| /* |
| * Copyright (c) 2018-2019 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/NELSTMLayer.h" |
| |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/runtime/common/LSTMParams.h" |
| |
| #include <cmath> |
| #include <memory> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| NELSTMLayer::NELSTMLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _accum_input_gate1(), _subtract_input_gate(), _pixelwise_mul_input_gate(), _activation_input_gate(), |
| _fully_connected_forget_gate(), _accum_forget_gate1(), _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _transpose_cell_state(), |
| _accum_cell_state1(), _accum_cell_state2(), _pixelwise_mul_cell_state1(), _activation_cell_state(), _cell_clip(), _pixelwise_mul_cell_state2(), _fully_connected_output(), |
| _pixelwise_mul_output_state1(), _accum_output1(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _projection_clip(), |
| _copy_cell_state(), _copy_output(), _concat_scratch_buffer(), _concat_inputs_forget_gate(), _concat_weights_forget_gate(), _concat_weights_input_gate(), _concat_weights_output(), |
| _mean_std_norm_input_gate(), _pixelwise_mul_input_gate_coeff(), _accum_input_gate_bias(), _mean_std_norm_forget_gate(), _pixelwise_mul_forget_gate_coeff(), _accum_forget_gate_bias(), |
| _mean_std_norm_cell_gate(), _pixelwise_mul_cell_gate_coeff(), _accum_cell_gate_bias(), _mean_std_norm_output_gate(), _pixelwise_mul_output_gate_coeff(), _accum_output_gate_bias(), _input_gate_out1(), |
| _input_gate_out2(), _input_gate_out3(), _input_gate_out4(), _forget_gate_out1(), _forget_gate_out2(), _forget_gate_out3(), _forget_gate_out4(), _forget_gate_out5(), _forget_gate_out6(), |
| _cell_state_out1(), _cell_state_out2(), _cell_state_out3(), _cell_state_out4(), _cell_state_out5(), _output1(), _output2(), _output3(), _output4(), _cell_state_activation(), _output_state1(), _ones(), |
| _input_layer_norm_out1(), _input_layer_norm_out2(), _forget_layer_norm_out1(), _forget_layer_norm_out2(), _cell_layer_norm_out1(), _cell_layer_norm_out2(), _output_layer_norm_out1(), |
| _output_layer_norm_out2(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false), _is_prepared(false), |
| _is_layer_norm_lstm(false) |
| { |
| } |
| |
| void NELSTMLayer::configure(const ITensor *input, |
| const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights, |
| const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights, |
| const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias, |
| const ITensor *output_state_in, const ITensor *cell_state_in, |
| ITensor *scratch_buffer, ITensor *output_state_out, ITensor *cell_state_out, ITensor *output, |
| const LSTMParams<ITensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, |
| input_to_forget_weights, input_to_cell_weights, input_to_output_weights, |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, |
| forget_gate_bias, cell_bias, output_gate_bias, |
| output_state_in, cell_state_in, |
| scratch_buffer, output_state_out, cell_state_out, output); |
| |
| _is_layer_norm_lstm = lstm_params.use_layer_norm(); |
| |
| // Set lstm parameters |
| LSTMParams<ITensorInfo> lstm_params_info; |
| if(lstm_params.has_peephole_opt()) |
| { |
| lstm_params_info.set_peephole_params(lstm_params.cell_to_forget_weights()->info(), lstm_params.cell_to_output_weights()->info()); |
| } |
| if(lstm_params.has_projection()) |
| { |
| lstm_params_info.set_projection_params(lstm_params.projection_weights()->info(), |
| lstm_params.projection_bias() != nullptr ? lstm_params.projection_bias()->info() : nullptr); |
| } |
| if(!lstm_params.has_cifg_opt()) |
| { |
| const ITensorInfo *cell_to_input_weights_info = (lstm_params.has_peephole_opt()) ? lstm_params.cell_to_input_weights()->info() : nullptr; |
| lstm_params_info.set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(), |
| cell_to_input_weights_info, lstm_params.input_gate_bias()->info()); |
| } |
| |
| // Validate |
| ARM_COMPUTE_ERROR_THROW_ON(NELSTMLayer::validate(input->info(), input_to_forget_weights->info(), |
| input_to_cell_weights->info(), input_to_output_weights->info(), |
| recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(), |
| forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(), |
