/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "SVDF.h"
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#include "CpuExecutor.h"
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#include "CpuOperationUtils.h"
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#include "HalInterfaces.h"
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#include "Tracing.h"
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namespace android {
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namespace nn {
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SVDF::SVDF(const Operation& operation,
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std::vector<RunTimeOperandInfo>& operands) {
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NNTRACE_TRANS("SVDF::SVDF");
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input_ = GetInput(operation, operands, kInputTensor);
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weights_feature_ = GetInput(operation, operands, kWeightsFeatureTensor);
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weights_time_ = GetInput(operation, operands, kWeightsTimeTensor);
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bias_ = GetInput(operation, operands, kBiasTensor);
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state_in_ = GetInput(operation, operands, kStateInTensor);
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params_.rank_ = getScalarData<int>(*GetInput(operation, operands, kRankParam));
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params_.activation_ = static_cast<TfLiteFusedActivation>(getScalarData<int>(
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*GetInput(operation, operands, kActivationParam)));
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state_out_ = GetOutput(operation, operands, kStateOutTensor);
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output_ = GetOutput(operation, operands, kOutputTensor);
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}
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bool SVDF::Prepare(const Operation &operation,
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std::vector<RunTimeOperandInfo> &operands,
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Shape *stateShape,
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Shape *outputShape) {
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NNTRACE_TRANS("SVDF::Prepare");
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// Check we have all the inputs and outputs we need.
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const int num_inputs = NumInputsWithValues(operation, operands);
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NN_CHECK(num_inputs == 6 || num_inputs == 7);
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NN_CHECK_EQ(NumOutputs(operation), 2);
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const RunTimeOperandInfo *input =
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GetInput(operation, operands, SVDF::kInputTensor);
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const RunTimeOperandInfo *weights_feature =
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GetInput(operation, operands, SVDF::kWeightsFeatureTensor);
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const RunTimeOperandInfo *weights_time =
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GetInput(operation, operands, SVDF::kWeightsTimeTensor);
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// Check all the parameters of tensor match within themselves and match the
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// input configuration.
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const int rank = getScalarData<int>(*GetInput(operation, operands, kRankParam));
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const uint32_t batch_size = SizeOfDimension(input, 0);
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const uint32_t num_filters = SizeOfDimension(weights_feature, 0);
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NN_CHECK_EQ(num_filters % rank, 0);
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const uint32_t num_units = num_filters / rank;
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const uint32_t memory_size = SizeOfDimension(weights_time, 1);
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NN_CHECK_EQ(SizeOfDimension(input, 1), SizeOfDimension(weights_feature, 1));
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NN_CHECK_EQ(SizeOfDimension(weights_time, 0), num_filters);
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const RunTimeOperandInfo *bias =
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GetInput(operation, operands, kBiasTensor);
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if (!IsNullInput(bias)) {
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NN_CHECK_EQ(SizeOfDimension(bias, 0), num_units);
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}
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// Resize state.
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const Shape &inputShape = input->shape();
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stateShape->type = inputShape.type;
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stateShape->dimensions = { batch_size, memory_size * num_filters };
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stateShape->offset = inputShape.offset;
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stateShape->scale = inputShape.scale;
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// Resize output.
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outputShape->type = inputShape.type;
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outputShape->dimensions = { batch_size, num_units };
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outputShape->offset = inputShape.offset;
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outputShape->scale = inputShape.scale;
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return true;
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}
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bool SVDF::Eval() {
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NNTRACE_TRANS("SVDF::Eval");
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switch (input_->type) {
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case OperandType::TENSOR_FLOAT16: {
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std::vector<float> inputDataFloat32(getNumberOfElements(input_->shape()));
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convertFloat16ToFloat32(reinterpret_cast<_Float16*>(input_->buffer), &inputDataFloat32);
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std::vector<float> inputStateDataFloat32(getNumberOfElements(state_in_->shape()));
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convertFloat16ToFloat32(reinterpret_cast<_Float16*>(state_in_->buffer),
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&inputStateDataFloat32);
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std::vector<float> biasDataFloat32(getNumberOfElements(bias_->shape()));
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if (!IsNullInput(bias_)) {
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convertFloat16ToFloat32(reinterpret_cast<_Float16*>(bias_->buffer),
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&biasDataFloat32);
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}
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std::vector<float> weightsFeatureDataFloat32(
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getNumberOfElements(weights_feature_->shape()));
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convertFloat16ToFloat32(reinterpret_cast<_Float16*>(weights_feature_->buffer),
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&weightsFeatureDataFloat32);
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std::vector<float> weightsTimeDataFloat32(getNumberOfElements(weights_time_->shape()));
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convertFloat16ToFloat32(reinterpret_cast<_Float16*>(weights_time_->buffer),
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&weightsTimeDataFloat32);
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std::vector<float> outputDataFloat32(getNumberOfElements(output_->shape()));
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std::vector<float> outputStateDataFloat32(getNumberOfElements(state_out_->shape()));
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EvalFloat32(inputDataFloat32.data(), inputStateDataFloat32.data(),
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biasDataFloat32.data(), weightsFeatureDataFloat32.data(),
