
    sg6*                     f    d Z ddlmZ ddlmZ ddlmZ  ej                  e      Z	 G d de      Z
y)zStableLM model configuration   )PretrainedConfig)rope_config_validation)loggingc                   Z     e Zd ZdZdZdgZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )StableLmConfiga	  
    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()
    ```stablelmpast_key_valuesc                    || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        || _        || _        || _        | j                  *d| j                  v r| j                  d   | j                  d<   t'        |        t)        | T  d|||d| y )Ntype	rope_type)bos_token_ideos_token_idtie_word_embeddings )
vocab_sizemax_position_embeddingshidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads
hidden_actinitializer_rangelayer_norm_eps	use_cache
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