fc4e37920b
* Prepare for transformers v5 upgrade * fix hf cli * update for hf hub changes * fix tokenizer apply_chat_template args * remap include_tokens_per_second * fix tps * handle migration for warmup * use latest hf hub * Fix scan -> ls * fix import * fix for renaming of mistral common tokenizer -> backend * update for fixed tokenziation for llama * Skip phi35 tests for now * remove mistral patch fixed upstream in huggingface/transformers#41439 * use namespacing for patch * don't rely on sdist for e2e tests for now * run modal ci without waiting too * Fix dep for ci * fix imports * Fix fp8 check * fsdp2 fixes * fix version handling * update fsdp version tests for new v5 behavior * Fail multigpu tests after 3 failures * skip known v5 broken tests for now and cleanup * bump deps * unmark skipped test * re-enable test_fsdp_qlora_prequant_packed test * increase multigpu ci timeout * skip broken gemma3 test * reduce timout back to original 120min now that the hanging test is skipped * fix for un-necessary collator for pretraining with bsz=1 * fix: safe_serialization deprecated in transformers v5 rc01 (#3318) * torch_dtype deprecated * load model in float32 for consistency with tests * revert some test fixtures back * use hf cache ls instead of scan * don't strip fsdp_version more fdsp_Version fixes for v5 fix version in fsdp_config fix aliasing fix fsdp_version check check fsdp_version is 2 in both places * Transformers v5 rc2 (#3347) * bump dep * use latest fbgemm, grab model config as part of fixture, un-skip test * import AutoConfig * don't need more problematic autoconfig when specifying config.json manually * add fixtures for argilla ultrafeedback datasets * download phi4-reasoning * fix arg * update tests for phi fast tokenizer changes * use explicit model types for gemma3 --------- Co-authored-by: Wing Lian <wing@axolotl.ai> * fix: AutoModelForVision2Seq -> AutoModelForImageTextToText * chore: remove duplicate * fix: attempt fix gemma3 text mode * chore: lint * ga release of v5 * need property setter for name_or_path for mistral tokenizer * vllm not compatible with transformers v5 * setter for chat_template w mistral too --------- Co-authored-by: NanoCode012 <nano@axolotl.ai> Co-authored-by: salman <salman.mohammadi@outlook.com>
485 lines
17 KiB
Python
485 lines
17 KiB
Python
"""Test module for FSDP2 multi-GPU functionality."""
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import os
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from pathlib import Path
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import pytest
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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_training_success(temp_dir):
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"""Verify that 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 len(checkpoint_files) > 0, (
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"No checkpoint files found - training may have failed"
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)
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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(torch.tensor(final_loss)), (
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f"Training loss is NaN: {final_loss}"
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)
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class TestFSDP2:
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"""Test class for FSDP2 functionality."""
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@require_torch_2_7_0
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@pytest.mark.parametrize(
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"fsdp_cpu_ram_efficient_loading",
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[True, False],
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)
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def test_fft_sft(self, temp_dir, fsdp_cpu_ram_efficient_loading):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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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[:10%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": fsdp_cpu_ram_efficient_loading,
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"transformer_layer_cls_to_wrap": "Qwen2DecoderLayer",
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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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"bf16": True,
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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_training_success(temp_dir)
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@require_torch_2_7_0
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@pytest.mark.parametrize("peft_use_dora", [True, False])
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def test_lora_sft(self, temp_dir, peft_use_dora):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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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[:10%]",
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},
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],
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"peft_use_dora": peft_use_dora,
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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_dropout": 0.05,
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"lora_target_linear": True,
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": "Qwen2DecoderLayer",
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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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"bf16": True,
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# explicitly disable LORA kernels, as they may be auto-enabled
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"lora_mlp_kernel": False,
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"lora_qkv_kernel": False,
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"lora_o_kernel": 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_training_success(temp_dir)
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@require_torch_2_7_0
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def test_lora_sft_kernels(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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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[:10%]",
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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": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": "Qwen2DecoderLayer",
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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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"bf16": True,
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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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}
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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_training_success(temp_dir)
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@require_torch_2_7_0
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def test_qlora_sft(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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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[:10%]",
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},
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],
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": "Qwen2DecoderLayer",
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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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"bf16": True,
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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_training_success(temp_dir)
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@require_torch_2_7_0
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def test_qlora_sft_kernels(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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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[:10%]",
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},
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],
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"load_in_4bit": True,
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"adapter": "qlora",
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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": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": "Qwen2DecoderLayer",
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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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"bf16": True,
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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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}
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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_training_success(temp_dir)
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@require_torch_2_7_0
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def test_dpo_fft(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"val_set_size": 0.01,
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"rl": "dpo",
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"chat_template": "chatml",
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"datasets": [
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{
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"path": "Intel/orca_dpo_pairs",
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"split": "train",
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"type": "chatml.intel",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": "Qwen2DecoderLayer",
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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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}
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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_training_success(temp_dir)
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@require_torch_2_7_0
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def test_dpo_lora(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "Qwen/Qwen2.5-0.5B",
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"sequence_len": 2048,
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"rl": "dpo",
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"chat_template": "chatml",
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"datasets": [
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{
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"path": "Intel/orca_dpo_pairs",
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"split": "train",
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"type": "chatml.intel",
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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_dropout": 0.05,
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"lora_target_linear": True,
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 2,
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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",
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"lr_scheduler": "cosine",
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"flash_attention": 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": "Qwen2DecoderLayer",
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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,
|
|
}
|
|
)
|
|
|
|
# write cfg to yaml file
|
|
Path(temp_dir).mkdir(parents=True, exist_ok=True)
|
|
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
|
|
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
|
|
|
|
execute_subprocess_async(
|
|
[
|
|
"axolotl",
|
|
"train",
|
|
str(Path(temp_dir) / "config.yaml"),
|
|
"--num-processes",
|
|
"2",
|
|
"--main-process-port",
|
|
f"{get_torch_dist_unique_port()}",
|
|
]
|
|
)
|
|
|
|
verify_training_success(temp_dir)
|