Get rid of server_args in internal iterfaces

.github/workflows/e2e-test.yml

@@ -35,6 +35,14 @@ jobs:
         pip install -e "python[all]"
         pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ --force-reinstall
 
+    - name: Set PYTHONPATH
+      run: |
+        echo "PYTHONPATH=$PYTHONPATH:$(pwd)/python" >> $GITHUB_ENV      
+
+    - name: Verify import
+      run: |
+        python3 -c "import sglang.srt.serving"    
+      
     - name: Benchmark Serving Throughput
       run: |
         cd test/srt

.gitignore

@@ -50,6 +50,7 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 cover/
+human-eval/
 
 # Translations
 *.mo

examples/usage/llava_video/srt_example_llava_v.py

@@ -184,20 +184,20 @@ def batch(video_dir, save_dir, cur_chunk, num_chunks, num_frames=16, batch_size=
         print("Invalid model path. Please specify a valid model path.")
         exit()
 
-    model_overide_args = {}
+    model_override_args = {}
 
-    model_overide_args["mm_spatial_pool_stride"] = args.mm_spatial_pool_stride
-    model_overide_args["architectures"] = ["LlavaVidForCausalLM"]
-    model_overide_args["num_frames"] = args.num_frames
-    model_overide_args["model_type"] = "llava"
+    model_override_args["mm_spatial_pool_stride"] = args.mm_spatial_pool_stride
+    model_override_args["architectures"] = ["LlavaVidForCausalLM"]
+    model_override_args["num_frames"] = args.num_frames
+    model_override_args["model_type"] = "llava"
 
     if "34b" in args.model_path.lower():
-        model_overide_args["image_token_index"] = 64002
+        model_override_args["image_token_index"] = 64002
 
     if args.num_frames == 32:
-        model_overide_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
-        model_overide_args["max_sequence_length"] = 4096 * 2
-        model_overide_args["tokenizer_model_max_length"] = 4096 * 2
+        model_override_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
+        model_override_args["max_sequence_length"] = 4096 * 2
+        model_override_args["tokenizer_model_max_length"] = 4096 * 2
     elif args.num_frames < 32:
         pass
     else:
@@ -211,7 +211,7 @@ def batch(video_dir, save_dir, cur_chunk, num_chunks, num_frames=16, batch_size=
         tokenizer_path=tokenizer_path,
         port=cur_port,
         additional_ports=[cur_port + 1, cur_port + 2, cur_port + 3, cur_port + 4],
-        model_overide_args=model_overide_args,
+        model_override_args=model_override_args,
         tp_size=1,
     )
     sgl.set_default_backend(runtime)

examples/usage/llm_engine.py

@@ -16,5 +16,5 @@
 outputs = llm.generate(prompts, sampling_params)
 # Print the outputs.
 for prompt, output in zip(prompts, outputs):
-    print('===============================')
-    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
+    print("===============================")
+    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

python/sglang/__init__.py

@@ -2,8 +2,8 @@
 
 from sglang.api import (
     LLM,
-    SamplingParams,
     Runtime,
+    SamplingParams,
     assistant,
     assistant_begin,
     assistant_end,

python/sglang/api.py

@@ -18,8 +18,9 @@
     SglSelect,
     SglVideo,
 )
-from sglang.srt.serving.engine import LLM
 from sglang.srt.sampling_params import SamplingParams
+from sglang.srt.serving.engine import LLM
+
 
 def function(
     func: Optional[Callable] = None, num_api_spec_tokens: Optional[int] = None
@@ -36,7 +37,7 @@ def decorator(func):
 def Runtime(*args, **kwargs):
     # Avoid importing unnecessary dependency
     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-    from sglang.srt.server import Runtime
+    from sglang.srt.serving.server import Runtime
 
     return Runtime(*args, **kwargs)
 

python/sglang/bench_latency.py

@@ -49,13 +49,18 @@
 import torch
 import torch.distributed as dist
 
+from sglang.srt.config import (
+    ModelConfig,
+    OptimizationConfig,
+    ParallelConfig,
+    ScheduleConfig,
+)
 from sglang.srt.hf_transformers_utils import get_tokenizer
 from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
-from sglang.srt.model_config import ModelConfig
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.model_executor.model_runner import ModelRunner
 from sglang.srt.sampling_params import SamplingParams
-from sglang.srt.server_args import ServerArgs
+from sglang.srt.serving.server_args import ServerArgs
 from sglang.srt.utils import suppress_other_loggers
 
