forked from jiuyuan/CPM-9G-8B
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
import torch
|
|
|
|
|
|
def pad(orig_items, key, padding_value=0, padding_side="left"):
|
|
items = []
|
|
if isinstance(orig_items[0][key], list):
|
|
assert isinstance(orig_items[0][key][0], torch.Tensor)
|
|
for it in orig_items:
|
|
for tr in it[key]:
|
|
items.append({key: tr})
|
|
else:
|
|
assert isinstance(orig_items[0][key], torch.Tensor)
|
|
items = orig_items
|
|
|
|
batch_size = len(items)
|
|
shape = items[0][key].shape
|
|
dim = len(shape)
|
|
assert dim <= 3
|
|
max_length = max(item[key].shape[-1] for item in items)
|
|
min_length = min(item[key].shape[-1] for item in items)
|
|
dtype = items[0][key].dtype
|
|
|
|
if dim == 1:
|
|
return torch.cat([item[key] for item in items], dim=0)
|
|
elif dim == 2:
|
|
if max_length == min_length:
|
|
return torch.cat([item[key] for item in items], dim=0)
|
|
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
|
|
else:
|
|
tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value
|
|
|
|
for i, item in enumerate(items):
|
|
if dim == 2:
|
|
if padding_side == "left":
|
|
tensor[i, -len(item[key][0]) :] = item[key][0].clone()
|
|
else:
|
|
tensor[i, : len(item[key][0])] = item[key][0].clone()
|
|
elif dim == 3:
|
|
if padding_side == "left":
|
|
tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
|
|
else:
|
|
tensor[i, : len(item[key][0]), :] = item[key][0].clone()
|
|
|
|
return tensor
|
|
|
|
|
|
def pad_raw(orig_items, max_length=1024, padding_value=0, padding_side="left"):
|
|
max_cols = max(tensor.size(1) for tensor in orig_items)
|
|
|
|
padded_arrays = []
|
|
for tensor in orig_items:
|
|
pad_cols = max_cols - tensor.size(1)
|
|
if padding_side == "left":
|
|
padded_tensor = torch.cat([torch.zeros(tensor.size(0), pad_cols), tensor], dim=1)
|
|
elif padding_side == "right":
|
|
padded_tensor = torch.cat([tensor, torch.zeros(tensor.size(0), pad_cols)], dim=1)
|
|
else:
|
|
raise ValueError("Invalid 'side' parameter. Must be 'left' or 'right'.")
|
|
padded_arrays.append(padded_tensor)
|
|
|
|
padded_tensor = torch.cat(padded_arrays, dim=0).to(dtype=torch.int32)
|
|
return padded_tensor
|