diff options
Diffstat (limited to 'tensorflow/models/embedding')
| -rw-r--r-- | tensorflow/models/embedding/BUILD | 74 | ||||
| -rwxr-xr-x | tensorflow/models/embedding/__init__.py | 0 | ||||
| -rw-r--r-- | tensorflow/models/embedding/word2vec.py | 503 | ||||
| -rw-r--r-- | tensorflow/models/embedding/word2vec_kernels.cc | 287 | ||||
| -rw-r--r-- | tensorflow/models/embedding/word2vec_ops.cc | 56 | ||||
| -rw-r--r-- | tensorflow/models/embedding/word2vec_optimized.py | 405 |
6 files changed, 1325 insertions, 0 deletions
diff --git a/tensorflow/models/embedding/BUILD b/tensorflow/models/embedding/BUILD new file mode 100644 index 0000000000..0fb164b05e --- /dev/null +++ b/tensorflow/models/embedding/BUILD @@ -0,0 +1,74 @@ +# Description: +# TensorFlow model for word2vec + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("/tensorflow/tensorflow", "tf_gen_op_wrapper_py") + +py_binary( + name = "word2vec", + srcs = [ + "word2vec.py", + ], + deps = [ + ":gen_word2vec", + "//tensorflow:tensorflow_py", + "//tensorflow/python:platform", + ], +) + +py_binary( + name = "word2vec_optimized", + srcs = [ + "word2vec_optimized.py", + ], + deps = [ + ":gen_word2vec", + "//tensorflow:tensorflow_py", + "//tensorflow/python:platform", + ], +) + +cc_library( + name = "word2vec_ops", + srcs = [ + "word2vec_ops.cc", + ], + visibility = ["//tensorflow:internal"], + deps = [ + "//tensorflow/core:framework", + ], + alwayslink = 1, +) + +cc_library( + name = "word2vec_kernels", + srcs = [ + "word2vec_kernels.cc", + ], + visibility = ["//tensorflow:internal"], + deps = [ + "//tensorflow/core", + ], + alwayslink = 1, +) + +tf_gen_op_wrapper_py( + name = "gen_word2vec", + out = "gen_word2vec.py", + deps = [":word2vec_ops"], +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/models/embedding/__init__.py b/tensorflow/models/embedding/__init__.py new file mode 100755 index 0000000000..e69de29bb2 --- /dev/null +++ b/tensorflow/models/embedding/__init__.py diff --git a/tensorflow/models/embedding/word2vec.py b/tensorflow/models/embedding/word2vec.py new file mode 100644 index 0000000000..4ebf3d6f27 --- /dev/null +++ b/tensorflow/models/embedding/word2vec.py @@ -0,0 +1,503 @@ +"""Multi-threaded word2vec mini-batched skip-gram model. + +Trains the model described in: +(Mikolov, et. al.) Efficient Estimation of Word Representations in Vector Space +ICLR 2013. +http://arxiv.org/abs/1301.3781 +This model does traditional minibatching. + +The key ops used are: +* placeholder for feeding in tensors for each example. +* embedding_lookup for fetching rows from the embedding matrix. +* sigmoid_cross_entropy_with_logits to calculate the loss. +* GradientDescentOptimizer for optimizing the loss. +* skipgram custom op that does input processing. +""" + +import sys +import threading +import time + +import tensorflow.python.platform + +import numpy as np +import tensorflow as tf + +from tensorflow.models.embedding import gen_word2vec as word2vec + +flags = tf.app.flags + +flags.DEFINE_string("save_path", None, "Directory to write the model and " + "training summaries.") +flags.DEFINE_string("train_data", None, "Training text file. " + "E.g., unzipped file http://mattmahoney.net/dc/text8.zip.") +flags.DEFINE_string( + "eval_data", None, "File consisting of analogies of four tokens." + "embedding 2 - embedding 1 + embedding 3 should be close " + "to embedding 4." + "E.g. https://word2vec.googlecode.com/svn/trunk/questions-words.txt.") +flags.DEFINE_integer("embedding_size", 200, "The embedding dimension size.") +flags.DEFINE_integer( + "epochs_to_train", 15, + "Number of epochs to train. Each epoch processes the training data once " + "completely.") +flags.DEFINE_float("learning_rate", 0.2, "Initial learning rate.") +flags.DEFINE_integer("num_neg_samples", 100, + "Negative samples per training example.") +flags.DEFINE_integer("batch_size", 16, + "Number of training examples processed per step " + "(size of a minibatch).") +flags.DEFINE_integer("concurrent_steps", 12, + "The number of concurrent training steps.") +flags.DEFINE_integer("window_size", 5, + "The number of words to predict to the left and right " + "of the target word.") +flags.DEFINE_integer("min_count", 5, + "The minimum number of word occurrences for it to be " + "included in the vocabulary.") +flags.DEFINE_float("subsample", 1e-3, + "Subsample threshold for word occurrence. Words that appear " + "with higher frequency will be randomly down-sampled. Set " + "to 0 to disable.") +flags.DEFINE_boolean( + "interactive", False, + "If true, enters an IPython interactive session to play with the trained " + "model. E.g., try model.analogy('france', 'paris', 'russia') and " + "model.nearby(['proton', 'elephant', 'maxwell']") +flags.DEFINE_integer("statistics_interval", 5, + "Print statistics every n seconds.") +flags.DEFINE_integer("summary_interval", 5, + "Save training summary to file every n seconds (rounded " + "up to statistics interval.") +flags.DEFINE_integer("checkpoint_interval", 600, + "Checkpoint the model (i.e. save the parameters) every n " + "seconds (rounded up to statistics interval.") + +FLAGS = flags.FLAGS + + +class Options(object): + """Options used by our word2vec model.""" + + def __init__(self): + # Model options. + + # Embedding dimension. + self.emb_dim = FLAGS.embedding_size + + # Training options. + # The training text file. + self.train_data = FLAGS.train_data + + # Number of negative samples per example. + self.num_samples = FLAGS.num_neg_samples + + # The initial learning rate. + self.learning_rate = FLAGS.learning_rate + + # Number of epochs to train. After these many epochs, the learning + # rate decays linearly to zero and the training stops. + self.epochs_to_train = FLAGS.epochs_to_train + + # Concurrent training steps. + self.concurrent_steps = FLAGS.concurrent_steps + + # Number of examples for one training step. + self.batch_size = FLAGS.batch_size + + # The number of words to predict to the left and right of the target word. + self.window_size = FLAGS.window_size + + # The minimum number of word occurrences for it to be included in the + # vocabulary. + self.min_count = FLAGS.min_count + + # Subsampling threshold for word occurrence. + self.subsample = FLAGS.subsample + + # How often to print statistics. + self.statistics_interval = FLAGS.statistics_interval + + # How often to write to the summary file (rounds up to the nearest + # statistics_interval). + self.summary_interval = FLAGS.summary_interval + + # How often to write checkpoints (rounds up to the nearest statistics + # interval). + self.checkpoint_interval = FLAGS.checkpoint_interval + + # Where to write out summaries. + self.save_path = FLAGS.save_path + + # Eval options. + # The text file for eval. + self.eval_data = FLAGS.eval_data + + +class Word2Vec(object): + """Word2Vec model (Skipgram).""" + + def __init__(self, options, session): + self._options = options + self._session = session + self._word2id = {} + self._id2word = [] + self.build_graph() + self.build_eval_graph() + self.save_vocab() + self._read_analogies() + + def _read_analogies(self): + """Reads through the analogy question file. + + Returns: + questions: a [n, 4] numpy array containing the analogy question's + word ids. + questions_skipped: questions skipped due to unknown words. + """ + questions = [] + questions_skipped = 0 + with open(self._options.eval_data) as analogy_f: + for line in analogy_f: + if line.startswith(":"): # Skip comments. + continue + words = line.strip().lower().split(" ") + ids = [self._word2id.get(w.strip()) for w in words] + if None in ids or len(ids) != 4: + questions_skipped += 1 + else: + questions.append(np.array(ids)) + print "Eval analogy file: ", self._options.eval_data + print "Questions: ", len(questions) + print "Skipped: ", questions_skipped + self._analogy_questions = np.array(questions, dtype=np.int32) + + def forward(self, examples, labels): + """Build the graph for the forward pass.""" + opts = self._options + + # Declare all variables we need. + # Embedding: [vocab_size, emb_dim] + init_width = 0.5 / opts.emb_dim + emb = tf.Variable( + tf.random_uniform( + [opts.vocab_size, opts.emb_dim], -init_width, init_width), + name="emb") + self._emb = emb + + # Softmax weight: [vocab_size, emb_dim]. Transposed. + sm_w_t = tf.Variable( + tf.zeros([opts.vocab_size, opts.emb_dim]), + name="sm_w_t") + + # Softmax bias: [emb_dim]. + sm_b = tf.Variable(tf.zeros([opts.vocab_size]), name="sm_b") + + # Global step: scalar, i.e., shape []. + self.global_step = tf.Variable(0, name="global_step") + + # Nodes to compute the nce loss w/ candidate sampling. + labels_matrix = tf.reshape( + tf.cast(labels, + dtype=tf.int64), + [opts.batch_size, 1]) + + # Negative sampling. + sampled_ids, _, _ = (tf.nn.fixed_unigram_candidate_sampler( + true_classes=labels_matrix, + num_true=1, + num_sampled=opts.num_samples, + unique=True, + range_max=opts.vocab_size, + distortion=0.75, + unigrams=opts.vocab_counts.tolist())) + + # Embeddings for examples: [batch_size, emb_dim] + example_emb = tf.nn.embedding_lookup(emb, examples) + + # Weights for labels: [batch_size, emb_dim] + true_w = tf.nn.embedding_lookup(sm_w_t, labels) + # Biases for labels: [batch_size, 1] + true_b = tf.nn.embedding_lookup(sm_b, labels) + + # Weights for sampled ids: [num_sampled, emb_dim] + sampled_w = tf.nn.embedding_lookup(sm_w_t, sampled_ids) + # Biases for sampled ids: [num_sampled, 1] + sampled_b = tf.nn.embedding_lookup(sm_b, sampled_ids) + + # True logits: [batch_size, 1] + true_logits = tf.reduce_sum(tf.mul(example_emb, true_w), 1) + true_b + + # Sampled logits: [batch_size, num_sampled] + # We replicate sampled noise lables for all examples in the batch + # using the matmul. + sampled_b_vec = tf.reshape(sampled_b, [opts.num_samples]) + sampled_logits = tf.matmul(example_emb, + sampled_w, + transpose_b=True) + sampled_b_vec + return true_logits, sampled_logits + + def nce_loss(self, true_logits, sampled_logits): + """Build the graph for the NCE loss.""" + + # cross-entropy(logits, labels) + opts = self._options + true_xent = tf.nn.sigmoid_cross_entropy_with_logits( + true_logits, tf.ones_like(true_logits)) + sampled_xent = tf.nn.sigmoid_cross_entropy_with_logits( + sampled_logits, tf.zeros_like(sampled_logits)) + + # NCE-loss is the sum of the true and noise (sampled words) + # contributions, averaged over the batch. + nce_loss_tensor = (tf.reduce_sum(true_xent) + + tf.reduce_sum(sampled_xent)) / opts.batch_size + return nce_loss_tensor + + def optimize(self, loss): + """Build the graph to optimize the loss function.""" + + # Optimizer nodes. + # Linear learning rate decay. + opts = self._options + words_to_train = float(opts.words_per_epoch * opts.epochs_to_train) + lr = opts.learning_rate * tf.maximum( + 0.0001, 1.0 - tf.cast(self._words, tf.float32) / words_to_train) + self._lr = lr + optimizer = tf.train.GradientDescentOptimizer(lr) + train = optimizer.minimize(loss, + global_step=self.global_step, + gate_gradients=optimizer.GATE_NONE) + self._train = train + + def build_eval_graph(self): + """Build the eval graph.""" + # Eval graph + + # Each analogy task is to predict the 4th word (d) given three + # words: a, b, c. E.g., a=italy, b=rome, c=france, we should + # predict d=paris. + + # The eval feeds three vectors of word ids for a, b, c, each of + # which is of size N, where N is the number of analogies we want to + # evaluate in one batch. + analogy_a = tf.placeholder(dtype=tf.int32) # [N] + analogy_b = tf.placeholder(dtype=tf.int32) # [N] + analogy_c = tf.placeholder(dtype=tf.int32) # [N] + + # Normalized word embeddings of shape [vocab_size, emb_dim]. + nemb = tf.nn.l2_normalize(self._emb, 1) + + # Each row of a_emb, b_emb, c_emb is a word's embedding vector. + # They all have the shape [N, emb_dim] + a_emb = tf.gather(nemb, analogy_a) # a's embs + b_emb = tf.gather(nemb, analogy_b) # b's embs + c_emb = tf.gather(nemb, analogy_c) # c's embs + + # We expect that d's embedding vectors on the unit hyper-sphere is + # near: c_emb + (b_emb - a_emb), which has the shape [N, emb_dim]. + target = c_emb + (b_emb - a_emb) + + # Compute cosine distance between each pair of target and vocab. + # dist has shape [N, vocab_size]. + dist = tf.matmul(target, nemb, transpose_b=True) + + # For each question (row in dist), find the top 4 words. + _, pred_idx = tf.nn.top_k(dist, 4) + + # Nodes for computing neighbors for a given word according to + # their cosine distance. + nearby_word = tf.placeholder(dtype=tf.int32) # word id + nearby_emb = tf.gather(nemb, nearby_word) + nearby_dist = tf.matmul(nearby_emb, nemb, transpose_b=True) + nearby_val, nearby_idx = tf.nn.top_k(nearby_dist, + min(1000, self._options.vocab_size)) + + # Nodes in the construct graph which are used by training and + # evaluation to run/feed/fetch. + self._analogy_a = analogy_a + self._analogy_b = analogy_b + self._analogy_c = analogy_c + self._analogy_pred_idx = pred_idx + self._nearby_word = nearby_word + self._nearby_val = nearby_val + self._nearby_idx = nearby_idx + + def build_graph(self): + """Build the graph for the full model.""" + opts = self._options + # The training data. A text file. + (words, counts, words_per_epoch, self._epoch, self._words, examples, + labels) = word2vec.skipgram(filename=opts.train_data, + batch_size=opts.batch_size, + window_size=opts.window_size, + min_count=opts.min_count, + subsample=opts.subsample) + (opts.vocab_words, opts.vocab_counts, + opts.words_per_epoch) = self._session.run([words, counts, words_per_epoch]) + opts.vocab_size = len(opts.vocab_words) + print "Data file: ", opts.train_data + print "Vocab size: ", opts.vocab_size - 1, " + UNK" + print "Words per epoch: ", opts.words_per_epoch + self._examples = examples + self._labels = labels + self._id2word = opts.vocab_words + for i, w in enumerate(self._id2word): + self._word2id[w] = i + true_logits, sampled_logits = self.forward(examples, labels) + loss = self.nce_loss(true_logits, sampled_logits) + tf.scalar_summary("NCE loss", loss) + self._loss = loss + self.optimize(loss) + + # Properly initialize all variables. + tf.initialize_all_variables().run() + + self.saver = tf.train.Saver() + + def save_vocab(self): + """Save the vocabulary to a file so the model can be reloaded.""" + opts = self._options + with open(opts.save_path + "/vocab.txt", "w") as f: + for i in xrange(opts.vocab_size): + f.write(opts.vocab_words[i] + " " + str(opts.vocab_counts[i]) + "\n") + + def _train_thread_body(self): + initial_epoch, = self._session.run([self._epoch]) + while True: + _, epoch = self._session.run([self._train, self._epoch]) + if epoch != initial_epoch: + break + + def train(self): + """Train the model.""" + opts = self._options + + initial_epoch, initial_words = self._session.run([self._epoch, self._words]) + + summary_op = tf.merge_all_summaries() + summary_writer = tf.train.SummaryWriter(opts.save_path, + graph_def=self._session.graph_def) + workers = [] + for _ in xrange(opts.concurrent_steps): + t = threading.Thread(target=self._train_thread_body) + t.start() + workers.append(t) + + last_words, last_time, last_summary_time = initial_words, time.time(), 0 + last_checkpoint_time = 0 + while True: + time.sleep(opts.statistics_interval) # Reports our progress once a while. + (epoch, step, loss, words, lr) = self._session.run( + [self._epoch, self.global_step, self._loss, self._words, self._lr]) + now = time.time() + last_words, last_time, rate = words, now, (words - last_words) / ( + now - last_time) + print("Epoch %4d Step %8d: lr = %5.3f loss = %6.2f words/sec = %8.0f\r" % + (epoch, step, lr, loss, rate)), + sys.stdout.flush() + if now - last_summary_time > opts.summary_interval: + summary_str = self._session.run(summary_op) + summary_writer.add_summary(summary_str, step) + last_summary_time = now + if now - last_checkpoint_time > opts.checkpoint_interval: + self.saver.save(self._session, + opts.save_path + "model", + global_step=step) + last_checkpoint_time = now + if epoch != initial_epoch: + break + + for t in workers: + t.join() + + return epoch + + def _predict(self, analogy): + """Predict the top 4 answers for analogy questions.""" + idx, = self._session.run([self._analogy_pred_idx], { + self._analogy_a: analogy[:, 0], + self._analogy_b: analogy[:, 1], + self._analogy_c: analogy[:, 2] + }) + return idx + + def eval(self): + """Evaluate analogy questions and reports