[Model] Add support for Gemma 3 (#14660)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Signed-off-by: Roger Wang <ywang@roblox.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Roger Wang <ywang@roblox.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/models/supported_models.md

@@ -263,10 +263,15 @@ See [this page](#generative-models) for more information on how to use generativ
   * ✅︎
   * ✅︎
 - * `Gemma2ForCausalLM`
-  * Gemma2
+  * Gemma 2
   * `google/gemma-2-9b`, `google/gemma-2-27b`, etc.
   * ✅︎
   * ✅︎
+- * `Gemma3ForCausalLM`
+  * Gemma 3
+  * `google/gemma-3-1b-it`, etc.
+  * ✅︎
+  * ✅︎
 - * `GlmForCausalLM`
   * GLM-4
   * `THUDM/glm-4-9b-chat-hf`, etc.
@@ -504,7 +509,7 @@ you should explicitly specify the task type to ensure that the model is used in
   *
   *
 - * `Gemma2Model`
-  * Gemma2-based
+  * Gemma 2-based
   * `BAAI/bge-multilingual-gemma2`, etc.
   *
   * ✅︎
@@ -752,6 +757,13 @@ See [this page](#generative-models) for more information on how to use generativ
   *
   * ✅︎
   * ✅︎
+- * `Gemma3ForConditionalGeneration`
+  * Gemma 3
+  * T + I<sup>+</sup>
+  * `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc.
+  * ✅︎
+  * ✅︎
+  * ✅︎\*
 - * `GLM4VForCausalLM`<sup>^</sup>
   * GLM-4V
   * T + I
@@ -937,6 +949,31 @@ For more details, please see: <gh-pr:4087#issuecomment-2250397630>
 To use Qwen2.5-VL series models, you have to install Hugging Face Transformers library from source via `pip install git+https://github.com/huggingface/transformers`.
 :::
 
+:::{note}
+To use Gemma3 series models, you have to install Hugging Face Transformers library from source via
+`pip install git+https://github.com/huggingface/transformers`.
+The earliest commit that supports this is [`50d3530aa04e7a7d003e6b255a98f79fd0447357`](https://github.com/huggingface/transformers/commit/50d3530aa04e7a7d003e6b255a98f79fd0447357).
+
+Both V0 and V1 support `Gemma3ForConditionalGeneration` for text-only inputs.
+However, there are differences in how they handle text + image inputs:
+
+V0 correctly implements the model's attention pattern:
+- Uses bidirectional attention between the image tokens corresponding to the same image
+- Uses causal attention for other tokens
+- Implemented via (naive) PyTorch SDPA with masking tensors
+- Note: May use significant memory for long prompts with image
+
+V1 currently uses a simplified attention pattern:
+- Uses causal attention for all tokens, including image tokens
+- Generates reasonable outputs but does not match the original model's attention for text + image inputs
+- Will be updated in the future to support the correct behavior
+
+This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends.
+
+Additionally, vLLM's current Gemma 3 implementation does not support the pan-and-scan image pre-processing algorithm, which helps handle images with skewed aspect ratios by intelligently cropping them into multiple views.
+Without this feature, model performance may degrade when processing images that deviate significantly from square dimensions.
+:::
+
 ### Pooling Models
 
 See [this page](pooling-models) for more information on how to use pooling models.

examples/offline_inference/vision_language.py

@@ -118,6 +118,23 @@ def run_fuyu(questions: list[str], modality: str):
     return llm, prompts, stop_token_ids
 
 
+# Gemma 3
+def run_gemma3(questions: list[str], modality: str):
+    assert modality == "image"
+    model_name = "google/gemma-3-4b-it"
+
+    llm = LLM(model=model_name,
+              max_model_len=2048,
+              max_num_seqs=2,
+              disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache)
+
+    prompts = [("<bos><start_of_turn>user\n"
+                f"<start_of_image>{question}<end_of_turn>\n"
+                "<start_of_turn>model\n") for question in questions]
+    stop_token_ids = None
+    return llm, prompts, stop_token_ids
+
+
 # GLM-4v
 def run_glm4v(questions: list[str], modality: str):
     assert modality == "image"
@@ -405,7 +422,7 @@ def run_mllama(questions: list[str], modality: str):
             "type": "image"
         }, {
             "type": "text",
-            "text": f"{question}"
+            "text": question
         }]
     }] for question in questions]
     prompts = tokenizer.apply_chat_template(messages,
@@ -664,6 +681,7 @@ model_example_map = {
     "deepseek_vl_v2": run_deepseek_vl2,
     "florence2": run_florence2,
     "fuyu": run_fuyu,
+    "gemma3": run_gemma3,
     "glm4v": run_glm4v,
     "h2ovl_chat": run_h2ovl,
     "idefics3": run_idefics3,

