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tb_framework: Replace references to thinc.backends.linalg with CBlas

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shademe 2022-08-09 16:34:39 +02:00
parent f4d0e85e1b
commit 00243c0757
1 changed files with 15 additions and 5 deletions
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20
spacy/ml/tb_framework.pyx
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@ -8,7 +8,6 @@ cimport numpy as np
from thinc.api import Model, normal_init, chain, list2array, Linear from thinc.api import Model, normal_init, chain, list2array, Linear
from thinc.api import uniform_init, glorot_uniform_init, zero_init from thinc.api import uniform_init, glorot_uniform_init, zero_init
from thinc.api import NumpyOps from thinc.api import NumpyOps
from thinc.backends.linalg cimport Vec, VecVec
from thinc.backends.cblas cimport CBlas, saxpy, sgemm from thinc.backends.cblas cimport CBlas, saxpy, sgemm
from thinc.types import Floats1d, Floats2d, Floats3d, Floats4d from thinc.types import Floats1d, Floats2d, Floats3d, Floats4d
from thinc.types import Ints1d, Ints2d from thinc.types import Ints1d, Ints2d
@ -512,11 +511,10 @@ cdef void _predict_states(CBlas cblas, ActivationsC* A, float* scores, StateC**
memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float)) memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float))
_sum_state_features(cblas, A.unmaxed, W.feat_weights, A.token_ids, n) _sum_state_features(cblas, A.unmaxed, W.feat_weights, A.token_ids, n)
for i in range(n.states): for i in range(n.states):
VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces], saxpy(cblas)(n.hiddens * n.pieces, 1., W.feat_bias, 1, &A.unmaxed[i*n.hiddens*n.pieces], 1)
W.feat_bias, 1., n.hiddens * n.pieces)
for j in range(n.hiddens): for j in range(n.hiddens):
index = i * n.hiddens * n.pieces + j * n.pieces index = i * n.hiddens * n.pieces + j * n.pieces
which = Vec.arg_max(&A.unmaxed[index], n.pieces) which = _arg_max(&A.unmaxed[index], n.pieces)
A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which] A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which]
if W.hidden_weights == NULL: if W.hidden_weights == NULL:
memcpy(scores, A.hiddens, n.states * n.classes * sizeof(float)) memcpy(scores, A.hiddens, n.states * n.classes * sizeof(float))
@ -528,7 +526,7 @@ cdef void _predict_states(CBlas cblas, ActivationsC* A, float* scores, StateC**
0.0, scores, n.classes) 0.0, scores, n.classes)
# Add bias # Add bias
for i in range(n.states): for i in range(n.states):
VecVec.add_i(&scores[i*n.classes], W.hidden_bias, 1., n.classes) saxpy(cblas)(n.classes, 1., W.hidden_bias, 1, &scores[i*n.classes], 1)
# Set unseen classes to minimum value # Set unseen classes to minimum value
i = 0 i = 0
min_ = scores[0] min_ = scores[0]
@ -561,3 +559,15 @@ cdef void _sum_state_features(CBlas cblas, float* output,
feature = &cached[idx] feature = &cached[idx]
saxpy(cblas)(O, one, <const float*>feature, 1, &output[b*O], 1) saxpy(cblas)(O, one, <const float*>feature, 1, &output[b*O], 1)
token_ids += F token_ids += F
cdef inline int _arg_max(const float* scores, const int n_classes) nogil:
if n_classes == 2:
return 0 if scores[0] > scores[1] else 1
cdef int i
cdef int best = 0
cdef float mode = scores[0]
for i in range(1, n_classes):
if scores[i] > mode:
mode = scores[i]
best = i
return best