PyTorch Contribution Roadmap

PyTorch Contribution Roadmap: Learn ATen, C10, CUDA, and Submit Your First PR

Contributing to PyTorch is much easier when you understand the framework from the bottom up. Instead of studying individual pull requests randomly, follow a structured path that starts with modern C++, moves into PyTorch internals, and gradually introduces ATen, C10, the dispatcher, TensorIterator, CUDA, numerical correctness, testing, and operator development.

The roadmap below is designed for developers who want to understand real PyTorch pull requests and eventually contribute meaningful bug fixes, improvements, and features to the PyTorch codebase.

What You Should Learn Before Contributing to PyTorch

Many PyTorch contributions involve multiple layers of the framework:

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1Python API
2     ↓
3torch / torch.nn
4     ↓
5torch.library / torchgen
6     ↓
7Dispatcher
8     ↓
9ATen
10     ↓
11C10
12     ↓
13CPU / CUDA Kernels
14     ↓
15Hardware

A small Python API change can therefore involve considerably more than Python code. Understanding how these layers interact will make PyTorch pull requests much easier to read and review.

Phase 1: Master Modern C++ Fundamentals

Goal

The first goal is to become comfortable reading and modifying modern C++ code.

PyTorch contains a large amount of C++17 code, including templates, resource-management patterns, device abstractions, dispatch infrastructure, and low-level tensor operations.

Topics to Learn

Study:

• C++17 fundamentals
• Classes and inheritance
• Templates
• Function overloading
• RAII
• Smart pointers
• Move semantics
• Copy semantics
• References and pointers
• Exceptions
• STL containers and algorithms
• constexpr
• enum class
• Lambdas
• Namespaces
• Header and source files
• const correctness
• Compile-time versus runtime behavior

Practical Projects

Do not learn these concepts only from tutorials. Build small programs such as:

1. A custom dynamic array
2. A simple memory allocator
3. An RAII resource wrapper
4. A template-based mathematical function
5. A small device/resource guard
6. A program demonstrating move semantics
7. A small C++ unit-testing project

These exercises make PyTorch's C++ code significantly easier to understand.

Phase 2: Understand the PyTorch Repository

Before investigating individual PRs, learn how the repository is organized.

A simplified view is:

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1pytorch/
2│
3├── aten/
4├── c10/
5├── torch/
6├── torchgen/
7├── test/
8├── benchmarks/
9├── tools/
10└── functorch/

ATen

ATen is the core tensor library used by PyTorch.

It contains tensor operations, operators, native implementations, dispatch-related infrastructure, and CPU/CUDA implementations.

A useful mental model is:

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1ATen
2  ↓
3Tensor operations
4  ↓
5Operator implementations
6  ↓
7CPU / CUDA kernels

C10

C10 provides important low-level infrastructure used throughout PyTorch.

It includes concepts related to:

• Devices
• Data types
• Dispatch
• Threading
• Guards
• Error handling
• Core abstractions

torch/csrc

The torch/csrc area contains important C++ components that connect PyTorch's C++ implementation with Python.

Understanding this layer becomes increasingly useful when investigating Python-to-C++ execution paths.

torchgen

torchgen is involved in generating parts of PyTorch's operator infrastructure from operator definitions and schemas.

Understanding it becomes important when working on operator registration and generated code.

test

PyTorch has an extensive test suite. Learning how tests are organized is essential because most useful contributions should include appropriate regression coverage.

Phase 3: Understand Tensor Internals

Before studying advanced operators, understand what a tensor actually represents.

Study:

• Shape
• Stride
• Storage
• Data type
• Device
• Layout
• Memory format
• Contiguous tensors
• Non-contiguous tensors
• Views
• Broadcasting

For example, conceptually:

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1Tensor
2 ├── Shape
3 ├── Stride
4 ├── Dtype
5 ├── Device
6 ├── Storage
7 └── Layout

Tensor Devices

Understand the differences between:

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1CPU Tensor
2CUDA Tensor
3Meta Tensor
4Fake Tensor

These tensor types serve different purposes and are important when understanding dispatch, compilation, testing, and operator behavior.

