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optim opencl conv2d transpose

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pull/3823/head
chenzupeng 5 years ago
parent 45ae76e86a
commit 39e4900e4b
4 changed files with 117 additions and 98 deletions
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79
mindspore/lite/src/runtime/kernel/opencl/cl/fp16/conv2d_transpose2x2.cl
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@ -1,52 +1,59 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define FLT half
#define FLT4 half4
#define FLT16 half16
__kernel void conv2d_transpose2x2(__global FLT4 *inputx, __global FLT16 *weight, __global FLT4 *bias,
__global FLT4 *output, int2 kernel_size, int2 stride, int2 padding, int4 src_size,
int4 dst_size) {
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void conv2d_transpose2x2(__read_only image2d_t src_data, __global FLT16 *weight, __read_only image2d_t biases,
__write_only image2d_t dst_data, int2 kernel_size, int2 stride, int2 padding,
int4 src_size, int4 dst_size) {
int h = get_global_id(0);
int kh = h % 2;
int src_h = h / 2;
src_h = src_h * 2;
int w = get_global_id(1);
int kw = w % 2;
int src_w = w / 2;
src_w = src_w * 2;
int co = get_global_id(2);
if (h * 2 >= dst_size.x || w * 2 >= dst_size.y || co >= dst_size.z) return;
FLT4 r0 = (FLT4)(0.f);
FLT4 r1 = (FLT4)(0.f);
FLT4 r2 = (FLT4)(0.f);
FLT4 r3 = (FLT4)(0.f);
int base_x = (h * src_size.y + w) * src_size.z;
int base_w = co * src_size.z;
int base_w = (co * 4 + kh + kw * 2) * src_size.z;
for (int ci = 0; ci < src_size.z; ++ci) {
FLT4 x = inputx[base_x + ci];
FLT16 w0 = weight[(base_w + ci) * 4];
FLT16 w1 = weight[(base_w + ci) * 4 + 1];
FLT16 w2 = weight[(base_w + ci) * 4 + 2];
FLT16 w3 = weight[(base_w + ci) * 4 + 3];
r0 += x.x * w0.s0123;
r0 += x.y * w0.s4567;
r0 += x.z * w0.s89ab;
r0 += x.w * w0.scdef;
FLT4 x0 = read_imagef(src_data, smp_zero, (int2)(src_w * src_size.z + ci, src_h));
FLT4 x1 = read_imagef(src_data, smp_zero, (int2)(src_w * src_size.z + ci, src_h + 1));
FLT4 x2 = read_imagef(src_data, smp_zero, (int2)((src_w + 1) * src_size.z + ci, src_h));
FLT4 x3 = read_imagef(src_data, smp_zero, (int2)((src_w + 1) * src_size.z + ci, src_h + 1));
FLT16 weight_cache = weight[base_w++];
r0 += x0.x * weight_cache.s0123;
r0 += x0.y * weight_cache.s4567;
r0 += x0.z * weight_cache.s89ab;
r0 += x0.w * weight_cache.scdef;
r1 += x.x * w1.s0123;
r1 += x.y * w1.s4567;
r1 += x.z * w1.s89ab;
r1 += x.w * w1.scdef;
r1 += x1.x * weight_cache.s0123;
r1 += x1.y * weight_cache.s4567;
r1 += x1.z * weight_cache.s89ab;
r1 += x1.w * weight_cache.scdef;
r2 += x.x * w2.s0123;
r2 += x.y * w2.s4567;
r2 += x.z * w2.s89ab;
r2 += x.w * w2.scdef;
r2 += x2.x * weight_cache.s0123;
r2 += x2.y * weight_cache.s4567;
r2 += x2.z * weight_cache.s89ab;
r2 += x2.w * weight_cache.scdef;
r3 += x.x * w3.s0123;
r3 += x.y * w3.s4567;
r3 += x.z * w3.s89ab;
r3 += x.w * w3.scdef;
r3 += x3.x * weight_cache.s0123;
r3 += x3.y * weight_cache.s4567;
r3 += x3.z * weight_cache.s89ab;
r3 += x3.w * weight_cache.scdef;
}
r0 += bias[co];
r1 += bias[co];
r2 += bias[co];
r3 += bias[co];
output[((2 * h + 0) * dst_size.y + 2 * w + 0) * dst_size.z + co] = r0;
output[((2 * h + 0) * dst_size.y + 2 * w + 1) * dst_size.z + co] = r1;
output[((2 * h + 1) * dst_size.y + 2 * w + 0) * dst_size.z + co] = r2;
