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Improved performance of convolution on AMD.

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PiperOrigin-RevId: 293066841
Change-Id: Ifb8927606cea81897b9dee25b70918c585ba8c0b
This commit is contained in:
Raman Sarokin 2020-02-03 20:42:28 -08:00 committed by TensorFlower Gardener
parent 2b3dddff9c
commit 7a5bd40b13
3 changed files with 49 additions and 10 deletions
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56
tensorflow/lite/delegates/gpu/cl/kernels/conv_powervr.cc
View File

@ -169,7 +169,8 @@ Status ConvPowerVR::Tune(const TuningParameters& params) {
if (conv_params_.weights_upload_type ==
WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP ||
conv_params_.weights_upload_type ==
WeightsUploadType::LOCAL_MEM_BY_THREADS) {
WeightsUploadType::LOCAL_MEM_BY_THREADS ||
conv_params_.fixed_work_group_size) {
return OkStatus();
}
if (conv_params_.work_group_launch_order[0] == 0 &&
@ -212,9 +213,15 @@ std::string GenerateConvPowerVR1x1(
conv_params.weights_upload_type ==
ConvPowerVR::WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP;
const std::string weights_space =
conv_params.weights_upload_type ==
ConvPowerVR::WeightsUploadType::CONSTANT_MEM
? "__constant"
: "__global";
const int3 work_group_size = conv_params.work_group_size;
const int3 block_size = conv_params.block_size;
if (need_local_mem) { // we use fixed workgroup size when use local mem
if (conv_params.fixed_work_group_size) {
c += "__attribute__((reqd_work_group_size(" +
std::to_string(work_group_size.x) + ", " +
std::to_string(work_group_size.y) + ", " +
@ -222,8 +229,8 @@ std::string GenerateConvPowerVR1x1(
}
c += "__kernel void main_function(\n";
c += src_tensor.GetDeclaration(AccessType::READ) + ",\n";
c += " __global ACCUM_FLT4* filters_buffer, \n";
c += " __global ACCUM_FLT4* biases \n";
c += " " + weights_space + " ACCUM_FLT4* filters_buffer, \n";
c += " " + weights_space + " ACCUM_FLT4* biases \n";
c += GetArgsDeclaration(linked_operations);
c += dst_tensor.GetDeclaration(AccessType::WRITE) + ",\n";
if (!is1x1) {
@ -301,14 +308,18 @@ std::string GenerateConvPowerVR1x1(
"];\n";
}
if (conv_params.weights_upload_type ==
ConvPowerVR::WeightsUploadType::GLOBAL_MEM) {
c += " __global ACCUM_FLT4* weights_cache;\n";
ConvPowerVR::WeightsUploadType::GLOBAL_MEM ||
conv_params.weights_upload_type ==
ConvPowerVR::WeightsUploadType::CONSTANT_MEM) {
c += " " + weights_space + " ACCUM_FLT4* weights_cache;\n";
}
if (is1x1) {
c += " __global ACCUM_FLT4* filters_loc = filters_buffer + Z * 4 * "
c += " " + weights_space +
" ACCUM_FLT4* filters_loc = filters_buffer + Z * 4 * "
"src_size.z;\n";
} else {
c += " __global ACCUM_FLT4* filters_loc = filters_buffer + Z * 4 * "
c += " " + weights_space +
" ACCUM_FLT4* filters_loc = filters_buffer + Z * 4 * "
"src_size.z * kernel_dilation.x * kernel_dilation.y;\n";
}
if (buffer_type) {
@ -445,7 +456,7 @@ std::string GenerateConvPowerVR1x1(
"weights_cache", "filters_loc",
/*global_offset_name*/ "", "lid", total_work_items,
block_size.z * 4 * conv_params.src_depth_loop_size);
} else { // GLOBAL_MEM
} else { // GLOBAL_MEM/CONSTANT_MEM
c += " weights_cache = filters_loc;\n";
}
read_src();
