Add sample GPT training run

sparse_autoencoder/training_runs/__init__.py

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+"""Training runs."""

sparse_autoencoder/training_runs/gpt2.py

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+"""Run an sweep on all layers of GPT2 Small.
+
+Command:
+
+```bash
+git clone https://github.com/ai-safety-foundation/sparse_autoencoder.git && cd sparse_autoencoder &&
+poetry env use python3.11 && poetry install &&
+poetry run python sparse_autoencoder/training_runs/gpt2.py
+```
+"""
+import os
+
+from sparse_autoencoder import (
+    ActivationResamplerHyperparameters,
+    AutoencoderHyperparameters,
+    Hyperparameters,
+    LossHyperparameters,
+    Method,
+    OptimizerHyperparameters,
+    Parameter,
+    PipelineHyperparameters,
+    SourceDataHyperparameters,
+    SourceModelHyperparameters,
+    SweepConfig,
+    sweep,
+)
+
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+def train() -> None:
+    """Train."""
+    sweep_config = SweepConfig(
+        parameters=Hyperparameters(
+            loss=LossHyperparameters(
+                l1_coefficient=Parameter(values=[0.0001]),
+            ),
+            optimizer=OptimizerHyperparameters(
+                lr=Parameter(value=0.0001),
+            ),
+            source_model=SourceModelHyperparameters(
+                name=Parameter("gpt2"),
+                cache_names=Parameter(
+                    value=[f"blocks.{layer}.hook_mlp_out" for layer in range(12)]
+                ),
+                hook_dimension=Parameter(768),
+            ),
+            source_data=SourceDataHyperparameters(
+                dataset_path=Parameter("alancooney/sae-monology-pile-uncopyrighted-tokenizer-gpt2"),
+                context_size=Parameter(256),
+                pre_tokenized=Parameter(value=True),
+                pre_download=Parameter(value=True),
+                # Total dataset is c.7bn activations (64 files)
+                # C. 1.5TB needed to store all activations
+                dataset_files=Parameter(
+                    [f"data/train-{str(i).zfill(5)}-of-00064.parquet" for i in range(20)]
+                ),
+            ),
+            autoencoder=AutoencoderHyperparameters(expansion_factor=Parameter(values=[32, 64])),
+            pipeline=PipelineHyperparameters(),
+            activation_resampler=ActivationResamplerHyperparameters(
+                threshold_is_dead_portion_fires=Parameter(1e-5),
+            ),
+        ),
+        method=Method.GRID,
+    )
+
+    sweep(sweep_config=sweep_config)
+
+
+if __name__ == "__main__":
+    train()