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提交 c0a3506f 编写于 作者: Y Yuan Shuai 提交者： GitHub
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[LITE][OPENCL] Add 3 kernels of ElementwiseAdd/FusionElemenwiseAddAct op with... 

[LITE][OPENCL] Add 3 kernels of ElementwiseAdd/FusionElemenwiseAddAct op with opencl image format (#2844)

* [LITE][OPENCL] Add 3 kernels of ElementwiseAdd/FuseElementwiseAdd op. test=develop
上级 4c267e92
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Showing with 513 addition and 37 deletion
lite/backends/opencl/cl_kernel/image/elementwise_add_kernel.cl
浏览文件 @ c0a3506f
......@@ -14,15 +14,72 @@ limitations under the License. */
#include <cl_common.h>
__kernel void elementwise_add(__read_only image2d_t input, __read_only image2d_t bias, __write_only image2d_t outputImage) {
__kernel void elementwise_add(__read_only image2d_t input,
__read_only image2d_t bias,
__write_only image2d_t outputImage) {
int x = get_global_id(0);
int y = get_global_id(1);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
int2 coords;
coords.x = x;
coords.y = y;
CL_DTYPE4 in = READ_IMG_TYPE(CL_DTYPE_CHAR, input, sampler, coords);
CL_DTYPE4 biase = READ_IMG_TYPE(CL_DTYPE_CHAR, bias, sampler, coords);
CL_DTYPE4 output = activation_type4(in + biase);
WRITE_IMG_TYPE(CL_DTYPE_CHAR, outputImage,coords,output);
}
__kernel void channel_add(__read_only image2d_t input,
__read_only image2d_t bias,
__write_only image2d_t outputImage,
int w) {
int x = get_global_id(0);
int y = get_global_id(1);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
int2 coords;
coords.x = x;
coords.y = y;
float4 in = read_imagef(input, sampler, coords);
float4 biase = read_imagef(bias, sampler, coords);
float4 output = in + biase;
write_imagef(outputImage,coords,output);
int2 coords_bias;
coords_bias.x = x % w;
coords_bias.y = 0;
CL_DTYPE4 in = READ_IMG_TYPE(CL_DTYPE_CHAR, input, sampler, coords);
CL_DTYPE4 biase = READ_IMG_TYPE(CL_DTYPE_CHAR, bias, sampler, coords_bias);
CL_DTYPE4 output = in + (CL_DTYPE4)(biase.x);
WRITE_IMG_TYPE(CL_DTYPE_CHAR, outputImage, coords, output);
}
__kernel void width_add(__read_only image2d_t input,
__read_only image2d_t bias,
__write_only image2d_t outputImage,
int w) {
int x = get_global_id(0);
int y = get_global_id(1);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
int2 coords;
coords.x = x;
coords.y = y;
int2 coords_bias;
coords_bias.x = x % w;
coords_bias.y = 0;
CL_DTYPE4 in = READ_IMG_TYPE(CL_DTYPE_CHAR, input, sampler, coords);
CL_DTYPE4 biase = READ_IMG_TYPE(CL_DTYPE_CHAR, bias, sampler, coords_bias);
CL_DTYPE4 output;
output.x = in.x + biase.x;
output.y = in.y + biase.x;
output.z = in.z + biase.x;
output.w = in.w + biase.x;
WRITE_IMG_TYPE(CL_DTYPE_CHAR, outputImage, coords, output);
}
lite/kernels/opencl/elementwise_add_compute.cc
浏览文件 @ c0a3506f
......@@ -23,6 +23,8 @@ namespace lite {
namespace kernels {
namespace opencl {
/* Buffer */
#if 0
void ElementwiseAddCompute::PrepareForRun() {
auto& context = ctx_->As<OpenCLContext>();
context.cl_context()->AddKernel(
......@@ -92,6 +94,124 @@ void ElementwiseAddCompute::UpdateParams() {
VLOG(4) << "channels: " << channels_;
VLOG(4) << "num: " << num_;
}
#endif
/* Image2D */
void ElementwiseAddImageCompute::PrepareForRun() {
ele_param_ = param_.get_mutable<param_t>();
auto* x = ele_param_->X;
auto* y = ele_param_->Y;
auto axis = ele_param_->axis;
if (y->dims().size() == 4) {
kernel_func_name_ = "elementwise_add"; // y: ImageDefault
} else if (y->dims().size() == 1) {
if (axis == x->dims().size() - 1) {
kernel_func_name_ = "width_add"; // y: ImageDefault
} else if (axis == x->dims().size() - 3) {
kernel_func_name_ = "channel_add"; // y: ImageFolder
} else {
LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
<< ", x->dims().size():" << x->dims().size()
<< ", y->dims.size():" << y->dims().size();
}
} else {
LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
