diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9385943da9..90c25e4350 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,18 +7,14 @@ hooks: - id: yapf - repo: https://github.com/pre-commit/pre-commit-hooks - sha: 4ef03c4223ad322c7adaa6c6c0efb26b57df3b71 + sha: 7539d8bd1a00a3c1bfd34cdb606d3a6372e83469 hooks: - id: check-added-large-files - id: check-merge-conflict - id: check-symlinks - id: detect-private-key - id: end-of-file-fixer -# TODO(yuyang): trailing whitespace has some bugs on markdown -# files now, please not add it to pre-commit hook now -# - id: trailing-whitespace -# -# TODO(yuyang): debug-statements not fit for Paddle, because -# not all of our python code is runnable. Some are used for -# documenation -# - id: debug-statements +- repo: https://github.com/PaddlePaddle/clang-format-pre-commit-hook.git + sha: 28c0ea8a67a3e2dbbf4822ef44e85b63a0080a29 + hooks: + - id: clang-formater diff --git a/README.md b/README.md index e8679fb55f..8a8e158415 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,13 @@ # PaddlePaddle -[![Build Status](https://travis-ci.org/baidu/Paddle.svg?branch=master)](https://travis-ci.org/baidu/Paddle) -[![Coverage Status](https://coveralls.io/repos/github/baidu/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/baidu/Paddle?branch=develop) -[![Join the chat at https://gitter.im/PaddlePaddle/Deep_Learning](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/PaddlePaddle/Deep_Learning?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](LICENSE) +[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle) +[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/) +[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/cn/index.html) +[![Coverage Status](https://coveralls.io/repos/github/PaddlePaddle/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/PaddlePaddle/Paddle?branch=develop) +[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases) +[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) + Welcome to the PaddlePaddle GitHub. @@ -14,7 +17,7 @@ developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu. Our vision is to enable deep learning for everyone via PaddlePaddle. -Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle. +Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle. ## Features @@ -89,7 +92,7 @@ Both [English Docs](http://paddlepaddle.org/doc/) and [Chinese Docs](http://padd ## Ask Questions -You are welcome to submit questions and bug reports as [Github Issues](https://github.com/baidu/paddle/issues). +You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues). ## Copyright and License PaddlePaddle is provided under the [Apache-2.0 license](LICENSE). diff --git a/demo/semantic_role_labeling/data/extract_dict_feature.py b/demo/semantic_role_labeling/data/extract_dict_feature.py index 2982e54c66..daca5f01cf 100644 --- a/demo/semantic_role_labeling/data/extract_dict_feature.py +++ b/demo/semantic_role_labeling/data/extract_dict_feature.py @@ -17,24 +17,15 @@ import os from optparse import OptionParser -def extract_dict_features(pair_file, feature_file, src_dict_file, - tgt_dict_file): - src_dict = set() - tgt_dict = set() - - with open(pair_file) as fin, open(feature_file, 'w') as feature_out, open( - src_dict_file, 'w') as src_dict_out, open(tgt_dict_file, - 'w') as tgt_dict_out: +def extract_dict_features(pair_file, feature_file): + + with open(pair_file) as fin, open(feature_file, 'w') as feature_out: for line in fin: - sentence, labels = line.strip().split('\t') + sentence, predicate, labels = line.strip().split('\t') sentence_list = sentence.split() labels_list = labels.split() - src_dict.update(sentence_list) - tgt_dict.update(labels_list) - verb_index = labels_list.index('B-V') - verb_feature = sentence_list[verb_index] mark = [0] * len(labels_list) if verb_index > 0: @@ -42,47 +33,50 @@ def extract_dict_features(pair_file, feature_file, src_dict_file, ctx_n1 = sentence_list[verb_index - 1] else: ctx_n1 = 'bos' - ctx_n1_feature = ctx_n1 + + if verb_index > 1: + mark[verb_index - 2] = 1 + ctx_n2 = sentence_list[verb_index - 2] + else: + ctx_n2 = 'bos' mark[verb_index] = 1 - ctx_0_feature = sentence_list[verb_index] + ctx_0 = sentence_list[verb_index] if verb_index < len(labels_list) - 2: mark[verb_index + 1] = 1 ctx_p1 = sentence_list[verb_index + 1] else: ctx_p1 = 'eos' - ctx_p1_feature = ctx_p1 + + if verb_index < len(labels_list) - 3: + mark[verb_index + 2] = 1 + ctx_p2 = sentence_list[verb_index + 2] + else: + ctx_p2 = 'eos' + feature_str = sentence + '\t' \ - + verb_feature + '\t' \ - + ctx_n1_feature + '\t' \ - + ctx_0_feature + '\t' \ - + ctx_p1_feature + '\t' \ + + predicate + '\t' \ + + ctx_n2 + '\t' \ + + ctx_n1 + '\t' \ + + ctx_0 + '\t' \ + + ctx_p1 + '\t' \ + + ctx_p2 + '\t' \ + ' '.join([str(i) for i in mark]) + '\t' \ + labels feature_out.write(feature_str + '\n') - src_dict_out.write('\n') - src_dict_out.write('\n'.join(list(src_dict))) - - tgt_dict_out.write('\n'.join(list(tgt_dict))) if __name__ == '__main__': - usage = '-p pair_file -f feature_file -s source dictionary -t target dictionary ' + usage = '-p pair_file -f feature_file' parser = OptionParser(usage) parser.add_option('-p', dest='pair_file', help='the pair file') - parser.add_option( - '-f', dest='feature_file', help='the file to store feature') - parser.add_option( - '-s', dest='src_dict', help='the file to store source dictionary') - parser.add_option( - '-t', dest='tgt_dict', help='the file to store target dictionary') + parser.add_option('-f', dest='feature_file', help='the feature file') (options, args) = parser.parse_args() - extract_dict_features(options.pair_file, options.feature_file, - options.src_dict, options.tgt_dict) + extract_dict_features(options.pair_file, options.feature_file) diff --git a/demo/semantic_role_labeling/data/extract_pairs.py b/demo/semantic_role_labeling/data/extract_pairs.py index 4d1bef8f95..86ab00ce41 100644 --- a/demo/semantic_role_labeling/data/extract_pairs.py +++ b/demo/semantic_role_labeling/data/extract_pairs.py @@ -51,7 +51,7 @@ def read_sentences(words_file): for line in fin: line = line.strip() if line == '': - sentences.append(s.lower()) + sentences.append(s) s = '' else: s += line + ' ' @@ -64,6 +64,11 @@ def transform_labels(sentences, labels): if len(labels[i]) == 1: continue else: + verb_list = [] + for x in labels[i][0]: + if x !='-': + verb_list.append(x) + for j in xrange(1, len(labels[i])): label_list = labels[i][j] current_tag = 'O' @@ -88,8 +93,7 @@ def transform_labels(sentences, labels): is_in_bracket = True else: print 'error:', ll - - sen_lab_pair.append((sentences[i], label_seq)) + sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq)) return sen_lab_pair @@ -97,9 +101,9 @@ def write_file(sen_lab_pair, output_file): with open(output_file, 'w') as fout: for x in sen_lab_pair: sentence = x[0] - label_seq = ' '.join(x[1]) - assert len(sentence.split()) == len(x[1]) - fout.write(sentence + '\t' + label_seq + '\n') + label_seq = ' '.join(x[2]) + assert len(sentence.split()) == len(x[2]) + fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n') if __name__ == '__main__': diff --git a/demo/semantic_role_labeling/data/get_data.sh b/demo/semantic_role_labeling/data/get_data.sh index 268c0995e2..55e33f4685 100644 --- a/demo/semantic_role_labeling/data/get_data.sh +++ b/demo/semantic_role_labeling/data/get_data.sh @@ -14,6 +14,10 @@ # limitations under the License. set -e wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz +wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/verbDict.txt --no-check-certificate +wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/targetDict.txt --no-check-certificate +wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/wordDict.txt --no-check-certificate +wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/emb --no-check-certificate tar -xzvf conll05st-tests.tar.gz rm conll05st-tests.tar.gz cp ./conll05st-release/test.wsj/words/test.wsj.words.gz . @@ -22,4 +26,4 @@ gunzip test.wsj.words.gz gunzip test.wsj.props.gz python extract_pairs.py -w test.wsj.words -p test.wsj.props -o test.wsj.seq_pair -python extract_dict_feature.py -p test.wsj.seq_pair -f feature -s src.dict -t tgt.dict +python extract_dict_feature.py -p test.wsj.seq_pair -f feature diff --git a/demo/semantic_role_labeling/dataprovider.py b/demo/semantic_role_labeling/dataprovider.py index 5c003584a5..d4c137ef42 100644 --- a/demo/semantic_role_labeling/dataprovider.py +++ b/demo/semantic_role_labeling/dataprovider.py @@ -17,11 +17,15 @@ from paddle.trainer.PyDataProvider2 import * UNK_IDX = 0 -def hook(settings, word_dict, label_dict, **kwargs): +def hook(settings, word_dict, label_dict, predicate_dict, **kwargs): settings.word_dict = word_dict settings.label_dict = label_dict + settings.predicate_dict = predicate_dict + #all inputs are integral and sequential type settings.slots = [ + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(predicate_dict)), integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)), @@ -31,27 +35,33 @@ def hook(settings, word_dict, label_dict, **kwargs): ] -@provider(init_hook=hook) -def