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laywerrobot/lib/python3.6/site-packages/gensim/models/fasttext_inner.pyx

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2020-08-27 21:55:39 +02:00
#!/usr/bin/env cython
# cython: boundscheck=False
# cython: wraparound=False
# cython: cdivision=True
# cython: embedsignature=True
# coding: utf-8
"""Optimized cython functions for training :class:`~gensim.models.fasttext.FastText` model."""
import cython
import numpy as np
cimport numpy as np
from libc.math cimport exp
from libc.math cimport log
from libc.string cimport memset
# scipy <= 0.15
try:
from scipy.linalg.blas import fblas
except ImportError:
# in scipy > 0.15, fblas function has been removed
import scipy.linalg.blas as fblas
from word2vec_inner cimport bisect_left, random_int32, scopy, saxpy, dsdot, sscal, \
REAL_t, EXP_TABLE, our_dot, our_saxpy, our_dot_double, our_dot_float, our_dot_noblas, our_saxpy_noblas
REAL = np.float32
DEF MAX_SENTENCE_LEN = 10000
DEF MAX_SUBWORDS = 1000
DEF EXP_TABLE_SIZE = 1000
DEF MAX_EXP = 6
cdef REAL_t[EXP_TABLE_SIZE] EXP_TABLE
cdef REAL_t[EXP_TABLE_SIZE] LOG_TABLE
cdef int ONE = 1
cdef REAL_t ONEF = <REAL_t>1.0
cdef unsigned long long fast_sentence_sg_neg(
const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len,
REAL_t *syn0_vocab, REAL_t *syn0_ngrams, REAL_t *syn1neg, const int size,
const np.uint32_t word_index, const np.uint32_t *subwords_index, const np.uint32_t subwords_len,
const REAL_t alpha, REAL_t *work, REAL_t *l1, unsigned long long next_random, REAL_t *word_locks_vocab,
REAL_t *word_locks_ngrams) nogil:
cdef long long a
cdef np.uint32_t word2_index = subwords_index[0]
cdef long long row1 = word2_index * size, row2
cdef unsigned long long modulo = 281474976710655ULL
cdef REAL_t f, g, label, f_dot, log_e_f_dot
cdef np.uint32_t target_index
cdef int d
memset(work, 0, size * cython.sizeof(REAL_t))
memset(l1, 0, size * cython.sizeof(REAL_t))
scopy(&size, &syn0_vocab[row1], &ONE, l1, &ONE)
for d in range(1, subwords_len):
our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_index[d] * size], &ONE, l1, &ONE)
cdef REAL_t norm_factor = ONEF / subwords_len
sscal(&size, &norm_factor, l1 , &ONE)
for d in range(negative+1):
if d == 0:
target_index = word_index
label = ONEF
else:
target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len)
next_random = (next_random * <unsigned long long>25214903917ULL + 11) & modulo
if target_index == word_index:
continue
label = <REAL_t>0.0
row2 = target_index * size
f_dot = our_dot(&size, l1, &ONE, &syn1neg[row2], &ONE)
if f_dot <= -MAX_EXP or f_dot >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (label - f) * alpha
our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE)
our_saxpy(&size, &g, l1, &ONE, &syn1neg[row2], &ONE)
our_saxpy(&size, &word_locks_vocab[word2_index], work, &ONE, &syn0_vocab[row1], &ONE)
for d in range(1, subwords_len):
our_saxpy(&size, &word_locks_ngrams[subwords_index[d]], work, &ONE, &syn0_ngrams[subwords_index[d]*size], &ONE)
return next_random
cdef void fast_sentence_sg_hs(
const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen,
REAL_t *syn0_vocab, REAL_t *syn0_ngrams, REAL_t *syn1, const int size,
const np.uint32_t *subwords_index, const np.uint32_t subwords_len,
const REAL_t alpha, REAL_t *work, REAL_t *l1, REAL_t *word_locks_vocab,
REAL_t *word_locks_ngrams) nogil:
cdef long long a, b
cdef np.uint32_t word2_index = subwords_index[0]
cdef long long row1 = word2_index * size, row2, sgn
cdef REAL_t f, g, f_dot, lprob
memset(work, 0, size * cython.sizeof(REAL_t))
memset(l1, 0, size * cython.sizeof(REAL_t))
scopy(&size, &syn0_vocab[row1], &ONE, l1, &ONE)
for d in range(1, subwords_len):
our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_index[d] * size], &ONE, l1, &ONE)
cdef REAL_t norm_factor = ONEF / subwords_len
sscal(&size, &norm_factor, l1 , &ONE)
for b in range(codelen):
row2 = word_point[b] * size
f_dot = our_dot(&size, l1, &ONE, &syn1[row2], &ONE)
if f_dot <= -MAX_EXP or f_dot >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (1 - word_code[b] - f) * alpha
our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE)
our_saxpy(&size, &g, l1, &ONE, &syn1[row2], &ONE)
our_saxpy(&size, &word_locks_vocab[word2_index], work, &ONE, &syn0_vocab[row1], &ONE)
for d in range(1, subwords_len):