<< ", x->dims().size():" << x->dims().size()
<< ", y->dims.size():" << y->dims().size();
}
VLOG(4) << "kernel_func_name_:" << kernel_func_name_;
auto& context = ctx_->As<OpenCLContext>();
context.cl_context()->AddKernel(
kernel_func_name_, "image/elementwise_add_kernel.cl", build_options_);
}
void ElementwiseAddImageCompute::Run() {
auto& context = ctx_->As<OpenCLContext>();
CHECK(context.cl_context() != nullptr);
auto* x = ele_param_->X;
auto* y = ele_param_->Y;
auto* out = ele_param_->Out;
auto axis = ele_param_->axis;
VLOG(4) << "x->target():" << TargetToStr(x->target());
VLOG(4) << "y->target():" << TargetToStr(y->target());
VLOG(4) << "out->target():" << TargetToStr(out->target());
VLOG(4) << "x->dims():" << x->dims();
VLOG(4) << "y->dims():" << y->dims();
VLOG(4) << "out->dims():" << out->dims();
VLOG(4) << "axis:" << axis;
paddle::lite::CLImageConverterDefault default_convertor;
auto x_img_shape = default_convertor.InitImageDimInfoWith(x->dims()); // w, h
auto x_img_width = x_img_shape[0];
auto x_img_height = x_img_shape[1];
auto out_img_shape =
default_convertor.InitImageDimInfoWith(out->dims()); // w, h
auto y_img_shape = default_convertor.InitImageDimInfoWith(y->dims());
auto* x_img = x->data<float, cl::Image2D>();
auto* y_img = y->data<float, cl::Image2D>();
auto* out_img =
out->mutable_data<float, cl::Image2D>(out_img_shape[0], out_img_shape[1]);
VLOG(4) << "x_img_shape[w,h]:" << x_img_width << " " << x_img_height;
VLOG(4) << "y_img_shape[w,h]:" << y_img_shape[0] << " " << y_img_shape[1];
VLOG(4) << "out_img_shape[w,h]:" << out_img_shape[0] << " "
<< out_img_shape[1];
STL::stringstream kernel_key;
kernel_key << kernel_func_name_ << build_options_;
auto kernel = context.cl_context()->GetKernel(kernel_key.str());
int arg_idx = 0;
auto y_dims = y->dims();
if (y_dims.size() == 4) {
cl_int status = kernel.setArg(arg_idx, *x_img);
CL_CHECK_FATAL(status);
status = kernel.setArg(++arg_idx, *y_img);
CL_CHECK_FATAL(status);
status = kernel.setArg(++arg_idx, *out_img);
CL_CHECK_FATAL(status);
} else if (y_dims.size() == 1) {
if (axis == x->dims().size() - 1 || axis == x->dims().size() - 3) {
int tensor_w = x->dims()[x->dims().size() - 1];
VLOG(4) << "tensor_w:" << tensor_w;
cl_int status = kernel.setArg(arg_idx, *x_img);
CL_CHECK_FATAL(status);
status = kernel.setArg(++arg_idx, *y_img);
CL_CHECK_FATAL(status);
status = kernel.setArg(++arg_idx, *out_img);
CL_CHECK_FATAL(status);
status = kernel.setArg(++arg_idx, static_cast<const int>(tensor_w));
CL_CHECK_FATAL(status);
} else {
LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
<< ", x->dims().size():" << x->dims().size()
<< ", y->dims.size():" << y->dims().size();
}
} else {
LOG(FATAL) << "ElementwiseAddImage doesn't support axis:" << axis
<< ", x->dims().size():" << x->dims().size()
<< ", y->dims.size():" << y->dims().size();
}
auto global_work_size = cl::NDRange{static_cast<cl::size_type>(x_img_width),
static_cast<cl::size_type>(x_img_height)};
VLOG(4) << "global_work_size:[2D]:" << x_img_width << " " << x_img_height;
auto status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel(
kernel,
cl::NullRange,
global_work_size,
cl::NullRange,
nullptr,
event_.get());
CL_CHECK_FATAL(status);
context.cl_wait_list()->emplace(out_img, event_);
}
} // namespace opencl
} // namespace kernels
......@@ -99,9 +219,36 @@ void ElementwiseAddCompute::UpdateParams() {
} // namespace paddle
namespace ocl = paddle::lite::kernels::opencl;
REGISTER_LITE_KERNEL(
elementwise_add, kOpenCL, kFloat, kNCHW, ocl::ElementwiseAddCompute, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kOpenCL))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// REGISTER_LITE_KERNEL(
// elementwise_add, kOpenCL, kFloat, kNCHW, ocl::ElementwiseAddCompute, def)
// .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// .BindInput("Y", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// .Finalize();
// TODO(ysh329): Not fix.
