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Mace
提交 08533627 编写于 作者: L liuqi
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Rebase winograd convolution code and rename gemm to matmul.

上级 bcd624f8
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Showing with 165 addition and 178 deletion
mace/core/operator.cc
浏览文件 @ 08533627
......@@ -77,7 +77,7 @@ extern void Register_Pooling(OperatorRegistry *op_registry);
extern void Register_ResizeBilinear(OperatorRegistry *op_registry);
extern void Register_Softmax(OperatorRegistry *op_registry);
extern void Register_SpaceToBatchND(OperatorRegistry *op_registry);
extern void Register_GEMM(OperatorRegistry *op_registry);
extern void Register_MatMul(OperatorRegistry *op_registry);
extern void Register_WinogradTransform(OperatorRegistry *op_registry);
extern void Register_WinogradInverseTransform(OperatorRegistry *op_registry);
......@@ -100,7 +100,7 @@ OperatorRegistry::OperatorRegistry() {
Register_ResizeBilinear(this);
Register_Softmax(this);
Register_SpaceToBatchND(this);
Register_GEMM(this);
Register_MatMul(this);
Register_WinogradTransform(this);
Register_WinogradInverseTransform(this);
}
......
mace/kernels/gemm.h → mace/kernels/matmul.h
浏览文件 @ 08533627
......@@ -2,8 +2,8 @@
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#ifndef MACE_KERNELS_GEMM_H_
#define MACE_KERNELS_GEMM_H_
#ifndef MACE_KERNELS_MATMUL_H_
#define MACE_KERNELS_MATMUL_H_
#include "mace/core/future.h"
#include "mace/core/tensor.h"
......@@ -13,7 +13,7 @@ namespace kernels {
template <DeviceType D, typename T>
struct GEMMFunctor {
struct MatMulFunctor {
void operator()(const Tensor *A,
const Tensor *B,
Tensor *C,
......@@ -53,7 +53,7 @@ struct GEMMFunctor {
template <typename T>
struct GEMMFunctor<DeviceType::OPENCL, T> {
struct MatMulFunctor<DeviceType::OPENCL, T> {
void operator()(const Tensor *A,
const Tensor *B,
Tensor *C,
......@@ -63,4 +63,4 @@ struct GEMMFunctor<DeviceType::OPENCL, T> {
} // namespace kernels
} // namespace mace
#endif // MACE_KERNELS_GEMM_H_
#endif // MACE_KERNELS_MATMUL_H_
mace/kernels/opencl/activation_opencl.cc
浏览文件 @ 08533627
......@@ -63,7 +63,7 @@ void ActivationFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::string tuning_key =
Concat("relu_opencl_kernel_", activation_, output->dim(0), output->dim(1),
output->dim(2), output->dim(3));
......
mace/kernels/opencl/addn.cc
浏览文件 @ 08533627
......@@ -48,7 +48,7 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
static_cast<uint32_t>(width_pixels),
static_cast<uint32_t>(batch_height_pixels)
};
std::vector<uint32_t> lws = {64, 16, 1};
const std::vector<uint32_t> lws = {64, 16, 1};
std::stringstream ss;
ss << "addn_opencl_kernel_"
<< output->dim(0) << "_"
......
mace/kernels/opencl/batch_norm_opencl.cc
浏览文件 @ 08533627
......@@ -83,7 +83,7 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::string tuning_key =
Concat("batch_norm_opencl_kernel_", activation_, output->dim(0),
output->dim(1), output->dim(2), output->dim(3), folded_constant_);
......
mace/kernels/opencl/cl/gemm.cl → mace/kernels/opencl/cl/matmul.cl
浏览文件 @ 08533627
#include <common.h>
// C = A * B
__kernel void gemm(__read_only image2d_t A,
__kernel void matmul(__read_only image2d_t A,
__read_only image2d_t B,
__write_only image2d_t C,
__private const int M,
......@@ -17,7 +17,6 @@ __kernel void gemm(__read_only image2d_t A,
const int bm = mad24(batch, M, ty << 2);
const int bk = mul24(batch, k_blocks);
float4 a0, a1, a2, a3;
float4 b0, b1, b2, b3;
float4 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
......
