Add minimal vLLM 0.16.1 build repo for BI-V150

vllm/transformers_utils/__init__.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm import envs
+
+if envs.VLLM_USE_MODELSCOPE:
+    try:
+        # Patch here, before each import happens
+        import modelscope
+        from packaging import version
+
+        # patch_hub begins from modelscope>=1.18.1
+        if version.parse(modelscope.__version__) <= version.parse("1.18.0"):
+            raise ImportError(
+                "Using vLLM with ModelScope needs modelscope>=1.18.1, please "
+                "install by `pip install modelscope -U`"
+            )
+        from modelscope.utils.hf_util import patch_hub
+
+        # Patch hub to download models from modelscope to speed up.
+        patch_hub()
+    except ImportError as err:
+        raise ImportError(
+            "Please install modelscope>=1.18.1 via "
+            "`pip install modelscope>=1.18.1` to use ModelScope."
+        ) from err

vllm/transformers_utils/chat_templates/__init__.py

@@ -0,0 +1,5 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from .registry import get_chat_template_fallback_path
+
+__all__ = ["get_chat_template_fallback_path"]

vllm/transformers_utils/chat_templates/registry.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+from pathlib import Path
+from typing import TypeAlias
+
+from vllm.logger import init_logger
+
+logger = init_logger(__file__)
+
+CHAT_TEMPLATES_DIR = Path(__file__).parent
+
+ChatTemplatePath: TypeAlias = Path | Callable[[str], Path | None]
+
+
+def _get_qwen_chat_template_fallback(tokenizer_name_or_path: str) -> Path | None:
+    if tokenizer_name_or_path.endswith("-Chat"):
+        return CHAT_TEMPLATES_DIR / "template_chatml.jinja"
+
+    return CHAT_TEMPLATES_DIR / "template_basic.jinja"
+
+
+def _get_minicpmv_chat_template_fallback(tokenizer_name_or_path: str) -> Path | None:
+    # MiniCPM-V-4.5 version uses a dedicated template
+    if "4.5" in tokenizer_name_or_path or "4_5" in tokenizer_name_or_path:
+        return CHAT_TEMPLATES_DIR / "template_minicpmv45.jinja"
+
+    # Other versions use chatml template
+    return CHAT_TEMPLATES_DIR / "template_chatml.jinja"
+
+
+_MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK: dict[str, ChatTemplatePath] = {
+    "blip-2": CHAT_TEMPLATES_DIR / "template_blip2.jinja",
+    "chameleon": CHAT_TEMPLATES_DIR / "template_basic.jinja",
+    "clip": CHAT_TEMPLATES_DIR / "template_basic.jinja",
+    "deepseek_ocr": CHAT_TEMPLATES_DIR / "template_deepseek_ocr.jinja",
+    "deepseek_ocr2": CHAT_TEMPLATES_DIR / "template_deepseek_ocr.jinja",
+    "deepseek_vl_v2": CHAT_TEMPLATES_DIR / "template_deepseek_vl2.jinja",
+    "fuyu": CHAT_TEMPLATES_DIR / "template_fuyu.jinja",
+    "minicpmv": _get_minicpmv_chat_template_fallback,
+    "paligemma": CHAT_TEMPLATES_DIR / "template_basic.jinja",
+    "qwen": _get_qwen_chat_template_fallback,
+    "siglip": CHAT_TEMPLATES_DIR / "template_basic.jinja",
+    "siglip2": CHAT_TEMPLATES_DIR / "template_basic.jinja",
+}
+
+
+def register_chat_template_fallback_path(
+    model_type: str,
+    chat_template: ChatTemplatePath,
+) -> None:
+    if model_type in _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK:
+        logger.warning(
+            "Model type %s already has a chat template registered. "
+            "It will be overwritten by the new chat template %s.",
+            model_type,
+            chat_template,
+        )
+
+    _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK[model_type] = chat_template
+
+
+def get_chat_template_fallback_path(
+    model_type: str,
+    tokenizer_name_or_path: str,
+) -> Path | None:
+    chat_template = _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK.get(model_type)
+    if callable(chat_template):
+        chat_template = chat_template(tokenizer_name_or_path)
+
+    if chat_template is None:
+        return None
+
+    return chat_template

vllm/transformers_utils/chat_templates/template_basic.jinja

@@ -0,0 +1,3 @@
+{%- for message in messages -%}
+    {{- message['content'] -}}
+{%- endfor -%}

vllm/transformers_utils/chat_templates/template_blip2.jinja

@@ -0,0 +1,11 @@
+{%- for message in messages -%}
+    {%- if message['role'] == 'user' -%}
+        {{- 'Question: ' + message['content'] + ' ' -}}
+    {%- elif message['role'] == 'assistant' -%}
+        {{- 'Answer: ' + message['content'] + ' ' -}}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {{- 'Answer:' -}}
+{% endif %}

vllm/transformers_utils/chat_templates/template_chatml.jinja

@@ -0,0 +1,10 @@
+{%- for message in messages -%}
+    {{- '<|im_start|>' + message['role'] + '\n' + message['content'] -}}
+    {%- if (loop.last and add_generation_prompt) or not loop.last -%}
+        {{- '<|im_end|>' + '\n' -}}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt and messages[-1]['role'] != 'assistant' -%}
+    {{- '<|im_start|>assistant\n' -}}
+{%- endif -%}

vllm/transformers_utils/chat_templates/template_deepseek_ocr.jinja

@@ -0,0 +1,14 @@
+{%- if messages[0]['role'] == 'system' -%}
+    {%- set system_message = messages[0]['content'] -%}
+    {%- set messages = messages[1:] -%}
+{%- else -%}
+    {% set system_message = '' -%}
+{%- endif -%}
+
+{{ bos_token + system_message }}
+{%- for message in messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
+    {%- endif -%}
+    {{ message['content'] }}
+{%- endfor -%}

vllm/transformers_utils/chat_templates/template_deepseek_vl2.jinja

@@ -0,0 +1,23 @@
+{%- if messages[0]['role'] == 'system' -%}
+    {%- set system_message = messages[0]['content'] -%}
+    {%- set messages = messages[1:] -%}
+{%- else -%}
+    {% set system_message = '' -%}
+{%- endif -%}
+
+{{ bos_token + system_message }}
+{%- for message in messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
+    {%- endif -%}
+
+    {%- if message['role'] == 'user' -%}
+        {{ '<|User|>: ' + message['content'] + '\n\n' }}
+    {%- elif message['role'] == 'assistant' -%}
+        {{ '<|Assistant|>: ' + message['content'] + eos_token + '\n\n' }}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {{ '<|Assistant|>: ' }}
+{%- endif -%}

vllm/transformers_utils/chat_templates/template_fuyu.jinja

@@ -0,0 +1,3 @@
+{%- for message in messages -%}
+    {{- message['content'] + '\n' -}}
+{%- endfor -%}

vllm/transformers_utils/chat_templates/template_minicpmv45.jinja

@@ -0,0 +1,93 @@
+{%- set enable_thinking = enable_thinking | default(false) %}
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning = '' %}
+        {%- if message.reasoning is defined and message.reasoning is not none %}
+            {%- set reasoning = message.reasoning %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

vllm/transformers_utils/config_parser_base.py

@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from transformers import PretrainedConfig
+
+
+class ConfigParserBase(ABC):
+    @abstractmethod
+    def parse(
+        self,
+        model: str | Path,
+        trust_remote_code: bool,
+        revision: str | None = None,
+        code_revision: str | None = None,
+        **kwargs,
+    ) -> tuple[dict, PretrainedConfig]:
+        raise NotImplementedError

vllm/transformers_utils/configs/__init__.py

@@ -0,0 +1,132 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Model configs may be defined in this directory for the following reasons:
+
+- There is no configuration file defined by HF Hub or Transformers library.
+- There is a need to override the existing config to support vLLM.
+- The HF model_type isn't recognized by the Transformers library but can
+  be mapped to an existing Transformers config, such as
+  deepseek-ai/DeepSeek-V3.2-Exp.
+"""
+
+from __future__ import annotations
+
+import importlib
+
+_CLASS_TO_MODULE: dict[str, str] = {
+    "AfmoeConfig": "vllm.transformers_utils.configs.afmoe",
+    "BagelConfig": "vllm.transformers_utils.configs.bagel",
+    "ChatGLMConfig": "vllm.transformers_utils.configs.chatglm",
+    "ColModernVBertConfig": "vllm.transformers_utils.configs.colmodernvbert",
+    "ColQwen3Config": "vllm.transformers_utils.configs.colqwen3",
+    "OpsColQwen3Config": "vllm.transformers_utils.configs.colqwen3",
+    "Qwen3VLNemotronEmbedConfig": "vllm.transformers_utils.configs.colqwen3",
+    "DeepseekVLV2Config": "vllm.transformers_utils.configs.deepseek_vl2",
+    "DotsOCRConfig": "vllm.transformers_utils.configs.dotsocr",
+    "EAGLEConfig": "vllm.transformers_utils.configs.eagle",
+    "FlexOlmoConfig": "vllm.transformers_utils.configs.flex_olmo",
+    "FunAudioChatConfig": "vllm.transformers_utils.configs.funaudiochat",
+    "FunAudioChatAudioEncoderConfig": "vllm.transformers_utils.configs.funaudiochat",
+    "HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
+    "HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
+    "HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
+    "IsaacConfig": "vllm.transformers_utils.configs.isaac",
+    # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
+    # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
+    # `FalconConfig` class from the official HuggingFace transformers library.
+    "RWConfig": "vllm.transformers_utils.configs.falcon",
+    "JAISConfig": "vllm.transformers_utils.configs.jais",
+    "Lfm2MoeConfig": "vllm.transformers_utils.configs.lfm2_moe",
+    "MedusaConfig": "vllm.transformers_utils.configs.medusa",
+    "MiDashengLMConfig": "vllm.transformers_utils.configs.midashenglm",
+    "MLPSpeculatorConfig": "vllm.transformers_utils.configs.mlp_speculator",
+    "MoonViTConfig": "vllm.transformers_utils.configs.moonvit",
+    "KimiLinearConfig": "vllm.transformers_utils.configs.kimi_linear",
+    "KimiVLConfig": "vllm.transformers_utils.configs.kimi_vl",
+    "KimiK25Config": "vllm.transformers_utils.configs.kimi_k25",
+    "NemotronConfig": "vllm.transformers_utils.configs.nemotron",
+    "NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
+    "Olmo3Config": "vllm.transformers_utils.configs.olmo3",
+    "OvisConfig": "vllm.transformers_utils.configs.ovis",
+    "PixelShuffleSiglip2VisionConfig": "vllm.transformers_utils.configs.isaac",
+    "RadioConfig": "vllm.transformers_utils.configs.radio",
+    "SpeculatorsConfig": "vllm.transformers_utils.configs.speculators.base",
+    "UltravoxConfig": "vllm.transformers_utils.configs.ultravox",
+    "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",
+    "Qwen3_5Config": "vllm.transformers_utils.configs.qwen3_5",
+    "Qwen3_5TextConfig": "vllm.transformers_utils.configs.qwen3_5",
+    "Qwen3_5MoeConfig": "vllm.transformers_utils.configs.qwen3_5_moe",
+    "Qwen3_5MoeTextConfig": "vllm.transformers_utils.configs.qwen3_5_moe",
+    "Tarsier2Config": "vllm.transformers_utils.configs.tarsier2",
+    # Special case: DeepseekV3Config is from HuggingFace Transformers
+    "DeepseekV3Config": "transformers",
+}
+
+__all__ = [
+    "AfmoeConfig",
+    "BagelConfig",
+    "ChatGLMConfig",
+    "ColModernVBertConfig",
+    "ColQwen3Config",
+    "OpsColQwen3Config",
+    "Qwen3VLNemotronEmbedConfig",
+    "DeepseekVLV2Config",
+    "DeepseekV3Config",
+    "DotsOCRConfig",
+    "EAGLEConfig",
+    "FlexOlmoConfig",
+    "FunAudioChatConfig",
+    "FunAudioChatAudioEncoderConfig",
+    "HunYuanVLConfig",
+    "HunYuanVLTextConfig",
+    "HunYuanVLVisionConfig",
+    "IsaacConfig",
+    "RWConfig",
+    "JAISConfig",
+    "Lfm2MoeConfig",
+    "MedusaConfig",
+    "MiDashengLMConfig",
+    "MLPSpeculatorConfig",
+    "MoonViTConfig",
+    "KimiLinearConfig",
+    "KimiVLConfig",
+    "KimiK25Config",
+    "NemotronConfig",
+    "NemotronHConfig",
+    "Olmo3Config",
+    "OvisConfig",
+    "PixelShuffleSiglip2VisionConfig",
+    "RadioConfig",
+    "SpeculatorsConfig",
+    "UltravoxConfig",
+    "Step3VLConfig",
+    "Step3VisionEncoderConfig",
+    "Step3TextConfig",
+    "Step3p5Config",
+    "Qwen3ASRConfig",
+    "Qwen3NextConfig",
+    "Qwen3_5Config",
+    "Qwen3_5TextConfig",
+    "Qwen3_5MoeConfig",
+    "Qwen3_5MoeTextConfig",
+    "Tarsier2Config",
+]
+
+
+def __getattr__(name: str):
+    if name in _CLASS_TO_MODULE:
+        module_name = _CLASS_TO_MODULE[name]
+        module = importlib.import_module(module_name)
+        return getattr(module, name)
+
+    raise AttributeError(f"module 'configs' has no attribute '{name}'")
+
+
+def __dir__():
+    return sorted(list(__all__))

vllm/transformers_utils/configs/afmoe.py

@@ -0,0 +1,87 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class AfmoeConfig(PretrainedConfig):
+    model_type = "afmoe"
+
+    def __init__(
+        self,
+        vocab_size: int = 200_192,
+        hidden_size: int = 2048,
+        intermediate_size: int = 6144,
+        moe_intermediate_size: int = 1408,
+        num_hidden_layers: int = 32,
+        num_dense_layers: int = 1,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int | None = None,
+        head_dim: int = 128,
+        hidden_act: str = "silu",
+        max_position_embeddings: int = 131072,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-5,
+        use_cache: bool = True,
+        tie_word_embeddings: bool = False,
+        rope_parameters: dict | None = None,
+        rope_scaling: dict | None = None,
+        num_experts: int = 64,
+        num_experts_per_tok: int = 6,
+        num_shared_experts: int = 2,
+        num_expert_groups: int = 1,
+        num_limited_groups: int = 1,
+        score_func: str = "sigmoid",
+        route_norm: bool = True,
+        route_scale: float = 1.0,
+        global_attn_every_n_layers: int = 4,
+        sliding_window: int = 2048,
+        layer_types: list[str] | None = None,
+        attention_dropout: float = 0.0,
+        mup_enabled: bool = False,
+        n_group: int = 1,
+        topk_group: int = 1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_dense_layers = num_dense_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads or num_attention_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        rope_theta = kwargs.pop("rope_theta", 10000.0)
+        if rope_parameters is None:
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+        self.rope_parameters = rope_parameters
+        self.rope_scaling = rope_scaling
+
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_shared_experts = num_shared_experts
+        self.num_expert_groups = num_expert_groups
+        self.num_limited_groups = num_limited_groups
+        self.score_func = score_func
+        self.route_norm = route_norm
+        self.route_scale = route_scale
+
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.sliding_window = sliding_window
+        self.layer_types = layer_types
+        self.attention_dropout = attention_dropout
+
+        self.mup_enabled = mup_enabled
+        self.n_group = n_group
+        self.topk_group = topk_group
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["AfmoeConfig"]

vllm/transformers_utils/configs/arctic.py

@@ -0,0 +1,216 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# ruff: noqa: E501
+# coding=utf-8
+# Copied from
+# https://huggingface.co/Snowflake/snowflake-arctic-instruct/blob/main/configuration_arctic.py
+"""Arctic model configuration"""
+
+from dataclasses import asdict, dataclass
+from typing import Any
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json",
+}
+
+
+@dataclass
+class ArcticLoRAConfig:
+    lora_r: int = 64
+    lora_alpha: float = 16
+    shard_base_weights: bool = False
+
+
+@dataclass
+class ArcticQuantizationConfig:
+    q_bits: int = 8
+    rounding: str = "nearest"
+    mantissa_bits: int = 3
+    group_size: int = 128
+
+
+class ArcticConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an
+    Arctic model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the #TODO(rsamdani): add what model has the default config..
+
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ArcticModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Arctic's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_parameters (`dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_theta` (`float`): The base period of the RoPE embeddings.
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+
+    ```python
+    >>> from transformers import ArcticModel, ArcticConfig
+
+    >>> # Initializing a Arctic 7B style configuration TODO(rsamdani): verify which model does the default configuration correspond to.
+    >>> configuration = ArcticConfig()
+
+    >>> # Initializing a model from the Arctic 7B style configuration
+    >>> model = ArcticModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "arctic"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_parameters: dict[str, Any] | None = None,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=1,
+        num_local_experts=8,
+        router_aux_loss_coef=0.001,
+        moe_layer_frequency=2,
+        parallel_attn_mlp_res=False,
+        moe_train_capacity_factor=1,
+        moe_eval_capacity_factor=1,
+        enable_expert_tensor_parallelism=False,
+        moe_min_capacity=0,
+        moe_token_dropping=True,
+        quantization=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        rope_theta = kwargs.pop("rope_theta", 1e6)
+        if rope_parameters is None:
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+        self.rope_parameters = rope_parameters
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.moe_layer_frequency = moe_layer_frequency
+        self.moe_train_capacity_factor = moe_train_capacity_factor
+        self.moe_eval_capacity_factor = moe_eval_capacity_factor
+        self.enable_expert_tensor_parallelism = enable_expert_tensor_parallelism
+        self.moe_min_capacity = moe_min_capacity
+        self.moe_token_dropping = moe_token_dropping
+        self.parallel_attn_mlp_res = parallel_attn_mlp_res
+        if isinstance(quantization, dict):
+            self.quantization = ArcticQuantizationConfig(**quantization)
+        else:
+            self.quantization = quantization
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_dict(cls, config_dict: dict[str, Any], **kwargs) -> "ArcticConfig":
+        result = super().from_dict(config_dict, **kwargs)
+        config = result[0] if isinstance(result, tuple) else result
+        if isinstance(config.quantization, dict):
+            config.quantization = ArcticQuantizationConfig(**config.quantization)
+        return result
+
+    def to_dict(self) -> dict[str, Any]:
+        ret = super().to_dict()
+        if isinstance(ret["quantization"], ArcticQuantizationConfig):
+            ret["quantization"] = asdict(ret["quantization"])
+        return ret

vllm/transformers_utils/configs/bagel.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from transformers import PretrainedConfig, SiglipVisionConfig
+from transformers.models.qwen2 import Qwen2Config
+
+
+class BagelConfig(PretrainedConfig):
+    """Configuration class for BAGEL model."""
+
+    model_type = "bagel"
+
+    def __init__(
+        self,
+        visual_gen: bool = True,
+        visual_und: bool = True,
+        llm_config: dict | Qwen2Config | None = None,
+        vit_config: dict | SiglipVisionConfig | None = None,
+        vae_config: dict | None = None,
+        latent_patch_size: int = 2,
+        max_latent_size: int = 32,
+        vit_max_num_patch_per_side: int = 70,
+        connector_act: str = "gelu_pytorch_tanh",
+        interpolate_pos: bool = False,
+        timestep_shift: float = 1.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.visual_gen = visual_gen
+        self.visual_und = visual_und
+
+        # Convert dict configs to proper config objects
+        if isinstance(llm_config, dict):
+            self.llm_config = Qwen2Config(**llm_config)
+        else:
+            self.llm_config = llm_config or Qwen2Config()
+
+        if isinstance(vit_config, dict):
+            self.vit_config = SiglipVisionConfig(**vit_config)
+        else:
+            self.vit_config = vit_config or SiglipVisionConfig()
+
+        self.vae_config = vae_config or {"z_channels": 16, "downsample": 8}
+        self.latent_patch_size = latent_patch_size
+        self.max_latent_size = max_latent_size
+        self.vit_max_num_patch_per_side = vit_max_num_patch_per_side
+        self.connector_act = connector_act
+        self.interpolate_pos = interpolate_pos
+        self.timestep_shift = timestep_shift
+
+    @property
+    def hidden_size(self) -> int:
+        """Return the hidden size of the language model."""
+        return self.llm_config.hidden_size

vllm/transformers_utils/configs/chatglm.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/zai-org/ChatGLM2-6B
+from transformers import PretrainedConfig
+
+
+class ChatGLMConfig(PretrainedConfig):
+    model_type = "chatglm"
+    attribute_map = {
+        "num_hidden_layers": "num_layers",
+        "n_head_kv": "multi_query_group_num",
+    }
+
+    def __init__(
+        self,
+        num_layers=28,
+        padded_vocab_size=65024,
+        hidden_size=4096,
+        ffn_hidden_size=13696,
+        kv_channels=128,
+        num_attention_heads=32,
+        seq_length=2048,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        layernorm_epsilon=1e-5,
+        rmsnorm=True,
+        apply_residual_connection_post_layernorm=False,
+        post_layer_norm=True,
+        add_bias_linear=False,
+        add_qkv_bias=False,
+        interleaved_qkv=False,
+        bias_dropout_fusion=True,
+        multi_query_attention=False,
+        multi_query_group_num=1,
+        apply_query_key_layer_scaling=True,
+        attention_softmax_in_fp32=True,
+        fp32_residual_connection=False,
+        quantization_bit=0,
+        pre_seq_len=None,
+        prefix_projection=False,
+        **kwargs,
+    ):
+        self.num_layers = num_layers
+        self.vocab_size = padded_vocab_size
+        self.padded_vocab_size = padded_vocab_size
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_attention_heads
+        self.seq_length = seq_length
+        # It is to be compatible with long lora.
+        self.max_position_embeddings = seq_length
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.layernorm_epsilon = layernorm_epsilon
+        self.rmsnorm = rmsnorm
+        self.apply_residual_connection_post_layernorm = (
+            apply_residual_connection_post_layernorm
+        )
+        self.post_layer_norm = post_layer_norm
+        self.add_bias_linear = add_bias_linear
+        self.add_qkv_bias = add_qkv_bias
+        self.bias_dropout_fusion = bias_dropout_fusion
+        self.multi_query_attention = multi_query_attention
+        self.multi_query_group_num = multi_query_group_num
+        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.fp32_residual_connection = fp32_residual_connection
+        self.quantization_bit = quantization_bit
+        self.pre_seq_len = pre_seq_len
+        self.prefix_projection = prefix_projection
+        self.interleaved_qkv = interleaved_qkv
+        super().__init__(**kwargs)

vllm/transformers_utils/configs/colmodernvbert.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Configuration for ColModernVBERT visual document retrieval model.
+
+ColModernVBERT combines SigLIP vision encoder + ModernBERT text encoder
+with a pixel shuffle connector and ColBERT-style 128-dim per-token embeddings.
+
+Reference: https://huggingface.co/ModernVBERT/colmodernvbert-merged
+"""
+
+from transformers import ModernBertConfig, PretrainedConfig, SiglipVisionConfig
+
+
+class ColModernVBertConfig(PretrainedConfig):
+    model_type = "colmodernvbert"
+
+    def __init__(
+        self,
+        embedding_dim: int = 128,
+        vlm_config: dict | None = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.embedding_dim = embedding_dim
+
+        if vlm_config is None:
+            vlm_config = {}
+
+        # Top-level VLM fields
+        self.image_token_id = vlm_config.get("image_token_id", 50407)
+        self.pixel_shuffle_factor = vlm_config.get("pixel_shuffle_factor", 4)
+        self.hidden_size = vlm_config.get("hidden_size", 768)
+        additional_vocab_size = vlm_config.get("additional_vocab_size", 40)
+
+        # Text config (ModernBERT)
+        text_cfg = vlm_config.get("text_config", {})
+        base_vocab = text_cfg.get("vocab_size", 50368)
+        self.text_config = ModernBertConfig(
+            vocab_size=base_vocab + additional_vocab_size,
+            hidden_size=text_cfg.get("hidden_size", 768),
+            intermediate_size=text_cfg.get("intermediate_size", 1152),
+            num_hidden_layers=text_cfg.get("num_hidden_layers", 22),
+            num_attention_heads=text_cfg.get("num_attention_heads", 12),
+            mlp_bias=text_cfg.get("mlp_bias", False),
+            max_position_embeddings=vlm_config.get("max_position_embeddings", 8192),
+        )
+
+        # Vision config (SigLIP)
+        vis_cfg = vlm_config.get("vision_config", {})
+        self.vision_config = SiglipVisionConfig(
+            hidden_size=vis_cfg.get("embed_dim", 768),
+            image_size=vis_cfg.get("image_size", 512),
+            patch_size=vis_cfg.get("patch_size", 16),
+            num_hidden_layers=vis_cfg.get("num_hidden_layers", 12),
+            intermediate_size=vis_cfg.get("intermediate_size", 3072),
+            num_attention_heads=vis_cfg.get("num_attention_heads", 12),
+        )
+
+    @property
+    def image_seq_len(self) -> int:
+        ps = self.vision_config.image_size // self.vision_config.patch_size
+        return (ps * ps) // (self.pixel_shuffle_factor**2)
+
+    def get_text_config(self, **kwargs):
+        return self.text_config

vllm/transformers_utils/configs/colqwen3.py

@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+ColQwen3 configuration that extends Qwen3VLConfig with embedding projection
+fields. This allows ColQwen3 models to be loaded without trust_remote_code
+by mapping their custom model_type (colqwen3, ops_colqwen3, etc.) to a
+standard config class that vLLM understands.
+
+Supported model_types:
+- colqwen3 (TomoroAI/tomoro-colqwen3-embed-8b)
+- ops_colqwen3 (OpenSearch-AI/Ops-Colqwen3-4B)
+- qwen3_vl_nemotron_embed (nvidia/nemotron-colembed-vl-8b-v2)
+"""
+
+from transformers.models.qwen3_vl.configuration_qwen3_vl import Qwen3VLConfig
+
+
+class ColQwen3Config(Qwen3VLConfig):
+    """Configuration class for ColQwen3 models.
+
+    Extends Qwen3VLConfig with additional fields used by ColQwen3 variants
+    for the embedding projection layer.
+    """
+
+    # Accept any ColQwen3 variant model_type
+    model_type = "colqwen3"
+
+    def __init__(
+        self,
+        embed_dim: int | None = None,
+        dims: int | None = None,
+        dim: int | None = None,
+        projection_dim: int | None = None,
+        colbert_dim: int | None = None,
+        pooling: str | None = None,
+        **kwargs,
+    ):
+        # Store embedding projection config fields
+        self.embed_dim = embed_dim
+        self.dims = dims
+        self.dim = dim
+        self.projection_dim = projection_dim
+        self.colbert_dim = colbert_dim
+        self.pooling = pooling
+
+        super().__init__(**kwargs)
+
+
+class OpsColQwen3Config(ColQwen3Config):
+    """Configuration for OpenSearch-AI ColQwen3 variants."""
+
+    model_type = "ops_colqwen3"
+
+
+class Qwen3VLNemotronEmbedConfig(ColQwen3Config):
+    """Configuration for NVIDIA Nemotron ColEmbed variants."""
+
+    model_type = "qwen3_vl_nemotron_embed"

vllm/transformers_utils/configs/deepseek_vl2.py

@@ -0,0 +1,130 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py#L115-L268
+
+from transformers import DeepseekV2Config, PretrainedConfig
+
+
+class VisionEncoderConfig(PretrainedConfig):
+    model_type: str = "vision"
+
+    model_name: str = "vit_so400m_patch14_siglip_384.webli"
+    image_size: int = 384
+    patch_size: int = 16
+    width: int = 1024
+    layers: int = 24
+    heads: int = 16
+    mlp_ratio: int = 4
+    global_pool: str = "map"
+    ignore_head: bool = True
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+    weight_init: str = "skip"
+    deterministic: bool = False
+    num_recomputing_layers: int = 0
+
+    def __init__(
+        self,
+        model_name: str = "vit_so400m_patch14_siglip_384.webli",
+        image_size: int = 384,
+        patch_size: int = 16,
+        width: int = 1024,
+        layers: int = 24,
+        heads: int = 16,
+        mlp_ratio: int = 4,
+        global_pool: str = "map",
+        ignore_head: bool = True,
+        class_token: bool = False,
+        num_classes: int = 0,
+        use_checkpoint: bool = False,
+        **kwargs,
+    ):
+        self.model_name = model_name
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.width = width
+        self.layers = layers
+        self.heads = heads
+        self.mlp_ratio = mlp_ratio
+        self.global_pool = global_pool
+        self.ignore_head = ignore_head
+        self.class_token = class_token
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+
+        super().__init__(**kwargs)
+
+
+class MlpProjectorConfig(PretrainedConfig):
+    model_type = "mlp_projector"
+    projector_type: str = "downsample_mlp_gelu"
+    input_dim: int = 1152
+    n_embed: int = 2048
+    depth: int = 2
+    mlp_ratio: int = 1
+    downsample_ratio: int = 2
+    token_pooling: bool = False
+
+    def __init__(
+        self,
+        projector_type: str = "downsample_mlp_gelu",
+        input_dim: int = 1152,
+        n_embed: int = 2048,
+        depth: int = 2,
+        mlp_ratio: int = 1,
+        downsample_ratio: int = 2,
+        **kwargs,
+    ):
+        self.projector_type = projector_type
+        self.input_dim = input_dim
+        self.n_embed = n_embed
+        self.depth = depth
+        self.mlp_ratio = mlp_ratio
+        self.downsample_ratio = downsample_ratio
+
+        super().__init__(**kwargs)
+
+
+class DeepseekVLV2Config(PretrainedConfig):
+    model_type = "deepseek_vl_v2"
+    architectures: list[str] | None = None
+    vision_config: VisionEncoderConfig
+    projector_config: MlpProjectorConfig
+
+    tile_tag: str = "2D"
+    global_view_pos: str = "head"
+    candidate_resolutions: tuple[tuple[int, int]] = ((384, 384),)
+
+    def __init__(
+        self,
+        tile_tag: str = "tile_tag",
+        global_view_pos: str = "head",
+        candidate_resolutions: tuple[tuple[int, int]] = ((384, 384),),
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        if self.architectures is None:
+            self.architectures = ["DeepseekVLV2ForCausalLM"]
+
+        vision_config = kwargs.get("vision_config", {})
+        self.vision_config = VisionEncoderConfig(**vision_config)
+
+        projector_config = kwargs.get("projector_config", {})
+        self.projector_config = MlpProjectorConfig(**projector_config)
+
+        language_config = kwargs.get("language_config", {})
+        self.text_config = DeepseekV2Config(**language_config)
+
+        self.tile_tag = tile_tag
+        self.global_view_pos = global_view_pos
+        self.candidate_resolutions = candidate_resolutions
+        self.vocab_size = self.text_config.vocab_size
+
+        # update model_type for OCR models
+        if "DeepseekOCRForCausalLM" in self.architectures:
+            self.model_type = "deepseek_ocr"
+        elif "DeepseekOCR2ForCausalLM" in self.architectures:
+            self.model_type = "deepseek_ocr2"

vllm/transformers_utils/configs/dotsocr.py

@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.qwen2 import Qwen2Config
+
+
+class DotsVisionConfig(PretrainedConfig):
+    model_type: str = "dots_vit"
+
+    def __init__(
+        self,
+        embed_dim: int = 1536,  # vision encoder embed size
+        hidden_size: int = 1536,  # after merger hidden size
+        intermediate_size: int = 4224,
+        num_hidden_layers: int = 42,
+        num_attention_heads: int = 12,
+        num_channels: int = 3,
+        patch_size: int = 14,
+        spatial_merge_size: int = 2,
+        temporal_patch_size: int = 1,
+        rms_norm_eps: float = 1e-5,
+        use_bias: bool = False,
+        attn_implementation="flash_attention_2",
+        initializer_range=0.02,
+        init_merger_std=0.02,
+        is_causal=False,  # ve causal forward
+        post_norm=True,
+        gradient_checkpointing=False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_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.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.rms_norm_eps = rms_norm_eps
+        self.use_bias = use_bias
+        self.attn_implementation = attn_implementation
+        self.initializer_range = initializer_range
+        self.init_merger_std = init_merger_std
+        self.is_causal = is_causal
+        self.post_norm = post_norm
+        self.gradient_checkpointing = gradient_checkpointing
+
+
+class DotsOCRConfig(Qwen2Config):
+    model_type = "dots_ocr"
+
+    def __init__(
+        self,
+        image_token_id=151665,
+        video_token_id=151656,
+        vision_config: dict | None = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_config = DotsVisionConfig(**(vision_config or {}))
+
+    def save_pretrained(self, save_directory, **kwargs):
+        self._auto_class = None
+        super().save_pretrained(save_directory, **kwargs)

vllm/transformers_utils/configs/eagle.py

@@ -0,0 +1,90 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+
+from transformers import AutoConfig, DeepseekV2Config, PretrainedConfig
+
+
+class EAGLEConfig(PretrainedConfig):
+    model_type = "eagle"
+
+    def __init__(
+        self,
+        model: PretrainedConfig | dict | None = None,
+        truncated_vocab_size: int | None = None,
+        method: str | None = "eagle",
+        **kwargs,
+    ):
+        model_config: PretrainedConfig | DeepseekV2Config | None
+        if isinstance(model, dict):
+            model_config = AutoConfig.for_model(**model)
+        else:
+            model_config = model
+
+        for k, v in kwargs.items():
+            if k != "architectures" and k != "model_type" and hasattr(model_config, k):
+                setattr(model_config, k, v)
+
+        self.model = model_config
+
+        if self.model is None:
+            self.truncated_vocab_size = None
+        else:
+            self.truncated_vocab_size = (
+                self.model.vocab_size
+                if truncated_vocab_size is None
+                else truncated_vocab_size
+            )
+
+        # Eagle model name should follow naming convention of
+        # LlamaForCausalLM -> EagleLlamaForCausalLM
+        # LlamaForCausalLM -> Eagle3LlamaForCausalLM
+        # LlamaForCausalLMEagle3 -> LlamaForCausalLMEagle3
+        if method == "eagle":
+            assert self.model is not None, (
+                "model should not be None when method is eagle"
+            )
+            kwargs["architectures"] = [
+                f"Eagle{arch}" if not arch.startswith("Eagle") else arch
+                for arch in self.model.architectures
+            ]
+
+        elif method == "eagle3":
+            assert self.model is not None, (
+                "model should not be None when method is eagle3"
+            )
+            kwargs["architectures"] = [
+                arch
+                if arch.startswith("Eagle3") or arch.endswith("Eagle3")
+                else f"Eagle3{arch}"
+                for arch in self.model.architectures
+            ]
+        else:
+            raise ValueError(
+                f"Invalid method {method}. Supported methods are eagle and eagle3."
+            )
+
+        super().__init__(**kwargs)
+
+        if self.model is not None:
+            for k, v in self.model.to_dict().items():
+                if k not in kwargs:
+                    setattr(self, k, v)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: str | os.PathLike,
+        **kwargs,
+    ) -> "EAGLEConfig":
+        config_dict, kwargs = cls.get_config_dict(
+            pretrained_model_name_or_path, **kwargs
+        )
+        return cls.from_dict(config_dict, **kwargs)
+
+    def to_json_string(self, use_diff: bool = True) -> str:
+        # we override use_diff to False as initializing
+        # EAGLEConfig with default arguments is not supported
+        del use_diff
+        return super().to_json_string(use_diff=False)

vllm/transformers_utils/configs/falcon.py

@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/tiiuae/falcon-7b/blob/main/configuration_RW.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Falcon configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class RWConfig(PretrainedConfig):
+    model_type = "falcon"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_hidden_layers": "n_layer",
+        "num_attention_heads": "n_head",
+        "num_kv_heads": "n_head_kv",
+    }
+
+    def __init__(
+        self,
+        vocab_size=250880,
+        hidden_size=64,
+        n_layer=2,
+        n_head=8,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        multi_query=True,
+        n_head_kv=None,
+        alibi=False,
+        bias=False,
+        parallel_attn=False,
+        new_decoder_architecture=False,
+        **kwargs,
+    ) -> None:
+        self.vocab_size = vocab_size
+        # Backward compatibility with n_embed kwarg
+        n_embed = kwargs.pop("n_embed", None)
+        self.hidden_size = hidden_size if n_embed is None else n_embed
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.multi_query = multi_query
+        self.n_head_kv = 1 if n_head_kv is None else n_head_kv
+        self.alibi = alibi
+        self.bias = bias
+        self.parallel_attn = parallel_attn
+        self.new_decoder_architecture = new_decoder_architecture
+
+        if self.hidden_size == 8192:
+            # Hack for falcon-40b
+            self.new_decoder_architecture = True
+
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+    @property
+    def head_dim(self):
+        return self.hidden_size // self.n_head
+
+    @property
+    def rotary(self):
+        return not self.alibi

vllm/transformers_utils/configs/flex_olmo.py

@@ -0,0 +1,82 @@
+# 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 FlexOlmoConfig(PretrainedConfig):
+    model_type = "flex_olmo"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=100352,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-06,
+        use_cache=True,
+        pad_token_id=100277,
+        bos_token_id=None,
+        eos_token_id=100257,
+        tie_word_embeddings=False,
+        rope_parameters: dict[str, Any] | None = None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_experts_per_tok=5,
+        num_experts=7,
+        output_router_logits=False,
+        router_aux_loss_coef=0.01,
+        norm_topk_prob=False,
+        **kwargs,
+    ):
+        if "architectures" not in kwargs:
+            kwargs["architectures"] = ["FlexOlmoForCausalLM"]
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
+        rope_theta = kwargs.pop("rope_theta", 500000.0)
+        if "rope_theta" not in rope_parameters:
+            rope_parameters["rope_theta"] = rope_theta
+        self.rope_parameters = rope_parameters
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.norm_topk_prob = norm_topk_prob
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_parameters is not None and "type" in self.rope_parameters:
+            self.rope_parameters["rope_type"] = self.rope_parameters["type"]

vllm/transformers_utils/configs/funaudiochat.py

@@ -0,0 +1,124 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from transformers import PretrainedConfig
+
+# NOTE: Temporary shim for FunAudioChat checkpoints.
+# These checkpoints use `model_type="funaudiochat"`, which is not currently
+# recognized by released Transformers, and the public checkpoint does not
+# provide an `auto_map` to enable `trust_remote_code=True`.
+# Remove this file once Transformers adds native support (or the checkpoint
+# provides an `auto_map`) and vLLM can rely on `AutoConfig.from_pretrained()`.
+
+
+class FunAudioChatAudioEncoderConfig(PretrainedConfig):
+    model_type = "funaudiochat_audio_encoder"
+
+    def __init__(
+        self,
+        _attn_implementation: str | None = None,
+        num_mel_bins: int = 128,
+        encoder_layers: int = 32,
+        encoder_attention_heads: int = 20,
+        encoder_ffn_dim: int = 5120,
+        d_model: int = 1280,
+        dropout: float = 0.0,
+        attention_dropout: float = 0.0,
+        activation_function: str = "gelu",
+        activation_dropout: float = 0.0,
+        scale_embedding: bool = False,
+        initializer_range: float = 0.02,
+        max_source_positions: int = 1500,
+        n_window: int = 100,
+        output_dim: int = 3584,
+        bos_token_id: int | None = None,
+        codebook_size: int | None = None,
+        continuous_features_mode: str = "replace",
+        crq_transformer_config: dict | None = None,
+        eos_token_id: int | None = None,
+        group_size: int = 5,
+        enable_audio_invert_tower: bool = True,
+        pad_token_id: int | None = None,
+        **kwargs,
+    ) -> None:
+        attn_impl = kwargs.pop("_attn_implementation", None) or _attn_implementation
+        super().__init__(**kwargs)
+        # Match HF default for attention implementation selection.
+        self._attn_implementation = attn_impl or "sdpa"
+
+        self.num_mel_bins = num_mel_bins
+        self.d_model = d_model
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_function = activation_function
+        self.activation_dropout = activation_dropout
+        self.num_hidden_layers = encoder_layers
+        self.initializer_range = initializer_range
+        self.scale_embedding = scale_embedding
+        self.max_source_positions = max_source_positions
+        self.n_window = n_window
+        self.output_dim = output_dim
+
+        self.bos_token_id = bos_token_id
+        self.codebook_size = codebook_size
+        self.continuous_features_mode = continuous_features_mode
+        self.crq_transformer_config = crq_transformer_config
+        self.eos_token_id = eos_token_id
+        self.group_size = group_size
+        self.enable_audio_invert_tower = enable_audio_invert_tower
+        self.pad_token_id = pad_token_id
+
+
+class FunAudioChatConfig(PretrainedConfig):
+    model_type = "funaudiochat"
+    attribute_map = {
+        "audio_token_id": "audio_token_index",
+    }
+
+    def __init__(
+        self,
+        audio_config: PretrainedConfig | dict | None = None,
+        text_config: PretrainedConfig | dict | None = None,
+        audio_token_index: int = 151646,
+        ignore_index: int = -100,
+        hidden_size: int | None = None,
+        **kwargs,
+    ) -> None:
+        self.audio_token_index = audio_token_index
+        self.ignore_index = ignore_index
+
+        if isinstance(audio_config, dict):
+            audio_config.setdefault(
+                "model_type", FunAudioChatAudioEncoderConfig.model_type
+            )
+            audio_config = FunAudioChatAudioEncoderConfig(**audio_config)
+        elif audio_config is None:
+            audio_config = FunAudioChatAudioEncoderConfig()
+        self.audio_config = audio_config
+
+        if isinstance(text_config, dict):
+            # Default to qwen2 for backwards compatibility; FunAudioChat uses
+            # qwen3 in practice for recent checkpoints.
+            text_config.setdefault("model_type", "qwen2")
+            import transformers
+
+            text_cls = transformers.CONFIG_MAPPING[text_config["model_type"]]
+            text_config = text_cls(**text_config)
+        elif text_config is None:
+            import transformers
+
+            text_config = transformers.CONFIG_MAPPING["qwen2"]()
+        self.text_config = text_config
+
+        self.hidden_size = (
+            int(self.text_config.hidden_size)
+            if hidden_size is None
+            else int(hidden_size)
+        )
+
+        super().__init__(**kwargs)

vllm/transformers_utils/configs/hunyuan_vl.py

@@ -0,0 +1,322 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py
+
+from transformers import PretrainedConfig
+
+
+class HunYuanVLVisionConfig(PretrainedConfig):
+    model_type = "hunyuan_vl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        hidden_act="gelu",
+        hidden_size=1152,
+        intermediate_size=4304,
+        interpolate_mode="bilinear",
+        rms_norm_eps=1e-05,
+        learnable_mlp_pooling_size=0,
+        num_attention_heads=16,
+        num_key_value_heads=None,
+        num_channels=3,
+        num_hidden_layers=27,
+        out_hidden_size=4096,
+        patch_size=16,
+        remove_prenorm=True,
+        spatial_merge_size=2,
+        temporal_patch_size=1,
+        resize_resolution=2048,
+        img_max_token_num=4096,
+        max_image_size=2048,
+        video_max_image_size=768,
+        video_min_image_size=256,
+        min_image_size=512,
+        anyres_vit_max_image_size=2048,
+        max_vit_seq_len=16384,
+        text_hidden_size=3072,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.interpolate_mode = interpolate_mode
+        self.learnable_mlp_pooling_size = learnable_mlp_pooling_size
+        self.num_attention_heads = num_attention_heads
+        if not num_key_value_heads:
+            self.num_key_value_heads = num_attention_heads
+        else:
+            self.num_key_value_heads = num_key_value_heads
+        self.num_channels = num_channels
+        self.num_hidden_layers = num_hidden_layers
+        self.out_hidden_size = out_hidden_size
+        self.patch_size = patch_size
+        self.remove_prenorm = remove_prenorm
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.rms_norm_eps = rms_norm_eps
+
+        self.resize_resolution = resize_resolution
+        self.img_max_token_num = img_max_token_num
+        self.max_image_size = max_image_size
+        self.min_image_size = min_image_size
+        self.video_max_image_size = video_max_image_size
+        self.video_min_image_size = video_min_image_size
+        self.anyres_vit_max_image_size = anyres_vit_max_image_size
+        self.max_vit_seq_len = max_vit_seq_len
+        self.text_hidden_size = text_hidden_size
+
+
+class HunYuanVLTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`HunYuanVLTextConfig`]. It is used to instantiate an
+    HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the HunYuan-7B.
+    Hunyuan-7B-Instruct [tencent/Hunyuan-7B-Instruct](https://huggingface.co/tencent/Hunyuan-7B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 290943):
+            Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`HunYuanVLTextConfig`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations or shared MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        eod_token_id (int, *optional*, defaults to 3):
+            Token ID representing the end-of-document marker. Used to indicate the termination of a text sequence.
+            Example: In multi-document processing, this token helps the model distinguish between separate documents.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        head_dim (`int`, *optional*, defaults to 128):
+            The attention head dimension.
+    """  # noqa: E501
+
+    model_type = "hunyuan_vl_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=290943,
+        hidden_size=4096,
+        intermediate_size: int = 11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        eod_token_id=3,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = head_dim
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        # self._rope_scaling_validation()   # TODO: Need validation?
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and "
+                f"`factor` or `type` and `alpha`, got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        rope_scaling_alpha = self.rope_scaling.get("alpha", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                "`rope_scaling`'s type field must be one of ['linear', 'dynamic'], "
+                f"got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None and rope_scaling_alpha is None:
+            raise ValueError(
+                "`rope_scaling`'s factor or alpha field must be have one, "
+                "got both of none"
+            )
+        if rope_scaling_factor is not None and (
+            not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                "`rope_scaling`'s factor field must be a float > 1.0, "
+                f"got {rope_scaling_factor}"
+            )
+        if rope_scaling_alpha is not None and (
+            not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0
+        ):
+            raise ValueError(
+                "`rope_scaling`'s alpha field must be a float > 1.0, "
+                f"got {rope_scaling_alpha}"
+            )
+
+
+class HunYuanVLConfig(PretrainedConfig):
+    model_type = "hunyuan_vl"
+    sub_configs = {
+        "vision_config": HunYuanVLVisionConfig,
+        "text_config": HunYuanVLTextConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        im_start_id=120118,
+        im_end_id=120119,
+        image_token_id=120120,
+        im_newline_id=120121,
+        video_start_id=120122,
+        video_end_id=120123,
+        **kwargs,
+    ):
+        # We need to init super() here so that it does not reset values
+        # that are in text config to the BaseClass defaults. The Base
+        # config has many text related defaults and not all defaults are
+        # same as for `HunYuanVLTextConfig`.
+        super().__init__(**kwargs)
+
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            # For BC use all kwargs to init `TextConfig`
+            self.text_config = self.sub_configs["text_config"](**kwargs)
+
+        self.image_token_id = image_token_id
+        self.im_start_id = im_start_id
+        self.im_end_id = im_end_id
+        self.im_newline_id = im_newline_id
+        self.video_start_id = video_start_id
+        self.video_end_id = video_end_id
+
+        self.vision_config.text_hidden_size = self.text_config.hidden_size
+
+        # Attention implementation to use. It sets it recursively on sub-configs
+        # so we call it again in the end.
+        self._attn_implementation = kwargs.pop("attn_implementation", None)
+
+    def __setattr__(self, key, value):
+        if (
+            (text_config := super().__getattribute__("__dict__").get("text_config"))
+            is not None
+            and key not in ["dtype", "_attn_implementation_internal"]
+            and key in text_config.__dict__
+        ):
+            setattr(text_config, key, value)
+        else:
+            super().__setattr__(key, value)
+
+    def __getattribute__(self, key):
+        if "text_config" in super().__getattribute__("__dict__") and key not in [
+            "_name_or_path",
+            "model_type",
+            "dtype",
+            "_attn_implementation_internal",
+        ]:
+            text_config = super().__getattribute__("text_config")
+            if key in text_config.__dict__:
+                return getattr(text_config, key)
+
+        return super().__getattribute__(key)

vllm/transformers_utils/configs/isaac.py

@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+from transformers import Qwen3Config
+from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
+
+
+class PixelShuffleSiglip2VisionConfig(Siglip2VisionConfig):
+    """Vision configuration for Isaac with Pixel Shuffle support.
+
+    Extends Siglip2VisionConfig with additional fields for pixel shuffle.
+    """
+
+    model_type = "pixel_shuffle_siglip2"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        pixel_shuffle_scale_factor: int = 1,
+        num_patches: int = 256,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # Add our custom fields
+        self.pixel_shuffle_scale_factor = pixel_shuffle_scale_factor
+        self.num_patches = num_patches
+
+
+class IsaacConfig(Qwen3Config):
+    """Configuration class for Isaac multimodal model."""
+
+    model_type = "isaac"
+    sub_configs = {
+        "vision_config": PixelShuffleSiglip2VisionConfig,
+        "text_config": Qwen3Config,
+    }
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        vision_patch_size: int = 16,
+        vision_max_num_patches: int = 256,
+        vision_min_num_patches: int | None = None,
+        pixel_shuffle_scale: int = 1,
+        max_sequence_length: int = 16384,
+        vision_token: str = "<image>",
+        vision_attn_implementation: str | None = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        if isinstance(text_config, dict):
+            # from HF config
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            # For BC use all kwargs to init text config.
+            self.text_config = self.sub_configs["text_config"](**kwargs)
+        else:
+            # from Qwen3Config
+            self.text_config = text_config
+
+        # EventStreamProcessor parameters (for backward compatibility)
+        self.video_patch_size = vision_patch_size
+        self.vision_max_num_patches = vision_max_num_patches
+        self.vision_min_num_patches = vision_min_num_patches
+        self.pixel_shuffle_scale = pixel_shuffle_scale
+
+        # Processing parameters
+        self.max_sequence_length = max_sequence_length
+        self.vision_token = vision_token
+
+        # Handle vision config - PixelShuffleSiglip2VisionConfig instance
+        if isinstance(vision_config, dict):
+            self.vision_config = PixelShuffleSiglip2VisionConfig(**vision_config)
+        elif vision_config is None:
+            self.vision_config = PixelShuffleSiglip2VisionConfig()
+        else:
+            self.vision_config = vision_config
+
+        # Ensure compatibility with pretrained checkpoints
+        self.vision_config.pixel_shuffle_scale_factor = getattr(
+            self.vision_config,
+            "pixel_shuffle_scale_factor",
+            pixel_shuffle_scale,
+        )
+        self.vision_config.num_patches = getattr(
+            self.vision_config,
+            "num_patches",
+            vision_max_num_patches,
+        )
+        self.vision_attn_implementation = vision_attn_implementation
+
+
+__all__ = [
+    "IsaacConfig",
+    "PixelShuffleSiglip2VisionConfig",
+]

vllm/transformers_utils/configs/jais.py

@@ -0,0 +1,243 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+# Copyright 2023 Cerebras Systems.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""JAIS configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class JAISConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a
+    [`JAISModel`]. It is used to instantiate a JAIS model according to the
+    specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used
+    to control the model outputs. Read the documentation from
+    [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50257):
+            Vocabulary size of the JAIS model. Defines the number of different
+            tokens that can be represented by the
+            `inputs_ids` passed when calling [`JAISModel`].
+        n_positions (`int`, *optional*, defaults to 1024):
+            The maximum sequence length that this model might ever be used
+            with. Typically set this to something large just in case
+            (e.g., 512 or 1024 or 2048).
+        n_embd (`int`, *optional*, defaults to 768):
+            Dimensionality of the embeddings and hidden states.
+        n_layer (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        n_head (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the
+            Transformer encoder.
+        n_inner (`int`, *optional*, defaults to None):
+            Dimensionality of the inner feed-forward layers. `None` will set
+            it to 4 times n_embd
+        activation_function (`str`, *optional*, defaults to `"gelu"`):
+            Activation function, to be selected in the list
+            `["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`.
+        resid_pdrop (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in
+            the embeddings, encoder, and pooler.
+        embd_pdrop (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the embeddings.
+        attn_pdrop (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+            The epsilon to use in the layer normalization layers.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for
+            initializing all weight matrices.
+        scale_attn_weights (`bool`, *optional*, defaults to `True`):
+            Scale attention weights by dividing by sqrt(hidden_size)..
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values
+            attentions (not used by all models).
+        scale_attn_by_inverse_layer_idx (`bool`, *optional*, default `True`):
+            Whether to additionally scale attention weights
+            by `1 / layer_idx + 1`.
+        reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
+            Whether to scale keys (K) prior to computing attention
+            (dot-product)
+            and upcast attention dot-product/softmax to float() when training
+            with mixed precision.
+        position_embedding_type (`str`, *optional*, defaults to `"learned"`):
+            Positional embedding can be either `"alibi"` or `"learned"`.
+        mup_width_scale (`float`, *optional*, defaults to 1.0):
+            muP parameter to scale learning rate and initializers. Calculated
+            as (`d_model,0 / d_model`), where
+            `d_model` is the model's width and `d_model,0` is the proxy
+            model's width.
+        mup_embeddings_scale (`float`, *optional*, defaults to 1.0):
+            muP parameter to scale token and position embeddings.
+        mup_output_alpha (`float`, *optional*, defaults to 1.0):
+            muP parameter to scale output logits
+            (`output_logits_scale = mup_output_alpha * mup_width_scale`).
+        mup_scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`):
+            Scale attention weights by dividing by hidden_size instead of
+            sqrt(hidden_size). Need to set scale_attn_weights to `True` as
+            well.
+        alibi_scaling (`dict`, *optional*):
+            Dictionary containing the scaling configuration for ALiBi
+            embeddings. Currently only supports linear
+            scaling strategy. Can specify either the scaling `factor` (must be
+            a float greater than 1) for fixed scaling
+            or `train_seq_len` for dynamic scaling on input samples with
+            sequence length > `train_seq_len`. The expected
+            formats are `{"type": strategy name, "factor": scaling factor}` or
+            `{"type": strategy name,
+            "train_seq_len": training sequence length}`.
+        architectures (`list`, *optional*, defaults to ['JAISLMHeadModel']):
+            architecture names for Jais.
+
+    Example:
+
+    ```python
+    >>> from transformers import JAISConfig, JAISModel
+
+    >>> # Initializing a JAIS configuration
+    >>> configuration = JAISConfig()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = JAISModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "jais"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "hidden_size": "n_embd",
+        "max_position_embeddings": "n_positions",
+        "num_attention_heads": "n_head",
+        "num_hidden_layers": "n_layer",
+    }
+
+    def __init__(
+        self,
+        vocab_size=50257,
+        n_positions=1024,
+        n_embd=768,
+        n_layer=12,
+        n_head=12,
+        n_inner=None,
+        activation_function="gelu_new",
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        scale_attn_weights=True,
+        use_cache=True,
+        bos_token_id=50256,
+        eos_token_id=50256,
+        scale_attn_by_inverse_layer_idx=False,
+        reorder_and_upcast_attn=False,
+        position_embedding_type="learned",
+        mup_width_scale=1.0,
+        mup_embeddings_scale=1.0,
+        mup_output_alpha=1.0,
+        mup_scale_qk_dot_by_d=False,
+        alibi_scaling=None,
+        architectures=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.scale_attn_weights = scale_attn_weights
+        self.use_cache = use_cache
+        self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
+        self.reorder_and_upcast_attn = reorder_and_upcast_attn
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        self.position_embedding_type = position_embedding_type
+        self.mup_width_scale = mup_width_scale
+        self.mup_embeddings_scale = mup_embeddings_scale
+        self.mup_output_alpha = mup_output_alpha
+        self.mup_scale_qk_dot_by_d = mup_scale_qk_dot_by_d
+
+        self.alibi_scaling = alibi_scaling
+        self._alibi_scaling_validation()
+        if architectures is None:
+            architectures = ["JAISLMHeadModel"]
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            architectures=architectures,
+            **kwargs,
+        )
+
+    def _alibi_scaling_validation(self):
+        """
+        Validate the `alibi_scaling` configuration.
+        """
+        if self.alibi_scaling is None:
+            return
+
+        if not isinstance(self.alibi_scaling, dict) or len(self.alibi_scaling) != 2:
+            raise ValueError(
+                "`alibi_scaling` must be a dictionary with two fields, "
+                "`type` and `factor` or `type` and `train_seq_len`, "
+                f"got {self.alibi_scaling}"
+            )
+        alibi_scaling_type = self.alibi_scaling.get("type", None)
+        alibi_scaling_factor = self.alibi_scaling.get("factor", None)
+        alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None)
+        if alibi_scaling_type is None or alibi_scaling_type != "linear":
+            raise ValueError(
+                f"`alibi_scaling`'s type field must be 'linear', "
+                f"got {alibi_scaling_type}"
+            )
+        if (
+            alibi_scaling_factor is not None
+            and not isinstance(alibi_scaling_factor, float)
+            or (alibi_scaling_factor is not None and alibi_scaling_factor <= 1.0)
+        ):
+            raise ValueError(
+                f"`alibi_scaling`'s factor field must be a float > 1.0, "
+                f"got {alibi_scaling_factor}"
+            )
+        if (
+            alibi_dynamic_scaling is not None
+            and not isinstance(alibi_dynamic_scaling, int)
+            or (alibi_dynamic_scaling is not None and alibi_dynamic_scaling <= 1)
+        ):
+            raise ValueError(
+                f"`alibi_scaling`'s `train_seq_len` field must be an "
+                f"integer > 1, got {alibi_dynamic_scaling}"
+            )

