Skip to content

Support modules that output scalar in Gather (and data parallel) #7973

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ssnl merged 2 commits into from
Jun 1, 2018
Merged

Support modules that output scalar in Gather (and data parallel) #7973

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c285b36
Support modules that output scalar in Gather (and data parallel)
ssnl May 30, 2018
36da6db
Improve warning msg
ssnl Jun 1, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 23 additions & 2 deletions test/test_nn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2287,8 +2287,8 @@ def test_scatter_gpu(self):

def _test_gather(self, output_device):
inputs = (
Variable(torch.randn(2, 4).cuda(0), requires_grad=True),
Variable(torch.randn(2, 4).cuda(1), requires_grad=True)
torch.randn(2, 4, device='cuda:0', requires_grad=True),
torch.randn(2, 4, device='cuda:1', requires_grad=True),
)
result = dp.gather(inputs, output_device)
self.assertEqual(result.size(), torch.Size([4, 4]))
Expand All @@ -2306,6 +2306,27 @@ def _test_gather(self, output_device):
self.assertEqual(inputs[1].grad.data, grad[2:])
_assertGradAndGradgradChecks(self, lambda x, y: dp.gather((x, y), output_device), inputs)

# test scalar inputs, should stack into a vector in this case
inputs = (
torch.randn((), device='cuda:0', requires_grad=True),
torch.randn((), device='cuda:1', requires_grad=True),
)
result = dp.gather(inputs, output_device)
self.assertEqual(result.size(), torch.Size([2]))
self.assertEqual(result[0], inputs[0])
self.assertEqual(result[1], inputs[1])
if output_device != -1:
self.assertEqual(result.get_device(), output_device)
else:
self.assertFalse(result.is_cuda)
grad = torch.randn(2)
if output_device != -1:
grad = grad.cuda(output_device)
result.backward(grad)
self.assertEqual(inputs[0].grad, grad[0])
self.assertEqual(inputs[1].grad, grad[1])
_assertGradAndGradgradChecks(self, lambda x, y: dp.gather((x, y), output_device), inputs)

@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_gather_cpu(self):
self._test_gather(-1)
Expand Down
15 changes: 14 additions & 1 deletion torch/nn/parallel/_functions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import warnings

import torch
import torch.cuda.comm as comm
from torch.autograd import Function
Expand Down Expand Up @@ -51,12 +53,23 @@ def forward(ctx, target_device, dim, *inputs):
ctx.target_device = target_device
ctx.dim = dim
ctx.input_gpus = tuple(map(lambda i: i.get_device(), inputs))
if all(t.dim() == 0 for t in inputs) and dim == 0:

This comment was marked as off-topic.

Sign in to view

This comment was marked as off-topic.

Sign in to view

This comment was marked as off-topic.

Sign in to view

This comment was marked as off-topic.

Sign in to view

This comment was marked as off-topic.

Sign in to view

This comment was marked as off-topic.

Sign in to view

inputs = tuple(t.view(1) for t in inputs)
warnings.warn('Was asked to gather along dimension 0, but all '
'input tensors were scalars; will instead unsqueeze '
'and return a vector.')
ctx.unsqueezed_scalar = True
else:
ctx.unsqueezed_scalar = False
ctx.input_sizes = tuple(map(lambda i: i.size(ctx.dim), inputs))
return comm.gather(inputs, ctx.dim, ctx.target_device)

@staticmethod
def backward(ctx, grad_output):
return (None, None) + Scatter.apply(ctx.input_gpus, ctx.input_sizes, ctx.dim, grad_output)
scattered_grads = Scatter.apply(ctx.input_gpus, ctx.input_sizes, ctx.dim, grad_output)
if ctx.unsqueezed_scalar:
scattered_grads = tuple(g[0] for g in scattered_grads)
return (None, None) + scattered_grads


class Scatter(Function):
Expand Down
6 changes: 6 additions & 0 deletions torch/nn/parallel/data_parallel.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,12 @@ class DataParallel(Module):
that each such hook be executed before the corresponding
:meth:`~torch.nn.Module.forward` call of that device.

.. warning::
When :attr:`module` returns a scalar (i.e., 0-dimensional tensor) in
:func:`forward`, this wrapper will return a vector of length equal to
number of devices used in data parallelism, containing the result from
each device.

.. note::
There is a subtlety in using the
``pack sequence -> recurrent network -> unpack sequence`` pattern in a
Expand Down