[Deprecation][2/N] Replace `--task` with `--runner` and `--convert` (#21470)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/v1/spec_decode/test_eagle.py

@@ -24,13 +24,8 @@ eagle3_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
 
 def _create_proposer(method: str, k: int) -> EagleProposer:
     model_config = ModelConfig(model=model_dir,
-                               task="generate",
-                               max_model_len=100,
-                               tokenizer=model_dir,
-                               tokenizer_mode="auto",
-                               dtype="auto",
-                               seed=None,
-                               trust_remote_code=False)
+                               runner="generate",
+                               max_model_len=100)
 
     # Choose model directory based on method
     draft_model_dir = eagle_dir if method == "eagle" else eagle3_dir

tests/v1/spec_decode/test_ngram.py

@@ -44,14 +44,7 @@ def test_ngram_proposer():
 
     def ngram_proposer(min_n: int, max_n: int, k: int) -> NgramProposer:
         # Dummy model config. Just to set max_model_len.
-        model_config = ModelConfig(model="facebook/opt-125m",
-                                   task="generate",
-                                   max_model_len=100,
-                                   tokenizer="facebook/opt-125m",
-                                   tokenizer_mode="auto",
-                                   dtype="auto",
-                                   seed=None,
-                                   trust_remote_code=False)
+        model_config = ModelConfig(model="facebook/opt-125m")
         return NgramProposer(
             vllm_config=VllmConfig(model_config=model_config,
                                    speculative_config=SpeculativeConfig.

tests/v1/tpu/worker/test_tpu_model_runner.py

@@ -26,10 +26,6 @@ def get_vllm_config():
     )
     model_config = ModelConfig(
         model="facebook/opt-125m",
-        task="generate",
-        tokenizer="facebook/opt-125m",
-        tokenizer_mode="auto",
-        trust_remote_code=True,
         dtype="bfloat16",  # TPUs typically use bfloat16
         seed=42,
     )

tests/v1/worker/test_gpu_model_runner.py

@@ -76,10 +76,6 @@ def get_vllm_config():
     )
     model_config = ModelConfig(
         model="facebook/opt-125m",
-        task="generate",
-        tokenizer="facebook/opt-125m",
-        tokenizer_mode="auto",
-        trust_remote_code=True,
         dtype="float16",
         seed=42,
     )

vllm/engine/arg_utils.py

@@ -22,14 +22,15 @@ from typing_extensions import TypeIs
 
 import vllm.envs as envs
 from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
-                         ConfigFormat, ConfigType, DecodingConfig,
-                         DetailedTraceModules, Device, DeviceConfig,
-                         DistributedExecutorBackend, GuidedDecodingBackend,
-                         GuidedDecodingBackendV1, HfOverrides, KVEventsConfig,
-                         KVTransferConfig, LoadConfig, LogprobsMode,
-                         LoRAConfig, ModelConfig, ModelDType, ModelImpl,
-                         MultiModalConfig, ObservabilityConfig, ParallelConfig,
-                         PoolerConfig, PrefixCachingHashAlgo, SchedulerConfig,
+                         ConfigFormat, ConfigType, ConvertOption,
+                         DecodingConfig, DetailedTraceModules, Device,
+                         DeviceConfig, DistributedExecutorBackend,
+                         GuidedDecodingBackend, GuidedDecodingBackendV1,
+                         HfOverrides, KVEventsConfig, KVTransferConfig,
+                         LoadConfig, LogprobsMode, LoRAConfig, ModelConfig,
+                         ModelDType, ModelImpl, MultiModalConfig,
+                         ObservabilityConfig, ParallelConfig, PoolerConfig,
+                         PrefixCachingHashAlgo, RunnerOption, SchedulerConfig,
                          SchedulerPolicy, SpeculativeConfig, TaskOption,
                          TokenizerMode, VllmConfig, get_attr_docs, get_field)
 from vllm.logger import init_logger
@@ -270,7 +271,9 @@ class EngineArgs:
         str, List[str]]] = ModelConfig.served_model_name
     tokenizer: Optional[str] = ModelConfig.tokenizer
     hf_config_path: Optional[str] = ModelConfig.hf_config_path
-    task: TaskOption = ModelConfig.task
+    runner: RunnerOption = ModelConfig.runner
+    convert: ConvertOption = ModelConfig.convert
+    task: Optional[TaskOption] = ModelConfig.task
     skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
     enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
     tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
@@ -461,7 +464,11 @@ class EngineArgs:
         )
         if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]):
             model_group.add_argument("--model", **model_kwargs["model"])
-        model_group.add_argument("--task", **model_kwargs["task"])
+        model_group.add_argument("--runner", **model_kwargs["runner"])
+        model_group.add_argument("--convert", **model_kwargs["convert"])
+        model_group.add_argument("--task",
+                                 **model_kwargs["task"],
+                                 deprecated=True)
         model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
         model_group.add_argument("--tokenizer-mode",
                                  **model_kwargs["tokenizer_mode"])
@@ -870,6 +877,8 @@ class EngineArgs:
         return ModelConfig(
             model=self.model,
             hf_config_path=self.hf_config_path,
+            runner=self.runner,
+            convert=self.convert,
             task=self.task,
             tokenizer=self.tokenizer,
             tokenizer_mode=self.tokenizer_mode,

