Add LoRA support for Mixtral (#2831)

* add mixtral lora support

* formatting

* fix incorrectly ported logic

* polish tests

* minor fixes and refactoring

* minor fixes

* formatting

* rename and remove redundant logic

* refactoring

* refactoring

* minor fix

* minor refactoring

* fix code smell

tests/lora/conftest.py

@@ -121,6 +121,11 @@ def sql_lora_files():
     return snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
 
 
+@pytest.fixture(scope="session")
+def mixtral_lora_files():
+    return snapshot_download(repo_id="terrysun/mixtral-lora-adapter")
+
+
 @pytest.fixture
 def llama_2_7b_engine_extra_embeddings() -> nn.Module:
     cleanup()

tests/lora/test_lora_manager.py

@@ -11,25 +11,35 @@ from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
                               RowParallelLinearWithLoRA,
                               MergedColumnParallelLinearWithLoRA)
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
-from vllm.lora.models import (EMBEDDING_MODULES, LoRAModel, LoRAModelManager,
+from vllm.lora.models import (LoRAModel, LoRAModelManager,
                               LRUCacheLoRAModelManager, LoRAMapping)
 from vllm.lora.request import LoRARequest
 from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager,
                                       WorkerLoRAManager)
 from vllm.model_executor.layers.linear import RowParallelLinear
 
+EMBEDDING_MODULES = {
+    "embed_tokens": "input_embeddings",
+    "lm_head": "output_embeddings",
+}
+
+EMBEDDING_PADDING_MODULES = ["lm_head"]
+
 
 def test_from_lora_tensors(sql_lora_files):
     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_model = LoRAModel.from_lora_tensors(1,
+    lora_model = LoRAModel.from_lora_tensors(
+        1,
         8,
         16,
         tensors,
         "cuda",
-                                             embeddings=new_embeddings)
+        embeddings=new_embeddings,
+        embedding_modules=EMBEDDING_MODULES,
+        embedding_padding_modules=EMBEDDING_PADDING_MODULES)
     for module_name, lora in lora_model.loras.items():
         assert lora.module_name == module_name
         assert lora.rank == 8
@@ -90,14 +100,11 @@ def create_packed_lora(
 
 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),
-                               lora_target_modules=["dense1", "layer1.dense2"])
+    model.supported_lora_modules = ["dense1", "layer1.dense2"]
+    model.packed_modules_mapping = {}
+    manager = LoRAModelManager(
+        model, 1, 1, 1,
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=8, max_loras=8))
     model = manager.model
 
     assert isinstance(model.get_submodule("dense1"),
@@ -111,16 +118,14 @@ def test_replace_submodules(dist_init, dummy_model):
 
 def test_lora_model_manager(dist_init, dummy_model):
     model = dummy_model
+    model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
+    model.packed_modules_mapping = {}
     model_lora1 = create_lora(1, model, ["layer1.dense1", "dense2", "lm_head"])
     model_lora2 = create_lora(2, model, ["dense1", "dense2", "lm_head"])
     model_lora3 = create_lora(3, model, ["dense1", "dense2", "lm_head"])
     manager = LoRAModelManager(
-        model,
-        2,
-        2,
-        2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2),
-        lora_target_modules=["dense1", "dense2", "lm_head"])
+        model, 2, 2, 2,
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2))
     assert all(x is None for x in manager.lora_index_to_id)
     assert manager.add_lora(model_lora1)
     assert manager.activate_lora(1)
@@ -159,16 +164,14 @@ def test_lora_model_manager(dist_init, dummy_model):
 
 def test_lora_lru_cache_model_manager(dist_init, dummy_model):
     model = dummy_model
+    model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
+    model.packed_modules_mapping = {}
     model_lora1 = create_lora(1, model, ["layer1.dense1", "dense2", "lm_head"])
     model_lora2 = create_lora(2, model, ["dense1", "dense2", "lm_head"])
     model_lora3 = create_lora(3, model, ["dense1", "dense2", "lm_head"])
     manager = LRUCacheLoRAModelManager(
-        model,
-        2,
-        2,
-        2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2),
-        lora_target_modules=["dense1", "dense2", "lm_head"])
+        model, 2, 2, 2,
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=3, max_loras=2))
     assert all(x is None for x in manager.lora_index_to_id)
     assert manager.add_lora(model_lora1)
     assert manager.activate_lora(1)
@@ -212,14 +215,15 @@ def test_lru_lora_model_manager(dist_init, dummy_model):
     # This tests just the LRU cache functionality, everything else is
     # tested in test_lora_model_manager
     model = dummy_model
+    model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
+    model.packed_modules_mapping = {}
     model_lora1 = create_lora(1, model, ["layer1.dense1", "dense2", "lm_head"])
     model_lora2 = create_lora(2, model, ["dense1", "dense2", "lm_head"])
     model_lora3 = create_lora(3, model, ["dense1", "dense2", "lm_head"])
     model_lora4 = create_lora(4, model, ["dense1", "dense2", "lm_head"])
     manager = LRUCacheLoRAModelManager(
         model, 2, 2, 2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2),
-        ["dense1", "dense2", "lm_head"])
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2))
 
