[Misc][LoRA] Add PEFTHelper for LoRA

tests/lora/test_lora_manager.py

@@ -1,3 +1,4 @@
+import json
 import os
 from typing import Dict, List
 
@@ -13,6 +14,7 @@
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
 from vllm.lora.models import (LoRAMapping, LoRAModel, LoRAModelManager,
                               LRUCacheLoRAModelManager)
+from vllm.lora.peft_helper import PEFTHelper
 from vllm.lora.request import LoRARequest
 from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager,
                                       WorkerLoRAManager)
@@ -30,18 +32,68 @@
 ]
 
 
+def test_peft_helper(sql_lora_files):
+    lora_config_path = os.path.join(sql_lora_files, "adapter_config.json")
+    with open(lora_config_path) as f:
+        config = json.load(f)
+    peft_helper = PEFTHelper.from_dict(config)
+    assert peft_helper.r == 8
+    assert peft_helper.lora_alpha == 16
+    assert peft_helper.target_modules == [
+        "q_proj",
+        "v_proj",
+        "k_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+        "embed_tokens",
+        "lm_head",
+    ]
+
+    expected_error = "vLLM only supports modules_to_save being None."
+    with pytest.raises(ValueError, match=expected_error):
+        config = dict(
+            r=8,
+            lora_alpha=16,
+            target_modules=["gate_proj"],
+            modules_to_save=["lm_head"],
+        )
+        PEFTHelper.from_dict(config)
+    expected_error = "vLLM does not yet support RSLoRA."
+    with pytest.raises(ValueError, match=expected_error):
+        config = dict(r=8,
+                      lora_alpha=16,
+                      target_modules=["gate_proj"],
+                      use_rslora=True)
+        PEFTHelper.from_dict(config)
+
+    expected_error = "vLLM does not yet support DoRA."
+    with pytest.raises(ValueError, match=expected_error):
+        config = dict(r=8,
+                      lora_alpha=16,
+                      target_modules=["gate_proj"],
+                      use_dora=True)
+        PEFTHelper.from_dict(config)
+
+
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 def test_from_lora_tensors(sql_lora_files, device):
     tensors = load_file(
         os.path.join(sql_lora_files, "adapter_model.safetensors"))
     new_embeddings = load_file(
         os.path.join(sql_lora_files, "new_embeddings.safetensors"))
+
+    lora_config_path = os.path.join(sql_lora_files, "adapter_config.json")
+    with open(lora_config_path) as f:
+        config = json.load(f)
+
+    peft_helper = PEFTHelper.from_dict(config)
     lora_model = LoRAModel.from_lora_tensors(
         1,
-        8,
-        16,
         tensors,
-        device,
+        peft_helper=peft_helper,
+        device=device,
         embeddings=new_embeddings,
         embedding_modules=EMBEDDING_MODULES,
         embedding_padding_modules=EMBEDDING_PADDING_MODULES)

vllm/lora/lora.py

@@ -4,6 +4,7 @@
 import torch
 import torch.types
 
+from vllm.lora.peft_helper import PEFTHelper
 from vllm.utils import is_pin_memory_available
 
 
@@ -59,6 +60,23 @@ def extra_vocab_size(self) -> int:
         return self.embeddings_tensor.shape[
             0] if self.embeddings_tensor is not None else 0
 
+    @classmethod
+    def from_config(
+        cls,
+        module_name: str,
+        peft_helper: PEFTHelper,
+        embeddings_tensor: Optional[torch.Tensor] = None,
+    ) -> "LoRALayerWeights":
+        return cls(
+            module_name,
+            peft_helper.r,
+            peft_helper.lora_alpha,
+            None,
+            None,
+            None,
+            embeddings_tensor,
+        )
+
     @classmethod
     def create_dummy_lora_weights(
             cls,

vllm/lora/models.py

@@ -21,6 +21,7 @@
                               LinearScalingRotaryEmbeddingWithLora,
                               LoRAMapping)
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
+from vllm.lora.peft_helper import PEFTHelper
 from vllm.lora.punica_wrapper import get_punica_wrapper
 from vllm.lora.utils import (from_layer, from_layer_logits_processor,
                              is_regex_target_modules,
@@ -104,14 +105,12 @@ def get_lora(self, module_name: str) -> Optional[LoRALayerWeights]:
     def from_lora_tensors(
         cls,
         lora_model_id: int,
-        rank: int,
-        lora_alpha: int,
         tensors: Dict[str, torch.Tensor],
+        peft_helper: PEFTHelper,
         device: str = "cuda",
         dtype: Optional[torch.dtype] = None,
         embeddings: Optional[Dict[str, torch.Tensor]] = None,
         target_embedding_padding: Optional[int] = None,
-        scaling_factor: Optional[float] = None,
         embedding_modules: Optional[Dict[str, str]] = None,
         embedding_padding_modules: Optional[List[str]] = None,
     ) -> "LoRAModel":
@@ -135,10 +134,9 @@ def from_lora_tensors(
                         if pin_memory:
                             lora_embeddings_tensor = (
                                 lora_embeddings_tensor.pin_memory())
-                loras[module_name] = LoRALayerWeights(module_name, rank,
-                                                      lora_alpha, None, None,
-                                                      None,
-                                                      lora_embeddings_tensor)
+                loras[module_name] = LoRALayerWeights.from_config(
+                    module_name, peft_helper, lora_embeddings_tensor)
+
             if is_bias:
                 loras[module_name].bias = tensor.to(device=device,
                                                     dtype=dtype).t()
@@ -170,7 +168,11 @@ def from_lora_tensors(
 
         for lora in loras.values():
             lora.optimize()
-        return cls(lora_model_id, rank, loras, scaling_factor=scaling_factor)
+
+        return cls(lora_model_id,
+                   peft_helper.r,
+                   loras,
+                   scaling_factor=peft_helper.vllm_scaling_factor)
 
