[New Model]: google/embeddinggemma-300m (#24318)

Signed-off-by: wang.yuqi <noooop@126.com>

docs/models/supported_models.md

@@ -440,6 +440,7 @@ These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) A
 |--------------|--------|-------------------|----------------------|---------------------------|---------------------|
 | `BertModel`<sup>C</sup> | BERT-based | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc. | | | ✅︎ |
 | `Gemma2Model`<sup>C</sup> | Gemma 2-based | `BAAI/bge-multilingual-gemma2`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `Gemma3TextModel`<sup>C</sup> | Gemma 3-based | `google/embeddinggemma-300m`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ | ✅︎ |
 | `GteModel`<sup>C</sup> | Arctic-Embed-2.0-M | `Snowflake/snowflake-arctic-embed-m-v2.0`. |  |  | ✅︎ |
 | `GteNewModel`<sup>C</sup> | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc. |  |  | ✅︎ |

tests/models/language/pooling/mteb_utils.py

@@ -10,7 +10,8 @@ import numpy as np
 import pytest
 import requests
 
-from tests.models.utils import EmbedModelInfo, RerankModelInfo
+from tests.models.utils import (EmbedModelInfo, RerankModelInfo,
+                                check_embeddings_close)
 
 # Most embedding models on the STS12 task (See #17175):
 # - Model implementation and minor changes in tensor dtype
@@ -163,12 +164,14 @@ def mteb_test_embed_models(hf_runner,
                            model_info: EmbedModelInfo,
                            vllm_extra_kwargs=None,
                            hf_model_callback=None,
-                           atol=MTEB_RERANK_TOL):
+                           atol=MTEB_EMBED_TOL):
     if not model_info.enable_test:
         # A model family has many models with the same architecture,
         # and we don't need to test each one.
         pytest.skip("Skipping test.")
 
+    example_prompts = ["The chef prepared a delicious meal."]
+
     vllm_extra_kwargs = vllm_extra_kwargs or {}
     vllm_extra_kwargs["dtype"] = model_info.dtype
 
@@ -191,6 +194,7 @@ def mteb_test_embed_models(hf_runner,
         vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
                                               MTEB_EMBED_TASKS)
         vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
+        vllm_outputs = vllm_model.embed(example_prompts)
 
     if model_info.mteb_score is None:
         with hf_runner(model_info.name,
@@ -202,6 +206,16 @@ def mteb_test_embed_models(hf_runner,
 
             st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
             st_dtype = next(hf_model.model.parameters()).dtype
+
+            # Test embed_dims and whether to use normalize
+            hf_outputs = hf_model.encode(example_prompts)
+            check_embeddings_close(
+                embeddings_0_lst=hf_outputs,
+                embeddings_1_lst=vllm_outputs,
+                name_0="hf",
+                name_1="vllm",
+                tol=1e-2,
+            )
     else:
         st_main_score = model_info.mteb_score
         st_dtype = "Constant"

tests/models/language/pooling/test_st_projector.py

@@ -2,7 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import pytest
 
-from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
+from ...utils import (CLSPoolingEmbedModelInfo, EmbedModelInfo,
+                      LASTPoolingEmbedModelInfo)
 from .mteb_utils import mteb_test_embed_models
 
 # ST models with projector (Dense) layers
@@ -13,6 +14,10 @@ ST_PROJECTOR_MODELS = [
         mteb_score=0.688611955,
         enable_test=True,
     ),
+    LASTPoolingEmbedModelInfo("google/embeddinggemma-300m",
+                              architecture="Gemma3TextModel",
+                              mteb_score=0.7473819294684156,
+                              enable_test=True)
 ]
 
 

tests/models/registry.py

@@ -352,6 +352,7 @@ _EMBEDDING_EXAMPLE_MODELS = {
     # [Text-only]
     "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
     "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2"),  # noqa: E501
+    "Gemma3TextModel": _HfExamplesInfo("google/embeddinggemma-300m"),
     "GritLM": _HfExamplesInfo("parasail-ai/GritLM-7B-vllm"),
     "GteModel": _HfExamplesInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
                                                trust_remote_code=True),

vllm/config/__init__.py

@@ -2750,6 +2750,8 @@ _STR_DTYPE_TO_TORCH_DTYPE = {
 _FLOAT16_NOT_SUPPORTED_MODELS = {
     "gemma2": "Numerical instability. Please use bfloat16 or float32 instead.",
     "gemma3": "Numerical instability. Please use bfloat16 or float32 instead.",
+    "gemma3_text":
+    "Numerical instability. Please use bfloat16 or float32 instead.",
     "plamo2": "Numerical instability. Please use bfloat16 or float32 instead.",
     "glm4": "Numerical instability. Please use bfloat16 or float32 instead.",
 }

vllm/model_executor/models/adapters.py

@@ -49,26 +49,28 @@ def _load_st_projector(model_config: "ModelConfig") -> Optional[nn.Module]:
         if not dense_modules:
             return None
 