accuracy.""" + + # How many questions we get right at precision@1. + correct = 0 + + total = self._analogy_questions.shape[0] + start = 0 + while start < total: + limit = start + 2500 + sub = self._analogy_questions[start:limit, :] + idx = self._predict(sub) + start = limit + for question in xrange(sub.shape[0]): + for j in xrange(4): + if idx[question, j] == sub[question, 3]: + # Bingo! We predicted correctly. E.g., [italy, rome, france, paris]. + correct += 1 + break + elif idx[question, j] in sub[question, :3]: + # We need to skip words already in the question. + continue + else: + # The correct label is not the precision@1 + break + print + print "Eval %4d/%d accuracy = %4.1f%%" % (correct, total, + correct * 100.0 / total) + + def analogy(self, w0, w1, w2): + """Predict word w3 as in w0:w1 vs w2:w3.""" + wid = np.array([[self._word2id.get(w, 0) for w in [w0, w1, w2]]]) + idx = self._predict(wid) + for c in [self._id2word[i] for i in idx[0, :]]: + if c not in [w0, w1, w2]: + return c + return "unknown" + + def nearby(self, words, num=20): + """Prints out nearby words given a list of words.""" + ids = np.array([self._word2id.get(x, 0) for x in words]) + vals, idx = self._session.run( + [self._nearby_val, self._nearby_idx], {self._nearby_word: ids}) + for i in xrange(len(words)): + print "\n%s\n=====================================" % (words[i]) + for (neighbor, distance) in zip(idx[i, :num], vals[i, :num]): + print "%-20s %6.4f" % (self._id2word[neighbor], distance) + + +def _start_shell(local_ns=None): + # An interactive shell is useful for debugging/development. + import IPython + user_ns = {} + if local_ns: + user_ns.update(local_ns) + user_ns.update(globals()) + IPython.start_ipython(argv=[], user_ns=user_ns) + + +def main(_): + """Train a word2vec model.""" + opts = Options() + with tf.Graph().as_default(), tf.Session() as session: + model = Word2Vec(opts, session) + for _ in xrange(opts.epochs_to_train): + model.train() # Process one epoch + model.eval() # Eval analogies. + # Perform a final save. + model.saver.save(session, + opts.save_path + "model", + global_step=model.global_step) + if FLAGS.interactive: + # E.g., + # [0]: model.analogy('france', 'paris', 'russia') + # [1]: model.nearby(['proton', 'elephant', 'maxwell']) + _start_shell(locals()) + + +if __name__ == "__main__": + tf.app.run() diff --git a/tensorflow/models/embedding/word2vec_kernels.cc b/tensorflow/models/embedding/word2vec_kernels.cc new file mode 100644 index 0000000000..f68139fc91 --- /dev/null +++ b/tensorflow/models/embedding/word2vec_kernels.cc @@ -0,0 +1,287 @@ +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/lib/gtl/map_util.h" +#include "tensorflow/core/lib/random/distribution_sampler.h" +#include "tensorflow/core/lib/random/philox_random.h" +#include "tensorflow/core/lib/random/simple_philox.h" +#include "tensorflow/core/platform/regexp.h" +#include "tensorflow/core/platform/thread_annotations.h" +#include "tensorflow/core/util/guarded_philox_random.h" + +namespace tensorflow { + +class SkipgramOp : public OpKernel { + public: + explicit SkipgramOp(OpKernelConstruction* ctx) + : OpKernel(ctx), rng_(&philox_) { + string filename; + OP_REQUIRES_OK(ctx, ctx->GetAttr("filename", &filename)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("batch_size", &batch_size_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("window_size", &window_size_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("min_count", &min_count_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("subsample", &subsample_)); + OP_REQUIRES_OK(ctx, Init(ctx->env(), filename)); + + mutex_lock l(mu_); + example_pos_ = corpus_size_; + label_pos_ = corpus_size_; + label_limit_ = corpus_size_; + } + + void Compute(OpKernelContext* ctx) override { + Tensor words_per_epoch(DT_INT64, TensorShape({})); + Tensor current_epoch(DT_INT32, TensorShape({})); + Tensor total_words_processed(DT_INT64, TensorShape({})); + Tensor examples(DT_INT32, TensorShape({batch_size_})); + auto Texamples = examples.flat<int32>(); + Tensor labels(DT_INT32, TensorShape({batch_size_})); + auto Tlabels = labels.flat<int32>(); + { + mutex_lock l(mu_); + for (int i = 0; i < batch_size_; ++i) { + NextExample(&Texamples(i), &Tlabels(i)); + } + words_per_epoch.scalar<int64>()() = corpus_size_; + current_epoch.scalar<int32>()() = current_epoch_; + total_words_processed.scalar<int64>()() = total_words_processed_; + } + ctx->set_output(0, word_); + ctx->set_output(1, freq_); + ctx->set_output(2, words_per_epoch); + ctx->set_output(3, current_epoch); + ctx->set_output(4, total_words_processed); + ctx->set_output(5, examples); + ctx->set_output(6, labels); + } + + private: + int32 batch_size_ = 0; + int32 window_size_ = 5; + float subsample_ = 1e-3; + int min_count_ = 5; + int32 vocab_size_ = 0; + Tensor word_; + Tensor freq_; + int32 corpus_size_ = 0; + std::vector<int32> corpus_; + + mutex mu_; + random::PhiloxRandom philox_ GUARDED_BY(mu_); + random::SimplePhilox rng_ GUARDED_BY(mu_); + int32 current_epoch_ GUARDED_BY(mu_) = -1; + int64 total_words_processed_ GUARDED_BY(mu_) = 0; + int32 example_pos_ GUARDED_BY(mu_); + int32 label_pos_ GUARDED_BY(mu_); + int32 label_limit_ GUARDED_BY(mu_); + + // {example_pos_, label_pos_} is the cursor for the next example. + // example_pos_ wrapps around at the end of corpus_. For each + // example, we randomly generate [label_pos_, label_limit) for + // labels. + void