src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2017-2019 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.
  */
 #include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h"

 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CPP/Validate.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/NEON/NEAsymm.h"
 #include "arm_compute/core/NEON/NEFixedPoint.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"

 #include <arm_neon.h>
 #include <cstddef>
 #include <cstdint>

 using namespace arm_compute;

 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
                           int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
     ARM_COMPUTE_UNUSED(result_offset_after_shift);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8,
                                                          DataType::F16,
                                                          DataType::S32, DataType::F32);

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(result_shift < 0, "Result shift must be a non negative integer");
     if(bias != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::F16, DataType::S32, DataType::F32);

         if(is_data_type_quantized_asymmetric(input->data_type()))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
         }

         ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)));
         ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
     }
     else
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_float(input->data_type()), "Calling output stage kernel with floating point arguments");
     }

     // Checks performed when output is configured
     if((output != nullptr) && (output->total_size() != 0))
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);

         if(is_data_type_quantized_asymmetric(output->data_type()))
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::S32 && output->data_type() != DataType::QASYMM8, "Wrong data type for bias");
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         }
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
 {
     ARM_COMPUTE_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);

     bool         window_changed                    = false;
     unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->data_type());

     // Update processed elements when input is S32 (comes from quantization input)
     if(input->data_type() == DataType::S32)
     {
         num_elems_processed_per_iteration = 16;
     }

     // Configure kernel window
     Window                 win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
     AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

     if(output != nullptr && (output->total_size() != 0))
     {
         AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

         if(bias == nullptr)
         {
             window_changed = update_window_and_padding(win, input_access, output_access);
         }
         else
         {
             AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
             window_changed = update_window_and_padding(win, input_access, output_access, bias_access);
         }

         output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
     }
     else
     {
         if(bias == nullptr)
         {
             window_changed = update_window_and_padding(win, input_access);
         }
         else
         {
             if(input->data_layout() == DataLayout::NCHW)
             {
                 AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
                 window_changed = update_window_and_padding(win, input_access, bias_access);
             }
             else
             {
                 AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration);
                 window_changed = update_window_and_padding(win, input_access, bias_access);
             }
         }

         input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }

 // Internal load
 inline float32x4_t internal_vld1q(const float *in)
 {
     return vld1q_f32(in);
 }

 // Internal store
 inline void internal_vst1q(float *p, const float32x4_t &v)
 {
     vst1q_f32(p, v);
 }

 // Internal vdup
 inline float32x4_t internal_vdupq_n(float v)
 {
     return vdupq_n_f32(v);
 }

 // Internal vadd
 inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y)
 {
     return vaddq_f32(x, y);
 }

 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
 inline float16x8_t internal_vld1q(const float16_t *in)
 {
     return vld1q_f16(in);
 }
 inline void internal_vst1q(float16_t *p, const float16x8_t &v)
 {
     vst1q_f16(p, v);
 }
 inline float16x8_t internal_vdupq_n(float16_t v)
 {
     return vdupq_n_f16(v);
 }
 inline float16x8_t internal_vqaddq(const float16x8_t &x, const float16x8_t &y)
 {
     return vaddq_f16(x, y);
 }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

 template <typename T1, typename T2, bool in_place, bool has_bias>
 void output_stage_nchw(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                        int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
     ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
     ARM_COMPUTE_UNUSED(result_shift);
     ARM_COMPUTE_UNUSED(result_offset_after_shift);

     Iterator in(input, window);

     if(in_place) // In place accumulate
     {
         execute_window_loop(window, [&](const Coordinates & id)
         {
             // Get bias and pointer to input
             const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());

             // Accumulate bias
             if(has_bias)
             {
                 const auto vb = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
                 internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
             }
             else
             {
                 internal_vst1q(in_ptr, internal_vld1q(in_ptr));
             }
         },
         in);
     }
     else // Out of place accumulate
     {
         Iterator out(output, window);
         execute_window_loop(window, [&](const Coordinates & id)
         {
             // Get bias and pointer to input
             const auto in_ptr  = reinterpret_cast<const T1 *>(in.ptr());
             const auto out_ptr = reinterpret_cast<T2 *>(out.ptr());

