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_diag { |
28 | |
29 | constexpr int kInputTensor = 0; |
30 | constexpr int kOutputTensor = 0; |
31 | |
32 | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
33 | TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); |
34 | TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
35 | const TfLiteTensor* input; |
36 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input)); |
37 | TfLiteIntArray* input_dims = input->dims; |
38 | int input_dims_size = input_dims->size; |
39 | TF_LITE_ENSURE(context, input_dims_size >= 1); |
40 | |
41 | TfLiteTensor* output; |
42 | TF_LITE_ENSURE_OK(context, |
43 | GetOutputSafe(context, node, kOutputTensor, &output)); |
44 | // Resize the output tensor. |
45 | TfLiteIntArray* output_shape = TfLiteIntArrayCreate(input_dims_size + 1); |
46 | for (int i = 0; i < input_dims_size; i++) { |
47 | output_shape->data[i] = input_dims->data[i]; |
48 | } |
49 | // Last dimension in the output is the same as the last dimension in the |
50 | // input. |
51 | output_shape->data[input_dims_size] = input_dims->data[input_dims_size - 1]; |
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 input value. |
61 | // All other entries get zero. |
62 | // TODO(b/128636574) Move to reference_ops. |
63 | template <typename T> |
64 | void FillDiagImpl(const T* in, T* out, const int batch_size, const int row_size, |
65 | 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 | // input values go on the diagonal, 0 elsewhere |
71 | if (i == j) { |
72 | out[i * col_size + j] = in[idx]; |
73 | idx++; |
74 | } else { |
75 | out[i * col_size + j] = 0; |
76 | } |
77 | } |
78 | } |
79 | out += row_size * col_size; |
80 | } |
81 | } |
82 | |
83 | template <typename T> |
84 | void FillDiag(const TfLiteTensor* input, TfLiteTensor* output, |
85 | const int batch_size, const int row_size, const int col_size) { |
86 | FillDiagImpl<T>(GetTensorData<T>(input), GetTensorData<T>(output), batch_size, |
87 | row_size, col_size); |
88 | } |
89 | |
90 | // Fill a tensor with given input on the diagonal, zero elsewhere |
91 | void FillDiagHelper(const TfLiteTensor* input, TfLiteTensor* output) { |
92 | const int num_output_dims = output->dims->size; |
93 | int batch_size = 1; |
94 | for (int i = 0; i < num_output_dims - 2; ++i) { |
95 | batch_size *= output->dims->data[i]; |
96 | } |
97 | |
98 | const int row_size = output->dims->data[num_output_dims - 2]; |
99 | const int col_size = output->dims->data[num_output_dims - 1]; |
100 | switch (output->type) { |
101 | case kTfLiteInt64: { |
102 | return FillDiag<int64_t>(input, output, batch_size, row_size, col_size); |
103 | } |
104 | case kTfLiteInt32: { |
105 | return FillDiag<int32_t>(input, output, batch_size, row_size, col_size); |
106 | } |
107 | case kTfLiteInt16: { |
108 | return FillDiag<int16_t>(input, output, batch_size, row_size, col_size); |
109 | } |
110 | case kTfLiteInt8: { |
111 | return FillDiag<int8_t>(input, output, batch_size, row_size, col_size); |
112 | } |
113 | case kTfLiteUInt8: { |
114 | return FillDiag<uint8_t>(input, output, batch_size, row_size, col_size); |
115 | } |
116 | default: |
117 | return FillDiag<float>(input, output, batch_size, row_size, col_size); |
118 | } |
119 | } |
120 | |
121 | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
122 | TfLiteTensor* output; |
123 | TF_LITE_ENSURE_OK(context, |
124 | GetOutputSafe(context, node, kOutputTensor, &output)); |
125 | const TfLiteTensor* input; |
126 | TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input)); |
127 | FillDiagHelper(input, output); |
128 | return kTfLiteOk; |
129 | } |
130 | |
131 | } // namespace matrix_diag |
132 | |
133 | TfLiteRegistration* Register_MATRIX_DIAG() { |
134 | static TfLiteRegistration r = {nullptr, nullptr, matrix_diag::Prepare, |
135 | matrix_diag::Eval}; |
136 | return &r; |
137 | } |
138 | |
139 | } // namespace builtin |
140 | } // namespace ops |
141 | } // namespace tflite |
142 | |