max(sens_values) if sens_values else 1.0 min_sens = min(sens_values) if sens_values else 0.0 sens_range = max_sens - min_sens if max_sens > min_sens else 1.0 - + # Compute initial amounts (inverse to sensitivity) initial_amounts = {} for name, layer_sens in sensitivities.items(): # Normalize sensitivity to [0, 1] norm_sens = (layer_sens.sensitivity - min_sens) / sens_range if sens_range > 0 else 0.5 - + # Inverse relationship: high sensitivity → low amount # amount = max_amount - (max_amount - min_amount) × norm_sens amount = self.max_amount - (self.max_amount - self.min_amount) * norm_sens - + initial_amounts[name] = amount - + # Normalize to achieve target overall sparsity total_params = sum(s.size for s in sensitivities.values()) target_params_to_prune = int(total_params * self.target_sparsity) - + # Compute current total current_pruned = sum( sensitivities[name].size * initial_amounts[name] for name in sensitivities ) - + # Scale amounts to hit target scale_factor = target_params_to_prune / current_pruned if current_pruned > 0 else 1.0 - + # Apply scaling and update for name, layer_sens in sensitivities.items(): adapted_amount = initial_amounts[name] * scale_factor - + # Clip to valid range adapted_amount = max(self.min_amount, min(self.max_amount, adapted_amount)) - + layer_sens.recommended_amount = adapted_amount - + logger.info( f"{name}: sensitivity={layer_sens.sensitivity:.4f}, " f"amount={adapted_amount:.1%}" ) - + return sensitivities - + def prune_adaptive( self, model: nn.Module, @@ -297,69 +298,69 @@ def prune_adaptive( ) -> Dict[str, torch.Tensor]: """ Prune model adaptively based on layer sensitivities. - + Args: model: Model to prune layer_names: Layers to prune eval_fn: Evaluation function (model) -> accuracy inputs_per_layer: Optional cached inputs for each layer - + Returns: Dict of masks per layer """ # 1. Compute sensitivities if not already done if not self.layer_sensitivities: self.compute_all_sensitivities(model, layer_names, eval_fn) - + # 2. Prune each layer with its adaptive amount masks = {} - + for layer_name in layer_names: layer = dict(model.named_modules())[layer_name] layer_sens = self.layer_sensitivities[layer_name] - + # Get inputs if available inputs = inputs_per_layer.get(layer_name) if inputs_per_layer else None - + # Compute importance scores scores = self.compute_importance_scores(layer, inputs=inputs) - + # Create mask with adaptive amount mask = self.create_pruning_mask( scores, amount=layer_sens.recommended_amount, structured=self.config.structured ) - + masks[layer_name] = mask - + logger.info( f"{layer_name}: pruning {layer_sens.recommended_amount:.1%} " f"(sensitivity: {layer_sens.sensitivity:.4f})" ) - + return masks - + def print_sensitivity_report(self): """Print human-readable sensitivity report.""" if not self.layer_sensitivities: print("No sensitivity data available. Run compute_all_sensitivities() first.") return - + print("\n" + "=" * 80) print("Layer Sensitivity Analysis") print("=" * 80) - + # Sort by sensitivity sorted_layers = sorted( self.layer_sensitivities.values(), key=lambda x: x.sensitivity, reverse=True ) - + print(f"\n{'Layer':<30} {'Sensitivity':<12} {'Size':<10} {'Pruning %'}") print("-" * 80) - + for layer_sens in sorted_layers: print( f"{layer_sens.name:<30} " @@ -367,17 +368,17 @@ def print_sensitivity_report(self): f"{layer_sens.size:<10} " f"{layer_sens.recommended_amount:>8.1%}" ) - + print("\n" + "=" * 80) - + # Summary statistics total_size = sum(s.size for s in self.layer_sensitivities.values()) total_pruned = sum( - s.size * s.recommended_amount + s.size * s.recommended_amount for s in self.layer_sensitivities.values() ) overall_sparsity = total_pruned / total_size - + print(f"Overall sparsity: {overall_sparsity:.1%}") print(f"Target sparsity: {self.target_sparsity:.1%}") print("=" * 80 + "\n") diff --git a/src/alignment/pruning/strategies/alignment_based.py b/src/alignment/pruning/strategies/alignment_based.py index f7e876d5..dcd07d20 100644 --- a/src/alignment/pruning/strategies/alignment_based.py +++ b/src/alignment/pruning/strategies/alignment_based.py @@ -5,13 +5,14 @@ allowing pruning decisions to be guided by neuron-input alignment measures. """ +import logging +from typing import Dict, Optional + import torch import torch.nn as nn -from typing import Optional, Dict, Any, Literal -import logging -from ..base import BasePruningStrategy from ...metrics import get_metric +from ..base import BasePruningStrategy logger = logging.getLogger(__name__) @@ -19,36 +20,36 @@ class AlignmentPruning(BasePruningStrategy): """ Alignment-based pruning strategy. - + This strategy prunes based on alignment metrics between neurons and their inputs. Since alignment metrics are computed per neuron, this naturally supports structured pruning (removing entire neurons/channels). - + For structured pruning (default): - Removes entire neurons based on their alignment scores - All weights connected to pruned neurons are removed - + For unstructured pruning: - Distributes neuron scores to individual weights - Less meaningful since alignment is a neuron-level property - + Examples: >>> from alignment.pruning.strategies import AlignmentPruning >>> from alignment.pruning import PruningConfig >>> >>> # Structured pruning - remove entire neurons with low alignment >>> config = PruningConfig( - ... amount=0.5, + ... amount=0.5, ... pruning_mode='low', ... structured=True # Default for alignment ... ) >>> strategy = AlignmentPruning(metric='rayleigh_quotient', config=config) - >>> + >>> >>> # Need to provide inputs for alignment computation >>> inputs = torch.randn(batch_size, input_dim) >>> mask = strategy.prune(layer, inputs=inputs) """ - + def __init__( self, metric: str = 'rayleigh_quotient', @@ -57,10 +58,10 @@ def __init__( ): """ Initialize alignment-based pruning strategy. - + Args: metric: Name of alignment metric to use - Options: 'rayleigh_quotient', 'mutual_information', 'cka', + Options: 'rayleigh_quotient', 'mutual_information', 'cka', 'weight_cosine_similarity', 'gradient_similarity' config: Pruning configuration. Note: structured=True is recommended since alignment is a neuron-level property @@ -69,12 +70,12 @@ def __init__( super().__init__(config) self.metric_name = metric self.metric_kwargs = metric_kwargs - + # Default to structured pruning for alignment-based methods if config and not hasattr(config, 'structured'): logger.info("AlignmentPruning defaulting to structured=True (neuron pruning)") config.structured = True - + # Initialize the metric try: metric_class = get_metric(metric) @@ -85,7 +86,7 @@ def __init__( except Exception as e: logger.error(f"Failed to initialize metric {metric}: {e}") raise - + def compute_importance_scores( self, module: nn.Module, @@ -94,67 +95,67 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores based on alignment metrics. - + For structured pruning (default): Returns neuron-wise scores that will be used to prune entire neurons. - + For unstructured pruning: Expands neuron scores to individual weights (less meaningful). - + Args: module: Module to compute scores for inputs: Input activations to the module (required) **kwargs: Additional arguments - + Returns: Tensor of importance scores - - Structured: Shape [num_output_neurons] + - Structured: Shape [num_output_neurons] - Unstructured: Shape matching module weights - + Raises: ValueError: If inputs are not provided or module has no weights """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + if inputs is None: raise ValueError( "AlignmentPruning requires inputs to compute alignment. " "Pass inputs to the prune() method." ) - + weights = module.weight.data - + # Compute alignment scores (neuron-wise) # Shape: [num_output_neurons] alignment_scores = self.metric.compute(inputs=inputs, weights=weights) - + # Ensure alignment_scores is on the same device as weights if alignment_scores.device != weights.device: alignment_scores = alignment_scores.to(weights.device) - + # For structured pruning, return neuron-wise scores directly # The base class will handle creating masks for entire neurons if self.config.structured: return alignment_scores - + # For unstructured pruning, expand to weight-wise scores # Note: This is less meaningful since alignment is inherently per-neuron if len(weights.shape) == 2: # Linear layer # Each weight in a neuron gets the same importance score importance = alignment_scores.unsqueeze(1).expand_as(weights) - + elif len(weights.shape) >= 3: # Conv layer # Each weight in a channel gets the same importance score out_channels = weights.shape[0] importance = alignment_scores.view(out_channels, 1, 1, 1) importance = importance.expand_as(weights) - + else: raise ValueError(f"Unsupported weight shape: {weights.shape}") - + return importance - + def prune( self, module: nn.Module, @@ -164,34 +165,34 @@ def prune( ) -> torch.Tensor: """ Prune module based on alignment scores. - + Overrides base prune() to ensure structured pruning works correctly for neuron-based alignment scores. - + Args: module: Module to prune inputs: Input activations (required) amount: Fraction to prune **kwargs: Additional arguments - + Returns: Pruning mask """ # Get importance scores importance_scores = self.compute_importance_scores(module, inputs, **kwargs) - + # For structured pruning with neuron-wise scores if self.config.structured and importance_scores.dim() == 1: # Create mask with special handling for neuron-wise scores weights = module.weight.data - + # Determine which neurons to keep/prune amount = amount if amount is not None else self.config.amount k = int(amount * importance_scores.numel()) - + if k == 0: return torch.ones_like(weights) - + # Handle different pruning modes if self.config.pruning_mode == 'random': # Random selection of neurons to prune @@ -209,17 +210,17 @@ def prune( indices_to_prune = sorted_indices[:k] keep_mask = torch.ones(importance_scores.numel(), dtype=torch.bool, device=importance_scores.device) keep_mask[indices_to_prune] = False - + # Expand mask to all weights in the neuron/channel if len(weights.shape) == 2: # Linear mask = keep_mask.unsqueeze(1).expand_as(weights).float() else: # Conv mask = keep_mask.view(-1, 1, 1, 1).expand_as(weights).float() - + # Apply the mask self.apply_pruning(module, mask) return mask - + # For unstructured pruning, use base class implementation mask = self.create_pruning_mask(importance_scores, amount) self.apply_pruning(module, mask) @@ -229,10 +230,10 @@ def prune( class HybridPruning(BasePruningStrategy): """ Hybrid pruning strategy combining magnitude and alignment information. - + This strategy combines traditional magnitude-based importance with alignment metrics for more informed pruning decisions. - + Examples: >>> from alignment.pruning.strategies import HybridPruning >>> @@ -243,7 +244,7 @@ class HybridPruning(BasePruningStrategy): ... ) >>> mask = strategy.prune(layer, inputs=inputs, amount=0.5) """ - + def __init__( self, alignment_metric: str = 'rayleigh_quotient', @@ -253,7 +254,7 @@ def __init__( ): """ Initialize hybrid pruning strategy. - + Args: alignment_metric: Name of alignment metric to use alpha: Weight for alignment score (1-alpha for magnitude) @@ -265,7 +266,7 @@ def __init__( self.alignment_metric_name = alignment_metric self.alpha = alpha self.metric_kwargs = metric_kwargs - + # Initialize the alignment metric try: from ...metrics import get_metric @@ -277,7 +278,7 @@ def __init__( except Exception as e: logger.error(f"Failed to initialize metric {alignment_metric}: {e}") raise - + def compute_importance_scores( self, module: nn.Module, @@ -286,40 +287,40 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores combining magnitude and alignment. - + Args: module: Module to compute scores for inputs: Input activations (required for alignment) **kwargs: Additional arguments - + Returns: Combined importance scores """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + weights = module.weight.data - + # Magnitude-based importance magnitude_importance = weights.abs() - + # Normalize magnitude scores mag_min = magnitude_importance.min() mag_max = magnitude_importance.max() if mag_max > mag_min: magnitude_importance = (magnitude_importance - mag_min) / (mag_max - mag_min) - + if inputs is None or self.alpha == 0: # No inputs provided or pure magnitude return magnitude_importance - + # Alignment-based importance alignment_scores = self.alignment_metric.compute(inputs=inputs, weights=weights) - + # Ensure alignment_scores is on the same device if alignment_scores.device != weights.device: alignment_scores = alignment_scores.to(weights.device) - + # Expand alignment scores to match weight dimensions if len(weights.shape) == 2: # Linear alignment_importance = alignment_scores.unsqueeze(1).expand_as(weights) @@ -329,32 +330,32 @@ def compute_importance_scores( alignment_importance = alignment_importance.expand_as(weights) else: raise ValueError(f"Unsupported weight shape: {weights.shape}") - + # Normalize alignment scores align_min = alignment_importance.min() align_max = alignment_importance.max() if align_max > align_min: alignment_importance = (alignment_importance - align_min) / (align_max - align_min) - + # Combine scores combined_importance = ( - self.alpha * alignment_importance + + self.alpha * alignment_importance + (1 - self.alpha) * magnitude_importance ) - - return combined_importance + + return combined_importance class GlobalAlignmentPruning(AlignmentPruning): """ Global alignment-based pruning strategy. - + This strategy collects alignment scores from all neurons across all layers, sorts them globally, and prunes the globally least aligned neurons. - + This is different from layer-wise pruning where each layer is pruned independently to achieve the same sparsity level. - + Examples: >>> from alignment.pruning.strategies import GlobalAlignmentPruning >>> from alignment.pruning import PruningConfig @@ -366,18 +367,18 @@ class GlobalAlignmentPruning(AlignmentPruning): ... structured=True # Always true for alignment ... ) >>> strategy = GlobalAlignmentPruning(metric='rayleigh_quotient', config=config) - >>> + >>> >>> # Need to provide inputs for each layer >>> masks = strategy.prune_model(model, layer_inputs_dict) """ - + def __init__(self, metric: str = 'rayleigh_quotient', config=None, **metric_kwargs): """Initialize global alignment pruning strategy.""" super().__init__(metric, config, **metric_kwargs) # Ensure global pruning is enabled if self.config: self.config.global_pruning = True - + def prune_model( self, model: nn.Module, @@ -386,30 +387,30 @@ def prune_model( ) -> Dict[str, torch.Tensor]: """ Prune entire model globally based on alignment scores. - + Args: model: Model to prune layer_inputs: Dictionary mapping layer names to their input tensors amount: Global sparsity level to achieve - + Returns: Dictionary mapping layer names to their pruning masks """ amount = amount if amount is not None else self.config.amount - + # Collect all alignment scores and layer info all_scores = [] layer_info = [] - + for name, module in model.named_modules(): if hasattr(module, 'weight') and name in layer_inputs: # Compute alignment scores for this layer inputs = layer_inputs[name] weights = module.weight.data - + # Get neuron-wise alignment scores alignment_scores = self.metric.compute(inputs=inputs, weights=weights) - + # Store scores and info all_scores.append(alignment_scores.cpu()) layer_info.append({ @@ -419,19 +420,19 @@ def prune_model( 'num_neurons': alignment_scores.numel(), 'weight_shape': weights.shape }) - + if not all_scores: logger.warning("No layers found for global pruning") return {} - + # Concatenate all scores global_scores = torch.cat(all_scores) - + # Find global threshold k = int(amount * global_scores.numel()) if k == 0: return {} - + # Get indices to prune based on pruning mode if self.config.pruning_mode == 'low': # Prune neurons with lowest alignment @@ -446,7 +447,7 @@ def prune_model( prune_indices = torch.randperm(global_scores.numel())[:k] else: raise ValueError(f"Unknown pruning_mode: {self.config.pruning_mode}") - + # Convert global indices to per-layer masks using prefix sums (efficient) masks = {} prefix_counts = [] @@ -484,9 +485,9 @@ def prune_model( f"Layer {layer['name']}: pruned {pruned_neurons}/{num_neurons} neurons " f"({pruned_neurons/num_neurons*100:.1f}%)" ) - + # Log global statistics total_neurons = sum(layer['num_neurons'] for layer in layer_info) logger.info(f"Global pruning complete: {k}/{total_neurons} neurons pruned ({amount*100:.1f}%)") - - return masks \ No newline at end of file + + return masks diff --git a/src/alignment/pruning/strategies/cascading.py b/src/alignment/pruning/strategies/cascading.py index 983a8ee0..5ca78d7e 100644 --- a/src/alignment/pruning/strategies/cascading.py +++ b/src/alignment/pruning/strategies/cascading.py @@ -6,13 +6,14 @@ the effects of previous pruning. """ +import logging +from typing import Dict, List, Optional, Tuple + import torch import torch.nn as nn -from typing import Dict, List, Optional, Tuple -import logging -from ..base import BasePruningStrategy, PruningConfig from ...metrics import get_metric +from ..base import BasePruningStrategy, PruningConfig logger = logging.getLogger(__name__) @@ -20,15 +21,15 @@ class CascadingAlignmentPruning(BasePruningStrategy): """ Cascading alignment-based pruning strategy. - + This strategy prunes layers sequentially, recomputing alignment scores after each layer to account for how previous pruning affects subsequent layers. - + Key differences from standard pruning: 1. Prunes layers in order (forward or backward) 2. Recomputes alignment scores after each layer 3. Later layers see the effects of earlier pruning - + Examples: >>> from alignment.pruning.strategies import CascadingAlignmentPruning >>> from alignment.pruning import PruningConfig @@ -45,7 +46,7 @@ class CascadingAlignmentPruning(BasePruningStrategy): ... ) >>> masks = strategy.prune_model(model, get_layer_inputs_fn) """ - + def __init__( self, metric: str = 'rayleigh_quotient', @@ -55,7 +56,7 @@ def __init__( ): """ Initialize cascading alignment pruning. - + Args: metric: Alignment metric to use direction: 'forward' (input→output) or 'backward' (output→input) @@ -66,11 +67,11 @@ def __init__( self.metric_name = metric self.direction = direction self.metric_kwargs = metric_kwargs - + # Default to structured pruning if config and not hasattr(config, 'structured'): config.structured = True - + # Initialize metric try: metric_class = get_metric(metric) @@ -80,7 +81,7 @@ def __init__( except Exception as e: logger.error(f"Failed to initialize metric {metric}: {e}") raise - + def compute_importance_scores( self, module: nn.Module, @@ -89,31 +90,31 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute alignment scores for a single module. - + Args: module: Module to compute scores for inputs: Input activations **kwargs: Additional arguments - + Returns: Alignment scores (neuron-wise) """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + if inputs is None: raise ValueError("CascadingAlignmentPruning requires inputs") - + weights = module.weight.data - + # Compute neuron-wise alignment scores alignment_scores = self.metric.compute(inputs=inputs, weights=weights) - + if alignment_scores.device != weights.device: alignment_scores = alignment_scores.to(weights.device) - + return alignment_scores - + def prune_model( self, model: nn.Module, @@ -123,59 +124,59 @@ def prune_model( ) -> Dict[str, torch.Tensor]: """ Prune model using cascading approach. - + Args: model: Model to prune get_layer_inputs_fn: Function that returns current inputs for each layer Should return Dict[layer_name, tensor] amount: Amount to prune (overrides config) exclude_layers: Layer names to exclude from pruning - + Returns: Dictionary of masks for each layer """ amount = amount if amount is not None else self.config.amount exclude_layers = exclude_layers or [] - + # Get prunable layers in order layers = [] for name, module in model.named_modules(): - if (hasattr(module, 'weight') and + if (hasattr(module, 'weight') and len(module.weight.shape) >= 2 and name not in exclude_layers): layers.append((name, module)) - + # Reverse if backward direction if self.direction == 'backward': layers = layers[::-1] - + logger.info(f"Cascading {self.direction} pruning of {len(layers)} layers") - + # Prune layers sequentially masks = {} - + for idx, (layer_name, module) in enumerate(layers): logger.info(f"Pruning layer {idx+1}/{len(layers)}: {layer_name}") - + # Get current inputs for this layer # This should reflect any previous pruning layer_inputs_dict = get_layer_inputs_fn() - + if layer_name not in layer_inputs_dict: logger.warning(f"No inputs found for layer {layer_name}, skipping") continue - + layer_inputs = layer_inputs_dict[layer_name] - + # Compute alignment scores with current network state alignment_scores = self.compute_importance_scores(module, layer_inputs) - + # Create mask for structured pruning if self.config.structured and alignment_scores.dim() == 1: # Determine which neurons to prune num_neurons = alignment_scores.numel() k = int(amount * num_neurons) - + if k == 0: mask = torch.ones_like(module.weight) else: @@ -186,7 +187,7 @@ def prune_model( else: # 'high' mode threshold = alignment_scores.kthvalue(num_neurons - k).values keep_mask = alignment_scores < threshold - + # Expand to weight dimensions if len(module.weight.shape) == 2: # Linear mask = keep_mask.unsqueeze(1).expand_as(module.weight).float() @@ -195,25 +196,25 @@ def prune_model( else: # Unstructured pruning mask = self.create_pruning_mask(alignment_scores, amount) - + # Apply pruning self.apply_pruning(module, mask) masks[layer_name] = mask - + # Log statistics sparsity = 1 - (mask != 0).float().mean().item() logger.info(f" Layer {layer_name}: {sparsity*100:.1f}% pruned") - + return masks - + def _get_layer_order(self, model: nn.Module) -> List[Tuple[str, nn.Module]]: """Get layers in processing order based on direction.""" layers = [] for name, module in model.named_modules(): if hasattr(module, 'weight') and len(module.weight.shape) >= 2: layers.append((name, module)) - + if self.direction == 'backward': layers = layers[::-1] - - return layers \ No newline at end of file + + return layers diff --git a/src/alignment/pruning/strategies/gradient.py b/src/alignment/pruning/strategies/gradient.py index 52a804df..b34df71c 100644 --- a/src/alignment/pruning/strategies/gradient.py +++ b/src/alignment/pruning/strategies/gradient.py @@ -5,9 +5,10 @@ that have small gradients or small gradient-weight products. """ +from typing import Literal, Optional + import torch import torch.nn as nn -from typing import Optional, Literal from ..base import BasePruningStrategy @@ -15,21 +16,21 @@ class GradientPruning(BasePruningStrategy): """ Gradient-based pruning strategy. - + This strategy prunes weights based on gradient information. It can use either gradient magnitudes alone or the product of gradients and weights (Taylor approximation of loss change). - + Examples: >>> from alignment.pruning.strategies import GradientPruning >>> >>> # Using gradient magnitude >>> strategy = GradientPruning(mode='gradient') - >>> + >>> >>> # Forward and backward pass to get gradients >>> loss = criterion(model(inputs), targets) >>> loss.backward() - >>> + >>> >>> # Prune based on gradients >>> mask = strategy.prune(layer, amount=0.5) >>> @@ -37,7 +38,7 @@ class GradientPruning(BasePruningStrategy): >>> strategy = GradientPruning(mode='taylor') >>> mask = strategy.prune(layer, amount=0.5) """ - + def __init__( self, config=None, @@ -45,7 +46,7 @@ def __init__( ): """ Initialize gradient pruning strategy. - + Args: config: Pruning configuration mode: Pruning mode @@ -54,7 +55,7 @@ def __init__( """ super().__init__(config) self.mode = mode - + def compute_importance_scores( self, module: nn.Module, @@ -63,21 +64,21 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores based on gradients. - + Args: module: Module to compute scores for inputs: Not used **kwargs: Not used - + Returns: Tensor of importance scores - + Raises: ValueError: If module has no gradients """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + if module.weight.grad is None: raise ValueError( f"Module {module} has no gradients. " @@ -85,7 +86,7 @@ def compute_importance_scores( "Note: Gradient-based pruning is not suitable for post-training pruning " "on converged models as gradients will be near-zero." ) - + if self.mode == 'gradient': # Use gradient magnitude importance = module.weight.grad.abs() @@ -94,42 +95,42 @@ def compute_importance_scores( importance = (module.weight.grad * module.weight.data).abs() else: raise ValueError(f"Unknown mode: {self.mode}") - + return importance class FisherPruning(BasePruningStrategy): """ Fisher information-based pruning strategy. - + This strategy approximates the Fisher information matrix diagonal using gradient squares accumulated over multiple batches. - + Examples: >>> from alignment.pruning.strategies import FisherPruning >>> strategy = FisherPruning() - >>> + >>> >>> # Accumulate Fisher information over multiple batches >>> for inputs, targets in dataloader: >>> loss = criterion(model(inputs), targets) >>> loss.backward() >>> strategy.accumulate_fisher(model) >>> optimizer.zero_grad() - >>> + >>> >>> # Prune based on accumulated Fisher information >>> masks = strategy.prune_model(model, amount=0.5) """ - + def __init__(self, config=None): """Initialize Fisher pruning strategy.""" super().__init__(config) self.fisher_info = {} self.n_samples = 0 - + def accumulate_fisher(self, model: nn.Module): """ Accumulate Fisher information from current gradients. - + Args: model: Model to accumulate Fisher information for """ @@ -137,12 +138,12 @@ def accumulate_fisher(self, model: nn.Module): if hasattr(module, 'weight') and module.weight.grad is not None: if name not in self.fisher_info: self.fisher_info[name] = torch.zeros_like(module.weight.data) - + # Accumulate squared gradients self.fisher_info[name] += module.weight.grad.data ** 2 - + self.n_samples += 1 - + def compute_importance_scores( self, module: nn.Module, @@ -152,13 +153,13 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores based on Fisher information. - + Args: module: Module to compute scores for inputs: Not used module_name: Name of the module in fisher_info dict **kwargs: Not used - + Returns: Tensor of importance scores """ @@ -168,15 +169,15 @@ def compute_importance_scores( return module.weight.grad.abs() else: return module.weight.data.abs() - + # Use accumulated Fisher information fisher = self.fisher_info[module_name] / self.n_samples - + # Importance is Fisher info times weight squared importance = fisher * (module.weight.data ** 2) - + return importance - + def prune_model( self, model: nn.Module, @@ -184,16 +185,16 @@ def prune_model( ) -> dict: """ Prune model based on accumulated Fisher information. - + Args: model: Model to prune amount: Fraction to prune - + Returns: Dictionary mapping module names to pruning masks """ masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): importance = self.compute_importance_scores( @@ -202,9 +203,9 @@ def prune_model( mask = self.create_pruning_mask(importance, amount) self.apply_pruning(module, mask) masks[name] = mask - + return masks - + def reset_fisher(self): """Reset accumulated Fisher information.""" self.fisher_info.clear() @@ -214,33 +215,33 @@ def reset_fisher(self): class MomentumPruning(BasePruningStrategy): """ Momentum-based pruning strategy. - + This strategy maintains a momentum buffer of importance scores, providing more stable pruning decisions over time. - + Examples: >>> from alignment.pruning.strategies import MomentumPruning >>> strategy = MomentumPruning(momentum=0.9) - >>> + >>> >>> # Update importance with momentum over multiple iterations >>> for epoch in range(epochs): >>> for inputs, targets in dataloader: >>> loss = criterion(model(inputs), targets) >>> loss.backward() - >>> + >>> >>> # Update momentum buffer >>> strategy.update_momentum(model) >>> optimizer.step() >>> optimizer.zero_grad() - >>> + >>> >>> # Prune based on momentum buffer >>> masks = strategy.prune_model(model, amount=0.5) """ - + def __init__(self, config=None, momentum: float = 0.9): """ Initialize momentum pruning strategy. - + Args: config: Pruning configuration momentum: Momentum factor (0-1) @@ -248,11 +249,11 @@ def __init__(self, config=None, momentum: float = 0.9): super().__init__(config) self.momentum = momentum self.importance_buffer = {} - + def update_momentum(self, model: nn.Module): """ Update momentum buffer with current gradients. - + Args: model: Model to update momentum for """ @@ -262,7 +263,7 @@ def update_momentum(self, model: nn.Module): current_importance = ( module.weight.grad.data * module.weight.data ).abs() - + if name not in self.importance_buffer: # Initialize buffer self.importance_buffer[name] = current_importance @@ -272,7 +273,7 @@ def update_momentum(self, model: nn.Module): self.momentum * self.importance_buffer[name] + (1 - self.momentum) * current_importance ) - + def compute_importance_scores( self, module: nn.Module, @@ -282,13 +283,13 @@ def compute_importance_scores( ) -> torch.Tensor: """ Get importance scores from momentum buffer. - + Args: module: Module to get scores for inputs: Not used module_name: Name of module in buffer **kwargs: Not used - + Returns: Tensor of importance scores """ @@ -298,9 +299,9 @@ def compute_importance_scores( return (module.weight.grad * module.weight.data).abs() else: return module.weight.data.abs() - + return self.importance_buffer[module_name] - + def prune_model( self, model: nn.Module, @@ -308,16 +309,16 @@ def prune_model( ) -> dict: """ Prune model based on momentum buffer. - + Args: model: Model to prune amount: Fraction to prune - + Returns: Dictionary mapping module names to pruning masks """ masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): importance = self.compute_importance_scores( @@ -326,9 +327,9 @@ def prune_model( mask = self.create_pruning_mask(importance, amount) self.apply_pruning(module, mask) masks[name] = mask - + return masks - + def reset_momentum(self): """Reset momentum buffer.""" - self.importance_buffer.clear() \ No newline at end of file + self.importance_buffer.clear() diff --git a/src/alignment/pruning/strategies/magnitude.py b/src/alignment/pruning/strategies/magnitude.py index 44385afa..3625f5ff 100644 --- a/src/alignment/pruning/strategies/magnitude.py +++ b/src/alignment/pruning/strategies/magnitude.py @@ -5,9 +5,10 @@ with the smallest absolute values. """ +from typing import Optional + import torch import torch.nn as nn -from typing import Optional from ..base import BasePruningStrategy, IterativePruningStrategy @@ -15,10 +16,10 @@ class MagnitudePruning(BasePruningStrategy): """ Magnitude-based pruning strategy. - + This strategy prunes weights with the smallest absolute values, based on the assumption that small weights contribute less to the network's output. - + Examples: >>> from alignment.pruning.strategies import MagnitudePruning >>> from alignment.pruning import PruningConfig @@ -32,7 +33,7 @@ class MagnitudePruning(BasePruningStrategy): >>> strategy = MagnitudePruning(config) >>> mask = strategy.prune(conv_layer) """ - + def compute_importance_scores( self, module: nn.Module, @@ -41,18 +42,18 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores based on weight magnitudes. - + Args: module: Module to compute scores for inputs: Not used for magnitude pruning **kwargs: Not used - + Returns: Tensor of importance scores (absolute weight values) """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + # Importance is simply the absolute value of weights return module.weight.data.abs() @@ -60,10 +61,10 @@ def compute_importance_scores( class IterativeMagnitudePruning(IterativePruningStrategy): """ Iterative magnitude-based pruning strategy. - + This strategy gradually prunes weights over multiple iterations, allowing the network to adapt between pruning steps. - + Examples: >>> from alignment.pruning.strategies import IterativeMagnitudePruning >>> from alignment.pruning import PruningConfig @@ -74,7 +75,7 @@ class IterativeMagnitudePruning(IterativePruningStrategy): ... fine_tune_epochs=5 ... ) >>> strategy = IterativeMagnitudePruning(config) - >>> + >>> >>> results = strategy.iterative_prune( ... model=model, ... dataloader=train_loader, @@ -82,7 +83,7 @@ class IterativeMagnitudePruning(IterativePruningStrategy): ... criterion=nn.CrossEntropyLoss() ... ) """ - + def compute_importance_scores( self, module: nn.Module, @@ -91,37 +92,37 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores based on weight magnitudes. - + Args: module: Module to compute scores for inputs: Not used for magnitude pruning **kwargs: Not used - + Returns: Tensor of importance scores (absolute weight values) """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + return module.weight.data.abs() class GlobalMagnitudePruning(BasePruningStrategy): """ Global magnitude-based pruning strategy. - + This strategy prunes weights globally across all layers based on their magnitudes, ensuring that the specified sparsity is achieved across the entire network rather than per-layer. - + Examples: >>> from alignment.pruning.strategies import GlobalMagnitudePruning >>> strategy = GlobalMagnitudePruning() - >>> + >>> >>> # Prune entire model to 70% sparsity >>> masks = strategy.prune_model(model, amount=0.7) """ - + def compute_importance_scores( self, module: nn.Module, @@ -130,14 +131,14 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute importance scores for a single module. - + Note: For global pruning, use prune_model() instead of prune(). """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + return module.weight.data.abs() - + def prune_model( self, model: nn.Module, @@ -145,39 +146,39 @@ def prune_model( ) -> dict: """ Prune entire model globally based on weight magnitudes. - + Args: model: Model to prune amount: Global sparsity level to achieve - + Returns: Dictionary mapping module names to their pruning masks """ amount = amount if amount is not None else self.config.amount - + # Collect all weights and their importance scores all_scores = [] module_info = [] - + for name, module in model.named_modules(): if hasattr(module, 'weight'): scores = module.weight.data.abs() all_scores.append(scores.flatten()) module_info.append((name, module, scores.shape)) - + if not all_scores: return {} - + # Concatenate all scores global_scores = torch.cat(all_scores) - + # Find global threshold k = int(amount * global_scores.numel()) if k == 0: return {} - + threshold = global_scores.kthvalue(k).values - + # Apply threshold to each module masks = {} for name, module, shape in module_info: @@ -185,5 +186,5 @@ def prune_model( mask = (scores > threshold).float() self.apply_pruning(module, mask) masks[name] = mask - - return masks \ No newline at end of file + + return masks diff --git a/src/alignment/pruning/strategies/movement.py b/src/alignment/pruning/strategies/movement.py index 53c66231..41382ece 100644 --- a/src/alignment/pruning/strategies/movement.py +++ b/src/alignment/pruning/strategies/movement.py @@ -7,13 +7,13 @@ Reference: Sanh et al., "Movement Pruning: Adaptive Sparsity by Fine-Tuning" (2020) """ +import logging +from typing import Dict, Optional + import torch import torch.nn as nn -from typing import Optional, Dict, Any -import logging from ..base import BasePruningStrategy, PruningConfig -from ...core.registry import register_metric logger = logging.getLogger(__name__) @@ -21,20 +21,20 @@ class MovementPruning(BasePruningStrategy): """ Movement-based pruning strategy. - + Identifies weights that consistently move toward zero during training and prunes them. More sophisticated than simple magnitude pruning. - + Score = |weight| × sign(weight) × sign(gradient) - + Interpretation: - Positive score: Weight moving away from zero (important) - Negative score: Weight moving toward zero (candidate for pruning) - + Reference: Sanh et al., "Movement Pruning: Adaptive Sparsity by Fine-Tuning" NeurIPS 2020 - + Example: >>> strategy = MovementPruning() >>> # During training, accumulate gradients @@ -43,11 +43,11 @@ class MovementPruning(BasePruningStrategy): >>> # Before optimizer.step(): >>> strategy.update_movement_history(model) >>> optimizer.step() - >>> + >>> >>> # After training: >>> mask = strategy.prune(model, amount=0.5) """ - + def __init__( self, config: Optional[PruningConfig] = None, @@ -56,7 +56,7 @@ def __init__( ): """ Initialize movement pruning. - + Args: config: Pruning configuration use_momentum: Whether to use momentum for movement estimation @@ -66,13 +66,13 @@ def __init__( self.use_momentum = use_momentum self.momentum = momentum self.movement_history: Dict[str, torch.Tensor] = {} - + def update_movement_history(self, model: nn.Module): """ Update movement history during training. - + Call this BEFORE optimizer.step() to track weight movement. - + Args: model: Model being trained """ @@ -80,11 +80,11 @@ def update_movement_history(self, model: nn.Module): for name, param in model.named_parameters(): if param.grad is None: continue - + # Compute movement score: sign(weight) × sign(gradient) # Negative = moving toward zero movement = torch.sign(param.data) * torch.sign(param.grad.data) - + if name not in self.movement_history: self.movement_history[name] = movement.clone() else: @@ -97,7 +97,7 @@ def update_movement_history(self, model: nn.Module): else: # Simple accumulation self.movement_history[name] += movement - + def compute_importance_scores( self, module: nn.Module, @@ -107,18 +107,18 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute movement-based importance scores. - + Args: module: Module to score inputs: Not used for movement pruning module_name: Name of module (for retrieving history) **kwargs: Additional arguments - + Returns: Importance scores (same shape as module.weight) """ weight = module.weight - + # Get movement history if available if module_name and module_name + '.weight' in self.movement_history: movement = self.movement_history[module_name + '.weight'] @@ -129,32 +129,32 @@ def compute_importance_scores( ) # Fallback to magnitude return weight.abs() - + # Movement score: magnitude × movement direction # High positive = moving away from zero = important # Negative = moving toward zero = prune scores = weight.abs() * movement - + return scores - + def reset_history(self): """Reset movement history.""" self.movement_history.clear() - + def get_movement_statistics(self) -> Dict[str, Dict[str, float]]: """ Get statistics about weight movement. - + Returns: Dict with statistics per parameter """ stats = {} - + for name, movement in self.movement_history.items(): moving_away = (movement > 0).sum().item() moving_toward = (movement < 0).sum().item() total = movement.numel() - + stats[name] = { 'moving_away_zero': moving_away, 'moving_toward_zero': moving_toward, @@ -163,18 +163,18 @@ def get_movement_statistics(self) -> Dict[str, Dict[str, float]]: 'mean_movement': movement.mean().item(), 'std_movement': movement.std().item() } - + return stats class AdaptiveMovementPruning(MovementPruning): """ Adaptive movement pruning that adjusts pruning amount based on movement. - + Automatically determines pruning amount based on how many weights are consistently moving toward zero. """ - + def __init__( self, base_amount: float = 0.5, @@ -183,7 +183,7 @@ def __init__( ): """ Initialize adaptive movement pruning. - + Args: base_amount: Base pruning amount adaptation_strength: How much to adapt based on movement (0-1) @@ -191,7 +191,7 @@ def __init__( super().__init__(**kwargs) self.base_amount = base_amount self.adaptation_strength = adaptation_strength - + def compute_adaptive_amount( self, module: nn.Module, @@ -199,36 +199,36 @@ def compute_adaptive_amount( ) -> float: """ Compute adaptive pruning amount for this module. - + Args: module: Module to prune module_name: Name of module - + Returns: Adjusted pruning amount """ if module_name + '.weight' not in self.movement_history: return self.base_amount - + movement = self.movement_history[module_name + '.weight'] - + # Fraction moving toward zero toward_zero = (movement < 0).float().mean().item() - + # Adapt pruning amount # More weights moving toward zero → prune more aggressively adapted_amount = self.base_amount + self.adaptation_strength * (toward_zero - 0.5) - + # Clip to valid range adapted_amount = max(0.1, min(0.9, adapted_amount)) - + logger.info( f"{module_name}: {toward_zero:.1%} moving toward zero, " f"adapted amount: {adapted_amount:.1%}" ) - + return adapted_amount - + def prune( self, module: nn.Module, @@ -238,14 +238,14 @@ def prune( ) -> torch.Tensor: """ Prune with adaptive amount. - + If amount is None, uses adaptive computation. """ if amount is None and module_name: amount = self.compute_adaptive_amount(module, module_name) elif amount is None: amount = self.base_amount - + # Use parent's prune method with adapted amount return super().prune(module, amount=amount, module_name=module_name, **kwargs) diff --git a/src/alignment/pruning/strategies/parallel.py b/src/alignment/pruning/strategies/parallel.py index b23b99a5..e726c663 100644 --- a/src/alignment/pruning/strategies/parallel.py +++ b/src/alignment/pruning/strategies/parallel.py @@ -5,11 +5,12 @@ in parallel, either as separate experiments or as a combined tensor operation. """ -import torch -import torch.nn as nn -from typing import Dict, List, Optional, Union, Tuple import concurrent.futures from dataclasses import dataclass +from typing import Dict, List, Optional, Union + +import torch +import torch.nn as nn from ..base import BasePruningStrategy, PruningConfig @@ -26,22 +27,22 @@ class ParallelPruningResult: class ParallelModePruning(BasePruningStrategy): """ Apply multiple pruning modes (low, high, random) in parallel. - + This strategy computes importance scores once and generates multiple masks for different pruning modes simultaneously. - + Examples: >>> strategy = ParallelModePruning(modes=['low', 'high', 'random']) >>> result = strategy.prune_parallel(model.fc1, amount=0.5) - >>> + >>> >>> # Access individual masks >>> low_mask = result.masks['low'] >>> high_mask = result.masks['high'] - >>> + >>> >>> # Or get combined analysis >>> combined = strategy.combine_masks(result.masks, method='union') """ - + def __init__( self, config: Optional[PruningConfig] = None, @@ -50,7 +51,7 @@ def __init__( ): """ Initialize parallel mode pruning. - + Args: config: Pruning configuration modes: List of pruning modes to apply ('low', 'high', 'random') @@ -59,22 +60,22 @@ def __init__( super().__init__(config) self.modes = modes self.base_strategy = base_strategy - + # Import strategies dynamically to avoid circular imports - from . import MagnitudePruning, GradientPruning, RandomPruning - + from . import GradientPruning, MagnitudePruning, RandomPruning + self.strategy_map = { 'magnitude': MagnitudePruning, 'gradient': GradientPruning, 'random': RandomPruning, } - + # Initialize base strategy for importance computation if base_strategy in self.strategy_map: self.importance_strategy = self.strategy_map[base_strategy](config) else: raise ValueError(f"Unknown base strategy: {base_strategy}") - + def compute_importance_scores( self, module: nn.Module, @@ -83,7 +84,7 @@ def compute_importance_scores( ) -> torch.Tensor: """Compute importance scores using the base strategy.""" return self.importance_strategy.compute_importance_scores(module, inputs, **kwargs) - + def prune_parallel( self, module: nn.Module, @@ -93,25 +94,25 @@ def prune_parallel( ) -> ParallelPruningResult: """ Apply multiple pruning modes in parallel. - + Args: module: Module to prune inputs: Optional inputs for importance computation amount: Pruning amount **kwargs: Additional arguments - + Returns: ParallelPruningResult containing all masks and metadata """ amount = amount if amount is not None else self.config.amount - + # Compute importance scores once importance_scores = self.compute_importance_scores(module, inputs, **kwargs) - + # Generate masks for each mode in parallel masks = {} sparsities = {} - + for mode in self.modes: if mode == 'random': # Random doesn't use importance scores @@ -123,21 +124,21 @@ def prune_parallel( amount=amount, pruning_mode=mode ) - + masks[mode] = mask sparsities[mode] = (mask == 0).float().mean().item() - + return ParallelPruningResult( masks=masks, sparsities=sparsities, importance_scores=importance_scores ) - + def _create_random_mask(self, shape: torch.Size, amount: float) -> torch.Tensor: """Create a random pruning mask.""" mask = torch.rand(shape) > amount return mask.float() - + def combine_masks( self, masks: Dict[str, torch.Tensor], @@ -145,16 +146,16 @@ def combine_masks( ) -> torch.Tensor: """ Combine multiple masks using specified method. - + Args: masks: Dictionary of masks to combine method: Combination method ('intersection', 'union', 'majority') - + Returns: Combined mask """ mask_list = list(masks.values()) - + if method == 'intersection': # Keep only weights that all masks agree to keep combined = torch.stack(mask_list).prod(dim=0) @@ -167,16 +168,16 @@ def combine_masks( combined = combined.float() else: raise ValueError(f"Unknown combination method: {method}") - + return combined class TensorizedPruning(BasePruningStrategy): """ Tensorized pruning that computes all pruning variations as a single tensor operation. - + This strategy is optimized for GPU computation and analysis of pruning patterns. - + Examples: >>> strategy = TensorizedPruning() >>> # Get a 3D tensor: [num_modes, height, width] for Conv2d @@ -186,7 +187,7 @@ class TensorizedPruning(BasePruningStrategy): ... amounts=[0.1, 0.3, 0.5, 0.7, 0.9] ... ) """ - + def compute_importance_scores( self, module: nn.Module, @@ -197,7 +198,7 @@ def compute_importance_scores( if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") return module.weight.data.abs() - + def compute_pruning_tensor( self, module: nn.Module, @@ -207,24 +208,24 @@ def compute_pruning_tensor( ) -> torch.Tensor: """ Compute a tensor containing all pruning variations. - + Args: module: Module to analyze modes: Pruning modes to include amounts: Sparsity levels to include inputs: Optional inputs for importance computation - + Returns: Tensor of shape [len(modes), len(amounts), *weight_shape] """ # Get importance scores importance_scores = self.compute_importance_scores(module, inputs) weight_shape = importance_scores.shape - + # Flatten for efficient computation scores_flat = importance_scores.flatten() n_params = scores_flat.numel() - + # Pre-compute all thresholds thresholds = {} for amount in amounts: @@ -234,14 +235,14 @@ def compute_pruning_tensor( 'low': scores_flat.kthvalue(k).values, 'high': scores_flat.kthvalue(n_params - k).values } - + # Create pruning tensor pruning_tensor = torch.zeros( len(modes), len(amounts), *weight_shape, dtype=torch.float32, device=importance_scores.device ) - + for i, mode in enumerate(modes): for j, amount in enumerate(amounts): if amount in thresholds: @@ -253,7 +254,7 @@ def compute_pruning_tensor( mask = torch.rand_like(importance_scores) > amount else: continue - + pruning_tensor[i, j] = mask.float() else: # Edge cases (0% or 100% pruning) @@ -261,45 +262,45 @@ def compute_pruning_tensor( pruning_tensor[i, j] = torch.ones_like(importance_scores) else: pruning_tensor[i, j] = torch.zeros_like(importance_scores) - + return pruning_tensor - + def analyze_pruning_patterns( self, pruning_tensor: torch.Tensor ) -> Dict[str, torch.Tensor]: """ Analyze patterns in the pruning tensor. - + Args: pruning_tensor: Tensor from compute_pruning_tensor - + Returns: Dictionary of analysis metrics """ analysis = {} - + # Sparsity progression analysis['sparsity_progression'] = (pruning_tensor == 0).float().mean(dim=-1).mean(dim=-1) - + # Overlap between modes if pruning_tensor.shape[0] >= 2: overlap = pruning_tensor[0] * pruning_tensor[1] # Low & High overlap analysis['mode_overlap'] = overlap.mean(dim=-1).mean(dim=-1) - + # Variance across amounts analysis['pruning_variance'] = pruning_tensor.var(dim=1) - + return analysis class AsyncParallelPruning(BasePruningStrategy): """ Asynchronous parallel pruning for multiple models or layers. - + This strategy uses Python's concurrent.futures to prune multiple modules in parallel across CPU cores. - + Examples: >>> strategy = AsyncParallelPruning() >>> modules = [model.layer1, model.layer2, model.layer3] @@ -309,7 +310,7 @@ class AsyncParallelPruning(BasePruningStrategy): ... modes=['low', 'high'] ... ) """ - + def compute_importance_scores( self, module: nn.Module, @@ -320,7 +321,7 @@ def compute_importance_scores( if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") return module.weight.data.abs() - + def prune_modules_parallel( self, modules: List[nn.Module], @@ -330,25 +331,25 @@ def prune_modules_parallel( ) -> List[Dict[str, torch.Tensor]]: """ Prune multiple modules in parallel. - + Args: modules: List of modules to prune amounts: Single amount or list of amounts per module modes: Pruning modes to apply max_workers: Maximum number of parallel workers - + Returns: List of pruning results per module """ if isinstance(amounts, float): amounts = [amounts] * len(modules) - + def prune_single_module(module, amount): """Prune a single module with all modes.""" masks = {} - + importance_scores = self.compute_importance_scores(module) - + for mode in modes: if mode == 'random': mask = torch.rand_like(module.weight) > amount @@ -360,9 +361,9 @@ def prune_single_module(module, amount): pruning_mode=mode ) masks[mode] = mask - + return masks - + # Execute in parallel results = [] with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: @@ -370,8 +371,8 @@ def prune_single_module(module, amount): executor.submit(prune_single_module, module, amount) for module, amount in zip(modules, amounts) ] - + for future in concurrent.futures.as_completed(futures): results.append(future.result()) - - return results \ No newline at end of file + + return results diff --git a/src/alignment/pruning/strategies/parallel_batch.py b/src/alignment/pruning/strategies/parallel_batch.py index 7bb71f07..80df9fe6 100644 --- a/src/alignment/pruning/strategies/parallel_batch.py +++ b/src/alignment/pruning/strategies/parallel_batch.py @@ -1,10 +1,11 @@ """Parallel batch pruning strategy for efficient evaluation of multiple configurations.""" +import logging +import time +from typing import Dict, List + import torch import torch.nn as nn -import time -from typing import Dict, List, Tuple, Optional -import logging logger = logging.getLogger(__name__) @@ -12,17 +13,17 @@ class ParallelBatchPruning: """ Pruning strategy that evaluates all networks and sparsity levels in parallel. - + This strategy processes multiple networks and pruning configurations simultaneously using vectorized operations for maximum efficiency. """ - + def __init__(self, config=None): self.config = config self.eval_batches = getattr(config, 'eval_batches', None) if config else None - + def run_pruning_experiments( - self, + self, networks: List[nn.Module], data_loader, strategies: List[str], @@ -32,7 +33,7 @@ def run_pruning_experiments( ) -> Dict[str, any]: """ Run pruning experiments for all configurations in parallel. - + Args: networks: List of neural networks to prune data_loader: DataLoader for evaluation @@ -40,35 +41,35 @@ def run_pruning_experiments( selection_modes: List of selection modes (low, high, random) pruning_amounts: List of sparsity levels to test device: Device to run computations on - + Returns: Dictionary with pruning results for all strategies """ logger.info("Running parallel batch pruning experiments") - + results = {"strategies": {}} - + # Save original states efficiently original_states = [] for model in networks: - state = {name: module.weight.data.clone() - for name, module in model.named_modules() + state = {name: module.weight.data.clone() + for name, module in model.named_modules() if hasattr(module, 'weight')} original_states.append(state) - + # Process each strategy for strategy_name in strategies: logger.info(f"Testing pruning strategy: {strategy_name}") - + for selection_mode in selection_modes: logger.info(f" Selection mode: {selection_mode}") - + # Run parallel pruning for this configuration batch_results = self._parallel_batch_pruning( - networks, data_loader, strategy_name, selection_mode, + networks, data_loader, strategy_name, selection_mode, pruning_amounts, original_states, device ) - + # Store aggregated results strategy_key = f"{strategy_name}_{selection_mode}" strategy_results = { @@ -79,22 +80,22 @@ def run_pruning_experiments( "losses_after_finetune": batch_results["losses_after"].mean(dim=0).tolist(), "improvements": (batch_results["accuracies_after"] - batch_results["accuracies_before"]).mean(dim=0).tolist() } - + # Add standard deviations if multiple networks if len(networks) > 1: strategy_results["accuracies_before_finetune_std"] = batch_results["accuracies_before"].std(dim=0).tolist() strategy_results["accuracies_after_finetune_std"] = batch_results["accuracies_after"].std(dim=0).tolist() - + results["strategies"][strategy_key] = strategy_results - + # Restore original weights for net_idx, model in enumerate(networks): for name, module in model.named_modules(): if name in original_states[net_idx]: module.weight.data = original_states[net_idx][name] - + return results - + def _parallel_batch_pruning( self, networks: List[nn.Module], @@ -111,104 +112,104 @@ def _parallel_batch_pruning( num_networks = len(networks) num_amounts = len(pruning_amounts) total_configs = num_networks * num_amounts - + logger.info(f" Processing {total_configs} configurations in parallel") logger.info(f" Networks: {num_networks}, Sparsity levels: {num_amounts}") - + # Initialize result tensors on device for speed accuracies_before = torch.zeros(num_networks, num_amounts, device=device) losses_before = torch.zeros(num_networks, num_amounts, device=device) sparsities = torch.zeros(num_networks, num_amounts) - + # Create all masks upfront logger.info(" Creating masks for all configurations...") all_masks = self._create_all_masks(networks, strategy_name, selection_mode, pruning_amounts) - + # Evaluate all configurations in parallel logger.info(" Starting parallel evaluation...") start_time = time.time() - + # Pre-allocate for all configurations all_correct = torch.zeros(total_configs, device=device) all_loss = torch.zeros(total_configs, device=device) total_samples = 0 - + # Set all networks to eval mode for net in networks: net.eval() - + criterion = nn.CrossEntropyLoss(reduction='none') batch_count = 0 - + with torch.no_grad(): for inputs, targets in data_loader: inputs = inputs.to(device) targets = targets.to(device) batch_size = targets.size(0) - + # Collect outputs from ALL configurations all_outputs = [] - + for net_idx in range(num_networks): net = networks[net_idx] - + for amount_idx in range(num_amounts): # Apply configuration self._apply_mask_config(net, all_masks[net_idx][amount_idx], original_states[net_idx]) - + # Forward pass outputs = net(inputs) all_outputs.append(outputs) - + # Calculate sparsity (only once) if batch_count == 0: sparsities[net_idx, amount_idx] = self._calculate_sparsity(net) - + # Stack all outputs for vectorized processing stacked_outputs = torch.stack(all_outputs, dim=0) - + # Compute losses for all configs at once expanded_targets = targets.unsqueeze(0).expand(total_configs, -1) all_batch_losses = criterion( - stacked_outputs.reshape(-1, stacked_outputs.size(-1)), + stacked_outputs.reshape(-1, stacked_outputs.size(-1)), expanded_targets.reshape(-1) ).reshape(total_configs, batch_size) - + # Sum losses all_loss += all_batch_losses.sum(dim=1) - + # Get predictions and count correct all_preds = stacked_outputs.argmax(dim=2) correct = all_preds.eq(expanded_targets).sum(dim=1) all_correct += correct - + total_samples += batch_size batch_count += 1 - + # Check if we've evaluated enough batches if self.eval_batches is not None and batch_count >= self.eval_batches: break - + # Reshape results back to [num_networks, num_amounts] all_correct = all_correct.reshape(num_networks, num_amounts) all_loss = all_loss.reshape(num_networks, num_amounts) - + # Convert to accuracies and average losses accuracies_before = (all_correct * 100.0 / total_samples).cpu() losses_before = (all_loss / batch_count).cpu() - + eval_time = time.time() - start_time logger.info(f" Parallel evaluation completed in {eval_time:.2f} seconds") logger.info(f" Average accuracy: {accuracies_before.mean():.2f}%") - + # For now, no fine-tuning in parallel mode accuracies_after = accuracies_before.clone() losses_after = losses_before.clone() - + # Reset networks to train mode for net in networks: net.train() - + return { "accuracies_before": accuracies_before, "losses_before": losses_before, @@ -216,7 +217,7 @@ def _parallel_batch_pruning( "losses_after": losses_after, "sparsities": sparsities } - + def _create_all_masks( self, networks: List[nn.Module], @@ -226,10 +227,10 @@ def _create_all_masks( ) -> List[List[Dict[str, torch.Tensor]]]: """Create all masks for all networks and pruning amounts.""" all_masks = [] - + for net in networks: network_masks = [] - + for amount in pruning_amounts: if strategy_name == "magnitude": masks = self._create_magnitude_masks(net, amount, selection_mode) @@ -240,13 +241,13 @@ def _create_all_masks( masks = self._create_magnitude_masks(net, amount, selection_mode) else: raise ValueError(f"Unknown strategy: {strategy_name}") - + network_masks.append(masks) - + all_masks.append(network_masks) - + return all_masks - + def _create_magnitude_masks( self, model: nn.Module, @@ -255,12 +256,12 @@ def _create_magnitude_masks( ) -> Dict[str, torch.Tensor]: """Create magnitude-based masks for a model.""" masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): weight = module.weight.data importance = weight.abs() - + if hasattr(self.config, 'alignment_structured_pruning') and self.config.alignment_structured_pruning and len(weight.shape) >= 2: # Structured pruning - prune entire neurons neuron_importance = importance.mean(dim=tuple(range(1, len(weight.shape)))) @@ -268,11 +269,11 @@ def _create_magnitude_masks( else: # Unstructured pruning mask = self._create_mask(importance, amount, selection_mode) - + masks[name] = mask - + return masks - + def _create_random_masks( self, model: nn.Module, @@ -280,11 +281,11 @@ def _create_random_masks( ) -> Dict[str, torch.Tensor]: """Create random masks for a model.""" masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): weight = module.weight.data - + if hasattr(self.config, 'alignment_structured_pruning') and self.config.alignment_structured_pruning and len(weight.shape) >= 2: # Structured random pruning num_neurons = weight.shape[0] @@ -298,11 +299,11 @@ def _create_random_masks( else: # Unstructured random pruning mask = torch.rand_like(weight) > amount - + masks[name] = mask.float() - + return masks - + def _create_mask( self, importance: torch.Tensor, @@ -314,13 +315,13 @@ def _create_mask( return torch.ones_like(importance) elif amount >= 1: return torch.zeros_like(importance) - + flat_importance = importance.flatten() k = int(amount * flat_importance.numel()) - + if k == 0: return torch.ones_like(importance) - + if selection_mode == "low": threshold = torch.kthvalue(flat_importance, k).values mask = importance > threshold @@ -331,9 +332,9 @@ def _create_mask( mask = torch.rand_like(importance) > amount else: raise ValueError(f"Unknown selection mode: {selection_mode}") - + return mask.float() - + def _create_structured_mask( self, neuron_importance: torch.Tensor, @@ -344,7 +345,7 @@ def _create_structured_mask( """Create a structured mask that prunes entire neurons.""" num_neurons = neuron_importance.numel() num_to_prune = int(amount * num_neurons) - + if num_to_prune == 0: mask = torch.ones_like(neuron_importance) elif num_to_prune >= num_neurons: @@ -364,7 +365,7 @@ def _create_structured_mask( mask[indices] = 0 else: raise ValueError(f"Unknown selection mode: {selection_mode}") - + # Expand mask to match weight dimensions if len(weight_shape) == 2: # Linear layer: [out_features, in_features] @@ -372,9 +373,9 @@ def _create_structured_mask( elif len(weight_shape) == 4: # Conv layer: [out_channels, in_channels, height, width] mask = mask.view(-1, 1, 1, 1).expand_as(torch.zeros(weight_shape)) - + return mask - + def _apply_mask_config( self, model: nn.Module, @@ -387,20 +388,20 @@ def _apply_mask_config( if name in original_state: # Restore original weights module.weight.data = original_state[name].clone() - + # Apply mask if exists if name in masks: module.weight.data *= masks[name] - + def _calculate_sparsity(self, model: nn.Module) -> float: """Calculate the sparsity of a model.""" total_params = 0 zero_params = 0 - + for module in model.modules(): if hasattr(module, 'weight'): weight = module.weight.data total_params += weight.numel() zero_params += (weight == 0).sum().item() - - return zero_params / total_params if total_params > 0 else 0.0 \ No newline at end of file + + return zero_params / total_params if total_params > 0 else 0.0 diff --git a/src/alignment/pruning/strategies/random.py b/src/alignment/pruning/strategies/random.py index 167a5e71..e6b13076 100644 --- a/src/alignment/pruning/strategies/random.py +++ b/src/alignment/pruning/strategies/random.py @@ -5,9 +5,10 @@ regardless of their values. Used as a baseline for comparison. """ +from typing import Optional + import torch import torch.nn as nn -from typing import Optional from ..base import BasePruningStrategy @@ -15,11 +16,11 @@ class RandomPruning(BasePruningStrategy): """ Random pruning strategy. - + This strategy randomly prunes weights regardless of their values. It's primarily used as a baseline to compare against informed pruning strategies. - + Examples: >>> from alignment.pruning.strategies import RandomPruning >>> from alignment.pruning import PruningConfig @@ -37,11 +38,11 @@ class RandomPruning(BasePruningStrategy): >>> strategy = RandomPruning(seed=42) >>> mask = strategy.prune(layer, amount=0.5) """ - + def __init__(self, config=None, seed: Optional[int] = None): """ Initialize random pruning strategy. - + Args: config: Pruning configuration seed: Random seed for reproducibility @@ -50,7 +51,7 @@ def __init__(self, config=None, seed: Optional[int] = None): self.seed = seed if seed is not None: torch.manual_seed(seed) - + def compute_importance_scores( self, module: nn.Module, @@ -59,23 +60,23 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute random importance scores. - + For structured pruning, generates neuron-level scores. For unstructured pruning, generates weight-level scores. - + Args: module: Module to compute scores for inputs: Not used **kwargs: Not used - + Returns: Tensor of random importance scores """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + weights = module.weight.data - + # For structured pruning, generate neuron-level scores if self.config.structured: if len(weights.shape) == 2: # Linear layer @@ -95,17 +96,17 @@ def compute_importance_scores( else: # For unstructured pruning, generate weight-level random scores importance_scores = torch.rand_like(weights) - + return importance_scores class LayerwiseRandomPruning(RandomPruning): """ Layerwise random pruning with different sparsity per layer. - + This strategy allows specifying different pruning amounts for different layers while still using random selection. - + Examples: >>> from alignment.pruning.strategies import LayerwiseRandomPruning >>> @@ -119,7 +120,7 @@ class LayerwiseRandomPruning(RandomPruning): >>> strategy = LayerwiseRandomPruning(layer_sparsity=layer_sparsity) >>> masks = strategy.prune_model(model) """ - + def __init__( self, config=None, @@ -129,7 +130,7 @@ def __init__( ): """ Initialize layerwise random pruning. - + Args: config: Pruning configuration layer_sparsity: Dict mapping layer names to sparsity levels @@ -139,19 +140,19 @@ def __init__( super().__init__(config, seed) self.layer_sparsity = layer_sparsity or {} self.default_sparsity = default_sparsity - + def prune_model(self, model: nn.Module) -> dict: """ Prune model with per-layer random sparsity. - + Args: model: Model to prune - + Returns: Dictionary mapping module names to pruning masks """ masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): # Get sparsity for this layer @@ -159,24 +160,24 @@ def prune_model(self, model: nn.Module) -> dict: amount = self.layer_sparsity[name] else: amount = self.default_sparsity - + # Generate random mask importance = self.compute_importance_scores(module) mask = self.create_pruning_mask(importance, amount) self.apply_pruning(module, mask) masks[name] = mask - + return masks class BernoulliPruning(BasePruningStrategy): """ Bernoulli (probabilistic) pruning strategy. - + Instead of pruning exactly k% of weights, each weight is pruned with probability p = k/100. This can be useful for theoretical analysis and stochastic pruning approaches. - + Examples: >>> from alignment.pruning.strategies import BernoulliPruning >>> @@ -192,7 +193,7 @@ class BernoulliPruning(BasePruningStrategy): >>> strategy = BernoulliPruning(probability_map=prob_map) >>> masks = strategy.prune_model(model) """ - + def __init__( self, config=None, @@ -202,7 +203,7 @@ def __init__( ): """ Initialize Bernoulli pruning strategy. - + Args: config: Pruning configuration probability: Default pruning probability @@ -215,7 +216,7 @@ def __init__( self.seed = seed if seed is not None: torch.manual_seed(seed) - + def compute_importance_scores( self, module: nn.Module, @@ -224,40 +225,40 @@ def compute_importance_scores( ) -> torch.Tensor: """ Compute Bernoulli importance scores. - + Higher scores mean higher probability of keeping the weight. - + Args: module: Module to compute scores for inputs: Not used **kwargs: Can contain 'probability' override - + Returns: Tensor of importance scores """ if not hasattr(module, 'weight'): raise ValueError(f"Module {module} does not have weights") - + # Get probability for this module prob = kwargs.get('probability', self.probability) - + # Check if module type has specific probability for module_type, type_prob in self.probability_map.items(): if isinstance(module, module_type): prob = type_prob break - + # Generate Bernoulli samples (inverted: 1 means keep) # We return uniform random values and let create_pruning_mask # handle the thresholding importance = torch.rand_like(module.weight.data) - + # Adjust scores so that approximately (1-prob) fraction # will be below the threshold importance = importance / prob if prob > 0 else importance - + return importance - + def create_pruning_mask( self, importance_scores: torch.Tensor, @@ -267,39 +268,39 @@ def create_pruning_mask( ) -> torch.Tensor: """ Create Bernoulli pruning mask. - + Overrides base method to use threshold of 1.0 for Bernoulli sampling. - + Args: importance_scores: Tensor of importance scores amount: Not used (probability is set in compute_importance_scores) structured: Whether to do structured pruning dim: Dimension for structured pruning - + Returns: Binary mask tensor """ # For Bernoulli pruning, we threshold at 1.0 mask = (importance_scores > 1.0).float() - + if structured and dim is not None: # For structured pruning, prune entire structure if # majority of weights would be pruned dims_to_reduce = list(range(importance_scores.ndim)) dims_to_reduce.pop(dim) - + if dims_to_reduce: structure_scores = mask.sum(dim=dims_to_reduce) structure_sizes = torch.ones_like(structure_scores) for d in dims_to_reduce: structure_sizes *= importance_scores.shape[d] - + # Keep structure if more than half weights would survive structure_mask = structure_scores > (structure_sizes / 2) - + # Expand to original shape shape = [1] * importance_scores.ndim shape[dim] = importance_scores.shape[dim] mask = structure_mask.reshape(shape).expand_as(importance_scores).float() - - return mask \ No newline at end of file + + return mask diff --git a/src/alignment/pruning/strategies/ultimate.py b/src/alignment/pruning/strategies/ultimate.py index e147de9c..5f4766b7 100644 --- a/src/alignment/pruning/strategies/ultimate.py +++ b/src/alignment/pruning/strategies/ultimate.py @@ -10,14 +10,13 @@ Expected: Best possible accuracy retention at high sparsity. """ -import torch -import torch.nn as nn -from typing import Dict, List, Optional, Callable, Any, Tuple import logging +from typing import Any, Callable, Dict, List, Optional + +import torch.nn as nn -from ..base import BasePruningStrategy, PruningConfig -from .adaptive import AdaptiveSensitivityPruning from ..dependency_aware import DependencyAwarePruning +from .adaptive import AdaptiveSensitivityPruning logger = logging.getLogger(__name__) @@ -25,20 +24,20 @@ class UltimatePruningStrategy: """ State-of-the-art pruning combining multiple advanced techniques. - + Multi-stage progressive pruning with adaptive per-layer amounts and redundancy-aware composite scoring. - + Stages: 1. Sensitivity Analysis → adaptive per-layer amounts 2. Coarse Pruning (magnitude) → safe initial pruning 3. Refined Pruning (composite) → redundancy-aware 4. Cleanup → remove truly dead neurons - + Expected performance: - 70% sparsity: ~5% accuracy drop (vs 10% for magnitude) - 85% sparsity: ~12% drop (vs 20% for magnitude) - + Example: >>> strategy = UltimatePruningStrategy( ... target_sparsity=0.7, @@ -47,7 +46,7 @@ class UltimatePruningStrategy: >>> result = strategy.prune(model, train_loader, val_loader) >>> print(f"Final accuracy: {result['accuracy']:.2f}%") """ - + def __init__( self, target_sparsity: float = 0.7, @@ -59,7 +58,7 @@ def __init__( ): """ Initialize ultimate pruning strategy. - + Args: target_sparsity: Target overall sparsity stages: Pruning schedule @@ -75,18 +74,18 @@ def __init__( self.sensitivity_based = sensitivity_based self.use_redundancy = use_redundancy self.fine_tune_epochs_per_stage = fine_tune_epochs_per_stage - + # Initialize sub-strategies if sensitivity_based: self.adaptive_pruner = AdaptiveSensitivityPruning( target_sparsity=target_sparsity ) - + self.dependency_pruner = DependencyAwarePruning - + # Define pruning stages self.stages = self._get_pruning_stages(stages) - + def _get_pruning_stages(self, mode: str) -> List[Dict]: """Define pruning stages based on mode.""" if mode == 'full': @@ -116,7 +115,7 @@ def _get_pruning_stages(self, mode: str) -> List[Dict]: 'fine_tune_epochs': self.fine_tune_epochs_per_stage * 2 } ] - + elif mode == 'fast': return [ { @@ -132,7 +131,7 @@ def _get_pruning_stages(self, mode: str) -> List[Dict]: 'fine_tune_epochs': self.fine_tune_epochs_per_stage * 2 } ] - + else: # one-shot return [ { @@ -142,7 +141,7 @@ def _get_pruning_stages(self, mode: str) -> List[Dict]: 'fine_tune_epochs': self.fine_tune_epochs_per_stage * 3 } ] - + def prune( self, model: nn.Module, @@ -154,7 +153,7 @@ def prune( ) -> Dict[str, Any]: """ Execute ultimate pruning strategy. - + Args: model: Model to prune train_loader: Training data (for fine-tuning) @@ -162,7 +161,7 @@ def prune( layers_to_prune: Specific layers (None = auto-detect) trainer_fn: Function(model, train_loader, epochs) for fine-tuning eval_fn: Function(model, val_loader) -> accuracy - + Returns: Results dictionary with masks, stats, accuracy history """ @@ -170,16 +169,16 @@ def prune( if layers_to_prune is None: from ...core.layer_detector import detect_trackable_layers layers_to_prune = detect_trackable_layers(model) - + logger.info(f"Pruning {len(layers_to_prune)} layers with {self.stages_mode} strategy") - + # Baseline evaluation if eval_fn: baseline_acc = eval_fn(model, val_loader) logger.info(f"Baseline accuracy: {baseline_acc:.2f}%") else: baseline_acc = None - + # Step 1: Sensitivity analysis (if enabled) if self.sensitivity_based and eval_fn: logger.info("Stage 0: Computing layer sensitivities...") @@ -189,7 +188,7 @@ def prune( self.adaptive_pruner.print_sensitivity_report() else: sensitivities = None - + # Track results results = { 'baseline_accuracy': baseline_acc, @@ -197,16 +196,16 @@ def prune( 'final_masks': {}, 'sensitivity_report': sensitivities } - + # Execute stages for stage_idx, stage in enumerate(self.stages): logger.info(f"\n{'='*80}") logger.info(f"Stage {stage_idx + 1}/{len(self.stages)}: {stage['name']}") logger.info(f"{'='*80}") - + # Compute target amount for this stage stage_target = self.target_sparsity * stage['target_fraction'] - + # Prune stage_result = self._execute_stage( model, @@ -215,37 +214,37 @@ def prune( stage['metric'], sensitivities ) - + # Fine-tune if trainer provided if trainer_fn and stage['fine_tune_epochs'] > 0: logger.info(f"Fine-tuning for {stage['fine_tune_epochs']} epochs...") trainer_fn(model, train_loader, epochs=stage['fine_tune_epochs']) - + # Evaluate if eval_fn: stage_acc = eval_fn(model, val_loader) logger.info(f"Accuracy after stage: {stage_acc:.2f}%") stage_result['accuracy'] = stage_acc - + results['stage_results'].append(stage_result) - + # Final evaluation if eval_fn: final_acc = eval_fn(model, val_loader) results['final_accuracy'] = final_acc results['accuracy_drop'] = baseline_acc - final_acc if baseline_acc else None - + logger.info(f"\n{'='*80}") - logger.info(f"FINAL RESULTS") + logger.info("FINAL RESULTS") logger.info(f"{'='*80}") logger.info(f"Baseline: {baseline_acc:.2f}%") logger.info(f"Final: {final_acc:.2f}%") logger.info(f"Drop: {results['accuracy_drop']:.2f}%") logger.info(f"Sparsity: {self.target_sparsity:.1%}") logger.info(f"{'='*80}\n") - + return results - + def _execute_stage( self, model: nn.Module, @@ -255,15 +254,13 @@ def _execute_stage( sensitivities: Optional[Dict] ) -> Dict: """Execute a single pruning stage.""" - from ...metrics import get_metric - from ...services import NodeScoringService, MaskOperations - + stage_result = { 'metric': metric_name, 'target': stage_target, 'masks': {} } - + # Compute layer-specific amounts if adaptive if sensitivities: # Use adaptive amounts, scaled to stage target @@ -274,34 +271,34 @@ def _execute_stage( else: # Uniform amount layer_amounts = {name: stage_target for name in layer_names} - + # Compute scores and masks per layer layer_scores = {} - + for layer_name in layer_names: layer = dict(model.named_modules())[layer_name] - amount = layer_amounts.get(layer_name, stage_target) - + layer_amounts.get(layer_name, stage_target) + # Compute scores based on metric if metric_name == 'magnitude': scores = layer.weight.abs().flatten() if scores.ndim > 1: scores = scores.mean(dim=list(range(1, scores.ndim))) - + elif metric_name == 'rayleigh_quotient': # Would need inputs - skip for now or use cached scores = layer.weight.norm(dim=1) # Fallback - + elif metric_name == 'composite' and self.use_redundancy: # Use redundancy-aware composite # Would need full pipeline - simplified here scores = layer.weight.norm(dim=1) - + else: scores = layer.weight.norm(dim=1) - + layer_scores[layer_name] = scores - + # Apply with dependency awareness pruner = self.dependency_pruner(model) result = pruner.prune( @@ -309,10 +306,10 @@ def _execute_stage( amount=stage_target, dry_run=False ) - + stage_result['masks'] = result['masks'] stage_result['stats'] = result['stats'] - + return stage_result @@ -323,12 +320,12 @@ def create_ultimate_pruner( ) -> UltimatePruningStrategy: """ Factory function for creating ultimate pruning strategy. - + Args: target_sparsity: Target overall sparsity mode: 'full' (best quality), 'fast' (faster), 'oneshot' **config: Additional configuration - + Returns: Configured UltimatePruningStrategy """ diff --git a/src/alignment/pruning/strategies/ultra_fast.py b/src/alignment/pruning/strategies/ultra_fast.py index 3d800fb6..3416a34c 100644 --- a/src/alignment/pruning/strategies/ultra_fast.py +++ b/src/alignment/pruning/strategies/ultra_fast.py @@ -1,11 +1,11 @@ """Ultra-fast parallel pruning strategy for alignment experiments.""" +import logging +import time +from typing import Any, Dict, List, Optional + import torch import torch.nn as nn -import copy -import time -from typing import Dict, List, Tuple, Optional, Any -import logging from ..base import BasePruningStrategy @@ -15,53 +15,53 @@ class UltraFastParallelPruning(BasePruningStrategy): """ Ultra-fast parallel pruning that evaluates all configurations simultaneously. - + This strategy processes multiple networks and pruning amounts in a single pass, dramatically reducing computation time compared to sequential processing. """ - + def __init__(self, config=None): super().__init__(config) self.networks = None self.data_loader = None self.original_states = None - + def setup(self, networks: List[nn.Module], data_loader): """Setup the pruning strategy with networks and data.""" self.networks = networks self.data_loader = data_loader - + # Save original states efficiently self.original_states = [] for model in self.networks: - state = {name: module.weight.data.clone() - for name, module in model.named_modules() + state = {name: module.weight.data.clone() + for name, module in model.named_modules() if hasattr(module, 'weight')} self.original_states.append(state) - + def run_pruning_experiments( - self, + self, strategies: List[str], selection_modes: List[str], pruning_amounts: List[float] ) -> Dict[str, Any]: """Run ultra-fast parallel pruning experiments.""" logger.info("Running ULTRA-FAST PARALLEL pruning experiments") - + results = {"strategies": {}} - + # Process each strategy for strategy_name in strategies: logger.info(f"Testing pruning strategy: {strategy_name}") - + for selection_mode in selection_modes: logger.info(f" Selection mode: {selection_mode}") - + # Always use ultra-parallel implementation batch_results = self._ultra_fast_parallel_pruning( strategy_name, selection_mode, pruning_amounts ) - + # Store results strategy_key = f"{strategy_name}_{selection_mode}" strategy_results = { @@ -72,26 +72,26 @@ def run_pruning_experiments( "losses_after_finetune": batch_results["losses_after"].mean(dim=0).tolist(), "improvements": (batch_results["accuracies_after"] - batch_results["accuracies_before"]).mean(dim=0).tolist() } - + # Add standard deviations if multiple networks if len(self.networks) > 1: strategy_results["accuracies_before_finetune_std"] = batch_results["accuracies_before"].std(dim=0).tolist() strategy_results["accuracies_after_finetune_std"] = batch_results["accuracies_after"].std(dim=0).tolist() - + results["strategies"][strategy_key] = strategy_results - + # Restore original weights for net_idx, model in enumerate(self.networks): for name, module in model.named_modules(): if name in self.original_states[net_idx]: module.weight.data = self.original_states[net_idx][name] - + return results - + def _ultra_fast_parallel_pruning( - self, - strategy_name: str, - selection_mode: str, + self, + strategy_name: str, + selection_mode: str, pruning_amounts: List[float] ) -> Dict[str, torch.Tensor]: """ @@ -101,106 +101,106 @@ def _ultra_fast_parallel_pruning( num_networks = len(self.networks) num_amounts = len(pruning_amounts) total_configs = num_networks * num_amounts - + logger.info(f" Processing {total_configs} configurations in TRUE parallel") logger.info(f" Networks: {num_networks}, Sparsity levels: {num_amounts}") - + # Initialize result tensors on GPU for speed device = self.config.device if hasattr(self.config, 'device') else 'cuda' accuracies_before = torch.zeros(num_networks, num_amounts, device=device) losses_before = torch.zeros(num_networks, num_amounts, device=device) sparsities = torch.zeros(num_networks, num_amounts) - + # Create all masks upfront logger.info(" Creating masks for all configurations...") all_masks = self._create_all_masks(strategy_name, selection_mode, pruning_amounts) - + # Evaluate all configurations in a single pass per batch logger.info(" Starting TRULY PARALLEL evaluation...") start_time = time.time() - + # Pre-allocate for all configurations all_correct = torch.zeros(total_configs, device=device) all_loss = torch.zeros(total_configs, device=device) total_samples = 0 - + # Set all networks to eval mode for net in self.networks: net.eval() - + criterion = nn.CrossEntropyLoss(reduction='none') eval_batches = getattr(self.config, 'eval_batches', None) if hasattr(self.config, 'eval_batches') else None batch_count = 0 - + with torch.no_grad(): for inputs, targets in self.data_loader: inputs = inputs.to(device) targets = targets.to(device) batch_size = targets.size(0) - + # Collect outputs from ALL configurations in one go all_outputs = [] - + for net_idx in range(num_networks): net = self.networks[net_idx] - + for amount_idx in range(num_amounts): # Apply configuration self._apply_mask_config(net, all_masks[net_idx][amount_idx], self.original_states[net_idx]) - + # Forward pass outputs = net(inputs) all_outputs.append(outputs) - + # Calculate sparsity (only once) if batch_count == 0: sparsities[net_idx, amount_idx] = self._calculate_sparsity(net) - + # Stack all outputs for vectorized processing stacked_outputs = torch.stack(all_outputs, dim=0) # [total_configs, batch_size, num_classes] - + # Compute losses for all configs at once expanded_targets = targets.unsqueeze(0).expand(total_configs, -1) all_batch_losses = criterion( - stacked_outputs.reshape(-1, stacked_outputs.size(-1)), + stacked_outputs.reshape(-1, stacked_outputs.size(-1)), expanded_targets.reshape(-1) ).reshape(total_configs, batch_size) - + # Sum losses all_loss += all_batch_losses.sum(dim=1) - + # Get predictions and count correct all_preds = stacked_outputs.argmax(dim=2) # [total_configs, batch_size] correct = all_preds.eq(expanded_targets).sum(dim=1) all_correct += correct - + total_samples += batch_size batch_count += 1 - + # Check if we've evaluated enough batches if eval_batches is not None and batch_count >= eval_batches: break - + # Reshape results back to [num_networks, num_amounts] all_correct = all_correct.reshape(num_networks, num_amounts) all_loss = all_loss.reshape(num_networks, num_amounts) - + # Convert to accuracies and average losses accuracies_before = (all_correct * 100.0 / total_samples).cpu() losses_before = (all_loss / batch_count).cpu() - + eval_time = time.time() - start_time logger.info(f" Parallel evaluation completed in {eval_time:.2f} seconds") logger.info(f" Average accuracy: {accuracies_before.mean():.2f}%") - + # For now, no fine-tuning in ultra-fast mode (can be added if needed) accuracies_after = accuracies_before.clone() losses_after = losses_before.clone() - + # Reset networks to train mode for net in self.networks: net.train() - + return { "accuracies_before": accuracies_before, "losses_before": losses_before, @@ -208,25 +208,25 @@ def _ultra_fast_parallel_pruning( "losses_after": losses_after, "sparsities": sparsities } - + def compute_importance_scores(self, module: nn.Module, inputs: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor: """Compute importance scores for a module (not used in parallel mode).""" # This is implemented for compatibility with BasePruningStrategy # The actual importance computation happens in _create_all_masks return module.weight.data.abs() - + def _create_all_masks( - self, - strategy_name: str, - selection_mode: str, + self, + strategy_name: str, + selection_mode: str, pruning_amounts: List[float] ) -> List[List[Dict[str, torch.Tensor]]]: """Create all masks for all networks and pruning amounts.""" all_masks = [] - + for net_idx, net in enumerate(self.networks): network_masks = [] - + for amount in pruning_amounts: if strategy_name == "magnitude": masks = self._create_magnitude_masks(net, amount, selection_mode) @@ -237,29 +237,29 @@ def _create_all_masks( masks = self._create_alignment_masks(net, amount, selection_mode) else: raise ValueError(f"Unknown strategy: {strategy_name}") - + network_masks.append(masks) - + all_masks.append(network_masks) - + return all_masks - + def _create_magnitude_masks( - self, - model: nn.Module, - amount: float, + self, + model: nn.Module, + amount: float, selection_mode: str ) -> Dict[str, torch.Tensor]: """Create magnitude-based masks for a model.""" masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): weight = module.weight.data importance = weight.abs() - + structured = getattr(self.config, 'alignment_structured_pruning', True) if hasattr(self.config, 'alignment_structured_pruning') else True - + if structured and len(weight.shape) >= 2: # Structured pruning - prune entire neurons neuron_importance = importance.mean(dim=tuple(range(1, len(weight.shape)))) @@ -267,25 +267,25 @@ def _create_magnitude_masks( else: # Unstructured pruning mask = self._create_mask(importance, amount, selection_mode) - + masks[name] = mask - + return masks - + def _create_random_masks( - self, - model: nn.Module, + self, + model: nn.Module, amount: float ) -> Dict[str, torch.Tensor]: """Create random masks for a model.""" masks = {} - + for name, module in model.named_modules(): if hasattr(module, 'weight'): weight = module.weight.data - + structured = getattr(self.config, 'alignment_structured_pruning', True) if hasattr(self.config, 'alignment_structured_pruning') else True - + if structured and len(weight.shape) >= 2: # Structured random pruning num_neurons = weight.shape[0] @@ -299,15 +299,15 @@ def _create_random_masks( else: # Unstructured random pruning mask = torch.rand_like(weight) > amount - + masks[name] = mask.float() - + return masks - + def _create_alignment_masks( - self, - model: nn.Module, - amount: float, + self, + model: nn.Module, + amount: float, selection_mode: str ) -> Dict[str, torch.Tensor]: """Create alignment-based masks (simplified for speed).""" @@ -315,11 +315,11 @@ def _create_alignment_masks( # based on weight magnitudes weighted by gradient magnitudes # (Full alignment computation would require forward passes) return self._create_magnitude_masks(model, amount, selection_mode) - + def _create_mask( - self, - importance: torch.Tensor, - amount: float, + self, + importance: torch.Tensor, + amount: float, selection_mode: str ) -> torch.Tensor: """Create a binary mask based on importance scores.""" @@ -327,13 +327,13 @@ def _create_mask( return torch.ones_like(importance) elif amount >= 1: return torch.zeros_like(importance) - + flat_importance = importance.flatten() k = int(amount * flat_importance.numel()) - + if k == 0: return torch.ones_like(importance) - + if selection_mode == "low": threshold = torch.kthvalue(flat_importance, k).values mask = importance > threshold @@ -344,20 +344,20 @@ def _create_mask( mask = torch.rand_like(importance) > amount else: raise ValueError(f"Unknown selection mode: {selection_mode}") - + return mask.float() - + def _create_structured_mask( - self, - neuron_importance: torch.Tensor, - amount: float, + self, + neuron_importance: torch.Tensor, + amount: float, selection_mode: str, weight_shape: torch.Size ) -> torch.Tensor: """Create a structured mask that prunes entire neurons.""" num_neurons = neuron_importance.numel() num_to_prune = int(amount * num_neurons) - + if num_to_prune == 0: mask = torch.ones_like(neuron_importance) elif num_to_prune >= num_neurons: @@ -377,7 +377,7 @@ def _create_structured_mask( mask[indices] = 0 else: raise ValueError(f"Unknown selection mode: {selection_mode}") - + # Expand mask to match weight dimensions if len(weight_shape) == 2: # Linear layer: [out_features, in_features] @@ -385,13 +385,13 @@ def _create_structured_mask( elif len(weight_shape) == 4: # Conv layer: [out_channels, in_channels, height, width] mask = mask.view(-1, 1, 1, 1).expand_as(torch.zeros(weight_shape)) - + return mask - + def _apply_mask_config( - self, - model: nn.Module, - masks: Dict[str, torch.Tensor], + self, + model: nn.Module, + masks: Dict[str, torch.Tensor], original_state: Dict[str, torch.Tensor] ): """Apply masks to a model after restoring original weights.""" @@ -400,20 +400,20 @@ def _apply_mask_config( if name in original_state: # Restore original weights module.weight.data = original_state[name].clone() - + # Apply mask if exists if name in masks: module.weight.data *= masks[name] - + def _calculate_sparsity(self, model: nn.Module) -> float: """Calculate the sparsity of a model.""" total_params = 0 zero_params = 0 - + for module in model.modules(): if hasattr(module, 'weight'): weight = module.weight.data total_params += weight.numel() zero_params += (weight == 0).sum().item() - - return zero_params / total_params if total_params > 0 else 0.0 \ No newline at end of file + + return zero_params / total_params if total_params > 0 else 0.0 diff --git a/src/alignment/quantization/__init__.py b/src/alignment/quantization/__init__.py index feb55578..30d3f6b3 100644 --- a/src/alignment/quantization/__init__.py +++ b/src/alignment/quantization/__init__.py @@ -13,18 +13,17 @@ - Symmetric and asymmetric quantization """ -from .ptq import ( - quantize_model, - quantize_layer, - INT8Quantizer, - INT4Quantizer, - MixedPrecisionQuantizer -) - from .analysis import ( analyze_quantization_sensitivity, compute_quantization_error, - find_optimal_bit_allocation + find_optimal_bit_allocation, +) +from .ptq import ( + INT4Quantizer, + INT8Quantizer, + MixedPrecisionQuantizer, + quantize_layer, + quantize_model, ) __all__ = [ diff --git a/src/alignment/quantization/analysis.py b/src/alignment/quantization/analysis.py index 99a3673f..ffe154f2 100644 --- a/src/alignment/quantization/analysis.py +++ b/src/alignment/quantization/analysis.py @@ -4,10 +4,11 @@ Analyze quantization effects on model performance and neuron importance. """ +import logging +from typing import Callable, Dict, List + import torch import torch.nn as nn -from typing import Dict, List, Optional, Callable -import logging logger = logging.getLogger(__name__) @@ -20,35 +21,35 @@ def analyze_quantization_sensitivity( ) -> Dict[str, Dict]: """ Analyze how sensitive each layer is to quantization. - + Args: model: Model to analyze data_loader: Validation data eval_fn: Evaluation function precision_levels: Precisions to test - + Returns: Sensitivity scores per layer per precision """ from .ptq import quantize_layer - + baseline_score = eval_fn(model, data_loader) - + results = {} - + for name, module in model.named_modules(): if not isinstance(module, nn.Linear): continue - + layer_results = {} - + for precision in precision_levels: # Save original weight original_weight = module.weight.data.clone() - + # Quantize and dequantize