     @classmethod
     def from_local_checkpoint(
@@ -212,6 +214,9 @@ def from_local_checkpoint(
                                                     "new_embeddings.bin")
         with open(lora_config_path) as f:
             config = json.load(f)
+
+        config["vllm_max_position_embeddings"] = max_position_embeddings
+        peft_helper = PEFTHelper.from_dict(config)
         if os.path.isfile(lora_tensor_path):
             tensors: Dict[str, torch.Tensor] = {}
             # Find unexpected modules.
@@ -242,7 +247,7 @@ def from_local_checkpoint(
             # When a bin file is provided, we rely on config to find unexpected
             # modules.
             unexpected_modules = []
-            target_modules = config["target_modules"]
+            target_modules = peft_helper.target_modules
             if not isinstance(target_modules, list):
                 target_modules = [target_modules]
             for module in target_modules:
@@ -256,7 +261,7 @@ def from_local_checkpoint(
             # https://github.com/vllm-project/vllm/pull/5909. But there's no
             # other better mechanism.
             if unexpected_modules and not is_regex_target_modules(
-                    config["target_modules"], expected_lora_modules):
+                    peft_helper.target_modules, expected_lora_modules):
                 raise ValueError(
                     f"While loading {lora_dir}, expected"
                     f" target modules in {expected_lora_modules}"
@@ -274,30 +279,17 @@ def from_local_checkpoint(
             embeddings = torch.load(new_embeddings_bin_file_path,
                                     map_location=device)
 
-        rank = config["r"]
-        lora_alpha = config["lora_alpha"]
-        context_length = config.get("context_length", None)
-        scaling_factor = None
-        if context_length:
-            if max_position_embeddings is None:
-                max_position_embeddings = context_length
-            scaling_factor = float(
-                math.ceil(context_length / max_position_embeddings))
-
         return cls.from_lora_tensors(
             lora_model_id=get_lora_id()
             if lora_model_id is None else lora_model_id,
-            rank=rank,
-            lora_alpha=lora_alpha,
             tensors=tensors,
+            peft_helper=peft_helper,
             device=device,
             dtype=dtype,
             embeddings=embeddings,
             target_embedding_padding=target_embedding_padding,
-            scaling_factor=scaling_factor,
             embedding_modules=embedding_modules,
-            embedding_padding_modules=embedding_padding_modules,
-        )
+            embedding_padding_modules=embedding_padding_modules)
 
 
 class LoRAModelManager(AdapterModelManager):

vllm/lora/peft_helper.py

@@ -0,0 +1,70 @@
+# Adapted from: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/config.py
+
+import math
+from dataclasses import MISSING, dataclass, field, fields
+from typing import Literal, Optional, Union
+
+
+@dataclass
+class PEFTHelper:
+    # Required fields
+    r: int
+    lora_alpha: int
+    target_modules: Union[list[str], str]
+
+    bias: Literal["none", "all", "lora_only"] = field(default="none")
+    modules_to_save: Optional[list[str]] = field(default=None)
+    use_rslora: bool = field(default=False)
+    use_dora: bool = field(default=False)
+    # long lora field
+    context_length: int = field(default=0)
+    # Extra vllm field, start with 'vllm_' to avoid conflict
+    vllm_max_position_embeddings: Optional[int] = field(default=False)
+    vllm_scaling_factor: Optional[float] = field(default=None)
+
+    def _validate_features(self):
+        error_msg = []
+
+        if self.modules_to_save:
+            error_msg.append("vLLM only supports modules_to_save being None.")
+        if self.use_rslora:
+            error_msg.append("vLLM does not yet support RSLoRA.")
+
+        if self.use_dora:
+            error_msg.append("vLLM does not yet support DoRA.")
+
+        if error_msg:
+            raise ValueError(f"{', '.join(error_msg)}")
+
+    def __post_init__(self):
+        self._validate_features()
+        if self.context_length:
+            if self.vllm_max_position_embeddings is None:
+                self.vllm_max_position_embeddings = self.context_length
+            self.vllm_scaling_factor = float(
+                math.ceil(self.context_length /
+                          self.vllm_max_position_embeddings))
+
+    @classmethod
+    def from_dict(cls, config_dict: dict) -> "PEFTHelper":
+        # Get all field information from the class
+        class_fields = {f.name: f for f in fields(cls)}
+        # Check for required fields
+        required_fields = {
+            name
+            for name, f in class_fields.items()
+            if f.default is MISSING and f.default_factory is MISSING
+        }
+
+        # Identify any missing required fields
+        missing_fields = required_fields - set(config_dict.keys())
+        if missing_fields:
+            raise ValueError(
+                f"Missing required configuration fields: {missing_fields}")
+
+        # Filter out fields that aren't defined in the class
+        filtered_dict = {
+            k: v
+            for k, v in config_dict.items() if k in class_fields
+        }
+        return cls(**filtered_dict)