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未验证 提交 1db2b370 编写于 作者: R Ray Liu 提交者： GitHub
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Merge pull request #1124 from codeWorm2015/opencl 

 update conv kernel cl
上级 1ddfd4a1 b56c0739
展开全部 隐藏空白更改
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Showing with 195 addition and 1110 deletion
src/operators/kernel/cl/cl_kernel/conv_add_bn_relu_kernel.cl
浏览文件 @ 1db2b370
......@@ -17,323 +17,4 @@ limitations under the License. */
#define BATCH_NORM
#define RELU
#include "cl_kernel/cl_common.h"
__kernel void 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_image,
__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) {
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 stride_xy;
stride_xy.x = stride;
stride_xy.y = stride;
int2 ouput_pos_in_one_block;
ouput_pos_in_one_block.x = out_w;
ouput_pos_in_one_block.y = out_nh;
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
int2 in_pos_in_one_block;
in_pos_in_one_block.x = ouput_pos_in_one_block.x * stride + offset;
in_pos_in_one_block.y = ouput_pos_in_one_block.y * stride + offset;
#ifdef BIASE
half4 output = read_imageh(bias, sampler, (int2)(out_c, 0));
#else
half4 output = 0.0f;
#endif
half4 input[9];
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
input[0] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[1] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[2] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[3] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[4] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.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));
input[5] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[6] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[7] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[8] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(pos_in.x + dilation < 0 || in_pos_in_one_block.y + dilation < 0 || pos_in.x + dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
for (int j = 0; j < 9; ++j) {
int2 fuck;
fuck.x = i * 3 + j % 3;
fuck.y = out_c * 4 * 3 + 0 * out_c * 3 + j / 3;
half4 weight_x = read_imageh(filter, sampler, fuck);
output.x += dot(input[j], weight_x);
fuck.y = out_c * 4 * 3 + 1 * out_c * 3 + j / 3;
half4 weight_y = read_imageh(filter, sampler, fuck);
output.y += dot(input[j], weight_y);
fuck.y = out_c * 4 * 3 + 2 * out_c * 3 + j / 3;
half4 weight_z = read_imageh(filter, sampler, fuck);
output.z += dot(input[j], weight_z);
fuck.y = out_c * 4 * 3 + 3 * out_c * 3 + j / 3;
half4 weight_w = read_imageh(filter, sampler, fuck);
output.w += dot(input[j], weight_w);
}
}
#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
write_imageh(output_image, (int2)(out_c * global_size_dim1 + out_w, out_nh), output);
}
__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) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
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;
const uint kernelHXW = 1;
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);
int weight_x_to = out_c * 3;
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));
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));
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));
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));
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));
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));
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));
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));
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));
for (int j = 0; j < 9; ++j) {
half4 input = inputs[j];
half4 weight = read_imageh(filter, sampler, (int2)(weight_x_to + j % 3, j / 3));
output.x += input.x * weight.x;
output.y += input.y * weight.y;
output.z += input.z * weight.z;
output.w += input.w * weight.w;
}
#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
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
write_imageh(output_image, output_pos, output);
}
__kernel void conv_1x1(__private const int global_size_dim0,
__private const int global_size_dim1,
__private const int global_size_dim2,
__read_only image2d_t input_image,
__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) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
const uint kernelHXW = 1;
int2 stride_xy = (int2)(stride, stride);
int2 ouput_pos_in_one_block = (int2)(out_w, out_nh);
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
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
if (pos_in.x >=0 && pos_in.y >= 0 && pos_in.x < input_width && pos_in.y < input_height) {
half4 input = read_imageh(input_image, sampler, pos_in);
half4 weight_x = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 0));
output.x += dot(input, weight_x);
half4 weight_y = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 1));
output.y += dot(input, weight_y);
half4 weight_z = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 2));
output.z += dot(input, weight_z);
half4 weight_w = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 3));
output.w += dot(input, weight_w);
}
}
#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
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
write_imageh(output_image, output_pos, output);
}
#include "cl_kernel/conv_kernel.inc.cl"
src/operators/kernel/cl/cl_kernel/conv_add_kernel.cl
浏览文件 @ 1db2b370
......@@ -12,324 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define BIASE
__kernel void 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_image,
__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) {
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 stride_xy;
stride_xy.x = stride;
stride_xy.y = stride;
int2 ouput_pos_in_one_block;
ouput_pos_in_one_block.x = out_w;
ouput_pos_in_one_block.y = out_nh;
int2 in_pos_in_one_block;
in_pos_in_one_block.x = ouput_pos_in_one_block.x * stride + offset;
in_pos_in_one_block.y = ouput_pos_in_one_block.y * stride + offset;
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
#ifdef BIASE
half4 output = read_imageh(bias, sampler, (int2)(out_c, 0));
#else
half4 output = 0.0f;
#endif
half4 input[9];
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
input[0] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[1] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[2] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[3] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[4] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.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));
input[5] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[6] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[7] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[8] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(pos_in.x + dilation < 0 || in_pos_in_one_block.y + dilation < 0 || pos_in.x + dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
for (int j = 0; j < 9; ++j) {
int2 fuck;
fuck.x = i * 3 + j % 3;
fuck.y = out_c * 4 * 3 + 0 * out_c * 3 + j / 3;
half4 weight_x = read_imageh(filter, sampler, fuck);
output.x += dot(input[j], weight_x);
fuck.y = out_c * 4 * 3 + 1 * out_c * 3 + j / 3;
half4 weight_y = read_imageh(filter, sampler, fuck);
output.y += dot(input[j], weight_y);
fuck.y = out_c * 4 * 3 + 2 * out_c * 3 + j / 3;
half4 weight_z = read_imageh(filter, sampler, fuck);
output.z += dot(input[j], weight_z);
fuck.y = out_c * 4 * 3 + 3 * out_c * 3 + j / 3;
half4 weight_w = read_imageh(filter, sampler, fuck);
output.w += dot(input[j], weight_w);
}
}
#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