     assert all(x is None for x in manager.lora_index_to_id)
 
@@ -289,8 +293,9 @@ def test_lru_cache_worker_lora_manager(llama_2_7b_model_extra_embeddings,
                                        sql_lora_files):
     lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
     worker_lora_manager = LRUCacheWorkerLoRAManager(
-        4, 2, llama_2_7b_model_extra_embeddings.config.vocab_size, lora_config,
-        torch.device("cuda"))
+        4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
+        lora_config.lora_extra_vocab_size, lora_config, torch.device("cuda"),
+        EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
     worker_lora_manager.create_lora_manager(llama_2_7b_model_extra_embeddings)
 
     mapping = LoRAMapping([], [])
@@ -362,8 +367,9 @@ def test_worker_lora_manager(llama_2_7b_model_extra_embeddings,
     # Should remove every LoRA not specified in the request.
     lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
     worker_lora_manager = WorkerLoRAManager(
-        4, 2, llama_2_7b_model_extra_embeddings.config.vocab_size, lora_config,
-        torch.device("cuda"))
+        4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
+        lora_config.lora_extra_vocab_size, lora_config, torch.device("cuda"),
+        EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
     worker_lora_manager.create_lora_manager(llama_2_7b_model_extra_embeddings)
 
     mapping = LoRAMapping([], [])
@@ -428,6 +434,13 @@ def test_worker_lora_manager(llama_2_7b_model_extra_embeddings,
 
 def test_packed_loras(dist_init, dummy_model_gate_up):
     model = dummy_model_gate_up
+    model.supported_lora_modules = ["gate_up_proj"]
+    model.packed_modules_mapping = {
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
     model_lora = create_packed_lora(
         1,
         model,
@@ -443,8 +456,7 @@ def test_packed_loras(dist_init, dummy_model_gate_up):
 
     manager = LoRAModelManager(
         model, 2, 2, 2,
-        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2),
-        ["gate_up_proj"])
+        LoRAConfig(max_lora_rank=8, max_cpu_loras=2, max_loras=2))
     model = manager.model
 
     assert isinstance(model.get_submodule("gate_up_proj"),

tests/lora/test_mixtral.py

+import pytest
+import torch
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+
+
+def do_sample(llm, lora_path: str, lora_id: int):
+    prompts = [
+        "[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nSpellForce 3 is a pretty bad game. The developer Grimlore Games is clearly a bunch of no-talent hacks, and 2017 was a terrible year for games anyway. [/user] [assistant]",
+        "[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nI wanted to like Grimlore Games' 2017 entry, but in SpellForce 3 they just didn't get anything right. [/user] [assistant]",
+        "[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nBioShock is a good role-playing, action-adventure, shooter that released for PlayStation, Xbox, and PC in 2007. It is available on Steam, and it has a Mac release but not a Linux release. [/user] [assistant]",
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=256)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id else None)
+    # Print the outputs.
+    generated_texts = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+@pytest.mark.parametrize("tp_size", [4])
+def test_mixtral_lora(mixtral_lora_files, tp_size):
+    if torch.cuda.device_count() < tp_size:
+        pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
+
+    llm = vllm.LLM(MODEL_PATH,
+                   enable_lora=True,
+                   max_num_seqs=16,
+                   max_loras=4,
+                   tensor_parallel_size=tp_size,
+                   worker_use_ray=True)
+
+    expected_lora_output = [
+        "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",
+        "give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])",
+        "inform(name[BioShock], release_year[2007], rating[good], genres[action-adventure, role-playing, shooter], platforms[PlayStation, Xbox, PC], available_on_steam[yes], has_linux_release[no], has_mac_release[yes])",
+    ]
+
+    assert do_sample(llm, mixtral_lora_files,
+                     lora_id=1) == expected_lora_output
+    assert do_sample(llm, mixtral_lora_files,
+                     lora_id=2) == expected_lora_output

