Partial merge - Clarify weight being sought for target pixel

/* Deathray - An Avisynth plug-in filter for spatial/temporal non-local means de-noising.
 *
 * version 1.01
 *
 * Copyright 2013, Jawed Ashraf - Deathray@cupidity.f9.co.uk
 */

__attribute__((reqd_work_group_size(8, 32, 1)))
__kernel void NLMMultiFrameFourPixel(
	read_only 	image2d_t 	target_plane,			// plane being filtered
	read_only 	image2d_t 	sample_plane,			// any other plane
	const		int			sample_equals_target,	// 1 when sample plane is target plane, 0 otherwise
	const		int			width,					// width in pixels
	const		int			height,					// height in pixels
	const		float		h,						// strength of denoising
	const		int			sample_expand,			// factor to expand sample radius
	constant	float		*g_gaussian,			// 49 weights of guassian kernel
	const		int			intermediate_width,		// width, in float4s, of intermediate buffers
	const		int			linear,					// process plane in linear space instead of gamma space
	global 		float4		*intermediate_average,	// intermediate average for 4 pixels
	global 		float4		*intermediate_weight,	// intermediate weight for 4 pixels
	global		float4		*intermediate_target) {	// intermediate target weights for 4 pixels

	// Each work group produces 1024 filtered pixels, organised as a tile
	// of 32x32, for a single iteration of multi-pass filtering. Each
	// iteration computes average and weight for each pixel based upon
	// the target plane and one other plane. After all passes have
	// completed, another kernel is used to compute the final average
	// for each pixel based upon the intermediate average and weights
	// cumulatively produced by each pass.
	//
	// Each work item computes 4 pixels in a contiguous horizontal strip.
	//
	// The tile is bordered with an apron that's 8 pixels wide. This apron
	// consists of pixels from adjoining tiles, where available. If the
	// tile is at the frame edge, the apron is filled with pixels mirrored
	// from just inside the frame.
	//
	// Input plane contains pixels as uchars. UNORM8 format is defined,
	// so a read converts uchar into a normalised float of range 0.f to 1.f.
	//
	// Destination is a triplet of float4 formatted buffers for average
	// (weighted running sum), weight (running sum of weights) and
	// target weight (weight that will be used at end for target pixel).

	__local float tile[TILE_SIDE * TILE_SIDE];

	int2 local_id;
	int2 source;
	Coordinates32x32(&local_id, &source);

	// Inside local memory the top-left corner of the tile is at (8,8)
	int2 target = (int2)((local_id.x << 2) + 8, local_id.y + 8);

	// The tile is 48x48 pixels which is entirely filled from the source
	FetchAndMirror48x48(target_plane, width, height, local_id, source, linear, tile) ;

	// Populate the 10x7 target window from the tile
	int kernel_radius = 3;
	float16 target_window[7];
	for (int y = 0; y < 2 * kernel_radius + 1; ++y) {
		target_window[y] = ReadTile16(target.x - kernel_radius,
									  target.y + y - kernel_radius,
									  tile);
	}

	// Most planes are planes other than the target plane, which need
	// to be fetched into the tile for sampling
	if (!sample_equals_target)
		FetchAndMirror48x48(sample_plane, width, height, local_id, source, linear, tile);

	int linear_address = source.y * intermediate_width + source.x;
	float4 average = intermediate_average[linear_address];
	float4 weight = intermediate_weight[linear_address];
	float4 target_weight = intermediate_target[linear_address];

	Filter4(target, h, sample_expand, target_window, tile, g_gaussian, sample_equals_target, &average, &weight, &target_weight);

	if (target.y < height) {
		intermediate_average[linear_address] = average;
		intermediate_weight[linear_address] = weight;
		intermediate_target[linear_address] = target_weight;
	}
}

__attribute__((reqd_work_group_size(8, 32, 1)))
__kernel void NLMFinalise(
	read_only 				image2d_t 	target_plane,			// plane being filtered
	const		global 		float4		*intermediate_average,	// final average for 4 pixels
	const		global 		float4		*intermediate_weight,	// final weight for 4 pixels
	const					int			intermediate_width,		// width, in float4s, of intermediate buffers
	const					int			linear,					// process plane in linear space instead of gamma space
	const					int			correction,				// apply a post-filtering correction
	write_only 				image2d_t 	destination_plane) {	// final result

	// Computes the final pixel value based upon the average and weight
	// values for each pixel generated by multiple filtering passes.
	//
	// Each work item computes 4 pixels in the result.
	//
	// Destination plane is formatted as UNORM8 uchar. The device
	// automatically converts a pixel in range 0.f to 1.f into 0 to 255.

	int2 local_id;
	int2 destination;
	Coordinates32x32(&local_id, &destination);

	int linear_address = destination.y * intermediate_width + destination.x;

	float4 average = intermediate_average[linear_address];
	float4 weight = intermediate_weight[linear_address];

	float4 filtered_pixels = average / weight;

	if (correction) {
		float4 original = ReadPixel4(target_plane, destination, linear);

		float4 difference = filtered_pixels - original;
		float4 correction = (difference * original * original) -
							((difference * original) * (difference * original));

		filtered_pixels = filtered_pixels - correction;
	}
	WritePixel4(filtered_pixels, destination, linear, destination_plane);
}

/* Deathray - An Avisynth plug-in filter for spatial/temporal non-local means de-noising.
 *
 * version 1.01
 *
 * Copyright 2013, Jawed Ashraf - Deathray@cupidity.f9.co.uk
 */

__attribute__((reqd_work_group_size(8, 32, 1)))
__kernel void NLMMultiFrameFourPixel(
	read_only 	image2d_t 	target_plane,			// plane being filtered
	read_only 	image2d_t 	sample_plane,			// any other plane
	const		int			sample_equals_target,	// 1 when sample plane is target plane, 0 otherwise
	const		int			width,					// width in pixels
	const		int			height,					// height in pixels
	const		float		h,						// strength of denoising
	const		int			sample_expand,			// factor to expand sample radius
	constant	float		*g_gaussian,			// 49 weights of guassian kernel
	const		int			intermediate_width,		// width, in float4s, of intermediate buffers
	const		int			linear,					// process plane in linear space instead of gamma space
	global 		float4		*intermediate_average,	// intermediate average for 4 pixels
	global 		float4		*intermediate_weight,	// intermediate weight for 4 pixels
	global		float4		*intermediate_max) {	// intermediate maximum weights for 4 pixels

	// Each work group produces 1024 filtered pixels, organised as a tile
	// of 32x32, for a single iteration of multi-pass filtering. Each
	// iteration computes average and weight for each pixel based upon
	// the target plane and one other plane. After all passes have
	// completed, another kernel is used to compute the final average
	// for each pixel based upon the intermediate average and weights
	// cumulatively produced by each pass.
	//
	// Each work item computes 4 pixels in a contiguous horizontal strip.
	//
	// The tile is bordered with an apron that's 8 pixels wide. This apron
	// consists of pixels from adjoining tiles, where available. If the
	// tile is at the frame edge, the apron is filled with pixels mirrored
	// from just inside the frame.
	//
	// Input plane contains pixels as uchars. UNORM8 format is defined,
	// so a read converts uchar into a normalised float of range 0.f to 1.f.
	//
	// Destination is a pair of float4 formatted buffers for average
	// (weighted running sum) and weight (running sum of weights).

	__local float tile[TILE_SIDE * TILE_SIDE];

	int2 local_id;
	int2 source;
	Coordinates32x32(&local_id, &source);

	// Inside local memory the top-left corner of the tile is at (8,8)
	int2 target = (int2)((local_id.x << 2) + 8, local_id.y + 8);

	// The tile is 48x48 pixels which is entirely filled from the source
	FetchAndMirror48x48(target_plane, width, height, local_id, source, linear, tile) ;

	// Populate the 10x7 target window from the tile
	int kernel_radius = 3;
	float16 target_window[7];
	for (int y = 0; y < 2 * kernel_radius + 1; ++y) {
		target_window[y] = ReadTile16(target.x - kernel_radius,
									  target.y + y - kernel_radius,
									  tile);
	}

	// Most planes are planes other than the target plane, which need
	// to be fetched into the tile for sampling
	if (!sample_equals_target)
		FetchAndMirror48x48(sample_plane, width, height, local_id, source, linear, tile);

	int linear_address = source.y * intermediate_width + source.x;
	float4 average = intermediate_average[linear_address];
	float4 weight = intermediate_weight[linear_address];
	float4 weight_max = intermediate_max[linear_address];

	Filter4(target, h, sample_expand, target_window, tile, g_gaussian, sample_equals_target, &average, &weight, &weight_max);

	if (target.y < height) {
		intermediate_average[linear_address] = average;
		intermediate_weight[linear_address] = weight;
		intermediate_max[linear_address] = weight_max;
	}
}

__attribute__((reqd_work_group_size(8, 32, 1)))
__kernel void NLMFinalise(
	read_only 				image2d_t 	target_plane,			// plane being filtered
	const		global 		float4		*intermediate_average,	// final average for 4 pixels
	const		global 		float4		*intermediate_weight,	// final weight for 4 pixels
	const					int			intermediate_width,		// width, in float4s, of intermediate buffers
	const					int			linear,					// process plane in linear space instead of gamma space
	const					int			correction,				// apply a post-filtering correction
	write_only 				image2d_t 	destination_plane) {	// final result

	// Computes the final pixel value based upon the average and weight
	// values for each pixel generated by multiple filtering passes.
	//
	// Each work item computes 4 pixels in the result.
	//
	// Destination plane is formatted as UNORM8 uchar. The device
	// automatically converts a pixel in range 0.f to 1.f into 0 to 255.

	int2 local_id;
	int2 destination;
	Coordinates32x32(&local_id, &destination);

	int linear_address = destination.y * intermediate_width + destination.x;

	float4 average = intermediate_average[linear_address];
	float4 weight = intermediate_weight[linear_address];

	float4 filtered_pixels = average / weight;

	if (correction) {
		float4 original = ReadPixel4(target_plane, destination, linear);

		float4 difference = filtered_pixels - original;
		float4 correction = (difference * original * original) -
							((difference * original) * (difference * original));

		filtered_pixels = filtered_pixels - correction;
	}
	WritePixel4(filtered_pixels, destination, linear, destination_plane);
}


fix MLO_N_LCL_IN_MAPS_ONCE definition

fix MLO_N_LCL_IN_MAPS_ONCE definition

Definition was erroneously located after 1st use

/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (c) 2017 Advanced Micro Devices, Inc.
 *
 * 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.
 *
 *******************************************************************************/

