cleaning up run_glue example

examples/run_glue.py

@@ -20,7 +20,6 @@ from __future__ import absolute_import, division, print_function
 import argparse
 import logging
 import os
-import sys
 import random
 from tqdm import tqdm, trange
 
@@ -30,7 +29,6 @@ import torch
 from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                               TensorDataset)
 from torch.utils.data.distributed import DistributedSampler
-from torch.nn import CrossEntropyLoss, MSELoss
 
 from tensorboardX import SummaryWriter
 
@@ -45,6 +43,186 @@ from utils_glue import processors, output_modes, convert_examples_to_features, c
 logger = logging.getLogger(__name__)
 
 
+def train(args, train_features, model):
+    """ Train the model """
+    if args.local_rank in [-1, 0]:
+        tb_writer = SummaryWriter()
+
+    # Convert in tensors and build dataloader
+    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)
+    if args.output_mode == "classification":
+        all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
+    elif args.output_mode == "regression":
+        all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
+
+    args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
+
+    train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
+    train_sampler = RandomSampler(train_data) if args.local_rank == -1 else 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
+
+    # Prepare optimizer
+    param_optimizer = list(model.named_parameters())
+    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, FusedAdam
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use 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)
+
+    # Train!
+    logger.info("***** Running training *****")
+    logger.info("  Num examples = %d", len(train_features))
+    logger.info("  Batch size = %d", args.train_batch_size)
+    logger.info("  Num steps = %d", num_train_optimization_steps)
+
+    global_step = 0
+    tr_loss = 0
+    model.train()
+    for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
+        for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
+            batch = tuple(t.to(args.device) for t in batch)
+            input_ids, input_mask, segment_ids, label_ids = batch
+
+            ouputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
+            loss = ouputs[0]
+
+            if args.n_gpu > 1:
+                loss = loss.mean() # mean() to average on multi-gpu parallel training
+            if args.gradient_accumulation_steps > 1:
+                loss = loss / args.gradient_accumulation_steps
+
+            loss.backward() if not args.fp16 else optimizer.backward(loss)
+
+            tr_loss += loss.item()
+            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
+                if args.local_rank in [-1, 0]:
+                    if not args.fp16:
+                        tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
+                    tb_writer.add_scalar('loss', loss.item(), global_step)
+
+    return global_step, tr_loss / global_step
+
+
+def evalutate(args, eval_task, eval_output_dir, eval_features, model):
+    """ Evaluate the model """
+    if os.path.exists(eval_output_dir) and os.listdir(eval_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(eval_output_dir))
+    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
+        os.makedirs(eval_output_dir)
+
+    # Convert in tensors and build dataloader
+    all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
+    all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
+    all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
+    if args.output_mode == "classification":
+        all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
+    elif args.output_mode == "regression":
+        all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
+
+    eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
+    # Note that DistributedSampler samples randomly
+    eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
+    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
+
+    # Eval!
+    logger.info("***** Running evaluation *****")
+    logger.info("  Num examples = %d", len(eval_examples))
+    logger.info("  Batch size = %d", args.eval_batch_size)
+    model.eval()
+    eval_loss = 0
+    nb_eval_steps = 0
+    preds = None
+    out_label_ids = None
+    for batch in tqdm(eval_dataloader, desc="Evaluating"):
+        batch = tuple(t.to(args.device) for t in batch)
+        input_ids, input_mask, segment_ids, label_ids = batch
+
+        with torch.no_grad():
+            outputs = model(input_ids,
+                            token_type_ids=segment_ids,
+                            attention_mask=input_mask,
+                            labels=label_ids)
+            tmp_eval_loss, logits = outputs[:2]
+
+        eval_loss += tmp_eval_loss.mean().item()
+        nb_eval_steps += 1
+        if preds is None:
+            preds = logits.detach().cpu().numpy()
+            out_label_ids = label_ids.detach().cpu().numpy()
+        else:
+            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
+            out_label_ids = np.append(out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
+
+    eval_loss = eval_loss / nb_eval_steps
+    if args.output_mode == "classification":
+        preds = np.argmax(preds, axis=1)
+    elif args.output_mode == "regression":
+        preds = np.squeeze(preds)
+    result = compute_metrics(eval_task, preds, out_label_ids)
+
+    output_eval_file = os.path.join(eval_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])))
+
+
+def load_and_cache_examples(args, task, tokenizer, eval=False):
+    processor = processors[task]()
+    output_mode = output_modes[task]
+    label_list = processor.get_labels()
+
+    # Load and cache data
+    processor = processors[task]()
+    examples = processor.get_dev_examples(args.data_dir)
+    cached_features_file = os.path.join(args.data_dir, '{}_{}_{}_{}'.format(
+        'dev' if eval else 'train',
+        list(filter(None, args.bert_model.split('/'))).pop(),
+        str(args.max_seq_length),
+        str(task)))
+
+    if os.path.exists(cached_features_file):
+        features = torch.load(cached_features_file)
+    else:
+        features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer, output_mode)
+        if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+            logger.info("  Saving eval features into cached file %s", cached_features_file)
+            torch.save(features, cached_features_file)
+
+    return features
+
+
 def main():
     parser = argparse.ArgumentParser()
 
