Merge branch 'v0.9.2-dev' of http://10.16.6.30/dcutoolkit/deeplearing/vllm into v0.9.2-dev

docs/models/supported_models.md

@@ -598,6 +598,7 @@ Specified using `--task generate`.
 | `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni                                                             | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup>                   | `Qwen/Qwen2.5-Omni-7B`                                                                                                                                  |                        | ✅︎                          | ✅︎\*                  |
 | `SkyworkR1VChatModel`                        | Skywork-R1V-38B                                                          | T + I                                                                 | `Skywork/Skywork-R1V-38B`                                                                                                                               |                        | ✅︎                          | ✅︎                    |
 | `SmolVLMForConditionalGeneration`            | SmolVLM2                                                                 | T + I                                                                 | `SmolVLM2-2.2B-Instruct`                                                                                                                                | ✅︎                     |                             | ✅︎                    |
+| `Step3VLForConditionalGeneration` | Step3-VL | T + I<sup>+</sup> | `stepfun-ai/step3` | | ✅︎ | ✅︎ |
 | `TarsierForConditionalGeneration`            | Tarsier                                                                  | T + I<sup>E+</sup>                                                    | `omni-search/Tarsier-7b`,`omni-search/Tarsier-34b`                                                                                                      |                        | ✅︎                          | ✅︎                    |
 | `Tarsier2ForConditionalGeneration`<sup>^</sup> | Tarsier2                                                                 | T + I<sup>E+</sup> + V<sup>E+</sup>                                                | `omni-research/Tarsier2-Recap-7b`,`omni-research/Tarsier2-7b-0115`                                                                                      |                        | ✅︎                          | ✅︎                    |
 

tests/models/registry.py

@@ -266,6 +266,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "StableLMEpochForCausalLM": _HfExamplesInfo(os.path.join(models_path_prefix,"stabilityai/stablelm-zephyr-3b")),  # noqa: E501
     "StableLmForCausalLM": _HfExamplesInfo(os.path.join(models_path_prefix,"stabilityai/stablelm-3b-4e1t")),
     "Starcoder2ForCausalLM": _HfExamplesInfo(os.path.join(models_path_prefix,"bigcode/starcoder2-3b")),
+    "Step3TextForCausalLM": _HfExamplesInfo(os.path.join(models_path_prefix, "stepfun-ai/step3"),
+                                            trust_remote_code=True,
+                                            is_available_online=False),
     "SolarForCausalLM": _HfExamplesInfo(os.path.join(models_path_prefix,"upstage/solar-pro-preview-instruct")),
     "TeleChat2ForCausalLM": _HfExamplesInfo(os.path.join(models_path_prefix,"Tele-AI/TeleChat2-3B"),
                                             trust_remote_code=True),
@@ -423,6 +426,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "Qwen2_5OmniForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "Qwen/Qwen2.5-Omni-7B-AWQ")),  # noqa: E501
     "SkyworkR1VChatModel": _HfExamplesInfo(os.path.join(models_path_prefix, "Skywork/Skywork-R1V-38B")),
     "SmolVLMForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "HuggingFaceTB/SmolVLM2-2.2B-Instruct")),  # noqa: E501
+    "Step3VLForConditionalGeneration": _HfExamplesInfo(os.path.join(models_path_prefix, "stepfun-ai/step3"),
+                                                        trust_remote_code=True,
+                                                        is_available_online=False),
     "UltravoxModel": _HfExamplesInfo(os.path.join(models_path_prefix, "fixie-ai/ultravox-v0_5-llama-3_2-1b"),  # noqa: E501
                                      trust_remote_code=True),
     "TarsierForConditionalGeneration": _HfExamplesInfo("omni-research/Tarsier-7b",  # noqa: E501

vllm/entrypoints/openai/tool_parsers/__init__.py

@@ -15,6 +15,7 @@ from .minimax_tool_parser import MinimaxToolParser
 from .mistral_tool_parser import MistralToolParser
 from .phi4mini_tool_parser import Phi4MiniJsonToolParser
 from .pythonic_tool_parser import PythonicToolParser
+from .step3_tool_parser import Step3ToolParser
 from .xlam_tool_parser import xLAMToolParser
 