// Trying to use float ATOMIC_ADD to increase total waves
// For example,  7x7 : 49
// Global 0:  H * W * N * (K/ 16 outplane per threads ) * (( Inputlanes/ 128 )   or 1)
// N * H * W =
//    [groupId / ((K/ 16 outplane per threads ) * (( Inputlanes/ 128 )   or 1))] * 64 + localId
// For example, H7*W7*N*C256*K64
// FIRST 4 waves
// Hit L2 for read
// try to Hit L2 for Atomic_ADD
// Assembly Shader can utlize LDS to Reductiion
// However Shader Compiler of OpenCL will compile any localId[1] to FLAT_BUFFER_LOAD not constant
// load

// WAVE 0  == N0_7x7 + N1_(7x2+1),  output  0-15  from K=64,  Inputplanes from 0-127
// WAVE 1  == N0_7x7 + N1_(7x2+1),  output  16-31 from K=64,  Inputplanes from 0-127
// WAVE 2  == N0_7x7 + N1_(7x2+1),  output  31-47 from K=64,  Inputplanes from 0-127
// WAVE 3  == N0_7x7 + N1_(7x2+1),  output  48-63 from K=64,  Inputplanes from 0-127
// 2nd 4 waves
// Hit L2 for read
// try to Hit L2 for Atomic_ADD

// WAVE 4  == N0_7x7 + N1_(7x2+1),  output  0-15  from K=64,  Inputplanes from 128-255
// WAVE 5  == N0_7x7 + N1_(7x2+1),  output  16-31 from K=64,  Inputplanes from 128-255
// WAVE 6  == N0_7x7 + N1_(7x2+1),  output  31-47 from K=64,  Inputplanes from 128-255
// WAVE 7  == N0_7x7 + N1_(7x2+1),  output  48-63 from K=64,  Inputplanes from 128-255

// example
#if 0 // ndef MLopen_RUNNING
#define BATCHSIZE 32
#define MLO_N_LCL_IN_MAPS_ONCE 8

#define H 28
#define W 28
#define C 192
#define K 64

#define MLO_IN_HEIGHT H
#define MLO_IN_WIDTH W
#define MLO_N_INPUTS C

//128 or MLO_N_INPUTS
#define MLO_N_LCL_IN_MAPS 192

#define MLO_N_OUTPUTS K

#define H_out 28
#define W_out 28
#define MLO_N_LCL_OUT_MAPS 16

#define MLO_N_IN_GROUPS ((MLO_N_INPUTS + MLO_N_LCL_IN_MAPS - 1) / MLO_N_LCL_IN_MAPS)
#define MLO_CLOOP0 (MLO_N_LCL_IN_MAPS / MLO_N_LCL_IN_MAPS_ONCE)
#define MLO_CLOOP2 \
    ((MLO_N_INPUTS - MLO_N_LCL_IN_MAPS * (MLO_N_IN_GROUPS - 1)) / MLO_N_LCL_IN_MAPS_ONCE)
#define MLO_CHEAT_SHADER_COMPILER 1

#endif

#define MLO_IN_CHANNEL_STRIDE (H * W)
#define MLO_IN_BATCH_STRIDE (H * W * C)

#define MLO_WEI_BSTRIDE (1 * 1 * C * K)
#define MLO_WEI_CHANNEL_STRIDE (1 * 1 * C)

#define MLO_OUT_BATCH_STRIDE (H_out * W_out * K)
#define MLO_OUT_CHANNEL_STRIDE (H_out * W_out)

#define FIXED_WORKGROUP_SIZE 64

#define MLO_N_OUT_GROUPS (MLO_N_OUTPUTS / MLO_N_LCL_OUT_MAPS)

#define MLO_GRP_SZ0 64
#define MLO_GRP_SZ1 1
#define MLO_GRP_SZ2 1

#define PPCAT_NX(A, B) A##B
#define PPCAT(A, B) PPCAT_NX(A, B)
#define TWO 2
#define FOUR 4
#define EIGHT 8

#if MIOPEN_USE_FP16 == 1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define _FLOAT half
#define _UNION_FLOAT_T half2
#define INIT(A) ((half2)(A[0], A[1]))
#ifndef HALF_MAX
#define MAX_VAL 65504 /* max value */
#else
#define MAX_VAL HALF_MAX
#endif
#endif
#if MIOPEN_USE_FP32 == 1
#define _FLOAT float
#define _UNION_FLOAT_T float
#define INIT(A) (A[0])
#ifndef FLT_MAX
#define MAX_VAL 3.402823466e+38F /* max value */
#else
#define MAX_VAL FLT_MAX
#endif
#endif

#define _FLOAT2 PPCAT(_FLOAT, TWO)
#define _FLOAT4 PPCAT(_FLOAT, FOUR)
#define _FLOAT8 PPCAT(_FLOAT, EIGHT)

#define MLO_CONV_BIAS 0
#define UNUSED __attribute((__unused__))

typedef union
{
    unsigned int intVal;
    _UNION_FLOAT_T floatVal;
} starVal;

inline void AtomicAdd(volatile __global _FLOAT* source, const _FLOAT operand)
{
    starVal newVal, prevVal;

    prevVal.floatVal = INIT(source);
    while(true)
    {
#if MIOPEN_USE_FP16 == 1
        newVal.floatVal = (_FLOAT2)(prevVal.floatVal.x + operand, source[1]);
#endif
#if MIOPEN_USE_FP32 == 1
        newVal.floatVal = prevVal.floatVal + operand;
#endif
        newVal.intVal =
            atomic_cmpxchg((volatile __global unsigned int*)source, prevVal.intVal, newVal.intVal);

        // equal to pass
        if(newVal.intVal == prevVal.intVal)
            break;

        prevVal.intVal = newVal.intVal;
    }
}

__attribute__((reqd_work_group_size(MLO_GRP_SZ0, MLO_GRP_SZ1, MLO_GRP_SZ2))) __kernel void
MIOpenConv1x1(const __global _FLOAT* __restrict in_ptr,
              __constant _FLOAT* __restrict wei_ptr,
#if MLO_CONV_BIAS
              const __global _FLOAT* __restrict bias,
#endif
              __global _FLOAT* __restrict out_ptr,
              UNUSED _FLOAT dummy_val // nothing
              )
{

    uint grp_id0       = get_group_id(0);
    uint out_grp_block = grp_id0 % MLO_N_OUT_GROUPS;
    uint in_grp_block  = (uint)(grp_id0 / MLO_N_OUT_GROUPS) % MLO_N_IN_GROUPS;
    uint grp_id0_faked = (uint)(grp_id0 / MLO_N_OUT_GROUPS) / MLO_N_IN_GROUPS;

    uint local_id0 = get_local_id(0);
#if MLO_CHEAT_SHADER_COMPILER == 1
    uint grp_id2 = get_group_id(2);
#endif

    uint pos      = (grp_id0_faked * FIXED_WORKGROUP_SIZE + local_id0) % MLO_IN_CHANNEL_STRIDE;
    uint batch_id = (grp_id0_faked * FIXED_WORKGROUP_SIZE + local_id0) / MLO_IN_CHANNEL_STRIDE;

    if(batch_id >= BATCHSIZE)
        return;

    uint out_id = out_grp_block * MLO_N_LCL_OUT_MAPS;

    uint gbl_in_off = batch_id * MLO_IN_BATCH_STRIDE +
                      in_grp_block * MLO_N_LCL_IN_MAPS * MLO_IN_CHANNEL_STRIDE + pos;

    uint wei_off = out_id * MLO_WEI_CHANNEL_STRIDE + in_grp_block * MLO_N_LCL_IN_MAPS;

    _FLOAT accum[MLO_N_LCL_OUT_MAPS];
    _FLOAT weights[MLO_N_LCL_IN_MAPS_ONCE];
    _FLOAT dat[MLO_N_LCL_IN_MAPS_ONCE];
    _FLOAT dat2[MLO_N_LCL_IN_MAPS_ONCE];

//

// ATOMIC is needed if INPUTS in many waves
#if(MLO_N_LCL_IN_MAPS != MLO_N_INPUTS)

    if(in_grp_block == 0)
    {
        uint gbl_out_off = batch_id * MLO_OUT_BATCH_STRIDE + out_id * MLO_OUT_CHANNEL_STRIDE + pos;
        __global _FLOAT* q = out_ptr + gbl_out_off;

        for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
        {
            *q = (_FLOAT)0;
            q += MLO_OUT_CHANNEL_STRIDE;
        }
    }
#endif

    for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
    {
        accum[o] = (_FLOAT)0;
    }

#if MLO_N_INPUTS == ((MLO_N_INPUTS / MLO_N_LCL_IN_MAPS) * MLO_N_LCL_IN_MAPS)
    // if(1)
    int loops = MLO_CLOOP0;

#if MLO_CHEAT_SHADER_COMPILER == 1
    // cheat shader compiler to disable loop unroll.  it will have better SQC performance
    if(grp_id2 == 0x1F)
    {
        loops = 377; // strange not to unroll loop
    }
#endif
#else
    int loops = MLO_CLOOP0;

    if(in_grp_block == (MLO_N_IN_GROUPS - 1))
    {
        loops = MLO_CLOOP2;
    }

#if MLO_CHEAT_SHADER_COMPILER == 1
    // cheat shader compiler to disable loop unroll.  it will have better SQC performance
    if(grp_id2 == 0x1F)
    {
        loops = 377; // strange not to unroll loop
    }
#endif

#endif
    {
        __global const _FLOAT* p = in_ptr + gbl_in_off;
        __constant _FLOAT* w     = wei_ptr + wei_off;

        // read data
        for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
        {

            dat[j] = *p;
            p += MLO_IN_CHANNEL_STRIDE;
        }

        for(uint ci = 0; ci < (loops - 2); ci += 2)
        {
            // read data
            for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
            {
                dat2[j] = *p;
                p += MLO_IN_CHANNEL_STRIDE;
            }

            // convolve
            __constant _FLOAT* w1 = w;
            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;

            // convolve
            w1 = w;
            for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
            {
                dat[j] = *p;
                p += MLO_IN_CHANNEL_STRIDE;
            }

