Multi-Gpu loss - Cleaning

run_squad_pytorch.py

@@ -27,6 +27,7 @@ import math
 import os
 from tqdm import tqdm, trange
 import random
+import numpy as np
 
 import torch
 from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
@@ -718,23 +719,6 @@ def main():
     parser.add_argument("--max_answer_length", default=30, type=int,
                         help="The maximum length of an answer that can be generated. This is needed because the start "
                              "and end predictions are not conditioned on one another.")
-
-    ### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
-    # parser.add_argument("--use_tpu", default=False, action='store_true', help="Whether to use TPU or GPU/CPU.")
-    # parser.add_argument("--tpu_name", default=None, type=str,
-    #                     help="The Cloud TPU to use for training. This should be either the name used when creating the "
-    #                          "Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
-    # parser.add_argument("--tpu_zone", default=None, type=str,
-    #                     help="[Optional] GCE zone where the Cloud TPU is located in. If not specified, we will attempt "
-    #                          "to automatically detect the GCE project from metadata.")
-    # parser.add_argument("--gcp_project", default=None, type=str,
-    #                     help="[Optional] Project name for the Cloud TPU-enabled project. If not specified, we will attempt "
-    #                          "to automatically detect the GCE project from metadata.")
-    # parser.add_argument("--master", default=None, type=str, help="[Optional] TensorFlow master URL.")
-    # parser.add_argument("--num_tpu_cores", default=8, type=int, help="Only used if `use_tpu` is True. "
-    #                                                                  "Total number of TPU cores to use.")
-    ### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
-
     parser.add_argument("--verbose_logging", default=False, action='store_true',
                         help="If true, all of the warnings related to data processing will be printed. "
                              "A number of warnings are expected for a normal SQuAD evaluation.")
@@ -836,16 +820,12 @@ def main():
         logger.info("  Batch size = %d", args.train_batch_size)
         logger.info("  Num steps = %d", num_train_steps)
 
-        logger.info("HHHHH Loading data")
         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)
-        #all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
         all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
         all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
 
-        logger.info("HHHHH Creating dataset")
-        #train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
         train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions)
         if args.local_rank == -1:
             train_sampler = RandomSampler(train_data)
@@ -869,15 +849,11 @@ def main():
                 start_positions = start_positions.view(-1, 1)
                 end_positions = end_positions.view(-1, 1)
                 
-                logger.info("HHHHH Forward")
                 loss, _ = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
                 model.zero_grad()
-                logger.info("HHHHH Backward")
+                loss.mean().backward()
-                loss.backward()
-                logger.info("HHHHH Loading data")
                 optimizer.step()
                 global_step += 1
-                logger.info("Done %s steps", global_step)
 
     if args.do_predict:
         eval_examples = read_squad_examples(
@@ -898,10 +874,8 @@ def main():
         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)
-        #all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
         all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
 
-        #eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_example_index)
         eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
         if args.local_rank == -1:
             eval_sampler = SequentialSampler(eval_data)
@@ -912,7 +886,6 @@ def main():
         model.eval()
         all_results = []
         logger.info("Start evaulating")
-        #for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
         for input_ids, input_mask, segment_ids, example_index in eval_dataloader:
             if len(all_results) % 1000 == 0:
                 logger.info("Processing example: %d" % (len(all_results)))
@@ -924,9 +897,7 @@ def main():
             start_logits, end_logits = model(input_ids, segment_ids, input_mask)
 
             unique_id = [int(eval_features[e.item()].unique_id) for e in example_index]
-            #start_logits = [x.item() for x in start_logits]
             start_logits = [x.view(-1).detach().cpu().numpy() for x in start_logits]
-            #end_logits = [x.item() for x in end_logits]
             end_logits = [x.view(-1).detach().cpu().numpy() for x in end_logits]
             for idx, i in enumerate(unique_id):
                 s = [float(x) for x in start_logits[idx]]
@@ -938,11 +909,6 @@ def main():
                         end_logits=e
                     )
                 )
-            # all_results.append(
-            #     RawResult(
-            #         unique_id=unique_id,
-            #         start_logits=start_logits,
-            #         end_logits=end_logits))
 
         output_prediction_file = os.path.join(args.output_dir, "predictions.json")
         output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")