81893c775c
* update accelerate to v1.8.0 * update bnb also * fix multigpu ci timeout * fix test set size * use latest accelerate 1.8.1 * disable default dtype
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
"""
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E2E tests for multigpu lora tinyllama
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"""
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from pathlib import Path
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import pytest
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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from huggingface_hub import snapshot_download
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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 check_tensorboard
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AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
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@pytest.fixture(scope="session", autouse=True)
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def download_model():
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# download the model
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snapshot_download("axolotl-mirrors/gemma-3-4b-pt", repo_type="model")
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class TestMultiGPUGemma3:
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"""
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Test case for Gemma3 models using LoRA
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"""
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def test_lora_ddp_packed(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "axolotl-mirrors/gemma-3-4b-pt",
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"sequence_len": 2048,
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"ddp_find_unused_parameters": True,
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"sample_packing": True,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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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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"val_set_size": 0.0,
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"chat_template": "gemma3",
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"datasets": [
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{
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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": 2,
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"micro_batch_size": 4,
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"gradient_checkpointing": True,
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"gradient_checkpointing_kwargs": {
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"use_reentrant": False,
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},
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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"learning_rate": 0.0001,
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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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"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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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 1.8, "Train Loss (%s) is too high"
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)
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