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import os |
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import pytest |
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import torch.multiprocessing as mp |
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from llamafactory.v1.accelerator.helper import ReduceOp |
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from llamafactory.v1.accelerator.interface import DistributedInterface |
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from llamafactory.v1.utils.env import find_available_port |
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from llamafactory.v1.utils.pytest import dist_env |
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def _all_reduce_tests(local_rank: int, world_size: int, master_port: int): |
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with dist_env(local_rank, world_size, master_port): |
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rank = DistributedInterface().get_rank() |
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world_size = DistributedInterface().get_world_size() |
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assert world_size == 2 |
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y_sum = DistributedInterface().all_reduce(rank + 1.0, op=ReduceOp.SUM) |
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assert y_sum == pytest.approx(3.0) |
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y_mean = DistributedInterface().all_reduce(rank + 1.0, op=ReduceOp.MEAN) |
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assert y_mean == pytest.approx(1.5) |
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y_max = DistributedInterface().all_reduce(rank + 1.0, op=ReduceOp.MAX) |
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assert y_max == pytest.approx(2.0) |
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z = DistributedInterface().all_gather(rank + 1.0) |
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assert z == pytest.approx([1.0, 2.0]) |
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z = DistributedInterface().broadcast(rank + 1.0) |
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assert z == pytest.approx(1.0) |
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def test_all_device(): |
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assert DistributedInterface().get_rank() == int(os.getenv("RANK", "0")) |
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assert DistributedInterface().get_world_size() == int(os.getenv("WORLD_SIZE", "1")) |
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assert DistributedInterface().get_local_rank() == int(os.getenv("LOCAL_RANK", "0")) |
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assert DistributedInterface().get_local_world_size() == int(os.getenv("LOCAL_WORLD_SIZE", "1")) |
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@pytest.mark.runs_on(["cuda", "npu"]) |
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@pytest.mark.require_distributed(2) |
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def test_multi_device(): |
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master_port = find_available_port() |
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world_size = 2 |
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mp.spawn(_all_reduce_tests, args=(world_size, master_port), nprocs=world_size) |
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if __name__ == "__main__": |
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""" |
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python tests_v1/accelerator/test_interface.py |
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""" |
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test_all_device() |
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