vllm/transformers_utils/configs/kimi_k25.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Kimi-K2.5 Model Configuration.
+
+This configuration supports video-chunk as an internal modality type.
+A video-chunk is the smallest independently processable unit of video.
+"""
+
+from transformers import DeepseekV3Config
+from transformers.configuration_utils import PretrainedConfig
+
+
+class KimiK25VisionConfig(PretrainedConfig):
+    model_type = "kimi_k25_vision"
+
+    def __init__(
+        self,
+        # Vision Tower
+        patch_size: int = 14,
+        init_pos_emb_height: int = 64,
+        init_pos_emb_width: int = 64,
+        init_pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+        num_attention_heads: int = 16,
+        num_hidden_layers: int = 27,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        merge_kernel_size: tuple[int, int] = (2, 2),
+        video_attn_type: str = "spatial_temporal",
+        merge_type: str = "sd2_tpool",
+        # MM Projector
+        mm_projector_type: str = "patchmerger",
+        mm_hidden_size: int | None = None,
+        projector_hidden_act: str = "gelu",
+        projector_ln_eps: float = 1e-5,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        # Vision Tower
+        self.patch_size = patch_size
+        self.init_pos_emb_height = init_pos_emb_height
+        self.init_pos_emb_width = init_pos_emb_width
+        self.init_pos_emb_time = init_pos_emb_time
+        self.pos_emb_type = pos_emb_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.merge_kernel_size = merge_kernel_size
+        self.video_attn_type = video_attn_type
+        self.merge_type = merge_type
+        # MM Projector
+        self.mm_projector_type = mm_projector_type
+        if mm_hidden_size is not None:
+            self.mm_hidden_size = mm_hidden_size
+        else:
+            self.mm_hidden_size = hidden_size
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_ln_eps = projector_ln_eps
+
+
+class KimiK25Config(PretrainedConfig):
+    """Kimi-K2.5 model configuration.
+
+    Kimi-K2.5 extends Kimi-K2 with vision support using video-chunks.
+    A video-chunk consists of multiple consecutive frames
+    that are processed together with temporal pooling.
+
+    Args:
+        vision_config: Configuration for the vision tower and projector.
+        text_config: Configuration for the text model (DeepseekV3).
+        ignore_index: The ignore index for the loss function.
+        media_placeholder_token_id: The token ID for media placeholders.
+        pad_token_id: The token ID for padding.
+    """
+
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        vision_config: dict | KimiK25VisionConfig | None = None,
+        text_config: dict | DeepseekV3Config | None = None,
+        ignore_index: int = -100,
+        media_placeholder_token_id: int = 163605,
+        pad_token_id: int = 0,
+        use_unified_vision_chunk: bool = False,
+        video_placeholder: str = "<|kimi_k25_video_placeholder|>",
+        **kwargs,
+    ):
+        # Vision config
+        if vision_config is None:
+            vision_config = KimiK25VisionConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = KimiK25VisionConfig(**vision_config)
+        self.vision_config: KimiK25VisionConfig = vision_config
+
+        # Text config
+        if text_config is None:
+            text_config = DeepseekV3Config()
+        elif isinstance(text_config, dict):
+            text_config = DeepseekV3Config(**text_config)
+        self.text_config: DeepseekV3Config = text_config
+
+        # Set mm_hidden_size to text hidden size if not explicitly set
+        if self.vision_config.mm_hidden_size == self.vision_config.hidden_size:
+            self.vision_config.mm_hidden_size = self.text_config.hidden_size
+
+        # Other config
+        self.ignore_index = ignore_index
+        self.media_placeholder_token_id = media_placeholder_token_id
+        self.use_unified_vision_chunk = use_unified_vision_chunk
+        self.video_placeholder = video_placeholder
+
+        # Propagate quantization config from text model
+        if getattr(self.text_config, "quantization_config", None) is not None:
+            self.quantization_config = self.text_config.quantization_config
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+    @property
+    def hidden_size(self) -> int:
+        """Get hidden size from text config for compatibility."""
+        return self.text_config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        """Get vocab size from text config for compatibility."""
+        return self.text_config.vocab_size

vllm/transformers_utils/configs/kimi_linear.py

@@ -0,0 +1,148 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class KimiLinearConfig(PretrainedConfig):
+    model_type = "kimi_linear"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        model_type="kimi_linear",
+        vocab_size=163840,
+        hidden_size=4096,
+        head_dim=None,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        rope_parameters=None,
+        tie_word_embeddings=False,
+        moe_intermediate_size: int | None = None,
+        moe_renormalize: bool = True,
+        moe_router_activation_func: str = "sigmoid",
+        num_experts: int | None = None,
+        num_experts_per_token: int | None = None,
+        num_shared_experts: int = 0,
+        routed_scaling_factor: float = 1.0,
+        first_k_dense_replace: int = 0,
+        moe_layer_freq: int = 1,
+        use_grouped_topk: bool = True,
+        num_expert_group: int = 1,
+        topk_group: int = 1,
+        q_lora_rank: int | None = None,
+        kv_lora_rank: int | None = None,
+        qk_nope_head_dim: int | None = None,
+        qk_rope_head_dim: int | None = None,
+        v_head_dim: int | None = None,
+        mla_use_nope: bool | None = False,
+        num_nextn_predict_layers: int = 0,
+        linear_attn_config: dict | None = None,
+        **kwargs,
+    ):
+        self.model_type = model_type
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.head_dim = (
+            head_dim if head_dim is not None else hidden_size // num_attention_heads
+        )
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
+        rope_theta = kwargs.pop("rope_theta", 10000.0)
+        if "rope_theta" not in rope_parameters:
+            rope_parameters["rope_theta"] = rope_theta
+        self.rope_parameters = rope_parameters
+
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.mla_use_nope = mla_use_nope
+        # moe config
+        self.num_experts = num_experts
+        self.num_experts_per_token = num_experts_per_token
+        self.moe_renormalize = moe_renormalize
+        self.num_shared_experts = num_shared_experts
+        self.routed_scaling_factor = routed_scaling_factor
+        self.moe_router_activation_func = moe_router_activation_func
+        assert self.moe_router_activation_func in ("softmax", "sigmoid")
+        self.moe_intermediate_size = moe_intermediate_size
+        self.first_k_dense_replace = first_k_dense_replace
+        self.moe_layer_freq = moe_layer_freq
+        self.use_grouped_topk = use_grouped_topk
+        self.num_expert_group = num_expert_group
+        self.topk_group = topk_group
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+
+        if linear_attn_config is not None:
+            assert linear_attn_config["kda_layers"] is not None
+            assert linear_attn_config["full_attn_layers"] is not None
+        self.linear_attn_config = linear_attn_config
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @property
+    def is_mla(self):
+        return (
+            self.q_lora_rank is not None
+            or self.kv_lora_rank is not None
+            or self.qk_nope_head_dim is not None
+            or self.qk_rope_head_dim is not None
+            or self.v_head_dim is not None
+            or self.mla_use_nope is True
+        )
+
+    @property
+    def is_moe(self):
+        return self.num_experts is not None
+
+    @property
+    def is_linear_attn(self) -> bool:
+        return not (
+            self.linear_attn_config is None
+            or (
+                isinstance(self.linear_attn_config, dict)
+                and self.linear_attn_config["kda_layers"] is not None
+                and len(self.linear_attn_config["kda_layers"]) == 0
+            )
+        )
+
+    def is_kda_layer(self, layer_idx: int):
+        return (
+            self.linear_attn_config is not None
+            and (layer_idx + 1) in self.linear_attn_config["kda_layers"]
+        )

vllm/transformers_utils/configs/kimi_vl.py

@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
+
+from transformers import DeepseekV2Config
+from transformers.configuration_utils import PretrainedConfig
+
+from vllm.transformers_utils.configs.moonvit import MoonViTConfig
+
+
+class KimiVLConfig(PretrainedConfig):
+    model_type = "kimi_vl"
+
+    def __init__(
+        self,
+        vision_config: dict | MoonViTConfig | None = None,
+        text_config: dict | DeepseekV2Config | None = None,
+        ignore_index: int = -100,
+        media_placeholder_token_id: int = 163605,
+        pad_token_id: int = 0,
+        **kwargs,
+    ):
+        if vision_config is None:
+            vision_config = MoonViTConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = MoonViTConfig(**vision_config)
+        self.vision_config = vision_config
+
+        if text_config is None:
+            text_config = DeepseekV2Config()
+        elif isinstance(text_config, dict):
+            text_config = DeepseekV2Config(**text_config)
+        self.text_config = text_config
+
+        self.ignore_index = ignore_index
+        self.media_placeholder_token_id = media_placeholder_token_id
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)