 __all__ = [
@@ -31,6 +32,7 @@ __all__ = [
     "PythonicToolParser", 
     "Phi4MiniJsonToolParser",
     "DeepSeekV3ToolParser", 
+    "Step3ToolParser",
     "xLAMToolParser", 
     "MinimaxToolParser",
     "Glm4MoeModelToolParser",

vllm/entrypoints/openai/tool_parsers/step3_tool_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import contextlib
+import json
+from collections.abc import Sequence
+from typing import Any, Optional, Union
+
+import regex as re
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module(["step3"])
+class Step3ToolParser(ToolParser):
+    """
+    Tool parser for a model that uses a specific XML-like format for tool calls.
+    This version uses a robust, stateful, cursor-based streaming parser and
+    consolidates tool arguments into a single message.
+    """
+
+    TOOL_CALLS_BEGIN = "<｜tool_calls_begin｜>"
+    TOOL_CALLS_END = "<｜tool_calls_end｜>"
+    TOOL_CALL_BEGIN = "<｜tool_call_begin｜>"
+    TOOL_CALL_END = "<｜tool_call_end｜>"
+    TOOL_SEP = "<｜tool_sep｜>"
+    SPECIAL_TOKENS = [
+        TOOL_CALLS_BEGIN, TOOL_CALLS_END, TOOL_CALL_BEGIN, TOOL_CALL_END
+    ]
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+        self.position = 0
+        # Explicit state flags for robust streaming
+        self.tool_block_started = False
+        self.tool_block_finished = False
+
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        if request.tools and request.tool_choice != 'none':
+            request.skip_special_tokens = False
+        return request
+
+    @staticmethod
+    def _parse_steptml_invoke(
+            action_text: str
+    ) -> tuple[Optional[str], Optional[dict[str, str]]]:
+        func_name_match = re.search(r'<steptml:invoke name="([^"]+)">',
+                                    action_text)
+        if not func_name_match:
+            return None, None
+        func_name = func_name_match.group(1)
+
+        params: dict[str, str] = {}
+        param_matches = re.findall(
+            r'<steptml:parameter name="([^"]+)">([^<]*)</steptml:parameter>',
+            action_text)
+        for name, value in param_matches:
+            params[name] = value.strip()
+        return func_name, params
+
+    def _cast_arguments(
+        self,
+        func_name: str,
+        params: dict[str, Any],
+        request: ChatCompletionRequest,
+    ) -> dict[str, Any]:
+        for tool in request.tools or []:
+            if tool.function.name == func_name:
+                schema = tool.function.parameters or {}
+                properties = schema.get("properties", {})
+                for key, value in params.items():
+                    if not isinstance(value, str):
+                        continue
+                    prop = properties.get(key, {})
+                    typ = prop.get("type")
+                    if typ == "string":
+                        params[key] = value.strip()
+                    elif typ == "integer":
+                        with contextlib.suppress(ValueError):
+                            params[key] = int(value)
+                    elif typ == "number":
+                        with contextlib.suppress(ValueError):
+                            params[key] = float(value)
+                    elif typ == "boolean":
+                        lower_val = value.lower()
+                        params[key] = lower_val == "true" if lower_val in (
+                            "true", "false") else value
+                    elif typ == "null":
+                        params[key] = None if value.lower(
+                        ) == "null" else value
+                break
+        return params
+
+    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,
+    ) -> Union[DeltaMessage, None]:
+
+        # The main loop processes the stream from the last known position.
+        while True:
+            if self.position >= len(current_text):
+                return None  # We've processed the entire stream.
+
+            unprocessed_text = current_text[self.position:]
+
+            # STATE: After all tools are done, all subsequent text is content.
+            if self.tool_block_finished:
+                self.position = len(current_text)
+                return DeltaMessage(content=unprocessed_text)
+
+            # STATE: Before the tool block has started.
+            if not self.tool_block_started:
+                if unprocessed_text.startswith(self.TOOL_CALLS_BEGIN):
+                    self.position += len(self.TOOL_CALLS_BEGIN)
+                    self.tool_block_started = True
+                    continue  # Token consumed, re-loop.
+
+                start_pos = unprocessed_text.find(self.TOOL_CALLS_BEGIN)
+                if start_pos == -1:
+                    if self.TOOL_CALLS_BEGIN.startswith(
+                            unprocessed_text.strip()) and unprocessed_text:
+                        return None  # It's a prefix, wait.
+                    self.position = len(current_text)
+                    return DeltaMessage(content=unprocessed_text)
+                else:
+                    content = unprocessed_text[:start_pos]
+                    self.position += len(content)
+                    return DeltaMessage(content=content)
+
+            # STATE: Inside the main tool block.
+            offset = len(unprocessed_text) - len(unprocessed_text.lstrip())
+            unprocessed_text = unprocessed_text.lstrip()
+            self.position += offset
+
+            if unprocessed_text.startswith(self.TOOL_CALLS_END):
