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Merge 3dadad8d55 into b3c46c315e

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envolution 2025-02-03 13:14:04 -08:00 committed by GitHub
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1 changed files with 16 additions and 9 deletions
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25
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, &A.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.
@ -232,6 +227,17 @@ cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) no
best = i best = i
return best return best
cdef int _arg_max(const float* scores, const int n_classes) nogil:
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
class ParserStepModel(Model): class ParserStepModel(Model):
def __init__( def __init__(
@ -531,3 +537,4 @@ cdef class precompute_hiddens:
return d_best.reshape((d_best.shape + (1,))) return d_best.reshape((d_best.shape + (1,)))
return state_vector, backprop_relu return state_vector, backprop_relu