diff --git a/python/sglang/srt/configs/__init__.py b/python/sglang/srt/configs/__init__.py
index 53fecdb38..8f86dcbef 100644
--- a/python/sglang/srt/configs/__init__.py
+++ b/python/sglang/srt/configs/__init__.py
@@ -57,6 +57,7 @@ from sglang.srt.configs.qwen3_5 import (
)
from sglang.srt.configs.qwen3_asr import Qwen3ASRConfig
from sglang.srt.configs.qwen3_next import Qwen3NextConfig
+from sglang.srt.configs.spark3 import Spark3Config
from sglang.srt.configs.step3_vl import (
Step3TextConfig,
Step3VisionEncoderConfig,
@@ -117,6 +118,7 @@ __all__ = [
"MiniCPMHybridConfig",
"Step3p5Config",
"MiniMaxM3VLConfig",
+ "Spark3Config",
"Step3p7Config",
"Qwen3ASRConfig",
"InklingAudioConfig",
diff --git a/python/sglang/srt/configs/spark3.py b/python/sglang/srt/configs/spark3.py
new file mode 100644
index 000000000..65842dc9f
--- /dev/null
+++ b/python/sglang/srt/configs/spark3.py
@@ -0,0 +1,63 @@
+from typing import Any, Optional
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Spark3Config(PretrainedConfig):
+ model_type = "spark3"
+ architectures = ["Spark3ForCausalLM"]
+
+ def __init__(
+ self,
+ hidden_size: int = 2048,
+ intermediate_size: int = 6656,
+ num_attention_heads: int = 8,
+ num_key_value_heads: int = 2,
+ num_hidden_layers: int = 28,
+ head_dim: int = 256,
+ headwise_attn_output_gate: bool = True,
+ sliding_window: int = 512,
+ vocab_size: int = 133120,
+ rms_norm_eps: float = 1e-6,
+ max_position_embeddings: int = 8192,
+ rope_parameters: Optional[dict[str, Any]] = None,
+ layer_types: list[str] = None,
+ tie_word_embeddings: Optional[bool] = None,
+ **kwargs,
+ ) -> None:
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads
+ self.num_hidden_layers = num_hidden_layers
+ self.head_dim = head_dim
+ self.headwise_attn_output_gate = headwise_attn_output_gate
+ self.sliding_window = sliding_window
+ self.vocab_size = vocab_size
+ self.rms_norm_eps = rms_norm_eps
+ self.max_position_embeddings = max_position_embeddings
+
+ if layer_types is not None:
+ layer_types = layer_types[: self.num_hidden_layers]
+ else:
+ layer_types = [
+ "sliding_attention" if bool((i + 1) % 4) else "full_attention"
+ for i in range(self.num_hidden_layers)
+ ]
+ self.layer_types = layer_types
+
+ if rope_parameters is not None:
+ self.rope_parameters = rope_parameters
+ else:
+ self.rope_parameters = {
+ "full_attention": {
+ "rope_theta": 5000000,
+ "partial_rotary_factor": 0.25,
+ },
+ "sliding_attention": {
+ "rope_theta": 10000,
+ "partial_rotary_factor": 1.0,
+ },
+ }
+
+ super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
diff --git a/python/sglang/srt/function_call/function_call_parser.py b/python/sglang/srt/function_call/function_call_parser.py
index 1530227b8..529c6e876 100644
--- a/python/sglang/srt/function_call/function_call_parser.py
+++ b/python/sglang/srt/function_call/function_call_parser.py
@@ -43,6 +43,7 @@ from sglang.srt.function_call.poolside_v1_detector import PoolsideV1Detector
from sglang.srt.function_call.pythonic_detector import PythonicDetector
from sglang.srt.function_call.qwen3_coder_detector import Qwen3CoderDetector
from sglang.srt.function_call.qwen25_detector import Qwen25Detector
+from sglang.srt.function_call.spark3_detector import Spark3Detector
from sglang.srt.function_call.step3_detector import Step3Detector
from sglang.srt.function_call.trinity_detector import TrinityDetector
from sglang.srt.function_call.utils import (
@@ -87,6 +88,7 @@ class FunctionCallParser:
"qwen": Qwen25Detector,
"qwen25": Qwen25Detector,
"qwen3_coder": Qwen3CoderDetector,
+ "spark": Spark3Detector,
"step3": Step3Detector,
"step3p5": Qwen3CoderDetector,
"minimax-m2": MinimaxM2Detector,
diff --git a/python/sglang/srt/function_call/spark3_detector.py b/python/sglang/srt/function_call/spark3_detector.py
new file mode 100644
index 000000000..17fea5b3c
--- /dev/null
+++ b/python/sglang/srt/function_call/spark3_detector.py
@@ -0,0 +1,263 @@
+import json
+import re
+from dataclasses import dataclass
+from typing import Any
+
+from sglang.srt.entrypoints.openai.protocol import Tool
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+ StreamingParseResult,
+ ToolCallItem,
+ _GetInfoFunc,
+)
+
+TOOL_CALL_BEGIN = ""
+TOOL_CALL_END = ""
+ARG_KEY_BEGIN = ""
+ARG_KEY_END = ""
+ARG_VALUE_BEGIN = ""
+ARG_VALUE_END = ""
+
+ARG_PAIR_PATTERN = re.compile(
+ rf"{re.escape(ARG_KEY_BEGIN)}(.*?){re.escape(ARG_KEY_END)}"
+ rf"{re.escape(ARG_VALUE_BEGIN)}(.*?){re.escape(ARG_VALUE_END)}",
+ re.DOTALL,
+)
+
+
+@dataclass(frozen=True)
+class _Spark3ToolCall:
+ name: str
+ arguments: dict[str, Any]
+
+ def arguments_json(self) -> str:
+ return json.dumps(
+ self.arguments,
+ ensure_ascii=False,
+ separators=(",", ":"),
+ )
+
+
+def _get_param_type(tools: list[Tool], function_name: str, param_name: str) -> str:
+ """Return a parameter's declared JSON Schema type, or ``string``."""