@@ -118,40 +296,32 @@ def main():
     # 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()
+        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)
         torch.distributed.init_process_group(backend='nccl')
-        n_gpu = 1
+        args.n_gpu = 1
     args.device = device
 
     # Setup logging
     logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
     logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
-                device, n_gpu, bool(args.local_rank != -1), args.fp16))
+        device, args.n_gpu, bool(args.local_rank != -1), args.fp16))
 
     # Setup seeds
     random.seed(args.seed)
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
-    if n_gpu > 0:
+    if args.n_gpu > 0:
         torch.cuda.manual_seed_all(args.seed)
 
-    # Safety checks and create output directory
-    if not args.do_train and not args.do_eval:
-        raise ValueError("At least one of `do_train` or `do_eval` must be True.")
-    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))
-    if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-        os.makedirs(args.output_dir)
-
     # Prepare GLUE task
-    task_name = args.task_name.lower()
-    if task_name not in processors:
-        raise ValueError("Task not found: %s" % (task_name))
-    processor = processors[task_name]()
-    output_mode = output_modes[task_name]
+    args.task_name = args.task_name.lower()
+    if args.task_name not in processors:
+        raise ValueError("Task not found: %s" % (args.task_name))
+    processor = processors[args.task_name]()
+    args.output_mode = output_modes[args.task_name]
     label_list = processor.get_labels()
     num_labels = len(label_list)
 
@@ -169,122 +339,23 @@ def main():
     # Distributed, parrallel and fp16 model
     if args.fp16:
         model.half()
-    model.to(device)
+    model.to(args.device)
     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)
-    elif n_gpu > 1:
+    elif args.n_gpu > 1:
         model = torch.nn.DataParallel(model)
 
-    global_step = 0
-    tr_loss = 0
+    # Training
     if args.do_train:
-        if args.local_rank in [-1, 0]:
-            tb_writer = SummaryWriter()
+        train_features = load_and_cache_examples(args, args.task_name, tokenizer, eval=False)
+        global_step, tr_loss = train(args, train_features, model)
+        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
-        # Load and cache data
-        train_examples = processor.get_train_examples(args.data_dir)
-        cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
-            list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(task_name)))
-        if os.path.exists(cached_train_features_file):
-            train_features = torch.load(cached_train_features_file)
-        else:
-            train_features = convert_examples_to_features(
-                train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
-            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
-                logger.info("  Saving train features into cached file %s", cached_train_features_file)
-                torch.save(train_features, cached_train_features_file)
 
