1/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#include "tensorflow/core/framework/op_kernel.h"
17#include "tensorflow/core/framework/register_types.h"
18#include "tensorflow/core/framework/tensor.h"
19#include "tensorflow/core/framework/tensor_util.h"
20#include "tensorflow/core/framework/types.h"
21#include "tensorflow/core/util/sparse/sparse_tensor.h"
22
23namespace tensorflow {
24
25template <typename T>
26class SparseAddGradOp : public OpKernel {
27 public:
28 explicit SparseAddGradOp(OpKernelConstruction *ctx) : OpKernel(ctx) {}
29
30 void Compute(OpKernelContext *ctx) override {
31 // Gradient for op: SparseAdd(a, b) == sum.
32 const Tensor *backprop_val_grad, *a_indices, *b_indices, *sum_indices;
33 OP_REQUIRES_OK(ctx, ctx->input("backprop_val_grad", &backprop_val_grad));
34 OP_REQUIRES_OK(ctx, ctx->input("a_indices", &a_indices));
35 OP_REQUIRES_OK(ctx, ctx->input("b_indices", &b_indices));
36 OP_REQUIRES_OK(ctx, ctx->input("sum_indices", &sum_indices));
37
38 OP_REQUIRES(ctx,
39 TensorShapeUtils::IsMatrix(a_indices->shape()) &&
40 TensorShapeUtils::IsMatrix(b_indices->shape()) &&
41 TensorShapeUtils::IsMatrix(sum_indices->shape()),
42 errors::InvalidArgument(
43 "Input indices should be matrices but received shapes: ",
44 a_indices->shape().DebugString(), " and ",
45 b_indices->shape().DebugString(), " and ",
46 sum_indices->shape().DebugString()));
47 OP_REQUIRES(
48 ctx, TensorShapeUtils::IsVector(backprop_val_grad->shape()),
49 errors::InvalidArgument(
50 "Input backprop_val_grad should be a vector but received shape: ",
51 backprop_val_grad->shape().DebugString()));
52 OP_REQUIRES(
53 ctx,
54 a_indices->dim_size(1) == b_indices->dim_size(1) &&
55 b_indices->dim_size(1) == sum_indices->dim_size(1),
56 errors::InvalidArgument("The densified operands should have the same "
57 "ndims; for A, B, sum got: ",
58 a_indices->dim_size(1), b_indices->dim_size(1),
59 sum_indices->dim_size(1)));
60 OP_REQUIRES(
61 ctx, backprop_val_grad->NumElements() == sum_indices->dim_size(0),
62 errors::InvalidArgument("# elements of backprop_val_grad and # rows of "
63 "sum_indices should match (#nnz of sum): got ",
64 backprop_val_grad->NumElements(), " and ",
65 sum_indices->dim_size(0)));
66
67 const int num_dims = a_indices->dim_size(1);
68 const int64_t a_nnz = a_indices->dim_size(0);
69 const int64_t b_nnz = b_indices->dim_size(0);
70 const int64_t sum_nnz = backprop_val_grad->NumElements();
71
72 const auto a_indices_mat = a_indices->matrix<int64_t>();
73 const auto b_indices_mat = b_indices->matrix<int64_t>();
74 const auto sum_indices_mat = sum_indices->matrix<int64_t>();
75
76 Tensor *a_val_grad, *b_val_grad;
77 OP_REQUIRES_OK(ctx,
78 ctx->allocate_output(0, TensorShape({a_nnz}), &a_val_grad));
79 OP_REQUIRES_OK(ctx,
80 ctx->allocate_output(1, TensorShape({b_nnz}), &b_val_grad));
81
82 T *a_val_grad_flat = a_val_grad->flat<T>().data();
83 T *b_val_grad_flat = b_val_grad->flat<T>().data();
84 const T *backprop_val_grad_flat = backprop_val_grad->flat<T>().data();
85 memset(a_val_grad_flat, 0, sizeof(T) * a_nnz);
86 memset(b_val_grad_flat, 0, sizeof(T) * b_nnz);
87
88#define COMPARE(a_or_b, idx) \
89 switch (sparse::DimComparator::cmp(a_or_b##_indices_mat, sum_indices_mat, \
90 idx, k, num_dims)) { \
91 case 0: \
92 a_or_b##_val_grad_flat[idx] = backprop_val_grad_flat[k]; \
93 ++idx; \
94 break; \
95 case -1: \
96 ++idx; \
97 a_or_b##_idx_geq = false; \
98 break; \
99 case 1: \
100 break; \
101 }
102
103 // Set-intersect the indices; fill in grads for positions in the
104 // intersection.
105 int64_t i = 0, j = 0, k = 0;
106 bool a_idx_geq, b_idx_geq;
107 while (i < a_nnz && j < b_nnz && k < sum_nnz) {
108 a_idx_geq = b_idx_geq = true;
109 COMPARE(a, i);
110 COMPARE(b, j);
111 // increment pointer into sum_indices iff both the current A, B indices >=
112 // the current sum index.
113 if (a_idx_geq && b_idx_geq) ++k;
114 }
115
116 // at most one loop below will run
117 while (i < a_nnz && k < sum_nnz) {
118 a_idx_geq = true;
119 COMPARE(a, i);
120 if (a_idx_geq) ++k;
121 }
122 while (j < b_nnz && k < sum_nnz) {
123 b_idx_geq = true;
124 COMPARE(b, j);
125 if (b_idx_geq) ++k;
126 }
127#undef COMPARE
128 }
129};
130
131#define REGISTER_KERNELS(type) \
132 REGISTER_KERNEL_BUILDER( \
133 Name("SparseAddGrad").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
134 SparseAddGradOp<type>)
135
136// This op should work for any T that SparseAdd is registered with.
137REGISTER_KERNELS(float);
138REGISTER_KERNELS(double);
139REGISTER_KERNELS(int64_t);
140REGISTER_KERNELS(int32);
141REGISTER_KERNELS(int16);
142REGISTER_KERNELS(int8);
143REGISTER_KERNELS(complex64);
144REGISTER_KERNELS(complex128);
145#undef REGISTER_KERNELS
146} // namespace tensorflow
147