Skip to content

Mace
Mace

慢慢CG / Mace
与 Fork 源项目一致

Fork自 Xiaomi / Mace

通知 1
Star 0
Fork 0
Mace
Mace
“aef98218836c4d487d0a61616dbf75c5eb8e932f”上不存在“mobile/src/operators/kernel/instancenorm_kernel.h”
提交 b41f6418 编写于 作者: U Unknown 提交者： liutuo
浏览文件
操作

change scalar math to cwise

上级 b90f0ad4
隐藏空白更改
内联 并排
Showing with 0 addition and 509 deletion
mace/core/operator.cc
浏览文件 @ b41f6418
...@@ -89,7 +89,6 @@ extern void Register_PSROIAlign(OperatorRegistry *op_registry); ...@@ -89,7 +89,6 @@ extern void Register_PSROIAlign(OperatorRegistry *op_registry);
extern void Register_ReOrganize(OperatorRegistry *op_registry); extern void Register_ReOrganize(OperatorRegistry *op_registry);
extern void Register_Reshape(OperatorRegistry *op_registry); extern void Register_Reshape(OperatorRegistry *op_registry);
extern void Register_ResizeBilinear(OperatorRegistry *op_registry); extern void Register_ResizeBilinear(OperatorRegistry *op_registry);
extern void Register_ScalarMath(OperatorRegistry *op_registry);
extern void Register_Slice(OperatorRegistry *op_registry); extern void Register_Slice(OperatorRegistry *op_registry);
extern void Register_Softmax(OperatorRegistry *op_registry); extern void Register_Softmax(OperatorRegistry *op_registry);
extern void Register_SpaceToBatchND(OperatorRegistry *op_registry); extern void Register_SpaceToBatchND(OperatorRegistry *op_registry);
...@@ -127,7 +126,6 @@ OperatorRegistry::OperatorRegistry() { ...@@ -127,7 +126,6 @@ OperatorRegistry::OperatorRegistry() {
ops::Register_ReOrganize(this); ops::Register_ReOrganize(this);
ops::Register_Reshape(this); ops::Register_Reshape(this);
ops::Register_ResizeBilinear(this); ops::Register_ResizeBilinear(this);
ops::Register_ScalarMath(this);
ops::Register_Slice(this); ops::Register_Slice(this);
ops::Register_Softmax(this); ops::Register_Softmax(this);
ops::Register_SpaceToBatchND(this); ops::Register_SpaceToBatchND(this);
......
mace/kernels/opencl/cl/scalar_math.cl 已删除 100644 → 0
浏览文件 @ b90f0ad4
#include <common.h>
__kernel void scalar_math(__read_only image2d_t input, /* [c%4 * w * c/4, h * b] */
__private const float scalar,
__write_only image2d_t output) {
const int w = get_global_id(0);
const int hb = get_global_id(1);
DATA_TYPE4 in0 = READ_IMAGET(input, SAMPLER, (int2)(w, hb));
DATA_TYPE4 in1;
in1.x = scalar;
in1.y = scalar;
in1.z = scalar;
in1.w = scalar;
DATA_TYPE4 out;
#if SCALAR_MATH_TYPE == 1
out = in0 + in1;
#elif SCALAR_MATH_TYPE == 4
out = in0 - in1;
#elif SCALAR_MATH_TYPE == 0
out = in0 * in1;
#elif SCALAR_MATH_TYPE == 5
out = in0 / in1;
#endif
WRITE_IMAGET(output, (int2)(w, hb), out);
}
mace/kernels/opencl/scalar_math_opencl.cc 已删除 100644 → 0
浏览文件 @ b90f0ad4
//
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include "mace/kernels/scalar_math.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/tuner.h"
namespace mace {
namespace kernels {
template <typename T>
void ScalarMathFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
Tensor *output,
StatsFuture *future) {
const index_t batch = input->dim(0);
const index_t height = input->dim(1);
const index_t width = input->dim(2);
