# coding=utf-8 # Copyright 2024 BigCode 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. """ Starcoder2 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) from ..deprecated._archive_maps import STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 class Starcoder2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a Starcoder2 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 [bigcode/starcoder2-7b_16k](https://huggingface.co/bigcode/starcoder2-7b_16k) model. 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 49152): Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Starcoder2Model`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 12288): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 24): 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 `8`. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): 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. Starcoder2'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. norm_epsilon (`float`, *optional*, defaults to 1e-05): Epsilon value for the layer norm 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`. bos_token_id (`int`, *optional*, defaults to 50256): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 50256): The id of the "end-of-sequence" token. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `None` (no sliding window). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. residual_dropout (`float`, *optional*, defaults to 0.0): Residual connection dropout value. embedding_dropout (`float`, *optional*, defaults to 0.0): Embedding dropout. use_bias (`bool`, *optional*, defaults to `True`): Whether to use bias term on linear layers of the model. ```python >>> from transformers import Starcoder2Model, Starcoder2Config >>> # Initializing a Starcoder2 7B style configuration >>> configuration = Starcoder2Config() >>> # Initializing a model from the Starcoder2 7B style configuration >>> model = Starcoder2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "starcoder2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=49152, hidden_size=3072, intermediate_size=12288, num_hidden_layers=30, num_attention_heads=24, num_key_value_heads=2, hidden_act="gelu_pytorch_tanh", max_position_embeddings=4096, initializer_range=0.018042, norm_epsilon=1e-5, use_cache=True, bos_token_id=50256, eos_token_id=50256, rope_theta=10000.0, sliding_window=None, attention_dropout=0.0, residual_dropout=0.0, embedding_dropout=0.0, use_bias=True, **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 self.use_bias = use_bias self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.norm_epsilon = norm_epsilon self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.residual_dropout = residual_dropout self.embedding_dropout = embedding_dropout super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, )