def sample_negatives(self, y):
bsz, fsz, tsz = y.shape
y = y.transpose(0, 1) # BCT -> CBT
y = y.contiguous().view(fsz, -1) # CBT => C(BxT)
cross_high = tsz * bsz
high = tsz if self.sample_distance is None else min(tsz, self.sample_distance)
assert high > 1
neg_idxs = torch.randint(low=0, high=high, size=(bsz, self.n_negatives * tsz))
with torch.no_grad():
if self.n_negatives > 0:
tszs = (
buffered_arange(tsz)
.unsqueeze(-1)
.expand(-1, self.n_negatives)
.flatten()
)
neg_idxs = torch.randint(
low=0, high=high - 1, size=(bsz, self.n_negatives * tsz)
)
neg_idxs[neg_idxs >= tszs] += 1
if self.cross_sample_negatives > 0:
tszs = (
buffered_arange(tsz)
.unsqueeze(-1)
.expand(-1, self.cross_sample_negatives)
.flatten()
)
cross_neg_idxs = torch.randint(
low=0,
high=cross_high - 1,
size=(bsz, self.cross_sample_negatives * tsz),
)
cross_neg_idxs[cross_neg_idxs >= tszs] += 1
if self.n_negatives > 0:
for i in range(1, bsz):
neg_idxs[i] += i * high
else:
neg_idxs = cross_neg_idxs
if self.cross_sample_negatives > 0 and self.n_negatives > 0:
neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
negs = y[..., neg_idxs.view(-1)]
negs = negs.view(
fsz, bsz, self.n_negatives + self.cross_sample_negatives, tsz
).permute(
2, 1, 0, 3
) # to NxBxCxT
return negs
def sample_negatives(self, y, num):
if self.n_negatives == 0 and self.cross_sample_negatives == 0:
return y.new(0)
bsz, tsz, fsz = y.shape
y = y.view(-1, fsz) # BTC => (BxT)C
cross_high = tsz * bsz
high = tsz
with torch.no_grad():
assert high > 1, f"{bsz,tsz,fsz}"
if self.n_negatives > 0:
tszs = (
buffered_arange(num)
.unsqueeze(-1)
.expand(-1, self.n_negatives)
.flatten()
)
neg_idxs = torch.randint(
low=0, high=high - 1, size=(bsz, self.n_negatives * num)
)
neg_idxs[neg_idxs >= tszs] += 1
if self.cross_sample_negatives > 0:
tszs = (
buffered_arange(num)
.unsqueeze(-1)
.expand(-1, self.cross_sample_negatives)
.flatten()
)
cross_neg_idxs = torch.randint(
low=0,
high=cross_high - 1,
size=(bsz, self.cross_sample_negatives * num),
)
cross_neg_idxs[cross_neg_idxs >= tszs] += 1
if self.n_negatives > 0:
for i in range(1, bsz):
neg_idxs[i] += i * high
else:
neg_idxs = cross_neg_idxs
if self.cross_sample_negatives > 0 and self.n_negatives > 0:
neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
negs = y[neg_idxs.view(-1)]
negs = negs.view(
bsz, num, self.n_negatives + self.cross_sample_negatives, fsz
).permute(
2, 0, 1, 3
) # to NxBxTxC
return negs, neg_idxs
def sample_negatives(self, y, num, padding_count=None):
if self.n_negatives == 0 and self.cross_sample_negatives == 0:
return y.new(0)
