xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-10 03:05:02 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Misc] Remove OLMo2 config copy (#17066)

Browse Source 
Signed-off-by: Isotr0py <2037008807@qq.com>
This commit is contained in:
Isotr0py 2025-04-24 21:14:32 +08:00 committed by GitHub
parent c0dfd97519
commit b0c1f6202d
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 4 additions and 176 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
vllm/model_executor/models/olmo2.py
View File

@ -28,6 +28,7 @@ from typing import Iterable, Optional, Tuple, Union
import torch
from torch import nn
from transformers import Olmo2Config
from vllm.attention import Attention
from vllm.config import VllmConfig
@ -51,7 +52,6 @@ from vllm.model_executor.models.utils import (
make_layers, maybe_prefix)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.olmo2 import Olmo2Config
class Olmo2Attention(nn.Module):

8
vllm/transformers_utils/config.py
View File

@ -36,10 +36,9 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
KimiVLConfig, MedusaConfig,
MllamaConfig, MLPSpeculatorConfig,
MPTConfig, NemotronConfig,
NVLM_D_Config, Olmo2Config,
RWConfig, SkyworkR1VChatConfig,
SolarConfig, Telechat2Config,
UltravoxConfig)
NVLM_D_Config, RWConfig,
SkyworkR1VChatConfig, SolarConfig,
Telechat2Config, UltravoxConfig)
# yapf: enable
from vllm.transformers_utils.utils import check_gguf_file
from vllm.utils import resolve_obj_by_qualname
@ -76,7 +75,6 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
"internvl_chat": InternVLChatConfig,
"nemotron": NemotronConfig,
"NVLM_D": NVLM_D_Config,
"olmo2": Olmo2Config,
"solar": SolarConfig,
"skywork_chat": SkyworkR1VChatConfig,
"telechat": Telechat2Config,

2
vllm/transformers_utils/configs/__init__.py
View File

@ -21,7 +21,6 @@ from vllm.transformers_utils.configs.moonvit import MoonViTConfig
from vllm.transformers_utils.configs.mpt import MPTConfig
from vllm.transformers_utils.configs.nemotron import NemotronConfig
from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
from vllm.transformers_utils.configs.olmo2 import Olmo2Config
from vllm.transformers_utils.configs.skyworkr1v import SkyworkR1VChatConfig
from vllm.transformers_utils.configs.solar import SolarConfig
from vllm.transformers_utils.configs.telechat2 import Telechat2Config
@ -46,7 +45,6 @@ __all__ = [
"KimiVLConfig",
"NemotronConfig",
"NVLM_D_Config",
"Olmo2Config",
"SkyworkR1VChatConfig",
"SolarConfig",
"Telechat2Config",

168
vllm/transformers_utils/configs/olmo2.py
View File

@ -1,168 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# yapf: disable
# ruff: noqa: E501
# coding=utf-8
# Copied from
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/olmo2/configuration_olmo2.py
"""OLMo 2 configuration."""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Olmo2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
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 [allenai/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-hf).
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 50304):
Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Olmo2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
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 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://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 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.
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 1):
Padding token id.
bos_token_id (`int`, *optional*):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 50279):
End of stream token id.
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.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
```python
>>> from transformers import Olmo2Model, Olmo2Config
>>> # Initializing a Olmo2 7B style configuration
>>> configuration = Olmo2Config()
>>> # Initializing a model from the Olmo2 7B style configuration
>>> model = Olmo2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "olmo2"
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_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
rms_norm_eps=1e-5,
**kwargs,
):
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
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.rms_norm_eps = rms_norm_eps
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 two fields, `type` and `factor`, " f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")