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