When TorchDynamo Struggles: Fine-Grained Control for PyTorch Tracing

What is TorchDynamo?

• These graphs represent the sequence of operations performed on tensors during a PyTorch program's execution.
• It's responsible for capturing PyTorch computational graphs using CPython's Frame Evaluation API.
• TorchDynamo is an internal PyTorch component that plays a crucial role in the torch.compiler module.

Why Fine-Grained Tracing?

• In these cases, fine-grained tracing allows you to manually control how TorchDynamo treats specific parts of your code.
• However, there might be certain situations where you have custom operations or features that TorchDynamo struggles to handle:
  • Custom hooks
  • torch.autograd.Function subclasses
• By default, TorchDynamo aims to automatically trace as much of your PyTorch code as possible to optimize it for performance.

allow_in_graph Decorator

• The operations within the decorated function are not traced or optimized, and they're directly included in the generated computational graph.
• When you decorate a function with @allow_in_graph, TorchDynamo essentially treats it as a black box.
• The torch.compiler.allow_in_graph decorator is the primary tool for fine-grained tracing.

Use Cases for allow_in_graph

• Preserving Functionality
  In some cases, the automatic tracing might inadvertently alter the behavior of your code. Using allow_in_graph prevents these unintended changes.
• Known TorchDynamo Hard-to-Support Features
  If you're using custom hooks or torch.autograd.Function subclasses that TorchDynamo has difficulty handling, decorating them with allow_in_graph ensures they're incorporated into the graph as-is.

Cautions with allow_in_graph

• Downstream Compatibility
  Ensure that the operations within the decorated function are compatible with the tools or techniques you're using for compilation or optimization after tracing.
• Careful Screening is Essential
  It's critical to carefully assess each function you decorate with allow_in_graph. The decorated function and any code it calls should not introduce graph breaks or closures that would prevent downstream PyTorch components (like AOTAutograd) from working correctly.

• Use allow_in_graph judiciously and with careful consideration for downstream compatibility.
• This allows you to manage how TorchDynamo handles specific parts of your PyTorch code, especially when dealing with unsupported features or situations where you need to preserve exact functionality.
• torch.compiler.TorchDynamo APIs offer fine-grained tracing capabilities through the allow_in_graph decorator.

---

import torch
from torch.compiler import torchdynamo


# Custom function that TorchDynamo might struggle with (replace with your actual logic)
@torchdynamo.allow_in_graph
def custom_hook_function(x):
    # This function might use custom hooks or have other unsupported features
    result = x * 2
    # Simulate a custom hook that modifies the result
    result += 10
    return result

def my_model(x):
    x = torch.relu(x)  # Standard PyTorch operation, traced by TorchDynamo
    x = custom_hook_function(x)  # Treated as a black box by TorchDynamo
    return x

# Compile the model for performance optimization
compiled_model = torch.jit.trace(my_model, torch.randn(5))

# Run the compiled model
output = compiled_model(torch.randn(5))
print(output)

1. We define a custom function custom_hook_function that might have features unsupported by TorchDynamo (like custom hooks in this example).
2. The @torchdynamo.allow_in_graph decorator ensures this function is included in the compiled graph as-is.
3. The my_model function performs a standard PyTorch operation (torch.relu) followed by the custom function.
4. When my_model is compiled with torch.jit.trace, TorchDynamo will trace torch.relu but leave custom_hook_function untouched due to the decorator.
5. The compiled model can be used for faster inference, and the custom logic within custom_hook_function will be preserved.

---

Manual Scripting (torch.jit.script)

• However, manually scripting requires ensuring all operations used are compatible with TorchScript. This might involve refactoring parts of your code.
• This allows you to write your model code directly in a PyTorch-like syntax that can be efficiently compiled.
• If you have full control over your model's code and it doesn't rely on unsupported features, consider manual scripting with torch.jit.script.

ONNX Export and Runtime

• This approach offers portability but may require additional tools and frameworks for execution.
• You can then use an ONNX runtime environment for efficient inference on your target platform.
• This involves using torch.onnx.export to convert your model into an intermediate representation.
• If you need to deploy your model across different platforms or frameworks that don't natively support PyTorch, consider exporting it to the Open Neural Network Exchange (ONNX) format.

Eager Execution for Flexibility

• This allows you to leverage the full power of Python for control flow and custom operations, but it might be less performant compared to compiled or optimized approaches.
• In situations where performance isn't the primary concern, or you're still in the development phase and need flexibility, consider using eager execution with PyTorch directly.

Choosing the Right Approach

The best alternative for your use case depends on your specific needs:

• If you prioritize flexibility during development, eager execution might be suitable.
• For cross-platform deployment, ONNX export offers portability.
• If performance is critical, and you can refactor your code for TorchScript compatibility, manual scripting can be a good choice.

Approach	Advantages	Disadvantages
Manual Scripting (JIT)	High performance, full control	Requires TorchScript compatibility, less flexibility
ONNX Export and Runtime	Portability across platforms	Requires additional ONNX runtime environment
Eager Execution (PyTorch)	Flexibility, easy for development	Less performant compared to compiled or optimized code