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106
vllm/transformers_utils/configs/__init__.py
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@@ -1,16 +1,102 @@
from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
from vllm.transformers_utils.configs.dbrx import DbrxConfig
# RWConfig is for the original tiiuae/falcon-40b(-instruct) and
# tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
# `FalconConfig` class from the official HuggingFace transformers library.
from vllm.transformers_utils.configs.falcon import RWConfig
from vllm.transformers_utils.configs.jais import JAISConfig
from vllm.transformers_utils.configs.mpt import MPTConfig
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Model configs may be defined in this directory for the following reasons:
- There is no configuration file defined by HF Hub or Transformers library.
- There is a need to override the existing config to support vLLM.
- The HF model_type isn't recognized by the Transformers library but can
be mapped to an existing Transformers config, such as
deepseek-ai/DeepSeek-V3.2-Exp.
"""
from __future__ import annotations
import importlib
_CLASS_TO_MODULE: dict[str, str] = {
"AfmoeConfig": "vllm.transformers_utils.configs.afmoe",
"BagelConfig": "vllm.transformers_utils.configs.bagel",
"ChatGLMConfig": "vllm.transformers_utils.configs.chatglm",
"DeepseekVLV2Config": "vllm.transformers_utils.configs.deepseek_vl2",
"DotsOCRConfig": "vllm.transformers_utils.configs.dotsocr",
"EAGLEConfig": "vllm.transformers_utils.configs.eagle",
"FlexOlmoConfig": "vllm.transformers_utils.configs.flex_olmo",
"HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
"HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
"HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
# RWConfig is for the original tiiuae/falcon-40b(-instruct) and
# tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
# `FalconConfig` class from the official HuggingFace transformers library.
"RWConfig": "vllm.transformers_utils.configs.falcon",
"JAISConfig": "vllm.transformers_utils.configs.jais",
"Lfm2MoeConfig": "vllm.transformers_utils.configs.lfm2_moe",
"MedusaConfig": "vllm.transformers_utils.configs.medusa",
"MiDashengLMConfig": "vllm.transformers_utils.configs.midashenglm",
"MLPSpeculatorConfig": "vllm.transformers_utils.configs.mlp_speculator",
"MoonViTConfig": "vllm.transformers_utils.configs.moonvit",
"KimiLinearConfig": "vllm.transformers_utils.configs.kimi_linear",
"KimiVLConfig": "vllm.transformers_utils.configs.kimi_vl",
"NemotronConfig": "vllm.transformers_utils.configs.nemotron",
"NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
"Olmo3Config": "vllm.transformers_utils.configs.olmo3",
"OvisConfig": "vllm.transformers_utils.configs.ovis",
"RadioConfig": "vllm.transformers_utils.configs.radio",
"SpeculatorsConfig": "vllm.transformers_utils.configs.speculators.base",
"UltravoxConfig": "vllm.transformers_utils.configs.ultravox",
"Step3VLConfig": "vllm.transformers_utils.configs.step3_vl",
"Step3VisionEncoderConfig": "vllm.transformers_utils.configs.step3_vl",
"Step3TextConfig": "vllm.transformers_utils.configs.step3_vl",
"Qwen3NextConfig": "vllm.transformers_utils.configs.qwen3_next",
"Tarsier2Config": "vllm.transformers_utils.configs.tarsier2",
# Special case: DeepseekV3Config is from HuggingFace Transformers
"DeepseekV3Config": "transformers",
}
__all__ = [
"AfmoeConfig",
"BagelConfig",
"ChatGLMConfig",
"DbrxConfig",
"MPTConfig",
"DeepseekVLV2Config",
"DeepseekV3Config",
"DotsOCRConfig",
"EAGLEConfig",
"FlexOlmoConfig",
"HunYuanVLConfig",
"HunYuanVLTextConfig",
"HunYuanVLVisionConfig",
"RWConfig",
"JAISConfig",
"Lfm2MoeConfig",
"MedusaConfig",
"MiDashengLMConfig",
"MLPSpeculatorConfig",
"MoonViTConfig",
"KimiLinearConfig",
"KimiVLConfig",
"NemotronConfig",
"NemotronHConfig",
"Olmo3Config",
"OvisConfig",
"RadioConfig",
"SpeculatorsConfig",
"UltravoxConfig",
"Step3VLConfig",
"Step3VisionEncoderConfig",
"Step3TextConfig",
"Qwen3NextConfig",
"Tarsier2Config",
]
def __getattr__(name: str):
if name in _CLASS_TO_MODULE:
module_name = _CLASS_TO_MODULE[name]
module = importlib.import_module(module_name)
return getattr(module, name)
raise AttributeError(f"module 'configs' has no attribute '{name}'")
def __dir__():
return sorted(list(__all__))

87
vllm/transformers_utils/configs/afmoe.py Normal file
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@@ -0,0 +1,87 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.configuration_utils import PretrainedConfig
class AfmoeConfig(PretrainedConfig):
model_type = "afmoe"
def __init__(
self,
vocab_size: int = 200_192,
hidden_size: int = 2048,
intermediate_size: int = 6144,
moe_intermediate_size: int = 1408,
num_hidden_layers: int = 32,
num_dense_layers: int = 1,
num_attention_heads: int = 16,
num_key_value_heads: int | None = None,
head_dim: int = 128,
hidden_act: str = "silu",
max_position_embeddings: int = 131072,
initializer_range: float = 0.02,
rms_norm_eps: float = 1e-5,
use_cache: bool = True,
tie_word_embeddings: bool = False,
rope_parameters: dict | None = None,
rope_scaling: dict | None = None,
num_experts: int = 64,
num_experts_per_tok: int = 6,
num_shared_experts: int = 2,
num_expert_groups: int = 1,
num_limited_groups: int = 1,
score_func: str = "sigmoid",
route_norm: bool = True,
route_scale: float = 1.0,
global_attn_every_n_layers: int = 4,
sliding_window: int = 2048,
layer_types: list[str] | None = None,
attention_dropout: float = 0.0,
mup_enabled: bool = False,
n_group: int = 1,
topk_group: int = 1,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_dense_layers = num_dense_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads or num_attention_heads
self.head_dim = head_dim
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
rope_theta = kwargs.pop("rope_theta", 10000.0)
if rope_parameters is None:
rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
self.rope_parameters = rope_parameters
self.rope_scaling = rope_scaling
self.moe_intermediate_size = moe_intermediate_size
self.num_experts = num_experts
self.num_experts_per_tok = num_experts_per_tok
self.num_shared_experts = num_shared_experts
self.num_expert_groups = num_expert_groups
self.num_limited_groups = num_limited_groups
self.score_func = score_func
self.route_norm = route_norm
self.route_scale = route_scale
self.global_attn_every_n_layers = global_attn_every_n_layers
self.sliding_window = sliding_window
self.layer_types = layer_types
self.attention_dropout = attention_dropout
self.mup_enabled = mup_enabled
self.n_group = n_group
self.topk_group = topk_group
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
__all__ = ["AfmoeConfig"]

