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