From 1579c5363566a6d39fe901ddcc80a3581aad2461 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 5 Feb 2019 15:36:33 +0100 Subject: [PATCH] more explicit notation: num_train_step => num_train_optimization_steps --- .gitignore | 5 ++++- examples/run_classifier.py | 17 ++++++++--------- examples/run_lm_finetuning.py | 17 ++++++++--------- examples/run_squad.py | 17 ++++++++--------- examples/run_squad2.py | 17 ++++++++--------- examples/run_swag.py | 17 ++++++++--------- 6 files changed, 44 insertions(+), 46 deletions(-) diff --git a/.gitignore b/.gitignore index 56a5f0d38a..aeff829aa0 100644 --- a/.gitignore +++ b/.gitignore @@ -119,4 +119,7 @@ dmypy.json .vscode # TF code -tensorflow_code \ No newline at end of file +tensorflow_code + +# Models +models \ No newline at end of file diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 52205552ca..52550e85fa 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -438,11 +438,13 @@ def main(): tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None - num_train_steps = None + num_train_optimization_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) - num_train_steps = - len(train_examples) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs + num_train_optimization_steps = int( + len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs + if args.local_rank != -1: + num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model model = BertForSequenceClassification.from_pretrained(args.bert_model, @@ -468,9 +470,6 @@ def main(): {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] - t_total = num_train_steps - if args.local_rank != -1: - t_total = t_total // torch.distributed.get_world_size() if args.fp16: try: from apex.optimizers import FP16_Optimizer @@ -491,7 +490,7 @@ def main(): optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, - t_total=t_total) + t_total=num_train_optimization_steps) global_step = 0 nb_tr_steps = 0 @@ -502,7 +501,7 @@ def main(): logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) - logger.info(" Num steps = %d", num_train_steps) + logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) @@ -539,7 +538,7 @@ def main(): if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically - lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index c9c71ad5a1..0ae5bcbb56 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -515,13 +515,15 @@ def main(): tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) #train_examples = None - num_train_steps = None + num_train_optimization_steps = None if args.do_train: print("Loading Train Dataset", args.train_file) train_dataset = BERTDataset(args.train_file, tokenizer, seq_len=args.max_seq_length, corpus_lines=None, on_memory=args.on_memory) - num_train_steps = - len(train_dataset) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs + num_train_optimization_steps = int( + len(train_dataset) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs + if args.local_rank != -1: + num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model model = BertForPreTraining.from_pretrained(args.bert_model) @@ -545,9 +547,6 @@ def main(): {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] - t_total = num_train_steps - if args.local_rank != -1: - t_total = t_total // torch.distributed.get_world_size() if args.fp16: try: from apex.optimizers import FP16_Optimizer @@ -568,14 +567,14 @@ def main(): optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, - t_total=t_total) + t_total=num_train_optimization_steps) global_step = 0 if args.do_train: logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Batch size = %d", args.train_batch_size) - logger.info(" Num steps = %d", num_train_steps) + logger.info(" Num steps = %d", num_train_optimization_steps) if args.local_rank == -1: train_sampler = RandomSampler(train_dataset) @@ -608,7 +607,7 @@ def main(): if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically - lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() diff --git a/examples/run_squad.py b/examples/run_squad.py index 421821006e..86f49df942 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -784,12 +784,14 @@ def main(): tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None - num_train_steps = None + num_train_optimization_steps = None if args.do_train: train_examples = read_squad_examples( input_file=args.train_file, is_training=True) - num_train_steps = - len(train_examples) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs + num_train_optimization_steps = int( + len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs + if args.local_rank != -1: + num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model model = BertForQuestionAnswering.from_pretrained(args.bert_model, @@ -821,9 +823,6 @@ def main(): {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] - t_total = num_train_steps - if args.local_rank != -1: - t_total = t_total // torch.distributed.get_world_size() if args.fp16: try: from apex.optimizers import FP16_Optimizer @@ -843,7 +842,7 @@ def main(): optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, - t_total=t_total) + t_total=num_train_optimization_steps) global_step = 0 if args.do_train: @@ -869,7 +868,7 @@ def main(): logger.info(" Num orig examples = %d", len(train_examples)) logger.info(" Num split examples = %d", len(train_features)) logger.info(" Batch size = %d", args.train_batch_size) - logger.info(" Num steps = %d", num_train_steps) + logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) @@ -903,7 +902,7 @@ def main(): if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically - lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() diff --git a/examples/run_squad2.py b/examples/run_squad2.py index 6adad7d8ea..ba96a81b98 100644 --- a/examples/run_squad2.py +++ b/examples/run_squad2.py @@ -877,12 +877,14 @@ def main(): tokenizer = BertTokenizer.from_pretrained(args.bert_model) train_examples = None - num_train_steps = None + num_train_optimization_steps = None if args.do_train: train_examples = read_squad_examples( input_file=args.train_file, is_training=True) - num_train_steps = - len(train_examples) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs + num_train_optimization_steps = int( + len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs + if args.local_rank != -1: + num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model model = BertForQuestionAnswering.from_pretrained(args.bert_model, @@ -914,9 +916,6 @@ def main(): {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] - t_total = num_train_steps - if args.local_rank != -1: - t_total = t_total // torch.distributed.get_world_size() if args.fp16: try: from apex.optimizers import FP16_Optimizer @@ -936,7 +935,7 @@ def main(): optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, - t_total=t_total) + t_total=num_train_optimization_steps) global_step = 0 if args.do_train: @@ -962,7 +961,7 @@ def main(): logger.info(" Num orig examples = %d", len(train_examples)) logger.info(" Num split examples = %d", len(train_features)) logger.info(" Batch size = %d", args.train_batch_size) - logger.info(" Num steps = %d", num_train_steps) + logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) @@ -997,7 +996,7 @@ def main(): if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically - lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() diff --git a/examples/run_swag.py b/examples/run_swag.py index 1856118ac5..c3cc69ae6d 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -349,11 +349,13 @@ def main(): tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None - num_train_steps = None + num_train_optimization_steps = None if args.do_train: train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True) - num_train_steps = - len(train_examples) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs + num_train_optimization_steps = int( + len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs + if args.local_rank != -1: + num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model model = BertForMultipleChoice.from_pretrained(args.bert_model, @@ -384,9 +386,6 @@ def main(): {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] - t_total = num_train_steps - if args.local_rank != -1: - t_total = t_total // torch.distributed.get_world_size() if args.fp16: try: from apex.optimizers import FP16_Optimizer @@ -406,7 +405,7 @@ def main(): optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, - t_total=t_total) + t_total=num_train_optimization_steps) global_step = 0 if args.do_train: @@ -415,7 +414,7 @@ def main(): logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) - logger.info(" Num steps = %d", num_train_steps) + logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long) all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long) all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long) @@ -455,7 +454,7 @@ def main(): if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically - lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step()