Torch Compile Issues With Selective Checkpoints

Sep 4, 2025 by Lucas 48 views

Torch Compile Not Working Properly with Selective Activation Checkpoint

The Problem: Torch Compile and Selective Checkpointing Don't Play Nice

Hey folks! 👋 We've got a head-scratcher for you today. It looks like torch.compile in PyTorch isn't quite jiving with selective activation checkpoints. Basically, when you try to use the two together, the compilation process seems to get bypassed, and you don't get the performance boost you'd expect from optimized Triton kernels. This means that your code might be running slower than it could be, and who wants that? 🐌

Let's break down what's happening. Selective activation checkpoints are a clever way to save memory by recomputing certain parts of a neural network during the backward pass, rather than storing all the intermediate activations. This is super helpful when you're dealing with large models or limited memory. However, when you throw torch.compile into the mix, it seems like the compilation either doesn't kick in, or it doesn't optimize the parts of the code that use these checkpoints. The result? You miss out on those juicy, hardware-accelerated kernels that torch.compile is supposed to generate. This behavior is confirmed by profiling results, which clearly show that the expected optimizations aren't being applied.

Imagine you're trying to build a super-fast race car 🏎️, but you accidentally put on the wrong tires. The engine is capable of amazing speeds, but the tires are holding you back. That's kind of what's happening here. torch.compile is the powerful engine, and selective checkpointing is like a set of features that helps manage the resources efficiently. When these two are not working together as expected, the entire system is affected.

Let's also consider the impact of this issue. For developers, it can mean a significant decrease in performance, especially in memory-intensive tasks. The problem is that you can't just switch on the checkpointing feature because it hurts your performance, even with torch.compile. It might also lead to frustration and a lot of head-scratching trying to figure out why your models aren't running as fast as they should be. Therefore, this issue affects training and inference, impacting model efficiency and usability.

So, the takeaway? If you're using selective activation checkpoints, be aware that they might currently interfere with torch.compile. This means you'll either have to find a workaround, reconsider your memory optimization strategy, or wait for a fix. Stay tuned, we'll keep you updated on the progress!

Reproducing the Bug

To see this issue for yourself, we've got some sample code for you. This is a simplified example that should help you see the problem. This code creates a small function f that performs a simple matrix multiplication. The main part of the script sets up two different scenarios: one that uses torch.compile without selective checkpointing, and another that incorporates checkpointing using create_selective_checkpoint_contexts. You can then run the provided script and profile it using nsys. This should demonstrate the impact of torch.compile when working with and without selective activation checkpoints. The core of the problem lies in the interaction between torch.compile and the checkpoint mechanism.

Here's the code snippet:

import functools

import torch
from torch.utils.checkpoint import checkpoint, CheckpointPolicy, create_selective_checkpoint_contexts

# Define which operations to save during checkpointing
ops_to_save = [
    torch.ops.aten.mm.default,
]

# Define the policy for checkpointing
def policy_fn(ctx, op, *args, **kwargs):
    if op in ops_to_save:
        return CheckpointPolicy.MUST_SAVE
    else:
        return CheckpointPolicy.PREFER_RECOMPUTE

# Create the context function for selective checkpointing
context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)

# Define a checkpointing function with the context
def checkpoint_fn_with_context(fn, *args, **kwargs):
    return checkpoint(fn, *args, **kwargs, use_reentrant=False, context_fn=context_fn)

# Define a checkpointing function without the context
def checkpoint_fn_without_context(fn, *args, **kwargs):
    return checkpoint(fn, *args, **kwargs, use_reentrant=False)

# Define the function to be compiled
def f(x, y):
    x = 3 * x + 1
    y = 2 * y + 1
    return x @ y

# Create the input tensors
x = torch.ones(10, 12, device="cuda", requires_grad=True)
y = torch.ones(12, 10, device="cuda", requires_grad=True)

# Compile the function
f_compiled = torch.compile(f, fullgraph=True, dynamic=True)

# Profile the function calls
for i in range(50):
    torch.cuda.nvtx.range_push(f"f_compiled_without_context {i}")
    checkpoint_fn_without_context(f_compiled, x, y)
    torch.cuda.nvtx.range_pop()

for i in range(50):
    torch.cuda.nvtx.range_push(f"f_compiled_with_context {i}")
    checkpoint_fn_with_context(f_compiled, x, y)
    torch.cuda.nvtx.range_pop()

This code creates two loops: one for calls without checkpointing, and the other for calls with checkpointing. This allows you to profile and compare the performance differences. The profiling data will then show you the effect of selective activation checkpointing, specifically, that torch.compile doesn't apply its optimizations correctly when it is used.