             // Accumulate bias
             if(has_bias)
             {
                 const auto vb = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
                 internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
             }
             else
             {
                 internal_vst1q(out_ptr, internal_vld1q(in_ptr));
             }
         },
         in, out);
     }
 }

 template <typename T1, typename T2, bool in_place, bool has_bias>
 void output_stage_nhwc(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                        int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
     ARM_COMPUTE_UNUSED(result_shift);
     ARM_COMPUTE_UNUSED(result_offset_after_shift);

     Window window_bias = window;
     window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
     window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
     window_bias.set(3, Window::Dimension(0, 0, 0));

     Iterator in(input, window);
     Iterator bi(bias, window_bias);

     if(in_place) // In place accumulate
     {
         execute_window_loop(window, [&](const Coordinates &)
         {
             // Get bias and pointer to input
             const auto in_ptr   = reinterpret_cast<T1 *>(in.ptr());
             const auto bias_ptr = reinterpret_cast<T2 *>(bi.ptr());

             // Accumulate bias
             if(has_bias)
             {
                 internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), internal_vld1q(bias_ptr)));
             }
             else
             {
                 internal_vst1q(in_ptr, internal_vld1q(in_ptr));
             }
         },
         in, bi);
     }
     else // Out of place accumulate
     {
         Iterator out(output, window);
         execute_window_loop(window, [&](const Coordinates &)
         {
             // Get bias and pointer to input
             const auto in_ptr   = reinterpret_cast<T1 *>(in.ptr());
             const auto out_ptr  = reinterpret_cast<T2 *>(out.ptr());
             const auto bias_ptr = reinterpret_cast<T2 *>(bi.ptr());

             // Accumulate bias
             if(has_bias)
             {
                 internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), internal_vld1q(bias_ptr)));
             }
             else
             {
                 internal_vst1q(out_ptr, internal_vld1q(in_ptr));
             }
         },
         in, bi, out);
     }
 }

 // QASYMM8 specializations
 template <>
 void output_stage_nchw<int32_t, uint8_t, false, true>(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                                                       int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
     uint8x16_t      min                           = vdupq_n_u8(0);
     uint8x16_t      max                           = vdupq_n_u8(255);

     Iterator in(input, window);
     Iterator out(output, window);

     execute_window_loop(window, [&](const Coordinates & id)
     {
         // Get bias and pointer to input
         const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr());
         int32x4x4_t v_in =
         {
             {
                 vld1q_s32(in_ptr),
                 vld1q_s32(in_ptr + 4),
                 vld1q_s32(in_ptr + 8),
                 vld1q_s32(in_ptr + 12)
             }
         };

         // Accumulate bias
         const auto vb = vdupq_n_s32(*reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))));
         v_in =
         {
             {
                 vaddq_s32(v_in.val[0], vb),
                 vaddq_s32(v_in.val[1], vb),
                 vaddq_s32(v_in.val[2], vb),
                 vaddq_s32(v_in.val[3], vb)
             }
         };

         const auto out_ptr = reinterpret_cast<uint8_t *>(out.ptr());
         vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
     },
     in, out);
 }
 template <>
 void output_stage_nchw<int32_t, uint8_t, false, false>(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                                                        int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_UNUSED(bias);

     const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
     uint8x16_t      min                           = vdupq_n_u8(0);
     uint8x16_t      max                           = vdupq_n_u8(255);

