# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import paddle as paddle import paddle.fluid as fluid import six import numpy import sys import math import argparse EMBED_SIZE = 32 HIDDEN_SIZE = 256 N = 5 BATCH_SIZE = 100 word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) def parse_args(): parser = argparse.ArgumentParser("word2vec") parser.add_argument( '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.') parser.add_argument( '--use_gpu', type=int, default=0, help='whether to use gpu') parser.add_argument( '--num_epochs', type=int, default=100, help='number of epoch') args = parser.parse_args() return args def inference_program(words, is_sparse): embed_first = fluid.layers.embedding( input=words[0], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_second = fluid.layers.embedding( input=words[1], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_third = fluid.layers.embedding( input=words[2], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_fourth = fluid.layers.embedding( input=words[3], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') concat_embed = fluid.layers.concat( input=[embed_first, embed_second, embed_third, embed_fourth], axis=1) hidden1 = fluid.layers.fc( input=concat_embed, size=HIDDEN_SIZE, act='sigmoid') predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax') return predict_word def train_program(predict_word): # The declaration of 'next_word' must be after the invoking of inference_program, # or the data input order of train program would be [next_word, firstw, secondw, # thirdw, fourthw], which is not correct. next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') cost = fluid.layers.cross_entropy(input=predict_word, label=next_word) avg_cost = fluid.layers.mean(cost) return avg_cost def optimizer_func(): return fluid.optimizer.AdagradOptimizer( learning_rate=3e-3, regularization=fluid.regularizer.L2DecayRegularizer(8e-4)) def train(if_use_cuda, params_dirname, is_sparse=True): place = fluid.CUDAPlace(0) if if_use_cuda else fluid.CPUPlace() train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) test_reader = paddle.batch( paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE) first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64') second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64') third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64') forth_word = fluid.layers.data(name='fourthw', shape=[1], dtype='int64') next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') word_list = [first_word, second_word, third_word, forth_word, next_word] feed_order = ['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw'] main_program = fluid.default_main_program() star_program = fluid.default_startup_program() if args.enable_ce: main_program.random_seed = 90 star_program.random_seed = 90 predict_word = inference_program(word_list, is_sparse) avg_cost = train_program(predict_word) test_program = main_program.clone(for_test=True) optimizer = optimizer_func() optimizer.minimize(avg_cost) exe = fluid.Executor(place) def train_test(program, reader): count = 0 feed_var_list = [ program.global_block().var(var_name) for var_name in feed_order ] feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place) test_exe = fluid.Executor(place) accumulated = len([avg_cost]) * [0] for test_data in reader(): avg_cost_np = test_exe.run( program=program, feed=feeder_test.feed(test_data), fetch_list=[avg_cost]) accumulated = [ x[0] + x[1][0] for x in zip(accumulated, avg_cost_np) ] count += 1 return [x / count for x in accumulated] def train_loop(): step = 0 feed_var_list_loop = [ main_program.global_block().var(var_name) for var_name in feed_order ] feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place) exe.run(star_program) for pass_id in range(PASS_NUM): for data in train_reader(): avg_cost_np = exe.run( main_program, feed=feeder.feed(data), fetch_list=[avg_cost]) if step % 10 == 0: outs = train_test(test_program, test_reader) # print("Step %d: Average Cost %f" % (step, avg_cost_np[0])) print("Step %d: Average Cost %f" % (step, outs[0])) # print(outs) # it will take a few hours. # If average cost is lower than 5.8, we consider the model good enough to stop. # Note 5.8 is a relatively high value. In order to get a better model, one should # aim for avg_cost lower than 3.5. But the training could take longer time. if outs[0] < 5.8: if args.enable_ce: print("kpis\ttrain_cost\t%f" % outs[0]) if params_dirname is not None: fluid.io.save_inference_model(params_dirname, [ 'firstw', 'secondw', 'thirdw', 'fourthw' ], [predict_word], exe) return step += 1 if math.isnan(float(avg_cost_np[0])): sys.exit("got NaN loss, training failed.") raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0])) train_loop() def infer(use_cuda, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() with fluid.scope_guard(inference_scope): # Use fluid.io.load_inference_model to obtain the inference program desc, # the feed_target_names (the names of variables that will be feeded # data using feed operators), and the fetch_targets (variables that # we want to obtain data from using fetch operators). [inferencer, feed_target_names, fetch_targets] = fluid.io.load_inference_model(params_dirname, exe) # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word # is simply an index to look up for the corresponding word vector and hence # the shape of word (base_shape) should be [1]. The recursive_sequence_lengths, # which is length-based level of detail (lod) of each LoDTensor, should be [[1]] # meaning there is only one level of detail and there is only one sequence of # one word on this level. # Note that recursive_sequence_lengths should be a list of lists. data1 = numpy.asarray([[211]], dtype=numpy.int64) # 'among' data2 = numpy.asarray([[6]], dtype=numpy.int64) # 'a' data3 = numpy.asarray([[96]], dtype=numpy.int64) # 'group' data4 = numpy.asarray([[4]], dtype=numpy.int64) # 'of' lod = numpy.asarray([[1]], dtype=numpy.int64) first_word = fluid.create_lod_tensor(data1, lod, place) second_word = fluid.create_lod_tensor(data2, lod, place) third_word = fluid.create_lod_tensor(data3, lod, place) fourth_word = fluid.create_lod_tensor(data4, lod, place) assert feed_target_names[0] == 'firstw' assert feed_target_names[1] == 'secondw' assert feed_target_names[2] == 'thirdw' assert feed_target_names[3] == 'fourthw' # Construct feed as a dictionary of {feed_target_name: feed_target_data} # and results will contain a list of data corresponding to fetch_targets. results = exe.run( inferencer, feed={ feed_target_names[0]: first_word, feed_target_names[1]: second_word, feed_target_names[2]: third_word, feed_target_names[3]: fourth_word }, fetch_list=fetch_targets, return_numpy=False) print(numpy.array(results[0])) most_possible_word_index = numpy.argmax(results[0]) print(most_possible_word_index) print([ key for key, value in six.iteritems(word_dict) if value == most_possible_word_index ][0]) print(results[0].recursive_sequence_lengths()) np_data = numpy.array(results[0]) print("Inference Shape: ", np_data.shape) def main(use_cuda, is_sparse): if use_cuda and not fluid.core.is_compiled_with_cuda(): return params_dirname = "word2vec.inference.model" train( if_use_cuda=use_cuda, params_dirname=params_dirname, is_sparse=is_sparse) infer(use_cuda=use_cuda, params_dirname=params_dirname) if __name__ == '__main__': args = parse_args() PASS_NUM = args.num_epochs use_cuda = args.use_gpu # set to True if training with GPU main(use_cuda=use_cuda, is_sparse=True)