const index_t channels = input->dim(3);
const index_t channel_blocks = RoundUpDiv4(channels);
const index_t width_pixels = channel_blocks * width;
const index_t batch_height_pixels = batch * height;
if (kernel_.get() == nullptr) {
auto runtime = OpenCLRuntime::Global();
std::set<std::string> built_options;
auto dt = DataTypeToEnum<T>::value;
std::string kernel_name = MACE_OBFUSCATE_SYMBOL("scalar_math");
built_options.emplace("-Dscalar_math=" + kernel_name);
built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
built_options.emplace(MakeString("-DSCALAR_MATH_TYPE=", type_));
kernel_ = runtime->BuildKernel("scalar_math", kernel_name, built_options);
}
if (!IsVecEqual(input_shape_, input->shape())) {
uint32_t idx = 0;
kernel_.setArg(idx++, *(input->opencl_image()));
kernel_.setArg(idx++, static_cast<float>(coeff_));
kernel_.setArg(idx++, *(output->opencl_image()));
input_shape_ = input->shape();
}
const uint32_t gws[2] = {static_cast<uint32_t>(width_pixels),
static_cast<uint32_t>(batch_height_pixels)};
const std::vector<uint32_t> lws = {64, 16, 1};
std::stringstream ss;
ss << "eltwise_opencl_kernel_" << output->dim(0) << "_" << output->dim(1)
<< "_" << output->dim(2) << "_" << output->dim(3);
TuningOrRun2DKernel(kernel_, ss.str(), gws, lws, future);
}
template struct ScalarMathFunctor<DeviceType::OPENCL, float>;
template struct ScalarMathFunctor<DeviceType::OPENCL, half>;
} // namespace kernels
} // namespace mace
mace/kernels/scalar_math.h 已删除 100644 → 0
浏览文件 @ b90f0ad4
//
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#ifndef MACE_KERNELS_SCALAR_MATH_H_
#define MACE_KERNELS_SCALAR_MATH_H_
#include <algorithm>
#include <vector>
#include "mace/core/future.h"
#include "mace/core/runtime/opencl/cl2_header.h"
#include "mace/core/tensor.h"
namespace mace {
namespace kernels {
enum ScalarMathType {
MUL = 0,
ADD = 1,
MAX = 2,
MIN = 3,
SUB = 4,
DIV = 5,
};
struct ScalarMathFunctorBase {
ScalarMathFunctorBase(const ScalarMathType type, const float coeff)
: type_(type), coeff_(coeff) {}
ScalarMathType type_;
float coeff_;
};
template <DeviceType D, typename T>
struct ScalarMathFunctor : ScalarMathFunctorBase {
ScalarMathFunctor(const ScalarMathType type, const float coeff)
: ScalarMathFunctorBase(type, coeff) {}
void operator()(const Tensor *input,
Tensor *output,
StatsFuture *future) {
Tensor::MappingGuard input_guard(input);
Tensor::MappingGuard output_guard(output);
const T *input_ptr = input->data<T>();
T *output_ptr = output->mutable_data<T>();
const index_t size = input->size();
switch (type_) {
case MUL:
#pragma omp parallel for
for (index_t i = 0; i < size; ++i) {
output_ptr[i] = coeff_ * input_ptr[i];
}
break;
case ADD:
#pragma omp parallel for
for (index_t i = 0; i < size; ++i) {
output_ptr[i] = coeff_ + input_ptr[i];
}
break;
case SUB:
#pragma omp parallel for
for (index_t i = 0; i < size; ++i) {
output_ptr[i] = input_ptr[i] - coeff_;
}
break;
case DIV:
#pragma omp parallel for
for (index_t i = 0; i < size; ++i) {
output_ptr[i] = input_ptr[i] / coeff_;
}
break;
default:
LOG(FATAL) << "ScalarMath op not support type " << type_;