            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat2[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;
        }

        //
        // last 2 iterations
        { // read data
            for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
            {
                dat2[j] = *p;
                p += MLO_IN_CHANNEL_STRIDE;
            }

            // convolve
            __constant _FLOAT* w1 = w;
            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;

            // convolve
            w1 = w;

            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat2[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;
        }
    }

    uint gbl_out_off   = batch_id * MLO_OUT_BATCH_STRIDE + out_id * MLO_OUT_CHANNEL_STRIDE + pos;
    __global _FLOAT* q = out_ptr + gbl_out_off;

    for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
    {
#if(MLO_N_LCL_IN_MAPS == MLO_N_INPUTS)
        *q = accum[o];
        q += MLO_OUT_CHANNEL_STRIDE;
#else
        AtomicAdd(q, accum[o]);
        q += MLO_OUT_CHANNEL_STRIDE;
#endif
    }
}

/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (c) 2017 Advanced Micro Devices, Inc.
 *
 * 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.
 *
 *******************************************************************************/

// Trying to use float ATOMIC_ADD to increase total waves
// For example,  7x7 : 49
// Global 0:  H * W * N * (K/ 16 outplane per threads ) * (( Inputlanes/ 128 )   or 1)
// N * H * W =
//    [groupId / ((K/ 16 outplane per threads ) * (( Inputlanes/ 128 )   or 1))] * 64 + localId
// For example, H7*W7*N*C256*K64
// FIRST 4 waves
// Hit L2 for read
// try to Hit L2 for Atomic_ADD
// Assembly Shader can utlize LDS to Reductiion
// However Shader Compiler of OpenCL will compile any localId[1] to FLAT_BUFFER_LOAD not constant
// load

// WAVE 0  == N0_7x7 + N1_(7x2+1),  output  0-15  from K=64,  Inputplanes from 0-127
// WAVE 1  == N0_7x7 + N1_(7x2+1),  output  16-31 from K=64,  Inputplanes from 0-127
// WAVE 2  == N0_7x7 + N1_(7x2+1),  output  31-47 from K=64,  Inputplanes from 0-127
// WAVE 3  == N0_7x7 + N1_(7x2+1),  output  48-63 from K=64,  Inputplanes from 0-127
// 2nd 4 waves
// Hit L2 for read
// try to Hit L2 for Atomic_ADD

// WAVE 4  == N0_7x7 + N1_(7x2+1),  output  0-15  from K=64,  Inputplanes from 128-255
// WAVE 5  == N0_7x7 + N1_(7x2+1),  output  16-31 from K=64,  Inputplanes from 128-255
// WAVE 6  == N0_7x7 + N1_(7x2+1),  output  31-47 from K=64,  Inputplanes from 128-255
// WAVE 7  == N0_7x7 + N1_(7x2+1),  output  48-63 from K=64,  Inputplanes from 128-255

// example
#if 0 // ndef MLopen_RUNNING
#define BATCHSIZE 32
#define H 28
#define W 28
#define C 192
#define K 64

#define MLO_IN_HEIGHT H
#define MLO_IN_WIDTH W
#define MLO_N_INPUTS C

//128 or MLO_N_INPUTS
#define MLO_N_LCL_IN_MAPS 192

#define MLO_N_OUTPUTS K

#define H_out 28
#define W_out 28
#define MLO_N_LCL_OUT_MAPS 16

#define MLO_N_IN_GROUPS ((MLO_N_INPUTS + MLO_N_LCL_IN_MAPS - 1) / MLO_N_LCL_IN_MAPS)
#define MLO_CLOOP0 (MLO_N_LCL_IN_MAPS / MLO_N_LCL_IN_MAPS_ONCE)
#define MLO_CLOOP2 \
    ((MLO_N_INPUTS - MLO_N_LCL_IN_MAPS * (MLO_N_IN_GROUPS - 1)) / MLO_N_LCL_IN_MAPS_ONCE)
#define MLO_CHEAT_SHADER_COMPILER 1

#endif

#define MLO_IN_CHANNEL_STRIDE (H * W)
#define MLO_IN_BATCH_STRIDE (H * W * C)

#define MLO_WEI_BSTRIDE (1 * 1 * C * K)
#define MLO_WEI_CHANNEL_STRIDE (1 * 1 * C)

#define MLO_N_LCL_IN_MAPS_ONCE 8

#define MLO_OUT_BATCH_STRIDE (H_out * W_out * K)
#define MLO_OUT_CHANNEL_STRIDE (H_out * W_out)

#define FIXED_WORKGROUP_SIZE 64

#define MLO_N_OUT_GROUPS (MLO_N_OUTPUTS / MLO_N_LCL_OUT_MAPS)

#define MLO_GRP_SZ0 64
#define MLO_GRP_SZ1 1
#define MLO_GRP_SZ2 1

#define PPCAT_NX(A, B) A##B
#define PPCAT(A, B) PPCAT_NX(A, B)
#define TWO 2
#define FOUR 4
#define EIGHT 8

#if MIOPEN_USE_FP16 == 1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define _FLOAT half
#define _UNION_FLOAT_T half2
#define INIT(A) ((half2)(A[0], A[1]))
#ifndef HALF_MAX
#define MAX_VAL 65504 /* max value */
#else
#define MAX_VAL HALF_MAX
#endif
#endif
#if MIOPEN_USE_FP32 == 1
#define _FLOAT float
#define _UNION_FLOAT_T float
#define INIT(A) (A[0])
#ifndef FLT_MAX
#define MAX_VAL 3.402823466e+38F /* max value */
#else
#define MAX_VAL FLT_MAX
#endif
#endif

#define _FLOAT2 PPCAT(_FLOAT, TWO)
#define _FLOAT4 PPCAT(_FLOAT, FOUR)
#define _FLOAT8 PPCAT(_FLOAT, EIGHT)

#define MLO_CONV_BIAS 0
#define UNUSED __attribute((__unused__))

typedef union
{
    unsigned int intVal;
    _UNION_FLOAT_T floatVal;
} starVal;

inline void AtomicAdd(volatile __global _FLOAT* source, const _FLOAT operand)
{
    starVal newVal, prevVal;

    prevVal.floatVal = INIT(source);
    while(true)
    {
#if MIOPEN_USE_FP16 == 1
        newVal.floatVal = (_FLOAT2)(prevVal.floatVal.x + operand, source[1]);
#endif
#if MIOPEN_USE_FP32 == 1
        newVal.floatVal = prevVal.floatVal + operand;
#endif
        newVal.intVal =
            atomic_cmpxchg((volatile __global unsigned int*)source, prevVal.intVal, newVal.intVal);

        // equal to pass
        if(newVal.intVal == prevVal.intVal)
            break;

        prevVal.intVal = newVal.intVal;
    }
}

__attribute__((reqd_work_group_size(MLO_GRP_SZ0, MLO_GRP_SZ1, MLO_GRP_SZ2))) __kernel void
MIOpenConv1x1(const __global _FLOAT* __restrict in_ptr,
              __constant _FLOAT* __restrict wei_ptr,
#if MLO_CONV_BIAS
              const __global _FLOAT* __restrict bias,
#endif
              __global _FLOAT* __restrict out_ptr,
              UNUSED _FLOAT dummy_val // nothing
              )
{

    uint grp_id0       = get_group_id(0);
    uint out_grp_block = grp_id0 % MLO_N_OUT_GROUPS;
    uint in_grp_block  = (uint)(grp_id0 / MLO_N_OUT_GROUPS) % MLO_N_IN_GROUPS;
    uint grp_id0_faked = (uint)(grp_id0 / MLO_N_OUT_GROUPS) / MLO_N_IN_GROUPS;

    uint local_id0 = get_local_id(0);
#if MLO_CHEAT_SHADER_COMPILER == 1
    uint grp_id2 = get_group_id(2);
#endif

    uint pos      = (grp_id0_faked * FIXED_WORKGROUP_SIZE + local_id0) % MLO_IN_CHANNEL_STRIDE;
    uint batch_id = (grp_id0_faked * FIXED_WORKGROUP_SIZE + local_id0) / MLO_IN_CHANNEL_STRIDE;

    if(batch_id >= BATCHSIZE)
        return;

    uint out_id = out_grp_block * MLO_N_LCL_OUT_MAPS;

    uint gbl_in_off = batch_id * MLO_IN_BATCH_STRIDE +
                      in_grp_block * MLO_N_LCL_IN_MAPS * MLO_IN_CHANNEL_STRIDE + pos;

    uint wei_off = out_id * MLO_WEI_CHANNEL_STRIDE + in_grp_block * MLO_N_LCL_IN_MAPS;

    _FLOAT accum[MLO_N_LCL_OUT_MAPS];
    _FLOAT weights[MLO_N_LCL_IN_MAPS_ONCE];
    _FLOAT dat[MLO_N_LCL_IN_MAPS_ONCE];
    _FLOAT dat2[MLO_N_LCL_IN_MAPS_ONCE];

//

// ATOMIC is needed if INPUTS in many waves
#if(MLO_N_LCL_IN_MAPS != MLO_N_INPUTS)

    if(in_grp_block == 0)
    {
        uint gbl_out_off = batch_id * MLO_OUT_BATCH_STRIDE + out_id * MLO_OUT_CHANNEL_STRIDE + pos;
        __global _FLOAT* q = out_ptr + gbl_out_off;

        for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
        {
            *q = (_FLOAT)0;
            q += MLO_OUT_CHANNEL_STRIDE;
        }
    }
#endif

    for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
    {
        accum[o] = (_FLOAT)0;
    }

#if MLO_N_INPUTS == ((MLO_N_INPUTS / MLO_N_LCL_IN_MAPS) * MLO_N_LCL_IN_MAPS)
    // if(1)
    int loops = MLO_CLOOP0;

#if MLO_CHEAT_SHADER_COMPILER == 1
    // cheat shader compiler to disable loop unroll.  it will have better SQC performance
    if(grp_id2 == 0x1F)
    {
        loops = 377; // strange not to unroll loop
    }
#endif
#else
    int loops = MLO_CLOOP0;

    if(in_grp_block == (MLO_N_IN_GROUPS - 1))
    {
        loops = MLO_CLOOP2;
    }

