From b219029c4566eebd4183219719c8e86f4671163c Mon Sep 17 00:00:00 2001 From: erenup <43887288+erenup@users.noreply.github.com> Date: Sun, 11 Aug 2019 15:20:37 +0800 Subject: [PATCH] refactoring old run_swag. This script is mainly refatored from run_squad in pytorch_transformers --- examples/single_model_scripts/run_swag.py | 684 +++++++++++++--------- 1 file changed, 401 insertions(+), 283 deletions(-) diff --git a/examples/single_model_scripts/run_swag.py b/examples/single_model_scripts/run_swag.py index fdda56e40b..4857c4b41b 100644 --- a/examples/single_model_scripts/run_swag.py +++ b/examples/single_model_scripts/run_swag.py @@ -13,17 +13,18 @@ # 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. -"""BERT finetuning runner.""" - -from __future__ import absolute_import +"""BERT finetuning runner. + Finetuning the library models for multiple choice on SWAG (Bert). +""" +from __future__ import absolute_import, division, print_function import argparse -import csv import logging +import csv import os import random import sys -from io import open +import glob import numpy as np import torch @@ -32,16 +33,21 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange -from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME -from pytorch_transformers.modeling_bert import BertForMultipleChoice, BertConfig -from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule -from pytorch_transformers.tokenization_bert import BertTokenizer +from tensorboardX import SummaryWriter + +from pytorch_transformers import (WEIGHTS_NAME, BertConfig, + BertForMultipleChoice, BertTokenizer) + +from pytorch_transformers import AdamW, WarmupLinearSchedule -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO) logger = logging.getLogger(__name__) +ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \ + for conf in [BertConfig]), ()) + +MODEL_CLASSES = { + 'bert': (BertConfig, BertForMultipleChoice, BertTokenizer), +} class SwagExample(object): """A single training/test example for the SWAG dataset.""" @@ -84,7 +90,6 @@ class SwagExample(object): return ", ".join(l) - class InputFeatures(object): def __init__(self, example_id, @@ -103,8 +108,7 @@ class InputFeatures(object): ] self.label = label - -def read_swag_examples(input_file, is_training): +def read_swag_examples(input_file, is_training=True): with open(input_file, 'r', encoding='utf-8') as f: reader = csv.reader(f) lines = [] @@ -156,7 +160,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, # final decision of the model, we will run a softmax over these 4 # outputs. features = [] - for example_index, example in enumerate(examples): + for example_index, example in tqdm(enumerate(examples)): context_tokens = tokenizer.tokenize(example.context_sentence) start_ending_tokens = tokenizer.tokenize(example.start_ending) @@ -242,314 +246,428 @@ def select_field(features, field): for feature in features ] + +def set_seed(args): + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if args.n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + +def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache + + # Load data features from cache or dataset file + input_file = args.predict_file if evaluate else args.train_file + cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format( + 'dev' if evaluate else 'train', + list(filter(None, args.model_name_or_path.split('/'))).pop(), + str(args.max_seq_length))) + if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples: + logger.info("Loading features from cached file %s", cached_features_file) + features = torch.load(cached_features_file) + else: + logger.info("Creating features from dataset file at %s", input_file) + examples = read_swag_examples(input_file) + features = convert_examples_to_features( + examples, tokenizer, args.max_seq_length, not evaluate) + + if args.local_rank in [-1, 0]: + logger.info("Saving features into cached file %s", cached_features_file) + torch.save(features, cached_features_file) + + if args.local_rank == 0: + torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache + + # Convert to Tensors and build dataset + all_input_ids = torch.tensor(select_field(features, 'input_ids'), dtype=torch.long) + all_input_mask = torch.tensor(select_field(features, 'input_mask'), dtype=torch.long) + all_segment_ids = torch.tensor(select_field(features, 'segment_ids'), dtype=torch.long) + all_label = torch.tensor([f.label for f in features], dtype=torch.long) + + if evaluate: + dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, + all_label) + else: + dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, + all_label) + + if output_examples: + return dataset, examples, features + return dataset +def train(args, train_dataset, model, tokenizer): + """ Train the model """ + if args.local_rank in [-1, 0]: + tb_writer = SummaryWriter() + + args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) + train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) + train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) + + if args.max_steps > 0: + t_total = args.max_steps + args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 + else: + t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs + + # Prepare optimizer and schedule (linear warmup and decay) + no_decay = ['bias', 'LayerNorm.weight'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, + {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) + scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total) + if args.fp16: + try: + from apex import amp + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") + model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) + + # multi-gpu