-        module = dense_modules[0]
-        folder = module.get("path", "")
+        layers = []
+        for module in dense_modules:
+            folder = module.get("path", "")
+
+            config_path = f"{folder}/config.json" if folder else "config.json"
+            layer_config = get_hf_file_to_dict(config_path, model_config.model,
+                                               model_config.revision)
+            if not layer_config:
+                continue
 
-        config_path = f"{folder}/config.json" if folder else "config.json"
-        layer_config = get_hf_file_to_dict(config_path, model_config.model,
-                                           model_config.revision)
-        if not layer_config:
-            return None
+            linear = nn.Linear(layer_config.get("in_features", 768),
+                               layer_config.get("out_features", 768),
+                               bias=layer_config.get("bias", True),
+                               dtype=torch.float32)
 
-        linear = nn.Linear(layer_config.get("in_features", 768),
-                           layer_config.get("out_features", 768),
-                           bias=layer_config.get("bias", True),
-                           dtype=torch.float32)
+            if not _load_dense_weights(linear, folder, model_config):
+                continue
 
-        if _load_dense_weights(linear, folder, model_config):
-            layers = [linear]
+            layers.append(linear)
             if act_name := layer_config.get("activation_function"):
                 layers.append(get_act_fn(act_name))
-            return nn.Sequential(*layers).to(dtype=torch.float32)
-
+        return nn.Sequential(*layers).to(dtype=torch.float32)
     except Exception:
         logger.exception("ST projector loading failed")
 

vllm/model_executor/models/config.py

@@ -24,6 +24,14 @@ class VerifyAndUpdateConfig:
         raise NotImplementedError
 
 
+class Gemma3TextModelConfig:
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        hf_config = vllm_config.model_config.hf_config
+        hf_config.is_causal = not hf_config.use_bidirectional_attention
+
+
 class GteNewModelConfig(VerifyAndUpdateConfig):
 
     @staticmethod
@@ -409,6 +417,7 @@ MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
     "GteModel": SnowflakeGteNewModelConfig,
     "GteNewModel": GteNewModelConfig,
     "GteNewForSequenceClassification": GteNewModelConfig,
+    "Gemma3TextModel": Gemma3TextModelConfig,
     "NomicBertModel": NomicBertModelConfig,
     "Qwen2ForProcessRewardModel": Qwen2ForProcessRewardModelConfig,
     "Qwen2ForRewardModel": Qwen2ForRewardModelConfig,

vllm/model_executor/models/gemma3.py

@@ -24,7 +24,7 @@ import torch.nn.functional as F
 from torch import nn
 from transformers import Gemma3TextConfig
 
-from vllm.attention import Attention
+from vllm.attention import Attention, AttentionType
 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
@@ -44,6 +44,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
+from ...attention.layers.encoder_only_attention import EncoderOnlyAttention
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, extract_layer_index,
                     is_pp_missing_parameter,
@@ -169,16 +170,24 @@ class Gemma3Attention(nn.Module):
             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=sliding_window,
-                              prefix=f"{prefix}.attn")
+        if getattr(config, "is_causal", True):
+            attn_type = AttentionType.DECODER
+        else:
+            attn_type = AttentionType.ENCODER_ONLY
+
+        attn_cls = (EncoderOnlyAttention
+                    if attn_type == AttentionType.ENCODER_ONLY else Attention)
+
+        self.attn = attn_cls(self.num_heads,
+                             self.head_dim,
+                             self.scaling,
+                             num_kv_heads=self.num_kv_heads,
+                             cache_config=cache_config,
+                             quant_config=quant_config,
+                             attn_type=attn_type,
+                             logits_soft_cap=attn_logits_soft_cap,
+                             per_layer_sliding_window=sliding_window,
+                             prefix=f"{prefix}.attn")
 
     def forward(
         self,

vllm/model_executor/models/registry.py

@@ -155,6 +155,7 @@ _EMBEDDING_MODELS = {
     "BertModel": ("bert", "BertEmbeddingModel"),
     "DeciLMForCausalLM": ("nemotron_nas", "DeciLMForCausalLM"),
     "Gemma2Model": ("gemma2", "Gemma2ForCausalLM"),
+    "Gemma3TextModel": ("gemma3", "Gemma3Model"),
     "GlmForCausalLM": ("glm", "GlmForCausalLM"),
     "GPT2ForSequenceClassification": ("gpt2", "GPT2ForSequenceClassification"),
     "GritLM": ("gritlm", "GritLM"),