69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
"""multigpu e2e test for tensor parallelism."""
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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, 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, require_torch_2_7_0
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class TestTensorParallel:
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"""Test class for Tensor Parallel functionality."""
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@pytest.mark.skip(
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reason="TP doesn't work with models with tied weights (embeddings)"
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)
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@require_torch_2_7_0
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def test_fft_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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"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",
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"tensor_parallel_size": 2,
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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/loss", 1.0, "Train Loss (%s) is too high"
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)
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