process(obj, file_name): +def get_batch_size(yeild_data): + return len(yeild_data[0]) + + +@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, + can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM) +def process(settings, file_name): with open(file_name, 'r') as fdata: for line in fdata: - sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = \ + sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \ line.strip().split('\t') - + words = sentence.split() sen_len = len(words) - word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words] + word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words] - predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len - ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX)] * sen_len - ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX)] * sen_len - ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX)] * sen_len + predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len + ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len + ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len + ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len + ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len + ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len marks = mark.split() mark_slot = [int(w) for w in marks] label_list = label.split() - label_slot = [obj.label_dict.get(w) for w in label_list] - - yield word_slot, predicate_slot, ctx_n1_slot, \ - ctx_0_slot, ctx_p1_slot, mark_slot, label_slot + label_slot = [settings.label_dict.get(w) for w in label_list] + yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ + ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot diff --git a/demo/semantic_role_labeling/db_lstm.py b/demo/semantic_role_labeling/db_lstm.py index e3f6edad69..54ceff0e72 100644 --- a/demo/semantic_role_labeling/db_lstm.py +++ b/demo/semantic_role_labeling/db_lstm.py @@ -18,8 +18,9 @@ import sys from paddle.trainer_config_helpers import * #file paths -word_dict_file = './data/src.dict' -label_dict_file = './data/tgt.dict' +word_dict_file = './data/wordDict.txt' +label_dict_file = './data/targetDict.txt' +predicate_file= './data/verbDict.txt' train_list_file = './data/train.list' test_list_file = './data/test.list' @@ -30,8 +31,10 @@ if not is_predict: #load dictionaries word_dict = dict() label_dict = dict() + predicate_dict = dict() with open(word_dict_file, 'r') as f_word, \ - open(label_dict_file, 'r') as f_label: + open(label_dict_file, 'r') as f_label, \ + open(predicate_file, 'r') as f_pre: for i, line in enumerate(f_word): w = line.strip() word_dict[w] = i @@ -40,6 +43,11 @@ if not is_predict: w = line.strip() label_dict[w] = i + for i, line in enumerate(f_pre): + w = line.strip() + predicate_dict[w] = i + + if is_test: train_list_file = None @@ -50,91 +58,157 @@ if not is_predict: module='dataprovider', obj='process', args={'word_dict': word_dict, - 'label_dict': label_dict}) + 'label_dict': label_dict, + 'predicate_dict': predicate_dict }) word_dict_len = len(word_dict) label_dict_len = len(label_dict) + pred_len = len(predicate_dict) else: word_dict_len = get_config_arg('dict_len', int) label_dict_len = get_config_arg('label_len', int) + pred_len = get_config_arg('pred_len', int) +############################## Hyper-parameters ################################## mark_dict_len = 2 word_dim = 32 mark_dim = 5 -hidden_dim = 128 +hidden_dim = 512 depth = 8 -emb_lr = 1e-2 -fc_lr = 1e-2 -lstm_lr = 2e-2 + + + +########################### Optimizer ####################################### + settings( batch_size=150, - learning_method=AdamOptimizer(), - learning_rate=1e-3, + learning_method=MomentumOptimizer(momentum=0), + learning_rate=2e-2, regularization=L2Regularization(8e-4), - gradient_clipping_threshold=25) + is_async=False, + model_average=ModelAverage(average_window=0.5, + max_average_window=10000), + +) -#6 features + + + +####################################### network ############################## +#8 features and 1 target word = data_layer(name='word_data', size=word_dict_len) -predicate = data_layer(name='verb_data', size=word_dict_len) +predicate = data_layer(name='verb_data', size=pred_len) + +ctx_n2 = data_layer(name='ctx_n2_data', size=word_dict_len) ctx_n1 = data_layer(name='ctx_n1_data', size=word_dict_len) ctx_0 = data_layer(name='ctx_0_data', size=word_dict_len) ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len) +ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len) mark = data_layer(name='mark_data', size=mark_dict_len) + if not is_predict: target = data_layer(name='target', size=label_dict_len) -ptt = ParameterAttribute(name='src_emb', learning_rate=emb_lr) -layer_attr = ExtraLayerAttribute(drop_rate=0.5) -fc_para_attr = ParameterAttribute(learning_rate=fc_lr) -lstm_para_attr = ParameterAttribute(initial_std=0., learning_rate=lstm_lr) -para_attr = [fc_para_attr, lstm_para_attr] -word_embedding = embedding_layer(size=word_dim, input=word, param_attr=ptt) -predicate_embedding = embedding_layer( - size=word_dim, input=predicate, param_attr=ptt) -ctx_n1_embedding = embedding_layer(size=word_dim, input=ctx_n1, param_attr=ptt) -ctx_0_embedding = embedding_layer(size=word_dim, input=ctx_0, param_attr=ptt) -ctx_p1_embedding = embedding_layer(size=word_dim, input=ctx_p1, param_attr=ptt) -mark_embedding = embedding_layer(size=mark_dim, input=mark) +default_std=1/math.sqrt(hidden_dim)/3.0 + +emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.) +std_0 = ParameterAttribute(initial_std=0.) +std_default = ParameterAttribute(initial_std=default_std) + +predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std)) +mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0) + +word_input=[word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2] +emb_layers = [embedding_layer(size=word_dim, input=x, param_attr=emb_para) for x in word_input] +emb_layers.append(predicate_embedding) +emb_layers.append(mark_embedding) hidden_0 = mixed_layer( + name='hidden0', size=hidden_dim, - input=[ - full_matrix_projection(input=word_embedding), - full_matrix_projection(input=predicate_embedding), - full_matrix_projection(input=ctx_n1_embedding), - full_matrix_projection(input=ctx_0_embedding), - full_matrix_projection(input=ctx_p1_embedding), - full_matrix_projection(input=mark_embedding), - ]) + bias_attr=std_default, + input=[ full_matrix_projection(input=emb, param_attr=std_default ) for emb in emb_layers ]) + -lstm_0 = lstmemory(input=hidden_0, layer_attr=layer_attr) +mix_hidden_lr = 1e-3 +lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0) +hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr) + +lstm_0 = lstmemory(name='lstm0', + input=hidden_0, + act=ReluActivation(), + gate_act=SigmoidActivation(), + state_act=SigmoidActivation(), + bias_attr=std_0, + param_attr=lstm_para_attr) #stack L-LSTM and R-LSTM with direct edges input_tmp = [hidden_0, lstm_0] + for i in range(1, depth): - fc = fc_layer(input=input_tmp, size=hidden_dim, param_attr=para_attr) + mix_hidden = mixed_layer(name='hidden'+str(i), + size=hidden_dim, + bias_attr=std_default, + input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr), + full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr) + ] + ) + + lstm = lstmemory(name='lstm'+str(i), + input=mix_hidden, + act=ReluActivation(), + gate_act=SigmoidActivation(), + state_act=SigmoidActivation(), + reverse=((i % 2)==1), + bias_attr=std_0, + param_attr=lstm_para_attr) + + input_tmp = [mix_hidden, lstm] + +feature_out = mixed_layer(name='output', + size=label_dict_len, + bias_attr=std_default, + input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr), + full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr) + ], + ) - lstm = lstmemory( - input=fc, - act=ReluActivation(), - reverse=(i % 2) == 1, - layer_attr=layer_attr) - input_tmp = [fc, lstm] -prob = fc_layer( - input=input_tmp, - size=label_dict_len, - act=SoftmaxActivation(), - param_attr=para_attr) if not is_predict: - cls = classification_cost(input=prob, label=target) - outputs(cls) + crf_l = crf_layer( name = 'crf', + size = label_dict_len, + input = feature_out, + label = target, + param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr) + + ) + + + crf_dec_l = crf_decoding_layer(name = 'crf_dec_l', + size = label_dict_len, + input = feature_out, + label = target, + param_attr=ParameterAttribute(name='crfw') + ) + + + eval = sum_evaluator(input=crf_dec_l) + + outputs(crf_l) + else: - outputs(prob) + crf_dec_l = crf_decoding_layer(name = 'crf_dec_l', + size = label_dict_len, + input = feature_out, + param_attr=ParameterAttribute(name='crfw') + ) + + outputs(crf_dec_l) + diff --git a/demo/semantic_role_labeling/predict.py b/demo/semantic_role_labeling/predict.py index f051d4175c..2761814e18 100644 --- a/demo/semantic_role_labeling/predict.py +++ b/demo/semantic_role_labeling/predict.py @@ -26,7 +26,7 @@ UNK_IDX = 0 class Prediction(): - def __init__(self, train_conf, dict_file, model_dir, label_file): + def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file): """ train_conf: trainer