### What this PR does / why we need it? - Keeps enable_cpu_binding default on, but skips binding on non‑ARM CPUs inside bind_cpus, with a clear log. - Uses a table-driven binding policy: A3 uses NUMA‑balanced binding; other device types use NUMA‑affinity binding. - Updates docs to reflect the exact behavior and adds/updates unit tests for the new logic. ### Does this PR introduce _any_ user-facing change? - Yes. CPU binding is now enabled by default via additional_config, and documented in the user guide. - CPU binding behavior differs by device type (A3 vs. others). ### How was this patch tested? Added/updated unit tests: test_cpu_binding.py 1. test_binding_mode_table covers A2 vs A3 binding mode mapping. 2. test_build_cpu_pools_fallback_to_numa_balanced covers fallback when affinity info is missing. 3. TestBindingSwitch.test_is_arm_cpu covers ARM/x86/unknown arch detection. 4. test_bind_cpus_skip_non_arm covers non‑ARM skip path in bind_cpus. test_worker_v1.py 1. Updated mocks for enable_cpu_binding default True to align with new config default. - vLLM version: v0.14.1 - vLLM main: d7de043 --------- Signed-off-by: chenchuw886 <chenchuw@huawei.com> Co-authored-by: chenchuw886 <chenchuw@huawei.com>
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Additional Configuration
Additional configuration is a mechanism provided by vLLM to allow plugins to control internal behavior by themselves. VLLM Ascend uses this mechanism to make the project more flexible.
How to use
With either online mode or offline mode, users can use additional configuration. Take Qwen3 as an example:
Online mode:
vllm serve Qwen/Qwen3-8B --additional-config='{"config_key":"config_value"}'
Offline mode:
from vllm import LLM
LLM(model="Qwen/Qwen3-8B", additional_config={"config_key":"config_value"})
Configuration options
The following table lists additional configuration options available in vLLM Ascend:
| Name | Type | Default | Description |
|---|---|---|---|
xlite_graph_config |
dict | {} |
Configuration options for Xlite graph mode |
weight_prefetch_config |
dict | {} |
Configuration options for weight prefetch |
finegrained_tp_config |
dict | {} |
Configuration options for module tensor parallelism |
ascend_compilation_config |
dict | {} |
Configuration options for ascend compilation |
eplb_config |
dict | {} |
Configuration options for ascend compilation |
npugraph_ex_config |
dict | {} |
Configuration options for Npugraph_ex backend |
refresh |
bool | false |
Whether to refresh global Ascend configuration content. This is usually used by rlhf or ut/e2e test case. |
dump_config_path |
str | None |
Configuration file path for msprobe dump(eager mode). |
enable_async_exponential |
bool | False |
Whether to enable asynchronous exponential overlap. To enable asynchronous exponential, set this config to True. |
enable_shared_expert_dp |
bool | False |
When the expert is shared in DP, it delivers better performance but consumes more memory. Currently only DeepSeek series models are supported. |
multistream_overlap_shared_expert |
bool | False |
Whether to enable multi-stream shared expert. This option only takes effect on MoE models with shared experts. |
multistream_overlap_gate |
bool | False |
Whether to enable multi-stream overlap gate. This option only takes effect on MoE models with shared experts. |
recompute_scheduler_enable |
bool | False |
Whether to enable recompute scheduler. |
enable_cpu_binding |
bool | True |
Whether to enable CPU binding. Only takes effect on ARM CPUs; when enabled, A3 uses NUMA-balanced binding strategy and other device types use NUMA-affinity's. |
SLO_limits_for_dynamic_batch |
int | -1 |
SLO limits for dynamic batch. This is new scheduler to support dynamic batch feature |