#if MLO_CHEAT_SHADER_COMPILER == 1
    // cheat shader compiler to disable loop unroll.  it will have better SQC performance
    if(grp_id2 == 0x1F)
    {
        loops = 377; // strange not to unroll loop
    }
#endif

#endif
    {
        __global const _FLOAT* p = in_ptr + gbl_in_off;
        __constant _FLOAT* w     = wei_ptr + wei_off;

        // read data
        for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
        {

            dat[j] = *p;
            p += MLO_IN_CHANNEL_STRIDE;
        }

        for(uint ci = 0; ci < (loops - 2); ci += 2)
        {
            // read data
            for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
            {
                dat2[j] = *p;
                p += MLO_IN_CHANNEL_STRIDE;
            }

            // convolve
            __constant _FLOAT* w1 = w;
            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;

            // convolve
            w1 = w;
            for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
            {
                dat[j] = *p;
                p += MLO_IN_CHANNEL_STRIDE;
            }

            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat2[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;
        }

        //
        // last 2 iterations
        { // read data
            for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
            {
                dat2[j] = *p;
                p += MLO_IN_CHANNEL_STRIDE;
            }

            // convolve
            __constant _FLOAT* w1 = w;
            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;

            // convolve
            w1 = w;

            for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
            {

                __constant _FLOAT* w2 = w1;

                for(uint j = 0; j < MLO_N_LCL_IN_MAPS_ONCE; ++j)
                {
                    weights[j] = *w2;
                    w2++;
                }
                w1 += MLO_WEI_CHANNEL_STRIDE;

                for(uint c = 0; c < MLO_N_LCL_IN_MAPS_ONCE; ++c)
                {
                    accum[o] += dat2[c] * weights[c];
                }
            }

            // move weights offset
            w += MLO_N_LCL_IN_MAPS_ONCE;
        }
    }

    uint gbl_out_off   = batch_id * MLO_OUT_BATCH_STRIDE + out_id * MLO_OUT_CHANNEL_STRIDE + pos;
    __global _FLOAT* q = out_ptr + gbl_out_off;

    for(uint o = 0; o < MLO_N_LCL_OUT_MAPS; ++o)
    {
#if(MLO_N_LCL_IN_MAPS == MLO_N_INPUTS)
        *q = accum[o];
        q += MLO_OUT_CHANNEL_STRIDE;
#else
        AtomicAdd(q, accum[o]);
        q += MLO_OUT_CHANNEL_STRIDE;
#endif
    }
}


clean up

clean up

/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (c) 2017 Advanced Micro Devices, Inc.
 *
 * 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.
 *
 *******************************************************************************/

#define PPCAT_NX(A, B) A##B
#define PPCAT(A, B) PPCAT_NX(A, B)
#define TWO 2
#define FOUR 4
#define EIGHT 8

#if MIOPEN_USE_FP16 == 1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define _FLOAT half
#define _FLOAT_ACCUM float
#ifndef HALF_MAX
#define MAX_VAL 65504 /* max value */
#else
#define MAX_VAL HALF_MAX
#endif

#endif
#if MIOPEN_USE_FP32 == 1
#define _FLOAT float
#define _FLOAT_ACCUM float
#ifndef FLT_MAX
#define MAX_VAL 3.402823466e+38F /* max value */
#else
#define MAX_VAL FLT_MAX
#endif
#endif

#define _FLOAT2 PPCAT(_FLOAT, TWO)
#define _FLOAT4 PPCAT(_FLOAT, FOUR)
#define _FLOAT8 PPCAT(_FLOAT, EIGHT)

#define UNUSED __attribute__((__unused__))

#define MLO_N_OUT_HORIZ_READS ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1) / MLO_IN_TILE0)
#define MLO_N_SPANS_PER_SCAN (MLO_N_OUT_HORIZ_READS)
#define MLO_N_OUT_HORIZ_PIX_READS (MLO_N_OUT_HORIZ_READS * MLO_IN_TILE0)
#define MLO_OUT_N_PIXS_OFF (MLO_OUT_WIDTH - ((MLO_OUT_WIDTH / MLO_IN_TILE0) * MLO_IN_TILE0))
#define MLO_N_OUT_VERTICAL_READS (MLO_FILTER_SIZE1)
// won't run non-border blocks if  MLO_IN_N_VERT_LOOPS < 2
//

#define MLO_IN_VERT_READS MLO_IN_EXTENT1

#if MLO_IN_N_VERT_LOOPS >= 2
#define MLO_N_GENERIC_LOOPS (MLO_IN_N_VERT_LOOPS - 2)
#else
#define MLO_N_GENERIC_LOOPS 0
#endif

// there is an assumption that the scanline fits into LDS
#define MLO_N_IN_HORIZ_PIX_READS (MLO_IN_WIDTH)
#define MLO_N_IN_HORIZ_READS ((MLO_N_IN_HORIZ_PIX_READS + MLO_READ_UNIT - 1) / MLO_READ_UNIT)
#define MLO_IN_N_PIXS_OFF \
    (MLO_N_IN_HORIZ_PIX_READS - (MLO_N_IN_HORIZ_PIX_READS / MLO_READ_UNIT) * MLO_READ_UNIT)
#define MLO_IN_LCL_WIDTH (MLO_N_IN_HORIZ_READS * MLO_READ_UNIT + 2 * MLO_FILTER_PAD0)
#define MLO_IN_LCL_HEIGHT MLO_IN_VERT_READS
#define MLO_IN_LCL_SZ (MLO_IN_LCL_WIDTH * MLO_IN_LCL_HEIGHT)
#define MLO_TOTAL_IN_LCL_SZ (MLO_N_LCL_BATCHS * MLO_N_LCL_IN_MAPS * MLO_IN_LCL_SZ)

#define MLO_WEI_LCL_SZ (MLO_GRP_SZ * MLO_FILTER_SIZE0)
#if MLO_TOTAL_IN_LCL_SZ > MLO_WEI_LCL_SZ
#define MLO_LCL_SZ (MLO_TOTAL_IN_LCL_SZ)
#else
#define MLO_LCL_SZ (MLO_WEI_LCL_SZ)
#endif

__attribute__((always_inline)) uint iDiv(uint v, uint d)
{
    uint r = v / d;
    return (r);
}

__attribute__((always_inline)) uint iMod(uint v, uint u, uint d)
{
    uint r = v - mul24((uint)u, (uint)d);
    return (r);
}

#if 0
// This function is for log-reduction of content in LDS,
// it's supposed to be called by MIOpenCvBwdWrW,
// However, it's no longer used and not tested.
__attribute__((always_inline)) void ReduceKernel(__local _FLOAT* lcl_blob,
                                                 __private _FLOAT* weights_accum,
                                                 uint lcl_id,
                                                 uint scan_lcl,
                                                 uint sum_stride,
                                                 uint unit_len,
                                                 UNUSED bool debug)
{
    // read first half
    if(scan_lcl < (sum_stride >> 1))
    {
        for(uint i = 0; i < unit_len; ++i)
        {
            weights_accum[i] = lcl_blob[(lcl_id + scan_lcl) * unit_len + i];
        }
    }
    // add second half
    // appload accumulated value so far
    for(uint j = (sum_stride >> 1); j > 0; j >>= 1)
    {
        barrier(CLK_LOCAL_MEM_FENCE);
        if(scan_lcl < j)
        {
            for(uint i = 0; i < unit_len; ++i)
            {
                weights_accum[i] += lcl_blob[(lcl_id + j) * unit_len + i];

                lcl_blob[lcl_id * unit_len + i] = weights_accum[i];
            }
        }
    }
}
#endif

/*
        group cooperative read
        read by MLO_READ_UNIT
        handle out of range both horizontally and vertically (by fixed number of veryical reads)

        no guard against number of inputs
*/
__attribute__((always_inline)) void readInput(uint lcl_id,
                                              uint gbl_in_scan_off,
                                              uint n_in_map_reads,
                                              uint n_v_reads,
                                              const __global _FLOAT* __restrict bot,
                                              __local _FLOAT* __restrict lcl_bot)
{
    for(uint p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * n_v_reads;
        p4 += MLO_GRP_SZ)
    {
        uint c    = 0;
        uint t_p4 = p4;
#if MLO_N_LCL_IN_MAPS > 1
        c    = p4 / (MLO_N_IN_HORIZ_READS * n_v_reads);
        t_p4 = iMod(p4, c, (MLO_N_IN_HORIZ_READS * n_v_reads));
#endif

        uint c_scan = t_p4 / (MLO_N_IN_HORIZ_READS);

#if MLO_N_IN_HORIZ_READS & (MLO_N_IN_HORIZ_READS - 1)
        uint c_pix4 = iMod(t_p4, c_scan, (MLO_N_IN_HORIZ_READS));
#else
        uint c_pix4 = t_p4 & (MLO_N_IN_HORIZ_READS - 1);
#endif

        uint bot_off = gbl_in_scan_off + c * MLO_IN_CHANNEL_STRIDE + c_scan * MLO_IN_STRIDE +
                       c_pix4 * MLO_READ_UNIT;
        const __global _FLOAT* bot_p = &bot[bot_off];

        __private _FLOAT in_rd_data[MLO_READ_UNIT];

        for(uint i = 0; i < MLO_READ_UNIT; ++i)
        {
            in_rd_data[i] = 0;
        }

        if(c < n_in_map_reads)
        {
#if MLO_IN_N_PIXS_OFF > 0
            if(c_pix4 == MLO_N_IN_HORIZ_READS - 1)
            {
                for(uint i = 0; i < MLO_IN_N_PIXS_OFF; ++i)
                {
                    in_rd_data[i] = bot_p[i];
                }
            }
            else
#endif
            {
                for(uint i = 0; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = bot_p[i];
                }
            }
        }

        // MLO_N_LCL_IN_MAPS inputs
        for(uint i = 0; i < MLO_READ_UNIT; ++i)
        {
            int lcl_in_off = c * MLO_IN_LCL_SZ + c_scan * MLO_IN_LCL_WIDTH + MLO_FILTER_PAD0 +
                             c_pix4 * MLO_READ_UNIT + i;
            lcl_bot[lcl_in_off] = in_rd_data[i];
        }

