803 lines
35 KiB
Python
803 lines
35 KiB
Python
# 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,
|
|
LoRAConfig, ModelConfig, ParallelConfig,
|
|
SchedulerConfig, VisionLanguageConfig)
|
|
from vllm.envs import VLLM_USE_MODELSCOPE
|
|
from vllm.logger import init_logger
|
|
from vllm.model_executor.layers.quantization.base_config import (
|
|
QuantizationConfig)
|
|
from vllm.model_executor.model_loader.tensorizer import (
|
|
TensorizerConfig, is_vllm_tensorized, load_with_tensorizer,
|
|
tensorizer_weights_iterator)
|
|
from vllm.model_executor.model_loader.utils import (get_model_architecture,
|
|
set_default_torch_dtype)
|
|
from vllm.model_executor.model_loader.weight_utils import (
|
|
download_safetensors_index_file_from_hf, download_weights_from_hf,
|
|
filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
|
|
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__)
|
|
|
|
|
|
def _get_quantization_config(
|
|
model_config: ModelConfig,
|
|
load_config: LoadConfig) -> Optional[QuantizationConfig]:
|
|
"""Get the quantization config."""
|
|
if model_config.quantization is not None:
|
|
quant_config = get_quant_config(model_config, load_config)
|
|
capability = torch.cuda.get_device_capability()
|
|
capability = capability[0] * 10 + capability[1]
|
|
if capability < quant_config.get_min_capability():
|
|
raise ValueError(
|
|
f"The quantization method {model_config.quantization} is not "
|
|
"supported for the current GPU. "
|
|
f"Minimum capability: {quant_config.get_min_capability()}. "
|
|
f"Current capability: {capability}.")
|
|
supported_dtypes = quant_config.get_supported_act_dtypes()
|
|
if model_config.dtype not in supported_dtypes:
|
|
raise ValueError(
|
|
f"{model_config.dtype} is not supported for quantization "
|
|
f"method {model_config.quantization}. Supported dtypes: "
|
|
f"{supported_dtypes}")
|
|
return quant_config
|
|
return None
|
|
|
|
|
|
def _get_model_initialization_kwargs(
|
|
model_class: Type[nn.Module], lora_config: Optional[LoRAConfig],
|
|
vision_language_config: Optional[VisionLanguageConfig]
|
|
) -> Dict[str, Any]:
|
|
"""Get extra kwargs for model initialization."""
|
|
extra_kwargs = {}
|
|
if hasattr(model_class, "supported_lora_modules"):
|
|
extra_kwargs["lora_config"] = lora_config
|
|
elif lora_config:
|
|
raise ValueError(
|
|
f"Model {model_class.__name__} does not support LoRA, "
|
|
"but LoRA is enabled. Support for this model may "
|
|
"be added in the future. If this is important to you, "
|
|
"please open an issue on github.")
|
|
elif issubclass(model_class, VisionLanguageModelBase):
|
|
if vision_language_config is None:
|
|
raise ValueError("Provide `image_input_type` and other vision "
|
|
"related configurations through LLM entrypoint "
|
|
"or engine arguments.")
|
|
|
|
extra_kwargs["vision_language_config"] = vision_language_config
|
|
return extra_kwargs
|
|
|
|
|
|
def _initialize_model(model_config: ModelConfig, load_config: LoadConfig,
|
|
lora_config: Optional[LoRAConfig],
|
|
vision_language_config: Optional[VisionLanguageConfig],
|
|
cache_config: CacheConfig) -> nn.Module:
|
|
"""Initialize a model with the given configurations."""
|
|
model_class = get_model_architecture(model_config)[0]
|
|
quant_config = _get_quantization_config(model_config, load_config)
|
|
|
|
return model_class(config=model_config.hf_config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
**_get_model_initialization_kwargs(
|
|
model_class, lora_config, vision_language_config))
|
|
|
|
|
|
class BaseModelLoader(ABC):
|
|
"""Base class for model loaders."""
|
|
|
|
def __init__(self, load_config: LoadConfig):
|
|
self.load_config = load_config
|
|
|
|
@abstractmethod
|
|
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:
|
|
"""Load a model with the given configurations."""