vllm/transformers_utils/configs/lfm2_moe.py

@@ -0,0 +1,163 @@
+# 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 Lfm2MoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Lfm2MoeModel`]. It is used to instantiate a LFM2 Moe
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LFM2-8B-A1B model.
+    e.g. [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 65536):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Lfm2Model`]
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 7168):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1792):
+            Intermediate size of the routed expert.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether to tie weight embeddings
+        rope_parameters (`dict`, *optional*):
+            The parameters of the RoPE embeddings.
+        max_position_embeddings (`int`, *optional*, defaults to 128000):
+            The maximum sequence length that this model might ever be used with.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+            `num_attention_heads`.
+        conv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the conv layers.
+        conv_L_cache (`int`, *optional*, defaults to 3):
+            L_cache dim in the conv layers.
+        num_dense_layers (`int`, *optional*, defaults to 2):
+            Number of dense Lfm2MoeMLP layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+        num_experts_per_tok (`int`, *optional*, defaults to 4):
+            Number of selected experts.
+        num_experts (`int`, *optional*, defaults to 32):
+            Number of routed experts.
+        use_expert_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use the expert bias on the routing weights.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for routed experts in MoE models.
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the topk probabilities.
+        layer_types (`Optional`, *optional*):
+            Type of each layers.
+
+    ```python
+    >>> from transformers import Lfm2MoeModel, Lfm2MoeConfig
+
+    >>> # Initializing a LFM2 Moe model
+    >>> configuration = Lfm2MoeConfig()
+
+    >>> # Initializing a model from the LFM2-8B-A1B style configuration
+    >>> model = Lfm2MoeModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""  # noqa: E501
+
+    model_type = "lfm2_moe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 65536,
+        hidden_size: int = 2048,
+        intermediate_size: int = 7168,
+        moe_intermediate_size: int = 1792,
+        num_hidden_layers: int = 32,
+        pad_token_id: int = 0,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        tie_word_embeddings: bool = True,
+        rope_parameters: dict[str, Any] | None = None,
+        max_position_embeddings: int = 128_000,
+        use_cache: bool = True,
+        norm_eps: float = 0.00001,
+        num_attention_heads: int = 32,
+        num_key_value_heads: int = 8,
+        conv_bias: bool = False,
+        conv_L_cache: int = 3,
+        num_dense_layers: int = 2,
+        num_experts_per_tok: int = 4,
+        num_experts: int = 32,
+        use_expert_bias: bool = True,
+        routed_scaling_factor: float = 1.0,
+        norm_topk_prob: bool = True,
+        layer_types: list[str] | None = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        rope_theta = kwargs.pop("rope_theta", 1000000.0)
+        if rope_parameters is None:
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+        self.rope_parameters = rope_parameters
+        self.max_position_embeddings = max_position_embeddings
+        self.use_cache = use_cache
+        self.norm_eps = norm_eps
+
+        # attn operator config
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        # custom operator config
+        self.conv_bias = conv_bias
+        self.conv_L_cache = conv_L_cache
+
+        # moe config
+        self.num_dense_layers = num_dense_layers
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.use_expert_bias = use_expert_bias
+        self.routed_scaling_factor = routed_scaling_factor
+        self.norm_topk_prob = norm_topk_prob
+        self.layer_types = layer_types
+
+        tie_word_embeddings = kwargs.get(
+            "tie_embedding", tie_word_embeddings
+        )  # to fit original config keys
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["Lfm2MoeConfig"]

vllm/transformers_utils/configs/medusa.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+
+from transformers import PretrainedConfig
+
+
+class MedusaConfig(PretrainedConfig):
+    model_type = "medusa"
+
+    def __init__(
+        self,
+        hidden_size: int = 4096,
+        vocab_size: int = 32001,
+        num_heads: int = 5,
+        num_hidden_layers: int = 1,
+        max_paths: int = 64,
+        topk: int = 10,
+        truncated_vocab_size: int | None = None,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.num_heads = num_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.max_paths = max_paths
+        self.topk = topk
+        self.max_seq_len = int(2**20)
+        self.truncated_vocab_size = (
+            vocab_size if truncated_vocab_size is None else truncated_vocab_size
+        )
+        if "architectures" not in kwargs:
+            kwargs["architectures"] = ["MedusaModel"]
+
+        super().__init__(**kwargs)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: str | os.PathLike,
+        **kwargs,
+    ) -> "MedusaConfig":
+        config_dict, kwargs = cls.get_config_dict(
+            pretrained_model_name_or_path, **kwargs
+        )
+        for k in list(config_dict.keys()):
+            if "num" in k:
+                if "heads" in k:
+                    config_dict["num_heads"] = config_dict.pop(k)
+                elif "layers" in k:
+                    config_dict["num_hidden_layers"] = config_dict.pop(k)
+        return cls.from_dict(config_dict, **kwargs)
+
+    @property
+    def num_attention_heads(self):
+        return 0
+
+    @property
+    def num_lookahead_tokens(self):
+        return self.num_heads
+
+    @num_lookahead_tokens.setter
+    def num_lookahead_tokens(self, num_lookahead_tokens: int):
+        self.num_heads = num_lookahead_tokens

vllm/transformers_utils/configs/midashenglm.py

@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 Horizon team, Xiaomi MiLM Plus.
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers import PretrainedConfig
+from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import (
+    Qwen2_5OmniTextConfig,
+)
+
+
+class DashengConfig(PretrainedConfig):
+    model_type = "midashenglm_dasheng_encoder"
+
+    def __init__(
+        self,
+        embed_dim: int = 768,
+        outputdim: int = 527,
+        patch_size: int | tuple[int, int] = 16,
+        patch_stride: int | tuple[int, int] = 16,
+        input_channels: int = 1,
+        target_length: int = 1012,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        init_values: float | None = None,
+        drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        f_min: float = 0.0,
+        f_max: float = 8000.0,
+        center: bool = True,
+        win_length: int = 512,
+        hop_length: int = 160,
+        sample_rate: int = 16000,
+        n_fft: int = 512,
+        n_mels: int = 64,
+        **kwargs,
+    ):
+        self.embed_dim = embed_dim
+        self.outputdim = outputdim
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        self.input_channels = input_channels
+        self.target_length = target_length
+        self.depth = depth
+        self.num_heads = num_heads
+        self.mlp_ratio = mlp_ratio
+        self.qkv_bias = qkv_bias
+        self.init_values = init_values
+        self.drop_rate = drop_rate
+        self.attn_drop_rate = attn_drop_rate
+        self.f_min = f_min
+        self.f_max = f_max
+        self.center = center
+        self.win_length = win_length
+        self.hop_length = hop_length
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.n_mels = n_mels
+        super().__init__(**kwargs)
+
+
+class MiDashengLMConfig(PretrainedConfig):
+    model_type = "midashenglm"
+
+    def __init__(
+        self,
+        audio_encoder_config: dict | None = None,
+        subsample_factor: int = 5,
+        text_config: dict | None = None,
+        audio_token_id: int | None = None,
+        **kwargs,
+    ):
+        self.audio_encoder_config = DashengConfig(**(audio_encoder_config or {}))
+        self.subsample_factor = subsample_factor
+        self.text_config = (
+            Qwen2_5OmniTextConfig(**text_config)
+            if text_config
+            else Qwen2_5OmniTextConfig()
+        )
+        self.text_config.rope_parameters = None  # uses_mrope is false
+        self.audio_token_id = audio_token_id
+        super().__init__(**kwargs)

vllm/transformers_utils/configs/mistral.py

@@ -0,0 +1,293 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers import PretrainedConfig, WhisperConfig
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def adapt_config_dict(
+    config_dict: dict[str, Any],
+    defaults: dict[str, Any],
+) -> PretrainedConfig:
+    config_dict = _remap_general_mistral_args(config_dict)
+    config_dict = _remap_mistral_sliding_window(config_dict)
+
+    if bool(config_dict.get("quantization")):
+        config_dict = _remap_mistral_quantization_args(config_dict)
+
+    is_moe = bool(config_dict.get("moe"))
+    is_mistral_large_3 = (
+        is_moe and (config_dict["moe"].get("num_shared_experts") or 0) > 0
+    )
+    if config_dict.get("model_type") == "mamba":
+        config_dict["architectures"] = ["Mamba2ForCausalLM"]
+    elif is_moe and is_mistral_large_3:
+        config_dict = _remap_moe_args(config_dict)
+        config_dict["model_type"] = "deepseek_v3"
+        config_dict["architectures"] = ["MistralLarge3ForCausalLM"]
+
+        assert "llama_4_scaling" in config_dict, (
+            "MistralLarge3 expect llama4 scaling config."
+        )
+        llama_4_scaling_config_keys = ["original_max_position_embeddings", "beta"]
+        assert all(
+            [
+                key in config_dict["llama_4_scaling"]
+                for key in llama_4_scaling_config_keys
+            ]
+        ), (
+            "llama_4_scaling config should define the keys: "
+            f"{','.join(llama_4_scaling_config_keys)}"
+        )
+    elif is_moe:
+        config_dict["architectures"] = ["MixtralForCausalLM"]
+    else:
+        config_dict["architectures"] = ["MistralForCausalLM"]
+
+    if bool(config_dict.get("yarn")):
+        config_dict = _remap_mistral_yarn_args(config_dict)
+
+    if bool(config_dict.get("llama_4_scaling")):
+        llama_4_scaling_config_keys = ["original_max_position_embeddings", "beta"]
+        assert all(
+            [
+                key in config_dict["llama_4_scaling"]
+                for key in llama_4_scaling_config_keys
+            ]
+        ), (
+            "llama_4_scaling config should define the keys: "
+            f"{','.join(llama_4_scaling_config_keys)}"
+        )
+
+    is_vision = (config_dict.get("multimodal") or {}).get(
+        "vision_encoder_args"
+    ) or config_dict.get("vision_encoder")
+    is_audio = bool(
+        ((config_dict.get("multimodal") or {}).get("whisper_model_args") or {}).get(
+            "encoder_args"
+        )
+    )
+
+    assert not (is_vision and is_audio), "Vision and audio are mutually exclusive"
+
+    if is_vision:
+        config_dict = _remap_mistral_vision_args(config_dict)
+    if is_audio:
+        config_dict = _remap_mistral_audio_args(config_dict)
+
+    for k, v in defaults.items():
+        config_dict.setdefault(k, v)
+
+    config = PretrainedConfig.from_dict(config_dict)
+
+    logger.debug("Initialized config %s", config)
+
+    return config
+
+
+def _remap_mistral_vision_args(config: dict) -> dict:
+    if config.get("multimodal"):
+        vision_config = config.pop("multimodal")
+    else:
+        vision_config = config.pop("vision_encoder")
+
+    quant_config = config.get("quantization_config")
+    config = {
+        "model_type": "pixtral",
+        "architectures": ["PixtralForConditionalGeneration"],
+        "text_config": PretrainedConfig.from_dict(config),
+        "vision_config": PretrainedConfig.from_dict(vision_config),
+    }
+    if quant_config:
+        config["quantization_config"] = quant_config
+    return config
+
+
+def _remap_mistral_yarn_args(config: dict) -> dict:
+    yarn_config_map = {
+        "factor": "factor",
+        "original_max_position_embeddings": "original_max_position_embeddings",
+        "beta": "beta_fast",
+        "alpha": "beta_slow",
+        "apply_scale": "apply_yarn_scaling",
+    }
+    yarn_config = config.get("yarn") or {}
+    config["rope_parameters"] = {
+        "rope_type": "yarn",
+        "mscale_all_dim": 1,
+    }
+
+    if rope_theta := config.pop("rope_theta", None):
+        config["rope_parameters"]["rope_theta"] = rope_theta
+
+    for old_name, new_name in yarn_config_map.items():
+        if old_name in yarn_config:
+            config["rope_parameters"][new_name] = yarn_config.pop(old_name)
+
+    assert len(yarn_config) == 0, f"Unparsed yarn config: {yarn_config}"
+
+    return config
+
+
+def _remap_general_mistral_args(config: dict) -> dict:
+    # Mistral key -> HF key
+    config_mapping = {
+        "dim": "hidden_size",
+        "norm_eps": "rms_norm_eps",
+        "n_kv_heads": "num_key_value_heads",
+        "n_layers": "num_hidden_layers",
+        "n_heads": "num_attention_heads",
+        "hidden_dim": "intermediate_size",
+    }
+    # HF key -> (Mistral key, default value)
+    top_level_mapping_with_default = {
+        "model_type": ("model_type", "transformer"),
+        "hidden_act": ("activation", "silu"),
+        "tie_word_embeddings": ("tied_embeddings", False),
+        "max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)),
+        "max_position_embeddings": ("max_position_embeddings", 128_000),
+    }
+
+    for key, new_key in config_mapping.items():
+        if key in config:
+            config[new_key] = config.pop(key)
+
+    for new_key, (key, default_value) in top_level_mapping_with_default.items():
+        config[new_key] = config.pop(key, default_value)
+
+    return config
+
+
+def _remap_mistral_sliding_window(config: dict) -> dict:
+    # Remap sliding_window (list) -> layer_types (list) + sliding window (int)
+    # for HF compatibility
+    # Mistral configs may define sliding_window as list[int]. Convert it
+    # to int and add the layer_types list[str] to make it HF compatible
+    if sliding_window := config.get("sliding_window"):
+        if isinstance(sliding_window, list):
+            pattern_repeats = config["num_hidden_layers"] // len(sliding_window)
+            layer_types = sliding_window * pattern_repeats
+            config["layer_types"] = [
+                "full_attention" if layer_type is None else "sliding_attention"
+                for layer_type in layer_types
+            ]
+            assert len(set(sliding_window) - {None}) <= 1, sliding_window
+            config["sliding_window"] = next(filter(None, sliding_window), None)
+        elif isinstance(sliding_window, int) and config.get("layer_types") is None:
+            config["layer_types"] = ["sliding_attention"] * config["num_hidden_layers"]
+        else:
+            raise ValueError(f"Unsupported sliding_window type: {sliding_window}")
+
+    return config
+
+
+def _remap_mistral_quantization_args(config: dict) -> dict:
+    if config.get("quantization"):
+        quantization = config.pop("quantization", {})
+        if quantization.get("qformat_weight") == "fp8_e4m3":
+            qscheme_act = quantization.get("qscheme_act")
+            assert qscheme_act in ("NO_SCALES", "TENSOR", None), (
+                "Only NO_SCALES and TENSOR (default) are supported for qscheme_act"
+            )
+            is_dynamic = qscheme_act == "NO_SCALES"
+            config["quantization_config"] = {
+                "quant_method": "fp8",
+                "activation_scheme": "dynamic" if is_dynamic else "static",
+            }
+        elif (
+            str(quantization.get("quant_method", "")).lower().replace("_", "-")
+            == "compressed-tensors"
+        ):
+            # Pass through compressed-tensors config, while normalizing
+            # quant_method to the canonical community spelling.
+            quantization["quant_method"] = "compressed-tensors"
+            config["quantization_config"] = quantization
+        else:
+            raise ValueError(f"Found unknown quantization='{quantization}' in config")
+
+    return config
+
+
+def _remap_mistral_audio_args(config: dict) -> dict:
+    whisper_args = config["multimodal"].pop("whisper_model_args")
+    encoder_args = whisper_args["encoder_args"]
+    downsample_args = whisper_args["downsample_args"]
+    downsample_factor = downsample_args["downsample_factor"]
+
+    # make sure that k/v blocks can be allocated with
+    # unified k/v cache class and pool whisper k/v cache blocks
+    # with downsample_factor:1 ratio
+    if encoder_args.get("causal"):
+        block_pool_size = downsample_factor
+        config["projection_size"] = downsample_factor * encoder_args["dim"]
+    else:
+        block_pool_size = 1
+
+    architecture = (
+        "VoxtralRealtimeGeneration"
+        if encoder_args.get("causal")
+        else "VoxtralForConditionalGeneration"
+    )
+
+    quant_config = config.get("quantization_config")
+    config = {
+        "model_type": "voxtral",
+        "architectures": [architecture],
+        "text_config": PretrainedConfig.from_dict(config),
+        "audio_config": WhisperConfig(
+            num_mel_bins=encoder_args["audio_encoding_args"]["num_mel_bins"],
+            window_size=encoder_args["audio_encoding_args"]["window_size"],
+            sampling_rate=encoder_args["audio_encoding_args"]["sampling_rate"],
+            hop_length=encoder_args["audio_encoding_args"]["hop_length"],
+            downsample_factor=downsample_factor,
+            d_model=encoder_args["dim"],
+            encoder_layers=encoder_args["n_layers"],
+            encoder_ffn_dim=encoder_args["hidden_dim"],
+            encoder_attention_heads=encoder_args["n_heads"],
+            encoder_head_dim=encoder_args["head_dim"],
+            vocab_size=encoder_args["vocab_size"],
+            max_source_positions=encoder_args["max_source_positions"],
+            is_encoder_decoder=False,  # Override WhisperConfig default
+            is_causal=encoder_args.get("causal", False),
+            sliding_window=encoder_args.get("sliding_window", None),
+            block_pool_size=block_pool_size,
+            pos_embed=encoder_args.get("pos_embed", "sinusoidal"),
+            global_log_mel_max=encoder_args["audio_encoding_args"].get(
+                "global_log_mel_max"
+            ),
+            # only needed for RoPE
+            max_position_embeddings=block_pool_size * config["max_position_embeddings"],
+        ),
+    }
+    if quant_config:
+        config["quantization_config"] = quant_config
+    return config
+
+
+def _remap_moe_args(config: dict) -> dict:
+    moe_config_map = {
+        "route_every_n": "moe_layer_freq",
+        "first_k_dense_replace": "first_k_dense_replace",
+        "num_experts_per_tok": "num_experts_per_tok",
+        "num_experts": "n_routed_experts",
+        "expert_hidden_dim": "moe_intermediate_size",
+        "routed_scale": "routed_scaling_factor",
+        "num_shared_experts": "n_shared_experts",
+        "num_expert_groups": "n_group",
+        "num_expert_groups_per_tok": "topk_group",
+    }
+    moe_config = config.get("moe", {})
+    for old_name, new_name in moe_config_map.items():
+        if old_name in moe_config:
+            value = moe_config.pop(old_name)
+            config[new_name] = value
+
+    config["topk_method"] = None
+    config["norm_topk_prob"] = True
+    config["scoring_func"] = "softmax"
+
+    return config

vllm/transformers_utils/configs/mlp_speculator.py

@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+from transformers import PretrainedConfig
+
+
+class MLPSpeculatorConfig(PretrainedConfig):
+    model_type = "mlp_speculator"
+
+    attribute_map = {
+        "hidden_size": "emb_dim",
+    }
+
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        emb_dim: int = 4096,
+        inner_dim: int = 0,
+        n_predict: int = 3,
+        top_k_tokens_per_head: list[int] | None = None,
+        n_candidates: int = 5,
+        tie_weights: bool = False,
+        scale_input: bool = False,
+        **kwargs,
+    ):
+        """
+        Initialize an MLPSpeculatorConfig
+
+        Args:
+            vocab_size: int
+                the model vocab size
+            emb_dim: int
+                the model embedding dimension
+            inner_dim: int
+                the inner dimension of the model. If 0, will be the emb_dim.
+            n_predict: int
+                the number of lookaheads for the speculator
+            top_k_tokens_per_head: list[int]
+                Number of tokens to consider from each head when forming the
+                candidate tree.
+                For each candidate branch in the tree, head n produces topk[n]
+                additional sub-branches.
+                NOTE: This parameter is currently unused.
+            n_candidates: int
+                number of child candidates to create per sequence
+            tie_weights: bool
+                If true, use a single set of weights for every model
+                head/stage after the first. The initial projection
+                from the base model may have a different size, so that
+                stays separate.
+            scale_input: bool
+                if True, will scale the initial hidden states from
+                the base model.
+        """
+        if top_k_tokens_per_head is None:
+            top_k_tokens_per_head = [5, 4, 3]
+        assert len(top_k_tokens_per_head) == n_predict
+        self.vocab_size = vocab_size
+        self.emb_dim = emb_dim
+        self.inner_dim = inner_dim
+        self.n_predict = n_predict
+        self.top_k_tokens_per_head = top_k_tokens_per_head
+        self.n_candidates = n_candidates
+        self.num_lookahead_tokens = n_predict
+        self.tie_weights = tie_weights
+        self.scale_input = scale_input
+
+        super().__init__(**kwargs)

vllm/transformers_utils/configs/moonvit.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
+from transformers.configuration_utils import PretrainedConfig
+
+
+class MoonViTConfig(PretrainedConfig):
+    model_type = "moonvit"
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        init_pos_emb_height: int = 64,
+        init_pos_emb_width: int = 64,
+        num_attention_heads: int = 16,
+        num_hidden_layers: int = 27,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        merge_kernel_size: tuple[int, int] = (2, 2),
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.patch_size = patch_size
+        # Positional embedding config
+        self.init_pos_emb_height = init_pos_emb_height
+        self.init_pos_emb_width = init_pos_emb_width
+        # Transformer config
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        # Patch merger config
+        self.merge_kernel_size = merge_kernel_size

vllm/transformers_utils/configs/nemotron.py

@@ -0,0 +1,220 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Nemotron model configuration"""
+
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class NemotronConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a
+    [`NemotronModel`]. It is used to instantiate a Nemotron model
+    according to the specified arguments, defining the model architecture.
+    Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the Nemotron-8B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be
+    used to control the model outputs. Read the documentation from
+    [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 256000):
+            Vocabulary size of the Nemotron model. Defines the number of
+            different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NemotronModel`]
+        hidden_size (`int`, *optional*, defaults to 6144):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 24576):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 48):
+            Number of attention heads for each attention layer in the
+            Transformer decoder.
+        head_dim (`int`, *optional*):
+            Projection weights dimension in multi-head attention. Set to
+            hidden_size // num_attention_heads if None
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to
+            implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use
+            Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention
+            (MQA) otherwise GQA is used. When converting a multi-head
+            checkpoint to a GQA checkpoint, each group key and value
+            head should be constructed by meanpooling all the original
+            heads within that group. For more details checkout
+            [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it
+            is not specified, will default to `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
+            The non-linear activation function (function or string) in the
+            decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used
+            with.
+        initializer_range (`float`, *optional*, defaults to 0.0134):
+            The standard deviation of the truncated_normal_initializer for
+            initializing all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values
+            attentions (not used by all models). Only relevant if
+            `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 2):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 3):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_parameters (`dict`, *optional*):
+            The parameters of the RoPE embeddings. Expected contents:
+                `rope_theta` (`float`): The base period of the RoPE embeddings.
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear',
+                    'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the
+                    original RoPE implementation.
+                `partial_rotary_factor` (`float`, *optional*, defaults to 0.5):
+                    Percentage of the query and keys which will have rotary embedding.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output
+            projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj and down_proj layers in the MLP
+            layers.
+
+    ```python
+    >>> from transformers import NemotronModel, NemotronConfig
+    >>> # Initializing a Nemotron nemotron-15b style configuration
+    >>> configuration = NemotronConfig()
+    >>> # Initializing a model from the nemotron-15b style configuration
+    >>> model = NemotronModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "nemotron"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=256000,
+        hidden_size=6144,
+        intermediate_size=24576,
+        num_hidden_layers=32,
+        num_attention_heads=48,
+        head_dim=None,
+        num_key_value_heads=None,
+        hidden_act="relu2",
+        max_position_embeddings=4096,
+        initializer_range=0.0134,
+        norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=2,
+        eos_token_id=3,
+        tie_word_embeddings=False,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        head_dim = head_dim or kwargs.get("kv_channels")
+        self.head_dim = (
+            head_dim if head_dim is not None else (hidden_size // num_attention_heads)
+        )
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
+        rope_theta = kwargs.pop("rope_theta", 10000.0)
+        if "rope_theta" not in rope_parameters:
+            rope_parameters["rope_theta"] = rope_theta
+        # for backward compatibility
+        partial_rotary_factor = (
+            kwargs.get("rope_percent")
+            or kwargs.get("rope_percentage")
+            or kwargs.get("partial_rotary_factor")
+            or 0.5
+        )
+        if "partial_rotary_factor" not in rope_parameters:
+            rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+        self.rope_parameters = rope_parameters
+        self._rope_parameters_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_parameters_validation(self):
+        """
+        Validate the `rope_parameters` configuration.
+        """
+        if self.rope_parameters is None:
+            return
+
+        rope_type: str | None = self.rope_parameters.get("rope_type", None)
+        factor: float | None = self.rope_parameters.get("factor", None)
+
+        if rope_type not in {"default", "linear", "dynamic"}:
+            raise ValueError(
+                "`rope_type` must be one of ['default', 'linear', 'dynamic'], "
+                f"got {rope_type}"
+            )
+        if rope_type != "default":
+            if factor is None:
+                raise ValueError(
+                    "If `rope_type` is not 'default', `rope_parameters` "
+                    "must include a `factor` field. Got `None`."
+                )
+            if not isinstance(factor, float) or factor <= 1.0:
+                raise ValueError(
+                    "`rope_parameters`'s factor field must be a float > 1, got "
+                    f"{factor}"
+                )

vllm/transformers_utils/configs/nemotron_h.py

@@ -0,0 +1,287 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""NemotronH model configuration"""
+
+import regex as re
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class NemotronHConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a
+    [`NemotronHModel`]. It is used to instantiate a NemotronH model according
+    to the specified arguments, defining the model architecture. Instantiating
+    a configuration with the defaults will yield a similar configuration to
+    that of the NemotronH-v0.1 model.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 131072):
+            Vocabulary size of the NemotronH model. Defines the number of
+            different tokens that can be represented by the `inputs_ids`
+            passed when calling [`NemotronHModel`]
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be
+            tied. Note that this is only relevant if the model has an output
+            word embedding layer.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 21504):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 52):
+            Number of hidden layers in the Transformer encoder.
+        hybrid_override_pattern (`str`, *optional*, defaults to
+            `"M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-"`):
+            The pattern of the hybrid model. The pattern is a string of
+            characters where each character represents
+            M: Mamba2, *: Attention, -: MLP
+        mtp_hybrid_override_pattern (`str`, *optional*, defaults to `"*E"`):
+            The pattern of the MTP layers.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the
+            Transformer encoder.
+        attention_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each attention head.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to
+            implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use
+            Multi Head Attention (MHA), if `num_key_value_heads=1` the model
+            will use Multi Query Attention (MQA) otherwise GQA is used.
+        mlp_hidden_act (`str`, *optional*, defaults to "relu2"):
+            The non-linear activation function in the MLP layers.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in attention layers.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in MLP layers.
+        use_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the model.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for
+            initializing all weight matrices.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        residual_in_fp32 (`bool`, *optional*, defaults to `False`):
+            Whether or not residuals should be in `float32`. If set to `False`
+            residuals will keep the same `dtype` as the rest of the model.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values
+            attentions (not used by all models). Only relevant if
+            `config.is_decoder=True`.
+        num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
+            Number of prompt logits to calculate during generation. If `None`,
+            all logits will be calculated. If an integer value, only last
+            `num_logits_to_keep` logits will be calculated.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        sliding_window (`int`, *optional*, defaults to None):
+            Sliding window attention window size.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used
+            with.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        hidden_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the hidden states.
+        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use the fast mamba kernels.
+            These are available only if `mamba-ssm` and `causal-conv1d`
+            are installed, and the mamba modules are running on a CUDA device.
+        ssm_state_size (`int`, *optional*, defaults to 128):
+            The dimension of the mamba state space latents.
+        mamba_num_heads (`int`, *optional*, defaults to 128):
+            Number of heads in Mamba layers.
+        mamba_n_groups (`int`, *optional*, defaults to 8):
+            Number of groups in Mamba layers.
+        mamba_head_dim (`int`, *optional*, defaults to 64):
+            Dimension of each Mamba head.
+        mamba_d_conv (`int`, *optional*, defaults to 4):
+            The size of the mamba convolution kernel.
+        mamba_expand (`int`, *optional*, defaults to 2):
+            Expanding factor used to determine the mamba intermediate size.
+        mamba_hidden_act (`str`, *optional*, defaults to "silu"):
+            The non-linear activation function in the Mamba layers.
+        mamba_dt_min (`float`, *optional*, defaults to 0.001):
+            Minimum value for the time step in Mamba.
+        mamba_dt_max (`float`, *optional*, defaults to 0.1):
+            Maximum value for the time step in Mamba.
+        mamba_dt_limit (`tuple`, *optional*, defaults to (0.0, float("inf"))):
+            Limits for the time step in Mamba.
+        mamba_dt_init_floor (`float`, *optional*, defaults to 1e-4):
+            Floor value for time step initialization in Mamba.
+        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the convolution layer of the mamba mixer
+            block.
+        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the input and output projections of the
+            mamba mixer block.
+        mamba_chunk_size (`int`, *optional*, defaults to 256):
+            Size of chunks for Mamba processing.
+        rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the pre-normalization residual connections.
+    """
+
+    model_type = "nemotron_h"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=131072,
+        tie_word_embeddings=False,
+        hidden_size=4096,
+        intermediate_size=21504,
+        num_hidden_layers=52,
+        hybrid_override_pattern="M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-",
+        mtp_hybrid_override_pattern="*E",
+        num_attention_heads=32,
+        head_dim=128,
+        num_key_value_heads=8,  # nemo: num_query_groups
+        mlp_hidden_act="relu2",
+        attention_bias=False,
+        mlp_bias=False,
+        use_bias=False,
+        initializer_range=0.02,  # nemo: init_method_std
+        layer_norm_epsilon=1e-5,  # nemo: layernorm_epsilon
+        residual_in_fp32=False,  #  Megatron Core default value
+        use_cache=True,
+        num_logits_to_keep=1,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        sliding_window=None,
+        max_position_embeddings=4096,
+        attention_dropout=0.0,
+        hidden_dropout=0.0,  # * ADDED
+        use_mamba_kernels=True,
+        ssm_state_size=128,  # mamba_state_size
+        mamba_num_heads=128,
+        mamba_n_groups=8,  # nemo: mamba_ssm_ngroups = num_heads
+        mamba_head_dim=64,
+        mamba_d_conv=4,
+        mamba_expand=2,
+        mamba_hidden_act="silu",
+        mamba_dt_min=0.001,
+        mamba_dt_max=0.1,
+        mamba_dt_limit=(0.0, float("inf")),
+        mamba_dt_init_floor=1e-4,
+        mamba_conv_bias=True,
+        mamba_proj_bias=False,
+        mamba_chunk_size=256,
+        rescale_prenorm_residual=True,
+        n_routed_experts=8,
+        n_shared_experts=1,
+        moe_intermediate_size=7688,
+        moe_shared_expert_intermediate_size=7688,
+        moe_latent_size=None,
+        num_experts_per_tok=2,
+        routed_scaling_factor=1.0,
+        n_group=1,
+        topk_group=1,
+        norm_topk_prob=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.tie_word_embeddings = tie_word_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.hybrid_override_pattern = hybrid_override_pattern
+        self.mtp_hybrid_override_pattern = mtp_hybrid_override_pattern
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = head_dim
+        self.sliding_window = sliding_window
+        self.max_position_embeddings = max_position_embeddings
+        self.attention_dropout = attention_dropout
+        self.hidden_dropout = hidden_dropout
+
+        # Validate hybrid_override_pattern
+        # M: Mamba2, *: Attention, -: MLP
+        assert len(self.hybrid_override_pattern) == self.num_hidden_layers, (
+            "hybrid_override_pattern must have same length as num_hidden_layers"
+        )
+        assert re.match(r"^[*-ME]+$", self.hybrid_override_pattern), (
+            "hybrid_override_pattern must only contain characters 'M', '*', '-', or 'E'"
+        )
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.mlp_hidden_act = mlp_hidden_act
+        self.attention_bias = attention_bias
+        self.mlp_bias = mlp_bias
+        self.use_bias = use_bias
+        self.initializer_range = initializer_range
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.residual_in_fp32 = residual_in_fp32
+
+        self.use_cache = use_cache
+        self.num_logits_to_keep = num_logits_to_keep
+
+        self.use_mamba_kernels = use_mamba_kernels
+        self.n_groups = mamba_n_groups
+        self.mamba_head_dim = mamba_head_dim
+        self.ssm_state_size = ssm_state_size
+        self.mamba_num_heads = mamba_num_heads
+        self.conv_kernel = mamba_d_conv
+        self.expand = mamba_expand
+        self.mamba_hidden_act = mamba_hidden_act
+        self.time_step_min = mamba_dt_min
+        self.time_step_max = mamba_dt_max
+        self.time_step_limit = mamba_dt_limit
+        self.time_step_floor = mamba_dt_init_floor
+        self.use_conv_bias = mamba_conv_bias
+        self.mamba_proj_bias = mamba_proj_bias
+        self.chunk_size = mamba_chunk_size
+        self.rescale_prenorm_residual = rescale_prenorm_residual
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size  # noqa: E501
+        self.moe_latent_size = moe_latent_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.routed_scaling_factor = routed_scaling_factor
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.norm_topk_prob = norm_topk_prob
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @property
+    def layers_block_type(self):
+        return [
+            "mamba"
+            if self.hybrid_override_pattern[i] == "M"
+            else "attention"
+            if self.hybrid_override_pattern[i] == "*"
+            else "mlp"
+            if self.hybrid_override_pattern[i] == "-"
+            else "moe"
+            for i in range(self.num_hidden_layers)
+        ]

vllm/transformers_utils/configs/olmo3.py

@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Olmo3Config(PretrainedConfig):
+    model_type = "olmo3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50304,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        use_cache=True,
+        pad_token_id=1,
+        bos_token_id=None,
+        eos_token_id=50279,
+        tie_word_embeddings=False,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        rms_norm_eps=1e-5,
+        sliding_window=4096,
+        layer_types=None,
+        **kwargs,
+    ):
+        # This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
+        # in vLLM.
+        if "architectures" not in kwargs:
+            kwargs["architectures"] = ["Olmo2ForCausalLM"]
+        elif "Olmo3ForCausalLM" in kwargs["architectures"]:
+            kwargs["architectures"].remove("Olmo3ForCausalLM")
+            kwargs["architectures"].append("Olmo2ForCausalLM")
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
+        rope_theta = kwargs.pop("rope_theta", 10000.0)
+        if "rope_theta" not in rope_parameters:
+            rope_parameters["rope_theta"] = rope_theta
+        self.rope_parameters = rope_parameters
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        self.rms_norm_eps = rms_norm_eps
+
+        self.sliding_window = sliding_window
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]

vllm/transformers_utils/configs/ovis.py

@@ -0,0 +1,182 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# ruff: noqa: E501
+# adapted from https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_aimv2.py
+# and https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_ovis.py
+# Ovis Config with AimV2 config registration removed for Transformers compatibility
+from typing import Any
+
+from transformers import AutoConfig, PretrainedConfig
+
+
+class AIMv2Config(PretrainedConfig):
+    """This is the configuration class to store the configuration of an [`AIMv2Model`].
+    Instantiating a configuration with the defaults will yield a similar configuration
+    to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
+    Args:
+        hidden_size: Dimension of the hidden representations.
+        intermediate_size: Dimension of the SwiGLU representations.
+        num_hidden_layers: Number of hidden layers in the Transformer.
+        num_attention_heads: Number of attention heads for each attention layer
+            in the Transformer.
+        num_channels: Number of input channels.
+        image_size: Image size.
+        patch_size: Patch size.
+        rms_norm_eps: Epsilon value used for the RMS normalization layer.
+        attention_dropout: Dropout ratio for attention probabilities.
+        projection_dropout: Dropout ratio for the projection layer after the attention.
+        qkv_bias: Whether to add a bias to the queries, keys and values.
+        use_bias: Whether to add a bias in the feed-forward and projection layers.
+        kwargs: Keyword arguments for the [`PretrainedConfig`].
+    """
+
+    model_type: str = "aimv2"
+
+    def __init__(
+        self,
+        hidden_size: int = 1024,
+        intermediate_size: int = 2816,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 8,
+        num_channels: int = 3,
+        image_size: int = 224,
+        patch_size: int = 14,
+        rms_norm_eps: float = 1e-5,
+        attention_dropout: float = 0.0,
+        projection_dropout: float = 0.0,
+        qkv_bias: bool = False,
+        use_bias: bool = False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_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.attention_dropout = attention_dropout
+        self.rms_norm_eps = rms_norm_eps
+
+        self.projection_dropout = projection_dropout
+        self.qkv_bias = qkv_bias
+        self.use_bias = use_bias
+
+
+# ----------------------------------------------------------------------
+#                     Visual Tokenizer Configuration
+# ----------------------------------------------------------------------
+class BaseVisualTokenizerConfig(PretrainedConfig):
+    def __init__(
+        self,
+        vocab_size=16384,
+        tokenize_function="softmax",
+        tau=1.0,
+        depths=None,
+        drop_cls_token=False,
+        backbone_config: PretrainedConfig | dict | None = None,
+        hidden_stride: int = 1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.tokenize_function = tokenize_function
+        self.tau = tau
+        if isinstance(depths, str):
+            depths = [int(x) for x in depths.split("|")]
+        self.depths = depths
+        self.backbone_kwargs = dict[str, Any]()
+        self.drop_cls_token = drop_cls_token
+        if backbone_config is not None:
+            assert isinstance(backbone_config, (PretrainedConfig, dict)), (
+                f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
+            )
+            if not isinstance(backbone_config, PretrainedConfig):
+                model_type = backbone_config["model_type"]
+                if model_type != "aimv2":
+                    backbone_config.pop("model_type")
+                    backbone_config = AutoConfig.for_model(
+                        model_type, **backbone_config
+                    )
+                else:
+                    backbone_config = AIMv2Config(**backbone_config)
+        self.backbone_config = backbone_config
+        self.hidden_stride = hidden_stride
+
+
+class Aimv2VisualTokenizerConfig(BaseVisualTokenizerConfig):
+    model_type = "aimv2_visual_tokenizer"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if self.drop_cls_token:
+            self.drop_cls_token = False
+        if self.depths:
+            assert len(self.depths) == 1
+            self.backbone_kwargs["num_hidden_layers"] = self.depths[0]
+
+
+class SiglipVisualTokenizerConfig(BaseVisualTokenizerConfig):
+    model_type = "siglip_visual_tokenizer"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if self.drop_cls_token:
+            self.drop_cls_token = False
+        if self.depths:
+            assert len(self.depths) == 1
+            self.backbone_kwargs["num_hidden_layers"] = self.depths[0]
+
+
+AutoConfig.register("siglip_visual_tokenizer", SiglipVisualTokenizerConfig)
+AutoConfig.register("aimv2_visual_tokenizer", Aimv2VisualTokenizerConfig)
+
+
+# ----------------------------------------------------------------------
+#                           Ovis Configuration
+# ----------------------------------------------------------------------
+class OvisConfig(PretrainedConfig):
+    model_type = "ovis"
+
+    def __init__(
+        self,
+        llm_config: PretrainedConfig | dict | None = None,
+        visual_tokenizer_config: PretrainedConfig | dict | None = None,
+        multimodal_max_length=8192,
+        hidden_size=None,
+        conversation_formatter_class=None,
+        llm_attn_implementation=None,
+        disable_tie_weight=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if llm_config is not None:
+            assert isinstance(llm_config, (PretrainedConfig, dict)), (
+                f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
+            )
+            if not isinstance(llm_config, PretrainedConfig):
+                model_type = llm_config["model_type"]
+                llm_config.pop("model_type")
+                llm_config = AutoConfig.for_model(model_type, **llm_config)
+
+        # map llm_config to text_config
+        self.text_config = llm_config
+        if visual_tokenizer_config is not None:
+            assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), (
+                f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
+            )
+            if not isinstance(visual_tokenizer_config, PretrainedConfig):
+                model_type = visual_tokenizer_config["model_type"]
+                visual_tokenizer_config.pop("model_type")
+                visual_tokenizer_config = AutoConfig.for_model(
+                    model_type, **visual_tokenizer_config
+                )
+
+        self.visual_tokenizer_config = visual_tokenizer_config
+        self.multimodal_max_length = multimodal_max_length
+        self.hidden_size = hidden_size
+        self.conversation_formatter_class = conversation_formatter_class
+        self.llm_attn_implementation = llm_attn_implementation
+        self.disable_tie_weight = disable_tie_weight

vllm/transformers_utils/configs/qwen3_5.py

@@ -0,0 +1,193 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Qwen3.5 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig, layer_type_validation
+
+
+class Qwen3_5TextConfig(PretrainedConfig):
+    model_type = "qwen3_5_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=248320,
+        hidden_size=4096,
+        intermediate_size=12288,
+        num_hidden_layers=32,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=256,
+        linear_conv_kernel_dim=4,
+        linear_key_head_dim=128,
+        linear_value_head_dim=128,
+        linear_num_key_heads=16,
+        linear_num_value_heads=32,
+        layer_types=None,
+        pad_token_id=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        **kwargs,
+    ):
+        kwargs["ignore_keys_at_rope_validation"] = [
+            "mrope_section",
+            "mrope_interleaved",
+        ]
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
+        self.rope_parameters = rope_parameters
+        kwargs.setdefault("partial_rotary_factor", 0.25)
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            interval_pattern = kwargs.get("full_attention_interval", 4)
+            self.layer_types = [
+                "linear_attention"
+                if bool((i + 1) % interval_pattern)
+                else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+        # linear attention part
+        self.linear_conv_kernel_dim = linear_conv_kernel_dim
+        self.linear_key_head_dim = linear_key_head_dim
+        self.linear_value_head_dim = linear_value_head_dim
+        self.linear_num_key_heads = linear_num_key_heads
+        self.linear_num_value_heads = linear_num_value_heads
+        super().__init__(**kwargs)
+        # Set these AFTER super().__init__() because transformers v4's
+        # PretrainedConfig.__init__ has these as explicit params with different
+        # defaults (e.g. tie_word_embeddings=True) that would overwrite our values.
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+
+
+class Qwen3_5VisionConfig(PretrainedConfig):
+    model_type = "qwen3_5"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+
+
+class Qwen3_5Config(PretrainedConfig):
+    model_type = "qwen3_5"
+    sub_configs = {
+        "vision_config": Qwen3_5VisionConfig,
+        "text_config": Qwen3_5TextConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_token_id=248056,
+        video_token_id=248057,
+        vision_start_token_id=248053,
+        vision_end_token_id=248054,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        super().__init__(**kwargs)
+        # Set after super().__init__() to avoid v4 PretrainedConfig overwrite
+        self.tie_word_embeddings = tie_word_embeddings
+
+
+__all__ = ["Qwen3_5Config", "Qwen3_5TextConfig"]

