CANN图引擎GE的编译优化与高效执行机制深度解析

前言

Graph Engine（GE）是CANN框架的核心图编译和执行引擎，负责将深度学习计算图转换为高效的NPU执行计划。GE提供了一套完整的图优化、编译和执行框架，通过图级别的优化实现性能提升。本文将深入剖析GE的架构设计、优化机制和执行流程。

相关链接：

CANN组织链接：https://atomgit.com/cann
GE仓库链接：https://atomgit.com/cann/ge

一、GE架构概述

1.1 设计理念

GE遵循以下核心设计理念：

计算图抽象：将模型表示为数据流图
多层IR体系：不同阶段的中间表示支持不同级别的优化
Pass流水线：模块化的优化Pass可灵活组合
后端解耦：图优化与后端执行分离
延迟编译：支持运行时JIT编译

1.2 架构层次

复制代码

GE/
├── 前端接口层
│   ├── Graph API - 图构建接口
│   ├── Tensor API - 张量定义接口
│   └── Op API - 算子定义接口
├── 中间表示层
│   ├── Graph IR - 计算图中间表示
│   ├── IR Builder - IR构建器
│   └── IR Printer - IR打印/调试
├── 图优化层
│   ├── 优化Pass框架
│   ├── 图变换Pass
│   │   ├── 算子融合 (Operator Fusion)
│   │   ├── 常量折叠 (Constant Folding)
│   │   ├── 死代码消除 (DCE)
│   │   ├── 公共子表达式消除 (CSE)
│   │   └── 循环展开 (Loop Unrolling)
│   ├── 内存优化Pass
│   │   ├── 内存复用 (Memory Reuse)
│   │   ├── 内存分配 (Memory Allocation)
│   │   └── 缓冲区融合 (Buffer Fusion)
│   └── 布局优化Pass
│       ├── 数据布局转换 (Layout Transform)
│       └── 格式优化 (Format Optimization)
├── 编译层
│   ├── 图分区 (Graph Partitioning)
│   ├── 流水线调度 (Pipeline Scheduling)
│   ├── 代码生成 (Code Generation)
│   └── 二进制生成 (Binary Generation)
└── 执行层
    ├── 图执行器 (Graph Executor)
    ├── 流管理 (Stream Management)
    ├── 事件同步 (Event Synchronization)
    └── 内存管理 (Memory Management)

二、核心API详解

2.1 图构建API

cpp 复制代码

#include "ge/ge_api.h"

/**
 * @brief 计算图类
 * 表示一个完整的计算图
 */
class Graph {
public:
    // 构造函数
    Graph(const std::string& name = "graph");

    // 获取图名称
    const std::string& GetName() const { return name_; }

    // 添加算子节点
    Status AddOp(OpPtr op);

    // 添加数据边
    Status AddDataEdge(const OpPtr& src_op, uint32_t src_index,
                      const OpPtr& dst_op, uint32_t dst_index);

    // 添加控制边
    Status AddControlEdge(const OpPtr& src_op, const OpPtr& dst_op);

    // 获取所有算子
    const std::vector<OpPtr>& GetAllOps() const { return ops_; }

    // 获取输入节点
    std::vector<OpPtr> GetInputNodes() const;

    // 获取输出节点
    std::vector<OpPtr> GetOutputNodes() const;

    // 图验证
    Status Validate() const;

    // 拓扑排序
    std::vector<OpPtr> TopologicalSort() const;

private:
    std::string name_;
    std::vector<OpPtr> ops_;
    std::vector<DataEdge> data_edges_;
    std::vector<ControlEdge> control_edges_;
};

/**
 * @brief 算子节点类
 */
class Op {
public:
    // 构造函数
    Op(const std::string& name, const std::string& type);

    // 获取算子名称
    const std::string& GetName() const { return name_; }

    // 获取算子类型
    const std::string& GetType() const { return type_; }

    // 添加输入张量描述
    Status AddInputDesc(const TensorDesc& desc);

    // 添加输出张量描述
    Status AddOutputDesc(const TensorDesc& desc);

    // 获取输入描述
    const TensorDesc& GetInputDesc(uint32_t index) const;

    // 获取输出描述
    const TensorDesc& GetOutputDesc(uint32_t index) const;

    // 设置属性
    template<typename T>
    Status SetAttr(const std::string& name, const T& value);

    // 获取属性
    template<typename T>
    Status GetAttr(const std::string& name, T* value) const;

    // 获取所有输入节点
    std::vector<OpPtr> GetInputs() const;

    // 获取所有输出节点
    std::vector<OpPtr> GetOutputs() const;

private:
    std::string name_;
    std::string type_;
    std::vector<TensorDesc> input_descs_;
    std::vector<TensorDesc> output_descs_;
    std::unordered_map<std::string, Attribute> attrs_;
};

/**
 * @brief 张量描述类
 */
class TensorDesc {
public:
    // 构造函数
    TensorDesc(const Shape& shape, DataType dtype, Format format);

    // 获取形状
    const Shape& GetShape() const { return shape_; }

    // 获取数据类型
    DataType GetDataType() const { return dtype_; }

    // 获取数据格式
    Format GetFormat() const { return format_; }

    // 设置形状
    void SetShape(const Shape& shape) { shape_ = shape; }

    // 设置存储格式
    void SetFormat(Format format) { format_ = format; }

    // 获取存储大小
    size_t GetBytes() const;

