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# coding=utf-8
from transformers.configuration_utils import PretrainedConfig
from open_clip import get_model_config
import math
from typing import Optional
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 = 51200,
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)
class LlavaConfig(PretrainedConfig):
model_type = "llava"
is_composition = False
def __init__(
self,
text_config=None,
vision_tower_name="ViT-SO400M-14-SigLIP-384",
ignore_index=-100,
image_token_index=50297,
projector_hidden_act="gelu",
projector_tokens_num=1,
vocab_size=51200,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.projector_tokens_num = projector_tokens_num
self.vocab_size = vocab_size
self.vision_tower_name = vision_tower_name
vision_config = get_model_config(vision_tower_name)
self.vision_embed_dim = vision_config["embed_dim"]
self.vocab_size = self.vocab_size
self.text_config = text_config
if isinstance(self.text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
self.text_config = PhiConfig(**text_config)
self.vocab_size = self.text_config.vocab_size
super().__init__(**kwargs) |