Unlocking Performance: Alternatives to torch.cuda.make_graphed_callables in PyTorch

Purpose

• Aims to reduce CPU overhead associated with launching individual kernel executions.
• Optimizes the execution of PyTorch operations on GPUs (Graphics Processing Units) by leveraging CUDA graphs.

What are CUDA Graphs?

• They allow for efficient execution by avoiding the overhead of dynamically scheduling kernels at runtime.
• CUDA graphs are pre-defined sequences of CUDA operations that are captured and stored on the GPU.

How torch.cuda.make_graphed_callables Works

1. • It takes functions (including nn.Module instances) as input.

2. Creates Graphed Versions
   

   • For each input callable, it generates a corresponding "graphed" version.
   • This graphed version encapsulates the original callable's forward pass (and optionally, backward pass) as a CUDA graph.

3. Forward Pass as a CUDA Graph
   

   • When the graphed callable's forward method is called:
     • The operations from the original callable's forward pass are executed as a single CUDA graph within an autograd node.
   • This reduces CPU overhead compared to launching individual kernels for each operation.

4. Backward Pass (Optional)
   

   • The graphed callable's forward pass also adds a backward node to the autograd graph.
   • During backward propagation, if applicable, the callable's backward work is also executed as a CUDA graph.

5. Drop-in Replacement
   

   • The graphed callables are designed to be functionally equivalent to their original counterparts.
   • You can typically use them as direct replacements in your autograd-enabled training loops for potential performance gains.

Things to Consider

• Long-lived Tensors
  
  • Since the graph captures memory addresses, you need to maintain references to tensors used as input and output during capture. These tensors should persist throughout the graph's usage.
• Warmup
  
  • Before capturing a CUDA graph, it's often beneficial to run the code a few times (warmup) to ensure all necessary operations are included in the graph.
• Graph-Safe Operations
  
  • torch.cuda.make_graphed_callables works best with operations that have static shapes (tensor dimensions) and control flow (no conditional branching).
  • Dynamic operations or branching might not be suitable for CUDA graphs.

In Summary

---

import torch

def simple_model(x):
  y = x * 2
  z = y.sin()
  return z

# Move the model and input to the GPU
device = "cuda" if torch.cuda.is_available() else "cpu"
model = simple_model.to(device)
x = torch.randn(5, requires_grad=True).to(device)

# Create a graphed version of the model (warmup included)
num_warmup_iters = 3
with torch.cuda.stream(torch.cuda.Stream()):
  for _ in range(num_warmup_iters):
    model(x)

graphed_model = torch.cuda.make_graphed_callables(model, sample_args=(x,))

# Use the graphed model for inference
y = graphed_model(x.clone())  # Use a clone to avoid modifying original input
print(y)

# You can also use the graphed model in a training loop with autograd:
optimizer = torch.optim.Adam(model.parameters())
for epoch in range(10):
  optimizer.zero_grad()
  y = graphed_model(x)
  loss = (y - torch.ones_like(y)).sum()
  loss.backward()
  optimizer.step()

1. • The simple_model function performs a multiplication and sine operation on the input tensor x.

2. Move to Device
   

   • Checks for GPU availability and moves the model and input tensor to the appropriate device (cuda or cpu).

3. Warmup and Graph Creation
   

   • Creates a CUDA stream and performs warmup iterations by running the original model with x.
   • This ensures the necessary operations are captured in the graph.
   • torch.cuda.make_graphed_callables creates a graphed_model that encapsulates the warmed-up forward pass (and backward pass if applicable) as a CUDA graph.

4. Inference with Graphed Model
   

   • Creates a copy of x (to avoid modifying the original) and uses graphed_model for forward pass execution.
   • The graph execution is optimized using the captured CUDA operations.

5. Training Loop (Optional)
   

   • Demonstrates how you can use graphed_model in an autograd-enabled training loop.
   • The forward pass leverages the CUDA graph, potentially improving performance.

Key Points

• Ensure your model operations are compatible with CUDA graphs (static shapes, control flow).
• Adapt the warmup iterations (num_warmup_iters) based on your model and workload.
• In practice, consider using torch.nn.Module for more complex models.
• This example uses a simple model for illustration purposes.

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Automatic Fusion

• This can already provide performance benefits without explicitly defining CUDA graphs.
• PyTorch's autograd engine performs some level of automatic fusion by combining multiple kernel launches into a single one under certain conditions.

Native PyTorch Optimizations

• Leverage PyTorch's built-in optimizations like:
  • Fused kernels: Combine multiple operations into a single kernel for efficiency (e.g., nn.functional.conv2d with bias).
  • Tensor cores: Utilize hardware acceleration for specific operations on modern NVIDIA GPUs (e.g., nn.functional.conv2d with appropriate dtypes).

AOT Autograd (Advanced)

• However, AOT Autograd has a steeper learning curve and might require additional effort compared to torch.cuda.make_graphed_callables.
• It allows statically compiling computation graphs, leading to potentially higher performance for complex models.
• PyTorch's functorch library offers AOT (Ahead-of-Time) Autograd for advanced users.

Hardware-Specific Optimizations

• Explore vendor-specific tools for GPU optimization:
  • NVIDIA's TensorRT: Offers a framework for deploying trained PyTorch models for high-performance inference.
  • AMD ROCm: Provides tools like the ROCm compiler (ROCC) for optimizing code for AMD GPUs.

Choosing the Right Option

The best alternative depends on your specific needs and expertise:

• Hardware-specific tools are suitable if you're targeting specific platforms for deployment.
• AOT Autograd is a potential avenue for advanced users willing to delve deeper.
• Consider torch.cuda.make_graphed_callables for more control over graph creation, but be mindful of its limitations.
• For simpler cases, automatic fusion and native PyTorch optimizations might suffice.

• Experiment with different approaches to find the most effective one for your scenario.
• Profile your code to identify performance bottlenecks before applying optimizations.