[Feature][Kernel] Support bitsandbytes quantization and QLoRA (#4776)

examples/lora_with_quantization_inference.py

+"""
+This example shows how to use LoRA with different quantization techniques
+for offline inference.
+
+Requires HuggingFace credentials for access.
+"""
+
+import gc
+from typing import List, Optional, Tuple
+
+import torch
+from huggingface_hub import snapshot_download
+
+from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
+from vllm.lora.request import LoRARequest
+
+
+def create_test_prompts(
+        lora_path: str
+) -> List[Tuple[str, SamplingParams, Optional[LoRARequest]]]:
+    return [
+        # this is an example of using quantization without LoRA
+        ("My name is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128), None),
+        # the next three examples use quantization with LoRA
+        ("my name is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128),
+         LoRARequest("lora-test-1", 1, lora_path)),
+        ("The capital of USA is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128),
+         LoRARequest("lora-test-2", 1, lora_path)),
+        ("The capital of France is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128),
+         LoRARequest("lora-test-3", 1, lora_path)),
+    ]
+
+
+def process_requests(engine: LLMEngine,
+                     test_prompts: List[Tuple[str, SamplingParams,
+                                              Optional[LoRARequest]]]):
+    """Continuously process a list of prompts and handle the outputs."""
+    request_id = 0
+
+    while test_prompts or engine.has_unfinished_requests():
+        if test_prompts:
+            prompt, sampling_params, lora_request = test_prompts.pop(0)
+            engine.add_request(str(request_id),
+                               prompt,
+                               sampling_params,
+                               lora_request=lora_request)
+            request_id += 1
+
+        request_outputs: List[RequestOutput] = engine.step()
+        for request_output in request_outputs:
+            if request_output.finished:
+                print("----------------------------------------------------")
+                print(f"Prompt: {request_output.prompt}")
+                print(f"Output: {request_output.outputs[0].text}")
+
+
+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,
+            load_format="bitsandbytes",
+            enable_lora=True,
+            max_lora_rank=64,
+            # set it only in GPUs of limited memory
+            enforce_eager=True)
+    else:
+        engine_args = EngineArgs(
+            model=model,
+            quantization=quantization,
+            enable_lora=True,
+            max_loras=4,
+            # set it only in GPUs of limited memory
+            enforce_eager=True)
+    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'
+    }]
+
+    for test_config in test_configs:
+        print(
+            f"~~~~~~~~~~~~~~~~ Running: {test_config['name']} ~~~~~~~~~~~~~~~~"
+        )
+        engine = initialize_engine(test_config['model'],
+                                   test_config['quantization'],
+                                   test_config['lora_repo'])
+        lora_path = snapshot_download(repo_id=test_config['lora_repo'])
+        test_prompts = create_test_prompts(lora_path)
+        process_requests(engine, test_prompts)
+
+        # Clean up the GPU memory for the next test
+        del engine
+        gc.collect()
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    main()

