3.【系统架构】CANN运行时组件架构设计与API实战：runtime运行时完全指南

一、项目简介

runtime 是CANN提供的运行时组件，为AI应用提供高效的硬件资源管理、模型推理、单算子执行等核心能力。作为连接上层应用和底层硬件的桥梁，runtime组件封装了复杂的硬件操作细节，提供简洁易用的API接口，使开发者能够专注于业务逻辑而非底层实现。

runtime组件提供了从设备初始化、内存管理、模型加载到推理执行的完整流程支持，是构建高性能AI应用的基础设施。无论是单算子测试、模型推理，还是资源监控与管理，runtime都提供了完善的解决方案。

相关链接：

• CANN组织链接：https://atomgit.com/cann
• runtime仓库链接：https://atomgit.com/cann/runtime

---

二、核心功能与特性

2.1 Runtime核心模块

模块	功能	主要API
设备管理	设备初始化、资源分配	aclrtSetDevice, aclrtGetDevice
内存管理	内存分配、释放、拷贝	aclrtMalloc, aclrtMemcpy
模型加载	模型加载、卸载、描述	aclmdlLoadFromFile, aclmdlExecute
数据预处理	图像预处理、格式转换	aclvpp
算子执行	单算子执行	aclopExecute
流程管理	Stream和Event管理	aclrtCreateStream, aclrtRecordEvent

2.2 技术特性

1. 统一接口：提供统一的C/C++/Python API
2. 异步执行：支持Stream异步执行提高效率
3. 内存优化：支持零拷贝、内存复用等优化
4. 多设备支持：支持单机多卡并行计算
5. 维测能力：内置性能分析和调试工具

---

三、环境准备

3.1 系统要求

• 操作系统：Ubuntu 18.04/20.04/22.04
• 处理器：Atlas系列AI加速器
• CANN版本：CANN 8.0.RC3及以上
• Python版本：3.8-3.10
• 驱动版本：配套的驱动固件

3.2 安装配置

# 克隆runtime仓库
git clone https://atomgit.com/cann/runtime.git
cd runtime

# 安装Python依赖
pip install numpy opencv-python pillow

# 编译安装
mkdir build && cd build
cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DCANN_INSTALL_PATH=/usr/local/Ascend \
    -DBUILD_PYTHON_BINDINGS=ON
make -j$(nproc)
make install

# 配置环境变量
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/lib64:$LD_LIBRARY_PATH
export PYTHONPATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages:$PYTHONPATH

# 验证安装
python3 -c "import acl; print('runtime installed successfully')"

---

四、设备管理示例

4.1 设备初始化与资源管理

import acl
import numpy as np

class DeviceManager:
    """设备管理器"""

    def __init__(self, device_id=0):
        self.device_id = device_id
        self.context = None
        self.is_initialized = False

    def init(self):
        """初始化设备"""
        # 初始化ACL
        ret = acl.init()
        if ret != 0:
            raise RuntimeError(f"Failed to initialize ACL, ret={ret}")

        # 指定计算设备
        ret = acl.rt.set_device(self.device_id)
        if ret != 0:
            raise RuntimeError(f"Failed to set device, ret={ret}")

        # 创建Context
        self.context = acl.rt.create_context(self.device_id)
        if self.context is None:
            raise RuntimeError("Failed to create context")

        self.is_initialized = True
        print(f"Device {self.device_id} initialized successfully")

    def get_device_count(self):
        """获取设备数量"""
        device_count = 0
        ret = acl.rt.get_device_count(device_count)
        if ret == 0:
            return device_count
        return 0

    def get_device_info(self):
        """获取设备信息"""
        # 获取设备版本信息
        version_info = acl.rt.get_version()
        print(f"Device Version: {version_info}")

        # 获取设备内存信息
        memory_info = acl.rt.get_mem_info()
        print(f"Total Memory: {memory_info['total'] / 1024**3:.2f} GB")
        print(f"Free Memory: {memory_info['free'] / 1024**3:.2f} GB")

    def reset(self):
        """重置设备"""
        if self.context is not None:
            acl.rt.destroy_context(self.context)
            self.context = None

        acl.rt.reset_device(self.device_id)
        self.is_initialized = False
        print(f"Device {self.device_id} reset successfully")

    def __del__(self):
        """析构函数"""
        if self.is_initialized:
            self.reset()
        acl.finalize()

