Frequently Asked Questions
==========================

My model reports "cuda runtime error(2): out of memory"
-------------------------------------------------------

As the error message suggests, you have run out of memory on your
GPU.  Since we often deal with large amounts of data in PyTorch,
small mistakes can rapidly cause your program to use up all of your
GPU; fortunately, the fixes in these cases are often simple.
Here are a few common things to check:

**Don't accumulate history across your training loop.**
By default, computations involving variables that require gradients
will keep history.  This means that you should avoid using such
variables in computations which will live beyond your training loops,
e.g., when tracking statistics. Instead, you should detach the variable
or access its underlying data.

Sometimes, it can be non-obvious when differentiable variables can
occur.  Consider the following training loop (abridged from `source
<https://discuss.pytorch.org/t/high-memory-usage-while-training/162>`_):

.. code-block:: python

    total_loss = 0
    for i in range(10000):
        optimizer.zero_grad()
        output = model(input)
        loss = criterion(output)
        loss.backward()
        optimizer.step()
        total_loss += loss

Here, ``total_loss`` is accumulating history across your training loop, since
``loss`` is a differentiable variable with autograd history. You can fix this by
writing `total_loss += float(loss)` instead.

Other instances of this problem:
`1 <https://discuss.pytorch.org/t/resolved-gpu-out-of-memory-error-with-batch-size-1/3719>`_.

**Don't hold onto tensors and variables you don't need.**
If you assign a Tensor or Variable to a local, Python will not
deallocate until the local goes out of scope.  You can free
this reference by using ``del x``.  Similarly, if you assign
a Tensor or Variable to a member variable of an object, it will
not deallocate until the object goes out of scope.  You will
get the best memory usage if you don't hold onto temporaries
you don't need.

The scopes of locals can be larger than you expect.  For example:

.. code-block:: python

    for i in range(5):
        intermediate = f(input[i])
        result += g(intermediate)
    output = h(result)
    return output

Here, ``intermediate`` remains live even while ``h`` is executing,
because its scope extrudes past the end of the loop.  To free it
earlier, you should ``del intermediate`` when you are done with it.

**Don't run RNNs on sequences that are too large.**
The amount of memory required to backpropagate through an RNN scales
linearly with the length of the RNN input; thus, you will run out of memory
if you try to feed an RNN a sequence that is too long.

The technical term for this phenomenon is `backpropagation through time
<https://en.wikipedia.org/wiki/Backpropagation_through_time>`_,
and there are plenty of references for how to implement truncated
BPTT, including in the `word language model <https://github.com/pytorch/examples/tree/master/word_language_model>`_ example; truncation is handled by the
``repackage`` function as described in
`this forum post <https://discuss.pytorch.org/t/help-clarifying-repackage-hidden-in-word-language-model/226>`_.

**Don't use linear layers that are too large.**
A linear layer ``nn.Linear(m, n)`` uses :math:`O(nm)` memory: that is to say,
the memory requirements of the weights
scales quadratically with the number of features.  It is very easy
to `blow through your memory <https://github.com/pytorch/pytorch/issues/958>`_
this way (and remember that you will need at least twice the size of the
weights, since you also need to store the gradients.)

My GPU memory isn't freed properly
----------------------------------
PyTorch uses a caching memory allocator to speed up memory allocations. As a
result, the values shown in ``nvidia-smi`` usually don't reflect the true
memory usage. See :ref:`cuda-memory-management` for more details about GPU
memory management.

If your GPU memory isn't freed even after Python quits, it is very likely that
some Python subprocesses are still alive. You may find them via
``ps -elf | grep python`` and manually kill them with ``kill -9 [pid]``.

My out of memory exception handler can't allocate memory
--------------------------------------------------------
You may have some code that tries to recover from out of memory errors.

.. code-block:: python

    try:
        run_model(batch_size)
    except RuntimeError: # Out of memory
        for _ in range(batch_size):
            run_model(1)

But find that when you do run out of memory, your recovery code can't allocate
either. That's because the python exception object holds a reference to the
stack frame where the error was raised. Which prevents the original tensor
objects from being freed. The solution is to move you OOM recovery code outside
of the ``except`` clause.

.. code-block:: python

    oom = False
    try:
        run_model(batch_size)
    except RuntimeError: # Out of memory
        oom = True

    if oom:
        for _ in range(batch_size):
            run_model(1)


.. _dataloader-workers-random-seed:

My data loader workers return identical random numbers
-------------------------------------------------------
You are likely using other libraries to generate random numbers in the dataset.
For example, NumPy's RNG is duplicated when worker subprocesses are started via
``fork``. See :class:`torch.utils.data.DataLoader`'s documentation for how to
properly set up random seeds in workers with its :attr:`worker_init_fn` option.

.. _pack-rnn-unpack-with-data-parallelism:

My recurrent network doesn't work with data parallelism
-------------------------------------------------------
There is a subtlety in using the
``pack sequence -> recurrent network -> unpack sequence`` pattern in a
:class:`~torch.nn.Module` with :class:`~torch.nn.DataParallel` or
:func:`~torch.nn.parallel.data_parallel`. Input to each the :meth:`forward` on
each device will only be part of the entire input. Because the unpack operation
:func:`torch.nn.utils.rnn.pad_packed_sequence` by default only pads up to the
longest input it sees, i.e., the longest on that particular device, size
mismatches will happen when results are gathered together. Therefore, you can
instead take advantage of the :attr:`total_length` argument of
:func:`~torch.nn.utils.rnn.pad_packed_sequence` to make sure that the
:meth:`forward` calls return sequences of same length. For example, you can
write::

    from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

    class MyModule(nn.Module):
        # ... __init__, other methods, etc.

        # padded_input is of shape [B x T x *] (batch_first mode) and contains
        # the sequences sorted by lengths
        #   B is the batch size
        #   T is max sequence length
        def forward(self, padded_input, input_lengths):
            total_length = padded_input.size(1)  # get the max sequence length
            packed_input = pack_padded_sequence(padded_input, input_lengths,
                                                batch_first=True)
            packed_output, _ = self.my_lstm(packed_input)
            output, _ = pad_packed_sequence(packed_output, batch_first=True,
                                            total_length=total_length)
            return output


    m = MyModule().cuda()
    dp_m = nn.DataParallel(m)


Additionally, extra care needs to be taken when batch dimension is dim ``1``
(i.e., ``batch_first=False``) with data parallelism. In this case, the first
argument of pack_padded_sequence ``padding_input`` will be of shape
``[T x B x *]`` and should be scattered along dim ``1``, but the second argument
``input_lengths`` will be of shape ``[B]`` and should be scattered along dim
``0``. Extra code to manipulate the tensor shapes will be needed.