| output_state_in->info(), cell_state_in->info(), |
| scratch_buffer->info(), output_state_out->info(), cell_state_out->info(), output->info(), |
| lstm_params_info, activation_info, cell_threshold, projection_threshold)); |
| |
| const TensorShape cell_state_shape = cell_state_in->info()->tensor_shape(); |
| |
| // Configure block that calculates the forget gate |
| // forget_gate = Activation(input * input_to_forget_weights + output_state_in * recurrent_to_forget_weights + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias) |
| // We optimize this as follows: |
| // forget_gate = Activation( (input,output_state_in) * (input_to_forget_weights,recurrent_to_forget_weights) + PixelWiseMul(cell_state, cell_to_forget_weights) + forget_gate_bias) |
| _forget_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _forget_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _forget_gate_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| |
| std::vector<const ITensor *> inputs_vector; |
| inputs_vector.emplace_back(input); |
| inputs_vector.emplace_back(output_state_in); |
| |
| _memory_group.manage(&_forget_gate_out2); |
| _concat_inputs_forget_gate.configure(inputs_vector, &_forget_gate_out2, Window::DimX); |
| |
| std::vector<const ITensor *> weights_vector; |
| |
| weights_vector.emplace_back(input_to_forget_weights); |
| weights_vector.emplace_back(recurrent_to_forget_weights); |
| |
| _concat_weights_forget_gate.configure(weights_vector, &_forget_gate_out6, Window::DimX); |
| |
| _memory_group.manage(&_forget_gate_out5); |
| _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, (_is_layer_norm_lstm) ? nullptr : forget_gate_bias, &_forget_gate_out5); |
| _memory_group.manage(&_forget_gate_out1); |
| _memory_group.manage(&_forget_gate_out3); |
| _forget_gate_out6.allocator()->allocate(); |
| |
| Tensor *forget_gate_out = &_forget_gate_out5; |
| if(lstm_params.has_peephole_opt()) |
| { |
| _forget_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| |
| _run_peephole_opt = true; |
| _memory_group.manage(&_forget_gate_out4); |
| _pixelwise_mul_forget_gate.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_forget_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| _accum_forget_gate1.configure(&_forget_gate_out5, &_forget_gate_out4, &_forget_gate_out3, ConvertPolicy::SATURATE); |
| _forget_gate_out4.allocator()->allocate(); |
| _forget_gate_out5.allocator()->allocate(); |
| forget_gate_out = &_forget_gate_out3; |
| } |
| else |
| { |
| _forget_gate_out3.allocator()->allocate(); |
| } |
| if(_is_layer_norm_lstm) |
| { |
| _forget_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _forget_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _memory_group.manage(&_forget_layer_norm_out1); |
| _memory_group.manage(&_forget_layer_norm_out2); |
| _mean_std_norm_forget_gate.configure(forget_gate_out); |
| _pixelwise_mul_forget_gate_coeff.configure(forget_gate_out, lstm_params.forget_layer_norm_weights(), &_forget_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| // forget_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before |
| forget_gate_out->allocator()->allocate(); |
| _accum_forget_gate_bias.configure(&_forget_layer_norm_out1, forget_gate_bias, &_forget_layer_norm_out2, ConvertPolicy::SATURATE); |
| _forget_layer_norm_out1.allocator()->allocate(); |
| forget_gate_out = &_forget_layer_norm_out2; |
| } |
| _activation_forget_gate.configure(forget_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); |
| |
| // Configure block that calculates the input gate |
| // input_gate = Activation(input * input_to_input_weights + output_state * recurrent_to_input_weights + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG |
| // input_gate = 1 - forget_gate, with CIFG |
| // We optimize this as follows: |
| // input_gate = Activation((input,output_state) * (input_to_input_weights,recurrent_to_input_weights) + PixelWiseMul(cell_state, cell_to_input_weights) + input_gate_bias), without CIFG |
| _input_gate_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| Tensor *input_gate_out = &_input_gate_out1; |
| if(lstm_params.has_cifg_opt()) |
| { |
| _memory_group.manage(&_input_gate_out1); |
