90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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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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#
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from collections.abc import Callable
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import torch
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import torch_npu
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from vllm_ascend.ops.fused_moe.experts_selector import _native_select_experts, _renormalize_topk_weights
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def select_experts(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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topk_group: int | None = None,
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num_expert_group: int | None = None,
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custom_routing_function: Callable | None = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: torch.Tensor | None = None,
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global_num_experts: int = -1,
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):
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"""
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Fused experts with select experts.
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Args:
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router_logits: router logits of shape (num_tokens, hidden_size).
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hidden_states: Hidden states of shape (num_tokens, hidden_size).
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top_k: number of top k experts.
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use_grouped_topk: Whether to group experts before selecting top-k.
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renormalize: Whether to renormalize the routing weights.
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topk_group: Number of expert groups to select from.
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num_expert_group: Number of experts in each group.
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custom_routing_function: Custom routing function.
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scoring_func: Scoring function to use.
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e_score_correction_bias: Correction bias to apply to expert scores.
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routed_scaling_factor: Scaling factor applied to routing weights.
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global_num_experts: Global number of experts.
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Returns:
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topk_weights: router weights of shape (num_tokens, top_k).
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topk_ids: selected expert IDs of shape (num_tokens, top_k).
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"""
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if scoring_func == "softmax" and not use_grouped_topk and custom_routing_function is None:
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# 310P returns invalid routing results when this op receives more than 1024 tokens.
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if router_logits.shape[0] > 1024:
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topk_results = [
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torch_npu.npu_moe_gating_top_k_softmax(router_logits_chunk, k=top_k)
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for router_logits_chunk in router_logits.split(1024, dim=0)
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]
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topk_weights = torch.cat([result[0] for result in topk_results], dim=0)
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topk_ids = torch.cat([result[1] for result in topk_results], dim=0)
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else:
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topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k_softmax(router_logits, k=top_k)
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topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
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else:
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topk_weights, topk_ids = _native_select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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e_score_correction_bias=e_score_correction_bias,
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)
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# Apply routed scaling factor to weights
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if routed_scaling_factor != 1.0:
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topk_weights = topk_weights * routed_scaling_factor
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return topk_weights, topk_ids
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