scatter_ and scatter

This commit is contained in:
VictorSanh
2018-11-03 10:27:38 -04:00
parent a6efe1235f
commit 2c55568c40

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@@ -502,7 +502,7 @@ class BertForQuestionAnswering(nn.Module):
def compute_loss(logits, positions): def compute_loss(logits, positions):
max_position = positions.max().item() max_position = positions.max().item()
one_hot = torch.FloatTensor(batch_size, max(max_position, seq_length) +1).zero_() one_hot = torch.FloatTensor(batch_size, max(max_position, seq_length) +1).zero_()
one_hot = one_hot.scatter(1, positions.cpu(), 1) # Second argument need to be LongTensor and not cuda.LongTensor one_hot = one_hot.scatter_(1, positions.cpu(), 1) # Second argument need to be LongTensor and not cuda.LongTensor
one_hot = one_hot[:, :seq_length].to(input_ids.device) one_hot = one_hot[:, :seq_length].to(input_ids.device)
log_probs = nn.functional.log_softmax(logits, dim = -1).view(batch_size, seq_length) log_probs = nn.functional.log_softmax(logits, dim = -1).view(batch_size, seq_length)
loss = -torch.mean(torch.sum(one_hot*log_probs), dim = -1) loss = -torch.mean(torch.sum(one_hot*log_probs), dim = -1)