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提交 2a178e98 编写于 作者: Y yangfei
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optimize depthwise_conv3x3

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src/operators/kernel/cl/cl_kernel/depthwise_conv_kernel.cl 0 → 100644
浏览文件 @ 2a178e98
#define BIASE
#define BATCH_NORM
#define RELU
#include "cl_common.h"
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__kernel void depth_conv_3x3(__private const int global_size_dim0,
__private const int global_size_dim1,
__private const int global_size_dim2,
__read_only image2d_t input,
__read_only image2d_t filter,
#ifdef BIASE
__read_only image2d_t bias,
#endif
#ifdef BATCH_NORM
__read_only image2d_t new_scale,
__read_only image2d_t new_biase,
#endif
__write_only image2d_t output_image,
__private const int stride,
__private const int offset,
__private const int input_c,
__private const int dilation,
__private const int input_width,/* of one block */
__private const int input_height, /* of one block */
__private const int output_width,
__private const int output_height,
__private const int filter_width,
__private const int filter_height) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
const int batch_index = out_nh / output_height;
const int out_nh_in_one_batch = out_nh % output_height;
int2 stride_xy = (int2)(stride, stride);
int2 ouput_pos_in_one_block = (int2)(out_w, out_nh_in_one_batch);
int2 in_pos_in_one_block = ouput_pos_in_one_block * stride_xy + (int2)(offset, offset);
#ifdef BIASE
half4 output = read_imageh(bias, sampler, (int2)(out_c, 0));
#else
half4 output = 0.0f;
#endif
int2 pos_in_input_block = (int2)(out_c * input_width, batch_index * input_height);
int2 pos_in_filter_block = (int2)(out_c * filter_width, batch_index * filter_height);
int filter_x = pos_in_filter_block.x ;
int filter_y = pos_in_filter_block.y ;
half4 inputs[9];
inputs[0] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x - 1, pos_in_input_block.y + in_pos_in_one_block.y - 1)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x - 1 < 0 || in_pos_in_one_block.y - 1 < 0 || in_pos_in_one_block.x - 1 >= input_width || in_pos_in_one_block.y - 1 >= input_height) << 15));
inputs[1] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x, pos_in_input_block.y + in_pos_in_one_block.y - 1)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x < 0 || in_pos_in_one_block.y - 1 < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y - 1 >= input_height) << 15));
inputs[2] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x + 1, pos_in_input_block.y + in_pos_in_one_block.y - 1)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x + 1 < 0 || in_pos_in_one_block.y - 1 < 0 || in_pos_in_one_block.x + 1 >= input_width || in_pos_in_one_block.y - 1 >= input_height) << 15));
inputs[3] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x - 1, pos_in_input_block.y + in_pos_in_one_block.y)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x - 1 < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x - 1 >= input_width || in_pos_in_one_block.y >= input_height) << 15));
/*
if (output_pos.x == 112 && output_pos.y == 0) {
half4 input1 = inputs[3];
float4 in = (float4)(input1.x, input1.y, input1.z, input1.w);
printf(" input4 3 - %v4hlf \n", in);
printf(" --- %d ---\n", in_pos_in_one_block.x - 1);
}
*/
inputs[4] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x, pos_in_input_block.y + in_pos_in_one_block.y)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y >= input_height) << 15));
inputs[5] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x + 1, pos_in_input_block.y + in_pos_in_one_block.y)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x + 1 < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x + 1 >= input_width || in_pos_in_one_block.y >= input_height) << 15));
inputs[6] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x - 1, pos_in_input_block.y + in_pos_in_one_block.y + 1)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x - 1 < 0 || in_pos_in_one_block.y + 1 < 0 || in_pos_in_one_block.x - 1 >= input_width || in_pos_in_one_block.y + 1 >= input_height) << 15));
