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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {} |
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class DeepseekV2Config(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek |
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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defaults will yield a similar configuration to that of the DeepSeek-V2 with multi-latent attention. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 102400): |
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Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`DeepseekV2Model`] |
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hidden_size (`int`, *optional*, defaults to 4096): |
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Dimension of the hidden representations. |
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intermediate_size (`int`, *optional*, defaults to 11008): |
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Dimension of the MLP representations. |
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moe_intermediate_size (`int`, *optional*, defaults to 1407): |
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Dimension of the MoE representations. |
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num_hidden_layers (`int`, *optional*, defaults to 32): |
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Number of hidden layers in the Transformer decoder. |
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num_attention_heads (`int`, *optional*, defaults to 32): |
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Number of attention heads for each attention layer in the Transformer decoder. |
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n_shared_experts (`int`, *optional*, defaults to None): |
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Number of shared experts, None means dense model. |
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n_routed_experts (`int`, *optional*, defaults to None): |
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Number of routed experts, None means dense model. |
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routed_scaling_factor (`float`, *optional*, defaults to 1.0): |
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Scaling factor or routed experts. |
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topk_method (`str`, *optional*, defaults to `gready`): |
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Topk method used in routed gate. |
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n_group (`int`, *optional*, defaults to None): |
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Number of groups for routed experts. |
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topk_group (`int`, *optional*, defaults to None): |
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Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups). |
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num_experts_per_tok (`int`, *optional*, defaults to None): |
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Number of selected experts, None means dense model. |
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moe_layer_freq (`int`, *optional*, defaults to 1): |
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The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers. |
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first_k_dense_replace (`int`, *optional*, defaults to 0): |
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Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). |
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\--k dense layers--/ |
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norm_topk_prob (`bool`, *optional*, defaults to False): |
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Whether to normalize the weights of the routed experts. |
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scoring_func (`str`, *optional*, defaults to 'softmax'): |
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Method of computing expert weights. |
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aux_loss_alpha (`float`, *optional*, defaults to 0.001): |
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Auxiliary loss weight coefficient. |
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seq_aux = (`bool`, *optional*, defaults to True): |
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Whether to compute the auxiliary loss for each individual sample. |
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num_key_value_heads (`int`, *optional*): |
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If |
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if |
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When |
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed |
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by meanpooling all the original heads within that group. For more details checkout [this |
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to |
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`num_attention_heads`. |
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): |
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The non-linear activation function (function or string) in the decoder. |
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max_position_embeddings (`int`, *optional*, defaults to 2048): |
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The maximum sequence length that this model might ever be used with. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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rms_norm_eps (`float`, *optional*, defaults to 1e-06): |
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The epsilon used by the rms normalization layers. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). Only |
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relevant if `config.is_decoder=True`. |
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pad_token_id (`int`, *optional*): |
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Padding token id. |
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bos_token_id (`int`, *optional*, defaults to 1): |
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Beginning of stream token id. |
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eos_token_id (`int`, *optional*, defaults to 2): |
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End of stream token id. |
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pretraining_tp (`int`, *optional*, defaults to 1): |
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this |
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document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is |
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this |
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issue](https://github.com/pytorch/pytorch/issues/76232). |
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tie_word_embeddings (`bool`, *optional*, defaults to `False`): |
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Whether to tie weight embeddings |
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rope_theta (`float`, *optional*, defaults to 10000.0): |
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The base period of the RoPE embeddings. |
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rope_scaling (`Dict`, *optional*): |
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling |
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is |
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update |
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`max_position_embeddings` to the expected new maximum. |
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): |
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Whether to use a bias in the query, key, value and output projection layers during self-attention. |
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attention_dropout (`float`, *optional*, defaults to 0.0): |
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The dropout ratio for the attention probabilities. |
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use_mla (`bool`, *optional*, defaults to `True`): Use multi-latent attention or multi-head attention. If True, |
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the model will use multi-latent attention, otherwise, it will use multi-head attention. |
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```python |
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>>> from transformers import DeepseekV2Model, DeepseekV2Config |
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>>> # Initializing a Deepseek-V2 style configuration |
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>>> configuration = DeepseekV2Config() |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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model_type = "deepseek_v2" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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vocab_size=102400, |
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hidden_size=4096, |
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intermediate_size=11008, |
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moe_intermediate_size = 1407, |
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num_hidden_layers=30, |
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num_attention_heads=32, |
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num_key_value_heads=32, |
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n_shared_experts = None, |
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n_routed_experts = None, |
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ep_size = 1, |
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routed_scaling_factor = 1.0, |
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kv_lora_rank = 512, |
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q_lora_rank = 1536, |
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qk_rope_head_dim = 64, |
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v_head_dim = 128, |
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qk_nope_head_dim = 128, |
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topk_method = 'gready', |
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n_group = None, |
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topk_group = None, |
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num_experts_per_tok = None, |
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moe_layer_freq = 1, |
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first_k_dense_replace = 0, |
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norm_topk_prob = False, |
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scoring_func = 'softmax', |
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aux_loss_alpha = 0.001, |
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seq_aux = True, |
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hidden_act="silu", |
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max_position_embeddings=2048, |
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initializer_range=0.02, |
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rms_norm_eps=1e-6, |
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use_cache=True, |
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pad_token_id=None, |
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bos_token_id=100000, |
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eos_token_id=100001, |
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pretraining_tp=1, |
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tie_word_embeddings=False, |
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rope_theta=10000.0, |
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rope_scaling=None, |
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attention_bias=False, |
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attention_dropout=0.0, |
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use_mla=True, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.max_position_embeddings = max_position_embeddings |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.moe_intermediate_size = moe_intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.n_shared_experts = n_shared_experts |
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self.n_routed_experts = n_routed_experts |
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self.ep_size = ep_size |
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self.routed_scaling_factor = routed_scaling_factor |
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self.kv_lora_rank = kv_lora_rank |
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self.q_lora_rank = q_lora_rank |
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self.qk_rope_head_dim = qk_rope_head_dim |
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self.v_head_dim = v_head_dim |
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self.qk_nope_head_dim = qk_nope_head_dim |
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self.topk_method = topk_method |
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self.n_group = n_group |
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self.topk_group = topk_group |
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self.num_experts_per_tok = num_experts_per_tok |
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self.moe_layer_freq = moe_layer_freq |
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self.first_k_dense_replace = first_k_dense_replace |
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self.norm_topk_prob = norm_topk_prob |
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self.scoring_func = scoring_func |
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self.aux_loss_alpha = aux_loss_alpha |
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self.seq_aux = seq_aux |
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if num_key_value_heads is None: |
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num_key_value_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.hidden_act = hidden_act |
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self.initializer_range = initializer_range |
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self.rms_norm_eps = float(rms_norm_eps) |
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self.pretraining_tp = pretraining_tp |
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self.use_cache = use_cache |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.attention_bias = attention_bias |
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self.attention_dropout = attention_dropout |
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self.use_mla = use_mla |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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