    } // for (int p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * MLO_IN_VERT_READS;

    barrier(CLK_LOCAL_MEM_FENCE);
}

/*
        core processing loop
        bot - input, from local (1 span)
        top - output diff, from global (array of spans, filters vertical size)

        loop over filter vertical size
*/
__attribute__((always_inline)) void
Processing(UNUSED uint sc,
           uint sc_lcl_off,
           uint top_lim,
           int bot_lim, // bot_lim could be negative at lower boundary padding
           __private _FLOAT_ACCUM* __restrict pvt_accum,
           __local _FLOAT* __restrict lcl_bot,
           __private _FLOAT* __restrict top_dat)
{
    for(int l = top_lim; l >= bot_lim; --l)
    {
        for(uint m = 0; m < MLO_IN_TILE0; ++m)
        {
            for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
            {
                for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
                {
                    uint bot_off = sc_lcl_off + c * MLO_IN_LCL_SZ + n + m;

                    _FLOAT bot_val = lcl_bot[bot_off];

                    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                    {
                        uint pvt_top_off =
                            k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (top_lim - l) * MLO_IN_TILE0 + m;
                        uint pvt_accum_off =
                            (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                            l * MLO_FILTER_SIZE0 + n;

                        _FLOAT top_val = top_dat[pvt_top_off];

                        pvt_accum[pvt_accum_off]
                            // each wk-it process an input
                            += (_FLOAT_ACCUM)(bot_val * top_val);
                    }
                }
            }
        }
    }
}

__attribute__((always_inline)) void moveOutputUp(__private _FLOAT* __restrict top_dat)
{
    // move up output to reduce overfetch
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
        {
            for(uint i = 0; i < MLO_IN_TILE0; ++i)
            {
                uint pvt_off_n = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0 + i;
                uint pvt_off_o = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (j + 1) * MLO_IN_TILE0 + i;
                top_dat[pvt_off_n] = top_dat[pvt_off_o];
            }
        }
    }
}

__attribute__((always_inline)) void spanReadingOutput(int spn,
                                                      int k,
                                                      int j,
                                                      int top_df_off,
                                                      _FLOAT mask,
                                                      __private _FLOAT* __restrict top_dat,
                                                      const __global _FLOAT* __restrict top_df)
{
    int pvt_off                     = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0;
    const __global _FLOAT* top_df_p = &top_df[top_df_off];
#if MLO_OUT_N_PIXS_OFF > 0
    if(spn == MLO_N_SPANS_PER_SCAN - 1)
    {
        uint i = 0;
        for(; i < MLO_OUT_N_PIXS_OFF; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;
        }
        for(; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = 0;
        }
    }
    else
#else
    (void)spn;
#endif
    {
        for(uint i = 0; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;
        }
    }
}

/*********************************************************************************************************
// wrw algorithm for large filters
// idea:
// split output scan-line on number of spans by the  MLO_IN_TILE0 (2 for example)
// 1 scan-line has ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) spans
// group will process MLO_GRP_SZ/((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) output maps

// alg
// load a block of input map (or full map) into LDS
// loop
// read MLO_FILTER_SIZE1 number of spans from output map into VGPRs (for example 5 *2 = 10)
// read 1 input line for  maps into LDS
// accumulate

// accumulate all spans at the end
// start new loop for the next batch (if defined)
// write out

// kerenl handles 5x5, 3x3 with padding
// small images in 1 short- MLO_N_GENERIC_LOOPS == 0
// big images  in 2 blocks - MLO_IN_N_VERT_LOOPS == 2 or multiple blocks - MLO_IN_N_VERT_LOOPS > 2
// there are prolog and apilog that deal with top/bottom padding.
// left/right padding handles as a LDS border pixels zeroed at the beginning.

**********************************************************************************************************/

__attribute__((reqd_work_group_size(MLO_GRP_SZ0, MLO_GRP_SZ1, MLO_GRP_SZ2))) __kernel void
MIOpenCvBwdWrW(const __global _FLOAT* __restrict top_df,
               const __global _FLOAT* __restrict bot,
               __global _FLOAT* __restrict weights_df,
#if MLO_CONV_BIAS
               __global _FLOAT* __restrict bias_df,
#endif
               UNUSED _FLOAT padding_val)
{

    // input/output tiles + reduce buffer

    __local _FLOAT lcl[(MLO_LCL_SZ) + 1];
    __local _FLOAT* lcl_bot = lcl;

    uint lcl_id = get_local_id(0);

    uint c_idx_base = get_group_id(0); // input map index base

    uint o_idx_base = get_group_id(1); // output map index base

    uint ib_base = get_group_id(2);

    uint ib = ib_base * (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    uint c_idx = c_idx_base * MLO_N_LCL_IN_MAPS; // input map index

    uint o_idx = o_idx_base * (MLO_N_LCL_OUT_MAPS * MLO_OUT_STACKS); // output map index

    uint n_in_map_reads = MLO_N_INPUTS >= c_idx + MLO_N_LCL_IN_MAPS
                              ? MLO_N_LCL_IN_MAPS
                              : (MLO_N_INPUTS >= c_idx ? MLO_N_INPUTS - c_idx : 0);

    uint gbl_in_off  = c_idx * MLO_IN_CHANNEL_STRIDE + ib * MLO_IN_BATCH_STRIDE;
    uint gbl_out_off = o_idx * MLO_OUT_CHANNEL_STRIDE + ib * MLO_OUT_BATCH_STRIDE;
    // 1 span per wk_item, total scanline with MLO_N_SPANS_PER_SCAN spans
    // TODO: more than 1 input
    uint o = lcl_id / MLO_N_SPANS_PER_SCAN;
#if MLO_N_SPANS_PER_SCAN & (MLO_N_SPANS_PER_SCAN - 1)
    uint spn = iMod(lcl_id, o, MLO_N_SPANS_PER_SCAN);
#else
    uint spn = lcl_id & (MLO_N_SPANS_PER_SCAN - 1);
#endif
    //	bool scan_lead = (o*MLO_N_SPANS_PER_SCAN == lcl_id);

    uint lcl_bot_off     = spn * MLO_IN_TILE0;
    uint out_wk_item_off = o * MLO_OUT_CHANNEL_STRIDE + lcl_bot_off;
    gbl_out_off += out_wk_item_off;
    // no output out of range
    gbl_out_off = (o_idx + o < MLO_N_OUTPUTS && o < MLO_OUT_STACKS) ? gbl_out_off : 0;

#define MLO_TOP_DAT_SZ (MLO_N_LCL_OUT_MAPS * MLO_IN_TILE0 * MLO_FILTER_SIZE1)

    __private _FLOAT top_dat[MLO_TOP_DAT_SZ];

    for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
    {
        top_dat[i] = 0;
    }

#define MLO_ACCUM_SZ (MLO_N_LCL_OUT_MAPS * MLO_N_LCL_IN_MAPS * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0)

    __private _FLOAT_ACCUM pvt_accum[MLO_ACCUM_SZ];

    for(uint i = 0; i < MLO_ACCUM_SZ; ++i)
    {
        pvt_accum[i] = 0;
    }

    // zero out LDS
    for(uint i = lcl_id; i < (MLO_LCL_SZ); i += MLO_GRP_SZ)
    {
        lcl[i] = 0;
    }

    // over all batches
    uint bend = ib + MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS;
    bend      = bend > MLO_BATCH_SZ ? MLO_BATCH_SZ : bend;

    for(uint b = ib; b < bend; ++b,
             gbl_in_off += MLO_N_LCL_BATCHS * MLO_IN_BATCH_STRIDE,
             gbl_out_off += MLO_N_LCL_BATCHS * MLO_OUT_BATCH_STRIDE)
    {
        barrier(CLK_LOCAL_MEM_FENCE);

        // top border input block
        uint gbl_in_scan_off  = gbl_in_off;
        uint gbl_out_scan_off = gbl_out_off;

        // read input map
        readInput(lcl_id, gbl_in_scan_off, n_in_map_reads, MLO_IN_VERT_READS, bot, lcl_bot);

        // move input pointer
        gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1;

        for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
        {
            top_dat[i] = 0;
        }

        // prefetch output
        uint gbl_out_scan_off1 = gbl_out_scan_off;
        for(uint k = 0; k < MLO_N_LCL_OUT_MAPS;
            ++k, gbl_out_scan_off1 += MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE)
        {
            for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
            {
                // loop around all output maps
                uint top_df_off = gbl_out_scan_off1 + j * MLO_OUT_STRIDE;
                _FLOAT mask     = 1;
#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = (j < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = (j < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(spn, k, j, top_df_off, mask, top_dat, top_df);
            }
        }

        gbl_out_scan_off += (MLO_FILTER_SIZE1 - 1) * MLO_OUT_STRIDE;

        uint sc         = 0;
        uint sc_lcl_off = lcl_bot_off;

        // prolog
        // handling padding

        // top padding
        for(; sc < MLO_FILTER_SIZE1 - MLO_FILTER_PAD1 - 1; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {
            Processing(sc, sc_lcl_off, sc + MLO_FILTER_PAD1, 0, pvt_accum, lcl_bot, top_dat);
        }

#ifdef __AMDGCN__
#pragma unroll 2
#endif

#if MLO_IN_N_VERT_LOOPS == 1
        for(; sc < MLO_IN_HEIGHT + MLO_FILTER_PAD1 - MLO_FILTER_SIZE1 + 1;
#else
        for(; sc < MLO_IN_EXTENT1;
#endif
            ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
            {
                uint top_df_off = gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                _FLOAT mask     = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(
                    spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
            }

            // processing
            Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

            // move up output to reduce overfetch
            moveOutputUp(top_dat);
        }

        // non-border input blocks
        for(uint i_loop = 0; i_loop < MLO_N_GENERIC_LOOPS;
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

            readInput(lcl_id, gbl_in_scan_off, n_in_map_reads, MLO_IN_VERT_READS, bot, lcl_bot);

            // point to the start of the local buffer

            sc_lcl_off = lcl_bot_off;

            for(; sc < (i_loop + 2) * MLO_IN_EXTENT1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT
                    top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                    mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // bottom border block

        for(int i_loop = 0; i_loop < (MLO_IN_N_VERT_LOOPS - MLO_N_GENERIC_LOOPS - 1);
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

// read 1 scan line less
// padding processing takes care of the bottom border.

