Merge tag 'v0.10.1.1' into v0.10.1.1-dev

vllm/entrypoints/constants.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Shared constants for vLLM entrypoints.
+"""
+
+# HTTP header limits for h11 parser
+# These constants help mitigate header abuse attacks
+H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT = 4194304  # 4 MB
+H11_MAX_HEADER_COUNT_DEFAULT = 256

vllm/entrypoints/launcher.py

@@ -14,6 +14,8 @@ from vllm import envs
 from vllm.engine.async_llm_engine import AsyncEngineDeadError
 from vllm.engine.multiprocessing import MQEngineDeadError
 from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.constants import (H11_MAX_HEADER_COUNT_DEFAULT,
+                                        H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT)
 from vllm.entrypoints.ssl import SSLCertRefresher
 from vllm.logger import init_logger
 from vllm.utils import find_process_using_port
@@ -26,6 +28,11 @@ async def serve_http(app: FastAPI,
                      sock: Optional[socket.socket],
                      enable_ssl_refresh: bool = False,
                      **uvicorn_kwargs: Any):
+    """
+    Start a FastAPI app using Uvicorn, with support for custom Uvicorn config
+    options.  Supports http header limits via h11_max_incomplete_event_size and
+    h11_max_header_count.
+    """
     logger.info("Available routes are:")
     for route in app.routes:
         methods = getattr(route, "methods", None)
@@ -36,7 +43,21 @@ async def serve_http(app: FastAPI,
 
         logger.info("Route: %s, Methods: %s", path, ', '.join(methods))
 
+    # Extract header limit options if present
+    h11_max_incomplete_event_size = uvicorn_kwargs.pop(
+        "h11_max_incomplete_event_size", None)
+    h11_max_header_count = uvicorn_kwargs.pop("h11_max_header_count", None)
+
+    # Set safe defaults if not provided
+    if h11_max_incomplete_event_size is None:
+        h11_max_incomplete_event_size = H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT
+    if h11_max_header_count is None:
+        h11_max_header_count = H11_MAX_HEADER_COUNT_DEFAULT
+
     config = uvicorn.Config(app, **uvicorn_kwargs)
+    # Set header limits
+    config.h11_max_incomplete_event_size = h11_max_incomplete_event_size
+    config.h11_max_header_count = h11_max_header_count
     config.load()
     server = uvicorn.Server(config)
     _add_shutdown_handlers(app, server)

vllm/entrypoints/openai/api_server.py

@@ -1894,6 +1894,8 @@ async def run_server_worker(listen_address,
             ssl_certfile=args.ssl_certfile,
             ssl_ca_certs=args.ssl_ca_certs,
             ssl_cert_reqs=args.ssl_cert_reqs,
+            h11_max_incomplete_event_size=args.h11_max_incomplete_event_size,
+            h11_max_header_count=args.h11_max_header_count,
             **uvicorn_kwargs,
         )
 

vllm/entrypoints/openai/cli_args.py

@@ -20,6 +20,8 @@ from vllm.config import config
 from vllm.engine.arg_utils import AsyncEngineArgs, optional_type
 from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
                                          validate_chat_template)
+from vllm.entrypoints.constants import (H11_MAX_HEADER_COUNT_DEFAULT,
+                                        H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT)
 from vllm.entrypoints.openai.serving_models import LoRAModulePath
 from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.logger import init_logger
@@ -172,6 +174,12 @@ schema. Example: `[{"type": "text", "text": "Hello world!"}]`"""
     enable_log_outputs: bool = False
     """If set to True, enable logging of model outputs (generations) 
     in addition to the input logging that is enabled by default."""
+    h11_max_incomplete_event_size: int = H11_MAX_INCOMPLETE_EVENT_SIZE_DEFAULT
+    """Maximum size (bytes) of an incomplete HTTP event (header or body) for
+    h11 parser. Helps mitigate header abuse. Default: 4194304 (4 MB)."""
+    h11_max_header_count: int = H11_MAX_HEADER_COUNT_DEFAULT
+    """Maximum number of HTTP headers allowed in a request for h11 parser.
+    Helps mitigate header abuse. Default: 256."""
 
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:

vllm/entrypoints/openai/protocol.py

@@ -20,7 +20,15 @@ from openai.types.chat.chat_completion_message import (
 from openai.types.responses import (ResponseFunctionToolCall,
                                     ResponseInputItemParam, ResponseOutputItem,
                                     ResponsePrompt, ResponseReasoningItem,
-                                    ResponseStatus, ResponseTextConfig)
+                                    ResponseStatus)
+
+# Backward compatibility for OpenAI client versions
+try:  # For older openai versions (< 1.100.0)
+    from openai.types.responses import ResponseTextConfig
+except ImportError:  # For newer openai versions (>= 1.100.0)
+    from openai.types.responses import (ResponseFormatTextConfig as
+                                        ResponseTextConfig)
+
 from openai.types.responses.response import ToolChoice
 from openai.types.responses.tool import Tool
 from openai.types.shared import Metadata, Reasoning

vllm/entrypoints/openai/tool_parsers/qwen3coder_tool_parser.py

@@ -208,15 +208,10 @@ class Qwen3CoderToolParser(ToolParser):
                             "valid JSON object in tool '%s', will try other "
                             "methods to parse it.", param_value, param_name,
                             func_name)
-                try:
-                    converted_value = eval(param_value)
-                    return converted_value
-                except Exception:
-                    logger.warning(
-                        "Parsed value '%s' of parameter '%s' cannot be "
-                        "converted via Python `eval()` in tool '%s', "
-                        "degenerating to string.", param_value, param_name,
-                        func_name)
+                logger.warning(
+                    "Parameter '%s' has unknown type '%s'. "
+                    "The value will be treated as a string.", param_name,
+                    param_type)
                 return param_value
 