vllm/transformers_utils/configs/qwen3_5_moe.py

@@ -0,0 +1,205 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Qwen3.5-MoE model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig, layer_type_validation
+
+
+class Qwen3_5MoeTextConfig(PretrainedConfig):
+    model_type = "qwen3_5_moe_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.shared_expert.gate_proj": "colwise",
+        "layers.*.mlp.shared_expert.up_proj": "colwise",
+        "layers.*.mlp.shared_expert.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=248320,
+        hidden_size=2048,
+        num_hidden_layers=40,
+        num_attention_heads=16,
+        num_key_value_heads=2,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=256,
+        linear_conv_kernel_dim=4,
+        linear_key_head_dim=128,
+        linear_value_head_dim=128,
+        linear_num_key_heads=16,
+        linear_num_value_heads=32,
+        moe_intermediate_size=512,
+        shared_expert_intermediate_size=512,
+        num_experts_per_tok=8,
+        num_experts=256,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        layer_types=None,
+        pad_token_id=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        **kwargs,
+    ):
+        kwargs["ignore_keys_at_rope_validation"] = [
+            "mrope_section",
+            "mrope_interleaved",
+        ]
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
+        self.rope_parameters = rope_parameters
+        kwargs.setdefault("partial_rotary_factor", 0.25)
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            interval_pattern = kwargs.get("full_attention_interval", 4)
+            self.layer_types = [
+                "linear_attention"
+                if bool((i + 1) % interval_pattern)
+                else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+        # linear attention part
+        self.linear_conv_kernel_dim = linear_conv_kernel_dim
+        self.linear_key_head_dim = linear_key_head_dim
+        self.linear_value_head_dim = linear_value_head_dim
+        self.linear_num_key_heads = linear_num_key_heads
+        self.linear_num_value_heads = linear_num_value_heads
+        self.moe_intermediate_size = moe_intermediate_size
+        self.shared_expert_intermediate_size = shared_expert_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        super().__init__(**kwargs)
+        # Set these AFTER super().__init__() because transformers v4's
+        # PretrainedConfig.__init__ has these as explicit params with different
+        # defaults (e.g. tie_word_embeddings=True) that would overwrite our values.
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+
+
+class Qwen3_5MoeVisionConfig(PretrainedConfig):
+    model_type = "qwen3_5_moe"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+
+
+class Qwen3_5MoeConfig(PretrainedConfig):
+    model_type = "qwen3_5_moe"
+    sub_configs = {
+        "vision_config": Qwen3_5MoeVisionConfig,
+        "text_config": Qwen3_5MoeTextConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_token_id=248056,
+        video_token_id=248057,
+        vision_start_token_id=248053,
+        vision_end_token_id=248054,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        super().__init__(**kwargs)
+        # Set after super().__init__() to avoid v4 PretrainedConfig overwrite
+        self.tie_word_embeddings = tie_word_embeddings
+
+
+__all__ = ["Qwen3_5MoeConfig", "Qwen3_5MoeTextConfig"]

vllm/transformers_utils/configs/qwen3_asr.py

@@ -0,0 +1,436 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# ruff: noqa
+# mypy: ignore-errors
+# coding=utf-8
+# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen3ASRAudioEncoderConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3ASRAudioEncoder`]. It is used to instantiate a
+    Qwen3-ASR audio encoder according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the audio encoder of the Qwen2-Audio
+    architecture.
+
+    e.g. [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_mel_bins (`int`, *optional*, defaults to 128):
+            Number of mel features used per input features. Should correspond to the value used in the
+            `Qwen3ASRProcessor` class.
+        encoder_layers (`int`, *optional*, defaults to 32):
+            Number of encoder layers.
+        encoder_attention_heads (`int`, *optional*, defaults to 20):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        encoder_ffn_dim (`int`, *optional*, defaults to 5120):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
+        d_model (`int`, *optional*, defaults to 1280):
+            Dimensionality of the layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        activation_function (`str`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        activation_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for activations inside the fully connected layer.
+        scale_embedding (`bool`, *optional*, defaults to `False`):
+            Scale embeddings by diving by sqrt(d_model).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        max_source_positions (`int`, *optional*, defaults to 1500):
+            The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
+        n_window (`int`, *optional*, defaults to 100):
+            The chunk for conv and flash attn in AudioEncoder.
+        output_dim (`int`, *optional*, defaults to 3584):
+            The output dimension of AudioEncoder.
+
+    Example:
+
+    ```python
+    >>> from transformers import Qwen3ASRAudioEncoderConfig, Qwen3ASRAudioEncoder
+
+    >>> # Initializing a Qwen3ASRAudioEncoderConfig
+    >>> configuration = Qwen3ASRAudioEncoderConfig()
+
+    >>> # Initializing a Qwen3ASRAudioEncoder (with random weights)
+    >>> model = Qwen3ASRAudioEncoder(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_asr_audio_encoder"
+
+    def __init__(
+        self,
+        num_mel_bins=128,
+        encoder_layers=32,
+        encoder_attention_heads=20,
+        encoder_ffn_dim=5120,
+        d_model=1280,
+        dropout=0,
+        attention_dropout=0,
+        activation_function="gelu",
+        activation_dropout=0,
+        scale_embedding=False,
+        initializer_range=0.02,
+        max_source_positions=1500,
+        n_window=100,
+        output_dim=3584,
+        n_window_infer=400,
+        conv_chunksize=500,
+        downsample_hidden_size=480,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.num_mel_bins = num_mel_bins
+        self.d_model = d_model
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_function = activation_function
+        self.activation_dropout = activation_dropout
+        self.num_hidden_layers = encoder_layers
+        self.initializer_range = initializer_range
+        self.scale_embedding = (
+            scale_embedding  # scale factor will be sqrt(d_model) if True
+        )
+        self.max_source_positions = max_source_positions
+        self.n_window = n_window
+        self.output_dim = output_dim
+        self.n_window_infer = n_window_infer
+        self.conv_chunksize = conv_chunksize
+        self.downsample_hidden_size = downsample_hidden_size
+
+
+class Qwen3ASRTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3ASRTextModel`]. It is used to instantiate a
+    Qwen3-ASR model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen3-ASR-1.7B [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen3ASR model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen3ASRModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+        head_dim (`int`, *optional*, defaults to 128):
+            The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 128000):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 5000000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`list[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import Qwen3ASRTextModel, Qwen3ASRTextConfig
+
+    >>> # Initializing a Qwen3ASR style configuration
+    >>> configuration = Qwen3ASRTextConfig()
+
+    >>> # Initializing a model from the Qwen3-VL-7B style configuration
+    >>> model = Qwen3ASRTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_asr_text"
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=128000,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=5000000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class Qwen3ASRThinkerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3ASRThinker`]. It is used to instantiate a
+    Qwen3-ASR-Thinker model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the thinker component of the Qwen3-Omni
+    architecture.
+
+    e.g. [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        audio_config (`dict`, *optional*):
+            The config dictionary of the audio backbone.
+        text_config (`dict`, *optional*):
+            The config dictionary of the text backbone.
+        audio_token_id (`int`, *optional*, defaults to 151646):
+            The audio token id to encode the audio prompt.
+        audio_start_token_id (`int`, *optional*, defaults to 151647):
+            The audio start token id to encode the audio prompt.
+        user_token_id (`int`, *optional*, defaults to 872):
+            The user token id to encode the user token.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+    Example:
+
+    ```python
+    >>> from transformers import Qwen3ASRThinkerModel, Qwen3ASRThinkerConfig
+
+    >>> # Initializing a default Qwen3ASRThinkerConfig
+    >>> configuration = Qwen3ASRThinkerConfig()
+
+    >>> # Initializing a model (with random weights) from the default configuration
+    >>> model = Qwen3ASRThinkerModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_asr_thinker"
+
+    attribute_map = {}
+    sub_configs = {
+        "audio_config": Qwen3ASRAudioEncoderConfig,
+        "text_config": Qwen3ASRTextConfig,
+    }
+
+    def __init__(
+        self,
+        audio_config=None,
+        text_config=None,
+        audio_token_id=151646,
+        audio_start_token_id=151647,
+        user_token_id=872,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.user_token_id = user_token_id
+        self.audio_start_token_id = audio_start_token_id
+        self.initializer_range = initializer_range
+
+        if isinstance(audio_config, dict):
+            audio_config = Qwen3ASRAudioEncoderConfig(**audio_config)
+        elif audio_config is None:
+            audio_config = Qwen3ASRAudioEncoderConfig()
+        self.audio_config = audio_config
+
+        if isinstance(text_config, dict):
+            text_config = Qwen3ASRTextConfig(**text_config)
+        elif text_config is None:
+            text_config = Qwen3ASRTextConfig()
+        self.text_config = text_config
+        self.audio_token_id = audio_token_id
+
+
+class Qwen3ASRConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`Qwen3ASRForConditionalGeneration`]. It is used to instantiate a Qwen3ASR
+    model according to the specified sub-models configurations, defining the model architecture.
+
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the
+    [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        thinker_config (`dict`, *optional*): Configuration of the underlying thinker sub-model.
+        support_languages (`List[str]`, *optional*): The languages supported by the model.
+
+    Example:
+
+    ```python
+    >>> from transformers import (
+    ...     Qwen3ASRThinkerConfig,
+    ...     Qwen3ASRForConditionalGeneration,
+    ...     Qwen3ASRConfig,
+    ... )
+
+    >>> # Initializing a Qwen3ASR style configuration
+    >>> configuration = Qwen3ASRConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = Qwen3ASRForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_asr"
+    sub_configs = {
+        "thinker_config": Qwen3ASRThinkerConfig,
+    }
+
+    def __init__(
+        self,
+        thinker_config=None,
+        support_languages=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if thinker_config is None:
+            thinker_config = {}
+            logger.info(
+                "thinker_config is None. Initializing thinker model with default values"
+            )
+
+        self.thinker_config = Qwen3ASRThinkerConfig(**thinker_config)
+        self.support_languages = support_languages
+
+    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+        """
+        Returns the config that is meant to be used with text IO. On most models, it is the original config instance
+        itself. On specific composite models, it is under a set of valid names.
+
+        Args:
+            decoder (`Optional[bool]`, *optional*, defaults to `False`):
+                If set to `True`, then only search for decoder config names.
+        """
+        # Overridden for deeply nested config like Qwen2.5-Omni. We don't have any omni model
+        # except for Qwen yet. This has to be generalized if more deeply nested configs are
+        # added. NOTE: currently method used only by vLLM
+        return self.thinker_config.get_text_config()
+
+
+__all__ = ["Qwen3ASRConfig", "Qwen3ASRThinkerConfig", "Qwen3ASRAudioEncoderConfig"]

vllm/transformers_utils/configs/qwen3_next.py

@@ -0,0 +1,277 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Qwen3-Next model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig, layer_type_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen3NextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3NextModel`]. It is used to instantiate a
+    Qwen3-Next model according to the specified arguments, defining the model architecture.
+    Instantiating a configuration with the defaults will yield a similar configuration to that of
+    Qwen3-Next-80B-A3B-Instruct [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the model. Defines the number of different tokens that can be represented by the
+            `inputs_ids`.
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 5632):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 2):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_parameters (`dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_theta` (`float`): The base period of the RoPE embeddings.
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+                `partial_rotary_factor` (`float`, *optional*, defaults to 0.25):
+                    Percentage of the query and keys which will have rotary embedding.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        head_dim (`int`, *optional*, defaults to 256):
+            Projection weights dimension in multi-head attention.
+        linear_conv_kernel_dim (`int`, *optional*, defaults to 4):
+            Kernel size of the convolution used in linear attention layers.
+        linear_key_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each key head in linear attention.
+        linear_value_head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each value head in linear attention.
+        linear_num_key_heads (`int`, *optional*, defaults to 16):
+            Number of key heads used in linear attention layers.
+        linear_num_value_heads (`int`, *optional*, defaults to 32):
+            Number of value heads used in linear attention layers.
+        decoder_sparse_step (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer.
+        moe_intermediate_size (`int`, *optional*, defaults to 512):
+            Intermediate size of the routed expert.
+        shared_expert_intermediate_size (`int`, *optional*, defaults to 512):
+            Intermediate size of the shared expert.
+        num_experts_per_tok (`int`, *optional*, defaults to 10):
+            Number of selected experts.
+        num_experts (`int`, *optional*, defaults to 512):
+            Number of routed experts.
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the topk probabilities.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabling this will also
+            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+        mlp_only_layers (`list[int]`, *optional*, defaults to `[]`):
+            Indicate which layers use Qwen3NextMLP rather than Qwen3NextSparseMoeBlock
+            The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
+            If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
+        layer_types (`list[str]`, *optional*):
+            Types of each layer (attention or linear).
+
+    ```python
+    >>> from transformers import Qwen3NextModel, Qwen3NextConfig
+
+    >>> # Initializing a Qwen3Next style configuration
+    >>> configuration =  Qwen3NextConfig()
+
+    >>> # Initializing a model from the Qwen3-Next-80B-A3B style configuration
+    >>> model = Qwen3NextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """  # noqa: E501
+
+    model_type = "qwen3_next"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.*.gate_proj": "colwise",
+        "layers.*.mlp.experts.*.up_proj": "colwise",
+        "layers.*.mlp.experts.*.down_proj": "rowwise",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=2048,
+        intermediate_size=5632,
+        num_hidden_layers=48,
+        num_attention_heads=16,
+        num_key_value_heads=2,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=256,
+        linear_conv_kernel_dim=4,
+        linear_key_head_dim=128,
+        linear_value_head_dim=128,
+        linear_num_key_heads=16,
+        linear_num_value_heads=32,
+        decoder_sparse_step=1,
+        moe_intermediate_size=512,
+        shared_expert_intermediate_size=512,
+        num_experts_per_tok=10,
+        num_experts=512,
+        norm_topk_prob=True,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        mlp_only_layers=None,
+        layer_types=None,
+        **kwargs,
+    ):
+        if mlp_only_layers is None:
+            mlp_only_layers = []
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
+        rope_theta = kwargs.pop("rope_theta", 10000.0)
+        if "rope_theta" not in rope_parameters:
+            rope_parameters["rope_theta"] = rope_theta
+        partial_rotary_factor = kwargs.pop("partial_rotary_factor", 0.25)
+        if "partial_rotary_factor" not in rope_parameters:
+            rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+        self.rope_parameters = rope_parameters
+        self.partial_rotary_factor = partial_rotary_factor
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                "linear_attention" if bool((i + 1) % 4) else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types)
+
+        # linear attention part
+        self.linear_conv_kernel_dim = linear_conv_kernel_dim
+        self.linear_key_head_dim = linear_key_head_dim
+        self.linear_value_head_dim = linear_value_head_dim
+        self.linear_num_key_heads = linear_num_key_heads
+        self.linear_num_value_heads = linear_num_value_heads
+
+        # MoE arguments
+        self.decoder_sparse_step = decoder_sparse_step
+        self.moe_intermediate_size = moe_intermediate_size
+        self.shared_expert_intermediate_size = shared_expert_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.norm_topk_prob = norm_topk_prob
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.mlp_only_layers = mlp_only_layers
+
+
+__all__ = ["Qwen3NextConfig"]

vllm/transformers_utils/configs/radio.py

@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Radio vision model configuration"""
+
+from typing import Any
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+VIT_TIMM_DIM_BY_NAME: dict[str, tuple[int, int, int, int]] = {
+    "vit_small_patch16_224": (384, 12, 6, 1536),
+    "vit_base_patch16_224": (768, 12, 12, 3072),
+    "vit_large_patch16_224": (1024, 24, 16, 4096),
+    "vit_huge_patch16_224": (1280, 32, 16, 5120),
+}
+
+OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073)
+OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711)
+
+
+class RadioConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a Radio
+    vision model. It is used to instantiate a Radio model according to the
+    specified arguments, defining the model architecture.
+
+    Args:
+        model_name: Name of the vision transformer model
+            (e.g., "vit_base_patch16_224"). Used to determine architecture
+            dimensions from `VIT_TIMM_DIM_BY_NAME`.
+        image_size: The size (resolution) of each image.
+        patch_size: The size (resolution) of each patch.
+        qkv_bias: Whether to add a bias to the queries, keys and values.
+        qk_normalization: Whether to apply normalization to queries and keys.
+        norm_type: The normalization type to use.
+        layer_norm_eps: The epsilon used by the layer normalization layers.
+        initializer_factor: A factor for initializing all weight matrices.
+        hidden_act: The non-linear activation function in the encoder.
+        cpe_max_size: Maximum image size for position embeddings.
+        norm_mean: Mean values for image normalization (RGB channels).
+            Defaults to (0.48145466, 0.4578275, 0.40821073)).
+        norm_std: Standard deviation values for image normalization
+            (RGB channels). Defaults to (0.26862954, 0.26130258, 0.27577711)).
+        register_multiple: Number of register tokens to use.
+        teachers: A list of teacher model configurations. Each teacher configuration is
+            a dict with keys like "name" and some may have "use_summary".
+        cls_token_per_teacher: Whether to use a separate CLS token for each teacher.
+    """
+
+    model_type = "radio"
+
+    def __init__(
+        self,
+        model_name: str,
+        image_size: int = 224,
+        patch_size: int = 16,
+        qkv_bias: bool = True,
+        qk_normalization: bool = False,
+        norm_type: str = "layer_norm",
+        layer_norm_eps: float = 1e-6,
+        initializer_factor: float = 1.0,
+        hidden_act: str = "gelu",
+        cpe_max_size: int = 2048,
+        norm_mean: tuple[float, float, float] | list = OPENAI_CLIP_MEAN,
+        norm_std: tuple[float, float, float] | list = OPENAI_CLIP_STD,
+        register_multiple: int | None = None,
+        teachers: list[dict[str, Any]] | None = None,
+        cls_token_per_teacher: bool = False,
+        **kwargs,
+    ):
+        self.model_name = model_name
+        (
+            self.hidden_size,
+            self.num_hidden_layers,
+            self.num_attention_heads,
+            self.intermediate_size,
+        ) = VIT_TIMM_DIM_BY_NAME[model_name]
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.norm_type = norm_type
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_factor = initializer_factor
+        self.hidden_act = hidden_act
+        self.cpe_max_size = cpe_max_size
+        self.norm_mean = (
+            list(norm_mean) if isinstance(norm_mean, (tuple, list)) else norm_mean
+        )
+        self.norm_std = (
+            list(norm_std) if isinstance(norm_std, (tuple, list)) else norm_std
+        )
+        self.register_multiple = register_multiple
+        self.teachers = teachers if teachers is not None else []
+        self.cls_token_per_teacher = cls_token_per_teacher
+        super().__init__(**kwargs)

vllm/transformers_utils/configs/speculators/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

vllm/transformers_utils/configs/speculators/algos.py

@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+SUPPORTED_SPECULATORS_TYPES = {}
+
+
+def register_speculator(name):
+    def decorator(fn):
+        SUPPORTED_SPECULATORS_TYPES[name] = fn
+        return fn
+
+    return decorator
+
+
+@register_speculator("eagle3")
+def update_eagle3(config_dict: dict, pre_trained_config: dict) -> None:
+    """
+    Apply Eagle-3 specific configuration transformations to the `dict` used to
+    construct the Transformers PreTrainedConfig.
+
+    Eagle-3 specific fields:
+    - draft_vocab_size: Size of the draft model's vocabulary
+    - target_hidden_size: Hidden size of the target model
+    - norm_before_residual: Whether to apply norm before residual connection
+    - eagle_aux_hidden_state_layer_ids: List of layer indices from the base
+        model to use as auxiliary inputs for the Eagle3 drafter. These layers
+        provide intermediate hidden states that help the drafter make better
+        predictions. This is the standard field used in Eagle3 checkpoints.
+    """
+
+    pre_trained_config["draft_vocab_size"] = config_dict.get("draft_vocab_size")
+    if config_dict.get("target_hidden_size") is not None:
+        pre_trained_config["target_hidden_size"] = config_dict["target_hidden_size"]
+    pre_trained_config["norm_before_residual"] = config_dict.get(
+        "norm_before_residual", True
+    )
+    pre_trained_config["architectures"] = ["Eagle3LlamaForCausalLM"]
+    if config_dict.get("eagle_aux_hidden_state_layer_ids"):
+        pre_trained_config["eagle_aux_hidden_state_layer_ids"] = config_dict[
+            "eagle_aux_hidden_state_layer_ids"
+        ]

vllm/transformers_utils/configs/speculators/base.py

@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+from typing import Any
+
+from transformers import PretrainedConfig
+
+from vllm.transformers_utils.configs.speculators.algos import (
+    SUPPORTED_SPECULATORS_TYPES,
+)
+
+__all__ = ["SpeculatorsConfig"]
+
+
+class SpeculatorsConfig(PretrainedConfig):
+    model_type = "speculators"
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: str | os.PathLike,
+        **kwargs,
+    ) -> "SpeculatorsConfig":
+        """Load speculators Eagle config and convert to vLLM format."""
+        config_dict, _ = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        vllm_config = cls.extract_transformers_pre_trained_config(config_dict)
+        return cls(**vllm_config)
+
+    @classmethod
+    def extract_transformers_pre_trained_config(
+        cls, config_dict: dict[str, Any]
+    ) -> dict[str, Any]:
+        """
+        Extract standard Transformers PreTrainedConfig config from speculators config.
+        """
+        speculators_model_type = config_dict.get("speculators_model_type")
+        if speculators_model_type not in SUPPORTED_SPECULATORS_TYPES:
+            raise ValueError(
+                f"Expected one of: {SUPPORTED_SPECULATORS_TYPES}. "
+                "Please ensure you're loading a speculators-format model."
+            )
+
+        # Start with transformer layer configuration if present
+        pre_trained_config = config_dict.get("transformer_layer_config", {})
+        # Apply anything specific to the supported algorithm
+        algo_updater = SUPPORTED_SPECULATORS_TYPES[speculators_model_type]
+        algo_updater(config_dict=config_dict, pre_trained_config=pre_trained_config)
+        return pre_trained_config
+
+    @classmethod
+    def extract_vllm_speculative_config(
+        cls, config_dict: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Extract vLLM speculative config from speculators config."""
+        # validate fields
+        # TODO: @dsikka - use speculators pydantic model to validate
+        cls.validate_speculators_config(config_dict=config_dict)
+        # Convert from speculators config -> format that can be ingested by vLLM
+        return cls.build_vllm_speculative_config(config_dict=config_dict)
+
+    @classmethod
+    def validate_speculators_config(cls, config_dict: dict[str, Any]) -> None:
+        try:
+            spec_config = config_dict["speculators_config"]
+            methods = spec_config["proposal_methods"]
+            first_method = methods[0]
+            _ = first_method["speculative_tokens"]
+            _ = spec_config["verifier"]["name_or_path"]
+            _ = config_dict["speculators_model_type"]
+        except (KeyError, IndexError, TypeError) as e:
+            raise ValueError("Invalid speculators config structure") from e
+
+        if "transformer_layer_config" not in config_dict:
+            raise ValueError("Must provide transformer_layer_config")
+
+        if not isinstance(config_dict["transformer_layer_config"], dict):
+            raise TypeError(
+                "'transformer_layer_config' must be a dictionary if provided"
+            )
+
+    @classmethod
+    def build_vllm_speculative_config(
+        cls, config_dict: dict[str, Any]
+    ) -> dict[str, Any]:
+        """
+        Build vLLM-compatible speculative configuration from speculators format.
+
+        This method extracts and transforms speculative configuration from the
+        speculators format into the structure expected by vLLM.
+
+        Args:
+            config_dict: Configuration dictionary in speculators format
+
+        Returns:
+            Dictionary with vLLM-compatible speculative configuration
+        """
+        # Extract speculators configuration
+        spec_config = config_dict["speculators_config"]
+
+        # Currently we only support one proposal method
+        proposal_methods = spec_config.get("proposal_methods")
+        if not proposal_methods:
+            raise ValueError("No proposal methods found in speculators config")
+
+        first_method = proposal_methods[0]
+        num_speculative_tokens = first_method.get("speculative_tokens")
+
+        if num_speculative_tokens is None:
+            raise ValueError(
+                f"Missing 'speculative_tokens' in proposal method. Got: {first_method}"
+            )
+
+        # Build base vLLM speculative configuration
+        return {
+            "method": config_dict.get("speculators_model_type"),
+            "num_speculative_tokens": num_speculative_tokens,
+        }

vllm/transformers_utils/configs/step3_vl.py

@@ -0,0 +1,178 @@
+# 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 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_parameters: dict[str, Any] | None = 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
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
+        rope_theta = kwargs.pop("rope_theta", 500000.0)
+        if "rope_theta" not in rope_parameters:
+            rope_parameters["rope_theta"] = rope_theta
+        self.rope_parameters = rope_parameters
+        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: dict | Step3VisionEncoderConfig | None = None,
+        text_config: dict | Step3TextConfig | None = 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)

vllm/transformers_utils/configs/step3p5.py

@@ -0,0 +1,100 @@
+# 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,
+        )

vllm/transformers_utils/configs/tarsier2.py

@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from transformers import Qwen2VLConfig
+
+
+class Tarsier2Config(Qwen2VLConfig):
+    """
+    Tarsier2's config.json is written such that AutoConfig.from_pretrained will create
+    a deeply nested config consisting of:
+
+    - LlavaConfig
+      - Qwen2VLConfig
+        - Qwen2VLTextConfig
+        - Qwen2VLVisionConfig
+      - Qwen2VLConfig
+        - Qwen2VLTextConfig
+        - Qwen2VLVisionConfig
+
+    When it should really just be a single Qwen2VLConfig.
+
+    This class is a hack to stop AutoConfig from creating the nested config structure.
+    """
+
+    model_type = "tarsier2"

vllm/transformers_utils/configs/ultravox.py

@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_config.py
+from typing import Any
+
+import transformers
+
+
+class UltravoxConfig(transformers.PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a
+    [`UltravoxForConditionalGeneration`]. It is used to instantiate an
+    Ultravox model according to the specified arguments, defining the model
+    architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to
+    control the model outputs. Read the documentation from [`PretrainedConfig`]
+    for more information.
+
+    Args:
+        audio_config (`Union[AutoConfig, dict]`,  *optional*):
+            Custom audio config or dict.
+        text_config (`Union[AutoConfig, dict]`, *optional*):
+            The config object of the text backbone.
+        audio_model_id (`str`, *optional*):
+            The model ID of the audio backbone.
+        text_model_id (`str`, *optional*):
+            The model ID of the text backbone.
+        ignore_index (`int`, *optional*, defaults to -100):
+            The ignore index for the loss function.
+        audio_token_index (`int`, *optional*, defaults to 32000):
+            The audio token index to encode the audio prompt.
+        stack_factor (`int`, *optional*, defaults to 8):
+            Audio downsampling factor for the multimodal projector.
+        norm_init (`float`, *optional*, defaults to 0.4):
+            The initialization value for the layer normalization.
+        projector_act (`str`, *optional*, defaults to `"swiglu"`):
+            The activation function used by the multimodal projector.
+        projector_ln_mid (`bool`, *optional*, defaults to `False`):
+            Whether to apply layer normalization at the middle of the
+            projector or at the end. Versions v0.4.1 and below
+            use `False`, but v0.5 and above use `True`.
+    """
+
+    wrapped_model_config: transformers.PretrainedConfig
+    model_type = "ultravox"
+    audio_token = "<|audio|>"
+    is_composition = False
+
+    def __init__(
+        self,
+        audio_config: dict[str, Any] | None = None,
+        text_config: dict[str, Any] | None = None,
+        audio_model_id: str | None = None,
+        text_model_id: str | None = None,
+        ignore_index: int = -100,
+        audio_token_index: int = 32000,
+        hidden_size: int = 4096,
+        stack_factor: int = 8,
+        norm_init: float = 0.4,
+        projector_act: str = "swiglu",
+        projector_ln_mid: bool = False,
+        num_projector_layers: int = 0,
+        **kwargs,
+    ):
+        self.ignore_index = ignore_index
+        self.audio_token_index = audio_token_index
+
+        self.hidden_size = hidden_size
+        self.stack_factor = stack_factor
+        self.norm_init = norm_init
+        self.projector_act = projector_act
+        self.projector_ln_mid = projector_ln_mid
+        self.num_projector_layers = num_projector_layers
+
+        # N.B. May set the wrapped_model_config below.
+        self.text_model_id = text_model_id
+        if text_model_id is None:
+            text_config = text_config or {}
+            self.wrapped_model_config = transformers.CONFIG_MAPPING[
+                text_config.get("model_type", "llama")
+            ](**text_config)
+
+        # N.B. May set the audio_config below.
+        self.audio_model_id = audio_model_id
+        if audio_model_id is None:
+            self.audio_model_id = None
+            audio_config = audio_config or {}
+            self.audio_config = transformers.CONFIG_MAPPING[
+                audio_config.get("model_type", "whisper")
+            ](**audio_config)
+
+        super().__init__(**kwargs)
+
+    def __setattr__(self, key, value):
+        # Since --hf-overrides are applied _after_ the UltravoxConfig is
+        # instantiated, load the configs implicitly when assigning text_model_id
+        # or audio_model_id. This allows:
+        #
+        #   --hf-overrides.text_model_id=<quantized variant>
+        #
+        # to behave as intended.
+        if key == "text_model_id" and value is not None:
+            from vllm.transformers_utils.config import get_config
+
+            self.wrapped_model_config = get_config(value, trust_remote_code=False)
+        elif key == "audio_model_id" and value is not None:
+            from vllm.transformers_utils.config import get_config
+
+            self.audio_config = get_config(value, trust_remote_code=False)
+
+        return super().__setattr__(key, value)
+
+    @property
+    def text_config(self) -> transformers.PretrainedConfig:
+        # When Ultravox wraps a multi-modal model (e.g. Gemma), we instantiate
+        # the full model, but the text config is the text config of the inner
+        # model.
+        return self.wrapped_model_config.get_text_config()

vllm/transformers_utils/dynamic_module.py

@@ -0,0 +1,70 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+
+from transformers.dynamic_module_utils import (
+    get_class_from_dynamic_module,
+    resolve_trust_remote_code,
+)
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def try_get_class_from_dynamic_module(
+    class_reference: str,
+    pretrained_model_name_or_path: str,
+    trust_remote_code: bool,
+    cache_dir: str | os.PathLike | None = None,
+    force_download: bool = False,
+    resume_download: bool | None = None,
+    proxies: dict[str, str] | None = None,
+    token: bool | str | None = None,
+    revision: str | None = None,
+    local_files_only: bool = False,
+    repo_type: str | None = None,
+    code_revision: str | None = None,
+    warn_on_fail: bool = True,
+    **kwargs,
+) -> type | None:
+    """
+    As `transformers.dynamic_module_utils.get_class_from_dynamic_module`,
+    but ignoring any errors.
+    """
+    try:
+        resolve_trust_remote_code(
+            trust_remote_code,
+            pretrained_model_name_or_path,
+            has_local_code=False,
+            has_remote_code=True,
+        )
+
+        return get_class_from_dynamic_module(
+            class_reference,
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            local_files_only=local_files_only,
+            repo_type=repo_type,
+            code_revision=code_revision,
+            **kwargs,
+        )
+    except Exception:
+        location = "ModelScope" if envs.VLLM_USE_MODELSCOPE else "HF Hub"
+
+        if warn_on_fail:
+            logger.warning(
+                "Unable to load %s from %s on %s.",
+                class_reference,
+                pretrained_model_name_or_path,
+                location,
+                exc_info=True,
+            )
+
+        return None

vllm/transformers_utils/gguf_utils.py

@@ -0,0 +1,301 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""GGUF utility functions."""
+
+from functools import cache
+from os import PathLike
+from pathlib import Path
+
+import gguf
+import regex as re
+from gguf.constants import Keys, VisionProjectorType
+from gguf.quants import GGMLQuantizationType
+from transformers import Gemma3Config, PretrainedConfig, SiglipVisionConfig
+
+from vllm.logger import init_logger
+
+from .repo_utils import list_filtered_repo_files
+
+logger = init_logger(__name__)
+
+
+@cache
+def check_gguf_file(model: str | PathLike) -> bool:
+    """Check if the file is a GGUF model."""
+    model = Path(model)
+    if not model.is_file():
+        return False
+    elif model.suffix == ".gguf":
+        return True
+
+    try:
+        with model.open("rb") as f:
+            header = f.read(4)
+
+        return header == b"GGUF"
+    except Exception as e:
+        logger.debug("Error reading file %s: %s", model, e)
+        return False
+
+
+@cache
+def is_remote_gguf(model: str | Path) -> bool:
+    """Check if the model is a remote GGUF model."""
+    pattern = r"^[a-zA-Z0-9][a-zA-Z0-9._-]*/[a-zA-Z0-9][a-zA-Z0-9._-]*:[A-Za-z0-9_+-]+$"
+    model = str(model)
+    if re.fullmatch(pattern, model):
+        _, quant_type = model.rsplit(":", 1)
+        return is_valid_gguf_quant_type(quant_type)
+    return False
+
+
+# Common suffixes used in GGUF file naming conventions
+# e.g., Q4_K_M, Q3_K_S, Q5_K_L, Q2_K_XL
+_GGUF_QUANT_SUFFIXES = ("_M", "_S", "_L", "_XL", "_XS", "_XXS")
+
+
+def is_valid_gguf_quant_type(gguf_quant_type: str) -> bool:
+    """Check if the quant type is a valid GGUF quant type.
+
+    Supports both exact GGML quant types (e.g., Q4_K, IQ1_S) and
+    extended naming conventions (e.g., Q4_K_M, Q3_K_S, Q5_K_L).
+    """
+    # Check for exact match first
+    if getattr(GGMLQuantizationType, gguf_quant_type, None) is not None:
+        return True
+
+    # Check for extended naming conventions (e.g., Q4_K_M -> Q4_K)
+    for suffix in _GGUF_QUANT_SUFFIXES:
+        if gguf_quant_type.endswith(suffix):
+            base_type = gguf_quant_type[: -len(suffix)]
+            if getattr(GGMLQuantizationType, base_type, None) is not None:
+                return True
+
+    return False
+
+
+def split_remote_gguf(model: str | Path) -> tuple[str, str]:
+    """Split the model into repo_id and quant type."""
+    model = str(model)
+    if is_remote_gguf(model):
+        parts = model.rsplit(":", 1)
+        return (parts[0], parts[1])
+    raise ValueError(
+        f"Wrong GGUF model or invalid GGUF quant type: {model}.\n"
+        "- It should be in repo_id:quant_type format.\n"
+        f"- Valid base quant types: {GGMLQuantizationType._member_names_}\n"
+        f"- Extended suffixes also supported: {_GGUF_QUANT_SUFFIXES}",
+    )
+
+
+def is_gguf(model: str | Path) -> bool:
+    """Check if the model is a GGUF model.
+
+    Args:
+        model: Model name, path, or Path object to check.
+
+    Returns:
+        True if the model is a GGUF model, False otherwise.
+    """
+    model = str(model)
+
+    # Check if it's a local GGUF file
+    if check_gguf_file(model):
+        return True
+
+    # Check if it's a remote GGUF model (repo_id:quant_type format)
+    return is_remote_gguf(model)
+
+
+def detect_gguf_multimodal(model: str) -> Path | None:
+    """Check if GGUF model has multimodal projector file.
+
+    Args:
+        model: Model path string
+
+    Returns:
+        Path to mmproj file if found, None otherwise
+    """
+    if not model.endswith(".gguf"):
+        return None
+
+    try:
+        model_path = Path(model)
+        if not model_path.is_file():
+            return None
+
+        model_dir = model_path.parent
+        mmproj_patterns = ["mmproj.gguf", "mmproj-*.gguf", "*mmproj*.gguf"]
+        for pattern in mmproj_patterns:
+            mmproj_files = list(model_dir.glob(pattern))
+            if mmproj_files:
+                return mmproj_files[0]
+        return None
+    except Exception:
+        return None
+
+
+def extract_vision_config_from_gguf(mmproj_path: str) -> "SiglipVisionConfig | None":
+    """Extract vision config parameters from mmproj.gguf metadata.
+
+    Reads vision encoder configuration from GGUF metadata fields using
+    standardized GGUF constants. Automatically detects the projector type
+    (e.g., gemma3, llama4) and applies model-specific parameters accordingly.
+
+    The function extracts standard CLIP vision parameters from GGUF metadata
+    and applies projector-type-specific customizations. For unknown projector
+    types, it uses safe defaults from SiglipVisionConfig.
+
+    Args:
+        mmproj_path: Path to mmproj.gguf file (str or Path)
+
+    Returns:
+        SiglipVisionConfig if extraction succeeds, None if any required
+        field is missing from the GGUF metadata
+
+    Raises:
+        Exception: Exceptions from GGUF reading (file not found, corrupted
+            file, etc.) propagate directly from gguf.GGUFReader
+    """
+    reader = gguf.GGUFReader(str(mmproj_path))
+
+    # Detect projector type to apply model-specific parameters
+    projector_type = None
+    projector_type_field = reader.get_field(Keys.Clip.PROJECTOR_TYPE)
+    if projector_type_field:
+        try:
+            projector_type = bytes(projector_type_field.parts[-1]).decode("utf-8")
+        except (AttributeError, UnicodeDecodeError) as e:
+            logger.warning("Failed to decode projector type from GGUF: %s", e)
+
+    # Map GGUF field constants to SiglipVisionConfig parameters.
+    # Uses official GGUF constants from gguf-py for standardization.
+    # Format: {gguf_constant: (param_name, dtype)}
+    VISION_CONFIG_FIELDS = {
+        Keys.ClipVision.EMBEDDING_LENGTH: ("hidden_size", int),
+        Keys.ClipVision.FEED_FORWARD_LENGTH: ("intermediate_size", int),
+        Keys.ClipVision.BLOCK_COUNT: ("num_hidden_layers", int),
+        Keys.ClipVision.Attention.HEAD_COUNT: ("num_attention_heads", int),
+        Keys.ClipVision.IMAGE_SIZE: ("image_size", int),
+        Keys.ClipVision.PATCH_SIZE: ("patch_size", int),
+        Keys.ClipVision.Attention.LAYERNORM_EPS: ("layer_norm_eps", float),
+    }
+
+    # Extract and validate all required fields
+    config_params = {}
+    for gguf_key, (param_name, dtype) in VISION_CONFIG_FIELDS.items():
+        field = reader.get_field(gguf_key)
+        if field is None:
+            logger.warning(
+                "Missing required vision config field '%s' in mmproj.gguf",
+                gguf_key,
+            )
+            return None
+        # Extract scalar value from GGUF field and convert to target type
+        config_params[param_name] = dtype(field.parts[-1])
+
+    # Apply model-specific parameters based on projector type
+    if projector_type == VisionProjectorType.GEMMA3:
+        # Gemma3 doesn't use the vision pooling head (multihead attention)
+        # This is a vLLM-specific parameter used in SiglipVisionTransformer
+        config_params["vision_use_head"] = False
+        logger.info("Detected Gemma3 projector, disabling vision pooling head")
+    # Add other projector-type-specific customizations here as needed
+    # elif projector_type == VisionProjectorType.LLAMA4:
+    #     config_params["vision_use_head"] = ...
+
+    # Create config with extracted parameters
+    # Note: num_channels and attention_dropout use SiglipVisionConfig defaults
+    # (3 and 0.0 respectively) which are correct for all models
+    config = SiglipVisionConfig(**config_params)
+
+    if projector_type:
+        logger.info(
+            "Extracted vision config from mmproj.gguf (projector_type: %s)",
+            projector_type,
+        )
+    else:
+        logger.info("Extracted vision config from mmproj.gguf metadata")
+
+    return config
+
+
+def maybe_patch_hf_config_from_gguf(
+    model: str,
+    hf_config: PretrainedConfig,
+) -> PretrainedConfig:
+    """Patch HF config for GGUF models.
+
+    Applies GGUF-specific patches to HuggingFace config:
+    1. For multimodal models: patches architecture and vision config
+    2. For all GGUF models: overrides vocab_size from embedding tensor
+
+    This ensures compatibility with GGUF models that have extended
+    vocabularies (e.g., Unsloth) where the GGUF file contains more
+    tokens than the HuggingFace tokenizer config specifies.
+
+    Args:
+        model: Model path string
+        hf_config: HuggingFace config to patch in-place
+
+    Returns:
+        Updated HuggingFace config
+    """
+    # Patch multimodal config if mmproj.gguf exists
+    mmproj_path = detect_gguf_multimodal(model)
+    if mmproj_path is not None:
+        vision_config = extract_vision_config_from_gguf(str(mmproj_path))
+
+        # Create HF config for Gemma3 multimodal
+        text_config = hf_config.get_text_config()
+        is_gemma3 = hf_config.model_type in ("gemma3", "gemma3_text")
+        if vision_config is not None and is_gemma3:
+            new_hf_config = Gemma3Config(
+                text_config=text_config,
+                vision_config=vision_config,
+                architectures=["Gemma3ForConditionalGeneration"],
+            )
+            hf_config = new_hf_config
+
+    return hf_config
+
+
+def get_gguf_file_path_from_hf(
+    repo_id: str | Path,
+    quant_type: str,
+    revision: str | None = None,
+) -> str:
+    """Get the GGUF file path from HuggingFace Hub based on repo_id and quant_type.
+
+    Args:
+        repo_id: The HuggingFace repository ID (e.g., "Qwen/Qwen3-0.6B")
+        quant_type: The quantization type (e.g., "Q4_K_M", "F16")
+        revision: Optional revision/branch name
+
+    Returns:
+        The path to the GGUF file on HuggingFace Hub (e.g., "filename.gguf"),
+    """
+    repo_id = str(repo_id)
+    gguf_patterns = [
+        f"*-{quant_type}.gguf",
+        f"*-{quant_type}-*.gguf",
+        f"*/*-{quant_type}.gguf",
+        f"*/*-{quant_type}-*.gguf",
+    ]
+    matching_files = list_filtered_repo_files(
+        repo_id,
+        allow_patterns=gguf_patterns,
+        revision=revision,
+    )
+
+    if len(matching_files) == 0:
+        raise ValueError(
+            "Could not find GGUF file for repo %s with quantization %s.",
+            repo_id,
+            quant_type,
+        )
+
+    # Sort to ensure consistent ordering (prefer non-sharded files)
+    matching_files.sort(key=lambda x: (x.count("-"), x))
+    gguf_filename = matching_files[0]
+    return gguf_filename