vllm/entrypoints/llm.py

@@ -20,8 +20,8 @@ from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
                               create_sort_beams_key_function)
 from vllm.config import (CompilationConfig, ModelDType, TokenizerMode,
                          is_init_field)
-from vllm.engine.arg_utils import (EngineArgs, HfOverrides, PoolerConfig,
-                                   TaskOption)
+from vllm.engine.arg_utils import (ConvertOption, EngineArgs, HfOverrides,
+                                   PoolerConfig, RunnerOption)
 from vllm.engine.llm_engine import LLMEngine
 from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
                                          ChatTemplateContentFormatOption,
@@ -170,7 +170,8 @@ class LLM:
         self,
         model: str,
         *,
-        task: TaskOption = "auto",
+        runner: RunnerOption = "auto",
+        convert: ConvertOption = "auto",
         tokenizer: Optional[str] = None,
         tokenizer_mode: TokenizerMode = "auto",
         skip_tokenizer_init: bool = False,
@@ -244,7 +245,8 @@ class LLM:
 
         engine_args = EngineArgs(
             model=model,
-            task=task,
+            runner=runner,
+            convert=convert,
             tokenizer=tokenizer,
             tokenizer_mode=tokenizer_mode,
             skip_tokenizer_init=skip_tokenizer_init,
@@ -459,18 +461,10 @@ class LLM:
         model_config = self.llm_engine.model_config
         runner_type = model_config.runner_type
         if runner_type != "generate":
-            messages = [
-                "LLM.generate() is only supported for generative models."
-            ]
-
-            if "generate" in model_config.supported_runner_types:
-                messages.append(
-                    "Your model supports the 'generate' runner, but is "
-                    f"currently initialized for the '{runner_type}' runner. "
-                    "Please initialize vLLM using `--task generate` or "
-                    "`--task transcription`.")
-
-            raise ValueError(" ".join(messages))
+            raise ValueError(
+                "LLM.generate() is only supported for generative models. "
+                "Try passing `--runner generate` to use the model as a "
+                "generative model.")
 
         if prompt_token_ids is not None:
             parsed_prompts = self._convert_v1_inputs(
@@ -497,7 +491,8 @@ class LLM:
         truncate_prompt_tokens = None
         if isinstance(sampling_params, SamplingParams):
             truncate_prompt_tokens = sampling_params.truncate_prompt_tokens
-        _validate_truncation_size(self.llm_engine.model_config.max_model_len,
+
+        _validate_truncation_size(model_config.max_model_len,
                                   truncate_prompt_tokens, tokenization_kwargs)
 
         # Add any modality specific loras to the corresponding prompts
@@ -1100,16 +1095,10 @@ class LLM:
         model_config = self.llm_engine.model_config
         runner_type = model_config.runner_type
         if runner_type != "pooling":
-            messages = ["LLM.encode() is only supported for pooling models."]
-
-            if "pooling" in model_config.supported_runner_types:
-                messages.append(
-                    "Your model supports the 'pooling' runner, but is "
-                    f"currently initialized for the '{runner_type}' runner. "
-                    "Please initialize vLLM using `--task embed`, "
-                    "`--task classify`, `--task score` etc.")
-
-            raise ValueError(" ".join(messages))
+            raise ValueError(
+                "LLM.encode() is only supported for pooling models. "
+                "Try passing `--runner pooling` to use the model as a "
+                "pooling model.")
 
