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Avoid use of numpy.tensordot

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Matthew Honnibal 2017-10-27 10:18:36 +00:00
parent 642eb28c16
commit c9987cf131
1 changed files with 22 additions and 13 deletions
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35
spacy/_ml.py
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@ -127,24 +127,34 @@ class PrecomputableAffine(Model):
self.nF = nF
def begin_update(self, X, drop=0.):
tensordot = self.ops.xp.tensordot
ascontiguous = self.ops.xp.ascontiguousarray
Yf = tensordot(X, self.W, axes=[[1], [3]])
Yf = self.ops.dot(X,
self.W.reshape((self.nF*self.nO*self.nP, self.nI)).T)
Yf = Yf.reshape((X.shape[0], self.nF, self.nO, self.nP))
def backward(dY_ids, sgd=None):
dY, ids = dY_ids
Xf = X[ids]
Xf = Xf.reshape((Xf.shape[0], self.nF * self.nI))
dXf = tensordot(dY, self.W, axes=[[1,2], [1,2]])
dW = tensordot(dY, Xf, axes=[[0], [0]])
# (o, p, f, i) --> (f, o, p, i)
self.d_W += dW.transpose((2, 0, 1, 3))
self.d_b += dY.sum(axis=0)
dY = dY.reshape((dY.shape[0], self.nO*self.nP))
Wopfi = self.W.transpose((1, 2, 0, 3))
Wopfi = self.ops.xp.ascontiguousarray(Wopfi)
Wopfi = Wopfi.reshape((self.nO*self.nP, self.nF * self.nI))
dXf = self.ops.dot(dY.reshape((dY.shape[0], self.nO*self.nP)), Wopfi)
# Reuse the buffer
dWopfi = Wopfi; dWopfi.fill(0.)
self.ops.xp.dot(dY.T, Xf, out=dWopfi)
dWopfi = dWopfi.reshape((self.nO, self.nP, self.nF, self.nI))
# (o, p, f, i) --> (f, o, p, i)
self.d_W += dWopfi.transpose((2, 0, 1, 3))
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return dXf
return dXf.reshape((dXf.shape[0], self.nF, self.nI))
return Yf, backward
@staticmethod
@ -168,9 +178,9 @@ class PrecomputableAffine(Model):
size=tokvecs.size).reshape(tokvecs.shape)
def predict(ids, tokvecs):
hiddens = model(tokvecs)
hiddens = model(tokvecs) # (b, f, o, p)
vector = model.ops.allocate((hiddens.shape[0], model.nO, model.nP))
model.ops.scatter_add(vector, ids, hiddens)
model.ops.xp.add.at(vector, ids, hiddens)
vector += model.b
if model.nP >= 2:
return model.ops.maxout(vector)[0]
@ -318,8 +328,7 @@ def Tok2Vec(width, embed_size, **kwargs):
tok2vec = (
FeatureExtracter(cols)
>> with_flatten(
embed >> (convolution ** 4), pad=4)
>> with_flatten(embed >> (convolution ** 4), pad=4)
)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7