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[fix]fix tests of kernels

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# SPDX-License-Identifier: Apache-2.0
# ruff: noqa: SIM117
import collections
import copy
import dataclasses
import fnmatch
import glob
import inspect
import itertools
import math
import os
import time
import warnings
from abc import ABC, abstractmethod
from contextlib import contextmanager
from typing import (Any, Callable, Dict, Generator, Iterable, List, Optional,
Tuple, cast)
import gguf
import huggingface_hub
import numpy as np
import torch
from huggingface_hub import HfApi
from torch import nn
from transformers import AutoModelForCausalLM
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
from vllm.attention import Attention
from vllm.config import (LoadConfig, LoadFormat, ModelConfig, ParallelConfig,
VllmConfig, set_current_vllm_config)
from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.envs import VLLM_USE_MODELSCOPE
from vllm.logger import init_logger
# yapf conflicts with isort for this block
# yapf: disable
from vllm.model_executor.layers.linear import (LinearBase,
MergedColumnParallelLinear,
QKVCrossParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
# yapf: enable
from vllm.model_executor.layers.quantization.base_config import (
QuantizeMethodBase)
from vllm.model_executor.model_loader.tensorizer import (
TensorizerConfig, is_vllm_tensorized, load_with_tensorizer,
serialize_vllm_model, tensorizer_weights_iterator)
from vllm.model_executor.model_loader.utils import (ParamMapping,
configure_quant_config,
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,
fastsafetensors_weights_iterator, filter_duplicate_safetensors_files,
filter_files_not_needed_for_inference, get_gguf_extra_tensor_names,
get_lock, gguf_quant_weights_iterator, initialize_dummy_weights,
np_cache_weights_iterator, pt_weights_iterator,
runai_safetensors_weights_iterator, safetensors_weights_iterator)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.transformers_utils.s3_utils import glob as s3_glob
from vllm.transformers_utils.utils import is_s3
from vllm.utils import is_pin_memory_available
@contextmanager
def device_loading_context(module: torch.nn.Module,
target_device: torch.device):
if target_device.type == "cpu":
# If target is CPU, no need to move anything
yield module
return
original_device_states: Dict[str, torch.device] = {}
# Store original device states and move parameters to GPU if they're on CPU
for name, p in module.named_parameters():
if p.device.type == "cpu":
original_device_states[name] = p.device
p.data = p.data.to(target_device)
# Parameters already on target device are not touched
try:
yield module
finally:
# Restore parameters to their original devices, ignoring new parameters
pin_memory = is_pin_memory_available()
for name, p in module.named_parameters():
if name in original_device_states:
original_device: torch.device = original_device_states[name]
if original_device.type == "cpu":
# `torch.empty_like` does not support `pin_memory` argument
cpu_data = torch.empty_strided(
size=p.data.size(),
stride=p.data.stride(),
dtype=p.data.dtype,
layout=p.data.layout,
device="cpu",
pin_memory=pin_memory,
)
cpu_data.copy_(p.data)
p.data = cpu_data
else:
p.data = p.data.to(original_device)
# New parameters or parameters already on target device are untouched
logger = init_logger(__name__)
def _initialize_model(
vllm_config: VllmConfig,
*,
prefix: str = "",
model_class: Optional[type[nn.Module]] = None,
) -> nn.Module:
"""Initialize a model with the given configurations."""
model_config = vllm_config.model_config
if model_class is None:
model_class, _ = get_model_architecture(model_config)
if vllm_config.quant_config is not None:
configure_quant_config(vllm_config.quant_config, model_class)
signatures = inspect.signature(model_class.__init__)
all_params = [param.name for param in signatures.parameters.values()]
if "vllm_config" in all_params and "prefix" in all_params:
# new-style model class
with set_current_vllm_config(vllm_config, check_compile=True):
return model_class(vllm_config=vllm_config, prefix=prefix)
msg = ("vLLM model class should accept `vllm_config` and `prefix` as "
"input arguments. Possibly you have an old-style model class"
" registered from out of tree and it is used for new vLLM version. "
"Check https://docs.vllm.ai/en/latest/design/arch_overview.html "
"for the design and update the model class accordingly.")
