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+/*
+ * Copyright (c) 2020-2021 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_NEQLSTMLAYERNORMALIZATIONKERNEL_H
+#define ARM_COMPUTE_NEQLSTMLAYERNORMALIZATIONKERNEL_H
+
+#include "src/core/NEON/INEKernel.h"
+
+#include <functional>
+
+namespace arm_compute
+{
+class ITensor;
+
+/** Kernel to perform layer normalization for QLSTM. */
+class NEQLSTMLayerNormalizationKernel : public INEKernel
+{
+public:
+ const char *name() const override
+ {
+ return "NEQLSTMLayerNormalizationKernel";
+ }
+ /** Default constructor */
+ NEQLSTMLayerNormalizationKernel() = default;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ NEQLSTMLayerNormalizationKernel(const NEQLSTMLayerNormalizationKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ NEQLSTMLayerNormalizationKernel &operator=(const NEQLSTMLayerNormalizationKernel &) = delete;
+ /** Default Move Constructor. */
+ NEQLSTMLayerNormalizationKernel(NEQLSTMLayerNormalizationKernel &&) = default;
+ /** Default move assignment operator */
+ NEQLSTMLayerNormalizationKernel &operator=(NEQLSTMLayerNormalizationKernel &&) = default;
+ /** Default destructor */
+ ~NEQLSTMLayerNormalizationKernel() = default;
+
+ /** Set the input and output tensors.
+ *
+ * @param[in] input Source tensor. Data types supported: QSYMM16.
+ * @param[out] output Destination tensor. Data types supported: Same as @p input.
+ * @param[in] weight Weight tensor. Data types supported: Same as @p input.
+ * @param[in] bias Bias tensor. Data types supported: S32
+ */
+ void configure(const ITensor *input, ITensor *output, const ITensor *weight, const ITensor *bias);
+ /** Static function to check if given info will lead to a valid configuration of @ref NEQLSTMLayerNormalizationKernel
+ *
+ * @param[in] input Source tensor info. Data types supported: QSYMM16.
+ * @param[in] output Destination tensor info. Data types supported: Same as @p input.
+ * @param[in] weight Weight tensor info. Data types supported: Same as @p input.
+ * @param[in] bias Bias tensor info. Data types supported: S32
+ *
+ * @return a status
+ */
+ static Status
+ validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *weight, const ITensorInfo *bias);
+ // Inherited methods overridden:
+ void run(const Window &window, const ThreadInfo &info) override;
+
+private:
+ // constants
+ static constexpr uint32_t max_input_dimension{2}; /**< The maximum input dimension supported */
+ static constexpr uint32_t max_weight_dimension{1}; /**< The maximum weight dimension supported */
+ static constexpr uint32_t max_bias_dimension{1}; /**< The maximum bias dimension supported */
+ static constexpr uint32_t vector_size_byte{16}; /**< Computation vector size in byte */
+
+ using ComputeFuncType = std::function<void(NEQLSTMLayerNormalizationKernel &)>;
+
+ ComputeFuncType _fn{}; /**< Function pointer to computation function */
+
+ const ITensor *_input{nullptr}; /**< Input tensor */
+ const ITensor *_weight{nullptr}; /**< Weight tensor */
+ const ITensor *_bias{nullptr}; /**< Bias tensor */
+ ITensor *_output{nullptr}; /**< Output tensor */
+
+ int32_t _output_multiplier{}; /**< Multiplier for output values */
+ int32_t _output_shift{}; /**< Shift value for output values */
+
+ int32_t _window_start_x{}; /**< The beginning of x-axis iteration */
+ int32_t _window_end_x{}; /**< The end of x-axis iteration */
+ int32_t _window_step_x{}; /**< The size of x-axis iteration's step */
+
+ Window _inout_window{}; /**< Window for input and output tensor */
+ Window _weight_window{}; /**< Window for weight and bias tensor */
+
+ /** Function to configure initial windows for destination of computation
+ *
+ * @param[in] Target destination tensor to use for output window
+ *
+ * @return configured window
+ */
+ Window configure_window(ITensor *target);
+ // Function to compute for data type QSYMM16
+ void compute_qsymm16();
+ /** Function to compute summation and summation of squared input of the given input pointer
+ *
+ * @param[in] Input_ptr pointer to input array
+ *
+ */
+ std::pair<int64_t, int64_t> sum_qsymm16(const int16_t *input_ptr);
+ /** Function to normalize values using computed mean and standard deviation
+ *
+ * @param[in] input_ptr Pointer to input array
+ * @param[in] output_ptr Pointer to output array
+ * @param[in] weight_ptr Pointer to weight array
+ * @param[in] bias_ptr Pointer to bias array
+ * @param[in] mean Mean value
+ * @param[in] inv_std_mul Quantized multiplier for standard deviation
+ * @param[in] inv_std_shift Shift for standard deviation
+ *
+ */
+ void normalize_qasymm16(const int16_t *input_ptr,
+ int16_t *output_ptr,
+ const int16_t *weight_ptr,
+ const int32_t *bias_ptr,
+ int32_t mean,
+ int32_t inv_std_mul,
+ int32_t inv_std_shift);
+ /** Function to compute output quantization information */
+ QuantizationInfo compute_output_qinfo();
+};
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_NEQLSTMLAYERNORMALIZATIONKERNEL_H */