mace/kernels/opencl/concat.cc
浏览文件 @ 08533627
......@@ -50,7 +50,7 @@ static void Concat2(const Tensor *input0,
static_cast<uint32_t>(width),
static_cast<uint32_t>(batch * height),
};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::stringstream ss;
ss << "concat_opencl_kernel_"
<< output->dim(0) << "_"
......
mace/kernels/opencl/conv_2d_opencl_1x1.cc
浏览文件 @ 08533627
......@@ -96,7 +96,7 @@ void Conv1x1(const Tensor *input,
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width_blocks),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {8, 15, 8, 1};
const std::vector<uint32_t> lws = {8, 15, 8, 1};
std::string tuning_key =
Concat("conv2d_1x1_opencl_kernel_", activation, output->dim(0),
output->dim(1), output->dim(2), output->dim(3));
......
mace/kernels/opencl/conv_2d_opencl_3x3.cc
浏览文件 @ 08533627
......@@ -94,7 +94,7 @@ static void Conv2d3x3S12(const Tensor *input,
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width_blocks),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {4, 15, 8, 1};
const std::vector<uint32_t> lws = {4, 15, 8, 1};
std::string tuning_key =
Concat("conv2d_3x3_opencl_kernel_", activation, output->dim(0),
output->dim(1), output->dim(2), output->dim(3));
......
mace/kernels/opencl/conv_2d_opencl_general.cc
浏览文件 @ 08533627
......@@ -97,7 +97,7 @@ void Conv2dOpencl(const Tensor *input,
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width_blocks),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::string tuning_key =
Concat("conv2d_general_opencl_kernel_", activation, output->dim(0),
output->dim(1), output->dim(2), output->dim(3));
......
mace/kernels/opencl/depthwise_conv_opencl.cc
浏览文件 @ 08533627
......@@ -106,7 +106,7 @@ void DepthwiseConv2d(const Tensor *input, // NHWC
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width_blocks),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::string tuning_key = Concat("depthwise_conv2d_ocl_kernel_", activation,
batch, height, width, channels, multiplier);
TuningOrRun3DKernel(dw_conv2d_kernel, tuning_key, gws, lws, future);
......@@ -150,7 +150,7 @@ void DepthwiseConv2dFunctor<DeviceType::OPENCL, T>::operator()(
padding_, output_shape.data(), paddings.data());
std::vector<size_t> output_image_shape;
CalImage2DShape(output_shape, BufferType::IN_OUT, output_image_shape);
CalImage2DShape(output_shape, BufferType::IN_OUT_CHANNEL, output_image_shape);
output->ResizeImage(output_shape, output_image_shape);
DepthwiseConv2d(input, filter, bias, strides_[0], paddings.data(), dilations_,
......
mace/kernels/opencl/helper.cc
浏览文件 @ 08533627
......@@ -158,7 +158,7 @@ std::string DtToUpstreamCLCMDDt(const DataType dt) {
void TuningOrRun3DKernel(cl::Kernel &kernel,
const std::string tuning_key,
const uint32_t *gws,
std::vector<uint32_t> &lws,
const std::vector<uint32_t> &lws,
StatsFuture *future) {
auto runtime = OpenCLRuntime::Global();
const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(kernel);
......@@ -255,7 +255,7 @@ void TuningOrRun3DKernel(cl::Kernel &kernel,
void TuningOrRun2DKernel(cl::Kernel &kernel,
const std::string tuning_key,
const uint32_t *gws,
std::vector<uint32_t> &lws,
const std::vector<uint32_t> &lws,
StatsFuture *future) {
auto runtime = OpenCLRuntime::Global();
const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(kernel);
......
mace/kernels/opencl/helper.h
浏览文件 @ 08533627
......@@ -44,14 +44,14 @@ std::string DtToUpstreamCLDt(const DataType dt);
void TuningOrRun3DKernel(cl::Kernel &kernel,
const std::string tuning_key,
const uint32_t *gws,
std::vector<uint32_t> &lws,
const std::vector<uint32_t> &lws,
StatsFuture *future);
void TuningOrRun2DKernel(cl::Kernel &kernel,
const std::string tuning_key,
const uint32_t *gws,
std::vector<uint32_t> &lws,
const std::vector<uint32_t> &lws,
StatsFuture *future);
inline void SetFuture(StatsFuture *future, const cl::Event &event) {
......
