src/runtime/NEON/functions/NELSTMLayerQuantized.cpp - platform/external/ComputeLibrary - Git at Google


            
            /*

            
             * Copyright (c) 2019-2021 Arm Limited.

            
             *

            
             * SPDX-License-Identifier: MIT

            
             *

            
             * Permission is hereby granted, free of charge, to any person obtaining a copy

            
             * of this software and associated documentation files (the "Software"), to

            
             * deal in the Software without restriction, including without limitation the

            
             * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or

            
             * sell copies of the Software, and to permit persons to whom the Software is

            
             * furnished to do so, subject to the following conditions:

            
             *

            
             * The above copyright notice and this permission notice shall be included in all

            
             * copies or substantial portions of the Software.

            
             *

            
             * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

            
             * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

            
             * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

            
             * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

            
             * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

            
             * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

            
             * SOFTWARE.

            
             */

            
            #include "arm_compute/runtime/NEON/functions/NELSTMLayerQuantized.h"


            #include "arm_compute/core/Utils.h"

            
            #include "arm_compute/core/Validate.h"

            
            #include "arm_compute/core/utils/quantization/AsymmHelpers.h"

            
            #include "src/common/utils/Log.h"

            
            #include "src/core/helpers/AutoConfiguration.h"


            #include <cmath>

            
            #include <memory>

            
            #include <tuple>


            namespace arm_compute

            
            {

            
            namespace

            
            {

            
            // Quantization info structures used in the LSTMQuantize layer

            
            const QuantizationInfo qasymm(1.f / 128.f, 128);

            
            const QuantizationInfo qsymm_3(8.f / 32768.f, 0);  // qsymm16 with 3 integer bit

            
            const QuantizationInfo qsymm_4(16.f / 32768.f, 0); // qsymm16 with 4 integer bit

            
            const QuantizationInfo qsymm_0(1.f / 32768.f, 0);  // qsymm16 with 0 integer bit

            
            } // namespace

            
            NELSTMLayerQuantized::~NELSTMLayerQuantized() = default;


            NELSTMLayerQuantized::NELSTMLayerQuantized(std::shared_ptr<IMemoryManager> memory_manager)

            
                : _memory_group(std::move(memory_manager)), _gemmlowp(), _output_stage(), _transpose_weights(), _concat_input_weights(), _concat_recurrent_weights(), _concat_weights(), _concat_inputs(),

            
                  _concat_bias(), _sigmoid_forget_gate(), _sigmoid_input_gate(), _sigmoid_output_gate(), _tanh_modulation_gate(), _tanh_output_state(), _add1(), _add2(), _mul1(), _mul2(), _mul3(),

            
                  _slice_input_tensor(), _slice_forget_tensor(), _slice_cell_tensor(), _slice_output_tensor(), _dequantize(), _quantize(), _input_to_input_weights(nullptr), _input_to_forget_weights(nullptr),

            
                  _input_to_cell_weights(nullptr), _input_to_output_weights(nullptr), _recurrent_to_input_weights(nullptr), _recurrent_to_forget_weights(nullptr), _recurrent_to_cell_weights(nullptr),

            
                  _recurrent_to_output_weights(nullptr), _input_gate_bias(nullptr), _forget_gate_bias(nullptr), _cell_bias(nullptr), _output_gate_bias(nullptr), _recurrent_weights(), _input_weights(), _weights(),

            
                  _input(), _weights_transposed(), _output_highp(), _output_lowp(), _bias(), _forget_gate_input(), _input_gate_input(), _output_gate_input(), _input_modulation_gate_input(), _forget_gate_output(),

            
                  _input_gate_output(), _output_gate_output(), _input_modulation_gate_output(), _cell_state1(), _cell_state2(), _output_state_tmp(), _output_state_out_symm(), _output_state_out_f32(),

            
                  _is_prepared(false)

            
            {

            
            }


            void NELSTMLayerQuantized::configure(const ITensor *input,

            
                                                 const ITensor *input_to_input_weights, const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights,

            
                                                 const ITensor *recurrent_to_input_weights, const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights,

            
                                                 const ITensor *input_gate_bias, const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias,

            
                                                 ITensor *cell_state_in, const ITensor *output_state_in,

            
                                                 ITensor *cell_state_out, ITensor *output_state_out)

            
            {

            
                ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,

            
                                             recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,

            
                                             input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);


                ARM_COMPUTE_ERROR_THROW_ON(NELSTMLayerQuantized::validate(input->info(), input_to_input_weights->info(), input_to_forget_weights->info(), input_to_cell_weights->info(),

