chore: import upstream snapshot with attribution

python/paddle/incubate/distributed/models/moe/utils.py

@@ -0,0 +1,87 @@
+# Copyright (c) 2022 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.
+#
+# The file has been adapted from the file:
+#     https://github.com/laekov/fastmoe/blob/master/fmoe/functions.py
+#     Git commit hash: 295a615aacce7e54a37e7935274ba15e901c78e4
+# We retain the following license from the original files:
+#     Copyright 2021, Jiaao He. All rights reserved.
+#   Licensed under the Apache License, Version 2.0 (the "License").
+
+import paddle
+from paddle.distributed.models.moe.utils import (
+    _assign_pos,
+    _limit_by_capacity,
+    _number_count,
+    _prune_gate_by_capacity,
+)
+from paddle.framework import in_dynamic_mode
+
+
+def _alltoall(in_tensor_list, group=None, use_calc_stream=True):
+    if group is not None and not group.is_member():
+        return
+
+    if in_dynamic_mode():
+        group = (
+            paddle.distributed.collective._get_default_group()
+            if group is None
+            else group
+        )
+        out = paddle.empty(in_tensor_list.shape, in_tensor_list.dtype)
+        task = group.process_group.alltoall(out, in_tensor_list)
+        task.wait()
+        return out
+    else:
+        ring_id = 0 if group is None else group.id
+        return paddle._C_ops.all_to_all(in_tensor_list, ring_id)
+
+
+def count_by_gate(gate, num_expert, world_size, require_pos=True, group=None):
+    total_expert_count = num_expert * world_size
+    with paddle.no_grad():
+        local_expert_count = _number_count(gate, total_expert_count)
+
+        if world_size > 1:
+            global_expert_count = _alltoall(local_expert_count, group=group)
+        else:
+            global_expert_count = local_expert_count
+        if not require_pos:
+            pos = None
+        else:
+            lec_cum = paddle.cumsum(local_expert_count, axis=0)
+            pos = _assign_pos(gate, lec_cum)
+    return pos, local_expert_count, global_expert_count
+
+
+def limit_by_capacity(topk_idx, num_expert, world_size, capacity, group=None):
+    with paddle.no_grad():
+        capacity = (
+            paddle.ones(shape=[num_expert], dtype=paddle.int64) * capacity
+        )
+        pos, lec, gec = count_by_gate(
+            topk_idx, num_expert, world_size, require_pos=False, group=group
+        )
+        new_gec = _limit_by_capacity(gec, capacity, world_size)
+        if world_size > 1:
+            assert group.nranks == world_size
+            new_lec = _alltoall(new_gec, group=group)
+        else:
+            new_lec = new_gec
+
+        topk_idx = _prune_gate_by_capacity(
+            topk_idx, new_lec, num_expert, world_size
+        )
+
+    return new_lec, new_gec, topk_idx