CANN图编译器GE全面解析：构建高效异构计算图的核心引擎

User_芊芊君子2026-02-07 16:18

引言

在现代深度学习框架中，计算图是表示和优化神经网络计算的核心抽象。计算图将复杂的神经网络分解为基本计算单元（算子）和数据流动（张量），通过图级别的优化实现显著的性能提升。

GE（Graph Engine）是CANN生态系统的图编译和执行引擎，负责将高层框架（如TensorFlow、PyTorch）定义的计算图转换为异构硬件上可执行的高效代码。本文将深入解析GE的核心架构、编译流程和优化技术。

一、GE图编译器概述

1.1 设计目标

GE作为连接深度学习框架和底层硬件的桥梁，主要设计目标包括：

  • 框架无关：支持多种主流深度学习框架
  • 硬件优化：针对异构计算平台优化图执行
  • 性能极致：通过图级别优化提升计算效率
  • 灵活扩展：支持自定义算子和优化策略

1.2 核心功能模块

模块 功能 技术要点
图构建 解析原始计算图 IR表示、算子注册
图优化 图级别变换 算子融合、常量折叠
内存规划 张量内存分配 内存复用、生命周期分析
代码生成 硬件代码生成 核函数生成、启动配置
图执行 运行时执行引擎 流水线调度、异步执行

1.3 计算图表示

GE使用中间表示（IR）来表示计算图：

复制代码 
Graph:
  - Nodes (算子节点)
    - Data: Conv2D, BiasAdd, ReLU, ...
    - Control: If, While, For, ...
  - Edges (数据边)
    - Tensor: 多维数组
  - Attributes (属性)
    - 算子参数、配置信息

二、GE核心架构详解

2.1 图IR（中间表示）定义

cpp 复制代码 
#include "ge_ir.h"
#include <string>
#include <vector>
#include <unordered_map>
#include <memory>

namespace ge {

// 基础数据类型
enum class DataType {
    DT_FLOAT = 0,
    DT_FLOAT16 = 1,
    DT_INT8 = 2,
    DT_INT16 = 3,
    DT_INT32 = 4,
    DT_INT64 = 5,
    DT_UINT8 = 6,
    DT_UINT16 = 7,
    DT_UINT32 = 8,
    DT_BOOL = 9,
    DT_DOUBLE = 10
};

// 张量形状
class TensorShape {
public:
    TensorShape() = default;
    explicit TensorShape(const std::vector<int64_t>& dims) : dims_(dims) {}

    void add_dim(int64_t dim) { dims_.push_back(dim); }
    int64_t dim_size(int index) const { return dims_[index]; }
    int64_t num_dims() const { return dims_.size(); }
    int64_t num_elements() const {
        int64_t total = 1;
        for (auto dim : dims_) total *= dim;
        return total;
    }

    const std::vector<int64_t>& dims() const { return dims_; }

    bool unknown_rank() const { return dims_.empty(); }
    bool has_unknown_dim() const {
        for (auto dim : dims_) {
            if (dim == -1) return true;
        }
        return false;
    }

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

// 张量描述
class TensorDesc {
public:
    TensorDesc() = default;
    TensorDesc(const TensorShape& shape, DataType dtype)
        : shape_(shape), dtype_(dtype) {}

    void set_shape(const TensorShape& shape) { shape_ = shape; }
    const TensorShape& shape() const { return shape_; }

    void set_data_type(DataType dtype) { dtype_ = dtype; }
    DataType data_type() const { return dtype_; }

    void set_format(const std::string& format) { format_ = format; }
    const std::string& format() const { return format_; }

    void set_layout(const std::string& layout) { layout_ = layout; }
    const std::string& layout() const { return layout_; }

private:
    TensorShape shape_;
    DataType dtype_ = DataType::DT_FLOAT;
    std::string format_ = "NCHW";  // NHWC, NCHW, etc.
    std::string layout_ = "Any";    // 特定布局优化
};

// 属性值（支持多种类型）
class AttrValue {
public:
    enum class Type {
        INT,
        FLOAT,
        STRING,
        BOOL,
        TENSOR,
        INT_LIST,
        FLOAT_LIST,
        STRING_LIST,
        TENSOR_LIST
    };

    AttrValue() : type_(Type::INT) {}

    static AttrValue create_int(int64_t value) {
        AttrValue attr;
        attr.type_ = Type::INT;
        attr.int_value_ = value;
        return attr;
    }

    static AttrValue create_float(float value) {
        AttrValue attr;
        attr.type_ = Type::FLOAT;
        attr.float_value_ = value;
        return attr;
    }

    static AttrValue create_string(const std::string& value) {
        AttrValue attr;
        attr.type_ = Type::STRING;
        attr.string_value_ = value;
        return attr;
    }

    static AttrValue create_bool(bool value) {
        AttrValue attr;
        attr.type_ = Type::BOOL;
        attr.bool_value_ = value;
        return attr;
    }

    static AttrValue create_int_list(const std::vector<int64_t>& value) {
        AttrValue attr;
        attr.type_ = Type::INT_LIST;
        attr.int_list_value_ = value;
        return attr;
    }