+ for tool in tools:
+ function = getattr(tool, "function", None)
+ if function is None or function.name != function_name:
+ continue
+ parameters = getattr(function, "parameters", None)
+ if not isinstance(parameters, dict):
+ continue
+ properties = parameters.get("properties")
+ if not isinstance(properties, dict):
+ continue
+ definition = properties.get(param_name)
+ if isinstance(definition, dict) and isinstance(definition.get("type"), str):
+ return definition["type"]
+ return "string"
+
+
+def _convert_value(value: str, param_type: str) -> Any:
+ """Convert Spark3 XML text according to the model's tool protocol."""
+ if value.lower() == "null":
+ return None
+
+ normalized_type = param_type.lower()
+ try:
+ if normalized_type in {"string", "str", "text"}:
+ return value
+ if normalized_type in {"integer", "int"}:
+ return int(value)
+ if normalized_type in {"number", "float"}:
+ number = float(value)
+ return int(number) if number.is_integer() else number
+ if normalized_type in {"boolean", "bool"}:
+ normalized_value = value.strip().lower()
+ if normalized_value not in {"true", "1", "false", "0"}:
+ raise ValueError(f"invalid boolean: {value}")
+ return normalized_value in {"true", "1"}
+ return json.loads(value)
+ except (TypeError, ValueError, json.JSONDecodeError):
+ try:
+ return json.loads(value)
+ except (TypeError, ValueError, json.JSONDecodeError):
+ return value
+
+
+def _parse_tool_call_xml(tool_xml: str, tools: list[Tool]) -> _Spark3ToolCall | None:
+ if not tool_xml.startswith(TOOL_CALL_BEGIN) or not tool_xml.endswith(TOOL_CALL_END):
+ return None
+
+ body = tool_xml[len(TOOL_CALL_BEGIN) : -len(TOOL_CALL_END)]
+ first_arg = body.find(ARG_KEY_BEGIN)
+ function_name = (body if first_arg < 0 else body[:first_arg]).strip()
+ if not function_name:
+ return None
+
+ arguments: dict[str, Any] = {}
+ for match in ARG_PAIR_PATTERN.finditer(body):
+ key, raw_value = match.group(1), match.group(2)
+ if not key:
+ continue
+ arguments[key] = _convert_value(
+ raw_value,
+ _get_param_type(tools, function_name, key),
+ )
+ return _Spark3ToolCall(name=function_name, arguments=arguments)
+
+
+def _partial_marker_suffix_length(text: str, marker: str) -> int:
+ """Length of the suffix that may become ``marker`` in the next chunk."""
+ for size in range(min(len(text), len(marker) - 1), 0, -1):
+ if text.endswith(marker[:size]):
+ return size
+ return 0
+
+
+class Spark3Detector(BaseFormatDetector):
+ """Detector for Spark3's XML-KV tool-call format.
