Merge tag 'v0.8.5' into v0.8.5-dev

tests/models/decoder_only/vision_language/vlm_utils/model_utils.py

@@ -68,6 +68,17 @@ def qwen2_vllm_to_hf_output(
     return output_ids, hf_output_str, out_logprobs
 
 
+def kimiv_vl_vllm_to_hf_output(
+        vllm_output: RunnerOutput,
+        model: str) -> tuple[list[int], str, Optional[SampleLogprobs]]:
+    """Sanitize vllm output [kimi_vl models] to be comparable with hf output."""
+    output_ids, output_str, out_logprobs = vllm_output
+
+    hf_output_str = output_str + "<|im_end|>[EOS]"
+
+    return output_ids, hf_output_str, out_logprobs
+
+
 def llava_image_vllm_to_hf_output(vllm_output: RunnerOutput,
                                   model: str) -> RunnerOutput:
     config = AutoConfig.from_pretrained(model)

tests/models/embedding/language/test_gritlm.py

@@ -59,24 +59,25 @@ def test_find_array(monkeypatch: pytest.MonkeyPatch):
 def server_embedding():
     # GritLM embedding implementation is only supported by XFormers backend.
     args = ["--task", "embed", "--max_model_len", str(MAX_MODEL_LEN)]
-    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
-        yield remote_server
+    with pytest.MonkeyPatch.context() as m:
+        m.setenv(STR_BACKEND_ENV_VAR, "XFORMERS")
+        with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+            yield remote_server
 
 
 @pytest.fixture(scope="module")
 def server_generate():
     args = ["--task", "generate", "--max_model_len", str(MAX_MODEL_LEN)]
-    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
-        yield remote_server
+    with pytest.MonkeyPatch.context() as m:
+        m.setenv(STR_BACKEND_ENV_VAR, "XFORMERS")
+        with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+            yield remote_server
 
 
 @pytest_asyncio.fixture
-async def client_embedding(monkeypatch: pytest.MonkeyPatch,
-                           server_embedding: RemoteOpenAIServer):
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_ATTENTION_BACKEND", "XFORMERS")
-        async with server_embedding.get_async_client() as async_client:
-            yield async_client
+async def client_embedding(server_embedding: RemoteOpenAIServer):
+    async with server_embedding.get_async_client() as async_client:
+        yield async_client
 
 
 @pytest_asyncio.fixture

tests/models/embedding/language/test_jina.py

@@ -153,14 +153,24 @@ def test_matryoshka(
 
     with vllm_runner(model, task="embed", dtype=dtype,
                      max_model_len=None) as vllm_model:
-        vllm_outputs = vllm_model.encode(
-            example_prompts,
-            pooling_params=PoolingParams(dimensions=dimensions))
-
-    check_embeddings_close(
-        embeddings_0_lst=hf_outputs,
-        embeddings_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-        tol=1e-2,
-    )
+        matryoshka_dimensions = (
+            vllm_model.model.llm_engine.model_config.matryoshka_dimensions)
+        assert matryoshka_dimensions is not None
+
+        if dimensions not in matryoshka_dimensions:
+            with pytest.raises(ValueError):
+                vllm_model.encode(
+                    example_prompts,
+                    pooling_params=PoolingParams(dimensions=dimensions))
+        else:
+            vllm_outputs = vllm_model.encode(
+                example_prompts,
+                pooling_params=PoolingParams(dimensions=dimensions))
+
+            check_embeddings_close(
+                embeddings_0_lst=hf_outputs,
+                embeddings_1_lst=vllm_outputs,
+                name_0="hf",
+                name_1="vllm",
+                tol=1e-2,
+            )

tests/models/embedding/language/test_snowflake_arctic_embed.py

+# SPDX-License-Identifier: Apache-2.0
+"""Compare the embedding outputs of HF and vLLM models.
+
+Run `pytest tests/models/embedding/language/test_snowflake_arctic_embed.py`.
+"""
+import pytest
+
+from tests.models.embedding.utils import EmbedModelInfo
+
+from ..utils import check_embeddings_close
+
+EMBEDDING_PROMPTS = [
+    'what is snowflake?', 'Where can I get the best tacos?', 'The Data Cloud!',
+    'Mexico City of Course!'
+]
+
+MODELS = [
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
+                   is_matryoshka=False,
+                   architecture="BertModel",
+                   enable_test=True),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-s",
+                   is_matryoshka=False,
+                   architecture="BertModel",
+                   enable_test=False),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-m",
+                   is_matryoshka=False,
+                   architecture="BertModel",
+                   enable_test=False),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long",
+                   is_matryoshka=False,
+                   architecture="NomicBertModel",
+                   enable_test=True),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-l",
+                   is_matryoshka=False,
+                   architecture="BertModel",
+                   enable_test=False),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v1.5",
+                   is_matryoshka=True,
+                   architecture="BertModel",
+                   enable_test=True),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-l-v2.0",
+                   is_matryoshka=True,
+                   architecture="XLMRobertaModel",
+                   enable_test=True),
+    EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
+                   is_matryoshka=True,
+                   architecture="GteModel",
+                   enable_test=True),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model_info: EmbedModelInfo,
+    dtype: str,
+    monkeypatch,
+) -> None:
+    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 = example_prompts + EMBEDDING_PROMPTS
+
+    vllm_extra_kwargs = {
+        "hf_overrides": {
+            "is_matryoshka": model_info.is_matryoshka
+        }
+    }
+
+    with hf_runner(model_info.name, dtype=dtype,
+                   is_sentence_transformer=True) as hf_model:
+        hf_outputs = hf_model.encode(example_prompts)
+
+    with vllm_runner(model_info.name,
+                     task="embed",
+                     dtype=dtype,
+                     max_model_len=None,
+                     **vllm_extra_kwargs) as vllm_model:
+
+        assert (vllm_model.model.llm_engine.model_config.is_matryoshka ==
+                model_info.is_matryoshka)
+
+        if model_info.architecture:
+            assert (model_info.architecture
+                    in vllm_model.model.llm_engine.model_config.architectures)
+
+        vllm_outputs = vllm_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,
+    )

tests/models/embedding/utils.py

 # SPDX-License-Identifier: Apache-2.0
 
 from collections.abc import Sequence
+from typing import NamedTuple, Optional
 
 import torch
 import torch.nn.functional as F
@@ -37,3 +38,29 @@ def matryoshka_fy(tensor, dimensions):
     tensor = tensor[..., :dimensions]
     tensor = F.normalize(tensor, p=2, dim=1)
     return tensor
+
+
+class EmbedModelInfo(NamedTuple):
+    name: str
+    is_matryoshka: bool
+    matryoshka_dimensions: Optional[list[int]] = None
+    architecture: str = ""
+    enable_test: bool = True
+
+
+def correctness_test(hf_model,
+                     inputs,
+                     vllm_outputs: Sequence[list[float]],
+                     dimensions: Optional[int] = None):
+
+    hf_outputs = hf_model.encode(inputs)
+    if dimensions:
+        hf_outputs = matryoshka_fy(hf_outputs, dimensions)
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=1e-2,
+    )

tests/models/encoder_decoder/vision_language/test_florence2.py

@@ -15,12 +15,12 @@ from ...utils import check_logprobs_close
 from ....utils import models_path_prefix
 
