Spaces:
Paused
Paused
File size: 4,839 Bytes
7396aab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
import torch
from transformers import PretrainedConfig, BitsAndBytesConfig
import math
from typing import Optional
class VisionProjectorConfig(PretrainedConfig):
def __init__(
self,
input_dim=768,
hidden_dim=256,
num_tokens=1,
output_dim=2560,
**kwargs
):
#super.__init__(**kwargs)
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.num_tokens = num_tokens
self.kwargs = kwargs
class CustomPhiConfig(PretrainedConfig):
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 = 2560,
n_layer: int = 32,
n_inner: Optional[int] = None,
n_head: int = 32,
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 = 0.1,
layer_norm_epsilon: float = 1e-05,
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 CLIPVisionToPhiConfig(PretrainedConfig):
def __init__(self,
vision_projector_config: VisionProjectorConfig,
phi_config: CustomPhiConfig,
**kwargs
):
#super().__init__(**kwargs)
self.vision_projector_config = vision_projector_config
self.phi_config = phi_config
self.tokenizer = kwargs.get('tokenizer')
self.freeze_phi_model = True
'''
phi_config_obj = CustomPhiConfig(
**{
"_name_or_path": "microsoft/phi-2",
"architectures": [
"PhiForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_phi.PhiConfig",
"AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
},
"img_processor": None,
"model_type": "phi-msft",
"torch_dtype": "float16",
"transformers_version": "4.35.2"
}
)
'''
from peft import LoraConfig
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16
)
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
task_type="CAUSAL_LM",
target_modules=[
"q_proj",
"k_proj",
"v_proj",
"dense",
"fc1",
"fc2"
]
)
class MultiInstructModelConfig(PretrainedConfig):
def __init__(self,
vision_projector_config: Optional[VisionProjectorConfig] = None,
**kwargs
):
self.vision_projector_config = vision_projector_config
self.quantization_config = bnb_config
self.peft_config = peft_config
self.tokenizer = kwargs.get('tokenizer')
self.freeze_vision_projector = True
extra = dict(
num_epochs=1,
resume=False,
data_dir='../data',
checkpoint_dir='../checkpoints',
max_seqlen=80,
batch_size=2,
live_image_processing=True,
vision_projector_file='/Users/piyushgrover/Downloads/old_vt_proj/vp_ckpt_0.pth',
validation_phase=False
)
qlora_config = dict(
num_steps=1000,
max_seqlen=512,
max_caption_len=100,
batch_size=8,
micro_batch_size=2,
data_dir='../data',
output_dir="./results",
vision_model=True,
vision_projector_file='models/vision_projector/vp_ckpt_0.pth',
resume=False
) |