wehub-resource-sync
127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import defaultdict
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from .metadata import LocalTensorIndex, LocalTensorMetadata, Metadata
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TensorLocation = tuple[str, str]
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class MetadataManager:
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def __init__(self):
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self._metadata_list: list[Metadata] = []
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self.local_tensor_metadata: dict[
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TensorLocation, LocalTensorMetadata
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] = {}
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self.has_flattened_tensors: bool = False
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self.file_storage_info: defaultdict[str, set[LocalTensorIndex]] = (
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defaultdict(set)
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)
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def set_metadata_list(self, metadata_list: list[Metadata]):
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assert len(metadata_list) == 1, "Only support single metadata list"
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self.clear()
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self.local_tensor_metadata = {}
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self.has_flattened_tensors = False
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self._metadata_list = metadata_list
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self._extract_local_tensor_metadata()
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self._extract_file_storage_info()
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def get_metadata_list(self) -> list[Metadata]:
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return self._metadata_list
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def is_metadata_list_empty(self) -> bool:
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return not self._metadata_list
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def get_flat_mapping(self) -> dict:
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if self.is_metadata_list_empty():
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raise ValueError(
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"Cannot get flat mapping because metadata list is empty."
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)
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return self._metadata_list[0].flat_mapping
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def get_file_storage_info(self) -> defaultdict:
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if self.is_metadata_list_empty():
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raise ValueError(
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"Cannot get file_storage_info because metadata list is empty."
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)
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return self.file_storage_info
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def _extract_local_tensor_metadata(self):
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if self.is_metadata_list_empty():
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return
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metadata = self._metadata_list[0]
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state_dict_metadata = metadata.state_dict_metadata
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storage_metadata = metadata.storage_metadata
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storage_metadata_split_replica_id = {}
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for local_tensor_index, file_name in storage_metadata.items():
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local_tensor_index = LocalTensorIndex(
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tensor_key=local_tensor_index.tensor_key,
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global_offset=local_tensor_index.global_offset,
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is_flattened=local_tensor_index.is_flattened,
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flattened_range=local_tensor_index.flattened_range,
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local_shape=local_tensor_index.local_shape,
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)
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replica_id = local_tensor_index.replica_id
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storage_metadata_split_replica_id[local_tensor_index] = (
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file_name,
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replica_id,
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)
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for k, local_tensor_meta_list in state_dict_metadata.items():
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for local_tensor_meta in local_tensor_meta_list:
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local_tensor_index = LocalTensorIndex(
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tensor_key=k,
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global_offset=local_tensor_meta.global_offset,
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is_flattened=local_tensor_meta.is_flattened,
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flattened_range=local_tensor_meta.flattened_range,
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local_shape=local_tensor_meta.local_shape,
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)
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if local_tensor_meta.is_flattened:
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self.has_flattened_tensors = True
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if local_tensor_index not in storage_metadata_split_replica_id:
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continue
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file_name, replica_id = storage_metadata_split_replica_id[
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local_tensor_index
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]
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if replica_id is not None and replica_id > 0:
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continue
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location_key: TensorLocation = (k, file_name)
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self.local_tensor_metadata[location_key] = local_tensor_meta
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def _extract_file_storage_info(self):
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if self.is_metadata_list_empty():
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return
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metadata = self._metadata_list[0]
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storage_metadata = metadata.storage_metadata
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for local_tensor_index, file_name in storage_metadata.items():
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self.file_storage_info[file_name].add(local_tensor_index)
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def clear(self):
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self._metadata_list = []
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self.local_tensor_metadata = {}
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self.has_flattened_tensors = False
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self.file_storage_info = defaultdict(set)
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