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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 _all_to_all_fp8
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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@parameterize("shape", [(16, 8, 4)])
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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("fp8_format", ["e4m3", "e5m2"])
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def check_4gpu(shape, scatter_dim, dtype, fp8_format):
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world_size = dist.get_world_size()
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input_tensor = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
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input_tensor_list = list(torch.chunk(input_tensor, world_size, scatter_dim))
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input_tensor_list = [x.contiguous() for x in input_tensor_list]
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output_tensor_list_fp8 = [torch.empty_like(x) for x in input_tensor_list]
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output_tensor_list = [torch.empty_like(x) for x in input_tensor_list]
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_all_to_all_fp8(output_tensor_list_fp8, input_tensor_list, group=_get_default_group(), fp8_format=fp8_format)
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dist.all_to_all(output_tensor_list, input_tensor_list, group=_get_default_group())
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assert_close(output_tensor_list_fp8, output_tensor_list, 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_to_all():
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spawn(run_dist, 4)
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if __name__ == "__main__":
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test_all_to_all()
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