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import math |
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from typing import Optional |
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from transformers import PretrainedConfig |
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class PhiConfig(PretrainedConfig): |
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model_type = "phi-msft" |
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attribute_map = { |
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"max_position_embeddings": "n_positions", |
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"hidden_size": "n_embd", |
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"num_attention_heads": "n_head", |
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"num_hidden_layers": "n_layer", |
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} |
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def __init__( |
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self, |
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vocab_size: int = 50304, |
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n_positions: int = 2048, |
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n_embd: int = 1024, |
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n_layer: int = 20, |
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n_inner: Optional[int] = None, |
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n_head: int = 16, |
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n_head_kv: Optional[int] = None, |
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num_experts_per_tok: int = 2, |
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num_local_experts: int = 4, |
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rotary_dim: Optional[int] = 32, |
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activation_function: Optional[str] = "gelu_new", |
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flash_attn: bool = False, |
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flash_rotary: bool = False, |
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fused_dense: bool = False, |
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attn_pdrop: float = 0.0, |
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embd_pdrop: float = 0.0, |
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resid_pdrop: float = 0.0, |
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layer_norm_epsilon: float = 1e-5, |
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initializer_range: float = 0.02, |
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tie_word_embeddings: bool = False, |
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pad_vocab_size_multiple: int = 64, |
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**kwargs |
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) -> None: |
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self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple) |
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self.n_positions = n_positions |
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self.n_embd = n_embd |
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self.n_layer = n_layer |
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self.n_inner = n_inner |
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self.n_head = n_head |
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self.n_head_kv = n_head_kv |
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self.num_experts_per_tok = num_experts_per_tok |
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self.num_local_experts = num_local_experts |
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self.rotary_dim = min(rotary_dim, n_embd // n_head) |
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self.activation_function = activation_function |
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self.flash_attn = flash_attn |
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self.flash_rotary = flash_rotary |
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self.fused_dense = fused_dense |
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self.attn_pdrop = attn_pdrop |
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self.embd_pdrop = embd_pdrop |
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self.resid_pdrop = resid_pdrop |
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self.layer_norm_epsilon = layer_norm_epsilon |
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self.initializer_range = initializer_range |
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) |
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