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c5fc8e03ff
opencv/modules/dnn/src/opencl/conv_layer_spatial.cl
Li Peng c5fc8e03ff cleanup unnecessary macros in convolution ocl kernel 
Signed-off-by: Li Peng <peng.li@intel.com>
2017-12-21 20:32:36 +08:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if APPLY_BIAS
#define BIAS_KERNEL_ARG __global Dtype * biases_base,
#else
#define BIAS_KERNEL_ARG
#endif
#if defined(FUSED_CONV_RELU)
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (Dtype)(negative_slope)))
#define NEGATIVE_SLOPE_ARG Dtype negative_slope,
#elif defined(FUSED_CONV_PRELU)
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (Dtype)(negative_slope[c])))
#define NEGATIVE_SLOPE_ARG __global const Dtype *negative_slope,
#elif defined(FUSED_CONV_POWER)
#define ACTIVATION_RELU_FUNCTION(x, c) pow(x, power)
#define NEGATIVE_SLOPE_ARG Dtype power,
#else
#define ACTIVATION_RELU_FUNCTION(x, c) (x)
#define NEGATIVE_SLOPE_ARG
#endif
#ifdef FUSED_CONV_ELTWISE
#define ACTIVATION_FUNCTION(_dst_, _offset_, _data_, _channel_) do { (_dst_)[(_offset_)] = ACTIVATION_RELU_FUNCTION(eltwise_data[(_offset_)] + (_data_), _channel_);} while(0)
#define ELTWISE_DATA_ARG __global Dtype* eltwise_data,
#else
#define ACTIVATION_FUNCTION(_dst_, _offset_, _data_, _channel_) do { (_dst_)[(_offset_)] = ACTIVATION_RELU_FUNCTION(_data_, _channel_);} while(0)
#define ELTWISE_DATA_ARG
#endif
#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
#define LOOP0(VAR, STMT)
#define LOOP1(VAR, STMT) (STMT); (VAR)++;
#define LOOP2(VAR, STMT) LOOP1(VAR, STMT); (STMT); (VAR)++;
#define LOOP3(VAR, STMT) LOOP2(VAR, STMT); (STMT); (VAR)++;
#define LOOP4(VAR, STMT) LOOP3(VAR, STMT); (STMT); (VAR)++;
#define LOOP5(VAR, STMT) LOOP4(VAR, STMT); (STMT); (VAR)++;
#define LOOP6(VAR, STMT) LOOP5(VAR, STMT); (STMT); (VAR)++;
#define LOOP7(VAR, STMT) LOOP6(VAR, STMT); (STMT); (VAR)++;
#define LOOP8(VAR, STMT) LOOP7(VAR, STMT); (STMT); (VAR)++;
#define LOOP9(VAR, STMT) LOOP8(VAR, STMT); (STMT); (VAR)++;
#define LOOP10(VAR, STMT) LOOP9(VAR, STMT); (STMT); (VAR)++;
#define LOOP11(VAR, STMT) LOOP10(VAR, STMT); (STMT); (VAR)++;
#define LOOP12(VAR, STMT) LOOP11(VAR, STMT); (STMT); (VAR)++;
#define LOOP13(VAR, STMT) LOOP12(VAR, STMT); (STMT); (VAR)++;
#define LOOP14(VAR, STMT) LOOP13(VAR, STMT); (STMT); (VAR)++;
#define LOOP15(VAR, STMT) LOOP14(VAR, STMT); (STMT); (VAR)++;
#define LOOP16(VAR, STMT) LOOP15(VAR, STMT); (STMT); (VAR)++;
#define LOOP(N, VAR, STMT) CAT(LOOP, N)((VAR), (STMT))
#if defined(convolve_simd) || defined(Conv_Interleaved)
#define INT_TYPE uint
#define INT_TYPE2 uint2
#define INT_TYPE4 uint4
#define INT_TYPE8 uint8
#define SUB_GROUP_BLOCK_READ2 intel_sub_group_block_read2
#define SUB_GROUP_BLOCK_READ4 intel_sub_group_block_read4
#define SUB_GROUP_BLOCK_READ8 intel_sub_group_block_read8
#define SUB_GROUP_BLOCK_READ intel_sub_group_block_read
#endif
#ifdef KERNEL_BASIC
__kernel void ConvolveBasic(
ELTWISE_DATA_ARG
NEGATIVE_SLOPE_ARG
__global Dtype* image_data,
int image_offset,
__global Dtype* kernel_data,
int kernel_offset,
__global Dtype* bias,
const int bias_offset,
__global Dtype* convolved_image,
const int convolved_image_offset,
const ushort input_width,
const ushort input_height,
const ushort output_width,
const ushort output_height,
const ushort pad_w,
const ushort pad_h
)
{
const int outputX = get_global_id(0);
const int outputY = get_global_id(1);
const int kernelNum = get_global_id(2) * ZPAR;
if (outputX < output_width && outputY < output_height)
{
Dtype sum[ZPAR];
for (int kern = 0; kern < ZPAR; kern++)
{
sum[kern] = 0.0f;
}
const int org_y = outputY * STRIDE_Y - pad_h;
const int org_x = outputX * STRIDE_X - pad_w;
const int currentKernelOffset = kernel_offset + kernelNum*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS;
#if APPLY_BIAS
const int biasIndex = bias_offset + kernelNum;
#endif
const int local_image_offset = org_y * input_width + org_x;
const int imageSize = input_width * input_height;
__global Dtype* image_dataPtr = (image_data + (image_offset + local_image_offset));
__global Dtype* kernel_dataPtr = (kernel_data + (currentKernelOffset));
for (int c = 0; c < CHANNELS; c++)
{
for (int y = 0; y < KERNEL_HEIGHT; y++)
{
for (int x = 0; x < KERNEL_WIDTH; x++)
{
int y_ = org_y + y * DILATION_Y;
int x_ = org_x + x * DILATION_X;
if (!(y_ >= 0 && y_ < input_height && x_ >= 0 && x_ < input_width))
{
continue;
}
for (int kern = 0; kern < ZPAR; kern++)
{
sum[kern] += image_dataPtr[x * DILATION_X] * kernel_dataPtr[kern*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS + x];
}
}
image_dataPtr += input_width * DILATION_Y;
kernel_dataPtr += KERNEL_WIDTH;
}
image_dataPtr += imageSize - input_width*KERNEL_HEIGHT*DILATION_Y;
}
for (int kern = 0; kern < ZPAR; kern++)
{
if (kernelNum + kern < OUTPUT_Z)
{
int offset = convolved_image_offset + (kernelNum+kern)*output_height*output_width + outputY*output_width + outputX;
#if APPLY_BIAS
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern] + bias[biasIndex + kern], biasIndex + kern);
#else
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], biasIndex + kern);
#endif
}
}
}
}
#elif defined KERNEL_IDLF
#define VLOAD4(_v, _p) do { _v = vload4(0, _p); } while(0)
// Each work-item computes a OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT region of one output map.
// Each work-group (which will be mapped to 1 SIMD16/SIMD8 EU thread) will compute 16/8 different feature maps, but each feature map is for the same region of the imput image.
// NDRange: (output_width+pad)/ OUT_BLOCK_WIDTH, (output_height+pad)/OUT_BLOCK_HEIGHT, NUM_FILTERS/OUT_BLOCK_DEPTH
// NOTE: for beignet this reqd_work_group_size does not guarantee that SIMD16 mode will be used, the compiler could choose to use two SIMD8 threads, and if that happens the code will break.