+
+ Wire format::
+
+ function_name
+ keyvalue
+
+
+ Values are converted with the parameter's JSON Schema type. A complete
+ block is emitted atomically in streaming mode so XML fragments are never
+ exposed as JSON argument deltas.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.bot_token = TOOL_CALL_BEGIN
+ self.eot_token = TOOL_CALL_END
+
+ def has_tool_call(self, text: str) -> bool:
+ return TOOL_CALL_BEGIN in text
+
+ def _build_item(
+ self,
+ parsed: _Spark3ToolCall,
+ tools: list[Tool],
+ tool_index: int,
+ ) -> ToolCallItem | None:
+ validated = self.parse_base_json(
+ {"name": parsed.name, "arguments": parsed.arguments}, tools
+ )
+ if not validated:
+ return None
+ return ToolCallItem(
+ tool_index=tool_index,
+ name=parsed.name,
+ parameters=parsed.arguments_json(),
+ )
+
+ def _record_streamed_item(
+ self, parsed: _Spark3ToolCall, item: ToolCallItem
+ ) -> None:
+ self.prev_tool_call_arr.append(
+ {"name": parsed.name, "arguments": parsed.arguments}
+ )
+ self.streamed_args_for_tool.append(item.parameters)
+
+ def detect_and_parse(self, text: str, tools: list[Tool]) -> StreamingParseResult:
+ calls: list[ToolCallItem] = []
+ normal_parts: list[str] = []
+ cursor = 0
+
+ while cursor < len(text):
+ start = text.find(TOOL_CALL_BEGIN, cursor)
+ if start < 0:
+ normal_parts.append(text[cursor:])
+ break
+
+ normal_parts.append(text[cursor:start])
+ end = text.find(TOOL_CALL_END, start + len(TOOL_CALL_BEGIN))
+ if end < 0:
+ normal_parts.append(text[start:])
+ break
+
+ end += len(TOOL_CALL_END)
+ raw_tool_call = text[start:end]
+ parsed = _parse_tool_call_xml(raw_tool_call, tools)
+ if parsed is None:
+ normal_parts.append(raw_tool_call)
+ else:
+ item = self._build_item(parsed, tools, len(calls))
+ if item is not None:
+ calls.append(item)
+ cursor = end
+
+ return StreamingParseResult(
+ normal_text="".join(normal_parts),
+ calls=calls,
+ )
+
+ def parse_streaming_increment(
+ self, new_text: str, tools: list[Tool]
+ ) -> StreamingParseResult:
+ self._buffer += new_text
+ calls: list[ToolCallItem] = []
+ normal_parts: list[str] = []
+
+ while self._buffer:
+ start = self._buffer.find(TOOL_CALL_BEGIN)
+ if start < 0:
+ keep = _partial_marker_suffix_length(self._buffer, TOOL_CALL_BEGIN)
+ if keep:
+ normal_parts.append(self._buffer[:-keep])
+ self._buffer = self._buffer[-keep:]
+ else:
+ normal_parts.append(self._buffer)
+ self._buffer = ""
+ break
+
+ if start > 0:
+ normal_parts.append(self._buffer[:start])
+ self._buffer = self._buffer[start:]
+
+ end = self._buffer.find(TOOL_CALL_END, len(TOOL_CALL_BEGIN))
+ if end < 0:
+ break
+
+ end += len(TOOL_CALL_END)
+ raw_tool_call = self._buffer[:end]
+ self._buffer = self._buffer[end:]
+ parsed = _parse_tool_call_xml(raw_tool_call, tools)
+ if parsed is None:
+ normal_parts.append(raw_tool_call)
+ continue
+
+ item = self._build_item(parsed, tools, self.current_tool_id + 1)
+ if item is not None:
+ self.current_tool_id += 1
+ self._record_streamed_item(parsed, item)
+ calls.append(item)
+
+ return StreamingParseResult(
+ normal_text="".join(normal_parts),
+ calls=calls,
+ )
+
+ def finish(self, tools: list[Tool]) -> StreamingParseResult:
+ del tools
+ pending = self._buffer
+ self._buffer = ""
+ if TOOL_CALL_BEGIN in pending:
+ # A complete opening marker means this is a truncated protocol
+ # block, not user-visible text. Partial marker prefixes are still
+ # released because the stream has ended and they cannot become a
+ # tool call anymore.
