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Merged
merged 11 commits into from
Aug 29, 2018
Merged

Skip KLDivLoss_cuda tests due to hang #161

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627b808
Make sure we don't push whitespace.
iotamudelta Aug 27, 2018
a75f8e4
Remove macro duplication
iotamudelta Aug 27, 2018
e68953f
fixed flake8 issues
lcskrishna Aug 27, 2018
3f93283
Merge pull request #155 from lcskrishna/cl/indentation
iotamudelta Aug 27, 2018
c69dc3d
Skip KLDivLoss_cuda tests because of recently observed hangs
jithunnair-amd Aug 28, 2018
eb81bae
Merge branch 'dockerpackages' into skip_tests
jithunnair-amd Aug 28, 2018
a37d62e
Skip KLDivLoss_no_reduce_cuda test for ROCm due to recently observed …
jithunnair-amd Aug 29, 2018
314f37d
Merge branch 'skip_tests' of https://github.com/jithunnair-amd/pytorc…
jithunnair-amd Aug 29, 2018
58426b3
fixed merge conflicts
lcskrishna Aug 29, 2018
be8e093
Merge pull request #1 from lcskrishna/cl/merge-conflicts
jithunnair-amd Aug 29, 2018
f53f3b9
Skip test_jit.py TestEndToEndHybridFrontendModels.test_super_resoluti…
jithunnair-amd Aug 29, 2018
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2 changes: 1 addition & 1 deletion aten/src/ATen/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -247,7 +247,7 @@ IF(USE_CUDA AND NOT USE_ROCM)
ENDIF()

IF(USE_ROCM)
### Link in the ROCm libraries BLAS / RNG .
### Link in the ROCm libraries BLAS / RNG.
FIND_LIBRARY(ROCBLAS_LIBRARY rocblas HINTS ${ROCBLAS_PATH}/lib)
FIND_LIBRARY(HIPRAND_LIBRARY hiprand HINTS ${HIPRAND_PATH}/lib)

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1 change: 1 addition & 0 deletions test/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,6 +107,7 @@ def wrapper(*args, **kwargs):
fn(*args, **kwargs)
return wrapper


def skipIfNoLapack(fn):
@wraps(fn)
def wrapper(*args, **kwargs):
Expand Down
45 changes: 22 additions & 23 deletions test/common_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ def get_weight(m):
constructor_args=(10, 8),
input_size=(4, 10),
reference_fn=lambda i, p: torch.mm(i, p[0].t()) + p[1].view(1, -1).expand(4, 8),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='Linear',
Expand Down Expand Up @@ -103,28 +103,28 @@ def get_weight(m):
constructor_args=(1,),
input_size=(10, 20),
reference_fn=lambda i, _: torch.exp(i).div(torch.exp(i).sum(1, True).expand(10, 20)),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='Softmax2d',
input_size=(1, 3, 10, 20),
reference_fn=lambda i, _: torch.exp(i).div(torch.exp(i).sum(1, False)),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='LogSoftmax',
constructor_args=(1,),
input_size=(10, 20),
reference_fn=lambda i, _: torch.exp(i).div_(torch.exp(i).sum(1, True).expand(10, 20)).log_(),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='LogSoftmax',
constructor_args=(1,),
input_size=(1, 3, 10, 20),
reference_fn=lambda i, _: torch.exp(i).div_(torch.exp(i).sum(1, False)).log_(),
desc='multiparam',
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='ELU',
Expand Down Expand Up @@ -204,61 +204,59 @@ def get_weight(m):
input_size=(2, 3, 4),
desc='1d_multiparam',
reference_fn=lambda i, p: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0],
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='PReLU',
input_size=(2, 3, 4, 5),
desc='2d',
reference_fn=lambda i, p: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0],
#test_cuda = (not TEST_WITH_ROCM)
),
dict(
module_name='PReLU',
constructor_args=(3,),
input_size=(2, 3, 4, 5),
desc='2d_multiparam',
reference_fn=lambda i, p: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0],
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='PReLU',
input_size=(2, 3, 4, 5, 6),
reference_fn=lambda i, p: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0],
desc='3d',
#test_cuda = (not TEST_WITH_ROCM)
),
dict(
module_name='PReLU',
constructor_args=(3,),
input_size=(2, 3, 4, 5, 6),
desc='3d_multiparam',
reference_fn=lambda i, p: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0],
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='Softsign',
input_size=(3, 2, 5),
reference_fn=lambda i, _: i.div(1 + torch.abs(i)),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='Softmin',
constructor_args=(1,),
input_size=(10, 20),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='Softmin',
constructor_args=(1,),
input_size=(2, 3, 5, 10),
desc='multidim',
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='Tanhshrink',
input_size=(2, 3, 4, 5),
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
]

Expand Down Expand Up @@ -575,6 +573,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
reference_fn=lambda i, t, m:
kldivloss_reference(i, t, get_reduction(m)),
check_sum_reduction=True,
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='MSELoss',
Expand All @@ -591,7 +590,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
reference_fn=lambda i, t, m: -(t * i.log() + (1 - t) * (1 - i).log()).sum() /
(i.numel() if get_reduction(m) else 1),
check_gradgrad=False,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='BCELoss',
Expand All @@ -602,7 +601,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
(i.numel() if get_reduction(m) else 1),
desc='weights',
check_gradgrad=False,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='CrossEntropyLoss',
Expand All @@ -623,7 +622,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
reference_fn=lambda i, t, m:
hingeembeddingloss_reference(i, t, reduction=get_reduction(m)),
check_sum_reduction=True,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='HingeEmbeddingLoss',
Expand All @@ -634,7 +633,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
hingeembeddingloss_reference(i, t, margin=0.5, reduction=get_reduction(m)),
desc='margin',
check_sum_reduction=True,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='MultiLabelMarginLoss',
Expand All @@ -661,7 +660,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
target_fn=lambda: torch.rand(5, 10).mul(2).floor(),
reference_fn=lambda i, t, m: -(t * i.sigmoid().log() + (1 - t) * (-i).sigmoid().log()).sum() / i.numel(),
check_gradgrad=False,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='MultiMarginLoss',
Expand Down Expand Up @@ -740,7 +739,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
reference_fn=lambda i, t, m:
cosineembeddingloss_reference(i[0], i[1], t, reduction=get_reduction(m)),
check_sum_reduction=True,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='CosineEmbeddingLoss',
Expand All @@ -751,7 +750,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
cosineembeddingloss_reference(i[0], i[1], t, margin=0.7, reduction=get_reduction(m)),
desc='margin',
check_sum_reduction=True,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='MarginRankingLoss',
Expand All @@ -760,7 +759,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
reference_fn=lambda i, t, m:
marginrankingloss_reference(i[0], i[1], t, reduction=get_reduction(m)),
check_sum_reduction=True,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
dict(
module_name='MarginRankingLoss',
Expand All @@ -771,7 +770,7 @@ def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0
marginrankingloss_reference(i[0], i[1], t, margin=0.5, reduction=get_reduction(m)),
desc='margin',
check_sum_reduction=True,
test_cuda = (not TEST_WITH_ROCM)
test_cuda=(not TEST_WITH_ROCM)
),
]

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1 change: 1 addition & 0 deletions test/test_jit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6324,6 +6324,7 @@ class Config:

self.checkTrace(SNLIClassifier(Config()), (premise, hypothesis), inputs_require_grads=False)

@skipIfRocm
def test_super_resolution(self):
import torch.nn.init as init

Expand Down
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