opencv/modules/dnn/src/opencl/conv_layer_spatial.cl

/*M///////////////////////////////////////////////////////////////////////////////////////
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//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
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//     this list of conditions and the following disclaimer in the documentation
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//     derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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#if APPLY_BIAS
#define BIAS_KERNEL_ARG __global Dtype * biases_base,
#else
#define BIAS_KERNEL_ARG
#endif

#if defined(FUSED_CONV_RELU)
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (Dtype)(negative_slope)))
#define NEGATIVE_SLOPE_ARG Dtype negative_slope,
#elif defined(FUSED_CONV_PRELU)
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (Dtype)(negative_slope[c])))
#define NEGATIVE_SLOPE_ARG __global const Dtype *negative_slope,
#elif defined(FUSED_CONV_POWER)
#define ACTIVATION_RELU_FUNCTION(x, c) pow(x, power)
#define NEGATIVE_SLOPE_ARG Dtype power,
#else
#define ACTIVATION_RELU_FUNCTION(x, c) (x)
#define NEGATIVE_SLOPE_ARG
#endif

#ifdef FUSED_CONV_ELTWISE
#define ACTIVATION_FUNCTION(_dst_, _offset_, _data_, _channel_) do { (_dst_)[(_offset_)] = ACTIVATION_RELU_FUNCTION(eltwise_data[(_offset_)] + (_data_), _channel_);} while(0)
#define ELTWISE_DATA_ARG __global Dtype* eltwise_data,
#else
#define ACTIVATION_FUNCTION(_dst_, _offset_, _data_, _channel_) do { (_dst_)[(_offset_)] = ACTIVATION_RELU_FUNCTION(_data_, _channel_);} while(0)
#define ELTWISE_DATA_ARG
#endif


#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
#define LOOP0(VAR, STMT)
#define LOOP1(VAR, STMT) (STMT); (VAR)++;
#define LOOP2(VAR, STMT) LOOP1(VAR, STMT); (STMT); (VAR)++;
#define LOOP3(VAR, STMT) LOOP2(VAR, STMT); (STMT); (VAR)++;
#define LOOP4(VAR, STMT) LOOP3(VAR, STMT); (STMT); (VAR)++;
#define LOOP5(VAR, STMT) LOOP4(VAR, STMT); (STMT); (VAR)++;
#define LOOP6(VAR, STMT) LOOP5(VAR, STMT); (STMT); (VAR)++;
#define LOOP7(VAR, STMT) LOOP6(VAR, STMT); (STMT); (VAR)++;
#define LOOP8(VAR, STMT) LOOP7(VAR, STMT); (STMT); (VAR)++;
#define LOOP9(VAR, STMT) LOOP8(VAR, STMT); (STMT); (VAR)++;
#define LOOP10(VAR, STMT) LOOP9(VAR, STMT); (STMT); (VAR)++;
#define LOOP11(VAR, STMT) LOOP10(VAR, STMT); (STMT); (VAR)++;
#define LOOP12(VAR, STMT) LOOP11(VAR, STMT); (STMT); (VAR)++;
#define LOOP13(VAR, STMT) LOOP12(VAR, STMT); (STMT); (VAR)++;
#define LOOP14(VAR, STMT) LOOP13(VAR, STMT); (STMT); (VAR)++;
#define LOOP15(VAR, STMT) LOOP14(VAR, STMT); (STMT); (VAR)++;
#define LOOP16(VAR, STMT) LOOP15(VAR, STMT); (STMT); (VAR)++;
#define LOOP(N, VAR, STMT) CAT(LOOP, N)((VAR), (STMT))

#if defined(convolve_simd) || defined(Conv_Interleaved)
#define INT_TYPE uint
#define INT_TYPE2 uint2
#define INT_TYPE4 uint4
#define INT_TYPE8 uint8
#define SUB_GROUP_BLOCK_READ2 intel_sub_group_block_read2
#define SUB_GROUP_BLOCK_READ4 intel_sub_group_block_read4
#define SUB_GROUP_BLOCK_READ8 intel_sub_group_block_read8
#define SUB_GROUP_BLOCK_READ intel_sub_group_block_read
#endif

#ifdef KERNEL_BASIC

__kernel void ConvolveBasic(
    ELTWISE_DATA_ARG
    NEGATIVE_SLOPE_ARG
    __global Dtype* image_data,
    int image_offset,
    __global Dtype* kernel_data,
    int kernel_offset,
    __global Dtype* bias,
    const int bias_offset,
    __global Dtype* convolved_image,
    const int convolved_image_offset,
    const ushort input_width,
    const ushort input_height,
    const ushort output_width,
    const ushort output_height,
    const ushort pad_w,
    const ushort pad_h
)
{
    const int outputX = get_global_id(0);
    const int outputY = get_global_id(1);
    const int kernelNum = get_global_id(2) * ZPAR;
    if (outputX < output_width && outputY < output_height)
    {
        Dtype sum[ZPAR];
        for (int kern = 0; kern < ZPAR; kern++)
        {
            sum[kern] = 0.0f;
        }
        const int org_y = outputY * STRIDE_Y - pad_h;
        const int org_x = outputX * STRIDE_X - pad_w;
        const int currentKernelOffset = kernel_offset + kernelNum*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS;
#if APPLY_BIAS
        const int biasIndex = bias_offset + kernelNum;
#endif
        const int local_image_offset = org_y * input_width + org_x;
        const int imageSize = input_width * input_height;
        __global Dtype* image_dataPtr = (image_data + (image_offset + local_image_offset));
        __global Dtype* kernel_dataPtr = (kernel_data + (currentKernelOffset));
        for (int c = 0; c < CHANNELS; c++)
        {
            for (int y = 0; y < KERNEL_HEIGHT; y++)
            {
                for (int x = 0; x < KERNEL_WIDTH; x++)
                {
                    int y_ = org_y + y * DILATION_Y;
                    int x_ = org_x + x * DILATION_X;
                    if (!(y_ >= 0 && y_ < input_height && x_ >= 0 && x_ < input_width))
                    {
                        continue;
                    }
                    for (int kern = 0; kern < ZPAR; kern++)
                    {
                        sum[kern] += image_dataPtr[x * DILATION_X] * kernel_dataPtr[kern*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS + x];
                    }
                }
                image_dataPtr += input_width * DILATION_Y;
                kernel_dataPtr += KERNEL_WIDTH;
            }
            image_dataPtr += imageSize - input_width*KERNEL_HEIGHT*DILATION_Y;
        }

        for (int kern = 0; kern < ZPAR; kern++)
        {
            if (kernelNum + kern < OUTPUT_Z)
            {
                int offset = convolved_image_offset + (kernelNum+kern)*output_height*output_width + outputY*output_width + outputX;
#if APPLY_BIAS
                ACTIVATION_FUNCTION(convolved_image, offset, sum[kern] + bias[biasIndex + kern], biasIndex + kern);
#else
                ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], biasIndex + kern);
#endif
            }
        }
    }
}

#elif defined KERNEL_IDLF

#define VLOAD4(_v, _p) do { _v = vload4(0, _p); } while(0)

// Each work-item computes a OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT region of one output map.
// Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the imput image.
// NDRange:  (output_width+pad)/ OUT_BLOCK_WIDTH, (output_height+pad)/OUT_BLOCK_HEIGHT, NUM_FILTERS/OUT_BLOCK_DEPTH

// NOTE: for beignet this reqd_work_group_size does not guarantee that SIMD16 mode will be used, the compiler could choose to use two SIMD8 threads, and if that happens the code will break.
#ifndef __BEIGNET__
__attribute__((reqd_work_group_size(1, 1, SIMD_SIZE)))
__attribute__((intel_reqd_sub_group_size(SIMD_SIZE)))
#endif
__kernel void
convolve_simd(
    ELTWISE_DATA_ARG
    NEGATIVE_SLOPE_ARG
    __global Dtype* inputs_base,
    filter_qualifier Dtype* weights_base,
    BIAS_KERNEL_ARG
    __global Dtype* outputs_base,
    const ushort input_width,
    const ushort input_height,
    const ushort output_width,
    const ushort output_height)
{
  __global Dtype* outputs = outputs_base;
  __global Dtype* inputs = inputs_base;
  filter_qualifier Dtype* weights = weights_base;
  unsigned int oc = get_global_id(0) * OUT_BLOCK_WIDTH;  // oc = Output Column
  unsigned int or = get_global_id(1) * OUT_BLOCK_HEIGHT;// or = Output Row
  unsigned int fm = get_global_id(2);// fm = Feature Map = od = Output Depth
  unsigned int fmg = get_group_id(2);
  unsigned int lid = get_local_id(2);

