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53 lines
1.6 KiB
53 lines
1.6 KiB
import torch
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import torch.distributed as dist
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from torch.distributed.distributed_c10d import _get_default_group
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from torch.testing import assert_close
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from colossalai import launch
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from colossalai.accelerator import get_accelerator
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from colossalai.quantization.fp8 import gather_fp8
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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@parameterize(
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"shape",
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[
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(3, 7),
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(2, 1),
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(1, 2),
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(2, 2),
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(4, 2),
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(5,),
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(4,),
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(2,),
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],
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)
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@parameterize("dtype", [torch.bfloat16, torch.float16])
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@parameterize("fp8_format", ["e4m3", "e5m2"])
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@parameterize("async_op", [True, False])
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def check_4gpu(shape, dtype, fp8_format, async_op):
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world_size = dist.get_world_size()
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x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
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output_list = [torch.empty_like(x) for _ in range(world_size)]
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output_list_fp8 = [torch.empty_like(x) for _ in range(world_size)]
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fp8_handle = gather_fp8(output_list_fp8, x, group=_get_default_group(), fp8_format=fp8_format, async_op=async_op)
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origin_hanle = dist.all_gather(output_list, x, group=_get_default_group(), async_op=async_op)
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if async_op:
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fp8_handle.wait()
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origin_hanle.wait()
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assert_close(output_list, output_list_fp8, rtol=0.1, atol=0.1)
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def run_dist(rank, world_size, port):
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launch(rank=rank, world_size=world_size, port=port, host="localhost")
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check_4gpu()
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@rerun_if_address_is_in_use()
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def test_all_gather():
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spawn(run_dist, 4)
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if __name__ == "__main__":
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test_all_gather()
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