configure. dict_file: word dictionary file name. @@ -35,26 +35,41 @@ class Prediction(): self.dict = {} self.labels = {} + self.predicate_dict={} self.labels_reverse = {} - self.load_dict_label(dict_file, label_file) + self.load_dict_label(dict_file, label_file, predicate_dict_file) len_dict = len(self.dict) len_label = len(self.labels) - - conf = parse_config(train_conf, 'dict_len=' + str(len_dict) + - ',label_len=' + str(len_label) + ',is_predict=True') + len_pred = len(self.predicate_dict) + + conf = parse_config( + train_conf, + 'dict_len=' + str(len_dict) + + ',label_len=' + str(len_label) + + ',pred_len=' + str(len_pred) + + ',is_predict=True') self.network = swig_paddle.GradientMachine.createFromConfigProto( conf.model_config) self.network.loadParameters(model_dir) slots = [ + integer_value_sequence(len_dict), + integer_value_sequence(len_pred), + integer_value_sequence(len_dict), + integer_value_sequence(len_dict), + integer_value_sequence(len_dict), + integer_value_sequence(len_dict), + integer_value_sequence(len_dict), + integer_value_sequence(2) + ] integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(2) ] self.converter = DataProviderConverter(slots) - def load_dict_label(self, dict_file, label_file): + def load_dict_label(self, dict_file, label_file, predicate_dict_file): """ Load dictionary from self.dict_file. """ @@ -65,39 +80,42 @@ class Prediction(): self.labels[line.strip()] = line_count self.labels_reverse[line_count] = line.strip() + for line_count, line in enumerate(open(predicate_dict_file, 'r')): + self.predicate_dict[line.strip()] = line_count def get_data(self, data_file): """ Get input data of paddle format. """ with open(data_file, 'r') as fdata: for line in fdata: - sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip( + sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = line.strip( ).split('\t') words = sentence.split() sen_len = len(words) - + word_slot = [self.dict.get(w, UNK_IDX) for w in words] - predicate_slot = [self.dict.get(predicate, UNK_IDX)] * sen_len + predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len + ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len ctx_p1_slot = [self.dict.get(ctx_p1, UNK_IDX)] * sen_len + ctx_p2_slot = [self.dict.get(ctx_p2, UNK_IDX)] * sen_len marks = mark.split() mark_slot = [int(w) for w in marks] + + yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ + ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot - yield word_slot, predicate_slot, ctx_n1_slot, \ - ctx_0_slot, ctx_p1_slot, mark_slot - - def predict(self, data_file): + def predict(self, data_file, output_file): """ data_file: file name of input data. """ input = self.converter(self.get_data(data_file)) output = self.network.forwardTest(input) - prob = output[0]["value"] - lab = list(np.argsort(-prob)[:, 0]) + lab = output[0]["id"].tolist() - with open(data_file, 'r') as fin, open('predict.res', 'w') as fout: + with open(data_file, 'r') as fin, open(output_file, 'w') as fout: index = 0 for line in fin: sen = line.split('\t')[0] @@ -109,8 +127,8 @@ class Prediction(): def option_parser(): - usage = ("python predict.py -c config -w model_dir " - "-d word dictionary -l label_file -i input_file") + usage = ("python predict.py -c config -w model_dir " + "-d word dictionary -l label_file -i input_file -p pred_dict_file") parser = OptionParser(usage="usage: %s [options]" % usage) parser.add_option( "-c", @@ -131,6 +149,13 @@ def option_parser(): dest="label_file", default=None, help="label file") + parser.add_option( + "-p", + "--predict_dict_file", + action="store", + dest="predict_dict_file", + default=None, + help="predict_dict_file") parser.add_option( "-i", "--data", @@ -144,6 +169,14 @@ def option_parser(): dest="model_path", default=None, help="model path") + + parser.add_option( + "-o", + "--output_file", + action="store", + dest="output_file", + default=None, + help="output file") return parser.parse_args() @@ -154,10 +187,12 @@ def main(): dict_file = options.dict_file model_path = options.model_path label_file = options.label_file + predict_dict_file = options.predict_dict_file + output_file = options.output_file swig_paddle.initPaddle("--use_gpu=0") - predict = Prediction(train_conf, dict_file, model_path, label_file) - predict.predict(data_file) + predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file) + predict.predict(data_file,output_file) if __name__ == '__main__': diff --git a/demo/semantic_role_labeling/predict.sh b/demo/semantic_role_labeling/predict.sh index a545b9a5d5..d0acdb0bd0 100644 --- a/demo/semantic_role_labeling/predict.sh +++ b/demo/semantic_role_labeling/predict.sh @@ -26,15 +26,18 @@ LOG=`get_best_pass $log` LOG=(${LOG}) best_model_path="output/pass-${LOG[1]}" - config_file=db_lstm.py -dict_file=./data/src.dict -label_file=./data/tgt.dict +dict_file=./data/wordDict.txt +label_file=./data/targetDict.txt +predicate_dict_file=./data/verbDict.txt input_file=./data/feature +output_file=predict.res python predict.py \ -c $config_file \ -w $best_model_path \ -l $label_file \ + -p $predicate_dict_file \ -d $dict_file \ - -i $input_file + -i $input_file \ + -o $output_file diff --git a/demo/semantic_role_labeling/test.sh b/demo/semantic_role_labeling/test.sh index 844649e8c0..c4ab44f5ca 100644 --- a/demo/semantic_role_labeling/test.sh +++ b/demo/semantic_role_labeling/test.sh @@ -36,4 +36,5 @@ paddle train \ --job=test \ --use_gpu=false \ --config_args=is_test=1 \ + --test_all_data_in_one_period=1 \ 2>&1 | tee 'test.log' diff --git a/demo/semantic_role_labeling/train.sh b/demo/semantic_role_labeling/train.sh index c3a22b644b..420768bb2b 100644 --- a/demo/semantic_role_labeling/train.sh +++ b/demo/semantic_role_labeling/train.sh @@ -16,11 +16,14 @@ set -e paddle train \ --config=./db_lstm.py \ + --use_gpu=0 \ + --log_period=5000 \ + --trainer_count=1 \ + --show_parameter_stats_period=5000 \ --save_dir=./output \ - --trainer_count=4 \ - --log_period=10 \ - --num_passes=500 \ - --use_gpu=false \ - --show_parameter_stats_period=10 \ + --num_passes=10000 \ + --average_test_period=10000000 \ + --init_model_path=./data \ + --load_missing_parameter_strategy=rand \ --test_all_data_in_one_period=1 \ -2>&1 | tee 'train.log' + 2>&1 | tee 'train.log' diff --git a/doc/demo/semantic_role_labeling/curve.jpg b/doc/demo/semantic_role_labeling/curve.jpg new file mode 100644 index 0000000000..baa35ae7f0 Binary files /dev/null and b/doc/demo/semantic_role_labeling/curve.jpg differ diff --git a/doc/demo/semantic_role_labeling/semantic_role_labeling.md b/doc/demo/semantic_role_labeling/semantic_role_labeling.md index 890f731458..e2793b2b34 100644 --- a/doc/demo/semantic_role_labeling/semantic_role_labeling.md +++ b/doc/demo/semantic_role_labeling/semantic_role_labeling.md @@ -1,183 +1,200 @@ -# Semantic Role labeling Tutorial # - -Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering. An instance is as following [1]: - - [ A0 He ] [ AM-MOD would ][ AM-NEG n’t ] [ V accept] [ A1 anything of value ] from [A2 those he was writing about ]. - -- V: verb -- A0: acceptor -- A1: thing accepted -- A2: accepted-from -- A3: Attribute -- AM-MOD: modal -- AM-NEG: negation - -Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. - -To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. - -## Data Description -The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license, the demo adopts the test data set of CoNLL-2005, which can be reached on website. - -To download and process the original data, user just need to execute the following command: - -```bash -cd data -./get_data.sh -``` -Several new files appear in the `data `directory as follows. -```bash -conll05st-release：the test data set of CoNll-2005 shared task -test.wsj.words：the Wall Street Journal data sentences -test.wsj.props: the propositional arguments -src.dict：the dictionary of words in sentences -tgt.dict：the labels dictionary -feature: the extracted features from data set -``` - -## Training -### DB-LSTM -Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. - -Unlike Bidirectional-LSTM that used in Sentiment Analysis demo, the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. - -The following figure shows a temporal expanded 2-layer DB-LSTM network. -
-![pic](./network_arch.png) -
- -### Features -Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark mr = 1 to denote the argument position if it locates in the predicate context region, or mr = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]: -
-![pic](./feature.jpg) -