    // 获取形状维度数
    int GetDimNum() const { return shape_.GetDimNum(); }

private:
    Shape shape_;
    DataType dtype_;
    Format format_;
};

/**
 * @brief 形状类
 */
class Shape {
public:
    // 构造函数
    Shape(const std::vector<int64_t>& dims);

    // 获取维度
    const std::vector<int64_t>& GetDims() const { return dims_; }

    // 获取维度数
    int GetDimNum() const { return dims_.size(); }

    // 获取总元素数
    int64_t GetTotalNum() const;

    // 获取指定维度的值
    int64_t GetDim(int index) const { return dims_[index]; }

    // 设置维度
    void SetDim(int index, int64_t value);

    // 形状比较
    bool operator==(const Shape& other) const;

    // 未知维度标记
    static constexpr int64_t kUnknownDim = -1;

private:
    std::vector<int64_t> dims_;
};

2.2 图优化API

cpp 复制代码

#include "ge/graph_optimizer.h"

/**
 * @brief 图优化器
 * 负责执行一系列优化Pass
 */
class GraphOptimizer {
public:
    // 构造函数
    GraphOptimizer();

    // 添加优化Pass
    Status AddPass(PassPtr pass);

    // 执行所有Pass
    Status Optimize(Graph* graph);

    // 获取优化报告
    const OptimizationReport& GetReport() const { return report_; }

private:
    std::vector<PassPtr> passes_;
    OptimizationReport report_;
};

/**
 * @brief 优化Pass基类
 * 所有优化Pass的基类
 */
class Pass {
public:
    // 构造函数
    Pass(const std::string& name) : name_(name) {}

    // 析构函数
    virtual ~Pass() = default;

    // 执行优化
    virtual Status Run(Graph* graph) = 0;

    // 获取Pass名称
    const std::string& GetName() const { return name_; }

    // 获取Pass类型
    virtual PassType GetType() const = 0;

protected:
    std::string name_;
};

// Pass类型枚举
enum PassType {
    PASS_GRAPH_TRANSFORM,     // 图变换
    PASS_MEMORY_OPT,          // 内存优化
    PASS_LAYOUT_OPT,          // 布局优化
    PASS_FUSION,              // 算子融合
    PASS_PARTITION            // 图分区
};

/**
 * @brief 算子融合Pass
 * 将多个连续算子融合为单个融合算子
 */
class OperatorFusionPass : public Pass {
public:
    struct FusionPattern {
        std::string name;                      // 模式名称
        std::vector<std::string> op_types;     // 算子类型序列
        std::function<bool(const std::vector<OpPtr>&)> matcher;  // 匹配器
        std::string fused_op_type;             // 融合后的算子类型
    };

    OperatorFusionPass() : Pass("OperatorFusion") {}

    Status Run(Graph* graph) override {
        bool changed = true;
        while (changed) {
            changed = false;
            for (const auto& pattern : patterns_) {
                if (TryApplyPattern(graph, pattern)) {
                    changed = true;
                    break;
                }
            }
        }
        return Status::OK();
    }

    PassType GetType() const override { return PASS_FUSION; }

    // 注册融合模式
    void RegisterPattern(const FusionPattern& pattern) {
        patterns_.push_back(pattern);
    }

private:
    bool TryApplyPattern(Graph* graph, const FusionPattern& pattern) {
        // 查找所有匹配的子图
        std::vector<std::vector<OpPtr>> matches;
        FindPatternMatches(graph, pattern, &matches);

        if (matches.empty()) {
            return false;
        }

        // 应用融合（这里简化处理第一个匹配）
        return ApplyFusion(graph, matches[0], pattern);
    }

    void FindPatternMatches(
        Graph* graph,
        const FusionPattern& pattern,
        std::vector<std::vector<OpPtr>>* matches) {

        // 遍历图中的所有算子序列
        auto ops = graph->GetAllOps();
        for (size_t i = 0; i + pattern.op_types.size() <= ops.size(); ++i) {
            std::vector<OpPtr> candidate(
                ops.begin() + i,
                ops.begin() + i + pattern.op_types.size()
            );

            // 检查类型匹配
            bool type_match = true;
            for (size_t j = 0; j < candidate.size(); ++j) {
                if (candidate[j]->GetType() != pattern.op_types[j]) {
                    type_match = false;
                    break;
                }
            }

            if (type_match && pattern.matcher(candidate)) {
                matches->push_back(candidate);
            }
        }
    }

    bool ApplyFusion(
        Graph* graph,
        const std::vector<OpPtr>& ops,
        const FusionPattern& pattern) {

        // 创建融合算子
        std::string fused_name = "Fused_" + ops[0]->GetName();
        for (size_t i = 1; i < ops.size(); ++i) {
            fused_name += "_" + ops[i]->GetName();
        }

        auto fused_op = std::make_shared<Op>(fused_name, pattern.fused_op_type);

        // 复制输入描述
        fused_op->AddInputDesc(ops[0]->GetInputDesc(0));

