Improve configs - LoRAConfig + PromptAdapterConfig (#16980)

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

vllm/config.py

@@ -2565,18 +2565,41 @@ class SpeculativeConfig:
         return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
 
 
+LoRADType = Literal["auto", "float16", "bfloat16"]
+
+
+@config
 @dataclass
 class LoRAConfig:
-    max_lora_rank: int
-    max_loras: int
+    """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
-    lora_dtype: Optional[Union[torch.dtype, str]] = 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
+    """Maximum size of extra vocabulary that can be present in a LoRA adapter
+    (added to the base model vocabulary)."""
     # This is a constant.
     lora_vocab_padding_size: ClassVar[int] = 256
-    long_lora_scaling_factors: Optional[tuple[float]] = None
+    long_lora_scaling_factors: Optional[tuple[float, ...]] = None
+    """Specify multiple scaling factors (which can be different from base model
+    scaling factor - see eg. Long LoRA) to allow for multiple LoRA adapters
+    trained with those scaling factors to be used at the same time. If not
+    specified, only adapters trained with the base model scaling factor are
+    allowed."""
     bias_enabled: bool = False
+    """Enable bias for LoRA adapters."""
 
     def compute_hash(self) -> str:
         """
@@ -2641,12 +2664,19 @@ class LoRAConfig:
                 "V1 LoRA does not support long LoRA, please use V0.")
 
 
+@config
 @dataclass
 class PromptAdapterConfig:
-    max_prompt_adapters: int
-    max_prompt_adapter_token: int
+    max_prompt_adapters: int = 1
+    """Max number of PromptAdapters in a batch."""
+    max_prompt_adapter_token: int = 0
+    """Max number of PromptAdapters tokens."""
     max_cpu_prompt_adapters: Optional[int] = None
-    prompt_adapter_dtype: Optional[torch.dtype] = None
+    """Maximum number of PromptAdapters to store in CPU memory. Must be >= than
+    `max_prompt_adapters`."""
+    prompt_adapter_dtype: Union[torch.dtype, str] = "auto"
+    """Data type for PromptAdapter. If auto, will default to base model dtype.
+    """
 
     def compute_hash(self) -> str:
         """
@@ -2678,7 +2708,7 @@ class PromptAdapterConfig:
             self.max_cpu_prompt_adapters = self.max_prompt_adapters
 
     def verify_with_model_config(self, model_config: ModelConfig):
-        if self.prompt_adapter_dtype in (None, "auto"):
+        if self.prompt_adapter_dtype == "auto":
             self.prompt_adapter_dtype = model_config.dtype
         elif isinstance(self.prompt_adapter_dtype, str):
             self.prompt_adapter_dtype = getattr(torch,