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import torch | |
import torch.nn.functional as F | |
from collections import defaultdict | |
def make_positions(tensor, padding_idx): | |
"""Replace non-padding symbols with their position numbers. | |
Position numbers begin at padding_idx+1. Padding symbols are ignored. | |
""" | |
# The series of casts and type-conversions here are carefully | |
# balanced to both work with ONNX export and XLA. In particular XLA | |
# prefers ints, cumsum defaults to output longs, and ONNX doesn't know | |
# how to handle the dtype kwarg in cumsum. | |
mask = tensor.ne(padding_idx).int() | |
return ( | |
torch.cumsum(mask, dim=1).type_as(mask) * mask | |
).long() + padding_idx | |
def softmax(x, dim): | |
return F.softmax(x, dim=dim, dtype=torch.float32) | |