diff --git a/vllm/model_executor/models/olmo2.py b/vllm/model_executor/models/olmo2.py index e7a5f91a3dae..44beae5726dc 100644 --- a/vllm/model_executor/models/olmo2.py +++ b/vllm/model_executor/models/olmo2.py @@ -28,6 +28,7 @@ import torch from torch import nn +from transformers import Olmo2Config from vllm.attention import Attention from vllm.config import VllmConfig @@ -51,7 +52,6 @@ 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): diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 4e2a31ce6729..c9f9af45044e 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -36,10 +36,9 @@ 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 @@ "internvl_chat": InternVLChatConfig, "nemotron": NemotronConfig, "NVLM_D": NVLM_D_Config, - "olmo2": Olmo2Config, "solar": SolarConfig, "skywork_chat": SkyworkR1VChatConfig, "telechat": Telechat2Config, diff --git a/vllm/transformers_utils/configs/__init__.py b/vllm/transformers_utils/configs/__init__.py index 739eea5cba51..8812d4c484b1 100644 --- a/vllm/transformers_utils/configs/__init__.py +++ b/vllm/transformers_utils/configs/__init__.py @@ -21,7 +21,6 @@ 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 @@ "KimiVLConfig", "NemotronConfig", "NVLM_D_Config", - "Olmo2Config", "SkyworkR1VChatConfig", "SolarConfig", "Telechat2Config", diff --git a/vllm/transformers_utils/configs/olmo2.py b/vllm/transformers_utils/configs/olmo2.py deleted file mode 100644 index c6e446333b43..000000000000 --- a/vllm/transformers_utils/configs/olmo2.py +++ /dev/null @@ -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}")