     Iterator in(input, window);
     Iterator out(output, window);
     execute_window_loop(window, [&](const Coordinates &)
     {
         // Get bias and pointer to input
         const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr());
         int32x4x4_t v_in =
         {
             {
                 vld1q_s32(in_ptr),
                 vld1q_s32(in_ptr + 4),
                 vld1q_s32(in_ptr + 8),
                 vld1q_s32(in_ptr + 12)
             }
         };

         const auto out_ptr = reinterpret_cast<uint8_t *>(out.ptr());
         vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
     },
     in, out);
 }
 template <>
 void output_stage_nhwc<int32_t, uint8_t, false, true>(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                                                       int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
     uint8x16_t      min                           = vdupq_n_u8(0);
     uint8x16_t      max                           = vdupq_n_u8(255);

     Window window_bias = window;
     window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
     window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
     window_bias.set(3, Window::Dimension(0, 0, 0));

     Iterator in(input, window);
     Iterator bi(bias, window_bias);

     Iterator out(output, window);
     execute_window_loop(window, [&](const Coordinates &)
     {
         // Get bias and pointer to input
         const auto in_ptr   = reinterpret_cast<int32_t *>(in.ptr());
         const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr());

         // Accumulate bias
         int32x4x4_t v_in =
         {
             {
                 vaddq_s32(vld1q_s32(in_ptr), vld1q_s32(bias_ptr)),
                 vaddq_s32(vld1q_s32(in_ptr + 4), vld1q_s32(bias_ptr + 4)),
                 vaddq_s32(vld1q_s32(in_ptr + 8), vld1q_s32(bias_ptr + 8)),
                 vaddq_s32(vld1q_s32(in_ptr + 12), vld1q_s32(bias_ptr + 12))
             }
         };

         const auto out_ptr = out.ptr();
         vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
     },
     in, bi, out);
 }
 template <>
 void output_stage_nhwc<int32_t, uint8_t, false, false>(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
                                                        int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_UNUSED(bias);

     const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
     uint8x16_t      min                           = vdupq_n_u8(0);
     uint8x16_t      max                           = vdupq_n_u8(255);

     Iterator in(input, window);
     Iterator out(output, window);
     execute_window_loop(window, [&](const Coordinates &)
     {
         // Get pointer to input
         const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());

         int32x4x4_t v_in =
         {
             {
                 vld1q_s32(in_ptr),
                 vld1q_s32(in_ptr + 4),
                 vld1q_s32(in_ptr + 8),
                 vld1q_s32(in_ptr + 12)
             }
         };

         const auto out_ptr = out.ptr();
         vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
     },
     in, out);
 }
 } // namespace

 NEDirectConvolutionLayerOutputStageKernel::NEDirectConvolutionLayerOutputStageKernel()
     : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0)
 {
 }

 void NEDirectConvolutionLayerOutputStageKernel::configure(ITensor *input, const ITensor *bias, ITensor *output,
                                                           int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input);

     // Auto-initialize output output if required
     if(output != nullptr)
     {
         // Work out expected output data type
         const DataType output_dt = (input->info()->data_type() == DataType::S32) ? DataType::QASYMM8 : input->info()->data_type();
         // Output tensor auto initialization if not yet initialized
         auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_dt));
     }

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info(),
                                                   result_fixedpoint_multiplier, result_shift, result_offset_after_shift));

     _func                         = nullptr;
     _bias                         = bias;
     _input                        = input;
     _output                       = output;
     _result_fixedpoint_multiplier = result_fixedpoint_multiplier;
     _result_shift                 = result_shift;
     _result_offset_after_shift    = result_offset_after_shift;

     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     INEKernel::configure(win_config.second);

     const bool has_bias = bias != nullptr;

