# 达芬奇架构下深度学习模型异构算力迁移技术解析
## 一、达芬奇架构与CANN软件栈技术体系
### 达芬奇架构核心特性
达芬奇架构作为异构计算的核心，采用**多维立体计算单元**设计，实现不同精度算力的灵活调配：
```c
// 达芬奇架构计算单元示意
struct DaVinciCore {
// 计算能力配置
ComputeCapability capability;
// 多维计算单元
CubeUnit cube_units[16]; // 矩阵计算单元
VectorUnit vector_units[8]; // 向量计算单元
ScalarUnit scalar_units; // 标量处理单元
// 内存层次结构
LocalMemory l1_cache[32KB]; // L1缓存
SharedMemory l2_cache[512KB]; // L2共享内存
GlobalMemory hbm_memory; // 高带宽内存
// 数据通路
DataPath matrix_data_path; // 矩阵数据通路
DataPath vector_data_path; // 向量数据通路
DataPath scalar_data_path; // 标量数据通路
};
// 计算精度支持
enum ComputePrecision {
FP32 = 0, // 单精度浮点
FP16 = 1, // 半精度浮点
INT8 = 2, // 8位整数
INT4 = 3, // 4位整数
BF16 = 4 // 脑浮点格式
};
```
### CANN软件栈整体架构
```
CANN软件栈层次结构：
├── 应用框架层
│ ├── TensorFlow插件
│ ├── PyTorch扩展
│ └── MindSpore原生支持
├── 运行时管理层
│ ├── 图编译器（GE）
│ ├── 运行时引擎（RT）
│ └── 任务调度器
├── 算子开发层
│ ├️── 算子库（AICPU/AIV）
│ ├── 自定义算子开发工具
│ └── 性能分析工具
├── 驱动层
│ ├── 设备驱动
│ └── 系统调用接口
└── 硬件抽象层
├── 达芬奇核心抽象
└── 内存管理单元
```
## 二、模型迁移全链路技术流程
### 模型分析与准备阶段
```python
# model_analyzer.py - 模型分析工具
import torch
import onnx
import numpy as np
from typing import Dict, List, Tuple
class ModelMigrationAnalyzer:
def __init__(self):
self.supported_ops = self._load_supported_operations()
self.precision_requirements = {}
def analyze_model(self, model_path: str, framework: str = "pytorch") -> Dict:
"""分析模型结构"""
analysis_result = {
"model_info": {},
"operator_analysis": [],
"memory_requirements": {},
"performance_estimation": {},
"migration_risks": []
}
# 加载模型
if framework == "pytorch":
model = torch.load(model_path)
analysis_result["model_info"] = self._analyze_pytorch_model(model)
elif framework == "onnx":
model = onnx.load(model_path)
analysis_result["model_info"] = self._analyze_onnx_model(model)
# 算子兼容性分析
ops_analysis = self._analyze_operators(analysis_result["model_info"]["operators"])
analysis_result["operator_analysis"] = ops_analysis
# 内存需求估算
memory_req = self._estimate_memory_requirements(model)
analysis_result["memory_requirements"] = memory_req
# 性能预估
perf_est = self._estimate_performance(model)
analysis_result["performance_estimation"] = perf_est
# 迁移风险评估
risks = self._identify_migration_risks(ops_analysis)
analysis_result["migration_risks"] = risks
return analysis_result
def _analyze_operators(self, operators: List[Dict]) -> List[Dict]:
"""分析算子兼容性"""
analysis_results = []
for op in operators:
op_name = op["name"]
op_type = op["type"]
# 检查算子是否支持
is_supported = op_type in self.supported_ops
# 检查精度要求
precision_support = self._check_precision_support(op)
# 检查性能特性
performance_chars = self._analyze_performance_characteristics(op)
analysis_results.append({
"operator": op_name,
"type": op_type,
"supported": is_supported,
"precision_support": precision_support,
"performance_characteristics": performance_chars,
"custom_required": not is_supported,
"recommended_action": self._get_recommendation(is_supported, precision_support)
})
return analysis_results
def _check_precision_support(self, operator: Dict) -> Dict:
"""检查精度支持情况"""
precision_req = operator.get("precision", "FP32")
davinci_support = {
"FP32": True,
"FP16": True,
"INT8": True,
"INT4": True,
"BF16": True
}
return {
"required": precision_req,
"supported": davinci_support.get(precision_req, False),
"alternative": self._find_precision_alternative(precision_req)