output[((2 * h + 1) * dst_size.y + 2 * w + 1) * dst_size.z + co] = r3;
}
FLT4 bias_val = read_imagef(biases, smp_zero, (int2)(co, 0));
r0 += bias_val;
r1 += bias_val;
r2 += bias_val;
r3 += bias_val;
write_imagef(dst_data, (int2)((2 * src_w + kw) * dst_size.z + co, 2 * src_h + kh), r0);
write_imagef(dst_data, (int2)((2 * src_w + kw) * dst_size.z + co, 2 * src_h + kh + 2), r1);
write_imagef(dst_data, (int2)((2 * src_w + kw + 2) * dst_size.z + co, 2 * src_h + kh), r2);
write_imagef(dst_data, (int2)((2 * src_w + kw + 2) * dst_size.z + co, 2 * src_h + kh + 2), r3);
}

78
mindspore/lite/src/runtime/kernel/opencl/cl/fp32/conv2d_transpose2x2.cl
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@ -1,51 +1,59 @@
#define FLT float
#define FLT4 float4
#define FLT16 float16
__kernel void conv2d_transpose2x2(__global FLT4 *inputx, __global FLT16 *weight, __global FLT4 *bias,
__global FLT4 *output, int2 kernel_size, int2 stride, int2 padding, int4 src_size,
int4 dst_size) {
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
__kernel void conv2d_transpose2x2(__read_only image2d_t src_data, __global FLT16 *weight, __read_only image2d_t biases,
__write_only image2d_t dst_data, int2 kernel_size, int2 stride, int2 padding,
int4 src_size, int4 dst_size) {
int h = get_global_id(0);
int kh = h % 2;
int src_h = h / 2;
src_h = src_h * 2;
int w = get_global_id(1);
int kw = w % 2;
int src_w = w / 2;
src_w = src_w * 2;
int co = get_global_id(2);
if (h * 2 >= dst_size.x || w * 2 >= dst_size.y || co >= dst_size.z) return;
FLT4 r0 = (FLT4)(0.f);
FLT4 r1 = (FLT4)(0.f);
FLT4 r2 = (FLT4)(0.f);
FLT4 r3 = (FLT4)(0.f);
int base_x = (h * src_size.y + w) * src_size.z;
int base_w = co * src_size.z;
int base_w = (co * 4 + kh + kw * 2) * src_size.z;
for (int ci = 0; ci < src_size.z; ++ci) {
FLT4 x = inputx[base_x + ci];
FLT16 w0 = weight[(base_w + ci) * 4];
FLT16 w1 = weight[(base_w + ci) * 4 + 1];
FLT16 w2 = weight[(base_w + ci) * 4 + 2];
FLT16 w3 = weight[(base_w + ci) * 4 + 3];
r0 += x.x * w0.s0123;
r0 += x.y * w0.s4567;
r0 += x.z * w0.s89ab;
r0 += x.w * w0.scdef;
FLT4 x0 = read_imagef(src_data, smp_zero, (int2)(src_w * src_size.z + ci, src_h));
FLT4 x1 = read_imagef(src_data, smp_zero, (int2)(src_w * src_size.z + ci, src_h + 1));
FLT4 x2 = read_imagef(src_data, smp_zero, (int2)((src_w + 1) * src_size.z + ci, src_h));
FLT4 x3 = read_imagef(src_data, smp_zero, (int2)((src_w + 1) * src_size.z + ci, src_h + 1));
FLT16 weight_cache = weight[base_w++];
r0 += x0.x * weight_cache.s0123;
r0 += x0.y * weight_cache.s4567;
r0 += x0.z * weight_cache.s89ab;
r0 += x0.w * weight_cache.scdef;
r1 += x.x * w1.s0123;
r1 += x.y * w1.s4567;
r1 += x.z * w1.s89ab;
r1 += x.w * w1.scdef;
r1 += x1.x * weight_cache.s0123;
r1 += x1.y * weight_cache.s4567;
r1 += x1.z * weight_cache.s89ab;
r1 += x1.w * weight_cache.scdef;
r2 += x.x * w2.s0123;
r2 += x.y * w2.s4567;
r2 += x.z * w2.s89ab;
r2 += x.w * w2.scdef;
r2 += x2.x * weight_cache.s0123;
r2 += x2.y * weight_cache.s4567;
r2 += x2.z * weight_cache.s89ab;
r2 += x2.w * weight_cache.scdef;
r3 += x.x * w3.s0123;
r3 += x.y * w3.s4567;
r3 += x.z * w3.s89ab;
r3 += x.w * w3.scdef;
r3 += x3.x * weight_cache.s0123;
r3 += x3.y * weight_cache.s4567;
r3 += x3.z * weight_cache.s89ab;
r3 += x3.w * weight_cache.scdef;
}
r0 += bias[co];
r1 += bias[co];
r2 += bias[co];
r3 += bias[co];