- -In this sample, the coresponding labelled sentence is: - -[ A1 A record date ] has [ AM-NEG n't ] been [ V set ] . - -In the demo, we adopt the feature template as above, consists of : `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`. - -### Data Provider - -`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The Six features and label are all IndexSlots. -``` -def hook(settings, word_dict, label_dict, **kwargs): - settings.word_dict = word_dict - settings.label_dict = label_dict - #all inputs are integral and sequential type - settings.slots = [ - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(2), - integer_value_sequence(len(label_dict))] -``` -The corresponding data iterator is as following: -``` -@provider(use_seq=True, init_hook=hook) -def process(obj, file_name): - with open(file_name, 'r') as fdata: - for line in fdata: - sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip().split('\t') - words = sentence.split() - sen_len = len(words) - word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words] - - predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len - ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX) ] * sen_len - ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX) ] * sen_len - ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX) ] * sen_len - - marks = mark.split() - mark_slot = [int(w) for w in marks] - - label_list = label.split() - label_slot = [obj.label_dict.get(w) for w in label_list] - - yield word_slot, predicate_slot, ctx_n1_slot, ctx_0_slot, ctx_p1_slot, mark_slot, label_slot -``` -The `process`function yield 7 lists which are six features and labels. - -### Neural Network Config -`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure. - -Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels. - -### Run Training -The script for training is `train.sh`, user just need to execute: -```bash - ./train.sh -``` -The content in `train.sh`: -``` -paddle train \ - --config=./db_lstm.py \ - --save_dir=./output \ - --trainer_count=4 \ - --log_period=10 \ - --num_passes=500 \ - --use_gpu=false \ - --show_parameter_stats_period=10 \ - --test_all_data_in_one_period=1 \ -2>&1 | tee 'train.log' -``` - -- \--config=./db_lstm.py : network config file. -- \--save_di=./output: output path to save models. -- \--trainer_count=4 : set thread number (or GPU count). -- \--log_period=10 : print log every 20 batches. -- \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time. -- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train. -- \--show_parameter_stats_period=10: show parameter statistic every 100 batches. -- \--test_all_data_in_one_period=1: test all data in every testing. - - -After training, the models will be saved in directory `output`. - -### Run testing -The script for testing is `test.sh`, user just need to execute: -```bash - ./test.sh -``` -The main part in `tesh.sh` -``` -paddle train \ - --config=./db_lstm.py \ - --model_list=$model_list \ - --job=test \ - --config_args=is_test=1 \ -``` - - - \--config=./db_lstm.py: network config file - - \--model_list=$model_list.list: model list file - - \--job=test: indicate the test job - - \--config_args=is_test=1: flag to indicate test - - -### Run prediction -The script for prediction is `predict.sh`, user just need to execute: -```bash - ./predict.sh - -``` -In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file -``` -python predict.py - -c $config_file - -w $model_path - -l $label_file - -d $dict_file - -i $input_file -``` - -`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix. - -After prediction, the result is saved in `predict.res`. - -## Reference -[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. - -[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015. +# Semantic Role labeling Tutorial # + +Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering. An instance is as following [1]: + + [ A0 He ] [ AM-MOD would ][ AM-NEG n’t ] [ V accept] [ A1 anything of value ] from [A2 those he was writing about ]. + +- V: verb +- A0: acceptor +- A1: thing accepted +- A2: accepted-from +- A3: Attribute +- AM-MOD: modal +- AM-NEG: negation + +Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. + +To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. + +## Data Description +The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license, the demo adopts the test data set of CoNLL-2005, which can be reached on website. + +To download and process the original data, user just need to execute the following command: + +```bash +cd data +./get_data.sh +``` +Several new files appear in the `data `directory as follows. +```bash +conll05st-release：the test data set of CoNll-2005 shared task +test.wsj.words：the Wall Street Journal data sentences +test.wsj.props: the propositional arguments +feature: the extracted features from data set +``` + +## Training +### DB-LSTM +Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. + +Unlike Bidirectional-LSTM that used in Sentiment Analysis demo, the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. + +The following figure shows a temporal expanded 2-layer DB-LSTM network. +
+![pic](./network_arch.png) +
+ +### Features +Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark mr = 1 to denote the argument position if it locates in the predicate context region, or mr = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]: +
+![pic](./feature.jpg) +
+ +In this sample, the coresponding labelled sentence is: + +[ A1 A record date ] has [ AM-NEG n't ] been [ V set ] . + +In the demo, we adopt the feature template as above, consists of : `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`. + +### Data Provider + +`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The Six features and label are all IndexSlots. +``` +def hook(settings, word_dict, label_dict, **kwargs): + settings.word_dict = word_dict + settings.label_dict = label_dict + #all inputs are integral and sequential type + settings.slots = [ + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(predicate_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(2), + integer_value_sequence(len(label_dict))] +``` +The corresponding data iterator is as following: +``` +@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, + can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM) +def process(settings, file_name): + with open(file_name, 'r') as fdata: + for line in fdata: + sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \ + line.strip().split('\t') + + words = sentence.split() + sen_len = len(words) + word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words] + + predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len + ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len + ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len + ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len + ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len + ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len + + marks = mark.split() + mark_slot = [int(w) for w in marks] + + label_list = label.split() + label_slot = [settings.label_dict.get(w) for w in label_list] + yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ + ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot +``` +The `process`function yield 9 lists which are 8 features and label. + +### Neural Network Config +`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure. + +Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels. + +### Run Training +The script for training is `train.sh`, user just need to execute: +```bash + ./train.sh +``` +The content in `train.sh`: +``` +paddle train \ + --config=./db_lstm.py \ + --use_gpu=0 \ + --log_period=5000 \ + --trainer_count=1 \ + --show_parameter_stats_period=5000 \ + --save_dir=./output \ + --num_passes=10000 \ + --average_test_period=10000000 \ + --init_model_path=./data \ + --load_missing_parameter_strategy=rand \ + --test_all_data_in_one_period=1 \ +2>&1 | tee 'train.log' +``` + +- \--config=./db_lstm.py : network config file. +- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU +- \--log_period=500: print log every 20 batches. +- \--trainer_count=1: set thread number (or GPU count). +- \--show_parameter_stats_period=5000: show parameter statistic every 100 batches. +- \--save_dir=./output: output path to save models. +- \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time. +- \--average_test_period=10000000: do test on average parameter every average_test_period batches +- \--init_model_path=./data: parameter initialization path +- \--load_missing_parameter_strategy=rand: random initialization unexisted parameters +- \--test_all_data_in_one_period=1: test all data in one period + + +After training, the models will be saved in directory `output`. Our training curve is as following: +
+![pic](./curve.jpg) +
