Difference between detach, clone, and deepcopy in PyTorch tensors
Last Updated :
23 Jul, 2025
In PyTorch, managing tensors efficiently while ensuring correct gradient propagation and data manipulation is crucial in deep learning workflows. Three important operations that deal with tensor handling in PyTorch are detach(), clone(), and deepcopy(). Each serves a unique purpose when working with tensors, especially regarding autograd (automatic differentiation) and memory management.
In this article, we will explore the differences between these three functions in PyTorch, breaking down their roles with relevant examples.
Introduction to Tensor Operations in PyTorch
Before diving into the specifics, let’s briefly summarize the key concepts:
- detach(): Generates a tensor with the same value as the input tensor with respect to which it depends but is no longer connected to the graph.
- clone(): Returns a copy of the tensor which has the same value as the current tensor but the gradients will keep flowing through it.
- deepcopy(): Returns a new tensor that shares the same data and computations as the original tensor, and each of its references.
These operations are basic in managing the tensor characteristics with respect to memory usage and gradient tracking during backpropagation.
What is detach() in PyTorch?
The detach() function returns a new tensor which has the same data as the input tensor but is not linked to the computation graph anymore. This is especially seen in PyTorch back-propagation in autograd where gradients are calculated during the process. When a tensor is detached, it stops keeping track of operations that could be used for calculating gradients in the backward pass, which in essence ‘freezes’ its influence on the backward computation graph.
Key Features of detach():
- Detaches a tensor from the computational graph.
- Does not copy the data; it returns a view of the original tensor.
- Does not affect the original tensor’s gradient requirement.
When to Use:
detach() is useful when you want to perform operations on a tensor but don’t want those operations to affect gradient calculations.
Example of detach():
Python
import torch
# Create a tensor with requires_grad=True to track computation
x = torch.tensor([2.0, 3.0], requires_grad=True)
# Perform some operations
y = x ** 2
# Detach y from the computational graph
y_detached = y.detach()
# Backpropagation will not affect y_detached
y.sum().backward()
print("Original tensor:", x.grad) # Shows gradient
print("Detached tensor:", y_detached.requires_grad) # False, as it no longer requires gradient
Output:
Original tensor: tensor([4., 6.])
Detached tensor: False
Here, y_detached is no longer part of the computational graph and does not require gradients. The original tensor x still has its gradients intact.
What is clone() in PyTorch?
clone() generates a new tensor that is semantically identical to the tensor and which shares its computational graph. This operation can be used when the client wishes to have a separate copy of the tensor while at the same time being able to backpropagate gradients.
Key Features of clone():
- Creates a copy of the tensor, keeping the computational graph intact.
- The clone has requires_grad set to the same value as the original tensor.
- Can optionally detach the copy by passing detach=True inside the clone() method.
When to Use:
clone() used when you want to copy the tensor which you want to get same reference as well as both should be able to compute gradient independently.
Example of clone():
Python
# Create a tensor with requires_grad=True
x = torch.tensor([2.0, 3.0], requires_grad=True)
# Clone the tensor
x_clone = x.clone()
# Perform operations on both the original and cloned tensors
y = x ** 2
z = x_clone ** 3
# Backpropagate both operations
y.sum().backward()
z.sum().backward()
print("Original tensor gradient:", x.grad) # Gradients from both y and z
print("Cloned tensor gradient:", x_clone.grad) # Independent gradients for the cloned tensor
Output:
Original tensor gradient: tensor([16., 36.])
Cloned tensor gradient: tensor([12., 27.])
In this case, both x and x_clone independently track gradients. clone() ensures that any operation on the cloned tensor doesn’t affect the original tensor's computational graph.
What is deepcopy() in PyTorch?
deepcopy() is imported under Python’s standard library under copy and deepcop(). It is deployed to create object copy in depth. If applied to PyTorch tensors, it clones the tensor’s data as well as any computational graph tied to it.
Key Features of deepcopy():
- Creates a completely independent copy of the tensor, including its computational graph and all associated tensors.
- Used when a full separation of tensors is required, including their history for gradient computation.
When to Use:
deepcopy() is very important to create a new completely separate copy of the tensor along with its computation history as well as computation graph.
Example of deepcopy():
Python
import torch
import copy
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = copy.deepcopy(x).detach() # Manually performing a deepcopy and detaching
y[0] = 10 # Modifying y does not affect x, and y does not require gradients
print("x:", x) # x remains unchanged
print("y:", y) # y is independent and does not track gradients
Output:
x: tensor([1., 2., 3.], requires_grad=True)
y: tensor([10., 2., 3.])
deepcopy() ensures that the new tensor is independent and retains its computation graph. This operation creates a full separation of data and graph, making it more memory-intensive compared to clone().
Practical Example: Comparing detach(), clone(), and deepcopy()
Consider the following example where we combine all three operations to demonstrate their effects.
Python
import torch
import copy
# Create an initial tensor
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
# Clone the tensor (with gradient tracking)
x_clone = x.clone()
# Detach the tensor (no gradient tracking)
x_detached = x.detach()
# Deepcopy the tensor (independent copy, with gradient tracking)
x_deepcopy = copy.deepcopy(x)
# Perform operations on all
y_original = x ** 2
y_clone = x_clone ** 2
y_detached = x_detached ** 2
y_deepcopy = x_deepcopy ** 2
# Backpropagate on original, clone, and deepcopy (not detached)
y_original.sum().backward()
y_clone.sum().backward()
y_deepcopy.sum().backward()
# Display gradients
print("Original tensor gradient:", x.grad) # Gradients from y_original
print("Cloned tensor gradient:", x_clone.grad) # Gradients from y_clone
print("Detached tensor gradient:", x_detached.requires_grad) # No gradient tracking
print("Deepcopied tensor gradient:", x_deepcopy.grad) # Gradients from y_deepcopy
Output:
Original tensor gradient: tensor([2., 4., 6.])
Cloned tensor gradient: tensor([2., 4., 6.])
Detached tensor gradient: False
Deepcopied tensor gradient: tensor([2., 4., 6.])
- detach() doesn’t track gradients at all, as shown by the False in requires_grad.
- clone() tracks gradients just like the original tensor.
- deepcopy() creates an independent copy that also tracks gradients, but independently from the original.
Differences Between detach(), clone(), and deepcopy()
Let’s now summarize the differences with a practical comparison:
Operation | Effect on Tensor | Data Effect on Computational Graph | Gradient Tracking | Use Case |
---|
detach() | Does not copy data, returns a view | Detaches from the computational graph | No gradient tracking | When you want to stop gradients for a tensor but keep the original |
---|
clone() | Creates a copy of tensor data | Retains the computational graph | Keeps gradient tracking intact | When you need a copy that still requires gradients |
---|
deepcopy() | Creates a completely independent copy | Copies the computational graph | Independent gradient tracking | When you need a full, independent copy including its history |
---|
Conclusion
It is crucial to differentiate between detach(), clone(), and deepcopy() operations in PyTorch to manage tensors effectively, particularly in elaborate models. Whereas detach() interrupt the gradient calculation process, clone() enables independent gradient tracking, and deepen() provides a complete copy with no connections. Every one of them is designed for a particular use case based on whether you want to keep, monitor, or completely cut off gradient computations and tensor history.
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