535 lines
30 KiB
C++
535 lines
30 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
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#define ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
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#include "tests/Globals.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Fixture.h"
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#include "tests/validation/reference/ActivationLayer.h"
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#include "tests/validation/reference/ArithmeticOperations.h"
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#include "tests/validation/reference/ConcatenateLayer.h"
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#include "tests/validation/reference/FullyConnectedLayer.h"
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#include "tests/validation/reference/GEMM.h"
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#include "tests/validation/reference/MeanStdDevNormalizationLayer.h"
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#include "tests/validation/reference/PixelWiseMultiplication.h"
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#include "tests/validation/reference/Transpose.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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template <typename TensorType, typename AccessorType, typename FunctionType, typename FunctionParams, typename T>
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class LSTMLayerValidationFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape input_shape, TensorShape input_weights_shape, TensorShape recurrent_weights_shape, TensorShape cell_bias_shape, TensorShape output_cell_shape, TensorShape output_shape,
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TensorShape scratch_shape, ActivationLayerInfo info, float cell_threshold, float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt,
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bool use_layer_norm)
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{
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_target = compute_target(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
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data_type, projection_opt, peephole_opt, use_layer_norm);
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_reference = compute_reference(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
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data_type, projection_opt, peephole_opt, use_layer_norm);
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}
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protected:
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template <typename U>
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void fill(U &&tensor, int i)
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{
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std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
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library->fill(tensor, distribution, i);
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}
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template <typename U>
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void fill_custom_val(U &&tensor, float num, int i)
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{
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std::uniform_real_distribution<> distribution(num, num);
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library->fill(tensor, distribution, i);
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}
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TensorType compute_target(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
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const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
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float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt, bool use_layer_norm)
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{
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const unsigned int num_cells = input_weights_shape.y();
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const unsigned int num_outputs = recurrent_weights_shape.x();
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// Create tensors
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TensorType input = create_tensor<TensorType>(input_shape, data_type);
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TensorType input_to_forget_w = create_tensor<TensorType>(input_weights_shape, data_type);
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TensorType input_to_cell_w = create_tensor<TensorType>(input_weights_shape, data_type);
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TensorType input_to_output_w = create_tensor<TensorType>(input_weights_shape, data_type);
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TensorType recurrent_to_forget_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
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TensorType recurrent_to_cell_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
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TensorType recurrent_to_output_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
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TensorType forget_gate_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
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TensorType cell_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
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TensorType output_gate_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
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TensorType output_state_in = create_tensor<TensorType>(output_shape, data_type);
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TensorType cell_state_in = create_tensor<TensorType>(output_cell_shape, data_type);
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TensorType scratch = create_tensor<TensorType>(scratch_shape, data_type);
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TensorType output_state_out = create_tensor<TensorType>(output_shape, data_type);
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TensorType cell_state_out = create_tensor<TensorType>(output_cell_shape, data_type);
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TensorType output = create_tensor<TensorType>(output_shape, data_type);
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TensorType input_to_input_w;
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TensorType recurrent_to_input_w;
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TensorType cell_to_input_w;
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TensorType cell_to_forget_w;
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TensorType input_gate_bias;
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TensorType cell_to_output_w;
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TensorType projection_w;
