新增OP
以下以添加argmax为例,详细说明新增op的方法。
1. 添加OpParam 结构体以传导 Op 的输入和输出
这里命名为
ArgmaxParam
在
paddlelite/lite/operators/op_params.h
中添加ArgmaxParam
结构体,代码如下:struct ArgmaxParam {
lite::Tensor* X{};
lite::Tensor* Out{};
int Axis{0};
};
2. 添加 Argmax Op 并注册
在paddlelite/lite/operators/目录下新建argmax_op.h文件,主要代码如下:
class ArgmaxOpLite : public OpLite {
public:
ArgmaxOpLite() {}
explicit ArgmaxOpLite(const std::string &op_type) : OpLite(op_type) {}
bool CheckShape() const override;
bool InferShape() const override;
bool AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) override;
void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
std::string DebugString() const override { return "argmax"; }
private:
mutable ArgmaxParam param_;
};
ArgmaxOpLite
继承OpLite
,成员变量包括ArgmaxParam
结构体,需要实现的接口包括CheckShape()
、InferShape()
、AttachImp()
、AttachKernel()
和DebugString()
函数。AttachKernel()
和DebugString()
函数较为简单,此处直接实现;在
paddlelite/lite/operators/
目录下新建argmax_op.cc文件,需要具体实现CheckShape()
、InferShape()
和AttachImp()
函数。CheckShape()
函数检查输入是否符合要求,InferShape()
函数基于输入推断得到输出的维度,AttachImp()
函数绑定Op的输入输出。然后在argmax_op.cc文件中注册argmax,核心代码如下:bool ArgmaxOpLite::CheckShape() const {
CHECK_OR_FALSE(param_.X);
CHECK_OR_FALSE(param_.Out);
CHECK_OR_FALSE(param_.Axis < (param_.X)->dims().size());
return true;
}
bool ArgmaxOpLite::InferShape() const {
auto x_dims = param_.X->dims();
int x_rank = x_dims.size();
int axis = param_.Axis;
if (axis < 0) axis += x_rank;
std::vector<int64_t> out_dims;
for (int64_t i = 0; i < axis; i++) {
out_dims.push_back(x_dims[i]);
}
for (int64_t i = axis + 1; i < x_rank; i++) {
out_dims.push_back(x_dims[i]);
}
// Set output dims
param_.Out->Resize(lite::DDim(out_dims));
return true;
}
bool ArgmaxOpLite::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
auto x = op_desc.Input("X").front();
auto out = op_desc.Output("Out").front();
param_.X = scope->FindVar(x)->GetMutable<lite::Tensor>();
param_.Out = scope->FindVar(out)->GetMutable<lite::Tensor>();
param_.Axis = op_desc.GetAttr<int>("Axis");
return true;
}
REGISTER_LITE_OP(argmax, paddle::lite::operators::ArgmaxOpLite);
在paddlelite/lite/operators/CMakeLists.txt中添加
add_operator(argmax_op basic SRCS argmax_op.cc DEPS ${op_DEPS})
3. 添加Argmax Kernel并绑定
以下以arm端argmax实现为例说明
在paddlelite/lite/kernels/arm/目录下新建argmax_compute.h文件,声明ArgmaxCompute类,并继承KernelLite,主要代码如下:
class ArgmaxCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
public:
using param_t = operators::ArgmaxParam;
void Run() override;
virtual ~ArgmaxCompute() = default;
};
在paddlelite/lite/kernels/arm/目录下新建argmax_compute.cc文件,主要实现Run函数。
Run()
函数调用paddlelite/lite/bachends/arm/math/argmax.h中的argmax_func()
函数,根据输入计算输出。最后在argmax_compute.cc文件中,我们绑定argmax的输入输出(为tensor的输入参数都需要绑定),代码如下:void ArgmaxCompute::Run() {
auto& param = Param<operators::ArgmaxParam>();
lite::Tensor* input = param.X;
lite::Tensor* output = param.Out;
int axis = param.Axis;
lite::arm::math::argmax_func(input, axis, output);
return;
}
REGISTER_LITE_KERNEL(
argmax, kARM, kFloat, kNCHW, paddle::lite::kernels::arm::ArgmaxCompute, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
.Finalize();
在paddlelite/lite/kernels/arm/CMakeLists.txt中添加
add_kernel(argmax_compute_arm ARM basic SRCS argmax_compute.cc DEPS ${lite_kernel_deps} math_arm)
4. 添加Argmax实现
在paddlelite/lite/backends/arm/math/目录下新建argmax.h文件,声明
argmax_func()
函数,代码如下:void argmax_func(const lite::Tensor* input, const int axis, lite::Tensor* output);
在paddlelite/lite/backends/arm/math/目录下新建argmax.cc文件,具体实现
argmax_func()
函数,代码如下:void argmax_func(const lite::Tensor *input,
const int axis,
lite::Tensor *output) {
auto input_ddim = input->dims();
auto output_ddim = output->dims();
const int size = input_ddim[axis];
const int in_channel = input_ddim.count(axis, input_ddim.size());
const int out_channel = output_ddim.count(axis, output_ddim.size());
const int in_stride = input_ddim.count(axis + 1, input_ddim.size());
const int out_stride = input_ddim.count(0, axis);
for (int n = 0; n < out_stride; n++) {
for (int k = 0; k < in_stride; k++) {
const float *in_ptr = input->data<float>() + n * in_channel + k;
std::vector<std::pair<float, int>> vec;
vec.resize(size);
for (int i = 0; i < size; i++) {
vec[i] = std::make_pair(in_ptr[i * in_stride], i);
}
// sort
std::partial_sort(vec.begin(),
vec.begin() + 1,
vec.end(),
std::greater<std::pair<float, int>>());
// out
float *out_ptr = output->mutable_data<float>() + n * out_channel + k;
*out_ptr = vec[0].second;
}
}
}
在paddlelite/lite/backends/arm/math/CMakeFile.txt中的
math_arm library
中添加argmax.cc,在paddlelite/lite/backends/arm/math/funcs.h中添加#include "lite/arm/math/argmax.h"
5. 添加Argmax单测
在paddlelite/lite/tests/kernels目录下新建argmax_compute_test.cc文件,声明并实现ArgmaxComputeTester类;
ArgmaxComputeTester类中主要包括PrepareOpDesc、PrepareData和RunBaseline函数。PrepareOpDesc函数设定单测op的类型和输入输出参数,PrepareData函数对输入tensor进行初始化,RunBaseline是基于输入计算得到输出,用于和框架计算的输出进行对比;
使用gtest添加单测,代码如下:
TEST(Argmax, precision) {
#ifdef LITE_WITH_ARM
LOG(INFO) << "test argmax arm";
Place place(TARGET(kARM));
for (int axis : {0, 1, 2, 3}) {
for (int n : {1, 3}) {
for (int c : {3, 6}) {
for (int h : {9, 18}) {
for (int w : {9, 18}) {
std::unique_ptr<arena::TestCase> tester(
new ArgmaxComputeTester(place, "def", axis, n, c, h, w));
arena::Arena arena(std::move(tester), place, 2e-5);
arena.TestPrecision();
}
}
}
}
}
#endif
}
在paddlelite/lite/tests/kernels/CMakeLists.txt中添加
lite_cc_test(test_kernel_argmax_compute SRCS argmax_compute_test.cc DEPS arena_framework ${x86_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
6. 编译运行
- 在paddlelite目录中,执行
./lite/tools/ci_build.sh build_test_arm
,该脚本会创建手机模拟器,并编译运行所有单测(花费时间较久)。如果运行无误,则表明添加argmax成功。