 MODELS = [os.path.join(models_path_prefix, "microsoft/Florence-2-base")]
-# Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
-# Therefore, we borrow the BartTokenizer from the original Bart model
-TOKENIZER = os.path.join(models_path_prefix, "facebook/bart-base")
+# Florence-2 model repo's tokenizer config is missing some special tokens.
+# Therefore, we use a converted tokenizer from a forked repo
+TOKENIZER = os.path.join(models_path_prefix, "Isotr0py/Florence-2-tokenizer")
 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
     "stop_sign":
-    "<CAPTION>",  # special task token
+    "<OD>",  # special task token which will output special tokens
     "cherry_blossom":
     "Describe in detail what is shown in the image.",
 })
@@ -47,7 +47,6 @@ def hf_to_vllm_output(hf_output: tuple[list[int], str,
     output_ids, output_str, out_logprobs = hf_output
 
     output_str = output_str.replace("</s>", "").replace("<s>", "")
-    output_ids = [ids for ids in output_ids if ids not in [0, 2]]
 
     return output_ids, output_str, out_logprobs
 
@@ -73,8 +72,11 @@ def run_test(
                      enforce_eager=True) as vllm_model:
         vllm_outputs_per_case = [
             vllm_model.generate_encoder_decoder_greedy_logprobs(
-                prompts, max_tokens, num_logprobs=num_logprobs)
-            for prompts in inputs
+                prompts,
+                max_tokens,
+                num_logprobs=num_logprobs,
+                skip_special_tokens=False,
+            ) for prompts in inputs
         ]
 
     hf_inputs = [get_hf_images_prompts(prompts) for prompts in inputs]
@@ -95,6 +97,7 @@ def run_test(
             outputs_1_lst=vllm_outputs,
             name_0="hf",
             name_1="vllm",
+            num_outputs_0_skip_tokens=1,
         )
 
 

tests/models/multimodal/processing/test_common.py

@@ -254,10 +254,12 @@ def _test_processing_correctness_mistral(
     "adept/fuyu-8b",
     "google/gemma-3-4b-it",
     "THUDM/glm-4v-9b",
+    "ibm-granite/granite-speech-3.3-8b",
     "h2oai/h2ovl-mississippi-800m",
     "OpenGVLab/InternVL2-1B",
     "HuggingFaceM4/Idefics3-8B-Llama3",
     "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
+    "moonshotai/Kimi-VL-A3B-Instruct",
     "meta-llama/Llama-4-Scout-17B-16E-Instruct",
     "llava-hf/llava-1.5-7b-hf",
     "llava-hf/llava-v1.6-mistral-7b-hf",
@@ -273,12 +275,14 @@ def _test_processing_correctness_mistral(
     "nvidia/NVLM-D-72B",
     "google/paligemma-3b-mix-224",
     "google/paligemma2-3b-ft-docci-448",
+    "microsoft/Phi-4-multimodal-instruct",
     "mistralai/Pixtral-12B-2409",
     "mistral-community/pixtral-12b",
     "Qwen/Qwen-VL-Chat",
     "Qwen/Qwen2-VL-2B-Instruct",
     "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
+    "Qwen/Qwen2.5-Omni-7B",
     "Skywork/Skywork-R1V-38B",
     "fixie-ai/ultravox-v0_5-llama-3_2-1b",
     "openai/whisper-large-v3",

tests/models/multimodal/processing/test_phi4mm.py

+# SPDX-License-Identifier: Apache-2.0
+"""Tests for phi4mm's multimodal preprocessing kwargs."""
+import pytest
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["microsoft/Phi-4-multimodal-instruct"])
+# yapf: disable
+@pytest.mark.parametrize(
+    ("mm_processor_kwargs", "expected_toks_per_img"),
+    [
+        ({"dynamic_hd": 4}, 1329),
+        ({"dynamic_hd": 16}, 4433),
+        # the default num_crops of phi-4-multimodal is 36
+        ({}, 9585),
+    ])
+# yapf: enable
+@pytest.mark.parametrize("num_imgs", [1, 2])
+@pytest.mark.parametrize("kwargs_on_init", [True, False])
+def test_processor_override(
+    image_assets: _ImageAssets,
+    model_id: str,
+    mm_processor_kwargs: dict[str, int],
+    expected_toks_per_img: int,
+    num_imgs: int,
+    kwargs_on_init: bool,
+):
+    """Ensure Phi4MMMultiModalProcessor handles dynamic_hd properly."""
+    # Avoid initializing CUDA early
+    from vllm.model_executor.models.phi4mm import _IMAGE_PLACEHOLDER_TOKEN_ID
+
+    ctx = build_model_context(
+        model_id,
+        mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
+        limit_mm_per_prompt={"image": num_imgs},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    hf_processor_mm_kwargs = {} if kwargs_on_init else mm_processor_kwargs
+
+    # Build the image str / prompt based on the number of images we pass
+    img_str = "".join([f"<|image_{idx}|>\n" for idx in range(1, num_imgs + 1)])
+    prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n"
+
+    image_size = ctx.get_hf_config(
+    ).embd_layer["image_embd_layer"]["crop_size"]
+    dummy_image_size = (image_size * 7, image_size * 7)
+    dummy_image = image_assets[0].pil_image.resize(dummy_image_size)
+    mm_data = {"image": [dummy_image] * num_imgs}
+
+    processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
+
+    # Ensure we have the right number of placeholders per num_crops size
+    img_tok_count = processed_inputs["prompt_token_ids"].count(
+        _IMAGE_PLACEHOLDER_TOKEN_ID)
+    assert img_tok_count == expected_toks_per_img * num_imgs

