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Add forward AD formulas for {adaptive_,fractional_,}max_pool{2,3}d_{backward,} (#69884)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69884 Also fixes: https://github.com/pytorch/pytorch/issues/69322, https://github.com/pytorch/pytorch/issues/69325 Test Plan: Imported from OSS Reviewed By: bdhirsh Differential Revision: D33093039 Pulled By: soulitzer fbshipit-source-id: b9a522a00f4e9e85974888de5058de07280f8f66
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soulitzer
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3116d87024
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@ -11941,18 +11941,21 @@ class TestNNInit(TestCase):
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self.assertEqual(F.max_unpool1d(output, indices, 2), F.max_unpool1d(output, indices, 2, stride=2))
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# Test list / tuple passed as argument to max_unpool1d
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input = torch.randn([1, 1, 5])
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input = torch.randn([1, 1, 5], requires_grad=True)
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output, indices = F.max_pool1d(input, 2, stride=2, return_indices=True)
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self.assertEqual(F.max_unpool1d(output, indices, 2, stride=2, output_size=input.shape),
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F.max_unpool1d(output, indices, 2, stride=2, output_size=input.size()))
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gradcheck(F.max_unpool1d, (output, indices, 2), check_forward_ad=True)
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# Test 2D
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output, indices = F.max_pool2d(torch.randn([1, 1, 4, 4]), 2, stride=2, return_indices=True)
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output, indices = F.max_pool2d(torch.randn([1, 1, 4, 4], requires_grad=True), 2, stride=2, return_indices=True)
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self.assertEqual(F.max_unpool2d(output, indices, 2), F.max_unpool2d(output, indices, 2, stride=2))
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gradcheck(F.max_unpool2d, (output, indices, 2), check_forward_ad=True)
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# Test 3D
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output, indices = F.max_pool3d(torch.randn([4, 4, 4, 4, 4]), 2, stride=2, return_indices=True)
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output, indices = F.max_pool3d(torch.randn([4, 4, 4, 4, 4], requires_grad=True), 2, stride=2, return_indices=True)
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self.assertEqual(F.max_unpool3d(output, indices, 2), F.max_unpool3d(output, indices, 2, stride=2))
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gradcheck(F.max_unpool3d, (output, indices, 2), check_forward_ad=True)
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def test_dirac_properties(self):
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for dims in [3, 4, 5]:
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@ -1962,9 +1962,13 @@
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- name: adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
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self: adaptive_max_pool2d_backward(grad, self, result1)
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result0: gather(self_t.flatten(-2), -1, result1.flatten(-2)).view_as(result1)
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output_differentiability: [True, False]
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- name: adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)
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self: adaptive_max_pool3d_backward(grad, self, result1)
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result0: gather(self_t.flatten(-3), -1, result1.flatten(-3)).view_as(result1)
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output_differentiability: [True, False]
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- name: avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
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self: avg_pool2d_backward(grad, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override)
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@ -1976,25 +1980,33 @@
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- name: fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor)
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self: fractional_max_pool2d_backward(grad, self, kernel_size, output_size, result1)
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result0: gather(self_t.flatten(-2), -1, result1.flatten(-2)).view_as(result1)
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output_differentiability: [True, False]
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- name: fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor)
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self: fractional_max_pool3d_backward(grad, self, kernel_size, output_size, result1)
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result0: gather(self_t.flatten(-3), -1, result1.flatten(-3)).view_as(result1)
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output_differentiability: [True, False]
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- name: max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
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self: max_pool2d_with_indices_backward(grad, self, kernel_size, stride, padding, dilation, ceil_mode, result1)
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result0: gather(self_t.flatten(-2), -1, result1.flatten(-2)).view_as(result1)
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output_differentiability: [True, False]
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- name: max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
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self: max_pool3d_with_indices_backward(grad, self, kernel_size, stride, padding, dilation, ceil_mode, result1)
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result0: gather(self_t.flatten(-3), -1, result1.flatten(-3)).view_as(result1)
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output_differentiability: [True, False]
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- name: max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor
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self: max_unpool2d_backward(grad, self, indices, output_size)
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indices: non_differentiable
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result: auto_linear
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- name: max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor
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self: max_unpool3d_backward(grad, self, indices, output_size, stride, padding)
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indices: non_differentiable
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result: auto_linear
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- name: convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensor
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input, weight, bias: "grad.defined() ? convolution_backward(grad, input, weight, bias->sizes(), stride, padding, dilation, transposed, output_padding, groups, grad_input_mask) : std::tuple<Tensor, Tensor, Tensor>()"
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@ -2086,10 +2098,12 @@
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- name: adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
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grad_output: max_pool_double_backward(grad, indices, 2)
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self: zeros_like(self)
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result: auto_linear
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- name: adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
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grad_output: max_pool_double_backward(grad, indices, 3)
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self: zeros_like(self)
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result: auto_linear
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- name: avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
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grad_output: avg_pool2d(grad, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override)
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@ -2108,10 +2122,12 @@
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- name: fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor
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grad_output: max_pool_double_backward(grad, indices, 2)
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self: zeros_like(self)
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result: auto_linear
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- name: fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor
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grad_output: max_pool_double_backward(grad, indices, 3)
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self: zeros_like(self)
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result: auto_linear
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- name: glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor
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grad_output: glu_double_backward_grad_output(grad, self, dim)
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@ -2148,11 +2164,13 @@
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grad_output: max_pool_double_backward(grad, indices, 2)
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self: zeros_like(self)
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indices: non_differentiable
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result: auto_linear
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- name: max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor
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grad_output: max_pool_double_backward(grad, indices, 3)
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self: zeros_like(self)
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indices: non_differentiable
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result: auto_linear
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- name: max_unpool2d_backward(Tensor grad_output, Tensor self, Tensor indices, int[2] output_size) -> Tensor
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grad_output: max_unpool2d(grad, indices, output_size)