inputs[7] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x, pos_in_input_block.y + in_pos_in_one_block.y + 1)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x < 0 || in_pos_in_one_block.y + 1 < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y + 1 >= input_height) << 15));
inputs[8] = select(read_imageh(input, sampler, (int2)(pos_in_input_block.x + in_pos_in_one_block.x + 1, pos_in_input_block.y + in_pos_in_one_block.y + 1)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x + 1 < 0 || in_pos_in_one_block.y + 1 < 0 || in_pos_in_one_block.x + 1 >= input_width || in_pos_in_one_block.y + 1 >= input_height) << 15));
half4 filters[9];
filters[0] = read_imageh(filter, sampler,(int2)(filter_x,filter_y));
filters[1] = read_imageh(filter, sampler,(int2)(filter_x + 1,filter_y));
filters[2] = read_imageh(filter, sampler,(int2)(filter_x + 2,filter_y));
filters[3] = read_imageh(filter, sampler,(int2)(filter_x,filter_y + 1));
filters[4] = read_imageh(filter, sampler,(int2)(filter_x + 1,filter_y + 1));
filters[5] = read_imageh(filter, sampler,(int2)(filter_x + 2,filter_y + 1));
filters[6] = read_imageh(filter, sampler,(int2)(filter_x,filter_y + 2));
filters[7] = read_imageh(filter, sampler,(int2)(filter_x + 1,filter_y + 2));
filters[8] = read_imageh(filter, sampler,(int2)(filter_x + 2,filter_y + 2));
for(int i = 0 ;i < 9 ; i++){
output += inputs[i] * filters[i];
}
#ifdef BATCH_NORM
output = output * read_imageh(new_scale, sampler, (int2)(out_c, 0)) + read_imageh(new_biase, sampler, (int2)(out_c, 0));
#endif
#ifdef RELU
output = activation(output);
#endif
/*
if (output_pos.x == 112 && output_pos.y == 0) {
for (int i = 0; i < 9; ++i) {
half4 input1 = inputs[i];
float4 in = (float4)(input1.x, input1.y, input1.z, input1.w);
printf(" input4 %d - %v4hlf \n", i, in);
}
float4 out = (float4)(output.x, output.y, output.z, output.w);
printf(" depth wise output output4 = %v4hlf \n", out);
printf(" pos_in_input_block -x %d \n ", pos_in_input_block.x);
printf(" pos_in_input_block -y %d \n ", pos_in_input_block.y);
printf(" in_pos_in_one_block - x %d \n", in_pos_in_one_block.x);
printf(" in_pos_in_one_block - y %d \n", in_pos_in_one_block.y);
}
*/
write_imageh(output_image, output_pos, output);
}
\ No newline at end of file
src/operators/kernel/cl/conv_add_bn_relu_kernel.cpp
浏览文件 @ 2a178e98
......@@ -29,6 +29,14 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
param->Paddings()[0] == param->Paddings()[1],
"need equal");
auto filter_ddim = param->Filter()->dims();
std::vector<int64_t> filter_shape(
{filter_ddim[1], filter_ddim[0], filter_ddim[2], filter_ddim[3]});
framework::DDim ddim = framework::make_ddim(filter_shape);
if (filter_ddim[1] == 1) {
param->Filter()->Resize(ddim);
}
param->Filter()->InitCLImage(cl_helper_.CLContext(),
cl_helper_.CLCommandQueue());
param->Bias()->InitCLImage(cl_helper_.CLContext(),
......@@ -43,21 +51,21 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
const int C = mean->numel();
// for (int j = 0; j < C; ++j) {
// DLOG << " mean - " << j << mean->data<float>()[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " variance - " << j << variance->data<float>()[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " scale - " << j << scale->data<float>()[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " bias - " << j << bias->data<float>()[j];
// }
// for (int j = 0; j < C; ++j) {
// DLOG << " mean - " << j << mean->data<float>()[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " variance - " << j << variance->data<float>()[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " scale - " << j << scale->data<float>()[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " bias - " << j << bias->data<float>()[j];
// }
//
// DLOG << " climage mean: " << *mean;
......@@ -85,21 +93,21 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
framework::CLImage *new_scale = new framework::CLImage();
// for (int j = 0; j < C; ++j) {
// DLOG << " new scale - " << j << new_scale_ptr[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " new bias - " << j << new_bias_ptr[j];
// }
// for (int j = 0; j < C; ++j) {
// DLOG << " new scale - " << j << new_scale_ptr[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " new bias - " << j << new_bias_ptr[j];
