1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
|
/*
* Copyright (C) 2019 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "TestHarness.h"
#include <gmock/gmock-matchers.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <cmath>
#include <functional>
#include <numeric>
#include <string>
#include <vector>
namespace test_helper {
namespace {
constexpr uint32_t kMaxNumberOfPrintedErrors = 10;
// TODO(b/139442217): Allow passing accuracy criteria from spec.
// Currently we only need relaxed accuracy criteria on mobilenet tests, so we return the quant8
// tolerance simply based on the current test name.
int getQuant8AllowedError() {
const ::testing::TestInfo* const testInfo =
::testing::UnitTest::GetInstance()->current_test_info();
const std::string testCaseName = testInfo->test_case_name();
const std::string testName = testInfo->name();
// We relax the quant8 precision for all tests with mobilenet:
// - CTS/VTS GeneratedTest and DynamicOutputShapeTest with mobilenet
// - VTS CompilationCachingTest and CompilationCachingSecurityTest except for TOCTOU tests
if (testName.find("mobilenet") != std::string::npos ||
(testCaseName.find("CompilationCaching") != std::string::npos &&
testName.find("TOCTOU") == std::string::npos)) {
return 2;
} else {
return 1;
}
}
uint32_t getNumberOfElements(const TestOperand& op) {
return std::reduce(op.dimensions.begin(), op.dimensions.end(), 1u, std::multiplies<uint32_t>());
}
// Check if the actual results meet the accuracy criterion.
template <typename T>
void expectNear(const TestOperand& op, const TestBuffer& result, double atol, double rtol) {
const T* actualBuffer = result.get<T>();
const T* expectedBuffer = op.data.get<T>();
uint32_t len = getNumberOfElements(op), numErrors = 0;
for (uint32_t i = 0; i < len; i++) {
// Compare all data types in double for precision and signed arithmetic.
double actual = static_cast<double>(actualBuffer[i]);
double expected = static_cast<double>(expectedBuffer[i]);
double tolerableRange = atol + rtol * std::fabs(expected);
// Print at most kMaxNumberOfPrintedErrors errors by EXPECT_NEAR.
if (numErrors < kMaxNumberOfPrintedErrors) {
EXPECT_NEAR(expected, actual, tolerableRange) << "When comparing element " << i;
}
if (std::fabs(actual - expected) > tolerableRange) numErrors++;
}
EXPECT_EQ(numErrors, 0u);
}
// For boolean values, we expect exact match.
void expectBooleanEqual(const TestOperand& op, const TestBuffer& result) {
const bool8* actualBuffer = result.get<bool8>();
const bool8* expectedBuffer = op.data.get<bool8>();
uint32_t len = getNumberOfElements(op), numErrors = 0;
for (uint32_t i = 0; i < len; i++) {
bool actual = static_cast<bool>(actualBuffer[i]);
bool expected = static_cast<bool>(expectedBuffer[i]);
// Print at most kMaxNumberOfPrintedErrors errors by EXPECT_NEAR.
if (numErrors < kMaxNumberOfPrintedErrors) {
EXPECT_EQ(expected, actual) << "When comparing element " << i;
}
if (expected != actual) numErrors++;
}
EXPECT_EQ(numErrors, 0u);
}
// Calculates the expected probability from the unnormalized log-probability of
// each class in the input and compares it to the actual occurrence of that class
// in the output.
void expectMultinomialDistributionWithinTolerance(const TestModel& model,
const std::vector<TestBuffer>& buffers) {
// This function is only for RANDOM_MULTINOMIAL single-operation test.
