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Upgrade to vllm 0.17.0 corex v4.1 overlay

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2026-04-29 19:38:22 +08:00
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215
vllm/transformers_utils/configs/AXK1.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
class AXK1Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AXK1Model`].
It is used to instantiate an A.X 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 A.X K1.
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 163840):
Vocabulary size of the A.X K1 model. Defines the number of different
tokens that can be represented by the `inputs_ids` passed when calling
[`AXK1Model`]
hidden_size (`int`, *optional*, defaults to 7168):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 18432):
Dimension of the MLP representations.
moe_intermediate_size (`int`, *optional*, defaults to 2048):
Dimension of the MoE representations.
num_hidden_layers (`int`, *optional*, defaults to 61):
Number of hidden layers in the Transformer decoder.
num_nextn_predict_layers (`int`, *optional*, defaults to 1):
Number of nextn predict layers in the AXK1 Model.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer
decoder.
n_shared_experts (`int`, *optional*, defaults to 1):
Number of shared experts, None means dense model.
n_routed_experts (`int`, *optional*, defaults to 192):
Number of routed experts, None means dense model.
routed_scaling_factor (`float`, *optional*, defaults to 2.5):
Scaling factor or routed experts.
topk_method (`str`, *optional*, defaults to `noaux_tc`):
Topk method used in routed gate.
n_group (`int`, *optional*, defaults to 8):
Number of groups for routed experts.
topk_group (`int`, *optional*, defaults to 4):
Number of selected groups for each token(for each token, ensuring the
selected experts is only within `topk_group` groups).
num_experts_per_tok (`int`, *optional*, defaults to 8):
Number of selected experts, None means dense model.
moe_layer_freq (`int`, *optional*, defaults to 1):
The frequency of the MoE layer: one expert layer for every
`moe_layer_freq - 1` dense layers.
first_k_dense_replace (`int`, *optional*, defaults to 1):
Number of dense layers in shallow layers
(embed->dense->dense->...->dense->moe->moe...->lm_head).
\--k dense layers--/
norm_topk_prob (`bool`, *optional*, defaults to True):
Whether to normalize the weights of the routed experts.
scoring_func (`str`, *optional*, defaults to 'sigmoid'):
Method of computing expert weights.
aux_loss_alpha (`float`, *optional*, defaults to 0.0001):
Auxiliary loss weight coefficient.
seq_aux = (`bool`, *optional*, defaults to True):
Whether to compute the auxiliary loss for each individual sample.
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 `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 131072):
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`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 163691):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 163691):
End of stream token id.
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.
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.
"""
model_type = "AXK1"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size: int = 163840,
hidden_size: int = 7168,
intermediate_size: int = 18432,
moe_intermediate_size: int = 2048,
num_hidden_layers: int = 61,
num_nextn_predict_layers: int | None = 1,
num_attention_heads: int = 64,
num_key_value_heads: int = 64,
n_shared_experts: int | None = 1,
n_routed_experts: int | None = 192,
ep_size: int | None = 8, ## Ignored - Expert parallel size
routed_scaling_factor: float | None = 2.5,
kv_lora_rank: int | None = 512,
q_lora_rank: int | None = 1536,
qk_rope_head_dim: int | None = 64,
v_head_dim: int | None = 128,
qk_nope_head_dim: int | None = 128,
topk_method: str | None = "noaux_tc",
n_group: int | None = 8,
topk_group: int | None = 4,
num_experts_per_tok: int | None = 8,
moe_layer_freq: int | None = 1,
first_k_dense_replace: int = 1,
norm_topk_prob: bool = True,
scoring_func: str | None = "sigmoid",
aux_loss_alpha: float | None = 0.0001,
seq_aux: float | None = True,
hidden_act: str | None = "silu",
max_position_embeddings: int | None = 131072,
initializer_range: float | None = 0.02,
rms_norm_eps: float = 1e-6,
use_cache: bool | None = True,
pad_token_id: int | None = None,
bos_token_id: int | None = 163691,
eos_token_id: int | None = 163691,
pretraining_tp: int | None = 1,
tie_word_embeddings: bool | None = False,
rope_theta: float | None = 10000.0,
rope_scaling: dict[str, Any] | None = None,
rope_parameters: dict[str, Any] | None = None,
attention_bias: bool | None = False,
attention_dropout: float | None = 0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_nextn_predict_layers = num_nextn_predict_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
self.aux_loss_alpha = aux_loss_alpha
self.seq_aux = seq_aux
# 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_parameters = rope_parameters
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,
)

