Fix incorrect data type in OpenCL

Fix incorrect data type in OpenCL

__kernel void rgba_to_uint8_rgba_buffer(read_only image2d_t src_image,
                                        __global uchar* const buf) {
	int2 coords = (int2)(get_global_id(0), get_global_id(1));
	int w = get_image_width(src_image);

	float4 pixel = read_imagef(src_image, coords) * 255.0f;

	int base = (coords.y * w + coords.x) * 4;
	buf[base] = round(pixel.x);
	buf[base + 1] = round(pixel.y);
	buf[base + 2] = round(pixel.z);
	buf[base + 3] = round(pixel.w);
}

__kernel void rgb_to_yuv444_601_limited(read_only image2d_t src_image,
                                        __private uint const Y_stride,
                                        __private uint const U_stride,
                                        __private uint const V_stride,
                                        __global uchar* const Y_buf,
                                        __global uchar* const U_buf,
                                        __global uchar* const V_buf) {
	int2 coords = (int2)(get_global_id(0), get_global_id(1));

	float4 pixel = read_imagef(src_image, coords);
	float Y = 16 + pixel.x * 65.481 + pixel.y * 128.553 + pixel.z * 24.966;
	float U = 128 - pixel.x * 37.797 - pixel.y * 74.203 + pixel.z * 112.0;
	float V = 128 + pixel.x * 112.0 - pixel.y * 93.786 - pixel.z * 18.214;

	// FFMpeg frames are flipped.
	coords.y = get_image_height(src_image) - coords.y - 1;

	Y_buf[coords.y * Y_stride + coords.x] = round(Y);
	U_buf[coords.y * U_stride + coords.x] = round(U);
	V_buf[coords.y * V_stride + coords.x] = round(V);
}

__kernel void rgb_to_yuv420_601_limited(read_only image2d_t src_image,
                                        __private uint const Y_stride,
                                        __private uint const U_stride,
                                        __private uint const V_stride,
                                        __global uchar* const Y_buf,
                                        __global uchar* const U_buf,
                                        __global uchar* const V_buf) {
	int2 coords = (int2)(get_global_id(0), get_global_id(1));
	int h = get_image_height(src_image);

	float4 pixel = read_imagef(src_image, coords);

	float Y = 16 + pixel.x * 65.481 + pixel.y * 128.553 + pixel.z * 24.966;
	Y_buf[(h - coords.y - 1) * Y_stride + coords.x] = round(Y);

	if ((coords.x & 1) == 0 && (coords.y & 1) == 0) {
		// Average the 4 pixel values for better results.
		int w = get_image_width(src_image);
		float4 top_right, bottom_left, bottom_right;

		if (coords.x + 1 < w) {
			top_right = read_imagef(src_image, coords + (int2)(1, 0));
		} else {
			top_right = pixel;
		}

		if (coords.y + 1 < h) {
			bottom_left = read_imagef(src_image, coords + (int2)(0, 1));
		} else {
			bottom_left = pixel;
		}

		if (coords.x + 1 < w && coords.y + 1 < h) {
			bottom_right = read_imagef(src_image, coords + (int2)(1, 1));
		} else {
			bottom_right = pixel;
		}

		pixel = (pixel + top_right + bottom_left + bottom_right) / (float4)(4.0, 4.0, 4.0, 4.0);

		coords.x >>= 1;
		coords.y >>= 1;

		coords.y = (h >> 1) - coords.y - 1;

		float U = 128 - pixel.x * 37.797 - pixel.y * 74.203 + pixel.z * 112.0;
		float V = 128 + pixel.x * 112.0 - pixel.y * 93.786 - pixel.z * 18.214;

		U_buf[coords.y * U_stride + coords.x] = round(U);
		V_buf[coords.y * V_stride + coords.x] = round(V);
	}
}

__kernel void rgba_to_uint8_rgba_buffer(read_only image2d_t src_image,
                                        __global uchar* const buf) {
	int2 coords = (int2)(get_global_id(0), get_global_id(1));
	int w = get_image_width(src_image);

	float4 pixel = read_imagef(src_image, coords);

	int base = (coords.y * w + coords.x) * 4;
	buf[base] = round(pixel.x);
	buf[base + 1] = round(pixel.y);
	buf[base + 2] = round(pixel.z);
	buf[base + 3] = round(pixel.w);
}

__kernel void rgb_to_yuv444_601_limited(read_only image2d_t src_image,
                                        __private uint const Y_stride,
                                        __private uint const U_stride,
                                        __private uint const V_stride,
                                        __global uchar* const Y_buf,
                                        __global uchar* const U_buf,
                                        __global uchar* const V_buf) {
	int2 coords = (int2)(get_global_id(0), get_global_id(1));

	float4 pixel = read_imagef(src_image, coords);
	float Y = 16 + pixel.x * 65.481 + pixel.y * 128.553 + pixel.z * 24.966;
	float U = 128 - pixel.x * 37.797 - pixel.y * 74.203 + pixel.z * 112.0;
	float V = 128 + pixel.x * 112.0 - pixel.y * 93.786 - pixel.z * 18.214;

	// FFMpeg frames are flipped.
	coords.y = get_image_height(src_image) - coords.y - 1;

	Y_buf[coords.y * Y_stride + coords.x] = round(Y);
	U_buf[coords.y * U_stride + coords.x] = round(U);
	V_buf[coords.y * V_stride + coords.x] = round(V);
}

__kernel void rgb_to_yuv420_601_limited(read_only image2d_t src_image,
                                        __private uint const Y_stride,
                                        __private uint const U_stride,
                                        __private uint const V_stride,
                                        __global uchar* const Y_buf,
                                        __global uchar* const U_buf,
                                        __global uchar* const V_buf) {
	int2 coords = (int2)(get_global_id(0), get_global_id(1));
	int h = get_image_height(src_image);

	float4 pixel = read_imagef(src_image, coords);

	float Y = 16 + pixel.x * 65.481 + pixel.y * 128.553 + pixel.z * 24.966;
	Y_buf[(h - coords.y - 1) * Y_stride + coords.x] = round(Y);

	if ((coords.x & 1) == 0 && (coords.y & 1) == 0) {
		// Average the 4 pixel values for better results.
		int w = get_image_width(src_image);
		float4 top_right, bottom_left, bottom_right;

		if (coords.x + 1 < w) {
			top_right = read_imagef(src_image, coords + (int2)(1, 0));
		} else {
			top_right = pixel;
		}

		if (coords.y + 1 < h) {
			bottom_left = read_imagef(src_image, coords + (int2)(0, 1));
		} else {
			bottom_left = pixel;
		}

		if (coords.x + 1 < w && coords.y + 1 < h) {
			bottom_right = read_imagef(src_image, coords + (int2)(1, 1));
		} else {
			bottom_right = pixel;
		}

		pixel = (pixel + top_right + bottom_left + bottom_right) / (float4)(4.0, 4.0, 4.0, 4.0);

		coords.x >>= 1;
		coords.y >>= 1;

		coords.y = (h >> 1) - coords.y - 1;

		float U = 128 - pixel.x * 37.797 - pixel.y * 74.203 + pixel.z * 112.0;
		float V = 128 + pixel.x * 112.0 - pixel.y * 93.786 - pixel.z * 18.214;