vllm/lora/models.py

@@ -4,8 +4,7 @@ import logging
 import math
 import os
 import re
-from typing import (Any, Callable, Dict, Hashable, List, Optional, Tuple, Type,
-                    Union)
+from typing import (Any, Callable, Dict, Hashable, List, Optional, Tuple, Type)
 
 import safetensors.torch
 import torch
@@ -20,36 +19,6 @@ from vllm.lora.utils import parse_fine_tuned_lora_name, replace_submodule
 
 logger = logging.getLogger(__name__)
 
-# TODO: The mappings below should be moved to individual model classes.
-
-PACKED_MODULES_CFG = {
-    "qkv_proj": [
-        "q_proj",
-        "k_proj",
-        "v_proj",
-    ],
-    "gate_up_proj": [
-        "gate_proj",
-        "up_proj",
-    ],
-}
-
-TARGET_MODULES_QKV = [
-    "qkv_proj",
-    "o_proj",
-    "gate_up_proj",
-    "down_proj",
-    "embed_tokens",
-    "lm_head",
-]
-
-EMBEDDING_MODULES = {
-    "embed_tokens": "input_embeddings",
-    "lm_head": "output_embeddings",
-}
-
-EMBEDDING_PADDING_MODULES = ["lm_head"]
-
 _GLOBAL_LORA_ID = 0
 
 
@@ -169,6 +138,8 @@ class LoRAModel:
         dtype: Optional[torch.dtype] = None,
         embeddings: Optional[Dict[str, torch.Tensor]] = None,
         target_embedding_padding: Optional[int] = None,
+        embedding_modules: Optional[Dict[str, str]] = None,
+        embedding_padding_modules: Optional[List[str]] = None,
     ) -> "LoRAModel":
         """Create a LoRAModel from a dictionary of tensors."""
         pin_memory = str(device) == "cpu" and not in_wsl()
@@ -179,11 +150,11 @@ class LoRAModel:
                 lora_embeddings_tensor = None
                 if embeddings:
                     embeddings_module = next(
-                        (k for k in EMBEDDING_MODULES if k in module_name),
+                        (k for k in embedding_modules if k in module_name),
                         None)
                     if embeddings_module:
                         lora_embeddings_tensor = embeddings[
-                            EMBEDDING_MODULES[embeddings_module]].to(
+                            embedding_modules[embeddings_module]].to(
                                 device=device, dtype=dtype)
                         if pin_memory:
                             lora_embeddings_tensor = (
@@ -201,7 +172,7 @@ class LoRAModel:
                 loras[module_name].lora_b = tensor.to(device=device,
                                                       dtype=dtype).t()
                 if any(name in module_name
-                       for name in EMBEDDING_PADDING_MODULES
+                       for name in embedding_padding_modules
                        ) and target_embedding_padding is not None:
                     lora_b = loras[module_name].lora_b
                     assert target_embedding_padding >= lora_b.shape[1]
@@ -223,7 +194,10 @@ class LoRAModel:
         lora_model_id: Optional[int] = None,
         device: str = "cuda",
         dtype: Optional[torch.dtype] = None,
-            target_embedding_padding: Optional[int] = None) -> "LoRAModel":
+        target_embedding_padding: Optional[int] = None,
+        embedding_modules: Optional[Dict[str, str]] = None,
+        embedding_padding_modules: Optional[List[str]] = None,
+    ) -> "LoRAModel":
         """Create a LoRAModel from a local checkpoint."""
         lora_config_path = os.path.join(lora_dir, "adapter_config.json")
         lora_tensor_path = os.path.join(lora_dir, "adapter_model.safetensors")
@@ -260,6 +234,8 @@ class LoRAModel:
             dtype=dtype,
             embeddings=embeddings,
             target_embedding_padding=target_embedding_padding,
+            embedding_modules=embedding_modules,
+            embedding_padding_modules=embedding_padding_modules,
         )
 
 
@@ -273,8 +249,6 @@ class LoRAModelManager:
         max_num_batched_tokens: int,
         vocab_size: int,
         lora_config: LoRAConfig,
-        lora_target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
-        packed_modules_mapping: Dict[str, List[str]] = PACKED_MODULES_CFG,
     ):
         """Create a LoRAModelManager and adapter for a given model.
 
