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218 lines
9.1 KiB
218 lines
9.1 KiB
/*
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* Copyright (C) 2018 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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#define LOG_TAG "Operations"
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#include <algorithm>
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#include <utility>
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#include <vector>
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#include "OperationResolver.h"
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#include "RNN.h"
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#include "nnapi/TypeUtils.h"
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namespace android {
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namespace nn {
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namespace unidirectional_sequence_rnn {
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constexpr uint32_t kNumInputs = 7;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kWeightsTensor = 1;
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constexpr uint32_t kRecurrentWeightsTensor = 2;
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constexpr uint32_t kBiasTensor = 3;
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constexpr uint32_t kHiddenStateTensor = 4;
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constexpr uint32_t kActivationParam = 5;
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constexpr uint32_t kTimeMajorParam = 6;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kNumOutputsWithState = 2;
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constexpr uint32_t kOutputTensor = 0;
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constexpr uint32_t kStateOutputTensor = 1;
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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namespace {
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template <typename T>
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void transposeFirstTwoDims(const T* input, const Shape& inputShape, T* output) {
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const uint32_t firstDimSize = getSizeOfDimension(inputShape, 0);
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const uint32_t secondDimSize = getSizeOfDimension(inputShape, 1);
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const uint32_t inputSize = getSizeOfDimension(inputShape, 2);
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for (int f = 0; f < firstDimSize; ++f) {
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for (int s = 0; s < secondDimSize; ++s) {
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for (int i = 0; i < inputSize; ++i) {
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const uint32_t inputIndex = f * secondDimSize * inputSize + s * inputSize + i;
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const uint32_t outputIndex = s * firstDimSize * inputSize + f * inputSize + i;
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output[outputIndex] = input[inputIndex];
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}
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}
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}
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}
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template <typename T>
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bool executeTyped(IOperationExecutionContext* context) {
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const T* input = context->getInputBuffer<T>(kInputTensor);
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Shape inputShape = context->getInputShape(kInputTensor);
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const T* weights = context->getInputBuffer<T>(kWeightsTensor);
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Shape weightsShape = context->getInputShape(kWeightsTensor);
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const T* recurrentWeights = context->getInputBuffer<T>(kRecurrentWeightsTensor);
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Shape recurrentWeightsShape = context->getInputShape(kRecurrentWeightsTensor);
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const T* bias = context->getInputBuffer<T>(kBiasTensor);
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const T* hiddenState = context->getInputBuffer<T>(kHiddenStateTensor);
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int32_t activation = context->getInputValue<int32_t>(kActivationParam);
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T* output = context->getOutputBuffer<T>(kOutputTensor);
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Shape outputShape = context->getOutputShape(kOutputTensor);
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int32_t timeMajor = context->getInputValue<int32_t>(kTimeMajorParam);
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// If the input tensors are not in time major format, we transpose the first
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// two dimensions, and set input and output pointers to temporary vectors
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// which are transposed back after the RNN is applied.
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std::vector<T> inputTransposed;
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std::vector<T> outputTransposed;
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if (!timeMajor) {
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// Convert input and output to time major format.
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inputTransposed.resize(getNumberOfElements(inputShape));
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outputTransposed.resize(getNumberOfElements(outputShape));
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transposeFirstTwoDims(input, inputShape, inputTransposed.data());
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input = inputTransposed.data();
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output = outputTransposed.data();
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std::swap(inputShape.dimensions[0], inputShape.dimensions[1]);
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std::swap(outputShape.dimensions[0], outputShape.dimensions[1]);
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}
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const uint32_t maxTime = getSizeOfDimension(inputShape, 0);
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const uint32_t batchSize = getSizeOfDimension(inputShape, 1);
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const uint32_t inputSize = getSizeOfDimension(inputShape, 2);
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const uint32_t numUnits = getSizeOfDimension(weightsShape, 0);
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// A shape at a fixed step (removed time dimension).
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Shape fixedTimeInputShape = inputShape;
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fixedTimeInputShape.dimensions.resize(2);
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fixedTimeInputShape.dimensions[0] = inputShape.dimensions[1];
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fixedTimeInputShape.dimensions[1] = inputShape.dimensions[2];
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for (int i = 0; i < maxTime; ++i) {
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RNN::RNNStep<T>(input, fixedTimeInputShape, hiddenState, bias, weights, weightsShape,
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recurrentWeights, recurrentWeightsShape, activation, output);
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input += batchSize * inputSize;
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hiddenState = output;
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output += batchSize * numUnits;
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}
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if (!timeMajor) {
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transposeFirstTwoDims(outputTransposed.data(), outputShape,
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context->getOutputBuffer<T>(kOutputTensor));
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}
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if (context->getNumOutputs() == kNumOutputsWithState) {
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// We checked that the state output is not omitted during preparation.
