nudles commented on a change in pull request #587: SINGA-504 Add Gemm operator
for autograd and onnx
URL: https://github.com/apache/singa/pull/587#discussion_r373778730
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File path: python/singa/autograd.py
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@@ -2760,3 +2760,107 @@ def backward(self, dy):
def reciprocal(x):
return Reciprocal()(x)[0]
+
+
+class Gemm(Operation):
+ def __init__(self, alpha=1.0, beta=1.0, transA=0, transB=0):
+ """
+ init a General Matrix multiplication(Gemm) operator
+ Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape
(M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is
broadcastable to shape (M, N), and output tensor Y has shape (M, N).
+ A' = transpose(A) if transA else A
+ B' = transpose(B) if transB else B
+ Args:alpha:
+ float, Scalar multiplier for the product of input tensors A * B.
+ Args:beta:
+ float, Scalar multiplier for input tensor C.
+ Args:transA:
+ int, Whether A should be transposed
+ Args:transB:
+ int, Whether B should be transposed
+ Returns:
+ tensor, the output
+ """
+ super(Gemm, self).__init__()
+ self.alpha = alpha
+ self.beta = beta
+ self.transA = transA
+ self.transB = transB
+
+ def forward(self, A, B, C=None):
+ """
+ forward propogation of Gemm
+ Args:A:
+ tensor, The shape of A should be (M, K) if transA is 0, or (K, M)
if transA is non-zero.
+ Args:B:
+ tensor, The shape of B should be (K, N) if transB is 0, or (N, K)
if transB is non-zero.
+ Args:C:
+ tensor(optional), Optional input tensor C. If not specified, the
computation is done as if C is a scalar 0. The shape of C should be
unidirectional broadcastable to (M, N).
+ Returns:
+ tensor, the output
+ """
+ _A = singa.DefaultTranspose(A) if self.transA == 1 else A
+ _B = singa.DefaultTranspose(B) if self.transB == 1 else B
+ if training:
+ self.inputs = (_A, _B, C)
+ tmpM = singa.MultFloat(singa.Mult(_A, _B), self.alpha)
Review comment:
how to optimize it?
I think MultiWithScale is more efficient as it calls the cpp/cuda gemm
directly instead of doing the matrix multiplication and addition separately..
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