def run_benchmark(args): from copy import deepcopy from bert_serving.server import BertServer # load vocabulary with open(args.client_vocab_file, encoding='utf8') as fp: vocab = list(set(vv for v in fp for vv in v.strip().split())) print('vocabulary size: %d' % len(vocab)) # select those non-empty test cases all_exp_names = [ k.replace('test_', '') for k, v in vars(args).items() if k.startswith('test_') and v ] for exp_name in all_exp_names: # set common args cargs = deepcopy(args) exp_vars = vars(args)['test_%s' % exp_name] avg_speed = [] for cvar in exp_vars: # override exp args setattr(cargs, exp_name, cvar) server = BertServer(cargs) server.start() time.sleep(cargs.wait_till_ready) # sleep until server is ready all_clients = [ BenchmarkClient(cargs, vocab) for _ in range(cargs.num_client) ] for bc in all_clients: bc.start() clients_speed = [] for bc in all_clients: bc.join() clients_speed.append(cargs.client_batch_size / bc.avg_time) server.close() max_speed, min_speed, cavg_speed = int(max(clients_speed)), int( min(clients_speed)), int(mean(clients_speed)) print('avg speed: %d\tmax speed: %d\tmin speed: %d' % (cavg_speed, max_speed, min_speed), flush=True) avg_speed.append(cavg_speed) with open( 'benchmark-%d%s.result' % (args.num_worker, '-fp16' if args.fp16 else ''), 'a') as fw: print('\n|`%s`\t|samples/s|\n|---|---|' % exp_name, file=fw) for cvar, cavg_speed in zip(exp_vars, avg_speed): print('|%s\t|%d|' % (cvar, cavg_speed), file=fw) # for additional plotting print('\n%s = %s\n%s = %s' % (exp_name, exp_vars, 'speed', avg_speed), file=fw)
def run_benchmark(args): from copy import deepcopy from bert_serving.server import BertServer # load vocabulary with open(args.client_vocab_file, encoding='utf8') as fp: vocab = list(set(vv for v in fp for vv in v.strip().split())) print('vocabulary size: %d' % len(vocab)) all_exp_names = [ k.replace('test_', '') for k in vars(args).keys() if k.startswith('test_') ] fp = open( 'benchmark-%d%s.result' % (args.num_worker, '-fp16' if args.fp16 else ''), 'w') for exp_name in all_exp_names: # set common args cargs = deepcopy(args) exp_vars = vars(args)['test_%s' % exp_name] avg_speed = [] fp.write('\n%s\tsamples/s\n' % exp_name) for cvar in exp_vars: # override exp args setattr(cargs, exp_name, cvar) server = BertServer(cargs) server.start() time.sleep(cargs.wait_till_ready) # sleep until server is ready all_clients = [ BenchmarkClient(cargs, vocab) for _ in range(cargs.num_client) ] for bc in all_clients: bc.start() clients_speed = [] for bc in all_clients: bc.join() clients_speed.append(cargs.client_batch_size / bc.avg_time) server.close() max_speed, min_speed, cavg_speed = int(max(clients_speed)), int( min(clients_speed)), int(mean(clients_speed)) print('avg speed: %d\tmax speed: %d\tmin speed: %d' % (cavg_speed, max_speed, min_speed), flush=True) fp.write('%s\t%d\n' % (cvar, cavg_speed)) fp.flush() avg_speed.append(cavg_speed) # for plotting fp.write('%s\n%s\n' % (exp_vars, avg_speed)) fp.flush() fp.close()
def save_emb(): common = [ '-model_dir', '/home/ydu/BERT/uncased_L-12_H-768_A-12/', '-num_worker', '2', '-port', '5555', '-port_out', '5556', '-max_seq_len', '128', '-max_batch_size', '256', # '-tuned_model_dir', '/home/ydu/BERT/bert_mgpu/pretrain_output/10k-32b-all4data/', # '-ckpt_name', 'model.ckpt-2500', ] args = get_args_parser().parse_args(common) # folder = ['books', 'dvd', 'electronics', 'kitchen'] data_path = '/home/ydu/BERT/DATA/' data_folder = ['metacritic', 'imdb', 'amazon', 'reddit'] # model_path = 'home/ydu/BERT/bert_mgpu/results/' # model_folder = 'amazon-balanced/' # model_type = 'bert-tune' data = {} # setattr(args, 'tuned_model_dir', '/home/ydu/BERT/bert_mgpu/pretrain_output/reddit-pretrain') # setattr(args, 'ckpt_name', 'model.ckpt-2500') setattr(args, 'tuned_model_dir', '/home/ydu/BERT/bert_mgpu/pretrain_output/10k-32b-all4data') setattr(args, 'ckpt_name', 'model.ckpt-2500') for d in data_folder: fn = data_path + d + '/all.tsv' print("===========", fn, "================") text = read_tsv(fn) server = BertServer(args) server.start() print('wait until server is ready...') time.sleep(20) print('encoding...') bc = BertClient() data[d] = bc.encode(text) bc.close() server.close() pickle_name = data_path + 'EMB/allpre_emb.pickle' with open(pickle_name, 'wb') as handle: pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL) return pickle_name
