















"""InternLM3 model configuration"""

from transformers .configuration_utils import PretrainedConfig 
from transformers .modeling_rope_utils import rope_config_validation 
from transformers .utils import logging 


logger =logging .get_logger (__name__ )


class InternLM3Config (PretrainedConfig ):
    r"""
    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
    an InternLM2 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 InternLM2-7B.

    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 151936):
            Vocabulary size of the InternLM3 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`InternLM3Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 22016):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        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 `32`.
        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 32768):
            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-06):
            The epsilon used by the rms 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
        qkv_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key and value projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in o_proj, up_proj, down_proj and gate_proj layers.
        head_dim (`int`, *optional*):
            The attention head dimension. If None, it will default to hidden_size // num_heads

    ```python
    >>> from transformers import InternLM3Model, InternLM3Config

    >>> # Initializing a InternLM3 style configuration
    >>> configuration = InternLM3Config()

    >>> # Initializing a model from the InternLM3-8B style configuration
    >>> model = InternLM3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type ="internlm3"
    keys_to_ignore_at_inference =["past_key_values"]


    base_model_tp_plan ={
    "layers.*.self_attn.q_proj":"colwise",
    "layers.*.self_attn.k_proj":"colwise",
    "layers.*.self_attn.v_proj":"colwise",
    "layers.*.self_attn.o_proj":"rowwise",
    "layers.*.mlp.gate_proj":"colwise",
    "layers.*.mlp.up_proj":"colwise",
    "layers.*.mlp.down_proj":"rowwise",
    }

    def __init__ (
    self ,
    vocab_size =128512 ,
    hidden_size =4096 ,
    intermediate_size =11008 ,
    num_hidden_layers =32 ,
    num_attention_heads =32 ,
    num_key_value_heads =32 ,
    hidden_act ="silu",
    max_position_embeddings =32768 ,
    initializer_range =0.02 ,
    rms_norm_eps =1e-6 ,
    use_cache =True ,
    tie_word_embeddings =False ,
    rope_theta =10000.0 ,
    rope_scaling =None ,
    qkv_bias =False ,
    attention_dropout =0.0 ,
    bias =False ,
    head_dim =None ,
    **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 


        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 .rms_norm_eps =rms_norm_eps 
        self .use_cache =use_cache 
        self .rope_theta =rope_theta 
        self .rope_scaling =rope_scaling 
        self .qkv_bias =qkv_bias 
        self .attention_dropout =attention_dropout 
        self .bias =bias 
        self .head_dim =(
        head_dim 
        if head_dim is not None 
        else self .hidden_size //self .num_attention_heads 
        )


        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__ (
        tie_word_embeddings =tie_word_embeddings ,
        **kwargs ,
        )