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TensorType projection_bias;
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TensorType input_layer_norm_w;
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TensorType forget_layer_norm_w;
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TensorType cell_layer_norm_w;
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TensorType output_layer_norm_w;
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bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? false : true;
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FunctionParams lstm_params;
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if(!cifg_opt)
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{
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input_to_input_w = create_tensor<TensorType>(input_weights_shape, data_type);
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recurrent_to_input_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
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if(peephole_opt)
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{
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cell_to_input_w = create_tensor<TensorType>(cell_bias_shape, data_type);
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}
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input_gate_bias = create_tensor<TensorType>(cell_bias_shape, data_type);
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lstm_params.set_cifg_params(&input_to_input_w, &recurrent_to_input_w, &cell_to_input_w, &input_gate_bias);
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}
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if(peephole_opt)
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{
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cell_to_forget_w = create_tensor<TensorType>(cell_bias_shape, data_type);
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cell_to_output_w = create_tensor<TensorType>(cell_bias_shape, data_type);
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lstm_params.set_peephole_params(&cell_to_forget_w, &cell_to_output_w);
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}
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if(projection_opt)
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{
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projection_w = create_tensor<TensorType>(TensorShape(num_cells, num_outputs), data_type);
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projection_bias = create_tensor<TensorType>(TensorShape(num_outputs), data_type);
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lstm_params.set_projection_params(&projection_w, &projection_bias);
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}
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if(use_layer_norm)
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{
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forget_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type);
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cell_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type);
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output_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type);
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if(!cifg_opt)
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{
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input_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type);
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lstm_params.set_layer_normalization_params(&input_layer_norm_w, &forget_layer_norm_w, &cell_layer_norm_w, &output_layer_norm_w);
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}
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else
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{
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lstm_params.set_layer_normalization_params(nullptr, &forget_layer_norm_w, &cell_layer_norm_w, &output_layer_norm_w);
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}
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}
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// Create and configure function
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FunctionType lstm;
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lstm.configure(&input, &input_to_forget_w, &input_to_cell_w, &input_to_output_w, &recurrent_to_forget_w,
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&recurrent_to_cell_w, &recurrent_to_output_w, &forget_gate_bias, &cell_bias, &output_gate_bias,
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&output_state_in, &cell_state_in,
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&scratch, &output_state_out, &cell_state_out, &output,
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lstm_params, info, cell_threshold, projection_threshold);
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ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(output_state_in.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cell_state_in.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(output_state_out.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cell_state_out.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Allocate tensors
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input.allocator()->allocate();
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input_to_forget_w.allocator()->allocate();
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input_to_cell_w.allocator()->allocate();
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input_to_output_w.allocator()->allocate();
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recurrent_to_forget_w.allocator()->allocate();
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recurrent_to_cell_w.allocator()->allocate();
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recurrent_to_output_w.allocator()->allocate();
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forget_gate_bias.allocator()->allocate();
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cell_bias.allocator()->allocate();
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output_gate_bias.allocator()->allocate();
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output_state_in.allocator()->allocate();
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cell_state_in.allocator()->allocate();
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scratch.allocator()->allocate();
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output_state_out.allocator()->allocate();
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cell_state_out.allocator()->allocate();
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output.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!output_state_in.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!cell_state_in.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!output_state_out.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!cell_state_out.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
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// Fill tensors