mace/kernels/opencl/gemm.cc → mace/kernels/opencl/matmul.cc
浏览文件 @ 08533627
......@@ -2,7 +2,7 @@
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include "mace/kernels/gemm.h"
#include "mace/kernels/matmul.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/tuner.h"
......@@ -11,7 +11,7 @@ namespace mace {
namespace kernels {
template <typename T>
void GEMMFunctor<DeviceType::OPENCL, T>::operator()(
void MatMulFunctor<DeviceType::OPENCL, T>::operator()(
const Tensor *A,
const Tensor *B,
Tensor *C,
......@@ -32,87 +32,44 @@ void GEMMFunctor<DeviceType::OPENCL, T>::operator()(
auto runtime = OpenCLRuntime::Global();
std::set<std::string> built_options;
auto dt = DataTypeToEnum<T>::value;
std::string kernel_name = MACE_OBFUSCATE_SYMBOL("gemm");
built_options.emplace("-Dgemm=" + kernel_name);
std::string kernel_name = MACE_OBFUSCATE_SYMBOL("matmul");
built_options.emplace("-Dmatmul=" + kernel_name);
built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
auto gemm_kernel = runtime->BuildKernel("gemm", kernel_name, built_options);
auto matmul_kernel = runtime->BuildKernel("matmul", kernel_name, built_options);
uint32_t idx = 0;
gemm_kernel.setArg(idx++,
matmul_kernel.setArg(idx++,
*(static_cast<const cl::Image2D *>(A->buffer())));
gemm_kernel.setArg(idx++,
matmul_kernel.setArg(idx++,
*(static_cast<const cl::Image2D *>(B->buffer())));
gemm_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(C->buffer())));
gemm_kernel.setArg(idx++, static_cast<int>(height));
gemm_kernel.setArg(idx++, static_cast<int>(width));
gemm_kernel.setArg(idx++, static_cast<int>(A->dim(2)));
gemm_kernel.setArg(idx++, static_cast<int>(height_blocks));
gemm_kernel.setArg(idx++, static_cast<int>(RoundUpDiv4(A->dim(2))));
matmul_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(C->buffer())));
matmul_kernel.setArg(idx++, static_cast<int>(height));
matmul_kernel.setArg(idx++, static_cast<int>(width));
matmul_kernel.setArg(idx++, static_cast<int>(A->dim(2)));
matmul_kernel.setArg(idx++, static_cast<int>(height_blocks));
matmul_kernel.setArg(idx++, static_cast<int>(RoundUpDiv4(A->dim(2))));
const uint32_t gws[3] = {
const uint32_t gws[2] = {
static_cast<uint32_t>(width_blocks),
static_cast<uint32_t>(height_blocks * batch),
};
const std::vector<uint32_t> lws = {16, 64};
const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(gemm_kernel);
auto params_generator = [&]()->std::vector<std::vector<uint32_t>> {
std::vector<uint32_t> local_ws(2, 0);
local_ws[0] = std::min<uint32_t>(width_blocks, kwg_size);
local_ws[1] = std::min<uint32_t>(height_blocks * batch, kwg_size / local_ws[0]);
return {{local_ws[0], local_ws[1]},
{local_ws[1], local_ws[0]},
{kwg_size / 4, 4},
{kwg_size / 8, 8},
{kwg_size / 16, 16},
{kwg_size / 32, 32},
{kwg_size / 64, 64},
{kwg_size / 128, 128},
{kwg_size / 256, 256},
{kwg_size / 512, 512},
{kwg_size, 1},
{1, kwg_size}
};
};
cl::Event event;
auto func = [&](const std::vector<uint32_t>& params)->cl_int {
cl_int error = runtime->command_queue().enqueueNDRangeKernel(
gemm_kernel, cl::NullRange,
cl::NDRange(gws[0], gws[1]),
cl::NDRange(params[0], params[1]),
nullptr, &event);
MACE_CHECK(error == CL_SUCCESS) << "Error code: " << error;
return error;
};
const std::vector<uint32_t> lws = {16, 64, 1};
std::stringstream ss;
ss << "gemm_opencl_kernel_"
ss << "matmul_opencl_kernel_"
<< C->dim(0) << "_"
<< C->dim(1) << "_"
<< C->dim(2) << "_"
<< C->dim(3);
OpenCLProfilingTimer timer(&event);
Tuner<uint32_t>::Get()->template TuneOrRun<cl_int>(ss.str(),
lws,
params_generator,
func,
&timer);
if (future != nullptr) {
future->wait_fn = [runtime, event](CallStats *stats) {
event.wait();
if (stats != nullptr) {
runtime->GetCallStats(event, stats);
}
};
}
TuningOrRun2DKernel(matmul_kernel, ss.str(), gws, lws, future);
};
template
struct GEMMFunctor<DeviceType::OPENCL, float>;
struct MatMulFunctor<DeviceType::OPENCL, float>;
template
struct GEMMFunctor<DeviceType::OPENCL, half>;
struct MatMulFunctor<DeviceType::OPENCL, half>;
} // namespace kernels
} // namespace mace
mace/kernels/opencl/resize_bilinear_opencl.cc
浏览文件 @ 08533627
......@@ -59,7 +59,7 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(out_width),
static_cast<uint32_t>(out_height * batch)};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::stringstream ss;
ss << "resize_bilinear_opencl_kernel_"
<< output->dim(0) << "_"
......