warnings.warn(msg, DeprecationWarning, stacklevel=2)
logger.warning(
"Trying to guess the arguments for old-style model class %s",
model_class,
)
# try to be compatible with old-style model class
kwargs = {}
if "prefix" in all_params:
kwargs["prefix"] = prefix
if "config" in all_params:
kwargs["config"] = model_config.hf_config
if "cache_config" in all_params:
kwargs["cache_config"] = vllm_config.cache_config
if "quant_config" in all_params:
kwargs["quant_config"] = vllm_config.quant_config
if "lora_config" in all_params:
kwargs["lora_config"] = vllm_config.lora_config
if "scheduler_config" in all_params:
kwargs["scheduler_config"] = vllm_config.scheduler_config
if "parallel_config" in all_params:
kwargs["parallel_config"] = vllm_config.parallel_config
with set_current_vllm_config(vllm_config, check_compile=True):
return model_class(**kwargs)
def _process_weights_after_loading(model: nn.Module, model_config: ModelConfig,
target_device: torch.device) -> None:
for _, module in model.named_modules():
if isinstance(module, QKVCrossParallelLinear):
# NOTE(Isotr0py): special case for cross QKV layer because
# q and kv proj aren't registered as submodules intentionally
module.process_weights_after_loading()
continue
quant_method = getattr(module, "quant_method", None)
if isinstance(quant_method, QuantizeMethodBase):
# When quant methods need to process weights after loading
# (for repacking, quantizing, etc), they expect parameters
# to be on the global target device. This scope is for the
# case where cpu offloading is used, where we will move the
# parameters onto device for processing and back off after.
with device_loading_context(module, target_device):
quant_method.process_weights_after_loading(module)
# Currently only used by MLA.
# NOTE: This intentionally happens after other modules so we can easily
# decompress the weights for MLA.
for _, module in model.named_modules():
if isinstance(module, Attention) and \
hasattr(module, "process_weights_after_loading"):
# TODO(lucas): see if there is a way to unify the signatures
# of process_weights_after_loading
module.process_weights_after_loading(model_config.dtype)
class BaseModelLoader(ABC):
"""Base class for model loaders."""
def __init__(self, load_config: LoadConfig):
self.load_config = load_config
@abstractmethod
def download_model(self, model_config: ModelConfig) -> None:
"""Download a model so that it can be immediately loaded."""
raise NotImplementedError
@abstractmethod
def load_model(self, *, vllm_config: VllmConfig) -> nn.Module:
"""Load a model with the given configurations."""
raise NotImplementedError
class DefaultModelLoader(BaseModelLoader):
"""Model loader that can load different file types from disk."""
@dataclasses.dataclass
class Source:
"""A source for weights."""
model_or_path: str
"""The model ID or path."""
revision: Optional[str]
"""The optional model revision."""
prefix: str = ""
"""A prefix to prepend to all weights."""
fall_back_to_pt: bool = True
"""Whether .pt weights can be used."""
allow_patterns_overrides: Optional[list[str]] = None
"""If defined, weights will load exclusively using these patterns."""
counter_before_loading_weights: float = 0.0
counter_after_loading_weights: float = 0.0
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):
# Use file lock to prevent multiple processes from
# downloading the same model weights at the same time.
with get_lock(model, self.load_config.download_dir):
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,
ignore_file_pattern=self.load_config.ignore_patterns,
)
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,
allow_patterns_overrides: Optional[list[str]],
) -> 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
index_file = SAFE_WEIGHTS_INDEX_NAME
# Some quantized models use .pt files for storing the weights.
if load_format == LoadFormat.AUTO:
allow_patterns = ["*.safetensors", "*.bin"]
elif (load_format == LoadFormat.SAFETENSORS
or load_format == LoadFormat.FASTSAFETENSORS):
use_safetensors = True
allow_patterns = ["*.safetensors"]
elif load_format == LoadFormat.MISTRAL:
use_safetensors = True
allow_patterns = ["consolidated*.safetensors"]
index_file = "consolidated.safetensors.index.json"
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 allow_patterns_overrides is not None:
allow_patterns = allow_patterns_overrides
if not is_local:
hf_folder = download_weights_from_hf(
model_name_or_path,
self.load_config.download_dir,
allow_patterns,
revision,
ignore_patterns=self.load_config.ignore_patterns,
)
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,
index_file,
self.load_config.download_dir,
revision,
)
hf_weights_files = filter_duplicate_safetensors_files(
hf_weights_files, hf_folder, index_file)
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, source: "Source"
) -> 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(
source.model_or_path, source.revision, source.fall_back_to_pt,
source.allow_patterns_overrides)
if self.load_config.load_format == LoadFormat.NPCACHE:
# Currently np_cache only support *.bin checkpoints
assert use_safetensors is False
weights_iterator = np_cache_weights_iterator(
source.model_or_path,
self.load_config.download_dir,
hf_folder,
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
elif use_safetensors:
if self.load_config.load_format == LoadFormat.FASTSAFETENSORS:
weights_iterator = fastsafetensors_weights_iterator(
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
else:
weights_iterator = safetensors_weights_iterator(
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
else:
weights_iterator = pt_weights_iterator(
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
if current_platform.is_tpu():
