992271b027
### What this PR does / why we need it?
This PR refactors the MoE (Mixture of Experts) communication logic by
introducing a strategy pattern. It defines an abstract base class,
`MoECommMethod`, which encapsulates different communication strategies
for MoE layers. By decoupling the MoE implementation from any single
communication method, this change makes it simpler to add, replace, or
optimize communication strategies in the future.
Plan / Roadmap
1. Introduce `MoECommMethod`, implement `AllGatherImpl`, and adapt ACL
Graph handling to cover all scenarios (this PR).
2. Implement `MC2CommImpl` and `AllToAllCommImpl` to optimize
performance in specific scenarios.
3. Enable W8A8 / Int8 models to use `unified_fused_experts`.
Other notes
* Data-parallel (DP) communication currently does not work with vLLM's
dispatch/combine mechanisms; an alternative approach is required to
resolve this incompatibility.
- vLLM version: v0.10.0
- vLLM main:
f7ad6a1eb3
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
115 lines
4.1 KiB
Python
115 lines
4.1 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.forward_context import get_forward_context
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from vllm.model_executor.layers.fused_moe.layer import \
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UnquantizedFusedMoEMethod
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.ops.fused_moe import (fused_experts_moge, select_experts,
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unified_fused_experts)
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from vllm_ascend.utils import is_310p
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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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ascend_config = get_ascend_config()
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if ascend_config.torchair_graph_config.enabled:
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self.use_aclgraph = False
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else:
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self.use_aclgraph = (vllm_config.compilation_config.level
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== CompilationLevel.PIECEWISE
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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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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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moe_comm_method = get_forward_context().moe_comm_method
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return unified_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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global_num_experts=global_num_experts,
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expert_map=expert_map,
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moe_comm_method=moe_comm_method,
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)
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UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
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UnquantizedFusedMoEMethod.forward_oot = forward_oot
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