            
                                                                          input_to_output_weights->info(),

            
                                                                          recurrent_to_input_weights->info(), recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),

            
                                                                          input_gate_bias->info(), forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(), cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info()));


                ARM_COMPUTE_LOG_PARAMS(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,

            
                                       recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,

            
                                       input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);


                const int input_size  = input->info()->dimension(0);

            
                const int batch_size  = input->info()->dimension(1);

            
                const int output_size = input_to_input_weights->info()->dimension(1);


                const QuantizationInfo qweights = input_to_input_weights->info()->quantization_info(); // Weights quantization


                auto_init_if_empty(*cell_state_out->info(), TensorInfo(TensorShape(batch_size, output_size), 1, DataType::QSYMM16, qsymm_4));

            
                auto_init_if_empty(*output_state_out->info(), TensorInfo(TensorShape(batch_size, output_size), 1, DataType::QASYMM8, qasymm));


                _input_to_input_weights      = input_to_input_weights;

            
                _input_to_forget_weights     = input_to_forget_weights;

            
                _input_to_cell_weights       = input_to_cell_weights;

            
                _input_to_output_weights     = input_to_output_weights;

            
                _recurrent_to_input_weights  = recurrent_to_input_weights;

            
                _recurrent_to_forget_weights = recurrent_to_forget_weights;

            
                _recurrent_to_cell_weights   = recurrent_to_cell_weights;

            
                _recurrent_to_output_weights = recurrent_to_output_weights;

            
                _input_gate_bias             = input_gate_bias;

            
                _forget_gate_bias            = forget_gate_bias;

            
                _cell_bias                   = cell_bias;

            
                _output_gate_bias            = output_gate_bias;


                // Weights concatenation

            
                std::vector<const ITensor *> inputs_weights_vector{ input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights };

            
                std::vector<const ITensor *> recurrent_weights_vector{ recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights };


                _input_weights.allocator()->init(TensorInfo(TensorShape(input_size, 4 * output_size), 1, DataType::QASYMM8, qweights));

            
                _concat_input_weights.configure(inputs_weights_vector, &_input_weights, Window::DimY);


                _recurrent_weights.allocator()->init(TensorInfo(TensorShape(output_size, 4 * output_size), 1, DataType::QASYMM8, qweights));

            
                _concat_recurrent_weights.configure(recurrent_weights_vector, &_recurrent_weights, Window::DimY);


                std::vector<const ITensor *> weights_vector{ &_recurrent_weights, &_input_weights };

            
                _weights.allocator()->init(TensorInfo(TensorShape(output_size + input_size, 4 * output_size), 1, DataType::QASYMM8, qweights));

            
                _concat_weights.configure(weights_vector, &_weights, Window::DimX);

            
                _transpose_weights.configure(&_weights, &_weights_transposed);


                // Input concatenation

            
                std::vector<const ITensor *> input_vector{ input, output_state_in };

            
                _memory_group.manage(&_input);

            
                _input.allocator()->init(TensorInfo(TensorShape(output_size + input_size, batch_size), 1, DataType::QASYMM8, qasymm));

            
                _concat_inputs.configure(input_vector, &_input, Window::DimX);


                // Bias concatenation

            
                std::vector<const ITensor *> bias_vector{ input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias };

            
                _bias.allocator()->init(TensorInfo(TensorShape(4 * output_size), 1, DataType::S32));

            
                _concat_bias.configure(bias_vector, &_bias, Window::DimX);


                // Invert the offset for gemmlowp

            
                _input.info()->set_quantization_info(QuantizationInfo(qasymm.uniform().scale, -qasymm.uniform().offset));

            
                _weights_transposed.info()->set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset));


                // Run gemmlowp

            
                _memory_group.manage(&_output_highp);

            
                _output_highp.allocator()->init(TensorInfo(TensorShape(4 * output_size, batch_size), 1, DataType::S32));

            
                _gemmlowp.configure(&_input, &_weights_transposed, nullptr, &_output_highp);

            
                _input.allocator()->allocate();


                // Set the offset back

            
                _input.info()->set_quantization_info(QuantizationInfo(qasymm.uniform().scale, qasymm.uniform().offset));

            
                _weights_transposed.info()->set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset));


                // multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12))

            
                _output_lowp.allocator()->init(TensorInfo(_output_highp.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_3));


                const float multiplier        = 4096.f * qasymm.uniform().scale * qweights.uniform().scale;

