File size: 5,899 Bytes
b5beb60 | 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 | from PIL import Image
import torch
from .base import BaseModel
from ..smp import *
class Phi3Vision(BaseModel):
INSTALL_REQ = False
INTERLEAVE = False
def __init__(self, model_path='microsoft/Phi-3-vision-128k-instruct', **kwargs):
try:
from transformers import AutoProcessor, AutoModelForCausalLM
except Exception as e:
logging.critical('Please install the latest version transformers.')
raise e
model = AutoModelForCausalLM.from_pretrained(
model_path, device_map='cuda', trust_remote_code=True, torch_dtype='auto').eval()
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
self.model = model
self.processor = processor
self.kwargs = kwargs
def generate_inner(self, message, dataset=None):
prompt, image_path = self.message_to_promptimg(message, dataset=dataset)
image = Image.open(image_path).convert('RGB')
messages = [
{'role': 'user', 'content': f'<|image_1|>\n{prompt}'}
]
prompt = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = self.processor(prompt, [image], return_tensors='pt').to('cuda')
generation_args = {
'max_new_tokens': 2048,
'temperature': 0.0,
'do_sample': False,
}
generation_args.update(self.kwargs)
generate_ids = self.model.generate(
**inputs,
eos_token_id=self.processor.tokenizer.eos_token_id,
**generation_args
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = self.processor.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)[0]
return response
def chat_inner(self, message, dataset=None):
messages = []
image_cnt = 1
image_list = []
for msg in message:
content = ''
# If message is just text in the conversation
if len(msg['content']) == 1 and msg['content'][0]['type'] == 'text':
msg_new = {'role': msg['role'], 'content': msg['content'][0]['value']}
messages.append(msg_new)
continue
# If both image & text is present
for x in msg['content']:
if x['type'] == 'text':
content += x['value']
elif x['type'] == 'image':
image = Image.open(x['value']).convert('RGB')
content += f'<|image_{image_cnt}|>\n'
image_list.append(image)
image_cnt += 1
msg_new = {'role': msg['role'], 'content': content}
messages.append(msg_new)
prompt = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = self.processor(prompt, image_list, return_tensors='pt').to('cuda')
generation_args = {
'max_new_tokens': 2048,
'temperature': 0.0,
'do_sample': False,
}
generation_args.update(self.kwargs)
generate_ids = self.model.generate(
**inputs,
eos_token_id=self.processor.tokenizer.eos_token_id,
**generation_args
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = self.processor.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)[0]
return response
class Phi3_5Vision(BaseModel):
INSTALL_REQ = False
INTERLEAVE = False
def __init__(self, model_path='microsoft/Phi-3.5-vision-instruct', **kwargs):
try:
from transformers import AutoProcessor, AutoModelForCausalLM
except Exception as e:
logging.critical('Please install the latest version transformers.')
raise e
model = AutoModelForCausalLM.from_pretrained(
model_path, device_map='cuda', trust_remote_code=True, torch_dtype='auto',
_attn_implementation='flash_attention_2').eval()
# for best performance, use num_crops=4 for multi-frame, num_crops=16 for single-frame.
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, num_crops=4)
self.model = model
self.processor = processor
self.kwargs = kwargs
def generate_inner(self, message, dataset=None):
prompt = '\n'.join([msg['value'] for msg in message if msg['type'] == 'text'])
images = [Image.open(msg['value']).convert('RGB') for msg in message if msg['type'] == 'image']
num_images = len(images)
placeholder = ''
for i in range(1, num_images + 1):
placeholder += f'<|image_{i}|>\n'
messages = [
{'role': 'user', 'content': placeholder + prompt}
]
prompt = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = self.processor(prompt, images, return_tensors='pt').to('cuda')
generation_args = {
'max_new_tokens': 2048,
'temperature': 0.0,
'do_sample': False,
}
generation_args.update(self.kwargs)
generate_ids = self.model.generate(
**inputs,
eos_token_id=self.processor.tokenizer.eos_token_id,
**generation_args
)
# remove input tokens
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = self.processor.batch_decode(
generate_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)[0]
return response
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