bsz, tsz, fsz = y.shape
y = y.view(-1, fsz) # BTC => (BxT)C
# FIXME: what happens if padding_count is specified?
cross_high = tsz * bsz
high = tsz - (padding_count or 0)
with torch.no_grad():
assert high > 1, f"{bsz,tsz,fsz}"
if self.n_negatives > 0:
tszs = (
buffered_arange(num)
.unsqueeze(-1)
.expand(-1, self.n_negatives)
.flatten()
)
neg_idxs = torch.randint(
low=0, high=high - 1, size=(bsz, self.n_negatives * num)
)
neg_idxs[neg_idxs >= tszs] += 1
if self.cross_sample_negatives > 0:
tszs = (
buffered_arange(num)
.unsqueeze(-1)
.expand(-1, self.cross_sample_negatives)
.flatten()
)
cross_neg_idxs = torch.randint(
low=0,
high=cross_high - 1,
size=(bsz, self.cross_sample_negatives * num),
)
cross_neg_idxs[cross_neg_idxs >= tszs] += 1
if self.n_negatives > 0:
neg_idxs = neg_idxs + (torch.arange(bsz).unsqueeze(1) * high)
else:
neg_idxs = cross_neg_idxs
if self.cross_sample_negatives > 0 and self.n_negatives > 0:
neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1)
negs = y[neg_idxs.view(-1)]
negs = negs.view(
bsz, num, self.n_negatives + self.cross_sample_negatives, fsz
).permute(
2, 0, 1, 3
) # to NxBxTxC
return negs, neg_idxs
def convert_padding_direction(
src_frames,
src_lengths,
right_to_left=False,
left_to_right=False,
):
"""Counterpart of :func:`~fairseq.utils.convert_padding_direction`,
operating on 3d tensors of size B x T x C. Note that this function is unware
of whether it has already been right padded or left padded (since any real
value is legal for non-padded elements), so be clear of the actual padding
direction before calling this function.
"""
assert right_to_left ^ left_to_right
assert src_frames.size(0) == src_lengths.size(0)
max_len = src_frames.size(1)
if not src_lengths.eq(max_len).any():
# no padding, return early
return src_frames
range = utils.buffered_arange(max_len).unsqueeze(-1).expand_as(src_frames)
num_pads = (max_len -
src_lengths.type_as(range)).unsqueeze(-1).unsqueeze(-1)
if right_to_left:
index = torch.remainder(range - num_pads, max_len)
else:
index = torch.remainder(range + num_pads, max_len)
return src_frames.gather(1, index)
def _with_sentence_boundaries(
self,
input: torch.Tensor,
):
if not self.add_bos and not self.add_eos:
return input
zero_block = input.new(input.size(0), 0)
bos_block = input.new_full(
(input.size(0), 1), self.eos_idx) if self.add_bos else zero_block
pad_block = input.new_full(
(input.size(0),
1), self.padding_idx) if self.add_eos else zero_block
# add eos in the beginning and pad to the end of the sentence
input = torch.cat([bos_block, input, pad_block], dim=1)
if self.add_eos:
num_pads = input.eq(self.padding_idx).long().sum(dim=1,
keepdim=True)
max_len = input.size(1)
# index of the first pad
first_pads = buffered_arange(max_len).type_as(input).view(
1, -1).expand(input.size(0), -1).eq(max_len - num_pads)
input[first_pads] = self.eos_idx
return input
def _with_sentence_boundaries(
self,
input: torch.Tensor):
"""
Args:
input: the sentence Tensor
it's bs * seq_len * num_chars in case of char input and bs*seq_len in case of token input
Returns:
tuple,
1) processed input,
2) tensor mask for the eos position of each sentence,
None if did not add eos
"""
if not self.add_bos and not self.add_eos:
return input, None
zero_block = input.new(0, 0)
block_size = (input.size(0), 1, input.size(2)) if self.char_inputs else (input.size(0), 1)
bos_block = torch.full(block_size, self.eos_idx).type_as(input) if self.add_bos else zero_block
pad_block = torch.full(block_size, self.padding_idx).type_as(input) if self.add_eos else zero_block
# add eos in the beginning and pad to the end of the sentence
input = torch.cat([bos_block, input, pad_block], dim=1)
first_pads = None # if not add_eos, then first_pads is not valid, set to None
if self.add_eos:
index_block = input[:, :, 0] if self.char_inputs else input
padding_mask = index_block.eq(self.padding_idx)
num_pads = padding_mask.long().sum(dim=1, keepdim=True)
max_len = input.size(1)
# index of the first pad
if self.onnx_trace:
first_pads = torch._dim_arange(input, 1).type_as(input).view(1, -1).\
repeat(input.size(0), 1).eq(max_len - num_pads)
eos_indices = first_pads
if self.char_inputs:
eos_indices = eos_indices.unsqueeze(2).repeat(1, 1, input.size(-1))
input = torch.where(eos_indices, torch.Tensor([self.eos_idx]).type_as(input), input)
else:
first_pads = buffered_arange(max_len).type_as(input).view(1, -1).\
expand(input.size(0), -1).eq(max_len - num_pads)
eos_indices = first_pads
if self.char_inputs:
eos_indices = eos_indices.unsqueeze(2).expand_as(input)
input[eos_indices] = self.eos_idx
return input, first_pads