__attribute__((reqd_work_group_size(1, 1, SIMD_SIZE)))
__attribute__((intel_reqd_sub_group_size(SIMD_SIZE)))
__kernel void
convolve_simd(
ELTWISE_DATA_ARG
NEGATIVE_SLOPE_ARG
__global Dtype* inputs_base,
filter_qualifier Dtype* weights_base,
BIAS_KERNEL_ARG
__global Dtype* outputs_base,
const ushort input_width,
const ushort input_height,
const ushort output_width,
const ushort output_height)
{
__global Dtype* outputs = outputs_base;
__global Dtype* inputs = inputs_base;
filter_qualifier Dtype* weights = weights_base;
unsigned int oc = get_global_id(0) * OUT_BLOCK_WIDTH; // oc = Output Column
unsigned int or = get_global_id(1) * OUT_BLOCK_HEIGHT;// or = Output Row
unsigned int fm = get_global_id(2);// fm = Feature Map = od = Output Depth
unsigned int fmg = get_group_id(2);
unsigned int lid = get_local_id(2);
Dtype out[OUT_BLOCK_WIDTH * OUT_BLOCK_HEIGHT];
int in_addr;
// find weights adress of given neuron (lid is index)
unsigned int weight_addr = (fmg % (ALIGNED_NUM_FILTERS/SIMD_SIZE)) * INPUT_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT * SIMD_SIZE + lid;
for(int i=0;i<OUT_BLOCK_SIZE;i++) {
out[i]=0.0f;
}
unsigned int num_in_batch = ( fm ) / ALIGNED_NUM_FILTERS;
unsigned int input_batch_offset = num_in_batch * input_height * input_width * TOTAL_INPUT_DEPTH_SIZE;
int curr_local_y = ( lid / ( TILE_X / 4 ) );
int curr_local_x = ( lid % ( TILE_X / 4 ) ) * 4;
int curr_y = or * STRIDE_Y + curr_local_y;
int curr_x = oc * STRIDE_X + curr_local_x;
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0
int saved_y = curr_y;
#endif
in_addr = input_batch_offset
+ (curr_y - INPUT_PAD_H) * input_width // y tile offset
+ curr_x - INPUT_PAD_W; // x tile offset
union {
Dtype4 in_vec[INVEC_SIZE];
Dtype in_array[INVEC_SIZE * 4];
} in_buf;
for(int kd = 0; kd < INPUT_DEPTH; kd++)
{
int in_offset = in_addr;
int reg = 0;
LOOP(INVEC_SIZE, reg,
{
if (curr_local_y + reg * TILE_Y_STRIDE < TILE_Y || INVEC_SIZE * TILE_Y_STRIDE <= (TILE_Y + 2) || reg < INVEC_SIZE - 1) {
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + 3 >= INPUT_PAD_W && curr_x < input_width + INPUT_PAD_W) {
if (curr_x < INPUT_PAD_W) {
in_buf.in_vec[reg].s0 = 0;
if (curr_x + 1 >= INPUT_PAD_W)
in_buf.in_vec[reg].s1 = *(inputs + in_offset + 1);
else
in_buf.in_vec[reg].s1 = 0;
if (curr_x + 2 >= INPUT_PAD_W)
in_buf.in_vec[reg].s2 = *(inputs + in_offset + 2);
else
in_buf.in_vec[reg].s2 = 0;
in_buf.in_vec[reg].s3 = *(inputs + in_offset + 3);
} else {
VLOAD4(in_buf.in_vec[reg], inputs + in_offset);
if (curr_x + 1 >= input_width + INPUT_PAD_W)
in_buf.in_vec[reg].s1 = 0;
if (curr_x + 2 >= input_width + INPUT_PAD_W)
in_buf.in_vec[reg].s2 = 0;
if (curr_x + 3 >= input_width + INPUT_PAD_W)
in_buf.in_vec[reg].s3 = 0;
}
} else {
in_buf.in_vec[reg] = 0;
}
curr_y += TILE_Y_STRIDE;
#else
VLOAD4(in_buf.in_vec[reg], inputs + in_offset);
#endif
}
in_offset += input_width * TILE_Y_STRIDE;
});
in_addr += input_height * input_width;
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0
curr_y = saved_y;
#endif
#if KERNEL_WIDTH * KERNEL_HEIGHT != 1
#define WEIGHT_PREF 8
#else
#define WEIGHT_PREF 1
#endif
union {
Dtype w[WEIGHT_PREF];
#if KERNEL_WIDTH * KERNEL_HEIGHT != 1
INT_TYPE8 ui8;
#endif
} weight_buf;
int w_idx=0;
unsigned int orig_weight_addr = weight_addr;
#if KERNEL_WIDTH * KERNEL_HEIGHT != 1
weight_buf.ui8 = SUB_GROUP_BLOCK_READ8((__global INT_TYPE *)&weights[weight_addr]);
weight_addr += SIMD_SIZE * WEIGHT_PREF;
#else
weight_buf.w[0] = as_Dtype(SUB_GROUP_BLOCK_READ((__global INT_TYPE *)&weights[weight_addr]));
weight_addr += SIMD_SIZE * 1;
#endif
#define BLOCK_IN(n) sub_group_broadcast( in_buf.in_array[((n)%4) + ((n) / (TILE_Y_STRIDE * TILE_X)) * 4], (((n) % (TILE_Y_STRIDE * TILE_X))/4))
int kr = 0; // kr = Kernel Row
LOOP(KERNEL_HEIGHT, kr,// LOOP is a macro that unrolls the loop.
{
int kc = 0; // kc = Kernel Column
LOOP(KERNEL_WIDTH, kc,
{
for(int br=0; br < OUT_BLOCK_HEIGHT; br++) {
for(int bc=0; bc < OUT_BLOCK_WIDTH; bc++) {
Dtype input = BLOCK_IN((br * STRIDE_Y + kr * DILATION_Y) * TILE_X + bc * STRIDE_X + kc * DILATION_X);
out[br * OUT_BLOCK_WIDTH + bc] = mad(weight_buf.w[w_idx % WEIGHT_PREF], input, out[br * OUT_BLOCK_WIDTH + bc]);
}
}
#if KERNEL_WIDTH * KERNEL_HEIGHT > WEIGHT_PREF
// We assume KERNEL_W is equal to KERNEL_H here.
if ((w_idx + 1) % WEIGHT_PREF == 0
#if KERNEL_WIDTH * KERNEL_HEIGHT % 8 != 0
&& ((w_idx + 1) <= (KERNEL_WIDTH * KERNEL_HEIGHT - WEIGHT_PREF))
#endif
) {
weight_buf.ui8 = SUB_GROUP_BLOCK_READ8((__global INT_TYPE *)&weights[weight_addr]);
weight_addr += SIMD_SIZE * WEIGHT_PREF; // weights must be stored in just the right SIMD swizzled format for this to work, see host code for details.
