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word2vec_inner_cbow_negs.pyx
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word2vec_inner_cbow_negs.pyx
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#!/usr/bin/env cython
# cython: boundscheck=False
# cython: wraparound=False
# cython: cdivision=True
# coding: utf-8
#
# Copyright (C) 2013 Radim Rehurek <me@radimrehurek.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
#
# Modified by Sébastien Jean
#Make neu1 replicate the behavior of work
import cython
import numpy as np
cimport numpy as np
from libc.math cimport exp
from libc.string cimport memset
from cpython cimport PyCObject_AsVoidPtr
from scipy.linalg.blas import fblas
REAL = np.float32
ctypedef np.float32_t REAL_t
ctypedef void (*scopy_ptr) (const int *N, const float *X, const int *incX, float *Y, const int *incY) nogil
ctypedef void (*saxpy_ptr) (const int *N, const float *alpha, const float *X, const int *incX, float *Y, const int *incY) nogil
ctypedef float (*sdot_ptr) (const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil
ctypedef double (*dsdot_ptr) (const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil
ctypedef double (*snrm2_ptr) (const int *N, const float *X, const int *incX) nogil
ctypedef void (*fast_sentence_ptr) (
const int neg_samples, int codelens[1000], np.uint32_t *table,
REAL_t *neu1, REAL_t *syn0, REAL_t *syn1neg, const int size,
np.uint32_t indexes[1000], const REAL_t alpha, REAL_t *work, int i, int j, int k, np.uint32_t *random_numbers) nogil
cdef scopy_ptr scopy=<scopy_ptr>PyCObject_AsVoidPtr(fblas.scopy._cpointer) # y = x
cdef saxpy_ptr saxpy=<saxpy_ptr>PyCObject_AsVoidPtr(fblas.saxpy._cpointer) # y += alpha * x
cdef sdot_ptr sdot=<sdot_ptr>PyCObject_AsVoidPtr(fblas.sdot._cpointer) # float = dot(x, y)
cdef dsdot_ptr dsdot=<dsdot_ptr>PyCObject_AsVoidPtr(fblas.sdot._cpointer) # double = dot(x, y)
cdef snrm2_ptr snrm2=<snrm2_ptr>PyCObject_AsVoidPtr(fblas.snrm2._cpointer) # sqrt(x^2)
cdef fast_sentence_ptr fast_sentence
DEF EXP_TABLE_SIZE = 1000
DEF MAX_EXP = 6
cdef REAL_t[EXP_TABLE_SIZE] EXP_TABLE
cdef int ONE = 1
cdef REAL_t ONEF = <REAL_t>1.0
cdef void fast_sentence0(
const int neg_samples, int codelens[1000], np.uint32_t *table,
REAL_t *neu1, REAL_t *syn0, REAL_t *syn1neg, const int size,
np.uint32_t indexes[1000], const REAL_t alpha, REAL_t *work, int i, int j, int k, np.uint32_t *random_numbers) nogil:
cdef long long a, b
cdef long long row2
cdef REAL_t f, g
cdef int m
cdef int d
cdef int random_integer
cdef np.int32_t target_index
cdef REAL_t label
cdef np.int32_t word_index
word_index = indexes[i]
cdef int count = 0
memset(neu1, 0, size * cython.sizeof(REAL_t))
for m in range(j, k):
if m == i or codelens[m] == 0:
continue
else:
count = count + 1
saxpy(&size,&ONEF, &syn0[indexes[m] * size], &ONE, neu1, &ONE)
if count > 0:
for c in range(size):
neu1[c] = neu1[c] / count
memset(work, 0, size * cython.sizeof(REAL_t))
for d in range(neg_samples+1):
if d == 0:
target_index = word_index
label = <REAL_t>1
else:
