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56 lines
1.5 KiB
56 lines
1.5 KiB
import torch
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import torch.distributed as dist
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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_reduce_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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(4, 7),
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(7, 4),
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(8, 9),
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(3),
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(7,),
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(8,),
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],
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)
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@parameterize("dtype", [torch.float16, torch.bfloat16])
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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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x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
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x_fp8 = x.clone()
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origin_handle = dist.all_reduce(x, async_op=async_op)
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fp8_handle = all_reduce_fp8(x_fp8, fp8_format=fp8_format, async_op=async_op)
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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(x, x_fp8, rtol=0.1, atol=0.1)
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origin_handle = dist.all_reduce(x, op=dist.ReduceOp.AVG, async_op=async_op)
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fp8_handle = all_reduce_fp8(x_fp8, op=dist.ReduceOp.AVG, fp8_format=fp8_format, async_op=async_op)
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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(x, x_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_reduce():
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spawn(run_dist, 4)
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if __name__ == "__main__":
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test_all_reduce()
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