#define MLO_LAST_VERT_READS (MLO_IN_HEIGHT - MLO_IN_EXTENT1 * (MLO_IN_N_VERT_LOOPS - 1))

            readInput(lcl_id, gbl_in_scan_off, n_in_map_reads, MLO_LAST_VERT_READS, bot, lcl_bot);

            // point to the start of the local buffer
            sc_lcl_off = lcl_bot_off;

#pragma unroll 3
            for(; sc < MLO_IN_HEIGHT + MLO_FILTER_PAD1 - MLO_FILTER_SIZE1 + 1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // epilog
        // handling padding

        for(; sc < MLO_IN_HEIGHT; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            // processing
            Processing(sc,
                       sc_lcl_off,
                       MLO_FILTER_SIZE1 - 1,
                       MLO_FILTER_SIZE1 - (MLO_IN_HEIGHT + MLO_FILTER_PAD1 - sc),
                       pvt_accum,
                       lcl_bot,
                       top_dat);

            // move up output to reduce overfetch
            moveOutputUp(top_dat);

        } // for (; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1 + 2; ++sc, gbl_out_scan_off +=
          // MLO_OUT_CHANNEL_STRIDE, gbl_in_scan_off += MLO_IN_CHANNEL_STRIDE)
    }     // 	for (int b = 0;

    // final summation over all output maps and each filter row
    // this coudl be done with log but it negligeble anyway
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
        {

            for(uint l = 0; l < MLO_FILTER_SIZE1; ++l)
            {
                barrier(CLK_LOCAL_MEM_FENCE);

                for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                {
                    uint pvt_off =
                        (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                        l * MLO_FILTER_SIZE0 + n;

                    lcl[lcl_id * MLO_FILTER_SIZE0 + n] = pvt_accum[pvt_off];
                }

                barrier(CLK_LOCAL_MEM_FENCE);

                if(spn == 0)
                {
                    for(uint s = 0; s < MLO_N_SPANS_PER_SCAN - 1; ++s)
                    {

                        for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                        {
                            uint pvt_off =
                                (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                                l * MLO_FILTER_SIZE0 + n;
                            pvt_accum[pvt_off] += lcl[(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n];
                        }
                    }
                }
            }
        }
    }

    // output
    // inputs are outputs
    // TODO : for more than 1 input

    uint wei_df_off =
        (((ib / MLO_N_BATCH_LOOPS) * MLO_N_OUTPUTS + o_idx + o) * (uint)MLO_WEI_BATCH_STRIDE)
        // this input channel
        + mul24(c_idx, (uint)MLO_WEI_CHANNEL_STRIDE);

    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
        {
            if(spn == 0 && c < n_in_map_reads && o_idx + o + k * MLO_OUT_STACKS < MLO_N_OUTPUTS &&
               o < MLO_OUT_STACKS)
            {
                for(uint i = 0; i < (MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0); ++i)
                {
                    weights_df[wei_df_off + k * MLO_OUT_STACKS * MLO_WEI_BATCH_STRIDE +
                               c * MLO_WEI_CHANNEL_STRIDE + i] =
                        pvt_accum[(k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 *
                                      MLO_FILTER_SIZE0 +
                                  i];
                }
            }
        }
    }
}

// final reduction kernel
// add filters over batches
__attribute__((reqd_work_group_size(MLO_UT_GRP_SZ0, 1, 1))) __kernel void
MIOpenCvBwdWrW_rdc(const __global _FLOAT* __restrict weight_df_tmp,
                   __global _FLOAT* __restrict weights_df)
{
    uint gbl_id   = get_global_id(0);
    uint wei_idx0 = gbl_id * MLO_UT_READ_UNIT;

#if MLO_WEI_CHANNEL_STRIDE & (MLO_WEI_CHANNEL_STRIDE - 1)
    uint wei_blk_idx = iDiv(wei_idx0, MLO_WEI_CHANNEL_STRIDE);
    uint wei_idx     = iMod(wei_idx0, wei_blk_idx, MLO_WEI_CHANNEL_STRIDE);
#else
    uint wei_blk_idx = wei_idx0 / MLO_WEI_CHANNEL_STRIDE;
    uint wei_idx     = wei_idx0 & (MLO_WEI_CHANNEL_STRIDE - 1);
#endif

    _FLOAT pvt_accum_wei[MLO_UT_READ_UNIT] = {MLO_UT_READ_UNIT * (_FLOAT)0};
    //	for (uint i = 0; i < MLO_UT_READ_UNIT; ++i)
    //	{
    //		pvt_accum_wei[i] = 0;
    //	}

    int batch_loop = (MLO_BATCH_SZ + (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS) - 1) /
                     (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    for(uint i = 0; i < batch_loop; ++i)
    {
        for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
        {
            pvt_accum_wei[j] += weight_df_tmp[(wei_blk_idx * MLO_WEI_CHANNEL_STRIDE +
                                               i * MLO_N_OUTPUTS * MLO_WEI_BATCH_STRIDE) +
                                              wei_idx + j];
        }
    }

    for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
    {
        weights_df[wei_idx0 + j] = pvt_accum_wei[j];
    }
}

/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (c) 2017 Advanced Micro Devices, Inc.
 *
 * 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.
 *
 *******************************************************************************/

#define PPCAT_NX(A, B) A##B
#define PPCAT(A, B) PPCAT_NX(A, B)
#define TWO 2
#define FOUR 4
#define EIGHT 8

#if MIOPEN_USE_FP16 == 1
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define _FLOAT half
#define _FLOAT_ACCUM float
#ifndef HALF_MAX
#define MAX_VAL 65504 /* max value */
#else
#define MAX_VAL HALF_MAX
#endif

#endif
#if MIOPEN_USE_FP32 == 1
#define _FLOAT float
#define _FLOAT_ACCUM float
#ifndef FLT_MAX
#define MAX_VAL 3.402823466e+38F /* max value */
#else
#define MAX_VAL FLT_MAX
#endif
#endif

#define _FLOAT2 PPCAT(_FLOAT, TWO)
#define _FLOAT4 PPCAT(_FLOAT, FOUR)
#define _FLOAT8 PPCAT(_FLOAT, EIGHT)

#define UNUSED __attribute__((__unused__))

#define DBG_OUT_OF_RNGE 0

#define MLO_N_OUT_HORIZ_READS ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1) / MLO_IN_TILE0)
#define MLO_N_SPANS_PER_SCAN (MLO_N_OUT_HORIZ_READS)
#define MLO_N_OUT_HORIZ_PIX_READS (MLO_N_OUT_HORIZ_READS * MLO_IN_TILE0)
#define MLO_OUT_N_PIXS_OFF (MLO_OUT_WIDTH - ((MLO_OUT_WIDTH / MLO_IN_TILE0) * MLO_IN_TILE0))
#define MLO_N_OUT_VERTICAL_READS (MLO_FILTER_SIZE1)
// won't run non-border blocks if  MLO_IN_N_VERT_LOOPS < 2
//

#define MLO_IN_VERT_READS MLO_IN_EXTENT1

#if MLO_IN_N_VERT_LOOPS >= 2
#define MLO_N_GENERIC_LOOPS (MLO_IN_N_VERT_LOOPS - 2)
#else
#define MLO_N_GENERIC_LOOPS 0
#endif

// there is an assumption that the scanline fits into LDS
#define MLO_N_IN_HORIZ_PIX_READS (MLO_IN_WIDTH)
#define MLO_N_IN_HORIZ_READS ((MLO_N_IN_HORIZ_PIX_READS + MLO_READ_UNIT - 1) / MLO_READ_UNIT)
#define MLO_IN_N_PIXS_OFF \
    (MLO_N_IN_HORIZ_PIX_READS - (MLO_N_IN_HORIZ_PIX_READS / MLO_READ_UNIT) * MLO_READ_UNIT)
#define MLO_IN_LCL_WIDTH (MLO_N_IN_HORIZ_READS * MLO_READ_UNIT + 2 * MLO_FILTER_PAD0)
#define MLO_IN_LCL_HEIGHT MLO_IN_VERT_READS
#define MLO_IN_LCL_SZ (MLO_IN_LCL_WIDTH * MLO_IN_LCL_HEIGHT)
#define MLO_TOTAL_IN_LCL_SZ (MLO_N_LCL_BATCHS * MLO_N_LCL_IN_MAPS * MLO_IN_LCL_SZ)

#define MLO_WEI_LCL_SZ (MLO_GRP_SZ * MLO_FILTER_SIZE0)
#if MLO_TOTAL_IN_LCL_SZ > MLO_WEI_LCL_SZ
#define MLO_LCL_SZ (MLO_TOTAL_IN_LCL_SZ)
#else
#define MLO_LCL_SZ (MLO_WEI_LCL_SZ)
#endif

__attribute__((always_inline)) uint iDiv(uint v, uint d)
{
    uint r = v / d;
    return (r);
}

__attribute__((always_inline)) uint iMod(uint v, uint u, uint d)
{
    uint r = v - mul24((uint)u, (uint)d);
    return (r);
}

#if 0
// This function is for log-reduction of content in LDS,
// it's supposed to be called by MIOpenCvBwdWrW,
// However, it's no longer used and not tested.
__attribute__((always_inline)) void ReduceKernel(__local _FLOAT* lcl_blob,
                                                 __private _FLOAT* weights_accum,
                                                 uint lcl_id,
                                                 uint scan_lcl,
                                                 uint sum_stride,
                                                 uint unit_len,
                                                 UNUSED bool debug)
{
    // read first half
    if(scan_lcl < (sum_stride >> 1))
    {
        for(uint i = 0; i < unit_len; ++i)
        {
            weights_accum[i] = lcl_blob[(lcl_id + scan_lcl) * unit_len + i];
        }
    }
    // add second half
    // appload accumulated value so far
    for(uint j = (sum_stride >> 1); j > 0; j >>= 1)
    {
        barrier(CLK_LOCAL_MEM_FENCE);
        if(scan_lcl < j)
        {
            for(uint i = 0; i < unit_len; ++i)
            {
                weights_accum[i] += lcl_blob[(lcl_id + j) * unit_len + i];

                lcl_blob[lcl_id * unit_len + i] = weights_accum[i];
            }
        }
    }
}
#endif