}
#if KERNEL_WIDTH*KERNEL_HEIGHT % 8 == 0
// need to do nothing
#else
else if ((w_idx + 1) % WEIGHT_PREF == 0 && ((w_idx + 1) > (KERNEL_WIDTH * KERNEL_HEIGHT - WEIGHT_PREF)))
#if KERNEL_WIDTH * KERNEL_HEIGHT % 8 == 1
weight_buf.w[0] = weights[weight_addr];
#elif KERNEL_WIDTH * KERNEL_HEIGHT % 8 == 2
weight_buf.ui8.s01 = SUB_GROUP_BLOCK_READ2((__global INT_TYPE *)&weights[weight_addr]);
#elif KERNEL_WIDTH * KERNEL_HEIGHT % 8 <= 4
weight_buf.ui8.s0123 = SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)&weights[weight_addr]);
#else
weight_buf.ui8 = SUB_GROUP_BLOCK_READ8((__global INT_TYPE *)&weights[weight_addr]);
#endif
#endif
#endif
++w_idx;
});
});
weight_addr = orig_weight_addr + KERNEL_WIDTH * KERNEL_HEIGHT * SIMD_SIZE;
}
// dead code to work around possible compiler bug.
if (ALIGNED_NUM_FILTERS != NUM_FILTERS && fm > 0xfffffffeul) {
outputs[0] = BLOCK_IN(fm % SIMD_SIZE);
}
fm = fm % ALIGNED_NUM_FILTERS;
if ((ALIGNED_NUM_FILTERS == NUM_FILTERS || fm < NUM_FILTERS)) {
unsigned int out_addr = ( num_in_batch * TOTAL_OUTPUT_DEPTH + fm ) * output_width * output_height;
out_addr += or * output_width + oc;
// we need this address calculation for biases because we support views and batching
#if APPLY_BIAS
Dtype bias = biases_base[fm];
#else
Dtype bias = 0;
#endif
for(unsigned int r = 0; r < OUT_BLOCK_HEIGHT; r++) {
if (r + or >= output_height) break;
for(unsigned int c = 0; c < OUT_BLOCK_WIDTH; c++) {
if (c + oc >= output_width) break;
// this does a scattered write to SIMD_SIZE different feature maps, so that data within one map is contiguous, thus ready for input to next layer.
ACTIVATION_FUNCTION(outputs, out_addr + r * output_width + c, bias + out[r * OUT_BLOCK_WIDTH + c], fm);
}
}
}
}
#elif defined KERNEL_GEMM_LIKE
#if APPLY_BIAS
// Dtype bias[4];
#define SUBGROUP_GET_BIAS(k, i) intel_sub_group_shuffle(bias[k], i)
#else
#define SUBGROUP_GET_BIAS(k, i) ((Dtype)0)
#endif
#ifdef Conv_Interleaved
typedef struct float1 { float s0; } float1;
typedef struct float5 { float s0; float s1; float s2; float s3; float s4; } float5;
typedef struct float6 { float s0; float s1; float s2; float s3; float s4; float s5; } float6;
typedef struct float7 { float s0; float s1; float s2; float s3; float s4; float s5; float s6; } float7;
typedef struct float9 { float s0; float s1; float s2; float s3; float s4; float s5; float s6; float s7; float s8; } float9;
typedef struct float10 { float s0; float s1; float s2; float s3; float s4; float s5;
float s6; float s7; float s8; float s9;} float10;
typedef struct float11 { float s0; float s1; float s2; float s3; float s4; float s5;
float s6; float s7; float s8; float s9; float sa;} float11;
typedef struct float12 { float s0; float s1; float s2; float s3; float s4; float s5;
float s6; float s7; float s8; float s9; float sa; float sb; } float12;
typedef struct float13 { float s0; float s1; float s2; float s3; float s4; float s5;
float s6; float s7; float s8; float s9; float sa; float sb; float sc;} float13;
typedef struct float14 { float s0; float s1; float s2; float s3; float s4; float s5;
float s6; float s7; float s8; float s9; float sa; float sb; float sc; float sd; } float14;
typedef struct float15 { float s0; float s1; float s2; float s3; float s4; float s5;
float s6; float s7; float s8; float s9; float sa; float sb; float sc; float sd; float se; } float15;
typedef struct float0 { float s0; } float0; //never used but makes compiler happy.
#define OUT_PITCH_X output_width
#define ROW_PITCH input_width
#define GEMM_LIKE_KERNEL_ARGS \
ELTWISE_DATA_ARG \
NEGATIVE_SLOPE_ARG \
const __global Dtype *src0, \
const __global Dtype *src1, \
BIAS_KERNEL_ARG \
__global Dtype *dst, \
const ushort input_width, \
const ushort input_height, \
const ushort output_width, \
const ushort output_height, \
const int out_pitch_y, \
const int out_pitch_z, \
const int aligned_input_size, \
const int slice_pitch
#endif
#ifdef GEMM_LIKE_CONV_32_1
//////////////////////////////////////////////////////////////////////////////
// Conv_Interleaved_32_1_flex
//
// Convolution: each workitem computes 1 patch x 32 filters worth of output
// data. Kernel's inner loop works on a single tile consisting of one
// row from each patch and the filter data corresponding to that row. Filter
// matrix is interleaved to reduce GRF bank conflicts. Patches are walked
// by rows and then by slices. Relies on sub_group extension for block
// reads and SIMD broadcast. Allows flexible sizing of TILE width (TILE_N)
// by dynamically selecting one of two code paths: one uses TILE_N = 32 and
// the other uses TILE_N = 8, 16, or 24.
#define TILE_M 1
#define TILE_K KERNEL_WIDTH
#define TILE_N 32
#ifndef __BEIGNET__
__attribute__((intel_reqd_sub_group_size(8)))
#endif
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
const int global_y = get_global_id(1);
const int global_z = get_global_id(2);
int interleaved_y;
int kernel_y;
int kernel_idx;
#define DOT_PRODUCT_8( _result, _rowA, colB ) \
{ \
_result.s0 = mad( _rowA, sub_group_broadcast( colB, 0 ), _result.s0 ); \
_result.s1 = mad( _rowA, sub_group_broadcast( colB, 1 ), _result.s1 ); \
_result.s2 = mad( _rowA, sub_group_broadcast( colB, 2 ), _result.s2 ); \
_result.s3 = mad( _rowA, sub_group_broadcast( colB, 3 ), _result.s3 ); \
_result.s4 = mad( _rowA, sub_group_broadcast( colB, 4 ), _result.s4 ); \
_result.s5 = mad( _rowA, sub_group_broadcast( colB, 5 ), _result.s5 ); \
_result.s6 = mad( _rowA, sub_group_broadcast( colB, 6 ), _result.s6 ); \
_result.s7 = mad( _rowA, sub_group_broadcast( colB, 7 ), _result.s7 ); \
}
typedef CAT( Dtype, KERNEL_WIDTH ) Dtype_t;
// True for all threads if filter_width is multiple of TILE_N
// else, true for all but right-most column of threads.
if( TILE_N_LAST == 0 || global_x < WIDTH1 / TILE_N )
{
// Result ctile (*dst) is M rows x N columns
// LWG size is 1x8. Thus each thread calculates 8*M rows x N cols of ctile.
Dtype8 blockC00 = 0.f;
Dtype8 blockC10 = 0.f;
Dtype8 blockC20 = 0.f;
Dtype8 blockC30 = 0.f;
// Src0 (patch input) is directly used as atile.
// Each work item points to the start of a different patch.
// atile is M rows x K columns.
int curr_x = ( global_y % output_width ) * STRIDE_X;
int curr_y = ( global_y / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
int saved_y = curr_y;
#endif
const __global Dtype *src0_read = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y - INPUT_PAD_H) * ROW_PITCH // y offset
+ (curr_x - INPUT_PAD_W); // x offset
// Src1 (filter) is directly used as btile.
// It starts at the top of src1 and walks down.
// btile is K rows x N columns.
const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2);
// Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
// Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
// and KERNEL_WIDTH/2 rows of interleaved filter.
int patch_depth = 0;
do
{
int patch_row = 0;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
curr_y = saved_y;
#endif
do
{
// Load atile and btile.
