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