ASSERT_EQ(model.operations.size(), 1u);
ASSERT_EQ(model.operations[0].type, TestOperationType::RANDOM_MULTINOMIAL);
ASSERT_EQ(model.inputIndexes.size(), 1u);
ASSERT_EQ(model.outputIndexes.size(), 1u);
ASSERT_EQ(buffers.size(), 1u);
const auto& inputOperand = model.operands[model.inputIndexes[0]];
const auto& outputOperand = model.operands[model.outputIndexes[0]];
ASSERT_EQ(inputOperand.dimensions.size(), 2u);
ASSERT_EQ(outputOperand.dimensions.size(), 2u);
const int kBatchSize = inputOperand.dimensions[0];
const int kNumClasses = inputOperand.dimensions[1];
const int kNumSamples = outputOperand.dimensions[1];
const uint32_t outputLength = getNumberOfElements(outputOperand);
const int32_t* outputData = buffers[0].get<int32_t>();
std::vector<int> classCounts(kNumClasses);
for (uint32_t i = 0; i < outputLength; i++) {
classCounts[outputData[i]]++;
}
const uint32_t inputLength = getNumberOfElements(inputOperand);
std::vector<float> inputData(inputLength);
if (inputOperand.type == TestOperandType::TENSOR_FLOAT32) {
const float* inputRaw = inputOperand.data.get<float>();
std::copy(inputRaw, inputRaw + inputLength, inputData.begin());
} else if (inputOperand.type == TestOperandType::TENSOR_FLOAT16) {
const _Float16* inputRaw = inputOperand.data.get<_Float16>();
std::transform(inputRaw, inputRaw + inputLength, inputData.begin(),
[](_Float16 fp16) { return static_cast<float>(fp16); });
} else {
FAIL() << "Unknown input operand type for RANDOM_MULTINOMIAL.";
}
for (int b = 0; b < kBatchSize; ++b) {
float probabilitySum = 0;
const int batchIndex = kBatchSize * b;
for (int i = 0; i < kNumClasses; ++i) {
probabilitySum += expf(inputData[batchIndex + i]);
}
for (int i = 0; i < kNumClasses; ++i) {
float probability =
static_cast<float>(classCounts[i]) / static_cast<float>(kNumSamples);
float probabilityExpected = expf(inputData[batchIndex + i]) / probabilitySum;
EXPECT_THAT(probability,
::testing::FloatNear(probabilityExpected,
model.expectedMultinomialDistributionTolerance));
}
}
}
} // namespace
void checkResults(const TestModel& model, const std::vector<TestBuffer>& buffers) {
// For RANDOM_MULTINOMIAL test only.
if (model.expectedMultinomialDistributionTolerance > 0.0f) {
expectMultinomialDistributionWithinTolerance(model, buffers);
return;
}
// Decide the tolerable range.
//
// For floating-point models, we use the relaxed precision if either
// - relaxed computation flag is set
// - the model has at least one TENSOR_FLOAT16 operand
double fpAtol = 1e-5;
double fpRtol = 5.0f * 1.1920928955078125e-7;
const bool hasFloat16Inputs = std::any_of(
model.operands.begin(), model.operands.end(),
[](const TestOperand& op) { return op.type == TestOperandType::TENSOR_FLOAT16; });
if (model.isRelaxed || hasFloat16Inputs) {
// TODO: Adjust the error limit based on testing.
// If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.
fpAtol = 5.0f * 0.0009765625;
// Set the relative tolerance to be 5ULP of the corresponding FP precision.
fpRtol = 5.0f * 0.0009765625;
}
const double quant8AllowedError = getQuant8AllowedError();
ASSERT_EQ(model.outputIndexes.size(), buffers.size());
for (uint32_t i = 0; i < model.outputIndexes.size(); i++) {
SCOPED_TRACE(testing::Message() << "When comparing output " << i);
const auto& operand = model.operands[model.outputIndexes[i]];
const auto& result = buffers[i];
if (operand.isIgnored) continue;
switch (operand.type) {
case TestOperandType::TENSOR_FLOAT32:
expectNear<float>(operand, result, fpAtol, fpRtol);
break;
case TestOperandType::TENSOR_FLOAT16:
expectNear<_Float16>(operand, result, fpAtol, fpRtol);
break;
case TestOperandType::TENSOR_INT32:
expectNear<int32_t>(operand, result, 0, 0);
break;
case TestOperandType::TENSOR_QUANT8_ASYMM:
expectNear<uint8_t>(operand, result, quant8AllowedError, 0);
break;
case TestOperandType::TENSOR_QUANT8_SYMM:
expectNear<int8_t>(operand, result, quant8AllowedError, 0);
break;
case TestOperandType::TENSOR_QUANT16_ASYMM:
expectNear<uint16_t>(operand, result, 1, 0);
break;
case TestOperandType::TENSOR_QUANT16_SYMM:
expectNear<int16_t>(operand, result, 1, 0);
break;
case TestOperandType::TENSOR_BOOL8:
expectBooleanEqual(operand, result);
break;
default:
FAIL() << "Data type not supported.";
}
}
}
} // namespace test_helper
|