// }
new_scale->SetTensorData(new_scale_ptr, variance->dims());
new_scale->InitCLImage(this->cl_helper_.CLContext(),
cl_helper_.CLCommandQueue());
// DLOG << " climage - y bias: " << *(param->Bias());
//
// DLOG << " climage - new scale: " << *new_scale;
// DLOG << " climage - y bias: " << *(param->Bias());
//
// DLOG << " climage - new scale: " << *new_scale;
framework::CLImage *new_bias = new framework::CLImage();
......@@ -107,9 +115,9 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
new_bias->InitCLImage(this->cl_helper_.CLContext(),
cl_helper_.CLCommandQueue());
// DLOG << " climage - new bias: " << *new_bias;
//
// DLOG << " climage - filter: " << *(param->Filter());
// DLOG << " climage - new bias: " << *new_bias;
//
// DLOG << " climage - filter: " << *(param->Filter());
param->SetNewScale(new_scale);
param->SetNewBias(new_bias);
......@@ -131,8 +139,12 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
param->Filter()->HeightOfOneBlock() == 1) {
this->cl_helper_.AddKernel("conv_1x1", "conv_add_bn_relu_kernel.cl");
DLOG << " conv add bn relu conv 1x1";
} else if (param->Filter()->dims()[1] == 1) {
this->cl_helper_.AddKernel("depth_conv_3x3", "conv_add_bn_relu_kernel.cl");
} else if (param->Filter()->dims()[0] == 1 &&
param->Input()->dims()[1] == param->Output()->dims()[1] &&
param->Filter()->dims()[2] == 3) {
// this->cl_helper_.AddKernel("depth_conv_3x3",
// "conv_add_bn_relu_kernel.cl");
this->cl_helper_.AddKernel("depth_conv_3x3", "depthwise_conv_kernel.cl");
DLOG << " conv add bn relu depth_conv_3x3";
} else if (param->Filter()->WidthOfOneBlock() == 3 &&
param->Filter()->HeightOfOneBlock() == 3) {
......@@ -167,21 +179,23 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
int input_height = param.Input()->HeightOfOneBlock();
int output_width = param.Output()->WidthOfOneBlock();
int output_height = param.Output()->HeightOfOneBlock();
// DLOG << " c block " << c_block;
// DLOG << " w " << w;
// DLOG << " nh " << nh;
// DLOG << " stride " << stride;
// DLOG << " offset " << offset;
// DLOG << " input_c " << input_c;
// DLOG << " dilation " << dilation;
// DLOG << " input width " << input_width;
// DLOG << " input height " << input_height;
// DLOG << " output width " << output_width;
// DLOG << " output height " << output_height;
// DLOG << " input dim " << param.Input()->dims();
// DLOG << " output dim " << param.Output()->dims();
// DLOG << " filter dim " << param.Filter()->dims();
int filter_width = param.Filter()->WidthOfOneBlock();
int filter_height = param.Filter()->HeightOfOneBlock();
// DLOG << " c block " << c_block;
// DLOG << " w " << w;
// DLOG << " nh " << nh;
// DLOG << " stride " << stride;
// DLOG << " offset " << offset;
// DLOG << " input_c " << input_c;
// DLOG << " dilation " << dilation;
// DLOG << " input width " << input_width;
// DLOG << " input height " << input_height;
// DLOG << " output width " << output_width;
// DLOG << " output height " << output_height;
// DLOG << " input dim " << param.Input()->dims();
// DLOG << " output dim " << param.Output()->dims();
// DLOG << " filter dim " << param.Filter()->dims();
cl_int status;
......@@ -236,11 +250,20 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
status = clSetKernelArg(kernel, 16, sizeof(int), &output_height);
CL_CHECK_ERRORS(status);
// cl_event out_event = param.Output()->GetClEvent();
// cl_event wait_event = param.Input()->GetClEvent();
if (param.Filter()->dims()[0] == 1 &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == 3) {
status = clSetKernelArg(kernel, 17, sizeof(int), &filter_width);
CL_CHECK_ERRORS(status);
status = clSetKernelArg(kernel, 18, sizeof(int), &filter_height);
CL_CHECK_ERRORS(status);
}
// cl_event out_event = param.Output()->GetClEvent();
// cl_event wait_event = param.Input()->GetClEvent();
status =
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
status = clEnqueueNDRangeKernel(
this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(), NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
CL_CHECK_ERRORS(status);
}
......
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