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Running
on
Zero
File size: 5,781 Bytes
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import torch
import numpy as np
def _sampler(pdf: torch.Tensor, num_samples: int,
device=torch.device('cpu')) -> torch.Tensor:
size = pdf.size()
z = -torch.log(torch.rand(size, device=device))
_, indices = torch.topk(pdf + z, num_samples)
return indices
def compute_mask_indices(
size: torch.Size,
mask_prob: float,
mask_length: int,
min_masks: int = 0,
device=torch.device('cpu'),
) -> torch.Tensor:
assert len(size) == 2
batch_size, seq_length = size
# compute number of masked span in batch
num_masked_spans = mask_prob * float(seq_length) / float(
mask_length) + torch.rand(1)[0]
num_masked_spans = int(num_masked_spans)
num_masked_spans = max(num_masked_spans, min_masks)
# num_masked <= seq_length
if num_masked_spans * mask_length > seq_length:
num_masked_spans = seq_length // mask_length
pdf = torch.ones(batch_size, seq_length - (mask_length - 1), device=device)
mask_idxs = _sampler(pdf, num_masked_spans, device=device)
mask_idxs = mask_idxs.unsqueeze(-1).repeat(1, 1, mask_length).view(
batch_size,
num_masked_spans * mask_length) # [B,num_masked_spans*mask_length]
offset = torch.arange(mask_length, device=device).view(1, 1, -1).repeat(
1, num_masked_spans, 1) # [1,num_masked_spans,mask_length]
offset = offset.view(1, num_masked_spans * mask_length)
mask_idxs = mask_idxs + offset # [B,num_masked_spans, mask_length]
ones = torch.ones(batch_size,
seq_length,
dtype=torch.bool,
device=mask_idxs.device)
# masks to fill
full_mask = torch.zeros_like(ones,
dtype=torch.bool,
device=mask_idxs.device)
return torch.scatter(full_mask, dim=1, index=mask_idxs, src=ones)
def compute_mask_indices_v2(
shape,
padding_mask,
mask_prob: float,
mask_length: int,
mask_type: str = 'static',
mask_other: float = 0.0,
min_masks: int = 2,
no_overlap: bool = False,
min_space: int = 1,
device=torch.device('cpu'),
):
bsz, all_sz = shape
mask = np.full((bsz, all_sz), False)
padding_mask = padding_mask.cpu().numpy()
all_num_mask = int(
# add a random number for probabilistic rounding
mask_prob * all_sz / float(mask_length) + np.random.rand())
all_num_mask = max(min_masks, all_num_mask)
mask_idcs = []
for i in range(bsz):
if padding_mask is not None and not isinstance(padding_mask, bytes):
sz = all_sz - padding_mask[i].sum()
num_mask = int(
# add a random number for probabilistic rounding
mask_prob * sz / float(mask_length) + np.random.rand())
num_mask = max(min_masks, num_mask)
else:
sz = all_sz
num_mask = all_num_mask
if mask_type == 'static':
lengths = np.full(num_mask, mask_length)
elif mask_type == 'uniform':
lengths = np.random.randint(mask_other,
mask_length * 2 + 1,
size=num_mask)
elif mask_type == 'normal':
lengths = np.random.normal(mask_length, mask_other, size=num_mask)
lengths = [max(1, int(round(x))) for x in lengths]
elif mask_type == 'poisson':
lengths = np.random.poisson(mask_length, size=num_mask)
lengths = [int(round(x)) for x in lengths]
else:
raise Exception('unknown mask selection ' + mask_type)
if sum(lengths) == 0:
lengths[0] = min(mask_length, sz - 1)
if no_overlap:
mask_idc = []
def arrange(s, e, length, keep_length, mask_idc):
span_start = np.random.randint(s, e - length)
mask_idc.extend(span_start + i for i in range(length))
new_parts = []
if span_start - s - min_space >= keep_length:
new_parts.append((s, span_start - min_space + 1))
if e - span_start - keep_length - min_space > keep_length:
new_parts.append((span_start + length + min_space, e))
return new_parts
parts = [(0, sz)]
min_length = min(lengths)
for length in sorted(lengths, reverse=True):
lens = np.fromiter(
(e - s if e - s >= length + min_space else 0
for s, e in parts),
np.int,
)
l_sum = np.sum(lens)
if l_sum == 0:
break
probs = lens / np.sum(lens)
c = np.random.choice(len(parts), p=probs)
s, e = parts.pop(c)
parts.extend(arrange(s, e, length, min_length, mask_idc))
mask_idc = np.asarray(mask_idc)
else:
min_len = min(lengths)
if sz - min_len <= num_mask:
min_len = sz - num_mask - 1
mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
mask_idc = np.asarray([
mask_idc[j] + offset for j in range(len(mask_idc))
for offset in range(lengths[j])
])
mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
min_len = min([len(m) for m in mask_idcs])
for i, mask_idc in enumerate(mask_idcs):
if len(mask_idc) > min_len:
mask_idc = np.random.choice(mask_idc, min_len, replace=False)
mask[i, mask_idc] = True
mask = torch.from_numpy(mask).to(device)
return mask
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