def _symbolic_pad_packed_sequence(g, input, batch_first=False, padding_value=0.0):
# See comment on _symbolic_pack_padded_sequence
data, lengths = g.op("PadPacked", input.data, input.batch_sizes, outputs=2)
if batch_first:
from torch.onnx import symbolic
data = symbolic.t(data)
return data, lengths
def _symbolic_pack_padded_sequence(g, input, lengths, batch_first=False):
if batch_first:
from torch.onnx import symbolic
input = symbolic.t(g, input)
# There currently is no PackPadded operator in ONNX. We rely on an
# optimization pass to remove this later. It is an error if all
# PackPadded operators cannot be optimized out.
return g.op("PackPadded", input, lengths, outputs=2)
def _symbolic_pack_padded_sequence(g, input, lengths, batch_first=False):
if batch_first:
from torch.onnx import symbolic
input = symbolic.t(input)
# There currently is no PackPadded operator in ONNX. We rely on an
# optimization pass to remove this later. It is an error if all
# PackPadded operators cannot be optimized out.
return g.op("PackPadded", input, lengths, outputs=2)
def _symbolic_pad_packed_sequence(g,
input,
batch_first=False,
padding_value=0.0):
# See comment on _symbolic_pack_padded_sequence
data, lengths = g.op("PadPacked", input.data, input.batch_sizes, outputs=2)
if batch_first:
from torch.onnx import symbolic
data = symbolic.t(g, data)
return data, lengths
def nonzero(g, input):
return t(g, g.op('NonZero', _cast_Float(g, input, False)))