tests/models/registry.py

@@ -121,9 +121,11 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "BaichuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan2-7B-chat",
                                          trust_remote_code=True),
     "BambaForCausalLM": _HfExamplesInfo("ibm-ai-platform/Bamba-9B"),
-    "BloomForCausalLM": _HfExamplesInfo("bigscience/bloomz-1b1"),
+    "BloomForCausalLM": _HfExamplesInfo("bigscience/bloom-560m",
+                                        {"1b": "bigscience/bloomz-1b1"}),
     "ChatGLMModel": _HfExamplesInfo("THUDM/chatglm3-6b",
-                                    trust_remote_code=True),
+                                    trust_remote_code=True,
+                                    max_transformers_version="4.48"),
     "ChatGLMForConditionalGeneration": _HfExamplesInfo("thu-coai/ShieldLM-6B-chatglm3",  # noqa: E501
                                                        trust_remote_code=True),
     "CohereForCausalLM": _HfExamplesInfo("CohereForAI/c4ai-command-r-v01",
@@ -141,24 +143,26 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
     "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),  # noqa: E501
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
-    "GemmaForCausalLM": _HfExamplesInfo("google/gemma-2b"),
+    "GemmaForCausalLM": _HfExamplesInfo("google/gemma-1.1-2b-it"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
-    "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it",
-                                         min_transformers_version="4.50"),
+    "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it"),
     "GlmForCausalLM": _HfExamplesInfo("THUDM/glm-4-9b-chat-hf"),
     "Glm4ForCausalLM": _HfExamplesInfo(
         "THUDM/GLM-4-32B-0414",
         is_available_online=False,
         min_transformers_version="4.52.dev0"
     ),
-    "GPT2LMHeadModel": _HfExamplesInfo("gpt2"),
-    "GPTBigCodeForCausalLM": _HfExamplesInfo("bigcode/starcoder"),
-    "GPTJForCausalLM": _HfExamplesInfo("EleutherAI/gpt-j-6b"),
-    "GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-160m"),
+    "GPT2LMHeadModel": _HfExamplesInfo("openai-community/gpt2",
+                                       {"alias": "gpt2"}),
+    "GPTBigCodeForCausalLM": _HfExamplesInfo("bigcode/starcoder",
+                                             {"tiny": "bigcode/tiny_starcoder_py"}),  # noqa: E501
+    "GPTJForCausalLM": _HfExamplesInfo("Milos/slovak-gpt-j-405M",
+                                       {"6b": "EleutherAI/gpt-j-6b"}),
+    "GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-70m",
+                                          {"1b": "EleutherAI/pythia-1.4b"}),
     "GraniteForCausalLM": _HfExamplesInfo("ibm/PowerLM-3b"),
     "GraniteMoeForCausalLM": _HfExamplesInfo("ibm/PowerMoE-3b"),
-    "GraniteMoeSharedForCausalLM": _HfExamplesInfo("ibm-research/moe-7b-1b-active-shared-experts",  # noqa: E501
-                                                   min_transformers_version="4.49"),  # noqa: E501
+    "GraniteMoeSharedForCausalLM": _HfExamplesInfo("ibm-research/moe-7b-1b-active-shared-experts"),  # noqa: E501
     "Grok1ModelForCausalLM": _HfExamplesInfo("hpcai-tech/grok-1",
                                              trust_remote_code=True),
     "InternLMForCausalLM": _HfExamplesInfo("internlm/internlm-chat-7b",
@@ -186,7 +190,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "MiniMaxText01ForCausalLM": _HfExamplesInfo("MiniMaxAI/MiniMax-Text-01",
                                                 trust_remote_code=True),
     "MistralForCausalLM": _HfExamplesInfo("mistralai/Mistral-7B-Instruct-v0.1"),
-    "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1"),  # noqa: E501
+    "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1",  # noqa: E501
+                                          {"falcon3": "ehristoforu/Falcon3-MoE-2x7B-Insruct"}),  # noqa: E501
     "QuantMixtralForCausalLM": _HfExamplesInfo("mistral-community/Mixtral-8x22B-v0.1-AWQ"),  # noqa: E501
     "MptForCausalLM": _HfExamplesInfo("mpt", is_available_online=False),
     "MPTForCausalLM": _HfExamplesInfo("mosaicml/mpt-7b"),
@@ -194,7 +199,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "OlmoForCausalLM": _HfExamplesInfo("allenai/OLMo-1B-hf"),
     "Olmo2ForCausalLM": _HfExamplesInfo("shanearora/OLMo-7B-1124-hf"),
     "OlmoeForCausalLM": _HfExamplesInfo("allenai/OLMoE-1B-7B-0924-Instruct"),
-    "OPTForCausalLM": _HfExamplesInfo("facebook/opt-iml-max-1.3b"),
+    "OPTForCausalLM": _HfExamplesInfo("facebook/opt-125m",
+                                      {"1b": "facebook/opt-iml-max-1.3b"}),
     "OrionForCausalLM": _HfExamplesInfo("OrionStarAI/Orion-14B-Chat",
                                         trust_remote_code=True),
     "PersimmonForCausalLM": _HfExamplesInfo("adept/persimmon-8b-chat"),
@@ -204,10 +210,12 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                             trust_remote_code=True),
     "PhiMoEForCausalLM": _HfExamplesInfo("microsoft/Phi-3.5-MoE-instruct",
                                          trust_remote_code=True),
+    "Plamo2ForCausalLM": _HfExamplesInfo("pfnet/plamo-2-1b",
+                                        trust_remote_code=True),
     "QWenLMHeadModel": _HfExamplesInfo("Qwen/Qwen-7B-Chat",
                                        trust_remote_code=True),
-    "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-7B-Instruct",
-                                        extras={"2.5": "Qwen/Qwen2.5-7B-Instruct"}), # noqa: E501
+    "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-0.5B-Instruct",
+                                        extras={"2.5": "Qwen/Qwen2.5-0.5B-Instruct"}), # noqa: E501
     "Qwen2MoeForCausalLM": _HfExamplesInfo("Qwen/Qwen1.5-MoE-A2.7B-Chat"),
     "Qwen3ForCausalLM": _HfExamplesInfo(
         "Qwen/Qwen3-8B",
@@ -233,8 +241,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "XverseForCausalLM": _HfExamplesInfo("xverse/XVERSE-7B-Chat",
                                          is_available_online=False,
                                          trust_remote_code=True),
-    "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct",
-                                         min_transformers_version="4.49"),
+    "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct"),
     # [Encoder-decoder]
     "BartModel": _HfExamplesInfo("facebook/bart-base"),
     "BartForConditionalGeneration": _HfExamplesInfo("facebook/bart-large-cnn"),
@@ -245,11 +252,15 @@ _EMBEDDING_EXAMPLE_MODELS = {
     "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
     "Gemma2Model": _HfExamplesInfo("BAAI/bge-multilingual-gemma2"),
     "GritLM": _HfExamplesInfo("parasail-ai/GritLM-7B-vllm"),
+    "GteModel": _HfExamplesInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
+                                               trust_remote_code=True),
     "InternLM2ForRewardModel": _HfExamplesInfo("internlm/internlm2-1_8b-reward",
                                                trust_remote_code=True),
     "JambaForSequenceClassification": _HfExamplesInfo("ai21labs/Jamba-tiny-reward-dev"),  # noqa: E501
     "LlamaModel": _HfExamplesInfo("llama", is_available_online=False),
     "MistralModel": _HfExamplesInfo("intfloat/e5-mistral-7b-instruct"),
+    "NomicBertModel": _HfExamplesInfo("Snowflake/snowflake-arctic-embed-m-long",  # noqa: E501
+                                               trust_remote_code=True),
     "Qwen2Model": _HfExamplesInfo("ssmits/Qwen2-7B-Instruct-embed-base"),
     "Qwen2ForRewardModel": _HfExamplesInfo("Qwen/Qwen2.5-Math-RM-72B"),
     "Qwen2ForProcessRewardModel": _HfExamplesInfo("Qwen/Qwen2.5-Math-PRM-7B"),
@@ -273,6 +284,7 @@ _CROSS_ENCODER_EXAMPLE_MODELS = {
     "BertForSequenceClassification": _HfExamplesInfo("cross-encoder/ms-marco-MiniLM-L-6-v2"),  # noqa: E501
     "RobertaForSequenceClassification": _HfExamplesInfo("cross-encoder/quora-roberta-base"),  # noqa: E501
     "XLMRobertaForSequenceClassification": _HfExamplesInfo("BAAI/bge-reranker-v2-m3"),  # noqa: E501
+    "ModernBertForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-reranker-modernbert-base"),  # noqa: E501
 }
 