training (should be after apex fp16 initialization) + if args.n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Distributed training (should be after apex fp16 initialization) + if args.local_rank != -1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], + output_device=args.local_rank, + find_unused_parameters=True) + + # Train! + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_dataset)) + logger.info(" Num Epochs = %d", args.num_train_epochs) + logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) + logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", + args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) + logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) + logger.info(" Total optimization steps = %d", t_total) + + global_step = 0 + tr_loss, logging_loss = 0.0, 0.0 + model.zero_grad() + train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) + set_seed(args) # Added here for reproductibility (even between python 2 and 3) + for _ in train_iterator: + epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) + for step, batch in enumerate(epoch_iterator): + model.train() + batch = tuple(t.to(args.device) for t in batch) + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + #'token_type_ids': None if args.model_type == 'xlm' else batch[2], + 'token_type_ids': batch[2], + 'labels': batch[3]} + # if args.model_type in ['xlnet', 'xlm']: + # inputs.update({'cls_index': batch[5], + # 'p_mask': batch[6]}) + outputs = model(**inputs) + loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) + + if args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + + if args.fp16: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) + else: + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) + + tr_loss += loss.item() + if (step + 1) % args.gradient_accumulation_steps == 0: + optimizer.step() + scheduler.step() # Update learning rate schedule + model.zero_grad() + global_step += 1 + + if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: + # Log metrics + if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well + results = evaluate(args, model, tokenizer) + for key, value in results.items(): + tb_writer.add_scalar('eval_{}'.format(key), value, global_step) + tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step) + tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step) + logging_loss = tr_loss + + if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: + # Save model checkpoint + output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step)) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_save.save_pretrained(output_dir) + tokenizer.save_vocabulary(output_dir) + torch.save(args, os.path.join(output_dir, 'training_args.bin')) + logger.info("Saving model checkpoint to %s", output_dir) + + if args.max_steps > 0 and global_step > args.max_steps: + epoch_iterator.close() + break + if args.max_steps > 0 and global_step > args.max_steps: + train_iterator.close() + break + + if args.local_rank in [-1, 0]: + tb_writer.close() + + return global_step, tr_loss / global_step + +def evaluate(args, model, tokenizer, prefix=""): + dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) + + if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: + os.makedirs(args.output_dir) + + args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) + # Note that DistributedSampler samples randomly + eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset) + eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) + + # Eval! + logger.info("***** Running evaluation {} *****".format(prefix)) + logger.info(" Num examples = %d", len(dataset)) + logger.info(" Batch size = %d", args.eval_batch_size) + + + eval_loss, eval_accuracy = 0, 0 + nb_eval_steps, nb_eval_examples = 0, 0 + + for batch in tqdm(eval_dataloader, desc="Evaluating"): + model.eval() + batch = tuple(t.to(args.device) for t in batch) + with torch.no_grad(): + inputs = {'input_ids': batch[0], + 'attention_mask': batch[1], + # 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids + 'token_type_ids': batch[2], + 'labels': batch[3]} + + # if args.model_type in ['xlnet', 'xlm']: + # inputs.update({'cls_index': batch[4], + # 'p_mask': batch[5]}) + outputs = model(**inputs) + tmp_eval_loss, logits = outputs[:2] + eval_loss += tmp_eval_loss.mean().item() + + logits = logits.detach().cpu().numpy() + label_ids = inputs['labels'].to('cpu').numpy() + tmp_eval_accuracy = accuracy(logits, label_ids) + eval_accuracy += tmp_eval_accuracy + + nb_eval_steps += 1 + nb_eval_examples += inputs['input_ids'].size(0) + + eval_loss = eval_loss / nb_eval_steps + eval_accuracy = eval_accuracy / nb_eval_examples + result = {'eval_loss': eval_loss, + 'eval_accuracy': eval_accuracy} + + output_eval_file = os.path.join(args.output_dir, "eval_results.txt") + with open(output_eval_file, "w") as writer: + logger.info("***** Eval results *****") + for key in sorted(result.keys()): + logger.info("%s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + + return result + def main(): parser = argparse.ArgumentParser() ## Required parameters - parser.add_argument("--data_dir", - default=None, - type=str, - required=True, - help="The input data dir. Should contain the .csv files (or other data files) for the task.") - parser.add_argument("--bert_model", default=None, type=str, required=True, - help="Bert pre-trained model selected in the list: bert-base-uncased, " - "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " - "bert-base-multilingual-cased, bert-base-chinese.") - parser.add_argument("--output_dir", - default=None, - type=str, - required=True, - help="The