    Type type() const { return type_; }

    int64_t int_value() const { return int_value_; }
    float float_value() const { return float_value_; }
    const std::string& string_value() const { return string_value_; }
    bool bool_value() const { return bool_value_; }
    const std::vector<int64_t>& int_list_value() const { return int_list_value_; }

private:
    Type type_;
    int64_t int_value_ = 0;
    float float_value_ = 0.0f;
    std::string string_value_;
    bool bool_value_ = false;
    std::vector<int64_t> int_list_value_;
};

// 算子节点
class Node {
public:
    Node(const std::string& name, const std::string& type)
        : name_(name), type_(type) {}

    const std::string& name() const { return name_; }
    const std::string& type() const { return type_; }

    // 输入输出
    void add_input(Node* node, int out_index) {
        inputs_.push_back({node, out_index});
    }

    void add_output(const TensorDesc& desc) {
        outputs_.push_back(desc);
    }

    const std::vector<std::pair<Node*, int>>& inputs() const { return inputs_; }
    const std::vector<TensorDesc>& outputs() const { return outputs_; }

    // 属性操作
    void set_attr(const std::string& key, const AttrValue& value) {
        attrs_[key] = value;
    }

    bool has_attr(const std::string& key) const {
        return attrs_.find(key) != attrs_.end();
    }

    const AttrValue& get_attr(const std::string& key) const {
        static AttrValue default_attr;
        auto it = attrs_.find(key);
        return it != attrs_.end() ? it->second : default_attr;
    }

    // 设备信息
    void set_device(const std::string& device) { device_ = device; }
    const std::string& device() const { return device_; }

private:
    std::string name_;
    std::string type_;  // 算子类型：Conv2D, MatMul, etc.
    std::vector<std::pair<Node*, int>> inputs_;  // (节点, 输出索引)
    std::vector<TensorDesc> outputs_;
    std::unordered_map<std::string, AttrValue> attrs_;
    std::string device_ = "default";
};

// 计算图
class Graph {
public:
    Graph() = default;
    explicit Graph(const std::string& name) : name_(name) {}

    const std::string& name() const { return name_; }

    // 节点操作
    Node* add_node(const std::string& name, const std::string& type) {
        nodes_.push_back(std::make_unique<Node>(name, type));
        return nodes_.back().get();
    }

    Node* find_node(const std::string& name) const {
        for (const auto& node : nodes_) {
            if (node->name() == name) {
                return node.get();
            }
        }
        return nullptr;
    }

    const std::vector<std::unique_ptr<Node>>& nodes() const { return nodes_; }

    // 输入输出
    void add_input(Node* node, int index) {
        inputs_.push_back({node, index});
    }

    void add_output(Node* node, int index) {
        outputs_.push_back({node, index});
    }

    const std::vector<std::pair<Node*, int>>& inputs() const { return inputs_; }
    const std::vector<std::pair<Node*, int>>& outputs() const { return outputs_; }

    // 图分析
    void topological_sort(std::vector<Node*>& sorted_nodes) const {
        std::unordered_map<Node*, int> in_degree;
        std::vector<Node*> sources;

        // 计算入度
        for (const auto& node : nodes_) {
            in_degree[node.get()] = node->inputs().size();
            if (node->inputs().empty()) {
                sources.push_back(node.get());
            }
        }

        // 拓扑排序
        while (!sources.empty()) {
            Node* node = sources.back();
            sources.pop_back();
            sorted_nodes.push_back(node);

            // 找到所有使用此节点输出的节点
            for (const auto& other_node : nodes_) {
                for (const auto& input : other_node->inputs()) {
                    if (input.first == node) {
                        in_degree[other_node.get()]--;
                        if (in_degree[other_node.get()] == 0) {
                            sources.push_back(other_node.get());
                        }
                    }
                }
            }
        }
    }

    // 图验证
    bool validate() const {
        // 检查循环引用
        std::unordered_set<Node*> visited;
        std::unordered_set<Node*> rec_stack;

        for (const auto& node : nodes_) {
            if (has_cycle(node.get(), visited, rec_stack)) {
                return false;
            }
        }