		U_buf[coords.y * U_stride + coords.x] = round(U);
		V_buf[coords.y * V_stride + coords.x] = round(V);
	}
}


fixed out-of-range

fixed out-of-range

/*
 * Copyright (c) 2016 AMD Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and/or associated documentation files (the
 * "Materials"), to deal in the Materials without restriction, including
 * without limitation the rights to use, copy, modify, merge, publish,
 * distribute, sublicense, and/or sell copies of the Materials, and to
 * permit persons to whom the Materials are 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 Materials.
 *
 * THE MATERIALS ARE 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
 * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
 */

#define _FLOAT					float
#define _FLOAT2					float2
#define _FLOAT4					float4
#define _FLOAT8					float8

#ifndef FLT_MAX
#define FLT_MAX         3.402823466e+38F        /* max value */
#endif

#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
#if MLO_FILTER_PAD1 > 0

#if  MLO_IN_N_VERT_LOOPS >= 2
#define MLO_N_GENERIC_LOOPS ((int)(MLO_IN_N_VERT_LOOPS - 2))
#define MLO_IN_VERT_READS (MLO_IN_EXTENT1 + MLO_FILTER_PAD1)
#else
#define MLO_N_GENERIC_LOOPS 0
#define MLO_IN_VERT_READS MLO_IN_EXTENT1
#endif

#else
#define MLO_N_GENERIC_LOOPS (MLO_IN_N_VERT_LOOPS)
#define MLO_IN_VERT_READS MLO_IN_EXTENT1
#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

#define MLO_HW_WAVE_ID_SETTING 0

#if MLO_HW_WAVE_ID_SETTING
extern __attribute__((const)) uint __hsail_get_dynwave_id(void);
static inline int getWaveId()
{
	int wave_id = 0;

	wave_id = __hsail_get_dynwave_id();
	wave_id = wave_id & MLO_N_PHYS_WAVES_MASK;
	return(wave_id);
}
#else
static inline int getWaveId()
{
	int wave_id = 0;

	wave_id = (get_local_id(0) >> MLO_LG2_PHYS_WAVE_SZ);

	return(wave_id);
}
#endif

static inline int gePhysLocalId()
{
	int lcl_wave_id = get_local_id(0) - ((get_local_id(0) >> MLO_LG2_PHYS_WAVE_SZ) << MLO_LG2_PHYS_WAVE_SZ);
	return(lcl_wave_id);
}

static inline int iDiv(int v, int d)
{
	int r = (int)((float)v / d + 0.00001f);
	return(r);
}

static inline int iMod(int v, int u, int d)
{
	int r = v - mul24((int)u, (int)d);
	return(r);
}

static inline void ReduceKernel(__local _FLOAT * lcl_blob, __private _FLOAT *weights_accum, int lcl_id, int scan_lcl, int sum_stride, int unit_len, bool debug)
{
// read first half
	if (scan_lcl < (sum_stride >> 1))
	{
		for (int 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 (int j = (sum_stride >> 1); j > 0; j >>= 1)
	{
		barrier(CLK_LOCAL_MEM_FENCE);
		if (scan_lcl < j)
		{
			for (int 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];
			}

		}
	}
}

static inline void  Kahan_summation(__private _FLOAT *sum, __private _FLOAT * c, _FLOAT v)
{
	_FLOAT y = v - *c;    //So far, so good: c is zero.
	_FLOAT t = *sum + y;         //Alas, sum is big, y small, so low-order digits of y are lost.
	*c = (t - *sum) - y;   //(t - sum) recovers the high-order part of y; subtracting y recovers -(low part of y)
	*sum = t;             //Algebraically, c should always be zero. Beware eagerly optimising compilers!
}

/*
	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
*/

static inline void readInput(int lcl_id, int gbl_in_scan_off, int n_v_reads, const __global _FLOAT * bot, __local _FLOAT *lcl_bot)
{
	for (int p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * n_v_reads;
		p4 += MLO_GRP_SZ)
	{
		__private _FLOAT in_rd_data[MLO_READ_UNIT];
// TODO : more than 1 input
		int c = 0;
		int t_p4 = p4;
#if MLO_N_LCL_IN_MAPS > 1
		c = iDiv(p4, (MLO_N_IN_HORIZ_READS * n_v_reads));
		t_p4 = iMod(p4, c, (MLO_N_IN_HORIZ_READS * n_v_reads));
#endif

		int c_scan = iDiv(t_p4, (MLO_N_IN_HORIZ_READS));

		int c_pix4 = iMod(t_p4, c_scan, (MLO_N_IN_HORIZ_READS));

//		if (c < MLO_N_INPUTS)

		{

#if MLO_IN_N_PIXS_OFF > 0

			if (c_pix4 == MLO_N_IN_HORIZ_READS - 1)
			{
				for (int i = 0; i < MLO_IN_N_PIXS_OFF; ++i)
				{

					in_rd_data[i] = bot[gbl_in_scan_off + c*MLO_IN_CHANNEL_STRIDE + c_scan* MLO_IN_STRIDE + c_pix4*MLO_READ_UNIT + i];
#if 0
					if (gbl_in_scan_off + c*MLO_IN_CHANNEL_STRIDE + c_scan* MLO_IN_STRIDE + c_pix4*MLO_READ_UNIT + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
					{
						printf("k:err:in-of-range\n");
					}
#endif
				}

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

			}
			else
#endif
			{

				for (int i = 0; i < MLO_READ_UNIT; ++i)
				{
					in_rd_data[i] = bot[gbl_in_scan_off + c*MLO_IN_CHANNEL_STRIDE + c_scan* MLO_IN_STRIDE + c_pix4*MLO_READ_UNIT + i];
#if 0
					if (gbl_in_scan_off + c*MLO_IN_CHANNEL_STRIDE + c_scan* MLO_IN_STRIDE + c_pix4*MLO_READ_UNIT + i >= MLO_IN_BATCH_STRIDE * MLO_BATCH_SZ)
					{
						printf("k:err:in-of-range\n");
					}
#endif
				}
			}

// MLO_N_LCL_IN_MAPS inputs
			for (int 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

*/
static inline void Processing(int sc, int sc_lcl_off, int top_lim, int bot_lim, __private _FLOAT * pvt_accum, __local _FLOAT * lcl_bot, __private _FLOAT * top_dat)
{
	for (int l = top_lim; l >= bot_lim; --l)
	{
		for (int m = 0; m < MLO_IN_TILE0; ++m)
		{
			for (int n = 0; n < MLO_FILTER_SIZE0; ++n)
			{
				for(int c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
				{
					_FLOAT bot_val = lcl_bot[sc_lcl_off + c*MLO_IN_LCL_SZ + n + m];
					for(int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
					{
						int pvt_top_off =  k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (top_lim - l) * MLO_IN_TILE0 + m;
						int 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
							+= bot_val*top_val;
#if 0
					if (bot_val * top_val != 0 && get_global_id(1) == 0 && get_global_id(2) == 0 && (get_local_id(0) == 0  || get_local_id(0) == 1 || get_local_id(0) == 2)&& k == 0 && l == 0 && n == 0)
					{
						printf("G: %d %d %d %d  %f %f %f %f\n",
							get_local_id(0),
							sc,
							sc_lcl_off + c*MLO_IN_LCL_SZ + n + m,
							pvt_top_off,
							pvt_accum[pvt_accum_off],
							bot_val * top_val,
							bot_val,
							top_val
						);
					}
#endif
					}
				}
			}