  Dtype out[OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT];

  int in_addr;

  // find weights adress of given neuron (lid is index)
  unsigned int weight_addr = (fmg % (ALIGNED_NUM_FILTERS/SIMD_SIZE)) * INPUT_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT * SIMD_SIZE + lid;

  for(int i=0;i<OUT_BLOCK_SIZE;i++) {
    out[i]=0.0f;
  }

  unsigned int num_in_batch = ( fm ) / ALIGNED_NUM_FILTERS;

  unsigned int input_batch_offset = num_in_batch * input_height * input_width * TOTAL_INPUT_DEPTH_SIZE;

  int curr_local_y = ( lid / ( TILE_X / 4 ) );
  int curr_local_x = ( lid % ( TILE_X / 4 ) ) * 4;
  int curr_y = or * STRIDE_Y + INPUT_START_Y + curr_local_y;
  int curr_x = oc * STRIDE_X + INPUT_START_X + curr_local_x;
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0
  int saved_y = curr_y;
#endif
  in_addr = input_batch_offset + INPUT_START_Z * input_height * input_width
            +  (curr_y - INPUT_PAD_H) * input_width             // y tile offset
            +   curr_x - INPUT_PAD_W;                        // x tile offset
  union {
    Dtype4 in_vec[INVEC_SIZE];
    Dtype in_array[INVEC_SIZE * 4];
  } in_buf;

  for(int kd = 0; kd < INPUT_DEPTH; kd++)
  {
    int in_offset = in_addr;
    int reg = 0;
    LOOP(INVEC_SIZE, reg,
      {
        if (curr_local_y + reg * TILE_Y_STRIDE < TILE_Y || INVEC_SIZE * TILE_Y_STRIDE <= (TILE_Y + 2) || reg < INVEC_SIZE - 1) {
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0
        if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + 3 >= INPUT_PAD_W && curr_x < input_width + INPUT_PAD_W) {
          if (curr_x < INPUT_PAD_W) {
            in_buf.in_vec[reg].s0 = 0;
            if (curr_x + 1 >= INPUT_PAD_W)
              in_buf.in_vec[reg].s1 = *(inputs + in_offset + 1);
            else
              in_buf.in_vec[reg].s1 = 0;
            if (curr_x + 2 >= INPUT_PAD_W)
              in_buf.in_vec[reg].s2 = *(inputs + in_offset + 2);
            else
              in_buf.in_vec[reg].s2 = 0;
            in_buf.in_vec[reg].s3 = *(inputs + in_offset + 3);
          } else {
            VLOAD4(in_buf.in_vec[reg], inputs + in_offset);
            if (curr_x + 1 >= input_width + INPUT_PAD_W)
              in_buf.in_vec[reg].s1 = 0;
            if (curr_x + 2 >= input_width + INPUT_PAD_W)
              in_buf.in_vec[reg].s2 = 0;
            if (curr_x + 3 >= input_width + INPUT_PAD_W)
              in_buf.in_vec[reg].s3 = 0;
          }
        } else {
          in_buf.in_vec[reg] = 0;
        }
        curr_y += TILE_Y_STRIDE;
#else
        VLOAD4(in_buf.in_vec[reg], inputs + in_offset);
#endif
        }
        in_offset += input_width * TILE_Y_STRIDE;
      });
    in_addr += input_height * input_width;
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0
    curr_y = saved_y;
#endif

#if KERNEL_WIDTH * KERNEL_HEIGHT != 1
#define WEIGHT_PREF 8
#else
#define WEIGHT_PREF 1
#endif
    union {
      Dtype w[WEIGHT_PREF];
#if KERNEL_WIDTH * KERNEL_HEIGHT != 1
      INT_TYPE8 ui8;
#endif
    } weight_buf;
    int w_idx=0;

    unsigned int orig_weight_addr = weight_addr;
#if KERNEL_WIDTH * KERNEL_HEIGHT != 1
    weight_buf.ui8 = SUB_GROUP_BLOCK_READ8((__global INT_TYPE *)&weights[weight_addr]);
    weight_addr += SIMD_SIZE * WEIGHT_PREF;
#else
    weight_buf.w[0] = as_Dtype(SUB_GROUP_BLOCK_READ((__global INT_TYPE *)&weights[weight_addr]));
    weight_addr += SIMD_SIZE * 1;
#endif

#define BLOCK_IN(n) sub_group_broadcast( in_buf.in_array[((n)%4) + ((n) / (TILE_Y_STRIDE * TILE_X)) * 4], (((n) % (TILE_Y_STRIDE * TILE_X))/4))

    int kr = 0;  // kr = Kernel Row
    LOOP(KERNEL_HEIGHT, kr,// LOOP is a macro that unrolls the loop.
        {
          int kc = 0;  // kc = Kernel Column
          LOOP(KERNEL_WIDTH, kc,
              {
                for(int br=0; br < OUT_BLOCK_HEIGHT; br++) {
                  for(int bc=0; bc < OUT_BLOCK_WIDTH; bc++) {
                    Dtype input = BLOCK_IN((br * STRIDE_Y + kr * DILATION_Y) * TILE_X + bc * STRIDE_X + kc * DILATION_X);
                    out[br * OUT_BLOCK_WIDTH + bc] = mad(weight_buf.w[w_idx % WEIGHT_PREF], input, out[br * OUT_BLOCK_WIDTH + bc]);
                  }
                }
#if KERNEL_WIDTH * KERNEL_HEIGHT > WEIGHT_PREF
                // We assume KERNEL_W is equal to KERNEL_H here.
                if ((w_idx + 1) % WEIGHT_PREF == 0
                #if KERNEL_WIDTH * KERNEL_HEIGHT % 8 != 0
                && ((w_idx + 1) <= (KERNEL_WIDTH * KERNEL_HEIGHT - WEIGHT_PREF))
                #endif
                    ) {
                  weight_buf.ui8 = SUB_GROUP_BLOCK_READ8((__global INT_TYPE *)&weights[weight_addr]);
                  weight_addr += SIMD_SIZE * WEIGHT_PREF;  // weights must be stored in just the right SIMD swizzled format for this to work, see host code for details.
                }
              #if KERNEL_WIDTH*KERNEL_HEIGHT % 8 == 0
                // need to do nothing
              #else
                else if ((w_idx + 1) %  WEIGHT_PREF == 0 && ((w_idx + 1) > (KERNEL_WIDTH * KERNEL_HEIGHT - WEIGHT_PREF)))
                #if KERNEL_WIDTH * KERNEL_HEIGHT % 8 == 1
                  weight_buf.w[0] = weights[weight_addr];
                #elif KERNEL_WIDTH * KERNEL_HEIGHT % 8 == 2
                  weight_buf.ui8.s01 = SUB_GROUP_BLOCK_READ2((__global INT_TYPE *)&weights[weight_addr]);
                #elif KERNEL_WIDTH * KERNEL_HEIGHT % 8 <= 4
                  weight_buf.ui8.s0123 = SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)&weights[weight_addr]);
                #else
                  weight_buf.ui8 = SUB_GROUP_BLOCK_READ8((__global INT_TYPE *)&weights[weight_addr]);
                #endif
              #endif
#endif
                ++w_idx;
              });
        });
    weight_addr = orig_weight_addr + KERNEL_WIDTH * KERNEL_HEIGHT * SIMD_SIZE;

  }
  // dead code to work around possible compiler bug.
  if (ALIGNED_NUM_FILTERS != NUM_FILTERS && fm > 0xfffffffeul) {
    outputs[0] = BLOCK_IN(fm % SIMD_SIZE);
  }
  fm = fm % ALIGNED_NUM_FILTERS;

  if ((ALIGNED_NUM_FILTERS == NUM_FILTERS || fm < NUM_FILTERS)) {
  unsigned int out_addr = OUT_BUFF_OFFSET + ( num_in_batch * TOTAL_OUTPUT_DEPTH + fm ) * output_width * output_height;
  out_addr += or * output_width + oc;
  // we need this address calculation for biases because we support views and batching
#if APPLY_BIAS
  Dtype bias = biases_base[fm];
#else
  Dtype bias = 0;
#endif
    for(unsigned int r = 0; r < OUT_BLOCK_HEIGHT; r++) {
      if (r + or >= output_height) break;
      for(unsigned int c = 0; c < OUT_BLOCK_WIDTH; c++) {
        if (c + oc >= output_width) break;
        // this does a scattered write to SIMD_SIZE different feature maps, so that data within one map is contiguous, thus ready for input to next layer.
        ACTIVATION_FUNCTION(outputs, out_addr + r * output_width + c, bias + out[r * OUT_BLOCK_WIDTH + c], fm);

      }
    }
  }
}

#elif defined KERNEL_GEMM_LIKE

#if APPLY_BIAS
// Dtype bias[4];
#define SUBGROUP_GET_BIAS(k, i) intel_sub_group_shuffle(bias[k], i)
#else
#define SUBGROUP_GET_BIAS(k, i) ((Dtype)0)
#endif

#ifdef Conv_Interleaved
typedef struct float1 { float s0; } float1;
typedef struct float5 { float s0; float s1; float s2; float s3; float s4; } float5;
typedef struct float6 { float s0; float s1; float s2; float s3; float s4; float s5; } float6;
typedef struct float7 { float s0; float s1; float s2; float s3; float s4; float s5; float s6; } float7;
typedef struct float9 { float s0; float s1; float s2; float s3; float s4; float s5; float s6; float s7; float s8; } float9;
typedef struct float10 { float s0; float s1; float s2; float s3; float s4; float s5;
                         float s6; float s7; float s8; float s9;} float10;
typedef struct float11 { float s0; float s1; float s2; float s3; float s4; float s5;
                         float s6; float s7; float s8; float s9; float sa;} float11;
typedef struct float12 { float s0; float s1; float s2; float s3; float s4; float s5;
                         float s6; float s7; float s8; float s9; float sa; float sb; } float12;
typedef struct float13 { float s0; float s1; float s2; float s3; float s4; float s5;
                         float s6; float s7; float s8; float s9; float sa; float sb; float sc;} float13;
typedef struct float14 { float s0; float s1; float s2; float s3; float s4; float s5;
                         float s6; float s7; float s8; float s9; float sa; float sb; float sc; float sd; } float14;
typedef struct float15 { float s0; float s1; float s2; float s3; float s4; float s5;
                         float s6; float s7; float s8; float s9; float sa; float sb; float sc; float sd; float se; } float15;
typedef struct float0 { float s0; } float0; //never used but makes compiler happy.

#define OUT_PITCH_X output_width
#define ROW_PITCH input_width

#define GEMM_LIKE_KERNEL_ARGS     \
    ELTWISE_DATA_ARG              \
    NEGATIVE_SLOPE_ARG            \
    const __global Dtype *src0,   \
    const __global Dtype *src1,   \
    BIAS_KERNEL_ARG               \
    __global Dtype *dst,          \
    const ushort input_width,     \
    const ushort input_height,    \
    const ushort output_width,    \
    const ushort output_height,   \
    const int out_pitch_y,     \
    const int out_pitch_z,     \
    const int aligned_input_size, \
    const int slice_pitch
#endif

#ifdef GEMM_LIKE_CONV_32_1
//////////////////////////////////////////////////////////////////////////////
// Conv_Interleaved_32_1_flex
//
// Convolution: each workitem computes 1 patch x 32 filters worth of output
// data.  Kernel's inner loop works on a single tile consisting of one
// row from each patch and the filter data corresponding to that row.  Filter
// matrix is interleaved to reduce GRF bank conflicts.  Patches are walked
// by rows and then by slices.  Relies on sub_group extension for block
// reads and SIMD broadcast.  Allows flexible sizing of TILE width (TILE_N)
// by dynamically selecting one of two code paths: one uses TILE_N = 32 and
// the other uses TILE_N = 8, 16, or 24.
#define TILE_M          1
#define TILE_K          KERNEL_WIDTH
#define TILE_N          32

#ifndef __BEIGNET__
__attribute__((intel_reqd_sub_group_size(8)))
#endif
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
    const int group_x = get_group_id(0);
    const int group_y = get_group_id(1);
    const int global_x = get_global_id(0);
    const int global_y = get_global_id(1);
    const int global_z = get_global_id(2);
    int interleaved_y;
    int kernel_y;
    int kernel_idx;

#define DOT_PRODUCT_8( _result, _rowA, colB )    \
    {   \
        _result.s0 = mad( _rowA, sub_group_broadcast( colB, 0 ), _result.s0 );  \
        _result.s1 = mad( _rowA, sub_group_broadcast( colB, 1 ), _result.s1 );  \
        _result.s2 = mad( _rowA, sub_group_broadcast( colB, 2 ), _result.s2 );  \
        _result.s3 = mad( _rowA, sub_group_broadcast( colB, 3 ), _result.s3 );  \
        _result.s4 = mad( _rowA, sub_group_broadcast( colB, 4 ), _result.s4 );  \
        _result.s5 = mad( _rowA, sub_group_broadcast( colB, 5 ), _result.s5 );  \
        _result.s6 = mad( _rowA, sub_group_broadcast( colB, 6 ), _result.s6 );  \
        _result.s7 = mad( _rowA, sub_group_broadcast( colB, 7 ), _result.s7 );  \
    }
    typedef CAT( Dtype, KERNEL_WIDTH ) Dtype_t;

    // True for all threads if filter_width is multiple of TILE_N
    // else, true for all but right-most column of threads.
    if( TILE_N_LAST == 0 || global_x < WIDTH1 / TILE_N )
    {
        // Result ctile (*dst) is M rows x N columns
        // LWG size is 1x8.  Thus each thread calculates 8*M rows x N cols of ctile.
        Dtype8  blockC00 = 0.f;
        Dtype8  blockC10 = 0.f;
        Dtype8  blockC20 = 0.f;
        Dtype8  blockC30 = 0.f;

        // Src0 (patch input) is directly used as atile.
        // Each work item points to the start of a different patch.
        // atile is M rows x K columns.
        int curr_x = ( global_y % output_width ) * STRIDE_X;
        int curr_y = ( global_y / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
        int saved_y = curr_y;
#endif
        const __global Dtype *src0_read = src0
          + aligned_input_size * global_z                            // batch offset
          + (curr_y - INPUT_PAD_H) * ROW_PITCH      // y offset
          + (curr_x - INPUT_PAD_W);                 // x offset

        // Src1 (filter) is directly used as btile.
        // It starts at the top of src1 and walks down.
        // btile is K rows x N columns.
        const __global Dtype *src1_read = src1 + ( global_x * TILE_N  * 2);

        // Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
        // Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
        // and KERNEL_WIDTH/2 rows of interleaved filter.
        int patch_depth = 0;
        do
        {
            int patch_row = 0;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
            curr_y = saved_y;
#endif

            do
            {
                // Load atile and btile.
                // Kernel data is partially interleaved.  Every 2 rows are interleaved at Dtype8 granularity.
                // The exception is that if KERNEL_WIDTH is odd the last row is not interleaved.  The non
                // interleaved row is padded with zero to ensure same size as interleaved rows. This
                // interleaving is done to ensure 0% GDR bank conflicts.  For example, this is how the
                // kernel data would be arranged before/after interleaving for KERNEL_WIDTH=3.
                // (0, 0) (8, 0) (16, 0) (24, 0) ...       (0, 0) (0, 1) (8, 0) (0, 1) (16, 0) (0, 1) (24, 0) ..
                // (0, 1) (8, 1) (16, 1) (24, 1) ... =>    (0, 2) (8, 2) (16, 2) (24, 2) ...
                // (0, 2) (8, 2) (16, 2) (24, 2) ...       ...
                // ...
                const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;

