Merge tag 'v0.15.1' into v0.15.1-dev

vllm/model_executor/layers/fused_moe/utils.py

@@ -370,6 +370,11 @@ def apply_moe_activation(
         torch.ops._C.gelu_and_mul(output, input)
     elif activation == "swigluoai":
         torch.ops._C.swigluoai_and_mul(output, input)
+    elif activation == "swiglustep":
+        from vllm.model_executor.layers.activation import swiglustep_and_mul_triton
+
+        swiglustep_and_mul_triton(output, input)
+
     # Activations without gated multiplication
     elif activation == SILU_NO_MUL:
         output.copy_(F.silu(input))

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -27,7 +27,6 @@ from vllm.model_executor.layers.fused_moe import (
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEQuantConfig,
-    RoutingMethodType,
     int4_w4a16_moe_quant_config,
     int4_w4afp8_moe_quant_config,
     int8_w8a8_moe_quant_config,
@@ -1043,17 +1042,9 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
         if self.block_quant:
             import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe  # noqa: E501, F401
 
-            e_score_correction_bias = (
-                layer.e_score_correction_bias.to(x.dtype)
-                if layer.e_score_correction_bias is not None
-                else None
-            )
-            routing_method_type = layer.routing_method_type
             return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
-                routing_logits=router_logits.to(torch.float32)
-                if routing_method_type == RoutingMethodType.DeepSeekV3
-                else router_logits,
-                routing_bias=e_score_correction_bias,
+                routing_logits=router_logits,
+                routing_bias=layer.e_score_correction_bias,
                 x=x,
                 w13_weight=layer.w13_weight,
                 w13_weight_scale_inv=layer.w13_weight_scale,
@@ -1067,7 +1058,7 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                 expert_offset=layer.ep_rank * layer.local_num_experts,
                 local_num_experts=layer.local_num_experts,
                 block_shape=self.weight_block_size,
-                routing_method_type=routing_method_type,
+                routing_method_type=layer.routing_method_type,
                 routed_scaling=layer.routed_scaling_factor,
             )
         else:

vllm/model_executor/layers/quantization/fp8.py

@@ -26,7 +26,6 @@ from vllm.model_executor.layers.fused_moe import (
 )
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
-    RoutingMethodType,
 )
 from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
 from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
@@ -964,17 +963,9 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         if self.block_quant:
             import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe  # noqa: E501, F401
 
-            e_score_correction_bias = (
-                layer.e_score_correction_bias.to(x.dtype)
-                if layer.e_score_correction_bias is not None
-                else None
-            )
-            routing_method_type = layer.routing_method_type
             return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
-                routing_logits=router_logits.to(torch.float32)
-                if routing_method_type == RoutingMethodType.DeepSeekV3
-                else router_logits,
-                routing_bias=e_score_correction_bias,
+                routing_logits=router_logits,
+                routing_bias=layer.e_score_correction_bias,
                 x=x,
                 w13_weight=layer.w13_weight,
                 w13_weight_scale_inv=layer.w13_weight_scale_inv,
@@ -988,7 +979,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                 expert_offset=layer.ep_rank * layer.local_num_experts,
                 local_num_experts=layer.local_num_experts,
                 block_shape=self.weight_block_size,
-                routing_method_type=routing_method_type,
+                routing_method_type=layer.routing_method_type,
                 routed_scaling=layer.routed_scaling_factor,
             )
         else:

vllm/model_executor/layers/quantization/kernels/scaled_mm/rocm.py

@@ -28,6 +28,7 @@ def rocm_per_tensor_float_w8a8_scaled_mm_impl(
         A.shape[0] == 1
         and B.shape[1] % 16 == 0
         and ((bias is None) or (bias.dtype == out_dtype))
+        and A.is_contiguous()
     ):
         output = ops.wvSplitKQ(
             B.t(),

vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py

@@ -6,7 +6,6 @@ from typing import TYPE_CHECKING
 
 import torch
 
-import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
@@ -22,10 +21,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     swizzle_blockscale,
 )
 from vllm.platforms import current_platform
-from vllm.utils.flashinfer import (
-    has_flashinfer_cutedsl_grouped_gemm_nt_masked,
-    has_flashinfer_cutlass_fused_moe,
-)
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
@@ -36,8 +31,6 @@ logger = init_logger(__name__)
 
 
 __all__ = [
-    "is_flashinfer_fp4_cutlass_moe_available",
-    "is_flashinfer_fp4_cutedsl_moe_available",
     "reorder_w1w3_to_w3w1",
 ]
 
@@ -122,26 +115,6 @@ def is_supported_config_trtllm(
     return True, None
 
 
-def is_flashinfer_fp4_cutlass_moe_available() -> bool:
-    """Return `True` when FlashInfer CUTLASS NV-FP4 kernels can be used."""
-    return (
-        envs.VLLM_USE_FLASHINFER_MOE_FP4
-        and has_flashinfer_cutlass_fused_moe()
-        and current_platform.is_cuda()
-        and current_platform.has_device_capability(100)
-    )
-
-
-def is_flashinfer_fp4_cutedsl_moe_available() -> bool:
-    """Return ``True`` when FlashInfer CUTEDSL NV-FP4 kernels can be used."""
-    return (
-        envs.VLLM_USE_FLASHINFER_MOE_FP4
-        and has_flashinfer_cutedsl_grouped_gemm_nt_masked()
-        and current_platform.is_cuda()
-        and current_platform.is_device_capability_family(100)
-    )
-
-
 def reorder_w1w3_to_w3w1(
     weight: torch.Tensor, scale: torch.Tensor, dim: int = -2
 ) -> tuple[torch.Tensor, torch.Tensor]:

vllm/model_executor/layers/quantization/utils/nvfp4_moe_support.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from dataclasses import dataclass
-
-import vllm.envs as envs
-from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
-    is_flashinfer_fp4_cutedsl_moe_available,
-    is_flashinfer_fp4_cutlass_moe_available,
-)
-from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
-    is_fp4_marlin_supported,
-)
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    cutlass_fp4_supported,
-)
-
-__all__ = ["detect_nvfp4_moe_support", "NvFp4Support"]
-
-_logger = init_logger(__name__)
-
-
-@dataclass(frozen=True)
-class NvFp4Support:
-    """Result container for NV-FP4 capability probing."""
-
-    cutlass_supported: bool
-    allow_flashinfer: bool
-    use_marlin: bool
-
-
-def detect_nvfp4_moe_support(class_name: str = "") -> NvFp4Support:
-    """Detect platform support for NV-FP4 fused-MoE path"""
-    cutlass_supported = cutlass_fp4_supported()
-
-    allow_flashinfer = cutlass_supported and (
-        is_flashinfer_fp4_cutlass_moe_available()
-        or is_flashinfer_fp4_cutedsl_moe_available()
-    )
-
-    if allow_flashinfer:
-        _logger.info_once(
-            "Using FlashInfer kernels for %s.", class_name or "NVFP4 path"
-        )
-    else:
-        if envs.VLLM_USE_FLASHINFER_MOE_FP4:
-            _logger.warning_once(
-                "FlashInfer kernels unavailable for %s on current platform.",
-                class_name or "NVFP4 path",
-            )
-
-    use_marlin = False
-    if not cutlass_supported:
-        if is_fp4_marlin_supported():
-            use_marlin = True
-            _logger.info_once("Falling back to Marlin FP4 MoE kernel.")
-        else:
-            raise ValueError(
-                "Current platform does not support NVFP4 quantization. "
-                "Please use Blackwell GPUs or enable FlashInfer."
-            )
-
-    return NvFp4Support(
-        cutlass_supported=cutlass_supported,
-        allow_flashinfer=allow_flashinfer,
-        use_marlin=use_marlin,
-    )

vllm/model_executor/layers/utils.py

@@ -146,6 +146,7 @@ def rocm_unquantized_gemm_impl(
             and n <= 128
             and k > 512
             and math.ceil(k / 512) * math.ceil(m / 16) < get_cu_count()
+            and x.is_contiguous()
         )
         # k == 2880 and (m == 640 or m == 128))
     )
@@ -165,6 +166,7 @@ def rocm_unquantized_gemm_impl(
         and on_gfx9()
         and x.dtype in [torch.float16, torch.bfloat16]
         and k % 8 == 0
+        and x.is_contiguous()
     )
 
     if use_skinny is not True:

vllm/model_executor/models/adapters.py

@@ -466,6 +466,7 @@ def load_weights_using_from_2_way_softmax(
 
     language_model = _get_language_model_for_seq_cls(model)
     is_vlm = language_model is not model
+    using_vlm_head = is_vlm and hasattr(language_model, "score")
 
     language_model.lm_head = ParallelLMHead(
         text_config.vocab_size, text_config.hidden_size, quant_config=quant_config
@@ -506,14 +507,16 @@ def load_weights_using_from_2_way_softmax(
         torch.float32
     ) - lm_head_weight.data[[false_id]].to(torch.float32)
 
-    score_layer = language_model.score if is_vlm else model.score
+    score_layer = language_model.score if using_vlm_head else model.score
     param = score_layer.weight
     weight_loader = getattr(param, "weight_loader", default_weight_loader)
     weight_loader(param, score_weight)
 
     del language_model.lm_head
 
-    score_weight_name = "language_model.score.weight" if is_vlm else "score.weight"
+    score_weight_name = (
+        "language_model.score.weight" if using_vlm_head else "score.weight"
+    )
     loaded_weights.add(score_weight_name)
 
     lm_head_name = "lm_head.weight"
@@ -537,6 +540,7 @@ def load_weights_no_post_processing(model, weights: Iterable[tuple[str, torch.Te
 
     language_model = _get_language_model_for_seq_cls(model)
     is_vlm = language_model is not model
+    using_vlm_head = is_vlm and hasattr(language_model, "score")
 
     language_model.lm_head = ParallelLMHead(
         text_config.vocab_size, text_config.hidden_size, quant_config=quant_config
@@ -572,14 +576,16 @@ def load_weights_no_post_processing(model, weights: Iterable[tuple[str, torch.Te
     token_ids = [tokenizer.convert_tokens_to_ids(t) for t in tokens]
     score_weight = language_model.lm_head.weight.data[token_ids]
 
-    score_layer = language_model.score if is_vlm else model.score
+    score_layer = language_model.score if using_vlm_head else model.score
     param = score_layer.weight
     weight_loader = getattr(param, "weight_loader", default_weight_loader)
     weight_loader(param, score_weight)
 
     del language_model.lm_head
 
-    score_weight_name = "language_model.score.weight" if is_vlm else "score.weight"
+    score_weight_name = (
+        "language_model.score.weight" if using_vlm_head else "score.weight"
+    )
     loaded_weights.add(score_weight_name)
 
     lm_head_name = "lm_head.weight"

vllm/model_executor/models/minimax_m2.py

@@ -107,6 +107,7 @@ class MiniMaxM2MoE(nn.Module):
             renormalize=True,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
+            router_logits_dtype=torch.float32,
         )
 
         self.gate = ReplicatedLinear(

vllm/model_executor/models/nemotron_parse.py

@@ -11,7 +11,6 @@ import math
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Literal
 
-import cv2
 import numpy as np
 import torch
 import torch.nn as nn
@@ -416,6 +415,8 @@ class NemotronParseImageProcessor:
         else:
             self.target_height = self.target_width = int(self.final_size)
 
+        import cv2
+
         self.transform = A.Compose(
             [
                 A.PadIfNeeded(
@@ -457,6 +458,8 @@ class NemotronParseImageProcessor:
             new_height = int(new_width / aspect_ratio)
 
