COMPMID-3236: Implement CLQLSTMLayer

COMPMID-3081: Extend CLQLSTMLayer with enhancements

Change-Id: Idb7aaaacdba957e5ad61e94edeab2e898730a109
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3057
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 330 insertions, 0 deletions

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<IMemoryManager> 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<ICLTensor> &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<ITensorInfo> &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 */