         if prompt_token_ids is not None:
             parsed_prompts = self._convert_v1_inputs(
@@ -1183,8 +1172,9 @@ class LLM:
             embedding vectors in the same order as the input prompts.
         """
         if "embed" not in self.supported_tasks:
-            raise ValueError("Embedding API is not supported by this model. "
-                             "Please set `--task embed`.")
+            raise ValueError(
+                "Embedding API is not supported by this model. "
+                "Try converting the model using `--convert embed`.")
 
         items = self.encode(
             prompts,
@@ -1229,7 +1219,7 @@ class LLM:
         if "classify" not in self.supported_tasks:
             raise ValueError(
                 "Classification API is not supported by this model. "
-                "Please set `--task classify`.")
+                "Try converting the model using `--convert classify`.")
 
         items = self.encode(
             prompts,
@@ -1283,27 +1273,26 @@ class LLM:
         use_tqdm: Union[bool, Callable[..., tqdm]] = True,
         lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
     ) -> list[ScoringRequestOutput]:
+        model_config = self.llm_engine.model_config
 
         if isinstance(tokenizer, MistralTokenizer):
             raise ValueError(
-                "Score API is only enabled for `--task embed or score`")
+                "Score API is not supported for Mistral tokenizer")
 
         if len(data_1) == 1:
             data_1 = data_1 * len(data_2)
 
         pooling_params = PoolingParams(task="score")
         tokenization_kwargs: dict[str, Any] = {}
-        _validate_truncation_size(self.llm_engine.model_config.max_model_len,
+
+        _validate_truncation_size(model_config.max_model_len,
                                   truncate_prompt_tokens, tokenization_kwargs)
 
         parsed_prompts = []
 
         input_pairs = [(t1, t2) for t1, t2 in zip(data_1, data_2)]
 
-        if self.llm_engine.model_config.is_multimodal_model:
-
-            model_config = self.llm_engine.model_config
-
+        if model_config.is_multimodal_model:
             for q, d in input_pairs:
                 _, engine_prompt = get_score_prompt(
                     model_config=model_config,
@@ -1314,11 +1303,9 @@ class LLM:
                 )
 
                 parsed_prompts.append(engine_prompt)
-
         else:
-
             for q, t in input_pairs:
-                if self.llm_engine.model_config.use_pad_token:
+                if model_config.use_pad_token:
                     # cross_encoder models defaults to using pad_token.
                     prompt_inputs = tokenizer(
                         text=q,  # type: ignore[arg-type]
@@ -1396,23 +1383,18 @@ class LLM:
         model_config = self.llm_engine.model_config
         runner_type = model_config.runner_type
         if runner_type != "pooling":
-            messages = ["LLM.score() is only supported for pooling models."]
-
-            if "pooling" in model_config.supported_runner_types:
-                messages.append(
-                    "Your model supports the 'pooling' runner, but is "
-                    f"currently initialized for the '{runner_type}' runner. "
-                    "Please initialize vLLM using `--task embed`, "
-                    "`--task classify`, `--task score` etc.")
-
-            raise ValueError(" ".join(messages))
+            raise ValueError(
+                "LLM.score() is only supported for pooling models. "
+                "Try passing `--runner pooling` to use the model as a "
+                "pooling model.")
 
         supported_tasks = self.supported_tasks
         if all(t not in supported_tasks for t in ("embed", "classify")):
             raise ValueError("Score API is not supported by this model. "
-                             "Please set `--task embed` or `--task classify`.")
+                             "Try converting the model using "
+                             "`--convert embed` or `--convert classify`.")
 
-        if (model_config.task == "classify"
+        if (model_config.is_cross_encoder
                 and getattr(model_config.hf_config, "num_labels", 0) != 1):
             raise ValueError("Score API is only enabled for num_labels == 1.")
 