mace/kernels/opencl/softmax_opencl.cc
浏览文件 @ 08533627
......@@ -41,7 +41,7 @@ void SoftmaxFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *logits,
const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width),
static_cast<uint32_t>(height * batch)};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::stringstream ss;
ss << "softmax_opencl_kernel_"
<< output->dim(0) << "_"
......
mace/kernels/opencl/space_to_batch_opencl.cc
浏览文件 @ 08533627
......@@ -61,7 +61,7 @@ void SpaceToBatchFunctor<DeviceType::OPENCL, T>::operator()(Tensor *space_tensor
const uint32_t gws[3] = {chan_blk,
static_cast<uint32_t>(batch_tensor->dim(2)),
static_cast<uint32_t>(batch_tensor->dim(0) * batch_tensor->dim(1))};
std::vector<uint32_t> lws = {8, 16, 8, 1};
const std::vector<uint32_t> lws = {8, 16, 8, 1};
std::stringstream ss;
ss << kernel_name << "_"
<< batch_tensor->dim(0) << "_"
......
mace/kernels/opencl/winograd_transform.cc
浏览文件 @ 08533627
......@@ -51,60 +51,16 @@ void WinogradTransformFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *i
wino_kernel.setArg(idx++, static_cast<uint32_t>(paddings[0] / 2));
wino_kernel.setArg(idx++, static_cast<uint32_t>(paddings[1] / 2));
const size_t gws[2] = {static_cast<size_t>(out_width),
const uint32_t gws[2] = {static_cast<size_t>(out_width),
static_cast<size_t>(RoundUpDiv4(input_tensor->dim(3)))};
const std::vector<uint32_t> lws = {128, 8};
const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(wino_kernel);
auto params_generator = [&]()->std::vector<std::vector<uint32_t>> {
std::vector<uint32_t> local_ws(2, 0);
local_ws[0] = std::min<uint32_t>(gws[0], kwg_size);
local_ws[1] = std::min<uint32_t>(gws[1], kwg_size / local_ws[0]);
return {{local_ws[0], local_ws[1]},
{local_ws[1], local_ws[0]},
{kwg_size / 4, 4},
{kwg_size / 8, 8},
{kwg_size / 16, 16},
{kwg_size / 32, 32},
{kwg_size / 64, 64},
{kwg_size / 128, 128},
{kwg_size / 256, 256},
{kwg_size / 512, 512},
{kwg_size, 1},
{1, kwg_size}
};
};
cl::Event event;
auto func = [&](const std::vector<uint32_t>& params)->cl_int {
cl_int error = runtime->command_queue().enqueueNDRangeKernel(
wino_kernel, cl::NullRange,
cl::NDRange(gws[0], gws[1]),
cl::NDRange(params[0], params[1]),
nullptr, &event);
MACE_CHECK(error == CL_SUCCESS) << "Error code: " << error;
return error;
};
const std::vector<uint32_t> lws = {128, 8, 1};
std::stringstream ss;
ss << "winograd_transform_kernel_"
<< input_tensor->dim(0) << "_"
<< input_tensor->dim(1) << "_"
<< input_tensor->dim(2) << "_"
<< input_tensor->dim(3);
OpenCLProfilingTimer timer(&event);
Tuner<uint32_t>::Get()->template TuneOrRun<cl_int>(ss.str(),
lws,
params_generator,
func,
&timer);
if (future != nullptr) {
future->wait_fn = [runtime, event](CallStats *stats) {
event.wait();
if (stats != nullptr) {
runtime->GetCallStats(event, stats);
}
};
}
TuningOrRun2DKernel(wino_kernel, ss.str(), gws, lws, future);
}
template<typename T>
......@@ -165,60 +121,17 @@ void WinogradInverseTransformFunctor<DeviceType::OPENCL, T>::operator()(const Te
wino_kernel.setArg(idx++, relux_max_limit_);
wino_kernel.setArg(idx++, prelu_alpha_);
const size_t gws[2] = {static_cast<size_t>(input_tensor->dim(2)),
const uint32_t gws[2] = {static_cast<size_t>(input_tensor->dim(2)),
static_cast<size_t>(RoundUpDiv4(input_tensor->dim(1)))};
const std::vector<uint32_t> lws = {128, 8};
