[NPU] [DOC] fix typos, heading levels and terminology in NPU docs (#40402)

docs/docs/hardware-platforms/ascend-npus/development/operator_development.mdx

@@ -37,7 +37,7 @@ sgl-kernel-npu/
 │       ├── op_host/               # Host-side code (validation, tiling, launch)
 │       │   ├── <op_name>.cpp
 │       │   └── tiling/            # Optional: tiling data
-│       └── op_kernel/             # Device-side code (Ascend C kernel on AICore)
+│       └── op_kernel/             # Device-side code (Ascend C kernel on AI Core)
 │           └── <op_name>_kernel.cpp
 ├── include/
 │   └── sgl_kenel_npu_ops.h        # C++ interface declarations
@@ -62,7 +62,7 @@ sgl-kernel-npu/
 
 A complete Ascend C operator consists of two parts:
 
-- **Device part**: Kernel code running on the NPU AICore, responsible for actual
+- **Device part**: Kernel code running on the NPU AI Core, responsible for actual
   computation. Written using the Ascend C API.
 - **Host part**: Code running on the CPU, responsible for parameter validation,
   data pre-processing, tiling, and kernel launch.
@@ -86,7 +86,7 @@ csrc/<op_name>/
 
 ### Step 2: Write the Device-side Kernel (op_kernel)
 
-Device-side code runs on AICore and follows the Ascend C programming model. The
+Device-side code runs on AI Core and follows the Ascend C programming model. The
 core structure is a class with `Init()` and `Process()` methods, plus an
 `extern "C"` entry function.
 
@@ -155,7 +155,7 @@ extern "C" __global__ __aicore__ void helloworld(
 
 **Key points:**
 
-- Class methods must be marked with `__aicore__`, indicating they run on AICore.
+- Class methods must be marked with `__aicore__`, indicating they run on AI Core.
 - Use `AscendC::TPipe` + `AscendC::TQue` to build a pipeline that overlaps data
   movement and computation.
 - The entry function must be declared `extern "C" __global__ __aicore__`. The

docs/docs/hardware-platforms/ascend-npus/evaluation/accuracy_evaluation.mdx

@@ -267,6 +267,7 @@ This is a common workaround for corporate proxy environments. If it does not res
 If you cannot obtain the CA certificate, download datasets manually as shown in [Download Dataset Error](#download-dataset-error) below.
 
