Understanding PyTorch’s ‘Storage Not Resizable’ Error: A Deep Dive into Memory, Multiprocessing, and Tensor Views

Introduction: When Simple Code Meets Complex Reality

You’re training a neural network. Your data loading code looks perfectly reasonable. Then suddenly, your training crashes with a cryptic error about “storage that is not resizable.” Welcome to one of PyTorch’s most confusing errors—one that sits at the intersection of computer architecture, memory management, and multiprocessing.

This error isn’t just a PyTorch quirk. It’s a window into how modern computers manage memory, how Python’s multiprocessing works, and why seemingly innocent operations can break in unexpected ways. By the end of this post, you’ll not only know how to fix this error, but understand why it happens and how to prevent it.

Foundation: How Computer Memory Actually Works

Before diving into PyTorch specifics, let’s establish the foundation. When your program creates data, that data lives in your computer’s RAM (Random Access Memory). But here’s the crucial part: memory isn’t just storage—it’s addressable storage.

Memory Addresses: The Postal System of Computing

Every piece of data in RAM has an address—think of it like a house address. When your program wants to access data, it asks the operating system: “Give me the data at address 0x7f8b8c000000.”

Memory Layout Example:
Address     | Data
------------|-------------
0x1000      | [1.0, 2.0, 3.0]  ← Original tensor data
0x1012      | [4.0, 5.0, 6.0]  ← More tensor data
0x1024      | Metadata         ← Information about the tensor

Now here’s where it gets interesting. When you have data at one address, there are two ways to create “another version” of it:

Copy: Allocate new memory, duplicate the data
- Pro: Independent—changes to one don’t affect the other
- Con: Uses more memory and CPU time
View: Create new metadata pointing to the same memory address
- Pro: Fast and memory-efficient
- Con: Changes to one affect the other (shared storage)

This distinction is fundamental to understanding our error.

PyTorch’s Memory Model: Storage, Tensors, and Views

PyTorch builds on this foundation with a sophisticated three-layer memory model:

Layer 1: Storage - The Raw Memory Block

At the bottom level is Storage—a contiguous block of memory holding raw numerical data. Think of it as a simple array of bytes:

# Simplified view of what Storage looks like internally
storage = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]  # Raw floating-point data

Layer 2: Tensor - The Interpretation Layer

A Tensor is metadata that interprets the storage. It contains:

Pointer to storage
Shape information (dimensions)
Stride information (how to navigate the data)
Data type

# Example: Same storage, different tensor interpretations
storage = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
 
# Tensor A: 2x3 matrix
tensor_a = Tensor(storage, shape=(2, 3), stride=(3, 1))
# Interprets as: [[1.0, 2.0, 3.0],
#                 [4.0, 5.0, 6.0]]
 
# Tensor B: 3x2 matrix (SAME storage!)
tensor_b = Tensor(storage, shape=(3, 2), stride=(2, 1))  
# Interprets as: [[1.0, 2.0],
#                 [3.0, 4.0],
#                 [5.0, 6.0]]

Layer 3: Views - Multiple Windows Into Storage

Here’s the crucial concept: multiple tensors can share the same storage. These are called views.

original = torch.tensor([1, 2, 3, 4, 5, 6])
reshaped = original.view(2, 3)  # Same storage, different shape
sliced = original[1:4]         # Same storage, different slice
 
# They all share storage!
print(original.storage().data_ptr() == reshaped.storage().data_ptr())  # True
print(original.storage().data_ptr() == sliced.storage().data_ptr())    # True

TODO(human): Add pedagogical explanation of tensor views vs copies

The “Resizable Storage” Problem Emerges

Before diving into the technical details, let’s understand why PyTorch implements this optimization at all.

The DataLoader Multiprocessing Architecture

When you use DataLoader(num_workers=4), here’s what happens:

# PyTorch creates this process architecture:
Main Process: Coordinates training, receives batches
├── Worker 1: Processes samples [0, 4, 8, 12, ...] 
├── Worker 2: Processes samples [1, 5, 9, 13, ...]
├── Worker 3: Processes samples [2, 6, 10, 14, ...]
└── Worker 4: Processes samples [3, 7, 11, 15, ...]

Each worker:

Loads dataset samples assigned to it
Applies transforms (data augmentation, preprocessing)
Collates samples into batches
Transfers batches to main process ← This is the bottleneck

The Data Transfer Challenge

Without optimization (expensive):

# Each worker process:
batch = create_batch()           # Worker creates batch in its memory
serialized = pickle.dumps(batch) # Serialize tensor data  
send_via_ipc(serialized)         # Send to main process via inter-process communication
# Main process:
batch = pickle.loads(serialized) # Deserialize (creates copy in main process memory)

Result: Every batch gets copied and serialized/deserialized!

