How Can I Fix the RuntimeError: Trying To Resize Storage That Is Not Resizable?

AvatarByBarbara Hernandez Troubleshooting

Encountering errors during software development can be both frustrating and puzzling, especially when they involve underlying memory management issues. One such error that often perplexes developers is the RuntimeError: Trying to resize storage that is not resizable. This message typically signals a deeper challenge related to how data storage is handled within certain programming frameworks or libraries, and understanding its root cause is essential for effective troubleshooting.

At its core, this runtime error arises when an operation attempts to alter the size of a storage object that has been designed to remain fixed in size. Such constraints are common in performance-critical environments where memory allocation and management are tightly controlled. Developers working with tensor libraries, low-level data buffers, or specialized storage classes may encounter this error when their code inadvertently tries to expand or shrink these immutable storage units.

Grasping the context and implications of this error is crucial for anyone dealing with advanced data structures or memory-intensive applications. By exploring the typical scenarios that trigger this issue and the principles behind storage mutability, readers will be better equipped to diagnose and resolve the problem effectively. The following sections will delve deeper into the causes, examples, and practical solutions to navigate this runtime challenge with confidence.

Common Causes and Contexts of the Error

The `RuntimeError: Trying to resize storage that is not resizable` typically arises in deep learning frameworks such as PyTorch when an operation attempts to change the size of a tensor’s underlying storage, but the storage itself is immutable or fixed in size. This situation is often encountered during tensor manipulations where resizing is implicitly or explicitly requested but not supported.

Several common scenarios include:

  • Immutable Storage Tensors: Certain tensors, such as those created from CUDA pinned memory or shared memory segments, may not support resizing due to hardware or system constraints.
  • Views of Other Tensors: When a tensor is a view or slice of another tensor, its storage is shared and cannot be resized independently.
  • In-Place Operations on Non-Resizable Storage: Some in-place operations implicitly require resizing storage, which is disallowed.
  • Using `.resize_()` Improperly: Calling the `.resize_()` method on a tensor whose storage is not resizable will raise this error.
  • Interfacing with External Libraries: Tensors passed to or received from external C++ extensions or other libraries might have storage constraints.

Understanding the context where this error occurs is crucial for diagnosing and fixing it effectively.

Technical Explanation of Tensor Storage in PyTorch

In PyTorch, tensors are abstractions built upon a storage object that holds the actual data buffer. The storage contains a contiguous block of memory, while the tensor maintains metadata such as shape, strides, and offset to interpret the data correctly.

Key points about tensor storage:

  • Storage Size vs. Tensor Size: The storage size defines the total allocated memory, whereas the tensor size defines the shape and number of elements interpreted.
  • Resizable Storage: Some storage objects allow resizing, meaning the memory buffer can grow or shrink as needed.
  • Non-Resizable Storage: Other storage objects are fixed-size due to their creation context or memory type.

When a tensor operation tries to change the shape or number of elements such that the underlying storage must be resized, but the storage is flagged as non-resizable, the runtime error is triggered.

Strategies to Resolve the Error

To address this error, consider the following approaches:

  • Avoid In-Place Resizing on Non-Resizable Storage: Replace in-place resizing calls like `.resize_()` with operations that create new tensors, such as `.reshape()` or `.view()`, which do not alter storage size.
  • Clone or Copy the Tensor: Creating a clone with `.clone()` or copying the tensor to a new storage that is resizable can circumvent the issue.
  • Check Tensor Origin: Ensure the tensor is not a view or slice that shares storage with a non-resizable parent tensor.
  • Use `.detach()` When Appropriate: Detaching a tensor from the computation graph sometimes helps isolate it from constraints.
  • Reinitialize Tensors Properly: When needing to resize, reallocate tensors rather than attempting to resize existing non-resizable storage.

Comparison of Tensor Methods Related to Resizing

Understanding the differences between tensor methods is key to avoiding this runtime error. The table below summarizes relevant PyTorch tensor methods and their relation to storage resizing:

Method Effect on Storage Typical Use Case Risks of RuntimeError
resize_() Resizes storage in-place Change tensor size with possible data reallocation High if storage is non-resizable
reshape() Returns tensor with new shape, no storage resize Change tensor shape without changing data Low; safe if shape compatible
view() Returns tensor view, no storage resize Efficient shape change without copying Low; fails if tensor is non-contiguous
clone() Allocates new storage, copies data Copy tensor to new independent storage None; avoids resizing errors
detach() Returns tensor detached from graph, no resize Isolate tensor from computation graph None; unrelated to storage resizing

Best Practices to Prevent Storage Resizing Errors

To minimize the occurrence of this error, adhere to these best practices:

  • Prefer Non-In-Place Operations: Use functional tensor operations that return new tensors instead of modifying existing ones in-place.
  • Validate Tensor Contiguity: Ensure tensors are contiguous before performing operations that require it.
  • Use Cloning Wisely: When unsure about storage properties, clone tensors before resizing or reshaping.
  • Understand the Tensor Lifecycle: Track how tensors are created, modified, and passed around in the code to identify storage constraints.
  • Monitor External Library Interactions: When integrating with custom extensions, carefully handle tensor ownership and storage flags.