enable_npugraph_ex |
bool | False |
Whether to enable npugraph_ex graph mode. |
pa_shape_list |
list | [] |
The custom shape list of page attention ops. |
enable_kv_nz |
bool | False |
Whether to enable KV cache NZ layout. This option only takes effects on models using MLA (e.g., DeepSeek). |
layer_sharding |
dict | {} |
Configuration options for Layer Sharding Linear |
The details of each configuration option are as follows:
xlite_graph_config
| Name | Type | Default | Description |
|---|---|---|---|
enabled |
bool | False |
Whether to enable Xlite graph mode. Currently only Llama, Qwen dense series models, and Qwen3-VL are supported. |
full_mode |
bool | False |
Whether to enable Xlite for both the prefill and decode stages. By default, Xlite is only enabled for the decode stage. |
weight_prefetch_config
| Name | Type | Default | Description |
|---|---|---|---|
enabled |
bool | False |
Whether to enable weight prefetch. |
prefetch_ratio |
dict | {"attn": {"qkv": 1.0, "o": 1.0}, "moe": {"gate_up": 0.8}, "mlp": { "gate_up": 1.0, "down": 1.0}} |
Prefetch ratio of each weight. |
finegrained_tp_config
| Name | Type | Default | Description |
|---|---|---|---|
lmhead_tensor_parallel_size |
int | 0 |
The custom tensor parallel size of lm_head. |
oproj_tensor_parallel_size |
int | 0 |
The custom tensor parallel size of o_proj. |
embedding_tensor_parallel_size |
int | 0 |
The custom tensor parallel size of embedding. |
mlp_tensor_parallel_size |
int | 0 |
The custom tensor parallel size of mlp. |
ascend_compilation_config
| Name | Type | Default | Description |
|---|---|---|---|
fuse_norm_quant |
bool | True |
Whether to enable fuse_norm_quant pass. |
fuse_qknorm_rope |
bool | True |
Whether to enable fuse_qknorm_rope pass. If Triton is not in the environment, set it to False. |
fuse_allreduce_rms |
bool | False |
Whether to enable fuse_allreduce_rms pass. It's set to False because of conflict with SP. |
eplb_config
| Name | Type | Default | Description |
|---|---|---|---|
dynamic_eplb |
bool | False |
Whether to enable dynamic EPLB. |
expert_map_path |
str | None |
When using expert load balancing for an MoE model, an expert map path needs to be passed in. |
expert_heat_collection_interval |
int | 400 |
Forward iterations when EPLB begins. |
algorithm_execution_interval |
int | 30 |
The forward iterations when the EPLB worker will finish CPU tasks. |
expert_map_record_path |
str | None |
Save the expert load calculation results to a new expert table in the specified directory. |
num_redundant_experts |
int | 0 |
Specify redundant experts during initialization. |
npugraph_ex_config
| Name | Type | Default | Description |
|---|---|---|---|
enable |
bool | True |
Whether to enable npugraph_ex backend. |
enable_static_kernel |
bool | False |
Whether to enable static kernel. Suitable for scenarios where shape changes are minimal and some time is available for static kernel compilation. |
fuse_norm_quant |
bool | True |
Whether to enable fuse_norm_quant pass. |
fuse_qknorm_rope |
bool | True |
Whether to enable fuse_qknorm_rope pass. If Triton is not in the environment, set it to False. |
fuse_allreduce_rms |
bool | False |
Whether to enable fuse_allreduce_rms pass. It's set to False because of conflict with SP. |
Example
An example of additional configuration is as follows:
{
"weight_prefetch_config": {
"enabled": True,
"prefetch_ratio": {
"attn": {
"qkv": 1.0,
"o": 1.0,
},
"moe": {
"gate_up": 0.8
},
"mlp": {
"gate_up": 1.0,
"down": 1.0
}
},
},
"finegrained_tp_config": {
"lmhead_tensor_parallel_size": 8,
"oproj_tensor_parallel_size": 8,
"embedding_tensor_parallel_size": 8,
"mlp_tensor_parallel_size": 8,
},
"enable_kv_nz": False,
"multistream_overlap_shared_expert": True,
"refresh": False
}