With shared memory optimization (efficient):

# Each worker process:
shared_storage = create_shared_memory()  # Create memory accessible by both processes
batch = create_batch(storage=shared_storage)  # Write directly to shared memory
notify_main_process()                    # Signal that data is ready
# Main process:
batch = read_from_shared_memory()        # Read directly, no copying needed

Result: Zero-copy data transfer between worker and main process!

When the Optimization Breaks

Now we can understand the error. The DataLoader’s default_collate function tries to create batches directly in shared memory to avoid expensive copying. But this fails when your dataset samples are tensor views with non-resizable storage.

According to the official PyTorch source code, when running with multiprocessing (num_workers > 0), the DataLoader uses this optimization:

# From PyTorch source: torch/utils/data/_utils/collate.py
if torch.utils.data.get_worker_info() is not None:
    # If we're in a background process, concatenate directly into a
    # shared memory tensor to avoid an extra copy
    numel = sum(x.numel() for x in batch)
    storage = elem.typed_storage()._new_shared(numel, device=elem.device)
    out = elem.new(storage).resize_(len(batch), *list(elem.size()))
    return torch.stack(batch, 0, out=out)

Let’s decode what each variable represents:

batch: A list of individual tensor samples from your dataset, e.g., [sample_1, sample_2, sample_3]
elem: The first tensor in the batch (batch[0]), used as a template for the batch tensor’s properties (dtype, device, etc.)
numel: Total number of elements needed for the entire batch (sum of all elements across all samples)

Now let’s trace through the complete sequence with a concrete example:

# Your dataset returns these samples:
batch = [
    torch.tensor([1.0, 2.0, 3.0, 4.0]),  # elem (first sample) - shape (4,)
    torch.tensor([5.0, 6.0, 7.0, 8.0]),  # second sample - shape (4,)  
    torch.tensor([9.0, 10.0, 11.0, 12.0]) # third sample - shape (4,)
]
 
elem = batch[0]  # [1.0, 2.0, 3.0, 4.0]
numel = 4 + 4 + 4 = 12  # total elements needed

Step 1: Create Shared Memory Storage

storage = elem.typed_storage()._new_shared(numel, device=elem.device)

This creates a new shared memory block containing 12 uninitialized elements:

storage = [?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?]  # 12 slots of garbage data

Step 2: Create Template Tensor

out = elem.new(storage)

The elem.new(storage) method is a legacy PyTorch method that:

Inherits metadata from elem: same dtype (float32), device (CPU), requires_grad, etc.
Uses the pro+vided storage instead of elem’s original storage
Contains uninitialized data from the storage (garbage values)
Has undefined shape initially

Step 3: Resize for Batch Dimensions

out = out.resize_(len(batch), *list(elem.size()))  # resize_(3, 4)

This reshapes out to (3, 4) to hold the entire batch:

out = [[?, ?, ?, ?],    # will hold sample 0: [1,2,3,4]
       [?, ?, ?, ?],    # will hold sample 1: [5,6,7,8]  
       [?, ?, ?, ?]]    # will hold sample 2: [9,10,11,12]

Step 4: Copy Real Data

return torch.stack(batch, 0, out=out)

Finally, torch.stack() copies the actual sample data into the pre-allocated tensor:

out = [[1.0, 2.0, 3.0, 4.0],
       [5.0, 6.0, 7.0, 8.0],
       [9.0, 10.0, 11.0, 12.0]]

Why This Optimization Matters

Without this optimization:

torch.stack() creates its own temporary tensor
Copy temporary tensor to shared memory for multiprocessing
Two memory allocations + one extra copy

With this optimization:

Pre-allocate shared memory tensor
torch.stack() writes directly into it
One memory allocation + zero extra copies

When the Error Occurs: The Storage Ownership Problem

The error happens when elem is a tensor view - but what does this actually mean for storage resizing?

What Makes Storage “Non-Resizable”?

Storage becomes non-resizable when multiple tensors share ownership of the same memory block. Here’s the fundamental issue:

# Resizable storage (one owner):
original = torch.tensor([1, 2, 3, 4, 5, 6])
print(original.storage()._cdata)  # Memory address: 0x7f8b8c000000
# Only 'original' points to this storage → PyTorch can safely resize it
 
# Non-resizable storage (multiple owners):
original = torch.tensor([1, 2, 3, 4, 5, 6]) 
view1 = original[::2]        # [1, 3, 5] - every 2nd element
view2 = original.view(2, 3)  # [[1, 2, 3], [4, 5, 6]] - reshaped
 
# All three tensors share the same storage!
print(original.storage()._cdata == view1.storage()._cdata)  # True
print(original.storage()._cdata == view2.storage()._cdata)  # True

Now imagine PyTorch tries to resize this shared storage:

# If PyTorch resized the storage from 6 to 12 elements:
# original.storage(): [1, 2, 3, 4, 5, 6] → [1, 2, 3, 4, 5, 6, ?, ?, ?, ?, ?, ?]
 
# What happens to the views?
# view1 expects every 2nd element: [1, 3, 5] → [1, 3, 5, ?, ?, ?] ❌ BROKEN!
# view2 expects 2x3 shape: [[1,2,3],[4,5,6]] → [[1,2,3],[4,5,6],[?,?,?],[?,?,?]] ❌ BROKEN!

PyTorch cannot safely resize storage when multiple tensors depend on its current size and layout.

The `elem.new(storage)` Failure: Inherited Constraints

Here’s the counterintuitive part: even though we create brand new storage, the new tensor inherits behavioral constraints from the template tensor.

The elem.new(storage).resize_() operation fails because:

elem is a view → It has non-resizable storage characteristics
elem.new(storage) creates a new tensor using new storage BUT inherits elem’s properties
Inherited properties include resizability constraints → The new tensor becomes non-resizable despite having its own storage
.resize_() fails → PyTorch refuses to resize the new tensor because it inherited the constraint from the template

This is PyTorch’s design: tensor.new() inherits behavioral constraints from the template tensor, not just data properties like dtype and device. The inheritance mechanism doesn’t distinguish between storage-specific and tensor-specific constraints.

Concrete Example of the Failure

# This is what breaks in your dataset:
def problematic_transform(audio_data):
    # Some operation that creates a view:
    rolled = torch.roll(audio_data, shifts=1, dims=0)  # Creates view in some versions
    normalized = rolled / rolled.max()
    return normalized  # Returns tensor that's a view with non-resizable constraints
 
# In DataLoader worker:
sample = problematic_transform(raw_audio)  # sample is a view!
batch = [sample, other_samples...]
elem = batch[0]  # elem is the view (template tensor)
 
# During collation:
storage = elem.typed_storage()._new_shared(numel)  # Creates NEW storage - no problem here
out = elem.new(storage)  # Creates new tensor with new storage BUT inherits elem's constraints
# The new tensor is now non-resizable because elem was non-resizable
out.resize_(len(batch), *elem.size())  # FAILS! New tensor inherited non-resizable constraint

The error occurs because elem.new(storage) inherits behavioral constraints from the template tensor, making the new tensor non-resizable even though it uses completely different storage. This inheritance mechanism ensures tensor consistency but creates the counterintuitive situation where fresh storage doesn’t guarantee resizability.

When Processes Collide: Multiprocessing and Shared Memory

Original Traceback (most recent call last):

  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
           ^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 54, in fetch
    return self.collate_fn(data)
           ^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\collate.py", line 265, in default_collate
    return collate(batch, collate_fn_map=default_collate_fn_map)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\collate.py", line 142, in collate
    return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed]  # Backwards compatibility.
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\collate.py", line 142, in <listcomp>
    return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed]  # Backwards compatibility.
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\collate.py", line 119, in collate
    return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "c:\Users\User\.conda\envs\birdsongs\Lib\site-packages\torch\utils\data\_utils\collate.py", line 161, in collate_tensor_fn
    out = elem.new(storage).resize_(len(batch), *list(elem.size()))
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Trying to resize storage that is not resizable

The error occurs during the collation phase when PyTorch tries to stack tensors into batches, but fails because some tensors have non-resizable storage that can’t be modified.

Root Cause Analysis

The error occurs due to tensor operations that create views sharing memory storage, which become problematic in multiprocessing environments. When PyTorch workers try to modify these shared tensors, they encounter storage that cannot be resized.

Key Problematic Operations

Numpy operations on tensors converted to arrays
Tensor dtype conversions that create views
Element-wise operations that return views
One-hot encoding operations

Problematic Code Patterns

Pattern 1: Using np.roll on tensor data

# PROBLEMATIC
if audio_data is not None and len(audio_data) > 0:
    audio_data = np.roll(audio_data, random.randint(0, len(audio_data) - 1))

Pattern 2: Dtype conversion creating views

# PROBLEMATIC  
audio_data = audio_data.to(torch.float32)

Pattern 3: torch.maximum operations

# PROBLEMATIC
target_tensor = torch.maximum(target_tensor, other_target_tensor_component)

Pattern 4: One-hot encoding

# POTENTIALLY PROBLEMATIC
target_tensor = torch.nn.functional.one_hot(
    torch.tensor(target), num_classes=self.num_classes)