requirements-dev.txt

@@ -35,3 +35,6 @@ aiohttp
 
 # Multimodal
 pillow
+
+# quantization
+bitsandbytes==0.42.0

tests/quantization/test_bitsandbytes.py

+'''Tests whether bitsandbytes computation is enabled correctly.
+
+Run `pytest tests/quantization/test_bitsandbytes.py`.
+'''
+import pytest
+import torch
+
+from vllm import SamplingParams
+from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+
+capability = torch.cuda.get_device_capability()
+capability = capability[0] * 10 + capability[1]
+
+
+@pytest.mark.skipif(
+    capability < QUANTIZATION_METHODS['bitsandbytes'].get_min_capability(),
+    reason='bitsandbytes is not supported on this GPU type.')
+def test_load_bnb_model(vllm_runner) -> None:
+    llm = vllm_runner('huggyllama/llama-7b',
+                      quantization='bitsandbytes',
+                      load_format='bitsandbytes',
+                      enforce_eager=True)
+
+    model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model
+
+    # check the weights in MLP & SelfAttention are quantized to torch.uint8
+    qweight = model.model.layers[0].mlp.gate_up_proj.qweight
+    assert qweight.dtype == torch.uint8, (
+        f'Expected gate_up_proj dtype torch.uint8 but got {qweight.dtype}')
+
+    qweight = model.model.layers[0].mlp.down_proj.qweight
+    assert qweight.dtype == torch.uint8, (
+        f'Expected down_proj dtype torch.uint8 but got {qweight.dtype}')
+
+    qweight = model.model.layers[0].self_attn.o_proj.qweight
+    assert qweight.dtype == torch.uint8, (
+        f'Expected o_proj dtype torch.uint8 but got {qweight.dtype}')
+
+    qweight = model.model.layers[0].self_attn.qkv_proj.qweight
+    assert qweight.dtype == torch.uint8, (
+        f'Expected qkv_proj dtype torch.uint8 but got {qweight.dtype}')
+
+    # some weights should not be quantized
+    weight = model.lm_head.weight
+    assert weight.dtype != torch.uint8, (
+        'lm_head weight dtype should not be torch.uint8')
+
+    weight = model.model.embed_tokens.weight
+    assert weight.dtype != torch.uint8, (
+        'embed_tokens weight dtype should not be torch.uint8')
+
+    weight = model.model.layers[0].input_layernorm.weight
+    assert weight.dtype != torch.uint8, (
+        'input_layernorm weight dtype should not be torch.uint8')
+
+    weight = model.model.layers[0].post_attention_layernorm.weight
+    assert weight.dtype != torch.uint8, (
+        'input_layernorm weight dtype should not be torch.uint8')
+
+    # check the output of the model is expected
+    sampling_params = SamplingParams(temperature=0.0,
+                                     logprobs=1,
+                                     prompt_logprobs=1,
+                                     max_tokens=8)
+
+    prompts = ['That which does not kill us', 'To be or not to be,']
+    expected_outputs = [
+        'That which does not kill us makes us stronger.',
+        'To be or not to be, that is the question.'
+    ]
+    outputs = llm.generate(prompts, sampling_params=sampling_params)
+
+    assert len(outputs) == len(prompts)
+
+    for index in range(len(outputs)):
+        # compare the first line of the output
+        actual_output = outputs[index][1][0].split('\n', 1)[0]
+        expected_output = expected_outputs[index].split('\n', 1)[0]
+        assert actual_output == expected_output, (
+            f'Expected: {expected_output}, but got: {actual_output}')

vllm/config.py

@@ -241,6 +241,12 @@ class ModelConfig:
                 "must be divisible by pipeline parallel size "
                 f"({pipeline_parallel_size}).")
 
+        if self.quantization == "bitsandbytes" and (
+                parallel_config.tensor_parallel_size > 1
+                or parallel_config.pipeline_parallel_size > 1):
+            raise ValueError(
+                "BitAndBytes quantization with TP or PP is not supported yet.")
+
     def get_hf_config_sliding_window(self) -> Optional[int]:
         """Get the sliding window size, or None if disabled.
         """
@@ -487,6 +493,7 @@ class LoadFormat(str, enum.Enum):
     DUMMY = "dummy"
     TENSORIZER = "tensorizer"
     SHARDED_STATE = "sharded_state"
+    BITSANDBYTES = "bitsandbytes"
 
 
 @dataclass

vllm/engine/arg_utils.py

@@ -92,6 +92,8 @@ class EngineArgs:
     ngram_prompt_lookup_max: Optional[int] = None
     ngram_prompt_lookup_min: Optional[int] = None
 
+    qlora_adapter_name_or_path: Optional[str] = None
+
     def __post_init__(self):
         if self.tokenizer is None:
             self.tokenizer = self.model
@@ -159,7 +161,8 @@ class EngineArgs:
             type=str,
             default=EngineArgs.load_format,
             choices=[
-                'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer'
+                'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer',
+                'bitsandbytes'
             ],
             help='The format of the model weights to load.\n\n'
             '* "auto" will try to load the weights in the safetensors format '
@@ -173,7 +176,9 @@ class EngineArgs:
             'which is mainly for profiling.\n'
             '* "tensorizer" will load the weights using tensorizer from '
             'CoreWeave. See the Tensorize vLLM Model script in the Examples'
-            'section for more information.\n')
+            'section for more information.\n'
+            '* "bitsandbytes" will load the weights using bitsandbytes '
+            'quantization.\n')
         parser.add_argument(
             '--dtype',
             type=str,
@@ -543,7 +548,10 @@ class EngineArgs:
             "will also be used in `model_name` tag content of "
             "prometheus metrics, if multiple names provided, metrics"
             "tag will take the first one.")
-
+        parser.add_argument('--qlora-adapter-name-or-path',
+                            type=str,
+                            default=None,
+                            help='Name or path of the QLoRA adapter.')
         return parser
 
