1/* Copyright 2018 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#include "tensorflow/lite/c/builtin_op_data.h"
16#include "tensorflow/lite/c/common.h"
17#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
18#include "tensorflow/lite/kernels/internal/tensor.h"
19#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
20#include "tensorflow/lite/kernels/internal/types.h"
21#include "tensorflow/lite/kernels/kernel_util.h"
22
23namespace tflite {
24namespace ops {
25namespace builtin {
26namespace fake_quant {
27
28// This file has reference implementation of FakeQuant.
29enum KernelType {
30 kReference,
31};
32
33struct OpContext {
34 OpContext(TfLiteContext* context, TfLiteNode* node) {
35 input = GetInput(context, node, 0);
36 output = GetOutput(context, node, 0);
37 }
38 const TfLiteTensor* input;
39 TfLiteTensor* output;
40};
41
42TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
43 TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
44 TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
45
46 const auto* params =
47 reinterpret_cast<TfLiteFakeQuantParams*>(node->builtin_data);
48
49 if (params->narrow_range) {
50 TF_LITE_KERNEL_LOG(
51 context,
52 "narrow_range FakeQuant is not currently supported at runtime. "
53 "narrow_range is only meant to be applied to weights, not activations");
54 return kTfLiteError;
55 }
56
57 OpContext op_context(context, node);
58 TfLiteIntArray* output_dims = TfLiteIntArrayCopy(op_context.input->dims);
59 op_context.output->type = op_context.input->type;
60 return context->ResizeTensor(context, op_context.output, output_dims);
61}
62
63template <KernelType kernel_type>
64TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
65 OpContext op_context(context, node);
66
67 const auto* params =
68 reinterpret_cast<TfLiteFakeQuantParams*>(node->builtin_data);
69
70 tflite::FakeQuantParams op_params;
71 op_params.num_bits = params->num_bits;
72 op_params.minmax.min = params->min;
73 op_params.minmax.max = params->max;
74 reference_ops::FakeQuant(op_params, GetTensorShape(op_context.input),
75 GetTensorData<float>(op_context.input),
76 GetTensorShape(op_context.output),
77 GetTensorData<float>(op_context.output));
78
79 return kTfLiteOk;
80}
81
82} // namespace fake_quant
83
84TfLiteRegistration* Register_FAKE_QUANT_REF() {
85 static TfLiteRegistration r = {nullptr, nullptr, fake_quant::Prepare,
86 fake_quant::Eval<fake_quant::kReference>};
87 return &r;
88}
89
90TfLiteRegistration* Register_FAKE_QUANT() { return Register_FAKE_QUANT_REF(); }
91
92} // namespace builtin
93} // namespace ops
94} // namespace tflite
95