examples/offline_inference/vision_language_multi_image.py

@@ -80,6 +80,42 @@ def load_deepseek_vl2(question: str, image_urls: list[str]):
     )
 
 
+def load_gemma3(question, image_urls: list[str]) -> ModelRequestData:
+    model_name = "google/gemma-3-4b-it"
+
+    llm = LLM(model=model_name,
+              max_model_len=8192,
+              max_num_seqs=2,
+              limit_mm_per_prompt={"image": len(image_urls)})
+
+    placeholders = [{"type": "image", "image": url} for url in image_urls]
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            *placeholders,
+            {
+                "type": "text",
+                "text": question
+            },
+        ],
+    }]
+
+    processor = AutoProcessor.from_pretrained(model_name)
+
+    prompt = processor.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+
+    return ModelRequestData(
+        llm=llm,
+        prompt=prompt,
+        stop_token_ids=None,
+        image_data=[fetch_image(url) for url in image_urls],
+        chat_template=None,
+    )
+
+
 def load_h2ovl(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "h2oai/h2ovl-mississippi-800m"
 
@@ -496,6 +532,7 @@ def load_qwen2_5_vl(question, image_urls: list[str]) -> ModelRequestData:
 model_example_map = {
     "aria": load_aria,
     "deepseek_vl_v2": load_deepseek_vl2,
+    "gemma3": load_gemma3,
     "h2ovl_chat": load_h2ovl,
     "idefics3": load_idefics3,
     "internvl_chat": load_internvl,

tests/models/multimodal/processing/test_common.py

@@ -162,6 +162,7 @@ def _test_processing_correctness(
     "deepseek-ai/deepseek-vl2-tiny",
     "microsoft/Florence-2-base",
     "adept/fuyu-8b",
+    "google/gemma-3-4b-it",
     "THUDM/glm-4v-9b",
     "h2oai/h2ovl-mississippi-800m",
     "OpenGVLab/InternVL2-1B",

tests/models/registry.py

@@ -124,6 +124,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
     "GemmaForCausalLM": _HfExamplesInfo("google/gemma-2b"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
+    "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it",
+                                         min_transformers_version="4.50"),
     "GlmForCausalLM": _HfExamplesInfo("THUDM/glm-4-9b-chat-hf"),
     "GPT2LMHeadModel": _HfExamplesInfo("gpt2"),
     "GPTBigCodeForCausalLM": _HfExamplesInfo("bigcode/starcoder"),
@@ -241,6 +243,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "DeepseekVLV2ForCausalLM": _HfExamplesInfo("deepseek-ai/deepseek-vl2-tiny",  # noqa: E501
                                                hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]}),  # noqa: E501
     "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
+    "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it",
+                                                      min_transformers_version="4.50"),
     "GLM4VForCausalLM": _HfExamplesInfo("THUDM/glm-4v-9b",
                                         trust_remote_code=True,
                                         hf_overrides={"architectures": ["GLM4VForCausalLM"]}),  # noqa: E501

vllm/config.py

@@ -350,10 +350,11 @@ class ModelConfig:
         if self.enforce_eager is None:
             self.enforce_eager = False
 