|
|
...
|
|
|
|
|
|
class DefaultModelLoader(BaseModelLoader):
|
|
"""Model loader that can load different file types from disk."""
|
|
|
|
def __init__(self, load_config: LoadConfig):
|
|
super().__init__(load_config)
|
|
if load_config.model_loader_extra_config:
|
|
raise ValueError(f"Model loader extra config is not supported for "
|
|
f"load format {load_config.load_format}")
|
|
|
|
def _maybe_download_from_modelscope(
|
|
self, model: str, revision: Optional[str]) -> Optional[str]:
|
|
"""Download model from ModelScope hub if VLLM_USE_MODELSCOPE is True.
|
|
|
|
Returns the path to the downloaded model, or None if the model is not
|
|
downloaded from ModelScope."""
|
|
if VLLM_USE_MODELSCOPE:
|
|
# download model from ModelScope hub,
|
|
# lazy import so that modelscope is not required for normal use.
|
|
# pylint: disable=C.
|
|
from modelscope.hub.snapshot_download import snapshot_download
|
|
|
|
if not os.path.exists(model):
|
|
model_path = snapshot_download(
|
|
model_id=model,
|
|
cache_dir=self.load_config.download_dir,
|
|
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
|
|
revision=revision,
|
|
)
|
|
else:
|
|
model_path = model
|
|
return model_path
|
|
return None
|
|
|
|
def _prepare_weights(self, model_name_or_path: str,
|
|
revision: Optional[str],
|
|
fall_back_to_pt: bool) -> Tuple[str, List[str], bool]:
|
|
"""Prepare weights for the model.
|
|
|
|
If the model is not local, it will be downloaded."""
|
|
model_name_or_path = self._maybe_download_from_modelscope(
|
|
model_name_or_path, revision) or model_name_or_path
|
|
|
|
is_local = os.path.isdir(model_name_or_path)
|
|
load_format = self.load_config.load_format
|
|
use_safetensors = False
|
|
# Some quantized models use .pt files for storing the weights.
|
|
if load_format == LoadFormat.AUTO:
|
|
allow_patterns = ["*.safetensors", "*.bin"]
|
|
elif load_format == LoadFormat.SAFETENSORS:
|
|
use_safetensors = True
|
|
allow_patterns = ["*.safetensors"]
|
|
elif load_format == LoadFormat.PT:
|
|
allow_patterns = ["*.pt"]
|
|
elif load_format == LoadFormat.NPCACHE:
|
|
allow_patterns = ["*.bin"]
|
|
else:
|
|
raise ValueError(f"Unknown load_format: {load_format}")
|
|
|
|
if fall_back_to_pt:
|
|
allow_patterns += ["*.pt"]
|
|
|
|
if not is_local:
|
|
hf_folder = download_weights_from_hf(model_name_or_path,
|
|
self.load_config.download_dir,
|
|
allow_patterns, revision)
|
|
else:
|
|
hf_folder = model_name_or_path
|
|
|
|
hf_weights_files: List[str] = []
|
|
for pattern in allow_patterns:
|
|
hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
|
|
if len(hf_weights_files) > 0:
|
|
if pattern == "*.safetensors":
|
|
use_safetensors = True
|
|
break
|
|
|
|
if use_safetensors:
|
|
# For models like Mistral-7B-Instruct-v0.3
|
|
# there are both sharded safetensors files and a consolidated
|
|
# safetensors file. Using both breaks.
|
|
# Here, we download the `model.safetensors.index.json` and filter
|
|
# any files not found in the index.
|
|
if not is_local:
|
|
download_safetensors_index_file_from_hf(
|
|
model_name_or_path, self.load_config.download_dir,
|
|
revision)
|
|
hf_weights_files = filter_duplicate_safetensors_files(
|
|
hf_weights_files, hf_folder)
|
|
else:
|
|
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_folder, hf_weights_files, use_safetensors
|
|
|
|
def _get_weights_iterator(
|
|
self, model_name_or_path: str, revision: Optional[str],
|
|
fall_back_to_pt: bool
|
|
) -> Generator[Tuple[str, torch.Tensor], None, None]:
|
|
"""Get an iterator for the model weights based on the load format."""