@@ -1421,15 +1403,14 @@ class LLM:
         # lists of tokens to the `text` and `text_pair` kwargs
         tokenizer = self.get_tokenizer()
 
-        if not self.llm_engine.model_config.is_multimodal_model:
+        if not model_config.is_multimodal_model:
 
             def check_data_type(data: Union[SingletonPrompt,
                                             Sequence[SingletonPrompt],
                                             ScoreMultiModalParam]):
                 if isinstance(data, dict) and "content" in data:
-                    raise ValueError(
-                        f"ScoreMultiModalParam is not supported for {self.llm_engine.model_config.architecture}",  # noqa: E501
-                    )
+                    raise ValueError("ScoreMultiModalParam is not supported "
+                                     f"for {model_config.architecture}")
 
             check_data_type(data_1)
             check_data_type(data_2)
@@ -1471,7 +1452,7 @@ class LLM:
 
         _validate_score_input_lens(data_1, data_2)  # type: ignore[arg-type]
 
-        if self.llm_engine.model_config.is_cross_encoder:
+        if model_config.is_cross_encoder:
             return self._cross_encoding_score(
                 tokenizer,
                 data_1,  # type: ignore[arg-type]

vllm/entrypoints/openai/api_server.py

@@ -1734,7 +1734,6 @@ async def init_app_state(
         state.openai_serving_models,
         request_logger=request_logger,
     ) if "transcription" in supported_tasks else None
-    state.task = model_config.task
 
     state.enable_server_load_tracking = args.enable_server_load_tracking
     state.server_load_metrics = 0

vllm/model_executor/model_loader/utils.py

@@ -9,9 +9,8 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import torch
-import transformers
 from torch import nn
-from transformers.dynamic_module_utils import get_class_from_dynamic_module
+from typing_extensions import assert_never
 
 from vllm.attention import Attention
 from vllm.config import (ModelConfig, ModelImpl, VllmConfig,
@@ -20,13 +19,10 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import QKVCrossParallelLinear
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
-from vllm.model_executor.models import ModelRegistry
 from vllm.model_executor.models.adapters import (as_embedding_model,
                                                  as_reward_model,
                                                  as_seq_cls_model)
 from vllm.model_executor.models.interfaces import SupportsQuant
-from vllm.model_executor.models.registry import (_PREVIOUSLY_SUPPORTED_MODELS,
-                                                 _TRANSFORMERS_BACKEND_MODELS)
 from vllm.utils import is_pin_memory_available
 
 logger = init_logger(__name__)
@@ -169,61 +165,6 @@ def device_loading_context(module: torch.nn.Module,
         # New parameters or parameters already on target device are untouched
 
 
-def resolve_transformers_arch(model_config: ModelConfig,
-                              architectures: list[str]):
-    if model_config.model_impl == ModelImpl.VLLM:
-        raise ValueError(
-            "Attempting to resolve architecture from the Transformers library "
-            "but the model implementation is set to vLLM. This should never "
-            "happen.")
-
-    for i, arch in enumerate(architectures):
-        if arch in _TRANSFORMERS_BACKEND_MODELS:
-            continue
-
-        if model_config.model_impl == ModelImpl.AUTO:
-            logger.warning(
-                "%s has no vLLM implementation, falling back to Transformers "
-                "implementation. Some features may not be supported and "
-                "performance may not be optimal.", arch)
-
-        auto_map: dict[str, str] = getattr(model_config.hf_config, "auto_map",
-                                           None) or dict()
-        # Make sure that config class is always initialized before model class,
-        # otherwise the model class won't be able to access the config class,
-        # the expected auto_map should have correct order like:
-        # "auto_map": {
-        #     "AutoConfig": "<your-repo-name>--<config-name>",
-        #     "AutoModel": "<your-repo-name>--<config-name>",
-        #     "AutoModelFor<Task>": "<your-repo-name>--<config-name>",
-        # },
-        auto_modules = {
-            name:
-            get_class_from_dynamic_module(module,
-                                          model_config.model,
-                                          revision=model_config.revision)
-            for name, module in sorted(auto_map.items(), key=lambda x: x[0])
-        }
-        model_module = getattr(transformers, arch, None)
-        if model_module is None:
-            if "AutoModel" not in auto_map:
-                raise ValueError(
-                    f"Cannot find model module. '{arch}' is not a registered "
-                    "model in the Transformers library (only relevant if the "
-                    "model is meant to be in Transformers) and 'AutoModel' is "
-                    "not present in the model config's 'auto_map' (relevant "
-                    "if the model is custom).")
-            model_module = auto_modules["AutoModel"]
-
-        if not model_module.is_backend_compatible():
-            raise ValueError(
-                f"The Transformers implementation of '{arch}' is not "
-                "compatible with vLLM.")
-
-        architectures[i] = model_config._get_transformers_backend_cls()
-    return architectures
-
-
 def get_model_architecture(
         model_config: ModelConfig) -> tuple[type[nn.Module], str]:
     architectures = getattr(model_config.hf_config, "architectures", [])
@@ -239,56 +180,38 @@ def get_model_architecture(
         "bitsandbytes",
     ]
 
