Torch-XPU Reshape Accuracy Error Investigation With Shape (48, 64, 64, 64, 64) To (48, 4096, 4096)

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Introduction

This article delves into a reshape accuracy error encountered within the torch-xpu framework, specifically when reshaping a tensor from the shape (48, 64, 64, 64, 64) to (48, 4096, 4096). This issue was initially discovered during the execution of a transformers pytest case, namely "pytest -rA tests/models/got_ocr2/test_modeling_got_ocr2.py::GotOcr2IntegrationTest::test_small_model_integration_test_got_ocr_crop_to_patches". To facilitate replication and investigation, a dedicated test case was constructed. This article provides a detailed explanation of the bug, the steps taken to reproduce it, and the system environment in which it was observed. Understanding these intricacies is crucial for developers and researchers working with large tensors and reshape operations in the context of deep learning and PyTorch. We aim to provide a comprehensive guide to address this issue and contribute to the robustness of the torch-xpu framework.

Background on Tensor Reshaping and Accuracy

Before diving into the specifics of the bug, it is essential to understand the concept of tensor reshaping and its implications for numerical accuracy. In the realm of deep learning, tensors are fundamental data structures used to represent everything from input images and text to model weights and activations. Tensor reshaping is the process of changing the dimensions of a tensor without altering its underlying data. This is a common operation in neural networks, allowing data to be rearranged to suit the requirements of different layers and operations. However, reshaping can sometimes lead to accuracy issues, particularly when dealing with very large tensors or when the reshaping involves a significant change in dimensionality.

When a tensor is reshaped, the underlying data elements remain the same, but their arrangement in memory may change. This can affect how subsequent computations are performed, especially when using specialized hardware accelerators like GPUs or XPUs. Numerical accuracy can be compromised due to the way these accelerators handle memory access and data alignment. For instance, certain reshaping operations might cause data to be accessed in a non-contiguous manner, leading to performance bottlenecks and potential inaccuracies. Additionally, the order in which floating-point operations are performed can influence the final result due to the non-associativity of floating-point arithmetic. Therefore, it is crucial to carefully analyze and test reshape operations, especially when working with high-dimensional tensors and custom hardware implementations.

In the context of torch-xpu, which is a PyTorch extension designed to leverage Intel's XPU architecture, these considerations are even more pertinent. The XPU architecture has its own unique memory hierarchy and computational characteristics, which can impact the behavior of reshape operations. Ensuring the accuracy of these operations is vital for the overall reliability and performance of models deployed on XPU-based systems. The bug described in this article highlights the importance of rigorous testing and debugging of tensor manipulations in specialized hardware environments.

Bug Description: Reshape Accuracy Error

The bug manifests as an accuracy error during a reshape operation when transforming a tensor with the shape (48, 64, 64, 64, 64) into a tensor with the shape (48, 4096, 4096). This specific transformation is triggered within a transformers pytest case, GotOcr2IntegrationTest, indicating that the issue arises in a real-world application context. The reshape operation itself is a common one in deep learning, often used to flatten or rearrange data for processing by different layers in a neural network. However, in this particular scenario, the combination of the input shape, the target shape, and the underlying torch-xpu implementation leads to an accuracy anomaly. This suggests that there might be an optimization or a memory management issue within torch-xpu that is exposed by this specific reshape operation.

The significance of this bug lies in its potential to impact the correctness of model outputs. If a reshape operation introduces even a small amount of error, it can propagate through subsequent layers and computations, potentially leading to a significant deviation in the final result. This is particularly concerning in applications where precision is critical, such as optical character recognition (OCR) or other tasks involving complex data transformations. Therefore, identifying and resolving this accuracy error is essential for ensuring the reliability of models running on torch-xpu.

To better understand the nature of the bug, it is important to consider the dimensions involved. The transformation from (48, 64, 64, 64, 64) to (48, 4096, 4096) involves a substantial change in the shape of the tensor. The original tensor has five dimensions, while the reshaped tensor has only three. The key change is the flattening of the last four dimensions (64, 64, 64, 64) into a single dimension of size 4096 * 4096. This flattening process might be exposing a corner case in the torch-xpu implementation, possibly related to how it handles large, contiguous memory blocks or how it optimizes memory access patterns. Further investigation is needed to pinpoint the exact cause of the accuracy error and to develop an appropriate fix.

Steps to Reproduce the Bug

To reproduce the reshape accuracy error, the following steps were meticulously documented and should be followed precisely:

  1. Clone the Repository: The first step involves cloning a specific repository that contains the necessary test case and data. Execute the following command in your terminal:

    git clone https://github.com/yuanwu2017/llm-dbg.git

    This command will download the repository to your local machine. The repository contains the test case and the data necessary to trigger the bug.

  2. Navigate to the Directory: After cloning the repository, change your current directory to the newly cloned directory using the following command:

    cd llm-dbg

    This ensures that all subsequent commands are executed in the correct context.

  3. Download Large Files with Git LFS: The repository uses Git Large File Storage (LFS) to manage large data files. To download these files, you need to use the git lfs pull command:

    git lfs pull

    This command will download the large data files required for the test case. Git LFS is essential for handling files that exceed the size limits of regular Git repositories.

  4. Concatenate Archive Parts: The data files are split into multiple parts to facilitate easier distribution and storage. These parts need to be concatenated back into single archive files. Use the following commands to concatenate the parts for attn_weight.tar.gz and attn_de.tar.gz:

    cat attn_weight.tar.gz.part.a* > attn_weight.tar.gz
cat attn_de.tar.gz.part.a* > attn_de.tar.gz

    These commands combine the individual parts into complete .tar.gz archives.