+        interleaved_attn_models = ["gemma2", "gemma3_text", "cohere2"]
         sliding_window = getattr(self.hf_text_config, "sliding_window", None)
         has_interleaved_attention = (sliding_window is not None) and (
             isinstance(sliding_window, list) or
-            (self.hf_text_config.model_type in ["gemma2", "cohere2"]))
+            (self.hf_text_config.model_type in interleaved_attn_models))
 
         if (not self.disable_sliding_window and has_interleaved_attention):
             if (backend :=
@@ -2501,11 +2502,11 @@ def _get_and_verify_dtype(
         dtype = dtype.lower()
         if dtype == "auto":
             if config_dtype == torch.float32:
-                if config.model_type == "gemma2":
+                if config.model_type in ("gemma2", "gemma3", "gemma3_text"):
                     logger.info(
-                        "For Gemma 2, we downcast float32 to bfloat16 instead "
-                        "of float16 by default. Please specify `dtype` if you "
-                        "want to use float16.")
+                        "For Gemma 2 and 3, we downcast float32 to bfloat16 "
+                        "instead of float16 by default. Please specify `dtype` "
+                        "if you want to use float16.")
                     torch_dtype = torch.bfloat16
                 else:
                     # Following the common practice, we use float16 for float32
@@ -2637,7 +2638,9 @@ def _get_and_verify_max_len(
         derived_max_model_len = default_max_len
 
     rope_scaling = getattr(hf_config, "rope_scaling", None)
-    if rope_scaling is not None:
+    # NOTE(woosuk): Gemma3's max_model_len (128K) is already scaled by RoPE
+    # scaling, so we skip applying the scaling factor again.
+    if rope_scaling is not None and "gemma3" not in hf_config.model_type:
         # No need to consider "type" key because of patch_rope_scaling when
         # loading HF config
         rope_type = rope_scaling["rope_type"]

vllm/entrypoints/chat_utils.py

@@ -433,6 +433,8 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                 return "<image>"
             if model_type == "aria":
                 return "<|fim_prefix|><|img|><|fim_suffix|>"
+            if model_type == "gemma3":
+                return "<start_of_image>"
 
             raise TypeError(f"Unknown {modality} model type: {model_type}")
         elif modality == "audio":