		}

	}

}

static inline void moveOutputUp(__private _FLOAT * top_dat)
{
	// move up output to reduce overfetch
	for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
	{
		for (int j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
		{
			for (int i = 0; i < MLO_IN_TILE0; ++i)
			{
				int pvt_off_n = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0 + i;
				int 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];
			}
		}
	}
}

static inline void spanRightSiding5x5(int k, int top_df_off, int j, _FLOAT mask, __private _FLOAT * top_dat, __global const _FLOAT * top_df)
{
	int i = 0;
	int pvt_off = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0;
	for (; i < MLO_OUT_N_PIXS_OFF; ++i)
	{
		top_dat[pvt_off + i] = top_df[top_df_off + i] * mask;
#if 0
		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;
	}

}

static inline void spanReadingOutput(int k, int j, int top_df_off, _FLOAT mask,
	__private _FLOAT * top_dat, const __global _FLOAT * top_df)
{
	int pvt_off = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0;
	for (int i = 0; i < MLO_IN_TILE0
		; ++i)
	{
		top_dat[pvt_off + i] = top_df[top_df_off + i] * mask;
#if 0
		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 MLOpenCvBwdWrW(
	const __global _FLOAT * top_df,
	const __global _FLOAT * bot,
	__global _FLOAT * weights_df,
#if MLO_CONV_BIAS
	__global _FLOAT * bias_df,
#endif
	_FLOAT padding_val
)
{

	// input/output tiles + reduce buffer

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

	// guarnteeing an uniformity over a wave
	int wave_id = getWaveId();
	int lcl_id = get_local_id(0);
	int lcl_wv_id = gePhysLocalId();

	int c_idx_base = get_group_id(1); // input map index base

	int o_idx_base = iDiv(get_group_id(2), (MLO_BATCH_SZ / (MLO_N_BATCH_LOOPS*MLO_N_LCL_BATCHS))); // output map index base
	int ib_base = iMod(get_group_id(2), o_idx_base, (MLO_BATCH_SZ / (MLO_N_BATCH_LOOPS*MLO_N_LCL_BATCHS)));

	int ib = ib_base*(MLO_N_BATCH_LOOPS*MLO_N_LCL_BATCHS);

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

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

	int gbl_in_off = c_idx * MLO_IN_CHANNEL_STRIDE + ib * MLO_IN_BATCH_STRIDE;
	int 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
	int o = iDiv(lcl_id, MLO_N_SPANS_PER_SCAN);
	//	bool scan_lead = (o*MLO_N_SPANS_PER_SCAN == lcl_id);
	int spn = iMod(lcl_id, o, MLO_N_SPANS_PER_SCAN);

	int lcl_bot_off = spn * MLO_IN_TILE0;
	int 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 (int 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 pvt_accum[MLO_ACCUM_SZ];

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

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

	// over all batches
	for (int b = 0;
		b < MLO_N_BATCH_LOOPS;
		++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
		int gbl_in_scan_off = gbl_in_off;
		int gbl_out_scan_off = gbl_out_off;

		// read input map
		readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);
		// move input pointer
		gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1;

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

		// prefetch output

		int gbl_out_scan_off1 = gbl_out_scan_off;
		for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k, gbl_out_scan_off1 += MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE)
		{
			for (int j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
			{
				// loop around all output maps
				int top_df_off = gbl_out_scan_off1 + j*MLO_OUT_STRIDE;
				_FLOAT mask = 1;
#if 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

// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0
				if (spn == MLO_N_SPANS_PER_SCAN - 1)
				{

					spanRightSiding5x5(k, top_df_off, j, mask, top_dat, top_df);

				}
				else
#endif
				{
					spanReadingOutput(k, j, top_df_off, mask,
						top_dat, top_df);
				}

			}
		}

		barrier(CLK_LOCAL_MEM_FENCE);

		gbl_out_scan_off += (MLO_FILTER_SIZE1 - 1) * MLO_OUT_STRIDE;

		int sc = 0;
		int sc_lcl_off = lcl_bot_off;

		// prolog
		// handling padding

		// pad0
		for (; sc < MLO_FILTER_PAD1; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
		{
			Processing(sc, sc_lcl_off, sc + MLO_FILTER_PAD1, 0, pvt_accum, lcl_bot, top_dat);
		}

		for (; sc < MLO_IN_EXTENT1
#if MLO_IN_N_VERT_LOOPS == 1
			- MLO_FILTER_PAD1
			// 3x3 out of range
#if MLO_OUT_N_PIXS_OFF > 0 && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
			- 1
#endif
#endif
			; ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
		{
			for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
			{
				int top_df_off = gbl_out_scan_off + k*MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
				_FLOAT mask = 1;

#if MLO_FILTER_SIZE1 > 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
				// move in the last output scans
// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0
				if (spn == MLO_N_SPANS_PER_SCAN - 1)
				{
					spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
				}
				else
#endif
				{
					spanReadingOutput(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 (int 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, MLO_IN_VERT_READS, bot, lcl_bot);

// point to the start of the local buffer

			sc_lcl_off = lcl_bot_off;

			barrier(CLK_LOCAL_MEM_FENCE);

			for (; sc < (i_loop + 2) * MLO_IN_EXTENT1
				// 3x3 out of range
				; ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
			{

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

#if MLO_FILTER_SIZE1 > 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
					// move in the last output scans
					// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0
					if (spn == MLO_N_SPANS_PER_SCAN - 1)
					{
						spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
					}
					else
#endif
					{
						spanReadingOutput(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.
// TO DD: verify it with 5x5
            // do need sync with the real read: non intersecting areas.
#define MLO_LAST_VERT_READS (MLO_IN_HEIGHT - MLO_IN_EXTENT1 * (MLO_IN_N_VERT_LOOPS - 1))

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

			// point to the start of the local buffer

			sc_lcl_off = lcl_bot_off;

			barrier(CLK_LOCAL_MEM_FENCE);

			for (; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1
				// 3x3 out of range
#if MLO_OUT_N_PIXS_OFF > 0 && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
				- 1
#endif
				; ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
			{

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

#if MLO_FILTER_SIZE1 > 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
					// move in the last output scans
					// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0
					if (spn == MLO_N_SPANS_PER_SCAN - 1)
					{
						spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
					}
					else
#endif
					{
						spanReadingOutput(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);

			}
		}

// handling 3x3 out of range
#if MLO_OUT_N_PIXS_OFF > 0  && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
		{
			for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
			{
				int top_df_off = gbl_out_scan_off + k*MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
				_FLOAT mask = 1;

#if MLO_FILTER_SIZE1 > 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
				// move in the last output scans

				if (spn == MLO_N_SPANS_PER_SCAN - 1 )
				{
					spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
				}
				else
				{

					spanReadingOutput(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);