         # Use cv2.INTER_LINEAR like the original
+        import cv2
+
         return cv2.resize(
             image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
         )

vllm/model_executor/models/registry.py

@@ -189,6 +189,7 @@ _TEXT_GENERATION_MODELS = {
     "SeedOssForCausalLM": ("seed_oss", "SeedOssForCausalLM"),
     "Step1ForCausalLM": ("step1", "Step1ForCausalLM"),
     "Step3TextForCausalLM": ("step3_text", "Step3TextForCausalLM"),
+    "Step3p5ForCausalLM": ("step3p5", "Step3p5ForCausalLM"),
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
@@ -479,6 +480,7 @@ _SPECULATIVE_DECODING_MODELS = {
     "MedusaModel": ("medusa", "Medusa"),
     "OpenPanguMTPModel": ("openpangu_mtp", "OpenPanguMTP"),
     "Qwen3NextMTP": ("qwen3_next_mtp", "Qwen3NextMTP"),
+    "Step3p5MTP": ("step3p5_mtp", "Step3p5MTP"),
     # Temporarily disabled.
     # # TODO(woosuk): Re-enable this once the MLP Speculator is supported in V1.
     # "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),

vllm/model_executor/models/step3p5.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Jurassic model."""
+
+from collections.abc import Iterable
+from typing import Any
+
+import torch
+from torch import nn
+from torch.nn.parameter import Parameter
+
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import (
+    get_dp_group,
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    get_tp_group,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE,
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
+
+from .interfaces import MixtureOfExperts, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    WeightsMapper,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class FP32ReplicatedLinear(ReplicatedLinear):
+    """
+    Use FP32 for higher precision.
+    """
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor | tuple[torch.Tensor, Parameter | None]:
+        assert self.params_dtype == torch.float32
+        return super().forward(x.to(torch.float32))
+
+
+class Step3p5MLP(nn.Module):
+    def __init__(
+        self,
+        config: ModelConfig,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+        self.prefix = prefix
+        self.hidden_size = hidden_size
+        self.limit = None
+        layer_idx = extract_layer_index(prefix)
+        if (
+            config.swiglu_limits_shared
+            and config.swiglu_limits_shared[layer_idx] is not None
+            and config.swiglu_limits_shared[layer_idx] != 0
+        ):
+            self.limit = config.swiglu_limits_shared[layer_idx]
+            self.act_fn = SwigluStepAndMul(limit=self.limit)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(hidden_states)
+        intermediate_act = self.act_fn(gate_up)
+        output, _ = self.down_proj(intermediate_act)
+        return output
+
+
+class Step3p5Attention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        head_dim: int | None = None,
+        rms_norm_eps: float = 1e-06,
+        qkv_bias: bool = False,
+        rope_theta: float | list[float] | None = 10000,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        rope_scaling: dict[str, Any] | None = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+        # Step3p5 specific args
+        sliding_window: int | None = None,
+        use_head_wise_attn_gate: bool = False,
+        layer_types: list = None,
+        use_rope_layers: list = None,
+        yarn_only_types: list = None,
+        swa_num_attention_heads: int | None = None,
+        partial_rotary_factor: float = 1.0,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.total_num_heads = num_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        self.layer_idx = extract_layer_index(prefix)
+        if layer_types:
+            enable_sliding_window = layer_types[self.layer_idx] == "sliding_attention"
+        else:
+            enable_sliding_window = self.layer_idx % 2 == 0
+        if yarn_only_types and layer_types[self.layer_idx] not in yarn_only_types:
+            rope_scaling = None
+
+        if sliding_window is not None and enable_sliding_window:
+            sliding_window = sliding_window
+            if swa_num_attention_heads is not None:
+                num_heads = swa_num_attention_heads
+                self.total_num_heads = swa_num_attention_heads
+        else:
+            sliding_window = None
+
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[self.layer_idx]
+
+        self.rank = get_tensor_model_parallel_rank()
+        self.partial_rotary_factor = partial_rotary_factor
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=qkv_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        if rope_scaling is not None and not isinstance(rope_scaling, dict):
+            raise ValueError("rope_scaling must be a dict for Step3p5Attention.")
+
+        rope_parameters: dict[str, Any] = (
+            dict(rope_scaling) if rope_scaling is not None else {}
+        )
+        rope_parameters.setdefault("rope_type", "default")
+        rope_parameters["rope_theta"] = self.rope_theta
+        rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            max_position=max_position,
+            rope_parameters=rope_parameters,
+        )
+
+        self.q_norm = GemmaRMSNorm(self.head_dim, rms_norm_eps)
+        self.k_norm = GemmaRMSNorm(self.head_dim, rms_norm_eps)
+        self.use_head_wise_attn_gate = use_head_wise_attn_gate
+        if use_head_wise_attn_gate:
+            self.g_proj = ColumnParallelLinear(
+                hidden_size,
+                self.total_num_heads,
+                bias=False,
+                prefix=f"{prefix}.g_proj",
+            )
+
+        self.use_rope = True
+        if use_rope_layers:
+            self.use_rope = use_rope_layers[self.layer_idx]
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            per_layer_sliding_window=sliding_window,
+            attn_type=attn_type,
+        )
+
+        self.max_position_embeddings = max_position
+        assert self.partial_rotary_factor == 1 or self.partial_rotary_factor == 0.5
+        self.rotary_dim = (
+            self.head_dim if self.partial_rotary_factor == 1 else self.head_dim // 2
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        # Add qk-norm inline similar to Qwen3 MOE attention
+        q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
+        q_by_head = self.q_norm(q_by_head.contiguous())
+        q = q_by_head.view(q.shape)
+
+        k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
+        k_by_head = self.k_norm(k_by_head.contiguous())
+        k = k_by_head.view(k.shape)
+        if self.use_rope:
+            q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        if self.use_head_wise_attn_gate:
+            extra_dims, _ = self.g_proj(hidden_states)
+            output = (
+                attn_output.view(*attn_output.shape[:-1], self.num_heads, self.head_dim)
+                * extra_dims.unsqueeze(-1).sigmoid()
+            )
+            attn_output = output.view(*attn_output.shape)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class FusedMoEBlock(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.layer_idx = extract_layer_index(prefix)
+
+        self.ep_size = get_ep_group().device_group.size()
+        self.ep_rank = get_ep_group().device_group.rank()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        parallel_config = vllm_config.parallel_config
+
+        self.hidden_size = config.hidden_size
+        self.enable_eplb = parallel_config.enable_eplb
+        self.n_routed_experts = config.moe_num_experts
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = parallel_config.eplb_config.num_redundant_experts
+        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+
+        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
+        self.physical_expert_end = (
+            self.physical_expert_start + self.n_local_physical_experts
+        )
+
+        if self.tp_size > config.moe_num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.moe_num_experts}."
+            )
+
+        self.gate = FP32ReplicatedLinear(
+            config.hidden_size,
+            config.moe_num_experts,
+            bias=False,
+            quant_config=None,
+            params_dtype=torch.float32,  # Use FP32 for higher precision.
+            prefix=f"{prefix}.gate",
+        )
+        self.use_moe_router_bias = config.use_moe_router_bias
+        assert self.use_moe_router_bias, "Only support use_moe_router_bias is true."
+        self.routed_scaling_factor = config.moe_router_scaling_factor
+        self.router_bias = nn.Parameter(
+            torch.zeros(config.moe_num_experts, dtype=torch.float32),
+            requires_grad=False,
+        )
+        self.need_fp32_gate = config.need_fp32_gate
+        assert self.need_fp32_gate, (
+            "Router logits must use FP32 precision for numerical stability."
+        )
+
+        activation = "silu"
+        swiglu_limits = config.swiglu_limits or []
+        swiglu_limit = (
+            swiglu_limits[self.layer_idx]
+            if self.layer_idx < len(swiglu_limits)
+            else None
+        )
+        if swiglu_limit not in (None, 0):
+            swiglu_limit = float(swiglu_limit)
+            assert swiglu_limit == 7.0, (
+                "Swiglu limit in fused moe block only suport 7.0 now."
+            )
+            activation = "swiglustep"
+            logger.debug(
+                "step3p5 layer_idx: %s, activation: %s, limit: %s",
+                self.layer_idx,
+                activation,
+                swiglu_limit,
+            )
+
+        self.share_expert = Step3p5MLP(
+            config=config,
+            hidden_size=self.hidden_size,
+            intermediate_size=config.share_expert_dim,
+            hidden_act="silu",
+            reduce_results=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.share_expert",
+        )
+        self.experts = SharedFusedMoE(
+            shared_experts=self.share_expert,
+            gate=self.gate,
+            num_experts=config.moe_num_experts,
+            top_k=config.moe_top_k,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_expert_weight,
+            quant_config=quant_config,
+            activation=activation,
+            prefix=f"{prefix}.experts",
+            scoring_func=getattr(config, "moe_router_activation", "sigmoid"),
+            e_score_correction_bias=self.router_bias,
+            routed_scaling_factor=config.moe_router_scaling_factor,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        if self.experts.is_internal_router:
+            # In this case, the gate/router runs inside the FusedMoE class
+            fused_moe_out = self.experts(
+                hidden_states=hidden_states, router_logits=hidden_states
+            )
+        else:
+            # router_logits: (num_tokens, n_experts)
+            router_logits, _ = self.gate(hidden_states)
+            fused_moe_out = self.experts(
+                hidden_states=hidden_states, router_logits=router_logits
+            )
+
+        shared_output, final_hidden_states = fused_moe_out
+        if self.share_expert is None:
+            assert shared_output is None
+
+        if self.share_expert is not None:
+            assert shared_output is not None
+            final_hidden_states += shared_output
+
+        if self.tp_size > 1:
+            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
+                final_hidden_states
+            )
+
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+
+class Step3p5DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.hidden_size = config.hidden_size
+        layer_idx = extract_layer_index(prefix)
+        self.layer_idx = layer_idx
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        if cache_config is not None:
+            cache_config.sliding_window = None
+        if config.att_impl_type == "GQA":
+            num_attention_heads = None
+            num_attention_groups = None
+            head_dim = None
+            if (
+                getattr(config, "attention_other_setting", None)
+                and getattr(config, "layer_types", [])
+                and config.layer_types[layer_idx]
+                == config.attention_other_setting["attention_type"]
+            ):
+                num_attention_heads = config.attention_other_setting[
+                    "num_attention_heads"
+                ]
+                num_attention_groups = config.attention_other_setting[
+                    "num_attention_groups"
+                ]
+                head_dim = config.attention_other_setting["head_dim"]
+            partial_rotary_factors = getattr(config, "partial_rotary_factors", [])
+            self.self_attn = Step3p5Attention(
+                hidden_size=self.hidden_size,
+                num_heads=num_attention_heads
+                if num_attention_heads
+                else config.num_attention_heads,
+                max_position=config.max_position_embeddings,
+                num_kv_heads=num_attention_groups
+                if num_attention_groups
+                else config.num_attention_groups,
+                rope_theta=config.rope_theta,
+                rms_norm_eps=config.rms_norm_eps,
+                qkv_bias=getattr(config, "attention_bias", False),
+                head_dim=head_dim if head_dim else getattr(config, "head_dim", None),
+                cache_config=cache_config,
+                quant_config=quant_config,
+                rope_scaling=getattr(config, "rope_scaling", None),
+                sliding_window=getattr(config, "sliding_window", None),
+                use_head_wise_attn_gate=getattr(
+                    config, "use_head_wise_attn_gate", False
+                ),
+                layer_types=getattr(config, "layer_types", []),
+                use_rope_layers=getattr(config, "use_rope_layers", []),
+                yarn_only_types=getattr(config, "yarn_only_types", []),
+                partial_rotary_factor=partial_rotary_factors[layer_idx]
+                if partial_rotary_factors
+                else 1.0,
+                prefix=f"{prefix}.self_attn",
+            )
+        else:
+            raise ValueError(
+                f"Unsupported attention implementation: {config.att_impl_type}"
+            )
+        self.use_moe = False
+        self.tp_group = get_tp_group()
+        self.use_fused_all_reduce = (
+            get_tensor_model_parallel_world_size() > 1
+            and get_dp_group().world_size == 1
+        )
+        if self.use_fused_all_reduce:
+            logger.warning_once("Enable custom fused all reduce...")
+        else:
+            logger.warning_once("Disable custom fused all reduce...")
+
+        moe_layers_enum = getattr(config, "moe_layers_enum", None)
+        if moe_layers_enum is not None:
+            moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
+        else:
+            moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+        if layer_idx in moe_layers_idx:
+            self.moe = FusedMoEBlock(
+                vllm_config,
+                prefix=f"{prefix}.moe",
+            )
+            self.use_moe = True
+        else:
+            self.mlp = Step3p5MLP(
+                config=config,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act="silu",
+                quant_config=quant_config,
+                reduce_results=True,
+                prefix=f"{prefix}.mlp",
+            )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(
+            config.hidden_size, config.rms_norm_eps
+        )
+        self.prefix = prefix
+
+    def add_and_maybe_inplace_all_reduce(
+        self, in1: torch.Tensor, in2: torch.Tensor
+    ) -> torch.Tensor:
+        if not self.use_fused_all_reduce:
+            return in1 + in2
+        return self.tp_group.all_reduce(in1 + in2)
+
+    def forward(
+        self, positions: torch.Tensor, hidden_states: torch.Tensor
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states += residual
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        if self.use_moe:
+            ffn_output = self.moe(hidden_states)
+        else:
+            ffn_output = self.mlp(hidden_states)
+        hidden_states = ffn_output + residual
+        return hidden_states
+
+
+@support_torch_compile
+class Step3p5Model(nn.Module):
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        self.vllm_config = vllm_config
+        config = vllm_config.model_config.hf_config
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.moe_num_experts = config.moe_num_experts
+
+        if get_pp_group().is_first_rank or (
+            config.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Step3p5DecoderLayer(
+                vllm_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {
+                    "hidden_states": hidden_states,
+                }
+            )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        config = self.config
+        assert config.num_attention_groups > 1, "Only support GQA"
+        qkv_params_mapping = []
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        expert_params_mapping = [
+            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
+            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
+            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+        ]
+
+        disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
+
+        for name, loaded_weight in weights:
+            if name.startswith("model."):
+                local_name = name[len("model.") :]
+                full_name = name
+            else:
+                local_name = name
+                full_name = f"model.{name}" if name else "model"
+
+            spec_layer = get_spec_layer_idx_from_weight_name(config, full_name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+
+            # Skip any layers beyond the main model's depth (e.g., MTP layers)
+            if full_name.startswith("model.layers."):
+                parts = full_name.split(".")
+                if len(parts) > 2 and parts[2].isdigit():
+                    layer_idx = int(parts[2])
+                    if layer_idx >= config.num_hidden_layers:
+                        continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in local_name:
+                    continue
+                if any(
+                    disable_moe_stacked_param in local_name
+                    for disable_moe_stacked_param in disable_moe_stacked_params
+                ):
+                    continue
+                replaced_name = local_name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(replaced_name, self):
+                    continue
+                if replaced_name not in params_dict:
+                    continue
+                param = params_dict[replaced_name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(replaced_name)
+                break
+            else:
+                for param_name, weight_name, shard_id in expert_params_mapping:
+                    if weight_name not in local_name:
+                        continue
+                    replaced_name = local_name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(replaced_name, self):
+                        continue
+                    if (
+                        replaced_name.endswith(".bias")
+                        or replaced_name.endswith("_bias")
+                    ) and replaced_name not in params_dict:
+                        continue
+                    if replaced_name not in params_dict:
+                        continue
+                    param = params_dict[replaced_name]
+                    weight_loader = param.weight_loader
+                    moe_expert_num = self.moe_num_experts
+                    assert loaded_weight.shape[0] == moe_expert_num
+                    for expert_id in range(moe_expert_num):
+                        loaded_weight_expert = loaded_weight[expert_id]
+                        weight_loader(
+                            param,
+                            loaded_weight_expert,
+                            replaced_name,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                        )
+                    loaded_params.add(replaced_name)
+                    break
+                else:
+                    for (
+                        param_name,
+                        weight_name,
+                        start_idx,
+                        end_idx,
+                    ) in qkv_params_mapping:
+                        if weight_name not in local_name:
+                            continue
+                        replaced_name = local_name.replace(weight_name, param_name)
+                        if is_pp_missing_parameter(replaced_name, self):
+                            continue
+                        if replaced_name not in params_dict:
+                            continue
+                        param = params_dict[replaced_name]
+                        dim = param.shape[param.output_dim]
+                        begin_idx = int(start_idx * dim)
+                        end_idx = int(end_idx * dim)
+                        param_slice = param.narrow(
+                            param.output_dim, begin_idx, end_idx - begin_idx
+                        )
+                        param_slice.copy_(loaded_weight)
+                        loaded_params.add(replaced_name)
+                        break
+                    else:
+                        if is_pp_missing_parameter(local_name, self):
+                            continue
+                        if "expert_bias" in local_name:
+                            logger.warning_once("ignore expert_bias")
+                            continue
+                        if local_name not in params_dict:
+                            continue
+                        param = params_dict[local_name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+                        loaded_params.add(local_name)
+        return loaded_params
+
+
+class Step3p5ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={".share_expert.": ".moe.share_expert."}
+    )
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.vllm_config = vllm_config
+
+        self.model = Step3p5Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        self.moe_layers: list[FusedMoEBlock] = []
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+            assert isinstance(layer, Step3p5DecoderLayer)
+            if hasattr(layer, "moe") and isinstance(layer.moe, FusedMoEBlock):
+                self.moe_layers.append(layer.moe)
+
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                if not lora_config
+                else lora_config.lora_vocab_padding_size,
+            )
+            self.logits_processor = LogitsProcessor(
+                self.unpadded_vocab_size, config.vocab_size
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+        # Set MoE hyperparameters
+        self.expert_weights = []
+        assert len(self.moe_layers) > 0, "No MoE layers found in the model."
+        example_layer = self.moe_layers[0]
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0
+        self.num_logical_experts = example_layer.n_logical_experts
+        self.num_physical_experts = example_layer.n_physical_experts
+        self.num_local_physical_experts = example_layer.n_local_physical_experts
+        self.num_routed_experts = example_layer.n_routed_experts
+        self.num_redundant_experts = example_layer.n_redundant_experts
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ):
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.model.norm(hidden_states)
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_tokens(input_ids)
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        for layer_idx, layer in enumerate(self.moe_layers):
+            experts = layer.experts
+            assert isinstance(experts, FusedMoE)
+            # Register the expert weights.
+            self.expert_weights.append(experts.get_expert_weights())
+            experts.set_eplb_state(
+                moe_layer_idx=layer_idx,
+                expert_load_view=expert_load_view,
+                logical_to_physical_map=logical_to_physical_map,
+                logical_replica_count=logical_replica_count,
+            )
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for layer in self.moe_layers:
+            assert isinstance(layer, FusedMoEBlock)
+            layer.n_local_physical_experts = num_local_physical_experts
+            layer.n_physical_experts = num_physical_experts
+            layer.n_redundant_experts = self.num_redundant_experts
+            layer.experts.update_expert_map()
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: ModelConfig, weight_name: str
+) -> int | None:
+    if hasattr(config, "num_nextn_predict_layers") and (
+        config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            if weight_name.startswith(
+                f"layers.{layer_idx + i}."  # Step3p5Model
+            ) or weight_name.startswith(f"model.layers.{layer_idx + i}."):  # Step3p5MTP
+                return layer_idx + i
+    return None