        // 检查输入输出连接
        for (const auto& node : nodes_) {
            for (const auto& input : node->inputs()) {
                if (input.first == nullptr) {
                    return false;
                }
            }
        }

        return true;
    }

private:
    std::string name_;
    std::vector<std::unique_ptr<Node>> nodes_;
    std::vector<std::pair<Node*, int>> inputs_;   // 图输入
    std::vector<std::pair<Node*, int>> outputs_;  // 图输出

    bool has_cycle(Node* node,
                   std::unordered_set<Node*>& visited,
                   std::unordered_set<Node*>& rec_stack) const {
        if (rec_stack.count(node)) return true;
        if (visited.count(node)) return false;

        visited.insert(node);
        rec_stack.insert(node);

        for (const auto& input : node->inputs()) {
            if (has_cycle(input.first, visited, rec_stack)) {
                return true;
            }
        }

        rec_stack.erase(node);
        return false;
    }
};

} // namespace ge

2.2 图优化Pass

cpp 复制代码 
#include "ge_optimizer.h"

namespace ge {

// 优化Pass基类
class GraphOptimizationPass {
public:
    virtual ~GraphOptimizationPass() = default;
    virtual bool run(Graph* graph) = 0;
    virtual const char* name() const = 0;
};

// 常量折叠
class ConstantFoldingPass : public GraphOptimizationPass {
public:
    bool run(Graph* graph) override {
        bool changed = false;
        std::vector<Node*> to_remove;

        for (const auto& node : graph->nodes()) {
            if (is_constant_op(node->type())) {
                // 尝试计算常量表达式的值
                auto result = evaluate_constant_expression(node);
                if (result.has_value()) {
                    // 创建常量节点替换原表达式
                    auto const_node = create_constant_node(
                        graph, node->name() + "_folded", result.value()
                    );

                    // 替换所有使用此节点的边
                    replace_uses_of_with(node, const_node);
                    to_remove.push_back(node.get());
                    changed = true;
                }
            }
        }

        // 移除已折叠的节点
        for (auto* node : to_remove) {
            graph->remove_node(node);
        }

        return changed;
    }

    const char* name() const override { return "ConstantFolding"; }

private:
    bool is_constant_op(const std::string& type) {
        static const std::unordered_set<std::string> constant_ops = {
            "Add", "Sub", "Mul", "Div", "Pow",
            "Sin", "Cos", "Exp", "Log",
            "Greater", "Less", "Equal"
        };
        return constant_ops.count(type) > 0;
    }

    std::optional<TensorDesc> evaluate_constant_expression(Node* node) {
        // 检查所有输入是否都是常量
        for (const auto& input : node->inputs()) {
            if (input.first->type() != "Const") {
                return std::nullopt;
            }
        }

        // 执行计算（简化实现）
        TensorDesc result;
        // ... 实际计算逻辑
        return result;
    }

    Node* create_constant_node(Graph* graph, const std::string& name,
                              const TensorDesc& value) {
        auto* node = graph->add_node(name, "Const");
        node->add_output(value);
        return node;
    }

    void replace_uses_of_with(Node* old_node, Node* new_node) {
        // 替换所有使用old_node的边
        // 实现略
    }
};

// 死代码消除
class DeadCodeEliminationPass : public GraphOptimizationPass {
public:
    bool run(Graph* graph) override {
        std::unordered_set<Node*> live;

        // 标记所有可达节点
        for (const auto& output : graph->outputs()) {
            mark_reachable(output.first, live);
        }

        // 移除不可达节点
        bool changed = false;
        std::vector<Node*> to_remove;
        for (const auto& node : graph->nodes()) {
            if (!live.count(node.get())) {
                to_remove.push_back(node.get());
                changed = true;
            }
        }

        for (auto* node : to_remove) {
            graph->remove_node(node);
        }

        return changed;
    }

    const char* name() const override { return "DeadCodeElimination"; }

private:
    void mark_reachable(Node* node, std::unordered_set<Node*>& live) {
        if (live.count(node)) return;
        live.insert(node);

        for (const auto& input : node->inputs()) {
            mark_reachable(input.first, live);
        }
    }
};

// 算子融合
class OperatorFusionPass : public GraphOptimizationPass {
public:
    bool run(Graph* graph) override {
        bool changed = false;

        // Conv2D + BiasAdd + ReLU 融合
        changed |= fuse_conv_bias_relu(graph);

        // MatMul + Add 融合
        changed |= fuse_matmul_add(graph);

        // 其他融合模式
        // ...

        return changed;
    }

    const char* name() const override { return "OperatorFusion"; }

private:
    bool fuse_conv_bias_relu(Graph* graph) {
        std::vector<Node*> to_remove;
        bool changed = false;

        for (const auto& node : graph->nodes()) {
            // 查找模式: Conv2D -> BiasAdd -> ReLU
            if (node->type() == "Relu") {
                auto* bias_add = get_single_input_node(node);
                if (bias_add && bias_add->type() == "BiasAdd") {
                    auto* conv2d = get_single_input_node(bias_add);
                    if (conv2d && conv2d->type() == "Conv2D") {
                        // 创建融合算子
                        auto* fused = create_fused_conv_bias_relu(
                            graph, conv2d, bias_add, node
                        );

