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optimize arithmetic and pooling

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pull/7890/head
chenzupeng 4 years ago
parent ea4df3c3c2
commit 53d0e9c22a
6 changed files with 56 additions and 176 deletions
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68
mindspore/lite/src/runtime/kernel/opencl/cl/avg_pool2d.cl
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@ -2,46 +2,14 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#define divide_no_check(a, b) (a / b)
__kernel void AvgPooling2d_BUF(__global FLT4 *input, __global FLT4 *output, const int4 input_shape,
const int4 output_shape, const int2 stride, const int2 kernel_size, const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
return;
}
FLT4 r = (FLT4)(0.0f);
FLT window_size = 0.0f;
int xs = X * stride.x - padding.x;
int ys = Y * stride.y - padding.y;
for (int kx = 0; kx < kernel_size.x; ++kx) {
int x_c = xs + kx;
bool outside_x = x_c < 0 || x_c >= input_shape.x;
for (int ky = 0; ky < kernel_size.y; ++ky) {
int y_c = ys + ky;
bool outside = outside_x || y_c < 0 || y_c >= input_shape.y;
r += !outside ? input[(input_shape.y * x_c + y_c) * output_shape.w + Z] : (FLT4)(0.0f);
window_size += !outside ? 1.0f : 0.0f;
}
}
FLT4 result = TO_FLT4(r / window_size);
output[(output_shape.y * X + Y) * output_shape.w + Z] = result;
}
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void AvgPooling2d_NHWC4_IMG(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
const int4 output_shape, const int2 stride, const int2 kernel_size,
const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int X = get_global_id(2);
int Y = get_global_id(1);
int Z = get_global_id(2);
int Z = get_global_id(0);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
@ -66,35 +34,3 @@ __kernel void AvgPooling2d_NHWC4_IMG(__read_only image2d_t input, __write_only i
FLT4 result = TO_FLT4(divide_no_check(r, window_size));
WRITE_IMAGE(output, (int2)(Y * output_shape.w + Z, X), result);
}
__kernel void AvgPooling2d_NC4HW4_IMG(__read_only image2d_t input, __write_only image2d_t output,
const int4 input_shape, const int4 output_shape, const int2 stride,
const int2 kernel_size, const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
return;
}
FLT4 r = (FLT4)(0.0f);
FLT window_size = 0.0f;
int xs = X * stride.x - padding.x;
int ys = Y * stride.y - padding.y;
for (int ky = 0; ky < kernel_size.y; ++ky) {
int y_c = ys + ky;
bool outside_y = y_c < 0 || y_c >= input_shape.y;
for (int kx = 0; kx < kernel_size.x; ++kx) {
int x_c = xs + kx;
bool outside = outside_y || x_c < 0 || x_c >= input_shape.x;
r += !outside ? READ_IMAGE(input, smp_zero, (int2)(y_c, Z * input_shape.x + x_c)) : (FLT4)(0.0f);
window_size += !outside ? 1.0f : 0.0f;
}
}
FLT4 result = TO_FLT4(divide_no_check(r, window_size));
WRITE_IMAGE(output, (int2)(Y, Z * output_shape.x + X), result);
}

