| /aosp_15_r20/external/pytorch/aten/src/ATen/native/ |
| H A D | GridSampler.h | 39 // `grad_in`.
43 bool align_corners, scalar_t *grad_in) { in grid_sampler_unnormalize_set_grad() argument
46 *grad_in = static_cast<scalar_t>(size - 1) / 2; in grid_sampler_unnormalize_set_grad()
50 *grad_in = static_cast<scalar_t>(size) / 2; in grid_sampler_unnormalize_set_grad()
62 // it also returns the `d output / d input` via pointer argument `grad_in`.
66 scalar_t *grad_in) { in clip_coordinates_set_grad() argument
70 *grad_in = static_cast<scalar_t>(0); in clip_coordinates_set_grad()
75 *grad_in = static_cast<scalar_t>(0); in clip_coordinates_set_grad()
78 *grad_in = static_cast<scalar_t>(1); in clip_coordinates_set_grad()
108 // `grad_in`.
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| H A D | UnfoldBackward.h | 17 Tensor& grad_in,
29 // grad_in does not mean that it is a gradient wrt to input,
30 // grad_in/grad_out is just an input/output of unfold_backward kernel.
34 const Tensor& grad_in, in _make_unfold_backward_iter_over_grad_out() argument
43 auto grad_in_dim_size = ensure_nonempty_size(grad_in, dim); in _make_unfold_backward_iter_over_grad_out()
60 /* prepare grad_in for TensorIterator { */ in _make_unfold_backward_iter_over_grad_out()
61 auto grad_in_strides = ensure_nonempty_vec(grad_in.strides().vec()); in _make_unfold_backward_iter_over_grad_out()
62 auto grad_in_sizes = ensure_nonempty_vec(grad_in.sizes().vec()); in _make_unfold_backward_iter_over_grad_out()
73 auto grad_in_restrided = grad_in.squeeze(-1).as_strided( in _make_unfold_backward_iter_over_grad_out()
83 0, iter_dim_size, grad_in.options().dtype(at::kLong) in _make_unfold_backward_iter_over_grad_out()
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| H A D | ReduceOps.cpp | 2301 auto grad_in = at::zeros_symint(sizes, grad_out.options()); in value_selecting_reduction_backward_symint() local
2303 return grad_in.scatter(dim, indices_, grad_out); in value_selecting_reduction_backward_symint()
2305 return grad_in.scatter_(dim, indices_, grad_out); in value_selecting_reduction_backward_symint()
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| H A D | native_functions.yaml | 10114 - func: unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/ |
| H A D | GridSampler.cuh | 34 // `grad_in`.
39 bool align_corners, scalar_t *grad_in) { in grid_sampler_unnormalize_set_grad() argument
42 *grad_in = static_cast<scalar_t>(size - 1) / 2; in grid_sampler_unnormalize_set_grad()
46 *grad_in = static_cast<scalar_t>(size) / 2; in grid_sampler_unnormalize_set_grad()
59 // it also returns the `d output / d input` via pointer argument `grad_in`.
63 scalar_t clip_coordinates_set_grad(scalar_t in, int clip_limit, scalar_t *grad_in) { in clip_coordinates_set_grad() argument
67 *grad_in = static_cast<scalar_t>(0); in clip_coordinates_set_grad()
72 *grad_in = static_cast<scalar_t>(0); in clip_coordinates_set_grad()
75 *grad_in = static_cast<scalar_t>(1); in clip_coordinates_set_grad()
105 // `grad_in`.
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| H A D | UnfoldBackwardKernel.cu | 12 // grad_in does not mean that it is a gradient wrt to input,
13 // grad_in/grad_out is just an input/output of unfold_backward kernel.
123 const Tensor& grad_in, in unfold_backward_cuda_kernel() argument
130 auto last_dim = maybe_wrap_dim(-1, grad_in.dim()); in unfold_backward_cuda_kernel()
132 auto grad_in_dim_stride = ensure_nonempty_stride(grad_in, dim); in unfold_backward_cuda_kernel()
133 auto grad_in_last_dim_stride = ensure_nonempty_stride(grad_in, last_dim); in unfold_backward_cuda_kernel()
134 auto grad_in_dim_size = ensure_nonempty_size(grad_in, dim); in unfold_backward_cuda_kernel()
139 grad_out, grad_in, dim, size, step); in unfold_backward_cuda_kernel()
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/cpu/ |
| H A D | UnfoldBackwardKernel.cpp | 17 // grad_in does not mean that it is a gradient wrt to input,
18 // grad_in/grad_out is just an input/output of unfold_backward kernel.