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vllm/transformers_utils/configs/arctic.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa: E501
# coding=utf-8
# Copied from
# https://huggingface.co/Snowflake/snowflake-arctic-instruct/blob/main/configuration_arctic.py
"""Arctic model configuration"""
from dataclasses import asdict, dataclass
from typing import Any
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json",
}
@dataclass
class ArcticLoRAConfig:
lora_r: int = 64
lora_alpha: float = 16
shard_base_weights: bool = False
@dataclass
class ArcticQuantizationConfig:
q_bits: int = 8
rounding: str = "nearest"
mantissa_bits: int = 3
group_size: int = 128
class ArcticConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an
Arctic model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the #TODO(rsamdani): add what model has the default config..
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ArcticModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
The maximum sequence length that this model might ever be used with. Arctic's sliding window attention
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_parameters (`dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_theta` (`float`): The base period of the RoPE embeddings.
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
sliding_window (`int`, *optional*):
Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
num_experts_per_tok (`int`, *optional*, defaults to 2):
The number of experts to root per-token, can be also interpreted as the `top-p` routing
parameter
num_local_experts (`int`, *optional*, defaults to 8):
Number of experts per Sparse MLP layer.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
```python
>>> from transformers import ArcticModel, ArcticConfig
>>> # Initializing a Arctic 7B style configuration TODO(rsamdani): verify which model does the default configuration correspond to.
>>> configuration = ArcticConfig()
>>> # Initializing a model from the Arctic 7B style configuration
>>> model = ArcticModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "arctic"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_parameters: dict[str, Any] | None = None,
sliding_window=None,
attention_dropout=0.0,
num_experts_per_tok=1,
num_local_experts=8,
router_aux_loss_coef=0.001,
moe_layer_frequency=2,
parallel_attn_mlp_res=False,
moe_train_capacity_factor=1,
moe_eval_capacity_factor=1,
enable_expert_tensor_parallelism=False,
moe_min_capacity=0,
moe_token_dropping=True,
quantization=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
rope_theta = kwargs.pop("rope_theta", 1e6)
if rope_parameters is None:
rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
self.rope_parameters = rope_parameters
self.attention_dropout = attention_dropout
self.num_experts_per_tok = num_experts_per_tok
self.num_local_experts = num_local_experts
self.router_aux_loss_coef = router_aux_loss_coef
self.moe_layer_frequency = moe_layer_frequency
self.moe_train_capacity_factor = moe_train_capacity_factor
self.moe_eval_capacity_factor = moe_eval_capacity_factor
self.enable_expert_tensor_parallelism = enable_expert_tensor_parallelism
self.moe_min_capacity = moe_min_capacity
self.moe_token_dropping = moe_token_dropping
self.parallel_attn_mlp_res = parallel_attn_mlp_res
if isinstance(quantization, dict):
self.quantization = ArcticQuantizationConfig(**quantization)
else:
self.quantization = quantization
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@classmethod
def from_dict(cls, config_dict: dict[str, Any], **kwargs) -> "ArcticConfig":
result = super().from_dict(config_dict, **kwargs)
config = result[0] if isinstance(result, tuple) else result
if isinstance(config.quantization, dict):
config.quantization = ArcticQuantizationConfig(**config.quantization)
return result
def to_dict(self) -> dict[str, Any]:
ret = super().to_dict()
if isinstance(ret["quantization"], ArcticQuantizationConfig):
ret["quantization"] = asdict(ret["quantization"])
return ret

53
vllm/transformers_utils/configs/bagel.py Normal file
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@@ -0,0 +1,53 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers import PretrainedConfig, SiglipVisionConfig
from transformers.models.qwen2 import Qwen2Config
class BagelConfig(PretrainedConfig):
"""Configuration class for BAGEL model."""
model_type = "bagel"
def __init__(
self,
visual_gen: bool = True,
visual_und: bool = True,
llm_config: dict | Qwen2Config | None = None,
vit_config: dict | SiglipVisionConfig | None = None,
vae_config: dict | None = None,
latent_patch_size: int = 2,
max_latent_size: int = 32,
vit_max_num_patch_per_side: int = 70,
connector_act: str = "gelu_pytorch_tanh",
interpolate_pos: bool = False,
timestep_shift: float = 1.0,
**kwargs,
):
super().__init__(**kwargs)
self.visual_gen = visual_gen
self.visual_und = visual_und
# Convert dict configs to proper config objects
if isinstance(llm_config, dict):
self.llm_config = Qwen2Config(**llm_config)
else:
self.llm_config = llm_config or Qwen2Config()
if isinstance(vit_config, dict):
self.vit_config = SiglipVisionConfig(**vit_config)
else:
self.vit_config = vit_config or SiglipVisionConfig()
self.vae_config = vae_config or {"z_channels": 16, "downsample": 8}
self.latent_patch_size = latent_patch_size
self.max_latent_size = max_latent_size
self.vit_max_num_patch_per_side = vit_max_num_patch_per_side
self.connector_act = connector_act
self.interpolate_pos = interpolate_pos
self.timestep_shift = timestep_shift
@property
def hidden_size(self) -> int:
"""Return the hidden size of the language model."""
return self.llm_config.hidden_size

67
vllm/transformers_utils/configs/chatglm.py
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@@ -1,6 +1,8 @@
# coding=utf-8
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://github.com/THUDM/ChatGLM2-6B
# https://github.com/zai-org/ChatGLM2-6B
from transformers import PretrainedConfig
@@ -11,33 +13,35 @@ class ChatGLMConfig(PretrainedConfig):
"n_head_kv": "multi_query_group_num",
}
def __init__(self,
num_layers=28,
padded_vocab_size=65024,
hidden_size=4096,
ffn_hidden_size=13696,
kv_channels=128,
num_attention_heads=32,
seq_length=2048,
hidden_dropout=0.0,
attention_dropout=0.0,
layernorm_epsilon=1e-5,
rmsnorm=True,
apply_residual_connection_post_layernorm=False,
post_layer_norm=True,
add_bias_linear=False,
add_qkv_bias=False,
interleaved_qkv=False,
bias_dropout_fusion=True,
multi_query_attention=False,
multi_query_group_num=1,
apply_query_key_layer_scaling=True,
attention_softmax_in_fp32=True,
fp32_residual_connection=False,
quantization_bit=0,
pre_seq_len=None,
prefix_projection=False,
**kwargs):
def __init__(
self,
num_layers=28,
padded_vocab_size=65024,
hidden_size=4096,
ffn_hidden_size=13696,
kv_channels=128,
num_attention_heads=32,
seq_length=2048,
hidden_dropout=0.0,
attention_dropout=0.0,
layernorm_epsilon=1e-5,
rmsnorm=True,
apply_residual_connection_post_layernorm=False,
post_layer_norm=True,
add_bias_linear=False,
add_qkv_bias=False,
interleaved_qkv=False,
bias_dropout_fusion=True,
multi_query_attention=False,
multi_query_group_num=1,
apply_query_key_layer_scaling=True,
attention_softmax_in_fp32=True,
fp32_residual_connection=False,
quantization_bit=0,
pre_seq_len=None,
prefix_projection=False,
**kwargs,
):
self.num_layers = num_layers
self.vocab_size = padded_vocab_size
self.padded_vocab_size = padded_vocab_size
@@ -46,12 +50,15 @@ class ChatGLMConfig(PretrainedConfig):
self.kv_channels = kv_channels
self.num_attention_heads = num_attention_heads
self.seq_length = seq_length
# It is to be compatible with long lora.
self.max_position_embeddings = seq_length
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.layernorm_epsilon = layernorm_epsilon
self.rmsnorm = rmsnorm
self.apply_residual_connection_post_layernorm = (
apply_residual_connection_post_layernorm)
apply_residual_connection_post_layernorm
)
self.post_layer_norm = post_layer_norm
self.add_bias_linear = add_bias_linear
self.add_qkv_bias = add_qkv_bias

278
vllm/transformers_utils/configs/dbrx.py
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@@ -1,278 +0,0 @@
# yapf: disable
# ruff: noqa: E501
# coding=utf-8
# Copied from
# https://huggingface.co/databricks/dbrx-base/blob/main/configuration_dbrx.py
"""Dbrx configuration."""
from typing import Any, Optional
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP = {} # type: ignore
class DbrxAttentionConfig(PretrainedConfig):
"""Configuration class for Dbrx Attention.
[`DbrxAttention`] class. It is used to instantiate attention layers
according to the specified arguments, defining the layers architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
attn_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for the attention layers.
clip_qkv (`float`, *optional*, defaults to None):
If not `None`, clip the queries, keys, and values in the attention layer to this value.
kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
rope_theta (float): The base frequency for rope.
"""
def __init__(
self,
attn_pdrop: float = 0,
clip_qkv: Optional[float] = None,
kv_n_heads: int = 1,
rope_theta: float = 10000.0,
**kwargs: Any,
):
super().__init__(**kwargs)
self.attn_pdrop = attn_pdrop
self.clip_qkv = clip_qkv
self.kv_n_heads = kv_n_heads
self.rope_theta = rope_theta
for k in ["model_type"]:
if k in kwargs:
kwargs.pop(k)
if len(kwargs) != 0:
raise ValueError(f"Found unknown {kwargs=}")
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: str, **kwargs: Any
) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
if config_dict.get("model_type") == "dbrx":
config_dict = config_dict["attn_config"]
if (
"model_type" in config_dict
and hasattr(cls, "model_type")
and config_dict["model_type"] != cls.model_type
):
logger.warning(
"You are using a model of type %s to instantiate a model of "
"type %s. This is not supported for all configurations of "
"models and can yield errors.",
config_dict["model_type"], cls.model_type)
return cls.from_dict(config_dict, **kwargs)
class DbrxFFNConfig(PretrainedConfig):
"""Configuration class for Dbrx FFN.
[`DbrxFFN`] class. It is used to instantiate feedforward layers according to
the specified arguments, defining the layers architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
ffn_act_fn (dict, optional): A dict specifying activation function for the FFN.
The dict should have a key 'name' with the value being the name of
the activation function along with any additional keyword arguments.
ffn_hidden_size (int, optional): The hidden size of the feedforward network.
moe_num_experts (int, optional): The number of experts in the mixture of experts layer.
moe_top_k (int, optional): The number of experts to use in the mixture of experts layer.
moe_jitter_eps (float, optional): The jitter epsilon for the mixture of experts layer.
moe_loss_weight (float, optional): The loss weight for the mixture of experts layer.
moe_normalize_expert_weights (float, optional): The normalization factor for the expert weights.
uniform_expert_assignment (bool, optional): Whether to use uniform expert assignment.
This should only be used for benchmarking purposes.
"""
def __init__(
self,
ffn_act_fn: Optional[dict] = None,
ffn_hidden_size: int = 3584,
moe_num_experts: int = 4,
moe_top_k: int = 1,
moe_jitter_eps: Optional[float] = None,
moe_loss_weight: float = 0.01,
moe_normalize_expert_weights: Optional[float] = 1,
uniform_expert_assignment: bool = False,
**kwargs: Any,
):
super().__init__()
if ffn_act_fn is None:
ffn_act_fn = {"name": "silu"}
self.ffn_act_fn = ffn_act_fn
self.ffn_hidden_size = ffn_hidden_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.moe_jitter_eps = moe_jitter_eps
self.moe_loss_weight = moe_loss_weight
self.moe_normalize_expert_weights = moe_normalize_expert_weights
self.uniform_expert_assignment = uniform_expert_assignment
for k in ["model_type"]:
if k in kwargs:
kwargs.pop(k)
if len(kwargs) != 0:
raise ValueError(f"Found unknown {kwargs=}")
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: str, **kwargs: Any
) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
if config_dict.get("model_type") == "dbrx":
config_dict = config_dict["ffn_config"]
if (
"model_type" in config_dict
and hasattr(cls, "model_type")
and config_dict["model_type"] != cls.model_type
):
logger.warning(
"You are using a model of type %s to instantiate a model of "
"type %s. This is not supported for all "
"configurations of models and can yield errors.", config_dict["model_type"], cls.model_type)
return cls.from_dict(config_dict, **kwargs)
class DbrxConfig(PretrainedConfig):
"""Configuration class for Dbrx.
[`DbrxModel`]. It is used to instantiate a Dbrx model according to the
specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
d_model (`int`, *optional*, defaults to 6144):
Dimensionality of the embeddings and hidden states.
n_heads (`int`, *optional*, defaults to 48):
Number of attention heads for each attention layer in the Transformer encoder.
n_layers (`int`, *optional*, defaults to 40):
Number of hidden layers in the Transformer encoder.
max_seq_len (`int`, *optional*, defaults to 32768):
The maximum sequence length of the model.
vocab_size (`int`, *optional*, defaults to 100352):
Vocabulary size of the Dbrx model. Defines the maximum number of different tokens that can be represented by
the `inputs_ids` passed when calling [`DbrxModel`].
resid_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability applied to the attention output before combining with residual.
emb_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for the embedding layer.
attn_config (`dict`, *optional*):
A dictionary used to configure the model's attention module.
ffn_config (`dict`, *optional*):
A dictionary used to configure the model's FFN module.
use_cache (`bool`, *optional*, defaults to `False`):
Whether or not the model should return the last key/values attentions (not used by all models).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabling this will also
allow the model to output the auxiliary loss. See [here]() for more details
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
Example:
```python
>>> from transformers import DbrxConfig, DbrxModel
>>> # Initializing a Dbrx configuration
>>> configuration = DbrxConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = DbrxModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "dbrx"
attribute_map = {
"num_attention_heads": "n_heads",
"hidden_size": "d_model",
"num_hidden_layers": "n_layers",
"max_position_embeddings": "max_seq_len",
}
def __init__(
self,
d_model: int = 2048,
n_heads: int = 16,
n_layers: int = 24,
max_seq_len: int = 2048,
vocab_size: int = 32000,
resid_pdrop: float = 0.0,
emb_pdrop: float = 0.0,
attn_config: Optional[DbrxAttentionConfig] = None,
ffn_config: Optional[DbrxFFNConfig] = None,
use_cache: bool = True,
initializer_range: float = 0.02,
output_router_logits: bool = False,
router_aux_loss_coef: float = 0.05,
**kwargs: Any,
):
if attn_config is None:
self.attn_config = DbrxAttentionConfig()
elif isinstance(attn_config, dict):
self.attn_config = DbrxAttentionConfig(**attn_config)
else:
self.attn_config = attn_config
if ffn_config is None:
self.ffn_config = DbrxFFNConfig()
elif isinstance(ffn_config, dict):
self.ffn_config = DbrxFFNConfig(**ffn_config)
else:
self.ffn_config = ffn_config
self.d_model = d_model
self.n_heads = n_heads
self.n_layers = n_layers
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.resid_pdrop = resid_pdrop
self.emb_pdrop = emb_pdrop
self.use_cache = use_cache
self.initializer_range = initializer_range
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
tie_word_embeddings = kwargs.pop("tie_word_embeddings", False)
if tie_word_embeddings:
raise ValueError(
"tie_word_embeddings is not supported for Dbrx models."
)
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

126
vllm/transformers_utils/configs/deepseek_vl2.py Normal file
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@@ -0,0 +1,126 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py#L115-L268
from transformers import DeepseekV2Config, PretrainedConfig
class VisionEncoderConfig(PretrainedConfig):
model_type: str = "vision"
model_name: str = "vit_so400m_patch14_siglip_384.webli"
image_size: int = 384
patch_size: int = 16
width: int = 1024
layers: int = 24
heads: int = 16
mlp_ratio: int = 4
global_pool: str = "map"
ignore_head: bool = True
class_token: bool = False
num_classes: int = 0
use_checkpoint: bool = False
weight_init: str = "skip"
deterministic: bool = False
num_recomputing_layers: int = 0
def __init__(
self,
model_name: str = "vit_so400m_patch14_siglip_384.webli",
image_size: int = 384,
patch_size: int = 16,
width: int = 1024,
layers: int = 24,
heads: int = 16,
mlp_ratio: int = 4,
global_pool: str = "map",
ignore_head: bool = True,
class_token: bool = False,
num_classes: int = 0,
use_checkpoint: bool = False,
**kwargs,
):
self.model_name = model_name
self.image_size = image_size
self.patch_size = patch_size
self.width = width
self.layers = layers
self.heads = heads
self.mlp_ratio = mlp_ratio
self.global_pool = global_pool
self.ignore_head = ignore_head
self.class_token = class_token
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
super().__init__(**kwargs)
class MlpProjectorConfig(PretrainedConfig):
model_type = "mlp_projector"
projector_type: str = "downsample_mlp_gelu"
input_dim: int = 1152
n_embed: int = 2048
depth: int = 2
mlp_ratio: int = 1
downsample_ratio: int = 2
token_pooling: bool = False
def __init__(
self,
projector_type: str = "downsample_mlp_gelu",
input_dim: int = 1152,
n_embed: int = 2048,
depth: int = 2,
mlp_ratio: int = 1,
downsample_ratio: int = 2,
**kwargs,
):
self.projector_type = projector_type
self.input_dim = input_dim
self.n_embed = n_embed
self.depth = depth
self.mlp_ratio = mlp_ratio
self.downsample_ratio = downsample_ratio
super().__init__(**kwargs)
class DeepseekVLV2Config(PretrainedConfig):
model_type = "deepseek_vl_v2"
vision_config: VisionEncoderConfig
projector_config: MlpProjectorConfig
tile_tag: str = "2D"
global_view_pos: str = "head"
candidate_resolutions: tuple[tuple[int, int]] = ((384, 384),)
def __init__(
self,
tile_tag: str = "tile_tag",
global_view_pos: str = "head",
candidate_resolutions: tuple[tuple[int, int]] = ((384, 384),),
**kwargs,
):
super().__init__(**kwargs)
vision_config = kwargs.get("vision_config", {})
self.vision_config = VisionEncoderConfig(**vision_config)
projector_config = kwargs.get("projector_config", {})
self.projector_config = MlpProjectorConfig(**projector_config)
language_config = kwargs.get("language_config", {})
self.text_config = DeepseekV2Config(**language_config)
self.tile_tag = tile_tag
self.global_view_pos = global_view_pos
self.candidate_resolutions = candidate_resolutions
self.vocab_size = self.text_config.vocab_size
# update model_type for OCR model
if "DeepseekOCRForCausalLM" in (
self.architectures or kwargs.get("architectures", [])
):
self.model_type = "deepseek_ocr"

