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Skip some test_nn tests which don't work on ROCm… #131

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Merged
merged 1 commit into from
Aug 17, 2018
Merged

Skip some test_nn tests which don't work on ROCm… #131

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3 changes: 2 additions & 1 deletion test/common_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,13 @@

import torch
import torch.cuda
from common import TEST_WITH_ROCM


TEST_CUDA = torch.cuda.is_available()
TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
CUDA_DEVICE = TEST_CUDA and torch.device("cuda:0")
TEST_CUDNN = TEST_CUDA and torch.backends.cudnn.is_acceptable(torch.tensor(1., device=CUDA_DEVICE))
TEST_CUDNN = TEST_CUDA and (TEST_WITH_ROCM or torch.backends.cudnn.is_acceptable(torch.tensor(1., device=CUDA_DEVICE)))
TEST_CUDNN_VERSION = TEST_CUDNN and torch.backends.cudnn.version()


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7 changes: 5 additions & 2 deletions test/common_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
import torch
import torch.cuda
from torch.nn.functional import _Reduction
from common import TestCase, to_gpu, freeze_rng_state, is_iterable
from common import TestCase, to_gpu, freeze_rng_state, is_iterable, TEST_WITH_ROCM
from common_cuda import TEST_CUDA
from torch.autograd.gradcheck import get_numerical_jacobian, iter_tensors
import torch.backends.cudnn
Expand Down Expand Up @@ -125,7 +125,8 @@ def get_weight(m):
module_name='ELU',
constructor_args=(2.,),
input_size=(3, 2, 5),
reference_fn=lambda x, _: torch.where(x >= 0, x, 2 * (x.exp() - 1))
reference_fn=lambda x, _: torch.where(x >= 0, x, 2 * (x.exp() - 1)),
test_cuda=(not TEST_WITH_ROCM),
),
# TODO: reference function
dict(
Expand Down Expand Up @@ -720,6 +721,7 @@ 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),
),
dict(
module_name='CosineEmbeddingLoss',
Expand All @@ -730,6 +732,7 @@ 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),
),
dict(
module_name='MarginRankingLoss',
Expand Down
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