[gpt-oss] Add gpt-oss bf16 support
This commit is contained in:
572
vllm/model_executor/models/interfaces.py
Normal file
572
vllm/model_executor/models/interfaces.py
Normal file
@@ -0,0 +1,572 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol,
|
||||
Union, overload, runtime_checkable)
|
||||
|
||||
import torch
|
||||
from torch import Tensor
|
||||
from typing_extensions import Self, TypeIs
|
||||
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.layers.quantization.base_config import (
|
||||
QuantizationConfig)
|
||||
from vllm.utils import supports_kw
|
||||
|
||||
from .interfaces_base import is_pooling_model
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.attention import AttentionMetadata
|
||||
from vllm.sequence import IntermediateTensors
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
MultiModalEmbeddings = Union[list[Tensor], Tensor, tuple[Tensor, ...]]
|
||||
"""
|
||||
The output embeddings must be one of the following formats:
|
||||
|
||||
- A list or tuple of 2D tensors, where each tensor corresponds to
|
||||
each input multimodal data item (e.g, image).
|
||||
- A single 3D tensor, with the batch dimension grouping the 2D tensors.
|
||||
"""
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class SupportsMultiModal(Protocol):
|
||||
"""The interface required for all multi-modal models."""
|
||||
|
||||
supports_multimodal: ClassVar[Literal[True]] = True
|
||||
"""
|
||||
A flag that indicates this model supports multi-modal inputs.
|
||||
|
||||
Note:
|
||||
There is no need to redefine this flag if this class is in the
|
||||
MRO of your model class.
|
||||
"""
|
||||
|
||||
def get_multimodal_embeddings(
|
||||
self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
|
||||
"""
|
||||
Returns multimodal embeddings generated from multimodal kwargs
|
||||
to be merged with text embeddings.
|
||||
|
||||
Note:
|
||||
The returned multimodal embeddings must be in the same order as
|
||||
the appearances of their corresponding multimodal data item in the
|
||||
input prompt.
|
||||
"""
|
||||
...
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
"""
|
||||
Returns the underlying language model used for text generation.
|
||||
|
||||
This is typically the `torch.nn.Module` instance responsible for
|
||||
processing the merged multimodal embeddings and producing hidden states
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The core language model component.
|
||||
"""
|
||||
...
|
||||
|
||||
# Only for models that support v0 chunked prefill
|
||||
# TODO(ywang96): Remove this overload once v0 is deprecated
|
||||
@overload
|
||||
def get_input_embeddings(
|
||||
self,
|
||||
input_ids: Tensor,
|
||||
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
||||
attn_metadata: Optional["AttentionMetadata"] = None,
|
||||
) -> Tensor:
|
||||
...
|
||||
|
||||
@overload
|
||||
def get_input_embeddings(
|
||||
self,
|
||||
input_ids: Tensor,
|
||||
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
||||
) -> Tensor:
|
||||
"""
|
||||
Returns the input embeddings merged from the text embeddings from
|
||||
input_ids and the multimodal embeddings generated from multimodal
|
||||
kwargs.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
# We can't use runtime_checkable with ClassVar for issubclass checks
|
||||
# so we need to treat the class as an instance and use isinstance instead
|
||||
@runtime_checkable
|
||||
class _SupportsMultiModalType(Protocol):
|
||||
supports_multimodal: Literal[True]
|
||||
|
||||
|
||||
@overload
|
||||
def supports_multimodal(
|
||||
model: type[object]) -> TypeIs[type[SupportsMultiModal]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_multimodal(model: object) -> TypeIs[SupportsMultiModal]:
|
||||
...
|
||||
|
||||
|
||||
def supports_multimodal(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[TypeIs[type[SupportsMultiModal]], TypeIs[SupportsMultiModal]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _SupportsMultiModalType)
|
||||
|
||||
return isinstance(model, SupportsMultiModal)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class SupportsLoRA(Protocol):
|
||||
"""The interface required for all models that support LoRA."""
|
||||
|
||||
supports_lora: ClassVar[Literal[True]] = True
|
||||
"""
|
||||
A flag that indicates this model supports LoRA.
|
||||
|
||||
Note:
|
||||
There is no need to redefine this flag if this class is in the
|
||||
MRO of your model class.