vllm/model_executor/models/gemma3.py

@@ -0,0 +1,533 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2025 The vLLM team.
+# Copyright 2025 Google Inc. 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 typing import Iterable, Optional, Set, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import Gemma3TextConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Gemma3MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_activation: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config)
+        if hidden_activation != "gelu_pytorch_tanh":
+            raise ValueError(
+                "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
+                "function. Please set `hidden_act` and `hidden_activation` to "
+                "`gelu_pytorch_tanh`.")
+        self.act_fn = GeluAndMul(approximate="tanh")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Gemma3Attention(nn.Module):
+
+    def __init__(self,
+                 config: Gemma3TextConfig,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 head_dim: int,
+                 max_position_embeddings: int,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 attn_logits_soft_cap: Optional[float] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = config.query_pre_attn_scalar**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+
+        self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+        # TODO(woosuk): Add reference to the original HF implementation.
+        layer_idx = extract_layer_index(prefix)
+        self.is_sliding = bool((layer_idx + 1) % config.sliding_window_pattern)
+        # Initialize the rotary embedding.
+        if self.is_sliding:
+            # Local attention. Override the values in config.json.
+            self.rope_theta = config.rope_local_base_freq
+            self.rope_scaling = {"rope_type": "default"}
+            self.sliding_window = config.interleaved_sliding_window
+        else:
+            # Global attention. Use the values in config.json.
+            self.rope_theta = config.rope_theta
+            self.rope_scaling = config.rope_scaling
+            self.sliding_window = None
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=self.rope_theta,
+            is_neox_style=True,
+            rope_scaling=self.rope_scaling,
+        )
+
+        # Initialize the attention.
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              logits_soft_cap=attn_logits_soft_cap,
+                              per_layer_sliding_window=self.sliding_window,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q = q.unflatten(-1, (self.num_heads, self.head_dim))
+        q = self.q_norm(q)
+        q = q.flatten(-2, -1)
+        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
+        k = self.k_norm(k)
+        k = k.flatten(-2, -1)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+
+        if not kwargs.get("has_images", False):
+            # Fast path for text-only inputs. The performance for the text-only
+            # inputs are not affected by the naive attention below.
+            output, _ = self.o_proj(attn_output)
+            return output
+
+        # NOTE(woosuk): Gemma3 uses bidirectional attention between image tokens
+        # that correspond to the same image while using causal attention
+        # otherwise. Current attention backends cannot handle this pattern, so
+        # we temporarily use a naive attention implementation with mask tensors.
+
+        # We intentionally keep the attention backend as-is and only override
+        # `attn_output` with the naive implementation's output. This minimizes
+        # changes to existing model runners and attention backends. The call to
+        # `self.attn(q, k, v)` is only used to populate the KV cache - its
+        # output is discarded and overwritten below. While this duplicates
+        # computation, it maintains compatibility.
+        # TODO(woosuk): Optimize by implementing custom attention kernels.
+        attn_output = self.naive_attn_with_masks(q,
+                                                 k,
+                                                 v,
+                                                 out=attn_output,
+                                                 **kwargs)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def naive_attn_with_masks(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        out: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        # NOTE(woosuk): As described in the comment above, this code is not
+        # meant to be performant. It is only meant to be correct.
+        q = q.view(-1, self.num_heads, self.head_dim)
+        # Expand the key and value to handle GQA.
+        num_queries_per_kv = self.num_heads // self.num_kv_heads
+        k = k.view(-1, self.num_kv_heads, self.head_dim)
+        k = k.repeat_interleave(num_queries_per_kv, dim=-2)
+        v = v.view(-1, self.num_kv_heads, self.head_dim)
+        v = v.repeat_interleave(num_queries_per_kv, dim=-2)
+
+        if self.is_sliding:
+            attn_masks = kwargs["local_attn_masks"]
+        else:
+            attn_masks = kwargs["global_attn_masks"]
+
+        seq_lens = kwargs["seq_lens"]
+        start_idx = 0
+        for seq_len, attn_mask in zip(seq_lens, attn_masks):
+            end_idx = start_idx + seq_len
+            query = q[start_idx:end_idx].unsqueeze(0)
+            key = k[start_idx:end_idx].unsqueeze(0)
+            value = v[start_idx:end_idx].unsqueeze(0)
+
+            # Transpose.
+            query = query.transpose(1, 2)
+            key = key.transpose(1, 2)
+            value = value.transpose(1, 2)
+
+            output = F.scaled_dot_product_attention(
+                query,
+                key,
+                value,
+                attn_mask,
+                self.scaling,
+            )
+            output = output.transpose(1, 2).flatten(-2, -1)
+            out[start_idx:end_idx] = output
+            start_idx = end_idx
+        return out
+
+
+class Gemma3DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Gemma3TextConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Gemma3Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_logits_soft_cap=None,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.hidden_size = config.hidden_size
+        self.mlp = Gemma3MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_activation=config.hidden_activation,
+            quant_config=quant_config,
+        )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size,
+                                            eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                     eps=config.rms_norm_eps)
+        self.pre_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                      eps=config.rms_norm_eps)
+        self.post_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                       eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        **kwargs,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            **kwargs,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states, residual = self.pre_feedforward_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Gemma3Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Gemma3DecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        # Normalize the embedding by sqrt(hidden_size)
+        # The normalizer's data type should be downcasted to the model's
+        # data type such as bfloat16, not float32.
+        # See https://github.com/huggingface/transformers/pull/29402
+        normalizer = self.config.hidden_size**0.5
+        self.register_buffer("normalizer", torch.tensor(normalizer))
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        # NOTE(woosuk): Only apply the normalizer to the output of
+        # vocab embedding. Don't apply it to the vision embedding.
+        return self.embed_tokens(input_ids) * self.normalizer
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+                **kwargs,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (param_name, shard_name, shard_id) in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        unloaded_params = params_dict.keys() - loaded_params
+        if unloaded_params:
+            logger.warning(
+                "Some weights are not initialized from checkpoints: %s",
+                unloaded_params)
+        return loaded_params
+
+
+class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        del lora_config  # Unused.
+        super().__init__()
+        self.config = config
+        # currently all existing Gemma models have `tie_word_embeddings` enabled
+        assert config.tie_word_embeddings
+        self.quant_config = quant_config
+        self.model = Gemma3Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+        self.logits_processor = LogitsProcessor(
+            config.vocab_size, soft_cap=config.final_logit_softcapping)
+        self.sampler = get_sampler()
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds, **kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.model.embed_tokens, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)

