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[EPLB]Record expert map without dynamic eplb. (#3409)

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What this PR does / why we need it?
1.Record expert map without dynamic eplb.
2.Add export PYTHONOPTIMIZE=1  when using dynamic eplb.
3.change eplb doc

Does this PR introduce any user-facing change?
How was this patch tested?
Qwen3_moe in A3.

- vLLM version: v0.11.0

---------

Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
This commit is contained in:
offline893
2025-10-15 14:21:15 +08:00
committed by GitHub
parent 4f937f561d
commit 5a3082cd15
9 changed files with 49 additions and 15 deletions
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21
vllm_ascend/ops/common_fused_moe.py
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@@ -26,7 +26,8 @@ from vllm.forward_context import get_forward_context
from vllm.logger import logger
from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
from vllm.model_executor.layers.fused_moe.layer import (
FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map)
FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map,
get_compressed_expert_map)
from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE
from vllm_ascend.ascend_config import get_ascend_config
@@ -176,7 +177,7 @@ class AscendFusedMoE(FusedMoE):
self.moe_config.ep_group = get_ep_group()
self.moe_config.mc2_group = get_mc2_group()
ascend_config = get_ascend_config()
self.dynamic_eplb = ascend_config.dynamic_eplb
self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
self.expert_map_path = ascend_config.expert_map_path
self.global_redundant_expert_num = ascend_config.init_redundancy_expert
self.global_num_experts = num_experts + self.global_redundant_expert_num
@@ -203,6 +204,14 @@ class AscendFusedMoE(FusedMoE):
self.log2phy = determine_default_log2phy_map(
self.global_num_experts, self.ep_size, self.ep_rank,
self.global_redundant_expert_num).npu()
if self.expert_map is not None and isinstance(
self.expert_map, torch.Tensor):
logger.info_once(
"[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
" number of experts: %s/%s. Experts local to global index map:"
" %s.", self.ep_rank, self.ep_size, self.local_num_experts,
self.global_num_experts,
get_compressed_expert_map(self.expert_map))
else:
# init moe.
self.local_num_experts, self.expert_map = determine_expert_map(
@@ -216,6 +225,14 @@ class AscendFusedMoE(FusedMoE):
self.log2phy = determine_default_log2phy_map(
self.global_num_experts, self.ep_size, self.ep_rank,
self.global_redundant_expert_num).npu()
if self.expert_map is not None and isinstance(
self.expert_map, torch.Tensor):
logger.info_once(
"[EP Rank %s/%s] Expert parallelism is enabled. Local/global"
" number of experts: %s/%s. Experts local to global index map:"
" %s.", self.ep_rank, self.ep_size, self.local_num_experts,
self.global_num_experts,
get_compressed_expert_map(self.expert_map))
local_num_experts = (torch.sum(
self.expert_map != -1) if self.expert_map is not None else
self.global_num_experts)