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| # Copyright (c) Microsoft Corporation. | |
| # Licensed under the MIT license. | |
| import math | |
| from typing import Optional | |
| from transformers import PretrainedConfig | |
| class PhiConfig(PretrainedConfig): | |
| """Phi configuration.""" | |
| model_type = "phi-msft" | |
| 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) | |