# 使用示例
if __name__ == "__main__":
    device = DeviceManager(device_id=0)
    device.init()
    device.get_device_info()

    # 执行其他操作...

    device.reset()

4.2 多设备并行管理

import acl
from concurrent.futures import ThreadPoolExecutor

class MultiDeviceManager:
    """多设备管理器"""

    def __init__(self, device_ids):
        self.device_ids = device_ids
        self.devices = {}

    def init_all(self):
        """初始化所有设备"""
        acl.init()

        for device_id in self.device_ids:
            device = DeviceManager(device_id)
            device.init()
            self.devices[device_id] = device

        print(f"Initialized {len(self.devices)} devices")

    def execute_on_device(self, device_id, func, *args, **kwargs):
        """在指定设备上执行函数"""
        if device_id not in self.devices:
            raise ValueError(f"Device {device_id} not initialized")

        device = self.devices[device_id]
        return func(device, *args, **kwargs)

    def parallel_execute(self, func, *args, **kwargs):
        """在所有设备上并行执行"""
        with ThreadPoolExecutor(max_workers=len(self.device_ids)) as executor:
            futures = []
            for device_id in self.device_ids:
                future = executor.submit(
                    self.execute_on_device, device_id, func, *args, **kwargs
                )
                futures.append(future)

            results = [future.result() for future in futures]
            return results

    def release_all(self):
        """释放所有设备"""
        for device in self.devices.values():
            device.reset()

        self.devices.clear()
        acl.finalize()

# 使用示例
def process_on_device(device, data):
    """在设备上处理数据"""
    print(f"Processing on device {device.device_id}")
    # 执行计算...
    return f"Result from device {device.device_id}"

if __name__ == "__main__":
    device_ids = [0, 1, 2, 3]
    multi_device = MultiDeviceManager(device_ids)
    multi_device.init_all()

    # 并行执行
    results = multi_device.parallel_execute(process_on_device, "test_data")
    print("Results:", results)

    multi_device.release_all()

---

五、内存管理示例

5.1 基础内存操作

import acl
import numpy as np

class MemoryManager:
    """内存管理器"""

    def __init__(self, device_id=0):
        self.device_id = device_id
        self.allocations = {}

    def malloc(self, size, desc=""):
        """分配Device内存"""
        ptr, size = acl.rt.malloc(size)
        self.allocations[ptr] = {
            'size': size,
            'desc': desc
        }
        print(f"Allocated {size} bytes ({size/1024**2:.2f} MB) for {desc}")
        return ptr, size

    def free(self, ptr):
        """释放Device内存"""
        if ptr in self.allocations:
            size = self.allocations[ptr]['size']
            desc = self.allocations[ptr]['desc']
            acl.rt.free(ptr)
            del self.allocations[ptr]
            print(f"Freed {size} bytes ({size/1024**2:.2f} MB) for {desc}")

    def memcpy_h2d(self, host_data, desc=""):
        """Host到Device内存拷贝"""
        # 获取数据大小
        if isinstance(host_data, np.ndarray):
            size = host_data.nbytes
        else:
            size = len(host_data)

        # 分配Device内存
        device_ptr, _ = self.malloc(size, desc)

        # 执行拷贝
        acl.rt.memcpy(
            device_ptr,           # 目标地址（Device）
            host_data,            # 源地址（Host）
            size,                 # 拷贝大小
            acl.rt.MEMCPY_HOST_TO_DEVICE  # 拷贝方向
        )

        return device_ptr

    def memcpy_d2h(self, device_ptr, size, desc=""):
        """Device到Host内存拷贝"""
        # 分配Host内存
        host_data = np.zeros(size // 4, dtype=np.float32)

        # 执行拷贝
        acl.rt.memcpy(
            host_data,            # 目标地址（Host）
            device_ptr,           # 源地址（Device）
            size,                 # 拷贝大小
            acl.rt.MEMCPY_DEVICE_TO_HOST  # 拷贝方向
        )

        return host_data

    def memcpy_d2d(self, src_ptr, dst_ptr, size):
        """Device到Device内存拷贝"""
        acl.rt.memcpy(
            dst_ptr,
            src_ptr,
            size,
            acl.rt.MEMCPY_DEVICE_TO_DEVICE
        )

    def get_memory_usage(self):
        """获取当前内存使用情况"""
        total, free = acl.rt.get_mem_info(self.device_id)
        used = total - free
        print(f"Memory Usage: {used/1024**3:.2f} GB / {total/1024**3:.2f} GB "
              f"({used/total*100:.1f}%)")

