[Core] Support dynamically loading Lora adapter from HuggingFace (#6234)

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

tests/core/test_scheduler.py

@@ -462,7 +462,7 @@ def test_prefill_schedule_max_lora():
                                            lora_request=LoRARequest(
                                                lora_name=str(i),
                                                lora_int_id=i + 1,
-                                               lora_local_path="abc"))
+                                               lora_path="abc"))
         waiting.append(seq_group)
     # Add two more requests to verify lora is prioritized.
     # 0: Lora, 1: Lora, 2: regular, 3: regular
@@ -760,7 +760,7 @@ def test_schedule_swapped_max_loras():
                                            lora_request=LoRARequest(
                                                lora_name=str(i),
                                                lora_int_id=i + 1,
-                                               lora_local_path="abc"))
+                                               lora_path="abc"))
         scheduler._allocate_and_set_running(seq_group)
         append_new_token_seq_group(60, seq_group, 1)
         scheduler._swap_out(seq_group, blocks_to_swap_out)

tests/lora/conftest.py

@@ -159,8 +159,14 @@ def dummy_model_gate_up() -> nn.Module:
 
 
 @pytest.fixture(scope="session")
-def sql_lora_files():
-    return snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
+def sql_lora_huggingface_id():
+    # huggingface repo id is used to test lora runtime downloading.
+    return "yard1/llama-2-7b-sql-lora-test"
+
+
+@pytest.fixture(scope="session")
+def sql_lora_files(sql_lora_huggingface_id):
+    return snapshot_download(repo_id=sql_lora_huggingface_id)
 
 
 @pytest.fixture(scope="session")

tests/lora/test_long_context.py

@@ -29,7 +29,7 @@ def _create_lora_request(lora_id, long_context_infos):
     context_len = long_context_infos[lora_id]["context_length"]
     scaling_factor = context_len_to_scaling_factor[context_len]
     return LoRARequest(context_len, lora_id,
-                       long_context_infos[lora_id]["lora"],
+                       long_context_infos[lora_id]["lora"], None,
                        4096 * scaling_factor)
 
 

tests/lora/test_lora_huggingface.py

+from typing import List
+
+import pytest
+
+from vllm.lora.models import LoRAModel
+from vllm.lora.utils import get_adapter_absolute_path
+from vllm.model_executor.models.llama import LlamaForCausalLM
+
+# Provide absolute path and huggingface lora ids
+lora_fixture_name = ["sql_lora_files", "sql_lora_huggingface_id"]
+
+
+@pytest.mark.parametrize("lora_fixture_name", lora_fixture_name)
+def test_load_checkpoints_from_huggingface(lora_fixture_name, request):
+    lora_name = request.getfixturevalue(lora_fixture_name)
+    supported_lora_modules = LlamaForCausalLM.supported_lora_modules
+    packed_modules_mapping = LlamaForCausalLM.packed_modules_mapping
+    embedding_modules = LlamaForCausalLM.embedding_modules
+    embed_padding_modules = LlamaForCausalLM.embedding_padding_modules
+    expected_lora_modules: List[str] = []
+    for module in supported_lora_modules:
+        if module in packed_modules_mapping:
+            expected_lora_modules.extend(packed_modules_mapping[module])
+        else:
+            expected_lora_modules.append(module)
+
+    lora_path = get_adapter_absolute_path(lora_name)
+
+    # lora loading should work for either absolute path and hugggingface id.
+    lora_model = LoRAModel.from_local_checkpoint(
+        lora_path,
+        expected_lora_modules,
+        lora_model_id=1,
+        device="cpu",
+        embedding_modules=embedding_modules,
+        embedding_padding_modules=embed_padding_modules)
+
+    # Assertions to ensure the model is loaded correctly
+    assert lora_model is not None, "LoRAModel is not loaded correctly"

tests/lora/test_utils.py

 from collections import OrderedDict
+from unittest.mock import patch
 
 import pytest
+from huggingface_hub.utils import HfHubHTTPError
 from torch import nn
 
-from vllm.lora.utils import parse_fine_tuned_lora_name, replace_submodule
+from vllm.lora.utils import (get_adapter_absolute_path,
+                             parse_fine_tuned_lora_name, replace_submodule)
 from vllm.utils import LRUCache
 