2
vllm/transformers_utils/configs/__init__.py
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@@ -16,6 +16,7 @@ import importlib
_CLASS_TO_MODULE: dict[str, str] = {
"AfmoeConfig": "vllm.transformers_utils.configs.afmoe",
"AXK1Config": "vllm.transformers_utils.configs.AXK1",
"BagelConfig": "vllm.transformers_utils.configs.bagel",
"ChatGLMConfig": "vllm.transformers_utils.configs.chatglm",
"ColModernVBertConfig": "vllm.transformers_utils.configs.colmodernvbert",
@@ -70,6 +71,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
__all__ = [
"AfmoeConfig",
"AXK1Config",
"BagelConfig",
"ChatGLMConfig",
"ColModernVBertConfig",

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vllm/transformers_utils/configs/extract_hidden_states.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Config definitions for ExtractHiddenStatesModel, to be used with
the extract_hidden_states spec decoding method."""
import os
from transformers import PretrainedConfig
class ExtractHiddenStatesConfig(PretrainedConfig):
model_type = "extract_hidden_states"
def __init__(
self,
model: PretrainedConfig | dict | None = None,
method: str | None = "extract_hidden_states",
**kwargs,
):
assert method == "extract_hidden_states"
if isinstance(model, dict):
model_dict = model
elif isinstance(model, PretrainedConfig):
model_dict = model.to_dict()
else:
model_dict = {}
# Combine: model_dict first, then kwargs override
combined = {**model_dict, **kwargs}
# Remove architectures from the base, we'll set it explicitly
combined = {k: v for k, v in combined.items() if k != "architectures"}
combined["architectures"] = ["ExtractHiddenStatesModel"]
super().__init__(**combined)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str | os.PathLike,
**kwargs,
) -> "ExtractHiddenStatesConfig":
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
# ExtractHiddenStatesConfig with default arguments is not supported
del use_diff
return super().to_json_string(use_diff=False)

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vllm/transformers_utils/configs/parakeet.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from transformers import ParakeetEncoderConfig, PretrainedConfig
class ParakeetConfig(ParakeetEncoderConfig):
llm_hidden_size: int
projection_hidden_size: int
projection_bias: bool
projection_eps: float = 1e-5
sampling_rate: int
@staticmethod
def from_hf_config(
config: PretrainedConfig, *, llm_hidden_size: int, max_model_len: int
) -> "ParakeetConfig":
assert isinstance(config, PretrainedConfig)
return ParakeetConfig(
**config.to_dict(),
scale_input=False,
attention_bias=False,
llm_hidden_size=llm_hidden_size,
max_position_embeddings=max_model_len
+ 1, # + 1 because it seems like max_model_len+1 can be passed
)
@dataclass(kw_only=True, frozen=True)
class ExtractorConfig:
feature_size: int
sampling_rate: int
subsampling_factor: int
subsampling_conv_kernel_size: int
subsampling_conv_stride: int
clip_duration_s: int = 30
clip_min_duration_s: float = 0.1
@staticmethod
def from_hf_config(config: PretrainedConfig) -> "ExtractorConfig":
assert isinstance(config, PretrainedConfig)
return ExtractorConfig(
feature_size=config.num_mel_bins,
sampling_rate=config.sampling_rate,
subsampling_factor=config.subsampling_factor,
subsampling_conv_kernel_size=config.subsampling_conv_kernel_size,
subsampling_conv_stride=config.subsampling_conv_stride,
)