+ pending = pending[: pending.find(TOOL_CALL_BEGIN)]
+ return StreamingParseResult(normal_text=pending)
+
+ def supports_structural_tag(self) -> bool:
+ return False
+
+ def parses_required_natively(self) -> bool:
+ return True
+
+ def structure_info(self) -> _GetInfoFunc:
+ raise NotImplementedError(
+ "Spark3 XML arguments cannot be represented by legacy structural tags"
+ )
diff --git a/python/sglang/srt/models/spark3.py b/python/sglang/srt/models/spark3.py
new file mode 100644
index 000000000..f5fd231b3
--- /dev/null
+++ b/python/sglang/srt/models/spark3.py
@@ -0,0 +1,564 @@
+import logging
+from typing import Iterable, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from sglang.srt.distributed import get_pp_group
+from sglang.srt.layers.activation import GeluAndMul
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+ ColumnParallelLinear,
+ MergedColumnParallelLinear,
+ QKVParallelLinear,
+ RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
+from sglang.srt.layers.vocab_parallel_embedding import (
+ ParallelLMHead,
+ VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import (
+ default_weight_loader,
+)
+from sglang.srt.platforms import current_platform
+from sglang.srt.runtime_context import get_parallel
+from sglang.srt.utils import add_prefix, make_layers
+
+Spark3Config = None
+
+logger = logging.getLogger(__name__)
+
+
+# Aligned with HF's implementation, using sliding window inclusive with the last token
+# SGLang assumes exclusive
+def _get_attention_sliding_window_size(config):
+ return config.sliding_window - 1
+
+
+class Spark3MLP(nn.Module):
+ def __init__(
+ self,
+ hidden_size: int,
+ intermediate_size: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ reduce_results: bool = True,
+ ) -> None:
+ super().__init__()
+ self.gate_up_proj = MergedColumnParallelLinear(
+ hidden_size,
+ [intermediate_size] * 2,
+ bias=False,
+ quant_config=quant_config,
+ prefix=add_prefix("gate_up_proj", prefix),
+ )
+ self.down_proj = RowParallelLinear(
+ intermediate_size,
+ hidden_size,
+ bias=False,
+ quant_config=quant_config,
+ prefix=add_prefix("down_proj", prefix),
+ reduce_results=reduce_results,
+ )
+ self.act_fn = GeluAndMul()
+
+ def forward(
+ self,
+ x,
+ forward_batch=None,
+ ):
+ gate_up, _ = self.gate_up_proj(x)
+ x = self.act_fn(gate_up)
+ x, _ = self.down_proj(x)
+ return x
+
+
+class Spark3Attention(nn.Module):
+ def __init__(
+ self,
+ hidden_size: int,
+ num_heads: int,
+ num_kv_heads: int,
+ head_dim: Optional[int] = None,
+ layer_id: int = 0,
+ rope_theta: float = 10000,
+ partial_rotary_factor: float = 1.0,
+ max_position_embeddings: int = 8192,
+ quant_config: Optional[QuantizationConfig] = None,
+ sliding_window: int = 512,
+ layer_type: str = "sliding_attention",
+ headwise_attn_output_gate: bool = True,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.hidden_size = hidden_size
+ self.total_num_heads = num_heads
+ attn_tp_rank = get_parallel().attn_tp_rank
+ attn_tp_size = get_parallel().attn_tp_size
+
+ assert self.total_num_heads % attn_tp_size == 0
+ self.num_heads = self.total_num_heads // attn_tp_size
+ self.total_num_kv_heads = num_kv_heads
+ if self.total_num_kv_heads >= attn_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 % attn_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 attn_tp_size % self.total_num_kv_heads == 0
+ self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
+ if head_dim is not None:
+ self.head_dim = head_dim
+ else:
+ self.head_dim = 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.max_position_embeddings = max_position_embeddings
+ self.partial_rotary_factor = partial_rotary_factor
+ self.headwise_attn_output_gate = headwise_attn_output_gate
+
+ self.q_k_v_proj = QKVParallelLinear(
+ hidden_size,
+ self.head_dim,
+ self.total_num_heads,
+ self.total_num_kv_heads,
+ bias=False,
+ quant_config=quant_config,
+ tp_rank=attn_tp_rank,
+ tp_size=attn_tp_size,
+ prefix=add_prefix("q_k_v_proj", prefix),
+ )
+ if self.headwise_attn_output_gate:
+ self.g_proj = ColumnParallelLinear(
+ hidden_size,
+ self.total_num_heads,
+ bias=False,
+ quant_config=None, # g_proj keeps bf16.
