Revert "[complex] conv_transpose2d (#81805)"
This reverts commit 528dd05108cdac6726748c34e385b5c3136256df.
Reverted https://github.com/pytorch/pytorch/pull/81805 on behalf of https://github.com/jeanschmidt due to Breaking internal builds - D40534110 - android-java-tests-0
diff --git a/aten/src/ATen/native/Convolution.cpp b/aten/src/ATen/native/Convolution.cpp
index 36ea8ee..4d68f23 100644
--- a/aten/src/ATen/native/Convolution.cpp
+++ b/aten/src/ATen/native/Convolution.cpp
@@ -1066,14 +1066,8 @@
Tensor input;
bool is_batched;
std::tie(input, is_batched) = batchify(input_, /*num_spatial_dims=*/ 2, "conv_transpose2d");
- Tensor output;
- if (at::isComplexType(input_.scalar_type())) {
- output = complex_convolution(
+ auto output = at::convolution(
input, weight, bias, stride, padding, dilation, true, output_padding, groups);
- } else {
- output = at::convolution(
- input, weight, bias, stride, padding, dilation, true, output_padding, groups);
- }
return is_batched ? output : output.squeeze(0);
}
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
index fbf4eb6..2130eee 100644
--- a/torch/testing/_internal/common_methods_invocations.py
+++ b/torch/testing/_internal/common_methods_invocations.py
@@ -3069,12 +3069,8 @@
assert fn is not None
- grad_fn_map = {torch.nn.functional.conv_transpose1d: torch.nn.grad.conv1d_input,
- torch.nn.functional.conv_transpose2d: torch.nn.grad.conv2d_input,
- torch.nn.functional.conv_transpose3d: torch.nn.grad.conv3d_input}
- batched_dim_map = {torch.nn.functional.conv_transpose1d: 3,
- torch.nn.functional.conv_transpose2d: 4,
- torch.nn.functional.conv_transpose3d: 5}
+ grad_fn_map = {torch.nn.functional.conv_transpose1d: torch.nn.grad.conv1d_input}
+ batched_dim_map = {torch.nn.functional.conv_transpose1d: 3}
# Input for `ref` is ndarray.
input, weight = torch.from_numpy(input), torch.from_numpy(weight)
@@ -3084,10 +3080,7 @@
input = input.unsqueeze(0)
if bias is not None:
- bias = torch.from_numpy(bias)
- unsqueeze_dims = input.ndim - 2
- for _ in range(unsqueeze_dims):
- bias = bias.unsqueeze(1)
+ bias = torch.from_numpy(bias).unsqueeze(1)
grad_output = input
# Get the input shape for grad_fn.
@@ -3154,7 +3147,8 @@
((1, 1, 4, 3), (1, 2, 3, 4), None,
{'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1}),
((2, 8, 4, 4), (8, 1, 3, 3), None, {'groups': 4}),
- ((1, 4, 5, 5), (4, 8, 3, 3), None, {})
+ ((1, 4, 5, 5), (4, 8, 3, 3), None,
+ {})
)
for input_shape, weight, bias, kwargs in cases:
@@ -10644,12 +10638,9 @@
OpInfo('nn.functional.conv_transpose2d',
aten_name='conv_transpose2d',
aliases=('conv_transpose2d',),
- # `ref` for this function is backward of
- # corresponding `conv*d`
- ref=partial(conv_transpose_ref, fn=torch.nn.functional.conv_transpose2d),
- dtypes=floating_and_complex_types_and(torch.int64),
- dtypesIfCUDA=floating_and_complex_types_and(torch.float16, torch.chalf,
- *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []),
+ dtypes=floating_types_and(torch.int64),
+ dtypesIfCUDA=floating_types_and(torch.float16,
+ *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []),
sample_inputs_func=sample_inputs_conv_transpose2d,
supports_forward_ad=True,
supports_fwgrad_bwgrad=True,
@@ -10658,32 +10649,11 @@
decorators=[
DecorateInfo(
toleranceOverride({torch.float32: tol(atol=1e-04, rtol=1.3e-06), }),
- 'TestCommon', 'test_variant_consistency_eager', device_type='cuda'),
- DecorateInfo(
- toleranceOverride({torch.float32: tol(atol=2e-05, rtol=5e-05), }),
- 'TestCommon', 'test_noncontiguous_samples', device_type='cuda'),
- DecorateInfo(
- toleranceOverride({torch.complex32: tol(atol=5e-2, rtol=5e-2)}),
- "TestCudaFuserOpInfo", "test_nvfuser_correctness"),
- DecorateInfo(
- toleranceOverride({torch.chalf: tol(atol=5e-2, rtol=5e-2), }),
- 'TestCommon', 'test_complex_half_reference_testing')],
+ 'TestCommon', 'test_variant_consistency_eager', device_type='cuda')],
skips=(
# RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at
# "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":104, please report a bug to PyTorch.
DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
- # RuntimeError: UNSUPPORTED DTYPE: complex
- DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness',
- dtypes=(torch.complex64, torch.complex128)),
- # RuntimeError: "slow_conv2d_cpu_grad_input" not implemented for 'Long'
- DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_numpy_ref',
- dtypes=(torch.int64,)),
- # Reference: https://github.com/pytorch/pytorch/issues/86356
- DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_numpy_ref',
- dtypes=(torch.double, torch.cdouble)),
- # AssertionError: None mismatch: torch.complex64 is not None
- DecorateInfo(unittest.expectedFailure, 'TestDtypeCustomRules', 'test_custom_rules',
- dtypes=(torch.complex64, torch.complex128)),
),
supports_out=False,),
OpInfo('nn.functional.conv_transpose3d',
diff --git a/torch/testing/_internal/common_modules.py b/torch/testing/_internal/common_modules.py
index fed908e..f214ffb 100644
--- a/torch/testing/_internal/common_modules.py
+++ b/torch/testing/_internal/common_modules.py
@@ -1143,7 +1143,6 @@
module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=2, lazy=False, transposed=True),
gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
module_memformat_affects_out=True,
- dtypes=floating_and_complex_types_and(torch.chalf),
skips=(
# channels_last support on cuda requires cudnn >= 7603
DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'),
@@ -1154,25 +1153,7 @@
# See https://github.com/pytorch/pytorch/issues/80247
DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cpu'),
DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
- dtypes=[torch.float64, torch.complex128]),
- # These fail only on ROCm
- DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
- dtypes=[torch.complex32, torch.complex64], active_if=TEST_WITH_ROCM),
- # Not implmented for chalf on CPU
- DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_forward',
- dtypes=(torch.chalf,), device_type='cpu'),
- DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_memory_format',
- dtypes=(torch.chalf,), device_type='cpu'),
- DecorateInfo(unittest.expectedFailure, 'TestModule',
- 'test_if_train_and_eval_modes_differ', dtypes=(torch.chalf,), device_type='cpu'),
- DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_non_contiguous_tensors',
- dtypes=(torch.chalf,), device_type='cpu'),
- DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity',
- dtypes=(torch.chalf,), device_type='cuda'),
- DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_multiple_device_transfer',
- dtypes=(torch.chalf,), device_type='cuda'),
- # Ref: https://github.com/pytorch/pytorch/issues/73502
- DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_pickle', dtypes=(torch.chalf,)),
+ dtypes=[torch.float64]),
),
decorators=(
DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
