# 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 import paddle.fluid as fluid from paddle.fluid.layers.device import get_places import unittest import os import numpy as np import math import sys def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True): PASS_NUM = 100 EMBED_SIZE = 32 HIDDEN_SIZE = 256 N = 5 BATCH_SIZE = 32 IS_SPARSE = is_sparse def __network__(words): 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_forth = 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_forth], 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') cost = fluid.layers.cross_entropy(input=predict_word, label=words[4]) avg_cost = fluid.layers.mean(cost) return avg_cost, predict_word word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) 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='forthw', shape=[1], dtype='int64') next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') if not is_parallel: avg_cost, predict_word = __network__( [first_word, second_word, third_word, forth_word, next_word]) else: raise NotImplementedError() sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_cost) train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) feeder = fluid.DataFeeder( feed_list=[first_word, second_word, third_word, forth_word, next_word], place=place) def train_loop(main_program): exe.run(fluid.default_startup_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 avg_cost_np[0] < 5.0: if save_dirname is not None: fluid.io.save_inference_model(save_dirname, [ 'firstw', 'secondw', 'thirdw', 'forthw' ], [predict_word], exe) return 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])) if is_local: train_loop(fluid.default_main_program()) else: port = os.getenv("PADDLE_PSERVER_PORT", "6174") pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip... eplist = [] for ip in pserver_ips.split(","): eplist.append(':'.join([ip, port])) pserver_endpoints = ",".join(eplist) # ip:port,ip:port... trainers = int(os.getenv("PADDLE_TRAINERS")) current_endpoint = os.getenv("POD_IP") + ":" + port trainer_id = int(os.getenv("PADDLE_TRAINER_ID")) training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER") t = fluid.DistributeTranspiler() t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers) if training_role == "PSERVER": pserver_prog = t.get_pserver_program(current_endpoint) pserver_startup = t.get_startup_program(current_endpoint, pserver_prog) exe.run(pserver_startup) exe.run(pserver_prog) elif training_role == "TRAINER": train_loop(t.get_trainer_program()) def infer(use_cuda, save_dirname=None): if save_dirname is None: return 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). [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(save_dirname, exe) word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) # 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. recursive_seq_lens = [[1]] base_shape = [1] # The range of random integers is [low, high] first_word = fluid.create_random_int_lodtensor( recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1) second_word = fluid.create_random_int_lodtensor( recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1) third_word = fluid.create_random_int_lodtensor( recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1) fourth_word = fluid.create_random_int_lodtensor( recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1) assert feed_target_names[0] == 'firstw' assert feed_target_names[1] == 'secondw' assert feed_target_names[2] == 'thirdw' assert feed_target_names[3] == 'forthw' # 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(inference_program, 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(results[0].recursive_sequence_lengths()) np_data = np.array(results[0]) print("Inference Shape: ", np_data.shape) def main(use_cuda, is_sparse, is_parallel): if use_cuda and not fluid.core.is_compiled_with_cuda(): return if not is_parallel: save_dirname = "word2vec.inference.model" else: save_dirname = None train(use_cuda, is_sparse, is_parallel, save_dirname) infer(use_cuda, save_dirname) FULL_TEST = os.getenv('FULL_TEST', '0').lower() in ['true', '1', 't', 'y', 'yes', 'on'] SKIP_REASON = "Only run minimum number of tests in CI server, to make CI faster" class W2VTest(unittest.TestCase): pass def inject_test_method(use_cuda, is_sparse, is_parallel): fn_name = "test_{0}_{1}_{2}".format("cuda" if use_cuda else "cpu", "sparse" if is_sparse else "dense", "parallel" if is_parallel else "normal") def __impl__(*args, **kwargs): prog = fluid.Program() startup_prog = fluid.Program() scope = fluid.core.Scope() with fluid.scope_guard(scope): with fluid.program_guard(prog, startup_prog): main( use_cuda=use_cuda, is_sparse=is_sparse, is_parallel=is_parallel) if (not fluid.core.is_compiled_with_cuda() or use_cuda) and is_sparse: fn = __impl__ else: # skip the other test when on CI server fn = unittest.skipUnless( condition=FULL_TEST, reason=SKIP_REASON)(__impl__) setattr(W2VTest, fn_name, fn) for use_cuda in (False, True): for is_sparse in (False, True): for is_parallel in (False, True): inject_test_method(use_cuda, is_sparse, is_parallel) if __name__ == '__main__': unittest.main()