 
@@ -111,15 +116,19 @@ def load_model(server_args, tp_rank):
     suppress_other_loggers()
     rank_print = print if tp_rank == 0 else lambda *args, **kwargs: None
 
-    model_config = ModelConfig(path=server_args.model_path)
+    model_config = ModelConfig(model_path=server_args.model_path)
+    optimization_config = OptimizationConfig()
+    parallel_config = ParallelConfig(tp_size=server_args.tp_size, nccl_ports=[28888])
+    schedule_config = ScheduleConfig(
+        mem_fraction_static=server_args.mem_fraction_static
+    )
     model_runner = ModelRunner(
         model_config=model_config,
-        mem_fraction_static=server_args.mem_fraction_static,
+        optimization_config=optimization_config,
+        parallel_config=parallel_config,
+        schedule_config=schedule_config,
         gpu_id=tp_rank,
         tp_rank=tp_rank,
-        tp_size=server_args.tp_size,
-        nccl_port=28888,
-        server_args=server_args,
     )
     rank_print(f"max_total_num_tokens={model_runner.max_total_num_tokens}")
     tokenizer = get_tokenizer(

python/sglang/launch_server.py

@@ -2,8 +2,8 @@
 
 import argparse
 
-from sglang.srt.server import launch_server
-from sglang.srt.server_args import ServerArgs
+from sglang.srt.serving.server import launch_server
+from sglang.srt.serving.server_args import ServerArgs
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

python/sglang/launch_server_llavavid.py

@@ -2,28 +2,29 @@
 
 import argparse
 
-from sglang.srt.server import ServerArgs, launch_server
+from sglang.srt.serving.server import ServerArgs, launch_server
 
 if __name__ == "__main__":
-    model_overide_args = {}
-
-    model_overide_args["mm_spatial_pool_stride"] = 2
-    model_overide_args["architectures"] = ["LlavaVidForCausalLM"]
-    model_overide_args["num_frames"] = 16
-    model_overide_args["model_type"] = "llavavid"
-    if model_overide_args["num_frames"] == 32:
-        model_overide_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
-        model_overide_args["max_sequence_length"] = 4096 * 2
-        model_overide_args["tokenizer_model_max_length"] = 4096 * 2
-        model_overide_args["model_max_length"] = 4096 * 2
+    model_override_args = {}
+
+    model_override_args["mm_spatial_pool_stride"] = 2
+    model_override_args["architectures"] = ["LlavaVidForCausalLM"]
+    model_override_args["num_frames"] = 16
+    model_override_args["model_type"] = "llavavid"
+    if model_override_args["num_frames"] == 32:
+        model_override_args["rope_scaling"] = {"factor": 2.0, "type": "linear"}
+        model_override_args["max_sequence_length"] = 4096 * 2
+        model_override_args["tokenizer_model_max_length"] = 4096 * 2
+        model_override_args["model_max_length"] = 4096 * 2
 
     parser = argparse.ArgumentParser()
     ServerArgs.add_cli_args(parser)
     args = parser.parse_args()
 
     if "34b" in args.model_path.lower():
-        model_overide_args["image_token_index"] = 64002
+        model_override_args["image_token_index"] = 64002
 
     server_args = ServerArgs.from_cli_args(args)
+    server_args.model_override_args = model_override_args
 
-    launch_server(server_args, model_overide_args, None)
+    launch_server(server_args, None)