From 1c1b3aa470f3854000be22edb61991f6210e5605 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Thu, 2 Apr 2020 17:35:42 +0100 Subject: COMPMID-3236: Implement CLQLSTMLayer COMPMID-3081: Extend CLQLSTMLayer with enhancements Change-Id: Idb7aaaacdba957e5ad61e94edeab2e898730a109 Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3057 Tested-by: Arm Jenkins Reviewed-by: Sang-Hoon Park Comments-Addressed: Arm Jenkins --- arm_compute/runtime/CL/functions/CLQLSTMLayer.h | 330 ++++++++++++++++++++++++ 1 file changed, 330 insertions(+) create mode 100644 arm_compute/runtime/CL/functions/CLQLSTMLayer.h (limited to 'arm_compute/runtime/CL/functions/CLQLSTMLayer.h') diff --git a/arm_compute/runtime/CL/functions/CLQLSTMLayer.h b/arm_compute/runtime/CL/functions/CLQLSTMLayer.h new file mode 100644 index 0000000000..ab34135ff5 --- /dev/null +++ b/arm_compute/runtime/CL/functions/CLQLSTMLayer.h @@ -0,0 +1,330 @@ +/* + * Copyright (c) 2020 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_CLQLSTMLAYER_H +#define ARM_COMPUTE_CLQLSTMLAYER_H + +#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h" +#include "arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h" +#include "arm_compute/core/CL/kernels/CLPixelWiseMultiplicationKernel.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/functions/CLActivationLayer.h" +#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h" +#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h" +#include "arm_compute/runtime/CL/functions/CLTranspose.h" + +#include "arm_compute/runtime/common/LSTMParams.h" + +namespace arm_compute +{ +// Forward declarations +class ICLTensor; + +/** Basic function to run @ref CLQLSTMLayer + * + * This function calls the following CL functions/kernels: + * + * -# @ref CLActivationLayer Activation functions (tanh and logistic) + * -# @ref CLSaturatedArithmeticOperationKernel Elementwise addition and subtraction + * -# @ref CLGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers + * -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint Convert 32-bit integers into QSYMM16 + * -# @ref CLGEMMLowpMatrixAReductionKernel For precomputing effective biases to use + * -# @ref CLPixelWiseMultiplicationKernel Elementwise multiplication + * -# @ref CLTranspose Transpose function for reshaping the weights + * */ +class CLQLSTMLayer : public IFunction +{ +public: + /** Default constructor */ + CLQLSTMLayer(std::shared_ptr memory_manager = nullptr); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLQLSTMLayer(const CLQLSTMLayer &) = delete; + /** Default move constructor */ + CLQLSTMLayer(CLQLSTMLayer &&) = default; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLQLSTMLayer &operator=(const CLQLSTMLayer &) = delete; + /** Default move assignment operator */ + CLQLSTMLayer &operator=(CLQLSTMLayer &&) = default; + /** Initialize function's tensors. + * + * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED. + * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32. + * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32. + * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. 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 [num_units, 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 [num_units, batch_size].Data types supported: Same as @p input. + * @param[in] lstm_params Weights tensors used in peephole, CIFG and layer normalization optimizations: + * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. + * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. + * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate. + * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate. + * hidden_state_zero The zero point of the hidden state. + * hidden_state_scale The scale of the hidden state. + * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16. + * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32. + * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32. + * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. + * If set to 0.0 then clipping is disabled. + * 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 0.0 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 *cell_state_in, const ICLTensor *output_state_in, + ICLTensor *cell_state_out, ICLTensor *output_state_out, + const LSTMParams &lstm_params); + + /** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer + * + * @param[in] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED. + * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. + * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. + * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32. + * @param[in] cell_state_in 2D tensor info with dimensions [num_units, 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 [num_units, 