vllm/model_executor/models/step3p5_mtp.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+
+from .step3p5 import Step3p5DecoderLayer, get_spec_layer_idx_from_weight_name
+from .utils import maybe_prefix
+
+logger = init_logger(__name__)
+
+
+class SharedHead(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        super().__init__()
+        self.norm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.head = ParallelLMHead(
+            config.vocab_size, config.hidden_size, quant_config=quant_config
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.norm(hidden_states)
+
+
+class Step3p5AMultiTokenPredictorLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str,
+    ) -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.enorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.hnorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
+        self.shared_head = SharedHead(config=config, quant_config=quant_config)
+        self.mtp_block = Step3p5DecoderLayer(
+            vllm_config,
+            prefix=f"{prefix}.mtp_block",
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        inputs_embeds = self.enorm(inputs_embeds)
+        previous_hidden_states = self.hnorm(previous_hidden_states)
+
+        hidden_states = self.eh_proj(
+            torch.cat([inputs_embeds, previous_hidden_states], dim=-1)
+        )
+
+        hidden_states = self.mtp_block(positions=positions, hidden_states=hidden_states)
+        return hidden_states
+
+
+class Step3p5AMultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = config.num_nextn_predict_layers
+        # to map the exact layer index from weights
+        self.layers = torch.nn.ModuleDict(
+            {
+                str(idx): Step3p5AMultiTokenPredictorLayer(
+                    vllm_config,
+                    f"{prefix}.layers.{idx}",
+                )
+                for idx in range(
+                    self.mtp_start_layer_idx,
+                    self.mtp_start_layer_idx + self.num_mtp_layers,
+                )
+            }
+        )
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
+            input_ids,
+            positions,
+            previous_hidden_states,
+            inputs_embeds,
+            current_step_idx,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
+        logits = self.logits_processor(
+            mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states)
+        )
+        return logits
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+
+class Step3p5MTP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.vllm_config = vllm_config
+        self.model = Step3p5AMultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        return self.model.compute_logits(hidden_states, spec_step_idx)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        expert_params_mapping = [
+            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
+            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
+            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if "embed_tokens" not in name and spec_layer is None:
+                continue
+            name = self._rewrite_spec_layer_name(spec_layer, name)
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if ("mlp.experts." in name) and name not in params_dict:
+                    continue
+                if "experts" in name or "moe" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip loading extra bias for GPTQ models.
+                    if (
+                        name.endswith(".bias") or name.endswith("_bias")
+                    ) and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    for expert_id in range(loaded_weight.shape[0]):
+                        loaded_weight_expert = loaded_weight[expert_id]
+                        weight_loader(
+                            param,
+                            loaded_weight_expert,
+                            name,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                        )
+                    loaded_params.add(name)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if (
+                        name.endswith(".bias")
+                        and name not in params_dict
+                        or "tok_embeddings" in name
+                    ):
+                        continue
+
+                    if spec_layer is not None and ".transformer." in name:
+                        name = name.replace(".transformer.", ".")
+                    if "shared_head" in name:
+                        name = name.replace("shared_head.output", "shared_head.head")
+                    if "embed_tokens" in name:
+                        assert (
+                            hasattr(self.config, "num_nextn_predict_layers")
+                            and self.config.num_nextn_predict_layers > 0
+                        )
+                        name = "model.embed_tokens.weight"
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        params_need_to_load = set(params_dict.keys())
+        # Some KV cache scales are optional: checkpoints may omit them and vLLM
+        # will fall back to default scales during initialization.
+        optional_params = {
+            name
+            for name, param in params_dict.items()
+            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale"))
+            and getattr(param, "numel", lambda: 0)() == 1
+            and getattr(param, "requires_grad", False) is False
+        }
+        params_need_to_load -= optional_params
+        if params_need_to_load != loaded_params:
+            missing_params = list(params_need_to_load - loaded_params)
+            param_name_example = missing_params[0]
+            raise RuntimeError(
+                "Some parameters like "
+                f"{param_name_example} are not in the checkpoint and will falsely "
+                "use random initialization"
+            )
+        return loaded_params
+
+    def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
+        """
+        Rewrite the weight name to match the format of the original model.
+        Add .mtp_block for modules in transformer layer block for spec layer
+        """
+        spec_layer_weight_names = [
+            "embed_tokens",
+            "enorm",
+            "hnorm",
+            "eh_proj",
+            "shared_head",
+        ]
+        spec_layer_weight = False
+        for weight_name in spec_layer_weight_names:
+            if weight_name in name:
+                spec_layer_weight = True
+                break
+        if not spec_layer_weight:
+            # treat rest weights as weights for transformer layer block
+            name = name.replace(
+                f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
+            )
+        return name

vllm/reasoning/__init__.py

@@ -85,6 +85,10 @@ _REASONING_PARSERS_TO_REGISTER = {
         "step3_reasoning_parser",
         "Step3ReasoningParser",
     ),
+    "step3p5": (
+        "step3p5_reasoning_parser",
+        "Step3p5ReasoningParser",
+    ),
 }
 