vllm/transformers_utils/model_arch_config_convertor.py

@@ -0,0 +1,460 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterator
+from contextlib import contextmanager
+from typing import final
+
+import torch
+from huggingface_hub import constants
+from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
+from transformers import PretrainedConfig
+
+from vllm import envs
+from vllm.config.model_arch import (
+    ModelArchitectureConfig,
+)
+from vllm.config.utils import getattr_iter
+from vllm.logger import init_logger
+from vllm.transformers_utils.config import (
+    ConfigFormat,
+    try_get_safetensors_metadata,
+)
+from vllm.utils.torch_utils import common_broadcastable_dtype
+
+logger = init_logger(__name__)
+
+
+@contextmanager
+def _maybe_patch_hf_hub_constants(config_format: ConfigFormat) -> Iterator[None]:
+    if config_format == "mistral":
+        hf_safetensors_single_file = constants.SAFETENSORS_SINGLE_FILE
+        hf_safetensors_index_file = constants.SAFETENSORS_INDEX_FILE
+        constants.SAFETENSORS_SINGLE_FILE = "consolidated.safetensors"
+        constants.SAFETENSORS_INDEX_FILE = "consolidated.safetensors.index.json"
+        try:
+            yield
+        finally:
+            constants.SAFETENSORS_SINGLE_FILE = hf_safetensors_single_file
+            constants.SAFETENSORS_INDEX_FILE = hf_safetensors_index_file
+    else:
+        yield
+
+
+class ModelArchConfigConvertorBase:
+    def __init__(self, hf_config: PretrainedConfig, hf_text_config: PretrainedConfig):
+        self.hf_config = hf_config
+        self.hf_text_config = hf_text_config
+
+    def get_architectures(self) -> list[str]:
+        return getattr(self.hf_config, "architectures", [])
+
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_hidden_layers", 0)
+
+    def get_total_num_attention_heads(self) -> int:
+        return getattr(self.hf_text_config, "num_attention_heads", 0)
+
+    def get_vocab_size(self) -> int:
+        return getattr(self.hf_text_config, "vocab_size", 0)
+
+    def get_hidden_size(self) -> int:
+        return getattr(self.hf_text_config, "hidden_size", 0)
+
+    def get_head_size(self) -> int:
+        if self.is_deepseek_mla():
+            qk_rope_head_dim = getattr(self.hf_text_config, "qk_rope_head_dim", 0)
+            if not envs.VLLM_MLA_DISABLE:
+                return self.hf_text_config.kv_lora_rank + qk_rope_head_dim
+            else:
+                qk_nope_head_dim = getattr(self.hf_text_config, "qk_nope_head_dim", 0)
+                if qk_rope_head_dim and qk_nope_head_dim:
+                    return qk_rope_head_dim + qk_nope_head_dim
+
+        # NOTE: Some configs may set head_dim=None in the config
+        if getattr(self.hf_text_config, "head_dim", None) is not None:
+            return self.hf_text_config.head_dim
+
+        # NOTE: Some models (such as PLaMo2.1) use `hidden_size_per_head`
+        if getattr(self.hf_text_config, "hidden_size_per_head", None) is not None:
+            return self.hf_text_config.hidden_size_per_head
+
+        # FIXME(woosuk): This may not be true for all models.
+        return (
+            self.hf_text_config.hidden_size // self.hf_text_config.num_attention_heads
+        )
+
+    def get_total_num_kv_heads(self) -> int:
+        attributes = [
+            # For Falcon:
+            "n_head_kv",
+            "num_kv_heads",
+            # For LLaMA-2:
+            "num_key_value_heads",
+            # For ChatGLM:
+            "multi_query_group_num",
+        ]
+        # For non-grouped-query attention models, the number of KV heads is
+        # equal to the number of attention heads.
+        default_factory = lambda: self.hf_text_config.num_attention_heads
+        return getattr_iter(
+            self.hf_text_config, attributes, default_factory=default_factory
+        )
+
+    def get_num_experts_from_block_configs(self) -> int:
+        """Check block_configs for heterogeneous models (e.g., NemotronH).
+
+        For heterogeneous models with varying expert counts per layer,
+        returns the MAX to ensure all expert weights can be loaded.
+        """
+        max_experts = 0
+        block_configs = getattr(self.hf_text_config, "block_configs", None)
+        if block_configs:
+            for block in block_configs:
+                if isinstance(block, dict):
+                    if block.get("block_type", "") == "moe":
+                        max_experts = max(max_experts, block.get("n_routed_experts", 0))
+                else:
+                    if getattr(block, "block_type", "") == "moe":
+                        max_experts = max(
+                            max_experts, getattr(block, "n_routed_experts", 0)
+                        )
+        return max_experts
+
+    def get_num_experts(self) -> int:
+        """Returns the number of experts in the model."""
+        num_expert_names = [
+            "num_experts",  # Jamba
+            "moe_num_experts",  # Dbrx
+            "n_routed_experts",  # DeepSeek
+            "num_local_experts",  # Mixtral
+        ]
+
+        num_experts = getattr_iter(self.hf_text_config, num_expert_names, 0)
+        if isinstance(num_experts, list):
+            # Ernie VL's remote code uses list[int]...
+            # The values are always the same so we just take the first one.
+            return num_experts[0]
+
+        if not num_experts:
+            num_experts = self.get_num_experts_from_block_configs()
+        return num_experts
+
+    @final
+    @classmethod
+    def get_torch_dtype(
+        cls,
+        hf_config: PretrainedConfig,
+        model_id: str,
+        revision: str | None,
+        config_format: ConfigFormat,
+    ):
+        # NOTE: getattr(config, "dtype", torch.float32) is not correct
+        # because config.dtype can be None.
+        config_dtype = getattr(hf_config, "dtype", None)
+
+        # Fallbacks for multi-modal models if the root config
+        # does not define dtype
+        if config_dtype is None:
+            config_dtype = getattr(hf_config.get_text_config(), "dtype", None)
+        if config_dtype is None and hasattr(hf_config, "vision_config"):
+            config_dtype = getattr(hf_config.vision_config, "dtype", None)
+        if config_dtype is None and hasattr(hf_config, "encoder_config"):
+            config_dtype = getattr(hf_config.encoder_config, "dtype", None)
+
+        # Try to read the dtype of the weights if they are in safetensors format
+        if config_dtype is None:
+            with _maybe_patch_hf_hub_constants(config_format):
+                repo_mt = try_get_safetensors_metadata(model_id, revision=revision)
+
+            if repo_mt and (files_mt := repo_mt.files_metadata):
+                param_dtypes: set[torch.dtype] = {
+                    _SAFETENSORS_TO_TORCH_DTYPE[dtype_str]
+                    for file_mt in files_mt.values()
+                    for dtype_str in file_mt.parameter_count
+                    if dtype_str in _SAFETENSORS_TO_TORCH_DTYPE
+                }
+
+                if param_dtypes:
+                    return common_broadcastable_dtype(param_dtypes)
+
+        if config_dtype is None:
+            config_dtype = torch.float32
+
+        return config_dtype
+
+    def _normalize_quantization_config(self, config: PretrainedConfig):
+        quant_cfg = getattr(config, "quantization_config", None)
+        if quant_cfg is None:
+            # compressed-tensors uses a "compression_config" key
+            quant_cfg = getattr(config, "compression_config", None)
+
+        else:
+            # Set quant_method for ModelOpt models.
+            producer_name = quant_cfg.get("producer", {}).get("name")
+            if producer_name == "modelopt":
+                quant_algo = quant_cfg.get("quantization", {}).get("quant_algo")
+                if quant_algo is not None:
+                    quant_algo_upper = str(quant_algo).upper()
+                    if quant_algo_upper in {
+                        "FP8",
+                        "FP8_PER_CHANNEL_PER_TOKEN",
+                        "FP8_PB_WO",
+                    }:
+                        quant_cfg["quant_method"] = "modelopt"
+                    elif quant_algo_upper == "NVFP4":
+                        quant_cfg["quant_method"] = "modelopt_fp4"
+                    else:
+                        raise ValueError(f"Unknown ModelOpt quant algo: {quant_algo}")
+
+        if quant_cfg is not None:
+            # Use the community standard 'quant_method'
+            quant_method = quant_cfg.get("quant_method", "").lower()
+
+            # Normalize library names
+            quant_method = quant_method.replace(
+                "compressed_tensors", "compressed-tensors"
+            )
+
+            quant_cfg["quant_method"] = quant_method
+
+        return quant_cfg
+
+    def get_quantization_config(self):
+        quant_cfg = self._normalize_quantization_config(self.hf_config)
+        if quant_cfg is None and (
+            text_config := getattr(self.hf_config, "text_config", None)
+        ):
+            # Check the text config as well for multi-modal models.
+            quant_cfg = self._normalize_quantization_config(text_config)
+        return quant_cfg
+
+    def is_deepseek_mla(self) -> bool:
+        if not hasattr(self.hf_text_config, "model_type"):
+            return False
+        elif self.hf_text_config.model_type in (
+            "deepseek_v2",
+            "deepseek_v3",
+            "deepseek_v32",
+            "deepseek_mtp",
+            "glm_moe_dsa",
+            "glm4_moe_lite",
+            "glm4_moe_lite_mtp",
+            "kimi_k2",
+            "kimi_linear",
+            "longcat_flash",
+            "pangu_ultra_moe",
+            "pangu_ultra_moe_mtp",
+        ):
+            return self.hf_text_config.kv_lora_rank is not None
+        elif self.hf_text_config.model_type == "eagle":
+            # if the model is an EAGLE module, check for the
+            # underlying architecture
+            return (
+                self.hf_text_config.model.model_type
+                in ("deepseek_v2", "deepseek_v3", "deepseek_v32", "deepseek_mtp")
+                and self.hf_text_config.kv_lora_rank is not None
+            )
+        return False
+
+    def derive_max_model_len_and_key(self) -> tuple[float, str | None]:
+        derived_max_model_len = float("inf")
+        possible_keys = [
+            # OPT
+            "max_position_embeddings",
+            # GPT-2
+            "n_positions",
+            # MPT
+            "max_seq_len",
+            # ChatGLM2
+            "seq_length",
+            # Command-R
+            "model_max_length",
+            # Whisper
+            "max_target_positions",
+            # Others
+            "max_sequence_length",
+            "max_seq_length",
+            "seq_len",
+        ]
+        # Choose the smallest "max_length" from the possible keys
+        max_len_key = None
+        for key in possible_keys:
+            max_len = getattr(self.hf_text_config, key, None)
+            if max_len is not None:
+                if max_len < derived_max_model_len:
+                    max_len_key = key
+                derived_max_model_len = min(derived_max_model_len, max_len)
+
+        # For Command-R / Cohere, Cohere2 / Aya Vision models
+        if tmp_max_len := getattr(self.hf_text_config, "model_max_length", None):
+            max_len_key = "model_max_length"
+            derived_max_model_len = tmp_max_len
+        return derived_max_model_len, max_len_key
+
+    def convert(self) -> ModelArchitectureConfig:
+        model_arch_config = ModelArchitectureConfig(
+            architectures=self.get_architectures(),
+            model_type=self.hf_config.model_type,
+            text_model_type=getattr(self.hf_text_config, "model_type", None),
+            hidden_size=self.get_hidden_size(),
+            total_num_hidden_layers=self.get_num_hidden_layers(),
+            total_num_attention_heads=self.get_total_num_attention_heads(),
+            head_size=self.get_head_size(),
+            vocab_size=self.get_vocab_size(),
+            total_num_kv_heads=self.get_total_num_kv_heads(),
+            num_experts=self.get_num_experts(),
+            quantization_config=self.get_quantization_config(),
+            is_deepseek_mla=self.is_deepseek_mla(),
+            derived_max_model_len_and_key=self.derive_max_model_len_and_key(),
+        )
+
+        return model_arch_config
+
+
+class MambaModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_head_size(self) -> int:
+        return 0
+
+    def get_total_num_kv_heads(self) -> int:
+        return 0
+
+
+class TerratorchModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_head_size(self) -> int:
+        return 0
+
+    def get_total_num_kv_heads(self) -> int:
+        return 0
+
+
+class MedusaModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_head_size(self) -> int:
+        return 0
+
+    def get_total_num_kv_heads(self) -> int:
+        return 0
+
+
+class Zamba2ModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_head_size(self) -> int:
+        return getattr(self.hf_text_config, "attention_head_dim", 0)
+
+
+class FalconModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_total_num_kv_heads(self) -> int:
+        # NOTE: for falcon, when new_decoder_architecture is True, the
+        # multi_query flag is ignored and we use n_head_kv for the number of
+        # KV heads.
+        new_decoder_arch_falcon = getattr(
+            self.hf_text_config, "new_decoder_architecture", False
+        )
+
+        if not new_decoder_arch_falcon and getattr(
+            self.hf_text_config, "multi_query", False
+        ):
+            # Multi-query attention, only one KV head.
+            return 1
+
+        # Use the base implementation which checks n_head_kv, num_kv_heads, etc.
+        return super().get_total_num_kv_heads()
+
+
+class MPTModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_total_num_kv_heads(self) -> int:
+        if "kv_n_heads" in self.hf_text_config.attn_config:
+            return self.hf_text_config.attn_config["kv_n_heads"]
+        return self.hf_text_config.num_attention_heads
+
+
+class DbrxModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_total_num_kv_heads(self) -> int:
+        return getattr(
+            self.hf_text_config.attn_config,
+            "kv_n_heads",
+            self.hf_text_config.num_attention_heads,
+        )
+
+
+class NemotronNasModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_total_num_kv_heads(self) -> int:
+        for block in self.hf_text_config.block_configs:
+            if not block.attention.no_op:
+                return (
+                    self.hf_text_config.num_attention_heads
+                    // block.attention.n_heads_in_group
+                )
+        raise RuntimeError(
+            "Could not determine the number of key-value attention heads "
+            "from model configuration. "
+            f"Architecture: {self.get_architectures()}. "
+            "This usually indicates an unsupported model architecture or "
+            "missing configuration. "
+            "Please check if your model is supported at: "
+            "https://docs.vllm.ai/en/latest/models/supported_models.html"
+        )
+
+
+class DeepSeekMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
+
+
+class MimoMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
+
+
+class GLM4MoeMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
+
+
+class ErnieMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
+
+
+class Qwen3NextMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
+
+
+class Qwen3_5MTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "mtp_num_hidden_layers", 0)
+
+
+class PanguUltraMoeMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
+
+
+class LongCatFlashMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "num_nextn_predict_layers", 1)
+
+
+# hf_config.model_type -> convertor class
+MODEL_ARCH_CONFIG_CONVERTORS = {
+    "mamba": MambaModelArchConfigConvertor,
+    "falcon_mamba": MambaModelArchConfigConvertor,
+    "timm_wrapper": TerratorchModelArchConfigConvertor,
+    "medusa": MedusaModelArchConfigConvertor,
+    "zamba2": Zamba2ModelArchConfigConvertor,
+    "mpt": MPTModelArchConfigConvertor,
+    "dbrx": DbrxModelArchConfigConvertor,
+    "falcon": FalconModelArchConfigConvertor,
+    "RefinedWeb": FalconModelArchConfigConvertor,
+    "RefinedWebModel": FalconModelArchConfigConvertor,
+    "nemotron-nas": NemotronNasModelArchConfigConvertor,
+    "deepseek_mtp": DeepSeekMTPModelArchConfigConvertor,
+    "qwen3_next_mtp": Qwen3NextMTPModelArchConfigConvertor,
+    "qwen3_5_mtp": Qwen3_5MTPModelArchConfigConvertor,
+    "mimo_mtp": MimoMTPModelArchConfigConvertor,
+    "glm4_moe_mtp": GLM4MoeMTPModelArchConfigConvertor,
+    "glm_ocr_mtp": GLM4MoeMTPModelArchConfigConvertor,
+    "ernie_mtp": ErnieMTPModelArchConfigConvertor,
+    "pangu_ultra_moe_mtp": PanguUltraMoeMTPModelArchConfigConvertor,
+    "longcat_flash_mtp": LongCatFlashMTPModelArchConfigConvertor,
+}

vllm/transformers_utils/processor.py

@@ -0,0 +1,484 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import importlib
+import inspect
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any, cast, get_args, get_type_hints
+
+from transformers import (
+    AutoFeatureExtractor,
+    AutoImageProcessor,
+    AutoProcessor,
+    AutoVideoProcessor,
+    processing_utils,
+)
+from transformers.feature_extraction_utils import FeatureExtractionMixin
+from transformers.image_processing_utils import BaseImageProcessor
+from transformers.processing_utils import ProcessorMixin
+from transformers.video_processing_utils import BaseVideoProcessor
+from typing_extensions import TypeVar
+
+from vllm.logger import init_logger
+from vllm.transformers_utils.gguf_utils import is_gguf
+from vllm.transformers_utils.utils import convert_model_repo_to_path
+from vllm.utils.func_utils import get_allowed_kwarg_only_overrides
+
+logger = init_logger(__name__)
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig
+
+
+def _transformers_v4_compatibility_import():
+    """Some remote code processors still import `ChatTemplateLoadKwargs` which was a
+    subset of `ProcessorChatTemplateKwargs` as defined in Transformers v4.
+    In Transformers v5 these were merged into `ProcessorChatTemplateKwargs` and
+    `ChatTemplateLoadKwargs` was removed. For backward compatibility, we add an alias
+    for `ChatTemplateLoadKwargs` if it doesn't exist.
+
+    This can be removed if `HCXVisionForCausalLM` is upstreamed to Transformers."""
+    old_import = getattr(processing_utils, "ChatTemplateLoadKwargs", None)
+    new_import = getattr(processing_utils, "ProcessorChatTemplateKwargs", None)
+    if old_import is None and new_import is not None:
+        processing_utils.ChatTemplateLoadKwargs = new_import
+
+
+def _transformers_v4_compatibility_init() -> Any:
+    """Some remote code processors may define `optional_attributes` in their
+    `ProcessorMixin` subclass, and then pass these arbitrary attributes directly to
+    `ProcessorMixin.__init__`, which is no longer allowed in Transformers v5. For
+    backward compatibility, we intercept these optional attributes and set them on the
+    processor instance before calling the original `ProcessorMixin.__init__`.
+
+    This can be removed if `Molmo2ForConditionalGeneration` is upstreamed to
+    Transformers."""
+    # Transformers v4
+    if hasattr(ProcessorMixin, "optional_attributes"):
+        return
+    # Transformers v5
+    if hasattr(ProcessorMixin.__init__, "_vllm_patched"):
+        return
+
+    original_init = ProcessorMixin.__init__
+
+    def __init__(self, *args, **kwargs):
+        for optional_attribute in getattr(self, "optional_attributes", []):
+            if optional_attribute in kwargs:
+                setattr(self, optional_attribute, kwargs.pop(optional_attribute))
+
+        original_init(self, *args, **kwargs)
+
+    # Only patch if ProcessorMixin is not mocked (for docs builds)
+    if not hasattr(ProcessorMixin, "_mock_name"):
+        __init__._vllm_patched = True  # type: ignore[attr-defined]
+        ProcessorMixin.__init__ = __init__
+
+
+_transformers_v4_compatibility_import()
+_transformers_v4_compatibility_init()
+
+_P = TypeVar("_P", bound=ProcessorMixin, default=ProcessorMixin)
+_V = TypeVar("_V", bound=BaseVideoProcessor, default=BaseVideoProcessor)
+
+
+class HashableDict(dict):
+    """
+    A dictionary that can be hashed by lru_cache.
+    """
+
+    # NOTE: pythonic dict is not hashable,
+    # we override on it directly for simplicity
+    def __hash__(self) -> int:  # type: ignore[override]
+        return hash(frozenset(self.items()))
+
+
+class HashableList(list):
+    """
+    A list that can be hashed by lru_cache.
+    """
+
+    def __hash__(self) -> int:  # type: ignore[override]
+        return hash(tuple(self))
+
+
+def _get_processor_factory_fn(processor_cls: type | tuple[type, ...]):
+    if isinstance(processor_cls, tuple) or processor_cls == ProcessorMixin:
+        return AutoProcessor.from_pretrained
+    if hasattr(processor_cls, "from_pretrained"):
+        return processor_cls.from_pretrained
+
+    return processor_cls
+
+
+@lru_cache
+def _collect_dynamic_keys_from_processing_kwargs(kwargs_cls: type) -> set[str]:
+    dynamic_kwargs: set[str] = set()
+    if kwargs_cls is None:
+        return dynamic_kwargs
+    # get kwargs annotations in processor
+    # merge text_kwargs / images_kwargs / videos_kwargs / audio_kwargs
+    kwargs_type_annotations = get_type_hints(kwargs_cls)
+    for kw_type in ("text_kwargs", "images_kwargs", "videos_kwargs", "audio_kwargs"):
+        if kw_type in kwargs_type_annotations:
+            # Use __annotations__ instead of get_type_hints() to avoid
+            # NameError from unresolved forward references (e.g.
+            # PILImageResampling). We only need key names, not types.
+            kw_cls = kwargs_type_annotations[kw_type]
+            kw_annotations: dict[str, Any] = {}
+            for base in reversed(kw_cls.__mro__):
+                kw_annotations.update(getattr(base, "__annotations__", {}))
+            for kw_name in kw_annotations:
+                dynamic_kwargs.add(kw_name)
+    dynamic_kwargs |= {"text_kwargs", "images_kwargs", "videos_kwargs", "audio_kwargs"}
+    return dynamic_kwargs
+
+
+def _merge_mm_kwargs(
+    model_config: "ModelConfig",
+    processor_cls: type | tuple[type, ...],
+    /,
+    **kwargs,
+):
+    mm_config = model_config.get_multimodal_config()
+    merged_kwargs = mm_config.merge_mm_processor_kwargs(kwargs)
+
+    factory = _get_processor_factory_fn(processor_cls)
+    allowed_kwargs = get_allowed_kwarg_only_overrides(
+        factory,
+        merged_kwargs,
+        requires_kw_only=False,
+        allow_var_kwargs=True,
+    )
+    # NOTE: Pythonic dict is not hashable and will raise unhashable type
+    # error when calling `cached_get_processor`, therefore we need to
+    # wrap it to a hashable dict.
+    for key, value in allowed_kwargs.items():
+        if isinstance(value, dict):
+            allowed_kwargs[key] = HashableDict(value)
+        if isinstance(value, list):
+            allowed_kwargs[key] = HashableList(value)
+
+    return allowed_kwargs
+
+
+def get_processor(
+    processor_name: str,
+    *args: Any,
+    revision: str | None = None,
+    trust_remote_code: bool = False,
+    processor_cls: type[_P] | tuple[type[_P], ...] = ProcessorMixin,
+    **kwargs: Any,
+) -> _P:
+    """Load a processor for the given model name via HuggingFace."""
+    if revision is None:
+        revision = "main"
+    try:
+        processor_name = convert_model_repo_to_path(processor_name)
+        if isinstance(processor_cls, tuple) or processor_cls == ProcessorMixin:
+            processor = AutoProcessor.from_pretrained(
+                processor_name,
+                *args,
+                revision=revision,
+                trust_remote_code=trust_remote_code,
+                **kwargs,
+            )
+        elif issubclass(processor_cls, ProcessorMixin):
+            processor = processor_cls.from_pretrained(
+                processor_name,
+                *args,
+                revision=revision,
+                trust_remote_code=trust_remote_code,
+                **kwargs,
+            )
+        else:
+            # Processors that are standalone classes unrelated to HF
+            processor = processor_cls(*args, **kwargs)
+    except ValueError as e:
+        # If the error pertains to the processor class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        # Unlike AutoTokenizer, AutoProcessor does not separate such errors
+        if not trust_remote_code:
+            err_msg = (
+                "Failed to load the processor. If the processor is "
+                "a custom processor not yet available in the HuggingFace "
+                "transformers library, consider setting "
+                "`trust_remote_code=True` in LLM or using the "
+                "`--trust-remote-code` flag in the CLI."
+            )
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+
+    if not isinstance(processor, processor_cls):
+        raise TypeError(
+            "Invalid type of HuggingFace processor. "
+            f"Expected type: {processor_cls}, but "
+            f"found type: {type(processor)}"
+        )
+
+    return processor
+
+
+cached_get_processor = lru_cache(get_processor)
+
+
+@lru_cache
+def get_processor_kwargs_from_processor(processor: _P) -> set[str]:
+    try:
+        # get kwargs annotations in processor
+        call_kwargs = inspect.signature(type(processor).__call__).parameters.get(
+            "kwargs"
+        )
+        call_kwargs_annotations = call_kwargs.annotation if call_kwargs else None
+        # if the processor has explicit kwargs annotation, use it
+        if call_kwargs_annotations not in (None, inspect._empty):
+            # get_type_hints will parse all type annotations at runtime,
+            # and if an annotation refers to a type or
+            # name that hasn’t been imported or defined, it will raise an error.
+            # So we use __annotations__ to get the raw annotations directly.
+            return _collect_dynamic_keys_from_processing_kwargs(
+                get_args(call_kwargs_annotations)[0]
+            )
+        # otherwise, try to get from ProcessingKwargs
+        else:
+            module_name = type(processor).__module__
+            mod = importlib.import_module(module_name)
+            # find *ProcessingKwargs in the module
+            processor_kwargs: set[str] = set()
+            for name, obj in vars(mod).items():
+                if name.endswith("ProcessingKwargs"):
+                    processor_kwargs = (
+                        processor_kwargs
+                        | _collect_dynamic_keys_from_processing_kwargs(obj)
+                    )
+            return processor_kwargs
+    except Exception:
+        logger.exception("Failed to collect processor kwargs")
+        return set()
+
+
+def cached_get_processor_without_dynamic_kwargs(
+    processor_name: str,
+    *args: Any,
+    revision: str | None = None,
+    trust_remote_code: bool = False,
+    processor_cls: type[_P] | tuple[type[_P], ...] = ProcessorMixin,
+    **kwargs: Any,
+) -> _P:
+    # Step 1: use default kwargs to get a temporary processor instance
+    processor = cached_get_processor(
+        processor_name,
+        revision=revision,
+        trust_remote_code=trust_remote_code,
+        processor_cls=processor_cls,  # type: ignore[arg-type]
+    )
+
+    # Step 2: use temporary processor collect dynamic keys
+    dynamic_keys = get_processor_kwargs_from_processor(processor)
+
+    # Step 3: use dynamic_keys filter kwargs
+    filtered_kwargs = {k: v for k, v in kwargs.items() if k not in dynamic_keys}
+
+    # Step 4: use filtered kwargs to get final processor instance
+    final_processor = cached_get_processor(
+        processor_name,
+        revision=revision,
+        trust_remote_code=trust_remote_code,
+        processor_cls=processor_cls,  # type: ignore[arg-type]
+        **filtered_kwargs,
+    )
+
+    return final_processor
+
+
+def cached_processor_from_config(
+    model_config: "ModelConfig",
+    processor_cls: type[_P] | tuple[type[_P], ...] = ProcessorMixin,
+    **kwargs: Any,
+) -> _P:
+    if is_gguf(model_config.model):
+        assert not is_gguf(model_config.tokenizer), (
+            "For multimodal GGUF models, the original tokenizer "
+            "should be used to correctly load processor."
+        )
+        model = model_config.tokenizer
+        revision = model_config.tokenizer_revision
+    else:
+        model = model_config.model
+        revision = model_config.revision
+
+    return cached_get_processor_without_dynamic_kwargs(
+        model,
+        revision=revision,
+        trust_remote_code=model_config.trust_remote_code,
+        processor_cls=processor_cls,  # type: ignore[arg-type]
+        **_merge_mm_kwargs(model_config, processor_cls, **kwargs),
+    )
+
+
+def get_feature_extractor(
+    processor_name: str,
+    *args: Any,
+    revision: str | None = None,
+    trust_remote_code: bool = False,
+    **kwargs: Any,
+):
+    """Load an audio feature extractor for the given model name
+    via HuggingFace."""
+    try:
+        processor_name = convert_model_repo_to_path(processor_name)
+        feature_extractor = AutoFeatureExtractor.from_pretrained(
+            processor_name,
+            *args,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+            **kwargs,
+        )
+    except ValueError as e:
+        # If the error pertains to the processor class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        # Unlike AutoTokenizer, AutoImageProcessor does not separate such errors
+        if not trust_remote_code:
+            err_msg = (
+                "Failed to load the feature extractor. If the feature "
+                "extractor is a custom extractor not yet available in the "
+                "HuggingFace transformers library, consider setting "
+                "`trust_remote_code=True` in LLM or using the "
+                "`--trust-remote-code` flag in the CLI."
+            )
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+    return cast(FeatureExtractionMixin, feature_extractor)
+
+
+cached_get_feature_extractor = lru_cache(get_feature_extractor)
+
+
+def cached_feature_extractor_from_config(
+    model_config: "ModelConfig",
+    **kwargs: Any,
+):
+    return cached_get_feature_extractor(
+        model_config.model,
+        revision=model_config.revision,
+        trust_remote_code=model_config.trust_remote_code,
+        **_merge_mm_kwargs(model_config, AutoFeatureExtractor, **kwargs),
+    )
+
+
+def get_image_processor(
+    processor_name: str,
+    *args: Any,
+    revision: str | None = None,
+    trust_remote_code: bool = False,
+    **kwargs: Any,
+):
+    """Load an image processor for the given model name via HuggingFace."""
+    try:
+        processor_name = convert_model_repo_to_path(processor_name)
+        processor = AutoImageProcessor.from_pretrained(
+            processor_name,
+            *args,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+            **kwargs,
+        )
+    except ValueError as e:
+        # If the error pertains to the processor class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        # Unlike AutoTokenizer, AutoImageProcessor does not separate such errors
+        if not trust_remote_code:
+            err_msg = (
+                "Failed to load the image processor. If the image processor is "
+                "a custom processor not yet available in the HuggingFace "
+                "transformers library, consider setting "
+                "`trust_remote_code=True` in LLM or using the "
+                "`--trust-remote-code` flag in the CLI."
+            )
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+
+    return cast(BaseImageProcessor, processor)
+
+
+cached_get_image_processor = lru_cache(get_image_processor)
+
+
+def cached_image_processor_from_config(
+    model_config: "ModelConfig",
+    **kwargs: Any,
+):
+    if is_gguf(model_config.model):
+        assert not is_gguf(model_config.tokenizer), (
+            "For multimodal GGUF models, the original tokenizer "
+            "should be used to correctly load image processor."
+        )
+        model = model_config.tokenizer
+        revision = model_config.tokenizer_revision
+    else:
+        model = model_config.model
+        revision = model_config.revision
+    return cached_get_image_processor(
+        model,
+        revision=revision,
+        trust_remote_code=model_config.trust_remote_code,
+        **_merge_mm_kwargs(model_config, AutoImageProcessor, **kwargs),
+    )
+
+
+def get_video_processor(
+    processor_name: str,
+    *args: Any,
+    revision: str | None = None,
+    trust_remote_code: bool = False,
+    processor_cls_overrides: type[_V] | None = None,
+    **kwargs: Any,
+):
+    """Load a video processor for the given model name via HuggingFace."""
+    try:
+        processor_name = convert_model_repo_to_path(processor_name)
+        processor_cls = processor_cls_overrides or AutoVideoProcessor
+        processor = processor_cls.from_pretrained(
+            processor_name,
+            *args,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+            **kwargs,
+        )
+    except ValueError as e:
+        # If the error pertains to the processor class not existing or not
+        # currently being imported, suggest using the --trust-remote-code flag.
+        # Unlike AutoTokenizer, AutoVideoProcessor does not separate such errors
+        if not trust_remote_code:
+            err_msg = (
+                "Failed to load the video processor. If the video processor is "
+                "a custom processor not yet available in the HuggingFace "
+                "transformers library, consider setting "
+                "`trust_remote_code=True` in LLM or using the "
+                "`--trust-remote-code` flag in the CLI."
+            )
+            raise RuntimeError(err_msg) from e
+        else:
+            raise e
+
+    return cast(BaseVideoProcessor, processor)
+
+
+cached_get_video_processor = lru_cache(get_video_processor)
+
+
+def cached_video_processor_from_config(
+    model_config: "ModelConfig",
+    processor_cls: type[_V] | None = None,
+    **kwargs: Any,
+):
+    return cached_get_video_processor(
+        model_config.model,
+        revision=model_config.revision,
+        trust_remote_code=model_config.trust_remote_code,
+        processor_cls_overrides=processor_cls,  # type: ignore[arg-type]
+        **_merge_mm_kwargs(model_config, AutoVideoProcessor, **kwargs),
+    )

