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Paddle Eager Graph by paddlepaddle — Agent Skill

Name: Paddle Eager Graph
Author: PaddlePaddle

Paddle Eager Graph

Use when navigating Paddle eager-mode (dynamic graph) source code, tracing forward/backward execution, debugging autograd issues, understanding PyLayer, or investigating complex-valued gradient computation. Covers Python API to C++ kernel call chain, backward graph topology sort, and inplace version tracking.

PaddlePaddle db51f3c 7 files · 39.9 KB Updated Sep 17, 2026

File contents

Paddle 动态图（Eager Mode）导航索引

Paddle 动态图边执行边建图：前向执行时构建反向图，调用 backward() 时按拓扑序执行反向图。

前向调用链路

Python paddle.add(x, y)
  │
  ▼
① ops_api.cc          ─ Python-C 映射，GetTensorFromArgs 提取 Tensor
  ▼
② eager_op_function.cc ─ 参数解析 / Dist Tensor / 释放 GIL / backend 选择
  ▼
③ dygraph_functions.cc ─ AMP / Type Promotion / 创建 GradNode / 构建反向图
  ▼
④ api.cc              ─ KernelKey 构造 / Kernel 选择 / PrepareData / InferMeta
  ▼
⑤ PHI Kernel 执行

关键文件表

层级	代码路径	代码生成器
① Python-C 映射	`paddle/fluid/pybind/ops_api.cc`	`ops_api_gen.py`
② 动态图 C++ 接口	`paddle/fluid/pybind/eager_op_function.cc`	`python_c_gen.py`
③ 自动微分函数	`paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.cc`	`eager_gen.py`
④ PHI 算子库接口	`paddle/phi/api/lib/api.cc`	`api_gen.py`

反向关键数据结构

数据结构	一句话描述
AutogradMeta	Tensor 持有的反向元信息：梯度、来源 GradNode、slot/rank 位置
GradNodeBase	反向节点基类，纯虚 `operator()` 执行反向计算
GradSlotMeta	描述 GradNode 某个 slot 的 meta 信息与出边 Edge
Edge	指向后继 GradNode 的边，含 `in_slot_id_` 和 `in_rank_`
TensorWrapper	GradNode 中保存前向 Tensor 的包装器，含 inplace 版本快照
GradTensorHolder	反向执行期间临时存放与聚合梯度的二维 buffer

调试场景速查

调试场景	阅读哪个参考文档
前向调用链路 / Kernel 选择问题	forward-call-chain.md
反向梯度不正确 / 拓扑排序问题	backward-execution.md
数据结构成员 / 内存泄漏排查	autograd-data-structs.md
自定义反向 PyLayer	pylayer.md
复数梯度 / Wirtinger 导数	complex-autograd.md
Inplace 操作 / 版本追踪	inplace.md

社区资料（L3 层）

PFCC 动态图源码阅读

---name: paddle-eager-graphdescription: Use when navigating Paddle eager-mode (dynamic graph) source code, tracing forward/backward execution, debugging autograd issues, understanding PyLayer, or investigating complex-valued gradient computation. Covers Python API to C++ kernel call chain, backward graph topology sort, and inplace version tracking.---# Paddle 动态图（Eager Mode）导航索引Paddle 动态图**边执行边建图**：前向执行时构建反向图，调用 `backward()` 时按拓扑序执行反向图。## 前向调用链路```Python paddle.add(x, y) │ ▼① ops_api.cc ─ Python-C 映射，GetTensorFromArgs 提取 Tensor ▼② eager_op_function.cc ─ 参数解析 / Dist Tensor / 释放 GIL / backend 选择 ▼③ dygraph_functions.cc ─ AMP / Type Promotion / 创建 GradNode / 构建反向图 ▼④ api.cc ─ KernelKey 构造 / Kernel 选择 / PrepareData / InferMeta ▼⑤ PHI Kernel 执行```## 关键文件表| 层级 | 代码路径 | 代码生成器 ||------|---------|-----------|| ① Python-C 映射 | `paddle/fluid/pybind/ops_api.cc` | `ops_api_gen.py` || ② 动态图 C++ 接口 | `paddle/fluid/pybind/eager_op_function.cc` | `python_c_gen.py` || ③ 自动微分函数 | `paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.cc` | `eager_gen.py` || ④ PHI 算子库接口 | `paddle/phi/api/lib/api.cc` | `api_gen.py` |## 反向关键数据结构| 数据结构 | 一句话描述 ||---------|-----------|| **AutogradMeta** | Tensor 持有的反向元信息：梯度、来源 GradNode、slot/rank 位置 || **GradNodeBase** | 反向节点基类，纯虚 `operator()` 执行反向计算 || **GradSlotMeta** | 描述 GradNode 某个 slot 的 meta 信息与出边 Edge || **Edge** | 指向后继 GradNode 的边，含 `in_slot_id_` 和 `in_rank_` || **TensorWrapper** | GradNode 中保存前向 Tensor 的包装器，含 inplace 版本快照 || **GradTensorHolder** | 反向执行期间临时存放与聚合梯度的二维 buffer |## 调试场景速查| 调试场景 | 阅读哪个参考文档 ||---------|----------------|| 前向调用链路 / Kernel 选择问题 | [forward-call-chain.md](references/forward-call-chain.md) || 反向梯度不正确 / 拓扑排序问题 | [backward-execution.md](references/backward-execution.md) || 数据结构成员 / 内存泄漏排查 | [autograd-data-structs.md](references/autograd-data-structs.md) || 自定义反向 PyLayer | [pylayer.md](references/pylayer.md) || 复数梯度 / Wirtinger 导数 | [complex-autograd.md](references/complex-autograd.md) || Inplace 操作 / 版本追踪 | [inplace.md](references/inplace.md) |## 社区资料（L3 层）- [PFCC 动态图源码阅读](https://github.com/PaddlePaddle/community/tree/master/pfcc/paddle-code-reading/Dygraph)

Frequently asked questions