 

vllm/reasoning/step3p5_reasoning_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.entrypoints.openai.responses.protocol import (
+    ResponsesRequest,
+)
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+from vllm.tokenizers import TokenizerLike
+
+
+class Step3p5ReasoningParser(BaseThinkingReasoningParser):
+    """
+    Reasoning parser for Step3p5 model.
+
+    Step3p5 uses the <think>...</think> format, but it tends to emit an extra
+    newline immediately before and/or after the </think> token. This parser trims:
+      - the newline right before </think>
+      - the newline right after </think>
+    """
+
+    @property
+    def start_token(self) -> str:
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        return "</think>"
+
+    def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        # Used to hold a trailing "\n" from reasoning content so we can decide
+        # whether it is immediately before </think>.
+        self._pending_reasoning_newline = False
+
+        # Used to delay the reasoning end detection.
+        # This is necessary to remove the newline appears immediately after </think>,
+        # which may cause the end detection to be delayed by one round.
+        self.end_offset = 1
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        if self.end_token_id in input_ids and self.end_offset > 0:
+            self.end_offset -= 1
+            return False
+        return self.end_offset < 1
+
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Sequence[int]
+    ) -> bool:
+        if self.end_token_id in input_ids and self.end_offset > 0:
+            self.end_offset -= 1
+            return False
+        return self.end_offset < 1
+
+    def extract_reasoning(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest | ResponsesRequest,
+    ) -> tuple[str | None, str | None]:
+        reasoning, content = super().extract_reasoning(model_output, request)
+        if reasoning is not None:
+            reasoning = reasoning.removesuffix("\n")
+        if content is not None:
+            content = content.removeprefix("\n")
+        return reasoning or None, content or None
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> DeltaMessage | None:
+        # Drop the immediate newline that models often emit after </think>.
+        if previous_text.endswith(self.end_token) and delta_text:
+            if delta_text == "\n":
+                return None
+            elif delta_text.startswith("\n"):
+                remaining = delta_text.removeprefix("\n")
+                return DeltaMessage(content=remaining) if remaining else None
+
+        ret = super().extract_reasoning_streaming(
+            previous_text,
+            current_text,
+            delta_text,
+            previous_token_ids,
+            current_token_ids,
+            delta_token_ids,
+        )
+
+        if ret is None:
+            return None
+
+        # Compatibility path for models that don't generate the start token:
+        # treat everything before </think> as reasoning and everything after
+        # as content.
+        if (
+            self.start_token_id not in previous_token_ids
+            and self.start_token_id not in delta_token_ids
+        ):
+            if self.end_token_id in delta_token_ids:
+                end_index = delta_text.find(self.end_token)
+                reasoning = delta_text[:end_index]
+                content = delta_text[end_index + len(self.end_token) :]
+                ret = DeltaMessage(reasoning=reasoning, content=content or None)
+            elif self.end_token_id in previous_token_ids:
+                ret = DeltaMessage(content=delta_text)
+            else:
+                ret = DeltaMessage(reasoning=delta_text)
+
+        reasoning_to_output = ret.reasoning
+        content_to_output = ret.content
+
+        # Reasoning: handle the newline immediately before </think>.
+        if reasoning_to_output is not None:
+            if self._pending_reasoning_newline:
+                reasoning_to_output = "\n" + reasoning_to_output
+                self._pending_reasoning_newline = False
+
+            if reasoning_to_output.endswith("\n"):
+                reasoning_to_output = reasoning_to_output.removesuffix("\n")
+                if self.end_token in delta_text:
+                    # Trailing "\n" is right before </think>, drop it.
+                    self._pending_reasoning_newline = False
+                else:
+                    # Hold the trailing "\n" until we know whether </think> follows.
+                    self._pending_reasoning_newline = True
+
+        # Content: handle the newline immediately after </think>.
+        if content_to_output is not None:
+            # No need to get into parser again to remove newline after </think>.
+            self.end_offset -= 1
+
+            # If we have content, reasoning must have ended.
+            self._pending_reasoning_newline = False
+
+            if self.end_token in delta_text and content_to_output.startswith("\n"):
+                content_to_output = content_to_output.removeprefix("\n")
+
+        reasoning_to_output = reasoning_to_output or None
+        content_to_output = content_to_output or None
+        if reasoning_to_output is None and content_to_output is None:
+            return None
+
+        return DeltaMessage(reasoning=reasoning_to_output, content=content_to_output)

vllm/tool_parsers/__init__.py

@@ -134,6 +134,10 @@ _TOOL_PARSERS_TO_REGISTER = {
         "step3_tool_parser",
         "Step3ToolParser",
     ),
+    "step3p5": (
+        "step3p5_tool_parser",
+        "Step3p5ToolParser",
+    ),
     "xlam": (
         "xlam_tool_parser",
         "xLAMToolParser",

