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!7913 [MS][LITE][GPU]optimize activation

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Merge pull request !7913 from chenzupeng/master-lite
pull/7913/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 8f44074796 3a47029423
commit 84a315279b
3 changed files with 49 additions and 92 deletions
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74
mindspore/lite/src/runtime/kernel/opencl/cl/activation.cl
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@ -1,15 +1,12 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define SLICES 4
#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
#define MIN(X, Y) (X < Y ? X : Y)
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void LeakyRelu(__read_only image2d_t input, __write_only image2d_t output, const int4 img_shape,
__kernel void LeakyRelu(__read_only image2d_t input, __write_only image2d_t output, const int2 img_shape,
const float alpha) {
int Y = get_global_id(0); // H
int X = get_global_id(1); // W C4
if (X >= img_shape.z || Y >= img_shape.y) return;
int X = get_global_id(0);
int Y = get_global_id(1);
if (X >= img_shape.x || Y >= img_shape.y) return;
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
FLT4 tmp;
FLT alpha_f = TO_FLT(alpha);
@ -20,53 +17,40 @@ __kernel void LeakyRelu(__read_only image2d_t input, __write_only image2d_t outp
WRITE_IMAGE(output, (int2)(X, Y), tmp);
}
__kernel void Relu(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
int Y = get_global_id(0);
int X = get_global_id(1);
if (X >= input_shape.z || Y >= input_shape.y) return;
__kernel void Relu(__read_only image2d_t input, __write_only image2d_t output, const int2 img_shape) {
int X = get_global_id(0);
int Y = get_global_id(1);
if (X >= img_shape.x || Y >= img_shape.y) return;
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
FLT4 tmp;
tmp.x = in_c4.x > 0.0f ? in_c4.x : 0.0f;
tmp.y = in_c4.y > 0.0f ? in_c4.y : 0.0f;
tmp.z = in_c4.z > 0.0f ? in_c4.z : 0.0f;
tmp.w = in_c4.w > 0.0f ? in_c4.w : 0.0f;
WRITE_IMAGE(output, (int2)(X, Y), tmp);
in_c4 = max(in_c4, (FLT)(0.f));
WRITE_IMAGE(output, (int2)(X, Y), in_c4);
}
__kernel void Relu6(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
int Y = get_global_id(0);
int X = get_global_id(1);
if (X >= input_shape.z || Y >= input_shape.y) return;
__kernel void Relu6(__read_only image2d_t input, __write_only image2d_t output, const int2 img_shape) {
int X = get_global_id(0);
int Y = get_global_id(1);
if (X >= img_shape.x || Y >= img_shape.y) return;
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
FLT4 tmp;
tmp.x = in_c4.x > 0.0f ? MIN(in_c4.x, 6.0f) : 0.0f;
tmp.y = in_c4.y > 0.0f ? MIN(in_c4.y, 6.0f) : 0.0f;
tmp.z = in_c4.z > 0.0f ? MIN(in_c4.z, 6.0f) : 0.0f;
tmp.w = in_c4.w > 0.0f ? MIN(in_c4.w, 6.0f) : 0.0f;
WRITE_IMAGE(output, (int2)(X, Y), tmp);
in_c4 = clamp(in_c4, (FLT)(0.f), (FLT)(6.f));
WRITE_IMAGE(output, (int2)(X, Y), in_c4);
}
__kernel void Sigmoid(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
int Y = get_global_id(0);
int X = get_global_id(1);
if (X >= input_shape.z || Y >= input_shape.y) return;
__kernel void Sigmoid(__read_only image2d_t input, __write_only image2d_t output, const int2 img_shape) {
int X = get_global_id(0);
int Y = get_global_id(1);
if (X >= img_shape.x || Y >= img_shape.y) return;
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
FLT4 tmp;
tmp.x = 1.0f / (1.0f + exp(-in_c4.x));
tmp.y = 1.0f / (1.0f + exp(-in_c4.y));
tmp.z = 1.0f / (1.0f + exp(-in_c4.z));
tmp.w = 1.0f / (1.0f + exp(-in_c4.w));
WRITE_IMAGE(output, (int2)(X, Y), tmp);
in_c4 = (FLT4)(1.f) / ((FLT4)(1.f) + exp(-in_c4));
WRITE_IMAGE(output, (int2)(X, Y), in_c4);
}
__kernel void Tanh(__read_only image2d_t input, __write_only image2d_t output, int4 input_shape) {
int Y = get_global_id(0);
int X = get_global_id(1);
if (X >= input_shape.z || Y >= input_shape.y) return;
__kernel void Tanh(__read_only image2d_t input, __write_only image2d_t output, const int2 img_shape) {
int X = get_global_id(0);
int Y = get_global_id(1);
if (X >= img_shape.x || Y >= img_shape.y) return;
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
in_c4.x = (exp(in_c4.x) - exp(-in_c4.x)) / (exp(in_c4.x) + exp(-in_c4.x));
in_c4.y = (exp(in_c4.y) - exp(-in_c4.y)) / (exp(in_c4.y) + exp(-in_c4.y));
in_c4.z = (exp(in_c4.z) - exp(-in_c4.z)) / (exp(in_c4.z) + exp(-in_c4.z));
in_c4.w = (exp(in_c4.w) - exp(-in_c4.w)) / (exp(in_c4.w) + exp(-in_c4.w));
FLT4 exp0 = exp(in_c4);
FLT4 exp1 = exp(-in_c4);
in_c4 = (exp0 - exp1) / (exp0 + exp1);
WRITE_IMAGE(output, (int2)(X, Y), in_c4);
}

