renamed 'free' -> 'deallocate'

#         )

#         # <<<

# <<<

def free_output_tensor(out):

    '''Pseudo-free (i.e., set to scalar) the output tensor's '.data' field.

def deallocate_output_tensor(out):

    '''Pseudo-deallocate (i.e., set to scalar) the output tensor's '.data' field.

    This method should be called right after the output tensor has been

    sent to the next pipeline stage. At this point, the output tensor is

def custom_backward(output, grad_output):

    '''Directly call C++ autograd engine.

    To make the 'free_output_tensor' (above) optimization work, the C++

    To make the 'deallocate_output_tensor' (above) optimization work, the C++

    autograd engine must be called directly, bypassing Pytorch's

    torch.autograd.backward. Pytorch's 'backward' checks that the output and

    grad have the same shape, while C++'s 'backward' does not.

        # >>>

        pax({"output_tensor": output_tensor})

        # <<<

        free_output_tensor(output_tensor)

        deallocate_output_tensor(output_tensor)

    # Run 1F1B in steady state.

    for k in range(num_microbatches_remaining):

                    output_tensor, input_tensor_grad,

                    recv_prev=recv_prev, recv_next=recv_next,

                    tensor_shape=tensor_shape, timers=timers)

        free_output_tensor(output_tensor)

        deallocate_output_tensor(output_tensor)

        # Put input_tensor and output_tensor_grad in data structures in the

        # right location.

        if not forward_only:

            input_tensors.append(input_tensor)

            output_tensors.append(output_tensor)

            free_output_tensor(output_tensor[0])

            deallocate_output_tensor(output_tensor[0])

    # Before running 1F1B, need to receive first forward tensor.

    # If all microbatches are run in warmup / cooldown phase, then no need to

            # Add input_tensor and output_tensor to end of list.

            input_tensors.append(input_tensor)

            output_tensors.append(output_tensor)

            free_output_tensor(output_tensor[0])

            deallocate_output_tensor(output_tensor[0])

            # Pop input_tensor and output_tensor from the start of the list for

            # the backward pass.