fix: align enable_overlap_scheduler naming between code and docs (#2038)

docs/backend/backend.md

@@ -79,7 +79,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 ```
 python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
 ```
-- To enable the experimental overlapped scheduler, add `--enable-overlap-scheduler`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly.
+- To enable the experimental overlapped scheduler, add `--enable-overlap-schedule`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currenly.
 - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.

docs/references/hyperparameter_tuning.md

@@ -31,7 +31,7 @@ If you see out of memory (OOM) errors, you can try to tune the following paramet
 - You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding.
 
 ### Try Advanced Options
-- To enable the experimental overlapped scheduler, add `--enable-overlap-scheduler`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly.
+- To enable the experimental overlapped scheduler, add `--enable-overlap-schedule`. It overlaps CPU scheduler with GPU computation and can accelerate almost all workloads. This does not work for constrained decoding currenly.
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. This does not work for FP8 currenly.
 
 ### Tune `--schedule-policy`

python/sglang/test/test_utils.py

@@ -673,7 +673,7 @@ def run_and_check_memory_leak(
     if enable_mixed_chunk:
         other_args += ["--enable-mixed-chunk"]
     if enable_overlap:
-        other_args += ["--enable-overlap-scheduler"]
+        other_args += ["--enable-overlap-schedule"]
 
     model = DEFAULT_MODEL_NAME_FOR_TEST
     port = random.randint(4000, 5000)