def forward_impl(self, input, hx, batch_sizes, max_batch_size,
sorted_indices):
# type: (Tensor, Optional[Tuple[Tensor, Tensor]], Optional[Tensor], int, Optional[Tensor]) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa
if hx is None:
num_directions = 2 if self.bidirectional else 1
zeros = torch.zeros(self.num_layers * num_directions,
max_batch_size,
self.hidden_size,
dtype=input.dtype,
device=input.device)
hx = (zeros, zeros)
else:
hx = self.permute_hidden(hx, sorted_indices)
self.check_forward_args(input, hx, batch_sizes)
if batch_sizes is None:
result = _VF.lstm(input, hx, self._get_flat_weights(), self.bias,
self.num_layers, self.dropout, self.training,
self.bidirectional, self.batch_first)
else:
result = _VF.lstm(input, batch_sizes, hx, self._get_flat_weights(),
self.bias, self.num_layers, self.dropout,
self.training, self.bidirectional)
output = result[0]
hidden = result[1:]
return output, hidden
def forward(self,
input,
params=None,
hx=None,
embeddings=None): # noqa: F811
if params is None:
params = [(lambda wn: getattr(self, wn)
if hasattr(self, wn) else None)(wn)]
orig_input = input
# xxx: isinstance check needs to be in conditional for TorchScript to compile
if isinstance(orig_input, PackedSequence):
input, batch_sizes, sorted_indices, unsorted_indices = input
max_batch_size = batch_sizes[0]
max_batch_size = int(max_batch_size)
else:
batch_sizes = None
max_batch_size = input.size(0) if self.batch_first else input.size(
1)
sorted_indices = None
unsorted_indices = None
if hx is None:
num_directions = 2 if self.bidirectional else 1
zeros = torch.zeros(self.num_layers * num_directions,
max_batch_size,
self.hidden_size,
dtype=input.dtype,
device=input.device)
hx = (zeros, zeros)
else:
# Each batch of the hidden state should match the input sequence that
# the user believes he/she is passing in.
hx = self.permute_hidden(hx, sorted_indices)
self.check_forward_args(input, hx, batch_sizes)
if batch_sizes is None:
result = _VF.lstm(input, hx, params, self.bias, self.num_layers,
self.dropout, self.training, self.bidirectional,
self.batch_first)
else:
result = _VF.lstm(input, batch_sizes, hx, params, bias,
self.num_layers, self.dropout, self.training,
self.bidirectional)
output = result[0]
hidden = result[1:]
# xxx: isinstance check needs to be in conditional for TorchScript to compile
if isinstance(orig_input, PackedSequence):
output_packed = PackedSequence(output, batch_sizes, sorted_indices,
unsorted_indices)
return output_packed, self.permute_hidden(hidden, unsorted_indices)
else:
return output, self.permute_hidden(hidden, unsorted_indices)
def forward(self, inputs, hx):
self.flatten_parameters()
result = _VF.lstm(inputs, hx, self._flat_weights(), self.bias, self.num_layers,
self.dropout, self.training, self.bidirectional, self.batch_first)
output = result[0]
hidden = result[1:]
return output, hidden
def forward(self, input, state=None):
H = self.hidden_dim
if state is None:
state = self.new_state(input)
state = (state[:, :H].unsqueeze(0), state[:, H:].unsqueeze(1))
res = _VF.lstm(input, state,
(self.w_ih, self.w_hh, self.b_ih, self.b_hh), True, 1,
0, self.training, False, True)
output, hidden1, hidden2 = res
outstate = torch.cat([hidden1[0], hidden2[0]], 1)
return output, outstate