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from transformers.configuration_utils import PretrainedConfig |
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from loguru import logger as logging |
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class BuddyGPTConfig(PretrainedConfig): |
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""" TinyLLM 配置文件 |
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""" |
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model_type = "buddygpt" |
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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=151669, |
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hidden_size=4096, |
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intermediate_size=4096, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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num_key_value_heads=None, |
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hidden_act="silu", |
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num_seq_len=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=None, |
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eos_token_id=None, |
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tie_word_embeddings=False, |
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rope_theta=10000.0, |
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attention_dropout=0.0, |
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_attn_implementation="sdpa", |
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q_lora_rank: int = 16, |
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qk_rope_head_dim: int = 4, |
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kv_lora_rank: int = 16, |
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v_head_dim: int = 16, |
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qk_nope_head_dim: int = 12, |
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n_expert=None, |
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n_expert_per_token=2, |
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n_group=2, |
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n_topk_group=1, |
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norm_topk_prob=True, |
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routed_scaling_factor=0.2, |
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scoring_func='sigmoid', |
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topk_method='noaux_tc', |
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moe_intermediate_size=10, |
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n_shared_experts=2, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.num_seq_len = num_seq_len |
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self.hidden_size = hidden_size |
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self.intermediate_size = 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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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.attention_dropout = attention_dropout |
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self._attn_implementation = _attn_implementation |
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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.kv_lora_rank = kv_lora_rank |
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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.n_expert = n_expert |
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self.n_expert_per_token = n_expert_per_token |
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self.n_group = n_group |
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self.n_topk_group = n_topk_group |
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self.norm_topk_prob = norm_topk_prob |
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self.routed_scaling_factor=routed_scaling_factor |
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self.scoring_func = scoring_func |
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self.topk_method = topk_method |
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self.moe_intermediate_size = moe_intermediate_size |
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self.n_shared_experts = n_shared_experts |
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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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) |