# In PyTorch XLA, we should call `xm.mark_step` frequently so that
# not too many ops are accumulated in the XLA program.
import torch_xla.core.xla_model as xm
def _xla_weights_iterator(iterator: Generator):
for weights in iterator:
yield weights
xm.mark_step()
weights_iterator = _xla_weights_iterator(weights_iterator)
elif current_platform.is_hpu():
import habana_frameworks.torch.core as htcore
def _hpu_weights_iterator(iterator: Generator):
for weights in iterator:
yield weights
htcore.mark_step()
weights_iterator = _hpu_weights_iterator(weights_iterator)
if self.counter_before_loading_weights == 0.0:
self.counter_before_loading_weights = time.perf_counter()
# Apply the prefix.
return ((source.prefix + name, tensor)
for (name, tensor) in weights_iterator)
def get_all_weights(
self,
model_config: ModelConfig,
model: nn.Module,
) -> Generator[Tuple[str, torch.Tensor], None, None]:
primary_weights = DefaultModelLoader.Source(
model_config.model,
model_config.revision,
prefix="",
fall_back_to_pt=getattr(model, "fall_back_to_pt_during_load",
True),
allow_patterns_overrides=getattr(model, "allow_patterns_overrides",
None),
)
yield from self._get_weights_iterator(primary_weights)
secondary_weights = cast(
Iterable[DefaultModelLoader.Source],
getattr(model, "secondary_weights", ()),
)
for source in secondary_weights:
yield from self._get_weights_iterator(source)
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model,
model_config.revision,
fall_back_to_pt=True,
allow_patterns_overrides=None)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = _initialize_model(vllm_config=vllm_config)
weights_to_load = {name for name, _ in model.named_parameters()}
loaded_weights = model.load_weights(
self.get_all_weights(model_config, model))
self.counter_after_loading_weights = time.perf_counter()
logger.info(
"Loading weights took %.2f seconds",
self.counter_after_loading_weights -
self.counter_before_loading_weights)
# We only enable strict check for non-quantized models
# that have loaded weights tracking currently.
if model_config.quantization is None and loaded_weights is not None:
weights_not_loaded = weights_to_load - loaded_weights
if weights_not_loaded:
raise ValueError(
"Following weights were not initialized from "
f"checkpoint: {weights_not_loaded}")
_process_weights_after_loading(model, model_config, target_device)
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 download_model(self, model_config: ModelConfig) -> None:
pass # Nothing to download
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = _initialize_model(vllm_config=vllm_config)
# NOTE(woosuk): For accurate performance evaluation, we assign
# random values to the weights.
initialize_dummy_weights(model)
_process_weights_after_loading(model, model_config, target_device)
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,
vllm_config: VllmConfig,
) -> nn.Module:
"""Load a serialized model with tensorizer to the CPU.
This is only necessary when the model isn't vLLM-tensorized (see
examples/other/tensorize_vllm_model.py) This should still
be faster than default HuggingFace loading, but will be slower than
loading a vLLM-tensorized model.
"""
device_config = vllm_config.device_config
model_config = vllm_config.model_config
with set_default_torch_dtype(model_config.dtype):
with torch.device(device_config.device):
model = _initialize_model(vllm_config=vllm_config)
model.load_weights(self._get_weights_iterator())
return model.eval()
def _load_model_serialized(
self,
vllm_config: VllmConfig,
) -> nn.Module:
"""Load a serialized model with tensorizer.
Expects a vLLM-tensorized model. See the
examples/other/tensorize_vllm_model.py example script
for serializing vLLM models."""
device_config = vllm_config.device_config
model_config = vllm_config.model_config
with set_default_torch_dtype(model_config.dtype):
with torch.device(device_config.device):
model_class = get_model_architecture(model_config)[0]
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,
vllm_config=vllm_config)
return model.eval()
def download_model(self, model_config: ModelConfig) -> None:
self.tensorizer_config.verify_with_model_config(model_config)
with self.tensorizer_config.open_stream():
pass
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
model_config = vllm_config.model_config
parallel_config = vllm_config.parallel_config
self._verify_config(model_config, parallel_config)
if parallel_config.tensor_parallel_size > 1:
from vllm.distributed import get_tensor_model_parallel_rank
self.tensorizer_config.tensorizer_uri = (
self.tensorizer_config.tensorizer_uri %
get_tensor_model_parallel_rank())
if is_vllm_tensorized(self.tensorizer_config):
return self._load_model_serialized(vllm_config=vllm_config)
return self._load_model_serialized_cpu(vllm_config=vllm_config)
@staticmethod
def save_model(
model: torch.nn.Module,
tensorizer_config: TensorizerConfig,
) -> None:
serialize_vllm_model(
model=model,
tensorizer_config=tensorizer_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/offline_inference/save_sharded_state.py` for creating a sharded
checkpoint.
