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未验证 提交 857b7116 编写于 作者: X xiebaiyuan 提交者： GitHub
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[LITE][OPENCL][Image] fix issue in concat and nearest_interp thx for… (#3011) 

* [LITE][OPENCL][Image] fix issue in concat and nearest_interp  thx for chenj and ys,test=develop

* [LITE][OPENCL][Image] fix issue in concat and nearest_interp  thx for chenj and ys,test=develop
上级 a797ed0a
隐藏空白更改
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Showing with 120 addition and 95 deletion
lite/backends/opencl/cl_kernel/image/concat_kernel.cl
浏览文件 @ 857b7116
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
......@@ -15,9 +12,9 @@ limitations under the License. */
#include <cl_common.h>
__kernel void concat2(__read_only image2d_t input0,
__read_only image2d_t input1,
__write_only image2d_t output,
int flag, int C_0, int out_C, int out_W, int width) {
__read_only image2d_t input1,
__write_only image2d_t output,
int flag, int C_0, int out_C, int out_W, int width) {
const int out_w = get_global_id(0); // image_width cxw/4
const int out_c = get_global_id(1); // image_width cxw/4
const int out_nh = get_global_id(2); // image_height nxh
......@@ -32,51 +29,51 @@ __kernel void concat2(__read_only image2d_t input0,
output_pos.y = out_nh;
CL_DTYPE4 output_data;
for (int i = 0; i < 4; i++) {
int c = out_c * 4 + i;
if (c >= out_C) {
break;
}
int c_in;
CL_DTYPE4 input_data;
if (c < C_0) {
c_in = c;
int2 input_pos;
input_pos.x = (c_in / 4) * out_W + out_w;
input_pos.y = out_nh;
input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input0, sampler, input_pos);
} else {
c_in = c - C_0;
int2 input_pos;
input_pos.x = (c_in / 4) * out_W + out_w;
input_pos.y = out_nh;
input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input1, sampler, input_pos);
}
int value_offset = c_in % 4;
float value;
if (value_offset == 0) {
value = input_data.x;
} else if (value_offset == 1) {
value = input_data.y;
} else if (value_offset == 2) {
value = input_data.z;
} else if (value_offset == 3) {
value = input_data.w;
}
if (i == 0) {
output_data.x = value;
} else if (i == 1) {
output_data.y = value;
} else if (i == 2) {
output_data.z = value;
} else if (i == 3) {
output_data.w = value;
int c = out_c * 4 + i;
if (c >= out_C) {
break;
}
int c_in;
CL_DTYPE4 input_data;
if (c < C_0) {
c_in = c;
int2 input_pos;
input_pos.x = (c_in / 4) * out_W + out_w;
input_pos.y = out_nh;
input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input0, sampler, input_pos);
} else {
c_in = c - C_0;
int2 input_pos;
input_pos.x = (c_in / 4) * out_W + out_w;
input_pos.y = out_nh;
input_data = READ_IMG_TYPE(CL_DTYPE_CHAR, input1, sampler, input_pos);
}
int value_offset = c_in % 4;
CL_DTYPE value;
if (value_offset == 0) {
value = input_data.x;
} else if (value_offset == 1) {
value = input_data.y;
} else if (value_offset == 2) {
value = input_data.z;
} else if (value_offset == 3) {
value = input_data.w;
}
if (i == 0) {
output_data.x = value;
} else if (i == 1) {
output_data.y = value;
} else if (i == 2) {
output_data.z = value;
} else if (i == 3) {
output_data.w = value;
}
}
WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, output_data);
}else if (flag == 2){ // by height, width == n
int2 input_pos;
input_pos.x = out_c * out_W + out_w;
int h = out_nh / width;
int h = out_nh / width;
CL_DTYPE4 input;
if (h < C_0){
input_pos.y = out_nh;
......@@ -108,8 +105,8 @@ __kernel void concat2(__read_only image2d_t input0,
}
__kernel void concat_mul(__read_only image2d_t input,
__write_only image2d_t output,
int flag, int C_0, int out_C, int out_W, int in_W, int width) {
__write_only image2d_t output,
int flag, int C_0, int out_C, int out_W, int in_W, int width) {
const int in_w = get_global_id(0); // image_width cxw/4
const int in_c = get_global_id(1); // image_width cxw/4
const int in_nh = get_global_id(2); // image_height nxh