quant_result = quantize_layer(module, precision) - + # Dequantize for evaluation if precision == 'int8': dequant = quant_result['weight'].float() * quant_result['scale'] @@ -57,27 +58,27 @@ def analyze_quantization_sensitivity( else: # int4 # Simplified dequantization dequant = quant_result['weight'].float() * quant_result['scale'].mean() - + # Apply quantized weight module.weight.data = dequant - + # Evaluate quantized_score = eval_fn(model, data_loader) - + # Compute sensitivity sensitivity = abs(baseline_score - quantized_score) - + layer_results[precision] = { 'score': quantized_score, 'sensitivity': sensitivity, 'relative_error': sensitivity / baseline_score if baseline_score != 0 else 0 } - + # Restore original module.weight.data = original_weight - + results[name] = layer_results - + return results @@ -88,27 +89,27 @@ def compute_quantization_error( ) -> Dict[str, float]: """ Compute quantization error metrics. - + Args: original_weight: Original FP weights quantized_weight: Quantized weights scale: Quantization scale - + Returns: Error metrics (MSE, MAE, SNR) """ # Dequantize dequantized = quantized_weight.float() * scale - + # Compute errors mse = ((original_weight - dequantized) ** 2).mean().item() mae = (original_weight - dequantized).abs().mean().item() - + # Signal-to-noise ratio signal_power = (original_weight ** 2).mean().item() noise_power = mse snr = 10 * torch.log10(torch.tensor(signal_power / (noise_power + 1e-10))) - + return { 'mse': mse, 'mae': mae, @@ -125,17 +126,17 @@ def find_optimal_bit_allocation( ) -> Dict[str, int]: """ Find optimal bit allocation per layer based on importance. - + High importance layers get more bits, low importance get fewer bits, achieving target average bits overall. - + Args: model: Model layer_scores: Importance scores per layer target_avg_bits: Target average bits (e.g., 6.0) min_bits: Minimum bits per layer max_bits: Maximum bits per layer - + Returns: Bit allocation per layer """ @@ -143,47 +144,47 @@ def find_optimal_bit_allocation( all_scores = [] layer_names = [] layer_sizes = [] - + for name, module in model.named_modules(): if isinstance(module, nn.Linear) and name in layer_scores: score = layer_scores[name].mean().item() size = module.weight.numel() - + all_scores.append(score) layer_names.append(name) layer_sizes.append(size) - + if not all_scores: return {} - + scores_tensor = torch.tensor(all_scores) scores_norm = (scores_tensor - scores_tensor.min()) / (scores_tensor.max() - scores_tensor.min() + 1e-8) - + # Allocate bits proportionally to importance # Important layers get more bits bit_range = max_bits - min_bits bits_initial = (min_bits + bit_range * scores_norm).round().int() - + # Adjust to meet target average total_size = sum(layer_sizes) current_avg = sum(bits_initial[i].item() * layer_sizes[i] for i in range(len(layer_sizes))) / total_size - + # Scale to hit target if current_avg != target_avg_bits: adjustment = target_avg_bits / current_avg bits_adjusted = (bits_initial.float() * adjustment).round().clamp(min_bits, max_bits).int() else: bits_adjusted = bits_initial - + # Create allocation dict allocation = { layer_names[i]: bits_adjusted[i].item() for i in range(len(layer_names)) } - + # Verify average actual_avg = sum(allocation[name] * layer_sizes[i] for i, name in enumerate(layer_names)) / total_size logger.info(f"Bit allocation: target={target_avg_bits}, actual={actual_avg:.2f}") - + return allocation diff --git a/src/alignment/quantization/ptq.py b/src/alignment/quantization/ptq.py index 7dc828af..66ab8d95 100644 --- a/src/alignment/quantization/ptq.py +++ b/src/alignment/quantization/ptq.py @@ -4,10 +4,11 @@ Quantize trained models without retraining. """ +import logging +from typing import Dict, Literal, Optional + import torch import torch.nn as nn -from typing import Optional, Dict, List, Literal -import logging logger = logging.getLogger(__name__) @@ -15,10 +16,10 @@ class INT8Quantizer: """ INT8 post-training quantization. - + Converts FP32/FP16 weights and activations to INT8. """ - + def __init__( self, per_channel: bool = True, @@ -26,14 +27,14 @@ def __init__( ): """ Initialize INT8 quantizer. - + Args: per_channel: Per-channel vs per-tensor quantization symmetric: Symmetric vs asymmetric quantization """ self.per_channel = per_channel self.symmetric = symmetric - + def quantize_tensor( self, tensor: torch.Tensor, @@ -41,11 +42,11 @@ def quantize_tensor( ) -> tuple: """ Quantize a tensor to INT8. - + Args: tensor: Tensor to quantize dim: Channel dimension (for per-channel) - + Returns: (quantized_tensor, scale, zero_point) """ @@ -54,7 +55,7 @@ def quantize_tensor( # Compute scale per channel dims_to_reduce = list(range(tensor.ndim)) dims_to_reduce.remove(dim) - + if self.symmetric: # Symmetric: scale based on max absolute value max_val = tensor.abs().amax(dim=dims_to_reduce, keepdim=True) @@ -77,33 +78,33 @@ def quantize_tensor( max_val = tensor.max() scale = (max_val - min_val) / 255.0 zero_point = (-min_val / scale).round().to(torch.int8) - + # Quantize if self.symmetric: quantized = (tensor / (scale + 1e-8)).round().clamp(-127, 127).to(torch.int8) else: quantized = ((tensor / (scale + 1e-8)) + zero_point).round().clamp(0, 255).to(torch.int8) - + return quantized, scale, zero_point - + def quantize_linear_layer( self, layer: nn.Linear ) -> Dict: """ Quantize a Linear layer. - + Args: layer: Linear layer to quantize - + Returns: Dict with quantized weight, scale, zero_point """ weight = layer.weight.data - + # Quantize weights (per output channel) q_weight, scale, zero_point = self.quantize_tensor(weight, dim=0) - + result = { 'weight': q_weight, 'scale': scale, @@ -111,92 +112,92 @@ def quantize_linear_layer( 'original_dtype': weight.dtype, 'original_shape': weight.shape } - + if layer.bias is not None: result['bias'] = layer.bias.data - + return result class INT4Quantizer: """ INT4 quantization for aggressive compression. - + Commonly used for LLMs to reduce memory footprint. """ - + def __init__(self, block_size: int = 128): """ Initialize INT4 quantizer. - + Args: block_size: Block size for group quantization """ self.block_size = block_size - + def quantize_tensor( self, tensor: torch.Tensor ) -> tuple: """ Quantize tensor to INT4 using block-wise quantization. - + Args: tensor: Tensor to quantize - + Returns: (quantized, scales, zero_points) """ original_shape = tensor.shape tensor_flat = tensor.flatten() - + # Pad to block size pad_size = (self.block_size - len(tensor_flat) % self.block_size) % self.block_size if pad_size > 0: tensor_flat = torch.nn.functional.pad(tensor_flat, (0, pad_size)) - + # Reshape into blocks blocks = tensor_flat.reshape(-1, self.block_size) - num_blocks = blocks.shape[0] - + blocks.shape[0] + # Quantize each block quantized_blocks = [] scales = [] zero_points = [] - + for block in blocks: # Symmetric INT4: range [-7, 7] max_val = block.abs().max() scale = max_val / 7.0 - + q_block = (block / (scale + 1e-8)).round().clamp(-7, 7).to(torch.int8) - + quantized_blocks.append(q_block) scales.append(scale) zero_points.append(torch.tensor(0, dtype=torch.int8)) - + quantized = torch.cat(quantized_blocks) - + # Remove padding if pad_size > 0: quantized = quantized[:-pad_size] - + # Reshape back quantized = quantized.reshape(original_shape) scales = torch.tensor(scales, dtype=tensor.dtype) - + return quantized, scales, torch.zeros_like(scales, dtype=torch.int8) class MixedPrecisionQuantizer: """ Mixed-precision quantization. - + Uses alignment metrics to decide precision per layer: - Important layers: Higher precision (FP16/INT8) - Less important: Lower precision (INT4) """ - + def __init__( self, importance_threshold: float = 0.5, @@ -205,7 +206,7 @@ def __init__( ): """ Initialize mixed-precision quantizer. - + Args: importance_threshold: Threshold for high vs low precision high_precision: Format for important layers @@ -214,10 +215,10 @@ def __init__( self.importance_threshold = importance_threshold self.high_precision = high_precision self.low_precision = low_precision - + self.int8_quantizer = INT8Quantizer() self.int4_quantizer = INT4Quantizer() - + def quantize_model_adaptive( self, model: nn.Module, @@ -225,25 +226,25 @@ def quantize_model_adaptive( ) -> Dict: """ Quantize model with mixed precision based on importance. - + Args: model: Model to quantize layer_importance: Importance score per layer - + Returns: Quantization results per layer """ results = {} - + for name, module in model.named_modules(): if not isinstance(module, nn.Linear): continue - + if name not in layer_importance: continue - + importance = layer_importance[name] - + # Choose precision based on importance if importance > self.importance_threshold: # High importance -> higher precision @@ -254,11 +255,11 @@ def quantize_model_adaptive( precision = self.low_precision result = self.int4_quantizer.quantize_tensor(module.weight.data) result = {'weight': result[0], 'scale': result[1], 'zero_point': result[2]} - + result['precision'] = precision result['importance'] = importance results[name] = result - + return results @@ -269,12 +270,12 @@ def quantize_model( ) -> Dict: """ Quantize entire model. - + Args: model: Model to quantize precision: Quantization precision layer_importance: For mixed-precision (optional) - + Returns: Quantization results per layer """ @@ -289,12 +290,12 @@ def quantize_model( return quantizer.quantize_model_adaptive(model, layer_importance) else: raise ValueError(f"Unknown precision: {precision}") - + results = {} for name, module in model.named_modules(): if isinstance(module, nn.Linear): results[name] = quantizer.quantize_linear_layer(module) - + return results @@ -304,11 +305,11 @@ def quantize_layer( ) -> Dict: """ Quantize a single layer. - + Args: layer: Layer to quantize precision: Quantization precision - + Returns: Quantization result """ @@ -318,7 +319,7 @@ def quantize_layer( quantizer = INT4Quantizer() else: raise ValueError(f"Unknown precision: {precision}") - + if isinstance(layer, nn.Linear): return quantizer.quantize_linear_layer(layer) else: diff --git a/src/alignment/services/__init__.py b/src/alignment/services/__init__.py index 90dd855d..669aead9 100644 --- a/src/alignment/services/__init__.py +++ b/src/alignment/services/__init__.py @@ -8,26 +8,22 @@ from .activation_capture import ( ActivationCaptureService, ActivationData, - create_capture_service -) -from .scoring import ( - NodeScoringService, - CompositeScores, - create_scoring_service + create_capture_service, ) from .mask_ops import MaskOperations +from .scoring import CompositeScores, NodeScoringService, create_scoring_service __all__ = [ # Activation capture 'ActivationCaptureService', 'ActivationData', 'create_capture_service', - + # Scoring 'NodeScoringService', 'CompositeScores', 'create_scoring_service', - + # Mask operations 'MaskOperations', ] diff --git a/src/alignment/services/activation_capture.py b/src/alignment/services/activation_capture.py index ea03f4c9..698325ba 100644 --- a/src/alignment/services/activation_capture.py +++ b/src/alignment/services/activation_capture.py @@ -5,11 +5,11 @@ lifecycle management and preprocessing. """ -from typing import Dict, List, Optional, Any, Tuple +import logging from dataclasses import dataclass +from typing import Any, Dict, List, Optional + import torch -import torch.nn as nn -import logging logger = logging.getLogger(__name__) @@ -26,20 +26,20 @@ class ActivationData: class ActivationCaptureService: """ Centralized service for activation and weight collection. - + This service provides: - Automatic hook lifecycle management - Preprocessing options - Memory-efficient batch processing - Clean API for experiments - + Example: >>> service = ActivationCaptureService(model_wrapper) >>> data = service.capture(input_batch, layers=['conv1', 'fc1']) >>> print(data.inputs['conv1'].shape) >>> print(data.outputs['conv1'].shape) """ - + def __init__( self, model_wrapper: Any, # BaseModelWrapper or similar @@ -48,7 +48,7 @@ def __init__( ): """ Initialize activation capture service. - + Args: model_wrapper: Model wrapper with HookManager support default_mode: Default preprocessing mode ('flatten', 'preserve_spatial') @@ -57,7 +57,7 @@ def __init__( self.model_wrapper = model_wrapper self.default_mode = default_mode self.conv_spatial = conv_spatial - + def capture( self, input_batch: torch.Tensor, @@ -68,20 +68,20 @@ def capture( ) -> ActivationData: """ Capture activations and weights for specified layers. - + Args: input_batch: Input tensor [batch, ...] layers: Layers to capture (None = all tracked) mode: Preprocessing mode (None = use default) include_weights: Whether to capture weights preprocess: Whether to preprocess activations - + Returns: ActivationData with inputs, outputs, and weights """ mode = mode or self.default_mode layers = layers or self.model_wrapper.tracked_layers - + # Capture activations using model wrapper try: output, activations = self.model_wrapper.forward_with_activations( @@ -90,36 +90,36 @@ def capture( except Exception as e: logger.error(f"Failed to capture activations: {e}") raise - + # Separate inputs and outputs inputs = {} outputs = {} for layer in layers: input_key = f"{layer}_input" output_key = f"{layer}_output" - + if input_key in activations: inputs[layer] = activations[input_key] if output_key in activations: outputs[layer] = activations[output_key] - + # Preprocess if requested if preprocess: inputs = self._preprocess_activations(inputs, mode) outputs = self._preprocess_activations(outputs, mode) - + # Capture weights if requested weights = {} if include_weights: weights = self.model_wrapper.get_layer_weights(layers=layers) - + return ActivationData( inputs=inputs, outputs=outputs, weights=weights, layer_names=layers ) - + def capture_batch_aggregated( self, dataloader, @@ -129,46 +129,46 @@ def capture_batch_aggregated( ) -> ActivationData: """ Capture activations across multiple batches with aggregation. - + Args: dataloader: DataLoader providing batches layers: Layers to capture max_batches: Maximum number of batches to process aggregation: How to aggregate ('concatenate', 'mean', 'none') - + Returns: Aggregated ActivationData """ layers = layers or self.model_wrapper.tracked_layers - + all_inputs = {layer: [] for layer in layers} all_outputs = {layer: [] for layer in layers} - + for batch_idx, (inputs, _) in enumerate(dataloader): if max_batches and batch_idx >= max_batches: break - + # Move to model device inputs = inputs.to(next(self.model_wrapper.model.parameters()).device) - + # Capture data = self.capture( - inputs, - layers=layers, + inputs, + layers=layers, include_weights=(batch_idx == 0) # Only get weights once ) - + # Accumulate for layer in layers: if layer in data.inputs: all_inputs[layer].append(data.inputs[layer]) if layer in data.outputs: all_outputs[layer].append(data.outputs[layer]) - + # Aggregate if aggregation == 'concatenate': aggregated_inputs = { - layer: torch.cat(tensors, dim=0) + layer: torch.cat(tensors, dim=0) for layer, tensors in all_inputs.items() if tensors } aggregated_outputs = { @@ -187,17 +187,17 @@ def capture_batch_aggregated( else: # 'none' aggregated_inputs = all_inputs aggregated_outputs = all_outputs - + # Get weights from first batch weights = data.weights if 'data' in locals() else {} - + return ActivationData( inputs=aggregated_inputs, outputs=aggregated_outputs, weights=weights, layer_names=layers ) - + def _preprocess_activations( self, activations: Dict[str, torch.Tensor], @@ -205,17 +205,17 @@ def _preprocess_activations( ) -> Dict[str, torch.Tensor]: """ Preprocess activations based on mode. - + Args: activations: Raw activations mode: Preprocessing mode - + Returns: Preprocessed activations """ if mode == 'none': return activations - + processed = {} for name, tensor in activations.items(): if mode == 'flatten': @@ -224,11 +224,11 @@ def _preprocess_activations( processed[name] = tensor.reshape(tensor.shape[0], -1) else: processed[name] = tensor - + elif mode == 'preserve_spatial': # Keep original shape processed[name] = tensor - + elif mode == 'patchwise': # For Conv: [B, C, H, W] -> [B, C, H*W] if tensor.ndim == 4: # Conv2d @@ -238,17 +238,17 @@ def _preprocess_activations( processed[name] = tensor else: processed[name] = tensor - + else: logger.warning(f"Unknown preprocessing mode: {mode}, using 'flatten'") processed[name] = tensor.reshape(tensor.shape[0], -1) if tensor.ndim > 2 else tensor - + return processed - + def __enter__(self): """Support context manager usage.""" return self - + def __exit__(self, exc_type, exc_val, exc_tb): """Cleanup on exit.""" if hasattr(self.model_wrapper, 'hook_manager'): @@ -259,11 +259,11 @@ def __exit__(self, exc_type, exc_val, exc_tb): def create_capture_service(model_wrapper, **config) -> ActivationCaptureService: """ Factory function for creating ActivationCaptureService. - + Args: model_wrapper: Model wrapper instance **config: Configuration options - + Returns: Configured ActivationCaptureService """ diff --git a/src/alignment/services/mask_ops.py b/src/alignment/services/mask_ops.py index a53061c3..c5df4260 100644 --- a/src/alignment/services/mask_ops.py +++ b/src/alignment/services/mask_ops.py @@ -5,9 +5,10 @@ and unstructured pruning. """ -from typing import Tuple, Optional, Union -import torch import logging +from typing import Tuple + +import torch logger = logging.getLogger(__name__) @@ -15,14 +16,14 @@ class MaskOperations: """ Utilities for creating and manipulating pruning masks. - + Provides methods for: - Creating structured (neuron/channel) masks - Creating unstructured (weight-level) masks - Expanding masks to match weight shapes - Applying masks to modules """ - + @staticmethod def create_structured_mask( scores: torch.Tensor, @@ -32,49 +33,49 @@ def create_structured_mask( ) -> torch.Tensor: """ Create structured mask (neuron/channel level) from importance scores. - + Args: scores: Importance scores [num_neurons] amount: Fraction to prune (0 to 1) mode: 'low' (prune low scores), 'high' (prune high scores), 'random' min_keep: Minimum number of neurons to keep - + Returns: Binary mask [num_neurons] where 1=keep, 0=prune """ num_neurons = scores.shape[0] num_to_prune = max(0, int(num_neurons * amount)) num_to_keep = max(min_keep, num_neurons - num_to_prune) - + mask = torch.ones_like(scores, dtype=torch.bool) - + if num_to_prune == 0: return mask - + if mode == 'low': # Prune lowest scores _, indices = torch.topk(scores, num_to_keep, largest=True) mask = torch.zeros_like(scores, dtype=torch.bool) mask[indices] = True - + elif mode == 'high': # Prune highest scores _, indices = torch.topk(scores, num_to_keep, largest=False) mask = torch.zeros_like(scores, dtype=torch.bool) mask[indices] = True - + elif mode == 'random': # Random pruning indices = torch.randperm(num_neurons)[:num_to_keep] mask = torch.zeros_like(scores, dtype=torch.bool) mask[indices] = True - + else: raise ValueError(f"Unknown pruning mode: {mode}") - + logger.debug(f"Created structured mask: {mask.sum()}/{num_neurons} neurons kept") return mask - + @staticmethod def create_unstructured_mask( scores: torch.Tensor, @@ -83,25 +84,25 @@ def create_unstructured_mask( ) -> torch.Tensor: """ Create unstructured mask (weight level) from importance scores. - + Args: scores: Importance scores (any shape) amount: Fraction to prune mode: 'low', 'high', or 'random' - + Returns: Binary mask same shape as scores """ num_weights = scores.numel() num_to_prune = int(num_weights * amount) num_to_keep = num_weights - num_to_prune - + if num_to_prune == 0: return torch.ones_like(scores, dtype=torch.bool) - + # Flatten for processing scores_flat = scores.flatten() - + if mode == 'low': _, indices = torch.topk(scores_flat, num_to_keep, largest=True) elif mode == 'high': @@ -110,17 +111,17 @@ def create_unstructured_mask( indices = torch.randperm(num_weights)[:num_to_keep] else: raise ValueError(f"Unknown mode: {mode}") - + # Create flat mask mask_flat = torch.zeros_like(scores_flat, dtype=torch.bool) mask_flat[indices] = True - + # Reshape to original shape mask = mask_flat.reshape(scores.shape) - + logger.debug(f"Created unstructured mask: {mask.sum()}/{num_weights} weights kept") return mask - + @staticmethod def expand_neuron_mask_to_weights( neuron_mask: torch.Tensor, @@ -129,18 +130,18 @@ def expand_neuron_mask_to_weights( ) -> torch.Tensor: """ Expand neuron/channel mask to full weight tensor. - + Args: neuron_mask: Binary mask [num_neurons] weight_shape: Target weight shape, e.g., [out, in] or [out, in, k, k] dim: Dimension corresponding to neurons (usually 0 for output neurons) - + Returns: Expanded mask matching weight_shape """ # Create base mask mask = torch.ones(weight_shape, dtype=torch.bool, device=neuron_mask.device) - + # Apply neuron mask along specified dimension if dim == 0: # Mask output neurons/channels @@ -152,7 +153,7 @@ def expand_neuron_mask_to_weights( mask[i, :, :, :] = False elif len(weight_shape) == 3: # Conv1d: [out, in, k] mask[i, :, :] = False - + elif dim == 1: # Mask input neurons/channels (less common) for i, keep in enumerate(neuron_mask): @@ -163,9 +164,9 @@ def expand_neuron_mask_to_weights( mask[:, i, :, :] = False elif len(weight_shape) == 3: mask[:, i, :] = False - + return mask - + @staticmethod def apply_mask_to_weights( weights: torch.Tensor, @@ -174,12 +175,12 @@ def apply_mask_to_weights( ) -> torch.Tensor: """ Apply mask to weights. - + Args: weights: Weight tensor mask: Binary mask (same shape as weights) mode: 'multiply' or 'zero' - + Returns: Masked weights """ @@ -187,7 +188,7 @@ def apply_mask_to_weights( raise ValueError( f"Mask shape {mask.shape} doesn't match weights shape {weights.shape}" ) - + if mode == 'multiply': return weights * mask.float() elif mode == 'zero': @@ -196,22 +197,22 @@ def apply_mask_to_weights( return masked_weights else: raise ValueError(f"Unknown mode: {mode}") - + @staticmethod def get_mask_statistics(mask: torch.Tensor) -> dict: """ Compute statistics about a mask. - + Args: mask: Binary mask - + Returns: Dictionary with statistics """ total = mask.numel() kept = mask.sum().item() pruned = total - kept - + return { 'total_elements': total, 'kept_elements': kept, @@ -219,7 +220,7 @@ def get_mask_statistics(mask: torch.Tensor) -> dict: 'sparsity': pruned / total if total > 0 else 0.0, 'density': kept / total if total > 0 else 0.0 } - + @staticmethod def combine_masks( masks: list, @@ -227,32 +228,32 @@ def combine_masks( ) -> torch.Tensor: """ Combine multiple masks. - + Args: masks: List of binary masks (same shape) operation: 'and' (intersection) or 'or' (union) - + Returns: Combined mask """ if not masks: raise ValueError("No masks provided") - + result = masks[0].clone() - + for mask in masks[1:]: if mask.shape != result.shape: raise ValueError("All masks must have the same shape") - + if operation == 'and': result = result & mask elif operation == 'or': result = result | mask else: raise ValueError(f"Unknown operation: {operation}") - + return result - + @staticmethod def global_threshold_mask( layer_scores: dict, @@ -261,29 +262,29 @@ def global_threshold_mask( ) -> dict: """ Create masks using a global threshold across all layers. - + Args: layer_scores: Dict mapping layer names to score tensors global_amount: Global fraction to prune mode: Pruning mode - + Returns: Dict mapping layer names to masks """ # Concatenate all scores all_scores = [] layer_info = [] - + for layer_name, scores in layer_scores.items(): all_scores.append(scores.flatten()) layer_info.append((layer_name, scores.shape, len(scores.flatten()))) - + all_scores_cat = torch.cat(all_scores) - + # Compute global threshold num_total = len(all_scores_cat) num_to_keep = int(num_total * (1 - global_amount)) - + if mode == 'low': threshold = torch.topk(all_scores_cat, num_to_keep, largest=True)[0][-1] threshold_fn = lambda s: s >= threshold @@ -292,15 +293,15 @@ def global_threshold_mask( threshold_fn = lambda s: s <= threshold else: raise ValueError(f"Global thresholding not supported for mode: {mode}") - + # Create per-layer masks masks = {} for layer_name, shape, _ in layer_info: scores = layer_scores[layer_name] mask = threshold_fn(scores) masks[layer_name] = mask - + logger.info(f"Global thresholding: {num_to_keep}/{num_total} elements kept") - + return masks diff --git a/src/alignment/services/scoring.py b/src/alignment/services/scoring.py index 430fc5ce..30f89d0d 100644 --- a/src/alignment/services/scoring.py +++ b/src/alignment/services/scoring.py @@ -5,10 +5,11 @@ into composite neuron importance scores for pruning and analysis. """ -from typing import Dict, List, Optional, Tuple, Any -import torch import logging from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple + +import torch logger = logging.getLogger(__name__) @@ -27,16 +28,16 @@ class CompositeScores: class NodeScoringService: """ Service for computing composite neuron importance scores. - + Combines multiple metrics: - Rayleigh Quotient (RQ): Alignment with input covariance - Mutual Information (MI): Information about target - Redundancy: Overlap with other neurons - Synergy: Complementary information with others - + Composite score: Score = α·MI + β·Synergy - γ·Redundancy + δ·log(RQ) - + Example: >>> scorer = NodeScoringService(metrics={'rq': rq_metric, 'mi': mi_metric}) >>> scores = scorer.compute_composite_scores( @@ -46,7 +47,7 @@ class NodeScoringService: ... ) >>> print(scores.composite) # Final importance scores """ - + def __init__( self, metrics: Dict[str, Any], @@ -57,7 +58,7 @@ def __init__( ): """ Initialize node scoring service. - + Args: metrics: Dictionary of initialized metrics Expected keys: 'rq', 'mi', 'redundancy', 'synergy' @@ -71,7 +72,7 @@ def __init__( self.beta_synergy = beta_synergy self.gamma_redundancy = gamma_redundancy self.delta_rq = delta_rq - + # Normalize weights total = alpha_mi + beta_synergy + gamma_redundancy + delta_rq if total > 0: @@ -79,7 +80,7 @@ def __init__( self.beta_synergy /= total self.gamma_redundancy /= total self.delta_rq /= total - + def compute_composite_scores( self, inputs: torch.Tensor, @@ -93,7 +94,7 @@ def compute_composite_scores( ) -> CompositeScores: """ Compute composite importance scores for all neurons in a layer. - + Args: inputs: Input activations [batch, features] weights: Layer weights [num_neurons, features] @@ -103,15 +104,15 @@ def compute_composite_scores( include_synergy: Whether to compute synergy (requires targets) layer_name: Optional layer name for logging **metric_kwargs: Additional arguments for metrics - + Returns: CompositeScores with individual and composite scores """ num_neurons = weights.shape[0] device = weights.device - + scores = CompositeScores(layer_name=layer_name) - + # 1. Compute RQ (always available) if 'rq' in self.metrics: try: @@ -125,7 +126,7 @@ def compute_composite_scores( except Exception as e: logger.warning(f"Failed to compute RQ: {e}") scores.rq = torch.zeros(num_neurons, device=device) - + # 2. Compute MI (if targets available) if 'mi' in self.metrics and targets is not None: try: @@ -140,7 +141,7 @@ def compute_composite_scores( except Exception as e: logger.warning(f"Failed to compute MI: {e}") scores.mi = torch.zeros(num_neurons, device=device) - + # 3. Compute Redundancy (if requested and metric available) if include_redundancy and 'redundancy' in self.metrics: try: @@ -153,7 +154,7 @@ def compute_composite_scores( except Exception as e: logger.warning(f"Failed to compute redundancy: {e}") scores.redundancy = torch.zeros(num_neurons, device=device) - + # 4. Compute Synergy (if requested, targets available, and metric available) if include_synergy and targets is not None and 'synergy' in self.metrics: try: @@ -168,12 +169,12 @@ def compute_composite_scores( except Exception as e: logger.warning(f"Failed to compute synergy: {e}") scores.synergy = torch.zeros(num_neurons, device=device) - + # 5. Compute Composite Score scores.composite = self._compute_composite(scores, num_neurons, device) - + return scores - + def _compute_composite( self, scores: CompositeScores, @@ -182,64 +183,64 @@ def _compute_composite( ) -> torch.Tensor: """ Compute weighted combination of individual scores. - + Args: scores: Individual metric scores num_neurons: Number of neurons device: Target device - + Returns: Composite scores [num_neurons] """ composite = torch.zeros(num_neurons, device=device) - + # Add MI component if scores.mi is not None and self.alpha_mi > 0: # Normalize MI to [0, 1] range for combining mi_normalized = self._normalize_scores(scores.mi) composite += self.alpha_mi * mi_normalized - + # Add Synergy component (positive contribution) if scores.synergy is not None and self.beta_synergy > 0: synergy_normalized = self._normalize_scores(scores.synergy) composite += self.beta_synergy * synergy_normalized - + # Subtract Redundancy component (negative contribution) if scores.redundancy is not None and self.gamma_redundancy > 0: redundancy_normalized = self._normalize_scores(scores.redundancy) composite -= self.gamma_redundancy * redundancy_normalized - + # Add RQ component (log scale) if scores.rq is not None and self.delta_rq > 0: # Use log(RQ + eps) to handle scale rq_log = torch.log(scores.rq + 1e-8) rq_log_normalized = self._normalize_scores(rq_log) composite += self.delta_rq * rq_log_normalized - + return composite - + @staticmethod def _normalize_scores(scores: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: """ Normalize scores to [0, 1] range. - + Args: scores: Raw scores eps: Small value for numerical stability - + Returns: Normalized scores """ scores_min = scores.min() scores_max = scores.max() - + if (scores_max - scores_min) < eps: # All scores are the same return torch.ones_like(scores) * 0.5 - + normalized = (scores - scores_min) / (scores_max - scores_min + eps) return normalized - + def compute_layerwise_scores( self, activation_data, # ActivationData from capture service @@ -249,25 +250,25 @@ def compute_layerwise_scores( ) -> Dict[str, CompositeScores]: """ Compute composite scores for multiple layers. - + Args: activation_data: ActivationData with inputs/weights targets: Target labels layers: Layers to score (None = all) **kwargs: Additional arguments for scoring - + Returns: Dictionary mapping layer names to CompositeScores """ layers = layers or activation_data.layer_names - + layerwise_scores = {} - + for layer in layers: if layer not in activation_data.inputs or layer not in activation_data.weights: logger.warning(f"Skipping layer {layer}: missing inputs or weights") continue - + try: scores = self.compute_composite_scores( inputs=activation_data.inputs[layer], @@ -278,17 +279,17 @@ def compute_layerwise_scores( **kwargs ) layerwise_scores[layer] = scores - + logger.info( f"Layer {layer}: composite score range " f"[{scores.composite.min():.4f}, {scores.composite.max():.4f}]" ) - + except Exception as e: logger.error(f"Failed to score layer {layer}: {e}") - + return layerwise_scores - + def rank_neurons_globally( self, layerwise_scores: Dict[str, CompositeScores], @@ -296,28 +297,28 @@ def rank_neurons_globally( ) -> Tuple[List[Tuple[str, int, float]], Optional[Dict]]: """ Rank all neurons across all layers by composite score. - + Args: layerwise_scores: Scores for each layer return_indices: Whether to return dictionary of per-layer indices - + Returns: Tuple of (ranked_list, optional_indices_dict) ranked_list: List of (layer_name, neuron_idx, score) tuples, sorted indices_dict: Per-layer sorted indices (if requested) """ all_neurons = [] - + for layer_name, scores in layerwise_scores.items(): if scores.composite is None: continue - + for neuron_idx, score in enumerate(scores.composite): all_neurons.append((layer_name, neuron_idx, score.item())) - + # Sort by score (descending) all_neurons.sort(key=lambda x: x[2], reverse=True) - + # Create per-layer indices if requested indices_dict = None if return_indices: @@ -326,7 +327,7 @@ def rank_neurons_globally( if scores.composite is not None: sorted_indices = torch.argsort(scores.composite, descending=True) indices_dict[layer_name] = sorted_indices - + return all_neurons, indices_dict @@ -336,11 +337,11 @@ def create_scoring_service( ) -> NodeScoringService: """ Factory function for creating NodeScoringService. - + Args: metrics: Dictionary of initialized metrics **weights: Weights for composite score (alpha_mi, beta_synergy, etc.) - + Returns: Configured NodeScoringService """ diff --git a/src/alignment/training/__init__.py b/src/alignment/training/__init__.py index 548d3a9d..59ce38c9 100644 --- a/src/alignment/training/__init__.py +++ b/src/alignment/training/__init__.py @@ -3,8 +3,8 @@ """ from .base import BaseTrainer, TrainingConfig -from .multi_network import train_networks_fully_tensorized, TensorizedNetworkWrapper from .experiment_trainer import ExperimentTrainer, ExperimentTrainingConfig +from .multi_network import TensorizedNetworkWrapper, train_networks_fully_tensorized __all__ = [ 'BaseTrainer', @@ -13,4 +13,4 @@ 'TensorizedNetworkWrapper', 'ExperimentTrainer', 'ExperimentTrainingConfig', -] \ No newline at end of file +] diff --git a/src/alignment/training/base.py b/src/alignment/training/base.py index eb89372f..81b0f27a 100644 --- a/src/alignment/training/base.py +++ b/src/alignment/training/base.py @@ -2,14 +2,15 @@ Base training utilities for neural networks. """ -from typing import Dict, List, Optional, Callable, Any, Tuple, Union -import torch -import torch.nn as nn -import torch.optim as optim import logging -from pathlib import Path import time from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.optim as optim logger = logging.getLogger(__name__) @@ -35,11 +36,11 @@ class TrainingConfig: class BaseTrainer: """ Base trainer class for neural network training. - + This class provides standard training functionality that can be extended for specific training strategies. """ - + def __init__( self, model: nn.Module, @@ -49,7 +50,7 @@ def __init__( ): """ Initialize trainer. - + Args: model: Model to train config: Training configuration @@ -60,20 +61,20 @@ def __init__( self.config = config or TrainingConfig() self.loss_fn = loss_fn or nn.CrossEntropyLoss() self.callbacks = callbacks or [] - + self.device = torch.device(self.config.device) self.model.to(self.device) - + # Setup optimizer self.optimizer = self._create_optimizer() self.scheduler = self._create_scheduler() - + # Training state self.current_epoch = 0 self.global_step = 0 self.best_val_metric = float('inf') self.patience_counter = 0 - + # History self.history = { 'train_loss': [], @@ -83,7 +84,7 @@ def __init__( 'epoch_times': [], 'learning_rates': [] } - + def train( self, train_loader: torch.utils.data.DataLoader, @@ -92,45 +93,45 @@ def train( ) -> Dict[str, List[float]]: """ Train the model. - + Args: train_loader: Training data loader val_loader: Validation data loader metric_fn: Optional metric function - + Returns: Training history dictionary """ logger.info(f"Starting training for {self.config.epochs} epochs") - + for epoch in range(self.config.epochs): self.current_epoch = epoch epoch_start = time.time() - + # Training phase train_loss, train_metrics = self._train_epoch( train_loader, metric_fn ) - + # Validation phase val_loss, val_metrics = 0.0, {} if val_loader and (epoch + 1) % self.config.eval_interval == 0: val_loss, val_metrics = self._validate( val_loader, metric_fn ) - + # Early stopping check if self._should_stop_early(val_loss): logger.info(f"Early stopping triggered at epoch {epoch + 1}") break - + # Update scheduler if self.scheduler: if isinstance(self.scheduler, optim.lr_scheduler.ReduceLROnPlateau): self.scheduler.step(val_loss) else: self.scheduler.step() - + # Record history epoch_time = time.time() - epoch_start self.history['train_loss'].append(train_loss) @@ -141,22 +142,22 @@ def train( self.history['learning_rates'].append( self.optimizer.param_groups[0]['lr'] ) - + # Log progress self._log_epoch_progress( epoch, train_loss, train_metrics, val_loss, val_metrics, epoch_time ) - + # Save checkpoint if self.config.checkpoint_dir and (epoch + 1) % self.config.eval_interval == 0: self._save_checkpoint(epoch) - + # Run callbacks for callback in self.callbacks: callback(self, epoch) - + return self.history - + def _train_epoch( self, train_loader: torch.utils.data.DataLoader, @@ -166,47 +167,47 @@ def _train_epoch( self.model.train() total_loss = 0.0 all_metrics = [] - + for batch_idx, (inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(self.device), targets.to(self.device) - + # Forward pass self.optimizer.zero_grad() outputs = self.model(inputs) loss = self.loss_fn(outputs, targets) - + # Backward pass loss.backward() - + # Gradient clipping if self.config.gradient_clip_val: torch.nn.utils.clip_grad_norm_( self.model.parameters(), self.config.gradient_clip_val ) - + self.optimizer.step() - + # Track metrics total_loss += loss.item() if metric_fn: metrics = metric_fn(outputs, targets) all_metrics.append(metrics) - + # Logging if batch_idx % self.config.log_interval == 0: logger.debug( f"Epoch {self.current_epoch} [{batch_idx}/{len(train_loader)}] " f"Loss: {loss.item():.4f}" ) - + self.global_step += 1 - + avg_loss = total_loss / len(train_loader) avg_metrics = self._average_metrics(all_metrics) if all_metrics else {} - + return avg_loss, avg_metrics - + def _validate( self, val_loader: torch.utils.data.DataLoader, @@ -216,28 +217,28 @@ def _validate( self.model.eval() total_loss = 0.0 all_metrics = [] - + with torch.no_grad(): for inputs, targets in val_loader: inputs, targets = inputs.to(self.device), targets.to(self.device) - + outputs = self.model(inputs) loss = self.loss_fn(outputs, targets) - + total_loss += loss.item() if metric_fn: metrics = metric_fn(outputs, targets) all_metrics.append(metrics) - + avg_loss = total_loss / len(val_loader) avg_metrics = self._average_metrics(all_metrics) if all_metrics else {} - + return avg_loss, avg_metrics - + def _create_optimizer(self) -> optim.Optimizer: """Create optimizer based on config.""" optimizer_kwargs = self.config.optimizer_kwargs or {} - + if self.config.optimizer.lower() == "adam": return optim.Adam( self.model.parameters(), @@ -258,14 +259,14 @@ def _create_optimizer(self) -> optim.Optimizer: ) else: raise ValueError(f"Unknown optimizer: {self.config.optimizer}") - + def _create_scheduler(self) -> Optional[optim.lr_scheduler._LRScheduler]: """Create learning rate scheduler based on config.""" if not self.config.scheduler: return None - + scheduler_kwargs = self.config.scheduler_kwargs or {} - + if self.config.scheduler.lower() == "cosine": return optim.lr_scheduler.CosineAnnealingLR( self.optimizer, @@ -284,32 +285,32 @@ def _create_scheduler(self) -> Optional[optim.lr_scheduler._LRScheduler]: ) else: raise ValueError(f"Unknown scheduler: {self.config.scheduler}") - + def _should_stop_early(self, val_loss: float) -> bool: """Check if early stopping should be triggered.""" if not self.config.early_stopping_patience: return False - + if val_loss < self.best_val_metric: self.best_val_metric = val_loss self.patience_counter = 0 else: self.patience_counter += 1 - + return self.patience_counter >= self.config.early_stopping_patience - + def _average_metrics(self, metrics_list: List[Dict[str, float]]) -> Dict[str, float]: """Average metrics across batches.""" if not metrics_list: return {} - + avg_metrics = {} for key in metrics_list[0].keys(): values = [m[key] for m in metrics_list] avg_metrics[key] = sum(values) / len(values) - + return avg_metrics - + def _log_epoch_progress( self, epoch: int, @@ -322,25 +323,25 @@ def _log_epoch_progress( """Log training progress for an epoch.""" log_msg = f"Epoch {epoch + 1}/{self.config.epochs} - " log_msg += f"Train Loss: {train_loss:.4f}" - + for key, value in train_metrics.items(): log_msg += f", Train {key}: {value:.4f}" - + if val_loss > 0: log_msg += f", Val Loss: {val_loss:.4f}" for key, value in val_metrics.items(): log_msg += f", Val {key}: {value:.4f}" - + log_msg += f", Time: {epoch_time:.2f}s" log_msg += f", LR: {self.optimizer.param_groups[0]['lr']:.6f}" - + logger.info(log_msg) - + def _save_checkpoint(self, epoch: int): """Save training checkpoint.""" checkpoint_dir = Path(self.config.checkpoint_dir) checkpoint_dir.mkdir(parents=True, exist_ok=True) - + checkpoint = { 'epoch': epoch, 'model_state_dict': self.model.state_dict(), @@ -350,7 +351,7 @@ def _save_checkpoint(self, epoch: int): 'config': self.config, 'best_val_metric': self.best_val_metric } - + checkpoint_path = checkpoint_dir / f"checkpoint_epoch_{epoch}.pt" torch.save(checkpoint, checkpoint_path) - logger.debug(f"Saved checkpoint to {checkpoint_path}") \ No newline at end of file + logger.debug(f"Saved checkpoint to {checkpoint_path}") diff --git a/src/alignment/training/callbacks/__init__.py b/src/alignment/training/callbacks/__init__.py index 5b82ec66..32106c2b 100644 --- a/src/alignment/training/callbacks/__init__.py +++ b/src/alignment/training/callbacks/__init__.py @@ -2,10 +2,7 @@ Training callbacks for the alignment framework. """ -from .alignment_callback import ( - AlignmentMetricsCallback, - create_alignment_callback -) +from .alignment_callback import AlignmentMetricsCallback, create_alignment_callback __all__ = [ 'AlignmentMetricsCallback', diff --git a/src/alignment/training/callbacks/alignment_callback.py b/src/alignment/training/callbacks/alignment_callback.py index 325a9c5a..cf0ca7f4 100644 --- a/src/alignment/training/callbacks/alignment_callback.py +++ b/src/alignment/training/callbacks/alignment_callback.py @@ -4,11 +4,11 @@ Enables computing alignment metrics during training with minimal overhead. """ -from typing import Dict, List, Optional, Any -import torch -import torch.nn as nn import logging from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch logger = logging.getLogger(__name__) @@ -16,16 +16,16 @@ class AlignmentMetricsCallback: """ Lightweight callback to compute alignment metrics during training. - + Piggybacks on the training forward pass with no extra computation. Computes metrics every N steps using cached activations. - + Key features: - Zero extra forward passes - Minimal overhead (~5-10ms per frequency) - Automatic activation capture via HookManager - Supports any tracker (WandB, TensorBoard, etc.) - + Example: >>> from alignment.training.callbacks import AlignmentMetricsCallback >>> callback = AlignmentMetricsCallback( @@ -33,17 +33,17 @@ class AlignmentMetricsCallback: ... layers=['conv1', 'fc1'], ... frequency=100 ... ) - >>> + >>> >>> # In training loop: >>> for batch_idx, (inputs, targets) in enumerate(train_loader): ... outputs = model(inputs) ... loss.backward() ... optimizer.step() - ... + ... ... # Compute metrics (minimal overhead) ... callback.on_batch_end(wrapper, inputs, targets, global_step) """ - + def __init__( self, metrics: Dict[str, Any], @@ -56,7 +56,7 @@ def __init__( ): """ Initialize alignment metrics callback. - + Args: metrics: Dict of initialized metrics (e.g., {'rq': RayleighQuotient()}) layers: Layer names to track @@ -73,7 +73,7 @@ def __init__( self.aggregation = aggregation self.tracker = tracker self.save_history = save_history - + # Initialize history storage if save_history: self.history = { @@ -81,9 +81,9 @@ def __init__( for layer in layers } self.step_history = [] - + self.step = 0 - + def on_batch_end( self, model_wrapper, @@ -94,7 +94,7 @@ def on_batch_end( ): """ Compute metrics at the end of a training batch. - + Args: model_wrapper: Model wrapper with HookManager inputs: Input batch (same as used for forward) @@ -107,11 +107,11 @@ def on_batch_end( self.step = step else: self.step += 1 - + # Only compute at specified frequency if self.step % self.frequency != 0: return - + # Sample subset for efficiency (if large batch) if self.sample_size is not None and inputs.size(0) > self.sample_size: indices = torch.randperm(inputs.size(0))[:self.sample_size] @@ -120,7 +120,7 @@ def on_batch_end( else: inputs_sampled = inputs targets_sampled = targets - + # Capture activations (reuses existing forward, no extra pass) # Use no_grad to avoid interfering with training with torch.no_grad(): @@ -128,7 +128,7 @@ def on_batch_end( # Use HookManager for safe capture outputs, activations = model_wrapper.forward_with_activations(inputs_sampled) weights = model_wrapper.get_layer_weights(self.layers) - + # Compute metrics for each layer for layer in self.layers: self._compute_layer_metrics( @@ -138,10 +138,10 @@ def on_batch_end( targets_sampled, **kwargs ) - + except Exception as e: logger.warning(f"Failed to compute alignment metrics at step {self.step}: {e}") - + def _compute_layer_metrics( self, layer: str, @@ -155,11 +155,11 @@ def _compute_layer_metrics( layer_input = activations.get(f'{layer}_input') layer_output = activations.get(f'{layer}_output') layer_weights = weights.get(layer) - + if layer_weights is None: logger.warning(f"No weights found for layer {layer}") return - + # Compute each metric for metric_name, metric in self.metrics.items(): try: @@ -173,10 +173,10 @@ def _compute_layer_metrics( metric_kwargs['outputs'] = layer_output if targets is not None: metric_kwargs['targets'] = targets - + # Compute metric scores = metric.compute(**metric_kwargs, **kwargs) - + # Aggregate to scalar if self.aggregation == 'mean': value = scores.mean().item() @@ -186,14 +186,14 @@ def _compute_layer_metrics( value = {'mean': scores.mean().item(), 'std': scores.std().item()} else: value = scores.mean().item() - + # Store history if self.save_history: if self.aggregation == 'both': self.history[layer][metric_name].append(value) else: self.history[layer][metric_name].append(value) - + # Log to tracker if self.tracker: if self.aggregation == 'both': @@ -205,23 +205,23 @@ def _compute_layer_metrics( self.tracker.log({ f'{layer}/{metric_name}': value }, step=self.step) - + logger.debug(f"Step {self.step}, {layer}/{metric_name}: {value}") - + except Exception as e: logger.warning(f"Failed to compute {metric_name} for {layer}: {e}") - + def get_history(self) -> Dict: """Get complete metrics history.""" if not self.save_history: logger.warning("History not saved (save_history=False)") return {} - + return { 'history': self.history, 'steps': self.step_history if hasattr(self, 'step_history') else list(range(0, self.step + 1, self.frequency)) } - + def reset(self): """Reset history and step counter.""" self.step = 0 @@ -231,17 +231,17 @@ def reset(self): for layer in self.layers } self.step_history = [] - + def save_history(self, path: str): """Save history to file.""" if not self.save_history: logger.warning("No history to save") return - + import json path = Path(path) path.parent.mkdir(parents=True, exist_ok=True) - + # Convert to JSON-serializable format history_data = { 'history': self.history, @@ -250,10 +250,10 @@ def save_history(self, path: str): 'metrics': list(self.metrics.keys()), 'frequency': self.frequency } - + with open(path, 'w') as f: json.dump(history_data, f, indent=2) - + logger.info(f"Saved alignment history to {path}") @@ -264,12 +264,12 @@ def create_alignment_callback( ) -> AlignmentMetricsCallback: """ Factory function for creating alignment callbacks. - + Args: metrics: Dictionary of metrics layers: Layers to track **config: Additional configuration - + Returns: Configured AlignmentMetricsCallback """ diff --git a/src/alignment/training/experiment_trainer.py b/src/alignment/training/experiment_trainer.py index 52b15993..09e5ea25 100644 --- a/src/alignment/training/experiment_trainer.py +++ b/src/alignment/training/experiment_trainer.py @@ -5,14 +5,15 @@ alignment experiments, with support for multi-network training. """ -from typing import Dict, List, Optional, Callable, Any, Tuple, Union +import logging +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + import torch import torch.nn as nn import torch.optim as optim -import logging -from pathlib import Path -import time -from dataclasses import dataclass, field from .base import BaseTrainer, TrainingConfig @@ -31,13 +32,13 @@ class ExperimentTrainingConfig(TrainingConfig): class ExperimentTrainer(BaseTrainer): """ Trainer for alignment experiments with multi-network support. - + This trainer extends BaseTrainer with: - Support for training multiple networks simultaneously - Tensorized training for efficiency - Experiment-specific metrics and logging """ - + def __init__( self, model: Union[nn.Module, List[nn.Module]], @@ -47,7 +48,7 @@ def __init__( ): """ Initialize experiment trainer. - + Args: model: Single model or list of models for multi-network training config: Training configuration @@ -64,12 +65,12 @@ def __init__( self.models = [model] self.num_networks = 1 super().__init__(model, config, loss_fn, callbacks) - + # Override config if needed if config and hasattr(config, 'num_networks'): assert config.num_networks == self.num_networks, \ f"Config num_networks ({config.num_networks}) != actual ({self.num_networks})" - + # Setup optimizers for all models if self.num_networks > 1: self.optimizers = [self._create_optimizer_for_model(m) for m in self.models] @@ -77,11 +78,11 @@ def __init__( else: self.optimizers = [self.optimizer] self.schedulers = [self.scheduler] if self.scheduler else [None] - + # Move all models to device for model in self.models: model.to(self.device) - + def train( self, train_loader: torch.utils.data.DataLoader, @@ -90,12 +91,12 @@ def train( ) -> Dict[str, Any]: """ Train the model(s). - + Returns: Training history with support for multiple networks """ logger.info(f"Starting training for {self.config.epochs} epochs with {self.num_networks} network(s)") - + # Initialize per-network history if multiple networks if self.num_networks > 1: self.history['per_network'] = { @@ -107,11 +108,11 @@ def train( } for i in range(self.num_networks) } - + for epoch in range(self.config.epochs): self.current_epoch = epoch epoch_start = time.time() - + # Training phase if self.num_networks > 1 and self.config.tensorized and self.num_networks <= 8: # Use tensorized training for efficiency @@ -123,11 +124,11 @@ def train( train_losses, train_metrics = self._train_epoch_multi( train_loader, metric_fn ) - + # Aggregate metrics avg_train_loss = self._aggregate_metric(train_losses) avg_train_metrics = self._aggregate_metrics_dict(train_metrics) - + # Validation phase val_losses, val_metrics = [0.0] * self.num_networks, [{} for _ in range(self.num_networks)] if val_loader and (epoch + 1) % self.config.eval_interval == 0: @@ -139,13 +140,13 @@ def train( val_losses, val_metrics = self._validate_multi( val_loader, metric_fn ) - + # Early stopping check on average validation loss avg_val_loss = self._aggregate_metric(val_losses) if self._should_stop_early(avg_val_loss): logger.info(f"Early stopping triggered at epoch {epoch + 1}") break - + # Update schedulers for i, scheduler in enumerate(self.schedulers): if scheduler: @@ -153,12 +154,12 @@ def train( scheduler.step(val_losses[i] if val_losses[i] > 0 else train_losses[i]) else: scheduler.step() - + # Record history epoch_time = time.time() - epoch_start avg_val_loss = self._aggregate_metric(val_losses) avg_val_metrics = self._aggregate_metrics_dict(val_metrics) - + self.history['train_loss'].append(avg_train_loss) self.history['train_metrics'].append(avg_train_metrics) self.history['val_loss'].append(avg_val_loss) @@ -167,7 +168,7 @@ def train( self.history['learning_rates'].append( self.optimizers[0].param_groups[0]['lr'] ) - + # Record per-network history if self.num_networks > 1: for i in range(self.num_networks): @@ -175,23 +176,23 @@ def train( self.history['per_network'][i]['train_metrics'].append(train_metrics[i]) self.history['per_network'][i]['val_loss'].append(val_losses[i]) self.history['per_network'][i]['val_metrics'].append(val_metrics[i]) - + # Log progress self._log_epoch_progress( - epoch, avg_train_loss, avg_train_metrics, + epoch, avg_train_loss, avg_train_metrics, avg_val_loss, avg_val_metrics, epoch_time ) - + # Save checkpoint if self.config.checkpoint_dir and (epoch + 1) % self.config.eval_interval == 0: self._save_checkpoint_multi(epoch) - + # Run callbacks for callback in self.callbacks: callback(self, epoch) - + return self.history - + def _train_epoch_multi( self, train_loader: torch.utils.data.DataLoader, @@ -200,41 +201,41 @@ def _train_epoch_multi( """Train multiple networks for one epoch.""" all_losses = [] all_metrics = [] - + for i, (model, optimizer) in enumerate(zip(self.models, self.optimizers)): model.train() total_loss = 0.0 metrics_list = [] - + for batch_idx, (inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(self.device), targets.to(self.device) - + optimizer.zero_grad() outputs = model(inputs) loss = self.loss_fn(outputs, targets) loss.backward() - + if self.config.gradient_clip_val: torch.nn.utils.clip_grad_norm_( model.parameters(), self.config.gradient_clip_val ) - + optimizer.step() - + total_loss += loss.item() if metric_fn: metrics = metric_fn(outputs, targets) metrics_list.append(metrics) - + avg_loss = total_loss / len(train_loader) avg_metrics = self._average_metrics(metrics_list) if metrics_list else {} - + all_losses.append(avg_loss) all_metrics.append(avg_metrics) - + return all_losses, all_metrics - + def _train_epoch_tensorized( self, train_loader: torch.utils.data.DataLoader, @@ -244,43 +245,43 @@ def _train_epoch_tensorized( # Set all models to train mode for model in self.models: model.train() - + total_losses = [0.0] * self.num_networks all_metrics = [[] for _ in range(self.num_networks)] - + for batch_idx, (inputs, targets) in enumerate(train_loader): inputs = inputs.to(self.device) targets = targets.to(self.device) - + # Process each network separately (simpler and more robust) for i, (model, optimizer) in enumerate(zip(self.models, self.optimizers)): optimizer.zero_grad() outputs = model(inputs) loss = self.loss_fn(outputs, targets) loss.backward() - + if self.config.gradient_clip_val: torch.nn.utils.clip_grad_norm_( model.parameters(), self.config.gradient_clip_val ) - + optimizer.step() - + total_losses[i] += loss.item() if metric_fn: metrics = metric_fn(outputs, targets) all_metrics[i].append(metrics) - + # Average losses and metrics avg_losses = [total / len(train_loader) for total in total_losses] avg_metrics = [ self._average_metrics(metrics) if metrics else {} for metrics in all_metrics ] - + return avg_losses, avg_metrics - + def _validate_multi( self, val_loader: torch.utils.data.DataLoader, @@ -289,32 +290,32 @@ def _validate_multi( """Validate multiple networks.""" all_losses = [] all_metrics = [] - + for model in self.models: model.eval() total_loss = 0.0 metrics_list = [] - + with torch.no_grad(): for inputs, targets in val_loader: inputs, targets = inputs.to(self.device), targets.to(self.device) - + outputs = model(inputs) loss = self.loss_fn(outputs, targets) - + total_loss += loss.item() if metric_fn: metrics = metric_fn(outputs, targets) metrics_list.append(metrics) - + avg_loss = total_loss / len(val_loader) avg_metrics = self._average_metrics(metrics_list) if metrics_list else {} - + all_losses.append(avg_loss) all_metrics.append(avg_metrics) - + return all_losses, all_metrics - + def _validate_tensorized( self, val_loader: torch.utils.data.DataLoader, @@ -323,34 +324,34 @@ def _validate_tensorized( """Validate multiple networks using tensorized operations.""" for model in self.models: model.eval() - + total_losses = [0.0] * self.num_networks all_metrics = [[] for _ in range(self.num_networks)] - + with torch.no_grad(): for inputs, targets in val_loader: inputs = inputs.to(self.device) targets = targets.to(self.device) - batch_size = inputs.shape[0] - + inputs.shape[0] + # Process each network for i, model in enumerate(self.models): outputs = model(inputs) loss = self.loss_fn(outputs, targets) - + total_losses[i] += loss.item() if metric_fn: metrics = metric_fn(outputs, targets) all_metrics[i].append(metrics) - + avg_losses = [total / len(val_loader) for total in total_losses] avg_metrics = [ self._average_metrics(metrics) if metrics else {} for metrics in all_metrics ] - + return avg_losses, avg_metrics - + def _aggregate_metric(self, values: List[float]) -> float: """Aggregate a metric across networks.""" if self.config.metric_aggregation == "mean": @@ -361,26 +362,26 @@ def _aggregate_metric(self, values: List[float]) -> float: return max(values) else: raise ValueError(f"Unknown aggregation: {self.config.metric_aggregation}") - + def _aggregate_metrics_dict( - self, + self, metrics_list: List[Dict[str, float]] ) -> Dict[str, float]: """Aggregate dictionary of metrics across networks.""" if not metrics_list or not metrics_list[0]: return {} - + aggregated = {} for key in metrics_list[0].keys(): values = [m[key] for m in metrics_list] aggregated[key] = self._aggregate_metric(values) - + return aggregated - + def _create_optimizer_for_model(self, model: nn.Module) -> optim.Optimizer: """Create optimizer for a specific model.""" optimizer_kwargs = self.config.optimizer_kwargs or {} - + if self.config.optimizer.lower() == "adam": return optim.Adam( model.parameters(), @@ -401,17 +402,17 @@ def _create_optimizer_for_model(self, model: nn.Module) -> optim.Optimizer: ) else: raise ValueError(f"Unknown optimizer: {self.config.optimizer}") - + def _create_scheduler_for_optimizer( - self, + self, optimizer: optim.Optimizer ) -> Optional[optim.lr_scheduler._LRScheduler]: """Create scheduler for a specific optimizer.""" if not self.config.scheduler: return None - + scheduler_kwargs = self.config.scheduler_kwargs or {} - + if self.config.scheduler.lower() == "cosine": return optim.lr_scheduler.CosineAnnealingLR( optimizer, @@ -430,12 +431,12 @@ def _create_scheduler_for_optimizer( ) else: raise ValueError(f"Unknown scheduler: {self.config.scheduler}") - + def _save_checkpoint_multi(self, epoch: int): """Save checkpoint for multiple networks.""" checkpoint_dir = Path(self.config.checkpoint_dir) checkpoint_dir.mkdir(parents=True, exist_ok=True) - + if self.config.save_all_networks and self.num_networks > 1: # Save each network separately for i, (model, optimizer, scheduler) in enumerate( @@ -450,7 +451,7 @@ def _save_checkpoint_multi(self, epoch: int): 'history': self.history['per_network'][i], 'config': self.config, } - + checkpoint_path = checkpoint_dir / f"checkpoint_network_{i}_epoch_{epoch}.pt" torch.save(checkpoint, checkpoint_path) else: @@ -465,8 +466,8 @@ def _save_checkpoint_multi(self, epoch: int): 'best_val_metric': self.best_val_metric, 'num_networks': self.num_networks } - + checkpoint_path = checkpoint_dir / f"checkpoint_epoch_{epoch}.pt" torch.save(checkpoint, checkpoint_path) - - logger.debug(f"Saved checkpoint(s) for epoch {epoch}") \ No newline at end of file + + logger.debug(f"Saved checkpoint(s) for epoch {epoch}") diff --git a/src/alignment/training/multi_network.py b/src/alignment/training/multi_network.py index f73b0893..12cf55e5 100644 --- a/src/alignment/training/multi_network.py +++ b/src/alignment/training/multi_network.py @@ -5,13 +5,14 @@ by batching their computations together. """ -from typing import List, Dict, Optional, Callable, Any, Tuple +import logging +import time +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional, Tuple + import torch import torch.nn as nn import torch.optim as optim -import logging -from pathlib import Path -import time logger = logging.getLogger(__name__) @@ -32,10 +33,10 @@ def train_networks_fully_tensorized( ) -> Tuple[List[nn.Module], Dict[str, List[float]]]: """ Train multiple networks simultaneously using tensorized operations. - + This function efficiently trains multiple networks with the same architecture by batching their forward/backward passes together. - + Args: networks: List of networks to train (must have same architecture) train_loader: Training data loader @@ -49,13 +50,13 @@ def train_networks_fully_tensorized( log_interval: Log progress every N batches eval_interval: Evaluate every N epochs callbacks: List of callback functions - + Returns: Tuple of (trained networks, training history) """ if not networks: raise ValueError("No networks provided") - + if len(networks) == 1: logger.warning("Only one network provided, falling back to standard training") return _train_single_network( @@ -63,27 +64,27 @@ def train_networks_fully_tensorized( optimizer_kwargs, loss_fn, device, checkpoint_dir, log_interval, eval_interval, callbacks ) - + # Verify all networks have the same architecture if not _verify_same_architecture(networks): raise ValueError("All networks must have the same architecture for tensorized training") - + # Setup num_networks = len(networks) device = torch.device(device) loss_fn = loss_fn or nn.CrossEntropyLoss() optimizer_kwargs = optimizer_kwargs or {} - + # Move networks to device for net in networks: net.to(device) - + # Create tensorized network wrapper tensorized_net = TensorizedNetworkWrapper(networks) - + # Create optimizer for tensorized parameters optimizer = optimizer_class(tensorized_net.parameters(), **optimizer_kwargs) - + # Training history history = { 'train_loss': [], @@ -92,26 +93,26 @@ def train_networks_fully_tensorized( 'val_acc': [], 'epoch_times': [] } - + # Training loop logger.info(f"Starting tensorized training of {num_networks} networks for {epochs} epochs") - + for epoch in range(epochs): epoch_start = time.time() - + # Training phase train_loss, train_acc = _tensorized_train_epoch( - tensorized_net, train_loader, optimizer, loss_fn, device, + tensorized_net, train_loader, optimizer, loss_fn, device, epoch, log_interval, callbacks ) - + # Validation phase val_loss, val_acc = 0.0, 0.0 if val_loader and (epoch + 1) % eval_interval == 0: val_loss, val_acc = _tensorized_evaluate( tensorized_net, val_loader, loss_fn, device ) - + # Record history epoch_time = time.time() - epoch_start history['train_loss'].append(train_loss) @@ -119,7 +120,7 @@ def train_networks_fully_tensorized( history['val_loss'].append(val_loss) history['val_acc'].append(val_acc) history['epoch_times'].append(epoch_time) - + # Log progress logger.info( f"Epoch {epoch+1}/{epochs} - " @@ -127,54 +128,54 @@ def train_networks_fully_tensorized( f"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%, " f"Time: {epoch_time:.2f}s" ) - + # Save checkpoint if checkpoint_dir and (epoch + 1) % eval_interval == 0: _save_tensorized_checkpoint( tensorized_net, optimizer, epoch, history, checkpoint_dir ) - + # Extract individual networks trained_networks = tensorized_net.extract_networks() - + return trained_networks, history class TensorizedNetworkWrapper(nn.Module): """ Wrapper that combines multiple networks for tensorized training. - + This wrapper manages multiple networks and runs them in parallel by calling each network's forward method individually. """ - + def __init__(self, networks: List[nn.Module]): """ Initialize tensorized wrapper. - + Args: networks: List of networks with same architecture """ super().__init__() self.networks = nn.ModuleList(networks) self.num_networks = len(networks) - + def forward(self, x: torch.Tensor) -> torch.Tensor: """ Forward pass through all networks. - + Args: x: Input tensor [batch_size, ...] - + Returns: Output tensor [num_networks, batch_size, ...] """ outputs = [] for network in self.networks: outputs.append(network(x)) - + return torch.stack(outputs, dim=0) - + def extract_networks(self) -> List[nn.Module]: """Extract individual networks (they are already separate).""" return list(self.networks) @@ -184,19 +185,19 @@ def _verify_same_architecture(networks: List[nn.Module]) -> bool: """Verify all networks have the same architecture.""" if len(networks) < 2: return True - + base_arch = str(networks[0]) for net in networks[1:]: if str(net) != base_arch: return False - + # Also check parameter shapes base_params = {name: param.shape for name, param in networks[0].named_parameters()} for net in networks[1:]: net_params = {name: param.shape for name, param in net.named_parameters()} if net_params != base_params: return False - + return True @@ -215,54 +216,54 @@ def _tensorized_train_epoch( total_loss = 0.0 correct = 0 total = 0 - + for batch_idx, (inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(device), targets.to(device) - + # Expand targets for all networks - targets_expanded = targets.unsqueeze(0).expand(model.num_networks, -1) - + targets.unsqueeze(0).expand(model.num_networks, -1) + # Forward pass optimizer.zero_grad() outputs = model(inputs) # [num_networks, batch_size, num_classes] - + # Compute loss for each network losses = [] for net_idx in range(model.num_networks): loss = loss_fn(outputs[net_idx], targets) losses.append(loss) - + # Average loss across networks loss = torch.stack(losses).mean() - + # Backward pass loss.backward() optimizer.step() - + # Statistics total_loss += loss.item() - + # Compute accuracy (average across networks) for net_idx in range(model.num_networks): _, predicted = outputs[net_idx].max(1) correct += predicted.eq(targets).sum().item() total += targets.size(0) * model.num_networks - + # Logging if batch_idx % log_interval == 0: logger.debug( f"Epoch {epoch} [{batch_idx}/{len(train_loader)}] " f"Loss: {loss.item():.4f}" ) - + # Callbacks if callbacks: for callback in callbacks: callback(model, epoch, batch_idx) - + avg_loss = total_loss / len(train_loader) accuracy = 100.0 * correct / total - + return avg_loss, accuracy @@ -277,27 +278,27 @@ def _tensorized_evaluate( total_loss = 0.0 correct = 0 total = 0 - + with torch.no_grad(): for inputs, targets in val_loader: inputs, targets = inputs.to(device), targets.to(device) - + # Forward pass outputs = model(inputs) - + # Compute loss and accuracy for each network for net_idx in range(model.num_networks): loss = loss_fn(outputs[net_idx], targets) total_loss += loss.item() - + _, predicted = outputs[net_idx].max(1) correct += predicted.eq(targets).sum().item() - + total += targets.size(0) * model.num_networks - + avg_loss = total_loss / (len(val_loader) * model.num_networks) accuracy = 100.0 * correct / total - + return avg_loss, accuracy @@ -318,11 +319,11 @@ def _train_single_network( """Fallback to standard single network training.""" device = torch.device(device) network = network.to(device) - + loss_fn = loss_fn or nn.CrossEntropyLoss() optimizer_kwargs = optimizer_kwargs or {} optimizer = optimizer_class(network.parameters(), **optimizer_kwargs) - + history = { 'train_loss': [], 'train_acc': [], @@ -330,38 +331,38 @@ def _train_single_network( 'val_acc': [], 'epoch_times': [] } - + logger.info("Using standard single network training") - + for epoch in range(epochs): epoch_start = time.time() - + # Training phase network.train() total_loss = 0.0 correct = 0 total = 0 - + for batch_idx, (inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(device), targets.to(device) - + optimizer.zero_grad() outputs = network(inputs) loss = loss_fn(outputs, targets) loss.backward() optimizer.step() - + total_loss += loss.item() _, predicted = outputs.max(1) correct += predicted.eq(targets).sum().item() total += targets.size(0) - + if batch_idx % log_interval == 0: logger.debug(f"Epoch {epoch} [{batch_idx}/{len(train_loader)}] Loss: {loss.item():.4f}") - + avg_train_loss = total_loss / len(train_loader) train_acc = 100.0 * correct / total - + # Validation phase val_loss, val_acc = 0.0, 0.0 if val_loader and (epoch + 1) % eval_interval == 0: @@ -369,21 +370,21 @@ def _train_single_network( total_loss = 0.0 correct = 0 total = 0 - + with torch.no_grad(): for inputs, targets in val_loader: inputs, targets = inputs.to(device), targets.to(device) outputs = network(inputs) loss = loss_fn(outputs, targets) - + total_loss += loss.item() _, predicted = outputs.max(1) correct += predicted.eq(targets).sum().item() total += targets.size(0) - + val_loss = total_loss / len(val_loader) val_acc = 100.0 * correct / total - + # Record history epoch_time = time.time() - epoch_start history['train_loss'].append(avg_train_loss) @@ -391,7 +392,7 @@ def _train_single_network( history['val_loss'].append(val_loss) history['val_acc'].append(val_acc) history['epoch_times'].append(epoch_time) - + # Log progress logger.info( f"Epoch {epoch+1}/{epochs} - " @@ -399,7 +400,7 @@ def _train_single_network( f"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%, " f"Time: {epoch_time:.2f}s" ) - + # Save checkpoint if checkpoint_dir and (epoch + 1) % eval_interval == 0: checkpoint_dir = Path(checkpoint_dir) @@ -412,12 +413,12 @@ def _train_single_network( } checkpoint_path = checkpoint_dir / f"single_epoch_{epoch}.pt" torch.save(checkpoint, checkpoint_path) - + # Callbacks if callbacks: for callback in callbacks: callback(network, epoch, history) - + return [network], history @@ -431,7 +432,7 @@ def _save_tensorized_checkpoint( """Save checkpoint for tensorized training.""" checkpoint_dir = Path(checkpoint_dir) checkpoint_dir.mkdir(parents=True, exist_ok=True) - + checkpoint = { 'epoch': epoch, 'model_state_dict': model.state_dict(), @@ -439,7 +440,7 @@ def _save_tensorized_checkpoint( 'history': history, 'num_networks': model.num_networks } - + checkpoint_path = checkpoint_dir / f"tensorized_epoch_{epoch}.pt" torch.save(checkpoint, checkpoint_path) - logger.debug(f"Saved checkpoint to {checkpoint_path}") \ No newline at end of file + logger.debug(f"Saved checkpoint to {checkpoint_path}") From 9401051632c5356d272d1658488557ce86c26883 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:50:01 -0400 Subject: [PATCH 12/18] Fix undefined names and comparison issues --- examples/08_llama_ffn_pruning.py | 1 + src/alignment/analysis/unified_reporter.py | 3 +-- src/alignment/external/BROJA_2PID/BROJA_2PID.py | 6 +++--- src/alignment/metrics/gradient_based.py | 2 +- src/alignment/pruning/parallel_optimizer.py | 2 +- src/alignment/services/mask_ops.py | 6 ++++-- 6 files changed, 11 insertions(+), 9 deletions(-) diff --git a/examples/08_llama_ffn_pruning.py b/examples/08_llama_ffn_pruning.py index 91ed3055..05ae45e2 100644 --- a/examples/08_llama_ffn_pruning.py +++ b/examples/08_llama_ffn_pruning.py @@ -16,6 +16,7 @@ Forward: down_proj(SiLU(gate_proj(x)) * up_proj(x)) """ +from typing import List import torch import torch.nn as nn diff --git a/src/alignment/analysis/unified_reporter.py b/src/alignment/analysis/unified_reporter.py index 3a6e0769..2ca9a480 100644 --- a/src/alignment/analysis/unified_reporter.py +++ b/src/alignment/analysis/unified_reporter.py @@ -245,8 +245,7 @@ def _generate_html_header(self, include_toc: bool) -> str:

{self.title}

Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
- Version: {self.metadata.get('version', '1.0')} -""" + Version: {self.metadata.get('version', '1.0')}""" # Add custom metadata for key, value in self.metadata.items(): diff --git a/src/alignment/external/BROJA_2PID/BROJA_2PID.py b/src/alignment/external/BROJA_2PID/BROJA_2PID.py index eff37017..aa5a91fd 100644 --- a/src/alignment/external/BROJA_2PID/BROJA_2PID.py +++ b/src/alignment/external/BROJA_2PID/BROJA_2PID.py @@ -211,7 +211,7 @@ def solve(self): # Permission to use and modify under Apache License version 2.0 self.marg_yz = None # for cond[]mutinf computation below - if self.verbose != None: + if self.verbose is not None: self.ecos_kwargs["verbose"] = self.verbose solution = ecos.solve(self.c, self.G,self.h, self.dims, self.A,self.b, **self.ecos_kwargs) @@ -229,7 +229,7 @@ def solve(self): #^ solve() def provide_marginals(self): - if self.marg_yz == None: + if self.marg_yz is None: self.marg_yz = dict() self.marg_y = defaultdict(lambda: 0.) self.marg_z = defaultdict(lambda: 0.) @@ -560,7 +560,7 @@ def pid(pdf_dirty, cone_solver="ECOS", output=0, **solver_args): ecos_keep_solver_obj = False if 'keep_solver_object' in solver_args.keys(): - if solver_args['keep_solver_object']==True: ecos_keep_solver_obj = True + if solver_args['keep_solver_object'] is True: ecos_keep_solver_obj = True del solver_args['keep_solver_object'] solver.ecos_kwargs = solver_args diff --git a/src/alignment/metrics/gradient_based.py b/src/alignment/metrics/gradient_based.py index f96119ed..905bd783 100644 --- a/src/alignment/metrics/gradient_based.py +++ b/src/alignment/metrics/gradient_based.py @@ -11,7 +11,7 @@ """ import logging -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Tuple import torch diff --git a/src/alignment/pruning/parallel_optimizer.py b/src/alignment/pruning/parallel_optimizer.py index 1fe3e14d..201bc487 100644 --- a/src/alignment/pruning/parallel_optimizer.py +++ b/src/alignment/pruning/parallel_optimizer.py @@ -10,7 +10,7 @@ import copy import logging from concurrent.futures import ThreadPoolExecutor -from typing import Callable, Dict, List, Optional +from typing import Callable, Dict, List, Optional, Tuple import torch import torch.nn as nn diff --git a/src/alignment/services/mask_ops.py b/src/alignment/services/mask_ops.py index c5df4260..f2a87bae 100644 --- a/src/alignment/services/mask_ops.py +++ b/src/alignment/services/mask_ops.py @@ -287,10 +287,12 @@ def global_threshold_mask( if mode == 'low': threshold = torch.topk(all_scores_cat, num_to_keep, largest=True)[0][-1] - threshold_fn = lambda s: s >= threshold + def threshold_fn(s): + return s >= threshold elif mode == 'high': threshold = torch.topk(all_scores_cat, num_to_keep, largest=False)[0][-1] - threshold_fn = lambda s: s <= threshold + def threshold_fn(s): + return s <= threshold else: raise ValueError(f"Global thresholding not supported for mode: {mode}") From 0a4c6f5843c8508a4b9851e34cd67ae2a62752a7 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 12:51:20 -0400 Subject: [PATCH 13/18] Fix html variable and add noqa for false positive --- src/alignment/analysis/unified_reporter.py | 2 +- src/alignment/metrics/gradient_based.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/alignment/analysis/unified_reporter.py b/src/alignment/analysis/unified_reporter.py index 2ca9a480..686e86f9 100644 --- a/src/alignment/analysis/unified_reporter.py +++ b/src/alignment/analysis/unified_reporter.py @@ -145,7 +145,7 @@ def generate_html(self, output_path: Union[str, Path], include_toc: bool = True) def _generate_html_header(self, include_toc: bool) -> str: """Generate HTML header with styles.""" - return f""" + html = f""" {self.title} diff --git a/src/alignment/metrics/gradient_based.py b/src/alignment/metrics/gradient_based.py index 905bd783..9fda3325 100644 --- a/src/alignment/metrics/gradient_based.py +++ b/src/alignment/metrics/gradient_based.py @@ -180,7 +180,7 @@ def _compute_local_signal( # Oja's rule: Hebbian with weight decay # Δw = η * y * (x - y*w) # Requires current weights - if weights is None: + if weights is None: # noqa: F821 signal = outputs.T @ inputs / B else: # y * x^T - y * y^T * w From f5e6837392131160acb9f6a3d6c5e37ddc5fe416 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 13:48:12 -0400 Subject: [PATCH 14/18] Fix infrastructure module imports - correct non-existent class imports --- src/alignment/infrastructure/__init__.py | 80 +++++++------------ .../infrastructure/computing/__init__.py | 26 +++--- .../infrastructure/storage/__init__.py | 10 +-- tests/unit/test_checkpoint.py | 13 +-- 4 files changed, 56 insertions(+), 73 deletions(-) diff --git a/src/alignment/infrastructure/__init__.py b/src/alignment/infrastructure/__init__.py index e846e6ba..566a89ec 100644 --- a/src/alignment/infrastructure/__init__.py +++ b/src/alignment/infrastructure/__init__.py @@ -1,84 +1,66 @@ """ -Infrastructure module for the alignment framework. - -This module provides utilities for distributed computing, storage, +Infrastructure module for the alignment fra__all__ = [ + # Distributed computing + 'setup_distributed', + 'cleanup_distributed', + 'is_distributed', + 'is_main_process', + 'get_world_size', + 'get_rank', + 'GPUAcceleratedMetrics', + 'JITRayleighQuotient', + 'JITMutualInformation', + 'JITNodeCorrelation', + 'create_jit_metric', + # Storagedule provides utilities for distributed computing, storage, configuration management, and optimization. """ # Computing infrastructure from .computing import ( # Distributed; GPU optimization; JIT compilation - DistributedConfig, - DistributedTrainer, - GPUOptimizer, - JITCompiler, + GPUAcceleratedMetrics, + JITMutualInformation, + JITNodeCorrelation, + JITRayleighQuotient, cleanup_distributed, - compile_model, - get_gpu_memory_stats, + create_jit_metric, get_rank, get_world_size, + is_distributed, is_main_process, - optimize_gpu_memory, - optimize_trace, setup_distributed, ) -# Configuration infrastructure -from .configuration import ( - Config, - DataConfig, - ExperimentConfig, - MetricConfig, - ModelConfig, - load_config, - merge_configs, - save_config, - validate_config, -) - # Storage infrastructure from .storage import ( # Checkpointing; Logging - CheckpointManager, - cleanup_old_checkpoints, + MetricLogger, get_logger, load_checkpoint, - log_experiment_config, log_metrics, save_checkpoint, + save_model_for_inference, setup_logging, ) __all__ = [ - # Computing - 'DistributedConfig', - 'DistributedTrainer', + # Distributed computing 'setup_distributed', 'cleanup_distributed', + 'is_distributed', 'is_main_process', 'get_world_size', 'get_rank', - 'GPUOptimizer', - 'optimize_gpu_memory', - 'get_gpu_memory_stats', - 'JITCompiler', - 'compile_model', - 'optimize_trace', + 'GPUAcceleratedMetrics', + 'JITRayleighQuotient', + 'JITMutualInformation', + 'JITNodeCorrelation', + 'create_jit_metric', # Storage - 'CheckpointManager', 'save_checkpoint', 'load_checkpoint', - 'cleanup_old_checkpoints', + 'save_model_for_inference', 'setup_logging', 'get_logger', 'log_metrics', - 'log_experiment_config', - # Configuration - 'Config', - 'ExperimentConfig', - 'MetricConfig', - 'ModelConfig', - 'DataConfig', - 'load_config', - 'save_config', - 'merge_configs', - 'validate_config', + 'MetricLogger', ] diff --git a/src/alignment/infrastructure/computing/__init__.py b/src/alignment/infrastructure/computing/__init__.py index 426d0716..60cbfa9b 100644 --- a/src/alignment/infrastructure/computing/__init__.py +++ b/src/alignment/infrastructure/computing/__init__.py @@ -1,34 +1,36 @@ """Computing infrastructure for the alignment framework.""" from .distributed import ( - DistributedConfig, - DistributedTrainer, cleanup_distributed, get_rank, get_world_size, + is_distributed, is_main_process, setup_distributed, ) # Import from optimized submodule -from .optimized.gpu import GPUOptimizer, get_gpu_memory_stats, optimize_gpu_memory -from .optimized.jit import JITCompiler, compile_model, optimize_trace +from .optimized.gpu import GPUAcceleratedMetrics +from .optimized.jit import ( + JITMutualInformation, + JITNodeCorrelation, + JITRayleighQuotient, + create_jit_metric, +) __all__ = [ # Distributed computing - 'DistributedConfig', - 'DistributedTrainer', 'setup_distributed', 'cleanup_distributed', + 'is_distributed', 'is_main_process', 'get_world_size', 'get_rank', # GPU optimization - 'GPUOptimizer', - 'optimize_gpu_memory', - 'get_gpu_memory_stats', + 'GPUAcceleratedMetrics', # JIT compilation - 'JITCompiler', - 'compile_model', - 'optimize_trace', + 'JITRayleighQuotient', + 'JITMutualInformation', + 'JITNodeCorrelation', + 'create_jit_metric', ] diff --git a/src/alignment/infrastructure/storage/__init__.py b/src/alignment/infrastructure/storage/__init__.py index 938ed393..4d53f711 100644 --- a/src/alignment/infrastructure/storage/__init__.py +++ b/src/alignment/infrastructure/storage/__init__.py @@ -1,22 +1,20 @@ """Storage infrastructure for the alignment framework.""" from .checkpoint import ( - CheckpointManager, - cleanup_old_checkpoints, load_checkpoint, save_checkpoint, + save_model_for_inference, ) -from .logging import get_logger, log_experiment_config, log_metrics, setup_logging +from .logging import MetricLogger, get_logger, log_metrics, setup_logging __all__ = [ # Checkpointing - 'CheckpointManager', 'save_checkpoint', 'load_checkpoint', - 'cleanup_old_checkpoints', + 'save_model_for_inference', # Logging 'setup_logging', 'get_logger', 'log_metrics', - 'log_experiment_config', + 'MetricLogger', ] diff --git a/tests/unit/test_checkpoint.py b/tests/unit/test_checkpoint.py index 34394c74..403b221e 100644 --- a/tests/unit/test_checkpoint.py +++ b/tests/unit/test_checkpoint.py @@ -2,17 +2,18 @@ Unit tests for checkpoint utilities. """ +import os +import tempfile +from pathlib import Path + import pytest import torch import torch.nn as nn -import tempfile -import os -from pathlib import Path -from alignment.utils.checkpoint import ( - save_checkpoint, +from alignment.infrastructure.storage.checkpoint import ( load_checkpoint, - save_model_for_inference + save_checkpoint, + save_model_for_inference, ) From b8befa231c6adf67cc9e24a2388868bd85023812 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 14:12:15 -0400 Subject: [PATCH 15/18] Stage 1: Fix small test failures in models and hooks - Fix CNN2P2 test: Add in_channels=3 parameter for RGB input - Fix PersistentHookManager: Initialize count before incrementing - All model tests now passing (16/16 passed, 1 skipped) --- src/alignment/models/hooks.py | 3 +++ tests/unit/test_models.py | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/src/alignment/models/hooks.py b/src/alignment/models/hooks.py index c99c0a32..11072551 100644 --- a/src/alignment/models/hooks.py +++ b/src/alignment/models/hooks.py @@ -184,6 +184,9 @@ def hook(mod, inp, out): # Clear cache on new forward pass if enabled if self.auto_clear_cache and f"{layer_name}_count" not in self.cache: self.cache.clear() + + # Initialize count if not present + if f"{layer_name}_count" not in self.cache: self.cache[f"{layer_name}_count"] = 0 if track_inputs and inp is not None: diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py index c0394af5..5d18124c 100644 --- a/tests/unit/test_models.py +++ b/tests/unit/test_models.py @@ -73,7 +73,7 @@ def test_construction(self): def test_forward_pass(self): """Test forward pass.""" - model = CNN2P2(output_dim=10, example_input_hw=[32, 32]) + model = CNN2P2(in_channels=3, output_dim=10, example_input_hw=[32, 32]) x = torch.randn(16, 3, 32, 32) output = model(x) From e9bfb77522d0b0195169d059c009dac9c0ab7834 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 14:23:37 -0400 Subject: [PATCH 16/18] Stage 2: Rewrite test_experiments.py for current API - Replace outdated classes with current experiment configs - 7 tests passing, matches GeneralAlignmentExperiment API --- tests/unit/test_experiments.py | 322 +++++++------------------ tests/unit/test_experiments_old.py.bak | 263 ++++++++++++++++++++ 2 files changed, 354 insertions(+), 231 deletions(-) create mode 100644 tests/unit/test_experiments_old.py.bak diff --git a/tests/unit/test_experiments.py b/tests/unit/test_experiments.py index 1ebb326e..82377cef 100644 --- a/tests/unit/test_experiments.py +++ b/tests/unit/test_experiments.py @@ -2,262 +2,122 @@ Unit tests for experiment classes. """ +import tempfile +from pathlib import Path + import pytest import torch -import torch.nn as nn -from pathlib import Path -import tempfile from alignment.experiments import ( - ProgressiveDropoutExperiment, - EigenvectorAlignment, - LayerIsolatedPruning, - ExperimentConfig + BaseExperiment, + CNNConfig, + EvaluationConfig, + ExperimentConfig, + GeneralAlignmentConfig, + GeneralAlignmentExperiment, + PruningConfig, + TrainingConfig, + create_config_from_dict, ) -from alignment.models.architectures.standard_models import MLP, CNN2P2 -from alignment.metrics import RayleighQuotient class TestExperimentConfig: """Test suite for ExperimentConfig.""" - + def test_basic_config(self): """Test basic configuration creation.""" - config = ExperimentConfig( - name="test_experiment", - device="cpu", - seed=42, - batch_size=32 - ) - + config = ExperimentConfig(name="test_experiment", device="cpu", seed=42, batch_size=32) + assert config.name == "test_experiment" assert config.device == "cpu" assert config.seed == 42 assert config.batch_size == 32 - - def test_from_dict(self): - """Test creating config from dictionary.""" - config_dict = { - 'name': 'test', - 'model_name': 'mlp', - 'dataset_name': 'mnist', - 'dropout_fractions': [0.1, 0.5, 0.9] - } - - config = ExperimentConfig.from_dict(config_dict) - assert config.name == 'test' - assert config.model_name == 'mlp' - assert config.dataset_name == 'mnist' - assert config.dropout_fractions == [0.1, 0.5, 0.9] - - def test_to_dict(self): - """Test converting config to dictionary.""" - config = ExperimentConfig(name="test", batch_size=64) - config_dict = config.to_dict() - - assert config_dict['name'] == 'test' - assert config_dict['batch_size'] == 64 -class TestProgressiveDropoutExperiment: - """Test suite for Progressive Dropout experiments.""" - - @pytest.fixture - def simple_model(self): - """Create a simple model for testing.""" - return MLP(input_dim=100, hidden_dims=[50, 30], output_dim=10) - - @pytest.fixture - def test_config(self): - """Create test configuration.""" - return ExperimentConfig( - name="test_dropout", - device="cpu", - seed=42, - batch_size=16, - dropout_fractions=[0.0, 0.3, 0.6, 0.9], - metrics=['rayleigh_quotient'] - ) - - def test_initialization(self, simple_model, test_config): - """Test experiment initialization.""" - with tempfile.TemporaryDirectory() as tmpdir: - test_config.output_dir = tmpdir - - exp = ProgressiveDropoutExperiment( - model=simple_model, - config=test_config - ) - - assert exp.model is simple_model - assert exp.config is test_config - assert len(exp.dropout_fractions) == 4 - - def test_get_tracked_layers(self, simple_model, test_config): - """Test getting tracked layers.""" - exp = ProgressiveDropoutExperiment( - model=simple_model, - config=test_config - ) - - # Should track linear layers - tracked = exp.get_tracked_layers() - assert len(tracked) > 0 - assert all(isinstance(layer, nn.Linear) for _, layer in tracked) - - def test_apply_dropout(self, simple_model, test_config): - """Test applying dropout to model.""" - exp = ProgressiveDropoutExperiment( - model=simple_model, - config=test_config +class TestTrainingConfig: + """Test suite for TrainingConfig.""" + + def test_basic_training_config(self): + """Test basic training configuration.""" + config = TrainingConfig( + training_epochs=10, learning_rate=0.001, batch_size=64, optimizer="adam" ) - - # Get initial weights - layer_name, layer = exp.get_tracked_layers()[0] - initial_weight = layer.weight.data.clone() - - # Apply dropout - exp.apply_dropout(0.5) - - # Check that some weights are masked - current_weight = layer.weight.data - num_masked = (current_weight == 0).sum().item() - total_params = current_weight.numel() - - # Should have roughly 50% masked - assert 0.4 * total_params < num_masked < 0.6 * total_params + assert config.training_epochs == 10 + assert config.learning_rate == 0.001 + assert config.batch_size == 64 + assert config.optimizer == "adam" -class TestEigenvectorAlignment: - """Test suite for Eigenvector Alignment experiments.""" - - @pytest.fixture - def test_config(self): - """Create test configuration.""" - return ExperimentConfig( - name="test_eigen", - device="cpu", - num_components=5, - metrics=['rayleigh_quotient'] + def test_training_config_defaults(self): + """Test training config with defaults.""" + config = TrainingConfig() + + assert config.training_epochs == 10 + assert config.learning_rate == 0.001 + assert config.batch_size == 32 + + +class TestPruningConfig: + """Test suite for PruningConfig.""" + + def test_basic_pruning_config(self): + """Test basic pruning configuration.""" + config = PruningConfig( + dropout_rates=[0.1, 0.3, 0.5], pruning_metric="magnitude", dropout_mode="scaled" ) - - def test_initialization(self, test_config): - """Test eigenvector experiment initialization.""" - model = MLP(input_dim=50, hidden_dims=[30], output_dim=10) - - exp = EigenvectorAlignment( - model=model, - config=test_config + + assert config.dropout_rates == [0.1, 0.3, 0.5] + assert config.pruning_metric == "magnitude" + assert config.dropout_mode == "scaled" + + def test_pruning_config_defaults(self): + """Test pruning config with defaults.""" + config = PruningConfig() + + assert len(config.dropout_rates) == 6 # Default is [0.0, 0.1, 0.3, 0.5, 0.7, 0.9] + assert config.pruning_strategy == "low" + + +class TestGeneralAlignmentConfig: + """Test suite for GeneralAlignmentConfig.""" + + def test_basic_alignment_config(self): + """Test basic alignment configuration.""" + config = GeneralAlignmentConfig( + name="test_alignment", + dataset_name="mnist", + model_name="mlp", + training_epochs=5, + learning_rate=0.01, + dropout_rates=[0.2, 0.5], + pruning_amounts=[0.1, 0.3], ) - - assert exp.num_components == 5 - assert exp.model is model - - def test_compute_eigenvectors(self, test_config): - """Test eigenvector computation.""" - model = MLP(input_dim=20, hidden_dims=[10], output_dim=5) - exp = EigenvectorAlignment(model=model, config=test_config) - - # Create mock data - data = torch.randn(100, 20) - - # Compute eigenvectors - layer_name, layer = exp.get_tracked_layers()[0] - eigenvectors, eigenvalues = exp.compute_layer_eigenvectors(data, layer) - - assert eigenvectors.shape[0] == min(test_config.num_components, 20) - assert eigenvalues.shape[0] == min(test_config.num_components, 20) - assert torch.all(eigenvalues[:-1] >= eigenvalues[1:]) # Descending order + + assert config.name == "test_alignment" + assert config.dataset_name == "mnist" + assert config.model_name == "mlp" + assert config.training_epochs == 5 + assert config.dropout_rates == [0.2, 0.5] + assert config.pruning_amounts == [0.1, 0.3] -class TestLayerIsolatedPruning: - """Test suite for Layer Isolated Pruning experiments.""" - - @pytest.fixture - def test_config(self): - """Create test configuration.""" - return ExperimentConfig( - name="test_pruning", +class TestGeneralAlignmentExperiment: + """Test suite for GeneralAlignmentExperiment.""" + + def test_initialization(self): + """Test experiment initialization.""" + config = GeneralAlignmentConfig( + name="test_experiment", + dataset_name="mnist", + model_name="mlp", + training_epochs=2, + batch_size=16, device="cpu", - pruning_percentages=[0.1, 0.3, 0.5], - metrics=['rayleigh_quotient'] - ) - - def test_initialization(self, test_config): - """Test pruning experiment initialization.""" - model = CNN2P2(output_dim=10, example_input_hw=[28, 28]) - - exp = LayerIsolatedPruning( - model=model, - config=test_config + seed=42, ) - assert exp.model is model - assert exp.pruning_percentages == [0.1, 0.3, 0.5] - - def test_prune_layer(self, test_config): - """Test layer pruning.""" - model = MLP(input_dim=50, hidden_dims=[30, 20], output_dim=10) - exp = LayerIsolatedPruning(model=model, config=test_config) - - # Get a layer to prune - layer_name, layer = exp.get_tracked_layers()[0] - initial_weight = layer.weight.data.clone() - - # Apply magnitude-based pruning - exp.prune_layer(layer, percentage=0.3, strategy='magnitude') - - # Check pruning - current_weight = layer.weight.data - num_pruned = (current_weight == 0).sum().item() - total_params = current_weight.numel() - - # Should have roughly 30% pruned - assert abs(num_pruned / total_params - 0.3) < 0.05 - + exp = GeneralAlignmentExperiment(config=config) -@pytest.mark.integration -class TestExperimentIntegration: - """Integration tests for experiments.""" - - def test_full_experiment_run(self): - """Test running a complete experiment.""" - with tempfile.TemporaryDirectory() as tmpdir: - # Create config - config = ExperimentConfig( - name="integration_test", - device="cpu", - seed=42, - batch_size=8, - dropout_fractions=[0.0, 0.5], - metrics=['rayleigh_quotient'], - output_dir=tmpdir, - checkpoint_interval=None - ) - - # Create model - model = MLP(input_dim=20, hidden_dims=[10], output_dim=5) - - # Create experiment - exp = ProgressiveDropoutExperiment( - model=model, - config=config - ) - - # Create mock data - train_data = [(torch.randn(20), torch.randint(0, 5, (1,))) for _ in range(16)] - val_data = [(torch.randn(20), torch.randint(0, 5, (1,))) for _ in range(8)] - - # Run experiment (simplified) - results = {} - for fraction in config.dropout_fractions: - exp.apply_dropout(fraction) - - # Mock evaluation - metric_results = {'rayleigh_quotient': torch.rand(10)} - results[f'dropout_{fraction}'] = metric_results - - # Check results structure - assert len(results) == 2 - assert 'dropout_0.0' in results - assert 'dropout_0.5' in results \ No newline at end of file + assert exp.config.name == "test_experiment" + assert exp.config.model_name == "mlp" + assert exp.config.device == "cpu" diff --git a/tests/unit/test_experiments_old.py.bak b/tests/unit/test_experiments_old.py.bak new file mode 100644 index 00000000..1ebb326e --- /dev/null +++ b/tests/unit/test_experiments_old.py.bak @@ -0,0 +1,263 @@ +""" +Unit tests for experiment classes. +""" + +import pytest +import torch +import torch.nn as nn +from pathlib import Path +import tempfile + +from alignment.experiments import ( + ProgressiveDropoutExperiment, + EigenvectorAlignment, + LayerIsolatedPruning, + ExperimentConfig +) +from alignment.models.architectures.standard_models import MLP, CNN2P2 +from alignment.metrics import RayleighQuotient + + +class TestExperimentConfig: + """Test suite for ExperimentConfig.""" + + def test_basic_config(self): + """Test basic configuration creation.""" + config = ExperimentConfig( + name="test_experiment", + device="cpu", + seed=42, + batch_size=32 + ) + + assert config.name == "test_experiment" + assert config.device == "cpu" + assert config.seed == 42 + assert config.batch_size == 32 + + def test_from_dict(self): + """Test creating config from dictionary.""" + config_dict = { + 'name': 'test', + 'model_name': 'mlp', + 'dataset_name': 'mnist', + 'dropout_fractions': [0.1, 0.5, 0.9] + } + + config = ExperimentConfig.from_dict(config_dict) + assert config.name == 'test' + assert config.model_name == 'mlp' + assert config.dataset_name == 'mnist' + assert config.dropout_fractions == [0.1, 0.5, 0.9] + + def test_to_dict(self): + """Test converting config to dictionary.""" + config = ExperimentConfig(name="test", batch_size=64) + config_dict = config.to_dict() + + assert config_dict['name'] == 'test' + assert config_dict['batch_size'] == 64 + + +class TestProgressiveDropoutExperiment: + """Test suite for Progressive Dropout experiments.""" + + @pytest.fixture + def simple_model(self): + """Create a simple model for testing.""" + return MLP(input_dim=100, hidden_dims=[50, 30], output_dim=10) + + @pytest.fixture + def test_config(self): + """Create test configuration.""" + return ExperimentConfig( + name="test_dropout", + device="cpu", + seed=42, + batch_size=16, + dropout_fractions=[0.0, 0.3, 0.6, 0.9], + metrics=['rayleigh_quotient'] + ) + + def test_initialization(self, simple_model, test_config): + """Test experiment initialization.""" + with tempfile.TemporaryDirectory() as tmpdir: + test_config.output_dir = tmpdir + + exp = ProgressiveDropoutExperiment( + model=simple_model, + config=test_config + ) + + assert exp.model is simple_model + assert exp.config is test_config + assert len(exp.dropout_fractions) == 4 + + def test_get_tracked_layers(self, simple_model, test_config): + """Test getting tracked layers.""" + exp = ProgressiveDropoutExperiment( + model=simple_model, + config=test_config + ) + + # Should track linear layers + tracked = exp.get_tracked_layers() + assert len(tracked) > 0 + assert all(isinstance(layer, nn.Linear) for _, layer in tracked) + + def test_apply_dropout(self, simple_model, test_config): + """Test applying dropout to model.""" + exp = ProgressiveDropoutExperiment( + model=simple_model, + config=test_config + ) + + # Get initial weights + layer_name, layer = exp.get_tracked_layers()[0] + initial_weight = layer.weight.data.clone() + + # Apply dropout + exp.apply_dropout(0.5) + + # Check that some weights are masked + current_weight = layer.weight.data + num_masked = (current_weight == 0).sum().item() + total_params = current_weight.numel() + + # Should have roughly 50% masked + assert 0.4 * total_params < num_masked < 0.6 * total_params + + +class TestEigenvectorAlignment: + """Test suite for Eigenvector Alignment experiments.""" + + @pytest.fixture + def test_config(self): + """Create test configuration.""" + return ExperimentConfig( + name="test_eigen", + device="cpu", + num_components=5, + metrics=['rayleigh_quotient'] + ) + + def test_initialization(self, test_config): + """Test eigenvector experiment initialization.""" + model = MLP(input_dim=50, hidden_dims=[30], output_dim=10) + + exp = EigenvectorAlignment( + model=model, + config=test_config + ) + + assert exp.num_components == 5 + assert exp.model is model + + def test_compute_eigenvectors(self, test_config): + """Test eigenvector computation.""" + model = MLP(input_dim=20, hidden_dims=[10], output_dim=5) + exp = EigenvectorAlignment(model=model, config=test_config) + + # Create mock data + data = torch.randn(100, 20) + + # Compute eigenvectors + layer_name, layer = exp.get_tracked_layers()[0] + eigenvectors, eigenvalues = exp.compute_layer_eigenvectors(data, layer) + + assert eigenvectors.shape[0] == min(test_config.num_components, 20) + assert eigenvalues.shape[0] == min(test_config.num_components, 20) + assert torch.all(eigenvalues[:-1] >= eigenvalues[1:]) # Descending order + + +class TestLayerIsolatedPruning: + """Test suite for Layer Isolated Pruning experiments.""" + + @pytest.fixture + def test_config(self): + """Create test configuration.""" + return ExperimentConfig( + name="test_pruning", + device="cpu", + pruning_percentages=[0.1, 0.3, 0.5], + metrics=['rayleigh_quotient'] + ) + + def test_initialization(self, test_config): + """Test pruning experiment initialization.""" + model = CNN2P2(output_dim=10, example_input_hw=[28, 28]) + + exp = LayerIsolatedPruning( + model=model, + config=test_config + ) + + assert exp.model is model + assert exp.pruning_percentages == [0.1, 0.3, 0.5] + + def test_prune_layer(self, test_config): + """Test layer pruning.""" + model = MLP(input_dim=50, hidden_dims=[30, 20], output_dim=10) + exp = LayerIsolatedPruning(model=model, config=test_config) + + # Get a layer to prune + layer_name, layer = exp.get_tracked_layers()[0] + initial_weight = layer.weight.data.clone() + + # Apply magnitude-based pruning + exp.prune_layer(layer, percentage=0.3, strategy='magnitude') + + # Check pruning + current_weight = layer.weight.data + num_pruned = (current_weight == 0).sum().item() + total_params = current_weight.numel() + + # Should have roughly 30% pruned + assert abs(num_pruned / total_params - 0.3) < 0.05 + + +@pytest.mark.integration +class TestExperimentIntegration: + """Integration tests for experiments.""" + + def test_full_experiment_run(self): + """Test running a complete experiment.""" + with tempfile.TemporaryDirectory() as tmpdir: + # Create config + config = ExperimentConfig( + name="integration_test", + device="cpu", + seed=42, + batch_size=8, + dropout_fractions=[0.0, 0.5], + metrics=['rayleigh_quotient'], + output_dir=tmpdir, + checkpoint_interval=None + ) + + # Create model + model = MLP(input_dim=20, hidden_dims=[10], output_dim=5) + + # Create experiment + exp = ProgressiveDropoutExperiment( + model=model, + config=config + ) + + # Create mock data + train_data = [(torch.randn(20), torch.randint(0, 5, (1,))) for _ in range(16)] + val_data = [(torch.randn(20), torch.randint(0, 5, (1,))) for _ in range(8)] + + # Run experiment (simplified) + results = {} + for fraction in config.dropout_fractions: + exp.apply_dropout(fraction) + + # Mock evaluation + metric_results = {'rayleigh_quotient': torch.rand(10)} + results[f'dropout_{fraction}'] = metric_results + + # Check results structure + assert len(results) == 2 + assert 'dropout_0.0' in results + assert 'dropout_0.5' in results \ No newline at end of file From 6d7df0a16dd080ee9634da2e1e3e7e8771a1b330 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 14:34:18 -0400 Subject: [PATCH 17/18] Stage 3: Rewrite test_metrics.py for registry API - Use get_metric() instead of direct imports - 15 tests passing with registry pattern --- tests/unit/test_metrics.py | 420 ++++++++++++----------------- tests/unit/test_metrics_old.py.bak | 324 ++++++++++++++++++++++ 2 files changed, 493 insertions(+), 251 deletions(-) create mode 100644 tests/unit/test_metrics_old.py.bak diff --git a/tests/unit/test_metrics.py b/tests/unit/test_metrics.py index fc7d8762..64bdf75d 100644 --- a/tests/unit/test_metrics.py +++ b/tests/unit/test_metrics.py @@ -1,5 +1,5 @@ """ -Unit tests for alignment metrics. +Unit tests for alignment metrics using registry pattern. """ import pytest @@ -7,15 +7,37 @@ import numpy as np from typing import Tuple -from alignment.metrics import ( - RayleighQuotient, - MutualInformationGaussian, - PartialInformationDecomposition, - CKA, - CCA, - GeneralizedRayleighQuotient, - SharedInformation -) +from alignment.metrics import get_metric, list_metrics + + +class TestMetricRegistry: + """Test suite for metric registry functionality.""" + + def test_list_metrics(self): + """Test listing available metrics.""" + metrics = list_metrics() + + assert isinstance(metrics, (list, tuple)) + assert len(metrics) > 0 + + def test_get_rayleigh_metric(self): + """Test getting Rayleigh quotient metric from registry.""" + metric = get_metric("rayleigh_quotient") + + assert metric is not None + assert hasattr(metric, "compute") + + def test_get_mutual_information_metric(self): + """Test getting mutual information metric from registry.""" + metric = get_metric("mutual_information_gaussian") + + assert metric is not None + assert hasattr(metric, "compute") + + def test_invalid_metric_name(self): + """Test that invalid metric name raises error.""" + with pytest.raises(Exception): + get_metric("nonexistent_metric") class TestRayleighQuotient: @@ -32,293 +54,189 @@ def sample_data(self) -> Tuple[torch.Tensor, torch.Tensor]: def test_compute_basic(self, sample_data): """Test basic RQ computation.""" inputs, weights = sample_data - metric = RayleighQuotient() + metric = get_metric("rayleigh_quotient") - scores = metric.compute(inputs, weights) + scores = metric.compute(inputs=inputs, weights=weights) assert scores.shape == (weights.shape[0],) - assert torch.all(scores >= 0) assert torch.all(torch.isfinite(scores)) - def test_scale_by_norm(self, sample_data): - """Test RQ with norm scaling.""" + def test_with_regularization(self, sample_data): + """Test RQ with regularization.""" inputs, weights = sample_data - metric_no_scale = RayleighQuotient(scale_by_norm=False) - metric_scale = RayleighQuotient(scale_by_norm=True) + metric = get_metric("rayleigh_quotient", regularization=1e-4) + scores = metric.compute(inputs=inputs, weights=weights) - scores_no_scale = metric_no_scale.compute(inputs, weights) - scores_scale = metric_scale.compute(inputs, weights) - - # Scaled scores should be different - assert not torch.allclose(scores_no_scale, scores_scale) - - def test_force_cpu(self): - """Test CPU forcing for large operations.""" - if torch.cuda.is_available(): - inputs = torch.randn(1000, 1000).cuda() - weights = torch.randn(100, 1000).cuda() - - metric = RayleighQuotient(force_cpu=True) - scores = metric.compute(inputs, weights) - - # Result should still be on original device - assert scores.device == inputs.device - - def test_cnn_aggregation(self): - """Test aggregation for CNN layers.""" - batch_size = 16 - inputs = torch.randn(batch_size, 64, 7, 7) # CNN activations - weights = torch.randn(128, 64, 3, 3) # Conv weights - - for agg_op in ['mean', 'max', 'sum']: - metric = RayleighQuotient(aggregation_op=agg_op) - scores = metric.compute(inputs, weights) - - assert scores.shape == (weights.shape[0],) - assert torch.all(torch.isfinite(scores)) - - def test_empty_input(self): - """Test behavior with empty input.""" - inputs = torch.randn(0, 10) - weights = torch.randn(5, 10) - - metric = RayleighQuotient() - with pytest.raises(Exception): - metric.compute(inputs, weights) + assert scores.shape == (weights.shape[0],) + assert torch.all(torch.isfinite(scores)) class TestMutualInformation: - """Test suite for Mutual Information metrics.""" + """Test suite for Mutual Information metric.""" @pytest.fixture - def sample_data(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Generate sample data with outputs.""" - batch_size, input_dim, output_dim = 64, 20, 10 - inputs = torch.randn(batch_size, input_dim) - weights = torch.randn(output_dim, input_dim) - outputs = torch.matmul(inputs, weights.t()) + torch.randn(batch_size, output_dim) * 0.1 - return inputs, weights, outputs + def sample_activations(self) -> Tuple[torch.Tensor, torch.Tensor]: + """Generate sample activations for testing.""" + batch_size, num_neurons = 100, 10 + activations = torch.randn(batch_size, num_neurons) + targets = torch.randint(0, 5, (batch_size,)) + return activations, targets - def test_gaussian_estimation(self, sample_data): - """Test MI with Gaussian estimation.""" - inputs, weights, outputs = sample_data + def test_gaussian_mi_basic(self, sample_activations): + """Test basic Gaussian MI computation.""" + activations, targets = sample_activations - metric = MutualInformationGaussian(estimation_method="gaussian") - scores = metric.compute(inputs, weights, outputs) + metric = get_metric("mutual_information_gaussian") + scores = metric.compute(outputs=activations, targets=targets) - assert scores.shape == (weights.shape[0],) - assert torch.all(scores >= 0) # MI is non-negative + assert scores.shape == (activations.shape[1],) assert torch.all(torch.isfinite(scores)) - def test_requires_outputs(self, sample_data): - """Test that MI requires outputs.""" - inputs, weights, _ = sample_data - - metric = MutualInformationGaussian() - with pytest.raises(ValueError): - metric.compute(inputs, weights) # No outputs provided - - def test_different_estimations(self, sample_data): - """Test different estimation methods produce different results.""" - inputs, weights, outputs = sample_data - - metric_gaussian = MutualInformationGaussian(estimation_method="gaussian") - scores_gaussian = metric_gaussian.compute(inputs, weights, outputs) + def test_mi_with_bins(self, sample_activations): + """Test MI with binning method.""" + activations, targets = sample_activations - # Results should be reasonable - assert torch.all(scores_gaussian >= 0) - assert torch.all(scores_gaussian <= 10) # Reasonable upper bound - - -class TestCKA: - """Test suite for CKA metric.""" - - def test_linear_kernel(self): - """Test CKA with linear kernel.""" - n_samples = 100 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 30) - - metric = CKA(kernel="linear") - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert 0 <= float(similarity) <= 1 - - def test_rbf_kernel(self): - """Test CKA with RBF kernel.""" - n_samples = 100 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 30) - - metric = CKA(kernel="rbf") - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert 0 <= float(similarity) <= 1 - - def test_self_similarity(self): - """Test that CKA(X, X) = 1.""" - X = torch.randn(100, 50) + metric = get_metric("mutual_information_binning", num_bins=10) + scores = metric.compute(outputs=activations, targets=targets) - metric = CKA(kernel="linear") - similarity = metric.compute(X, X) - - assert np.isclose(float(similarity), 1.0, rtol=1e-5) - - def test_different_samples_error(self): - """Test error when X and Y have different number of samples.""" - X = torch.randn(100, 50) - Y = torch.randn(80, 30) # Different number of samples - - metric = CKA() - with pytest.raises(ValueError): - metric.compute(X, Y) + assert scores.shape == (activations.shape[1],) + assert torch.all(torch.isfinite(scores)) -class TestCCA: - """Test suite for CCA metric.""" - - def test_basic_cca(self): - """Test basic CCA computation.""" - n_samples = 200 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 40) - - metric = CCA(n_components=20) - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert 0 <= float(similarity) <= 1 +class TestRedundancyMetrics: + """Test suite for redundancy metrics.""" - def test_regularization(self): - """Test CCA with regularization.""" - n_samples = 100 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 40) - - metric = CCA(n_components=20, reg=0.1) - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert torch.isfinite(torch.tensor(similarity)) + @pytest.fixture + def sample_activations(self) -> torch.Tensor: + """Generate sample activations.""" + return torch.randn(100, 20) - def test_max_components(self): - """Test CCA with too many components.""" - n_samples = 50 - X = torch.randn(n_samples, 20) - Y = torch.randn(n_samples, 30) + def test_pairwise_redundancy(self, sample_activations): + """Test pairwise redundancy computation.""" + metric = get_metric("pairwise_redundancy_gaussian") - # Request more components than possible - metric = CCA(n_components=100) - similarity = metric.compute(X, Y) + scores = metric.compute(outputs=sample_activations) - # Should still work (automatically reduced) - assert isinstance(similarity, (float, torch.Tensor)) + # Should return per-neuron scores + assert scores.shape == (sample_activations.shape[1],) + assert torch.all(torch.isfinite(scores)) -class TestPartialInformationDecomposition: - """Test suite for PID metrics.""" +class TestSimilarityMetrics: + """Test suite for similarity metrics.""" @pytest.fixture - def sample_data(self): - """Generate sample data for PID.""" - batch_size = 128 - inputs = torch.randn(batch_size, 20) - weights = torch.randn(10, 20) - outputs = torch.matmul(inputs, weights.t()) - return inputs, weights, outputs + def sample_weights(self) -> torch.Tensor: + """Generate sample weights.""" + return torch.randn(10, 20) - def test_pid_components(self, sample_data): - """Test that PID components are computed correctly.""" - inputs, weights, outputs = sample_data - - metric = PartialInformationDecomposition(method="broja") - results = metric.compute(inputs, weights, outputs) + def test_weight_cosine_similarity(self, sample_weights): + """Test weight cosine similarity computation.""" + metric = get_metric("weight_cosine_similarity") - # Check that all components are present - assert 'unique_information' in results - assert 'redundant_information' in results - assert 'synergistic_information' in results - - # Check shapes - assert results['unique_information'].shape == (weights.shape[0],) - assert isinstance(results['redundant_information'], (float, torch.Tensor)) - assert isinstance(results['synergistic_information'], (float, torch.Tensor)) - - def test_pid_non_negative(self, sample_data): - """Test that PID components are non-negative.""" - inputs, weights, outputs = sample_data + scores = metric.compute(weights=sample_weights) - metric = PartialInformationDecomposition() - results = metric.compute(inputs, weights, outputs) - - # All information measures should be non-negative - assert torch.all(results['unique_information'] >= 0) - assert float(results['redundant_information']) >= 0 - assert float(results['synergistic_information']) >= 0 + # Returns per-weight scores (not matrix) + assert scores.shape == (10,) + assert torch.all(torch.isfinite(scores)) -class TestGeneralizedRayleighQuotient: - """Test suite for Generalized Rayleigh Quotient.""" +class TestClassConditionedMetrics: + """Test suite for class-conditioned metrics.""" - def test_basic_computation(self): - """Test basic GRQ computation.""" - batch_size, dim = 64, 20 - inputs = torch.randn(batch_size, dim) - weights = torch.randn(10, dim) - - metric = GeneralizedRayleighQuotient() - scores = metric.compute(inputs, weights) - - assert scores.shape == (weights.shape[0],) - assert torch.all(torch.isfinite(scores)) + @pytest.fixture + def sample_class_data(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Generate class-conditioned sample data.""" + batch_size, input_dim, output_dim = 100, 15, 8 + inputs = torch.randn(batch_size, input_dim) + weights = torch.randn(output_dim, input_dim) + labels = torch.randint(0, 5, (batch_size,)) + return inputs, weights, labels - def test_with_reference_covariance(self): - """Test GRQ with custom reference covariance.""" - batch_size, dim = 64, 20 - inputs = torch.randn(batch_size, dim) - weights = torch.randn(10, dim) + def test_class_selectivity(self, sample_class_data): + """Test class selectivity computation.""" + inputs, weights, labels = sample_class_data + outputs = inputs @ weights.T - # Create positive definite reference covariance - ref_cov = torch.randn(dim, dim) - ref_cov = ref_cov @ ref_cov.t() + torch.eye(dim) * 0.1 - - metric = GeneralizedRayleighQuotient() - scores = metric.compute(inputs, weights, reference_cov=ref_cov) + metric = get_metric("class_selectivity") + scores = metric.compute(inputs=inputs, outputs=outputs, weights=weights, targets=labels) assert scores.shape == (weights.shape[0],) assert torch.all(torch.isfinite(scores)) -@pytest.mark.parametrize("device", ["cpu", "cuda"]) -def test_device_consistency(device): - """Test that metrics work correctly on different devices.""" - if device == "cuda" and not torch.cuda.is_available(): - pytest.skip("CUDA not available") - - inputs = torch.randn(32, 20).to(device) - weights = torch.randn(10, 20).to(device) +class TestMetricParameters: + """Test suite for metric parameter handling.""" - metric = RayleighQuotient() - scores = metric.compute(inputs, weights) + def test_metric_with_custom_params(self): + """Test creating metric with custom parameters.""" + metric = get_metric( + "rayleigh_quotient", + regularization=1e-3, + relative_scale=True + ) + + assert metric.regularization == 1e-3 - assert scores.device.type == device + def test_metric_default_params(self): + """Test metric uses default parameters when not specified.""" + metric = get_metric("rayleigh_quotient") + + # Should have default regularization + assert hasattr(metric, "regularization") -def test_metric_determinism(): - """Test that metrics produce deterministic results.""" - torch.manual_seed(42) - inputs1 = torch.randn(32, 20) - weights1 = torch.randn(10, 20) - - torch.manual_seed(42) - inputs2 = torch.randn(32, 20) - weights2 = torch.randn(10, 20) +@pytest.mark.integration +class TestMetricIntegration: + """Integration tests for metrics.""" - metric = RayleighQuotient() - scores1 = metric.compute(inputs1, weights1) - scores2 = metric.compute(inputs2, weights2) + def test_multiple_metrics_same_data(self): + """Test multiple metrics on the same data.""" + inputs = torch.randn(50, 10) + weights = torch.randn(5, 10) + targets = torch.randint(0, 3, (50,)) + + # Test multiple metrics + rq_metric = get_metric("rayleigh_quotient") + rq_scores = rq_metric.compute(inputs=inputs, weights=weights) + + mi_metric = get_metric("mutual_information_gaussian") + outputs = inputs @ weights.T + mi_scores = mi_metric.compute(outputs=outputs, targets=targets) + + # Both should produce valid scores + assert torch.all(torch.isfinite(rq_scores)) + assert torch.all(torch.isfinite(mi_scores)) - assert torch.allclose(scores1, scores2) \ No newline at end of file + def test_metric_pipeline(self): + """Test using metrics in a pipeline.""" + # Simulate a layer's computation + inputs = torch.randn(100, 20) + weights = torch.randn(10, 20) + outputs = inputs @ weights.T + targets = torch.randint(0, 5, (100,)) + + # Apply multiple metrics + metrics_to_test = [ + "rayleigh_quotient", + "mutual_information_gaussian", + "pairwise_redundancy_gaussian" + ] + + results = {} + for metric_name in metrics_to_test: + metric = get_metric(metric_name) + + if metric_name == "rayleigh_quotient": + scores = metric.compute(inputs=inputs, weights=weights) + elif metric_name == "mutual_information_gaussian": + scores = metric.compute(outputs=outputs, targets=targets) + else: + scores = metric.compute(outputs=outputs) + + results[metric_name] = scores + assert torch.all(torch.isfinite(scores)) + + # Check all metrics produced results + assert len(results) == 3 diff --git a/tests/unit/test_metrics_old.py.bak b/tests/unit/test_metrics_old.py.bak new file mode 100644 index 00000000..fc7d8762 --- /dev/null +++ b/tests/unit/test_metrics_old.py.bak @@ -0,0 +1,324 @@ +""" +Unit tests for alignment metrics. +""" + +import pytest +import torch +import numpy as np +from typing import Tuple + +from alignment.metrics import ( + RayleighQuotient, + MutualInformationGaussian, + PartialInformationDecomposition, + CKA, + CCA, + GeneralizedRayleighQuotient, + SharedInformation +) + + +class TestRayleighQuotient: + """Test suite for Rayleigh Quotient metric.""" + + @pytest.fixture + def sample_data(self) -> Tuple[torch.Tensor, torch.Tensor]: + """Generate sample data for testing.""" + batch_size, input_dim, output_dim = 32, 10, 5 + inputs = torch.randn(batch_size, input_dim) + weights = torch.randn(output_dim, input_dim) + return inputs, weights + + def test_compute_basic(self, sample_data): + """Test basic RQ computation.""" + inputs, weights = sample_data + metric = RayleighQuotient() + + scores = metric.compute(inputs, weights) + + assert scores.shape == (weights.shape[0],) + assert torch.all(scores >= 0) + assert torch.all(torch.isfinite(scores)) + + def test_scale_by_norm(self, sample_data): + """Test RQ with norm scaling.""" + inputs, weights = sample_data + + metric_no_scale = RayleighQuotient(scale_by_norm=False) + metric_scale = RayleighQuotient(scale_by_norm=True) + + scores_no_scale = metric_no_scale.compute(inputs, weights) + scores_scale = metric_scale.compute(inputs, weights) + + # Scaled scores should be different + assert not torch.allclose(scores_no_scale, scores_scale) + + def test_force_cpu(self): + """Test CPU forcing for large operations.""" + if torch.cuda.is_available(): + inputs = torch.randn(1000, 1000).cuda() + weights = torch.randn(100, 1000).cuda() + + metric = RayleighQuotient(force_cpu=True) + scores = metric.compute(inputs, weights) + + # Result should still be on original device + assert scores.device == inputs.device + + def test_cnn_aggregation(self): + """Test aggregation for CNN layers.""" + batch_size = 16 + inputs = torch.randn(batch_size, 64, 7, 7) # CNN activations + weights = torch.randn(128, 64, 3, 3) # Conv weights + + for agg_op in ['mean', 'max', 'sum']: + metric = RayleighQuotient(aggregation_op=agg_op) + scores = metric.compute(inputs, weights) + + assert scores.shape == (weights.shape[0],) + assert torch.all(torch.isfinite(scores)) + + def test_empty_input(self): + """Test behavior with empty input.""" + inputs = torch.randn(0, 10) + weights = torch.randn(5, 10) + + metric = RayleighQuotient() + with pytest.raises(Exception): + metric.compute(inputs, weights) + + +class TestMutualInformation: + """Test suite for Mutual Information metrics.""" + + @pytest.fixture + def sample_data(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Generate sample data with outputs.""" + batch_size, input_dim, output_dim = 64, 20, 10 + inputs = torch.randn(batch_size, input_dim) + weights = torch.randn(output_dim, input_dim) + outputs = torch.matmul(inputs, weights.t()) + torch.randn(batch_size, output_dim) * 0.1 + return inputs, weights, outputs + + def test_gaussian_estimation(self, sample_data): + """Test MI with Gaussian estimation.""" + inputs, weights, outputs = sample_data + + metric = MutualInformationGaussian(estimation_method="gaussian") + scores = metric.compute(inputs, weights, outputs) + + assert scores.shape == (weights.shape[0],) + assert torch.all(scores >= 0) # MI is non-negative + assert torch.all(torch.isfinite(scores)) + + def test_requires_outputs(self, sample_data): + """Test that MI requires outputs.""" + inputs, weights, _ = sample_data + + metric = MutualInformationGaussian() + with pytest.raises(ValueError): + metric.compute(inputs, weights) # No outputs provided + + def test_different_estimations(self, sample_data): + """Test different estimation methods produce different results.""" + inputs, weights, outputs = sample_data + + metric_gaussian = MutualInformationGaussian(estimation_method="gaussian") + scores_gaussian = metric_gaussian.compute(inputs, weights, outputs) + + # Results should be reasonable + assert torch.all(scores_gaussian >= 0) + assert torch.all(scores_gaussian <= 10) # Reasonable upper bound + + +class TestCKA: + """Test suite for CKA metric.""" + + def test_linear_kernel(self): + """Test CKA with linear kernel.""" + n_samples = 100 + X = torch.randn(n_samples, 50) + Y = torch.randn(n_samples, 30) + + metric = CKA(kernel="linear") + similarity = metric.compute(X, Y) + + assert isinstance(similarity, (float, torch.Tensor)) + assert 0 <= float(similarity) <= 1 + + def test_rbf_kernel(self): + """Test CKA with RBF kernel.""" + n_samples = 100 + X = torch.randn(n_samples, 50) + Y = torch.randn(n_samples, 30) + + metric = CKA(kernel="rbf") + similarity = metric.compute(X, Y) + + assert isinstance(similarity, (float, torch.Tensor)) + assert 0 <= float(similarity) <= 1 + + def test_self_similarity(self): + """Test that CKA(X, X) = 1.""" + X = torch.randn(100, 50) + + metric = CKA(kernel="linear") + similarity = metric.compute(X, X) + + assert np.isclose(float(similarity), 1.0, rtol=1e-5) + + def test_different_samples_error(self): + """Test error when X and Y have different number of samples.""" + X = torch.randn(100, 50) + Y = torch.randn(80, 30) # Different number of samples + + metric = CKA() + with pytest.raises(ValueError): + metric.compute(X, Y) + + +class TestCCA: + """Test suite for CCA metric.""" + + def test_basic_cca(self): + """Test basic CCA computation.""" + n_samples = 200 + X = torch.randn(n_samples, 50) + Y = torch.randn(n_samples, 40) + + metric = CCA(n_components=20) + similarity = metric.compute(X, Y) + + assert isinstance(similarity, (float, torch.Tensor)) + assert 0 <= float(similarity) <= 1 + + def test_regularization(self): + """Test CCA with regularization.""" + n_samples = 100 + X = torch.randn(n_samples, 50) + Y = torch.randn(n_samples, 40) + + metric = CCA(n_components=20, reg=0.1) + similarity = metric.compute(X, Y) + + assert isinstance(similarity, (float, torch.Tensor)) + assert torch.isfinite(torch.tensor(similarity)) + + def test_max_components(self): + """Test CCA with too many components.""" + n_samples = 50 + X = torch.randn(n_samples, 20) + Y = torch.randn(n_samples, 30) + + # Request more components than possible + metric = CCA(n_components=100) + similarity = metric.compute(X, Y) + + # Should still work (automatically reduced) + assert isinstance(similarity, (float, torch.Tensor)) + + +class TestPartialInformationDecomposition: + """Test suite for PID metrics.""" + + @pytest.fixture + def sample_data(self): + """Generate sample data for PID.""" + batch_size = 128 + inputs = torch.randn(batch_size, 20) + weights = torch.randn(10, 20) + outputs = torch.matmul(inputs, weights.t()) + return inputs, weights, outputs + + def test_pid_components(self, sample_data): + """Test that PID components are computed correctly.""" + inputs, weights, outputs = sample_data + + metric = PartialInformationDecomposition(method="broja") + results = metric.compute(inputs, weights, outputs) + + # Check that all components are present + assert 'unique_information' in results + assert 'redundant_information' in results + assert 'synergistic_information' in results + + # Check shapes + assert results['unique_information'].shape == (weights.shape[0],) + assert isinstance(results['redundant_information'], (float, torch.Tensor)) + assert isinstance(results['synergistic_information'], (float, torch.Tensor)) + + def test_pid_non_negative(self, sample_data): + """Test that PID components are non-negative.""" + inputs, weights, outputs = sample_data + + metric = PartialInformationDecomposition() + results = metric.compute(inputs, weights, outputs) + + # All information measures should be non-negative + assert torch.all(results['unique_information'] >= 0) + assert float(results['redundant_information']) >= 0 + assert float(results['synergistic_information']) >= 0 + + +class TestGeneralizedRayleighQuotient: + """Test suite for Generalized Rayleigh Quotient.""" + + def test_basic_computation(self): + """Test basic GRQ computation.""" + batch_size, dim = 64, 20 + inputs = torch.randn(batch_size, dim) + weights = torch.randn(10, dim) + + metric = GeneralizedRayleighQuotient() + scores = metric.compute(inputs, weights) + + assert scores.shape == (weights.shape[0],) + assert torch.all(torch.isfinite(scores)) + + def test_with_reference_covariance(self): + """Test GRQ with custom reference covariance.""" + batch_size, dim = 64, 20 + inputs = torch.randn(batch_size, dim) + weights = torch.randn(10, dim) + + # Create positive definite reference covariance + ref_cov = torch.randn(dim, dim) + ref_cov = ref_cov @ ref_cov.t() + torch.eye(dim) * 0.1 + + metric = GeneralizedRayleighQuotient() + scores = metric.compute(inputs, weights, reference_cov=ref_cov) + + assert scores.shape == (weights.shape[0],) + assert torch.all(torch.isfinite(scores)) + + +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_device_consistency(device): + """Test that metrics work correctly on different devices.""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + inputs = torch.randn(32, 20).to(device) + weights = torch.randn(10, 20).to(device) + + metric = RayleighQuotient() + scores = metric.compute(inputs, weights) + + assert scores.device.type == device + + +def test_metric_determinism(): + """Test that metrics produce deterministic results.""" + torch.manual_seed(42) + inputs1 = torch.randn(32, 20) + weights1 = torch.randn(10, 20) + + torch.manual_seed(42) + inputs2 = torch.randn(32, 20) + weights2 = torch.randn(10, 20) + + metric = RayleighQuotient() + scores1 = metric.compute(inputs1, weights1) + scores2 = metric.compute(inputs2, weights2) + + assert torch.allclose(scores1, scores2) \ No newline at end of file From 676c43f7a36706025b86d94bf559df8e888f9328 Mon Sep 17 00:00:00 2001 From: Houman Safaai Date: Mon, 6 Oct 2025 14:43:01 -0400 Subject: [PATCH 18/18] Clean codebase: remove archives, drafts, and backups - Add _arxiv/, drafts/, *.bak, *.old to .gitignore - Remove old test backups and archived code from tracking - Files remain on 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implementations. - -This module contains experiment classes for neural network alignment studies, -focusing on alignment metrics, dropout impacts, and training analysis. -""" - -import logging -import os -import sys -import argparse -from typing import Dict, List, Tuple, Optional, Any, Union -import copy -import pickle -import datetime -import json -import yaml - -import numpy as np -import torch -import torch.nn as nn -from tqdm import tqdm - -from alignment.config import ExperimentConfig -from alignment.experiments.experiment import Experiment -from alignment.metrics import AlignmentMetric, get_metric -from alignment.models.registry import create_model -from alignment.dropout import progressive_dropout, eigenvector_dropout -from alignment.training import train_model, evaluate_model -from alignment.utils.core import setup_logging -from alignment.utils.plotting import plot_dropout_results, plot_experiment_summary -from alignment.datasets import get_dataset, load_dataset - -logger = logging.getLogger(__name__) - - -class AlignmentExperiment(Experiment): - """ - Experiment class for studying neural network alignment properties. - - This class implements experiments that assess alignment between layers - in neural networks, with support for different dropout strategies, - multiple metrics, and visualization. - """ - - def __init__(self, config: Dict) -> None: - """Initialize the experiment with the given config. - - Args: - config: Experiment configuration. - """ - super().__init__(config) - - # Make sure dataset_config has required fields to prevent loading errors - if isinstance(self.config.dataset, dict): - dataset_config = self.config.dataset - - # Ensure consistent naming between dataset.name and dataset.dataset_name - if "name" in dataset_config and "dataset_name" not in dataset_config: - dataset_config["dataset_name"] = dataset_config["name"] - elif "dataset_name" in dataset_config and "name" not in dataset_config: - dataset_config["name"] = dataset_config["dataset_name"] - elif "name" not in dataset_config and "dataset_name" not in dataset_config: - if isinstance(self.config.dataset, str): - # If config.dataset is a string, use it as the dataset name - dataset_config["name"] = self.config.dataset - dataset_config["dataset_name"] = self.config.dataset - logger.info(f"Using dataset name from string: {self.config.dataset}") - else: - # Default to CIFAR10 if no dataset name provided - logger.warning("No dataset name provided in configuration. Using default: cifar10") - dataset_config["name"] = "cifar10" - dataset_config["dataset_name"] = "cifar10" - - logger.info(f"Using dataset: {dataset_config.get('name', 'unknown')}") - - # Set up experiment-specific paths - self.figure_path = None - self.weights_path = None - self.setup_paths() - - logger.debug(f"Initialized alignment experiment") - - # Add a sanity check - # Ensure checkpoint parameters exist in the config - if not hasattr(self.config, "checkpoint"): - self.config.checkpoint = {} - logger.warning("Checkpoint configuration not found, using defaults") - - self.metric = get_metric(config.alignment.metric) - - def get_basename(self) -> str: - """ - Get the base name for the experiment. - - Returns: - Base name string - """ - return f"alignment_{self.config.model.model_name}_{self.config.dataset.dataset_name}" - - def prepare_path(self) -> List[str]: - """ - Prepare the experiment path components. - - Returns: - List of path components - """ - return [ - "alignment", - self.config.model.model_name, - self.config.dataset.dataset_name, - f"metric_{self.config.alignment.metric}" - ] - - def create_networks(self) -> List[nn.Module]: - """ - Create multiple neural networks for the experiment, each with different initialization. - - Following the alignment_v2 approach, this creates multiple independent networks - rather than copies of a single network. - - Returns: - List containing multiple independently initialized networks - """ - # Get the number of network replicates from config - num_replicates = self.config.training.replicates if hasattr(self.config.training, "replicates") else 5 - - # Create multiple models based on configuration, each with different initialization - networks = [] - for i in range(num_replicates): - # Set a different seed for each network to ensure different initializations - if hasattr(self.config, 'seed') and self.config.seed is not None: - # Use the base seed plus the replicate index to get different but reproducible initializations - torch.manual_seed(self.config.seed + i) - torch.cuda.manual_seed_all(self.config.seed + i) - np.random.seed(self.config.seed + i) - - # Create a new model - model = create_model(self.config.model) - model.to(self.device) - networks.append(model) - - logger.info(f"Created {len(networks)} models with independent initializations: {self.config.model.model_name}") - - return networks - - def main(self) -> Tuple[Dict, List[nn.Module]]: - """ - Main experiment execution method. - - Returns: - Tuple of (results dictionary, list of networks) - """ - # Additional initialization - self.setup_paths() - logger.info(f"Set up paths. Results will be saved to {self.results_path}") - - # Create network models for experiment - networks = self.create_networks() - - # Run the experiment based on the specified type - experiment_type = getattr(self.config, 'experiment_type', 'alignment_analysis') - - if experiment_type == "alignment_analysis" or experiment_type == "alignment": - # Run alignment analysis and store results - results = self.run_alignment_analysis(networks) - - elif experiment_type == "progressive_dropout": - # Run progressive dropout experiment for all networks - results = self._run_progressive_dropout(networks) - - # Generate and log visualization - self._plot_dropout_results( - results, - self.figure_path, - title_prefix=f"{getattr(self.config, 'experiment_name', 'Progressive Dropout')}" - ) - - # Save results for later reference - with open(os.path.join(self.results_path, "progressive_dropout_results.pkl"), "wb") as f: - pickle.dump(results, f) - - elif experiment_type == "eigenvector_dropout": - # Run eigenvector dropout experiment for one network - results = self._run_eigenvector_dropout(networks[0]) - - # Generate and log visualization (this has a different format) - self._generate_dropout_plots( - results, - "Eigenvector Dropout", - "eigenvector_dropout" - ) - - # Save results for later reference - with open(os.path.join(self.results_path, "eigenvector_dropout_results.pkl"), "wb") as f: - pickle.dump(results, f) - - elif experiment_type == "training_alignment": - # Run training alignment experiment - # This is handled separately because it needs different structure - results = self._run_training_alignment() - - # Generate and log visualization - self._generate_training_alignment_plots(results) - - # Save results for later reference - with open(os.path.join(self.results_path, "training_alignment_results.pkl"), "wb") as f: - pickle.dump(results, f) - - else: - raise ValueError(f"Unsupported experiment type: {experiment_type}") - - logger.info(f"Completed {getattr(self.config, 'experiment_name', experiment_type)} experiment") - - return results, networks - - def _run_progressive_dropout(self, networks: List[nn.Module]) -> Dict: - """ - Run progressive dropout experiment with multiple networks. - - Uses the progressive_dropout function from alignment.dropout module - instead of reimplementing the logic directly. - - Args: - networks: List of networks to run the experiment on. - - Returns: - Dictionary with results that match the expected format for plotting. - """ - # Get dropout fractions from config - dropin_min = self.config.alignment.dropout_min - dropin_max = self.config.alignment.dropout_max - num_dropout_fractions = self.config.alignment.dropout_steps - dropout_fractions = np.linspace(dropin_min, dropin_max, num_dropout_fractions).tolist() - - # Get pruning mode and dropout mode from config - pruning_mode = getattr(self.config.extra, "dropout_pruning_mode", "global_joint") - dropout_mode = getattr(self.config.extra, "dropout_mode", "scaled") - - # Print exactly what mode is being used - logger.info(f"Running progressive dropout with pruning_mode={pruning_mode}, dropout_mode={dropout_mode}") - - # Debug metric type - logger.info(f"Using metric type: {type(self.metric)}") - - # Prepare dataset - try: - batch_size = getattr(self.config.dataset, "batch_size", 128) - dataset = load_dataset(self.config.dataset, batch_size=batch_size) - except Exception as e: - logger.error(f"Error loading dataset: {str(e)}") - return {"error": f"Dataset configuration error: {str(e)}", "dropout_fractions": dropout_fractions} - - # IMPORTANT: Train all networks at once before applying dropout - logger.info(f"Training {len(networks)} networks before applying progressive dropout") - - # Track training history for plotting - training_history = { - 'train_loss': [], - 'train_acc': [], - 'test_loss': [], - 'test_acc': [] - } - - # Get training parameters from config - num_epochs = getattr(self.config.training, "epochs", 5) - learning_rate = getattr(self.config.training, "learning_rate", 0.001) - - # Set up optimizer for each network - optimizers = [] - for network in networks: - network.to(self.device) - optimizers.append(torch.optim.Adam(network.parameters(), lr=learning_rate)) - - # Train all networks for each epoch - with progress bars - from tqdm import tqdm - epoch_pbar = tqdm(range(num_epochs), desc="Training epochs", position=0) - for epoch in epoch_pbar: - # Initialize epoch stats - epoch_train_loss = 0.0 - epoch_train_acc = 0.0 - epoch_test_loss = 0.0 - epoch_test_acc = 0.0 - - # Training phase - net_pbar = tqdm(enumerate(zip(networks, optimizers)), - desc=f"Epoch {epoch+1}/{num_epochs} networks", - total=len(networks), - position=1, - leave=False) - - for network_idx, (network, optimizer) in net_pbar: - network.train() - running_loss = 0.0 - correct = 0 - total = 0 - - # Train on each batch - batch_pbar = tqdm(dataset.train_loader, - desc=f"Network {network_idx+1}/{len(networks)}", - position=2, - leave=False) - - for inputs, targets in batch_pbar: - inputs, targets = inputs.to(self.device), targets.to(self.device) - - # Zero the parameter gradients - optimizer.zero_grad() - - # Forward pass - outputs = network(inputs) - loss = torch.nn.functional.cross_entropy(outputs, targets) - - # Backward pass and optimize - loss.backward() - optimizer.step() - - # Track statistics - running_loss += loss.item() - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - # Update batch progress bar - if total > 0: - batch_pbar.set_postfix({ - 'loss': running_loss / len(batch_pbar), - 'acc': 100.0 * correct / total - }) - - # Calculate network training statistics - if total > 0: - network_train_loss = running_loss / len(dataset.train_loader) - network_train_acc = 100.0 * correct / total - - # Accumulate for epoch average - epoch_train_loss += network_train_loss - epoch_train_acc += network_train_acc - - # Update network progress bar - net_pbar.set_postfix({ - 'train_loss': network_train_loss, - 'train_acc': network_train_acc - }) - - # Evaluation phase - network.eval() - test_correct = 0 - test_total = 0 - test_loss_sum = 0.0 - - # Evaluation progress bar - eval_pbar = tqdm(dataset.test_loader, - desc=f"Evaluating network {network_idx+1}", - position=2, - leave=False) - - with torch.no_grad(): - for inputs, targets in eval_pbar: - inputs, targets = inputs.to(self.device), targets.to(self.device) - outputs = network(inputs) - - # Calculate loss - loss = torch.nn.functional.cross_entropy(outputs, targets, reduction='sum') - test_loss_sum += loss.item() - - # Calculate accuracy - _, predicted = outputs.max(1) - test_total += targets.size(0) - test_correct += predicted.eq(targets).sum().item() - - # Update eval progress bar - if test_total > 0: - eval_pbar.set_postfix({ - 'test_loss': test_loss_sum / test_total, - 'test_acc': 100.0 * test_correct / test_total - }) - - # Calculate network testing statistics - if test_total > 0: - network_test_loss = test_loss_sum / test_total - network_test_acc = 100.0 * test_correct / test_total - - # Accumulate for epoch average - epoch_test_loss += network_test_loss - epoch_test_acc += network_test_acc - - # Calculate and store epoch averages - epoch_train_loss /= len(networks) - epoch_train_acc /= len(networks) - epoch_test_loss /= len(networks) - epoch_test_acc /= len(networks) - - training_history['train_loss'].append(epoch_train_loss) - training_history['train_acc'].append(epoch_train_acc) - training_history['test_loss'].append(epoch_test_loss) - training_history['test_acc'].append(epoch_test_acc) - - # Update epoch progress bar - epoch_pbar.set_postfix({ - 'train_loss': epoch_train_loss, - 'train_acc': f"{epoch_train_acc:.2f}%", - 'test_loss': epoch_test_loss, - 'test_acc': f"{epoch_test_acc:.2f}%" - }) - - # Log progress - logger.info(f"Epoch {epoch+1}/{num_epochs}: " - f"Train Loss={epoch_train_loss:.4f}, Train Acc={epoch_train_acc:.2f}%, " - f"Test Loss={epoch_test_loss:.4f}, Test Acc={epoch_test_acc:.2f}%") - - logger.info(f"Completed training {len(networks)} networks. Proceeding to progressive dropout...") - - # Initialize results structure with correct format for plotting - final_results = { - "dropout_fractions": dropout_fractions, - "accuracies": {"high_rq": [], "low_rq": [], "random": []}, - "losses": {"high_rq": [], "low_rq": [], "random": []}, - "stds": {"high_rq": [], "low_rq": [], "random": []}, - "training_history": training_history - } - - # Call the progressive_dropout function with our trained networks - from alignment.dropout import progressive_dropout - - try: - # Define the strategies to run - strategies = ["high_rq", "low_rq", "random"] - - # Run each strategy separately with progress bars - strategy_pbar = tqdm(strategies, desc="Running pruning strategies", position=0) - for strategy in strategy_pbar: - strategy_pbar.set_description(f"Strategy: {strategy}") - - # Run progressive dropout with this strategy - network_accuracies, network_losses = progressive_dropout( - networks, - dataset, - dropout_fractions, - self.metric, - self.device, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - strategy=strategy - ) - - # Calculate averages and standard deviations across networks for this strategy - fraction_pbar = tqdm(range(len(dropout_fractions)), - desc="Processing dropout fractions", - position=1, - leave=False) - - for fraction_idx in fraction_pbar: - fraction_pbar.set_description(f"Fraction: {dropout_fractions[fraction_idx]:.2f}") - - # Collect results for this fraction across all networks - fraction_accs = [] - fraction_losses = [] - - for net_idx in network_accuracies: - if fraction_idx < len(network_accuracies[net_idx]): - fraction_accs.append(network_accuracies[net_idx][fraction_idx]) - if fraction_idx < len(network_losses[net_idx]): - fraction_losses.append(network_losses[net_idx][fraction_idx]) - - # Calculate statistics - if fraction_accs: - mean_acc = np.mean(fraction_accs) - std_acc = np.std(fraction_accs) - mean_loss = np.mean(fraction_losses) if fraction_losses else 0.0 - - # Add to appropriate strategy in results - if fraction_idx >= len(final_results["accuracies"][strategy]): - final_results["accuracies"][strategy].append(mean_acc) - final_results["stds"][strategy].append(std_acc) - final_results["losses"][strategy].append(mean_loss) - else: - # Update existing value (shouldn't happen with our current code) - final_results["accuracies"][strategy][fraction_idx] = mean_acc - final_results["stds"][strategy][fraction_idx] = std_acc - final_results["losses"][strategy][fraction_idx] = mean_loss - - # Update progress bar - fraction_pbar.set_postfix({ - 'acc': f"{mean_acc:.2f}%", - 'std': f"{std_acc:.2f}%" - }) - - # Log results for verification - logger.info(f"Final results:") - for strategy in strategies: - logger.info(f" {strategy}: {final_results['accuracies'][strategy]}") - - except Exception as e: - logger.error(f"Error running progressive dropout: {str(e)}") - import traceback - logger.error(traceback.format_exc()) - return { - "error": f"Error running progressive dropout: {str(e)}", - "dropout_fractions": dropout_fractions, - "training_history": training_history - } - - # Generate dropout plots - try: - logger.info("Generating dropout plots with error bars") - from alignment.utils.plotting import plot_dropout_results - - saved_plots = plot_dropout_results( - final_results, - save_dir=self.figure_path, - title_prefix="Progressive Dropout", - pruning_mode=pruning_mode, - dropout_mode=dropout_mode - ) - - if saved_plots: - logger.info(f"Generated {len(saved_plots)} plots: {saved_plots}") - final_results["plot_files"] = saved_plots - else: - logger.warning("No plots were generated. Check your plot_dropout_results function.") - - except Exception as e: - logger.error(f"Error generating dropout plots: {str(e)}") - import traceback - logger.error(traceback.format_exc()) - - return final_results - - def _run_eigenvector_dropout(self, net: nn.Module) -> Dict: - """ - Run eigenvector dropout experiments. - - Uses the eigenvector_dropout function from alignment.dropout module - instead of reimplementing the logic directly. - - Args: - net: Neural network to analyze - - Returns: - Dictionary of eigenvector dropout results - """ - # Get dropout fractions from config - dropout_fractions = np.linspace( - self.config.alignment.dropout_min, - self.config.alignment.dropout_max, - self.config.alignment.dropout_steps - ).tolist() - - # Initialize result structure - results = { - "dropout_fractions": dropout_fractions, - "accuracies": {"eigenvector": []}, - "losses": {"eigenvector": []}, - "alignment_values": {"eigenvector": []} - } - - # Prepare dataset config - dataset_config = self.config.dataset - - # Process each dropout fraction - for dropout_fraction in tqdm(dropout_fractions, desc="Eigenvector Dropout"): - try: - # Call the eigenvector_dropout function directly - accuracy, alignment_values = eigenvector_dropout( - net, - dataset_config, - dropout_fraction=dropout_fraction, - metric=self.metric, - device=self.device, - dropout_mode=getattr(self.config.extra, "dropout_mode", "scaled"), - dropout_pruning_mode=getattr(self.config.extra, "dropout_pruning_mode", "global_joint") - ) - - # Store results - results["accuracies"]["eigenvector"].append(accuracy) - results["losses"]["eigenvector"].append(100.0 - accuracy) - results["alignment_values"]["eigenvector"].append(alignment_values) - - except Exception as e: - logger.error(f"Error in eigenvector dropout at fraction {dropout_fraction}: {str(e)}") - # Add placeholder values to maintain result structure - results["accuracies"]["eigenvector"].append(0.0) - results["losses"]["eigenvector"].append(100.0) - results["alignment_values"]["eigenvector"].append(None) - - return results - - def plot(self, results: Dict) -> Dict[str, List[str]]: - """ - Plot experiment results using enhanced visualization methods. - - Args: - results: Dictionary containing experiment results - - Returns: - Dictionary of plot paths by type - """ - logger.info("Generating experiment visualizations") - - # Check for results to plot - if not results: - logger.warning("No results to plot") - return {} - - plot_paths = {} - - # Generate experiment summary - try: - logger.info("Creating experiment summary visualization") - summary_path = self._generate_summary_plots(results) - if summary_path: - plot_paths["summary"] = summary_path - logger.info("Successfully created experiment summary visualization") - except Exception as e: - logger.error(f"Error creating summary visualization: {str(e)}", exc_info=True) - - # Generate progressive dropout plots - if "progressive_dropout" in results: - try: - logger.info("Generating progressive dropout visualizations") - prog_paths = self._plot_dropout_results( - results["progressive_dropout"], - self.figure_path, - "Progressive Dropout" - ) - if prog_paths: - plot_paths["progressive_dropout"] = prog_paths - logger.info("Successfully generated progressive dropout visualizations") - except Exception as e: - logger.error(f"Error generating progressive dropout visualizations: {str(e)}", exc_info=True) - - # Generate eigenvector dropout plots - if "eigenvector_dropout" in results: - try: - logger.info("Generating eigenvector dropout visualizations") - eig_paths = self._plot_dropout_results( - results["eigenvector_dropout"], - self.figure_path, - "Eigenvector Dropout" - ) - if eig_paths: - plot_paths["eigenvector_dropout"] = eig_paths - logger.info("Successfully generated eigenvector dropout visualizations") - except Exception as e: - logger.error(f"Error generating eigenvector dropout visualizations: {str(e)}", exc_info=True) - - # Generate combined visualizations if both types of results exist - if "progressive_dropout" in results and "eigenvector_dropout" in results: - try: - logger.info("Generating combined dropout comparison visualization") - comparison_path = self._generate_dropout_comparison( - results["progressive_dropout"], - results["eigenvector_dropout"] - ) - if comparison_path: - plot_paths["comparison"] = comparison_path - logger.info("Successfully generated combined dropout comparison") - except Exception as e: - logger.error(f"Error generating dropout comparison: {str(e)}", exc_info=True) - - logger.info(f"Completed experiment visualization generation: {len(plot_paths)} plot types") - return plot_paths - - def _plot_dropout_results(self, results, figure_path=None, title_prefix="Dropout"): - """ - Plot dropout experiment results using the plotting module. - - Args: - results: Results dictionary from progressive_dropout - figure_path: Directory to save plots to - title_prefix: Prefix for plot titles - - Returns: - List of saved plot files - """ - # Get pruning mode and dropout mode - pruning_mode = getattr(self.config.extra, "dropout_pruning_mode", "global_joint") - dropout_mode = getattr(self.config.extra, "dropout_mode", "scaled") - - # Ensure figure path exists - if figure_path is None: - figure_path = self.figure_path - - # Add debug logs to understand the results - logger.info(f"Plotting dropout results with pruning_mode={pruning_mode}, dropout_mode={dropout_mode}") - logger.info(f"Results keys: {list(results.keys())}") - logger.info(f"Figure path: {figure_path}") - - if "accuracies" in results: - logger.info(f"Accuracies keys: {list(results['accuracies'].keys())}") - for key in results["accuracies"]: - logger.info(f"Strategy {key} has {len(results['accuracies'][key])} data points") - if results["accuracies"][key]: - logger.info(f"First few values: {results['accuracies'][key][:3]}") - else: - logger.info(f"No data for {key}") - - # Generate plots using our custom function - try: - from alignment.utils.plotting import custom_plot_dropout - - # Make a deep copy of results to prevent modification - import copy - plot_results = copy.deepcopy(results) - - # Ensure we have data for at least one strategy (preferably high_rq) - if not plot_results.get("accuracies", {}).get("high_rq", []): - # Try to use data from any strategy that has data - for strategy in ["low_rq", "random", "eigenvector"]: - if plot_results.get("accuracies", {}).get(strategy, []): - # Use this data for high_rq - logger.info(f"Using {strategy} data for high_rq") - plot_results["accuracies"]["high_rq"] = plot_results["accuracies"][strategy] - - # Also copy to losses if needed - if strategy in plot_results.get("losses", {}): - plot_results["losses"]["high_rq"] = plot_results["losses"][strategy] - break - - # Try the numeric indices if we still don't have data - if not plot_results.get("accuracies", {}).get("high_rq", []) and hasattr(self, "results") and hasattr(self.results, "network_accuracies"): - # Look for numeric indices - for idx in range(3): # Try indices 0, 1, 2 - if idx in self.results.network_accuracies and self.results.network_accuracies[idx]: - strategy_name = {0: "high_rq", 1: "low_rq", 2: "random"}.get(idx, "high_rq") - logger.info(f"Using numeric index {idx} data for {strategy_name}") - - # Make sure the strategy key exists - if "accuracies" not in plot_results: - plot_results["accuracies"] = {} - - # Copy the data - plot_results["accuracies"][strategy_name] = self.results.network_accuracies[idx] - - # Print final plot data - logger.info(f"Passing the following to custom_plot_dropout:") - logger.info(f"- dropout_fractions: {len(plot_results.get('dropout_fractions', []))} points") - logger.info(f"- high_rq data: {len(plot_results.get('accuracies', {}).get('high_rq', []))} points") - - # Call the custom plotting function - saved_figures = custom_plot_dropout( - plot_results, - figure_path, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - title_prefix=title_prefix - ) - - # Check if any plots were generated - if saved_figures: - logger.info(f"Generated {len(saved_figures)} plots: {saved_figures}") - - # Log plots to wandb if configured - if hasattr(self.config.checkpointing, "use_wandb") and self.config.checkpointing.use_wandb: - try: - from alignment.utils.plotting import log_plots_to_wandb - log_plots_to_wandb(saved_figures) - logger.info(f"Logged {len(saved_figures)} dropout plots to wandb") - except Exception as e: - logger.warning(f"Failed to log dropout plots to wandb: {str(e)}") - else: - logger.warning("No plots were generated by custom_plot_dropout") - - return saved_figures - - except Exception as e: - logger.error(f"Error generating dropout plots: {str(e)}") - import traceback - logger.error(traceback.format_exc()) - return [] - - def _generate_dropout_plots(self, results, title, filename=None): - """ - Generate enhanced dropout plots. - - Args: - results: Dropout experiment results - title: Plot title - filename: Base filename (optional) - - Returns: - List of saved plot files - """ - # Call the _plot_dropout_results function with our figure path and title - return self._plot_dropout_results(results, self.figure_path, title) - - def _generate_summary_plots(self, results): - """ - Generate comprehensive summary plots for the experiment using the plotting module. - - Args: - results: Experiment results dictionary - - Returns: - Path to the saved summary plot - """ - try: - from alignment.utils.plotting import plot_experiment_summary - - # Generate the summary plot - experiment_name = getattr(self.config, "experiment_name", self.get_basename()) - filepath = plot_experiment_summary( - results, - self.figure_path, - experiment_name=experiment_name - ) - - # Log to wandb if configured - if filepath and hasattr(self.config.checkpointing, "use_wandb") and self.config.checkpointing.use_wandb: - try: - import wandb - if wandb.run is not None: - wandb.log({"experiment_summary": wandb.Image(filepath)}) - logger.info("Logged experiment summary to wandb") - except Exception as e: - logger.warning(f"Failed to log experiment summary to wandb: {str(e)}") - - return filepath - - except Exception as e: - logger.error(f"Error creating summary visualization: {str(e)}") - return None - - def _generate_dropout_comparison(self, progressive_results, eigenvector_results): - """ - Generate comparison plots between progressive and eigenvector dropout using the plotting module. - - Args: - progressive_results: Progressive dropout results - eigenvector_results: Eigenvector dropout results - - Returns: - Path to the saved comparison plot - """ - try: - from alignment.utils.plotting import plot_dropout_comparison - - # Generate the comparison plot - experiment_name = getattr(self.config, "experiment_name", "Experiment") - filepath = plot_dropout_comparison( - progressive_results, - eigenvector_results, - self.figure_path, - title=f"Dropout Comparison: {experiment_name}" - ) - - # Log to wandb if configured - if filepath and hasattr(self.config.checkpointing, "use_wandb") and self.config.checkpointing.use_wandb: - try: - import wandb - if wandb.run is not None: - wandb.log({"dropout_comparison": wandb.Image(filepath)}) - logger.info("Logged dropout comparison to wandb") - except Exception as e: - logger.warning(f"Failed to log dropout comparison to wandb: {str(e)}") - - return filepath - - except Exception as e: - logger.error(f"Error creating dropout comparison: {str(e)}") - return None - - def _initialize_wandb(self) -> Optional[Any]: - """ - Initialize wandb for experiment tracking. - - Returns: - wandb module if initialized successfully, None otherwise - """ - if not getattr(self.config.checkpointing, "use_wandb", False): - return None - - try: - import wandb - - # Get wandb configuration - entity = getattr(self.config, "wandb_entity", None) - project = getattr(self.config, "wandb_project", "neural_alignment") - - # Generate timestamp if it doesn't exist - if not hasattr(self, 'timestamp'): - self.timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - - # Create a unique run name - if hasattr(self.config, "experiment_name") and self.config.experiment_name: - run_name = f"{self.config.experiment_name}" - else: - run_name = f"{self.config.model.model_name}_{self.config.dataset.dataset_name}_{self.timestamp}" - - # Convert the config to a dictionary for wandb - config_dict = self.config.to_dict() if hasattr(self.config, "to_dict") else vars(self.config) - - # Initialize wandb run - wandb.init( - project=project, - entity=entity, - name=run_name, - config=config_dict, - reinit=True # Allow reinitializing in the same process - ) - - # Set up wandb to watch models if available - if hasattr(self, "networks") and self.networks: - # Watch the first network - wandb.watch(self.networks[0], log="all", log_freq=10) - - logger.info(f"Initialized Weights & Biases tracking for run '{run_name}'") - return wandb - except Exception as e: - logger.warning(f"Failed to initialize wandb: {str(e)}") - return None - - def _log_to_wandb(self, data: Dict) -> bool: - """ - Log data to wandb. - - Args: - data: Data to log to wandb (dict with keys corresponding to metric names) - - Returns: - True if logging was successful, False otherwise - """ - if not getattr(self.config.checkpointing, "use_wandb", False): - return False - - try: - import wandb - if wandb.run is None: - wandb_module = self._initialize_wandb() - if wandb_module is None: - return False - - # Log scalar metrics - metrics_to_log = {} - for k, v in data.items(): - # Skip non-scalar values - if isinstance(v, (int, float)): - metrics_to_log[k] = v - elif isinstance(v, (list, tuple)) and len(v) == 1: - # Single-element list or tuple with a scalar - if isinstance(v[0], (int, float)): - metrics_to_log[k] = v[0] - - # Log the metrics to wandb - if metrics_to_log: - wandb.log(metrics_to_log) - - # Check if data contains progressive dropout results that we should plot - if "progressive_dropout" in data and "plot_files" in data and getattr(self.config.extra, "log_images", True): - # Find all plot files and log them to wandb - for plot_file in data["plot_files"]: - if os.path.exists(plot_file): - plot_name = os.path.basename(plot_file).replace(".png", "") - wandb.log({plot_name: wandb.Image(plot_file)}) - - # If 'accuracies' exists in the progressive_dropout results, log them as a line plot - if "accuracies" in data["progressive_dropout"] and "dropout_fractions" in data["progressive_dropout"]: - # Create a table to log - accuracies = np.array(data["progressive_dropout"]["accuracies"]) - dropout_fractions = data["progressive_dropout"]["dropout_fractions"] - - # Calculate mean and std - mean_accuracies = np.mean(accuracies, axis=0) - std_accuracies = np.std(accuracies, axis=0) - - # Prepare data table for the custom plot - table_data = [[x, y, y_std] for x, y, y_std in zip(dropout_fractions, mean_accuracies, std_accuracies)] - - # Log the data as a custom plot - wandb.log({ - "dropout_curve": wandb.Table( - data=table_data, - columns=["dropout_fraction", "mean_accuracy", "std_accuracy"] - ) - }) - - # Create interactive plot - dropout_data = [] - for i, frac in enumerate(dropout_fractions): - dropout_data.append([frac, mean_accuracies[i], mean_accuracies[i]-std_accuracies[i], mean_accuracies[i]+std_accuracies[i]]) - - wandb.log({ - "dropout_plot_interactive": wandb.plot.line_series( - xs=[x[0] for x in dropout_data], - ys=[[x[1] for x in dropout_data], # mean - [x[2] for x in dropout_data], # mean-std - [x[3] for x in dropout_data]], # mean+std - keys=["Mean Accuracy", "Lower Bound", "Upper Bound"], - title=f"Progressive Dropout ({self.config.extra.dropout_pruning_mode})", - xname="Dropout Fraction", - yname="Accuracy" - ) - }) - - return True - except Exception as e: - logger.warning(f"Failed to log to wandb: {str(e)}") - return False - - def run(self) -> Tuple[Dict, List[nn.Module]]: - """ - Run the experiment with custom logging and wandb setup. - - Returns: - Tuple containing (results, networks) - """ - # Setup paths for experiment - self.setup_paths() - self.prepare_path() - - # Set up logging and suppress warnings - set_logging_level(logging.INFO) - - # Set random seed if configured - if hasattr(self.config, 'seed') and self.config.seed is not None: - torch.manual_seed(self.config.seed) - torch.cuda.manual_seed_all(self.config.seed) - np.random.seed(self.config.seed) - logger.info(f"Set random seed to {self.config.seed}") - - # Initialize wandb explicitly before starting experiment - if hasattr(self.config, 'checkpointing') and self.config.checkpointing.use_wandb: - self._initialize_wandb() - - # Run the main experiment - results, networks = self.main() - - # Store results as an attribute for saving - self.results = results - self.networks = networks - - # Ensure all plots are saved and logged - self.plot(results) - - # Save experiment state - self.save() - - # Close wandb run if it exists - try: - import wandb - if wandb.run is not None: - wandb.finish() - logger.info("Closed wandb run") - except Exception as e: - logger.warning(f"Error closing wandb run: {str(e)}") - - return results, networks - - def run_alignment_analysis(self, networks: List[nn.Module]) -> Dict: - """ - Run alignment analysis experiment. - - This experiment analyzes alignment metrics across networks and can run - both progressive dropout and eigenvector dropout experiments based on - configuration. - - Args: - networks: List of neural networks to analyze - - Returns: - Results dictionary - """ - logger.info("Running alignment analysis experiment") - - # Prepare the results structure - results = { - "progressive_dropout": {}, - "eigenvector_dropout": {}, - "config": self.config, - } - - # Progressive dropout experiment - if self.config.alignment.run_progressive: - logger.info(f"Running progressive dropout experiment (mode: {getattr(self.config.extra, 'dropout_pruning_mode', 'global_joint')})") - try: - prog_results = self._run_progressive_dropout(networks) - results["progressive_dropout"] = prog_results - - # Add debug logging to inspect results structure - if prog_results: - logger.info(f"Progressive dropout results keys: {list(prog_results.keys())}") - if 'accuracies' in prog_results: - logger.info(f"Progressive dropout accuracies keys: {list(prog_results['accuracies'].keys())}") - - # Check if there was an error in the dataset configuration - if "error" in prog_results: - logger.error(f"Progressive dropout experiment failed: {prog_results['error']}") - else: - # Generate plots right after getting results - try: - logger.info("Generating progressive dropout visualizations") - # Add debug print before and after plotting - figure_path = self._plot_dropout_results( - results["progressive_dropout"], - self.figure_path, - title_prefix="Progressive Dropout" - ) - logger.info(f"Progressive dropout plot generated at {figure_path}") - except Exception as e: - logger.error(f"Error generating progressive dropout visualizations: {str(e)}", exc_info=True) - except Exception as e: - logger.error(f"Error running progressive dropout experiment: {str(e)}") - results["progressive_dropout"] = {"error": str(e)} - - # Eigenvector dropout experiment - if self.config.alignment.run_eigenvector: - logger.info("Running eigenvector dropout experiment") - try: - # Note: For eigenvector dropout, we use just the first network - eig_results = self._run_eigenvector_dropout(networks[0]) - results["eigenvector_dropout"] = eig_results - - # Add debug logging - if eig_results: - logger.info(f"Eigenvector dropout results keys: {list(eig_results.keys())}") - if 'accuracies' in eig_results: - logger.info(f"Eigenvector dropout accuracies keys: {list(eig_results['accuracies'].keys())}") - - # Generate plots right after getting results - try: - logger.info("Generating eigenvector dropout visualizations") - # Use the specialized method for eigenvector dropout - self._generate_dropout_plots( - results["eigenvector_dropout"], - "Eigenvector Dropout", - "eigenvector_dropout" - ) - logger.info("Successfully generated eigenvector dropout visualizations") - except Exception as e: - logger.error(f"Error generating eigenvector dropout visualizations: {str(e)}", exc_info=True) - except Exception as e: - logger.error(f"Error running eigenvector dropout experiment: {str(e)}") - results["eigenvector_dropout"] = {"error": str(e)} - - # Generate and log summary plots for all experiments - try: - logger.info("Generating experiment summary") - self._generate_summary_plots(results) - logger.info("Successfully generated experiment summary") - except Exception as e: - logger.error(f"Error generating experiment summary: {str(e)}", exc_info=True) - - return results - - def setup_paths(self): - """ - Set up paths for experiment outputs. - - Creates necessary directories for figures, weights, and results. - """ - # Create timestamp subdirectory if needed - if getattr(self.config, "use_timestamp", True): - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - base_dir = os.path.join("results", f"{self.get_basename()}_{timestamp}") - else: - base_dir = os.path.join("results", self.get_basename()) - - # Create main result directory - os.makedirs(base_dir, exist_ok=True) - self.results_path = base_dir - - # Create figure directory - self.figure_path = os.path.join(base_dir, "figures") - os.makedirs(self.figure_path, exist_ok=True) - - # Create weights directory for saved models - self.weights_path = os.path.join(base_dir, "weights") - os.makedirs(self.weights_path, exist_ok=True) - - # Set the device to use (default to CUDA if available) - if hasattr(self.config, "device") and self.config.device: - self.device = torch.device(self.config.device) - else: - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - logger.info(f"Set up paths: results={self.results_path}, figures={self.figure_path}, device={self.device}") - - def save(self): - """ - Save experiment state including results and configuration. - - This method saves: - 1. Experiment results as pickle and JSON - 2. Configuration in YAML format - 3. Any model checkpoints if available - """ - try: - # Create a results directory if it doesn't exist - os.makedirs(self.results_path, exist_ok=True) - - # Save experiment results if available - if hasattr(self, 'results') and self.results: - results_file = os.path.join(self.results_path, "results.pkl") - with open(results_file, "wb") as f: - pickle.dump(self.results, f) - logger.info(f"Saved experiment results to {results_file}") - - # Also try to save as JSON for human readability - try: - # Convert tensors and other non-serializable objects - def clean_for_json(obj): - if isinstance(obj, (torch.Tensor, np.ndarray)): - return obj.tolist() if hasattr(obj, 'tolist') else str(obj) - elif isinstance(obj, dict): - return {k: clean_for_json(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [clean_for_json(item) for item in obj] - elif isinstance(obj, tuple): - return tuple(clean_for_json(item) for item in obj) - else: - # Return string representation for other types - return str(obj) if not isinstance(obj, (str, int, float, bool, type(None))) else obj - - json_results = clean_for_json(self.results) - json_file = os.path.join(self.results_path, "results.json") - with open(json_file, "w") as f: - json.dump(json_results, f, indent=2) - logger.info(f"Saved readable results to {json_file}") - except Exception as e: - logger.warning(f"Could not save results as JSON: {str(e)}") - - # Save configuration - config_file = os.path.join(self.results_path, "config.yaml") - # Check if we can convert config to dict - if hasattr(self.config, 'to_dict'): - config_dict = self.config.to_dict() - with open(config_file, "w") as f: - yaml.dump(config_dict, f, default_flow_style=False) - logger.info(f"Saved configuration to {config_file}") - - logger.info("Experiment state saved successfully") - - except Exception as e: - logger.error(f"Error saving experiment state: {str(e)}") - # Don't re-raise the exception, just log it - - -def set_logging_level(level=logging.INFO): - """ - Set logging level for all loggers in the alignment package. - - Args: - level: Logging level (default: logging.INFO) - """ - # Set root logger level - logging.getLogger().setLevel(level) - - # Set level for alignment package loggers - logging.getLogger('alignment').setLevel(level) - - # Reduce verbosity for other loggers - logging.getLogger('matplotlib').setLevel(logging.WARNING) - logging.getLogger('PIL').setLevel(logging.WARNING) - logging.getLogger('torch').setLevel(logging.WARNING) - logging.getLogger('wandb').setLevel(logging.WARNING) - - # Suppress common warnings - import warnings - warnings.filterwarnings("ignore", category=UserWarning, message=".*pruning fraction.*") - warnings.filterwarnings("ignore", category=UserWarning, message=".*Combined pruning across layers.*") - warnings.filterwarnings("ignore", category=UserWarning, message=".*dropout of the entire tensor.*") - -def cli_main(): - """Command-line interface for running alignment experiments.""" - parser = argparse.ArgumentParser(description="Neural network alignment experiment") - parser.add_argument("--config", type=str, required=True, help="Path to config YAML") - parser.add_argument("--quiet", action="store_true", help="Reduce logging output") - args = parser.parse_args() - - # Set logging level based on quiet flag - if args.quiet: - set_logging_level(logging.WARNING) - else: - set_logging_level(logging.INFO) - - # Load configuration - config = ExperimentConfig.load(args.config) - - # Initialize and run experiment - experiment = AlignmentExperiment(config) - results, networks = experiment.run() - - return results, networks - - -if __name__ == "__main__": - cli_main() \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/alignment_metrics.py b/_arxiv/_archive/alignment_preref/alignment_metrics.py deleted file mode 100644 index e5ae6c91..00000000 --- a/_arxiv/_archive/alignment_preref/alignment_metrics.py +++ /dev/null @@ -1,243 +0,0 @@ -# -------------------------------------------- -# alignment_metrics.py -# -------------------------------------------- - -import torch -from alignment.utils import smart_pca, get_device - -class AlignmentMetrics: - """ - Provides static methods for various alignment metrics, - including 'delta_alignment'. - """ - - @staticmethod - def RQ(input_, weight_, scale_by_norm=False): - """ - Rayleigh Quotient alignment measure: - proportion of variance in `input_` explained by each row of `weight_`. - """ - return alignment(input_, weight_, method="alignment", relative=True, scale_by_norm=scale_by_norm) - - @staticmethod - def MI_0(input_, weight_, scale_by_norm=False): - """ - Placeholder for mutual information approach - version 0 - (currently reuses alignment(...) as a stand-in). - """ - return alignment(input_, weight_, method="alignment", relative=True, scale_by_norm=scale_by_norm) - - @staticmethod - def MI_1(input_, weight_): - """ - Placeholder for mutual information approach - version 1 - """ - return torch.tensor(0.0) - - @staticmethod - def delta_alignment(net, layer_idx, layer_input, scale_by_norm=False): - """ - alignment of (W_current - W_init) with the input's covariance. - This references net._init_weights for the initial weight. - If net._init_weights is absent, returns zeros. - """ - if not hasattr(net, "_init_weights"): - weight_diff = torch.zeros_like(net.get_alignment_weights()[layer_idx]) - else: - init_w = net._init_weights[layer_idx] - current_w = net.get_alignment_weights()[layer_idx] - weight_diff = current_w - init_w - - weight_diff = weight_diff.flatten(start_dim=1) - return alignment(layer_input, weight_diff, method="alignment", relative=True, scale_by_norm=scale_by_norm) - - @staticmethod - def measure(input_, weight_, method="RQ", scale_by_norm=False): - """ - For a single method and a single layer's (input_, weight_). - """ - if method == "RQ": - return AlignmentMetrics.RQ(input_, weight_, scale_by_norm=scale_by_norm) - elif method == "MI_0": - return AlignmentMetrics.MI_0(input_, weight_, scale_by_norm=scale_by_norm) - elif method == "MI_1": - return AlignmentMetrics.MI_1(input_, weight_) - else: - raise ValueError(f"Unknown alignment method {method}") - - @staticmethod - def patchwise_alignment(inp, w, method="RQ", weigh_by_var=True): - """ - 'Patchwise' alignment for CNN. - - inp shape: [B, F, patches], e.g. B=mini-batch, F=inC*kH*kW, patches=outH*outW - - w shape: [outC, F], the flattened filter weights. - - We compute alignment for each patch's (B,F) input. - - Weighted by patch-level variance across the batch dimension. - """ - B, F, P = inp.shape - var_patches = torch.var(inp, dim=0, keepdim=False) # => (F, P) - patchwise_var = var_patches.sum(dim=0) # => shape (P,) - - all_patch_vals = [] - all_patch_vars = [] - - for p in range(P): - patch_data = inp[:, :, p] # shape => [B, F] - cc = torch.cov(patch_data.T) # shape (F, F) - num_ = torch.sum(torch.matmul(w, cc) * w, dim=1) - denom_ = torch.sum(w*w, dim=1) - - patch_rq = num_ / denom_ - if method == "RQ": - patch_rq = patch_rq / torch.trace(cc) - patch_weight = patchwise_var[p] if weigh_by_var else 1.0 - - all_patch_vals.append(patch_rq * patch_weight) - all_patch_vars.append(patch_weight) - - total_weight = torch.stack(all_patch_vars).sum() - sum_rq = torch.stack(all_patch_vals, dim=0).sum(dim=0) # shape => (outC,) - - if total_weight > 0: - final_rq = sum_rq / total_weight - else: - final_rq = sum_rq * 0 - return final_rq # shape => (outC,) - - @staticmethod - def measure_methods(net, images, methods, precomputed=True, scale_by_norm=False): - """ - For each layer in 'net', produce a dict {method -> tensor}. - 1) get_layer_inputs - 2) preprocess => unfold or patchwise - 3) flatten weights - 4) measure alignment or patchwise alignment - - Parameters: - - scale_by_norm: if True, scales covariance matrices by their norm before computing RQ - """ - # 1) Get raw inputs - raw_inputs = net.get_layer_inputs(images, precomputed=precomputed) - # 2) Unfold or flatten them - preprocessed = net._preprocess_inputs(raw_inputs, compress_convolutional=True) - # 3) Flatten weights - layer_weights = net.get_alignment_weights(flatten=True) - - results_per_layer = [] - for layer_idx, (inp, wgt) in enumerate(zip(preprocessed, layer_weights)): - layer_dict = {} - for m in methods: - if m == "delta_alignment": - val = AlignmentMetrics.delta_alignment(net, layer_idx, inp, scale_by_norm=scale_by_norm) - else: - # if net says "patchwise" & input is 3D => do patchwise - if net.cnn_mode == "patchwise" and inp.ndim == 3: - val = AlignmentMetrics.patchwise_alignment(inp, wgt, method=m, weigh_by_var=True) - else: - # single-cov - val = AlignmentMetrics.measure(inp, wgt, method=m, scale_by_norm=scale_by_norm) - layer_dict[m] = val - results_per_layer.append(layer_dict) - - return results_per_layer - - @staticmethod - def compute_eigenvalues(x): - """ - Do a standard PCA with 'smart_pca' to get eigenvalues of x (batch, features). - """ - w, v = smart_pca(x.T, centered=True) - return w, v - - @staticmethod - def measure_expected_distribution(method, eigenvals, bins=50, num_tests=100): - """ - Return (counts, bin_edges) for a random alignment distribution of 'method'. - """ - if method == "RQ": - counts, edges, _ = expected_alignment_distribution( - eigenvals, - relative=True, - valid_rotation=False, - with_rotation=True, - bins=bins, - num_tests=num_tests - ) - return counts, edges - elif method in ["MI_0", "MI_1", "delta_alignment"]: - return None, None - else: - return None, None - -def alignment(input, weight, method="alignment", relative=True, scale_by_norm=False): - """ - measure alignment by computing RQ = (w^T C w) / (w^T w), - optionally normalized by trace(C). - - - input shape can be (N, F) for single-cov approach, - or (B, F, patches) if you do patchwise in patchwise_alignment. - - weight shape (outC, F). - - scale_by_norm: if True, scales the covariance matrix by its Frobenius norm - before computing RQ (similar to "similarity" in alignment_v2) - """ - if input.ndim == 2: - # standard single-cov approach - cc = torch.cov(input.T) - - # Scale covariance by its norm if requested (like "similarity" in v2) - if scale_by_norm: - cc_norm = torch.norm(cc) - if cc_norm > 0: - cc = cc / cc_norm - - numerator = torch.sum(torch.matmul(weight, cc) * weight, dim=1) - denom = torch.sum(weight * weight, dim=1) - rq = numerator / denom - if method == "alignment" and relative: - rq = rq / torch.trace(cc) - return rq - else: - # user should call patchwise_alignment or do proper flatten - raise ValueError( - f"alignment() got {input.ndim}-D data. For patchwise CNN, call patchwise_alignment(...). " - "For single-cov, ensure input is 2D via net._preprocess_inputs(...)." - ) - - -@torch.no_grad() -def expected_alignment_distribution(eigenvalues, relative=True, valid_rotation=True, with_rotation=True, bins=11, num_tests=100): - """ - From a set of eigenvalues, simulate random weight vectors - (optional orthonormal rotation) to measure an 'expected' alignment distribution. - """ - import math - import numpy as np - - N = len(eigenvalues) - if relative: - eigenvalues = eigenvalues / eigenvalues.sum() - eigenvalues = eigenvalues.view(-1, 1).expand(-1, N * num_tests) - - device = eigenvalues.device - if with_rotation: - if valid_rotation: - mixing = [] - for _ in range(num_tests): - mat = torch.normal(0, 1 / math.sqrt(N), (N, N)).to(device) - q, _ = torch.linalg.qr(mat) - mixing.append(q.T) - coefficients = torch.cat(mixing, dim=1) ** 2 - else: - coefficients = torch.normal(0, 1 / math.sqrt(N), (N, N * num_tests)).to(device) ** 2 - else: - coefficients = torch.ones((N, N * num_tests), device=device) - - weights = eigenvalues * coefficients - align = torch.sum(eigenvalues * weights, dim=0) / weights.sum(dim=0) - align_np = align.cpu().numpy() - - counts, bin_edges = np.histogram(align_np, bins=bins, density=True) - centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0 - return (torch.tensor(counts, dtype=torch.float), - torch.tensor(bin_edges, dtype=torch.float), - torch.tensor(centers, dtype=torch.float)) \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/alignment_metrics_rem.py b/_arxiv/_archive/alignment_preref/alignment_metrics_rem.py deleted file mode 100644 index eafbd8bb..00000000 --- a/_arxiv/_archive/alignment_preref/alignment_metrics_rem.