"""
DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors"
def __init__(self,
load_config: LoadConfig,
runai_model_streamer: bool = False):
super().__init__(load_config)
self.runai_model_streamer = runai_model_streamer
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: Dict[Any, List[Tuple[str, torch.Tensor]]] = (
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: Dict[str, torch.Tensor] = {}
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 is_s3(model_name_or_path) or 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,
ignore_patterns=self.load_config.ignore_patterns,
)
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model, model_config.revision)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
from vllm.distributed import get_tensor_model_parallel_rank
model_weights = model_config.model
if hasattr(model_config, "model_weights"):
model_weights = model_config.model_weights
local_model_path = model_weights
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = _initialize_model(vllm_config=vllm_config)
_process_weights_after_loading(model, model_config,
target_device)
rank = get_tensor_model_parallel_rank()
pattern = os.path.join(
local_model_path,
self.pattern.format(rank=rank, part="*"),
)
filepaths = []
if is_s3(local_model_path):
file_pattern = f"*{self.pattern.format(rank=rank, part=' * ')}"
filepaths = s3_glob(path=local_model_path,
allow_pattern=[file_pattern])
else:
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 key, tensor in self.iterate_over_files(filepaths):
# 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()
def iterate_over_files(
self, paths) -> Generator[Tuple[str, torch.Tensor], None, None]:
if self.runai_model_streamer:
yield from runai_safetensors_weights_iterator(paths, True)
else:
from safetensors.torch import safe_open
for path in paths:
with safe_open(path, framework="pt") as f:
for key in f.keys(): # noqa: SIM118
tensor = f.get_tensor(key)
yield key, tensor
@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."""
possible_config_file_names = ["adapter_config.json"]
def __init__(self, load_config: LoadConfig):
super().__init__(load_config)
# Save the module names without sharding.
self.unsharded_weights_modules: List[str] = []
# Save the module names that are sharded by column.
self.column_sharded_weights_modules: List[str] = []
# Store all module names (from transformers) that support
# BNB quantization.
self.target_modules: List[str] = []
# mapping weight names from transformers to vllm.
self.weight_mapper: Callable = lambda name: name
def _get_weight_files(
self,
model_name_or_path: str,
allowed_patterns: List[str],
revision: Optional[str] = None,
) -> Tuple[str, 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 model_name_or_path, 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,
ignore_patterns=self.load_config.ignore_patterns,
)
return hf_folder, 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_folder, hf_weights_files, matched_pattern = self._get_weight_files(
model_name_or_path, allowed_patterns, revision)
use_safetensors = matched_pattern == "*.safetensors"
is_local = os.path.isdir(model_name_or_path)
index_file = SAFE_WEIGHTS_INDEX_NAME
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,
index_file,
self.load_config.download_dir,
revision,
)
hf_weights_files = filter_duplicate_safetensors_files(
hf_weights_files, hf_folder, index_file)
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_weights_files, use_safetensors
def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool):
if use_safetensors:
iterator = safetensors_weights_iterator(
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
else:
iterator = pt_weights_iterator(
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
for org_name, param in iterator:
# mapping weight names from transformers to vllm while preserving
# original names.
mapped_name = self.weight_mapper(org_name)
yield org_name, mapped_name, param
def _get_quantized_weights_iterator(
self,
model_name_or_path: str,
revision: Optional[str],
pre_quant: bool,
load_8bit: bool,
) -> 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__ < "0.45.3":
raise ImportError("bitsandbytes version is wrong. Please "
"install bitsandbytes>=0.45.3.")