| _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _subtract_input_gate.configure(&_ones, forget_gate_out, &_input_gate_out1, ConvertPolicy::SATURATE); |
| _ones.allocator()->allocate(); |
| _run_cifg_opt = true; |
| } |
| else |
| { |
| _input_gate_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _input_gate_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| |
| std::vector<const ITensor *> lstm_weights; |
| lstm_weights.emplace_back(lstm_params.input_to_input_weights()); |
| lstm_weights.emplace_back(lstm_params.recurrent_to_input_weights()); |
| |
| _concat_weights_input_gate.configure(lstm_weights, &_input_gate_out2, Window::DimX); |
| |
| _memory_group.manage(&_input_gate_out1); |
| _memory_group.manage(&_input_gate_out4); |
| |
| _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, (_is_layer_norm_lstm) ? nullptr : lstm_params.input_gate_bias(), &_input_gate_out3); |
| _input_gate_out2.allocator()->allocate(); |
| input_gate_out = &_input_gate_out3; |
| |
| if(_run_peephole_opt) |
| { |
| _memory_group.manage(&_input_gate_out4); |
| _pixelwise_mul_input_gate.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_input_gate_out4, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| _accum_input_gate1.configure(&_input_gate_out3, &_input_gate_out4, &_input_gate_out1, ConvertPolicy::SATURATE); |
| _input_gate_out3.allocator()->allocate(); |
| _input_gate_out4.allocator()->allocate(); |
| input_gate_out = &_input_gate_out1; |
| } |
| else |
| { |
| _input_gate_out1.allocator()->allocate(); |
| } |
| |
| if(_is_layer_norm_lstm) |
| { |
| _input_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _input_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _memory_group.manage(&_input_layer_norm_out1); |
| _memory_group.manage(&_input_layer_norm_out2); |
| _mean_std_norm_input_gate.configure(input_gate_out); |
| _pixelwise_mul_input_gate_coeff.configure(input_gate_out, lstm_params.input_layer_norm_weights(), &_input_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| // input_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before |
| input_gate_out->allocator()->allocate(); |
| _accum_input_gate_bias.configure(&_input_layer_norm_out1, lstm_params.input_gate_bias(), &_input_layer_norm_out2, ConvertPolicy::SATURATE); |
| _input_layer_norm_out1.allocator()->allocate(); |
| input_gate_out = &_input_layer_norm_out2; |
| } |
| _activation_input_gate.configure(input_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); |
| } |
| |
| // Configure block that calculates the cell state |
| // cell_state = Clip((PixelwiseMul(input_gate, Activation(input * input_to_cell_weights + output_state_in * recurrent_to_cell_weights + cell_bias)) + PixelwiseMul(forget_gate, cell_state)), cell_threshold) |
| TensorShape cell_state1_shape = compute_transposed_shape(*recurrent_to_output_weights->info()); |
| _cell_state_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _cell_state_out2.allocator()->init(TensorInfo(cell_state1_shape, 1, input->info()->data_type())); |
| _cell_state_out3.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _cell_state_out4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _cell_state_out5.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| |
| _memory_group.manage(&_cell_state_out1); |
| _fully_connected_cell_state.configure(input, input_to_cell_weights, (_is_layer_norm_lstm) ? nullptr : cell_bias, &_cell_state_out1); |
| _memory_group.manage(&_cell_state_out2); |
| _transpose_cell_state.configure(recurrent_to_cell_weights, &_cell_state_out2); |
| _memory_group.manage(&_cell_state_out3); |
| _gemm_cell_state1.configure(output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f); |
| _cell_state_out2.allocator()->allocate(); |
| _memory_group.manage(&_cell_state_out4); |
| _accum_cell_state1.configure(&_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE); |
| Tensor *cell_state_out_ptr = &_cell_state_out4; |
| if(_is_layer_norm_lstm) |
| { |
| _cell_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _cell_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _memory_group.manage(&_cell_layer_norm_out1); |
| _memory_group.manage(&_cell_layer_norm_out2); |
| _mean_std_norm_cell_gate.configure(cell_state_out_ptr); |