write_imageh(output_image, (int2)(out_c * global_size_dim1 + out_w, out_nh), output);
}
__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) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
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;
const uint kernelHXW = 1;
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);
int weight_x_to = out_c * 3;
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));
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));
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));
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));
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));
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));
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));
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));
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));
for (int j = 0; j < 9; ++j) {
half4 input = inputs[j];
half4 weight = read_imageh(filter, sampler, (int2)(weight_x_to + j % 3, j / 3));
output.x += input.x * weight.x;
output.y += input.y * weight.y;
output.z += input.z * weight.z;
output.w += input.w * weight.w;
}
#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
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
write_imageh(output_image, output_pos, output);
}
__kernel void conv_1x1(__private const int global_size_dim0,
__private const int global_size_dim1,
__private const int global_size_dim2,
__read_only image2d_t input_image,
__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) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
const uint kernelHXW = 1;
int2 stride_xy = (int2)(stride, stride);
int2 ouput_pos_in_one_block = (int2)(out_w, out_nh);
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
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
if (pos_in.x >=0 && pos_in.y >= 0 && pos_in.x < input_width && pos_in.y < input_height) {
half4 input = read_imageh(input_image, sampler, pos_in);
half4 weight_x = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 0));
output.x += dot(input, weight_x);
half4 weight_y = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 1));
output.y += dot(input, weight_y);
half4 weight_z = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 2));
output.z += dot(input, weight_z);
half4 weight_w = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 3));
output.w += dot(input, weight_w);
}
}
#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
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
write_imageh(output_image, output_pos, output);
}
#include "cl_kernel/conv_kernel.inc.cl"
src/operators/kernel/cl/cl_kernel/conv_kernel.cl
浏览文件 @ 1db2b370
......@@ -12,320 +12,4 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__kernel void 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_image,
__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) {
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 stride_xy;
stride_xy.x = stride;
stride_xy.y = stride;
int2 ouput_pos_in_one_block;
ouput_pos_in_one_block.x = out_w;
ouput_pos_in_one_block.y = out_nh;
int2 in_pos_in_one_block;
in_pos_in_one_block.x = ouput_pos_in_one_block.x * stride + offset;
in_pos_in_one_block.y = ouput_pos_in_one_block.y * stride + offset;
#ifdef BIASE
half4 output = read_imageh(bias, sampler, int2(out_c, 0));
#else
half4 output = 0.0f;
#endif
half4 input[9];
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
input[0] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[1] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[2] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height));
input[3] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[4] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.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));
input[5] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y >= input_height));
input[6] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[7] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(in_pos_in_one_block.x < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y + dilation >= input_height));
input[8] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)(pos_in.x + dilation < 0 || in_pos_in_one_block.y + dilation < 0 || pos_in.x + dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height));
for (int j = 0; j < 9; ++j) {
int2 fuck;
fuck.x = i * 3 + j % 3;
fuck.y = out_c * 4 * 3 + 0 * out_c * 3 + j / 3;
half4 weight_x = read_imageh(filter, sampler, fuck);
output.x += dot(input[j], weight_x);
fuck.y = out_c * 4 * 3 + 1 * out_c * 3 + j / 3;
half4 weight_y = read_imageh(filter, sampler, fuck);
output.y += dot(input[j], weight_y);
fuck.y = out_c * 4 * 3 + 2 * out_c * 3 + j / 3;
half4 weight_z = read_imageh(filter, sampler, fuck);
output.z += dot(input[j], weight_z);
fuck.y = out_c * 4 * 3 + 3 * out_c * 3 + j / 3;
half4 weight_w = read_imageh(filter, sampler, fuck);
output.w += dot(input[j], weight_w);
}
}
#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
write_imageh(output_image, (int2)(out_c * global_size_dim1 + out_w, out_nh), output);
}
__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) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
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;
const uint kernelHXW = 1;
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);
int weight_x_to = out_c * 3;
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));
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));
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));
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));
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));
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));
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));
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));
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));
for (int j = 0; j < 9; ++j) {
half4 input = inputs[j];
half4 weight = read_imageh(filter, sampler, (int2)(weight_x_to + j % 3, j / 3));
output.x += input.x * weight.x;
output.y += input.y * weight.y;
output.z += input.z * weight.z;
output.w += input.w * weight.w;
}
#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
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
write_imageh(output_image, output_pos, output);
}
__kernel void conv_1x1(__private const int global_size_dim0,
__private const int global_size_dim1,
__private const int global_size_dim2,
__read_only image2d_t input_image,
__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) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
const uint kernelHXW = 1;
int2 stride_xy = (int2)(stride, stride);
int2 ouput_pos_in_one_block = (int2)(out_w, out_nh);
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
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
if (pos_in.x >=0 && pos_in.y >= 0 && pos_in.x < input_width && pos_in.y < input_height) {
half4 input = read_imageh(input_image, sampler, pos_in);
half4 weight_x = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 0));
output.x += dot(input, weight_x);
half4 weight_y = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 1));
output.y += dot(input, weight_y);
half4 weight_z = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 2));
output.z += dot(input, weight_z);
half4 weight_w = read_imageh(filter, sampler, (int2)(i, out_c * 4 + 3));
output.w += dot(input, weight_w);
}
}
#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
int2 output_pos = (int2)(out_c * global_size_dim1 + out_w, out_nh);
write_imageh(output_image, output_pos, output);
}
#include "cl_kernel/conv_kernel.inc.cl"
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