chore: import upstream snapshot with attribution

python/paddle/distributed/flex_checkpoint/dcp/metadata_manager.py

@@ -0,0 +1,126 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from collections import defaultdict
+
+from .metadata import LocalTensorIndex, LocalTensorMetadata, Metadata
+
+TensorLocation = tuple[str, str]
+
+
+class MetadataManager:
+    def __init__(self):
+        self._metadata_list: list[Metadata] = []
+        self.local_tensor_metadata: dict[
+            TensorLocation, LocalTensorMetadata
+        ] = {}
+        self.has_flattened_tensors: bool = False
+        self.file_storage_info: defaultdict[str, set[LocalTensorIndex]] = (
+            defaultdict(set)
+        )
+
+    def set_metadata_list(self, metadata_list: list[Metadata]):
+        assert len(metadata_list) == 1, "Only support single metadata list"
+        self.clear()
+
+        self.local_tensor_metadata = {}
+        self.has_flattened_tensors = False
+
+        self._metadata_list = metadata_list
+        self._extract_local_tensor_metadata()
+        self._extract_file_storage_info()
+
+    def get_metadata_list(self) -> list[Metadata]:
+        return self._metadata_list
+
+    def is_metadata_list_empty(self) -> bool:
+        return not self._metadata_list
+
+    def get_flat_mapping(self) -> dict:
+        if self.is_metadata_list_empty():
+            raise ValueError(
+                "Cannot get flat mapping because metadata list is empty."
+            )
+        return self._metadata_list[0].flat_mapping
+
+    def get_file_storage_info(self) -> defaultdict:
+        if self.is_metadata_list_empty():
+            raise ValueError(
+                "Cannot get file_storage_info because metadata list is empty."
+            )
+        return self.file_storage_info
+
+    def _extract_local_tensor_metadata(self):
+        if self.is_metadata_list_empty():
+            return
+
+        metadata = self._metadata_list[0]
+        state_dict_metadata = metadata.state_dict_metadata
+        storage_metadata = metadata.storage_metadata
+
+        storage_metadata_split_replica_id = {}
+        for local_tensor_index, file_name in storage_metadata.items():
+            local_tensor_index = LocalTensorIndex(
+                tensor_key=local_tensor_index.tensor_key,
+                global_offset=local_tensor_index.global_offset,
+                is_flattened=local_tensor_index.is_flattened,
+                flattened_range=local_tensor_index.flattened_range,
+                local_shape=local_tensor_index.local_shape,
+            )
+            replica_id = local_tensor_index.replica_id
+            storage_metadata_split_replica_id[local_tensor_index] = (
+                file_name,
+                replica_id,
+            )
+
+        for k, local_tensor_meta_list in state_dict_metadata.items():
+            for local_tensor_meta in local_tensor_meta_list:
+                local_tensor_index = LocalTensorIndex(
+                    tensor_key=k,
+                    global_offset=local_tensor_meta.global_offset,
+                    is_flattened=local_tensor_meta.is_flattened,
+                    flattened_range=local_tensor_meta.flattened_range,
+                    local_shape=local_tensor_meta.local_shape,
+                )
+
+                if local_tensor_meta.is_flattened:
+                    self.has_flattened_tensors = True
+
+                if local_tensor_index not in storage_metadata_split_replica_id:
+                    continue
+
+                file_name, replica_id = storage_metadata_split_replica_id[
+                    local_tensor_index
+                ]
+                if replica_id is not None and replica_id > 0:
+                    continue
+
+                location_key: TensorLocation = (k, file_name)
+
+                self.local_tensor_metadata[location_key] = local_tensor_meta
+
+    def _extract_file_storage_info(self):
+        if self.is_metadata_list_empty():
+            return
+
+        metadata = self._metadata_list[0]
+        storage_metadata = metadata.storage_metadata
+        for local_tensor_index, file_name in storage_metadata.items():
+            self.file_storage_info[file_name].add(local_tensor_index)
+
+    def clear(self):
+        self._metadata_list = []
+        self.local_tensor_metadata = {}
+        self.has_flattened_tensors = False
+        self.file_storage_info = defaultdict(set)