Beyond Static Tracing: Exploring TorchDynamo for Dynamic PyTorch Model Export

What is ONNX?

• It allows models created in different frameworks (like PyTorch) to be run on various inference engines, promoting portability and interoperability.
• Open Neural Network Exchange (ONNX) is a format for representing deep learning models.

What is PyTorch?

• PyTorch is a popular deep learning framework built for flexibility and research.

TorchDynamo-based ONNX Exporter

• This approach has several advantages over the traditional static tracing method:

  • Handles dynamic PyTorch constructs
    It can export models that use dynamic control flow (e.g., loops, conditionals) that might not be easily captured by static tracing.
  • Improved compatibility
    It often leads to more compatible and efficient ONNX models compared to static tracing.

• It leverages the TorchDynamo engine, which dynamically analyzes the model's Python bytecode.

• Introduced in PyTorch 2.1 (beta), this exporter offers a new approach to converting PyTorch models to ONNX format.

Key Components

• ONNX Registry
  A registry that maps PyTorch operations to their corresponding ONNX operators.
• ONNX Export Options
  A collection of options for customizing the export behavior (e.g., specifying input shapes, opset version).
• ONNX Exporter
  The main class that orchestrates the conversion process.

How It Works

1. TorchDynamo analysis
   
   • The TorchDynamo engine hooks into Python's frame evaluation API.
   • It analyzes the model's Python bytecode to understand its operations and control flow.
2. FX Graph generation
   
   • Based on the bytecode analysis, Dynamo constructs an FX Graph.
   • An FX Graph is a high-level representation of the model's computation, similar to a directed acyclic graph (DAG).
   • It captures the relationships between operations in the model.
3. Graph polishing
   
   • The FX Graph is optimized and refined before translation to ONNX.
   • This may involve simplifying nodes, merging operations, and addressing potential compatibility issues with ONNX.
4. ONNX graph generation
   
   • The polished FX Graph is translated into an equivalent ONNX graph.
   • Each operation in the FX Graph is mapped to the most suitable ONNX operator.
   • Necessary metadata and attributes are added to the ONNX graph for correct execution in different inference engines.

Benefits

• Improved compatibility
  Often leads to more compatible and efficient ONNX models.
• Dynamic model support
  Can export models with dynamic control flow not easily handled by static tracing.

• Not backward-compatible
  Models exported with this method may not be compatible with older versions of PyTorch or ONNX runtime environments.
• Experimental
  The TorchDynamo-based exporter is still under development and considered experimental. It might not be as stable or feature-complete as the static tracing-based exporter.

---

import torch
import torch.nn as nn
import torch.onnx

# Define a simple PyTorch model with a dynamic loop
class MyModel(nn.Module):
    def __init__(self, input_size):
        super(MyModel, self).__init__()
        self.linear = nn.Linear(input_size, 10)

    def forward(self, x):
        y = self.linear(x)
        for i in range(x.shape[0]):  # Dynamic loop based on input size
            y[i] += torch.sin(y[i])
        return y

# Create a sample model instance
model = MyModel(16)

# Define dummy input (replace with actual input shape for your model)
dummy_input = torch.randn(4, 16)  # Batch size 4, input size 16

# Export the model to ONNX format using TorchDynamo
torch.onnx.export(
    model,  # PyTorch model instance
    dummy_input,  # Sample input tensor
    "my_model.onnx",  # Output ONNX file name
    opset_version=12,  # Optional: Specify ONNX opset version
    dynamic_axes={'input_0': 0}  # Optional: Specify dynamic axes (if applicable)
)

print("Model exported successfully!")

1. We define a simple MyModel class that takes an input tensor and performs a linear transformation followed by a dynamic loop with a sine operation.
2. We create a model instance and a dummy input tensor.
3. The torch.onnx.export function is used for export. Here:
   • model: The PyTorch model instance to be exported.
   • dummy_input: A sample input tensor to guide the export process.
   • "my_model.onnx": The desired filename for the exported ONNX model.
   • opset_version=12: (Optional) The ONNX operator set version to target (check compatibility with your inference engine).
   • dynamic_axes={'input_0': 0}: (Optional) This specifies that the first dimension (batch size) of the input tensor is dynamic, allowing for variable batch sizes during inference.

• Adjust the dynamic_axes argument if your model has other dynamic dimensions.
• Replace dummy_input with an input shape that matches your actual model's requirements.

---

TorchScript-based ONNX Exporter (Existing Method)

• Disadvantages
  
  • Limited support for dynamic control flow (e.g., loops, conditionals).
  • Might require additional scripting or model modifications to work with specific control flow constructs.
• Advantages
  
  • More stable and mature compared to the TorchDynamo-based exporter.
  • May be sufficient for simpler models without dynamic control flow.
• It uses torch.jit.trace to trace the model's execution with a sample input.
• This is the traditional method available since PyTorch 1.2.0.

ONNX Runtime Conversion Tools

• Disadvantages
  
  • Less integrated with PyTorch compared to the built-in exporters.
  • May require additional steps and configuration.
• Advantages
  
  • May offer better compatibility with ONNX Runtime.
  • Can potentially handle some dynamic control flow scenarios.
• These tools typically use a combination of static tracing and conversion techniques.
• ONNX Runtime provides tools like onnx-cli and Python bindings to convert PyTorch models to ONNX format.

Third-party Conversion Tools

• Disadvantages
  
  • Can introduce additional dependencies and maintenance overhead.
  • Integration level with PyTorch workflows might vary.
• Advantages
  
  • May offer specialized features or better compatibility with specific hardware or inference engines.
• Several third-party libraries can convert PyTorch models to ONNX format, each with its own strengths and limitations.

Choosing the Right Alternative

The best choice depends on your specific needs:

• For optimized conversion with specific hardware or inference engines
  Explore third-party libraries, but be aware of potential integration challenges and version compatibility.
• For handling dynamic control flow
  If your model demands dynamic control flow and the TorchDynamo-based exporter (still experimental) doesn't meet your stability requirements, consider ONNX Runtime conversion tools or third-party libraries after evaluating their compatibility with your model and ONNX runtime environment.
• For stable exports with static models
  Stick with the TorchScript-based exporter.