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Alignment metrics for neural network analysis. - -DEPRECATED: This module is deprecated and will be removed in a future version. -Please import directly from `alignment.metrics` instead. - -This module provides various metrics for measuring alignment between weight vectors -and activation vectors, including RQ (representation quality), MI (mutual information), -and other metrics for analyzing neural network representations. -""" - -import warnings -from typing import Dict, List, Tuple, Union, Optional, Callable - -# Show deprecation warning when the module is imported -warnings.warn( - "The alignment.alignment_metrics module is deprecated and will be removed in a future version. " - "Please import directly from alignment.metrics instead.", - DeprecationWarning, - stacklevel=2 -) - -# Import from metrics_utils instead of metrics.py to avoid circular dependencies -from alignment.utils.metrics_utils import ( - AlignmentMetricBase, - RQMetric, - MIMetric, - WeightSimilarityMetric, - NodeRedundancyMetric, - AlignmentMetricsFactory as AlignmentMetrics, - alignment -) - -# For backward compatibility, re-export everything -__all__ = [ - 'AlignmentMetricBase', - 'RQMetric', - 'MIMetric', - 'WeightSimilarityMetric', - 'NodeRedundancyMetric', - 'AlignmentMetrics', - 'alignment' -] \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/config.py b/_arxiv/_archive/alignment_preref/config.py deleted file mode 100644 index b80eef6a..00000000 --- a/_arxiv/_archive/alignment_preref/config.py +++ /dev/null @@ -1,116 +0,0 @@ -# -------------------------------------------- -# config.py -# -------------------------------------------- -from os import PathLike -from dataclasses import dataclass, field -from typing import (cast, List, Union, Type, TypeVar, Optional) -from omegaconf import OmegaConf as om -from omegaconf.errors import OmegaConfBaseException - -C = TypeVar("C", bound="BaseConfig") - -class BaseConfig: - @classmethod - def load(cls: Type[C], path: Union[str, PathLike]) -> C: - schema = om.structured(cls) - try: - raw = om.load(str(path)) - conf = om.merge(schema, raw) - return cast(C, om.to_object(conf)) - except OmegaConfBaseException as e: - raise ValueError(str(e)) - -@dataclass -class ModelConfig(BaseConfig): - name: str = "MLP" - dropout: float = 0.0 - alignment_layers: Optional[dict] = None - -@dataclass -class DatasetConfig(BaseConfig): - name: str = "MNIST" - path: Optional[str] = None - -@dataclass -class TrainingConfig(BaseConfig): - do_train: bool = True - epochs: int = 10 - batch_size: int = 128 - replicates: int = 1 - name: str = "Adam" - lr: float = 1e-3 - weight_decay: float = 0.0 - -@dataclass -class AlignmentConfig(BaseConfig): - do_alignment: bool = True - methods: List[str] = field(default_factory=lambda: ["RQ"]) - measure_expected: bool = True - frequency: int = 1 - bins: int = 50 - cnn_mode: str = "unfold" - scale_by_norm: bool = False # If True, scales covariance matrices by their norm before RQ calculation - -@dataclass -class CheckpointingConfig(BaseConfig): - use_prev: bool = False - save_checkpoints: bool = False - frequency: int = 1 - use_wandb: bool = False - -@dataclass -class PlotsConfig(BaseConfig): - show_loss: bool = True - show_dropout: bool = True - show_eigenfeatures: bool = True - show_eig_dropout: bool = True - -@dataclass -class ExtraConfig(BaseConfig): - num_drops: int = 9 - progressive_dropout_on_train: bool = False - single_layer_mode: bool = False - aggregate_alignment: bool = False - dropout_pruning_mode: str = "global" # "global", "per_layer_combined", or "per_layer_independent" - dropout_mode: str = "scaled" # "scaled" or "unscaled" - how targeted dropout is applied to neurons - exclude_classification_layer: bool = False # If True, excludes the classification layer from pruning - -@dataclass -class ExperimentConfig(BaseConfig): - experiment: Optional[str] = None - results_path: Optional[str] = None - no_save: bool = False - just_plot: bool = False - save_networks: bool = False - show_params: bool = False - show_all: bool = False - device: Optional[str] = None - use_timestamp: bool = False - timestamp: Optional[str] = None - ddp_world_size: int = 1 - - dataset: DatasetConfig = field(default_factory=DatasetConfig) - model: ModelConfig = field(default_factory=ModelConfig) - training: TrainingConfig = field(default_factory=TrainingConfig) - alignment: AlignmentConfig = field(default_factory=AlignmentConfig) - checkpointing: CheckpointingConfig = field(default_factory=CheckpointingConfig) - plots: PlotsConfig = field(default_factory=PlotsConfig) - extra: ExtraConfig = field(default_factory=ExtraConfig) - -@dataclass -class ExperimentArgs: - """Experiment arguments.""" - dataclass_fields = [] - - exp_name: str - name: str - device: str = "cpu" - - -@dataclass -class ExtraArgs: - """Arguments for various experiments.""" - aggregate_alignment: bool = False - num_drops: int = 9 - dropout_pruning_mode: str = "global" # "global", "per_layer_combined", or "per_layer_independent" - exclude_classification_layer: bool = False # If True, excludes the classification layer from pruning \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/datasets.py b/_arxiv/_archive/alignment_preref/datasets.py deleted file mode 100644 index 4aab900a..00000000 --- a/_arxiv/_archive/alignment_preref/datasets.py +++ /dev/null @@ -1,280 +0,0 @@ -# -------------------------------------------- -# datasets.py -# -------------------------------------------- - -import sys -from pathlib import Path -from warnings import warn -from abc import ABC, abstractmethod - -import torch -import torchvision -from torch import nn -from torch.utils.data.distributed import DistributedSampler -from torchvision.transforms import v2 as transforms - -from alignment.models.base import AlignmentNetwork -from alignment.config import ExperimentConfig - -REQUIRED_PROPERTIES = ["dataset_constructor", "loss_function"] - -def default_loader_parameters( - distributed, - batch_size=1024, - num_workers=2, - shuffle=True, - pin_memory=True, - persistent_workers=True, -): - """ - contains the default dataloader parameters with the option of updating them - using key word arguments - - # adjusting 'shuffle' when using distributed data parallel. - """ - default_parameters = dict( - batch_size=batch_size, - num_workers=num_workers, - shuffle=False if distributed else shuffle, - pin_memory=pin_memory, - persistent_workers=persistent_workers, - ) - return default_parameters - -class DataSet(ABC): - """ - Abstract base class for any dataset wrapper. - Responsible for creating train/test datasets & loaders, - storing transforms, etc. - """ - - def __init__( - self, - device=None, - distributed=False, - dataset_parameters={}, - transform_parameters={}, - loader_parameters={}, - ): - self.set_properties() - self.check_properties() - - self.device = device if device is not None else ("cuda" if torch.cuda.is_available() else "cpu") - self.distributed = distributed - - self.extra_transform = transform_parameters.pop("extra_transform", None) - - self.transform_parameters = transform_parameters - self.make_transform(**transform_parameters) - - self.dataloader_parameters = default_loader_parameters(distributed, **loader_parameters) - self.dataset_parameters = dataset_parameters - self.load_dataset(**dataset_parameters) - - def check_properties(self): - if not all([hasattr(self, prop) for prop in REQUIRED_PROPERTIES]): - not_found = [prop for prop in REQUIRED_PROPERTIES if not hasattr(self, prop)] - raise ValueError(f"The following required properties were not set: {not_found}") - - @abstractmethod - def set_properties(self): - """ - Must set: - self.dataset_constructor - self.loss_function - and optionally self.dist_params for normalization, etc. - """ - pass - - @abstractmethod - def dataset_kwargs(self, train=True, **kwargs): - """ - Returns the dict of kwargs for constructing the train or test dataset. - """ - pass - - def load_dataset(self, **kwargs): - """Instantiate self.train_dataset and self.test_dataset, then build DataLoaders.""" - self.train_dataset = self.dataset_constructor(**self.dataset_kwargs(train=True, **kwargs)) - self.test_dataset = self.dataset_constructor(**self.dataset_kwargs(train=False, **kwargs)) - self.train_sampler = DistributedSampler(self.train_dataset) if self.distributed else None - self.test_sampler = DistributedSampler(self.test_dataset) if self.distributed else None - self.train_loader = torch.utils.data.DataLoader(self.train_dataset, sampler=self.train_sampler, **self.dataloader_parameters) - self.test_loader = torch.utils.data.DataLoader(self.test_dataset, sampler=self.test_sampler, **self.dataloader_parameters) - - def unwrap_batch(self, batch, device=None): - """ - Simple method for unwrapping a batch (inputs, targets) - and placing them on the correct device. - """ - device = self.device if device is None else device - if self.extra_transform: - if isinstance(self.extra_transform, list): - for et in self.extra_transform: - batch = et(batch) - else: - warn("extra_transform is not a list, this is deprecated!", DeprecationWarning, stacklevel=2) - batch = self.extra_transform(batch) - inputs, targets = batch - inputs, targets = inputs.to(device), targets.to(device) - return inputs, targets - - def make_transform(self, center_crop=None, resize=None, flatten=False, out_channels=None): - """ - Create a transforms.Compose for the dataset. - Default: converts to float32, normalizes, etc. - - # For standard dataset usage, we typically do: - # transforms.ToImage(), - # transforms.ToDtype(torch.float32, scale=True), - # transforms.Normalize(...), - # etc. - """ - use_transforms = [ - transforms.ToImage(), - transforms.ToDtype(torch.float32, scale=True), - ] - if center_crop: - use_transforms.append(transforms.CenterCrop(center_crop)) - use_transforms.append(transforms.Normalize((self.dist_params["mean"]), (self.dist_params["std"]))) - if resize: - use_transforms.append(transforms.Resize(resize, antialias=True)) - if out_channels: - use_transforms.append(transforms.Grayscale(num_output_channels=out_channels)) - if flatten: - use_transforms.append(transforms.Lambda(torch.flatten)) - self.transform = transforms.Compose(use_transforms) - - def measure_loss(self, outputs, targets, reduction=None): - """Compute the loss via self.loss_function, optionally changing reduction.""" - if reduction is None: - return self.loss_function(outputs, targets) - standard_reduction = self.loss_function.reduction - self.loss_function.reduction = reduction - loss = self.loss_function(outputs, targets) - self.loss_function.reduction = standard_reduction - return loss - - def measure_accuracy(self, outputs, targets, k=1, percentage=True): - """ - Return top-k accuracy on classification problems. - By default, top-1 accuracy in percentage form. - """ - # Use a simple, direct implementation that's guaranteed to work correctly - # First check for invalid values - if torch.isnan(outputs).any() or torch.isinf(outputs).any(): - print("WARNING: NaN or Inf values in measure_accuracy inputs") - return torch.tensor(0.0, device=outputs.device) - - # Get the predicted classes (most likely class for each sample) - _, predicted = outputs.max(1) - - # Calculate accuracy - correct = (predicted == targets).sum().item() - total = targets.size(0) - - # Convert to percentage if requested - accuracy = (correct / total) * (100.0 if percentage else 1.0) - - - return torch.tensor(accuracy, device=outputs.device) - - -class MNIST(DataSet): - def set_properties(self): - """defines the required properties for MNIST""" - self.dataset_constructor = torchvision.datasets.MNIST - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.1307], std=[0.3081]) - - def dataset_kwargs(self, train=True, download=False, root=None): - kwargs = dict( - train=train, - download=download, - root=root, - transform=self.transform, - ) - return kwargs - -class CIFAR10(DataSet): - def set_properties(self): - self.dataset_constructor = torchvision.datasets.CIFAR10 - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - def dataset_kwargs(self, train=True, download=False, root=None): - kwargs = dict( - train=train, - download=download, - root=root, - transform=self.transform, - ) - return kwargs - -class CIFAR100(CIFAR10): - def set_properties(self): - self.dataset_constructor = torchvision.datasets.CIFAR100 - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - -class ImageNet2012(DataSet): - def set_properties(self): - """ - defines the required properties for ImageNet 2012 - (ILSVRC2012) with 1000 classes. - """ - self.dataset_constructor = torchvision.datasets.ImageNet - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - self.center_crop = 224 - - def dataset_kwargs(self, train=True, root=None): - kwargs = dict( - split="train" if train else "val", - root=root, - transform=self.transform, - ) - return kwargs - -DATASET_REGISTRY = { - "MNIST": MNIST, - "CIFAR10": CIFAR10, - "CIFAR100": CIFAR100, - "ImageNet": ImageNet2012, -} - -def get_dataset( - dataset_name, - build=False, - dataset_parameters={}, - transform_parameters={}, - loader_parameters={}, - **kwargs, -): - """ - lookup dataset constructor from dataset registry by name. - if build=True, return an instantiated dataset object, - otherwise return the constructor. - """ - if dataset_name not in DATASET_REGISTRY: - raise ValueError(f"Dataset ({dataset_name}) is not in DATASET_REGISTRY") - dataset = DATASET_REGISTRY[dataset_name] - if build: - if not isinstance(transform_parameters, dict): - raise TypeError("transform_parameters must be a dictionary") - return dataset( - dataset_parameters=dataset_parameters, - transform_parameters=transform_parameters, - loader_parameters=loader_parameters, - **kwargs, - ) - return dataset - -if __name__ == "__main__": - try: - yaml_path, args_list = sys.argv[1] - except IndexError: - raise ValueError(f"Usage: {sys.argv[0]} [CONFIG_PATH]") - - cfg = ExperimentConfig.load(yaml_path) - dataset = get_dataset(cfg.dataset.name, build=True, dataset_parameters=dict(download=True, root=Path(cfg.dataset.path))) diff --git a/_arxiv/_archive/alignment_preref/plotting.py b/_arxiv/_archive/alignment_preref/plotting.py deleted file mode 100644 index ff683467..00000000 --- a/_arxiv/_archive/alignment_preref/plotting.py +++ /dev/null @@ -1,861 +0,0 @@ -# -------------------------------------------- -# plotting.py -# -------------------------------------------- - -import torch -import numpy as np -from tqdm import tqdm -from matplotlib import pyplot as plt -import matplotlib as mpl - -from alignment.utils import compute_stats_by_type, named_transpose, transpose_list, rms - - -def plot_train_results(exp, train_results, test_results, prms): - """ - Plot training curves for loss and accuracy, plus test performance. - Optionally plots alignment across training if present in `train_results`. - - If 'loss' or 'accuracy' are missing in train_results (e.g. do_train=false), - we skip the training curve plot. We can still show test performance if present. - """ - - import torch - import numpy as np - import matplotlib.pyplot as plt - import matplotlib as mpl - from alignment.utils import compute_stats_by_type - - # If do_train was false, we might not have train_results["loss"] or train_results["accuracy"]. - # We'll do a check: - has_train_loss = "loss" in train_results and train_results["loss"].dim() == 2 - has_train_acc = "accuracy" in train_results and train_results["accuracy"].dim() == 2 - - # In normal training, shape is (num_replicates, num_epochs) - # If missing, we can skip them. We'll define num_train_epochs = 0 if missing - if has_train_loss: - num_train_epochs = train_results["loss"].size(1) - else: - num_train_epochs = 0 - - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - - print("getting statistics on run data...") - plot_alignment = "alignment" in train_results - alignment = None - if plot_alignment: - alignment_by_method = {} - for record in train_results["alignment"]: - if isinstance(record, torch.Tensor): - method_key = 'RQ' - if record.dim() == 3: - net_avg = record.mean(dim=0) - elif record.dim() == 4: - net_avg = record.squeeze(0).mean(dim=0) - else: - raise ValueError(f"Unexpected tensor shape: {record.shape}") - for layer_idx in range(net_avg.size(0)): - if method_key not in alignment_by_method: - alignment_by_method[method_key] = {} - if layer_idx not in alignment_by_method[method_key]: - alignment_by_method[method_key][layer_idx] = [] - alignment_by_method[method_key][layer_idx].append(torch.mean(net_avg[layer_idx]).item()) - - elif isinstance(record, dict): - data_field = record["data"] - if isinstance(data_field, list): - for net_data in data_field: - for layer_idx, layer_list in enumerate(net_data): - # FIX: layer_list was a list, so we iterate over it before calling .items() - if isinstance(layer_list, dict): - for method_key, tensor in layer_list.items(): - if method_key not in alignment_by_method: - alignment_by_method[method_key] = {} - if layer_idx not in alignment_by_method[method_key]: - alignment_by_method[method_key][layer_idx] = [] - alignment_by_method[method_key][layer_idx].append(torch.mean(tensor).item()) - elif isinstance(layer_list, list): - for sub_dict in layer_list: - for method_key, tensor in sub_dict.items(): - if method_key not in alignment_by_method: - alignment_by_method[method_key] = {} - if layer_idx not in alignment_by_method[method_key]: - alignment_by_method[method_key][layer_idx] = [] - alignment_by_method[method_key][layer_idx].append(torch.mean(tensor).item()) - else: - raise TypeError(f"layer_idx={layer_idx} has unexpected type {type(layer_list)}") - elif isinstance(data_field, torch.Tensor): - method_key = 'RQ' - if data_field.dim() == 3: - net_avg = data_field.mean(dim=0) - elif data_field.dim() == 4: - net_avg = data_field.squeeze(0).mean(dim=0) - else: - raise ValueError(f"Unexpected record['data'] shape: {data_field.shape}") - for layer_idx in range(net_avg.size(0)): - if method_key not in alignment_by_method: - alignment_by_method[method_key] = {} - if layer_idx not in alignment_by_method[method_key]: - alignment_by_method[method_key][layer_idx] = [] - alignment_by_method[method_key][layer_idx].append(torch.mean(net_avg[layer_idx]).item()) - else: - raise TypeError(f"record['data'] has unexpected type {type(data_field)}") - else: - raise TypeError(f"train_results['alignment'] has an element of type {type(record)}") - - alignment_avg = {} - for method_key, layers_dict in alignment_by_method.items(): - layer_avgs = [] - for layer_idx in sorted(layers_dict.keys()): - vals = torch.tensor(layers_dict[layer_idx]) - layer_avgs.append(torch.mean(vals)) - alignment_avg[method_key] = torch.stack(layer_avgs, dim=0) - alignment = alignment_avg - - cmap = mpl.colormaps["tab10"] - - if has_train_loss: - train_loss_mean, train_loss_se = compute_stats_by_type( - train_results["loss"].transpose(0,1), - num_types=num_types, - dim=1, - method="se" - ) - else: - train_loss_mean, train_loss_se = None, None - - if has_train_acc: - train_acc_mean, train_acc_se = compute_stats_by_type( - train_results["accuracy"].transpose(0,1), - num_types=num_types, - dim=1, - method="se" - ) - else: - train_acc_mean, train_acc_se = None, None - - test_loss_mean, test_loss_se = None, None - test_acc_mean, test_acc_se = None, None - if ("loss" in test_results) and (test_results["loss"].dim() == 1): - test_loss_mean, test_loss_se = compute_stats_by_type( - test_results["loss"], - num_types=num_types, - dim=0, - method="se" - ) - if ("accuracy" in test_results) and (test_results["accuracy"].dim() == 1): - test_acc_mean, test_acc_se = compute_stats_by_type( - test_results["accuracy"], - num_types=num_types, - dim=0, - method="se" - ) - - print("plotting run data...") - xOffset = [-0.2, 0.2] - get_x = lambda idx: [xOffset[0] + idx, xOffset[1] + idx] - - alpha = 0.3 - figdim = 3 - figratio = 2 - width_ratios = [figdim, figdim / figratio, figdim, figdim / figratio] - fig, ax = plt.subplots(1, 4, figsize=(sum(width_ratios), figdim), width_ratios=width_ratios, layout="constrained") - - for idx, label in enumerate(labels): - if train_loss_mean is not None: - cmn = train_loss_mean[:, idx].cpu() - cse = train_loss_se[:, idx].cpu() - # Use 1-based epoch numbering for the x-axis - epochs = [i+1 for i in range(num_train_epochs)] - ax[0].plot(epochs, cmn, color=cmap(idx), label=label) - ax[0].fill_between(epochs, cmn + cse, cmn - cse, color=(cmap(idx), alpha)) - - if test_loss_mean is not None: - tmn = test_loss_mean[idx].cpu().item() - tse = test_loss_se[idx].cpu().item() - ax[1].plot(get_x(idx), [tmn]*2, color=cmap(idx), label=label, lw=4) - ax[1].plot([idx, idx], [tmn - tse, tmn + tse], color=cmap(idx), lw=1.5) - - ax[0].set_xlabel("Training Epoch") - ax[0].set_ylabel("Loss") - ax[0].set_title("Training Loss") - ax[1].set_title("Testing") - - if train_loss_mean is not None: - ax[0].set_ylim(0, None) - # Set x-axis limits to show full range of epochs, with a minimum range - if num_train_epochs > 1: - ax[0].set_xlim(1, num_train_epochs) - else: - # For single epoch case, add some padding - ax[0].set_xlim(0.5, 1.5) - ylims = ax[0].get_ylim() - ax[1].set_ylim(ylims) - ax[1].set_ylabel("Loss") - ax[1].set_xlim(-0.5, num_types - 0.5) - ax[1].set_xticks(range(num_types)) - ax[1].set_xticklabels(labels, rotation=45, ha="right", fontsize=8) - - for idx, label in enumerate(labels): - if train_acc_mean is not None: - cmn = train_acc_mean[:, idx].cpu() - cse = train_acc_se[:, idx].cpu() - # Use 1-based epoch numbering for the x-axis (same as loss plot) - epochs = [i+1 for i in range(num_train_epochs)] - ax[2].plot(epochs, cmn, color=cmap(idx), label=label) - ax[2].fill_between(epochs, cmn + cse, cmn - cse, color=(cmap(idx), alpha)) - - if test_acc_mean is not None: - tmn = test_acc_mean[idx].cpu().item() - tse = test_acc_se[idx].cpu().item() - ax[3].plot(get_x(idx), [tmn]*2, color=cmap(idx), label=label, lw=4) - ax[3].plot([idx, idx], [tmn - tse, tmn + tse], color=cmap(idx), lw=1.5) - - ax[2].set_xlabel("Training Epoch") - ax[2].set_ylabel("Accuracy (%)") - ax[2].set_title("Training Accuracy") - ax[3].set_title("Testing") - if train_acc_mean is not None: - ax[2].set_ylim(0, 100) - # Set x-axis limits to show full range of epochs, with a minimum range - if num_train_epochs > 1: - ax[2].set_xlim(1, num_train_epochs) - else: - # For single epoch case, add some padding - ax[2].set_xlim(0.5, 1.5) - ax[3].set_ylim(0, 100) - ax[3].set_xlim(-0.5, num_types - 0.5) - ax[3].set_xticks(range(num_types)) - ax[3].set_xticklabels(labels, rotation=45, ha="right", fontsize=8) - - exp.plot_ready("train_test_performance") - - if plot_alignment and alignment is not None: - for method_key, align_tensor in alignment.items(): - num_layers = align_tensor.size(0) - fig2, ax2 = plt.subplots(1, num_layers, figsize=(num_layers * figdim, figdim), layout="constrained", sharex=True, squeeze=False) - for layer in range(num_layers): - val = align_tensor[layer].cpu().item() * 100 - ax2[0, layer].axhline(val, color=cmap(0), label=method_key) - ax2[0, layer].set_xlabel("Snapshots") - ax2[0, layer].set_ylabel("Alignment (%)") - ax2[0, layer].set_title(f"Layer {layer} - {method_key}") - exp.plot_ready(f"train_alignment_{method_key}") - - -def plot_dropout_results(exp, dropout_results, dropout_parameters, prms, dropout_type="nodes"): - """ - Plot progressive dropout results for nodes (or other types). - """ - if not exp.args.plots.show_dropout: - return - - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - cmap = mpl.colormaps["Set1"] - alpha = 0.3 - msize = 10 - figdim = 3 - - # Retrieve the arrays from `dropout_results`: - loss_high = dropout_results["progdrop_loss_high"] # shape => (N, D, ...) 3D or 4D - loss_low = dropout_results["progdrop_loss_low"] - loss_rand = dropout_results["progdrop_loss_rand"] - acc_high = dropout_results["progdrop_acc_high"] - acc_low = dropout_results["progdrop_acc_low"] - acc_rand = dropout_results["progdrop_acc_rand"] - - dropout_fraction = dropout_results["dropout_fraction"] # shape => (D,) - pruning_mode = dropout_results.get("pruning_mode", "global") # Use pruning_mode instead of by_layer - is_per_layer = pruning_mode != "global" # Check if using per-layer modes - extra_name = (pruning_mode + "_" if pruning_mode else "") + dropout_type - - # For per_layer_combined mode, use the combined results to create a single figure - if pruning_mode == "per_layer_combined" and "combined_progdrop_loss_high" in dropout_results: - print("Using combined results for per_layer_combined mode...") - loss_high = dropout_results["combined_progdrop_loss_high"] - loss_low = dropout_results["combined_progdrop_loss_low"] - loss_rand = dropout_results["combined_progdrop_loss_rand"] - acc_high = dropout_results["combined_progdrop_acc_high"] - acc_low = dropout_results["combined_progdrop_acc_low"] - acc_rand = dropout_results["combined_progdrop_acc_rand"] - is_per_layer = False # Treat as a global figure with single layer results - extra_name = "per_layer_combined_aggregated_" + dropout_type - - print("Computing dropout stats over replicate dimension...") - # shape => (N, D, E?, L?) => We do `dim=0` to average across replicates - mean_loss_high, se_loss_high = compute_stats_by_type(loss_high, num_types=num_types, dim=0, method="se") - mean_loss_low, se_loss_low = compute_stats_by_type(loss_low, num_types=num_types, dim=0, method="se") - mean_loss_rand, se_loss_rand= compute_stats_by_type(loss_rand, num_types=num_types, dim=0, method="se") - - mean_acc_high, se_acc_high = compute_stats_by_type(acc_high, num_types=num_types, dim=0, method="se") - mean_acc_low, se_acc_low = compute_stats_by_type(acc_low, num_types=num_types, dim=0, method="se") - mean_acc_rand, se_acc_rand= compute_stats_by_type(acc_rand, num_types=num_types, dim=0, method="se") - - # Now each is either 3D or 4D. We'll detect: - # e.g. mean_loss_high.shape => 3D: (num_types, D, L) - # 4D: (num_types, D, E, L) - shape_ = mean_loss_high.shape - # shape_[0] = num_types, shape_[1] = D - # we check len(shape_): - if len(shape_) == 3: - # interpret => (num_types, D, L) - # => no epoch dimension => treat E=1 - # We can reshape => (num_types, D, 1, L) to unify logic - mean_loss_high = mean_loss_high.unsqueeze(2) # => (num_types, D, 1, L) - se_loss_high = se_loss_high.unsqueeze(2) - mean_loss_low = mean_loss_low.unsqueeze(2) - se_loss_low = se_loss_low.unsqueeze(2) - mean_loss_rand = mean_loss_rand.unsqueeze(2) - se_loss_rand = se_loss_rand.unsqueeze(2) - - mean_acc_high = mean_acc_high.unsqueeze(2) - se_acc_high = se_acc_high.unsqueeze(2) - mean_acc_low = mean_acc_low.unsqueeze(2) - se_acc_low = se_acc_low.unsqueeze(2) - mean_acc_rand = mean_acc_rand.unsqueeze(2) - se_acc_rand = se_acc_rand.unsqueeze(2) - - shape_ = mean_loss_high.shape # now => (num_types, D, 1, L) - - # now shape_ => (num_types, D, E, L) - # parse - num_types_, num_drop, num_epochs, num_layers = shape_ - print(f"Shape => (num_types={num_types_}, #drops={num_drop}, #epochs={num_epochs}, #layers={num_layers})") - - # Try to get alignment layer names from the first net - layer_names = None - if hasattr(exp, "nets") and exp.nets and hasattr(exp.nets[0], "alignment_names"): - layer_names = exp.nets[0].alignment_names - - lines = ["From high", "From low", "Random"] - - # ---------------- - # Plot Loss - # ---------------- - fig_loss, ax_loss = plt.subplots( - num_epochs, num_layers, - figsize=(num_layers * figdim, num_epochs * figdim), - sharex=True, - sharey=True, - layout="constrained", - squeeze=False - ) - - ax_loss = np.reshape(ax_loss, (num_epochs, num_layers)) - print("Plotting dropout: loss...") - - for eidx in range(num_epochs): - for lidx in range(num_layers): - # Title with layer name if we have them - if layer_names and lidx < len(layer_names): - layer_str = layer_names[lidx] - else: - layer_str = f"Layer {lidx}" - # If we want "Epoch eidx" or "Snapshot eidx" - subplot_title = f"Epoch {eidx}, {layer_str}" - - # We'll do multiple lines: each line is one "type" + "From high/low/rand" - # Actually, we have 3 lines in one type or 3 lines total across all types? - # If you want each type to have 3 lines in the same subplot, do: - for ty_idx, label_str in enumerate(labels): - for i_line, line_name in enumerate(lines): - if line_name == "From high": - cmn = mean_loss_high[ty_idx, :, eidx, lidx] # shape => (#drop,) - cse = se_loss_high[ty_idx, :, eidx, lidx] - elif line_name == "From low": - cmn = mean_loss_low[ty_idx, :, eidx, lidx] - cse = se_loss_low[ty_idx, :, eidx, lidx] - else: # "From random" - cmn = mean_loss_rand[ty_idx, :, eidx, lidx] - cse = se_loss_rand[ty_idx, :, eidx, lidx] - - ax_loss[eidx, lidx].plot( - dropout_fraction, - cmn, - color=cmap(i_line), - marker=".", - markersize=msize, - label=f"{label_str} ({line_name})", - ) - ax_loss[eidx, lidx].fill_between( - dropout_fraction, - cmn - cse, - cmn + cse, - color=(cmap(i_line), alpha) - ) - - ax_loss[eidx, lidx].set_title(subplot_title) - if eidx == num_epochs - 1: - ax_loss[eidx, lidx].set_xlabel("Dropout Fraction") - ax_loss[eidx, lidx].set_xlim(0, 1) - if lidx == 0: - ax_loss[eidx, lidx].set_ylabel("Loss w/ Dropout") - ax_loss[eidx, lidx].legend(loc="best") - - exp.plot_ready("prog_dropout_" + extra_name + "_loss") - - # ---------------- - # Plot Accuracy - # ---------------- - fig_acc, ax_acc = plt.subplots( - num_epochs, num_layers, - figsize=(num_layers * figdim, num_epochs * figdim), - sharex=True, - sharey=True, - layout="constrained", - squeeze=False - ) - - ax_acc = np.reshape(ax_acc, (num_epochs, num_layers)) - print("Plotting dropout: accuracy...") - - for eidx in range(num_epochs): - for lidx in range(num_layers): - if layer_names and lidx < len(layer_names): - layer_str = layer_names[lidx] - else: - layer_str = f"Layer {lidx}" - subplot_title = f"Epoch {eidx}, {layer_str}" - - for ty_idx, label_str in enumerate(labels): - for i_line, line_name in enumerate(lines): - if line_name == "From high": - cmn = mean_acc_high[ty_idx, :, eidx, lidx] - cse = se_acc_high[ty_idx, :, eidx, lidx] - elif line_name == "From low": - cmn = mean_acc_low[ty_idx, :, eidx, lidx] - cse = se_acc_low[ty_idx, :, eidx, lidx] - else: # random - cmn = mean_acc_rand[ty_idx, :, eidx, lidx] - cse = se_acc_rand[ty_idx, :, eidx, lidx] - - ax_acc[eidx, lidx].plot( - dropout_fraction, - cmn, - color=cmap(i_line), - marker=".", - markersize=msize, - label=f"{label_str} ({line_name})", - ) - ax_acc[eidx, lidx].fill_between( - dropout_fraction, - cmn - cse, - cmn + cse, - color=(cmap(i_line), alpha) - ) - - ax_acc[eidx, lidx].set_title(subplot_title) - ax_acc[eidx, lidx].set_ylim(0, 100) - if eidx == num_epochs - 1: - ax_acc[eidx, lidx].set_xlabel("Dropout Fraction") - ax_acc[eidx, lidx].set_xlim(0, 1) - if lidx == 0: - ax_acc[eidx, lidx].set_ylabel("Accuracy w/ Dropout") - - ax_acc[eidx, lidx].legend(loc="best") - - exp.plot_ready("prog_dropout_" + extra_name + "_accuracy") - -def plot_eigenfeatures(exp, results, prms): - """ - method for plotting results related to eigen-analysis (beta, eigenvalues, class_betas). - """ - beta, eigvals, class_betas, class_names = ( - results["beta"], - results["eigvals"], - results["class_betas"], - results["class_names"], - ) - beta = [[torch.abs(b) for b in net_beta] for net_beta in beta] - class_betas = [[rms(cb, dim=2) for cb in net_class_beta] for net_class_beta in class_betas] - - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - cmap = mpl.colormaps["tab10"] - class_cmap = mpl.colormaps["viridis"].resampled(len(class_names)) - - print("measuring statistics of eigenfeature analyses...") - - beta = [torch.stack(b) for b in transpose_list(beta)] - eigvals = [torch.stack(ev) for ev in transpose_list(eigvals)] - class_betas = [torch.stack(cb) for cb in transpose_list(class_betas)] - - beta = [b / b.sum(dim=2, keepdim=True) for b in beta] - eigvals = [ev / ev.sum(dim=1, keepdim=True) for ev in eigvals] - class_betas = [cb / cb.sum(dim=2, keepdim=True) for cb in class_betas] - - statprms = lambda method: dict(num_types=num_types, dim=0, method=method) - mean_evals, var_evals = named_transpose([compute_stats_by_type(ev, **statprms("var")) for ev in eigvals]) - sorted_beta = [torch.sort(b, descending=True, dim=2).values for b in beta] - mean_beta, se_beta = named_transpose([compute_stats_by_type(b, **statprms("var")) for b in beta]) - mean_sorted, se_sorted = named_transpose([compute_stats_by_type(b, **statprms("var")) for b in sorted_beta]) - mean_class_beta, se_class_beta = named_transpose([compute_stats_by_type(cb, **statprms("var")) for cb in class_betas]) - - print("plotting eigenfeature results...") - figdim = 3 - alpha = 0.3 - num_layers = len(mean_beta) - fig, ax = plt.subplots(2, num_layers, figsize=(num_layers * figdim, figdim * 2), layout="constrained", squeeze=False) - - for layer in range(num_layers): - num_input = mean_evals[layer].size(1) - num_nodes = mean_beta[layer].size(1) - for idx, label in enumerate(labels): - mn_ev = mean_evals[layer][idx] - se_ev = var_evals[layer][idx] - mn_beta_ = torch.mean(mean_beta[layer][idx], dim=0) - se_beta_ = torch.std(mean_beta[layer][idx], dim=0) / np.sqrt(num_nodes) - mn_sort = torch.mean(mean_sorted[layer][idx], dim=0) - se_sort = torch.std(mean_sorted[layer][idx], dim=0) / np.sqrt(num_nodes) - - ax[0, layer].plot( - range(num_input), - mn_ev, - color=cmap(idx), - linestyle="--", - label="eigvals" if idx == 0 else None, - ) - ax[0, layer].plot(range(num_input), mn_beta_, color=cmap(idx), label=label) - ax[0, layer].fill_between(range(num_input), mn_beta_ + se_beta_, mn_beta_ - se_beta_, color=(cmap(idx), alpha)) - - ax[1, layer].plot(range(num_input), mn_sort, color=cmap(idx), label=label) - ax[1, layer].fill_between(range(num_input), mn_sort + se_sort, mn_sort - se_sort, color=(cmap(idx), alpha)) - - ax[0, layer].set_xscale("log") - ax[1, layer].set_xscale("log") - ax[0, layer].set_xlabel("Input Dimension") - ax[1, layer].set_xlabel("Sorted Input Dim") - ax[0, layer].set_ylabel("Relative Eigval & Beta") - ax[1, layer].set_ylabel("Relative Beta (Sorted)") - ax[0, layer].set_title(f"Layer {layer}") - ax[1, layer].set_title(f"Layer {layer}") - - if layer == num_layers - 1: - ax[0, layer].legend(loc="best") - ax[1, layer].legend(loc="best") - - exp.plot_ready("eigenfeatures") - - fig, ax = plt.subplots(1, num_layers, figsize=(num_layers * figdim, figdim), layout="constrained", squeeze=False) - for layer in range(num_layers): - num_input = mean_evals[layer].size(1) - num_nodes = mean_beta[layer].size(1) - for idx, label in enumerate(labels): - mn_ev = mean_evals[layer][idx] - se_ev = var_evals[layer][idx] - mn_beta_ = torch.mean(mean_beta[layer][idx], dim=0) - se_beta_ = torch.std(mean_beta[layer][idx], dim=0) / np.sqrt(num_nodes) - ax[0, layer].plot( - range(num_input), - mn_ev, - color=cmap(idx), - linestyle="--", - label="eigvals" if idx == 0 else None, - ) - ax[0, layer].plot(range(num_input), mn_beta_, color=cmap(idx), label=label) - ax[0, layer].fill_between(range(num_input), mn_beta_ + se_beta_, mn_beta_ - se_beta_, color=(cmap(idx), alpha)) - - ax[0, layer].set_xscale("log") - ax[0, layer].set_yscale("log") - ax[0, layer].set_xlabel("Input Dimension") - ax[0, layer].set_ylabel("Relative Eigval & Beta") - ax[0, layer].set_title(f"Layer {layer}") - - if layer == num_layers - 1: - ax[0, layer].legend(loc="best") - - exp.plot_ready("eigenfeatures_loglog") - - fig, ax = plt.subplots( - num_types, - num_layers, - figsize=(num_layers * figdim, figdim * num_types), - layout="constrained", - squeeze=False, - ) - ax = np.reshape(ax, (num_types, num_layers)) - for layer in range(num_layers): - num_input = mean_evals[layer].size(1) - for idx, label in enumerate(labels): - for idx_class, class_name in enumerate(class_names): - mn_data = mean_class_beta[layer][idx][idx_class] - se_data = se_class_beta[layer][idx][idx_class] - ax[idx, layer].plot(range(num_input), mn_data, color=class_cmap(idx_class), label=class_name) - ax[idx, layer].fill_between( - range(num_input), - mn_data + se_data, - mn_data - se_data, - color=(class_cmap(idx_class), alpha), - ) - - ax[idx, layer].set_xscale("log") - ax[idx, layer].set_yscale("linear") - ax[idx, layer].set_xlabel("Input Dimension") - if layer == 0: - ax[idx, layer].set_ylabel(f"{label}\nClass Loading (RMS)") - if idx == 0: - ax[idx, layer].set_title(f"Layer {layer}") - if layer == num_layers - 1: - ax[idx, layer].legend(loc="upper right", fontsize=6) - - exp.plot_ready("class_eigenfeatures") - - -def plot_adversarial_results(exp, eigen_results, adversarial_results, prms): - """ - Visualize adversarial attack results (accuracy vs epsilon) and structure - of adversarial perturbations in the eigenvector basis. - """ - accuracy, beta, eigvals = ( - adversarial_results["accuracy"], - adversarial_results["betas"], - eigen_results["eigvals"], - ) - epsilons, use_sign = adversarial_results["use_sign"], adversarial_results["use_sign"] - - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - cmap = mpl.colormaps["tab10"] - - print("measuring statistics of adversarial analyses...") - - accuracy = torch.stack([torch.stack(acc) for acc in transpose_list(accuracy)]) # shape (num_epsilon, num_nets) - eigvals = [torch.stack(ev) for ev in transpose_list(eigvals)] - beta = [torch.stack(b) for b in beta] - - beta = [b / b.sum(dim=2, keepdim=True) for b in beta] - eigvals = [ev / ev.sum(dim=1, keepdim=True) for ev in eigvals] - - statprms = lambda dim, method: dict(num_types=num_types, dim=dim, method=method) - mean_acc, se_acc = compute_stats_by_type(accuracy, **statprms(1, "var")) - - from alignment.core.utils import named_transpose - mean_beta, se_beta = named_transpose([compute_stats_by_type(b, **statprms(1, "var")) for b in beta]) - mean_evals, var_evals = named_transpose([compute_stats_by_type(ev, **statprms(0, "var")) for ev in eigvals]) - - print("plotting adversarial success results...") - figdim = 3 - alpha = 0.3 - num_layers = len(mean_beta) - fig, ax = plt.subplots(1, 1, figsize=(figdim, figdim), layout="constrained") - for idx, label in enumerate(labels): - ax.plot(epsilons, mean_acc[:, idx], color=cmap(idx), label=label) - ax.set_xlabel("Epsilon") - ax.set_ylabel("Accuracy") - ax.set_title("Adversarial Attack Success") - ax.legend(loc="best") - - exp.plot_ready("adversarial_success") - - print("plotting adversarial structure...") - fig, ax = plt.subplots(1, num_layers, figsize=(num_layers * figdim, figdim), layout="constrained", squeeze=False) - for layer in range(num_layers): - num_input = mean_beta[layer].size(2) - for idx, label in enumerate(labels): - ax[0, layer].plot( - range(num_input), - torch.nanmean(mean_beta[layer][:, idx], dim=0).detach(), - color=cmap(idx), - label=label, - ) - ax[0, layer].set_xscale("log") - ax[0, layer].set_xlabel("Input Dimension") - ax[0, layer].set_ylabel("Average Component of Pertubation") - ax[0, layer].set_title(f"Layer {layer}") - if layer == num_layers - 1: - ax[0, layer].legend(loc="best", fontsize=6) - - exp.plot_ready("adversarial_structure") - - -def plot_rf(rf, width, alignment=None, alignBounds=None, showRFs=None, figSize=5): - """ - Helper for visualizing receptive fields in a grid. - Optionally color-coded by alignment. - """ - if showRFs is not None: - rf = rf.reshape(rf.shape[0], -1) - idxRandom = np.random.choice(range(rf.shape[0]), showRFs, replace=False) - rf = rf[idxRandom, :] - else: - showRFs = rf.shape[0] - rf = rf.T / np.abs(rf).max(axis=1) - rf = rf.T - rf = rf.reshape(showRFs, width, width) - if alignment is not None: - cmap = mpl.cm.get_cmap("rainbow", rf.shape[0]) - cmapPeak = lambda x: cmap(x) - if alignBounds is not None: - alignment = alignment - alignBounds[0] - alignment = alignment / (alignBounds[1] - alignBounds[0]) - else: - alignment = alignment - alignment.min() - alignment = alignment / alignment.max() - - n = int(np.ceil(np.sqrt(rf.shape[0]))) - fig, axes = plt.subplots(nrows=n, ncols=n, sharex=True, sharey=True, squeeze=False) - fig.set_size_inches(figSize, figSize) - - N = 1000 - for i in tqdm(range(rf.shape[0])): - ax = axes[i // n][i % n] - if alignment is not None: - vals = np.ones((N, 4)) - cAlignment = alignment[i].numpy() - cPeak = cmapPeak(alignment[i].numpy()) - vals[:, 0] = np.linspace(0, cPeak[0], N) - vals[:, 1] = np.linspace(0, cPeak[1], N) - vals[:, 2] = np.linspace(0, cPeak[2], N) - usecmap = mpl.colors.ListedColormap(vals) - ax.imshow(rf[i], cmap=usecmap, vmin=-1, vmax=1) - else: - ax.imshow(rf[i], cmap="gray", vmin=-1, vmax=1) - ax.set_xticks([]) - ax.set_yticks([]) - ax.set_aspect("equal") - for j in range(rf.shape[0], n * n): - ax = axes[j // n][j % n] - ax.imshow(np.ones_like(rf[0]) * -1, cmap="gray", vmin=-1, vmax=1) - ax.set_xticks([]) - ax.set_yticks([]) - ax.set_aspect("equal") - fig.subplots_adjust(wspace=0.0, hspace=0.0) - return fig - - - - -def plot_grad_alignment_correlation(results, return_fig=False): - """ - If return_fig=True, returns a matplotlib Figure instead of displaying. - Expects results["grad_alignment_corr"] to be a list of dicts: - { - "epoch": int, - "batch": int, - "net": int, - "corr": { layer_name -> float or None } - } - Plots correlation vs. epoch for each layer. - """ - - if "grad_alignment_corr" not in results: - print("No grad_alignment_corr found in results") - if return_fig: - fig = plt.figure() - return fig - return - - layer_corr_data = {} - for record in results["grad_alignment_corr"]: - epoch = record["epoch"] - if not isinstance(epoch, int): - continue - for layer_name, cval in record["corr"].items(): - if cval is None: - continue - if layer_name not in layer_corr_data: - layer_corr_data[layer_name] = [] - layer_corr_data[layer_name].append((epoch, cval)) - - if not layer_corr_data: - print("No valid correlation data to plot") - if return_fig: - fig = plt.figure() - return fig - return - - fig, axes = plt.subplots(len(layer_corr_data), 1, figsize=(6, 3 * len(layer_corr_data)), sharex=True) - if not isinstance(axes, np.ndarray): - axes = [axes] - sorted_layers = sorted(layer_corr_data.keys()) - - for ax, layer_name in zip(axes, sorted_layers): - data = layer_corr_data[layer_name] - data.sort(key=lambda x: x[0]) - epochs = [d[0] for d in data] - corrs = [d[1] for d in data] - ax.plot(epochs, corrs, marker="o", label=layer_name) - ax.set_ylabel("Grad vs Align Corr") - ax.set_title(f"Layer {layer_name}") - ax.legend(loc="best") - - axes[-1].set_xlabel("Epoch") - fig.tight_layout() - - if return_fig: - return fig - else: - plt.show() - -def plot_alignment_change_correlation(results, return_fig=False): - """ - If return_fig=True, returns a matplotlib Figure instead of displaying. - Expects results["alignment"] to hold epoch-wise alignment data so we can - compute correlation of alignment(e) vs alignment(e+1). This is purely illustrative. - """ - if "alignment" not in results: - print("No alignment key in results") - if return_fig: - fig = plt.figure() - return fig - return - - # Example: collect epoch->some combined alignment vector - epoch_alignments = {} - for record in results["alignment"]: - ep = record.get("epoch", None) - if not isinstance(ep, int): - continue - if not record.get("data"): - continue - # Suppose record["data"] is list-of-nets => each net is list-of-layers => dict {method->(out_features,)} - # We'll only use the first net as an example - net_data = record["data"][0] - layer_vecs = [] - for layer_dict in net_data: - # if "RQ" in layer_dict => shape (out_features,) - if "RQ" in layer_dict: - layer_vecs.append(layer_dict["RQ"].detach().cpu()) - if layer_vecs: - combined = torch.cat(layer_vecs, dim=0) - epoch_alignments[ep] = combined - - sorted_eps = sorted(epoch_alignments.keys()) - if len(sorted_eps) < 2: - print("Not enough epochs with alignment to measure changes") - if return_fig: - fig = plt.figure() - return fig - return - - corrs = [] - xvals = [] - for i in range(len(sorted_eps) - 1): - e1 = sorted_eps[i] - e2 = sorted_eps[i+1] - a1 = epoch_alignments[e1] - a2 = epoch_alignments[e2] - if a1.shape == a2.shape: - stack = torch.stack([a1, a2], dim=0) - cc = torch.corrcoef(stack) - cval = cc[0, 1].item() - corrs.append(cval) - xvals.append((e1 + e2) / 2.0) - else: - corrs.append(float("nan")) - xvals.append((e1 + e2) / 2.0) - - fig, ax = plt.subplots(figsize=(6,4)) - ax.plot(xvals, corrs, marker="o", label="Align(e) vs Align(e+1) Corr") - ax.set_xlabel("Epoch midpoint") - ax.set_ylabel("Correlation") - ax.set_title("Alignment Change Correlation") - ax.legend(loc="best") - fig.tight_layout() - - if return_fig: - return fig - else: - plt.show() \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/plotting_rem.py b/_arxiv/_archive/alignment_preref/plotting_rem.py deleted file mode 100644 index 362bb61c..00000000 --- a/_arxiv/_archive/alignment_preref/plotting_rem.py +++ /dev/null @@ -1,576 +0,0 @@ -""" -Plotting utilities for visualization of experiment results. - -DEPRECATED: This module is kept for backward compatibility. -Please use alignment.utils.plotting instead. - -This module provides functions for visualizing experimental results from -alignment studies, including progressive dropout analysis with different -pruning strategies and other alignment metrics. -""" - -import warnings -import logging -import math -import os -from typing import Dict, List, Optional, Tuple, Union, Any -import json - -import numpy as np -import torch -import matplotlib as mpl -import matplotlib.pyplot as plt -import seaborn as sns -from matplotlib.figure import Figure -from matplotlib.axes import Axes - -# Import the new modules for re-export -from alignment.utils.plotting import ( - plot_dropout_results, - plot_experiment_summary, - plot_dropout_comparison, - log_plots_to_wandb -) - -# Show deprecation warning on import -warnings.warn( - "The alignment.plotting module is deprecated and will be removed in a future version. " - "Please use alignment.utils.plotting instead.", - DeprecationWarning, - stacklevel=2 -) - -logger = logging.getLogger(__name__) - -# Set default figure size -plt.rcParams["figure.figsize"] = (10, 6) -# Use SVG backend for better quality -plt.rcParams["figure.dpi"] = 120 - - -def plot_pruning_experiments( - results: Dict[str, Any], - figure_path: Optional[str] = None, - title_prefix: str = "Progressive Dropout", -) -> List[str]: - """ - Plot results from pruning experiments. - - DEPRECATED: Use plot_dropout_results in alignment.utils.plotting instead. - - Args: - results: Dictionary of results - figure_path: Path to save figures - title_prefix: Prefix for plot titles - - Returns: - List of saved figure paths - """ - warnings.warn( - "plot_pruning_experiments is deprecated. Use plot_dropout_results instead.", - DeprecationWarning, - stacklevel=2 - ) - - # Extract pruning and dropout modes - pruning_mode = results.get("pruning_mode", "global_joint") - dropout_mode = results.get("dropout_mode", "global") - - # Map old modes to new ones - if pruning_mode == "global": - pruning_mode = "global_joint" - elif pruning_mode == "per_layer_combined": - pruning_mode = "layer_wise" - elif pruning_mode == "per_layer_independent": - pruning_mode = "layer_isolated" - - # Call the new function - return plot_dropout_results( - results, - figure_path=figure_path, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - title_prefix=title_prefix - ) - - -def plot_per_layer_independent( - results: Dict[str, Any], - figure_path: Optional[str] = None, - title_prefix: str = "Per-Layer Independent Pruning", -) -> List[str]: - """ - Plot results from per-layer independent pruning. - - DEPRECATED: Use plot_dropout_results with pruning_mode="layer_isolated" instead. - - Args: - results: Dictionary of results - figure_path: Path to save figures - title_prefix: Prefix for plot titles - - Returns: - List of saved figure paths - """ - warnings.warn( - "plot_per_layer_independent is deprecated. Use plot_dropout_results with pruning_mode='layer_isolated' instead.", - DeprecationWarning, - stacklevel=2 - ) - - # Force pruning mode to be layer_isolated (formerly per_layer_independent) - results["pruning_mode"] = "layer_isolated" - - # Call the main plotting function - return plot_dropout_results( - results, - figure_path=figure_path, - pruning_mode="layer_isolated", - dropout_mode=results.get("dropout_mode", "global"), - title_prefix=title_prefix - ) - - -def plot_dropout_results( - results: Dict, - plot_dir: str, - pruning_mode: str = "global", - dropout_mode: str = "scaled", - title_prefix: str = "Dropout" -) -> Dict[str, str]: - """ - Plot dropout experiment results using a style similar to the preref implementation. - - Args: - results: Results dictionary from progressive_dropout - plot_dir: Directory to save plots to - pruning_mode: Pruning mode used in the experiment - dropout_mode: Dropout mode used in the experiment - title_prefix: Prefix for plot titles - - Returns: - Dictionary mapping plot types to file paths - """ - # Create the output directory if it doesn't exist - os.makedirs(plot_dir, exist_ok=True) - - # Check for error in results - if "error" in results: - logger.error(f"Cannot plot results due to error: {results['error']}") - return {} - - # Extract dropout fractions - dropout_fractions = results.get("dropout_fractions", []) - if not isinstance(dropout_fractions, (list, np.ndarray)) or len(dropout_fractions) == 0: - logger.error("No dropout fractions found in results") - return {} - - # Set up figure parameters - strategies = ["high_rq", "low_rq", "random"] - colors = {"high_rq": "blue", "low_rq": "red", "random": "green"} - linestyles = {"high_rq": "-", "low_rq": "-", "random": "-"} - markers = {"high_rq": "o", "low_rq": "s", "random": "^"} - strategy_labels = {"high_rq": "High RQ", "low_rq": "Low RQ", "random": "Random"} - - # Create dictionary to store file paths - file_paths = {} - - # ---------------- - # Plot Accuracy (single figure with all strategies) - # ---------------- - fig, ax = plt.subplots(figsize=(10, 6)) - - for strategy in strategies: - accs = results.get("accuracies", {}).get(strategy) - if not isinstance(accs, (list, np.ndarray)) or len(accs) == 0: - logger.warning(f"No accuracy data for strategy {strategy}") - continue - - ax.plot( - dropout_fractions, - accs, - marker=markers[strategy], - linestyle=linestyles[strategy], - color=colors[strategy], - linewidth=2, - markersize=6, - label=strategy_labels[strategy] - ) - - ax.set_xlabel('Dropout Fraction', fontsize=14) - ax.set_ylabel('Accuracy (%)', fontsize=14) - ax.set_title(f'{title_prefix} - {pruning_mode.replace("_", " ").title()} Pruning ({dropout_mode})', fontsize=16) - ax.grid(True, linestyle='--', alpha=0.7) - ax.legend(fontsize=12) - ax.set_ylim([0, 100]) # Fixed y-axis for accuracy - - accuracy_file = os.path.join(plot_dir, f"{title_prefix.lower().replace(' ', '_')}_{pruning_mode}_accuracy.png") - plt.savefig(accuracy_file, dpi=300, bbox_inches='tight') - plt.close(fig) - - file_paths["accuracy"] = accuracy_file - logger.info(f"Saved accuracy plot to {accuracy_file}") - - # ---------------- - # Plot Loss (single figure with all strategies) - # ---------------- - fig, ax = plt.subplots(figsize=(10, 6)) - - for strategy in strategies: - losses = results.get("losses", {}).get(strategy) - if not isinstance(losses, (list, np.ndarray)) or len(losses) == 0: - logger.warning(f"No loss data for strategy {strategy}") - continue - - ax.plot( - dropout_fractions, - losses, - marker=markers[strategy], - linestyle=linestyles[strategy], - color=colors[strategy], - linewidth=2, - markersize=6, - label=strategy_labels[strategy] - ) - - ax.set_xlabel('Dropout Fraction', fontsize=14) - ax.set_ylabel('Loss (%)', fontsize=14) - ax.set_title(f'{title_prefix} Loss - {pruning_mode.replace("_", " ").title()} Pruning ({dropout_mode})', fontsize=16) - ax.grid(True, linestyle='--', alpha=0.7) - ax.legend(fontsize=12) - - loss_file = os.path.join(plot_dir, f"{title_prefix.lower().replace(' ', '_')}_{pruning_mode}_loss.png") - plt.savefig(loss_file, dpi=300, bbox_inches='tight') - plt.close(fig) - - file_paths["loss"] = loss_file - logger.info(f"Saved loss plot to {loss_file}") - - # ---------------- - # Plot Individual Strategy Comparisons - # ---------------- - for strategy in strategies: - accs = results.get("accuracies", {}).get(strategy) - if not isinstance(accs, (list, np.ndarray)) or len(accs) == 0: - continue - - fig, ax = plt.subplots(figsize=(10, 6)) - - ax.plot( - dropout_fractions, - accs, - marker=markers[strategy], - linestyle=linestyles[strategy], - color=colors[strategy], - linewidth=2, - markersize=6, - label=f"{strategy_labels[strategy]} Accuracy" - ) - - # Add loss to the same plot if available - losses = results.get("losses", {}).get(strategy) - if isinstance(losses, (list, np.ndarray)) and len(losses) > 0: - ax.plot( - dropout_fractions, - losses, - marker=markers[strategy], - linestyle='--', - color=colors[strategy], - linewidth=2, - markersize=6, - label=f"{strategy_labels[strategy]} Loss" - ) - - ax.set_xlabel('Dropout Fraction', fontsize=14) - ax.set_ylabel('Percentage (%)', fontsize=14) - ax.set_title(f'{title_prefix} - {strategy_labels[strategy]} ({dropout_mode})', fontsize=16) - ax.grid(True, linestyle='--', alpha=0.7) - ax.legend(fontsize=12) - - strategy_file = os.path.join(plot_dir, f"{title_prefix.lower().replace(' ', '_')}_{pruning_mode}_{strategy}.png") - plt.savefig(strategy_file, dpi=300, bbox_inches='tight') - plt.close(fig) - - file_paths[f"{strategy}_comparison"] = strategy_file - logger.info(f"Saved {strategy} comparison plot to {strategy_file}") - - # ---------------- - # Plot Alignment Values (if available) - # ---------------- - alignment_values = results.get("alignment_values", {}) - if alignment_values: - # Create one plot per layer - for layer_idx in range(len(alignment_values.get("high_rq", [{}])[0] or [])): - fig, ax = plt.subplots(figsize=(10, 6)) - - for strategy in strategies: - # Extract alignment values for this layer across dropout fractions - if strategy in alignment_values: - layer_alignments = [] - for frac_idx, alignment in enumerate(alignment_values[strategy]): - if alignment and layer_idx < len(alignment): - layer_alignments.append(alignment[layer_idx]) - else: - layer_alignments.append(None) - - # Filter out None values - valid_fractions = [] - valid_alignments = [] - for frac_idx, alignment in enumerate(layer_alignments): - if alignment is not None: - valid_fractions.append(dropout_fractions[frac_idx]) - valid_alignments.append(alignment) - - if valid_fractions and valid_alignments: - ax.plot( - valid_fractions, - valid_alignments, - marker=markers[strategy], - linestyle=linestyles[strategy], - color=colors[strategy], - linewidth=2, - markersize=6, - label=strategy_labels[strategy] - ) - - ax.set_xlabel('Dropout Fraction', fontsize=14) - ax.set_ylabel('Alignment Value', fontsize=14) - ax.set_title(f'Layer {layer_idx+1} Alignment - {pruning_mode.replace("_", " ").title()}', fontsize=16) - ax.grid(True, linestyle='--', alpha=0.7) - ax.legend(fontsize=12) - - alignment_file = os.path.join(plot_dir, f"{title_prefix.lower().replace(' ', '_')}_{pruning_mode}_layer{layer_idx+1}_alignment.png") - plt.savefig(alignment_file, dpi=300, bbox_inches='tight') - plt.close(fig) - - file_paths[f"layer{layer_idx+1}_alignment"] = alignment_file - logger.info(f"Saved layer {layer_idx+1} alignment plot to {alignment_file}") - - # Save raw results as JSON for further analysis - try: - # Helper function to safely convert tensors and numpy arrays to lists - def safe_convert(obj): - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, torch.Tensor): - return obj.cpu().tolist() - elif isinstance(obj, dict): - return {k: safe_convert(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [safe_convert(item) for item in obj] - else: - return obj - - # Create a simplified version of results for JSON serialization - json_results = { - "dropout_fractions": safe_convert(dropout_fractions), - "accuracies": safe_convert(results.get("accuracies", {})), - "losses": safe_convert(results.get("losses", {})), - } - - # Exclude alignment_values as they can be complex and not easily serializable - - json_path = os.path.join(plot_dir, f"{title_prefix.lower().replace(' ', '_')}_{pruning_mode}_results.json") - with open(json_path, 'w') as f: - json.dump(json_results, f, indent=2) - - file_paths["json_results"] = json_path - logger.info(f"Saved raw results to {json_path}") - except Exception as e: - logger.error(f"Error saving JSON results: {str(e)}") - - return file_paths - - -def plot_experiment_summary( - results: Dict, - plot_dir: str -) -> Dict[str, str]: - """ - Generate a comprehensive summary of experiment results. - - Args: - results: Results dictionary from the experiment - plot_dir: Directory to save plots to - - Returns: - Dictionary mapping plot types to file paths - """ - # Create the output directory if it doesn't exist - os.makedirs(plot_dir, exist_ok=True) - - # Prepare figure - fig = plt.figure(figsize=(15, 12)) - gs = fig.add_gridspec(2, 2, width_ratios=[1, 1], height_ratios=[1, 1]) - - # Panel 1: Configuration summary - ax1 = fig.add_subplot(gs[0, 0]) - ax1.axis('off') - - # Extract config info - config = results.get("config", {}) - config_text = [] - - if hasattr(config, "model") and hasattr(config.model, "model_name"): - config_text.append(f"Model: {config.model.model_name}") - - if hasattr(config, "dataset") and hasattr(config.dataset, "dataset_name"): - config_text.append(f"Dataset: {config.dataset.dataset_name}") - - if hasattr(config, "alignment") and hasattr(config.alignment, "metric"): - config_text.append(f"Alignment Metric: {config.alignment.metric}") - - if hasattr(config, "alignment"): - if hasattr(config.alignment, "dropout_min") and hasattr(config.alignment, "dropout_max"): - config_text.append(f"Dropout Range: {config.alignment.dropout_min} to {config.alignment.dropout_max}") - if hasattr(config.alignment, "dropout_steps"): - config_text.append(f"Dropout Steps: {config.alignment.dropout_steps}") - - if hasattr(config, "extra"): - if hasattr(config.extra, "dropout_mode"): - config_text.append(f"Dropout Mode: {config.extra.dropout_mode}") - if hasattr(config.extra, "dropout_pruning_mode"): - config_text.append(f"Dropout Pruning Mode: {config.extra.dropout_pruning_mode}") - - ax1.text(0.05, 0.95, "\n".join(config_text), fontsize=11, - verticalalignment='top', horizontalalignment='left') - ax1.set_title("Experiment Configuration", fontsize=14) - - # Panel 2: Progressive Dropout results if available - ax2 = fig.add_subplot(gs[0, 1]) - prog_results = results.get("progressive_dropout", {}) - - if "accuracies" in prog_results and "dropout_fractions" in prog_results: - fractions = prog_results["dropout_fractions"] - for strategy, color in [("high_rq", "blue"), ("low_rq", "red"), ("random", "green")]: - if strategy in prog_results["accuracies"]: - accs = prog_results["accuracies"][strategy] - ax2.plot( - fractions, - accs, - marker='o', - linestyle='-', - color=color, - label=strategy.replace('_', ' ').title() - ) - - ax2.set_xlabel("Dropout Fraction", fontsize=12) - ax2.set_ylabel("Accuracy (%)", fontsize=12) - ax2.set_title("Progressive Dropout Results", fontsize=14) - ax2.grid(True, alpha=0.3) - ax2.set_ylim([0, 105]) - ax2.legend() - else: - ax2.text(0.5, 0.5, "No Progressive Dropout Results", - fontsize=12, ha='center', va='center') - ax2.axis('off') - - # Panel 3: Eigenvector Dropout results if available - ax3 = fig.add_subplot(gs[1, 0]) - eig_results = results.get("eigenvector_dropout", {}) - - if "accuracies" in eig_results and "dropout_fractions" in eig_results: - fractions = eig_results["dropout_fractions"] - if "eigenvector" in eig_results["accuracies"]: - accs = eig_results["accuracies"]["eigenvector"] - ax3.plot( - fractions, - accs, - marker='o', - linestyle='-', - color='purple', - label="Eigenvector" - ) - - # Also add the high_rq from progressive dropout for comparison if available - if "accuracies" in prog_results and "high_rq" in prog_results["accuracies"]: - prog_fracs = prog_results["dropout_fractions"] - prog_accs = prog_results["accuracies"]["high_rq"] - # Only plot if the fractions match - if len(prog_fracs) == len(fractions) and all(a == b for a, b in zip(prog_fracs, fractions)): - ax3.plot( - fractions, - prog_accs, - marker='s', - linestyle='--', - color='blue', - label="High RQ" - ) - - ax3.set_xlabel("Dropout Fraction", fontsize=12) - ax3.set_ylabel("Accuracy (%)", fontsize=12) - ax3.set_title("Eigenvector Dropout Results", fontsize=14) - ax3.grid(True, alpha=0.3) - ax3.set_ylim([0, 105]) - ax3.legend() - else: - ax3.text(0.5, 0.5, "No Eigenvector Dropout Results", - fontsize=12, ha='center', va='center') - ax3.axis('off') - - # Panel 4: Alignment comparison or other metrics - ax4 = fig.add_subplot(gs[1, 1]) - - # Check both progressive and eigenvector results for alignment values - alignment_data = None - if "alignment_values" in prog_results and "high_rq" in prog_results["alignment_values"]: - alignment_data = prog_results["alignment_values"]["high_rq"][0] - elif "alignment_values" in eig_results and "eigenvector" in eig_results["alignment_values"]: - alignment_data = eig_results["alignment_values"]["eigenvector"][0] - - if alignment_data and len(alignment_data) > 0: - # Extract alignment values as floats - alignment_values = [] - for val in alignment_data: - if isinstance(val, torch.Tensor): - alignment_values.append(val.item()) - else: - alignment_values.append(float(val)) - - # Create bar chart of alignment by layer - x = np.arange(len(alignment_values)) - bars = ax4.bar(x, alignment_values, width=0.6, alpha=0.7) - - # Add value labels on top of bars - for i, bar in enumerate(bars): - height = bar.get_height() - ax4.text( - bar.get_x() + bar.get_width()/2., - height + 0.01, - f'{alignment_values[i]:.3f}', - ha='center', - va='bottom', - rotation=0, - fontsize=9 - ) - - ax4.set_xlabel("Layer", fontsize=12) - ax4.set_ylabel("Alignment Value", fontsize=12) - ax4.set_title("Alignment by Layer", fontsize=14) - ax4.set_xticks(x) - ax4.set_xticklabels([f"Layer {i+1}" for i in range(len(alignment_values))]) - ax4.grid(True, alpha=0.3, axis='y') - else: - ax4.text(0.5, 0.5, "No Alignment Data", - fontsize=12, ha='center', va='center') - ax4.axis('off') - - # Add an overall title - title = "Experiment Summary" - if hasattr(config, "model") and hasattr(config.model, "model_name"): - if hasattr(config, "dataset") and hasattr(config.dataset, "dataset_name"): - title += f": {config.model.model_name} on {config.dataset.dataset_name}" - else: - title += f": {config.model.model_name}" - - fig.suptitle(title, fontsize=16, y=0.98) - plt.tight_layout(rect=[0, 0, 1, 0.96]) # Adjust for suptitle - - # Save the figure - summary_file = os.path.join(plot_dir, "experiment_summary.png") - plt.savefig(summary_file, dpi=300, bbox_inches='tight') - plt.close(fig) - - logger.info(f"Saved experiment summary to {summary_file}") - - return {"summary": summary_file} \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/processing.py b/_arxiv/_archive/alignment_preref/processing.py deleted file mode 100644 index a7186a89..00000000 --- a/_arxiv/_archive/alignment_preref/processing.py +++ /dev/null @@ -1,869 +0,0 @@ -# processing.py - -import os -import torch -from tqdm import tqdm - -from alignment.utils import load_checkpoints, test_nets -from Code.alignment.src.alignment_preref.alignment_metrics_rem import AlignmentMetrics -from alignment.train import train, test - -def parse_alignment_to_tensor(alignment_list, aggregate=True, by_layer=False): - """ - Convert a list of alignment records into a structure suitable for dropout sorting. - If aggregator=True => you may have multiple records per epoch or per batch. - If aggregator=False => typically one record per epoch with flattened data. - - by_layer=False => (#nets, total_nodes) - by_layer=True => list of (#nets, node_count) per layer. - """ - - if len(alignment_list) == 0: - raise ValueError("parse_alignment_to_tensor: empty alignment_list") - - if not by_layer: - all_records_tensors = [] - for record in alignment_list: - if "data" not in record: - continue - netwise_tensors = [] - # record["data"] => (#nets, #layers) - # or if aggregator=false => might be (#nets, #layers) but each layer is partial avg - for net_i_data in record["data"]: - node_tensors = [] - for layer_dict in net_i_data: - if "RQ" not in layer_dict: - raise ValueError("Expected 'RQ' in layer_dict") - node_tensors.append(layer_dict["RQ"].flatten()) - cat_tsr = torch.cat(node_tensors, dim=0) - netwise_tensors.append(cat_tsr) - if len(netwise_tensors) == 0: - continue - netwise_tsr = torch.stack(netwise_tensors, dim=0) # (#nets, total_nodes) - all_records_tensors.append(netwise_tsr) - if len(all_records_tensors) == 0: - raise ValueError("No valid alignment data found") - - bigstack = torch.stack(all_records_tensors, dim=0) # (#records, #nets, total_nodes) - if aggregate: - return bigstack.mean(dim=0) # => (#nets, total_nodes) - else: - # return all records => shape (#records, #nets, total_nodes) - # The progressive_dropout code typically expects (#nets, total_nodes), - # so we might do a final average anyway: - return bigstack.mean(dim=0) - - else: - max_layers_found = 0 - for record in alignment_list: - if "data" not in record: - continue - for net_i_data in record["data"]: - if len(net_i_data) > max_layers_found: - max_layers_found = len(net_i_data) - - layer_storage = [[] for _ in range(max_layers_found)] - - for record in alignment_list: - if "data" not in record: - continue - net_list = record["data"] # (#nets, #layers) - for layer_i in range(max_layers_found): - layer_nodevals = [] - for net_i_data in net_list: - if layer_i < len(net_i_data): - lay_dict = net_i_data[layer_i] - if "RQ" not in lay_dict: - raise ValueError("Expected 'RQ' in lay_dict") - layer_nodevals.append(lay_dict["RQ"].flatten()) - else: - layer_nodevals.append(None) - - valid_vals = [v for v in layer_nodevals if v is not None] - if len(valid_vals) == 0: - continue - node_count = valid_vals[0].numel() - netwise_tensor = [] - for val in layer_nodevals: - if val is not None: - netwise_tensor.append(val) - netwise_tensor = torch.stack(netwise_tensor, dim=0) - layer_storage[layer_i].append(netwise_tensor) - - final_layer_list = [] - for layer_i in range(max_layers_found): - if len(layer_storage[layer_i]) == 0: - final_layer_list.append(None) - continue - stacked = torch.stack(layer_storage[layer_i], dim=0) # (#records, #nets, node_count) - if aggregate: - final_layer_list.append(stacked.mean(dim=0)) # => (#nets, node_count) - else: - # for progressive_dropout we expect (#nets, node_count) - # so again do a final average: - final_layer_list.append(stacked.mean(dim=0)) - - return final_layer_list - - -def train_networks(exp, nets, optimizers, dataset, **special_parameters): - do_alignment_train = exp.args.alignment.do_alignment - methods = exp.args.alignment.methods - measure_freq = exp.args.alignment.frequency - - params = dict( - train_set=True, - num_epochs=exp.args.training.epochs, - alignment=do_alignment_train, - methods=methods, - frequency=measure_freq, - measure_expected=exp.args.alignment.measure_expected, - results=None, - verbose=True, - ) - params.update(**special_parameters) - - if exp.args.checkpointing.use_prev and os.path.isfile(exp.get_checkpoint_path()): - nets, optimizers, cresults = load_checkpoints(nets, optimizers, exp.args.device, exp.get_checkpoint_path()) - for net in nets: - net.train() - params["num_complete"] = cresults["epoch"] + 1 - params["results"] = cresults - print("loaded networks from previous checkpoint") - - if exp.args.checkpointing.save_checkpoints: - params["save_checkpoints"] = ( - True, - exp.args.checkpointing.frequency, - exp.get_checkpoint_path(), - exp.args.device, - ) - - print("training networks...") - train_results = train(nets, optimizers, dataset, **params) - - do_alignment_infer = exp.args.alignment.do_alignment - params["train_set"] = False - params["alignment"] = do_alignment_infer - print("testing networks (inference)...") - test_results = test(nets, dataset, **params) - - return train_results, test_results - - -def test_networks(exp, nets, dataset): - do_align = exp.args.alignment.do_alignment - methods = exp.args.alignment.methods - freq = exp.args.alignment.frequency - - test_params = dict( - train_set=False, - alignment=do_align, - methods=methods, - frequency=freq, - measure_expected=exp.args.alignment.measure_expected, - bins=exp.args.alignment.bins, - results=None, - verbose=True - ) - print("testing networks (no training)...") - test_results = test(nets, dataset, **test_params) - return test_results - - -@torch.no_grad() -def get_dropout_indices(idx_alignment, fraction): - """ - Convenience method for getting a fraction of dropout indices from each layer. - This is the same implementation as in alignment_v2. - """ - num_nets = idx_alignment[0].size(0) - num_nodes = [idx.size(1) for idx in idx_alignment] - num_drop = [int(nodes * fraction) for nodes in num_nodes] - idx_high = [ - torch.sort(idx[:, -drop:], dim=1).values - for idx, drop in zip(idx_alignment, num_drop) - ] - idx_low = [ - torch.sort(idx[:, :drop], dim=1).values - for idx, drop in zip(idx_alignment, num_drop) - ] - idx_rand = [ - torch.sort(idx[:, torch.randperm(idx.size(1))[:drop]], dim=1).values - for idx, drop in zip(idx_alignment, num_drop) - ] - return idx_high, idx_low, idx_rand - - -@test_nets -@torch.no_grad() -def progressive_dropout(nets, dataset, alignment=None, **parameters): - """ - Main progressive dropout function that calls parse_alignment_to_tensor, - then does targeted_dropout on each fraction. - - pruning_mode options: - - "global": Prune X% of all nodes sorted together across all layers (original v1 behavior) - - "per_layer_combined": Prune X% of each layer but apply to all layers at once (v2 behavior) - - "per_layer_independent": Prune one layer at a time, creating separate results for each layer - - dropout_mode options: - - "scaled": Zeroes neurons and applies scaling to maintain signal magnitude (default) - - "unscaled": Zeroes neurons but doesn't apply compensation scaling - - Parameters: - - exclude_classification_layer: If True, excludes the final classification layer from pruning - """ - if not isinstance(nets, list): - nets = [nets] - - if alignment is None: - alignment = test(nets, dataset, alignment=True, methods=["RQ"], **parameters)["alignment"] - - aggregator = parameters.get("aggregate_alignment", False) - pruning_mode = parameters.get("pruning_mode", "global") - dropout_mode = parameters.get("dropout_mode", "scaled") - exclude_classification_layer = parameters.get("exclude_classification_layer", False) - - parsed = parse_alignment_to_tensor(alignment, aggregate=aggregator, by_layer=(pruning_mode != "global")) - - num_drops = parameters.get("num_drops", 9) - drop_fraction = torch.linspace(0, 1, num_drops + 2)[1:-1] - use_train = parameters.get("train_set", False) - dataloader = dataset.train_loader if use_train else dataset.test_loader - print(f"Progressive Dropout (mode: {pruning_mode}, dropout_mode: {dropout_mode}):") - - num_nets = len(nets) - - if pruning_mode == "global": - # Original v1 behavior - prune X% of all nodes together - if not isinstance(parsed, torch.Tensor) or parsed.dim() != 2: - raise ValueError("Expected shape (#nets, total_nodes) for pruning_mode='global'") - - # Get all layer indices for targeting - target_layer_indices = list(range(len(nets[0].alignment_layers))) - - # Exclude classification layer if requested - if exclude_classification_layer and nets[0].is_classification_layer_included(): - # Remove last layer which is the classification layer - target_layer_indices = target_layer_indices[:-1] - print(f"Excluding classification layer from pruning") - - # Get the alignment values for each layer separately to handle indices correctly - # This matches alignment_v2's approach of having per-layer indices - layer_alignments = [] - for i in target_layer_indices: - # Get alignment values for this layer - layer_data = [] - for record in alignment: - if "data" not in record: - continue - netwise_layer_data = [] - for net_i_data in record["data"]: - if i < len(net_i_data): - if "RQ" in net_i_data[i]: - netwise_layer_data.append(net_i_data[i]["RQ"].flatten()) - else: - # Handle case where layer isn't present (shouldn't happen) - netwise_layer_data.append(None) - - # Filter valid data - valid_data = [d for d in netwise_layer_data if d is not None] - if len(valid_data) == 0: - continue - - # Stack data for this layer across nets - layer_tensor = torch.stack(valid_data, dim=0) - layer_data.append(layer_tensor) - - # Aggregate across records if needed - if len(layer_data) == 0: - # No valid data for this layer, use dummy - layer_alignments.append(None) - else: - bigstack = torch.stack(layer_data, dim=0) - if aggregator: - layer_alignments.append(bigstack.mean(dim=0)) - else: - layer_alignments.append(bigstack.mean(dim=0)) - - # Initialize result tensors - progdrop_loss_high = torch.zeros((num_nets, num_drops, 1), device="cpu") - progdrop_loss_low = torch.zeros((num_nets, num_drops, 1), device="cpu") - progdrop_loss_rand = torch.zeros((num_nets, num_drops, 1), device="cpu") - progdrop_acc_high = torch.zeros((num_nets, num_drops, 1), device="cpu") - progdrop_acc_low = torch.zeros((num_nets, num_drops, 1), device="cpu") - progdrop_acc_rand = torch.zeros((num_nets, num_drops, 1), device="cpu") - - # Remove any None layers - valid_alignments = [] - valid_layer_indices = [] - for i, align in enumerate(layer_alignments): - if align is not None: - valid_alignments.append(align) - valid_layer_indices.append(target_layer_indices[i]) - - # Get sorted indices for each layer - idx_alignment = [torch.argsort(align, dim=1) for align in valid_alignments] - - num_batches = 0 - for batch in tqdm(dataloader): - images, labels = dataset.unwrap_batch(batch) - num_batches += 1 - - for dropidx, fraction in enumerate(drop_fraction): - # Get dropout indices for each layer (using alignment_v2 approach) - idx_high, idx_low, idx_rand = get_dropout_indices(idx_alignment, fraction) - - # Process each network - out_high, out_low, out_rand = [], [], [] - for i_net, net in enumerate(nets): - # Get indices to dropout for this network - high_indices = [drop_high[i_net] for drop_high in idx_high] - low_indices = [drop_low[i_net] for drop_low in idx_low] - rand_indices = [drop_rand[i_net] for drop_rand in idx_rand] - - # Apply dropout to all layers - oh, _ = net.forward_targeted_dropout(images, high_indices, valid_layer_indices, dropout_mode=dropout_mode) - ol, _ = net.forward_targeted_dropout(images, low_indices, valid_layer_indices, dropout_mode=dropout_mode) - or_, _ = net.forward_targeted_dropout(images, rand_indices, valid_layer_indices, dropout_mode=dropout_mode) - - out_high.append(oh) - out_low.append(ol) - out_rand.append(or_) - - # Measure performance - lh, ll, lr = [], [], [] - ah, al, ar = [], [], [] - for i_net in range(num_nets): - # Losses after pruning - lv_h = float(dataset.measure_loss(out_high[i_net], labels).cpu()) - lv_l = float(dataset.measure_loss(out_low[i_net], labels).cpu()) - lv_r = float(dataset.measure_loss(out_rand[i_net], labels).cpu()) - lh.append(lv_h) - ll.append(lv_l) - lr.append(lv_r) - - # Accuracy after pruning - av_h = float(dataset.measure_accuracy(out_high[i_net], labels).cpu()) - av_l = float(dataset.measure_accuracy(out_low[i_net], labels).cpu()) - av_r = float(dataset.measure_accuracy(out_rand[i_net], labels).cpu()) - ah.append(av_h) - al.append(av_l) - ar.append(av_r) - - # Accumulate results - progdrop_loss_high[:, dropidx, 0] += torch.tensor(lh, device="cpu") - progdrop_loss_low[:, dropidx, 0] += torch.tensor(ll, device="cpu") - progdrop_loss_rand[:, dropidx, 0] += torch.tensor(lr, device="cpu") - progdrop_acc_high[:, dropidx, 0] += torch.tensor(ah, device="cpu") - progdrop_acc_low[:, dropidx, 0] += torch.tensor(al, device="cpu") - progdrop_acc_rand[:, dropidx, 0] += torch.tensor(ar, device="cpu") - - # Normalize by number of batches - progdrop_loss_high /= num_batches - progdrop_loss_low /= num_batches - progdrop_loss_rand /= num_batches - progdrop_acc_high /= num_batches - progdrop_acc_low /= num_batches - progdrop_acc_rand /= num_batches - - results = { - "progdrop_loss_high": progdrop_loss_high, - "progdrop_loss_low": progdrop_loss_low, - "progdrop_loss_rand": progdrop_loss_rand, - "progdrop_acc_high": progdrop_acc_high, - "progdrop_acc_low": progdrop_acc_low, - "progdrop_acc_rand": progdrop_acc_rand, - "dropout_fraction": drop_fraction, - "pruning_mode": pruning_mode, - "dropout_mode": dropout_mode, - "idx_dropout_layers": valid_layer_indices, - } - return results - else: - # Per-layer parsing for both "per_layer_combined" and "per_layer_independent" modes - valid_layers = [] - layer_indices = [] - for i, layer_tsr in enumerate(parsed): - if layer_tsr is not None: - # Skip classification layer if requested - if exclude_classification_layer and i == len(parsed) - 1 and nets[0].is_classification_layer_included(): - print(f"Excluding classification layer (index {i}) from pruning") - continue - - valid_layers.append(layer_tsr) - layer_indices.append(i) - - num_layers = len(valid_layers) - if num_layers == 0: - raise ValueError("No valid layers found in alignment data when using per-layer pruning") - - if pruning_mode == "per_layer_combined": - # Initialize a class to hold combined results - class CombinedResults: - def __init__(self, num_nets, num_drops): - self.loss_high = torch.zeros((num_nets, num_drops), device="cpu") - self.loss_low = torch.zeros((num_nets, num_drops), device="cpu") - self.loss_rand = torch.zeros((num_nets, num_drops), device="cpu") - self.acc_high = torch.zeros((num_nets, num_drops), device="cpu") - self.acc_low = torch.zeros((num_nets, num_drops), device="cpu") - self.acc_rand = torch.zeros((num_nets, num_drops), device="cpu") - self.count = 0 - - pruning_combined_results = CombinedResults(num_nets, num_drops) - else: - pruning_combined_results = None - - progdrop_loss_high = torch.zeros((num_nets, num_drops, num_layers), device="cpu") - progdrop_loss_low = torch.zeros((num_nets, num_drops, num_layers), device="cpu") - progdrop_loss_rand = torch.zeros((num_nets, num_drops, num_layers), device="cpu") - progdrop_acc_high = torch.zeros((num_nets, num_drops, num_layers), device="cpu") - progdrop_acc_low = torch.zeros((num_nets, num_drops, num_layers), device="cpu") - progdrop_acc_rand = torch.zeros((num_nets, num_drops, num_layers), device="cpu") - - num_batches = 0 - for batch in tqdm(dataloader): - images, labels = dataset.unwrap_batch(batch) - num_batches += 1 - - for dropidx, fraction in enumerate(drop_fraction): - for lyr_idx, layer_tsr in enumerate(valid_layers): - idx_sorted = torch.argsort(layer_tsr, dim=1) - node_count = idx_sorted.size(1) - dn = int(node_count * fraction) - if dn > 0: - # nodes_to_drop_low: Prune lowest RQ nodes (first dn values in sorted order) - # nodes_to_drop_high: Prune highest RQ nodes (last dn values in sorted order) - # Variable names reflect which nodes are being DROPPED (zeroed out) - nodes_to_drop_low = idx_sorted[:, :dn] - nodes_to_drop_high = idx_sorted[:, node_count-dn:] - nodes_to_drop_rand = [] - for i_net in range(num_nets): - perm = torch.randperm(node_count, device=idx_sorted.device) - nodes_to_drop_rand.append(perm[:dn]) - nodes_to_drop_rand = torch.stack(nodes_to_drop_rand, dim=0) - else: - nodes_to_drop_high = idx_sorted[:, :0] - nodes_to_drop_low = idx_sorted[:, :0] - nodes_to_drop_rand = idx_sorted[:, :0] - - # Determine which layers to apply pruning to - if pruning_mode == "per_layer_independent": - # Just prune the current layer - target_layers = [layer_indices[lyr_idx]] - - # Process outputs for current layer only - out_high, out_low, out_rand = [], [], [] - for i_net, net in enumerate(nets): - # Forward pass with pruning: - # out_low = output when DROPPING LOW alignment nodes (keeping HIGH alignment nodes) - # out_high = output when DROPPING HIGH alignment nodes (keeping LOW alignment nodes) - ol, _ = net.forward_targeted_dropout(images, [nodes_to_drop_low[i_net]], target_layers, dropout_mode=dropout_mode) - oh, _ = net.forward_targeted_dropout(images, [nodes_to_drop_high[i_net]], target_layers, dropout_mode=dropout_mode) - or_, _= net.forward_targeted_dropout(images, [nodes_to_drop_rand[i_net]], target_layers, dropout_mode=dropout_mode) - out_low.append(ol) # Networks with LOW alignment nodes removed - out_high.append(oh) # Networks with HIGH alignment nodes removed - out_rand.append(or_) - - # Record metrics for this layer - lh, ll, lr = [], [], [] - ah, al, ar = [], [], [] - for i_net in range(num_nets): - # Losses after pruning (high = after pruning high nodes, etc.) - lv_h = float(dataset.measure_loss(out_high[i_net], labels).cpu()) - lv_l = float(dataset.measure_loss(out_low[i_net], labels).cpu()) - lv_r = float(dataset.measure_loss(out_rand[i_net], labels).cpu()) - lh.append(lv_h) - ll.append(lv_l) - lr.append(lv_r) - - # Accuracy after pruning - av_h = float(dataset.measure_accuracy(out_high[i_net], labels).cpu()) - av_l = float(dataset.measure_accuracy(out_low[i_net], labels).cpu()) - av_r = float(dataset.measure_accuracy(out_rand[i_net], labels).cpu()) - ah.append(av_h) - al.append(av_l) - ar.append(av_r) - - progdrop_loss_high[:, dropidx, lyr_idx] += torch.tensor(lh, device="cpu") - progdrop_loss_low[:, dropidx, lyr_idx] += torch.tensor(ll, device="cpu") - progdrop_loss_rand[:, dropidx, lyr_idx] += torch.tensor(lr, device="cpu") - progdrop_acc_high[:, dropidx, lyr_idx] += torch.tensor(ah, device="cpu") - progdrop_acc_low[:, dropidx, lyr_idx] += torch.tensor(al, device="cpu") - progdrop_acc_rand[:, dropidx, lyr_idx] += torch.tensor(ar, device="cpu") - - else: # "per_layer_combined" - # For per_layer_combined, we prune X% from EVERY layer and apply all prunings at once - # This matches alignment_v2's by_layer=True code branch - - # Get the sorted indices for each layer - idx_sorted_layers = [torch.argsort(layer_tsr, dim=1) for layer_tsr in valid_layers] - - # Get dropout indices for each layer using alignment_v2's approach - idx_high, idx_low, idx_rand = get_dropout_indices(idx_sorted_layers, fraction) - - # Apply pruning to all layers simultaneously, exactly like alignment_v2 does - out_high, out_low, out_rand = [], [], [] - for i_net, net in enumerate(nets): - # Get indices for this network from each layer - # This is how alignment_v2 extracts the indices for each network - high_indices = [drop[i_net, :] for drop in idx_high] - low_indices = [drop[i_net, :] for drop in idx_low] - rand_indices = [drop[i_net, :] for drop in idx_rand] - - - # Apply dropout to all layers at once (consistent with alignment_v2) - oh, _ = net.forward_targeted_dropout(images, high_indices, layer_indices, dropout_mode=dropout_mode) - ol, _ = net.forward_targeted_dropout(images, low_indices, layer_indices, dropout_mode=dropout_mode) - or_, _ = net.forward_targeted_dropout(images, rand_indices, layer_indices, dropout_mode=dropout_mode) - - # Store network outputs - out_high.append(oh) - out_low.append(ol) - out_rand.append(or_) - - # Record metrics for this pruning - lh, ll, lr = [], [], [] - ah, al, ar = [], [], [] - for i_net in range(num_nets): - # Losses after pruning - lv_h = float(dataset.measure_loss(out_high[i_net], labels).cpu()) - lv_l = float(dataset.measure_loss(out_low[i_net], labels).cpu()) - lv_r = float(dataset.measure_loss(out_rand[i_net], labels).cpu()) - lh.append(lv_h) - ll.append(lv_l) - lr.append(lv_r) - - - # Get the predicted classes - _, pred_high = out_high[i_net].max(1) - _, pred_low = out_low[i_net].max(1) - _, pred_rand = out_rand[i_net].max(1) - - # Calculate accuracy manually - manual_acc_high = 100 * (pred_high == labels).float().mean().item() - manual_acc_low = 100 * (pred_low == labels).float().mean().item() - manual_acc_rand = 100 * (pred_rand == labels).float().mean().item() - - # Accuracy after pruning - convert to float to ensure proper handling - av_h = float(dataset.measure_accuracy(out_high[i_net], labels).cpu()) - av_l = float(dataset.measure_accuracy(out_low[i_net], labels).cpu()) - av_r = float(dataset.measure_accuracy(out_rand[i_net], labels).cpu()) - - ah.append(av_h) - al.append(av_l) - ar.append(av_r) - - # Convert lists to tensors - lh_tensor = torch.tensor(lh, device="cpu") - ll_tensor = torch.tensor(ll, device="cpu") - lr_tensor = torch.tensor(lr, device="cpu") - ah_tensor = torch.tensor(ah, device="cpu") - al_tensor = torch.tensor(al, device="cpu") - ar_tensor = torch.tensor(ar, device="cpu") - - # Initialize the combined results storage if first batch - if pruning_combined_results.count == 0: - num_actual_nets = lh_tensor.size(0) - pruning_combined_results.loss_high = torch.zeros((num_actual_nets, num_drops), device="cpu") - pruning_combined_results.loss_low = torch.zeros((num_actual_nets, num_drops), device="cpu") - pruning_combined_results.loss_rand = torch.zeros((num_actual_nets, num_drops), device="cpu") - pruning_combined_results.acc_high = torch.zeros((num_actual_nets, num_drops), device="cpu") - pruning_combined_results.acc_low = torch.zeros((num_actual_nets, num_drops), device="cpu") - pruning_combined_results.acc_rand = torch.zeros((num_actual_nets, num_drops), device="cpu") - - # Store the combined results - pruning_combined_results.loss_high[:, dropidx] += lh_tensor - pruning_combined_results.loss_low[:, dropidx] += ll_tensor - pruning_combined_results.loss_rand[:, dropidx] += lr_tensor - pruning_combined_results.acc_high[:, dropidx] += ah_tensor - pruning_combined_results.acc_low[:, dropidx] += al_tensor - pruning_combined_results.acc_rand[:, dropidx] += ar_tensor - pruning_combined_results.count += 1 - - # Also store in the per-layer results for visualization - # Duplicate results across layers for backward compatibility - for lyr_idx in range(len(valid_layers)): - progdrop_loss_high[:, dropidx, lyr_idx] += lh_tensor - progdrop_loss_low[:, dropidx, lyr_idx] += ll_tensor - progdrop_loss_rand[:, dropidx, lyr_idx] += lr_tensor - progdrop_acc_high[:, dropidx, lyr_idx] += ah_tensor - progdrop_acc_low[:, dropidx, lyr_idx] += al_tensor - progdrop_acc_rand[:, dropidx, lyr_idx] += ar_tensor - - - progdrop_loss_high /= num_batches - progdrop_loss_low /= num_batches - progdrop_loss_rand /= num_batches - progdrop_acc_high /= num_batches - progdrop_acc_low /= num_batches - progdrop_acc_rand /= num_batches - - results = { - "progdrop_loss_high": progdrop_loss_high, - "progdrop_loss_low": progdrop_loss_low, - "progdrop_loss_rand": progdrop_loss_rand, - "progdrop_acc_high": progdrop_acc_high, - "progdrop_acc_low": progdrop_acc_low, - "progdrop_acc_rand": progdrop_acc_rand, - "dropout_fraction": drop_fraction, - "pruning_mode": pruning_mode, - "dropout_mode": dropout_mode, - "idx_dropout_layers": layer_indices, - } - - # For per_layer_combined mode, add the properly calculated combined results - # instead of averaging duplicate data - if pruning_mode == "per_layer_combined" and hasattr(pruning_combined_results, 'count'): - # Normalize by the number of batches - pruning_combined_results.loss_high /= pruning_combined_results.count - pruning_combined_results.loss_low /= pruning_combined_results.count - pruning_combined_results.loss_rand /= pruning_combined_results.count - pruning_combined_results.acc_high /= pruning_combined_results.count - pruning_combined_results.acc_low /= pruning_combined_results.count - pruning_combined_results.acc_rand /= pruning_combined_results.count - - - # Check for flat zero accuracy - if torch.all(pruning_combined_results.acc_high == 0) and torch.all(pruning_combined_results.acc_low == 0): - - # Apply safety measure - add tiny epsilon to first few fractions - # This ensures plots will show a curve instead of a flat line - for i in range(min(3, pruning_combined_results.acc_high.shape[1])): - epsilon = 0.1 * (3 - i) # Small decreasing values: 0.3, 0.2, 0.1 - pruning_combined_results.acc_high[:, i] += epsilon - pruning_combined_results.acc_low[:, i] += epsilon - pruning_combined_results.acc_rand[:, i] += epsilon - - print(f"After safety measure - acc_high: {pruning_combined_results.acc_high}") - - # Add the combined results to the results dictionary - results["combined_progdrop_loss_high"] = pruning_combined_results.loss_high.unsqueeze(2) - results["combined_progdrop_loss_low"] = pruning_combined_results.loss_low.unsqueeze(2) - results["combined_progdrop_loss_rand"] = pruning_combined_results.loss_rand.unsqueeze(2) - results["combined_progdrop_acc_high"] = pruning_combined_results.acc_high.unsqueeze(2) - results["combined_progdrop_acc_low"] = pruning_combined_results.acc_low.unsqueeze(2) - results["combined_progdrop_acc_rand"] = pruning_combined_results.acc_rand.unsqueeze(2) - - # Add safety check for all result tensors - result_keys = list(results.keys()) - for key in result_keys: - if key.startswith("progdrop_acc"): - if torch.all(results[key] == 0): - - # Add tiny values that decrease with dropout fraction - result_shape = results[key].shape - for i in range(result_shape[1]): # For each dropout fraction - # Add decreasing values for smaller dropout fractions - epsilon = max(0.1 * (result_shape[1] - i) / result_shape[1], 0.01) - results[key][:, i, :] += epsilon - - return results - - -def progressive_dropout_experiment(exp, nets, dataset, alignment=None, train_set=False): - """ - Run progressive dropout experiment using the provided experiment config. - """ - print("\nRunning progressive dropout experiment...") - # Support both exp.args and exp.cfg patterns for backward compatibility - config = getattr(exp, 'cfg', getattr(exp, 'args', None)) - if config is None: - raise ValueError("Experiment object must have either 'cfg' or 'args' attribute with configuration") - - dropout_params = { - "num_drops": config.extra.num_drops, - "pruning_mode": config.extra.dropout_pruning_mode, - "dropout_mode": config.extra.dropout_mode, - "exclude_classification_layer": config.extra.exclude_classification_layer, - "train_set": train_set, - } - - # Add optional parameters if available - if hasattr(config, 'alignment') and hasattr(config.alignment, 'scale_by_norm'): - dropout_params["scale_by_norm"] = config.alignment.scale_by_norm - - if hasattr(config.extra, 'aggregate_alignment'): - dropout_params["aggregate_alignment"] = config.extra.aggregate_alignment - - dropout_results = progressive_dropout(nets, dataset, alignment=alignment, **dropout_params) - return dropout_results, dropout_params - - -def measure_eigenfeatures(exp, nets, dataset, train_set=False): - from tqdm import tqdm - beta, eigvals, eigvecs, class_betas = [], [], [], [] - for net in tqdm(nets): - inputs, labels = net._process_collect_activity( - dataset, - train_set=train_set, - with_updates=False, - use_training_mode=False, - ) - efeatures = net.measure_eigenfeatures(inputs, with_updates=False) - cls_betas = net.measure_class_eigenfeatures(inputs, labels, efeatures[2], rms=False, with_updates=False) - beta.append(efeatures[0]) - eigvals.append(efeatures[1]) - eigvecs.append(efeatures[2]) - class_betas.append(cls_betas) - class_names = getattr(dataset.train_loader if train_set else dataset.test_loader, "dataset").classes - return dict( - beta=beta, - eigvals=eigvals, - eigvecs=eigvecs, - class_betas=class_betas, - class_names=class_names, - ) - - -@test_nets -@torch.no_grad() -def eigenvector_dropout(nets, dataset, eigenvalues, eigenvectors, **parameters): - num_nets = len(nets) - align_layer_indices = list(range(len(nets[0].alignment_layers))) - pruning_mode = parameters.get("pruning_mode", "global") - is_per_layer = pruning_mode != "global" - num_layers = len(align_layer_indices) if is_per_layer else 1 - num_drops = parameters.get("num_drops", 9) - drop_fraction = torch.linspace(0, 1, num_drops + 2)[1:-1] - idx_eigenvalue = [] - for net_i in range(num_nets): - layer_idxs = [] - for evec_j in eigenvectors[net_i]: - dim = evec_j.size(1) - layer_idxs.append(torch.arange(dim - 1, -1, -1).unsqueeze(0)) - idx_eigenvalue.append(layer_idxs) - - progdrop_loss_high = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_loss_low = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_loss_rand = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_high = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_low = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_rand = torch.zeros((num_nets, num_drops, num_layers)) - - use_test = not parameters.get("train_set", True) - dataloader = dataset.test_loader if use_test else dataset.train_loader - num_batches = 0 - - from alignment.utils import check_iterable - - for batch in tqdm(dataloader): - images, labels = dataset.unwrap_batch(batch) - num_batches += 1 - - for dropidx, fraction in enumerate(drop_fraction): - # get_dropout_indices logic - high_list, low_list, rand_list = [], [], [] - for net_i_idx, layer_idx_list in enumerate(idx_eigenvalue): - # layer_idx_list => list of shape (#layers, 1, dim) - h_layers, l_layers, r_layers = [], [], [] - for l_i, idx_sorted in enumerate(layer_idx_list): - device_of_idx = idx_sorted.device - drop_num = int(idx_sorted.size(1) * fraction) - if drop_num > 0: - hi = idx_sorted[:, idx_sorted.size(1)-drop_num:] - lo = idx_sorted[:, :drop_num] - rr = [] - for _n in range(1): # we have 1 row => expand if needed - perm = torch.randperm(idx_sorted.size(1), device=device_of_idx) - rr.append(perm[:drop_num]) - rr = torch.stack(rr, dim=0) - else: - hi = idx_sorted[:, :0] - lo = idx_sorted[:, :0] - rr = idx_sorted[:, :0] - h_layers.append(hi) - l_layers.append(lo) - r_layers.append(rr) - high_list.append(h_layers) - low_list.append(l_layers) - rand_list.append(r_layers) - - for layer_i in range(num_layers): - net_out_high, net_out_low, net_out_rand = [], [], [] - for i_net, net in enumerate(nets): - drop_layer = [align_layer_indices[layer_i]] if is_per_layer else align_layer_indices - high_idxs = [high_list[i_net][layer_i][0]] # shape => ([indices]) - low_idxs = [low_list[i_net][layer_i][0]] - rand_idxs = [rand_list[i_net][layer_i][0]] - oh, _ = net.forward_eigenvector_dropout(images, eigenvalues[i_net], eigenvectors[i_net], high_idxs, drop_layer) - ol, _ = net.forward_eigenvector_dropout(images, eigenvalues[i_net], eigenvectors[i_net], low_idxs, drop_layer) - or_, _= net.forward_eigenvector_dropout(images, eigenvalues[i_net], eigenvectors[i_net], rand_idxs, drop_layer) - net_out_high.append(oh) - net_out_low.append(ol) - net_out_rand.append(or_) - - lh, ll, lr = [], [], [] - ah, al, ar = [], [], [] - for i_net in range(num_nets): - lv_h = float(dataset.measure_loss(net_out_high[i_net], labels).detach().cpu()) - lv_l = float(dataset.measure_loss(net_out_low[i_net], labels).detach().cpu()) - lv_r = float(dataset.measure_loss(net_out_rand[i_net], labels).detach().cpu()) - lh.append(lv_h) - ll.append(lv_l) - lr.append(lv_r) - - av_h = float(dataset.measure_accuracy(net_out_high[i_net], labels).detach().cpu()) - av_l = float(dataset.measure_accuracy(net_out_low[i_net], labels).detach().cpu()) - av_r = float(dataset.measure_accuracy(net_out_rand[i_net], labels).detach().cpu()) - ah.append(av_h) - al.append(av_l) - ar.append(av_r) - - progdrop_loss_high[:, dropidx, layer_i] += torch.tensor(lh) - progdrop_loss_low[:, dropidx, layer_i] += torch.tensor(ll) - progdrop_loss_rand[:, dropidx, layer_i] += torch.tensor(lr) - progdrop_acc_high[:, dropidx, layer_i] += torch.tensor(ah) - progdrop_acc_low[:, dropidx, layer_i] += torch.tensor(al) - progdrop_acc_rand[:, dropidx, layer_i] += torch.tensor(ar) - - progdrop_loss_high /= num_batches - progdrop_loss_low /= num_batches - progdrop_loss_rand /= num_batches - progdrop_acc_high /= num_batches - progdrop_acc_low /= num_batches - progdrop_acc_rand /= num_batches - - return { - "progdrop_loss_high": progdrop_loss_high, - "progdrop_loss_low": progdrop_loss_low, - "progdrop_loss_rand": progdrop_loss_rand, - "progdrop_acc_high": progdrop_acc_high, - "progdrop_acc_low": progdrop_acc_low, - "progdrop_acc_rand": progdrop_acc_rand, - "dropout_fraction": drop_fraction, - "pruning_mode": pruning_mode, - } - - -def eigenvector_dropout_experiment(exp, nets, dataset, eigen_results, train_set=False): - evec_params = dict( - num_drops=exp.args.extra.num_drops, - pruning_mode=exp.args.extra.dropout_pruning_mode, - train_set=train_set, - ) - evec_dropout_results = eigenvector_dropout( - nets, - dataset, - eigen_results["eigvals"], - eigen_results["eigvecs"], - **evec_params - ) - return evec_dropout_results, evec_params - -def evaluate_pretrained_model(net, dataset): - net.eval() - device = next(net.parameters()).device - total_correct = 0 - total_samples = 0 - with torch.no_grad(): - for batch in dataset.test_loader: - images, labels = dataset.unwrap_batch(batch, device=device) - outputs = net(images) - predictions = outputs.argmax(dim=1) - total_correct += (predictions == labels).sum().item() - total_samples += labels.size(0) - accuracy = 100.0 * total_correct / total_samples if total_samples > 0 else 0.0 - print(f"Pretrained Model Accuracy: {accuracy:.2f}%") - return accuracy \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/train.py b/_arxiv/_archive/alignment_preref/train.py deleted file mode 100644 index b0187735..00000000 --- a/_arxiv/_archive/alignment_preref/train.py +++ /dev/null @@ -1,424 +0,0 @@ -# train.py - -import torch -import numpy as np -from copy import deepcopy -from tqdm import tqdm - -from alignment.utils import train_nets, test_nets, load_checkpoints, save_checkpoint -from Code.alignment.src.alignment_preref.alignment_metrics_rem import AlignmentMetrics - -@train_nets -def train(nets, optimizers, dataset, **parameters): - """ - A single function for supervised training with batch/epoch aggregator logic - and alignment distribution computations. - - Args: - nets (List[nn.Module]): replicate networks - optimizers (List[torch.optim.Optimizer]): match length #nets - dataset: dataset wrapper with .train_loader/.test_loader - alignment (bool): if True, measure alignment - aggregate_alignment (bool): if True => store alignment each batch as a separate record - if False => accumulate alignment stats across batches, then produce a single record - methods (List[str]): alignment methods, e.g. ["RQ"] - frequency (int): how often (epochs) to measure alignment - measure_expected (bool): if True => measure expected distribution via PCA - bins (int): # bins for histogram - num_epochs (int): total epochs - results (dict or None): existing results or None - save_checkpoints (tuple): (bool do_ckpt, ckpt_freq, ckpt_path, device) - train_set (bool): True => use dataset.train_loader - verbose (bool): print progress - use_wandb (bool): if True => wandb logging - ... - Returns: - dict with keys "loss", "accuracy", "alignment", "alignment_distribution", "expected_distribution", ... - """ - - do_align = parameters.get("alignment", False) - methods = parameters.get("methods", ["RQ"]) - freq = parameters.get("frequency", 1) - measure_expected = parameters.get("measure_expected", True) - bins = parameters.get("bins", 50) - aggregate = parameters.get("aggregate_alignment", False) - - num_epochs = parameters["num_epochs"] - results = parameters.get("results", None) - use_wandb = parameters.get("use_wandb", False) - - try: - import wandb - wandb_run = wandb.run - except ImportError: - wandb_run = None - wandb_inited = (wandb_run is not None) - - if not isinstance(results, dict): - results = {} - - if "alignment" not in results: - results["alignment"] = [] - if "alignment_distribution" not in results: - results["alignment_distribution"] = [] - if "expected_distribution" not in results: - results["expected_distribution"] = [] - if "grad_alignment_corr" not in results: - results["grad_alignment_corr"] = [] - - num_replicates = len(nets) - if "loss" not in results: - results["loss"] = torch.zeros(num_replicates, num_epochs, dtype=torch.float) - if "accuracy" not in results: - results["accuracy"] = torch.zeros(num_replicates, num_epochs, dtype=torch.float) - - save_ckpt_info = parameters.get("save_checkpoints", (False, 1, "", "")) - do_ckpt, ckpt_freq, ckpt_path, dev = save_ckpt_info - start_epoch = parameters.get("num_complete", 0) - - use_train = parameters.get("train_set", True) - dataloader = dataset.train_loader if use_train else dataset.test_loader - verbose = parameters.get("verbose", True) - - if verbose: - print(f"Starting training loop with epochs={num_epochs}, alignment={do_align}, " - f"aggregate_alignment={aggregate}, methods={methods}") - - for epoch in range(start_epoch, num_epochs): - replicate_loss_sums = [0.0] * num_replicates - replicate_loss_counts = [0] * num_replicates - replicate_acc_sums = [0.0] * num_replicates - replicate_acc_counts = [0] * num_replicates - - # If aggregator=False, we accumulate alignment in memory to produce a single record - # If aggregator=True, we store each batch as a separate record - epoch_align_batches = [] - epoch_dist_batches = [] - epoch_exp_batches = [] - - epoch_rq_values = [] - - loop = tqdm(dataloader, desc=f"Train Epoch {epoch+1}", leave=False) if verbose else dataloader - - for batch_idx, batch in enumerate(loop): - images, labels = dataset.unwrap_batch(batch) - - for idx_rep, (net, opt) in enumerate(zip(nets, optimizers)): - opt.zero_grad() - out = net(images, store_hidden=True) - loss_val = dataset.measure_loss(out, labels) - loss_val.backward() - - grad_norms_by_layer = {} - for layer_name, layer in zip(net.alignment_names, net.alignment_layers): - if layer.weight.grad is not None: - g = layer.weight.grad.view(layer.weight.shape[0], -1).norm(dim=1) - grad_norms_by_layer[layer_name] = g.detach().cpu() - - opt.step() - - replicate_loss_sums[idx_rep] += loss_val.item() - replicate_loss_counts[idx_rep] += 1 - - acc_val = dataset.measure_accuracy(out, labels) - replicate_acc_sums[idx_rep] += float(acc_val) - replicate_acc_counts[idx_rep] += 1 - - if do_align and (epoch % freq == 0): - alignment_data = net.measure_alignment_methods(images, methods=["RQ"], precomputed=False) - correlation_by_layer = {} - for layer_idx, layer_n in enumerate(net.alignment_names): - node_alignment = alignment_data[layer_idx]["RQ"].cpu() - if layer_n in grad_norms_by_layer: - node_gradnorm = grad_norms_by_layer[layer_n] - if node_alignment.shape == node_gradnorm.shape: - stack = torch.stack([node_alignment, node_gradnorm], dim=0) - corr_mat = torch.corrcoef(stack) - correlation_by_layer[layer_n] = corr_mat[0, 1].item() - else: - correlation_by_layer[layer_n] = None - else: - correlation_by_layer[layer_n] = None - results["grad_alignment_corr"].append({ - "epoch": epoch, - "batch": batch_idx, - "net": idx_rep, - "corr": correlation_by_layer - }) - - if do_align and (epoch % freq == 0): - batch_align_data = [] - for net in nets: - layer_metrics = AlignmentMetrics.measure_methods(net, images, methods=methods, precomputed=False) - batch_align_data.append(layer_metrics) - - dist_data = [] - for net_layer_list in batch_align_data: - layer_dists = [] - for layer_dict in net_layer_list: - method_dists = {} - for m, val_tensor in layer_dict.items(): - val_cpu = val_tensor.detach().cpu() - c, e = torch.histogram(val_cpu, bins=bins, density=True) - method_dists[m] = (c, e) - layer_dists.append(method_dists) - dist_data.append(layer_dists) - - exp_data = [] - if measure_expected: - for net in nets: - layer_inps = net.get_layer_inputs(images, precomputed=False) - layer_exp_list = [] - for inp in layer_inps: - if inp.ndim == 4: - inp = inp.flatten(start_dim=1) - w_vals, _ = AlignmentMetrics.compute_eigenvalues(inp) - method_exp = {} - for m in methods: - ccounts, cedges = AlignmentMetrics.measure_expected_distribution(m, w_vals, bins=bins) - method_exp[m] = (ccounts, cedges) - layer_exp_list.append(method_exp) - exp_data.append(layer_exp_list) - - # aggregator logic - if aggregate: - # store each batch separately - results["alignment"].append({ - "epoch": epoch, - "batch": batch_idx, - "data": batch_align_data - }) - results["alignment_distribution"].append({ - "epoch": epoch, - "batch": batch_idx, - "data": dist_data - }) - if measure_expected: - results["expected_distribution"].append({ - "epoch": epoch, - "batch": batch_idx, - "data": exp_data - }) - else: - # store them in memory for now - epoch_align_batches.append(batch_align_data) - epoch_dist_batches.append(dist_data) - epoch_exp_batches.append(exp_data) - - if batch_align_data and batch_align_data[0]: - first_net_first_layer = batch_align_data[0][0] - if "RQ" in first_net_first_layer: - rq_val = first_net_first_layer["RQ"].mean().item() - epoch_rq_values.append(rq_val) - - for idx_rep in range(num_replicates): - if replicate_loss_counts[idx_rep] > 0: - avg_loss = replicate_loss_sums[idx_rep] / replicate_loss_counts[idx_rep] - else: - avg_loss = 0.0 - results["loss"][idx_rep, epoch] = avg_loss - if replicate_acc_counts[idx_rep] > 0: - avg_acc = replicate_acc_sums[idx_rep] / replicate_acc_counts[idx_rep] - else: - avg_acc = 0.0 - results["accuracy"][idx_rep, epoch] = avg_acc - - if do_align and (epoch % freq == 0) and not aggregate: - # we produce a single record for the entire epoch - if epoch_align_batches: - # Flatten or unify them - # We'll combine all batch_align_data into one large net-layers structure - # We do so by concatenating node-level alignment across batches - # Then average node-level alignment - # This can replicate aggregator=True final average - - # shape => list of (#batches) elements, each => [net_i_data], net_i_data => list of layer_dict - # we'll unify them as if we had large data - combined_net_data = [] - for _net_i in range(num_replicates): - combined_net_data.append([]) # each net => layers - - # for each batch => batch_align_data is shape (#nets, #layers) - for batch_align_data in epoch_align_batches: - for net_idx, layer_list in enumerate(batch_align_data): - # layer_list => list of dicts, each => method->Tensor - combined_net_data[net_idx].append(layer_list) - - # now combined_net_data[net_idx] => list of (#batches) layer_list - # we unify them layer by layer, node by node - final_epoch_align_data = [] - for net_idx in range(num_replicates): - # gather all batch-layers => flatten into per-layer accum - all_layers = list(zip(*combined_net_data[net_idx])) - # each element of all_layers => list of dicts from each batch - net_layer_list = [] - for layer_items in all_layers: - # layer_items => each batch a dict like {"RQ": Tensor(...)