Phase 4: Learn the PyTorch Dispatch System

The dispatcher is one of the most important concepts for PyTorch contributors.

Study:

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1Operator Schema
2      ↓
3Dispatcher
4      ↓
5Dispatch Key
6      ↓
7Selected Kernel
8      ↓
9CPU / CUDA / Other Implementation

Important Concepts

Learn:

• Dispatcher
• DispatchKey
• DispatchKeySet
• Kernel registration
• Native functions
• Operator schemas
• Dispatch rules
• Backend-specific kernels
• Autograd dispatch
• Meta dispatch

Useful areas to explore include:

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1aten/src/ATen/core/
2aten/src/ATen/native/
3torchgen/

Do not try to memorize every dispatcher implementation. Instead, learn how an operator moves from its public API to the appropriate implementation.

Phase 5: Learn TensorIterator

TensorIterator is especially important when investigating elementwise and broadcasting operations.

Study:

• Broadcasting
• Tensor iteration
• Type promotion
• Scalar operands
• Dtype handling
• CPU kernels
• CUDA kernels
• Kernel dispatch

A useful conceptual model is:

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1Input Tensors
2      ↓
3Shape / Broadcasting
4      ↓
5Dtype Analysis
6      ↓
7TensorIterator
8      ↓
9Kernel
10      ↓
11Output Tensor

Once TensorIterator makes sense, many seemingly complicated ATen operators become easier to follow.

Phase 6: Learn CPU and CUDA Kernels

After understanding ATen and dispatch, move into kernel implementations.

CPU Kernel Concepts

Study:

• Elementwise operations
• Vectorization
• Memory access
• SIMD
• Type specialization
• Parallel execution
• TensorIterator-based kernels

CUDA Kernel Concepts

Study:

• CUDA threads
• Blocks
• Grids
• CUDA streams
• Device selection
• Synchronization
• Memory transfers
• Kernel launches
• GPU memory
• CUDA error handling

You do not need to become an advanced CUDA programmer before your first PyTorch contribution. Basic CUDA knowledge is enough for many beginner and intermediate issues.

Phase 7: Learn Numerical Correctness and Dtypes

A significant class of PyTorch bugs involves numerical behavior.

Study:

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1float32
2float16
3bfloat16
4integer types
5complex types

Also understand:

• IEEE 754 floating-point representation
• Precision
• Overflow
• Underflow
• NaN
• Infinity
• Type conversion
• Type promotion
• Scalar conversion
• Casting rules

This knowledge is especially useful for issues involving float16, bfloat16, torch.where, clamp, reductions, and mathematical operators.

Phase 8: Learn PyTorch Error Handling and Validation

Beginner-friendly contributions often involve improving validation or error messages.

Study:

• Python exceptions
• ValueError
• TypeError
• Argument validation
• Shape validation
• Dtype validation
• Device validation
• Backward compatibility
• Regression testing

Good error messages should tell users:

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1What went wrong?
2Why did it happen?
3What input was expected?
4What should the user do instead?

This makes validation work more important than it may initially appear.

Phase 9: Study Real PyTorch Pull Requests

Once the fundamentals are in place, analyze PRs according to their technical difficulty.

PR 1: Improve infer_schema Error Messages

Learn:

• Python type annotations
• inspect
• typing
• typing.get_type_hints()
• Future annotations
• Function signatures
• Operator schemas
• torch.library
• torch._library
• torchgen

The key concept is understanding how Python function information can be transformed into an operator schema.

Difficulty: Beginner

PR 2: lp_pool Parameter Validation

Study:

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1torch.nn.functional
2torch.nn.modules.pooling

Learn:

• Functional APIs
• nn.Module
• Parameter validation
• Input validation
• Exception handling
• Backward compatibility
• Regression tests

The goal is to understand where validation belongs and how incorrect parameters should be reported to users.