output directory where the model checkpoints will be written.") + parser.add_argument("--train_file", default=None, type=str, required=True, + help="SWAG csv for training. E.g., train.csv") + parser.add_argument("--predict_file", default=None, type=str, required=True, + help="SWAG csv for predictions. E.g., val.csv or test.csv") + parser.add_argument("--model_type", default=None, type=str, required=True, + help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) + parser.add_argument("--model_name_or_path", default=None, type=str, required=True, + help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) + parser.add_argument("--output_dir", default=None, type=str, required=True, + help="The output directory where the model checkpoints and predictions will be written.") ## Other parameters - parser.add_argument("--max_seq_length", - default=128, - type=int, - help="The maximum total input sequence length after WordPiece tokenization. \n" - "Sequences longer than this will be truncated, and sequences shorter \n" - "than this will be padded.") - parser.add_argument("--do_train", - action='store_true', + parser.add_argument("--config_name", default="", type=str, + help="Pretrained config name or path if not the same as model_name") + parser.add_argument("--tokenizer_name", default="", type=str, + help="Pretrained tokenizer name or path if not the same as model_name") + parser.add_argument("--max_seq_length", default=384, type=int, + help="The maximum total input sequence length after tokenization. Sequences " + "longer than this will be truncated, and sequences shorter than this will be padded.") + parser.add_argument("--do_train", action='store_true', help="Whether to run training.") - parser.add_argument("--do_eval", - action='store_true', + parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") - parser.add_argument("--do_lower_case", - action='store_true', + parser.add_argument("--evaluate_during_training", action='store_true', + help="Rul evaluation during training at each logging step.") + parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") - parser.add_argument("--train_batch_size", - default=32, - type=int, - help="Total batch size for training.") - parser.add_argument("--eval_batch_size", - default=8, - type=int, - help="Total batch size for eval.") - parser.add_argument("--learning_rate", - default=5e-5, - type=float, - help="The initial learning rate for Adam.") - parser.add_argument("--num_train_epochs", - default=3.0, - type=float, - help="Total number of training epochs to perform.") - parser.add_argument("--warmup_proportion", - default=0.1, - type=float, - help="Proportion of training to perform linear learning rate warmup for. " - "E.g., 0.1 = 10%% of training.") - parser.add_argument("--no_cuda", - action='store_true', - help="Whether not to use CUDA when available") - parser.add_argument("--local_rank", - type=int, - default=-1, - help="local_rank for distributed training on gpus") - parser.add_argument('--seed', - type=int, - default=42, - help="random seed for initialization") - parser.add_argument('--gradient_accumulation_steps', - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.") - parser.add_argument('--fp16', - action='store_true', - help="Whether to use 16-bit float precision instead of 32-bit") - parser.add_argument('--loss_scale', - type=float, default=0, - help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" - "0 (default value): dynamic loss scaling.\n" - "Positive power of 2: static loss scaling value.\n") + parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, + help="Batch size per GPU/CPU for training.") + parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, + help="Batch size per GPU/CPU for evaluation.") + parser.add_argument("--learning_rate", default=5e-5, type=float, + help="The initial learning rate for Adam.") + parser.add_argument('--gradient_accumulation_steps', type=int, default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.") + parser.add_argument("--weight_decay", default=0.0, type=float, + help="Weight deay if we apply some.") + parser.add_argument("--adam_epsilon", default=1e-8, type=float, + help="Epsilon for Adam optimizer.") + parser.add_argument("--max_grad_norm", default=1.0, type=float, + help="Max gradient norm.") + parser.add_argument("--num_train_epochs", default=3.0, type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--max_steps", default=-1, type=int, + help="If > 0: set total number of training steps to perform. Override num_train_epochs.") + parser.add_argument("--warmup_steps", default=0, type=int, + help="Linear warmup over warmup_steps.") + + parser.add_argument('--logging_steps', type=int, default=50, + help="Log every X updates steps.") + parser.add_argument('--save_steps', type=int, default=50, + help="Save checkpoint every X updates steps.") + parser.add_argument("--eval_all_checkpoints", action='store_true', + help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number") + parser.add_argument("--no_cuda", action='store_true', + help="Whether not to use CUDA when available") + parser.add_argument('--overwrite_output_dir', action='store_true', + help="Overwrite the content of the output directory") + parser.add_argument('--overwrite_cache', action='store_true', + help="Overwrite the cached training and evaluation sets") + parser.add_argument('--seed', type=int, default=42, + help="random seed for initialization") + + parser.add_argument("--local_rank", type=int, default=-1, + help="local_rank for distributed