-    vllm_supported_archs = ModelRegistry.get_supported_archs()
-    is_supported = lambda arch: (arch in vllm_supported_archs and arch not in
-                                 _TRANSFORMERS_BACKEND_MODELS)
-    vllm_not_supported = not any(is_supported(arch) for arch in architectures)
-
-    if vllm_not_supported:
-        # try automatic conversion in adapters.py
-        for arch in architectures:
-            if not arch.endswith("ForSequenceClassification"):
-                continue
+    if (model_config.quantization is not None
+            and model_config.quantization not in mixtral_supported
+            and "MixtralForCausalLM" in architectures):
+        architectures = ["QuantMixtralForCausalLM"]
 
-            assert model_config.task == "classify"
-            causal_lm_arch = arch.replace("ForSequenceClassification",
-                                          "ForCausalLM")
-            causal_lm_arch_vllm_supported = (causal_lm_arch
-                                             in vllm_supported_archs)
-            if not causal_lm_arch_vllm_supported:
-                continue
+    model_cls, arch = model_config.registry.resolve_model_cls(
+        architectures,
+        model_config=model_config,
+    )
 
-            architectures = [causal_lm_arch]
-            vllm_not_supported = False
-            break
-
-    if any(arch in _PREVIOUSLY_SUPPORTED_MODELS for arch in architectures):
-        previous_version = _PREVIOUSLY_SUPPORTED_MODELS[architectures[0]]
-        raise ValueError(
-            f"Model architecture {architectures[0]} was supported"
-            f" in vLLM until version {previous_version}, and is "
-            "not supported anymore. Please use an older version"
-            " of vLLM if you want to use this model architecture.")
-
-    if (model_config.model_impl == ModelImpl.TRANSFORMERS or
-            model_config.model_impl == ModelImpl.AUTO and vllm_not_supported):
-        architectures = resolve_transformers_arch(model_config, architectures)
-        logger.debug_once("Resolve transformers arch %s", str(architectures))
-    elif (model_config.quantization is not None
-          and model_config.quantization not in mixtral_supported
-          and "MixtralForCausalLM" in architectures):
-        architectures = ["QuantMixtralForCausalLM"]
+    if arch == model_config._get_transformers_backend_cls():
+        assert model_config.model_impl != ModelImpl.VLLM
+        if model_config.model_impl == ModelImpl.AUTO:
+            logger.warning_once(
+                "%s has no vLLM implementation, falling back to Transformers "
+                "implementation. Some features may not be supported and "
+                "performance may not be optimal.", arch)
 
-    model_cls, arch = ModelRegistry.resolve_model_cls(architectures)
-    if model_config.task == "embed":
-        logger.debug_once("Automatic conversion using `as_embedding_model`.")
+    convert_type = model_config.convert_type
+    if convert_type == "none":
+        pass
+    elif convert_type == "embed":
+        logger.debug_once("Converting to embedding model.")
         model_cls = as_embedding_model(model_cls)
-    elif model_config.task == "classify":
-        logger.debug_once("Automatic conversion using `as_seq_cls_model`.")
+    elif convert_type == "classify":
+        logger.debug_once("Converting to sequence classification model.")
         model_cls = as_seq_cls_model(model_cls)
-    elif model_config.task == "reward":
-        logger.debug_once("Automatic conversion using `as_reward_model`.")
+    elif convert_type == "reward":
+        logger.debug_once("Converting to reward model.")
         model_cls = as_reward_model(model_cls)
+    else:
+        assert_never(convert_type)
 
     return model_cls, arch
 

vllm/model_executor/models/config.py

@@ -253,8 +253,10 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
             dtype=kv_cache_dtype,
             use_mla=model_config.use_mla).page_size_bytes
 
-        model_cls = ModelRegistry.resolve_model_cls(
-            model_config._model_info.architecture)[0]
+        model_cls, _ = ModelRegistry.resolve_model_cls(
+            model_config.architecture,
+            model_config=model_config,
+        )
 
