[Misc] Remove qlora_adapter_name_or_path (#17699)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

Author: jeejeelee

examples/offline_inference/lora_with_quantization_inference.py

@@ -75,43 +75,38 @@ def initialize_engine(model: str, quantization: str,
                       lora_repo: Optional[str]) -> LLMEngine:
     """Initialize the LLMEngine."""
 
-    if quantization == "bitsandbytes":
-        # QLoRA (https://arxiv.org/abs/2305.14314) is a quantization technique.
-        # It quantizes the model when loading, with some config info from the
-        # LoRA adapter repo. So need to set the parameter of load_format and
-        # qlora_adapter_name_or_path as below.
-        engine_args = EngineArgs(model=model,
-                                 quantization=quantization,
-                                 qlora_adapter_name_or_path=lora_repo,
-                                 enable_lora=True,
-                                 max_lora_rank=64)
-    else:
-        engine_args = EngineArgs(model=model,
-                                 quantization=quantization,
-                                 enable_lora=True,
-                                 max_loras=4)
+    engine_args = EngineArgs(model=model,
+                             quantization=quantization,
+                             enable_lora=True,
+                             max_lora_rank=64,
+                             max_loras=4)
     return LLMEngine.from_engine_args(engine_args)
 
 
 def main():
     """Main function that sets up and runs the prompt processing."""
 
-    test_configs = [{
-        "name": "qlora_inference_example",
-        'model': "huggyllama/llama-7b",
-        'quantization': "bitsandbytes",
-        'lora_repo': 'timdettmers/qlora-flan-7b'
-    }, {
-        "name": "AWQ_inference_with_lora_example",
-        'model': 'TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ',
-        'quantization': "awq",
-        'lora_repo': 'jashing/tinyllama-colorist-lora'
-    }, {
-        "name": "GPTQ_inference_with_lora_example",
-        'model': 'TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ',
-        'quantization': "gptq",
-        'lora_repo': 'jashing/tinyllama-colorist-lora'
-    }]
+    test_configs = [
+        # QLoRA (https://arxiv.org/abs/2305.14314)
+        {
+            "name": "qlora_inference_example",
+            'model': "huggyllama/llama-7b",
+            'quantization': "bitsandbytes",
+            'lora_repo': 'timdettmers/qlora-flan-7b'
+        },
+        {
+            "name": "AWQ_inference_with_lora_example",
+            'model': 'TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ',
+            'quantization': "awq",
+            'lora_repo': 'jashing/tinyllama-colorist-lora'
+        },
+        {
+            "name": "GPTQ_inference_with_lora_example",
+            'model': 'TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ',
+            'quantization': "gptq",
+            'lora_repo': 'jashing/tinyllama-colorist-lora'
+        }
+    ]
 
     for test_config in test_configs:
         print(

vllm/engine/arg_utils.py

@@ -6,6 +6,7 @@
 import json
 import re
 import threading
+import warnings
 from dataclasses import MISSING, dataclass, fields
 from itertools import permutations
 from typing import (Any, Callable, Dict, List, Literal, Optional, Type,
@@ -394,7 +395,13 @@ def __post_init__(self):
         if isinstance(self.compilation_config, (int, dict)):
             self.compilation_config = CompilationConfig.from_cli(
                 str(self.compilation_config))
-
+        if self.qlora_adapter_name_or_path is not None:
+            warnings.warn(
+                "The `qlora_adapter_name_or_path` is deprecated "
+                "and will be removed in v0.10.0. ",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         # Setup plugins
         from vllm.plugins import load_general_plugins
         load_general_plugins()
@@ -504,10 +511,14 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
                                 **load_kwargs["ignore_patterns"])
         load_group.add_argument("--use-tqdm-on-load",
                                 **load_kwargs["use_tqdm_on_load"])
-        load_group.add_argument('--qlora-adapter-name-or-path',
-                                type=str,
-                                default=None,
-                                help='Name or path of the QLoRA adapter.')
+        load_group.add_argument(
+            "--qlora-adapter-name-or-path",
+            type=str,
+            default=None,
+            help="The `--qlora-adapter-name-or-path` has no effect, do not set"
+            " it, and it  will be removed in v0.10.0.",
+            deprecated=True,
+        )
         load_group.add_argument('--pt-load-map-location',
                                 **load_kwargs["pt_load_map_location"])
 
