Speed up word2vec binary model loading (piskvorky#2642)

gensim/models/utils_any2vec.py

@@ -28,7 +28,7 @@
 import logging
 from gensim import utils
 
-from numpy import zeros, dtype, float32 as REAL, ascontiguousarray, fromstring
+from numpy import zeros, dtype, float32 as REAL, ascontiguousarray, frombuffer
 
 from six.moves import range
 from six import iteritems, PY2
@@ -147,7 +147,7 @@ def _save_word2vec_format(fname, vocab, vectors, fvocab=None, binary=False, tota
 
 
 def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',
-                          limit=None, datatype=REAL):
+                          limit=None, datatype=REAL, binary_chunk_size=10 * 1024):
     """Load the input-hidden weight matrix from the original C word2vec-tool format.
 
     Note that the information stored in the file is incomplete (the binary tree is missing),
@@ -176,14 +176,60 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8'
     datatype : type, optional
         (Experimental) Can coerce dimensions to a non-default float type (such as `np.float16`) to save memory.
         Such types may result in much slower bulk operations or incompatibility with optimized routines.)
+    binary_chunk_size : int, optional
+        Size of chunk in which binary files are read. Used mostly for testing. Defalut value 10 kB.
 
     Returns
     -------
     object
         Returns the loaded model as an instance of :class:`cls`.
 
     """
+
+    def __add_word_to_result(result, counts, word, weights):
+        word_id = len(result.vocab)
+        if word in result.vocab:
+            logger.warning("duplicate word '%s' in %s, ignoring all but first", word, fname)
+            return
+        if counts is None:
+            # most common scenario: no vocab file given. just make up some bogus counts, in descending order
+            result.vocab[word] = Vocab(index=word_id, count=vocab_size - word_id)
+        elif word in counts:
+            # use count from the vocab file
+            result.vocab[word] = Vocab(index=word_id, count=counts[word])
+        else:
+            # vocab file given, but word is missing -- set count to None (TODO: or raise?)
+            logger.warning("vocabulary file is incomplete: '%s' is missing", word)
+            result.vocab[word] = Vocab(index=word_id, count=None)
+        result.vectors[word_id] = weights
+        result.index2word.append(word)
+
+    def __find_space(buffer, start):
+        for i in range(start, len(buffer)):
+            if buffer[i] == 32:
+                return i
+        return -1
+
+    def __add_words_from_binary_chunk_to_result(result, counts, max_words, chunk, n_bytes_per_vector, datatype):
+        start = 0
+        n = len(chunk)
+
+        for _ in range(0, max_words):
+            i_space = __find_space(chunk, start)
+            i_vector = i_space + 1
+            if i_space != -1 and (n - i_vector) >= n_bytes_per_vector:
+                # It was reported that sometimes words start with "\n", hence the strip() call
+                word = chunk[start:i_space].decode("utf-8").strip()
+                vector = frombuffer(chunk[i_vector:i_vector + n_bytes_per_vector], dtype=REAL).astype(datatype)
+                __add_word_to_result(result, counts, word, vector)
+                start = i_vector + n_bytes_per_vector
+            else:
+                break
+
+        return chunk[start:]
+
     from gensim.models.keyedvectors import Vocab
+
     counts = None
     if fvocab is not None:
         logger.info("loading word counts from %s", fvocab)
@@ -203,42 +249,16 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8'
         result.vector_size = vector_size
         result.vectors = zeros((vocab_size, vector_size), dtype=datatype)
 
-        def add_word(word, weights):
-            word_id = len(result.vocab)
-            if word in result.vocab:
-                logger.warning("duplicate word '%s' in %s, ignoring all but first", word, fname)
-                return
-            if counts is None:
-                # most common scenario: no vocab file given. just make up some bogus counts, in descending order
-                result.vocab[word] = Vocab(index=word_id, count=vocab_size - word_id)
-            elif word in counts:
-                # use count from the vocab file
-                result.vocab[word] = Vocab(index=word_id, count=counts[word])
-            else:
-                # vocab file given, but word is missing -- set count to None (TODO: or raise?)
-                logger.warning("vocabulary file is incomplete: '%s' is missing", word)
-                result.vocab[word] = Vocab(index=word_id, count=None)
-            result.vectors[word_id] = weights
-            result.index2word.append(word)
-
         if binary:
-            binary_len = dtype(REAL).itemsize * vector_size
-            for _ in range(vocab_size):
-                # mixed text and binary: read text first, then binary
-                word = []
-                while True:
-                    ch = fin.read(1)  # Python uses I/O buffering internally
-                    if ch == b' ':
-                        break
-                    if ch == b'':
-                        raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?")
-                    if ch != b'\n':  # ignore newlines in front of words (some binary files have)
-                        word.append(ch)
-                word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors)
-                with utils.ignore_deprecation_warning():
-                    # TODO use frombuffer or something similar
-                    weights = fromstring(fin.read(binary_len), dtype=REAL).astype(datatype)
-                add_word(word, weights)
+            n_bytes_per_vector = vector_size * dtype(REAL).itemsize
+            chunk = b''
+
+            while len(result.vocab) < vocab_size:
+                new_chunk = fin.read(binary_chunk_size)
+                chunk = chunk + new_chunk
+                max_words = vocab_size - len(result.vocab)
+                chunk = __add_words_from_binary_chunk_to_result(result, counts, max_words,
+                                                                chunk, n_bytes_per_vector, datatype)
         else:
             for line_no in range(vocab_size):
                 line = fin.readline()
@@ -248,7 +268,7 @@ def add_word(word, weights):
                 if len(parts) != vector_size + 1:
                     raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no)
                 word, weights = parts[0], [datatype(x) for x in parts[1:]]
-                add_word(word, weights)
+                __add_word_to_result(result, counts, word, weights)
     if result.vectors.shape[0] != len(result.vocab):
         logger.info(
             "duplicate words detected, shrinking matrix size from %i to %i",