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fill(AccessorType(input), 0);
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fill(AccessorType(input_to_forget_w), 1);
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fill(AccessorType(input_to_cell_w), 2);
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fill(AccessorType(input_to_output_w), 3);
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fill(AccessorType(recurrent_to_forget_w), 4);
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fill(AccessorType(recurrent_to_cell_w), 5);
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fill(AccessorType(recurrent_to_output_w), 6);
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fill(AccessorType(forget_gate_bias), 7);
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fill(AccessorType(cell_bias), 8);
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fill(AccessorType(output_gate_bias), 9);
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fill(AccessorType(output_state_in), 10);
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fill(AccessorType(cell_state_in), 11);
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fill(AccessorType(scratch), 12);
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if(!cifg_opt)
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{
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ARM_COMPUTE_EXPECT(input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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input_to_input_w.allocator()->allocate();
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recurrent_to_input_w.allocator()->allocate();
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cell_to_input_w.allocator()->allocate();
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input_gate_bias.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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fill(AccessorType(input_to_input_w), 13);
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fill(AccessorType(recurrent_to_input_w), 14);
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if(peephole_opt)
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{
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fill(AccessorType(cell_to_input_w), 15);
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}
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fill(AccessorType(recurrent_to_input_w), 16);
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fill(AccessorType(input_gate_bias), 17);
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}
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if(peephole_opt)
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{
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ARM_COMPUTE_EXPECT(cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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cell_to_forget_w.allocator()->allocate();
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cell_to_output_w.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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fill(AccessorType(cell_to_forget_w), 18);
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fill(AccessorType(cell_to_output_w), 19);
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}
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if(projection_opt)
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{
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ARM_COMPUTE_EXPECT(projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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projection_w.allocator()->allocate();
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projection_bias.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
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fill(AccessorType(projection_w), 20);
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fill(AccessorType(projection_bias), 21);
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}
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if(use_layer_norm)
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{
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if(!cifg_opt)
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{
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ARM_COMPUTE_EXPECT(input_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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input_layer_norm_w.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!input_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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fill(AccessorType(input_layer_norm_w), 22);
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}
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ARM_COMPUTE_EXPECT(forget_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(cell_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(output_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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forget_layer_norm_w.allocator()->allocate();
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cell_layer_norm_w.allocator()->allocate();
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output_layer_norm_w.allocator()->allocate();
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ARM_COMPUTE_EXPECT(!forget_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!cell_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(!output_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS);
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fill(AccessorType(forget_layer_norm_w), 23);
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fill(AccessorType(cell_layer_norm_w), 24);
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fill(AccessorType(output_layer_norm_w), 25);
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}
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// Compute function
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lstm.run();
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_target_scratch = std::move(scratch);
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return output;
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}
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SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
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const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
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float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt, bool use_layer_norm)
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{
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const unsigned int num_cells = input_weights_shape.y();
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const unsigned int num_outputs = recurrent_weights_shape.x();
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// Create projection weights shape
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TensorShape projection_weights_shape(num_cells, num_outputs);