@ -477,7 +488,7 @@ std::string GenerateConvPowerVR1x1(
c += GenerateUploadByThreads("weights_cache", "biases", "Z", "lid",
total_work_items, block_size.z);
c += " barrier(CLK_LOCAL_MEM_FENCE);\n";
} else { // GLOBAL_MEM
} else { // GLOBAL_MEM/CONSTANT_MEM
c += " weights_cache = biases + Z;\n";
}
if (need_local_mem) {
@ -528,6 +539,7 @@ ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(
conv_params.block_size = int3(1, 1, 4);
conv_params.work_group_size = int3(8, 4, 1);
conv_params.work_group_launch_order = int3(2, 0, 1);
conv_params.fixed_work_group_size = true;
conv_params.src_depth_loop_size = 1;
conv_params.weights_upload_type = WeightsUploadType::LOCAL_MEM_BY_THREADS;
if (dst_depth % 4 == 0 || dst_depth >= 8) {
@ -547,6 +559,7 @@ ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(
conv_params.block_size = int3(1, 1, 4);
conv_params.work_group_size = int3(8, 4, 1);
conv_params.work_group_launch_order = int3(2, 0, 1);
conv_params.fixed_work_group_size = true;
conv_params.src_depth_loop_size = 1;
conv_params.weights_upload_type =
WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP;
@ -581,10 +594,33 @@ ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(
conv_params.block_size.x = 2;
conv_params.work_group_size = int3(4, 8, 1);
}
} else if (device.IsAMD()) {
conv_params.block_size = int3(2, 1, 1);
if (x_kernel_is_1 && y_kernel_is_1) {
conv_params.block_size.y = 2;
}
conv_params.work_group_size = int3(8, 4, 1);
conv_params.work_group_launch_order = int3(2, 0, 1);
conv_params.fixed_work_group_size = true;
conv_params.src_depth_loop_size = 1;
conv_params.weights_upload_type = WeightsUploadType::CONSTANT_MEM;
if (dst_depth % 8 == 0 || dst_depth >= 32) {
conv_params.block_size.z = 8;
} else if (dst_depth % 4 == 0 || dst_depth >= 8) {
conv_params.block_size.z = 4;
} else if (dst_depth % 2 == 0 || dst_depth >= 4) {
conv_params.block_size.z = 2;
} else {
conv_params.block_size.z = 1;
}
if (src_depth % 2 == 0 && src_depth >= 16) {
conv_params.src_depth_loop_size = 2;
}
} else {
conv_params.block_size = int3(1, 1, 4);
conv_params.work_group_size = int3(8, 4, 1);
conv_params.work_group_launch_order = int3(0, 1, 2);
conv_params.fixed_work_group_size = false;
conv_params.src_depth_loop_size = 1;
conv_params.weights_upload_type = WeightsUploadType::GLOBAL_MEM;
if (dst_depth % 4 == 0 || dst_depth >= 8) {

2
tensorflow/lite/delegates/gpu/cl/kernels/conv_powervr.h
View File

@ -54,12 +54,14 @@ class ConvPowerVR : public GPUOperation {
LOCAL_MEM_ASYNC_SUBGROUP, // we use it for PowerVR with workgroup size = 32
LOCAL_MEM_BY_THREADS,
GLOBAL_MEM,
CONSTANT_MEM,
};
struct ConvParams {
int3 block_size;
int3 work_group_size;
int3 work_group_launch_order;
bool fixed_work_group_size;
int src_depth_loop_size;
WeightsUploadType weights_upload_type;
bool x_kernel_is_1;

1
tensorflow/lite/delegates/gpu/cl/selectors/convolution_selector.cc
View File

@ -106,6 +106,7 @@ Status SelectConvolution(const Convolution2DAttributes& attr,
return SelectConvolutionAdreno(attr, dst_shape, creation_context, op_def,
hints, ptr);
case Vendor::POWERVR:
case Vendor::AMD:
return SelectConvolutionPowerVR(attr, creation_context, op_def, ptr);
case Vendor::NVIDIA:
return SelectConvolutionNVidia(attr, creation_context, op_def, ptr);