vllm/transformers_utils/processors/__init__.py

@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Multi-modal processors may be defined in this directory for the following
+reasons:
+
+- There is no processing file defined by HF Hub or Transformers library.
+- There is a need to override the existing processor to support vLLM.
+"""
+
+from vllm.transformers_utils.processors.bagel import BagelProcessor
+from vllm.transformers_utils.processors.deepseek_vl2 import DeepseekVLV2Processor
+from vllm.transformers_utils.processors.funasr_processor import FunASRProcessor
+from vllm.transformers_utils.processors.hunyuan_vl import HunYuanVLProcessor
+from vllm.transformers_utils.processors.hunyuan_vl_image import HunYuanVLImageProcessor
+from vllm.transformers_utils.processors.ovis import OvisProcessor
+from vllm.transformers_utils.processors.ovis2_5 import Ovis2_5Processor
+
+__all__ = [
+    "BagelProcessor",
+    "DeepseekVLV2Processor",
+    "FunASRProcessor",
+    "HunYuanVLProcessor",
+    "HunYuanVLImageProcessor",
+    "OvisProcessor",
+    "Ovis2_5Processor",
+]

vllm/transformers_utils/processors/bagel.py

@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 Bytedance Ltd. and/or its affiliates.
+"""BAGEL processor for image and text inputs."""
+
+from transformers import AutoProcessor
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+class BagelProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[call-arg]
+    _defaults = {
+        "images_kwargs": {
+            "return_tensors": "pt",
+        },
+    }
+
+
+class BagelProcessor(ProcessorMixin):
+    """
+    Constructs a BAGEL processor which wraps a
+    SigLIP image processor and a Qwen2 tokenizer.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "SiglipImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __call__(
+        self,
+        text: TextInput
+        | PreTokenizedInput
+        | list[TextInput]
+        | list[PreTokenizedInput] = None,
+        images: ImageInput = None,
+        **kwargs: Unpack[BagelProcessorKwargs],
+    ):
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s).
+        """
+        output_kwargs = self._merge_kwargs(
+            BagelProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if images is not None:
+            # Process images with the image processor
+            pixel_values = self.image_processor(
+                images, **output_kwargs["images_kwargs"]
+            )
+        else:
+            pixel_values = {}
+
+        text_inputs = (
+            self.tokenizer(text, **output_kwargs["text_kwargs"])
+            if text is not None
+            else {}
+        )
+
+        return BatchFeature(data={**pixel_values, **text_inputs})
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's batch_decode.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's decode.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+AutoProcessor.register("BagelProcessor", BagelProcessor)

vllm/transformers_utils/processors/deepseek_ocr.py

@@ -0,0 +1,458 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# adapted from https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/image_process.py
+# and https://github.com/deepseek-ai/DeepSeek-OCR-2/blob/main/DeepSeek-OCR2-master/DeepSeek-OCR2-vllm/process/image_process.py
+import math
+from typing import Literal
+
+import torch
+import torchvision.transforms as T
+from PIL import Image, ImageOps
+from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+# TODO(Isotr0py): change modes for variants
+# see: https://github.com/deepseek-ai/DeepSeek-OCR/blob/8cf003d38821fa1b19c73da3bd1b0dc262ea8136/DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py#L1-L6
+# Tiny: base_size = 512, image_size = 512, crop_mode = False
+# Small: base_size = 640, image_size = 640, crop_mode = False
+# Base: base_size = 1024, image_size = 1024, crop_mode = False
+# Large: base_size = 1280, image_size = 1280, crop_mode = False
+# Gundam: base_size = 1024, image_size = 640, crop_mode = True
+BASE_SIZE = 1024
+IMAGE_SIZE = 640
+CROP_MODE = True
+
+# TODO(Isotr0py): Expose as mm_kwargs
+MIN_CROPS = 2
+MAX_CROPS = 6  # max:9; If your GPU memory is small, it is recommended to set it to 6.
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float("inf")
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def calculate_aspect_ratios(
+    min_num: int = MIN_CROPS, max_num: int = MAX_CROPS
+) -> list[tuple[int, int]]:
+    target_ratios: set[tuple[int, int]] = set(
+        (i, j)
+        for n in range(min_num, max_num + 1)
+        for i in range(1, n + 1)
+        for j in range(1, n + 1)
+        if i * j <= max_num and i * j >= min_num
+    )
+    sorted_target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+    return sorted_target_ratios
+
+
+def count_tiles(
+    orig_width,
+    orig_height,
+    min_num=MIN_CROPS,
+    max_num=MAX_CROPS,
+    image_size=640,
+    use_thumbnail=False,
+):
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = calculate_aspect_ratios(min_num, max_num)
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+    )
+
+    return target_aspect_ratio
+
+
+def dynamic_preprocess(
+    image, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False
+):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = calculate_aspect_ratios(min_num, max_num)
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size,
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images, target_aspect_ratio
+
+
+class ImageTransform:
+    def __init__(
+        self,
+        mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        std: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+    ):
+        self.mean = mean
+        self.std = std
+        self.normalize = normalize
+
+        transform_pipelines = [T.ToTensor()]
+
+        if normalize:
+            transform_pipelines.append(T.Normalize(mean, std))
+
+        self.transform = T.Compose(transform_pipelines)
+
+    def __call__(self, pil_img: Image.Image):
+        x = self.transform(pil_img)
+        return x
+
+
+class DeepseekOCRProcessor(ProcessorMixin):
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    attributes = ["tokenizer"]
+
+    def __init__(
+        self,
+        tokenizer: LlamaTokenizerFast,
+        patch_size: int = 16,
+        downsample_ratio: int = 4,
+        image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+        image_token: str = "<image>",
+        pad_token: str = "<｜▁pad▁｜>",
+        add_special_token: bool = False,
+        sft_format: str = "deepseek",
+        mask_prompt: bool = True,
+        ignore_id: int = -100,
+        image_size: int = IMAGE_SIZE,
+        base_size: int = BASE_SIZE,
+        strategy: Literal["v1", "v2"] = "v1",
+        **kwargs,
+    ):
+        self.image_size = image_size
+        self.base_size = base_size
+
+        # image token calculation strategy for
+        # Deepseek-OCR and Deepseek-OCR-2
+        self.strategy = strategy
+        assert strategy in ["v1", "v2"], "Only 'v1' and 'v2' strategies are supported."
+
+        self.patch_size = 16
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.normalize = normalize
+        self.downsample_ratio = 4
+
+        self.image_transform = ImageTransform(
+            mean=image_mean, std=image_std, normalize=normalize
+        )
+
+        self.tokenizer = tokenizer
+        self.tokenizer.padding_side = "left"  # must set this，padding side with make a difference in batch inference # noqa: E501
+
+        # add the pad_token as special token to use 'tokenizer.pad_token'
+        # and 'tokenizer.pad_token_id'
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.add_special_tokens({"pad_token": pad_token})
+
+        # add image token
+        self.image_token_id = self.tokenizer.vocab.get(image_token)
+        self.image_token = image_token
+        self.pad_token = pad_token
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+
+        super().__init__(
+            tokenizer,
+            **kwargs,
+        )
+
+    @property
+    def bos_id(self):
+        return self.tokenizer.bos_token_id
+
+    @property
+    def eos_id(self):
+        return self.tokenizer.eos_token_id
+
+    @property
+    def pad_id(self):
+        return self.tokenizer.pad_token_id
+
+    def encode(self, text: str, bos: bool = True, eos: bool = False):
+        t = self.tokenizer.encode(text, add_special_tokens=False)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: list[int], **kwargs) -> str:
+        return self.tokenizer.decode(t, **kwargs)
+
+    def process_one(
+        self,
+        prompt: str,
+        images: list[Image.Image],
+        crop_mode: bool = CROP_MODE,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            images (List[ImageType]): the list of images;
+            crop_mode (bool): if True, then crop the image;
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        assert prompt is not None and images is not None, (
+            "prompt and images must be used at the same time."
+        )
+
+        sft_format = prompt
+
+        (
+            input_ids,
+            pixel_values,
+            images_crop,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+            _,
+        ) = self.tokenize_with_images(
+            conversation=sft_format,
+            images=images,
+            bos=True,
+            eos=True,
+            cropping=crop_mode,
+        )
+
+        prepare = BatchFeature(
+            data=dict(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                images_crop=images_crop,
+                images_seq_mask=images_seq_mask,
+                images_spatial_crop=images_spatial_crop,
+                num_image_tokens=num_image_tokens,
+            ),
+            tensor_type="pt",
+        )
+        return prepare
+
+    def __call__(
+        self,
+        *,
+        prompt: str,
+        images: list[Image.Image],
+        crop_mode: bool = CROP_MODE,
+        **kwargs,
+    ):
+        prepare = self.process_one(
+            prompt=prompt,
+            images=images,
+            crop_mode=crop_mode,
+        )
+
+        return prepare
+
+    def tokenize_with_images(
+        self,
+        conversation: str,
+        images: list[Image.Image],
+        bos: bool = True,
+        eos: bool = True,
+        cropping: bool = True,
+    ):
+        """Tokenize text with <image> tags."""
+
+        assert conversation.count(self.image_token) == len(images)
+        text_splits = conversation.split(self.image_token)
+        images_list, images_crop_list, images_seq_mask, images_spatial_crop = (
+            [],
+            [],
+            [],
+            [],
+        )
+        image_shapes = []
+        num_image_tokens = []
+        tokenized_str = []
+        for text_sep, image in zip(text_splits, images):
+            tokenized_sep = self.encode(text_sep, bos=False, eos=False)
+            tokenized_str += tokenized_sep
+            images_seq_mask += [False] * len(tokenized_sep)
+
+            image_shapes.append(image.size)
+
+            images_crop_raw = []
+            if image.size[0] <= self.image_size and image.size[1] <= self.image_size:
+                crop_ratio = [1, 1]
+            elif cropping:
+                images_crop_raw, crop_ratio = dynamic_preprocess(
+                    image, image_size=self.image_size
+                )
+            else:
+                crop_ratio = [1, 1]
+
+            if not cropping:
+                image = image.resize((self.image_size, self.image_size))
+
+            global_view = ImageOps.pad(
+                image,
+                (self.base_size, self.base_size),
+                color=tuple(int(x * 255) for x in self.image_transform.mean),
+            )
+            images_list.append(self.image_transform(global_view))
+
+            num_width_tiles, num_height_tiles = crop_ratio
+            images_spatial_crop.append([num_width_tiles, num_height_tiles])
+
+            if num_width_tiles > 1 or num_height_tiles > 1:
+                for cropped_image in images_crop_raw:
+                    images_crop_list.append(self.image_transform(cropped_image))
+
+            num_queries = math.ceil(
+                (self.image_size // self.patch_size) / self.downsample_ratio
+            )
+            num_queries_base = math.ceil(
+                (self.base_size // self.patch_size) / self.downsample_ratio
+            )
+
+            num_tokens_base = (
+                (num_queries_base * (num_queries_base + 1))
+                if self.strategy == "v1"
+                else num_queries_base * num_queries_base
+            )
+            tokenized_image = [self.image_token_id] * num_tokens_base
+
+            tokenized_image += [self.image_token_id]
+            if num_width_tiles > 1 or num_height_tiles > 1:
+                num_tokens_per_row = (
+                    num_queries * num_width_tiles + 1
+                    if self.strategy == "v1"
+                    else num_queries * num_width_tiles
+                )
+                local_row = [self.image_token_id] * num_tokens_per_row
+                tokenized_image += local_row * (num_queries * num_height_tiles)
+            tokenized_str += tokenized_image
+            images_seq_mask += [True] * len(tokenized_image)
+            num_image_tokens.append(len(tokenized_image))
+
+        """process the last text split"""
+        tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos and eos tokens"""
+        if bos:
+            tokenized_str = [self.bos_id] + tokenized_str
+            images_seq_mask = [False] + images_seq_mask
+        if eos:
+            tokenized_str = tokenized_str + [self.eos_id]
+            images_seq_mask = images_seq_mask + [False]
+
+        assert len(tokenized_str) == len(images_seq_mask), (
+            f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} "
+            f"is not equal to images_seq_mask's length {len(images_seq_mask)}."
+        )
+
+        masked_tokenized_str = []
+        for token_index in tokenized_str:
+            if token_index != self.image_token_id:
+                masked_tokenized_str.append(token_index)
+            else:
+                masked_tokenized_str.append(self.ignore_id)
+
+        assert (
+            len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
+        ), (
+            f"tokenized_str's length {len(tokenized_str)}, "
+            f"input_ids' length {len(masked_tokenized_str)}, "
+            f"images_seq_mask's length {len(images_seq_mask)}, are not equal."
+        )
+
+        input_ids = torch.LongTensor(tokenized_str)
+        target_ids = torch.LongTensor(masked_tokenized_str)
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+        # set input_ids < 0 | input_ids == self.image_token_id as ignore_id
+        target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
+            self.ignore_id
+        )
+        input_ids[input_ids < 0] = self.pad_id
+
+        # Remove the ending eos token
+        assert input_ids[-1] == self.eos_id
+        input_ids = input_ids[:-1]
+        target_ids = target_ids[:-1]
+        images_seq_mask = images_seq_mask[:-1]
+
+        if len(images_list) == 0:
+            pixel_values = torch.zeros((0, 3, self.base_size, self.base_size))
+            images_spatial_crop = torch.zeros((0, 2), dtype=torch.long)
+            images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
+        else:
+            pixel_values = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+            if images_crop_list:
+                images_crop = torch.stack(images_crop_list, dim=0)
+            else:
+                images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
+
+        input_ids = input_ids.unsqueeze(0)
+
+        return (
+            input_ids,
+            pixel_values,
+            images_crop,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+            image_shapes,
+        )
+
+
+AutoProcessor.register("DeepseekOCRProcessor", DeepseekOCRProcessor)

vllm/transformers_utils/processors/deepseek_vl2.py

@@ -0,0 +1,406 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# ruff: noqa: E501
+# coding=utf-8
+# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/ff23960c5cf9e6874b44be38af930cfb0ccbb620/deepseek_vl2/models/processing_deepseek_vl_v2.py
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import math
+from typing import Any
+
+import torch
+import torchvision.transforms as T
+from PIL import Image, ImageOps
+from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+
+class ImageTransform:
+    def __init__(
+        self,
+        mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        std: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+    ):
+        self.mean = mean
+        self.std = std
+        self.normalize = normalize
+
+        transform_pipelines = [T.ToTensor()]
+
+        if normalize:
+            transform_pipelines.append(T.Normalize(mean, std))
+
+        self.transform = T.Compose(transform_pipelines)
+
+    def __call__(self, pil_img: Image.Image):
+        x = self.transform(pil_img)
+        return x
+
+
+class DeepseekVLV2Processor(ProcessorMixin):
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    attributes = ["tokenizer"]
+
+    def __init__(
+        self,
+        tokenizer: LlamaTokenizerFast,
+        candidate_resolutions: tuple[tuple[int, int]],
+        patch_size: int,
+        downsample_ratio: int,
+        image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+        image_token: str = "<image>",
+        pad_token: str = "<｜▁pad▁｜>",
+        add_special_token: bool = False,
+        sft_format: str = "deepseek",
+        mask_prompt: bool = True,
+        ignore_id: int = -100,
+        **kwargs,
+    ):
+        self.candidate_resolutions = candidate_resolutions
+        self.image_size = candidate_resolutions[0][0]
+        self.patch_size = patch_size
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.normalize = normalize
+        self.downsample_ratio = downsample_ratio
+
+        self.image_transform = ImageTransform(
+            mean=image_mean, std=image_std, normalize=normalize
+        )
+        self.tokenizer = tokenizer
+        self.tokenizer.padding_side = "left"  # must set this，padding side with make a difference in batch inference
+
+        # add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
+        if tokenizer.pad_token is None:
+            self.tokenizer.add_special_tokens({"pad_token": pad_token})
+
+        # add image token
+        image_token_id = self.tokenizer.vocab.get(image_token)
+        if image_token_id is None:
+            special_tokens = [image_token]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+        self.image_token_id = self.tokenizer.vocab.get(image_token)
+
+        # add five special tokens for grounding-related tasks
+        # <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
+        special_tokens = ["<|ref|>", "<|/ref|>", "<|det|>", "<|/det|>", "<|grounding|>"]
+        special_tokens_dict = {"additional_special_tokens": special_tokens}
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+
+        # add special tokens for SFT data
+        special_tokens = ["<|User|>", "<|Assistant|>"]
+        special_tokens_dict = {"additional_special_tokens": special_tokens}
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+
+        self.image_token = image_token
+        self.pad_token = pad_token
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+
+        super().__init__(
+            tokenizer,
+            **kwargs,
+        )
+
+    def select_best_resolution(self, image_size):
+        # used for cropping
+        original_width, original_height = image_size
+        best_fit = None
+        max_effective_resolution = 0
+        min_wasted_resolution = float("inf")
+
+        for width, height in self.candidate_resolutions:
+            scale = min(width / original_width, height / original_height)
+            downscaled_width, downscaled_height = (
+                int(original_width * scale),
+                int(original_height * scale),
+            )
+            effective_resolution = min(
+                downscaled_width * downscaled_height, original_width * original_height
+            )
+            wasted_resolution = (width * height) - effective_resolution
+
+            if effective_resolution > max_effective_resolution or (
+                effective_resolution == max_effective_resolution
+                and wasted_resolution < min_wasted_resolution
+            ):
+                max_effective_resolution = effective_resolution
+                min_wasted_resolution = wasted_resolution
+                best_fit = (width, height)
+
+        return best_fit
+
+    @property
+    def bos_id(self):
+        return self.tokenizer.bos_token_id
+
+    @property
+    def eos_id(self):
+        return self.tokenizer.eos_token_id
+
+    @property
+    def pad_id(self):
+        return self.tokenizer.pad_token_id
+
+    def encode(self, text: str, bos: bool = True, eos: bool = False):
+        t = self.tokenizer.encode(text, add_special_tokens=False)
+
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+
+        return t
+
+    def decode(self, t: list[int], **kwargs) -> str:
+        return self.tokenizer.decode(t, **kwargs)
+
+    def process_one(
+        self,
+        prompt: str,
+        images: list[Image.Image],
+        inference_mode: bool = True,
+        **kwargs: Any,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            images (list[ImageType]): the list of images;
+            inference_mode (bool): if True, then remove the last eos token;
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (list[int]): the number of image tokens
+        """
+
+        assert prompt is not None and images is not None, (
+            "prompt and images must be used at the same time."
+        )
+
+        sft_format = prompt
+        (
+            tokenized_str,
+            images_list,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+        ) = self.tokenize_with_images(
+            sft_format, images, bos=True, eos=True, cropping=len(images) <= 2
+        )
+        masked_tokenized_str = []
+        for token_index in tokenized_str:
+            if token_index != self.image_token_id:
+                masked_tokenized_str.append(token_index)
+            else:
+                masked_tokenized_str.append(self.ignore_id)
+
+        assert (
+            len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
+        ), (
+            f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
+            f"imags_seq_mask's length {len(images_seq_mask)}, are not equal"
+        )
+
+        input_ids = torch.LongTensor(tokenized_str)
+        target_ids = torch.LongTensor(masked_tokenized_str)
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+        # set input_ids < 0 | input_ids == self.image_token_id as ignore_id
+        target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
+            self.ignore_id
+        )
+        input_ids[input_ids < 0] = self.pad_id
+
+        if inference_mode:
+            # Remove the ending eos token
+            assert input_ids[-1] == self.eos_id
+            input_ids = input_ids[:-1]
+            target_ids = target_ids[:-1]
+            images_seq_mask = images_seq_mask[:-1]
+
+        if len(images_list) == 0:
+            pixel_values = torch.zeros((1, 3, self.image_size, self.image_size))
+            images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
+        else:
+            pixel_values = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+
+        input_ids = input_ids.unsqueeze(0)
+
+        prepare = BatchFeature(
+            data=dict(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                images_seq_mask=images_seq_mask,
+                images_spatial_crop=images_spatial_crop,
+                num_image_tokens=num_image_tokens,
+            ),
+            tensor_type="pt",
+        )
+        return prepare
+
+    def __call__(
+        self,
+        *,
+        text: str,
+        images: list[Image.Image],
+        inference_mode: bool = True,
+        **kwargs: Any,
+    ):
+        """
+
+        Args:
+            text (str): the formatted prompt;
+            images (list[ImageType]): the list of images;
+            inference_mode (bool): if True, then remove the last eos token;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (list[int]): the number of image tokens
+        """
+
+        prepare = self.process_one(
+            prompt=text,
+            images=images,
+            inference_mode=inference_mode,
+        )
+
+        return prepare
+
+    def tokenize_with_images(
+        self,
+        conversation: str,
+        images: list[Image.Image],
+        bos: bool = True,
+        eos: bool = True,
+        cropping: bool = True,
+    ):
+        """Tokenize text with <image> tags."""
+        assert conversation.count(self.image_token) == len(images)
+        text_splits = conversation.split(self.image_token)
+        images_list, images_seq_mask, images_spatial_crop = [], [], []
+        num_image_tokens = []
+        tokenized_str = []
+        for text_sep, image in zip(text_splits, images):
+            """encode text_sep"""
+            tokenized_sep = self.encode(text_sep, bos=False, eos=False)
+            tokenized_str += tokenized_sep
+            images_seq_mask += [False] * len(tokenized_sep)
+
+            """select best resolution for anyres"""
+            if cropping:
+                best_width, best_height = self.select_best_resolution(image.size)
+            else:
+                best_width, best_height = self.image_size, self.image_size
+
+            """process the global view"""
+            global_view = ImageOps.pad(
+                image,
+                (self.image_size, self.image_size),
+                color=tuple(int(x * 255) for x in self.image_transform.mean),
+            )
+            images_list.append(self.image_transform(global_view))
+
+            """process the local views"""
+            local_view = ImageOps.pad(
+                image,
+                (best_width, best_height),
+                color=tuple(int(x * 255) for x in self.image_transform.mean),
+            )
+            for i in range(0, best_height, self.image_size):
+                for j in range(0, best_width, self.image_size):
+                    images_list.append(
+                        self.image_transform(
+                            local_view.crop(
+                                (j, i, j + self.image_size, i + self.image_size)
+                            )
+                        )
+                    )
+
+            """record height / width crop num"""
+            num_width_tiles, num_height_tiles = (
+                best_width // self.image_size,
+                best_height // self.image_size,
+            )
+            images_spatial_crop.append([num_width_tiles, num_height_tiles])
+
+            """add image tokens"""
+            h = w = math.ceil(
+                (self.image_size // self.patch_size) / self.downsample_ratio
+            )
+            # global views tokens h * (w + 1), 1 is for line separator
+            tokenized_image = [self.image_token_id] * h * (w + 1)
+            # add a separator between global and local views
+            tokenized_image += [self.image_token_id]
+            # local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1)
+            tokenized_image += (
+                [self.image_token_id]
+                * (num_height_tiles * h)
+                * (num_width_tiles * w + 1)
+            )
+
+            tokenized_str += tokenized_image
+            images_seq_mask += [True] * len(tokenized_image)
+            num_image_tokens.append(len(tokenized_image))
+
+        """process the last text split"""
+        tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos and eos tokens"""
+        if bos:
+            tokenized_str = [self.bos_id] + tokenized_str
+            images_seq_mask = [False] + images_seq_mask
+        if eos:
+            tokenized_str = tokenized_str + [self.eos_id]
+            images_seq_mask = images_seq_mask + [False]
+
+        assert len(tokenized_str) == len(images_seq_mask), (
+            f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
+        )
+
+        return (
+            tokenized_str,
+            images_list,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+        )
+
+
+AutoProcessor.register("DeepseekVLV2Processor", DeepseekVLV2Processor)

vllm/transformers_utils/processors/funasr_processor.py

@@ -0,0 +1,504 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchaudio.compliance.kaldi as kaldi
+from torch.nn.utils.rnn import pad_sequence
+from transformers import (
+    AutoFeatureExtractor,
+    AutoProcessor,
+    BatchFeature,
+)
+from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
+from transformers.processing_utils import ProcessorMixin
+from transformers.utils import TensorType
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def apply_cmvn(inputs, cmvn):  # noqa
+    """
+    Apply CMVN with mvn data
+    """
+
+    device = inputs.device
+    # dtype = inputs.dtype
+    frame, dim = inputs.shape
+
+    means = cmvn[0:1, :dim]
+    vars = cmvn[1:2, :dim]
+    inputs += means.to(device)
+    inputs *= vars.to(device)
+
+    return inputs.type(torch.float32)
+
+
+def apply_lfr(inputs, lfr_m, lfr_n):
+    # LFR_inputs = []
+    T = inputs.shape[0]
+    T_lfr = int(np.ceil(T / lfr_n))
+    left_padding = inputs[0].repeat((lfr_m - 1) // 2, 1)
+    inputs = torch.vstack((left_padding, inputs))
+    T = T + (lfr_m - 1) // 2
+    feat_dim = inputs.shape[-1]
+    strides = (lfr_n * feat_dim, 1)
+    sizes = (T_lfr, lfr_m * feat_dim)
+    last_idx = (T - lfr_m) // lfr_n + 1
+    num_padding = lfr_m - (T - last_idx * lfr_n)
+    if num_padding > 0:
+        num_padding = (
+            (2 * lfr_m - 2 * T + (T_lfr - 1 + last_idx) * lfr_n)
+            / 2
+            * (T_lfr - last_idx)
+        )
+        inputs = torch.vstack([inputs] + [inputs[-1:]] * int(num_padding))
+    LFR_outputs = inputs.as_strided(sizes, strides)
+    return LFR_outputs.clone().type(torch.float32)
+
+
+def load_cmvn(cmvn_file):
+    with open(cmvn_file, encoding="utf-8") as f:
+        lines = f.readlines()
+    means_list = []
+    vars_list = []
+    for i in range(len(lines)):
+        line_item = lines[i].split()
+        if line_item[0] == "<AddShift>":
+            line_item = lines[i + 1].split()
+            if line_item[0] == "<LearnRateCoef>":
+                add_shift_line = line_item[3 : (len(line_item) - 1)]
+                means_list = list(add_shift_line)
+                continue
+        elif line_item[0] == "<Rescale>":
+            line_item = lines[i + 1].split()
+            if line_item[0] == "<LearnRateCoef>":
+                rescale_line = line_item[3 : (len(line_item) - 1)]
+                vars_list = list(rescale_line)
+                continue
+    means = np.array(means_list).astype(np.float32)
+    vars = np.array(vars_list).astype(np.float32)
+    cmvn = np.array([means, vars])
+    cmvn = torch.as_tensor(cmvn, dtype=torch.float32)
+    return cmvn
+
+
+class WavFrontend(nn.Module):
+    """Conventional frontend structure for ASR."""
+
+    def __init__(
+        self,
+        cmvn_file: str = "null",
+        fs: int = 16000,
+        window: str = "hamming",
+        n_mels: int = 80,
+        frame_length: int = 25,
+        frame_shift: int = 10,
+        filter_length_min: int = -1,
+        filter_length_max: int = -1,
+        lfr_m: int = 1,
+        lfr_n: int = 1,
+        dither: float = 1.0,
+        snip_edges: bool = True,
+        upsacle_samples: bool = True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.fs = fs
+        self.window = window
+        self.n_mels = n_mels
+        self.frame_length = frame_length
+        self.frame_shift = frame_shift
+        self.filter_length_min = filter_length_min
+        self.filter_length_max = filter_length_max
+        self.lfr_m = lfr_m
+        self.lfr_n = lfr_n
+        self.cmvn_file = cmvn_file
+        self.dither = dither
+        self.snip_edges = snip_edges
+        self.upsacle_samples = upsacle_samples
+        self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
+
+    def output_size(self) -> int:
+        return self.n_mels * self.lfr_m
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        input_lengths,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size = input.size(0)
+        feats = []
+        feats_lens = []
+        for i in range(batch_size):
+            waveform_length = input_lengths[i]
+            waveform = input[i][:waveform_length]
+            if self.upsacle_samples:
+                waveform = waveform * (1 << 15)
+            waveform = waveform.unsqueeze(0)
+            mat = kaldi.fbank(
+                waveform,
+                num_mel_bins=self.n_mels,
+                frame_length=min(self.frame_length, waveform_length / self.fs * 1000),
+                frame_shift=self.frame_shift,
+                dither=self.dither,
+                energy_floor=0.0,
+                window_type=self.window,
+                sample_frequency=self.fs,
+                snip_edges=self.snip_edges,
+            )
+
+            if self.lfr_m != 1 or self.lfr_n != 1:
+                mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
+            if self.cmvn is not None:
+                mat = apply_cmvn(mat, self.cmvn)
+            feat_length = mat.size(0)
+            feats.append(mat)
+            feats_lens.append(feat_length)
+
+        feats_lens = torch.as_tensor(feats_lens)
+        if batch_size == 1:
+            feats_pad = feats[0][None, :, :]
+        else:
+            feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+        return feats_pad, feats_lens
+
+    def forward_fbank(
+        self, input: torch.Tensor, input_lengths: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size = input.size(0)
+        feats = []
+        feats_lens = []
+        for i in range(batch_size):
+            waveform_length = input_lengths[i]
+            waveform = input[i][:waveform_length]
+            waveform = waveform * (1 << 15)
+            waveform = waveform.unsqueeze(0)
+            mat = kaldi.fbank(
+                waveform,
+                num_mel_bins=self.n_mels,
+                frame_length=self.frame_length,
+                frame_shift=self.frame_shift,
+                dither=self.dither,
+                energy_floor=0.0,
+                window_type=self.window,
+                sample_frequency=self.fs,
+            )
+
+            feat_length = mat.size(0)
+            feats.append(mat)
+            feats_lens.append(feat_length)
+
+        feats_lens = torch.as_tensor(feats_lens)
+        feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+        return feats_pad, feats_lens
+
+    def forward_lfr_cmvn(
+        self, input: torch.Tensor, input_lengths: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size = input.size(0)
+        feats = []
+        feats_lens = []
+        for i in range(batch_size):
+            mat = input[i, : input_lengths[i], :]
+            if self.lfr_m != 1 or self.lfr_n != 1:
+                mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
+            if self.cmvn is not None:
+                mat = apply_cmvn(mat, self.cmvn)
+            feat_length = mat.size(0)
+            feats.append(mat)
+            feats_lens.append(feat_length)
+
+        feats_lens = torch.as_tensor(feats_lens)
+        feats_pad = pad_sequence(feats, batch_first=True, padding_value=0.0)
+        return feats_pad, feats_lens
+
+
+class FunASRFeatureExtractor(SequenceFeatureExtractor):
+    r"""
+    Constructs a FunASR feature extractor.
+
+    This feature extractor inherits from [`~feature_extraction_sequence_
+        utils.SequenceFeatureExtractor`] which contains most of the main
+        methods. Users should refer to this superclass for more information
+        regarding those methods.
+
+    This class extracts mel-filter bank features from raw speech using a custom
+    numpy implementation of the `Short Time Fourier Transform` which should
+    match pytorch's `torch.stft` equivalent.
+
+    Args:
+        feature_size (`int`, *optional*, defaults to 80):
+            The feature dimension of the extracted features.
+        sampling_rate (`int`, *optional*, defaults to 16000):
+            The sampling rate at which the audio files should be digitalized
+            expressed in hertz (Hz).
+        hop_length (`int`, *optional*, defaults to 160):
+            Length of the overlapping windows for the STFT used to obtain the
+            Mel Frequency coefficients.
+        chunk_length (`int`, *optional*, defaults to 30):
+            The maximum number of chunks of `sampling_rate` samples used to
+            trim and pad longer or shorter audio sequences.
+        n_fft (`int`, *optional*, defaults to 400):
+            Size of the Fourier transform.
+        padding_value (`float`, *optional*, defaults to 0.0):
+            Padding value used to pad the audio. Should correspond to silences.
+        dither (`float`, *optional*, defaults to 0.0):
+            Adds dithering. In other words, adds a small Gaussian noise to each frame.
+            E.g. use 0.0001 to add dithering with a normal distribution centered
+            around 0.0 with standard deviation 0.0001 (assuming [-1,+1] range
+            of raw_speech). The value 0.0 means no dithering.
+            Dithering has similar effect as `spectrogram(mel_floor=...)`. It reduces
+            the high log_mel_fbank values for signals with hard-zero sections,
+            when VAD cutoff is present in the signal.
+    """
+
+    model_input_names = ["input_features"]
+
+    def __init__(
+        self,
+        feature_size=80,
+        sampling_rate=16000,
+        hop_length=160,
+        chunk_length=30,
+        n_fft=400,
+        padding_value=0.0,
+        dither=0.0,
+        return_attention_mask=False,
+        **kwargs,
+    ):
+        super().__init__(
+            feature_size=feature_size,
+            sampling_rate=sampling_rate,
+            padding_value=padding_value,
+            return_attention_mask=return_attention_mask,
+            **kwargs,
+        )
+        self.frontend_conf = kwargs.get("frontend_conf", {})
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.chunk_length = chunk_length
+        self.n_samples = chunk_length * sampling_rate
+        self.nb_max_frames = self.n_samples // hop_length
+        self.sampling_rate = sampling_rate
+        self.dither = dither
+
+    def extract_fbank(
+        self, data, data_len=None, data_type: str = "sound", frontend=None, **kwargs
+    ):
+        if isinstance(data, np.ndarray):
+            data = torch.from_numpy(data)
+            if len(data.shape) < 2:
+                data = data[None, :]  # data: [batch, N]
+            data_len = [data.shape[1]] if data_len is None else data_len
+        elif isinstance(data, torch.Tensor):
+            if len(data.shape) < 2:
+                data = data[None, :]  # data: [batch, N]
+            data_len = [data.shape[1]] if data_len is None else data_len
+        elif isinstance(data, (list, tuple)):
+            data_list, data_len = [], []
+            for data_i in data:
+                if isinstance(data_i, np.ndarray):
+                    data_i = torch.from_numpy(data_i)
+                data_list.append(data_i)
+                data_len.append(data_i.shape[0])
+            data = pad_sequence(data_list, batch_first=True)
+
+        data, data_len = frontend(data, data_len, **kwargs)
+
+        if isinstance(data_len, (list, tuple)):
+            data_len = torch.tensor([data_len])
+        return data.to(torch.float32), data_len.to(torch.int32)
+
+    def __call__(
+        self,
+        raw_speech: np.ndarray | list[float] | list[np.ndarray] | list[list[float]],
+        truncation: bool = True,
+        pad_to_multiple_of: int | None = None,
+        return_tensors: str | TensorType | None = None,
+        return_attention_mask: bool | None = None,
+        padding: str | None = "max_length",
+        max_length: int | None = None,
+        sampling_rate: int | None = None,
+        do_normalize: bool | None = None,
+        device: str | None = "cpu",
+        return_token_timestamps: bool | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        is_batched = isinstance(raw_speech, (list, tuple)) and (
+            isinstance(raw_speech[0], (np.ndarray, tuple, list))
+        )
+
+        if is_batched:
+            raw_speech = [
+                np.asarray([speech], dtype=np.float32).T for speech in raw_speech
+            ]
+        elif not is_batched and not isinstance(raw_speech, np.ndarray):
+            raw_speech = np.asarray(raw_speech, dtype=np.float32)
+        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(
+            np.float64
+        ):
+            raw_speech = raw_speech.astype(np.float32)
+
+        if not is_batched:
+            raw_speech = [np.asarray([raw_speech]).T]
+
+        batched_speech = BatchFeature({"input_features": raw_speech})
+
+        padded_inputs = self.pad(
+            batched_speech,
+            padding=padding,
+            max_length=max_length if max_length else self.n_samples,
+            truncation=truncation,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=return_attention_mask or do_normalize,
+        )
+
+        input_features = padded_inputs.get("input_features").transpose(2, 0, 1)
+
+        frontend = WavFrontend(**self.frontend_conf, dither=self.dither)
+        input_features, speech_lengths = self.extract_fbank(
+            input_features[0],
+            data_type=kwargs.get("data_type", "sound"),
+            frontend=frontend,
+            is_final=True,
+        )
+        olens = 1 + (speech_lengths - 3 + 2 * 1) // 2
+        olens = 1 + (olens - 3 + 2 * 1) // 2
+        fake_token_len = (olens - 1) // 2 + 1
+        if isinstance(input_features[0], list):
+            padded_inputs["input_features"] = [
+                np.asarray(feature, dtype=np.float32) for feature in input_features
+            ]
+
+        else:
+            padded_inputs["input_features"] = input_features
+
+        if return_tensors is not None:
+            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+        padded_inputs["speech_lengths"] = speech_lengths
+        padded_inputs["fake_token_len"] = fake_token_len
+
+        return padded_inputs
+
+
+class FunASRProcessor(ProcessorMixin):
+    r"""
+    Constructs a FunASR processor which wraps a FunASR feature extractor and
+    a FunASR tokenizer into a single processor.
+
+    [`FunASRProcessor`] offers all the functionalities of
+    [`FunASRFeatureExtractor`] and [`Qwen2Tokenizer`]. See the
+    [`~FunASRProcessor.__call__`] and [`~FunASRProcessor.decode`] for more
+    information.
+
+    Args:
+        feature_extractor (`FunASRFeatureExtractor`): An instance of
+            [`FunASRFeatureExtractor`].
+            The feature extractor is a required input.
+        tokenizer (`Qwen2Tokenizer`):
+            An instance of [`Qwen2Tokenizer`]. The tokenizer is a required
+            input.
+    """
+
+    feature_extractor_class = "FunASRFeatureExtractor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(
+        self,
+        feature_extractor,
+        tokenizer,
+        audio_token="<|AUDIO|>",
+    ):
+        super().__init__(feature_extractor, tokenizer)
+        self.current_processor = self.feature_extractor
+        self._in_target_context_manager = False
+        self.audio_token = (
+            tokenizer.audio_token if hasattr(tokenizer, "audio_token") else audio_token
+        )
+        self.audio_token_id = tokenizer.convert_tokens_to_ids(self.audio_token)
+
+    def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
+        return self.tokenizer.get_decoder_prompt_ids(
+            task=task, language=language, no_timestamps=no_timestamps
+        )
+
+    def __call__(self, *args, **kwargs):
+        """
+        Forwards the `audio` argument to FunASRFeatureExtractor's
+        [`~FunASRFeatureExtractor.__call__`] and the `text` argument to
+        [`~Qwen2Tokenizer.__call__`]. Please refer to the docstring of the
+        above two methods for more information.
+        """
+        if self._in_target_context_manager:
+            return self.current_processor(*args, **kwargs)
+
+        audio = kwargs.pop("audio", None)
+        sampling_rate = kwargs.pop("sampling_rate", None)
+        text = kwargs.pop("text", None)
+        if len(args) > 0:
+            audio = args[0]
+            args = args[1:]
+
+        if text is None:
+            raise ValueError("You need to specify `text` input to process.")
+        elif isinstance(text, str):
+            text = [text]
+        elif not isinstance(text, list) and not isinstance(text[0], str):
+            raise ValueError(
+                "Invalid input text. Please provide a string, or a list of strings"
+            )
+
+        if audio is not None:
+            # ensure we have as much audios as audio tokens
+            num_audio_tokens = sum(sample.count(self.audio_token) for sample in text)
+            num_audios = 1 if type(audio) is np.ndarray else len(audio)
+            if num_audio_tokens != num_audios:
+                raise ValueError(
+                    f"Found {num_audio_tokens} {self.audio_token} token{'s' if num_audio_tokens > 1 else ''} in provided text but received {num_audios} audio{'s' if num_audios > 1 else ''}"  # noqa: E501
+                )
+            inputs = self.feature_extractor(
+                audio, *args, sampling_rate=sampling_rate, **kwargs
+            )
+
+            expanded_text = []
+            for sample in text:
+                replace_str = []
+                while self.audio_token in sample:
+                    num_audio_tokens = inputs["fake_token_len"].item()
+
+                    expanded_audio_token = self.audio_token * num_audio_tokens
+
+                    replace_str.append(expanded_audio_token)
+                    sample = sample.replace(self.audio_token, "<placeholder>", 1)
+
+                while "<placeholder>" in sample:
+                    sample = sample.replace("<placeholder>", replace_str.pop(0), 1)
+                expanded_text.append(sample)
+            text = expanded_text
+
+        if text is not None:
+            encodings = self.tokenizer(text, **kwargs)
+
+        if text is None:
+            return inputs
+
+        elif audio is None:
+            return encodings
+        else:
+            inputs["labels"] = encodings["input_ids"]
+
+            return inputs
+
+    def get_prompt_ids(self, text: str, return_tensors="np"):
+        return self.tokenizer.get_prompt_ids(text, return_tensors=return_tensors)
+
+
+AutoFeatureExtractor.register("FunASRFeatureExtractor", FunASRFeatureExtractor)
+AutoProcessor.register("FunASRProcessor", FunASRProcessor)