 _MULTIMODAL_EXAMPLE_MODELS = {
@@ -286,10 +298,11 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                 extras={"fork": "Isotr0py/deepseek-vl2-tiny"},  # noqa: E501
                                                 max_transformers_version="4.48",  # noqa: E501
                                                 transformers_version_reason="HF model is not compatible.",  # noqa: E501
-                                               hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]}),  # 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"),
+    "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it"),
+    "GraniteSpeechForConditionalGeneration": _HfExamplesInfo("ibm-granite/granite-speech-3.3-8b",  # noqa: E501
+                                                             min_transformers_version="4.52.0"),  # noqa: E501
     "GLM4VForCausalLM": _HfExamplesInfo("THUDM/glm-4v-9b",
                                         trust_remote_code=True,
                                         hf_overrides={"architectures": ["GLM4VForCausalLM"]}),  # noqa: E501
@@ -302,6 +315,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                          trust_remote_code=True),
     "Idefics3ForConditionalGeneration": _HfExamplesInfo("HuggingFaceM4/Idefics3-8B-Llama3",  # noqa: E501
                                                         {"tiny": "HuggingFaceTB/SmolVLM-256M-Instruct"}),  # noqa: E501
+    "KimiVLForConditionalGeneration": _HfExamplesInfo("moonshotai/Kimi-VL-A3B-Instruct",  # noqa: E501
+                                                      extras={"thinking": "moonshotai/Kimi-VL-A3B-Thinking"},  # noqa: E501
+                                                      trust_remote_code=True),
     "Llama4ForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-4-Scout-17B-16E-Instruct",   # noqa: E501
                                                       min_transformers_version="4.51"),
     "LlavaForConditionalGeneration": _HfExamplesInfo("llava-hf/llava-1.5-7b-hf",
@@ -322,7 +338,6 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                 extras={"2.6": "openbmb/MiniCPM-V-2_6"},  # noqa: E501
                                 trust_remote_code=True),
     "Mistral3ForConditionalGeneration": _HfExamplesInfo("mistralai/Mistral-Small-3.1-24B-Instruct-2503",  # noqa: E501
-                                                        min_transformers_version="4.50",  # noqa: E501
                                                         extras={"fp8": "nm-testing/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic"}),  # noqa: E501
     "MolmoForCausalLM": _HfExamplesInfo("allenai/Molmo-7B-D-0924",
                                         max_transformers_version="4.48",
@@ -348,8 +363,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                       hf_overrides={"architectures": ["QwenVLForConditionalGeneration"]}),  # noqa: E501
     "Qwen2AudioForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-Audio-7B-Instruct"),  # noqa: E501
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-VL-2B-Instruct"),  # noqa: E501
-    "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct",  # noqa: E501
-                                                          min_transformers_version="4.49"),  # noqa: E501
+    "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct"),  # noqa: E501
+    "Qwen2_5OmniModel": _HfExamplesInfo("Qwen/Qwen2.5-Omni-7B",  # noqa: E501
+                                                                  min_transformers_version="4.52"),  # noqa: E501
     "SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B"),
     "SmolVLMForConditionalGeneration": _HfExamplesInfo("HuggingFaceTB/SmolVLM2-2.2B-Instruct"),  # noqa: E501
     "UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_5-llama-3_2-1b",  # noqa: E501
@@ -358,7 +374,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     # Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
     # Therefore, we borrow the BartTokenizer from the original Bart model
     "Florence2ForConditionalGeneration": _HfExamplesInfo("microsoft/Florence-2-base",  # noqa: E501
-                                                         tokenizer="facebook/bart-base",
+                                                         tokenizer="Isotr0py/Florence-2-tokenizer",
                                                          trust_remote_code=True),  # noqa: E501
     "MllamaForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-3.2-11B-Vision-Instruct"),  # noqa: E501
     "Llama4ForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-4-Scout-17B-16E-Instruct"),  # noqa: E501
@@ -379,6 +395,10 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
                                              trust_remote_code=True,
                                              speculative_model="yuhuili/EAGLE-LLaMA3-Instruct-8B",
                                              tokenizer="meta-llama/Meta-Llama-3-8B-Instruct"),  # noqa: E501
+    "Eagle3LlamaForCausalLM": _HfExamplesInfo("yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",  # noqa: E501
+                                            trust_remote_code=True,
+                                            speculative_model="yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
+                                            tokenizer="meta-llama/Llama-3.1-8B-Instruct"),
 }
 
 _TRANSFORMERS_MODELS = {

tests/models/test_bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+"""Compare the outputs of a GPTQ model to a bitblas model.
+
+Note: GPTQ and bitblas do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+bitblas/GPTQ models are in the top 3 selections of each other.
+
+Note: bitblas internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for bitblas. As a result, we re-run the 
+test up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_bitblas.py`.
+"""
+from dataclasses import dataclass
+
+import pytest
+
+from .utils import check_logprobs_close
+
+
+@dataclass
+class ModelPair:
+    model_bitblas: str
+    model_gptq: str
+
+
+model_pairs = [
+    ModelPair(model_bitblas="hxbgsyxh/opt-125m-4bit-128g-bitblas",
+              model_gptq="hxbgsyxh/opt-125m-4bit-128g"),
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(True, reason="BitBLAS takes too much time for tuning.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model_pair: ModelPair,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with vllm_runner(model_pair.model_bitblas,
+                     dtype=dtype,
+                     quantization="bitblas") as bitblas_model:
+        bitblas_outputs = bitblas_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model_pair.model_gptq, dtype=dtype,
+                     quantization="gptq") as gptq_model:
+        gptq_outputs = gptq_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=gptq_outputs,
+        outputs_1_lst=bitblas_outputs,
+        name_0="gptq",
+        name_1="bitblas",
+    )

tests/models/test_gptq_bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+"""Compare the outputs of a GPTQ model to a bitblas model.
+
+Note: GPTQ and bitblas do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+bitblas/GPTQ models are in the top 3 selections of each other.
+
+Note: bitblas internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for bitblas. As a result, we re-run the 
+test up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_bitblas.py`.
+"""
+from dataclasses import dataclass
+
+import pytest
+
+from .utils import check_logprobs_close
+
+
+@dataclass
+class ModelPair:
+    model_gptq: str
+
+
+model_pairs = [
+    ModelPair(model_gptq="hxbgsyxh/opt-125m-4bit-128g"),
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(True, reason="BitBLAS takes too much time for tuning.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model_pair: ModelPair,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with vllm_runner(model_pair.model_gptq,
+                     dtype=dtype,
+                     quantization="bitblas") as bitblas_model:
+        bitblas_outputs = bitblas_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model_pair.model_gptq, dtype=dtype,
+                     quantization="gptq") as gptq_model:
+        gptq_outputs = gptq_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=gptq_outputs,
+        outputs_1_lst=bitblas_outputs,
+        name_0="gptq",
+        name_1="gptq_bitblas",
+    )

tests/models/test_initialization.py

@@ -24,10 +24,7 @@ def test_can_initialize(model_arch):
     def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
         hf_config.update(model_info.hf_overrides)
 
-        if hasattr(hf_config, "text_config"):
-            text_config: PretrainedConfig = hf_config.text_config
-        else:
-            text_config = hf_config
+        text_config = hf_config.get_text_config()
 
         text_config.update({
             "num_layers": 1,

tests/models/test_oot_registration.py

@@ -18,10 +18,9 @@ def test_plugin(
         m.setenv("VLLM_USE_V1", "0")
         m.setenv("VLLM_PLUGINS", "")
 
-        with pytest.raises(Exception) as excinfo:
+        match = "Cannot find model module"
+        with pytest.raises(ValueError, match=match):
             LLM(model=dummy_opt_path, load_format="dummy")
-        error_msg = "has no vLLM implementation and the Transformers implementation is not compatible with vLLM"  # noqa: E501
-        assert (error_msg in str(excinfo.value))
 
