configuration_deepseek.py
10.4 KB · 215 lines · python Raw
1 # Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py
2
3 from transformers.configuration_utils import PretrainedConfig
4 from transformers.utils import logging
5
6 logger = logging.get_logger(__name__)
7
8 DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
9
10
11 class DeepseekV3Config(PretrainedConfig):
12 r"""
13 This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
14 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
15 defaults will yield a similar configuration to that of the DeepSeek-V3.
16
17 Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
18 documentation from [`PretrainedConfig`] for more information.
19
20
21 Args:
22 vocab_size (`int`, *optional*, defaults to 129280):
23 Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
24 `inputs_ids` passed when calling [`DeepseekV3Model`]
25 hidden_size (`int`, *optional*, defaults to 4096):
26 Dimension of the hidden representations.
27 intermediate_size (`int`, *optional*, defaults to 11008):
28 Dimension of the MLP representations.
29 moe_intermediate_size (`int`, *optional*, defaults to 1407):
30 Dimension of the MoE representations.
31 num_hidden_layers (`int`, *optional*, defaults to 32):
32 Number of hidden layers in the Transformer decoder.
33 num_nextn_predict_layers (`int`, *optional*, defaults to 1):
34 Number of nextn predict layers in the DeepSeekV3 Model.
35 num_attention_heads (`int`, *optional*, defaults to 32):
36 Number of attention heads for each attention layer in the Transformer decoder.
37 n_shared_experts (`int`, *optional*, defaults to None):
38 Number of shared experts, None means dense model.
39 n_routed_experts (`int`, *optional*, defaults to None):
40 Number of routed experts, None means dense model.
41 routed_scaling_factor (`float`, *optional*, defaults to 1.0):
42 Scaling factor or routed experts.
43 topk_method (`str`, *optional*, defaults to `gready`):
44 Topk method used in routed gate.
45 n_group (`int`, *optional*, defaults to None):
46 Number of groups for routed experts.
47 topk_group (`int`, *optional*, defaults to None):
48 Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
49 num_experts_per_tok (`int`, *optional*, defaults to None):
50 Number of selected experts, None means dense model.
51 moe_layer_freq (`int`, *optional*, defaults to 1):
52 The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
53 first_k_dense_replace (`int`, *optional*, defaults to 0):
54 Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
55 \--k dense layers--/
56 norm_topk_prob (`bool`, *optional*, defaults to False):
57 Whether to normalize the weights of the routed experts.
58 scoring_func (`str`, *optional*, defaults to 'softmax'):
59 Method of computing expert weights.
60 aux_loss_alpha (`float`, *optional*, defaults to 0.001):
61 Auxiliary loss weight coefficient.
62 seq_aux = (`bool`, *optional*, defaults to True):
63 Whether to compute the auxiliary loss for each individual sample.
64 num_key_value_heads (`int`, *optional*):
65 This is the number of key_value heads that should be used to implement Grouped Query Attention. If
66 `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
67 `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
68 converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
69 by meanpooling all the original heads within that group. For more details checkout [this
70 paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
71 `num_attention_heads`.
72 hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
73 The non-linear activation function (function or string) in the decoder.
74 max_position_embeddings (`int`, *optional*, defaults to 2048):
75 The maximum sequence length that this model might ever be used with.
76 initializer_range (`float`, *optional*, defaults to 0.02):
77 The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
78 rms_norm_eps (`float`, *optional*, defaults to 1e-06):
79 The epsilon used by the rms normalization layers.
80 use_cache (`bool`, *optional*, defaults to `True`):
81 Whether or not the model should return the last key/values attentions (not used by all models). Only
82 relevant if `config.is_decoder=True`.
83 pad_token_id (`int`, *optional*):
84 Padding token id.
85 bos_token_id (`int`, *optional*, defaults to 1):
86 Beginning of stream token id.
87 eos_token_id (`int`, *optional*, defaults to 2):
88 End of stream token id.
89 pretraining_tp (`int`, *optional*, defaults to 1):
90 Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
91 document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
92 necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
93 issue](https://github.com/pytorch/pytorch/issues/76232).
94 tie_word_embeddings (`bool`, *optional*, defaults to `False`):
95 Whether to tie weight embeddings
96 rope_theta (`float`, *optional*, defaults to 10000.0):
97 The base period of the RoPE embeddings.
98 rope_scaling (`Dict`, *optional*):
99 Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
100 strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
101 `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
102 `max_position_embeddings` to the expected new maximum.