# 使用示例
if __name__ == "__main__":
    device_id = 0
    acl.rt.set_device(device_id)

    mem_mgr = MemoryManager(device_id)

    # 分配并拷贝数据
    data = np.random.rand(1024, 1024).astype(np.float32)
    device_ptr = mem_mgr.memcpy_h2d(data, "input_data")

    # 查看内存使用
    mem_mgr.get_memory_usage()

    # 处理数据...

    # 拷回结果
    result = mem_mgr.memcpy_d2h(device_ptr, data.nbytes, "result")

    # 释放内存
    mem_mgr.free(device_ptr)

5.2 内存池管理

import acl
import numpy as np
from collections import defaultdict

class MemoryPool:
    """内存池管理器"""

    def __init__(self, device_id=0, init_size=1024*1024*1024):
        self.device_id = device_id
        self.init_size = init_size
        self.pool = {}
        self.used_blocks = defaultdict(dict)
        self.block_size = 64 * 1024 * 1024  # 64MB块大小

        # 初始化内存池
        self._init_pool()

    def _init_pool(self):
        """初始化内存池"""
        num_blocks = (self.init_size + self.block_size - 1) // self.block_size

        for i in range(num_blocks):
            ptr, size = acl.rt.malloc(self.block_size)
            self.pool[ptr] = {
                'size': self.block_size,
                'allocated': False
            }

        print(f"Memory pool initialized with {len(self.pool)} blocks")

    def allocate(self, size, desc=""):
        """从内存池分配内存"""
        # 计算需要的块数量
        num_blocks = (size + self.block_size - 1) // self.block_size

        # 查找连续的空闲块
        free_blocks = [ptr for ptr, info in self.pool.items()
                      if not info['allocated']]

        if len(free_blocks) < num_blocks:
            raise RuntimeError("Insufficient memory in pool")

        # 分配块
        allocated_blocks = free_blocks[:num_blocks]
        for ptr in allocated_blocks:
            self.pool[ptr]['allocated'] = True
            self.used_blocks[ptr] = desc

        return allocated_blocks[0], num_blocks * self.block_size

    def free(self, ptr):
        """释放内存到内存池"""
        # 释放该指针及其后续连续块
        for block_ptr in self.pool.keys():
            if self.pool[block_ptr]['allocated'] and block_ptr in self.used_blocks:
                # 简化实现：释放所有块
                self.pool[block_ptr]['allocated'] = False
                del self.used_blocks[block_ptr]

    def get_pool_stats(self):
        """获取内存池统计信息"""
        total_blocks = len(self.pool)
        used_blocks = sum(1 for info in self.pool.values() if info['allocated'])
        free_blocks = total_blocks - used_blocks

        total_memory = total_blocks * self.block_size
        used_memory = used_blocks * self.block_size
        free_memory = free_blocks * self.block_size

        print(f"Memory Pool Stats:")
        print(f"  Total: {total_memory/1024**3:.2f} GB ({total_blocks} blocks)")
        print(f"  Used:  {used_memory/1024**3:.2f} GB ({used_blocks} blocks)")
        print(f"  Free:  {free_memory/1024**3:.2f} GB ({free_blocks} blocks)")
        print(f"  Usage: {used_blocks/total_blocks*100:.1f}%")

# 使用示例
if __name__ == "__main__":
    device_id = 0
    acl.rt.set_device(device_id)

    pool = MemoryPool(device_id, init_size=512*1024*1024)

    # 分配内存
    ptr1, size1 = pool.allocate(100*1024*1024, "tensor_a")
    ptr2, size2 = pool.allocate(50*1024*1024, "tensor_b")

    pool.get_pool_stats()

    # 释放内存
    pool.free(ptr1)
    pool.free(ptr2)

    pool.get_pool_stats()

---

六、模型推理示例

6.1 模型加载与推理

import acl
import numpy as np
import cv2

class ModelInference:
    """模型推理类"""

    def __init__(self, model_path, device_id=0):
        self.model_path = model_path
        self.device_id = device_id
        self.model_id = None
        self.model_desc = None
        self.dataset = None
        self.context = None

    def init(self):
        """初始化"""
        # 初始化ACL
        acl.init()

