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Simplify PrecomputableAffine slightly
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@ -49,17 +49,14 @@ def forward(model, X, is_train):
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model.inc_grad("b", dY.sum(axis=0))
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dY = dY.reshape((dY.shape[0], nO * nP))
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Wopfi = model.ops.as_contig(W.transpose((1, 2, 0, 3)))
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Wopfi = W.transpose((1, 2, 0, 3))
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Wopfi = Wopfi.reshape((nO * nP, nF * nI))
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dXf = model.ops.gemm(dY.reshape((dY.shape[0], nO * nP)), Wopfi)
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# Reuse the buffer
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dWopfi = Wopfi
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dWopfi.fill(0.0)
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model.ops.gemm(dY, Xf, out=dWopfi, trans1=True)
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dWopfi = model.ops.gemm(dY, Xf, trans1=True)
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dWopfi = dWopfi.reshape((nO, nP, nF, nI))
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# (o, p, f, i) --> (f, o, p, i)
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dWopfi = model.ops.as_contig(dWopfi.transpose((2, 0, 1, 3)))
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dWopfi = dWopfi.transpose((2, 0, 1, 3))
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model.inc_grad("W", dWopfi)
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return dXf.reshape((dXf.shape[0], nF, nI))
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