| _pixelwise_mul_cell_gate_coeff.configure(cell_state_out_ptr, lstm_params.cell_layer_norm_weights(), &_cell_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| // cell_state_out_ptr is going to be reassigned, so allocate the tensor that it was assigned to before |
| cell_state_out_ptr->allocator()->allocate(); |
| _accum_cell_gate_bias.configure(&_cell_layer_norm_out1, cell_bias, &_cell_layer_norm_out2, ConvertPolicy::SATURATE); |
| _cell_layer_norm_out1.allocator()->allocate(); |
| cell_state_out_ptr = &_cell_layer_norm_out2; |
| } |
| _activation_cell_state.configure(cell_state_out_ptr, nullptr, activation_info); |
| _memory_group.manage(&_cell_state_out5); |
| _pixelwise_mul_cell_state1.configure(cell_state_out_ptr, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| cell_state_out_ptr->allocator()->allocate(); |
| _pixelwise_mul_cell_state2.configure(forget_gate_out, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| _accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE); |
| _cell_state_out3.allocator()->allocate(); |
| _cell_state_out5.allocator()->allocate(); |
| // Perform clipping |
| if(cell_threshold != 0.f) |
| { |
| _perform_cell_clipping = true; |
| _cell_clip.configure(&_cell_state_out1, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold)); |
| } |
| |
| // Configure block that calculates the output |
| // output_state_out = Activation(input * input_to_output_weights + output_state_in * recurrent_to_output_weights + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias) |
| // We optimize this as follows: |
| // output_state_out = Activation( (input,output_state_in) * (input_to_output_weights, recurrent_to_output_weights) + PixelWiseMul(cell_state, cell_to_output_weights) + output_gate_bias) |
| _output1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _output4.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| |
| std::vector<const ITensor *> in_out_weights; |
| in_out_weights.emplace_back(input_to_output_weights); |
| in_out_weights.emplace_back(recurrent_to_output_weights); |
| |
| _concat_weights_output.configure(in_out_weights, &_output2, Window::DimX); |
| _memory_group.manage(&_output1); |
| _memory_group.manage(&_output4); |
| |
| _fully_connected_output.configure(&_forget_gate_out2, &_output2, (_is_layer_norm_lstm) ? nullptr : output_gate_bias, &_output4); |
| |
| _output2.allocator()->allocate(); |
| _forget_gate_out2.allocator()->allocate(); |
| |
| Tensor *output_gate_out = &_output4; |
| if(lstm_params.has_peephole_opt()) |
| { |
| _output3.allocator()->init(TensorInfo(_cell_state_out1.info()->tensor_shape(), 1, input->info()->data_type())); |
| |
| _memory_group.manage(&_output3); |
| _pixelwise_mul_output_state1.configure(&_cell_state_out1, lstm_params.cell_to_output_weights(), &_output3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| _accum_output1.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE); |
| _output4.allocator()->allocate(); |
| output_gate_out = &_output1; |
| |
| // Allocate intermediate buffers |
| _output3.allocator()->allocate(); |
| } |
| else |
| { |
| _output1.allocator()->allocate(); |
| } |
| if(_is_layer_norm_lstm) |
| { |
| _output_layer_norm_out1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _output_layer_norm_out2.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _memory_group.manage(&_output_layer_norm_out1); |
| _memory_group.manage(&_output_layer_norm_out2); |
| _mean_std_norm_output_gate.configure(output_gate_out); |
| _pixelwise_mul_output_gate_coeff.configure(output_gate_out, lstm_params.output_layer_norm_weights(), &_output_layer_norm_out1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| // output_gate_out is going to be reassigned, so allocate the tensor that it was assigned to before |
| output_gate_out->allocator()->allocate(); |
| _accum_output_gate_bias.configure(&_output_layer_norm_out1, output_gate_bias, &_output_layer_norm_out2, ConvertPolicy::SATURATE); |
| _output_layer_norm_out1.allocator()->allocate(); |
| output_gate_out = &_output_layer_norm_out2; |
| } |
| _activation_output.configure(output_gate_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); |
| |
| // Configure block that calculates the output state |