// Kernel data is partially interleaved. Every 2 rows are interleaved at Dtype8 granularity.
// The exception is that if KERNEL_WIDTH is odd the last row is not interleaved. The non
// interleaved row is padded with zero to ensure same size as interleaved rows. This
// interleaving is done to ensure 0% GDR bank conflicts. For example, this is how the
// kernel data would be arranged before/after interleaving for KERNEL_WIDTH=3.
// (0, 0) (8, 0) (16, 0) (24, 0) ... (0, 0) (0, 1) (8, 0) (0, 1) (16, 0) (0, 1) (24, 0) ..
// (0, 1) (8, 1) (16, 1) (24, 1) ... => (0, 2) (8, 2) (16, 2) (24, 2) ...
// (0, 2) (8, 2) (16, 2) (24, 2) ... ...
// ...
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[ 0 ];
Dtype* pblockA00 = (Dtype*)(&blockA00);
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read[pos * DILATION_X];
else
pblockA00[pos] = 0;
})
curr_y += DILATION_Y;
#endif
src0_read += (ROW_PITCH * DILATION_Y);
Dtype blockB00[KERNEL_WIDTH*4];
Dtype8* p8BlockB00 = (Dtype8*)blockB00;
Dtype4* p4BlockB00 = (Dtype4*)blockB00;
Dtype* pBlockB00 = (Dtype* )blockB00;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
p8BlockB00[interleaved_y] = as_Dtype8( SUB_GROUP_BLOCK_READ8( (const __global INT_TYPE *)src1_read ) );
src1_read += WIDTH1 * 2;
} )
if ( kernel_width_is_odd )
{
p4BlockB00[KERNEL_WIDTH - 1] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE *)src1_read ) );
src1_read += WIDTH1 * 2;
}
// Perform MADs
kernel_idx = 0;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_8( blockC00, pblockA00[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC00, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC10, pblockA00[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC10, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC20, pblockA00[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC20, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC30, pblockA00[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC30, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
} )
kernel_y = interleaved_y * 2;
if ( kernel_width_is_odd )
{
DOT_PRODUCT_8( blockC00, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC10, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC20, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC30, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
}
}
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y); // reset to start of next slice of patch
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out = dst + out_offset;
#if APPLY_BIAS
Dtype bias[4];
Dtype4 *bias_vec;
bias_vec = (Dtype4*)bias;
*bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
if (global_y * TILE_M < output_width * output_height )
{
for (int i = 0; i < 8; i++)
{
ACTIVATION_FUNCTION(dst, out_offset + ( 0 + i ) * out_pitch_y, blockC00[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
ACTIVATION_FUNCTION(dst, out_offset + ( 8 + i ) * out_pitch_y, blockC10[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + 8 + i);
ACTIVATION_FUNCTION(dst, out_offset + ( 16 + i ) * out_pitch_y, blockC20[i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + 16 + i);
ACTIVATION_FUNCTION(dst, out_offset + ( 24 + i ) * out_pitch_y, blockC30[i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + 24 + i);
}
}
}
#if TILE_N_LAST > 0
else
{
// Result ctile (*dst) is M rows x N columns
// LWG size is 1x8. Thus each thread calculates 8*M rows x N cols of ctile.
int i = 0;
Dtype8 blockC[TILE_N_LAST_DIV8];
LOOP(TILE_N_LAST_DIV8, i,
{
blockC[i] = 0.f;
} )
// Src0 (patch input) is directly used as atile.
// Each work item points to the start of a different patch.
// atile is M rows x K columns.
int curr_x = ( global_y % output_width ) * STRIDE_X;
int curr_y = ( global_y / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
int saved_y = curr_y;
#endif
const __global Dtype *src0_read = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y - INPUT_PAD_H) * ROW_PITCH // y offset
+ (curr_x - INPUT_PAD_W); // x offset
// Src1 (filter) is directly used as btile.
// It starts at the top of src1 and walks down.
// btile is K rows x N columns.
const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2);
// Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
// Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
// and KERNEL_WIDTH/2 rows of interleaved filter.
int patch_depth = 0;
do
{
int patch_row = 0;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
curr_y = saved_y;
#endif
do
{
// Load atile and interleaved btile.
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[ 0 ];
Dtype* pblockA00 = (Dtype*)(&blockA00);
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read[pos * DILATION_X];
else
pblockA00[pos] = 0;
})
curr_y += DILATION_Y;
#endif
src0_read += (ROW_PITCH * DILATION_Y);
Dtype blockB[KERNEL_WIDTH * TILE_N_LAST_DIV8];
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
#if TILE_N_LAST_DIV8 == 1
Dtype2* p2BlockB = (Dtype2* )blockB;
p2BlockB[interleaved_y] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
Dtype4* p4BlockB = (Dtype4* )blockB;
p4BlockB[interleaved_y] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
//TODO: broken. No block_read6
Dtype6* p6BlockB = (Dtype6* )blockB;
(*((Dtype8*)(&p6BlockB[interleaved_y]))).s0123 = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
(*((Dtype8*)(&p6BlockB[interleaved_y]))).s45 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)(src1_read + 4 * 8) ) );
#endif
src1_read += WIDTH1 * 2;
} )
if ( kernel_width_is_odd )
{
#if TILE_N_LAST_DIV8 == 1
Dtype* pBlockB = (Dtype* )blockB;
pBlockB[KERNEL_WIDTH - 1] = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
Dtype2* p2BlockB = (Dtype2* )blockB;
p2BlockB[KERNEL_WIDTH - 1] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
Dtype3* p3BlockB = (Dtype3* )blockB;
p3BlockB[KERNEL_WIDTH - 1].s01 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
p3BlockB[KERNEL_WIDTH - 1].s2 = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*) (src1_read + 2 * 8) ) );
#endif
src1_read += WIDTH1 * 2;
}
// Perform MADs
Dtype* pBlockB = (Dtype*)blockB;
kernel_idx = 0;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_8( blockC[0], pblockA00[kernel_y ], pBlockB[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC[0], pblockA00[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
DOT_PRODUCT_8( blockC[1], pblockA00[kernel_y ], pBlockB[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC[1], pblockA00[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
DOT_PRODUCT_8( blockC[2], pblockA00[kernel_y ], pBlockB[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC[2], pblockA00[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
} )
kernel_y = interleaved_y * 2;
if ( kernel_width_is_odd )
{
DOT_PRODUCT_8( blockC[0], pblockA00[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
DOT_PRODUCT_8( blockC[1], pblockA00[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
DOT_PRODUCT_8( blockC[2], pblockA00[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
}
}
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out = dst + out_offset;
#if APPLY_BIAS
Dtype bias[4];
Dtype4 *bias_vec;
bias_vec = (Dtype4*)bias;
*bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
if (global_y * TILE_M < output_width * output_height )
{
for (int i = 0; i < 8; i++)
{
if ( TILE_N_LAST_DIV8 > 0 )
{
ACTIVATION_FUNCTION(dst, out_offset + ( 0+i) * out_pitch_y, blockC[0][i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
}
if ( TILE_N_LAST_DIV8 > 1 )
{
ACTIVATION_FUNCTION(dst, out_offset + ( 8+i) * out_pitch_y, blockC[1][i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
}
if ( TILE_N_LAST_DIV8 > 2 )
{
ACTIVATION_FUNCTION(dst, out_offset + (16+i) * out_pitch_y, blockC[2][i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
}
if ( TILE_N_LAST_DIV8 > 3 )
{
ACTIVATION_FUNCTION(dst, out_offset + (24+i) * out_pitch_y, blockC[3][i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
}
}
}
}
#endif
}
#endif
#ifdef GEMM_LIKE_CONV_32_2
//////////////////////////////////////////////////////////////////////////////
// Conv_Interleaved_32_2_flex
//
// Convolution: each workitem computes 1 patch x 32 filters worth of output
// data. Kernel's inner loop works on a single tile consisting of one
// row from each patch and the filter data corresponding to that row. Filter
// matrix is interleaved to reduce GRF bank conflicts. Patches are walked
// by rows and then by slices. Relies on sub_group extension for block
// reads and SIMD broadcast. Allows flexible sizing of TILE width (TILE_N)
// by dynamically selecting one of two code paths: one uses TILE_N = 32 and
// the other uses TILE_N = 8, 16, or 24.