-        # Convert in tensors and build dataloader
-        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)
-        if output_mode == "classification":
-            all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
-        elif output_mode == "regression":
-            all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
-
-        args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
-
-        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
-        train_sampler = RandomSampler(train_data) if args.local_rank == -1 else 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
-
-        # Prepare optimizer
-        param_optimizer = list(model.named_parameters())
-        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, FusedAdam
-            except ImportError:
-                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use 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)
-
-        # Train!
-        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", disable=args.local_rank not in [-1, 0]):
-            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
-                batch = tuple(t.to(device) for t in batch)
-                input_ids, input_mask, segment_ids, label_ids = batch
-
-                ouputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
-                loss = ouputs[0]
-
-                if n_gpu > 1:
-                    loss = loss.mean() # mean() to average on multi-gpu parallel training
-                if args.gradient_accumulation_steps > 1:
-                    loss = loss / args.gradient_accumulation_steps
-
-                if args.fp16:
-                    optimizer.backward(loss)
-                else:
-                    loss.backward()
-
-                tr_loss += loss.item()
-                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
-                    if args.local_rank in [-1, 0]:
-                        if not args.fp16:
-                            tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
-                        tb_writer.add_scalar('loss', loss.item(), global_step)
-
-    ### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
-    ### Example:
+    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         # Save a trained model, configuration and tokenizer
         model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
@@ -307,94 +378,18 @@ def main():
     else:
         model = BertForSequenceClassification.from_pretrained(args.bert_model)
 
-    model.to(device)
+    model.to(args.device)
 
-    ### Evaluation
+    # Evaluation
     if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        eval_task_names = ("mnli", "mnli-mm") if task_name == "mnli" else (task_name,)
-        eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if task_name == "mnli" else (args.output_dir,)
-        for eval_task, output_dir in zip(eval_task_names, eval_outputs_dirs):
-            if os.path.exists(output_dir) and os.listdir(output_dir) and args.do_train:
-                raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
+        # Handle MNLI double evaluation
+        eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
+        eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)
 
-            # Load and cache data
-            processor = processors[eval_task]()
-            eval_examples = processor.get_dev_examples(args.data_dir)
-            cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
-                list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(eval_task)))
-            if os.path.exists(cached_eval_features_file):
-                eval_features = torch.load(cached_eval_features_file)
-            else:
-                eval_features = convert_examples_to_features(
-                    eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
-                if args.local_rank == -1 or torch.distributed.get_rank() == 0:
-                    logger.info("  Saving eval features into cached file %s", cached_eval_features_file)
-                torch.save(eval_features, cached_eval_features_file)
+        for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
+            eval_features = load_and_cache_examples(args, eval_task, tokenizer, eval=True)
 
-            # Convert in tensors and build dataloader
-            all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
-            all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
-            all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
-            if output_mode == "classification":
-                all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
-            elif output_mode == "regression":
-                all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
-
-            eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
-            # Note that DistributedSampler samples randomly
-            eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
-            eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-            # Eval!
-            logger.info("***** Running evaluation *****")
-            logger.info("  Num examples = %d", len(eval_examples))
-            logger.info("  Batch size = %d", args.eval_batch_size)
-            model.eval()
-            eval_loss = 0
-            nb_eval_steps = 0
-            preds = None
-            out_label_ids = None
-            for batch in tqdm(eval_dataloader, desc="Evaluating"):
-                batch = tuple(t.to(device) for t in batch)
-                input_ids, input_mask, segment_ids, label_ids = batch
-
-                with torch.no_grad():
-                    outputs = model(input_ids,
-                                    token_type_ids=segment_ids,
-                                    attention_mask=input_mask,
-                                    labels=label_ids)
-                    tmp_eval_loss, logits = outputs[:2]
-
-                eval_loss += tmp_eval_loss.mean().item()
-                nb_eval_steps += 1
-                if preds is None:
-                    preds = logits.detach().cpu().numpy()
-                    out_label_ids = label_ids.detach().cpu().numpy()
-                else:
-                    preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-                    out_label_ids = np.append(out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
-
-            eval_loss = eval_loss / nb_eval_steps
-            if output_mode == "classification":
-                preds = np.argmax(preds, axis=1)
-            elif output_mode == "regression":
-                preds = np.squeeze(preds)
-            result = compute_metrics(eval_task, preds, out_label_ids)
-
-            loss = tr_loss/global_step if args.do_train else None
-
-            result['eval_loss'] = eval_loss
-            result['global_step'] = global_step
-            result['loss'] = loss
-
-            output_eval_file = os.path.join(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])))
+            evalutate(args, eval_task, eval_output_dir, eval_features, model)
 
 
 if __name__ == "__main__":