                        // 替换原图
                        replace_node_with(graph, node, fused);
                        to_remove.push_back(conv2d);
                        to_remove.push_back(bias_add);
                        to_remove.push_back(node);
                        changed = true;
                    }
                }
            }
        }

        for (auto* node : to_remove) {
            graph->remove_node(node);
        }

        return changed;
    }

    Node* get_single_input_node(Node* node) {
        if (node->inputs().size() == 1) {
            return node->inputs()[0].first;
        }
        return nullptr;
    }

    Node* create_fused_conv_bias_relu(Graph* graph, Node* conv,
                                     Node* bias, Node* relu) {
        auto* fused = graph->add_node(
            conv->name() + "_fused",
            "FusedConvBiasAddRelu"
        );

        // 复制Conv的属性
        for (const auto& attr : conv->attrs()) {
            fused->set_attr(attr.first, attr.second);
        }

        // 设置偏置
        fused->set_attr("bias", bias->get_attr("bias"));

        // 设置输入输出
        fused->add_input(conv->inputs()[0].first, conv->inputs()[0].second);
        fused->add_input(conv->inputs()[1].first, conv->inputs()[1].second);
        fused->add_output(conv->outputs()[0]);

        return fused;
    }

    void replace_node_with(Graph* graph, Node* old_node, Node* new_node) {
        // 替换所有使用old_node的边
        // 实现略
    }

    bool fuse_matmul_add(Graph* graph) {
        // MatMul + BiasAdd融合实现
        // 类似于上面的Conv融合
        return false;
    }
};

// 公共子表达式消除
class CommonSubexpressionEliminationPass : public GraphOptimizationPass {
public:
    bool run(Graph* graph) override {
        // 构建表达式哈希
        std::unordered_map<size_t, std::vector<Node*>> expr_map;

        for (const auto& node : graph->nodes()) {
            size_t hash = compute_expression_hash(node);
            expr_map[hash].push_back(node.get());
        }

        bool changed = false;
        std::vector<Node*> to_remove;

        // 查找可合并的表达式
        for (const auto& [hash, nodes] : expr_map) {
            if (nodes.size() > 1) {
                // 找到候选，进一步验证
                for (size_t i = 1; i < nodes.size(); ++i) {
                    if (are_equivalent(nodes[0], nodes[i])) {
                        // 合并到nodes[0]
                        replace_uses_of_with(nodes[i], nodes[0]);
                        to_remove.push_back(nodes[i]);
                        changed = true;
                    }
                }
            }
        }

        for (auto* node : to_remove) {
            graph->remove_node(node);
        }

        return changed;
    }

    const char* name() const override { return "CommonSubexpressionElimination"; }

private:
    size_t compute_expression_hash(Node* node) {
        size_t hash = std::hash<std::string>{}(node->type());

        for (const auto& input : node->inputs()) {
            hash ^= std::hash<Node*>{}(input.first);
            hash ^= std::hash<int>{}(input.second);
        }

        for (const auto& attr : node->attrs()) {
            hash ^= std::hash<std::string>{}(attr.first);
            // 哈希属性值（简化）
        }

        return hash;
    }

    bool are_equivalent(Node* a, Node* b) {
        if (a->type() != b->type()) return false;
        if (a->inputs().size() != b->inputs().size()) return false;

        for (size_t i = 0; i < a->inputs().size(); ++i) {
            if (a->inputs()[i].first != b->inputs()[i].first) return false;
            if (a->inputs()[i].second != b->inputs()[i].second) return false;
        }

        return true;
    }

    void replace_uses_of_with(Node* old_node, Node* new_node) {
        // 实现略
    }
};

// 优化Pass管理器
class OptimizationPassManager {
public:
    void add_pass(std::unique_ptr<GraphOptimizationPass> pass) {
        passes_.push_back(std::move(pass));
    }

    bool run(Graph* graph) {
        bool changed = false;
        int iteration = 0;
        const int max_iterations = 10;

        while (iteration < max_iterations) {
            bool iter_changed = false;

            for (auto& pass : passes_) {
                bool pass_changed = pass->run(graph);
                if (pass_changed) {
                    iter_changed = true;
                }
            }

            if (!iter_changed) break;
            changed = true;
            iteration++;
        }

        return changed;
    }

private:
    std::vector<std::unique_ptr<GraphOptimizationPass>> passes_;
};

