Merge branch 'lmcafee/t5-free-pipe-output-true-fix' into 'main'

                       help='Call torch.cuda.empty_cache() each iteration '

                       '(training and eval), to reduce fragmentation.'

                       '0=off, 1=moderate, 2=aggressive.')

    group.add_argument('--deallocate-pipeline-outputs', action='store_true',

                       default=False, help='If set, pipeline output tensors '

                       'are deallocated during the forward pass.')

    return parser

        forward_backward_func = forward_backward_no_pipelining

    return forward_backward_func

def free_output_tensor(output_tensors, deallocate_pipeline_outputs):

    '''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

    only useful for its '.grad_fn' field, and not its '.data'.

    '''

    if not deallocate_pipeline_outputs or output_tensors is None:

    if out is None:

        return

    if isinstance(output_tensors, torch.Tensor):

        output_tensors = [output_tensors]

    for output_tensor in output_tensors:

        output_tensor.data = torch.cuda.FloatTensor([0])

    assert isinstance(out, torch.Tensor), \

        "expected Tensor, found %s." % type(out).__name__

    assert out._base is None, \

        "counter-productive to free a view of another tensor."

    out.data = torch.empty(

        (1,),

        device = out.device,

        dtype = out.dtype,

    )

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.

        accumulate_grad=True,

    )

def forward_step(forward_step_func, data_iterator, model, input_tensor, losses_reduced):

    """Forward step for passed-in model.

    # Backward pass.

    if output_tensor_grad[0] is None:

        output_tensor = optimizer.scale_loss(output_tensor[0])

    if args.deallocate_pipeline_outputs:

    custom_backward(output_tensor[0], output_tensor_grad[0])

    else:

        torch.autograd.backward(output_tensor[0],

                                grad_tensors=output_tensor_grad[0])

    # Collect the grad of the input_tensor.

    input_tensor_grad = [None]

                    output_tensor, recv_prev=recv_prev,

                    tensor_shape=tensor_shape,

                    timers=timers)

        free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)

        input_tensors[next_forward_model_chunk_id].append(input_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, args.deallocate_pipeline_outputs)

        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, args.deallocate_pipeline_outputs)

            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, args.deallocate_pipeline_outputs)

            deallocate_output_tensor(output_tensor[0])

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

            # the backward pass.