 
 @create_new_process_for_each_test()

tests/quantization/test_compressed_tensors.py

@@ -266,16 +266,23 @@ def test_compressed_tensors_w8a8_dynamic_per_token(
                     reason="WNA16 is not supported on ROCm.")
 @pytest.mark.parametrize(
     "wNa16_args",
-    [
-     (os.path.join(models_path_prefix,"nm-testing/tinyllama-oneshot-w4a16-channel-v2"), "channel", None, 8),
-     (os.path.join(models_path_prefix,"nm-testing/tinyllama-oneshot-w4a16-group128-v2"), "group", 128, 8),
-     (os.path.join(models_path_prefix,"nm-testing/tinyllama-oneshot-w8a16-per-channel"), "channel", None, 4),
-    ],
+    [(os.path.join(models_path_prefix, "nm-testing/tinyllama-oneshot-w4a16-channel-v2"), "channel", None, 8,
+      True, False),
+     (os.path.join(models_path_prefix, "nm-testing/tinyllama-oneshot-w4a16-group128-v2"), "group", 128, 8, True,
+      False),
+     (os.path.join(models_path_prefix, "nm-testing/tinyllama-oneshot-w8a16-per-channel"), "channel", None, 4,
+      True, False),
+     (os.path.join(models_path_prefix, "nm-testing/TinyLlama-1.1B-Chat-v1.0-awq-group128-asym256"), "group", 128,
+      8, False, False),
+     (os.path.join(models_path_prefix, "nm-testing/TinyLlama-1.1B-Chat-v1.0-W4A16-G128-Asym-Updated-Channel"),
+      "channel", None, 8, False, False),
+     (os.path.join(models_path_prefix, "nm-testing/TinyLlama-1.1B-Chat-v1.0-W4A16-G128-Asym-Updated-ActOrder"),
+      "group", 128, 8, False, True)],
 )
 @pytest.mark.skipif(not current_platform.is_cuda(),
                     reason="The tests are skipped on non-CUDA platform.")
 def test_compressed_tensors_wNa16(vllm_runner, wNa16_args):
-    model, strategy, group, pack_factor = wNa16_args
+    model, strategy, group, pack_factor, symmetric, has_g_idx = wNa16_args
     with vllm_runner(model) as llm:
 
         def check_model(model):
@@ -291,6 +298,8 @@ def test_compressed_tensors_wNa16(vllm_runner, wNa16_args):
                                                   if group is None else group)
 
             assert qkv_proj.scheme.pack_factor == pack_factor
+            assert qkv_proj.scheme.symmetric == symmetric
+            assert qkv_proj.scheme.has_g_idx == has_g_idx
 
         llm.apply_model(check_model)
 

tests/samplers/test_beam_search.py

@@ -7,6 +7,9 @@ Run `pytest tests/samplers/test_beam_search.py`.
 import pytest
 import os
 from ..utils import models_path_prefix
+from transformers import AutoModelForSeq2SeqLM
+
+from vllm.assets.audio import AudioAsset
 
 
 @pytest.fixture(autouse=True)
@@ -21,7 +24,8 @@ def v1(run_with_both_engines):
 #   3. Use the model "huggyllama/llama-7b".
 MAX_TOKENS = [64]
 BEAM_WIDTHS = [4]
-MODELS = [os.path.join(models_path_prefix, "TinyLlama/TinyLlama-1.1B-Chat-v1.0")]
+MM_BEAM_WIDTHS = [2]
+MODELS = [os.path.join(models_path_prefix, "TinyLlama/TinyLlama-1.1B-Chat-v1.0")] 
 
 
 @pytest.mark.skip_v1  # FIXME: This fails on V1 right now.
@@ -50,15 +54,90 @@ def test_beam_search_single_input(
     for i in range(len(example_prompts)):
         hf_output_ids, hf_output_texts = hf_outputs[i]
         vllm_output_ids, vllm_output_texts = vllm_outputs[i]
-        for i, (hf_text,
+        for j, (hf_text,
                 vllm_text) in enumerate(zip(hf_output_texts,
                                             vllm_output_texts)):
-            print(f">>>{i}-th hf output:")
+            print(f">>>{j}-th hf output:")
             print(hf_text)
-            print(f">>>{i}-th vllm output:")
+            print(f">>>{j}-th vllm output:")
             print(vllm_text)
         assert len(hf_output_ids) == len(vllm_output_ids)
         for j in range(len(hf_output_ids)):
             assert hf_output_ids[j] == vllm_output_ids[j], (
                 f"Test{i} output{j}:\nHF: {hf_output_ids}\n"
                 f"vLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", MAX_TOKENS)
+@pytest.mark.parametrize("beam_width", MM_BEAM_WIDTHS)
+def test_beam_search_passes_multimodal_data(
+    hf_runner,
+    vllm_runner,
+    dtype: str,
+    max_tokens: int,
+    beam_width: int,
+) -> None:
+    """Ensure that beam search passes multimodal data through correctly."""
+    # NOTE - this test is primarily to check that mm data is passed to beams
+    # correctly. As such, we just need to check one extra modality to make
+    # sure things pass through properly.
+    audios = [AudioAsset("mary_had_lamb").audio_and_sample_rate]
+    model = "Qwen/Qwen2-Audio-7B-Instruct"
+    audio_seq = "<|audio_bos|><|AUDIO|><|audio_eos|>"
+    prompts = [
+        f"<|im_start|>user\n{audio_seq}Can you transcribe this?<|im_end|>\n<|im_start|>assistant\n"  #noqa: E501
+    ]
+
+    with hf_runner(model, dtype=dtype,
+                   auto_cls=AutoModelForSeq2SeqLM) as hf_model:
+        audio_token_id = hf_model.config.audio_token_index
+        eos_token_id = hf_model.tokenizer.eos_token_id  # <|im_end|>
+        hf_outputs = hf_model.generate_beam_search(
+            prompts,
+            beam_width=beam_width,
+            max_tokens=max_tokens,
+            audios=audios,
+        )
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.generate_beam_search(
+            prompts,
+            beam_width=beam_width,
+            max_tokens=max_tokens,
+            audios=audios,
+        )
+
+    seq_with_no_audio_toks = lambda seq: [
+        tok for tok in seq if tok != audio_token_id
+    ]
+
+    for i in range(len(prompts)):
+        hf_output_ids, hf_output_texts = hf_outputs[i]
+        vllm_output_ids, vllm_output_texts = vllm_outputs[i]
+
+        for j, (hf_text,
+                vllm_text) in enumerate(zip(hf_output_texts,
+                                            vllm_output_texts)):
+            print(f">>>{j}-th hf output [NOTE: special tokens are filtered]:")
+            print(hf_text)
+            print(f">>>{j}-th vllm output:")
+            print(vllm_text)
+        assert len(hf_output_ids) == len(vllm_output_ids)
+
+        for j in range(len(hf_output_ids)):
+            # Compare everything except for the audio tokens; we do this since
+            # the IDs returned from the transformers helper expands the audio
+            # token to match features, while the vLLM helper maintains the
+            # single audio token in the input text
+            filtered_hf_output_ids = seq_with_no_audio_toks(hf_output_ids[j])
+            filtered_vllm_output_ids = seq_with_no_audio_toks(
+                vllm_output_ids[j])
+
+            # HF output IDs may contain the end of sequence
+            if len(filtered_hf_output_ids
+                   ) == len(filtered_vllm_output_ids) + 1:
+                assert filtered_hf_output_ids[-1] == eos_token_id
+                filtered_hf_output_ids = filtered_hf_output_ids[:-1]
+
+            assert filtered_hf_output_ids == filtered_vllm_output_ids