vllm/transformers_utils/processors/hunyuan_vl.py

@@ -0,0 +1,230 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/processing_hunyuan_vl.py
+
+import numpy as np
+import torch
+from transformers import AutoProcessor
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.video_utils import VideoInput
+
+
+class HunYuanVLProcessor(ProcessorMixin):
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"  # ("AutoTokenizer", None)
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        # TODO Fix the init
+        self.tokenizer = tokenizer
+        self.image_token_id = 120120  # self.tokenizer.image_token_id
+        self.image_token = self.tokenizer.convert_ids_to_tokens(self.image_token_id)
+        self.im_start_token_id = 120118  # self.tokenizer.im_start_id
+        self.im_start_token = self.tokenizer.convert_ids_to_tokens(
+            self.im_start_token_id
+        )
+        self.im_end_token_id = 120119  # self.tokenizer.im_end_id
+        self.im_end_token = self.tokenizer.convert_ids_to_tokens(self.im_end_token_id)
+        self.placeholder_token = self.tokenizer.convert_ids_to_tokens(
+            self.tokenizer.vocab_size - 1
+        )
+        self.pad_id = 120002  # self.tokenizer.pad_token_id
+
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput
+        | PreTokenizedInput
+        | list[TextInput]
+        | list[PreTokenizedInput] = None,
+        videos: VideoInput = None,
+        **kwargs,
+    ) -> BatchFeature:
+        image_inputs = {}
+        if images is not None:
+            image_inputs = self.image_processor(images=images)
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+
+        image_tokens_cumsum = [0]
+        if images is not None:
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    grid_h, grid_w = image_grid_thw[index][-2:]
+                    patch_h = grid_h // self.image_processor.merge_size
+                    patch_w = grid_w // self.image_processor.merge_size
+                    num_image_tokens = patch_h * (patch_w + 1) + 2
+                    image_tokens_cumsum.append(
+                        image_tokens_cumsum[-1] + num_image_tokens
+                    )
+                    # text[i] = text[i].replace(self.image_token, self.im_start_token + self.placeholder_token * num_image_tokens + self.im_end_token, 1) # noqa: E501
+                    text[i] = text[i].replace(
+                        self.image_token, self.placeholder_token * num_image_tokens, 1
+                    )
+                    index += 1
+                text[i] = text[i].replace(self.placeholder_token, self.image_token)
+                # text[i] = self.tokenizer.bos_token + text[i]
+
+        text_inputs = self.tokenizer(text, add_special_tokens=False, **kwargs)
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+
+        input_ids = text_inputs["input_ids"]
+        position_ids = torch.arange(len(input_ids[0]))
+        position_ids_w = torch.arange(len(input_ids[0]))
+        position_ids_h = torch.arange(len(input_ids[0]))
+        position_ids_t = torch.arange(len(input_ids[0]))
+
+        if images is not None:
+            image_token_pos_indices = torch.where(input_ids[0] == self.image_token_id)[
+                0
+            ]
+            for i in range(len(image_grid_thw)):
+                grid_h, grid_w = image_grid_thw[i][-2:]
+                patch_h = grid_h // self.image_processor.merge_size
+                patch_w = grid_w // self.image_processor.merge_size
+                start_pos = image_token_pos_indices[image_tokens_cumsum[i]].item() + 1
+                replace_num = (patch_w + 1) * patch_h
+                position_ids_w[start_pos : start_pos + replace_num] = torch.tensor(
+                    list(range(patch_w + 1)) * patch_h, dtype=torch.int64
+                )
+                patch_h_list = []
+                for h in range(patch_h):
+                    patch_h_list += [h] * (patch_w + 1)
+                position_ids_h[start_pos : start_pos + replace_num] = torch.tensor(
+                    patch_h_list, dtype=torch.int64
+                )
+                position_ids_t[start_pos : start_pos + replace_num] = 0
+
+        position_ids = torch.stack(
+            [position_ids, position_ids_w, position_ids_h, position_ids_t]
+        ).unsqueeze(0)
+        text_inputs["position_ids"] = position_ids
+
+        attention_mask = input_ids.ne(self.pad_id)
+        text_inputs["attention_mask"] = attention_mask
+        text_inputs["imgs_pos"] = [self.get_imgs_pos(e) for e in input_ids]
+        # image_inputs["imgs"] = [[image_inputs["pixel_values"]]]
+
+        return_tensors = kwargs.pop("return_tensors", None)
+        return BatchFeature(
+            data={**text_inputs, **image_inputs},
+            tensor_type=return_tensors,
+        )
+
+    def batch_decode(self, *args, **kwargs):
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def post_process_image_text_to_text(
+        self,
+        generated_outputs,
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        assert 0
+
+    def apply_chat_template(self, *args, **kwargs):
+        kwargs["return_dict"] = False
+        return self.tokenizer.apply_chat_template(*args, **kwargs)
+
+    def get_imgs_pos(self, doc_ids):
+        doc_ids = np.array(doc_ids, dtype=np.int64)
+        img_begin_index = np.where(doc_ids == self.im_start_token_id)[0]
+        img_end_index = np.where(doc_ids == self.im_end_token_id)[0]
+        imgs_pos = np.concatenate(
+            (
+                np.reshape(img_begin_index + 1, (-1, 1)),
+                np.reshape(img_end_index, (-1, 1)),
+            ),
+            axis=-1,
+        ).tolist()
+        return imgs_pos
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+def split_image_into_patch_blocks(
+    pixel_values: torch.Tensor,  # shape: [batch_size, 3, H, W]
+    patch_size: int = 16,  # e.g. 16
+    adaptor_patch_div: int = 4,  # e.g. 4 --> each patch_size is cut into 4x4 small regions, i.e. patch_size // 4 # noqa: E501
+) -> torch.Tensor:
+    """
+    Split the input image tensor (supporting batch) into large patches of size `patch_size`,
+    and then further divide each large patch into smaller regions of size
+    (patch_size // adaptor_patch_div) x (patch_size // adaptor_patch_div).
+    Each small region is extracted as a tensor of shape [3, patch_size, patch_size].
+    The final output contains all such small region tensors.
+
+    Args:
+        pixel_values: Input image tensor of shape [batch_size, 3, H, W].
+        patch_size: Size of the large patch, e.g., 16.
+        adaptor_patch_div: Each large patch is divided into
+                          (patch_size // adaptor_patch_div) x (patch_size // adaptor_patch_div)
+                          smaller regions.
+
+    Returns:
+        patches: A tensor of shape [N, 3, patch_size, patch_size],
+                 where N = batch_size * (H // patch_size) * (W // patch_size) * (patch_size // adaptor_patch_div)^2.
+                 Each element in the batch corresponds to one small image region.
+    """  # noqa: E501
+    batch_size, channels, height, width = pixel_values.shape
+    assert channels == 3, "Pixel values must have 3 channels in dim=1"
+    assert height % patch_size == 0 and width % patch_size == 0, (
+        "H and W must be divisible by patch_size"
+    )
+
+    patch_height_num = height // patch_size
+    patch_width_num = width // patch_size
+
+    # Reshape to [B, 3, ph, ps, pw, ps]
+    img = pixel_values.reshape(
+        batch_size, 3, patch_height_num, patch_size, patch_width_num, patch_size
+    )
+
+    # Further split each psxps patch into (ps//aps)x(ps//aps) small regions
+    img = img.reshape(
+        batch_size,
+        3,
+        patch_height_num,
+        patch_size // adaptor_patch_div,  # ps // aps
+        adaptor_patch_div,
+        patch_width_num,
+        patch_size // adaptor_patch_div,  # ps // aps
+        adaptor_patch_div,
+    )
+
+    # Permute to group the small regions: [B, ph, pw, ps//aps, ps//aps, 3, aps, aps]
+    img = img.permute(0, 2, 5, 3, 6, 1, 4, 7)
+
+    # Reshape into [B * ph * pw * (ps//aps)^2, 3, patch_size, patch_size]
+    patches = img.reshape(-1, 3, patch_size, patch_size)
+
+    return patches
+
+
+AutoProcessor.register("HunYuanVLProcessor", HunYuanVLProcessor)

vllm/transformers_utils/processors/hunyuan_vl_image.py

@@ -0,0 +1,477 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/image_processing_hunyuan_vl.py
+"""Image processor class for HunYuanVL."""
+
+# isort conflicts with ruff for transformers imports
+# isort: skip_file
+import math
+
+import numpy as np
+import torchvision.transforms as transforms
+from transformers import AutoImageProcessor
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_transforms import (
+    convert_to_rgb,
+)
+from transformers.image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    make_flat_list_of_images,
+    make_list_of_images,
+    valid_images,
+    validate_preprocess_arguments,
+)
+from transformers.utils import TensorType, logging
+from transformers.video_utils import VideoInput, make_batched_videos
+
+logger = logging.get_logger(__name__)
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 16,
+    min_pixels: int = 512 * 512,
+    max_pixels: int = 2048 * 2048,
+):
+    """Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+
+    """
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError(
+            "absolute aspect ratio must be smaller than 200, got "
+            f"{max(height, width) / min(height, width)}"
+        )
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = max(factor, math.floor(height / beta / factor) * factor)
+        w_bar = max(factor, math.floor(width / beta / factor) * factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+class HunYuanVLImageProcessor(BaseImageProcessor):
+    model_input_names = [
+        "pixel_values",
+        "image_grid_thw",
+        "pixel_values_videos",
+        "video_grid_thw",
+    ]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_rescale: bool = True,
+        rescale_factor: int | float = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool = True,
+        min_pixels: int | None = None,
+        max_pixels: int | None = None,
+        patch_size: int = 16,
+        temporal_patch_size: int = 2,
+        merge_size: int = 2,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        if size is not None and (
+            "shortest_edge" not in size or "longest_edge" not in size
+        ):
+            raise ValueError(
+                "size must contain 'shortest_edge' and 'longest_edge' keys."
+            )
+        else:
+            size = {"shortest_edge": 512 * 512, "longest_edge": 2048 * 2048}
+        # backward compatibility: override size with min_pixels and max_pixels
+        # if they are provided.
+        if min_pixels is not None:
+            size["shortest_edge"] = min_pixels
+        if max_pixels is not None:
+            size["longest_edge"] = max_pixels
+        self.min_pixels = size["shortest_edge"]
+        self.max_pixels = size["longest_edge"]
+        self.size = size
+
+        self.do_resize = do_resize
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.merge_size = merge_size
+        self.do_convert_rgb = do_convert_rgb
+
+        # hard-code
+
+    def _preprocess(
+        self,
+        images: ImageInput | VideoInput,
+        do_resize: bool | None = None,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool | None = None,
+        rescale_factor: float | None = None,
+        do_normalize: bool | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        patch_size: int = 16,
+        temporal_patch_size: int = 2,
+        merge_size: int = 2,
+        do_convert_rgb: bool | None = None,
+        data_format: ChannelDimension | None = ChannelDimension.FIRST,
+        input_data_format: str | ChannelDimension | None = None,
+    ):
+        """
+        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+
+        Args:
+            images (`ImageInput`):
+                Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing. `shortest_edge` and `longest_edge` keys must be present.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Scale factor to use if rescaling the image.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            patch_size (`int`, *optional*, defaults to `self.patch_size`):
+                The spatial patch size of the vision encoder.
+            temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
+                The temporal patch size of the vision encoder.
+            merge_size (`int`, *optional*, defaults to `self.merge_size`):
+                The merge size of the vision encoder to llm encoder.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.   - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """  # noqa: E501
+        images = make_list_of_images(images)
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        width, height = images[0].width, images[0].height
+        resized_width, resized_height = width, height
+        processed_images = []
+        for image in images:
+            if do_resize:
+                resized_height, resized_width = smart_resize(
+                    height=height,
+                    width=width,
+                    factor=patch_size * merge_size,
+                    min_pixels=self.min_pixels,
+                    max_pixels=self.max_pixels,
+                )
+                image = image.resize((resized_width, resized_height))
+
+            if do_normalize:
+                image = transforms.Compose(
+                    [
+                        transforms.ToTensor(),
+                        transforms.Normalize(self.image_mean, self.image_std),
+                    ]
+                )(image)
+            processed_images.append(image)
+
+        patches = np.array(processed_images)
+        channel = patches.shape[1]
+        grid_t = patches.shape[0] // temporal_patch_size
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        patches = patches.reshape(
+            1,
+            channel,
+            grid_h // merge_size,
+            merge_size,
+            patch_size,
+            grid_w // merge_size,
+            merge_size,
+            patch_size,
+        )
+        patches = patches.transpose(0, 2, 3, 5, 6, 1, 4, 7)
+        flatten_patches = patches.reshape(
+            1 * grid_h * grid_w, channel * patch_size * patch_size
+        )
+
+        return flatten_patches, (grid_t, grid_h, grid_w)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        videos: VideoInput = None,
+        do_resize: bool | None = None,
+        size: dict[str, int] | None = None,
+        min_pixels: int | None = None,
+        max_pixels: int | None = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool | None = None,
+        rescale_factor: float | None = None,
+        do_normalize: bool | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        patch_size: int | None = None,
+        temporal_patch_size: int | None = None,
+        merge_size: int | None = None,
+        do_convert_rgb: bool | None = None,
+        return_tensors: str | TensorType | None = None,
+        data_format: ChannelDimension | None = ChannelDimension.FIRST,
+        input_data_format: str | ChannelDimension | None = None,
+    ):
+        """
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            videos (`VideoInput`):
+                Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
+                passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+                the longest edge resized to keep the input aspect ratio.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            min_pixels (`int`, *optional*, defaults to `self.min_pixels`):
+                The min pixels of the image to resize the image.
+            max_pixels (`int`, *optional*, defaults to `self.max_pixels`):
+                The max pixels of the image to resize the image.
+            patch_size (`int`, *optional*, defaults to `self.patch_size`):
+                The spatial patch size of the vision encoder.
+            temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
+                The temporal patch size of the vision encoder.
+            merge_size (`int`, *optional*, defaults to `self.merge_size`):
+                The merge size of the vision encoder to llm encoder.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+        """  # noqa: E501
+        min_pixels = min_pixels if min_pixels is not None else self.min_pixels
+        max_pixels = max_pixels if max_pixels is not None else self.max_pixels
+
+        if size is not None:
+            if "shortest_edge" not in size or "longest_edge" not in size:
+                raise ValueError(
+                    "size must contain 'shortest_edge' and 'longest_edge' keys."
+                )
+            min_pixels = size["shortest_edge"]
+        elif min_pixels is not None and max_pixels is not None:
+            # backward compatibility: override size with min_pixels and max_pixels
+            # if they are provided.
+            size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
+        else:
+            size = {**self.size}
+
+        do_resize = do_resize if do_resize is not None else self.do_resize
+
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = (
+            rescale_factor if rescale_factor is not None else self.rescale_factor
+        )
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        patch_size = patch_size if patch_size is not None else self.patch_size
+        temporal_patch_size = (
+            temporal_patch_size
+            if temporal_patch_size is not None
+            else self.temporal_patch_size
+        )
+        merge_size = merge_size if merge_size is not None else self.merge_size
+        do_convert_rgb = (
+            do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+        )
+
+        if images is not None:
+            images = make_flat_list_of_images(images)
+
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        validate_preprocess_arguments(
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+
+        data = {}
+        if images is not None:
+            pixel_values, vision_grid_thws = [], []
+            for image in images:
+                patches, image_grid_thw = self._preprocess(
+                    image,
+                    do_resize=do_resize,
+                    size=size,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    patch_size=patch_size,
+                    temporal_patch_size=temporal_patch_size,
+                    merge_size=merge_size,
+                    data_format=data_format,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                )
+                pixel_values.extend(patches)
+                vision_grid_thws.append(image_grid_thw)
+            pixel_values = np.array(pixel_values)
+            vision_grid_thws = np.array(vision_grid_thws)
+            data.update(
+                {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
+            )
+
+        # kept for BC only and should be removed after v5.0
+        if videos is not None:
+            logger.warning(
+                "`HunYuanVLV1ImageProcessor` works only with image inputs "
+                "and doesn't process videos anymore. "
+                "This is a deprecated behavior and will be removed in v5.0. "
+                "Your videos should be forwarded to `HunYuanVLV1VideoProcessor`. "
+            )
+            videos = make_batched_videos(videos)
+            pixel_values_videos, vision_grid_thws_videos = [], []
+            for images in videos:
+                patches, video_grid_thw = self._preprocess(
+                    images,
+                    do_resize=do_resize,
+                    size=size,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    patch_size=patch_size,
+                    temporal_patch_size=temporal_patch_size,
+                    merge_size=merge_size,
+                    data_format=data_format,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                )
+                pixel_values_videos.extend(patches)
+                vision_grid_thws_videos.append(video_grid_thw)
+            data.update(
+                {
+                    "pixel_values_videos": np.array(pixel_values_videos),
+                    "video_grid_thw": np.array(vision_grid_thws_videos),
+                }
+            )
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*):
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of image patches per image.
+        """
+        min_pixels = (
+            images_kwargs["min_pixels"]
+            if "min_pixels" in images_kwargs
+            else self.size["shortest_edge"]
+        )
+        max_pixels = (
+            images_kwargs["max_pixels"]
+            if "max_pixels" in images_kwargs
+            else self.size["longest_edge"]
+        )
+        patch_size = images_kwargs.get("patch_size", self.patch_size)
+        merge_size = images_kwargs.get("merge_size", self.merge_size)
+
+        factor = patch_size * merge_size
+        resized_height, resized_width = smart_resize(
+            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
+        )
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        return grid_h * (grid_w + 1) + 2
+
+
+AutoImageProcessor.register("HunYuanVLImageProcessor", HunYuanVLImageProcessor)

vllm/transformers_utils/processors/ovis.py

@@ -0,0 +1,458 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# ruff: noqa: E501
+# coding=utf-8
+# adapted from https://github.com/AIDC-AI/Ovis/blob/35ab51a1a1e3542fa6db260a1084cefbc8f164bb/ovis/vllm/processing_ovis.py
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from functools import cached_property
+
+import PIL
+import torch
+from transformers import AutoProcessor, BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+from vllm.multimodal.image import convert_image_mode
+
+__all__ = ["OvisProcessor"]
+IGNORE_ID = -100
+
+
+class OvisProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[call-arg]
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            "do_convert_rgb": True,
+            "return_tensors": "pt",
+        },
+    }
+
+
+class OvisProcessor(ProcessorMixin):
+    r"""
+    Constructs an Ovis processor which wraps an Ovis image processor and a Qwen2 tokenizer into a single processor.
+    [`OvisProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~OvisProcessor.__call__`] and [`~OvisProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template", "image_pad_token", "image_segment_len"]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        image_pad_token=None,
+        image_segment_len=255,
+        **kwargs,
+    ):
+        self.image_token = "<image>"
+        self.image_pad_token = image_pad_token
+        self.image_segment_len = image_segment_len
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    @cached_property
+    def extra_special_tokens(self):
+        image_pad_token_id = self.tokenizer.get_vocab()[self.image_pad_token]
+        extra_special_tokens = {
+            "image_token": -200,
+            "image_atom": -300,
+            "image_start": -301,
+            "image_prefix": -302,
+            "image_col_sep": -303,
+            "image_row_sep": -304,
+            "image_end": -305,
+            "image_pad": image_pad_token_id,
+        }
+        return extra_special_tokens
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput
+        | PreTokenizedInput
+        | list[TextInput]
+        | list[PreTokenizedInput] = None,
+        **kwargs: Unpack[OvisProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
+            Args:
+                images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                    The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                    tensor. Both channels-first and channels-last formats are supported.
+                text (`str`, `list[str]`, `list[list[str]]`):
+                    The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                    (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                    `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+                videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                    The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                    tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+                return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                    If set, will return tensors of a particular framework. Acceptable values are:
+                    - `'tf'`: Return TensorFlow `tf.constant` objects.
+                    - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                    - `'np'`: Return NumPy `np.ndarray` objects.
+                    - `'jax'`: Return JAX `jnp.ndarray` objects.
+            Returns:
+                [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+                - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+                - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+                  `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+                  `None`).
+                - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+                - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+                - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+                - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+                - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+
+        max_partition = kwargs.pop("max_partition", 9)
+        covering_threshold = kwargs.pop("covering_threshold", 0.9)
+
+        output_kwargs = self._merge_kwargs(
+            OvisProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        # Process all images first
+        image_features = {}
+        if images is not None:
+            processed_images = []
+            image_placeholders_list = []
+            grids = []
+
+            # Process each image
+            for image in images if isinstance(images, list) else [images]:
+                pixel_values, image_placeholders, grid = self.preprocess_image(
+                    image=image,
+                    max_partition=max_partition,
+                    covering_threshold=covering_threshold,
+                    **output_kwargs["images_kwargs"],
+                )
+                processed_images.append(pixel_values)
+                image_placeholders_list.append(image_placeholders)
+                grids.append(grid)
+
+            # assign all processed images
+            if processed_images:
+                image_features["image_placeholders"] = image_placeholders_list
+
+        # Process text input
+        if text is not None:
+            if not isinstance(text, list):
+                text = [text]
+
+            tokenized_batched_text = self._tokenize_with_image_symbol(text)
+            image_token_id = self.get_token_value("image_token")
+            replaced_ids_list = []
+            idx = 0
+            for ids_tensor in tokenized_batched_text:
+                if (
+                    image_token_id in ids_tensor
+                    and "image_placeholders" in image_features
+                ):
+                    if idx < len(image_features["image_placeholders"]):
+                        # Converts in list for ease of use
+                        ids_list = ids_tensor.tolist()
+
+                        new_ids = []
+
+                        # replace placeholders
+                        for i, token_id in enumerate(ids_list):
+                            if token_id == image_token_id:
+                                placeholder_ids = image_features["image_placeholders"][
+                                    idx
+                                ]
+                                new_ids.extend(placeholder_ids)
+                                idx += 1
+                            else:
+                                new_ids.append(token_id)
+
+                        # Converts back to tensors
+                        ids_tensor = torch.tensor(new_ids, dtype=torch.long)
+                    else:
+                        raise RuntimeError(
+                            "Mismatch between the images you provided and the number of placeholder present in the text"
+                        )
+
+                replaced_ids_list.append(ids_tensor)
+
+            if replaced_ids_list:
+                replaced_and_tokenized_ids = torch.stack(replaced_ids_list)
+            else:
+                replaced_and_tokenized_ids = torch.tensor([], dtype=torch.long)
+
+            # Create the output with text features
+            output = BatchFeature(
+                data={
+                    "input_ids": replaced_and_tokenized_ids,
+                }
+            )
+
+            # Add image features if present
+            if image_features:
+                output["pixel_values"] = processed_images
+                output["grids"] = grids
+
+            return output
+
+        # If only images were provided
+        return BatchFeature(data=image_features)
+
+    def _tokenize_with_image_symbol(self, text_list: list[str]) -> torch.LongTensor:
+        batch_token_ids = []
+        for text in text_list:
+            text_chunks = [
+                self.tokenizer(chunk, add_special_tokens=False).input_ids
+                for chunk in text.split(self.image_token)
+            ]
+            token_ids = []
+            num_chuck = len(text_chunks)
+            for i, chunk in enumerate(text_chunks):
+                token_ids.extend(chunk)
+                if i < num_chuck - 1:
+                    token_ids.append(self.get_token_value("image_token"))
+            batch_token_ids.append(token_ids)
+        return torch.tensor(batch_token_ids, dtype=torch.long)
+
+    def get_image_size(self):
+        size = self.image_processor.size
+        if "shortest_edge" in size:
+            width = height = size["shortest_edge"]
+        elif "height" in size and "width" in size:
+            width = size["width"]
+            height = size["height"]
+        else:
+            raise ValueError("Can't parse image size from image_processor config.")
+        return height, width
+
+    def get_token_value(self, tok):
+        return self.extra_special_tokens[tok]
+
+    def construct_image_indicators(self, grid):
+        image_placeholders = [
+            self.get_token_value("image_start"),
+            self.get_token_value("image_atom"),
+            self.get_token_value("image_prefix"),
+        ]
+        if grid[0] * grid[1] > 1:
+            for r in range(grid[0]):
+                for c in range(grid[1]):
+                    image_placeholders.append(self.get_token_value("image_atom"))
+                    if c < grid[1] - 1:
+                        image_placeholders.append(self.get_token_value("image_col_sep"))
+                if r < grid[0] - 1:
+                    image_placeholders.append(self.get_token_value("image_row_sep"))
+        image_placeholders.append(self.get_token_value("image_end"))
+        return image_placeholders
+
+    def construct_image_placeholders(self, grid):
+        image_placeholders = self.construct_image_indicators(grid)
+
+        image_atom_token_id = self.get_token_value("image_atom")
+        # Extract the padding token ID from tokenizer
+        image_padding_token_id = self.get_token_value("image_pad")
+
+        # Create a new list with padding tokens inserted
+        padded_placeholder_tokens = []
+        for token in image_placeholders:
+            padded_placeholder_tokens.append(image_padding_token_id)
+            if token == image_atom_token_id:
+                padded_placeholder_tokens.extend(
+                    [image_padding_token_id] * self.image_segment_len
+                )
+        return padded_placeholder_tokens
+
+    def preprocess_image(
+        self,
+        image: PIL.Image.Image,
+        max_partition,
+        covering_threshold,
+        do_convert_rgb,
+        return_tensors,
+    ):
+        def _preprocess(img: PIL.Image.Image, side):
+            # first resize and preprocess
+            w, h = img.size
+            if w == h:
+                new_width = new_height = side
+            elif w > h:
+                new_width = side
+                new_height = int(h / w * new_width)
+            else:
+                new_height = side
+                new_width = int(w / h * new_height)
+            new_size = dict(height=new_height, width=new_width)
+            pixel_values = self.image_processor.preprocess(
+                img, size=new_size, return_tensors=return_tensors
+            )["pixel_values"]
+
+            # then pad to square
+            square_values = torch.zeros(
+                [1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device
+            )
+            new_height, new_width = pixel_values.shape[2:]
+            if new_height == new_width:
+                square_values[:, :, :, :] = pixel_values
+            elif new_height > new_width:
+                from_index = (side - new_width) // 2
+                square_values[:, :, :, from_index : from_index + new_width] = (
+                    pixel_values
+                )
+            else:
+                from_index = (side - new_height) // 2
+                square_values[:, :, from_index : from_index + new_height, :] = (
+                    pixel_values
+                )
+
+            return square_values
+
+        def _partition(img, grid) -> list[tuple[int, int, int, int]]:
+            w, h = img.size
+            row_height = h // grid[0]
+            col_width = w // grid[1]
+
+            partition = []
+            for row in range(grid[0]):
+                for col in range(grid[1]):
+                    left = col * col_width
+                    upper = row * row_height
+                    right = w if col == grid[1] - 1 else (col + 1) * col_width
+                    lower = h if row == grid[0] - 1 else (row + 1) * row_height
+                    partition.append((left, upper, right, lower))
+
+            return partition
+
+        def _covering_area(left, upper, right, lower, side):
+            w = right - left
+            h = lower - upper
+            w, h = max(w, h), min(w, h)
+            if w > side:
+                h = h / w * side
+                w = side
+            return w * h
+
+        def _get_best_grid(img, side):
+            img_area = img.size[0] * img.size[1]
+
+            candidate_grids = []
+            for i in range(1, max_partition + 1):
+                for j in range(1, max_partition + 1):
+                    if i * j <= max_partition:
+                        candidate_grids.append((i, j))
+
+            all_grids = []
+            good_grids = []
+            for grid in candidate_grids:
+                partition = _partition(img, grid)
+                covering_ratio = (
+                    sum([_covering_area(*p, side) for p in partition]) / img_area
+                )
+                assert covering_ratio <= 1.0
+                all_grids.append((grid, covering_ratio))
+                if covering_ratio > covering_threshold:
+                    good_grids.append((grid, covering_ratio))
+
+            if len(good_grids) > 0:
+                # pick the good partition with minimum #sub_images and break the tie using covering_ratio
+                return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][
+                    0
+                ]
+            else:
+                # pick the partition with maximum covering_ratio and break the tie using #sub_images
+                return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
+
+        if do_convert_rgb:
+            image = convert_image_mode(image, "RGB")
+
+        sides = self.get_image_size()
+        if sides[0] != sides[1]:
+            raise ValueError("get_image_size() returns non-square size")
+        side = sides[0]
+        grid = _get_best_grid(image, side)
+        partition = _partition(image, grid)
+        crops = [image.crop(p) for p in partition]
+        if len(crops) > 1:
+            crops.insert(0, image)
+        pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0)
+        image_placeholders = self.construct_image_placeholders(grid)
+        return torch.tensor(pixel_values), image_placeholders, torch.tensor(grid)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def post_process_image_text_to_text(self, generated_outputs):
+        """
+        Post-process the output of the model to decode the text.
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=False,
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(
+            dict.fromkeys(tokenizer_input_names + image_processor_input_names)
+        )
+        return names_from_processor + ["second_per_grid_ts"]
+
+
+AutoProcessor.register("OvisProcessor", OvisProcessor)