except ImportError as err:
raise ImportError("Please install bitsandbytes>=0.45.3 via "
"`pip install bitsandbytes>=0.45.3` to use "
"bitsandbytes quantizer.") from err
hf_weights_files, use_safetensors = self._prepare_weights(
model_name_or_path, revision)
quant_state_dict: Dict[str, Any] = {}
if pre_quant:
if load_8bit:
return self._quantized_8bit_generator(
hf_weights_files, use_safetensors,
quant_state_dict), quant_state_dict
else:
return self._quantized_4bit_generator(
hf_weights_files, use_safetensors,
quant_state_dict), quant_state_dict
return self._unquantized_generator(hf_weights_files, use_safetensors,
quant_state_dict), quant_state_dict
def _is_8bit_weight_name(self, weight_name: str):
quantized_suffix = {".scb", ".weight_format"}
return any(weight_name.lower().endswith(suffix)
for suffix in quantized_suffix)
def _is_4bit_weight_name(self, weight_name: str):
quantized_suffix = {
"absmax",
"quant_map",
"nested_absmax",
"nested_quant_map",
"bitsandbytes",
}
suffix = weight_name.split(".")[-1]
return any(q_suffix in suffix for q_suffix in quantized_suffix)
def _quantized_8bit_generator(self, hf_weights_files, use_safetensors,
quant_state_dict) -> Generator:
for (
org_weight_name,
mapped_weight_name,
weight_tensor,
) in self._hf_weight_iter(hf_weights_files, use_safetensors):
if not mapped_weight_name.lower().endswith(".scb"):
continue
weight_key = mapped_weight_name.lower().replace(".scb", ".weight")
quant_state_dict[weight_key] = weight_tensor
for (
org_weight_name,
mapped_weight_name,
weight_tensor,
) in self._hf_weight_iter(hf_weights_files, use_safetensors):
if self._is_8bit_weight_name(mapped_weight_name):
continue
if mapped_weight_name in quant_state_dict:
set_weight_attrs(weight_tensor, {"load_in_8bit": True})
yield org_weight_name, weight_tensor
else:
yield org_weight_name, weight_tensor
def _quantized_4bit_generator(self, hf_weights_files, use_safetensors,
quant_state_dict) -> Generator:
from bitsandbytes.functional import QuantState
# First iterate over all quant state weights
weight_iterator = self._hf_weight_iter(hf_weights_files,
use_safetensors)
temp_state_dict = {}
for (
org_weight_name,
mapped_weight_name,
weight_tensor,
) in weight_iterator:
if not self._is_4bit_weight_name(mapped_weight_name):
continue
# bitsandbytes library requires
# weight.quant_state.bitsandbytes__* in CPU
if "quant_state.bitsandbytes" in mapped_weight_name:
temp_state_dict[mapped_weight_name] = weight_tensor.cpu().data
else:
temp_state_dict[mapped_weight_name] = weight_tensor
# Closure to parse quant_state for each prequant weight
def _parse_quant_state(param_name: str,
temp_state_dict: Dict) -> QuantState:
quant_state = {}
for k in temp_state_dict:
if param_name + "." in k:
quant_state[k] = temp_state_dict[k]
return QuantState.from_dict(quant_state,
device=current_platform.device_type)
# Second iterate over all prequant and normal weights
# pre quantized weights would have a quant_state
for (
org_weight_name,
mapped_weight_name,
weight_tensor,
) in self._hf_weight_iter(hf_weights_files, use_safetensors):
if self._is_4bit_weight_name(mapped_weight_name):
continue
if (f"{mapped_weight_name}.quant_state.bitsandbytes__nf4"
in temp_state_dict) or (
f"{mapped_weight_name}.quant_state.bitsandbytes__fp4"
in temp_state_dict):
quant_state = _parse_quant_state(mapped_weight_name,
temp_state_dict)
quant_state_dict[mapped_weight_name] = quant_state
yield org_weight_name, weight_tensor
else:
yield org_weight_name, weight_tensor
def _unquantized_generator(self, hf_weights_files, use_safetensors,
quant_state_dict) -> Generator:
from bitsandbytes.functional import quantize_4bit
tp_size = get_tensor_model_parallel_world_size()
tp_rank = get_tensor_model_parallel_rank()
for (
org_weight_name,
mapped_weight_name,
weight_tensor,
) in self._hf_weight_iter(hf_weights_files, use_safetensors):
if any(target_module in mapped_weight_name
for target_module in self.target_modules
) and mapped_weight_name.endswith(".weight"):
# Without sharding
if any(
mapped_weight_name.startswith(module)
for module in self.unsharded_weights_modules):
weight_sub_tensor = weight_tensor
# Shard by column
elif any(
mapped_weight_name.startswith(module)
for module in self.column_sharded_weights_modules):
total_size = weight_tensor.size(-1)
start_index = total_size // tp_size * tp_rank
end_index = total_size // tp_size * (tp_rank + 1)
weight_sub_tensor = weight_tensor[...,
start_index:end_index]
# Weights have fused on disk. In this case, we assume that the
# weight and module use same name.
elif any(
mapped_weight_name.startswith(module)
for module in self.maybe_fused_weights_modules):
# special case for fused weights
# get the size of each shard weight tensor
total_shard_sizes = next(
(sizes for module, sizes in
self.maybe_fused_weights_modules.items()
if mapped_weight_name.startswith(module)))
total_size = weight_tensor.size(0)
assert total_size == sum(total_shard_sizes)
# get the start/end index of each shard weight tensor
total_start_index = list(
itertools.accumulate([0] + total_shard_sizes))[:-1]
shard_weights_index = [(
idx + size // tp_size * tp_rank,
idx + size // tp_size * (tp_rank + 1),
) for idx, size in zip(total_start_index,
total_shard_sizes)]
# slice and reorder the weight tensor
weight_tensor = [
weight_tensor[start_index:end_index, ...]
for start_index, end_index in shard_weights_index
]
weight_sub_tensor = torch.cat(weight_tensor, dim=0)
# Shard by row
else:
total_size = weight_tensor.size(0)
start_index = total_size // tp_size * tp_rank
end_index = total_size // tp_size * (tp_rank + 1)
weight_sub_tensor = weight_tensor[start_index:end_index,
...]
