/* * Copyright (c) 2018-2021 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #ifndef ARM_COMPUTE_CLLSTMLAYER_H #define ARM_COMPUTE_CLLSTMLAYER_H #include "arm_compute/runtime/IFunction.h" #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/functions/CLActivationLayer.h" #include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h" #include "arm_compute/runtime/CL/functions/CLCopy.h" #include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h" #include "arm_compute/runtime/CL/functions/CLFill.h" #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h" #include "arm_compute/runtime/CL/functions/CLGEMM.h" #include "arm_compute/runtime/CL/functions/CLMeanStdDevNormalizationLayer.h" #include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h" #include "arm_compute/runtime/IMemoryManager.h" #include "arm_compute/runtime/MemoryGroup.h" #include "arm_compute/runtime/common/LSTMParams.h" #include namespace arm_compute { class CLCompileContext; class ICLTensor; namespace opencl { namespace kernels { class ClTransposeKernel; } } /** This function performs a single time step in a Long Short-Term Memory (LSTM) layer. * */ class CLLSTMLayer : public IFunction { public: /** Default constructor */ CLLSTMLayer(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copied */ CLLSTMLayer(const CLLSTMLayer &) = delete; /** Prevent instances of this class from being copied */ CLLSTMLayer &operator=(const CLLSTMLayer &) = delete; /** Prevent instances of this class to be moved */ CLLSTMLayer(CLLSTMLayer &&) = delete; /** Prevent instances of this class to be moved */ CLLSTMLayer &operator=(CLLSTMLayer &&) = delete; /** Default destructor */ ~CLLSTMLayer(); /** Initialize function's tensors. * * Valid data layouts: * - All * * Valid data type configurations: * |src0 - src13 | dst0 - dst3 | * |:------------|:------------| * |F16 |F16 | * |F32 |F32 | * * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32. * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input. * @param[out] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input. * @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input. * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. * Data types supported: Same as @p input. * @param[in] lstm_params Weights tensors used in peephole optimization: * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input. * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input. * input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo. * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. * If set to 0.0f then clipping is disabled. * @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. * If set to 0.0f then clipping is disabled. */ void configure(const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, const ICLTensor *output_state_in, ICLTensor *cell_state_in, ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output, const LSTMParams &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f); /** Initialize function's tensors. * * @param[in] compile_context The compile context to be used. * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32. * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input. * @param[out] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input. * @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input. * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. * Data types supported: Same as @p input. * @param[in] lstm_params Weights tensors used in peephole optimization: * input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input. * recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input. * cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input * projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input. * projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input. * input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo. * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. * If set to 0.0f then clipping is disabled. * @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. * If set to 0.0f then clipping is disabled. */ void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, const ICLTensor *output_state_in, ICLTensor *cell_state_in, ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output, const LSTMParams &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f); /** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer * * @param[in] input Source tensor info. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32. * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input. * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] output_state_in 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input. * @param[in] scratch_buffer 2D tensor info with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. * Data type supported: Same as @p input. * @param[in] output_state_out 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[in] cell_state_out 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input. * @param[in] output Destination tensor info. Output is a 2D tensor with dimensions [output_size, batch_size]. Data types supported: Same as @p input. * @param[in] lstm_params Weights tensors info used in peephole optimization: * input_to_input_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input. * recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input. * cell_to_input_weights 1D weights tensor info with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input. * cell_to_forget_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * cell_to_output_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * input_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input * projection_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input. * projection_bias 1D weights tensor info with dimensions [output_size]. Data type supported: Same as @p input. * input_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * forget_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * cell_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * output_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input. * @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo. * @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. * If set to 0.0f then clipping is disabled. * @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. * If set to 0.0f then clipping is disabled. * * @return a status */ static Status 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 = 0.f, float projection_threshold = 0.f); // Inherited methods overridden: void run() override; void prepare() override; private: MemoryGroup _memory_group; CLFullyConnectedLayer _fully_connected_input_gate; CLArithmeticAddition _accum_input_gate1; CLArithmeticSubtraction _subtract_input_gate; CLPixelWiseMultiplication _pixelwise_mul_input_gate; CLActivationLayer _activation_input_gate; CLFullyConnectedLayer _fully_connected_forget_gate; CLArithmeticAddition _accum_forget_gate1; CLPixelWiseMultiplication _pixelwise_mul_forget_gate; CLActivationLayer _activation_forget_gate; CLFullyConnectedLayer _fully_connected_cell_state; CLGEMM _gemm_cell_state1; std::unique_ptr _transpose_cell_state; CLArithmeticAddition _accum_cell_state1; CLArithmeticAddition _accum_cell_state2; CLPixelWiseMultiplication _pixelwise_mul_cell_state1; CLActivationLayer _activation_cell_state; CLActivationLayer _cell_clip; CLPixelWiseMultiplication _pixelwise_mul_cell_state2; CLFullyConnectedLayer _fully_connected_output; CLPixelWiseMultiplication _pixelwise_mul_output_state1; CLArithmeticAddition _accum_output1; CLActivationLayer _activation_output; CLActivationLayer _activation_output_state; CLPixelWiseMultiplication _pixelwise_mul_output_state2; CLFullyConnectedLayer _fully_connected_output_state; CLActivationLayer _projection_clip; CLCopy _copy_cell_state; CLCopy _copy_output; CLConcatenateLayer _concat_scratch_buffer; CLConcatenateLayer _concat_inputs_forget_gate; CLConcatenateLayer _concat_weights_forget_gate; CLConcatenateLayer _concat_weights_input_gate; CLConcatenateLayer _concat_weights_output; CLFill _ones_fill; CLMeanStdDevNormalizationLayer _mean_std_norm_input_gate; CLPixelWiseMultiplication _pixelwise_mul_input_gate_coeff; CLArithmeticAddition _accum_input_gate_bias; CLMeanStdDevNormalizationLayer _mean_std_norm_forget_gate; CLPixelWiseMultiplication _pixelwise_mul_forget_gate_coeff; CLArithmeticAddition _accum_forget_gate_bias; CLMeanStdDevNormalizationLayer _mean_std_norm_cell_gate; CLPixelWiseMultiplication _pixelwise_mul_cell_gate_coeff; CLArithmeticAddition _accum_cell_gate_bias; CLMeanStdDevNormalizationLayer _mean_std_norm_output_gate; CLPixelWiseMultiplication _pixelwise_mul_output_gate_coeff; CLArithmeticAddition _accum_output_gate_bias; CLTensor _input_gate_out1; CLTensor _input_gate_out2; CLTensor _input_gate_out3; CLTensor _input_gate_out4; CLTensor _forget_gate_out1; CLTensor _forget_gate_out2; CLTensor _forget_gate_out3; CLTensor _forget_gate_out4; CLTensor _forget_gate_out5; CLTensor _forget_gate_out6; CLTensor _cell_state_out1; CLTensor _cell_state_out2; CLTensor _cell_state_out3; CLTensor _cell_state_out4; CLTensor _cell_state_out5; CLTensor _output1; CLTensor _output2; CLTensor _output3; CLTensor _output4; CLTensor _cell_state_activation; CLTensor _output_state1; CLTensor _ones; CLTensor _input_layer_norm_out1; CLTensor _input_layer_norm_out2; CLTensor _forget_layer_norm_out1; CLTensor _forget_layer_norm_out2; CLTensor _cell_layer_norm_out1; CLTensor _cell_layer_norm_out2; CLTensor _output_layer_norm_out1; CLTensor _output_layer_norm_out2; bool _run_peephole_opt; bool _run_cifg_opt; bool _perform_cell_clipping; bool _has_projection_weights; bool _perform_projection_clipping; bool _is_prepared; bool _is_layer_norm_lstm; const ICLTensor *_recurrent_to_cell_weights{ nullptr }; }; } // namespace arm_compute #endif /* ARM_COMPUTE_CLLSTMLAYER_H */