}
}
};
template <typename T>
struct ScalarMathFunctor<DeviceType::OPENCL, T> : ScalarMathFunctorBase {
ScalarMathFunctor(const ScalarMathType type, const float coeff)
: ScalarMathFunctorBase(type, coeff) {}
void operator()(const Tensor *input,
Tensor *output,
StatsFuture *future);
cl::Kernel kernel_;
std::vector<index_t> input_shape_;
};
} // namespace kernels
} // namespace mace
#endif // MACE_KERNELS_SCALAR_MATH_H_
mace/ops/scalar_math.cc 已删除 100644 → 0
浏览文件 @ b90f0ad4
//
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include "mace/ops/scalar_math.h"
namespace mace {
namespace ops {
void Register_ScalarMath(OperatorRegistry *op_registry) {
REGISTER_OPERATOR(op_registry, OpKeyBuilder("ScalarMath")
.Device(DeviceType::CPU)
.TypeConstraint<float>("T")
.Build(),
ScalarMathOp<DeviceType::CPU, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("ScalarMath")
.Device(DeviceType::OPENCL)
.TypeConstraint<float>("T")
.Build(),
ScalarMathOp<DeviceType::OPENCL, float>);
REGISTER_OPERATOR(op_registry, OpKeyBuilder("ScalarMath")
.Device(DeviceType::OPENCL)
.TypeConstraint<half>("T")
.Build(),
ScalarMathOp<DeviceType::OPENCL, half>);
}
} // namespace ops
} // namespace mace
mace/ops/scalar_math.h 已删除 100644 → 0
浏览文件 @ b90f0ad4
//
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#ifndef MACE_OPS_SCALAR_MATH_H_
#define MACE_OPS_SCALAR_MATH_H_
#include <string>
#include "mace/core/operator.h"
#include "mace/kernels/scalar_math.h"
namespace mace {
namespace ops {
template <DeviceType D, class T>
class ScalarMathOp : public Operator<D, T> {
public:
ScalarMathOp(const OperatorDef &operator_def, Workspace *ws)
: Operator<D, T>(operator_def, ws),
x_(OperatorBase::GetSingleArgument<float>("x", 1.0)),
functor_(static_cast<kernels::ScalarMathType>(
OperatorBase::GetSingleArgument<int>(
"type", static_cast<int>(
kernels::ScalarMathType::ADD))),
this->x_) {}
bool Run(StatsFuture *future) override {
const Tensor *input_tensor = this->Input(INPUT);
Tensor *output_tensor = this->Output(OUTPUT);
output_tensor->ResizeLike(input_tensor);
functor_(input_tensor, output_tensor, future);
return true;
}
protected:
const float x_;
OP_INPUT_TAGS(INPUT);
OP_OUTPUT_TAGS(OUTPUT);
private:
kernels::ScalarMathFunctor<D, T> functor_;
};
} // namespace ops
} // namespace mace
#endif // MACE_OPS_SCALAR_MATH_H_
mace/ops/scalar_math_benchmark.cc 已删除 100644 → 0
浏览文件 @ b90f0ad4
//
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include "mace/core/operator.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/core/testing/test_benchmark.h"
#include "mace/ops/ops_test_util.h"
namespace mace {
namespace ops {
namespace test {
template <DeviceType D, typename T>
static void ScalarMath(int iters, int batch, int channels,
int height, int width, float x, int type) {
mace::testing::StopTiming();
OpsTestNet net;
// Add input data
net.AddRandomInput<D, T>("Input", {batch, height, width, channels});
if (D == DeviceType::OPENCL) {
BufferToImage<D, T>(&net, "Input", "InputImage",
kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("ScalarMath", "ScalarMathBM")
.Input("InputImage")