|
||||
"""
|
||||
# The `embedding_module` and `embedding_padding_modules`
|
||||
# are empty by default.
|
||||
embedding_modules: ClassVar[dict[str, str]] = {}
|
||||
embedding_padding_modules: ClassVar[list[str]] = []
|
||||
packed_modules_mapping: ClassVar[dict[str, list[str]]] = {}
|
||||
|
||||
|
||||
# We can't use runtime_checkable with ClassVar for issubclass checks
|
||||
# so we need to treat the class as an instance and use isinstance instead
|
||||
@runtime_checkable
|
||||
class _SupportsLoRAType(Protocol):
|
||||
supports_lora: Literal[True]
|
||||
|
||||
packed_modules_mapping: dict[str, list[str]]
|
||||
embedding_modules: dict[str, str]
|
||||
embedding_padding_modules: list[str]
|
||||
|
||||
|
||||
@overload
|
||||
def supports_lora(model: type[object]) -> TypeIs[type[SupportsLoRA]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_lora(model: object) -> TypeIs[SupportsLoRA]:
|
||||
...
|
||||
|
||||
|
||||
def supports_lora(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[TypeIs[type[SupportsLoRA]], TypeIs[SupportsLoRA]]:
|
||||
result = _supports_lora(model)
|
||||
|
||||
if not result:
|
||||
lora_attrs = (
|
||||
"packed_modules_mapping",
|
||||
"embedding_modules",
|
||||
"embedding_padding_modules",
|
||||
)
|
||||
missing_attrs = tuple(attr for attr in lora_attrs
|
||||
if not hasattr(model, attr))
|
||||
|
||||
if getattr(model, "supports_lora", False):
|
||||
if missing_attrs:
|
||||
logger.warning(
|
||||
"The model (%s) sets `supports_lora=True`, "
|
||||
"but is missing LoRA-specific attributes: %s",
|
||||
model,
|
||||
missing_attrs,
|
||||
)
|
||||
else:
|
||||
if not missing_attrs:
|
||||
logger.warning(
|
||||
"The model (%s) contains all LoRA-specific attributes, "
|
||||
"but does not set `supports_lora=True`.", model)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def _supports_lora(model: Union[type[object], object]) -> bool:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _SupportsLoRAType)
|
||||
|
||||
return isinstance(model, SupportsLoRA)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class SupportsPP(Protocol):
|
||||
"""The interface required for all models that support pipeline parallel."""
|
||||
|
||||
supports_pp: ClassVar[Literal[True]] = True
|
||||
"""
|
||||
A flag that indicates this model supports pipeline parallel.
|
||||
|
||||
Note:
|
||||
There is no need to redefine this flag if this class is in the
|
||||
MRO of your model class.
|
||||
"""
|
||||
|
||||
def make_empty_intermediate_tensors(
|
||||
self,
|
||||
batch_size: int,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
) -> "IntermediateTensors":
|
||||
"""Called when PP rank > 0 for profiling purposes."""
|
||||
...
|
||||
|
||||
def forward(
|
||||
self,
|
||||
*,
|
||||
intermediate_tensors: Optional["IntermediateTensors"],
|
||||
) -> Union[Tensor, "IntermediateTensors"]:
|
||||
"""
|
||||
Accept [`IntermediateTensors`][vllm.sequence.IntermediateTensors] when
|
||||
PP rank > 0.
|
||||
|
||||
Return [`IntermediateTensors`][vllm.sequence.IntermediateTensors] only
|
||||
for the last PP rank.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
# We can't use runtime_checkable with ClassVar for issubclass checks
|
||||
# so we need to treat the class as an instance and use isinstance instead
|
||||
@runtime_checkable
|
||||
class _SupportsPPType(Protocol):
|
||||
supports_pp: Literal[True]
|
||||
|
||||
def make_empty_intermediate_tensors(
|
||||
self,
|
||||
batch_size: int,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
) -> "IntermediateTensors":
|
||||
...
|
||||
|
||||
def forward(
|
||||
self,
|
||||
*,
|
||||
intermediate_tensors: Optional["IntermediateTensors"],
|
||||
) -> Union[Tensor, "IntermediateTensors"]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_pp(model: type[object]) -> TypeIs[type[SupportsPP]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_pp(model: object) -> TypeIs[SupportsPP]:
|
||||
...
