Move LoRAConfig from config/__init__.py to config/lora.py (#24644)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/config/__init__.py

@@ -36,6 +36,7 @@ from vllm.config.compilation import (CompilationConfig, CompilationLevel,
 from vllm.config.kv_events import KVEventsConfig
 from vllm.config.kv_transfer import KVTransferConfig
 from vllm.config.load import LoadConfig
+from vllm.config.lora import LoRAConfig
 from vllm.config.parallel import (DistributedExecutorBackend, EPLBConfig,
                                   ParallelConfig)
 from vllm.config.scheduler import SchedulerConfig, SchedulerPolicy
@@ -2400,116 +2401,6 @@ class SpeculativeConfig:
         return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
 
 
-LoRADType = Literal["auto", "float16", "bfloat16"]
-
-
-@config
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
-class LoRAConfig:
-    """Configuration for LoRA."""
-
-    max_lora_rank: int = 16
-    """Max LoRA rank."""
-    max_loras: int = 1
-    """Max number of LoRAs in a single batch."""
-    fully_sharded_loras: bool = False
-    """By default, only half of the LoRA computation is sharded with tensor
-    parallelism. Enabling this will use the fully sharded layers. At high
-    sequence length, max rank or tensor parallel size, this is likely faster.
-    """
-    max_cpu_loras: Optional[int] = None
-    """Maximum number of LoRAs to store in CPU memory. Must be >= than
-    `max_loras`."""
-    lora_dtype: Union[torch.dtype, LoRADType] = "auto"
-    """Data type for LoRA. If auto, will default to base model dtype."""
-    lora_extra_vocab_size: int = 256
-    """(Deprecated) Maximum size of extra vocabulary that can be present in a 
-    LoRA adapter. Will be removed in v0.12.0."""
-    lora_vocab_padding_size: ClassVar[int] = current_platform\
-        .get_lora_vocab_padding_size()
-    default_mm_loras: Optional[dict[str, str]] = None
-    """Dictionary mapping specific modalities to LoRA model paths; this field
-    is only applicable to multimodal models and should be leveraged when a
-    model always expects a LoRA to be active when a given modality is present.
-    Note that currently, if a request provides multiple additional
-    modalities, each of which have their own LoRA, we do NOT apply
-    default_mm_loras because we currently only support one lora adapter
-    per prompt. When run in offline mode, the lora IDs for n modalities
-    will be automatically assigned to 1-n with the names of the modalities
-    in alphabetic order."""
-    bias_enabled: bool = False
-    """[DEPRECATED] Enable bias for LoRA adapters. This option will be
-    removed in v0.12.0."""
-
-    def compute_hash(self) -> str:
-        """
-        WARNING: Whenever a new field is added to this config,
-        ensure that it is included in the factors list if
-        it affects the computation graph.
-
-        Provide a hash that uniquely identifies all the configs
-        that affect the structure of the computation
-        graph from input ids/embeddings to the final hidden states,
-        excluding anything before input ids/embeddings and after
-        the final hidden states.
-        """
-        factors: list[Any] = []
-        factors.append(self.max_lora_rank)
-        factors.append(self.max_loras)
-        factors.append(self.fully_sharded_loras)
-        factors.append(self.lora_dtype)
-        factors.append(self.lora_extra_vocab_size)
-        factors.append(self.lora_vocab_padding_size)
-        factors.append(self.bias_enabled)
-        hash_str = hashlib.md5(str(factors).encode(),
-                               usedforsecurity=False).hexdigest()
-        return hash_str
-
-    def __post_init__(self):
-        # Deprecation warning for lora_extra_vocab_size
-        logger.warning(
-            "`lora_extra_vocab_size` is deprecated and will be removed "
-            "in v0.12.0. Additional vocabulary support for "
-            "LoRA adapters is being phased out.")
-
-        # Deprecation warning for enable_lora_bias
-        if self.bias_enabled:
-            logger.warning("`enable_lora_bias` is deprecated "
-                           "and will be removed in v0.12.0.")
-
-        # Setting the maximum rank to 512 should be able to satisfy the vast
-        # majority of applications.
-        possible_max_ranks = (8, 16, 32, 64, 128, 256, 320, 512)
-        possible_lora_extra_vocab_size = (256, 512)
-        if self.max_lora_rank not in possible_max_ranks:
-            raise ValueError(
-                f"max_lora_rank ({self.max_lora_rank}) must be one of "
-                f"{possible_max_ranks}.")
-        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
-            raise ValueError(
-                f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
-                f"must be one of {possible_lora_extra_vocab_size}.")
-        if self.max_loras < 1:
-            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
-        if self.max_cpu_loras is None:
-            self.max_cpu_loras = self.max_loras
-        elif self.max_cpu_loras < self.max_loras:
-            raise ValueError(
-                f"max_cpu_loras ({self.max_cpu_loras}) must be >= "
-                f"max_loras ({self.max_loras})")
-
-    def verify_with_cache_config(self, cache_config: CacheConfig):
-        if cache_config.cpu_offload_gb > 0 and not envs.VLLM_USE_V1:
-            raise ValueError(
-                "V0 LoRA does not support CPU offload, please use V1.")
-
-    def verify_with_model_config(self, model_config: ModelConfig):
-        if self.lora_dtype in (None, "auto"):
-            self.lora_dtype = model_config.dtype
-        elif isinstance(self.lora_dtype, str):
-            self.lora_dtype = getattr(torch, self.lora_dtype)
-
-
 @config
 @dataclass
 class MultiModalConfig: