diff --git a/pyproject.toml b/pyproject.toml index e8e3e2de..2950f664 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,7 @@ dependencies = [ [project.optional-dependencies] dev = ["pytest>=8.4.2", "pytest-cov>=3.0.0", "ruff>=0.0.285"] +hpo = ["optuna>=3.6.0", "optuna-integration>=3.6.0"] docs = [ "mkdocs-material>=9.4.0", "mkdocstrings[python]>=0.22.0", diff --git a/src/electrai/configs/MP/config_hpo.yaml b/src/electrai/configs/MP/config_hpo.yaml new file mode 100644 index 00000000..b08dab02 --- /dev/null +++ b/src/electrai/configs/MP/config_hpo.yaml @@ -0,0 +1,101 @@ +# Hyperparameter Optimization Configuration for ResUNet +# This config defines both the search space and fixed parameters + +# HPO settings +hpo: + study_name: resunet_hpo + n_trials: 50 + timeout: null # seconds, null for no timeout + storage: sqlite:///hpo_study.db + direction: minimize # minimize val_loss + pruner: + type: median + n_startup_trials: 5 + n_warmup_steps: 3 # epochs before pruning starts + sampler: + type: tpe # tpe, random, or grid + + # Search space definitions + search_space: + # Model architecture + depth: + type: int + low: 2 + high: 4 + n_channels: + type: categorical + choices: [16, 32, 64] + n_residual_blocks: + type: int + low: 1 + high: 3 + kernel_size: + type: categorical + choices: [3, 5] + + # Training parameters + lr: + type: float + low: 1.0e-4 + high: 1.0e-2 + log: true + weight_decay: + type: categorical + choices: [0.0, 1.0e-5, 1.0e-4] + warmup_length: + type: int + low: 1 + high: 10 + gradient_clip_value: + type: categorical + choices: [1.0, 5.0, 20.0] + + # Data parameters + batch_size: + type: categorical + choices: [1, 2, 4] + augmentation: + type: categorical + choices: [true, false] + +# Fixed dataset parameters (not tuned) +data: + _target_: electrai.dataloader.dataset.RhoRead + root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/dataset_2/mp_filelist.txt + split_file: null + precision: f32 + batch_size: 1 # overridden by search_space + train_workers: 8 + val_workers: 2 + pin_memory: false + val_frac: 0.1 # larger val set for reliable HPO signal + drop_last: false + augmentation: false # overridden by search_space + random_seed: 42 + +# Fixed model parameters +model: + _target_: electrai.model.resunet.ResUNet3D + in_channels: 1 + out_channels: 1 + n_channels: 32 # overridden by search_space + n_residual_blocks: 1 # overridden by search_space + kernel_size: 5 # overridden by search_space + depth: 2 # overridden by search_space + +# Fixed training parameters +precision: 32 +epochs: 15 # reduced for HPO iterations +lr: 0.001 # overridden by search_space +weight_decay: 0.0 # overridden by search_space +warmup_length: 3 # overridden by search_space +beta1: 0.9 +beta2: 0.99 + +# Logging - W&B for tracking HPO trials +wandb_mode: online +entity: PrinceOA +wb_pname: mp-hpo + +# Checkpoints - minimal saving during HPO +ckpt_path: ./hpo_checkpoints diff --git a/src/electrai/configs/MP/config_hpo_tier1.yaml b/src/electrai/configs/MP/config_hpo_tier1.yaml new file mode 100644 index 00000000..58c940f0 --- /dev/null +++ b/src/electrai/configs/MP/config_hpo_tier1.yaml @@ -0,0 +1,81 @@ +# Tier 1 Coarse Search - Critical hyperparameters only +# Focus: model architecture + learning rate + +hpo: + study_name: resunet_hpo_tier1 + n_trials: 50 + timeout: null + storage: sqlite:///hpo_tier1.db + direction: minimize + pruner: + type: median + n_startup_trials: 5 + n_warmup_steps: 3 + sampler: + type: tpe + + # Tier 1 search space only + search_space: + # Model architecture (critical) + depth: + type: int + low: 2 + high: 4 + n_channels: + type: categorical + choices: [16, 32, 64] + n_residual_blocks: + type: int + low: 1 + high: 3 + kernel_size: + type: categorical + choices: [3, 5] + + # Learning rate (critical) + lr: + type: float + low: 1.0e-4 + high: 1.0e-2 + log: true + +# Fixed Tier 2 parameters (reasonable defaults) +data: + _target_: electrai.dataloader.dataset.RhoRead + root: /scratch/gpfs/ROSENGROUP/common/globus_share_OA/mp/dataset_2/mp_filelist.txt + split_file: null + precision: f32 + batch_size: 1 # fixed + train_workers: 8 + val_workers: 2 + pin_memory: false + val_frac: 0.1 + drop_last: false + augmentation: false # fixed: disabled + random_seed: 42 + +model: + _target_: electrai.model.resunet.ResUNet3D + in_channels: 1 + out_channels: 1 + n_channels: 32 + n_residual_blocks: 1 + kernel_size: 5 + depth: 2 + +# Fixed training parameters +precision: 32 +epochs: 15 +lr: 0.001 +weight_decay: 0.0 # fixed +warmup_length: 5 # fixed +gradient_clip_value: 5.0 # fixed +beta1: 0.9 +beta2: 0.99 + +# Logging +wandb_mode: online +entity: PrinceOA +wb_pname: betsy-mp-hpo-tier1 + +ckpt_path: ./hpo_tier1_checkpoints diff --git a/src/electrai/entrypoints/hpo.py b/src/electrai/entrypoints/hpo.py new file mode 100644 index 00000000..a490f884 --- /dev/null +++ b/src/electrai/entrypoints/hpo.py @@ -0,0 +1,341 @@ +"""Hyperparameter optimization entrypoint using Optuna. + +Installation: This module requires the optional ``hpo`` extra (optuna and +optuna-integration). Install with: + uv sync --extra hpo + +Run with regular python (NOT torchrun): + uv run python src/electrai/entrypoints/hpo.py --config path/to/config.yaml + +For multi-GPU training within each trial, set devices in config or use --devices flag: + uv run python src/electrai/entrypoints/hpo.py --config path/to/config.yaml --devices 8 +""" + +from __future__ import annotations + +import copy +import logging +import os +from pathlib import Path +from types import SimpleNamespace +from typing import TYPE_CHECKING + +import optuna +import torch +import yaml +from hydra.utils import instantiate +from lightning.pytorch import Trainer +from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint +from lightning.pytorch.loggers import WandbLogger +from optuna.integration import PyTorchLightningPruningCallback + +from electrai.lightning import LightningGenerator + +if TYPE_CHECKING: + from optuna import Trial + +logger = logging.getLogger(__name__) + + +def get_gpu_config(hpo_cfg: dict, args) -> tuple[int, str]: + """Determine number of devices and strategy for training. + + Returns (num_devices, strategy) tuple. + + Note: We use ddp_spawn instead of ddp because the regular ddp strategy + uses subprocess launching which re-runs the entire script on each GPU. + This causes each process to create different Optuna trials with different + hyperparameters, leading to model mismatch errors across ranks. + ddp_spawn uses torch.multiprocessing.spawn which creates child processes + from within the training call, ensuring all ranks use the same model. + """ + # Priority: CLI args > config > auto-detect + if hasattr(args, "devices") and args.devices is not None: + num_devices = args.devices + else: + num_devices = hpo_cfg.get("devices", "auto") + + if num_devices == "auto": + num_devices = torch.cuda.device_count() if torch.cuda.is_available() else 1 + + # Use ddp_spawn for multi-GPU (not ddp which uses subprocess launcher) + strategy = ( + "ddp_spawn" if isinstance(num_devices, int) and num_devices > 1 else "auto" + ) + + return num_devices, strategy + + +def suggest_hyperparameters(trial: Trial, search_space: dict) -> dict: + """Suggest hyperparameters from the search space using Optuna trial.""" + params = {} + for name, spec in search_space.items(): + param_type = spec["type"] + if param_type == "float": + params[name] = trial.suggest_float( + name, spec["low"], spec["high"], log=spec.get("log", False) + ) + elif param_type == "int": + params[name] = trial.suggest_int(name, spec["low"], spec["high"]) + elif param_type == "categorical": + params[name] = trial.suggest_categorical(name, spec["choices"]) + else: + msg = f"Unknown parameter type: {param_type}" + raise ValueError(msg) + return params + + +def apply_hyperparameters(cfg_dict: dict, params: dict) -> dict: + """Apply suggested hyperparameters to the config dictionary.""" + cfg = copy.deepcopy(cfg_dict) + + # Model parameters + if "depth" in params: + cfg["model"]["depth"] = params["depth"] + if "n_channels" in params: + cfg["model"]["n_channels"] = params["n_channels"] + if "n_residual_blocks" in params: + cfg["model"]["n_residual_blocks"] = params["n_residual_blocks"] + if "kernel_size" in params: + cfg["model"]["kernel_size"] = params["kernel_size"] + + # Training parameters + if "lr" in params: + cfg["lr"] = params["lr"] + if "weight_decay" in params: + cfg["weight_decay"] = params["weight_decay"] + if "warmup_length" in params: + cfg["warmup_length"] = params["warmup_length"] + if "gradient_clip_value" in params: + cfg["gradient_clip_value"] = params["gradient_clip_value"] + + # Data parameters + if "batch_size" in params: + cfg["data"]["batch_size"] = params["batch_size"] + if "augmentation" in params: + cfg["data"]["augmentation"] = params["augmentation"] + + return cfg + + +def create_objective(cfg_dict: dict, hpo_cfg: dict, args): + """Create the Optuna objective function.""" + # Determine GPU configuration once for all trials + num_devices, strategy = get_gpu_config(hpo_cfg, args) + logger.info(f"Training config: {num_devices} device(s), strategy={strategy}") + + def objective(trial: Trial) -> float: + # Suggest hyperparameters + params = suggest_hyperparameters(trial, hpo_cfg["search_space"]) + + # Apply to config + trial_cfg_dict = apply_hyperparameters(cfg_dict, params) + cfg = SimpleNamespace(**trial_cfg_dict) + + # Check memory constraints: depth vs n_channels + depth = trial_cfg_dict["model"]["depth"] + n_channels = trial_cfg_dict["model"]["n_channels"] + max_channels_at_bottleneck = n_channels * (2**depth) + if max_channels_at_bottleneck > 512: + # Skip configurations likely to OOM + logger.warning( + f"Pruning trial {trial.number}: depth={depth}, n_channels={n_channels} " + f"would create {max_channels_at_bottleneck} channels at bottleneck" + ) + raise optuna.TrialPruned + + # Create data module + datamodule = instantiate(cfg.data) + + # Create model + lit_model = LightningGenerator(cfg) + + # Set up W&B logging if enabled + wandb_mode = getattr(cfg, "wandb_mode", "disabled").lower() + os.environ["WANDB_MODE"] = wandb_mode + if wandb_mode != "disabled": + wandb_logger = WandbLogger( + project=getattr(cfg, "wb_pname", "mp-hpo"), + entity=getattr(cfg, "entity", None), + name=f"trial_{trial.number}", + group=hpo_cfg.get("study_name", "resunet_hpo"), + config={ + **params, + "trial_number": trial.number, + "num_devices": num_devices, + }, + reinit="finish_previous", + ) + else: + wandb_logger = None + + # Callbacks - only use pruning callback on single GPU (DDP has issues with it) + callbacks = [ + ModelCheckpoint( + dirpath=Path(cfg.ckpt_path) / f"trial_{trial.number}", + monitor="val_loss", + save_top_k=1, + mode="min", + ), + EarlyStopping(monitor="val_loss", patience=5, mode="min"), + ] + # Optuna pruning callback doesn't work well with DDP strategies + if strategy not in ("ddp", "ddp_spawn"): + callbacks.insert( + 0, PyTorchLightningPruningCallback(trial, monitor="val_loss") + ) + + # Trainer with multi-GPU support + trainer = Trainer( + max_epochs=int(cfg.epochs), + callbacks=callbacks, + accelerator="gpu" if torch.cuda.is_available() else "cpu", + devices=num_devices, + num_nodes=1, + strategy=strategy, + precision=cfg.precision, + enable_progress_bar=True, + enable_model_summary=trial.number == 0, # Only show summary for first trial + logger=wandb_logger, + gradient_clip_val=getattr(cfg, "gradient_clip_value", 1.0), + ) + + try: + trainer.fit(lit_model, datamodule=datamodule) + except RuntimeError as e: + if "out of memory" in str(e).lower(): + logger.warning(f"Trial {trial.number} OOM: {e}") + torch.cuda.empty_cache() + if wandb_logger is not None: + wandb_logger.experiment.finish(exit_code=1) + raise optuna.TrialPruned from e + raise + + # Return best validation loss + val_loss = trainer.callback_metrics.get("val_loss") + + # Log final metrics and finish W&B run + if wandb_logger is not None: + if val_loss is not None: + wandb_logger.experiment.summary["best_val_loss"] = val_loss.item() + wandb_logger.experiment.finish() + + if val_loss is None: + raise optuna.TrialPruned + + return val_loss.item() + + return objective + + +def create_sampler(sampler_cfg: dict) -> optuna.samplers.BaseSampler: + """Create Optuna sampler from config.""" + sampler_type = sampler_cfg.get("type", "tpe").lower() + if sampler_type == "tpe": + return optuna.samplers.TPESampler() + if sampler_type == "random": + return optuna.samplers.RandomSampler() + if sampler_type == "grid": + msg = "Grid sampler requires explicit search_space definition" + raise ValueError(msg) + msg = f"Unknown sampler type: {sampler_type}" + raise ValueError(msg) + + +def create_pruner(pruner_cfg: dict) -> optuna.pruners.BasePruner: + """Create Optuna pruner from config.""" + pruner_type = pruner_cfg.get("type", "median").lower() + n_startup = pruner_cfg.get("n_startup_trials", 5) + n_warmup = pruner_cfg.get("n_warmup_steps", 3) + + if pruner_type == "median": + return optuna.pruners.MedianPruner( + n_startup_trials=n_startup, n_warmup_steps=n_warmup + ) + if pruner_type == "hyperband": + return optuna.pruners.HyperbandPruner() + if pruner_type == "none": + return optuna.pruners.NopPruner() + msg = f"Unknown pruner type: {pruner_type}" + raise ValueError(msg) + + +def run_hpo(args): + """Run hyperparameter optimization study.""" + # Load config + config_path = Path(args.config) + with Path.open(config_path) as f: + cfg_dict = yaml.safe_load(f) + + hpo_cfg = cfg_dict.get("hpo", {}) + + # Create study + sampler = create_sampler(hpo_cfg.get("sampler", {})) + pruner = create_pruner(hpo_cfg.get("pruner", {})) + + study = optuna.create_study( + study_name=hpo_cfg.get("study_name", "resunet_hpo"), + storage=hpo_cfg.get("storage"), + direction=hpo_cfg.get("direction", "minimize"), + sampler=sampler, + pruner=pruner, + load_if_exists=True, + ) + + # Create objective + objective = create_objective(cfg_dict, hpo_cfg, args) + + # Run optimization + study.optimize( + objective, + n_trials=hpo_cfg.get("n_trials", 50), + timeout=hpo_cfg.get("timeout"), + gc_after_trial=True, + show_progress_bar=True, + ) + + # Report results + logger.info("=" * 60) + logger.info("HPO Study Complete") + logger.info("=" * 60) + logger.info(f"Best trial: {study.best_trial.number}") + logger.info(f"Best value: {study.best_value:.6f}") + logger.info("Best hyperparameters:") + for key, value in study.best_params.items(): + logger.info(f" {key}: {value}") + + # Save best config + best_cfg = apply_hyperparameters(cfg_dict, study.best_params) + del best_cfg["hpo"] # Remove HPO section from final config + best_config_path = config_path.parent / f"{config_path.stem}_best.yaml" + with Path.open(best_config_path, "w") as f: + yaml.dump(best_cfg, f, default_flow_style=False) + logger.info(f"Best config saved to: {best_config_path}") + + return study + + +if __name__ == "__main__": + import argparse + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + + parser = argparse.ArgumentParser(description="Run HPO for ElectrAI ResUNet") + parser.add_argument( + "--config", + type=str, + default="src/electrai/configs/MP/config_hpo.yaml", + help="Path to HPO config file", + ) + parser.add_argument( + "--devices", + type=int, + default=None, + help="Number of GPUs to use per trial (default: auto-detect all available)", + ) + args = parser.parse_args() + + run_hpo(args) diff --git a/src/electrai/lightning.py b/src/electrai/lightning.py index 560288fb..405dab4d 100644 --- a/src/electrai/lightning.py +++ b/src/electrai/lightning.py @@ -38,7 +38,12 @@ def training_step(self, batch): def validation_step(self, batch): loss = self._loss_calculation(batch) self.log( - "val_loss", loss, prog_bar=True, on_step=True, on_epoch=True, sync_dist=True + "val_loss", + loss, + prog_bar=True, + on_step=False, + on_epoch=True, + sync_dist=True, ) return loss diff --git a/uv.lock b/uv.lock index f7d606fa..d4378826 100644 --- a/uv.lock +++ 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