Note

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KPU Runtime API Manual

Overview

KPU runtime APIs are used to load kmodel on AI devices, set input data, execute KPU/CPU computations, retrieve output data, etc. This document provides C++ APIs. The related header files and static libraries are located in the src/rtsmart/libs/nncase/riscv64 directory. The APIs provided in this document are used to write code in C++ on the local PC that runs on k230, compile it into an executable file, and then copy it to k230 to run.

$ tree -L 3 riscv64/
riscv64/
├── gsl
│   └── gsl-lite.hpp
├── nncase
│   ├── include
│   │   └── nncase
│   └── lib
│       ├── cmake
│       ├── libfunctional_k230.a
│       ├── libnncase.rt_modules.k230.a
│       └── libNncase.Runtime.Native.a
└── rvvlib
    ├── include
    │   ├── k230_math.h
    │   ├── nms.h
    │   └── rvv_math.h
    └── librvv.a

8 directories, 8 files

API Introduction

hrt::create

【Description】

Creates a runtime_tensor.

【Definition】

(1) NNCASE_API result<runtime_tensor> create(typecode_t datatype, dims_t shape, memory_pool_t pool = pool_shared_first) noexcept;
(2) NNCASE_API result<runtime_tensor> create(typecode_t datatype, dims_t shape, gsl::span<gsl::byte> data, bool copy,
       memory_pool_t pool = pool_shared_first) noexcept;
(3)NNCASE_API result<runtime_tensor>create(typecode_t datatype, dims_t shape, strides_t strides, gsl::span<gsl::byte> data, bool copy, memory_pool_t pool = pool_shared_first, uintptr_t physical_address = 0) noexcept;

【Parameters】

Name	Type	Description
datatype	typecode_t	Data type, such as dt_float32, dt_uint8, etc.
shape	dims_t	Shape of the tensor
data	gsl::span<gsl::byte>	User-space data buffer
copy	bool	Whether to copy
pool	memory_pool_t	Memory pool type, default value is pool_shared_first
physical_address	uintptr_t	Physical address of the user-specified buffer

【Return Value】

result<runtime_tensor>

【Example】

// create input tensor
auto input_desc = interp.input_desc(0);
auto input_shape = interp.input_shape(0);
auto input_tensor = host_runtime_tensor::create(input_desc.datatype, input_shape, hrt::pool_shared).expect("cannot create input tensor");

hrt::sync

【Description】

Synchronizes the cache of a tensor.

• For user input data, you need to call the sync_write_back of this interface to ensure that the data has been flushed into ddr.

• For the output data after gnne/ai2d computation, the gnne/ai2d runtime has done sync_invalidate processing by default.

【Definition】

NNCASE_API result<void> sync(runtime_tensor &tensor, sync_op_t op, bool force = false) noexcept;

【Parameters】

Name	Type	Description
tensor	runtime_tensor	The tensor to operate on
op	sync_op_t	sync_invalidate (invalidates the tensor’s cache) or sync_write_back (writes the tensor’s cache to ddr)
force	bool	Whether to force execution

【Return Value】

result<void>

【Example】

hrt::sync(input_tensor, sync_op_t::sync_write_back, true).expect("sync write_back failed");

interpreter::load_model

【Description】

Loads a kmodel.

【Definition】

NNCASE_NODISCARD result<void> load_model(gsl::span<const gsl::byte> buffer) noexcept;

【Parameters】

Name	Type	Description
buffer	gsl::span <const gsl::byte>	kmodel buffer

【Return Value】

result<void>

【Example】

interpreter interp;
auto model = read_binary_file<unsigned char>(kmodel);
interp.load_model({(const gsl::byte *)model.data(), model.size()}).expect("cannot load model.");

interpreter::inputs_size

【Description】

Gets the number of model inputs.

【Definition】

size_t inputs_size() const noexcept;

【Parameters】

None.

【Return Value】

size_t

【Example】

auto inputs_size = interp.inputs_size();

interpreter::outputs_size

【Description】

Gets the number of model outputs.

【Definition】

size_t outputs_size() const noexcept;

【Parameters】

None.

【Return Value】

size_t

【Example】

auto outputs_size = interp.outputs_size();

interpreter:: input_shape

【Description】

Gets the shape of the specified input of the model.