/*
        group cooperative read
        read by MLO_READ_UNIT
        handle out of range both horizontally and vertically (by fixed number of veryical reads)

        no guard against number of inputs
*/
__attribute__((always_inline)) void readInput(uint lcl_id,
                                              uint gbl_in_scan_off,
#if 1 // debug
                                              uint n_in_map_reads,
#endif
                                              uint n_v_reads,
                                              const __global _FLOAT* __restrict bot,
                                              __local _FLOAT* __restrict lcl_bot)
{
    for(uint p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * n_v_reads;
        p4 += MLO_GRP_SZ)
    {
        uint c    = 0;
        uint t_p4 = p4;
#if MLO_N_LCL_IN_MAPS > 1
        c    = p4 / (MLO_N_IN_HORIZ_READS * n_v_reads);
        t_p4 = iMod(p4, c, (MLO_N_IN_HORIZ_READS * n_v_reads));
#endif

        uint c_scan = t_p4 / (MLO_N_IN_HORIZ_READS);

#if MLO_N_IN_HORIZ_READS & (MLO_N_IN_HORIZ_READS - 1)
        uint c_pix4 = iMod(t_p4, c_scan, (MLO_N_IN_HORIZ_READS));
#else
        uint c_pix4 = t_p4 & (MLO_N_IN_HORIZ_READS - 1);
#endif

        uint bot_off = gbl_in_scan_off + c * MLO_IN_CHANNEL_STRIDE + c_scan * MLO_IN_STRIDE +
                       c_pix4 * MLO_READ_UNIT;
        const __global _FLOAT* bot_p = &bot[bot_off];

        __private _FLOAT in_rd_data[MLO_READ_UNIT];

#if 1 // debug
        for(uint i = 0; i < MLO_READ_UNIT; ++i)
        {
            in_rd_data[i] = 0;
        }
#endif

#if 1 // debug
        if(c < n_in_map_reads)
#endif

        {
#if MLO_IN_N_PIXS_OFF > 0
            if(c_pix4 == MLO_N_IN_HORIZ_READS - 1)
            {
                for(uint i = 0; i < MLO_IN_N_PIXS_OFF; ++i)
                {
                    in_rd_data[i] = bot_p[i];

#if DBG_OUT_OF_RNGE
                    if(bot_off + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
                    {
                        printf("k:err:out-of-range\n");
                    }
#endif
                }

#if 0 // not neccessay any more
                for(uint i = MLO_IN_N_PIXS_OFF; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = 0;
                }
#endif
            }
            else
#endif
            {

                for(uint i = 0; i < MLO_READ_UNIT; ++i)
                {
                    in_rd_data[i] = bot_p[i];

#if DBG_OUT_OF_RNGE
                    if(bot_off + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
                    {
                        printf("k:err:out-of-range\n");
                    }
#endif
                }
            }
        }

        // MLO_N_LCL_IN_MAPS inputs
        for(uint i = 0; i < MLO_READ_UNIT; ++i)
        {
            int lcl_in_off = c * MLO_IN_LCL_SZ + c_scan * MLO_IN_LCL_WIDTH + MLO_FILTER_PAD0 +
                             c_pix4 * MLO_READ_UNIT + i;
            lcl_bot[lcl_in_off] = in_rd_data[i];
        }

    } // for (int p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * MLO_IN_VERT_READS;

    barrier(CLK_LOCAL_MEM_FENCE);
}

/*
        core processing loop
        bot - input, from local (1 span)
        top - output diff, from global (array of spans, filters vertical size)

        loop over filter vertical size
*/
__attribute__((always_inline)) void
Processing(UNUSED uint sc,
           uint sc_lcl_off,
           uint top_lim,
           int bot_lim, // bot_lim could be negative at lower boundary padding
           __private _FLOAT_ACCUM* __restrict pvt_accum,
           __local _FLOAT* __restrict lcl_bot,
           __private _FLOAT* __restrict top_dat)
{
    for(int l = top_lim; l >= bot_lim; --l)
    {
        for(uint m = 0; m < MLO_IN_TILE0; ++m)
        {
            for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
            {
                for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
                {
                    uint bot_off = sc_lcl_off + c * MLO_IN_LCL_SZ + n + m;

                    _FLOAT bot_val = lcl_bot[bot_off];

                    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                    {
                        uint pvt_top_off =
                            k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (top_lim - l) * MLO_IN_TILE0 + m;
                        uint pvt_accum_off =
                            (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                            l * MLO_FILTER_SIZE0 + n;

                        _FLOAT top_val = top_dat[pvt_top_off];

                        pvt_accum[pvt_accum_off]
                            // each wk-it process an input
                            += (_FLOAT_ACCUM)(bot_val * top_val);
                    }
                }
            }
        }
    }
}

__attribute__((always_inline)) void moveOutputUp(__private _FLOAT* __restrict top_dat)
{
    // move up output to reduce overfetch
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
        {
            for(uint i = 0; i < MLO_IN_TILE0; ++i)
            {
                uint pvt_off_n = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0 + i;
                uint pvt_off_o = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (j + 1) * MLO_IN_TILE0 + i;
                top_dat[pvt_off_n] = top_dat[pvt_off_o];
            }
        }
    }
}

__attribute__((always_inline)) void spanReadingOutput(int spn,
                                                      int k,
                                                      int j,
                                                      int top_df_off,
                                                      _FLOAT mask,
                                                      __private _FLOAT* __restrict top_dat,
                                                      const __global _FLOAT* __restrict top_df)
{
    int pvt_off                     = k * MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j * MLO_IN_TILE0;
    const __global _FLOAT* top_df_p = &top_df[top_df_off];
#if MLO_OUT_N_PIXS_OFF > 0
    if(spn == MLO_N_SPANS_PER_SCAN - 1)
    {
        uint i = 0;
        for(; i < MLO_OUT_N_PIXS_OFF; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;

#if DBG_OUT_OF_RNGE
            if(top_df_off + i >= MLO_OUT_BATCH_STRIDE * MLO_BATCH_SZ)
            {
                printf("k:err:out-of-range\n");
            }
#endif
        }
        for(; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = 0;
        }
    }
    else
#else
    (void)spn;
#endif
    {
        for(uint i = 0; i < MLO_IN_TILE0; ++i)
        {
            top_dat[pvt_off + i] = top_df_p[i] * mask;

#if DBG_OUT_OF_RNGE
            if(top_df_off + i >= MLO_OUT_BATCH_STRIDE * MLO_BATCH_SZ)
            {
                printf("k:err:out-of-range\n");
            }
#endif
        }
    }
}

/*********************************************************************************************************
// wrw algorithm for large filters
// idea:
// split output scan-line on number of spans by the  MLO_IN_TILE0 (2 for example)
// 1 scan-line has ((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) spans
// group will process MLO_GRP_SZ/((MLO_OUT_WIDTH + MLO_IN_TILE0 - 1/MLO_IN_TILE0) output maps

// alg
// load a block of input map (or full map) into LDS
// loop
// read MLO_FILTER_SIZE1 number of spans from output map into VGPRs (for example 5 *2 = 10)
// read 1 input line for  maps into LDS
// accumulate

// accumulate all spans at the end
// start new loop for the next batch (if defined)
// write out

// kerenl handles 5x5, 3x3 with padding
// small images in 1 short- MLO_N_GENERIC_LOOPS == 0
// big images  in 2 blocks - MLO_IN_N_VERT_LOOPS == 2 or multiple blocks - MLO_IN_N_VERT_LOOPS > 2
// there are prolog and apilog that deal with top/bottom padding.
// left/right padding handles as a LDS border pixels zeroed at the beginning.

**********************************************************************************************************/

__attribute__((reqd_work_group_size(MLO_GRP_SZ0, MLO_GRP_SZ1, MLO_GRP_SZ2))) __kernel void
MIOpenCvBwdWrW(const __global _FLOAT* __restrict top_df,
               const __global _FLOAT* __restrict bot,
               __global _FLOAT* __restrict weights_df,
#if MLO_CONV_BIAS
               __global _FLOAT* __restrict bias_df,
#endif
               UNUSED _FLOAT padding_val)
{

    // input/output tiles + reduce buffer

    __local _FLOAT lcl[(MLO_LCL_SZ) + 1];
    __local _FLOAT* lcl_bot = lcl;

    uint lcl_id = get_local_id(0);

    uint c_idx_base = get_group_id(0); // input map index base

    uint o_idx_base = get_group_id(1); // output map index base

    uint ib_base = get_group_id(2);

    uint ib = ib_base * (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    uint c_idx = c_idx_base * MLO_N_LCL_IN_MAPS; // input map index

    uint o_idx = o_idx_base * (MLO_N_LCL_OUT_MAPS * MLO_OUT_STACKS); // output map index

#if 1 // debug
    uint n_in_map_reads = MLO_N_INPUTS >= c_idx + MLO_N_LCL_IN_MAPS
                              ? MLO_N_LCL_IN_MAPS
                              : (MLO_N_INPUTS >= c_idx ? MLO_N_INPUTS - c_idx : 0);
#endif

    uint gbl_in_off  = c_idx * MLO_IN_CHANNEL_STRIDE + ib * MLO_IN_BATCH_STRIDE;
    uint gbl_out_off = o_idx * MLO_OUT_CHANNEL_STRIDE + ib * MLO_OUT_BATCH_STRIDE;
    // 1 span per wk_item, total scanline with MLO_N_SPANS_PER_SCAN spans
    // TODO: more than 1 input
    uint o = lcl_id / MLO_N_SPANS_PER_SCAN;
#if MLO_N_SPANS_PER_SCAN & (MLO_N_SPANS_PER_SCAN - 1)
    uint spn = iMod(lcl_id, o, MLO_N_SPANS_PER_SCAN);
#else
    uint spn = lcl_id & (MLO_N_SPANS_PER_SCAN - 1);
#endif
    //	bool scan_lead = (o*MLO_N_SPANS_PER_SCAN == lcl_id);

    uint lcl_bot_off     = spn * MLO_IN_TILE0;
    uint out_wk_item_off = o * MLO_OUT_CHANNEL_STRIDE + lcl_bot_off;
    gbl_out_off += out_wk_item_off;
    // no output out of range
    gbl_out_off = (o_idx + o < MLO_N_OUTPUTS && o < MLO_OUT_STACKS) ? gbl_out_off : 0;

#define MLO_TOP_DAT_SZ (MLO_N_LCL_OUT_MAPS * MLO_IN_TILE0 * MLO_FILTER_SIZE1)

    __private _FLOAT top_dat[MLO_TOP_DAT_SZ];

    for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
    {
        top_dat[i] = 0;
    }

#define MLO_ACCUM_SZ (MLO_N_LCL_OUT_MAPS * MLO_N_LCL_IN_MAPS * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0)