NextExample(int32* example, int32* label) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + while (true) { + if (label_pos_ >= label_limit_) { + if (example_pos_ + 1 >= corpus_size_) { + ++current_epoch_; + example_pos_ = 0; + } else { + ++example_pos_; + } + ++total_words_processed_; + int32 word_freq = freq_.flat<int32>()(corpus_[example_pos_]); + if (subsample_ > 0) { + // See Eq. 5 in http://arxiv.org/abs/1310.4546 + float keep_prob = + (std::sqrt(word_freq / (subsample_ * corpus_size_)) + 1) * + (subsample_ * corpus_size_) / word_freq; + if (rng_.RandFloat() > keep_prob) continue; + } + const int32 skip = 1 + rng_.Uniform(window_size_); + label_pos_ = std::max<int32>(0, example_pos_ - skip); + label_limit_ = std::min<int32>(corpus_size_, example_pos_ + skip + 1); + } + if (example_pos_ != label_pos_) { + break; + } + ++label_pos_; + } + *example = corpus_[example_pos_]; + *label = corpus_[label_pos_++]; + } + + Status Init(Env* env, const string& filename) { + string data; + TF_RETURN_IF_ERROR(ReadFileToString(env, filename, &data)); + RE2 kWord("\\s*(\\S+)"); + auto input = ToRegexpStringPiece(data); + string w; + corpus_size_ = 0; + std::unordered_map<string, int32> word_freq; + while (RE2::Consume(&input, kWord, &w)) { + ++(word_freq[w]); + ++corpus_size_; + } + if (corpus_size_ < window_size_ * 10) { + return errors::InvalidArgument("The text file ", filename, + " contains too little data: ", + corpus_size_, " words"); + } + typedef std::pair<string, int32> WordFreq; + std::vector<WordFreq> ordered; + for (const auto& p : word_freq) { + if (p.second >= min_count_) ordered.push_back(p); + } + LOG(INFO) << "Data file: " << filename << " contains " << data.size() + << " bytes, " << corpus_size_ << " words, " << word_freq.size() + << " unique words, " << ordered.size() + << " unique frequent words."; + word_freq.clear(); + std::sort(ordered.begin(), ordered.end(), + [](const WordFreq& x, const WordFreq& y) { + return x.second > y.second; + }); + vocab_size_ = static_cast<int32>(1 + ordered.size()); + Tensor word(DT_STRING, TensorShape({vocab_size_})); + Tensor freq(DT_INT32, TensorShape({vocab_size_})); + word.flat<string>()(0) = "UNK"; + static const int32 kUnkId = 0; + std::unordered_map<string, int32> word_id; + int64 total_counted = 0; + for (std::size_t i = 0; i < ordered.size(); ++i) { + const auto& w = ordered[i].first; + auto id = i + 1; + word.flat<string>()(id) = w; + auto word_count = ordered[i].second; + freq.flat<int32>()(id) = word_count; + total_counted += word_count; + word_id[w] = id; + } + freq.flat<int32>()(kUnkId) = corpus_size_ - total_counted; + word_ = word; + freq_ = freq; + corpus_.reserve(corpus_size_); + input = ToRegexpStringPiece(data); + while (RE2::Consume(&input, kWord, &w)) { + corpus_.push_back(gtl::FindWithDefault(word_id, w, kUnkId)); + } + return Status::OK(); + } +}; + +REGISTER_KERNEL_BUILDER(Name("Skipgram").Device(DEVICE_CPU), SkipgramOp); + +class NegTrainOp : public OpKernel { + public: + explicit NegTrainOp(OpKernelConstruction* ctx) : OpKernel(ctx) { + base_.Init(0, 0); + + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_negative_samples", &num_samples_)); + + std::vector<int32> vocab_count; + OP_REQUIRES_OK(ctx, ctx->GetAttr("vocab_count", &vocab_count)); + + std::vector<float> vocab_weights; + vocab_weights.reserve(vocab_count.size()); + for (const auto& f : vocab_count) { + float r = std::pow(static_cast<float>(f), 0.75f); + vocab_weights.push_back(r); + } + sampler_ = new random::DistributionSampler(vocab_weights); + } + + ~NegTrainOp() { delete sampler_; } + + void Compute(OpKernelContext* ctx) override { + Tensor w_in = ctx->mutable_input(0, false); + OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(w_in.shape()), + errors::InvalidArgument("Must be a matrix")); + Tensor w_out = ctx->mutable_input(1, false); + OP_REQUIRES(ctx, w_in.shape() == w_out.shape(), + errors::InvalidArgument("w_in.shape == w_out.shape")); + const Tensor& examples = ctx->input(2); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(examples.shape()), + errors::InvalidArgument("Must be a vector")); + const Tensor& labels = ctx->input(3); + OP_REQUIRES(ctx, examples.shape() == labels.shape(), + errors::InvalidArgument("examples.shape == labels.shape")); + const Tensor& learning_rate = ctx->input(4); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(learning_rate.shape()), + errors::InvalidArgument("Must be a scalar")); + + auto Tw_in = w_in.matrix<float>(); + auto Tw_out = w_out.matrix<float>(); + auto Texamples = examples.flat<int32>(); + auto Tlabels = labels.flat<int32>(); + auto lr = learning_rate.scalar<float>()(); + const int64 vocab_size = w_in.dim_size(0); + const int64 dims = w_in.dim_size(1); + const int64 batch_size = examples.dim_size(0); + OP_REQUIRES(ctx, vocab_size == sampler_->num(), + errors::InvalidArgument("vocab_size mismatches: ", vocab_size, + " vs. ", sampler_->num())); + + // Gradient accumulator for v_in. + Tensor buf(DT_FLOAT, TensorShape({dims})); + auto Tbuf = buf.flat<float>(); + + // Scalar buffer to hold sigmoid(+/- dot). + Tensor g_buf(DT_FLOAT, TensorShape({})); + auto g = g_buf.scalar<float>(); + + // The following loop needs 2 random 32-bit values per negative + // sample. We reserve 8 values per sample just in case the + // underlying implementation changes. + auto rnd = base_.ReserveSamples32(batch_size * num_samples_ * 8); + random::SimplePhilox srnd(&rnd); + + for (int64 i = 0; i < batch_size; ++i) { + const int32 example = Texamples(i); + DCHECK(0 <= example && example < vocab_size) << example; + const int32 label = Tlabels(i); + DCHECK(0 <= label && label < vocab_size) << label; + auto v_in = Tw_in.chip<0>(example); + + // Positive: example predicts label. + // forward: x = v_in' * v_out + // l = log(sigmoid(x)) + // backward: dl/dx = g = sigmoid(-x) + // dl/d(v_in) = g * v_out' + // dl/d(v_out) = v_in' * g + { + auto v_out = Tw_out.chip<0>(label); + auto dot = (v_in * v_out).sum(); + g = (dot.exp() + 1.f).inverse(); + Tbuf = v_out * (g() * lr); + v_out += v_in * (g() * lr); + } + + // Negative samples: + // forward: x = v_in' * v_sample + // l = log(sigmoid(-x)) + // backward: dl/dx = g = -sigmoid(x) + // dl/d(v_in) = g * v_out' + // dl/d(v_out) = v_in' * g + for (int j = 0; j < num_samples_; ++j) { + const int sample = sampler_->Sample(&srnd); + if (sample == label) continue; // Skip. + auto v_sample = Tw_out.chip<0>(sample); + auto dot = (v_in * v_sample).sum(); + g = -((-dot).exp() + 1.f).inverse(); + Tbuf += v_sample * (g() * lr); + v_sample += v_in * (g() * lr); + } + + // Applies the gradient on v_in. + v_in += Tbuf; + } + } + + private: + int32 num_samples_ = 0; + random::DistributionSampler* sampler_ = nullptr; + GuardedPhiloxRandom base_; +}; + +REGISTER_KERNEL_BUILDER(Name("NegTrain").Device(DEVICE_CPU), NegTrainOp); + +} // end namespace tensorflow diff --git a/tensorflow/models/embedding/word2vec_ops.cc b/tensorflow/models/embedding/word2vec_ops.cc new file mode 100644 index 0000000000..abe03baaf4 --- /dev/null +++ b/tensorflow/models/embedding/word2vec_ops.cc @@ -0,0 +1,56 @@ +#include "tensorflow/core/framework/op.h" + +namespace tensorflow { + +REGISTER_OP("Skipgram") + .Output("vocab_word: string") + .Output("vocab_freq: int32") + .Output("words_per_epoch: int64") + .Output("current_epoch: int32") + .Output("total_words_processed: int64") + .Output("examples: int32") + .Output("labels: int32") + .Attr("filename: string") + .Attr("batch_size: int") + .Attr("window_size: int = 5") + .Attr("min_count: int = 5") + .Attr("subsample: float = 1e-3") + .Doc(R"doc( +Parses a text file and creates a batch of examples. + +vocab_word: A vector of words in the corpus. +vocab_freq: Frequencies of words. Sorted in the non-ascending order. +words_per_epoch: Number of words per epoch in the data file. +current_epoch: The current epoch number. +total_words_processed: The total number of words processed so far. +examples: A vector of word ids. +labels: A vector of word ids. +filename: The corpus's text file name. +batch_size: The size of produced batch. +window_size: The number of words to predict to the left and right of the target. +min_count: The minimum number of word occurrences for it to be included in the + vocabulary. +subsample: Threshold for word occurrence. Words that appear with higher + frequency will be randomly down-sampled. Set to 0 to disable. +)doc"); + +REGISTER_OP("NegTrain") + .Input("w_in: Ref(float)") + .Input("w_out: Ref(float)") + .Input("examples: int32") + .Input("labels: int32") + .Input("lr: float") + .Attr("vocab_count: list(int)") + .Attr("num_negative_samples: int") + .Doc(R"doc( +Training via negative sampling. + +w_in: input word embedding. +w_out: output word embedding. +examples: A vector of word ids. +labels: A vector of word ids. +vocab_count: Count of words in the vocabulary. +num_negative_samples: Number of negative samples per exaple. +)doc"); + +} // end namespace tensorflow diff --git a/tensorflow/models/embedding/word2vec_optimized.py b/tensorflow/models/embedding/word2vec_optimized.py new file mode 100644 index 0000000000..23e7645a0b --- /dev/null +++ b/tensorflow/models/embedding/word2vec_optimized.py @@ -0,0 +1,405 @@ +"""Multi-threaded word2vec unbatched skip-gram model. + +Trains the model described in: +(Mikolov, et. al.) Efficient Estimation of Word Representations in Vector Space +ICLR 2013. +http://arxiv.org/abs/1301.3781 +This model does true SGD (i.e. no minibatching). To do this efficiently, custom +ops are used to sequentially process data within a 'batch'. + +The key ops used are: +* skipgram custom op that does input processing. +* neg_train custom op that efficiently calculates and applies the gradient using + true SGD. +""" + +import sys +import threading +import time + +import tensorflow.python.platform + +import numpy as np +import tensorflow as tf + +from tensorflow.models.embedding import gen_word2vec as word2vec + +flags = tf.app.flags + +flags.DEFINE_string("save_path", None, "Directory to write the model.") +flags.DEFINE_string( + "train_data", None, + "Training data. E.g., unzipped file http://mattmahoney.net/dc/text8.zip.") +flags.DEFINE_string( + "eval_data", None, "Analogy questions. " + "https://word2vec.googlecode.com/svn/trunk/questions-words.txt.") +flags.DEFINE_integer("embedding_size", 200, "The embedding dimension size.") +flags.DEFINE_integer( + "epochs_to_train", 15, + "Number of epochs to train. Each epoch processes the training data once " + "completely.") +flags.DEFINE_float("learning_rate", 0.025, "Initial learning rate.") +flags.DEFINE_integer("num_neg_samples", 25, + "Negative samples per training example.") +flags.DEFINE_integer("batch_size", 500, + "Numbers of training examples each step processes " + "(no minibatching).") +flags.DEFINE_integer("concurrent_steps", 12, + "The number of concurrent training steps.") +flags.DEFINE_integer("window_size", 5, + "The number of words to predict to the left