71
mindspore/lite/src/runtime/kernel/opencl/cl/max_pool2d.cl
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@ -1,48 +1,14 @@
#ifdef cl_khr_fp16
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void MaxPooling2d_BUF(__global FLT4 *input, __global FLT4 *output, const int4 input_shape,
const int4 output_shape, const int2 stride, const int2 kernel_size, const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
return;
}
FLT4 maximum = (FLT4)(-10000.0f);
int xs = X * stride.x - padding.x;
int ys = Y * stride.y - padding.y;
for (int kx = 0; kx < kernel_size.x; ++kx) {
int x_c = xs + kx;
if (x_c < 0 || x_c >= input_shape.x) {
continue;
}
for (int ky = 0; ky < kernel_size.y; ++ky) {
int y_c = ys + ky;
if (y_c < 0 || y_c >= input_shape.y) {
continue;
}
FLT4 src = input[(input_shape.y * x_c + y_c) * input_shape.w + Z];
maximum = max(src, maximum);
}
}
output[(output_shape.y * X + Y) * output_shape.w + Z] = maximum;
}
__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
__kernel void MaxPooling2d_NHWC4_IMG(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
const int4 output_shape, const int2 stride, const int2 kernel_size,
const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int X = get_global_id(2);
int Y = get_global_id(1);
int Z = get_global_id(2);
int Z = get_global_id(0);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
@ -69,9 +35,9 @@ __kernel void MaxPooling2d_ReLU_NHWC4_IMG(__read_only image2d_t input, __write_o
const int4 input_shape, const int4 output_shape, const int2 stride,
const int2 kernel_size, const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int X = get_global_id(2);
int Y = get_global_id(1);
int Z = get_global_id(2);
int Z = get_global_id(0);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
@ -93,32 +59,3 @@ __kernel void MaxPooling2d_ReLU_NHWC4_IMG(__read_only image2d_t input, __write_o
}
WRITE_IMAGE(output, (int2)(Y * output_shape.w + Z, X), max(maximum, (FLT4)(0.f)));
}
__kernel void MaxPooling2d_NC4HW4_IMG(__read_only image2d_t input, __write_only image2d_t output,
const int4 input_shape, const int4 output_shape, const int2 stride,
const int2 kernel_size, const int2 padding) {
// axis to dst tensor coordinate
int X = get_global_id(0);
int Y = get_global_id(1);
int Z = get_global_id(2);
// boundary check
if (X >= output_shape.x || Y >= output_shape.y || Z >= output_shape.w) {
return;
}
FLT4 maximum = (FLT4)(-10000.0f);
int xs = X * stride.x - padding.x;
int ys = Y * stride.y - padding.y;
for (int ky = 0; ky < kernel_size.y; ++ky) {
int y_c = ys + ky;
if (y_c < 0 || y_c >= input_shape.y) continue;
for (int kx = 0; kx < kernel_size.x; ++kx) {
int x_c = xs + kx;
if (x_c < 0 || x_c >= input_shape.x) continue;
FLT4 src = READ_IMAGE(input, smp_none, (int2)(y_c, Z * input_shape.x + x_c));
maximum = max(src, maximum);
}
}
WRITE_IMAGE(output, (int2)(Y, Z * output_shape.x + X), maximum);
}

71
mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.cc
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@ -52,16 +52,23 @@ std::vector<size_t> ArithmeticOpenCLKernel::InitGlobalSize() const {
}
void ArithmeticOpenCLKernel::Image2dGetWorkGroupSize() {
local_size_ = {16, 16};
auto out_shape = out_tensors_[0]->shape();
if (out_shape.size() == 2) {
size_t H = out_shape[0];
size_t W = UP_DIV(out_shape[1], C4NUM);
global_size_ = {W, H};
if (element_flag_) {
local_size_ = {16, 16};
auto out_shape = out_tensors_[0]->shape();
if (out_shape.size() == 2) {
size_t H = out_shape[0];
size_t W = UP_DIV(out_shape[1], C4NUM);
global_size_ = {W, H};
} else {
size_t H = out_shape[0] * out_shape[1];
size_t W = out_shape[2] * UP_DIV(out_shape[3], C4NUM);
global_size_ = {W, H};
}
} else {
size_t H = out_shape[0] * out_shape[1];
size_t W = out_shape[2] * UP_DIV(out_shape[3], C4NUM);
global_size_ = {W, H};
local_size_ = {};
auto out_shape = GetNHWCShape(out_tensors_[0]->shape());
global_size_ = {static_cast<size_t>(UP_DIV(out_shape[3], C4NUM)), static_cast<size_t>(out_shape[2]),
static_cast<size_t>(out_shape[1] * out_shape[0])};
}
}
@ -129,6 +136,27 @@ int ArithmeticOpenCLKernel::InitBuffer() {
return RET_OK;
}
int ArithmeticOpenCLKernel::SetArgs() {
int arg_idx = 3;
if (!element_flag_) {
cl_int4 input0_shape = {inputs_nhwc_shapes_[0][0], inputs_nhwc_shapes_[0][1], inputs_nhwc_shapes_[0][2],
UP_DIV(inputs_nhwc_shapes_[0][3], C4NUM)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input0_shape);
cl_int4 input1_shape = {inputs_nhwc_shapes_[1][0], inputs_nhwc_shapes_[1][1], inputs_nhwc_shapes_[1][2],
UP_DIV(inputs_nhwc_shapes_[1][3], C4NUM)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input1_shape);
auto out_shape = GetNHWCShape(out_tensors_[0]->shape());
cl_int4 output_shape{out_shape[0], out_shape[1], out_shape[2], UP_DIV(out_shape[3], C4NUM)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, output_shape);
} else {
cl_int2 output_shape{static_cast<int>(global_size_[0]), static_cast<int>(global_size_[1])};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, output_shape);
}
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_min_);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_max_);
return RET_OK;
}
int ArithmeticOpenCLKernel::Init() {
std::string kernel_name;
auto *arithmetic_parameter = reinterpret_cast<const ArithmeticParameter *>(op_parameter_);
@ -237,6 +265,7 @@ int ArithmeticOpenCLKernel::Init() {
Image2dGetWorkGroupSize();
InitBuffer();
SetArgs();
MS_LOG(DEBUG) << kernel_name << " Init Done!";
return RET_OK;
}
@ -250,29 +279,7 @@ int ArithmeticOpenCLKernel::Run() {
auto input_1_ptr = inputs_weight_ptrs_[1] == nullptr ? in_tensors_[1]->data_c() : inputs_weight_ptrs_[1];
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input_1_ptr);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->data_c());
if (!element_flag_) {
cl_int4 input0_shape = {inputs_nhwc_shapes_[0][0], inputs_nhwc_shapes_[0][1], inputs_nhwc_shapes_[0][2],
UP_DIV(inputs_nhwc_shapes_[0][3], C4NUM)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input0_shape);
cl_int4 input1_shape = {inputs_nhwc_shapes_[1][0], inputs_nhwc_shapes_[1][1], inputs_nhwc_shapes_[1][2],
UP_DIV(inputs_nhwc_shapes_[1][3], C4NUM)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input1_shape);
auto out_shape = GetNHWCShape(out_tensors_[0]->shape());
cl_int4 output_shape{out_shape[0], out_shape[1], out_shape[2], UP_DIV(out_shape[3], C4NUM)};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, output_shape);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_min_);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_max_);
ocl_runtime_->RunKernel(kernel_,
{static_cast<size_t>(UP_DIV(out_shape[3], C4NUM)), static_cast<size_t>(out_shape[2]),
static_cast<size_t>(out_shape[1] * out_shape[0])},
{}, nullptr);
} else {
cl_int2 output_shape{static_cast<int>(global_size_[0]), static_cast<int>(global_size_[1])};
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, output_shape);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_min_);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_max_);
ocl_runtime_->RunKernel(kernel_, global_size_, local_size_, nullptr);
}
ocl_runtime_->RunKernel(kernel_, global_size_, local_size_, nullptr);
return RET_OK;
}