training on gpus") + parser.add_argument('--fp16', action='store_true', + help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") + parser.add_argument('--fp16_opt_level', type=str, default='O1', + help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." + "See details at https://nvidia.github.io/apex/amp.html") + parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") + parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: + raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir)) + + # Setup distant debugging if needed + if args.server_ip and args.server_port: + # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script + import ptvsd + print("Waiting for debugger attach") + ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) + ptvsd.wait_for_attach() + + # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") - n_gpu = torch.cuda.device_count() - else: + args.n_gpu = torch.cuda.device_count() + else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) - n_gpu = 1 - # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( - device, n_gpu, bool(args.local_rank != -1), args.fp16)) + args.n_gpu = 1 + args.device = device - if args.gradient_accumulation_steps < 1: - raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( - args.gradient_accumulation_steps)) + # Setup logging + logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) + logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", + args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16) - args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps + # Set seed + set_seed(args) - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - if n_gpu > 0: - torch.cuda.manual_seed_all(args.seed) + # Load pretrained model and tokenizer + if args.local_rank not in [-1, 0]: + torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab - if not args.do_train and not args.do_eval: - raise ValueError("At least one of `do_train` or `do_eval` must be True.") + args.model_type = args.model_type.lower() + config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] + config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path) + tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) + model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) - if os.path.exists(args.output_dir) and os.listdir(args.output_dir): - raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) - if not os.path.exists(args.output_dir): - os.makedirs(args.output_dir) + if args.local_rank == 0: + torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab - tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) + model.to(args.device) - # Prepare model - model = BertForMultipleChoice.from_pretrained(args.bert_model, - cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)), - num_choices=4) - if args.fp16: - model.half() - model.to(device) - if args.local_rank != -1: - try: - from apex.parallel import DistributedDataParallel as DDP - except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") - - model = DDP(model) - elif n_gpu > 1: - model = torch.nn.DataParallel(model) + logger.info("Training/evaluation parameters %s", args) + # Training if args.do_train: - - # Prepare data loader - - train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True) - train_features = convert_examples_to_features( - train_examples, tokenizer, args.max_seq_length, True) - 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) - all_label = torch.tensor([f.label for f in train_features], dtype=torch.long) - train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) - if args.local_rank == -1: - train_sampler = RandomSampler(train_data) - else: - train_sampler = DistributedSampler(train_data) - train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) - - num_train_optimization_steps = len(train_dataloader) // 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 optimizer - - param_optimizer = list(model.named_parameters()) - - # hack to remove pooler, which is not used - # thus it produce None grad that break apex - param_optimizer = [n for n in param_optimizer] - - no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] - optimizer_grouped_parameters = [ - {'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} - ] - if args.fp16: - try: - from apex.optimizers import FP16_Optimizer - from apex.optimizers import FusedAdam - except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") - - optimizer = FusedAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - bias_correction=False, - max_grad_norm=1.0) - if args.loss_scale == 0: - optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) - else: - optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) - warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion, - t_total=num_train_optimization_steps) - else: - optimizer = BertAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - warmup=args.warmup_proportion, - t_total=num_train_optimization_steps) - - global_step = 0 - - 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_optimization_steps) - - model.train() - for _ in trange(int(args.num_train_epochs), desc="Epoch"): - tr_loss = 0 - nb_tr_examples, nb_tr_steps = 0, 0 - for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): - batch = tuple(t.to(device) for t in batch) - input_ids, input_mask, segment_ids, label_ids = batch - loss = model(input_ids, segment_ids, input_mask, label_ids) - if n_gpu > 1: - loss = loss.mean() # mean() to average on multi-gpu. - if args.fp16 and args.loss_scale != 1.0: - # rescale loss for fp16 training - # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html - loss = loss * args.loss_scale - if args.gradient_accumulation_steps > 1: - loss = loss / args.gradient_accumulation_steps - tr_loss += loss.item() - nb_tr_examples += input_ids.size(0) - nb_tr_steps += 1 - - if args.fp16: - optimizer.backward(loss) - else: - loss.backward() - if (step + 1) % args.gradient_accumulation_steps == 0: - 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.get_lr(global_step, args.warmup_proportion) - for param_group in optimizer.param_groups: - param_group['lr'] = lr_this_step - optimizer.step() - optimizer.zero_grad() - global_step += 1 + train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) + global_step, tr_loss = train(args, train_dataset, model, tokenizer) + logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) - if args.do_train: - # Save a trained model, configuration and tokenizer - model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + # Save the trained model and the tokenizer + if args.local_rank == -1 or torch.distributed.get_rank() == 0: + # Create output directory if needed + if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: + os.makedirs(args.output_dir) - # If we save using the predefined names, we can load using `from_pretrained` - output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) - output_config_file = os.path.join(args.output_dir, CONFIG_NAME) + logger.info("Saving model checkpoint to %s", args.output_dir) + # Save a trained model, configuration and tokenizer using `save_pretrained()`. + # They can then be reloaded using `from_pretrained()` + model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training + model_to_save.save_pretrained(args.output_dir) + tokenizer.save_pretrained(args.output_dir) - torch.save(model_to_save.state_dict(), output_model_file) - model_to_save.config.to_json_file(output_config_file) - tokenizer.save_vocabulary(args.output_dir) + # Good practice: save your training arguments together with the trained model + torch.save(args, os.path.join(args.output_dir, 'training_args.bin')) # Load a trained model and vocabulary that you have fine-tuned - model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4) - tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) - else: - model = BertForMultipleChoice.from_pretrained(args.bert_model, num_choices=4) - model.to(device) + model = model_class.from_pretrained(args.output_dir) + tokenizer = tokenizer_class.from_pretrained(args.output_dir) + model.to(args.device) - if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): - eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True) - eval_features = convert_examples_to_features( - eval_examples, tokenizer, args.max_seq_length, True) - logger.info("***** Running evaluation *****") - logger.info(" Num examples = %d", len(eval_examples)) - logger.info(" Batch size = %d", args.eval_batch_size) - all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long) - all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long) - all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long) - all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long) - eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) - # Run prediction for full data - eval_sampler = SequentialSampler(eval_data) - eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) + # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory + results = {} + if args.do_eval and args.local_rank in [-1, 0]: + if args.do_train: + checkpoints = [args.output_dir] + else: + # if do_train is False and do_eval is true, load model directly from pretrained. + checkpoints = [args.model_name_or_path] - model.eval() - eval_loss, eval_accuracy = 0, 0 - nb_eval_steps, nb_eval_examples = 0, 0 - for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): - input_ids = input_ids.to(device) - input_mask = input_mask.to(device) - segment_ids = segment_ids.to(device) - label_ids = label_ids.to(device) + if args.eval_all_checkpoints: + checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) + logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs - with torch.no_grad(): - tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) - logits = model(input_ids, segment_ids, input_mask) + logger.info("Evaluate the following checkpoints: %s", checkpoints) - logits = logits.detach().cpu().numpy() - label_ids = label_ids.to('cpu').numpy() - tmp_eval_accuracy = accuracy(logits, label_ids) + for checkpoint in checkpoints: + # Reload the model + global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" + model = model_class.from_pretrained(checkpoint) + tokenizer = tokenizer_class.from_pretrained(checkpoint) + model.to(args.device) - eval_loss += tmp_eval_loss.mean().item() - eval_accuracy += tmp_eval_accuracy + # Evaluate + result = evaluate(args, model, tokenizer, prefix=global_step) - nb_eval_examples += input_ids.size(0) - nb_eval_steps += 1 + result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items()) + results.update(result) - eval_loss = eval_loss / nb_eval_steps - eval_accuracy = eval_accuracy / nb_eval_examples + logger.info("Results: {}".format(results)) - result = {'eval_loss': eval_loss, - 'eval_accuracy': eval_accuracy, - 'global_step': global_step, - 'loss': tr_loss/global_step} - - output_eval_file = os.path.join(args.output_dir, "eval_results.txt") - with open(output_eval_file, "w") as writer: - logger.info("***** Eval results *****") - for key in sorted(result.keys()): - logger.info(" %s = %s", key, str(result[key])) - writer.write("%s = %s\n" % (key, str(result[key]))) + return results if __name__ == "__main__": - main() + main() \ No newline at end of file