PyTorch中a.grad多次调用id()返回不同值的原因咨询

Great question—this behavior does seem counterintuitive at first, since we'd expect accessing the same property to return the same object reference. Let's break down what's happening here.

First, let's recap your code and observation: you manually assign a new tensor to a.grad after running backward(), but three consecutive calls to id(a.grad) return different values. This means each access is returning a new tensor object, not the same one you assigned.

What's causing this? #

The key lies in how PyTorch implements the grad property for tensors. While in newer PyTorch versions, accessing a.grad directly returns a reference to the internal _grad tensor (so IDs would stay the same), some older versions or specific edge cases have a different behavior:

• Safety through copying/detaching: PyTorch might return a clone or detached view of the underlying gradient tensor each time you access grad. This is a safeguard to prevent accidental modifications to the gradient data that could break subsequent backward passes or optimizations. Even though the object IDs are different, these tensors would share the same underlying data storage (you can verify this by checking a.grad.data_ptr()—it should be the same across all accesses).
• Dynamic gradient handling: For leaf tensors (like your a), PyTorch manages the gradient tensor internally. In some implementations, accessing grad triggers a lazy creation or wrapping of the stored gradient, resulting in a new object each time.

Let's test this to confirm #

You can add a few more checks to see what's really going on:

import torch
a = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
b = torch.tensor([5.0, 5.0, 5.0])
loss = sum(a*b)
loss.backward()
a.grad = torch.tensor([5., 6., 7.])

# Capture the grad objects
g1 = a.grad
g2 = a.grad
g3 = a.grad

# Check IDs and underlying data pointers
print(f"IDs: {id(g1)}, {id(g2)}, {id(g3)}")
print(f"Data pointers: {g1.data_ptr()}, {g2.data_ptr()}, {g3.data_ptr()}")
print(f"Values are equal: {torch.allclose(g1, g2) and torch.allclose(g2, g3)}")

If the data pointers are the same, that means all three tensors share the same underlying values—they're just different wrapper objects. The values will still be correct for your use case, even if the IDs differ.

Wrap-up #

The different IDs don't mean your gradient is being changed or lost—it's just PyTorch's way of managing gradient access to protect the integrity of the computation graph. If you need to hold onto a stable reference to the gradient, store it in a separate variable (like g1 in the test code) instead of repeatedly accessing a.grad.

内容的提问来源于stack exchange，提问作者shelvey jiang