from transformers.utils import logging from transformers.models.llama import LlamaConfig logger = logging.get_logger(__name__) class SLModelConfig(LlamaConfig): r""" This is the configuration class to store the configuration of a [`SLModelModel`]. It is used to instantiate an SLModel model according to the specified arguments, defining the model 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 128256): Vocabulary size of the SLModel model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SLModel`] hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. 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 encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 8192): 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. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): End of stream token id. pad_token_id (`int`, *optional*, defaults to 128001): Padding token id. mask_token_id (`int`, *optional*, defaults to 128002): Mask token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 250000.0): 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'], 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'. 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 attention_bias (`bool`, *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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads classifier_pooling (`str`, *optional*, defaults to `"late"`): The pooling strategy to use for the classifier. Can be one of ['mean', 'eos']. retrieval_pooling (`str`, *optional*, defaults to `"late"`): The pooling strategy to use for the retriever. Can be one of ['mean', 'eos']. """ model_type = "sl_model" def __init__( self, vocab_size=128256, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-05, bos_token_id=128000, eos_token_id=128001, pad_token_id=128001, mask_token_id=128002, pretraining_tp=1, tie_word_embeddings=False, rope_theta=250000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, mlp_bias=False, head_dim=None, classifier_pooling="mean", retrieval_pooling="mean", is_causal=False, **kwargs, ): if num_key_value_heads is None: num_key_value_heads = num_attention_heads if "use_cache" in kwargs: kwargs.pop("use_cache", None) super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, intermediate_size=intermediate_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, hidden_act=hidden_act, max_position_embeddings=max_position_embeddings, initializer_range=initializer_range, rms_norm_eps=rms_norm_eps, use_cache=False, bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, pretraining_tp=pretraining_tp, tie_word_embeddings=tie_word_embeddings, rope_theta=rope_theta, rope_scaling=rope_scaling, attention_bias=attention_bias, attention_dropout=attention_dropout, mlp_bias=mlp_bias, head_dim=head_dim, **kwargs, ) self.mask_token_id = mask_token_id self.classifier_pooling = classifier_pooling self.retrieval_pooling = retrieval_pooling self.is_causal = is_causal __all__ = ["SLModelConfig"]