def forward(self, input, hx=None, cx=None, doDropMC=False):
if self.dr > 0 and (doDropMC is True or self.training is True):
doDrop = True
else:
doDrop = False
batchSize = input.size(1)
if hx is None:
hx = input.new_zeros(
1, batchSize, self.hiddenSize, requires_grad=False)
if cx is None:
cx = input.new_zeros(
1, batchSize, self.hiddenSize, requires_grad=False)
# cuDNN backend - disabled flat weight
handle = torch.backends.cudnn.get_handle()
if doDrop is True:
self.reset_mask()
weight = [dropMask.apply(self.w_ih, self.maskW_ih, True),
dropMask.apply(self.w_hh, self.maskW_hh, True),
self.b_ih, self.b_hh]
else:
weight = [self.w_ih, self.w_hh, self.b_ih, self.b_hh]
output, hy, cy, reserve, new_weight_buf = torch._cudnn_rnn(
input, weight, 4, None, hx, cx,
torch.backends.cudnn.CUDNN_LSTM, self.hiddenSize,
1, False, 0, self.training, False, (), None)
return output, (hy, cy)
def forward(input, weight, hx, batch_sizes):
if mode == cudnn.CUDNN_LSTM:
hx, cx = hx
else:
cx = None
handle = cudnn.get_handle()
with torch.cuda.device(input.get_device()):
dropout_ts = cudnn.rnn.init_dropout_state(dropout, train, dropout_seed, dropout_state)
weight_arr = list(itertools.chain.from_iterable(weight))
weight_stride0 = len(weight[0])
output, hy, cy, reserve, new_weight_buf = torch._cudnn_rnn(
input, weight_arr, weight_stride0,
flat_weight,
hx, cx,
mode, hidden_size, num_layers,
batch_first, dropout, train, bool(bidirectional),
list(batch_sizes.data) if variable_length else (),
dropout_ts)
if cx is not None:
return (output, (hy, cy))
else:
return (output, hy)
def forward(self, input, hx=None, cx=None, do_drop_mc=False, dropout_false=False):
# dropout_false: it will ensure do_drop is false, unless do_drop_mc is true
if dropout_false and (not do_drop_mc):
do_drop = False
elif self.dr > 0 and (do_drop_mc is True or self.training is True):
do_drop = True
else:
do_drop = False
batch_size = input.size(1)
if hx is None:
hx = input.new_zeros(1, batch_size, self.hidden_size, requires_grad=False)
if cx is None:
cx = input.new_zeros(1, batch_size, self.hidden_size, requires_grad=False)
# cuDNN backend - disabled flat weight
freeze_mask = False
handle = torch.backends.cudnn.get_handle()
if do_drop is True:
if not freeze_mask:
self.reset_mask()
weight = [
DropMask.apply(self.w_ih, self.mask_w_ih, True),
DropMask.apply(self.w_hh, self.mask_w_hh, True), self.b_ih,
self.b_hh
]
else:
weight = [self.w_ih, self.w_hh, self.b_ih, self.b_hh]
output, hy, cy, reserve, new_weight_buf = torch._cudnn_rnn(
input, weight, 4, None, hx, cx, torch.backends.cudnn.CUDNN_LSTM,
self.hidden_size, 1, False, 0, self.training, False, (), None)
return output, (hy, cy)
def forward(self,
input,
hx=None,
cx=None,
doDropMC=False,
dropoutFalse=False):
# dropoutFalse: it will ensure doDrop is false, unless doDropMC is true
if dropoutFalse and (not doDropMC):
doDrop = False
elif self.dr > 0 and (doDropMC is True or self.training is True):
doDrop = True
else:
doDrop = False
batchSize = input.size(1)
if hx is None:
hx = input.new_zeros(1,
batchSize,
self.hiddenSize,
requires_grad=False)
if cx is None:
cx = input.new_zeros(1,
batchSize,
self.hiddenSize,
requires_grad=False)
# cuDNN backend - disabled flat weight
# handle = torch.backends.cudnn.get_handle()
if doDrop is True:
self.reset_mask()
weight = [
DropMask.apply(self.w_ih, self.maskW_ih, True),
DropMask.apply(self.w_hh, self.maskW_hh, True),
self.b_ih,
self.b_hh,
]
else:
weight = [self.w_ih, self.w_hh, self.b_ih, self.b_hh]
# output, hy, cy, reserve, new_weight_buf = torch._cudnn_rnn(
# input, weight, 4, None, hx, cx, torch.backends.cudnn.CUDNN_LSTM,
# self.hiddenSize, 1, False, 0, self.training, False, (), None)
if torch.__version__ < "1.8":
output, hy, cy, reserve, new_weight_buf = torch._cudnn_rnn(
input,
weight,
4,
None,
hx,
cx,
2, # 2 means LSTM
self.hiddenSize,
1,
False,
0,
self.training,
False,
(),
None,
)
else:
output, hy, cy, reserve, new_weight_buf = torch._cudnn_rnn(
input,
weight,
4,
None,
hx,
cx,
2, # 2 means LSTM
self.hiddenSize,
0,
1,
False,
0,
self.training,
False,
(),
None,
)
return output, (hy, cy)