output[((2 * h + 0) * dst_size.y + 2 * w + 0) * dst_size.z + co] = r0;
output[((2 * h + 0) * dst_size.y + 2 * w + 1) * dst_size.z + co] = r1;
output[((2 * h + 1) * dst_size.y + 2 * w + 0) * dst_size.z + co] = r2;
output[((2 * h + 1) * dst_size.y + 2 * w + 1) * dst_size.z + co] = r3;
}
FLT4 bias_val = read_imagef(biases, smp_zero, (int2)(co, 0));
r0 += bias_val;
r1 += bias_val;
r2 += bias_val;
r3 += bias_val;
write_imagef(dst_data, (int2)((2 * src_w + kw) * dst_size.z + co, 2 * src_h + kh), r0);
write_imagef(dst_data, (int2)((2 * src_w + kw) * dst_size.z + co, 2 * src_h + kh + 2), r1);
write_imagef(dst_data, (int2)((2 * src_w + kw + 2) * dst_size.z + co, 2 * src_h + kh), r2);
write_imagef(dst_data, (int2)((2 * src_w + kw + 2) * dst_size.z + co, 2 * src_h + kh + 2), r3);
}

52
mindspore/lite/src/runtime/kernel/opencl/kernel/conv2d_transpose.cc
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@ -64,11 +64,8 @@ int Conv2dTransposeOpenCLKernel::Init() {
auto allocator = ocl_runtime->GetAllocator();
padWeight_ = reinterpret_cast<FLOAT_T *>(allocator->Malloc(div_ci * div_co * 16 * kh * kw * sizeof(FLOAT_T)));
padWeight_ = reinterpret_cast<FLOAT_T *>(allocator->MapBuffer(padWeight_, CL_MAP_WRITE, nullptr, true));
bias_ = reinterpret_cast<FLOAT_T *>(allocator->Malloc(div_co * 4 * sizeof(FLOAT_T)));
bias_ = reinterpret_cast<FLOAT_T *>(allocator->MapBuffer(bias_, CL_MAP_WRITE, nullptr, true));
PadWeight();
allocator->UnmapBuffer(padWeight_);
allocator->UnmapBuffer(bias_);
outputs_[0]->SetFormat(schema::Format_NHWC4);
MS_LOG(DEBUG) << kernel_name << " Init Done!";
return 0;
@ -77,7 +74,7 @@ int Conv2dTransposeOpenCLKernel::Init() {
int Conv2dTransposeOpenCLKernel::ReSize() { return 0; }
void Conv2dTransposeOpenCLKernel::PadWeight() {
// OHWI to OIHW4(I)4(O)
// OHWI to OHWI4(I)4(O)
ConvParameter *param = reinterpret_cast<ConvParameter *>(opParameter);
int ci = param->input_channel_;
int co = param->output_channel_;
@ -86,13 +83,11 @@ void Conv2dTransposeOpenCLKernel::PadWeight() {
int div_ci = UP_DIV(ci, 4);
int div_co = UP_DIV(co, 4);
auto origin_weight = reinterpret_cast<FLOAT_T *>(inputs_.at(kWeightIndex)->Data());
auto origin_bias = reinterpret_cast<FLOAT_T *>(inputs_.at(kBiasIndex)->Data());
bool has_bias = origin_bias != nullptr;
int index = 0;
for (int co_i = 0; co_i < div_co; co_i++) {
for (int ci_i = 0; ci_i < div_ci; ci_i++) {
for (int kw_i = 0; kw_i < kw; kw_i++) {
for (int kh_i = 0; kh_i < kh; kh_i++) {
for (int kw_i = 0; kw_i < kw; kw_i++) {
for (int ci_i = 0; ci_i < div_ci; ci_i++) {
for (int ci4_i = 0; ci4_i < 4; ci4_i++) {
for (int co4_i = 0; co4_i < 4; co4_i++) {
int co_offset = co_i * 4 + co4_i;
@ -109,16 +104,6 @@ void Conv2dTransposeOpenCLKernel::PadWeight() {
}
}
}
for (int co_i = 0; co_i < div_co; co_i++) {
for (int co4_i = 0; co4_i < 4; co4_i++) {
int co_offset = co_i * 4 + co4_i;
if (has_bias && co_offset < co) {
bias_[co_offset] = origin_bias[co_offset];
} else {
bias_[co_offset] = 0.;
}
}
}
}
int Conv2dTransposeOpenCLKernel::Run() {
@ -134,14 +119,31 @@ int Conv2dTransposeOpenCLKernel::Run() {
int co = param->output_channel_;
int kh = param->kernel_h_;
int kw = param->kernel_w_;
int pad = kh - 1 - param->pad_h_;
int pad = param->pad_h_;
int oh = outputs_[0]->shape()[1];
int ow = outputs_[0]->shape()[2];
int h = inputs_[0]->shape()[1];