} - # we unify them across batches => cat their Tensors => average - # for safety, do so for each method - methods_dict = {} - for m in layer_items[0].keys(): - cat_list = [li[m].flatten() for li in layer_items] - big_cat = torch.cat(cat_list, dim=0) - mean_cat = big_cat.view(-1).mean(dim=0, keepdim=False) - # store shape => (some_nodes,) => we store as 1D - # or we can keep as single scalar => depends on usage - # typical aggregator => node-level? we might want to keep node-level means => but here - # we produce a single average => lose node granularity - # If we want node granularity => cat them without mean => but aggregator=False => unify them - # For this example => let's store node-level average if you want - # We'll keep as big_cat => or we do node-level? let's do full node-level cat - # That might produce big memory. We'll do a single average for each node across all batches: - # big_cat => shape (#batches * node_count,) => we can keep it or average => - # aggregator=False => we want final single "RQ" per node? => we'd do stack or cat? - # We'll do node-level average => big_cat is large => We'll keep the same # of nodes as 1 batch - # We can't guess node_count if each batch might have a different # of samples => - # but alignment is node-based, does not depend on samples, only on the node dimension => - # Actually alignment is node-based. We'll produce a single average: - # average across all batches => same shape as layer_items[0][m] - # We do a stack approach => - shapes = [li[m].shape for li in layer_items] - # assume all same shape => we stack - stacked = torch.stack([li[m] for li in layer_items], dim=0) # (#batches, node_count) - mean_per_node = stacked.mean(dim=0) # shape => (node_count,) - methods_dict[m] = mean_per_node - net_layer_list.append(methods_dict) - final_epoch_align_data.append(net_layer_list) - - # final_epoch_align_data => shape (#nets, #layers) - # store as a single record - results["alignment"].append({ - "epoch": epoch, - "batch": "aggregated", - "data": final_epoch_align_data - }) - - # we do the same for distribution & exp_data if needed - # skip for brevity => or replicate same logic - # or produce a single record "alignment_distribution" at epoch - # ignoring for shortness - - # optional distribution code - if epoch_dist_batches: - # we unify them in a simpler approach => just pick last batch or do an average - # for shortness, skip or do your logic - pass - - if measure_expected and epoch_exp_batches: - pass - - mean_loss_ep = float(torch.mean(results["loss"][:, epoch])) - mean_acc_ep = float(torch.mean(results["accuracy"][:, epoch])) - mean_rq = float(np.mean(epoch_rq_values)) if len(epoch_rq_values) > 0 else 0.0 - - if use_wandb and wandb_inited: - import wandb - wandb.log({ - "epoch": epoch, - "train_loss": mean_loss_ep, - "train_acc": mean_acc_ep, - "train_alignment_RQ_epoch": mean_rq - }) - - if do_ckpt and (epoch % ckpt_freq == 0): - cpy_res = deepcopy(results) - cpy_res["epoch"] = epoch - cpy_res["device"] = dev - cpy_res["prms"] = parameters - load_checkpoints(nets, optimizers, dev, None) - save_checkpoint(nets, optimizers, cpy_res, ckpt_path) - - if verbose: - print(f"Epoch {epoch+1}/{num_epochs} => loss={mean_loss_ep:.4f}, acc={mean_acc_ep:.2f}") - - results["loss"] = results["loss"].detach() - results["accuracy"] = results["accuracy"].detach() - return results - - -@test_nets -def test(nets, dataset, **parameters): - """ - A single function for testing/evaluation, possibly measuring alignment too. - Returns a dict with "loss", "accuracy", possibly "alignment" etc. - """ - - do_align = parameters.get("alignment", False) - methods = parameters.get("methods", ["RQ"]) - measure_expected = parameters.get("measure_expected", True) - bins = parameters.get("bins", 50) - train_set = parameters.get("train_set", False) - results = parameters.get("results", {}) - if not isinstance(results, dict): - results = {} - - num_reps = len(nets) - device = dataset.device - loader = dataset.train_loader if train_set else dataset.test_loader - - loss_vec = torch.zeros(num_reps) - acc_vec = torch.zeros(num_reps) - - total_correct = torch.zeros(num_reps, device=device) - total_samples = 0 - total_loss = torch.zeros(num_reps, device=device) - - for batch in loader: - images, labels = dataset.unwrap_batch(batch, device=device) - for idx_rep, net in enumerate(nets): - out = net(images) - loss_val = dataset.measure_loss(out, labels, reduction="sum") - total_loss[idx_rep] += loss_val.detach() - pred = out.argmax(dim=1) - total_correct[idx_rep] += (pred == labels).sum() - total_samples += labels.size(0) - - for idx_rep in range(num_reps): - if total_samples > 0: - loss_vec[idx_rep] = total_loss[idx_rep].cpu().item() / float(total_samples) - acc_vec[idx_rep] = (total_correct[idx_rep].cpu().item() / total_samples) * 100.0 - - results["loss"] = loss_vec - results["accuracy"] = acc_vec - - if do_align: - images, labels = next(iter(loader)) - images, labels = dataset.unwrap_batch((images, labels), device=device) - align_data = [] - dist_data = [] - exp_data = [] - for net in nets: - net.forward(images, store_hidden=True) - metrics = AlignmentMetrics.measure_methods(net, images, methods=methods, precomputed=False) - align_data.append(metrics) - - layer_dists = [] - for layer_dict in metrics: - m_d = {} - for m, val_tensor in layer_dict.items(): - val_cpu = val_tensor.detach().cpu() - c, e = torch.histogram(val_cpu, bins=bins, density=True) - m_d[m] = (c, e) - layer_dists.append(m_d) - dist_data.append(layer_dists) - - if measure_expected: - net_inps = net.get_layer_inputs(images, precomputed=False) - layer_exp_list = [] - for inp in net_inps: - if inp.ndim == 4: - inp = inp.flatten(start_dim=1) - wvals, _ = AlignmentMetrics.compute_eigenvalues(inp) - method_exp = {} - for m in methods: - ccounts, cedges = AlignmentMetrics.measure_expected_distribution(m, wvals, bins=bins) - method_exp[m] = (ccounts, cedges) - layer_exp_list.append(method_exp) - exp_data.append(layer_exp_list) - - results["alignment"] = [{"epoch":"test", "batch":"all", "data": align_data}] - results["alignment_distribution"] = [{"epoch":"test", "batch":"all", "data": dist_data}] - if measure_expected: - results["expected_distribution"] = [{"epoch":"test", "batch":"all", "data": exp_data}] - - return results \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/utils.py b/_arxiv/_archive/alignment_preref/utils.py deleted file mode 100644 index 8d6cbd1f..00000000 --- a/_arxiv/_archive/alignment_preref/utils.py +++ /dev/null @@ -1,631 +0,0 @@ -# -------------------------------------------- -# utils.py -# -------------------------------------------- - -import os -import math -import zipfile -from typing import List -from warnings import warn -from contextlib import contextmanager -from functools import wraps -from natsort import natsorted -from gitignore_parser import parse_gitignore - -import torch -import numpy as np -from scipy.linalg import null_space -from sklearn.decomposition import IncrementalPCA - -@contextmanager -def no_grad(no_grad=True): - if no_grad: - with torch.no_grad(): - yield - else: - yield - -def test_nets(func): - @wraps(func) - def wrapper(nets, *args, **kwargs): - # get original training mode and set to eval - in_training_mode = [set_net_mode(net, training=False) for net in nets] - func_outputs = func(nets, *args, **kwargs) - # return networks to whatever mode they used to be in - for train_mode, net in zip(in_training_mode, nets): - set_net_mode(net, training=train_mode) - return func_outputs - return wrapper - -def train_nets(func): - @wraps(func) - def wrapper(nets, *args, **kwargs): - # get original training mode and set to train - in_training_mode = [set_net_mode(net, training=True) for net in nets] - func_outputs = func(nets, *args, **kwargs) - # return networks to whatever mode they used to be in - for train_mode, net in zip(in_training_mode, nets): - set_net_mode(net, training=train_mode) - return func_outputs - return wrapper - -def set_net_mode(net, training=True): - """ - Helper for toggling train/eval mode of a network. - """ - in_training_mode = net.training - # set to training mode or evaluation mode - if training: - net.train() - else: - net.eval() - # return original mode of network - return in_training_mode - -def get_device(obj): - """ - Returns 'cuda' if the module or tensor is on GPU, else 'cpu'. - """ - if isinstance(obj, torch.nn.Module): - return next(obj.parameters()).device.type - elif isinstance(obj, torch.Tensor): - return "cuda" if obj.is_cuda else "cpu" - else: - raise ValueError("get_device: object must be nn.Module or torch.Tensor") - -def check_iterable(val): - """duck-type check if val is iterable""" - try: - _ = iter(val) - except: - return False - else: - return True - -def remove_by_idx(input, idx, dim): - """ - remove part of input along dimension 'dim' for the indices in 'idx' - """ - idx_keep = [i for i in range(input.size(dim)) if i not in idx] - return torch.index_select(input, dim, torch.tensor(idx_keep).to(input.device)) - -def get_eval_transform_by_cutoff(cutoff): - """ - Return a function that zeros out eigenvalues below a fraction 'cutoff'. - """ - def eval_transform(evals): - assert torch.all(evals >= 0), "found negative eigenvalues, doesn't work for 'cutoff' eval_transform" - evals = evals / torch.sum(evals) - return 1.0 * (evals > cutoff) - return eval_transform - -def fractional_histogram(*args, **kwargs): - """wrapper of np.histogram() with relative counts instead of total or density""" - counts, bins = np.histogram(*args, **kwargs) - counts = counts / np.sum(counts) - return counts, bins - -def edge2center(edges): - """from a list of edges of bins (e.g. for torch.histogram()), return the centers between the edges""" - assert edges.ndim == 1, "edges must be a 1-d array" - return edges[:-1] + np.diff(edges) / 2 - -def smartcorr(input): - """ - Wraps torch.corrcoef but zeros out rows/cols that have zero variance. - """ - idx_zeros = torch.var(input, dim=1) == 0 - cc = torch.corrcoef(input) - cc[idx_zeros, :] = 0 - cc[:, idx_zeros] = 0 - return cc - -def batch_cov(input, centered=True, correction=True): - """ - Batched covariance. - If input.ndim==3, shape is (batch, dim, samples). - If input.ndim==2, shape is (dim, samples). - """ - assert (input.ndim == 2) or (input.ndim == 3), "input must be a 2D or 3D tensor" - assert isinstance(correction, bool), "correction must be bool" - - no_batch = input.ndim == 2 - if no_batch: - input = input.unsqueeze(0) - - S = input.size(2) - if centered: - input = input - input.mean(dim=2, keepdim=True) - bcov = torch.bmm(input, input.transpose(1, 2)) - bcov /= (S - 1.0 * correction) - - if no_batch: - bcov = bcov.squeeze(0) - return bcov - -def smart_pcaOLD(input, centered=True, use_rank=True, correction=True): - """ - smart algorithm for pca optimized for speed - - input should either have shape (batch, dim, samples) or (dim, samples) - if dim > samples, will use svd and if samples < dim will use covariance/eigh method - - will center data when centered=True - - if it fails, will fall back on performing sklearns IncrementalPCA whenever forcetry=True - """ - assert (input.ndim == 2) or (input.ndim == 3), "input should be a matrix or batched matrices" - assert isinstance(correction, bool), "correction should be a boolean" - - if input.ndim == 2: - no_batch = True - input = input.unsqueeze(0) # create batch dimension for uniform code - else: - no_batch = False - - _, D, S = input.size() - if D > S: - # subtract mean if doing centered covariance - if centered: - input = input - input.mean(dim=2, keepdim=True) - # if more dimensions than samples, it's more efficient to run svd - v, w, _ = named_transpose([torch.linalg.svd(inp) for inp in input]) - # convert singular values to eigenvalues - w = [ww**2 / (S - 1.0 * correction) for ww in w] - # append zeros because svd returns w in R**k where k = min(D, S) - w = [torch.concatenate((ww, torch.zeros(D - S))) for ww in w] - - else: - # if more samples than dimensions, it's more efficient to run eigh - bcov = batch_cov(input, centered=centered, correction=correction) - w, v = named_transpose([eigendecomposition(C, use_rank=use_rank) for C in bcov]) - - # return to stacked tensor across batch dimension - w = torch.stack(w) - v = torch.stack(v) - - # if no batch originally provided, squeeze out batch dimension - if no_batch: - w = w.squeeze(0) - v = v.squeeze(0) - - # return eigenvalues and eigenvectors - return w, v - -def smart_pca(input, centered=True, use_rank=True, correction=True): - """ - Efficient PCA using either SVD or eigen-decomposition depending on shape. - Falls back on a known method (like sklearn) if it fails to converge. - - Args: - input (torch.Tensor): shape could be (D, S) or (B, D, S). - - If 2D, treat it as a single (D, S) case (no batch). - - If 3D, treat it as (batch, D, S). - centered (bool): subtract mean along samples dimension if True - use_rank (bool): zero out small eigenvalues beyond the matrix rank - correction (bool): if True, use (S-1) denominator - Returns: - w (torch.Tensor): eigen/singular values, shape is either (D,) or (B, D) - v (torch.Tensor): eigen/singular vectors, shape is either (D, D) or (B, D, D) - """ - # 1) We only handle 2D or 3D - assert input.ndim in (2, 3), "smart_pca: input must be 2D or 3D (D,S or B,D,S)." - assert isinstance(correction, bool), "correction must be bool (True/False)." - - # 2) Convert to batch form if 2D - no_batch = (input.ndim == 2) - if no_batch: - input = input.unsqueeze(0) # shape => (1, D, S) - - B, D, S = input.shape - - # 3) If #dims > #samples, use SVD on (D, S) for each batch - # else use covariance+eigendecomposition - if D > S: - # Possibly center the data - if centered: - mean_ = input.mean(dim=2, keepdim=True) # shape (B, D, 1) - input = input - mean_ - - # We'll collect eigenvalues and eigenvectors for each batch - w_list = [] - v_list = [] - for b in range(B): - inp_ = input[b] # shape (D, S) - # Using SVD => inp_ = U * S_ * V^T - # torch.linalg.svd => (U, S, Vh) - U, Svals, Vh = torch.linalg.svd(inp_, full_matrices=False) - # Convert singular values to eigenvalues - w_ = Svals**2 / (S - (1.0 if correction else 0.0)) - v_ = Vh.transpose(0, 1) # from shape (S, D) => (D, S) - - w_list.append(w_) - v_list.append(v_) - # Stack to shape => (B, D) or (B, S) … but in standard PCA we want dimension = D - w = torch.stack(w_list, dim=0) # shape => (B, k) - v = torch.stack(v_list, dim=0) # shape => (B, D, S) - # If we truly want (B, D, D) we might need to pad or handle the mismatch if D != S - - else: - # Use covariance => (D x D), then eigh - bcov = batch_cov(input, centered=centered, correction=correction) - # bcov shape => (B, D, D) - w_list = [] - v_list = [] - for b in range(B): - C = bcov[b] # shape (D, D) - w_, v_ = eigendecomposition(C, use_rank=use_rank) - w_list.append(w_) - v_list.append(v_) - w = torch.stack(w_list, dim=0) # => shape (B, D) - v = torch.stack(v_list, dim=0) # => shape (B, D, D) - - # 4) If we started with no batch, remove batch dim - if no_batch: - w = w.squeeze(0) # => (D,) - v = v.squeeze(0) # => (D, D) or possibly (D, S) in the SVD branch - - return w, v - -def eigendecomposition(C, use_rank=True): - """ - helper for getting eigenvalues and eigenvectors of covariance matrix - - will measure eigenvalues and eigenvectors with torch.linalg.eigh() - the output will be sorted from highest to lowest eigenvalue (& eigenvector) - - if use_rank=True, will measure the rank of the covariance matrix and zero - out any eigenvalues beyond the rank (that are usually nonzero numerical errors) - """ - try: - w, v = torch.linalg.eigh(C) - except torch._C._LinAlgError as error: - # this happens if the algorithm failed to converge - # try with sklearn's incrementalPCA algorithm - return sklearn_pca(C, use_rank=use_rank) - except Exception as error: - raise error - w_idx = torch.argsort(-w) - w = w[w_idx] - v = v[:, w_idx] - # iff use_rank=True, will set eigenvalues to 0 for probable numerical errors - if use_rank: - crank = torch.linalg.matrix_rank(C) - w[crank:] = 0 - return w, v - -def sklearn_pca(input, use_rank=True, rank=None): - """ - sklearn incrementalPCA algorithm serving as a replacement for eigh when it fails - - input should be a tensor with shape (num_samples, num_features) or it can be a - covariance matrix with (num_features, num_features) - - if use_rank=True, will set num_components to the rank of input and then fill out the - rest of the components with random orthogonal components in the null space of the true - components and set the eigenvalues to 0 - - if use_rank=False, will attempt to fit all the components - if rank is not None, will attempt to fit #=rank components without measuring the rank directly - (will ignore "rank" if use_rank=False) - - returns w, v where w is eigenvalues and v is eigenvectors sorted from highest to lowest - """ - # Move tensor to CPU if it's on GPU - if input.is_cuda: - input = input.cpu() - - num_samples, num_features = input.shape - rank = None if not use_rank else (rank if rank is not None else fast_rank(input)) - ipca = IncrementalPCA(n_components=rank).fit(input) - v = ipca.components_ - w = ipca.singular_values_**2 / num_samples - # if v is a subspace of input (e.g. not a full basis, fill it out) - if v.shape[0] < num_features: - msg = "this condition should always be true, and if not we have to find out why" - assert w.shape[0] == v.shape[0], msg - v_kernel = null_space(v).T - v = np.vstack((v, v_kernel)) - w = np.concatenate((w, np.zeros(v_kernel.shape[0]))) - return torch.tensor(w, dtype=torch.float), torch.tensor(v, dtype=torch.float).T - -def fast_rank(input): - """uses transpose to speed up rank computation, otherwise normal""" - # Move tensor to CPU if it's on GPU - if input.is_cuda: - input = input.cpu() - - if input.size(-2) < input.size(-1): - input = torch.transpose(input, -2, -1) - return int(torch.linalg.matrix_rank(input)) - -def get_maximum_strides(h_input, w_input, layer): - """ - Helper for computing the number of strides h_max, w_max after - convolution with given kernel/stride/padding/dilation. - """ - h_max = int(np.floor((h_input + 2 * layer.padding[0] - layer.dilation[0] * (layer.kernel_size[0] - 1) - 1) / layer.stride[0] + 1)) - w_max = int(np.floor((w_input + 2 * layer.padding[1] - layer.dilation[1] * (layer.kernel_size[1] - 1) - 1) / layer.stride[1] + 1)) - return h_max, w_max - -def get_unfold_params(layer): - return dict(stride=layer.stride, padding=layer.padding, dilation=layer.dilation) - -@torch.no_grad() -def cvPCA(X1, X2): - """X1, X2 are both (dimensions x samples)""" - D, B = X1.shape - assert X2.shape == (D, B), "shape mismatch" - _, u = smart_pca(X1) - cproj0 = X1.T @ u - cproj1 = X2.T @ u - ss = (cproj0 * cproj1).mean(axis=0) - return ss - -def get_num_components(nc, shape): - return nc if nc is not None else min(shape) - -@torch.no_grad() -def shuff_cvPCA(X1, X2, nshuff=5, cvmethod=cvPCA): - D, B = X1.shape - assert X2.shape == (D, B), "shape mismatch" - nc = get_num_components(None, (D, B)) - ss = torch.zeros((nshuff, nc)) - X = torch.stack((X1, X2)) - for k in range(nshuff): - iflip = 1 * (torch.rand(B) > 0.5) - X1c = torch.gather(X, 0, iflip.view(1, 1, -1).expand(1, D, -1)).squeeze(0) - X2c = torch.gather(X, 0, -(iflip - 1).view(1, 1, -1).expand(1, D, -1)).squeeze(0) - ss[k] = cvmethod(X1c, X2c) - return ss - -def avg_value_by_layer(full): - """ - Return average value per layer across a list of epochs or minibatches. - - **full** is a list of lists where the outer list is each snapshot through training or - minibatch etc and each inner list is the value for each node in the network across layers - of a particular measurement - - For example: - num_epochs = 1000 - nodes_per_layer = [50, 40, 30, 20] - len(full) == 1000 - len(full[i]) == 4 ... for all i - [f.shape for f in full[i]] = [50, 40, 30, 20] ... for all i - - this method will return a tensor of size (num_layers, num_epochs) of the average value (for - whatever value is in **full**) for each list/list of values in **full** - """ - num_epochs = len(full) - num_layers = len(full[0]) - avg_full = torch.zeros((num_layers, num_epochs)) - for layer in range(num_layers): - avg_full[layer, :] = torch.tensor([torch.mean(f[layer]) for f in full]) - return avg_full.cpu() - -def value_by_layer(full: List[List[torch.Tensor]], layer: int) -> torch.Tensor: - """ - return all value measurements for a particular layer from **full** - - **full** is a list of lists where the outer list is each snapshot through training or - minibatch etc and each inner list is the value for each node in the network across layers - - this method will return just the part of **full** corresponding to the layer indexed - by **layer** as a tensor of shape (num_epochs, num_nodes) - - see ``avg_value_by_layer`` for a little more explanation - """ - return torch.cat([f[layer].view(1, -1) for f in full], dim=0).cpu() - -def condense_values(full: List[List[List[torch.Tensor]]]) -> List[torch.Tensor]: - """ - condense List[List[List[Tensor]]] -> list of #=num_layers Tensors - shape: (num_networks, num_batches, num_nodes_per_layer) - - returns list of #=num_layers tensors, where each tensor has shape (num_networks, num_batches, num_nodes_per_layer) - - full should be a list of list of lists - the first list should have length = number of networks - the second list should have length = number of batches - the third list should have length = number of layers in the network (this has to be the same for each network!) - the tensor should have shape = number of nodes in this layer (also must be the same for each network) (or can be anything as long as consistent across layers) - """ - num_layers = len(full[0][0]) - return [torch.stack([value_by_layer(value, layer) for value in full]) for layer in range(num_layers)] - -def transpose_list(list_of_lists): - """helper function for transposing the order of a list of lists""" - return list(map(list, zip(*list_of_lists))) - -def named_transpose(list_of_lists, reduction=None): - """ - helper function for transposing lists without forcing the output to be a list like transpose_list - - for example, if list_of_lists contains 10 copies of lists that each have 3 iterable elements you - want to name "A", "B", and "C", then write: - A, B, C = named_transpose(list_of_lists) - - if reduction is used, it will be applied to each output, otherwise will make them lists - """ - if reduction is not None: - return map(reduction, zip(*list_of_lists)) - return map(list, zip(*list_of_lists)) - -def ptp(tensor, dim=None, keepdim=False): - """ - simple method for measuring range of tensor on requested dimension or on all data - """ - if dim is None: - return tensor.max() - tensor.min() - return tensor.max(dim, keepdim).values - tensor.min(dim, keepdim).values - -def rms(tensor, dim=None, keepdim=False): - """simple method for measuring root-mean-square on requested dimension or on all data in tensor""" - if dim is None: - return torch.sqrt(torch.mean(tensor**2)) - return torch.sqrt(torch.mean(tensor**2, dim=dim, keepdim=keepdim)) - -def compute_stats_by_type(tensor, num_types, dim, method="var"): - """ - helper method for returning the mean and variance across a certain dimension - where multiple types are concatenated on that dimension - - for example, suppose we trained 2 networks each with 3 sets of parameters - and concatenated the loss in a tensor like [set1-loss-net1, set1-loss-net2, set2-loss-net1, ...] - then this would contract across the nets from each set and return the mean and variance - """ - num_on_dim = tensor.size(dim) - num_per_type = int(num_on_dim / num_types) - tensor_by_type = tensor.unsqueeze(dim) - expand_shape = list(tensor_by_type.shape) - expand_shape[dim + 1] = num_per_type - expand_shape[dim] = num_types - tensor_by_type = tensor_by_type.view(expand_shape) - type_means = torch.mean(tensor_by_type, dim=dim + 1) - if method == "var": - type_dev = torch.var(tensor_by_type, dim=dim + 1) - elif method == "std": - type_dev = torch.std(tensor_by_type, dim=dim + 1) - elif method == "se": - type_dev = torch.std(tensor_by_type, dim=dim + 1) / np.sqrt(num_per_type) - elif method == "range": - type_dev = ptp(tensor_by_type, dim=dim + 1) - else: - raise ValueError(f"Method ({method}) not recognized.") - return type_means, type_dev - - -def weighted_average(data, weights, dim, keepdim=False, ignore_nan=False): - """ - take the weighted average of **data** on a certain dimension with **weights** - - weights should be a nonnegative vector that broadcasts into data - avg = sum_i(data_i * weight_i, dim) / sum_i(weight_i, dim) - - if ignore_nan=True, (default=False), will ignore nans in weighted average - """ - assert data.ndim == weights.ndim, "data and weights must have same number of dimensions" - assert torch.all(weights[~torch.isnan(weights)] >= 0), "weights must be nonnegative" - - for d in dim if check_iterable(dim) else [dim]: - assert data.size(d) == weights.size( - d - ), f"data and weights must have same size in averaging dimensions (data.size({d})={data.size(d)}, (weight.size({d})={weights.size(d)}))" - - # use normal sum if not ignore nan, otherwise use nansum - sum = torch.nansum if ignore_nan else torch.sum - - # make sure nans are in the same place in weights and data for accurate division by total weight - if ignore_nan: - weights = weights.expand(data.size()) - weights = torch.masked_fill(weights, torch.isnan(data), torch.nan) - - # numerator & denominator of weighted average - numerator = sum(data * weights, dim=dim, keepdim=keepdim) - denominator = sum(weights, dim=dim, keepdim=keepdim) - - # return weighted average - return numerator / denominator - - -def fgsm_attack(image, epsilon, data_grad, transform, sign): - """update an image with fast-gradient sign method""" - warn("fgsm_attack is only going to be in utils temporarily!", DeprecationWarning, stacklevel=2) - # Collect the element-wise sign of the data gradient - if sign: - data_grad = data_grad.sign() - else: - data_grad = data_grad.clone() - # Create the perturbed image by adjusting each pixel of the input image - perturbed_image = image + epsilon * data_grad - # Adding clipping to maintain [0,1] range - perturbed_image = transform(perturbed_image) - # Return the perturbed image - return perturbed_image - -def str2bool(str): - if isinstance(str, bool): - return str - if str.lower() in ("true", "1"): - return True - elif str.lower() in ("false", "0"): - return False - else: - raise TypeError("Boolean type expected") - -def save_checkpoint(nets, optimizers, results, path): - """ - Method for saving checkpoints for networks throughout training. - """ - multi_model_ckpt = {f"model_state_dict_{i}": net.state_dict() for i, net in enumerate(nets)} - multi_optimizer_ckpt = {f"optimizer_state_dict_{i}": opt.state_dict() for i, opt in enumerate(optimizers)} - checkpoint = results | multi_model_ckpt | multi_optimizer_ckpt - torch.save(checkpoint, path) - -def load_checkpoints(nets, optimizers, device, path): - """ - Method for loading presaved checkpoint during training. - """ - if device == "cpu": - checkpoint = torch.load(path, map_location=device) - elif device == "cuda": - checkpoint = torch.load(path) - - net_ids = natsorted([key for key in checkpoint if key.startswith("model_state_dict")]) - opt_ids = natsorted([key for key in checkpoint if key.startswith("optimizer_state_dict")]) - assert all( - [oi.split("_")[-1] == ni.split("_")[-1] for oi, ni in zip(opt_ids, net_ids)] - ), "nets and optimizers cannot be matched up from checkpoint" - - [net.load_state_dict(checkpoint.pop(net_id)) for net, net_id in zip(nets, net_ids)] - [opt.load_state_dict(checkpoint.pop(opt_id)) for opt, opt_id in zip(optimizers, opt_ids)] - - if device == "cuda": - [net.to(device) for net in nets] - return nets, optimizers, checkpoint - -def match_git(path): - """simple method for determining if a path is a git-related file or directory""" - if ".git" in path: - return True - return False - -def compress_directory(output_path, directory_path=None): - """ - Utility to compress entire directory into a zip while respecting .gitignore and ignoring .git. - """ - if directory_path is None: - directory_path = os.path.dirname(os.path.abspath(__file__)) + "/../.." - gitignore_path = os.path.join(directory_path, ".gitignore") - matches = parse_gitignore(gitignore_path) - - files_to_copy = [] - archive_names = [] - for dirpath, dirnames, files in os.walk(directory_path): - if matches(dirpath) or match_git(dirpath): - dirnames[:] = [] - else: - # Filter files based on .gitignore rules (and don't save any .git files) - keep_files = [f for f in files if not matches(f) and not match_git(f)] - full_files = [os.path.join(dirpath, f) for f in keep_files] - for file in full_files: - files_to_copy.append(file) - archive_names.append(os.path.relpath(file, directory_path)) - - with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zipf: - for file, name in zip(files_to_copy, archive_names): - zipf.write(file, arcname=name) - - -def condense_values(al_list): - data_list = [item["data"] for item in al_list] - aggregated = [] - for net_i in range(len(data_list[0])): - net_snapshots = [d[net_i] for d in data_list] - net_layers = [] - for layer_i in range(len(net_snapshots[0])): - layer_values = [snap[layer_i] for snap in net_snapshots] - net_layers.append(torch.stack(layer_values, dim=0).mean(dim=0)) - aggregated.append(net_layers) - return aggregated \ No newline at end of file diff --git a/_arxiv/_archive/alignment_preref/utils_rem.py b/_arxiv/_archive/alignment_preref/utils_rem.py deleted file mode 100644 index e933d1e2..00000000 --- a/_arxiv/_archive/alignment_preref/utils_rem.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Utilities for alignment research. - -DEPRECATED: This module is kept for backward compatibility. -Please use alignment.utils.core and alignment.utils.math instead. - -This module contains various utility functions for the alignment package. -""" - -import warnings -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -# Import from the new modules for re-export -from alignment.utils.core import ( - setup_logging, - timer, - debug, - to_numpy, - to_tensor, - check_iterable, - ensure_device, - timed -) - -from alignment.utils.math import ( - orthogonalize, - compute_correlation_matrix, - matrix_angles, - project_to_subspace -) - -# Show deprecation warning on import -warnings.warn( - "The alignment.utils module is deprecated and will be removed in a future version. " - "Please use alignment.utils.core and alignment.utils.math instead.", - DeprecationWarning, - stacklevel=2 -) - -# Re-export everything from the imported modules -__all__ = [ - # Core utilities - 'setup_logging', - 'timer', - 'debug', - 'to_numpy', - 'to_tensor', - 'check_iterable', - 'ensure_device', - 'timed', - - # Math utilities - 'orthogonalize', - 'compute_correlation_matrix', - 'matrix_angles', - 'project_to_subspace' -] \ No newline at end of file diff --git a/_arxiv/_archive/alignment_stats.py b/_arxiv/_archive/alignment_stats.py deleted file mode 100644 index 1f01906c..00000000 --- a/_arxiv/_archive/alignment_stats.py +++ /dev/null @@ -1,97 +0,0 @@ -import torch - - -from alignment_v2.datasets import get_dataset -from alignment_v2.models.registry import get_model, get_transform_parameters -from alignment_v2 import processing -from alignment_v2.config import ExperimentConfig -from alignment_v2.experiments.experiment import Experiment -from alignment_v2 import plotting - -class AlignmentStatistics(Experiment): - def get_basename(self): - return "alignment_stats" - - def prepare_path(self): - return [self.args.model.name, self.args.dataset.name, self.args.optimizer.name] - - def create_networks(self): - """ - method for creating networks - """ - if self.args.optimizer.name == "Adam": - optim = torch.optim.Adam - elif self.args.optimizer.name == "SGD": - optim = torch.optim.SGD - else: - raise ValueError(f"optimizer ({self.args.optimizer.name}) not recognized") - - nets = [ - get_model( - self.args.model.name, - alignment_layer_names=self.args.model.alignment_layers, - build=True, - dataset=self.args.dataset.name, - dropout=self.args.model.dropout, - ) - for _ in range(self.args.training.replicates) - ] - nets = [net.to(self.device) for net in nets] - - optimizers = [optim(net.parameters(), lr=self.args.optimizer.lr, weight_decay=self.args.optimizer.weight_decay) for net in nets] - - prms = { - "vals": [self.args.model.name], - "name": "network", - "dataset": self.args.dataset.name, - "dropout": self.args.model.dropout, - "lr": self.args.optimizer.lr, - "weight_decay": self.args.optimizer.weight_decay, - } - return nets, optimizers, prms - - def main(self): - nets, optimizers, prms = self.create_networks() - - dataset = self.prepare_dataset(get_transform_parameters(self.args.model.name, self.args.dataset.name)) - - train_results, test_results = processing.train_networks(self, nets, optimizers, dataset) - - dropout_results, dropout_parameters = processing.progressive_dropout_experiment( - self, nets, dataset, alignment=test_results.get("alignment", None), train_set=False - ) - - eigen_results = processing.measure_eigenfeatures(self, nets, dataset, train_set=False) - - evec_dropout_results, evec_dropout_parameters = processing.eigenvector_dropout(self, nets, dataset, eigen_results, train_set=False) - - results = dict( - prms=prms, - train_results=train_results, - test_results=test_results, - dropout_results=dropout_results, - dropout_parameters=dropout_parameters, - eigen_results=eigen_results, - evec_dropout_results=evec_dropout_results, - evec_dropout_parameters=evec_dropout_parameters, - ) - - return results, nets - - def plot(self, results): - plotting.plot_train_results(self, results["train_results"], results["test_results"], results["prms"]) - plotting.plot_dropout_results( - self, - results["dropout_results"], - results["dropout_parameters"], - results["prms"], - dropout_type="nodes", - ) - plotting.plot_eigenfeatures(self, results["eigen_results"], results["prms"]) - plotting.plot_dropout_results( - self, - results["evec_dropout_results"], - results["evec_dropout_parameters"], - results["prms"], - dropout_type="eigenvectors", - ) \ No newline at end of file diff --git a/_arxiv/_archive/alignment_v2/__init__.py b/_arxiv/_archive/alignment_v2/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/_arxiv/_archive/alignment_v2/datasets.py b/_arxiv/_archive/alignment_v2/datasets.py deleted file mode 100644 index 5f57be31..00000000 --- a/_arxiv/_archive/alignment_v2/datasets.py +++ /dev/null @@ -1,322 +0,0 @@ -from warnings import warn -from abc import ABC, abstractmethod - -import torch -import torchvision -from torch import nn -from torch.utils.data.distributed import DistributedSampler -from torchvision.transforms import v2 as transforms - -from alignment_v2 import files -from alignment_v2.models.base import AlignmentNetwork - -REQUIRED_PROPERTIES = ["dataset_path", "dataset_constructor", "loss_function"] - - -def default_loader_parameters( - distributed, - batch_size=1024*2, - num_workers=2, - shuffle=True, - pin_memory=True, - persistent_workers=True, -): - """ - contains the default dataloader parameters with the option of updating them - using key word argument - """ - default_parameters = dict( - batch_size=batch_size, - num_workers=num_workers, # usually 2 workers is appropriate for swapping loading during batch processing - shuffle=False if distributed else shuffle, # can't use shuffle=True if using DDP - pin_memory=pin_memory, - persistent_workers=persistent_workers, - ) - return default_parameters - - -class DataSet(ABC): - def __init__( - self, - device=None, - distributed=False, - dataset_parameters={}, - transform_parameters={}, - loader_parameters={}, - ): - # set properties of dataset and check that all required properties are defined - self.set_properties() - self.check_properties() - - # define device for dataloading - self.device = device if device is not None else ("cuda" if torch.cuda.is_available() else "cpu") - self.distributed = distributed - - # define extra transform (should be a callable method or None) for any transformations that - # can't go in the torchvision.transforms.Compose(...), hopefully this won't be needed later - # when that issue is resolved (grayscale to RGB transform isn't working in Compose right now) - self.extra_transform = transform_parameters.pop("extra_transform", None) - - # create transform for dataloader - self.transform_parameters = transform_parameters - self.make_transform(**transform_parameters) - - # define the dataloader parameters - self.dataloader_parameters = default_loader_parameters(distributed, **loader_parameters) # get dataloader parameters - - # load the dataset and create the dataloaders - self.dataset_parameters = dataset_parameters - self.load_dataset(**dataset_parameters) - - def check_properties(self): - """ - DataSet objects have a few properties that are required but need to be set - by the children. For simplicity, I want the properties to be attributes, - instead of @property methods, but I want to make sure that all the required - properties are loaded. Hence this method. - """ - if not all([hasattr(self, prop) for prop in REQUIRED_PROPERTIES]): - not_found = [prop for prop in REQUIRED_PROPERTIES if not hasattr(self, prop)] - raise ValueError(f"The following required properties were not set: {not_found}") - - @abstractmethod - def set_properties(self): - """ - DataSets have a few required properties (listed in the **REQUIRED_PROPERTIES** - global variable). This method is used to set them all. There is a check implemented - in the __init__ method to make sure all required properties are set. - - required - -------- - dataset_path: string, defines the local file location containing the relevant dataset - files. in particular, whatever filepath is returned by this method will - be passed into the "root" input for torch datasets that is required to - load one of the standard datasets (like MNIST, CIFAR, ImageNet...) - - dataset_constructor: callable method, defines the constructor object for the dataset - loss_function: callable method, defines how to evaluate the loss of the output and target - """ - pass - - @abstractmethod - def dataset_kwargs(self, train=True, **kwargs): - """ - keyword arguments passed into the torch dataset constructor - - different datasets have different kwarg requirements, including - the way train vs test is defined by the kwargs. This class calls - the train dataset and the test dataset using dataset_kwargs(train=True) - or train=False, so the children need to define whatever kwargs go - into the dataset_kwargs appropriately to be determined by the kwarg - train - """ - pass - - def load_dataset(self, **kwargs): - """load dataset using the established path and parameters""" - self.train_dataset = self.dataset_constructor(**self.dataset_kwargs(train=True, **kwargs)) - self.test_dataset = self.dataset_constructor(**self.dataset_kwargs(train=False, **kwargs)) - self.train_sampler = DistributedSampler(self.train_dataset) if self.distributed else None - self.test_sampler = DistributedSampler(self.test_dataset) if self.distributed else None - self.train_loader = torch.utils.data.DataLoader(self.train_dataset, sampler=self.train_sampler, **self.dataloader_parameters) - self.test_loader = torch.utils.data.DataLoader(self.test_dataset, sampler=self.test_sampler, **self.dataloader_parameters) - - def unwrap_batch(self, batch, device=None): - """simple method for unwrapping batch for simple training loops""" - device = self.device if device is None else device - if self.extra_transform: - if type(self.extra_transform) == list: - for et in self.extra_transform: - batch = et(batch) - else: - warn("extra_transform is not a list, this is deprecated!", DeprecationWarning, stacklevel=2) - batch = self.extra_transform(batch) - inputs, targets = batch - inputs, targets = inputs.to(device), targets.to(device) - return inputs, targets - - def make_transform(self, center_crop=None, resize=None, flatten=False, out_channels=None): - """ - create transform for dataloader - resize is the new (H, W) shape of the image for the transforms.Resize transform (or None) - flatten is a boolean indicating whether to flatten the image, (i.e. for a linear input layer) - """ - # default transforms - use_transforms = [ - # Convert PIL Image to PyTorch Tensor - transforms.ToImage(), - transforms.ToDtype(torch.float32, scale=True), - ] - - if center_crop: - use_transforms.append(transforms.CenterCrop(center_crop)) - - # Normalize inputs to canonical distribution - use_transforms.append(transforms.Normalize((self.dist_params["mean"]), (self.dist_params["std"]))) - - # extra transforms depending on network - if resize: - use_transforms.append(transforms.Resize(resize, antialias=True)) - if out_channels: - use_transforms.append(transforms.Grayscale(num_output_channels=out_channels)) - if flatten: - use_transforms.append(transforms.Lambda(torch.flatten)) - - # store composed transformation - self.transform = transforms.Compose(use_transforms) - - def measure_loss(self, outputs, targets, reduction=None): - """simple method for measuring loss with stored loss function""" - if reduction is None: - return self.loss_function(outputs, targets) - - standard_reduction = self.loss_function.reduction - self.loss_function.reduction = reduction - loss = self.loss_function(outputs, targets) - self.loss_function.reduction = standard_reduction - return loss - - def measure_accuracy(self, outputs, targets, k=1, percentage=True): - """ - simple method for measuring accuracy on a classification problem - - default output is top1 percentage, but k can set the top-k accuracy - and if percentage=False then returns the number correct (by topk) - """ - topk = outputs.topk(k, dim=1, sorted=True, largest=True)[1] # get topk indices - num_correct = torch.sum(torch.any(topk == targets.view(-1, 1), dim=1)) # num correct - if percentage: - return 100 * num_correct / outputs.size(0) # percentage - else: - return num_correct - - -class MNIST(DataSet): - def set_properties(self): - """defines the required properties for MNIST""" - self.dataset_path = files.dataset_path("MNIST") - self.dataset_constructor = torchvision.datasets.MNIST - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.1307], std=[0.3081]) - - def dataset_kwargs(self, train=True, download=False): - """set data constructor kwargs for MNIST""" - kwargs = dict( - train=train, - root=self.dataset_path, - download=download, - transform=self.transform, - ) - return kwargs - - -class CIFAR10(DataSet): - def set_properties(self): - """defines the required properties for CIFAR10""" - self.dataset_path = files.dataset_path("CIFAR10") - self.dataset_constructor = torchvision.datasets.CIFAR10 - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - def dataset_kwargs(self, train=True, download=False): - """set data constructor kwargs for CIFAR10""" - kwargs = dict( - train=train, - root=self.dataset_path, - download=download, - transform=self.transform, - ) - return kwargs - - -class CIFAR100(CIFAR10): - def set_properties(self): - """defines the required properties for CIFAR100""" - self.dataset_path = files.dataset_path("CIFAR100") - self.dataset_constructor = torchvision.datasets.CIFAR100 - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - -class ImageNet2012(DataSet): - def set_properties(self): - """ - defines the required properties for ImageNet 2012 (ILSVRC2012) with - 1000 classes. - preprocessing according to pytorch documentation: - https://pytorch.org/hub/pytorch_vision_alexnet/ - """ - self.dataset_path = files.dataset_path("ImageNet") - self.dataset_constructor = torchvision.datasets.ImageNet - self.loss_function = nn.CrossEntropyLoss() - self.dist_params = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - self.center_crop = 224 - - def dataset_kwargs(self, train=True): - """set data constructor kwargs for ImageNet2012""" - kwargs = dict( - split="train" if train else "val", - root=self.dataset_path, - transform=self.transform, - ) - return kwargs - - -DATASET_REGISTRY = { - "MNIST": MNIST, - "CIFAR10": CIFAR10, - "CIFAR100": CIFAR100, - "ImageNet": ImageNet2012, -} - - -def get_dataset( - dataset_name, - build=False, - dataset_parameters={}, - transform_parameters={}, - loader_parameters={}, - **kwargs, -): - """ - lookup dataset constructor from dataset registry by name - - if build=True, uses kwargs to build dataset and returns a dataset object - otherwise just returns the constructor - """ - if dataset_name not in DATASET_REGISTRY: - raise ValueError(f"Dataset ({dataset_name}) is not in DATASET_REGISTRY") - dataset = DATASET_REGISTRY[dataset_name] - if build: - if isinstance(transform_parameters, AlignmentNetwork): - # Can use an AlignmentNetwork instance to automatically retrieve transform parameters - transform_parameters = transform_parameters.get_transform_parameters(dataset_name) - else: - if not isinstance(transform_parameters, dict): - raise TypeError("transform_parameters must be a dictionary or an AlignmentNetwork") - - # Build the dataset - return dataset( - dataset_parameters=dataset_parameters, - transform_parameters=transform_parameters, - loader_parameters=loader_parameters, - **kwargs, - ) - - # Otherwise return the constructor - return dataset - - -if __name__ == "__main__": - """simple program for downloading a dataset""" - - from argparse import ArgumentParser - - def get_args(args=None): - parser = ArgumentParser(description="simple program for downloading a dataset to the local file location") - parser.add_argument("--dataset", type=str, default="MNIST") - return parser.parse_args(args=args) - - args = get_args() - - dataset = get_dataset(args.dataset, build=True, dataset_parameters=dict(download=True)) diff --git a/_arxiv/_archive/alignment_v2/files.py b/_arxiv/_archive/alignment_v2/files.py deleted file mode 100644 index 4aa4216c..00000000 --- a/_arxiv/_archive/alignment_v2/files.py +++ /dev/null @@ -1,55 +0,0 @@ -import getpass -import socket -from pathlib import Path - -PATH_REGISTRY = { - "DESKTOP-M2J64J2": Path("C:/Users/andrew/Documents/machineLearning"), - "Celia": Path("/Users/celiaberon/Documents/machine_learning"), - "cberon": Path("/n/home00/cberon/alignment/"), - "corwin": Path("/Users/corwin/Building"), - "atlandau": Path("/n/home05/atlandau/machine_learning"), - "landauland": Path("/Users/landauland/Documents/ML-Datasets"), - "nkhoshnevis": Path("/n/vast-scratch/kempner_dev/nkhoshnevis/projects/alignment_dir"), - "hsafaai": Path("/n/holylabs/LABS/kempner_dev/Users/hsafaai/datasets"), -} - - -def get_hostname(): - return socket.gethostname() - - -def get_username(): - return getpass.getuser() - - -def local_path(): - """method for defining the local root path for datasets and results""" - hostname = get_hostname() - if hostname.lower().startswith("celia"): - hostname = "Celia" - hostname = hostname if hostname in PATH_REGISTRY else get_username() - if hostname not in PATH_REGISTRY: - raise ValueError(f"hostname ({hostname}) is not registered in the path registry") - # return path - return PATH_REGISTRY[hostname] - - -def results_path(): - """method for returning relative path to results generated by this package""" - return local_path() / "results" - - -def data_path(): - """method for returning the relative path containing datasets""" - return local_path() / "datasets" - - -def dataset_path(dataset): - """path to specific stored datasets (they all have different requirements)""" - if dataset == "MNIST": - return data_path() - elif dataset == "CIFAR10" or dataset == "CIFAR100": - return data_path() - elif dataset == "ImageNet": - return Path("/n/holylfs06/LABS/kempner_shared/Lab/data/imagenet_1k/") - diff --git a/_arxiv/_archive/alignment_v2/plotting.py b/_arxiv/_archive/alignment_v2/plotting.py deleted file mode 100644 index c9c768e6..00000000 --- a/_arxiv/_archive/alignment_v2/plotting.py +++ /dev/null @@ -1,565 +0,0 @@ -import numpy as np -from tqdm import tqdm -from matplotlib import pyplot as plt -import matplotlib as mpl - -import torch -from alignment_v2.utils import compute_stats_by_type, named_transpose, transpose_list, rms - - -def plot_train_results(exp, train_results, test_results, prms): - """ - plotting method for training trajectories and testing data - """ - - num_train_epochs = train_results["loss"].size(0) - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - - print("getting statistics on run data...") - plot_alignment = "alignment" in train_results - if plot_alignment: - alignment = torch.stack([torch.mean(align, dim=2) for align in train_results["alignment"]]) - - cmap = mpl.colormaps["tab10"] - - train_loss_mean, train_loss_se = compute_stats_by_type(train_results["loss"], num_types=num_types, dim=1, method="se") - train_acc_mean, train_acc_se = compute_stats_by_type(train_results["accuracy"], num_types=num_types, dim=1, method="se") - - if plot_alignment: - align_mean, align_se = compute_stats_by_type(alignment, num_types=num_types, dim=1, method="se") - - test_loss_mean, test_loss_se = compute_stats_by_type(torch.tensor(test_results["loss"]), num_types=num_types, dim=0, method="se") - test_acc_mean, test_acc_se = compute_stats_by_type(torch.tensor(test_results["accuracy"]), num_types=num_types, dim=0, method="se") - - print("plotting run data...") - xOffset = [-0.2, 0.2] - get_x = lambda idx: [xOffset[0] + idx, xOffset[1] + idx] - - # Make Training and Testing Performance Figure - alpha = 0.3 - figdim = 3 - figratio = 2 - width_ratios = [figdim, figdim / figratio, figdim, figdim / figratio] - - fig, ax = plt.subplots(1, 4, figsize=(sum(width_ratios), figdim), width_ratios=width_ratios, layout="constrained") - - # plot loss results fot training and testing - for idx, label in enumerate(labels): - cmn = train_loss_mean[:, idx] - cse = train_loss_se[:, idx] - tmn = test_loss_mean[idx] - tse = test_loss_se[idx] - - ax[0].plot(range(num_train_epochs), cmn, color=cmap(idx), label=label) - ax[0].fill_between(range(num_train_epochs), cmn + cse, cmn - cse, color=(cmap(idx), alpha)) - ax[1].plot(get_x(idx), [tmn] * 2, color=cmap(idx), label=label, lw=4) - ax[1].plot([idx, idx], [tmn - tse, tmn + tse], color=cmap(idx), lw=1.5) - - ax[0].set_xlabel("Training Epoch") - ax[0].set_ylabel("Loss") - ax[0].set_title("Training Loss") - ax[0].set_ylim(0, None) - ylims = ax[0].get_ylim() - ax[1].set_xticks(range(num_types)) - ax[1].set_xticklabels(labels, rotation=45, ha="right", fontsize=8) - ax[1].set_ylabel("Loss") - ax[1].set_title("Testing") - ax[1].set_xlim(-0.5, num_types - 0.5) - ax[1].set_ylim(ylims) - - # plot loss results fot training and testing - for idx, label in enumerate(labels): - cmn = train_acc_mean[:, idx] - cse = train_acc_se[:, idx] - tmn = test_acc_mean[idx] - tse = test_acc_se[idx] - - ax[2].plot(range(num_train_epochs), cmn, color=cmap(idx), label=label) - ax[2].fill_between(range(num_train_epochs), cmn + cse, cmn - cse, color=(cmap(idx), alpha)) - ax[3].plot(get_x(idx), [tmn] * 2, color=cmap(idx), label=label, lw=4) - ax[3].plot([idx, idx], [tmn - tse, tmn + tse], color=cmap(idx), lw=1.5) - - ax[2].set_xlabel("Training Epoch") - ax[2].set_ylabel("Accuracy (%)") - ax[2].set_title("Training Accuracy") - ax[2].set_ylim(0, 100) - ax[3].set_xticks(range(num_types)) - ax[3].set_xticklabels(labels, rotation=45, ha="right", fontsize=8) - ax[3].set_ylabel("Accuracy (%)") - ax[3].set_title("Testing") - ax[3].set_xlim(-0.5, num_types - 0.5) - ax[3].set_ylim(0, 100) - - exp.plot_ready("train_test_performance") - - # Make Alignment Figure - if plot_alignment: - num_align_epochs = align_mean.size(2) - num_layers = align_mean.size(0) - fig, ax = plt.subplots(1, num_layers, figsize=(num_layers * figdim, figdim), layout="constrained", sharex=True) - for idx, label in enumerate(labels): - for layer in range(num_layers): - cmn = align_mean[layer, idx] * 100 - cse = align_se[layer, idx] * 100 - ax[layer].plot(range(num_align_epochs), cmn, color=cmap(idx), label=label) - ax[layer].fill_between(range(num_align_epochs), cmn + cse, cmn - cse, color=(cmap(idx), alpha)) - - for layer in range(num_layers): - ax[layer].set_ylim(0, None) - ax[layer].set_xlabel("Training Epoch") - ax[layer].set_ylabel("Alignment (%)") - ax[layer].set_title(f"Layer {layer}") - - ax[0].legend(loc="lower right") - - exp.plot_ready("train_alignment_by_layer") - - -def plot_dropout_results(exp, dropout_results, dropout_parameters, prms, dropout_type="nodes"): - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val} - dropout {dropout_type}" for val in prms["vals"]] - cmap = mpl.colormaps["Set1"] - alpha = 0.3 - msize = 10 - figdim = 3 - - num_layers = dropout_results["progdrop_loss_high"].size(2) - names = ["From high", "From low", "Random"] - names_diff = ["high-low"] - num_exp = len(names) - dropout_fraction = dropout_results["dropout_fraction"] - by_layer = dropout_results["by_layer"] - extra_name = "by_layer" if by_layer else "all_layers" - extra_name += dropout_type - - # Get statistics across each network type for progressive dropout experiment - print("measuring statistics on dropout analysis...") - loss_mean_high, loss_se_high = compute_stats_by_type(dropout_results["progdrop_loss_high"], num_types=num_types, dim=0, method="se") - loss_mean_low, loss_se_low = compute_stats_by_type(dropout_results["progdrop_loss_low"], num_types=num_types, dim=0, method="se") - loss_mean_rand, loss_se_rand = compute_stats_by_type(dropout_results["progdrop_loss_rand"], num_types=num_types, dim=0, method="se") - - acc_mean_high, acc_se_high = compute_stats_by_type(dropout_results["progdrop_acc_high"], num_types=num_types, dim=0, method="se") - acc_mean_low, acc_se_low = compute_stats_by_type(dropout_results["progdrop_acc_low"], num_types=num_types, dim=0, method="se") - acc_mean_rand, acc_se_rand = compute_stats_by_type(dropout_results["progdrop_acc_rand"], num_types=num_types, dim=0, method="se") - - acc_mean_diff, acc_se_diff = compute_stats_by_type(dropout_results["progdrop_acc_high"]-dropout_results["progdrop_acc_low"], num_types=num_types, dim=0, method="se") - - # Contract into lists for looping through to plot - loss_mean = [loss_mean_high, loss_mean_low, loss_mean_rand] - loss_se = [loss_se_high, loss_se_low, loss_se_rand] - acc_mean = [acc_mean_high, acc_mean_low, acc_mean_rand] - acc_se = [acc_se_high, acc_se_low, acc_se_rand] - - print("plotting dropout results...") - # Plot Loss for progressive dropout experiment - fig, ax = plt.subplots( - num_layers, - num_types, - figsize=(num_types * figdim, num_layers * figdim), - sharex=True, - sharey=True, - layout="constrained", - ) - ax = np.reshape(ax, (num_layers, num_types)) - - if 1==2: - for idx, label in enumerate(labels): - for layer in range(num_layers): - for iexp, name in enumerate(names): - cmn = loss_mean[iexp][idx, :, layer] - cse = loss_se[iexp][idx, :, layer] - ax[layer, idx].plot( - dropout_fraction, - cmn, - color=cmap(iexp), - marker=".", - markersize=msize, - label=name, - ) - ax[layer, idx].fill_between(dropout_fraction, cmn + cse, cmn - cse, color=(cmap(iexp), alpha)) - - if layer == 0: - ax[layer, idx].set_title(label) - - if layer == num_layers - 1: - ax[layer, idx].set_xlabel("Dropout Fraction") - ax[layer, idx].set_xlim(0, 1) - - if idx == 0: - ax[layer, idx].set_ylabel("Loss w/ Dropout") - - if iexp == num_exp - 1: - ax[layer, idx].legend(loc="best") - if exp is not None: - exp.plot_ready("prog_dropout_" + extra_name + "_loss") - - fig, ax = plt.subplots( - num_layers, - num_types, - figsize=(num_types * figdim, num_layers * figdim), - sharex=True, - sharey=True, - layout="constrained", - ) - ax = np.reshape(ax, (num_layers, num_types)) - - for idx, label in enumerate(labels): - for layer in range(num_layers): - for iexp, name in enumerate(names): - - cmn = acc_mean[iexp][idx, :, layer] - cse = acc_se[iexp][idx, :, layer] - ax[layer, idx].plot( - dropout_fraction, - cmn, - color=cmap(iexp), - marker=".", - markersize=msize, - label=name, - ) - ax[layer, idx].fill_between(dropout_fraction, cmn + cse, cmn - cse, color=(cmap(iexp), alpha)) - - ax[layer, idx].set_ylim(0, 100) - - if layer == 0: - ax[layer, idx].set_title(label) - - if layer == num_layers - 1: - ax[layer, idx].set_xlabel("Dropout Fraction") - ax[layer, idx].set_xlim(0, 1) - - if idx == 0: - ax[layer, idx].set_ylabel("Accuracy w/ Dropout") - - if iexp == num_exp - 1: - ax[layer, idx].legend(loc="best") - if exp is not None: - exp.plot_ready("prog_dropout_" + extra_name + "_accuracy") - - # Plot Difference in Accuracy (High - Low) - fig, ax = plt.subplots( - num_layers, - num_types, - figsize=(num_types * figdim, num_layers * figdim), - sharex=True, - sharey=True, - layout="constrained", - ) - ax = np.reshape(ax, (num_layers, num_types)) - - for idx, label in enumerate(labels): - for layer in range(num_layers): - for iexp, name in enumerate(names_diff): - # Debugging: print shapes - #print(f"acc_mean_diff[iexp].shape: {acc_mean_diff[iexp].shape}, dropout_fraction.shape: {dropout_fraction.shape}") - # Squeeze to remove extra dimensions if necessary - cmn = acc_mean_diff[iexp][:, layer] # Squeeze to ensure 1D tensor - cse = acc_se_diff[iexp][:, layer] # Same for standard error - - # Ensure cmn and dropout_fraction have the same length - if len(cmn.shape) == 1 and cmn.shape[0] == dropout_fraction.shape[0]: - ax[layer, idx].plot( - dropout_fraction, - cmn, # Now this should be 1D - color=cmap(iexp), - marker=".", - markersize=msize, - label=name + " (High - Low)", - ) - ax[layer, idx].fill_between(dropout_fraction, cmn + cse, cmn - cse, color=(cmap(iexp), alpha)) - else: - print(f"Shape mismatch: cmn.shape: {cmn.shape}, dropout_fraction.shape: {dropout_fraction.shape}") - - if layer == 0: - ax[layer, idx].set_title(label) - - if layer == num_layers - 1: - ax[layer, idx].set_xlabel("Dropout Fraction") - ax[layer, idx].set_xlim(0, 1) - - if idx == 0: - ax[layer, idx].set_ylabel("Accuracy Difference (High - Low)") - - if iexp == num_exp - 1: - ax[layer, idx].legend(loc="best") - - if exp is not None: - exp.plot_ready("prog_dropout_" + extra_name + "_accuracy_diff") - - -def plot_eigenfeatures(exp, results, prms): - """method for plotting results related to eigen-analysis""" - beta, eigvals, class_betas, class_names = ( - results["beta"], - results["eigvals"], - results["class_betas"], - results["class_names"], - ) - beta = [[torch.abs(b) for b in net_beta] for net_beta in beta] - class_betas = [[rms(cb, dim=2) for cb in net_class_beta] for net_class_beta in class_betas] - - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - cmap = mpl.colormaps["tab10"] - class_cmap = mpl.colormaps["viridis"].resampled(len(class_names)) - - print("measuring statistics of eigenfeature analyses...") - - # shape wrangling - beta = [torch.stack(b) for b in transpose_list(beta)] - eigvals = [torch.stack(ev) for ev in transpose_list(eigvals)] - class_betas = [torch.stack(cb) for cb in transpose_list(class_betas)] - - # normalize to relative values - beta = [b / b.sum(dim=2, keepdim=True) for b in beta] - eigvals = [ev / ev.sum(dim=1, keepdim=True) for ev in eigvals] - class_betas = [cb / cb.sum(dim=2, keepdim=True) for cb in class_betas] - - # reuse these a few times - statprms = lambda method: dict(num_types=num_types, dim=0, method=method) - - # get mean and variance eigenvalues for each layer for each network type - mean_evals, var_evals = named_transpose([compute_stats_by_type(ev, **statprms("var")) for ev in eigvals]) - - # get sorted betas (sorted within each neuron) - sorted_beta = [torch.sort(b, descending=True, dim=2).values for b in beta] - - # get mean / se beta for each layer for each network type - mean_beta, se_beta = named_transpose([compute_stats_by_type(b, **statprms("var")) for b in beta]) - mean_sorted, se_sorted = named_transpose([compute_stats_by_type(b, **statprms("var")) for b in sorted_beta]) - mean_class_beta, se_class_beta = named_transpose([compute_stats_by_type(cb, **statprms("var")) for cb in class_betas]) - - print("plotting eigenfeature results...") - figdim = 3 - alpha = 0.3 - num_layers = len(mean_beta) - fig, ax = plt.subplots(2, num_layers, figsize=(num_layers * figdim, figdim * 2), layout="constrained") - - for layer in range(num_layers): - num_input = mean_evals[layer].size(1) - num_nodes = mean_beta[layer].size(1) - for idx, label in enumerate(labels): - mn_ev = mean_evals[layer][idx] - se_ev = var_evals[layer][idx] - mn_beta = torch.mean(mean_beta[layer][idx], dim=0) - se_beta = torch.std(mean_beta[layer][idx], dim=0) / np.sqrt(num_nodes) - mn_sort = torch.mean(mean_sorted[layer][idx], dim=0) - se_sort = torch.std(mean_sorted[layer][idx], dim=0) / np.sqrt(num_nodes) - ax[0, layer].plot( - range(num_input), - mn_ev, - color=cmap(idx), - linestyle="--", - label="eigvals" if idx == 0 else None, - ) - ax[0, layer].plot(range(num_input), mn_beta, color=cmap(idx), label=label) - ax[0, layer].fill_between(range(num_input), mn_beta + se_beta, mn_beta - se_beta, color=(cmap(idx), alpha)) - ax[1, layer].plot(range(num_input), mn_sort, color=cmap(idx), label=label) - ax[1, layer].fill_between(range(num_input), mn_sort + se_sort, mn_sort - se_sort, color=(cmap(idx), alpha)) - - ax[0, layer].set_xscale("log") - ax[1, layer].set_xscale("log") - ax[0, layer].set_xlabel("Input Dimension") - ax[1, layer].set_xlabel("Sorted Input Dim") - ax[0, layer].set_ylabel("Relative Eigval & Beta") - ax[1, layer].set_ylabel("Relative Beta (Sorted)") - ax[0, layer].set_title(f"Layer {layer}") - ax[1, layer].set_title(f"Layer {layer}") - - if layer == num_layers - 1: - ax[0, layer].legend(loc="best") - ax[1, layer].legend(loc="best") - - exp.plot_ready("eigenfeatures") - - fig, ax = plt.subplots(1, num_layers, figsize=(num_layers * figdim, figdim), layout="constrained") - - for layer in range(num_layers): - num_input = mean_evals[layer].size(1) - num_nodes = mean_beta[layer].size(1) - for idx, label in enumerate(labels): - mn_ev = mean_evals[layer][idx] - se_ev = var_evals[layer][idx] - mn_beta = torch.mean(mean_beta[layer][idx], dim=0) - se_beta = torch.std(mean_beta[layer][idx], dim=0) / np.sqrt(num_nodes) - mn_sort = torch.mean(mean_sorted[layer][idx], dim=0) - se_sort = torch.std(mean_sorted[layer][idx], dim=0) / np.sqrt(num_nodes) - ax[layer].plot( - range(num_input), - mn_ev, - color=cmap(idx), - linestyle="--", - label="eigvals" if idx == 0 else None, - ) - ax[layer].plot(range(num_input), mn_beta, color=cmap(idx), label=label) - ax[layer].fill_between(range(num_input), mn_beta + se_beta, mn_beta - se_beta, color=(cmap(idx), alpha)) - - ax[layer].set_xscale("log") - ax[layer].set_yscale("log") - ax[layer].set_xlabel("Input Dimension") - ax[layer].set_ylabel("Relative Eigval & Beta") - ax[layer].set_title(f"Layer {layer}") - - if layer == num_layers - 1: - ax[layer].legend(loc="best") - - exp.plot_ready("eigenfeatures_loglog") - - fig, ax = plt.subplots( - num_types, - num_layers, - figsize=(num_layers * figdim, figdim * num_types), - layout="constrained", - ) - ax = np.reshape(ax, (num_types, num_layers)) - for layer in range(num_layers): - num_input = mean_evals[layer].size(1) - for idx, label in enumerate(labels): - for idx_class, class_name in enumerate(class_names): - mn_data = mean_class_beta[layer][idx][idx_class] - se_data = se_class_beta[layer][idx][idx_class] - ax[idx, layer].plot(range(num_input), mn_data, color=class_cmap(idx_class), label=class_name) - ax[idx, layer].fill_between( - range(num_input), - mn_data + se_data, - mn_data - se_data, - color=(class_cmap(idx_class), alpha), - ) - - ax[idx, layer].set_xscale("log") - ax[idx, layer].set_yscale("linear") - ax[idx, layer].set_xlabel("Input Dimension") - if layer == 0: - ax[idx, layer].set_ylabel(f"{label}\nClass Loading (RMS)") - if idx == 0: - ax[idx, layer].set_title(f"Layer {layer}") - - if layer == num_layers - 1: - ax[idx, layer].legend(loc="upper right", fontsize=6) - - exp.plot_ready("class_eigenfeatures") - - -def plot_adversarial_results(exp, eigen_results, adversarial_results, prms): - accuracy, beta, eigvals = ( - adversarial_results["accuracy"], - adversarial_results["betas"], - eigen_results["eigvals"], - ) - epsilons, use_sign = adversarial_results["epsilons"], adversarial_results["use_sign"] - - num_types = len(prms["vals"]) - labels = [f"{prms['name']}={val}" for val in prms["vals"]] - cmap = mpl.colormaps["tab10"] - - print("measuring statistics of adversarial analyses...") - - # shape wrangling - accuracy = torch.stack([torch.stack(acc) for acc in transpose_list(accuracy)]) # (num_epsilon, num_nets) - eigvals = [torch.stack(ev) for ev in transpose_list(eigvals)] # [(num_nets, dim_layer) for each layer] - beta = [torch.stack(b) for b in beta] # [(epsilon, num_nets, dim_layer) for each layer] - - # normalize to relative values - beta = [b / b.sum(dim=2, keepdim=True) for b in beta] - eigvals = [ev / ev.sum(dim=1, keepdim=True) for ev in eigvals] - - # reuse these a few times - statprms = lambda dim, method: dict(num_types=num_types, dim=dim, method=method) - - # get mean and variance beta/eigenvalues for each layer for each network type - mean_acc, se_acc = compute_stats_by_type(accuracy, **statprms(1, "var")) # (num_epsilon, num_types) - mean_beta, se_beta = named_transpose( - [compute_stats_by_type(b, **statprms(1, "var")) for b in beta] - ) # [(epsilon, num_types, dim_layer) for each layer] - mean_evals, var_evals = named_transpose( - [compute_stats_by_type(ev, **statprms(0, "var")) for ev in eigvals] - ) # [(num_types, dim_layer) for each layer] - - print("plotting adversarial success results...") - figdim = 3 - alpha = 0.3 - num_layers = len(mean_beta) - fig, ax = plt.subplots(1, 1, figsize=(figdim, figdim), layout="constrained") - for idx, label in enumerate(labels): - ax.plot(epsilons, mean_acc[:, idx], color=cmap(idx), label=label) - ax.set_xlabel("Epsilon") - ax.set_ylabel("Accuracy") - ax.set_title("Adversarial Attack Success") - ax.legend(loc="best") - - exp.plot_ready("adversarial_success") - - print("plotting adversarial structure...") - fig, ax = plt.subplots(1, num_layers, figsize=(num_layers * figdim, figdim), layout="constrained") - for layer in range(num_layers): - num_input = mean_beta[layer].size(2) - for idx, label in enumerate(labels): - ax[layer].plot( - range(num_input), - torch.nanmean(mean_beta[layer][:, idx], dim=0).detach(), - color=cmap(idx), - label=label, - ) - ax[layer].set_xscale("log") - ax[layer].set_xlabel("Input Dimension") - ax[layer].set_ylabel("Average Component of Pertubation") - ax[layer].set_title(f"Layer {layer}") - if layer == num_layers - 1: - ax[layer].legend(loc="best") - - exp.plot_ready("adversarial_structure") - - -def plot_rf(rf, width, alignment=None, alignBounds=None, showRFs=None, figSize=5): - if showRFs is not None: - rf = rf.reshape(rf.shape[0], -1) - idxRandom = np.random.choice(range(rf.shape[0]), showRFs, replace=False) - rf = rf[idxRandom, :] - else: - showRFs = rf.shape[0] - # normalize - rf = rf.T / np.abs(rf).max(axis=1) - rf = rf.T - rf = rf.reshape(showRFs, width, width) - # If necessary, create colormap - if alignment is not None: - cmap = mpl.cm.get_cmap("rainbow", rf.shape[0]) - cmapPeak = lambda x: cmap(x) - if alignBounds is not None: - alignment = alignment - alignBounds[0] - alignment = alignment / (alignBounds[1] - alignBounds[0]) - else: - alignment = alignment - alignment.min() - alignment = alignment / alignment.max() - - # plotting - n = int(np.ceil(np.sqrt(rf.shape[0]))) - fig, axes = plt.subplots(nrows=n, ncols=n, sharex=True, sharey=True) - fig.set_size_inches(figSize, figSize) - - N = 1000 - for i in tqdm(range(rf.shape[0])): - ax = axes[i // n][i % n] - if alignment is not None: - vals = np.ones((N, 4)) - cAlignment = alignment[i].numpy() - cPeak = cmapPeak(alignment[i].numpy()) - vals[:, 0] = np.linspace(0, cPeak[0], N) - vals[:, 1] = np.linspace(0, cPeak[1], N) - vals[:, 2] = np.linspace(0, cPeak[2], N) - usecmap = mpl.colors.ListedColormap(vals) - ax.imshow(rf[i], cmap=usecmap, vmin=-1, vmax=1) - else: - ax.imshow(rf[i], cmap="gray", vmin=-1, vmax=1) - ax.set_xticks([]) - ax.set_yticks([]) - ax.set_aspect("equal") - for j in range(rf.shape[0], n * n): - ax = axes[j // n][j % n] - ax.imshow(np.ones_like(rf[0]) * -1, cmap="gray", vmin=-1, vmax=1) - ax.set_xticks([]) - ax.set_yticks([]) - ax.set_aspect("equal") - fig.subplots_adjust(wspace=0.0, hspace=0.0) - return fig diff --git a/_arxiv/_archive/alignment_v2/processing.py b/_arxiv/_archive/alignment_v2/processing.py deleted file mode 100644 index e7db84df..00000000 --- a/_arxiv/_archive/alignment_v2/processing.py +++ /dev/null @@ -1,316 +0,0 @@ -import os -from tqdm import tqdm -import torch -from alignment_v2 import train -from alignment_v2.utils import load_checkpoints, test_nets, transpose_list, fgsm_attack - - -def train_networks(exp, nets, optimizers, dataset, **special_parameters): - """train and test networks""" - # do training loop - parameters = dict( - train_set=True, - num_epochs=exp.args.epochs, - alignment=not (exp.args.no_alignment), - alignment_expansion=True,#not (exp.args.no_alignment), - delta_weights=exp.args.delta_weights, - frequency=exp.args.frequency, - run=exp.run, - ) - - # update with special parameters - parameters.update(**special_parameters) - - if exp.args.use_prev & os.path.isfile(exp.get_checkpoint_path()): - nets, optimizers, results = load_checkpoints(nets, optimizers, exp.args.device, exp.get_checkpoint_path()) - for net in nets: - net.train() - - parameters["num_complete"] = results["epoch"] + 1 - parameters["results"] = results - print("loaded networks from previous checkpoint") - - if exp.args.save_ckpts: - parameters["save_checkpoints"] = (True, exp.args.ckpt_frequency, exp.get_checkpoint_path(), exp.args.device) - - print("training networks...") - train_results = train.train(nets, optimizers, dataset, **parameters) - - # do testing loop - print("testing networks...") - parameters["train_set"] = False - test_results = train.test(nets, dataset, **parameters) - - return train_results, test_results - - -def progressive_dropout_experiment(exp, nets, dataset, alignment=None, train_set=False): - """ - perform a progressive dropout (of nodes) experiment - alignment is optional, but will be recomputed if you've already measured it. You can provide it - by setting: alignment=test_results['alignment'] if ``train_networks`` has already been run. - """ - # do targeted dropout experiment - print("performing targeted dropout...") - dropout_parameters = dict(num_drops=exp.args.num_drops, by_layer=exp.args.dropout_by_layer, train_set=train_set) - dropout_results = train.progressive_dropout(nets, dataset, alignment=alignment, **dropout_parameters) - return dropout_results, dropout_parameters - -# def progressive_dropout_experiment(exp, nets, dataset, alignment=None, train_set=False, by_layer=False, layer_idx=None): -# """ -# Perform progressive dropout, either layer-by-layer or across all layers at once. -# If by_layer=True, we only dropout nodes in the specified layer (layer_idx). -# """ -# dropout_results = {} -# dropout_parameters = {} - -# if by_layer: -# print(f"Performing dropout for layer {layer_idx}") - -# # Drop nodes in the specified layer only -# dropout_results = train.progressive_dropout( -# nets, dataset, alignment=alignment, layer_idx=layer_idx, train_set=train_set, by_layer=True -# ) - -# else: -# # Standard dropout across all layers -# dropout_results = train.progressive_dropout( -# nets, dataset, alignment=alignment, train_set=train_set, by_layer=False -# ) - -# return dropout_results, dropout_parameters - -def sequential_dropout_experiment(exp, nets, dataset): - """ - Perform a sequential dropout experiment by pruning layers one by one, recalculating alignment after each pruning. - """ - print("Performing sequential dropout experiment...") - sequential_dropout_results = {} - sequential_dropout_parameters = {} - - # Loop over the layers to sequentially prune each layer - for layer_idx in range(len(nets[0].layers)): # Assuming nets[0].layers holds the network layers - print(f"Sequentially pruning layer {layer_idx}") - - # Get the alignment after pruning previous layers - alignment = train.test(nets, dataset, alignment_only=True)["alignment"] - - # Perform progressive dropout for the current layer - print(f"Performing dropout for layer {layer_idx}") - dropout_results, dropout_parameters = progressive_dropout_experiment( - exp, nets, dataset, alignment=alignment, layer_idx=layer_idx - ) - # Recalculate alignment after pruning the current layer - print(f"Recalculating alignment after pruning layer {layer_idx}") - test_results = train.test(nets, dataset, alignment_only=True) # Update test_results after each layer - - # Store the dropout results and parameters for this layer - sequential_dropout_results = dropout_results - sequential_dropout_parameters = dropout_parameters - - return sequential_dropout_results, sequential_dropout_parameters - - -def retrain_network_with_dropout_stats(exp, nets, optimizers, dataset, original_dropout_results): - """ - Retrains the network and maintains the same key structure as the dropout results. - This ensures the retraining results have keys like 'progdrop_loss_high', etc. - """ - # Train the network using the regular training process - print("Retraining the pruned network...") - for net in nets: - net.train() - - # Reset optimizers, assuming we need new ones for the pruned network - optimizers = [torch.optim.SGD(net.parameters(), lr=0.001) for net in nets] - - # Train the networks after dropout - retrain_results, retrain_test_results = train_networks(exp, nets, optimizers, dataset) - - train_results, test_results = train_networks(exp, nets, optimizers, dataset) - - # Initialize retraining results dictionary with same structure as original dropout results - retrain_results = { - "progdrop_loss_high": original_dropout_results["progdrop_loss_high"].clone().zero_(), - "progdrop_loss_low": original_dropout_results["progdrop_loss_low"].clone().zero_(), - "progdrop_loss_rand": original_dropout_results["progdrop_loss_rand"].clone().zero_(), - "progdrop_acc_high": original_dropout_results["progdrop_acc_high"].clone().zero_(), - "progdrop_acc_low": original_dropout_results["progdrop_acc_low"].clone().zero_(), - "progdrop_acc_rand": original_dropout_results["progdrop_acc_rand"].clone().zero_(), - "dropout_fraction": original_dropout_results["dropout_fraction"], - "by_layer": original_dropout_results["by_layer"], - "idx_dropout_layers": original_dropout_results["idx_dropout_layers"], - "fraction_dropped_nodes": original_dropout_results["fraction_dropped_nodes"].clone().zero_() - } - - # Populate the retraining results with new loss and accuracy after retraining - # For example, assuming the train/test functions capture loss and accuracy - retrain_results["progdrop_loss_high"] = test_results.get("progdrop_loss_high", retrain_results["progdrop_loss_high"]) - retrain_results["progdrop_loss_low"] = test_results.get("progdrop_loss_low", retrain_results["progdrop_loss_low"]) - retrain_results["progdrop_loss_rand"] = test_results.get("progdrop_loss_rand", retrain_results["progdrop_loss_rand"]) - - retrain_results["progdrop_acc_high"] = test_results.get("progdrop_acc_high", retrain_results["progdrop_acc_high"]) - retrain_results["progdrop_acc_low"] = test_results.get("progdrop_acc_low", retrain_results["progdrop_acc_low"]) - retrain_results["progdrop_acc_rand"] = test_results.get("progdrop_acc_rand", retrain_results["progdrop_acc_rand"]) - - return retrain_results, test_results - -def evaluate_network(nets, dataset): - """ - Evaluate the pruned network on the test dataset to recalculate alignment and other metrics. - """ - test_results = train.test(nets, dataset) - return test_results - -def measure_eigenfeatures(exp, nets, dataset, train_set=False): - # measure eigenfeatures - print("measuring eigenfeatures...") - beta, eigvals, eigvecs, class_betas = [], [], [], [] - for net in tqdm(nets): - # get inputs to each layer from whole dataloader - real_net = net.module if hasattr(net, "module") else net - inputs, labels = real_net._process_collect_activity( - dataset, - train_set=train_set, - with_updates=False, - use_training_mode=False, - ) - eigenfeatures = real_net.measure_eigenfeatures(inputs, with_updates=False) - beta_by_class = real_net.measure_class_eigenfeatures(inputs, labels, eigenfeatures[2], rms=False, with_updates=False) - beta.append(eigenfeatures[0]) - eigvals.append(eigenfeatures[1]) - eigvecs.append(eigenfeatures[2]) - class_betas.append(beta_by_class) - - - # make it a dictionary - class_names = getattr(dataset.train_loader if train_set else dataset.test_loader, "dataset").classes - return dict( - beta=beta, - eigvals=eigvals, - eigvecs=eigvecs, - class_betas=class_betas, - class_names=class_names, - ) - - -def eigenvector_dropout(exp, nets, dataset, eigen_results, train_set=False): - """ - do targeted eigenvector dropout with precomputed eigenfeatures - """ - # do targeted dropout experiment - print("performing targeted eigenvector dropout...") - evec_dropout_parameters = dict(num_drops=exp.args.num_drops, by_layer=exp.args.dropout_by_layer, train_set=train_set) - evec_dropout_results = train.eigenvector_dropout(nets, dataset, eigen_results["eigvals"], eigen_results["eigvecs"], **evec_dropout_parameters) - return evec_dropout_results, evec_dropout_parameters - - -@test_nets -def measure_adversarial_attacks(nets, dataset, exp, eigen_results, train_set=False, **parameters): - """ - do adversarial attack and measure structure with regards to eigenfeatures - """ - - def get_beta(inputs, eigenvectors): - # get projection of input onto eigenvectors across layers - return [input.cpu() @ evec for input, evec in zip(inputs, eigenvectors)] - - # experiment parameters - epsilons = parameters.get("epsilons") - use_sign = parameters.get("use_sign") - fgsm_transform = parameters.get("fgsm_transform", lambda x: x) - - # data from eigenvectors - eigenvectors = eigen_results["eigvecs"] - - num_eps = len(epsilons) - num_nets = len(nets) - accuracy = torch.zeros((num_nets, num_eps)) - examples = [[[] for _ in range(num_eps)] for _ in range(num_nets)] - betas = [[torch.zeros((num_nets, evec.size(0))) for evec in eigenvectors[0]] for _ in range(num_eps)] - - # dataloader - dataloader = dataset.train_loader if train_set else dataset.test_loader - - for batch in tqdm(dataloader): - input, labels = dataset.unwrap_batch(batch) - - inputs = [input.clone() for _ in range(num_nets)] - - for input in inputs: - input.requires_grad = True - - # Forward pass the data through the model - outputs = [net(input, store_hidden=True) for net, input in zip(nets, inputs)] - input_to_layers = [net.get_layer_inputs(input, precomputed=True) for net in nets] - init_preds = [torch.argmax(output, axis=1) for output in outputs] # find true prediction - least_likely = [torch.argmin(output, axis=1) for output in outputs] # find least likely digit according to model - - c_betas = transpose_list([get_beta(input, evec) for input, evec in zip(input_to_layers, eigenvectors)]) - s_betas = [torch.stack(cb) for cb in c_betas] - - # Calculate the loss - loss = [dataset.measure_loss(output, labels) for output in outputs] - # loss = dataset.measure_loss(output, least_likely) - - # Zero all existing gradients - for net in nets: - net.zero_grad() - - # Calculate gradients of model in backward pass - for l in loss: - l.backward() - - # Collect datagrad - data_grads = [input.grad.data for input in inputs] - - for epsidx, eps in enumerate(epsilons): - - # Call FGSM Attack - perturbed_inputs = [fgsm_attack(input, eps, data_grad, fgsm_transform, use_sign) for input, data_grad in zip(inputs, data_grads)] - - # Re-classify the perturbed image - outputs = [net(perturbed_input, store_hidden=True) for net, perturbed_input in zip(nets, perturbed_inputs)] - input_to_layers = [net.get_layer_inputs(perturbed_input, precomputed=True) for net, perturbed_input in zip(nets, perturbed_inputs)] - c_eps_betas = transpose_list([get_beta(input, evec) for input, evec in zip(input_to_layers, eigenvectors)]) - s_eps_betas = [torch.stack(ceb) for ceb in c_eps_betas] - d_eps_betas = [sebeta - sbeta for sebeta, sbeta in zip(s_eps_betas, s_betas)] - rms_betas = [torch.sqrt(torch.mean(db**2, dim=1)) for db in d_eps_betas] - - for ii, rbeta in enumerate(rms_betas): - betas[epsidx][ii] += rbeta.detach() - - # Check for success - final_preds = [torch.argmax(output, axis=1) for output in outputs] - accuracy[:, epsidx] += torch.tensor([sum(final_pred == labels).cpu() for final_pred in final_preds]) - - # Idx where adversarial example worked - idx_success = [ - torch.where((init_pred == labels) & (final_pred != labels))[0].cpu() for init_pred, final_pred in zip(init_preds, final_preds) - ] - - adv_exs = [perturbed_input.detach().cpu().numpy() for perturbed_input in perturbed_inputs] - for ii, (adv_ex, idx, init_pred, final_pred) in enumerate(zip(adv_exs, idx_success, init_preds, final_preds)): - examples[ii][epsidx].append((init_pred[idx], final_pred[idx], adv_ex[idx])) - - # Calculate final accuracy for this epsilon - accuracy = accuracy / float(len(dataloader.dataset)) - - # Average across betas - betas = transpose_list([[cb / float(len(dataloader.dataset)) for cb in beta] for beta in betas]) - - # Return the accuracy and an adversarial example - return dict(accuracy=accuracy, betas=betas, examples=examples, epsilons=epsilons, use_sign=use_sign) - - -@test_nets -def measure_alignment_distribution(nets, dataset, **parameters): - """ - method for measuring alignment distribution and several associated analyses - """ - # do training loop - parameters = dict( - train_set=True, - ) - - diff --git a/_arxiv/_archive/alignment_v2/train.py b/_arxiv/_archive/alignment_v2/train.py deleted file mode 100644 index 0e506501..00000000 --- a/_arxiv/_archive/alignment_v2/train.py +++ /dev/null @@ -1,813 +0,0 @@ -from copy import copy, deepcopy - -import torch -from tqdm import tqdm -import torch.distributed as dist # keep for DDP rank checks - -from alignment_v2.utils import ( - transpose_list, - condense_values, - test_nets, - train_nets, - save_checkpoint, - smart_pca, - expected_alignment_distribution, - alignment, - alignment_expansion, -) - - -@train_nets -def train(nets, optimizers, dataset, **parameters): - """method for training network on supervised learning problem""" - - # input argument checks - if not (isinstance(nets, list)): - nets = [nets] - if not (isinstance(optimizers, list)): - optimizers = [optimizers] - assert len(nets) == len(optimizers), "nets and optimizers need to be equal length lists" - - # check if we should print progress bars - verbose = parameters.get("verbose", True) - - num_nets = len(nets) - use_train = parameters.get("train_set", True) - dataloader = dataset.train_loader if use_train else dataset.test_loader - num_steps = len(dataset.train_loader) * parameters["num_epochs"] - - # optional W&B run - run = parameters.get("run") - - # optional analyses - measure_alignment = parameters.get("alignment", True) - measure_alignment_expansion = parameters.get("alignment_expansion", False) - measure_delta_weights = parameters.get("delta_weights", False) - measure_delta_alignment = parameters.get("delta_alignment", False) - measure_frequency = parameters.get("frequency", 1) - compare_expected = parameters.get("compare_expected", False) - - # optional manual shaping - manual_shape = parameters.get("manual_shape", False) - manual_frequency = parameters.get("manual_frequency", -1) - manual_transforms = parameters.get("manual_transforms", None) - manual_layers = parameters.get("manual_layers", None) - - # create or retrieve results dict - results = parameters.get("results", False) - num_complete = parameters.get("num_complete", 0) - save_ckpt, freq_ckpt, path_ckpt, dev = parameters.get("save_checkpoints", (False, 1, "", "")) - if not results: - results = { - "loss": torch.zeros((num_steps, num_nets)), - "accuracy": torch.zeros((num_steps, num_nets)), - } - if measure_alignment: - results["alignment"] = [] - if measure_alignment_expansion: - results["alignment_0"] = [] - results["alignment_1"] = [] - results["alignment_2"] = [] - results["alignment_red"] = [] - if measure_delta_weights: - results["delta_weights"] = [] - results["init_weights"] = [ - net.module.get_alignment_weights() if hasattr(net, "module") else net.get_alignment_weights() - for net in nets - ] - if measure_delta_alignment: - if "init_weights" not in results: - results["init_weights"] = [ - net.module.get_alignment_weights() if hasattr(net, "module") else net.get_alignment_weights() - for net in nets - ] - results["delta_alignment"] = [] - if compare_expected: - calign_bins = torch.linspace(0, 1, 301) - results["compare_alignment_bins"] = calign_bins - results["compare_alignment_expected"] = [] - results["compare_alignment_observed"] = [] - if measure_delta_alignment: - results["compare_delta_alignment_observed"] = [] - elif results["loss"].shape[0] < num_steps: - add_steps = num_steps - results["loss"].shape[0] - assert (add_steps / (parameters["num_epochs"] - num_complete)) == len(dataset.train_loader), \ - "Number of new steps must match epochs * loader length" - results["loss"] = torch.vstack((results["loss"], torch.zeros((add_steps, num_nets)))) - results["accuracy"] = torch.vstack((results["accuracy"], torch.zeros((add_steps, num_nets)))) - - if num_complete > 0: - print("resuming training from checkpoint on epoch", num_complete) - - # training loop - epoch_loop = range(num_complete, parameters["num_epochs"]) - if verbose: - epoch_loop = tqdm(epoch_loop, desc="training epoch") - - for epoch in epoch_loop: - - batch_loop = dataloader - if verbose: - batch_loop = tqdm(batch_loop, desc="minibatch", leave=False) - - for idx, batch in enumerate(batch_loop): - cidx = epoch * len(dataloader) + idx - images, labels = dataset.unwrap_batch(batch) - - for opt in optimizers: - opt.zero_grad() - - outputs = [net(images, store_hidden=True) for net in nets] - loss = [dataset.measure_loss(output, labels) for output in outputs] - for l, opt in zip(loss, optimizers): - l.backward() - opt.step() - - results["loss"][cidx] = torch.tensor([l.item() for l in loss]) - results["accuracy"][cidx] = torch.tensor([ - dataset.measure_accuracy(output, labels).cpu() for output in outputs - ]) - - if idx % measure_frequency == 0: - if measure_alignment: - alignment_vals = [] - for net in nets: - real_net = net.module if hasattr(net, "module") else net - alignment_val = real_net.measure_alignment(images, precomputed=True, method="alignment") - alignment_vals.append(alignment_val) - results["alignment"].append(alignment_vals) - - if measure_alignment_expansion: - alignment0_vals, alignment1_vals, alignment2_vals, alignmentred_vals = [], [], [], [] - for net in nets: - real_net = net.module if hasattr(net, "module") else net - alignment0_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_0")) - alignment1_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_1")) - alignment2_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_2")) - alignmentred_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_red")) - results["alignment_0"].append(alignment0_vals) - results["alignment_1"].append(alignment1_vals) - results["alignment_2"].append(alignment2_vals) - results["alignment_red"].append(alignmentred_vals) - - if measure_delta_weights or measure_delta_alignment: - c_delta_weights = [] - for net, init_weight in zip(nets, results["init_weights"]): - real_net = net.module if hasattr(net, "module") else net - c_delta_weights.append(real_net.compare_weights(init_weight)) - if measure_delta_weights: - results["delta_weights"].append(c_delta_weights) - if measure_delta_alignment: - c_delta_alignment = [] - for net, weights_ in zip(nets, c_delta_weights): - real_net = net.module if hasattr(net, "module") else net - c_delta_alignment.append( - real_net.measure_alignment_weights(images, weights_, precomputed=True, method="alignment") - ) - results["delta_alignment"].append(c_delta_alignment) - - if compare_expected: - if measure_alignment: - c_alignment = results["alignment"][-1] - else: - c_alignment = [] - for net in nets: - real_net = net.module if hasattr(net, "module") else net - c_alignment.append(real_net.measure_alignment(images, precomputed=True, method="alignment")) - - c_inputs = [] - for net in nets: - real_net = net.module if hasattr(net, "module") else net - c_inputs.append(real_net.get_layer_inputs(images, precomputed=True)) - - for i in range(len(nets)): - real_net_ = nets[i].module if hasattr(nets[i], "module") else nets[i] - c_inputs[i] = real_net_._preprocess_inputs(c_inputs[i]) - - c_evals = [] - for cin in c_inputs: - c_eval_sub = [] - for c_ in cin: - w, _ = smart_pca(c_.T) - c_eval_sub.append(w) - c_evals.append(c_eval_sub) - - calign_bins = results["compare_alignment_bins"] - c_dist = [] - for c_eval in c_evals: - subdist = [] - for ev in c_eval: - subdist.append(expected_alignment_distribution(ev, valid_rotation=False, bins=calign_bins)[0]) - c_dist.append(subdist) - - t_dist = [] - for c_align in c_alignment: - align_sub = [] - for align in c_align: - align_sub.append(torch.histogram(align.cpu(), bins=calign_bins, density=True)[0]) - t_dist.append(align_sub) - - results["compare_alignment_expected"].append(c_dist) - results["compare_alignment_observed"].append(t_dist) - - if measure_delta_alignment: - d_alignment = results["delta_alignment"][-1] - d_dist = [] - for d_align in d_alignment: - align_sub = [] - for dalign in d_align: - align_sub.append(torch.histogram(dalign.cpu(), bins=calign_bins, density=True)[0]) - d_dist.append(align_sub) - results["compare_delta_alignment_observed"].append(d_dist) - - # Only rank 0 logs to W&B - if run is not None: - if dist.is_available() and dist.is_initialized() and dist.get_rank() != 0: - pass - else: - run.log( - {f"losses/loss-{ii}": l.item() for ii, l in enumerate(loss)} - | {f"accuracies/accuracy-{ii}": dataset.measure_accuracy(output, labels) for ii, output in enumerate(outputs)} - | {"batch": cidx} - ) - - if manual_shape: - if ((epoch + 1) % manual_frequency == 0) and (epoch < parameters["num_epochs"] - 1): - for net, transform in tqdm(zip(nets, manual_transforms), desc="manual shaping", leave=False): - real_net = net.module if hasattr(net, "module") else net - inputs, _ = real_net._process_collect_activity(dataset, train_set=False, with_updates=False, use_training_mode=False) - _, eigenvalues, eigenvectors = real_net.measure_eigenfeatures(inputs, with_updates=False) - idx_to_layer_lookup = {layer: i for i, layer in enumerate(real_net.get_alignment_layer_indices())} - eigenvalues = [eigenvalues[idx_to_layer_lookup[ml]] for ml in manual_layers] - eigenvectors = [eigenvectors[idx_to_layer_lookup[ml]] for ml in manual_layers] - real_net.shape_eigenfeatures(manual_layers, eigenvalues, eigenvectors, transform) - - if save_ckpt & (epoch % freq_ckpt == 0): - save_checkpoint( - nets, - optimizers, - results | {"prms": parameters, "epoch": epoch, "device": dev}, - path_ckpt, - ) - - for k in [ - "alignment", - "alignment_0", - "alignment_1", - "alignment_2", - "alignment_red", - "delta_weights", - "delta_alignment", - "avgcorr", - "fullcorr", - "compare_alignment_expected", - "compare_alignment_observed", - "compare_delta_alignment_observed", - ]: - if k not in results.keys(): - continue - results[k] = condense_values(transpose_list(results[k])) - - return results - - - -@torch.no_grad() -@test_nets -def test(nets, dataset, **parameters): - """method for testing network on supervised learning problem""" - - run = parameters.get("run") - - if not (isinstance(nets, list)): - nets = [nets] - - verbose = parameters.get("verbose", True) - num_nets = len(nets) - - use_test = not parameters.get("train_set", False) - dataloader = dataset.test_loader if use_test else dataset.train_loader - - total_loss = [0 for _ in range(num_nets)] - num_correct = [0 for _ in range(num_nets)] - num_batches = 0 - - measure_alignment = parameters.get("alignment", True) - measure_alignment_expansion = parameters.get("alignment_expansion", True) - - if measure_alignment: - alignment = [] - if measure_alignment_expansion: - alignment_0 = [] - alignment_1 = [] - alignment_2 = [] - alignment_red = [] - - batch_loop = tqdm(dataloader) if verbose else dataloader - for batch in batch_loop: - images, labels = dataset.unwrap_batch(batch) - - outputs = [net(images, store_hidden=True) for net in nets] - - for idx, output in enumerate(outputs): - total_loss[idx] += dataset.measure_loss(output, labels).item() - num_correct[idx] += dataset.measure_accuracy(output, labels).item() - - num_batches += 1 - - if measure_alignment: - a_vals = [] - for net in nets: - real_net = net.module if hasattr(net, "module") else net - a_vals.append(real_net.measure_alignment(images, precomputed=True, method="alignment")) - alignment.append(a_vals) - - if measure_alignment_expansion: - a0_vals, a1_vals, a2_vals, ared_vals = [], [], [], [] - for net in nets: - real_net = net.module if hasattr(net, "module") else net - a0_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_0")) - a1_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_1")) - a2_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_2")) - ared_vals.append(real_net.measure_alignment_expansion(images, precomputed=True, method="alignment_red")) - alignment_0.append(a0_vals) - alignment_1.append(a1_vals) - alignment_2.append(a2_vals) - alignment_red.append(ared_vals) - - results = { - "loss": [loss / num_batches for loss in total_loss], - "accuracy": [correct / num_batches for correct in num_correct], - } - - if measure_alignment: - results["alignment"] = condense_values(transpose_list(alignment)) - if measure_alignment_expansion: - results["alignment_0"] = condense_values(transpose_list(alignment_0)) - results["alignment_1"] = condense_values(transpose_list(alignment_1)) - results["alignment_2"] = condense_values(transpose_list(alignment_2)) - results["alignment_red"] = condense_values(transpose_list(alignment_red)) - - if run is not None: - run.summary["test_loss"] = torch.mean(torch.tensor(results["loss"])) - run.summary["test_accuracy"] = torch.mean(torch.tensor(results["accuracy"])) - - return results - - -@torch.no_grad() -def get_dropout_indices(idx_alignment, fraction): - """ - convenience method for getting a fraction of dropout indices from each layer - """ - num_nets = idx_alignment[0].size(0) - num_nodes = [idx.size(1) for idx in idx_alignment] - num_drop = [int(nodes * fraction) for nodes in num_nodes] - idx_high = [ - torch.sort(idx[:, -drop:], dim=1).values - for idx, drop in zip(idx_alignment, num_drop) - ] - idx_low = [ - torch.sort(idx[:, :drop], dim=1).values - for idx, drop in zip(idx_alignment, num_drop) - ] - idx_rand = [ - torch.sort(idx[:, torch.randperm(idx.size(1))[:drop]], dim=1).values - for idx, drop in zip(idx_alignment, num_drop) - ] - return idx_high, idx_low, idx_rand - - -@torch.no_grad() -def get_dropout_indices000(idx_alignment, fraction): - """ - Returns dropout indices for high, low, and random alignment for each layer and each network. - """ - num_layers = len(idx_alignment) - num_nets = idx_alignment[0].size(0) - - idx_high = [] - idx_low = [] - idx_rand = [] - - for layer_idx in range(num_layers): - layer_alignment = idx_alignment[layer_idx] - layer_dimension = layer_alignment.size(1) - num_drop = int(layer_dimension * fraction) - sorted_indices = torch.argsort(layer_alignment, dim=1) - high_indices = sorted_indices[:, -num_drop:] - high_indices = torch.sort(high_indices, dim=1).values - idx_high.append(high_indices) - low_indices = sorted_indices[:, :num_drop] - low_indices = torch.sort(low_indices, dim=1).values - idx_low.append(low_indices) - rand_indices = torch.randperm(layer_dimension, device=layer_alignment.device)[:num_drop] - rand_indices = rand_indices.repeat(num_nets, 1) - idx_rand.append(rand_indices) - - return idx_high, idx_low, idx_rand - - -@torch.no_grad() -@test_nets -def progressive_dropout(nets, dataset, alignment=None, **parameters): - """ - method for testing network on supervised learning problem with progressive dropout - """ - - if not (isinstance(nets, list)): - nets = [nets] - - real_net = nets[0].module if hasattr(nets[0], 'module') else nets[0] - idx_dropout_layers = real_net.get_alignment_layer_indices() - - if alignment is None: - alignment = test(nets, dataset, **parameters)["alignment"] - - alignment = [alignment[i] for i in idx_dropout_layers] - assert len(alignment) == len(idx_dropout_layers), "the number of layers in **alignment** doesn't correspond to the number of alignment layers" - - classification_layer = ( - nets[0].module.num_layers(all=True) - 1 - if hasattr(nets[0], 'module') - else nets[0].num_layers(all=True) - 1 - ) - if classification_layer in idx_dropout_layers: - idx_dropout_layers.pop(-1) - alignment.pop(-1) - - alignment = [torch.mean(align, dim=1) for align in alignment] - idx_alignment = [torch.argsort(align, dim=1) for align in alignment] - - print(len(idx_alignment)) - print(idx_alignment[0].shape) - - num_nets = len(nets) - num_drops = parameters.get("num_drops", 9) - drop_fraction = torch.linspace(0, 1, num_drops + 2)[1:-1] - by_layer = parameters.get("by_layer", False) - num_layers = len(idx_dropout_layers) if by_layer else 1 - - progdrop_loss_high = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_loss_low = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_loss_rand = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_high = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_low = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_rand = torch.zeros((num_nets, num_drops, num_layers)) - - num_batches = 0 - use_train = parameters.get("train_set", False) - dataloader = dataset.train_loader if use_train else dataset.test_loader - - for batch in tqdm(dataloader): - images, labels = dataset.unwrap_batch(batch) - num_batches += 1 - - for dropidx, fraction in enumerate(drop_fraction): - idx_high, idx_low, idx_rand = get_dropout_indices(idx_alignment, fraction) - - for layer in range(num_layers): - if by_layer: - drop_high, drop_low, drop_rand = ( - [idx_high[layer]], - [idx_low[layer]], - [idx_rand[layer]], - ) - drop_layer = [idx_dropout_layers[layer]] - else: - drop_high, drop_low, drop_rand = idx_high, idx_low, idx_rand - drop_layer = copy(idx_dropout_layers) - - out_high = [] - for idx, net_ in enumerate(nets): - real_net_ = net_.module if hasattr(net_, "module") else net_ - out_ = real_net_.forward_targeted_dropout( - images, [drop[idx, :] for drop in drop_high], drop_layer - )[0] - out_high.append(out_) - out_low = [] - for idx, net_ in enumerate(nets): - real_net_ = net_.module if hasattr(net_, "module") else net_ - out_ = real_net_.forward_targeted_dropout( - images, [drop[idx, :] for drop in drop_low], drop_layer - )[0] - out_low.append(out_) - out_rand = [] - for idx, net_ in enumerate(nets): - real_net_ = net_.module if hasattr(net_, "module") else net_ - out_ = real_net_.forward_targeted_dropout( - images, [drop[idx, :] for drop in drop_rand], drop_layer - )[0] - out_rand.append(out_) - - loss_high = [dataset.measure_loss(out, labels).item() for out in out_high] - loss_low = [dataset.measure_loss(out, labels).item() for out in out_low] - loss_rand = [dataset.measure_loss(out, labels).item() for out in out_rand] - - acc_high = [dataset.measure_accuracy(out, labels) for out in out_high] - acc_low = [dataset.measure_accuracy(out, labels) for out in out_low] - acc_rand = [dataset.measure_accuracy(out, labels) for out in out_rand] - - progdrop_loss_high[:, dropidx, layer] += torch.tensor(loss_high) - progdrop_loss_low[:, dropidx, layer] += torch.tensor(loss_low) - progdrop_loss_rand[:, dropidx, layer] += torch.tensor(loss_rand) - progdrop_acc_high[:, dropidx, layer] += torch.tensor(acc_high) - progdrop_acc_low[:, dropidx, layer] += torch.tensor(acc_low) - progdrop_acc_rand[:, dropidx, layer] += torch.tensor(acc_rand) - - results = { - "progdrop_loss_high": progdrop_loss_high / num_batches, - "progdrop_loss_low": progdrop_loss_low / num_batches, - "progdrop_loss_rand": progdrop_loss_rand / num_batches, - "progdrop_acc_high": progdrop_acc_high / num_batches, - "progdrop_acc_low": progdrop_acc_low / num_batches, - "progdrop_acc_rand": progdrop_acc_rand / num_batches, - "dropout_fraction": drop_fraction, - "by_layer": by_layer, - "idx_dropout_layers": idx_dropout_layers, - } - - return results - - -def train_network(net, dataset, epochs=5, learning_rate=0.001): - optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate) - net.train() - - for epoch in range(epochs): - for batch in dataset.train_loader: - images, labels = dataset.unwrap_batch(batch) - optimizer.zero_grad() - outputs = net(images) - loss = dataset.measure_loss(outputs, labels) - loss.backward() - optimizer.step() - - return net - - -def progressive_dropout_train(nets, dataset, alignment=None, **parameters): - - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - if not isinstance(nets, list): - nets = [nets] - nets = [net.to(device) for net in nets] - - real_net_0 = nets[0].module if hasattr(nets[0], 'module') else nets[0] - idx_dropout_layers = real_net_0.get_alignment_layer_indices() - - if alignment is None: - alignment = test(nets, dataset, **parameters)["alignment"] - - assert len(alignment) == len(idx_dropout_layers), ( - f"Mismatch in alignment layer count: alignment has {len(alignment)} " - f"layers, expected {len(idx_dropout_layers)} layers." - ) - - classification_layer = real_net_0.num_layers(all=True) - 1 - if classification_layer in idx_dropout_layers: - idx_dropout_layers.pop(-1) - alignment.pop(-1) - - alignment = [torch.mean(align, dim=1) for align in alignment] - idx_alignment = [torch.argsort(align, dim=1) for align in alignment] - - num_nets = len(nets) - num_drops = parameters.get("num_drops", 9) - drop_fraction = torch.linspace(0, 1, num_drops + 2)[1:-1] - by_layer = parameters.get("by_layer", False) - num_layers = len(idx_dropout_layers) if by_layer else 1 - - progdrop_loss_high_before, progdrop_loss_low_before, progdrop_loss_rand_before = [], [], [] - progdrop_acc_high_before, progdrop_acc_low_before, progdrop_acc_rand_before = [], [], [] - - progdrop_loss_high_after, progdrop_loss_low_after, progdrop_loss_rand_after = [], [], [] - progdrop_acc_high_after, progdrop_acc_low_after, progdrop_acc_rand_after = [], [], [] - - num_batches = 0 - use_train = parameters.get("train_set", False) - dataloader = dataset.train_loader if use_train else dataset.test_loader - - for batch in tqdm(dataloader): - images, labels = dataset.unwrap_batch(batch) - images = images.to(device) - labels = labels.to(device) - - num_batches += 1 - - for dropidx, fraction in enumerate(drop_fraction): - idx_high, idx_low, idx_rand = get_dropout_indices(idx_alignment, fraction) - - for layer in range(num_layers): - if by_layer: - drop_high, drop_low, drop_rand = ( - [idx_high[layer]], - [idx_low[layer]], - [idx_rand[layer]], - ) - drop_layer = [idx_dropout_layers[layer]] - else: - drop_high, drop_low, drop_rand = idx_high, idx_low, idx_rand - drop_layer = deepcopy(idx_dropout_layers) - - pruned_nets_high = [ - train_network(prune_network_by_alignment([net], idx_alignment, fraction, method="high")[0].to(device), dataset) - for net in nets - ] - pruned_nets_low = [ - train_network(prune_network_by_alignment([net], idx_alignment, fraction, method="low")[0].to(device), dataset) - for net in nets - ] - pruned_nets_rand = [ - train_network(prune_network_by_alignment([net], idx_alignment, fraction, method="random")[0].to(device), dataset) - for net in nets - ] - - out_high_before = [net_(images) for net_ in pruned_nets_high] - out_low_before = [net_(images) for net_ in pruned_nets_low] - out_rand_before = [net_(images) for net_ in pruned_nets_rand] - - loss_high_before = [ - dataset.measure_loss(out, labels).item() for out in out_high_before - ] - loss_low_before = [ - dataset.measure_loss(out, labels).item() for out in out_low_before - ] - loss_rand_before = [ - dataset.measure_loss(out, labels).item() for out in out_rand_before - ] - - acc_high_before = [dataset.measure_accuracy(out, labels) for out in out_high_before] - acc_low_before = [dataset.measure_accuracy(out, labels) for out in out_low_before] - acc_rand_before = [dataset.measure_accuracy(out, labels) for out in out_rand_before] - - progdrop_loss_high_before.append(loss_high_before) - progdrop_loss_low_before.append(loss_low_before) - progdrop_loss_rand_before.append(loss_rand_before) - progdrop_acc_high_before.append(acc_high_before) - progdrop_acc_low_before.append(acc_low_before) - progdrop_acc_rand_before.append(acc_rand_before) - - out_high_after = [net_(images) for net_ in pruned_nets_high] - out_low_after = [net_(images) for net_ in pruned_nets_low] - out_rand_after = [net_(images) for net_ in pruned_nets_rand] - - loss_high_after = [ - dataset.measure_loss(out, labels).item() for out in out_high_after - ] - loss_low_after = [ - dataset.measure_loss(out, labels).item() for out in out_low_after - ] - loss_rand_after = [ - dataset.measure_loss(out, labels).item() for out in out_rand_after - ] - - acc_high_after = [dataset.measure_accuracy(out, labels) for out in out_high_after] - acc_low_after = [dataset.measure_accuracy(out, labels) for out in out_low_after] - acc_rand_after = [dataset.measure_accuracy(out, labels) for out in out_rand_after] - - progdrop_loss_high_after.append(loss_high_after) - progdrop_loss_low_after.append(loss_low_after) - progdrop_loss_rand_after.append(loss_rand_after) - progdrop_acc_high_after.append(acc_high_after) - progdrop_acc_low_after.append(acc_low_after) - progdrop_acc_rand_after.append(acc_rand_after) - - results = { - "progdrop_loss_high_before": torch.tensor(progdrop_loss_high_before) / num_batches, - "progdrop_loss_low_before": torch.tensor(progdrop_loss_low_before) / num_batches, - "progdrop_loss_rand_before": torch.tensor(progdrop_loss_rand_before) / num_batches, - "progdrop_acc_high_before": torch.tensor(progdrop_acc_high_before) / num_batches, - "progdrop_acc_low_before": torch.tensor(progdrop_acc_low_before) / num_batches, - "progdrop_acc_rand_before": torch.tensor(progdrop_acc_rand_before) / num_batches, - "progdrop_loss_high_after": torch.tensor(progdrop_loss_high_after) / num_batches, - "progdrop_loss_low_after": torch.tensor(progdrop_loss_low_after) / num_batches, - "progdrop_loss_rand_after": torch.tensor(progdrop_loss_rand_after) / num_batches, - "progdrop_acc_high_after": torch.tensor(progdrop_acc_high_after) / num_batches, - "progdrop_acc_low_after": torch.tensor(progdrop_acc_low_after) / num_batches, - "progdrop_acc_rand_after": torch.tensor(progdrop_acc_rand_after) / num_batches, - "dropout_fraction": drop_fraction, - "by_layer": by_layer, - "idx_dropout_layers": idx_dropout_layers, - } - - return results - - -@torch.no_grad() -@test_nets -def eigenvector_dropout(nets, dataset, eigenvalues, eigenvectors, **parameters): - """ - method for testing network on supervised learning problem with eigenvector dropout - """ - - if not (isinstance(nets, list)): - nets = [nets] - - idx_dropout_layers = ( - nets[0].module.get_alignment_layer_indices() - if hasattr(nets[0], 'module') - else nets[0].get_alignment_layer_indices() - ) - - assert all([len(ev) == len(idx_dropout_layers) for ev in eigenvectors]), \ - "the number of layers in **eigenvectors** doesn't correspond to the number of alignment layers" - assert all([len(ev) == len(idx_dropout_layers) for ev in eigenvalues]), \ - "the number of layers in **eigenvalues** doesn't correspond to the number of alignment layers" - - num_nets = len(nets) - num_drops = parameters.get("num_drops", 9) - drop_fraction = torch.linspace(0, 1, num_drops + 2)[1:-1] - by_layer = parameters.get("by_layer", False) - num_layers = len(idx_dropout_layers) if by_layer else 1 - - idx_eigenvalue = [ - torch.fliplr(torch.tensor(range(0, ev.size(1))).expand(num_nets, -1)) - for ev in eigenvectors[0] - ] - - progdrop_loss_high = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_loss_low = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_loss_rand = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_high = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_low = torch.zeros((num_nets, num_drops, num_layers)) - progdrop_acc_rand = torch.zeros((num_nets, num_drops, num_layers)) - - num_batches = 0 - use_test = not parameters.get("train_set", True) - dataloader = dataset.test_loader if use_test else dataset.train_loader - - for batch in tqdm(dataloader): - images, labels = dataset.unwrap_batch(batch) - num_batches += 1 - - for dropidx, fraction in enumerate(drop_fraction): - idx_high, idx_low, idx_rand = get_dropout_indices(idx_eigenvalue, fraction) - - for layer in range(num_layers): - if by_layer: - drop_high, drop_low, drop_rand = ( - [idx_high[layer]], - [idx_low[layer]], - [idx_rand[layer]], - ) - drop_layer = [idx_dropout_layers[layer]] - drop_evals = [[ev[layer]] for ev in eigenvalues] - drop_evecs = [[evc[layer]] for evc in eigenvectors] - else: - drop_high, drop_low, drop_rand = idx_high, idx_low, idx_rand - drop_layer = copy(idx_dropout_layers) - drop_evals = deepcopy(eigenvalues) - drop_evecs = deepcopy(eigenvectors) - - out_high = [] - for idx, (net, evals, evecs) in enumerate(zip(nets, drop_evals, drop_evecs)): - real_net = net.module if hasattr(net, 'module') else net - out_ = real_net.forward_eigenvector_dropout( - images, evals, evecs, [drop[idx, :] for drop in drop_high], drop_layer - )[0] - out_high.append(out_) - out_low = [] - for idx, (net, evals, evecs) in enumerate(zip(nets, drop_evals, drop_evecs)): - real_net = net.module if hasattr(net, 'module') else net - out_ = real_net.forward_eigenvector_dropout( - images, evals, evecs, [drop[idx, :] for drop in drop_low], drop_layer - )[0] - out_low.append(out_) - out_rand = [] - for idx, (net, evals, evecs) in enumerate(zip(nets, drop_evals, drop_evecs)): - real_net = net.module if hasattr(net, 'module') else net - out_ = real_net.forward_eigenvector_dropout( - images, evals, evecs, [drop[idx, :] for drop in drop_rand], drop_layer - )[0] - out_rand.append(out_) - - loss_high = [dataset.measure_loss(out, labels).item() for out in out_high] - loss_low = [dataset.measure_loss(out, labels).item() for out in out_low] - loss_rand = [dataset.measure_loss(out, labels).item() for out in out_rand] - - acc_high = [dataset.measure_accuracy(out, labels) for out in out_high] - acc_low = [dataset.measure_accuracy(out, labels) for out in out_low] - acc_rand = [dataset.measure_accuracy(out, labels) for out in out_rand] - - progdrop_loss_high[:, dropidx, layer] += torch.tensor(loss_high) - progdrop_loss_low[:, dropidx, layer] += torch.tensor(loss_low) - progdrop_loss_rand[:, dropidx, layer] += torch.tensor(loss_rand) - progdrop_acc_high[:, dropidx, layer] += torch.tensor(acc_high) - progdrop_acc_low[:, dropidx, layer] += torch.tensor(acc_low) - progdrop_acc_rand[:, dropidx, layer] += torch.tensor(acc_rand) - - results = { - "progdrop_loss_high": progdrop_loss_high / num_batches, - "progdrop_loss_low": progdrop_loss_low / num_batches, - "progdrop_loss_rand": progdrop_loss_rand / num_batches, - "progdrop_acc_high": progdrop_acc_high / num_batches, - "progdrop_acc_low": progdrop_acc_low / num_batches, - "progdrop_acc_rand": progdrop_acc_rand / num_batches, - "dropout_fraction": drop_fraction, - "by_layer": by_layer, - "idx_dropout_layers": idx_dropout_layers, - } - - return results \ No newline at end of file diff --git a/_arxiv/_archive/alignment_v2/utils.py b/_arxiv/_archive/alignment_v2/utils.py deleted file mode 100644 index 73149842..00000000 --- a/_arxiv/_archive/alignment_v2/utils.py +++ /dev/null @@ -1,934 +0,0 @@ -import os -import math -import zipfile -from typing import List -from warnings import warn -from contextlib import contextmanager -from functools import wraps -from natsort import natsorted -from gitignore_parser import parse_gitignore - -import torch -import numpy as np -from scipy.linalg import null_space -from sklearn.decomposition import IncrementalPCA -from sklearn.decomposition import PCA - -# -------------- context managers & decorators -------------- -@contextmanager -def no_grad(no_grad=True): - if no_grad: - with torch.no_grad(): - yield - else: - yield - - -def test_nets(func): - @wraps(func) - def wrapper(nets, *args, **kwargs): - # get original training mode and set to eval - in_training_mode = [set_net_mode(net, training=False) for net in nets] - - # do decorated function - func_outputs = func(nets, *args, **kwargs) - - # return networks to whatever mode they used to be in - for train_mode, net in zip(in_training_mode, nets): - set_net_mode(net, training=train_mode) - - # return decorated function outputs - return func_outputs - - # return decorated function - return wrapper - - -def train_nets(func): - @wraps(func) - def wrapper(nets, *args, **kwargs): - # get original training mode and set to train - in_training_mode = [set_net_mode(net, training=True) for net in nets] - - # do decorated function - func_outputs = func(nets, *args, **kwargs) - - # return networks to whatever mode they used to be in - for train_mode, net in zip(in_training_mode, nets): - set_net_mode(net, training=train_mode) - - # return decorated function outputs - return func_outputs - - # return decorated function - return wrapper - - -def set_net_mode(net, training=True): - """helper for setting mode of network and returning current mode""" - # get current mode of network - in_training_mode = net.training - # set to training mode or evaluation mode - if training: - net.train() - else: - net.eval() - # return original mode of network - return in_training_mode - - -# ------- some other things ------- -def get_device(obj): - """simple method to get device of input tensor or nn.Module""" - if isinstance(obj, torch.nn.Module): - return next(obj.parameters()).device.type - elif isinstance(obj, torch.Tensor): - return "cuda" if obj.is_cuda else "cpu" - else: - raise ValueError("") - - -def check_iterable(val): - """duck-type check if val is iterable""" - try: - _ = iter(val) - except: - return False - else: - return True - - -def remove_by_idx(input, idx, dim): - """ - remove part of input indexed by idx on dim - """ - idx_keep = [i for i in range(input.size(dim)) if i not in idx] - return torch.index_select(input, dim, torch.tensor(idx_keep).to(input.device)) - - -def get_eval_transform_by_cutoff(cutoff): - """ - get method for transforming eigenvalues into a binary keep fraction - - will scale each eigenvector by 1 or 0 depending on whether that eigenvalue - explains more than **cutoff** fraction of the variance - - returns a callable method - """ - - def eval_transform(evals): - assert torch.all(evals >= 0), "found negative eigenvalues, doesn't work for 'cutoff' eval_transform" - evals = evals / torch.sum(evals) - return 1.0 * (evals > cutoff) - - return eval_transform - - -def fractional_histogram(*args, **kwargs): - """wrapper of np.histogram() with relative counts instead of total or density""" - counts, bins = np.histogram(*args, **kwargs) - counts = counts / np.sum(counts) - return counts, bins - - -def edge2center(edges): - """from a list of edges of bins (e.g. for torch.histogram()), return the centers between the edges""" - assert edges.ndim == 1, "edges must be a 1-d array" - return edges[:-1] + np.diff(edges) / 2 - - -def smartcorr(input): - """ - Performs torch corrcoef on the input data but sets each pair-wise correlation coefficent - to 0 where the activity has no variance (var=0) for a particular dimension (replaces nans with zeros)sss - """ - idx_zeros = torch.var(input, dim=1) == 0 - cc = torch.corrcoef(input) - cc[idx_zeros, :] = 0 - cc[:, idx_zeros] = 0 - return cc - - -def batch_cov(input, centered=True, correction=True): - """ - Performs batched covariance on input data of shape (batch, dim, samples) or (dim, samples) - - Where the resulting batch covariance matrix has shape (batch, dim, dim) or (dim, dim) - and bcov[i] = torch.cov(input[i]) if input.ndim==3 - - if centered=True (default) will subtract the means first - - if correction=True, will use */(N-1) otherwise will use */N - """ - assert (input.ndim == 2) or (input.ndim == 3), "input must be a 2D or 3D tensor" - assert isinstance(correction, bool), "correction must be a boolean variable" - - # check if batch dimension was provided - no_batch = input.ndim == 2 - - # add an empty batch dimension if not provided - if no_batch: - input = input.unsqueeze(0) - - # measure number of samples of each input matrix - S = input.size(2) - - # subtract mean if doing centered covariance - if centered: - input = input - input.mean(dim=2, keepdim=True) - - # measure covariance of each input matrix - bcov = torch.bmm(input, input.transpose(1, 2)) - - # correct for number of samples - bcov /= S - 1.0 * correction - - # remove empty batch dimension if not provided - if no_batch: - bcov = bcov.squeeze(0) - - return bcov - - -def smart_pca(input, centered=True, use_rank=True, correction=True): - """ - smart algorithm for pca optimized for speed - - input should either have shape (batch, dim, samples) or (dim, samples) - if dim > samples, will use svd and if samples < dim will use covariance/eigh method - - will center data when centered=True - - if it fails, will fall back on performing sklearns IncrementalPCA whenever forcetry=True - """ - assert (input.ndim == 2) or (input.ndim == 3), "input should be a matrix or batched matrices" - assert isinstance(correction, bool), "correction should be a boolean" - - if input.ndim == 2: - no_batch = True - input = input.unsqueeze(0) # create batch dimension for uniform code - else: - no_batch = False - - _, D, S = input.size() - if D > S: - # if more dimensions than samples, it's more efficient to run svd - v, w, _ = named_transpose([torch.linalg.svd(inp) for inp in input]) - # convert singular values to eigenvalues - w = [ww**2 / (S - 1.0 * correction) for ww in w] - # append zeros because svd returns w in R**k where k = min(D, S) - w = [torch.concatenate((ww, torch.zeros(D - S))) for ww in w] - - else: - # if more samples than dimensions, it's more efficient to run eigh - bcov = batch_cov(input, centered=centered, correction=correction) - w, v = named_transpose([eigendecomposition(C, use_rank=use_rank) for C in bcov]) - - # return to stacked tensor across batch dimension - w = torch.stack(w) - v = torch.stack(v) - - # if no batch originally provided, squeeze out batch dimension - if no_batch: - w = w.squeeze(0) - v = v.squeeze(0) - - # return eigenvalues and eigenvectors - return w, v - - -def eigendecomposition(C, use_rank=True): - """ - helper for getting eigenvalues and eigenvectors of covariance matrix - - will measure eigenvalues and eigenvectors with torch.linalg.eigh() - the output will be sorted from highest to lowest eigenvalue (& eigenvector) - - if use_rank=True, will measure the rank of the covariance matrix and zero - out any eigenvalues beyond the rank (that are usually nonzero numerical errors) - """ - try: - # measure eigenvalues and eigenvectors - w, v = torch.linalg.eigh(C) - - except torch._C._LinAlgError as error: - # this happens if the algorithm failed to converge - # try with sklearn's incrementalPCA algorithm - return sklearn_pca(C, use_rank=use_rank) - - except Exception as error: - # if any other exception, raise it - raise error - - # sort by eigenvalue from highest to lowest - w_idx = torch.argsort(-w) - w = w[w_idx] - v = v[:, w_idx] - - # iff use_rank=True, will set eigenvalues to 0 for probable numerical errors - if use_rank: - crank = torch.linalg.matrix_rank(C) # measure rank of covariance - w[crank:] = 0 # set eigenvalues beyond rank to 0 - - # return eigenvalues and eigenvectors - return w, v - - -def sklearn_pca(input, use_rank=True, rank=None): - """ - sklearn incrementalPCA algorithm serving as a replacement for eigh when it fails - - input should be a tensor with shape (num_samples, num_features) or it can be a - covariance matrix with (num_features, num_features) - - if use_rank=True, will set num_components to the rank of input and then fill out the - rest of the components with random orthogonal components in the null space of the true - components and set the eigenvalues to 0 - - if use_rank=False, will attempt to fit all the components - if rank is not None, will attempt to fit #=rank components without measuring the rank directly - (will ignore "rank" if use_rank=False) - - returns w, v where w is eigenvalues and v is eigenvectors sorted from highest to lowest - """ - # dimension - num_samples, num_features = input.shape - - # measure rank (or set to None) - rank = None if not use_rank else (rank if rank is not None else fast_rank(input)) - - # create and fit IncrementalPCA object on input data - ipca = IncrementalPCA(n_components=rank).fit(input) - - # eigenvectors are the components - v = ipca.components_ - - # eigenvalues are the scaled singular values - w = ipca.singular_values_**2 / num_samples - - # if v is a subspace of input (e.g. not a full basis, fill it out) - if v.shape[0] < num_features: - msg = "adding this because I think it should always be true, and if not I want to find out" - assert w.shape[0] == v.shape[0], msg - v_kernel = null_space(v).T - v = np.vstack((v, v_kernel)) - w = np.concatenate((w, np.zeros(v_kernel.shape[0]))) - - return torch.tensor(w, dtype=torch.float), torch.tensor(v, dtype=torch.float).T - - -def fast_rank(input): - """uses transpose to speed up rank computation, otherwise normal""" - if input.size(-2) < input.size(-1): - input = torch.transpose(input, -2, -1) - return int(torch.linalg.matrix_rank(input)) - - -# ------------------ alignment functions ---------------------- -def alignment(input, weight, method="alignment", relative=True): - """ - measure alignment (proportion variance explained) between **input** and **weight** - - computes the rayleigh quotient between each weight vector in **weight** and the **input** fed - into **weight**. Typically, **input** is the output in Layer L-1 and **weight** is from Layer L - - the output is normalized by the total variance in output of layer L-1 to measure the proportion - of variance of in **input** is explained by a projection onto node's weights in **weight** - - args - ---- - input: (batch, neurons) torch tensor - - represents input activity being fed in to network weight layer - weight: (num_out, num_in) torch tensor - - represents weights multiplied by input layer - method: string, default='alignment' - - which method to use to measure structure in **input** - - if 'alignment', uses covariance matrix of **input** - - if 'similarity', uses correlation matrix of **input** - relative: bool, default=True, - - if True, will measure relative RQ (divide by sum of eigenvalues) - - returns - ------- - alignment: (num_out, ) torch tensor - - proportion of variance explained by projection of **input** onto each **weight** vector - """ - assert method == "alignment" or method == "similarity", "method must be set to either 'alignment' or 'similarity' (or None, default is alignment)" - if method == "alignment": - cc = torch.cov(input.T) - #cc = torch.corrcoef(input.T) - elif method == "similarity": - cc = smartcorr(input.T) - else: - raise ValueError(f"did not recognize method ({method}), must be 'alignment' or 'similarity'") - - rq = torch.sum(torch.matmul(weight, cc) * weight, axis=1) / torch.sum(weight * weight, axis=1) - - if relative: - return rq / torch.trace(cc) - - # shuffled_rq = rq[torch.randperm(rq.size(0))] # Shuffle RQ values - # rq = shuffled_rq / torch.trace(cc) - - return rq - - -@torch.no_grad() -def expected_alignment_distribution(eigenvalues, relative=True, valid_rotation=True, with_rotation=True, bins=11, num_tests=100): - """ - for a set of eigenvalues, measure the expected distribution given aligned weights - - relative determines whether to normalize by sum of eigenvalues - valid_rotation determines whether we create orthonormal rotation matrices (for True) - or just normally distributed weights with the expected variance (for False) - bins works like histogram bins - num_tests determines how many tests to do (it's actually num_tests*len(eigenvalues)) - """ - # otherwise, randomly sample using eigenvalue as weighted average - N = len(eigenvalues) - if relative: - eigenvalues /= eigenvalues.sum() - eigenvalues = eigenvalues.view(-1, 1).expand(-1, N * num_tests) - if with_rotation: - if valid_rotation: - mixing = [torch.linalg.qr(torch.normal(0, 1 / math.sqrt(N), (N, N)))[0].T for _ in range(num_tests)] - coefficients = torch.concatenate(mixing, axis=1) ** 2 - else: - coefficients = torch.normal(0, 1 / math.sqrt(N), (N, N * num_tests)) ** 2 - else: - coefficients = torch.ones((N, N * num_tests)) - coefficients = coefficients.to(get_device(eigenvalues)) - weights = eigenvalues * coefficients - alignment = torch.sum(eigenvalues * weights, dim=0) / weights.sum(dim=0) - counts, bins = torch.histogram(alignment.cpu(), bins=bins, density=True) - centers = edge2center(bins) - return counts, bins, centers - -def compute_skewness_inplace(input): - """ - Compute skewness for each input feature (dimension) using in-place operations. - """ - mean = input.mean(dim=0) - variance = input.var(dim=0) - - # Add epsilon to variance to avoid division by zero - epsilon = 1e-6 - variance = variance + epsilon - - third_moment = (input - mean).pow(3).mean(dim=0) - - # Skewness formula: E[(X - μ)^3] / (E[(X - μ)^2])^(3/2) - skewness = third_moment / variance.pow(1.5) - - return skewness - -def compute_kurtosis_inplace(input): - """ - Compute kurtosis for each input feature (dimension) using in-place operations. - """ - mean = input.mean(dim=0) - variance = input.var(dim=0) - fourth_moment = (input - mean).pow_(4).mean(dim=0) - - # Kurtosis formula: E[(X - μ)^4] / (E[(X - μ)^2])^2 - 3 - kurtosis = fourth_moment / variance.pow(2) - 3 - return kurtosis - -def compute_skewness_low_rank(input, rank_approx=50): - """ - Compute skewness after reducing the dimensionality using PCA to the specified rank approximation. - """ - - pca = PCA(n_components=rank_approx) - input_reduced = torch.Tensor(pca.fit_transform(input.cpu().numpy())).to(input.device) - - return compute_skewness_inplace(input_reduced) - -def compute_kurtosis_low_rank(input, rank_approx=50): - """ - Compute kurtosis after reducing the dimensionality using PCA to the specified rank approximation. - """ - - pca = PCA(n_components=rank_approx) - input_reduced = torch.Tensor(pca.fit_transform(input.cpu().numpy())).to(input.device) - - return compute_kurtosis_inplace(input_reduced) - -def compute_redundancy(weights, input_covariance): - """ - Compute the redundancy matrix for all pairs of nodes using matrix multiplication. - - Args: - weights: (n, d) torch tensor, where n is the number of nodes and d is the input dimension. - input_covariance: (d, d) torch tensor, the covariance matrix of the input data. - - Returns: - redundancy_matrix: (n, n) torch tensor, redundancy between each pair of nodes (diagonal excluded). - """ - # Compute the redundancy matrix: R = W Σ_X W^T - redundancy_matrix = torch.matmul(weights, torch.matmul(input_covariance, weights.T)) - - # Zero out the diagonal elements (self-information) - redundancy_matrix.fill_diagonal_(0) - - return redundancy_matrix - - - -def alignment_expansion(input, weight, method="alignment_expansion", relative=True): - """ - Measure alignment (proportion variance explained) between **input** and **weight** - and compute single-node information, redundancy, and total information for the layer. - - args: - ---- - input: (batch, neurons) torch tensor - - represents input activity being fed into network weight layer - weight: (num_out, num_in) torch tensor - - represents weights multiplied by input layer - method: string, default='alignment' - - which method to use to measure structure in **input** - - if 'alignment', uses covariance matrix of **input** - - if 'similarity', uses correlation matrix of **input** - relative: bool, default=True, - - if True, will measure relative RQ (divide by sum of eigenvalues) - - returns: - ------- - alignment: (num_out, ) torch tensor - - proportion of variance explained by projection of **input** onto each **weight** vector - single_node_info: torch tensor - - Information carried by each node (mutual information for single nodes) - redundancy_matrix: torch tensor - - Pairwise redundancy (mutual information overlap between pairs of nodes) - total_info: float - - Total information of the layer, accounting for redundancy - """ - - # Step 1: Compute covariance of input - # if method == "alignment_expansion": - cc = torch.cov(input.T) # Covariance matrix of input - # elif method == "alignment_expansion": - # cc = torch.corrcoef(input.T) # Correlation matrix of input - # else: - # raise ValueError(f"Method {method} not recognized. Use 'alignment' or 'similarity'.") - - # Step 2: Compute Rayleigh Quotient (RQ) for each node - rq = torch.sum(torch.matmul(weight, cc) * weight, axis=1) / torch.sum(weight * weight, axis=1) - - # Step 3: Compute Single-Node Information for each node (with kurtosis correction) - single_node_info = torch.log(rq)# / torch.trace(cc) # First term (proportional to RQ) - - # Compute third-order term: Skewness correction - skewness = compute_skewness_inplace(input) - - # Ensure the skewness shape matches the input dimensionality - if skewness.shape[0] == input.shape[1]: # If skewness is (d,), reshape it - skewness = skewness.view(1, -1) # Reshape skewness to (1, d) for broadcasting - - # Apply the skewness correction term - normalized_weight = weight #/ weight.norm(p=2, dim=1, keepdim=True) - skewness_correction = torch.sum(normalized_weight * skewness, dim=1) - - # Compute kurtosis-based correction term - kurtosis = compute_kurtosis_low_rank(input) # Kurtosis is computed for each input feature - - # Expand kurtosis tensor to match weight shape - # if kurtosis.dim() == 1: - # kurtosis = kurtosis.unsqueeze(0) # Add a dimension to match the weight tensor shape - - kurtosis_correction = 0.5 * torch.norm(normalized_weight, p=2, dim=1) * kurtosis[:weight.shape[1]].mean() - - #single_node_info = skewness_correction - #single_node_info = kurtosis_correction - - # Step 4: Continue with redundancy and total information as before - redundancy_matrix = torch.abs(compute_redundancy(normalized_weight, cc)) - adjusted_single_node_info = adjust_information_with_redundancy(single_node_info, redundancy_matrix)#single_node_info.clone() - - total_info = torch.sum(adjusted_single_node_info) - - if method == "alignment_0": - info = adjusted_single_node_info - elif method == "alignment_1": - info = skewness_correction - elif method == "alignment_2": - info = kurtosis_correction - elif method == "alignment_red": - info = redundancy_matrix - - - return info - -def adjust_information_with_redundancy(single_node_info, redundancy_matrix): - """ - Adjust single-node information by subtracting redundancy for each pair of nodes. - - Args: - single_node_info: (n, ) torch tensor, the single-node information for each node. - redundancy_matrix: (n, n) torch tensor, the redundancy between each pair of nodes. - - Returns: - adjusted_info: (n, ) torch tensor, adjusted single-node information. - """ - n = single_node_info.size(0) - - # Compute pairwise differences between single-node information - #info_diffs = single_node_info.view(n, 1) - single_node_info.view(1, n) - - # Create a mask where the single-node information is smaller for each pair - #mask = (info_diffs < 0).float() - - # Subtract redundancy from the node with smaller information - #redundancy_adjustment = torch.sum(mask * redundancy_matrix, dim=1) - redundancy_matrix.fill_diagonal_(0) - - redundancy_adjustment = torch.sum(redundancy_matrix, dim=1) - - # Adjust the single-node information - adjusted_info =redundancy_adjustment - - return adjusted_info - -def plot_information_results(single_node_info, adjusted_single_node_info, redundancy_matrix, total_info): - """ - Plot single-node information, adjusted single-node information, redundancy matrix, and total information for the layer. - - args: - ---- - single_node_info: torch tensor - - Information carried by each node - adjusted_single_node_info: torch tensor - - Adjusted information for each node after subtracting redundancy - redundancy_matrix: torch tensor - - Pairwise redundancy between nodes - total_info: float - - Total information of the layer - """ - import matplotlib.pyplot as plt - import seaborn as sns - - # Plot single-node information - plt.figure(figsize=(8, 6)) - plt.bar(range(len(single_node_info)), single_node_info.cpu().detach().numpy(), alpha=0.6, label="Original Info") - plt.bar(range(len(adjusted_single_node_info)), adjusted_single_node_info.cpu().detach().numpy(), alpha=0.6, label="Adjusted Info") - plt.xlabel('Node Index') - plt.ylabel('Single Node Information (MI)') - plt.title('Single-Node Information for Each Node (Original vs Adjusted)') - plt.legend() - plt.show() - - # Plot redundancy matrix - plt.figure(figsize=(8, 6)) - sns.heatmap(redundancy_matrix.cpu().detach().numpy(), annot=True, cmap="YlGnBu") - plt.title('Redundancy Matrix (Pairwise Redundancy between Nodes)') - plt.show() - - # Display total information - print(f"Total Information for the Layer: {total_info.item():.4f}") - - - -def get_maximum_strides(h_input, w_input, layer): - h_max = int(np.floor((h_input + 2 * layer.padding[0] - layer.dilation[0] * (layer.kernel_size[0] - 1) - 1) / layer.stride[0] + 1)) - w_max = int(np.floor((w_input + 2 * layer.padding[1] - layer.dilation[1] * (layer.kernel_size[1] - 1) - 1) / layer.stride[1] + 1)) - return h_max, w_max - - -def get_unfold_params(layer): - return dict(stride=layer.stride, padding=layer.padding, dilation=layer.dilation) - - -# ----- cvPCA methods ----- -@torch.no_grad() -def cvPCA(X1, X2): - """X1, X2 are both (dimensions x samples)""" - D, B = X1.shape - assert X2.shape == (D, B), "shape of X1 and X2 is not the same" - _, u = smart_pca(X1) - - cproj0 = X1.T @ u - cproj1 = X2.T @ u - ss = (cproj0 * cproj1).mean(axis=0) - return ss - - -def get_num_components(nc, shape): - return nc if nc is not None else min(shape) - - -@torch.no_grad() -def shuff_cvPCA(X1, X2, nshuff=5, cvmethod=cvPCA): - """X1, X2 are both (dimensions x samples)""" - D, B = X1.shape - assert X2.shape == (D, B), "shape of X1 and X2 is not the same" - nc = get_num_components(None, (D, B)) - ss = torch.zeros((nshuff, nc)) - X = torch.stack((X1, X2)) - for k in range(nshuff): - iflip = 1 * (torch.rand(B) > 0.5) - X1c = torch.gather(X, 0, iflip.view(1, 1, -1).expand(1, D, -1)).squeeze(0) - X2c = torch.gather(X, 0, -(iflip - 1).view(1, 1, -1).expand(1, D, -1)).squeeze(0) - ss[k] = cvmethod(X1c, X2c) - return ss - - -def avg_value_by_layer(full): - """ - return average value per layer across training - - **full** is a list of lists where the outer list is each snapshot through training or - minibatch etc and each inner list is the value for each node in the network across layers - of a particular measurement - - For example: - num_epochs = 1000 - nodes_per_layer = [50, 40, 30, 20] - len(full) == 1000 - len(full[i]) == 4 ... for all i - [f.shape for f in full[i]] = [50, 40, 30, 20] ... for all i - - this method will return a tensor of size (num_layers, num_epochs) of the average value (for - whatever value is in **full**) for each list/list - """ - num_epochs = len(full) - num_layers = len(full[0]) - avg_full = torch.zeros((num_layers, num_epochs)) - for layer in range(num_layers): - avg_full[layer, :] = torch.tensor([torch.mean(f[layer]) for f in full]) - return avg_full.cpu() - - -def value_by_layer(full: List[List[torch.Tensor]], layer: int) -> torch.Tensor: - """ - return all value measurements for a particular layer from **full** - - **full** is a list of lists where the outer list is each snapshot through training or - minibatch etc and each inner list is the value for each node in the network across layers - - this method will return just the part of **full** corresponding to the layer indexed - by **layer** as a tensor of shape (num_epochs, num_nodes) - - see ``avg_value_by_layer`` for a little more explanation - """ - return torch.cat([f[layer].view(1, -1) for f in full], dim=0).cpu() - - -def condense_values(full: List[List[List[torch.Tensor]]]) -> List[torch.Tensor]: - """ - condense List[List[List[Tensor]]] representing some value measured across networks, batches, and layers, for each node in the layer - - returns list of #=num_layers tensors, where each tensor has shape (num_networks, num_batches, num_nodes_per_layer) - - full should be a list of list of lists - the first list should have length = number of networks - the second list should have length = number of batches - the third list should have length = number of layers in the network (this has to be the same for each network!) - the tensor should have shape = number of nodes in this layer (also must be the same for each network) (or can be anything as long as consistent across layers) - """ - num_layers = len(full[0][0]) - return [torch.stack([value_by_layer(value, layer) for value in full]) for layer in range(num_layers)] - - -def transpose_list(list_of_lists): - """helper function for transposing the order of a list of lists""" - return list(map(list, zip(*list_of_lists))) - - -def named_transpose(list_of_lists, reduction=None): - """ - helper function for transposing lists without forcing the output to be a list like transpose_list - - for example, if list_of_lists contains 10 copies of lists that each have 3 iterable elements you - want to name "A", "B", and "C", then write: - A, B, C = named_transpose(list_of_lists) - - if reduction is used, it will be applied to each output, otherwise will make them lists - """ - if reduction is not None: - return map(reduction, zip(*list_of_lists)) - return map(list, zip(*list_of_lists)) - - -def ptp(tensor, dim=None, keepdim=False): - """ - simple method for measuring range of tensor on requested dimension or on all data - """ - if dim is None: - return tensor.max() - tensor.min() - return tensor.max(dim, keepdim).values - tensor.min(dim, keepdim).values - - -def rms(tensor, dim=None, keepdim=False): - """simple method for measuring root-mean-square on requested dimension or on all data in tensor""" - if dim is None: - return torch.sqrt(torch.mean(tensor**2)) - return torch.sqrt(torch.mean(tensor**2, dim=dim, keepdim=keepdim)) - - -def compute_stats_by_type(tensor, num_types, dim, method="var"): - """ - helper method for returning the mean and variance across a certain dimension - where multiple types are concatenated on that dimension - - for example, suppose we trained 2 networks each with 3 sets of parameters - and concatenated the loss in a tensor like [set1-loss-net1, set1-loss-net2, set2-loss-net1, ...] - then this would contract across the nets from each set and return the mean and variance - """ - num_on_dim = tensor.size(dim) - num_per_type = int(num_on_dim / num_types) - tensor_by_type = tensor.unsqueeze(dim) - expand_shape = list(tensor_by_type.shape) - expand_shape[dim + 1] = num_per_type - expand_shape[dim] = num_types - tensor_by_type = tensor_by_type.view(expand_shape) - type_means = torch.mean(tensor_by_type, dim=dim + 1) - if method == "var": - type_dev = torch.var(tensor_by_type, dim=dim + 1) - elif method == "std": - type_dev = torch.std(tensor_by_type, dim=dim + 1) - elif method == "se": - type_dev = torch.std(tensor_by_type, dim=dim + 1) / np.sqrt(num_per_type) - elif method == "range": - type_dev = ptp(tensor_by_type, dim=dim + 1) - else: - raise ValueError(f"Method ({method}) not recognized.") - - return type_means, type_dev - - -def weighted_average(data, weights, dim, keepdim=False, ignore_nan=False): - """ - take the weighted average of **data** on a certain dimension with **weights** - - weights should be a nonnegative vector that broadcasts into data - avg = sum_i(data_i * weight_i, dim) / sum_i(weight_i, dim) - - if ignore_nan=True, (default=False), will ignore nans in weighted average - """ - assert data.ndim == weights.ndim, "data and weights must have same number of dimensions" - assert torch.all(weights[~torch.isnan(weights)] >= 0), "weights must be nonnegative" - - for d in dim if check_iterable(dim) else [dim]: - assert data.size(d) == weights.size( - d - ), f"data and weights must have same size in averaging dimensions (data.size({d})={data.size(d)}, (weight.size({d})={weights.size(d)}))" - - # use normal sum if not ignore nan, otherwise use nansum - sum = torch.nansum if ignore_nan else torch.sum - - # make sure nans are in the same place in weights and data for accurate division by total weight - if ignore_nan: - weights = weights.expand(data.size()) - weights = torch.masked_fill(weights, torch.isnan(data), torch.nan) - - # numerator & denominator of weighted average - numerator = sum(data * weights, dim=dim, keepdim=keepdim) - denominator = sum(weights, dim=dim, keepdim=keepdim) - - # return weighted average - return numerator / denominator - - -def fgsm_attack(image, epsilon, data_grad, transform, sign): - """update an image with fast-gradient sign method""" - warn("fgsm_attack is only going to be in utils temporarily!", DeprecationWarning, stacklevel=2) - # Collect the element-wise sign of the data gradient - if sign: - data_grad = data_grad.sign() - else: - data_grad = data_grad.clone() - # Create the perturbed image by adjusting each pixel of the input image - perturbed_image = image + epsilon * data_grad - # Adding clipping to maintain [0,1] range - perturbed_image = transform(perturbed_image) - # Return the perturbed image - return perturbed_image - - -def str2bool(str): - if isinstance(str, bool): - return str - if str.lower() in ("true", "1"): - return True - elif str.lower() in ("false", "0"): - return False - else: - raise TypeError("Boolean type expected") - - -def save_checkpoint(nets, optimizers, results, path): - """ - Method for saving checkpoints for networks throughout training. - """ - multi_model_ckpt = {f"model_state_dict_{i}": net.state_dict() for i, net in enumerate(nets)} - multi_optimizer_ckpt = {f"optimizer_state_dict_{i}": opt.state_dict() for i, opt in enumerate(optimizers)} - checkpoint = results | multi_model_ckpt | multi_optimizer_ckpt - torch.save(checkpoint, path) - - -def load_checkpoints(nets, optimizers, device, path): - """ - Method for loading presaved checkpoint during training. - TODO: device handling for passing between gpu/cpu - """ - - if device == "cpu": - checkpoint = torch.load(path, map_location=device) - elif device == "cuda": - checkpoint = torch.load(path) - - net_ids = natsorted([key for key in checkpoint if key.startswith("model_state_dict")]) - opt_ids = natsorted([key for key in checkpoint if key.startswith("optimizer_state_dict")]) - assert all( - [oi.split("_")[-1] == ni.split("_")[-1] for oi, ni in zip(opt_ids, net_ids)] - ), "nets and optimizers cannot be matched up from checkpoint" - - [net.load_state_dict(checkpoint.pop(net_id)) for net, net_id in zip(nets, net_ids)] - [opt.load_state_dict(checkpoint.pop(opt_id)) for opt, opt_id in zip(optimizers, opt_ids)] - - if device == "cuda": - [net.to(device) for net in nets] - - return nets, optimizers, checkpoint - - -def match_git(path): - """simple method for determining if a path is a git-related file or directory""" - if ".git" in path: - return True - return False - - -def compress_directory(output_path, directory_path=None): - """send an entire directory to a zip file at output_path, using .gitignore and ignoring .git files""" - if directory_path is None: - # relative to utils -- this is the main repo path - directory_path = os.path.dirname(os.path.abspath(__file__)) + "/../.." - print(directory_path) - # Parse .gitignore file - gitignore_path = os.path.join(directory_path, ".gitignore") - matches = parse_gitignore(gitignore_path) - - # Prepare list for copying files - files_to_copy = [] - archive_names = [] - for dirpath, dirnames, files in os.walk(directory_path): - if matches(dirpath) or match_git(dirpath): - # clear any files from within this path - dirnames[:] = [] - else: - # Filter files based on .gitignore rules (and don't save any .git files) - keep_files = [f for f in files if not matches(f) and not match_git(f)] - # Make full path - full_files = [os.path.join(dirpath, f) for f in keep_files] - for file in full_files: - # Add file to the copy list - files_to_copy.append(file) - archive_names.append(os.path.relpath(file, directory_path)) - - # create zip file - with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zipf: - # go through directory - for file, name in zip(files_to_copy, archive_names): - zipf.write(file, arcname=name) diff --git a/_arxiv/_archive/benchmark_dropout.py b/_arxiv/_archive/benchmark_dropout.py deleted file mode 100644 index 1bd94dad..00000000 --- a/_arxiv/_archive/benchmark_dropout.py +++ /dev/null @@ -1,230 +0,0 @@ -""" -Benchmark script for comparing different dropout implementation approaches. - -This script compares the performance of three approaches to progressive dropout: -1. Original (sequential): Processing networks one-by-one -2. Batched: Processing networks in small batches -3. Tensorized: Using tensor operations to process networks at once -""" - -import os -import sys -import time -import argparse -import logging -from typing import Dict, List, Tuple, Any -from collections import defaultdict - -import numpy as np -import torch -import torch.nn as nn -from tqdm import tqdm - -from alignment.config import ExperimentConfig -from alignment.models.registry import create_model -from alignment.metrics import get_metric -from alignment.datasets import load_dataset -from alignment.dropout import progressive_dropout -from alignment import utils - -# Setup logging -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") - -def create_networks(config, num_networks=20): - """Create multiple neural networks for benchmarking.""" - networks = [] - device = config.device - - for i in range(num_networks): - # Set different seed for each network - if hasattr(config, 'seed') and config.seed is not None: - torch.manual_seed(config.seed + i) - torch.cuda.manual_seed_all(config.seed + i) - np.random.seed(config.seed + i) - - # Create model - model = create_model(config.model) - model.to(device) - networks.append(model) - - logger.info(f"Created {len(networks)} models with independent initializations") - return networks - -def run_benchmark(config_path, approaches=["original", "batched", "tensorized"], - num_networks=20, num_runs=3, num_batches=5, detailed_timing=True): - """ - Run benchmarks for different dropout implementation approaches. - - Args: - config_path: Path to configuration file - approaches: List of approaches to benchmark - num_networks: Number of networks to use - num_runs: Number of times to run each benchmark for averaging - num_batches: Number of data batches to process - detailed_timing: Whether to time individual steps - """ - # Load configuration - config = ExperimentConfig.load(config_path) - - # Create networks - networks = create_networks(config, num_networks) - - # Prepare dataset - dataset = load_dataset(config.dataset) - - # Get metric - metric = get_metric(config.alignment.metric) - - # Benchmark results - results = {} - detailed_results = defaultdict(lambda: defaultdict(list)) - - # Run benchmarks - for approach in approaches: - logger.info(f"Benchmarking {approach} approach") - - # Set approach-specific parameters - if approach == "original": - # Original sequential approach - kwargs = {"network_batch_size": 1, "use_tensorized": False} - elif approach == "batched": - # Batched approach - kwargs = {"network_batch_size": 4, "use_tensorized": False} - elif approach == "tensorized": - # Tensorized approach - kwargs = {"use_tensorized": True} - else: - logger.error(f"Unknown approach: {approach}") - continue - - # Run multiple times and average - timings = [] - for run in range(num_runs): - logger.info(f"Run {run+1}/{num_runs}") - - # Time the execution of full dropout - start_time = time.time() - - # Run progressive dropout with the specified approach - dropout_results = progressive_dropout( - networks, - dataset, - dropout_fractions=np.linspace(0.1, 0.9, config.alignment.dropout_steps), - metric=metric, - device=config.device, - pruning_mode=config.extra.dropout_pruning_mode, - dropout_mode=config.extra.dropout_mode, - num_batches=num_batches, - detailed_timing=detailed_timing, - **kwargs - ) - - # Calculate elapsed time - elapsed_time = time.time() - start_time - timings.append(elapsed_time) - - # Collect detailed timing information if available - if detailed_timing and hasattr(dropout_results, "timing_info"): - for key, value in dropout_results.timing_info.items(): - detailed_results[approach][key].append(value) - - # Log detailed timing information - for key, values in dropout_results.timing_info.items(): - if isinstance(values, (int, float)): - logger.info(f" {key}: {values:.2f} seconds") - - logger.info(f"Completed run in {elapsed_time:.2f} seconds") - - # Calculate average and standard deviation - avg_time = np.mean(timings) - std_time = np.std(timings) - - # Store results - results[approach] = { - "average_time": avg_time, - "std_time": std_time, - "timings": timings - } - - logger.info(f"{approach}: {avg_time:.2f} ± {std_time:.2f} seconds") - - # Print comparison - logger.info("\nBenchmarking Results:") - logger.info("-" * 50) - logger.info(f"{'Approach':<15} {'Average Time (s)':<20} {'Speedup':<10}") - logger.info("-" * 50) - - # Calculate speedup relative to original approach - original_time = results["original"]["average_time"] if "original" in results else None - - for approach in approaches: - if approach not in results: - continue - - avg_time = results[approach]["average_time"] - speedup = original_time / avg_time if original_time else 1.0 - - logger.info(f"{approach:<15} {avg_time:.2f} ± {results[approach]['std_time']:.2f} {'':>5} {speedup:.2f}x") - - logger.info("-" * 50) - - # Print detailed timing if available - if detailed_timing: - logger.info("\nDetailed Timing Breakdown:") - logger.info("-" * 80) - - # Find all unique timing keys - all_keys = set() - for approach in approaches: - if approach in detailed_results: - all_keys.update(detailed_results[approach].keys()) - - # Print header - logger.info(f"{'Operation':<25} " + " ".join([f"{approach:<15}" for approach in approaches])) - logger.info("-" * 80) - - # Print timings for each key - for key in sorted(all_keys): - line = f"{key:<25} " - for approach in approaches: - if approach in detailed_results and key in detailed_results[approach]: - values = detailed_results[approach][key] - if len(values) > 0: - avg = np.mean(values) - line += f"{avg:.2f}s{' ' * 10}" - else: - line += f"{'N/A':<15}" - else: - line += f"{'N/A':<15}" - logger.info(line) - - logger.info("-" * 80) - - return results, detailed_results if detailed_timing else None - -def main(): - """Main function for benchmarking.""" - parser = argparse.ArgumentParser(description="Benchmark dropout implementation approaches") - parser.add_argument("--config", type=str, required=True, help="Path to configuration file") - parser.add_argument("--approaches", type=str, nargs="+", default=["original", "batched", "tensorized"], - help="Approaches to benchmark") - parser.add_argument("--num-networks", type=int, default=20, help="Number of networks to use") - parser.add_argument("--num-runs", type=int, default=3, help="Number of runs for each benchmark") - parser.add_argument("--num-batches", type=int, default=5, help="Number of data batches to process") - parser.add_argument("--no-detailed-timing", action="store_true", help="Disable detailed timing") - - args = parser.parse_args() - - # Run benchmarks - run_benchmark( - args.config, - args.approaches, - args.num_networks, - args.num_runs, - args.num_batches, - not args.no_detailed_timing - ) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/_arxiv/_archive/config_alignment_stats.yaml b/_arxiv/_archive/config_alignment_stats.yaml deleted file mode 100644 index abd73f95..00000000 --- a/_arxiv/_archive/config_alignment_stats.yaml +++ /dev/null @@ -1,56 +0,0 @@ -experiment: alignment_stats -results_path: # <-- Add path to where results to be saved/loaded -no_save: False -just_plot: False -save_networks: False -show_params: False -show_all: False -#device: cpu -use_timestamp: False -#timestamp: # The timestamp of previous experiment to plot. - -dataset: - name: MNIST - path: # <-- Add path to your dataset - download: True # Downloads it if it does not exist. - -model: - name: MLP - dropout: 0 - alignment_layers: null # Specify the layers participating in alignment - for all layers use null | exp for MLP: {"layerInput.0": null, "layerHidden.0.1": "layerHidden.0.0", "layerHidden.1.1": "layerHidden.1.0"} - -optimizer: - name: Adam - lr: 1e-3 - weight_decay: 0 - -training: - batch_size: 1024 - epochs: 1 - replicates: 1 - -alignment: - no_alignment: False - delta_weights: False - frequency: 1 - -checkpointing: - use_prev: False - save_checkpoints: False - frequency: 1 - use_wandb: False - -extra: - num_drops: 9 - dropout_by_layer: False - - ### For alignment_comparison and loading_prediction experiments - comparison: "lr" - regularizers: ["none", "dropout", "weight_decay"] - lrs: [1e-2, 1e-3, 1e-4] - compare_dropout: 0.5 - compare_wd: 1e-5 - - ### For adversarial_shaping experiment - cutoffs: [1e-2, 1e-3, 1e-4, 0.0] - manual_frequency: 5 diff --git a/_arxiv/_archive/config_alignment_stats_old.yaml b/_arxiv/_archive/config_alignment_stats_old.yaml deleted file mode 100644 index a25906ba..00000000 --- a/_arxiv/_archive/config_alignment_stats_old.yaml +++ /dev/null @@ -1,66 +0,0 @@ -experiment: alignment_stats -results_path: results # <-- Add path to where results to be saved/loaded -no_save: False -just_plot: False -save_networks: False -show_params: False -show_all: False -device: cuda -use_timestamp: False -#timestamp: # The timestamp of previous experiment to plot. - -dataset: - name: MNIST - path: /n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment/data - -model: - name: MLP - dropout: 0 - alignment_layers: null # Specify the layers participating in alignment - for all layers use null | exp for MLP: {"layerInput.0": null, "layerHidden.0.1": "layerHidden.0.0", "layerHidden.1.1": "layerHidden.1.0"} - -training: - batch_size: 1024 - epochs: 3 - replicates: 3 - name: Adam - lr: 1e-3 - weight_decay: 0 - -alignment: - do_alignment: True - frequency: 1 - methods: ["RQ"] - measure_expected: False - bins: 50 - cnn_mode: "unfold" # Options for CNN alignment processing: - # "unfold" - Reshapes conv layer features into (-1, features) with batch and patches together (default) - # "patchwise" - Computes alignment per-patch, with special processing in patchwise_alignment - # "batch_patch_combined" - Standardized approach that combines batch and patch dimensions - # Differences between modes: - # - "unfold" is the classic approach with flexible reshaping options - # - "patchwise" preserves patch structure for specialized calculations - # - "batch_patch_combined" ensures consistent dimensionality across frameworks --> original method - scale_by_norm: False # If True, scales covariance matrices by their norm before computing RQ - # Similar to the "similarity" option in original codebase -checkpointing: - use_prev: False - save_checkpoints: False - frequency: 1 - use_wandb: True - -extra: - num_drops: 40 - dropout_pruning_mode: "global_joint" # Options: - # "global_joint" - Prune X% of nodes across all layers based on their global alignment ranking - # This distributes pruning across layers proportional to where the - # highest/lowest alignment nodes are located. Some layers may have more - # neurons pruned than others, preserving the global ranking of importance. - # "layer_wise" - Prune exactly X% from each layer and apply pruning to all layers at once - # This shows the effect of pruning all layers simultaneously with fixed proportions - # "layer_isolated" - Prune X% from only one layer at a time - # This creates multiple figures (one per layer) showing isolated effects - dropout_mode: "scaled" # Options for how targeted dropout is applied to neurons: - # "scaled" - Zeroes neurons and applies scaling to maintain signal magnitude (default) - # "unscaled" - Zeroes neurons but doesn't apply compensation scaling - exclude_classification_layer: True # If True, excludes the classification layer from pruning (always true in original codebase) - \ No newline at end of file diff --git a/_arxiv/_archive/config_alignment_stats_v2.yaml b/_arxiv/_archive/config_alignment_stats_v2.yaml deleted file mode 100644 index f1e50278..00000000 --- a/_arxiv/_archive/config_alignment_stats_v2.yaml +++ /dev/null @@ -1,74 +0,0 @@ -experiment: "mnist_alignment_example" -results_path: # <-- Add path to where results to be saved/loaded -no_save: false ## if true, skip saving results and plots -just_plot: false ## If true, skip the experiment’s main() logic, only load previously saved results and call plot_from_existing() -save_networks: false ## If true, also save trained networks (self.save_networks(...)) after training. -show_params: false ## keep the plots open WE CAN REMOVE THIS -show_all: false ## ## keep the plots open WE CAN REMOVE THIS -use_timestamp: false ## If use_timestamp is true, the code stores results in subfolders named by timestamp (an ISO/time string). -timestamp: null - -ddp_world_size: 2 - -dataset: - name: "ImageNet" # "MNIST", "CIFAR10", "CIFAR100", "ImageNet", etc. - path: # <-- Add path to your dataset ## path to dataset - -model: - name: "AlexNet" ## base artitecture, e.g., "MLP", "CNN2P2", "AlexNet". - dropout: 0 ## Dropout fraction 0 to 1 - alignment_layers: null - # "layer2.0": "layer1.0" - # a dict of layer names: source_layer. if null it will be set to all layers. - #"features.8": null # the null after "features.8" refers to the fact that we don't mention a particular input for this layer and use what it gets - -training: - do_train: true - epochs: 10 - batch_size: 100 - lr: 1e-3 - replicates: 2 - name: "Adam" - weight_decay: 0.0 - -alignment: -# do_train_alignment: false -# do_test_alignment: true - do_alignment: false - methods: ["RQ"] - measure_expected: False - frequency: 1 - bins: 50 - cnn_mode: "unfold" #"unfold: Single covariance across all patches", "patch: Patchwise alignment, weighting by variance", "old"" - -checkpointing: - use_wandb: true - use_prev: false - save_checkpoints: false - frequency: 1 - -plots: - show_loss: true - show_dropout: true - show_eigenfeatures: true - show_eig_dropout: true - -extra: - num_drops: 50 - dropout_by_layer: false - single_layer_mode: false # dropout_by_layer must be true to work - progressive_dropout_on_train: false - aggregate_alignment: true - -# • If aggregate_alignment=True, we directly append the alignment each batch to results["alignment"]. -# • If aggregate_alignment=False, we temporarily store in epoch_align_data and only append once at the end of the epoch. - - #comparison: "lr" ## for “alignment_comparison” style experiments - #regularizers: ["none", "dropout", "weight_decay"] - #lrs: [1e-3, 1e-4] - #compare_dropout: 0.5 - #compare_wd: 1e-5 - #cutoffs: [1e-2, 1e-3, 1e-4, 0.0] - #manual_frequency: 5 - - # python src/alignment_v2/experiments/alignment_experiments.py --config .vscode/config_alignment_stats_v2.yaml \ No newline at end of file diff --git a/_arxiv/_archive/config_pruning_modes.yaml b/_arxiv/_archive/config_pruning_modes.yaml deleted file mode 100644 index 9e5fb726..00000000 --- a/_arxiv/_archive/config_pruning_modes.yaml +++ /dev/null @@ -1,80 +0,0 @@ -# Pruning Modes Configuration Sample - -# This config demonstrates the different pruning modes available: -# 1. "global_joint": Concatenate all nodes and prune X% across all layers (original v1) -# 2. "layer_wise": Prune X% from each layer separately, but all at once (v2-like) -# 3. "layer_isolated": Prune X% from only one layer at a time (new option) -# 4. "cascading_layer": Prune layers progressively - prune layer 1, use pruned network for layer 2, etc. - -experiment: - name: pruning_modes_comparison - type: alignment_stats - -dataset: - name: MNIST - batch_size: 200 - test_batch_size: 200 - -model: - network: MLP - input_dim: 784 - hidden_dim: [512, 256, 128, 64] - output_dim: 10 - activation: ReLU - dropout: 0.2 - -alignment: - methods: - - "RQ" - frequency: 10 - measure_expected: true - do_alignment: true - bins: 21 - -training: - epochs: 50 - lr: 0.001 - optimizer: Adam - criterion: CrossEntropyLoss - device: cuda:0 - -checkpointing: - use_prev: false - save_checkpoints: false - frequency: 10 - -extra: - # Choose one of the pruning modes: - - # Option 1: Global Joint Pruning (Original v1) - # Concatenates all nodes from all layers and prunes the lowest X% across all nodes - # dropout_pruning_mode: "global_joint" - - # Option 2: Layer-wise Pruning (v2-like) - # Prunes X% from each layer separately, but all at once - # dropout_pruning_mode: "layer_wise" - - # Option 3: Layer Isolation Pruning (new) - # Prunes X% from only one layer at a time, generating separate results per layer - # dropout_pruning_mode: "layer_isolated" - - # Option 4: Cascading Layer Pruning (new) - # Progressively prunes layers - prune layer 1, use pruned network to compute alignments for layer 2, etc. - dropout_pruning_mode: "cascading_layer" - - aggregate_alignment: false - num_drops: 9 - -# Alternative configs you can uncomment to use: - -# Config for "layer_wise" mode (v2 behavior) -# extra: -# dropout_pruning_mode: "layer_wise" -# aggregate_alignment: false -# num_drops: 9 - -# Config for "layer_isolated" mode -# extra: -# dropout_pruning_mode: "layer_isolated" -# aggregate_alignment: false -# num_drops: 9 \ No newline at end of file diff --git a/_arxiv/_archive/config_refactored_test.yaml b/_arxiv/_archive/config_refactored_test.yaml deleted file mode 100644 index 5c409d7e..00000000 --- a/_arxiv/_archive/config_refactored_test.yaml +++ /dev/null @@ -1,65 +0,0 @@ -experiment: "alignment_experiment" -results_path: "results/pruning_test" -no_save: false -just_plot: false -save_networks: true -show_params: false -show_all: false -device: "cuda" -use_timestamp: true -timestamp: null -ddp_world_size: 1 - -dataset: - dataset_name: "MNIST" - dataset_path: "/n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment/data" - -model: - model_name: "mlp" - dropout: 0.0 - alignment_layers: null - -training: - do_train: true - epochs: 5 - batch_size: 1024 - replicates: 1 - name: "Adam" - lr: 1e-3 - weight_decay: 0.0 - -alignment: - do_alignment: true - metric: "RQ" - methods: ["RQ"] - measure_expected: false - frequency: 1 - bins: 50 - cnn_mode: "unfold" - scale_by_norm: false - run_progressive: true - run_eigenvector: true - dropout_min: 0.0 - dropout_max: 0.9 - dropout_steps: 10 - -checkpointing: - use_prev: false - save_checkpoints: true - frequency: 1 - use_wandb: false - -plots: - show_loss: true - show_dropout: true - show_eigenfeatures: true - show_eig_dropout: true - -extra: - num_drops: 10 - progressive_dropout_on_train: false - single_layer_mode: false - aggregate_alignment: false - dropout_pruning_mode: "per_layer_combined" - dropout_mode: "scaled" - exclude_classification_layer: true \ No newline at end of file diff --git a/_arxiv/_archive/config_with_eigenvector.yaml b/_arxiv/_archive/config_with_eigenvector.yaml deleted file mode 100644 index 5da61835..00000000 --- a/_arxiv/_archive/config_with_eigenvector.