#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1
                Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[  0  ];
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
#else
                Dtype_t blockA00;
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
                int pos = 0;
                LOOP(KERNEL_WIDTH, pos,
                {
                  if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
                    pblockA00[pos] = src0_read[pos * DILATION_X];
                  else
                    pblockA00[pos] = 0;
                })
                curr_y += DILATION_Y;
#endif
                src0_read += (ROW_PITCH * DILATION_Y);

                Dtype blockB00[KERNEL_WIDTH*4];
                Dtype8* p8BlockB00 = (Dtype8*)blockB00;
                Dtype4* p4BlockB00 = (Dtype4*)blockB00;
                Dtype*  pBlockB00 =  (Dtype* )blockB00;

                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
                    p8BlockB00[interleaved_y] = as_Dtype8( SUB_GROUP_BLOCK_READ8( (const __global INT_TYPE *)src1_read ) );
                    src1_read += WIDTH1 * 2;
                } )
                if ( kernel_width_is_odd )
                {
                    p4BlockB00[KERNEL_WIDTH - 1] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE *)src1_read ) );
                    src1_read += WIDTH1 * 2;
                }

                // Perform MADs
                kernel_idx = 0;
                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
                    kernel_y = interleaved_y * 2;
                    DOT_PRODUCT_8( blockC00, pblockA00[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC00, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC10, pblockA00[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC10, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC20, pblockA00[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC20, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC30, pblockA00[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC30, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                } )
                    kernel_y = interleaved_y * 2;
                if ( kernel_width_is_odd )
                {
                    DOT_PRODUCT_8( blockC00, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC10, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC20, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC30, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                }
            }

            //while( ++patch_row < 1 ); //debug
            while( ++patch_row < KERNEL_HEIGHT );

            src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y); // reset to start of next slice of patch
        }
        //while ( ++patch_depth < 1 ); //debug
        while ( ++patch_depth < INPUT_DEPTH );

        // Dst resembles a cube of width x height x (output channel * batches).  Each tile writes:
        // (SIMD * TILE_M) x 1 x TILE_N.  Partial writes most likely generated if padding used.
        int out_offset = global_z * out_pitch_z                                                   // batch offset
         + ( group_x * TILE_N ) * out_pitch_y                                       // channel offset
         + ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X  // y offset
         + ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT;               // x offset

        __global Dtype *out = dst + out_offset;
#if APPLY_BIAS
        Dtype bias[4];
        Dtype4 *bias_vec;
        bias_vec = (Dtype4*)bias;
        *bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
        if (global_y * TILE_M < output_width * output_height )
        {
            for (int i = 0; i < 8; i++)
            {
            ACTIVATION_FUNCTION(dst, out_offset + ( 0 + i ) * out_pitch_y, blockC00[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
            ACTIVATION_FUNCTION(dst, out_offset + ( 8 + i ) * out_pitch_y, blockC10[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + 8 + i);
            ACTIVATION_FUNCTION(dst, out_offset + ( 16 + i ) * out_pitch_y, blockC20[i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + 16 + i);
            ACTIVATION_FUNCTION(dst, out_offset + ( 24 + i ) * out_pitch_y, blockC30[i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + 24 + i);
            }
        }
    }
#if TILE_N_LAST > 0
    else
    {

        // Result ctile (*dst) is M rows x N columns
        // LWG size is 1x8.  Thus each thread calculates 8*M rows x N cols of ctile.
        int i = 0;
        Dtype8  blockC[TILE_N_LAST_DIV8];
        LOOP(TILE_N_LAST_DIV8, i,
        {
            blockC[i] = 0.f;
        } )

        // Src0 (patch input) is directly used as atile.
        // Each work item points to the start of a different patch.
        // atile is M rows x K columns.
        int curr_x = ( global_y % output_width ) * STRIDE_X;
        int curr_y = ( global_y / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
        int saved_y = curr_y;
#endif
        const __global Dtype *src0_read = src0
          + aligned_input_size * global_z                            // batch offset
          + (curr_y - INPUT_PAD_H) * ROW_PITCH      // y offset
          + (curr_x - INPUT_PAD_W);                 // x offset

        // Src1 (filter) is directly used as btile.
        // It starts at the top of src1 and walks down.
        // btile is K rows x N columns.
        const __global Dtype *src1_read = src1 + ( global_x * TILE_N  * 2);

        // Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
        // Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
        // and KERNEL_WIDTH/2 rows of interleaved filter.
        int patch_depth = 0;
        do
        {
            int patch_row = 0;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
            curr_y = saved_y;
#endif
            do
            {
                // Load atile and interleaved btile.
                const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1
                Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[  0  ];
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
#else
                Dtype_t blockA00;
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
                int pos = 0;
                LOOP(KERNEL_WIDTH, pos,
                {
                  if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
                    pblockA00[pos] = src0_read[pos * DILATION_X];
                  else
                    pblockA00[pos] = 0;
                })
                curr_y += DILATION_Y;
#endif
                src0_read += (ROW_PITCH * DILATION_Y);
                Dtype blockB[KERNEL_WIDTH * TILE_N_LAST_DIV8];

                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
#if TILE_N_LAST_DIV8 == 1
                    Dtype2* p2BlockB = (Dtype2* )blockB;
                    p2BlockB[interleaved_y] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
                    Dtype4* p4BlockB = (Dtype4* )blockB;
                    p4BlockB[interleaved_y] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
                    //TODO: broken.  No block_read6
                    Dtype6* p6BlockB = (Dtype6* )blockB;
                    (*((Dtype8*)(&p6BlockB[interleaved_y]))).s0123 = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
                    (*((Dtype8*)(&p6BlockB[interleaved_y]))).s45 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)(src1_read + 4 * 8) ) );
#endif
                    src1_read += WIDTH1 * 2;
                } )
                if ( kernel_width_is_odd )
                {
#if TILE_N_LAST_DIV8 == 1
                    Dtype* pBlockB = (Dtype* )blockB;
                    pBlockB[KERNEL_WIDTH - 1] = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
                    Dtype2* p2BlockB = (Dtype2* )blockB;
                    p2BlockB[KERNEL_WIDTH - 1] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
                    Dtype3* p3BlockB = (Dtype3* )blockB;
                    p3BlockB[KERNEL_WIDTH - 1].s01 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
                    p3BlockB[KERNEL_WIDTH - 1].s2 = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*) (src1_read + 2 * 8) ) );
#endif
                    src1_read += WIDTH1 * 2;
                }

                // Perform MADs
                Dtype* pBlockB = (Dtype*)blockB;
                kernel_idx = 0;
                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
                    kernel_y = interleaved_y * 2;
                    DOT_PRODUCT_8( blockC[0], pblockA00[kernel_y    ], pBlockB[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC[0], pblockA00[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
                    DOT_PRODUCT_8( blockC[1], pblockA00[kernel_y    ], pBlockB[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC[1], pblockA00[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
                    DOT_PRODUCT_8( blockC[2], pblockA00[kernel_y    ], pBlockB[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC[2], pblockA00[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
                } )
                    kernel_y = interleaved_y * 2;
                if ( kernel_width_is_odd )
                {
                    DOT_PRODUCT_8( blockC[0], pblockA00[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
                    DOT_PRODUCT_8( blockC[1], pblockA00[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
                    DOT_PRODUCT_8( blockC[2], pblockA00[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
                }
            }

            //while( ++patch_row < 1 ); //debug
            while( ++patch_row < KERNEL_HEIGHT );

            src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
        }
        //while ( ++patch_depth < 1 );  //debug
        while ( ++patch_depth < INPUT_DEPTH );

