1 | /* Copyright 2018 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 <stddef.h> |
16 | #include <stdint.h> |
17 | |
18 | #include "tensorflow/lite/c/common.h" |
19 | #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h" |
20 | #include "tensorflow/lite/kernels/internal/reference/reference_ops.h" |
21 | #include "tensorflow/lite/kernels/internal/tensor.h" |
22 | #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
23 | #include "tensorflow/lite/kernels/kernel_util.h" |
24 | |
25 | namespace tflite { |
26 | namespace ops { |
27 | namespace builtin { |
28 | namespace pow { |
29 | namespace { |
30 | |
31 | // Input/output tensor index. |
32 | constexpr int kInputTensor1 = 0; |
33 | constexpr int kInputTensor2 = 1; |
34 | constexpr int kOutputTensor = 0; |
35 | |
36 | // Op data for pow op. |
37 | struct OpData { |
38 | bool requires_broadcast; |
39 | }; |
40 | |
41 | void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
42 | auto* data = new OpData; |
43 | data->requires_broadcast = false; |
44 | return data; |
45 | } |
46 | |
47 | void Free(TfLiteContext* context, void* buffer) { |
48 | delete reinterpret_cast<OpData*>(buffer); |
49 | } |
50 | |
51 | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
52 | TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
53 | TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
54 | |
55 | OpData* data = reinterpret_cast<OpData*>(node->user_data); |
56 | |
57 | const TfLiteTensor* input1; |
58 | TF_LITE_ENSURE_OK(context, |
59 | GetInputSafe(context, node, kInputTensor1, &input1)); |
60 | const TfLiteTensor* input2; |
61 | TF_LITE_ENSURE_OK(context, |
62 | GetInputSafe(context, node, kInputTensor2, &input2)); |
63 | TfLiteTensor* output; |
64 | TF_LITE_ENSURE_OK(context, |
65 | GetOutputSafe(context, node, kOutputTensor, &output)); |
66 | |
67 | TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); |
68 | |
69 | const TfLiteType type = input1->type; |
70 | if (type != kTfLiteInt32 && type != kTfLiteFloat32) { |
71 | TF_LITE_KERNEL_LOG(context, "Unsupported data type %s." , |
72 | TfLiteTypeGetName(type)); |
73 | return kTfLiteError; |
74 | } |
75 | output->type = type; |
76 | |
77 | data->requires_broadcast = !HaveSameShapes(input1, input2); |
78 | |
79 | TfLiteIntArray* output_size = nullptr; |
80 | if (data->requires_broadcast) { |
81 | TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast( |
82 | context, input1, input2, &output_size)); |
83 | } else { |
84 | output_size = TfLiteIntArrayCopy(input1->dims); |
85 | } |
86 | |
87 | return context->ResizeTensor(context, output, output_size); |
88 | } |
89 | |
90 | template <typename T> |
91 | void PowImpl(const TfLiteTensor* input1, const TfLiteTensor* input2, |
92 | TfLiteTensor* output, bool requires_broadcast) { |
93 | if (requires_broadcast) { |
94 | optimized_ops::BroadcastPow4D( |
95 | GetTensorShape(input1), GetTensorData<T>(input1), |
96 | GetTensorShape(input2), GetTensorData<T>(input2), |
97 | GetTensorShape(output), GetTensorData<T>(output)); |
98 | } else { |
99 | reference_ops::Pow(GetTensorShape(input1), GetTensorData<T>(input1), |
100 | GetTensorShape(input2), GetTensorData<T>(input2), |
101 | GetTensorShape(output), GetTensorData<T>(output)); |
102 | } |
103 | } |
104 | |
105 | TfLiteStatus CheckValue(TfLiteContext* context, const TfLiteTensor* input) { |
106 | const int64_t num_elements = NumElements(input); |
107 | const int32_t* data = GetTensorData<int32_t>(input); |
108 | for (int i = 0; i < num_elements; ++i) { |
109 | if (data[i] < 0) { |
110 | TF_LITE_KERNEL_LOG(context, |
111 | "POW does not support negative value for int32." ); |
112 | return kTfLiteError; |
113 | } |
114 | } |
115 | return kTfLiteOk; |
116 | } |
117 | |
118 | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
119 | OpData* data = reinterpret_cast<OpData*>(node->user_data); |
120 | |
121 | const TfLiteTensor* input1; |
122 | TF_LITE_ENSURE_OK(context, |
123 | GetInputSafe(context, node, kInputTensor1, &input1)); |
124 | const TfLiteTensor* input2; |
125 | TF_LITE_ENSURE_OK(context, |
126 | GetInputSafe(context, node, kInputTensor2, &input2)); |
127 | TfLiteTensor* output; |
128 | TF_LITE_ENSURE_OK(context, |
129 | GetOutputSafe(context, node, kOutputTensor, &output)); |
130 | |
131 | switch (output->type) { |
132 | case kTfLiteInt32: { |
133 | // TensorFlow does not support negative for int32. |
134 | TF_LITE_ENSURE_OK(context, CheckValue(context, input2)); |
135 | PowImpl<int32_t>(input1, input2, output, data->requires_broadcast); |
136 | break; |
137 | } |
138 | case kTfLiteFloat32: { |
139 | PowImpl<float>(input1, input2, output, data->requires_broadcast); |
140 | break; |
141 | } |
142 | default: { |
143 | TF_LITE_KERNEL_LOG(context, "Unsupported data type: %d" , output->type); |
144 | return kTfLiteError; |
145 | } |
146 | } |
147 | return kTfLiteOk; |
148 | } |
149 | |
150 | } // namespace |
151 | } // namespace pow |
152 | |
153 | TfLiteRegistration* Register_POW() { |
154 | static TfLiteRegistration r = {pow::Init, pow::Free, pow::Prepare, pow::Eval}; |
155 | return &r; |
156 | } |
157 | |
158 | } // namespace builtin |
159 | } // namespace ops |
160 | } // namespace tflite |
161 | |