1/* Copyright 2015 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
16// See docs in ../ops/array_ops.cc.
17
18#define EIGEN_USE_THREADS
19
20#include <math.h>
21
22#include "tensorflow/core/framework/op.h"
23#include "tensorflow/core/framework/op_kernel.h"
24#include "tensorflow/core/framework/tensor.h"
25#include "tensorflow/core/framework/tensor_shape.h"
26#include "tensorflow/core/framework/type_traits.h"
27#include "tensorflow/core/framework/types.h"
28#include "tensorflow/core/kernels/meta_support.h"
29#include "tensorflow/core/kernels/quantization_utils.h"
30#include "tensorflow/core/lib/core/errors.h"
31
32namespace tensorflow {
33
34typedef Eigen::ThreadPoolDevice CPUDevice;
35
36template <class T1, class T2>
37class RequantizeOp : public OpKernel {
38 public:
39 explicit RequantizeOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
40
41 void Compute(OpKernelContext* ctx) override {
42 const Tensor& input = ctx->input(0);
43
44 const Tensor& input_min = ctx->input(1);
45 const Tensor& input_max = ctx->input(2);
46 const Tensor& requested_output_min = ctx->input(3);
47 const Tensor& requested_output_max = ctx->input(4);
48 OP_REQUIRES(
49 ctx, TensorShapeUtils::IsScalar(input_min.shape()),
50 errors::InvalidArgument("`input_min` must be rank 0 but is rank ",
51 input_min.dims()));
52 OP_REQUIRES(
53 ctx, TensorShapeUtils::IsScalar(input_max.shape()),
54 errors::InvalidArgument("`input_max` must be rank 0 but is rank ",
55 input_max.dims()));
56 OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(requested_output_min.shape()),
57 errors::InvalidArgument(
58 "`requested_output_min` must be rank 0 but is rank ",
59 requested_output_min.dims()));
60 OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(requested_output_max.shape()),
61 errors::InvalidArgument(
62 "`requested_output_max` must be rank 0 but is rank ",
63 requested_output_max.dims()));
64
65 const float input_min_float = input_min.flat<float>()(0);
66 const float input_max_float = input_max.flat<float>()(0);
67 const float requested_output_min_float =
68 requested_output_min.flat<float>()(0);
69 const float requested_output_max_float =
70 requested_output_max.flat<float>()(0);
71
72 Tensor* output = nullptr;
73 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
74 Tensor* output_min = nullptr;
75 OP_REQUIRES_OK(ctx, ctx->allocate_output(1, TensorShape({}), &output_min));
76 Tensor* output_max = nullptr;
77 OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({}), &output_max));
78
79 OP_REQUIRES(
80 ctx, requested_output_min_float <= 0.0f,
81 errors::InvalidArgument("requested_output_min must be <= 0, but got ",
82 requested_output_min_float));
83 OP_REQUIRES(
84 ctx, requested_output_max_float >= requested_output_min_float,
85 errors::InvalidArgument(
86 "requested_output_max must be >= requested_output_min, but got ",
87 requested_output_max_float, " and ", requested_output_min_float));
88
89 auto input_array = input.flat<T1>();
90
91#if 0
92 // This is the reference, non-eigen implementation:
93 auto output_array = output->flat<T2>();
94 RequantizeManyInNewRange<T1, T2>(
95 input_array.data(), input_array.size(),
96 input_min_float, input_max_float,
97 requested_output_min_float, requested_output_max_float,
98 output_array.data());
99#endif
100
101 if (input_array.size() > 0) {
102 if (meta::IsSupportedAndEnabled() && std::is_same<T1, qint32>() &&
103 std::is_same<T2, quint8>()) {
104 auto input_i32_array = input.flat<qint32>();
105 meta::Requantize(ctx, input_i32_array.data(), input_i32_array.size(),
106 input_min_float, input_max_float,
107 requested_output_min_float, requested_output_max_float,
108 output->flat<quint8>().data());
109 } else {
110 RequantizeManyInNewRangeUsingEigen<T1, T2>(
111 ctx->eigen_device<CPUDevice>(), input, input_min_float,
112 input_max_float, requested_output_min_float,
113 requested_output_max_float, output);
114 }
115 }
116
117 output_min->flat<float>().setConstant(requested_output_min_float);
118 output_max->flat<float>().setConstant(requested_output_max_float);
119 }
120};
121
122REGISTER_KERNEL_BUILDER(Name("Requantize")
123 .Device(DEVICE_CPU)
124 .TypeConstraint<qint32>("Tinput")
125 .TypeConstraint<quint8>("out_type"),
126 RequantizeOp<qint32, quint8>);
127
128REGISTER_KERNEL_BUILDER(Name("Requantize")
129 .Device(DEVICE_CPU)
130 .TypeConstraint<qint32>("Tinput")
131 .TypeConstraint<qint8>("out_type"),
132 RequantizeOp<qint32, qint8>);
133
134} // namespace tensorflow
135