vllm/tool_parsers/step3p5_tool_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import ast
+import json
+from collections.abc import Sequence
+from typing import Any
+from xml.parsers.expat import ParserCreate
+
+import regex as re
+
+from vllm.entrypoints.chat_utils import make_tool_call_id
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+    ChatCompletionToolsParam,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaFunctionCall,
+    DeltaMessage,
+    DeltaToolCall,
+    ExtractedToolCallInformation,
+    FunctionCall,
+    ToolCall,
+)
+from vllm.logger import init_logger
+from vllm.tokenizers import TokenizerLike
+from vllm.tool_parsers.abstract_tool_parser import (
+    ToolParser,
+    ToolParserManager,
+)
+
+logger = init_logger(__name__)
+
+
+class StreamingXMLToolCallParser:
+    """
+    Simplified streaming XML tool call parser
+    Supports streaming input, parsing, and output
+    """
+
+    def __init__(self):
+        self.reset_streaming_state()
+
+        # Tool configuration information
+        self.tools: list[ChatCompletionToolsParam] | None = None
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+        self.function_start_token: str = "<function="
+        self.function_end_token: str = "</function>"
+        self.parameter_start_token: str = "<parameter="
+        self.parameter_end_token: str = "</parameter>"
+
+    def reset_streaming_state(self):
+        """Reset streaming parsing state"""
+
+        self.deltas = []
+        # state for streaming
+        self.tool_call_index = 0
+        self.current_call_id = None
+        self.last_completed_call_id = None
+        self.current_function_name = None
+        self.current_function_open = False
+        self.parameters = {}
+        self.current_param_name = None
+        self.current_param_value = ""
+        self.current_param_value_converted = ""
+        self.current_param_is_first = False
+        self.should_emit_end_newline = False
+        self.start_quote_emitted = False
+
+        self.streaming_buffer = ""
+        self.last_processed_pos = 0
+
+        self.text_content_buffer = ""
+
+        # state for preprocessing and deferred parsing
+        self._pre_inside_parameter = False
+        self._pre_param_buffer = ""
+        self._pre_current_param_name = None
+        self.defer_current_parameter = False
+        self.deferred_param_raw_value = ""
+
+        # recreate parser
+        self.parser = ParserCreate()
+        self.setup_parser()
+
+    def parse_single_streaming_chunks(self, xml_chunk: str) -> DeltaMessage:
+        """
+        Parse single streaming XML chunk and return Delta response
+        This is the actual streaming interface that receives chunks
+        one by one and maintains internal state
+
+        Args:
+            xml_chunk: Single XML chunk string
+        Returns:
+            DeltaMessage: Contains delta information generated by this chunk,
+            returns empty response if no complete elements
+        """
+        # Record delta count before processing
+        initial_delta_count = len(self.deltas)
+
+        self.streaming_buffer += xml_chunk
+
+        found_elements = self._process_complete_xml_elements()
+
+        if found_elements:
+            # If complete elements found, check if end events were missed
+            # some tags may not have been triggered
+            try:
+                new_deltas = self.deltas[initial_delta_count:]
+                # If this chunk contains </function>
+                # but didn't generate '}', then complete it
+                if (
+                    self.current_call_id is not None
+                    and self.function_end_token in xml_chunk
+                ):
+                    # - Added '}' (non-empty parameter ending)
+                    # - Added '{}' (empty parameter function)
+                    has_function_close = any(
+                        (
+                            td.tool_calls
+                            and any(
+                                (
+                                    tc.function
+                                    and tc.id == self.current_call_id
+                                    and isinstance(tc.function.arguments, str)
+                                    and (tc.function.arguments in ("}", "{}"))
+                                )
+                                for tc in td.tool_calls
+                            )
+                        )
+                        for td in new_deltas
+                    )
+                    if not has_function_close:
+                        # Close potentially unclosed element
+                        if self.current_param_name:
+                            self._end_element("parameter")
+                        if self.current_function_name:
+                            self._end_element("function")
+                # If this chunk contains </tool_call>
+                # but didn't generate final empty delta, then complete it
+                if (
+                    self.current_call_id is not None
+                    and self.tool_call_end_token in xml_chunk
+                ):
+                    has_toolcall_close = any(
+                        (
+                            td.tool_calls
+                            and any(
+                                (
+                                    tc.type == "function"
+                                    and tc.function
+                                    and tc.function.arguments == ""
+                                    and tc.id == self.current_call_id
+                                )
+                                for tc in td.tool_calls
+                            )
+                        )
+                        for td in new_deltas
+                    )
+                    if not has_toolcall_close:
+                        # Close potentially unclosed element
+                        if self.current_param_name:
+                            self._end_element("parameter")
+                        if self.current_function_name:
+                            self._end_element("function")
+                        self._end_element("tool_call")
+            except Exception as e:
+                logger.warning("Error with fallback parsing: %s", e)
+            # Merge newly generated deltas into single response
+            result_delta = self._merge_new_deltas_to_single_response(
+                initial_delta_count
+            )
+            return result_delta
+        else:
+            # No complete elements, check if there's unoutput text content
+            if self.text_content_buffer and self.tool_call_index == 0:
+                # Has text content but no tool_call yet, output text content
+                text_delta = DeltaMessage(content=self.text_content_buffer)
+                self._emit_delta(text_delta)
+                # Clear buffer to avoid duplicate output
+                self.text_content_buffer = ""
+                return text_delta
+
+            # If this chunk contains end tags but wasn't triggered by parser,
+            # manually complete end events
+            # Only execute when still on the same call as when entered,
+            # to prevent accidentally closing new calls
+            # in multi <tool_call> scenarios
+            if self.current_call_id is not None and (
+                self.function_end_token in xml_chunk
+                or self.tool_call_end_token in xml_chunk
+            ):
+                # Close potentially unclosed element
+                if self.current_param_name:
+                    self._end_element("parameter")
+                if self.function_end_token in xml_chunk and self.current_function_name:
+                    self._end_element("function")
+                if self.tool_call_end_token in xml_chunk:
+                    self._end_element("tool_call")
+                # Return the merged delta result generated by this fallback
+                result_delta = self._merge_new_deltas_to_single_response(
+                    initial_delta_count
+                )
+                return result_delta
+
+            # No complete elements, return empty response
+            return DeltaMessage(content=None)
+
+    def _escape_xml_special_chars(self, text: str) -> str:
+        """
+        Escape XML special characters
+        Args:
+            text: Original text
+        Returns:
+            Escaped text
+        """
+        xml_escapes = {
+            "&": "&amp;",
+            "<": "&lt;",
+            ">": "&gt;",
+            '"': "&quot;",
+            "'": "&apos;",
+        }
+
+        for char, escape in xml_escapes.items():
+            text = text.replace(char, escape)
+
+        return text
+
+    def _process_complete_xml_elements(self) -> bool:
+        """
+        Process complete XML elements in buffer
+
+        Returns:
+            bool: Whether complete elements were found and processed
+        """
+        found_any = False
+
+        while self.last_processed_pos < len(self.streaming_buffer):
+            # Find next complete xml element
+            element, end_pos = self._find_next_complete_element(self.last_processed_pos)
+            if element is None:
+                # No complete element found, wait for more data
+                break
+
+            # Check if this element should be skipped
+            if self._should_skip_element(element):
+                self.last_processed_pos = end_pos
+                continue
+
+            # Found complete XML element, process it
+            try:
+                preprocessed_element = self._preprocess_xml_chunk(element)
+                # Check if this is the first tool_call start
+                if (
+                    (
+                        preprocessed_element.strip().startswith("<tool_call>")
+                        or preprocessed_element.strip().startswith("<function name=")
+                    )
+                    and self.tool_call_index == 0
+                ) and self.text_content_buffer:
+                    # First tool_call starts,
+                    # output previously collected text content first
+                    text_delta = DeltaMessage(content=self.text_content_buffer)
+                    self._emit_delta(text_delta)
+                    # Clear buffer for potential subsequent text content
+                    self.text_content_buffer = ""
+
+                # If a new tool_call starts and
+                # there are already completed tool_calls with function name
+                if (
+                    preprocessed_element.strip().startswith("<tool_call>")
+                    and self.tool_call_index > 0
+                    and self.current_call_id
+                    and self.current_function_name
+                ):
+                    # Reset parser state but preserve generated deltas
+                    if self.current_param_name:
+                        self._end_element("parameter")
+                    if self.current_function_open:
+                        self._end_element("function")
+                    # Output final tool_call tail delta
+                    final_delta = DeltaMessage(
+                        role=None,
+                        content=None,
+                        reasoning_content=None,
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.tool_call_index - 1,
+                                id=self.current_call_id,
+                                type="function",
+                                function=DeltaFunctionCall(name=None, arguments=""),
+                            )
+                        ],
+                    )
+                    self._emit_delta(final_delta)
+                    # Reset XML parser and current call state
+                    self._reset_xml_parser_after_tool_call()
+                # Parse preprocessed element
+                self.parser.Parse(preprocessed_element, False)
+                found_any = True
+
+            except Exception as e:
+                logger.warning("Error when parsing XML elements: %s", e)
+
+            # Update processed position
+            self.last_processed_pos = end_pos
+
+        return found_any
+
+    def _fix_incomplete_tag_in_chunk(self, chunk: str) -> str:
+        """
+        Fallback: fix incomplete <parameter=xxx or <function=xxx tags
+        (missing >)
+        Examples: <parameter=-C: -> <parameter=-C>, <parameter=parameter=-n:
+        -> <parameter=-n>
+        Also handles missing = cases: <function xxx> -> <function=xxx>,
+        <functionxxx> -> <function=xxx>
+        Only fixes tags that pass validation (parameter exists in tool definition)
+        """
+        # First, handle missing = cases for function tags
+        chunk = self._fix_missing_equals_in_function_tag(chunk)
+
+        for tag_type in ["parameter", "function"]:
+            pattern = f"<{tag_type}="
+            if pattern not in chunk:
+                continue
+
+            start_idx = chunk.find(pattern)
+            after_tag = chunk[start_idx:]
+            gt_pos = after_tag.find(">")
+            lt_pos = after_tag.find("<", len(pattern))
+
+            # Skip if already well-formed
+            if (
+                gt_pos != -1
+                and (lt_pos == -1 or gt_pos < lt_pos)
+                and pattern in after_tag[:gt_pos]
+            ):
+                continue
+
+            # Extract tag name (stop at space, newline, or <)
+            content = chunk[start_idx + len(pattern) :]
+            end_pos = next(
+                (i for i, ch in enumerate(content) if ch in (" ", "\n", "<")),
+                len(content),
+            )
+            tag_name = content[:end_pos]
+
+            if not tag_name:
+                continue
+
+            # Remove duplicate prefix: <parameter=parameter=xxx -> <parameter=xxx
+            if tag_name.startswith(f"{tag_type}="):
+                tag_name = tag_name[len(tag_type) + 1 :]
+
+            # Remove trailing non-alphanumeric chars (keep - and _)
+            while tag_name and not (
+                tag_name[-1].isalnum() or tag_name[-1] in ("-", "_")
+            ):
+                tag_name = tag_name[:-1]
+
+            if not tag_name:
+                continue
+
+            # Validate parameter exists in tool definition
+            if tag_type == "parameter" and not self._validate_parameter_name(tag_name):
+                continue
+
+            # Apply fix
+            chunk = chunk.replace(
+                f"<{tag_type}={content[:end_pos]}", f"<{tag_type}={tag_name}>", 1
+            )
+
+        return chunk
+
+    def _fix_missing_equals_in_function_tag(self, chunk: str) -> str:
+        """
+        Fix missing = in function tags: <function xxx> or <functionxxx>
+        Examples:
+          <function execute_bash> -> <function=execute_bash>
+          <functionexecute_bash> -> <function=execute_bash>
+        Only fixes if function name exists in tool definition
+        """
+        # already correct
+        if "<function=" in chunk:
+            return chunk
+
+        # Pattern 1: <function xxx> (with space/newline but no =)
+        pattern1 = r"<function\s+([a-zA-Z_][a-zA-Z0-9_]*)\s*>"
+        match1 = re.search(pattern1, chunk)
+        if match1:
+            func_name = match1.group(1).strip()
+            # must validate function name exists before fixing
+            if func_name and self._validate_function_name(func_name):
+                original = match1.group(0)
+                fixed = f"<function={func_name}>"
+                chunk = chunk.replace(original, fixed, 1)
+                return chunk
+
+        # Pattern 2: <functionxxx> (no space, no =)
+        # only match <function followed by letters
+        pattern2 = r"<function([a-zA-Z_][a-zA-Z0-9_]*)\s*>"
+        match2 = re.search(pattern2, chunk)
+        if match2:
+            func_name = match2.group(1).strip()
+            # must validate function name exists before fixing
+            if func_name and self._validate_function_name(func_name):
+                original = match2.group(0)
+                fixed = f"<function={func_name}>"
+                chunk = chunk.replace(original, fixed, 1)
+                return chunk
+
+        return chunk
+
+    def _validate_function_name(self, func_name: str) -> bool:
+        """Check if function name exists in tool definitions"""
+        if not self.tools:
+            return False
+
+        for tool in self.tools:
+            if (
+                hasattr(tool, "type")
+                and tool.type == "function"
+                and hasattr(tool, "function")
+                and hasattr(tool.function, "name")
+                and tool.function.name == func_name
+            ):
+                return True
+
+        return False
+
+    def _validate_parameter_name(self, param_name: str) -> bool:
+        """Check if parameter exists in current function's tool definition"""
+        if not self.tools or not self.current_function_name:
+            return True
+
+        for tool in self.tools:
+            if (
+                hasattr(tool, "type")
+                and tool.type == "function"
+                and hasattr(tool, "function")
+                and hasattr(tool.function, "name")
+                and tool.function.name == self.current_function_name
+            ):
+                if not hasattr(tool.function, "parameters"):
+                    return True
+                params = tool.function.parameters
+                if isinstance(params, dict):
+                    properties = params.get("properties", params)
+                    return param_name in properties
+                break
+
+        return True
+
+    def _should_skip_element(self, element: str) -> bool:
+        """
+        Determine whether an element should be skipped
+
+        Args:
+            element: Element to evaluate
+
+        Returns:
+            bool: True means should skip, False means should process
+        """
+
+        # If it's a tool_call XML tag, don't skip
+        if (
+            element.startswith(self.tool_call_start_token)
+            or element.startswith(self.function_start_token)
+            or element.startswith(self.parameter_start_token)
+        ):
+            return False
+
+        # If currently not parsing tool calls and not blank,
+        # collect this text instead of skipping
+        # Only process other XML elements after tool_call appears,
+        # otherwise treat as plain text
+        if self.current_call_id is None and element:
+            # Collect text content to buffer
+            self.text_content_buffer += element
+            return True  # Still skip, but content has been collected
+
+        # If currently parsing tool calls,
+        # this might be parameter value, don't skip
+        if self.current_call_id is not None:
+            return False
+
+        # Skip blank content
+        return not element
+
+    def _find_next_complete_element(self, start_pos: int) -> tuple[str | None, int]:
+        """
+        Find next complete XML element from specified position
+
+        Args:
+            start_pos: Position to start searching
+
+        Returns:
+            (Complete element string, element end position),
+            returns (None, start_pos) if no complete element found
+        """
+        buffer = self.streaming_buffer[start_pos:]
+
+        if not buffer:
+            return None, start_pos
+
+        if buffer.startswith("<"):
+            # Check if this is an incomplete parameter/function tag
+            # e.g., <parameter=-C: or <function=xxx
+            is_incomplete_param = (
+                buffer.startswith("<parameter=") and ">" not in buffer.split("\n")[0]
+            )
+            is_incomplete_func = (
+                buffer.startswith("<function=") and ">" not in buffer.split("\n")[0]
+            )
+
+            if is_incomplete_param or is_incomplete_func:
+                # Find the corresponding closing tag
+                tag_type = "parameter" if is_incomplete_param else "function"
+                closing_tag = f"</{tag_type}>"
+                closing_pos = buffer.find(closing_tag)
+
+                if closing_pos != -1:
+                    # Found closing tag, return complete element including closing tag
+                    complete_element = buffer[: closing_pos + len(closing_tag)]