     // Set appropriate function
     if(input->info()->data_layout() == DataLayout::NCHW)
     {
         switch(input->info()->data_type())
         {
             case DataType::S32:
             {
                 _func = (bias == nullptr) ? &output_stage_nchw<int32_t, uint8_t, false, false> : &output_stage_nchw<int32_t, uint8_t, false, true>;
                 break;
             }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
             case DataType::F16:
             {
                 if(has_bias)
                 {
                     _func = (output == nullptr) ? &output_stage_nchw<float16_t, float16_t, true, true> : &output_stage_nchw<float16_t, float16_t, false, true>;
                 }
                 else
                 {
                     _func = (output == nullptr) ? &output_stage_nchw<float16_t, float16_t, true, false> : &output_stage_nchw<float16_t, float16_t, false, false>;
                 }
                 break;
             }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
             case DataType::F32:
             {
                 if(has_bias)
                 {
                     _func = (output == nullptr) ? &output_stage_nchw<float, float, true, true> : &output_stage_nchw<float, float, false, true>;
                 }
                 else
                 {
                     _func = (output == nullptr) ? &output_stage_nchw<float, float, true, false> : &output_stage_nchw<float, float, false, false>;
                 }
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
             }
         }
     }
     else
     {
         switch(input->info()->data_type())
         {
             case DataType::S32:
             {
                 _func = (bias == nullptr) ? &output_stage_nhwc<int32_t, uint8_t, false, false> : &output_stage_nhwc<int32_t, uint8_t, false, true>;
                 break;
             }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
             case DataType::F16:
             {
                 if(has_bias)
                 {
                     _func = (output == nullptr) ? &output_stage_nhwc<float16_t, float16_t, true, true> : &output_stage_nhwc<float16_t, float16_t, false, true>;
                 }
                 else
                 {
                     _func = (output == nullptr) ? &output_stage_nhwc<float16_t, float16_t, true, false> : &output_stage_nhwc<float16_t, float16_t, false, false>;
                 }
                 break;
             }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
             case DataType::F32:
             {
                 if(has_bias)
                 {
                     _func = (output == nullptr) ? &output_stage_nhwc<float, float, true, true> : &output_stage_nhwc<float, float, false, true>;
                 }
                 else
                 {
                     _func = (output == nullptr) ? &output_stage_nhwc<float, float, true, false> : &output_stage_nhwc<float, float, false, false>;
                 }
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
             }
         }
     }
 }

 Status NEDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
                                                            int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), bias == nullptr ? nullptr : bias->clone().get(), output == nullptr ? nullptr : output->clone().get()).first);

     return Status{};
 }

 void NEDirectConvolutionLayerOutputStageKernel::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
     ARM_COMPUTE_ERROR_ON(_func == nullptr);

     (*_func)(_input, _bias, window, _output, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift);
 }
	/*
	* Copyright (c) 2017-2019 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.
	*/
	#include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h"

	#include "arm_compute/core/AccessWindowStatic.h"
	#include "arm_compute/core/CPP/Validate.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/ITensor.h"
	#include "arm_compute/core/NEON/NEAsymm.h"
	#include "arm_compute/core/NEON/NEFixedPoint.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Window.h"

	#include <arm_neon.h>
	#include <cstddef>
	#include <cstdint>

	using namespace arm_compute;

	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
	ARM_COMPUTE_UNUSED(result_offset_after_shift);
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8,
	DataType::F16,
	DataType::S32, DataType::F32);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(result_shift < 0, "Result shift must be a non negative integer");
	if(bias != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::F16, DataType::S32, DataType::F32);

	if(is_data_type_quantized_asymmetric(input->data_type()))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
	}

	ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)));
	ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_float(input->data_type()), "Calling output stage kernel with floating point arguments");
	}

	// Checks performed when output is configured
	if((output != nullptr) && (output->total_size() != 0))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);

	if(is_data_type_quantized_asymmetric(output->data_type()))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::S32 && output->data_type() != DataType::QASYMM8, "Wrong data type for bias");
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo input, ITensorInfo bias, ITensorInfo *output)
	{
	ARM_COMPUTE_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);

	bool window_changed = false;
	unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->data_type());

	// Update processed elements when input is S32 (comes from quantization input)
	if(input->data_type() == DataType::S32)
	{
	num_elems_processed_per_iteration = 16;
	}