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// Create projection bias shape
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TensorShape projection_bias_shape(num_outputs);
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TensorShape gemm_shape{ 1, output_shape.y() };
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SimpleTensor<T> gemm_out{ gemm_shape, data_type };
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// Create reference
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SimpleTensor<T> input{ input_shape, data_type };
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SimpleTensor<T> input_to_input_w{ input_weights_shape, data_type };
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SimpleTensor<T> input_to_forget_w{ input_weights_shape, data_type };
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SimpleTensor<T> input_to_cell_w{ input_weights_shape, data_type };
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SimpleTensor<T> input_to_output_w{ input_weights_shape, data_type };
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SimpleTensor<T> recurrent_to_input_w{ recurrent_weights_shape, data_type };
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SimpleTensor<T> recurrent_to_forget_w{ recurrent_weights_shape, data_type };
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SimpleTensor<T> recurrent_to_cell_w{ recurrent_weights_shape, data_type };
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SimpleTensor<T> recurrent_to_output_w{ recurrent_weights_shape, data_type };
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SimpleTensor<T> cell_to_input_w{ cell_bias_shape, data_type };
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SimpleTensor<T> cell_to_forget_w{ cell_bias_shape, data_type };
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SimpleTensor<T> cell_to_output_w{ cell_bias_shape, data_type };
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SimpleTensor<T> input_gate_bias{ cell_bias_shape, data_type };
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SimpleTensor<T> forget_gate_bias{ cell_bias_shape, data_type };
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SimpleTensor<T> cell_bias{ cell_bias_shape, data_type };
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SimpleTensor<T> output_gate_bias{ cell_bias_shape, data_type };
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SimpleTensor<T> projection_w{ projection_weights_shape, data_type };
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SimpleTensor<T> projection_bias{ projection_bias_shape, data_type };
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SimpleTensor<T> output_state_in{ output_shape, data_type };
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SimpleTensor<T> cell_state_in{ output_cell_shape, data_type };
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SimpleTensor<T> scratch{ scratch_shape, data_type };
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SimpleTensor<T> output_state_out{ output_shape, data_type };
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SimpleTensor<T> cell_state_out{ output_cell_shape, data_type };
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SimpleTensor<T> output{ output_shape, data_type };
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bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? false : true;
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// Fill reference
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fill(input, 0);
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fill(input_to_forget_w, 1);
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fill(input_to_cell_w, 2);
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fill(input_to_output_w, 3);
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fill(recurrent_to_forget_w, 4);
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fill(recurrent_to_cell_w, 5);
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fill(recurrent_to_output_w, 6);
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if(use_layer_norm)
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{
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fill_custom_val(forget_gate_bias, 0.f, 7);
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fill_custom_val(cell_bias, 0.f, 8);
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fill_custom_val(output_gate_bias, 0.f, 9);
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}
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else
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{
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fill(forget_gate_bias, 7);
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fill(cell_bias, 8);
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fill(output_gate_bias, 9);
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}
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fill(output_state_in, 10);
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fill(cell_state_in, 11);
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fill(scratch, 12);
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fill(input_to_input_w, 13);
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fill(recurrent_to_input_w, 14);
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fill(cell_to_input_w, 15);
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fill(recurrent_to_input_w, 16);
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if(!cifg_opt && use_layer_norm)
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|
{
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fill_custom_val(input_gate_bias, 0.f, 17);
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|
}
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else
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{
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fill(input_gate_bias, 17);
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}
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fill(cell_to_forget_w, 18);
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|
fill(cell_to_output_w, 19);
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|
fill(projection_w, 20);
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fill(projection_bias, 21);
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|
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// Compute forget_gate
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|
SimpleTensor<T> fully_connected_forget = reference::fully_connected_layer(input, input_to_forget_w, forget_gate_bias, output_cell_shape);
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|
SimpleTensor<T> transposed_weights = reference::transpose(recurrent_to_forget_w);
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|
SimpleTensor<T> gemm = reference::gemm(output_state_in, transposed_weights, cell_state_in, 1.f, 0.f);
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|
SimpleTensor<T> forget_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected_forget, gemm, data_type, ConvertPolicy::SATURATE);
|
|
|
|
