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Fix DataParallel scattering for empty lists / dicts / tuples #3769

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Merged
soumith merged 2 commits into from
Nov 22, 2017
Merged

Fix DataParallel scattering for empty lists / dicts / tuples #3769

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ece8244
Fix DataParallel scattering for empty lists and dicts
pemazare Nov 17, 2017
c76be41
Fix DataParallel scattering for empty tuples
pemazare Nov 21, 2017
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54 changes: 54 additions & 0 deletions test/test_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1614,6 +1614,60 @@ def forward(self, input):
self.assertEqual(out.get_device(), 0)
self.assertEqual(out.data, expected_out)

@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
def test_data_parallel_module_kwargs_only_empty_list(self):
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.l = l

def forward(self, input):
return self.l(input['data'])

l = nn.Linear(10, 5).float().cuda()
i = Variable(torch.randn(20, 10).float().cuda())
expected_out = l(i).data
n = nn.DataParallel(Net())
out = n(input={'data': i, 'unused': []})
self.assertEqual(out.get_device(), 0)
self.assertEqual(out.data, expected_out)

@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
def test_data_parallel_module_kwargs_only_empty_dict(self):
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.l = l

def forward(self, input):
return self.l(input['data'])

l = nn.Linear(10, 5).float().cuda()
i = Variable(torch.randn(20, 10).float().cuda())
expected_out = l(i).data
n = nn.DataParallel(Net())
out = n(input={'data': i, 'unused': {}})
self.assertEqual(out.get_device(), 0)
self.assertEqual(out.data, expected_out)

@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
def test_data_parallel_module_kwargs_only_empty_tuple(self):
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.l = l

def forward(self, input):
return self.l(input['data'])

l = nn.Linear(10, 5).float().cuda()
i = Variable(torch.randn(20, 10).float().cuda())
expected_out = l(i).data
n = nn.DataParallel(Net())
out = n(input={'data': i, 'unused': ()})
self.assertEqual(out.get_device(), 0)
self.assertEqual(out.data, expected_out)

def test_state_dict(self):
l = nn.Linear(5, 5)
block = nn.Module()
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6 changes: 3 additions & 3 deletions torch/nn/parallel/scatter_gather.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,11 @@ def scatter_map(obj):
if isinstance(obj, Variable):
return Scatter.apply(target_gpus, None, dim, obj)
assert not torch.is_tensor(obj), "Tensors not supported in scatter."
if isinstance(obj, tuple):
if isinstance(obj, tuple) and len(obj) > 0:
return list(zip(*map(scatter_map, obj)))
if isinstance(obj, list):
if isinstance(obj, list) and len(obj) > 0:
return list(map(list, zip(*map(scatter_map, obj))))
if isinstance(obj, dict):
if isinstance(obj, dict) and len(obj) > 0:
return list(map(type(obj), zip(*map(scatter_map, obj.items()))))
return [obj for targets in target_gpus]

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