            
                int32_t     output_multiplier = 0;

            
                int32_t     output_shift      = 0;

            
                quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);


                _memory_group.manage(&_output_lowp);


                GEMMLowpOutputStageInfo info;

            
                info.type                = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;

            
                info.gemmlowp_multiplier = output_multiplier;

            
                info.gemmlowp_shift      = output_shift;

            
                info.output_data_type    = DataType::QSYMM16;

            
                _output_stage.configure(&_output_highp, &_bias, &_output_lowp, info);

            
                _output_highp.allocator()->allocate();

            
                _bias.allocator()->allocate();


                // Get the gate tensors

            
                if(batch_size > 1)

            
                {

            
                    _memory_group.manage(&_input_gate_input);

            
                    _slice_input_tensor.configure(&_output_lowp, &_input_gate_input, { 0, 0 }, { output_size, batch_size });

            
                    _memory_group.manage(&_forget_gate_input);

            
                    _slice_forget_tensor.configure(&_output_lowp, &_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size });

            
                    _memory_group.manage(&_input_modulation_gate_input);

            
                    _slice_cell_tensor.configure(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size });

            
                    _memory_group.manage(&_output_gate_input);

            
                    _slice_output_tensor.configure(&_output_lowp, &_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size });

            
                    _output_lowp.allocator()->allocate();

            
                }

            
                else

            
                {

            
                    _memory_group.manage(&_input_gate_input);

            
                    _slice_input_tensor.configure(&_output_lowp, &_input_gate_input, { 0 }, { output_size });

            
                    _memory_group.manage(&_forget_gate_input);

            
                    _slice_forget_tensor.configure(&_output_lowp, &_forget_gate_input, { output_size }, { 2 * output_size });

            
                    _memory_group.manage(&_input_modulation_gate_input);

            
                    _slice_cell_tensor.configure(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size }, { 3 * output_size });

            
                    _memory_group.manage(&_output_gate_input);

            
                    _slice_output_tensor.configure(&_output_lowp, &_output_gate_input, { 3 * output_size }, { 4 * output_size });

            
                    _output_lowp.allocator()->allocate();

            
                }


                // Forget gate

            
                _memory_group.manage(&_forget_gate_output);

            
                _forget_gate_output.allocator()->init(TensorInfo(_forget_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));

            
                _sigmoid_forget_gate.configure(&_forget_gate_input, &_forget_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));

            
                _forget_gate_input.allocator()->allocate();


                // Input gate

            
                _memory_group.manage(&_input_gate_output);

            
                _input_gate_output.allocator()->init(TensorInfo(_input_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));

            
                _sigmoid_input_gate.configure(&_input_gate_input, &_input_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));

            
                _input_gate_input.allocator()->allocate();


                // Input modulation gate equation

            
                _memory_group.manage(&_input_modulation_gate_output);

            
                _input_modulation_gate_output.allocator()->init(TensorInfo(_input_modulation_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));

            
                _tanh_modulation_gate.configure(&_input_modulation_gate_input, &_input_modulation_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f));

            
                _input_modulation_gate_input.allocator()->allocate();


                // Output gate

            
                _memory_group.manage(&_output_gate_output);

            
                _output_gate_output.allocator()->init(TensorInfo(_output_gate_input.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));

            
                _sigmoid_output_gate.configure(&_output_gate_input, &_output_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));

            
                _output_gate_input.allocator()->allocate();


                // Long term memory

            
                _memory_group.manage(&_cell_state1);

            
                _cell_state1.allocator()->init(TensorInfo(_forget_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_4));

            
                _mul1.configure(&_forget_gate_output, cell_state_in, &_cell_state1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);

            
                _forget_gate_output.allocator()->allocate();


                _memory_group.manage(&_cell_state2);

            
                _cell_state2.allocator()->init(TensorInfo(_input_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_4));

            
                _mul2.configure(&_input_gate_output, &_input_modulation_gate_output, &_cell_state2, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);

            
                _input_modulation_gate_output.allocator()->allocate();

            
                _input_gate_output.allocator()->allocate();


                _add1.configure(&_cell_state1, &_cell_state2, cell_state_out, ConvertPolicy::SATURATE);

            
                _cell_state1.allocator()->allocate();

            
                _cell_state2.allocator()->allocate();


                // Short term memory

            
                _memory_group.manage(&_output_state_tmp);

            
                _output_state_tmp.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));

            
                _tanh_output_state.configure(cell_state_out, &_output_state_tmp, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f));


                _memory_group.manage(&_output_state_out_symm);

            
                _output_state_out_symm.allocator()->init(TensorInfo(_output_gate_output.info()->tensor_shape(), 1, DataType::QSYMM16, qsymm_0));

            
                _mul3.configure(&_output_state_tmp, &_output_gate_output, &_output_state_out_symm, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);

            
                _output_gate_output.allocator()->allocate();

            
                _output_state_tmp.allocator()->allocate();


                // Requantize the output state from QSYMM16 to QASYMM8

            
                _memory_group.manage(&_output_state_out_f32);

            
                _output_state_out_f32.allocator()->init(TensorInfo(_output_state_out_symm.info()->tensor_shape(), 1, DataType::F32));

            
                _dequantize.configure(&_output_state_out_symm, &_output_state_out_f32);

            
                _output_state_out_symm.allocator()->allocate();


                _quantize.configure(&_output_state_out_f32, output_state_out);

            
                _output_state_out_f32.allocator()->allocate();

            
            }


            Status NELSTMLayerQuantized::validate(const ITensorInfo *input,

            
                                                  const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,

            
                                                  const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,

            
                                                  const ITensorInfo *input_gate_bias, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,

            
                                                  const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,

            
                                                  const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out)

            
            {

            
                ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,

            
                                                    recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in,

            
                                                    output_state_in, cell_state_out, output_state_out);


                const int input_size  = input->dimension(0);

            
                const int batch_size  = input->dimension(1);

            
                const int output_size = input_to_input_weights->dimension(1);


                // Dimensionality checks

            
                ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2);

            
                ARM_COMPUTE_RETURN_ERROR_ON(input_to_input_weights->num_dimensions() > 2);

            
                ARM_COMPUTE_RETURN_ERROR_ON(input_gate_bias->num_dimensions() > 1);

            
                ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2);


                TensorInfo input_weights_info(input_to_input_weights->clone()->set_tensor_shape(TensorShape(input_size, output_size)).set_data_type(DataType::QASYMM8));

            
                TensorInfo recurrent_weights_info(input_to_input_weights->clone()->set_tensor_shape(TensorShape(output_size, output_size)).set_data_type(DataType::QASYMM8));

            
                TensorInfo bias_info(input_gate_bias->clone()->set_tensor_shape(TensorShape(output_size)).set_data_type(DataType::S32));

            
                TensorInfo output_state_info(cell_state_in->clone()->set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(DataType::QASYMM8).set_quantization_info(qasymm));

            
                TensorInfo cell_state_info(cell_state_in->clone()->set_tensor_shape(TensorShape(output_size, batch_size)).set_data_type(DataType::QSYMM16).set_quantization_info(qsymm_4));


                // Shape checks

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input_weights_info, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&recurrent_weights_info, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&bias_info, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&cell_state_info, cell_state_in);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&output_state_info, output_state_in);


                // Data type checks

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input_weights_info, input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&bias_info, input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_in);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&output_state_info, output_state_in);


                // Quantization checks

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input_weights_info, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_in);

            
                ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_in);


                // Validate internal functions

            
                // _concat_input_weights

            
                std::vector<const ITensorInfo *> inputs_weights_vector;

            
                inputs_weights_vector.emplace_back(input_to_input_weights);

            
                inputs_weights_vector.emplace_back(input_to_forget_weights);

            
                inputs_weights_vector.emplace_back(input_to_cell_weights);

            
                inputs_weights_vector.emplace_back(input_to_output_weights);

            
                const QuantizationInfo qweights = input_to_input_weights->quantization_info(); // Weights quantization

            
                const TensorInfo       input_weights(TensorShape(input_size, 4 * output_size), 1, DataType::QASYMM8, qweights);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(inputs_weights_vector, &input_weights, Window::DimY));


                // _concat_recurrent_weights

            
                std::vector<const ITensorInfo *> recurrent_weights_vector;

            
                recurrent_weights_vector.emplace_back(recurrent_to_input_weights);

            
                recurrent_weights_vector.emplace_back(recurrent_to_forget_weights);

            
                recurrent_weights_vector.emplace_back(recurrent_to_cell_weights);

            
                recurrent_weights_vector.emplace_back(recurrent_to_output_weights);

            
                const TensorInfo recurrent_weights(TensorShape(output_size, 4 * output_size), 1, DataType::QASYMM8, qweights);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(recurrent_weights_vector, &recurrent_weights, Window::DimY));