}
def _estimate_memory_requirements(self, model) -> Dict:
"""估算内存需求"""
# 估算模型权重内存
weight_memory = self._calculate_weight_memory(model)
# 估算中间激活值内存
activation_memory = self._calculate_activation_memory(model)
# 考虑达芬奇架构的内存特性
memory_requirements = {
"weight_memory_mb": weight_memory / (1024 * 1024),
"activation_memory_mb": activation_memory / (1024 * 1024),
"total_memory_mb": (weight_memory + activation_memory) / (1024 * 1024),
"hbm_requirements": self._calculate_hbm_requirements(weight_memory, activation_memory),
"l2_cache_utilization": self._estimate_cache_utilization(model)
}
return memory_requirements
def generate_migration_report(self, analysis_result: Dict) -> str:
"""生成迁移分析报告"""
report = f"""
========================================
模型迁移分析报告
========================================
模型基本信息:
- 框架: {analysis_result['model_info']['framework']}
- 参数量: {analysis_result['model_info']['parameter_count']:,}
- 算子总数: {len(analysis_result['model_info']['operators'])}
算子兼容性分析:
{self._format_operator_analysis(analysis_result['operator_analysis'])}
内存需求分析:
- 权重内存: {analysis_result['memory_requirements']['weight_memory_mb']:.2f} MB
- 激活内存: {analysis_result['memory_requirements']['activation_memory_mb']:.2f} MB
- 总内存: {analysis_result['memory_requirements']['total_memory_mb']:.2f} MB
性能预估:
- 理论算力需求: {analysis_result['performance_estimation']['theoretical_flops'] / 1e9:.2f} GFLOPS
- 预估推理时间: {analysis_result['performance_estimation']['estimated_inference_time']:.3f} ms
迁移风险评估:
{self._format_risk_analysis(analysis_result['migration_risks'])}
建议措施:
{self._generate_recommendations(analysis_result)}
"""
return report
```
### 模型转换与优化
```python
# model_converter.py - 模型转换工具
import onnx
import onnxruntime as ort
from typing import Optional
import numpy as np
class DavinciModelConverter:
def __init__(self, target_device: str = "Ascend910"):
self.target_device = target_device
self.conversion_config = self._load_conversion_config()
def convert_to_om(self,KF.R6T.HK|4V.P8H.HK|6J.E2C.HK
input_model_path: str,
output_model_path: str,
precision: str = "FP16",
input_shape: Optional[Dict] = None) -> bool:
"""转换模型到OM格式"""
try:
# 1. 加载原始模型
if input_model_path.endswith('.onnx'):
onnx_model = onnx.load(input_model_path)
elif input_model_path.endswith('.pb'):
onnx_model = self._convert_tf_to_onnx(input_model_path)
# 2. 模型优化
optimized_model = self._optimize_model(onnx_model, precision)
# 3. 生成ATC转换命令
atc_cmd = self._build_atc_command(
input_model_path=input_model_path,
output_model_path=output_model_path,
precision=precision,
input_shape=input_shape
)
# 4. 执行转换
conversion_success = self._execute_conversion(atc_cmd)
if conversion_success:
# 5. 验证转换结果
verification_result = self._verify_om_model(output_model_path)
return verification_result
return False
except Exception as e:
print(f"模型转换失败: {str(e)}")
return False
def _build_atc_command(self, **kwargs) -> str:
"""构建ATC转换命令"""
base_cmd = "atc"
cmd_params = {
'model': kwargs['input_model_path'],
'output': kwargs['output_model_path'].replace('.om', ''),
'framework': '5', # ONNX
'soc_version': self._get_soc_version(),
'input_format': 'ND',
'precision_mode': self._map_precision_mode(kwargs['precision']),
'log': 'error',
}
# 添加输入形状
if kwargs.get('input_shape'):
shape_str = self._format_input_shape(kwargs['input_shape'])
cmd_params['input_shape'] = shape_str
# 添加优化选项
optimization_options = self._get_optimization_options()
cmd_params.update(optimization_options)
# 构建完整命令
cmd_parts = [base_cmd]
for key, value in cmd_params.items():
if value is not None:
cmd_parts.append(f'--{key}={value}')
return ' '.join(cmd_parts)
def _optimize_model(self, model, precision: str):
"""模型优化"""
optimization_passes = []
# 精度转换优化
if precision in ["FP16", "INT8"]:
optimization_passes.append({
"name": "precision_conversion",
"config": {"target_precision": precision}
})
# 算子融合优化
optimization_passes.append({
"name": "operator_fusion",
"config": self._get_fusion_rules()
})
# 内存优化
optimization_passes.append({
"name": "memory_optimization",
"config": {"enable_memory_reuse": True}
})
# 执行优化
for pass_config in optimization_passes:
model = self._apply_optimization_pass(model, pass_config)
return model
def _apply_optimization_pass(self, model, pass_config: Dict):
"""应用优化过程"""
pass_name = pass_config["name"]
if pass_name == "precision_conversion":
return self._convert_model_precision(model, pass_config["config"])
elif pass_name == "operator_fusion":
return self._fuse_operators(model, pass_config["config"])
elif pass_name == "memory_optimization":
return self._optimize_memory(model, pass_config["config"])
return model
def _convert_model_precision(self, model, config: Dict):
"""转换模型精度"""
target_precision = config["target_precision"]
# 精度转换策略
precision_strategy = {
"FP32": self._convert_to_fp32,
"FP16": self._convert_to_fp16,
"INT8": self._convert_to_int8,
"mixed": self._apply_mixed_precision
}
converter = precision_strategy.get(target_precision)
if converter:
return converter(model)
return model
def _convert_to_fp16(self, model):
"""转换到FP16精度"""
# 识别适合FP16的算子
fp16_safe_ops = self._identify_fp16_safe_operators(model)
# 应用精度转换
converted_model = self._apply_precision_conversion(
model,
target_precision="FP16",
safe_ops=fp16_safe_ops
)
# 添加精度损失补偿
loss_compensation = self._calculate_precision_loss_compensation(converted_model)
return self._apply_loss_compensation(converted_model, loss_compensation)
def _fuse_operators(self, model, fusion_rules: Dict):
"""算子融合优化"""
fused_model = model
# 应用融合规则
for rule_name, rule_config in fusion_rules.items():
if rule_name == "conv_bn_relu":
fused_model = self._fuse_conv_bn_relu(fused_model, rule_config)
elif rule_name == "matmul_add":
fused_model = self._fuse_matmul_add(fused_model, rule_config)
elif rule_name == "elementwise_ops":
fused_model = self._fuse_elementwise_operations(fused_model, rule_config)
return fused_model
```
## 三、CANN运行时与算子开发
### 运行时环境配置
```python
# runtime_manager.py - 运行时管理
import acl
import numpy as np
from typing import List, Dict
class CannRuntimeManager:
def __init__(self, device_id: int = 0):
self.device_id = device_id
self.context = None
self.stream = None
self.initialized = False
def initialize(self) -> bool:
"""初始化CANN运行时"""
try:
# 1. 初始化ACL
ret = acl.init()
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"ACL初始化失败: {ret}")
# 2. 设置设备
ret = acl.rt.set_device(self.device_id)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"设置设备失败: {ret}")
# 3. 创建上下文
self.context, ret = acl.rt.create_context(self.device_id)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"创建上下文失败: {ret}")
