[Misc] Remove OLMo2 config copy (#17066)

Signed-off-by: Isotr0py <2037008807@qq.com>

vllm/model_executor/models/olmo2.py

@@ -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):

vllm/transformers_utils/config.py

@@ -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,

vllm/transformers_utils/configs/__init__.py

@@ -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",

vllm/transformers_utils/configs/olmo2.py

-# 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}")