|
||||
|
||||
|
||||
def supports_pp(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[bool, TypeIs[type[SupportsPP]], TypeIs[SupportsPP]]:
|
||||
supports_attributes = _supports_pp_attributes(model)
|
||||
supports_inspect = _supports_pp_inspect(model)
|
||||
|
||||
if supports_attributes and not supports_inspect:
|
||||
logger.warning(
|
||||
"The model (%s) sets `supports_pp=True`, but does not accept "
|
||||
"`intermediate_tensors` in its `forward` method", model)
|
||||
|
||||
if not supports_attributes:
|
||||
pp_attrs = ("make_empty_intermediate_tensors", )
|
||||
missing_attrs = tuple(attr for attr in pp_attrs
|
||||
if not hasattr(model, attr))
|
||||
|
||||
if getattr(model, "supports_pp", False):
|
||||
if missing_attrs:
|
||||
logger.warning(
|
||||
"The model (%s) sets `supports_pp=True`, "
|
||||
"but is missing PP-specific attributes: %s",
|
||||
model,
|
||||
missing_attrs,
|
||||
)
|
||||
else:
|
||||
if not missing_attrs:
|
||||
logger.warning(
|
||||
"The model (%s) contains all PP-specific attributes, "
|
||||
"but does not set `supports_pp=True`.", model)
|
||||
|
||||
return supports_attributes and supports_inspect
|
||||
|
||||
|
||||
def _supports_pp_attributes(model: Union[type[object], object]) -> bool:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _SupportsPPType)
|
||||
|
||||
return isinstance(model, SupportsPP)
|
||||
|
||||
|
||||
def _supports_pp_inspect(model: Union[type[object], object]) -> bool:
|
||||
model_forward = getattr(model, "forward", None)
|
||||
if not callable(model_forward):
|
||||
return False
|
||||
|
||||
return supports_kw(model_forward, "intermediate_tensors")
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class HasInnerState(Protocol):
|
||||
"""The interface required for all models that has inner state."""
|
||||
|
||||
has_inner_state: ClassVar[Literal[True]] = True
|
||||
"""
|
||||
A flag that indicates this model has inner state.
|
||||
Models that has inner state usually need access to the scheduler_config
|
||||
for max_num_seqs, etc. True for e.g. both Mamba and Jamba.
|
||||
"""
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class _HasInnerStateType(Protocol):
|
||||
has_inner_state: ClassVar[Literal[True]]
|
||||
|
||||
|
||||
@overload
|
||||
def has_inner_state(model: object) -> TypeIs[HasInnerState]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def has_inner_state(model: type[object]) -> TypeIs[type[HasInnerState]]:
|
||||
...
|
||||
|
||||
|
||||
def has_inner_state(
|
||||
model: Union[type[object], object]
|
||||
) -> Union[TypeIs[type[HasInnerState]], TypeIs[HasInnerState]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _HasInnerStateType)
|
||||
|
||||
return isinstance(model, HasInnerState)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class IsAttentionFree(Protocol):
|
||||
"""The interface required for all models like Mamba that lack attention,
|
||||
but do have state whose size is constant wrt the number of tokens."""
|
||||
|
||||
is_attention_free: ClassVar[Literal[True]] = True
|
||||
"""
|
||||
A flag that indicates this model has no attention.
|
||||
Used for block manager and attention backend selection.
|
||||
True for Mamba but not Jamba.
|
||||
"""
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class _IsAttentionFreeType(Protocol):
|
||||
is_attention_free: ClassVar[Literal[True]]
|
||||
|
||||
|
||||
@overload
|
||||
def is_attention_free(model: object) -> TypeIs[IsAttentionFree]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def is_attention_free(model: type[object]) -> TypeIs[type[IsAttentionFree]]:
|
||||
...
|
||||
|
||||
|
||||
def is_attention_free(
|
||||
model: Union[type[object], object]
|
||||
) -> Union[TypeIs[type[IsAttentionFree]], TypeIs[IsAttentionFree]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _IsAttentionFreeType)
|
||||
|
||||
return isinstance(model, IsAttentionFree)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class IsHybrid(Protocol):
|
||||
"""The interface required for all models like Jamba that have both
|
||||
attention and mamba blocks, indicates that
|
||||
hf_config has 'layers_block_type'"""
|
||||
|
||||
is_hybrid: ClassVar[Literal[True]] = True
|
||||
"""
|
||||
A flag that indicates this model has both mamba and attention blocks
|
||||
, also indicates that the model's hf_config has
|
||||
'layers_block_type' """
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class _IsHybridType(Protocol):
|
||||
is_hybrid: ClassVar[Literal[True]]
|
||||
|
||||
|
||||
@overload
|
||||
def is_hybrid(model: object) -> TypeIs[IsHybrid]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def is_hybrid(model: type[object]) -> TypeIs[type[IsHybrid]]:
|
||||
...
