77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
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from transformers import PretrainedConfig
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from transformers.modeling_rope_utils import rope_config_validation
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class KORMoConfig(PretrainedConfig):
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model_type = "kormo"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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def __init__(
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self,
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vocab_size=112576,
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hidden_size=6144,
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intermediate_size=21504,
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num_hidden_layers=48,
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num_attention_heads=40,
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num_key_value_heads=8,
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hidden_act="silu",
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max_position_embeddings=131072,
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initializer_range=0.02,
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rms_norm_eps=1e-05,
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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=500000.0,
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attention_bias=False,
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attention_dropout=0.0,
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rope_scaling=None,
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mlp_bias=False,
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head_dim=128,
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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.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.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.mlp_bias = mlp_bias
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self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
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self.mask_type = None
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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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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