# 4. 创建流
self.stream, ret = acl.rt.create_stream()
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"创建流失败: {ret}")
# 5. 设置内存管理回调
acl.rt.set_mem_callback(self._memory_callback)
self.initialized = True
print(f"成功初始化CANN运行时，设备ID: {self.device_id}")
return True
except Exception as e:
print(f"运行时初始化失败: {str(e)}")
return False
def load_model(self, model_path: str) -> Dict:
"""加载OM模型"""
if not self.initialized:
raise RuntimeError("运行时未初始化")
model_info = {}
try:
# 1. 加载模型文件
with open(model_path, 'rb') as f:
model_data = f.read()
model_size = len(model_data)
# 2. 在设备上分配模型内存
model_ptr, ret = acl.rt.malloc(model_size,
acl.rt.mem_type.MEMORY_DEVICE)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"分配模型内存失败: {ret}")
# 3. 复制模型到设备
ret = acl.rt.memcpy(model_ptr, model_size,
model_data, model_size,
acl.rt.memcpy_kind.MEMCPY_HOST_TO_DEVICE)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"复制模型到设备失败: {ret}")
# 4. 加载模型
model_id, ret = acl.mdl.load_from_mem(model_ptr, model_size)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"加载模型失败: {ret}")
# 5. 获取模型描述信息
model_desc = acl.mdl.create_desc()
ret = acl.mdl.get_desc(model_desc, model_id)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"获取模型描述失败: {ret}")
# 收集模型信息
model_info = {
"model_id": model_id,
"model_desc": model_desc,
"input_num": acl.mdl.get_num_inputs(model_desc),
"output_num": acl.mdl.get_num_outputs(model_desc),
"dynamic_batch": self._check_dynamic_batch(model_desc)
}
# 获取输入输出信息
model_info["inputs"] = self._get_model_io_info(model_desc, is_input=True)
model_info["outputs"] = self._get_model_io_info(model_desc, is_input=False)
return model_info
except Exception as e:
print(f"加载模型失败: {str(e)}")
self._cleanup_model_resources(model_info)
raise
def create_dataset(self, data_dict: Dict) -> List:
"""创建数据集"""
datasets = []
for name, data in data_dict.items():
# 根据数据类型创建Dataset
if isinstance(data, np.ndarray):
dataset = self._create_tensor_dataset(data, name)
elif isinstance(data, list):
dataset = self._create_batch_dataset(data, name)
else:
raise ValueError(f"不支持的数据类型: {type(data)}")
datasets.append(dataset)
return datasets
def _create_tensor_dataset(self, tensor: np.ndarray, name: str):
"""创建张量数据集"""
# 1. 获取张量信息
dtype_map = {
np.float32: acl.ACL_FLOAT,
np.float16: acl.ACL_FLOAT16,
np.int32: acl.ACL_INT32,
np.int8: acl.ACL_INT8
}
dtype = dtype_map.get(tensor.dtype.type)
if dtype is None:
raise ValueError(f"不支持的数据类型: {tensor.dtype}")
# 2. 在设备上分配内存
tensor_size = tensor.size * tensor.itemsize
dev_ptr, ret = acl.rt.malloc(tensor_size, acl.rt.mem_type.MEMORY_DEVICE)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"分配设备内存失败: {ret}")
# 3. 复制数据到设备
ret = acl.rt.memcpy(dev_ptr, tensor_size,
tensor.ctypes.data, tensor_size,
acl.rt.memcpy_kind.MEMCPY_HOST_TO_DEVICE)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"复制数据到设备失败: {ret}")
# 4. 创建TensorDesc
dims = list(tensor.shape)
tensor_desc = acl.create_tensor_desc(dtype, dims, acl.ACL_FORMAT_ND)
# 5. 创建DataBuffer
data_buffer = acl.create_data_buffer(dev_ptr, tensor_size)
# 6. 创建Dataset
dataset = acl.mdl.create_dataset()
acl.mdl.add_dataset_buffer(dataset, data_buffer)
# 设置TensorDesc
acl.set_tensor_desc(tensor_desc, dataset, 0)
return dataset
def execute_model(self, model_info: Dict, input_datasets: List):