        // 复制输出描述
        fused_op->AddOutputDesc(ops.back()->GetOutputDesc(0));

        // 复制属性
        for (auto* op : ops) {
            // 简化：实际需要合并属性
        }

        // 替换原图中的算子
        return graph->ReplaceSubgraph(ops, fused_op);
    }

    std::vector<FusionPattern> patterns_;
};

/**
 * @brief 常量折叠Pass
 * 在编译期计算常量表达式的值
 */
class ConstantFoldingPass : public Pass {
public:
    ConstantFoldingPass() : Pass("ConstantFolding") {}

    Status Run(Graph* graph) override {
        auto ops = graph->GetAllOps();
        for (auto& op : ops) {
            if (IsConstantOp(op)) {
                continue;  // 已经是常量
            }

            // 检查所有输入是否都是常量
            if (AllInputsAreConstants(op)) {
                // 执行算子计算
                Tensor result;
                Status s = EvaluateConstantOp(op, &result);
                if (s.ok()) {
                    // 创建常量算子替换原算子
                    auto const_op = CreateConstantOp(op->GetName(), result);
                    graph->ReplaceOp(op, const_op);
                }
            }
        }
        return Status::OK();
    }

    PassType GetType() const override { return PASS_GRAPH_TRANSFORM; }

private:
    bool IsConstantOp(const OpPtr& op) {
        return op->GetType() == "Const";
    }

    bool AllInputsAreConstants(const OpPtr& op) {
        auto inputs = op->GetInputs();
        for (auto& input : inputs) {
            if (!IsConstantOp(input)) {
                return false;
            }
        }
        return true;
    }

    Status EvaluateConstantOp(const OpPtr& op, Tensor* result) {
        // 简化：实际需要根据算子类型执行计算
        return Status::OK();
    }

    OpPtr CreateConstantOp(const std::string& name, const Tensor& value) {
        auto const_op = std::make_shared<Op>(name, "Const");
        // 设置常量值
        return const_op;
    }
};

/**
 * @brief 公共子表达式消除Pass
 */
class CommonSubexpressionEliminationPass : public Pass {
public:
    CommonSubexpressionEliminationPass() : Pass("CSE") {}

    Status Run(Graph* graph) override {
        // 构建表达式哈希表
        std::unordered_map<std::string, std::vector<OpPtr>> expr_map;

        for (auto& op : graph->GetAllOps()) {
            std::string hash = ComputeOpHash(op);
            expr_map[hash].push_back(op);
        }

        // 对于每个哈希值，只保留第一个算子，其他替换为引用
        for (auto& pair : expr_map) {
            if (pair.second.size() > 1) {
                auto& canonical = pair.second[0];
                for (size_t i = 1; i < pair.second.size(); ++i) {
                    graph->ReplaceOp(pair.second[i], canonical);
                }
            }
        }

        return Status::OK();
    }

    PassType GetType() const override { return PASS_GRAPH_TRANSFORM; }

private:
    std::string ComputeOpHash(const OpPtr& op) {
        // 计算算子的签名哈希
        std::string hash = op->GetType();
        for (auto& input : op->GetInputs()) {
            hash += "_" + input->GetName();
        }
        return hash;
    }
};

/**
 * @brief 内存复用Pass
 * 优化内存分配，复用不再使用的缓冲区
 */
class MemoryReusePass : public Pass {
public:
    MemoryReusePass() : Pass("MemoryReuse") {}

    Status Run(Graph* graph) override {
        // 1. 构建数据依赖图
        DependencyGraph dep_graph;
        BuildDependencyGraph(graph, &dep_graph);

        // 2. 计算每个张量的生命周期
        std::unordered_map<OpPtr, Lifetime> lifetimes;
        ComputeLifetimes(graph, &dep_graph, &lifetimes);

        // 3. 构建不相交集合用于内存复用
        std::vector<BufferGroup> groups;
        PartitionByLifetime(lifetimes, &groups);

        // 4. 为每个组分配共享内存
        AllocateSharedMemory(graph, groups);

        return Status::OK();
    }

    PassType GetType() const override { return PASS_MEMORY_OPT; }

private:
    struct Lifetime {
        int birth;
        int death;
    };

    struct BufferGroup {
        std::vector<OpPtr> ops;
        size_t max_size;
    };

    void BuildDependencyGraph(Graph* graph, DependencyGraph* dep_graph) {
        // 构建数据依赖关系
    }

    void ComputeLifetimes(
        Graph* graph,
        const DependencyGraph* dep_graph,
        std::unordered_map<OpPtr, Lifetime>* lifetimes) {

        auto ops = graph->TopologicalSort();
        for (size_t i = 0; i < ops.size(); ++i) {
            Lifetime lt;
            lt.birth = i;
            // 计算最后一次使用的时间
            lt.death = FindLastUse(ops[i], ops);
            (*lifetimes)[ops[i]] = lt;
        }
    }

    int FindLastUse(const OpPtr& op, const std::vector<OpPtr>& ops) {
        int last_use = -1;
        for (size_t i = 0; i < ops.size(); ++i) {
            if (HasDependency(ops[i], op)) {
                last_use = i;
            }
        }
        return last_use;
    }

    bool HasDependency(const OpPtr& op, const OpPtr& target) {
        auto inputs = op->GetInputs();
        for (auto& input : inputs) {
            if (input == target) {
                return true;
            }
        }
        return false;
    }

    void PartitionByLifetime(
        const std::unordered_map<OpPtr, Lifetime>& lifetimes,
        std::vector<BufferGroup>* groups) {
        // 将不重叠生命周期的张量分组
    }

    void AllocateSharedMemory(
        Graph* graph,
        const std::vector<BufferGroup>& groups) {
        // 为每组分配共享内存
    }
};