71
vllm/transformers_utils/configs/dotsocr.py Normal file
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@@ -0,0 +1,71 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
from transformers.models.qwen2 import Qwen2Config
class DotsVisionConfig(PretrainedConfig):
model_type: str = "dots_vit"
def __init__(
self,
embed_dim: int = 1536, # vision encoder embed size
hidden_size: int = 1536, # after merger hidden size
intermediate_size: int = 4224,
num_hidden_layers: int = 42,
num_attention_heads: int = 12,
num_channels: int = 3,
patch_size: int = 14,
spatial_merge_size: int = 2,
temporal_patch_size: int = 1,
rms_norm_eps: float = 1e-5,
use_bias: bool = False,
attn_implementation="flash_attention_2",
initializer_range=0.02,
init_merger_std=0.02,
is_causal=False, # ve causal forward
post_norm=True,
gradient_checkpointing=False,
**kwargs: Any,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.rms_norm_eps = rms_norm_eps
self.use_bias = use_bias
self.attn_implementation = attn_implementation
self.initializer_range = initializer_range
self.init_merger_std = init_merger_std
self.is_causal = is_causal
self.post_norm = post_norm
self.gradient_checkpointing = gradient_checkpointing
class DotsOCRConfig(Qwen2Config):
model_type = "dots_ocr"
def __init__(
self,
image_token_id=151665,
video_token_id=151656,
vision_config: dict | None = None,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self.image_token_id = image_token_id
self.video_token_id = video_token_id
self.vision_config = DotsVisionConfig(**(vision_config or {}))
def save_pretrained(self, save_directory, **kwargs):
self._auto_class = None
super().save_pretrained(save_directory, **kwargs)

90
vllm/transformers_utils/configs/eagle.py Normal file
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@@ -0,0 +1,90 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from transformers import AutoConfig, DeepseekV2Config, PretrainedConfig
class EAGLEConfig(PretrainedConfig):
model_type = "eagle"
def __init__(
self,
model: PretrainedConfig | dict | None = None,
truncated_vocab_size: int | None = None,
method: str | None = "eagle",
**kwargs,
):
model_config: PretrainedConfig | DeepseekV2Config | None
if isinstance(model, dict):
model_config = AutoConfig.for_model(**model)
else:
model_config = model
for k, v in kwargs.items():
if k != "architectures" and k != "model_type" and hasattr(model_config, k):
setattr(model_config, k, v)
self.model = model_config
if self.model is None:
self.truncated_vocab_size = None
else:
self.truncated_vocab_size = (
self.model.vocab_size
if truncated_vocab_size is None
else truncated_vocab_size
)
# Eagle model name should follow naming convention of
# LlamaForCausalLM -> EagleLlamaForCausalLM
# LlamaForCausalLM -> Eagle3LlamaForCausalLM
# LlamaForCausalLMEagle3 -> LlamaForCausalLMEagle3
if method == "eagle":
assert self.model is not None, (
"model should not be None when method is eagle"
)
kwargs["architectures"] = [
f"Eagle{arch}" if not arch.startswith("Eagle") else arch
for arch in self.model.architectures
]
elif method == "eagle3":
assert self.model is not None, (
"model should not be None when method is eagle3"
)
kwargs["architectures"] = [
arch
if arch.startswith("Eagle3") or arch.endswith("Eagle3")
else f"Eagle3{arch}"
for arch in self.model.architectures
]
else:
raise ValueError(
f"Invalid method {method}. Supported methods are eagle and eagle3."
)
super().__init__(**kwargs)
if self.model is not None:
for k, v in self.model.to_dict().items():
if k not in kwargs:
setattr(self, k, v)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str | os.PathLike,
**kwargs,
) -> "EAGLEConfig":
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
return cls.from_dict(config_dict, **kwargs)
def to_json_string(self, use_diff: bool = True) -> str:
# we override use_diff to False as initializing
# EAGLEConfig with default arguments is not supported
del use_diff
return super().to_json_string(use_diff=False)

8
vllm/transformers_utils/configs/falcon.py
View File

@@ -1,3 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from
# https://huggingface.co/tiiuae/falcon-7b/blob/main/configuration_RW.py
# Copyright 2023 The vLLM team.
@@ -16,6 +19,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Falcon configuration"""
from transformers.configuration_utils import PretrainedConfig
@@ -74,9 +78,7 @@ class RWConfig(PretrainedConfig):
# Hack for falcon-40b
self.new_decoder_architecture = True
super().__init__(bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs)
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@property
def head_dim(self):

82
vllm/transformers_utils/configs/flex_olmo.py Normal file
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@@ -0,0 +1,82 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
class FlexOlmoConfig(PretrainedConfig):
model_type = "flex_olmo"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=100352,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-06,
use_cache=True,
pad_token_id=100277,
bos_token_id=None,
eos_token_id=100257,
tie_word_embeddings=False,
rope_parameters: dict[str, Any] | None = None,
attention_bias=False,
attention_dropout=0.0,
num_experts_per_tok=5,
num_experts=7,
output_router_logits=False,
router_aux_loss_coef=0.01,
norm_topk_prob=False,
**kwargs,
):
if "architectures" not in kwargs:
kwargs["architectures"] = ["FlexOlmoForCausalLM"]
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
rope_theta = kwargs.pop("rope_theta", 500000.0)
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
self.rope_parameters = rope_parameters
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.norm_topk_prob = norm_topk_prob
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_parameters is not None and "type" in self.rope_parameters:
self.rope_parameters["rope_type"] = self.rope_parameters["type"]

322
vllm/transformers_utils/configs/hunyuan_vl.py Normal file
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@@ -0,0 +1,322 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://github.com/ManaEstras/transformers/blob/v4.57.1.hyvl/src/transformers/models/hunyuan_vl/configuration_hunyuan_vl.py
from transformers import PretrainedConfig
class HunYuanVLVisionConfig(PretrainedConfig):
model_type = "hunyuan_vl"
base_config_key = "vision_config"
def __init__(
self,
hidden_act="gelu",
hidden_size=1152,
intermediate_size=4304,
interpolate_mode="bilinear",
rms_norm_eps=1e-05,
learnable_mlp_pooling_size=0,
num_attention_heads=16,
num_key_value_heads=None,
num_channels=3,
num_hidden_layers=27,
out_hidden_size=4096,
patch_size=16,
remove_prenorm=True,
spatial_merge_size=2,
temporal_patch_size=1,
resize_resolution=2048,
img_max_token_num=4096,
max_image_size=2048,
video_max_image_size=768,
video_min_image_size=256,
min_image_size=512,
anyres_vit_max_image_size=2048,
max_vit_seq_len=16384,
text_hidden_size=3072,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_act = hidden_act
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.interpolate_mode = interpolate_mode
self.learnable_mlp_pooling_size = learnable_mlp_pooling_size
self.num_attention_heads = num_attention_heads
if not num_key_value_heads:
self.num_key_value_heads = num_attention_heads
else:
self.num_key_value_heads = num_key_value_heads
self.num_channels = num_channels
self.num_hidden_layers = num_hidden_layers
self.out_hidden_size = out_hidden_size
self.patch_size = patch_size
self.remove_prenorm = remove_prenorm
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.rms_norm_eps = rms_norm_eps
self.resize_resolution = resize_resolution
self.img_max_token_num = img_max_token_num
self.max_image_size = max_image_size
self.min_image_size = min_image_size
self.video_max_image_size = video_max_image_size
self.video_min_image_size = video_min_image_size
self.anyres_vit_max_image_size = anyres_vit_max_image_size
self.max_vit_seq_len = max_vit_seq_len
self.text_hidden_size = text_hidden_size
class HunYuanVLTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HunYuanVLTextConfig`]. It is used to instantiate an
HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the HunYuan-7B.
Hunyuan-7B-Instruct [tencent/Hunyuan-7B-Instruct](https://huggingface.co/tencent/Hunyuan-7B-Instruct).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 290943):
Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`HunYuanVLTextConfig`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations or shared MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
eod_token_id (int, *optional*, defaults to 3):
Token ID representing the end-of-document marker. Used to indicate the termination of a text sequence.
Example: In multi-document processing, this token helps the model distinguish between separate documents.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
head_dim (`int`, *optional*, defaults to 128):
The attention head dimension.
""" # noqa: E501
model_type = "hunyuan_vl_text"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=290943,
hidden_size=4096,
intermediate_size: int = 11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
eod_token_id=3,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
head_dim=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
# self._rope_scaling_validation() # TODO: Need validation?
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and "
f"`factor` or `type` and `alpha`, got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
rope_scaling_alpha = self.rope_scaling.get("alpha", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], "
f"got {rope_scaling_type}"
)
if rope_scaling_factor is None and rope_scaling_alpha is None:
raise ValueError(
"`rope_scaling`'s factor or alpha field must be have one, "
"got both of none"
)
if rope_scaling_factor is not None and (
not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0
):
raise ValueError(
"`rope_scaling`'s factor field must be a float > 1.0, "
f"got {rope_scaling_factor}"
)
if rope_scaling_alpha is not None and (
not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0
):
raise ValueError(
"`rope_scaling`'s alpha field must be a float > 1.0, "
f"got {rope_scaling_alpha}"
)
class HunYuanVLConfig(PretrainedConfig):
model_type = "hunyuan_vl"
sub_configs = {
"vision_config": HunYuanVLVisionConfig,
"text_config": HunYuanVLTextConfig,
}
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
text_config=None,
vision_config=None,
im_start_id=120118,
im_end_id=120119,
image_token_id=120120,
im_newline_id=120121,
video_start_id=120122,
video_end_id=120123,
**kwargs,
):
# We need to init super() here so that it does not reset values
# that are in text config to the BaseClass defaults. The Base
# config has many text related defaults and not all defaults are
# same as for `HunYuanVLTextConfig`.
super().__init__(**kwargs)
if isinstance(vision_config, dict):
self.vision_config = self.sub_configs["vision_config"](**vision_config)
elif vision_config is None:
self.vision_config = self.sub_configs["vision_config"]()
if isinstance(text_config, dict):
self.text_config = self.sub_configs["text_config"](**text_config)
elif text_config is None:
# For BC use all kwargs to init `TextConfig`
self.text_config = self.sub_configs["text_config"](**kwargs)
self.image_token_id = image_token_id
self.im_start_id = im_start_id
self.im_end_id = im_end_id
self.im_newline_id = im_newline_id
self.video_start_id = video_start_id
self.video_end_id = video_end_id
self.vision_config.text_hidden_size = self.text_config.hidden_size
# Attention implementation to use. It sets it recursively on sub-configs
# so we call it again in the end.
self._attn_implementation = kwargs.pop("attn_implementation", None)
def __setattr__(self, key, value):
if (
(text_config := super().__getattribute__("__dict__").get("text_config"))
is not None
and key not in ["dtype", "_attn_implementation_internal"]
and key in text_config.__dict__
):
setattr(text_config, key, value)
else:
super().__setattr__(key, value)
def __getattribute__(self, key):
if "text_config" in super().__getattribute__("__dict__") and key not in [
"_name_or_path",
"model_type",
"dtype",
"_attn_implementation_internal",
]:
text_config = super().__getattribute__("text_config")
if key in text_config.__dict__:
return getattr(text_config, key)
return super().__getattribute__(key)

58
vllm/transformers_utils/configs/jais.py
View File

@@ -1,6 +1,7 @@
# coding=utf-8
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2024 - 2024 Moore Threads Technology Co., Ltd("Moore Threads"). All rights reserved.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Copyright 2023 Cerebras Systems.
#
@@ -73,10 +74,9 @@ class JAISConfig(PretrainedConfig):
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values
attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*,
defaults to `False`):
Whether to additionally scale attention weights by
`1 / layer_idx + 1`.
scale_attn_by_inverse_layer_idx (`bool`, *optional*, default `True`):
Whether to additionally scale attention weights
by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention
(dot-product)
@@ -98,7 +98,7 @@ class JAISConfig(PretrainedConfig):
Scale attention weights by dividing by hidden_size instead of
sqrt(hidden_size). Need to set scale_attn_weights to `True` as
well.
alibi_scaling (`Dict`, *optional*):
alibi_scaling (`dict`, *optional*):
Dictionary containing the scaling configuration for ALiBi
embeddings. Currently only supports linear
scaling strategy. Can specify either the scaling `factor` (must be
@@ -108,7 +108,7 @@ class JAISConfig(PretrainedConfig):
formats are `{"type": strategy name, "factor": scaling factor}` or
`{"type": strategy name,
"train_seq_len": training sequence length}`.
architectures (`List`, *optional*, defaults to ['JAISLMHeadModel']):
architectures (`list`, *optional*, defaults to ['JAISLMHeadModel']):
architecture names for Jais.
Example:
@@ -209,29 +209,35 @@ class JAISConfig(PretrainedConfig):
if self.alibi_scaling is None:
return
if (not isinstance(self.alibi_scaling, dict)
or len(self.alibi_scaling) != 2):
if not isinstance(self.alibi_scaling, dict) or len(self.alibi_scaling) != 2:
raise ValueError(
"`alibi_scaling` must be a dictionary with two fields,"
"`alibi_scaling` must be a dictionary with two fields, "
"`type` and `factor` or `type` and `train_seq_len`, "
f"got {self.alibi_scaling}")
f"got {self.alibi_scaling}"
)
alibi_scaling_type = self.alibi_scaling.get("type", None)
alibi_scaling_factor = self.alibi_scaling.get("factor", None)
alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None)
if alibi_scaling_type is None or alibi_scaling_type != "linear":
raise ValueError(f"`alibi_scaling`'s type field must be 'linear',"
f"got {alibi_scaling_type}")
if (alibi_scaling_factor is not None
and not isinstance(alibi_scaling_factor, float)
or (alibi_scaling_factor is not None
and alibi_scaling_factor <= 1.0)):
raise ValueError(
f"`alibi_scaling`'s factor field must be a float > 1.0,"
f"got {alibi_scaling_factor}")
if (alibi_dynamic_scaling is not None
and not isinstance(alibi_dynamic_scaling, int)
or (alibi_dynamic_scaling is not None
and alibi_dynamic_scaling <= 1)):
f"`alibi_scaling`'s type field must be 'linear', "
f"got {alibi_scaling_type}"
)
if (
alibi_scaling_factor is not None
and not isinstance(alibi_scaling_factor, float)
or (alibi_scaling_factor is not None and alibi_scaling_factor <= 1.0)
):
raise ValueError(
f"`alibi_scaling`'s `train_seq_len` field must be an"
f"integer > 1, got {alibi_dynamic_scaling}")
f"`alibi_scaling`'s factor field must be a float > 1.0, "
f"got {alibi_scaling_factor}"
)
if (
alibi_dynamic_scaling is not None
and not isinstance(alibi_dynamic_scaling, int)
or (alibi_dynamic_scaling is not None and alibi_dynamic_scaling <= 1)
):
raise ValueError(
f"`alibi_scaling`'s `train_seq_len` field must be an "
f"integer > 1, got {alibi_dynamic_scaling}"
)