// "Y" may from constant value like conv bias (kARM, need do cl_image_converter
// on CPU);
// may from anther branch like "X" (kOpenCL, nothing to do).
// Consider 2 situations have different actions when pass running(pick kernel),
// set target of "Y" as kOpenCL temporarily.
REGISTER_LITE_KERNEL(elementwise_add,
kOpenCL,
kFloat,
kImageDefault,
ocl::ElementwiseAddImageCompute,
def)
.BindInput("X",
{LiteType::GetTensorTy(TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault))})
.BindInput("Y",
{LiteType::GetTensorTy(TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault))})
.BindOutput("Out",
{LiteType::GetTensorTy(TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault))})
.Finalize();
lite/kernels/opencl/elementwise_add_compute.h
浏览文件 @ c0a3506f
......@@ -33,6 +33,10 @@ class ElementwiseAddCompute
void Run() override;
std::string doc() const override {
return "ElementwiseAdd using cl::Buffer, kFloat";
}
protected:
void UpdateParams();
......@@ -45,6 +49,28 @@ class ElementwiseAddCompute
std::shared_ptr<cl::Event> event_{new cl::Event};
};
class ElementwiseAddImageCompute
: public KernelLite<TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault)> {
public:
using param_t = operators::ElementwiseParam;
void PrepareForRun() override;
void Run() override;
std::string doc() const override {
return "ElementwiseAdd using cl::Image2D, kFloat";
}
protected:
param_t* ele_param_{nullptr};
std::string kernel_func_name_{"elementwise_add"};
std::string build_options_{" -DCL_DTYPE_float"};
std::shared_ptr<cl::Event> event_{new cl::Event};
};
} // namespace opencl
} // namespace kernels
} // namespace lite
......
lite/kernels/opencl/elementwise_add_compute_test.cc
浏览文件 @ c0a3506f
......@@ -22,6 +22,19 @@
namespace paddle {
namespace lite {
template <typename dtype>
void fill_data(dtype *x, const int length, int set_value = -1) {
if (set_value == -1) {
for (size_t idx = 0; idx < length; ++idx) {
x[idx] = idx;
}
} else if (set_value != -1) {
for (size_t idx = 0; idx < length; ++idx) {
x[idx] = set_value;
}
}
}
template <typename dtype>
void elementwise_compute_ref(const dtype *x_data,
const dtype *y_data,
......@@ -46,25 +59,17 @@ void elementwise_compute_ref(const dtype *x_data,
for (int i = y_dims.size() + axis; i < x_dims.size(); ++i) {
num *= x_dims[i];
}
VLOG(4) << "axis:" << axis;
VLOG(4) << "batch:" << batch;
VLOG(4) << "cahnnels:" << channels;
VLOG(4) << "num:" << num;
// do elementwise add/sub/max/...