+                self.position += len(self.TOOL_CALLS_END)
+                self.tool_block_finished = True
+                self.current_tool_id = -1
+                continue
+
+            # Check if we are between tool calls.
+            tool_finished = (
+                self.current_tool_id != -1 and
+                self.prev_tool_call_arr[self.current_tool_id].get("finished"))
+            if self.current_tool_id == -1 or tool_finished:
+                if unprocessed_text.startswith(self.TOOL_CALL_BEGIN):
+                    self.position += len(self.TOOL_CALL_BEGIN)
+                    if self.current_tool_id == -1:
+                        self.current_tool_id = 0
+                    else:
+                        self.current_tool_id += 1
+                    self.current_tool_name_sent = False
+                    while len(self.prev_tool_call_arr) <= self.current_tool_id:
+                        self.prev_tool_call_arr.append({})
+                    self.prev_tool_call_arr[
+                        self.current_tool_id]["finished"] = False
+                    continue
+
+                if self.TOOL_CALL_BEGIN.startswith(unprocessed_text):
+                    return None
+
+            # STATE: Parsing an active tool call.
+            if self.current_tool_id != -1 and not self.prev_tool_call_arr[
+                    self.current_tool_id].get("finished", False):
+                end_tool_pos = unprocessed_text.find(self.TOOL_CALL_END)
+                if end_tool_pos == -1:
+                    tool_body = unprocessed_text
+                else:
+                    tool_body = unprocessed_text[:end_tool_pos]
+
+                if end_tool_pos == -1 and self.TOOL_CALL_END.startswith(
+                        tool_body):
+                    return None
+
+                function_name, arguments = self._parse_steptml_invoke(
+                    tool_body)
+                if not function_name:
+                    return None
+
+                tool_call_arr = {
+                    "name": function_name,
+                    "parameters": arguments or {}
+                }
+
+                # Send the function name as soon as it's parsed.
+                if not self.current_tool_name_sent:
+                    self.current_tool_name_sent = True
+                    self.prev_tool_call_arr[self.current_tool_id].update(
+                        tool_call_arr)
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name))
+                    ])
+
+                # Update our internal state with the latest parsed arguments.
+                self.prev_tool_call_arr[
+                    self.current_tool_id].update(  # noqa: E501
+                        tool_call_arr)
+
+                # Only send arguments when the tool call is complete.
+                if end_tool_pos != -1:
+                    self.position += end_tool_pos + len(self.TOOL_CALL_END)
+                    self.prev_tool_call_arr[
+                        self.current_tool_id]["finished"] = True
+
+                    final_args = self._cast_arguments(
+                        function_name,
+                        tool_call_arr.get("parameters", {}),  # type: ignore
+                        request)
+                    if final_args:
+                        final_args_json = json.dumps(final_args,
+                                                     ensure_ascii=False)
+                        return DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=final_args_json))
+                        ])
+
+                # If tool is not finished, return None to wait for more tokens.
+                return None
+
+            return None
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        if self.TOOL_CALLS_BEGIN not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        pre_text, rest = model_output.split(self.TOOL_CALLS_BEGIN, 1)
+        if self.TOOL_CALLS_END not in rest:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        tool_block, post_text = rest.split(self.TOOL_CALLS_END, 1)
+        content = (pre_text + post_text).strip()
+
+        tool_calls: list[ToolCall] = []
+        call_parts = tool_block.split(self.TOOL_CALL_BEGIN)
+
+        for part in call_parts:
+            if not part or self.TOOL_CALL_END not in part:
+                continue
+
+            call_content = part.split(self.TOOL_CALL_END, 1)[0]
+            if self.TOOL_SEP not in call_content:
+                continue
+
+            type_part, invoke_part = call_content.split(self.TOOL_SEP, 1)
+            if type_part.strip() != "function":
+                continue
+
+            function_name, params_dict = self._parse_steptml_invoke(
+                invoke_part)
+
+            if function_name and params_dict is not None:
+                params_dict = self._cast_arguments(function_name, params_dict,
+                                                   request)
+                params_str = json.dumps(params_dict, ensure_ascii=False)
+                tool_calls.append(
+                    ToolCall(function=FunctionCall(name=function_name,
+                                                   arguments=params_str)))
+        if tool_calls:
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=content if content else None)
+        return ExtractedToolCallInformation(tools_called=False,
+                                            tool_calls=[],
+                                            content=model_output)
\ No newline at end of file