148
vllm/transformers_utils/configs/kimi_linear.py Normal file
View File

@@ -0,0 +1,148 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.configuration_utils import PretrainedConfig
from vllm.logger import init_logger
logger = init_logger(__name__)
class KimiLinearConfig(PretrainedConfig):
model_type = "kimi_linear"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
model_type="kimi_linear",
vocab_size=163840,
hidden_size=4096,
head_dim=None,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
rope_parameters=None,
tie_word_embeddings=False,
moe_intermediate_size: int | None = None,
moe_renormalize: bool = True,
moe_router_activation_func: str = "sigmoid",
num_experts: int | None = None,
num_experts_per_token: int | None = None,
num_shared_experts: int = 0,
routed_scaling_factor: float = 1.0,
first_k_dense_replace: int = 0,
moe_layer_freq: int = 1,
use_grouped_topk: bool = True,
num_expert_group: int = 1,
topk_group: int = 1,
q_lora_rank: int | None = None,
kv_lora_rank: int | None = None,
qk_nope_head_dim: int | None = None,
qk_rope_head_dim: int | None = None,
v_head_dim: int | None = None,
mla_use_nope: bool | None = False,
num_nextn_predict_layers: int = 0,
linear_attn_config: dict | None = None,
**kwargs,
):
self.model_type = model_type
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.head_dim = (
head_dim if head_dim is not None else hidden_size // num_attention_heads
)
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
rope_theta = kwargs.pop("rope_theta", 10000.0)
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
self.rope_parameters = rope_parameters
self.q_lora_rank = q_lora_rank
self.kv_lora_rank = kv_lora_rank
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.mla_use_nope = mla_use_nope
# moe config
self.num_experts = num_experts
self.num_experts_per_token = num_experts_per_token
self.moe_renormalize = moe_renormalize
self.num_shared_experts = num_shared_experts
self.routed_scaling_factor = routed_scaling_factor
self.moe_router_activation_func = moe_router_activation_func
assert self.moe_router_activation_func in ("softmax", "sigmoid")
self.moe_intermediate_size = moe_intermediate_size
self.first_k_dense_replace = first_k_dense_replace
self.moe_layer_freq = moe_layer_freq
self.use_grouped_topk = use_grouped_topk
self.num_expert_group = num_expert_group
self.topk_group = topk_group
self.num_nextn_predict_layers = num_nextn_predict_layers
if linear_attn_config is not None:
assert linear_attn_config["kda_layers"] is not None
assert linear_attn_config["full_attn_layers"] is not None
self.linear_attn_config = linear_attn_config
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@property
def is_mla(self):
return (
self.q_lora_rank is not None
or self.kv_lora_rank is not None
or self.qk_nope_head_dim is not None
or self.qk_rope_head_dim is not None
or self.v_head_dim is not None
or self.mla_use_nope is True
)
@property
def is_moe(self):
return self.num_experts is not None
@property
def is_linear_attn(self) -> bool:
return not (
self.linear_attn_config is None
or (
isinstance(self.linear_attn_config, dict)
and self.linear_attn_config["kda_layers"] is not None
and len(self.linear_attn_config["kda_layers"]) == 0
)
)
def is_kda_layer(self, layer_idx: int):
return (
self.linear_attn_config is not None
and (layer_idx + 1) in self.linear_attn_config["kda_layers"]
)

38
vllm/transformers_utils/configs/kimi_vl.py Normal file
View File

@@ -0,0 +1,38 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
from transformers import DeepseekV2Config
from transformers.configuration_utils import PretrainedConfig
from vllm.transformers_utils.configs.moonvit import MoonViTConfig
class KimiVLConfig(PretrainedConfig):
model_type = "kimi_vl"
def __init__(
self,
vision_config: dict | MoonViTConfig | None = None,
text_config: dict | DeepseekV2Config | None = None,
ignore_index: int = -100,
media_placeholder_token_id: int = 163605,
pad_token_id: int = 0,
**kwargs,
):
if vision_config is None:
vision_config = MoonViTConfig()
elif isinstance(vision_config, dict):
vision_config = MoonViTConfig(**vision_config)
self.vision_config = vision_config
if text_config is None:
text_config = DeepseekV2Config()
elif isinstance(text_config, dict):
text_config = DeepseekV2Config(**text_config)
self.text_config = text_config
self.ignore_index = ignore_index
self.media_placeholder_token_id = media_placeholder_token_id
super().__init__(pad_token_id=pad_token_id, **kwargs)