int w = inputs_[0]->shape()[2];
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
cl::ImageFormat image_format;
{
image_format.image_channel_order = CL_RGBA;
#ifdef ENABLE_FP16
image_format.image_channel_data_type = CL_HALF_FLOAT;
#else
image_format.image_channel_data_type = CL_FLOAT;
#endif
}
cl_int in_error_code, in_error_code_weight, in_error_code_bias, out_error_code;
cl::Image2D img_x(*ocl_runtime->Context(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, image_format, w * ci / 4, h, 0,
inputs_[0]->Data(), &in_error_code);
cl::Image2D img_bias(*ocl_runtime->Context(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, image_format, co / 4, 1, 0,
inputs_[2]->Data(), &in_error_code_bias);
cl::Image2D out_mem(*ocl_runtime->Context(), CL_MEM_WRITE_ONLY, image_format, ow * co / 4, oh, 0, nullptr,
&out_error_code);
// local size should less than MAX_GROUP_SIZE
std::vector<size_t> local = {4, 4, 32};
std::vector<size_t> local = {16, 1, 16};
std::vector<size_t> global = {UP_ROUND((size_t)oh / 2, local[0]), UP_ROUND((size_t)ow / 2, local[1]),
UP_ROUND((size_t)co / 4, local[2])};
@ -150,16 +152,19 @@ int Conv2dTransposeOpenCLKernel::Run() {
cl_int2 padding = {pad, pad};
cl_int4 src_size = {h, w, UP_DIV(ci, 4), 1};
cl_int4 dst_size = {oh, ow, UP_DIV(co, 4), 1};
ocl_runtime->SetKernelArg(kernel_, 0, inputs_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, 0, img_x);
ocl_runtime->SetKernelArg(kernel_, 1, padWeight_);
ocl_runtime->SetKernelArg(kernel_, 2, bias_);
ocl_runtime->SetKernelArg(kernel_, 3, outputs_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, 2, img_bias);
ocl_runtime->SetKernelArg(kernel_, 3, out_mem);
ocl_runtime->SetKernelArg(kernel_, 4, kernel_size);
ocl_runtime->SetKernelArg(kernel_, 5, stride);
ocl_runtime->SetKernelArg(kernel_, 6, padding);
ocl_runtime->SetKernelArg(kernel_, 7, src_size);
ocl_runtime->SetKernelArg(kernel_, 8, dst_size);
ocl_runtime->RunKernel(kernel_, global, local, nullptr);
auto origin = cl::array<cl::size_type, 3U>{0, 0, 0};
auto region = cl::array<cl::size_type, 3U>{(size_t)(ow * co / 4), (size_t)(oh), 1};
ocl_runtime->GetDefaultCommandQueue()->enqueueReadImage(out_mem, CL_TRUE, origin, region, 0, 0, outputs_[0]->Data());
return 0;
}
@ -180,4 +185,3 @@ kernel::LiteKernel *OpenCLConv2dTransposeKernelCreator(const std::vector<lite::t
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_DeConv2D, OpenCLConv2dTransposeKernelCreator)
} // namespace mindspore::kernel

6
mindspore/lite/src/runtime/kernel/opencl/kernel/matmul.cc
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@ -94,8 +94,8 @@ void MatMulOpenCLKernel::PadWeight() {
}
}
if (hasBias_) {
memcpy(inputs_[2]->Data(), bias_, sizeof(FLOAT_T) * sizeCI.s[0]);
for (int i = sizeCI.s[0]; i < sizeCI.s[1] * 4; i++) {
memcpy(bias_, inputs_[2]->Data(), sizeof(FLOAT_T) * sizeCO.s[0]);
for (int i = sizeCO.s[0]; i < sizeCO.s[1] * 4; i++) {
bias_[i] = 0;
}
}
@ -118,7 +118,7 @@ int MatMulOpenCLKernel::Run() {
ocl_runtime->SetKernelArg(kernel_, 1, padWeight_);
ocl_runtime->SetKernelArg(kernel_, 2, outputs_[0]->Data());
if (hasBias_) {
ocl_runtime->SetKernelArg(kernel_, 3, inputs_[2]->Data());
ocl_runtime->SetKernelArg(kernel_, 3, bias_);
} else {
ocl_runtime->SetKernelArg(kernel_, 3, nullptr);
}

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