tests/spec_decode/test_scorer.py

@@ -64,9 +64,8 @@ def test_scorer(model_name: str, batch_size: int, max_propose_len: int,
     scorer_worker = create_worker(Worker, model_name, block_size,
                                   num_gpu_blocks, seed)
     scorer_worker.model_runner.disable_logprobs = True  # accessed by mqa_scorer
-    scorer_worker.model_runner.model.sampler.include_gpu_probs_tensor = True
-    scorer_worker.model_runner.model.sampler.\
-        should_modify_greedy_probs_inplace = True
+    scorer_worker.model_runner.sampler.include_gpu_probs_tensor = True
+    scorer_worker.model_runner.sampler.should_modify_greedy_probs_inplace = True
 
     vocab_size = scorer_worker.vocab_size
 

tests/test_config.py

 # SPDX-License-Identifier: Apache-2.0
 
-from dataclasses import asdict
+from dataclasses import MISSING, Field, asdict, dataclass, field
 
 import pytest
 import os
 
-from vllm.config import ModelConfig, PoolerConfig
+from vllm.config import ModelConfig, PoolerConfig, get_field
 from vllm.model_executor.layers.pooler import PoolingType
 from vllm.platforms import current_platform
 from utils import models_path_prefix
 
 
+def test_get_field():
+
+    @dataclass
+    class TestConfig:
+        a: int
+        b: dict = field(default_factory=dict)
+        c: str = "default"
+
+    with pytest.raises(ValueError):
+        get_field(TestConfig, "a")
+
+    b = get_field(TestConfig, "b")
+    assert isinstance(b, Field)
+    assert b.default is MISSING
+    assert b.default_factory is dict
+
+    c = get_field(TestConfig, "c")
+    assert isinstance(c, Field)
+    assert c.default == "default"
+    assert c.default_factory is MISSING
+
+
 @pytest.mark.parametrize(
     ("model_id", "expected_runner_type", "expected_task"),
     [

tests/test_utils.py

@@ -13,11 +13,11 @@ import torch
 from vllm_test_utils.monitor import monitor
 
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
-from vllm.utils import (FlexibleArgumentParser, MemorySnapshot,
-                        PlaceholderModule, StoreBoolean, bind_kv_cache,
-                        deprecate_kwargs, get_open_port, memory_profiling,
-                        merge_async_iterators, sha256, supports_kw,
-                        swap_dict_values)
+from vllm.utils import (CacheInfo, FlexibleArgumentParser, LRUCache,
+                        MemorySnapshot, PlaceholderModule, StoreBoolean,
+                        bind_kv_cache, deprecate_kwargs, get_open_port,
+                        memory_profiling, merge_async_iterators, sha256,
+                        supports_kw, swap_dict_values)
 
 from .utils import create_new_process_for_each_test, error_on_warning
 from .utils import models_path_prefix
@@ -419,6 +419,129 @@ def test_bind_kv_cache_pp():
         assert ctx['layers.0.self_attn'].kv_cache[1] is kv_cache[1][0]
 
 
+class TestLRUCache(LRUCache):
+
+    def _on_remove(self, key, value):
+        if not hasattr(self, "_remove_counter"):
+            self._remove_counter = 0
+        self._remove_counter += 1
+
+
+def test_lru_cache():
+    cache = TestLRUCache(3)
+    assert cache.stat() == CacheInfo(hits=0, total=0)
+    assert cache.stat(delta=True) == CacheInfo(hits=0, total=0)
+
+    cache.put(1, 1)
+    assert len(cache) == 1
+
+    cache.put(1, 1)
+    assert len(cache) == 1
+
+    cache.put(2, 2)
+    assert len(cache) == 2
+
+    cache.put(3, 3)
+    assert len(cache) == 3
+    assert set(cache.cache) == {1, 2, 3}
+
+    cache.put(4, 4)
+    assert len(cache) == 3
+    assert set(cache.cache) == {2, 3, 4}
+    assert cache._remove_counter == 1
+
+    assert cache.get(2) == 2
+    assert cache.stat() == CacheInfo(hits=1, total=1)
+    assert cache.stat(delta=True) == CacheInfo(hits=1, total=1)
+
+    assert cache[2] == 2
+    assert cache.stat() == CacheInfo(hits=2, total=2)
+    assert cache.stat(delta=True) == CacheInfo(hits=1, total=1)
+
+    cache.put(5, 5)
+    assert set(cache.cache) == {2, 4, 5}
+    assert cache._remove_counter == 2
+
+    assert cache.pop(5) == 5
+    assert len(cache) == 2
+    assert set(cache.cache) == {2, 4}
+    assert cache._remove_counter == 3
+
+    assert cache.get(-1) is None
+    assert cache.stat() == CacheInfo(hits=2, total=3)
+    assert cache.stat(delta=True) == CacheInfo(hits=0, total=1)
+
+    cache.pop(10)
+    assert len(cache) == 2
+    assert set(cache.cache) == {2, 4}
+    assert cache._remove_counter == 3
+
+    cache.get(10)
+    assert len(cache) == 2
+    assert set(cache.cache) == {2, 4}
+    assert cache._remove_counter == 3
+
+    cache.put(6, 6)
+    assert len(cache) == 3
+    assert set(cache.cache) == {2, 4, 6}
+    assert 2 in cache
+    assert 4 in cache
+    assert 6 in cache
+
+    cache.remove_oldest()
+    assert len(cache) == 2
+    assert set(cache.cache) == {2, 6}
+    assert cache._remove_counter == 4
+
+    cache.clear()
+    assert len(cache) == 0
+    assert cache._remove_counter == 6
+    assert cache.stat() == CacheInfo(hits=0, total=0)
+    assert cache.stat(delta=True) == CacheInfo(hits=0, total=0)
+
+    cache._remove_counter = 0
+
+    cache[1] = 1
+    assert len(cache) == 1
+
+    cache[1] = 1
+    assert len(cache) == 1
+
+    cache[2] = 2
+    assert len(cache) == 2
+
+    cache[3] = 3
+    assert len(cache) == 3
+    assert set(cache.cache) == {1, 2, 3}
+
+    cache[4] = 4
+    assert len(cache) == 3
+    assert set(cache.cache) == {2, 3, 4}
+    assert cache._remove_counter == 1
+    assert cache[2] == 2
+
+    cache[5] = 5
+    assert set(cache.cache) == {2, 4, 5}
+    assert cache._remove_counter == 2
+
+    del cache[5]
+    assert len(cache) == 2
+    assert set(cache.cache) == {2, 4}
+    assert cache._remove_counter == 3
+
+    cache.pop(10)
+    assert len(cache) == 2
+    assert set(cache.cache) == {2, 4}
+    assert cache._remove_counter == 3
+
+    cache[6] = 6
+    assert len(cache) == 3
+    assert set(cache.cache) == {2, 4, 6}
+    assert 2 in cache
+    assert 4 in cache
+    assert 6 in cache
+
+
 def test_placeholder_module_error_handling():
     placeholder = PlaceholderModule("placeholder_1234")
 

tests/tokenization/test_cached_tokenizer.py

 # SPDX-License-Identifier: Apache-2.0
-
+import pickle
 from copy import deepcopy
 import os
 
+import pytest
 from transformers import AutoTokenizer
-
-from vllm.transformers_utils.tokenizer import get_cached_tokenizer
 from ..utils import models_path_prefix
 