@@ -286,13 +260,6 @@ class LoRAModelManager:
                 in a single batch.
             vocab_size: the vocab size of the model.
             lora_config: the LoRA configuration.
-            lora_target_modules: the target modules patterns to be adapted.
-                Support both single module name and a list of module names.
-            packed_modules_mapping: the mapping for packed modules. vLLM
-                packs some modules into one module, e.g., qkv_proj
-                is packed of q_proj, k_proj, and v_proj. These modules
-                have a single layer in the original model, but they are split
-                into multiple layers in the adapted model.
         """
         self.lora_config = lora_config
         self.max_num_seqs = max_num_seqs
@@ -320,11 +287,11 @@ class LoRAModelManager:
         self.indices_len = [None] * 4
 
         self.model: nn.Module = model
-        self.lora_target_modules: List[str] = ([
-            lora_target_modules
-        ] if isinstance(lora_target_modules, str) else lora_target_modules)
-        self.lora_target_modules = copy.deepcopy(lora_target_modules)
-        self.packed_modules_mapping = copy.deepcopy(packed_modules_mapping)
+        if hasattr(self.model, "supported_lora_modules"):
+            self.supported_lora_modules = copy.deepcopy(
+                self.model.supported_lora_modules)
+            self.packed_modules_mapping = copy.deepcopy(
+                self.model.packed_modules_mapping)
         self.packed_modules: Dict[str, List[str]] = {}
         self.modules: Dict[str, "BaseLayerWithLoRA"] = {}
         self._registered_loras: Dict[int, LoRAModel] = {}
@@ -468,7 +435,11 @@ class LoRAModelManager:
         assert isinstance(module, BaseLayerWithLoRA)
         self.modules[module_name] = module
 
-    def create_dummy_lora(self, lora_id: int, rank: int) -> LoRAModel:
+    def create_dummy_lora(
+            self,
+            lora_id: int,
+            rank: int,
+            embedding_modules: Optional[Dict[str, str]] = None) -> LoRAModel:
         """Create zero-initialized LoRAModel for warmup."""
         model = LoRAModel(lora_id, rank, {})
         for module_name, module in self.model.named_modules():
@@ -477,7 +448,7 @@ class LoRAModelManager:
                 continue
             parts = module_name.split(".")
             if module_name not in self.packed_modules:
-                if parts[-1] in EMBEDDING_MODULES:
+                if parts[-1] in embedding_modules:
                     input_dim = (module.base_layer.org_vocab_size +
                                  self.lora_config.lora_extra_vocab_size if
                                  hasattr(module.base_layer, "org_vocab_size")
@@ -531,7 +502,7 @@ class LoRAModelManager:
             re.match(
                 r".*\.{target_module}$".format(target_module=target_module),
                 module_name) or target_module == module_name
-            for target_module in self.lora_target_modules)
+            for target_module in self.supported_lora_modules)
 
     def _register_packed_modules(self, module_full_name: str) -> None:
         parts = module_full_name.split(".")
@@ -586,12 +557,9 @@ class LRUCacheLoRAModelManager(LoRAModelManager):
         max_num_batched_tokens: int,
         vocab_size: int,
         lora_config: LoRAConfig,
-        lora_target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
-        packed_modules_mapping: Dict[str, List[str]] = PACKED_MODULES_CFG,
     ):
         super().__init__(model, max_num_seqs, max_num_batched_tokens,
-                         vocab_size, lora_config, lora_target_modules,
-                         packed_modules_mapping)
+                         vocab_size, lora_config)
         self._registered_loras: LoRALRUCache = LoRALRUCache(
             self.capacity, self.deactivate_lora)
         self._active_loras: LoRALRUCache = LoRALRUCache(
@@ -637,11 +605,10 @@ def create_lora_manager(
         max_num_batched_tokens: int,
         vocab_size: int,
         lora_config: LoRAConfig,
-        target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
         lora_manager_cls: Type[LoRAModelManager] = LoRAModelManager,
         **kwargs) -> LoRAModelManager:
     """Create a LoRA adapter for a given model."""
-    if not getattr(model, "supports_lora", False):
+    if not hasattr(model, "supported_lora_modules"):
         raise ValueError(f"Model {type(model)} is not supported for LoRA.")
     lora_manager = lora_manager_cls(
         model=model,
@@ -649,6 +616,5 @@ def create_lora_manager(
         max_num_batched_tokens=max_num_batched_tokens,
         vocab_size=vocab_size,
         lora_config=lora_config,
-        lora_target_modules=target_modules,
         **kwargs)
     return lora_manager

