biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Convert formatting to use ruff instead of yapf + isort (#26247)

Browse Source 
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
Download Patch File Download Diff File Expand all files Collapse all files

96
vllm/model_executor/model_loader/utils.py
View File

@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Utilities for selecting and loading models."""
import contextlib
import inspect
import warnings
@@ -17,12 +18,16 @@ from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
from vllm.logger import init_logger
from vllm.model_executor.layers.linear import QKVCrossParallelLinear
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig, QuantizeMethodBase)
QuantizationConfig,
QuantizeMethodBase,
)
from vllm.model_executor.models.adapters import (
as_embedding_model, as_reward_model, as_seq_cls_model,
try_create_mm_pooling_model_cls)
from vllm.model_executor.models.interfaces import (SupportsQuant,
supports_multimodal)
as_embedding_model,
as_reward_model,
as_seq_cls_model,
try_create_mm_pooling_model_cls,
)
from vllm.model_executor.models.interfaces import SupportsQuant, supports_multimodal
from vllm.utils import is_pin_memory_available
logger = init_logger(__name__)
@@ -57,16 +62,16 @@ def initialize_model(
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,
prefix=prefix):
with set_current_vllm_config(vllm_config, check_compile=True, prefix=prefix):
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.")
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(
@@ -87,20 +92,19 @@ def initialize_model(
kwargs["lora_config"] = vllm_config.lora_config
if "scheduler_config" in all_params:
kwargs["scheduler_config"] = vllm_config.scheduler_config
with set_current_vllm_config(vllm_config,
check_compile=True,
prefix=prefix):
with set_current_vllm_config(vllm_config, check_compile=True, prefix=prefix):
return model_class(**kwargs)
def process_weights_after_loading(model: nn.Module, model_config: ModelConfig,
target_device: torch.device) -> None:
def process_weights_after_loading(
model: nn.Module, model_config: ModelConfig, target_device: torch.device
) -> None:
# to avoid circular dependency
from vllm.model_executor.model_loader.online_quantization import (
maybe_save_metadata_and_attributes_for_weight_reloading)
maybe_save_metadata_and_attributes_for_weight_reloading(
model, model_config)
maybe_save_metadata_and_attributes_for_weight_reloading,
)
maybe_save_metadata_and_attributes_for_weight_reloading(model, model_config)
for _, module in model.named_modules():
if isinstance(module, QKVCrossParallelLinear):
@@ -122,16 +126,16 @@ def process_weights_after_loading(model: nn.Module, model_config: ModelConfig,
# 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"):
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)
@contextmanager
def device_loading_context(module: torch.nn.Module,
target_device: torch.device):
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
@@ -176,8 +180,7 @@ _MODEL_ARCH_BY_HASH = dict[int, tuple[type[nn.Module], str]]()
"""Caches the outputs of `_get_model_architecture`."""
def _get_model_architecture(
model_config: ModelConfig) -> tuple[type[nn.Module], str]:
def _get_model_architecture(model_config: ModelConfig) -> tuple[type[nn.Module], str]:
architectures = getattr(model_config.hf_config, "architectures", [])
model_cls, arch = model_config.registry.resolve_model_cls(
@@ -191,7 +194,9 @@ def _get_model_architecture(
logger.warning_once(
"%s has no vLLM implementation, falling back to Transformers "
"implementation. Some features may not be supported and "
"performance may not be optimal.", arch)
"performance may not be optimal.",
arch,
)
convert_type = model_config.convert_type
if convert_type != "none" and supports_multimodal(model_cls):
@@ -220,16 +225,17 @@ def _get_model_architecture(
return model_cls, arch
def get_model_architecture(
model_config: ModelConfig) -> tuple[type[nn.Module], str]:
key = hash((
model_config.model,
model_config.convert_type,
model_config.runner_type,
model_config.trust_remote_code,
model_config.model_impl,
tuple(getattr(model_config.hf_config, "architectures", [])),
))
def get_model_architecture(model_config: ModelConfig) -> tuple[type[nn.Module], str]:
key = hash(
(
model_config.model,
model_config.convert_type,
model_config.runner_type,
model_config.trust_remote_code,
model_config.model_impl,
tuple(getattr(model_config.hf_config, "architectures", [])),
)
)
if key in _MODEL_ARCH_BY_HASH:
return _MODEL_ARCH_BY_HASH[key]
@@ -253,9 +259,9 @@ class ParamMapping:
It creates a bidirectional mapping between packed parameters and their
constituent parts.
"""
packed_mapping: dict[str, list[str]]
inverse_packed_mapping: dict[str, tuple[str,
int]] = field(default_factory=dict)
inverse_packed_mapping: dict[str, tuple[str, int]] = field(default_factory=dict)
def __post_init__(self):
for packed_name, sub_params in self.packed_mapping.items():
@@ -268,16 +274,16 @@ class ParamMapping:
index,
)
def get_sub_modules(self,
module_name: str) -> Optional[tuple[str, list[str]]]:
def get_sub_modules(self, module_name: str) -> Optional[tuple[str, list[str]]]:
for key, value in self.packed_mapping.items():
if module_name.endswith(key):
return key, value
return None
def configure_quant_config(quant_config: QuantizationConfig,
model_class: type[nn.Module]):
def configure_quant_config(
quant_config: QuantizationConfig, model_class: type[nn.Module]
):
"""
Pass packed_modules_mapping by reference to quant_config so that
quant_config can properly match fused modules