        # 设置设备
        acl.rt.set_device(self.device_id)
        self.context = acl.rt.create_context(self.device_id)

        # 加载模型
        self._load_model()

    def _load_model(self):
        """加载模型文件"""
        # 从文件加载模型
        ret, self.model_id = acl.mdl.load_from_file(
            self.model_path,  # 模型文件路径
        )

        if ret != 0:
            raise RuntimeError(f"Failed to load model, ret={ret}")

        # 获取模型描述
        self.model_desc = acl.mdl.create_desc()
        ret = acl.mdl.get_desc(self.model_desc, self.model_id)

        if ret != 0:
            raise RuntimeError(f"Failed to get model desc, ret={ret}")

        print(f"Model loaded successfully, model_id={self.model_id}")

        # 打印模型信息
        self._print_model_info()

    def _print_model_info(self):
        """打印模型信息"""
        # 获取输入信息
        input_num = acl.mdl.get_num_inputs(self.model_desc)
        print(f"Model has {input_num} inputs")

        for i in range(input_num):
            name = acl.mdl.get_input_name_by_index(self.model_desc, i)
            size = acl.mdl.get_input_size_by_index(self.model_desc, i)
            dims = acl.mdl.get_input_dims(self.model_desc, i)
            print(f"  Input {i}: name={name}, size={size}, dims={dims}")

        # 获取输出信息
        output_num = acl.mdl.get_num_outputs(self.model_desc)
        print(f"Model has {output_num} outputs")

        for i in range(output_num):
            name = acl.mdl.get_output_name_by_index(self.model_desc, i)
            size = acl.mdl.get_output_size_by_index(self.model_desc, i)
            dims = acl.mdl.get_output_dims(self.model_desc, i)
            print(f"  Output {i}: name={name}, size={size}, dims={dims}")

    def preprocess(self, image_path):
        """预处理输入图像"""
        # 读取图像
        image = cv2.imread(image_path)
        if image is None:
            raise ValueError(f"Failed to load image: {image_path}")

        # 调整大小（假设模型需要224x224）
        resized = cv2.resize(image, (224, 224))

        # 归一化
        normalized = resized.astype(np.float32) / 255.0
        normalized = (normalized - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]

        # 转换为CHW格式
        chw = normalized.transpose(2, 0, 1)

        # 增加batch维度
        batched = np.expand_dims(chw, axis=0)

        return batched.astype(np.float32)

    def infer(self, input_data):
        """执行推理"""
        # 准备输入数据集
        self.dataset = acl.mdl.create_dataset()

        # 获取输入大小
        input_size = acl.mdl.get_input_size_by_index(
            self.model_desc, 0
        )

        # 分配Device内存并拷贝数据
        device_ptr, _ = acl.rt.malloc(input_size)
        acl.rt.memcpy(
            device_ptr,
            input_data,
            input_size,
            acl.rt.MEMCPY_HOST_TO_DEVICE
        )

        # 设置输入数据集
        acl.mdl.add_input_buffer(
            self.dataset,
            0,           # 输入索引
            device_ptr,  # 数据指针
            input_size   # 数据大小
        )

        # 创建输出数据集
        output_dataset = acl.mdl.create_dataset()

        # 获取输出信息并分配内存
        output_num = acl.mdl.get_num_outputs(self.model_desc)
        output_ptrs = []

        for i in range(output_num):
            output_size = acl.mdl.get_output_size_by_index(self.model_desc, i)
            device_ptr, _ = acl.rt.malloc(output_size)
            output_ptrs.append(device_ptr)

            acl.mdl.add_output_buffer(
                output_dataset,
                i,
                device_ptr,
                output_size
            )

        # 执行推理
        ret = acl.mdl.execute(
            self.model_id,
            self.dataset,
            output_dataset
        )

        if ret != 0:
            raise RuntimeError(f"Failed to execute model, ret={ret}")

        # 获取输出结果
        results = []
        for i, device_ptr in enumerate(output_ptrs):
            output_size = acl.mdl.get_output_size_by_index(self.model_desc, i)
            host_data = np.zeros(output_size // 4, dtype=np.float32)

            acl.rt.memcpy(
                host_data,
                device_ptr,
                output_size,
                acl.rt.MEMCPY_DEVICE_TO_HOST
            )

            results.append(host_data)