#define TILE_M 2
#define TILE_K KERNEL_WIDTH
#define TILE_N 32
#ifndef __BEIGNET__
__attribute__((intel_reqd_sub_group_size(8)))
#endif
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
const int global_y = get_global_id(1);
const int global_z = get_global_id(2);
int interleaved_y;
int kernel_y;
int kernel_idx;
#define DOT_PRODUCT_8( _result, _rowA, colB ) \
{ \
_result.s0 = mad( _rowA, sub_group_broadcast( colB, 0 ), _result.s0 ); \
_result.s1 = mad( _rowA, sub_group_broadcast( colB, 1 ), _result.s1 ); \
_result.s2 = mad( _rowA, sub_group_broadcast( colB, 2 ), _result.s2 ); \
_result.s3 = mad( _rowA, sub_group_broadcast( colB, 3 ), _result.s3 ); \
_result.s4 = mad( _rowA, sub_group_broadcast( colB, 4 ), _result.s4 ); \
_result.s5 = mad( _rowA, sub_group_broadcast( colB, 5 ), _result.s5 ); \
_result.s6 = mad( _rowA, sub_group_broadcast( colB, 6 ), _result.s6 ); \
_result.s7 = mad( _rowA, sub_group_broadcast( colB, 7 ), _result.s7 ); \
}
typedef CAT( Dtype, KERNEL_WIDTH ) Dtype_t;
// True for all threads if filter_width is multiple of TILE_N
// else, true for all but right-most column of threads.
if( TILE_N_LAST == 0 || global_x < WIDTH1 / TILE_N )
{
// Result ctile (*dst) is M rows x N columns
// LWG size is 1x8. Thus each thread calculates 8*M rows x N cols of ctile.
Dtype8 blockC00 = 0.f;
Dtype8 blockC10 = 0.f;
Dtype8 blockC20 = 0.f;
Dtype8 blockC30 = 0.f;
Dtype8 blockC01 = 0.f;
Dtype8 blockC11 = 0.f;
Dtype8 blockC21 = 0.f;
Dtype8 blockC31 = 0.f;
// Src0 (patch input) is directly used as atile.
// Each work item points to the start of a different patch.
// atile is M rows x K columns.
int curr_x0 = ( ( global_y * TILE_M + 0 ) % output_width ) * STRIDE_X;
int curr_x1 = ( ( global_y * TILE_M + 1 ) % output_width ) * STRIDE_X;
int curr_y0 = ( ( global_y * TILE_M + 0 ) / output_width ) * STRIDE_Y;
int curr_y1 = ( ( global_y * TILE_M + 1 ) / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
int saved_y0 = curr_y0;
int saved_y1 = curr_y1;
#endif
const __global Dtype *src0_read0 = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y0 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x0 - INPUT_PAD_W; // x offset
const __global Dtype *src0_read1 = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y1 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x1 - INPUT_PAD_W; // x offset
// Src1 (filter) is directly used as btile.
// It starts at the top of src1 and walks down.
// btile is K rows x N columns.
const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2);
// Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
// Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
// and KERNEL_WIDTH/2 rows of interleaved filter.
int patch_depth = 0;
do
{
int patch_row = 0;
do
{
// Load atile and btile.
// Kernel data is partially interleaved. Every 2 rows are interleaved at Dtype8 granularity.
// The exception is that if KERNEL_WIDTH is odd the last row is not interleaved. The non
// interleaved row is padded with zero to ensure same size as interleaved rows. This
// interleaving is done to ensure 0% GDR bank conflicts. For example, this is how the
// kernel data would be arranged before/after interleaving for KERNEL_WIDTH=3.
// (0, 0) (8, 0) (16, 0) (24, 0) ... (0, 0) (0, 1) (8, 0) (0, 1) (16, 0) (0, 1) (24, 0) ..
// (0, 1) (8, 1) (16, 1) (24, 1) ... => (0, 2) (8, 2) (16, 2) (24, 2) ...
// (0, 2) (8, 2) (16, 2) (24, 2) ... ...
// ...
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_H == 0 && INPUT_PAD_W == 0 && DILATION_X == 1 && DILATION_Y == 1
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read0 )[ 0 ]; src0_read0 += ROW_PITCH;
Dtype_t blockA01 = ( (const __global Dtype_t*)src0_read1 )[ 0 ]; src0_read1 += ROW_PITCH;
Dtype* pblockA00 = (Dtype*)(&blockA00);
Dtype* pblockA01 = (Dtype*)(&blockA01);
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read0[pos * DILATION_X];
else
pblockA00[pos] = 0;
})
curr_y0 += DILATION_Y;
Dtype_t blockA01;
Dtype* pblockA01 = (Dtype*)(&blockA01);
pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA01[pos] = src0_read1[pos * DILATION_X];
else
pblockA01[pos] = 0;
})
curr_y1 += DILATION_Y;
src0_read0 += (ROW_PITCH * DILATION_Y);
src0_read1 += (ROW_PITCH * DILATION_Y);
#endif
Dtype blockB00[KERNEL_WIDTH*4];
Dtype8* p8BlockB00 = (Dtype8*)blockB00;
Dtype4* p4BlockB00 = (Dtype4*)blockB00;
Dtype* pBlockB00 = (Dtype* )blockB00;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
p8BlockB00[interleaved_y] = as_Dtype8( SUB_GROUP_BLOCK_READ8( (const __global INT_TYPE*)src1_read ) );
src1_read += WIDTH1 * 2;
} )
if ( kernel_width_is_odd )
{
p4BlockB00[KERNEL_WIDTH - 1] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
src1_read += WIDTH1 * 2;
}
// Perform MADs
kernel_idx = 0;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_8( blockC00, pblockA00[kernel_y ], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC01, pblockA01[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC00, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC01, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC10, pblockA00[kernel_y ], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC11, pblockA01[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC10, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC11, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC20, pblockA00[kernel_y ], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC21, pblockA01[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC20, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC21, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC30, pblockA00[kernel_y ], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC31, pblockA01[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC30, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC31, pblockA01[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
} )
if ( kernel_width_is_odd )
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_8( blockC00, pblockA00[kernel_y], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC01, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC10, pblockA00[kernel_y], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC11, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC20, pblockA00[kernel_y], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC21, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC30, pblockA00[kernel_y], pBlockB00[kernel_idx] );
DOT_PRODUCT_8( blockC31, pblockA01[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
}
}
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0 || DILATION_X != 1 || DILATION_Y != 1
curr_y0 = saved_y0;
curr_y1 = saved_y1;
#endif
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
#if APPLY_BIAS
Dtype bias[4];
Dtype4 *bias_vec;
bias_vec = (Dtype4*)bias;
*bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
if( global_y * TILE_M < output_width * output_height )
{
for( int i = 0; i < 8; i++ )
{
ACTIVATION_FUNCTION(dst, out0_offset + ( 0+i) * out_pitch_y, blockC00[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
ACTIVATION_FUNCTION(dst, out0_offset + ( 8+i) * out_pitch_y, blockC10[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
ACTIVATION_FUNCTION(dst, out0_offset + (16+i) * out_pitch_y, blockC20[i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
ACTIVATION_FUNCTION(dst, out0_offset + (24+i) * out_pitch_y, blockC30[i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
}
}
if( global_y * TILE_M + 1 < output_width * output_height )
{
for( int i = 0; i < 8; i++ )
{
ACTIVATION_FUNCTION(dst, out1_offset + ( 0+i) * out_pitch_y, blockC01[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
ACTIVATION_FUNCTION(dst, out1_offset + ( 8+i) * out_pitch_y, blockC11[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
ACTIVATION_FUNCTION(dst, out1_offset + (16+i) * out_pitch_y, blockC21[i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
ACTIVATION_FUNCTION(dst, out1_offset + (24+i) * out_pitch_y, blockC31[i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
}
}
}
#if TILE_N_LAST > 0
else
{
// Result ctile (*dst) is M rows x N columns
// LWG size is 1x8. Thus each thread calculates 8*M rows x N cols of ctile.