         # get mamba page size
         mamba_page_size = MambaSpec(

vllm/model_executor/models/registry.py

@@ -12,19 +12,24 @@ import sys
 import tempfile
 from abc import ABC, abstractmethod
 from collections.abc import Set
-from dataclasses import asdict, dataclass, field
+from dataclasses import dataclass, field
 from functools import lru_cache
 from typing import Callable, Optional, TypeVar, Union
 
 import torch.nn as nn
+import transformers
 
+from vllm.config import (ModelConfig, ModelImpl, iter_architecture_defaults,
+                         try_match_architecture_defaults)
 from vllm.logger import init_logger
+from vllm.transformers_utils.dynamic_module import (
+    try_get_class_from_dynamic_module)
 
 from .interfaces import (has_inner_state, has_noops, is_attention_free,
                          is_hybrid, supports_cross_encoding,
                          supports_multimodal, supports_multimodal_raw_input,
                          supports_pp, supports_transcription, supports_v0_only)
-from .interfaces_base import is_text_generation_model
+from .interfaces_base import is_pooling_model, is_text_generation_model
 
 logger = init_logger(__name__)
 
@@ -311,7 +316,7 @@ class _ModelInfo:
         return _ModelInfo(
             architecture=model.__name__,
             is_text_generation_model=is_text_generation_model(model),
-            is_pooling_model=True,  # Can convert any model into a pooling model
+            is_pooling_model=is_pooling_model(model),
             supports_cross_encoding=supports_cross_encoding(model),
             supports_multimodal=supports_multimodal(model),
             supports_multimodal_raw_input=supports_multimodal_raw_input(model),
@@ -465,6 +470,16 @@ class _ModelRegistry:
                 f"Model architectures {architectures} failed "
                 "to be inspected. Please check the logs for more details.")
 
+        for arch in architectures:
+            if arch in _PREVIOUSLY_SUPPORTED_MODELS:
+                previous_version = _PREVIOUSLY_SUPPORTED_MODELS[arch]
+
+                raise ValueError(
+                    f"Model architecture {arch} was supported in vLLM until "
+                    f"v{previous_version}, and is not supported anymore. "
+                    "Please use an older version of vLLM if you want to "
+                    "use this model architecture.")
+
         raise ValueError(
             f"Model architectures {architectures} are not supported for now. "
             f"Supported architectures: {all_supported_archs}")
@@ -477,174 +492,284 @@ class _ModelRegistry:
         return _try_load_model_cls(model_arch, self.models[model_arch])
 
     def _try_inspect_model_cls(self, model_arch: str) -> Optional[_ModelInfo]:
-        if model_arch in self.models:
-            return _try_inspect_model_cls(model_arch, self.models[model_arch])
+        if model_arch not in self.models:
+            return None
 
-        if model_arch.endswith("ForSequenceClassification"):
-            causal_lm_arch = model_arch.replace("ForSequenceClassification",
-                                                "ForCausalLM")
-            if causal_lm_arch not in self.models:
+        return _try_inspect_model_cls(model_arch, self.models[model_arch])
+
+    def _try_resolve_transformers(
+        self,
+        architecture: str,
+        model_config: ModelConfig,
+    ) -> Optional[str]:
+        if architecture in _TRANSFORMERS_BACKEND_MODELS:
+            return architecture
+
+        auto_map: dict[str, str] = getattr(model_config.hf_config, "auto_map",
+                                           None) or dict()
+
+        # Make sure that config class is always initialized before model class,
+        # otherwise the model class won't be able to access the config class,
+        # the expected auto_map should have correct order like:
+        # "auto_map": {
+        #     "AutoConfig": "<your-repo-name>--<config-name>",
+        #     "AutoModel": "<your-repo-name>--<config-name>",
+        #     "AutoModelFor<Task>": "<your-repo-name>--<config-name>",
+        # },
+        for prefix in ("AutoConfig", "AutoModel"):
+            for name, module in auto_map.items():
+                if name.startswith(prefix):
+                    try_get_class_from_dynamic_module(
+                        module,
+                        model_config.model,
+                        revision=model_config.revision,
+                        warn_on_fail=False,
+                    )
+
+        model_module = getattr(transformers, architecture, None)
+
+        if model_module is None:
+            for name, module in auto_map.items():
+                if name.startswith("AutoModel"):
+                    model_module = try_get_class_from_dynamic_module(
+                        module,
+                        model_config.model,
+                        revision=model_config.revision,
+                        warn_on_fail=True,
+                    )
+                    if model_module is not None:
+                        break
+            else:
+                if model_config.model_impl != ModelImpl.TRANSFORMERS:
+                    return None
+
+                raise ValueError(
+                    f"Cannot find model module. {architecture!r} is not a "
+                    "registered model in the Transformers library (only "
+                    "relevant if the model is meant to be in Transformers) "
+                    "and 'AutoModel' is not present in the model config's "
+                    "'auto_map' (relevant if the model is custom).")
+
+        if not model_module.is_backend_compatible():
+            if model_config.model_impl != ModelImpl.TRANSFORMERS:
                 return None
 
