| /* | |
| * Copyright (c) 2018-2022 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/Utils.h" | |
| #include "arm_compute/core/Validate.h" | |
| #include "arm_compute/core/utils/misc/InfoHelpers.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 "src/common/utils/Log.h" | |
| namespace arm_compute | |
| { | |
| using namespace arm_compute::misc::shape_calculator; | |
| using namespace arm_compute::utils::info_helpers; | |
| NELSTMLayer::~NELSTMLayer() = default; | |
| 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); | |
| ARM_COMPUTE_LOG_PARAMS(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, | |
| lstm_params, activation_info, cell_threshold, projection_threshold); | |
| _is_layer_norm_lstm = lstm_params.use_layer_norm(); | |
| // Set lstm parameters | |
| LSTMParams<ITensorInfo> lstm_params_info{}; | |
| build_lstm_params_tensor_info(lstm_params, &lstm_params_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<const 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_cells); | |
| 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_cells); | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_cells); | |
| ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_cells); | |
| } | |
| // 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(NEPixelWiseMultiplication::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(NEPixelWiseMultiplication::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(NEActivationLayer::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(NEPixelWiseMultiplication::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(NEPixelWiseMultiplication::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(NEActivationLayer::validate(&input_gate, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticSubtraction::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(NEPixelWiseMultiplication::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(NEActivationLayer::validate(&cell_state_tmp, nullptr, activation_info)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&cell_state_tmp, &input_gate, &cell_state_tmp, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::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(NEActivationLayer::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(NEPixelWiseMultiplication::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(NEPixelWiseMultiplication::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(NEActivationLayer::validate(&output_gate_tmp, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); | |
| // Validate output state | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&cell_state_tmp, &cell_state_tmp, activation_info)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::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(NEActivationLayer::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(NECopy::validate(&cell_state_tmp, cell_state_out)); | |
| ARM_COMPUTE_RETURN_ON_ERROR(NECopy::validate(output_state_out, output)); | |
| // Validate scratch concatenation | |
| std::vector<const 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) | |
| { | |
| _pixelwise_mul_forget_gate.run(); | |
| _accum_forget_gate1.run(); | |
| } | |
| if(_is_layer_norm_lstm) | |
| { | |
| _mean_std_norm_forget_gate.run(); | |
| _pixelwise_mul_forget_gate_coeff.run(); | |
| _accum_forget_gate_bias.run(); | |
| } | |
| _activation_forget_gate.run(); | |
| 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); | |
| } | |
| _subtract_input_gate.run(); | |
| } | |
| else | |
| { | |
| _fully_connected_input_gate.run(); | |
| if(_run_peephole_opt) | |
| { | |
| _pixelwise_mul_input_gate.run(); | |
| _accum_input_gate1.run(); | |
| } | |
| if(_is_layer_norm_lstm) | |
| { | |
| _mean_std_norm_input_gate.run(); | |
| _pixelwise_mul_input_gate_coeff.run(); | |
| _accum_input_gate_bias.run(); | |
| } | |
| _activation_input_gate.run(); | |
| } | |
| _fully_connected_cell_state.run(); | |
| _transpose_cell_state.run(); | |
| _gemm_cell_state1.run(); | |
| _accum_cell_state1.run(); | |
| if(_is_layer_norm_lstm) | |
| { | |
| _mean_std_norm_cell_gate.run(); | |
| _pixelwise_mul_cell_gate_coeff.run(); | |
| _accum_cell_gate_bias.run(); | |
| } | |
| _activation_cell_state.run(); | |
| _pixelwise_mul_cell_state1.run(); | |
| _pixelwise_mul_cell_state2.run(); | |
| _accum_cell_state2.run(); | |
| if(_perform_cell_clipping) | |
| { | |
| _cell_clip.run(); | |
| } | |
| _fully_connected_output.run(); | |
| if(_run_peephole_opt) | |
| { | |
| _pixelwise_mul_output_state1.run(); | |
| _accum_output1.run(); | |
| } | |
| if(_is_layer_norm_lstm) | |
| { | |
| _mean_std_norm_output_gate.run(); | |
| _pixelwise_mul_output_gate_coeff.run(); | |
| _accum_output_gate_bias.run(); | |
| } | |
| _activation_output.run(); | |
| _activation_output_state.run(); | |
| _pixelwise_mul_output_state2.run(); | |
| if(_has_projection_weights) | |
| { | |
| _fully_connected_output_state.run(); | |
| if(_perform_projection_clipping) | |
| { | |
| _projection_clip.run(); | |
| } | |
| } | |
| _copy_cell_state.run(); | |
| _copy_output.run(); | |
| _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; | |
| } | |
| } | |
| } // namespace arm_compute |