163
vllm/transformers_utils/configs/lfm2_moe.py Normal file
View File

@@ -0,0 +1,163 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
class Lfm2MoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Lfm2MoeModel`]. It is used to instantiate a LFM2 Moe
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LFM2-8B-A1B model.
e.g. [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 65536):
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Lfm2Model`]
hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 7168):
Dimension of the MLP representations.
moe_intermediate_size (`int`, *optional*, defaults to 1792):
Intermediate size of the routed expert.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_parameters (`dict`, *optional*):
The parameters of the RoPE embeddings.
max_position_embeddings (`int`, *optional*, defaults to 128000):
The maximum sequence length that this model might ever be used with.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
`num_attention_heads`.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the conv layers.
conv_L_cache (`int`, *optional*, defaults to 3):
L_cache dim in the conv layers.
num_dense_layers (`int`, *optional*, defaults to 2):
Number of dense Lfm2MoeMLP layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
num_experts_per_tok (`int`, *optional*, defaults to 4):
Number of selected experts.
num_experts (`int`, *optional*, defaults to 32):
Number of routed experts.
use_expert_bias (`bool`, *optional*, defaults to `True`):
Whether to use the expert bias on the routing weights.
routed_scaling_factor (`float`, *optional*, defaults to 1.0):
Scaling factor for routed experts in MoE models.
norm_topk_prob (`bool`, *optional*, defaults to `True`):
Whether to normalize the topk probabilities.
layer_types (`Optional`, *optional*):
Type of each layers.
```python
>>> from transformers import Lfm2MoeModel, Lfm2MoeConfig
>>> # Initializing a LFM2 Moe model
>>> configuration = Lfm2MoeConfig()
>>> # Initializing a model from the LFM2-8B-A1B style configuration
>>> model = Lfm2MoeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```""" # noqa: E501
model_type = "lfm2_moe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size: int = 65536,
hidden_size: int = 2048,
intermediate_size: int = 7168,
moe_intermediate_size: int = 1792,
num_hidden_layers: int = 32,
pad_token_id: int = 0,
bos_token_id: int = 1,
eos_token_id: int = 2,
tie_word_embeddings: bool = True,
rope_parameters: dict[str, Any] | None = None,
max_position_embeddings: int = 128_000,
use_cache: bool = True,
norm_eps: float = 0.00001,
num_attention_heads: int = 32,
num_key_value_heads: int = 8,
conv_bias: bool = False,
conv_L_cache: int = 3,
num_dense_layers: int = 2,
num_experts_per_tok: int = 4,
num_experts: int = 32,
use_expert_bias: bool = True,
routed_scaling_factor: float = 1.0,
norm_topk_prob: bool = True,
layer_types: list[str] | None = None,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
rope_theta = kwargs.pop("rope_theta", 1000000.0)
if rope_parameters is None:
rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
self.rope_parameters = rope_parameters
self.max_position_embeddings = max_position_embeddings
self.use_cache = use_cache
self.norm_eps = norm_eps
# attn operator config
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
# custom operator config
self.conv_bias = conv_bias
self.conv_L_cache = conv_L_cache
# moe config
self.num_dense_layers = num_dense_layers
self.moe_intermediate_size = moe_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.use_expert_bias = use_expert_bias
self.routed_scaling_factor = routed_scaling_factor
self.norm_topk_prob = norm_topk_prob
self.layer_types = layer_types
tie_word_embeddings = kwargs.get(
"tie_embedding", tie_word_embeddings
) # to fit original config keys
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["Lfm2MoeConfig"]

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vllm/transformers_utils/configs/medusa.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from transformers import PretrainedConfig
class MedusaConfig(PretrainedConfig):
model_type = "medusa"
def __init__(
self,
hidden_size: int = 4096,
vocab_size: int = 32001,
num_heads: int = 5,
num_hidden_layers: int = 1,
max_paths: int = 64,
topk: int = 10,
truncated_vocab_size: int | None = None,
**kwargs,
):
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.num_heads = num_heads
self.num_hidden_layers = num_hidden_layers
self.max_paths = max_paths
self.topk = topk
self.max_seq_len = int(2**20)
self.truncated_vocab_size = (
vocab_size if truncated_vocab_size is None else truncated_vocab_size
)
if "architectures" not in kwargs:
kwargs["architectures"] = ["MedusaModel"]
super().__init__(**kwargs)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str | os.PathLike,
**kwargs,
) -> "MedusaConfig":
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs
)
for k in list(config_dict.keys()):
if "num" in k:
if "heads" in k:
config_dict["num_heads"] = config_dict.pop(k)
elif "layers" in k:
config_dict["num_hidden_layers"] = config_dict.pop(k)
return cls.from_dict(config_dict, **kwargs)
@property
def num_attention_heads(self):
return 0
@property
def num_lookahead_tokens(self):
return self.num_heads
@num_lookahead_tokens.setter
def num_lookahead_tokens(self, num_lookahead_tokens: int):
self.num_heads = num_lookahead_tokens

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 Horizon team, Xiaomi MiLM Plus.
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import PretrainedConfig
from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import (
Qwen2_5OmniTextConfig,
)
class DashengConfig(PretrainedConfig):
model_type = "midashenglm_dasheng_encoder"
def __init__(
self,
embed_dim: int = 768,
outputdim: int = 527,
patch_size: int | tuple[int, int] = 16,
patch_stride: int | tuple[int, int] = 16,
input_channels: int = 1,
target_length: int = 1012,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
init_values: float | None = None,
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
f_min: float = 0.0,
f_max: float = 8000.0,
center: bool = True,
win_length: int = 512,
hop_length: int = 160,
sample_rate: int = 16000,
n_fft: int = 512,
n_mels: int = 64,
**kwargs,
):
self.embed_dim = embed_dim
self.outputdim = outputdim
self.patch_size = patch_size
self.patch_stride = patch_stride
self.input_channels = input_channels
self.target_length = target_length
self.depth = depth
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.init_values = init_values
self.drop_rate = drop_rate
self.attn_drop_rate = attn_drop_rate
self.f_min = f_min
self.f_max = f_max
self.center = center
self.win_length = win_length
self.hop_length = hop_length
self.sample_rate = sample_rate
self.n_fft = n_fft
self.n_mels = n_mels
super().__init__(**kwargs)
class MiDashengLMConfig(PretrainedConfig):
model_type = "midashenglm"
def __init__(
self,
audio_encoder_config: dict | None = None,
subsample_factor: int = 5,
text_config: dict | None = None,
audio_token_id: int | None = None,
**kwargs,
):
self.audio_encoder_config = DashengConfig(**(audio_encoder_config or {}))
self.subsample_factor = subsample_factor
self.text_config = (
Qwen2_5OmniTextConfig(**text_config)
if text_config
else Qwen2_5OmniTextConfig()
)
self.text_config.rope_parameters = None # uses_mrope is false
self.audio_token_id = audio_token_id
super().__init__(**kwargs)

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vllm/transformers_utils/configs/mistral.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers import PretrainedConfig, WhisperConfig
from vllm.logger import init_logger
logger = init_logger(__name__)
def adapt_config_dict(
config_dict: dict[str, Any],
defaults: dict[str, Any],
) -> PretrainedConfig:
config_dict = _remap_general_mistral_args(config_dict)
if bool(config_dict.get("quantization")):
config_dict = _remap_mistral_quantization_args(config_dict)
is_moe = bool(config_dict.get("moe"))
is_mistral_large_3 = (
is_moe and (config_dict["moe"].get("num_shared_experts") or 0) > 0
)
if config_dict.get("model_type") == "mamba":
config_dict["architectures"] = ["Mamba2ForCausalLM"]
elif is_moe and is_mistral_large_3:
config_dict = _remap_moe_args(config_dict)
config_dict["model_type"] = "deepseek_v3"
config_dict["architectures"] = ["MistralLarge3ForCausalLM"]
assert "llama_4_scaling" in config_dict, (
"MistralLarge3 expect llama4 scaling config."
)
llama_4_scaling_config_keys = ["original_max_position_embeddings", "beta"]
assert all(
[
key in config_dict["llama_4_scaling"]
for key in llama_4_scaling_config_keys
]
), (
"llama_4_scaling config should define the keys: "
f"{','.join(llama_4_scaling_config_keys)}"
)
elif is_moe:
config_dict["architectures"] = ["MixtralForCausalLM"]
else:
config_dict["architectures"] = ["MistralForCausalLM"]
if bool(config_dict.get("yarn")):
config_dict = _remap_mistral_yarn_args(config_dict)
if bool(config_dict.get("llama_4_scaling")):
llama_4_scaling_config_keys = ["original_max_position_embeddings", "beta"]
assert all(
[
key in config_dict["llama_4_scaling"]
for key in llama_4_scaling_config_keys
]
), (
"llama_4_scaling config should define the keys: "
f"{','.join(llama_4_scaling_config_keys)}"
)
is_vision = (config_dict.get("multimodal") or {}).get(
"vision_encoder_args"
) or config_dict.get("vision_encoder")
is_audio = bool(
((config_dict.get("multimodal") or {}).get("whisper_model_args") or {}).get(
"encoder_args"
)
)
assert not (is_vision and is_audio), "Vision and audio are mutually exclusive"
if is_vision:
config_dict = _remap_mistral_vision_args(config_dict)
if is_audio:
config_dict = _remap_mistral_audio_args(config_dict)
for k, v in defaults.items():
config_dict.setdefault(k, v)
config = PretrainedConfig.from_dict(config_dict)
logger.debug("Initialized config %s", config)
return config
def _remap_mistral_vision_args(config: dict) -> dict:
if config.get("multimodal"):
vision_config = config.pop("multimodal")
else:
vision_config = config.pop("vision_encoder")
quant_config = config.get("quantization_config")
config = {
"model_type": "pixtral",
"architectures": ["PixtralForConditionalGeneration"],
"text_config": PretrainedConfig.from_dict(config),
"vision_config": PretrainedConfig.from_dict(vision_config),
}
if quant_config:
config["quantization_config"] = quant_config
return config
def _remap_mistral_yarn_args(config: dict) -> dict:
yarn_config_map = {
"factor": "factor",
"original_max_position_embeddings": "original_max_position_embeddings",
"beta": "beta_fast",
"alpha": "beta_slow",
"apply_scale": "apply_yarn_scaling",
}
yarn_config = config.get("yarn") or {}
config["rope_parameters"] = {
"rope_type": "yarn",
"mscale_all_dim": 1,
}
if rope_theta := config.pop("rope_theta", None):
config["rope_parameters"]["rope_theta"] = rope_theta
for old_name, new_name in yarn_config_map.items():
if old_name in yarn_config:
config["rope_parameters"][new_name] = yarn_config.pop(old_name)
assert len(yarn_config) == 0, f"Unparsed yarn config: {yarn_config}"
return config
def _remap_general_mistral_args(config: dict) -> dict:
# Mistral key -> HF key
config_mapping = {
"dim": "hidden_size",
"norm_eps": "rms_norm_eps",
"n_kv_heads": "num_key_value_heads",
"n_layers": "num_hidden_layers",
"n_heads": "num_attention_heads",
"hidden_dim": "intermediate_size",
}
# HF key -> (Mistral key, default value)
top_level_mapping_with_default = {
"model_type": ("model_type", "transformer"),
"hidden_act": ("activation", "silu"),
"tie_word_embeddings": ("tied_embeddings", False),
"max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)),
"max_position_embeddings": ("max_position_embeddings", 128_000),
}
for key, new_key in config_mapping.items():
if key in config:
config[new_key] = config.pop(key)
for new_key, (key, default_value) in top_level_mapping_with_default.items():
config[new_key] = config.pop(key, default_value)
return config
def _remap_mistral_quantization_args(config: dict) -> dict:
if config.get("quantization"):
quantization = config.pop("quantization", {})
if quantization.get("qformat_weight") == "fp8_e4m3":
qscheme_act = quantization.get("qscheme_act")
assert qscheme_act in ("NO_SCALES", "TENSOR", None), (
"Only NO_SCALES and TENSOR (default) are supported for qscheme_act"
)
is_dynamic = qscheme_act == "NO_SCALES"
config["quantization_config"] = {
"quant_method": "fp8",
"activation_scheme": "dynamic" if is_dynamic else "static",
}
else:
raise ValueError(f"Found unknown quantization='{quantization}' in config")
return config
def _remap_mistral_audio_args(config: dict) -> dict:
whisper_args = config["multimodal"].pop("whisper_model_args")
encoder_args = whisper_args["encoder_args"]
downsample_args = whisper_args["downsample_args"]
quant_config = config.get("quantization_config")
config = {
"model_type": "whixtral",
"architectures": ["VoxtralForConditionalGeneration"],
"text_config": PretrainedConfig.from_dict(config),
"audio_config": WhisperConfig(
num_mel_bins=encoder_args["audio_encoding_args"]["num_mel_bins"],
window_size=encoder_args["audio_encoding_args"]["window_size"],
sampling_rate=encoder_args["audio_encoding_args"]["sampling_rate"],
hop_length=encoder_args["audio_encoding_args"]["hop_length"],
downsample_factor=downsample_args["downsample_factor"],
d_model=encoder_args["dim"],
encoder_layers=encoder_args["n_layers"],
encoder_ffn_dim=encoder_args["hidden_dim"],
encoder_attention_heads=encoder_args["n_heads"],
vocab_size=encoder_args["vocab_size"],
max_source_positions=encoder_args["max_source_positions"],
is_encoder_decoder=False, # Override WhisperConfig default
),
}
if quant_config:
config["quantization_config"] = quant_config
return config
def _remap_moe_args(config: dict) -> dict:
moe_config_map = {
"route_every_n": "moe_layer_freq",
"first_k_dense_replace": "first_k_dense_replace",
"num_experts_per_tok": "num_experts_per_tok",
"num_experts": "n_routed_experts",
"expert_hidden_dim": "moe_intermediate_size",
"routed_scale": "routed_scaling_factor",
"num_shared_experts": "n_shared_experts",
"num_expert_groups": "n_group",
"num_expert_groups_per_tok": "topk_group",
}
moe_config = config.get("moe", {})
for old_name, new_name in moe_config_map.items():
if old_name in moe_config:
value = moe_config.pop(old_name)
config[new_name] = value
config["topk_method"] = None
config["norm_topk_prob"] = True
config["scoring_func"] = "softmax"
return config

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers import PretrainedConfig