 
-def test_cached_tokenizer():
-    reference_tokenizer = AutoTokenizer.from_pretrained(os.path.join(models_path_prefix, "gpt2"))
+from vllm.transformers_utils.tokenizer import (AnyTokenizer,
+                                               get_cached_tokenizer)
+
+
+@pytest.mark.parametrize("model_id", [os.path.join(models_path_prefix, "gpt2"), os.path.join(models_path_prefix, "THUDM/chatglm3-6b")])
+def test_cached_tokenizer(model_id: str):
+    reference_tokenizer = AutoTokenizer.from_pretrained(model_id,
+                                                        trust_remote_code=True)
     reference_tokenizer.add_special_tokens({"cls_token": "<CLS>"})
     reference_tokenizer.add_special_tokens(
         {"additional_special_tokens": ["<SEP>"]})
+
     cached_tokenizer = get_cached_tokenizer(deepcopy(reference_tokenizer))
+    _check_consistency(cached_tokenizer, reference_tokenizer)
+
+    pickled_tokenizer = pickle.dumps(cached_tokenizer)
+    unpickled_tokenizer = pickle.loads(pickled_tokenizer)
+    _check_consistency(unpickled_tokenizer, reference_tokenizer)
+
+
+def _check_consistency(target: AnyTokenizer, expected: AnyTokenizer):
+    assert isinstance(target, type(expected))
+
+    # Cached attributes
+    assert target.all_special_ids == expected.all_special_ids
+    assert target.all_special_tokens == expected.all_special_tokens
+    assert (target.all_special_tokens_extended ==
+            expected.all_special_tokens_extended)
+    assert target.get_vocab() == expected.get_vocab()
+    assert len(target) == len(expected)
+
+    # Other attributes
+    assert getattr(target, "padding_side",
+                   None) == getattr(expected, "padding_side", None)
 
-    assert reference_tokenizer.encode("prompt") == cached_tokenizer.encode(
-        "prompt")
-    assert set(reference_tokenizer.all_special_ids) == set(
-        cached_tokenizer.all_special_ids)
-    assert set(reference_tokenizer.all_special_tokens) == set(
-        cached_tokenizer.all_special_tokens)
-    assert set(reference_tokenizer.all_special_tokens_extended) == set(
-        cached_tokenizer.all_special_tokens_extended)
+    assert target.encode("prompt") == expected.encode("prompt")

tests/tokenization/test_detokenize.py

@@ -3,19 +3,26 @@
 from collections.abc import Generator
 from typing import Any, Optional
 
-import pytest
 import os
-from transformers import AutoTokenizer
+import pytest
+from transformers import (AutoTokenizer, PreTrainedTokenizer,
+                          PreTrainedTokenizerFast)
 
 from vllm.inputs import token_inputs
 from vllm.sequence import Logprob, SamplingParams, Sequence, SequenceGroup
-from vllm.transformers_utils.detokenizer import (Detokenizer,
-                                                 detokenize_incrementally)
-from vllm.transformers_utils.tokenizer_group import get_tokenizer_group
-
+from vllm.transformers_utils.detokenizer import Detokenizer
+from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.engine.detokenizer import (FastIncrementalDetokenizer,
+                                        IncrementalDetokenizer,
+                                        SlowIncrementalDetokenizer)
 from ..utils import models_path_prefix
 
+SPECIAL_TOKS_TRUTH = [
+    "Some text with adjacent special tokens                <|padding|><|padding|><fim_prefix><fim_middle><fim_suffix>other text<fim_pad>",  # noqa
+]
+
 TRUTH = [
     "Hello here, this is a simple test",
     "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be used in production environments, where inference and serving",  # noqa
@@ -25,7 +32,8 @@ TRUTH = [
     # incomplete UTF-8 characters
     # see https://github.com/vllm-project/vllm/pull/9625
     "ပုံပြင်လေးပြောပြပါ်",
-]
+] + SPECIAL_TOKS_TRUTH
+
 TOKENIZERS = [
     os.path.join(models_path_prefix, "facebook/opt-125m"),
     os.path.join(models_path_prefix, "gpt2"),
@@ -41,26 +49,37 @@ TOKENIZERS = [
 ]
 
 
-def _run_incremental_decode(tokenizer, all_input_ids,
-                            skip_special_tokens: bool, starting_index: int):
-    decoded_text = ""
-    offset = 0
-    token_offset = 0
-    prev_tokens = None
-    for i in range(starting_index, len(all_input_ids)):
-        new_tokens, text, offset, token_offset = detokenize_incrementally(
-            tokenizer,
-            all_input_ids[:i + 1],
-            prev_tokens,
-            offset,
-            token_offset,
-            skip_special_tokens=skip_special_tokens)
-        decoded_text += text
-        if prev_tokens is None:
-            prev_tokens = new_tokens
-        else:
-            prev_tokens += new_tokens
-    return decoded_text
+def _run_incremental_decode(tokenizer,
+                            all_input_ids,
+                            skip_special_tokens: bool,
+                            starting_index: int,
+                            spaces_between_special_tokens: bool = True,
+                            fast: Optional[bool] = None):
+
+    prompt_token_ids = all_input_ids[:starting_index]
+
+    params = SamplingParams(
+        skip_special_tokens=skip_special_tokens,
+        spaces_between_special_tokens=spaces_between_special_tokens,
+    )
+    request = EngineCoreRequest("", prompt_token_ids, None, None, None, params,
+                                None, 0.0, None)
+
+    if fast is None:
+        detokenizer = IncrementalDetokenizer.from_new_request(
+            tokenizer, request)
+    elif fast:
+        detokenizer = FastIncrementalDetokenizer(tokenizer, request)
+    else:
+        detokenizer = SlowIncrementalDetokenizer(tokenizer, request)
+
+    output_text = ""
+    for i, token_id in enumerate(all_input_ids[starting_index:]):
+        detokenizer.update([token_id], False)
+        finished = i == len(all_input_ids) - 1
+        output_text += detokenizer.get_next_output_text(finished, delta=True)
+
+    return output_text, detokenizer.output_token_ids
 
 
 @pytest.fixture
@@ -88,11 +107,13 @@ def test_mistral_edge_case(tokenizer, truth):
     starting_index = 0
     all_input_ids = tokenizer(truth, add_special_tokens=False).input_ids
 
-    decoded_text = _run_incremental_decode(tokenizer,
-                                           all_input_ids,
-                                           skip_special_tokens=True,
-                                           starting_index=starting_index)
+    decoded_text, out_ids = _run_incremental_decode(
+        tokenizer,
+        all_input_ids,
+        skip_special_tokens=True,
+        starting_index=starting_index)
     assert decoded_text == truth
+    assert out_ids == all_input_ids[starting_index:]
 
