[train] support megatron-bridge for PT/SFT training (#10645)

tests/train/megatron_bridge/conftest.py

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+# 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()