class MLPSpeculatorConfig(PretrainedConfig):
model_type = "mlp_speculator"
attribute_map = {
"hidden_size": "emb_dim",
}
def __init__(
self,
vocab_size: int = 32000,
emb_dim: int = 4096,
inner_dim: int = 0,
n_predict: int = 3,
top_k_tokens_per_head: list[int] | None = None,
n_candidates: int = 5,
tie_weights: bool = False,
scale_input: bool = False,
**kwargs,
):
"""
Initialize an MLPSpeculatorConfig
Args:
vocab_size: int
the model vocab size
emb_dim: int
the model embedding dimension
inner_dim: int
the inner dimension of the model. If 0, will be the emb_dim.
n_predict: int
the number of lookaheads for the speculator
top_k_tokens_per_head: list[int]
Number of tokens to consider from each head when forming the
candidate tree.
For each candidate branch in the tree, head n produces topk[n]
additional sub-branches.
NOTE: This parameter is currently unused.
n_candidates: int
number of child candidates to create per sequence
tie_weights: bool
If true, use a single set of weights for every model
head/stage after the first. The initial projection
from the base model may have a different size, so that
stays separate.
scale_input: bool
if True, will scale the initial hidden states from
the base model.
"""
if top_k_tokens_per_head is None:
top_k_tokens_per_head = [5, 4, 3]
assert len(top_k_tokens_per_head) == n_predict
self.vocab_size = vocab_size
self.emb_dim = emb_dim
self.inner_dim = inner_dim
self.n_predict = n_predict
self.top_k_tokens_per_head = top_k_tokens_per_head
self.n_candidates = n_candidates
self.num_lookahead_tokens = n_predict
self.tie_weights = tie_weights
self.scale_input = scale_input
super().__init__(**kwargs)

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vllm/transformers_utils/configs/moonvit.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/configuration_kimi_vl.py
from transformers.configuration_utils import PretrainedConfig
class MoonViTConfig(PretrainedConfig):
model_type = "moonvit"
def __init__(
self,
patch_size: int = 14,
init_pos_emb_height: int = 64,
init_pos_emb_width: int = 64,
num_attention_heads: int = 16,
num_hidden_layers: int = 27,
hidden_size: int = 1152,
intermediate_size: int = 4304,
merge_kernel_size: tuple[int, int] = (2, 2),
**kwargs,
):
super().__init__(**kwargs)
self.patch_size = patch_size
# Positional embedding config
self.init_pos_emb_height = init_pos_emb_height
self.init_pos_emb_width = init_pos_emb_width
# Transformer config
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
# Patch merger config
self.merge_kernel_size = merge_kernel_size

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vllm/transformers_utils/configs/mpt.py
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# coding=utf-8
# Copied from
# https://huggingface.co/mosaicml/mpt-7b/blob/main/configuration_mpt.py
"""A HuggingFace-style model configuration."""
import warnings
from typing import Any, Dict, Optional, Union
from transformers import PretrainedConfig
attn_config_defaults: Dict = {
'attn_type': 'multihead_attention',
'attn_pdrop': 0.0,
'attn_impl': 'triton',
'qk_ln': False,
'clip_qkv': None,
'softmax_scale': None,
'prefix_lm': False,
'attn_uses_sequence_id': False,
'alibi': False,
'alibi_bias_max': 8
}
ffn_config_defaults: Dict = {'ffn_type': 'mptmlp'}
init_config_defaults: Dict = {
'name': 'kaiming_normal_',
'fan_mode': 'fan_in',
'init_nonlinearity': 'relu',
'init_div_is_residual': True,
'emb_init_std': None,
'emb_init_uniform_lim': None,
'init_std': None,
'init_gain': 0.0
}
class MPTConfig(PretrainedConfig):
model_type = 'mpt'
attribute_map = {
'num_attention_heads': 'n_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'n_layers',
}
# pylint: disable=dangerous-default-value
def __init__(self,
d_model: int = 2048,
n_heads: int = 16,
n_layers: int = 24,
expansion_ratio: int = 4,
max_seq_len: int = 2048,
vocab_size: int = 50368,
resid_pdrop: float = 0.0,
emb_pdrop: float = 0.0,
learned_pos_emb: bool = True,
attn_config: Dict = attn_config_defaults,
ffn_config: Dict = ffn_config_defaults,
init_device: str = 'cpu',
logit_scale: Optional[Union[float, str]] = None,
no_bias: bool = False,
embedding_fraction: float = 1.0,
norm_type: str = 'low_precision_layernorm',
use_cache: bool = False,
init_config: Dict = init_config_defaults,
fc_type: str = 'torch',
verbose: Optional[int] = None,
**kwargs: Any):
self.d_model = d_model
self.n_heads = n_heads
self.n_layers = n_layers
self.expansion_ratio = expansion_ratio
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.resid_pdrop = resid_pdrop
self.emb_pdrop = emb_pdrop
self.learned_pos_emb = learned_pos_emb
self.attn_config = attn_config
self.ffn_config = ffn_config
self.init_device = init_device
self.logit_scale = logit_scale
self.no_bias = no_bias
self.embedding_fraction = embedding_fraction
self.norm_type = norm_type
self.use_cache = use_cache
self.init_config = init_config
self.fc_type = fc_type
if verbose is not None:
warnings.warn(DeprecationWarning(
'verbose argument for MPTConfig is now ignored and '
'will be removed. Use python_log_level instead.'),
stacklevel=2)
if 'name' in kwargs:
del kwargs['name']
if 'loss_fn' in kwargs:
del kwargs['loss_fn']
if self.attn_config.get('alibi', False):
self.learned_pos_emb = False
warnings.warn(
f'alibi is turned on, setting `learned_pos_emb` '
f'to {self.learned_pos_emb}`',
stacklevel=2)
super().__init__(**kwargs)
self._validate_config()
def _set_config_defaults(
self, config: Dict[str, Any],
config_defaults: Dict[str, Any]) -> Dict[str, Any]:
for (k, v) in config_defaults.items():
if k not in config:
config[k] = v
return config
def _validate_config(self) -> None:
self.attn_config = self._set_config_defaults(self.attn_config,
attn_config_defaults)
self.ffn_config = self._set_config_defaults(self.ffn_config,
ffn_config_defaults)
self.init_config = self._set_config_defaults(self.init_config,
init_config_defaults)
if self.d_model % self.n_heads != 0:
raise ValueError('d_model must be divisible by n_heads')
if any((
prob < 0 or prob > 1 for prob in
[self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop]
)):
raise ValueError(
"self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are "
"probabilities and must be between 0 and 1")
if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
raise ValueError(
f"Unknown attn_impl={self.attn_config['attn_impl']}")
if self.attn_config['prefix_lm'] and self.attn_config[
'attn_impl'] not in ['torch', 'triton']:
raise NotImplementedError(
'prefix_lm only implemented with torch and triton attention.')
if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in [
'torch', 'triton'
]:
raise NotImplementedError(
'alibi only implemented with torch and triton attention.')
if self.attn_config['attn_uses_sequence_id'] and self.attn_config[
'attn_impl'] not in ['torch', 'triton']:
raise NotImplementedError(
'attn_uses_sequence_id only implemented with torch '
'and triton attention.')
if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
raise ValueError(
'model.embedding_fraction must be between 0 (exclusive) '
'and 1 (inclusive)!')
if isinstance(self.logit_scale,
str) and self.logit_scale != 'inv_sqrt_d_model':
raise ValueError(
f"self.logit_scale={self.logit_scale!r} is not recognized as "
"an option; use numeric value or 'inv_sqrt_d_model'.")
if self.init_config.get('name', None) is None:
raise ValueError(
f"self.init_config={self.init_config!r} 'name' needs to be set."
)
if not self.learned_pos_emb and (not self.attn_config['alibi']):
warnings.warn(
'Positional information not being provided to the model.',
stacklevel=2)
if self.fc_type == 'te' or self.ffn_config['ffn_type'] == 'te_ln_mlp':
try:
# pylint: disable=import-outside-toplevel
import transformer_engine.pytorch as te
del te
except Exception as exc:
raise ImportError(
'TransformerEngine import fail. `fc_type: te` requires '
'TransformerEngine be installed. '
'The required version of transformer_engine also requires '
'FlashAttention v1.0.6 is installed:\n'
'pip install flash-attn==1.0.6 --no-build-isolation \n'
'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156'
) from exc
if self.ffn_config['ffn_type'] == 'mptmlp':
self.ffn_config['fc_type'] = self.fc_type
elif self.ffn_config['ffn_type'] == 'te_ln_mlp':
self.ffn_config['bias'] = not self.no_bias

220
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Nemotron model configuration"""
from transformers import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class NemotronConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a
[`NemotronModel`]. It is used to instantiate a Nemotron model
according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar
configuration to that of the Nemotron-8B.
Configuration objects inherit from [`PretrainedConfig`] and can be
used to control the model outputs. Read the documentation from
[`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 256000):
Vocabulary size of the Nemotron model. Defines the number of
different tokens that can be represented by the
`inputs_ids` passed when calling [`NemotronModel`]
hidden_size (`int`, *optional*, defaults to 6144):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 24576):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 48):
Number of attention heads for each attention layer in the
Transformer decoder.
head_dim (`int`, *optional*):
Projection weights dimension in multi-head attention. Set to
hidden_size // num_attention_heads if None
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to
implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use
Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention
(MQA) otherwise GQA is used. When converting a multi-head
checkpoint to a GQA checkpoint, each group key and value
head should be constructed by meanpooling all the original
heads within that group. For more details checkout
[this paper](https://arxiv.org/pdf/2305.13245.pdf). If it
is not specified, will default to `num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
The non-linear activation function (function or string) in the
decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used
with.
initializer_range (`float`, *optional*, defaults to 0.0134):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices.
norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values
attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 3):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_parameters (`dict`, *optional*):
The parameters of the RoPE embeddings. Expected contents:
`rope_theta` (`float`): The base period of the RoPE embeddings.
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear',
'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the
original RoPE implementation.
`partial_rotary_factor` (`float`, *optional*, defaults to 0.5):
Percentage of the query and keys which will have rotary embedding.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output
projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj and down_proj layers in the MLP
layers.
```python
>>> from transformers import NemotronModel, NemotronConfig
>>> # Initializing a Nemotron nemotron-15b style configuration
>>> configuration = NemotronConfig()
>>> # Initializing a model from the nemotron-15b style configuration
>>> model = NemotronModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "nemotron"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=256000,
hidden_size=6144,
intermediate_size=24576,
num_hidden_layers=32,
num_attention_heads=48,
head_dim=None,
num_key_value_heads=None,
hidden_act="relu2",
max_position_embeddings=4096,
initializer_range=0.0134,
norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=2,
eos_token_id=3,
tie_word_embeddings=False,
rope_parameters=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
head_dim = head_dim or kwargs.get("kv_channels")
self.head_dim = (
head_dim if head_dim is not None else (hidden_size // num_attention_heads)
)
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.norm_eps = norm_eps
self.use_cache = use_cache
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
rope_theta = kwargs.pop("rope_theta", 10000.0)
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
# for backward compatibility
partial_rotary_factor = (
kwargs.get("rope_percent")
or kwargs.get("rope_percentage")
or kwargs.get("partial_rotary_factor")
or 0.5
)
if "partial_rotary_factor" not in rope_parameters:
rope_parameters["partial_rotary_factor"] = partial_rotary_factor
self.rope_parameters = rope_parameters
self._rope_parameters_validation()
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_parameters_validation(self):
"""
Validate the `rope_parameters` configuration.
"""
if self.rope_parameters is None:
return
rope_type: str | None = self.rope_parameters.get("rope_type", None)
factor: float | None = self.rope_parameters.get("factor", None)
if rope_type not in {"default", "linear", "dynamic"}:
raise ValueError(
"`rope_type` must be one of ['default', 'linear', 'dynamic'], "
f"got {rope_type}"
)
if rope_type != "default":
if factor is None:
raise ValueError(
"If `rope_type` is not 'default', `rope_parameters` "
"must include a `factor` field. Got `None`."
)
if not isinstance(factor, float) or factor <= 1.0:
raise ValueError(
"`rope_parameters`'s factor field must be a float > 1, got "
f"{factor}"
)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""NemotronH model configuration"""
import regex as re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class NemotronHConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a
[`NemotronHModel`]. It is used to instantiate a NemotronH model according
to the specified arguments, defining the model architecture. Instantiating
a configuration with the defaults will yield a similar configuration to
that of the NemotronH-v0.1 model.
Args:
vocab_size (`int`, *optional*, defaults to 131072):
Vocabulary size of the NemotronH model. Defines the number of
different tokens that can be represented by the `inputs_ids`
passed when calling [`NemotronHModel`]
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be
tied. Note that this is only relevant if the model has an output
word embedding layer.
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 21504):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 52):
Number of hidden layers in the Transformer encoder.
hybrid_override_pattern (`str`, *optional*, defaults to