 
 @pytest.fixture
@@ -109,45 +130,91 @@ def skip_special_tokens(request, tokenizer_name) -> Generator[bool, Any, None]:
 @pytest.mark.parametrize("with_prompt", [True, False])
 @pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
 @pytest.mark.parametrize("skip_special_tokens", (True, False), indirect=True)
-def test_decode_streaming(tokenizer, truth, with_prompt, skip_special_tokens):
+@pytest.mark.parametrize("spaces_between_special_tokens", (True, False))
+@pytest.mark.parametrize("fast", (True, False))
+def test_decode_streaming(tokenizer, truth, with_prompt, skip_special_tokens,
+                          spaces_between_special_tokens, fast):
+    if fast and not isinstance(tokenizer, PreTrainedTokenizerFast):
+        pytest.skip()
+
+    if skip_special_tokens and not spaces_between_special_tokens:
+        pytest.skip()
+
+    if not fast and isinstance(tokenizer, PreTrainedTokenizerFast):
+        # Fix up inconsistency in fast/slow tokenizer behaviour.
+        tokenizer.add_special_tokens({
+            "additional_special_tokens": [
+                at for at in
+                tokenizer._tokenizer.get_added_tokens_decoder().values()
+                if at.special
+            ]
+        })
+
+    extra_decode_args = {} if not isinstance(tokenizer,  PreTrainedTokenizer) \
+        else {"spaces_between_special_tokens": spaces_between_special_tokens}
+
+    truth_tokens = tokenizer(truth, add_special_tokens=False).input_ids
+    if tokenizer.bos_token_id is not None:
+        truth_tokens.insert(0, tokenizer.bos_token_id)
+    truth_tokens.append(tokenizer.eos_token_id)
+
+    new_truth = tokenizer.decode(truth_tokens,
+                                 skip_special_tokens=skip_special_tokens,
+                                 **extra_decode_args)
+
     if with_prompt:
-        truth_tokens = tokenizer(truth, add_special_tokens=False).input_ids
-        prompt_input_ids = truth_tokens[:len(truth) // 2]
-        generated_input_ids = truth_tokens[len(truth) // 2:]
+        num_prompt_tokens = len(
+            tokenizer(truth[:len(truth) // 2],
+                      add_special_tokens=False).input_ids)
+        if tokenizer.bos_token_id is not None:
+            num_prompt_tokens += 1
+
+        prompt_input_ids = truth_tokens[:num_prompt_tokens]
+        generated_input_ids = truth_tokens[num_prompt_tokens:]
         all_input_ids = prompt_input_ids + generated_input_ids
         starting_index = len(prompt_input_ids)
         prompt = tokenizer.decode(prompt_input_ids,
-                                  skip_special_tokens=skip_special_tokens)
-        generated = truth[len(prompt):]
+                                  skip_special_tokens=skip_special_tokens,
+                                  **extra_decode_args)
+
+        generated = new_truth[len(prompt):]
     else:
-        generated = truth
+        generated = new_truth
         starting_index = 0
-        all_input_ids = tokenizer(truth, add_special_tokens=False).input_ids
-    if skip_special_tokens:
-        if tokenizer.bos_token_id is not None:
-            all_input_ids = [tokenizer.bos_token_id] + all_input_ids
-            starting_index += 1
-        all_input_ids = all_input_ids + [tokenizer.eos_token_id]
+        all_input_ids = truth_tokens
 
-    decoded_text = _run_incremental_decode(
+    decoded_text, out_ids = _run_incremental_decode(
         tokenizer,
         all_input_ids,
         skip_special_tokens=skip_special_tokens,
-        starting_index=starting_index)
+        starting_index=starting_index,
+        spaces_between_special_tokens=spaces_between_special_tokens,
+        fast=fast)
 
     assert decoded_text == generated
+    assert out_ids == all_input_ids[starting_index:]
+
+
+@pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
+@pytest.mark.parametrize("fast", (True, False))
+def test_oov_decode(tokenizer, fast):
+    if fast and not isinstance(tokenizer, PreTrainedTokenizerFast):
+        pytest.skip()
 
-    decoded_text = _run_incremental_decode(
+    decoded_text, out_ids = _run_incremental_decode(
         tokenizer, [len(tokenizer)],
-        skip_special_tokens=skip_special_tokens,
-        starting_index=starting_index)
+        skip_special_tokens=True,
+        starting_index=0,
+        spaces_between_special_tokens=True,
+        fast=fast)
 
     assert decoded_text == ''
+    assert out_ids == [len(tokenizer)]
 
 
 @pytest.fixture
 def detokenizer(tokenizer_name: str) -> Detokenizer:
-    init_kwargs = dict(
+    tokenizer_group = TokenizerGroup(
         tokenizer_id=tokenizer_name,
         enable_lora=False,
         max_num_seqs=100,
@@ -157,26 +224,20 @@ def detokenizer(tokenizer_name: str) -> Detokenizer:
         revision=None,
     )
 
-    tokenizer_group = get_tokenizer_group(
-        None,
-        **init_kwargs,
-    )
-
     return Detokenizer(tokenizer_group)
 
 
 @pytest.fixture(name="complete_sequence_token_ids")
 def create_complete_sequence_token_ids(complete_sequence: str,
                                        tokenizer) -> list[int]:
-    complete_sequence_token_ids = tokenizer(complete_sequence).input_ids
-    return complete_sequence_token_ids
+    return tokenizer(complete_sequence, add_special_tokens=False).input_ids
 
 
 def create_sequence(prompt_token_ids=None):
-    prompt_token_ids = prompt_token_ids or [1]
+    prompt_token_ids = prompt_token_ids or []
     return Sequence(
         seq_id=0,
-        inputs=token_inputs(prompt_token_ids, prompt="<s>"),
+        inputs=token_inputs(prompt_token_ids),
         block_size=16,
     )
 
@@ -227,7 +288,7 @@ def test_decode_sequence_logprobs(complete_sequence: str,
     assert sequential_result == "".join(sequential_logprobs_text_chosen_token)
     assert sequential_result != "".join(sequential_logprobs_text_other_token)
 
-    if skip_special_tokens:
+    if not skip_special_tokens:
         # Text for logprobs for the chosen token should be the same as the
         # generated text. Note that this will only be true if we skip
         # special tokens.
@@ -236,10 +297,23 @@ def test_decode_sequence_logprobs(complete_sequence: str,
 
 @pytest.mark.parametrize("complete_sequence", TRUTH)
 @pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
-def test_decode_prompt_logprobs(complete_sequence_token_ids: list[int],
+def test_decode_prompt_logprobs(complete_sequence: str,
+                                complete_sequence_token_ids: list[int],
                                 detokenizer: Detokenizer):
+
+    # We want to use skip_special_tokens=False here but Mistral tokenizers
+    # don't support that.
+    if complete_sequence not in SPECIAL_TOKS_TRUTH:
+        skip_special_tokens = True
+    elif not isinstance(detokenizer.tokenizer_group.get_lora_tokenizer(None),
+                        MistralTokenizer):
+        skip_special_tokens = False
+    else:
+        pytest.skip("MistralTokenizers don't support "
+                    "skip_special_tokens=False")
+        return
     """Verify Detokenizer decodes prompt logprobs correctly."""
-    sampling_params = SamplingParams(skip_special_tokens=True,
+    sampling_params = SamplingParams(skip_special_tokens=skip_special_tokens,
                                      prompt_logprobs=1)
 
     # Run sequentially.
@@ -259,8 +333,10 @@ def test_decode_prompt_logprobs(complete_sequence_token_ids: list[int],
     # decoded_prompt_logprobs doesn't contain the first token.
     token_ids = complete_sequence_token_ids
     tokenizer = detokenizer.get_tokenizer_for_seq(seq)
-    text_full = tokenizer.decode(token_ids, skip_special_tokens=True)
-    text_first = tokenizer.decode(token_ids[0], skip_special_tokens=True)
+    text_full = tokenizer.decode(token_ids,
+                                 skip_special_tokens=skip_special_tokens)
+    text_first = tokenizer.decode(token_ids[0],
+                                  skip_special_tokens=skip_special_tokens)
     text = text_full[len(text_first):]
 
     # Text for logprobs for the chosen token should be the same as the