PyTorch MPS: Troubleshooting `index_select` With Scalars

by Axel Sørensen 57 views

Hey everyone! Let's dive into an interesting issue encountered with PyTorch's index_select function, specifically when dealing with scalar index tensors on MPS (Metal Performance Shaders) devices. This might sound a bit technical, but we'll break it down in a way that's easy to understand.

The Curious Case of index_select and Scalar Tensors

So, what's the deal? The index_select function in PyTorch is a powerful tool for selecting specific elements from a tensor along a given dimension, it is a fundamental operation in tensor manipulation, allowing you to extract subsets of data based on indices. Think of it like picking specific rows or columns from a matrix. Now, the interesting part is that on CPUs, index_select can happily handle a scalar tensor (a zero-dimensional tensor, basically a single number) as the index. This means you can use a single number to select a specific row or column.

For example, if you have a matrix and you want to select the second row, you can use index_select with the index being a scalar tensor containing the number 1 (since indexing usually starts from 0). This flexibility makes index_select quite versatile in various scenarios, such as advanced indexing and data manipulation tasks.

However, when we move to MPS devices, which are designed for Apple's GPUs, things get a little tricky. MPS throws an error when you try to use a scalar tensor as the index in index_select. This inconsistency between CPU and MPS behavior can be a real head-scratcher, especially when you're trying to write code that runs seamlessly on different devices. Understanding this difference is crucial for developing robust and portable PyTorch applications. Imagine you've written a complex model that relies on this behavior, and suddenly it breaks when you switch to MPS – not a fun situation!

Diving Deeper: The Bug Report

Let's take a look at the original bug report to get a clearer picture. The user provided a concise code snippet that perfectly illustrates the issue. Here’s the code:

import torch

def test_index_select(device):
 x = torch.ones([2, 3], device=device)
 index = torch.tensor(1, device=device) # zero-dimensional index tensor
 try:
 output = torch.index_select(x, dim=0, index=index)
 print(f"index_select test succeeds for device: {device}. output shape: {output.shape}")
 except Exception as e:
 print(f"index_select test fails for device: {device}: {e}")

test_index_select(device = "cpu")
test_index_select(device = "mps")

This code creates a 2x3 tensor filled with ones and then attempts to use index_select to select a row using a scalar index tensor. As the user pointed out, this works flawlessly on the CPU, but it throws a Dimension specified as -1 but tensor has no dimensions error on MPS. This error message is a bit cryptic, but it essentially means that MPS is not correctly interpreting the scalar tensor as a valid index.

The error message “Dimension specified as -1 but tensor has no dimensions” can be particularly confusing if you're not familiar with the internals of tensor operations. In this context, it suggests that the MPS implementation of index_select might be expecting a tensor with a specific number of dimensions for the index, and a scalar tensor (which has zero dimensions) doesn't fit the bill. This could be due to differences in how the CPU and MPS implementations handle dimension checking or tensor reshaping during the index_select operation.

The terminal output from running this code clearly shows the discrepancy:

index_select test succeeds for device: cpu. output shape: torch.Size([1, 3])
index_select test fails for device: mps: Dimension specified as -1 but tensor has no dimensions

This stark contrast highlights the bug and the need for a fix to ensure consistent behavior across different devices.

Why This Matters: The Importance of Cross-Device Compatibility

You might be wondering,