|
|
hf_folder, hf_weights_files, use_safetensors = self._prepare_weights(
|
|
model_name_or_path, revision, fall_back_to_pt)
|
|
if self.load_config.load_format == LoadFormat.NPCACHE:
|
|
# Currently np_cache only support *.bin checkpoints
|
|
assert use_safetensors is False
|
|
return np_cache_weights_iterator(model_name_or_path,
|
|
self.load_config.download_dir,
|
|
hf_folder, hf_weights_files)
|
|
if use_safetensors:
|
|
return safetensors_weights_iterator(hf_weights_files)
|
|
return pt_weights_iterator(hf_weights_files)
|
|
|
|
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)
|
|
model.load_weights(
|
|
self._get_weights_iterator(model_config.model,
|
|
model_config.revision,
|
|
fall_back_to_pt=getattr(
|
|
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:
|
|
quant_method.process_weights_after_loading(module)
|
|
# FIXME: Remove this after Mixtral is updated
|
|
# to use quant_method.
|
|
if hasattr(module, "process_weights_after_loading"):
|
|
module.process_weights_after_loading()
|
|
return model.eval()
|
|
|
|
|
|
class DummyModelLoader(BaseModelLoader):
|
|
"""Model loader that will set model weights to random values."""
|
|
|
|
def __init__(self, load_config: LoadConfig):
|
|
super().__init__(load_config)
|
|
if load_config.model_loader_extra_config:
|
|
raise ValueError(f"Model loader extra config is not supported for "
|
|
f"load format {load_config.load_format}")
|
|
|
|
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)
|
|
# NOTE(woosuk): For accurate performance evaluation, we assign
|
|
# random values to the weights.
|
|
initialize_dummy_weights(model)
|
|
return model.eval()
|
|
|
|
|
|
class TensorizerLoader(BaseModelLoader):
|
|
"""Model loader using CoreWeave's tensorizer library."""
|
|
|
|
def __init__(self, load_config: LoadConfig):
|
|
super().__init__(load_config)
|
|
if isinstance(load_config.model_loader_extra_config, TensorizerConfig):
|
|
self.tensorizer_config = load_config.model_loader_extra_config
|
|
else:
|
|
self.tensorizer_config = TensorizerConfig(
|
|
**load_config.model_loader_extra_config)
|
|
|
|
def _verify_config(self, model_config: ModelConfig,
|
|
parallel_config: ParallelConfig):
|
|
self.tensorizer_config.verify_with_model_config(model_config)
|
|
self.tensorizer_config.verify_with_parallel_config(parallel_config)
|
|
|
|
def _get_weights_iterator(
|
|
self) -> Generator[Tuple[str, torch.Tensor], None, None]:
|
|
tensorizer_args = self.tensorizer_config._construct_tensorizer_args()
|
|
return tensorizer_weights_iterator(tensorizer_args)
|
|
|
|
def _load_model_serialized_cpu(
|
|
self,
|
|
model_config: ModelConfig,
|
|
device_config: DeviceConfig,
|
|
lora_config: Optional[LoRAConfig],
|
|
vision_language_config: Optional[VisionLanguageConfig],
|
|
cache_config: CacheConfig,
|
|
) -> nn.Module:
|
|
"""Load a serialized model with tensorizer to the CPU.
|
|
|
|
This is only necessary when the model isn't vLLM-tensorized (see
|
|
examples/tensorize_vllm_model.py) This should still be faster than
|
|
default HuggingFace loading, but will be slower than loading a
|
|
vLLM-tensorized model.
|
|
"""
|
|
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)
|
|
|
|
model.load_weights(self._get_weights_iterator())
|
|
return model.eval()
|
|
|
|
def _load_model_serialized(
|
|
self,
|
|
model_config: ModelConfig,
|
|
device_config: DeviceConfig,
|
|
lora_config: Optional[LoRAConfig],
|
|
vision_language_config: Optional[VisionLanguageConfig],
|
|
cache_config: CacheConfig,
|
|
) -> nn.Module:
|
|
"""Load a serialized model with tensorizer.
