/* * Copyright (c) 2019-2021 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_NELSTMLAYERQUANTIZED_H #define ARM_COMPUTE_NELSTMLAYERQUANTIZED_H #include "arm_compute/core/Types.h" #include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" #include "arm_compute/runtime/NEON/functions/NEArithmeticAddition.h" #include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h" #include "arm_compute/runtime/NEON/functions/NEDequantizationLayer.h" #include "arm_compute/runtime/NEON/functions/NEElementwiseOperations.h" #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" #include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h" #include "arm_compute/runtime/NEON/functions/NEQuantizationLayer.h" #include "arm_compute/runtime/NEON/functions/NESlice.h" #include "arm_compute/runtime/NEON/functions/NETranspose.h" #include "arm_compute/runtime/common/LSTMParams.h" namespace arm_compute { // Forward declarations class ITensor; /** Basic function to run @ref NELSTMLayerQuantized * * This function calls the following functions/kernels: * * -# @ref NEGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers * -# @ref NEGEMMLowpOutputStage Convert 32-bit integers into QSYMM16 * -# @ref NETranspose Matrix transpose * -# @ref NEConcatenateLayer Tensor concatenation * -# @ref NEActivationLayer Activation functions (tanh and logistic) * -# @ref NEArithmeticAddition Elementwise addition * -# @ref NEPixelWiseMultiplication Elementwise multiplication * -# @ref NESlice Tensor slicing * -# @ref NEDequantizationLayer Dequantize into float * -# @ref NEQuantizationLayer Quantize from float * */ class NELSTMLayerQuantized : public IFunction { public: /** Default constructor */ NELSTMLayerQuantized(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copied (As this class contains pointers) */ NELSTMLayerQuantized(const NELSTMLayerQuantized &) = delete; /** Prevent instances of this class from being moved (As this class contains pointers) */ NELSTMLayerQuantized(NELSTMLayerQuantized &&) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ NELSTMLayerQuantized &operator=(const NELSTMLayerQuantized &) = delete; /** Prevent instances of this class from being moved (As this class contains pointers) */ NELSTMLayerQuantized &operator=(NELSTMLayerQuantized &&) = delete; /** Default destructor */ ~NELSTMLayerQuantized(); /** Initialize function's tensors. * * Valid data layouts: * - All * * Valid data type configurations: * |src0 - src8 |src9 - src12 |src13 |src14 |dst0 |dst1 | * |:-----------|:------------|:-------|:------|:------|:------| * |QASYMM8 |S32 |QSYMM16 |QASYMM8|QSYMM16|QASYMM8| * * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8. * @param[in] input_to_input_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_input_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] input_gate_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32. * @param[in] forget_gate_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32. * @param[in] cell_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32. * @param[in] output_gate_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32. * @param[in] cell_state_in 2D tensor with dimensions [output_size, batch_size]. Data type supported: QSYMM16. * @param[in] output_state_in 2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[out] cell_state_out Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. Data type supported: QSYMM16. * @param[out] output_state_out Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input. */ void configure(const ITensor *input, const ITensor *input_to_input_weights, const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights, const ITensor *recurrent_to_input_weights, const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights, const ITensor *input_gate_bias, const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias, ITensor *cell_state_in, const ITensor *output_state_in, ITensor *cell_state_out, ITensor *output_state_out); /** Static function to check if given info will lead to a valid configuration of @ref NELSTMLayer * * @param[in] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8. * @param[in] input_to_input_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input. * @param[in] input_gate_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32. * @param[in] forget_gate_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32. * @param[in] cell_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32. * @param[in] output_gate_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32. * @param[in] cell_state_in 2D tensor info with dimensions [output_size, batch_size]. Data type supported: QSYMM16. * @param[in] output_state_in 2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input. * @param[out] cell_state_out Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size]. Data type supported: QSYMM16. * @param[out] output_state_out Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input. * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights, const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights, const ITensorInfo *input_gate_bias, const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias, const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in, const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out); // Inherited methods overridden: void run() override; void prepare() override; private: MemoryGroup _memory_group; // Functions used NEGEMMLowpMatrixMultiplyCore _gemmlowp; NEGEMMLowpOutputStage _output_stage; NETranspose _transpose_weights; NEConcatenateLayer _concat_input_weights; NEConcatenateLayer _concat_recurrent_weights; NEConcatenateLayer _concat_weights; NEConcatenateLayer _concat_inputs; NEConcatenateLayer _concat_bias; NEActivationLayer _sigmoid_forget_gate; NEActivationLayer _sigmoid_input_gate; NEActivationLayer _sigmoid_output_gate; NEActivationLayer _tanh_modulation_gate; NEActivationLayer _tanh_output_state; NEArithmeticAddition _add1; NEArithmeticAddition _add2; NEPixelWiseMultiplication _mul1; NEPixelWiseMultiplication _mul2; NEPixelWiseMultiplication _mul3; NESlice _slice_input_tensor; NESlice _slice_forget_tensor; NESlice _slice_cell_tensor; NESlice _slice_output_tensor; NEDequantizationLayer _dequantize; NEQuantizationLayer _quantize; // Tensor pointers const ITensor *_input_to_input_weights; const ITensor *_input_to_forget_weights; const ITensor *_input_to_cell_weights; const ITensor *_input_to_output_weights; const ITensor *_recurrent_to_input_weights; const ITensor *_recurrent_to_forget_weights; const ITensor *_recurrent_to_cell_weights; const ITensor *_recurrent_to_output_weights; const ITensor *_input_gate_bias; const ITensor *_forget_gate_bias; const ITensor *_cell_bias; const ITensor *_output_gate_bias; // Temporary tensors Tensor _recurrent_weights; Tensor _input_weights; Tensor _weights; Tensor _input; Tensor _weights_transposed; Tensor _output_highp; Tensor _output_lowp; Tensor _bias; Tensor _forget_gate_input; Tensor _input_gate_input; Tensor _output_gate_input; Tensor _input_modulation_gate_input; Tensor _forget_gate_output; Tensor _input_gate_output; Tensor _output_gate_output; Tensor _input_modulation_gate_output; Tensor _cell_state1; Tensor _cell_state2; Tensor _output_state_tmp; Tensor _output_state_out_symm; Tensor _output_state_out_f32; bool _is_prepared; }; } // namespace arm_compute #endif /* ARM_COMPUTE_NELSTMLAYERQUANTIZED_H */