2.3 图执行API

cpp 复制代码

#include "ge/graph_executor.h"

/**
 * @brief 图执行器
 * 负责编译后的图的执行
 */
class GraphExecutor {
public:
    // 构造函数
    GraphExecutor();

    // 析构函数
    ~GraphExecutor();

    // 加载编译后的图
    Status LoadGraph(const CompiledGraph& compiled_graph);

    // 设置输入数据
    Status SetInput(const std::string& input_name, const Tensor& data);

    // 设置输入数据（按索引）
    Status SetInput(uint32_t index, const Tensor& data);

    // 获取输出数据
    Status GetOutput(const std::string& output_name, Tensor* data);

    // 获取输出数据（按索引）
    Status GetOutput(uint32_t index, Tensor* data);

    // 执行图
    Status Execute();

    // 异步执行
    Status ExecuteAsync(aclrtStream stream);

    // 获取执行统计信息
    const ExecutionStats& GetStats() const { return stats_; }

private:
    CompiledGraph compiled_graph_;
    std::vector<Tensor> inputs_;
    std::vector<Tensor> outputs_;
    ExecutionStats stats_;
};

/**
 * @brief 图编译器
 * 将图编译为可执行格式
 */
class GraphCompiler {
public:
    // 构造函数
    GraphCompiler();

    // 配置选项
    struct CompileOptions {
        bool enable_fusion = true;          // 启用算子融合
        bool enable_optimization = true;    // 启用优化
        bool enable_precision_mode = false; // 精度模式
        std::string target_device = "NPU";  // 目标设备
        int opt_level = 3;                  // 优化级别 (0-3)
    };

    // 编译图
    Status Compile(
        const Graph& graph,
        const CompileOptions& options,
        CompiledGraph* compiled_graph);

    // 导出编译后的图
    Status Save(const CompiledGraph& graph, const std::string& path);

    // 加载编译后的图
    Status Load(const std::string& path, CompiledGraph* graph);

private:
    Status ApplyOptimizations(
        const Graph& input,
        const CompileOptions& options,
        Graph* optimized);

    Status GenerateExecutable(
        const Graph& optimized,
        const CompileOptions& options,
        CompiledGraph* executable);
};

/**
 * @brief 流管理器
 * 管理执行流和事件同步
 */
class StreamManager {
public:
    // 创建流
    Status CreateStream(aclrtStream* stream);

    // 销毁流
    Status DestroyStream(aclrtStream stream);

    // 创建事件
    Status CreateEvent(aclrtEvent* event);

    // 销毁事件
    Status DestroyEvent(aclrtEvent event);

    // 记录事件
    Status RecordEvent(aclrtEvent event, aclrtStream stream);

    // 等待事件
    Status WaitEvent(aclrtEvent event, aclrtStream stream);

    // 流同步
    Status StreamSynchronize(aclrtStream stream);

    // 设备同步
    Status DeviceSynchronize();

private:
    std::vector<aclrtStream> streams_;
    std::vector<aclrtEvent> events_;
};

三、应用实践

3.1 构建和优化计算图

cpp 复制代码

#include "ge/ge_api.h"

// 构建一个简单的卷积神经网络
Graph BuildConvNet() {
    Graph graph("ConvNet");

    // 创建数据输入节点
    auto data = std::make_shared<Op>("data", "Data");
    TensorDesc data_desc({1, 3, 224, 224}, DT_FLOAT, FORMAT_NCHW);
    data->AddOutputDesc(data_desc);
    graph.AddOp(data);

    // 创建卷积层1
    auto conv1 = std::make_shared<Op>("conv1", "Conv2D");
    conv1->AddInputDesc(data_desc);
    TensorDesc conv1_weight_desc({64, 3, 7, 7}, DT_FLOAT, FORMAT_NCHW);
    conv1->AddInputDesc(conv1_weight_desc);
    TensorDesc conv1_bias_desc({64}, DT_FLOAT, FORMAT_ND);
    conv1->AddInputDesc(conv1_bias_desc);
    TensorDesc conv1_output_desc({1, 64, 112, 112}, DT_FLOAT, FORMAT_NCHW);
    conv1->AddOutputDesc(conv1_output_desc);
    conv1->SetAttr("strides", std::vector<int>{2, 2});
    conv1->SetAttr("pads", std::vector<int>{3, 3, 3, 3});
    conv1->SetAttr("dilations", std::vector<int>{1, 1});
    conv1->SetAttr("groups", 1);
    graph.AddOp(conv1);