How to Profile the Code

To properly analyze this, we're going to use NVIDIA's Nsight Systems (nsys). This is a powerful profiling tool that gives you detailed insights into what your code is doing on the GPU. The provided command in the original report profiles your code and gives you detailed information about what happens when torch.compile meets selective activation checkpointing. This is how to do it:

nsys profile -w true -t cuda,nvtx,cudnn,cublas -s cpu --capture-range=cudaProfilerApi --capture-range-end=stop --kill=none --force-overwrite true -x true -o torch_compile_selective_activation_acheckpoint \
python3 torch_compile_selective_activation_acheckpoint.py

Let's break down this command. The -w true option allows you to wait for the profiling to finish, and -t cuda,nvtx,cudnn,cublas tells Nsight to trace CUDA calls, as well as cuDNN and cuBLAS calls. The --capture-range=cudaProfilerApi and --capture-range-end=stop options are used to specify the start and end points of the profiling. The -x true options are for exporting the result. This ensures the profiling data is saved and easy to analyze. The output file will be named torch_compile_selective_activation_acheckpoint.nsys-rep. Once the profiling finishes, you can open the report and analyze it.

The profiling output will show you exactly what's happening on the GPU during execution. You will see the time spent in each kernel, the number of kernel launches, and other useful metrics. This will help you understand if torch.compile is generating and using the optimized kernels, as you would expect. The profiling data will help confirm that selective activation checkpointing impacts the effectiveness of torch.compile.

Expected Behavior

When torch compile is working as expected, you should see a significant speedup when using f_compiled, compared to the uncompiled version. The profiling data should show the generation and execution of optimized Triton kernels. These kernels are designed to run efficiently on the GPU, therefore, the execution time should be reduced. The expected behavior is that torch.compile should effectively optimize the code, even when selective activation checkpointing is used. However, the issue is that this is not the case.

When comparing profiling runs with and without checkpointing, the one without checkpointing should show the optimized kernels. In contrast, the one with checkpointing will likely either not have these optimized kernels or they won't be fully optimized. This means the performance improvement expected from torch.compile won't be realized when checkpointing is enabled. This difference can be clearly seen in the profiling results.

The essence of the issue is in how the compiler handles the parts of the code that interact with checkpointing, in other words, when torch.compile encounters operations that are part of a checkpointing context. This is where the problem arises, leading to the lack of performance improvements when the code is compiled.

Current Status and Possible Workarounds

As of now, the interaction between torch.compile and selective activation checkpoints is not working correctly. The compiler appears to struggle when both are used together. This is an active area of investigation and improvement for PyTorch developers. Since the issue has been identified, the PyTorch team is probably working hard to resolve the issue. They may release an update or provide a fix for this in future versions.

If you're facing this issue, here are a few possible workarounds:

Avoid Using Selective Checkpointing with torch.compile: The simplest solution (though potentially the least desirable) is to avoid using selective checkpointing when you also need to use torch.compile. This will allow you to take full advantage of the compiler's optimizations, but you might need more memory. If your model is small, this workaround is feasible.
Alternative Checkpointing Strategies: Explore other checkpointing techniques that might be more compatible with torch.compile. Full checkpointing may be a viable option if you don't need the fine-grained memory optimization that selective checkpointing provides. Although full checkpointing is less memory-efficient, it might work well with the compiler. Depending on the model and task, this may not significantly impact performance.
Manual Optimization: Manually optimize your code. This could involve rewriting parts of your model, using different operations, or experimenting with different data layouts. This is time-consuming and may require a deep understanding of PyTorch and the underlying hardware. However, the performance gain could be very significant.

Conclusion: Stay Tuned!

So, there you have it! We've identified a bug. If you run into this issue, the key takeaway is to be aware that selective activation checkpoints and torch.compile may not work well together. Keep an eye out for updates from the PyTorch team. They are continuously working to improve their software. For now, consider the workarounds we've mentioned. You can avoid using selective checkpointing or experiment with alternatives. Remember to stay updated on the latest developments in PyTorch and keep an eye on the bug reports. If you have additional information, feel free to contribute. Thanks for reading, and happy coding! 🚀