            # 释放输出内存
            acl.rt.free(device_ptr)

        # 清理资源
        acl.mdl.destroy_dataset(output_dataset)
        acl.mdl.destroy_dataset(self.dataset)
        acl.rt.free(device_ptr)

        return results

    def finalize(self):
        """释放资源"""
        if self.model_desc is not None:
            acl.mdl.destroy_desc(self.model_desc)

        if self.model_id is not None:
            acl.mdl.unload(self.model_id)

        if self.context is not None:
            acl.rt.destroy_context(self.context)

        acl.rt.reset_device(self.device_id)
        acl.finalize()

# 使用示例
if __name__ == "__main__":
    model_path = "model/resnet50.om"
    image_path = "test_image.jpg"

    # 创建推理实例
    inference = ModelInference(model_path)
    inference.init()

    # 预处理
    input_data = inference.preprocess(image_path)

    # 推理
    outputs = inference.infer(input_data)

    # 处理输出
    print("Inference completed!")
    print(f"Output shape: {outputs[0].shape}")

    # 释放资源
    inference.finalize()

6.2 批量推理

import acl
import numpy as np

class BatchInference:
    """批量推理类"""

    def __init__(self, model_path, device_id=0, batch_size=8):
        self.model_path = model_path
        self.device_id = device_id
        self.batch_size = batch_size
        self.model_id = None
        self.model_desc = None

    def init(self):
        """初始化"""
        acl.init()
        acl.rt.set_device(self.device_id)
        self._load_model()

    def _load_model(self):
        """加载模型"""
        ret, self.model_id = acl.mdl.load_from_file(self.model_path)
        if ret != 0:
            raise RuntimeError(f"Failed to load model, ret={ret}")

        self.model_desc = acl.mdl.create_desc()
        ret = acl.mdl.get_desc(self.model_desc, self.model_id)
        if ret != 0:
            raise RuntimeError(f"Failed to get model desc, ret={ret}")

    def infer_batch(self, batch_data):
        """批量推理"""
        num_samples = len(batch_data)
        results = []

        # 分批处理
        for start_idx in range(0, num_samples, self.batch_size):
            end_idx = min(start_idx + self.batch_size, num_samples)
            current_batch = batch_data[start_idx:end_idx]

            # 构造batch输入
            actual_batch_size = len(current_batch)
            batch_input = np.stack(current_batch, axis=0)

            # 执行推理
            batch_result = self._infer_single(batch_input)
            results.extend(batch_result)

        return results

    def _infer_single(self, input_data):
        """单次推理"""
        dataset = acl.mdl.create_dataset()

        input_size = acl.mdl.get_input_size_by_index(self.model_desc, 0)
        device_ptr, _ = acl.rt.malloc(input_size)

        acl.rt.memcpy(
            device_ptr,
            input_data,
            input_size,
            acl.rt.MEMCPY_HOST_TO_DEVICE
        )

        acl.mdl.add_input_buffer(dataset, 0, device_ptr, input_size)

        # 执行推理
        output_dataset = acl.mdl.create_dataset()

        output_num = acl.mdl.get_num_outputs(self.model_desc)
        output_ptrs = []

        for i in range(output_num):
            output_size = acl.mdl.get_output_size_by_index(self.model_desc, i)
            device_ptr, _ = acl.rt.malloc(output_size)
            output_ptrs.append(device_ptr)
            acl.mdl.add_output_buffer(output_dataset, i, device_ptr, output_size)

        ret = acl.mdl.execute(self.model_id, dataset, output_dataset)

        if ret != 0:
            raise RuntimeError(f"Failed to execute model, ret={ret}")

        # 获取结果
        results = []
        for i, device_ptr in enumerate(output_ptrs):
            output_size = acl.mdl.get_output_size_by_index(self.model_desc, i)
            host_data = np.zeros(output_size // 4, dtype=np.float32)

            acl.rt.memcpy(
                host_data,
                device_ptr,
                output_size,
                acl.rt.MEMCPY_DEVICE_TO_HOST
            )

            results.append(host_data)
            acl.rt.free(device_ptr)

        # 清理
        acl.mdl.destroy_dataset(output_dataset)
        acl.mdl.destroy_dataset(dataset)

        return results

    def finalize(self):
        """释放资源"""
        if self.model_desc is not None:
            acl.mdl.destroy_desc(self.model_desc)
        if self.model_id is not None:
            acl.mdl.unload(self.model_id)

        acl.rt.reset_device(self.device_id)
        acl.finalize()

