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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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5
docs/source/user_guide/feature_guide/eplb_swift_balancer.md
View File

@ -16,7 +16,7 @@ Expert balancing for MoE models in LLM serving is essential for optimal performa
### Dynamic EPLB
Enable dynamic balancing with auto-tuned parameters. Adjust num_iterations_eplb_update and num_wait_worker_iterations based on workload patterns.
We need to add environment variable `export PYTHONOPTIMIZE=1` to get context of vllm process. Enable dynamic balancing with auto-tuned parameters. Adjust num_iterations_eplb_update and num_wait_worker_iterations based on workload patterns.
```shell
vllm serve Qwen/Qwen3-235B-A22 \
@ -25,7 +25,6 @@ vllm serve Qwen/Qwen3-235B-A22 \
--additional-config '{
"dynamic_eplb": true,
"num_iterations_eplb_update": 400,
"gate_eplb": true,
"num_wait_worker_iterations": 30
}'
```
@ -42,9 +41,7 @@ vllm serve Qwen/Qwen3-235B-A22 \
--additional-config '{
"expert_map_record_path": "/path/to/eplb.json",
"init_redundancy_expert": 16,
"dynamic_eplb": true,
"num_iterations_eplb_update": 400,
"gate_eplb": true,
"num_wait_worker_iterations": 30
}'
```

3
vllm_ascend/eplb/eplb_updator.py
View File

@ -137,7 +137,8 @@ class EplbUpdator:
self.compute_and_set_moe_load(is_clear=True)
self.wakeup_eplb_worker()
if self.update_expert_weight_flag():
if self.update_expert_weight_flag(
) and self.expert_map_record_path is None:
self.eplb_loader.update_expert_map_and_weight(self.reqs)
self.update_iteration()

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)

3
vllm_ascend/quantization/w4a8_dynamic.py
View File

@ -140,7 +140,8 @@ class AscendW4A8DynamicFusedMoEMethod:
# NOTE: new quantize weights: 2 int4 pack into int8
self.new_quant_version = quant_version == "1.0.0"
self.tp_size = 1 if vllm_config.parallel_config.enable_expert_parallel else self.ep_group.world_size
self.dynamic_eplb = get_ascend_config().dynamic_eplb
ascend_config = get_ascend_config()
self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
if self.new_quant_version and self.tp_size > 16:
raise ValueError(
"The current weight does not support moe part tp>16.")

2
vllm_ascend/quantization/w8a8_dynamic.py
View File

@ -124,7 +124,7 @@ class AscendW8A8DynamicFusedMoEMethod:
vllm_config.compilation_config.level == CompilationLevel.PIECEWISE
and not vllm_config.model_config.enforce_eager
and not ascend_config.torchair_graph_config.enabled)
self.dynamic_eplb = ascend_config.dynamic_eplb
self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
try:
device_group = get_mc2_group().device_group

21
vllm_ascend/torchair/ops/torchair_fused_moe.py
View File

@ -35,7 +35,8 @@ from vllm.model_executor.layers.fused_moe.config import \
from vllm.model_executor.layers.fused_moe.config import \
FusedMoEParallelConfig # isort: skip
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.quantization.base_config import \
QuantizationConfig
@ -1028,7 +1029,7 @@ class TorchairAscendFusedMoE(FusedMoE):
self.moe_parallel_config.ep_size, is_deepseek_v3_r1)
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
@ -1055,6 +1056,14 @@ class TorchairAscendFusedMoE(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(
@ -1068,6 +1077,14 @@ class TorchairAscendFusedMoE(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 num_experts)
if self.dynamic_eplb:

4
vllm_ascend/torchair/quantization/torchair_w4a8_dynamic.py
View File

@ -134,6 +134,7 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
self.ep_group = get_ep_group()
ascend_config = get_ascend_config()
self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
vllm_config = get_current_vllm_config()
@ -336,7 +337,8 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
is_torchair=self.torchair_graph_enabled,
quantized_x_for_share=shared_gate_up,
dynamic_scale_for_share=shared_dequant_scale,
mc2_mask=kwargs.get("mc2_mask", None))
mc2_mask=kwargs.get("mc2_mask", None),
dynamic_eplb=self.dynamic_eplb)
else:
# The current implementation of deepseek moe splits hidden_states
# according to tp_size before they are feed into layers module.

2
vllm_ascend/torchair/quantization/torchair_w8a8_dynamic.py
View File

@ -848,7 +848,7 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
self.ep_group = get_ep_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.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp

3
vllm_ascend/worker/model_runner_v1.py
View File

@ -475,8 +475,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
dtype=torch.bool,
device=self.device,
)
self.dynamic_eplb = self.ascend_config.dynamic_eplb
self.dynamic_eplb = self.ascend_config.dynamic_eplb or self.ascend_config.expert_map_record_path
if self.dynamic_eplb:
self.is_eplb_warmuped = False
self.policy_type = self.ascend_config.eplb_policy_type