vllm/model_executor/models/registry.py

@@ -120,6 +120,7 @@ _TEXT_GENERATION_MODELS = {
     "Qwen3ForCausalLM": ("qwen3", "Qwen3ForCausalLM"),
     "Qwen3MoeForCausalLM": ("qwen3_moe", "Qwen3MoeForCausalLM"),
     "RWForCausalLM": ("falcon", "FalconForCausalLM"),
+    "Step3TextForCausalLM": ("step3_text", "Step3TextForCausalLM"),
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
@@ -228,6 +229,7 @@ _MULTIMODAL_MODELS = {
     "Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
     "Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
     "UltravoxModel": ("ultravox", "UltravoxModel"),
+    "Step3VLForConditionalGeneration": ("step3_vl", "Step3VLForConditionalGeneration"),  # noqa: E501
     "Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
     "TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"),  # noqa: E501
     "Tarsier2ForConditionalGeneration": ("qwen2_vl", "Tarsier2ForConditionalGeneration"),  # noqa: E501

vllm/reasoning/__init__.py

@@ -6,6 +6,7 @@ from .deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
 from .glm4_moe_reasoning_parser import Glm4MoeModelReasoningParser
 from .granite_reasoning_parser import GraniteReasoningParser
 from .qwen3_reasoning_parser import Qwen3ReasoningParser
+from .step3_reasoning_parser import Step3ReasoningParser
 
 __all__ = [
     "ReasoningParser",
@@ -14,4 +15,5 @@ __all__ = [
     "GraniteReasoningParser",
     "Qwen3ReasoningParser",
     "Glm4MoeModelReasoningParser",
+    "Step3ReasoningParser",
 ]