| /** lstm_res = PixelwiseMul(output, Activation(cell_state)) |
| * |
| * -- Clip(lstm_res * projection_weights + projection_bias, projection_threshold) , if there is a projection |
| * / |
| * output_state = -- |
| * \ |
| * -- lstm_res , otherwise |
| */ |
| ITensor *output_state_out_tmp = lstm_params.has_projection() ? &_output_state1 : output_state_out; |
| _cell_state_activation.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| _output_state1.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); |
| |
| _memory_group.manage(&_cell_state_activation); |
| _activation_output_state.configure(&_cell_state_out1, &_cell_state_activation, activation_info); |
| _pixelwise_mul_output_state2.configure(&_cell_state_activation, output_gate_out, output_state_out_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); |
| _cell_state_activation.allocator()->allocate(); |
| output_gate_out->allocator()->allocate(); |
| |
| if(lstm_params.has_projection()) |
| { |
| _has_projection_weights = true; |
| _fully_connected_output_state.configure(output_state_out_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out); |
| _output_state1.allocator()->allocate(); |
| // Perform clipping |
| if(projection_threshold != 0.f) |
| { |
| _perform_projection_clipping = true; |
| _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold)); |
| } |
| } |
| |
| // Copy cell state and output |
| _copy_cell_state.configure(&_cell_state_out1, cell_state_out); |
| _copy_output.configure(output_state_out, output); |
| |
| // Vector for holding the tensors to store in scratch buffer |
| std::vector<ITensor *> scratch_inputs; |
| if(!lstm_params.has_cifg_opt()) |
| { |
| scratch_inputs.emplace_back(input_gate_out); |
| } |
| scratch_inputs.emplace_back(&_cell_state_out1); |
| scratch_inputs.emplace_back(forget_gate_out); |
| scratch_inputs.emplace_back(output_gate_out); |
| _concat_scratch_buffer.configure(scratch_inputs, scratch_buffer, Window::DimX); |
| input_gate_out->allocator()->allocate(); |
| _cell_state_out1.allocator()->allocate(); |
| forget_gate_out->allocator()->allocate(); |
| output_gate_out->allocator()->allocate(); |
| } |
| |
| Status NELSTMLayer::validate(const ITensorInfo *input, |
| const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, |
| const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, |
| const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, |
| const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in, |
| const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output, |
| const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold, float projection_threshold) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, |
| input_to_forget_weights, input_to_cell_weights, input_to_output_weights, |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, |
| forget_gate_bias, cell_bias, output_gate_bias, |
| output_state_in, cell_state_in, |
| scratch_buffer, output_state_out, cell_state_out, output); |
| |
| // Check data types |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, |
| input_to_forget_weights, input_to_cell_weights, input_to_output_weights, |
| recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, |
| forget_gate_bias, cell_bias, output_gate_bias, |
| output_state_in, cell_state_in, |
| scratch_buffer, output_state_out, cell_state_out, output); |
| |
| // Check dimensions |
| ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_to_forget_weights->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_to_cell_weights->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_forget_weights->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_cell_weights->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_gate_bias->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(scratch_buffer->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_state_out->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_state_out->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(cell_bias->dimension(0) * 4 != scratch_buffer->dimension(0) |
| && cell_bias->dimension(0) * 3 != scratch_buffer->dimension(0)); |
| |
| const unsigned int num_batches = input->dimension(1); |
| const unsigned int num_cells = input_to_output_weights->dimension(1); |