        // Dst resembles a cube of width x height x (output channel * batches).  Each tile writes:
        // (SIMD * TILE_M) x 1 x TILE_N.  Partial writes most likely generated if padding used.
        int out_offset = global_z * out_pitch_z                                                   // batch offset
         + ( group_x * TILE_N ) * out_pitch_y                                       // channel offset
         + ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X  // y offset
         + ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT;               // x offset
        __global Dtype *out = dst + out_offset;
#if APPLY_BIAS
        Dtype bias[4];
        Dtype4 *bias_vec;
        bias_vec = (Dtype4*)bias;
        *bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif

        if (global_y * TILE_M < output_width * output_height )
        {
            for (int i = 0; i < 8; i++)
            {
                if ( TILE_N_LAST_DIV8 > 0 )
                {
                  ACTIVATION_FUNCTION(dst, out_offset + ( 0+i) * out_pitch_y, blockC[0][i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
                }
                if ( TILE_N_LAST_DIV8 > 1 )
                {
                  ACTIVATION_FUNCTION(dst, out_offset + ( 8+i) * out_pitch_y, blockC[1][i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
                }
                if ( TILE_N_LAST_DIV8 > 2 )
                {
                  ACTIVATION_FUNCTION(dst, out_offset + (16+i) * out_pitch_y, blockC[2][i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
                }
                if ( TILE_N_LAST_DIV8 > 3 )
                {
                  ACTIVATION_FUNCTION(dst, out_offset + (24+i) * out_pitch_y, blockC[3][i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
                }
            }
        }
    }
#endif
}
#endif
#ifdef GEMM_LIKE_CONV_32_2

//////////////////////////////////////////////////////////////////////////////
// Conv_Interleaved_32_2_flex
//
// Convolution: each workitem computes 1 patch x 32 filters worth of output
// data.  Kernel's inner loop works on a single tile consisting of one
// row from each patch and the filter data corresponding to that row.  Filter
// matrix is interleaved to reduce GRF bank conflicts.  Patches are walked
// by rows and then by slices.  Relies on sub_group extension for block
// reads and SIMD broadcast.  Allows flexible sizing of TILE width (TILE_N)
// by dynamically selecting one of two code paths: one uses TILE_N = 32 and
// the other uses TILE_N = 8, 16, or 24.
#define TILE_M          2
#define TILE_K          KERNEL_WIDTH
#define TILE_N          32

#ifndef __BEIGNET__
__attribute__((intel_reqd_sub_group_size(8)))
#endif
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
    const int group_x = get_group_id(0);
    const int group_y = get_group_id(1);
    const int global_x = get_global_id(0);
    const int global_y = get_global_id(1);
    const int global_z = get_global_id(2);
    int interleaved_y;
    int kernel_y;
    int kernel_idx;

#define DOT_PRODUCT_8( _result, _rowA, colB )    \
    {   \
        _result.s0 = mad( _rowA, sub_group_broadcast( colB, 0 ), _result.s0 );  \
        _result.s1 = mad( _rowA, sub_group_broadcast( colB, 1 ), _result.s1 );  \
        _result.s2 = mad( _rowA, sub_group_broadcast( colB, 2 ), _result.s2 );  \
        _result.s3 = mad( _rowA, sub_group_broadcast( colB, 3 ), _result.s3 );  \
        _result.s4 = mad( _rowA, sub_group_broadcast( colB, 4 ), _result.s4 );  \
        _result.s5 = mad( _rowA, sub_group_broadcast( colB, 5 ), _result.s5 );  \
        _result.s6 = mad( _rowA, sub_group_broadcast( colB, 6 ), _result.s6 );  \
        _result.s7 = mad( _rowA, sub_group_broadcast( colB, 7 ), _result.s7 );  \
    }
        typedef CAT( Dtype, KERNEL_WIDTH ) Dtype_t;

    // True for all threads if filter_width is multiple of TILE_N
    // else, true for all but right-most column of threads.
    if( TILE_N_LAST == 0 || global_x < WIDTH1 / TILE_N )
    {
        // Result ctile (*dst) is M rows x N columns
        // LWG size is 1x8.  Thus each thread calculates 8*M rows x N cols of ctile.
        Dtype8  blockC00 = 0.f;
        Dtype8  blockC10 = 0.f;
        Dtype8  blockC20 = 0.f;
        Dtype8  blockC30 = 0.f;
        Dtype8  blockC01 = 0.f;
        Dtype8  blockC11 = 0.f;
        Dtype8  blockC21 = 0.f;
        Dtype8  blockC31 = 0.f;

        // Src0 (patch input) is directly used as atile.
        // Each work item points to the start of a different patch.
        // atile is M rows x K columns.
        int curr_x0 = ( ( global_y * TILE_M + 0 ) % output_width ) * STRIDE_X;
        int curr_x1 = ( ( global_y * TILE_M + 1 ) % output_width ) * STRIDE_X;
        int curr_y0 = ( ( global_y * TILE_M + 0 ) / output_width ) * STRIDE_Y;
        int curr_y1 = ( ( global_y * TILE_M + 1 ) / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
        int saved_y0 = curr_y0;
        int saved_y1 = curr_y1;
#endif
        const __global Dtype *src0_read0 = src0
         + aligned_input_size * global_z                                            // batch offset
         + (curr_y0 - INPUT_PAD_H) * ROW_PITCH   // y offset
         + curr_x0 - INPUT_PAD_W;                // x offset
        const __global Dtype *src0_read1 = src0
         + aligned_input_size * global_z                                            // batch offset
         + (curr_y1 - INPUT_PAD_H) * ROW_PITCH   // y offset
         + curr_x1 - INPUT_PAD_W;                // x offset

        // Src1 (filter) is directly used as btile.
        // It starts at the top of src1 and walks down.
        // btile is K rows x N columns.
        const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2);

        // Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
        // Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
        // and KERNEL_WIDTH/2 rows of interleaved filter.
        int patch_depth = 0;
        do
        {
            int patch_row = 0;
            do
            {
                // Load atile and btile.
                // Kernel data is partially interleaved.  Every 2 rows are interleaved at Dtype8 granularity.
                // The exception is that if KERNEL_WIDTH is odd the last row is not interleaved.  The non
                // interleaved row is padded with zero to ensure same size as interleaved rows. This
                // interleaving is done to ensure 0% GDR bank conflicts.  For example, this is how the
                // kernel data would be arranged before/after interleaving for KERNEL_WIDTH=3.
                // (0, 0) (8, 0) (16, 0) (24, 0) ...       (0, 0) (0, 1) (8, 0) (0, 1) (16, 0) (0, 1) (24, 0) ..
                // (0, 1) (8, 1) (16, 1) (24, 1) ... =>    (0, 2) (8, 2) (16, 2) (24, 2) ...
                // (0, 2) (8, 2) (16, 2) (24, 2) ...       ...
                // ...
                const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_H == 0 && INPUT_PAD_W == 0 && DILATION_X == 1 && DILATION_Y == 1
                Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read0 )[  0  ]; src0_read0 += ROW_PITCH;
                Dtype_t blockA01 = ( (const __global Dtype_t*)src0_read1 )[  0  ]; src0_read1 += ROW_PITCH;
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
                Dtype*  pblockA01 = (Dtype*)(&blockA01);
#else
                Dtype_t blockA00;
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
                int pos = 0;
                LOOP(KERNEL_WIDTH, pos,
                {
                  if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
                    pblockA00[pos] = src0_read0[pos * DILATION_X];
                  else
                    pblockA00[pos] = 0;
                })
                curr_y0 += DILATION_Y;
                Dtype_t blockA01;
                Dtype*  pblockA01 = (Dtype*)(&blockA01);
                pos = 0;
                LOOP(KERNEL_WIDTH, pos,
                {
                  if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
                    pblockA01[pos] = src0_read1[pos * DILATION_X];
                  else
                    pblockA01[pos] = 0;
                })
                curr_y1 += DILATION_Y;
                src0_read0 += (ROW_PITCH * DILATION_Y);
                src0_read1 += (ROW_PITCH * DILATION_Y);
#endif
                Dtype blockB00[KERNEL_WIDTH*4];
                Dtype8* p8BlockB00 = (Dtype8*)blockB00;
                Dtype4* p4BlockB00 = (Dtype4*)blockB00;
                Dtype*  pBlockB00 =  (Dtype* )blockB00;

