basic torchao fp8 mixed precision training (#2926)
* debug * debug * debug * revert unneeded change * add accelerator config to base trainer builder * add back accumulated_cache_size_limit setting * lint * accelerator constructor patch for single-GPU torch fp8 * lint * re-using existing fp8 code * lint * remove accelerate patch now fix in latest release * fix * docs * add fp8 + fsdp2 example * remove unused config * update config * smoke tests * add validator * add 2.7.0 guard for fsdp2 * fix * add config descriptions * add FSDP doc link * nit * set force_recompute_fp8_weight_in_bwd with enable_fsdp_float8_all_gather * better cfg for smoke tests * add test for accelerate patching * update fp8 validator
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Dan Saunders
GitHub
62
tests/e2e/integrations/test_fp8.py
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62
tests/e2e/integrations/test_fp8.py
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"""
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Simple end-to-end smoke tests for FP8 mixed precision training
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"""
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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 tests.e2e.utils import check_model_output_exists, require_torch_2_7_0
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class FP8IntegrationTestCase:
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"""
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e2e smoke tests for FP8 mixed precision training with Axolotl
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"""
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@require_torch_2_7_0
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def test_fp8_single_gpu_smoke(self, temp_dir):
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"""Smoke test for single GPU FP8 + torch.compile training"""
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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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"tokenizer_type": "AutoTokenizer",
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"trust_remote_code": True,
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"sequence_len": 512,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 3, # Very short smoke test
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"sdp_attention": True,
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"pad_to_seq_len": True,
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"sample_packing": True,
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"fp8": True,
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"torch_compile": True,
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"save_safetensors": True,
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"save_first_step": False,
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}
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)
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# pylint: disable=duplicate-code
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cfg = validate_config(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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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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120
tests/e2e/multigpu/test_fp8_fsdp2.py
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120
tests/e2e/multigpu/test_fp8_fsdp2.py
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"""Test module for FP8 mixed precision with FSDP2 multi-GPU functionality."""
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# pylint: disable=duplicate-code
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import os
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from pathlib import Path
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import torch
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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from tbparse import SummaryReader
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from transformers.testing_utils import get_torch_dist_unique_port
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import most_recent_subdir, require_torch_2_7_0
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AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
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def verify_fp8_training_success(temp_dir):
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"""Verify that FP8 training completed successfully by checking artifacts and loss."""
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output_path = Path(temp_dir)
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model_files = list(output_path.glob("*.bin")) + list(
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output_path.glob("*.safetensors")
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)
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assert len(model_files) > 0, "No model files found - training may have failed"
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checkpoint_files = list(output_path.glob("checkpoint-*"))
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assert (
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len(checkpoint_files) > 0
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), "No checkpoint files found - training may have failed"
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tb_log_path = most_recent_subdir(temp_dir + "/runs")
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if tb_log_path:
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event_files = sorted(os.listdir(tb_log_path))
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if event_files:
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event_file = os.path.join(tb_log_path, event_files[0])
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reader = SummaryReader(event_file)
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df = reader.scalars
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train_loss_df = df[df.tag == "train/train_loss"]
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if len(train_loss_df) > 0:
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final_loss = train_loss_df.value.values[-1]
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assert not torch.isnan(
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torch.tensor(final_loss)
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), f"Training loss is NaN: {final_loss}"
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class TestFP8FSDP2:
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"""Test class for FP8 mixed precision with FSDP2 functionality."""
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@require_torch_2_7_0
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def test_fp8_fsdp2_smoke(self, temp_dir):
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"""Smoke test for 2-GPU FP8 + torch.compile + FSDP2 training"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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"trust_remote_code": True,
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"sequence_len": 512,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 3, # Very short smoke test
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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_torch_fused", # Use standard optimizer for stability
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"lr_scheduler": "cosine",
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"sdp_attention": True,
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"pad_to_seq_len": True,
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"sample_packing": True,
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# FP8 configuration
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"fp8": True,
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"fp8_enable_fsdp_float8_all_gather": True,
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"torch_compile": True,
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# FSDP2 configuration
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"fsdp_version": 2,
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"fsdp_config": {
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"offload_params": False,
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"cpu_ram_efficient_loading": False,
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"transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
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"state_dict_type": "FULL_STATE_DICT",
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"auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
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"reshard_after_forward": True,
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},
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"use_tensorboard": True,
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"save_safetensors": True,
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"save_first_step": False,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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"--num-processes",
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"2",
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"--main-process-port",
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f"{get_torch_dist_unique_port()}",
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]
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)
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verify_fp8_training_success(temp_dir)
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