if (elt_type == "add") {
for (int i = 0; i < batch; ++i) {
for (int j = 0; j < channels; ++j) {
int offset = (i * channels + j) * num;
const dtype *din_ptr = x_data + offset;
const dtype diny_data = y_data[j];
dtype *dout_ptr = out_data + offset;
for (int k = 0; k < num; ++k) {
*dout_ptr = *din_ptr + diny_data;
if (use_relu) {
*dout_ptr = std::max(*dout_ptr, static_cast<dtype>(0));
}
dout_ptr++;
din_ptr++;
}
}
if (elt_type == "add" && axis == 1 && y_dims.size() == 1) {
for (int i = 0; i < x_dims.production(); ++i) {
auto w = i % y_dims.production();
out_data[i] = x_data[i] + y_data[w];
}
} else if (elt_type == "sub") {
} else if (elt_type == "add") {
for (int i = 0; i < batch; ++i) {
for (int j = 0; j < channels; ++j) {
int offset = (i * channels + j) * num;
......@@ -72,7 +77,7 @@ void elementwise_compute_ref(const dtype *x_data,
const dtype diny_data = y_data[j];
dtype *dout_ptr = out_data + offset;
for (int k = 0; k < num; ++k) {
*dout_ptr = *din_ptr - diny_data;
*dout_ptr = *din_ptr + diny_data;
if (use_relu) {
*dout_ptr = std::max(*dout_ptr, static_cast<dtype>(0));
}
......@@ -86,7 +91,9 @@ void elementwise_compute_ref(const dtype *x_data,
}
}
TEST(elementwise_add, compute) {
// buffer
#if 0
TEST(elementwise_add_buffer, compute) {
LOG(INFO) << "to get kernel ...";
auto kernels = KernelRegistry::Global().Create(
"elementwise_add", TARGET(kOpenCL), PRECISION(kFloat), DATALAYOUT(kNCHW));
......@@ -163,7 +170,7 @@ TEST(elementwise_add, compute) {
TargetWrapperCL::Unmap(out_data, mapped_out);
}
TEST(fusion_elementwise_add_activation, compute) {
TEST(fusion_elementwise_add_activation_buffer, compute) {
LOG(INFO) << "to get kernel ...";
auto kernels =
KernelRegistry::Global().Create("fusion_elementwise_add_activation",
......@@ -243,9 +250,204 @@ TEST(fusion_elementwise_add_activation, compute) {
}
TargetWrapperCL::Unmap(out_data, mapped_out);
}
#endif
// image
TEST(elementwise_add_image2d_fp32, compute) {
LOG(INFO) << "main steps of test: host -> layout(buf2img on cpu) -> "
"elementwise_add(img) -> "
"layout(img2buf on cpu) "
"-> host";
// elementwise_add's 3 kernels selection routing strategy:
// --------------------------------------------------------
// 1. elementwise_add: Need y_dim.size() == 4
// 2. elementwise_add (used by fuse_elementwise_activation op):
// Need y_dim.size() == 4 && act_type == "relu"
// 3. width_add: Need y_dim.size() == 1 && x_dim.size() == 4 && axis ==
// 3
// 4. channel_add: Need y_dim.size() == 1 && x_dim.size() == 4 && axis ==
// 1
// dims
const int n = 1;
const int c = 3;
const int h = 2;
const int w = 2;
const DDim x_dim = DDim(std::vector<DDim::value_type>{n, c, h, w});
auto out_dim = x_dim;
// y_dim / axis / relu_flag
std::vector<DDim> y_dim_v{DDim(std::vector<DDim::value_type>{n, c, h, w}),
DDim(std::vector<DDim::value_type>{n, c, h, w}),
DDim(std::vector<DDim::value_type>{w}),
DDim(std::vector<DDim::value_type>{w})};
std::vector<int> axis_v{-1, -1, 3, 1};
std::vector<bool> relu_flag_v{false, true, false, false};
CHECK(y_dim_v.size() == axis_v.size() && axis_v.size() == relu_flag_v.size())
<< "y_dim_v.size() == axis_v.size() == relu_flag_v.size() should be "
"same, and be corresponding "
"one by one";
// start loop
for (size_t case_idx = 0; case_idx < y_dim_v.size(); ++case_idx) {
auto y_dim = y_dim_v[case_idx];
auto axis = axis_v[case_idx];
auto relu_flag = relu_flag_v[case_idx];
LOG(INFO) << "================== elementwise_add, case_idx:" << case_idx + 1
<< "/" << y_dim_v.size() << " ===================";
LOG(INFO) << "x_dim:" << x_dim;
LOG(INFO) << "y_dim:" << y_dim;