27 // unfold_backward receives grad_in and returns grad_out such that
28 // grad_in.shape == out.shape,
36 // In this case the iteration takes over grad_in and performs the following copy:
37 // grad_out[..., i_out_dim,...] = grad_in[..., i_in_dim,..., i_in_last_dim],
43 // grad_in[...,i_in_dim,...,i_in_last_dim], where
51 // Simply put, given i_out_dim, we find which folds of grad_in
53 // and then the corresponding value of grad_in[...,i_in_dim,...,i_in_last_dim]
113 const Tensor& grad_in, in unfold_backward_cpu_kernel() argument
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| H A D | PaddingKernel.cpp | 115 static inline void add_stub(scalar_t* grad_in, const scalar_t* grad_out, int64_t size) { in add_stub() argument
119 Vec grad_vec = Vec::loadu(grad_in + d) + Vec::loadu(grad_out + d); in add_stub()
120 grad_vec.store(grad_in + d); in add_stub()
126 grad_in[d] += grad_out[d]; in add_stub()
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| /aosp_15_r20/external/tensorflow/tensorflow/core/kernels/ |
| H A D | maxpooling_op.cc | 1030 const Tensor& grad_in, const Tensor& argmax, in launch()
1035 auto shard = [&grad_in, &argmax, &grad_out, include_batch_in_index]( in launch()
1041 const int64_t input_size_per_batch = grad_in.NumElements() / batch_size; in launch()
1046 auto grad_in_flat = grad_in.flat<T>(); in launch()
1117 const Tensor& grad_in = context->input(1); in Compute() local
1130 OP_REQUIRES(context, grad_in.shape() == params.forward_output_shape(), in Compute()
1133 ", but got ", grad_in.shape())); in Compute()
1148 context, params, grad_in, argmax, grad_out, include_batch_in_index_); in Compute()
1185 const Tensor& grad_in = context->input(1); in Compute() local
1199 context, grad_in.shape() == tensor_in.shape(), in Compute()
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| /aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/nn_ops/ |
| H A D | pooling_ops_test.py | 936 grad_in = constant_op.constant(tensor_output, shape=output_shape)
937 out_op = gen_nn_ops.max_pool_grad_with_argmax(t, grad_in, argmax, ksize,
945 grad_in = constant_op.constant(tensor_output, shape=output_shape)
946 out_op = gen_nn_ops.max_pool_grad(t, orig_out, grad_in, ksize, strides,
968 grad_in = constant_op.constant(tensor_input, shape=input_shape)
970 t, grad_in, argmax, ksize, strides, padding)
977 grad_in = constant_op.constant(tensor_input, shape=input_shape)
978 out_op = gen_nn_ops.max_pool_grad_grad(t, orig_out, grad_in, ksize,
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| /aosp_15_r20/external/pytorch/test/ |
| H A D | test_fake_tensor.py | 877 grad_in = torch.ops.aten._adaptive_avg_pool2d_backward(grad_out, inp)
879 torch._prims_common.suggest_memory_format(grad_in)
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| H A D | test_nn.py | 395 def hook(self, module, grad_out, grad_in): argument
9854 grad_in = in_t.grad
9855 self.assertTrue(grad_in.is_contiguous(memory_format=memory_format))
9861 self.assertEqual(in_t.grad, grad_in)
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| H A D | test_autograd.py | 1395 def acc_grad_node_post_hook(grad_in, grad_out): argument
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| /aosp_15_r20/external/pytorch/test/nn/ |
| H A D | test_module_hooks.py | 1336 def bw_hook(m, grad_in, grad_output): argument
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| H A D | test_convolution.py | 505 grad_in = torch.ones(res.shape, dtype=torch.float32)
506 res.backward(grad_in)
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| /aosp_15_r20/external/pytorch/tools/autograd/ |
| H A D | derivatives.yaml | 1811 - name: unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor
1812 grad_in: grad.unfold(dim, size, step)
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