# bitsandbytes requires data in GPU
if weight_sub_tensor.is_cuda:
loaded_weight = weight_sub_tensor
else:
loaded_weight = weight_sub_tensor.cuda()
# remove the following after the issue is fixed:
# https://github.com/bitsandbytes-foundation/bitsandbytes/issues/1342
if loaded_weight.is_contiguous() is False:
loaded_weight = loaded_weight.contiguous()
with set_default_torch_dtype(torch.float32):
processed_weight, quant_state = quantize_4bit(
loaded_weight,
compress_statistics=True,
quant_type="nf4",
)
quant_state_dict[mapped_weight_name] = quant_state
else:
processed_weight = weight_tensor
yield org_weight_name, processed_weight
def _get_bnb_target_modules(self, model: nn.Module) -> None:
for name, module in model.named_modules():
if isinstance(module, (LinearBase, )):
if modules_info := self.modules_mapping.get_sub_modules(name):
# Map vllm's names to transformers's names.
rep_name, sub_modules = modules_info
for sub_name in sub_modules:
self.target_modules.append(
name.replace(rep_name, sub_name))
# Add original module name even if the module has stacked map,
# in case model has a mixture of disk-merged and disk-splitted
# weights with same last name.
self.target_modules.append(name)
assert (self.target_modules
), "vllm currently does not support BNB quantization for"
f" {type(model).__name__}"
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(model).__name__}.")
if not hasattr(model, "packed_modules_mapping"):
raise AttributeError(
f"Model {type(model).__name__} does not support BitsAndBytes "
"quantization yet. No 'packed_modules_mapping' found.")
self.modules_mapping = ParamMapping(
copy.deepcopy(model.packed_modules_mapping))
# For some models like Molmo, we need to use hf_to_vllm_mapper
# to ensure correct loading of weights.
if hf_to_vllm_mapper := getattr(model, "hf_to_vllm_mapper", None):
self.weight_mapper = lambda name: hf_to_vllm_mapper._map_name(name)
# Modules whose weights might have fused on disk
# we need their output_sizes to make shard in flight correctly with TP
self.maybe_fused_weights_modules: Dict[str, List[int]] = {}
self._get_bnb_target_modules(model)
for name, module in model.named_modules():
# Some modules like `ReplicatedLinear` should not have their weights
# sharded. The reason for implementing it this way is to avoid new
# static variable in the model implementation.
if isinstance(module, (ReplicatedLinear, )):
self.unsharded_weights_modules.append(name)
# `QKVParallelLinear` and `MergedColumnParallelLinear` might have
# fused weights on disk. We need to use the output sizes of these
# modules to shard the weights correctly.
elif isinstance(module,
(QKVParallelLinear, MergedColumnParallelLinear)):
self.maybe_fused_weights_modules[name] = module.output_sizes
# In TP, these weights are partitioned along the column
# dimension (dim=-1)
elif isinstance(module, (RowParallelLinear, )):
self.column_sharded_weights_modules.append(name)
self.model_type = type(model).__name__
logger.info("Loading weights with BitsAndBytes quantization. "
"May take a while ...")
quant_config = getattr(model_config.hf_config, "quantization_config",
None)
pre_quant = False
if quant_config is not None:
quant_method = quant_config.get("quant_method")
if quant_method == "bitsandbytes":
pre_quant = True
else:
raise ValueError(
f"BitsAndBytes loader does not support {quant_method} "
"quantization")
# The quant_states in pre_quantized models cannot work with a split
# weight tensor. So TP does not work with pre_quantized bnb models.
if pre_quant and get_tensor_model_parallel_world_size() > 1:
raise ValueError(
"Prequant BitsAndBytes models with tensor parallelism is not "
"supported. Please try with pipeline parallelism.")