const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(wino_kernel);
auto params_generator = [&]()->std::vector<std::vector<uint32_t>> {
std::vector<uint32_t> local_ws(2, 0);
local_ws[0] = std::min<uint32_t>(gws[0], kwg_size);
local_ws[1] = std::min<uint32_t>(gws[1], kwg_size / local_ws[0]);
return {{local_ws[0], local_ws[1]},
{local_ws[1], local_ws[0]},
{kwg_size / 4, 4},
{kwg_size / 8, 8},
{kwg_size / 16, 16},
{kwg_size / 32, 32},
{kwg_size / 64, 64},
{kwg_size / 128, 128},
{kwg_size / 256, 256},
{kwg_size / 512, 512},
{kwg_size, 1},
{1, kwg_size}
};
};
cl::Event event;
auto func = [&](const std::vector<uint32_t>& params)->cl_int {
cl_int error = runtime->command_queue().enqueueNDRangeKernel(
wino_kernel, cl::NullRange,
cl::NDRange(gws[0], gws[1]),
cl::NDRange(params[0], params[1]),
nullptr, &event);
MACE_CHECK(error == CL_SUCCESS) << "Error code: " << error;
return error;
};
const std::vector<uint32_t> lws = {128, 8, 1};
std::stringstream ss;
ss << "winograd_inverse_transform_kernel_"
<< input_tensor->dim(0) << "_"
<< input_tensor->dim(1) << "_"
<< input_tensor->dim(2) << "_"
<< input_tensor->dim(3);
OpenCLProfilingTimer timer(&event);
Tuner<uint32_t>::Get()->template TuneOrRun<cl_int>(ss.str(),
lws,
params_generator,
func,
&timer);
if (future != nullptr) {
future->wait_fn = [runtime, event](CallStats *stats) {
event.wait();
if (stats != nullptr) {
runtime->GetCallStats(event, stats);
}
};
}
TuningOrRun2DKernel(wino_kernel, ss.str(), gws, lws, future);
}
template
......
mace/ops/depthwise_conv2d_test.cc
浏览文件 @ 08533627
......@@ -26,7 +26,7 @@ void SimpleValidTest() {
net.AddInputFromArray<D, float>("Bias", {2}, {.1f, .2f});
if (D == DeviceType::OPENCL) {
BufferToImage<D, T>(net, "Input", "InputImage",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(net, "Filter", "FilterImage",
kernels::BufferType::DW_CONV2D_FILTER);
BufferToImage<D, T>(net, "Bias", "BiasImage",
......@@ -46,7 +46,7 @@ void SimpleValidTest() {
// Transfer output
ImageToBuffer<D, T>(net, "OutputImage", "Output",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
} else {
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
......@@ -129,7 +129,7 @@ void ComplexValidTest() {
{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f});
if (D == DeviceType::OPENCL) {
BufferToImage<D, T>(net, "Input", "InputImage",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(net, "Filter", "FilterImage",
kernels::BufferType::DW_CONV2D_FILTER);
BufferToImage<D, T>(net, "Bias", "BiasImage",
......@@ -149,7 +149,7 @@ void ComplexValidTest() {
// Transfer output
ImageToBuffer<D, T>(net, "OutputImage", "Output",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
} else {
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
......@@ -239,7 +239,7 @@ void TestNxNS12(const index_t height, const index_t width) {
if (D == DeviceType::OPENCL) {
BufferToImage<D, T>(net, "Input", "InputImage",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(net, "Filter", "FilterImage",
kernels::BufferType::DW_CONV2D_FILTER);
BufferToImage<D, T>(net, "Bias", "BiasImage",
......@@ -259,7 +259,7 @@ void TestNxNS12(const index_t height, const index_t width) {
// Transfer output
ImageToBuffer<D, float>(net, "OutputImage", "DeviceOutput",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
} else {
OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
.Input("Input")
......