     @classmethod
@@ -555,6 +563,23 @@ class EngineArgs:
         return engine_args
 
     def create_engine_config(self, ) -> EngineConfig:
+
+        # bitsandbytes quantization needs a specific model loader
+        # so we make sure the quant method and the load format are consistent
+        if (self.quantization == "bitsandbytes" or
+            self.qlora_adapter_name_or_path is not None) and \
+            self.load_format != "bitsandbytes":
+            raise ValueError(
+                "BitsAndBytes quantization and QLoRA adapter only support "
+                f"'bitsandbytes' load format, but got {self.load_format}")
+
+        if (self.load_format == "bitsandbytes" or
+            self.qlora_adapter_name_or_path is not None) and \
+            self.quantization != "bitsandbytes":
+            raise ValueError(
+                "BitsAndBytes load format and QLoRA adapter only support "
+                f"'bitsandbytes' quantization, but got {self.quantization}")
+
         device_config = DeviceConfig(self.device)
         model_config = ModelConfig(
             self.model, self.tokenizer, self.tokenizer_mode,
@@ -622,6 +647,13 @@ class EngineArgs:
             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 != "":
+            if self.model_loader_extra_config is None:
+                self.model_loader_extra_config = {}
+            self.model_loader_extra_config[
+                "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path
+
         load_config = LoadConfig(
             load_format=self.load_format,
             download_dir=self.download_dir,

vllm/model_executor/layers/linear.py

 from abc import abstractmethod
-from typing import List, Optional
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.nn.functional as F
@@ -26,6 +26,21 @@ def adjust_marlin_shard(param, shard_size, shard_offset):
     return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
 
 
+def adjust_bitsandbytes_shard(param: Parameter,
+                              qkv_offsets: Dict[str, Tuple[int, int]],
+                              loaded_shard_id: str) -> Tuple[int, int]:
+    """Adjust the quantization offsets and sizes for BitsAndBytes sharding."""
+
+    total, _ = qkv_offsets["total"]
+    orig_offset, orig_size = qkv_offsets[loaded_shard_id]
+
+    quantized_total = param.data.shape[0]
+    quantized_offset = orig_offset * quantized_total // total
+    quantized_size = orig_size * quantized_total // total
+
+    return quantized_size, quantized_offset
+
+
 class LinearMethodBase(QuantizeMethodBase):
     """Base class for different (maybe quantized) linear methods."""
 
@@ -416,6 +431,12 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                 shard_size, shard_offset = adjust_marlin_shard(
                     param, shard_size, shard_offset)
 
+            use_bitsandbytes = getattr(param, "use_bitsandbytes", False)
+            if use_bitsandbytes:
+                shard_size = loaded_weight.shape[output_dim]
+                shard_offset = loaded_weight.shape[output_dim] * \
+                    loaded_shard_id
+
             param_data = param_data.narrow(output_dim, shard_offset,
                                            shard_size)
             start_idx = tp_rank * shard_size
@@ -615,6 +636,22 @@ class QKVParallelLinear(ColumnParallelLinear):
                 shard_size, shard_offset = adjust_marlin_shard(
                     param, shard_size, shard_offset)
 
+            use_bitsandbytes = getattr(param, "use_bitsandbytes", False)
+            if use_bitsandbytes:
+                orig_qkv_offsets = {
+                    "q": (0, self.num_heads * self.head_size),
+                    "k": (self.num_heads * self.head_size,
+                          self.num_kv_heads * self.head_size),
+                    "v":
+                    ((self.num_heads + self.num_kv_heads) * self.head_size,
+                     self.num_kv_heads * self.head_size),
+                    "total":
+                    ((self.num_heads + 2 * self.num_kv_heads) * self.head_size,
+                     0)
+                }
+                shard_size, shard_offset = adjust_bitsandbytes_shard(
+                    param, orig_qkv_offsets, loaded_shard_id)
+
             param_data = param_data.narrow(output_dim, shard_offset,
                                            shard_size)
             if loaded_shard_id == "q":