-            info = _try_inspect_model_cls(causal_lm_arch,
-                                          self.models[causal_lm_arch])
+            raise ValueError(
+                f"The Transformers implementation of {architecture!r} "
+                "is not compatible with vLLM.")
 
-            info = _ModelInfo(**dict(
-                asdict(info), **{
-                    "architecture": model_arch,
-                    "supports_cross_encoding": True
-                }))
-            return info
+        return model_config._get_transformers_backend_cls()
 
-        return None
+    def _normalize_arch(
+        self,
+        architecture: str,
+        model_config: ModelConfig,
+    ) -> str:
+        if architecture in self.models:
+            return architecture
+
+        # This may be called in order to resolve runner_type and convert_type
+        # in the first place, in which case we consider the default match
+        match = try_match_architecture_defaults(
+            architecture,
+            runner_type=getattr(model_config, "runner_type", None),
+            convert_type=getattr(model_config, "convert_type", None),
+        )
+        if match:
+            suffix, _ = match
+
+            # Get the name of the base model to convert
+            for repl_suffix, _ in iter_architecture_defaults():
+                base_arch = architecture.replace(suffix, repl_suffix)
+                if base_arch in self.models:
+                    return base_arch
+
+        return architecture
 
     def _normalize_archs(
         self,
-        architectures: Union[str, list[str]],
+        architectures: list[str],
+        model_config: ModelConfig,
     ) -> list[str]:
-        if isinstance(architectures, str):
-            architectures = [architectures]
         if not architectures:
             logger.warning("No model architectures are specified")
 
-        # filter out support architectures
-        normalized_arch = list(
-            filter(lambda model: model in self.models, architectures))
-
-        # try automatic conversion in adapters.py
-        for arch in architectures:
-            if not arch.endswith("ForSequenceClassification"):
-                continue
-            causal_lm_arch = arch.replace("ForSequenceClassification",
-                                          "ForCausalLM")
-            if causal_lm_arch in self.models:
-                normalized_arch.append(arch)
-
-        # NOTE(Isotr0py): Be careful of architectures' order!
-        # Make sure Transformers backend architecture is at the end of the
-        # list, otherwise pooling models automatic conversion will fail!
-        for arch in normalized_arch:
-            if arch.startswith("TransformersFor"):
-                normalized_arch.remove(arch)
-                normalized_arch.append(arch)
-
-        return normalized_arch
+        return [
+            self._normalize_arch(arch, model_config) for arch in architectures
+        ]
 
     def inspect_model_cls(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> tuple[_ModelInfo, str]:
-        architectures = self._normalize_archs(architectures)
+        if isinstance(architectures, str):
+            architectures = [architectures]
 
-        for arch in architectures:
-            model_info = self._try_inspect_model_cls(arch)
+        normalized_archs = self._normalize_archs(architectures, model_config)
+
+        # Require transformers impl
+        if model_config.model_impl == ModelImpl.TRANSFORMERS:
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_info = self._try_inspect_model_cls(arch)
+                if model_info is not None:
+                    return (model_info, arch)
+
+        for arch, normalized_arch in zip(architectures, normalized_archs):
+            model_info = self._try_inspect_model_cls(normalized_arch)
             if model_info is not None:
                 return (model_info, arch)
 
+        # Fallback to transformers impl
+        if model_config.model_impl in (ModelImpl.AUTO, ModelImpl.TRANSFORMERS):
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_info = self._try_inspect_model_cls(arch)
+                if model_info is not None:
+                    return (model_info, arch)
+
         return self._raise_for_unsupported(architectures)
 
     def resolve_model_cls(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> tuple[type[nn.Module], str]:
-        architectures = self._normalize_archs(architectures)
+        if isinstance(architectures, str):
+            architectures = [architectures]
 