our_saxpy(&size, &word_locks_ngrams[subwords_index[d]], work, &ONE, &syn0_ngrams[subwords_index[d]*size], &ONE)
cdef unsigned long long fast_sentence_cbow_neg(
const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, int codelens[MAX_SENTENCE_LEN],
REAL_t *neu1, REAL_t *syn0_vocab, REAL_t *syn0_ngrams, REAL_t *syn1neg, const int size,
const np.uint32_t indexes[MAX_SENTENCE_LEN], const np.uint32_t *subwords_idx[MAX_SENTENCE_LEN],
const int subwords_idx_len[MAX_SENTENCE_LEN], const REAL_t alpha, REAL_t *work,
int i, int j, int k, int cbow_mean, unsigned long long next_random, REAL_t *word_locks_vocab, REAL_t *word_locks_ngrams) nogil:
cdef long long a
cdef long long row2
cdef unsigned long long modulo = 281474976710655ULL
cdef REAL_t f, g, count, inv_count = 1.0, label, log_e_f_dot, f_dot
cdef np.uint32_t target_index, word_index
cdef int d, m
word_index = indexes[i]
memset(neu1, 0, size * cython.sizeof(REAL_t))
count = <REAL_t>0.0
for m in range(j, k):
if m == i:
continue
count += ONEF
our_saxpy(&size, &ONEF, &syn0_vocab[indexes[m] * size], &ONE, neu1, &ONE)
for d in range(subwords_idx_len[m]):
count += ONEF
our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_idx[m][d] * size], &ONE, neu1, &ONE)
if count > (<REAL_t>0.5):
inv_count = ONEF / count
if cbow_mean:
sscal(&size, &inv_count, neu1, &ONE)
memset(work, 0, size * cython.sizeof(REAL_t))
for d in range(negative+1):
if d == 0:
target_index = word_index
label = ONEF
else:
target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len)
next_random = (next_random * <unsigned long long>25214903917ULL + 11) & modulo
if target_index == word_index:
continue
label = <REAL_t>0.0
row2 = target_index * size
f_dot = our_dot(&size, neu1, &ONE, &syn1neg[row2], &ONE)
if f_dot <= -MAX_EXP or f_dot >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (label - f) * alpha
our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE)
our_saxpy(&size, &g, neu1, &ONE, &syn1neg[row2], &ONE)
if not cbow_mean: # divide error over summed window vectors
sscal(&size, &inv_count, work, &ONE)
for m in range(j,k):
if m == i:
continue
our_saxpy(&size, &word_locks_vocab[indexes[m]], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE)
for d in range(subwords_idx_len[m]):
our_saxpy(&size, &word_locks_ngrams[subwords_idx[m][d]], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE)
return next_random
cdef void fast_sentence_cbow_hs(
const np.uint32_t *word_point, const np.uint8_t *word_code, int codelens[MAX_SENTENCE_LEN],
REAL_t *neu1, REAL_t *syn0_vocab, REAL_t *syn0_ngrams, REAL_t *syn1, const int size,
const np.uint32_t indexes[MAX_SENTENCE_LEN], const np.uint32_t *subwords_idx[MAX_SENTENCE_LEN],
const int subwords_idx_len[MAX_SENTENCE_LEN],const REAL_t alpha, REAL_t *work,
int i, int j, int k, int cbow_mean, REAL_t *word_locks_vocab, REAL_t *word_locks_ngrams) nogil:
cdef long long a, b
cdef long long row2, sgn
cdef REAL_t f, g, count, inv_count = 1.0, f_dot, lprob
cdef int m
memset(neu1, 0, size * cython.sizeof(REAL_t))
count = <REAL_t>0.0
for m in range(j, k):
if m == i:
continue
count += ONEF
our_saxpy(&size, &ONEF, &syn0_vocab[indexes[m] * size], &ONE, neu1, &ONE)
for d in range(subwords_idx_len[m]):
count += ONEF
our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_idx[m][d] * size], &ONE, neu1, &ONE)
if count > (<REAL_t>0.5):
inv_count = ONEF / count
if cbow_mean:
sscal(&size, &inv_count, neu1, &ONE)
memset(work, 0, size * cython.sizeof(REAL_t))
for b in range(codelens[i]):
row2 = word_point[b] * size
f_dot = our_dot(&size, neu1, &ONE, &syn1[row2], &ONE)
if f_dot <= -MAX_EXP or f_dot >= MAX_EXP:
continue
f = EXP_TABLE[<int>((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]
g = (1 - word_code[b] - f) * alpha
our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE)
our_saxpy(&size, &g, neu1, &ONE, &syn1[row2], &ONE)
if not cbow_mean: # divide error over summed window vectors
sscal(&size, &inv_count, work, &ONE)
for m in range(j,k):
if m == i:
continue
our_saxpy(&size, &word_locks_vocab[indexes[m]], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE)
for d in range(subwords_idx_len[m]):
our_saxpy(&size, &word_locks_ngrams[subwords_idx[m][d]], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE)
def train_batch_sg(model, sentences, alpha, _work, _l1):
"""Update skip-gram model by training on a sequence of sentences.