Solutions

Solution 1: Force tensor cloning and contiguous memory

# FIXED
audio_data = audio_data.clone().contiguous().to(torch.float32)

Solution 2: Replace numpy operations with PyTorch equivalents

# FIXED - Replace np.roll with PyTorch circular shift
if audio_data is not None and len(audio_data) > 0:
    shift_amount = random.randint(0, len(audio_data) - 1)
    audio_data = torch.roll(audio_data, shifts=shift_amount)

Solution 3: Clone results of tensor operations

# FIXED
target_tensor = torch.maximum(target_tensor, other_target_tensor_component).clone()

Solution 4: Quick workaround (performance cost)

# In your training script
NUM_WORKERS = 0  # Disables multiprocessing

Best Practices

Always use .clone() when uncertain about memory sharing
Use .contiguous() after operations that might create views
Prefer PyTorch operations over numpy when working with tensors
Test with num_workers > 0 during development
Use .detach() when breaking computation graphs

Key Takeaway

When working with PyTorch DataLoaders and multiprocessing, be mindful of operations that create tensor views. The rule of thumb: if you’re unsure whether an operation creates a view, add .clone().contiguous() to ensure independent memory storage.

Tiny Pivots

Explorer

Understanding PyTorch's 'Storage Not Resizable' Error: A Deep Dive into Memory, Multiprocessing, and Tensor Views

Understanding PyTorch’s ‘Storage Not Resizable’ Error: A Deep Dive into Memory, Multiprocessing, and Tensor Views

Introduction: When Simple Code Meets Complex Reality

Foundation: How Computer Memory Actually Works

Memory Addresses: The Postal System of Computing

PyTorch’s Memory Model: Storage, Tensors, and Views

Layer 1: Storage - The Raw Memory Block

Layer 2: Tensor - The Interpretation Layer

Layer 3: Views - Multiple Windows Into Storage

TODO(human): Add pedagogical explanation of tensor views vs copies

The “Resizable Storage” Problem Emerges

The DataLoader Multiprocessing Architecture

The Data Transfer Challenge

When the Optimization Breaks

Step 1: Create Shared Memory Storage

Step 2: Create Template Tensor

Step 3: Resize for Batch Dimensions

Step 4: Copy Real Data

Why This Optimization Matters

When the Error Occurs: The Storage Ownership Problem

What Makes Storage “Non-Resizable”?

The `elem.new(storage)` Failure: Inherited Constraints

Concrete Example of the Failure

When Processes Collide: Multiprocessing and Shared Memory

Root Cause Analysis

Key Problematic Operations

Problematic Code Patterns

Pattern 1: Using np.roll on tensor data

Pattern 2: Dtype conversion creating views

Pattern 3: torch.maximum operations

Pattern 4: One-hot encoding

Solutions

Solution 1: Force tensor cloning and contiguous memory

Solution 2: Replace numpy operations with PyTorch equivalents

Solution 3: Clone results of tensor operations

Solution 4: Quick workaround (performance cost)

Best Practices

Key Takeaway

Tiny Pivots

Explorer

Understanding PyTorch's 'Storage Not Resizable' Error: A Deep Dive into Memory, Multiprocessing, and Tensor Views

Understanding PyTorch’s ‘Storage Not Resizable’ Error: A Deep Dive into Memory, Multiprocessing, and Tensor Views

Introduction: When Simple Code Meets Complex Reality

Foundation: How Computer Memory Actually Works

Memory Addresses: The Postal System of Computing

Views vs Copies: Two Ways to Share Data

PyTorch’s Memory Model: Storage, Tensors, and Views

Layer 1: Storage - The Raw Memory Block

Layer 2: Tensor - The Interpretation Layer

Layer 3: Views - Multiple Windows Into Storage

TODO(human): Add pedagogical explanation of tensor views vs copies

The “Resizable Storage” Problem Emerges

The DataLoader Multiprocessing Architecture

The Data Transfer Challenge

When the Optimization Breaks

Step 1: Create Shared Memory Storage

Step 2: Create Template Tensor

Step 3: Resize for Batch Dimensions

Step 4: Copy Real Data

Why This Optimization Matters

When the Error Occurs: The Storage Ownership Problem

What Makes Storage “Non-Resizable”?

The elem.new(storage) Failure: Inherited Constraints

Concrete Example of the Failure

When Processes Collide: Multiprocessing and Shared Memory

Root Cause Analysis

Key Problematic Operations

Problematic Code Patterns

Pattern 1: Using np.roll on tensor data

Pattern 2: Dtype conversion creating views

Pattern 3: torch.maximum operations

Pattern 4: One-hot encoding

Solutions

Solution 1: Force tensor cloning and contiguous memory

Solution 2: Replace numpy operations with PyTorch equivalents

Solution 3: Clone results of tensor operations

Solution 4: Quick workaround (performance cost)

Best Practices

Key Takeaway

The `elem.new(storage)` Failure: Inherited Constraints