+ tp_rank=attn_tp_rank,
+ tp_size=attn_tp_size,
+ prefix=add_prefix("g_proj", prefix),
+ )
+
+ self.out_proj = RowParallelLinear(
+ self.total_num_heads * self.head_dim,
+ hidden_size,
+ bias=False,
+ quant_config=quant_config,
+ tp_rank=attn_tp_rank,
+ tp_size=attn_tp_size,
+ prefix=add_prefix("out_proj", prefix),
+ )
+
+ self.rotary_emb = get_rope(
+ self.head_dim,
+ rotary_dim=self.head_dim,
+ max_position=max_position_embeddings,
+ base=rope_theta,
+ rope_scaling=None,
+ partial_rotary_factor=partial_rotary_factor,
+ is_neox_style=True,
+ )
+ if layer_type not in ("sliding_attention", "full_attention"):
+ raise ValueError(f"Unsupported Spark3 layer_type: {layer_type}")
+ sliding_window_size = (
+ sliding_window if layer_type == "sliding_attention" else -1
+ )
+ self.attn = RadixAttention(
+ self.num_heads,
+ self.head_dim,
+ self.scaling,
+ num_kv_heads=self.num_kv_heads,
+ sliding_window_size=sliding_window_size,
+ layer_id=layer_id,
+ quant_config=quant_config,
+ prefix=add_prefix("attn", prefix),
+ )
+
+ def forward(
+ self,
+ positions: torch.Tensor,
+ hidden_states: torch.Tensor,
+ forward_batch: ForwardBatch,
+ ) -> torch.Tensor:
+ qkv, _ = self.q_k_v_proj(hidden_states)
+ q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+ q, k = self.rotary_emb(positions, q, k)
+ attn_output = self.attn(q, k, v, forward_batch)
+
+ if self.headwise_attn_output_gate:
+ g, _ = self.g_proj(hidden_states)
+ g = torch.sigmoid(g.float()).to(attn_output.dtype)
+ gate_output = attn_output.view(
+ attn_output.shape[0],
+ self.num_heads,
+ self.head_dim,
+ ) * g.unsqueeze(-1)
+ attn_output = gate_output.view(*attn_output.shape)
+
+ output, _ = self.out_proj(attn_output)
+ return output
+
+
+class Spark3DecoderLayer(nn.Module):
+ """A single transformer layer.
+
+ Transformer layer takes input with size [s, b, h] and returns an
+ output of the same size.
+ """
+
+ def __init__(
+ self,
+ config: Spark3Config,
+ layer_id: int = 0,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+
+ max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+ head_dim = getattr(config, "head_dim", None)
+ layer_type = config.layer_types[layer_id]
+ rope_params = config.rope_parameters[layer_type]
+ rope_theta = rope_params.get("rope_theta")
+ partial_rotary_factor = rope_params.get("partial_rotary_factor")
+ if partial_rotary_factor is None:
+ partial_rotary_factor = 1.0
+
+ self.self_attn = Spark3Attention(
+ hidden_size=config.hidden_size,
+ num_heads=config.num_attention_heads,
+ num_kv_heads=config.num_key_value_heads,
+ head_dim=head_dim,
+ layer_id=layer_id,
+ rope_theta=rope_theta,
+ partial_rotary_factor=partial_rotary_factor,
+ max_position_embeddings=max_position_embeddings,
+ quant_config=quant_config,
+ sliding_window=_get_attention_sliding_window_size(config),
+ layer_type=layer_type,
+ headwise_attn_output_gate=getattr(
+ config, "headwise_attn_output_gate", True
+ ),
+ prefix=add_prefix("self_attn", prefix),
+ )
+
+ # MLP
+ self.mlp = Spark3MLP(
+ config.hidden_size,
+ intermediate_size=config.intermediate_size,
+ quant_config=quant_config,
+ prefix=add_prefix("mlp", prefix),
+ )
+
+ self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = RMSNorm(
+ config.hidden_size, eps=config.rms_norm_eps
+ )
+
+ def forward(
+ self,
+ positions: torch.Tensor,
+ hidden_states: torch.Tensor,
+ forward_batch: ForwardBatch,
+ residual: Optional[torch.Tensor],
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ # Self Attention
+ if residual is None:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ else:
+ hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+ hidden_states = self.self_attn(
+ positions=positions,
+ hidden_states=hidden_states,
+ forward_batch=forward_batch,
+ )
+ hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+ hidden_states = self.mlp(hidden_states)
+
+ return hidden_states, residual
+
+
+class Spark3Model(nn.Module):
+ def __init__(
+ self,
+ config: Spark3Config,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.config = config
+ self.vocab_size = config.vocab_size
+ self.pp_group = get_pp_group()
+
+ if self.pp_group.is_first_rank:
+ self.embed_tokens = VocabParallelEmbedding(
