[Minor] Code style improvements (#2355)

python/sglang/srt/layers/torchao_utils.py

@@ -2,12 +2,10 @@
 Common utilities for torchao.
 """
 
-from typing import Dict, Set
-
 import torch
 
 
-def apply_torchao_config_to_model_(
+def apply_torchao_config_to_model(
     model: torch.nn.Module, torchao_config: str, filter_fn=None
 ):
     """Quantize a modelwith torchao quantization specified by torchao_config
@@ -21,13 +19,19 @@ def apply_torchao_config_to_model_(
     # Lazy import to suppress some warnings
     from torchao.quantization import (
         float8_dynamic_activation_float8_weight,
+        float8_weight_only,
         int4_weight_only,
         int8_dynamic_activation_int8_weight,
         int8_weight_only,
         quantize_,
     )
     from torchao.quantization.observer import PerRow, PerTensor
 
+    if filter_fn is None:
+
+        def filter_fn(module, fqn):
+            return "proj" in fqn
+
     if torchao_config == "" or torchao_config is None:
         return model
     elif "int8wo" in torchao_config:
@@ -44,8 +48,6 @@ def apply_torchao_config_to_model_(
         ], f"int4wo groupsize needs to be one of [32, 64, 128, 256] but got {group_size}"
         quantize_(model, int4_weight_only(group_size=group_size), filter_fn=filter_fn)
     elif "fp8wo" in torchao_config:
-        from torchao.quantization import float8_weight_only
-
         # this requires newer hardware
         # [rank0]: AssertionError: fp8e4nv data type is not supported on CUDA arch < 89
         quantize_(model, float8_weight_only(), filter_fn=filter_fn)

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -47,7 +47,7 @@ def _to_torch(model: torch.nn.Module, reverse: bool, batch_size: int):
                 if "FusedMoE" in sub.__class__.__name__:
                     if batch_size == 1:
                         # The performance of torch.compile on this layer is not always good when bs > 1,
-                        # so we decide to skip it for now.
+                        # so we decide to only use torch.compile when bs =1
                         sub._forward_method = fused_moe_forward_native
                 else:
                     sub._forward_method = sub.forward_native

python/sglang/srt/model_executor/model_runner.py

@@ -27,7 +27,6 @@
     initialize_model_parallel,
     set_custom_all_reduce,
 )
-from vllm.distributed.parallel_state import in_the_same_node_as
 
 from sglang.srt.configs.device_config import DeviceConfig
 from sglang.srt.configs.load_config import LoadConfig
@@ -38,7 +37,7 @@
 from sglang.srt.layers.attention.triton_backend import TritonAttnBackend
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.sampler import Sampler
-from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model_
+from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
 from sglang.srt.lora.lora_manager import LoRAManager
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.mem_cache.memory_pool import (
@@ -112,11 +111,13 @@ def __init__(
             )
 
         if self.is_multimodal:
-            logger.info(
-                "Automatically turn off --chunked-prefill-size and adjust --mem-fraction-static for multimodal models."
-            )
             server_args.chunked_prefill_size = -1
             self.mem_fraction_static *= 0.95
+            logger.info(
+                f"Automatically reduce --mem-fraction-static to {self.mem_fraction_static} "
+                f"and turn off chunked prefill "
+                f"because this is a multimodal model."
+            )
             # TODO: qwen2-vl does not support radix cache now, set disable_radix_cache=True automatically
             if self.model_config.hf_config.architectures == [
                 "Qwen2VLForConditionalGeneration"
@@ -160,11 +161,8 @@ def __init__(
         else:
             self.torch_tp_applied = False
 
-        def filter_fn(module, fqn):
-            return "proj" in fqn
-
-        apply_torchao_config_to_model_(
-            self.model, global_server_args_dict["torchao_config"], filter_fn
+        apply_torchao_config_to_model(
+            self.model, global_server_args_dict["torchao_config"]
         )
 
         # Init memory pool and attention backends