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ff8702f7de
### What problem does this PR solve? add support for LocalLLM ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
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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