    // 创建BN层1
    auto bn1 = std::make_shared<Op>("bn1", "BatchNorm");
    bn1->AddInputDesc(conv1_output_desc);
    TensorDesc bn1_output_desc({1, 64, 112, 112}, DT_FLOAT, FORMAT_NCHW);
    bn1->AddOutputDesc(bn1_output_desc);
    bn1->SetAttr("epsilon", 1e-5f);
    bn1->SetAttr("momentum", 0.9f);
    graph.AddOp(bn1);

    // 创建ReLU激活1
    auto relu1 = std::make_shared<Op>("relu1", "ReLU");
    relu1->AddInputDesc(bn1_output_desc);
    relu1->AddOutputDesc(bn1_output_desc);
    graph.AddOp(relu1);

    // 创建最大池化层1
    auto pool1 = std::make_shared<Op>("pool1", "MaxPool");
    pool1->AddInputDesc(bn1_output_desc);
    TensorDesc pool1_output_desc({1, 64, 56, 56}, DT_FLOAT, FORMAT_NCHW);
    pool1->AddOutputDesc(pool1_output_desc);
    pool1->SetAttr("kernels", std::vector<int>{3, 3});
    pool1->SetAttr("strides", std::vector<int>{2, 2});
    pool1->SetAttr("pads", std::vector<int>{1, 1, 1, 1});
    graph.AddOp(pool1);

    // 添加数据边
    graph.AddDataEdge(data, 0, conv1, 0);
    graph.AddDataEdge(conv1, 0, bn1, 0);
    graph.AddDataEdge(bn1, 0, relu1, 0);
    graph.AddDataEdge(relu1, 0, pool1, 0);

    return graph;
}

// 应用优化Pass
void OptimizeGraph(Graph* graph) {
    GraphOptimizer optimizer;

    // 添加算子融合Pass
    auto fusion_pass = std::make_shared<OperatorFusionPass>();

    // 注册融合模式：Conv + BN + ReLU
    OperatorFusionPass::FusionPattern conv_bn_relu;
    conv_bn_relu.name = "ConvBNRelu";
    conv_bn_relu.op_types = {"Conv2D", "BatchNorm", "ReLU"};
    conv_bn_relu.matcher = [](const std::vector<OpPtr>& ops) {
        // 检查是否可以融合（简化示例）
        return true;
    };
    conv_bn_relu.fused_op_type = "FusedConvBNRelu";
    fusion_pass->RegisterPattern(conv_bn_relu);

    optimizer.AddPass(fusion_pass);

    // 添加常量折叠Pass
    optimizer.AddPass(std::make_shared<ConstantFoldingPass>());

    // 添加公共子表达式消除Pass
    optimizer.AddPass(std::make_shared<CommonSubexpressionEliminationPass>());

    // 添加内存复用Pass
    optimizer.AddPass(std::make_shared<MemoryReusePass>());

    // 执行优化
    Status s = optimizer.Optimize(graph);
    if (!s.ok()) {
        std::cerr << "Optimization failed: " << s.ErrorMessage() << std::endl;
    } else {
        std::cout << "Optimization completed successfully" << std::endl;
        auto report = optimizer.GetReport();
        std::cout << "Passes executed: " << report.passes_executed << std::endl;
        std::cout << "Ops fused: " << report.ops_fused << std::endl;
        std::cout << "Memory saved: " << report.memory_saved << " bytes" << std::endl;
    }
}

// 编译和执行图
void CompileAndExecuteGraph(const Graph& graph) {
    GraphCompiler compiler;

    // 配置编译选项
    GraphCompiler::CompileOptions options;
    options.enable_fusion = true;
    options.enable_optimization = true;
    options.opt_level = 3;
    options.target_device = "NPU";

    // 编译图
    CompiledGraph compiled_graph;
    Status s = compiler.Compile(graph, options, &compiled_graph);
    if (!s.ok()) {
        std::cerr << "Compilation failed: " << s.ErrorMessage() << std::endl;
        return;
    }

    // 保存编译后的图
    compiler.Save(compiled_graph, "conv_net.om");

    // 创建执行器
    GraphExecutor executor;
    executor.LoadGraph(compiled_graph);

    // 设置输入
    Tensor input_tensor({1, 3, 224, 224}, DT_FLOAT, FORMAT_NCHW);
    // 填充输入数据...
    executor.SetInput(0, input_tensor);

    // 执行
    s = executor.Execute();
    if (!s.ok()) {
        std::cerr << "Execution failed: " << s.ErrorMessage() << std::endl;
        return;
    }

    // 获取输出
    Tensor output_tensor;
    executor.GetOutput(0, &output_tensor);

    // 打印执行统计
    auto stats = executor.GetStats();
    std::cout << "Execution time: " << stats.execution_time_us << " us" << std::endl;
    std::cout << "Memory used: " << stats.memory_used << " bytes" << std::endl;
}

3.2 自定义优化Pass

cpp 复制代码

/**
 * @brief 自定义布局转换Pass
 * 优化数据布局以提高缓存命中率
 */
class LayoutTransformPass : public Pass {
public:
    LayoutTransformPass() : Pass("LayoutTransform") {}

    Status Run(Graph* graph) override {
        // 分析每个算子的最优输入输出格式
        std::unordered_map<OpPtr, FormatPreference> prefs;
        AnalyzeFormatPreferences(graph, &prefs);

        // 插入必要的格式转换
        InsertFormatTransforms(graph, prefs);

        // 尽可能消除冗余的转换
        EliminateRedundantTransforms(graph);

        return Status::OK();
    }

    PassType GetType() const override { return PASS_LAYOUT_OPT; }

private:
    struct FormatPreference {
        Format input_format;
        Format output_format;
    };

    void AnalyzeFormatPreferences(
        Graph* graph,
        std::unordered_map<OpPtr, FormatPreference>* prefs) {

        for (auto& op : graph->GetAllOps()) {
            FormatPreference pref;

            // 根据算子类型确定最优格式
            if (op->GetType() == "Conv2D" || op->GetType() == "FusedConvBNRelu") {
                // 卷积算子偏好NCHW
                pref.input_format = FORMAT_NCHW;
                pref.output_format = FORMAT_NCHW;
            } else if (op->GetType() == "MatMul" || op->GetType() == "FullyConnection") {
                // 矩阵乘法偏好NC1HWC0（NPU专用格式）
                pref.input_format = FORMAT_NC1HWC0;
                pref.output_format = FORMAT_NC1HWC0;
            } else {
                // 默认NCHW
                pref.input_format = FORMAT_NCHW;
                pref.output_format = FORMAT_NCHW;
            }

            (*prefs)[op] = pref;
        }
    }

    void InsertFormatTransforms(
        Graph* graph,
        const std::unordered_map<OpPtr, FormatPreference>& prefs) {

        for (auto& op : graph->GetAllOps()) {
            auto pref = prefs.find(op);
            if (pref == prefs.end()) continue;

            // 检查输入格式
            auto inputs = op->GetInputs();
            for (size_t i = 0; i < inputs.size(); ++i) {
                Format input_format = op->GetInputDesc(i).GetFormat();
                if (input_format != pref->second.input_format) {
                    // 插入格式转换
                    auto trans_op = CreateFormatTransformOp(
                        inputs[i], input_format, pref->second.input_format);
                    graph->InsertOpBefore(op, trans_op, i);
                }
            }

            // 检查输出格式
            auto outputs = op->GetOutputs();
            for (size_t i = 0; i < outputs.size(); ++i) {
                Format output_format = op->GetOutputDesc(i).GetFormat();
                if (output_format != pref->second.output_format) {
                    // 插入格式转换
                    auto trans_op = CreateFormatTransformOp(
                        op, output_format, pref->second.output_format);
                    graph->InsertOpAfter(op, trans_op, i);
                }
            }
        }
    }

    void EliminateRedundantTransforms(Graph* graph) {
        // 移除相邻且相互抵消的格式转换
        bool changed = true;
        while (changed) {
            changed = false;
            auto ops = graph->GetAllOps();

            for (auto& op : ops) {
                if (op->GetType() != "FormatTransform") continue;

                auto consumers = op->GetOutputs();
                for (auto& consumer : consumers) {
                    if (consumer->GetType() == "FormatTransform") {
                        // 检查是否可以抵消
                        Format src_fmt = op->GetInputDesc(0).GetFormat();
                        Format mid_fmt = op->GetOutputDesc(0).GetFormat();
                        Format dst_fmt = consumer->GetOutputDesc(0).GetFormat();

                        if (src_fmt == dst_fmt) {
                            // 移除这两个转换
                            graph->BypassOp(op);
                            graph->BypassOp(consumer);
                            changed = true;
                            break;
                        }
                    }
                }
                if (changed) break;
            }
        }
    }

    OpPtr CreateFormatTransformOp(
        const OpPtr& input,
        Format src_format,
        Format dst_format) {

        std::string name = "FormatTransform_" + src_format + "_to_" + dst_format;
        auto trans_op = std::make_shared<Op>(name, "FormatTransform");
        trans_op->AddInputDesc(input->GetOutputDesc(0));

        TensorDesc output_desc = input->GetOutputDesc(0);
        output_desc.SetFormat(dst_format);
        trans_op->AddOutputDesc(output_desc);

        trans_op->SetAttr("src_format", static_cast<int>(src_format));
        trans_op->SetAttr("dst_format", static_cast<int>(dst_format));

        return trans_op;
    }
};

/**
 * @brief 循环展开Pass
 */
class LoopUnrollingPass : public Pass {
public:
    LoopUnrollingPass(int max_unroll_factor = 4)
        : Pass("LoopUnrolling"), max_unroll_factor_(max_unroll_factor) {}

    Status Run(Graph* graph) override {
        // 查找循环结构
        std::vector<LoopSubgraph> loops;
        FindLoops(graph, &loops);