		}

		++sc; gbl_out_scan_off += MLO_OUT_STRIDE; sc_lcl_off += MLO_IN_LCL_WIDTH;
#endif

		// epilog
		// handling padding
		// pad0/1

		for (; sc < MLO_OUT_HEIGHT; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
		{
			// processing
			Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, (MLO_FILTER_PAD1 + 1 - (MLO_OUT_HEIGHT - 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 (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
	{
		for(int c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
		{

			for (int l = 0; l < MLO_FILTER_SIZE1; ++l)
			{

				barrier(CLK_LOCAL_MEM_FENCE);
				for (int n = 0; n < MLO_FILTER_SIZE0; ++n)
				{
					int 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 (int s = 0; s < MLO_N_SPANS_PER_SCAN - 1; ++s)
					{

						for (int n = 0; n < MLO_FILTER_SIZE0; ++n)
						{
							int 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];
#if 0
						if (/*fabs(pvt_accum[pvt_off] - 0.020364f) < 0.0001f*/ pvt_off == 12 && get_global_id(1) == 0 && get_global_id(2) == 0 && get_local_id(0) == 0 && k == 0/* && l == 2 && n == 2*/)
						{
							printf("G:s: %d %d %d  %f %f\n",
								get_local_id(0),
								(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n,
								pvt_off,
								pvt_accum[pvt_off],
								lcl[(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n]
							);
						}
#endif

						}

					}
				}

//				barrier(CLK_LOCAL_MEM_FENCE);
			}
		}
	}

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

	int wei_df_off = (((ib/MLO_N_BATCH_LOOPS) * MLO_N_OUTPUTS + o_idx + o) * (int)MLO_WEI_BATCH_STRIDE)
		// this input channel
		+ mul24(c_idx, (int)MLO_WEI_CHANNEL_STRIDE);
	for (int k = 0; k < MLO_N_LCL_OUT_MAPS && spn == 0 && o_idx + o + k*MLO_OUT_STACKS < MLO_N_OUTPUTS && o < MLO_OUT_STACKS; ++k)
	{
		for (int c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
		{

			for (int 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];
#if 0
				if (((ib/MLO_N_BATCH_LOOPS) == 0 || (ib/MLO_N_BATCH_LOOPS) == 1) && i == 0)
				{
					printf("G:o: %d %d %d %d %d  %f\n",
						get_group_id(2),
						ib,
						k,
						c,
						wei_df_off + k*MLO_OUT_STACKS*MLO_WEI_BATCH_STRIDE + c*MLO_WEI_CHANNEL_STRIDE + i,
						weights_df[wei_df_off + k*MLO_OUT_STACKS*MLO_WEI_BATCH_STRIDE + c*MLO_WEI_CHANNEL_STRIDE + i]
					);
				}
#endif
			}

		}
	}

}

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

	int wei_blk_idx = iDiv(wei_idx0, MLO_WEI_CHANNEL_STRIDE);
	int wei_idx = iMod(wei_idx0, wei_blk_idx, MLO_WEI_CHANNEL_STRIDE);

	_FLOAT pvt_accum_wei[MLO_UT_READ_UNIT];
	for (int 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 (int i = 0; i < batch_loop; ++i)
	{
		*(MLO_UT_READ_TYPE*)pvt_accum_wei
			+= *(__global MLO_UT_READ_TYPE*)&weight_df_tmp[(wei_blk_idx * MLO_WEI_CHANNEL_STRIDE + i* MLO_N_OUTPUTS*MLO_WEI_BATCH_STRIDE)  + wei_idx];
	}

	*(__global MLO_UT_READ_TYPE*)&weights_df[wei_idx0] = *(MLO_UT_READ_TYPE*)pvt_accum_wei;

}

/*
 * Copyright (c) 2016 AMD Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and/or associated documentation files (the
 * "Materials"), to deal in the Materials without restriction, including
 * without limitation the rights to use, copy, modify, merge, publish,
 * distribute, sublicense, and/or sell copies of the Materials, and to
 * permit persons to whom the Materials are 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 Materials.
 *
 * THE MATERIALS ARE 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
 * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
 */

#define _FLOAT					float
#define _FLOAT2					float2
#define _FLOAT4					float4
#define _FLOAT8					float8

#ifndef FLT_MAX
#define FLT_MAX         3.402823466e+38F        /* max value */
#endif

#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
#if MLO_FILTER_PAD1 > 0

#if  MLO_IN_N_VERT_LOOPS >= 2
#define MLO_N_GENERIC_LOOPS ((int)(MLO_IN_N_VERT_LOOPS - 2))
#define MLO_IN_VERT_READS (MLO_IN_EXTENT1 + MLO_FILTER_PAD1)
#else
#define MLO_N_GENERIC_LOOPS 0
#define MLO_IN_VERT_READS MLO_IN_EXTENT1
#endif

#else
#define MLO_N_GENERIC_LOOPS (MLO_IN_N_VERT_LOOPS)
#define MLO_IN_VERT_READS MLO_IN_EXTENT1
#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

#define MLO_HW_WAVE_ID_SETTING 0

#if MLO_HW_WAVE_ID_SETTING
extern __attribute__((const)) uint __hsail_get_dynwave_id(void);
static inline int getWaveId()
{
	int wave_id = 0;

	wave_id = __hsail_get_dynwave_id();
	wave_id = wave_id & MLO_N_PHYS_WAVES_MASK;
	return(wave_id);
}
#else
static inline int getWaveId()
{
	int wave_id = 0;

	wave_id = (get_local_id(0) >> MLO_LG2_PHYS_WAVE_SZ);

	return(wave_id);
}
#endif

static inline int gePhysLocalId()
{
	int lcl_wave_id = get_local_id(0) - ((get_local_id(0) >> MLO_LG2_PHYS_WAVE_SZ) << MLO_LG2_PHYS_WAVE_SZ);
	return(lcl_wave_id);
}

static inline int iDiv(int v, int d)
{
	int r = (int)((float)v / d + 0.00001f);
	return(r);
}

static inline int iMod(int v, int u, int d)
{
	int r = v - mul24((int)u, (int)d);
	return(r);
}

static inline void ReduceKernel(__local _FLOAT * lcl_blob, __private _FLOAT *weights_accum, int lcl_id, int scan_lcl, int sum_stride, int unit_len, bool debug)
{
// read first half
	if (scan_lcl < (sum_stride >> 1))
	{
		for (int 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 (int j = (sum_stride >> 1); j > 0; j >>= 1)
	{
		barrier(CLK_LOCAL_MEM_FENCE);
		if (scan_lcl < j)
		{
			for (int 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];
			}

		}
	}
}

static inline void  Kahan_summation(__private _FLOAT *sum, __private _FLOAT * c, _FLOAT v)
{
	_FLOAT y = v - *c;    //So far, so good: c is zero.
	_FLOAT t = *sum + y;         //Alas, sum is big, y small, so low-order digits of y are lost.
	*c = (t - *sum) - y;   //(t - sum) recovers the high-order part of y; subtracting y recovers -(low part of y)
	*sum = t;             //Algebraically, c should always be zero. Beware eagerly optimising compilers!
}

/*
	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
*/

static inline void readInput(int lcl_id, int gbl_in_scan_off, int n_v_reads, const __global _FLOAT * bot, __local _FLOAT *lcl_bot)
{
	for (int p4 = lcl_id; p4 < MLO_N_LCL_IN_MAPS * MLO_N_IN_HORIZ_READS * n_v_reads;
		p4 += MLO_GRP_SZ)
	{
		__private _FLOAT in_rd_data[MLO_READ_UNIT];
// TODO : more than 1 input
		int c = 0;
		int t_p4 = p4;
#if MLO_N_LCL_IN_MAPS > 1
		c = iDiv(p4, (MLO_N_IN_HORIZ_READS * n_v_reads));
		t_p4 = iMod(p4, c, (MLO_N_IN_HORIZ_READS * n_v_reads));
#endif

		int c_scan = iDiv(t_p4, (MLO_N_IN_HORIZ_READS));

		int c_pix4 = iMod(t_p4, c_scan, (MLO_N_IN_HORIZ_READS));