|
||||
|
||||
|
||||
def is_hybrid(
|
||||
model: Union[type[object], object]
|
||||
) -> Union[TypeIs[type[IsHybrid]], TypeIs[IsHybrid]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _IsHybridType)
|
||||
|
||||
return isinstance(model, IsHybrid)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class HasNoOps(Protocol):
|
||||
has_noops: ClassVar[Literal[True]] = True
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class _HasNoOpsType(Protocol):
|
||||
has_noops: ClassVar[Literal[True]]
|
||||
|
||||
|
||||
@overload
|
||||
def has_noops(model: object) -> TypeIs[HasNoOps]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def has_noops(model: type[object]) -> TypeIs[type[HasNoOps]]:
|
||||
...
|
||||
|
||||
|
||||
def has_noops(
|
||||
model: Union[type[object], object]
|
||||
) -> Union[TypeIs[type[HasNoOps]], TypeIs[HasNoOps]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, _HasNoOpsType)
|
||||
|
||||
return isinstance(model, HasNoOps)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class SupportsCrossEncoding(Protocol):
|
||||
"""The interface required for all models that support cross encoding."""
|
||||
|
||||
supports_cross_encoding: ClassVar[Literal[True]] = True
|
||||
|
||||
|
||||
@overload
|
||||
def supports_cross_encoding(
|
||||
model: type[object]) -> TypeIs[type[SupportsCrossEncoding]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_cross_encoding(model: object) -> TypeIs[SupportsCrossEncoding]:
|
||||
...
|
||||
|
||||
|
||||
def _supports_cross_encoding(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[TypeIs[type[SupportsCrossEncoding]], TypeIs[SupportsCrossEncoding]]:
|
||||
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, SupportsCrossEncoding)
|
||||
|
||||
return isinstance(model, SupportsCrossEncoding)
|
||||
|
||||
|
||||
def supports_cross_encoding(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[TypeIs[type[SupportsCrossEncoding]], TypeIs[SupportsCrossEncoding]]:
|
||||
return is_pooling_model(model) and _supports_cross_encoding(model)
|
||||
|
||||
|
||||
class SupportsQuant:
|
||||
"""The interface required for all models that support quantization."""
|
||||
|
||||
packed_modules_mapping: ClassVar[dict[str, list[str]]] = {}
|
||||
quant_config: Optional[QuantizationConfig] = None
|
||||
|
||||
def __new__(cls, *args, **kwargs) -> Self:
|
||||
instance = super().__new__(cls)
|
||||
quant_config = cls._find_quant_config(*args, **kwargs)
|
||||
if quant_config is not None:
|
||||
instance.quant_config = quant_config
|
||||
instance.quant_config.packed_modules_mapping.update(
|
||||
cls.packed_modules_mapping)
|
||||
return instance
|
||||
|
||||
@staticmethod
|
||||
def _find_quant_config(*args, **kwargs) -> Optional[QuantizationConfig]:
|
||||
from vllm.config import VllmConfig # avoid circular import
|
||||
|
||||
args_values = list(args) + list(kwargs.values())
|
||||
for arg in args_values:
|
||||
if isinstance(arg, VllmConfig):
|
||||
return arg.quant_config
|
||||
|
||||
if isinstance(arg, QuantizationConfig):
|
||||
return arg
|
||||
|
||||
return None
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class SupportsTranscription(Protocol):
|
||||
"""The interface required for all models that support transcription."""
|
||||
|
||||
supports_transcription: ClassVar[Literal[True]] = True
|
||||
|
||||
|
||||
@overload
|
||||
def supports_transcription(
|
||||
model: type[object]) -> TypeIs[type[SupportsTranscription]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_transcription(model: object) -> TypeIs[SupportsTranscription]:
|
||||
...
|
||||
|
||||
|
||||
def supports_transcription(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[TypeIs[type[SupportsTranscription]], TypeIs[SupportsTranscription]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, SupportsTranscription)
|
||||
|
||||
return isinstance(model, SupportsTranscription)
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class SupportsV0Only(Protocol):
|
||||
"""Models with this interface are not compatible with V1 vLLM."""
|
||||
|
||||
supports_v0_only: ClassVar[Literal[True]] = True
|
||||
|
||||
|
||||
@overload
|
||||
def supports_v0_only(model: type[object]) -> TypeIs[type[SupportsV0Only]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def supports_v0_only(model: object) -> TypeIs[SupportsV0Only]:
|
||||
...
|
||||
|
||||
|
||||
def supports_v0_only(
|
||||
model: Union[type[object], object],
|
||||
) -> Union[TypeIs[type[SupportsV0Only]], TypeIs[SupportsV0Only]]:
|
||||
if isinstance(model, type):
|
||||
return isinstance(model, SupportsV0Only)
|
||||
|
||||
return isinstance(model, SupportsV0Only)
|
||||
Reference in New Issue
Block a user