418 lines
16 KiB
Python
418 lines
16 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 collections.abc import Callable
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import torch
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import torch.nn.functional as F
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from vllm.distributed import get_tp_group
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from vllm.forward_context import get_forward_context
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from vllm_ascend.ascend_forward_context import MoECommType
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from vllm_ascend.device.device_op import DeviceOperator
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from vllm_ascend.distributed.utils import split_tensor_along_first_dim
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from vllm_ascend.utils import get_weight_prefetch_method
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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=1.0,
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e_score_correction_bias: torch.Tensor | None = None,
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indices_type: torch.dtype | None = None,
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mix_placement: bool = False,
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num_logical_experts: int = -1,
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num_shared_experts: int = 0,
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num_experts: int = -1,
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input_ids: torch.Tensor | None = None,
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tid2eid: torch.Tensor | None = None,
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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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indices_type: dtype of indices
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num_experts: 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_weight_prefetch_method()
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if weight_prefetch_method:
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weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
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is_support_npu_moe_gating_top_k = check_npu_moe_gating_top_k(
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hidden_states=hidden_states,
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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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scoring_func=scoring_func,
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custom_routing_function=custom_routing_function,
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)
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if is_support_npu_moe_gating_top_k:
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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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scoring_func=scoring_func,
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routed_scaling_factor=routed_scaling_factor,
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tid2eid=tid2eid,
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input_ids=input_ids,
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)
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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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routed_scaling_factor=routed_scaling_factor,
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e_score_correction_bias=e_score_correction_bias,
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tid2eid=None,
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input_ids=None,
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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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if mix_placement:
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shared_expert_routing_factor = 1.0 if is_support_npu_moe_gating_top_k else (1 / routed_scaling_factor)
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batch_size = topk_ids.shape[0]
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pad_shared_expert_ids = torch.arange(
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num_logical_experts, num_logical_experts + num_shared_experts, dtype=topk_ids.dtype, device=topk_ids.device
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).repeat(batch_size, 1)
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pad_shared_expert_weights = torch.full(
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(topk_weights.shape[0], num_shared_experts),
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shared_expert_routing_factor,
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dtype=topk_weights.dtype,
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device=topk_weights.device,
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)
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topk_ids = torch.cat([topk_ids, pad_shared_expert_ids], dim=1)
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topk_weights = torch.cat([topk_weights, pad_shared_expert_weights], dim=1)
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return topk_weights, topk_ids
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def check_npu_moe_gating_top_k(
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hidden_states: torch.Tensor,
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top_k: int,
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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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scoring_func: str = "softmax",
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custom_routing_function: Callable | None = None,
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):
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if scoring_func == "sigmoid" and not renormalize: # sigmoid + renorm=0 is not supported in current branch
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return False
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if custom_routing_function is not None:
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return False
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if scoring_func != "softmax" and scoring_func != "sigmoid" and scoring_func != "sqrtsoftplus":
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return False
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topk_group = topk_group if topk_group is not None else 1
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num_expert_group = num_expert_group if num_expert_group is not None else 1
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if not (
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num_expert_group > 0
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and hidden_states.shape[-1] % num_expert_group == 0
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and hidden_states.shape[-1] // num_expert_group > 2
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):
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return False
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if topk_group < 1 or topk_group > num_expert_group:
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return False
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if top_k < 1 or top_k > (hidden_states.shape[-1] / (num_expert_group * topk_group)):
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return False
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if topk_group * hidden_states.shape[-1] / num_expert_group < top_k: # noqa: SIM103
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return False
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return True
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def _native_grouped_topk(
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topk_weights: torch.Tensor,
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num_expert_group: int | None,
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topk_group: int | None,
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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, -1).max(dim=-1).values
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topk_group_indices = torch.topk(grouped_weights.to(torch.float32), k=topk_group, dim=-1, 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 = (
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topk_group_mask.unsqueeze(-1)
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.expand(num_token, num_expert_group, topk_weights.shape[-1] // num_expert_group)
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.reshape(num_token, -1)
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)
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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,
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topk_group: int | None,
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renormalize: bool,
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top_k: int,
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num_expert_group: int | None,
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e_score_correction_bias: torch.Tensor | None,
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):