| |
| if(lstm_params.use_layer_norm()) |
| { |
| // If CIFG is used, input layer normalization weights tensor is omitted |
| if(lstm_params.has_cifg_opt()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights() != nullptr); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights()); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_batches); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.input_layer_norm_weights()); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.forget_layer_norm_weights(), lstm_params.cell_layer_norm_weights(), lstm_params.output_layer_norm_weights()); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_batches); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_batches); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_batches); |
| } |
| |
| // Check peephole optimization |
| if(lstm_params.has_peephole_opt()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_output_weights(), lstm_params.cell_to_forget_weights()); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_output_weights()->num_dimensions() > 1); |
| } |
| |
| TensorShape units_out_transposed_shape = compute_transposed_shape(*recurrent_to_output_weights); |
| TensorShape num_units_transposed_shape = compute_transposed_shape(*forget_gate_bias); |
| const TensorInfo units_out_transposed_info = TensorInfo(units_out_transposed_shape, 1, input->data_type()); |
| const TensorInfo num_units_transposed_info = TensorInfo(num_units_transposed_shape, 1, input->data_type()); |
| |
| TensorInfo input_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); |
| TensorInfo forget_gate = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); |
| TensorInfo output_gate_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); |
| TensorInfo cell_state_tmp = TensorInfo(TensorShape(num_cells, num_batches), 1, input->data_type()); |
| |
| std::vector<const ITensorInfo *> inputs_vector; |
| inputs_vector.emplace_back(input); |
| inputs_vector.emplace_back(output_state_in); |
| const TensorShape concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, 0); |
| TensorInfo forget_gate_concat = TensorInfo(concat_shape, 1, input->data_type()); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(inputs_vector, &forget_gate_concat, Window::DimX)); |
| |
| // Validate forget gate |
| ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_forget_weights, (lstm_params.use_layer_norm()) ? nullptr : forget_gate_bias, &forget_gate)); |
| |
| if(lstm_params.has_peephole_opt()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); |
| } |
| if(lstm_params.use_layer_norm()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&forget_gate)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&forget_gate, lstm_params.forget_layer_norm_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, |
| RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&forget_gate, forget_gate_bias, &forget_gate, ConvertPolicy::SATURATE)); |
| } |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&forget_gate, &forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); |
| |
| // Validate input gate |
| if(!lstm_params.has_cifg_opt()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), |
| lstm_params.recurrent_to_input_weights(), |
| lstm_params.input_gate_bias()); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_to_input_weights()->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.recurrent_to_input_weights()->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_gate_bias()->num_dimensions() > 1); |
| |
| std::vector<const ITensorInfo *> lstm_weights; |
| lstm_weights.emplace_back(lstm_params.input_to_input_weights()); |
| lstm_weights.emplace_back(lstm_params.recurrent_to_input_weights()); |
| TensorShape lstm_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(lstm_weights, 0); |
| TensorInfo lstm_gate_concat = TensorInfo(lstm_weights_concat_shape, 1, input->data_type()); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(lstm_weights, &lstm_gate_concat, Window::DimX)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), (lstm_params.use_layer_norm()) ? nullptr : lstm_params.input_gate_bias(), &input_gate)); |
| |