int i = 0;
Dtype8 blockC0[TILE_N_LAST_DIV8];
Dtype8 blockC1[TILE_N_LAST_DIV8];
LOOP(TILE_N_LAST_DIV8, i,
{
blockC0[i] = 0.f;
blockC1[i] = 0.f;
} )
// Src0 (patch input) is directly used as atile.
// Each work item points to the start of a different patch.
// atile is M rows x K columns.
int curr_x0 = ( ( global_y * TILE_M + 0 ) % output_width ) * STRIDE_X;
int curr_x1 = ( ( global_y * TILE_M + 1 ) % output_width ) * STRIDE_X;
int curr_y0 = ( ( global_y * TILE_M + 0 ) / output_width ) * STRIDE_Y;
int curr_y1 = ( ( global_y * TILE_M + 1 ) / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
int saved_y0 = curr_y0;
int saved_y1 = curr_y1;
#endif
const __global Dtype *src0_read0 = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y0 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x0 - INPUT_PAD_W; // x offset
const __global Dtype *src0_read1 = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y1 - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x1 - INPUT_PAD_W; // x offset
// Src1 (filter) is directly used as btile.
// It starts at the top of src1 and walks down.
// btile is K rows x N columns.
const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2);
// Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
// Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
// and KERNEL_WIDTH/2 rows of interleaved filter.
int patch_depth = 0;
do
{
int patch_row = 0;
do
{
// Load atile and interleaved btile.
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_H == 0 && INPUT_PAD_W == 0 && DILATION_X == 1 && DILATION_Y == 1
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read0 )[ 0 ]; src0_read0 += ROW_PITCH;
Dtype_t blockA01 = ( (const __global Dtype_t*)src0_read1 )[ 0 ]; src0_read1 += ROW_PITCH;
Dtype* pblockA00 = (Dtype*)(&blockA00);
Dtype* pblockA01 = (Dtype*)(&blockA01);
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y0 >= INPUT_PAD_H && curr_y0 < input_height + INPUT_PAD_H && curr_x0 + pos * DILATION_X >= INPUT_PAD_W && curr_x0 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read0[pos * DILATION_X];
else
pblockA00[pos] = 0;
})
curr_y0 += DILATION_Y;
Dtype_t blockA01;
Dtype* pblockA01 = (Dtype*)(&blockA01);
pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y1 >= INPUT_PAD_H && curr_y1 < input_height + INPUT_PAD_H && curr_x1 + pos * DILATION_X >= INPUT_PAD_W && curr_x1 + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA01[pos] = src0_read1[pos * DILATION_X];
else
pblockA01[pos] = 0;
})
curr_y1 += DILATION_Y;
src0_read0 += (ROW_PITCH * DILATION_Y);
src0_read1 += (ROW_PITCH * DILATION_Y);
#endif
Dtype blockB[KERNEL_WIDTH * TILE_N_LAST_DIV8];
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
#if TILE_N_LAST_DIV8 == 1
Dtype2* p2BlockB = (Dtype2* )blockB;
p2BlockB[interleaved_y] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
Dtype4* p4BlockB = (Dtype4* )blockB;
p4BlockB[interleaved_y] = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
//TODO: broken. No block_read6
Dtype6* p6BlockB = (Dtype6* )blockB;
(*((Dtype8*)(&p6BlockB[interleaved_y]))).s0123 = as_Dtype4( SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read ) );
(*((Dtype8*)(&p6BlockB[interleaved_y]))).s45 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)(src1_read + 4 * 8) ) );
#endif
src1_read += WIDTH1 * 2;
} )
if ( kernel_width_is_odd )
{
#if TILE_N_LAST_DIV8 == 1
Dtype* pBlockB = (Dtype* )blockB;
pBlockB[KERNEL_WIDTH - 1] = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 2
Dtype2* p2BlockB = (Dtype2* )blockB;
p2BlockB[KERNEL_WIDTH - 1] = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
#elif TILE_N_LAST_DIV8 == 3
Dtype3* p3BlockB = (Dtype3* )blockB;
p3BlockB[KERNEL_WIDTH - 1].s01 = as_Dtype2( SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read ) );
p3BlockB[KERNEL_WIDTH - 1].s2 = as_Dtype( SUB_GROUP_BLOCK_READ( (const __global INT_TYPE*) (src1_read + 8) ) );
#endif
src1_read += WIDTH1 * 2;
}
// Perform MADs
Dtype* pBlockB = (Dtype*)blockB;
kernel_idx = 0;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_8( blockC0[0], pblockA00[kernel_y ], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[0], pblockA01[kernel_y ], pBlockB[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC0[0], pblockA00[kernel_y + 1], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[0], pblockA01[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
DOT_PRODUCT_8( blockC0[1], pblockA00[kernel_y ], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[1], pblockA01[kernel_y ], pBlockB[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC0[1], pblockA00[kernel_y + 1], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[1], pblockA01[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
DOT_PRODUCT_8( blockC0[2], pblockA00[kernel_y ], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[2], pblockA01[kernel_y ], pBlockB[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_8( blockC0[2], pblockA00[kernel_y + 1], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[2], pblockA01[kernel_y + 1], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
} )
kernel_y = interleaved_y * 2;
if ( kernel_width_is_odd )
{
DOT_PRODUCT_8( blockC0[0], pblockA00[kernel_y], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[0], pblockA01[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 2
DOT_PRODUCT_8( blockC0[1], pblockA00[kernel_y], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[1], pblockA01[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#if TILE_N_LAST_DIV8 >= 3
DOT_PRODUCT_8( blockC0[2], pblockA00[kernel_y], pBlockB[kernel_idx] );
DOT_PRODUCT_8( blockC1[2], pblockA01[kernel_y], pBlockB[kernel_idx] ); kernel_idx++;
#endif
#endif
}
}
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
#if INPUT_PAD_W != 0 || INPUT_PAD_H != 0 || DILATION_X != 1 || DILATION_Y != 1
curr_y0 = saved_y0;
curr_y1 = saved_y1;
#endif
src0_read0 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
src0_read1 += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y );
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out0_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 0 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 0 ) % output_width ) + OUT_PADDING_LEFT; // x offset