//		if (c < MLO_N_INPUTS)

		{

#if MLO_IN_N_PIXS_OFF > 0

			if (c_pix4 == MLO_N_IN_HORIZ_READS - 1)
			{
				for (int i = 0; i < MLO_IN_N_PIXS_OFF; ++i)
				{

					in_rd_data[i] = bot[gbl_in_scan_off + c*MLO_IN_CHANNEL_STRIDE + c_scan* MLO_IN_STRIDE + c_pix4*MLO_READ_UNIT + i];
				}

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

			}
			else
#endif
			{

				for (int i = 0; i < MLO_READ_UNIT; ++i)
				{
					in_rd_data[i] = bot[gbl_in_scan_off + c*MLO_IN_CHANNEL_STRIDE + c_scan* MLO_IN_STRIDE + c_pix4*MLO_READ_UNIT + i];
				}
			}

// MLO_N_LCL_IN_MAPS inputs
			for (int 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

*/
static inline void Processing(int sc, int sc_lcl_off, int top_lim, int bot_lim, __private _FLOAT * pvt_accum, __local _FLOAT * lcl_bot, __private _FLOAT * top_dat)
{
	for (int l = top_lim; l >= bot_lim; --l)
	{
		for (int m = 0; m < MLO_IN_TILE0; ++m)
		{
			for (int n = 0; n < MLO_FILTER_SIZE0; ++n)
			{
				for(int c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
				{
					_FLOAT bot_val = lcl_bot[sc_lcl_off + c*MLO_IN_LCL_SZ + n + m];
					for(int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
					{
						int pvt_top_off =  k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (top_lim - l) * MLO_IN_TILE0 + m;
						int 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
							+= bot_val*top_val;
#if 0
					if (bot_val * top_val != 0 && get_global_id(1) == 0 && get_global_id(2) == 0 && (get_local_id(0) == 0  || get_local_id(0) == 1 || get_local_id(0) == 2)&& k == 0 && l == 0 && n == 0)
					{
						printf("G: %d %d %d %d  %f %f %f %f\n",
							get_local_id(0),
							sc,
							sc_lcl_off + c*MLO_IN_LCL_SZ + n + m,
							pvt_top_off,
							pvt_accum[pvt_accum_off],
							bot_val * top_val,
							bot_val,
							top_val
						);
					}
#endif
					}
				}
			}

		}

	}

}

static inline void moveOutputUp(__private _FLOAT * top_dat)
{
	// move up output to reduce overfetch
	for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
	{
		for (int j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
		{
			for (int i = 0; i < MLO_IN_TILE0; ++i)
			{
				int pvt_off_n = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0 + i;
				int 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];
			}
		}
	}
}

static inline void spanRightSiding5x5(int k, int top_df_off, int j, _FLOAT mask, __private _FLOAT * top_dat, __global const _FLOAT * top_df)
{
	int i = 0;
	int pvt_off = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0;
	for (; i < MLO_OUT_N_PIXS_OFF; ++i)
	{
		top_dat[pvt_off + i] = top_df[top_df_off + i] * mask;
	}
	for (; i < MLO_IN_TILE0; ++i)
	{
		top_dat[pvt_off + i] = 0;
	}

}
#if 1
#if (MLO_IN_TILE0 - MLO_OUT_N_PIXS_OFF <= MLO_FILTER_PAD0)
#define MLO_OUT_MASK_SZ (MLO_IN_TILE0 - MLO_OUT_N_PIXS_OFF)
#else
#define MLO_OUT_MASK_SZ (MLO_FILTER_PAD0)
#endif
#endif

static inline void spanReadingOutput(int k, int j, int top_df_off, _FLOAT mask,
	__private _FLOAT * top_dat, const __global _FLOAT * top_df)
{
	int pvt_off = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0;
	for (int i = 0; i < MLO_IN_TILE0; ++i)
	{
		top_dat[pvt_off + i] = top_df[top_df_off + i] * mask
			;
	}
}

//#define MLO_OUT_MASK_SZ (1)
static inline void spanReadingOutput3x3(int k, int j, int top_df_off, _FLOAT mask,
#if MLO_OUT_N_PIXS_OFF > 0  && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
	__private _FLOAT * out_mask,
#endif
	__private _FLOAT * top_dat, const __global _FLOAT * top_df)
{
	int pvt_off = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + j *MLO_IN_TILE0;
	for (int i = 0; i < MLO_IN_TILE0; ++i)
	{
		top_dat[pvt_off + i] = top_df[top_df_off + i] * mask
			;
	}
#if MLO_OUT_N_PIXS_OFF > 0  && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16

	for (int i = MLO_OUT_N_PIXS_OFF; i < MLO_OUT_N_PIXS_OFF + MLO_OUT_MASK_SZ; ++i)
	{
		top_dat[pvt_off + i] *= out_mask[i - MLO_OUT_N_PIXS_OFF];

	}
#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 MLOpenCvBwdWrW(
	const __global _FLOAT * top_df,
	const __global _FLOAT * bot,
	__global _FLOAT * weights_df,
#if MLO_CONV_BIAS
	__global _FLOAT * bias_df,
#endif
	_FLOAT padding_val
)
{

	// input/output tiles + reduce buffer

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

	// guarnteeing an uniformity over a wave
	int wave_id = getWaveId();
	int lcl_id = get_local_id(0);
	int lcl_wv_id = gePhysLocalId();

	int c_idx_base = get_group_id(1); // input map index base

	int o_idx_base = iDiv(get_group_id(2), (MLO_BATCH_SZ / (MLO_N_BATCH_LOOPS*MLO_N_LCL_BATCHS))); // output map index base
	int ib_base = iMod(get_group_id(2), o_idx_base, (MLO_BATCH_SZ / (MLO_N_BATCH_LOOPS*MLO_N_LCL_BATCHS)));

	int ib = ib_base*(MLO_N_BATCH_LOOPS*MLO_N_LCL_BATCHS);

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

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

	int gbl_in_off = c_idx * MLO_IN_CHANNEL_STRIDE + ib * MLO_IN_BATCH_STRIDE;
	int 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
	int o = iDiv(lcl_id, MLO_N_SPANS_PER_SCAN);
	//	bool scan_lead = (o*MLO_N_SPANS_PER_SCAN == lcl_id);
	int spn = iMod(lcl_id, o, MLO_N_SPANS_PER_SCAN);

	int lcl_bot_off = spn * MLO_IN_TILE0;
	int out_wk_item_off = o * MLO_OUT_CHANNEL_STRIDE + lcl_bot_off;
	gbl_out_off += out_wk_item_off;

#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 (int 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 pvt_accum[MLO_ACCUM_SZ];

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

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

// 3x3 out mask
#if MLO_OUT_N_PIXS_OFF > 0  && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16

	_FLOAT out_mask[MLO_OUT_MASK_SZ];
	if (spn == MLO_N_SPANS_PER_SCAN - 1)
	{
		for (int i = 0; i < MLO_OUT_MASK_SZ; ++i)
		{
			out_mask[i] = 0;
		}
	}
	else
	{
		for (int i = 0; i < MLO_OUT_MASK_SZ; ++i)
		{
			out_mask[i] = 1;
		}
	}

#endif

	// over all batches
	for (int b = 0;
		b < MLO_N_BATCH_LOOPS;
		++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
		int gbl_in_scan_off = gbl_in_off;
		int gbl_out_scan_off = gbl_out_off;