  5. Extract the Archives: Once the archive files are concatenated, they need to be extracted. Use the following commands to extract the contents of attn_weight.tar.gz and attn_de.tar.gz:

    tar -xvzf attn_weight.tar.gz
tar -xvzf attn_de.tar.gz

    These commands will extract the necessary data files into the current directory.

  6. Run the Test Script: Finally, execute the test script that reproduces the bug. Use the following command:

    python test_reshape.py

    This command will run the Python script that performs the reshape operation and checks for accuracy errors. If the bug is present, the script will report the error.

By following these steps, you should be able to reproduce the reshape accuracy error in your environment. This will allow you to further investigate the issue and potentially contribute to its resolution. The script test_reshape.py contains the specific code that triggers the bug, and the data files provide the necessary input tensors. This detailed reproduction procedure ensures that the bug can be reliably observed across different systems and configurations.

System Environment and Versions

The reshape accuracy error was observed in a specific system environment, which is crucial for understanding the context of the bug and for potential debugging efforts. The system's configuration, including the operating system, hardware, and software versions, can significantly influence the behavior of numerical computations, especially when dealing with specialized hardware accelerators like XPUs. Below is a detailed breakdown of the environment in which the bug was encountered:

Operating System

The operating system in use was Ubuntu 22.04.5 LTS (x86_64). This is a widely used Linux distribution known for its stability and support for a wide range of hardware and software. The LTS (Long Term Support) designation indicates that this version of Ubuntu receives updates and security patches for an extended period, making it a popular choice for development and production environments.

GCC Version

The GNU Compiler Collection (GCC) version installed on the system was (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0. GCC is a crucial toolchain component for compiling C and C++ code, which are commonly used in the development of high-performance computing libraries like PyTorch and its extensions. The specific version of GCC can impact the performance and behavior of compiled code, making it an important factor in debugging numerical issues.

Python Version

The Python interpreter used was version 3.11.13 (main, Jun 4 2025, 08:57:29) [GCC 11.4.0] (64-bit runtime). Python is the primary language used for interacting with PyTorch and for writing test scripts and applications. The version of Python can affect the behavior of the interpreter and the availability of certain libraries and features.

PyTorch and Related Libraries

The core libraries and their versions are as follows:

  • PyTorch version: 2.8.0.dev20250615+xpu
  • torch-xpu-ops: 3.3.1+gitb0e26b73
  • torchaudio: 2.8.0.dev20250615+xpu
  • torchvision: 0.23.0.dev20250615+xpu

These versions are particularly important because they represent the specific builds of PyTorch and its extensions that were in use when the bug was observed. The +xpu suffix indicates that these are builds optimized for Intel's XPU architecture. The version numbers provide crucial context for identifying potential bug fixes or regressions in specific releases.

Hardware Configuration

The system's hardware configuration is also a key factor. The CPU is an Intel(R) Xeon(R) Platinum 8480+, which is a high-end server processor with a large number of cores (224 CPUs). The system has a substantial amount of memory (210 MiB L3 cache) and is configured with two NUMA nodes. The absence of CUDA availability and GPU models in the environment information suggests that the system is primarily relying on the XPU for computations.

Significance of the Environment

The combination of these environment details provides a comprehensive picture of the system in which the reshape accuracy error occurred. This information is invaluable for developers and researchers attempting to reproduce the bug, diagnose its root cause, and develop a fix. The specific versions of PyTorch, torch-xpu-ops, and the underlying hardware architecture are all potential factors that could contribute to the issue. By carefully considering these details, it is possible to narrow down the scope of the investigation and identify the most relevant areas for further analysis.

Conclusion and Further Investigation

In conclusion, this article has detailed a reshape accuracy error encountered when reshaping a tensor from (48, 64, 64, 64, 64) to (48, 4096, 4096) within the torch-xpu framework. The bug was reproduced using a specific test case and observed in a well-defined system environment. The steps to reproduce the bug have been clearly outlined, and the relevant system versions and hardware configurations have been documented.

This issue highlights the importance of rigorous testing and debugging of tensor manipulations, particularly when dealing with specialized hardware accelerators like Intel's XPU. The reshape operation, while seemingly simple, can expose subtle issues in memory management and numerical computation, especially when applied to large tensors. The potential impact of this accuracy error on the correctness of model outputs underscores the need for a thorough investigation and a robust solution.

Further investigation is required to pinpoint the root cause of the bug. Potential areas of focus include:

  • Memory Access Patterns: Analyze how torch-xpu handles memory access during the reshape operation. Non-contiguous memory access or inefficient data alignment could be contributing factors.
  • Numerical Precision: Examine the precision of floating-point computations during the reshape and subsequent operations. Subtle differences in numerical precision can accumulate and lead to noticeable errors.
  • XPU-Specific Optimizations: Investigate whether XPU-specific optimizations are introducing the error. Certain optimizations might be valid for some tensor shapes but not for others.
  • Code Review: Conduct a detailed review of the torch-xpu code related to reshape operations, paying close attention to memory management and data manipulation logic.

By addressing these areas, it should be possible to identify the underlying cause of the reshape accuracy error and develop a fix that ensures the reliability and accuracy of tensor operations in torch-xpu. This will contribute to the overall robustness of the framework and enable developers and researchers to confidently deploy models on XPU-based systems.