+                    return complete_element, start_pos + closing_pos + len(closing_tag)
+
+            # Need to ensure no new < appears,
+            # find the nearest one between < and >
+            tag_end = buffer.find("<", 1)
+            tag_end2 = buffer.find(">", 1)
+            if tag_end != -1 and tag_end2 != -1:
+                # Next nearest is <
+                if tag_end < tag_end2:
+                    return buffer[:tag_end], start_pos + tag_end
+                # Next nearest is >, means found XML element
+                else:
+                    return buffer[: tag_end2 + 1], start_pos + tag_end2 + 1
+            elif tag_end != -1:
+                return buffer[:tag_end], start_pos + tag_end
+            elif tag_end2 != -1:
+                return buffer[: tag_end2 + 1], start_pos + tag_end2 + 1
+            else:
+                # If currently not parsing tool calls (entering a tool_call),
+                # check if starts with <tool_call> or <function=
+                if self.current_call_id is None:
+                    # Check if might be start of <tool_call>
+                    if buffer == "<tool_call>"[: len(buffer)]:
+                        # Might be start of <tool_call>, wait for more data
+                        return None, start_pos
+                    elif (
+                        buffer.startswith("<function=")
+                        or buffer == "<function="[: len(buffer)]
+                    ):
+                        # Might be start of <function=, wait for more data
+                        # to get the complete function tag
+                        return None, start_pos
+                    else:
+                        # Not start of <tool_call> or <function=, treat as text
+                        return buffer, start_pos + len(buffer)
+                else:
+                    # When parsing tool calls,
+                    # wait for more data to get complete tag
+                    return None, start_pos
+        else:
+            # Find text content (until next < or buffer end)
+            next_tag_pos = buffer.find("<")
+            if next_tag_pos != -1:
+                # Found text content
+                text_content = buffer[:next_tag_pos]
+                return text_content, start_pos + next_tag_pos
+            else:
+                # Buffer end is all text, process
+                # (no longer wait for more data)
+                remaining = buffer
+                return remaining, start_pos + len(remaining)
+
+    def _merge_new_deltas_to_single_response(self, initial_count: int) -> DeltaMessage:
+        """
+        Merge newly generated deltas from this processing
+        into a single DeltaMessage
+
+        Args:
+            initial_count: Delta count before processing
+
+        Returns:
+            Merged DeltaMessage containing all newly generated delta information
+        """
+        if len(self.deltas) <= initial_count:
+            return DeltaMessage(content=None)
+
+        # Get newly generated deltas
+        new_deltas = self.deltas[initial_count:]
+
+        if len(new_deltas) == 1:
+            # Only one new delta, return directly
+            return new_deltas[0]
+
+        # Merge multiple new deltas
+        merged_tool_calls: list[DeltaToolCall] = []
+        merged_content: str = ""
+
+        for delta in new_deltas:
+            if delta.content:
+                merged_content += delta.content
+            if delta.tool_calls:
+                # For tool_calls, we need to intelligently merge arguments
+                for tool_call in delta.tool_calls:
+                    # Find if there's already a tool_call with the same call_id
+                    existing_call = None
+                    for existing in merged_tool_calls:
+                        if existing.id == tool_call.id:
+                            existing_call = existing
+                            break
+
+                    if existing_call and existing_call.function:
+                        # Merge to existing tool_call
+                        if tool_call.function and tool_call.function.name:
+                            existing_call.function.name = tool_call.function.name
+                        if (
+                            tool_call.function
+                            and tool_call.function.arguments is not None
+                        ):
+                            if existing_call.function.arguments is None:
+                                existing_call.function.arguments = ""
+
+                            # For streaming JSON parameters,
+                            # simply concatenate in order
+                            new_args = tool_call.function.arguments
+                            existing_call.function.arguments += new_args
+                        if tool_call.type:
+                            existing_call.type = tool_call.type
+                    else:
+                        # Add new tool_call
+                        merged_tool_calls.append(tool_call)
+
+        return DeltaMessage(
+            content=merged_content if merged_content else None,
+            tool_calls=merged_tool_calls,
+        )
+
+    def _preprocess_xml_chunk(self, chunk: str) -> str:
+        """
+        Preprocess XML chunk, handle non-standard formats,
+        and escape special characters
+
+        Args:
+            chunk: Original XML chunk
+
+        Returns:
+            Processed XML chunk
+        """
+
+        # Check if this is a tool_call related element
+        is_tool_call = False
+        if chunk.startswith(self.tool_call_start_token) or chunk.startswith(
+            self.tool_call_end_token
+        ):
+            is_tool_call = True
+        # Check for function tags (including malformed ones without =)
+        # <function=xxx>, </function>, <function xxx>, <functionxxx>
+        if (
+            chunk.startswith(self.function_start_token)
+            or chunk.startswith(self.function_end_token)
+            or chunk.startswith("<function ")
+            or re.match(r"^<function[a-zA-Z_]", chunk)
+        ):  # <functionXXX without space or =
+            is_tool_call = True
+        if chunk.startswith(self.parameter_start_token) or chunk.startswith(
+            self.parameter_end_token
+        ):
+            is_tool_call = True
+
+        # Fallback: fix incomplete <parameter= or <function= tags without
+        # closing >
+        # This handles cases like: <parameter=-C:\n or <parameter=-B 5\n
+        # Apply when parsing tool calls OR when chunk looks like a function/
+        # parameter tag
+        if (
+            self.current_call_id is not None
+            or chunk.startswith("<function")
+            or chunk.startswith("<parameter")
+        ):
+            chunk = self._fix_incomplete_tag_in_chunk(chunk)
+
+        # Handle <function=name> format -> <function name="name">
+        processed = re.sub(r"<function=([^>]+)>", r'<function name="\1">', chunk)
+        # Handle <parameter=name> format -> <parameter name="name">
+        processed = re.sub(r"<parameter=([^>]+)>", r'<parameter name="\1">', processed)
+
+        original_chunk = chunk
+        # If in parameter value accumulation mode
+        if self._pre_inside_parameter:
+            # Parameter end: output accumulated raw text
+            # safely then return </parameter>
+            if processed.startswith("</parameter>"):
+                body_text = self._pre_param_buffer
+                # Trigger deferred parsing mode
+                # literal_eval+json output in end_element
+                self.defer_current_parameter = True
+                self.deferred_param_raw_value = body_text
+                # Clean up state
+                self._pre_inside_parameter = False
+                self._pre_param_buffer = ""
+                self._pre_current_param_name = None
+                safe_text = self._escape_xml_special_chars(body_text)
+                return f"{safe_text}</parameter>"
+            else:
+                # If this is the first block of content after entering parameter
+                # evaluate if deferred parsing is needed;
+                # If not needed, exit accumulation mode
+                # and pass through directly
+                if self._pre_param_buffer == "":
+                    # Get current parameter type
+                    param_type = (
+                        self._get_param_type(self._pre_current_param_name)
+                        if self._pre_current_param_name
+                        else "string"
+                    )
+                    # Only these types need deferred parsing to
+                    # handle Python literals containing single quotes
+                    is_object_type = param_type in ["object"]
+                    is_complex_type = (
+                        param_type in ["array", "arr", "sequence"]
+                        or param_type.startswith("dict")
+                        or param_type.startswith("list")
+                    )
+
+                    # Only delay when contains container symbols
+                    # and has single quotes and is complex type
+                    has_container_hint = (
+                        ("[" in original_chunk)
+                        or ("{" in original_chunk)
+                        or ("(" in original_chunk)
+                    )
+
+                    # Determine if deferred parsing is needed
+                    need_defer = False
+                    if is_complex_type:
+                        # Complex type, always need deferred parsing
+                        need_defer = True
+                    elif (
+                        is_object_type
+                        and has_container_hint
+                        and ("'" in original_chunk)
+                    ):
+                        # Object type with container symbols
+                        # and single quotes, need deferred parsing
+                        need_defer = True
+
+                    if not need_defer:
+                        # No need for deferred parsing,
+                        # exit parameter mode directly
+                        self._pre_inside_parameter = False
+                        return self._escape_xml_special_chars(original_chunk)
+                self._pre_param_buffer += original_chunk
+                return ""
+
+        # Parameter start: enable accumulation
+        if processed.startswith("<parameter name="):
+            m = re.match(r'<parameter name="([^"]+)">', processed)
+            if m:
+                self._pre_current_param_name = m.group(1)
+            self._pre_inside_parameter = True
+            self._pre_param_buffer = ""
+            return processed
+
+        # If processed doesn't contain special_token, escape processed
+        # This is because XML parsing encounters special characters
+        # and reports errors, so escaping is needed
+        if not is_tool_call:
+            processed = self._escape_xml_special_chars(processed)
+        return processed
+
+    def _emit_delta(self, delta: DeltaMessage):
+        """Emit Delta response (streaming output)"""
+        self.deltas.append(delta)
+
+    def _auto_close_open_parameter_if_needed(self, incoming_tag: str | None = None):
+        """Before starting to process new elements,
+        if there are unclosed tags from before,
+        automatically complete their endings to the parser.
+        - If there are unclosed parameters,
+        it's equivalent to feeding `</parameter>`
+        - When about to start a new function or tool_call,
+        if there are unclosed functions, complete `</function>`.
+        - When about to start a new tool_call,
+        if there are unclosed tool_calls, complete `</tool_call>`.
+        """
+        # First close unclosed parameters
+        if self.current_param_name:
+            self._end_element("parameter")
+
+        # If about to start new function or tool_call,
+        # and there are unclosed functions, close function first
+        if incoming_tag in ("function", "tool_call") and self.current_function_name:
+            self._end_element("function")
+
+        # If about to start new tool_call,
+        # and there are unclosed tool_calls, close tool_call first
+        if incoming_tag == "tool_call" and self.current_call_id:
+            self._end_element("tool_call")
+
+    def _start_element(self, name: str, attrs: dict[str, str]):
+        """Handle XML start element events"""
+
+        if name == "root":
+            return
+
+        if name == "tool_call":
+            # Before opening new tool_call,
+            # automatically complete previous unclosed tags
+            self._auto_close_open_parameter_if_needed("tool_call")
+
+            self.parameters = {}
+            self.current_call_id = make_tool_call_id()
+            self.current_param_is_first = True
+            self.tool_call_index += 1
+        elif name.startswith("function") or (name == "function"):
+            # If missing tool_call, manually complete
+            if not self.current_call_id:
+                self._start_element("tool_call", {})
+            # Before opening new function,
+            # automatically complete previous unclosed tags (parameter/function)
+            self._auto_close_open_parameter_if_needed("function")
+            function_name = self._extract_function_name(name, attrs)
+            self.current_function_name = function_name
+            self.current_function_open = True
+            if function_name:
+                delta = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.tool_call_index - 1,
+                            id=self.current_call_id,
+                            type="function",
+                            function=DeltaFunctionCall(
+                                name=function_name, arguments=""
+                            ),
+                        )
+                    ]
+                )
+                self._emit_delta(delta)
+        elif name.startswith("parameter") or (name == "parameter"):
+            # If previous parameter hasn't ended normally,
+            # complete its end first, then start new parameter
+            self._auto_close_open_parameter_if_needed("parameter")
+            param_name = self._extract_parameter_name(name, attrs)
+            self.current_param_name = param_name
+            self.current_param_value = ""
+            self.current_param_value_converted = ""
+            self.start_quote_emitted = False  # Reset start quote flag
+
+            # Only output parameter name and colon,
+            # don't output quotes
+            # decide after parameter value type is determined
+            if param_name:
+                if not self.parameters:
+                    # First parameter
+                    # start JSON, only output parameter name and colon
+                    json_start = f'{{"{param_name}": '
+                    delta = DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.tool_call_index - 1,
+                                id=self.current_call_id,
+                                type="function",
+                                function=DeltaFunctionCall(
+                                    name=None, arguments=json_start
+                                ),
+                            )
+                        ]
+                    )
+                    self._emit_delta(delta)
+                    self.current_param_is_first = True
+                else:
+                    # Subsequent parameters
+                    # add comma and parameter name, no quotes
+                    json_continue = f', "{param_name}": '
+                    delta = DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.tool_call_index - 1,
+                                id=self.current_call_id,
+                                type="function",
+                                function=DeltaFunctionCall(
+                                    name=None, arguments=json_continue
+                                ),
+                            )
+                        ]
+                    )
+                    self._emit_delta(delta)
+                    self.current_param_is_first = False
+
+    def _char_data(self, data: str):
+        """Handle XML character data events"""
+        if data and self.current_param_name:
+            # If preprocessing stage determines deferred parsing is needed,
+            # only cache character data, no streaming output
+            if self.defer_current_parameter:
+                original_data = data
+                if self.should_emit_end_newline:
+                    original_data = "\n" + original_data
+                    self.should_emit_end_newline = False
+                if original_data.endswith("\n"):
+                    self.should_emit_end_newline = True
+                    original_data = original_data[:-1]
+                self.current_param_value += original_data
+                return
+
+            param_type = self._get_param_type(self.current_param_name)
+
+            # Check if this is the first time receiving data for this parameter
+            # If this is the first packet of data and starts with \n, remove \n
+            if not self.current_param_value and data.startswith("\n"):
+                data = data[1:]
+
+            # Output start quote for string type (if not already output)
+            if (
+                param_type in ["string", "str", "text", "varchar", "char", "enum"]
+                and not self.start_quote_emitted
+            ):
+                quote_delta = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.tool_call_index - 1,
+                            id=self.current_call_id,
+                            type="function",
+                            function=DeltaFunctionCall(name=None, arguments='"'),
+                        )
+                    ]
+                )
+                self._emit_delta(quote_delta)
+                self.start_quote_emitted = True
+
+            if not data:
+                return
+
+            original_data = data
+            # Delay output of trailing newline
+            if self.should_emit_end_newline:
+                original_data = "\n" + original_data
+                self.should_emit_end_newline = False
+            if original_data.endswith("\n"):
+                self.should_emit_end_newline = True
+                original_data = original_data[:-1]
+            self.current_param_value += original_data
+
+            # convert parameter value by param_type
+            converted_value = self._convert_param_value(
+                self.current_param_value, param_type
+            )
+            output_data = self._convert_for_json_streaming(converted_value, param_type)
+
+            delta_data = output_data[len(self.current_param_value_converted) :]
+            self.current_param_value_converted = output_data
+
+            delta = DeltaMessage(
+                tool_calls=[
+                    DeltaToolCall(
+                        index=self.tool_call_index - 1,
+                        id=self.current_call_id,
+                        type="function",
+                        function=DeltaFunctionCall(name=None, arguments=delta_data),
+                    )
+                ]
+            )
+            self._emit_delta(delta)
+
+    def _end_element(self, name: str):
+        """Handle XML end element events"""
+
+        if name == "root":
+            return
+
+        # If function or tool_call ends and there are still unclosed parameters,
+        # complete parameter end first
+        if (
+            name.startswith("function") or name == "function" or name == "tool_call"
+        ) and self.current_param_name:
+            self._auto_close_open_parameter_if_needed()
+
+        if (
+            name.startswith("parameter") or name == "parameter"
+        ) and self.current_param_name:
+            # End current parameter
+            param_name = self.current_param_name
+            param_value = self.current_param_value
+
+            # If in deferred parsing mode,
+            # perform overall parsing on raw content
+            # accumulated in preprocessing stage and output once
+            if self.defer_current_parameter:
+                raw_text = (
+                    self.deferred_param_raw_value
+                    if self.deferred_param_raw_value