| if(lstm_params.has_peephole_opt()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights()); |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_input_weights()->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_gate, &input_gate, &input_gate, ConvertPolicy::SATURATE)); |
| } |
| |
| if(lstm_params.use_layer_norm()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&input_gate)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&input_gate, lstm_params.input_layer_norm_weights(), &input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&input_gate, lstm_params.input_gate_bias(), &input_gate, ConvertPolicy::SATURATE)); |
| } |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticSubtractionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); |
| } |
| |
| // Validate cell state |
| ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_cell_weights, (lstm_params.use_layer_norm()) ? nullptr : cell_bias, &cell_state_tmp)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &cell_state_tmp, 1.f, 0.f, GEMMInfo())); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE)); |
| if(lstm_params.use_layer_norm()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&cell_state_tmp)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_layer_norm_weights(), &cell_state_tmp, 1, ConvertPolicy::SATURATE, |
| RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp, cell_bias, &cell_state_tmp, ConvertPolicy::SATURATE)); |
| } |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&cell_state_tmp, nullptr, activation_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &forget_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp, &cell_state_tmp, &cell_state_tmp, ConvertPolicy::SATURATE)); |
| if(cell_threshold != 0.f) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&cell_state_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, |
| cell_threshold))); |
| } |
| |
| // Validate output gate tmp |
| std::vector<const ITensorInfo *> in_out_weights; |
| in_out_weights.emplace_back(input_to_output_weights); |
| in_out_weights.emplace_back(recurrent_to_output_weights); |
| TensorShape in_out_weights_concat_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(in_out_weights, 0); |
| TensorInfo in_out_gate_concat = TensorInfo(in_out_weights_concat_shape, 1, input->data_type()); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(in_out_weights, &in_out_gate_concat, Window::DimX)); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_output_weights, (lstm_params.use_layer_norm()) ? nullptr : output_gate_bias, &output_gate_tmp)); |
| |
| if(lstm_params.has_peephole_opt()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, lstm_params.cell_to_output_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE, |
| RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_gate_tmp, &output_gate_tmp, &output_gate_tmp, ConvertPolicy::SATURATE)); |
| } |
| if(lstm_params.use_layer_norm()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEMeanStdDevNormalizationLayer::validate(&output_gate_tmp)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&output_gate_tmp, lstm_params.output_layer_norm_weights(), &output_gate_tmp, 1, ConvertPolicy::SATURATE, |
| RoundingPolicy::TO_ZERO)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&output_gate_tmp, output_gate_bias, &output_gate_tmp, ConvertPolicy::SATURATE)); |
| } |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); |
| |
| // Validate output state |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&cell_state_tmp, &cell_state_tmp, activation_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&cell_state_tmp, &output_gate_tmp, &output_gate_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); |
| if(lstm_params.has_projection()) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(&output_gate_tmp, lstm_params.projection_weights(), lstm_params.projection_bias(), output_state_out)); |
| if(projection_threshold != 0.f) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(output_state_out, output_state_out, |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold))); |
| } |
| } |
| |
| // Validate copy kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(NECopyKernel::validate(&cell_state_tmp, cell_state_out)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NECopyKernel::validate(output_state_out, output)); |