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weightsTimeDataFloat32.data(), outputDataFloat32.data(),
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outputStateDataFloat32.data());
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convertFloat32ToFloat16(outputDataFloat32,
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reinterpret_cast<_Float16*>(output_->buffer));
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convertFloat32ToFloat16(outputStateDataFloat32,
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reinterpret_cast<_Float16*>(state_out_->buffer));
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break;
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}
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case OperandType::TENSOR_FLOAT32: {
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EvalFloat32(reinterpret_cast<float*>(input_->buffer),
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reinterpret_cast<float*>(state_in_->buffer),
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reinterpret_cast<float*>(bias_->buffer),
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reinterpret_cast<float*>(weights_feature_->buffer),
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reinterpret_cast<float*>(weights_time_->buffer),
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reinterpret_cast<float*>(output_->buffer),
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reinterpret_cast<float*>(state_out_->buffer));
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break;
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}
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default: {
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LOG(ERROR) << "Unsupported data type: " << static_cast<int>(input_->type);
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return false;
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}
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}
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return true;
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}
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void SVDF::EvalFloat32(const float* inputData, const float* inputStateData, const float* biasData,
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const float* weightsFeatureData, const float* weightsTimeData,
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float* outputData, float* outputStateData) {
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NNTRACE_COMP("SVDF::EvalFloat32");
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const int rank = params_.rank_;
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const int batch_size = SizeOfDimension(input_, 0);
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const int input_size = SizeOfDimension(input_, 1);
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const int num_filters = SizeOfDimension(weights_feature_, 0);
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const int num_units = num_filters / rank;
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const int memory_size = SizeOfDimension(weights_time_, 1);
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memcpy(outputStateData, inputStateData, sizeof(float) * batch_size * memory_size * num_filters);
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// Compute conv1d(inputs, weights_feature).
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for (int b = 0; b < batch_size; b++) {
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float* state_ptr_batch = outputStateData + b * memory_size * num_filters;
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for (int c = 0; c < num_filters; c++) {
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float* state_ptr = state_ptr_batch + c * memory_size;
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state_ptr[memory_size - 1] = 0.0;
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}
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}
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// The state left most column is used to save current cycle activation. This
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// is achieved by starting at state->data.f[memory_size - 1] and having the
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// stride equal to memory_size.
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tflite::tensor_utils::MatrixBatchVectorMultiplyAccumulate(
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weightsFeatureData, num_filters, input_size, inputData, batch_size,
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&outputStateData[memory_size - 1], memory_size);
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// Compute matmul(state, weights_time).
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// The right most column is used to save temporary output (with the size of
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// num_filters). This is achieved by starting at state->data.f and having the
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// stride equal to memory_size.
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float scratch[batch_size * num_filters];
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for (int b = 0; b < batch_size; b++) {
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float* state_out_ptr_batch = outputStateData + b * memory_size * num_filters;
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float* scratch_ptr_batch = scratch + b * num_filters;
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tflite::tensor_utils::BatchVectorBatchVectorDotProduct(
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weightsTimeData, state_out_ptr_batch, memory_size, num_filters, scratch_ptr_batch,
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/*result_stride=*/1);
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}
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// Initialize output with bias if provided.
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if (!IsNullInput(bias_)) {
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tflite::tensor_utils::VectorBatchVectorAssign(biasData, num_units, batch_size, outputData);
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} else {
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tflite::tensor_utils::ZeroVector(outputData, batch_size * num_units);
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}
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// Reduction sum
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for (int b = 0; b < batch_size; b++) {
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float* output_ptr_batch = outputData + b * num_units;
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float* scratch_ptr_batch = scratch + b * num_filters;
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tflite::tensor_utils::ReductionSumVector(scratch_ptr_batch, output_ptr_batch, num_units,
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rank);
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}
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// Apply activation.
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for (int b = 0; b < batch_size; b++) {
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float* output_ptr_batch = outputData + b * num_units;
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tflite::tensor_utils::ApplyActivationToVector(output_ptr_batch, num_units,
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params_.activation_, output_ptr_batch);
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}
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// Right shift the state.
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for (int b = 0; b < batch_size; b++) {
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float* state_out_ptr_batch = outputStateData + b * memory_size * num_filters;
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for (int f = 0; f < num_filters; f++) {
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tflite::tensor_utils::VectorShiftLeft(state_out_ptr_batch, memory_size,
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/*shift_value=*/0.0);
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state_out_ptr_batch += memory_size;
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}
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}
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}
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} // namespace nn
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} // namespace android
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