mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-04-28 11:45:23 +00:00
611a5a80ca
* [inference] add int8 rotary embedding kernel for smoothquant (#4843) * [inference] add smoothquant llama attention (#4850) * add smoothquant llama attention * remove uselss code * remove useless code * fix import error * rename file name * [inference] add silu linear fusion for smoothquant llama mlp (#4853) * add silu linear * update skip condition * catch smoothquant cuda lib exception * prcocess exception for tests * [inference] add llama mlp for smoothquant (#4854) * add llama mlp for smoothquant * fix down out scale * remove duplicate lines * add llama mlp check * delete useless code * [inference] add smoothquant llama (#4861) * add smoothquant llama * fix attention accuracy * fix accuracy * add kv cache and save pretrained * refactor example * delete smooth * refactor code * [inference] add smooth function and delete useless code for smoothquant (#4895) * add smooth function and delete useless code * update datasets * remove duplicate import * delete useless file * refactor codes (#4902) * rafactor code * add license * add torch-int and smoothquant license
40 lines
1.0 KiB
Python
40 lines
1.0 KiB
Python
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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