                // _concat_weights

            
                std::vector<const ITensorInfo *> weights_vector;

            
                weights_vector.emplace_back(&recurrent_weights);

            
                weights_vector.emplace_back(&input_weights);

            
                const TensorInfo weights(TensorShape(input_size + output_size, 4 * output_size), 1, DataType::QASYMM8, qweights);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(weights_vector, &weights, Window::DimX));

            
                // _transpose_weights

            
                const TensorShape weights_transposed_shape(weights.tensor_shape()[1], weights.tensor_shape()[0]);

            
                TensorInfo        weights_transposed = weights.clone()->set_is_resizable(true).set_tensor_shape(weights_transposed_shape);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(&weights, &weights_transposed));


                // _concat_inputs

            
                std::vector<const ITensorInfo *> input_vector;

            
                input_vector.emplace_back(input);

            
                input_vector.emplace_back(output_state_in);

            
                TensorInfo input_concatenated(TensorShape(output_size + input_size, batch_size), 1, DataType::QASYMM8, qasymm);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(input_vector, &input_concatenated, Window::DimX));


                // _concat_bias

            
                std::vector<const ITensorInfo *> bias_vector;

            
                bias_vector.emplace_back(input_gate_bias);

            
                bias_vector.emplace_back(forget_gate_bias);

            
                bias_vector.emplace_back(cell_bias);

            
                bias_vector.emplace_back(output_gate_bias);


                const TensorInfo bias_concatenated(TensorShape(4 * output_size), 1, DataType::S32);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(bias_vector, &bias_concatenated, Window::DimX));


                // Invert the offset for gemmlowp

            
                input_concatenated.set_quantization_info(QuantizationInfo(qasymm.uniform().scale, -qasymm.uniform().offset));

            
                weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset));


                // _gemmlowp

            
                const TensorInfo output_highp(TensorShape(4 * output_size, batch_size), 1, DataType::S32);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input_concatenated, &weights_transposed, nullptr, &output_highp));


                // Set the offset back

            
                input_concatenated.set_quantization_info(QuantizationInfo(qasymm.uniform().scale, qasymm.uniform().offset));

            
                weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset));


                const TensorInfo output_lowp(output_highp.tensor_shape(), 1, DataType::QSYMM16, qsymm_3);


                const float multiplier        = 4096.f * qasymm.uniform().scale * qweights.uniform().scale;

            
                int32_t     output_multiplier = 0;

            
                int32_t     output_shift      = 0;

            
                ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));


                // _output_stage

            
                GEMMLowpOutputStageInfo info;

            
                info.type                = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;

            
                info.gemmlowp_multiplier = output_multiplier;

            
                info.gemmlowp_shift      = output_shift;

            
                info.output_data_type    = DataType::QSYMM16;

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&output_highp, &bias_concatenated, &output_lowp, info));


                TensorInfo input_gate_input;

            
                TensorInfo forget_gate_input;

            
                TensorInfo input_modulation_gate_input;

            
                TensorInfo output_gate_input;


                if(batch_size > 1)

            
                {

            
                    // _slice_input_tensor

            
                    input_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_gate_input, { 0, 0 }, { output_size, batch_size }));

            
                    // _slice_forget_tensor

            
                    forget_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }));

            
                    // _slice_cell_tensor

            
                    input_modulation_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }));

            
                    // _slice_output_tensor

            
                    output_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }));

            
                }

            
                else

            
                {

            
                    // _slice_input_tensor

            
                    input_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_gate_input, { 0 }, { output_size }));

            
                    // _slice_forget_tensor

            
                    forget_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &forget_gate_input, { output_size }, { 2 * output_size }));

            
                    // _slice_cell_tensor

            
                    input_modulation_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_modulation_gate_input, { 2 * output_size }, { 3 * output_size }));

            
                    // _slice_output_tensor

            
                    output_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3);

            
                    ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &output_gate_input, { 3 * output_size }, { 4 * output_size }));

            
                }


                // _sigmoid_forget_gate

            
                const TensorInfo forget_gate_output(forget_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&forget_gate_input, &forget_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));

            
                // _sigmoid_input_gate

            
                const TensorInfo input_gate_output(input_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_gate_input, &input_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));

            
                // _tanh_modulation_gate

            
                const TensorInfo input_modulation_gate_output(input_modulation_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_modulation_gate_input, &input_modulation_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f)));

            
                // _sigmoid_output_gate

            
                const TensorInfo output_gate_output(output_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&output_gate_input, &output_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));