"""执行模型推理"""
try:
# 1. 创建输出Dataset
output_datasets = self._create_output_datasets(model_info)
# 2. 执行推理
ret = acl.mdl.execute(model_info["model_id"],
model_info["model_desc"],
input_datasets,
output_datasets)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"模型执行失败: {ret}")
# 3. 同步等待
ret = acl.rt.synchronize_stream(self.stream)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"流同步失败: {ret}")
# 4. 获取输出数据
outputs = self._extract_outputs(output_datasets, model_info)
# 5. 清理资源
self._destroy_datasets(output_datasets)
return outputs
except Exception as e:
print(f"模型执行失败: {str(e)}")
raise
def _create_output_datasets(self, model_info: Dict) -> List:
"""创建输出数据集"""
output_datasets = []
for i in range(model_info["output_num"]):
# 获取输出信息
output_desc = acl.mdl.get_output_dims(model_info["model_desc"], i)
output_dtype = acl.mdl.get_output_data_type(model_info["model_desc"], i)
# 计算输出大小
output_size = self._calculate_output_size(output_desc, output_dtype)
# 分配输出内存
output_ptr, ret = acl.rt.malloc(output_size, acl.rt.mem_type.MEMORY_DEVICE)
if ret != acl.ACL_SUCCESS:
raise RuntimeError(f"分配输出内存失败: {ret}")
# 创建DataBuffer和Dataset
data_buffer = acl.create_data_buffer(output_ptr, output_size)
dataset = acl.mdl.create_dataset()
acl.mdl.add_dataset_buffer(dataset, data_buffer)
output_datasets.append(dataset)
return output_datasets
```
### 自定义算子开发
```c++
// custom_operator.cpp - 自定义算子实现
#include <vector>
#include <memory>
#include "acl/acl.h"
#include "acl/acl_op.h"
// 自定义算子：深度可分离卷积
class DepthwiseSeparableConvOp {
public:HC.W4E.HK|QZ.E8P.HK|YW.R6T.HK
DepthwiseSeparableConvOp() {
// 初始化算子资源
op_desc_ = nullptr;
workspace_size_ = 0;
}
~DepthwiseSeparableConvOp() {
// 清理资源
if (op_desc_) {
aclopDestroyTensorDesc(op_desc_);
}
}
// 算子注册
static void Register() {
aclopRegisterOp(
"DepthwiseSeparableConv", // 算子名称
DepthwiseSeparableConvOp::ParseAndCompute, // 计算函数
DepthwiseSeparableConvOp::InferShape, // 形状推导函数
DepthwiseSeparableConvOp::InferType // 类型推导函数
);
}
// 计算函数
static aclError ParseAndCompute(
const aclopTensor* inputs[],
int num_inputs,
const aclopTensor* outputs[],
int num_outputs,
const aclopAttr* attr,
aclrtStream stream) {
// 解析输入
const aclopTensor* input = inputs[0];
const aclopTensor* depthwise_filter = inputs[1];
const aclopTensor* pointwise_filter = inputs[2];
// 获取张量信息
auto input_desc = aclopGetTensorDesc(input);
auto dw_filter_desc = aclopGetTensorDesc(depthwise_filter);
auto pw_filter_desc = aclopGetTensorDesc(pointwise_filter);
// 获取卷积参数
int stride_h = 1, stride_w = 1;
int dilation_h = 1, dilation_w = 1;
int pad_top = 0, pad_bottom = 0, pad_left = 0, pad_right = 0;
aclopGetAttrValue(attr, "strides", &stride_h, 2);
aclopGetAttrValue(attr, "dilations", &dilation_h, 2);
aclopGetAttrValue(attr, "pads", &pad_top, 4);
// 深度卷积计算
aclopTensor* depthwise_output = nullptr;
auto depthwise_attr = CreateDepthwiseConvAttr(
stride_h, stride_w,
dilation_h, dilation_w,
pad_top, pad_bottom, pad_left, pad_right
);
aclError ret = ComputeDepthwiseConv(
input, depthwise_filter,
depthwise_attr, &depthwise_output,
stream
);
if (ret != ACL_SUCCESS) {
return ret;
}
// 逐点卷积计算
ret = ComputePointwiseConv(
depthwise_output, pointwise_filter,
outputs[0], stream
);
// 清理中间结果
aclopDestroyTensor(depthwise_output);
return ret;
}
// 形状推导
