Improve configs - LoRAConfig + PromptAdapterConfig

tests/lora/test_lora_manager.py

@@ -31,6 +31,8 @@
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
 ] if current_platform.is_cuda_alike() else ["cpu"])
 
+DEFAULT_DTYPE = torch.get_default_dtype()
+
 
 @pytest.fixture(scope="function", autouse=True)
 def use_v0_only(monkeypatch: pytest.MonkeyPatch):
@@ -125,8 +127,10 @@ def test_replace_submodules(dist_init, dummy_model):
     model = dummy_model
     manager = LoRAModelManager(
         model, 1, 1, 1,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=8, max_loras=8),
-        torch.device(DEVICES[0]))
+        LoRAConfig(max_lora_rank=8,
+                   max_cpu_loras=8,
+                   max_loras=8,
+                   lora_dtype=DEFAULT_DTYPE), torch.device(DEVICES[0]))
     model = manager.model
     assert isinstance(model.get_submodule("dense1"),
                       ColumnParallelLinearWithLoRA)
@@ -155,7 +159,8 @@ def test_lora_model_manager(dist_init, dummy_model, device):
                                2,
                                LoRAConfig(max_lora_rank=8,
                                           max_cpu_loras=3,
-                                          max_loras=2),
+                                          max_loras=2,
+                                          lora_dtype=DEFAULT_DTYPE),
                                device=device)
     assert all(x is None for x in manager.lora_index_to_id)
     assert manager.add_adapter(model_lora1)
@@ -221,7 +226,8 @@ def test_lora_lru_cache_model_manager(dist_init, dummy_model, device):
                                        2,
                                        LoRAConfig(max_lora_rank=8,
                                                   max_cpu_loras=3,
-                                                  max_loras=2),
+                                                  max_loras=2,
+                                                  lora_dtype=DEFAULT_DTYPE),
                                        device=device)
     assert all(x is None for x in manager.lora_index_to_id)
     assert manager.add_adapter(model_lora1)
@@ -316,7 +322,8 @@ def test_lru_lora_model_manager(dist_init, dummy_model, device):
                                        2,
                                        LoRAConfig(max_lora_rank=8,
                                                   max_cpu_loras=2,
-                                                  max_loras=2),
+                                                  max_loras=2,
+                                                  lora_dtype=DEFAULT_DTYPE),
                                        device=device)
 
     assert all(x is None for x in manager.lora_index_to_id)
@@ -424,7 +431,10 @@ def test_lru_lora_model_manager(dist_init, dummy_model, device):
 @pytest.mark.parametrize("device", DEVICES)
 def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
                                           sql_lora_files, device):
-    lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
+    lora_config = LoRAConfig(max_lora_rank=8,
+                             max_cpu_loras=4,
+                             max_loras=4,
+                             lora_dtype=DEFAULT_DTYPE)
     worker_adapter_manager = LRUCacheWorkerLoRAManager(
         4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
         lora_config.lora_extra_vocab_size, lora_config, device,
@@ -504,7 +514,10 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
 def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
                                 sql_lora_files, device):
     # Should remove every LoRA not specified in the request.
-    lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
+    lora_config = LoRAConfig(max_lora_rank=8,
+                             max_cpu_loras=4,
+                             max_loras=4,
+                             lora_dtype=DEFAULT_DTYPE)
     worker_adapter_manager = WorkerLoRAManager(
         4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
         lora_config.lora_extra_vocab_size, lora_config, device,
@@ -600,7 +613,8 @@ def test_packed_loras(dist_init, dummy_model_gate_up, device):
                                2,
                                LoRAConfig(max_lora_rank=8,
                                           max_cpu_loras=2,
-                                          max_loras=2),
+                                          max_loras=2,
+                                          lora_dtype=DEFAULT_DTYPE),
                                device=device)
     model = manager.model
 

vllm/config.py

@@ -2565,18 +2565,41 @@ def __repr__(self) -> str:
         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 @@ def verify_lora_support(self):
                 "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 @@ def __post_init__(self):
             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,

vllm/engine/arg_utils.py

@@ -7,7 +7,7 @@
 import re
 import threading
 from dataclasses import MISSING, dataclass, fields
-from typing import (Any, Callable, Dict, List, Literal, Optional, Tuple, Type,
+from typing import (Any, Callable, Dict, List, Literal, Optional, Type,
                     TypeVar, Union, cast, get_args, get_origin)
 