mace/ops/depthwise_conv_2d_benchmark.cc
浏览文件 @ 08533627
......@@ -34,7 +34,7 @@ static void DepthwiseConv2d(int iters,
if (D == DeviceType::OPENCL) {
BufferToImage<D, T>(net, "Input", "InputImage",
kernels::BufferType::IN_OUT);
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(net, "Filter", "FilterImage",
kernels::BufferType::DW_CONV2D_FILTER);
BufferToImage<D, T>(net, "Bias", "BiasImage",
......
mace/ops/gemm.cc → mace/ops/matmul.cc
浏览文件 @ 08533627
......@@ -2,28 +2,28 @@
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include "mace/ops/gemm.h"
#include "mace/ops/matmul.h"
namespace mace {
void Register_GEMM(OperatorRegistry *op_registry) {
REGISTER_OPERATOR(op_registry, OpKeyBuilder("GEMM")
void Register_MatMul(OperatorRegistry *op_registry) {
REGISTER_OPERATOR(op_registry, OpKeyBuilder("MatMul")
.Device(DeviceType::CPU)
.TypeConstraint<float>("T")
.Build(),
GEMMOp<DeviceType::CPU, float>);
MatMulOp<DeviceType::CPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("GEMM")
REGISTER_OPERATOR(op_registry, OpKeyBuilder("MatMul")
.Device(DeviceType::OPENCL)
.TypeConstraint<float>("T")
.Build(),
GEMMOp<DeviceType::OPENCL, float>);
MatMulOp<DeviceType::OPENCL, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("GEMM")
REGISTER_OPERATOR(op_registry, OpKeyBuilder("MatMul")
.Device(DeviceType::OPENCL)
.TypeConstraint<half>("T")
.Build(),
GEMMOp<DeviceType::OPENCL, half>);
MatMulOp<DeviceType::OPENCL, half>);
}
} // namespace mace
mace/ops/gemm.h → mace/ops/matmul.h
浏览文件 @ 08533627
......@@ -2,18 +2,18 @@
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#ifndef MACE_OPS_GEMM_H_
#define MACE_OPS_GEMM_H_
#ifndef MACE_OPS_MATMUL_H_
#define MACE_OPS_MATMUL_H_
#include "mace/core/operator.h"
#include "mace/kernels/gemm.h"
#include "mace/kernels/matmul.h"
namespace mace {
template <DeviceType D, class T>
class GEMMOp : public Operator<D, T> {
class MatMulOp : public Operator<D, T> {
public:
GEMMOp(const OperatorDef &operator_def, Workspace *ws)
MatMulOp(const OperatorDef &operator_def, Workspace *ws)
: Operator<D, T>(operator_def, ws) {}
bool Run(StatsFuture *future) override {
......@@ -32,9 +32,9 @@ class GEMMOp : public Operator<D, T> {
}
private:
kernels::GEMMFunctor<D, T> functor_;
kernels::MatMulFunctor<D, T> functor_;
};
} // namespace mace
#endif // MACE_OPS_GEMM_H_
#endif // MACE_OPS_MATMUL_H_
mace/ops/gemm_benchmark.cc → mace/ops/matmul_benchmark.cc
浏览文件 @ 08533627
......@@ -9,7 +9,7 @@
namespace mace {
template <DeviceType D, typename T>
static void GEMMBenchmark(
static void MatMulBenchmark(
int iters, int batch, int height, int channels, int out_width) {
mace::testing::StopTiming();
......@@ -25,14 +25,14 @@ static void GEMMBenchmark(
BufferToImage<D, T>(net, "B", "BImage",
kernels::BufferType::IN_OUT_HEIGHT);
OpDefBuilder("GEMM", "GEMMBM")
OpDefBuilder("MatMul", "MatMulBM")
.Input("AImage")
.Input("BImage")
.Output("Output")
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.Finalize(net.NewOperatorDef());