         # Extract function name

vllm/model_executor/layers/fused_moe/layer.py

@@ -805,11 +805,11 @@ class FusedMoE(CustomOp):
         self.global_num_experts = num_experts + num_redundant_experts
 
         # we padding globally so EP buffer allocation works
-        if (quant_config and quant_config.get_name() == "mxfp4"
-                and (current_platform.is_rocm()
-                     or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
-                     or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16)):
-            hidden_size = round_up(hidden_size, 256)
+        if quant_config and quant_config.get_name() == "mxfp4":
+            from vllm.model_executor.layers.quantization.mxfp4 import (  # noqa: E501
+                should_use_flashinfer_mxfp4)
+            if current_platform.is_rocm() or should_use_flashinfer_mxfp4():
+                hidden_size = round_up(hidden_size, 256)
 
         # For smuggling this layer into the fused moe custom op
         compilation_config = vllm_config.compilation_config

vllm/model_executor/layers/quantization/mxfp4.py

@@ -6,6 +6,7 @@ import torch
 from torch.nn.parameter import Parameter
 
 from vllm import envs
+from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEConfig,
                                                   FusedMoEMethodBase)
 from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
@@ -26,12 +27,38 @@ from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils import (has_triton_kernels, is_torch_equal_or_newer,
                         next_power_of_2, round_up)
+from vllm.utils.flashinfer import has_flashinfer
 
-if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
-        or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
-    # from flashinfer.fused_moe import cutlass_fused_moe
-    from flashinfer import (mxfp8_quantize, shuffle_matrix_a,
-                            shuffle_matrix_sf_a, trtllm_fp4_block_scale_moe)
+logger = init_logger(__name__)
+
+
+def _should_use_flashinfer_mxfp4_bf16():
+    """Determine if FlashInfer MXFP4 BF16 should be used."""
+    # If explicitly set, respect the setting
+    if envs.is_set("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16"):
+        return envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16
+
+    # Enable by default on SM100 if MXFP8 is not explicitly enabled
+    if (current_platform.is_device_capability(100) and has_flashinfer()
+            and not envs.is_set("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8")):
+        logger.info_once(
+            "Enabling FlashInfer MXFP4 BF16 backend by default for Blackwell. "
+            "For faster performance, consider setting "
+            "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1, "
+            "though this may impact accuracy.")
+        return True
+
+    return False
+
+
+def _should_use_flashinfer_mxfp4_mxfp8():
+    """Determine if FlashInfer MXFP4 MXFP8 should be used."""
+    return envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
+
+
+def should_use_flashinfer_mxfp4():
+    return (_should_use_flashinfer_mxfp4_mxfp8()
+            or _should_use_flashinfer_mxfp4_bf16())
 
 
 class Mxfp4Config(QuantizationConfig):
@@ -87,12 +114,18 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         self.moe = moe
         self.use_marlin = self._should_use_marlin()
 
+        if current_platform.is_device_capability(100) and not has_flashinfer():
+            logger.warning_once(
+                "MXFP4 MoE is enabled on Blackwell but FlashInfer "
+                "is not available. This may result in degraded performance. "
+                "Please `pip install vllm[flashinfer]` for best results.")
+
     def _should_use_marlin(self):
         if envs.VLLM_MXFP4_USE_MARLIN is not None:
             return envs.VLLM_MXFP4_USE_MARLIN
         if current_platform.is_cuda() and \
-                not current_platform.has_device_capability(100):
-            if not current_platform.is_device_capability(90):
+                not current_platform.is_device_capability(100):
+            if not current_platform.has_device_capability(90):
                 # marlin kernel has better performance on ampere
                 return True
             if not has_triton_kernels():
@@ -138,8 +171,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             layer.hidden_size = hidden_size
             layer.intermediate_size_per_partition = \
                 intermediate_size_per_partition_after_pad
-        elif (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
-              or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+        elif should_use_flashinfer_mxfp4():
             # pad the intermediate size to be a multiple of 2 * mxfp4_block
             # for to hold non-uniform sharded tensor as well as swizzling
             # other padding to increase performance
@@ -230,8 +262,8 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
     def process_weights_after_loading(self, layer):
         if self.use_marlin:
             prepare_moe_fp4_layer_for_marlin(layer)
-        elif (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
-              or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+        elif should_use_flashinfer_mxfp4():
+            from flashinfer import shuffle_matrix_a, shuffle_matrix_sf_a
             layer.gemm1_alpha = Parameter(torch.tensor(
                 [1.702] * self.num_experts, dtype=torch.float32).cuda(),
                                           requires_grad=False)
@@ -478,11 +510,11 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             logical_replica_count), (
                 "MXFP4 are not supported with this configuration.")
 
-        if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
-                or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
+        if should_use_flashinfer_mxfp4():
+            from flashinfer import mxfp8_quantize, trtllm_fp4_block_scale_moe
             assert not self.moe.use_ep, (
                 "EP is not supported for flashinfer mxfp4 moe backend yet.")
-            if envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16:
+            if _should_use_flashinfer_mxfp4_bf16():
                 assert x.dtype == torch.bfloat16
                 x_quant = x
                 x_scale = None

vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -21,7 +21,7 @@ logger = init_logger(__name__)
 
 class CutlassMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]):
     # enable full CUDA Graph support for decode-only capture
-    attn_cudagraph_support: ClassVar[
+    cudagraph_support: ClassVar[
         AttentionCGSupport] = AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE