49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
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import os
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from typing import Union
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import ixformer._C as ops
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import torch
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__all__ = ["gemv", "ref_gemv"]
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def ref_gemv(x: torch.Tensor, A: torch.Tensor, gemv_max_batch: int = 1):
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output = torch.nn.functional.linear(x, A)
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return output
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def gemv_conditions(input, weight, gemv_max_batch):
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# gemv 使用的条件 input:[m,k] weight:[n,k]
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# 1. m<=gemv_max_batch
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# 2. k%2==0 n%2==0
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# 3. bias is None
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input = input.view(-1, input.shape[-1])
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weight = weight.view(-1, weight.shape[-1])
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m = input.shape[0]
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k = input.shape[1]
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n = weight.shape[0]
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if m <= gemv_max_batch and k % 2 == 0 and n % 2 == 0:
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return True
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return False
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def gemv(x: torch.Tensor, A: torch.Tensor, gemv_max_batch: int = 1):
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"""
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Args:
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x: (..., k) torch.float16, torch.bfloat16
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A: (n,k) torch.float16, torch.bfloat16, torch.float32
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gemv_max_batch: int
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用于是否满足gemv使用条件的判断,目前只支持到1
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Returns:
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Tensor: (..., n) torch.float16, torch.bfloat16
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"""
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disable_infer_gemm_ex = os.getenv("DISABLE_INFER_GEMM_EX", "0")
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use_gemv = gemv_conditions(x, A, gemv_max_batch) and disable_infer_gemm_ex != "1"
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assert use_gemv == True
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output_shape = list(x.shape)
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output_shape[-1] = A.shape[0]
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output = x.new_empty(output_shape)
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output = ops.infer.linear_ex(x, A, None, output)
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return output
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