} else {
OpDefBuilder("GEMM", "GEMMBM")
OpDefBuilder("MatMul", "MatMulBM")
.Input("A")
.Input("B")
.Output("Output")
......@@ -52,19 +52,19 @@ static void GEMMBenchmark(
net.Sync();
}
#define BM_GEMM_MACRO(N, H, C, W, TYPE, DEVICE) \
static void BM_GEMM_##N##_##H##_##C##_##W##_##TYPE##_##DEVICE(int iters) { \
#define BM_MATMUL_MACRO(N, H, C, W, TYPE, DEVICE) \
static void BM_MATMUL_##N##_##H##_##C##_##W##_##TYPE##_##DEVICE(int iters) { \
const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W; \
mace::testing::ItemsProcessed(tot); \
mace::testing::BytesProcessed(tot *(sizeof(TYPE))); \
GEMMBenchmark<DEVICE, TYPE>(iters, N, H, C, W); \
MatMulBenchmark<DEVICE, TYPE>(iters, N, H, C, W); \
} \
BENCHMARK(BM_GEMM_##N##_##H##_##C##_##W##_##TYPE##_##DEVICE)
BENCHMARK(BM_MATMUL_##N##_##H##_##C##_##W##_##TYPE##_##DEVICE)
#define BM_GEMM(N, H, C, W, TYPE) \
BM_GEMM_MACRO(N, H, C, W, TYPE, OPENCL);
#define BM_MATMUL(N, H, C, W, TYPE) \
BM_MATMUL_MACRO(N, H, C, W, TYPE, OPENCL);
BM_GEMM(16, 32, 128, 49, half);
BM_GEMM(16, 32, 128, 961, half);
BM_GEMM(16, 32, 128, 3969, half);
BM_MATMUL(16, 32, 128, 49, half);
BM_MATMUL(16, 32, 128, 961, half);
BM_MATMUL(16, 32, 128, 3969, half);
} // namespace mace
mace/ops/gemm_test.cc → mace/ops/matmul_test.cc
浏览文件 @ 08533627
......@@ -8,7 +8,7 @@
namespace mace {
class GEMMOpTest : public OpsTestBase {};
class MatMulOpTest : public OpsTestBase {};
template<DeviceType D>
void Simple(const std::vector<index_t> &A_shape,
......@@ -29,7 +29,7 @@ void Simple(const std::vector<index_t> &A_shape,
BufferToImage<D, float>(net, "B", "BImage",
kernels::BufferType::IN_OUT_HEIGHT);
OpDefBuilder("GEMM", "GEMMTest")
OpDefBuilder("MatMul", "MatMulTest")
.Input("AImage")
.Input("BImage")
.Output("OutputImage")
......@@ -41,7 +41,7 @@ void Simple(const std::vector<index_t> &A_shape,
ImageToBuffer<D, float>(net, "OutputImage", "Output",
kernels::BufferType::IN_OUT_HEIGHT);
} else {
OpDefBuilder("GEMM", "GEMMTest")
OpDefBuilder("MatMul", "MatMulTest")
.Input("A")
.Input("B")
.Output("Output")
......@@ -57,7 +57,7 @@ void Simple(const std::vector<index_t> &A_shape,
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
TEST_F(GEMMOpTest, SimpleCPU) {
TEST_F(MatMulOpTest, SimpleCPU) {
Simple<DeviceType::CPU>({1, 2, 3, 1}, {1, 2, 3, 4, 5, 6},
{1, 3, 2, 1}, {1, 2, 3, 4, 5, 6},
{1, 2, 2, 1}, {22, 28, 49, 64});
......@@ -74,13 +74,13 @@ TEST_F(GEMMOpTest, SimpleCPU) {
}
TEST_F(GEMMOpTest, SimpleCPUWithBatch) {
TEST_F(MatMulOpTest, SimpleCPUWithBatch) {
Simple<DeviceType::CPU>({2, 2, 3, 1}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6},
{2, 3, 2, 1}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6},
{2, 2, 2, 1}, {22, 28, 49, 64, 22, 28, 49, 64});
}
TEST_F(GEMMOpTest, SimpleOPENCL) {
TEST_F(MatMulOpTest, SimpleOPENCL) {
Simple<DeviceType::OPENCL>({1, 2, 3, 1}, {1, 2, 3, 4, 5, 6},
{1, 3, 2, 1}, {1, 2, 3, 4, 5, 6},
{1, 2, 2, 1}, {22, 28, 49, 64});
......@@ -96,7 +96,7 @@ TEST_F(GEMMOpTest, SimpleOPENCL) {
1315, 1430, 1545, 1660, 1775});
}
TEST_F(GEMMOpTest, SimpleGPUWithBatch) {
TEST_F(MatMulOpTest, SimpleGPUWithBatch) {
Simple<DeviceType::CPU>({2, 2, 3, 1}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6},
{2, 3, 2, 1}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6},
{2, 2, 2, 1}, {22, 28, 49, 64, 22, 28, 49, 64});
......@@ -111,7 +111,7 @@ void Complex(const index_t batch,
// Construct graph
OpsTestNet net;
OpDefBuilder("GEMM", "GEMMTest")
OpDefBuilder("MatMul", "MatMulTest")
.Input("A")
.Input("B")
.Output("Output")
......@@ -136,7 +136,7 @@ void Complex(const index_t batch,
BufferToImage<DeviceType::OPENCL, T>(net, "B", "BImage",
kernels::BufferType::IN_OUT_HEIGHT);
OpDefBuilder("GEMM", "GEMMTest")
OpDefBuilder("MatMul", "MatMulTest")
.Input("AImage")
.Input("BImage")
.Output("OutputImage")
......@@ -155,24 +155,24 @@ void Complex(const index_t batch,
}
}
TEST_F(GEMMOpTest, OPENCLAlignedWithoutBatch) {
TEST_F(MatMulOpTest, OPENCLAlignedWithoutBatch) {
Complex<float>(1, 64, 128, 32);
Complex<float>(1, 64, 32, 128);
}
TEST_F(GEMMOpTest, OPENCLUnAlignedWithoutBatch) {
TEST_F(MatMulOpTest, OPENCLUnAlignedWithoutBatch) {
Complex<float>(1, 31, 113, 61);
Complex<float>(1, 113, 31, 73);
}
TEST_F(GEMMOpTest, OPENCLUnAlignedWithBatch) {
TEST_F(MatMulOpTest, OPENCLUnAlignedWithBatch) {
Complex<float>(2, 3, 3, 3);
Complex<float>(16, 31, 61, 67);
Complex<float>(31, 31, 61, 67);
}
TEST_F(GEMMOpTest, OPENCLHalfAlignedWithoutBatch) {
TEST_F(MatMulOpTest, OPENCLHalfAlignedWithoutBatch) {
Complex<half>(1, 64, 128, 32);
Complex<half>(1, 64, 32, 128);
}
TEST_F(GEMMOpTest, OPENCLHalfUnAlignedWithBatch) {
TEST_F(MatMulOpTest, OPENCLHalfUnAlignedWithBatch) {
Complex<half>(2, 31, 113, 61);
Complex<half>(16, 32, 64, 64);
Complex<half>(31, 31, 61, 67);
......
mace/ops/winograd_convolution_test.cc
浏览文件 @ 08533627
......@@ -52,7 +52,7 @@ void WinogradConvolution(const index_t batch,
BufferToImage<D, T>(net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL);
BufferToImage<D, T>(net, "Filter", "FilterImage",
kernels::BufferType::FILTER);
kernels::BufferType::CONV2D_FILTER);
BufferToImage<D, T>(net, "Bias", "BiasImage",
kernels::BufferType::ARGUMENT);
OpDefBuilder("Conv2D", "Conv2dTest")
......@@ -92,8 +92,8 @@ void WinogradConvolution(const index_t batch,
// Run on opencl
net.RunOp(D);
// GEMM
OpDefBuilder("GEMM", "GEMMTest")
// MatMul
OpDefBuilder("MatMul", "MatMulTest")
.Input("WinoFilter")
.Input("WinoInput")
.Output("WinoGemm")
......
mace/utils/tuner.h
浏览文件 @ 08533627
......@@ -41,7 +41,7 @@ class Tuner {
template <typename RetType>
RetType TuneOrRun(
const std::string param_key,
std::vector<param_type> &default_param,
const std::vector<param_type> &default_param,
const std::function<std::vector<std::vector<param_type>>()>
&param_generator,
const std::function<RetType(const std::vector<param_type> &, Timer *, std::vector<param_type> *)> &func,
......
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