def init_rnn_descriptor(fn):
return cudnn.RNNDescriptor(
fn.hidden_size,
fn.num_layers,
fn.dropout_state['desc'].get(),
fn.input_mode,
fn.bidirectional,
fn.mode,
fn.datatype
)
def init_rnn_descriptor(fn, handle):
return cudnn.RNNDescriptor(
handle,
fn.hidden_size,
fn.num_layers,
fn.dropout_state['desc_' + str(torch.cuda.current_device())].get(),
fn.input_mode,
fn.bidirectional,
fn.mode,
fn.datatype
)
def init_rnn_descriptor(fn, handle):
dropout_desc_name = 'desc_' + str(torch.cuda.current_device())
dropout_p = fn.dropout if fn.train else 0
if (dropout_desc_name not in fn.dropout_state) or (
fn.dropout_state[dropout_desc_name].get() is None):
fn.dropout_state[dropout_desc_name] = Unserializable(
cudnn.DropoutDescriptor(handle, dropout_p, fn.dropout_seed))
dropout_desc = fn.dropout_state[dropout_desc_name].get()
dropout_desc.set_dropout(dropout_p, fn.dropout_seed)
return cudnn.RNNDescriptor(handle, fn.hidden_size, fn.num_layers,
dropout_desc, fn.input_mode, fn.bidirectional,
fn.mode, fn.datatype)
def init_rnn_descriptor(fn):
rnn_desc = cudnn.RNNDescriptor()
rnn_desc.set(fn.hidden_size, fn.num_layers, fn.dropout_desc, fn.input_mode,
fn.bidirectional, fn.mode, fn.datatype)
return rnn_desc