int out1_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M + 1 ) / output_width + OUT_PADDING_HEIGHT ) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M + 1 ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out1 = dst + out1_offset;
#if APPLY_BIAS
Dtype bias[4];
Dtype4 *bias_vec;
bias_vec = (Dtype4*)bias;
*bias_vec = as_Dtype4(SUB_GROUP_BLOCK_READ4((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
if( global_y * TILE_M < output_width * output_height )
{
for( int i = 0; i < 8; i++ )
{
if ( TILE_N_LAST_DIV8 > 0 )
{
ACTIVATION_FUNCTION(dst, out0_offset + ( 0+i) * out_pitch_y, blockC0[0][i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
}
if ( TILE_N_LAST_DIV8 > 1 )
{
ACTIVATION_FUNCTION(dst, out0_offset + ( 8+i) * out_pitch_y, blockC0[1][i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
}
if ( TILE_N_LAST_DIV8 > 2 )
{
ACTIVATION_FUNCTION(dst, out0_offset + (16+i) * out_pitch_y, blockC0[2][i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
}
if ( TILE_N_LAST_DIV8 > 3 )
{
ACTIVATION_FUNCTION(dst, out0_offset + (24+i) * out_pitch_y, blockC0[3][i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
}
}
}
if( global_y * TILE_M + 1 < output_width * output_height )
{
for( int i = 0; i < 8; i++ )
{
if ( TILE_N_LAST_DIV8 > 0 )
{
ACTIVATION_FUNCTION(dst, out1_offset + ( 0+i) * out_pitch_y, blockC1[0][i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i);
}
if ( TILE_N_LAST_DIV8 > 1 )
{
ACTIVATION_FUNCTION(dst, out1_offset + ( 8+i) * out_pitch_y, blockC1[1][i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 8);
}
if ( TILE_N_LAST_DIV8 > 2 )
{
ACTIVATION_FUNCTION(dst, out1_offset + (16+i) * out_pitch_y, blockC1[2][i] + SUBGROUP_GET_BIAS(2, i), group_x * TILE_N + i + 16);
}
if ( TILE_N_LAST_DIV8 > 3 )
{
ACTIVATION_FUNCTION(dst, out1_offset + (24+i) * out_pitch_y, blockC1[3][i] + SUBGROUP_GET_BIAS(3, i), group_x * TILE_N + i + 24);
}
}
}
}
#endif
}
#endif
#if defined(GEMM_LIKE_CONV_32_2_SIMD16) || defined(GEMM_LIKE_CONV_32_1_SIMD16)
#define INTERLEAVED_SIMD16_OUTPUT(_out_, _offset_, _m_) do {\
if (global_y * TILE_M < output_width * output_height ) \
{ \
if ( ( OUT_DEPTH % TILE_N ) == 0 ) {\
for (int i = 0; i < 16; i++) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_ [i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_ [i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
} \
} \
else if( ( OUT_DEPTH % 16 ) == 0 ) { \
if ( ( global_x + 1 ) < get_global_size(0) ) { \
for ( int i = 0; i < 16; i++ ) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_ [i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_ [i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
} \
} \
else { \
for (int i = 0; i < 16; i++) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_ [i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
} \
} \
} \
else { \
if ( ( global_x + 1 ) < get_global_size(0) ) \
{ \
for ( int i = 0; i < 16; i++ ) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
} \
} \
else { \
if ( (OUT_DEPTH % TILE_N) > 16 ) { \
for (int i = 0; i < 16 ; i++) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
} \
for (int i = 0; i < OUT_DEPTH % 16 ; i++) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + (16+i) * out_pitch_y, blockC1 ##_m_[i] + SUBGROUP_GET_BIAS(1, i), group_x * TILE_N + i + 16); \
} \
} \
else { \
for (int i = 0; i < OUT_DEPTH % 16 ; i++) \
{ \
ACTIVATION_FUNCTION(_out_, _offset_ + ( 0+i) * out_pitch_y, blockC0 ##_m_[i] + SUBGROUP_GET_BIAS(0, i), group_x * TILE_N + i); \
} \
} \
} \
} \
} \
}while(0)
#endif
#ifdef GEMM_LIKE_CONV_32_1_SIMD16
#define TILE_M 1
#define TILE_K KERNEL_WIDTH
#define TILE_N 32
#ifndef __BEIGNET__
__attribute__((intel_reqd_sub_group_size(16)))
#endif
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
const int global_y = get_global_id(1);
const int global_z = get_global_id(2);
int interleaved_y;
int kernel_y;
int kernel_idx;
// Result ctile (*dst) is M rows x N columns
// LWG size is 1x16. Thus each thread calculates 16*M rows x N cols of ctile.
Dtype16 blockC00 = 0.f;
Dtype16 blockC10 = 0.f;
// Src0 (patch input) is directly used as atile.
// Each work item points to the start of a different patch.
// atile is M rows x K columns.
int curr_x = ( global_y % output_width ) * STRIDE_X;
int curr_y = ( global_y / output_width ) * STRIDE_Y;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
int saved_y = curr_y;
#endif
const __global Dtype *src0_read = src0
+ aligned_input_size * global_z // batch offset
+ (curr_y - INPUT_PAD_H) * ROW_PITCH // y offset
+ curr_x - INPUT_PAD_W; // x offset
const __global Dtype *src0_read_orig = src0_read;
// Src1 (filter) is directly used as btile.
// It starts at the top of src1 and walks down.
// btile is K rows x N columns.
const __global Dtype *src1_read = src1 + ( global_x * TILE_N * 2 );
#define DOT_PRODUCT_16( _result, _rowA, colB ) \
{ \
_result.s0 = mad( _rowA, sub_group_broadcast( colB, 0 ), _result.s0 ); \
_result.s1 = mad( _rowA, sub_group_broadcast( colB, 1 ), _result.s1 ); \
_result.s2 = mad( _rowA, sub_group_broadcast( colB, 2 ), _result.s2 ); \
_result.s3 = mad( _rowA, sub_group_broadcast( colB, 3 ), _result.s3 ); \
_result.s4 = mad( _rowA, sub_group_broadcast( colB, 4 ), _result.s4 ); \
_result.s5 = mad( _rowA, sub_group_broadcast( colB, 5 ), _result.s5 ); \
_result.s6 = mad( _rowA, sub_group_broadcast( colB, 6 ), _result.s6 ); \
_result.s7 = mad( _rowA, sub_group_broadcast( colB, 7 ), _result.s7 ); \
_result.s8 = mad( _rowA, sub_group_broadcast( colB, 8 ), _result.s8 ); \
_result.s9 = mad( _rowA, sub_group_broadcast( colB, 9 ), _result.s9 ); \
_result.sa = mad( _rowA, sub_group_broadcast( colB, 10 ), _result.sa ); \
_result.sb = mad( _rowA, sub_group_broadcast( colB, 11 ), _result.sb ); \
_result.sc = mad( _rowA, sub_group_broadcast( colB, 12 ), _result.sc ); \
_result.sd = mad( _rowA, sub_group_broadcast( colB, 13 ), _result.sd ); \
_result.se = mad( _rowA, sub_group_broadcast( colB, 14 ), _result.se ); \
_result.sf = mad( _rowA, sub_group_broadcast( colB, 15 ), _result.sf ); \
}
typedef CAT( Dtype, KERNEL_WIDTH ) Dtype_t;
// Walk DOWN src0 (patch 0, 1, 2, ...) and DOWN src1.
