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/*
* Copyright (c) 2020 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "modules/audio_processing/agc2/rnn_vad/rnn_gru.h"
#include <array>
#include <memory>
#include <vector>
#include "api/array_view.h"
#include "modules/audio_processing/agc2/rnn_vad/test_utils.h"
#include "modules/audio_processing/test/performance_timer.h"
#include "rtc_base/checks.h"
#include "rtc_base/logging.h"
#include "test/gtest.h"
#include "third_party/rnnoise/src/rnn_vad_weights.h"
namespace webrtc {
namespace rnn_vad {
namespace {
void TestGatedRecurrentLayer(
GatedRecurrentLayer& gru,
rtc::ArrayView<const float> input_sequence,
rtc::ArrayView<const float> expected_output_sequence) {
const int input_sequence_length = rtc::CheckedDivExact(
rtc::dchecked_cast<int>(input_sequence.size()), gru.input_size());
const int output_sequence_length = rtc::CheckedDivExact(
rtc::dchecked_cast<int>(expected_output_sequence.size()), gru.size());
ASSERT_EQ(input_sequence_length, output_sequence_length)
<< "The test data length is invalid.";
// Feed the GRU layer and check the output at every step.
gru.Reset();
for (int i = 0; i < input_sequence_length; ++i) {
SCOPED_TRACE(i);
gru.ComputeOutput(
input_sequence.subview(i * gru.input_size(), gru.input_size()));
const auto expected_output =
expected_output_sequence.subview(i * gru.size(), gru.size());
ExpectNearAbsolute(expected_output, gru, 3e-6f);
}
}
// Gated recurrent units layer test data.
constexpr int kGruInputSize = 5;
constexpr int kGruOutputSize = 4;
constexpr std::array<int8_t, 12> kGruBias = {96, -99, -81, -114, 49, 119,
-118, 68, -76, 91, 121, 125};
constexpr std::array<int8_t, 60> kGruWeights = {
// Input 0.
124, 9, 1, 116, // Update.
-66, -21, -118, -110, // Reset.
104, 75, -23, -51, // Output.
// Input 1.
-72, -111, 47, 93, // Update.
77, -98, 41, -8, // Reset.
40, -23, -43, -107, // Output.
// Input 2.
9, -73, 30, -32, // Update.
-2, 64, -26, 91, // Reset.
-48, -24, -28, -104, // Output.
// Input 3.
74, -46, 116, 15, // Update.
32, 52, -126, -38, // Reset.
-121, 12, -16, 110, // Output.
// Input 4.
-95, 66, -103, -35, // Update.
-38, 3, -126, -61, // Reset.
28, 98, -117, -43 // Output.
};
constexpr std::array<int8_t, 48> kGruRecurrentWeights = {
// Output 0.
-3, 87, 50, 51, // Update.
-22, 27, -39, 62, // Reset.
31, -83, -52, -48, // Output.
// Output 1.
-6, 83, -19, 104, // Update.
105, 48, 23, 68, // Reset.
23, 40, 7, -120, // Output.
// Output 2.
64, -62, 117, 85, // Update.
51, -43, 54, -105, // Reset.
120, 56, -128, -107, // Output.
// Output 3.
39, 50, -17, -47, // Update.
-117, 14, 108, 12, // Reset.
-7, -72, 103, -87, // Output.
};
constexpr std::array<float, 20> kGruInputSequence = {
0.89395463f, 0.93224651f, 0.55788344f, 0.32341808f, 0.93355054f,
0.13475326f, 0.97370994f, 0.14253306f, 0.93710381f, 0.76093364f,
0.65780413f, 0.41657975f, 0.49403164f, 0.46843281f, 0.75138855f,
0.24517593f, 0.47657707f, 0.57064998f, 0.435184f, 0.19319285f};
constexpr std::array<float, 16> kGruExpectedOutputSequence = {
0.0239123f, 0.5773077f, 0.f, 0.f,
0.01282811f, 0.64330572f, 0.f, 0.04863098f,
0.00781069f, 0.75267816f, 0.f, 0.02579715f,
0.00471378f, 0.59162533f, 0.11087593f, 0.01334511f};
class RnnGruParametrization
: public ::testing::TestWithParam<AvailableCpuFeatures> {};
// Checks that the output of a GRU layer is within tolerance given test input
// data.
TEST_P(RnnGruParametrization, CheckGatedRecurrentLayer) {
GatedRecurrentLayer gru(kGruInputSize, kGruOutputSize, kGruBias, kGruWeights,
kGruRecurrentWeights,
/*cpu_features=*/GetParam(),
/*layer_name=*/"GRU");
TestGatedRecurrentLayer(gru, kGruInputSequence, kGruExpectedOutputSequence);
}
TEST_P(RnnGruParametrization, DISABLED_BenchmarkGatedRecurrentLayer) {
// Prefetch test data.
std::unique_ptr<FileReader> reader = CreateGruInputReader();
std::vector<float> gru_input_sequence(reader->size());
reader->ReadChunk(gru_input_sequence);
using ::rnnoise::kHiddenGruBias;
using ::rnnoise::kHiddenGruRecurrentWeights;
using ::rnnoise::kHiddenGruWeights;
using ::rnnoise::kHiddenLayerOutputSize;
using ::rnnoise::kInputLayerOutputSize;
GatedRecurrentLayer gru(kInputLayerOutputSize, kHiddenLayerOutputSize,
kHiddenGruBias, kHiddenGruWeights,
kHiddenGruRecurrentWeights,
/*cpu_features=*/GetParam(),
/*layer_name=*/"GRU");
rtc::ArrayView<const float> input_sequence(gru_input_sequence);
ASSERT_EQ(input_sequence.size() % kInputLayerOutputSize,
static_cast<size_t>(0));
const int input_sequence_length =
input_sequence.size() / kInputLayerOutputSize;
constexpr int kNumTests = 100;
::webrtc::test::PerformanceTimer perf_timer(kNumTests);
for (int k = 0; k < kNumTests; ++k) {
perf_timer.StartTimer();
for (int i = 0; i < input_sequence_length; ++i) {
gru.ComputeOutput(
input_sequence.subview(i * gru.input_size(), gru.input_size()));
}
perf_timer.StopTimer();
}
RTC_LOG(LS_INFO) << (perf_timer.GetDurationAverage() / 1000) << " +/- "
<< (perf_timer.GetDurationStandardDeviation() / 1000)
<< " ms";
}
// Finds the relevant CPU features combinations to test.
std::vector<AvailableCpuFeatures> GetCpuFeaturesToTest() {
std::vector<AvailableCpuFeatures> v;
v.push_back(NoAvailableCpuFeatures());
AvailableCpuFeatures available = GetAvailableCpuFeatures();
if (available.sse2) {
v.push_back({/*sse2=*/true, /*avx2=*/false, /*neon=*/false});
}
if (available.avx2) {
v.push_back({/*sse2=*/false, /*avx2=*/true, /*neon=*/false});
}
if (available.neon) {
v.push_back({/*sse2=*/false, /*avx2=*/false, /*neon=*/true});
}
return v;
}
INSTANTIATE_TEST_SUITE_P(
RnnVadTest,
RnnGruParametrization,
::testing::ValuesIn(GetCpuFeaturesToTest()),
[](const ::testing::TestParamInfo<AvailableCpuFeatures>& info) {
return info.param.ToString();
});
} // namespace
} // namespace rnn_vad
} // namespace webrtc
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