} // namespace ge

2.3 内存规划

cpp 复制代码 
#include "ge_memory_planner.h"

namespace ge {

// 张量生命周期分析
class TensorLifetimeAnalyzer {
public:
    struct LiveInterval {
        int start;  // 最早定义点
        int end;    // 最后使用点
        size_t size;
        int tensor_id;
    };

    std::vector<LiveInterval> analyze_lifetimes(const Graph* graph) {
        std::vector<LiveInterval> intervals;
        std::unordered_map<Node*, int> node_ids;
        std::unordered_map<std::pair<Node*, int>, int> output_ids;

        // 为节点分配ID
        int id = 0;
        for (const auto& node : graph->nodes()) {
            node_ids[node.get()] = id++;
        }

        // 分析每个张量的生命周期
        id = 0;
        for (const auto& node : graph->nodes()) {
            int def_point = node_ids[node.get()];

            for (size_t i = 0; i < node->outputs().size(); ++i) {
                LiveInterval interval;
                interval.start = def_point;
                interval.end = def_point;
                interval.size = compute_size(node->outputs()[i]);
                interval.tensor_id = id++;

                // 找到最后使用点
                for (const auto& user : graph->nodes()) {
                    for (const auto& input : user->inputs()) {
                        if (input.first == node.get() &&
                            static_cast<size_t>(input.second) == i) {
                            interval.end = std::max(
                                interval.end, node_ids[user.get()]
                            );
                        }
                    }
                }

                intervals.push_back(interval);
            }
        }

        return intervals;
    }

private:
    size_t compute_size(const TensorDesc& desc) {
        size_t element_size = 0;
        switch (desc.data_type()) {
            case DataType::DT_FLOAT:
            case DataType::DT_INT32:
                element_size = 4;
                break;
            case DataType::DT_FLOAT16:
            case DataType::DT_INT16:
                element_size = 2;
                break;
            case DataType::DT_INT8:
            case DataType::DT_UINT8:
            case DataType::DT_BOOL:
                element_size = 1;
                break;
            case DataType::DT_INT64:
            case DataType::DT_DOUBLE:
                element_size = 8;
                break;
        }

        return desc.shape().num_elements() * element_size;
    }
};

// 内存分配器
class MemoryAllocator {
public:
    struct MemoryBlock {
        size_t offset;
        size_t size;
        int tensor_id;
    };

    std::vector<MemoryBlock> allocate(const std::vector<LiveInterval>& intervals) {
        std::vector<MemoryBlock> allocations;
        std::vector<std::pair<size_t, size_t>> free_list;  // (offset, size)

        // 按开始时间排序
        auto sorted = intervals;
        std::sort(sorted.begin(), sorted.end(),
                 [](const auto& a, const auto& b) {
                     return a.start < b.start;
                 });

        size_t max_offset = 0;

        for (const auto& interval : sorted) {
            // 尝试从空闲列表分配
            bool allocated = false;
            for (auto it = free_list.begin(); it != free_list.end(); ++it) {
                if (it->second >= interval.size) {
                    // 找到合适的块
                    MemoryBlock block;
                    block.offset = it->first;
                    block.size = interval.size;
                    block.tensor_id = interval.tensor_id;
                    allocations.push_back(block);

                    // 更新空闲列表
                    if (it->second > interval.size) {
                        it->first += interval.size;
                        it->second -= interval.size;
                    } else {
                        free_list.erase(it);
                    }

                    allocated = true;
                    break;
                }
            }

            if (!allocated) {
                // 分配新空间
                MemoryBlock block;
                block.offset = max_offset;
                block.size = interval.size;
                block.tensor_id = interval.tensor_id;
                allocations.push_back(block);

                max_offset += interval.size;
            }

            // 回收过期的张量
            for (size_t i = 0; i < allocations.size(); ++i) {
                if (sorted[allocations[i].tensor_id].end < interval.start) {
                    // 加入空闲列表
                    free_list.push_back({
                        allocations[i].offset,
                        allocations[i].size
                    });
                }
            }
        }

        return allocations;
    }
};

// 内存规划器
class MemoryPlanner {
public:
    struct MemoryPlan {
        std::unordered_map<int, size_t> tensor_offsets;  // tensor_id -> offset
        size_t total_size;
    };

    MemoryPlan plan(const Graph* graph) {
        // 分析生命周期
        TensorLifetimeAnalyzer analyzer;
        auto intervals = analyzer.analyze_lifetimes(graph);

        // 分配内存
        MemoryAllocator allocator;
        auto allocations = allocator.allocate(intervals);

        // 构建规划
        MemoryPlan plan;
        plan.total_size = 0;

        for (const auto& alloc : allocations) {
            plan.tensor_offsets[alloc.tensor_id] = alloc.offset;
            plan.total_size = std::max(
                plan.total_size,
                alloc.offset + alloc.size
            );
        }

        return plan;
    }
};