 
@@ -182,3 +185,55 @@ def test_lru_cache():
     assert 2 in cache
     assert 4 in cache
     assert 6 in cache
+
+
+# Unit tests for get_adapter_absolute_path
+@patch('os.path.isabs')
+def test_get_adapter_absolute_path_absolute(mock_isabs):
+    path = '/absolute/path/to/lora'
+    mock_isabs.return_value = True
+    assert get_adapter_absolute_path(path) == path
+
+
+@patch('os.path.expanduser')
+def test_get_adapter_absolute_path_expanduser(mock_expanduser):
+    # Path with ~ that needs to be expanded
+    path = '~/relative/path/to/lora'
+    absolute_path = '/home/user/relative/path/to/lora'
+    mock_expanduser.return_value = absolute_path
+    assert get_adapter_absolute_path(path) == absolute_path
+
+
+@patch('os.path.exists')
+@patch('os.path.abspath')
+def test_get_adapter_absolute_path_local_existing(mock_abspath, mock_exist):
+    # Relative path that exists locally
+    path = 'relative/path/to/lora'
+    absolute_path = '/absolute/path/to/lora'
+    mock_exist.return_value = True
+    mock_abspath.return_value = absolute_path
+    assert get_adapter_absolute_path(path) == absolute_path
+
+
+@patch('huggingface_hub.snapshot_download')
+@patch('os.path.exists')
+def test_get_adapter_absolute_path_huggingface(mock_exist,
+                                               mock_snapshot_download):
+    # Hugging Face model identifier
+    path = 'org/repo'
+    absolute_path = '/mock/snapshot/path'
+    mock_exist.return_value = False
+    mock_snapshot_download.return_value = absolute_path
+    assert get_adapter_absolute_path(path) == absolute_path
+
+
+@patch('huggingface_hub.snapshot_download')
+@patch('os.path.exists')
+def test_get_adapter_absolute_path_huggingface_error(mock_exist,
+                                                     mock_snapshot_download):
+    # Hugging Face model identifier with download error
+    path = 'org/repo'
+    mock_exist.return_value = False
+    mock_snapshot_download.side_effect = HfHubHTTPError(
+        "failed to query model info")
+    assert get_adapter_absolute_path(path) == path

vllm/entrypoints/openai/serving_engine.py

@@ -43,7 +43,7 @@ class PromptAdapterPath:
 @dataclass
 class LoRAModulePath:
     name: str
-    local_path: str
+    path: str
 
 
 AnyRequest = Union[ChatCompletionRequest, CompletionRequest, DetokenizeRequest,
@@ -83,7 +83,7 @@ class OpenAIServing:
                 LoRARequest(
                     lora_name=lora.name,
                     lora_int_id=i,
-                    lora_local_path=lora.local_path,
+                    lora_path=lora.path,
                 ) for i, lora in enumerate(lora_modules, start=1)
             ]
 

vllm/lora/request.py

-from dataclasses import dataclass
+import warnings
+from dataclasses import dataclass, field
 from typing import Optional
 
 from vllm.adapter_commons.request import AdapterRequest
@@ -20,10 +21,25 @@ class LoRARequest(AdapterRequest):
 
     lora_name: str
     lora_int_id: int
-    lora_local_path: str
+    lora_path: str = ""
+    lora_local_path: Optional[str] = field(default=None, repr=False)
     long_lora_max_len: Optional[int] = None
     __hash__ = AdapterRequest.__hash__
 
+    def __post_init__(self):
+        if 'lora_local_path' in self.__dict__:
+            warnings.warn(
+                "The 'lora_local_path' attribute is deprecated "
+                "and will be removed in a future version. "
+                "Please use 'lora_path' instead.",
+                DeprecationWarning,
+                stacklevel=2)
+            if not self.lora_path:
+                self.lora_path = self.lora_local_path or ""
+
+        # Ensure lora_path is not empty
+        assert self.lora_path, "lora_path cannot be empty"
+
     @property
     def adapter_id(self):
         return self.lora_int_id
@@ -32,6 +48,26 @@ class LoRARequest(AdapterRequest):
     def name(self):
         return self.lora_name
 
+    @property
+    def path(self):
+        return self.lora_path
+
     @property
     def local_path(self):
-        return self.lora_local_path
+        warnings.warn(
+            "The 'local_path' attribute is deprecated "
+            "and will be removed in a future version. "
+            "Please use 'path' instead.",
+            DeprecationWarning,
+            stacklevel=2)
+        return self.lora_path
+
+    @local_path.setter
+    def local_path(self, value):
+        warnings.warn(
+            "The 'local_path' attribute is deprecated "
+            "and will be removed in a future version. "
+            "Please use 'path' instead.",
+            DeprecationWarning,
+            stacklevel=2)
+        self.lora_path = value

vllm/lora/utils.py

+import os
 from typing import List, Optional, Set, Tuple, Type
 
+import huggingface_hub
+from huggingface_hub.utils import (EntryNotFoundError, HfHubHTTPError,
+                                   HFValidationError, RepositoryNotFoundError)
 from torch import nn
 from transformers import PretrainedConfig
 
