102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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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class DeepseekV3Config(PretrainedConfig):
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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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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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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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# for backward compatibility
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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 = rms_norm_eps
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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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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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