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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, 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), k=top_k, dim=-1, 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), k=top_k, dim=-1, 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: torch.Tensor | None,
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topk_group: int | None,
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num_expert_group: int | None,
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scoring_func: str = "softmax",
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routed_scaling_factor=1.0,
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tid2eid=None,
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input_ids=None,
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):
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topk_group = topk_group if topk_group is not None else 1
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num_expert_group = num_expert_group if num_expert_group is not None else 1
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renorm = int(renormalize)
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if scoring_func == "sqrtsoftplus":
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if tid2eid is not None:
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forward_context = get_forward_context()
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input_ids = forward_context.input_ids.to(torch.int64)
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# tid2eid_ones = torch.ones(tid2eid.shape[0],tid2eid.shape[1],device=router_logits.device,dtype=torch.int32)
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tid2eid_ones = tid2eid.to(torch.int32)
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if forward_context.moe_comm_type == MoECommType.ALLGATHER:
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prepare_finalize = forward_context.moe_comm_method.prepare_finalize
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input_ids = prepare_finalize.all_gather_input_id_with_dp_group(input_ids)
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else:
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input_ids = forward_context.moe_comm_method.pad_and_split_input_ids(input_ids)
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if forward_context.flash_comm_v1_enabled and forward_context.moe_comm_type != MoECommType.ALLGATHER:
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# Process for Flash Comm V1
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tp_size = get_tp_group().world_size
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tp_rank = get_tp_group().rank_in_group
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splitted_input = split_tensor_along_first_dim(input_ids, num_partitions=tp_size)
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input_ids = splitted_input[tp_rank].contiguous()
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input_ids = torch.where(input_ids == -1, 0, input_ids)
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else:
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input_ids = None
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tid2eid_ones = None
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topk_weights, topk_ids, _ = torch.ops._C_ascend.moe_gating_top_k_hash(
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x=router_logits,
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k=top_k,
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bias=e_score_correction_bias,
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input_ids=input_ids,
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tid2eid=tid2eid_ones,
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k_group=topk_group,
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group_count=num_expert_group,
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routed_scaling_factor=routed_scaling_factor,
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eps=1e-20,
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group_select_mode=1,
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# The hash custom op currently rejects renorm != 0. Apply
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# norm_topk_prob in Python below before returning to MoE compute.
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renorm=0,
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norm_type=2,
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out_flag=False,
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)
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return topk_weights, topk_ids
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norm_type = 0 if scoring_func == "softmax" else 1
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if e_score_correction_bias is not None and e_score_correction_bias.dtype != router_logits.dtype:
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e_score_correction_bias = e_score_correction_bias.to(router_logits.dtype)
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topk_weights, topk_ids, _ = DeviceOperator.moe_gating_top_k(
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router_logits,
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k=top_k,
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k_group=topk_group,
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group_count=num_expert_group,
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group_select_mode=1,
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renorm=renorm,
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norm_type=norm_type, # 0: softmax; 1: sigmoid
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out_flag=False,
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routed_scaling_factor=routed_scaling_factor,
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eps=1e-20,
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bias_opt=e_score_correction_bias,
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)
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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: 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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use_hash: bool = False,
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tid2eid: dict[int, int] | None = None,
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input_ids: torch.Tensor | None = 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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elif scoring_func == "sqrtsoftplus":
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topk_weights = F.softplus(router_logits).sqrt()
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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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topk_weights, topk_ids = _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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)
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return topk_weights * routed_scaling_factor, topk_ids
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if e_score_correction_bias is not None:
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topk_weights = topk_weights + 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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)
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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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topk_weights = topk_weights * routed_scaling_factor
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return topk_weights, topk_ids
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def zero_experts_compute(
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expert_indices: torch.Tensor,
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expert_scales: torch.Tensor,
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num_experts: int,
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zero_expert_type: str,
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hidden_states: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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if zero_expert_type == "identity":
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zero_expert_mask = expert_indices < num_experts
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zero_expert_scales = expert_scales.clone()
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zero_expert_scales = torch.where(zero_expert_mask, 0.0, zero_expert_scales)
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hidden_states = hidden_states.unsqueeze(1)
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zero_expert_scales = zero_expert_scales.unsqueeze(2)
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result = hidden_states * zero_expert_scales
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result = result.sum(dim=1)
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normal_expert_mask = expert_indices >= num_experts
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expert_indices = torch.where(normal_expert_mask, 0, expert_indices)
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expert_scales = torch.where(normal_expert_mask, 0.0, expert_scales)
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return expert_indices, expert_scales, result
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