@@ -105,3 +109,46 @@ def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool]:
             return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A"
 
     raise ValueError(f"{name} is unsupported LoRA weight")
+
+
+def get_adapter_absolute_path(lora_path: str) -> str:
+    """
+    Resolves the given lora_path to an absolute local path.
+
+    If the lora_path is identified as a Hugging Face model identifier,
+    it will download the model and return the local snapshot path.
+    Otherwise, it treats the lora_path as a local file path and
+    converts it to an absolute path.
+
+    Parameters:
+    lora_path (str): The path to the lora model, which can be an absolute path,
+                     a relative path, or a Hugging Face model identifier.
+
+    Returns:
+    str: The resolved absolute local path to the lora model.
+    """
+
+    # Check if the path is an absolute path. Return it no matter exists or not.
+    if os.path.isabs(lora_path):
+        return lora_path
+
+    # If the path starts with ~, expand the user home directory.
+    if lora_path.startswith('~'):
+        return os.path.expanduser(lora_path)
+
+    # Check if the expanded relative path exists locally.
+    if os.path.exists(lora_path):
+        return os.path.abspath(lora_path)
+
+    # If the path does not exist locally, assume it's a Hugging Face repo.
+    try:
+        local_snapshot_path = huggingface_hub.snapshot_download(
+            repo_id=lora_path)
+    except (HfHubHTTPError, RepositoryNotFoundError, EntryNotFoundError,
+            HFValidationError):
+        # Handle errors that may occur during the download
+        # Return original path instead instead of throwing error here
+        logger.exception("Error downloading the HuggingFace model")
+        return lora_path
+
+    return local_snapshot_path

vllm/lora/worker_manager.py

@@ -13,6 +13,7 @@ from vllm.logger import init_logger
 from vllm.lora.models import (LoRAModel, LoRAModelManager,
                               LRUCacheLoRAModelManager, create_lora_manager)
 from vllm.lora.request import LoRARequest
+from vllm.lora.utils import get_adapter_absolute_path
 
 logger = init_logger(__name__)
 
@@ -89,8 +90,9 @@ class WorkerLoRAManager(AbstractWorkerManager):
                         packed_modules_mapping[module])
                 else:
                     expected_lora_modules.append(module)
+            lora_path = get_adapter_absolute_path(lora_request.lora_path)
             lora = self._lora_model_cls.from_local_checkpoint(
-                lora_request.lora_local_path,
+                lora_path,
                 expected_lora_modules,
                 max_position_embeddings=self.max_position_embeddings,
                 lora_model_id=lora_request.lora_int_id,
@@ -102,8 +104,7 @@ class WorkerLoRAManager(AbstractWorkerManager):
                 embedding_padding_modules=self.embedding_padding_modules,
             )
         except Exception as e:
-            raise RuntimeError(
-                f"Loading lora {lora_request.lora_local_path} failed") from e
+            raise RuntimeError(f"Loading lora {lora_path} failed") from e
         if lora.rank > self.lora_config.max_lora_rank:
             raise ValueError(
                 f"LoRA rank {lora.rank} is greater than max_lora_rank "

vllm/transformers_utils/tokenizer.py

@@ -137,14 +137,13 @@ def get_lora_tokenizer(lora_request: LoRARequest, *args,
     if lora_request is None:
         return None
     try:
-        tokenizer = get_tokenizer(lora_request.lora_local_path, *args,
-                                  **kwargs)
+        tokenizer = get_tokenizer(lora_request.lora_path, *args, **kwargs)
     except OSError as e:
         # No tokenizer was found in the LoRA folder,
         # use base model tokenizer
         logger.warning(
             "No tokenizer found in %s, using base model tokenizer instead. "
-            "(Exception: %s)", lora_request.lora_local_path, e)
+            "(Exception: %s)", lora_request.lora_path, e)
         tokenizer = None
     return tokenizer
 

vllm/worker/model_runner.py

@@ -691,7 +691,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                     dummy_lora_request = LoRARequest(
                         lora_name=f"warmup_{lora_id}",
                         lora_int_id=lora_id,
-                        lora_local_path="/not/a/real/path",
+                        lora_path="/not/a/real/path",
                     )
                     self.lora_manager.add_dummy_lora(dummy_lora_request,
                                                      rank=LORA_WARMUP_RANK)