    __private _FLOAT_ACCUM pvt_accum[MLO_ACCUM_SZ];

    for(uint i = 0; i < MLO_ACCUM_SZ; ++i)
    {
        pvt_accum[i] = 0;
    }

    // zero out LDS
    for(uint i = lcl_id; i < (MLO_LCL_SZ); i += MLO_GRP_SZ)
    {
        lcl[i] = 0;
    }

    // over all batches
    uint bend = ib + MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS;
    bend      = bend > MLO_BATCH_SZ ? MLO_BATCH_SZ : bend;

    for(uint b = ib; b < bend; ++b,
             gbl_in_off += MLO_N_LCL_BATCHS * MLO_IN_BATCH_STRIDE,
             gbl_out_off += MLO_N_LCL_BATCHS * MLO_OUT_BATCH_STRIDE)
    {
        barrier(CLK_LOCAL_MEM_FENCE);

        // top border input block
        uint gbl_in_scan_off  = gbl_in_off;
        uint gbl_out_scan_off = gbl_out_off;

// read input map
#if 0 // debug
        readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);
#else
        readInput(lcl_id, gbl_in_scan_off, n_in_map_reads, MLO_IN_VERT_READS, bot, lcl_bot);
#endif

        // move input pointer
        gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1;

        for(uint i = 0; i < MLO_TOP_DAT_SZ; ++i)
        {
            top_dat[i] = 0;
        }

        // prefetch output
        uint gbl_out_scan_off1 = gbl_out_scan_off;
        for(uint k = 0; k < MLO_N_LCL_OUT_MAPS;
            ++k, gbl_out_scan_off1 += MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE)
        {
            for(uint j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
            {
                // loop around all output maps
                uint top_df_off = gbl_out_scan_off1 + j * MLO_OUT_STRIDE;
                _FLOAT mask     = 1;
#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = (j < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = (j < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(spn, k, j, top_df_off, mask, top_dat, top_df);
            }
        }

        gbl_out_scan_off += (MLO_FILTER_SIZE1 - 1) * MLO_OUT_STRIDE;

        uint sc         = 0;
        uint sc_lcl_off = lcl_bot_off;

        // prolog
        // handling padding

        // top padding
        for(; sc < MLO_FILTER_SIZE1 - MLO_FILTER_PAD1 - 1; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {
            Processing(sc, sc_lcl_off, sc + MLO_FILTER_PAD1, 0, pvt_accum, lcl_bot, top_dat);
        }

#ifdef __AMDGCN__
#pragma unroll 2
#endif

#if MLO_IN_N_VERT_LOOPS == 1
        for(; sc < MLO_IN_HEIGHT + MLO_FILTER_PAD1 - MLO_FILTER_SIZE1 + 1;
#else
        for(; sc < MLO_IN_EXTENT1;
#endif
            ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
            {
                uint top_df_off = gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                _FLOAT mask     = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT || MLO_FILTER_SIZE1 - 1 > MLO_OUT_HEIGHT
                top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                spanReadingOutput(
                    spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
            }

            // processing
            Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

            // move up output to reduce overfetch
            moveOutputUp(top_dat);
        }

        // non-border input blocks
        for(uint i_loop = 0; i_loop < MLO_N_GENERIC_LOOPS;
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

#if 0 // debug
            readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);
#else
            readInput(lcl_id, gbl_in_scan_off, n_in_map_reads, MLO_IN_VERT_READS, bot, lcl_bot);
#endif

            // point to the start of the local buffer

            sc_lcl_off = lcl_bot_off;

            for(; sc < (i_loop + 2) * MLO_IN_EXTENT1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

#if MLO_IN_HEIGHT != MLO_OUT_HEIGHT
                    top_df_off = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? top_df_off : 0;
                    mask       = ((sc + MLO_FILTER_PAD1) < MLO_OUT_HEIGHT) ? 1 : 0;
#endif

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // bottom border block

        for(int i_loop = 0; i_loop < (MLO_IN_N_VERT_LOOPS - MLO_N_GENERIC_LOOPS - 1);
            ++i_loop, gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1)
        {
            barrier(CLK_LOCAL_MEM_FENCE);

// read 1 scan line less
// padding processing takes care of the bottom border.

#define MLO_LAST_VERT_READS (MLO_IN_HEIGHT - MLO_IN_EXTENT1 * (MLO_IN_N_VERT_LOOPS - 1))

#if 0 // debug
            readInput(lcl_id, gbl_in_scan_off, MLO_LAST_VERT_READS, bot, lcl_bot);
#else
            readInput(lcl_id, gbl_in_scan_off, n_in_map_reads, MLO_LAST_VERT_READS, bot, lcl_bot);
#endif

            // point to the start of the local buffer
            sc_lcl_off = lcl_bot_off;

#pragma unroll 3
            for(; sc < MLO_IN_HEIGHT + MLO_FILTER_PAD1 - MLO_FILTER_SIZE1 + 1;
                ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
            {

                for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
                {
                    uint top_df_off =
                        gbl_out_scan_off + k * MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
                    _FLOAT mask = 1;

                    spanReadingOutput(
                        spn, k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask, top_dat, top_df);
                }

                // processing
                Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, 0, pvt_accum, lcl_bot, top_dat);

                // move up output to reduce overfetch
                moveOutputUp(top_dat);
            }
        }

        // epilog
        // handling padding

        for(; sc < MLO_IN_HEIGHT; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
        {

            // processing
            Processing(sc,
                       sc_lcl_off,
                       MLO_FILTER_SIZE1 - 1,
                       MLO_FILTER_SIZE1 - (MLO_IN_HEIGHT + MLO_FILTER_PAD1 - sc),
                       pvt_accum,
                       lcl_bot,
                       top_dat);

            // move up output to reduce overfetch
            moveOutputUp(top_dat);

        } // for (; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1 + 2; ++sc, gbl_out_scan_off +=
          // MLO_OUT_CHANNEL_STRIDE, gbl_in_scan_off += MLO_IN_CHANNEL_STRIDE)
    }     // 	for (int b = 0;

    // final summation over all output maps and each filter row
    // this coudl be done with log but it negligeble anyway
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
        {

            for(uint l = 0; l < MLO_FILTER_SIZE1; ++l)
            {
                barrier(CLK_LOCAL_MEM_FENCE);

                for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                {
                    uint pvt_off =
                        (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                        l * MLO_FILTER_SIZE0 + n;

                    lcl[lcl_id * MLO_FILTER_SIZE0 + n] = pvt_accum[pvt_off];
                }

                barrier(CLK_LOCAL_MEM_FENCE);

                if(spn == 0)
                {
                    for(uint s = 0; s < MLO_N_SPANS_PER_SCAN - 1; ++s)
                    {

                        for(uint n = 0; n < MLO_FILTER_SIZE0; ++n)
                        {
                            uint pvt_off =
                                (k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 +
                                l * MLO_FILTER_SIZE0 + n;
                            pvt_accum[pvt_off] += lcl[(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n];
                        }
                    }
                }
            }
        }
    }

    // output
    // inputs are outputs
    // TODO : for more than 1 input

    uint wei_df_off =
        (((ib / MLO_N_BATCH_LOOPS) * MLO_N_OUTPUTS + o_idx + o) * (uint)MLO_WEI_BATCH_STRIDE)
        // this input channel
        + mul24(c_idx, (uint)MLO_WEI_CHANNEL_STRIDE);

#if 0 // debug
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        if(spn == 0 && o_idx + o + k * MLO_OUT_STACKS < MLO_N_OUTPUTS && o < MLO_OUT_STACKS)
        {
            for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
#else
    for(uint k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
    {
        for(uint c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
        {
            if(spn == 0 && c < n_in_map_reads && o_idx + o + k * MLO_OUT_STACKS < MLO_N_OUTPUTS &&
               o < MLO_OUT_STACKS)
#endif
            {
                for(uint i = 0; i < (MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0); ++i)
                {
                    weights_df[wei_df_off + k * MLO_OUT_STACKS * MLO_WEI_BATCH_STRIDE +
                               c * MLO_WEI_CHANNEL_STRIDE + i] =
                        pvt_accum[(k * MLO_N_LCL_IN_MAPS + c) * MLO_FILTER_SIZE1 * MLO_FILTER_SIZE0 + i];
                }
            }
        }
    }
}

// final reduction kernel
// add filters over batches
__attribute__((reqd_work_group_size(MLO_UT_GRP_SZ0, 1, 1))) __kernel void
MIOpenCvBwdWrW_rdc(const __global _FLOAT* __restrict weight_df_tmp,
                   __global _FLOAT* __restrict weights_df)
{
    uint gbl_id   = get_global_id(0);
    uint wei_idx0 = gbl_id * MLO_UT_READ_UNIT;

#if MLO_WEI_CHANNEL_STRIDE & (MLO_WEI_CHANNEL_STRIDE - 1)
    uint wei_blk_idx = iDiv(wei_idx0, MLO_WEI_CHANNEL_STRIDE);
    uint wei_idx     = iMod(wei_idx0, wei_blk_idx, MLO_WEI_CHANNEL_STRIDE);
#else
    uint wei_blk_idx = wei_idx0 / MLO_WEI_CHANNEL_STRIDE;
    uint wei_idx     = wei_idx0 & (MLO_WEI_CHANNEL_STRIDE - 1);
#endif

    _FLOAT pvt_accum_wei[MLO_UT_READ_UNIT] = {MLO_UT_READ_UNIT * (_FLOAT)0};
    //	for (uint i = 0; i < MLO_UT_READ_UNIT; ++i)
    //	{
    //		pvt_accum_wei[i] = 0;
    //	}

    int batch_loop = (MLO_BATCH_SZ + (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS) - 1) /
                     (MLO_N_BATCH_LOOPS * MLO_N_LCL_BATCHS);

    for(uint i = 0; i < batch_loop; ++i)
    {
        for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
        {
            pvt_accum_wei[j] += weight_df_tmp[(wei_blk_idx * MLO_WEI_CHANNEL_STRIDE +
                                               i * MLO_N_OUTPUTS * MLO_WEI_BATCH_STRIDE) +
                                              wei_idx + j];
        }
    }

    for(uint j = 0; j < MLO_UT_READ_UNIT; ++j)
    {
        weights_df[wei_idx0 + j] = pvt_accum_wei[j];
    }
}