Each sentence is a list of string tokens, which are looked up in the model's
vocab dictionary. Called internally from :meth:`gensim.models.fasttext.FastText.train`.
Parameters
----------
model : :class:`~gensim.models.fasttext.FastText`
Model to be trained.
sentences : iterable of list of str
Corpus streamed directly from disk/network.
alpha : float
Learning rate.
_work : np.ndarray
Private working memory for each worker.
_l1 : np.ndarray
Private working memory for each worker.
Returns
-------
int
Effective number of words trained.
"""
cdef int hs = model.hs
cdef int negative = model.negative
cdef int sample = (model.vocabulary.sample != 0)
cdef REAL_t *syn0_vocab = <REAL_t *>(np.PyArray_DATA(model.wv.vectors_vocab))
cdef REAL_t *word_locks_vocab = <REAL_t *>(np.PyArray_DATA(model.trainables.vectors_vocab_lockf))
cdef REAL_t *syn0_ngrams = <REAL_t *>(np.PyArray_DATA(model.wv.vectors_ngrams))
cdef REAL_t *word_locks_ngrams = <REAL_t *>(np.PyArray_DATA(model.trainables.vectors_ngrams_lockf))
cdef REAL_t *work
cdef REAL_t *l1
cdef REAL_t _alpha = alpha
cdef int size = model.wv.vector_size
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_idx[MAX_SENTENCE_LEN + 1]
cdef int window = model.window
cdef int i, j, k
cdef int effective_words = 0, effective_sentences = 0
cdef int sent_idx, idx_start, idx_end
# For hierarchical softmax
cdef REAL_t *syn1
cdef np.uint32_t *points[MAX_SENTENCE_LEN]
cdef np.uint8_t *codes[MAX_SENTENCE_LEN]
# For negative sampling
cdef REAL_t *syn1neg
cdef np.uint32_t *cum_table
cdef unsigned long long cum_table_len
# for sampling (negative and frequent-word downsampling)
cdef unsigned long long next_random
# For passing subwords information as C objects for nogil
cdef int subwords_idx_len[MAX_SENTENCE_LEN]
cdef np.uint32_t *subwords_idx[MAX_SENTENCE_LEN]
# dummy dictionary to ensure that the memory locations that subwords_idx point to
# are referenced throughout so that it isn't put back to free memory pool by Python's memory manager
subword_arrays = {}
if hs:
syn1 = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1))
if negative:
syn1neg = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1neg))
cum_table = <np.uint32_t *>(np.PyArray_DATA(model.vocabulary.cum_table))
cum_table_len = len(model.vocabulary.cum_table)
if negative or sample:
next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24)
# convert Python structures to primitive types, so we can release the GIL
work = <REAL_t *>np.PyArray_DATA(_work)
l1 = <REAL_t *>np.PyArray_DATA(_l1)
# prepare C structures so we can go "full C" and release the Python GIL
vlookup = model.wv.vocab
sentence_idx[0] = 0 # indices of the first sentence always start at 0
for sent in sentences:
if not sent:
continue # ignore empty sentences; leave effective_sentences unchanged
for token in sent:
word = vlookup[token] if token in vlookup else None
if word is None:
continue # leaving `effective_words` unchanged = shortening the sentence = expanding the window
if sample and word.sample_int < random_int32(&next_random):
continue
indexes[effective_words] = word.index
subwords = model.wv.buckets_word[word.index]
word_subwords = np.array((word.index,) + subwords, dtype=np.uint32)
subwords_idx_len[effective_words] = <int>(len(subwords) + 1)
subwords_idx[effective_words] = <np.uint32_t *>np.PyArray_DATA(word_subwords)
# ensures reference count of word_subwords doesn't reach 0
subword_arrays[effective_words] = word_subwords
if hs:
codelens[effective_words] = <int>len(word.code)
codes[effective_words] = <np.uint8_t *>np.PyArray_DATA(word.code)
points[effective_words] = <np.uint32_t *>np.PyArray_DATA(word.point)
effective_words += 1
if effective_words == MAX_SENTENCE_LEN:
break
# keep track of which words go into which sentence, so we don't train
# across sentence boundaries.
# indices of sentence number X are between <sentence_idx[X], sentence_idx[X])
effective_sentences += 1
sentence_idx[effective_sentences] = effective_words
if effective_words == MAX_SENTENCE_LEN:
break
# precompute "reduced window" offsets in a single randint() call
for i, item in enumerate(model.random.randint(0, window, effective_words)):
reduced_windows[i] = item
with nogil:
for sent_idx in range(effective_sentences):
idx_start = sentence_idx[sent_idx]
idx_end = sentence_idx[sent_idx + 1]
for i in range(idx_start, idx_end):
j = i - window + reduced_windows[i]
if j < idx_start:
j = idx_start
k = i + window + 1 - reduced_windows[i]
if k > idx_end:
k = idx_end
for j in range(j, k):
if j == i:
continue
if hs:
fast_sentence_sg_hs(
points[j], codes[j], codelens[j], syn0_vocab, syn0_ngrams, syn1, size,
subwords_idx[i], subwords_idx_len[i], _alpha, work, l1, word_locks_vocab,
word_locks_ngrams)
if negative:
next_random = fast_sentence_sg_neg(
negative, cum_table, cum_table_len, syn0_vocab, syn0_ngrams, syn1neg, size,
indexes[j], subwords_idx[i], subwords_idx_len[i], _alpha, work, l1,
next_random, word_locks_vocab, word_locks_ngrams)
return effective_words
def train_batch_cbow(model, sentences, alpha, _work, _neu1):
"""Update the CBOW model by training on a sequence of sentences.
Each sentence is a list of string tokens, which are looked up in the model's
vocab dictionary. Called internally from :meth:`gensim.models.fasttext.FastText.train`.
Parameters
----------
model : :class:`~gensim.models.fasttext.FastText`
Model to be trained.
sentences : iterable of list of str
Corpus streamed directly from disk/network.
alpha : float
Learning rate.
_work : np.ndarray
Private working memory for each worker.
_neu1 : np.ndarray
Private working memory for each worker.
Returns
-------
int
Effective number of words trained.
"""
cdef int hs = model.hs
cdef int negative = model.negative
cdef int sample = (model.vocabulary.sample != 0)
cdef int cbow_mean = model.cbow_mean
cdef REAL_t *syn0_vocab = <REAL_t *>(np.PyArray_DATA(model.wv.vectors_vocab))
cdef REAL_t *word_locks_vocab = <REAL_t *>(np.PyArray_DATA(model.trainables.vectors_vocab_lockf))
cdef REAL_t *syn0_ngrams = <REAL_t *>(np.PyArray_DATA(model.wv.vectors_ngrams))
cdef REAL_t *word_locks_ngrams = <REAL_t *>(np.PyArray_DATA(model.trainables.vectors_ngrams_lockf))
cdef REAL_t *work
cdef REAL_t _alpha = alpha
cdef int size = model.wv.vector_size
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_idx[MAX_SENTENCE_LEN + 1]
cdef int window = model.window
cdef int i, j, k
cdef int effective_words = 0, effective_sentences = 0
cdef int sent_idx, idx_start, idx_end
# For hierarchical softmax
cdef REAL_t *syn1
cdef np.uint32_t *points[MAX_SENTENCE_LEN]
cdef np.uint8_t *codes[MAX_SENTENCE_LEN]
# For negative sampling
cdef REAL_t *syn1neg
cdef np.uint32_t *cum_table
cdef unsigned long long cum_table_len
# for sampling (negative and frequent-word downsampling)
cdef unsigned long long next_random
# For passing subwords information as C objects for nogil
cdef int subwords_idx_len[MAX_SENTENCE_LEN]
cdef np.uint32_t *subwords_idx[MAX_SENTENCE_LEN]
# dummy dictionary to ensure that the memory locations that subwords_idx point to
# are referenced throughout so that it isn't put back to free memory pool by Python's memory manager
subword_arrays = {}
if hs:
syn1 = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1))
if negative:
syn1neg = <REAL_t *>(np.PyArray_DATA(model.trainables.syn1neg))
cum_table = <np.uint32_t *>(np.PyArray_DATA(model.vocabulary.cum_table))
cum_table_len = len(model.vocabulary.cum_table)
if negative or sample:
next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24)
# 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)
# prepare C structures so we can go "full C" and release the Python GIL
vlookup = model.wv.vocab
sentence_idx[0] = 0 # indices of the first sentence always start at 0
for sent in sentences:
if not sent:
continue # ignore empty sentences; leave effective_sentences unchanged
for token in sent:
word = vlookup[token] if token in vlookup else None
if word is None:
continue # leaving `effective_words` unchanged = shortening the sentence = expanding the window
if sample and word.sample_int < random_int32(&next_random):
continue
indexes[effective_words] = word.index
subwords = model.wv.buckets_word[word.index]
word_subwords = np.array(subwords, dtype=np.uint32)
subwords_idx_len[effective_words] = <int>len(subwords)
subwords_idx[effective_words] = <np.uint32_t *>np.PyArray_DATA(word_subwords)
# ensures reference count of word_subwords doesn't reach 0
subword_arrays[effective_words] = word_subwords
if hs:
codelens[effective_words] = <int>len(word.code)
codes[effective_words] = <np.uint8_t *>np.PyArray_DATA(word.code)
points[effective_words] = <np.uint32_t *>np.PyArray_DATA(word.point)
effective_words += 1
if effective_words == MAX_SENTENCE_LEN:
break
# keep track of which words go into which sentence, so we don't train
# across sentence boundaries.
# indices of sentence number X are between <sentence_idx[X], sentence_idx[X])
effective_sentences += 1
sentence_idx[effective_sentences] = effective_words
if effective_words == MAX_SENTENCE_LEN:
break
# precompute "reduced window" offsets in a single randint() call
for i, item in enumerate(model.random.randint(0, window, effective_words)):
reduced_windows[i] = item
# release GIL & train on all sentences
with nogil:
for sent_idx in range(effective_sentences):
idx_start = sentence_idx[sent_idx]
idx_end = sentence_idx[sent_idx + 1]
for i in range(idx_start, idx_end):
j = i - window + reduced_windows[i]
if j < idx_start:
j = idx_start
k = i + window + 1 - reduced_windows[i]
if k > idx_end:
k = idx_end
if hs:
fast_sentence_cbow_hs(
points[i], codes[i], codelens, neu1, syn0_vocab, syn0_ngrams, syn1, size,indexes,
subwords_idx,subwords_idx_len,_alpha, work, i, j, k, cbow_mean, word_locks_vocab,
word_locks_ngrams)
if negative:
next_random = fast_sentence_cbow_neg(
negative, cum_table, cum_table_len, codelens, neu1, syn0_vocab, syn0_ngrams,
syn1neg, size, indexes, subwords_idx, subwords_idx_len, _alpha, work, i, j, k,
cbow_mean, next_random, word_locks_vocab, word_locks_ngrams)
return effective_words
def init():
"""Precompute function `sigmoid(x) = 1 / (1 + exp(-x))`, for x values discretized into table EXP_TABLE.
Also calculate log(sigmoid(x)) into LOG_TABLE.
Returns
-------
{0, 1, 2}
Enumeration to signify underlying data type returned by the BLAS dot product calculation.
0 signifies double, 1 signifies double, and 2 signifies that custom cython loops were used
instead of BLAS.
"""
global our_dot
global our_saxpy
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))
LOG_TABLE[i] = <REAL_t>log( EXP_TABLE[i] )
# 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:
our_dot = our_dot_double
our_saxpy = saxpy
return 0 # double
elif abs(p_res[0] - expected) < 0.0001:
our_dot = our_dot_float
our_saxpy = saxpy
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
our_dot = our_dot_noblas
our_saxpy = our_saxpy_noblas
return 2
FAST_VERSION = init() # initialize the module
MAX_WORDS_IN_BATCH = MAX_SENTENCE_LEN
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