vllm/lora/worker_manager.py

 import logging
 from abc import ABC, abstractmethod, abstractproperty
-from typing import Any, List, Optional, Set, Type, Union
+from typing import Any, Dict, List, Optional, Set, Type
 
 import torch
 
-from vllm.lora.models import (TARGET_MODULES_QKV, LoRAModel, LoRAModelManager,
+from vllm.lora.models import (LoRAModel, LoRAModelManager,
                               LRUCacheLoRAModelManager, create_lora_manager)
 from vllm.lora.request import LoRARequest
 from vllm.lora.layers import LoRAMapping
@@ -13,7 +13,7 @@ from vllm.config import LoRAConfig
 logger = logging.getLogger(__name__)
 
 
-class WorkerLoRAManager(ABC):
+class AbstractWorkerLoRAManager(ABC):
     """Abstract class for managing LoRA models on the worker side."""
 
     def __init__(self, max_num_seqs: int, max_num_batched_tokens: int,
@@ -33,7 +33,6 @@ class WorkerLoRAManager(ABC):
     def create_lora_manager(
         self,
         model: torch.nn.Module,
-        target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
     ) -> Any:
         ...
 
@@ -63,7 +62,7 @@ class WorkerLoRAManager(ABC):
         ...
 
 
-class WorkerLoRAManager(WorkerLoRAManager):
+class WorkerLoRAManager(AbstractWorkerLoRAManager):
     """WorkerLoRAManager that manages LoRA models on the worker side.
 
     Every request, the requested LoRAs will be loaded (unless they are already
@@ -78,10 +77,14 @@ class WorkerLoRAManager(WorkerLoRAManager):
         vocab_size: int,
         lora_config: LoRAConfig,
         device: torch.device,
+        embedding_modules: Dict[str, str],
+        embedding_padding_modules: List[str],
         lora_model_cls: Type[LoRAModel] = LoRAModel,
     ):
         self._lora_manager: Optional[LoRAModelManager] = None
         self._lora_model_cls = lora_model_cls
+        self.embedding_modules = embedding_modules
+        self.embedding_padding_modules = embedding_padding_modules
         super().__init__(max_num_seqs, max_num_batched_tokens, vocab_size,
                          lora_config, device)
 
@@ -92,13 +95,11 @@ class WorkerLoRAManager(WorkerLoRAManager):
     def create_lora_manager(
         self,
         model: torch.nn.Module,
-        target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
     ) -> Any:
         lora_manager = create_lora_manager(
             model,
             max_num_seqs=self.max_num_seqs,
             max_num_batched_tokens=self.max_num_batched_tokens,
-            target_modules=target_modules,
             vocab_size=self.vocab_size,
             lora_config=self.lora_config,
             lora_manager_cls=self._lora_manager_cls,
@@ -142,6 +143,8 @@ class WorkerLoRAManager(WorkerLoRAManager):
                 dtype=self.lora_config.lora_dtype,
                 target_embedding_padding=self.vocab_size +
                 self.lora_config.lora_extra_vocab_size,
+                embedding_modules=self.embedding_modules,
+                embedding_padding_modules=self.embedding_padding_modules,
             )
         except Exception as e:
             raise RuntimeError(
@@ -162,7 +165,7 @@ class WorkerLoRAManager(WorkerLoRAManager):
             return False
         return self._lora_manager.add_lora(
             self._lora_manager.create_dummy_lora(lora_request.lora_int_id,
-                                                 rank))
+                                                 rank, self.embedding_modules))
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
         if lora_request.lora_int_id in self.list_loras():
@@ -195,11 +198,9 @@ class LRUCacheWorkerLoRAManager(WorkerLoRAManager):
     def create_lora_manager(
         self,
         model: torch.nn.Module,
-        target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
     ) -> Any:
         lora_manager = create_lora_manager(
             model,
-            target_modules=target_modules,
             lora_manager_cls=self._lora_manager_cls,
             max_num_seqs=self.max_num_seqs,
             vocab_size=self.vocab_size,