and right " + "of the target word.") +flags.DEFINE_integer("min_count", 5, + "The minimum number of word occurrences for it to be " + "included in the vocabulary.") +flags.DEFINE_float("subsample", 1e-3, + "Subsample threshold for word occurrence. Words that appear " + "with higher frequency will be randomly down-sampled. Set " + "to 0 to disable.") +flags.DEFINE_boolean( + "interactive", False, + "If true, enters an IPython interactive session to play with the trained " + "model. E.g., try model.analogy('france', 'paris', 'russia') and " + "model.nearby(['proton', 'elephant', 'maxwell']") + +FLAGS = flags.FLAGS + + +class Options(object): + """Options used by our word2vec model.""" + + def __init__(self): + # Model options. + + # Embedding dimension. + self.emb_dim = FLAGS.embedding_size + + # Training options. + + # The training text file. + self.train_data = FLAGS.train_data + + # Number of negative samples per example. + self.num_samples = FLAGS.num_neg_samples + + # The initial learning rate. + self.learning_rate = FLAGS.learning_rate + + # Number of epochs to train. After these many epochs, the learning + # rate decays linearly to zero and the training stops. + self.epochs_to_train = FLAGS.epochs_to_train + + # Concurrent training steps. + self.concurrent_steps = FLAGS.concurrent_steps + + # Number of examples for one training step. + self.batch_size = FLAGS.batch_size + + # The number of words to predict to the left and right of the target word. + self.window_size = FLAGS.window_size + + # The minimum number of word occurrences for it to be included in the + # vocabulary. + self.min_count = FLAGS.min_count + + # Subsampling threshold for word occurrence. + self.subsample = FLAGS.subsample + + # Where to write out summaries. + self.save_path = FLAGS.save_path + + # Eval options. + + # The text file for eval. + self.eval_data = FLAGS.eval_data + + +class Word2Vec(object): + """Word2Vec model (Skipgram).""" + + def __init__(self, options, session): + self._options = options + self._session = session + self._word2id = {} + self._id2word = [] + self.build_graph() + self.build_eval_graph() + self.save_vocab() + self._read_analogies() + + def _read_analogies(self): + """Reads through the analogy question file. + + Returns: + questions: a [n, 4] numpy array containing the analogy question's + word ids. + questions_skipped: questions skipped due to unknown words. + """ + questions = [] + questions_skipped = 0 + with open(self._options.eval_data) as analogy_f: + for line in analogy_f: + if line.startswith(":"): # Skip comments. + continue + words = line.strip().lower().split(" ") + ids = [self._word2id.get(w.strip()) for w in words] + if None in ids or len(ids) != 4: + questions_skipped += 1 + else: + questions.append(np.array(ids)) + print "Eval analogy file: ", self._options.eval_data + print "Questions: ", len(questions) + print "Skipped: ", questions_skipped + self._analogy_questions = np.array(questions, dtype=np.int32) + + def build_graph(self): + """Build the model graph.""" + opts = self._options + + # The training data. A text file. + (words, counts, words_per_epoch, current_epoch, total_words_processed, + examples, labels) = word2vec.skipgram(filename=opts.train_data, + batch_size=opts.batch_size, + window_size=opts.window_size, + min_count=opts.min_count, + subsample=opts.subsample) + (opts.vocab_words, opts.vocab_counts, + opts.words_per_epoch) = self._session.run([words, counts, words_per_epoch]) + opts.vocab_size = len(opts.vocab_words) + print "Data file: ", opts.train_data + print "Vocab size: ", opts.vocab_size - 1, " + UNK" + print "Words per epoch: ", opts.words_per_epoch + + self._id2word = opts.vocab_words + for i, w in enumerate(self._id2word): + self._word2id[w] = i + + # Declare all variables we need. + # Input words embedding: [vocab_size, emb_dim] + w_in = tf.Variable( + tf.random_uniform( + [opts.vocab_size, + opts.emb_dim], -0.5 / opts.emb_dim, 0.5 / opts.emb_dim), + name="w_in") + + # Global step: scalar, i.e., shape []. + w_out = tf.Variable(tf.zeros([opts.vocab_size, opts.emb_dim]), name="w_out") + + # Global step: [] + global_step = tf.Variable(0, name="global_step") + + # Linear learning rate decay. + words_to_train = float(opts.words_per_epoch * opts.epochs_to_train) + lr = opts.learning_rate * tf.maximum( + 0.0001, + 1.0 - tf.cast(total_words_processed, tf.float32) / words_to_train) + + # Training nodes. + inc = global_step.assign_add(1) + with tf.control_dependencies([inc]): + train = word2vec.neg_train(w_in, + w_out, + examples, + labels, + lr, + vocab_count=opts.vocab_counts.tolist(), + num_negative_samples=opts.num_samples) + + self._w_in = w_in + self._examples = examples + self._labels = labels + self._lr = lr + self._train = train + self.step = global_step + self._epoch = current_epoch + self._words = total_words_processed + + def save_vocab(self): + """Save the vocabulary to a file so the model can be reloaded.""" + opts = self._options + with open(opts.save_path + "/vocab.txt", "w") as f: + for i in xrange(opts.vocab_size): + f.write(opts.vocab_words[i] + " " + str(opts.vocab_counts[i]) + "\n") + + def build_eval_graph(self): + """Build the evaluation graph.""" + # Eval graph + opts = self._options + + # Each analogy task is to predict the 4th word (d) given three + # words: a, b, c. E.g., a=italy, b=rome, c=france, we should + # predict d=paris. + + # The eval feeds three vectors of word ids for a, b, c, each of + # which is