LOG(INFO) << "out_dim:" << out_dim;
LOG(INFO) << "axis:" << axis;
LOG(INFO) << "relu_flag:" << relu_flag;
// tensor
VLOG(4) << "set tensors about op param";
lite::Tensor eleadd_x, eleadd_y, eleadd_out;
eleadd_x.Resize(x_dim);
eleadd_y.Resize(y_dim);
eleadd_out.Resize(out_dim);
// initialize tensors
VLOG(4) << "initialize tensors";
paddle::lite::CLImageConverterDefault default_convertor;
// x
std::vector<float> x_v(x_dim.production());
fill_data<float>(x_v.data(), x_v.size()); // fill with index value
auto x_img_shape = default_convertor.InitImageDimInfoWith(x_dim); // w, h
auto x_img_w = x_img_shape[0];
auto x_img_h = x_img_shape[1];
std::vector<float> x_img_v(x_img_w * x_img_h * 4); // 4: RGBA
default_convertor.NCHWToImage(x_v.data(), x_img_v.data(), x_dim);
eleadd_x.mutable_data<float, cl::Image2D>(x_img_w, x_img_h, x_img_v.data());
// y
std::vector<float> y_v(y_dim.production());
fill_data<float>(y_v.data(), y_v.size()); // fill with index value
auto y_img_shape = default_convertor.InitImageDimInfoWith(y_dim); // w, h
auto y_img_w = y_img_shape[0];
auto y_img_h = y_img_shape[1];
std::vector<float> y_img_v(y_img_shape[0] * y_img_shape[1] * 4); // 4: RGBA
default_convertor.NCHWToImage(y_v.data(), y_img_v.data(), y_dim);
eleadd_y.mutable_data<float, cl::Image2D>(y_img_w, y_img_h, y_img_v.data());
// out
auto out_img_shape =
default_convertor.InitImageDimInfoWith(out_dim); // w, h
auto out_img_w = out_img_shape[0];
auto out_img_h = out_img_shape[1];
eleadd_out.mutable_data<float, cl::Image2D>(out_img_w, out_img_h);
std::vector<float> out_img_v(out_img_w * out_img_h * 4);
fill_data<float>(
out_img_v.data(), out_img_v.size(), 0); // fill with zero value
std::vector<float> out_v(out_dim.production());
// operator param
operators::FusionElementwiseActivationParam
fuseEleaddParam; // enabled if relu_flag is true
fuseEleaddParam.X = &eleadd_x;
fuseEleaddParam.Y = &eleadd_y;
fuseEleaddParam.Out = &eleadd_out;
fuseEleaddParam.axis = axis;
fuseEleaddParam.act_type = relu_flag ? "relu" : "";
operators::ElementwiseParam eleaddParam;
eleaddParam.X = &eleadd_x;
eleaddParam.Y = &eleadd_y;
eleaddParam.Out = &eleadd_out;
eleaddParam.axis = axis;
auto op_param = relu_flag ? fuseEleaddParam : eleaddParam;
// set kernel
auto eleadd_img_kernels =
KernelRegistry::Global().Create("elementwise_add",
TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault));
ASSERT_FALSE(eleadd_img_kernels.empty());
auto eleadd_img_kernel = std::move(eleadd_img_kernels.front());
VLOG(4) << "get eleadd kernel: " << eleadd_img_kernel->doc();
// set context and kernel args
VLOG(4) << "set context and kernel args";
std::unique_ptr<KernelContext> context(new KernelContext);
context->As<OpenCLContext>().InitOnce();
eleadd_img_kernel->SetParam(op_param);
std::unique_ptr<KernelContext> eleadd_img_context(new KernelContext);
context->As<OpenCLContext>().CopySharedTo(
&(eleadd_img_context->As<OpenCLContext>()));
eleadd_img_kernel->SetContext(std::move(eleadd_img_context));
// run kernel
VLOG(4) << "run kernel";
eleadd_img_kernel->Launch();
// download gpu result to cpu
const size_t cl_image2d_row_pitch{0};
const size_t cl_image2d_slice_pitch{0};
TargetWrapperCL::ImgcpySync(out_img_v.data(),
eleadd_out.data<float, cl::Image2D>(),
out_img_w,
out_img_h,
cl_image2d_row_pitch,
cl_image2d_slice_pitch,
IoDirection::DtoH);
default_convertor.ImageToNCHW(
out_img_v.data(), out_v.data(), out_img_shape, out_dim);
// compute cpu reference
std::unique_ptr<float[]> out_ref(new float[out_dim.production()]);
elementwise_compute_ref<float>(x_v.data(),
y_v.data(),
out_ref.get(),
x_dim,