| |
| // Validate scratch concatenation |
| std::vector<ITensorInfo *> inputs_vector_info_raw; |
| if(!lstm_params.has_cifg_opt()) |
| { |
| inputs_vector_info_raw.push_back(&input_gate); |
| } |
| inputs_vector_info_raw.push_back(&cell_state_tmp); |
| inputs_vector_info_raw.push_back(&forget_gate); |
| inputs_vector_info_raw.push_back(&output_gate_tmp); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer, Window::DimX)); |
| return Status{}; |
| } |
| |
| void NELSTMLayer::run() |
| { |
| prepare(); |
| |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| _concat_inputs_forget_gate.run(); |
| _fully_connected_forget_gate.run(); |
| |
| if(_run_peephole_opt) |
| { |
| NEScheduler::get().schedule(&_pixelwise_mul_forget_gate, Window::DimY); |
| _accum_forget_gate1.run(); |
| } |
| if(_is_layer_norm_lstm) |
| { |
| _mean_std_norm_forget_gate.run(); |
| NEScheduler::get().schedule(&_pixelwise_mul_forget_gate_coeff, Window::DimY); |
| NEScheduler::get().schedule(&_accum_forget_gate_bias, Window::DimY); |
| } |
| NEScheduler::get().schedule(&_activation_forget_gate, Window::DimY); |
| |
| if(_run_cifg_opt) |
| { |
| if(_ones.info()->data_type() == DataType::F16) |
| { |
| std::fill_n(reinterpret_cast<half *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); |
| } |
| else |
| { |
| std::fill_n(reinterpret_cast<float *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); |
| } |
| NEScheduler::get().schedule(&_subtract_input_gate, Window::DimY); |
| } |
| else |
| { |
| _fully_connected_input_gate.run(); |
| |
| if(_run_peephole_opt) |
| { |
| NEScheduler::get().schedule(&_pixelwise_mul_input_gate, Window::DimY); |
| _accum_input_gate1.run(); |
| } |
| |
| if(_is_layer_norm_lstm) |
| { |
| _mean_std_norm_input_gate.run(); |
| NEScheduler::get().schedule(&_pixelwise_mul_input_gate_coeff, Window::DimY); |
| NEScheduler::get().schedule(&_accum_input_gate_bias, Window::DimY); |
| } |
| NEScheduler::get().schedule(&_activation_input_gate, Window::DimY); |
| } |
| |
| _fully_connected_cell_state.run(); |
| NEScheduler::get().schedule(&_transpose_cell_state, Window::DimY); |
| _gemm_cell_state1.run(); |
| NEScheduler::get().schedule(&_accum_cell_state1, Window::DimY); |
| if(_is_layer_norm_lstm) |
| { |
| _mean_std_norm_cell_gate.run(); |
| NEScheduler::get().schedule(&_pixelwise_mul_cell_gate_coeff, Window::DimY); |
| NEScheduler::get().schedule(&_accum_cell_gate_bias, Window::DimY); |
| } |
| NEScheduler::get().schedule(&_activation_cell_state, Window::DimY); |
| NEScheduler::get().schedule(&_pixelwise_mul_cell_state1, Window::DimY); |
| NEScheduler::get().schedule(&_pixelwise_mul_cell_state2, Window::DimY); |
| NEScheduler::get().schedule(&_accum_cell_state2, Window::DimY); |
| |
| if(_perform_cell_clipping) |
| { |
| NEScheduler::get().schedule(&_cell_clip, Window::DimY); |
| } |
| |
| _fully_connected_output.run(); |
| if(_run_peephole_opt) |
| { |
| NEScheduler::get().schedule(&_pixelwise_mul_output_state1, Window::DimY); |
| _accum_output1.run(); |
| } |
| if(_is_layer_norm_lstm) |
| { |
| _mean_std_norm_output_gate.run(); |
| NEScheduler::get().schedule(&_pixelwise_mul_output_gate_coeff, Window::DimY); |
| NEScheduler::get().schedule(&_accum_output_gate_bias, Window::DimY); |
| } |
| NEScheduler::get().schedule(&_activation_output, Window::DimY); |
| |
| NEScheduler::get().schedule(&_activation_output_state, Window::DimY); |
| NEScheduler::get().schedule(&_pixelwise_mul_output_state2, Window::DimY); |
| |
| if(_has_projection_weights) |
| { |
| _fully_connected_output_state.run(); |
| if(_perform_projection_clipping) |
| { |
| NEScheduler::get().schedule(&_projection_clip, Window::DimY); |
| } |
| } |
| |
| NEScheduler::get().schedule(&_copy_cell_state, Window::DimY); |
| NEScheduler::get().schedule(&_copy_output, Window::DimY); |
| |
| _concat_scratch_buffer.run(); |
| } |
| |
| void NELSTMLayer::prepare() |
| { |
| if(!_is_prepared) |
| { |
| _concat_weights_forget_gate.run(); |
| if(!_run_cifg_opt) |
| { |
| _concat_weights_input_gate.run(); |
| } |
| _concat_weights_output.run(); |
| _is_prepared = true; |
| } |
| } |