.Output("Output")
.AddIntArg("type", type)
.AddFloatArg("x", x)
.Finalize(net.NewOperatorDef());
} else {
OpDefBuilder("ScalarMath", "ScalarMathBM")
.Input("Input")
.Output("Output")
.AddIntArg("type", type)
.AddFloatArg("x", x)
.Finalize(net.NewOperatorDef());
}
// Warm-up
for (int i = 0; i < 5; ++i) {
net.RunOp(D);
}
net.Sync();
mace::testing::StartTiming();
while (iters--) {
net.RunOp(D);
}
net.Sync();
}
#define BM_SCALAR_MATH_MACRO(N, C, H, W, X, G, TYPE, DEVICE) \
static void \
BM_SCALAR_MATH_##N##_##C##_##H##_##W##_##X##_##G##_##TYPE##_##DEVICE( \
int iters) { \
const int64_t tot = static_cast<int64_t>(iters) * N * C * H * W; \
mace::testing::MaccProcessed(tot); \
mace::testing::BytesProcessed(tot *(sizeof(TYPE))); \
ScalarMath<DEVICE, TYPE>(iters, N, C, H, W, X, G); \
} \
BENCHMARK( \
BM_SCALAR_MATH_##N##_##C##_##H##_##W##_##X##_##G##_##TYPE##_##DEVICE)
#define BM_SCALAR_MATH(N, C, H, W, X, G) \
BM_SCALAR_MATH_MACRO(N, C, H, W, X, G, float, CPU); \
BM_SCALAR_MATH_MACRO(N, C, H, W, X, G, float, OPENCL); \
BM_SCALAR_MATH_MACRO(N, C, H, W, X, G, half, OPENCL);
BM_SCALAR_MATH(1, 1, 512, 512, 2, 0);
BM_SCALAR_MATH(1, 3, 128, 128, 2, 1);
BM_SCALAR_MATH(1, 3, 512, 512, 2, 4);
BM_SCALAR_MATH(1, 32, 112, 112, 2, 5);
BM_SCALAR_MATH(1, 64, 256, 256, 3, 0);
BM_SCALAR_MATH(1, 64, 512, 512, 3, 1);
BM_SCALAR_MATH(1, 128, 56, 56, 3, 4);
BM_SCALAR_MATH(1, 128, 256, 256, 3, 5);
BM_SCALAR_MATH(1, 256, 14, 14, 3, 0);
BM_SCALAR_MATH(1, 512, 14, 14, 3, 1);
BM_SCALAR_MATH(1, 1024, 7, 7, 3, 4);
BM_SCALAR_MATH(32, 1, 256, 256, 3, 5);
} // namespace test
} // namespace ops
} // namespace mace
mace/ops/scalar_math_test.cc 已删除 100644 → 0
浏览文件 @ b90f0ad4
//
// Copyright (c) 2017 XiaoMi All rights reserved.
//
#include "mace/core/operator.h"
#include "mace/ops/ops_test_util.h"
#include "../kernels/scalar_math.h"
namespace mace {
namespace ops {
namespace test {
class ScalarMathOpTest : public OpsTestBase {};
template <DeviceType D>
void Simple(const kernels::ScalarMathType type,
const std::vector<index_t> &shape,
const std::vector<float> &input0,
const float x,
const std::vector<float> &output) {
// Construct graph
OpsTestNet net;
// Add input data
net.AddInputFromArray<D, float>("Input1", shape, input0);
if (D == DeviceType::CPU) {
OpDefBuilder("ScalarMath", "ScalarMathTest")
.Input("Input1")
.AddIntArg("type", static_cast<int>(type))
.AddFloatArg("x", x)
.Output("Output")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
} else {
BufferToImage<D, half>(&net, "Input1", "InputImg1",
kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("ScalarMath", "ScalarMathTest")
.Input("InputImg1")
.AddIntArg("type", static_cast<int>(type))
.AddFloatArg("x", x)
.Output("OutputImg")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
ImageToBuffer<D, float>(&net, "OutputImg", "Output",
kernels::BufferType::IN_OUT_CHANNEL);
}
auto expected = CreateTensor<float>(shape, output);
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-3);
}
TEST_F(ScalarMathOpTest, CPUSimple) {
Simple<DeviceType::CPU>(kernels::ScalarMathType::MUL, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 0.1, {0.1, 0.2, .3, .4, .5, .6});