static void InferShape(
const aclopTensorDesc* input_desc,
const aclopTensorDesc* dw_filter_desc,
const aclopTensorDesc* pw_filter_desc,
const aclopAttr* attr,
aclopTensorDesc* output_desc) {
// 获取输入形状
std::vector<int64_t> input_shape;
aclopGetTensorDescShape(input_desc, input_shape);
int batch = input_shape[0];
int input_channels = input_shape[1];
int input_h = input_shape[2];
int input_w = input_shape[3];
// 获取滤波器形状
std::vector<int64_t> pw_filter_shape;
aclopGetTensorDescShape(pw_filter_desc, pw_filter_shape);
int output_channels = pw_filter_shape[0];
// 计算输出形状
int stride_h = 1, stride_w = 1;
int pad_top = 0, pad_bottom = 0, pad_left = 0, pad_right = 0;
aclopGetAttrValue(attr, "strides", &stride_h, 2);
aclopGetAttrValue(attr, "pads", &pad_top, 4);
int output_h = (input_h + pad_top + pad_bottom - 3) / stride_h + 1;
int output_w = (input_w + pad_left + pad_right - 3) / stride_w + 1;
// 设置输出形状
std::vector<int64_t> output_shape = {
batch, output_channels, output_h, output_w
};
aclopSetTensorDescShape(output_desc, output_shape);
}
private:
aclopTensorDesc* op_desc_;
size_t workspace_size_;
// 创建深度卷积属性
static aclopAttr* CreateDepthwiseConvAttr(
int stride_h, int stride_w,
int dilation_h, int dilation_w,
int pad_top, int pad_bottom, int pad_left, int pad_right) {
aclopAttr* attr = aclopCreateAttr();
int strides[] = {stride_h, stride_w};
aclopSetAttrListInt(attr, "strides", strides, 2);
int dilations[] = {dilation_h, dilation_w};
aclopSetAttrListInt(attr, "dilations", dilations, 2);
int pads[] = {pad_top, pad_bottom, pad_left, pad_right};
aclopSetAttrListInt(attr, "pads", pads, 4);
aclopSetAttrInt(attr, "group", 1);
return attr;
}
// 计算深度卷积
static aclError ComputeDepthwiseConv(
const aclopTensor* input,
const aclopTensor* filter,
const aclopAttr* attr,
aclopTensor** output,
aclrtStream stream) {
// 调用ACL深度卷积算子
return aclopConv2D(
input, filter, nullptr,
*output, attr,
stream
);
}
// 计算逐点卷积
static aclError ComputePointwiseConv(
const aclopTensor* input,
const aclopTensor* filter,
const aclopTensor* output,
aclrtStream stream) {
// 创建卷积属性
aclopAttr* attr = aclopCreateAttr();
int strides[] = {1, 1};
aclopSetAttrListInt(attr, "strides", strides, 2);
int pads[] = {0, 0, 0, 0};
aclopSetAttrListInt(attr, "pads", pads, 4);
// 调用标准卷积算子
aclError ret = aclopConv2D(
input, filter, nullptr,
output, attr,
stream
);
aclopDestroyAttr(attr);
return ret;
}
};
// 算子注册入口
extern "C" void RegisterCustomOperators() {
DepthwiseSeparableConvOp::Register();
}
```
## 四、性能优化与调试
### 性能分析工具
```python
# performance_analyzer.py - 性能分析
import json
import time
from dataclasses import dataclass
from typing import List, Dict
import numpy as np
@dataclass
class PerformanceMetrics:
latency: float # 延迟（毫秒）
throughput: float # 吞吐量（FPS）
memory_usage: Dict[str, float] # 内存使用
compute_utilization: float # 计算利用率
power_consumption: float # 功耗
class DavinciPerformanceAnalyzer:
def __init__(self, device_id: int = 0):
self.device_id = device_id
self.metrics_history = []
def analyze_inference_performance(self, model_executor,
input_data: List[np.ndarray],
warmup_iterations: int = 10,
test_iterations: int = 100) -> PerformanceMetrics:
"""分析推理性能"""
# 预热阶段
print("开始预热...")
for i in range(warmup_iterations):
_ = model_executor.execute(input_data)
# 性能测试
print("开始性能测试...")