@@ -534,7 +545,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             deprecated=True,
             help="[DEPRECATED] The `--enable-reasoning` flag is deprecated as "
             "of v0.8.6. Use `--reasoning-parser` to specify the reasoning "
-            "parser backend insteadThis flag (`--enable-reasoning`) will be "
+            "parser backend instead. This flag (`--enable-reasoning`) will be "
             "removed in v0.10.0. When `--reasoning-parser` is specified, "
             "reasoning mode is automatically enabled.")
         guided_decoding_group.add_argument(
@@ -896,12 +907,6 @@ def create_model_config(self) -> ModelConfig:
 
     def create_load_config(self) -> LoadConfig:
 
-        if(self.qlora_adapter_name_or_path is not None) and \
-            self.quantization != "bitsandbytes":
-            raise ValueError(
-                "QLoRA adapter only support "
-                f"'bitsandbytes' quantization, but got {self.quantization}")
-
         if self.quantization == "bitsandbytes":
             self.load_format = "bitsandbytes"
 
@@ -1098,11 +1103,6 @@ def create_engine_config(
             max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
             and self.max_cpu_loras > 0 else None) if self.enable_lora else None
 
-        if self.qlora_adapter_name_or_path is not None and \
-            self.qlora_adapter_name_or_path != "":
-            self.model_loader_extra_config[
-                "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path
-
         # bitsandbytes pre-quantized model need a specific model loader
         if model_config.quantization == "bitsandbytes":
             self.quantization = self.load_format = "bitsandbytes"

vllm/model_executor/model_loader/weight_utils.py

@@ -162,31 +162,23 @@ def get_quant_config(model_config: ModelConfig,
                                   None)
     if hf_quant_config is not None:
         return quant_cls.from_config(hf_quant_config)
-    # In case of bitsandbytes/QLoRA, get quant config from the adapter model.
+    # Inflight BNB quantization
     if model_config.quantization == "bitsandbytes":
-        if (not load_config.model_loader_extra_config
-                or "qlora_adapter_name_or_path"
-                not in load_config.model_loader_extra_config):
-            return quant_cls.from_config({"adapter_name_or_path": ""})
-        model_name_or_path = load_config.model_loader_extra_config[
-            "qlora_adapter_name_or_path"]
-
-    else:
-        model_name_or_path = model_config.model
-    is_local = os.path.isdir(model_name_or_path)
+        return quant_cls.from_config({})
+    is_local = os.path.isdir(model_config.model)
     if not is_local:
         # Download the config files.
-        with get_lock(model_name_or_path, load_config.download_dir):
+        with get_lock(model_config.model, load_config.download_dir):
             hf_folder = snapshot_download(
-                model_name_or_path,
+                model_config.model,
                 revision=model_config.revision,
                 allow_patterns="*.json",
                 cache_dir=load_config.download_dir,
                 local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
                 tqdm_class=DisabledTqdm,
             )
     else:
-        hf_folder = model_name_or_path
+        hf_folder = model_config.model
 
     possible_config_filenames = quant_cls.get_config_filenames()
 
@@ -213,7 +205,7 @@ def get_quant_config(model_config: ModelConfig,
         config = json.load(f)
 
         if model_config.quantization == "bitsandbytes":
-            config["adapter_name_or_path"] = model_name_or_path
+            config["adapter_name_or_path"] = model_config.model
         elif model_config.quantization == "modelopt":
             if config["producer"]["name"] == "modelopt":
                 return quant_cls.from_config(config)