vllm/transformers_utils/processors/ovis2_5.py

@@ -0,0 +1,481 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from functools import cached_property
+
+import numpy as np
+import PIL
+import torch
+from transformers import AutoProcessor, BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+__all__ = ["Ovis2_5Processor"]
+IMAGE_TOKEN = "<image>"
+VIDEO_TOKEN = "<video>"
+MIN_PIXELS = 448 * 448
+MAX_PIXELS = 1792 * 1792
+
+
+class Ovis2_5ProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[call-arg]
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            "do_convert_rgb": True,
+        },
+        "videos_kwargs": {
+            "do_convert_rgb": True,
+        },
+    }
+
+
+class Ovis2_5Processor(ProcessorMixin):
+    r"""
+    Constructs an Ovis processor which wraps an Ovis image processor
+    and a Qwen2 tokenizer into a single processor.
+    [`OvisProcessor`] offers all the functionalities of
+    [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`].
+    See the [`~OvisProcessor.__call__`] and [`~OvisProcessor.decode`]
+    for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will
+            be used to convert lists of messages in a chat into
+            a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template", "image_pad_token"]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        image_pad_token=None,
+        patch_size=16,
+        hidden_stride=2,
+        temporal_patch_size=1,
+        **kwargs,
+    ):
+        self.image_token = IMAGE_TOKEN
+        self.video_token = VIDEO_TOKEN
+        self.image_pad_token = "<|image_pad|>"
+
+        self.patch_size = patch_size
+        self.hidden_stride = hidden_stride
+        self.temporal_patch_size = temporal_patch_size
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    @cached_property
+    def extra_special_tokens(self):
+        vocab = self.tokenizer.get_vocab()
+        required_tokens = {
+            "image_token": "<image>",
+            "video_token": "<video>",
+            "visual_atom": "<ovis_visual_atom>",
+            "image_start": "<ovis_image_start>",
+            "image_end": "<ovis_image_end>",
+            "video_start": "<ovis_video_start>",
+            "video_end": "<ovis_video_end>",
+            "image_pad": "<|image_pad|>",
+        }
+
+        extra_special_tokens = {}
+        suggestion = (
+            "please add '<image>', '<video>', '<ovis_visual_atom>', "
+            "'<ovis_image_start>', '<ovis_image_end>', '<ovis_video_start>', "
+            "'<ovis_video_end>' in 'additional_special_tokens' of "
+            "tokenizer_config.json, You can refer to "
+            "https://huggingface.co/AIDC-AI/Ovis2.6-30B-A3B/blob/main/tokenizer_config.json"
+        )
+
+        for key, token_name in required_tokens.items():
+            if token_name not in vocab:
+                raise ValueError(f"Can not find {token_name}, {suggestion}")
+            extra_special_tokens[key] = vocab[token_name]
+
+        return extra_special_tokens
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        videos: np.ndarray | list[ImageInput] = None,
+        text: TextInput
+        | PreTokenizedInput
+        | list[TextInput]
+        | list[PreTokenizedInput] = None,
+        **kwargs: Unpack[Ovis2_5ProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s)
+        and image(s). This method forwards the `text`and `kwargs` arguments
+        to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text`
+        is not `None` to encode the text. To prepare the vision inputs,
+        this method forwards the `vision_infos` and `kwrags` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`]
+        if `vision_infos` is not `None`.
+            Args:
+                images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`,
+                    `list[PIL.Image.Image]`, `list[np.ndarray]`,
+                    `list[torch.Tensor]`):
+                    The image or batch of images to be prepared.
+                    Each image can be a PIL image, NumPy array or PyTorch
+                    tensor. Both channels-first and channels-last formats
+                    are supported.
+                text (`str`, `list[str]`, `list[list[str]]`):
+                    The sequence or batch of sequences to be encoded.
+                    Each sequence can be a string or a list of strings
+                    (pretokenized string). If the sequences are provided as
+                    list of strings (pretokenized), you must set
+                    `is_split_into_words=True` (to lift the ambiguity with
+                    a batch of sequences).
+                videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`,
+                    `list[torch.Tensor]`):
+                    The image or batch of videos to be prepared. Each video
+                    can be a 4D NumPy array or PyTorch tensor, or a nested
+                    list of 3D frames. Both channels-first and channels-last
+                    formats are supported.
+                return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                    If set, will return tensors of a particular framework.
+                    Acceptable values are:
+                    - `'tf'`: Return TensorFlow `tf.constant` objects.
+                    - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                    - `'np'`: Return NumPy `np.ndarray` objects.
+                    - `'jax'`: Return JAX `jnp.ndarray` objects.
+            Returns:
+                [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+                - **input_ids** -- list of token ids to be fed to a model.
+                  Returned when `text` is not `None`.
+                - **attention_mask** -- list of indices specifying which tokens
+                  should be attended to by the model (when
+                  `return_attention_mask=True` or if *"attention_mask"*
+                  is in `self.model_input_names` and if `text` is not `None`).
+                - **pixel_values** -- Pixel values to be fed to a model.
+                  Returned when `images` is not `None`.
+                - **pixel_values_videos** -- Pixel values of videos to be fed to
+                  a model. Returned when `videos` is not `None`.
+                - **image_grid_thw** -- list of image 3D grid in LLM. Returned
+                  when `images` is not `None`.
+                - **video_grid_thw** -- list of video 3D grid in LLM. Returned
+                  when `videos` is not `None`.
+                - **second_per_grid_ts** -- list of video seconds per time grid.
+                  Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Ovis2_5ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        # Process all images first
+        visual_features = {}
+        output = BatchFeature()
+        if images is not None:
+            processed_images = []
+            image_placeholders_list = []
+            grids = []
+            # Process each image
+            for image in images if isinstance(images, list) else [images]:
+                pixel_values, image_placeholders, grid = self.preprocess_multidata(
+                    images=image,
+                    **output_kwargs["images_kwargs"],
+                )
+                processed_images.append(pixel_values)
+                image_placeholders_list.append(image_placeholders)
+                grids.append(grid)
+
+            # assign all processed images
+            if processed_images:
+                visual_features["image_placeholders"] = image_placeholders_list
+            output["pixel_values"] = processed_images
+            output["grids"] = grids
+
+        if videos is not None:
+            processed_videos = []
+            videos_placeholders_list = []
+            grids = []
+            # Process each video
+            for video in videos if isinstance(videos, list) else [videos]:
+                pixel_values, video_placeholders, grid = self.preprocess_multidata(
+                    video=video,
+                    **output_kwargs["videos_kwargs"],
+                )
+                processed_videos.append(pixel_values)
+                videos_placeholders_list.append(video_placeholders)
+                grids.append(grid)
+            # assign all processed videos
+            if processed_videos:
+                visual_features["video_placeholders"] = videos_placeholders_list
+            output["video_pixel_values"] = processed_videos
+            output["video_grids"] = grids
+
+        # Process text input
+        if text is not None:
+            if not isinstance(text, list):
+                text = [text]
+            tokenized_batched_text = self._tokenize_with_visual_symbol(text)
+            image_token_id = self.get_token_value("image_token")
+            video_token_id = self.get_token_value("video_token")
+            replaced_ids_list = []
+            image_idx = 0
+            video_idx = 0
+            for ids_tensor in tokenized_batched_text:
+                has_image_tokens = (
+                    image_token_id in ids_tensor
+                    and "image_placeholders" in visual_features
+                    and image_idx < len(visual_features["image_placeholders"])
+                )
+                has_video_tokens = (
+                    video_token_id in ids_tensor
+                    and "video_placeholders" in visual_features
+                    and video_idx < len(visual_features["video_placeholders"])
+                )
+                if has_image_tokens or has_video_tokens:
+                    # Convert to list for easier manipulation
+                    ids_list = ids_tensor.tolist()
+                    new_ids = []
+
+                    # Replace placeholders
+                    for token_id in ids_list:
+                        if token_id == image_token_id:
+                            new_ids.extend(
+                                visual_features["image_placeholders"][image_idx]
+                            )
+                            image_idx += 1
+                        elif token_id == video_token_id:
+                            new_ids.extend(
+                                visual_features["video_placeholders"][video_idx]
+                            )
+                            video_idx += 1
+                        else:
+                            new_ids.append(token_id)
+                    # Convert back to tensor
+                    ids_tensor = torch.tensor(new_ids, dtype=torch.long)
+                replaced_ids_list.append(ids_tensor)
+            if replaced_ids_list:
+                replaced_and_tokenized_ids = torch.stack(replaced_ids_list)
+            else:
+                replaced_and_tokenized_ids = torch.tensor([], dtype=torch.long)
+            output["input_ids"] = replaced_and_tokenized_ids
+
+            return output
+        # If only images were provided
+        return BatchFeature(data=visual_features)
+
+    def _tokenize_with_visual_symbol(self, text_list: list[str]) -> torch.LongTensor:
+        batch_token_ids = []
+        for text in text_list:
+            token_ids = []
+            video_token_id = self.get_token_value("video_token")
+            image_token_id = self.get_token_value("image_token")
+            video_split_texts = text.split(self.video_token)
+
+            for j, video_segment in enumerate(video_split_texts):
+                image_split_texts = video_segment.split(self.image_token)
+                text_chunks = [
+                    self.tokenizer(chunk, add_special_tokens=False).input_ids
+                    for chunk in image_split_texts
+                ]
+                segment_tokens = []
+                for i, chunk in enumerate(text_chunks):
+                    segment_tokens.extend(chunk)
+                    if i < len(text_chunks) - 1:
+                        segment_tokens.append(image_token_id)
+                token_ids.extend(segment_tokens)
+                if j < len(video_split_texts) - 1:
+                    token_ids.append(video_token_id)
+
+            batch_token_ids.append(token_ids)
+        return torch.tensor(batch_token_ids, dtype=torch.long)
+
+    # Copied from qwen2_vl
+    def smart_resize(
+        self,
+        height: int,
+        width: int,
+        factor: int = 28,
+        min_pixels: int = MIN_PIXELS,
+        max_pixels: int = MAX_PIXELS,
+    ):
+        """Rescales the image so that the following conditions are met:
+        1. Both dimensions (height and width) are divisible by 'factor'.
+        2. The total number of pixels is within the range
+            ['min_pixels', 'max_pixels'].
+        3. The aspect ratio of the image is maintained as closely as possible.
+        """
+        if height < factor or width < factor:
+            print(
+                f"height:{height} or width:{width} must be larger than factor:{factor}"
+            )
+            if height < width:
+                width = round(factor / height * width)
+                height = factor
+            else:
+                height = round(factor / width * height)
+                width = factor
+
+        elif max(height, width) / min(height, width) > 200:
+            print(
+                f"absolute aspect ratio must be smaller than 200, "
+                f"got {max(height, width) / min(height, width)}"
+            )
+            if height > width:
+                height = 200 * width
+            else:
+                width = 200 * height
+
+        h_bar = round(height / factor) * factor
+        w_bar = round(width / factor) * factor
+        if h_bar * w_bar > max_pixels:
+            beta = math.sqrt((height * width) / max_pixels)
+            h_bar = math.floor(height / beta / factor) * factor
+            w_bar = math.floor(width / beta / factor) * factor
+        elif h_bar * w_bar < min_pixels:
+            beta = math.sqrt(min_pixels / (height * width))
+            h_bar = math.ceil(height * beta / factor) * factor
+            w_bar = math.ceil(width * beta / factor) * factor
+        return h_bar, w_bar
+
+    def get_token_value(self, tok):
+        return self.extra_special_tokens[tok]
+
+    def construct_visual_indicators(self, grid, is_video: bool = False):
+        if is_video:
+            start_token = self.get_token_value("video_start")
+            end_token = self.get_token_value("video_end")
+        else:
+            start_token = self.get_token_value("image_start")
+            end_token = self.get_token_value("image_end")
+
+        image_placeholders = [start_token, self.get_token_value("visual_atom")]
+        if grid[0] * grid[1] > 1:
+            for r in range(grid[0]):
+                for c in range(grid[1]):
+                    image_placeholders.append(self.get_token_value("visual_atom"))
+
+        image_placeholders.append(end_token)
+        return image_placeholders
+
+    def construct_visual_placeholders(self, grid, is_video: bool = False):
+        visual_placeholders = self.construct_visual_indicators((1, 1), is_video)
+
+        image_atom_token_id = self.get_token_value("visual_atom")
+        # Extract the padding token ID from tokenizer
+        image_padding_token_id = self.get_token_value("image_pad")
+
+        num_image_atoms = grid[0] * grid[1] * grid[2]
+        num_image_atoms //= self.hidden_stride**2
+        num_image_atoms //= self.temporal_patch_size
+
+        # Create a new list with padding tokens inserted
+        padded_placeholder_tokens = []
+        for token in visual_placeholders:
+            if token == image_atom_token_id:
+                padded_placeholder_tokens.extend(
+                    [image_padding_token_id] * num_image_atoms
+                )
+            else:
+                padded_placeholder_tokens.append(image_padding_token_id)
+        return padded_placeholder_tokens
+
+    def preprocess_multidata(
+        self,
+        images: PIL.Image.Image | list[PIL.Image.Image] | None = None,
+        video: list[PIL.Image.Image] | np.ndarray | None = None,
+        do_convert_rgb: bool | None = True,
+        min_pixels: int = MIN_PIXELS,
+        max_pixels: int = MAX_PIXELS,
+        return_tensors: str | None = "pt",
+    ):
+        is_video = False
+        if images is not None:
+            if not isinstance(images, list):
+                images = [images]
+        elif video is not None:
+            is_video = True
+            # type of vidoe in dummy_mm_data is np.ndarray
+            if isinstance(video, np.ndarray):
+                images = []
+                for i in range(video.shape[0]):
+                    image = PIL.Image.fromarray(video[i].astype(np.uint8))
+                    images.append(image)
+            elif isinstance(video, list):
+                images = video
+        else:
+            raise ValueError("Either images or video should be provided.")
+        min_pixels = min(
+            max_pixels if max_pixels is not None else MAX_PIXELS,
+            min_pixels if min_pixels is not None else MIN_PIXELS,
+        )
+        images = [
+            image.convert("RGB") if do_convert_rgb and image.mode != "RGB" else image
+            for image in images
+        ]
+
+        width, height = images[0].size
+        resized_height, resized_width = height, width
+        processed_images = []
+        for image in images:
+            resized_height, resized_width = self.smart_resize(
+                height,
+                width,
+                factor=self.patch_size * self.hidden_stride,
+                min_pixels=min_pixels,
+                max_pixels=max_pixels,
+            )
+            new_size = dict(height=resized_height, width=resized_width)
+            image_pt = self.image_processor.preprocess(image, size=new_size)[
+                "pixel_values"
+            ][0]
+
+            processed_images.append(image_pt)
+
+        patches = np.array(processed_images)
+        if patches.shape[0] % self.temporal_patch_size != 0:
+            num_to_pad = self.temporal_patch_size - (
+                patches.shape[0] % self.temporal_patch_size
+            )
+            repeats = np.repeat(patches[-1][np.newaxis], num_to_pad, axis=0)
+            patches = np.concatenate([patches, repeats], axis=0)
+        channel = patches.shape[1]
+        grid_t = patches.shape[0] // self.temporal_patch_size
+        grid_h = resized_height // self.patch_size
+        grid_w = resized_width // self.patch_size
+
+        patches = patches.reshape(
+            grid_t,
+            self.temporal_patch_size,
+            channel,
+            grid_h // self.hidden_stride,
+            self.hidden_stride,
+            self.patch_size,
+            grid_w // self.hidden_stride,
+            self.hidden_stride,
+            self.patch_size,
+        )
+        patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
+        flatten_patches = patches.reshape(
+            grid_t * grid_h * grid_w,
+            channel * self.temporal_patch_size * self.patch_size * self.patch_size,
+        )
+
+        visual_placeholders = self.construct_visual_placeholders(
+            [grid_t, grid_h, grid_w], is_video
+        )
+        return (
+            torch.tensor(flatten_patches),
+            visual_placeholders,
+            torch.tensor([[grid_t, grid_h, grid_w]]),
+        )
+
+
+AutoProcessor.register("Ovis2_5Processor", Ovis2_5Processor)

vllm/transformers_utils/processors/qwen3_asr.py

@@ -0,0 +1,232 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# ruff: noqa
+# mypy: ignore-errors
+# coding=utf-8
+# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import regex as re
+
+import numpy as np
+
+from transformers import AutoProcessor
+from transformers.audio_utils import AudioInput
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
+from transformers.tokenization_utils_base import TextInput
+
+
+class Qwen3ASRProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "padding_side": "left",
+        },
+        "audio_kwargs": {
+            "sampling_rate": 16000,
+            "padding": True,
+            "return_attention_mask": True,
+        },
+    }
+
+
+def _get_feat_extract_output_lengths(input_lengths):
+    """
+    Computes the output length of the convolutional layers and the output length of the audio encoder
+    """
+
+    input_lengths_leave = input_lengths % 100
+    feat_lengths = (input_lengths_leave - 1) // 2 + 1
+    output_lengths = (
+        ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
+    )
+    return output_lengths
+
+
+class Qwen3ASRProcessor(ProcessorMixin):
+    r"""
+    Constructs a Qwen3ASR processor.
+    [`Qwen3ASRProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`], and [`Qwen2TokenizerFast`]. See the
+    [`~Qwen3ASRProcessor.__call__`] and [`~Qwen3ASRProcessor.decode`] for more information.
+
+    Args:
+        feature_extractor ([`WhisperFeatureExtractor`], *optional*):
+            The audio feature extractor.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The text tokenizer.
+        chat_template (`Optional[str]`, *optional*):
+            The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
+    """
+
+    attributes = ["feature_extractor", "tokenizer"]
+    feature_extractor_class = "WhisperFeatureExtractor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(self, feature_extractor=None, tokenizer=None, chat_template=None):
+        super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
+        self.audio_token = self.tokenizer.audio_token
+        self.audio_bos_token = self.tokenizer.audio_bos_token
+        self.audio_eos_token = self.tokenizer.audio_eos_token
+
+    def __call__(
+        self,
+        text: TextInput = None,
+        audio: AudioInput = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the audio(s), this method forwards the `audio` and `kwargs` arguments to
+        WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] if `audio` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            audio (`np.ndarray`, `List[np.ndarray]`):
+                The audio or batch of audio to be prepared. Each audio can be a NumPy array.
+        """
+
+        if text is None:
+            raise ValueError("You need to specify either a `text` input to process.")
+
+        output_kwargs = self._merge_kwargs(
+            Qwen3ASRProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if audio is not None:
+            output_kwargs["audio_kwargs"]["padding"] = True
+            output_kwargs["audio_kwargs"]["truncation"] = False
+            audio_inputs = self.feature_extractor(
+                audio, **output_kwargs["audio_kwargs"]
+            )
+            audio_inputs["feature_attention_mask"] = audio_inputs.pop(
+                "attention_mask"
+            )  # rename feature_attention_mask to prevent conflicts later on
+            audio_inputs["input_features"] = audio_inputs.pop(
+                "input_features"
+            )  # rename input_features to prevent conflicts later on
+            audio_lengths = iter(
+                _get_feat_extract_output_lengths(
+                    audio_inputs["feature_attention_mask"].sum(-1)
+                )
+            )
+        else:
+            audio_inputs = {}
+            audio_lengths = iter([])
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = self.replace_multimodal_special_tokens(
+            text,
+            audio_lengths,
+        )
+
+        texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(
+            data={**texts_inputs, **audio_inputs},
+            tensor_type=kwargs.get("return_tensors"),
+        )
+
+    def replace_multimodal_special_tokens(
+        self,
+        text,
+        audio_lengths,
+    ):
+        processed_text = []
+        for sample in text:
+            positions = []
+            special_tokens = [re.escape(tok) for tok in [self.audio_token]]
+            pattern = "|".join(special_tokens)
+            positions = sorted(
+                [
+                    (match.start(), match.group())
+                    for match in re.finditer(pattern, sample)
+                ]
+            )
+            positions.sort(key=lambda x: x[0])
+
+            for _, special_token in positions:
+                if special_token == self.audio_token:
+                    sample = sample.replace(
+                        self.audio_token,
+                        "<|audio_placeholder|>" * next(audio_lengths),
+                        1,
+                    )
+
+            sample = sample.replace("<|audio_placeholder|>", self.audio_token)
+            processed_text.append(sample)
+        return processed_text
+
+    def get_chunked_index(
+        self, token_indices: np.ndarray, tokens_per_chunk: int
+    ) -> list[tuple[int, int]]:
+        """
+        Splits token index list into chunks based on token value ranges.
+
+        Given a list of token indices, returns a list of (start, end) index tuples representing
+        slices of the list where the token values fall within successive ranges of `tokens_per_chunk`.
+
+        For example, if `tokens_per_chunk` is 1000, the function will create chunks such that:
+        - the first chunk contains token values < 1000,
+        - the second chunk contains values >= 1000 and < 2000, and so on.
+
+        Parameters:
+            token_indices (`np.ndarray`): A monotonically increasing list of token index values.
+            tokens_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
+
+        Returns:
+            `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive)
+                                and end (exclusive) indices of a chunk in `token_indices`.
+        """
+
+        def _iter():
+            i, start_idx = 0, 0  # skip bos token
+            current_chunk = 1
+            while i < len(token_indices):  # skip eos token
+                if token_indices[i] >= current_chunk * tokens_per_chunk:
+                    yield (start_idx, i)
+                    start_idx = i
+                    current_chunk += 1
+                i += 1
+            yield (start_idx, len(token_indices))
+
+        return list(_iter())
+
+    def apply_chat_template(self, conversations, chat_template=None, **kwargs):
+        kwargs["return_dict"] = False
+        return super().apply_chat_template(conversations, chat_template, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        feature_extractor_input_names = self.feature_extractor.model_input_names
+        return list(
+            dict.fromkeys(
+                tokenizer_input_names
+                + feature_extractor_input_names
+                + ["feature_attention_mask"]
+            )
+        )
+
+
+AutoProcessor.register("Qwen3ASRProcessor", Qwen3ASRProcessor)

vllm/transformers_utils/repo_utils.py

@@ -0,0 +1,303 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utilities for model repo interaction."""
+
+import fnmatch
+import json
+import os
+import time
+from collections.abc import Callable
+from functools import cache
+from pathlib import Path
+from typing import TypeVar
+
+import huggingface_hub
+from huggingface_hub import hf_hub_download, try_to_load_from_cache
+from huggingface_hub import list_repo_files as hf_list_repo_files
+from huggingface_hub.utils import (
+    EntryNotFoundError,
+    HfHubHTTPError,
+    LocalEntryNotFoundError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+)
+
+from vllm import envs
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+_R = TypeVar("_R")
+
+
+def with_retry(
+    func: Callable[[], _R],
+    log_msg: str,
+    max_retries: int = 2,
+    retry_delay: int = 2,
+) -> _R:
+    for attempt in range(max_retries):
+        try:
+            return func()
+        except Exception as e:
+            if attempt == max_retries - 1:
+                logger.error("%s: %s", log_msg, e)
+                raise
+            logger.error(
+                "%s: %s, retrying %d of %d", log_msg, e, attempt + 1, max_retries
+            )
+            time.sleep(retry_delay)
+            retry_delay *= 2
+
+    raise AssertionError("Should not be reached")
+
+
+# @cache doesn't cache exceptions
+@cache
+def list_repo_files(
+    repo_id: str,
+    *,
+    revision: str | None = None,
+    repo_type: str | None = None,
+    token: str | bool | None = None,
+) -> list[str]:
+    def lookup_files() -> list[str]:
+        # directly list files if model is local
+        if (local_path := Path(repo_id)).exists():
+            return [
+                str(file.relative_to(local_path))
+                for file in local_path.rglob("*")
+                if file.is_file()
+            ]
+        # if model is remote, use hf_hub api to list files
+        try:
+            if envs.VLLM_USE_MODELSCOPE:
+                from vllm.transformers_utils.utils import modelscope_list_repo_files
+
+                return modelscope_list_repo_files(
+                    repo_id,
+                    revision=revision,
+                    token=os.getenv("MODELSCOPE_API_TOKEN", None),
+                )
+            return hf_list_repo_files(
+                repo_id, revision=revision, repo_type=repo_type, token=token
+            )
+        except huggingface_hub.errors.OfflineModeIsEnabled:
+            # Don't raise in offline mode,
+            # all we know is that we don't have this
+            # file cached.
+            return []
+
+    return with_retry(lookup_files, "Error retrieving file list")
+
+
+def list_filtered_repo_files(
+    model_name_or_path: str,
+    allow_patterns: list[str],
+    revision: str | None = None,
+    repo_type: str | None = None,
+    token: str | bool | None = None,
+) -> list[str]:
+    try:
+        all_files = list_repo_files(
+            repo_id=model_name_or_path,
+            revision=revision,
+            token=token,
+            repo_type=repo_type,
+        )
+    except Exception:
+        logger.error(
+            "Error retrieving file list. Please ensure your `model_name_or_path`"
+            "`repo_type`, `token` and `revision` arguments are correctly set. "
+            "Returning an empty list."
+        )
+        return []
+
+    file_list = []
+    # Filter patterns on filenames
+    for pattern in allow_patterns:
+        file_list.extend(
+            [
+                file
+                for file in all_files
+                if fnmatch.fnmatch(os.path.basename(file), pattern)
+            ]
+        )
+    return file_list
+
+
+def any_pattern_in_repo_files(
+    model_name_or_path: str,
+    allow_patterns: list[str],
+    revision: str | None = None,
+    repo_type: str | None = None,
+    token: str | bool | None = None,
+):
+    return (
+        len(
+            list_filtered_repo_files(
+                model_name_or_path=model_name_or_path,
+                allow_patterns=allow_patterns,
+                revision=revision,
+                repo_type=repo_type,
+                token=token,
+            )
+        )
+        > 0
+    )
+
+
+def is_mistral_model_repo(
+    model_name_or_path: str,
+    revision: str | None = None,
+    repo_type: str | None = None,
+    token: str | bool | None = None,
+) -> bool:
+    return any_pattern_in_repo_files(
+        model_name_or_path=model_name_or_path,
+        allow_patterns=["consolidated*.safetensors"],
+        revision=revision,
+        repo_type=repo_type,
+        token=token,
+    )
+
+
+def file_exists(
+    repo_id: str,
+    file_name: str,
+    *,
+    repo_type: str | None = None,
+    revision: str | None = None,
+    token: str | bool | None = None,
+) -> bool:
+    # `list_repo_files` is cached and retried on error, so this is more efficient than
+    # huggingface_hub.file_exists default implementation when looking for multiple files
+    file_list = list_repo_files(
+        repo_id, repo_type=repo_type, revision=revision, token=token
+    )
+    return file_name in file_list
+
+
+# In offline mode the result can be a false negative
+def file_or_path_exists(
+    model: str | Path, config_name: str, revision: str | None
+) -> bool:
+    if (local_path := Path(model)).exists():
+        return (local_path / config_name).is_file()
+
+    # Offline mode support: Check if config file is cached already
+    cached_filepath = try_to_load_from_cache(
+        repo_id=model, filename=config_name, revision=revision
+    )
+    if isinstance(cached_filepath, str):
+        # The config file exists in cache - we can continue trying to load
+        return True
+
+    # NB: file_exists will only check for the existence of the config file on
+    # hf_hub. This will fail in offline mode.
+
+    # Call HF to check if the file exists
+    return file_exists(str(model), config_name, revision=revision)
+
+
+def get_model_path(model: str | Path, revision: str | None = None):
+    if os.path.exists(model):
+        return model
+    assert huggingface_hub.constants.HF_HUB_OFFLINE
+    common_kwargs = {
+        "local_files_only": huggingface_hub.constants.HF_HUB_OFFLINE,
+        "revision": revision,
+    }
+
+    if envs.VLLM_USE_MODELSCOPE:
+        from modelscope.hub.snapshot_download import snapshot_download
+
+        return snapshot_download(model_id=model, **common_kwargs)
+
+    from huggingface_hub import snapshot_download
+
+    return snapshot_download(repo_id=model, **common_kwargs)
+
+
+def get_hf_file_bytes(
+    file_name: str, model: str | Path, revision: str | None = "main"
+) -> bytes | None:
+    """Get file contents from HuggingFace repository as bytes."""
+    file_path = try_get_local_file(model=model, file_name=file_name, revision=revision)
+
+    if file_path is None:
+        hf_hub_file = hf_hub_download(model, file_name, revision=revision)
+        file_path = Path(hf_hub_file)
+
+    if file_path is not None and file_path.is_file():
+        with open(file_path, "rb") as file:
+            return file.read()
+
+    return None
+
+
+def try_get_local_file(
+    model: str | Path, file_name: str, revision: str | None = "main"
+) -> Path | None:
+    file_path = Path(model) / file_name
+    if file_path.is_file():
+        return file_path
+    else:
+        try:
+            cached_filepath = try_to_load_from_cache(
+                repo_id=model, filename=file_name, revision=revision
+            )
+            if isinstance(cached_filepath, str):
+                return Path(cached_filepath)
+        except ValueError:
+            ...
+    return None
+
+
+def get_hf_file_to_dict(
+    file_name: str, model: str | Path, revision: str | None = "main"
+):
+    """
+    Downloads a file from the Hugging Face Hub and returns
+    its contents as a dictionary.
+
+    Parameters:
+    - file_name (str): The name of the file to download.
+    - model (str): The name of the model on the Hugging Face Hub.
+    - revision (str): The specific version of the model.
+
+    Returns:
+    - config_dict (dict): A dictionary containing
+    the contents of the downloaded file.
+    """
+
+    file_path = try_get_local_file(model=model, file_name=file_name, revision=revision)
+
+    if file_path is None:
+        try:
+            hf_hub_file = hf_hub_download(model, file_name, revision=revision)
+        except huggingface_hub.errors.OfflineModeIsEnabled:
+            return None
+        except (
+            RepositoryNotFoundError,
+            RevisionNotFoundError,
+            EntryNotFoundError,
+            LocalEntryNotFoundError,
+        ) as e:
+            logger.debug("File or repository not found in hf_hub_download", e)
+            return None
+        except HfHubHTTPError as e:
+            logger.warning(
+                "Cannot connect to Hugging Face Hub. Skipping file download for '%s':",
+                file_name,
+                exc_info=e,
+            )
+            return None
+        file_path = Path(hf_hub_file)
+
+    if file_path is not None and file_path.is_file():
+        with open(file_path) as file:
+            return json.load(file)
+
+    return None

vllm/transformers_utils/runai_utils.py

@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import hashlib
+import os
+import shutil
+import signal
+
+from vllm import envs
+from vllm.assets.base import get_cache_dir
+from vllm.logger import init_logger
+from vllm.utils.import_utils import PlaceholderModule
+
+logger = init_logger(__name__)
+
+SUPPORTED_SCHEMES = ["s3://", "gs://"]
+
+try:
+    from runai_model_streamer import list_safetensors as runai_list_safetensors
+    from runai_model_streamer import pull_files as runai_pull_files
+except ImportError:
+    runai_model_streamer = PlaceholderModule("runai_model_streamer")  # type: ignore[assignment]
+    runai_pull_files = runai_model_streamer.placeholder_attr("pull_files")
+    runai_list_safetensors = runai_model_streamer.placeholder_attr("list_safetensors")
+
+
+def list_safetensors(path: str = "") -> list[str]:
+    """
+    List full file names from object path and filter by allow pattern.
+
+    Args:
+        path: The object storage path to list from.
+
+    Returns:
+        list[str]: List of full object storage paths allowed by the pattern
+    """
+    return runai_list_safetensors(path)
+
+
+def is_runai_obj_uri(model_or_path: str) -> bool:
+    return model_or_path.lower().startswith(tuple(SUPPORTED_SCHEMES))
+
+
+class ObjectStorageModel:
+    """
+    A class representing an ObjectStorage model mirrored into a
+    temporary directory.
+
+    Attributes:
+        dir: The temporary created directory.
+
+    Methods:
+        pull_files(): Pull model from object storage to the temporary directory.
+    """
+
+    def __init__(self, url: str) -> None:
+        if envs.VLLM_ASSETS_CACHE_MODEL_CLEAN:
+            for sig in (signal.SIGINT, signal.SIGTERM):
+                existing_handler = signal.getsignal(sig)
+                signal.signal(sig, self._close_by_signal(existing_handler))
+
+        dir_name = os.path.join(
+            get_cache_dir(),
+            "model_streamer",
+            hashlib.sha256(str(url).encode()).hexdigest()[:8],
+        )
+        os.makedirs(dir_name, exist_ok=True)
+        self.dir = dir_name
+        logger.debug("Init object storage, model cache path is: %s", dir_name)
+
+    def _close(self) -> None:
+        if os.path.exists(self.dir):
+            shutil.rmtree(self.dir)
+
+    def _close_by_signal(self, existing_handler=None):
+        def new_handler(signum, frame):
+            self._close()
+            if existing_handler:
+                existing_handler(signum, frame)
+
+        return new_handler
+
+    def pull_files(
+        self,
+        model_path: str = "",
+        allow_pattern: list[str] | None = None,
+        ignore_pattern: list[str] | None = None,
+    ) -> None:
+        """
+        Pull files from object storage into the temporary directory.
+
+        Args:
+            model_path: The object storage path of the model.
+            allow_pattern: A list of patterns of which files to pull.
+            ignore_pattern: A list of patterns of which files not to pull.
+
+        """
+        if not model_path.endswith("/"):
+            model_path = model_path + "/"
+        runai_pull_files(model_path, self.dir, allow_pattern, ignore_pattern)

vllm/transformers_utils/s3_utils.py

@@ -0,0 +1,95 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import fnmatch
+from typing import TYPE_CHECKING
+
+from vllm.utils.import_utils import PlaceholderModule
+
+if TYPE_CHECKING:
+    from botocore.client import BaseClient
+
+try:
+    import boto3
+except ImportError:
+    boto3 = PlaceholderModule("boto3")  # type: ignore[assignment]
+
+
+def _filter_allow(paths: list[str], patterns: list[str]) -> list[str]:
+    return [
+        path
+        for path in paths
+        if any(fnmatch.fnmatch(path, pattern) for pattern in patterns)
+    ]
+
+
+def _filter_ignore(paths: list[str], patterns: list[str]) -> list[str]:
+    return [
+        path
+        for path in paths
+        if not any(fnmatch.fnmatch(path, pattern) for pattern in patterns)
+    ]
+
+
+def glob(
+    s3: "BaseClient | None" = None,
+    path: str = "",
+    allow_pattern: list[str] | None = None,
+) -> list[str]:
+    """
+    List full file names from S3 path and filter by allow pattern.
+
+    Args:
+        s3: S3 client to use.
+        path: The S3 path to list from.
+        allow_pattern: A list of patterns of which files to pull.
+
+    Returns:
+        list[str]: List of full S3 paths allowed by the pattern
+    """
+    if s3 is None:
+        s3 = boto3.client("s3")
+    if not path.endswith("/"):
+        path = path + "/"
+    bucket_name, _, paths = list_files(s3, path=path, allow_pattern=allow_pattern)
+    return [f"s3://{bucket_name}/{path}" for path in paths]
+
+
+def list_files(
+    s3: "BaseClient",
+    path: str,
+    allow_pattern: list[str] | None = None,
+    ignore_pattern: list[str] | None = None,
+) -> tuple[str, str, list[str]]:
+    """
+    List files from S3 path and filter by pattern.
+
+    Args:
+        s3: S3 client to use.
+        path: The S3 path to list from.
+        allow_pattern: A list of patterns of which files to pull.
+        ignore_pattern: A list of patterns of which files not to pull.
+
+    Returns:
+        tuple[str, str, list[str]]: A tuple where:
+            - The first element is the bucket name
+            - The second element is string represent the bucket
+              and the prefix as a dir like string
+            - The third element is a list of files allowed or
+              disallowed by pattern
+    """
+    parts = path.removeprefix("s3://").split("/")
+    prefix = "/".join(parts[1:])
+    bucket_name = parts[0]
+
+    objects = s3.list_objects_v2(Bucket=bucket_name, Prefix=prefix)
+    paths = [obj["Key"] for obj in objects.get("Contents", [])]
+
+    paths = _filter_ignore(paths, ["*/"])
+    if allow_pattern is not None:
+        paths = _filter_allow(paths, allow_pattern)
+
+    if ignore_pattern is not None:
+        paths = _filter_ignore(paths, ignore_pattern)
+
+    return bucket_name, prefix, paths

vllm/transformers_utils/tokenizer.py

@@ -0,0 +1,21 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import warnings
+
+
+def __getattr__(name: str):
+    # Keep until lm-eval is updated
+    if name == "get_tokenizer":
+        from vllm.tokenizers import get_tokenizer
+
+        warnings.warn(
+            "`vllm.transformers_utils.tokenizer.get_tokenizer` "
+            "has been moved to `vllm.tokenizers.get_tokenizer`. "
+            "The old name will be removed in a future version.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+        return get_tokenizer
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/transformers_utils/utils.py

@@ -0,0 +1,112 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+import os
+import struct
+from functools import cache
+from os import PathLike
+from pathlib import Path
+from typing import Any
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def is_s3(model_or_path: str) -> bool:
+    return model_or_path.lower().startswith("s3://")
+
+
+def is_gcs(model_or_path: str) -> bool:
+    return model_or_path.lower().startswith("gs://")
+
+
+def is_cloud_storage(model_or_path: str) -> bool:
+    return is_s3(model_or_path) or is_gcs(model_or_path)
+
+
+def modelscope_list_repo_files(
+    repo_id: str,
+    revision: str | None = None,
+    token: str | bool | None = None,
+) -> list[str]:
+    """List files in a modelscope repo."""
+    from modelscope.hub.api import HubApi
+
+    api = HubApi()
+    api.login(token)
+    # same as huggingface_hub.list_repo_files
+    files = [
+        file["Path"]
+        for file in api.get_model_files(
+            model_id=repo_id, revision=revision, recursive=True
+        )
+        if file["Type"] == "blob"
+    ]
+    return files
+
+
+def _maybe_json_dict(path: str | PathLike) -> dict[str, str]:
+    with open(path) as f:
+        try:
+            return json.loads(f.read())
+        except Exception:
+            return dict[str, str]()
+
+
+def _maybe_space_split_dict(path: str | PathLike) -> dict[str, str]:
+    parsed_dict = dict[str, str]()
+    with open(path) as f:
+        for line in f.readlines():
+            try:
+                model_name, redirect_name = line.strip().split()
+                parsed_dict[model_name] = redirect_name
+            except Exception:
+                pass
+    return parsed_dict
+
+
+@cache
+def maybe_model_redirect(model: str) -> str:
+    """
+    Use model_redirect to redirect the model name to a local folder.
+
+    :param model: hf model name
+    :return: maybe redirect to a local folder
+    """
+
+    model_redirect_path = envs.VLLM_MODEL_REDIRECT_PATH
+
+    if not model_redirect_path:
+        return model
+
+    if not Path(model_redirect_path).exists():
+        return model
+
+    redirect_dict = _maybe_json_dict(model_redirect_path) or _maybe_space_split_dict(
+        model_redirect_path
+    )
+    if redirect_model := redirect_dict.get(model):
+        logger.info("model redirect: [ %s ] -> [ %s ]", model, redirect_model)
+        return redirect_model
+
+    return model
+
+
+def parse_safetensors_file_metadata(path: str | PathLike) -> dict[str, Any]:
+    with open(path, "rb") as f:
+        length_of_metadata = struct.unpack("<Q", f.read(8))[0]
+        metadata = json.loads(f.read(length_of_metadata).decode("utf-8"))
+        return metadata
+
+
+def convert_model_repo_to_path(model_repo: str) -> str:
+    """When VLLM_USE_MODELSCOPE is True convert a model
+    repository string to a Path str."""
+    if not envs.VLLM_USE_MODELSCOPE or Path(model_repo).exists():
+        return model_repo
+    from modelscope.utils.file_utils import get_model_cache_root
+
+    return os.path.join(get_model_cache_root(), model_repo)