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Zero
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import torch
def casual_chunk_mask(ilens, chunk_size, left_chunks=1):
# type: (List[int], int, int) -> Tensor
# param ilens: list, (B, )
# param chunk_size: int
# return chunk_mask: torch.Tensor, (B, T, T)
B = len(ilens)
T = max(ilens)
chunk_mask = torch.zeros(B, T, T)
for b in range(0, B):
if chunk_size == -1 :
chunk_mask[b, 0:ilens[b], 0:ilens[b]] = 1
else:
for t in range(0, ilens[b], chunk_size):
ty_start = t
ty_end = min(t + chunk_size, ilens[b])
tx_start = max(t - chunk_size * left_chunks, 0)
tx_end = min(t + chunk_size, ilens[b])
chunk_mask[b, ty_start:ty_end, tx_start:tx_end] = 1
return chunk_mask
def subsequent_chunk_mask(
size: int,
chunk_size: int,
num_left_chunks: int = -1
) -> torch.Tensor:
"""Create mask for subsequent steps (size, size) with chunk size,
this is for streaming encoder
Args:
size (int): size of mask
chunk_size (int): size of chunk
num_left_chunks (int): number of left chunks
<0: use full chunk
>=0: use num_left_chunks
device (torch.device): "cpu" or "cuda" or torch.Tensor.device
Returns:
torch.Tensor: mask
Examples:
>>> subsequent_chunk_mask(4, 2)
[[1, 1, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 1],
[1, 1, 1, 1]]
"""
ret = torch.zeros(size, size, dtype=torch.bool)
for i in range(size):
if num_left_chunks < 0:
start = 0
else:
start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
ending = min((i // chunk_size + 1) * chunk_size, size)
ret[i, start:ending] = torch.ones(ending-start, dtype=torch.bool)
return ret
def add_optional_chunk_mask(xs: torch.Tensor, masks: torch.Tensor,
use_dynamic_chunk: bool,
use_dynamic_left_chunk: bool,
decoding_chunk_size: int, static_chunk_size: int,
num_decoding_left_chunks: int):
""" Apply optional mask for encoder.
Args:
xs (torch.Tensor): padded input, (B, L, D), L for max length
mask (torch.Tensor): mask for xs, (B, 1, L)
use_dynamic_chunk (bool): whether to use dynamic chunk or not
use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
training.
decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
static_chunk_size (int): chunk size for static chunk training/decoding
if it's greater than 0, if use_dynamic_chunk is true,
this parameter will be ignored
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks
Returns:
torch.Tensor: chunk mask of the input xs.
"""
# Whether to use chunk mask or not
if use_dynamic_chunk:
max_len = xs.size(1)
if decoding_chunk_size < 0:
chunk_size = max_len
num_left_chunks = -1
elif decoding_chunk_size > 0:
chunk_size = decoding_chunk_size
num_left_chunks = num_decoding_left_chunks
else:
# chunk size is either [1, 25] or full context(max_len).
# Since we use 4 times subsampling and allow up to 1s(100 frames)
# delay, the maximum frame is 100 / 4 = 25.
chunk_size = torch.randint(1, max_len, (1, )).item()
num_left_chunks = -1
if chunk_size > max_len // 2:
chunk_size = max_len
else:
chunk_size = chunk_size % 25 + 1
if use_dynamic_left_chunk:
max_left_chunks = (max_len - 1) // chunk_size
num_left_chunks = torch.randint(0, max_left_chunks,
(1, )).item()
chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size,
num_left_chunks) # (L, L)
chunk_masks = chunk_masks.to(xs.device)
chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
chunk_masks = masks & chunk_masks # (B, L, L)
elif static_chunk_size > 0:
num_left_chunks = num_decoding_left_chunks
chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size,
num_left_chunks) # (L, L)
chunk_masks = chunk_masks.unsqueeze(0).to(masks.device) # (1, L, L)
chunk_masks = masks & chunk_masks # (B, L, L)
else:
chunk_masks = masks
return chunk_masks
def make_pad_mask(lengths: torch.Tensor) -> torch.Tensor:
"""Make mask tensor containing indices of padded part.
See description of make_non_pad_mask.
Args:
lengths (torch.Tensor): Batch of lengths (B,).
Returns:
torch.Tensor: Mask tensor containing indices of padded part.
Examples:
>>> lengths = [5, 3, 2]
>>> make_pad_mask(lengths)
masks = [[0, 0, 0, 0 ,0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1]]
"""
batch_size = int(lengths.size(0))
max_len = int(lengths.max().item())
seq_range = torch.arange(0,
max_len,
dtype=torch.int64,
device=lengths.device)
seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
seq_length_expand = lengths.unsqueeze(-1)
mask = seq_range_expand >= seq_length_expand
return mask
def subsequent_mask(
size: int,
device: torch.device = torch.device("cpu"),
) -> torch.Tensor:
"""Create mask for subsequent steps (size, size).
This mask is used only in decoder which works in an auto-regressive mode.
This means the current step could only do attention with its left steps.
In encoder, fully attention is used when streaming is not necessary and
the sequence is not long. In this case, no attention mask is needed.
When streaming is need, chunk-based attention is used in encoder. See
subsequent_chunk_mask for the chunk-based attention mask.
Args:
size (int): size of mask
str device (str): "cpu" or "cuda" or torch.Tensor.device
dtype (torch.device): result dtype
Returns:
torch.Tensor: mask
Examples:
>>> subsequent_mask(3)
[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]
"""
ret = torch.ones(size, size, device=device, dtype=torch.bool)
return torch.tril(ret, out=ret)
def target_mask(ys_in_pad, ignore_id):
# type: (Tensor, int) -> Tensor
# Create mask for decoder self-attention.
# :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax)
# :param int ignore_id: index of padding
# :param torch.dtype dtype: result dtype
# :rtype: torch.Tensor
ys_mask = ys_in_pad != ignore_id
m = subsequent_mask(ys_mask.size(-1), device=ys_mask.device).unsqueeze(0)
return ys_mask.unsqueeze(-2) & m
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