c0a0c7534c
* offload activations to disk instead of CPU RAM * add prefetch * Disco :dance: * include offload_disk in e2e test for AC * document and make sure to cleanup * fix annotation to match docs * fix docs build * address PR feedback
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
"""
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E2E tests for activation checkpointing
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"""
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import pytest
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import transformers
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from torch.utils.checkpoint import checkpoint
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from axolotl.cli.args import TrainerCliArgs
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from axolotl.common.datasets import load_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from ..utils import check_model_output_exists
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@pytest.fixture()
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def fix_checkpoint_after_test():
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yield
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transformers.modeling_utils.checkpoint = checkpoint
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class TestActivationCheckpointing:
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"""
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E2E tests for activation checkpointing
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"""
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@pytest.mark.parametrize(
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"gradient_checkpointing",
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["offload", "offload_disk"],
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)
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def test_activation_checkpointing_offload(
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self,
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temp_dir,
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fix_checkpoint_after_test, # pylint: disable=unused-argument,redefined-outer-name
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gradient_checkpointing,
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):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 1024,
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"val_set_size": 0.0,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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"eos_token": "<|im_end|>",
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},
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"datasets": [
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{
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"chat_template": "chatml",
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"path": "mlabonne/FineTome-100k",
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"type": "chat_template",
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"split": "train[:10%]",
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"field_messages": "conversations",
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"message_field_role": "from",
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"message_field_content": "value",
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},
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],
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"num_epochs": 1,
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"max_steps": 5,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"sample_packing": True,
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"bf16": True,
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"save_safetensors": True,
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"gradient_checkpointing": gradient_checkpointing,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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