                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
                    p8BlockB00[interleaved_y] = as_Dtype8( SUB_GROUP_BLOCK_READ8( (const __global INT_TYPE*)src1_read ) );
                    src1_read += WIDTH1 * 2;
                } )
                if ( kernel_width_is_odd )
                {
                    p4BlockB00[KERNEL_WIDTH - 1] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
                    src1_read += WIDTH1 * 2;
                }
                // Perform MADs
                kernel_idx = 0;
                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
                    kernel_y = interleaved_y * 2;
                    DOT_PRODUCT_8( blockC00, pblockA00[kernel_y    ], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC01, pblockA01[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC00, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC01, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC10, pblockA00[kernel_y    ], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC11, pblockA01[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC10, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC11, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC20, pblockA00[kernel_y    ], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC21, pblockA01[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC20, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC21, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC30, pblockA00[kernel_y    ], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC31, pblockA01[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC30, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC31, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                } )
                if ( kernel_width_is_odd )
                {
                    kernel_y = interleaved_y * 2;
                    DOT_PRODUCT_8( blockC00, pblockA00[kernel_y], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC01, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC10, pblockA00[kernel_y], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC11, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC20, pblockA00[kernel_y], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC21, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC30, pblockA00[kernel_y], pBlockB00[kernel_idx] );
                    DOT_PRODUCT_8( blockC31, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                }
            }

            //while( ++patch_row < 1 ); //debug
            while( ++patch_row < KERNEL_HEIGHT );
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0 || DILATION_X != 1 || DILATION_Y != 1
            curr_y0 = saved_y0;
            curr_y1 = saved_y1;
#endif
            src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
            src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
        }
        //while ( ++patch_depth < 1 );  //debug
        while ( ++patch_depth < INPUT_DEPTH );

        // Dst resembles a cube of width x height x (output channel * batches).  Each tile writes:
        // (SIMD * TILE_M) x 1 x TILE_N.  Partial writes most likely generated if padding used.
        int out0_offset = global_z * out_pitch_z                                                       // batch offset
         + ( group_x * TILE_N ) * out_pitch_y                                           // channel offset
         + ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
         + ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT;               // x offset
        int out1_offset = global_z * out_pitch_z                                                       // batch offset
         + ( group_x * TILE_N ) * out_pitch_y                                           // channel offset
         + ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
         + ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT;               // x offset

#if APPLY_BIAS
        Dtype bias[4];
        Dtype4 *bias_vec;
        bias_vec = (Dtype4*)bias;
        *bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif

        if( global_y * TILE_M < output_width * output_height )
        {
            for( int i = 0; i < 8; i++ )
            {
                ACTIVATION_FUNCTION(dst, out0_offset + ( 0+i) * out_pitch_y, blockC00[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
                ACTIVATION_FUNCTION(dst, out0_offset + ( 8+i) * out_pitch_y, blockC10[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
                ACTIVATION_FUNCTION(dst, out0_offset + (16+i) * out_pitch_y, blockC20[i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
                ACTIVATION_FUNCTION(dst, out0_offset + (24+i) * out_pitch_y, blockC30[i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
            }
        }
        if( global_y * TILE_M + 1 < output_width * output_height )
        {
            for( int i = 0; i < 8; i++ )
            {
                ACTIVATION_FUNCTION(dst, out1_offset + ( 0+i) * out_pitch_y, blockC01[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
                ACTIVATION_FUNCTION(dst, out1_offset + ( 8+i) * out_pitch_y, blockC11[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
                ACTIVATION_FUNCTION(dst, out1_offset + (16+i) * out_pitch_y, blockC21[i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
                ACTIVATION_FUNCTION(dst, out1_offset + (24+i) * out_pitch_y, blockC31[i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
            }
        }
    }
#if TILE_N_LAST > 0
    else
    {

        // Result ctile (*dst) is M rows x N columns
        // LWG size is 1x8.  Thus each thread calculates 8*M rows x N cols of ctile.
        int i = 0;
        Dtype8  blockC0[TILE_N_LAST_DIV8];
        Dtype8  blockC1[TILE_N_LAST_DIV8];
        LOOP(TILE_N_LAST_DIV8, i,
        {
            blockC0[i] = 0.f;
            blockC1[i] = 0.f;
        } )

        // Src0 (patch input) is directly used as atile.
        // Each work item points to the start of a different patch.
        // atile is M rows x K columns.
        int curr_x0 = ( ( global_y * TILE_M + 0 ) % output_width ) * STRIDE_X;
        int curr_x1 = ( ( global_y * TILE_M + 1 ) % output_width ) * STRIDE_X;
        int curr_y0 = ( ( global_y * TILE_M + 0 ) / output_width ) * STRIDE_Y;
        int curr_y1 = ( ( global_y * TILE_M + 1 ) / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
        int saved_y0 = curr_y0;
        int saved_y1 = curr_y1;
#endif
        const __global Dtype *src0_read0 = src0
         + aligned_input_size * global_z                                            // batch offset
         + (curr_y0 - INPUT_PAD_H) * ROW_PITCH   // y offset
         + curr_x0 - INPUT_PAD_W;                // x offset
        const __global Dtype *src0_read1 = src0
         + aligned_input_size * global_z                                            // batch offset
         + (curr_y1 - INPUT_PAD_H) * ROW_PITCH   // y offset
         + curr_x1 - INPUT_PAD_W;                // x offset

        // Src1 (filter) is directly used as btile.
        // It starts at the top of src1 and walks down.
        // btile is K rows x N columns.
        const __global Dtype *src1_read = src1 + ( global_x * TILE_N  * 2);

        // Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
        // Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
        // and KERNEL_WIDTH/2 rows of interleaved filter.
        int patch_depth = 0;
        do
        {
            int patch_row = 0;
            do
            {
                // Load atile and interleaved btile.
                const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_H == 0 && INPUT_PAD_W == 0 && DILATION_X == 1 && DILATION_Y == 1
                Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read0 )[  0  ]; src0_read0 += ROW_PITCH;
                Dtype_t blockA01 = ( (const __global Dtype_t*)src0_read1 )[  0  ]; src0_read1 += ROW_PITCH;
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
                Dtype*  pblockA01 = (Dtype*)(&blockA01);
#else
                Dtype_t blockA00;
                Dtype*  pblockA00 = (Dtype*)(&blockA00);
                int pos = 0;
                LOOP(KERNEL_WIDTH, pos,
                {
                  if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
                    pblockA00[pos] = src0_read0[pos * DILATION_X];
                  else
                    pblockA00[pos] = 0;
                })
                curr_y0 += DILATION_Y;
                Dtype_t blockA01;
                Dtype*  pblockA01 = (Dtype*)(&blockA01);
                pos = 0;
                LOOP(KERNEL_WIDTH, pos,
                {
                  if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
                    pblockA01[pos] = src0_read1[pos * DILATION_X];
                  else
                    pblockA01[pos] = 0;
                })
                curr_y1 += DILATION_Y;
                src0_read0 += (ROW_PITCH * DILATION_Y);
                src0_read1 += (ROW_PITCH * DILATION_Y);
#endif
                Dtype blockB[KERNEL_WIDTH * TILE_N_LAST_DIV8];

                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
#if TILE_N_LAST_DIV8 == 1
                    Dtype2* p2BlockB = (Dtype2* )blockB;
                    p2BlockB[interleaved_y] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
                    Dtype4* p4BlockB = (Dtype4* )blockB;
                    p4BlockB[interleaved_y] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
                    //TODO: broken.  No block_read6
                    Dtype6* p6BlockB = (Dtype6* )blockB;
                    (*((Dtype8*)(&p6BlockB[interleaved_y]))).s0123 = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
                    (*((Dtype8*)(&p6BlockB[interleaved_y]))).s45 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)(src1_read + 4 * 8) ) );
#endif
                    src1_read += WIDTH1 * 2;
                } )
                if ( kernel_width_is_odd )
                {
#if TILE_N_LAST_DIV8 == 1
                    Dtype* pBlockB = (Dtype* )blockB;
                    pBlockB[KERNEL_WIDTH - 1] = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
                    Dtype2* p2BlockB = (Dtype2* )blockB;
                    p2BlockB[KERNEL_WIDTH - 1] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
                    Dtype3* p3BlockB = (Dtype3* )blockB;
                    p3BlockB[KERNEL_WIDTH - 1].s01 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
                    p3BlockB[KERNEL_WIDTH - 1].s2 = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*) (src1_read + 8) ) );
#endif
                    src1_read += WIDTH1 * 2;
                }