random_integer = random_numbers[i*neg_samples + d - 1]
target_index = table[random_integer]
if target_index == word_index:
continue
label = <REAL_t>0
row2 = target_index * size
f = <REAL_t>dsdot(&size, neu1, &ONE, &syn1neg[row2], &ONE)
#if f <= -MAX_EXP or f >= MAX_EXP:
# continue
#f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
f = (<REAL_t>1.0)/(<REAL_t>1.0 + <REAL_t>exp(-f))
g = (label - f) * alpha
saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE)
saxpy(&size, &g, neu1, &ONE, &syn1neg[row2], &ONE)
for m in range(j,k):
if m == i or codelens[m] == 0:
continue
else:
saxpy(&size, &ONEF, work, &ONE, &syn0[indexes[m]*size], &ONE)
cdef void fast_sentence1(
const int neg_samples, int codelens[1000], np.uint32_t *table,
REAL_t *neu1, REAL_t *syn0, REAL_t *syn1neg, const int size,
np.uint32_t indexes[1000], const REAL_t alpha, REAL_t *work, int i, int j, int k, np.uint32_t *random_numbers) nogil:
cdef long long a, b
cdef long long row2
cdef REAL_t f, g
cdef int m
cdef int d
cdef int random_integer
cdef np.int32_t target_index
cdef REAL_t label
cdef np.int32_t word_index
word_index = indexes[i]
cdef int count = 0
memset(neu1, 0, size * cython.sizeof(REAL_t))
for m in range(j, k):
if m == i or codelens[m] == 0:
continue
else:
count = count + 1
saxpy(&size,&ONEF, &syn0[indexes[m] * size], &ONE, neu1, &ONE)
if count > 0:
for c in range(size):
neu1[c] = neu1[c] / count
memset(work, 0, size * cython.sizeof(REAL_t))
for d in range(neg_samples+1):
if d == 0:
target_index = word_index
label = <REAL_t>1
else:
random_integer = random_numbers[i*neg_samples + d - 1]
target_index = table[random_integer]
if target_index == word_index:
continue
label = <REAL_t>0
row2 = target_index * size
f = <REAL_t>sdot(&size, neu1, &ONE, &syn1neg[row2], &ONE)
#if f <= -MAX_EXP or f >= MAX_EXP:
# continue
#f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
f = (<REAL_t>1.0)/(<REAL_t>1.0 + <REAL_t>exp(-f))
g = (label - f) * alpha
saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE)
saxpy(&size, &g, neu1, &ONE, &syn1neg[row2], &ONE)
for m in range(j,k):
if m == i or codelens[m] == 0:
continue
else:
saxpy(&size, &ONEF, work, &ONE, &syn0[indexes[m]*size], &ONE)
"""
cdef void fast_sentence2(
#const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen,
const int neg_samples, int codelens[1000], np.uint32_t *table,
REAL_t *neu1, REAL_t *syn0, REAL_t *syn1neg, const int size,
#const REAL_t alpha, REAL_t *work, int i, int j, int k) nogil:
#np.uint32_t indexes[MAX_SENTENCE_LEN], const REAL_t alpha, REAL_t *work, int i, int j, int k) nogil:
np.uint32_t indexes[1000], const REAL_t alpha, REAL_t *work, int i, int j, int k, np.uint32_t *random_numbers) nogil:
cdef long long a, b
cdef long long row2
cdef REAL_t f, g
cdef int m
cdef int count = 0
memset(neu1, 0, size * cython.sizeof(REAL_t)) #set work to zero?
for m in range(j, k):
if m == i or codelens[m] == 0:
continue
else:
count = count + 1
#saxpy(&size,&ONEF, &syn0[indexes[m] * size], &ONE, neu1, &ONE)
for c in range(size):
neu1[c] = neu1[c] + syn0[indexes[m] * size + c]
# neu1_copy[c] = neu1_copy[c] + syn0[indexes[m] * size + c]
# if count > 0: #divide or not?
# for c in range(size):
# neu1[c] = neu1[c] / count
for a in range(size):
work[a] = <REAL_t>0.0
# neu1[a] = <REAL_t>0.0
for b in range(codelens[i]):
row2 = word_point[b] * size
#f = <REAL_t>0.2
for a in range(size):
f += neu1[a] * syn1neg[row2 + a]
if f <= -MAX_EXP or f >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (1 - word_code[b] - f) * alpha
for a in range(size):
work[a] += g * syn1neg[row2 + a]
for a in range(size):
syn1neg[row2 + a] += g * neu1[a]
#for a in range(size):
# neu1[a] += work[a]
for m in range(j,k):
if m == i or codelens[m] == 0:
continue
else:
# #saxpy(&size, &ONEF, work, &ONE, &syn0[indexes[m]*size], &ONE)
for a in range(size):
syn0[indexes[m] * size + a] = syn0[indexes[m] * size + a] + work[a]
"""
DEF MAX_SENTENCE_LEN = 1000
def train_sentence(model, sentence, alpha, _work, _neu1):
cdef REAL_t *syn0 = <REAL_t *>(np.PyArray_DATA(model.syn0))
cdef REAL_t *syn1neg = <REAL_t *>(np.PyArray_DATA(model.syn1neg))
cdef REAL_t *work
cdef REAL_t *neu1
cdef np.uint32_t word2_index
cdef REAL_t _alpha = alpha
cdef int size = model.layer1_size
cdef int neg_samples = model.neg_samples
cdef int table_size = model.table_size
cdef np.uint32_t *table = <np.uint32_t *>(np.PyArray_DATA(model.table))
cdef int reduce = model.reduce
cdef int direction = model.direction
#cdef np.uint32_t *points[MAX_SENTENCE_LEN]
#cdef np.uint8_t *codes[MAX_SENTENCE_LEN]
cdef int codelens[MAX_SENTENCE_LEN]
cdef np.uint32_t indexes[MAX_SENTENCE_LEN]
cdef np.uint32_t reduced_windows[MAX_SENTENCE_LEN]
cdef int sentence_len
cdef int window = model.window
cdef np.uint32_t *random_numbers = <np.uint32_t *>(np.PyArray_DATA(model.random_numbers))
cdef int i, j, k, m
cdef long result = 0
cdef int c, count #Added this line
# convert Python structures to primitive types, so we can release the GIL
work = <REAL_t *>np.PyArray_DATA(_work)
neu1 = <REAL_t *>np.PyArray_DATA(_neu1)
sentence_len = <int>min(MAX_SENTENCE_LEN, len(sentence))
cdef int a
for a in range(sentence_len*neg_samples):
random_numbers[a] = np.random.randint(table_size)
for i in range(sentence_len):
word = sentence[i]
if word is None:
codelens[i] = 0
else:
indexes[i] = word.index
codelens[i] = <int>len(word.code)
#codes[i] = <np.uint8_t *>np.PyArray_DATA(word.code)
#points[i] = <np.uint32_t *>np.PyArray_DATA(word.point)
if reduce > 0:
reduced_windows[i] = np.random.randint(window)
else:
reduced_windows[i] = 0
result += 1
# release GIL & train on the sentence
with nogil:
for i in range(sentence_len):
if codelens[i] == 0: #out of vocabulary
continue
if direction < 0:
j = i - window + reduced_windows[i]
if j < 0:
j = 0
k = i
elif direction == 0:
j = i - window + reduced_windows[i]
if j < 0:
j = 0
k = i + window + 1 - reduced_windows[i]
if k > sentence_len:
k = sentence_len
else:
j = i+1
k = i + window + 1 - reduced_windows[i]
if k > sentence_len:
k = sentence_len
fast_sentence(neg_samples, codelens, table, neu1, syn0, syn1neg, size, indexes, _alpha, work, i, j, k, random_numbers) #need a way to access stuff
return result
def init():
#Precompute function `sigmoid(x) = 1 / (1 + exp(-x))`, for x values discretized
#into table EXP_TABLE.
global fast_sentence
cdef int i
cdef float *x = [<float>10.0]
cdef float *y = [<float>0.01]
cdef float expected = <float>0.1
cdef int size = 1
cdef double d_res
cdef float *p_res
# build the sigmoid table
for i in range(EXP_TABLE_SIZE):
EXP_TABLE[i] = <REAL_t>exp((i / <REAL_t>EXP_TABLE_SIZE * 2 - 1) * MAX_EXP)
EXP_TABLE[i] = <REAL_t>(EXP_TABLE[i] / (EXP_TABLE[i] + 1))
# check whether sdot returns double or float
d_res = dsdot(&size, x, &ONE, y, &ONE)
p_res = <float *>&d_res
if (abs(d_res - expected) < 0.0001):
fast_sentence = fast_sentence0
print "0"
return 0 # double
elif (abs(p_res[0] - expected) < 0.0001):
fast_sentence = fast_sentence1
print "1"
return 1 # float
else:
# neither => use cython loops, no BLAS
# actually, the BLAS is so messed up we'll probably have segfaulted above and never even reach here
fast_sentence = fast_sentence1 #modified (and false) The last optimization has not been implemented.
"print 2"
return 2
FAST_VERSION = init() # initialize the module