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Increase Channels Last test coverage (#35504)

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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35504

Test Plan: Imported from OSS

Differential Revision: D20682117

Pulled By: VitalyFedyunin

fbshipit-source-id: ddd7ef1f075ea2c5c35df7bd698974fc5c59bc40
This commit is contained in:
Vitaly Fedyunin 2020-03-27 12:01:44 -07:00 committed by Facebook GitHub Bot
parent 3af46c90bd
commit 930d218fbf
1 changed files with 34 additions and 8 deletions
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42
test/test_torch.py
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@ -12790,64 +12790,90 @@ class TestTorchDeviceType(TestCase):
def test_helper(x, y, memory_format):
fns = [
# lambda x, y: x.clone(),
lambda x, y: x.clone(),
lambda x, y: x + 3,
lambda x, y: 3 * x,
lambda x, y: x + y,
lambda x, y: y + x,
lambda x, y: x * y,
lambda x, y: y * x,
lambda x, y: x.sin(),
lambda x, y: x.sinh(),
lambda x, y: x.sqrt(),
# lambda x, y: abs(x), # https://github.com/pytorch/pytorch/issues/24531
# lambda x, y: x.abs(), # https://github.com/pytorch/pytorch/issues/24531
# lambda x, y: x.acos(), # https://github.com/pytorch/pytorch/issues/24532
lambda x, y: abs(x),
lambda x, y: x.abs(),
lambda x, y: x.abs_(),
lambda x, y: x.acos(),
lambda x, y: x.acos_(),
lambda x, y: x.add(y, alpha=3),
lambda x, y: x.add_(y, alpha=3),
lambda x, y: x.addcdiv(y, y, value=2),
lambda x, y: x.addcdiv_(y, y, value=2),
lambda x, y: y.addcdiv(x, y, value=2),
lambda x, y: x.addcmul(y, y, value=2),
lambda x, y: x.addcmul_(y, y, value=2),
lambda x, y: y.addcmul(x, y, value=2),
lambda x, y: x.asin(),
lambda x, y: x.asin_(),
# lambda x, y: x.atan(), # https://github.com/pytorch/pytorch/issues/24538
lambda x, y: x.atan2(y),
lambda x, y: x.atan2_(y),
lambda x, y: x.ceil(),
lambda x, y: x.ceil_(),
# lambda x, y: x.clamp(-1, 1), # https://github.com/pytorch/pytorch/issues/24544
# lambda x, y: x.cos(), # https://github.com/pytorch/pytorch/issues/24545
# lambda x, y: x.cosh(), # https://github.com/pytorch/pytorch/issues/24546
lambda x, y: x.div(0.5),
lambda x, y: x.div_(0.5),
lambda x, y: x.div(y),
lambda x, y: x.div_(y),
lambda x, y: x.digamma(),
lambda x, y: x.digamma_(),
# lambda x, y: x.erf(), # https://github.com/pytorch/pytorch/issues/24558
# lambda x, y: x.erfc(), # https://github.com/pytorch/pytorch/issues/24559
lambda x, y: x.erfinv(),
lambda x, y: x.erfinv_(),
# lambda x, y: x.exp(), # https://github.com/pytorch/pytorch/issues/24561
lambda x, y: x.expm1(),
lambda x, y: x.expm1_(),
lambda x, y: x.floor(),
lambda x, y: x.floor_(),
# lambda x, y: x.fmod(2), # https://github.com/pytorch/pytorch/issues/24565
# lambda x, y: x.frac(), # https://github.com/pytorch/pytorch/issues/24566
# lambda x, y: x.lerp(y, 0.5), # Need to update Lerp.cu with TensorIterator
lambda x, y: x.log(),
lambda x, y: x.log_(),
lambda x, y: x.log10(),
lambda x, y: x.log10_(),
lambda x, y: x.log1p(),
lambda x, y: x.log1p_(),
lambda x, y: x.log2(),
lambda x, y: x.log2_(),
lambda x, y: x.mul(3),
lambda x, y: x.mul_(3),
lambda x, y: x.neg(),
lambda x, y: x.neg_(),
lambda x, y: x.pow(3),
lambda x, y: x.pow_(3),
# lambda x, y: x.pow(0.0), # Need to make resize_as_ memory format aware
# lambda x, y: x.pow(1.0), # Need to make resize_as_ memory format aware
# lambda x, y: x.reciprocal(), # Not migrated for CUDA
# lambda x, y: x.remainder(2), # https://github.com/pytorch/pytorch/issues/24615
lambda x, y: x.round(),
lambda x, y: x.round_(),
lambda x, y: x.rsqrt(),
# lambda x, y: x.sigmoid(), # https://github.com/pytorch/pytorch/issues/24624
lambda x, y: x.rsqrt_(),
lambda x, y: x.sigmoid(),
lambda x, y: x.sigmoid_(),
lambda x, y: x.sign(),
lambda x, y: x.sign_(),
lambda x, y: x.sin(),
lambda x, y: x.sin_(),
lambda x, y: x.sinh(),
lambda x, y: x.sinh_(),
lambda x, y: x.sqrt(),
lambda x, y: x.sqrt_(),
# lambda x, y: x.tan(), # https://github.com/pytorch/pytorch/issues/24641
# lambda x, y: x.tanh(), # https://github.com/pytorch/pytorch/issues/24642
lambda x, y: x.trunc(),
lambda x, y: x.trunc_(),
chunk_op,
unsqueeze_op_add,
# unsqueeze_op_clone,