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96d97a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // Licensed under the Apache License, Version 2.0 (the "License");
# // you may not use this file except in compliance with the License.
# // You may obtain a copy of the License at
# //
# // http://www.apache.org/licenses/LICENSE-2.0
# //
# // Unless required by applicable law or agreed to in writing, software
# // distributed under the License is distributed on an "AS IS" BASIS,
# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# // See the License for the specific language governing permissions and
# // limitations under the License.
import torch
from rotary_embedding_torch import RotaryEmbedding
from torch import nn
__all__ = ["meta_non_persistent_buffer_init_fn"]
def meta_non_persistent_buffer_init_fn(module: nn.Module) -> nn.Module:
"""
Used for materializing `non-persistent tensor buffers` while model resuming.
Since non-persistent tensor buffers are not saved in state_dict,
when initializing model with meta device, user should materialize those buffers manually.
Currently, only `rope.dummy` is this special case.
"""
with torch.no_grad():
for submodule in module.modules():
if not isinstance(submodule, RotaryEmbedding):
continue
for buffer_name, buffer in submodule.named_buffers(recurse=False):
if buffer.is_meta and "dummy" in buffer_name:
materialized_buffer = torch.zeros_like(buffer, device="cpu")
setattr(submodule, buffer_name, materialized_buffer)
assert not any(b.is_meta for n, b in module.named_buffers())
return module
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