+ config.vocab_size,
+ config.hidden_size,
+ quant_config=quant_config,
+ use_attn_tp_group=is_dp_attention_enabled(),
+ prefix=add_prefix("embed_tokens", prefix),
+ )
+ else:
+ self.embed_tokens = PPMissingLayer()
+
+ self.layers, self.start_layer, self.end_layer = make_layers(
+ config.num_hidden_layers,
+ lambda idx, prefix: Spark3DecoderLayer(
+ layer_id=idx,
+ config=config,
+ quant_config=quant_config,
+ prefix=prefix,
+ ),
+ pp_rank=self.pp_group.rank_in_group,
+ pp_size=self.pp_group.world_size,
+ prefix=add_prefix("layers", prefix),
+ )
+ if self.pp_group.is_last_rank:
+ self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ else:
+ self.norm = PPMissingLayer(return_tuple=True)
+
+ def get_input_embeddings(self) -> nn.Embedding:
+ return self.embed_tokens
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ forward_batch: ForwardBatch,
+ input_embeds: torch.Tensor = None,
+ pp_proxy_tensors: Optional[PPProxyTensors] = None,
+ ) -> Union[torch.Tensor, PPProxyTensors]:
+ if self.pp_group.is_first_rank:
+ if input_embeds is None:
+ hidden_states = self.embed_tokens(input_ids)
+ else:
+ hidden_states = input_embeds
+ residual = None
+ else:
+ assert pp_proxy_tensors is not None
+ hidden_states = pp_proxy_tensors["hidden_states"]
+ residual = pp_proxy_tensors["residual"]
+
+ for i in range(self.start_layer, self.end_layer):
+ layer = self.layers[i]
+ hidden_states, residual = layer(
+ positions,
+ hidden_states,
+ forward_batch,
+ residual,
+ )
+ if not self.pp_group.is_last_rank:
+ return PPProxyTensors(
+ {
+ "hidden_states": hidden_states,
+ "residual": residual,
+ }
+ )
+ else:
+ if hidden_states.shape[0] != 0:
+ if residual is None:
+ hidden_states = self.norm(hidden_states)
+ else:
+ hidden_states, _ = self.norm(hidden_states, residual)
+
+ return hidden_states
+
+
+class Spark3ForCausalLM(nn.Module):
+ def __init__(
+ self,
+ config: Spark3Config,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.pp_group = get_pp_group()
+ self.config = config
+ self.quant_config = quant_config
+ self.model = Spark3Model(
+ config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+ )
+
+ # handle the lm head on different pp ranks
+ if self.pp_group.is_last_rank:
+ if self.pp_group.world_size == 1 and config.tie_word_embeddings:
+ self.lm_head = self.model.embed_tokens
+ else:
+ self.lm_head = ParallelLMHead(
+ config.vocab_size,
+ config.hidden_size,
+ quant_config=quant_config,
+ prefix=add_prefix("lm_head", prefix),
+ )
+ else:
+ # ranks other than the last rank will have a placeholder layer
+ self.lm_head = PPMissingLayer()
+
+ self.logits_processor = LogitsProcessor(config)
+
+ def get_input_embeddings(self) -> nn.Embedding:
+ return self.model.embed_tokens
+
+ @torch.no_grad()
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ forward_batch: ForwardBatch,
+ input_embeds: torch.Tensor = None,
+ pp_proxy_tensors: Optional[PPProxyTensors] = None,
+ ) -> torch.Tensor:
+ hidden_states = self.model(
+ input_ids,
+ positions,
+ forward_batch,
+ input_embeds,
+ pp_proxy_tensors=pp_proxy_tensors,
+ )
+
+ if self.pp_group.is_last_rank:
+ return self.logits_processor(
+ input_ids,
+ hidden_states,
+ self.lm_head,
+ forward_batch,
+ )
+ else:
+ return hidden_states
+
+ @torch.no_grad()
+ def forward_split_prefill(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ forward_batch: ForwardBatch,
+ split_interval: Tuple[int, int], # [start, end) 0-based
+ input_embeds: torch.Tensor = None,
+ ):
+ start, end = split_interval
+ # embed
+ if start == 0:
+ if input_embeds is None:
+ forward_batch.hidden_states = self.model.embed_tokens(input_ids)
+ else:
+ forward_batch.hidden_states = input_embeds
+ # decoder layer
+ for i in range(start, end):
+ layer = self.model.layers[i]
+ forward_batch.hidden_states, forward_batch.residual = layer(
+ positions,
+ forward_batch.hidden_states,
+ forward_batch,
+ forward_batch.residual,
+ )
+
+ if end == self.model.config.num_hidden_layers:
+ # norm
+ hidden_states, _ = self.model.norm(
+ forward_batch.hidden_states, forward_batch.residual
+ )
+ forward_batch.hidden_states = hidden_states
+ # logits process
+ result = self.logits_processor(