vllm/model_executor/layers/quantization/__init__.py

@@ -4,6 +4,8 @@ from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.quantization.bitsandbytes import (
+    BitsAndBytesConfig)
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
     CompressedTensorsConfig)
 from vllm.model_executor.layers.quantization.deepspeedfp import (
@@ -30,6 +32,7 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "gptq": GPTQConfig,
     "squeezellm": SqueezeLLMConfig,
     "sparseml": CompressedTensorsConfig,
+    "bitsandbytes": BitsAndBytesConfig,
 }
 
 

vllm/model_executor/layers/quantization/bitsandbytes.py

+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+
+class BitsAndBytesConfig(QuantizationConfig):
+    """Config class for BitsAndBytes Quantization.
+
+    Reference: https://arxiv.org/abs/2305.14314
+    """
+
+    def __init__(
+        self,
+        adapter_name_or_path: str,
+        target_modules: List[str],
+    ) -> None:
+
+        self.adapter_name_or_path = adapter_name_or_path
+        self.target_modules = target_modules
+
+    def __repr__(self) -> str:
+        return (
+            f"BitsAndBytesConfig(adapter_name_or_path={self.adapter_name_or_path}"
+        )
+
+    @classmethod
+    def get_name(self) -> str:
+        return "bitsandbytes"
+
+    @classmethod
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.float32, torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(self) -> int:
+        return 70
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "adapter_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "BitsAndBytesConfig":
+        adapter_name = cls.get_from_keys(config, ["adapter_name_or_path"])
+        default_target_modules = [
+            "gate_proj", "down_proj", "up_proj", "q_proj", "k_proj", "v_proj",
+            "o_proj"
+        ]
+        if adapter_name == "":
+            target_modules = default_target_modules
+        else:
+            target_modules = cls.get_from_keys(config, ["target_modules"])
+        return cls(adapter_name, target_modules)
+
+    def get_quant_method(
+            self,
+            layer: torch.nn.Module) -> Optional["BitsAndBytesLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return BitsAndBytesLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+
+
+class BitsAndBytesLinearMethod(LinearMethodBase):
+    """Linear method for BitsAndBytes.
+
+    Args:
+       quant_config: The BitsAndBytes quantization config.
+    """
+
+    def __init__(self, quant_config: BitsAndBytesConfig):
+        try:
+            import bitsandbytes
+            if bitsandbytes.__version__ < "0.42.0":
+                raise ImportError("bitsandbytes version is wrong. Please "
+                                  "install bitsandbytes>=0.42.0.")
+        except ImportError as err:
+            raise ImportError("Please install bitsandbytes>=0.42.0 via "
+                              "`pip install bitsandbytes>=0.42.0` to use "
+                              "bitsandbytes quantizer.") from err
+
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: List[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        quant_ratio = 0
+        if params_dtype.is_floating_point:
+            quant_ratio = torch.finfo(params_dtype).bits // torch.iinfo(
+                torch.uint8).bits
+        else:
+            quant_ratio = torch.iinfo(params_dtype).bits // torch.iinfo(
+                torch.uint8).bits
+
+        if input_size_per_partition * sum(
+                output_partition_sizes) % quant_ratio != 0:
+            raise ValueError(
+                "The input size is not aligned with the quantized "
+                "weight shape. ")
+        qweight = Parameter(
+            torch.empty(
+                input_size_per_partition * sum(output_partition_sizes) //
+                quant_ratio,
+                1,
+                dtype=torch.uint8,
+            ),
+            requires_grad=False,
+        )
+
+        set_weight_attrs(
+            qweight,
+            {
+                "input_dim": 0,
+                # In bitsandbytes, a tensor of shape [n,m] is quantized to
+                #[n*m/pack_ratio, 1],so the output_dim is 0
+                "output_dim": 0,
+                "pack_factor": quant_ratio,
+                "use_bitsandbytes": True,
+            })
+        layer.register_parameter("qweight", qweight)
+        set_weight_attrs(qweight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        # only load the bitsandbytes module when needed
+        from bitsandbytes import matmul_4bit
+
+        original_type = x.dtype
+        bf_x = x.to(torch.bfloat16)
+
+        qweight = layer.qweight
+        quant_states = qweight.bnb_quant_state
+        offsets = qweight.bnb_shard_offsets
+
+        out_dim_0 = x.shape[0]
+        out_dim_1 = sum(
+            [quant_state[1].shape[0] for quant_state in quant_states.items()])
+        out = torch.empty(out_dim_0,
+                          out_dim_1,
+                          dtype=torch.bfloat16,
+                          device=x.device)
+
+        current_index = 0
+        for i in range(len(quant_states)):
+            output_size = quant_states[i].shape[0]
+            # It is more efficient to use out kwarg like
+            # matmul_4bit(..., out = ...).  Infeasible now due to the bug
+            # https://github.com/TimDettmers/bitsandbytes/issues/1235.
+            # Need to change  after the bug is fixed.
+            out[:, current_index:current_index + output_size] = matmul_4bit(
+                bf_x, qweight[offsets[i]:offsets[i + 1]].t(), quant_states[i])
+
+            current_index += output_size
+
+        out = out.to(original_type)
+
+        if bias is not None:
+            out += bias
+
+        return out