vllm/model_executor/models/gemma3_mm.py

@@ -0,0 +1,425 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import (Any, Iterable, Literal, Mapping, Optional, Sequence, Set,
+                    Tuple, TypedDict, Union)
+
+import torch
+from torch import nn
+from transformers import BatchFeature, Gemma3Config, ProcessorMixin
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
+                                    NestedTensors)
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsMultiModal, SupportsPP
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+logger = init_logger(__name__)
+
+
+class Gemma3ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: `(batch_size * num_images, num_channels, height, width)`"""
+
+
+Gemma3ImageInputs = Gemma3ImagePixelInputs
+
+
+class Gemma3ProcessingInfo(BaseProcessingInfo):
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        hf_config = self.ctx.get_hf_config()
+        return {"image": hf_config.mm_tokens_per_image}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[ProcessorMixin],
+    ) -> int:
+        hf_config = self.ctx.get_hf_config()
+        return hf_config.mm_tokens_per_image
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        # Result in the max possible feature size (h:w = 16:1)
+        return ImageSize(height=8000, width=50)
+
+
+class Gemma3DummyInputsBuilder(BaseDummyInputsBuilder[Gemma3ProcessingInfo]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        tokenizer = self.info.get_tokenizer()
+        boi_token = tokenizer.boi_token
+
+        num_images = mm_counts.get("image", 0)
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+        return ProcessorInputs(
+            prompt_text=" ".join([boi_token] * num_images),
+            mm_data=mm_data,
+        )
+
+
+class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        # TODO(woosuk): Support pan-and-scan.
+        img_kwargs = mm_kwargs.get("images_kwargs", {})
+        img_kwargs["do_pan_and_scan"] = False
+        mm_kwargs["images_kwargs"] = img_kwargs
+        return super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+        )
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(pixel_values=MultiModalFieldConfig.batched("image"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        tokenizer = self.info.get_tokenizer()
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        hf_config = self.info.get_hf_config()
+
+        boi_token = tokenizer.boi_token
+        image_token = tokenizer.image_token
+        mm_tokens_per_image = hf_config.mm_tokens_per_image
+        image_tokens_expanded = "".join([image_token] * mm_tokens_per_image)
+
+        def get_replacement_gemma3(item_idx: int):
+            return PromptUpdateDetails(
+                full=hf_processor.full_image_sequence,
+                features=image_tokens_expanded,
+            )
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=boi_token,
+                replacement=get_replacement_gemma3,
+            )
+        ]
+
+
+class Gemma3MultiModalProjector(nn.Module):
+
+    def __init__(self, config: Gemma3Config):
+        super().__init__()
+
+        self.mm_input_projection_weight = nn.Parameter(
+            torch.zeros(config.vision_config.hidden_size,
+                        config.text_config.hidden_size))
+
+        self.mm_soft_emb_norm = GemmaRMSNorm(
+            config.vision_config.hidden_size,
+            eps=config.vision_config.layer_norm_eps)
+
+        self.patches_per_image = int(config.vision_config.image_size //
+                                     config.vision_config.patch_size)
+        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
+        self.kernel_size = self.patches_per_image // self.tokens_per_side
+        self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size,
+                                     stride=self.kernel_size)
+
+    def forward(self, vision_outputs: torch.Tensor):
+        batch_size, _, seq_length = vision_outputs.shape
+
+        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
+        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
+            batch_size, seq_length, self.patches_per_image,
+            self.patches_per_image)
+        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
+
+        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
+        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
+        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
+
+        normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
+
+        projected_vision_outputs = torch.matmul(
+            normed_vision_outputs, self.mm_input_projection_weight)
+        return projected_vision_outputs.type_as(vision_outputs)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Gemma3MultiModalProcessor,
+                                        info=Gemma3ProcessingInfo,
+                                        dummy_inputs=Gemma3DummyInputsBuilder)
+class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                     SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+        self.sliding_window = config.text_config.interleaved_sliding_window
+
+        self.vision_tower = SiglipVisionModel(config.vision_config,
+                                              quant_config,
+                                              prefix=maybe_prefix(
+                                                  prefix, "vision_tower"))
+        self.multi_modal_projector = Gemma3MultiModalProjector(config)
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Gemma3ForCausalLM"],
+        )
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.language_model.logits_processor.scale *= logit_scale