【Definition】

const runtime_shape_t &input_shape(size_t index) const noexcept;

【Parameters】

Name	Type	Description
index	size_t	Index of the input

【Return Value】

runtime_shape_t

【Example】

auto shape = interp.input_shape(0);

interpreter:: output_shape

【Description】

Gets the shape of the specified output of the model.

【Definition】

const runtime_shape_t &output_shape(size_t index) const noexcept;

【Parameters】

Name	Type	Description
index	size_t	Index of the output

【Return Value】

runtime_shape_t

【Example】

auto shape = interp.output_shape(0);

interpreter:: input_tensor

【Description】

Gets/sets the input tensor of the specified index.

【Definition】

(1) result<runtime_tensor> input_tensor(size_t index) noexcept;
(2) result<void> input_tensor(size_t index, runtime_tensor tensor) noexcept;

【Parameters】

Name	Type	Description
index	size_t	Index of the input
tensor	runtime_tensor	The runtime tensor corresponding to the input

【Return Value】

(1) result<runtime_tensor>
(2) result<void>

【Example】

// set input
interp.input_tensor(0, input_tensor).expect("cannot set input tensor");

interpreter:: output_tensor

【Description】

Gets/sets the output tensor of the specified index.

【Definition】

(1) result<runtime_tensor> output_tensor(size_t index) noexcept;
(2) result<void> output_tensor(size_t index, runtime_tensor tensor) noexcept;

【Parameters】

Name	Type	Description
index	size_t	Index of the output
tensor	runtime_tensor	The runtime tensor corresponding to the output

【Return Value】

(1) result<runtime_tensor>
(2) result<void>

【Example】

// get output
auto output_tensor = interp.output_tensor(0).expect("cannot get output tensor");

interpreter:: run

【Description】

Executes KPU computation.

【Definition】

result<void> run() noexcept;

【Parameters】

None.

【Return Value】

Returns result <void>.

【Example】

// run
interp.run().expect("error occurred in running model");

Example

#include <chrono>
#include <fstream>
#include <iostream>
#include <nncase/runtime/interpreter.h>
#include <nncase/runtime/runtime_op_utility.h>

#define USE_OPENCV 1
#define preprocess 1

#if USE_OPENCV
#include <opencv2/highgui.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc.hpp>
#endif

using namespace nncase;
using namespace nncase::runtime;
using namespace nncase::runtime::detail;

// Model input resolution
#define INTPUT_HEIGHT 224
#define INTPUT_WIDTH 224
#define INTPUT_CHANNELS 3

template <class T>
std::vector<T> read_binary_file(const std::string &file_name)
{
    std::ifstream ifs(file_name, std::ios::binary);
    ifs.seekg(0, ifs.end);
    size_t len = ifs.tellg();
    std::vector<T> vec(len / sizeof(T), 0);
    ifs.seekg(0, ifs.beg);
    ifs.read(reinterpret_cast<char *>(vec.data()), len);
    ifs.close();
    return vec;
}

void read_binary_file(const char *file_name, char *buffer)
{
    std::ifstream ifs(file_name, std::ios::binary);
    ifs.seekg(0, ifs.end);
    size_t len = ifs.tellg();
    ifs.seekg(0, ifs.beg);
    ifs.read(buffer, len);
    ifs.close();
}

static std::vector<std::string> read_txt_file(const char *file_name)
{
    std::vector<std::string> vec;
    vec.reserve(1024);
    std::ifstream fp(file_name);
    std::string label;
    while (getline(fp, label))
    {
        vec.push_back(label);
    }
    return vec;
}

template<typename T>
static int softmax(const T* src, T* dst, int length)
{
    const T alpha = *std::max_element(src, src + length);
    T denominator{ 0 };

    for (int i = 0; i < length; ++i) {
        dst[i] = std::exp(src[i] - alpha);
        denominator += dst[i];
    }

    for (int i = 0; i < length; ++i) {
        dst[i] /= denominator;
    }

    return 0;
}

#if USE_OPENCV
std::vector<uint8_t> hwc2chw(cv::Mat &img)
{
    std::vector<uint8_t> vec;
    std::vector<cv::Mat> rgbChannels(3);
    cv::split(img, rgbChannels);
    for (auto i = 0; i < rgbChannels.size(); i++)
    {
        std::vector<uint8_t> data = std::vector<uint8_t>(rgbChannels[i].reshape(1, 1));
        vec.insert(vec.end(), data.begin(), data.end());
    }

    return vec;
}
#endif

static int inference(const char *kmodel_file, const char *image_file, const char *label_file)
{
    // load kmodel
    interpreter interp;