		// read input map
		readInput(lcl_id, gbl_in_scan_off, MLO_IN_VERT_READS, bot, lcl_bot);
		// move input pointer
		gbl_in_scan_off += MLO_IN_STRIDE * MLO_IN_EXTENT1;

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

		// prefetch output

		int gbl_out_scan_off1 = gbl_out_scan_off;
		for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k, gbl_out_scan_off1 += MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE)
		{
			for (int j = 0; j < MLO_FILTER_SIZE1 - 1; ++j)
			{
				// loop around all output maps
				int top_df_off = gbl_out_scan_off1 + j*MLO_OUT_STRIDE;
				_FLOAT mask = 1;
#if 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

// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0 //&& (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) > 16
				if (spn == MLO_N_SPANS_PER_SCAN - 1)
				{

					spanRightSiding5x5(k, top_df_off, j, mask, top_dat, top_df);

				}
				else
#endif
				{
					spanReadingOutput(k, j, top_df_off, mask,
						top_dat, top_df);
				}

			}
		}

		barrier(CLK_LOCAL_MEM_FENCE);

		gbl_out_scan_off += (MLO_FILTER_SIZE1 - 1) * MLO_OUT_STRIDE;

		int sc = 0;
		int sc_lcl_off = lcl_bot_off;

		// prolog
		// handling padding

		// pad0
		for (; sc < MLO_FILTER_PAD1; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
		{
			Processing(sc, sc_lcl_off, sc + MLO_FILTER_PAD1, 0, pvt_accum, lcl_bot, top_dat);
		}

		for (; sc < MLO_IN_EXTENT1
#if MLO_IN_N_VERT_LOOPS == 1
			- MLO_FILTER_PAD1
			// 3x3 out of range
#if MLO_OUT_N_PIXS_OFF > 0 && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
			- 1
#endif
#endif
			; ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
		{
			for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
			{
				int top_df_off = gbl_out_scan_off + k*MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
				_FLOAT mask = 1;

#if MLO_FILTER_SIZE1 > 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
				// move in the last output scans
// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0 //&& (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) > 16
				if (spn == MLO_N_SPANS_PER_SCAN - 1)
				{
					spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
				}
				else
#endif
				{
					spanReadingOutput(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 (int 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, MLO_IN_VERT_READS, bot, lcl_bot);

// point to the start of the local buffer

			sc_lcl_off = lcl_bot_off;

			barrier(CLK_LOCAL_MEM_FENCE);

			for (; sc < (i_loop + 2) * MLO_IN_EXTENT1
				// 3x3 out of range
				; ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
			{

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

#if MLO_FILTER_SIZE1 > 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
					// move in the last output scans
					// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0 // && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) > 16
					if (spn == MLO_N_SPANS_PER_SCAN - 1)
					{
						spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
					}
					else
#endif
					{
						spanReadingOutput(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.
// TO DD: verify it with 5x5
            // do need sync with the real read: non intersecting areas.
#define MLO_LAST_VERT_READS (MLO_IN_HEIGHT - MLO_IN_EXTENT1 * (MLO_IN_N_VERT_LOOPS - 1))

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

			// point to the start of the local buffer

			sc_lcl_off = lcl_bot_off;

			barrier(CLK_LOCAL_MEM_FENCE);

			for (; sc < MLO_OUT_HEIGHT - MLO_FILTER_PAD1
				// 3x3 out of range
#if MLO_OUT_N_PIXS_OFF > 0 && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
				- 1
#endif
				; ++sc, gbl_out_scan_off += MLO_OUT_STRIDE, sc_lcl_off += MLO_IN_LCL_WIDTH)
			{

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

#if MLO_FILTER_SIZE1 > 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
					// move in the last output scans
					// 5x5 out of range
#if MLO_OUT_N_PIXS_OFF > 0 //&& (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) > 16
					if (spn == MLO_N_SPANS_PER_SCAN - 1)
					{
						spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
					}
					else
#endif
					{
						spanReadingOutput(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);

			}
		}

// handling 3x3 out of range
#if MLO_OUT_N_PIXS_OFF > 0  && (MLO_FILTER_SIZE1*MLO_FILTER_SIZE0) <= 16
		{
			for (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
			{
				int top_df_off = gbl_out_scan_off + k*MLO_OUT_STACKS * MLO_OUT_CHANNEL_STRIDE;
				_FLOAT mask = 1;

#if MLO_FILTER_SIZE1 > 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
				// move in the last output scans

				if (spn == MLO_N_SPANS_PER_SCAN - 1 )
				{
					spanRightSiding5x5(k, top_df_off, (MLO_FILTER_SIZE1 - 1), mask, top_dat, top_df);
				}
				else
				{
#if 1
					spanReadingOutput(k, (MLO_FILTER_SIZE1 - 1), top_df_off, mask,
						top_dat, top_df);
#else

					int pvt_off = k*MLO_IN_TILE0 * MLO_FILTER_SIZE1 + (MLO_FILTER_SIZE1 - 1) *MLO_IN_TILE0;
					for (int i = 0; i < MLO_IN_TILE0; ++i)
					{
						top_dat[pvt_off + i] = top_df[top_df_off + i] * mask;
					}
#endif
				}

			}

			// 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);

		}

		++sc; gbl_out_scan_off += MLO_OUT_STRIDE; sc_lcl_off += MLO_IN_LCL_WIDTH;
#endif

		// epilog
		// handling padding
		// pad0/1

		for (; sc < MLO_OUT_HEIGHT; ++sc, sc_lcl_off += MLO_IN_LCL_WIDTH)
		{
			// processing
			Processing(sc, sc_lcl_off, MLO_FILTER_SIZE1 - 1, (MLO_FILTER_PAD1 + 1 - (MLO_OUT_HEIGHT - 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 (int k = 0; k < MLO_N_LCL_OUT_MAPS; ++k)
	{
		for(int c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
		{

			for (int l = 0; l < MLO_FILTER_SIZE1; ++l)
			{

				barrier(CLK_LOCAL_MEM_FENCE);
				for (int n = 0; n < MLO_FILTER_SIZE0; ++n)
				{
					int 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 (int s = 0; s < MLO_N_SPANS_PER_SCAN - 1; ++s)
					{

						for (int n = 0; n < MLO_FILTER_SIZE0; ++n)
						{
							int 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];
#if 0
						if (/*fabs(pvt_accum[pvt_off] - 0.020364f) < 0.0001f*/ pvt_off == 12 && get_global_id(1) == 0 && get_global_id(2) == 0 && get_local_id(0) == 0 && k == 0/* && l == 2 && n == 2*/)
						{
							printf("G:s: %d %d %d  %f %f\n",
								get_local_id(0),
								(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n,
								pvt_off,
								pvt_accum[pvt_off],
								lcl[(lcl_id + s + 1) * MLO_FILTER_SIZE0 + n]
							);
						}
#endif