+ +### Run testing +The script for testing is `test.sh`, user just need to execute: +```bash + ./test.sh +``` +The main part in `tesh.sh` +``` +paddle train \ + --config=./db_lstm.py \ + --model_list=$model_list \ + --job=test \ + --config_args=is_test=1 \ +``` + + - \--config=./db_lstm.py: network config file + - \--model_list=$model_list.list: model list file + - \--job=test: indicate the test job + - \--config_args=is_test=1: flag to indicate test + - \--test_all_data_in_one_period=1: test all data in 1 period + + +### Run prediction +The script for prediction is `predict.sh`, user just need to execute: +```bash + ./predict.sh + +``` +In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file +``` +python predict.py + -c $config_file \ + -w $best_model_path \ + -l $label_file \ + -p $predicate_dict_file \ + -d $dict_file \ + -i $input_file \ + -o $output_file +``` + +`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix. + +After prediction, the result is saved in `predict.res`. + +## Reference +[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. + +[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015. diff --git a/paddle/cuda/CMakeLists.txt b/paddle/cuda/CMakeLists.txt index cdb730bb3c..11dbfb54b2 100755 --- a/paddle/cuda/CMakeLists.txt +++ b/paddle/cuda/CMakeLists.txt @@ -81,5 +81,8 @@ else() add_library(paddle_cuda ${CUDA_SOURCES}) endif() -add_style_check_target(paddle_cuda ${CUDA_SOURCES}) -add_style_check_target(paddle_cuda ${CUDA_HEADERS}) +add_style_check_target(paddle_cuda + ${CUDA_SOURCES} + ${CUDA_HEADERS} + ${CUDA_DSO_SOURCES} + ${CUDA_CXX_WITH_GPU_SOURCES}) diff --git a/paddle/cuda/src/hl_cuda_cublas.cc b/paddle/cuda/src/hl_cuda_cublas.cc index f16376ec93..abf6afadc2 100644 --- a/paddle/cuda/src/hl_cuda_cublas.cc +++ b/paddle/cuda/src/hl_cuda_cublas.cc @@ -104,7 +104,7 @@ CUBLAS_BLAS_ROUTINE_EACH(DYNAMIC_LOAD_CUBLAS_V2_WRAP) #endif const char* hl_cublas_get_error_string(cublasStatus_t status) { - switch(status) { + switch (status) { case CUBLAS_STATUS_NOT_INITIALIZED: return "[cublas status]: not initialized"; case CUBLAS_STATUS_ALLOC_FAILED: @@ -181,7 +181,7 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) { real **inout_d = (real **)hl_malloc_device(sizeof(real *)); hl_memcpy(inout_d, inout_h, sizeof(real *)); - int *pivot_d = (int *)hl_malloc_device(dimN*sizeof(int)); + int *pivot_d = (int *)hl_malloc_device(dimN * sizeof(int)); int *info_d = (int *)t_resource.gpu_mem; /* Note: cublasSgetrfBatched is used to calculate a number of @@ -189,10 +189,9 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) { the API for better performance. */ CHECK_CUBLAS(CUBLAS_GETRF(t_resource.handle, - dimN, inout_d, lda, pivot_d, - info_d, 1)); + dimN, inout_d, lda, pivot_d, info_d, 1)); - int info_h; + int info_h; hl_memcpy(&info_h, info_d, sizeof(int)); if (info_h != 0) { LOG(FATAL) << "Factorization of matrix failed: matrix may be singular.\n"; @@ -204,8 +203,8 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) { hl_memcpy(out_d, out_h, sizeof(real *)); CHECK_CUBLAS(CUBLAS_GETRI(t_resource.handle, - dimN, (const real **)inout_d, lda, pivot_d, - out_d, ldc, info_d, 1)); + dimN, (const real **)inout_d, lda, pivot_d, + out_d, ldc, info_d, 1)); hl_memcpy(&info_h, info_d, sizeof(int)); if (info_h != 0) { @@ -215,7 +214,7 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) { hl_free_mem_device(inout_d); hl_free_mem_device(pivot_d); hl_free_mem_device(out_d); - + CHECK_SYNC("hl_matrix_inverse failed"); } diff --git a/paddle/cuda/src/hl_cuda_cudnn.cc b/paddle/cuda/src/hl_cuda_cudnn.cc index 92b28e4345..1829fe23ac 100644 --- a/paddle/cuda/src/hl_cuda_cudnn.cc +++ b/paddle/cuda/src/hl_cuda_cudnn.cc @@ -159,13 +159,11 @@ CUDNN_DNN_ROUTINE_EACH_R5(DYNAMIC_LOAD_CUDNN_WRAP) bool g_is_libcudnn_init = false; int g_cudnn_lib_version = 0; -void hl_cudnn_desc_init(cudnnTensorDescriptor_t* cudnn_desc) -{ +void hl_cudnn_desc_init(cudnnTensorDescriptor_t* cudnn_desc) { CHECK_CUDNN(dynload::cudnnCreateTensorDescriptor(cudnn_desc)); } -void hl_cudnn_init(cudnnHandle_t *cudnn_handle, cudaStream_t stream) -{ +void hl_cudnn_init(cudnnHandle_t *cudnn_handle, cudaStream_t stream) { size_t cudnn_dso_ver = dynload::cudnnGetVersion(); size_t cudnn_dso_major = cudnn_dso_ver / 1000; size_t cudnn_cuh_major = CUDNN_VERSION / 1000; @@ -212,13 +210,18 @@ void hl_conv_workspace(hl_tensor_descriptor input, CHECK_NOTNULL(conv); // Specify workspace limit directly - size_t memoryLimitBytes = (1LL << 20) * FLAGS_cudnn_conv_workspace_limit_in_mb; + size_t memoryLimitBytes = + (1LL << 20) * FLAGS_cudnn_conv_workspace_limit_in_mb; // cudnn convolution forward configuration - cudnnTensorDescriptor_t fwd_src_desc = GET_TENSOR_DESCRIPTOR(input); - cudnnTensorDescriptor_t fwd_dest_desc = GET_TENSOR_DESCRIPTOR(output); - cudnnFilterDescriptor_t fwd_filter_desc = GET_FILTER_DESCRIPTOR(filter); - cudnnConvolutionDescriptor_t fwd_conv_desc = GET_CONVOLUTION_DESCRIPTOR(conv); + cudnnTensorDescriptor_t fwd_src_desc = + GET_TENSOR_DESCRIPTOR(input); + cudnnTensorDescriptor_t fwd_dest_desc = + GET_TENSOR_DESCRIPTOR(output); + cudnnFilterDescriptor_t fwd_filter_desc = + GET_FILTER_DESCRIPTOR(filter); + cudnnConvolutionDescriptor_t fwd_conv_desc = + GET_CONVOLUTION_DESCRIPTOR(conv); CHECK_CUDNN(dynload::cudnnGetConvolutionForwardAlgorithm( t_resource.cudnn_handle, @@ -250,23 +253,23 @@ void hl_conv_workspace(hl_tensor_descriptor input, GET_CONVOLUTION_DESCRIPTOR(conv); CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataAlgorithm( - t_resource.cudnn_handle, - bwd_data_filter_desc, - bwd_data_diff_desc, - bwd_data_conv_desc, - bwd_data_grad_desc, - CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, - memoryLimitBytes, - reinterpret_cast(convBwdDataAlgo))); + t_resource.cudnn_handle, + bwd_data_filter_desc, + bwd_data_diff_desc, + bwd_data_conv_desc, + bwd_data_grad_desc, + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + memoryLimitBytes, + reinterpret_cast(convBwdDataAlgo))); CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( - t_resource.cudnn_handle, - bwd_data_filter_desc, - bwd_data_diff_desc, - bwd_data_conv_desc, - bwd_data_grad_desc, - static_cast(*convBwdDataAlgo), - bwdDataLimitBytes)); + t_resource.cudnn_handle, + bwd_data_filter_desc, + bwd_data_diff_desc, + bwd_data_conv_desc, + bwd_data_grad_desc, + static_cast(*convBwdDataAlgo), + bwdDataLimitBytes)); // cudnn convolution backward filter configuration cudnnTensorDescriptor_t bwd_filter_src_desc = @@ -279,21 +282,21 @@ void hl_conv_workspace(hl_tensor_descriptor input, GET_FILTER_DESCRIPTOR(filter); CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterAlgorithm( - t_resource.cudnn_handle, - bwd_filter_src_desc, - bwd_filter_diff_desc, - bwd_filter_conv_desc, - bwd_filter_grad_desc, - CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, - memoryLimitBytes, - reinterpret_cast(convBwdFilterAlgo))); + t_resource.cudnn_handle, + bwd_filter_src_desc, + bwd_filter_diff_desc, + bwd_filter_conv_desc, + bwd_filter_grad_desc, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + memoryLimitBytes, + reinterpret_cast(convBwdFilterAlgo))); CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( - t_resource.cudnn_handle, bwd_filter_src_desc, - bwd_filter_diff_desc, bwd_filter_conv_desc, - bwd_filter_grad_desc, - static_cast(*convBwdFilterAlgo), - bwdFilterLimitBytes)); + t_resource.cudnn_handle, bwd_filter_src_desc, + bwd_filter_diff_desc, bwd_filter_conv_desc, + bwd_filter_grad_desc, + static_cast(*convBwdFilterAlgo), + bwdFilterLimitBytes)); #endif } @@ -302,8 +305,7 @@ void hl_create_tensor_descriptor(hl_tensor_descriptor* image_desc, int batch_size, int feature_maps, int height, - int width) -{ + int width) { CHECK_NOTNULL(image_desc); cudnn_tensor_descriptor hl_desc = @@ -359,8 +361,7 @@ void hl_tensor_reshape(hl_tensor_descriptor image_desc, int batch_size, int feature_maps, int height, - int width) -{ + int width) { const int stride_w = 1; const int stride_h = width * stride_w; const int stride_c = height * stride_h; @@ -384,8 +385,7 @@ void hl_tensor_reshape(hl_tensor_descriptor image_desc, int nStride, int cStride, int hStride, - int wStride) -{ + int wStride) { CHECK_NOTNULL(image_desc); cudnn_tensor_descriptor hl_desc = (cudnn_tensor_descriptor)image_desc; @@ -408,8 +408,7 @@ void hl_tensor_reshape(hl_tensor_descriptor image_desc, hl_desc->width = width; } -void hl_destroy_tensor_descriptor(hl_tensor_descriptor image_desc) -{ +void hl_destroy_tensor_descriptor(hl_tensor_descriptor image_desc) { CHECK_NOTNULL(image_desc); cudnn_tensor_descriptor hl_desc = (cudnn_tensor_descriptor)image_desc; @@ -430,11 +429,9 @@ void hl_create_pooling_descriptor(hl_pooling_descriptor* pooling_desc, int height_padding, int width_padding, int stride_height, - int stride_width) -{ + int stride_width) { cudnnPoolingMode_t cudnn_mode; - switch (mode) - { + switch (mode) { case HL_POOLING_MAX: cudnn_mode = CUDNN_POOLING_MAX; break; @@ -478,13 +475,13 @@ void hl_create_pooling_descriptor(hl_pooling_descriptor* pooling_desc, *pooling_desc = (hl_pooling_descriptor)hl_pooling_desc; } -void hl_destroy_pooling_descriptor(hl_pooling_descriptor pooling_desc) -{ +void hl_destroy_pooling_descriptor(hl_pooling_descriptor pooling_desc) { CHECK_NOTNULL(pooling_desc); - cudnn_pooling_descriptor hl_pooling = (cudnn_pooling_descriptor)pooling_desc; - CHECK_NOTNULL(hl_pooling->desc); + cudnn_pooling_descriptor hl_pooling = + (cudnn_pooling_descriptor)pooling_desc; + CHECK_NOTNULL(hl_pooling->desc); CHECK_CUDNN(dynload::cudnnDestroyPoolingDescriptor(hl_pooling->desc)); hl_pooling->desc = NULL; @@ -496,8 +493,7 @@ void hl_pooling_forward(hl_tensor_descriptor input, real* input_image, hl_tensor_descriptor output, real* output_image, - hl_pooling_descriptor pooling) -{ + hl_pooling_descriptor pooling) { cudnnPoolingDescriptor_t pooling_desc; cudnnTensorDescriptor_t input_desc; cudnnTensorDescriptor_t output_desc; @@ -531,8 +527,7 @@ void hl_pooling_backward(hl_tensor_descriptor input, hl_tensor_descriptor output, real* output_image, real* output_image_grad, - hl_pooling_descriptor pooling) -{ + hl_pooling_descriptor pooling) { cudnnPoolingDescriptor_t pooling_desc; cudnnTensorDescriptor_t input_desc; cudnnTensorDescriptor_t output_desc; @@ -571,8 +566,7 @@ void hl_create_filter_descriptor(hl_filter_descriptor* filter, int input_feature_maps, int output_feature_maps, int height, - int width) -{ + int width) { CHECK_NOTNULL(filter); cudnn_filter_descriptor hl_filter = @@ -607,8 +601,7 @@ void hl_create_filter_descriptor(hl_filter_descriptor* filter, } -void hl_destroy_filter_descriptor(hl_filter_descriptor filter) -{ +void hl_destroy_filter_descriptor(hl_filter_descriptor filter) { CHECK_NOTNULL(filter); cudnn_filter_descriptor hl_filter = (cudnn_filter_descriptor)filter; @@ -627,14 +620,13 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv, int padding_height, int padding_width, int stride_height, - int stride_width) -{ + int stride_width) { CHECK_NOTNULL(conv); - cudnn_convolution_descriptor hl_conv = - (cudnn_convolution_descriptor)malloc(sizeof(_cudnn_convolution_descriptor)); - CHECK_NOTNULL(hl_conv); + cudnn_convolution_descriptor hl_conv = (cudnn_convolution_descriptor) + malloc(sizeof(_cudnn_convolution_descriptor)); + CHECK_NOTNULL(hl_conv); CHECK_CUDNN(dynload::cudnnCreateConvolutionDescriptor(&hl_conv->desc)); cudnnConvolutionMode_t mode = CUDNN_CROSS_CORRELATION; @@ -667,8 +659,7 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, int padding_height, int padding_width, int stride_height, - int stride_width) -{ + int stride_width) { CHECK_NOTNULL(conv); CHECK_NOTNULL(image); CHECK_NOTNULL(filter); @@ -697,8 +688,7 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, hl_conv->mode = mode; } -void hl_destroy_convolution_descriptor(hl_convolution_descriptor conv) -{ +void hl_destroy_convolution_descriptor(hl_convolution_descriptor conv) { CHECK_NOTNULL(conv); cudnn_convolution_descriptor hl_conv = (cudnn_convolution_descriptor)conv; @@ -753,8 +743,7 @@ void hl_convolution_forward(hl_tensor_descriptor input, void hl_convolution_forward_add_bias(hl_tensor_descriptor bias, real* bias_data, hl_tensor_descriptor output, - real* output_data) -{ + real* output_data) { CHECK_NOTNULL(bias); CHECK_NOTNULL(output); CHECK_NOTNULL(bias_data); @@ -782,8 +771,7 @@ void hl_convolution_forward_add_bias(hl_tensor_descriptor bias, void hl_convolution_backward_bias(hl_tensor_descriptor bias, real* bias_grad_data, hl_tensor_descriptor output, - real* output_grad_data) -{ + real* output_grad_data) { CHECK_NOTNULL(bias); CHECK_NOTNULL(output); CHECK_NOTNULL(bias_grad_data); @@ -814,7 +802,6 @@ void hl_convolution_backward_filter(hl_tensor_descriptor input, void* gpuWorkSpace, size_t sizeInBytes, int convBwdFilterAlgo) { - CHECK_NOTNULL(input); CHECK_NOTNULL(output); CHECK_NOTNULL(filter); @@ -889,8 +876,7 @@ void hl_convolution_backward_data(hl_tensor_descriptor input, void hl_softmax_forward(real *input, real *output, int height, - int width) -{ + int width) { #ifndef PADDLE_TYPE_DOUBLE cudnnDataType_t data_type = CUDNN_DATA_FLOAT; #else @@ -923,8 +909,7 @@ void hl_softmax_forward(real *input, void hl_softmax_backward(real *output_value, real *output_grad, int height, - int width) -{ + int width) { #ifndef PADDLE_TYPE_DOUBLE cudnnDataType_t data_type = CUDNN_DATA_FLOAT; #else diff --git a/paddle/cuda/src/hl_cuda_device.cc b/paddle/cuda/src/hl_cuda_device.cc index 3ea2c91bd5..ca19f210c5 100644 --- a/paddle/cuda/src/hl_cuda_device.cc +++ b/paddle/cuda/src/hl_cuda_device.cc @@ -203,8 +203,8 @@ inline pid_t gettid() { #endif pid_t tid = syscall(__NR_gettid); #endif - CHECK_NE(tid, -1); - return tid; + CHECK_NE((int)tid, -1); + return tid; } void hl_init(int device) { @@ -355,7 +355,8 @@ void* hl_malloc_host(size_t size) { void *dest_h; CHECK(size) << __func__ << ": the size for device memory is 0, please check."; - CHECK_CUDA(dynload::cudaHostAlloc((void**)&dest_h, size, cudaHostAllocDefault)); + CHECK_CUDA(dynload::cudaHostAlloc( + (void**)&dest_h, size, cudaHostAllocDefault)); return dest_h; } @@ -364,7 +365,7 @@ void hl_free_mem_host(void *dest_h) { CHECK_NOTNULL(dest_h); cudaError_t err = dynload::cudaFreeHost(dest_h); - CHECK (cudaSuccess == err || cudaErrorCudartUnloading == err) + CHECK(cudaSuccess == err || cudaErrorCudartUnloading == err) << hl_get_device_error_string(); } @@ -502,7 +503,8 @@ int hl_get_cuda_version() { return g_cuda_lib_version; } -void hl_create_thread_resources(int device, thread_device_resources device_res) { +void hl_create_thread_resources(int device, + thread_device_resources device_res) { CHECK_CUDA(dynload::cudaSetDevice(device)); /* create thread stream */ diff --git a/paddle/cuda/src/hl_cudart_wrap.cc b/paddle/cuda/src/hl_cudart_wrap.cc index 27bbd03bc3..fe755b8c26 100644 --- a/paddle/cuda/src/hl_cudart_wrap.cc +++ b/paddle/cuda/src/hl_cudart_wrap.cc @@ -78,48 +78,38 @@ __host__ cudaError_t CUDARTAPI cudaLaunchKernel(const void *func, dim3 blockDim, void **args, size_t sharedMem, - cudaStream_t stream) -{ - return dynload::cudaLaunchKernel(func, gridDim, blockDim, args, sharedMem, stream); + cudaStream_t stream) { + return dynload::cudaLaunchKernel(func, gridDim, blockDim, + args, sharedMem, stream); } #endif /* CUDART_VERSION >= 7000 */ -__host__ cudaError_t CUDARTAPI cudaLaunch(const void *func) -{ +__host__ cudaError_t CUDARTAPI cudaLaunch(const void *func) { return dynload::cudaLaunch(func); } __host__ cudaError_t CUDARTAPI cudaSetupArgument(const void *arg, size_t size, - size_t offset) -{ + size_t offset) { return dynload::cudaSetupArgument(arg, size, offset); } __host__ cudaError_t CUDARTAPI cudaConfigureCall(dim3 gridDim, dim3 blockDim, size_t sharedMem, - cudaStream_t stream) -{ + cudaStream_t stream) { return dynload::cudaConfigureCall(gridDim, blockDim, sharedMem, stream); } extern "C" { -void** CUDARTAPI __cudaRegisterFatBinary( - void *fatCubin -) -{ +void** CUDARTAPI __cudaRegisterFatBinary(void *fatCubin) { return dynload::__cudaRegisterFatBinary(fatCubin); - } -void CUDARTAPI __cudaUnregisterFatBinary( - void **fatCubinHandle -) -{ +void CUDARTAPI __cudaUnregisterFatBinary(void **fatCubinHandle) { return dynload::__cudaUnregisterFatBinary(fatCubinHandle); } diff --git a/paddle/cuda/src/hl_dso_loader.cc b/paddle/cuda/src/hl_dso_loader.cc index b564b96903..1a3ce08619 100644 --- a/paddle/cuda/src/hl_dso_loader.cc +++ b/paddle/cuda/src/hl_dso_loader.cc @@ -12,27 +12,28 @@ 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. */ - #include "hl_dso_loader.h" -#include "paddle/utils/Logging.h" #include "paddle/utils/CommandLineParser.h" +#include "paddle/utils/Logging.h" -P_DEFINE_string(cudnn_dir, "", +P_DEFINE_string(cudnn_dir, + "", "Specify path for loading libcudnn.so. For instance, " - "/usr/local/cudnn/lib64. If empty [default], dlopen will search " - "cudnn from LD_LIBRARY_PATH"); + "/usr/local/cudnn/lib. If empty [default], dlopen " + "will search cudnn from LD_LIBRARY_PATH"); -P_DEFINE_string(cuda_dir, "", +P_DEFINE_string(cuda_dir, + "", "Specify path for loading cuda library, such as libcublas, " - "libcurand. For instance, /usr/local/cuda/lib64. " - "(Note: libcudart can not be specified by cuda_dir, since some " + "libcurand. For instance, /usr/local/cuda/lib64. (Note: " + "libcudart can not be specified by cuda_dir, since some " "build-in function in cudart already ran before main entry). " - "If empty [default], dlopen will search cuda from LD_LIBRARY_PATH"); + "If default, dlopen will search cuda from LD_LIBRARY_PATH"); -static inline std::string join(const std::string& part1, const std::string& part2) { +static inline std::string join(const std::string& part1, + const std::string& part2) { // directory separator const char sep = '/'; - if (!part2.empty() && part2.front() == sep) { return part2; } @@ -46,100 +47,115 @@ static inline std::string join(const std::string& part1, const std::string& part return ret; } -static inline void GetDsoHandleFromDefaultPath( - std::string& dso_path, void** dso_handle, int dynload_flags) { - VLOG(3) << "Try to find cuda library: " << dso_path - << " from default system path."; - // default search from LD_LIBRARY_PATH/DYLD_LIBRARY_PATH +static inline void GetDsoHandleFromDefaultPath(std::string& dso_path, + void** dso_handle, + int dynload_flags) { + VLOG(3) << "Try to find cuda library: " << dso_path + << " from default system path."; + // default search from LD_LIBRARY_PATH/DYLD_LIBRARY_PATH + *dso_handle = dlopen(dso_path.c_str(), dynload_flags); + +// DYLD_LIBRARY_PATH is disabled after Mac OS 10.11 to +// bring System Integrity Projection (SIP), if dso_handle +// is null, search from default package path in Mac OS. +#if defined(__APPLE__) || defined(__OSX__) + if (nullptr == *dso_handle) { + dso_path = join("/usr/local/cuda/lib/", dso_path); *dso_handle = dlopen(dso_path.c_str(), dynload_flags); - - // DYLD_LIBRARY_PATH is disabled after Mac OS 10.11 to - // bring System Integrity Projection (SIP), if dso_handle - // is null, search from default