vllm/model_executor/model_loader/loader.py

 # ruff: noqa: SIM117
 import collections
 import copy
+import fnmatch
 import glob
+import json
+import math
 import os
 from abc import ABC, abstractmethod
 from typing import Any, Dict, Generator, List, Optional, Tuple, Type
 
 import huggingface_hub
+import numpy as np
 import torch
+from huggingface_hub import HfApi, hf_hub_download
 from torch import nn
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoadFormat,
@@ -28,6 +33,7 @@ from vllm.model_executor.model_loader.weight_utils import (
     get_quant_config, initialize_dummy_weights, np_cache_weights_iterator,
     pt_weights_iterator, safetensors_weights_iterator)
 from vllm.model_executor.models.vlm_base import VisionLanguageModelBase
+from vllm.model_executor.utils import set_weight_attrs
 
 logger = init_logger(__name__)
 
@@ -247,6 +253,7 @@ class DefaultModelLoader(BaseModelLoader):
                                                model,
                                                "fall_back_to_pt_during_load",
                                                True)), )
+
             for _, module in model.named_modules():
                 quant_method = getattr(module, "quant_method", None)
                 if quant_method is not None:
@@ -539,6 +546,241 @@ class ShardedStateLoader(BaseModelLoader):
             )
 
 
+class BitsAndBytesModelLoader(BaseModelLoader):
+    """Model loader to load model weights with BitAndBytes quantization."""
+
+    default_target_modules = [
+        "gate_proj", "down_proj", "up_proj", "q_proj", "k_proj", "v_proj",
+        "o_proj"
+    ]
+
+    possible_config_file_names = ["adapter_config.json"]
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+
+        # we don't need to quantize the whole model, only the target modules
+        # that are specified in the adapter config file. If the adapter config
+        # file is not provided, we will quantize the default modules.
+        if (not load_config.model_loader_extra_config
+                or "qlora_adapter_name_or_path"
+                not in load_config.model_loader_extra_config):
+            self.target_modules = self.default_target_modules
+            return
+
+        qlora_adapter = load_config.model_loader_extra_config[
+            "qlora_adapter_name_or_path"]
+
+        config_file_path = self._get_config_file(qlora_adapter)
+
+        with open(config_file_path, "r") as f:
+            config = json.load(f)
+            self.target_modules = config["target_modules"]
+
+    def _get_config_file(self, qlora_adapter: str) -> str:
+        is_local = os.path.isdir(qlora_adapter)
+        config_file_path = None
+        if is_local:
+            for file in self.possible_config_file_names:
+                config_file_path = os.path.join(qlora_adapter, file)
+                if os.path.exists(config_file_path):
+                    break
+        else:
+            hf_api = HfApi()
+            repo_files = hf_api.list_repo_files(repo_id=qlora_adapter)
+            for file in self.possible_config_file_names:
+                if file in repo_files:
+                    config_file_path = hf_hub_download(repo_id=qlora_adapter,
+                                                       filename=file)
+                    break
+
+        if not config_file_path:
+            raise ValueError(
+                f"Cannot find adapter config file in {qlora_adapter}")
+
+        return config_file_path
+
+    def _get_weight_files(
+            self,
+            model_name_or_path: str,
+            allowed_patterns: List[str],
+            revision: Optional[str] = None) -> Tuple[List[str], str]:
+        """Retrieve weight files. Download the files if necessary. 
+        
+        Return the weight files and the file pattern."""
+        is_local = os.path.isdir(model_name_or_path)
+
+        if is_local:
+            for pattern in allowed_patterns:
+                weight_files = glob.glob(
+                    os.path.join(model_name_or_path, pattern))
+                if weight_files:
+                    return weight_files, pattern
+        else:
+            hf_api = HfApi()
+            repo_files = hf_api.list_repo_files(repo_id=model_name_or_path)
+            for pattern in allowed_patterns:
