from typing import Optional from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) DEEPSEEK_FIXES_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class DeepseekFixedConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a DeepseekWithConcentrationekModel`]. It is used to instantiate an DeepSeek 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 DeepseekWithConcentration-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 102400): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DeepseekWithConcentrationModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1407): Dimension of the MoE 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. n_shared_experts (`int`, *optional*, defaults to None): Number of shared experts, None means dense model. n_routed_experts (`int`, *optional*, defaults to None): Number of routed experts, None means dense model. num_experts_per_tok (`int`, *optional*, defaults to None): Number of selected experts, None means dense model. moe_layer_freq (`int`, *optional*, defaults to 1): The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers. first_k_dense_replace (`int`, *optional*, defaults to 0): Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). \--k dense layers--/ norm_topk_prob (`bool`, *optional*, defaults to False): Whether to normalize the weights of the routed experts. scoring_func (`str`, *optional*, defaults to 'softmax'): Method of computing expert weights. aux_loss_alpha (`float`, *optional*, defaults to 0.001): Auxiliary loss weight coefficient. seq_aux = (`bool`, *optional*, defaults to True): Whether to compute the auxiliary loss for each individual sample. 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 decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): 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`. pad_token_id (`int`, *optional*): 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. 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/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 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. attention_bias (`bool`, defaults to `False`, *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. ```python >>> from transformers import DeepseekWithConcentrationModel, DeepseekWithConcentrationConfig >>> # Initializing a DeepseekWithConcentration DeepseekWithConcentration-7b style configuration >>> configuration = DeepseekWithConcentrationConfig() >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "deepseek_with_concentration" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=102400, hidden_size=4096, intermediate_size=11008, moe_intermediate_size = 1407, num_hidden_layers=30, num_attention_heads=32, num_key_value_heads=32, n_shared_experts = None, n_routed_experts = None, num_experts_per_tok = None, moe_layer_freq = 1, first_k_dense_replace = 0, norm_topk_prob = False, scoring_func = 'softmax', aux_loss_alpha = 0.001, seq_aux = True, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=None, bos_token_id=100000, eos_token_id=100001, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, moe_implementation="eager", **kwargs, ): assert moe_implementation in ('eager', 'megablocks'), "Invalid moe_implementation value. Choose from 'eager' or 'megablocks'." self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.moe_intermediate_size = moe_intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.n_shared_experts = n_shared_experts self.n_routed_experts = n_routed_experts self.num_experts_per_tok = num_experts_per_tok self.moe_layer_freq = moe_layer_freq self.first_k_dense_replace = first_k_dense_replace self.norm_topk_prob = norm_topk_prob self.scoring_func = scoring_func self.aux_loss_alpha = aux_loss_alpha self.seq_aux = seq_aux # for backward compatibility 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.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self._rope_scaling_validation() self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.moe_implementation = moe_implementation 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, ) def _rope_scaling_validation(self): """ Validate the `rope_scaling` configuration. """ if self.rope_scaling is None: return if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: raise ValueError( "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " f"got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_factor = self.rope_scaling.get("factor", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" ) if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")