+ input_ids, forward_batch.hidden_states, self.lm_head, forward_batch
+ )
+ else:
+ result = None
+
+ return result
+
+ @property
+ def start_layer(self):
+ return self.model.start_layer
+
+ @property
+ def end_layer(self):
+ return self.model.end_layer
+
+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+ stacked_params_mapping = [
+ ("gate_up_proj", "gate_proj", 0),
+ ("gate_up_proj", "up_proj", 1),
+ ]
+ params_dict = dict(self.named_parameters())
+ for name, loaded_weight in weights:
+ original_name = name
+ layer_id = get_layer_id(name)
+ if (
+ layer_id is not None
+ and hasattr(self.model, "start_layer")
+ and (
+ layer_id < self.model.start_layer
+ or layer_id >= self.model.end_layer
+ )
+ ):
+ continue
+
+ if self.config.tie_word_embeddings and "lm_head.weight" in name:
+ continue
+
+ if name in ("model.embedding.weight",):
+ name = "model.embed_tokens.weight"
+ if (
+ name == "model.embed_tokens.weight"
+ and self.config.tie_word_embeddings
+ and self.pp_group.world_size > 1
+ ):
+ if self.pp_group.is_last_rank:
+ name = "lm_head.weight"
+ elif not self.pp_group.is_first_rank:
+ continue
+
+ loaded = False
+ for param_name, weight_name, shard_id in stacked_params_mapping:
+ if weight_name not in name:
+ continue
+ mapped_name = name.replace(weight_name, param_name)
+ if mapped_name not in params_dict:
+ continue
+ param = params_dict[mapped_name]
+ weight_loader = getattr(param, "weight_loader", default_weight_loader)
+ weight_loader(param, loaded_weight, shard_id)
+ loaded = True
+ break
+ if loaded:
+ continue
+
+ if name in params_dict:
+ param = params_dict[name]
+ weight_loader = getattr(param, "weight_loader", default_weight_loader)
+ weight_loader(param, loaded_weight)
+ elif original_name in ("model.embedding.weight",):
+ continue
+ else:
+ logger.warning(f"Parameter {name} not found in params_dict")
+
+ def get_embed_and_head(self):
+ return self.model.embed_tokens.weight, self.lm_head.weight
+
+ def set_embed_and_head(self, embed, head):
+ del self.model.embed_tokens.weight
+ del self.lm_head.weight
+ self.model.embed_tokens.weight = embed
+ self.lm_head.weight = head
+ current_platform.empty_cache()
+ current_platform.synchronize()
+
+ def get_attention_sliding_window_size(self) -> int:
+ return _get_attention_sliding_window_size(self.config)
+
+
+EntryClass = [Spark3ForCausalLM]
diff --git a/python/sglang/srt/utils/hf_transformers/common.py b/python/sglang/srt/utils/hf_transformers/common.py
index c5f346786..4606733fb 100644
--- a/python/sglang/srt/utils/hf_transformers/common.py
+++ b/python/sglang/srt/utils/hf_transformers/common.py
@@ -66,6 +66,7 @@ from sglang.srt.configs import (
Qwen3_5MoeTextConfig,
Qwen3_5TextConfig,
Qwen3NextConfig,
+ Spark3Config,
Step3p5Config,
Step3p7Config,
Step3VLConfig,
@@ -102,6 +103,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
LocateAnythingConfig,
InternVLChatConfig,
LagunaConfig,
+ Spark3Config,
Step3VLConfig,
LongcatFlashConfig,
Olmo3Config,
diff --git a/test/registered/unit/function_call/test_spark3_detector.py b/test/registered/unit/function_call/test_spark3_detector.py
new file mode 100644
index 000000000..950e2d7fe
--- /dev/null
+++ b/test/registered/unit/function_call/test_spark3_detector.py
@@ -0,0 +1,202 @@
+"""Unit tests for Spark3Detector - no server, no model loading."""
+
+import json
+import unittest
+
+from sglang.srt.entrypoints.openai.protocol import Function, Tool
+from sglang.srt.environ import envs
+from sglang.srt.function_call.function_call_parser import FunctionCallParser
+from sglang.srt.function_call.spark3_detector import Spark3Detector
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=3, suite="base-a-test-cpu")
+
+
+def _xml(name: str, arguments: list[tuple[str, str]]) -> str:
+ pairs = "".join(
+ f"{key}{value}"
+ for key, value in arguments
+ )
+ return f"{name}{pairs}"
+
+
+def _tools():
+ return [
+ Tool(
+ type="function",
+ function=Function(
+ name="set_state",
+ parameters={
+ "type": "object",
+ "properties": {
+ "name": {"type": "string"},
+ "count": {"type": "integer"},
+ "ratio": {"type": "number"},
+ "active": {"type": "boolean"},
+ "items": {"type": "array"},
+ "metadata": {"type": "object"},
+ },