of size N, where N is the number of analogies we want to + # evaluate in one batch. + analogy_a = tf.placeholder(dtype=tf.int32) # [N] + analogy_b = tf.placeholder(dtype=tf.int32) # [N] + analogy_c = tf.placeholder(dtype=tf.int32) # [N] + + # Normalized word embeddings of shape [vocab_size, emb_dim]. + nemb = tf.nn.l2_normalize(self._w_in, 1) + + # Each row of a_emb, b_emb, c_emb is a word's embedding vector. + # They all have the shape [N, emb_dim] + a_emb = tf.gather(nemb, analogy_a) # a's embs + b_emb = tf.gather(nemb, analogy_b) # b's embs + c_emb = tf.gather(nemb, analogy_c) # c's embs + + # We expect that d's embedding vectors on the unit hyper-sphere is + # near: c_emb + (b_emb - a_emb), which has the shape [N, emb_dim]. + target = c_emb + (b_emb - a_emb) + + # Compute cosine distance between each pair of target and vocab. + # dist has shape [N, vocab_size]. + dist = tf.matmul(target, nemb, transpose_b=True) + + # For each question (row in dist), find the top 4 words. + _, pred_idx = tf.nn.top_k(dist, 4) + + # Nodes for computing neighbors for a given word according to + # their cosine distance. + nearby_word = tf.placeholder(dtype=tf.int32) # word id + nearby_emb = tf.gather(nemb, nearby_word) + nearby_dist = tf.matmul(nearby_emb, nemb, transpose_b=True) + nearby_val, nearby_idx = tf.nn.top_k(nearby_dist, + min(1000, opts.vocab_size)) + + # Nodes in the construct graph which are used by training and + # evaluation to run/feed/fetch. + self._analogy_a = analogy_a + self._analogy_b = analogy_b + self._analogy_c = analogy_c + self._analogy_pred_idx = pred_idx + self._nearby_word = nearby_word + self._nearby_val = nearby_val + self._nearby_idx = nearby_idx + + # Properly initialize all variables. + tf.initialize_all_variables().run() + + self.saver = tf.train.Saver() + + def _train_thread_body(self): + initial_epoch, = self._session.run([self._epoch]) + while True: + _, epoch = self._session.run([self._train, self._epoch]) + if epoch != initial_epoch: + break + + def train(self): + """Train the model.""" + opts = self._options + + initial_epoch, initial_words = self._session.run([self._epoch, self._words]) + + workers = [] + for _ in xrange(opts.concurrent_steps): + t = threading.Thread(target=self._train_thread_body) + t.start() + workers.append(t) + + last_words, last_time = initial_words, time.time() + while True: + time.sleep(5) # Reports our progress once a while. + (epoch, step, words, + lr) = self._session.run([self._epoch, self.step, self._words, self._lr]) + now = time.time() + last_words, last_time, rate = words, now, (words - last_words) / ( + now - last_time) + print "Epoch %4d Step %8d: lr = %5.3f words/sec = %8.0f\r" % (epoch, step, + lr, rate), + sys.stdout.flush() + if epoch != initial_epoch: + break + + for t in workers: + t.join() + + def _predict(self, analogy): + """Predict the top 4 answers for analogy questions.""" + idx, = self._session.run([self._analogy_pred_idx], { + self._analogy_a: analogy[:, 0], + self._analogy_b: analogy[:, 1], + self._analogy_c: analogy[:, 2] + }) + return idx + + def eval(self): + """Evaluate analogy questions and reports accuracy.""" + + # How many questions we get right at precision@1. + correct = 0 + + total = self._analogy_questions.shape[0] + start = 0 + while start < total: + limit = start + 2500 + sub = self._analogy_questions[start:limit, :] + idx = self._predict(sub) + start = limit + for question in xrange(sub.shape[0]): + for j in xrange(4): + if idx[question, j] == sub[question, 3]: + # Bingo! We predicted correctly. E.g., [italy, rome, france, paris]. + correct += 1 + break + elif idx[question, j] in sub[question, :3]: + # We need to skip words already in the question. + continue + else: + # The correct label is not the precision@1 + break + print + print "Eval %4d/%d accuracy = %4.1f%%" % (correct, total, + correct * 100.0 / total) + + def analogy(self, w0, w1, w2): + """Predict word w3 as in w0:w1 vs w2:w3.""" + wid = np.array([[self._word2id.get(w, 0) for w in [w0, w1, w2]]]) + idx = self._predict(wid) + for c in [self._id2word[i] for i in idx[0, :]]: + if c not in [w0, w1, w2]: + return c + return "unknown" + + def nearby(self, words, num=20): + """Prints out nearby words given a list of words.""" + ids = np.array([self._word2id.get(x, 0) for x in words]) + vals, idx = self._session.run( + [self._nearby_val, self._nearby_idx], {self._nearby_word: ids}) + for i in xrange(len(words)): + print "\n%s\n=====================================" % (words[i]) + for (neighbor, distance) in zip(idx[i, :num], vals[i, :num]): + print "%-20s %6.4f" % (self._id2word[neighbor], distance) + + +def _start_shell(local_ns=None): + # An interactive shell is useful for debugging/development. + import IPython + user_ns = {} + if local_ns: + user_ns.update(local_ns) + user_ns.update(globals()) + IPython.start_ipython(argv=[], user_ns=user_ns) + + +def main(_): + """Train a word2vec model.""" + opts = Options() + with tf.Graph().as_default(), tf.Session() as session: + model = Word2Vec(opts, session) + for _ in xrange(opts.epochs_to_train): + model.train() # Process one epoch + model.eval() # Eval analogies. + # Perform a final save. + model.saver.save(session, opts.save_path + "model", global_step=model.step) + if FLAGS.interactive: + # E.g., + # [0]: model.Analogy('france', 'paris', 'russia') + # [1]: model.Nearby(['proton', 'elephant', 'maxwell']) + _start_shell(locals()) + + +if __name__ == "__main__": + tf.app.run() |