def masked_topk(
input_: torch.FloatTensor,
mask: torch.BoolTensor,
k: Union[int, torch.LongTensor],
dim: int = -1,
) -> Tuple[torch.LongTensor, torch.LongTensor, torch.FloatTensor]:
if input_.size() != mask.size():
raise ValueError("`input_` and `mask` must have the same shape.")
if not -input_.dim() <= dim < input_.dim():
raise ValueError("`dim` must be in `[-input_.dim(), input_.dim())`")
dim = (dim + input_.dim()) % input_.dim()
max_k = k if isinstance(k, int) else k.max()
permutation = list(range(input_.dim()))
permutation.pop(dim)
permutation += [dim]
reverse_permutation = list(range(input_.dim() - 1))
reverse_permutation.insert(dim, -1)
other_dims_size = list(input_.size())
other_dims_size.pop(dim)
permuted_size = other_dims_size + [max_k] # for restoration
if isinstance(k, int):
k = k * torch.ones(*other_dims_size, dtype=torch.long, device=mask.device)
else:
if list(k.size()) != other_dims_size:
raise ValueError(
"`k` must have the same shape as `input_` with dimension `dim` removed."
)
num_items = input_.size(dim)
input_ = input_.permute(*permutation).reshape(-1, num_items)
mask = mask.permute(*permutation).reshape(-1, num_items)
k = k.reshape(-1)
input_ = replace_masked_values(input_, mask, min_value_of_dtype(input_.dtype))
_, top_indices = input_.topk(max_k, 1)
top_indices_mask = get_mask_from_sequence_lengths(k, max_k).bool()
fill_value, _ = top_indices.max(dim=1, keepdim=True)
top_indices = torch.where(top_indices_mask, top_indices, fill_value)
top_indices, _ = top_indices.sort(1)
sequence_mask = mask.gather(1, top_indices)
top_mask = top_indices_mask & sequence_mask
top_input = input_.gather(1, top_indices)
return (
top_input.reshape(*permuted_size).permute(*reverse_permutation),
top_mask.reshape(*permuted_size).permute(*reverse_permutation),
top_indices.reshape(*permuted_size).permute(*reverse_permutation),
)
def forward(self,
input_ids,
past=None,
mask: torch.BoolTensor = None,
token_type_ids=None,
position_ids=None):
"""
mask: [batch_size, seq_length] is attention mask
"""
# past length calculation and dealing with past
if past is None:
past_length = input_ids.shape[1]
past = [None] * 12
else:
# count self
past_length = past[0].shape[3] + input_ids.shape[1]
if mask is None:
# print("mask is not provided")
mask = torch.ones(input_ids.shape[0],
past_length,
dtype=torch.bool,
device=input_ids.device)
# Fast way to compute lower triangle attention mask
mask = mask.view(input_ids.shape[0], 1, 1,
mask.shape[1]).repeat(1, self.num_attention_heads,
mask.shape[1], 1)
mask = mask & mask.permute(0, 1, 3, 2)
mask = torch.tril(mask)
# calculate embedding output
embedding_output = self.embeddings(input_ids,
position_ids=position_ids)
# Transformer layer
last_layer_output, presents = self.encoder(embedding_output,
mask=mask,
past=past)
return last_layer_output, presents