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2026-07-13 12:40:42 +08:00

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Python

# 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 __future__ import annotations
from typing import TYPE_CHECKING
from paddle import _C_ops
# from ....framework import LayerHelper, in_dynamic_or_pir_mode
from paddle.base.framework import in_dynamic_or_pir_mode
from paddle.base.layer_helper import LayerHelper
if TYPE_CHECKING:
from paddle import Tensor
def moe_combine(
x: Tensor,
combine_weights: Tensor,
scatter_index: Tensor,
name: str | None = None,
) -> Tensor:
"""
Args:
x: Input tensor [seq, dim]
combine_weights: Combination weights [s, k]
scatter_index: Scatter indices [k, s] dtype=int32
Returns:
Output Combined output [s, dim]
"""
if in_dynamic_or_pir_mode():
if not (
x.process_mesh is None
and combine_weights.process_mesh is None
and scatter_index.process_mesh is None
):
# auto parallel mode
return _C_ops.moe_combine_auto(x, combine_weights, scatter_index)
return _C_ops.moe_combine(x, combine_weights, scatter_index)
helper = LayerHelper('moe_combine', **locals())
y = helper.create_variable_for_type_inference(dtype=x.dtype)
inputs = {
'x': x,
'combine_weights': combine_weights,
'scatter_index': scatter_index,
}
helper.append_op(type='moe_combine', inputs=inputs, outputs={'y': y})
return y