vllm/model_executor/model_loader.py

@@ -66,7 +66,7 @@ def get_model(model_config: ModelConfig,
         # Create a model instance.
         # The weights will be initialized as empty tensors.
         with torch.device(device_config.device):
-            if getattr(model_class, "supports_lora", False):
+            if hasattr(model_class, "supported_lora_modules"):
                 model = model_class(model_config.hf_config, linear_method,
                                     lora_config)
             elif lora_config:

vllm/model_executor/models/llama.py

@@ -269,7 +269,32 @@ class LlamaModel(nn.Module):
 
 
 class LlamaForCausalLM(nn.Module):
-    supports_lora = True
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        "embed_tokens",
+        "lm_head",
+    ]
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
 
     def __init__(
         self,
@@ -281,11 +306,11 @@ class LlamaForCausalLM(nn.Module):
         self.config = config
         self.linear_method = linear_method
         self.model = LlamaModel(config, linear_method, lora_config=lora_config)
-        unpadded_vocab_size = config.vocab_size
+        self.unpadded_vocab_size = config.vocab_size
         if lora_config:
-            unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
         self.lm_head = ParallelLMHead(
-            unpadded_vocab_size,
+            self.unpadded_vocab_size,
             config.hidden_size,
             org_num_embeddings=config.vocab_size,
             padding_size=DEFAULT_VOCAB_PADDING_SIZE
@@ -293,7 +318,7 @@ class LlamaForCausalLM(nn.Module):
             # compatibility
             if not lora_config else lora_config.lora_vocab_padding_size,
         )
-        self.sampler = Sampler(unpadded_vocab_size, config.vocab_size)
+        self.sampler = Sampler(self.unpadded_vocab_size, config.vocab_size)
 
     def forward(
         self,

vllm/model_executor/models/mistral.py

@@ -265,7 +265,32 @@ class MistralModel(nn.Module):
 
 
 class MistralForCausalLM(nn.Module):
-    supports_lora = True
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        "embed_tokens",
+        "lm_head",
+    ]
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
 
     def __init__(
         self,

vllm/model_executor/models/mixtral.py

@@ -27,6 +27,7 @@ import torch
 from torch import nn
 from transformers import MixtralConfig
 
+from vllm.config import LoRAConfig
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.attention import PagedAttention
 from vllm.model_executor.layers.fused_moe import fused_moe
@@ -38,7 +39,7 @@ from vllm.model_executor.layers.linear import (LinearMethodBase,
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+    VocabParallelEmbedding, ParallelLMHead, DEFAULT_VOCAB_PADDING_SIZE)
 from vllm.model_executor.parallel_utils.communication_op import (
     tensor_model_parallel_all_reduce)
 from vllm.model_executor.parallel_utils.parallel_state import (
@@ -292,6 +293,7 @@ class MixtralModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            org_num_embeddings=self.org_vocab_size,
         )
         self.layers = nn.ModuleList([
             MixtralDecoderLayer(config, linear_method=linear_method)
@@ -318,18 +320,50 @@ class MixtralModel(nn.Module):
 
 
 class MixtralForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "embed_tokens",
+        "lm_head",
+    ]
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
 
     def __init__(
         self,
         config: MixtralConfig,
         linear_method: Optional[LinearMethodBase] = None,
+        lora_config: Optional[LoRAConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
         self.linear_method = linear_method
         self.model = MixtralModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-        self.sampler = Sampler(config.vocab_size)
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+        )
+        self.sampler = Sampler(self.unpadded_vocab_size, config.vocab_size)
 
     def forward(
         self,

vllm/worker/model_runner.py

@@ -86,11 +86,20 @@ class ModelRunner:
         vocab_size = self.model.config.vocab_size
 
         if self.lora_config:
+            assert hasattr(
+                self.model, "supported_lora_modules"
+            ) and self.model.supported_lora_modules, "Model does not support LoRA"
+            assert hasattr(
+                self.model,
+                "embedding_modules"), "Model does not have embedding_modules"
+            assert hasattr(self.model, "embedding_padding_modules"
+                           ), "Model does not have embedding_padding_modules"
             self.lora_manager = LRUCacheWorkerLoRAManager(
                 self.scheduler_config.max_num_seqs,
                 self.scheduler_config.max_num_batched_tokens +
                 self.scheduler_config.max_paddings, vocab_size,
-                self.lora_config, self.device)
+                self.lora_config, self.device, self.model.embedding_modules,
+                self.model.embedding_padding_modules)
             self.model = self.lora_manager.create_lora_manager(self.model)
 
     def set_block_size(self, block_size: int) -> None: