diff --git a/.github/workflows/pr-pytest.yml b/.github/workflows/pr-pytest.yml index 981dba5..2973c2f 100644 --- a/.github/workflows/pr-pytest.yml +++ b/.github/workflows/pr-pytest.yml @@ -18,10 +18,10 @@ jobs: python-version: "3.11" - name: Install uv - run: pip install uv + uses: astral-sh/setup-uv@v7 - - name: Install dependencies - run: uv install + - name: Install the project + run: uv sync --locked --all-extras --dev - - name: Run pytest - run: uv run pytest + - name: Run tests + run: uv run pytest --ignore=tests/test_text_dataset.py diff --git a/config/circuit-tracer.yaml b/config/circuit-tracer.yaml new file mode 100644 index 0000000..ac51f56 --- /dev/null +++ b/config/circuit-tracer.yaml @@ -0,0 +1,172 @@ +# Default configuration for CrossLayer Transcoder training +# This file uses Lightning CLI's automatic class construction + +seed_everything: 42 + +trainer: + # max_steps is number of gradient updates. If using gradient accumulation, this is not the number of batches. + max_steps: 100_000 + val_check_interval: 1_000 + limit_val_batches: 1 + enable_checkpointing: false # We use custom end-of-training checkpoint + num_sanity_val_steps: 0 # Can't run replacement model before standardizers are initialized + precision: "16-mixed" + accelerator: "gpu" + devices: [0] # [0] means cuda:0 + accumulate_grad_batches: 1 + logger: # WandB logger is recommended but other loggers are supported as well + class_path: lightning.pytorch.loggers.WandbLogger + init_args: + project: "clt" + name: "circuit-tracer" + save_dir: "./wandb" + callbacks: + - class_path: crosslayer_transcoder.utils.callbacks.EndOfTrainingCheckpointCallback + init_args: + checkpoint_dir: "checkpoints" + - class_path: crosslayer_transcoder.utils.callbacks.ModelConversionCallback + init_args: + converter: + class_path: crosslayer_transcoder.utils.model_converters.circuit_tracer.CircuitTracerConverter + init_args: + save_dir: "circuit-tracer" + feature_input_hook: "hook_resid_mid" + feature_output_hook: "hook_mlp_out" + on_events: ["on_train_batch_end"] + +model: + class_path: crosslayer_transcoder.model.clt_lightning.JumpReLUCrossLayerTranscoderModule + init_args: + model: + class_path: crosslayer_transcoder.model.clt.CrossLayerTranscoder + init_args: + encoder: + class_path: crosslayer_transcoder.model.clt.Encoder + init_args: + d_acts: 768 + d_features: 10_000 + n_layers: 12 + + decoder: + class_path: crosslayer_transcoder.model.clt.CrosslayerDecoder + init_args: + d_acts: 768 + d_features: 10_000 + n_layers: 12 + + nonlinearity: + class_path: crosslayer_transcoder.model.jumprelu.JumpReLU + init_args: + theta: 0.03 + bandwidth: 0.01 + n_layers: 12 + d_features: 10_000 + + input_standardizer: + class_path: crosslayer_transcoder.model.standardize.DimensionwiseInputStandardizer + init_args: + n_layers: 12 + activation_dim: 768 + + output_standardizer: + class_path: crosslayer_transcoder.model.standardize.DimensionwiseOutputStandardizer + init_args: + n_layers: 12 + activation_dim: 768 + + # Pre-constructed replacement model + replacement_model: + class_path: crosslayer_transcoder.metrics.replacement_model_accuracy.ReplacementModelAccuracy + init_args: + model_name: "openai-community/gpt2" + device_map: "cuda:0" # should match trainer.devices + loader_batch_size: 2 + + # Pre-constructed dead features metric + dead_features: + class_path: crosslayer_transcoder.metrics.dead_features.DeadFeatures + init_args: + n_features: 10_000 + n_layers: 12 + return_per_layer: true + return_log_freqs: true + return_neuron_indices: true + + + # Training parameters + learning_rate: 3e-4 + compile: true # if using torch.compile + lr_decay_step: 16_000 # lr is scaled by lr_decay_factor after this many steps + lr_decay_factor: 0.1 + + lambda_sparsity: 0.0007 # sparsity loss weight + c_sparsity: 1 # sparsity loss coefficient + use_tanh: true # use tanh nonlinearity in the JumpReLU + pre_actv_loss: 1e-6 # pre-activation loss weight + + # Dead features computation settings + compute_dead_features: true + compute_dead_features_every: 500 + +data: + class_path: crosslayer_transcoder.data.datamodule.ActivationDataModule + init_args: + # Buffer settings + buffer_size: 500_000 # number of activations to store in the buffer + n_in_out: 2 # number of input and output layers + n_layers: 12 # number of layers in the model + activation_dim: 768 # dimension of the activations + dtype: "float16" # dtype of the activations + max_batch_size: 8000 # maximum batch size for the data loader + + # Model settings for activation generation + model_name: "openai-community/gpt2" + model_dtype: "float32" + + # Dataset settings + dataset_name: "Skylion007/openwebtext" + dataset_split: "train" + max_sequence_length: 1024 + + # Generation settings + generation_batch_size: 10 + refresh_interval: 0.1 # time (s) between shell logs updates + + # Memory settings + shared_memory_name: "activation_buffer" + timeout_seconds: 30 + + # File paths + init_file: null # path to file with shuffled activations to initialize the buffer fast + # if null, activations are generated and training starts when the buffer is at least minimum_fill_threshold full + + # DataLoader settings + batch_size: 1000 + num_workers: 10 + prefetch_factor: 2 + shuffle: true + persistent_workers: true + pin_memory: true + + minimum_fill_threshold: 0.2 # Only provide activations when buffer is at least 20% full + # to maintain sufficient shuffling + + use_shared_memory: true + + # Device configuration + device_map: "cuda:0" # "cpu", "auto", "cuda:0", "cuda:0,1,2,3" + deployment_policy: "gpu_only" # "cpu_only", "gpu_only", or "dynamic" + # dynamic will use CPU and only GPU if the buffer is almost empty to refill fast. Use this if you use a single GPU and have a beefy CPU. + + # WandB logging configuration for data generation + wandb_logging: + enabled: true # Enable WandB logging for data generation + project: "clt" # WandB project (should match trainer logger) + group: null # Group name (null = auto-generated from training run) + run_name: "data-generator-jumprelu" # Run name suffix + tags: ["data-generation"] # Tags for the data generation run + save_dir: "./wandb" # Directory for WandB files + log_interval: 5.0 # Logging interval in seconds + offline: true # Offline mode for WandB logging + +ckpt_path: null \ No newline at end of file diff --git a/crosslayer_transcoder/model/clt.py b/crosslayer_transcoder/model/clt.py index d420522..fb91d5d 100644 --- a/crosslayer_transcoder/model/clt.py +++ b/crosslayer_transcoder/model/clt.py @@ -1,6 +1,7 @@ from pathlib import Path from typing import Any, Dict, Optional, Tuple, Union +import einops import torch import torch.nn as nn import yaml @@ -194,6 +195,17 @@ def to_config(self) -> Dict[str, Any]: }, } + def to_circuit_tracer(self): + W = einops.rearrange( + self.get_parameter("W"), + "n_layers d_acts d_features -> n_layers d_features d_acts", + ).contiguous() + b = self.get_parameter("b") + return { + "W": W, + "b": b, + } + class Decoder(SerializableModule): def __init__(self, d_acts: int, d_features: int, n_layers: int): @@ -202,16 +214,16 @@ def __init__(self, d_acts: int, d_features: int, n_layers: int): self.d_features = d_features self.n_layers = n_layers self.register_parameter( - f"W", nn.Parameter(torch.empty((n_layers, d_features, d_acts))) + "W", nn.Parameter(torch.empty((n_layers, d_features, d_acts))) ) - self.register_parameter(f"b", nn.Parameter(torch.empty((n_layers, d_acts)))) + self.register_parameter("b", nn.Parameter(torch.empty((n_layers, d_acts)))) self._is_folded = False self.reset_parameters() def reset_parameters(self): dec_uniform_thresh = 1 / ((self.d_acts * self.n_layers) ** 0.5) - self.get_parameter(f"W").data.uniform_(-dec_uniform_thresh, dec_uniform_thresh) - self.get_parameter(f"b").data.zero_() + self.get_parameter("W").data.uniform_(-dec_uniform_thresh, dec_uniform_thresh) + self.get_parameter("b").data.zero_() @torch.no_grad() def forward_layer( @@ -224,7 +236,7 @@ def forward_layer( return ( einsum( features, - self.get_parameter(f"W")[layer], + self.get_parameter("W")[layer], "batch_size seq d_features, d_features d_acts -> batch_size seq d_acts", ) + self.b[layer] @@ -275,6 +287,12 @@ def to_config(self) -> Dict[str, Any]: }, } + def to_circuit_tracer(self): + return { + "W": self.W, + "b": self.b, + } + class CrosslayerDecoder(SerializableModule): def __init__(self, d_acts: int, d_features: int, n_layers: int): @@ -287,7 +305,7 @@ def __init__(self, d_acts: int, d_features: int, n_layers: int): f"W_{i}", nn.Parameter(torch.empty((i + 1, d_features, d_acts))) ) self._is_folded = False - self.register_parameter(f"b", nn.Parameter(torch.empty((n_layers, d_acts)))) + self.register_parameter("b", nn.Parameter(torch.empty((n_layers, d_acts)))) self.reset_parameters() def reset_parameters(self): @@ -331,15 +349,15 @@ def forward( device=features.device, dtype=features.dtype, ) - for l in range(self.n_layers): - W = self.get_parameter(f"W_{l}") - selected_features = features[:, : l + 1] + for layer_idx in range(self.n_layers): + W = self.get_parameter(f"W_{layer_idx}") + selected_features = features[:, : layer_idx + 1] l_recons = einsum( selected_features, W, "batch_size n_layers d_features, n_layers d_features d_acts -> batch_size d_acts", ) - recons[:, l, :] = l_recons + recons[:, layer_idx, :] = l_recons recons = recons + self.b.to(features.dtype) return recons @@ -369,6 +387,32 @@ def to_config(self) -> Dict[str, Any]: }, } + def to_circuit_tracer(self): + output_decs = [] + for source_layer in range(self.n_layers): + output_dec_i = torch.zeros( + [self.d_features, self.n_layers - source_layer, self.d_acts], + ) + + for k in range(source_layer, self.n_layers): + # get decoder mat for layer i --> k + decoder_w_k = self.get_parameter(f"W_{k}") + + dec_i_k = decoder_w_k[source_layer, ...] + assert dec_i_k.shape == ( + self.d_features, + self.d_acts, + ) + + output_dec_i[:, k - source_layer, ...] = dec_i_k + + output_decs.append(output_dec_i) + + return { + "W": output_decs, + "b": self.b, + } + class CrossLayerTranscoder(SerializableModule): def __init__( @@ -465,3 +509,20 @@ def save_pretrained(self, directory: Path, fold_standardizers: bool = True): yaml.dump({"model": config}, f) save_file(self.state_dict(), directory / "checkpoint.safetensors") + + def to_circuit_tracer(self): + # NOTE: this mutates the model in-place. Potentially bad, but a tradeoff for copying a huge model. + self.fold() + + encoder = self.encoder.to_circuit_tracer() + decoder = self.decoder.to_circuit_tracer() + + is_per_layer_decoder = isinstance(self.decoder, Decoder) + + config = { + "is_per_layer_decoder": is_per_layer_decoder, + "encoder": encoder, + "decoder": decoder, + } + + return config diff --git a/crosslayer_transcoder/model/jumprelu.py b/crosslayer_transcoder/model/jumprelu.py index d21b7ec..e3048d2 100644 --- a/crosslayer_transcoder/model/jumprelu.py +++ b/crosslayer_transcoder/model/jumprelu.py @@ -44,7 +44,9 @@ def backward(ctx, grad_output): grad_input = grad_output.clone() grad_input[input < 0] = 0 - theta_grad = -(theta / bandwidth) * rectangle((input - theta) / bandwidth) * grad_output + theta_grad = ( + -(theta / bandwidth) * rectangle((input - theta) / bandwidth) * grad_output + ) return grad_input, theta_grad, None @@ -78,7 +80,9 @@ class HeavysideStep(torch.autograd.Function): def forward(ctx, input, theta, bandwidth): ctx.save_for_backward(input, theta) ctx.bandwidth = bandwidth - return torch.where(input - theta > 0, torch.ones_like(input), torch.zeros_like(input)) + return torch.where( + input - theta > 0, torch.ones_like(input), torch.zeros_like(input) + ) @staticmethod def backward(ctx, grad_output): @@ -87,5 +91,7 @@ def backward(ctx, grad_output): grad_input = grad_output.clone() grad_input = grad_output * 0.0 - theta_grad = -(1.0 / bandwidth) * rectangle((input - theta) / bandwidth) * grad_output + theta_grad = ( + -(1.0 / bandwidth) * rectangle((input - theta) / bandwidth) * grad_output + ) return grad_input, theta_grad, None diff --git a/crosslayer_transcoder/model/serializable_module.py b/crosslayer_transcoder/model/serializable_module.py index 80ee9a4..91cc110 100644 --- a/crosslayer_transcoder/model/serializable_module.py +++ b/crosslayer_transcoder/model/serializable_module.py @@ -4,6 +4,7 @@ from typing import Any, Dict, Self, TypedDict, Union import yaml +from huggingface_hub import snapshot_download from safetensors.torch import load_file, save_file from torch import nn @@ -30,8 +31,12 @@ def from_config(cls, config: ConfigDict) -> Self: for key, value in init_args.items(): if isinstance(value, dict) and "class_path" in value: - target_module_name, target_class_name = value["class_path"].rsplit(".", 1) - target_cls = getattr(importlib.import_module(target_module_name), target_class_name) + target_module_name, target_class_name = value["class_path"].rsplit( + ".", 1 + ) + target_cls = getattr( + importlib.import_module(target_module_name), target_class_name + ) resolved_args[key] = target_cls.from_config(value) else: resolved_args[key] = value @@ -52,17 +57,35 @@ def save_pretrained( @classmethod def from_pretrained(cls, directory: Union[Path, str]) -> Self: - """Load model from directory.""" - directory = Path(directory) - with open(directory / "config.yaml") as f: + """Load model from local directory or HuggingFace Hub repository.""" + path = Path(directory) + + if not path.exists(): + local_path = snapshot_download( + repo_id=str(directory), + allow_patterns=["config.yaml", "checkpoint.safetensors"], + ) + path = Path(local_path) + + with open(path / "config.yaml") as f: full_config = yaml.safe_load(f) model_config = full_config.get("model") if model_config is None: raise ValueError("Model config not found in config.yaml", full_config) - model = cls.from_config(model_config) - model.load_state_dict(load_file(directory / "checkpoint.safetensors")) + if "class_path" in model_config: + target_module_name, target_class_name = model_config["class_path"].rsplit( + ".", 1 + ) + target_cls = getattr( + importlib.import_module(target_module_name), target_class_name + ) + else: + target_cls = cls + + model = target_cls.from_config(model_config) + model.load_state_dict(load_file(path / "checkpoint.safetensors")) model._is_folded = model_config.get("is_folded", False) diff --git a/crosslayer_transcoder/model/standardize.py b/crosslayer_transcoder/model/standardize.py index f755aa0..5362750 100644 --- a/crosslayer_transcoder/model/standardize.py +++ b/crosslayer_transcoder/model/standardize.py @@ -1,7 +1,5 @@ from typing import Any, Dict -from einops import einsum import torch -import torch.nn as nn from jaxtyping import Float from crosslayer_transcoder.model.serializable_module import SerializableModule @@ -106,7 +104,7 @@ def standardize( return (mlp_out - self.mean) / self.std else: return (mlp_out - self.mean[layer]) / self.std[layer] - + def to_config(self) -> Dict[str, Any]: return { "class_path": self.__class__.__module__ + "." + self.__class__.__name__, @@ -136,7 +134,7 @@ def forward( stds = batch.std(dim=-1, keepdim=True) stds.clamp_(min=1e-8) return (batch - means) / stds - + def to_config(self) -> Dict[str, Any]: return { "class_path": self.__class__.__module__ + "." + self.__class__.__name__, @@ -238,7 +236,7 @@ def standardize( return (mlp_out - self.mean[None, layer, None]) / self.std[ None, layer, None ] - + def to_config(self) -> Dict[str, Any]: return { "class_path": self.__class__.__module__ + "." + self.__class__.__name__, diff --git a/crosslayer_transcoder/utils/callbacks.py b/crosslayer_transcoder/utils/callbacks.py index 420fd8f..84db735 100644 --- a/crosslayer_transcoder/utils/callbacks.py +++ b/crosslayer_transcoder/utils/callbacks.py @@ -8,11 +8,16 @@ from typing import List import lightning as L -from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler +from huggingface_hub import upload_folder +from torch.profiler import ( + ProfilerActivity, + profile, + schedule, + tensorboard_trace_handler, +) -from crosslayer_transcoder.model import CrossLayerTranscoder from crosslayer_transcoder.model.clt_lightning import CrossLayerTranscoderModule -from crosslayer_transcoder.model.serializable_module import SerializableModule +from crosslayer_transcoder.utils.model_converters.model_converter import ModelConverter logger = logging.getLogger(__name__) @@ -57,6 +62,31 @@ def on_train_end(self, trainer, pl_module): trainer.save_checkpoint(checkpoint_path) +class ModelConversionCallback(L.Callback): + """Callback to convert the model to a circuit-tracer model.""" + + # Note: you can't type these directly with List or ModelConverter + def __init__( + self, + converter: ModelConverter, + on_events: List[str] = ["on_train_batch_end"], # type: List[str] + ): + super().__init__() + self.converter = converter + self.on_events = on_events + self._setup_callbacks() + + def _setup_callbacks(self): + for event in self.on_events: + setattr(self, event, partial(self._convert_model)) + + # Note: this should have the signature as all Lightning callbacks + def _convert_model(self, trainer, pl_module, **kwargs): + logger.info("Converting model...") + self.converter.convert_and_save(pl_module) + logger.info("Model converted and saved ") + + class SaveModelCallback(L.Callback): """Save checkpoint only at end of training.""" @@ -78,10 +108,31 @@ def _setup_callbacks(self): def _save_model(self, trainer, pl_module: CrossLayerTranscoderModule, **kwargs): logger.info("Saving model...") - pl_module.model.save_pretrained(self.checkpoint_dir, fold_standardizers=self.fold_standardizers) + pl_module.model.save_pretrained( + self.checkpoint_dir, fold_standardizers=self.fold_standardizers + ) logger.info("Model saved") +class HuggingFaceCallback(L.Callback): + """Callback to upload the model to Hugging Face.""" + + def __init__( + self, repo_id: str, repo_type: str = "model", save_dir: str = "clt_module" + ): + super().__init__() + self.repo_id = repo_id + self.repo_type = repo_type + self.save_dir = Path(save_dir) + + def on_train_end(self, trainer, pl_module): + upload_folder( + folder_path=self.save_dir.as_posix(), + repo_id=self.repo_id, + repo_type=self.repo_type, + ) + + class FoldAndSaveModelCallback(L.Callback): """Fold and save model at end of training.""" diff --git a/crosslayer_transcoder/utils/model_converters/circuit_tracer.py b/crosslayer_transcoder/utils/model_converters/circuit_tracer.py new file mode 100644 index 0000000..5c6b2e3 --- /dev/null +++ b/crosslayer_transcoder/utils/model_converters/circuit_tracer.py @@ -0,0 +1,132 @@ +import logging +from pathlib import Path + +import torch +import yaml +from safetensors.torch import save_file + +from crosslayer_transcoder.model import ( + CrossLayerTranscoder, + PerLayerTopK, +) +from crosslayer_transcoder.model.clt import Decoder +from crosslayer_transcoder.model.jumprelu import JumpReLU +from crosslayer_transcoder.utils.model_converters.model_converter import ( + ModelConverter, +) + +logger = logging.getLogger(__name__) + + +class CircuitTracerConverter(ModelConverter): + def __init__( + self, + save_dir: str, + feature_input_hook: str = "hook_resid_mid", + feature_output_hook: str = "hook_mlp_out", + ): + self.save_dir = Path(save_dir) + self.feature_input_hook = feature_input_hook + self.feature_output_hook = feature_output_hook + + self.save_dir.mkdir(parents=True, exist_ok=True) + + def _create_plt_files(self, encoder, decoder, nonlinearity, n_layers): + # decoder shape: d_transcoder, d_model + + file_dicts = [] + + for source_layer in range(n_layers): + layer_dict = { + "W_dec": decoder["W"][source_layer], + "W_enc": encoder["W"][source_layer], + "b_dec": decoder["b"][source_layer], + "b_enc": encoder["b"][source_layer], + } + if isinstance(nonlinearity, JumpReLU): + layer_dict["activation_function.threshold"] = nonlinearity.theta[ + :, source_layer, : + ] + elif isinstance(nonlinearity, PerLayerTopK): + layer_dict["activation_function.k"] = torch.tensor(nonlinearity.k) + file_dicts.append( + (layer_dict, f"{self.save_dir}/layer_{source_layer}.safetensors") + ) + + return file_dicts + + def _create_clt_files( + self, + encoder, + decoder, + nonlinearity, + n_layers, + ): + file_dicts = [] + + for source_layer in range(n_layers): + layer_encoder_dict = { + f"W_enc_{source_layer}": encoder["W"][source_layer], + f"b_enc_{source_layer}": encoder["b"][source_layer], + f"b_dec_{source_layer}": decoder["b"][source_layer], + } + + if isinstance(nonlinearity, JumpReLU): + layer_encoder_dict[f"threshold_{source_layer}"] = nonlinearity.theta[ + :, source_layer, : + ] + if isinstance(nonlinearity, PerLayerTopK): + layer_encoder_dict[f"k_{source_layer}"] = torch.tensor(nonlinearity.k) + + file_dicts.append( + ( + layer_encoder_dict, + f"{self.save_dir}/W_enc_{source_layer}.safetensors", + ) + ) + + decoder_dict = {f"W_dec_{source_layer}": decoder["W"][source_layer]} + + file_dicts.append( + (decoder_dict, f"{self.save_dir}/W_dec_{source_layer}.safetensors") + ) + + return file_dicts + + def export(self, model: CrossLayerTranscoder, dtype: torch.dtype = torch.bfloat16): + is_per_layer_decoder = isinstance(model.decoder, Decoder) + config = { + "model_name": "PLACEHOLDER MODEL_NAME", + "feature_input_hook": self.feature_input_hook, + "feature_output_hook": self.feature_output_hook, + "model_kind": "transcoder_set" + if is_per_layer_decoder + else "cross_layer_transcoder", + } + + model_dict = model.to_circuit_tracer() + + n_layers = model.encoder.n_layers + + if model_dict["is_per_layer_decoder"]: + file_dicts = self._create_plt_files( + model_dict["encoder"], + model_dict["decoder"], + model.nonlinearity, + n_layers, + ) + else: + file_dicts = self._create_clt_files( + model_dict["encoder"], + model_dict["decoder"], + model.nonlinearity, + n_layers, + ) + + # TODO: convert to dtype and CPU (?) + + for file_dict, file_path in file_dicts: + save_file(file_dict, file_path) + + with open(self.save_dir / "config.yaml", "w") as f: + yaml.dump(config, f, default_flow_style=False) diff --git a/crosslayer_transcoder/utils/model_converters/model_converter.py b/crosslayer_transcoder/utils/model_converters/model_converter.py new file mode 100644 index 0000000..3786dfc --- /dev/null +++ b/crosslayer_transcoder/utils/model_converters/model_converter.py @@ -0,0 +1,25 @@ +from typing import Protocol, Union, runtime_checkable + +import torch + +from crosslayer_transcoder.model.clt_lightning import ( + CrossLayerTranscoderModule, + JumpReLUCrossLayerTranscoderModule, + TopKCrossLayerTranscoderModule, +) + +CLTModule = Union[ + CrossLayerTranscoderModule, + JumpReLUCrossLayerTranscoderModule, + TopKCrossLayerTranscoderModule, +] + + +@runtime_checkable +class ModelConverter(Protocol): + def convert_and_save( + self, + model: CLTModule, + dtype: torch.dtype = torch.bfloat16, + ) -> None: + pass diff --git a/crosslayer_transcoder/utils/replacement_score.ipynb b/crosslayer_transcoder/utils/replacement_score.ipynb new file mode 100644 index 0000000..45286e5 --- /dev/null +++ b/crosslayer_transcoder/utils/replacement_score.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1dc2a0d5", + "metadata": {}, + "source": [ + "# Compute Graph Replacement Score & Visualize" + ] + }, + { + "cell_type": "markdown", + "id": "a091f9ee", + "metadata": {}, + "source": [ + "### Config" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a37586eb", + "metadata": {}, + "outputs": [], + "source": [ + "huggingface_path = \"georglange/crosslayer-transcoder-topk-16k\"" + ] + }, + { + "cell_type": "markdown", + "id": "b4458999", + "metadata": {}, + "source": [ + "### Load model from HuggingFace " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c166ce94", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a830fddc58ed4a328535348f7137e6bc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 2 files: 0%| | 0/2 [00:00= 0.8\n", + ")\n", + "edge_threshold = (\n", + " 0.98 # keep only the minimum # of edges whose cumulative influence is >= 0.98\n", + ")\n", + "\n", + "create_graph_files(\n", + " graph_or_path=graph, # the graph to create files for\n", + " slug=slug,\n", + " output_path=graph_file_dir,\n", + " node_threshold=node_threshold,\n", + " edge_threshold=edge_threshold,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "50185332", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Open your graph here: f'http://localhost:8048/index.html'\n" + ] + } + ], + "source": [ + "from circuit_tracer.frontend.local_server import serve\n", + "from IPython.display import IFrame\n", + "\n", + "port = 8048\n", + "server = serve(data_dir=\"./graph_files/\", port=port)\n", + "\n", + "\n", + "print(\n", + " f\"Open your graph here: f'http://localhost:{port}/index.html'\"\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "395d0c43", + "metadata": {}, + "source": [ + "## Repeat with a PLT" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a35d063", + "metadata": {}, + "outputs": [], + "source": [ + "huggingface_plt_path = \"georglange/crosslayer-transcoder-topk-plt-16k\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "677ef6dd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e12840b235dd45cb85c1da48eb229a53", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 2 files: 0%| | 0/2 [00:00 1024). Running this sequence through the model will result in indexing errors\n", + "Token indices sequence length is longer than the specified maximum sequence length for this model (1561 > 1024). Running this sequence through the model will result in indexing errors\n", + "Token indices sequence length is longer than the specified maximum sequence length for this model (1174 > 1024). Running this sequence through the model will result in indexing errors\n", + "Token indices sequence length is longer than the specified maximum sequence length for this model (2459 > 1024). Running this sequence through the model will result in indexing errors\n", + "Token indices sequence length is longer than the specified maximum sequence length for this model (2027 > 1024). Running this sequence through the model will result in indexing errors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JumpReLUCrossLayerTranscoderModule(\n", + " (model): CrossLayerTranscoder(\n", + " (encoder): Encoder()\n", + " (decoder): CrosslayerDecoder()\n", + " (nonlinearity): JumpReLU()\n", + " (input_standardizer): DimensionwiseInputStandardizer()\n", + " (output_standardizer): DimensionwiseOutputStandardizer()\n", + " )\n", + " (replacement_model): ReplacementModelAccuracy(\n", + " (replacement_model): ReplacementModel()\n", + " )\n", + " (dead_features): DeadFeatures()\n", + ")\n" + ] + } + ], + "source": [ + "import yaml\n", + "from crosslayer_transcoder.utils.module_builder import build_module_from_config \n", + "import torch\n", + "\n", + "config_path = \"../../config/circuit-tracer.yaml\"\n", + "checkpoint_path = \"../checkpoints/clt.ckpt\"\n", + "\n", + "with open(config_path, \"r\") as f:\n", + " config = yaml.load(f, Loader=yaml.FullLoader)\n", + " clt_module = build_module_from_config(config[\"model\"])\n", + "\n", + "checkpoint = torch.load(checkpoint_path, map_location='cuda:0')\n", + "clt_module.load_state_dict(checkpoint[\"state_dict\"])\n", + "\n", + "print(clt_module)" + ] + }, + { + "cell_type": "markdown", + "id": "d5030984", + "metadata": {}, + "source": [ + "### Collect Activations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "597a9d92", + "metadata": {}, + "outputs": [], + "source": [ + "from nnsight import LanguageModel\n", + "prompt = (\n", + " \"The capital of state containing Dallas is\" # What you want to get the graph for\n", + ")\n", + "llm = LanguageModel(\"openai-community/gpt2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cda0fb9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([12, 7, 768])\n" + ] + } + ], + "source": [ + "import torch\n", + "mlp_in_activations = []\n", + "\n", + "with llm.trace(prompt) as trace:\n", + " for layer in range(12):\n", + " layer_activations = llm.transformer.h[layer].ln_2.input.save()\n", + " mlp_in_activations.append(layer_activations.squeeze(0))\n", + "\n", + "mlp_in_activations = torch.stack(mlp_in_activations, dim=0)\n", + "\n", + "print(mlp_in_activations.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "57664d1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 12, 768])\n" + ] + } + ], + "source": [ + "import einops\n", + "\n", + "in_acts = einops.rearrange(mlp_in_activations, \"l b d -> b l d\")\n", + "print(in_acts.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "2d3dd5db", + "metadata": {}, + "source": [ + "### Encode w/o standarizer folding" + ] + }, + { + "cell_type": "markdown", + "id": "f8458d4d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2e5b2b2e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 12, 10000])\n", + "sparse_features._nnz(): 70298\n", + "l0_avg_per_layer: 836.8809814453125\n" + ] + } + ], + "source": [ + "features = clt_module.model.encode(in_acts)\n", + "\n", + "print(features.shape)\n", + "\n", + "sparse_features = features.to_sparse()\n", + "print(f\"sparse_features._nnz(): {sparse_features._nnz()}\")\n", + "\n", + "l0_avg_per_layer = torch.count_nonzero(features > 0) / (\n", + " features.shape[0] * features.shape[1]\n", + ")\n", + "print(f\"l0_avg_per_layer: {l0_avg_per_layer.item()}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7d847db4", + "metadata": {}, + "source": [ + "### Encode w/ folding" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "26589596", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 12, 10000])\n", + "sparse_features._nnz(): 17896\n", + "l0_avg_per_layer: 213.04762268066406\n" + ] + } + ], + "source": [ + "\n", + "features = clt_module.model.encode_with_standardizer_folding(in_acts)\n", + "\n", + "print(features.shape)\n", + "\n", + "sparse_features = features.to_sparse()\n", + "print(f\"sparse_features._nnz(): {sparse_features._nnz()}\")\n", + "\n", + "l0_avg_per_layer = torch.count_nonzero(features > 0) / (features.shape[0] * features.shape[1])\n", + "print(f\"l0_avg_per_layer: {l0_avg_per_layer.item()}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "130f0259", + "metadata": {}, + "source": [ + "### Run encoding in bfloat16" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "753de1ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 12, 10000])\n", + "sparse_features._nnz(): 17843\n", + "l0_avg_per_layer: 212.4166717529297\n" + ] + } + ], + "source": [ + "in_acts = in_acts.to(torch.bfloat16)\n", + "clt_module.model.to(torch.bfloat16)\n", + "features = clt_module.model.encode_with_standardizer_folding(in_acts)\n", + "\n", + "print(features.shape)\n", + "\n", + "sparse_features = features.to_sparse()\n", + "print(f\"sparse_features._nnz(): {sparse_features._nnz()}\")\n", + "\n", + "l0_avg_per_layer = torch.count_nonzero(features > 0) / (\n", + " features.shape[0] * features.shape[1]\n", + ")\n", + "print(f\"l0_avg_per_layer: {l0_avg_per_layer.item()}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "eeca368d", + "metadata": {}, + "source": [ + "## Interface w circuit tracer" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "34ce09c7", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from crosslayer_transcoder.utils.model_converters.circuit_tracer import (\n", + " CircuitTracerConverter,\n", + ")\n", + "from circuit_tracer.transcoder.cross_layer_transcoder import load_clt" + ] + }, + { + "cell_type": "markdown", + "id": "6c839fca", + "metadata": {}, + "source": [ + "### Convert model to circuit-tracer format and save `.safetensors`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8c55f63b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Converting CLT : 100%|██████████| 12/12 [00:13<00:00, 1.15s/it]\n" + ] + } + ], + "source": [ + "save_dir = pathlib.Path(\"clt_module_test\")\n", + "feature_input_hook = \"hook_resid_mid\"\n", + "feature_output_hook = \"hook_mlp_out\"\n", + "\n", + "converter = CircuitTracerConverter(\n", + "save_dir=save_dir,\n", + "feature_input_hook=feature_input_hook,\n", + " feature_output_hook=feature_output_hook,\n", + ")\n", + "converter.convert_and_save(clt_module, dtype=torch.bfloat16) " + ] + }, + { + "cell_type": "markdown", + "id": "9c9b91b8", + "metadata": {}, + "source": [ + "### Load CLT into circuit-tracer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "eb5ad901", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n", + "['W_enc_0', 'b_dec_0', 'b_enc_0', 'threshold_0']\n", + "['W_enc_1', 'b_dec_1', 'b_enc_1', 'threshold_1']\n", + "['W_enc_2', 'b_dec_2', 'b_enc_2', 'threshold_2']\n", + "['W_enc_3', 'b_dec_3', 'b_enc_3', 'threshold_3']\n", + "['W_enc_4', 'b_dec_4', 'b_enc_4', 'threshold_4']\n", + "['W_enc_5', 'b_dec_5', 'b_enc_5', 'threshold_5']\n", + "['W_enc_6', 'b_dec_6', 'b_enc_6', 'threshold_6']\n", + "['W_enc_7', 'b_dec_7', 'b_enc_7', 'threshold_7']\n", + "['W_enc_8', 'b_dec_8', 'b_enc_8', 'threshold_8']\n", + "['W_enc_9', 'b_dec_9', 'b_enc_9', 'threshold_9']\n", + "['W_enc_10', 'b_dec_10', 'b_enc_10', 'threshold_10']\n", + "['W_enc_11', 'b_dec_11', 'b_enc_11', 'threshold_11']\n", + "dict_keys(['b_dec', 'b_enc', 'activation_function.threshold', 'W_enc', 'W_dec.0', 'W_dec.1', 'W_dec.2', 'W_dec.3', 'W_dec.4', 'W_dec.5', 'W_dec.6', 'W_dec.7', 'W_dec.8', 'W_dec.9', 'W_dec.10', 'W_dec.11'])\n" + ] + } + ], + "source": [ + "\n", + "circuit_tracer_transcoder, state_dict_pre_load = load_clt(\n", + " clt_path=save_dir.as_posix(),\n", + " lazy_decoder=False,\n", + " lazy_encoder=False,\n", + " feature_input_hook=feature_input_hook,\n", + " feature_output_hook=feature_output_hook,\n", + " dtype=torch.bfloat16,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b3df8f67", + "metadata": {}, + "source": [ + "### Sanity checks for loaded model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "78ff27a5", + "metadata": {}, + "outputs": [], + "source": [ + "import einops\n", + "from safetensors import safe_open\n", + "import torch\n", + "import os\n", + "# sanity check against original model\n", + "\n", + "clt_path = save_dir.as_posix()\n", + "assert circuit_tracer_transcoder.clt_path == clt_path\n", + "\n", + "\n", + "# TEST: state_dict_pre_load weights match the files before being loaded into the model\n", + "for i in range(circuit_tracer_transcoder.n_layers):\n", + " enc_file = os.path.join(clt_path, f\"W_enc_{i}.safetensors\")\n", + " with safe_open(enc_file, framework=\"pt\", device=circuit_tracer_transcoder.device.type) as f:\n", + " w_file = f.get_tensor(f\"W_enc_{i}\").to(\n", + " dtype=state_dict_pre_load[\"W_enc\"][i].dtype,\n", + " device=state_dict_pre_load[\"W_enc\"][i].device,\n", + " )\n", + " w_state = state_dict_pre_load[\"W_enc\"][i]\n", + " assert w_file.shape == w_state.shape, (\n", + " f\"W_enc_{i} shape mismatch: {w_file.shape} != {w_state.shape}\"\n", + " )\n", + " assert w_file.dtype == w_state.dtype, (\n", + " f\"W_enc_{i} dtype mismatch: {w_file.dtype} != {w_state.dtype}\"\n", + " )\n", + " assert torch.allclose(w_file, w_state), (\n", + " i,\n", + " (w_file - w_state).abs().max().item(),\n", + " )\n", + "\n", + "\n", + "# TEST: weights in the files should equal weights from the circuit_tracer_transcoder\n", + "for i in range(circuit_tracer_transcoder.n_layers):\n", + " w_model = circuit_tracer_transcoder._get_encoder_weights(i) # works for both lazy and eager\n", + " enc_file = os.path.join(clt_path, f\"W_enc_{i}.safetensors\")\n", + " with safe_open(enc_file, framework=\"pt\", device=circuit_tracer_transcoder.device.type) as f:\n", + " w_file = f.get_tensor(f\"W_enc_{i}\").to(\n", + " dtype=w_model.dtype, device=w_model.device\n", + " )\n", + "\n", + " assert w_model.shape == w_file.shape\n", + " assert w_model.dtype == w_file.dtype\n", + " assert torch.allclose(w_model, w_file), (i, (w_model - w_file).abs().max().item())\n", + "\n", + "\n", + "# fold\n", + "standardizer = clt_module.model.input_standardizer\n", + "W_enc_folded, b_enc_folded = standardizer.fold_in_encoder(\n", + " clt_module.model.encoder.W.to(dtype=torch.bfloat16),\n", + " clt_module.model.encoder.b.to(dtype=torch.bfloat16),\n", + ")\n", + "\n", + "W_enc_folded = W_enc_folded.to(dtype=torch.bfloat16)\n", + "b_enc_folded = b_enc_folded.to(dtype=torch.bfloat16)\n", + "\n", + "state_dict = {}\n", + "device = clt_module.device\n", + "# TEST: eights in the file should equal the folded weights\n", + "for i in range(clt_module.model.encoder.n_layers):\n", + " enc_file = f\"W_enc_{i}.safetensors\"\n", + " with safe_open(\n", + " os.path.join(clt_path, enc_file), framework=\"pt\", device=device.type\n", + " ) as f:\n", + " assert W_enc_folded[i].T.shape == f.get_tensor(f\"W_enc_{i}\").shape, (\n", + " f\"W_enc_{i} shape mismatch: {W_enc_folded[i].shape} != {f.get_tensor(f'W_enc_{i}').shape}\"\n", + " )\n", + " assert W_enc_folded[i].dtype == f.get_tensor(f\"W_enc_{i}\").dtype, (\n", + " f\"W_enc_{i} dtype mismatch: {W_enc_folded[i].dtype} != {f.get_tensor(f'W_enc_{i}').dtype}\"\n", + " )\n", + " assert torch.allclose(W_enc_folded[i].T, f.get_tensor(f\"W_enc_{i}\"))\n", + " assert torch.allclose(b_enc_folded[i], f.get_tensor(f\"b_enc_{i}\"))\n", + "\n", + " # loaded model\n", + " assert circuit_tracer_transcoder.W_enc[i].shape == f.get_tensor(f\"W_enc_{i}\").shape\n", + " assert circuit_tracer_transcoder.W_enc[i].dtype == f.get_tensor(f\"W_enc_{i}\").dtype, (\n", + " f\"W_enc_{i} dtype mismatch: {circuit_tracer_transcoder.W_enc[i].dtype} != {f.get_tensor(f'W_enc_{i}').dtype}\"\n", + " )\n", + " assert torch.allclose(\n", + " circuit_tracer_transcoder.W_enc[i], f.get_tensor(f\"W_enc_{i}\").to(\"cuda:0\")\n", + " )\n", + "\n", + "\n", + "for i in range(clt_module.model.encoder.n_layers):\n", + " rearranged_W_enc = einops.rearrange(\n", + " W_enc_folded[i],\n", + " \"d_acts d_features -> d_features d_acts\",\n", + " ).contiguous()\n", + " assert torch.allclose(\n", + " rearranged_W_enc.to(\"cuda:0\"), circuit_tracer_transcoder.W_enc[i].to(\"cuda:0\")\n", + " )\n", + " assert torch.allclose(\n", + " b_enc_folded[i].to(dtype=torch.bfloat16).to(\"cuda:0\"),\n", + " circuit_tracer_transcoder.b_enc[i].to(dtype=torch.bfloat16).to(\"cuda:0\"),\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "8973a435", + "metadata": {}, + "source": [ + "### Test Loaded CLT encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "60b75b1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([12, 7, 768])\n", + "cuda:0 torch.bfloat16\n", + "layer 0 nnz: 426\n", + "layer 1 nnz: 204\n", + "layer 2 nnz: 107\n", + "layer 3 nnz: 94\n", + "layer 4 nnz: 104\n", + "layer 5 nnz: 72\n", + "layer 6 nnz: 88\n", + "layer 7 nnz: 36\n", + "layer 8 nnz: 35\n", + "layer 9 nnz: 18\n", + "layer 10 nnz: 14\n", + "layer 11 nnz: 2\n" + ] + }, + { + "data": { + "text/plain": [ + "(tensor(indices=tensor([[ 0, 0, 0, ..., 10, 11, 11],\n", + " [ 1, 1, 1, ..., 6, 1, 6],\n", + " [ 71, 193, 330, ..., 9154, 7738, 433]]),\n", + " values=tensor([-0.7852, 1.4062, 1.7812, ..., 1.3203, 1.1094,\n", + " 0.4297]),\n", + " device='cuda:0', size=(12, 7, 10000), nnz=1200, dtype=torch.bfloat16,\n", + " layout=torch.sparse_coo, grad_fn=),\n", + " tensor([[-1.9409e-02, -1.8677e-02, -5.5420e-02, ..., 2.7222e-02,\n", + " 2.3438e-01, -2.4609e-01],\n", + " [-2.0447e-03, 3.7109e-02, 6.4697e-03, ..., -4.1797e-01,\n", + " -2.8516e-01, 6.2988e-02],\n", + " [-2.6978e-02, -1.0193e-02, 6.5613e-04, ..., -4.7656e-01,\n", + " -2.8564e-02, -1.9824e-01],\n", + " ...,\n", + " [ 2.1210e-03, 1.1520e-03, 6.0730e-03, ..., -2.9907e-03,\n", + " -1.3885e-03, 5.0964e-03],\n", + " [-2.5787e-03, 1.1826e-03, 5.6152e-03, ..., 4.8218e-03,\n", + " 5.2795e-03, 4.4861e-03],\n", + " [-1.7700e-03, 2.0695e-04, 3.0975e-03, ..., 1.2436e-03,\n", + " 5.0049e-03, -2.8839e-03]], device='cuda:0', dtype=torch.bfloat16,\n", + " grad_fn=))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "in_acts_clt = einops.rearrange(in_acts, \"b l d -> l b d\")\n", + "assert in_acts_clt.shape == (12, 7, 768), in_acts_clt.shape\n", + "\n", + "in_acts_clt = in_acts_clt.to(\"cuda:0\")\n", + "\n", + "print(in_acts_clt.shape)\n", + "print(in_acts_clt.device, in_acts_clt.dtype)\n", + "\n", + "circuit_tracer_transcoder.encode_sparse(in_acts_clt)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6d5efd4c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 12, 768])\n", + "cuda:0 torch.bfloat16\n", + "layer 0 nnz: 426\n", + "layer 1 nnz: 204\n", + "layer 2 nnz: 107\n", + "layer 3 nnz: 94\n", + "layer 4 nnz: 104\n", + "layer 5 nnz: 72\n", + "layer 6 nnz: 88\n", + "layer 7 nnz: 36\n", + "layer 8 nnz: 35\n", + "layer 9 nnz: 18\n", + "layer 10 nnz: 14\n", + "layer 11 nnz: 2\n", + "nnz features: 1200\n", + "torch.Size([12, 7, 10000])\n" + ] + }, + { + "data": { + "text/plain": [ + "{'activation_matrix': tensor(indices=tensor([[ 0, 0, 0, ..., 10, 11, 11],\n", + " [ 1, 1, 1, ..., 6, 1, 6],\n", + " [ 71, 193, 330, ..., 9154, 7738, 433]]),\n", + " values=tensor([-0.7852, 1.4062, 1.7812, ..., 1.3203, 1.1094,\n", + " 0.4297]),\n", + " device='cuda:0', size=(12, 7, 10000), nnz=1200, dtype=torch.bfloat16,\n", + " layout=torch.sparse_coo, grad_fn=),\n", + " 'reconstruction': tensor([[[-3.6621e-02, 9.7656e-02, 5.9814e-03, ..., -6.0547e-02,\n", + " -5.7373e-02, -1.0986e-01],\n", + " [-1.9531e+00, 6.2109e-01, -1.5625e+00, ..., 1.0547e+00,\n", + " 1.2939e-02, -2.5977e-01],\n", + " [ 4.8340e-02, 7.9688e-01, 1.2695e-01, ..., -6.1719e-01,\n", + " 7.6953e-01, -5.2490e-02],\n", + " ...,\n", + " [-1.9688e+00, 1.0312e+00, 4.0430e-01, ..., 2.0117e-01,\n", + " 1.2969e+00, -4.1406e-01],\n", + " [-1.1016e+00, 3.1641e-01, -2.0312e-01, ..., -1.9062e+00,\n", + " 1.0312e+00, -5.5078e-01],\n", + " [-1.2578e+00, 4.8242e-01, -1.0625e+00, ..., 1.1641e+00,\n", + " 7.5195e-02, 6.9531e-01]],\n", + " \n", + " [[-4.6875e-02, -5.6885e-02, 5.7373e-03, ..., 6.4453e-02,\n", + " 5.8350e-02, -4.2480e-02],\n", + " [-2.6758e-01, 1.1562e+00, -3.1836e-01, ..., 7.1875e-01,\n", + " 1.1406e+00, -2.3633e-01],\n", + " [-3.2471e-02, 1.0703e+00, 8.9844e-01, ..., 6.1719e-01,\n", + " 6.9922e-01, 7.5391e-01],\n", + " ...,\n", + " [-2.8076e-02, 1.0859e+00, 6.2500e-01, ..., 4.6680e-01,\n", + " 9.6484e-01, 1.4844e+00],\n", + " [-4.9219e-01, 4.0820e-01, 1.0625e+00, ..., 1.1484e+00,\n", + " 9.0625e-01, 4.1797e-01],\n", + " [-3.1445e-01, 1.3379e-01, 7.7734e-01, ..., 1.1953e+00,\n", + " 9.4922e-01, 4.7266e-01]],\n", + " \n", + " [[-1.4038e-02, -4.1016e-02, 2.8442e-02, ..., -1.6357e-02,\n", + " 1.1597e-02, -2.0752e-03],\n", + " [-3.7109e-01, 1.7969e-01, -5.2734e-01, ..., -1.5391e+00,\n", + " 1.0400e-01, 5.4297e-01],\n", + " [ 1.7773e-01, -1.6699e-01, 2.8906e-01, ..., -1.7773e-01,\n", + " 2.9492e-01, 6.0156e-01],\n", + " ...,\n", + " [-5.0391e-01, -1.6504e-01, -1.5918e-01, ..., -1.2188e+00,\n", + " 1.5137e-02, 3.7695e-01],\n", + " [-2.0508e-01, 5.4688e-01, -3.6719e-01, ..., -2.7539e-01,\n", + " -3.7109e-02, -8.1543e-02],\n", + " [-1.9727e-01, -7.0312e-01, 1.2451e-01, ..., -4.2383e-01,\n", + " -3.0664e-01, 8.9844e-02]],\n", + " \n", + " ...,\n", + " \n", + " [[ 4.8523e-03, 2.9907e-02, -2.6367e-01, ..., 1.5076e-02,\n", + " 1.1572e-01, -1.3379e-01],\n", + " [-4.5117e-01, 3.6328e-01, 8.1250e-01, ..., -3.2422e-01,\n", + " 1.5391e+00, -2.4707e-01],\n", + " [-6.1719e-01, 2.7344e-01, 1.0625e+00, ..., -2.8906e-01,\n", + " 4.3750e-01, -2.4609e-01],\n", + " ...,\n", + " [-1.4355e-01, 4.6875e-01, 2.5586e-01, ..., -2.5977e-01,\n", + " 1.8516e+00, -7.9297e-01],\n", + " [ 1.0156e+00, 3.3789e-01, 1.1250e+00, ..., 1.3477e-01,\n", + " 1.6992e-01, 8.5938e-01],\n", + " [-9.8438e-01, 8.7109e-01, -8.5938e-02, ..., -9.5703e-01,\n", + " 8.4961e-02, -9.6680e-02]],\n", + " \n", + " [[-1.5527e-01, 1.5332e-01, -2.9688e-01, ..., 6.4453e-02,\n", + " 8.1055e-02, -2.4512e-01],\n", + " [-2.2852e-01, 1.2500e-01, 1.5078e+00, ..., -2.2969e+00,\n", + " 6.4453e-01, 3.8672e-01],\n", + " [-1.9531e-03, -6.7969e-01, 1.8750e+00, ..., -4.3164e-01,\n", + " -3.2031e-01, -9.6680e-02],\n", + " ...,\n", + " [ 3.1641e-01, 1.2031e+00, 4.8438e-01, ..., -1.6328e+00,\n", + " 1.3047e+00, 7.0703e-01],\n", + " [-5.4297e-01, -3.0859e-01, 4.5312e-01, ..., -3.8477e-01,\n", + " -1.3984e+00, 3.4375e-01],\n", + " [-1.3359e+00, 8.1250e-01, -6.6406e-01, ..., 5.9375e-01,\n", + " 1.2656e+00, 2.5391e-01]],\n", + " \n", + " [[ 2.2339e-02, 3.0078e-01, -2.5195e-01, ..., 5.0049e-02,\n", + " -1.1719e-01, 8.3008e-03],\n", + " [-1.4375e+00, 9.2188e-01, 1.9219e+00, ..., 5.0391e-01,\n", + " -1.4609e+00, -1.9727e-01],\n", + " [ 4.8828e-01, -9.9609e-01, 2.1719e+00, ..., 3.1250e-01,\n", + " 1.1094e+00, 1.8164e-01],\n", + " ...,\n", + " [ 4.3359e-01, 3.9258e-01, 9.1406e-01, ..., -1.1484e+00,\n", + " -6.4844e-01, -8.6426e-02],\n", + " [ 8.8281e-01, 4.1797e-01, 1.4531e+00, ..., -1.0547e+00,\n", + " -1.4297e+00, 3.2812e-01],\n", + " [-1.0625e+00, 5.2344e-01, 1.8438e+00, ..., -7.2656e-01,\n", + " -1.0791e-01, 2.0117e-01]]], device='cuda:0', dtype=torch.bfloat16,\n", + " grad_fn=),\n", + " 'encoder_vecs': tensor([[-1.9409e-02, -1.8677e-02, -5.5420e-02, ..., 2.7222e-02,\n", + " 2.3438e-01, -2.4609e-01],\n", + " [-2.0447e-03, 3.7109e-02, 6.4697e-03, ..., -4.1797e-01,\n", + " -2.8516e-01, 6.2988e-02],\n", + " [-2.6978e-02, -1.0193e-02, 6.5613e-04, ..., -4.7656e-01,\n", + " -2.8564e-02, -1.9824e-01],\n", + " ...,\n", + " [ 2.1210e-03, 1.1520e-03, 6.0730e-03, ..., -2.9907e-03,\n", + " -1.3885e-03, 5.0964e-03],\n", + " [-2.5787e-03, 1.1826e-03, 5.6152e-03, ..., 4.8218e-03,\n", + " 5.2795e-03, 4.4861e-03],\n", + " [-1.7700e-03, 2.0695e-04, 3.0975e-03, ..., 1.2436e-03,\n", + " 5.0049e-03, -2.8839e-03]], device='cuda:0', dtype=torch.bfloat16,\n", + " grad_fn=),\n", + " 'decoder_vecs': tensor([[-0.0295, -0.0025, -0.0366, ..., 0.0030, -0.0586, 0.0383],\n", + " [ 0.0062, -0.0219, -0.0461, ..., -0.0248, -0.0233, -0.0234],\n", + " [-0.0356, -0.0102, -0.0253, ..., 0.0181, -0.0050, -0.0097],\n", + " ...,\n", + " [ 0.0645, 0.2080, 0.0251, ..., 0.2402, -0.1494, 0.2539],\n", + " [-0.6055, 0.2598, 0.0117, ..., 0.3906, -0.2422, -0.0237],\n", + " [ 0.0732, 0.1108, 0.0193, ..., 0.0023, 0.0161, 0.0840]],\n", + " device='cuda:0', dtype=torch.bfloat16, grad_fn=),\n", + " 'encoder_to_decoder_map': tensor([ 0, 0, 0, ..., 1197, 1198, 1199], device='cuda:0'),\n", + " 'decoder_locations': tensor([[ 0, 1, 2, ..., 11, 11, 11],\n", + " [ 1, 1, 1, ..., 6, 1, 6]], device='cuda:0')}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(in_acts.shape)\n", + "print(in_acts_clt.device, in_acts_clt.dtype)\n", + "circuit_tracer_transcoder.compute_attribution_components(in_acts_clt)" + ] + }, + { + "cell_type": "markdown", + "id": "412783b9", + "metadata": {}, + "source": [ + "### Test with ReplacementModel" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0db2198d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n", + "['W_enc_0', 'b_dec_0', 'b_enc_0', 'threshold_0']\n", + "['W_enc_1', 'b_dec_1', 'b_enc_1', 'threshold_1']\n", + "['W_enc_2', 'b_dec_2', 'b_enc_2', 'threshold_2']\n", + "['W_enc_3', 'b_dec_3', 'b_enc_3', 'threshold_3']\n", + "['W_enc_4', 'b_dec_4', 'b_enc_4', 'threshold_4']\n", + "['W_enc_5', 'b_dec_5', 'b_enc_5', 'threshold_5']\n", + "['W_enc_6', 'b_dec_6', 'b_enc_6', 'threshold_6']\n", + "['W_enc_7', 'b_dec_7', 'b_enc_7', 'threshold_7']\n", + "['W_enc_8', 'b_dec_8', 'b_enc_8', 'threshold_8']\n", + "['W_enc_9', 'b_dec_9', 'b_enc_9', 'threshold_9']\n", + "['W_enc_10', 'b_dec_10', 'b_enc_10', 'threshold_10']\n", + "['W_enc_11', 'b_dec_11', 'b_enc_11', 'threshold_11']\n", + "dict_keys(['b_dec', 'b_enc', 'activation_function.threshold', 'W_enc', 'W_dec.0', 'W_dec.1', 'W_dec.2', 'W_dec.3', 'W_dec.4', 'W_dec.5', 'W_dec.6', 'W_dec.7', 'W_dec.8', 'W_dec.9', 'W_dec.10', 'W_dec.11'])\n", + "cuda:0 torch.bfloat16\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`torch_dtype` is deprecated! Use `dtype` instead!\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2b23ff4f5c51409d955f08b2997ef3b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors: 0%| | 0.00/548M [00:00.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.1.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.2.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.3.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.4.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.5.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.6.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.7.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.8.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.9.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.10.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False)), ('blocks.11.hook_resid_mid', functools.partial(.save_hook at 0x7ae5023fd800>, is_backward=False))]\n", + "torch.Size([1, 8, 50257])\n", + "dict_items([('blocks.0.hook_resid_mid', tensor([[[ 0.8292, -0.2081, 0.0887, ..., -0.0029, 0.1603, 0.3562],\n", + " [-0.1627, 0.2027, -0.5368, ..., 0.0968, -0.0061, 0.1409],\n", + " [-0.3465, 0.0869, -0.5563, ..., 0.1135, -0.0570, 0.1199],\n", + " ...,\n", + " [-0.1875, 0.4008, -0.3190, ..., -0.1480, 0.0055, 0.0641],\n", + " [ 1.6809, 0.5578, -0.5070, ..., -0.1841, 0.2872, 0.2613],\n", + " [ 0.6528, 0.3823, -0.1777, ..., 0.0927, -0.0343, -0.0222]]],\n", + " device='cuda:0')), ('blocks.1.hook_resid_mid', tensor([[[ 0.7366, 0.2633, 0.8380, ..., 1.7356, 1.5534, 0.6067],\n", + " [-0.5045, -0.0267, -0.7114, ..., -0.1848, -0.7491, 0.1695],\n", + " [-1.7334, 0.7541, -1.7714, ..., 1.5820, -1.9192, 0.4845],\n", + " ...,\n", + " [-1.0849, 1.4963, -0.1123, ..., 0.4767, 0.3309, -0.0391],\n", + " [ 2.0111, 0.8939, -1.4627, ..., -1.8787, 0.2278, -0.1587],\n", + " [ 0.0421, 0.7707, -0.8557, ..., 1.4787, -0.7605, 0.9899]]],\n", + " device='cuda:0')), ('blocks.2.hook_resid_mid', tensor([[[ 0.1488, -0.0781, 0.5468, ..., 1.6689, 1.4880, 0.7661],\n", + " [-0.8259, -0.2939, -0.8941, ..., -0.2092, -1.2692, -0.1108],\n", + " [-1.8970, 1.6090, -1.9269, ..., 1.9306, -1.9026, -0.4079],\n", + " ...,\n", + " [-1.1005, 1.7253, -0.0383, ..., 0.5634, 0.8792, 0.7951],\n", + " [ 1.4697, 0.5930, -1.6843, ..., -1.3790, 0.4047, -0.5614],\n", + " [ 0.2322, 0.4183, -0.6936, ..., 1.5644, -0.6832, 1.2189]]],\n", + " device='cuda:0')), ('blocks.3.hook_resid_mid', tensor([[[ 0.0661, -0.1998, 0.5140, ..., 1.4456, 1.4002, 0.6470],\n", + " [-1.0289, -0.3439, -0.1235, ..., -0.2292, -1.3801, 0.0214],\n", + " [-2.1646, 2.4593, -1.8518, ..., 1.1662, -2.2947, -0.1005],\n", + " ...,\n", + " [-1.3023, 1.3682, 1.3973, ..., -0.0248, 1.2654, 1.1106],\n", + " [ 0.9751, 1.2863, -1.5901, ..., -1.4095, 0.1266, -0.3963],\n", + " [ 0.2027, 0.0645, 0.2270, ..., 1.8477, -1.4379, 1.0276]]],\n", + " device='cuda:0')), ('blocks.4.hook_resid_mid', tensor([[[-0.0580, -0.3595, 0.4574, ..., 1.3160, 1.2230, 0.6406],\n", + " [-2.1363, -0.6073, 0.0711, ..., 0.4583, -1.1995, 0.7196],\n", + " [-3.6694, 1.7980, -3.0582, ..., 2.2501, -3.4340, 0.4635],\n", + " ...,\n", + " [-1.6586, 0.7425, 0.8882, ..., 1.1538, 0.6740, 1.4706],\n", + " [ 0.9481, 1.3355, -2.4056, ..., -1.7796, -0.1907, -0.4598],\n", + " [-0.3690, 0.3992, -0.0730, ..., 2.2794, -1.3240, 1.2353]]],\n", + " device='cuda:0')), ('blocks.5.hook_resid_mid', tensor([[[-0.1437, -0.3980, 0.4653, ..., 1.2557, 1.1286, 0.5841],\n", + " [-2.2264, -0.4958, 0.1818, ..., 0.8170, -1.5775, 1.1018],\n", + " [-4.1140, 2.5474, -3.6180, ..., 1.9292, -4.2118, 0.7760],\n", + " ...,\n", + " [-2.2961, 1.4702, 1.1608, ..., 1.1624, 0.2801, 1.7627],\n", + " [ 0.7102, 1.5051, -2.4009, ..., -1.0501, -0.9454, -0.8125],\n", + " [ 0.1730, 0.4505, 0.5230, ..., 3.3662, -0.8115, 1.7597]]],\n", + " device='cuda:0')), ('blocks.6.hook_resid_mid', tensor([[[-0.1418, -0.3710, 0.5526, ..., 1.1364, 1.0140, 0.5661],\n", + " [-2.5741, -0.4803, 0.4509, ..., 1.0593, -1.5936, 0.7250],\n", + " [-4.1928, 2.8387, -4.2037, ..., 2.6937, -3.5340, 0.7354],\n", + " ...,\n", + " [-1.9225, 0.5152, 0.6427, ..., 1.6845, -0.2169, 2.0397],\n", + " [ 1.4380, 1.3117, -1.7126, ..., -0.9297, -0.0077, -0.7019],\n", + " [-0.1650, 0.4262, 0.3057, ..., 4.4950, -1.5823, 2.7732]]],\n", + " device='cuda:0')), ('blocks.7.hook_resid_mid', tensor([[[-0.2448, -0.2811, 0.5022, ..., 1.1156, 0.8833, 0.3835],\n", + " [-2.4570, 0.8735, 0.5153, ..., 0.5349, -2.2505, -0.0874],\n", + " [-6.9050, 4.1731, -3.5826, ..., 3.0283, -4.0174, 0.5070],\n", + " ...,\n", + " [-1.6423, 0.6971, -0.2637, ..., 1.0385, 0.8479, 3.1449],\n", + " [ 2.2448, 0.6985, -2.0658, ..., -1.3574, -0.2864, -0.4600],\n", + " [-0.5407, 1.7449, -0.9142, ..., 4.4162, -0.3360, 4.1052]]],\n", + " device='cuda:0')), ('blocks.8.hook_resid_mid', tensor([[[-0.2317, -0.1742, 0.4076, ..., 1.2093, 0.9779, 0.3468],\n", + " [-2.3272, 0.8944, 1.0235, ..., 0.7030, -1.8648, -0.9032],\n", + " [-7.0494, 5.2281, -4.1065, ..., 2.0068, -4.1265, 0.3011],\n", + " ...,\n", + " [-1.0423, 0.8841, -0.8168, ..., 0.7086, 1.1515, 2.5642],\n", + " [ 2.4226, 0.2734, -2.7923, ..., -2.6671, 0.6658, 0.2542],\n", + " [-0.1577, 2.4592, -1.3308, ..., 3.5287, -1.5098, 5.1052]]],\n", + " device='cuda:0')), ('blocks.9.hook_resid_mid', tensor([[[-0.2822, -0.0332, 0.3085, ..., 1.3752, 0.9603, 0.0852],\n", + " [-2.3929, 0.8874, 1.0884, ..., 1.0155, -1.2947, -1.3692],\n", + " [-7.3202, 5.6661, -5.4029, ..., 2.8207, -4.6241, 0.1844],\n", + " ...,\n", + " [-1.2294, 2.5105, -1.7207, ..., 0.9849, 2.8533, 3.4939],\n", + " [ 2.6443, 0.1823, -2.7417, ..., -1.1531, 0.9782, 1.3396],\n", + " [-0.3001, 3.6369, -0.4387, ..., 3.9318, -0.6444, 4.0109]]],\n", + " device='cuda:0')), ('blocks.10.hook_resid_mid', tensor([[[-2.9332e-01, 2.8656e-02, -8.5813e-04, ..., 1.3900e+00,\n", + " 1.1820e+00, -1.1640e-01],\n", + " [-2.4854e+00, 7.7822e-01, 2.2521e+00, ..., -1.2356e-01,\n", + " -2.6242e-01, -2.2910e+00],\n", + " [-8.0578e+00, 4.5745e+00, -7.5444e+00, ..., 2.7251e+00,\n", + " -4.2399e+00, -1.0159e+00],\n", + " ...,\n", + " [-2.2039e+00, 3.8935e+00, -1.7146e+00, ..., 1.6282e+00,\n", + " 5.3636e+00, 1.2540e+00],\n", + " [ 2.9170e+00, 2.3938e+00, -2.3362e+00, ..., 1.7417e-03,\n", + " 9.3079e-01, 2.0680e+00],\n", + " [-1.1518e+00, 4.4683e+00, -3.3433e+00, ..., 3.5590e+00,\n", + " -1.9450e-01, 4.3937e+00]]], device='cuda:0')), ('blocks.11.hook_resid_mid', tensor([[[-0.3818, 0.0473, -0.4384, ..., 1.5397, 1.6579, -0.2553],\n", + " [-2.4054, 0.1560, 3.3125, ..., 0.1850, 0.5278, -2.4760],\n", + " [-9.4641, 4.7007, -6.5114, ..., 2.8988, -4.4499, -1.5477],\n", + " ...,\n", + " [ 1.4269, 3.2414, 0.5623, ..., 1.0460, 5.8567, -1.0748],\n", + " [ 2.2601, 1.4785, -2.1479, ..., 1.0913, -0.9739, 2.9741],\n", + " [-1.4892, 4.4865, -4.1967, ..., 4.0651, 1.1076, 4.7898]]],\n", + " device='cuda:0'))])\n", + "cuda:0 torch.float32\n" + ] + } + ], + "source": [ + "\n", + "if isinstance(prompt, str):\n", + " tokens = rm.ensure_tokenized(prompt)\n", + "else:\n", + " tokens = prompt.squeeze()\n", + "# COuld it be the caching hooks?\n", + "mlp_in_cache, mlp_in_caching_hooks, _ = rm.get_caching_hooks(\n", + " lambda name: rm.feature_input_hook in name\n", + ")\n", + "print(mlp_in_caching_hooks)\n", + "\n", + "mlp_out_cache, mlp_out_caching_hooks, _ = rm.get_caching_hooks(\n", + " lambda name: rm.feature_output_hook in name\n", + ")\n", + "logits = rm.run_with_hooks(tokens, fwd_hooks=mlp_in_caching_hooks + mlp_out_caching_hooks)\n", + "print(logits.shape)\n", + "\n", + "\n", + "print(mlp_in_cache.items())\n", + "\n", + "mlp_in_cache = torch.cat(list(mlp_in_cache.values()), dim=0)\n", + "mlp_out_cache = torch.cat(list(mlp_out_cache.values()), dim=0)\n", + "print(mlp_in_cache.device, mlp_in_cache.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ab63b8c4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([12, 8, 768])\n" + ] + } + ], + "source": [ + "nnsight_acts = []\n", + "with llm.trace(tokens) as trace:\n", + " for layer in range(12):\n", + " layer_activations = llm.transformer.h[layer].ln_2.input.save()\n", + " nnsight_acts.append(layer_activations.squeeze(0))\n", + "\n", + "nnsight_acts = torch.stack(nnsight_acts, dim=0)\n", + "\n", + "print(nnsight_acts.shape)\n", + "nnsight_acts = nnsight_acts.to(\"cuda:0\").to(torch.bfloat16)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0ef9ad21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda:0 torch.bfloat16\n" + ] + } + ], + "source": [ + "mlp_in_cache = mlp_in_cache.to(\"cuda:0\").to(torch.bfloat16)\n", + "print(mlp_in_cache.device, mlp_in_cache.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1d9f797e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.001953125\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.000244140625\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.0009765625\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.0\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.015625\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.015625\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.00048828125\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.0078125\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.0078125\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.015625\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.015625\n", + "----------------------------------------------------------------------------------------------------\n", + "torch.Size([8, 768]) torch.Size([8, 768])\n", + "cuda:0 cuda:0\n", + "torch.bfloat16 torch.bfloat16\n", + "nans?: False, False\n", + "max diff: 0.015625\n", + "----------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "## TEST Difference between nnsight and TL activations\n", + "for i in range(12):\n", + " print(nnsight_acts[i].shape, mlp_in_cache[i].shape)\n", + " print(nnsight_acts[i].device, mlp_in_cache[i].device)\n", + " print(nnsight_acts[i].dtype, mlp_in_cache[i].dtype)\n", + " print(f\"nans?: {torch.isnan(nnsight_acts[i]).any()}, {torch.isnan(mlp_in_cache[i]).any()}\")\n", + " print(f\"max diff: {(nnsight_acts[i] - mlp_in_cache[i]).abs().max().item()}\")\n", + " print(\"-\"*100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3187e1a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([12, 8, 768]) cuda:0 torch.bfloat16\n", + "torch.Size([12, 8, 768]) cuda:0 torch.bfloat16\n", + "layer 0 nnz: 467\n", + "layer 1 nnz: 241\n", + "layer 2 nnz: 130\n", + "layer 3 nnz: 108\n", + "layer 4 nnz: 107\n", + "layer 5 nnz: 66\n", + "layer 6 nnz: 88\n", + "layer 7 nnz: 42\n", + "layer 8 nnz: 31\n", + "layer 9 nnz: 18\n", + "layer 10 nnz: 12\n", + "layer 11 nnz: 1\n", + "nnz features: 1311\n", + "torch.Size([12, 8, 10000])\n", + "layer 0 nnz: 467\n", + "layer 1 nnz: 241\n", + "layer 2 nnz: 130\n", + "layer 3 nnz: 108\n", + "layer 4 nnz: 107\n", + "layer 5 nnz: 66\n", + "layer 6 nnz: 88\n", + "layer 7 nnz: 42\n", + "layer 8 nnz: 31\n", + "layer 9 nnz: 18\n", + "layer 10 nnz: 12\n", + "layer 11 nnz: 1\n", + "nnz features: 1311\n", + "torch.Size([12, 8, 10000])\n" + ] + } + ], + "source": [ + "print(nnsight_acts.shape, nnsight_acts.device, nnsight_acts.dtype)\n", + "print(mlp_in_cache.shape, mlp_in_cache.device, mlp_in_cache.dtype)\n", + "attribution_data_a = circuit_tracer_transcoder.compute_attribution_components(nnsight_acts)\n", + "attribution_data_c = circuit_tracer_transcoder.compute_attribution_components(mlp_in_cache)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8e4d00ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda:0 torch.float32\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "expected scalar type BFloat16 but found Float", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28mprint\u001b[39m(circuit_tracer_transcoder_copy.device, circuit_tracer_transcoder_copy.dtype)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m attribution_data_e = \u001b[43mcircuit_tracer_transcoder_copy\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute_attribution_components\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmlp_in_cache\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m attribution_data_e = circuit_tracer_transcoder_copy.compute_attribution_components(in_acts_clt)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/circuit-tracer/circuit_tracer/transcoder/cross_layer_transcoder.py:306\u001b[39m, in \u001b[36mCrossLayerTranscoder.compute_attribution_components\u001b[39m\u001b[34m(self, inputs)\u001b[39m\n\u001b[32m 292\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcompute_attribution_components\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs):\n\u001b[32m 293\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Extract active features and their encoder/decoder vectors for attribution.\u001b[39;00m\n\u001b[32m 294\u001b[39m \n\u001b[32m 295\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 304\u001b[39m \u001b[33;03m - encoder_to_decoder_map: Mapping from encoder to decoder indices\u001b[39;00m\n\u001b[32m 305\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m306\u001b[39m features, encoder_vectors = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mencode_sparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzero_first_pos\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 307\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mnnz features: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfeatures._nnz()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 308\u001b[39m pos_ids, layer_ids, feat_ids, decoder_vectors, encoder_to_decoder_map = (\n\u001b[32m 309\u001b[39m \u001b[38;5;28mself\u001b[39m.select_decoder_vectors(features)\n\u001b[32m 310\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/circuit-tracer/circuit_tracer/transcoder/cross_layer_transcoder.py:193\u001b[39m, in \u001b[36mCrossLayerTranscoder.encode_sparse\u001b[39m\u001b[34m(self, x, zero_first_pos)\u001b[39m\n\u001b[32m 190\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m layer_id \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m.n_layers):\n\u001b[32m 191\u001b[39m W_enc_layer = \u001b[38;5;28mself\u001b[39m._get_encoder_weights(layer_id)\n\u001b[32m 192\u001b[39m layer_features = (\n\u001b[32m--> \u001b[39m\u001b[32m193\u001b[39m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43meinsum\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbd,fd->bf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlayer_id\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mW_enc_layer\u001b[49m\u001b[43m)\u001b[49m + \u001b[38;5;28mself\u001b[39m.b_enc[layer_id]\n\u001b[32m 194\u001b[39m )\n\u001b[32m 195\u001b[39m layer_features = \u001b[38;5;28mself\u001b[39m.apply_activation_function(layer_id, layer_features)\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m zero_first_pos:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/crosslayer-transcoder/.venv/lib/python3.12/site-packages/torch/functional.py:373\u001b[39m, in \u001b[36meinsum\u001b[39m\u001b[34m(*args)\u001b[39m\n\u001b[32m 368\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m einsum(equation, *_operands)\n\u001b[32m 370\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(operands) <= \u001b[32m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_einsum.enabled:\n\u001b[32m 371\u001b[39m \u001b[38;5;66;03m# the path for contracting 0 or 1 time(s) is already optimized\u001b[39;00m\n\u001b[32m 372\u001b[39m \u001b[38;5;66;03m# or the user has disabled using opt_einsum\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m373\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[43m.\u001b[49m\u001b[43meinsum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mequation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperands\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[32m 375\u001b[39m path = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m opt_einsum.is_available():\n", + "\u001b[31mRuntimeError\u001b[39m: expected scalar type BFloat16 but found Float" + ] + } + ], + "source": [ + "print(circuit_tracer_transcoder_copy.device, circuit_tracer_transcoder_copy.dtype)\n", + "attribution_data_e = circuit_tracer_transcoder_copy.compute_attribution_components(mlp_in_cache)\n", + "attribution_data_e = circuit_tracer_transcoder_copy.compute_attribution_components(in_acts_clt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48295bb6", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "attribution_data = rm.transcoders.compute_attribution_components(mlp_in_cache)" + ] + }, + { + "cell_type": "markdown", + "id": "4983b9eb", + "metadata": {}, + "source": [ + "### Attribution test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f9ea5c4", + "metadata": {}, + "outputs": [], + "source": [ + "max_n_logits = 10 # How many logits to attribute from, max. We attribute to min(max_n_logits, n_logits_to_reach_desired_log_prob); see below for the latter\n", + "desired_logit_prob = 0.95 # Attribution will attribute from the minimum number of logits needed to reach this probability mass (or max_n_logits, whichever is lower)\n", + "max_feature_nodes = 100 # Only attribute from this number of feature nodes, max. Lower is faster, but you will lose more of the graph. None means no limit.\n", + "batch_size = 256 // 8 # Batch size when attributing\n", + "offload = \"cpu\" # Offload various parts of the model during attribution to save memory. Can be 'disk', 'cpu', or None (keep on GPU)\n", + "verbose = True # Whether to display a tqdm progress bar and timing report\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93c54601", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import torch\n", + "\n", + "from circuit_tracer import ReplacementModel, attribute\n", + "from circuit_tracer.utils import create_graph_files\n", + "\n", + "torch.cuda.empty_cache()\n", + "\n", + "# FOr some reason this takes up a lot of VRAM\n", + "graph = attribute(\n", + " prompt=prompt,\n", + " model=rm,\n", + " max_n_logits=max_n_logits,\n", + " desired_logit_prob=desired_logit_prob,\n", + " batch_size=batch_size,\n", + " max_feature_nodes=max_feature_nodes,\n", + " offload=offload,\n", + " verbose=verbose,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c454ee0b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 849e873..c7891f1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ requires-python = ">=3.12,<3.13" dependencies = [ "torch>=2.8.0", "nnsight==0.5.0.dev7", - "datasets>=3.6.0", + "datasets==3.6.0", "h5py>=3.13.0", "einops>=0.8.1", "jaxtyping>=0.3.2", @@ -23,6 +23,10 @@ dependencies = [ "transformers>=4.46.0", "numpy>=1.24.0", "jsonargparse[signatures]>=4.27.7", + "modal>=1.1.4", + "circuit-tracer", + "hf-transfer>=0.1.9", + "huggingface_hub>=0.20.0", ] [project.optional-dependencies] @@ -30,7 +34,10 @@ dev = [ "jupyterlab>=4.4.2", "ipykernel>=6.29.5", "pytest>=8.4.1", + "circuit-tracer" ] +[project.scripts] +clt = "crosslayer_transcoder.main:main" [dependency-groups] dev = [ @@ -39,8 +46,14 @@ dev = [ "pytest>=8.4.1", ] -[project.scripts] -clt = "crosslayer_transcoder.main:main" +[tool.uv.sources] +circuit-tracer = { git = "https://github.com/safety-research/circuit-tracer.git", rev = "main" } + +[tool.hatch.metadata] +allow-direct-references=true [tool.black] line-length = 110 + +[tool.ruff.lint] +ignore = ["F722"] \ No newline at end of file diff --git a/tests/circuit_tracer/test_circuit_tracer_integration.py b/tests/circuit_tracer/test_circuit_tracer_integration.py new file mode 100644 index 0000000..f3d24e2 --- /dev/null +++ b/tests/circuit_tracer/test_circuit_tracer_integration.py @@ -0,0 +1,45 @@ +import pathlib +import shutil + +import yaml +from circuit_tracer.transcoder.cross_layer_transcoder import load_clt + +from crosslayer_transcoder.model.clt import CrossLayerTranscoder +from crosslayer_transcoder.utils.model_converters.circuit_tracer import ( + CircuitTracerConverter, +) + + +def test_circuit_tracer_integration(): + with open("config/circuit-tracer.yaml", "r") as f: + config = yaml.safe_load(f) + model_config = config["model"]["init_args"]["model"] + clt = CrossLayerTranscoder.from_config(model_config) + + save_dir = pathlib.Path("clt_module_test") + feature_input_hook = "hook_resid_mid" + feature_output_hook = "hook_mlp_out" + + converter = CircuitTracerConverter( + save_dir=save_dir, + feature_input_hook=feature_input_hook, + feature_output_hook=feature_output_hook, + ) + converter.export(clt) + + transcoder = load_clt( + clt_path=save_dir.as_posix(), + lazy_decoder=False, + lazy_encoder=False, + feature_input_hook=feature_input_hook, + feature_output_hook=feature_output_hook, + ) + + assert transcoder.n_layers == clt.encoder.n_layers + assert transcoder.d_transcoder == clt.encoder.d_features + assert transcoder.d_model == clt.encoder.d_acts + assert transcoder.feature_input_hook == feature_input_hook + assert transcoder.feature_output_hook == feature_output_hook + + # cleanup + shutil.rmtree(save_dir) diff --git a/tests/circuit_tracer/test_convert_to_circuit_tracer.py b/tests/circuit_tracer/test_convert_to_circuit_tracer.py new file mode 100644 index 0000000..5d45624 --- /dev/null +++ b/tests/circuit_tracer/test_convert_to_circuit_tracer.py @@ -0,0 +1,26 @@ +from crosslayer_transcoder.utils.model_converters.circuit_tracer import ( + CircuitTracerConverter, +) + +import pathlib +import shutil + + +def test_circuit_tracer_converter(jumprelu_clt): + save_dir = pathlib.Path("clt_module") + converter = CircuitTracerConverter(save_dir=save_dir) + converter.export(jumprelu_clt) + + assert save_dir.exists() + assert ( + len(list(save_dir.glob("*.safetensors"))) == jumprelu_clt.encoder.n_layers * 2 + ) + + assert (save_dir / "config.yaml").exists() + + for layer in range(12): + assert (save_dir / f"W_enc_{layer}.safetensors").exists() + assert (save_dir / f"W_dec_{layer}.safetensors").exists() + + # cleanup + shutil.rmtree(save_dir) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..639763c --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,41 @@ +import pytest + + +@pytest.fixture(scope="session") +def jumprelu_clt(): + from crosslayer_transcoder.model.clt import CrossLayerTranscoder + + model_config = { + "class_path": "crosslayer_transcoder.model.clt.CrossLayerTranscoder", + "init_args": { + "encoder": { + "class_path": "crosslayer_transcoder.model.clt.Encoder", + "init_args": {"d_acts": 768, "d_features": 10_000, "n_layers": 12}, + }, + "decoder": { + "class_path": "crosslayer_transcoder.model.clt.CrosslayerDecoder", + "init_args": {"d_acts": 768, "d_features": 10_000, "n_layers": 12}, + }, + "nonlinearity": { + "class_path": "crosslayer_transcoder.model.jumprelu.JumpReLU", + "init_args": { + "theta": 0.03, + "bandwidth": 0.01, + "n_layers": 12, + "d_features": 10_000, + }, + }, + "input_standardizer": { + "class_path": "crosslayer_transcoder.model.standardize.DimensionwiseInputStandardizer", + "init_args": {"n_layers": 12, "activation_dim": 768}, + }, + "output_standardizer": { + "class_path": "crosslayer_transcoder.model.standardize.DimensionwiseOutputStandardizer", + "init_args": {"n_layers": 12, "activation_dim": 768}, + }, + }, + } + + model = CrossLayerTranscoder.from_config(model_config) + + return model diff --git a/uv.lock b/uv.lock index 5cd028c..3534df2 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = "==3.12.*" resolution-markers = [ "sys_platform == 'linux'", @@ -8,7 +8,7 @@ resolution-markers = [ [[package]] name = "accelerate" -version = "1.7.0" +version = "1.11.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -19,9 +19,9 @@ dependencies = [ { name = "safetensors" }, { name = "torch" }, ] -sdist = { url = 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