`"M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-"`):
The pattern of the hybrid model. The pattern is a string of
characters where each character represents
M: Mamba2, *: Attention, -: MLP
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the
Transformer encoder.
attention_head_dim (`int`, *optional*, defaults to 128):
Dimension of each attention head.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to
implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use
Multi Head Attention (MHA), if `num_key_value_heads=1` the model
will use Multi Query Attention (MQA) otherwise GQA is used.
mlp_hidden_act (`str`, *optional*, defaults to "relu2"):
The non-linear activation function in the MLP layers.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in attention layers.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in MLP layers.
use_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the model.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
residual_in_fp32 (`bool`, *optional*, defaults to `False`):
Whether or not residuals should be in `float32`. If set to `False`
residuals will keep the same `dtype` as the rest of the model.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values
attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
Number of prompt logits to calculate during generation. If `None`,
all logits will be calculated. If an integer value, only last
`num_logits_to_keep` logits will be calculated.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
sliding_window (`int`, *optional*, defaults to None):
Sliding window attention window size.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used
with.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
hidden_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the hidden states.
use_mamba_kernels (`bool`, *optional*, defaults to `True`):
Flag indicating whether or not to use the fast mamba kernels.
These are available only if `mamba-ssm` and `causal-conv1d`
are installed, and the mamba modules are running on a CUDA device.
ssm_state_size (`int`, *optional*, defaults to 128):
The dimension of the mamba state space latents.
mamba_num_heads (`int`, *optional*, defaults to 128):
Number of heads in Mamba layers.
mamba_n_groups (`int`, *optional*, defaults to 8):
Number of groups in Mamba layers.
mamba_head_dim (`int`, *optional*, defaults to 64):
Dimension of each Mamba head.
mamba_d_conv (`int`, *optional*, defaults to 4):
The size of the mamba convolution kernel.
mamba_expand (`int`, *optional*, defaults to 2):
Expanding factor used to determine the mamba intermediate size.
mamba_hidden_act (`str`, *optional*, defaults to "silu"):
The non-linear activation function in the Mamba layers.
mamba_dt_min (`float`, *optional*, defaults to 0.001):
Minimum value for the time step in Mamba.
mamba_dt_max (`float`, *optional*, defaults to 0.1):
Maximum value for the time step in Mamba.
mamba_dt_limit (`tuple`, *optional*, defaults to (0.0, float("inf"))):
Limits for the time step in Mamba.
mamba_dt_init_floor (`float`, *optional*, defaults to 1e-4):
Floor value for time step initialization in Mamba.
mamba_conv_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the convolution layer of the mamba mixer
block.
mamba_proj_bias (`bool`, *optional*, defaults to `False`):
Whether to use bias in the input and output projections of the
mamba mixer block.
mamba_chunk_size (`int`, *optional*, defaults to 256):
Size of chunks for Mamba processing.
rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
Whether to rescale the pre-normalization residual connections.
"""
model_type = "nemotron_h"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=131072,
tie_word_embeddings=False,
hidden_size=4096,
intermediate_size=21504,
num_hidden_layers=52,
hybrid_override_pattern="M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-",
num_attention_heads=32,
head_dim=128,
num_key_value_heads=8, # nemo: num_query_groups
mlp_hidden_act="relu2",
attention_bias=False,
mlp_bias=False,
use_bias=False,
initializer_range=0.02, # nemo: init_method_std
layer_norm_epsilon=1e-5, # nemo: layernorm_epsilon
residual_in_fp32=False, # Megatron Core default value
use_cache=True,
num_logits_to_keep=1,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
sliding_window=None,
max_position_embeddings=4096,
attention_dropout=0.0,
hidden_dropout=0.0, # * ADDED
use_mamba_kernels=True,
ssm_state_size=128, # mamba_state_size
mamba_num_heads=128,
mamba_n_groups=8, # nemo: mamba_ssm_ngroups = num_heads
mamba_head_dim=64,
mamba_d_conv=4,
mamba_expand=2,
mamba_hidden_act="silu",
mamba_dt_min=0.001,
mamba_dt_max=0.1,
mamba_dt_limit=(0.0, float("inf")),
mamba_dt_init_floor=1e-4,
mamba_conv_bias=True,
mamba_proj_bias=False,
mamba_chunk_size=256,
rescale_prenorm_residual=True,
n_routed_experts=8,
n_shared_experts=1,
moe_intermediate_size=7688,
moe_shared_expert_intermediate_size=7688,
moe_latent_size=None,
num_experts_per_tok=2,
routed_scaling_factor=1.0,
n_group=1,
topk_group=1,
norm_topk_prob=True,
**kwargs,
):
self.vocab_size = vocab_size
self.tie_word_embeddings = tie_word_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.hybrid_override_pattern = hybrid_override_pattern
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.sliding_window = sliding_window
self.max_position_embeddings = max_position_embeddings
self.attention_dropout = attention_dropout
self.hidden_dropout = hidden_dropout
# Validate hybrid_override_pattern
# M: Mamba2, *: Attention, -: MLP
assert len(self.hybrid_override_pattern) == self.num_hidden_layers, (
"hybrid_override_pattern must have same length as num_hidden_layers"
)
assert re.match(r"^[*-M]+$", self.hybrid_override_pattern), (
"hybrid_override_pattern must only contain characters 'M', '*', or '-'"
)
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.mlp_hidden_act = mlp_hidden_act
self.attention_bias = attention_bias
self.mlp_bias = mlp_bias
self.use_bias = use_bias
self.initializer_range = initializer_range
self.layer_norm_epsilon = layer_norm_epsilon
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.num_logits_to_keep = num_logits_to_keep
self.use_mamba_kernels = use_mamba_kernels
self.n_groups = mamba_n_groups
self.mamba_head_dim = mamba_head_dim
self.ssm_state_size = ssm_state_size
self.mamba_num_heads = mamba_num_heads
self.conv_kernel = mamba_d_conv
self.expand = mamba_expand
self.mamba_hidden_act = mamba_hidden_act
self.time_step_min = mamba_dt_min
self.time_step_max = mamba_dt_max
self.time_step_limit = mamba_dt_limit
self.time_step_floor = mamba_dt_init_floor
self.use_conv_bias = mamba_conv_bias
self.mamba_proj_bias = mamba_proj_bias
self.chunk_size = mamba_chunk_size
self.rescale_prenorm_residual = rescale_prenorm_residual
self.n_routed_experts = n_routed_experts
self.n_shared_experts = n_shared_experts
self.moe_intermediate_size = moe_intermediate_size
self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size # noqa: E501
self.moe_latent_size = moe_latent_size
self.num_experts_per_tok = num_experts_per_tok
self.routed_scaling_factor = routed_scaling_factor
self.n_group = n_group
self.topk_group = topk_group
self.norm_topk_prob = norm_topk_prob
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@property
def layers_block_type(self):
return [
"mamba"
if self.hybrid_override_pattern[i] == "M"
else "attention"
if self.hybrid_override_pattern[i] == "*"
else "mlp"
if self.hybrid_override_pattern[i] == "-"
else "moe"
for i in range(self.num_hidden_layers)
]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers.configuration_utils import PretrainedConfig
class Olmo3Config(PretrainedConfig):
model_type = "olmo3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=50304,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
use_cache=True,
pad_token_id=1,
bos_token_id=None,
eos_token_id=50279,
tie_word_embeddings=False,
rope_parameters=None,
attention_bias=False,
attention_dropout=0.0,
rms_norm_eps=1e-5,
sliding_window=4096,
layer_types=None,
**kwargs,
):
# This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
# in vLLM.
if "architectures" not in kwargs:
kwargs["architectures"] = ["Olmo2ForCausalLM"]
elif "Olmo3ForCausalLM" in kwargs["architectures"]:
kwargs["architectures"].remove("Olmo3ForCausalLM")
kwargs["architectures"].append("Olmo2ForCausalLM")
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.use_cache = use_cache
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
rope_theta = kwargs.pop("rope_theta", 10000.0)
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
self.rope_parameters = rope_parameters
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.rms_norm_eps = rms_norm_eps
self.sliding_window = sliding_window
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
for i in range(self.num_hidden_layers)
]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa: E501
# adapted from https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_aimv2.py
# and https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_ovis.py
# Ovis Config with AimV2 config registration removed for Transformers compatibility
from typing import Any
from transformers import AutoConfig, PretrainedConfig
class AIMv2Config(PretrainedConfig):
"""This is the configuration class to store the configuration of an [`AIMv2Model`].
Instantiating a configuration with the defaults will yield a similar configuration
to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
Args:
hidden_size: Dimension of the hidden representations.
intermediate_size: Dimension of the SwiGLU representations.
num_hidden_layers: Number of hidden layers in the Transformer.
num_attention_heads: Number of attention heads for each attention layer
in the Transformer.
num_channels: Number of input channels.
image_size: Image size.
patch_size: Patch size.
rms_norm_eps: Epsilon value used for the RMS normalization layer.
attention_dropout: Dropout ratio for attention probabilities.
projection_dropout: Dropout ratio for the projection layer after the attention.
qkv_bias: Whether to add a bias to the queries, keys and values.
use_bias: Whether to add a bias in the feed-forward and projection layers.
kwargs: Keyword arguments for the [`PretrainedConfig`].
"""
model_type: str = "aimv2"
def __init__(
self,
hidden_size: int = 1024,
intermediate_size: int = 2816,
num_hidden_layers: int = 24,
num_attention_heads: int = 8,
num_channels: int = 3,
image_size: int = 224,
patch_size: int = 14,
rms_norm_eps: float = 1e-5,
attention_dropout: float = 0.0,
projection_dropout: float = 0.0,
qkv_bias: bool = False,
use_bias: bool = False,
**kwargs: Any,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.attention_dropout = attention_dropout
self.rms_norm_eps = rms_norm_eps
self.projection_dropout = projection_dropout
self.qkv_bias = qkv_bias
self.use_bias = use_bias
# ----------------------------------------------------------------------
# Visual Tokenizer Configuration
# ----------------------------------------------------------------------
class BaseVisualTokenizerConfig(PretrainedConfig):
def __init__(
self,
vocab_size=16384,
tokenize_function="softmax",
tau=1.0,
depths=None,
drop_cls_token=False,
backbone_config: PretrainedConfig | dict | None = None,
hidden_stride: int = 1,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.tokenize_function = tokenize_function
self.tau = tau
if isinstance(depths, str):
depths = [int(x) for x in depths.split("|")]
self.depths = depths
self.backbone_kwargs = dict[str, Any]()
self.drop_cls_token = drop_cls_token
if backbone_config is not None:
assert isinstance(backbone_config, (PretrainedConfig, dict)), (
f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
)
if not isinstance(backbone_config, PretrainedConfig):
model_type = backbone_config["model_type"]
if model_type != "aimv2":
backbone_config.pop("model_type")
backbone_config = AutoConfig.for_model(
model_type, **backbone_config
)
else:
backbone_config = AIMv2Config(**backbone_config)
self.backbone_config = backbone_config
self.hidden_stride = hidden_stride
class Aimv2VisualTokenizerConfig(BaseVisualTokenizerConfig):
model_type = "aimv2_visual_tokenizer"
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.drop_cls_token:
self.drop_cls_token = False
if self.depths:
assert len(self.depths) == 1
self.backbone_kwargs["num_hidden_layers"] = self.depths[0]
class SiglipVisualTokenizerConfig(BaseVisualTokenizerConfig):
model_type = "siglip_visual_tokenizer"
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.drop_cls_token:
self.drop_cls_token = False
if self.depths:
assert len(self.depths) == 1
self.backbone_kwargs["num_hidden_layers"] = self.depths[0]
AutoConfig.register("siglip_visual_tokenizer", SiglipVisualTokenizerConfig)
AutoConfig.register("aimv2_visual_tokenizer", Aimv2VisualTokenizerConfig)
# ----------------------------------------------------------------------
# Ovis Configuration
# ----------------------------------------------------------------------
class OvisConfig(PretrainedConfig):
model_type = "ovis"
def __init__(
self,
llm_config: PretrainedConfig | dict | None = None,
visual_tokenizer_config: PretrainedConfig | dict | None = None,
multimodal_max_length=8192,
hidden_size=None,
conversation_formatter_class=None,
llm_attn_implementation=None,
disable_tie_weight=False,
**kwargs,
):
super().__init__(**kwargs)
if llm_config is not None:
assert isinstance(llm_config, (PretrainedConfig, dict)), (
f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
)
if not isinstance(llm_config, PretrainedConfig):
model_type = llm_config["model_type"]
llm_config.pop("model_type")
llm_config = AutoConfig.for_model(model_type, **llm_config)
# map llm_config to text_config
self.text_config = llm_config
if visual_tokenizer_config is not None:
assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), (
f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
)
if not isinstance(visual_tokenizer_config, PretrainedConfig):
model_type = visual_tokenizer_config["model_type"]
visual_tokenizer_config.pop("model_type")
visual_tokenizer_config = AutoConfig.for_model(
model_type, **visual_tokenizer_config
)
self.visual_tokenizer_config = visual_tokenizer_config
self.multimodal_max_length = multimodal_max_length
self.hidden_size = hidden_size
self.conversation_formatter_class = conversation_formatter_class
self.llm_attn_implementation = llm_attn_implementation
self.disable_tie_weight = disable_tie_weight