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# Alignment Experiment Configuration -# This configuration file defines parameters for neural network alignment experiments -# that analyze the relationship between alignment, dropout, and network performance. - -# Experiment metadata -experiment_type: "alignment" # Type of experiment to run (options: "alignment", "training", "analysis") -results_path: "results" # Base path for saving results (can be relative or absolute path) -use_timestamp: true # Whether to include timestamp in result paths for unique experiment tracking - -# Core execution options -device: "cuda" # Device to run on (options: "cuda", "cpu", or specific GPU like "cuda:0") -no_save: false # If true, don't save any results to disk (useful for debug runs) -just_plot: false # If true, only load and plot existing results without running experiments -save_networks: true # Whether to save trained network checkpoints for later use -show_all: false # Whether to display all plots during execution (vs. just saving them) - -# Dataset configuration -dataset: - dataset_name: "MNIST" # Name of the dataset to use (options: "MNIST", "CIFAR10", "CIFAR100") - data_path: "/n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment/data" # Path to dataset files - batch_size: 1024 # Batch size for training and evaluation (higher values use more memory but train faster) - -# Model configuration -model: - model_name: "MLP" # Model architecture to use (options: "MLP", "CNN2P2", "alexnet") - dropout_rate: 0.0 # Base dropout rate during training (range: 0.0-1.0, 0.0 means no dropout) - alignment_layers: null # Layers to measure alignment on (null for all layers, or specify as [0,1,2]) - -# Training configuration -training: - epochs: 5 # Number of training epochs (more epochs can improve performance but take longer) - replicates: 10 # Number of network replicates to train (for statistical analysis) - optimizer: "Adam" # Optimizer to use (options: "Adam", "SGD", "RMSprop") - learning_rate: 1.0e-3 # Learning rate for optimizer (typical range: 1e-4 to 1e-2) - weight_decay: 0.0 # L2 regularization strength (higher values prevent overfitting) - -# Checkpointing configuration -checkpointing: - save_checkpoints: true # Whether to save training checkpoints to disk - checkpoint_frequency: 1 # How often to save checkpoints (in epochs, 1 means every epoch) - use_wandb: true # Whether to use Weights & Biases for experiment tracking and visualization - load_checkpoint: false # Whether to load from previous checkpoint (for resuming training) - -# Alignment analysis configuration -alignment: - metric: "RQ" # Alignment metric to use (options: "RQ", "NullSpace", "Delta", "R2") - run_progressive: true # Whether to run progressive dropout experiment (node pruning) - run_eigenvector: true # Whether to run eigenvector dropout experiment (feature pruning based on PCA components) - dropout_min: 0.0 # Minimum dropout fraction to test (range: 0.0-1.0) - dropout_max: 0.95 # Maximum dropout fraction to test (range: 0.0-1.0, <1.0 to avoid complete dropout) - dropout_steps: 20 # Number of dropout fractions to test (more steps = higher resolution but slower) - scale_by_norm: false # Whether to scale covariance matrices by norm (can improve numerics) - -# Extra configuration parameters -extra: - dropout_mode: "scaled" # How dropout is applied (options: "scaled" = multiply by 1/(1-p), "unscaled" = no scaling) - dropout_pruning_mode: "global_joint" # How pruning is distributed (options: "global_joint" = across all neurons, "layer_wise" = layer-by-layer, "layer_isolated" = one layer at a time) - exclude_classification_layer: true # Whether to exclude the output layer from pruning (usually true for classification tasks) - cnn_mode: "unfold" # How CNN features are processed for alignment (options: "unfold", "patchwise", "batch_patch_combined") \ No newline at end of file diff --git a/_arxiv/_archive/configs/config_alignment_experiment.yaml b/_arxiv/_archive/configs/config_alignment_experiment.yaml deleted file mode 100644 index 1850e894..00000000 --- a/_arxiv/_archive/configs/config_alignment_experiment.yaml +++ /dev/null @@ -1,203 +0,0 @@ -# Alignment Experiment Configuration -# This configuration file defines parameters for neural network alignment experiments -# that analyze the relationship between alignment, dropout, and network performance. - -# ----------------------------------------------------------------------------- -# I. Experiment Metadata & Core Execution -# ----------------------------------------------------------------------------- -experiment_name: "alexnet_imagenet_alignment_test" -# experiment_name: "cnn_mnist_nested_config_test" -experiment_type: "AUTO" # Options: - # "AUTO" - Automatically selects the best experiment type based on the dataset. - # "progressive_dropout" - Prunes nodes iteratively and measures performance. - # "eigenvector_dropout" - Prunes based on eigenvectors of alignment metric. - # "alignment_analysis" - Runs both progressive and (if configured) eigenvector dropout. - # "layer_isolated_pruning" - Prunes/evaluates one layer at a time (generates N plots). - # "cascading_layer_pruning" - Prunes layers sequentially, L1 then L2 etc (simple version). -results_path: "results" # Base path for saving all outputs. -use_timestamp: true # If true, appends a YYYYMMDD_HHMMSS timestamp to `results_path`. -debug_mode: false # MODIFIED: Set to true for testing callbacks - -device: "cuda" # "cuda", "cuda:0", "cpu". Ignored if use_ddp is true (device set by local_rank). -save_networks: true # If true, final trained/pruned network models are saved. -no_save: false # Master switch: if true, nothing is saved to disk. -just_plot: false # If true, loads existing results and only generates plots. -show_all: false # If true, matplotlib plots will be displayed interactively. -seed: 422 # MODIFIED: Set a seed for reproducibility during testing - -# --- NEW: DDP (Distributed Data Parallel) Configuration --- -use_ddp: false # Set to true to enable DDP for multi-GPU training. - # Requires launching with torchrun or similar. - # e.g., torchrun --nproc_per_node=NUM_GPUS your_script.py --config this_file.yaml -ddp_backend: "nccl" # DDP backend. "nccl" is recommended for NVIDIA GPUs. -# ddp_rank, ddp_world_size, ddp_local_rank are set automatically by the launch script if use_ddp is true. -# --- End DDP Configuration --- - -# ----------------------------------------------------------------------------- -# II. Dataset Configuration -# ----------------------------------------------------------------------------- -# Maps to DatasetConfig in config.py -dataset: - dataset_name: "ImageNet" # Options: "MNIST", "CIFAR10", "CIFAR100", "ImageNet". - data_path: "/n/holylfs06/LABS/kempner_shared/Lab/data/imagenet_1k/" # Root directory for dataset files. - batch_size: 256 # Batch size for DataLoaders (reduced for ImageNet due to larger images). - num_workers: 8 # Number of worker processes for DataLoader (increased for ImageNet). - -# ----------------------------------------------------------------------------- -# III. Model Configuration (NEW STRUCTURE) -# ----------------------------------------------------------------------------- -# Maps to ModelConfig in config.py -model: - # --- Identifier and Common Parameters --- - model_name: "torchvision_alexnet" # Options: "MLP", "CNN2P2", "torchvision_alexnet", "torchvision_resnet18", "hf_bert-base-uncased", etc. - output_dim: 1000 # Number of output classes. Used by the final layer of the model. - dropout_rate: 0.0 # General dropout rate that internal model constructors might use. - - # --- CNN Mode for AlignmentNetwork Wrapper --- - # Defines how the AlignmentNetwork wrapper (if the model is a CNN) preprocesses its inputs for metric calculation. - # Options: "unfold", "patchwise", "batch_patch_combined". Default in code is "unfold". - cnn_mode: "unfold" - - # --- Alignment Layers Specification (Common) --- - # Optional: Specify layers for AlignmentNetwork to analyze. Names must match model.named_modules(). - # If null, AlignmentNetwork attempts to find all layers with weights. - alignment_layers: null - # Example for AlexNet: - # alignment_layers: - # "features.0": 0 - # "features.3": 1 - # "classifier.6": 5 - - # --- Model-Specific Parameter Blocks --- - # Only include and populate ONE of the following blocks based on model_name. - - # (Example 1) MLP Parameters (if model_name: "MLP") - mlp_params: - input_dim: 784 - hidden_dims: [100, 100, 50] # Example: Two hidden layers - activation: "relu" - - # CNN2P2 Parameters (if model_name: "CNN2P2") - Comment out if using MLP or external - cnn2p2_params: - in_channels: 1 - conv_channels: [32, 64] - kernel_sizes: [5, 5] - strides: [1, 1] - paddings: [2, 2] - pool_kernel_size: 2 - pool_stride: 2 - hidden_fc_dim: 128 - example_input_hw: [28, 28] - - # (Example 3) External Model Parameters (if model_name indicates an external model, e.g., "torchvision_alexnet") - external_params: # Used if model_name is "torchvision_alexnet", "hf_bert-base-uncased", or just "external" - source: "torchvision" # Options: "torchvision", "huggingface_transformers" - name_or_path: "alexnet" # e.g., "alexnet", "resnet18" for torchvision; "bert-base-uncased" for hf - pretrained: true # Load pretrained weights (True/False) - freeze_feature_extractor: false # Freeze all layers except the classifier (True/False) - - # --- Ad-hoc Parameters for Internal Constructors (Use Sparingly) --- - extra_model_params: {} # For any other specific kwargs to pass to internal model constructors (create_mlp, create_cnn2p2) - -# ----------------------------------------------------------------------------- -# IV. Training Configuration -# ----------------------------------------------------------------------------- -# Maps to TrainingConfig in config.py -training: - epochs: 5 - replicates: 1 # For DDP, typically train 1 replicate, DDP handles data parallelism. - # If not DDP, can set >1 for multiple independent training runs. - optimizer: "Adam" - learning_rate: 0.001 - momentum: 0.9 - loss: "cross_entropy" - training_method: "auto" # "auto", "sequential", "fully_tensorized". - # "fully_tensorized" is for non-DDP multi-replicate speedup on one GPU. - # With DDP, "sequential" is typically used for the single replicate. - train_before_dropout: false # Whether to run initial training phase before dropout experiments. - -# ----------------------------------------------------------------------------- -# V. Alignment Metric Calculation Settings -# ----------------------------------------------------------------------------- -# Maps to alignment_settings: AlignmentConfig in config.py -alignment_settings: - metric: - - "RQ" - #- "MI_G" - #- "Node_Redundancy" - scale_by_norm: false # Global default for scale_by_norm - - # cnn_mode below is for how metric calculation functions (e.g. compute_all_node_scores) - # process CNN features IF they receive them in a specific format (e.g. from AlignmentNetwork's precomputation). - # The ModelConfig.cnn_mode determines how AlignmentNetwork itself preprocesses and provides these features. - # These two should ideally be consistent or ModelConfig.cnn_mode dictates the format received by metric functions. - cnn_mode: "unfold" # Options: "unfold", "patchwise", "batch_patch_combined", "filter_patch_summary", "filter_specific_covariance_rq" - # Detailed options for alignment_settings.cnn_mode (how metrics process CNN features): - # "unfold": Assumes features are [batch_size * num_patches, feature_dim] (e.g., from AlignmentNetwork cnn_mode="unfold"). - # For RQ, computes ONE covariance matrix from ALL these patch features (from ONE original data batch). - # Each filter's RQ is then computed against this single, global covariance matrix. - # Matches typical RQ definition but can be memory-intensive. - # "filter_patch_summary": (Specific to RQ metric) For each filter, computes a score based on its interaction - # with every input patch it sees (e.g., mean/max of SQUARED COSINE SIMILARITIES - # between filter and patches). Memory-efficient. Not mathematically identical to covariance-based RQ. - # "filter_specific_covariance_rq": (Specific to RQ metric) For each filter, computes its full covariance-based RQ. - # The covariance is derived from all unfolded input patches (from ONE original batch) - # that the filter processes. Mathematically standard RQ per filter. Computationally intensive. - # "patchwise": Expects features like [batch_size, num_patches, feature_dim]. Intended for metrics that process per-patch. - # (Currently requires metric-specific support not fully integrated into general path for all metrics). - # "batch_patch_combined": Similar effect to "unfold" for how data is shaped for global covariance calculations. - - cnn_rq_aggregation_op: "mean" # If cnn_mode for metric is "filter_patch_summary" for RQ: "mean", "max", "var", "sum". - force_cpu_for_large_metric_ops: false # If true, attempts to offload large tensor ops in metrics (like cov mat) to CPU. - - run_progressive: true # For experiment_type="alignment_analysis", whether to run progressive dropout part. - run_eigenvector: false # For experiment_type="alignment_analysis", whether to run eigenvector dropout part. - - callbacks: # Configure metrics to track during training - alignment_metrics: - - name: "RQ" - num_batches: 5 # Batches for callback metrics. null for all test batches. - - name: "MI_G" # Gaussian MI - num_batches: 5 - # - name: "PID_SI" # PID Shared Info (Requires BROJA_2PID to be correctly placed and importable) - # num_batches: 1 - -# ----------------------------------------------------------------------------- -# VI. Pruning Experiment Settings -# ----------------------------------------------------------------------------- -# Maps to pruning_settings: PruningConfig in config.py -pruning_settings: - dropout_min: 0.0 - dropout_max: 1.0 - dropout_steps: 30 # Reduced for faster testing - - dropout_mode: "scaled" # "scaled" or "unscaled" - # "scaled": Remaining weights are scaled by 1/(1-p) (Inverted Dropout principle). - # "unscaled" (or "zero"): Weights are simply zeroed out. - dropout_pruning_mode: "layer_isolated" # "global_joint", "layer_wise", "layer_isolated", "cascading_layer" - # "global_joint": Considers all nodes from all prunable layers together. Ranks globally by score and prunes top X% overall. - # "layer_wise": Prunes X% from *each* prunable layer independently, based on scores within that layer. - # "layer_isolated": (For experiment_type="layer_isolated_pruning") Prunes X% in one layer, evaluates, restores, then moves to next layer. - # "cascading_layer": Prunes L1, its output influences pruning of L2, etc. (Note: current batched impl. has limitations). - exclude_classification_layer: true - use_multi_strategy_dropout: true - num_batches_for_scores: null # Number of batches to compute pruning scores on. - -# ----------------------------------------------------------------------------- -# VII. Checkpointing & Logging Configuration -# ----------------------------------------------------------------------------- -checkpointing: - save_checkpoints: false - checkpoint_frequency: 1 - load_checkpoint: false - -wandb: - use_wandb: true - wandb_project: "neural_alignment" - wandb_entity: "your_wandb_entity" # <<< IMPORTANT: Update with your W&B entity or remove line for default - -extra: - log_frequency: 1 - log_images: true - detailed_logging: true - # dummy_extra_param: null \ No newline at end of file diff --git a/_arxiv/_archive/configs/config_alignment_stats.yaml b/_arxiv/_archive/configs/config_alignment_stats.yaml deleted file mode 100644 index abd73f95..00000000 --- a/_arxiv/_archive/configs/config_alignment_stats.yaml +++ /dev/null @@ -1,56 +0,0 @@ -experiment: alignment_stats -results_path: # <-- Add path to where results to be saved/loaded -no_save: False -just_plot: False -save_networks: False -show_params: False -show_all: False -#device: cpu -use_timestamp: False -#timestamp: # The timestamp of previous experiment to plot. - -dataset: - name: MNIST - path: # <-- Add path to your dataset - download: True # Downloads it if it does not exist. - -model: - name: MLP - dropout: 0 - alignment_layers: null # Specify the layers participating in alignment - for all layers use null | exp for MLP: {"layerInput.0": null, "layerHidden.0.1": "layerHidden.0.0", "layerHidden.1.1": "layerHidden.1.0"} - -optimizer: - name: Adam - lr: 1e-3 - weight_decay: 0 - -training: - batch_size: 1024 - epochs: 1 - replicates: 1 - -alignment: - no_alignment: False - delta_weights: False - frequency: 1 - -checkpointing: - use_prev: False - save_checkpoints: False - frequency: 1 - use_wandb: False - -extra: - num_drops: 9 - dropout_by_layer: False - - ### For alignment_comparison and loading_prediction experiments - comparison: "lr" - regularizers: ["none", "dropout", "weight_decay"] - lrs: [1e-2, 1e-3, 1e-4] - compare_dropout: 0.5 - compare_wd: 1e-5 - - ### For adversarial_shaping experiment - cutoffs: [1e-2, 1e-3, 1e-4, 0.0] - manual_frequency: 5 diff --git a/_arxiv/_archive/configs/config_alignment_stats_old.yaml b/_arxiv/_archive/configs/config_alignment_stats_old.yaml deleted file mode 100644 index a25906ba..00000000 --- a/_arxiv/_archive/configs/config_alignment_stats_old.yaml +++ /dev/null @@ -1,66 +0,0 @@ -experiment: alignment_stats -results_path: results # <-- Add path to where results to be saved/loaded -no_save: False -just_plot: False -save_networks: False -show_params: False -show_all: False -device: cuda -use_timestamp: False -#timestamp: # The timestamp of previous experiment to plot. - -dataset: - name: MNIST - path: /n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment/data - -model: - name: MLP - dropout: 0 - alignment_layers: null # Specify the layers participating in alignment - for all layers use null | exp for MLP: {"layerInput.0": null, "layerHidden.0.1": "layerHidden.0.0", "layerHidden.1.1": "layerHidden.1.0"} - -training: - batch_size: 1024 - epochs: 3 - replicates: 3 - name: Adam - lr: 1e-3 - weight_decay: 0 - -alignment: - do_alignment: True - frequency: 1 - methods: ["RQ"] - measure_expected: False - bins: 50 - cnn_mode: "unfold" # Options for CNN alignment processing: - # "unfold" - Reshapes conv layer features into (-1, features) with batch and patches together (default) - # "patchwise" - Computes alignment per-patch, with special processing in patchwise_alignment - # "batch_patch_combined" - Standardized approach that combines batch and patch dimensions - # Differences between modes: - # - "unfold" is the classic approach with flexible reshaping options - # - "patchwise" preserves patch structure for specialized calculations - # - "batch_patch_combined" ensures consistent dimensionality across frameworks --> original method - scale_by_norm: False # If True, scales covariance matrices by their norm before computing RQ - # Similar to the "similarity" option in original codebase -checkpointing: - use_prev: False - save_checkpoints: False - frequency: 1 - use_wandb: True - -extra: - num_drops: 40 - dropout_pruning_mode: "global_joint" # Options: - # "global_joint" - Prune X% of nodes across all layers based on their global alignment ranking - # This distributes pruning across layers proportional to where the - # highest/lowest alignment nodes are located. Some layers may have more - # neurons pruned than others, preserving the global ranking of importance. - # "layer_wise" - Prune exactly X% from each layer and apply pruning to all layers at once - # This shows the effect of pruning all layers simultaneously with fixed proportions - # "layer_isolated" - Prune X% from only one layer at a time - # This creates multiple figures (one per layer) showing isolated effects - dropout_mode: "scaled" # Options for how targeted dropout is applied to neurons: - # "scaled" - Zeroes neurons and applies scaling to maintain signal magnitude (default) - # "unscaled" - Zeroes neurons but doesn't apply compensation scaling - exclude_classification_layer: True # If True, excludes the classification layer from pruning (always true in original codebase) - \ No newline at end of file diff --git a/_arxiv/_archive/configs/config_alignment_stats_v2.yaml b/_arxiv/_archive/configs/config_alignment_stats_v2.yaml deleted file mode 100644 index f1e50278..00000000 --- a/_arxiv/_archive/configs/config_alignment_stats_v2.yaml +++ /dev/null @@ -1,74 +0,0 @@ -experiment: "mnist_alignment_example" -results_path: # <-- Add path to where results to be saved/loaded -no_save: false ## if true, skip saving results and plots -just_plot: false ## If true, skip the experiment’s main() logic, only load previously saved results and call plot_from_existing() -save_networks: false ## If true, also save trained networks (self.save_networks(...)) after training. -show_params: false ## keep the plots open WE CAN REMOVE THIS -show_all: false ## ## keep the plots open WE CAN REMOVE THIS -use_timestamp: false ## If use_timestamp is true, the code stores results in subfolders named by timestamp (an ISO/time string). -timestamp: null - -ddp_world_size: 2 - -dataset: - name: "ImageNet" # "MNIST", "CIFAR10", "CIFAR100", "ImageNet", etc. - path: # <-- Add path to your dataset ## path to dataset - -model: - name: "AlexNet" ## base artitecture, e.g., "MLP", "CNN2P2", "AlexNet". - dropout: 0 ## Dropout fraction 0 to 1 - alignment_layers: null - # "layer2.0": "layer1.0" - # a dict of layer names: source_layer. if null it will be set to all layers. - #"features.8": null # the null after "features.8" refers to the fact that we don't mention a particular input for this layer and use what it gets - -training: - do_train: true - epochs: 10 - batch_size: 100 - lr: 1e-3 - replicates: 2 - name: "Adam" - weight_decay: 0.0 - -alignment: -# do_train_alignment: false -# do_test_alignment: true - do_alignment: false - methods: ["RQ"] - measure_expected: False - frequency: 1 - bins: 50 - cnn_mode: "unfold" #"unfold: Single covariance across all patches", "patch: Patchwise alignment, weighting by variance", "old"" - -checkpointing: - use_wandb: true - use_prev: false - save_checkpoints: false - frequency: 1 - -plots: - show_loss: true - show_dropout: true - show_eigenfeatures: true - show_eig_dropout: true - -extra: - num_drops: 50 - dropout_by_layer: false - single_layer_mode: false # dropout_by_layer must be true to work - progressive_dropout_on_train: false - aggregate_alignment: true - -# • If aggregate_alignment=True, we directly append the alignment each batch to results["alignment"]. -# • If aggregate_alignment=False, we temporarily store in epoch_align_data and only append once at the end of the epoch. - - #comparison: "lr" ## for “alignment_comparison” style experiments - #regularizers: ["none", "dropout", "weight_decay"] - #lrs: [1e-3, 1e-4] - #compare_dropout: 0.5 - #compare_wd: 1e-5 - #cutoffs: [1e-2, 1e-3, 1e-4, 0.0] - #manual_frequency: 5 - - # python src/alignment_v2/experiments/alignment_experiments.py --config .vscode/config_alignment_stats_v2.yaml \ No newline at end of file diff --git a/_arxiv/_archive/configs/config_pruning_modes.yaml b/_arxiv/_archive/configs/config_pruning_modes.yaml deleted file mode 100644 index 9e5fb726..00000000 --- a/_arxiv/_archive/configs/config_pruning_modes.yaml +++ /dev/null @@ -1,80 +0,0 @@ -# Pruning Modes Configuration Sample - -# This config demonstrates the different pruning modes available: -# 1. "global_joint": Concatenate all nodes and prune X% across all layers (original v1) -# 2. "layer_wise": Prune X% from each layer separately, but all at once (v2-like) -# 3. "layer_isolated": Prune X% from only one layer at a time (new option) -# 4. "cascading_layer": Prune layers progressively - prune layer 1, use pruned network for layer 2, etc. - -experiment: - name: pruning_modes_comparison - type: alignment_stats - -dataset: - name: MNIST - batch_size: 200 - test_batch_size: 200 - -model: - network: MLP - input_dim: 784 - hidden_dim: [512, 256, 128, 64] - output_dim: 10 - activation: ReLU - dropout: 0.2 - -alignment: - methods: - - "RQ" - frequency: 10 - measure_expected: true - do_alignment: true - bins: 21 - -training: - epochs: 50 - lr: 0.001 - optimizer: Adam - criterion: CrossEntropyLoss - device: cuda:0 - -checkpointing: - use_prev: false - save_checkpoints: false - frequency: 10 - -extra: - # Choose one of the pruning modes: - - # Option 1: Global Joint Pruning (Original v1) - # Concatenates all nodes from all layers and prunes the lowest X% across all nodes - # dropout_pruning_mode: "global_joint" - - # Option 2: Layer-wise Pruning (v2-like) - # Prunes X% from each layer separately, but all at once - # dropout_pruning_mode: "layer_wise" - - # Option 3: Layer Isolation Pruning (new) - # Prunes X% from only one layer at a time, generating separate results per layer - # dropout_pruning_mode: "layer_isolated" - - # Option 4: Cascading Layer Pruning (new) - # Progressively prunes layers - prune layer 1, use pruned network to compute alignments for layer 2, etc. - dropout_pruning_mode: "cascading_layer" - - aggregate_alignment: false - num_drops: 9 - -# Alternative configs you can uncomment to use: - -# Config for "layer_wise" mode (v2 behavior) -# extra: -# dropout_pruning_mode: "layer_wise" -# aggregate_alignment: false -# num_drops: 9 - -# Config for "layer_isolated" mode -# extra: -# dropout_pruning_mode: "layer_isolated" -# aggregate_alignment: false -# num_drops: 9 \ No newline at end of file diff --git a/_arxiv/_archive/configs/config_refactored_test.yaml b/_arxiv/_archive/configs/config_refactored_test.yaml deleted file mode 100644 index 5c409d7e..00000000 --- a/_arxiv/_archive/configs/config_refactored_test.yaml +++ /dev/null @@ -1,65 +0,0 @@ -experiment: "alignment_experiment" -results_path: "results/pruning_test" -no_save: false -just_plot: false -save_networks: true -show_params: false -show_all: false -device: "cuda" -use_timestamp: true -timestamp: null -ddp_world_size: 1 - -dataset: - dataset_name: "MNIST" - dataset_path: "/n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment/data" - -model: - model_name: "mlp" - dropout: 0.0 - alignment_layers: null - -training: - do_train: true - epochs: 5 - batch_size: 1024 - replicates: 1 - name: "Adam" - lr: 1e-3 - weight_decay: 0.0 - -alignment: - do_alignment: true - metric: "RQ" - methods: ["RQ"] - measure_expected: false - frequency: 1 - bins: 50 - cnn_mode: "unfold" - scale_by_norm: false - run_progressive: true - run_eigenvector: true - dropout_min: 0.0 - dropout_max: 0.9 - dropout_steps: 10 - -checkpointing: - use_prev: false - save_checkpoints: true - frequency: 1 - use_wandb: false - -plots: - show_loss: true - show_dropout: true - show_eigenfeatures: true - show_eig_dropout: true - -extra: - num_drops: 10 - progressive_dropout_on_train: false - single_layer_mode: false - aggregate_alignment: false - dropout_pruning_mode: "per_layer_combined" - dropout_mode: "scaled" - exclude_classification_layer: true \ No newline at end of file diff --git a/_arxiv/_archive/configs/config_with_eigenvector.yaml b/_arxiv/_archive/configs/config_with_eigenvector.yaml deleted file mode 100644 index 5da61835..00000000 --- a/_arxiv/_archive/configs/config_with_eigenvector.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# Alignment Experiment Configuration -# This configuration file defines parameters for neural network alignment experiments -# that analyze the relationship between alignment, dropout, and network performance. - -# Experiment metadata -experiment_type: "alignment" # Type of experiment to run (options: "alignment", "training", "analysis") -results_path: "results" # Base path for saving results (can be relative or absolute path) -use_timestamp: true # Whether to include timestamp in result paths for unique experiment tracking - -# Core execution options -device: "cuda" # Device to run on (options: "cuda", "cpu", or specific GPU like "cuda:0") -no_save: false # If true, don't save any results to disk (useful for debug runs) -just_plot: false # If true, only load and plot existing results without running experiments -save_networks: true # Whether to save trained network checkpoints for later use -show_all: false # Whether to display all plots during execution (vs. just saving them) - -# Dataset configuration -dataset: - dataset_name: "MNIST" # Name of the dataset to use (options: "MNIST", "CIFAR10", "CIFAR100") - data_path: "/n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment/data" # Path to dataset files - batch_size: 1024 # Batch size for training and evaluation (higher values use more memory but train faster) - -# Model configuration -model: - model_name: "MLP" # Model architecture to use (options: "MLP", "CNN2P2", "alexnet") - dropout_rate: 0.0 # Base dropout rate during training (range: 0.0-1.0, 0.0 means no dropout) - alignment_layers: null # Layers to measure alignment on (null for all layers, or specify as [0,1,2]) - -# Training configuration -training: - epochs: 5 # Number of training epochs (more epochs can improve performance but take longer) - replicates: 10 # Number of network replicates to train (for statistical analysis) - optimizer: "Adam" # Optimizer to use (options: "Adam", "SGD", "RMSprop") - learning_rate: 1.0e-3 # Learning rate for optimizer (typical range: 1e-4 to 1e-2) - weight_decay: 0.0 # L2 regularization strength (higher values prevent overfitting) - -# Checkpointing configuration -checkpointing: - save_checkpoints: true # Whether to save training checkpoints to disk - checkpoint_frequency: 1 # How often to save checkpoints (in epochs, 1 means every epoch) - use_wandb: true # Whether to use Weights & Biases for experiment tracking and visualization - load_checkpoint: false # Whether to load from previous checkpoint (for resuming training) - -# Alignment analysis configuration -alignment: - metric: "RQ" # Alignment metric to use (options: "RQ", "NullSpace", "Delta", "R2") - run_progressive: true # Whether to run progressive dropout experiment (node pruning) - run_eigenvector: true # Whether to run eigenvector dropout experiment (feature pruning based on PCA components) - dropout_min: 0.0 # Minimum dropout fraction to test (range: 0.0-1.0) - dropout_max: 0.95 # Maximum dropout fraction to test (range: 0.0-1.0, <1.0 to avoid complete dropout) - dropout_steps: 20 # Number of dropout fractions to test (more steps = higher resolution but slower) - scale_by_norm: false # Whether to scale covariance matrices by norm (can improve numerics) - -# Extra configuration parameters -extra: - dropout_mode: "scaled" # How dropout is applied (options: "scaled" = multiply by 1/(1-p), "unscaled" = no scaling) - dropout_pruning_mode: "global_joint" # How pruning is distributed (options: "global_joint" = across all neurons, "layer_wise" = layer-by-layer, "layer_isolated" = one layer at a time) - exclude_classification_layer: true # Whether to exclude the output layer from pruning (usually true for classification tasks) - cnn_mode: "unfold" # How CNN features are processed for alignment (options: "unfold", "patchwise", "batch_patch_combined") \ No newline at end of file diff --git a/_arxiv/_archive/configs/training_example.yaml b/_arxiv/_archive/configs/training_example.yaml deleted file mode 100644 index 886566d1..00000000 --- a/_arxiv/_archive/configs/training_example.yaml +++ /dev/null @@ -1,47 +0,0 @@ -# Example configuration with tensorized training options -experiment_type: "progressive_dropout" -experiment_name: "Tensorized Training Example" -use_timestamp: true -device: "cuda" - -# Dataset configuration -dataset: - dataset_name: "MNIST" - batch_size: 128 - data_path: "./data" - -# Model configuration -model: - model_name: "MLP" - dropout_rate: 0.0 - -# Training configuration -training: - epochs: 5 - replicates: 10 # Number of network copies to train - learning_rate: 0.001 - weight_decay: 0.0 - optimizer: "Adam" - train_before_dropout: true - -# Alignment configuration -alignment: - metric: "RQ" - run_progressive: true - run_eigenvector: false - dropout_min: 0.0 - dropout_max: 0.95 - dropout_steps: 20 - -# Extra configuration options -extra: - dropout_mode: "scaled" - dropout_pruning_mode: "global_joint" - exclude_classification_layer: true - # Available training methods: "auto", "sequential", "tensorized", "fully_tensorized" - training_method: "fully_tensorized" # Using the fastest method - -# Checkpoint configuration -checkpointing: - save_checkpoints: false - use_wandb: false \ No newline at end of file diff --git a/_arxiv/_archive/debug_pruning_strategies.py b/_arxiv/_archive/debug_pruning_strategies.py deleted file mode 100644 index 3eb729d7..00000000 --- a/_arxiv/_archive/debug_pruning_strategies.py +++ /dev/null @@ -1,235 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np -import matplotlib.pyplot as plt -import logging -import os -import sys -import copy - -# Add parent directory to path to access src -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))) - -from src.alignment.models.models import MLP -from src.alignment.datasets import load_dataset -from src.alignment.dropout import test_pruning_strategies - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s [%(levelname)s] %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', -) -logger = logging.getLogger(__name__) - -def main(): - # Set random seed for reproducibility - torch.manual_seed(42) - np.random.seed(42) - - # Determine device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - logger.info(f"Using device: {device}") - - # Load MNIST dataset for testing - dataset_config = { - "dataset_name": "MNIST", - "data_path": "data", - "batch_size": 1024, - "model_name": "mlp", - } - dataset = load_dataset(dataset_config, device=device) - logger.info(f"Loaded {dataset_config['dataset_name']} dataset") - - # Create a simple MLP model for MNIST - network = MLP( - input_dim=784, # 28x28 for MNIST - num_hidden=[100, 100], - output_dim=10, - dropout_rate=0.0, - ) - - # Get linear layers from the MLP model - # First find all linear layers in the network - linear_layers = [] - for name, module in network.named_modules(): - if isinstance(module, nn.Linear): - linear_layers.append(module) - - # Set alignment layers for pruning (all linear layers) - network.alignment_layers = linear_layers - network.alignment_layer_names = [f"Layer {i}" for i in range(len(network.alignment_layers))] - - network.to(device) - logger.info(f"Created MLP model with {len(network.alignment_layers)} alignment layers") - - # Train the network for a single epoch - logger.info("Training network...") - train_network(network, dataset, device) - - # Print statistics about the original model - logger.info("Evaluating original model...") - print_layer_stats(network) - orig_accuracy, orig_loss = dataset.evaluate(network, device) - logger.info(f"Original model - Accuracy: {orig_accuracy:.2f}%, Loss: {orig_loss:.4f}") - - # Save original weights for verification - original_weights = {} - for i, layer in enumerate(network.alignment_layers): - if hasattr(layer, "weight") and layer.weight is not None: - original_weights[i] = layer.weight.data.clone() - - # Test different pruning strategies - logger.info("Testing pruning strategies...") - pruning_percents = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9] - - # Test each strategy individually to confirm they differ - strategies = ["high_rq", "low_rq", "random"] - all_results = {} - - for strategy in strategies: - logger.info(f"Testing {strategy} pruning strategy...") - - # Make a fresh copy of the network for each strategy - network_copy = copy.deepcopy(network).to(device) - - # Verify the copy has the same weights - for i, layer in enumerate(network_copy.alignment_layers): - if i in original_weights: - weight_diff = torch.sum(torch.abs(layer.weight.data - original_weights[i])).item() - if weight_diff > 1e-5: - logger.error(f"Weights for layer {i} changed before pruning! Diff: {weight_diff}") - - # Test this specific strategy - strategy_results = test_pruning_strategies( - network_copy, - dataset, - pruning_percents=pruning_percents, - device=device, - strategy=strategy - ) - - # Store results for this strategy - all_results[strategy] = strategy_results - - # Print results - logger.info(f"{strategy} pruning results:") - for i, percent in enumerate(pruning_percents): - acc = strategy_results[f"{strategy}_acc"][i] - loss = strategy_results[f"{strategy}_loss"][i] - logger.info(f" {percent*100:.0f}% pruned: Accuracy = {acc:.2f}%, Loss = {loss:.4f}") - - # Plot results - plt.figure(figsize=(12, 6)) - for strategy in strategies: - results = all_results[strategy] - plt.plot( - [p*100 for p in pruning_percents], - results[f"{strategy}_acc"], - marker="o" if strategy == "high_rq" else ("s" if strategy == "low_rq" else "^"), - label=f"{strategy}", - linewidth=2 - ) - - plt.xlabel("Pruning Percentage", fontsize=12) - plt.ylabel("Accuracy (%)", fontsize=12) - plt.title("Pruning Strategies Comparison", fontsize=14) - plt.grid(True, linestyle="--", alpha=0.7) - plt.legend(fontsize=11) - plt.tight_layout() - - # Save plot - plt.savefig("pruning_strategies_debug.png", dpi=300, bbox_inches="tight") - logger.info("Saved plot to pruning_strategies_debug.png") - - # Check if strategies are different - if len(strategies) > 1: - are_different = False - for i in range(len(strategies)-1): - for j in range(i+1, len(strategies)): - s1, s2 = strategies[i], strategies[j] - accs1 = all_results[s1][f"{s1}_acc"] - accs2 = all_results[s2][f"{s2}_acc"] - - # Calculate difference - diff = np.mean(np.abs(np.array(accs1) - np.array(accs2))) - logger.info(f"Mean accuracy difference between {s1} and {s2}: {diff:.2f}%") - - if diff > 1.0: # Consider different if average difference > 1% - are_different = True - - if are_different: - logger.info("SUCCESS: Pruning strategies show different results!") - else: - logger.error("FAILURE: Pruning strategies show very similar results.") - -def train_network(network, dataset, device, num_epochs=3): - """Train a network for a specified number of epochs""" - logger.info(f"Training a single network") - optimizer = torch.optim.Adam(network.parameters(), lr=0.001) - - # Training loop - for epoch in range(num_epochs): - running_loss = 0.0 - correct = 0 - total = 0 - - # Create DataLoader for training - train_loader = torch.utils.data.DataLoader( - dataset.train_dataset, batch_size=1024, shuffle=True - ) - - # Train for one epoch - network.train() - for i, (inputs, targets) in enumerate(train_loader): - # Move data to device - inputs, targets = inputs.to(device), targets.to(device) - - # Zero the parameter gradients - optimizer.zero_grad() - - # Forward pass - outputs = network(inputs) - loss = torch.nn.functional.cross_entropy(outputs, targets) - - # Backward pass and optimize - loss.backward() - optimizer.step() - - # Calculate accuracy - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - # Update running loss - running_loss += loss.item() - - # Print epoch statistics - epoch_loss = running_loss / len(train_loader) - epoch_acc = 100. * correct / total - logger.info(f"Epoch {epoch+1}/{num_epochs}: Loss={epoch_loss:.4f}, Acc={epoch_acc:.2f}%") - - # Evaluate on test set - test_acc, test_loss = dataset.evaluate(network, device) - logger.info(f"Epoch {epoch+1}/{num_epochs}: Train Loss={epoch_loss:.4f}, Train Acc={epoch_acc:.2f}%, Test Loss={test_loss:.4f}, Test Acc={test_acc:.2f}%") - -def print_layer_stats(network): - """Print statistics about each alignment layer in the network""" - for i, layer in enumerate(network.alignment_layers): - if hasattr(layer, "weight") and layer.weight is not None: - weights = layer.weight.data - total_weights = weights.numel() - zero_weights = (weights == 0).sum().item() - zero_percent = 100.0 * zero_weights / total_weights - - # Count pruned neurons (rows with all zeros) - pruned_neurons = 0 - total_neurons = weights.size(0) - for j in range(total_neurons): - if torch.all(weights[j] == 0): - pruned_neurons += 1 - - logger.info(f"Layer {i}: Shape {weights.shape}, zeros: {zero_weights}/{total_weights} ({zero_percent:.2f}%), pruned neurons: {pruned_neurons}/{total_neurons} ({100.0 * pruned_neurons / total_neurons:.2f}%)") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/_arxiv/_archive/md_files/DOCUMENTATION.md b/_arxiv/_archive/md_files/DOCUMENTATION.md deleted file mode 100644 index 01a39254..00000000 --- a/_arxiv/_archive/md_files/DOCUMENTATION.md +++ /dev/null @@ -1,52 +0,0 @@ -# Network Alignment Analysis Documentation - -This document provides a comprehensive index to all documentation for the Network Alignment Analysis codebase. - -## Getting Started - -- [README](README.md) - Main repository README with setup instructions -- [Usage Guide](doc/usage.md) - How to use the codebase -- [Installation Guide](README.md#installation) - How to install the package - -## Core Documentation - -- [Main Documentation](doc/DOCUMENTATION.md) - Overview of codebase architecture and capabilities -- [API Reference](doc/api/README.md) - Comprehensive API reference -- [Metrics System](doc/metrics/README.md) - Documentation for the metrics system -- [Experiment Framework](doc/experiment/README.md) - Documentation for the experiment framework -- [Performance Optimizations](doc/performance/README.md) - Documentation for performance optimizations -- [Tensorized Implementations](doc/tensorized/README.md) - Documentation for tensorized implementations - -## Guides and References - -- [Pruning Modes](doc/pruning_modes.md) - Documentation for different pruning strategies -- [Background](doc/background.md) - Background information on alignment analysis -- [Development Roadmap](doc/ROADMAP.md) - Future development plans - -## Refactoring and Cleanup - -- [Metrics Refactoring Summary](METRICS_REFACTORING_SUMMARY.md) - Summary of metrics system refactoring -- [Codebase Cleanup Summary](CODEBASE_CLEANUP_SUMMARY.md) - Summary of codebase cleanup - -## Directory Structure - -The codebase is organized into the following directories: - -- `src/alignment/`: Core source code implementing alignment metrics and algorithms - - `metrics.py`: Implementation of all alignment metrics - - `utils/`: Utility functions for data handling, visualization, etc. - - `experiment/`: Experiment framework for running alignment experiments - - `networks/`: Network architectures and training utilities - - `pruning/`: Implementation of various pruning strategies - -- `tests/`: Unit and integration tests -- `scripts/`: Utility scripts for running experiments and analysis -- `benchmarks/`: Performance evaluation scripts -- `configs/`: Configuration files for experiments -- `results/`: Output directory for experiment results -- `doc/`: Documentation - -## Additional Resources - -- [CHANGELOG.md](CHANGELOG.md) - History of changes to the codebase -- [LICENSE](LICENSE) - MIT License for the project \ No newline at end of file diff --git a/_arxiv/_archive/md_files/README_tensorized_dropout.md b/_arxiv/_archive/md_files/README_tensorized_dropout.md deleted file mode 100644 index 4daf37ff..00000000 --- a/_arxiv/_archive/md_files/README_tensorized_dropout.md +++ /dev/null @@ -1,93 +0,0 @@ -# Tensorized Dropout and Training Documentation - -This document explains the tensorized implementations for training and dropout operations in the alignment project. - -## Tensorized Training Methods - -The project now includes optimized tensorized training methods for efficiently training multiple networks in parallel. This is particularly useful for experiments that require training multiple networks with the same architecture but different initializations. - -### Available Training Methods - -1. **Sequential Training** (`sequential`): Original method that trains each network one at a time. -2. **Tensorized Training** (`tensorized`): Trains networks in parallel by batching their training steps. -3. **Fully Tensorized Training** (`fully_tensorized`): Most efficient method that combines networks into a single model ensemble. -4. **Auto-select** (`auto`): Automatically selects the most efficient method based on the number of networks and their architectures. - -### Configuration - -You can specify which training method to use in your experiment configuration: - -```yaml -extra: - training_method: "fully_tensorized" # Options: "auto", "sequential", "tensorized", "fully_tensorized" -``` - -By default, the system will use `"auto"` which automatically selects the most efficient method. - -### Example Config - -An example configuration file is provided at `configs/training_example.yaml` showing how to use the tensorized training methods. - -### Performance - -Our benchmark tests show that the fully tensorized approach can be up to 3x faster than sequential training for larger numbers of networks (e.g., 10+ networks). The speed improvement comes from: - -- Reduced overhead from parallel computation -- Better utilization of GPU resources -- Minimized data transfer between CPU and GPU - -## Tensorized Progressive Dropout - -This document describes the improvements made to the progressive dropout implementation in the alignment codebase, along with benchmark results comparing different approaches. - -### Overview - -Progressive dropout is a technique used to analyze the alignment of neural networks by gradually dropping out neurons and measuring the impact on performance. Three major optimizations are now available: - -1. **Tensorized Implementation**: Process all networks at once using tensor operations -2. **Multi-Strategy Implementation**: Process all strategies (high_rq, low_rq, random) simultaneously -3. **Combined Optimization**: Apply both optimizations together for maximum performance - -### Multi-Strategy Dropout - -The multi-strategy optimization allows processing all three pruning strategies (high_rq, low_rq, random) together, which provides a significant speedup when you need results for all strategies: - -#### Configuration - -```yaml -extra: - use_multi_strategy_dropout: true # Process all strategies simultaneously -``` - -#### Implementation Details - -The implementation creates separate network copies for each strategy, but computes the neuron scores only once. Then it applies different pruning strategies to each network copy and evaluates them in parallel. - -#### Benchmark Results - -Benchmark results for multi-strategy dropout show significant speedups (typically 2.5-3x) compared to running the three strategies sequentially. - -| Configuration | Sequential Time | Multi-Strategy Time | Speedup | -|---------------|----------------|---------------------|---------| -| 5 networks, 10 dropout steps | 240s | 89s | 2.7x | - -### Benchmark Scripts - -Two benchmark scripts are provided to test the performance of these optimizations: - -1. `benchmark_network_training.py`: Compare training methods (sequential, tensorized, fully_tensorized) -2. `benchmark_dropout_strategies.py`: Compare dropout strategy processing (sequential vs. multi-strategy) - -To run the benchmarks: - -```bash -python benchmark_network_training.py --num_networks 10 --epochs 1 -python benchmark_dropout_strategies.py --config configs/config_alignment_experiment.yaml --runs 1 -``` - -### Conclusion - -These optimizations provide substantial performance improvements for alignment experiments, allowing you to run more experiments with larger networks in less time. For the best performance: - -1. Use `training_method: "fully_tensorized"` for training multiple networks -2. Use `use_multi_strategy_dropout: true` when running progressive dropout with multiple strategies \ No newline at end of file diff --git a/_arxiv/_archive/md_files/REFACTORING_METRICS.md b/_arxiv/_archive/md_files/REFACTORING_METRICS.md deleted file mode 100644 index daa2d165..00000000 --- a/_arxiv/_archive/md_files/REFACTORING_METRICS.md +++ /dev/null @@ -1,66 +0,0 @@ -# Metrics System Refactoring - -## Overview - -This document summarizes the refactoring of the alignment metrics system, which has been fully consolidated into a single, unified metrics infrastructure under `src/alignment/metrics.py`. - -## Completed Refactoring Tasks - -- [x] Critical Bug Fixes & Import Issues -- [x] Import Hoisting (general pass) -- [x] Logging in DDP Runner -- [x] Optional transformers -- [x] Plotting for Metric Evolution & Conditional Legends -- [x] Configuration Access in ExperimentRunner -- [x] Refactor Pruning Logic in dropout_manager.py -- [x] Consolidated Metrics Systems (metrics_utils.py vs. metrics.py) - - [x] Ported WeightSimilarityMetric to weight_cosine_similarity, weight_dot_similarity, and weight_euclidean_distance - - [x] Ported MIMetric to mi_proj_vs_mean_input - - [x] Ported RQMetric alternative formulation to rq_alt_denom - - [x] Ported NodeRedundancyMetric to node_redundancy - - [x] Updated tests to use the new metrics.py system (get_metric) - - [x] Deprecated and removed metrics_utils.py - -## Architecture Overview - -The new metrics system is built around: - -1. **Central Registry**: `ALIGNMENT_METRICS_REGISTRY` maps metric names to metric functions -2. **Metric Protocol**: `AlignmentMetric` protocol defines the interface for all metrics -3. **Metric Implementation**: `_AlignmentMetricImpl` provides consistent dispatch for all metrics -4. **Factory Function**: `get_metric()` creates properly configured metric objects -5. **High-Level Functions**: - - `compute_metrics_for_layers()`: Process multiple layers at once - - `compute_all_node_scores()`: Process the whole network - - `compute_pairwise_metric()`: Compute metrics between pairs of data - -## Documentation - -A comprehensive metrics system documentation has been added at `src/alignment/README_metrics.md`, which includes: - -- Available metrics and their purposes -- Usage examples for each metric type -- Architecture overview -- Guidelines for metric selection - -## Testing - -All metrics have corresponding tests in `tests/test_benchmark.py` which verify the correct functionality of: - -- Standard RQ and alternative RQ metrics -- MI metrics including the specialized MI projection metric -- Weight similarity metrics -- Node redundancy metrics - -## Future Improvements - -Potential areas for future enhancement: - -1. Add more test coverage for the remaining metrics -2. Consider adding more metrics for specific use cases -3. Performance optimizations for large-scale networks -4. Integration with visualization tools for better insights - -## Conclusion - -The metrics system refactoring has successfully unified all alignment metrics under a single, consistent API. This will make the codebase more maintainable, easier to extend, and provide clearer documentation for users. \ No newline at end of file diff --git a/_arxiv/archive/benchmarks/README.md b/_arxiv/archive/benchmarks/README.md deleted file mode 100644 index 5e7498d0..00000000 --- a/_arxiv/archive/benchmarks/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Alignment Benchmarks - -This directory contains benchmarking scripts for the Alignment library. These scripts are designed to assess the performance of various components of the codebase. - -## Contents - -- **benchmark_dropout_strategies.py**: Compares the performance of different dropout strategies (sequential vs. multi-strategy). -- **benchmark_network_training.py**: Benchmarks network training with different configurations. - -## Usage - -Most benchmark scripts can be run directly from the command line: - -```bash -python benchmark_dropout_strategies.py --config configs/config_alignment_experiment.yaml -``` - -## Adding New Benchmarks - -When adding new benchmarks, please follow these guidelines: - -1. Include clear documentation within the script about what is being measured -2. Add command-line arguments for configuration -3. Include a simple way to output/visualize the benchmark results -4. Update this README with information about the new benchmark - -## Results - -Benchmark results should be saved to the `results/` directory, not committed directly to the repository unless they represent an important baseline. \ No newline at end of file diff --git a/_arxiv/archive/benchmarks/benchmark_dropout_strategies.py b/_arxiv/archive/benchmarks/benchmark_dropout_strategies.py deleted file mode 100755 index 392afece..00000000 --- a/_arxiv/archive/benchmarks/benchmark_dropout_strategies.py +++ /dev/null @@ -1,178 +0,0 @@ -#!/usr/bin/env python -""" -Benchmark script to compare original sequential dropout approach vs. multi-strategy approach. - -This script measures the performance difference between: -1. Processing strategies (high_rq, low_rq, random) sequentially -2. Processing all strategies simultaneously with the new tensorized multi-strategy approach -""" - -import os -import sys -import time -import argparse -import logging -import torch -import numpy as np -from tqdm import tqdm - -from alignment.experiments.alignment_experiments import AlignmentExperiment -from alignment.config import ExperimentConfig -from alignment.datasets import load_dataset -from alignment.dropout import progressive_dropout, progressive_dropout_multi_strategy - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s [%(levelname)s] %(message)s', - handlers=[ - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -def run_benchmark(config_path, num_runs=3): - """Run benchmark comparing sequential vs. multi-strategy dropout.""" - # Load configuration - logger.info(f"Loading configuration from {config_path}") - config = ExperimentConfig.load(config_path) - - # Force the device to be cuda if available - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - config.device = str(device) - - # Create experiment - experiment = AlignmentExperiment(config) - - # Create networks - logger.info("Creating networks...") - networks = experiment.create_networks() - logger.info(f"Created {len(networks)} networks") - - # Load dataset - logger.info("Loading dataset...") - dataset = load_dataset(config.dataset) - - # Setup dropout parameters - dropout_fractions = np.linspace( - config.alignment.dropout_min, - config.alignment.dropout_max, - config.alignment.dropout_steps - ).tolist() - - pruning_mode = getattr(config.extra, "dropout_pruning_mode", "global_joint") - dropout_mode = getattr(config.extra, "dropout_mode", "scaled") - metric = experiment.metric - - # Initialize timing results - sequential_times = [] - multi_strategy_times = [] - - # Run benchmarks multiple times - for run in range(num_runs): - logger.info(f"\nRun {run+1}/{num_runs}") - - # Make copies of networks to ensure fair comparison - networks_sequential = [net.clone() if hasattr(net, 'clone') else net for net in networks] - networks_multi = [net.clone() if hasattr(net, 'clone') else net for net in networks] - - # Process strategies sequentially (original approach) - logger.info("Running sequential approach...") - sequential_start = time.time() - - strategies = ["high_rq", "low_rq", "random"] - for strategy in tqdm(strategies, desc="Strategies"): - # Clone networks for this strategy to avoid interference - strategy_networks = [net.clone() if hasattr(net, 'clone') else net for net in networks_sequential] - - # Run progressive dropout with this strategy - network_accuracies, network_losses = progressive_dropout( - strategy_networks, - dataset, - dropout_fractions, - metric, - device, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - strategy=strategy, - show_progress=False # Disable progress bars for cleaner output - ) - - sequential_time = time.time() - sequential_start - sequential_times.append(sequential_time) - logger.info(f"Sequential approach took {sequential_time:.2f} seconds") - - # Process all strategies at once (new approach) - logger.info("Running multi-strategy approach...") - multi_start = time.time() - - # Run with multi-strategy mode - network_accuracies, network_losses = progressive_dropout( - networks_multi, - dataset, - dropout_fractions, - metric, - device, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - show_progress=False, # Disable progress bars for cleaner output - use_multi_strategy=True - ) - - multi_time = time.time() - multi_start - multi_strategy_times.append(multi_time) - logger.info(f"Multi-strategy approach took {multi_time:.2f} seconds") - - # Calculate speedup - speedup = sequential_time / multi_time if multi_time > 0 else float('inf') - logger.info(f"Speedup: {speedup:.2f}x") - - # Calculate average times and speedup - avg_sequential = np.mean(sequential_times) - avg_multi = np.mean(multi_strategy_times) - avg_speedup = avg_sequential / avg_multi if avg_multi > 0 else float('inf') - - std_sequential = np.std(sequential_times) - std_multi = np.std(multi_strategy_times) - - # Print summary - logger.info("\n" + "="*50) - logger.info("BENCHMARK SUMMARY") - logger.info("="*50) - logger.info(f"Network count: {len(networks)}") - logger.info(f"Dropout steps: {len(dropout_fractions)}") - logger.info(f"Pruning mode: {pruning_mode}") - logger.info(f"Dropout mode: {dropout_mode}") - logger.info(f"Device: {device}") - logger.info("-"*50) - logger.info(f"Sequential approach: {avg_sequential:.2f} ± {std_sequential:.2f} seconds") - logger.info(f"Multi-strategy approach: {avg_multi:.2f} ± {std_multi:.2f} seconds") - logger.info(f"Average speedup: {avg_speedup:.2f}x") - logger.info("="*50) - - return { - "sequential": sequential_times, - "multi_strategy": multi_strategy_times, - "speedup": avg_speedup - } - -def main(): - parser = argparse.ArgumentParser(description="Benchmark dropout strategies") - parser.add_argument("--config", type=str, default="configs/config_alignment_experiment.yaml", - help="Path to config file") - parser.add_argument("--runs", type=int, default=1, - help="Number of benchmark runs") - - args = parser.parse_args() - - # Run the benchmark - results = run_benchmark(args.config, args.runs) - - # Print the recommendation - if results["speedup"] > 1.5: - print("\nRECOMMENDATION: Use multi-strategy approach for significant speedup.") - else: - print("\nRECOMMENDATION: Both approaches have similar performance in this configuration.") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/_arxiv/archive/benchmarks/benchmark_network_training.py b/_arxiv/archive/benchmarks/benchmark_network_training.py deleted file mode 100755 index 72d1b49c..00000000 --- a/_arxiv/archive/benchmarks/benchmark_network_training.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python -""" -Benchmark script for comparing network training methods. - -This script benchmarks different training methods for multiple networks: -1. Sequential training (original approach) -2. Tensorized training (improved parallel approach) -3. Fully tensorized training (optimized ensemble approach) -""" - -import os -import time -import argparse -import logging -import torch -import torch.nn as nn -from tqdm import tqdm - -from alignment.metrics import get_metric -from alignment.datasets import load_dataset -from alignment.models.models import MLP, create_mlp -from alignment.models.base import AlignmentNetwork -from alignment.training import ( - train_networks_sequential, - train_networks_tensorized, - train_networks_fully_tensorized, - train_networks -) - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s [%(levelname)s] %(message)s', - handlers=[ - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -def create_networks(num_networks, input_size=784, hidden_sizes=[512, 256], output_size=10, seed=42): - """Create multiple networks with identical architecture but different initializations.""" - networks = [] - - for i in range(num_networks): - # Set seed for reproducibility, but different for each network - torch.manual_seed(seed + i) - - # Create a simple MLP network - base_model = MLP( - input_dim=input_size, - output_dim=output_size, - num_hidden=hidden_sizes, - dropout_rate=0.0 - ) - - # Get all linear layers for alignment - linear_layers = {} - for name, module in base_model.named_modules(): - if isinstance(module, nn.Linear): - if name != "layers.0": # Skip the first layer - linear_layers[name] = None # Use the layer's own input - - # Create AlignmentNetwork wrapper - network = AlignmentNetwork(base_model=base_model, alignment_layer_names=linear_layers) - - # Move to device (will be moved again in training, but good practice) - if torch.cuda.is_available(): - network.to('cuda') - - networks.append(network) - - logger.info(f"Created {num_networks} networks with architecture: {input_size} -> {hidden_sizes} -> {output_size}") - return networks - -def run_benchmark(networks, dataset, num_epochs=2, learning_rate=0.001, device=None, show_progress=True): - """Run benchmark comparing different training methods.""" - if device is None: - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - logger.info(f"Running benchmark on device: {device}") - - # Define methods to benchmark - methods = [ - ("Sequential", train_networks_sequential), - ("Tensorized", train_networks_tensorized), - ("Fully Tensorized", train_networks_fully_tensorized), - ("Auto-select", train_networks) - ] - - results = {} - - # Clone networks for each method to ensure fair comparison - for method_name, method_func in methods: - logger.info(f"Benchmarking {method_name} training...") - - # Create copies of networks for this method - method_networks = [] - - # For each network in the original list - for i, source_net in enumerate(networks): - # Get the architecture of the source network - input_dim = 784 # MNIST standard - output_dim = 10 # MNIST standard - - # Extract hidden sizes by examining the linear layers - linear_layers = [ - module for module in source_net.base_model.modules() - if isinstance(module, nn.Linear) - ] - - # Get all but the last layer's output dimensions for hidden sizes - hidden_sizes = [layer.out_features for layer in linear_layers[:-1]] - - logger.debug(f"Network {i}: input_dim={input_dim}, hidden_sizes={hidden_sizes}, output_dim={output_dim}") - - # Create base model with identical architecture - base_model = MLP( - input_dim=input_dim, - output_dim=output_dim, - num_hidden=hidden_sizes, - dropout_rate=0.0 - ) - - # Create alignment layer mapping identical to source network - # Use the layer_to_input_names attribute which is the internal representation - layer_to_input_names = source_net.layer_to_input_names - - # Create new network with same architecture and alignment layers - target_net = AlignmentNetwork( - base_model=base_model, - alignment_layer_names=layer_to_input_names - ) - - # Copy parameters from source to target - # This would be the most reliable way to ensure identical networks - target_net.load_state_dict(source_net.state_dict()) - - # Move to device - target_net.to(device) - - # Add to method networks - method_networks.append(target_net) - - # Measure time - start_time = time.time() - - # Train networks using this method - training_history = method_func( - networks=method_networks, - dataset=dataset, - num_epochs=num_epochs, - learning_rate=learning_rate, - device=device, - show_progress=show_progress - ) - - # Calculate elapsed time - elapsed_time = time.time() - start_time - - # Store results - results[method_name] = { - "time": elapsed_time, - "history": training_history, - "final_acc": training_history["test_acc"][-1] if training_history["test_acc"] else 0.0 - } - - logger.info(f"{method_name} training completed in {elapsed_time:.2f} seconds (final acc: {results[method_name]['final_acc']:.2f}%)") - - # Print summary - print("\nBenchmark Results:") - print("==================") - print(f"Networks: {len(networks)}, Epochs: {num_epochs}") - print("------------------") - - # Find the fastest method to calculate speedup - fastest_time = min(results[method]["time"] for method in results) - - for method_name in results: - elapsed_time = results[method_name]["time"] - final_acc = results[method_name]["final_acc"] - speedup = fastest_time / elapsed_time if elapsed_time > 0 else 0 - - print(f"{method_name:16s}: {elapsed_time:.2f}s ({speedup:.2f}x), Acc: {final_acc:.2f}%") - - return results - -def main(): - parser = argparse.ArgumentParser(description="Benchmark network training methods.") - parser.add_argument("--num_networks", type=int, default=5, help="Number of networks to train") - parser.add_argument("--hidden_sizes", type=str, default="512,256", help="Hidden layer sizes (comma-separated)") - parser.add_argument("--epochs", type=int, default=2, help="Number of epochs to train") - parser.add_argument("--device", type=str, default="cuda", help="Device to run benchmark on") - parser.add_argument("--batch_size", type=int, default=128, help="Batch size for training") - parser.add_argument("--seed", type=int, default=42, help="Random seed for reproducibility") - - args = parser.parse_args() - - # Parse hidden sizes - hidden_sizes = [int(size) for size in args.hidden_sizes.split(",")] - - # Set device - device = torch.device(args.device if torch.cuda.is_available() and args.device == "cuda" else "cpu") - - # Set random seed - torch.manual_seed(args.seed) - - # Create networks - networks = create_networks( - num_networks=args.num_networks, - hidden_sizes=hidden_sizes, - seed=args.seed - ) - - # Load dataset - logger.info("Loading MNIST dataset") - transform_params = { - "center_crop": None, - "resize": None, - "flatten": True, # Flatten for MLP - "normalize": False - } - dataset_config = { - "dataset_name": "MNIST", - "batch_size": args.batch_size, - "data_path": "./data", - "transform_params": transform_params - } - dataset = load_dataset(dataset_config) - - # Run benchmark - run_benchmark( - networks=networks, - dataset=dataset, - num_epochs=args.epochs, - device=device - ) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/_arxiv/archive/cluster/alignment-ddp-example.slurm b/_arxiv/archive/cluster/alignment-ddp-example.slurm deleted file mode 100644 index 13f6226c..00000000 --- a/_arxiv/archive/cluster/alignment-ddp-example.slurm +++ /dev/null @@ -1,157 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=alignment-ddp # Job name -#SBATCH --partition=kempner # Partition with GPUs -#SBATCH --nodes=2 # Number of nodes -#SBATCH --ntasks-per-node=4 # Tasks per node (match GPUs) -#SBATCH --cpus-per-task=16 # CPUs per task -#SBATCH --mem=512G # Memory per node -#SBATCH --gres=gpu:4 # GPUs per node -#SBATCH --time=24:00:00 # Time limit -#SBATCH --mail-type=begin,end # Email notifications -#SBATCH --account=kempner_lab # Account to charge - -# Set up job directory -JOB_NAME="alignment-experiment" -source cluster/slurm_settings.txt -JOB_DIR=${JOB_FOLDER}/${JOB_NAME}-${SLURM_JOB_ID} -mkdir -p $JOB_DIR - -# Set up logging -logfile="${JOB_DIR}/slurm-${SLURM_JOB_ID}.out" -echo "Job started at $(date)" > $logfile -echo "Running on nodes: $SLURM_JOB_NODELIST" >> $logfile - -# Set up DDP environment variables -export MASTER_PORT=12355 -export WORLD_SIZE=$(($SLURM_NNODES * $SLURM_NTASKS_PER_NODE)) -master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) -export MASTER_ADDR=$master_addr - -echo "WORLD_SIZE=$WORLD_SIZE" >> $logfile -echo "MASTER_ADDR=$MASTER_ADDR" >> $logfile -echo "MASTER_PORT=$MASTER_PORT" >> $logfile - -# Load modules and activate environment -module purge -module load python/3.9.12 -module load cuda/11.7 - -# Activate conda environment -conda activate alignment_env - -# Record start time -start_time=$(date +%s) - -# Create Python script for the experiment -cat > ${JOB_DIR}/run_experiment.py << 'EOF' -import os -import torch -import torch.distributed as dist -from pathlib import Path - -# Import from alignment codebase -from alignment import ModelWrapper, DatasetWrapper -from alignment.experiments import ProgressiveDropoutExperiment -from alignment.experiments.base import ExperimentConfig -from alignment.infrastructure.computing.distributed import setup_distributed, cleanup_distributed -from alignment.infrastructure.storage.logging import setup_logging - -def main(): - # Setup distributed environment - setup_distributed() - - # Get rank and world size - rank = dist.get_rank() if dist.is_initialized() else 0 - world_size = dist.get_world_size() if dist.is_initialized() else 1 - - # Setup logging (only on main process) - if rank == 0: - setup_logging(Path(os.environ.get('JOB_DIR', '.'))) - - # Configuration - config = ExperimentConfig( - name=f"resnet50_imagenet_ddp", - description="Progressive dropout analysis on ResNet50", - - # Model settings - model_name="resnet50", - pretrained=True, - - # Dataset settings - dataset_name="imagenet", - data_path="/n/holylabs/LABS/kempner_shared/datasets/imagenet", - batch_size=256 // world_size, # Scale batch size per GPU - num_workers=8, - - # Experiment settings - metrics=["rayleigh_quotient", "mi_gaussian", "pid_shared"], - tracked_layers=[ - "layer1.0.conv1", "layer1.1.conv1", "layer1.2.conv1", - "layer2.0.conv1", "layer2.1.conv1", "layer2.2.conv1", - "layer3.0.conv1", "layer3.1.conv1", "layer3.2.conv1", - "layer4.0.conv1", "layer4.1.conv1", "layer4.2.conv1", - ], - - # Training settings - train_before_dropout=True, - training_epochs=90, - learning_rate=0.1 * (world_size * 256 / 256), # Linear LR scaling - optimizer="sgd", - - # Distributed settings - distributed=True, - world_size=world_size, - rank=rank, - - # Other settings - checkpoint_dir=f"{os.environ.get('JOB_DIR', '.')}/checkpoints", - log_dir=f"{os.environ.get('JOB_DIR', '.')}/logs", - exclude_classification_layer=True, - scale_by_norm=False, - force_cpu_for_large_metric_ops=True, - ) - - # Create experiment - experiment = ProgressiveDropoutExperiment( - config=config, - dropout_range=(0.0, 0.9, 10), # 10 dropout levels from 0% to 90% - dropout_mode="magnitude", # Drop based on metric magnitude - compute_metrics_during_training=True, - metric_computation_interval=10, # Compute metrics every 10 epochs - ) - - # Run experiment - print(f"Rank {rank}: Starting experiment...") - results = experiment.run() - - # Save results (only on main process) - if rank == 0: - experiment.save_results() - print("Experiment completed successfully!") - - # Cleanup - cleanup_distributed() - -if __name__ == "__main__": - main() -EOF - -# Run the experiment using srun -# Each process will be launched on the appropriate node/GPU -echo "Starting distributed training..." >> $logfile -srun python ${JOB_DIR}/run_experiment.py >> $logfile 2>&1 - -# Record end time -end_time=$(date +%s) -total_time=$((end_time - start_time)) - -echo "Job completed at $(date)" >> $logfile -echo "Total runtime: $total_time seconds" >> $logfile - -# Copy results to a permanent location (only from rank 0 node) -if [[ $SLURM_PROCID -eq 0 ]]; then - RESULTS_DIR="/n/holylabs/LABS/kempner_lab/Users/$USER/alignment_results/${SLURM_JOB_ID}" - mkdir -p $RESULTS_DIR - cp -r ${JOB_DIR}/* $RESULTS_DIR/ - echo "Results copied to: $RESULTS_DIR" >> $logfile -fi \ No newline at end of file diff --git a/_arxiv/archive/cluster/alignment_stats.slurm b/_arxiv/archive/cluster/alignment_stats.slurm deleted file mode 100644 index 5b3d2ad4..00000000 --- a/_arxiv/archive/cluster/alignment_stats.slurm +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=alignment_stats # create a short name for your job -#SBATCH --partition=kempner # partition -#SBATCH --account=kempner_bsabatini_lab # account needed for kempner partition -#SBATCH --nodes=1 # node count -#SBATCH --ntasks-per-node=1 # total number of tasks per node -#SBATCH --cpus-per-task=64 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --gres=gpu:1 # number of allocated gpus per node -#SBATCH --mem=1000G # total memory per node (4 GB per cpu-core is default) -#SBATCH --time=04:00:00 # total run time limit (HH:MM:SS) -#SBATCH --mail-type=begin # send email when job begins -#SBATCH --mail-type=end # send email when job ends - -# we need to define the job name directly since it isn't a slurm environment variable -JOB_NAME="alignment_stats" - -# this text file has some settings in it (like the standard job directory) -source cluster/slurm_settings.txt -JOB_DIR=${JOB_FOLDER}/${JOB_NAME}-${SLURM_JOB_ID} # make a specific directory for this particular job -mkdir -p $JOB_DIR - -# define a unique log file in the right place -logfile="${JOB_DIR}/slurm-${SLURM_JOB_ID}.out" -echo "Writing to ${logfile}" - -# load python and activate our conda environment -module purge -module load python -conda activate networkAlignmentAnalysis - -# record the start time -start_time=$(date +%s) - -# this is the command that initiates the processes -python experiment.py alignment_stats --network CNN2P2 --dataset CIFAR100 --use_wandb --epochs 200 >> $logfile - -# record the end time -end_time=$(date +%s) - -# measure the time elapsed for the core part of the job in the logfile -total_time=$((end_time-start_time)) -echo "Total Time= "$total_time" seconds" >> $logfile diff --git a/_arxiv/archive/cluster/ddp-example/README.md b/_arxiv/archive/cluster/ddp-example/README.md deleted file mode 100644 index f559b6d8..00000000 --- a/_arxiv/archive/cluster/ddp-example/README.md +++ /dev/null @@ -1,13 +0,0 @@ -## Distributed Data Parallel Example on Slurm Cluster - -The contents of this folder contain a MWE for getting DDP to work on a cluster. If you want to try -it yourself, start from the main networkAlignmentAnalysis folder (with the .gitignore etc.). The -file called [ddp.slurm](ddp.slurm) is an ``sbatch`` script you can call with: - -```shell -sbatch cluster/ddp-example/ddp.slurm -``` - -This prepares some environment variables, creates a job folder, then calls the python script -[ddp.py](ddp.py) which downloads MNIST to the job folder, trains it across N GPUs (where N is -equal to the number of nodes times the number of GPUs per node), and saves it if requested. diff --git a/_arxiv/archive/cluster/ddp-example/ddp.py b/_arxiv/archive/cluster/ddp-example/ddp.py deleted file mode 100644 index 9eb76ebb..00000000 --- a/_arxiv/archive/cluster/ddp-example/ddp.py +++ /dev/null @@ -1,218 +0,0 @@ -""" -Massive thank you to the Princeton cluster engineers for providing this example (we've only made small changes) -https://github.com/PrincetonUniversity/multi_gpu_training/blob/main/02_pytorch_ddp/mnist_classify_ddp.py -""" - -import argparse -import shutil -import torch -from torch import nn -import torch.nn.functional as F -import torch.optim as optim -from torchvision import datasets, transforms -from torch.optim.lr_scheduler import StepLR - -import os -import time -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from socket import gethostname - - -class Net(nn.Module): - def __init__(self): - super(Net, self).__init__() - self.conv1 = nn.Conv2d(1, 32, 3, 1) - self.conv2 = nn.Conv2d(32, 64, 3, 1) - self.dropout1 = nn.Dropout(0.25) - self.dropout2 = nn.Dropout(0.5) - self.fc1 = nn.Linear(9216, 128) - self.fc2 = nn.Linear(128, 10) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(x) - x = self.conv2(x) - x = F.relu(x) - x = F.max_pool2d(x, 2) - x = self.dropout1(x) - x = torch.flatten(x, 1) - x = self.fc1(x) - x = F.relu(x) - x = self.dropout2(x) - x = self.fc2(x) - output = F.log_softmax(x, dim=1) - return output - - -def train(args, model, device, dataloader, datasampler, optimizer, epoch, rank): - """basic training script""" - # required for different shuffle order of examples in dataset each epoch - datasampler.set_epoch(epoch) - - if rank == 0 and epoch == 1: - first_batch_timer = time.time() - - model.train() - for batch_idx, (data, target) in enumerate(dataloader): - data, target = data.to(device), target.to(device) - if rank == 0 and epoch == 1 and batch_idx == 0: - print(f"Train-- epoch {epoch}, rank {rank}, first batch loaded in {time.time() - first_batch_timer} seconds.") - optimizer.zero_grad() - output = model(data) - loss = F.nll_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - if rank == 0: - print(f"Train Epoch: {epoch} [{batch_idx}/{len(dataloader)} ({100.*batch_idx/len(dataloader):.0f}%)] \t Loss: {loss.item():.6f}") - if args.dry_run: - break - - -def test(model, device, dataloader): - model.eval() - test_loss = 0 - correct = 0 - attempts = 0 - with torch.no_grad(): - for data, target in dataloader: - data, target = data.to(device), target.to(device) - output = model(data) - test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss - pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability - correct += pred.eq(target.view_as(pred)).sum().item() - attempts += data.size(0) - - test_loss /= attempts - - print(f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{attempts} ({100.*correct/attempts:.0f}%)\n") - - -def setup(rank, world_size): - # initialize the process group - dist.init_process_group("nccl", rank=rank, world_size=world_size) - - -def main(): - # Training settings - parser = argparse.ArgumentParser(description="PyTorch MNIST Example with DDP") - parser.add_argument( - "--job-folder", - type=str, - default=".", - help="job folder for storing data", - ) - parser.add_argument( - "--batch-size", - type=int, - default=64, - metavar="N", - help="input batch size for training (default: 64)", - ) - parser.add_argument( - "--epochs", - type=int, - default=14, - metavar="N", - help="number of epochs to train (default: 14)", - ) - parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)") - parser.add_argument( - "--gamma", - type=float, - default=0.9, - metavar="M", - help="Learning rate step gamma (default: 0.9)", - ) - parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training") - parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass") - parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)") - parser.add_argument( - "--log-interval", - type=int, - default=10, - metavar="N", - help="how many batches to wait before logging training status", - ) - parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model") - args = parser.parse_args() - - print("job folder:", args.job_folder) - data_folder = os.path.join(args.job_folder, "data") - - torch.manual_seed(args.seed) - - world_size = int(os.environ["WORLD_SIZE"]) - rank = int(os.environ["SLURM_PROCID"]) - gpus_per_node = int(os.environ["SLURM_GPUS_ON_NODE"]) - print("gpus_per_node:", gpus_per_node) - print("device_count:", torch.cuda.device_count()) - - assert gpus_per_node == torch.cuda.device_count() - print( - f"Hello from rank {rank} of {world_size} on {gethostname()} where there are" f" {gpus_per_node} allocated GPUs per node.", - flush=True, - ) - - if world_size > 1: - setup(rank, world_size) - if rank == 0: - print(f"Group initialized? {dist.is_initialized()}", flush=True) - - local_rank = rank - gpus_per_node * (rank // gpus_per_node) - torch.cuda.set_device(local_rank) - print(f"host: {gethostname()}, rank: {rank}, local_rank: {local_rank}") - - # Create network - net = Net() - model = net.to(local_rank) - - # Make it a DDP object for distributed processing and training - ddp_model = DDP(model, device_ids=[local_rank]) if world_size > 1 else model - optimizer = optim.Adadelta(ddp_model.parameters(), lr=args.lr) - scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) - - transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) - - train_data = datasets.MNIST(data_folder, train=True, download=True, transform=transform) - test_data = datasets.MNIST(data_folder, train=False, download=True, transform=transform) - - train_sampler = torch.utils.data.distributed.DistributedSampler(train_data, num_replicas=world_size, rank=rank) - train_loader = torch.utils.data.DataLoader( - train_data, - batch_size=args.batch_size, - sampler=train_sampler, - num_workers=int(os.environ["SLURM_CPUS_PER_TASK"]), - pin_memory=True, - ) - test_loader = torch.utils.data.DataLoader( - test_data, - batch_size=args.batch_size, - num_workers=int(os.environ["SLURM_CPUS_PER_TASK"]), - pin_memory=True, - ) - - for epoch in range(1, args.epochs + 1): - if rank == 0: - epoch_time = time.time() - train(args, ddp_model, local_rank, train_loader, train_sampler, optimizer, epoch, rank) - if rank == 0: - test(ddp_model, local_rank, test_loader) - scheduler.step() - if rank == 0: - epoch_time = time.time() - epoch_time - print(f"\nEpoch {epoch}, Train & Test Time = {epoch_time:.1f} seconds (measured from rank {rank}).\n") - - if args.save_model and rank == 0: - torch.save(model.state_dict(), os.path.join(args.job_folder, "test_model_ddp.pt")) - - if world_size > 1: - dist.destroy_process_group() - - # clear locally downloaded MNIST data - shutil.rmtree(data_folder) - - -if __name__ == "__main__": - main() diff --git a/_arxiv/archive/cluster/ddp-example/ddp.slurm b/_arxiv/archive/cluster/ddp-example/ddp.slurm deleted file mode 100644 index 43cdd746..00000000 --- a/_arxiv/archive/cluster/ddp-example/ddp.slurm +++ /dev/null @@ -1,56 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=ddp-example # create a short name for your job -#SBATCH --partition=kempner # partition to use (need one with GPUs for this) -#SBATCH --nodes=2 # node count -#SBATCH --ntasks-per-node=4 # total number of tasks per node -#SBATCH --cpus-per-task=16 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --mem=512G # total memory per node (4 GB per cpu-core is default) -#SBATCH --gres=gpu:4 # number of allocated gpus per node -#SBATCH --time=00:10:00 # total run time limit (HH:MM:SS) -#SBATCH --mail-type=begin # send email when job begins -#SBATCH --mail-type=end # send email when job ends -#SBATCH --account=kempner_bsabatini_lab # the account needs to be specified for the kempner partition - -# we need to define the job name directly since it isn't a slurm environment variable -JOB_NAME="DDP-Example" - -# this text file has some settings in it (like the standard job directory) -source cluster/slurm_settings.txt -JOB_DIR=${JOB_FOLDER}/${JOB_NAME}-${SLURM_JOB_ID} # make a specific directory for this particular job -mkdir -p $JOB_DIR - -# define a unique log file in the right place -logfile="${JOB_DIR}/slurm-${SLURM_JOB_ID}.out" -echo $logfile - -# note: -# it's important for ntasks-per-node to be equal to the number of GPUs per node, which is set with --gres=gpu:4 -export MASTER_PORT=12355 # there may be a smarter way to set this (e.g. with code), but this port is almost always open (maybe 100% of the time) -export WORLD_SIZE=$(($SLURM_NNODES * $SLURM_NTASKS_PER_NODE)) # world size is equal to number of nodes and number of tasks per node -echo "WORLD_SIZE="$WORLD_SIZE >> $logfile -echo "MASTER_PORT="$MASTER_PORT >> $logfile - -# define a master address for communication between GPUs -master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) -export MASTER_ADDR=$master_addr -echo "MASTER_ADDR="$MASTER_ADDR >> $logfile - -# load python and activate our conda environment -module purge -module load python -conda activate networkAlignmentAnalysis - -# record the start time -start_time=$(date +%s) - -# this is the command that initiates the processes -# srun will send each command to each task on each node for a total of WORLD_SIZE tasks -# (in this case the script it runs is python cluster/ddp.py) -srun python cluster/ddp-example/ddp.py --epochs=50 --job-folder=${JOB_DIR} >> $logfile - -# record the end time -end_time=$(date +%s) - -# measure the time elapsed for the core part of the job in the logfile -total_time=$((end_time-start_time)) -echo "Total Time= "$total_time" seconds" >> $logfile diff --git a/_arxiv/archive/cluster/imagenet-example/imagenet.py b/_arxiv/archive/cluster/imagenet-example/imagenet.py deleted file mode 100644 index 08a2abfd..00000000 --- a/_arxiv/archive/cluster/imagenet-example/imagenet.py +++ /dev/null @@ -1,217 +0,0 @@ -import argparse -import wandb -import torch -import torch.nn.functional as F -import torch.optim as optim -from torch.optim.lr_scheduler import StepLR - -import os -import sys -import time -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from socket import gethostname - -mainPath = os.path.dirname(os.path.abspath(__file__)) + "/.." -sys.path.append(mainPath) - -from networkAlignmentAnalysis import datasets -from networkAlignmentAnalysis.models.registry import get_model - - -def configure_wandb(args): - """create a wandb run file and set environment parameters appropriately""" - if args.use_wandb: - wandb.login() - run = wandb.init( - project=args.job_name, - name=args.job_folder, - ) - os.environ["WANDB_MODE"] = "offline" - return run - - return None - - -def train(args, model, device, dataset, optimizer, epoch, rank, run, train=True): - dataloader = dataset.train_loader if train else dataset.test_loader - if dataset.distributed: - if train: - dataset.train_sampler.set_epoch(epoch) - else: - dataset.test_sampler.set_epoch(epoch) - - if rank == 0: - first_batch_timer = time.time() - - model.train() - for batch_idx, batch in enumerate(dataloader): - data, target = dataset.unwrap_batch(batch, device=device) - if rank == 0 and batch_idx == 0: - print(f"Train-- epoch {epoch}, rank {rank}, first batch loaded in {time.time() - first_batch_timer} seconds.") - optimizer.zero_grad() - output = model(data) - loss = dataset.measure_loss(output, target) - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - if rank == 0: - print(f"Train Epoch: {epoch} [{batch_idx}/{len(dataloader)} ({100.*batch_idx/len(dataloader):.0f}%)] \t Loss: {loss.item():.6f}") - if run is not None: - run.log(dict(epoch=epoch, batch_idx=batch_idx, train_loss=loss.item())) - if args.dry_run: - break - - -def test(model, device, dataset, run, train=False): - dataloader = dataset.train_loader if train else dataset.test_loader - model.eval() - test_loss = 0 - correct = 0 - attempts = 0 - with torch.no_grad(): - for batch in dataloader: - data, target = dataset.unwrap_batch(batch, device=device) - output = model(data) - test_loss += dataset.measure_loss(output, target, reduction="sum").item() - pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability - correct += pred.eq(target.view_as(pred)).sum().item() - attempts += data.size(0) - - test_loss /= attempts - - print(f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{attempts} ({100.*correct/attempts:.0f}%)\n") - - if run is not None: - run.log(dict(test_loss=test_loss, test_accuracy=100.0 * correct / attempts)) - - -def create_dataset(name, net, distributed=True, loader_parameters={}): - return datasets.get_dataset( - name, - build=True, - distributed=distributed, - transform_parameters=net, - loader_parameters=loader_parameters, - ) - - -def setup(rank, world_size): - # initialize the process group - dist.init_process_group("nccl", rank=rank, world_size=world_size) - - -def main(): - # Training settings - parser = argparse.ArgumentParser(description="PyTorch ImageNet Example") - parser.add_argument( - "--job-name", - type=str, - default="ImageNet-Example", - help="job name for this project", - ) - parser.add_argument( - "--job-folder", - type=str, - default=".", - help="job folder for storing data", - ) - parser.add_argument( - "--use_wandb", - default=False, - action="store_true", - help="if used, will log experiment to WandB", - ) - parser.add_argument( - "--batch-size", - type=int, - default=64, - metavar="N", - help="input batch size for training (default: 64)", - ) - parser.add_argument( - "--epochs", - type=int, - default=14, - metavar="N", - help="number of epochs to train (default: 14)", - ) - parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)") - parser.add_argument("--wd", type=float, default=0.0, metavar="WD", help="weight decay (default: 0.0)") - parser.add_argument( - "--gamma", - type=float, - default=0.9, - metavar="M", - help="Learning rate step gamma (default: 0.99)", - ) - parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training") - parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass") - parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)") - parser.add_argument( - "--log-interval", - type=int, - default=10, - metavar="N", - help="how many batches to wait before logging training status", - ) - parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model") - args = parser.parse_args() - - run = configure_wandb(args) - - torch.manual_seed(args.seed) - - world_size = int(os.environ["WORLD_SIZE"]) - rank = int(os.environ["SLURM_PROCID"]) - gpus_per_node = int(os.environ["SLURM_GPUS_ON_NODE"]) - assert gpus_per_node == torch.cuda.device_count() - print( - f"Hello from rank {rank} of {world_size} on {gethostname()} where there are" f" {gpus_per_node} allocated GPUs per node.", - flush=True, - ) - - loader_parameters = dict( - batch_size=args.batch_size, - num_workers=max(int(os.environ["SLURM_CPUS_PER_TASK"]) - 1, 1), # save a little headroom - ) - - if world_size > 1: - setup(rank, world_size) - if rank == 0: - print(f"Group initialized? {dist.is_initialized()}", flush=True) - - local_rank = rank - gpus_per_node * (rank // gpus_per_node) - torch.cuda.set_device(local_rank) - print(f"host: {gethostname()}, rank: {rank}, local_rank: {local_rank}") - - model_name = "AlexNet" - dataset_name = "ImageNet" - net = get_model(model_name, build=True, dataset=dataset_name) - dataset = create_dataset(dataset_name, net, distributed=world_size > 1, loader_parameters=loader_parameters) - - model = net.to(local_rank) - ddp_model = DDP(model, device_ids=[local_rank]) if world_size > 1 else model - optimizer = optim.Adadelta(ddp_model.parameters(), lr=args.lr, weight_decay=args.wd) - - scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) - - for epoch in range(1, args.epochs + 1): - epoch_time = time.time() - train(args, ddp_model, local_rank, dataset, optimizer, epoch, rank, run) - if rank == 0: - test(ddp_model, local_rank, dataset, run) - scheduler.step() - epoch_time = time.time() - epoch_time - if rank == 0: - print(f"Epoch {epoch}, Train & Test Time = {epoch_time:.1f} seconds (measured from rank {rank}).\n") - - if args.save_model and rank == 0: - torch.save(model.state_dict(), os.path.join(args.job_folder, "alexnet_imagenet.pt")) - - if world_size > 1: - dist.destroy_process_group() - - -if __name__ == "__main__": - main() diff --git a/_arxiv/archive/cluster/imagenet-example/imagenet.slurm b/_arxiv/archive/cluster/imagenet-example/imagenet.slurm deleted file mode 100644 index fe4e8976..00000000 --- a/_arxiv/archive/cluster/imagenet-example/imagenet.slurm +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=ImageNet-Example # create a short name for your job -#SBATCH --partition=kempner # partition -#SBATCH --account=kempner_bsabatini_lab # account needed for kempner partition -#SBATCH --nodes=4 # node count -#SBATCH --ntasks-per-node=4 # total number of tasks per node -#SBATCH --cpus-per-task=16 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --gres=gpu:4 # number of allocated gpus per node -#SBATCH --mem=1000G # total memory per node (4 GB per cpu-core is default) -#SBATCH --time=01:00:00 # total run time limit (HH:MM:SS) -#SBATCH --mail-type=begin # send email when job begins -#SBATCH --mail-type=end # send email when job ends - - -# we need to define the job name directly since it isn't a slurm environment variable -JOB_NAME="ImageNet-Example" - -# this text file has some settings in it (like the standard job directory) -source cluster/slurm_settings.txt -JOB_DIR=${JOB_FOLDER}/${JOB_NAME}-${SLURM_JOB_ID} # make a specific directory for this particular job -mkdir -p $JOB_DIR - -# define a unique log file in the right place -logfile="${JOB_DIR}/slurm-${SLURM_JOB_ID}.out" -echo "Writing to "$logfile - -export MASTER_PORT=12355 -export WORLD_SIZE=$(($SLURM_NNODES * $SLURM_NTASKS_PER_NODE)) -echo "WORLD_SIZE="$WORLD_SIZE >> $logfile -echo "MASTER_PORT="$MASTER_PORT >> $logfile - -master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) -export MASTER_ADDR=$master_addr -echo "MASTER_ADDR="$MASTER_ADDR >> $logfile - -module purge -module load python -conda activate networkAlignmentAnalysis - -start_time=$(date +%s) - -# (nvidia-smi --query-gpu=utilization.gpu --format=csv --loop=1 --filename=gpu_utilization.log) - -srun python cluster/imagenet-example/imagenet.py --epochs=10 --use_wandb --log-interval=100 --job-folder=${JOB_DIR} --job-name=${JOB_NAME} --save-model >> $logfile - -end_time=$(date +%s) - -total_time=$((end_time-start_time)) -echo "Total Time= "$total_time" seconds" >> $logfile -echo "Total Time= "$total_time" seconds" diff --git a/_arxiv/archive/cluster/lightning-example/lightning_train.sbatch b/_arxiv/archive/cluster/lightning-example/lightning_train.sbatch deleted file mode 100644 index 86a127f2..00000000 --- a/_arxiv/archive/cluster/lightning-example/lightning_train.sbatch +++ /dev/null @@ -1,35 +0,0 @@ -#! /bin/bash -#SBATCH --job-name=vast_benchmark -#SBATCH --time=1-00:00:00 -#SBATCH --partition=kempner_requeue -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=4 -#SBATCH --mem=1000G -#SBATCH --output=benchmark_lightning.out -#SBATCH --error=benchmark_lightning.err -#SBATCH --account=kempner_dev -#SBATCH --dependency=singleton -#SBATCH --gres=gpu:1 - -if [[ -z $1 ]] ; then - model="alexnet" -else - model=$1 -fi - - -VAST_CREDENTIALS_PATH=/n/holylabs/LABS/kempner_dev/Users/$USER/vast-benchmarking/.load_credentials.sh -METRIC_GATHERING_SCRIPT_PATH=/n/holylabs/LABS/kempner_dev/Users/$USER/vast-benchmarking/get_vast_stats.sh -DATADIR=/n/fas-vast/kempner_benchmark/preproccessed_imagenet/imagenet21k_resized -CONTAINER_PATH=/n/holylabs/LABS/kempner_dev/Lab/containers/pytorch_2.1.2-cuda12.1-cudnn8-runtime-lightning.sif - - -source $VAST_CREDENTIALS_PATH -metrics_file=vast_metrics/vast_metrics_${SLURM_NNODES}nodes_${SLURM_GPUS_ON_NODE}gpus_${model}_${SLURM_JOB_ID}.csv - -$METRIC_GATHERING_SCRIPT_PATH $metrics_file 30 & - -time srun singularity exec --bind $DATADIR --nv $CONTAINER_PATH python3 train_model_lightning.py --dataset $DATADIR/imagenet21k_train \ - --epochs 1 --batch_size 64 --num_workers $SLURM_CPUS_PER_TASK --num_gpus $SLURM_GPUS_ON_NODE --num_nodes $SLURM_NNODES \ - --model $model \ No newline at end of file diff --git a/_arxiv/archive/cluster/lightning-example/submit_lightning.sh b/_arxiv/archive/cluster/lightning-example/submit_lightning.sh deleted file mode 100644 index 3e246f8f..00000000 --- a/_arxiv/archive/cluster/lightning-example/submit_lightning.sh +++ /dev/null @@ -1,26 +0,0 @@ -#! /bin/bash -# Script used to submit a matrix of jobs to the cluster - -mkdir -p logs -mkdir -p vast_metrics - -for model in alexnet resnet50 ; do - for nodes in 1 2 4 8 16 32 ; do - if [[ $nodes -eq 1 ]]; then - for gpus in 1 2 4 ; do - sbatch -N $nodes --ntasks-per-node $gpus --gres gpu:$gpus \ - -o logs/vast_benchmark_${nodes}nodes_${gpus}gpus_${model}.out \ - -e logs/vast_benchmark_${nodes}nodes_${gpus}gpus_${model}.err \ - lightning_train.sbatch $model - sleep 3 - done - else - gpus=4 - sbatch -N $nodes --ntasks-per-node $gpus --gres gpu:$gpus \ - -o logs/vast_benchmark_${nodes}nodes_${gpus}gpus_${model}.out \ - -e logs/vast_benchmark_${nodes}nodes_${gpus}gpus_${model}.err \ - lightning_train.sbatch $model - sleep 3 - fi - done -done \ No newline at end of file diff --git a/_arxiv/archive/cluster/lightning-example/train_model_lightning.py b/_arxiv/archive/cluster/lightning-example/train_model_lightning.py deleted file mode 100644 index 3589f688..00000000 --- a/_arxiv/archive/cluster/lightning-example/train_model_lightning.py +++ /dev/null @@ -1,110 +0,0 @@ -from typing import Any -from lightning.pytorch.utilities.types import STEP_OUTPUT -import torch -import torchvision -from torchvision import datasets, transforms -from torch.utils.data import DataLoader - -import lightning as L - -import argparse -import logging - - -class LightningModel(L.LightningModule): - def __init__(self, model, optimizer, lr) -> None: - super().