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    @property
+    def sampler(self):
+        return self.language_model.sampler
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            if d.shape != expected_dims:
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f"is {expected_dims}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Gemma3ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        assert image_embeds is None, "Gemma3 does not support image_embeds."
+        if pixel_values is None:
+            return None
+
+        if not isinstance(pixel_values, (torch.Tensor, list[torch.Tensor])):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        pixel_values = flatten_bn(pixel_values, concat=True)
+        return Gemma3ImagePixelInputs(
+            type="pixel_values",
+            data=self._validate_pixel_values(pixel_values),
+        )
+
+    def _image_pixels_to_features(
+        self,
+        vision_tower: SiglipVisionModel,
+        pixel_values: torch.Tensor,
+    ) -> torch.Tensor:
+        target_dtype = vision_tower.get_input_embeddings().weight.dtype
+        image_features = vision_tower(pixel_values.to(dtype=target_dtype))
+        return image_features
+
+    def _process_image_input(
+        self,
+        image_input: Gemma3ImageInputs,
+    ) -> torch.Tensor:
+        assert self.vision_tower is not None
+        pixel_values = image_input["data"]
+        vision_outputs = self._image_pixels_to_features(
+            self.vision_tower,
+            pixel_values,
+        )
+        return self.multi_modal_projector(vision_outputs)
+
+    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        if multimodal_embeddings is None:
+            inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        else:
+            inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.image_token_index)
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object) -> Union[SamplerOutput, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            if vision_embeddings is not None:
+                kwargs = self.prepare_attn_masks(
+                    input_ids,
+                    positions,
+                    mask_dtype=vision_embeddings.dtype,
+                    **kwargs)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds,
+                                                  **kwargs)
+
+        return hidden_states
+
+    def prepare_attn_masks(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        mask_dtype: torch.dtype,
+        **kwargs,
+    ):
+        kwargs["has_images"] = True
+        # NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
+        # This is a HACK. Fix this.
+        start_idices = (positions == 0).cpu().nonzero()
+        num_seqs = len(start_idices)
+        seq_lens = []
+        for i in range(num_seqs):
+            start_idx = start_idices[i].item()
+            if i < num_seqs - 1:
+                end_idx = start_idices[i + 1].item()
+            else:
+                end_idx = len(input_ids)
+            seq_lens.append(end_idx - start_idx)
+        kwargs["seq_lens"] = seq_lens
+
+        global_attn_masks = []
+        local_attn_masks = []
+        start_idx = 0
+        for seq_len in seq_lens:
+            end_idx = start_idx + seq_len
+            input_token_ids = input_ids[start_idx:end_idx]
+            start_idx = end_idx
+            # Create a global causal mask.
+            global_attn_mask = torch.empty(
+                1,
+                1,
+                seq_len,
+                seq_len,
+                dtype=mask_dtype,
+                device=input_ids.device,
+            )
+            global_attn_mask.fill_(float("-inf"))
+            # Fill the lower triangle with 0.
+            global_attn_mask = global_attn_mask.triu(diagonal=1)
+
+            # Consider the bidirectional attention between image tokens.
+            img_mask = torch.zeros_like(global_attn_mask)
+            img_pos = (input_token_ids == self.config.image_token_index)
+            img_mask[:, :, :, img_pos] += 1
+            img_mask[:, :, img_pos, :] += 1
+            global_attn_mask = torch.where(img_mask == 2, 0, global_attn_mask)
+            global_attn_masks.append(global_attn_mask)
+
+            # Create a local causal mask with sliding window (1024).
+            local_attn_mask = torch.ones_like(global_attn_mask)
+            local_attn_mask = torch.tril(local_attn_mask,
+                                         diagonal=-self.sliding_window)
+            local_attn_mask = torch.where(local_attn_mask == 0,
+                                          global_attn_mask, float("-inf"))
+            local_attn_masks.append(local_attn_mask)
+        kwargs["global_attn_masks"] = global_attn_masks
+        kwargs["local_attn_masks"] = local_attn_masks
+        return kwargs
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -53,6 +53,7 @@ _TEXT_GENERATION_MODELS = {
     "Fairseq2LlamaForCausalLM": ("fairseq2_llama", "Fairseq2LlamaForCausalLM"),
     "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
     "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
+    "Gemma3ForCausalLM": ("gemma3", "Gemma3ForCausalLM"),
     "GlmForCausalLM": ("glm", "GlmForCausalLM"),
     "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
     "GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
@@ -161,6 +162,7 @@ _MULTIMODAL_MODELS = {
     "ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"),  # noqa: E501
     "DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
+    "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
     "GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),