Revert "drop ascend scheduler" (#4580)

Reverts vllm-project/vllm-ascend#4498
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

docs/source/tutorials/DeepSeek-V3.2-Exp.md

@@ -108,7 +108,7 @@ vllm serve vllm-ascend/DeepSeek-V3.2-Exp-W8A8 \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.92 \
---additional-config '{"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
 ```
 
 ### Multi-node Deployment
@@ -160,7 +160,7 @@ vllm serve /root/.cache/Modelers_Park/DeepSeek-V3.2-Exp \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.9 \
---additional-config '{"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
 ```
 
 **Node 1**
@@ -204,7 +204,7 @@ vllm serve /root/.cache/Modelers_Park/DeepSeek-V3.2-Exp \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.92 \
---additional-config '{"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
 ```
 
 ::::
@@ -252,7 +252,7 @@ vllm serve vllm-ascend/DeepSeek-V3.2-Exp-W8A8 \
 --quantization ascend \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.9 \
---additional-config '{"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
 ```
 
 **Node 1**
@@ -299,7 +299,7 @@ vllm serve vllm-ascend/DeepSeek-V3.2-Exp-W8A8 \
 --quantization ascend \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.92 \
---additional-config '{"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]}}'
 ```
 
 ::::

docs/source/tutorials/multi_node.md

@@ -137,7 +137,7 @@ vllm serve vllm-ascend/DeepSeek-V3.1-W8A8 \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.9 \
---additional-config '{"torchair_graph_config":{"enabled":true}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
 ```
 
 **Node 1**
@@ -182,7 +182,7 @@ vllm serve vllm-ascend/DeepSeek-V3.1-W8A8 \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.92 \
---additional-config '{"torchair_graph_config":{"enabled":true}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
 ```
 
 The deployment view looks like:

docs/source/tutorials/multi_node_kimi.md

@@ -93,7 +93,7 @@ vllm serve /home/cache/weights/Kimi-K2-Instruct-W8A8 \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.9 \
---additional-config '{"torchair_graph_config":{"enabled":true}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
 ```
 
 **Node 1**
@@ -137,7 +137,7 @@ vllm serve /home/cache/weights/Kimi-K2-Instruct-W8A8 \
 --trust-remote-code \
 --no-enable-prefix-caching \
 --gpu-memory-utilization 0.92 \
---additional-config '{"torchair_graph_config":{"enabled":true}}'
+--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
 ```
 
 The deployment view looks like:

docs/source/tutorials/multi_npu_moge.md

@@ -158,6 +158,11 @@ if __name__ == "__main__":
             'torchair_graph_config': {
             'enabled': True,
             },
+            'ascend_scheduler_config':{
+            'enabled': True,
+            'enable_chunked_prefill' : False,
+            'chunked_prefill_enabled': False
+            },
             })
 
     outputs = llm.generate(prompts, sampling_params)