 import torch
@@ -192,18 +192,23 @@ class EngineArgs:
         get_field(MultiModalConfig, "limit_per_prompt")
     mm_processor_kwargs: Optional[Dict[str, Any]] = None
     disable_mm_preprocessor_cache: bool = False
+    # LoRA fields
     enable_lora: bool = False
-    enable_lora_bias: bool = False
-    max_loras: int = 1
-    max_lora_rank: int = 16
+    enable_lora_bias: bool = LoRAConfig.bias_enabled
+    max_loras: int = LoRAConfig.max_loras
+    max_lora_rank: int = LoRAConfig.max_lora_rank
+    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
+    max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
+    lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
+    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
+    long_lora_scaling_factors: Optional[tuple[float, ...]] = \
+        LoRAConfig.long_lora_scaling_factors
+    # PromptAdapter fields
     enable_prompt_adapter: bool = False
-    max_prompt_adapters: int = 1
-    max_prompt_adapter_token: int = 0
-    fully_sharded_loras: bool = False
-    lora_extra_vocab_size: int = 256
-    long_lora_scaling_factors: Optional[Tuple[float]] = None
-    lora_dtype: Optional[Union[str, torch.dtype]] = 'auto'
-    max_cpu_loras: Optional[int] = None
+    max_prompt_adapters: int = PromptAdapterConfig.max_prompt_adapters
+    max_prompt_adapter_token: int = \
+        PromptAdapterConfig.max_prompt_adapter_token
+
     device: Device = DeviceConfig.device
     num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
     multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
@@ -338,10 +343,21 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
                     kwargs[name]["choices"] = choices
                     choice_type = type(choices[0])
                     assert all(type(c) is choice_type for c in choices), (
-                        f"All choices must be of the same type. "
+                        "All choices must be of the same type. "
                         f"Got {choices} with types {[type(c) for c in choices]}"
                     )
                     kwargs[name]["type"] = choice_type
+                elif can_be_type(field_type, tuple):
+                    if is_type_in_union(field_type, tuple):
+                        field_type = get_type_from_union(field_type, tuple)
+                    dtypes = get_args(field_type)
+                    dtype = dtypes[0]
+                    assert all(
+                        d is dtype for d in dtypes if d is not Ellipsis
+                    ), ("All non-Ellipsis tuple elements must be of the same "
+                        f"type. Got {dtypes}.")
+                    kwargs[name]["type"] = dtype
+                    kwargs[name]["nargs"] = "+"
                 elif can_be_type(field_type, int):
                     kwargs[name]["type"] = optional_int if optional else int
                 elif can_be_type(field_type, float):
@@ -685,70 +701,49 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
             'inputs.')
 
         # LoRA related configs
-        parser.add_argument('--enable-lora',
-                            action='store_true',
-                            help='If True, enable handling of LoRA adapters.')
-        parser.add_argument('--enable-lora-bias',
-                            action='store_true',
-                            help='If True, enable bias for LoRA adapters.')
-        parser.add_argument('--max-loras',
-                            type=int,
-                            default=EngineArgs.max_loras,
-                            help='Max number of LoRAs in a single batch.')
-        parser.add_argument('--max-lora-rank',
-                            type=int,
-                            default=EngineArgs.max_lora_rank,
-                            help='Max LoRA rank.')
-        parser.add_argument(
-            '--lora-extra-vocab-size',
-            type=int,
-            default=EngineArgs.lora_extra_vocab_size,
-            help=('Maximum size of extra vocabulary that can be '
-                  'present in a LoRA adapter (added to the base '
-                  'model vocabulary).'))
-        parser.add_argument(
+        lora_kwargs = get_kwargs(LoRAConfig)
+        lora_group = parser.add_argument_group(
+            title="LoRAConfig",
+            description=LoRAConfig.__doc__,
+        )
+        lora_group.add_argument(
+            '--enable-lora',
+            action=argparse.BooleanOptionalAction,
+            help='If True, enable handling of LoRA adapters.')
+        lora_group.add_argument('--enable-lora-bias',
+                                **lora_kwargs["bias_enabled"])
+        lora_group.add_argument('--max-loras', **lora_kwargs["max_loras"])
+        lora_group.add_argument('--max-lora-rank',
+                                **lora_kwargs["max_lora_rank"])
+        lora_group.add_argument('--lora-extra-vocab-size',
+                                **lora_kwargs["lora_extra_vocab_size"])
+        lora_group.add_argument(
             '--lora-dtype',
-            type=str,
-            default=EngineArgs.lora_dtype,
-            choices=['auto', 'float16', 'bfloat16'],
-            help=('Data type for LoRA. If auto, will default to '
-                  'base model dtype.'))
-        parser.add_argument(
-            '--long-lora-scaling-factors',
-            type=optional_str,
-            default=EngineArgs.long_lora_scaling_factors,
-            help=('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.'))
-        parser.add_argument(
-            '--max-cpu-loras',
-            type=int,
-            default=EngineArgs.max_cpu_loras,
-            help=('Maximum number of LoRAs to store in CPU memory. '
-                  'Must be >= than max_loras.'))
-        parser.add_argument(
-            '--fully-sharded-loras',
-            action='store_true',
-            help=('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.'))
-        parser.add_argument('--enable-prompt-adapter',
-                            action='store_true',
-                            help='If True, enable handling of PromptAdapters.')
-        parser.add_argument('--max-prompt-adapters',
-                            type=int,
-                            default=EngineArgs.max_prompt_adapters,
-                            help='Max number of PromptAdapters in a batch.')
-        parser.add_argument('--max-prompt-adapter-token',
-                            type=int,
-                            default=EngineArgs.max_prompt_adapter_token,
-                            help='Max number of PromptAdapters tokens')
+            **lora_kwargs["lora_dtype"],
+        )
+        lora_group.add_argument('--long-lora-scaling-factors',
+                                **lora_kwargs["long_lora_scaling_factors"])
+        lora_group.add_argument('--max-cpu-loras',
+                                **lora_kwargs["max_cpu_loras"])
+        lora_group.add_argument('--fully-sharded-loras',
+                                **lora_kwargs["fully_sharded_loras"])
+
+        # PromptAdapter related configs
+        prompt_adapter_kwargs = get_kwargs(PromptAdapterConfig)
+        prompt_adapter_group = parser.add_argument_group(
+            title="PromptAdapterConfig",
+            description=PromptAdapterConfig.__doc__,
+        )
+        prompt_adapter_group.add_argument(
+            '--enable-prompt-adapter',
+            action=argparse.BooleanOptionalAction,
+            help='If True, enable handling of PromptAdapters.')
+        prompt_adapter_group.add_argument(
+            '--max-prompt-adapters',
+            **prompt_adapter_kwargs["max_prompt_adapters"])
+        prompt_adapter_group.add_argument(
+            '--max-prompt-adapter-token',
+            **prompt_adapter_kwargs["max_prompt_adapter_token"])
 
         # Device arguments
         device_kwargs = get_kwargs(DeviceConfig)