                // Perform MADs
                Dtype* pBlockB = (Dtype*)blockB;
                kernel_idx = 0;
                interleaved_y = 0;
                LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
                {
                    kernel_y = interleaved_y * 2;
                    DOT_PRODUCT_8( blockC0[0], pblockA00[kernel_y    ], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[0], pblockA01[kernel_y    ], pBlockB[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC0[0], pblockA00[kernel_y + 1], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[0], pblockA01[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
                    DOT_PRODUCT_8( blockC0[1], pblockA00[kernel_y    ], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[1], pblockA01[kernel_y    ], pBlockB[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC0[1], pblockA00[kernel_y + 1], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[1], pblockA01[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
                    DOT_PRODUCT_8( blockC0[2], pblockA00[kernel_y    ], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[2], pblockA01[kernel_y    ], pBlockB[kernel_idx] ); kernel_idx++;
                    DOT_PRODUCT_8( blockC0[2], pblockA00[kernel_y + 1], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[2], pblockA01[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
                } )
                    kernel_y = interleaved_y * 2;
                if ( kernel_width_is_odd )
                {
                    DOT_PRODUCT_8( blockC0[0], pblockA00[kernel_y], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[0], pblockA01[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
                    DOT_PRODUCT_8( blockC0[1], pblockA00[kernel_y], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[1], pblockA01[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
                    DOT_PRODUCT_8( blockC0[2], pblockA00[kernel_y], pBlockB[kernel_idx] );
                    DOT_PRODUCT_8( blockC1[2], pblockA01[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
                }
            }

            //while( ++patch_row < 1 ); //debug
            while( ++patch_row < KERNEL_HEIGHT );
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0 || DILATION_X != 1 || DILATION_Y != 1
            curr_y0 = saved_y0;
            curr_y1 = saved_y1;
#endif
            src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
            src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
        }
        //while ( ++patch_depth < 1 );  //debug
        while ( ++patch_depth < INPUT_DEPTH );

        // Dst resembles a cube of width x height x (output channel * batches).  Each tile writes:
        // (SIMD * TILE_M) x 1 x TILE_N.  Partial writes most likely generated if padding used.
        int out0_offset = global_z * out_pitch_z                                                       // batch offset
         + ( group_x * TILE_N ) * out_pitch_y                                           // channel offset
         + ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
         + ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT;               // x offset
        int out1_offset = global_z * out_pitch_z                                                       // batch offset
         + ( group_x * TILE_N ) * out_pitch_y                                           // channel offset
         + ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
         + ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT;               // x offset
        __global Dtype *out1 = dst + out1_offset;

#if APPLY_BIAS
        Dtype bias[4];
        Dtype4 *bias_vec;
        bias_vec = (Dtype4*)bias;
        *bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
        if( global_y * TILE_M < output_width * output_height )
        {
            for( int i = 0; i < 8; i++ )
            {
                if ( TILE_N_LAST_DIV8 > 0 )
                {
                  ACTIVATION_FUNCTION(dst, out0_offset + ( 0+i) * out_pitch_y, blockC0[0][i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
                }
                if ( TILE_N_LAST_DIV8 > 1 )
                {
                  ACTIVATION_FUNCTION(dst, out0_offset + ( 8+i) * out_pitch_y, blockC0[1][i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
                }
                if ( TILE_N_LAST_DIV8 > 2 )
                {
                  ACTIVATION_FUNCTION(dst, out0_offset + (16+i) * out_pitch_y, blockC0[2][i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
                }
                if ( TILE_N_LAST_DIV8 > 3 )
                {
                  ACTIVATION_FUNCTION(dst, out0_offset + (24+i) * out_pitch_y, blockC0[3][i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
                }
            }
        }
        if( global_y * TILE_M + 1 < output_width * output_height )
        {
            for( int i = 0; i < 8; i++ )
            {
                if ( TILE_N_LAST_DIV8 > 0 )
                {
                  ACTIVATION_FUNCTION(dst, out1_offset + ( 0+i) * out_pitch_y, blockC1[0][i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
                }
                if ( TILE_N_LAST_DIV8 > 1 )
                {
                  ACTIVATION_FUNCTION(dst, out1_offset + ( 8+i) * out_pitch_y, blockC1[1][i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
                }
                if ( TILE_N_LAST_DIV8 > 2 )
                {
                  ACTIVATION_FUNCTION(dst, out1_offset + (16+i) * out_pitch_y, blockC1[2][i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
                }
                if ( TILE_N_LAST_DIV8 > 3 )
                {
                  ACTIVATION_FUNCTION(dst, out1_offset + (24+i) * out_pitch_y, blockC1[3][i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
                }
            }
        }
    }
#endif
}
#endif

#if defined(GEMM_LIKE_CONV_32_2_SIMD16) || defined(GEMM_LIKE_CONV_32_1_SIMD16)
#define INTERLEAVED_SIMD16_OUTPUT(_out_, _offset_,  _m_) do {\
    if (global_y * TILE_M < output_width * output_height ) \
    { \
      if ( ( OUT_DEPTH % TILE_N ) == 0 ) {\
        for (int i = 0; i < 16; i++) \
        { \
          ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_ [i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
          ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_ [i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
        } \
      } \
      else if( ( OUT_DEPTH % 16 ) == 0 ) { \
        if ( ( global_x + 1 ) < get_global_size(0) ) { \
          for ( int i = 0; i < 16; i++ ) \
          { \
            ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_ [i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
            ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_ [i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
          } \
        } \
        else { \
          for (int i = 0; i < 16; i++) \
          { \
            ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_ [i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
          } \
        } \
      } \
      else { \
        if ( ( global_x + 1 ) < get_global_size(0) ) \
        { \
          for ( int i = 0; i < 16; i++ ) \
          { \
            ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
            ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
          } \
        } \
        else { \
          if ( (OUT_DEPTH % TILE_N) > 16 ) { \
            for (int i = 0; i < 16 ; i++) \
            { \
              ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
            } \
            for (int i = 0; i < OUT_DEPTH % 16 ; i++) \
            { \
              ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
            } \
          } \
          else { \
            for (int i = 0; i < OUT_DEPTH % 16 ; i++) \
            { \
              ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
            } \
          } \
        } \
      } \
    } \
 }while(0)
#endif

#ifdef GEMM_LIKE_CONV_32_1_SIMD16
#define TILE_M          1
#define TILE_K          KERNEL_WIDTH
#define TILE_N          32

#ifndef __BEIGNET__
__attribute__((intel_reqd_sub_group_size(16)))
#endif
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
    const int group_x = get_group_id(0);
    const int group_y = get_group_id(1);
    const int global_x = get_global_id(0);
    const int global_y = get_global_id(1);
    const int global_z = get_global_id(2);
    int interleaved_y;
    int kernel_y;
    int kernel_idx;

    // Result ctile (*dst) is M rows x N columns
    // LWG size is 1x16.  Thus each thread calculates 16*M rows x N cols of ctile.
    Dtype16  blockC00 = 0.f;
    Dtype16  blockC10 = 0.f;

    // Src0 (patch input) is directly used as atile.
    // Each work item points to the start of a different patch.
    // atile is M rows x K columns.
    int curr_x = ( global_y % output_width ) * STRIDE_X;
    int curr_y = ( global_y / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
    int saved_y = curr_y;
#endif
    const __global Dtype *src0_read = src0
     + aligned_input_size * global_z                            // batch offset
     + (curr_y - INPUT_PAD_H) * ROW_PITCH      // y offset
     + curr_x - INPUT_PAD_W;                 // x offset
     const __global Dtype *src0_read_orig = src0_read;

    // Src1 (filter) is directly used as btile.
    // It starts at the top of src1 and walks down.
    // btile is K rows x N columns.
    const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2 );