+                matching_files = fnmatch.filter(repo_files, pattern)
+                if matching_files:
+                    hf_folder = download_weights_from_hf(
+                        model_name_or_path, self.load_config.download_dir,
+                        [pattern], revision)
+                    return glob.glob(os.path.join(hf_folder, pattern)), pattern
+
+        raise RuntimeError(
+            f"No model weights found in: `{model_name_or_path}`")
+
+    def _prepare_weights(self, model_name_or_path: str,
+                         revision: Optional[str]) -> Tuple[List[str], bool]:
+        """Prepare weight files for the model."""
+
+        allowed_patterns = ["*.safetensors", "*.bin", "*.pt"]
+
+        hf_weights_files, matched_pattern = self._get_weight_files(
+            model_name_or_path, allowed_patterns, revision)
+
+        if matched_pattern != "*.safetensors":
+            hf_weights_files = filter_files_not_needed_for_inference(
+                hf_weights_files)
+
+        if len(hf_weights_files) == 0:
+            raise RuntimeError(
+                f"Cannot find any model weights with `{model_name_or_path}`")
+
+        return hf_weights_files, matched_pattern == "*.safetensors"
+
+    def _get_quantized_weights_iterator(
+        self, model_name_or_path: str, revision: Optional[str]
+    ) -> Tuple[Generator[Tuple[str, torch.Tensor], None, None], Dict[str,
+                                                                     Any]]:
+        """Get an iterator to the model weights with bitsandbytes quantization,
+        as well as the quantization state dictionary."""
+
+        # only load the bitsandbytes module when needed
+        try:
+            import bitsandbytes
+            if bitsandbytes.__version__ < "0.42.0":
+                raise ImportError("bitsandbytes version is wrong. Please "
+                                  "install bitsandbytes>=0.42.0.")
+            from bitsandbytes.functional import quantize_4bit
+        except ImportError as err:
+            raise ImportError("Please install bitsandbytes>=0.42.0 via "
+                              "`pip install bitsandbytes>=0.42.0` to use "
+                              "bitsandbytes quantizer.") from err
+
+        hf_weights_files, use_safetensors = self._prepare_weights(
+            model_name_or_path, revision)
+
+        quant_state_dict = {}
+        if use_safetensors:
+            weight_iterator = safetensors_weights_iterator(hf_weights_files)
+        else:
+            weight_iterator = pt_weights_iterator(hf_weights_files)
+
+        def generator():
+            for weight_name, weight_tensor in weight_iterator:
+                if any(target_module in weight_name
+                       for target_module in self.target_modules):
+                    weight_name = weight_name.replace(".weight", ".qweight")
+                    #  bitsandbytes requires data in GPU
+                    loaded_weight = weight_tensor.cuda().data
+                    with set_default_torch_dtype(torch.float32):
+                        processed_weight, quant_state = quantize_4bit(
+                            loaded_weight,
+                            compress_statistics=True,
+                            quant_type="nf4")
+
+                    quant_state_dict[weight_name] = quant_state
+                else:
+                    processed_weight = weight_tensor
+
+                yield weight_name, processed_weight
+
+        return generator(), quant_state_dict
+
+    def _load_weights(self, model_config: ModelConfig,
+                      model: nn.Module) -> None:
+        if not hasattr(model, 'load_weights'):
+            raise AttributeError(
+                "The required method 'load_weights' is not defined in class"
+                f" {type(self).__name__}.")
+
+        if not hasattr(model, 'bitsandbytes_stacked_params_mapping'):
+            raise AttributeError(
+                f"Model {type(self).__name__} does not support BitsAndBytes "
+                "quantization yet.")
+
+        logger.info("Loading weights with BitsAndBytes quantization. "
+                    " May take a while ...")
+
+        qweight_iterator, quant_state_dict = (
+            self._get_quantized_weights_iterator(model_config.model,
