explosion/spaCy
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Replace einsums

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This commit is contained in:
Matthew Honnibal 2017-05-08 14:46:50 +02:00
parent 8d2eab74da
commit a66a4a4d0f
3 changed files with 12 additions and 6 deletions
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12
spacy/_ml.py
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@ -93,7 +93,10 @@ class PrecomputableMaxouts(Model):
# W: (f, o, p, i)
# b: (o, p)
Yfp = numpy.einsum('bi,fopi->fbop', X, self.W)
# Yfp = numpy.einsum('bi,fopi->fbop', X, self.W)
Yfp = self.ops.xp.tensordot(X, self.W,
axes=[[1], [3]]).transpose((1, 0, 2, 3))
Yfp = self.ops.xp.ascontiguousarray(Yfp)
Yfp += self.b
Yf = self.ops.allocate((self.nF, X.shape[0], self.nO))
which = self.ops.allocate((self.nF, X.shape[0], self.nO), dtype='i')
@ -106,8 +109,11 @@ class PrecomputableMaxouts(Model):
for i in range(self.nF):
dYp += self.ops.backprop_maxout(dY, which[i], self.nP)
dXf = numpy.einsum('bop,fopi->bfi', dYp, self.W)
dW = numpy.einsum('bop,bfi->fopi', dYp, Xf)
#dXf = numpy.einsum('bop,fopi->bfi', dYp, self.W)
dXf = self.ops.xp.tensordot(dYp, self.W, axes=[[1,2], [1,2]])
#dW = numpy.einsum('bfi,bop->fopi', Xf, dYp)
dW = self.ops.xp.tensordot(Xf, dYp, axes=[[0], [0]])
dW = dW.transpose((0, 2, 3, 1))
db = dYp.sum(axis=0)
self.d_W += dW

2
spacy/pipeline.pyx
View File

@ -21,7 +21,7 @@ class TokenVectorEncoder(object):
'''Assign position-sensitive vectors to tokens, using a CNN or RNN.'''
def __init__(self, vocab, **cfg):
self.vocab = vocab
self.model = build_tok2vec(vocab.lang, 64, **cfg)
self.model = build_tok2vec(vocab.lang, **cfg)
self.tagger = chain(
self.model,
Softmax(self.vocab.morphology.n_tags))

4
spacy/syntax/parser.pyx
View File

@ -89,7 +89,7 @@ def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, upper_model, low
for i, offset in enumerate(offsets):
adjusted_ids[i] *= token_ids[i] >= 0
adjusted_ids[i] += offset
features = upper_model.ops.allocate((len(states), 64), dtype='f')
features = upper_model.ops.allocate((len(states), lower_model.nO), dtype='f')
for i in range(len(states)):
for j, tok_i in enumerate(adjusted_ids[i]):
if tok_i >= 0:
@ -222,7 +222,7 @@ cdef class Parser:
nr_context_tokens = StateClass.nr_context_tokens(nF, nB, nS, nL, nR)
upper = chain(Maxout(width, width), Maxout(self.moves.n_moves, width))
lower = PrecomputableMaxouts(width, nF=nr_context_tokens, nI=width)
lower = PrecomputableMaxouts(width, nF=nr_context_tokens, nI=width*2)
return upper, lower
def __call__(self, Doc tokens):