|
|
|
|
Expects a vLLM-tensorized model. See the
|
|
examples/tensorize_vllm_model.py example script
|
|
for serializing vLLM models."""
|
|
with set_default_torch_dtype(model_config.dtype):
|
|
with torch.device(device_config.device):
|
|
model_class = get_model_architecture(model_config)[0]
|
|
quant_config = _get_quantization_config(
|
|
model_config, self.load_config)
|
|
extra_kwargs = _get_model_initialization_kwargs(
|
|
model_class, lora_config, vision_language_config)
|
|
extra_kwargs["quant_config"] = quant_config
|
|
extra_kwargs["cache_config"] = cache_config
|
|
|
|
tensorizer_config = copy.copy(self.tensorizer_config)
|
|
tensorizer_config.model_class = model_class
|
|
tensorizer_config.hf_config = model_config.hf_config
|
|
tensorizer_config.dtype = model_config.dtype
|
|
|
|
model = load_with_tensorizer(tensorizer_config, **extra_kwargs)
|
|
return model.eval()
|
|
|
|
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:
|
|
self._verify_config(model_config, parallel_config)
|
|
|
|
if is_vllm_tensorized(self.tensorizer_config):
|
|
return self._load_model_serialized(model_config, device_config,
|
|
lora_config,
|
|
vision_language_config,
|
|
cache_config)
|
|
return self._load_model_serialized_cpu(model_config, device_config,
|
|
lora_config,
|
|
vision_language_config,
|
|
cache_config)
|
|
|
|
|
|
class ShardedStateLoader(BaseModelLoader):
|
|
"""
|
|
Model loader that directly loads each worker's model state dict, which
|
|
enables a fast load path for large tensor-parallel models where each worker
|
|
only needs to read its own shard rather than the entire checkpoint. See
|
|
`examples/save_sharded_state.py` for creating a sharded checkpoint.
|
|
"""
|
|
|
|
DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors"
|
|
|
|
def __init__(self, load_config: LoadConfig):
|
|
super().__init__(load_config)
|
|
extra_config = ({} if load_config.model_loader_extra_config is None
|
|
else load_config.model_loader_extra_config.copy())
|
|
self.pattern = extra_config.pop("pattern", self.DEFAULT_PATTERN)
|
|
if extra_config:
|
|
raise ValueError(f"Unexpected extra config keys for load format "
|
|
f"{load_config.load_format}: "
|
|
f"{load_config.model_loader_extra_config.keys()}")
|
|
|
|
@staticmethod
|
|
def _filter_subtensors(
|
|
tensors: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
|
|
"""
|
|
Filter out all tensors that share the same memory or a subset of the
|
|
memory of another tensor.
|
|
"""
|
|
same_storage_groups = collections.defaultdict(list)
|
|
for key, tensor in tensors.items():
|
|
if tensor.numel():
|
|
ptr = tensor.untyped_storage().data_ptr()
|
|
same_storage_groups[tensor.device, ptr].append((key, tensor))
|
|
|
|
def get_end_ptr(tensor: torch.Tensor) -> int:
|
|
return tensor.view(-1)[-1].data_ptr() + tensor.element_size()
|
|
|
|
result = {}
|
|
for group in same_storage_groups.values():
|
|
for k, t in group:
|
|
a, b = t.data_ptr(), get_end_ptr(t)
|
|
for k2, t2 in group:
|
|
if not t2.is_contiguous():
|
|
continue
|
|
a2, b2 = t2.data_ptr(), get_end_ptr(t2)
|
|
if a < a2 or b2 < b:
|
|
continue
|
|
if a2 < a or b < b2 or not t.is_contiguous():
|
|
break # t2 covers strictly more memory than t.