+                                                 model_config.revision))
+
+        model.load_weights(qweight_iterator)
+
+        param_dict = dict(model.named_parameters())
+        stacked_quant_state_dict: Dict[str, Dict[int, Any]] = {}
+        for quant_param_name in quant_state_dict:
+            non_stacked_param_name = quant_param_name
+
+            shard_index = 0
+            for shard_name, (
+                    weight_name, index
+            ) in model.bitsandbytes_stacked_params_mapping.items():
+                if shard_name in quant_param_name:
+                    shard_index = index
+                    quant_param_name = quant_param_name.replace(
+                        shard_name, weight_name)
+                    break
+
+            if quant_param_name not in param_dict:
+                raise ValueError(
+                    f"Parameter {quant_param_name} not found in the model.")
+
+            if quant_param_name not in stacked_quant_state_dict:
+                stacked_quant_state_dict[quant_param_name] = {}
+
+            stacked_quant_state_dict[quant_param_name][shard_index] = (
+                quant_state_dict[non_stacked_param_name])
+
+        # save quant_states and offsets as the attributes of the parameters
+        for param_name, param in param_dict.items():
+            if param_name in stacked_quant_state_dict:
+                quant_states = stacked_quant_state_dict[param_name]
+                set_weight_attrs(param, {"bnb_quant_state": quant_states})
+
+                pack_ratio = getattr(param, "pack_factor", -1)
+                if pack_ratio == -1:
+                    raise ValueError(
+                        f"pack_factor not set for parameter {param_name}.")
+
+                num_elements = [0] * len(quant_states)
+                for seq, quant_state in enumerate(quant_states.items()):
+                    num_elements[seq] = math.prod(
+                        quant_state[1].shape) // pack_ratio
+
+                offsets = np.concatenate(([0], np.cumsum(num_elements)))
+                set_weight_attrs(param, {"bnb_shard_offsets": offsets})
+
+    def load_model(self, *, model_config: ModelConfig,
+                   device_config: DeviceConfig,
+                   lora_config: Optional[LoRAConfig],
+                   vision_language_config: Optional[VisionLanguageConfig],
+                   parallel_config: ParallelConfig,
+                   scheduler_config: SchedulerConfig,
+                   cache_config: CacheConfig) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
+                model = _initialize_model(model_config, self.load_config,
+                                          lora_config, vision_language_config,
+                                          cache_config)
+
+                self._load_weights(model_config, model)
+
+        return model.eval()
+
+
 def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     """Get a model loader based on the load format."""
 
@@ -554,4 +796,7 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     if load_config.load_format == LoadFormat.SHARDED_STATE:
         return ShardedStateLoader(load_config)
 
+    if load_config.load_format == LoadFormat.BITSANDBYTES:
+        return BitsAndBytesModelLoader(load_config)
+
     return DefaultModelLoader(load_config)

vllm/model_executor/model_loader/weight_utils.py

@@ -130,6 +130,16 @@ def get_quant_config(model_config: ModelConfig,
 
     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.
+    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)
     if not is_local:
@@ -169,6 +179,10 @@ def get_quant_config(model_config: ModelConfig,
     quant_config_file = quant_config_files[0]
     with open(quant_config_file, "r") as f:
         config = json.load(f)
+
+        if model_config.quantization == "bitsandbytes":
+            config["adapter_name_or_path"] = model_name_or_path
+
     return quant_cls.from_config(config)
 
 

vllm/model_executor/models/llama.py

@@ -319,6 +319,14 @@ class LlamaForCausalLM(nn.Module):
         "lm_head": "output_embeddings",
     }
     embedding_padding_modules = ["lm_head"]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "gate_proj": ("gate_up_proj", 0),
+        "up_proj": ("gate_up_proj", 1),
+    }
 
     def __init__(
         self,