Simple<DeviceType::CPU>(kernels::ScalarMathType::ADD, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 2.0, {3, 4, 5, 6, 7, 8});
Simple<DeviceType::CPU>(kernels::ScalarMathType::DIV, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 0.1, {10, 20, 30, 40, 50, 60});
Simple<DeviceType::CPU>(kernels::ScalarMathType::SUB, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 2.0, {-1, 0, 1, 2, 3, 4});
}
TEST_F(ScalarMathOpTest, GPUSimple) {
Simple<DeviceType::OPENCL>(kernels::ScalarMathType::MUL, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 0.1, {0.1, 0.2, .3, .4, .5, .6});
Simple<DeviceType::OPENCL>(kernels::ScalarMathType::ADD, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 2.0, {3, 4, 5, 6, 7, 8});
Simple<DeviceType::OPENCL>(kernels::ScalarMathType::DIV, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 0.1, {10, 20, 30, 40, 50, 60});
Simple<DeviceType::OPENCL>(kernels::ScalarMathType::SUB, {1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}, 2.0, {-1, 0, 1, 2, 3, 4});
}
template <DeviceType D, typename T>
void RandomTest(const kernels::ScalarMathType type,
const std::vector<index_t> &shape) {
testing::internal::LogToStderr();
srand(time(NULL));
// Construct graph
OpsTestNet net;
// Add input data
net.AddRandomInput<D, float>("Input1", shape);
OpDefBuilder("ScalarMath", "ScalarMathTest")
.Input("Input1")
.AddIntArg("type", static_cast<int>(type))
.AddFloatArg("x", 1.2)
.Output("Output")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp();
BufferToImage<D, T>(&net, "Input1", "InputImg1",
kernels::BufferType::IN_OUT_CHANNEL);
OpDefBuilder("ScalarMath", "ScalarMathTest")
.Input("InputImg1")
.AddIntArg("type", static_cast<int>(type))
.AddFloatArg("x", 1.2)
.AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
.Output("OutputImg")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
ImageToBuffer<D, float>(&net, "OutputImg", "OPENCLOutput",
kernels::BufferType::IN_OUT_CHANNEL);
if (DataTypeToEnum<T>::value == DT_FLOAT) {
ExpectTensorNear<float>(*net.GetTensor("Output"),
*net.GetOutput("OPENCLOutput"), 1e-3);
} else {
ExpectTensorNear<float>(*net.GetTensor("Output"),
*net.GetOutput("OPENCLOutput"), 1e-1);
}
}
TEST_F(ScalarMathOpTest, OPENCLRandomFloat) {
RandomTest<DeviceType::OPENCL, float>(kernels::ScalarMathType::MUL,
{3, 23, 37, 19});
RandomTest<DeviceType::OPENCL, float>(kernels::ScalarMathType::ADD,
{13, 32, 32, 64});
RandomTest<DeviceType::OPENCL, float>(kernels::ScalarMathType::SUB,
{3, 32, 32, 64});
RandomTest<DeviceType::OPENCL, float>(kernels::ScalarMathType::DIV,
{13, 32, 32, 64});
}
TEST_F(ScalarMathOpTest, OPENCLRandomHalf) {
RandomTest<DeviceType::OPENCL, half>(kernels::ScalarMathType::MUL,
{3, 23, 37, 19});
RandomTest<DeviceType::OPENCL, half>(kernels::ScalarMathType::ADD,
{13, 32, 32, 64});
RandomTest<DeviceType::OPENCL, half>(kernels::ScalarMathType::SUB,
{3, 32, 32, 64});
RandomTest<DeviceType::OPENCL, half>(kernels::ScalarMathType::DIV,
{13, 32, 32, 64});
}
} // namespace test
} // namespace ops
} // namespace mace
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或