	// Configure kernel window
	Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
	AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);

	if(output != nullptr && (output->total_size() != 0))
	{
	AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

	if(bias == nullptr)
	{
	window_changed = update_window_and_padding(win, input_access, output_access);
	}
	else
	{
	AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
	window_changed = update_window_and_padding(win, input_access, output_access, bias_access);
	}

	output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
	}
	else
	{
	if(bias == nullptr)
	{
	window_changed = update_window_and_padding(win, input_access);
	}
	else
	{
	if(input->data_layout() == DataLayout::NCHW)
	{
	AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
	window_changed = update_window_and_padding(win, input_access, bias_access);
	}
	else
	{
	AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration);
	window_changed = update_window_and_padding(win, input_access, bias_access);
	}
	}

	input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}

	// Internal load
	inline float32x4_t internal_vld1q(const float *in)
	{
	return vld1q_f32(in);
	}

	// Internal store
	inline void internal_vst1q(float *p, const float32x4_t &v)
	{
	vst1q_f32(p, v);
	}

	// Internal vdup
	inline float32x4_t internal_vdupq_n(float v)
	{
	return vdupq_n_f32(v);
	}

	// Internal vadd
	inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y)
	{
	return vaddq_f32(x, y);
	}

	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	inline float16x8_t internal_vld1q(const float16_t *in)
	{
	return vld1q_f16(in);
	}
	inline void internal_vst1q(float16_t *p, const float16x8_t &v)
	{
	vst1q_f16(p, v);
	}
	inline float16x8_t internal_vdupq_n(float16_t v)
	{
	return vdupq_n_f16(v);
	}
	inline float16x8_t internal_vqaddq(const float16x8_t &x, const float16x8_t &y)
	{
	return vaddq_f16(x, y);
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

	template <typename T1, typename T2, bool in_place, bool has_bias>
	void output_stage_nchw(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
	ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
	ARM_COMPUTE_UNUSED(result_shift);
	ARM_COMPUTE_UNUSED(result_offset_after_shift);

	Iterator in(input, window);

	if(in_place) // In place accumulate
	{
	execute_window_loop(window, [&](const Coordinates & id)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());

	// Accumulate bias
	if(has_bias)
	{
	const auto vb = internal_vdupq_n(static_cast<T1>(reinterpret_cast<const T2 >(bias->ptr_to_element(Coordinates(id.z())))));
	internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
	}
	else
	{
	internal_vst1q(in_ptr, internal_vld1q(in_ptr));
	}
	},
	in);
	}
	else // Out of place accumulate
	{
	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates & id)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<const T1 *>(in.ptr());
	const auto out_ptr = reinterpret_cast<T2 *>(out.ptr());

	// Accumulate bias
	if(has_bias)
	{
	const auto vb = internal_vdupq_n(static_cast<T1>(reinterpret_cast<const T2 >(bias->ptr_to_element(Coordinates(id.z())))));
	internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
	}
	else
	{
	internal_vst1q(out_ptr, internal_vld1q(in_ptr));
	}
	},
	in, out);
	}
	}

	template <typename T1, typename T2, bool in_place, bool has_bias>
	void output_stage_nhwc(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_UNUSED(result_fixedpoint_multiplier);
	ARM_COMPUTE_UNUSED(result_shift);
	ARM_COMPUTE_UNUSED(result_offset_after_shift);

	Window window_bias = window;
	window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
	window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
	window_bias.set(3, Window::Dimension(0, 0, 0));

	Iterator in(input, window);
	Iterator bi(bias, window_bias);

	if(in_place) // In place accumulate
	{
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());
	const auto bias_ptr = reinterpret_cast<T2 *>(bi.ptr());

	// Accumulate bias
	if(has_bias)
	{
	internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), internal_vld1q(bias_ptr)));
	}
	else
	{
	internal_vst1q(in_ptr, internal_vld1q(in_ptr));
	}
	},
	in, bi);
	}
	else // Out of place accumulate
	{
	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());
	const auto out_ptr = reinterpret_cast<T2 *>(out.ptr());
	const auto bias_ptr = reinterpret_cast<T2 *>(bi.ptr());