+ },
+ ),
+ ),
+ Tool(
+ type="function",
+ function=Function(
+ name="now",
+ parameters={"type": "object", "properties": {}},
+ ),
+ ),
+ ]
+
+
+class TestSpark3DetectorDetectAndParse(CustomTestCase):
+ def setUp(self):
+ self.tools = _tools()
+ self.detector = Spark3Detector()
+
+ def test_spark3_parser_is_registered(self):
+ self.assertIs(FunctionCallParser.ToolCallParserEnum["spark"], Spark3Detector)
+
+ def test_nonstream_parses_multiple_calls_and_preserves_normal_text(self):
+ text = (
+ "before"
+ + _xml(
+ "set_state",
+ [
+ ("name", "上海"),
+ ("count", "42"),
+ ("ratio", "2.5"),
+ ("active", "1"),
+ ("items", '["a", "b"]'),
+ ("metadata", '{"source":"spark"}'),
+ ],
+ )
+ + "middle"
+ + _xml("now", [])
+ + "after"
+ )
+
+ result = self.detector.detect_and_parse(text, self.tools)
+
+ self.assertEqual(result.normal_text, "beforemiddleafter")
+ self.assertEqual([call.tool_index for call in result.calls], [0, 1])
+ self.assertEqual([call.name for call in result.calls], ["set_state", "now"])
+ self.assertEqual(
+ json.loads(result.calls[0].parameters),
+ {
+ "name": "上海",
+ "count": 42,
+ "ratio": 2.5,
+ "active": True,
+ "items": ["a", "b"],
+ "metadata": {"source": "spark"},
+ },
+ )
+ self.assertEqual(json.loads(result.calls[1].parameters), {})
+
+ def test_null_and_conversion_fallbacks_match_spark3_protocol(self):
+ text = _xml(
+ "set_state",
+ [
+ ("name", "null"),
+ ("count", "not-an-int"),
+ ("active", "false"),
+ ("undeclared", "42"),
+ ],
+ )
+
+ result = self.detector.detect_and_parse(text, self.tools)
+
+ self.assertEqual(
+ json.loads(result.calls[0].parameters),
+ {
+ "name": None,
+ "count": "not-an-int",
+ "active": False,
+ "undeclared": "42",
+ },
+ )
+
+ def test_malformed_block_is_text_and_unknown_tool_honors_policy(self):
+ malformed = "xy"
+ result = self.detector.detect_and_parse(malformed, self.tools)
+ self.assertEqual(result.normal_text, malformed)
+ self.assertEqual(result.calls, [])
+
+ unknown = _xml("missing", [("value", "1")])
+ with envs.SGLANG_FORWARD_UNKNOWN_TOOLS.override(False):
+ result = self.detector.detect_and_parse(unknown, self.tools)
+ self.assertEqual(result.normal_text, "")
+ self.assertEqual(result.calls, [])
+ with envs.SGLANG_FORWARD_UNKNOWN_TOOLS.override(True):
+ result = self.detector.detect_and_parse(unknown, self.tools)
+ self.assertEqual(result.calls[0].name, "missing")
+ self.assertEqual(json.loads(result.calls[0].parameters), {"value": "1"})
+
+ def test_stream_end_flushes_partial_marker_and_required_stays_native(self):
+ result = self.detector.parse_streaming_increment("plainset_statecount", self.tools
+ )
+ self.assertEqual(result.normal_text, "plain")
+ self.assertEqual(truncated.finish(self.tools).normal_text, "")
+
+ self.assertFalse(self.detector.supports_structural_tag())
+ self.assertTrue(self.detector.parses_required_natively())
+ self.assertIs(
+ FunctionCallParser(self.tools, "spark").get_structure_constraint(
+ "required"
+ ),
+ None,
+ )
+
+
+class TestSpark3DetectorStreaming(CustomTestCase):
+ def setUp(self):
+ self.tools = _tools()
+
+ def test_streaming_character_chunks_match_nonstream_result(self):
+ text = (
+ "answer:"
+ + _xml("set_state", [("count", "42"), ("active", "0")])
+ + _xml("now", [])
+ + "done"
+ )
+ detector = Spark3Detector()
+ normal_parts = []
+ calls = []
+
+ for character in text:
+ result = detector.parse_streaming_increment(character, self.tools)
+ normal_parts.append(result.normal_text)
+ calls.extend(result.calls)
+ end = detector.finish(self.tools)
+ normal_parts.append(end.normal_text)
+ calls.extend(end.calls)
+
+ self.assertEqual("".join(normal_parts), "answer:done")
+ self.assertEqual([call.tool_index for call in calls], [0, 1])
+ self.assertEqual(
+ json.loads(calls[0].parameters), {"count": 42, "active": False}
+ )
+ self.assertEqual(json.loads(calls[1].parameters), {})
+ self.assertEqual(
+ detector.prev_tool_call_arr,
+ [
+ {"name": "set_state", "arguments": {"count": 42, "active": False}},
+ {"name": "now", "arguments": {}},
+ ],
+ )
+ self.assertEqual(
+ detector.streamed_args_for_tool,
+ ['{"count":42,"active":false}', "{}"],
+ )
+
+
+if __name__ == "__main__":
+ unittest.main()