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):
total_loss = 0 for i in range(10000): optimizer.zero_grad() output = model(input) loss = criterion(output) loss.backward() optimizer.step() total_loss += loss
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.
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:
for i in range(5): intermediate = f(input[i]) result += g(intermediate) output = h(result) return output
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,
and there are plenty of references for how to implement truncated
BPTT, including in the word language model example; truncation is handled by the
repackage function as described in
this forum post.
Don’t use linear layers that are too large.
A linear layer
nn.Linear(m, n) uses \(O(nm)\) 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
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 Memory management for more details about GPU
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 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
torch.utils.data.DataLoader’s documentation for how to
properly set up random seeds in workers with its
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
data_parallel(). Input to each the
each device will only be part of the entire input. Because the unpack operation
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
total_length argument of
pad_packed_sequence() to make sure that the
forward() calls return sequences of same length. For example, you can
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
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.