1
mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.h
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@ -33,6 +33,7 @@ class ArithmeticOpenCLKernel : public OpenCLKernel {
int Init() override;
int Run() override;
int InitBuffer() override;
int SetArgs();
private:
std::vector<size_t> InitGlobalSize() const;

17
mindspore/lite/src/runtime/kernel/opencl/kernel/pooling2d.cc
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@ -83,17 +83,20 @@ int PoolingOpenCLKernel::Init() {
ocl_runtime_->LoadSource(program_name, source);
ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, build_options);
#endif
InitGlobalSize();
MS_LOG(DEBUG) << kernel_name << " Init Done!";
return mindspore::lite::RET_OK;
}
std::vector<size_t> PoolingOpenCLKernel::InitGlobalSize() const {
void PoolingOpenCLKernel::InitGlobalSize() {
const size_t global_x = out_tensors_[0]->shape()[1];
const size_t global_y = out_tensors_[0]->shape()[2];
const size_t global_z = UP_DIV(out_tensors_[0]->shape()[3], C4NUM);
std::vector<size_t> global = {global_x, global_y, global_z};
return global;
global_size_ = {global_z, global_y, global_x};
int max_work_group_size = ocl_runtime_->GetKernelMaxWorkGroupSize(kernel_(), (*ocl_runtime_->Device())());
local_size_ = GetCommonLocalSize(global_size_, max_work_group_size);
global_size_ = GetCommonGlobalSize(local_size_, global_size_);
}
int PoolingOpenCLKernel::Run() {
@ -116,13 +119,7 @@ int PoolingOpenCLKernel::Run() {
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, kernel_size);
ocl_runtime_->SetKernelArg(kernel_, arg_idx++, padding);
std::vector<size_t> local_size;
std::vector<size_t> global_size = InitGlobalSize();
int max_work_group_size = ocl_runtime_->GetKernelMaxWorkGroupSize(kernel_(), (*ocl_runtime_->Device())());
local_size = GetCommonLocalSize(global_size, max_work_group_size);
global_size = GetCommonGlobalSize(local_size, global_size);
ocl_runtime_->RunKernel(kernel_, global_size, local_size, nullptr);
ocl_runtime_->RunKernel(kernel_, global_size_, local_size_, nullptr);
return mindspore::lite::RET_OK;
}

4
mindspore/lite/src/runtime/kernel/opencl/kernel/pooling2d.h
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@ -35,10 +35,12 @@ class PoolingOpenCLKernel : public OpenCLKernel {
int Run() override;
private:
std::vector<size_t> InitGlobalSize() const;
void InitGlobalSize();
PoolingParameter *parameter_;
cl::Kernel kernel_;
bool enable_fp16_{false};
std::vector<size_t> local_size_;
std::vector<size_t> global_size_;
};
} // namespace mindspore::kernel

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