						}

					}
				}

//				barrier(CLK_LOCAL_MEM_FENCE);
			}
		}
	}

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

	int wei_df_off = (((ib/MLO_N_BATCH_LOOPS) * MLO_N_OUTPUTS + o_idx + o) * (int)MLO_WEI_BATCH_STRIDE)
		// this input channel
		+ mul24(c_idx, (int)MLO_WEI_CHANNEL_STRIDE);
	for (int k = 0; k < MLO_N_LCL_OUT_MAPS && spn == 0 && o_idx + o + k*MLO_OUT_STACKS < MLO_N_OUTPUTS && o < MLO_OUT_STACKS; ++k)
	{
		for (int c = 0; c < MLO_N_LCL_IN_MAPS; ++c)
		{

			for (int 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];
#if 0
				if (((ib/MLO_N_BATCH_LOOPS) == 0 || (ib/MLO_N_BATCH_LOOPS) == 1) && i == 0)
				{
					printf("G:o: %d %d %d %d %d  %f\n",
						get_group_id(2),
						ib,
						k,
						c,
						wei_df_off + k*MLO_OUT_STACKS*MLO_WEI_BATCH_STRIDE + c*MLO_WEI_CHANNEL_STRIDE + i,
						weights_df[wei_df_off + k*MLO_OUT_STACKS*MLO_WEI_BATCH_STRIDE + c*MLO_WEI_CHANNEL_STRIDE + i]
					);
				}
#endif
			}

		}
	}

}

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

	int wei_blk_idx = iDiv(wei_idx0, MLO_WEI_CHANNEL_STRIDE);
	int wei_idx = iMod(wei_idx0, wei_blk_idx, MLO_WEI_CHANNEL_STRIDE);

	_FLOAT pvt_accum_wei[MLO_UT_READ_UNIT];
	for (int 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 (int i = 0; i < batch_loop; ++i)
	{
		*(MLO_UT_READ_TYPE*)pvt_accum_wei
			+= *(__global MLO_UT_READ_TYPE*)&weight_df_tmp[(wei_blk_idx * MLO_WEI_CHANNEL_STRIDE + i* MLO_N_OUTPUTS*MLO_WEI_BATCH_STRIDE)  + wei_idx];
	}

	*(__global MLO_UT_READ_TYPE*)&weights_df[wei_idx0] = *(MLO_UT_READ_TYPE*)pvt_accum_wei;

}


trying stuff

trying stuff

// Copyright Hugh Perkins 2014, 2015 hughperkins at gmail
//
// This Source Code Form is subject to the terms of the Mozilla Public License,
// v. 2.0. If a copy of the MPL was not distributed with this file, You can
// obtain one at http://mozilla.org/MPL/2.0/.

// expected defines:
// one of: [ TANH | RELU | LINEAR ]
// BIASED (or not)

#ifdef TANH
    #define ACTIVATION_FUNCTION(output) (tanh(output))
#elif defined SCALEDTANH
    #define ACTIVATION_FUNCTION(output) ( 1.7159f * tanh( 0.66667f * output))
#elif SIGMOID
    #define ACTIVATION_FUNCTION(output) (1.0f / (1 + exp(-output)))
#elif defined RELU
    #define ACTIVATION_FUNCTION(output) (output> 0 ? output : 0)
#elif defined LINEAR
    #define ACTIVATION_FUNCTION(output) (output)
#endif

#ifdef gOutputBoardSize // for previous tests that dont define it
#ifdef ACTIVATION_FUNCTION // protect against not defined
// workgroup id organized like: [imageid][outplane]
// local id organized like: [outrow][outcol]
// each thread iterates over: [upstreamplane][filterrow][filtercol]
// number workgroups = 32
// one filter plane takes up 5 * 5 * 4 = 100 bytes
// one filter cube (corresponding to one outplane) = 5*5 * 32 * 4 = 3.2KB (ok)
// all filter cubes = 3.2KB * 32 = 102KB (too big)
// results are organized like [imageid][filterid][row][col]
void kernel propagate_4_by_n_outplane_smallercache( const int batchSize,
    const int pixelsPerThread,
      global const float *images, global const float *filters,
        #ifdef BIASED
            global const float*biases,
        #endif
    global float *results,
    local float *_upstreamBoard, local float *_filterCube, local float *_pixelSums ) {
    const int globalId = get_global_id(0);

    const int evenPadding = gFilterSize % 2 == 0 ? 1 : 0;

    const int localId = get_local_id(0);
    const int workgroupId = get_group_id(0);
    const int workgroupSize = get_local_size(0);
    const int n = workgroupId / gNumFilters;
    const int outPlane = workgroupId % gNumFilters;

    const int numUpstreamsPerThread = ( gInputBoardSizeSquared + workgroupSize - 1 ) / workgroupSize;
    const int numFilterPixelsPerThread = ( gFilterSizeSquared + workgroupSize - 1 ) / workgroupSize;

    local float *_myPixelSums = _pixelSums + pixelsPerThread * localId;
    //local float *_myPixelSums = _pixelSums;

    for( int pixel = 0; pixel < pixelsPerThread; pixel++ ) {
        _myPixelSums[pixel] = 0.0f;
    }

    const int outputRow = localId / gOutputBoardSize;
    const int outputCol = localId % gOutputBoardSize;

    const int minu = gPadZeros ? max( -gHalfFilterSize, -outputRow ) : -gHalfFilterSize;
    const int maxu = gPadZeros ? min( gHalfFilterSize - evenPadding, gOutputBoardSize - 1 - outputRow  - evenPadding) : gHalfFilterSize - evenPadding;
    const int minv = gPadZeros ? max( -gHalfFilterSize, -outputCol ) : - gHalfFilterSize;
    const int maxv = gPadZeros ? min( gHalfFilterSize - evenPadding, gOutputBoardSize - 1 - outputCol - evenPadding) : gHalfFilterSize - evenPadding;

    for( int upstreamPlane = 0; upstreamPlane < gInputPlanes; upstreamPlane++ ) {
        int thisUpstreamBoardOffset = ( n * gInputPlanes + upstreamPlane ) * gInputBoardSizeSquared;
        barrier(CLK_LOCAL_MEM_FENCE);
        for( int i = 0; i < numUpstreamsPerThread; i++ ) {
            int thisOffset = workgroupSize * i + localId;
            if( thisOffset < gInputBoardSizeSquared ) {
                _upstreamBoard[ thisOffset ] = images[ thisUpstreamBoardOffset + thisOffset ];
            }
        }
        const int filterGlobalOffset = ( outPlane * gInputPlanes + upstreamPlane ) * gFilterSizeSquared;
        for( int i = 0; i < numFilterPixelsPerThread; i++ ) {
            int thisOffset = workgroupSize * i + localId;
            if( thisOffset < gFilterSizeSquared ) {
                _filterCube[thisOffset] = filters[filterGlobalOffset + thisOffset];
            }
        }
        barrier(CLK_LOCAL_MEM_FENCE);

