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import torch.cuda
try:
from torch._C import _cudnn
except ImportError:
# Uses of all the functions below should be guarded by torch.backends.cudnn.is_available(),
# so it's safe to not emit any checks here.
_cudnn = None # type: ignore[assignment]
def get_cudnn_mode(mode):
if mode == 'RNN_RELU':
return int(_cudnn.RNNMode.rnn_relu)
elif mode == 'RNN_TANH':
return int(_cudnn.RNNMode.rnn_tanh)
elif mode == 'LSTM':
return int(_cudnn.RNNMode.lstm)
elif mode == 'GRU':
return int(_cudnn.RNNMode.gru)
else:
raise Exception("Unknown mode: {}".format(mode))
# NB: We don't actually need this class anymore (in fact, we could serialize the
# dropout state for even better reproducibility), but it is kept for backwards
# compatibility for old models.
class Unserializable(object):
def __init__(self, inner):
self.inner = inner
def get(self):
return self.inner
def __getstate__(self):
# Note: can't return {}, because python2 won't call __setstate__
# if the value evaluates to False
return "<unserializable>"
def __setstate__(self, state):
self.inner = None
def init_dropout_state(dropout, train, dropout_seed, dropout_state):
dropout_desc_name = 'desc_' + str(torch.cuda.current_device())
dropout_p = dropout if train else 0
if (dropout_desc_name not in dropout_state) or (dropout_state[dropout_desc_name].get() is None):
if dropout_p == 0:
dropout_state[dropout_desc_name] = Unserializable(None)
else:
dropout_state[dropout_desc_name] = Unserializable(torch._cudnn_init_dropout_state( # type: ignore[call-arg]
dropout_p,
train,
dropout_seed,
self_ty=torch.uint8,
device=torch.device('cuda')))
dropout_ts = dropout_state[dropout_desc_name].get()
return dropout_ts
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