package path in Mac OS. - #if defined(__APPLE__) || defined(__OSX__) if (nullptr == *dso_handle) { - dso_path = join("/usr/local/cuda/lib/", dso_path); - *dso_handle = dlopen(dso_path.c_str(), dynload_flags); - if (nullptr == *dso_handle) { - if (dso_path == "libcudnn.dylib") { - LOG(FATAL) << "Note: [Recommend] copy cudnn into /usr/local/cuda/ \n" - << "For instance, sudo tar -xzf cudnn-7.5-osx-x64-v5.0-ga.tgz -C " - << "/usr/local \n sudo chmod a+r /usr/local/cuda/include/cudnn.h " - << "/usr/local/cuda/lib/libcudnn*"; - } - } - } - #endif + if (dso_path == "libcudnn.dylib") { + LOG(FATAL) + << "Note: [Recommend] copy cudnn into /usr/local/cuda/ \n" // NOLINT + << "For instance, sudo tar -xzf " + "cudnn-7.5-osx-x64-v5.0-ga.tgz -C " // NOLINT + << "/usr/local \n sudo chmod a+r " + "/usr/local/cuda/include/cudnn.h " // NOLINT + << "/usr/local/cuda/lib/libcudnn*"; + } + } + } +#endif } -static inline void GetDsoHandleFromSearchPath( - const std::string& search_root, - const std::string& dso_name, - void** dso_handle) { - int dynload_flags = RTLD_LAZY | RTLD_LOCAL; - *dso_handle = nullptr; - - std::string dlPath = dso_name; - if (search_root.empty()) { - GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags); - } else { - // search xxx.so from custom path - dlPath = join(search_root, dso_name); - *dso_handle = dlopen(dlPath.c_str(), dynload_flags); - // if not found, search from default path - if (nullptr == dso_handle) { - LOG(WARNING) << "Failed to find cuda library: " << dlPath; - dlPath = dso_name; - GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags); - } +static inline void GetDsoHandleFromSearchPath(const std::string& search_root, + const std::string& dso_name, + void** dso_handle) { + int dynload_flags = RTLD_LAZY | RTLD_LOCAL; + *dso_handle = nullptr; + + std::string dlPath = dso_name; + if (search_root.empty()) { + GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags); + } else { + // search xxx.so from custom path + dlPath = join(search_root, dso_name); + *dso_handle = dlopen(dlPath.c_str(), dynload_flags); + // if not found, search from default path + if (nullptr == *dso_handle) { + LOG(WARNING) << "Failed to find cuda library: " << dlPath; + dlPath = dso_name; + GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags); } + } - CHECK(nullptr != *dso_handle) - << "Failed to find cuda library: " << dlPath << std::endl - << "Please specify its path correctly using one of the following ideas: \n" - - << "Idea 1. set cuda and cudnn lib path at runtime. " - << "http://www.paddlepaddle.org/doc/ui/cmd_argument/argument_outline.html \n" - << "For instance, issue command: paddle train --use_gpu=1 " - << "--cuda_dir=/usr/local/cudnn/lib --cudnn_dir=/usr/local/cudnn/lib ...\n" - - << "Idea 2. set environment variable LD_LIBRARY_PATH on Linux or " - << "DYLD_LIBRARY_PATH on Mac OS. \n" - << "For instance, issue command: export LD_LIBRARY_PATH=... \n" - - << "Note: After Mac OS 10.11, using the DYLD_LIBRARY_PATH is impossible " - << "unless System Integrity Protection (SIP) is disabled. However, @Idea 1" - << "always work well."; + CHECK(nullptr != *dso_handle) << "Failed to find cuda library: " << dlPath + << std::endl + << "Please specify its path correctly using " + "one of the following ways: \n" // NOLINT + + << "Method 1. set cuda and cudnn lib path at " + "runtime. " + << "http://www.paddlepaddle.org/doc/ui/" + "cmd_argument/" + "argument_outline.html \n" // NOLINT + << "For instance, issue command: paddle train " + "--use_gpu=1 " + << "--cuda_dir=/usr/local/cuda/lib64 " + "--cudnn_dir=/usr/local/cudnn/lib " + "...\n" // NOLINT + + << "Method 2. set environment variable " + "LD_LIBRARY_PATH on Linux or " + << "DYLD_LIBRARY_PATH on Mac OS. \n" + << "For instance, issue command: export " + "LD_LIBRARY_PATH=... \n" + + << "Note: After Mac OS 10.11, using the " + "DYLD_LIBRARY_PATH is impossible " + << "unless System Integrity Protection (SIP) " + "is disabled. However, " + "method 1 " // NOLINT + << "always work well."; } void GetCublasDsoHandle(void** dso_handle) { #if defined(__APPLE__) || defined(__OSX__) - GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.dylib", dso_handle); + GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.dylib", dso_handle); #else - GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.so", dso_handle); + GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.so", dso_handle); #endif } void GetCudnnDsoHandle(void** dso_handle) { #if defined(__APPLE__) || defined(__OSX__) - GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.dylib", dso_handle); + GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.dylib", dso_handle); #else - GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.so", dso_handle); + GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.so", dso_handle); #endif } void GetCudartDsoHandle(void** dso_handle) { #if defined(__APPLE__) || defined(__OSX__) - GetDsoHandleFromSearchPath("", "libcudart.dylib", dso_handle); + GetDsoHandleFromSearchPath("", "libcudart.dylib", dso_handle); #else - GetDsoHandleFromSearchPath("", "libcudart.so", dso_handle); + GetDsoHandleFromSearchPath("", "libcudart.so", dso_handle); #endif } void GetCurandDsoHandle(void** dso_handle) { #if defined(__APPLE__) || defined(__OSX__) - GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.dylib", dso_handle); + GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.dylib", dso_handle); #else - GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.so", dso_handle); + GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.so", dso_handle); #endif } diff --git a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp index e397c71c87..c2625bce9a 100644 --- a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp +++ b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp @@ -240,7 +240,7 @@ public: seqClassficationError_ = 0; } - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { os << config_.name() << "=" << (numSequences_ ? totalScore_ / numSequences_ : 0); os << " deletions error" diff --git a/paddle/gserver/evaluators/ChunkEvaluator.cpp b/paddle/gserver/evaluators/ChunkEvaluator.cpp index 22579891f3..6f5d2b47c3 100644 --- a/paddle/gserver/evaluators/ChunkEvaluator.cpp +++ b/paddle/gserver/evaluators/ChunkEvaluator.cpp @@ -114,7 +114,7 @@ public: numCorrect_ = 0; } - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { double precision = (double)numCorrect_ / numOutputSegments_; double recall = (double)numCorrect_ / numLabelSegments_; double f1 = diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 7bdcdaae53..d43dceea74 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -315,7 +315,7 @@ public: return 0; } - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { CHECK(colIdx_ + (int32_t)colNum_ >= 0 && colIdx_ - (int32_t)colNum_ < 0) << "column index [" << colIdx_ << "] out of range [-" << colNum_ << ", " << colNum_ << ")"; @@ -421,7 +421,7 @@ void AucEvaluator::distributeEval(ParameterClient2* client) { client->reduce(statNeg_, statNeg_, kBinNum_ + 1, FLAGS_trainer_id, 0); } -double AucEvaluator::calcAuc() { +double AucEvaluator::calcAuc() const { double totPos = 0.0; double totNeg = 0.0; double totPosPrev = 0.0; @@ -584,7 +584,7 @@ real PrecisionRecallEvaluator::evalImp(std::vector& arguments) { return 0; } -void PrecisionRecallEvaluator::printStats(std::ostream& os) { +void PrecisionRecallEvaluator::printStats(std::ostream& os) const { int label = config_.positive_label(); if (label != -1) { CHECK(label >= 0 && label < (int)statsInfo_.size()) diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h index b79a539384..e9957a5ce2 100644 --- a/paddle/gserver/evaluators/Evaluator.h +++ b/paddle/gserver/evaluators/Evaluator.h @@ -99,19 +99,19 @@ public: * @brief print the statistics of evaluate result * @note finish() should be called before printStats */ - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { os << config_.name() << "=" << (numSamples_ ? totalScore_ / numSamples_ : 0); } friend std::ostream& operator<<(std::ostream& os, - Evaluator& evaluator) { + const Evaluator& evaluator) { evaluator.printStats(os); return os; } friend std::ostream&& operator<<(std::ostream&& os, // NOLINT - Evaluator& evaluator) { + const Evaluator& evaluator) { evaluator.printStats(os); return std::move(os); } @@ -135,7 +135,7 @@ public: return -1; } virtual void finish() {} - virtual void printStats(std::ostream&) {} + virtual void printStats(std::ostream&) const {} }; /** * @brief evaluate AUC using colIdx-th column as prediction. @@ -165,7 +165,7 @@ public: virtual real evalImp(std::vector& arguments); - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { os << config_.name() << "=" << calcAuc(); } @@ -189,7 +189,7 @@ private: return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0; } - double calcAuc(); + double calcAuc() const; }; /** @@ -244,7 +244,7 @@ public: virtual real evalImp(std::vector& arguments); - virtual void printStats(std::ostream& os); + virtual void printStats(std::ostream& os) const; virtual void distributeEval(ParameterClient2* client); @@ -339,7 +339,7 @@ public: virtual void finish() { calc(predictArray_); } - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { os << " pos/neg" << "=" << pairArray_[0] / ((pairArray_[1] <= 0) ? 