chore: import upstream snapshot with attribution

python/paddle/distributed/fleet/dataset/__init__.py

@@ -0,0 +1,22 @@
+#   Copyright (c) 2019 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
+
+from .dataset import (  # noqa: F401
+    DatasetBase,
+    FileInstantDataset,
+    InMemoryDataset,
+    QueueDataset,
+)
+from .index_dataset import TreeIndex  # noqa: F401
+
+__all__ = []

python/paddle/distributed/fleet/dataset/index_dataset.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2021 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 __future__ import annotations
+
+from typing import Any
+
+from paddle.base import core
+
+__all__ = []
+
+
+class Index:
+    def __init__(self, name: str) -> None:
+        self._name = name
+
+
+class TreeIndex(Index):
+    def __init__(self, name: str, path: str) -> None:
+        super().__init__(name)
+        self._wrapper = core.IndexWrapper()
+        self._wrapper.insert_tree_index(name, path)
+        self._tree = self._wrapper.get_tree_index(name)
+        self._height = self._tree.height()
+        self._branch = self._tree.branch()
+        self._total_node_nums = self._tree.total_node_nums()
+        self._emb_size = self._tree.emb_size()
+        self._layerwise_sampler = None
+
+    def height(self) -> int:
+        return self._height
+
+    def branch(self) -> int:
+        return self._branch
+
+    def total_node_nums(self) -> int:
+        return self._total_node_nums
+
+    def emb_size(self) -> int:
+        return self._emb_size
+
+    def get_all_leaves(self) -> list[Any]:
+        return self._tree.get_all_leaves()
+
+    def get_nodes(self, codes: list[int]) -> list[Any]:
+        return self._tree.get_nodes(codes)
+
+    def get_layer_codes(self, level: int) -> list[int]:
+        return self._tree.get_layer_codes(level)
+
+    def get_travel_codes(self, id: int, start_level: int = 0) -> list[int]:
+        return self._tree.get_travel_codes(id, start_level)
+
+    def get_ancestor_codes(self, ids: list[int], level: int) -> list[int]:
+        return self._tree.get_ancestor_codes(ids, level)
+
+    def get_children_codes(self, ancestor: int, level: int) -> list[int]:
+        return self._tree.get_children_codes(ancestor, level)
+
+    def get_travel_path(self, child: int, ancestor: int) -> list[int]:
+        res = []
+        while child > ancestor:
+            res.append(child)
+            child = int((child - 1) / self._branch)
+        return res
+
+    def get_pi_relation(self, ids: list[int], level: int) -> dict[int, int]:
+        codes = self.get_ancestor_codes(ids, level)
+        return dict(zip(ids, codes))
+
+    def init_layerwise_sampler(
+        self,
+        layer_sample_counts: list[int],
+        start_sample_layer: int = 1,
+        seed: int = 0,
+    ) -> None:
+        assert self._layerwise_sampler is None
+        self._layerwise_sampler = core.IndexSampler("by_layerwise", self._name)
+        self._layerwise_sampler.init_layerwise_conf(
+            layer_sample_counts, start_sample_layer, seed
+        )
+
+    def layerwise_sample(
+        self,
+        user_input: list[list[int]],
+        index_input: list[int],
+        with_hierarchy: bool = False,
+    ) -> list[list[int]]:
+        if self._layerwise_sampler is None:
+            raise ValueError("please init layerwise_sampler first.")
+        return self._layerwise_sampler.sample(
+            user_input, index_input, with_hierarchy
+        )