	// Accumulate bias
	if(has_bias)
	{
	internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), internal_vld1q(bias_ptr)));
	}
	else
	{
	internal_vst1q(out_ptr, internal_vld1q(in_ptr));
	}
	},
	in, bi, out);
	}
	}

	// QASYMM8 specializations
	template <>
	void output_stage_nchw<int32_t, uint8_t, false, true>(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
	uint8x16_t min = vdupq_n_u8(0);
	uint8x16_t max = vdupq_n_u8(255);

	Iterator in(input, window);
	Iterator out(output, window);

	execute_window_loop(window, [&](const Coordinates & id)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());
	int32x4x4_t v_in =
	{
	{
	vld1q_s32(in_ptr),
	vld1q_s32(in_ptr + 4),
	vld1q_s32(in_ptr + 8),
	vld1q_s32(in_ptr + 12)
	}
	};

	// Accumulate bias
	const auto vb = vdupq_n_s32(reinterpret_cast<const int32_t >(bias->ptr_to_element(Coordinates(id.z()))));
	v_in =
	{
	{
	vaddq_s32(v_in.val[0], vb),
	vaddq_s32(v_in.val[1], vb),
	vaddq_s32(v_in.val[2], vb),
	vaddq_s32(v_in.val[3], vb)
	}
	};

	const auto out_ptr = reinterpret_cast<uint8_t *>(out.ptr());
	vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
	},
	in, out);
	}
	template <>
	void output_stage_nchw<int32_t, uint8_t, false, false>(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_UNUSED(bias);

	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
	uint8x16_t min = vdupq_n_u8(0);
	uint8x16_t max = vdupq_n_u8(255);

	Iterator in(input, window);
	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());
	int32x4x4_t v_in =
	{
	{
	vld1q_s32(in_ptr),
	vld1q_s32(in_ptr + 4),
	vld1q_s32(in_ptr + 8),
	vld1q_s32(in_ptr + 12)
	}
	};

	const auto out_ptr = reinterpret_cast<uint8_t *>(out.ptr());
	vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
	},
	in, out);
	}
	template <>
	void output_stage_nhwc<int32_t, uint8_t, false, true>(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
	uint8x16_t min = vdupq_n_u8(0);
	uint8x16_t max = vdupq_n_u8(255);

	Window window_bias = window;
	window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
	window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
	window_bias.set(3, Window::Dimension(0, 0, 0));

	Iterator in(input, window);
	Iterator bi(bias, window_bias);

	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get bias and pointer to input
	const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());
	const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr());

	// Accumulate bias
	int32x4x4_t v_in =
	{
	{
	vaddq_s32(vld1q_s32(in_ptr), vld1q_s32(bias_ptr)),
	vaddq_s32(vld1q_s32(in_ptr + 4), vld1q_s32(bias_ptr + 4)),
	vaddq_s32(vld1q_s32(in_ptr + 8), vld1q_s32(bias_ptr + 8)),
	vaddq_s32(vld1q_s32(in_ptr + 12), vld1q_s32(bias_ptr + 12))
	}
	};

	const auto out_ptr = out.ptr();
	vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
	},
	in, bi, out);
	}
	template <>
	void output_stage_nhwc<int32_t, uint8_t, false, false>(ITensor input, const ITensor bias, const Window &window, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_UNUSED(bias);

	const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(result_offset_after_shift);
	uint8x16_t min = vdupq_n_u8(0);
	uint8x16_t max = vdupq_n_u8(255);

	Iterator in(input, window);
	Iterator out(output, window);
	execute_window_loop(window, [&](const Coordinates &)
	{
	// Get pointer to input
	const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr());

	int32x4x4_t v_in =
	{
	{
	vld1q_s32(in_ptr),
	vld1q_s32(in_ptr + 4),
	vld1q_s32(in_ptr + 8),
	vld1q_s32(in_ptr + 12)
	}
	};

	const auto out_ptr = out.ptr();
	vst1q_u8(out_ptr, finalize_quantization<false>(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max));
	},
	in, out);
	}
	} // namespace

