multi-gpu cleanup
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@ -539,9 +539,11 @@ def main():
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label_ids = label_ids.to(device)
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loss, _ = model(input_ids, segment_ids, input_mask, label_ids)
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loss = loss.mean() # sum() is to account for multi-gpu support.
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu.
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tr_loss += loss.item()
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nb_tr_examples += input_ids.size(0)
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model.zero_grad()
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loss.backward()
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optimizer.step()
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13
run_squad.py
13
run_squad.py
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@ -837,25 +837,19 @@ def main():
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logger.info(" Batch size = %d", args.train_batch_size)
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logger.info(" Num steps = %d", num_train_steps)
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logger.info("HHHHH Loading data")
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all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
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all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
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all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
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#all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
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all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
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all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
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logger.info("HHHHH Creating dataset")
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#train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
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train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions)
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if args.local_rank == -1:
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train_sampler = RandomSampler(train_data)
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else:
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train_sampler = DistributedSampler(train_data)
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logger.info("HHHHH Dataloader")
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train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
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logger.info("HHHHH Starting Traing")
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model.train()
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for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
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for input_ids, input_mask, segment_ids, start_positions, end_positions in tqdm(train_dataloader,
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@ -863,19 +857,18 @@ def main():
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input_ids = input_ids.to(device)
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input_mask = input_mask.float().to(device)
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segment_ids = segment_ids.to(device)
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#label_ids = label_ids.to(device)
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start_positions = start_positions.to(device)
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end_positions = start_positions.to(device)
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start_positions = start_positions.view(-1, 1)
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end_positions = end_positions.view(-1, 1)
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logger.info("HHHHH Forward")
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loss, _ = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu.
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model.zero_grad()
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logger.info("HHHHH Backward, loss: {}".format(loss))
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loss.backward()
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logger.info("HHHHH Loading data")
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optimizer.step()
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global_step += 1
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logger.info("Done %s steps", global_step)
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