ac77da96da
* use smaller pretrained models for ci * more steps for loss check * fix tests * more train steps * fix losses
121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
"""Test LoRA kernels under FSDP2 multi-GPU training.
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Verifies that lora_qkv_kernel, lora_o_kernel, lora_mlp_kernel, and
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lora_embedding_kernel work correctly with FSDP2 sharding, including
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with bias, dropout, and DoRA enabled.
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"""
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from pathlib import Path
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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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 require_torch_2_7_0
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AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
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def _run_training(temp_dir, cfg):
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"""Write config and launch multi-GPU training."""
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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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def _base_lora_fsdp2_config(temp_dir, **overrides):
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"""Base config for LoRA + FSDP2 + kernel tests."""
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cfg = {
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"base_model": "axolotl-ai-co/tiny-qwen3-129m",
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"sequence_len": 512,
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"val_set_size": 0.0,
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"datasets": [
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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"split": "train[:1%]",
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},
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],
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_target_linear": True,
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"num_epochs": 1,
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"max_steps": 3,
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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": 1e-4,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"bf16": True,
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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": "Qwen3DecoderLayer",
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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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# Enable all LoRA kernels
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"lora_mlp_kernel": True,
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"lora_qkv_kernel": True,
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"lora_o_kernel": True,
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"lora_embedding_kernel": True,
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"save_safetensors": True,
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}
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cfg.update(overrides)
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return DictDefault(cfg)
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class TestFSDP2LoRAKernels:
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"""Test LoRA kernels under FSDP2."""
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@require_torch_2_7_0
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def test_lora_kernels_basic(self, temp_dir):
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"""Basic LoRA + kernels + FSDP2: no dropout, no bias, no DoRA."""
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cfg = _base_lora_fsdp2_config(temp_dir)
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_run_training(temp_dir, cfg)
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assert (Path(temp_dir) / "adapter_model.safetensors").exists()
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@require_torch_2_7_0
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def test_lora_kernels_with_dropout(self, temp_dir):
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"""LoRA kernels + dropout + FSDP2."""
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cfg = _base_lora_fsdp2_config(temp_dir, lora_dropout=0.1)
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_run_training(temp_dir, cfg)
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assert (Path(temp_dir) / "adapter_model.safetensors").exists()
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@require_torch_2_7_0
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def test_lora_kernels_with_dora(self, temp_dir):
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"""LoRA kernels + DoRA + FSDP2."""
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cfg = _base_lora_fsdp2_config(temp_dir, peft_use_dora=True)
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_run_training(temp_dir, cfg)
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assert (Path(temp_dir) / "adapter_model.safetensors").exists()
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@require_torch_2_7_0
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def test_lora_kernels_with_dora_and_dropout(self, temp_dir):
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"""LoRA kernels + DoRA + dropout + FSDP2."""
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cfg = _base_lora_fsdp2_config(
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temp_dir,
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peft_use_dora=True,
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lora_dropout=0.05,
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)
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_run_training(temp_dir, cfg)
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assert (Path(temp_dir) / "adapter_model.safetensors").exists()
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