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@ -10775,6 +10775,10 @@ op_db: List[OpInfo] = [
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dtypes=floating_types(),
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dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16),
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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# got: Batching rule not implemented for aten::flatten.using_ints
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check_batched_forward_grad=False,
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gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
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sample_inputs_func=sample_inputs_adaptive_max_pool1d),
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OpInfo('nn.functional.adaptive_max_pool2d',
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@ -10792,6 +10796,10 @@ op_db: List[OpInfo] = [
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DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),
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),
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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# got: Batching rule not implemented for aten::flatten.using_ints
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check_batched_forward_grad=False,
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gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
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sample_inputs_func=sample_inputs_adaptive_max_pool2d),
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OpInfo('nn.functional.adaptive_max_pool3d',
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@ -10811,6 +10819,10 @@ op_db: List[OpInfo] = [
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DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),
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),
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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# got: Batching rule not implemented for aten::flatten.using_ints
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check_batched_forward_grad=False,
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gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
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sample_inputs_func=sample_inputs_adaptive_max_pool3d),
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OpInfo('nn.functional.avg_pool1d',
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@ -11201,49 +11213,54 @@ op_db: List[OpInfo] = [
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OpInfo('nn.functional.fractional_max_pool2d',
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supports_autograd=True,
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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op=lambda input, *args, **kwargs:
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wrapper_set_seed(torch.nn.functional.fractional_max_pool2d, input, *args, **kwargs),
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# vmap does not support random operations
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check_batched_forward_grad=False,
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dtypes=floating_types(),
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dtypesIfCUDA=floating_types_and(torch.float16),
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test_neg_view=False,
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sample_inputs_func=sample_inputs_fractional_max_pool2d,
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decorators=[
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# FIXME: both derivatives are implemented incorrectly
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# https://github.com/pytorch/pytorch/issues/69322
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DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_grad'),
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DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_gradgrad'),
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# FIXME: produces incorrect output on non-contiguous inputs
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# https://github.com/pytorch/pytorch/issues/69325
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DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_noncontiguous_samples'),
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decorators=(
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# FIXME: AssertionError: False is not true : Tensors failed to compare as equal!
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DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'),
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# RuntimeError: input->type()->kind() == TypeKind::OptionalType
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# INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270
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DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
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], ),
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DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'))),
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OpInfo('nn.functional.fractional_max_pool3d',
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supports_autograd=True,
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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op=lambda input, *args, **kwargs:
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wrapper_set_seed(torch.nn.functional.fractional_max_pool3d, input, *args, **kwargs),
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# vmap does not support random operations
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check_batched_forward_grad=False,
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dtypes=floating_types(),
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dtypesIfCUDA=floating_types_and(torch.float16),
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test_neg_view=False,
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sample_inputs_func=sample_inputs_fractional_max_pool3d,
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decorators=[
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decorators=(
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# FIXME: both derivatives are implemented incorrectly
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# https://github.com/pytorch/pytorch/issues/69322
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DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_grad'),
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# RuntimeError: cannot reshape tensor of 0 elements into shape [0, 1, -1] because the
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# unspecified dimension size -1 can be any value and is ambiguous
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DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_gradgrad'),
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# FIXME: produces incorrect output on non-contiguous inputs
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# https://github.com/pytorch/pytorch/issues/69325
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DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_noncontiguous_samples'),
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# FIXME: AssertionError: False is not true : Tensors failed to compare as equal!
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DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'),
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# RuntimeError: input->type()->kind() == TypeKind::OptionalType
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# INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270
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DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),
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], ),
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DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'),)),
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OpInfo('nn.functional.max_pool1d',
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aten_name='max_pool1d',
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supports_autograd=True,
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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# got: Batching rule not implemented for aten::flatten.using_ints
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check_batched_forward_grad=False,
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# TODO: add shape checks
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assert_jit_shape_analysis=False,
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dtypes=floating_types(),
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@ -11259,6 +11276,10 @@ op_db: List[OpInfo] = [
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# Vmap is not happy with non-contiguous (channels_last) inputs
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check_batched_gradgrad=False,
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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# got: Batching rule not implemented for aten::flatten.using_ints
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check_batched_forward_grad=False,
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assert_jit_shape_analysis=True,
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dtypes=floating_types(),
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dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16),
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@ -11267,6 +11288,10 @@ op_db: List[OpInfo] = [
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aten_name='max_pool3d',
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supports_autograd=True,
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supports_out=False,
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supports_forward_ad=True,
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supports_fwgrad_bwgrad=True,
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# got: Batching rule not implemented for aten::flatten.using_ints
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check_batched_forward_grad=False,
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# TODO: add shape checks
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assert_jit_shape_analysis=False,
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dtypes=floating_types(),
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