58
mindspore/lite/src/runtime/kernel/opencl/kernel/activation.cc
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@ -41,26 +41,6 @@ using mindspore::schema::PrimitiveType_Activation;
namespace mindspore::kernel {
int ActivationOpenClKernel::Init() {
in_size_ = in_tensors_[0]->shape().size();
out_size_ = out_tensors_[0]->shape().size();
size_t n, h, w, c;
if (in_size_ == 2) {
n = in_tensors_[0]->shape()[0];
c = in_tensors_[0]->shape()[1];
h = w = 1;
} else {
n = in_tensors_[0]->shape()[0];
h = in_tensors_[0]->shape()[1];
w = in_tensors_[0]->shape()[2];
c = in_tensors_[0]->shape()[3];
}
nhwc_shape_ = {n, h, w, c};
enable_fp16_ = ocl_runtime_->GetFp16Enable();
fp_size = enable_fp16_ ? sizeof(uint16_t) : sizeof(float);
if (in_size_ != 2 && in_size_ != 4) {
MS_LOG(ERROR) << "Activate fun only support dim=4 or 2, but your dim=" << in_size_;
return mindspore::lite::RET_ERROR;
}
std::map<int, std::string> kernel_names{{ActivationType_LEAKY_RELU, "LeakyRelu"},
{ActivationType_RELU, "Relu"},
{ActivationType_SIGMOID, "Sigmoid"},
@ -70,45 +50,43 @@ int ActivationOpenClKernel::Init() {
MS_LOG(ERROR) << "schema::ActivationType:" << type_ << "not found";
return mindspore::lite::RET_ERROR;
}
outShape = Image2DInfo(out_tensors_[0]);
local_size_ = {};
global_size_ = {outShape.width, outShape.height};
std::string source = activation_source;
std::set<std::string> build_options;
std::string program_name = "Activation";
ocl_runtime_->LoadSource(program_name, source);
std::string kernel_name = kernel_names[type_];
ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, build_options);
MS_LOG(DEBUG) << op_parameter_->name_ << " init Done!";
SetArgs();
MS_LOG(DEBUG) << kernel_name << " init Done!";
return mindspore::lite::RET_OK;
}
int ActivationOpenClKernel::SetArgs() {
int arg_idx = 2;
cl_int2 image_size = {static_cast<int>(outShape.width), static_cast<int>(outShape.height)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, image_size);
if (type_ == ActivationType_LEAKY_RELU) {
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, alpha_);
}
return RET_OK;
}
int ActivationOpenClKernel::Run() {
MS_LOG(DEBUG) << op_parameter_->name_ << " begin running!";
cl_int4 img2d_shape = GetImg2dShape();
MS_LOG(DEBUG) << this->name() << " begin running!";
int arg_idx = 0;
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->data_c());
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->data_c());
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, img2d_shape);
if (type_ == ActivationType_LEAKY_RELU) {
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, alpha_);
}
std::vector<size_t> local = {};
std::vector<size_t> global = {static_cast<size_t>(img2d_shape.s[1]), static_cast<size_t>(img2d_shape.s[2])};
auto ret = ocl_runtime_->RunKernel(kernel_, global, local, nullptr);
auto ret = ocl_runtime_->RunKernel(kernel_, global_size_, local_size_, nullptr);
if (ret != mindspore::lite::RET_OK) {
MS_LOG(ERROR) << "Run kernel:" << op_parameter_->name_ << " fail.";
MS_LOG(ERROR) << "Run kernel:" << this->name() << " fail.";
return mindspore::lite::RET_ERROR;
}
return mindspore::lite::RET_OK;
}
cl_int4 ActivationOpenClKernel::GetImg2dShape() {
cl_int4 img2d_shape = {1, 1, 1, 1};
img2d_shape.s[1] = nhwc_shape_[1];
img2d_shape.s[2] = nhwc_shape_[2] * UP_DIV(nhwc_shape_[3], C4NUM);
img2d_shape.s[3] = C4NUM;
return img2d_shape;
}
kernel::LiteKernel *OpenClActivationKernelCreator(const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter,
const lite::InnerContext *ctx, const kernel::KernelKey &desc,

9
mindspore/lite/src/runtime/kernel/opencl/kernel/activation.h
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@ -37,16 +37,11 @@ class ActivationOpenClKernel : public OpenCLKernel {
int Run() override;
private:
cl_int4 GetImg2dShape();
int SetArgs();
cl::Kernel kernel_;
int type_;
float alpha_;
int in_size_{};
int out_size_{};
size_t fp_size{};
bool enable_fp16_{false};
std::vector<size_t> nhwc_shape_;
Image2DInfo outShape = Image2DInfo(nullptr);
};
} // namespace mindspore::kernel

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