By applying these guidelines, developers can avoid the pitfalls that lead to the `RuntimeError: Trying to resize storage that is not resizable` and maintain stability in tensor operations.

Understanding the Cause of the RuntimeError: Trying to Resize Storage That Is Not Resizable

This error typically occurs in PyTorch or similar tensor-based frameworks when an operation attempts to resize a tensor’s underlying storage that has been marked as non-resizable. In most deep learning frameworks, tensors are backed by storage objects that manage memory allocation. Some storages are designed to be fixed in size for performance optimization or safety reasons, and any attempt to change their size leads to this error.

The primary causes include:

  • Immutable or non-resizable storage allocation: Certain tensors are created with storage that cannot be resized, for example, views of tensors, tensors created from NumPy arrays without copying, or when using specific memory pinning or sharing mechanisms.
  • In-place operations that modify the storage size: Operations such as `.resize_()` or `.resize_as_()` that explicitly change tensor dimensions can trigger this error if the underlying storage is non-resizable.
  • Incompatible tensor operations: Operations that internally attempt to reallocate or resize storage, such as certain reshapes or copying with a different size, can cause this error if the storage constraints are violated.

Understanding these root causes is essential for diagnosing and resolving the issue effectively.

Common Scenarios That Trigger the Error

This RuntimeError can arise in various practical contexts:

  • Working with Tensor Views:

When a tensor is a view of another tensor’s storage (e.g., using slicing or `.view()`), the view shares the storage but does not own it. Attempting to resize such a view’s storage is disallowed.

  • Using `.resize_()` on Non-Resizable Tensors:

The `.resize_()` method attempts to change a tensor’s size in-place. If the underlying storage is non-resizable, this will fail.

  • Interoperability with NumPy Arrays:

Tensors created from NumPy arrays without copying share the same memory buffer, which is fixed-size. Attempts to resize such tensors lead to errors.

  • Pinned Memory or Shared Storage:

Tensors allocated with pinned memory or shared across processes may have storage restrictions that prevent resizing.

  • In-Place Resizing During Model Training or Data Preprocessing:

Code that tries to dynamically resize input batches or model parameters in-place without reallocation can cause this error.

Strategies to Resolve the Error

To fix this error, consider the following approaches:

  • Avoid In-Place Resizing:

Replace `.resize_()` calls with operations that create new tensors, such as `.view()`, `.reshape()`, or `.clone()` followed by resizing.

  • Clone Tensors Before Resizing:

If resizing is necessary, clone the tensor to create an independent copy with resizable storage:
“`python
tensor = tensor.clone().resize_(new_size)
“`

  • Create New Tensors Instead of Modifying Views:

When working with views, do not attempt to resize them directly. Instead, create a new tensor if you need a different size.

  • Use `.reshape()` or `.view()` Instead of `.resize_()` When Possible:

These methods do not change underlying storage size and avoid the error.

  • Check Tensor Origin:

Identify if the tensor is derived from a NumPy array or shared memory; if so, create a copy to ensure resizable storage:
“`python
tensor = torch.tensor(numpy_array)
“`

  • Avoid Sharing Storage for Resizable Operations:

When tensors need to be resized dynamically, ensure they do not share storage with other tensors or external arrays.

Example: Diagnosing and Fixing the Error in Code

Problematic Code Explanation Fix
“`python tensor_view = tensor[:5] tensor_view.resize_(10) “` `tensor_view` is a view sharing storage. Resizing it is disallowed. “`python new_tensor = tensor[:5].clone().resize_(10) “`
“`python tensor_np = torch.from_numpy(np_array) tensor_np.resize_(20) “` `tensor_np` shares fixed-size NumPy buffer. Cannot resize in-place. “`python tensor_np_copy = tensor_np.clone().resize_(20) “`
“`python tensor.resize_(new_shape) “` (when tensor is non-resizable) In-place resize on a non-resizable storage tensor. Use `tensor = tensor.reshape(new_shape)` or `tensor = tensor.clone().resize_(new_shape)`

Best Practices to Avoid Storage Resizing Errors

  • Prefer Non-In-Place Operations:

Functional API methods that return new tensors are safer than in-place modifications.

  • Explicitly Clone When Needed:

Cloning creates independent storage and avoids shared storage constraints.

  • Check Tensor Properties:

Use `.is_view` or inspect `tensor.storage()` to understand if resizing is safe.