#define DOT_PRODUCT_16( _result, _rowA, colB )    \
    {   \
        _result.s0 = mad( _rowA, sub_group_broadcast( colB,  0 ), _result.s0 );  \
        _result.s1 = mad( _rowA, sub_group_broadcast( colB,  1 ), _result.s1 );  \
        _result.s2 = mad( _rowA, sub_group_broadcast( colB,  2 ), _result.s2 );  \
        _result.s3 = mad( _rowA, sub_group_broadcast( colB,  3 ), _result.s3 );  \
        _result.s4 = mad( _rowA, sub_group_broadcast( colB,  4 ), _result.s4 );  \
        _result.s5 = mad( _rowA, sub_group_broadcast( colB,  5 ), _result.s5 );  \
        _result.s6 = mad( _rowA, sub_group_broadcast( colB,  6 ), _result.s6 );  \
        _result.s7 = mad( _rowA, sub_group_broadcast( colB,  7 ), _result.s7 );  \
        _result.s8 = mad( _rowA, sub_group_broadcast( colB,  8 ), _result.s8 );  \
        _result.s9 = mad( _rowA, sub_group_broadcast( colB,  9 ), _result.s9 );  \
        _result.sa = mad( _rowA, sub_group_broadcast( colB, 10 ), _result.sa );  \
        _result.sb = mad( _rowA, sub_group_broadcast( colB, 11 ), _result.sb );  \
        _result.sc = mad( _rowA, sub_group_broadcast( colB, 12 ), _result.sc );  \
        _result.sd = mad( _rowA, sub_group_broadcast( colB, 13 ), _result.sd );  \
        _result.se = mad( _rowA, sub_group_broadcast( colB, 14 ), _result.se );  \
        _result.sf = mad( _rowA, sub_group_broadcast( colB, 15 ), _result.sf );  \
    }
    typedef CAT( Dtype, KERNEL_WIDTH ) Dtype_t;
    // Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
    // Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
    // and KERNEL_WIDTH/2 rows of interleaved filter.
    int patch_depth = 0;
#ifndef __BEIGNET__
    __attribute__((opencl_unroll_hint(1)))
#endif
    do
    {
        int patch_row = 0;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
        curr_y = saved_y;
#endif
#ifndef __BEIGNET__
        __attribute__((opencl_unroll_hint(1)))
#endif
        do
        {
            // Load atile and btile.
            // Kernel data is partially interleaved.  Every 2 rows are interleaved at Dtype16 granularity.
            // The exception is that if KERNEL_WIDTH is odd the last row is not interleaved.  The non
            // interleaved row is padded with zero to ensure same size as interleaved rows. This
            // interleaving is done to ensure 0% GDR bank conflicts.  For example, this is how the
            // kernel data would be arranged before/after interleaving for KERNEL_WIDTH=3.
            // (0, 0) (16, 0) (32, 0) (48, 0) ...     (0, 0) ( 0, 1) (16, 0) ( 0, 1) (32, 0) (0, 1) (48, 0) ...
            // (0, 1) (16, 1) (32, 1) (48, 1) ... =>  (0, 2) (16, 2) (32, 2) (48, 2) ...
            // (0, 2) (16, 2) (32, 2) (48, 2) ...     ...
            // ...
            const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;

#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1
            Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[  0  ];
            Dtype*  pblockA00 = (Dtype*)(&blockA00);
#else
            Dtype_t blockA00;
            Dtype*  pblockA00 = (Dtype*)(&blockA00);
            int pos = 0;
            LOOP(KERNEL_WIDTH, pos,
            {
              if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
                pblockA00[pos] = src0_read[pos * DILATION_X];
              else
                pblockA00[pos] = 0;
            })
            curr_y += DILATION_Y;
#endif
            src0_read += ROW_PITCH * DILATION_Y;
            INT_TYPE blockB00[KERNEL_WIDTH * 2];
            INT_TYPE4* p4BlockB00 = (INT_TYPE4*)blockB00;
            INT_TYPE2* p2BlockB00 = (INT_TYPE2*)blockB00;
            Dtype* pBlockB00  = (Dtype*)blockB00;
            interleaved_y = 0;
            LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
            {
                p4BlockB00[interleaved_y] = SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read );
                src1_read += WIDTH1 * 2;
            } )
            if ( kernel_width_is_odd )
            {
                p2BlockB00[KERNEL_WIDTH - 1] = SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read );
                src1_read += WIDTH1 * 2;
            }

            // Perform MADs
            kernel_idx = 0;
            interleaved_y = 0;
            LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
            {
                kernel_y = interleaved_y * 2;
                DOT_PRODUCT_16( blockC00, pblockA00[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                DOT_PRODUCT_16( blockC00, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
                DOT_PRODUCT_16( blockC10, pblockA00[kernel_y    ], pBlockB00[kernel_idx] ); kernel_idx++;
                DOT_PRODUCT_16( blockC10, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
            } )
            if ( kernel_width_is_odd )
            {
                kernel_y = interleaved_y * 2;
                DOT_PRODUCT_16( blockC00, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
                DOT_PRODUCT_16( blockC10, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
            }
        }

        //while( ++patch_row < 1 ); //debug
        while( ++patch_row < KERNEL_HEIGHT );

        src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
    }
    //while ( ++patch_depth < 1 );  //debug
    while ( ++patch_depth < INPUT_DEPTH );

    // Dst resembles a cube of width x height x (output channel * batches).  Each tile writes:
    // (SIMD * TILE_M) x 1 x TILE_N.  Partial writes most likely generated if padding used.
    int out_offset = global_z * out_pitch_z                                                   // batch offset
     + ( group_x * TILE_N ) * out_pitch_y                                       // channel offset
     + ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X  // y offset
     + ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT;               // x offset
    __global Dtype *out = dst + out_offset;

#if APPLY_BIAS
    Dtype bias[2];
    Dtype2 *bias_vec;
    bias_vec = (Dtype2*)bias;
    *bias_vec = as_Dtype2(SUB_GROUP_BLOCK_READ2((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
    INTERLEAVED_SIMD16_OUTPUT(dst, out_offset, 0);
}
#endif

#elif defined KERNEL_DWCONV

__kernel void DWCONV(
    ELTWISE_DATA_ARG
    NEGATIVE_SLOPE_ARG
    __global Dtype* image_data,
    __global Dtype* kernel_data,
    BIAS_KERNEL_ARG
    __global Dtype* convolved_image,
    const ushort input_width,
    const ushort input_height,
    const ushort output_width,
    const ushort output_height) {

  const int outputX = get_global_id(0);
  const int outputY = get_global_id(1);
  const int outputZ = get_global_id(2);
  if(outputX < output_width && outputY < output_height)
  {
    Dtype sum = 0.;

    const int org_y = outputY * STRIDE_Y - INPUT_PAD_H;
    const int org_x = outputX * STRIDE_X - INPUT_PAD_W;
    const int currentKernelOffset = KERNEL_SIZE*(outputZ%CHANNELS);
    const int biasIndex=outputZ%CHANNELS;
    const int local_image_offset = org_y*input_width + org_x;
    const int imageSize = input_width*input_height;

    __global Dtype* image_dataPtrFloat = (image_data + (imageSize*outputZ + local_image_offset));
    __global Dtype* kernel_dataPtrFloat = (kernel_data + (currentKernelOffset));

    for(int y = 0; y < KERNEL_H; y++)
    {
      for(int x = 0; x < KERNEL_W; x++)
      {
        if(!(org_y + y * DILATION_Y >= 0 && org_y + y * DILATION_Y < input_height && org_x + x * DILATION_X >= 0 && org_x + x * DILATION_X < input_width))
        {
          continue;
        }
        sum += image_dataPtrFloat[x * DILATION_X] * kernel_dataPtrFloat[x];
      }
      image_dataPtrFloat += input_width * DILATION_Y;
      kernel_dataPtrFloat += KERNEL_W;
    }

    #if APPLY_BIAS
    int offset = outputZ*output_height*output_width + outputY*output_width + outputX;
    ACTIVATION_FUNCTION(convolved_image, offset, sum + biases_base[biasIndex], biasIndex);
    #else
    int offset = outputZ*output_height*output_width + outputY*output_width + outputX;
    ACTIVATION_FUNCTION(convolved_image, offset, sum, biasIndex);
    #endif
  }
}
#endif // KERNEL_BASIC/IDLF/GEMM_LIKE/DWCONV