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T* stateOutput = context->getOutputBuffer<T>(kStateOutputTensor);
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std::copy(hiddenState, hiddenState + batchSize * numUnits, stateOutput);
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}
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return true;
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}
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} // namespace
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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Result<Version> validate(const IOperationValidationContext* context) {
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NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
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const int numOutputs = context->getNumOutputs();
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NN_RET_CHECK(numOutputs == kNumOutputs || numOutputs == kNumOutputsWithState);
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OperandType inputType = context->getInputType(kInputTensor);
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if (inputType != OperandType::TENSOR_FLOAT16 && inputType != OperandType::TENSOR_FLOAT32) {
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return NN_ERROR() << "Unsupported input operand type for UNIDIRECTIONAL_SEQUENCE_RNN op: "
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<< inputType;
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}
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NN_RET_CHECK(validateInputTypes(context, {inputType, inputType, inputType, inputType, inputType,
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OperandType::INT32, OperandType::INT32}));
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std::vector<OperandType> outputTypes = {inputType};
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Version minVersionSupported = Version::ANDROID_Q;
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if (numOutputs == kNumOutputsWithState) {
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minVersionSupported = Version::ANDROID_R;
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outputTypes.push_back(inputType);
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}
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NN_RET_CHECK(validateOutputTypes(context, outputTypes));
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return minVersionSupported;
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}
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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bool prepare(IOperationExecutionContext* context) {
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Shape input = context->getInputShape(kInputTensor);
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Shape weights = context->getInputShape(kWeightsTensor);
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Shape recurrentWeights = context->getInputShape(kRecurrentWeightsTensor);
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Shape bias = context->getInputShape(kBiasTensor);
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Shape hiddenState = context->getInputShape(kHiddenStateTensor);
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int32_t timeMajor = context->getInputValue<int32_t>(kTimeMajorParam);
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NN_RET_CHECK(timeMajor == 0 || timeMajor == 1);
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const uint32_t batchSize =
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timeMajor ? getSizeOfDimension(input, 1) : getSizeOfDimension(input, 0);
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const uint32_t maxTime =
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timeMajor ? getSizeOfDimension(input, 0) : getSizeOfDimension(input, 1);
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const uint32_t numUnits = getSizeOfDimension(weights, 0);
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const uint32_t inputSize = getSizeOfDimension(input, 2);
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NN_RET_CHECK_EQ(getNumberOfDimensions(input), 3);
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NN_RET_CHECK_EQ(getNumberOfDimensions(weights), 2);
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NN_RET_CHECK_EQ(getNumberOfDimensions(recurrentWeights), 2);
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NN_RET_CHECK_EQ(getNumberOfDimensions(bias), 1);
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NN_RET_CHECK_EQ(getNumberOfDimensions(hiddenState), 2);
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NN_RET_CHECK_EQ(inputSize, getSizeOfDimension(weights, 1));
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NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(bias, 0));
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NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(recurrentWeights, 0));
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NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(recurrentWeights, 1));
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NN_RET_CHECK_EQ(batchSize, getSizeOfDimension(hiddenState, 0));
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NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(hiddenState, 1));
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Shape output = context->getOutputShape(kOutputTensor);
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output.dimensions.resize(3);
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output.dimensions[0] = timeMajor ? maxTime : batchSize;
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output.dimensions[1] = timeMajor ? batchSize : maxTime;
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output.dimensions[2] = numUnits;
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if (context->getNumOutputs() == kNumOutputsWithState) {
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NN_RET_CHECK(!context->isOmittedOutput(kStateOutputTensor));
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Shape outputStateShape = context->getInputShape(kHiddenStateTensor);
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outputStateShape.dimensions.resize(2);
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outputStateShape.dimensions[0] = batchSize;
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outputStateShape.dimensions[1] = numUnits;
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NN_RET_CHECK(context->setOutputShape(kStateOutputTensor, outputStateShape));
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}
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return context->setOutputShape(kOutputTensor, output);
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}
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bool execute(IOperationExecutionContext* context) {
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if (context->getInputType(kInputTensor) == OperandType::TENSOR_FLOAT16) {
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executeTyped<_Float16>(context);
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} else {
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executeTyped<float>(context);
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}
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return true;
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}
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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} // namespace unidirectional_sequence_rnn
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NN_REGISTER_OPERATION(UNIDIRECTIONAL_SEQUENCE_RNN, "UNIDIRECTIONAL_SEQUENCE_RNN",
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unidirectional_sequence_rnn::validate, unidirectional_sequence_rnn::prepare,
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unidirectional_sequence_rnn::execute);
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} // namespace nn
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} // namespace android
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