  //      for( int pixel = 0; pixel < pixelsPerThread; pixel++ ) {
        int pixel = 0;
            const int virtualLocalId = localId + pixel * workgroupSize;

            float thissum = 0;
            if( virtualLocalId < gOutputBoardSizeSquared ) {
                for( int u = minu; u <= maxu; u++ ) {
                    int inputRow = outputRow + u + ( gPadZeros ? 0 : gHalfFilterSize );
                    int inputboardrowoffset = inputRow * gInputBoardSize;
                    int filterrowoffset = (u+gHalfFilterSize) * gFilterSize + gHalfFilterSize;
                    for( int v = minv; v <= maxv; v++ ) {
                        int inputCol = outputCol + v + ( gPadZeros ? 0 : gHalfFilterSize );
                        thissum += _upstreamBoard[ inputboardrowoffset + inputCol] * _filterCube[ filterrowoffset + v ];
                    }
                }
            }
            _myPixelSums[pixel] += thissum;
//            if( globalId == 0 ) results[0] = _upstreamBoard[pixel];
       // }
    }
//    for( int pixel = 0; pixel < pixelsPerThread; pixel++ ) {
        int pixel = 0;
        const int virtualLocalId = localId + pixel * workgroupSize;
        if( virtualLocalId < gOutputBoardSizeSquared ) {
            float sum = _myPixelSums[pixel];
            #ifdef BIASED
                sum += biases[outPlane];
            #endif
            // results are organized like [imageid][filterid][row][col]
            int resultIndex = ( n * gNumFilters + outPlane ) * gOutputBoardSizeSquared + virtualLocalId;
            results[resultIndex ] = ACTIVATION_FUNCTION(sum);
            // results[resultIndex ] = 123;
            //if( globalId == 0 ) results[0] += 0.000001f + _perPixelSums[0];
        }
//    }
}
#endif
#endif

// Copyright Hugh Perkins 2014, 2015 hughperkins at gmail
//
// This Source Code Form is subject to the terms of the Mozilla Public License,
// v. 2.0. If a copy of the MPL was not distributed with this file, You can
// obtain one at http://mozilla.org/MPL/2.0/.

// expected defines:
// one of: [ TANH | RELU | LINEAR ]
// BIASED (or not)

#ifdef TANH
    #define ACTIVATION_FUNCTION(output) (tanh(output))
#elif defined SCALEDTANH
    #define ACTIVATION_FUNCTION(output) ( 1.7159f * tanh( 0.66667f * output))
#elif SIGMOID
    #define ACTIVATION_FUNCTION(output) (1.0f / (1 + exp(-output)))
#elif defined RELU
    #define ACTIVATION_FUNCTION(output) (output> 0 ? output : 0)
#elif defined LINEAR
    #define ACTIVATION_FUNCTION(output) (output)
#endif

#ifdef gOutputBoardSize // for previous tests that dont define it
#ifdef ACTIVATION_FUNCTION // protect against not defined
// workgroup id organized like: [imageid][outplane]
// local id organized like: [outrow][outcol]
// each thread iterates over: [upstreamplane][filterrow][filtercol]
// number workgroups = 32
// one filter plane takes up 5 * 5 * 4 = 100 bytes
// one filter cube (corresponding to one outplane) = 5*5 * 32 * 4 = 3.2KB (ok)
// all filter cubes = 3.2KB * 32 = 102KB (too big)
// results are organized like [imageid][filterid][row][col]
void kernel propagate_4_by_n_outplane_smallercache( const int batchSize,
    const int pixelsPerThread,
      global const float *images, global const float *filters,
        #ifdef BIASED
            global const float*biases,
        #endif
    global float *results,
    local float *_upstreamBoard, local float *_filterCube, local float *_pixelSums ) {
    const int globalId = get_global_id(0);

    const int evenPadding = gFilterSize % 2 == 0 ? 1 : 0;

    const int localId = get_local_id(0);
    const int workgroupId = get_group_id(0);
    const int workgroupSize = get_local_size(0);
    const int n = workgroupId / gNumFilters;
    const int outPlane = workgroupId % gNumFilters;

    const int numUpstreamsPerThread = ( gInputBoardSizeSquared + workgroupSize - 1 ) / workgroupSize;
    const int numFilterPixelsPerThread = ( gFilterSizeSquared + workgroupSize - 1 ) / workgroupSize;

    local float *_myPixelSums = _pixelSums + pixelsPerThread * localId;
    //local float *_myPixelSums = _pixelSums;

    for( int pixel = 0; pixel < pixelsPerThread; pixel++ ) {
        _myPixelSums[pixel] = 0.0f;
    }

    const int outputRow = localId / gOutputBoardSize;
    const int outputCol = localId % gOutputBoardSize;

    const int minu = gPadZeros ? max( -gHalfFilterSize, -outputRow ) : -gHalfFilterSize;
    const int maxu = gPadZeros ? min( gHalfFilterSize - evenPadding, gOutputBoardSize - 1 - outputRow  - evenPadding) : gHalfFilterSize - evenPadding;
    const int minv = gPadZeros ? max( -gHalfFilterSize, -outputCol ) : - gHalfFilterSize;
    const int maxv = gPadZeros ? min( gHalfFilterSize - evenPadding, gOutputBoardSize - 1 - outputCol - evenPadding) : gHalfFilterSize - evenPadding;

    for( int upstreamPlane = 0; upstreamPlane < gInputPlanes; upstreamPlane++ ) {
        int thisUpstreamBoardOffset = ( n * gInputPlanes + upstreamPlane ) * gInputBoardSizeSquared;
        barrier(CLK_LOCAL_MEM_FENCE);
        for( int i = 0; i < numUpstreamsPerThread; i++ ) {
            int thisOffset = workgroupSize * i + localId;
            if( thisOffset < gInputBoardSizeSquared ) {
                _upstreamBoard[ thisOffset ] = images[ thisUpstreamBoardOffset + thisOffset ];
            }
        }
        const int filterGlobalOffset = ( outPlane * gInputPlanes + upstreamPlane ) * gFilterSizeSquared;
        for( int i = 0; i < numFilterPixelsPerThread; i++ ) {
            int thisOffset = workgroupSize * i + localId;
            if( thisOffset < gFilterSizeSquared ) {
                _filterCube[thisOffset] = filters[filterGlobalOffset + thisOffset];
            }
        }
        barrier(CLK_LOCAL_MEM_FENCE);

  //      for( int pixel = 0; pixel < pixelsPerThread; pixel++ ) {
        int pixel = 0;
            const int virtualLocalId = localId + pixel * workgroupSize;

            float thissum = 0;
            if( virtualLocalId < gOutputBoardSizeSquared ) {
                for( int u = minu; u <= maxu; u++ ) {
                    int inputRow = outputRow + u + ( gPadZeros ? 0 : gHalfFilterSize );
                    int inputboardrowoffset = inputRow * gInputBoardSize;
                    int filterrowoffset = (u+gHalfFilterSize) * gFilterSize + gHalfFilterSize;
                    for( int v = minv; v <= maxv; v++ ) {
                        int inputCol = outputCol + v + ( gPadZeros ? 0 : gHalfFilterSize );
                        thissum += _upstreamBoard[ inputboardrowoffset + inputCol] * _filterCube[ filterrowoffset + v ];
                    }
                }
            }
            _myPixelSums[pixel] += thissum;
//            if( globalId == 0 ) results[0] = _upstreamBoard[pixel];
        }
    }
//    for( int pixel = 0; pixel < pixelsPerThread; pixel++ ) {
        int pixel = 0;
        const int virtualLocalId = localId + pixel * workgroupSize;
        if( virtualLocalId < gOutputBoardSizeSquared ) {
            float sum = _myPixelSums[pixel];
            #ifdef BIASED
                sum += biases[outPlane];
            #endif
            // results are organized like [imageid][filterid][row][col]
            int resultIndex = ( n * gNumFilters + outPlane ) * gOutputBoardSizeSquared + virtualLocalId;
            results[resultIndex ] = ACTIVATION_FUNCTION(sum);
            // results[resultIndex ] = 123;
            //if( globalId == 0 ) results[0] += 0.000001f + _perPixelSums[0];
        }
//    }
}
#endif
#endif