1 /*
2 * Copyright (C) 2018 The Android Open Source Project
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 #define LOG_TAG "Operations"
18
19 #include "HalInterfaces.h"
20 #include "OperationResolver.h"
21 #include "OperationsUtils.h"
22 #include "Tracing.h"
23
24 #include <cmath>
25
26 namespace android {
27 namespace nn {
28 namespace log_softmax {
29
30 using namespace hal;
31
32 constexpr char kOperationName[] = "LOG_SOFTMAX";
33
34 constexpr uint32_t kNumInputs = 3;
35 constexpr uint32_t kInputTensor = 0;
36 constexpr uint32_t kInputBeta = 1;
37 constexpr uint32_t kInputAxis = 2;
38
39 constexpr uint32_t kNumOutputs = 1;
40 constexpr uint32_t kOutputTensor = 0;
41
42 template <typename T>
compute(const T * input,const Shape & shape,T beta,uint32_t axis,T * output)43 inline bool compute(const T* input, const Shape& shape, T beta, uint32_t axis, T* output) {
44 const uint32_t outerSize = getNumberOfElements(shape, 0, axis);
45 const uint32_t axisSize = getSizeOfDimension(shape, axis);
46 const uint32_t innerSize = getNumberOfElements(shape, axis + 1, getNumberOfDimensions(shape));
47 for (uint32_t outer = 0; outer < outerSize; ++outer) {
48 for (uint32_t inner = 0; inner < innerSize; ++inner) {
49 // We subtract the maximum value from each element to ensure
50 // numerical stability, taking advantage of the following equality:
51 // exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C))
52 T maxValue = input[outer * axisSize * innerSize + inner];
53 for (uint32_t i = 1; i < axisSize; ++i) {
54 maxValue = std::max(maxValue, input[(outer * axisSize + i) * innerSize + inner]);
55 }
56
57 T sum = 0;
58 for (uint32_t i = 0; i < axisSize; ++i) {
59 sum += std::exp(static_cast<double>(
60 (input[(outer * axisSize + i) * innerSize + inner] - maxValue) * beta));
61 }
62
63 const T logSum = std::log(static_cast<double>(sum));
64 for (uint32_t i = 0; i < axisSize; ++i) {
65 output[(outer * axisSize + i) * innerSize + inner] =
66 (input[(outer * axisSize + i) * innerSize + inner] - maxValue) * beta -
67 logSum;
68 }
69 }
70 }
71 return true;
72 }
73
validate(const IOperationValidationContext * context)74 bool validate(const IOperationValidationContext* context) {
75 NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
76 NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
77 OperandType inputType = context->getInputType(kInputTensor);
78 std::vector<OperandType> inExpectedTypes;
79 std::vector<OperandType> outExpectedTypes;
80 if (inputType == OperandType::TENSOR_FLOAT32) {
81 inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::FLOAT32, OperandType::INT32};
82 outExpectedTypes = {OperandType::TENSOR_FLOAT32};
83 } else if (inputType == OperandType::TENSOR_FLOAT16) {
84 inExpectedTypes = {OperandType::TENSOR_FLOAT16, OperandType::FLOAT16, OperandType::INT32};
85 outExpectedTypes = {OperandType::TENSOR_FLOAT16};
86 } else {
87 LOG(ERROR) << "Unsupported input tensor type for operation " << kOperationName;
88 return false;
89 }
90 NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
91 NN_RET_CHECK(validateOutputTypes(context, outExpectedTypes));
92 return validateHalVersion(context, HalVersion::V1_2);
93 }
94
prepare(IOperationExecutionContext * context)95 bool prepare(IOperationExecutionContext* context) {
96 return context->setOutputShape(kOutputTensor, context->getInputShape(kInputTensor));
97 }
98
execute(IOperationExecutionContext * context)99 bool execute(IOperationExecutionContext* context) {
100 int32_t axis = context->getInputValue<int32_t>(kInputAxis);
101 NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
102 switch (context->getInputType(kInputTensor)) {
103 case OperandType::TENSOR_FLOAT16:
104 return compute(context->getInputBuffer<_Float16>(kInputTensor),
105 context->getInputShape(kInputTensor),
106 context->getInputValue<_Float16>(kInputBeta), axis,
107 context->getOutputBuffer<_Float16>(kOutputTensor));
108 case OperandType::TENSOR_FLOAT32:
109 return compute(context->getInputBuffer<float>(kInputTensor),
110 context->getInputShape(kInputTensor),
111 context->getInputValue<float>(kInputBeta), axis,
112 context->getOutputBuffer<float>(kOutputTensor));
113 default:
114 NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
115 }
116 }
117
118 } // namespace log_softmax
119
120 NN_REGISTER_OPERATION(LOG_SOFTMAX, log_softmax::kOperationName, log_softmax::validate,
121 log_softmax::prepare, log_softmax::execute);
122
123 } // namespace nn
124 } // namespace android
125