+                    else param_value
+                )
+                parsed_value = None
+                output_arguments = None
+                try:
+                    # If previously delayed trailing newline,
+                    # add it back before parsing
+                    if self.should_emit_end_newline:
+                        raw_for_parse = raw_text + "\n"
+                    else:
+                        raw_for_parse = raw_text
+                    parsed_value = ast.literal_eval(raw_for_parse)
+                    output_arguments = json.dumps(parsed_value, ensure_ascii=False)
+                except Exception:
+                    # Fallback: output as string as-is
+                    output_arguments = json.dumps(raw_text, ensure_ascii=False)
+                    parsed_value = raw_text
+
+                delta = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.tool_call_index - 1,
+                            id=self.current_call_id,
+                            type="function",
+                            function=DeltaFunctionCall(
+                                name=None, arguments=output_arguments
+                            ),
+                        )
+                    ]
+                )
+                self._emit_delta(delta)
+
+                # Clean up and store
+                self.should_emit_end_newline = False
+                self.parameters[param_name] = parsed_value
+                self.current_param_name = None
+                self.current_param_value = ""
+                self.current_param_value_converted = ""
+                self.start_quote_emitted = False
+                self.defer_current_parameter = False
+                self.deferred_param_raw_value = ""
+                return
+
+            param_type = self._get_param_type(param_name)
+
+            # convert complete parameter value by param_type
+            converted_value = self._convert_param_value(param_value, param_type)
+
+            # Decide whether to add end quote based on parameter type
+            if param_type in ["string", "str", "text", "varchar", "char", "enum"]:
+                # For empty string parameters, need special handling
+                if not param_value and not self.start_quote_emitted:
+                    # No start quote output,
+                    # directly output complete empty string
+                    delta = DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.tool_call_index - 1,
+                                id=self.current_call_id,
+                                type="function",
+                                function=DeltaFunctionCall(name=None, arguments='""'),
+                            )
+                        ]
+                    )
+                    self._emit_delta(delta)
+                else:
+                    # Non-empty parameter value, output end quote
+                    delta = DeltaMessage(
+                        tool_calls=[
+                            DeltaToolCall(
+                                index=self.tool_call_index - 1,
+                                id=self.current_call_id,
+                                type="function",
+                                function=DeltaFunctionCall(name=None, arguments='"'),
+                            )
+                        ]
+                    )
+                    self._emit_delta(delta)
+
+            self.should_emit_end_newline = False
+            # Store converted value
+            self.parameters[param_name] = converted_value
+            self.current_param_name = None
+            self.current_param_value = ""
+            self.current_param_value_converted = ""
+            self.start_quote_emitted = False
+
+        elif name.startswith("function") or name == "function":
+            # if there are parameters, close JSON object
+            if self.parameters:
+                delta = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.tool_call_index - 1,
+                            id=self.current_call_id,
+                            type="function",
+                            function=DeltaFunctionCall(name=None, arguments="}"),
+                        )
+                    ]
+                )
+                self._emit_delta(delta)
+            # return empty object
+            else:
+                delta = DeltaMessage(
+                    tool_calls=[
+                        DeltaToolCall(
+                            index=self.tool_call_index - 1,
+                            id=self.current_call_id,
+                            type="function",
+                            function=DeltaFunctionCall(name=None, arguments="{}"),
+                        )
+                    ]
+                )
+                self._emit_delta(delta)
+            self.current_function_open = False
+            self.current_function_name = (
+                None  # Clear function name to prevent duplicate closing
+            )
+
+        elif name == "tool_call":
+            # Before ending tool_call,
+            # ensure function is closed to complete missing right brace
+            if self.current_function_open:
+                # If there are still unclosed parameters, close them first
+                if self.current_param_name:
+                    self._end_element("parameter")
+                # Close function, ensure output '}' or '{}'
+                self._end_element("function")
+            # Final Delta
+            delta = DeltaMessage(
+                tool_calls=[
+                    DeltaToolCall(
+                        index=self.tool_call_index - 1,
+                        id=self.current_call_id,
+                        type="function",
+                        function=DeltaFunctionCall(name=None, arguments=""),
+                    )
+                ]
+            )
+            self._emit_delta(delta)
+
+            # Check if there's text content to output (between tool_calls)
+            if self.text_content_buffer.strip():
+                text_delta = DeltaMessage(content=self.text_content_buffer)
+                self._emit_delta(text_delta)
+
+            self._reset_xml_parser_after_tool_call()
+
+    def setup_parser(self):
+        """Set up XML parser event handlers"""
+        self.parser.buffer_text = True
+        self.parser.StartElementHandler = self._start_element
+        self.parser.EndElementHandler = self._end_element
+        self.parser.CharacterDataHandler = self._char_data
+
+    def set_tools(self, tools: list[ChatCompletionToolsParam] | None):
+        """Set tool configuration information"""
+        self.tools = tools
+
+    def _extract_function_name(self, name: str, attrs: dict[str, str]) -> str | None:
+        """Extract function name from various formats"""
+        if attrs and "name" in attrs:
+            return attrs["name"]
+
+        if "=" in name:
+            parts = name.split("=", 1)
+            if len(parts) == 2 and parts[0] == "function":
+                return parts[1]
+
+        return None
+
+    def _extract_parameter_name(self, name: str, attrs: dict[str, str]) -> str | None:
+        """Extract parameter name from various formats"""
+        if attrs and "name" in attrs:
+            return attrs["name"]
+
+        if "=" in name:
+            parts = name.split("=", 1)
+            if len(parts) == 2 and parts[0] == "parameter":
+                return parts[1]
+
+        return None
+
+    def _get_param_type(self, param_name: str) -> str:
+        """Get parameter type based on tool configuration, defaults to string
+        Args:
+            param_name: Parameter name
+
+        Returns:
+            Parameter type
+        """
+        if not self.tools or not self.current_function_name:
+            return "string"
+
+        for tool in self.tools:
+            if not hasattr(tool, "type") or not (
+                hasattr(tool, "function") and hasattr(tool.function, "name")
+            ):
+                continue
+            if (
+                tool.type == "function"
+                and tool.function.name == self.current_function_name
+            ):
+                if not hasattr(tool.function, "parameters"):
+                    return "string"
+                params = tool.function.parameters
+                if isinstance(params, dict) and "properties" in params:
+                    properties = params["properties"]
+                    if param_name in properties and isinstance(
+                        properties[param_name], dict
+                    ):
+                        return self.repair_param_type(
+                            str(properties[param_name].get("type", "string"))
+                        )
+                elif isinstance(params, dict) and param_name in params:
+                    param_config = params[param_name]
+                    if isinstance(param_config, dict):
+                        return self.repair_param_type(
+                            str(param_config.get("type", "string"))
+                        )
+                break
+        return "string"
+
+    def repair_param_type(self, param_type: str) -> str:
+        """Repair unknown parameter types by treating them as string
+        Args:
+            param_type: Parameter type
+
+        Returns:
+            Repaired parameter type
+        """
+        if (
+            param_type in ["string", "str", "text", "varchar", "char", "enum"]
+            or param_type.startswith("int")
+            or param_type.startswith("uint")
+            or param_type.startswith("long")
+            or param_type.startswith("short")
+            or param_type.startswith("unsigned")
+            or param_type.startswith("num")
+            or param_type.startswith("float")
+            or param_type in ["boolean", "bool", "binary"]
+            or (
+                param_type in ["object", "array", "arr", "sequence"]
+                or param_type.startswith("dict")
+                or param_type.startswith("list")
+            )
+        ):
+            return param_type
+        else:
+            return "string"
+
+    def _convert_param_value(self, param_value: str, param_type: str) -> Any:
+        """Convert value based on parameter type
+        Args:
+            param_value: Parameter value
+            param_type: Parameter type
+
+        Returns:
+            Converted value
+        """
+        if param_value.lower() == "null":
+            return None
+
+        param_type = param_type.strip().lower()
+        if param_type in ["string", "str", "text", "varchar", "char", "enum"]:
+            return param_value
+        elif (
+            param_type.startswith("int")
+            or param_type.startswith("uint")
+            or param_type.startswith("long")
+            or param_type.startswith("short")
+            or param_type.startswith("unsigned")
+        ):
+            try:
+                return int(param_value)
+            except (ValueError, TypeError):
+                logger.warning(
+                    "Parsed value '%s' is not an integer, degenerating to string.",
+                    param_value,
+                )
+            return param_value
+        elif param_type.startswith("num") or param_type.startswith("float"):
+            try:
+                float_param_value: float = float(param_value)
+                return (
+                    float_param_value
+                    if float_param_value - int(float_param_value) != 0
+                    else int(float_param_value)
+                )
+            except (ValueError, TypeError):
+                logger.warning(
+                    "Parsed value '%s' is not a float, degenerating to string.",
+                    param_value,
+                )
+            return param_value
+        elif param_type in ["boolean", "bool", "binary"]:
+            param_value = param_value.lower()
+            return param_value == "true"
+        else:
+            return param_value
+
+    def _convert_for_json_streaming(self, converted_value: Any, param_type: str) -> str:
+        """Convert converted_value based on
+        whether it's empty and if type is string
+        Args:
+            converted_value: Converted value
+            param_type: Parameter type
+
+        Returns:
+            Converted string for streaming output
+        """
+        # Check if value is empty, but exclude numeric 0
+        if converted_value is None or converted_value == "":
+            return ""
+
+        if param_type in ["string", "str", "text", "varchar", "char", "enum"]:
+            # String type, remove double quotes
+            return json.dumps(converted_value, ensure_ascii=False)[1:-1]
+        else:
+            # Non-string type, return complete JSON string
+            if not isinstance(converted_value, str):
+                return json.dumps(converted_value, ensure_ascii=False)
+            else:
+                return converted_value
+
+    def _reset_xml_parser_after_tool_call(self):
+        """
+        Each tool_call is treated as a separate XML document,
+        so we need to reset the parser after each tool_call.
+        """
+
+        # recreate XML parser
+        self.parser = ParserCreate()
+        self.setup_parser()
+
+        # Reset current tool_call state
+        if self.current_call_id:
+            self.last_completed_call_id = self.current_call_id
+        self.current_call_id = None
+        self.current_function_name = None
+        self.current_function_open = False
+        self.parameters = {}
+        self.current_param_name = None
+        self.current_param_value = ""
+        self.current_param_value_converted = ""
+        self.current_param_is_first = False
+        self.should_emit_end_newline = False
+        self.start_quote_emitted = False
+        self.text_content_buffer = ""
+
+        # Reset preprocessing and deferred parsing state
+        self._pre_inside_parameter = False
+        self._pre_param_buffer = ""
+        self._pre_current_param_name = None
+        self.defer_current_parameter = False
+        self.deferred_param_raw_value = ""
+
+
+@ToolParserManager.register_module("step3p5")
+class Step3p5ToolParser(ToolParser):
+    def __init__(self, tokenizer: TokenizerLike):
+        super().__init__(tokenizer)
+        self.parser = StreamingXMLToolCallParser()
+
+        # Add missing attributes for compatibility with serving_chat.py
+        self.prev_tool_call_arr: list[dict] = []
+        self.streamed_args_for_tool: list[str] = []
+
+        logger.info(
+            "vLLM Successfully import tool parser %s !", self.__class__.__name__
+        )
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        self.parser.reset_streaming_state()
+        # Reset tool call tracking arrays for new extraction
+        self.prev_tool_call_arr = []
+        self.streamed_args_for_tool = []
+        if request:
+            self.parser.set_tools(request.tools)
+        result = self.parser.parse_single_streaming_chunks(model_output)
+        if not result.tool_calls:
+            return ExtractedToolCallInformation(
+                tool_calls=[],
+                tools_called=False,
+                content=result.content,
+            )
+        else:
+            tool_calls = []
+            for tool_call in result.tool_calls:
+                if tool_call.function and tool_call.function.name:
+                    tool_calls.append(
+                        ToolCall(
+                            id=tool_call.id,
+                            type=tool_call.type,
+                            function=FunctionCall(
+                                name=tool_call.function.name,
+                                arguments=tool_call.function.arguments,
+                            ),
+                        )
+                    )
+
+                    # Update tool call tracking arrays for compatibility
+                    tool_index = (
+                        tool_call.index
+                        if tool_call.index is not None
+                        else len(self.prev_tool_call_arr) - 1
+                    )
+
+                    # Ensure we have enough entries in our tracking arrays
+                    while len(self.prev_tool_call_arr) <= tool_index:
+                        self.prev_tool_call_arr.append({"name": "", "arguments": ""})
+                    while len(self.streamed_args_for_tool) <= tool_index:
+                        self.streamed_args_for_tool.append("")
+
+                    # Update tool call information
+                    self.prev_tool_call_arr[tool_index]["name"] = (
+                        tool_call.function.name
+                    )
+                    self.prev_tool_call_arr[tool_index]["arguments"] = (
+                        tool_call.function.arguments
+                    )
+
+                    # Update streamed arguments
+                    if tool_call.function.arguments:
+                        self.streamed_args_for_tool[tool_index] = (
+                            tool_call.function.arguments
+                        )
+
+            return ExtractedToolCallInformation(
+                tool_calls=tool_calls,
+                tools_called=len(tool_calls) > 0,
+                content=result.content,
+            )
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> DeltaMessage | None:
+        if not previous_text:
+            self.parser.reset_streaming_state()
+            # Reset tool call tracking arrays for new streaming session
+            self.prev_tool_call_arr = []
+            self.streamed_args_for_tool = []
+            if request:
+                self.parser.set_tools(request.tools)
+
+        # Model sometimes outputs separately causing delta_text to be empty.
+        # If there were tool_calls before and all current tool_calls have ended,
+        # return an empty tool_call for outer streaming output
+        # to correctly output tool_call field
+        if not delta_text and delta_token_ids:
+            open_calls = current_text.count(
+                self.parser.tool_call_start_token
+            ) - current_text.count(self.parser.tool_call_end_token)
+            if (
+                open_calls == 0
+                and self.parser.tool_call_index > 0
+                or not self.parser.tool_call_index
+                and current_text
+            ):
+                return DeltaMessage(content="")
+            return None
+
+        # Parse the delta text and get the result
+        result = self.parser.parse_single_streaming_chunks(delta_text)
+
+        # Update tool call tracking arrays based on incremental parsing results
+        if result and result.tool_calls:
+            for tool_call in result.tool_calls:
+                if tool_call.function:
+                    tool_index = (
+                        tool_call.index
+                        if tool_call.index is not None
+                        else len(self.prev_tool_call_arr) - 1
+                    )
+
+                    # Ensure we have enough entries in our tracking arrays
+                    while len(self.prev_tool_call_arr) <= tool_index:
+                        self.prev_tool_call_arr.append({"name": "", "arguments": ""})
+                    while len(self.streamed_args_for_tool) <= tool_index:
+                        self.streamed_args_for_tool.append("")
+
+                    # Update tool name if provided
+                    if tool_call.function.name:
+                        self.prev_tool_call_arr[tool_index]["name"] = (
+                            tool_call.function.name
+                        )
+
+                    # Update arguments incrementally
+                    if tool_call.function.arguments is not None:
+                        # Concatenate the incremental arguments
+                        # to the existing streamed arguments
+                        self.prev_tool_call_arr[tool_index]["arguments"] += (
+                            tool_call.function.arguments
+                        )
+                        self.streamed_args_for_tool[tool_index] += (
+                            tool_call.function.arguments
+                        )
+        return result
+
+    def parser_should_check_for_unstreamed_tool_arg_tokens(self) -> bool:
+        """
+        Skip the remaining_call calculation in serving_chat
+        """
+        return False