        // 尝试展开小循环
        for (const auto& loop : loops) {
            if (ShouldUnroll(loop)) {
                UnrollLoop(graph, loop);
            }
        }

        return Status::OK();
    }

    PassType GetType() const override { return PASS_GRAPH_TRANSFORM; }

private:
    struct LoopSubgraph {
        OpPtr loop_var;
        OpPtr loop_body;
        int trip_count;
        std::vector<OpPtr> body_ops;
    };

    void FindLoops(Graph* graph, std::vector<LoopSubgraph>* loops) {
        // 识别循环结构（简化示例）
        for (auto& op : graph->GetAllOps()) {
            if (op->GetType() == "Loop" || op->GetType() == "While") {
                LoopSubgraph loop;
                loop.loop_var = op;
                // 提取循环体...
                loops->push_back(loop);
            }
        }
    }

    bool ShouldUnroll(const LoopSubgraph& loop) {
        // 展开条件：
        // 1. 循环次数较小且已知
        // 2. 循环体较小
        // 3. 展开后不会导致代码膨胀过多
        return loop.trip_count > 0 &&
               loop.trip_count <= max_unroll_factor_ &&
               loop.body_ops.size() <= 10;
    }

    void UnrollLoop(Graph* graph, const LoopSubgraph& loop) {
        // 复制循环体trip_count次
        for (int i = 0; i < loop.trip_count; ++i) {
            for (auto& op : loop.body_ops) {
                auto cloned_op = CloneOp(op);
                graph->AddOp(cloned_op);
                // 重新连接数据边...
            }
        }

        // 移除原始循环
        graph->RemoveOp(loop.loop_var);
    }

    OpPtr CloneOp(const OpPtr& op) {
        auto cloned = std::make_shared<Op>(
            op->GetName() + "_clone",
            op->GetType()
        );
        // 复制描述和属性
        return cloned;
    }

    int max_unroll_factor_;
};

3.3 图分区和分布式编译

cpp 复制代码

/**
 * @brief 图分区Pass
 * 将计算图分区以支持分布式训练
 */
class GraphPartitionPass : public Pass {
public:
    GraphPartitionPass(int num_devices = 4)
        : Pass("GraphPartition"), num_devices_(num_devices) {}

    Status Run(Graph* graph) override {
        // 分析计算代价
        std::unordered_map<OpPtr, Cost> costs;
        AnalyzeCosts(graph, &costs);

        // 执行图分区
        std::vector<GraphPartition> partitions;
        PartitionGraph(graph, costs, &partitions);

        // 插入通信算子
        InsertCommunicationOps(graph, partitions);

        // 为每个分区生成独立的子图
        GeneratePartitionedGraphs(graph, partitions);

        return Status::OK();
    }

    PassType GetType() const override { return PASS_PARTITION; }

private:
    struct Cost {
        float compute_cost;    // 计算代价
        float memory_cost;     // 内存代价
        float comm_cost;       // 通信代价
    };

    struct GraphPartition {
        int device_id;
        std::vector<OpPtr> ops;
        std::vector<OpPtr> send_ops;
        std::vector<OpPtr> recv_ops;
    };

    void AnalyzeCosts(
        Graph* graph,
        std::unordered_map<OpPtr, Cost>* costs) {

        for (auto& op : graph->GetAllOps()) {
            Cost cost;

            // 估算计算代价（根据FLOPs）
            cost.compute_cost = EstimateFLOPs(op);

            // 估算内存代价
            cost.memory_cost = EstimateMemoryUsage(op);

            // 通信代价（跨分区）
            cost.comm_cost = 0.0f;  // 初始为0，分区后计算

            (*costs)[op] = cost;
        }
    }

    float EstimateFLOPs(const OpPtr& op) {
        // 根据算子类型和输入形状估算FLOPs
        std::string type = op->GetType();

        if (type == "Conv2D") {
            auto output_shape = op->GetOutputDesc(0).GetShape();
            auto kernel_shape = op->GetInputDesc(1).GetShape();

            int64_t N = output_shape.GetDim(0);
            int64_t C = output_shape.GetDim(1);
            int64_t H = output_shape.GetDim(2);
            int64_t W = output_shape.GetDim(3);
            int64_t K = kernel_shape.GetDim(2);
            int64_t K_w = kernel_shape.GetDim(3);

            return N * C * H * W * K * K_w;
        } else if (type == "MatMul") {
            auto shape_a = op->GetInputDesc(0).GetShape();
            auto shape_b = op->GetInputDesc(1).GetShape();

            int64_t M = shape_a.GetDim(0);
            int64_t K = shape_a.GetDim(1);
            int64_t N = shape_b.GetDim(1);

            return M * K * N;
        }

        return 1000.0f;  // 默认代价
    }

    float EstimateMemoryUsage(const OpPtr& op) {
        float total = 0.0f;
        for (uint32_t i = 0; i < op->GetInputDescNum(); ++i) {
            total += op->GetInputDesc(i).GetBytes();
        }
        for (uint32_t i = 0; i < op->GetOutputDescNum(); ++i) {
            total += op->GetOutputDesc(i).GetBytes();
        }
        return total;
    }

    void PartitionGraph(
        Graph* graph,
        const std::unordered_map<OpPtr, Cost>& costs,
        std::vector<GraphPartition>* partitions) {

        // 使用简单的贪心算法分区
        // 实际可以使用更复杂的算法如Kernighan-Lin

        partitions->resize(num_devices_);
        std::vector<float> device_loads(num_devices_, 0.0f);

        // 按计算代价排序算子
        auto ops = graph->GetAllOps();
        std::sort(ops.begin(), ops.end(),
                 [&costs](const OpPtr& a, const OpPtr& b) {
                     return costs.at(a).compute_cost > costs.at(b).compute_cost;
                 });

