File size: 7,431 Bytes
b7cd909 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
import torch
from typing import List, Union, Literal, Optional
from transformers import PreTrainedModel
from PIL import Image
from .configuration_moondream import PhiConfig
from .configuration_moondream import MoondreamConfig
from .vision_encoder import VisionEncoder
from .region_model import RegionModel
from .modeling_phi import PhiForCausalLM
class Moondream(PreTrainedModel):
config_class = MoondreamConfig
_supports_flash_attn_2 = True
def __init__(self, config):
super().__init__(config)
self.vision_encoder = VisionEncoder(
use_flash_attn=config._attn_implementation == "flash_attention_2"
)
self.region_model = RegionModel()
if type(config.text_config) == dict:
phi_config = PhiConfig(
**config.text_config, attn_implementation=config._attn_implementation
)
else:
phi_config = config.text_config
self.text_model = PhiForCausalLM(phi_config)
@property
def device(self):
return self.text_model.device
def encode_image(self, image):
with torch.no_grad():
return self.vision_encoder(image)
def input_embeds(self, prompt, image_embeds, tokenizer):
def _tokenize(txt):
return tokenizer(
txt, return_tensors="pt", add_special_tokens=False
).input_ids.to(self.device)
text_emb = self.text_model.get_input_embeddings()
# Add BOS token
embeds = []
embeds.append(
text_emb((torch.tensor([[tokenizer.bos_token_id]], device=self.device)))
)
if "<image>" not in prompt:
embeds.append(text_emb(_tokenize(prompt)))
else:
assert prompt.count("<image>") == 1
before, after = prompt.split("<image>")
if len(before) > 0:
embeds.append(text_emb(_tokenize(before)))
embeds.append(image_embeds.to(self.device))
if len(after) > 0:
embeds.append(text_emb(_tokenize(after)))
return torch.cat(embeds, dim=1)
def get_input_embeddings(self):
return self.text_model.get_input_embeddings()
def generate(
self,
image_embeds,
prompt,
tokenizer,
max_new_tokens=128,
**kwargs,
):
generate_config = {
"eos_token_id": tokenizer.eos_token_id,
"bos_token_id": tokenizer.bos_token_id,
"pad_token_id": tokenizer.bos_token_id,
"max_new_tokens": max_new_tokens,
**kwargs,
}
with torch.no_grad():
inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
output_ids = self.text_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
**generate_config,
)
return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
# Note: Not ready for use yet, intended for September release.
def caption(
self,
images: List[Image.Image],
tokenizer,
length: Optional[Literal["short"]] = None,
**kwargs,
):
image_embeds = self.encode_image(images)
templated_prompts = [
f"<image>\n\n{'Short caption' if length == 'short' else 'Caption'}:" for _ in images
]
inputs_embeds = torch.stack([
self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
for prompt, image_embed in zip(templated_prompts, image_embeds)
])
attention_mask = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=self.device)
generate_config = {
"eos_token_id": tokenizer.eos_token_id,
"bos_token_id": tokenizer.bos_token_id,
"pad_token_id": tokenizer.bos_token_id,
"repetition_penalty": 1.2,
"max_new_tokens": 512,
**kwargs,
}
with torch.no_grad():
output_ids = self.text_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
**generate_config,
)
return [
x.strip()
for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
]
def answer_question(
self,
image_embeds,
question,
tokenizer,
chat_history="",
result_queue=None,
max_new_tokens=256,
**kwargs,
):
prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
answer = self.generate(
image_embeds,
prompt,
tokenizer=tokenizer,
max_new_tokens=max_new_tokens,
**kwargs,
)[0]
cleaned_answer = answer.strip()
# Use the result_queue to pass the result if it is provided
if result_queue:
result_queue.put(cleaned_answer)
else:
return cleaned_answer
def batch_answer(
self,
images,
prompts,
tokenizer,
**kwargs,
):
image_embeds = self.encode_image(images)
templated_prompts = [
f"<image>\n\nQuestion: {prompt}\n\nAnswer:" for prompt in prompts
]
prompt_embs = [
self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
for prompt, image_embed in zip(templated_prompts, image_embeds)
]
bos_emb = prompt_embs[0][0]
max_len = max([p.shape[0] for p in prompt_embs])
inputs_embeds = torch.cat(
[
torch.cat([bos_emb.repeat(max_len - p.shape[0], 1), p]).unsqueeze(0)
for p in prompt_embs
],
dim=0,
)
attention_mask = torch.cat(
[
torch.cat(
[
torch.zeros(
1,
max_len - p.shape[0],
device=self.device,
dtype=torch.long,
),
torch.ones(1, p.shape[0], device=self.device, dtype=torch.long),
],
dim=1,
)
for p in prompt_embs
],
dim=0,
)
generate_config = {
"eos_token_id": tokenizer.eos_token_id,
"bos_token_id": tokenizer.bos_token_id,
"pad_token_id": tokenizer.bos_token_id,
"max_new_tokens": 512,
**kwargs,
}
with torch.no_grad():
output_ids = self.text_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
**generate_config,
)
return [
x.strip()
for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
]
def detect(self, image: Image.Image, query: str, tokenizer):
pass
|