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4e9c5b592a
Using `VectorMath::DotProduct()` in GatedRecurrentLayer to reuse existing SIMD optimizations. Results: - When SSE2/AVX2 is avilable, the GRU layer takes 40% of the unoptimized code - The realtime factor for the VAD improved as follows - SSE2: from 570x to 630x - AVX2: from 610x to 680x This CL also improved the GRU layer benchmark by (i) benchmarking a GRU layer havibng the same size of that used in the VAD and (ii) by prefetching a long input sequence. Bug: webrtc:10480 Change-Id: I9716b15661e4c6b81592b4cf7c172d90e41b5223 Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/195545 Reviewed-by: Per Åhgren <peah@webrtc.org> Commit-Queue: Alessio Bazzica <alessiob@webrtc.org> Cr-Commit-Position: refs/heads/master@{#32803}
70 lines
2.5 KiB
C++
70 lines
2.5 KiB
C++
/*
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* Copyright (c) 2020 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#ifndef MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_GRU_H_
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#define MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_GRU_H_
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#include <array>
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#include <vector>
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#include "absl/strings/string_view.h"
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#include "api/array_view.h"
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#include "modules/audio_processing/agc2/cpu_features.h"
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#include "modules/audio_processing/agc2/rnn_vad/vector_math.h"
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namespace webrtc {
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namespace rnn_vad {
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// Maximum number of units for a GRU layer.
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constexpr int kGruLayerMaxUnits = 24;
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// Recurrent layer with gated recurrent units (GRUs) with sigmoid and ReLU as
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// activation functions for the update/reset and output gates respectively.
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class GatedRecurrentLayer {
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public:
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// Ctor. `output_size` cannot be greater than `kGruLayerMaxUnits`.
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GatedRecurrentLayer(int input_size,
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int output_size,
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rtc::ArrayView<const int8_t> bias,
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rtc::ArrayView<const int8_t> weights,
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rtc::ArrayView<const int8_t> recurrent_weights,
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const AvailableCpuFeatures& cpu_features,
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absl::string_view layer_name);
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GatedRecurrentLayer(const GatedRecurrentLayer&) = delete;
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GatedRecurrentLayer& operator=(const GatedRecurrentLayer&) = delete;
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~GatedRecurrentLayer();
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// Returns the size of the input vector.
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int input_size() const { return input_size_; }
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// Returns the pointer to the first element of the output buffer.
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const float* data() const { return state_.data(); }
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// Returns the size of the output buffer.
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int size() const { return output_size_; }
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// Resets the GRU state.
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void Reset();
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// Computes the recurrent layer output and updates the status.
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void ComputeOutput(rtc::ArrayView<const float> input);
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private:
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const int input_size_;
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const int output_size_;
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const std::vector<float> bias_;
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const std::vector<float> weights_;
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const std::vector<float> recurrent_weights_;
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const VectorMath vector_math_;
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// Over-allocated array with size equal to `output_size_`.
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std::array<float, kGruLayerMaxUnits> state_;
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};
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} // namespace rnn_vad
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} // namespace webrtc
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#endif // MODULES_AUDIO_PROCESSING_AGC2_RNN_VAD_RNN_GRU_H_
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