103 attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
104 Whether to use a bias in the query, key, value and output projection layers during self-attention.
105 attention_dropout (`float`, *optional*, defaults to 0.0):
106 The dropout ratio for the attention probabilities.
107
108 ```python
109 >>> from transformers import DeepseekV3Model, DeepseekV3Config
110
111 >>> # Initializing a Deepseek-V3 style configuration
112 >>> configuration = DeepseekV3Config()
113
114 >>> # Accessing the model configuration
115 >>> configuration = model.config
116 ```"""
117
118 model_type = "deepseek_v3"
119 keys_to_ignore_at_inference = ["past_key_values"]
120
121 def __init__(
122 self,
123 vocab_size=129280,
124 hidden_size=7168,
125 intermediate_size=18432,
126 moe_intermediate_size=2048,
127 num_hidden_layers=61,
128 num_nextn_predict_layers=1,
129 num_attention_heads=128,
130 num_key_value_heads=128,
131 n_shared_experts=1,
132 n_routed_experts=256,
133 ep_size=1,
134 routed_scaling_factor=2.5,
135 kv_lora_rank=512,
136 q_lora_rank=1536,
137 qk_rope_head_dim=64,
138 v_head_dim=128,
139 qk_nope_head_dim=128,
140 topk_method='noaux_tc',
141 n_group=8,
142 topk_group=4,
143 num_experts_per_tok=8,
144 moe_layer_freq=1,
145 first_k_dense_replace=3,
146 norm_topk_prob=True,
147 scoring_func='sigmoid',
148 aux_loss_alpha=0.001,
149 seq_aux=True,
150 hidden_act="silu",
151 max_position_embeddings=4096,
152 initializer_range=0.02,
153 rms_norm_eps=1e-6,
154 use_cache=True,
155 pad_token_id=None,
156 bos_token_id=0,
157 eos_token_id=1,
158 pretraining_tp=1,
159 tie_word_embeddings=False,
160 rope_theta=10000.0,
161 rope_scaling=None,
162 attention_bias=False,
163 attention_dropout=0.0,
164 **kwargs,
165 ):
166 self.vocab_size = vocab_size
167 self.max_position_embeddings = max_position_embeddings
168 self.hidden_size = hidden_size
169 self.intermediate_size = intermediate_size
170 self.moe_intermediate_size = moe_intermediate_size
171 self.num_hidden_layers = num_hidden_layers
172 self.num_nextn_predict_layers = num_nextn_predict_layers
173 self.num_attention_heads = num_attention_heads
174 self.n_shared_experts = n_shared_experts
175 self.n_routed_experts = n_routed_experts
176 self.ep_size = ep_size
177 self.routed_scaling_factor = routed_scaling_factor
178 self.kv_lora_rank = kv_lora_rank
179 self.q_lora_rank = q_lora_rank
180 self.qk_rope_head_dim = qk_rope_head_dim
181 self.v_head_dim = v_head_dim
182 self.qk_nope_head_dim = qk_nope_head_dim
183 self.topk_method = topk_method
184 self.n_group = n_group
185 self.topk_group = topk_group
186 self.num_experts_per_tok = num_experts_per_tok
187 self.moe_layer_freq = moe_layer_freq
188 self.first_k_dense_replace = first_k_dense_replace
189 self.norm_topk_prob = norm_topk_prob
190 self.scoring_func = scoring_func
191 self.aux_loss_alpha = aux_loss_alpha
192 self.seq_aux = seq_aux
193 # for backward compatibility
194 if num_key_value_heads is None:
195 num_key_value_heads = num_attention_heads
196
197 self.num_key_value_heads = num_key_value_heads
198 self.hidden_act = hidden_act
199 self.initializer_range = initializer_range
200 self.rms_norm_eps = rms_norm_eps
201 self.pretraining_tp = pretraining_tp
202 self.use_cache = use_cache
203 self.rope_theta = rope_theta
204 self.rope_scaling = rope_scaling
205 self.attention_bias = attention_bias
206 self.attention_dropout = attention_dropout
207
208 super().__init__(
209 pad_token_id=pad_token_id,
210 bos_token_id=bos_token_id,
211 eos_token_id=eos_token_id,
212 tie_word_embeddings=tie_word_embeddings,
213 **kwargs,
214 )
215