8cfd257992
### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced
This is a part of #1422 backport.
Fixes https://github.com/vllm-project/vllm-ascend/issues/1396
https://github.com/vllm-project/vllm-ascend/issues/1154
### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.
### How was this patch tested?
CI passed with new added and existing test.
- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a
Signed-off-by: MengqingCao <cmq0113@163.com>
117 lines
4.5 KiB
Python
117 lines
4.5 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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from vllm.config import CompilationLevel, get_current_vllm_config
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from vllm.model_executor.layers.fused_moe.layer import \
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UnquantizedFusedMoEMethod
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ops.fused_moe import (fused_experts, fused_experts_moge,
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select_experts,
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select_gating_top_k_softmax_experts)
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from vllm_ascend.utils import is_310p
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SELECT_GATING_TOPK_SOTFMAX_EXPERTS: bool = envs_ascend.SELECT_GATING_TOPK_SOTFMAX_EXPERTS
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original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__
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def unquantized_fused_moe_init_func(self, *args, **kwargs):
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original_unquantized_fused_moe_init_func(self, *args, **kwargs)
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vllm_config = get_current_vllm_config()
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self.max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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self.use_aclgraph = vllm_config.compilation_config.level == CompilationLevel.PIECEWISE and not vllm_config.model_config.enforce_eager
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def forward_oot(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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use_grouped_topk: bool,
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top_k: int,
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router_logits: torch.Tensor,
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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[int] = None,
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expert_map: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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activation: str = "silu",
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enable_eplb: bool = False,
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expert_load_view: Optional[torch.Tensor] = None,
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logical_to_physical_map: Optional[torch.Tensor] = None,
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logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:
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if SELECT_GATING_TOPK_SOTFMAX_EXPERTS:
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topk_weights, topk_ids = select_gating_top_k_softmax_experts(
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hidden_states=x,
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router_logits=router_logits,
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top_k=top_k,
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renormalize=renormalize)
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else:
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topk_weights, topk_ids = select_experts(
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global_num_experts=global_num_experts,
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hidden_states=x,
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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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if topk_ids.shape[1] < top_k or is_310p():
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assert global_num_experts is not None
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return fused_experts_moge(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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moe_parallel_config=self.moe.moe_parallel_config,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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top_k=top_k,
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global_num_experts=global_num_experts,
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expert_map=expert_map,
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apply_router_weight_on_input=apply_router_weight_on_input)
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# If use aclgraph, we need to set max_num_tokens to make
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# the input shape of `npu_moe_init_routing` fixed
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max_num_tokens = self.max_num_batched_tokens if self.use_aclgraph else None
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return fused_experts(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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top_k=top_k,
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expert_map=expert_map,
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apply_router_weight_on_input=apply_router_weight_on_input,
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max_num_tokens=max_num_tokens)
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UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
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UnquantizedFusedMoEMethod.forward_oot = forward_oot
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