Difficulty: Beginner

PR 3: CUDA Guard and RAII

This is a major step toward PyTorch's C++ and CUDA infrastructure.

Learn:

• CUDA device contexts
• cudaSetDevice()
• RAII
• Device guards
• CUDAGuard
• DeviceGuard
• Exception safety
• Current-device management

Explore areas such as:

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1c10/cuda/
2DeviceGuard
3CUDAGuard
4CUDAGuardImpl

The central concept is:

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1Save Current Device
2       ↓
3Switch Device
4       ↓
5Perform Operation
6       ↓
7Restore Previous Device

RAII allows cleanup and restoration to happen automatically when the guard leaves scope.

Difficulty: Intermediate

PR 4: Clamp and float16 Overflow

This type of PR requires a stronger understanding of numerical behavior.

Learn:

• IEEE 754
• FP16
• BF16
• Floating-point limits
• Scalar conversion
• Casting
• Type promotion
• Overflow detection
• CPU and CUDA behavior

Study relevant ATen and scalar implementations and compare how different dtypes behave.

Difficulty: Intermediate

PR 5: F.pad Validation

Study:

• Tensor dimensions
• Padding semantics
• Reflection padding
• Replication padding
• Circular padding
• Argument validation
• Dimension checking
• Error messages
• Regression tests

Relevant areas include the Python functional API and the corresponding native C++ implementation.

Difficulty: Beginner

PR 6: torch.where and float16 Overflow

This is a more advanced operator-level problem.

Learn:

• torch.where
• TensorIterator
• Broadcasting
• Type promotion
• Scalar conversion
• CPU kernels
• CUDA kernels
• Operator dispatch

A useful investigation path is:

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1torch.where()
2      ↓
3Operator registration
4      ↓
5Dispatcher
6      ↓
7ATen implementation
8      ↓
9TensorIterator
10      ↓
11CPU / CUDA kernel

Difficulty: Intermediate

Core PyTorch Components to Master

Recommended Learning Sequence

Follow this order rather than jumping directly into complex CUDA PRs:

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11. Modern C++
2       ↓
32. Python + PyTorch APIs
4       ↓
53. PyTorch Repository Structure
6       ↓
74. Tensor Internals
8       ↓
95. ATen
10       ↓
116. Dispatcher
12       ↓
137. TensorIterator
14       ↓
158. CPU Kernels
16       ↓
179. CUDA Fundamentals
18       ↓
1910. CUDA Kernels
20       ↓
2111. Operator Registration
22       ↓
2312. Autograd
24       ↓
2513. Unit Testing
26       ↓
2714. Small Bug Fixes
28       ↓
2915. Medium Complexity PRs

Recommended Contribution Progression

Level 1: Python Validation and Error Messages

Start with issues involving:

• Better error messages
• Argument validation
• Shape validation
• Parameter validation
• Python API behavior

Examples include infer_schema and F.pad-style improvements.

Skills gained:

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1Python
2Exceptions
3PyTorch APIs
4Testing
5API design

Level 2: PyTorch Functional APIs

Move into:

• torch.nn.functional
• nn.Module
• Operator semantics
• Parameter constraints
• Regression tests

This stage teaches how public PyTorch APIs map to internal implementations.

Level 3: Numerical Correctness

Next investigate:

• FP16
• BF16
• Overflow
• Underflow
• Type promotion
• Scalar conversion
• CPU/GPU differences

This stage introduces the interaction between mathematical behavior and low-level implementation details.

Level 4: ATen and C10

After becoming comfortable with operators, move deeper into:

• ATen
• C10
• Dispatcher
• Device guards
• Operator registration
• Native implementations

This is where PyTorch contribution starts becoming substantially more systems-oriented.

