1 | /* Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
2 | |
3 | Licensed under the Apache License, Version 2.0 (the "License"); |
4 | you may not use this file except in compliance with the License. |
5 | You may obtain a copy of the License at |
6 | |
7 | http://www.apache.org/licenses/LICENSE-2.0 |
8 | |
9 | Unless required by applicable law or agreed to in writing, software |
10 | distributed under the License is distributed on an "AS IS" BASIS, |
11 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
12 | See the License for the specific language governing permissions and |
13 | limitations under the License. |
14 | ==============================================================================*/ |
15 | #include <stdint.h> |
16 | |
17 | #include "tensorflow/lite/c/common.h" |
18 | #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h" |
19 | #include "tensorflow/lite/kernels/internal/reference/reference_ops.h" |
20 | #include "tensorflow/lite/kernels/internal/tensor.h" |
21 | #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
22 | #include "tensorflow/lite/kernels/kernel_util.h" |
23 | |
24 | namespace tflite { |
25 | namespace ops { |
26 | namespace builtin { |
27 | namespace matrix_set_diag { |
28 | |
29 | constexpr int kInputTensor = 0; |
30 | constexpr int kDiagonalTensor = 1; |
31 | constexpr int kOutputTensor = 0; |
32 | |
33 | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
34 | TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
35 | TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
36 | const TfLiteTensor* input; |
37 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input)); |
38 | TfLiteIntArray* input_dims = input->dims; |
39 | int input_dims_size = input_dims->size; |
40 | TF_LITE_ENSURE(context, input_dims_size >= 2); |
41 | |
42 | TfLiteTensor* output; |
43 | TF_LITE_ENSURE_OK(context, |
44 | GetOutputSafe(context, node, kOutputTensor, &output)); |
45 | |
46 | TfLiteIntArray* output_shape = TfLiteIntArrayCreate(input_dims_size); |
47 | for (int i = 0; i < input_dims_size; i++) { |
48 | output_shape->data[i] = input_dims->data[i]; |
49 | } |
50 | |
51 | // Resize the output tensor to the same size as the input tensor. |
52 | output->type = input->type; |
53 | TF_LITE_ENSURE_OK(context, |
54 | context->ResizeTensor(context, output, output_shape)); |
55 | |
56 | return kTfLiteOk; |
57 | } |
58 | |
59 | // Fill the tensor to make a diagonal matrix in each batch, i.e., when |
60 | // row index and column index are the same, fill with the next diagonal value. |
61 | // All other entries are the same as the input value. |
62 | // TODO(b/128636574) Move to reference_ops. |
63 | template <typename T> |
64 | void FillDiagImpl(const T* in, const T* diag, T* out, const int batch_size, |
65 | const int row_size, const int col_size) { |
66 | int idx = 0; |
67 | for (int b = 0; b < batch_size; b++) { |
68 | for (int i = 0; i < row_size; i++) { |
69 | for (int j = 0; j < col_size; ++j) { |
70 | // diag values go on the diagonal, in values elsewhere |
71 | if (i == j) { |
72 | out[i * col_size + j] = diag[idx]; |
73 | idx++; |
74 | } else { |
75 | out[i * col_size + j] = in[i * col_size + j]; |
76 | } |
77 | } |
78 | } |
79 | out += row_size * col_size; |
80 | in += row_size * col_size; |
81 | } |
82 | } |
83 | |
84 | template <typename T> |
85 | void FillDiag(const TfLiteTensor* input, const TfLiteTensor* diag, |
86 | TfLiteTensor* output, const int batch_size, const int row_size, |
87 | const int col_size) { |
88 | FillDiagImpl<T>(GetTensorData<T>(input), GetTensorData<T>(diag), |
89 | GetTensorData<T>(output), batch_size, row_size, col_size); |
90 | } |
91 | |
92 | // Fill a tensor with given "diag" values on the diagonal, input values |
93 | // elsewhere. |
94 | void FillDiagHelper(const TfLiteTensor* input, const TfLiteTensor* diag, |
95 | TfLiteTensor* output) { |
96 | const int num_output_dims = output->dims->size; |
97 | int batch_size = 1; |
98 | for (int i = 0; i < num_output_dims - 2; ++i) { |
99 | batch_size *= output->dims->data[i]; |
100 | } |
101 | |
102 | const int row_size = output->dims->data[num_output_dims - 2]; |
103 | const int col_size = output->dims->data[num_output_dims - 1]; |
104 | switch (output->type) { |
105 | case kTfLiteInt64: { |
106 | return FillDiag<int64_t>(input, diag, output, batch_size, row_size, |
107 | col_size); |
108 | } |
109 | case kTfLiteInt32: { |
110 | return FillDiag<int32_t>(input, diag, output, batch_size, row_size, |
111 | col_size); |
112 | } |
113 | case kTfLiteInt16: { |
114 | return FillDiag<int16_t>(input, diag, output, batch_size, row_size, |
115 | col_size); |
116 | } |
117 | case kTfLiteInt8: { |
118 | return FillDiag<int8_t>(input, diag, output, batch_size, row_size, |
119 | col_size); |
120 | } |
121 | case kTfLiteUInt8: { |
122 | return FillDiag<uint8_t>(input, diag, output, batch_size, row_size, |
123 | col_size); |
124 | } |
125 | default: |
126 | return FillDiag<float>(input, diag, output, batch_size, row_size, |
127 | col_size); |
128 | } |
129 | } |
130 | |
131 | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
132 | TfLiteTensor* output; |
133 | TF_LITE_ENSURE_OK(context, |
134 | GetOutputSafe(context, node, kOutputTensor, &output)); |
135 | const TfLiteTensor* input; |
136 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input)); |
137 | const TfLiteTensor* diag; |
138 | TF_LITE_ENSURE_OK(context, |
139 | GetInputSafe(context, node, kDiagonalTensor, &diag)); |
140 | FillDiagHelper(input, diag, output); |
141 | return kTfLiteOk; |
142 | } |
143 | |
144 | } // namespace matrix_set_diag |
145 | |
146 | TfLiteRegistration* Register_MATRIX_SET_DIAG() { |
147 | static TfLiteRegistration r = {nullptr, nullptr, matrix_set_diag::Prepare, |
148 | matrix_set_diag::Eval}; |
149 | return &r; |
150 | } |
151 | |
152 | } // namespace builtin |
153 | } // namespace ops |
154 | } // namespace tflite |
155 | |