latencies = []
memory_usages = []
for i in range(test_iterations):
start_time = time.perf_counter()
# 执行推理
outputs = model_executor.execute(input_data)
# 测量延迟
latency = (time.perf_counter() - start_time) * 1000 # 毫秒
latencies.append(latency)
# 获取内存使用情况
memory_usage = self._get_memory_usage()
memory_usages.append(memory_usage)
# 计算统计指标
avg_latency = np.mean(latencies)
latency_std = np.std(latencies)
throughput = 1000 / avg_latency # FPS
# 计算平均内存使用
avg_memory = self._average_memory_usage(memory_usages)
# 获取计算利用率
compute_util = self._get_compute_utilization()
# 获取功耗
power_cons = self._get_power_consumption()
metrics = PerformanceMetrics(
latency=avg_latency,
throughput=throughput,
memory_usage=avg_memory,
compute_utilization=compute_util,
power_consumption=power_cons
)
# 保存历史记录
self.metrics_history.append({
"timestamp": time.time(),
"metrics": metrics,
"latency_stats": {
"mean": avg_latency,
"std": latency_std,
"p50": np.percentile(latencies, 50),
"p95": np.percentile(latencies, 95),
"p99": np.percentile(latencies, 99)
}
})
return metrics
def _get_memory_usage(self) -> Dict[str, float]:
"""获取内存使用情况"""
import subprocess
try:
# 使用npu-smi获取内存信息
result = subprocess.run(
['npu-smi', 'info', '-t', 'memory', '-i', str(self.device_id)],
capture_output=True,
text=True
)
memory_info = {}
for line in result.stdout.split('\n'):
if 'Memory' in line:
parts = line.split(':')
if len(parts) >= 2:
key = parts[0].strip()
value = parts[1].strip()
memory_info[key] = float(value.split()[0])
return memory_info
except Exception as e:
print(f"获取内存信息失败: {str(e)}")
return {}
def _get_compute_utilization(self) -> float:
"""获取计算利用率"""
import subprocess
try:
# 获取AI Core利用率
result = subprocess.run(
['npu-smi', 'info', '-t', 'utilization', '-i', str(self.device_id)],
capture_output=True,
text=True
)
for line in result.stdout.split('\n'):
if 'AICore' in line:
parts = line.split(':')
if len(parts) >= 2:
util_str = parts[1].strip().replace('%', '')
return float(util_str) / 100
return 0.0
except Exception as e:
print(f"获取计算利用率失败: {str(e)}")
return 0.0
def _get_power_consumption(self) -> float:
"""获取功耗"""
import subprocess
try:
result = subprocess.run(
['npu-smi', 'info', '-t', 'power', '-i', str(self.device_id)],
capture_output=True,
text=True
)
for line in result.stdout.split('\n'):
if 'Power' in line:
parts = line.split(':')
if len(parts) >= 2:
power_str = parts[1].strip().split()[0]
return float(power_str)
return 0.0
except Exception as e:
print(f"获取功耗失败: {str(e)}")
return 0.0
def generate_performance_report(self, metrics: PerformanceMetrics) -> str:
"""生成性能报告"""
report = f"""
========================================
达芬奇架构性能分析报告
========================================
延迟分析:
- 平均延迟: {metrics.latency:.2f} ms
- 吞吐量: {metrics.throughput:.2f} FPS
内存使用分析:
- 设备内存: {metrics.memory_usage.get('Memory Usage', 0):.2f} MB
- HBM带宽: {metrics.memory_usage.get('HBM Bandwidth', 0):.2f} GB/s
计算效率分析:
- AI Core利用率: {metrics.compute_utilization*100:.1f}%
- 功耗: {metrics.power_consumption:.2f} W
- 能效比: {metrics.throughput/metrics.power_consumption:.2f} FPS/W
优化建议:
{self._generate_optimization_suggestions(metrics)}
"""
return report
def _generate_optimization_suggestions(self, metrics: PerformanceMetrics) -> str:
"""生成优化建议"""
suggestions = []
# 内存优化建议
if metrics.memory_