} // namespace ge

2.4 代码生成

cpp 复制代码 
#include "ge_code_generator.h"

namespace ge {

// 代码生成器基类
class CodeGenerator {
public:
    virtual ~CodeGenerator() = default;
    virtual std::string generate(const Graph* graph,
                                const MemoryPlan& memory_plan) = 0;
};

// 异构计算代码生成器
class HeteroComputeCodeGenerator : public CodeGenerator {
public:
    std::string generate(const Graph* graph,
                        const MemoryPlan& memory_plan) override {
        CodeEmitter emitter;

        // 生成头文件
        emit_headers(emitter);

        // 生成全局变量
        emit_globals(emitter, memory_plan);

        // 生成核函数
        emit_kernels(emitter, graph, memory_plan);

        // 生成主机端代码
        emit_host_code(emitter, graph, memory_plan);

        return emitter.str();
    }

private:
    void emit_headers(CodeEmitter& emitter) {
        emitter.emit("#include <hcl_api.h>\n");
        emitter.emit("#include <hcl_runtime.h>\n\n");
    }

    void emit_globals(CodeEmitter& emitter, const MemoryPlan& memory_plan) {
        emitter.emit("// 全局内存池\n");
        emitter.emit("static void* g_memory_pool = nullptr;\n");
        emitter.emit("static const size_t g_pool_size = ");
        emitter.emit(std::to_string(memory_plan.total_size));
        emitter.emit(";\n\n");
    }

    void emit_kernels(CodeEmitter& emitter, const Graph* graph,
                     const MemoryPlan& memory_plan) {
        for (const auto& node : graph->nodes()) {
            emit_single_kernel(emitter, node, memory_plan);
        }
    }

    void emit_single_kernel(CodeEmitter& emitter,
                           const std::unique_ptr<Node>& node,
                           const MemoryPlan& memory_plan) {
        // 生成核函数签名
        emitter.emit("__global__ void ");
        emitter.emit(node->name());
        emitter.emit("_kernel(\n");

        // 参数列表
        std::vector<std::string> params;
        for (size_t i = 0; i < node->inputs().size(); ++i) {
            params.push_back("void* input_" + std::to_string(i));
        }
        for (size_t i = 0; i < node->outputs().size(); ++i) {
            params.push_back("void* output_" + std::to_string(i));
        }

        for (size_t i = 0; i < params.size(); ++i) {
            emitter.emit("    ");
            emitter.emit(params[i]);
            if (i < params.size() - 1) {
                emitter.emit(",\n");
            }
        }
        emitter.emit("\n) {\n");

        // 生成核函数体
        emit_kernel_body(emitter, node, memory_plan);

        emitter.emit("}\n\n");
    }

    void emit_kernel_body(CodeEmitter& emitter,
                         const std::unique_ptr<Node>& node,
                         const MemoryPlan& memory_plan) {
        std::string op_type = node->type();

        if (op_type == "Conv2D") {
            emit_conv2d_kernel(emitter, node, memory_plan);
        } else if (op_type == "MatMul") {
            emit_matmul_kernel(emitter, node, memory_plan);
        } else if (op_type == "Add") {
            emit_add_kernel(emitter, node, memory_plan);
        } else {
            emit_generic_kernel(emitter, node, memory_plan);
        }
    }

    void emit_conv2d_kernel(CodeEmitter& emitter,
                           const std::unique_ptr<Node>& node,
                           const MemoryPlan& memory_plan) {
        // 获取Conv2D属性
        auto strides = node->get_attr("strides").int_list_value();
        auto pads = node->get_attr("pads").int_list_value();
        auto dilations = node->get_attr("dilations").int_list_value();
        auto groups = node->get_attr("groups").int_value();

        emitter.emit("    // Conv2D kernel\n");
        emitter.emit("    int idx = blockIdx.x * blockDim.x + threadIdx.x;\n");
        emitter.emit("    // ... Conv2D实现\n");
    }

    void emit_matmul_kernel(CodeEmitter& emitter,
                           const std::unique_ptr<Node>& node,
                           const MemoryPlan& memory_plan) {
        emitter.emit("    // MatMul kernel\n");
        emitter.emit("    int row = blockIdx.y * blockDim.y + threadIdx.y;\n");
        emitter.emit("    int col = blockIdx.x * blockDim.x + threadIdx.x;\n");
        emitter.emit("    // ... MatMul实现\n");
    }

    void emit_add_kernel(CodeEmitter& emitter,
                        const std::unique_ptr<Node>& node,
                        const MemoryPlan& memory_plan) {
        emitter.emit("    // Element-wise Add kernel\n");
        emitter.emit("    int idx = blockIdx.x * blockDim.x + threadIdx.x;\n");
        emitter.emit("    float* a = (float*)input_0;\n");
        emitter.emit("    float* b = (float*)input_1;\n");
        emitter.emit("    float* c = (float*)output_0;\n");
        emitter.emit("    c[idx] = a[idx] + b[idx];\n");
    }