vllm/reasoning/step3_reasoning_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+from typing import Optional, Union
+
+import regex as re
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaMessage)
+from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+
+logger = init_logger(__name__)
+
+
+@ReasoningParserManager.register_module("step3")
+class Step3ReasoningParser(ReasoningParser):
+    """
+    Reasoning parser for Step3 model.
+    The Step3 model uses </think> token to denote the end of reasoning 
+    text. This parser extracts all content before </think> as reasoning content.
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase):
+        super().__init__(tokenizer)
+        self.think_end_token = "</think>"
+
+        self.reasoning_regex = re.compile(rf"(.*?){self.think_end_token}",
+                                          re.DOTALL)
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ReasoningParser "
+                "constructor during construction.")
+
+        self.think_end_token_id = self.vocab.get(self.think_end_token)
+        if self.think_end_token_id is None:
+            raise RuntimeError(
+                "Step3 reasoning parser could not locate think end "
+                "token in the tokenizer!")
+
+    def extract_reasoning_content_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],
+    ) -> Union[DeltaMessage, None]:
+        """
+        Extract reasoning content from a delta message.
+        Handles streaming output where previous + delta = current.
+        Uses token IDs for faster processing.
+        For text "abc</think>xyz":
+        - 'abc' goes to reasoning_content
+        - 'xyz' goes to content
+        """
+        # Skip single special token
+        if len(delta_token_ids
+               ) == 1 and delta_token_ids[0] == self.think_end_token_id:
+            return None
+
+        if self.think_end_token_id in delta_token_ids:
+            # </think> in delta, extract reasoning content and remaining content
+            end_index = delta_text.find(self.think_end_token)
+            reasoning_content = delta_text[:end_index]
+            content = delta_text[end_index + len(self.think_end_token):]
+            return DeltaMessage(reasoning_content=reasoning_content,
+                                content=content if content else None)
+        elif self.think_end_token_id in previous_token_ids:
+            # </think> already seen in previous text, everything is content
+            return DeltaMessage(content=delta_text)
+        else:
+            # No </think> seen yet, everything is reasoning
+            return DeltaMessage(reasoning_content=delta_text)
+
+    def extract_reasoning_content(
+            self, model_output: str, request: ChatCompletionRequest
+    ) -> tuple[Optional[str], Optional[str]]:
+
+        # Check if the model output contains the </think> token
+        if self.think_end_token not in model_output:
+            # If no </think> token, everything is reasoning content
+            return model_output, None
+        else:
+            # Find the first occurrence of </think>
+            end_index = model_output.find(self.think_end_token)
+            reasoning_content = model_output[:end_index]
+
+            # Content after </think> token
+            content = model_output[end_index + len(self.think_end_token):]
+
+            if len(content) == 0:
+                content = None
+
+            return reasoning_content, content
+
+    def is_reasoning_end(self, input_ids: list[int]) -> bool:
+        return self.think_end_token_id in input_ids
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        if self.think_end_token_id not in input_ids[:-1]:
+            return []
+        else:
+            return input_ids[input_ids.index(self.think_end_token_id) + 1:]
\ No newline at end of file

vllm/transformers_utils/config.py

@@ -39,6 +39,7 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
                                              MLPSpeculatorConfig, MPTConfig,
                                              NemotronConfig, NVLM_D_Config,
                                              OvisConfig, RWConfig,
+                                             Step3TextConfig, Step3VLConfig, 
                                              SkyworkR1VChatConfig, SolarConfig,
                                              Telechat2Config, UltravoxConfig)
 # yapf: enable
@@ -97,6 +98,8 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = {
     "skywork_chat": SkyworkR1VChatConfig,
     "telechat": Telechat2Config,
     "ultravox": UltravoxConfig,
+    "step3_vl": Step3VLConfig,
+    "step3_text": Step3TextConfig,
     **_CONFIG_REGISTRY_OVERRIDE_HF
 }
 

vllm/transformers_utils/configs/__init__.py

@@ -28,6 +28,9 @@ from vllm.transformers_utils.configs.ovis import OvisConfig
 from vllm.transformers_utils.configs.skyworkr1v import SkyworkR1VChatConfig
 from vllm.transformers_utils.configs.solar import SolarConfig
 from vllm.transformers_utils.configs.telechat2 import Telechat2Config
+from vllm.transformers_utils.configs.step3_vl import (Step3TextConfig,
+                                                      Step3VisionEncoderConfig,
+                                                      Step3VLConfig)
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
 
