PyTorch MPS Inductor AdaptiveMaxPool Bugs

by Alex Johnson 42 views

When you're deep into your machine learning projects, especially those leveraging the power of Apple's Silicon (MPS) and PyTorch's cutting-edge torch.compile with the Inductor backend, you expect smooth sailing. However, a recent discovery highlights a critical issue: F.adaptive_max_pool1d and F.adaptive_max_pool2d can produce incorrect results when run on MPS devices with torch.compile(backend='inductor'). This isn't just a minor glitch; we're talking about significant numerical divergence compared to the standard eager mode execution, with differences exceeding 1.0. This article dives deep into this bug, exploring its implications and how to identify it in your own workflows.

The Nitty-Gritty: AdaptiveMaxPool and MPS Inductor Woes

The core of the problem lies in how adaptive_max_pool1d and adaptive_max_pool2d behave under the compiled Inductor backend on MPS devices. These pooling operations are fundamental in neural networks, often used to downsample feature maps to a fixed size, regardless of the input dimensions. This makes them incredibly versatile. For instance, in convolutional neural networks (CNNs), they can ensure that the output of a convolutional layer always has the same spatial dimensions before being fed into a fully connected layer. Similarly, in recurrent neural networks (RNNs), they can be used to pool features over time. The adaptive nature means you specify the output size, and the operation figures out the best way to pool the input to achieve it. This is a huge convenience, saving developers from manual calculations and complex slicing.

However, when torch.compile with the Inductor backend steps in to optimize these operations for MPS, something goes awry. The compiled code doesn't seem to perform the pooling calculation precisely as expected, leading to outputs that deviate substantially from what you'd get if you ran the same code in PyTorch's regular, uncompiled (eager) mode. The provided reproduction script vividly demonstrates this. It defines a simple function that applies F.adaptive_max_pool1d and then compares the output of the eager version with the compiled version. The result? A maximum difference of 2.4042608737945557, which is far from ideal and signals a serious numerical correctness issue. This discrepancy isn't limited to the 1D version; the bug also affects adaptive_max_pool2d, indicating a broader problem within the Inductor backend's implementation for these specific pooling functions on MPS.

Why Does This Matter? The Ripple Effect of Numerical Errors

Numerical correctness is not just a theoretical concern; it has very real-world implications for anyone training or deploying machine learning models. When your pooling operations yield incorrect results, even subtly, it can lead to a cascade of errors throughout your neural network. Imagine your model is trying to learn complex patterns. If the features are being aggregated incorrectly at an early stage, the subsequent layers will receive distorted information. This can manifest in several ways:

  • Degraded Model Performance: The most immediate consequence is a drop in accuracy or other performance metrics. Your model might struggle to converge during training, or it might perform poorly on validation and test sets. The subtle differences introduced by the buggy pooling can accumulate, making it harder for the model to discern meaningful patterns.
  • Training Instability: In some cases, numerical errors can lead to unstable training. Gradients might become erratic, causing the optimization process to diverge or oscillate, preventing the model from reaching its optimal state. This can be particularly frustrating, as it might appear as if the model is not learning, even with a well-designed architecture and dataset.
  • Inconsistent Results: Even if a model can be trained, the inconsistency introduced by numerical errors can be problematic. Running the same training process multiple times might yield vastly different results, making it difficult to reproduce findings or trust the deployed model's behavior. This is especially critical in research and production environments where reproducibility is paramount.
  • Subtle Bugs in Complex Models: In highly complex models, identifying the root cause of performance issues can be a daunting task. A bug in a seemingly standard operation like adaptive pooling might go unnoticed for a long time, masked by the intricate interactions of other components. This makes the discovery of such bugs even more crucial for the PyTorch community.

For developers working with MPS and torch.compile, this bug means that any model relying on adaptive_max_pool1d or adaptive_max_pool2d might be producing inaccurate outputs without you even realizing it. The torch.compile feature is designed to accelerate your code, but if the accelerated code is incorrect, the speedup comes at the cost of reliability. It's essential to be aware of this potential pitfall and to verify the numerical output of your models, especially when deploying compiled code on different hardware backends.

Reproducing the Bug: A Step-by-Step Guide

Reproducing the AdaptiveMaxPool bug on PyTorch MPS with the Inductor backend is straightforward, thanks to the clear example provided. Let's break down the script and understand what's happening. The script begins by importing the necessary libraries: torch for tensor operations and torch.nn.functional (aliased as F) for the pooling function.

import torch
import torch.nn.functional as F

Next, a simple function fn is defined. This function takes a tensor x as input and applies F.adaptive_max_pool1d to it, with the output_size set to 3. This means that regardless of the input tensor's spatial dimensions (the second dimension for 1D pooling), the output will always have a size of 3 in that dimension.

def fn(x):
    return F.adaptive_max_pool1d(x, output_size=3)

Then, a random input tensor x is created. It has a shape of (4, 10, 8) and is explicitly placed on the MPS device using device='mps'. The shape (4, 10, 8) implies a batch size of 4, a channel dimension of 10, and a sequence length (or spatial dimension for 1D) of 8. The adaptive_max_pool1d will operate on the last dimension (size 8) and downsample it to size 3.

x = torch.randn(4, 10, 8,device='mps')

The eager execution result is captured in eager_out. This is the baseline, the