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/reference/binary_function.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 | // TODO(b/117523611): We should factor out a binary_op and put binary ops there. |
26 | namespace tflite { |
27 | namespace ops { |
28 | namespace builtin { |
29 | namespace floor_mod { |
30 | namespace { |
31 | |
32 | // Input/output tensor index. |
33 | constexpr int kInputTensor1 = 0; |
34 | constexpr int kInputTensor2 = 1; |
35 | constexpr int kOutputTensor = 0; |
36 | |
37 | // Op data for floor_mod op. |
38 | struct OpData { |
39 | bool requires_broadcast; |
40 | }; |
41 | |
42 | // TODO(b/117912880): Support quantization. |
43 | |
44 | void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
45 | auto* data = new OpData; |
46 | data->requires_broadcast = false; |
47 | return data; |
48 | } |
49 | |
50 | void Free(TfLiteContext* context, void* buffer) { |
51 | delete reinterpret_cast<OpData*>(buffer); |
52 | } |
53 | |
54 | TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
55 | TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
56 | TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
57 | |
58 | // Reinterprete the opaque data provided by user. |
59 | OpData* data = reinterpret_cast<OpData*>(node->user_data); |
60 | |
61 | const TfLiteTensor* input1; |
62 | TF_LITE_ENSURE_OK(context, |
63 | GetInputSafe(context, node, kInputTensor1, &input1)); |
64 | const TfLiteTensor* input2; |
65 | TF_LITE_ENSURE_OK(context, |
66 | GetInputSafe(context, node, kInputTensor2, &input2)); |
67 | TfLiteTensor* output; |
68 | TF_LITE_ENSURE_OK(context, |
69 | GetOutputSafe(context, node, kOutputTensor, &output)); |
70 | |
71 | TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); |
72 | |
73 | const TfLiteType type = input1->type; |
74 | if (type != kTfLiteInt32 && type != kTfLiteFloat32 && type != kTfLiteInt64) { |
75 | TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported by floor_mod." , |
76 | TfLiteTypeGetName(type)); |
77 | return kTfLiteError; |
78 | } |
79 | output->type = type; |
80 | |
81 | data->requires_broadcast = !HaveSameShapes(input1, input2); |
82 | |
83 | TfLiteIntArray* output_size = nullptr; |
84 | if (data->requires_broadcast) { |
85 | TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast( |
86 | context, input1, input2, &output_size)); |
87 | } else { |
88 | output_size = TfLiteIntArrayCopy(input1->dims); |
89 | } |
90 | |
91 | return context->ResizeTensor(context, output, output_size); |
92 | } |
93 | |
94 | template <typename T> |
95 | TfLiteStatus EvalImpl(TfLiteContext* context, bool requires_broadcast, |
96 | const TfLiteTensor* input1, const TfLiteTensor* input2, |
97 | TfLiteTensor* output) { |
98 | const T* denominator_data = GetTensorData<T>(input2); |
99 | |
100 | if (input2->type == kTfLiteInt32 || input2->type == kTfLiteInt64) { |
101 | // Validate the denominator only for integer. |
102 | const int num_elements = NumElements(input2); |
103 | for (int i = 0; i < num_elements; ++i) { |
104 | if (denominator_data[i] == 0) { |
105 | TF_LITE_KERNEL_LOG(context, "Division by 0" ); |
106 | return kTfLiteError; |
107 | } |
108 | } |
109 | } |
110 | if (requires_broadcast) { |
111 | reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>( |
112 | GetTensorShape(input1), GetTensorData<T>(input1), |
113 | GetTensorShape(input2), denominator_data, GetTensorShape(output), |
114 | GetTensorData<T>(output), reference_ops::FloorMod<T>); |
115 | } else { |
116 | reference_ops::BinaryFunction<T, T, T>( |
117 | GetTensorShape(input1), GetTensorData<T>(input1), |
118 | GetTensorShape(input2), GetTensorData<T>(input2), |
119 | GetTensorShape(output), GetTensorData<T>(output), |
120 | reference_ops::FloorMod<T>); |
121 | } |
122 | |
123 | return kTfLiteOk; |
124 | } |
125 | |
126 | TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
127 | OpData* data = reinterpret_cast<OpData*>(node->user_data); |
128 | |
129 | const TfLiteTensor* input1; |
130 | TF_LITE_ENSURE_OK(context, |
131 | GetInputSafe(context, node, kInputTensor1, &input1)); |
132 | const TfLiteTensor* input2; |
133 | TF_LITE_ENSURE_OK(context, |
134 | GetInputSafe(context, node, kInputTensor2, &input2)); |
135 | TfLiteTensor* output; |
136 | TF_LITE_ENSURE_OK(context, |
137 | GetOutputSafe(context, node, kOutputTensor, &output)); |
138 | |
139 | switch (input1->type) { |
140 | case kTfLiteInt32: { |
141 | return EvalImpl<int32_t>(context, data->requires_broadcast, input1, |
142 | input2, output); |
143 | } |
144 | case kTfLiteInt64: { |
145 | return EvalImpl<int64_t>(context, data->requires_broadcast, input1, |
146 | input2, output); |
147 | } |
148 | case kTfLiteFloat32: { |
149 | return EvalImpl<float>(context, data->requires_broadcast, input1, input2, |
150 | output); |
151 | } |
152 | default: { |
153 | TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported by floor_mod." , |
154 | TfLiteTypeGetName(input1->type)); |
155 | return kTfLiteError; |
156 | } |
157 | } |
158 | } |
159 | |
160 | } // namespace |
161 | } // namespace floor_mod |
162 | |
163 | TfLiteRegistration* Register_FLOOR_MOD() { |
164 | // Init, Free, Prepare, Eval are satisfying the Interface required by |
165 | // TfLiteRegistration. |
166 | static TfLiteRegistration r = {floor_mod::Init, floor_mod::Free, |
167 | floor_mod::Prepare, floor_mod::Eval}; |
168 | return &r; |
169 | } |
170 | |
171 | } // namespace builtin |
172 | } // namespace ops |
173 | } // namespace tflite |
174 | |