mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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36 lines
1.1 KiB
36 lines
1.1 KiB
import pytest |
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import torch |
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from packaging import version |
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from torch import nn |
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try: |
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from colossalai.kernel.triton.softmax import softmax |
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HAS_TRITON = True |
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except ImportError: |
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HAS_TRITON = False |
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print("please install triton from https://github.com/openai/triton") |
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TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4") |
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@pytest.mark.skipif( |
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not TRITON_CUDA_SUPPORT or not HAS_TRITON, reason="triton requires cuda version to be higher than 11.4" |
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) |
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def test_softmax_op(): |
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data_samples = [ |
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torch.randn((3, 4, 5, 32), device="cuda", dtype=torch.float32), |
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torch.randn((320, 320, 78), device="cuda", dtype=torch.float32), |
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torch.randn((2345, 4, 5, 64), device="cuda", dtype=torch.float16), |
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] |
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for data in data_samples: |
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module = nn.Softmax(dim=-1) |
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data_torch_out = module(data) |
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data_triton_out = softmax(data) |
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check = torch.allclose(data_torch_out.cpu(), data_triton_out.cpu(), rtol=1e-3, atol=1e-3) |
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assert check is True, "softmax outputs from triton and torch are not matched" |
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if __name__ == "__main__": |
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test_softmax_op()
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