 __all__ = [
@@ -56,4 +59,7 @@ __all__ = [
     "SolarConfig",
     "Telechat2Config",
     "UltravoxConfig",
+    "Step3VLConfig",
+    "Step3VisionEncoderConfig",
+    "Step3TextConfig",
 ]

vllm/transformers_utils/configs/step3_vl.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any, Optional, Union
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Step3VisionEncoderConfig(PretrainedConfig):
+    model_type = "step3_vision_encoder"
+
+    def __init__(
+        self,
+        hidden_size=1792,
+        intermediate_size=3072,
+        output_hidden_size=4096,
+        num_hidden_layers=63,
+        num_attention_heads=16,
+        num_channels=3,
+        image_size=728,
+        patch_size=14,
+        hidden_act="quick_gelu",
+        layer_norm_eps=1e-5,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.output_hidden_size = output_hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        super().__init__(**kwargs)
+
+
+class Step3TextConfig(PretrainedConfig):
+    model_type = "step3_text"
+    architectures = ["Step3TextForCausalLM"]
+
+    def __init__(
+        self,
+        hidden_size: int = 7168,
+        intermediate_size: int = 18432,
+        num_attention_heads: int = 64,
+        num_attention_groups: int = 1,
+        num_hidden_layers: int = 61,
+        max_seq_len: int = 65536,
+        vocab_size: int = 128815,
+        rms_norm_eps: float = 1e-5,
+        moe_intermediate_size: int = 5120,
+        moe_num_experts: int = 48,
+        moe_top_k: int = 3,
+        rope_theta: float = 500000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embedding: int = 65536,
+        share_expert_dim: int = 5120,
+        share_q_dim: int = 2048,
+        head_dim: int = 256,
+        norm_expert_weight: bool = False,
+        moe_layers_enum: tuple[int,
+                               ...] = (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+                                       15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
+                                       25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
+                                       35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+                                       45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+                                       55, 56, 57, 58, 59),
+        **kwargs,
+    ) -> None:
+        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.moe_intermediate_size = moe_intermediate_size
+        self.moe_num_experts = moe_num_experts
+        self.moe_top_k = moe_top_k
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.max_position_embedding = max_position_embedding
+        self.share_expert_dim = share_expert_dim
+        self.share_q_dim = share_q_dim
+        self.head_dim = head_dim
+        self.norm_expert_weight = norm_expert_weight
+        self.moe_layers_enum = moe_layers_enum
+
+        super().__init__(**kwargs)
+
+
+class Step3VLConfig(PretrainedConfig):
+    model_type = "step3_vl"
+
+    def __init__(
+        self,
+        vision_config: Optional[Union[dict, Step3VisionEncoderConfig]] = None,
+        text_config: Optional[Union[dict, Step3TextConfig]] = None,
+        understand_projector_stride: int = 1,
+        projector_bias: bool = True,
+        image_token_id: int = 128001,
+        **kwargs,
+    ) -> None:
+        if vision_config is None:
+            vision_config = Step3VisionEncoderConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = Step3VisionEncoderConfig(**vision_config)
+        self.vision_config = vision_config
+
+        if text_config is None:
+            text_config = Step3TextConfig()
+        elif isinstance(text_config, dict):
+            text_config = Step3TextConfig(**text_config)
+        self.text_config = text_config
+
+        self.understand_projector_stride = understand_projector_stride
+        self.projector_bias = projector_bias
+        self.hidden_size = text_config.hidden_size
+        self.image_token_id = image_token_id
+
+        super().__init__(**kwargs)
\ No newline at end of file

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

@@ -690,7 +690,8 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
 
     def can_run_in_cudagraph(
             self, common_attn_metadata: CommonAttentionMetadata) -> bool:
-        #return common_attn_metadata.max_query_len == 1
+        if not self.use_spec_decode:
+            return common_attn_metadata.max_query_len == 1
         return self._num_prefills == 0