/* * 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 #include #include using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; NELSTMLayer::NELSTMLayer(std::shared_ptr memory_manager) : _memory_group(std::move(memory_manager)), _fully_connected_input_gate(), _gemm_input_gate(), _transpose_input_gate(), _accum_input_gate1(), _accum_input_gate2(), _subtract_input_gate(), _pixelwise_mul_input_gate(), _activation_input_gate(), _fully_connected_forget_gate(), _gemm_forget_gate(), _transpose_forget_gate(), _accum_forget_gate1(), _accum_forget_gate2(), _pixelwise_mul_forget_gate(), _activation_forget_gate(), _fully_connected_cell_state(), _gemm_cell_state1(), _gemm_cell_state2(), _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(), _gemm_output(), _pixelwise_mul_output_state1(), _transpose_output(), _accum_output1(), _accum_output2(), _activation_output(), _activation_output_state(), _pixelwise_mul_output_state2(), _fully_connected_output_state(), _gemm_output_state(), _accum_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(), _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(), _run_peephole_opt(false), _run_cifg_opt(false), _perform_cell_clipping(false), _has_projection_weights(false), _perform_projection_clipping(false), _is_prepared(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 &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); // Set lstm parameters LSTMParams 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 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); std::vector 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); _memory_group.manage(&_forget_gate_out5); _fully_connected_forget_gate.configure(&_forget_gate_out2, &_forget_gate_out6, 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_gate2.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(); } _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 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); _memory_group.manage(&_input_gate_out1); _memory_group.manage(&_input_gate_out4); _fully_connected_input_gate.configure(&_forget_gate_out2, &_input_gate_out2, 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_gate2.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(); } _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, 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); _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info); _memory_group.manage(&_cell_state_out5); _pixelwise_mul_cell_state1.configure(&_cell_state_out4, input_gate_out, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO); _cell_state_out4.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 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); _memory_group.manage(&_output1); _memory_group.manage(&_output4); _fully_connected_output.configure(&_forget_gate_out2, &_output2, 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_output2.configure(&_output4, &_output3, &_output1, ConvertPolicy::SATURATE); _output4.allocator()->allocate(); output_gate_out = &_output1; // Allocate intermediate buffers _output3.allocator()->allocate(); } else { _output1.allocator()->allocate(); } _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 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); 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 &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); // 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 inputs_vector; inputs_vector.emplace_back(input); inputs_vector.emplace_back(output_state_in); TensorInfo forget_gate_concat; ARM_COMPUTE_RETURN_ON_ERROR(NEWidthConcatenateLayer::validate(inputs_vector, &forget_gate_concat)); // Validate forget gate ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_forget_weights, 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)); } 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 lstm_weights; lstm_weights.emplace_back(lstm_params.input_to_input_weights()); lstm_weights.emplace_back(lstm_params.recurrent_to_input_weights()); TensorInfo lstm_gate_concat; ARM_COMPUTE_RETURN_ON_ERROR(NEWidthConcatenateLayer::validate(lstm_weights, &lstm_gate_concat)); ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, lstm_params.input_to_input_weights(), 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)); } 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, 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)); 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 in_out_weights; in_out_weights.emplace_back(input_to_output_weights); in_out_weights.emplace_back(recurrent_to_output_weights); TensorInfo in_out_gate_concat; ARM_COMPUTE_RETURN_ON_ERROR(NEWidthConcatenateLayer::validate(in_out_weights, &in_out_gate_concat)); ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, input_to_output_weights, 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)); } 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 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(NEWidthConcatenateLayer::validate(inputs_vector_info_raw, scratch_buffer)); 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_gate2.run(); } NEScheduler::get().schedule(&_activation_forget_gate, Window::DimY); if(_run_cifg_opt) { if(_ones.info()->data_type() == DataType::F16) { std::fill_n(reinterpret_cast(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 1); } else { std::fill_n(reinterpret_cast(_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_gate2.run(); } 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); 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_output2.run(); } 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; } }