| |
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| |
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| | from transformers.configuration_utils import PretrainedConfig
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| | from transformers.utils import logging
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| |
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| | logger = logging.get_logger(__name__)
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| |
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| | DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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| | class DeepseekV3Config(PretrainedConfig):
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| | r"""
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| | This is the configuration class to store the configuration of a [`DeepseekV3Model`]. 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-V3.
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| |
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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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| |
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| |
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| | Args:
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| | vocab_size (`int`, *optional*, defaults to 129280):
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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 [`DeepseekV3Model`]
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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_nextn_predict_layers (`int`, *optional*, defaults to 1):
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| | Number of nextn predict layers in the DeepSeekV3 Model.
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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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| |
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| | ```python
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| | >>> from transformers import DeepseekV3Model, DeepseekV3Config
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| |
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| | >>> # Initializing a Deepseek-V3 style configuration
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| | >>> configuration = DeepseekV3Config()
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| |
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| | >>> # Accessing the model configuration
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| | >>> configuration = model.config
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| | ```"""
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| |
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| | model_type = "deepseek_v3"
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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=129280,
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| | hidden_size=7168,
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| | intermediate_size=18432,
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| | moe_intermediate_size = 2048,
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| | num_hidden_layers=61,
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| | num_nextn_predict_layers=1,
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| | num_attention_heads=128,
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| | num_key_value_heads=128,
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| | n_shared_experts = 1,
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| | n_routed_experts = 256,
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| | ep_size = 1,
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| | routed_scaling_factor = 2.5,
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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 = 'noaux_tc',
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| | n_group = 8,
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| | topk_group = 4,
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| | num_experts_per_tok = 8,
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| | moe_layer_freq = 1,
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| | first_k_dense_replace = 3,
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| | norm_topk_prob = True,
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| | scoring_func = 'sigmoid',
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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=4096,
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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=0,
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| | eos_token_id=1,
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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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| | **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_nextn_predict_layers = num_nextn_predict_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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| |
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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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| |
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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 = 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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| |
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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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| | ) |
