mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2026-07-28 03:36:10 +08:00
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
# Copyright 2025 the LlamaFactory team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""Fixtures for Megatron Bridge GPU unit tests."""
|
|
|
|
import os
|
|
from pathlib import Path
|
|
from unittest import mock
|
|
|
|
import pytest
|
|
|
|
from llamafactory.extras.packages import is_megatron_bridge_available
|
|
|
|
|
|
pytestmark = pytest.mark.skipif(
|
|
not is_megatron_bridge_available(),
|
|
reason="megatron-bridge is not installed",
|
|
)
|
|
|
|
|
|
@pytest.fixture(scope="session")
|
|
def mb_model_path() -> str:
|
|
r"""Return the local Hugging Face model path used by Megatron Bridge tests."""
|
|
model_path = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
|
|
return model_path
|
|
|
|
|
|
@pytest.fixture
|
|
def mb_output_dir(tmp_path: Path) -> Path:
|
|
r"""Create a writable output directory for Megatron Bridge tests."""
|
|
output_dir = tmp_path / "output"
|
|
output_dir.mkdir(parents=True)
|
|
return output_dir
|
|
|
|
|
|
@pytest.fixture
|
|
def mb_training_args_factory(mb_output_dir: Path, mb_model_path: str):
|
|
r"""Build LLaMA-Factory dataclass arguments for Megatron Bridge tests."""
|
|
patchers: list[mock._patch] = []
|
|
|
|
def _factory(
|
|
*,
|
|
tensor_model_parallel_size: int = 1,
|
|
pipeline_model_parallel_size: int = 1,
|
|
context_parallel_size: int = 1,
|
|
sequence_parallel: bool = False,
|
|
use_distributed_optimizer: bool = False,
|
|
num_train_samples: int = 100,
|
|
cutoff_len: int = 512,
|
|
output_dir: Path | None = None,
|
|
):
|
|
from llamafactory.hparams.data_args import DataArguments
|
|
from llamafactory.hparams.finetuning_args import FinetuningArguments
|
|
from llamafactory.hparams.megatron_bridge_args import MegatronBridgeArguments
|
|
from llamafactory.hparams.model_args import ModelArguments
|
|
from llamafactory.hparams.training_args import TrainingArguments
|
|
|
|
resolved_output_dir = output_dir or mb_output_dir
|
|
model_args = ModelArguments(model_name_or_path=mb_model_path)
|
|
data_args = DataArguments(
|
|
dataset="alpaca_en_demo",
|
|
template="llama3",
|
|
cutoff_len=cutoff_len,
|
|
preprocessing_num_workers=1,
|
|
)
|
|
training_args = TrainingArguments(
|
|
output_dir=str(resolved_output_dir),
|
|
per_device_train_batch_size=1,
|
|
gradient_accumulation_steps=1,
|
|
num_train_epochs=1,
|
|
learning_rate=5e-6,
|
|
logging_steps=1,
|
|
save_steps=1000000,
|
|
report_to="none",
|
|
)
|
|
world_size = tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size
|
|
patcher = mock.patch.object(
|
|
type(training_args),
|
|
"world_size",
|
|
new_callable=mock.PropertyMock,
|
|
return_value=world_size,
|
|
)
|
|
patchers.append(patcher)
|
|
patcher.start()
|
|
finetuning_args = FinetuningArguments(stage="sft", finetuning_type="full")
|
|
mb_args = MegatronBridgeArguments(
|
|
tensor_model_parallel_size=tensor_model_parallel_size,
|
|
pipeline_model_parallel_size=pipeline_model_parallel_size,
|
|
context_parallel_size=context_parallel_size,
|
|
sequence_parallel=sequence_parallel,
|
|
use_distributed_optimizer=use_distributed_optimizer,
|
|
overlap_param_gather=False,
|
|
overlap_grad_reduce=False,
|
|
export_hf_on_finish=False,
|
|
)
|
|
return model_args, data_args, training_args, finetuning_args, mb_args, num_train_samples
|
|
|
|
yield _factory
|
|
|
|
for patcher in patchers:
|
|
patcher.stop()
|