pytorch sets grad attribute to none if I use simple minus instead of -=

This is a simple code to show the problem

import torch
X = torch.arange(-3, 3, step=0.1)
Y = X * 3
Y += 0.1 * torch.randn(Y.shape)

def my_train_model(iter):
    w = torch.tensor(-15.0, requires_grad=True)
    lr = 0.1
    for epoch in range(iter):
        print(w.grad)
        yhat = w * X
        loss = torch.mean((yhat - Y) ** 2)
        loss.backward()

        with torch.no_grad():
            print(w.grad)
            w = w - lr * w.grad # gradient exists if w-= lr*w.grad
            print(w.grad)
            w.grad.zero_()
        print(loss)

my_train_model(4)

This sets the w.grad to none after performing the w = w - lr * w.grad, but the problem will be solved if I use w -= lr * w.grad instead of the above expression!

What is the problem with the first expression which sets w.grad to none?

The function torch.no_grad() guarantees that no gradient is computed, which means any component wrapped in there is created with requires_grad=False, as you can see in this example.

The in-place operation only changes the value of the tensor, from this answer from forum:

An in-place operation is an operation that changes directly the content of a given Tensor without making a copy.

Therefore, with the problem that you proposed, w = w - lr * w.grad will replace w by the copy of itself without gradient, and w -= lr * w.grad only change the content of tensor which is still keep the gradient of itself from the operation outside of torch.no_grad().