if(peephole_opt)
|
|
{
|
|
SimpleTensor<T> pixelwise_mul_forget_gate = reference::pixel_wise_multiplication<T, T, T>(cell_state_in, cell_to_forget_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO, data_type);
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|
forget_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, forget_gate, pixelwise_mul_forget_gate, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
|
|
if(use_layer_norm)
|
|
{
|
|
SimpleTensor<T> forget_layer_norm_w{ cell_bias_shape, data_type };
|
|
fill(forget_layer_norm_w, 23);
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|
forget_gate = reference::mean_std_normalization_layer(forget_gate);
|
|
forget_gate = reference::pixel_wise_multiplication<T, T, T>(forget_gate, forget_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
fill(forget_gate_bias, 7);
|
|
forget_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, forget_gate, forget_gate_bias, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
forget_gate = reference::activation_layer(forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
|
|
|
|
// Compute input_gate
|
|
SimpleTensor<T> input_gate;
|
|
if(cifg_opt)
|
|
{
|
|
SimpleTensor<T> ones{ cell_bias_shape, data_type };
|
|
fill_custom_val(ones, 1.f, 0);
|
|
input_gate = reference::arithmetic_operation<T>(reference::ArithmeticOperation::SUB, ones, forget_gate, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
else
|
|
{
|
|
SimpleTensor<T> fully_connected_input = reference::fully_connected_layer(input, input_to_input_w, input_gate_bias, output_cell_shape);
|
|
transposed_weights = reference::transpose(recurrent_to_input_w);
|
|
gemm = reference::gemm(output_state_in, transposed_weights, cell_state_in, 1.f, 0.f);
|
|
input_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected_input, gemm, data_type, ConvertPolicy::SATURATE);
|
|
if(peephole_opt)
|
|
{
|
|
SimpleTensor<T> pixelwise_mul_input_gate = reference::pixel_wise_multiplication<T, T, T>(cell_state_in, cell_to_input_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
input_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, input_gate, pixelwise_mul_input_gate, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
if(use_layer_norm)
|
|
{
|
|
SimpleTensor<T> input_layer_norm_w{ cell_bias_shape, data_type };
|
|
fill(input_layer_norm_w, 22);
|
|
input_gate = reference::mean_std_normalization_layer(input_gate);
|
|
input_gate = reference::pixel_wise_multiplication<T, T, T>(input_gate, input_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
fill(input_gate_bias, 17);
|
|
input_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, input_gate, input_gate_bias, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
input_gate = reference::activation_layer(input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
|
|
}
|
|
|
|
// Compute cell_state
|
|
SimpleTensor<T> fully_connected_cell_state = reference::fully_connected_layer(input, input_to_cell_w, cell_bias, output_cell_shape);
|
|
transposed_weights = reference::transpose(recurrent_to_cell_w);
|
|
gemm = reference::gemm(output_state_in, transposed_weights, cell_state_out, 1.f, 0.f);
|
|
SimpleTensor<T> pixelwise_mul = reference::pixel_wise_multiplication<T, T, T>(cell_state_in, forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected_cell_state, gemm, data_type, ConvertPolicy::SATURATE);
|
|
if(use_layer_norm)
|
|
{
|
|
SimpleTensor<T> cell_layer_norm_w{ cell_bias_shape, data_type };
|
|
fill(cell_layer_norm_w, 24);
|
|
cell_state_out = reference::mean_std_normalization_layer(cell_state_out);
|
|
cell_state_out = reference::pixel_wise_multiplication<T, T, T>(cell_state_out, cell_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
fill(cell_bias, 8);
|
|
cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, cell_state_out, cell_bias, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
cell_state_out = reference::activation_layer(cell_state_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
|
|
cell_state_out = reference::pixel_wise_multiplication<T, T, T>(cell_state_out, input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, cell_state_out, pixelwise_mul, data_type, ConvertPolicy::SATURATE);
|
|
if(cell_threshold != 0.f)
|
|
{
|
|
cell_state_out = reference::activation_layer(cell_state_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold));
|
|
}
|
|
|
|
// Compute output
|
|
SimpleTensor<T> fully_connected_output = reference::fully_connected_layer(input, input_to_output_w, output_gate_bias, output_cell_shape);
|
|
transposed_weights = reference::transpose(recurrent_to_output_w);
|
|
gemm = reference::gemm(output_state_in, transposed_weights, cell_state_out, 1.f, 0.f);
|
|
output = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected_output, gemm, data_type, ConvertPolicy::SATURATE);
|
|
if(peephole_opt)
|
|
{
|
|
pixelwise_mul = reference::pixel_wise_multiplication<T, T, T>(cell_state_out, cell_to_output_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
output = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, output, pixelwise_mul, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
if(use_layer_norm)
|
|
{
|
|
SimpleTensor<T> output_layer_norm_w{ cell_bias_shape, data_type };
|
|
fill(output_layer_norm_w, 25);
|
|
output = reference::mean_std_normalization_layer(output);
|
|
output = reference::pixel_wise_multiplication<T, T, T>(output, output_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
fill(output_gate_bias, 9);
|
|
output = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, output, output_gate_bias, data_type, ConvertPolicy::SATURATE);
|
|
}
|
|
output = reference::activation_layer(output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
|
|
|
|
// Compute output state
|
|
SimpleTensor<T> cell_state_activation = reference::activation_layer(cell_state_out, info);
|
|
output_state_out = reference::pixel_wise_multiplication<T, T, T>(output, cell_state_activation, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN, data_type);
|
|
|
|
if(projection_opt)
|
|
{
|
|
SimpleTensor<T> fully_connected_projection = reference::fully_connected_layer(output_state_out, projection_w, projection_bias, output_cell_shape);
|
|
if(projection_threshold != 0.f)
|
|
{
|
|
output_state_out = reference::activation_layer(fully_connected_projection, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
|
|
}
|
|
}
|
|
|
|
std::vector<SimpleTensor<T>> scratch_inputs;
|
|
if(!cifg_opt)
|
|
{
|
|
scratch_inputs.emplace_back(std::move(input_gate));
|
|
}
|
|
scratch_inputs.emplace_back(std::move(cell_state_out));
|
|
scratch_inputs.emplace_back(std::move(forget_gate));
|
|
scratch_inputs.emplace_back(std::move(output));
|
|
scratch = reference::concatenate_layer(scratch_inputs, scratch, Window::DimX);
|
|
_reference_scratch = std::move(scratch);
|
|
return output_state_out;
|
|
}
|
|
|
|
TensorType _target{};
|
|
TensorType _target_scratch{};
|
|
SimpleTensor<T> _reference{};
|
|
SimpleTensor<T> _reference_scratch{};
|
|
};
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|
|
#endif /* ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE */
|