vllm/transformers_utils/config.py

@@ -96,6 +96,8 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     ultravox="UltravoxConfig",
     step3_vl="Step3VLConfig",
     step3_text="Step3TextConfig",
+    step3p5="Step3p5Config",
+    qwen3_asr="Qwen3ASRConfig",
     qwen3_next="Qwen3NextConfig",
     lfm2_moe="Lfm2MoeConfig",
     tarsier2="Tarsier2Config",

vllm/transformers_utils/configs/__init__.py

@@ -51,6 +51,8 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "Step3VLConfig": "vllm.transformers_utils.configs.step3_vl",
     "Step3VisionEncoderConfig": "vllm.transformers_utils.configs.step3_vl",
     "Step3TextConfig": "vllm.transformers_utils.configs.step3_vl",
+    "Step3p5Config": "vllm.transformers_utils.configs.step3p5",
+    "Qwen3ASRConfig": "vllm.transformers_utils.configs.qwen3_asr",
     "Qwen3NextConfig": "vllm.transformers_utils.configs.qwen3_next",
     "Tarsier2Config": "vllm.transformers_utils.configs.tarsier2",
     # Special case: DeepseekV3Config is from HuggingFace Transformers
@@ -91,6 +93,8 @@ __all__ = [
     "Step3VLConfig",
     "Step3VisionEncoderConfig",
     "Step3TextConfig",
+    "Step3p5Config",
+    "Qwen3ASRConfig",
     "Qwen3NextConfig",
     "Tarsier2Config",
 ]

vllm/transformers_utils/configs/step3p5.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Step3p5Config(PretrainedConfig):
+    model_type = "step3p5"
+
+    def __init__(
+        self,
+        hidden_size: int = 5120,
+        intermediate_size: int = 13312,
+        num_attention_heads: int = 40,
+        num_attention_groups: int = 8,
+        num_hidden_layers: int = 48,
+        max_seq_len: int = 4096,
+        vocab_size: int = 65536,
+        rms_norm_eps: float = 1e-5,
+        moe_every_n_layer: int = 2,
+        use_moe: bool = False,
+        moe_intermediate_size: int = 10240,
+        moe_num_experts: int = 16,
+        moe_top_k: int = 4,
+        moe_layer_offset: int = 0,
+        rope_theta: float | list[float] | None = 500000,
+        rope_scaling: dict[str, Any] | None = None,
+        head_dim: int | None = None,
+        share_expert_dim: int | None = None,
+        norm_expert_weight: bool = True,
+        bos_token_id: list[int] | int | None = None,
+        eos_token_id: list[int] | int | None = None,
+        moe_router_activation: str = "softmax",
+        moe_router_scaling_factor: float = 1.0,
+        att_impl_type: str = "GQA",
+        use_head_wise_attn_gate: bool = False,
+        use_moe_router_bias: bool = True,
+        need_fp32_gate: bool = True,
+        layer_types: list[str] | None = None,
+        use_rope_layers: list[bool] | None = None,
+        yarn_only_types: list[str] | None = None,
+        attention_other_setting: dict[str, Any] | None = None,
+        num_nextn_predict_layers: int = 0,
+        swiglu_limits: list[float] | None = None,
+        swiglu_limits_shared: list[float] | None = None,
+        max_position_embeddings: int | None = None,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_attention_groups = num_attention_groups
+        self.num_hidden_layers = num_hidden_layers
+        self.max_seq_len = max_seq_len
+        self.vocab_size = vocab_size
+        self.rms_norm_eps = rms_norm_eps
+        self.use_moe = use_moe
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_every_n_layer = moe_every_n_layer
+        self.moe_num_experts = moe_num_experts
+        self.num_experts_per_tok = moe_top_k
+        self.moe_top_k = moe_top_k
+        self.moe_layer_offset = moe_layer_offset
+
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.head_dim = head_dim
+        if share_expert_dim is None:
+            self.share_expert_dim = self.moe_intermediate_size * self.moe_top_k
+        else:
+            self.share_expert_dim = share_expert_dim
+        self.norm_expert_weight = norm_expert_weight
+
+        self.max_position_embeddings = max_position_embeddings
+        self.moe_router_activation = moe_router_activation
+        self.moe_router_scaling_factor = moe_router_scaling_factor
+        self.use_moe_router_bias = use_moe_router_bias
+        self.need_fp32_gate = need_fp32_gate
+
+        self.att_impl_type = att_impl_type
+        self.use_head_wise_attn_gate = use_head_wise_attn_gate
+        self.layer_types = layer_types
+        self.use_rope_layers = use_rope_layers
+        self.yarn_only_types = yarn_only_types
+        self.attention_other_setting = attention_other_setting
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.swiglu_limits = swiglu_limits
+        self.swiglu_limits_shared = swiglu_limits_shared
+
+        resolved_bos_token_id = 1 if bos_token_id is None else bos_token_id
+        resolved_eos_token_id = [2, 3] if eos_token_id is None else eos_token_id
+        self.bos_token_id = resolved_bos_token_id
+        self.eos_token_id = resolved_eos_token_id
+
+        super().__init__(
+            bos_token_id=resolved_bos_token_id,
+            eos_token_id=resolved_eos_token_id,
+            **kwargs,
+        )