# 使用示例
if __name__ == "__main__":
    model_path = "model/resnet50.om"

    batch_inference = BatchInference(model_path, batch_size=16)
    batch_inference.init()

    # 准备批量数据
    batch_data = [np.random.rand(3, 224, 224).astype(np.float32) for _ in range(32)]

    # 执行批量推理
    results = batch_inference.infer_batch(batch_data)

    print(f"Processed {len(results)} samples")

    batch_inference.finalize()

---

七、Stream与Event示例

7.1 Stream异步执行

import acl
import numpy as np

class StreamManager:
    """Stream管理器"""

    def __init__(self, device_id=0):
        self.device_id = device_id
        self.streams = {}
        self.events = {}
        self.stream_counter = 0
        self.event_counter = 0

    def create_stream(self, name=""):
        """创建Stream"""
        stream = acl.rt.create_stream(acl.rt.stream_id)
        stream_id = self.stream_counter
        self.streams[stream_id] = {
            'stream': stream,
            'name': name or f"stream_{stream_id}"
        }
        self.stream_counter += 1
        return stream_id

    def create_event(self, name=""):
        """创建Event"""
        event = acl.rt.create_event(acl.rt.event_id)
        event_id = self.event_counter
        self.events[event_id] = {
            'event': event,
            'name': name or f"event_{event_id}"
        }
        self.event_counter += 1
        return event_id

    def record_event(self, event_id, stream_id):
        """在Stream上记录Event"""
        if event_id not in self.events or stream_id not in self.streams:
            raise ValueError("Invalid event_id or stream_id")

        event = self.events[event_id]['event']
        stream = self.streams[stream_id]['stream']

        acl.rt.record_event(event, stream)
        print(f"Recorded event {event_id} on stream {stream_id}")

    def wait_event(self, event_id, stream_id):
        """Stream等待Event"""
        if event_id not in self.events or stream_id not in self.streams:
            raise ValueError("Invalid event_id or stream_id")

        event = self.events[event_id]['event']
        stream = self.streams[stream_id]['stream']

        acl.rt.wait_event(stream, event)
        print(f"Stream {stream_id} waited for event {event_id}")

    def synchronize_stream(self, stream_id):
        """同步Stream"""
        if stream_id not in self.streams:
            raise ValueError("Invalid stream_id")

        stream = self.streams[stream_id]['stream']
        acl.rt.synchronize_stream(stream)
        print(f"Synchronized stream {stream_id}")

    def async_memcpy_h2d(self, host_data, stream_id):
        """异步Host到Device拷贝"""
        if stream_id not in self.streams:
            raise ValueError("Invalid stream_id")

        size = host_data.nbytes if isinstance(host_data, np.ndarray) else len(host_data)
        device_ptr, _ = acl.rt.malloc(size)

        stream = self.streams[stream_id]['stream']
        acl.rt.memcpy_async(
            device_ptr,
            host_data,
            size,
            acl.rt.MEMCPY_HOST_TO_DEVICE,
            stream
        )

        return device_ptr

    def async_memcpy_d2h(self, device_ptr, size, stream_id):
        """异步Device到Host拷贝"""
        if stream_id not in self.streams:
            raise ValueError("Invalid stream_id")

        host_data = np.zeros(size // 4, dtype=np.float32)
        stream = self.streams[stream_id]['stream']

        acl.rt.memcpy_async(
            host_data,
            device_ptr,
            size,
            acl.rt.MEMCPY_DEVICE_TO_HOST,
            stream
        )

        return host_data

    def release(self):
        """释放所有资源"""
        for event_info in self.events.values():
            acl.rt.destroy_event(event_info['event'])

        for stream_info in self.streams.values():
            acl.rt.destroy_stream(stream_info['stream'])

        self.events.clear()
        self.streams.clear()

# 使用示例
if __name__ == "__main__":
    device_id = 0
    acl.init()
    acl.rt.set_device(device_id)

    stream_mgr = StreamManager(device_id)

    # 创建Stream和Event
    stream1 = stream_mgr.create_stream("compute_stream")
    stream2 = stream_mgr.create_stream("copy_stream")
    event1 = stream_mgr.create_event("compute_done")

