"""Deepseek Moe model configuration""" from transformers.utils import logging from transformers.configuration_utils import PretrainedConfig from transformers.modeling_rope_utils import rope_config_validation logger = logging.get_logger(__name__) DEEPSEEK_FIXES_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class DeepseekConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a DeepseekModel`]. 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 DeepseekModel-20b. 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 128256): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DeepseekModel`] 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 1792): 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/main/perf_train_gpu_many#tensor-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. 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 attention_bias (`bool`, *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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_heads ```python >>> from transformers import DeepseekModel, DeepseekConfig >>> configuration = DeepseekConfig() >>> model = DeepseekModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "deepseek" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=128256, hidden_size=2048, intermediate_size=14336, moe_intermediate_size = 1792, num_hidden_layers=28, num_attention_heads=16, num_key_value_heads=8, 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=1, eos_token_id=2, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, moe_implementation="eager", mlp_bias=False, head_dim=None, **kwargs, ): assert moe_implementation in ('eager', ), "Invalid moe_implementation value." 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.attention_bias = attention_bias self.attention_dropout = attention_dropout self.mlp_bias = mlp_bias self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, copy it it to 'rope_type'. 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) 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, )