-        for arch in architectures:
-            model_cls = self._try_load_model_cls(arch)
+        normalized_archs = self._normalize_archs(architectures, model_config)
+
+        # Require transformers impl
+        if model_config.model_impl == ModelImpl.TRANSFORMERS:
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_cls = self._try_load_model_cls(arch)
+                if model_cls is not None:
+                    return (model_cls, arch)
+
+        for arch, normalized_arch in zip(architectures, normalized_archs):
+            model_cls = self._try_load_model_cls(normalized_arch)
             if model_cls is not None:
                 return (model_cls, arch)
 
+        # Fallback to transformers impl
+        if model_config.model_impl in (ModelImpl.AUTO, ModelImpl.TRANSFORMERS):
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_cls = self._try_load_model_cls(arch)
+                if model_cls is not None:
+                    return (model_cls, arch)
+
         return self._raise_for_unsupported(architectures)
 
     def is_text_generation_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.is_text_generation_model
 
     def is_pooling_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.is_pooling_model
 
     def is_cross_encoder_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.supports_cross_encoding
 
     def is_multimodal_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.supports_multimodal
 
     def supports_multimodal_raw_input(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.supports_multimodal_raw_input
 
     def is_pp_supported_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.supports_pp
 
     def model_has_inner_state(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.has_inner_state
 
     def is_attention_free_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.is_attention_free
 
     def is_hybrid_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.is_hybrid
 
     def is_noops_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.has_noops
 
     def is_transcription_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.supports_transcription
 
     def is_transcription_only_model(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return model_cls.supports_transcription_only
 
     def is_v1_compatible(
         self,
         architectures: Union[str, list[str]],
+        model_config: ModelConfig,
     ) -> bool:
-        model_cls, _ = self.inspect_model_cls(architectures)
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
         return not model_cls.supports_v0_only
 
 

vllm/transformers_utils/dynamic_module.py

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

vllm/transformers_utils/tokenizer_group.py

@@ -3,6 +3,8 @@
 
 from typing import Optional
 
+from typing_extensions import assert_never
+
 from vllm.config import LoRAConfig, ModelConfig, SchedulerConfig
 from vllm.lora.request import LoRARequest
 from vllm.transformers_utils.tokenizer import (AnyTokenizer, encode_tokens,
@@ -108,6 +110,14 @@ class TokenizerGroup:
 def init_tokenizer_from_configs(model_config: ModelConfig,
                                 scheduler_config: SchedulerConfig,
                                 lora_config: Optional[LoRAConfig]):
+    runner_type = model_config.runner_type
+    if runner_type == "generate" or runner_type == "draft":
+        truncation_side = "left"
+    elif runner_type == "pooling":
+        truncation_side = "right"
+    else:
+        assert_never(runner_type)
+
     return TokenizerGroup(
         tokenizer_id=model_config.tokenizer,
         enable_lora=bool(lora_config),
@@ -117,4 +127,4 @@ def init_tokenizer_from_configs(model_config: ModelConfig,
         tokenizer_mode=model_config.tokenizer_mode,
         trust_remote_code=model_config.trust_remote_code,
         revision=model_config.tokenizer_revision,
-        truncation_side=model_config.truncation_side)
+        truncation_side=truncation_side)

vllm/v1/worker/gpu_model_runner.py

@@ -127,8 +127,8 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         self.is_multimodal_model = model_config.is_multimodal_model
         self.is_pooling_model = model_config.pooler_config is not None
         self.is_encoder_only_model = False
-        self.model_supports_multimodal_raw_input = (
-            model_config.model_supports_multimodal_raw_input)
+        self.is_multimodal_raw_input_supported = (
+            model_config.is_multimodal_raw_input_supported)
         self.max_model_len = model_config.max_model_len
         self.max_num_tokens = scheduler_config.max_num_batched_tokens
         self.max_num_reqs = scheduler_config.max_num_seqs
@@ -583,7 +583,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
     ) -> dict[str, Any]:
 
         model_kwargs: dict[str, Any] = {}
-        if self.model_supports_multimodal_raw_input:
+        if self.is_multimodal_raw_input_supported:
             # This model requires the raw multimodal data in input.
             if scheduler_output:
                 multi_modal_kwargs_list = []