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[complex] conv_transpose2d (#81805)
Reference: https://github.com/pytorch/pytorch/issues/71108 Fixes : #86414 Pull Request resolved: https://github.com/pytorch/pytorch/pull/81805 Approved by: https://github.com/anjali411
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kshitij12345
PyTorch MergeBot
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232fbd90ff
commit
528dd05108
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@ -1066,8 +1066,14 @@ at::Tensor conv_transpose2d(
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Tensor input;
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bool is_batched;
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std::tie(input, is_batched) = batchify(input_, /*num_spatial_dims=*/ 2, "conv_transpose2d");
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auto output = at::convolution(
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Tensor output;
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if (at::isComplexType(input_.scalar_type())) {
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output = complex_convolution(
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input, weight, bias, stride, padding, dilation, true, output_padding, groups);
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} else {
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output = at::convolution(
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input, weight, bias, stride, padding, dilation, true, output_padding, groups);
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}
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return is_batched ? output : output.squeeze(0);
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}
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@ -3069,8 +3069,12 @@ def conv_transpose_ref(input, weight, bias, stride=1, padding=0,
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assert fn is not None
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grad_fn_map = {torch.nn.functional.conv_transpose1d: torch.nn.grad.conv1d_input}
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batched_dim_map = {torch.nn.functional.conv_transpose1d: 3}
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grad_fn_map = {torch.nn.functional.conv_transpose1d: torch.nn.grad.conv1d_input,
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torch.nn.functional.conv_transpose2d: torch.nn.grad.conv2d_input,
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torch.nn.functional.conv_transpose3d: torch.nn.grad.conv3d_input}
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batched_dim_map = {torch.nn.functional.conv_transpose1d: 3,
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torch.nn.functional.conv_transpose2d: 4,
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torch.nn.functional.conv_transpose3d: 5}
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# Input for `ref` is ndarray.
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input, weight = torch.from_numpy(input), torch.from_numpy(weight)
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@ -3080,7 +3084,10 @@ def conv_transpose_ref(input, weight, bias, stride=1, padding=0,
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input = input.unsqueeze(0)
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if bias is not None:
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bias = torch.from_numpy(bias).unsqueeze(1)
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bias = torch.from_numpy(bias)
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unsqueeze_dims = input.ndim - 2
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for _ in range(unsqueeze_dims):
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bias = bias.unsqueeze(1)
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grad_output = input
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# Get the input shape for grad_fn.
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@ -3147,8 +3154,7 @@ def sample_inputs_conv_transpose2d(op_info, device, dtype, requires_grad, **kwar
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((1, 1, 4, 3), (1, 2, 3, 4), None,
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{'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1}),
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((2, 8, 4, 4), (8, 1, 3, 3), None, {'groups': 4}),
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((1, 4, 5, 5), (4, 8, 3, 3), None,
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{})
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((1, 4, 5, 5), (4, 8, 3, 3), None, {})
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)
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for input_shape, weight, bias, kwargs in cases:
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@ -10635,9 +10641,12 @@ op_db: List[OpInfo] = [
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OpInfo('nn.functional.conv_transpose2d',
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aten_name='conv_transpose2d',
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aliases=('conv_transpose2d',),
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dtypes=floating_types_and(torch.int64),
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dtypesIfCUDA=floating_types_and(torch.float16,
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*[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []),
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# `ref` for this function is backward of
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# corresponding `conv*d`
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ref=partial(conv_transpose_ref, fn=torch.nn.functional.conv_transpose2d),
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dtypes=floating_and_complex_types_and(torch.int64),
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dtypesIfCUDA=floating_and_complex_types_and(torch.float16, torch.chalf,
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*[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []),
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sample_inputs_func=sample_inputs_conv_transpose2d,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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@ -10646,11 +10655,32 @@ op_db: List[OpInfo] = [
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decorators=[
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DecorateInfo(
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toleranceOverride({torch.float32: tol(atol=1e-04, rtol=1.3e-06), }),
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'TestCommon', 'test_variant_consistency_eager', device_type='cuda')],
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'TestCommon', 'test_variant_consistency_eager', device_type='cuda'),
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DecorateInfo(
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toleranceOverride({torch.float32: tol(atol=2e-05, rtol=5e-05), }),
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'TestCommon', 'test_noncontiguous_samples', device_type='cuda'),
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DecorateInfo(
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toleranceOverride({torch.complex32: tol(atol=5e-2, rtol=5e-2)}),
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"TestCudaFuserOpInfo", "test_nvfuser_correctness"),
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DecorateInfo(
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toleranceOverride({torch.chalf: tol(atol=5e-2, rtol=5e-2), }),
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'TestCommon', 'test_complex_half_reference_testing')],
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skips=(
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# RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at
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# "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":104, please report a bug to PyTorch.
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DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
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# RuntimeError: UNSUPPORTED DTYPE: complex
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DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness',
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dtypes=(torch.complex64, torch.complex128)),
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# RuntimeError: "slow_conv2d_cpu_grad_input" not implemented for 'Long'
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DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_numpy_ref',
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dtypes=(torch.int64,)),
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# Reference: https://github.com/pytorch/pytorch/issues/86356
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DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_numpy_ref',
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dtypes=(torch.double, torch.cdouble)),
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# AssertionError: None mismatch: torch.complex64 is not None
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DecorateInfo(unittest.expectedFailure, 'TestDtypeCustomRules', 'test_custom_rules',
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dtypes=(torch.complex64, torch.complex128)),
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),
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supports_out=False,),
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OpInfo('nn.functional.conv_transpose3d',
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@ -1143,6 +1143,7 @@ module_db: List[ModuleInfo] = [
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module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=2, lazy=False, transposed=True),
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gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
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module_memformat_affects_out=True,
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dtypes=floating_and_complex_types_and(torch.chalf),
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skips=(
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# channels_last support on cuda requires cudnn >= 7603
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DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'),
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@ -1153,7 +1154,25 @@ module_db: List[ModuleInfo] = [
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
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dtypes=[torch.float64]),
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dtypes=[torch.float64, torch.complex128]),
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# These fail only on ROCm
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
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dtypes=[torch.complex32, torch.complex64], active_if=TEST_WITH_ROCM),
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# Not implmented for chalf on CPU
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DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_forward',
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dtypes=(torch.chalf,), device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_memory_format',
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dtypes=(torch.chalf,), device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, 'TestModule',
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'test_if_train_and_eval_modes_differ', dtypes=(torch.chalf,), device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_non_contiguous_tensors',
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dtypes=(torch.chalf,), device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity',
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dtypes=(torch.chalf,), device_type='cuda'),
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DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_multiple_device_transfer',
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dtypes=(torch.chalf,), device_type='cuda'),
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# Ref: https://github.com/pytorch/pytorch/issues/73502
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DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_pickle', dtypes=(torch.chalf,)),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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