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from jina import Deployment |
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from docarray import BaseDoc |
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from jina import Executor, requests |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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import argparse |
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import torch |
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class Prompt(BaseDoc): |
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message: list[dict] |
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gen_conf: dict |
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class Generation(BaseDoc): |
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text: str |
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tokenizer = None |
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model_name = "" |
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class TokenStreamingExecutor(Executor): |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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self.model = AutoModelForCausalLM.from_pretrained( |
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model_name, device_map="auto", torch_dtype="auto" |
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) |
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@requests(on="/chat") |
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async def generate(self, doc: Prompt, **kwargs) -> Generation: |
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text = tokenizer.apply_chat_template( |
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doc.message, |
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tokenize=False, |
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) |
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inputs = tokenizer([text], return_tensors="pt") |
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generation_config = GenerationConfig( |
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**doc.gen_conf, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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generated_ids = self.model.generate( |
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inputs.input_ids, generation_config=generation_config |
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) |
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generated_ids = [ |
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output_ids[len(input_ids) :] |
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for input_ids, output_ids in zip(inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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yield Generation(text=response) |
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@requests(on="/stream") |
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async def task(self, doc: Prompt, **kwargs) -> Generation: |
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text = tokenizer.apply_chat_template( |
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doc.message, |
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tokenize=False, |
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) |
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input = tokenizer([text], return_tensors="pt") |
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input_len = input["input_ids"].shape[1] |
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max_new_tokens = 512 |
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if "max_new_tokens" in doc.gen_conf: |
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max_new_tokens = doc.gen_conf.pop("max_new_tokens") |
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generation_config = GenerationConfig( |
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**doc.gen_conf, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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for _ in range(max_new_tokens): |
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output = self.model.generate( |
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**input, max_new_tokens=1, generation_config=generation_config |
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) |
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if output[0][-1] == tokenizer.eos_token_id: |
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break |
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yield Generation( |
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text=tokenizer.decode(output[0][input_len:], skip_special_tokens=True) |
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) |
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input = { |
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"input_ids": output, |
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"attention_mask": torch.ones(1, len(output[0])), |
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} |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_name", type=str, help="Model name or path") |
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parser.add_argument("--port", default=12345, type=int, help="Jina serving port") |
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args = parser.parse_args() |
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model_name = args.model_name |
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tokenizer = AutoTokenizer.from_pretrained(args.model_name) |
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with Deployment( |
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uses=TokenStreamingExecutor, port=args.port, protocol="grpc" |
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) as dep: |
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dep.block() |
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