from typing import Optional from transformers import PretrainedConfig from typing import Optional, TypedDict class RopeParameters(TypedDict, total=False): """ Args: rope_theta (`float`): The base period of the RoPE embeddings. rope_type (`str`, *optional*, defaults to "default"): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. partial_rotary_factor (`float`, *optional*): The percentage of the query and key head embedding on which RoPE will be applied. 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 'yarn', '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 """ rope_theta: float rope_type: str | None partial_rotary_factor: float | None factor: float | None original_max_position_embeddings: int | None attention_factor: float | None beta_fast: float | None beta_slow: float | None short_factor: list[float] | None long_factor: list[float] | None low_freq_factor: float | None high_freq_factor: float | None class Lfm2Prototype1Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Lfm2Model`]. It is used to instantiate a LFM2 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 LFM2-1.2B model. e.g. [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B) 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 65536): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Lfm2Model`] hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 12288): Dimension of the MLP representations. 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 decoder. num_key_value_heads (`int`, *optional*, defaults to 8): 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. max_position_embeddings (`int`, *optional*, defaults to 128000): The maximum sequence length that this model might ever be used with. Lfm2 1 supports up to 2048 tokens, Lfm2 2 up to 4096, CodeLfm2 up to 16384. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. norm_eps (`float`, *optional*, defaults to 1e-05): 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`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. conv_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the conv layers. conv_L_cache (`int`, *optional*, defaults to 3): L_cache dim in the conv layers. block_multiple_of (`int`, *optional*, defaults to 256): Multiple for the `intermediate_size`. block_ffn_dim_multiplier (`float`, *optional*, defaults to 1.0): Multiplier for the `intermediate_size`. block_auto_adjust_ff_dim (`bool`, *optional*, defaults to `True`): Whether to adjust the dim of the `intermediate_size`. full_attn_idxs (`Optional`, *optional*): Index of the layers which use attention. layer_types (`Optional`, *optional*): Type of each layers. ```python >>> from transformers import Lfm2Model, Lfm2Config >>> # Initializing a LFM2 model >>> configuration = Lfm2Config() >>> # Initializing a model from the LFM2-1.2B style configuration >>> model = Lfm2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "lfm2" keys_to_ignore_at_inference = ["past_key_values"] default_theta = 1000000.0 def __init__( self, vocab_size: Optional[int] = 65536, hidden_size: Optional[int] = 2560, intermediate_size: Optional[int] = 12288, num_hidden_layers: Optional[int] = 32, num_attention_heads: Optional[int] = 32, num_key_value_heads: Optional[int] = 8, max_position_embeddings: Optional[int] = 128_000, initializer_range: Optional[float] = 0.02, norm_eps: Optional[float] = 0.00001, use_cache: Optional[bool] = True, pad_token_id: Optional[int] = 0, bos_token_id: Optional[int] = 1, eos_token_id: Optional[int] = 2, tie_word_embeddings: Optional[bool] = True, rope_parameters: Optional[RopeParameters | dict[str, RopeParameters]] = None, conv_bias: Optional[bool] = False, conv_L_cache: Optional[int] = 3, block_multiple_of: Optional[int] = 256, block_ffn_dim_multiplier: Optional[float] = 1.0, block_auto_adjust_ff_dim: Optional[bool] = True, full_attn_idxs: Optional[list[int]] = None, layer_types: Optional[list[str]] = None, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.max_position_embeddings = max_position_embeddings self.use_cache = use_cache self.norm_eps = norm_eps self.initializer_range = initializer_range # attn operator config self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads # custom operator config self.conv_bias = conv_bias self.conv_L_cache = conv_L_cache # MLP config self.intermediate_size = kwargs.get("block_ff_dim", intermediate_size) # to fit original config keys self.block_multiple_of = block_multiple_of self.block_ffn_dim_multiplier = block_ffn_dim_multiplier self.block_auto_adjust_ff_dim = block_auto_adjust_ff_dim self.layer_types = layer_types if self.layer_types is None: full_attn_idxs = full_attn_idxs if full_attn_idxs is not None else list(range(num_hidden_layers)) self.layer_types = ["full_attention" if i in full_attn_idxs else "conv" for i in range(num_hidden_layers)] self.rope_parameters = rope_parameters tie_word_embeddings = kwargs.get("tie_embedding", tie_word_embeddings) # to fit original config keys super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) __all__ = ["Lfm2Config"]