    # 在stream1上执行计算
    data1 = np.random.rand(1024, 1024).astype(np.float32)
    device_ptr1 = stream_mgr.async_memcpy_h2d(data1, stream1)

    # 记录event
    stream_mgr.record_event(event1, stream1)

    # 在stream2上等待event1
    stream_mgr.wait_event(event1, stream2)

    # 在stream2上执行后续操作
    data2 = np.zeros((1024, 1024), dtype=np.float32)
    stream_mgr.async_memcpy_d2h(device_ptr1, data2.nbytes, stream2)

    # 同步stream2
    stream_mgr.synchronize_stream(stream2)

    print("Async operations completed")

    # 释放资源
    acl.rt.free(device_ptr1)
    stream_mgr.release()

    acl.rt.reset_device(device_id)
    acl.finalize()

---

八、性能分析示例

8.1 性能数据采集

import acl
import time
from contextlib import contextmanager

class PerformanceProfiler:
    """性能分析器"""

    def __init__(self, device_id=0):
        self.device_id = device_id
        self.metrics = {}

    @contextmanager
    def profile(self, name):
        """性能分析上下文管理器"""
        # 开始时间
        acl.rt.set_ts_mode(acl.rt.TIMESTAMP_MODE_NS)
        start_ns = acl.rt.get_timestamp()

        yield

        # 结束时间
        end_ns = acl.rt.get_timestamp()
        duration_us = (end_ns - start_ns) / 1000  # 转换为微秒

        self.metrics[name] = {
            'duration_us': duration_us,
            'duration_ms': duration_us / 1000
        }

        print(f"{name}: {duration_us:.2f} us ({duration_us/1000:.2f} ms)")

    def get_device_memory_info(self):
        """获取设备内存信息"""
        total, free = acl.rt.get_mem_info(self.device_id)
        used = total - free

        return {
            'total_gb': total / 1024**3,
            'used_gb': used / 1024**3,
            'free_gb': free / 1024**3,
            'usage_percent': (used / total) * 100
        }

    def print_summary(self):
        """打印性能汇总"""
        print("\n=== Performance Summary ===")

        if self.metrics:
            print("\nOperation Timing:")
            total_time = sum(m['duration_ms'] for m in self.metrics.values())
            for name, metric in self.metrics.items():
                pct = (metric['duration_ms'] / total_time) * 100
                print(f"  {name}: {metric['duration_ms']:.2f} ms ({pct:.1f}%)")
            print(f"  Total: {total_time:.2f} ms")

        mem_info = self.get_device_memory_info()
        print("\nMemory Usage:")
        print(f"  Total: {mem_info['total_gb']:.2f} GB")
        print(f"  Used:  {mem_info['used_gb']:.2f} GB")
        print(f"  Free:  {mem_info['free_gb']:.2f} GB")
        print(f"  Usage: {mem_info['usage_percent']:.1f}%")

# 使用示例
if __name__ == "__main__":
    device_id = 0
    acl.init()
    acl.rt.set_device(device_id)

    profiler = PerformanceProfiler(device_id)

    with profiler.profile("memory_allocation"):
        # 分配内存
        size = 100 * 1024 * 1024  # 100MB
        ptr, _ = acl.rt.malloc(size)

    with profiler.profile("computation"):
        # 执行计算...
        time.sleep(0.01)  # 模拟计算

    with profiler.profile("memory_free"):
        acl.rt.free(ptr)

    profiler.print_summary()

    acl.rt.reset_device(device_id)
    acl.finalize()

---

九、应用场景

runtime组件广泛应用于以下场景：

场景	描述	使用模块
模型推理部署	加载和执行AI模型推理	模型加载、推理执行
批量数据处理	高效处理大规模数据	内存管理、批量推理
多卡并行训练	分布式训练资源管理	多设备管理、Stream
实时推理服务	低延迟在线推理	异步执行、性能优化
资源监控	设备资源使用监控	性能分析、内存统计

---

十、总结

runtime作为CANN的运行时组件，为AI应用提供了完整的设备管理、内存管理、模型执行等基础能力。通过本文的介绍和示例代码，开发者可以全面了解runtime的架构设计和API使用方法，为构建高性能AI应用奠定坚实基础。

相关链接：

• CANN组织链接：https://atomgit.com/cann
• runtime仓库链接：https://atomgit.com/cann/runtime