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Implement backward pass for pack_padded_sequence #4512

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Merged
apaszke merged 1 commit into from
Jan 9, 2018
Merged

Implement backward pass for pack_padded_sequence #4512

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2 changes: 1 addition & 1 deletion test/test_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2795,7 +2795,7 @@ def forward_backward(cuda, rnn, input_val, hx_val, grad_output, grad_hy, weights

if isinstance(input_val, rnn_utils.PackedSequence):
input = rnn_utils.PackedSequence(
Variable(input_val.data, requires_grad=True), input_val.batch_sizes)
Variable(input_val.data.data, requires_grad=True), input_val.batch_sizes)
input_var = input.data
else:
input = Variable(input_val.clone(), requires_grad=True)
Expand Down
53 changes: 53 additions & 0 deletions torch/nn/_functions/packing.py
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@@ -0,0 +1,53 @@
import torch
from torch.autograd import Function


class PackPadded(Function):
@staticmethod
def forward(ctx, input, lengths, batch_first):
if batch_first:
input = input.transpose(0, 1)

if lengths[-1] <= 0:
raise ValueError("Length of all samples has to be greater than 0, "
"but found an element in 'lengths' that is <= 0")

steps = []
batch_sizes = []
lengths_iter = reversed(lengths)
batch_size = input.size(1)

if len(lengths) != batch_size:
raise ValueError("Expected `len(lengths)` to be equal to batch_size, but got "
"{} (batch_size={}).".format(len(lengths), batch_size))

prev_l = 0
for i, l in enumerate(lengths_iter):
if l > prev_l:
c_batch_size = batch_size - i
steps.append(input[prev_l:l, :c_batch_size].contiguous().view(-1, *input.size()[2:]))
batch_sizes.extend([c_batch_size] * (l - prev_l))
prev_l = l

elif prev_l > l:
raise ValueError("'lengths' array has to be sorted in decreasing order")

ctx.batch_sizes = batch_sizes
ctx.batch_first = batch_first
ctx.input_size = input.size()

return torch.cat(steps), torch.LongTensor(batch_sizes)

@staticmethod
def backward(ctx, grad_steps, grad_batch_sizes):
grad_input = grad_steps.new(*ctx.input_size).zero_()

offset = 0
for i, bs in enumerate(ctx.batch_sizes):
grad_input[i, :bs] = grad_steps[offset:offset + bs]
offset += bs

if ctx.batch_first:
grad_input = grad_input.transpose(0, 1)

return grad_input, None, None
29 changes: 5 additions & 24 deletions torch/nn/utils/rnn.py
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Expand Up @@ -4,6 +4,8 @@
from torch.autograd import Variable


from .._functions.packing import PackPadded

PackedSequence_ = namedtuple('PackedSequence', ['data', 'batch_sizes'])


Expand Down Expand Up @@ -56,30 +58,9 @@ def pack_padded_sequence(input, lengths, batch_first=False):
Returns:
a :class:`PackedSequence` object
"""
if lengths[-1] <= 0:
raise ValueError("length of all samples has to be greater than 0, "
"but found an element in 'lengths' that is <=0")
if batch_first:
input = input.transpose(0, 1)

steps = []
batch_sizes = []
lengths_iter = reversed(lengths)
batch_size = input.size(1)
if len(lengths) != batch_size:
raise ValueError("lengths array has incorrect size")

prev_l = 0
for i, l in enumerate(lengths_iter):
if l > prev_l:
c_batch_size = batch_size - i
steps.append(input[prev_l:l, :c_batch_size].contiguous().view(-1, *input.size()[2:]))
batch_sizes.extend([c_batch_size] * (l - prev_l))
prev_l = l
elif prev_l > l: # remember that new_length is the preceding length in the array
raise ValueError("lengths array has to be sorted in decreasing order")

return PackedSequence(torch.cat(steps), batch_sizes)
data, batch_sizes = PackPadded.apply(input, lengths, batch_first)

return PackedSequence(data, list(batch_sizes.data))


def pad_packed_sequence(sequence, batch_first=False, padding_value=0.0):
Expand Down