        // 贪心分配
        for (auto& op : ops) {
            // 找到当前负载最小的设备
            int min_device = 0;
            float min_load = device_loads[0];
            for (int i = 1; i < num_devices_; ++i) {
                if (device_loads[i] < min_load) {
                    min_load = device_loads[i];
                    min_device = i;
                }
            }

            (*partitions)[min_device].ops.push_back(op);
            device_loads[min_device] += costs.at(op).compute_cost;
        }
    }

    void InsertCommunicationOps(
        Graph* graph,
        std::vector<GraphPartition>& partitions) {

        // 分析跨分区边
        for (size_t i = 0; i < partitions.size(); ++i) {
            for (auto& op : partitions[i].ops) {
                auto outputs = op->GetOutputs();
                for (auto& output : outputs) {
                    // 检查输出是否在不同分区
                    int output_partition = FindPartition(output, partitions);
                    if (output_partition != i && output_partition >= 0) {
                        // 需要插入Send/Recv
                        auto send_op = CreateSendOp(op, output, i, output_partition);
                        auto recv_op = CreateRecvOp(op, output, i, output_partition);

                        partitions[i].send_ops.push_back(send_op);
                        partitions[output_partition].recv_ops.push_back(recv_op);

                        graph->InsertOpAfter(op, send_op, 0);
                        graph->InsertOpBefore(output, recv_op, 0);
                    }
                }
            }
        }
    }

    int FindPartition(
        const OpPtr& op,
        const std::vector<GraphPartition>& partitions) {

        for (size_t i = 0; i < partitions.size(); ++i) {
            if (std::find(partitions[i].ops.begin(),
                         partitions[i].ops.end(), op)
                != partitions[i].ops.end()) {
                return i;
            }
        }
        return -1;
    }

    OpPtr CreateSendOp(
        const OpPtr& src,
        const OpPtr& dst,
        int src_device,
        int dst_device) {

        std::string name = "Send_" + src->GetName() + "_to_" + dst->GetName();
        auto send_op = std::make_shared<Op>(name, "Send");
        send_op->AddInputDesc(src->GetOutputDesc(0));
        send_op->AddOutputDesc(src->GetOutputDesc(0));
        send_op->SetAttr("src_rank", src_device);
        send_op->SetAttr("dst_rank", dst_device);
        return send_op;
    }

    OpPtr CreateRecvOp(
        const OpPtr& src,
        const OpPtr& dst,
        int src_device,
        int dst_device) {

        std::string name = "Recv_" + src->GetName() + "_from_" + dst->GetName();
        auto recv_op = std::make_shared<Op>(name, "Recv");
        recv_op->AddInputDesc(src->GetOutputDesc(0));
        recv_op->AddOutputDesc(src->GetOutputDesc(0));
        recv_op->SetAttr("src_rank", src_device);
        recv_op->SetAttr("dst_rank", dst_device);
        return recv_op;
    }

    void GeneratePartitionedGraphs(
        Graph* graph,
        const std::vector<GraphPartition>& partitions) {

        for (size_t i = 0; i < partitions.size(); ++i) {
            Graph subgraph("Partition_" + std::to_string(i));
            subgraph.SetDeviceId(i);

            for (auto& op : partitions[i].ops) {
                subgraph.AddOp(op);
            }
            for (auto& op : partitions[i].send_ops) {
                subgraph.AddOp(op);
            }
            for (auto& op : partitions[i].recv_ops) {
                subgraph.AddOp(op);
            }

            partitioned_graphs_.push_back(subgraph);
        }
    }

    int num_devices_;
    std::vector<Graph> partitioned_graphs_;
};

四、性能优化技巧

4.1 编译时优化配置

cpp 复制代码

// 配置高级优化选项
GraphCompiler::CompileOptions options;

// 启用所有优化
options.opt_level = 3;  // 最高优化级别

// 启用特定优化
options.enable_fusion = true;
options.enable_precision_mode = true;  // 允许FP16
options.enable_const_folding = true;
options.enable_dead_code_elimination = true;

// 目标设备配置
options.target_device = "NPU";
options.target_core_type = "AI_CORE";

// 内存优化
options.enable_memory_optimize = true;
options.memory_optimize_level = 2;

// 并行配置
options.enable_multi_stream = true;
options.stream_num = 4;

4.2 融合策略配置

cpp 复制代码

// 自定义融合策略
FusionStrategy strategy;

// 启用的融合模式
strategy.enable_conv_bn_relu = true;
strategy.enable_conv_bias_add_relu = true;
strategy.enable_matmul_add_relu = true;
strategy.enable_batch_norm_act = true;

// 融合限制
strategy.max_fused_ops = 10;
strategy.max_fusion_depth = 5;

// 设置融合策略
optimizer.SetFusionStrategy(strategy);

五、总结

GE图引擎是CANN框架的核心组件，通过多层IR和模块化Pass架构实现了灵活高效的图优化。从算子融合、内存优化到图分区，GE提供了全方位的优化能力，使得深度学习模型能够在NPU上获得最佳性能。

相关链接：

CANN组织链接：https://atomgit.com/cann
GE仓库链接：https://atomgit.com/cann/ge