| /* |
| * Copyright (c) 2019 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 <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(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())); |
| |
| 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; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| |
| quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| |
| _memory_group.manage(&_output_lowp); |
| _output_stage.configure(&_output_highp, &_bias, &_output_lowp, output_multiplier, output_shift); |
| _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)); |
| |
| // multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12)) |
| const TensorInfo output_lowp(output_highp.tensor_shape(), 1, DataType::QSYMM16, qsymm_3); |
| |
| const float multiplier = 4096.f * qasymm.uniform().scale * qweights.uniform().scale; |
| ARM_COMPUTE_UNUSED(multiplier); |
| ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f); |
| // _output_stage |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(&output_highp, &bias_concatenated, &output_lowp)); |
| |
| 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 QASYMM16 |
| _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 |