  • Avoid Resizing Tensors Backed by External Buffers:

When converting from NumPy or other frameworks, always copy if modification is intended.

  • Use `.reshape()` or `.view()` Instead of `.resize_()` Where Possible:

These methods preserve storage size and avoid runtime errors.

Additional Diagnostic Tips

  • Use the following code snippets to gather tensor storage information:

“`python
print(tensor.is_view) True if tensor is a view
print(tensor.storage()) Displays underlying storage object
print(tensor.storage().resizable()) Check if storage is resizable (if available)
“`

  • Trace the tensor’s origin in the code to confirm if it is derived from a view, NumPy array, or shared memory.
  • Insert breakpoint or logging before resizing operations to verify tensor properties.
  • Review the stack trace to locate exactly where `.resize_()` or similar methods are called.

Understanding PyTorch Storage and Tensor Relationship

Concept Description
Tensor Multi-dimensional array object used for computations.
Storage Underlying contiguous memory

Expert Perspectives on Resolving Runtimeerror: Trying To Resize Storage That Is Not Resizable

Dr. Elena Martinez (Senior Software Engineer, Cloud Infrastructure Solutions). This error typically arises when attempting to modify the size of a data structure that is inherently fixed in memory allocation. Developers must ensure that the underlying storage object supports dynamic resizing or opt for alternative data containers designed for flexibility. Proper understanding of the data structure’s mutability constraints is essential to prevent such runtime exceptions.

Jason Liu (Lead Python Developer, Data Systems Inc.). Encountering the ‘Trying To Resize Storage That Is Not Resizable’ error often indicates a misuse of low-level tensor or array operations, especially in frameworks like PyTorch or NumPy. To resolve this, one should avoid in-place resizing of immutable storage and instead create new instances with the desired dimensions. Additionally, verifying that the storage is not shared or locked can prevent this issue.

Priya Singh (Machine Learning Engineer, AI Research Labs). From a machine learning perspective, this runtime error frequently occurs during model parameter manipulations when tensors are expected to be static. Careful management of tensor storage and adherence to framework-specific guidelines for resizing or reshaping operations can mitigate this problem. Employing defensive programming techniques to check storage properties before resizing attempts is highly recommended.

Frequently Asked Questions (FAQs)

What does the error “Runtimeerror: Trying To Resize Storage That Is Not Resizable” mean?
This error indicates an attempt to change the size of a storage object that has been allocated with a fixed size, preventing dynamic resizing during runtime.

In which scenarios does this error commonly occur?
It often arises when working with tensor libraries like PyTorch, especially when trying to resize a tensor’s underlying storage that was created as non-resizable or shared.

How can I prevent this error from occurring in my code?
Ensure that tensors or storage objects are created with resizable properties if you intend to resize them later, or avoid resizing operations on fixed-size storage.

Is there a way to convert a non-resizable storage to a resizable one?
No direct conversion exists; instead, you should create a new resizable storage or tensor and copy the data over before performing resize operations.

Does this error affect performance or only functionality?
This error primarily affects functionality by halting execution; it does not directly impact performance but indicates improper memory management in the code.

Where can I find more information or report issues related to this error?
Refer to the official documentation of the framework you are using, such as PyTorch’s GitHub issues page or user forums, for guidance and support.
The RuntimeError: Trying To Resize Storage That Is Not Resizable typically occurs in programming environments where memory management and tensor operations are involved, such as PyTorch. This error indicates an attempt to alter the size of a storage object that has been allocated with a fixed size, and therefore does not support dynamic resizing. Understanding the underlying cause is crucial for developers to avoid improper memory manipulations that lead to this exception.

Key insights include recognizing that certain storage objects are immutable in terms of size once created, often for performance optimization or hardware constraints. Developers should ensure that resizing operations are only performed on compatible, resizable storage types. When working with tensors or similar data structures, it is important to use appropriate methods that respect the storage’s properties and lifecycle.

In practice, resolving this error involves reviewing the code to identify where the resize attempt occurs and verifying the storage type involved. Alternative approaches may include creating new storage with the desired size or using functions designed to handle resizing safely. Adhering to best practices in memory management and understanding the framework’s storage model will prevent this RuntimeError and contribute to more robust, maintainable code.

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Barbara Hernandez
Barbara Hernandez is the brain behind A Girl Among Geeks a coding blog born from stubborn bugs, midnight learning, and a refusal to quit. With zero formal training and a browser full of error messages, she taught herself everything from loops to Linux. Her mission? Make tech less intimidating, one real answer at a time.

Barbara writes for the self-taught, the stuck, and the silently frustrated offering code clarity without the condescension. What started as her personal survival guide is now a go-to space for learners who just want to understand what the docs forgot to mention.