pytorch error: element 0 of tensors does not require grad and does not have a grad_fn

Trying to get used to pytorch syntax. Wrote a simple linear regression program.

# weights and biases
w = torch.randn(2,3, requires_grad=True)
b = torch.randn(2, requires_grad=True)
print(w.dtype, b.dtype)

# a linear function
def model(x):
  return x @ w.t() + b

# loss function
def mse(t1, t2):
  diff = (t1-t2)**2
  return torch.mean(diff)

# do this in a loop
for _ in range(100):
  preds = model(inputs_t)
  loss = mse(targets_t, preds)
  loss.backward()
  # print(loss)
  print(w.grad, b.grad)
  print("==============")
  with torch.no_grad():
    w = w - w.grad * 1e-5
    b = b - b.grad * 1e-5
    w.grad = None
    b.grad = None

Manually trying to zero the gradients of each variable since I read that pytorch stores all the past gradients inside the .grad variable. But I have this error: element 0 of tensors does not require grad and does not have a grad_fn

This is the output. It runs for one loop and then stops:

tensor([[  4958.0928,   6618.7686,   7047.0444],
        [-21244.5488, -32400.4414, -35345.9766]]) tensor([  58.3144, -298.9364])
==============

---------------------------------------------------------------------------

RuntimeError                              Traceback (most recent call last)

<ipython-input-122-eef55c1b4837> in <cell line: 16>()
     20   #   b.grad.zero_()
     21   loss = mse(targets_t, preds)
---> 22   loss.backward()
     23   # print(loss)
     24   print(w.grad, b.grad)

2 frames

/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py in _engine_run_backward(t_outputs, *args, **kwargs)
    742         unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)
    743     try:
--> 744         return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    745             t_outputs, *args, **kwargs
    746         )  # Calls into the C++ engine to run the backward pass

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

what should i change in this code to remove this error?

I tried searching the error but everyone else is either using more complex problems or its a version problem for most. I dont really understand how to interpret those posts since I am using whatever default google colabs uses for pytorch.

When you compute:

  with torch.no_grad():
    w = w - w.grad * 1e-5
    b = b - b.grad * 1e-5
    w.grad = None
    b.grad = None

You are creating a new object w that is different from the old object w. The new w is created under torch.no_grad(), so the new w doesn't have a gradient. This causes the error.

The solution is to update w and b with an in-place operation. There are several ways to do this:

with torch.no_grad():
    for param in [w, b]:
        
        # Option 1: in-place subtraction 
        param.sub_(param.grad*1e-2)
        
        # Option 2: assign new values to the .data attribute 
        param_new = param - param.grad * 1e-2
        param.data = param_new.data
        
        # Option 3: use the in-place copy 
        param_new = param - param.grad * 1e-2
        param.copy_(param_new)
        
        # Finally, set grad to None
        param.grad = None

Any one of those options will work