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https://github.com/handsomezhuzhu/vllm-npu-plugin.git
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278 lines
11 KiB
Python
278 lines
11 KiB
Python
#
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# Copyright (c) 2025 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 typing import Callable, Optional
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import torch
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import torch_npu
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from vllm.forward_context import get_forward_context
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from vllm_npu.ascend_config import get_ascend_config
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def select_experts(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: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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routed_scaling_factor=1.0,
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e_score_correction_bias: Optional[torch.Tensor] = None,
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indices_type: Optional[torch.dtype] = None,
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global_num_experts: int = -1):
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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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indices_type: dtype of indices
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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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# prefetch w1_w3_proj.weight preprocess
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weight_prefetch_method = get_forward_context().weight_prefetch_method
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if weight_prefetch_method:
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weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(
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hidden_states, "gate_up")
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topk_weights, topk_ids = _select_experts_with_fusion_ops(
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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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topk_group=topk_group,
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renormalize=renormalize,
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e_score_correction_bias=e_score_correction_bias,
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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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routed_scaling_factor=routed_scaling_factor,
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global_num_experts=global_num_experts)
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if topk_weights is None:
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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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global_num_experts=global_num_experts,
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)
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return topk_weights, topk_ids
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def _native_grouped_topk(
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topk_weights: torch.Tensor,
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num_expert_group: Optional[int],
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topk_group: Optional[int],
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):
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topk_group = 0 if topk_group is None else topk_group
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num_expert_group = 0 if num_expert_group is None else num_expert_group
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num_token = topk_weights.shape[0]
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grouped_weights = topk_weights.view(num_token, num_expert_group,
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-1).max(dim=-1).values
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topk_group_indices = torch.topk(grouped_weights.to(torch.float32),
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k=topk_group,
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dim=-1,
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sorted=False)[1]
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topk_group_mask = torch.zeros_like(grouped_weights)
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topk_group_mask.scatter_(1, topk_group_indices, 1)
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topk_weight_mask = (topk_group_mask.unsqueeze(-1).expand(
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num_token, num_expert_group,
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topk_weights.shape[-1] // num_expert_group).reshape(num_token, -1))
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topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0)
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return topk_weights
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def _renormalize_topk_weights(
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topk_weights: torch.Tensor,
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renormalize: bool,
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):
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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return topk_weights
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def _select_expert_use_group_topk(
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topk_weights: torch.Tensor, topk_group: Optional[int],
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renormalize: bool, top_k: int, num_expert_group: Optional[int],
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e_score_correction_bias: Optional[torch.Tensor]):
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assert topk_group is not None
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assert num_expert_group is not None
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if e_score_correction_bias is not None:
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# Store original scores before applying correction bias. We use biased
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# scores for expert selection but original scores for routing weights
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original_weights = topk_weights
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topk_weights = topk_weights + e_score_correction_bias.unsqueeze(0)
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# TODO: Change to npu_group_topk when the latest CANN and NNAL is available
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# >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group)
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topk_weights = _native_grouped_topk(topk_weights, num_expert_group,
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topk_group)
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# TODO bfloat16 is not supported in torch.topk with ge graph.
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if e_score_correction_bias is not None:
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topk_ids = torch.topk(topk_weights.to(torch.float32),
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k=top_k,
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dim=-1,
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sorted=False)[1]
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# Use original unbiased scores for the routing weights
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topk_weights = original_weights.gather(1, topk_ids)
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else:
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topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32),
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k=top_k,
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dim=-1,
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sorted=False)
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
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return topk_weights, topk_ids
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def _select_experts_with_fusion_ops(
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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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e_score_correction_bias: Optional[torch.Tensor],
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topk_group: Optional[int],
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num_expert_group: Optional[int],
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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routed_scaling_factor=1.0,
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global_num_experts: int = -1):
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topk_weights, topk_ids = None, None
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# NOTE: now npu_moe_gating_top_k can only support 'group_count=256' pattern
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global_redundant_expert_num = get_ascend_config().init_redundancy_expert
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is_deepseek_v3_r1 = global_num_experts - global_redundant_expert_num == 256
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if is_deepseek_v3_r1:
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topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
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router_logits,
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k=top_k, # topk currently 8
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bias=e_score_correction_bias,
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k_group=topk_group, # fix: 4
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group_count=num_expert_group, # fix 8
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group_select_mode=
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1, # 0: the maximum in the group; 1: topk2.sum(fix)
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renorm=0, # 0: softmax->topk(fix); 1: topk->softmax
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norm_type=1, # 0: softmax; 1: sigmoid(fix)
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# out_flag=False, # todo new api; should the third output be output
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# y2_flag=False, # old api; should the third output be output
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routed_scaling_factor=1,
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eps=float(1e-20))
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if not use_grouped_topk and custom_routing_function is None and scoring_func == "softmax":
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topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k_softmax(
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x=router_logits, finished=None, k=top_k)
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
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return topk_weights, topk_ids
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def _native_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: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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e_score_correction_bias: Optional[torch.Tensor] = None,
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global_num_experts: Optional[torch.Tensor] = None
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Select top-k experts based on router logits.
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Args:
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hidden_states: Hidden states of shape (num_tokens, hidden_size).
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router_logits: Router logits of shape (num_tokens, num_experts).
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top_k: Number of experts to select.
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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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Returns:
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topk_weights: Routing 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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Raises:
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ValueError: If an unsupported scoring function is provided.
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"""
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if scoring_func == "softmax":
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topk_weights = router_logits.softmax(dim=-1)
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elif scoring_func == "sigmoid":
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topk_weights = router_logits.sigmoid()
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else:
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raise ValueError(f"Unsupported scoring function: {scoring_func}")
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if use_grouped_topk:
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return _select_expert_use_group_topk(
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topk_weights=topk_weights,
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top_k=top_k,
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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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e_score_correction_bias=e_score_correction_bias)
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if custom_routing_function is not None:
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topk_weights, topk_ids = custom_routing_function(
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hidden_states=hidden_states,
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gating_output=router_logits,
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topk=top_k,
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renormalize=renormalize,
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global_num_experts=global_num_experts)
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# Required by npu_moe_init_routing
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topk_ids = topk_ids.to(torch.int32)
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return topk_weights, topk_ids
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topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1)
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topk_weights = topk_weights.to(hidden_states.dtype)
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# Required by npu_moe_init_routing
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
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return topk_weights, topk_ids
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