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Fix compatibility with CuPy 9.x (#11194) 

After the precomputable affine table of shape [nB, nF, nO, nP] is
computed, padding with shape [1, nF, nO, nP] is assigned to the first
row of the precomputed affine table. However, when we are indexing the
precomputed table, we get a row of shape [nF, nO, nP]. CuPy versions
before 10.0 cannot paper over this shape difference.

This change fixes compatibility with CuPy < 10.0 by squeezing the first
dimension of the padding before assignment.
This commit is contained in:
Daniël de Kok 2022-07-26 10:52:01 +02:00 committed by GitHub
parent 36ff2a5441
commit 4ee8a06149
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spacy/ml/_precomputable_affine.py
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@ -26,7 +26,11 @@ def forward(model, X, is_train):
Yf = model.ops.alloc2f(X.shape[0] + 1, nF * nO * nP, zeros=False)
model.ops.gemm(X, W.reshape((nF * nO * nP, nI)), trans2=True, out=Yf[1:])
Yf = Yf.reshape((Yf.shape[0], nF, nO, nP))
Yf[0] = model.get_param("pad")
# Set padding. Padding has shape (1, nF, nO, nP). Unfortunately, we cannot
# change its shape to (nF, nO, nP) without breaking existing models. So
# we'll squeeze the first dimension here.
Yf[0] = model.ops.xp.squeeze(model.get_param("pad"), 0)
def backward(dY_ids):
# This backprop is particularly tricky, because we get back a different