Level 5: CUDA and Performance

Finally, investigate:

• CUDA kernels
• GPU memory access
• CUDA streams
• Kernel launches
• Synchronization
• Vectorization
• Performance optimization
• Profiling

At this stage, you can begin working on more complex PyTorch infrastructure and performance issues.

12-Week PyTorch Contribution Plan

Weeks 1–2: Modern C++

Focus on:

• C++17
• Templates
• RAII
• STL
• Smart pointers
• Move semantics
• Exceptions
• Memory management

Build several small C++ projects rather than only watching tutorials.

Weeks 3–4: PyTorch Internals

Study:

• Repository structure
• Tensor
• Storage
• Stride
• Device
• Dtype
• ATen
• C10

Trace a few simple operators through the source code.

Weeks 5–6: Operators and Testing

Focus on:

• Python APIs
• Native operators
• Operator schemas
• Dispatcher
• Validation
• Unit tests
• Regression tests

At this stage, start reproducing small existing bugs locally.

Weeks 7–8: Numerical Behavior

Study:

• FP32
• FP16
• BF16
• Type promotion
• Broadcasting
• TensorIterator
• Overflow
• Underflow

Investigate issues similar to clamp and torch.where.

Weeks 9–10: CUDA and C10

Focus on:

• CUDA fundamentals
• CUDA device management
• Streams
• Device guards
• RAII
• CUDAGuard
• C10 CUDA infrastructure

Weeks 11–12: First Contributions

Start with small issues involving:

• Error messages
• Validation
• Documentation
• Tests
• Small correctness fixes

Then progress toward:

• ATen changes
• Operator fixes
• Numerical corrections
• C10 changes
• CUDA-related fixes

How to Read a PyTorch PR Efficiently

Do not read a large PR file by file without a plan.

Use this process:

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11. Read the PR description
2          ↓
32. Identify the reported bug
4          ↓
53. Reproduce the problem
6          ↓
74. Find the public API
8          ↓
95. Trace the operator
10          ↓
116. Identify dispatch behavior
12          ↓
137. Locate the implementation
14          ↓
158. Understand the failing case
16          ↓
179. Read the patch
18          ↓
1910. Read the regression test
20          ↓
2111. Run the relevant tests

The most important question is:

"Why does this particular line of code need to change?"

Understanding that question is more valuable than simply understanding what the patch changed.

Skills Required for Your First PyTorch PR

You do not need to master all of PyTorch before contributing.

For a first contribution, a strong foundation in these areas is enough:

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1Python
2C++
3Git
4PyTorch APIs
5Unit Testing
6Basic ATen knowledge
7Basic Tensor concepts

CUDA becomes increasingly important as you move toward GPU-specific issues.

What Not to Do

Avoid starting with the most complicated parts of PyTorch.

Do not begin by trying to understand:

• The entire dispatcher
• All of Autograd
• Every ATen operator
• The complete CUDA backend
• Every generated file
• The entire build system

Instead, pick one small operator or one small bug and trace only the code necessary to understand it.

A narrow investigation is much more effective than trying to understand the entire framework at once.

Final PyTorch Contribution Roadmap

The overall progression can be summarized as:

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1Modern C++
2    ↓
3Python + PyTorch APIs
4    ↓
5Tensor Fundamentals
6    ↓
7ATen
8    ↓
9C10
10    ↓
11Dispatcher
12    ↓
13TensorIterator
14    ↓
15CPU Kernels
16    ↓
17CUDA
18    ↓
19Numerical Correctness
20    ↓
21Operator Registration
22    ↓
23Testing
24    ↓
25Small Bug Fixes
26    ↓
27Medium PyTorch PRs

The best strategy is to learn one layer, inspect real source code, reproduce a small issue, write a test, and then make a small change.

You do not need to understand all of PyTorch before submitting your first contribution. Start with small validation, testing, documentation, or error-message improvements and gradually move toward ATen, numerical correctness, C10, and CUDA infrastructure.