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. + * @param[in] lstm_params Weights tensors info used in peephole, CIFG and layer normalization optimizations: + * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. + * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. + * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate. + * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate. + * hidden_state_zero The zero point of the hidden state. + * hidden_state_scale The scale of the hidden state. + * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8. + * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16. + * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32. + * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8. + * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32. + * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16. + * cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. + * If set to 0.0 then clipping is disabled. + * 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 0.0 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 *cell_state_in, const ITensorInfo *output_state_in, + const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out, + const LSTMParams &lstm_params); + + // Inherited methods overridden: + void run() override; + void prepare() override; + +private: + /** Internal method to configure matrix multiplication plus output stage of each gate. + * + * @param[in] mm Matrix multiplication function to use. + * @param[in] outstage Output stage function to use. + * @param[in] gemmlowp_info GEMMLowp metadata to be used by the output stage. + * @param[in] mm_input Input tensor to matrix multiplication function. + * @param[in] mm_weights Weights tensor to matrix multiplication function. + * @param[in] bias Bias tensor to matrix multiplication function. + * @param[in] outstage_res Tensor to be used for storing the result of the output stage. + * @param[in] gemmlowp_scale Real multiplier to be used computing multiplier and shift for requantization. + * @param[in] mm_res_info Tensor info to be used to initialize matrix multiplication result tensor. + * @param[in] mm_res_info Tensor info to be used to initialize output stage result tensor. + * + */ + void configure_mm(CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info, + const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias, CLTensor *mm_res, + CLTensor *outstage_res, float gemmlowp_scale, + const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info); + + MemoryGroup _memory_group{}; + + // Functions used + CLTranspose _transpose_input_to_forget_weights{}; + CLTranspose _transpose_input_to_cell_weights{}; + CLTranspose _transpose_input_to_output_weights{}; + CLTranspose _transpose_input_to_input_weights{}; + CLTranspose _transpose_recurrent_to_forget_weights{}; + CLTranspose _transpose_recurrent_to_cell_weights{}; + CLTranspose _transpose_recurrent_to_output_weights{}; + CLTranspose _transpose_recurrent_to_input_weights{}; + CLTranspose _transpose_projection_weights{}; + CLGEMMLowpMatrixAReductionKernel _input_to_input_reduction{}; + CLGEMMLowpMatrixAReductionKernel _recurrent_to_input_reduction{}; + CLGEMMLowpMatrixAReductionKernel _input_to_forget_reduction{}; + CLGEMMLowpMatrixAReductionKernel _recurrent_to_forget_reduction{}; + CLGEMMLowpMatrixAReductionKernel _input_to_cell_reduction{}; + CLGEMMLowpMatrixAReductionKernel _recurrent_to_cell_reduction{}; + CLGEMMLowpMatrixAReductionKernel _input_to_output_reduction{}; + CLGEMMLowpMatrixAReductionKernel _recurrent_to_output_reduction{}; + CLGEMMLowpMatrixAReductionKernel _projection_reduction{}; + CLSaturatedArithmeticOperationKernel _projection_bias_add{}; + CLGEMMLowpMatrixMultiplyCore _mm_input_to_forget{}; + CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_forget{}; + CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_to_forget{}; + CLGEMMLowpOutputStage _input_to_forget_outstage{}; + CLGEMMLowpOutputStage _recurrent_to_forget_outstage{}; + CLGEMMLowpOutputStage _cell_to_forget_outstage{}; + CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_forget{}; + CLSaturatedArithmeticOperationKernel _accumulate_cell_forget{}; + CLActivationLayer _forget_gate_sigmoid{}; + CLGEMMLowpMatrixMultiplyCore _mm_input_to_cell{}; + CLGEMMLowpOutputStage _input_to_cell_outstage{}; + CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_cell{}; + CLGEMMLowpOutputStage _recurrent_to_cell_outstage{}; + CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_modulation{}; + CLActivationLayer _cell_gate_tanh{}; + CLSaturatedArithmeticOperationKernel _input_gate_sub{}; + CLGEMMLowpMatrixMultiplyCore _mm_input_to_input{}; + CLGEMMLowpOutputStage _input_to_input_outstage{}; + CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_input{}; + CLGEMMLowpOutputStage _recurrent_to_input_outstage{}; + CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_input{}; + CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_to_input{}; + CLGEMMLowpOutputStage _cell_to_input_outstage{}; + CLSaturatedArithmeticOperationKernel _accumulate_cell_input{}; + CLActivationLayer _input_gate_tanh{}; + CLPixelWiseMultiplicationKernel _pixelwise_mul_forget_cell{}; + CLPixelWiseMultiplicationKernel _pixelwise_mul_input_cell{}; + CLSaturatedArithmeticOperationKernel _add_forget_cell{}; + CLActivationLayer _cell_clip{}; + CLGEMMLowpMatrixMultiplyCore _mm_input_to_output{}; + CLGEMMLowpOutputStage _input_to_output_outstage{}; + CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_output{}; + CLGEMMLowpOutputStage _recurrent_to_output_outstage{}; + CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_output{}; + CLPixelWiseMultiplicationKernel _pixelwise_mul_cell_to_output{}; + CLSaturatedArithmeticOperationKernel _accumulate_cell_to_output{}; + CLActivationLayer _output_gate_sigmoid{}; + CLActivationLayer _hidden_tanh{}; + CLPixelWiseMultiplicationKernel _pixelwise_mul_hidden{}; + CLGEMMLowpOutputStage _hidden_outstage{}; + CLGEMMLowpMatrixMultiplyCore _mm_projection{}; + CLGEMMLowpOutputStage _projection_outstage{}; + CLSaturatedArithmeticOperationKernel _accumulate_projection{}; + CLActivationLayer _projection_clip{}; + + // Tensor pointers + const ICLTensor *_input_to_input_weights + { + nullptr + }; + const ICLTensor *_recurrent_to_input_weights{ nullptr }; + const ICLTensor *_projection_bias{ nullptr }; + const ICLTensor *_input_to_forget_weights{ nullptr }; + const ICLTensor *_input_to_cell_weights{ nullptr }; + const ICLTensor *_input_to_output_weights{ nullptr }; + const ICLTensor *_recurrent_to_forget_weights{ nullptr }; + const ICLTensor *_recurrent_to_cell_weights{ nullptr }; + const ICLTensor *_recurrent_to_output_weights{ nullptr }; + const ICLTensor *_projection_weights{ nullptr }; + + // Temporary tensors + CLTensor _input_to_forget_weights_transposed{ nullptr }; + CLTensor _input_to_cell_weights_transposed{ nullptr }; + CLTensor _input_to_output_weights_transposed{ nullptr }; + CLTensor _input_to_input_weights_transposed{ nullptr }; + CLTensor _recurrent_to_forget_weights_transposed{ nullptr }; + CLTensor _recurrent_to_cell_weights_transposed{ nullptr }; + CLTensor _recurrent_to_output_weights_transposed{ nullptr }; + CLTensor _recurrent_to_input_weights_transposed{ nullptr }; + CLTensor _projection_weights_transposed{ nullptr }; + CLTensor _input_to_input_eff_bias{ nullptr }; + CLTensor _recurrent_to_input_eff_bias{ nullptr }; + CLTensor _input_to_forget_eff_bias{ nullptr }; + CLTensor _recurrent_to_forget_eff_bias{ nullptr }; + CLTensor _input_to_cell_eff_bias{ nullptr }; + CLTensor _recurrent_to_cell_eff_bias{ nullptr }; + CLTensor _input_to_output_eff_bias{ nullptr }; + CLTensor _recurrent_to_output_eff_bias{ nullptr }; + CLTensor _projection_reduction_res{ nullptr }; + CLTensor _projection_eff_bias{ nullptr }; + CLTensor _mm_input_to_forget_res{ nullptr }; + CLTensor _mm_recurrent_to_forget_res{ nullptr }; + CLTensor _mul_cell_to_forget_res{ nullptr }; + CLTensor _input_to_forget_outstage_res{ nullptr }; + CLTensor _cell_to_forget_outstage_res{ nullptr }; + CLTensor _recurrent_to_forget_outstage_res{ nullptr }; + CLTensor _forget_gate{ nullptr }; + CLTensor _mm_input_to_cell_res{ nullptr }; + CLTensor _input_to_cell_outstage_res{ nullptr }; + CLTensor _mm_recurrent_to_cell_res{ nullptr }; + CLTensor _recurrent_to_cell_outstage_res{ nullptr }; + CLTensor _cell_gate{ nullptr }; + CLTensor _mul_input_cell_res{ nullptr }; + CLTensor _mm_input_to_input_res{ nullptr }; + CLTensor _input_to_input_outstage_res{ nullptr }; + CLTensor _mm_recurrent_to_input_res{ nullptr }; + CLTensor _mul_cell_to_input_res{ nullptr }; + CLTensor _cell_to_input_outstage_res{ nullptr }; + CLTensor _recurrent_to_input_outstage_res{ nullptr }; + CLTensor _input_gate{ nullptr }; + CLTensor _mm_input_to_output_res{ nullptr }; + CLTensor _input_to_output_outstage_res{ nullptr }; + CLTensor _mm_recurrent_to_output_res{ nullptr }; + CLTensor _mul_cell_to_output_res{ nullptr }; + CLTensor _recurrent_to_output_outstage_res{ nullptr }; + CLTensor _output_gate{ nullptr }; + CLTensor _hidden_mul_res{ nullptr }; + CLTensor _mm_projection_res{ nullptr }; + CLTensor _projection_outstage_res{ nullptr }; + CLTensor _ones{ nullptr }; + + bool _is_prepared{ false }; + bool _has_cifg{ false }; + bool _has_cell_clipping{ false }; + bool _has_projection{ false }; + bool _has_projection_clipping{ false }; + bool _has_peephole{ false }; +}; +} // namespace arm_compute +#endif /* ARM_COMPUTE_CLQLSTMLAYER_H */ -- cgit v1.2.1