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@ -13,14 +13,20 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn |
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@parameterize("scatter_dim", [0, 1, 2]) |
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@parameterize("scatter_dim", [0, 1, 2]) |
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@parameterize("dtype", [torch.bfloat16, torch.float16]) |
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@parameterize("dtype", [torch.bfloat16, torch.float16]) |
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@parameterize("fp8_format", ["e4m3", "e5m2"]) |
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@parameterize("fp8_format", ["e4m3", "e5m2"]) |
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def check_4gpu(shape, scatter_dim, dtype, fp8_format): |
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@parameterize("async_op", [True, False]) |
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def check_4gpu(shape, scatter_dim, dtype, fp8_format, async_op): |
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x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device()) |
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x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device()) |
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input_list = list(torch.chunk(x, dim=scatter_dim, chunks=4)) |
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input_list = list(torch.chunk(x, dim=scatter_dim, chunks=4)) |
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input_list = [t.contiguous() for t in input_list] |
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input_list = [t.contiguous() for t in input_list] |
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output_origin = torch.empty_like(input_list[0]) |
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output_origin = torch.empty_like(input_list[0]) |
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output_fp8 = torch.empty_like(input_list[0]) |
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output_fp8 = torch.empty_like(input_list[0]) |
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reduce_scatter(output_origin, input_list, group=_get_default_group()) |
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origin_handle = reduce_scatter(output_origin, input_list, group=_get_default_group(), async_op=async_op) |
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reduce_scatter_fp8(output_fp8, input_list, group=_get_default_group(), fp8_format=fp8_format) |
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fp8_handle = reduce_scatter_fp8( |
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output_fp8, input_list, group=_get_default_group(), fp8_format=fp8_format, async_op=async_op |
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) |
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if async_op: |
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origin_handle.wait() |
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fp8_handle.wait() |
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assert_close(output_origin, output_fp8, rtol=0.1, atol=0.1) |
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assert_close(output_origin, output_fp8, rtol=0.1, atol=0.1) |
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