                // _mul_forget_gate_cell_state

            
                const TensorInfo cell_state_tmp1(forget_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_4);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&forget_gate_output, cell_state_in, &cell_state_tmp1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));


                // _mul_input_gate_input_mod_gate

            
                const TensorInfo cell_state_tmp2(input_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_4);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&input_gate_output, &input_modulation_gate_output, &cell_state_tmp2, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));


                // _add_cell_state_tmps

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp1, &cell_state_tmp2, cell_state_out, ConvertPolicy::SATURATE));


                // _tanh_modulation_gate

            
                const TensorInfo output_state_tmp(cell_state_out->tensor_shape(), 1, DataType::QSYMM16, qsymm_0);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, &output_state_tmp, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f)));


                // _mul_output_state_tmp_output_gate

            
                const TensorInfo output_state_out_symm(output_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_0);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&output_state_tmp, &output_gate_output, &output_state_out_symm, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));


                // _dequantize

            
                const TensorInfo output_state_out_f32(output_state_out_symm.tensor_shape(), 1, DataType::F32);

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEDequantizationLayer::validate(&output_state_out_symm, &output_state_out_f32));


                // _quantize

            
                ARM_COMPUTE_RETURN_ON_ERROR(NEQuantizationLayer::validate(&output_state_out_f32, output_state_out));


                if(cell_state_out->total_size() != 0)

            
                {

            
                    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_out);

            
                    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&cell_state_info, cell_state_out);

            
                    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_out);

            
                }


                if(output_state_out->total_size() != 0)

            
                {

            
                    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&output_state_info, output_state_out);

            
                    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&output_state_info, output_state_out);

            
                    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_out);

            
                }


                return Status{};

            
            }


            void NELSTMLayerQuantized::run()

            
            {

            
                prepare();


                // Acquire all the temporaries

            
                MemoryGroupResourceScope scope_mg(_memory_group);


                // Concat and transpose the input

            
                _concat_inputs.run();


                // Run gemmlowp

            
                _gemmlowp.run();

            
                _output_stage.run();


                // Slice the results

            
                _slice_input_tensor.run();

            
                _slice_forget_tensor.run();

            
                _slice_cell_tensor.run();

            
                _slice_output_tensor.run();


                // Gates

            
                // Forget gate

            
                _sigmoid_forget_gate.run();


                // Input gate

            
                _sigmoid_input_gate.run();


                // Input modulation gate

            
                _tanh_modulation_gate.run();


                // Output gate

            
                _sigmoid_output_gate.run();


                // Cell state (long term memory)

            
                _mul1.run();

            
                _mul2.run();

            
                _add1.run();


                // Output state (short term memory)

            
                _tanh_output_state.run();

            
                _mul3.run();


                // Requantize output state from QSYMM16 to QASYMM8

            
                _dequantize.run();

            
                _quantize.run();

            
            }


            void NELSTMLayerQuantized::prepare()

            
            {

            
                if(!_is_prepared)

            
                {

            
                    _input_weights.allocator()->allocate();

            
                    _concat_input_weights.run();


                    _input_to_input_weights->mark_as_unused();

            
                    _input_to_forget_weights->mark_as_unused();

            
                    _input_to_cell_weights->mark_as_unused();

            
                    _input_to_output_weights->mark_as_unused();


                    _recurrent_weights.allocator()->allocate();

            
                    _concat_recurrent_weights.run();

            
                    _recurrent_to_input_weights->mark_as_unused();

            
                    _recurrent_to_forget_weights->mark_as_unused();

            
                    _recurrent_to_cell_weights->mark_as_unused();

            
                    _recurrent_to_output_weights->mark_as_unused();


                    _weights.allocator()->allocate();

            
                    _concat_weights.run();


                    _input_weights.mark_as_unused();

            
                    _input_weights.allocator()->free();

            
                    _recurrent_weights.mark_as_unused();

            
                    _recurrent_weights.allocator()->free();


                    _weights_transposed.allocator()->allocate();

            
                    _transpose_weights.run();


                    _weights.mark_as_unused();

            
                    _weights.allocator()->free();


                    _bias.allocator()->allocate();

            
                    _concat_bias.run();

            
                    _input_gate_bias->mark_as_unused();

            
                    _forget_gate_bias->mark_as_unused();

            
                    _cell_bias->mark_as_unused();

            
                    _output_gate_bias->mark_as_unused();


                    _is_prepared = true;

            
                }

            
            }


            } // namespace arm_compute