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import math
from transformers import PretrainedConfig
class Phi2Config(PretrainedConfig):
model_type = "phi2" # not necessary unless you want to register model with auto classes
attribute_map = {
"max_position_embeddings": "initial_cos_sin_cache_len",
"hidden_size": "d_embedding",
"num_attention_heads": "n_attn_heads",
"num_hidden_layers": "n_attn_blocks",
}
def __init__(
self,
vocab_size: int, # this includes the extra tokens included by Phi2 in tokenizer_config.json
vocab_chunk_for_gpu_efficiency: int,
initial_cos_sin_cache_len: int,
d_embedding: int,
n_attn_blocks: int,
n_attn_heads: int,
use_flash_attn: bool,
use_flash_rotary: bool,
use_fused_dense: bool,
attn_pdrop: float,
embd_pdrop: float,
resid_pdrop: float,
layer_norm_epsilon: float,
weight_initialization_range: float,
tie_word_embeddings: bool, # whether embedding weights are shared between the encoder and decoder
checkpointing: bool, # whether to use gradient checkpointing to reduce memory usage (I think)
**kwargs
) -> None:
self.vocab_size = (
math.ceil(
vocab_size / vocab_chunk_for_gpu_efficiency
) * vocab_chunk_for_gpu_efficiency
)
self.initial_cos_sin_cache_len = initial_cos_sin_cache_len
self.d_embedding = d_embedding
self.n_attn_blocks = n_attn_blocks
self.n_attn_heads = n_attn_heads
self.use_flash_attn = use_flash_attn
self.use_flash_rotary = use_flash_rotary
self.use_fused_dense = use_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.weight_initialization_range = weight_initialization_range
self.checkpointing = checkpointing
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
if __name__ == "__main__":
phi2_config = Phi2Config()
# phi2_config.save_pretrained("phi2_config")
# phi2_config = Phi2Config.from_pretrained("phi2_config")
# phi2_config.push_to_hub("phi2_config")
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