__init__() - self.model = model - self.optimizer = optimizer - self.lr = lr - - def training_step(self, batch, batch_idx): - images, labels = batch - output = self.model(images) - loss = torch.nn.functional.cross_entropy(output, labels) - return loss - - def configure_optimizers(self): - optimizer = self.optimizer(self.parameters(), lr=self.lr) - return optimizer - - -# def train_one_epoch(model, loader, optimizer, device): -# model.train() -# batch_num = 0 -# for images, labels in loader: -# images, labels = images.to(device), labels.to(device) -# optimizer.zero_grad() -# output = model(images) -# loss = torch.nn.functional.cross_entropy(output, labels) -# loss.backward() -# optimizer.step() -# if batch_num % 100 == 0: -# logging.info(f"Batch Num: {batch_num} Loss: {loss.item()}") -# batch_num += 1 - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Train a model") - parser.add_argument("--dataset", type=str, default="imagenet21k_train", help="dataset to use") - parser.add_argument("--batch_size", type=int, default=64, help="batch size") - parser.add_argument("--epochs", type=int, default=10, help="number of epochs") - parser.add_argument("--lr", type=float, default=0.01, help="learning rate") - parser.add_argument("--model", type=str, default="alexnet", help="model to use") - parser.add_argument("--num_workers", type=int, default=4, help="number of workers") - parser.add_argument("--num_gpus", type=int, default=1, help="number of gpus per node") - parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes") - parser.add_argument( - "--checkpoint_steps", - type=int, - default=0, - help="number of training steps to save checkpoint after", - ) - parser.add_argument("--checkpoint_dir", type=str, default="checkpoints", help="directory to save checkpoints") - - return parser.parse_args() - - -def main(): - args = parse_args() - preprocess = transforms.Compose( - [ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ] - ) - dataset = datasets.ImageFolder(args.dataset, transform=preprocess) - logging.info(f"Data loaded, Found {len(dataset.classes)} classes, {len(dataset)} images") - num_classes = len(dataset.classes) - loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) - model = torchvision.models.get_model(args.model, num_classes=num_classes) - optimizer = torch.optim.SGD - lighting_model = LightningModel(model, optimizer, args.lr) - - callbacks = None - if args.checkpoint_steps > 0: - callbacks = [ - L.callbacks.ModelCheckpoint( - dirpath=args.checkpoint_dir, - save_top_k=-1, - save_last=True, - every_n_train_steps=args.checkpoint_steps, - ) - ] - - trainer = L.Trainer( - max_epochs=args.epochs, - min_epochs=args.epochs, - strategy="ddp", - devices=args.num_gpus, - num_nodes=args.num_nodes, - callbacks=callbacks, - ) - trainer.fit(lighting_model, train_dataloaders=loader) - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) - main() diff --git a/_arxiv/archive/cluster/slurm_settings.txt b/_arxiv/archive/cluster/slurm_settings.txt deleted file mode 100644 index 229c3711..00000000 --- a/_arxiv/archive/cluster/slurm_settings.txt +++ /dev/null @@ -1,3 +0,0 @@ -# Job Folder -JOB_FOLDER=./jobs - diff --git a/_arxiv/archive/cluster/testing-examples/test_job.slurm b/_arxiv/archive/cluster/testing-examples/test_job.slurm deleted file mode 100644 index 7ab81e4d..00000000 --- a/_arxiv/archive/cluster/testing-examples/test_job.slurm +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=test_files # create a short name for your job -#SBATCH --nodes=1 # node count -#SBATCH --ntasks-per-node=1 # total number of tasks per node -#SBATCH --cpus-per-task=1 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --mem=100M # total memory per node (4 GB per cpu-core is default) -#SBATCH --time=00:20:00 # total run time limit (HH:MM:SS) -#SBATCH --mail-type=begin # send email when job begins -#SBATCH --mail-type=end # send email when job ends -#SBATCH --output=/dev/null # no output message - -source cluster/slurm_settings.txt -mkdir -p $JOB_FOLDER -logfile="${JOB_FOLDER}/slurm-${SLURM_JOB_ID}.out" - -echo "Writing to ${logfile}" -echo "Writing in ${logfile}" >> $logfile - -module purge -module load python - -start_time=$(date +%s) - -python -c "print('hello world')" >> $logfile -python test_print.py >> $logfile - -end_time=$(date +%s) - -total_time=$((end_time-start_time)) -echo "Total Time= "$total_time" seconds" >> $logfile - -echo "Finished." diff --git a/_arxiv/archive/refactoring_docs/GAUSSIAN_MI_SUMMARY.md b/_arxiv/archive/refactoring_docs/GAUSSIAN_MI_SUMMARY.md deleted file mode 100644 index 58e65f3b..00000000 --- a/_arxiv/archive/refactoring_docs/GAUSSIAN_MI_SUMMARY.md +++ /dev/null @@ -1,84 +0,0 @@ -# Gaussian Mutual Information with Edgeworth Expansion - -## Overview -I've implemented a new metric `gaussian_mi_analytic` that computes mutual information between inputs and outputs of neural network nodes, assuming approximately Gaussian distributions with analytic expansions for non-Gaussian corrections. - -## Features - -### 1. **Analytic Gaussian MI Calculation** -For linear transformations Y = WX + ε where X and ε are Gaussian: -- Exact formula: I(X;Y) = 1/2 * log(det(Σ_X) * det(Σ_Y) / det(Σ_joint)) -- Efficient computation using covariance matrices -- Numerical stability with regularization - -### 2. **Edgeworth Expansion Corrections** -The metric includes corrections for non-Gaussian distributions up to order 3: - -- **Order 0**: Pure Gaussian assumption -- **Order 1**: First-order correction using third cumulants (skewness) -- **Order 2**: Second-order correction using fourth cumulants (kurtosis) and mixed terms -- **Order 3**: Third-order correction with cross-terms between skewness and kurtosis - -### 3. **Key Parameters** -- `expansion_order`: Controls the order of Edgeworth expansion (0-3) -- `noise_std`: Assumed noise level in the system -- `regularization`: Numerical stability parameter -- `per_neuron`: Compute MI for each neuron separately or jointly - -## Mathematical Foundation - -### Cumulants Used -- κ₂ = variance -- κ₃ = E[X³] (related to skewness) -- κ₄ = E[X⁴] - 3σ⁴ (excess kurtosis) - -### Edgeworth Corrections -The corrections capture deviations from Gaussianity: - -1. **First-order**: Involves normalized third cumulants (γ₁ = κ₃/σ³) -2. **Second-order**: Involves normalized fourth cumulants (γ₂ = κ₄/σ⁴) -3. **Third-order**: Cross-terms between different order cumulants - -## Test Results - -The metric was tested on: -1. **Pure Gaussian data**: Corrections are minimal as expected -2. **Skewed data (Chi-squared)**: Small corrections observed -3. **Heavy-tailed data (Student-t)**: Significant corrections, especially at orders 2 and 3 -4. **Different noise levels**: MI decreases with increasing noise as expected - -### Example Results -- Pure Gaussian (Order 0): MI ≈ 2.99 -- Heavy-tailed (Order 0): MI ≈ 2.99 -- Heavy-tailed (Order 2): MI ≈ 4.16 (showing significant correction) - -## Usage Example - -```python -from src.alignment.core.registry import METRIC_REGISTRY - -# Create metric with second-order corrections -metric = METRIC_REGISTRY.get("gaussian_mi_analytic")( - expansion_order=2, - noise_std=0.1, - per_neuron=True -) - -# Compute MI scores for each neuron -mi_scores = metric.compute(inputs=inputs, weights=weights) -``` - -## Benefits - -1. **Analytic computation**: Fast and exact for Gaussian case -2. **Non-Gaussian handling**: Edgeworth expansion captures deviations -3. **Flexible**: Can adjust expansion order based on data characteristics -4. **Per-neuron analysis**: Can analyze individual neuron information transfer - -## Integration - -The metric is fully integrated into the alignment framework: -- Registered as `gaussian_mi_analytic` -- Available through the metric registry -- Follows the standard BaseMetric interface -- Properly handles different tensor dimensions \ No newline at end of file diff --git a/_arxiv/archive/scripts/README.md b/_arxiv/archive/scripts/README.md deleted file mode 100644 index f5b9f704..00000000 --- a/_arxiv/archive/scripts/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Alignment Scripts - -This directory contains utility scripts for the Alignment library. These scripts provide various functionality for running experiments, demonstrations, and utilities. - -## Contents - -- **direct_pruning_test.py**: Tests pruning functionality directly without relying on the experiment infrastructure. -- **run_multi_strategy_experiment.py**: Runs experiments with multiple pruning strategies. -- **run_fixed_experiment.py**: Runs experiments with fixed configurations. -- **run_cascading_with_plots.py**: Runs cascading pruning experiments and generates plots. -- **run_cascading_test.py**: Tests cascading pruning methodology. -- **continual_mnist.py**: Implementation of continual learning on MNIST. -- **teacher_student.py**: Implementation of teacher-student neural network model. - -## Shell Scripts - -- **run_benchmark.sh**: Convenience script for running benchmarks. -- **run_cascading_test.sh**: Shell script for testing cascading pruning. - -## Usage - -Most scripts have command-line arguments for configuration: - -```bash -python scripts/run_fixed_experiment.py --config configs/config_alignment_experiment.yaml -``` - -## Adding New Scripts - -When adding new scripts, please follow these guidelines: - -1. Include clear documentation at the top of the script about its purpose and usage -2. Add command-line arguments for configuration using argparse -3. Include appropriate error handling and logging -4. Update this README with information about the new script \ No newline at end of file diff --git a/_arxiv/archive/scripts/__init__.py b/_arxiv/archive/scripts/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/_arxiv/archive/scripts/continual_mnist.py b/_arxiv/archive/scripts/continual_mnist.py deleted file mode 100644 index 021b5a55..00000000 --- a/_arxiv/archive/scripts/continual_mnist.py +++ /dev/null @@ -1,144 +0,0 @@ -# Some Code for continual learning with permuted MNIST -import time -from functools import partial -import numpy as np -import scipy as sp -import sklearn -import torch -import torch.nn.functional as F -from torch import nn -from torchvision.transforms import v2 as transforms - -from tqdm import tqdm -import matplotlib as mpl -from matplotlib import pyplot as plt - -import os -import sys - -mainPath = os.path.dirname(os.path.abspath(__file__)) + "/../.." -sys.path.append(mainPath) - -from alignment.models.registry import get_model -from alignment.datasets import get_dataset -from alignment.experiments.registry import get_experiment -from alignment import utils -from alignment import files -from alignment import train - - -def permute(batch, batch_dim=True, shuffle_idx=None): - if shuffle_idx is not None: - original_size = batch[0].shape - if batch_dim: - batch[0] = batch[0][:, shuffle_idx] - else: - batch[0] = batch[0][shuffle_idx] - batch[0].reshape(original_size) - return batch - - -def add_permutation(dataset, num_pixels=784): - perm = partial(permute, batch_dim=True, shuffle_idx=torch.randperm(num_pixels)) - if dataset.extra_transform is None: - dataset.extra_transform = [] - dataset.extra_transform.append(perm) - return dataset - - -def update_permutation(dataset, num_pixels=784): - perm = partial(permute, batch_dim=True, shuffle_idx=torch.randperm(num_pixels)) - dataset.extra_transform[-1] = perm - return dataset - - -def do_permuted_round(nets, optimizers, dataset, train_epochs=1, verbose=False): - parameters = dict( - verbose=verbose, - num_epochs=train_epochs, - alignment=False, - ) - dataset = update_permutation(dataset) - - # train and test - train_results = train.train(nets, optimizers, dataset, **parameters) - test_results = train.test(nets, dataset, **parameters) - - return train_results, test_results - - -def main(): - DEVICE = "cuda" if torch.cuda.is_available() else "cpu" - print("using device: ", DEVICE) - - model_name = "MLP" - dataset_name = "MNIST" - - hidden_widths = [200, 200, 200] - - lrs = [1e-1, 3e-2, 1e-2, 3e-3] - num_replicates = 3 - - nets = [] - optimizers = [] - net_lr = [] - for lr in lrs: - for _ in range(num_replicates): - net = get_model(model_name, build=True, dataset=dataset_name, hidden_widths=hidden_widths, dropout=0.0, ignore_flag=False) - net.to(DEVICE) - - optimizer = torch.optim.SGD(net.parameters(), lr=lr) - - nets.append(net) - optimizers.append(optimizer) - net_lr.append(lr) - - loader_parameters = dict( - shuffle=True, - batch_size=5, - ) - dataset = get_dataset(dataset_name, build=True, transform_parameters=net, loader_parameters=loader_parameters, device=DEVICE) - dataset = add_permutation(dataset) - - num_rounds = 200 - - train_loss = [] - train_accuracy = [] - test_loss = [] - test_accuracy = [] - for round in tqdm(range(num_rounds)): - c_train_res, c_test_res = do_permuted_round(nets, optimizers, dataset, verbose=False) - train_loss.append(c_train_res["loss"]) - train_accuracy.append(c_train_res["accuracy"]) - test_loss.append(c_test_res["loss"]) - test_accuracy.append(c_test_res["accuracy"]) - - loss = torch.stack([torch.tensor(l) for l in test_loss]) - accuracy = torch.stack([torch.tensor(a) for a in test_accuracy]) - - type_loss = utils.compute_stats_by_type(loss, len(lrs), 1)[0] - type_accuracy = utils.compute_stats_by_type(accuracy, len(lrs), 1)[0] - - # print(loss.shape, accuracy.shape, type_loss.shape, type_accuracy.shape) - - cols = mpl.colormaps["Set1"].resampled(len(lrs)) - names = [f"lr={lr}" for lr in lrs] - - fig, ax = plt.subplots(1, 2, figsize=(6, 3), layout="constrained") - for ii in range(len(lrs)): - ax[0].plot(range(num_rounds), type_loss[:, ii], c=cols(ii), label=names[ii]) - ax[1].plot(range(num_rounds), type_accuracy[:, ii], c=cols(ii), label=names[ii]) - - ax[0].set_xlabel("Rounds of Permuted MNIST") - ax[1].set_xlabel("Rounds of Permuted MNIST") - ax[0].set_ylabel("loss") - ax[1].set_ylabel("accuracy") - ax[0].legend(loc="best") - ax[1].legend(loc="best") - plt.show() - - print("Hello, add a debugger here to evaluate while testing") - - -if __name__ == "__main__": - main() diff --git a/_arxiv/archive/scripts/direct_pruning_test.py b/_arxiv/archive/scripts/direct_pruning_test.py deleted file mode 100644 index efb97975..00000000 --- a/_arxiv/archive/scripts/direct_pruning_test.py +++ /dev/null @@ -1,257 +0,0 @@ -#!/usr/bin/env python -""" -Direct Pruning Test - -This script tests pruning functionality directly, without relying on the complex -experiment infrastructure. It loads a model, applies pruning, and logs the results -including before/after weights and accuracies. -""" - -import os -import sys -import logging -import argparse -import copy -import numpy as np -import torch -import torch.nn as nn - -# Set up logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s [%(levelname)s] %(message)s', - handlers=[ - logging.StreamHandler(), - logging.FileHandler('direct_pruning_test.log', mode='w') - ] -) -logger = logging.getLogger(__name__) - -# Add src to path if not already there -src_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'src') -if src_path not in sys.path: - sys.path.insert(0, src_path) - -# Import alignment modules -from alignment.models.registry import create_model -from alignment.datasets import load_dataset -from alignment.metrics import get_metric -from alignment.dropout import progressive_dropout -from alignment.config import ExperimentConfig - -def count_zero_weights(model): - """Count zero weights in the model.""" - zero_weights = 0 - total_weights = 0 - - for name, param in model.named_parameters(): - if 'weight' in name: - layer_zeros = (param.data == 0).sum().item() - layer_total = param.data.numel() - zero_weights += layer_zeros - total_weights += layer_total - - if hasattr(param, 'shape'): - logger.info(f"Layer {name}: {layer_zeros}/{layer_total} zeros " - f"({100.0*layer_zeros/layer_total:.2f}% pruned), shape {param.shape}") - - if total_weights > 0: - logger.info(f"Total: {zero_weights}/{total_weights} zeros " - f"({100.0*zero_weights/total_weights:.2f}% pruned)") - - return zero_weights, total_weights - -def verify_pruning(config_path=None, model_name='mlp', dataset_name='mnist', strategy='low_rq', - pruning_mode='layer_wise', dropout_mode='unscaled', pruning_percent=0.5, - device=None): - """Test pruning directly.""" - # Use GPU if available - if device is None: - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - logger.info(f"Using device: {device}") - - # Fix dataset name casing - use uppercase for standard datasets - if dataset_name.lower() == 'mnist': - dataset_name = 'MNIST' - elif dataset_name.lower() == 'cifar10': - dataset_name = 'CIFAR10' - elif dataset_name.lower() == 'cifar100': - dataset_name = 'CIFAR100' - - # Define data path - data_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data') - - # Create dataset config - dataset_config = { - 'dataset_name': dataset_name, - 'batch_size': 128, - 'data_dir': data_root, - 'download': True - } - - # Make sure data directory exists - os.makedirs(data_root, exist_ok=True) - - dataset = load_dataset(dataset_config) - - # Get input dimension from dataset - if dataset_name.lower() in ['mnist', 'fashion_mnist']: - input_dim = 784 # 28*28 - else: - # Try to infer from the dataset - sample_data = next(iter(dataset.train_loader))[0] - input_dim = sample_data[0].flatten().shape[0] - logger.info(f"Inferred input dimension: {input_dim}") - - # Create a simple model - class MLP(nn.Module): - def __init__(self, input_dim=784, hidden_dim=100, output_dim=10): - super().__init__() - self.fc1 = nn.Linear(input_dim, hidden_dim) - self.fc2 = nn.Linear(hidden_dim, hidden_dim) - self.fc3 = nn.Linear(hidden_dim, output_dim) - self.relu = nn.ReLU() - - # Define alignment layers for pruning - self.alignment_layers = [self.fc1, self.fc2, self.fc3] - self.alignment_names = ['layer_0', 'layer_1', 'layer_2'] - self.hidden = {} - - def forward(self, x): - # Store inputs for hooking - batch_size = x.size(0) - x = x.view(batch_size, -1) # Flatten - - # Forward with storing activations - self.hidden['layer_0'] = x - x = self.relu(self.fc1(x)) - - self.hidden['layer_1'] = x - x = self.relu(self.fc2(x)) - - self.hidden['layer_2'] = x - x = self.fc3(x) - - return x - - # Create model based on dataset - model = MLP(input_dim=input_dim, hidden_dim=100, output_dim=10) - model.to(device) - - # Check for alignment layers - if not hasattr(model, 'alignment_layers'): - logger.error("Model doesn't have alignment_layers attribute") - return - - logger.info(f"Created model with {len(model.alignment_layers)} alignment layers") - - # Make a copy for pruning - pruned_model = copy.deepcopy(model) - pruned_model.to(device) - - # Make sure parameters are on device - for param in pruned_model.parameters(): - param.data = param.data.to(device) - - # Evaluate original model - model.eval() - orig_accuracy, orig_loss = dataset.evaluate(model, device) - logger.info(f"Original model: accuracy={orig_accuracy:.2f}%, loss={orig_loss:.4f}") - - # Check zero weights before pruning - logger.info("Weights before pruning:") - orig_zeros, orig_total = count_zero_weights(pruned_model) - - # Setup metric - metric = get_metric('rq') - - # Apply pruning - logger.info(f"Applying pruning: strategy={strategy}, mode={pruning_mode}, " - f"dropout_mode={dropout_mode}, percent={pruning_percent*100:.1f}%") - - # Call progressive_dropout directly - network_accuracies, network_losses = progressive_dropout( - [pruned_model], - dataset, - [0.0, pruning_percent], # Use just two fractions: 0% and the target % - metric, - device, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - strategy=strategy, - show_progress=False - ) - - # Check if pruning was applied - logger.info("Weights after pruning:") - pruned_zeros, pruned_total = count_zero_weights(pruned_model) - - # Calculate how many weights were pruned - weights_pruned = pruned_zeros - orig_zeros - pruned_percent = 100.0 * weights_pruned / pruned_total if pruned_total > 0 else 0 - logger.info(f"Weights pruned: {weights_pruned}/{pruned_total} ({pruned_percent:.2f}%)") - - # Evaluate pruned model - pruned_model.eval() - pruned_accuracy, pruned_loss = dataset.evaluate(pruned_model, device) - logger.info(f"Pruned model: accuracy={pruned_accuracy:.2f}%, loss={pruned_loss:.4f}") - - # Calculate accuracy change - acc_change = pruned_accuracy - orig_accuracy - logger.info(f"Accuracy change: {acc_change:.2f}% points") - - # Summary - logger.info("\nSUMMARY:") - logger.info(f"Strategy: {strategy}") - logger.info(f"Pruning mode: {pruning_mode}") - logger.info(f"Dropout mode: {dropout_mode}") - logger.info(f"Pruning percentage: {pruning_percent*100:.1f}%") - logger.info(f"Original accuracy: {orig_accuracy:.2f}%") - logger.info(f"Pruned accuracy: {pruned_accuracy:.2f}%") - logger.info(f"Accuracy change: {acc_change:.2f}% points") - logger.info(f"Weights pruned: {weights_pruned}/{pruned_total} ({pruned_percent:.2f}%)") - - return { - 'orig_accuracy': orig_accuracy, - 'pruned_accuracy': pruned_accuracy, - 'acc_change': acc_change, - 'weights_pruned': weights_pruned, - 'total_weights': pruned_total, - 'pruned_percent': pruned_percent - } - -def main(): - """Main function.""" - parser = argparse.ArgumentParser(description='Test pruning directly') - parser.add_argument('--model', type=str, default='mlp', help='Model name') - parser.add_argument('--dataset', type=str, default='mnist', help='Dataset name') - parser.add_argument('--strategy', type=str, default='low_rq', - choices=['high_rq', 'low_rq', 'random'], help='Pruning strategy') - parser.add_argument('--pruning-mode', type=str, default='layer_wise', - choices=['global_joint', 'layer_wise', 'layer_isolated'], - help='Pruning mode') - parser.add_argument('--dropout-mode', type=str, default='unscaled', - choices=['scaled', 'unscaled'], help='Dropout mode') - parser.add_argument('--pruning-percent', type=float, default=0.5, - help='Pruning percentage (0.0-1.0)') - parser.add_argument('--device', type=str, default=None, help='Device (cuda/cpu)') - - args = parser.parse_args() - - # Convert percent to fraction if needed - if args.pruning_percent > 1.0: - args.pruning_percent /= 100.0 - - # Run test - verify_pruning( - model_name=args.model, - dataset_name=args.dataset, - strategy=args.strategy, - pruning_mode=args.pruning_mode, - dropout_mode=args.dropout_mode, - pruning_percent=args.pruning_percent, - device=args.device - ) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/_arxiv/archive/scripts/run_benchmark.sh b/_arxiv/archive/scripts/run_benchmark.sh deleted file mode 100755 index db589054..00000000 --- a/_arxiv/archive/scripts/run_benchmark.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -# Benchmark script for testing tensorized network training -# This script runs multiple benchmark configurations to compare -# training speeds for different numbers of networks - -ROOT_DIR="/n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment" -cd $ROOT_DIR - -# Set up common parameters -EPOCHS=1 -DEVICE="cuda" -BATCH_SIZE=64 -HIDDEN_SIZES="32,16" - -echo "----------------------------------------------" -echo "Network Training Method Benchmark" -echo "----------------------------------------------" -echo "Date: $(date)" -echo "Device: $DEVICE" -echo "Hidden sizes: $HIDDEN_SIZES" -echo "Epochs: $EPOCHS" -echo "Batch size: $BATCH_SIZE" -echo "----------------------------------------------" -echo "" - -# Run benchmarks with different network counts -for NUM_NETWORKS in 1 3 5 10 20 -do - echo "=== Running benchmark with $NUM_NETWORKS networks ===" - python benchmark_network_training.py \ - --num_networks $NUM_NETWORKS \ - --hidden_sizes $HIDDEN_SIZES \ - --epochs $EPOCHS \ - --device $DEVICE \ - --batch_size $BATCH_SIZE - echo "" -done - -echo "----------------------------------------------" -echo "Benchmark completed" -echo "----------------------------------------------" \ No newline at end of file diff --git a/_arxiv/archive/scripts/run_cascading_test.py b/_arxiv/archive/scripts/run_cascading_test.py deleted file mode 100644 index 5e49e42f..00000000 --- a/_arxiv/archive/scripts/run_cascading_test.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python -""" -Test script for running alignment experiments with cascading layer pruning -""" - -import os -import sys -import logging -import time - -# Add the src directory to the Python path -script_dir = os.path.dirname(os.path.abspath(__file__)) -sys.path.insert(0, os.path.join(script_dir, "src")) - -try: - from alignment.config import ExperimentConfig - from alignment.experiments.alignment_experiments import AlignmentExperiment - from alignment.experiments.alignment_experiments import set_logging_level - print("Successfully imported alignment modules") -except ImportError as e: - print(f"Error importing alignment modules: {e}") - sys.exit(1) - -def main(): - # Define experiment parameters - config_file = "configs/config_alignment_experiment.yaml" - experiment_type = "progressive_dropout" - pruning_mode = "cascading_layer" - run_name = "cascading_test" - - print(f"Starting alignment experiment with cascading layer pruning at {time.ctime()}") - start_time = time.time() - - # Set up logging - set_logging_level(logging.INFO) - - # Load configuration - config_path = os.path.join(script_dir, config_file) - config = ExperimentConfig.load(config_path) - - # Set experiment parameters - config.experiment_type = experiment_type - - # Make sure the extra attribute exists - if not hasattr(config, 'extra'): - config.extra = type('ExtraConfig', (), {})() - - config.extra.dropout_pruning_mode = pruning_mode - - # Set experiment name - config.experiment_name = run_name - - # Modify configuration for faster testing - config.training.replicates = 3 # Use fewer networks - config.alignment.dropout_steps = 3 # Use fewer dropout steps - - # Print configuration for debugging - print(f"Using pruning mode: {pruning_mode}") - print(f"Number of replicates: {config.training.replicates}") - print(f"Number of dropout steps: {config.alignment.dropout_steps}") - - # Create and run experiment - experiment = AlignmentExperiment(config) - results, networks = experiment.run() - - end_time = time.time() - print(f"Alignment experiment finished at {time.ctime()}") - print(f"Total duration: {end_time - start_time:.2f} seconds") - - return 0 - -if __name__ == "__main__": - sys.exit(main()) \ No newline at end of file diff --git a/_arxiv/archive/scripts/run_cascading_test.sh b/_arxiv/archive/scripts/run_cascading_test.sh deleted file mode 100755 index a233ad25..00000000 --- a/_arxiv/archive/scripts/run_cascading_test.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash - -# Load necessary modules (if needed) -# module purge -# module load python/3.12.5-fasrc01 -# module load cuda/12.4.1-fasrc01 -# module load cudnn/8.9.2.26_cuda12-fasrc01 - -# Define the experiment parameters -CONFIG_FILE="configs/config_alignment_experiment.yaml" -EXPERIMENT_TYPE="progressive_dropout" -PRUNING_MODE="cascading_layer" -RUN_NAME="cascading_test" - -echo "Starting alignment experiment with cascading layer pruning at $(date)" -start_time=$(date +%s) - -# Add the alignment package to the Python path -export PYTHONPATH=/n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment - -# Execute the alignment experiment -cd /n/holylabs/LABS/kempner_dev/Users/hsafaai/Code/alignment -python -c " -import logging -import sys -from alignment.config import ExperimentConfig -from alignment.experiments.alignment_experiments import AlignmentExperiment, set_logging_level - -# Set up logging -set_logging_level(logging.INFO) - -# Load configuration -config_path = '${CONFIG_FILE}' -config = ExperimentConfig.load(config_path) - -# Set experiment parameters -config.experiment_type = '${EXPERIMENT_TYPE}' -config.extra.dropout_pruning_mode = '${PRUNING_MODE}' -config.run.name = '${RUN_NAME}' - -# Create and run experiment -experiment = AlignmentExperiment(config) -results, networks = experiment.run() - -print('Experiment completed successfully') -" - -end_time=$(date +%s) -echo "Alignment experiment finished at $(date)" -echo "Total duration: $((end_time - start_time)) seconds." \ No newline at end of file diff --git a/_arxiv/archive/scripts/run_cascading_with_plots.py b/_arxiv/archive/scripts/run_cascading_with_plots.py deleted file mode 100755 index e4cba3b2..00000000 --- a/_arxiv/archive/scripts/run_cascading_with_plots.py +++ /dev/null @@ -1,179 +0,0 @@ -#!/usr/bin/env python -""" -Run alignment experiment with cascading layer pruning and enhanced visualization -""" - -import os -import sys -import logging -import time -import matplotlib.pyplot as plt -import numpy as np -import torch - -# Add the src directory to the Python path -script_dir = os.path.dirname(os.path.abspath(__file__)) -sys.path.insert(0, os.path.join(script_dir, "src")) - -# Import alignment modules -from alignment.config import ExperimentConfig -from alignment.experiments.alignment_experiments import AlignmentExperiment, set_logging_level -from alignment.utils.plotting import plot_dropout_results - -# Set style to match the alignment_preref version -plt.style.use('seaborn-v0_8-whitegrid') -plt.rcParams['figure.figsize'] = (10, 6) -plt.rcParams['lines.linewidth'] = 2.5 -plt.rcParams['axes.grid'] = True -plt.rcParams['grid.alpha'] = 0.3 -plt.rcParams['axes.labelsize'] = 14 -plt.rcParams['xtick.labelsize'] = 12 -plt.rcParams['ytick.labelsize'] = 12 -plt.rcParams['legend.fontsize'] = 12 -plt.rcParams['figure.titlesize'] = 16 - -# Ensure wandb is available -try: - import wandb -except ImportError: - print("Warning: wandb not installed. Install with 'pip install wandb' for full functionality.") - wandb = None - -def enhanced_plotting(results, figure_path, pruning_mode, dropout_mode, experiment_name): - """Enhanced plotting function with consistent colors and styles""" - - # Define consistent colors and styles to match alignment_preref - saved_figures = [] - - # Extract data - dropout_fractions = results["dropout_fractions"] - accuracies = np.array(results["accuracies"]) - - # Plot mean accuracy vs. dropout fraction with enhanced styling - plt.figure(figsize=(12, 8)) - - # Use a richer color palette and add markers for better visibility - plt.plot(dropout_fractions, np.mean(accuracies, axis=0), 'o-', - color='#1f77b4', linewidth=2.5, markersize=8, - label="Mean Accuracy") - - # Add error bands with semi-transparency - plt.fill_between( - dropout_fractions, - np.mean(accuracies, axis=0) - np.std(accuracies, axis=0), - np.mean(accuracies, axis=0) + np.std(accuracies, axis=0), - alpha=0.25, color='#1f77b4' - ) - - # Enhance plot formatting - plt.xlabel("Dropout Fraction", fontsize=14) - plt.ylabel("Accuracy (%)", fontsize=14) - plt.title(f"{experiment_name}: {pruning_mode} Pruning", fontsize=16) - plt.grid(True, alpha=0.3) - plt.ylim(0, 100) # Set consistent y-axis range - plt.xlim(0, max(dropout_fractions) * 1.05) # Add a small margin - plt.legend(loc="upper right", fontsize=12) - - # Add more information to the plot - plt.text(0.02, 0.02, f"Dropout Mode: {dropout_mode}", transform=plt.gca().transAxes, - fontsize=10, bbox=dict(facecolor='white', alpha=0.8)) - - # Save the enhanced figure - if figure_path is not None: - os.makedirs(figure_path, exist_ok=True) - filename = os.path.join( - figure_path, - f"enhanced_{pruning_mode}_{dropout_mode}.png" - ) - plt.savefig(filename, dpi=300, bbox_inches="tight") - saved_figures.append(filename) - - # Log to wandb if available - if wandb is not None and wandb.run is not None: - wandb.log({"dropout_plot": wandb.Image(filename)}) - - plt.close() - else: - plt.show() - - # Also generate the standard plots for comparison - standard_plots = plot_dropout_results( - results, - figure_path, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode, - title_prefix=experiment_name - ) - - saved_figures.extend(standard_plots) - return saved_figures - -def run_experiment(config_path): - """Run the experiment with enhanced visualization""" - print(f"Starting cascading layer pruning experiment at {time.ctime()}") - start_time = time.time() - - # Set up logging - set_logging_level(logging.INFO) - - # Load configuration - config = ExperimentConfig.load(config_path) - - # Ensure we're using progressive dropout with cascading layer - config.experiment_type = "progressive_dropout" - - # Make sure the extra attribute exists - if not hasattr(config, 'extra'): - config.extra = type('ExtraConfig', (), {})() - - # Set the pruning mode to cascading layer - config.extra.dropout_pruning_mode = "cascading_layer" - - # Initialize wandb if available - if wandb is not None and getattr(config.checkpointing, "use_wandb", False): - wandb.init( - project=getattr(config, "wandb_project", "neural_alignment"), - entity=getattr(config, "wandb_entity", None), - name=getattr(config, "experiment_name", "cascading_layer_test"), - config=config.to_dict() - ) - wandb.run.log_code(".", include_fn=lambda path: path.endswith(".py")) - - # Create and run experiment - experiment = AlignmentExperiment(config) - results, networks = experiment.run() - - # Enhance the default plots - if "progressive_dropout" in results: - dropout_results = results["progressive_dropout"] - pruning_mode = config.extra.dropout_pruning_mode - dropout_mode = getattr(config.extra, "dropout_mode", "scaled") - - # Create enhanced plots - saved_figures = enhanced_plotting( - dropout_results, - experiment.figure_path, - pruning_mode, - dropout_mode, - getattr(config, "experiment_name", "Progressive Dropout") - ) - - print(f"Generated {len(saved_figures)} plot files") - - end_time = time.time() - print(f"Experiment finished at {time.ctime()}") - print(f"Total duration: {end_time - start_time:.2f} seconds") - - # Finish wandb run - if wandb is not None and wandb.run is not None: - wandb.finish() - - return results, networks - -if __name__ == "__main__": - if len(sys.argv) > 1: - config_path = sys.argv[1] - else: - config_path = "configs/config_alignment_experiment.yaml" - - results, _ = run_experiment(config_path) \ No newline at end of file diff --git a/_arxiv/archive/scripts/run_fixed_experiment.py b/_arxiv/archive/scripts/run_fixed_experiment.py deleted file mode 100644 index 2935332c..00000000 --- a/_arxiv/archive/scripts/run_fixed_experiment.py +++ /dev/null @@ -1,402 +0,0 @@ -#!/usr/bin/env python -""" -Run a fixed version of the alignment experiment. -""" - -import os -import sys -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import numpy as np -import logging -from tqdm import tqdm - -# Configure basic logging -logging.basicConfig(level=logging.INFO, - format='%(asctime)s [%(levelname)s] %(message)s', - handlers=[logging.StreamHandler()]) -logger = logging.getLogger(__name__) - -# Add the project to path -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "."))) - -# Import from the alignment package -from alignment.config import ExperimentConfig -from alignment.models.registry import create_model -from alignment.datasets import load_dataset -from alignment.metrics import get_metric -from alignment.utils.plotting import plot_dropout_results - -def train_networks(networks, dataset, device, num_epochs=5, learning_rate=0.001): - """Train multiple networks on the given dataset.""" - logger.info(f"Training {len(networks)} networks for {num_epochs} epochs") - - # Track training history for plotting - training_history = { - 'train_loss': [], - 'train_acc': [], - 'test_loss': [], - 'test_acc': [] - } - - # Train each network - trained_networks = [] - for i, network in enumerate(networks): - logger.info(f"Training network {i+1}/{len(networks)}") - - # Move network to the device - network = network.to(device) - - # Create optimizer - optimizer = optim.Adam(network.parameters(), lr=learning_rate) - - # Training loop - network.train() - history = { - 'train_loss': [], - 'train_acc': [], - 'test_loss': [], - 'test_acc': [] - } - - for epoch in range(num_epochs): - running_loss = 0.0 - correct = 0 - total = 0 - - # Train on each batch - for inputs, targets in tqdm(dataset.train_loader, desc=f"Network {i+1}, Epoch {epoch+1}/{num_epochs}"): - inputs, targets = inputs.to(device), targets.to(device) - - # Zero the parameter gradients - optimizer.zero_grad() - - # Forward pass - outputs = network(inputs) - loss = F.cross_entropy(outputs, targets) - - # Backward pass and optimize - loss.backward() - optimizer.step() - - # Track statistics - running_loss += loss.item() - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - # Calculate epoch statistics - train_loss = running_loss / len(dataset.train_loader) - train_acc = 100.0 * correct / total - - # Evaluate on test set - network.eval() - test_correct = 0 - test_total = 0 - test_loss_sum = 0.0 - - with torch.no_grad(): - for inputs, targets in dataset.test_loader: - inputs, targets = inputs.to(device), targets.to(device) - outputs = network(inputs) - - # Calculate loss - loss = F.cross_entropy(outputs, targets, reduction='sum') - test_loss_sum += loss.item() - - # Calculate accuracy - _, predicted = outputs.max(1) - test_total += targets.size(0) - test_correct += predicted.eq(targets).sum().item() - - test_acc = 100.0 * test_correct / test_total - test_loss = test_loss_sum / test_total - - # Store history - history['train_loss'].append(train_loss) - history['train_acc'].append(train_acc) - history['test_loss'].append(test_loss) - history['test_acc'].append(test_acc) - - # Log progress - logger.info(f"Network {i+1}, Epoch {epoch+1}/{num_epochs}: " - f"Train Loss={train_loss:.4f}, Train Acc={train_acc:.2f}%, " - f"Test Loss={test_loss:.4f}, Test Acc={test_acc:.2f}%") - - # Add to trained networks - trained_networks.append(network) - - # Accumulate training history (average across networks) - if i == 0: - # First network, initialize history - training_history = history - else: - # Average with previous networks - for key in training_history: - if key in history: - # Calculate running average - for epoch_idx in range(len(history[key])): - if epoch_idx < len(training_history[key]): - training_history[key][epoch_idx] = (training_history[key][epoch_idx] * i + history[key][epoch_idx]) / (i + 1) - - logger.info(f"Completed training {len(networks)} networks") - return trained_networks, training_history - -def test_pruning_strategies(networks, dataset, dropout_fractions, device): - """Test different pruning strategies on trained networks.""" - logger.info(f"Testing pruning strategies on {len(networks)} networks") - - # Initialize results - results = { - 'dropout_fractions': dropout_fractions, - 'accuracies': {'high_rq': [], 'low_rq': [], 'random': []}, - 'stds': {'high_rq': [], 'low_rq': [], 'random': []}, - 'losses': {'high_rq': [], 'low_rq': [], 'random': []} - } - - # For each pruning percentage, test all networks - for prune_idx, prune_percent in enumerate(dropout_fractions): - logger.info(f"Testing pruning percentage: {prune_percent*100:.1f}%") - - # Store results for each network at this pruning percentage - strategy_results = { - 'high_rq': {'acc': [], 'loss': []}, - 'low_rq': {'acc': [], 'loss': []}, - 'random': {'acc': [], 'loss': []} - } - - # Process each network - for net_idx, network in enumerate(networks): - logger.info(f"Processing network {net_idx+1}/{len(networks)}") - - # Save original weights - original_weights = {} - original_biases = {} - - for i, layer in enumerate(network.alignment_layers): - if hasattr(layer, "weight") and layer.weight is not None: - original_weights[i] = layer.weight.data.clone() - if hasattr(layer, "bias") and layer.bias is not None: - original_biases[i] = layer.bias.data.clone() - - # Get original accuracy - if prune_idx == 0 or net_idx == 0: - network.eval() - correct = 0 - total = 0 - total_loss = 0.0 - - with torch.no_grad(): - for inputs, targets in dataset.test_loader: - inputs, targets = inputs.to(device), targets.to(device) - outputs = network(inputs) - - # Compute loss - loss = F.cross_entropy(outputs, targets, reduction='sum') - total_loss += loss.item() - - # Compute accuracy - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - orig_accuracy = 100.0 * correct / total - orig_loss = total_loss / total - - logger.info(f"Original accuracy: {orig_accuracy:.2f}%, loss: {orig_loss:.4f}") - - # Test each strategy - for strategy in ["high_rq", "low_rq", "random"]: - # Restore original weights - for i, layer in enumerate(network.alignment_layers): - if i in original_weights: - layer.weight.data = original_weights[i].clone() - if i in original_biases and hasattr(layer, "bias") and layer.bias is not None: - layer.bias.data = original_biases[i].clone() - - # Skip pruning for 0% case - if prune_percent == 0.0: - acc = orig_accuracy - loss = orig_loss - else: - # Apply pruning based on strategy - total_neurons = 0 - total_pruned = 0 - - # Apply pruning to each layer - for i, layer in enumerate(network.alignment_layers): - if i not in original_weights: - continue - - # Compute neuron importance scores (using weight magnitude as proxy) - weights = layer.weight.data - input_dim = weights.shape[1] - total_neurons += input_dim - - # Calculate importance scores (weight magnitude) - neuron_scores = [torch.norm(weights[:, j]).item() for j in range(input_dim)] - - # Calculate how many neurons to prune - num_to_drop = max(1, int(input_dim * prune_percent)) if prune_percent > 0 else 0 - total_pruned += num_to_drop - - if num_to_drop > 0: - # Get indices to drop based on strategy - if strategy == "high_rq": # Drop highest alignment neurons - sorted_indices = np.argsort(neuron_scores)[::-1] # Sort descending - to_drop = sorted_indices[:num_to_drop] - elif strategy == "low_rq": # Drop lowest alignment neurons - sorted_indices = np.argsort(neuron_scores) # Sort ascending - to_drop = sorted_indices[:num_to_drop] - else: # Random pruning - all_indices = list(range(input_dim)) - np.random.shuffle(all_indices) - to_drop = all_indices[:num_to_drop] - - # Zero out weights for these neurons - for idx in to_drop: - if idx < weights.shape[1]: - layer.weight.data[:, idx] = 0.0 - if hasattr(layer, "bias") and layer.bias is not None and idx < layer.bias.data.shape[0]: - layer.bias.data[idx] = 0.0 - - logger.info(f"Pruned {total_pruned}/{total_neurons} neurons with strategy: {strategy}") - - # Evaluate pruned network - network.eval() - correct = 0 - total = 0 - total_loss = 0.0 - - with torch.no_grad(): - for inputs, targets in dataset.test_loader: - inputs, targets = inputs.to(device), targets.to(device) - outputs = network(inputs) - - # Compute loss - loss = F.cross_entropy(outputs, targets, reduction='sum') - total_loss += loss.item() - - # Compute accuracy - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - acc = 100.0 * correct / total - loss = total_loss / total - - # Store results for this network and strategy - strategy_results[strategy]['acc'].append(acc) - strategy_results[strategy]['loss'].append(loss) - - logger.info(f"Strategy {strategy}, Accuracy: {acc:.2f}%, Loss: {loss:.4f}") - - # Calculate mean and std for each strategy at this pruning percentage - for strategy in ["high_rq", "low_rq", "random"]: - # Get all networks' results for this strategy and pruning percentage - accs = strategy_results[strategy]['acc'] - losses = strategy_results[strategy]['loss'] - - # Calculate statistics - mean_acc = np.mean(accs) - std_acc = np.std(accs) - mean_loss = np.mean(losses) - - # Add to results - results['accuracies'][strategy].append(mean_acc) - results['stds'][strategy].append(std_acc) - results['losses'][strategy].append(mean_loss) - - logger.info(f"Pruning {prune_percent*100:.1f}%, Strategy {strategy}: " - f"Mean acc: {mean_acc:.2f}%, Std: {std_acc:.2f}%") - - return results - -def main(): - """Main function to run the experiment.""" - # Set random seed for reproducibility - torch.manual_seed(42) - np.random.seed(42) - - # Load configuration - config_path = "configs/config_alignment_experiment.yaml" - config = ExperimentConfig.load(config_path) - - # Set device - device = "cuda" if torch.cuda.is_available() else "cpu" - logger.info(f"Using device: {device}") - - # Load dataset - logger.info(f"Loading dataset: {config.dataset.dataset_name}") - dataset = load_dataset(config.dataset) - - # Create multiple networks (replicates) - num_networks = min(getattr(config.training, "replicates", 5), 2) # Limit to 2 networks for faster testing - logger.info(f"Creating {num_networks} networks") - - networks = [] - for i in range(num_networks): - logger.info(f"Creating model {i+1}/{num_networks}: {config.model.model_name}") - network = create_model(config.model) - networks.append(network) - - # Get dropout fractions from config - dropin_min = config.alignment.dropout_min - dropin_max = config.alignment.dropout_max - num_dropout_fractions = config.alignment.dropout_steps - dropout_fractions = np.linspace(dropin_min, dropin_max, num_dropout_fractions).tolist() - - # Train all networks - trained_networks, training_history = train_networks( - networks, - dataset, - device, - num_epochs=getattr(config.training, "epochs", 5), - learning_rate=getattr(config.training, "learning_rate", 0.001) - ) - - # Test pruning strategies - pruning_results = test_pruning_strategies( - trained_networks, - dataset, - dropout_fractions, - device - ) - - # Add training history to results - pruning_results['training_history'] = training_history - - # Create output directory - results_dir = "debug_output" - os.makedirs(results_dir, exist_ok=True) - - # Generate plots - try: - logger.info("Generating pruning plots with error bars") - - # Get pruning mode and dropout mode from config - pruning_mode = getattr(config.extra, "dropout_pruning_mode", "global_joint") - dropout_mode = getattr(config.extra, "dropout_mode", "scaled") - - saved_plots = plot_dropout_results( - pruning_results, - results_dir, - title_prefix="Fixed Pruning Test", - pruning_mode=pruning_mode, - dropout_mode=dropout_mode - ) - - if saved_plots: - logger.info(f"Generated {len(saved_plots)} plots: {saved_plots}") - - except Exception as e: - logger.error(f"Error generating dropout plots: {str(e)}") - import traceback - logger.error(traceback.format_exc()) - - logger.info("Experiment completed successfully!") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/_arxiv/archive/scripts/run_multi_strategy_experiment.py b/_arxiv/archive/scripts/run_multi_strategy_experiment.py deleted file mode 100644 index 95d2a8cc..00000000 --- a/_arxiv/archive/scripts/run_multi_strategy_experiment.py +++ /dev/null @@ -1,614 +0,0 @@ -#!/usr/bin/env python -""" -Run an experiment with multiple pruning strategies. - -This script executes neural network pruning using three different strategies: -1. high_rq: Prune neurons with highest alignment scores (weight magnitudes) -2. low_rq: Prune neurons with lowest alignment scores (weight magnitudes) -3. random: Prune neurons randomly - -The results are plotted for comparison. -""" - -import os -import sys -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -import numpy as np -import logging -from tqdm import tqdm -import matplotlib.pyplot as plt -import copy - -# Configure basic logging -logging.basicConfig(level=logging.INFO, - format='%(asctime)s [%(levelname)s] %(message)s', - handlers=[logging.StreamHandler()]) -logger = logging.getLogger(__name__) - -# Add the project to path -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "."))) - -# Import from the alignment package -from alignment.config import ExperimentConfig -from alignment.models.registry import create_model -from alignment.datasets import load_dataset -from alignment.metrics import get_metric -from alignment.utils.plotting import plot_dropout_results - -def train_networks(networks, dataset, device, num_epochs=5, learning_rate=0.001): - """Train multiple networks efficiently in one training loop.""" - logger.info(f"Training {len(networks)} networks for {num_epochs} epochs") - - # Track training history for plotting - training_history = { - 'train_loss': [], - 'train_acc': [], - 'test_loss': [], - 'test_acc': [] - } - - # Set up optimizer for each network - optimizers = [] - for network in networks: - network.to(device) - optimizers.append(torch.optim.Adam(network.parameters(), lr=learning_rate)) - - # Train all networks for each epoch - epoch_pbar = tqdm(range(num_epochs), desc="Training epochs", position=0) - for epoch in epoch_pbar: - # Initialize epoch stats - epoch_train_loss = 0.0 - epoch_train_acc = 0.0 - epoch_test_loss = 0.0 - epoch_test_acc = 0.0 - - # Training phase - net_pbar = tqdm(enumerate(zip(networks, optimizers)), - desc=f"Epoch {epoch+1}/{num_epochs} networks", - total=len(networks), - position=1, - leave=False) - - for network_idx, (network, optimizer) in net_pbar: - network.train() - running_loss = 0.0 - correct = 0 - total = 0 - - # Train on each batch - batch_pbar = tqdm(dataset.train_loader, - desc=f"Network {network_idx+1}/{len(networks)}", - position=2, - leave=False) - - for inputs, targets in batch_pbar: - inputs, targets = inputs.to(device), targets.to(device) - - # Zero the parameter gradients - optimizer.zero_grad() - - # Forward pass - outputs = network(inputs) - loss = F.cross_entropy(outputs, targets) - - # Backward pass and optimize - loss.backward() - optimizer.step() - - # Track statistics - running_loss += loss.item() - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - # Update batch progress bar - if total > 0: - batch_pbar.set_postfix({ - 'loss': running_loss / len(batch_pbar), - 'acc': 100.0 * correct / total - }) - - # Calculate network training statistics - if total > 0: - network_train_loss = running_loss / len(dataset.train_loader) - network_train_acc = 100.0 * correct / total - - # Accumulate for epoch average - epoch_train_loss += network_train_loss - epoch_train_acc += network_train_acc - - # Update network progress bar - net_pbar.set_postfix({ - 'train_loss': network_train_loss, - 'train_acc': network_train_acc - }) - - # Evaluation phase - network.eval() - test_correct = 0 - test_total = 0 - test_loss_sum = 0.0 - - # Evaluation progress bar - eval_pbar = tqdm(dataset.test_loader, - desc=f"Evaluating network {network_idx+1}", - position=2, - leave=False) - - with torch.no_grad(): - for inputs, targets in eval_pbar: - inputs, targets = inputs.to(device), targets.to(device) - outputs = network(inputs) - - # Calculate loss - loss = F.cross_entropy(outputs, targets, reduction='sum') - test_loss_sum += loss.item() - - # Calculate accuracy - _, predicted = outputs.max(1) - test_total += targets.size(0) - test_correct += predicted.eq(targets).sum().item() - - # Update eval progress bar - if test_total > 0: - eval_pbar.set_postfix({ - 'test_loss': test_loss_sum / test_total, - 'test_acc': 100.0 * test_correct / test_total - }) - - # Calculate network testing statistics - if test_total > 0: - network_test_loss = test_loss_sum / test_total - network_test_acc = 100.0 * test_correct / test_total - - # Accumulate for epoch average - epoch_test_loss += network_test_loss - epoch_test_acc += network_test_acc - - # Calculate and store epoch averages - epoch_train_loss /= len(networks) - epoch_train_acc /= len(networks) - epoch_test_loss /= len(networks) - epoch_test_acc /= len(networks) - - training_history['train_loss'].append(epoch_train_loss) - training_history['train_acc'].append(epoch_train_acc) - training_history['test_loss'].append(epoch_test_loss) - training_history['test_acc'].append(epoch_test_acc) - - # Update epoch progress bar - epoch_pbar.set_postfix({ - 'train_loss': epoch_train_loss, - 'train_acc': f"{epoch_train_acc:.2f}%", - 'test_loss': epoch_test_loss, - 'test_acc': f"{epoch_test_acc:.2f}%" - }) - - # Log progress - logger.info(f"Epoch {epoch+1}/{num_epochs}: " - f"Train Loss={epoch_train_loss:.4f}, Train Acc={epoch_train_acc:.2f}%, " - f"Test Loss={epoch_test_loss:.4f}, Test Acc={epoch_test_acc:.2f}%") - - logger.info(f"Completed training {len(networks)} networks.") - return networks, training_history - -def run_pruning(networks, dataset, dropout_fractions, device, pruning_mode="layer_wise", dropout_mode="scaled"): - """Run pruning with three different strategies and collect results in a more efficient way.""" - # Define the strategies to evaluate - strategies = ["high_rq", "low_rq", "random"] - - # Initialize results structure - results = { - 'dropout_fractions': dropout_fractions, - 'accuracies': {strategy: [] for strategy in strategies}, - 'stds': {strategy: [] for strategy in strategies}, - 'losses': {strategy: [] for strategy in strategies} - } - - # Store original weights and biases for all networks - logger.info("Saving original network weights") - original_weights = {} - original_biases = {} - - for net_idx, network in enumerate(networks): - original_weights[net_idx] = {} - original_biases[net_idx] = {} - - for layer_idx, layer in enumerate(network.alignment_layers): - if hasattr(layer, "weight") and layer.weight is not None: - original_weights[net_idx][layer_idx] = layer.weight.data.clone() - if hasattr(layer, "bias") and layer.bias is not None: - original_biases[net_idx][layer_idx] = layer.bias.data.clone() - - # First, get original accuracy for all networks (0% pruning) - logger.info("Evaluating original network performance (0% pruning)") - orig_accs = [] - orig_losses = [] - - # Move all networks to device - for network in networks: - network.to(device) - - # Evaluate all networks at baseline - for network in networks: - network.eval() - correct = 0 - total = 0 - total_loss = 0.0 - - with torch.no_grad(): - for inputs, targets in dataset.test_loader: - inputs, targets = inputs.to(device), targets.to(device) - outputs = network(inputs) - - # Compute loss - loss = F.cross_entropy(outputs, targets, reduction='sum') - total_loss += loss.item() - - # Compute accuracy - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - if total > 0: - orig_accs.append(100.0 * correct / total) - orig_losses.append(total_loss / total) - - # Calculate baseline statistics - orig_mean_acc = np.mean(orig_accs) - orig_std_acc = np.std(orig_accs) - orig_mean_loss = np.mean(orig_losses) - - # Add baseline values to all strategies (they start from the same point) - for strategy in strategies: - results['accuracies'][strategy].append(orig_mean_acc) - results['stds'][strategy].append(orig_std_acc) - results['losses'][strategy].append(orig_mean_loss) - - logger.info(f"Original accuracy: {orig_mean_acc:.2f}% ± {orig_std_acc:.2f}%") - - # Iterate through pruning fractions (skip the first one which is 0%) - fraction_pbar = tqdm(enumerate(dropout_fractions), - total=len(dropout_fractions), - desc="Pruning fractions", - position=0) - - for fraction_idx, fraction in fraction_pbar: - if fraction_idx == 0 and fraction == 0.0: - continue - - fraction_pbar.set_description(f"Pruning fraction: {fraction:.2f}") - - # For each strategy, process all networks at once - strategy_pbar = tqdm(strategies, desc=f"Pruning strategies", position=1, leave=False) - - for strategy in strategy_pbar: - strategy_pbar.set_description(f"Strategy: {strategy}") - - # Restore original weights for all networks at once - for net_idx, network in enumerate(networks): - for layer_idx in original_weights[net_idx]: - layer = network.alignment_layers[layer_idx] - layer.weight.data = original_weights[net_idx][layer_idx].clone() - if layer_idx in original_biases[net_idx] and hasattr(layer, "bias") and layer.bias is not None: - layer.bias.data = original_biases[net_idx][layer_idx].clone() - - # Apply pruning to all networks in parallel - for net_idx, network in enumerate(networks): - # Apply pruning based on the mode - if pruning_mode == "global_joint": - # Collect all neurons and their scores across all layers - all_neurons = [] - - for layer_idx, layer in enumerate(network.alignment_layers): - if layer_idx not in original_weights[net_idx]: - continue - - weights = layer.weight.data - input_dim = weights.shape[1] - - # Calculate importance scores - neuron_scores = torch.norm(weights, dim=0).cpu().numpy() - - # Store (layer_idx, neuron_idx, score) tuples - for j, score in enumerate(neuron_scores): - all_neurons.append((layer_idx, j, score)) - - # Sort neurons by score based on strategy - if strategy == "high_rq": - # Sort by highest scores first - all_neurons.sort(key=lambda x: x[2], reverse=True) - elif strategy == "low_rq": - # Sort by lowest scores first - all_neurons.sort(key=lambda x: x[2]) - elif strategy == "random": - # Shuffle neurons randomly - import random - random.shuffle(all_neurons) - - # Calculate how many neurons to prune - total_neurons = len(all_neurons) - num_to_drop = int(total_neurons * fraction) - - if num_to_drop > 0: - # Get indices to drop - to_drop = all_neurons[:num_to_drop] - - # Apply pruning - for layer_idx, neuron_idx, _ in to_drop: - layer = network.alignment_layers[layer_idx] - # Zero out weights for this neuron - if neuron_idx < layer.weight.data.shape[1]: - layer.weight.data[:, neuron_idx] = 0.0 - if hasattr(layer, "bias") and layer.bias is not None and neuron_idx < layer.bias.data.shape[0]: - layer.bias.data[neuron_idx] = 0.0 - - elif pruning_mode == "layer_wise": - # Apply pruning to each layer individually - can optimize with tensor operations - for layer_idx, layer in enumerate(network.alignment_layers): - if layer_idx not in original_weights[net_idx]: - continue - - weights = layer.weight.data - input_dim = weights.shape[1] - - # Calculate importance scores - vectorized version - neuron_scores = torch.norm(weights, dim=0).cpu().numpy() - - # Calculate how many neurons to prune in this layer - num_to_drop = int(input_dim * fraction) - - if num_to_drop > 0: - # Get neurons to drop based on strategy - if strategy == "high_rq": - # Sort by highest scores first (descending) - sorted_indices = np.argsort(neuron_scores)[::-1] - to_drop = sorted_indices[:num_to_drop] - elif strategy == "low_rq": - # Sort by lowest scores first (ascending) - sorted_indices = np.argsort(neuron_scores) - to_drop = sorted_indices[:num_to_drop] - elif strategy == "random": - # Choose random neurons - all_indices = np.arange(input_dim) - np.random.shuffle(all_indices) - to_drop = all_indices[:num_to_drop] - - # Apply pruning - if to_drop.size > 0: - # Create a mask tensor - mask = torch.ones_like(weights) - - # Convert numpy array to tensor and ensure it's properly formatted for indexing - to_drop_tensor = torch.tensor(to_drop.tolist(), device=weights.device, dtype=torch.long) - - # Use tensor indexing - mask[:, to_drop_tensor] = 0.0 - - # Apply mask to weights - layer.weight.data = weights * mask - - # Apply to bias if it exists - if hasattr(layer, "bias") and layer.bias is not None: - bias_mask = torch.ones_like(layer.bias.data) - valid_indices = [idx for idx in to_drop if idx < layer.bias.data.shape[0]] - if valid_indices: - bias_indices = torch.tensor(valid_indices, device=layer.bias.device, dtype=torch.long) - bias_mask[bias_indices] = 0.0 - layer.bias.data = layer.bias.data * bias_mask - - # Evaluate all pruned networks efficiently - network_accs = [] - network_losses = [] - - for network in networks: - # Evaluate - network.eval() - correct = 0 - total = 0 - total_loss = 0.0 - - with torch.no_grad(): - for inputs, targets in dataset.test_loader: - inputs, targets = inputs.to(device), targets.to(device) - outputs = network(inputs) - - # Compute loss - loss = F.cross_entropy(outputs, targets, reduction='sum') - total_loss += loss.item() - - # Compute accuracy - _, predicted = outputs.max(1) - total += targets.size(0) - correct += predicted.eq(targets).sum().item() - - if total > 0: - accuracy = 100.0 * correct / total - loss = total_loss / total - - network_accs.append(accuracy) - network_losses.append(loss) - - # Calculate statistics for this fraction and strategy - if network_accs: - mean_acc = np.mean(network_accs) - std_acc = np.std(network_accs) - mean_loss = np.mean(network_losses) - - # Add to results - results['accuracies'][strategy].append(mean_acc) - results['stds'][strategy].append(std_acc) - results['losses'][strategy].append(mean_loss) - - logger.info(f" {strategy}: Accuracy {mean_acc:.2f}% ± {std_acc:.2f}%") - - # Restore original weights for all networks - logger.info("Restoring original weights") - for net_idx, network in enumerate(networks): - for layer_idx in original_weights[net_idx]: - layer = network.alignment_layers[layer_idx] - layer.weight.data = original_weights[net_idx][layer_idx].clone() - if layer_idx in original_biases[net_idx] and hasattr(layer, "bias") and layer.bias is not None: - layer.bias.data = original_biases[net_idx][layer_idx].clone() - - return results - -def plot_results(results, pruning_mode, dropout_mode, output_dir="multi_strategy_output"): - """Create plots showing the results of different pruning strategies.""" - # Ensure output directory exists - os.makedirs(output_dir, exist_ok=True) - - # Extract data for plotting - fractions = results['dropout_fractions'] - strategies = list(results['accuracies'].keys()) - - # Set up plot colors and markers - colors = {'high_rq': 'red', 'low_rq': 'green', 'random': 'blue'} - markers = {'high_rq': 'o', 'low_rq': 's', 'random': '^'} - labels = { - 'high_rq': 'Prune Highest Magnitude', - 'low_rq': 'Prune Lowest Magnitude', - 'random': 'Prune Random' - } - - # Create accuracy plot - plt.figure(figsize=(10, 6)) - - for strategy in strategies: - accs = results['accuracies'][strategy] - stds = results['stds'][strategy] - - plt.errorbar( - fractions, - accs, - yerr=stds, - label=labels[strategy], - color=colors[strategy], - marker=markers[strategy], - capsize=4, - markersize=8, - linewidth=2 - ) - - plt.title(f'Accuracy vs. Pruning Fraction\n({pruning_mode} pruning, {dropout_mode} mode)', fontsize=14) - plt.xlabel('Pruning Fraction', fontsize=12) - plt.ylabel('Accuracy (%)', fontsize=12) - plt.grid(True, linestyle='--', alpha=0.7) - plt.legend(fontsize=12) - plt.tight_layout() - - # Save plot - accuracy_plot_file = os.path.join(output_dir, f'accuracy_plot_{pruning_mode}.png') - plt.savefig(accuracy_plot_file, dpi=300) - plt.close() - - # Also use the alignment plotting utility if available - try: - from alignment.utils.plotting import plot_dropout_results - - saved_plots = plot_dropout_results( - results, - output_dir, - title_prefix=f"Multi-Strategy Pruning Test", - pruning_mode=pruning_mode, - dropout_mode=dropout_mode - ) - - if saved_plots: - logger.info(f"Generated {len(saved_plots)} plots using alignment plotting utility") - return saved_plots - except Exception as e: - logger.warning(f"Could not use alignment plotting utility: {str(e)}") - - return [accuracy_plot_file] - -def main(): - """Main function to run the multi-strategy pruning experiment.""" - # Set random seed for reproducibility - torch.manual_seed(42) - np.random.seed(42) - - # Load configuration - config_path = "configs/config_alignment_experiment.yaml" - config = ExperimentConfig.load(config_path) - - # Set device - device = "cuda" if torch.cuda.is_available() else "cpu" - logger.info(f"Using device: {device}") - - # Load dataset - logger.info(f"Loading dataset: {config.dataset.dataset_name}") - dataset = load_dataset(config.dataset) - - # Create multiple networks (replicates) - num_networks = min(getattr(config.training, "replicates", 5), 3) # Limit to 3 networks for faster testing - logger.info(f"Creating {num_networks} networks") - - networks = [] - for i in range(num_networks): - logger.info(f"Creating model {i+1}/{num_networks}: {config.model.model_name}") - network = create_model(config.model) - networks.append(network) - - # Get dropout fractions from config - dropin_min = config.alignment.dropout_min - dropin_max = config.alignment.dropout_max - num_dropout_fractions = config.alignment.dropout_steps - dropout_fractions = np.linspace(dropin_min, dropin_max, num_dropout_fractions).tolist() - - # Get pruning and dropout modes - pruning_mode = getattr(config.extra, "dropout_pruning_mode", "layer_wise") - dropout_mode = getattr(config.extra, "dropout_mode", "scaled") - - # Train all networks - trained_networks, training_history = train_networks( - networks, - dataset, - device, - num_epochs=getattr(config.training, "epochs", 10), - learning_rate=getattr(config.training, "learning_rate", 0.001) - ) - - # Run pruning with different strategies - pruning_results = run_pruning( - trained_networks, - dataset, - dropout_fractions, - device, - pruning_mode=pruning_mode, - dropout_mode=dropout_mode - ) - - # Add training history to results - pruning_results['training_history'] = training_history - - # Create output directory - results_dir = "multi_strategy_output" - os.makedirs(results_dir, exist_ok=True) - - # Generate plots - try: - logger.info("Generating multi-strategy pruning plots") - - saved_plots = plot_results( - pruning_results, - pruning_mode, - dropout_mode, - output_dir=results_dir - ) - - if saved_plots: - logger.info(f"Generated {len(saved_plots)} plots: {saved_plots}") - - except Exception as e: - logger.error(f"Error generating plots: {str(e)}") - import traceback - logger.error(traceback.format_exc()) - - logger.info("Multi-strategy experiment completed successfully!") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/_arxiv/archive/scripts/teacher_student.py b/_arxiv/archive/scripts/teacher_student.py deleted file mode 100644 index 3109341d..00000000 --- a/_arxiv/archive/scripts/teacher_student.py +++ /dev/null @@ -1,93 +0,0 @@ -from tqdm import tqdm -import numpy as np -import torch - -from matplotlib import pyplot as plt - -import os -import sys - -mainPath = os.path.dirname(os.path.abspath(__file__)) + "/.." -sys.path.append(mainPath) - -from networkAlignmentAnalysis.models.registry import get_model -from networkAlignmentAnalysis import utils -from networkAlignmentAnalysis import train - -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" - - -def get_data(modes, signal_dist, noise_amplitude, batch_size): - signal = signal_dist.sample((batch_size,)) - data = signal @ modes + torch.normal(0, noise_amplitude, (batch_size, modes.size(1))) - return signal, data - - -def train(teacher, student, modes, signal_dist, noise_amplitude, batch_size=256, num_epochs=1000): - optimizer = torch.optim.SGD(student.parameters(), lr=1e-2) - lossfn = torch.nn.MSELoss() - track_loss = [] - alignment = [] - for epoch in tqdm(range(num_epochs)): - signal, data = get_data(modes, signal_dist, noise_amplitude, batch_size) - signal, data = signal.to(DEVICE), data.to(DEVICE) - - optimizer.zero_grad() - target = teacher(signal) - output = student(data, store_hidden=True) - loss = lossfn(output, target) - loss.backward() - optimizer.step() - track_loss.append(loss.item()) - alignment.append(student.measure_alignment(data, precomputed=True)) - - alignment = [torch.stack(a) for a in utils.transpose_list(alignment)] # list across layers, (epochs x dim) - return student, track_loss, alignment - - -if __name__ == "__main__": - print("using device: ", DEVICE) - - N = 100 - num_modes = 50 - shape = 0.7 * torch.ones((num_modes,)) - modes = torch.normal(0, 1, (num_modes, N)) - amplitude = torch.rand((num_modes,)) * 0.9 + 0.1 - signal_dist = torch.distributions.gamma.Gamma(shape, amplitude) - noise_amplitude = 0.2 - - model_name = "MLP" - teacher = get_model(model_name, build=True, input_dim=num_modes, dropout=0.0, ignore_flag=False, linear=True).to(DEVICE).eval() - student = get_model(model_name, build=True, input_dim=N, dropout=0.0, ignore_flag=False).to(DEVICE) - - student, track_loss, alignment = train(teacher, student, modes, signal_dist, noise_amplitude) - mean_alignment = torch.stack([torch.mean(align, dim=1) for align in alignment]) - - fig, ax = plt.subplots(1, 3, figsize=(9, 3), layout="constrained") - ax[0].plot(track_loss) - ax[0].set_ylim(0, 0.5) - for layer in range(len(alignment)): - ax[1].plot(mean_alignment[layer], label=f"layer{layer}") - ax[1].legend() - - signal, data = get_data(modes, signal_dist, noise_amplitude, 1) - ax[2].scatter(teacher(signal).detach(), student(data).detach()) - plt.show() - - N, S = 2, 10000 - num_modes = 2 - nonnormality = 1.5 - a = b = 1 / nonnormality - modes = np.random.normal(0, 1, (num_modes, N)) * np.random.normal(0, 1, num_modes).reshape(-1, 1) - signal = np.random.gamma(0.5, 1, (S, num_modes)) * np.sign(np.random.random((S, num_modes)) - 0.5) - data = signal @ modes - w, v = [np.array(o) for o in utils.smart_pca(torch.tensor(data).T)] - print(w.shape, v.shape) - - plt.scatter(data[:, 0], data[:, 1], s=1, c=("b", 0.4)) - for mode in modes: - plt.plot([0, mode[0]], [0, mode[1]], c="r") - for evec in v.T: - plt.plot([0, evec[0]], [0, evec[1]], c="k") - - plt.show() diff --git a/_arxiv/archive/scripts/timetests/test_batched_alignment.py b/_arxiv/archive/scripts/timetests/test_batched_alignment.py deleted file mode 100644 index 738ce96c..00000000 --- a/_arxiv/archive/scripts/timetests/test_batched_alignment.py +++ /dev/null @@ -1,41 +0,0 @@ -import time -import torch -from alignment import utils - -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" - -# B, D, S, C = 1024, 25, 784, 32 -B, D, S, C = 1024, 800, 196, 64 -input = torch.normal(0, 1, (B, D, S)).to(DEVICE) -weight = torch.normal(0, 1, (C, D)).to(DEVICE) - - -def get_align_usual(input, weight): - var_stride = torch.mean(torch.var(input, dim=1), dim=0) - align_stride = torch.stack([utils.alignment(input[:, :, i], weight) for i in range(S)], dim=1) - return utils.weighted_average(align_stride, var_stride.view(1, -1), 1, ignore_nan=True) - - -def get_align_new(input, weight): - var_stride = torch.mean(torch.var(input, dim=1), dim=0) - cc = utils.batch_cov(input.transpose(0, 2)) - rq = torch.sum(torch.matmul(weight, cc) * weight, axis=2) / torch.sum(weight * weight, axis=1) - prq = rq / torch.diagonal(cc, dim1=1, dim2=2).sum(1, keepdim=True) - return utils.weighted_average(prq, var_stride.view(-1, 1), 0, ignore_nan=True) - - -au = get_align_usual(input, weight) -an = get_align_new(input, weight) -print("align_usual and align_new produce same result?", torch.allclose(au, an)) - -num_tests = 100 - -t = time.time() -for _ in range(num_tests): - _ = get_align_usual(input, weight) -print("get_align_usual : time per test=", (time.time() - t) / num_tests) - -t = time.time() -for _ in range(num_tests): - _ = get_align_new(input, weight) -print("get_align_new : time per test=", (time.time() - t) / num_tests) diff --git a/_arxiv/archive/scripts/timetests/test_dataloader.py b/_arxiv/archive/scripts/timetests/test_dataloader.py deleted file mode 100644 index 130c2070..00000000 --- a/_arxiv/archive/scripts/timetests/test_dataloader.py +++ /dev/null @@ -1,106 +0,0 @@ -import os -import sys - -mainPath = os.path.dirname(os.path.abspath(__file__)) + "/.." -sys.path.append(mainPath) - -import time -from tqdm import tqdm - -import torch -import torchvision -from torchvision.transforms import v2 as transforms -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim - -from alignment import files - -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" - - -class Net(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(3, 6, 5) - self.pool = nn.MaxPool2d(2, 2) - self.conv2 = nn.Conv2d(6, 16, 5) - self.fc1 = nn.Linear(16 * 5 * 5, 120) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x): - x = self.pool(F.relu(self.conv1(x))) - x = self.pool(F.relu(self.conv2(x))) - x = torch.flatten(x, 1) # flatten all dimensions except batch - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - x = self.fc3(x) - return x - - -def check_time(num_workers, batch_size, pin_memory, fast_loader): - transform = transforms.Compose( - transforms.ToImage(), - transforms.ToDtype(torch.float32, scale=True), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), - ) - - trainset = torchvision.datasets.CIFAR10(root=files.data_path(), train=True, download=False, transform=transform) - - loader_kwargs = dict(batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=pin_memory) - - if fast_loader: - trainloader = torch.utils.data.DataLoader(trainset, **loader_kwargs, persistent_workers=fast_loader) - else: - trainloader = torch.utils.data.DataLoader(trainset, **loader_kwargs, persistent_workers=fast_loader) - - net = Net() - net.to(DEVICE) - - criterion = nn.CrossEntropyLoss() - optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) - - time_per_batch = torch.zeros((2, len(trainloader))) - batch_time = time.time() - for cycle in range(2): - for idx, (images, labels) in enumerate(trainloader): - images, labels = images.to(DEVICE), labels.to(DEVICE) - - optimizer.zero_grad() - - outputs = net(images) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - time_per_batch[cycle, idx] = time.time() - batch_time - batch_time = time.time() - - avg_batch = torch.mean(time_per_batch[:, 1:]) - total_time = torch.sum(time_per_batch) - init_time = time_per_batch[0, 0] - avg_batch # estimate of prepare time - second_init = time_per_batch[1, 0] - avg_batch - return init_time, avg_batch, total_time, second_init - - -if __name__ == "__main__": - print("using device: ", DEVICE) - - # tests - num_workers = [0, 2, 4] - batch_size = [8, 1024] - pin_memory = [True, False] - use_fast_loader = [True, False] - - for nw in num_workers: - for bs in batch_size: - for pin in pin_memory: - for fast in use_fast_loader: - if nw == 0 and fast: - continue # persistent memory is irrelevant when num_workers=0 - - init, avg, total, second = check_time(nw, bs, pin, fast) - print(f"NW={nw}, BS={bs}, Pin={pin}, Persistent={fast}") - print(f"Dur={total:.3f}, PerBatch={avg:.2f}, Prep={init:.2f}, SecondPrep={second:.2f}") - print("") diff --git a/drafts/alignment_notes b/drafts/alignment_notes deleted file mode 160000 index fea87596..00000000 --- a/drafts/alignment_notes +++ /dev/null @@ -1 +0,0 @@ -Subproject commit fea87596a5b84f587955c3af61fde1acc4359d5d diff --git a/tests/unit/test_experiments_old.py.bak b/tests/unit/test_experiments_old.py.bak deleted file mode 100644 index 1ebb326e..00000000 --- a/tests/unit/test_experiments_old.py.bak +++ /dev/null @@ -1,263 +0,0 @@ -""" -Unit tests for experiment classes. -""" - -import pytest -import torch -import torch.nn as nn -from pathlib import Path -import tempfile - -from alignment.experiments import ( - ProgressiveDropoutExperiment, - EigenvectorAlignment, - LayerIsolatedPruning, - ExperimentConfig -) -from alignment.models.architectures.standard_models import MLP, CNN2P2 -from alignment.metrics import RayleighQuotient - - -class TestExperimentConfig: - """Test suite for ExperimentConfig.""" - - def test_basic_config(self): - """Test basic configuration creation.""" - config = ExperimentConfig( - name="test_experiment", - device="cpu", - seed=42, - batch_size=32 - ) - - assert config.name == "test_experiment" - assert config.device == "cpu" - assert config.seed == 42 - assert config.batch_size == 32 - - def test_from_dict(self): - """Test creating config from dictionary.""" - config_dict = { - 'name': 'test', - 'model_name': 'mlp', - 'dataset_name': 'mnist', - 'dropout_fractions': [0.1, 0.5, 0.9] - } - - config = ExperimentConfig.from_dict(config_dict) - assert config.name == 'test' - assert config.model_name == 'mlp' - assert config.dataset_name == 'mnist' - assert config.dropout_fractions == [0.1, 0.5, 0.9] - - def test_to_dict(self): - """Test converting config to dictionary.""" - config = ExperimentConfig(name="test", batch_size=64) - config_dict = config.to_dict() - - assert config_dict['name'] == 'test' - assert config_dict['batch_size'] == 64 - - -class TestProgressiveDropoutExperiment: - """Test suite for Progressive Dropout experiments.""" - - @pytest.fixture - def simple_model(self): - """Create a simple model for testing.""" - return MLP(input_dim=100, hidden_dims=[50, 30], output_dim=10) - - @pytest.fixture - def test_config(self): - """Create test configuration.""" - return ExperimentConfig( - name="test_dropout", - device="cpu", - seed=42, - batch_size=16, - dropout_fractions=[0.0, 0.3, 0.6, 0.9], - metrics=['rayleigh_quotient'] - ) - - def test_initialization(self, simple_model, test_config): - """Test experiment initialization.""" - with tempfile.TemporaryDirectory() as tmpdir: - test_config.output_dir = tmpdir - - exp = ProgressiveDropoutExperiment( - model=simple_model, - config=test_config - ) - - assert exp.model is simple_model - assert exp.config is test_config - assert len(exp.dropout_fractions) == 4 - - def test_get_tracked_layers(self, simple_model, test_config): - """Test getting tracked layers.""" - exp = ProgressiveDropoutExperiment( - model=simple_model, - config=test_config - ) - - # Should track linear layers - tracked = exp.get_tracked_layers() - assert len(tracked) > 0 - assert all(isinstance(layer, nn.Linear) for _, layer in tracked) - - def test_apply_dropout(self, simple_model, test_config): - """Test applying dropout to model.""" - exp = ProgressiveDropoutExperiment( - model=simple_model, - config=test_config - ) - - # Get initial weights - layer_name, layer = exp.get_tracked_layers()[0] - initial_weight = layer.weight.data.clone() - - # Apply dropout - exp.apply_dropout(0.5) - - # Check that some weights are masked - current_weight = layer.weight.data - num_masked = (current_weight == 0).sum().item() - total_params = current_weight.numel() - - # Should have roughly 50% masked - assert 0.4 * total_params < num_masked < 0.6 * total_params - - -class TestEigenvectorAlignment: - """Test suite for Eigenvector Alignment experiments.""" - - @pytest.fixture - def test_config(self): - """Create test configuration.""" - return ExperimentConfig( - name="test_eigen", - device="cpu", - num_components=5, - metrics=['rayleigh_quotient'] - ) - - def test_initialization(self, test_config): - """Test eigenvector experiment initialization.""" - model = MLP(input_dim=50, hidden_dims=[30], output_dim=10) - - exp = EigenvectorAlignment( - model=model, - config=test_config - ) - - assert exp.num_components == 5 - assert exp.model is model - - def test_compute_eigenvectors(self, test_config): - """Test eigenvector computation.""" - model = MLP(input_dim=20, hidden_dims=[10], output_dim=5) - exp = EigenvectorAlignment(model=model, config=test_config) - - # Create mock data - data = torch.randn(100, 20) - - # Compute eigenvectors - layer_name, layer = exp.get_tracked_layers()[0] - eigenvectors, eigenvalues = exp.compute_layer_eigenvectors(data, layer) - - assert eigenvectors.shape[0] == min(test_config.num_components, 20) - assert eigenvalues.shape[0] == min(test_config.num_components, 20) - assert torch.all(eigenvalues[:-1] >= eigenvalues[1:]) # Descending order - - -class TestLayerIsolatedPruning: - """Test suite for Layer Isolated Pruning experiments.""" - - @pytest.fixture - def test_config(self): - """Create test configuration.""" - return ExperimentConfig( - name="test_pruning", - device="cpu", - pruning_percentages=[0.1, 0.3, 0.5], - metrics=['rayleigh_quotient'] - ) - - def test_initialization(self, test_config): - """Test pruning experiment initialization.""" - model = CNN2P2(output_dim=10, example_input_hw=[28, 28]) - - exp = LayerIsolatedPruning( - model=model, - config=test_config - ) - - assert exp.model is model - assert exp.pruning_percentages == [0.1, 0.3, 0.5] - - def test_prune_layer(self, test_config): - """Test layer pruning.""" - model = MLP(input_dim=50, hidden_dims=[30, 20], output_dim=10) - exp = LayerIsolatedPruning(model=model, config=test_config) - - # Get a layer to prune - layer_name, layer = exp.get_tracked_layers()[0] - initial_weight = layer.weight.data.clone() - - # Apply magnitude-based pruning - exp.prune_layer(layer, percentage=0.3, strategy='magnitude') - - # Check pruning - current_weight = layer.weight.data - num_pruned = (current_weight == 0).sum().item() - total_params = current_weight.numel() - - # Should have roughly 30% pruned - assert abs(num_pruned / total_params - 0.3) < 0.05 - - -@pytest.mark.integration -class TestExperimentIntegration: - """Integration tests for experiments.""" - - def test_full_experiment_run(self): - """Test running a complete experiment.""" - with tempfile.TemporaryDirectory() as tmpdir: - # Create config - config = ExperimentConfig( - name="integration_test", - device="cpu", - seed=42, - batch_size=8, - dropout_fractions=[0.0, 0.5], - metrics=['rayleigh_quotient'], - output_dir=tmpdir, - checkpoint_interval=None - ) - - # Create model - model = MLP(input_dim=20, hidden_dims=[10], output_dim=5) - - # Create experiment - exp = ProgressiveDropoutExperiment( - model=model, - config=config - ) - - # Create mock data - train_data = [(torch.randn(20), torch.randint(0, 5, (1,))) for _ in range(16)] - val_data = [(torch.randn(20), torch.randint(0, 5, (1,))) for _ in range(8)] - - # Run experiment (simplified) - results = {} - for fraction in config.dropout_fractions: - exp.apply_dropout(fraction) - - # Mock evaluation - metric_results = {'rayleigh_quotient': torch.rand(10)} - results[f'dropout_{fraction}'] = metric_results - - # Check results structure - assert len(results) == 2 - assert 'dropout_0.0' in results - assert 'dropout_0.5' in results \ No newline at end of file diff --git a/tests/unit/test_metrics_old.py.bak b/tests/unit/test_metrics_old.py.bak deleted file mode 100644 index fc7d8762..00000000 --- a/tests/unit/test_metrics_old.py.bak +++ /dev/null @@ -1,324 +0,0 @@ -""" -Unit tests for alignment metrics. -""" - -import pytest -import torch -import numpy as np -from typing import Tuple - -from alignment.metrics import ( - RayleighQuotient, - MutualInformationGaussian, - PartialInformationDecomposition, - CKA, - CCA, - GeneralizedRayleighQuotient, - SharedInformation -) - - -class TestRayleighQuotient: - """Test suite for Rayleigh Quotient metric.""" - - @pytest.fixture - def sample_data(self) -> Tuple[torch.Tensor, torch.Tensor]: - """Generate sample data for testing.""" - batch_size, input_dim, output_dim = 32, 10, 5 - inputs = torch.randn(batch_size, input_dim) - weights = torch.randn(output_dim, input_dim) - return inputs, weights - - def test_compute_basic(self, sample_data): - """Test basic RQ computation.""" - inputs, weights = sample_data - metric = RayleighQuotient() - - scores = metric.compute(inputs, weights) - - assert scores.shape == (weights.shape[0],) - assert torch.all(scores >= 0) - assert torch.all(torch.isfinite(scores)) - - def test_scale_by_norm(self, sample_data): - """Test RQ with norm scaling.""" - inputs, weights = sample_data - - metric_no_scale = RayleighQuotient(scale_by_norm=False) - metric_scale = RayleighQuotient(scale_by_norm=True) - - scores_no_scale = metric_no_scale.compute(inputs, weights) - scores_scale = metric_scale.compute(inputs, weights) - - # Scaled scores should be different - assert not torch.allclose(scores_no_scale, scores_scale) - - def test_force_cpu(self): - """Test CPU forcing for large operations.""" - if torch.cuda.is_available(): - inputs = torch.randn(1000, 1000).cuda() - weights = torch.randn(100, 1000).cuda() - - metric = RayleighQuotient(force_cpu=True) - scores = metric.compute(inputs, weights) - - # Result should still be on original device - assert scores.device == inputs.device - - def test_cnn_aggregation(self): - """Test aggregation for CNN layers.""" - batch_size = 16 - inputs = torch.randn(batch_size, 64, 7, 7) # CNN activations - weights = torch.randn(128, 64, 3, 3) # Conv weights - - for agg_op in ['mean', 'max', 'sum']: - metric = RayleighQuotient(aggregation_op=agg_op) - scores = metric.compute(inputs, weights) - - assert scores.shape == (weights.shape[0],) - assert torch.all(torch.isfinite(scores)) - - def test_empty_input(self): - """Test behavior with empty input.""" - inputs = torch.randn(0, 10) - weights = torch.randn(5, 10) - - metric = RayleighQuotient() - with pytest.raises(Exception): - metric.compute(inputs, weights) - - -class TestMutualInformation: - """Test suite for Mutual Information metrics.""" - - @pytest.fixture - def sample_data(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Generate sample data with outputs.""" - batch_size, input_dim, output_dim = 64, 20, 10 - inputs = torch.randn(batch_size, input_dim) - weights = torch.randn(output_dim, input_dim) - outputs = torch.matmul(inputs, weights.t()) + torch.randn(batch_size, output_dim) * 0.1 - return inputs, weights, outputs - - def test_gaussian_estimation(self, sample_data): - """Test MI with Gaussian estimation.""" - inputs, weights, outputs = sample_data - - metric = MutualInformationGaussian(estimation_method="gaussian") - scores = metric.compute(inputs, weights, outputs) - - assert scores.shape == (weights.shape[0],) - assert torch.all(scores >= 0) # MI is non-negative - assert torch.all(torch.isfinite(scores)) - - def test_requires_outputs(self, sample_data): - """Test that MI requires outputs.""" - inputs, weights, _ = sample_data - - metric = MutualInformationGaussian() - with pytest.raises(ValueError): - metric.compute(inputs, weights) # No outputs provided - - def test_different_estimations(self, sample_data): - """Test different estimation methods produce different results.""" - inputs, weights, outputs = sample_data - - metric_gaussian = MutualInformationGaussian(estimation_method="gaussian") - scores_gaussian = metric_gaussian.compute(inputs, weights, outputs) - - # Results should be reasonable - assert torch.all(scores_gaussian >= 0) - assert torch.all(scores_gaussian <= 10) # Reasonable upper bound - - -class TestCKA: - """Test suite for CKA metric.""" - - def test_linear_kernel(self): - """Test CKA with linear kernel.""" - n_samples = 100 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 30) - - metric = CKA(kernel="linear") - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert 0 <= float(similarity) <= 1 - - def test_rbf_kernel(self): - """Test CKA with RBF kernel.""" - n_samples = 100 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 30) - - metric = CKA(kernel="rbf") - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert 0 <= float(similarity) <= 1 - - def test_self_similarity(self): - """Test that CKA(X, X) = 1.""" - X = torch.randn(100, 50) - - metric = CKA(kernel="linear") - similarity = metric.compute(X, X) - - assert np.isclose(float(similarity), 1.0, rtol=1e-5) - - def test_different_samples_error(self): - """Test error when X and Y have different number of samples.""" - X = torch.randn(100, 50) - Y = torch.randn(80, 30) # Different number of samples - - metric = CKA() - with pytest.raises(ValueError): - metric.compute(X, Y) - - -class TestCCA: - """Test suite for CCA metric.""" - - def test_basic_cca(self): - """Test basic CCA computation.""" - n_samples = 200 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 40) - - metric = CCA(n_components=20) - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert 0 <= float(similarity) <= 1 - - def test_regularization(self): - """Test CCA with regularization.""" - n_samples = 100 - X = torch.randn(n_samples, 50) - Y = torch.randn(n_samples, 40) - - metric = CCA(n_components=20, reg=0.1) - similarity = metric.compute(X, Y) - - assert isinstance(similarity, (float, torch.Tensor)) - assert torch.isfinite(torch.tensor(similarity)) - - def test_max_components(self): - """Test CCA with too many components.""" - n_samples = 50 - X = torch.randn(n_samples, 20) - Y = torch.randn(n_samples, 30) - - # Request more components than possible - metric = CCA(n_components=100) - similarity = metric.compute(X, Y) - - # Should still work (automatically reduced) - assert isinstance(similarity, (float, torch.Tensor)) - - -class TestPartialInformationDecomposition: - """Test suite for PID metrics.""" - - @pytest.fixture - def sample_data(self): - """Generate sample data for PID.""" - batch_size = 128 - inputs = torch.randn(batch_size, 20) - weights = torch.randn(10, 20) - outputs = torch.matmul(inputs, weights.t()) - return inputs, weights, outputs - - def test_pid_components(self, sample_data): - """Test that PID components are computed correctly.""" - inputs, weights, outputs = sample_data - - metric = PartialInformationDecomposition(method="broja") - results = metric.compute(inputs, weights, outputs) - - # Check that all components are present - assert 'unique_information' in results - assert 'redundant_information' in results - assert 'synergistic_information' in results - - # Check shapes - assert results['unique_information'].shape == (weights.shape[0],) - assert isinstance(results['redundant_information'], (float, torch.Tensor)) - assert isinstance(results['synergistic_information'], (float, torch.Tensor)) - - def test_pid_non_negative(self, sample_data): - """Test that PID components are non-negative.""" - inputs, weights, outputs = sample_data - - metric = PartialInformationDecomposition() - results = metric.compute(inputs, weights, outputs) - - # All information measures should be non-negative - assert torch.all(results['unique_information'] >= 0) - assert float(results['redundant_information']) >= 0 - assert float(results['synergistic_information']) >= 0 - - -class TestGeneralizedRayleighQuotient: - """Test suite for Generalized Rayleigh Quotient.""" - - def test_basic_computation(self): - """Test basic GRQ computation.""" - batch_size, dim = 64, 20 - inputs = torch.randn(batch_size, dim) - weights = torch.randn(10, dim) - - metric = GeneralizedRayleighQuotient() - scores = metric.compute(inputs, weights) - - assert scores.shape == (weights.shape[0],) - assert torch.all(torch.isfinite(scores)) - - def test_with_reference_covariance(self): - """Test GRQ with custom reference covariance.""" - batch_size, dim = 64, 20 - inputs = torch.randn(batch_size, dim) - weights = torch.randn(10, dim) - - # Create positive definite reference covariance - ref_cov = torch.randn(dim, dim) - ref_cov = ref_cov @ ref_cov.t() + torch.eye(dim) * 0.1 - - metric = GeneralizedRayleighQuotient() - scores = metric.compute(inputs, weights, reference_cov=ref_cov) - - assert scores.shape == (weights.shape[0],) - assert torch.all(torch.isfinite(scores)) - - -@pytest.mark.parametrize("device", ["cpu", "cuda"]) -def test_device_consistency(device): - """Test that metrics work correctly on different devices.""" - if device == "cuda" and not torch.cuda.is_available(): - pytest.skip("CUDA not available") - - inputs = torch.randn(32, 20).to(device) - weights = torch.randn(10, 20).to(device) - - metric = RayleighQuotient() - scores = metric.compute(inputs, weights) - - assert scores.device.type == device - - -def test_metric_determinism(): - """Test that metrics produce deterministic results.""" - torch.manual_seed(42) - inputs1 = torch.randn(32, 20) - weights1 = torch.randn(10, 20) - - torch.manual_seed(42) - inputs2 = torch.randn(32, 20) - weights2 = torch.randn(10, 20) - - metric = RayleighQuotient() - scores1 = metric.compute(inputs1, weights1) - scores2 = metric.compute(inputs2, weights2) - - assert torch.allclose(scores1, scores2) \ No newline at end of file