Commit 185a0df5 authored by Myle Ott's avatar Myle Ott
Browse files

Fix warning about deprecated `volatile` kwarg for Variables

parent dccf7909

fairseq/criterions/label_smoothed_cross_entropy.py

+3 −1
Changes for fairseq/criterions/label_smoothed_cross_entropy.py: 3 added lines, 1 removed line.
Original line number Diff line number Diff line
@@ -11,6 +11,8 @@ import torch 
from torch.autograd.variable import Variable

import torch.nn.functional as F



from fairseq import utils



from .fairseq_criterion import FairseqCriterion
@@ -41,7 +43,7 @@ class LabelSmoothedNLLLoss(torch.autograd.Function): 


    @staticmethod

    def backward(ctx, grad):

        return Variable(ctx.grad_input, volatile=True) * grad, None, None, None, None, None

        return utils.volatile_variable(ctx.grad_input) * grad, None, None, None, None, None





class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):

fairseq/modules/conv_tbc.py

+6 −4
Changes for fairseq/modules/conv_tbc.py: 6 added lines, 4 removed lines.
Original line number Diff line number Diff line
@@ -7,9 +7,11 @@ 
#



import torch

from torch.autograd import Variable, Function

from torch.autograd import Function

from torch.nn.modules.utils import _single



from fairseq import utils



try:

    from fairseq import temporal_convolution_tbc

except ImportError as e:
@@ -93,9 +95,9 @@ class ConvTBCFunction(Function): 
            input,

            weight)



        grad_input = Variable(grad_input, volatile=True)

        grad_weight = Variable(grad_weight, volatile=True)

        grad_bias = Variable(grad_bias, volatile=True)

        grad_input = utils.volatile_variable(grad_input)

        grad_weight = utils.volatile_variable(grad_weight)

        grad_bias = utils.volatile_variable(grad_bias)



        return grad_input, grad_weight, grad_bias, None

fairseq/modules/linearized_convolution.py

+4 −1
Changes for fairseq/modules/linearized_convolution.py: 4 added lines, 1 removed line.
Original line number Diff line number Diff line
@@ -8,6 +8,9 @@ 


import torch

import torch.nn.functional as F



from fairseq import utils



from .conv_tbc import ConvTBC
@@ -65,7 +68,7 @@ class LinearizedConvolution(ConvTBC): 
                self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()

            # append next input

            self.input_buffer[:, -1, :] = input[:, -1, :]

            input = torch.autograd.Variable(self.input_buffer, volatile=True)

            input = utils.volatile_variable(self.input_buffer)

        output = F.linear(input.view(bsz, -1), weight, self.bias)

        return output.view(bsz, 1, -1)

fairseq/sequence_generator.py

+1 −1
Changes for fairseq/sequence_generator.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -322,7 +322,7 @@ class SequenceGenerator(object): 


    def _decode(self, tokens, encoder_outs):

        # wrap in Variable

        tokens = Variable(tokens, volatile=True)

        tokens = utils.volatile_variable(tokens)



        avg_probs = None

        avg_attn = None

fairseq/utils.py

+15 −8
Changes for fairseq/utils.py: 15 added lines, 8 removed lines.
Original line number Diff line number Diff line
@@ -176,6 +176,20 @@ def _upgrade_args(args): 
    return args





def maybe_no_grad(condition):

    if hasattr(torch, 'no_grad') and condition:

        return torch.no_grad()

    # no-op context manager

    return contextlib.ExitStack()





def volatile_variable(*args, **kwargs):

    if hasattr(torch, 'no_grad'):

        with torch.no_grad():

            return Variable(*args, **kwargs)

    return Variable(*args, **kwargs, volatile=True)





def make_variable(sample, volatile=False, cuda_device=None):

    """Wrap input tensors in Variable class."""
@@ -183,7 +197,7 @@ def make_variable(sample, volatile=False, cuda_device=None): 
        if torch.is_tensor(maybe_tensor):

            if cuda_device is not None and torch.cuda.is_available():

                maybe_tensor = maybe_tensor.cuda(async=True, device=cuda_device)

            return Variable(maybe_tensor, volatile=volatile)

            return volatile_variable(maybe_tensor)

        elif isinstance(maybe_tensor, dict):

            return {

                key: _make_variable(value)
@@ -255,10 +269,3 @@ def strip_pad(tensor, pad): 
    if tensor[-1] == pad:

        tensor = rstrip_pad(tensor, pad)

    return tensor
 




def maybe_no_grad(condition):

    if hasattr(torch, 'no_grad') and condition:

        return torch.no_grad()

    # no-op context manager

    return contextlib.ExitStack()