// Inner loop loads and FMADs one row (KERNEL_WIDTH) of each input patch
// and KERNEL_WIDTH/2 rows of interleaved filter.
int patch_depth = 0;
#ifndef __BEIGNET__
__attribute__((opencl_unroll_hint(1)))
#endif
do
{
int patch_row = 0;
#if INPUT_PAD_H != 0 || INPUT_PAD_W != 0 || DILATION_X != 1 || DILATION_Y != 1
curr_y = saved_y;
#endif
#ifndef __BEIGNET__
__attribute__((opencl_unroll_hint(1)))
#endif
do
{
// Load atile and btile.
// Kernel data is partially interleaved. Every 2 rows are interleaved at Dtype16 granularity.
// The exception is that if KERNEL_WIDTH is odd the last row is not interleaved. The non
// interleaved row is padded with zero to ensure same size as interleaved rows. This
// interleaving is done to ensure 0% GDR bank conflicts. For example, this is how the
// kernel data would be arranged before/after interleaving for KERNEL_WIDTH=3.
// (0, 0) (16, 0) (32, 0) (48, 0) ... (0, 0) ( 0, 1) (16, 0) ( 0, 1) (32, 0) (0, 1) (48, 0) ...
// (0, 1) (16, 1) (32, 1) (48, 1) ... => (0, 2) (16, 2) (32, 2) (48, 2) ...
// (0, 2) (16, 2) (32, 2) (48, 2) ... ...
// ...
const bool kernel_width_is_odd = KERNEL_WIDTH % 2 == 1;
#if INPUT_PAD_W == 0 && INPUT_PAD_H == 0 && DILATION_X == 1 && DILATION_Y == 1
Dtype_t blockA00 = ( (const __global Dtype_t*)src0_read )[ 0 ];
Dtype* pblockA00 = (Dtype*)(&blockA00);
#else
Dtype_t blockA00;
Dtype* pblockA00 = (Dtype*)(&blockA00);
int pos = 0;
LOOP(KERNEL_WIDTH, pos,
{
if (curr_y >= INPUT_PAD_H && curr_y < input_height + INPUT_PAD_H && curr_x + pos * DILATION_X >= INPUT_PAD_W && curr_x + pos * DILATION_X < input_width + INPUT_PAD_W)
pblockA00[pos] = src0_read[pos * DILATION_X];
else
pblockA00[pos] = 0;
})
curr_y += DILATION_Y;
#endif
src0_read += ROW_PITCH * DILATION_Y;
INT_TYPE blockB00[KERNEL_WIDTH * 2];
INT_TYPE4* p4BlockB00 = (INT_TYPE4*)blockB00;
INT_TYPE2* p2BlockB00 = (INT_TYPE2*)blockB00;
Dtype* pBlockB00 = (Dtype*)blockB00;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
p4BlockB00[interleaved_y] = SUB_GROUP_BLOCK_READ4( (const __global INT_TYPE*)src1_read );
src1_read += WIDTH1 * 2;
} )
if ( kernel_width_is_odd )
{
p2BlockB00[KERNEL_WIDTH - 1] = SUB_GROUP_BLOCK_READ2( (const __global INT_TYPE*)src1_read );
src1_read += WIDTH1 * 2;
}
// Perform MADs
kernel_idx = 0;
interleaved_y = 0;
LOOP(KERNEL_WIDTH_DIV2, interleaved_y,
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_16( blockC00, pblockA00[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_16( blockC00, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_16( blockC10, pblockA00[kernel_y ], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_16( blockC10, pblockA00[kernel_y + 1], pBlockB00[kernel_idx] ); kernel_idx++;
} )
if ( kernel_width_is_odd )
{
kernel_y = interleaved_y * 2;
DOT_PRODUCT_16( blockC00, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
DOT_PRODUCT_16( blockC10, pblockA00[kernel_y], pBlockB00[kernel_idx] ); kernel_idx++;
}
}
//while( ++patch_row < 1 ); //debug
while( ++patch_row < KERNEL_HEIGHT );
src0_read += slice_pitch - ( KERNEL_HEIGHT * ROW_PITCH * DILATION_Y ); // reset to start of next slice of patch
}
//while ( ++patch_depth < 1 ); //debug
while ( ++patch_depth < INPUT_DEPTH );
// Dst resembles a cube of width x height x (output channel * batches). Each tile writes:
// (SIMD * TILE_M) x 1 x TILE_N. Partial writes most likely generated if padding used.
int out_offset = global_z * out_pitch_z // batch offset
+ ( group_x * TILE_N ) * out_pitch_y // channel offset
+ ( ( global_y * TILE_M ) / output_width + OUT_PADDING_HEIGHT) * OUT_PITCH_X // y offset
+ ( ( global_y * TILE_M ) % output_width ) + OUT_PADDING_LEFT; // x offset
__global Dtype *out = dst + out_offset;
#if APPLY_BIAS
Dtype bias[2];
Dtype2 *bias_vec;
bias_vec = (Dtype2*)bias;
*bias_vec = as_Dtype2(SUB_GROUP_BLOCK_READ2((__global INT_TYPE *)biases_base + group_x * TILE_N));
#endif
INTERLEAVED_SIMD16_OUTPUT(dst, out_offset, 0);
}
#endif
#elif defined KERNEL_DWCONV
__kernel void DWCONV(
ELTWISE_DATA_ARG
NEGATIVE_SLOPE_ARG
__global Dtype* image_data,
__global Dtype* kernel_data,
BIAS_KERNEL_ARG
__global Dtype* convolved_image,
const ushort input_width,
const ushort input_height,
const ushort output_width,
const ushort output_height) {
const int outputX = get_global_id(0);
const int outputY = get_global_id(1);
const int outputZ = get_global_id(2);
if(outputX < output_width && outputY < output_height)
{
Dtype sum = 0.;
const int org_y = outputY * STRIDE_Y - INPUT_PAD_H;
const int org_x = outputX * STRIDE_X - INPUT_PAD_W;
const int currentKernelOffset = KERNEL_SIZE*(outputZ%CHANNELS);
const int biasIndex=outputZ%CHANNELS;
const int local_image_offset = org_y*input_width + org_x;
const int imageSize = input_width*input_height;
__global Dtype* image_dataPtrFloat = (image_data + (imageSize*outputZ + local_image_offset));
__global Dtype* kernel_dataPtrFloat = (kernel_data + (currentKernelOffset));
for(int y = 0; y < KERNEL_H; y++)
{
for(int x = 0; x < KERNEL_W; x++)
{
if(!(org_y + y * DILATION_Y >= 0 && org_y + y * DILATION_Y < input_height && org_x + x * DILATION_X >= 0 && org_x + x * DILATION_X < input_width))
{
continue;
}
sum += image_dataPtrFloat[x * DILATION_X] * kernel_dataPtrFloat[x];
}
image_dataPtrFloat += input_width * DILATION_Y;
kernel_dataPtrFloat += KERNEL_W;
}
#if APPLY_BIAS
int offset = outputZ*output_height*output_width + outputY*output_width + outputX;
ACTIVATION_FUNCTION(convolved_image, offset, sum + biases_base[biasIndex], biasIndex);
#else
int offset = outputZ*output_height*output_width + outputY*output_width + outputX;
ACTIVATION_FUNCTION(convolved_image, offset, sum, biasIndex);
#endif
}
}
#endif // KERNEL_BASIC/IDLF/GEMM_LIKE/DWCONV
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