    void emit_generic_kernel(CodeEmitter& emitter,
                            const std::unique_ptr<Node>& node,
                            const MemoryPlan& memory_plan) {
        emitter.emit("    // Generic kernel for ");
        emitter.emit(node->type());
        emitter.emit("\n");
        emitter.emit("    // TODO: implement\n");
    }

    void emit_host_code(CodeEmitter& emitter, const Graph* graph,
                       const MemoryPlan& memory_plan) {
        emitter.emit("// 主机端执行函数\n");
        emitter.emit("void execute_graph() {\n");
        emitter.emit("    // 分配内存池\n");
        emitter.emit("    hclMalloc(&g_memory_pool, g_pool_size);\n\n");

        emitter.emit("    // 执行计算图\n");
        std::vector<Node*> sorted_nodes;
        graph->topological_sort(sorted_nodes);

        for (Node* node : sorted_nodes) {
            emit_kernel_launch(emitter, node, memory_plan);
        }

        emitter.emit("\n    // 释放内存\n");
        emitter.emit("    hclFree(g_memory_pool);\n");
        emitter.emit("}\n");
    }

    void emit_kernel_launch(CodeEmitter& emitter, Node* node,
                           const MemoryPlan& memory_plan) {
        emitter.emit("    // Launch: ");
        emitter.emit(node->name());
        emitter.emit("\n");

        // 计算grid和block大小
        auto grid_block = compute_launch_config(node);

        emitter.emit("    ");
        emitter.emit(node->name());
        emitter.emit("_kernel<<<");
        emitter.emit(std::to_string(grid_block.first));
        emitter.emit(", ");
        emitter.emit(std::to_string(grid_block.second));
        emitter.emit(">>>(\n");

        // 传递参数（基于内存规划）
        for (size_t i = 0; i < node->inputs().size(); ++i) {
            auto offset = get_tensor_offset(node->inputs()[i], memory_plan);
            emitter.emit("        (void*)((char*)g_memory_pool + ");
            emitter.emit(std::to_string(offset));
            emitter.emit(")");
            if (i < node->inputs().size() - 1 || !node->outputs().empty()) {
                emitter.emit(",\n");
            }
        }

        for (size_t i = 0; i < node->outputs().size(); ++i) {
            auto offset = get_tensor_output_offset(node, i, memory_plan);
            emitter.emit("        (void*)((char*)g_memory_pool + ");
            emitter.emit(std::to_string(offset));
            emitter.emit(")");
            if (i < node->outputs().size() - 1) {
                emitter.emit(",\n");
            }
        }

        emitter.emit("\n    );\n");
    }

    std::pair<int, int> compute_launch_config(Node* node) {
        // 根据输出形状和硬件特性计算grid/block大小
        if (node->outputs().empty()) {
            return {1, 1};
        }

        const auto& shape = node->outputs()[0].shape();
        int total_elements = static_cast<int>(shape.num_elements());

        // 简化：每个线程处理一个元素
        int block_size = 256;
        int grid_size = (total_elements + block_size - 1) / block_size;

        return {grid_size, block_size};
    }

    size_t get_tensor_offset(const std::pair<Node*, int>& tensor,
                            const MemoryPlan& memory_plan) {
        // 根据内存规划获取偏移
        // 简化实现
        return 0;
    }

    size_t get_tensor_output_offset(Node* node, int output_index,
                                   const MemoryPlan& memory_plan) {
        // 根据内存规划获取输出偏移
        // 简化实现
        return 0;
    }
};

// 代码发射器
class CodeEmitter {
public:
    void emit(const std::string& code) {
        buffer_ += code;
    }

    std::string str() const {
        return buffer_;
    }

private:
    std::string buffer_;
};

} // namespace ge

三、Python API

GE提供了友好的Python接口：

python 复制代码 
import ge
import numpy as np

# ===== 构建计算图 =====
def build_simple_graph():
    # 创建图
    graph = ge.Graph("my_graph")

    # 创建占位符节点（输入）
    input_ph = graph.add_placeholder("input",
                                     shape=(1, 224, 224, 3),
                                     dtype=ge.DataType.DT_FLOAT)

    # 创建常量节点（权重）
    weights = graph.add_constant("weights",
                                value=np.random.randn(3, 3, 3, 16).astype(np.float32))

    # 创建算子节点
    conv = graph.add_op("conv",
                       op_type="Conv2D",
                       inputs=[input_ph, weights],
                       attrs={"strides": [1, 1, 1, 1],
                             "pads": [0, 0, 0, 0],
                             "dilations": [1, 1, 1, 1]})

    bias = graph.add_constant("bias",
                             value=np.zeros(16).astype(np.float32))

    bias_add = graph.add_op("bias_add",
                           op_type="BiasAdd",
                           inputs=[conv, bias])

    relu = graph.add_op("relu",
                       op_type="Relu",
                       inputs=[bias_add])

