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remove backend as per #13701

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envolution 2024-11-30 14:50:22 -05:00
parent 3e30b5bef6
commit e3eac03d40
1 changed files with 4 additions and 9 deletions
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13
spacy/ml/parser_model.pyx
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@ -5,7 +5,6 @@ from libc.math cimport exp
from libc.stdlib cimport calloc, free, realloc from libc.stdlib cimport calloc, free, realloc
from libc.string cimport memcpy, memset from libc.string cimport memcpy, memset
from thinc.backends.cblas cimport saxpy, sgemm from thinc.backends.cblas cimport saxpy, sgemm
from thinc.backends.linalg cimport Vec, VecVec
import numpy import numpy
import numpy.random import numpy.random
@ -116,14 +115,10 @@ cdef void predict_states(
n.hiddens * n.pieces n.hiddens * n.pieces
) )
for i in range(n.states): for i in range(n.states):
VecVec.add_i( saxpy(cblas)(n.hiddens * n.pieces, 1., W.feat_bias, 1, &A.unmaxed[i*n.hiddens*n.pieces], 1)
&A.unmaxed[i*n.hiddens*n.pieces],
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]
memset(A.scores, 0, n.states * n.classes * sizeof(float)) memset(A.scores, 0, n.states * n.classes * sizeof(float))
if W.hidden_weights == NULL: if W.hidden_weights == NULL:
@ -138,7 +133,7 @@ cdef void predict_states(
) )
# Add bias # Add bias
for i in range(n.states): for i in range(n.states):
VecVec.add_i(&A.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_ = A.scores[0] min_ = A.scores[0]
@ -187,7 +182,7 @@ cdef void cpu_log_loss(
"""Do multi-label log loss""" """Do multi-label log loss"""
cdef double max_, gmax, Z, gZ cdef double max_, gmax, Z, gZ
best = arg_max_if_gold(scores, costs, is_valid, O) best = arg_max_if_gold(scores, costs, is_valid, O)
guess = Vec.arg_max(scores, O) guess = _arg_max(scores, O)
if best == -1 or guess == -1: if best == -1 or guess == -1:
# These shouldn't happen, but if they do, we want to make sure we don't # These shouldn't happen, but if they do, we want to make sure we don't
# cause an OOB access. # cause an OOB access.