torch.compile Benchmarked

PyTorch recently introduced torch.compile, a method to JIT-compile PyTorch code into optimized kernels, in its version 2.0. In the official website, they boast about their achievements as:

Across these 163 open-source models torch.compile works 93% of time, and the model runs 43% faster in training on an NVIDIA A100 GPU. At Float32 precision, it runs 21% faster on average and at AMP Precision it runs 51% faster on average.

This is fantastic! In this blog post, I will try out this new feature and see what it can do.

Experiments

torch.compile provides several compilation modes: default, reduce-overhead, and max-autotune. The optimal choice depends on various configurations that define the bottleneck, such as model architecture and input tensor size. It is explained here as:

The default mode is a preset that tries to compile efficiently without taking too long to compile or using extra memory. Other modes such as reduce-overhead reduce the framework overhead by a lot more, but cost a small amount of extra memory. max-autotune compiles for a long time, trying to give you the fastest code it can generate.

Anyway, I measured following metrics against the eager mode and different compilation modes while running GPT-2 (1.5B parameters) on Colab A100.

• Initial training step time & memory (when JIT compiling happens)
• Training time
• Inference time

I used AMP for a more realistic experiment. The core part of the code looks like this:

def time_train(model, optimizer, scaler, inputs) -> float:
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(N_ITER):
        optimizer.zero_grad()
        with autocast():
            outputs = model(**inputs, labels=inputs["input_ids"])
        scaler.scale(outputs.loss).backward()
        scaler.step(optimizer)
        scaler.update()
    end.record()
    torch.cuda.synchronize()
    return start.elapsed_time(end) / N_ITER

@torch.no_grad()
def time_infer(model, inputs) -> float:
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(N_ITER):
        with autocast():
            _ = model(**inputs)
    end.record()
    torch.cuda.synchronize()
    return start.elapsed_time(end) / N_ITER

For more details about the experiment, please refer to the Colab notebook.

Results & Discussions

The table below summarizes the measured metrics.

Indeed, torch.compile reduced the training time by 40-44% (corresponding to 68-78% speedup).

However, it didn’t reduce the inference time in this setting. I haven’t yet figured out what is going on here, but it seems that some people have reported negative effects of torch.compile.

• https://github.com/pytorch/pytorch/issues/98441
• https://discuss.pytorch.org/t/torch-compile-negative-performance/173857

If you have any thoughts on this, I’d appreciate your comments.

References

[1] torch.compile Tutorial — PyTorch Tutorials 2.0.1+cu117 documentation
[2] Is PyTorch 2.0 Faster Than PyTorch 1.13? | PyTorch 2.0 Benchmarks v2 – Weights & Biases
[3] PyTorch 2.0の新機能「torch.compile」使ってみた - まったり勉強ノート