# coding=utf-8 # Copyright 2024 Stability AI 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. """StableLM model configuration""" from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation from ...utils import logging logger = logging.get_logger(__name__) class StableLmConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`~StableLmModel`]. It is used to instantiate an StableLM 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 StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture. 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 StableLM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`StableLmModel`]. intermediate_size (`int`, *optional*, defaults to 6912): Dimension of the MLP representations. hidden_size (`int`, *optional*, defaults to 2560): Number of hidden layers in the Transformer decoder. 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 encoder. num_key_value_heads (`int`, *optional*, defaults to 32): 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). max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. 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. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE use_qkv_bias (`bool`, *optional*, defaults to `False`): Whether or not the model should use bias for qkv layers. qk_layernorm (`bool`, *optional*, defaults to `False`): Whether or not to normalize, per head, the Queries and Keys after projecting the hidden states. use_parallel_residual (`bool`, *optional*, defaults to `False`): Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training speedup at large scales. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio after applying the MLP to the hidden states. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. partial_rotary_factor (`float`, *optional*, defaults to 0.25): Percentage of the query and keys which will have rotary embedding. bos_token_id (int, *optional*, defaults to 0): The id of the `BOS` token in the vocabulary. eos_token_id (int, *optional*, defaults to 0): The id of the `EOS` token in the vocabulary. Example: ```python >>> from transformers import StableLmModel, StableLmConfig >>> # Initializing a StableLM stablelm-3b style configuration >>> configuration = StableLmConfig() ```""" model_type = "stablelm" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=50304, intermediate_size=6912, hidden_size=2560, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_act="silu", max_position_embeddings=4096, initializer_range=0.02, layer_norm_eps=1.0e-5, use_cache=True, tie_word_embeddings=False, rope_theta=10_000, rope_scaling=None, use_qkv_bias=False, qk_layernorm=False, use_parallel_residual=False, hidden_dropout=0.0, attention_dropout=0.0, partial_rotary_factor=0.25, bos_token_id=0, eos_token_id=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.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.use_qkv_bias = use_qkv_bias self.qk_layernorm = qk_layernorm self.use_parallel_residual = use_parallel_residual self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.partial_rotary_factor = partial_rotary_factor # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self) super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) __all__ = ["StableLmConfig"]