|
|
if k2 < k:
|
|
# Same tensors, keep the one with the smaller key.
|
|
break
|
|
else:
|
|
result[k] = t
|
|
return result
|
|
|
|
def _prepare_weights(self, model_name_or_path: str,
|
|
revision: Optional[str]):
|
|
if os.path.isdir(model_name_or_path):
|
|
return model_name_or_path
|
|
else:
|
|
allow_patterns = ["*.safetensors"]
|
|
return download_weights_from_hf(model_name_or_path,
|
|
self.load_config.download_dir,
|
|
allow_patterns, revision)
|
|
|
|
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:
|
|
from safetensors.torch import safe_open
|
|
|
|
from vllm.distributed import get_tensor_model_parallel_rank
|
|
|
|
local_model_path = self._prepare_weights(model_config.model,
|
|
model_config.revision)
|
|
|
|
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)
|
|
rank = get_tensor_model_parallel_rank()
|
|
pattern = os.path.join(
|
|
local_model_path,
|
|
self.pattern.format(rank=rank, part="*"),
|
|
)
|
|
filepaths = glob.glob(pattern)
|
|
if not filepaths:
|
|
# TODO: support un-sharded checkpoints too
|
|
raise ValueError(
|
|
f"Could not find checkpoint files '{pattern}', only "
|
|
f"pre-sharded checkpoints are currently supported!")
|
|
state_dict = self._filter_subtensors(model.state_dict())
|
|
for path in filepaths:
|
|
with safe_open(path, framework="pt") as f:
|
|
for key in f.keys(): # noqa: SIM118
|
|
tensor = f.get_tensor(key)
|
|
# If loading with LoRA enabled, additional padding may
|
|
# be added to certain parameters. We only load into a
|
|
# narrowed view of the parameter data.
|
|
param_data = state_dict[key].data
|
|
param_shape = state_dict[key].shape
|
|
for dim, size in enumerate(tensor.shape):
|
|
if size < param_shape[dim]:
|
|
param_data = param_data.narrow(dim, 0, size)
|
|
if tensor.shape != param_shape:
|
|
logger.warning(
|
|
"loading tensor of shape %s into "
|
|
"parameter '%s' of shape %s", tensor.shape,
|
|
key, param_shape)
|
|
param_data.copy_(tensor)
|
|
state_dict.pop(key)
|
|
if state_dict:
|
|
raise ValueError(
|
|
f"Missing keys {tuple(state_dict)} in loaded state!")
|
|
return model.eval()
|
|
|
|
@staticmethod
|
|
def save_model(
|
|
model: torch.nn.Module,
|
|
path: str,
|
|
pattern: Optional[str] = None,
|
|
max_size: Optional[int] = None,
|
|
) -> None:
|
|
from safetensors.torch import save_file
|
|
|
|
from vllm.distributed import get_tensor_model_parallel_rank
|
|
if pattern is None:
|
|
pattern = ShardedStateLoader.DEFAULT_PATTERN
|
|
rank = get_tensor_model_parallel_rank()
|
|
part_idx = 0
|
|
total_size = 0
|
|
state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
|
|
state_dict_part: Dict[str, torch.Tensor] = {}
|
|
for key, tensor in state_dict.items():
|
|
param_size = tensor.nelement() * tensor.element_size()
|
|
if max_size is not None and total_size + param_size > max_size:
|
|
filename = pattern.format(rank=rank, part=part_idx)
|
|
save_file(
|
|
state_dict_part,
|
|
os.path.join(path, filename),
|
|
)
|
|
part_idx += 1
|
|
total_size = 0
|
|
state_dict_part = {}
|
|
state_dict_part[key] = tensor
|
|
total_size += param_size
|
|
if len(state_dict_part) > 0:
|
|
filename = pattern.format(rank=rank, part=part_idx)
|
|
save_file(
|
|
state_dict_part,
|
|
os.path.join(path, filename),
|
|
)
|
|
|
|
|
|
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."""
|
|
|
|
if isinstance(load_config.load_format, type):
|
|
return load_config.load_format(load_config)
|
|
|
|
if load_config.load_format == LoadFormat.DUMMY:
|
|
return DummyModelLoader(load_config)
|
|
|
|
if load_config.load_format == LoadFormat.TENSORIZER:
|
|
return TensorizerLoader(load_config)
|
|
|
|
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)
|