    // Load kmodel from memory
    auto kmodel = read_binary_file<unsigned char>(kmodel_file);
    interp.load_model({ (const gsl::byte *)kmodel.data(), kmodel.size() }).expect("cannot load kmodel.");
    // Load kmodel from file stream
    std::ifstream ifs(kmodel_file, std::ios::binary);
    interp.load_model(ifs).expect("cannot load kmodel");


    // create input tensor
    auto input_desc = interp.input_desc(0);
    auto input_shape = interp.input_shape(0);
    auto input_tensor = host_runtime_tensor::create(input_desc.datatype, input_shape, hrt::pool_shared).expect("cannot create input tensor");
    interp.input_tensor(0, input_tensor).expect("cannot set input tensor");

    // create output tensor
    // auto output_desc = interp.output_desc(0);
    // auto output_shape = interp.output_shape(0);
    // auto output_tensor = host_runtime_tensor::create(output_desc.datatype, output_shape, hrt::pool_shared).expect("cannot create output tensor");
    // interp.output_tensor(0, output_tensor).expect("cannot set output tensor");

    // set input data
    auto dst = input_tensor.impl()->to_host().unwrap()->buffer().as_host().unwrap().map(map_access_::map_write).unwrap().buffer();
#if USE_OPENCV
    cv::Mat img = cv::imread(image_file);
    cv::resize(img, img, cv::Size(INTPUT_WIDTH, INTPUT_HEIGHT), cv::INTER_NEAREST);
    auto input_vec = hwc2chw(img);
    memcpy(reinterpret_cast<char *>(dst.data()), input_vec.data(), input_vec.size());
#else
    read_binary_file(image_file, reinterpret_cast<char *>(dst.data()));
#endif
    hrt::sync(input_tensor, sync_op_t::sync_write_back, true).expect("sync write_back failed");

    // run
    size_t counter = 1;
    auto start = std::chrono::steady_clock::now();
    for (size_t c = 0; c < counter; c++)
    {
        interp.run().expect("error occurred in running model");
    }
    auto stop = std::chrono::steady_clock::now();
    double duration = std::chrono::duration<double, std::milli>(stop - start).count();
    std::cout << "interp.run() took: " << duration / counter << " ms" << std::endl;

    // get output data
    auto output_tensor = interp.output_tensor(0).expect("cannot set output tensor");
    dst = output_tensor.impl()->to_host().unwrap()->buffer().as_host().unwrap().map(map_access_::map_read).unwrap().buffer();
    float *output_data = reinterpret_cast<float *>(dst.data());
    auto out_shape = interp.output_shape(0);
    auto size = compute_size(out_shape);

    // postprogress softmax by cpu
    std::vector<float> softmax_vec(size, 0);
    auto buf = softmax_vec.data();
    softmax(output_data, buf, size);
    auto it = std::max_element(buf, buf + size);
    size_t idx = it - buf;

    // load label
    auto labels = read_txt_file(label_file);
    std::cout << "image classify result: " << labels[idx] << "(" << *it << ")" << std::endl;

    return 0;
}

int main(int argc, char *argv[])
{
    std::cout << "case " << argv[0] << " built at " << __DATE__ << " " << __TIME__ << std::endl;
    if (argc != 4)
    {
        std::cerr << "Usage: " << argv[0] << " <kmodel> <image> <label>" << std::endl;
        return -1;
    }

    int ret = inference(argv[1], argv[2], argv[3]);
    if (ret)
    {
        std::cerr << "inference failed: ret = " << ret << std::endl;
        return -2;
    }
    return 0;
}

The above code needs to be compiled into an elf executable using the compilation tools in the k230 sdk environment, and then copied to the development board to run.