o ¹ÈÏi’*ã@sLdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zStarcoder2 model configurationé)ÚPretrainedConfig)Úrope_config_validation)ÚloggingcsŒeZdZdZdZdgZdddddddœZdgdgfd d gd gfd gd gfd œZ                  d‡fdd„ Z‡Z S)ÚStarcoder2Configa§ This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a Starcoder2 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 [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b) model. 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 49152): Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Starcoder2Model`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 12288): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 24): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 2): 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 `8`. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. Starcoder2's sliding window attention allows sequence of up to 4096*32 tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. norm_epsilon (`float`, *optional*, defaults to 1e-05): Epsilon value for the layer norm 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`. bos_token_id (`int`, *optional*, defaults to 50256): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 50256): The id of the "end-of-sequence" token. 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 sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `None` (no sliding window). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. residual_dropout (`float`, *optional*, defaults to 0.0): Residual connection dropout value. embedding_dropout (`float`, *optional*, defaults to 0.0): Embedding dropout. use_bias (`bool`, *optional*, defaults to `True`): Whether to use bias term on linear layers of the model. ```python >>> from transformers import Starcoder2Model, Starcoder2Config >>> # Initializing a Starcoder2 7B style configuration >>> configuration = Starcoder2Config() >>> # Initializing a model from the Starcoder2 7B style configuration >>> model = Starcoder2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Ú starcoder2Úpast_key_valuesÚcolwiseÚrowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.c_fczlayers.*.mlp.c_projÚ input_idsÚ inputs_embedsÚ hidden_statesÚattention_mask)Ú embed_tokensÚlayersÚnorméÀé é0éééÚgelu_pytorch_tanhéçŸVÑšy’?çñh㈵øä>TéPÄçˆÃ@Nçc s´||_||_||_||_||_||_||_||_||_||_ | |_ | |_ | |_ ||_ ||_||_||_||_|jdurHd|jvrH|jd|jd<t|ƒtƒjd| | dœ|¤ŽdS)NÚtypeÚ rope_type)Ú bos_token_idÚ eos_token_id©)Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚsliding_windowÚuse_biasÚnum_key_value_headsÚ hidden_actÚinitializer_rangeÚ norm_epsilonÚ use_cacheÚ rope_thetaÚ rope_scalingÚattention_dropoutÚresidual_dropoutÚembedding_dropoutrÚsuperÚ__init__)Úselfr#r%r&r'r(r+r,r$r-r.r/r r!r0r1r)r2r3r4r*Úkwargs©Ú __class__r"úk/home/ubuntu/.local/lib/python3.10/site-packages/transformers/models/starcoder2/configuration_starcoder2.pyr6˜s6þ ýzStarcoder2Config.__init__)rrrrrrrrrrTrrrNNrrrT) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planr6Ú __classcell__r"r"r9r;rsHmú  ýërN) r?Úconfiguration_utilsrÚmodeling_rope_utilsrÚutilsrÚ get_loggerr<ÚloggerrÚ__all__r"r"r"r;Ús     7