mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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40 lines
1.0 KiB
40 lines
1.0 KiB
1 year ago
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import warnings
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import pytest
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import torch
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try:
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from colossalai.kernel.op_builder.smoothquant import SmoothquantBuilder
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smoothquant_cuda = SmoothquantBuilder().load()
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HAS_SMOOTHQUANT_CUDA = True
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except:
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warnings.warn("CUDA smoothquant linear is not installed")
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HAS_SMOOTHQUANT_CUDA = False
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@pytest.mark.skipif(
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not HAS_SMOOTHQUANT_CUDA,
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reason="smoothquant linear not installed properly",
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)
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def test_linear():
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a = torch.randint(-127, 127, (128, 512), dtype=torch.int8, device="cuda")
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b = torch.randint(-127, 127, (512, 256), dtype=torch.int8, device="cuda")
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c = torch.rand(256, dtype=torch.float, device="cuda")
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alpha = 1 / 127
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beta = 1.0
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torch_out = torch.mm(a.to(torch.float) * alpha, b.to(torch.float)) + c
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silu = torch.nn.SiLU()
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torch_out = silu(torch_out)
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b = b.transpose(0, 1).contiguous()
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cuda_out = smoothquant_cuda.linear_silu_a8_w8_bfp32_ofp32(a, b, c, alpha, beta)
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assert torch.allclose(torch_out, cuda_out, rtol=1e-02, atol=1e-02)
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if __name__ == "__main__":
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test_linear()
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