......@@ -125,32 +122,32 @@ __kernel void concat_mul(__read_only image2d_t input,
if (flag == 1){ // by channel
CL_DTYPE4 output_data;
for (int i = 0; i < 4; i++) {
int c_out = C_0 + in_c * 4 + i;
if (c_out >= out_C) {
break;
}
int2 output_pos;
output_pos.x = (c_out / 4) * in_W + in_w;
output_pos.y = in_nh;
float val;
if (i == 0) {
val = input_data.x;
} else if (i == 1) {
val = input_data.y;
} else if (i == 2) {
val = input_data.z;
} else if (i == 3) {
val = input_data.w;
int c_out = C_0 + in_c * 4 + i;
if (c_out >= out_C) {
break;
}
int2 output_pos;
output_pos.x = (c_out / 4) * in_W + in_w;
output_pos.y = in_nh;
CL_DTYPE val;
if (i == 0) {
val = input_data.x;
} else if (i == 1) {
val = input_data.y;
} else if (i == 2) {
val = input_data.z;
} else if (i == 3) {
val = input_data.w;
}
if (c_out % 4 == 0){
output_data.x = val;
output_data.x = val;
}else if (c_out % 4 == 1){
output_data.y = val;
}else if (c_out % 4 == 2){
output_data.z = val;
}else if (c_out % 4 == 3){
output_data.w = val;
}
output_data.y = val;
}else if (c_out % 4 == 2){
output_data.z = val;
}else if (c_out % 4 == 3){
output_data.w = val;
}
WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, output_data);
}
}else if (flag == 2){ // by height, width == n
......@@ -164,4 +161,4 @@ __kernel void concat_mul(__read_only image2d_t input,
output_pos.x = in_c * out_W + (in_w + C_0);
WRITE_IMG_TYPE(CL_DTYPE_CHAR, output, output_pos, input_data);
}
}
}
\ No newline at end of file
lite/kernels/opencl/concat_image_compute.cc
浏览文件 @ 857b7116
......@@ -96,7 +96,7 @@ class ConcatComputeImage : public KernelLite<TARGET(kOpenCL),
auto& param = *param_.get_mutable<param_t>();
const auto& x_dims = param.output->dims();
auto image_shape = InitImageDimInfoWith(x_dims);
auto* out_buf = param.output->mutable_data<float, cl::Image2D>(
auto* out_buf = param.output->mutable_data<half_t, cl::Image2D>(
image_shape["width"], image_shape["height"]);
const auto& y_dims = param.output->dims(); // useless: check dim only
......@@ -107,21 +107,41 @@ class ConcatComputeImage : public KernelLite<TARGET(kOpenCL),
auto inputs = param.x;
int arg_idx = 0;
int width = inputs[0]->dims()[-1];
auto global_work_size = cl::NDRange{
static_cast<cl::size_type>(x_dims[-1]),
static_cast<cl::size_type>(image_shape["width"] / x_dims[-1]),
static_cast<cl::size_type>(image_shape["height"])};
int width = inputs[0]->dims()[inputs[0]->dims().size() - 1];
LOG(INFO) << "concat 输入尺寸: ";
for (size_t i = 0; i < inputs.size(); i++) {
LOG(INFO) << "inputs [" << i << "]"
<< "[" << inputs[i]->dims().size() << "D]:"
<< " dims:" << inputs[i]->dims()[0] << " "
<< inputs[i]->dims()[1] << " " << inputs[i]->dims()[2] << " "
<< inputs[i]->dims()[3];
}
LOG(INFO) << "concat 输出尺寸: ";
LOG(INFO) << " out dims: "
<< "[" << x_dims.size() << "D]:" << x_dims[0] << " " << x_dims[1]
<< " " << x_dims[2] << " " << x_dims[3];
LOG(INFO) << "axis_: " << axis_;
LOG(INFO) << "flag_: " << flag_;
auto global_work_size =
cl::NDRange{static_cast<cl::size_type>(x_dims[x_dims.size() - 1]),
static_cast<cl::size_type>(image_shape["width"] /
x_dims[x_dims.size() - 1]),
static_cast<cl::size_type>(image_shape["height"])};
VLOG(4) << TargetToStr(param.output->target());
VLOG(4) << "image_shape(w,h):" << image_shape["width"] << " "
<< image_shape["height"];
VLOG(4) << "x_dims[" << x_dims.size() << "D]:" << x_dims[0] << " "
<< x_dims[1] << " " << x_dims[2] << " " << x_dims[3];
<< x_dims[1] << " " << x_dims[2] << " " << x_dims[3]
<< "x_dims[x_dims.size() - 1]" << x_dims[x_dims.size() - 1];
VLOG(4) << "y_dims[" << y_dims.size() << "D]:" << y_dims[0] << " "
<< y_dims[1] << " " << y_dims[2] << " " << y_dims[3];
VLOG(4) << "width_: " << width_ << ", flag_: " << flag_;
LOG(INFO) << "width_: " << width_ << ", flag_: " << flag_;
VLOG(4) << "global_work_size: " << x_dims[x_dims.size() - 1] << " "
<< (image_shape["width"] / x_dims[x_dims.size() - 1]) << " "
<< (image_shape["height"]);
auto kernel = context.cl_context()->GetKernel(kernel_key.str());
int out_w = x_dims[-1];
int out_w = x_dims[x_dims.size() - 1];
int out_c = x_dims[1];
if (inputs.size() == 2) {
auto* x_buf0 = inputs[0]->data<float, cl::Image2D>();
......@@ -159,13 +179,14 @@ class ConcatComputeImage : public KernelLite<TARGET(kOpenCL),
auto in_dims = inputs[i]->dims();
image_shape = InitImageDimInfoWith(in_dims);
auto* x_buf = inputs[i]->data<float, cl::Image2D>();
auto in_w = in_dims[-1];
int in_w = in_dims[in_dims.size() - 1];
VLOG(4) << "image_shape(w,h):" << image_shape["width"] << " "
<< image_shape["height"];
global_work_size = cl::NDRange{
static_cast<cl::size_type>(in_dims[-1]),
static_cast<cl::size_type>(image_shape["width"] / in_dims[-1]),
static_cast<cl::size_type>(image_shape["height"])};
global_work_size =
cl::NDRange{static_cast<cl::size_type>(in_dims[in_dims.size() - 1]),
static_cast<cl::size_type>(image_shape["width"] /
in_dims[in_dims.size() - 1]),
static_cast<cl::size_type>(image_shape["height"])};
cl_int status = kernel.setArg(arg_idx, *x_buf);
CL_CHECK_FATAL(status);
status = kernel.setArg(++arg_idx, *out_buf);
......@@ -205,7 +226,7 @@ class ConcatComputeImage : public KernelLite<TARGET(kOpenCL),
int width_ = 1;
param_t* concat_param_{nullptr};
std::string kernel_func_name_{};
std::string build_options_{"-DCL_DTYPE_half"};
std::string build_options_{" -DCL_DTYPE_half"};
std::shared_ptr<cl::Event> event_{new cl::Event};
};
......
lite/kernels/opencl/nearest_interp_image_compute.cc
浏览文件 @ 857b7116
......@@ -45,15 +45,16 @@ class NearestInterpComputeImageDefault
void Run() override {
auto& param = *param_.get_mutable<param_t>();
const auto& x_dims = param.X->dims();
const auto& y_dims = param.Out->dims();
auto* x_buf =
param.X->data<half_t,
cl::Image2D>(); // use half_t represents half float
auto image_shape = InitImageDimInfoWith(x_dims);
auto out_image_shape = InitImageDimInfoWith(y_dims);
auto* out_buf = param.Out->mutable_data<half_t, cl::Image2D>( // use half_t
// represents half float
image_shape["width"],
image_shape["height"]);
const auto& y_dims = param.Out->dims(); // useless: check dim only
out_image_shape["width"],
out_image_shape["height"]);
float scale_h = y_dims[2] / x_dims[2];
float scale_w = y_dims[3] / x_dims[3];
int in_dims_h = x_dims[2];
......@@ -87,16 +88,22 @@ class NearestInterpComputeImageDefault
VLOG(4) << TargetToStr(param.X->target());
VLOG(4) << TargetToStr(param.Out->target());
VLOG(4) << "image_shape(w,h):" << image_shape["width"] << " "
<< image_shape["height"];
VLOG(4) << "out_image_shape(w,h):" << out_image_shape["width"] << " "
<< out_image_shape["height"];
VLOG(4) << "x_dims[" << x_dims.size() << "D]:" << x_dims[0] << " "
<< x_dims[1] << " " << x_dims[2] << " " << x_dims[3];
VLOG(4) << "y_dims[" << y_dims.size() << "D]:" << y_dims[0] << " "
<< y_dims[1] << " " << y_dims[2] << " " << y_dims[3];
const std::vector<size_t>& default_work_size =
DefaultWorkSize(y_dims,
DDim(std::vector<DDim::value_type>{
static_cast<int64_t>(out_image_shape["width"]),
static_cast<int64_t>(out_image_shape["height"])}));
auto global_work_size =
cl::NDRange{static_cast<cl::size_type>(image_shape["width"]),
static_cast<cl::size_type>(image_shape["height"])};
cl::NDRange{static_cast<cl::size_type>(default_work_size.data()[0]),
static_cast<cl::size_type>(default_work_size.data()[1]),
static_cast<cl::size_type>(default_work_size.data()[2])};
status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel(
kernel,
cl::NullRange,
......@@ -112,7 +119,7 @@ class NearestInterpComputeImageDefault
private:
std::string kernel_func_name_{"nearest_interp"};
std::string build_options_{"-DCL_DTYPE_half"};
std::string build_options_{" -DCL_DTYPE_half"};
std::shared_ptr<cl::Event> event_{new cl::Event};
};
......
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