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Qwen3-Next model configuration"""
from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Qwen3NextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3NextModel`]. It is used to instantiate a
Qwen3-Next model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of
Qwen3-Next-80B-A3B-Instruct [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the model. Defines the number of different tokens that can be represented by the
`inputs_ids`.
hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 5632):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 48):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 2):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_parameters (`dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_theta` (`float`): The base period of the RoPE embeddings.
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
`partial_rotary_factor` (`float`, *optional*, defaults to 0.25):
Percentage of the query and keys which will have rotary embedding.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
head_dim (`int`, *optional*, defaults to 256):
Projection weights dimension in multi-head attention.
linear_conv_kernel_dim (`int`, *optional*, defaults to 4):
Kernel size of the convolution used in linear attention layers.
linear_key_head_dim (`int`, *optional*, defaults to 128):
Dimension of each key head in linear attention.
linear_value_head_dim (`int`, *optional*, defaults to 128):
Dimension of each value head in linear attention.
linear_num_key_heads (`int`, *optional*, defaults to 16):
Number of key heads used in linear attention layers.
linear_num_value_heads (`int`, *optional*, defaults to 32):
Number of value heads used in linear attention layers.
decoder_sparse_step (`int`, *optional*, defaults to 1):
The frequency of the MoE layer.
moe_intermediate_size (`int`, *optional*, defaults to 512):
Intermediate size of the routed expert.
shared_expert_intermediate_size (`int`, *optional*, defaults to 512):
Intermediate size of the shared expert.
num_experts_per_tok (`int`, *optional*, defaults to 10):
Number of selected experts.
num_experts (`int`, *optional*, defaults to 512):
Number of routed experts.
norm_topk_prob (`bool`, *optional*, defaults to `True`):
Whether to normalize the topk probabilities.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabling this will also
allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
mlp_only_layers (`list[int]`, *optional*, defaults to `[]`):
Indicate which layers use Qwen3NextMLP rather than Qwen3NextSparseMoeBlock
The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
layer_types (`list[str]`, *optional*):
Types of each layer (attention or linear).
```python
>>> from transformers import Qwen3NextModel, Qwen3NextConfig
>>> # Initializing a Qwen3Next style configuration
>>> configuration = Qwen3NextConfig()
>>> # Initializing a model from the Qwen3-Next-80B-A3B style configuration
>>> model = Qwen3NextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
""" # noqa: E501
model_type = "qwen3_next"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.experts.*.gate_proj": "colwise",
"layers.*.mlp.experts.*.up_proj": "colwise",
"layers.*.mlp.experts.*.down_proj": "rowwise",
"layers.*.mlp.shared_experts.gate_proj": "colwise",
"layers.*.mlp.shared_experts.up_proj": "colwise",
"layers.*.mlp.shared_experts.down_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=151936,
hidden_size=2048,
intermediate_size=5632,
num_hidden_layers=48,
num_attention_heads=16,
num_key_value_heads=2,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_parameters=None,
attention_bias=False,
attention_dropout=0.0,
head_dim=256,
linear_conv_kernel_dim=4,
linear_key_head_dim=128,
linear_value_head_dim=128,
linear_num_key_heads=16,
linear_num_value_heads=32,
decoder_sparse_step=1,
moe_intermediate_size=512,
shared_expert_intermediate_size=512,
num_experts_per_tok=10,
num_experts=512,
norm_topk_prob=True,
output_router_logits=False,
router_aux_loss_coef=0.001,
mlp_only_layers=None,
layer_types=None,
**kwargs,
):
if mlp_only_layers is None:
mlp_only_layers = []
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
rope_theta = kwargs.pop("rope_theta", 10000.0)
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
partial_rotary_factor = kwargs.pop("partial_rotary_factor", 0.25)
if "partial_rotary_factor" not in rope_parameters:
rope_parameters["partial_rotary_factor"] = partial_rotary_factor
self.rope_parameters = rope_parameters
self.partial_rotary_factor = partial_rotary_factor
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.head_dim = head_dim
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"linear_attention" if bool((i + 1) % 4) else "full_attention"
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
# linear attention part
self.linear_conv_kernel_dim = linear_conv_kernel_dim
self.linear_key_head_dim = linear_key_head_dim
self.linear_value_head_dim = linear_value_head_dim
self.linear_num_key_heads = linear_num_key_heads
self.linear_num_value_heads = linear_num_value_heads
# MoE arguments
self.decoder_sparse_step = decoder_sparse_step
self.moe_intermediate_size = moe_intermediate_size
self.shared_expert_intermediate_size = shared_expert_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm_topk_prob = norm_topk_prob
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.mlp_only_layers = mlp_only_layers
__all__ = ["Qwen3NextConfig"]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Radio vision model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
VIT_TIMM_DIM_BY_NAME: dict[str, tuple[int, int, int, int]] = {
"vit_small_patch16_224": (384, 12, 6, 1536),
"vit_base_patch16_224": (768, 12, 12, 3072),
"vit_large_patch16_224": (1024, 24, 16, 4096),
"vit_huge_patch16_224": (1280, 32, 16, 5120),
}
OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711)
class RadioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a Radio
vision model. It is used to instantiate a Radio model according to the
specified arguments, defining the model architecture.
Args:
model_name: Name of the vision transformer model
(e.g., "vit_base_patch16_224"). Used to determine architecture
dimensions from `VIT_TIMM_DIM_BY_NAME`.
image_size: The size (resolution) of each image.
patch_size: The size (resolution) of each patch.
qkv_bias: Whether to add a bias to the queries, keys and values.
qk_normalization: Whether to apply normalization to queries and keys.
norm_type: The normalization type to use.
layer_norm_eps: The epsilon used by the layer normalization layers.
initializer_factor: A factor for initializing all weight matrices.
hidden_act: The non-linear activation function in the encoder.
max_img_size: Maximum image size for position embeddings.
norm_mean: Mean values for image normalization (RGB channels).
Defaults to (0.48145466, 0.4578275, 0.40821073)).
norm_std: Standard deviation values for image normalization
(RGB channels). Defaults to (0.26862954, 0.26130258, 0.27577711)).
reg_tokens: Number of register tokens to use.
"""
model_type = "radio"
def __init__(
self,
model_name: str,
image_size: int = 224,
patch_size: int = 16,
qkv_bias: bool = True,
qk_normalization: bool = False,
norm_type: str = "layer_norm",
layer_norm_eps: float = 1e-6,
initializer_factor: float = 1.0,
hidden_act: str = "gelu",
max_img_size: int = 2048,
norm_mean: tuple[float, float, float] | list = OPENAI_CLIP_MEAN,
norm_std: tuple[float, float, float] | list = OPENAI_CLIP_STD,
reg_tokens: int | None = None,
**kwargs,
):
self.model_name = model_name
(
self.hidden_size,
self.num_hidden_layers,
self.num_attention_heads,
self.intermediate_size,
) = VIT_TIMM_DIM_BY_NAME[model_name]
self.image_size = image_size
self.patch_size = patch_size
self.qkv_bias = qkv_bias
self.qk_normalization = qk_normalization
self.norm_type = norm_type
self.layer_norm_eps = layer_norm_eps
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
self.max_img_size = max_img_size
self.norm_mean = (
list(norm_mean) if isinstance(norm_mean, (tuple, list)) else norm_mean
)
self.norm_std = (
list(norm_std) if isinstance(norm_std, (tuple, list)) else norm_std
)
self.reg_tokens = reg_tokens
super().__init__(**kwargs)

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vllm/transformers_utils/configs/speculators/__init__.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

38
vllm/transformers_utils/configs/speculators/algos.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
SUPPORTED_SPECULATORS_TYPES = {}
def register_speculator(name):
def decorator(fn):
SUPPORTED_SPECULATORS_TYPES[name] = fn
return fn
return decorator
@register_speculator("eagle3")
def update_eagle3(config_dict: dict, vllm_config: dict) -> None:
"""
Apply Eagle-3 specific configuration transformations.
Eagle-3 specific fields:
- draft_vocab_size: Size of the draft model's vocabulary
- target_hidden_size: Hidden size of the target model
- norm_before_residual: Whether to apply norm before residual connection
- eagle_aux_hidden_state_layer_ids: List of layer indices from the base
model to use as auxiliary inputs for the Eagle3 drafter. These layers
provide intermediate hidden states that help the drafter make better
predictions. This is the standard field used in Eagle3 checkpoints.
"""
vllm_config["draft_vocab_size"] = config_dict.get("draft_vocab_size")
if config_dict.get("target_hidden_size") is not None:
vllm_config["target_hidden_size"] = config_dict["target_hidden_size"]
vllm_config["norm_before_residual"] = config_dict.get("norm_before_residual", True)
vllm_config["architectures"] = ["Eagle3LlamaForCausalLM"]
if config_dict.get("eagle_aux_hidden_state_layer_ids"):
vllm_config["eagle_aux_hidden_state_layer_ids"] = config_dict[
"eagle_aux_hidden_state_layer_ids"
]

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vllm/transformers_utils/configs/speculators/base.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import os
from typing import Any
from transformers import PretrainedConfig
from vllm.transformers_utils.configs.speculators.algos import (
SUPPORTED_SPECULATORS_TYPES,
)
__all__ = ["SpeculatorsConfig"]
class SpeculatorsConfig(PretrainedConfig):
model_type = "speculators"
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str | os.PathLike,
**kwargs,
) -> "SpeculatorsConfig":
"""Load speculators Eagle config and convert to vLLM format."""
config_dict, _ = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
vllm_config = cls.extract_vllm_speculative_config(config_dict)
return cls(**vllm_config)
@classmethod
def extract_vllm_speculative_config(
cls, config_dict: dict[str, Any]
) -> dict[str, Any]:
speculators_model_type = config_dict.get("speculators_model_type")
if speculators_model_type not in SUPPORTED_SPECULATORS_TYPES:
raise ValueError(
f"Expected one of: {SUPPORTED_SPECULATORS_TYPES}. "
"Please ensure you're loading a speculators-format model."
)
# validate fields
# TODO: @dsikka - use speculators pydantic model to validate
cls.validate_speculators_config(config_dict=config_dict)
# Convert from speculators config -> format that can be ingested by vLLM
vllm_config = cls.build_vllm_speculative_config(config_dict=config_dict)
# Apply anything specific to the supported algorithm
algo_updater = SUPPORTED_SPECULATORS_TYPES[speculators_model_type]
algo_updater(config_dict=config_dict, vllm_config=vllm_config)
return vllm_config
@classmethod
def validate_speculators_config(cls, config_dict: dict[str, Any]) -> None:
try:
spec_config = config_dict["speculators_config"]
methods = spec_config["proposal_methods"]
first_method = methods[0]
_ = first_method["speculative_tokens"]
_ = spec_config["verifier"]["name_or_path"]
_ = config_dict["speculators_model_type"]
except (KeyError, IndexError, TypeError) as e:
raise ValueError("Invalid speculators config structure") from e
if "transformer_layer_config" not in config_dict:
raise ValueError("Must provide transformer_layer_config")
if not isinstance(config_dict["transformer_layer_config"], dict):
raise TypeError(
"'transformer_layer_config' must be a dictionary if provided"
)
@classmethod
def build_vllm_speculative_config(
cls, config_dict: dict[str, Any]
) -> dict[str, Any]:
"""
Build vLLM-compatible speculative configuration from speculators format.
This method extracts and transforms speculative configuration from the
speculators format into the structure expected by vLLM.
Args:
config_dict: Configuration dictionary in speculators format
Returns:
Dictionary with vLLM-compatible speculative configuration
"""
# Extract speculators configuration
spec_config = config_dict["speculators_config"]
# Currently we only support one proposal method
proposal_methods = spec_config.get("proposal_methods")
if not proposal_methods:
raise ValueError("No proposal methods found in speculators config")
first_method = proposal_methods[0]
num_speculative_tokens = first_method.get("speculative_tokens")
if num_speculative_tokens is None:
raise ValueError(
f"Missing 'speculative_tokens' in proposal method. Got: {first_method}"
)
# Build base vLLM speculative configuration
vllm_config = {
"method": config_dict.get("speculators_model_type"),
"num_speculative_tokens": num_speculative_tokens,
"target_model": spec_config.get("verifier")["name_or_path"],
}
# Merge transformer layer configuration if present
transformer_config = config_dict.get("transformer_layer_config", {})
vllm_config.update(transformer_config)
return vllm_config

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vllm/transformers_utils/configs/step3_vl.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any
from transformers.configuration_utils import PretrainedConfig
class Step3VisionEncoderConfig(PretrainedConfig):
model_type = "step3_vision_encoder"
def __init__(
self,
hidden_size=1792,
intermediate_size=3072,
output_hidden_size=4096,
num_hidden_layers=63,
num_attention_heads=16,
num_channels=3,
image_size=728,
patch_size=14,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
**kwargs,
):
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.output_hidden_size = output_hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
super().__init__(**kwargs)
class Step3TextConfig(PretrainedConfig):
model_type = "step3_text"
architectures = ["Step3TextForCausalLM"]
def __init__(
self,
hidden_size: int = 7168,
intermediate_size: int = 18432,
num_attention_heads: int = 64,
num_attention_groups: int = 1,
num_hidden_layers: int = 61,
max_seq_len: int = 65536,
vocab_size: int = 128815,
rms_norm_eps: float = 1e-5,
moe_intermediate_size: int = 5120,
moe_num_experts: int = 48,
moe_top_k: int = 3,
rope_parameters: dict[str, Any] | None = None,
max_position_embedding: int = 65536,
share_expert_dim: int = 5120,
share_q_dim: int = 2048,
head_dim: int = 256,
norm_expert_weight: bool = False,
moe_layers_enum: tuple[int, ...] = (
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
),
**kwargs,
) -> None:
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.num_attention_groups = num_attention_groups
self.num_hidden_layers = num_hidden_layers
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.rms_norm_eps = rms_norm_eps
self.moe_intermediate_size = moe_intermediate_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
rope_theta = kwargs.pop("rope_theta", 500000.0)
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
self.rope_parameters = rope_parameters
self.max_position_embedding = max_position_embedding
self.share_expert_dim = share_expert_dim
self.share_q_dim = share_q_dim
self.head_dim = head_dim
self.norm_expert_weight = norm_expert_weight
self.moe_layers_enum = moe_layers_enum
super().__init__(**kwargs)
class Step3VLConfig(PretrainedConfig):
model_type = "step3_vl"
def __init__(
self,
vision_config: dict | Step3VisionEncoderConfig | None = None,
text_config: dict | Step3TextConfig | None = None,
understand_projector_stride: int = 1,
projector_bias: bool = True,
image_token_id: int = 128001,
**kwargs,
) -> None:
if vision_config is None:
vision_config = Step3VisionEncoderConfig()
elif isinstance(vision_config, dict):
vision_config = Step3VisionEncoderConfig(**vision_config)
self.vision_config = vision_config
if text_config is None:
text_config = Step3TextConfig()
elif isinstance(text_config, dict):
text_config = Step3TextConfig(**text_config)
self.text_config = text_config
self.understand_projector_stride = understand_projector_stride
self.projector_bias = projector_bias
self.hidden_size = text_config.hidden_size
self.image_token_id = image_token_id
super().__init__(**kwargs)

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vllm/transformers_utils/configs/tarsier2.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from transformers import Qwen2VLConfig
class Tarsier2Config(Qwen2VLConfig):
"""
Tarsier2's config.json is written such that AutoConfig.from_pretrained will create
a deeply nested config consisting of:
- LlavaConfig
- Qwen2VLConfig
- Qwen2VLTextConfig
- Qwen2VLVisionConfig
- Qwen2VLConfig
- Qwen2VLTextConfig
- Qwen2VLVisionConfig
When it should really just be a single Qwen2VLConfig.
This class is a hack to stop AutoConfig from creating the nested config structure.
"""
model_type = "tarsier2"

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vllm/transformers_utils/configs/ultravox.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_config.py
from typing import Any
import transformers
class UltravoxConfig(transformers.PretrainedConfig):
r"""
This is the configuration class to store the configuration of a
[`UltravoxForConditionalGeneration`]. It is used to instantiate an
Ultravox model according to the specified arguments, defining the model
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to
control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
audio_config (`Union[AutoConfig, dict]`, *optional*):
Custom audio config or dict.
text_config (`Union[AutoConfig, dict]`, *optional*):
The config object of the text backbone.
audio_model_id (`str`, *optional*):
The model ID of the audio backbone.
text_model_id (`str`, *optional*):
The model ID of the text backbone.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
audio_token_index (`int`, *optional*, defaults to 32000):
The audio token index to encode the audio prompt.
stack_factor (`int`, *optional*, defaults to 8):
Audio downsampling factor for the multimodal projector.
norm_init (`float`, *optional*, defaults to 0.4):
The initialization value for the layer normalization.
projector_act (`str`, *optional*, defaults to `"swiglu"`):
The activation function used by the multimodal projector.
projector_ln_mid (`bool`, *optional*, defaults to `False`):
Whether to apply layer normalization at the middle of the
projector or at the end. Versions v0.4.1 and below
use `False`, but v0.5 and above use `True`.
"""
wrapped_model_config: transformers.PretrainedConfig
model_type = "ultravox"
audio_token = "<|audio|>"
is_composition = False
def __init__(
self,
audio_config: dict[str, Any] | None = None,
text_config: dict[str, Any] | None = None,
audio_model_id: str | None = None,
text_model_id: str | None = None,
ignore_index: int = -100,
audio_token_index: int = 32000,
hidden_size: int = 4096,
stack_factor: int = 8,
norm_init: float = 0.4,
projector_act: str = "swiglu",
projector_ln_mid: bool = False,
num_projector_layers: int = 0,
**kwargs,
):
self.ignore_index = ignore_index
self.audio_token_index = audio_token_index
self.hidden_size = hidden_size
self.stack_factor = stack_factor
self.norm_init = norm_init
self.projector_act = projector_act
self.projector_ln_mid = projector_ln_mid
self.num_projector_layers = num_projector_layers
# N.B. May set the wrapped_model_config below.
self.text_model_id = text_model_id
if text_model_id is None:
text_config = text_config or {}
self.wrapped_model_config = transformers.CONFIG_MAPPING[
text_config.get("model_type", "llama")
](**text_config)
# N.B. May set the audio_config below.
self.audio_model_id = audio_model_id
if audio_model_id is None:
self.audio_model_id = None
audio_config = audio_config or {}
self.audio_config = transformers.CONFIG_MAPPING[
audio_config.get("model_type", "whisper")
](**audio_config)
super().__init__(**kwargs)
def __setattr__(self, key, value):
# Since --hf-overrides are applied _after_ the UltravoxConfig is
# instantiated, load the configs implicitly when assigning text_model_id
# or audio_model_id. This allows:
#
# --hf-overrides.text_model_id=<quantized variant>
#
# to behave as intended.
if key == "text_model_id" and value is not None:
from vllm.transformers_utils.config import get_config
self.wrapped_model_config = get_config(value, trust_remote_code=False)
elif key == "audio_model_id" and value is not None:
from vllm.transformers_utils.config import get_config
self.audio_config = get_config(value, trust_remote_code=False)
return super().__setattr__(key, value)
@property
def text_config(self) -> transformers.PretrainedConfig:
# When Ultravox wraps a multi-modal model (e.g. Gemma), we instantiate
# the full model, but the text config is the text config of the inner
# model.
return self.wrapped_model_config.get_text_config()