	NEDirectConvolutionLayerOutputStageKernel::NEDirectConvolutionLayerOutputStageKernel()
	: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0)
	{
	}

	void NEDirectConvolutionLayerOutputStageKernel::configure(ITensor input, const ITensor bias, ITensor *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input);

	// Auto-initialize output output if required
	if(output != nullptr)
	{
	// Work out expected output data type
	const DataType output_dt = (input->info()->data_type() == DataType::S32) ? DataType::QASYMM8 : input->info()->data_type();
	// Output tensor auto initialization if not yet initialized
	auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_dt));
	}

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info(),
	result_fixedpoint_multiplier, result_shift, result_offset_after_shift));

	_func = nullptr;
	_bias = bias;
	_input = input;
	_output = output;
	_result_fixedpoint_multiplier = result_fixedpoint_multiplier;
	_result_shift = result_shift;
	_result_offset_after_shift = result_offset_after_shift;

	// Configure kernel window
	auto win_config = validate_and_configure_window(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	INEKernel::configure(win_config.second);

	const bool has_bias = bias != nullptr;

	// Set appropriate function
	if(input->info()->data_layout() == DataLayout::NCHW)
	{
	switch(input->info()->data_type())
	{
	case DataType::S32:
	{
	_func = (bias == nullptr) ? &output_stage_nchw<int32_t, uint8_t, false, false> : &output_stage_nchw<int32_t, uint8_t, false, true>;
	break;
	}
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	{
	if(has_bias)
	{
	_func = (output == nullptr) ? &output_stage_nchw<float16_t, float16_t, true, true> : &output_stage_nchw<float16_t, float16_t, false, true>;
	}
	else
	{
	_func = (output == nullptr) ? &output_stage_nchw<float16_t, float16_t, true, false> : &output_stage_nchw<float16_t, float16_t, false, false>;
	}
	break;
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
	case DataType::F32:
	{
	if(has_bias)
	{
	_func = (output == nullptr) ? &output_stage_nchw<float, float, true, true> : &output_stage_nchw<float, float, false, true>;
	}
	else
	{
	_func = (output == nullptr) ? &output_stage_nchw<float, float, true, false> : &output_stage_nchw<float, float, false, false>;
	}
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
	}
	}
	}
	else
	{
	switch(input->info()->data_type())
	{
	case DataType::S32:
	{
	_func = (bias == nullptr) ? &output_stage_nhwc<int32_t, uint8_t, false, false> : &output_stage_nhwc<int32_t, uint8_t, false, true>;
	break;
	}
	#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
	case DataType::F16:
	{
	if(has_bias)
	{
	_func = (output == nullptr) ? &output_stage_nhwc<float16_t, float16_t, true, true> : &output_stage_nhwc<float16_t, float16_t, false, true>;
	}
	else
	{
	_func = (output == nullptr) ? &output_stage_nhwc<float16_t, float16_t, true, false> : &output_stage_nhwc<float16_t, float16_t, false, false>;
	}
	break;
	}
	#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
	case DataType::F32:
	{
	if(has_bias)
	{
	_func = (output == nullptr) ? &output_stage_nhwc<float, float, true, true> : &output_stage_nhwc<float, float, false, true>;
	}
	else
	{
	_func = (output == nullptr) ? &output_stage_nhwc<float, float, true, false> : &output_stage_nhwc<float, float, false, false>;
	}
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
	}
	}
	}
	}

	Status NEDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo input, const ITensorInfo bias, const ITensorInfo *output,
	int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), bias == nullptr ? nullptr : bias->clone().get(), output == nullptr ? nullptr : output->clone().get()).first);

	return Status{};
	}

	void NEDirectConvolutionLayerOutputStageKernel::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
	ARM_COMPUTE_ERROR_ON(_func == nullptr);

	(*_func)(_input, _bias, window, _output, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift);
	}