File size: 7,887 Bytes
60cbb5b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
import torch
from torch import nn
from transformers import PreTrainedModel, PretrainedConfig
from safetensors.torch import load_file
# CLIP
from .modeling_clipPT import CLIPVisionTransformer
from transformers import CLIPImageProcessor
from transformers import AutoTokenizer
# Qwen
from .modeling_qwen2 import Qwen2Model
# Timer
from .modeling_timer import TimerForPrediction
class MulTiCastTimerConfig(PretrainedConfig):
def __init__(
self,
forecasting_length = None,
vision_model_name = None,
text_model_name = None,
vision_model_prompt_len = None,
text_model_prompt_len = None,
timer_prompt_len = None,
**kwargs
):
super().__init__(**kwargs)
self.forecasting_length = forecasting_length
self.vision_model_name = vision_model_name
self.text_model_name = text_model_name
self.vision_model_prompt_len = vision_model_prompt_len if vision_model_prompt_len is not None else 10
self.text_model_prompt_len = text_model_prompt_len if text_model_prompt_len is not None else 4
self.timer_prompt_len = timer_prompt_len if timer_prompt_len is not None else 4
class MulTiCastTimerModel(PreTrainedModel):
config_class = MulTiCastTimerConfig
def __init__(self, config):
super().__init__(config)
self.config = config
# Vision Model
if config.vision_model_name is None:
pass
elif config.vision_model_name == 'CLIP':
from transformers import AutoModel
vision_model = AutoModel.from_pretrained("openai/clip-vit-base-patch32").vision_model
state_dict = vision_model.state_dict()
state_dict = {k: v.to(torch.bfloat16) for k, v in state_dict.items()}
self.vision_model = CLIPVisionTransformer(vision_model.config, config.vision_model_prompt_len)
self.vision_model.load_state_dict(state_dict, strict=False)
self.processor = CLIPImageProcessor()
for name, param in self.vision_model.named_parameters(): # Freeze layers other than prompts
if "encoder.prompts" in name:
param.requires_grad = True
else:
param.requires_grad = False
else:
pass
# Text Model
if config.text_model_name is None:
pass
elif config.text_model_name == 'Qwen':
self.tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
from transformers import AutoModelForCausalLM
text_model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2-1.5B-Instruct",
torch_dtype=torch.bfloat16,
device_map="cpu",
attn_implementation="sdpa"
).model
state_dict = text_model.state_dict()
self.text_model = Qwen2Model(text_model.config, config.text_model_prompt_len)
self.text_model.load_state_dict(state_dict, strict=False)
for name, param in self.text_model.named_parameters(): # Freeze layers other than prompts
if "prompts" in name:
param.requires_grad = True
else:
param.requires_grad = False
else:
pass
# Timer
from transformers import AutoModelForCausalLM
timer = AutoModelForCausalLM.from_pretrained('thuml/timer-base-84m', trust_remote_code=True)
state_dict = timer.state_dict()
self.timer = TimerForPrediction(timer.config, config.timer_prompt_len)
self.timer.load_state_dict(state_dict, strict=False)
for name, param in self.timer.named_parameters(): # Freeze layers other than prompts
if "model.prompts" in name:
param.requires_grad = True
else:
param.requires_grad = False
# Vision Interaction Layer
if config.vision_model_name is None:
pass
else:
self.vision_interaction_layer = nn.Linear(self.vision_model.config.hidden_size, self.timer.config.hidden_size)
# Text Interaction Layer
if config.text_model_name is None:
pass
else:
self.text_interaction_layer = nn.Linear(self.text_model.config.hidden_size, self.timer.config.hidden_size)
def predict(self, input_ids = None, images = None, texts = None):
images = self.processor.preprocess(images)['pixel_values'][0]
images = torch.tensor(images)
images = images.unsqueeze(0)
if self.config.vision_model_name is None and images is None:
vision_embedding = None
else:
vision_output = self.vision_model(images, output_attentions=True)
vision_attentions = vision_output.attentions
vision_embedding = vision_output.pooler_output
vision_embedding = self.vision_interaction_layer(vision_embedding)
if self.config.text_model_name is None and all(x is None for x in texts):
text_embedding = None
else:
tokenized_texts = self.tokenizer(texts, return_tensors="pt")
text_embedding = self.text_model(**tokenized_texts)
text_embedding = text_embedding.last_hidden_state[:, 0 , :]
text_embedding = self.text_interaction_layer(text_embedding)
out = self.timer(input_ids=input_ids, vision_embedding=vision_embedding, text_embedding=text_embedding)
return {
"logits": out.logits,
"vision_attentions": vision_attentions,
"time_series_attentions": out.attentions
}
def forward(self, input_ids = None, images = None, texts = None, labels = None):
if self.config.vision_model_name is None and images is None:
vision_embedding = None
else:
vision_embedding = self.vision_model(images)
vision_embedding = vision_embedding.pooler_output
vision_embedding = self.vision_interaction_layer(vision_embedding)
if self.config.text_model_name is None and all(x is None for x in texts):
text_embedding = None
else:
tokenized_texts = self.tokenizer(texts, return_tensors="pt")
text_embedding = self.text_model(**tokenized_texts)
text_embedding = text_embedding.last_hidden_state[:, 0 , :]
text_embedding = self.text_interaction_layer(text_embedding)
out = self.timer(input_ids=input_ids, vision_embedding=vision_embedding, text_embedding=text_embedding)
out = out["logits"]
if labels is not None:
if self.config.forecasting_length == out.shape[-1]:
loss = torch.mean(torch.square(out-labels)) # MSE
else: # pretrained Timer has 96 forecasting length. This is in case of shorter forecasting length. Forecasting length larger than 96 will occure an error.
loss = torch.mean(torch.square(out[:, :self.config.forecasting_length]-labels))
else:
loss = None
return {
"loss": loss,
"logits": out
}
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
from transformers.utils import cached_file
config = MulTiCastTimerConfig.from_pretrained(pretrained_model_name_or_path)
model = MulTiCastTimerModel(config)
resolved_file = cached_file(pretrained_model_name_or_path, "model.safetensors")
state_dict = load_file(resolved_file)
model.load_state_dict(state_dict, strict=False)
return model |