 ### EvalScope Request Retry Timeout
+
 If EvalScope keeps retrying requests with errors like:
 ```
 2026-06-22 03:09:03 - evalscope - WARNING: Attempt 4 / 5 failed: ....... Retrying...

docs/docs/hardware-platforms/ascend-npus/faq.mdx

@@ -42,13 +42,13 @@ Thread 0x0000fff873f6f120 (most recent call first):
 (SGLangEngine pid=3872176) [ERROR] 2026-04-14-12:14:41 (PID:3874122, Device:0, RankID:-1) ERR00100 PTA call acl api failed
 (SGLangEngine pid=3872176) [Error]: An internal error occurs in the runtime module on the host.
 (SGLangEngine pid=3872176) Rectify the fault based on the error information in the ascend log.
-(SGLangEngine pid=3872176) [PID: 3874122] 2026-04-14-12:14:41.897.548 AclNN_Runtime_Error(EZ9903): aclrtLaunchKerneWithHostArgs failed, return: 507000
+(SGLangEngine pid=3872176) [PID: 3874122] 2026-04-14-12:14:41.897.548 AclNN_Runtime_Error(EZ9903): aclrtLaunchKernelWithHostArgs failed, return: 507000
 (SGLangEngine pid=3872176)  Solution: In this scenario, collect the plog when the fault occurs and locate the fault based on the plog.
 (SGLangEngine pid=3872176)  TraceBack (most recent call last):
 (SGLangEngine pid=3872176)  Check kernel task failed, stream_id=2028, task_id=48, retCode=0x7080005.[FUNC:LaunchKernel][FILE:context.cc][LINE:1585]
 (SGLangEngine pid=3872176)  rtsLaunchKernelWithHostArgs execution failed, reason=kernel type error[FUNC:FuncErrorReason][FILE:error_message_manage.cc][LINE:61]
 (SGLangEngine pid=3872176)  rtsLaunchKernelWithHostArgs failed, runtime result = 507000.[FUNC:ReportCallError][FILE:Log_inner.cpp][LINE:148]
-(SGLangEngine pid=3872176)  aclrtLaunchKerneWWithHostArgs failed, return: 507000
+(SGLangEngine pid=3872176)  aclrtLaunchKernelWithHostArgs failed, return: 507000
 (SGLangEngine pid=3872176)  Launch kernel failed.
 (SGLangEngine pid=3872176)  #### KernelLaunch failed: /home/850b160/cann-8.5.8/opp/built-in/op_impl/ai_core/tbe//kernel/ascend910_93/ops_legacy/fill/Fill_41dadce325bOf810d03359af2a38990b_high_performance.o
 (SGLangEngine pid=3872176)  Kernel Run failed. opType: 18, Fill
@@ -109,7 +109,7 @@ prefill 7.log:3094:RuntimeError: ACL stream synchronize failed, error code:50703
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.705 davinci kernel task.cc:14241170327 GetArasInfo:[AIC INFO] aras(0 to 9) after execute:0x3fffffb9000, 0, 0, 0x12c9323ff600, 0x12c93231ee00, 0x12c93f1d8800,0x12c93f3ff600,0x12c958200000,0x100000003, 0xaaaa00000001.
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.710 [davinci kernel task.cc:1427]170327 GetArgsInfo:tilingKey = 0, print 1 Times totalLen=(10*8), argsSize=80, blockDim=3
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.717 [davinci kernel task.cc:1468]170327 PrintErrorInfoForDavinciTask:[AIC INFO] after execute:arqs print end
-[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.751 davinci kernel task.cc:14981170327 PrintErrorInfoForDavinciTask:[DFX INFO]Aicore kernel execute failed, device id=13, stream id=43, report stream id=43, task id=13145, flip num=56, fault kernel_name=alloc_extend _kernel_18, fault kernel info ext=alloc_extend_kernel, program id=141, hash=14069671779787989248.
+[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.751 davinci kernel task.cc:14981170327 PrintErrorInfoForDavinciTask:[DFX INFO]Aicore kernel execute failed, device id=13, stream id=43, report stream id=43, task id=13145, flip num=56, fault kernel_name=alloc_extend_kernel_18, fault kernel info ext=alloc_extend_kernel, program id=141, hash=14069671779787989248.
 [ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.823 [dump manager.cpp:41][tid:170327] An exception callback message is received.
 [ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.971 [kernel info collector.cpp:384][tid:170327] Get error register information. coreNum=0
 [ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.981 kernel info collector.cpp:4771tid:1703271 It is Non-SuperKernel. functionCount=1, qlobalCount=1
@@ -119,7 +119,7 @@ prefill 7.log:3094:RuntimeError: ACL stream synchronize failed, error code:50703
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.911 [stream.cc:1335]170327 GetError:AIV Kernel happen error, retCode=0x31.
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.929 [stream.cc:1335]170327 GetError:[AIC_INFO] after execute:args print end
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.936 stream.cc:13351170327 GetError: DFX INFO1Aicore kernel execute failed, device id=13, stream id=43, report stream id=43, task id=13145, tlip num=56,
- fault kernel_name=alloc_extend_kernel_18, ault kernel info ext=alloc_extend_kernel, program id=141, hash=14069671779787989248.
+ fault kernel_name=alloc_extend_kernel_18, fault kernel info ext=alloc_extend_kernel, program id=141, hash=14069671779787989248.
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.943 [stream.cc:3549]170327 EnterFailureAbort:stream id=43 enter failure abort.
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.973 [stars_engine.cc:1427]170327 StarsResumeRtsa:stop scheduling in abort failure mode: stream id=43, sq id=6,sq head=801, task id=13145, taskType=66.
 [ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.972 [stream.cc:1463]164629 SynchronizeExecutedTask:context is abort, status=0x715005e.

docs/docs/hardware-platforms/ascend-npus/mindspore_backend.mdx

@@ -33,9 +33,9 @@ pip install -e .
 
 Current SGLang-MindSpore supports Qwen3 and DeepSeek V3/R1 models. This doc uses Qwen3-8B as an example.
 
-### Offline infer
+### Offline Inference
 
-Use the following script for offline infer:
+Use the following script for offline inference:
 
 <CodeGroup>
 ```python Offline Inference
@@ -107,7 +107,7 @@ python3 -m sglang.launch_server \
 
 ## Troubleshooting
 
-#### Debug Mode
+### Debug Mode
 
 Enable sglang debug logging by log-level argument.
 
@@ -132,7 +132,7 @@ export GLOG_v=0  # DEBUG
 ```
 </CodeGroup>
 
-#### Explicitly select devices
+### Explicitly select devices
 
 Use the following environment variable to explicitly select the devices to use.
 
@@ -142,7 +142,7 @@ export ASCEND_RT_VISIBLE_DEVICES=4,5,6,7  # to set device
 ```
 </CodeGroup>
 
-#### Some communication environment issues
+### Some communication environment issues
 
 In case of some environment with special communication environment, users need set some environment variables.
 
@@ -152,7 +152,7 @@ export MS_ENABLE_LCCL=off # current not support LCCL communication mode in SGLan
 ```
 </CodeGroup>
 
-#### Some dependencies of protobuf
+### Some dependencies of protobuf
 
 In case of some environment with special protobuf version, users need set some environment variables to avoid binary version mismatch.
 

docs/docs/hardware-platforms/ascend-npus/optimization/ring_sp_performance.mdx

@@ -1,7 +1,7 @@
 ---
 title: "Ring-SP Performance (Wan2.1-T2V-1.3B)"
 metatags:
-    description: "This page reports Ring-SP performance on Ascend NPU with torchnpu==2.10.0."
+    description: "This page reports Ring-SP performance on Ascend NPU with torch_npu==2.10.0."
 ---
 
 This page reports Ring-SP performance on Ascend NPU with `torch_npu==2.10.0`.

docs/docs/supported-models/mindspore_models.mdx

@@ -37,7 +37,7 @@ Current SGLang-MindSpore supports Qwen3 and DeepSeek V3/R1 models. This doc uses
 
 Use the following script for offline inference:
 
-```python Offline Infer
+```python Offline Inference
 import sglang as sgl
 
 # Initialize the engine with MindSpore backend