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from transformers import AutoTokenizer, PreTrainedTokenizerFast |
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from transformers.tokenization_utils_base import AddedToken |
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from http.server import HTTPServer, BaseHTTPRequestHandler |
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import json |
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import argparse |
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def _prompt_split_image( |
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image_seq_len, |
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image_rows, |
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image_cols, |
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fake_token_around_image, |
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image_token, |
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global_img_token, |
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): |
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"""Prompt with expanded image tokens for when the image is split into patches.""" |
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text_split_images = "" |
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for n_h in range(image_rows): |
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for n_w in range(image_cols): |
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text_split_images += ( |
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f"{fake_token_around_image}" |
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+ f"<row_{n_h + 1}_col_{n_w + 1}>" |
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+ f"{image_token}" * image_seq_len |
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) |
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text_split_images += "\n" |
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text_split_images += ( |
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f"\n{fake_token_around_image}" |
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+ f"{global_img_token}" |
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+ f"{image_token}" * image_seq_len |
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+ f"{fake_token_around_image}" |
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) |
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return text_split_images |
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def _prompt_single_image( |
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image_seq_len, fake_token_around_image, image_token, global_img_token |
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): |
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"""Prompt with expanded image tokens for a single image.""" |
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return ( |
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f"{fake_token_around_image}" |
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+ f"{global_img_token}" |
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+ f"{image_token}" * image_seq_len |
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+ f"{fake_token_around_image}" |
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) |
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def get_image_prompt_string( |
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image_rows, |
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image_cols, |
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image_seq_len, |
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fake_token_around_image, |
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image_token, |
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global_img_token, |
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): |
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if image_rows == 0 and image_cols == 0: |
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return _prompt_single_image( |
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image_seq_len, |
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fake_token_around_image=fake_token_around_image, |
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image_token=image_token, |
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global_img_token=global_img_token, |
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) |
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return _prompt_split_image( |
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image_seq_len, |
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image_rows, |
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image_cols, |
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fake_token_around_image, |
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image_token, |
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global_img_token, |
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) |
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class Tokenizer_Http(): |
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def __init__(self): |
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path = 'qwen2_5-vl-tokenizer' |
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self.tokenizer = AutoTokenizer.from_pretrained(path, |
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trust_remote_code=True, |
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use_fast=False) |
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def encode(self, content): |
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text = [f'<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{content}<|im_end|>\n<|im_start|>assistant\n'] |
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input_ids = self.tokenizer(text) |
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return input_ids["input_ids"][0] |
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def encode_vpm(self, content="Describe this image."): |
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text = f'<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>' + '<|image_pad|>' * 256 + f'<|vision_end|>{content}<|im_end|>\n<|im_start|>assistant\n' |
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output_kwargs = {'text_kwargs': {'padding': True, 'return_tensors': 'pt'}, 'images_kwargs': {'return_tensors': 'pt'}, 'audio_kwargs': {'padding': True, 'return_tensors': 'pt'}, 'videos_kwargs': {'fps': 2.0, 'return_tensors': 'pt'}, 'common_kwargs': {'return_tensors': 'pt'}} |
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) |
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return text_inputs["input_ids"].tolist()[0] |
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def decode(self, token_ids): |
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return self.tokenizer.decode(token_ids, |
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clean_up_tokenization_spaces=False) |
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@property |
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def bos_id(self): |
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return self.tokenizer.bos_token_id |
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@property |
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def eos_id(self): |
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return self.tokenizer.eos_token_id |
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@property |
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def bos_token(self): |
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return self.tokenizer.bos_token |
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@property |
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def eos_token(self): |
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return self.tokenizer.eos_token |
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@property |
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def img_start_token(self): |
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return self.tokenizer.encode("<|vision_start|>")[0] |
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@property |
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def img_context_token(self): |
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return self.tokenizer.encode("<|image_pad|>")[0] |
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tokenizer = Tokenizer_Http() |
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print(tokenizer.bos_id, tokenizer.bos_token, tokenizer.eos_id, |
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tokenizer.eos_token) |
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token_ids = tokenizer.encode_vpm() |
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print(token_ids) |
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print(len(token_ids)) |
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token_ids = tokenizer.encode("hello world") |
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print(token_ids) |
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print(len(token_ids)) |
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class Request(BaseHTTPRequestHandler): |
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timeout = 5 |
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server_version = 'Apache' |
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def do_GET(self): |
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print(self.path) |
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self.send_response(200) |
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self.send_header("type", "get") |
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self.end_headers() |
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if self.path == '/bos_id': |
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bos_id = tokenizer.bos_id |
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if bos_id is None: |
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msg = json.dumps({'bos_id': -1}) |
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else: |
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msg = json.dumps({'bos_id': bos_id}) |
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elif self.path == '/eos_id': |
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eos_id = tokenizer.eos_id |
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if eos_id is None: |
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msg = json.dumps({'eos_id': -1}) |
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else: |
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msg = json.dumps({'eos_id': eos_id}) |
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elif self.path == '/img_start_token': |
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img_start_token = tokenizer.img_start_token |
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if img_start_token is None: |
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msg = json.dumps({'img_start_token': -1}) |
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else: |
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msg = json.dumps({'img_start_token': img_start_token}) |
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elif self.path == '/img_context_token': |
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img_context_token = tokenizer.img_context_token |
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if img_context_token is None: |
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msg = json.dumps({'img_context_token': -1}) |
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else: |
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msg = json.dumps({'img_context_token': img_context_token}) |
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else: |
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msg = 'error' |
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print(msg) |
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msg = str(msg).encode() |
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self.wfile.write(msg) |
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def do_POST(self): |
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data = self.rfile.read(int( |
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self.headers['content-length'])) |
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data = data.decode() |
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self.send_response(200) |
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self.send_header("type", "post") |
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self.end_headers() |
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if self.path == '/encode': |
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req = json.loads(data) |
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print(req) |
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prompt = req['text'] |
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b_img_prompt = False |
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if 'img_prompt' in req: |
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b_img_prompt = req['img_prompt'] |
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if b_img_prompt: |
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token_ids = tokenizer.encode_vpm(prompt) |
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else: |
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token_ids = tokenizer.encode(prompt) |
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if token_ids is None: |
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msg = json.dumps({'token_ids': -1}) |
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else: |
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msg = json.dumps({'token_ids': token_ids}) |
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elif self.path == '/decode': |
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req = json.loads(data) |
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token_ids = req['token_ids'] |
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text = tokenizer.decode(token_ids) |
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if text is None: |
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msg = json.dumps({'text': ""}) |
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else: |
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msg = json.dumps({'text': text}) |
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else: |
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msg = 'error' |
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print(msg) |
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msg = str(msg).encode() |
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self.wfile.write(msg) |
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if __name__ == "__main__": |
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args = argparse.ArgumentParser() |
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args.add_argument('--host', type=str, default='localhost') |
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args.add_argument('--port', type=int, default=8080) |
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args = args.parse_args() |
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host = (args.host, args.port) |
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print('http://%s:%s' % host) |
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server = HTTPServer(host, Request) |
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server.serve_forever() |
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