y_dim,
op_param.axis,
"add",
relu_flag);
#if 0 // enable to check value of x and y
for (int eidx = 0; eidx < out_dim.production(); eidx++) {
auto value = out_v[eidx];
auto ref_value = out_ref.get()[eidx];
LOG(INFO) << "1st diff in this case at eidx[from 0]:" << eidx << " / "
<< out_dim.production() << ", x_v[" << eidx << "]:"
<< x_v[eidx] << ", value[" << eidx << "]:" << value
<< ", ref_value[" << eidx << "]:" << ref_value;
}
for (int i = 0; i < y_v.size(); i++) {
LOG(INFO) << "y_v[" << i << "]:" << y_v[i];
}
#endif
for (int eidx = 0; eidx < out_dim.production(); eidx++) {
auto value = out_v[eidx];
auto ref_value = out_ref.get()[eidx];
EXPECT_NEAR(value, ref_value, 1e-6);
if (abs(value - ref_value) > 1e-6) {
LOG(INFO) << "1st diff in this case at eidx[from 0]:" << eidx << " / "
<< out_dim.production() << ", value[" << eidx << "]:" << value
<< ", ref_value[" << eidx << "]:" << ref_value;
break;
}
}
}
}
} // namespace lite
} // namespace paddle
USE_LITE_KERNEL(elementwise_add, kOpenCL, kFloat, kNCHW, def);
USE_LITE_KERNEL(fusion_elementwise_add_activation, kOpenCL, kFloat, kNCHW, def);
// USE_LITE_KERNEL(elementwise_add, kOpenCL, kFloat, kNCHW, def);
// USE_LITE_KERNEL(fusion_elementwise_add_activation, kOpenCL, kFloat, kNCHW,
// def);
USE_LITE_KERNEL(elementwise_add, kOpenCL, kFloat, kImageDefault, def);
USE_LITE_KERNEL(
fusion_elementwise_add_activation, kOpenCL, kFloat, kImageDefault, def);
lite/kernels/opencl/fusion_elementwise_add_activation_compute.cc
浏览文件 @ c0a3506f
......@@ -20,6 +20,9 @@ namespace paddle {
namespace lite {
namespace kernels {
namespace opencl {
/* Buffer */
#if 0
class FusionElementwiseAddActivationCompute : public ElementwiseAddCompute {
public:
using param_t = operators::FusionElementwiseActivationParam;
......@@ -38,19 +41,60 @@ class FusionElementwiseAddActivationCompute : public ElementwiseAddCompute {
}
}
};
#endif
class FusionElementwiseAddActivationImageCompute
: public ElementwiseAddImageCompute {
public:
using param_t = operators::FusionElementwiseActivationParam;
void PrepareForRun() override {
build_options_ += " -DRELU";
auto& context = ctx_->As<OpenCLContext>();
context.cl_context()->AddKernel(
kernel_func_name_, "image/elementwise_add_kernel.cl", build_options_);
ele_param_ = param_.get_mutable<param_t>();
auto act_t = static_cast<param_t*>(ele_param_)->act_type;
VLOG(4) << "act: " << act_t;
if (act_t != "relu") {
LOG(FATAL) << "Unsupported Activation type: " << act_t;
}
}
};
} // namespace opencl
} // namespace kernels
} // namespace lite
} // namespace paddle
namespace ocl = paddle::lite::kernels::opencl;
// REGISTER_LITE_KERNEL(fusion_elementwise_add_activation,
// kOpenCL,
// kFloat,
// kNCHW,
// ocl::FusionElementwiseAddActivationCompute,
// def)
// .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// .BindInput("Y", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL))})
// .Finalize();
REGISTER_LITE_KERNEL(fusion_elementwise_add_activation,
kOpenCL,
kFloat,
kNCHW,
ocl::FusionElementwiseAddActivationCompute,
kImageDefault,
ocl::FusionElementwiseAddActivationImageCompute,
def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kOpenCL))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL))})
.BindInput("X",
{LiteType::GetTensorTy(TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault))})
.BindInput("Y",
{LiteType::GetTensorTy(TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault))})
.BindOutput("Out",
{LiteType::GetTensorTy(TARGET(kOpenCL),
PRECISION(kFloat),
DATALAYOUT(kImageDefault))})
.Finalize();
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