def extract_topics_all(issues_path, model_dir, topic_file, n_topics): """Extract topics for all issues with top n_topics topics""" topic_all = [] text_all, divide_list = combine_issues(issues_path) topics = tp.get_topic_list(topic_file) topic_embedding = tp.get_topic_embedding(topics, port=3500, port_out=3501, model_path=model_dir) #topic_embedding = np.load('../output/topic_embedding.npy') print('topic embedding shape = ', topic_embedding.shape) stop_words = tp.expand_stopwords() print(len(stop_words)) text_flat_tokenized, text_article_tokenized = tp.bert_tokens(text_all) tfidf_biglist = tp.tfidf_vec(text_flat_tokenized, stop_words) port_in = 6550 port_out = 6551 tmp_dir = './output/tmp' if not os.path.isdir(tmp_dir): os.makedirs(tmp_dir) ZEROMQ_SOCK_TMP_DIR=tmp_dir common = [ '-model_dir', model_dir, '-num_worker', '2', '-port', str(port_in), '-port_out', str(port_out), '-max_seq_len', '20', '-max_batch_size', '256', '-pooling_strategy', 'NONE', '-pooling_layer', '-2', '-graph_tmp_dir', tmp_dir, '-cpu', '-show_tokens_to_client', ] args = get_args_parser().parse_args(common) server = BertServer(args) server.start() print('wait until server is ready...') time.sleep(20) print('encoding...') for issue_num in range(len(text_all)): #issue_num = 0 divide_list_each = divide_list[issue_num] text_one_issue = text_all[issue_num] vec = tp.get_word_embedding_server_on(text_one_issue, port=port_in, port_out=port_out) topics_issue, sort_topic_sim = tp.get_topics_one_issue(vec,topic_embedding,topics, divide_list_each, tfidf_biglist, issue_num, n_topics) topic_all.append(topics_issue) server.close() topic_folder = './output/topic' if not os.path.isdir(topic_folder): os.makedirs(topic_folder) with open(topic_folder + '/topic.pkl', 'wb') as f: pickle.dump(topic_all, f) return topic_all
class CaissBertServer: def __init__(self, model_path): args = get_args_parser().parse_args([ '-num_worker', '4', '-model_dir', model_path, '-port', '5555', '-port_out', '5556', '-max_seq_len', 'NONE', '-mask_cls_sep', '-cpu' ]) # 详细说明,请参考:https://github.com/hanxiao/bert-as-service self._server = BertServer(args) def start(self): self._server.start() def close(self): self._server.close()
class BertEncoderServer(BaseTextEncoder): store_args_kwargs = True is_trained = True def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) bert_args = ['-%s' % v for v in args] for k, v in kwargs.items(): bert_args.append('-%s' % k) bert_args.append(str(v)) self._bert_args = bert_args def post_init(self): from bert_serving.server import BertServer from bert_serving.server import get_args_parser self.bert_server = BertServer(get_args_parser().parse_args(self._bert_args)) self.bert_server.start() self.bert_server.is_ready.wait() def close(self): self.bert_server.close()
for k, v in common.items(): setattr(args, k, v) for pool_layer in range(1, 13): setattr(args, 'pooling_layer', [-pool_layer]) server = BertServer(args) server.start() print('wait until server is ready...') time.sleep(15) print('encoding...') bc = BertClient(port=common['port'], port_out=common['port_out'], show_server_config=True) subset_vec_all_layers.append(bc.encode(subset_text)) bc.close() server.close() print('done at layer -%d' % pool_layer) def vis(embed, vis_alg='PCA', pool_alg='REDUCE_MEAN'): plt.close() fig = plt.figure() plt.rcParams['figure.figsize'] = [21, 7] for idx, ebd in enumerate(embed): ax = plt.subplot(2, 6, idx + 1) vis_x = ebd[:, 0] vis_y = ebd[:, 1] plt.scatter(vis_x, vis_y, c=subset_label, cmap=ListedColormap(["blue", "green", "yellow", "red"]),