load_8bit = False
if pre_quant:
load_8bit = quant_config.get("load_in_8bit", False)
qweight_iterator, quant_state_dict = (
self._get_quantized_weights_iterator(model_config.model,
model_config.revision,
pre_quant, load_8bit))
weights_to_load = {name for name, _ in model.named_parameters()}
loaded_weights = model.load_weights(qweight_iterator)
# Some models may have weights loading tracker unimplemented.
if loaded_weights is not None:
weights_not_loaded = weights_to_load - loaded_weights
if weights_not_loaded:
raise ValueError("Following weights were not initialized from "
f"checkpoint: {weights_not_loaded}")
torch.cuda.empty_cache()
param_dict = dict(model.named_parameters())
stacked_quant_state_dict: Dict[str, Dict[int, Any]] = {}
# TODO: Change this lazy import to normal import
# after the checks are updated to run on a new version
from vllm.model_executor.models.utils import is_pp_missing_parameter
for quant_param_name in quant_state_dict:
if is_pp_missing_parameter(quant_param_name, model):
continue
non_stacked_param_name = quant_param_name
shard_index = 0
for shard_name, (
weight_name,
index,
) in self.modules_mapping.inverse_packed_mapping.items():
# Some models, such as MiniCPM V2.5/2.6, contain both
# module names 'kv_proj' and 'qkv_proj'. To prevent 'kv_proj'
# from being incorrectly identified as being present in
# 'vpm.encoder.layers.0.self_attn.qkv_proj.weight
shard_pos = quant_param_name.find(shard_name)
can_correct_rename = (shard_pos
> 0) and (quant_param_name[shard_pos - 1]
== ".")
# If the quant_param_name is packed, it won't occur in the
# param_dict before renaming.
new_quant_param_name = quant_param_name.replace(
shard_name, weight_name)
need_rename = (quant_param_name not in param_dict) \
and (new_quant_param_name in param_dict)
if can_correct_rename and need_rename:
shard_index = index
quant_param_name = new_quant_param_name
break
# Models like Clip/Siglip may skip some layers in initialization,
# causing unused quant_param_name in state_dict.
if quant_param_name not in param_dict:
continue
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 quant_states.items():
num_elements[seq] = (math.prod(quant_state.shape) //
pack_ratio)
offsets = np.concatenate(([0], np.cumsum(num_elements)))
# Make torch infer_schema happy
offsets = torch.tensor(offsets).cpu()
set_weight_attrs(param, {"bnb_shard_offsets": offsets})
if load_8bit:
set_weight_attrs(
param, {"matmul_state": [None] * len(quant_states)})
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model, model_config.revision)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
with set_default_torch_dtype(model_config.dtype):
with torch.device(device_config.device):
model = _initialize_model(vllm_config=vllm_config)
self._load_weights(model_config, model)
return model.eval()
class GGUFModelLoader(BaseModelLoader):
"""
Model loader that can load GGUF files. This is useful for loading models
that are quantized with GGUF and saved in the GGUF format. This loader
supports loading both full models and sharded models.
"""
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 _prepare_weights(self, model_name_or_path: str):
if os.path.isfile(model_name_or_path):
return model_name_or_path
else:
raise ValueError(f"{model_name_or_path} is not a file.")
def _get_gguf_weights_map(self, model_config: ModelConfig):
"""
GGUF uses this naming convention for their tensors from HF checkpoint:
`blk.N.BB.weight` and `blk.N.BB.bias`
where N signifies the block number of a layer, and BB signifies the
attention/mlp layer components.
See "Standardized tensor names" in
https://github.com/ggerganov/ggml/blob/master/docs/gguf.md for details.
"""
config = model_config.hf_config
model_type = config.model_type
gguf_to_hf_name_map = {}
# hack: ggufs have a different name than transformers
if model_type == "cohere":
model_type = "command-r"
if model_type in ("deepseek_v3", "deepseek_v2"):
model_type = "deepseek2"
# GGUF layer map assumes that we will have a merged expert weights
# so we need to map them manually
for idx in range(config.num_hidden_layers):
gguf_to_hf_name_map[f"blk.{idx}.exp_probs_b.bias"] = \
f"model.layers.{idx}.mlp.gate.e_score_correction_bias"
gguf_to_hf_name_map[f"blk.{idx}.ffn_down_exps.weight"] = \
f"model.layers.{idx}.mlp.experts.0.down_proj.weight"
gguf_to_hf_name_map[f"blk.{idx}.ffn_gate_exps.weight"] = \
f"model.layers.{idx}.mlp.experts.0.gate_proj.weight"
gguf_to_hf_name_map[f"blk.{idx}.ffn_up_exps.weight"] = \
f"model.layers.{idx}.mlp.experts.0.up_proj.weight"
arch = None
for key, value in gguf.MODEL_ARCH_NAMES.items():
if value == model_type:
arch = key
break
if arch is None:
raise RuntimeError(f"Unknown gguf model_type: {model_type}")
num_layers = config.num_hidden_layers
name_map = gguf.get_tensor_name_map(arch, num_layers)
with torch.device("meta"):
dummy_model = AutoModelForCausalLM.from_config(
config, trust_remote_code=model_config.trust_remote_code)
state_dict = dummy_model.state_dict()
for hf_name in state_dict:
name, suffix = hf_name.rsplit(".", 1)
gguf_name = name_map.get_name(name)
gguf_to_hf_name_map[f"{gguf_name}.{suffix}"] = hf_name
return gguf_to_hf_name_map
def _get_weights_iterator(
self, model_name_or_path: str, gguf_to_hf_name_map: Dict[str, str]
) -> Generator[Tuple[str, torch.Tensor], None, None]:
return gguf_quant_weights_iterator(model_name_or_path,
gguf_to_hf_name_map)
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
device_config = vllm_config.device_config
model_config = vllm_config.model_config
local_model_path = self._prepare_weights(model_config.model)
gguf_weights_map = self._get_gguf_weights_map(model_config)
# we can only know if tie word embeddings after mapping weights
if "lm_head.weight" in get_gguf_extra_tensor_names(
local_model_path, gguf_weights_map):
model_config.hf_config.update({"tie_word_embeddings": True})
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = _initialize_model(vllm_config=vllm_config)
model.load_weights(
self._get_weights_iterator(local_model_path, gguf_weights_map))
_process_weights_after_loading(model, model_config, target_device)
return model
class RunaiModelStreamerLoader(BaseModelLoader):
"""
Model loader that can load safetensors
files from local FS or S3 bucket.