1.0 : pairArray_[1]); } diff --git a/paddle/gserver/gradientmachines/MultiNetwork.cpp b/paddle/gserver/gradientmachines/MultiNetwork.cpp index d30ca6f28e..b85d2e0c99 100644 --- a/paddle/gserver/gradientmachines/MultiNetwork.cpp +++ b/paddle/gserver/gradientmachines/MultiNetwork.cpp @@ -154,7 +154,7 @@ public: return -1; } - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { for (auto& evaluator : evaluators_) { evaluator->printStats(os); os << ' '; diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 3127b4dd9a..c77b00eb06 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -325,7 +325,7 @@ public: (void)arguments; return -1; } - virtual void printStats(std::ostream& os) { + virtual void printStats(std::ostream& os) const { for (auto& evaluator : evaluators_) { evaluator->printStats(os); os << ' '; diff --git a/paddle/math/BaseMatrix.cu b/paddle/math/BaseMatrix.cu index 54448bdb5a..2f32b3fdd1 100644 --- a/paddle/math/BaseMatrix.cu +++ b/paddle/math/BaseMatrix.cu @@ -1449,8 +1449,8 @@ template<> template int BaseMatrixT::applyRow(Agg agg, BaseMatrixT& b) { MatrixOffset offset(0, 0, 0, 0, 0, 0); - int numRows = b.height_; - int numCols = b.width_; + size_t numRows = b.height_; + size_t numCols = b.width_; CHECK_EQ(height_, numRows); CHECK_EQ(width_, 1UL); aggregate(agg, base::unary::identity(), base::binary::second(), b, numRows, @@ -1463,8 +1463,8 @@ template<> template int BaseMatrixT::applyRow(Agg agg, Saver sv, BaseMatrixT& b) { MatrixOffset offset(0, 0, 0, 0, 0, 0); - int numRows = b.height_; - int numCols = b.width_; + size_t numRows = b.height_; + size_t numCols = b.width_; CHECK_EQ(height_, numRows); CHECK_EQ(width_, 1UL); aggregate(agg, base::unary::identity(), sv, b, numRows, numCols, offset, @@ -1493,8 +1493,8 @@ template int BaseMatrixT::applyRow(Agg agg, Op op, Saver sv, BaseMatrixT& b, BaseMatrixT& c) { MatrixOffset offset(0, 0, 0, 0, 0, 0); - int numRows = b.height_; - int numCols = b.width_; + size_t numRows = b.height_; + size_t numCols = b.width_; CHECK_EQ(height_, numRows); CHECK_EQ(width_, 1UL); CHECK_EQ(c.height_, numRows); @@ -1524,8 +1524,8 @@ template<> template int BaseMatrixT::applyCol(Agg agg, BaseMatrixT& b) { MatrixOffset offset(0, 0, 0, 0, 0, 0); - int numRows = b.height_; - int numCols = b.width_; + size_t numRows = b.height_; + size_t numCols = b.width_; CHECK_EQ(width_, numCols); CHECK_EQ(height_, 1UL); aggregate(agg, base::unary::identity(), base::binary::second(), b, numRows, @@ -1538,8 +1538,8 @@ template<> template int BaseMatrixT::applyCol(Agg agg, Saver sv, BaseMatrixT& b) { MatrixOffset offset(0, 0, 0, 0, 0, 0); - int numRows = b.height_; - int numCols = b.width_; + size_t numRows = b.height_; + size_t numCols = b.width_; CHECK_EQ(width_, numCols); CHECK_EQ(height_, 1UL); aggregate(agg, base::unary::identity(), sv, b, numRows, numCols, offset, diff --git a/paddle/math/Vector.cpp b/paddle/math/Vector.cpp index 23c9caccea..68a1518d67 100644 --- a/paddle/math/Vector.cpp +++ b/paddle/math/Vector.cpp @@ -82,8 +82,8 @@ MatrixPtr VectorT::toOneHotSparseMatrix(size_t idRange, bool useGpu) { template <> MatrixPtr VectorT::toOneHotSparseMatrix(size_t idRange, bool useGpu) { - int height = getSize(); - int width = idRange; + size_t height = getSize(); + size_t width = idRange; MatrixPtr mat = Matrix::createSparseMatrix( height, idRange, height, NO_VALUE, SPARSE_CSR, false, useGpu); @@ -91,7 +91,7 @@ MatrixPtr VectorT::toOneHotSparseMatrix(size_t idRange, bool useGpu) { cpuIds.copyFrom(*this); int *idData = cpuIds.getData(); - for (int i = 0; i < height; i ++) { + for (decltype(height) i = 0; i < height; i ++) { const unsigned int id = idData[i]; CHECK_LT(id, width); mat->setRow(i, 1, &id, nullptr); diff --git a/paddle/pserver/ParameterServer2.cpp b/paddle/pserver/ParameterServer2.cpp index c8f37d0bf4..960fca2853 100644 --- a/paddle/pserver/ParameterServer2.cpp +++ b/paddle/pserver/ParameterServer2.cpp @@ -1469,7 +1469,6 @@ void ParameterServer2::waitPassFinish(const WaitPassFinishRequest& request, void ParameterServer2::synchronize(const SynchronizeRequest& request, ProtoResponseCallback callback) { - CHECK_LT(request.sync_object_id(), SyncObject_ARRAYSIZE); synchronizeBarriers_[request.sync_object_id()]->wait(); dataSize_ = 0; callback(SynchronizeResponse()); @@ -1477,7 +1476,6 @@ void ParameterServer2::synchronize(const SynchronizeRequest& request, void ParameterServer2::asyncFinishPass(const SynchronizeRequest& request, ProtoResponseCallback callback) { - CHECK_LT(request.sync_object_id(), SyncObject_ARRAYSIZE); synchronizeBarriers_[request.sync_object_id()]->wait(); callback(SynchronizeResponse()); diff --git a/paddle/utils/BarrierStat.cpp b/paddle/utils/BarrierStat.cpp index cbc738a839..f083ef3982 100644 --- a/paddle/utils/BarrierStat.cpp +++ b/paddle/utils/BarrierStat.cpp @@ -29,10 +29,10 @@ P_DEFINE_bool(log_barrier_show_log, false, // for performance tuning insight namespace paddle { -std::ostream &operator<<(std::ostream &output, BarrierStatBase &stat) { +std::ostream &operator<<(std::ostream &output, + const BarrierStatBase &stat) { if (FLAGS_log_barrier_abstract) { - std::lock_guard guard( - const_cast(stat).lock_); + std::lock_guard guard(stat.lock_); stat.showAbstract(output); } return output; @@ -136,7 +136,7 @@ void BarrierEndStat::reset(bool clearRawData) { totAbstract_.minDelta = UINT64_MAX; } -void BarrierEndStat::showAbstract(std::ostream &output) { +void BarrierEndStat::showAbstract(std::ostream &output) const { // do not support the case "<=2 pserver" if (numConnThreads_ <= 2 || !totSamples_) { return; @@ -272,7 +272,7 @@ void BarrierDeltaStat::reset(bool clearRawData) { totAbstract_.minDelta = UINT64_MAX; } -void BarrierDeltaStat::showAbstract(std::ostream &output) { +void BarrierDeltaStat::showAbstract(std::ostream &output) const { // do not support the case "<=2 pserver" if (numConnThreads_ <= 2 || !totSamples_) { return; diff --git a/paddle/utils/BarrierStat.h b/paddle/utils/BarrierStat.h index 22d6cc9bce..add1093758 100644 --- a/paddle/utils/BarrierStat.h +++ b/paddle/utils/BarrierStat.h @@ -218,11 +218,12 @@ public: } protected: - virtual void showAbstract(std::ostream &output) {} - friend std::ostream &operator<<(std::ostream &output, BarrierStatBase &stat); + virtual void showAbstract(std::ostream &output) const {} + friend std::ostream &operator<<(std::ostream &output, + const BarrierStatBase &stat); protected: - std::mutex lock_; + mutable std::mutex lock_; std::mutex abstractLock_; // see note on updaterStat // each freqency for each barrier trainer std::vector abstract_; @@ -262,7 +263,7 @@ protected: * log_barrier_abstract, log_barrier_lowest_nodes, log_barrier_threshold * control details. */ - virtual void showAbstract(std::ostream &output); + virtual void showAbstract(std::ostream &output) const; private: std::unique_ptr timeVector_; @@ -286,7 +287,7 @@ public: virtual bool checkPassBarrier() { return timeVector_->empty(); } protected: - virtual void showAbstract(std::ostream &outPut); + virtual void showAbstract(std::ostream &outPut) const; private: // store delta time in uint64_t, eg BP time of all trainers diff --git a/paddle/utils/CompilerMacros.h b/paddle/utils/CompilerMacros.h new file mode 100644 index 0000000000..4236d750c4 --- /dev/null +++ b/paddle/utils/CompilerMacros.h @@ -0,0 +1,17 @@ +/* Copyright (c) 2016 Baidu, Inc. 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. */ + +#pragma once + +#define ATTR_NORETURN __attribute__((noreturn)) diff --git a/paddle/utils/Logging.cpp b/paddle/utils/Logging.cpp index a0644940b5..9a6b1f2d83 100644 --- a/paddle/utils/Logging.cpp +++ b/paddle/utils/Logging.cpp @@ -134,7 +134,7 @@ static void initializeLogFds(char* argv0) { gLogInited = true; } -static void (*gFailureFunctionPtr)() __attribute__((noreturn)) = abort; +static void (*gFailureFunctionPtr)() ATTR_NORETURN = abort; LogMessage::LogMessage(const char* fname, int line, int severity) : fname_(fname), line_(line), severity_(severity) {} @@ -171,7 +171,7 @@ void setMinLogLevel(int level) { paddle::internal::gMinLogLevel = level; } -void installFailureFunction(void (*callback)()) { +void installFailureFunction(void (*callback)() ATTR_NORETURN) { paddle::internal::gFailureFunctionPtr = callback; } diff --git a/paddle/utils/Logging.h b/paddle/utils/Logging.h index 7fdfa3240c..46b6a7feeb 100644 --- a/paddle/utils/Logging.h +++ b/paddle/utils/Logging.h @@ -23,6 +23,7 @@ limitations under the License. */ #include #ifndef PADDLE_USE_GLOG +#include "CompilerMacros.h" //! TODO(yuyang18): Move this utility macro into some global header. #define PP_CAT(a, b) PP_CAT_I(a, b) @@ -168,7 +169,7 @@ void setMinLogLevel(int level); * @brief Install Log(Fatal) failure function. Default is abort(); * @param callback: The failure function. */ -void installFailureFunction(void (*callback)()); +void installFailureFunction(void (*callback)() ATTR_NORETURN); /** * @brief installFailureWriter