ColossalAI/colossalai/zero/low_level/bookkeeping/tensor_bucket.py

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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
class TensorBucket:
def __init__(self, size):
self._max_size = size
self._current_size = 0
self._bucket = []
@property
def max_size(self):
return self._max_size
@property
def current_size(self):
return self._current_size
def is_full_or_oversized(self):
return self._current_size >= self._max_size
def is_empty(self):
return len(self._bucket) == 0
def add_to_bucket(self, tensor, allow_oversize=False):
tensor_size = tensor.numel()
if not allow_oversize and self.will_exceed_max_size(tensor_size):
msg = f"The param bucket max size {self._max_size} is exceeded" + f"by tensor (size {tensor_size})"
raise RuntimeError(msg)
self._bucket.append(tensor)
self._current_size += tensor_size
def will_exceed_max_size(self, tensor_size):
expected_size = self._current_size + tensor_size
return expected_size > self._max_size
def get_bucket(self):
return self._bucket
def empty(self):
self._bucket = []
self._size = 0
def flatten(self):
return _flatten_dense_tensors(self._bucket)
def unflatten_and_copy(self, flat_tensor):
unflattened_tensor_list = _unflatten_dense_tensors(flat_tensor, self._bucket)
for old, new in zip(self._bucket, unflattened_tensor_list):
old.copy_(new)