"""
def __init__(self, load_config: LoadConfig):
super().__init__(load_config)
if load_config.model_loader_extra_config:
extra_config = load_config.model_loader_extra_config
if ("concurrency" in extra_config
and isinstance(extra_config.get("concurrency"), int)):
os.environ["RUNAI_STREAMER_CONCURRENCY"] = str(
extra_config.get("concurrency"))
if ("memory_limit" in extra_config
and isinstance(extra_config.get("memory_limit"), int)):
os.environ["RUNAI_STREAMER_MEMORY_LIMIT"] = str(
extra_config.get("memory_limit"))
runai_streamer_s3_endpoint = os.getenv(
'RUNAI_STREAMER_S3_ENDPOINT')
aws_endpoint_url = os.getenv('AWS_ENDPOINT_URL')
if (runai_streamer_s3_endpoint is None
and aws_endpoint_url is not None):
os.environ["RUNAI_STREAMER_S3_ENDPOINT"] = aws_endpoint_url
def _prepare_weights(self, model_name_or_path: str,
revision: Optional[str]) -> List[str]:
"""Prepare weights for the model.
If the model is not local, it will be downloaded."""
is_s3_path = is_s3(model_name_or_path)
is_local = os.path.isdir(model_name_or_path)
safetensors_pattern = "*.safetensors"
index_file = SAFE_WEIGHTS_INDEX_NAME
hf_folder = (model_name_or_path if
(is_local or is_s3_path) else download_weights_from_hf(
model_name_or_path,
self.load_config.download_dir,
[safetensors_pattern],
revision,
ignore_patterns=self.load_config.ignore_patterns,
))
if is_s3_path:
hf_weights_files = s3_glob(path=hf_folder,
allow_pattern=[safetensors_pattern])
else:
hf_weights_files = glob.glob(
os.path.join(hf_folder, safetensors_pattern))
if not is_local and not is_s3_path:
download_safetensors_index_file_from_hf(
model_name_or_path, index_file, self.load_config.download_dir,
revision)
if not hf_weights_files:
raise RuntimeError(
f"Cannot find any safetensors model weights with "
f"`{model_name_or_path}`")
return hf_weights_files
def _get_weights_iterator(
self, model_or_path: str,
revision: str) -> Generator[Tuple[str, torch.Tensor], None, None]:
"""Get an iterator for the model weights based on the load format."""
hf_weights_files = self._prepare_weights(model_or_path, revision)
return runai_safetensors_weights_iterator(
hf_weights_files,
self.load_config.use_tqdm_on_load,
)
def download_model(self, model_config: ModelConfig) -> None:
"""Download model if necessary"""
self._prepare_weights(model_config.model, model_config.revision)
def load_model(self, vllm_config: VllmConfig) -> nn.Module:
"""Perform streaming of the model to destination"""
device_config = vllm_config.device_config
model_config = vllm_config.model_config
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = _initialize_model(vllm_config=vllm_config)
model_weights = model_config.model
if hasattr(model_config, "model_weights"):
model_weights = model_config.model_weights
model.load_weights(
self._get_weights_iterator(model_weights,
model_config.revision))
_process_weights_after_loading(model, model_config, target_device)
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)
if load_config.load_format == LoadFormat.GGUF:
return GGUFModelLoader(load_config)
if load_config.load_format == LoadFormat.RUNAI_STREAMER:
return RunaiModelStreamerLoader(load_config)
if load_config.load_format == LoadFormat.RUNAI_STREAMER_SHARDED:
return ShardedStateLoader(load_config, runai_model_streamer=True)
return DefaultModelLoader(load_config)
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