    # 设置输出
    graph.set_outputs([relu])

    return graph

# ===== 图优化 =====
def optimize_graph(graph):
    # 创建优化管理器
    optimizer = ge.OptimizationPassManager()

    # 添加优化Pass
    optimizer.add_pass(ge.ConstantFoldingPass())
    optimizer.add_pass(ge.DeadCodeEliminationPass())
    optimizer.add_pass(ge.OperatorFusionPass())
    optimizer.add_pass(ge.CommonSubexpressionEliminationPass())

    # 运行优化
    optimized_graph = optimizer.run(graph)

    return optimized_graph

# ===== 内存规划 =====
def plan_memory(graph):
    planner = ge.MemoryPlanner()
    memory_plan = planner.plan(graph)

    print(f"Total memory required: {memory_plan.total_size / 1024 / 1024:.2f} MB")

    return memory_plan

# ===== 代码生成 =====
def generate_code(graph, memory_plan):
    codegen = ge.HeteroComputeCodeGenerator()
    code = codegen.generate(graph, memory_plan)

    print("Generated code:")
    print(code)

    return code

# ===== 完整示例 =====
def complete_example():
    # 1. 构建图
    print("=== Building Graph ===")
    graph = build_simple_graph()
    print(f"Graph has {len(graph.nodes())} nodes")

    # 2. 优化图
    print("\n=== Optimizing Graph ===")
    optimized_graph = optimize_graph(graph)
    print(f"Optimized graph has {len(optimized_graph.nodes())} nodes")

    # 3. 内存规划
    print("\n=== Memory Planning ===")
    memory_plan = plan_memory(optimized_graph)

    # 4. 生成代码
    print("\n=== Code Generation ===")
    code = generate_code(optimized_graph, memory_plan)

# ===== 自定义算子 =====
def custom_operator_example():
    graph = ge.Graph("custom_op_graph")

    # 定义自定义算子
    @ge.register_operator("MyCustomOp")
    class MyCustomOp(ge.Operator):
        def compute(self, inputs, attrs):
            # 自定义计算逻辑
            x = inputs[0]
            return x * 2 + 1

    # 使用自定义算子
    input_ph = graph.add_placeholder("input", shape=(10,), dtype=ge.DataType.DT_FLOAT)

    custom = graph.add_op("custom",
                         op_type="MyCustomOp",
                         inputs=[input_ph])

    graph.set_outputs([custom])

    return graph

# ===== 子图示例 =====
def subgraph_example():
    # 主图
    main_graph = ge.Graph("main_graph")

    input_ph = main_graph.add_placeholder("input", shape=(10, 10))

    # 创建子图（可重用）
    subgraph = ge.Graph("dense_block")
    sub_input = subgraph.add_placeholder("x", shape=(10, 10))

    w1 = subgraph.add_constant("w1", value=np.random.randn(10, 10))
    matmul1 = subgraph.add_op("matmul1", op_type="MatMul", inputs=[sub_input, w1])
    relu1 = subgraph.add_op("relu1", op_type="Relu", inputs=[matmul1])

    w2 = subgraph.add_constant("w2", value=np.random.randn(10, 10))
    matmul2 = subgraph.add_op("matmul2", op_type="MatMul", inputs=[relu1, w2])
    relu2 = subgraph.add_op("relu2", op_type="Relu", inputs=[matmul2])

    subgraph.set_outputs([relu2])

    # 在主图中调用子图
    call = main_graph.add_call("dense_block_call", subgraph, inputs=[input_ph])
    main_graph.set_outputs([call])

    return main_graph

if __name__ == "__main__":
    complete_example()
    print("\n=== Custom Operator Example ===")
    custom_graph = custom_operator_example()
    print("\n=== Subgraph Example ===")
    sub_graph = subgraph_example()

四、性能优化策略

4.1 常用优化技术

优化技术 效果 复杂度
算子融合 30-50% 中
常量折叠 10-20% 低
内存复用 20-40% 高
循环展开 5-15% 低
向量化 20-40% 中

4.2 优化建议

  1. 优先使用算子融合：减少内存访问
  2. 启用内存规划：降低显存占用
  3. 使用量化：压缩模型大小
  4. 图级别优化：充分利用数据流信息

五、总结

GE作为CANN的图编译和执行引擎，提供了：

  1. 完整的编译流程：从图构建到代码生成
  2. 丰富的优化Pass：算子融合、常量折叠等
  3. 智能内存规划：生命周期分析和内存复用
  4. 灵活的代码生成：支持多种后端

通过GE，开发者可以高效地将深度学习模型部署到异构计算平台上。

相关链接

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