285 lines
9.1 KiB
Python
285 lines
9.1 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import time
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import json
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from tqdm import tqdm
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from argparse import ArgumentParser
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from typing import Dict, List
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from threading import Thread
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import torch
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import uvicorn
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from fastapi import FastAPI
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from fastapi.responses import JSONResponse
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from transformers import AutoTokenizer
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# from transformers import AutoModelForCausalLM, AutoModel
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from ipex_llm.transformers import AutoModelForCausalLM, AutoModel
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from transformers.generation import GenerationConfig, TextIteratorStreamer
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from transformers import StoppingCriteriaList, StoppingCriteria
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from sse_starlette.sse import EventSourceResponse
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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class GenerationParameters(BaseModel):
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max_new_tokens: int
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temperature: float
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repetition_penalty: float
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top_p: float
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do_sample: bool
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stop: List[str]
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class GenerationRequest(BaseModel):
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inputs: str
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parameters: GenerationParameters
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class StopWordsCriteria(StoppingCriteria):
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"""Custom `StoppingCriteria` which checks if all generated functions in the batch are completed."""
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def __init__(self, input_length, stop_words, tokenizer):
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self.input_length = input_length
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self.stop_words = stop_words
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self.stop_words += ["|<end|", "|end>|"]
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self.tokenizer = tokenizer
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def __call__(self, input_ids, scores, **kwargs):
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"""Returns true if all generated sequences contain any of the end-of-function strings."""
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texts = [ self.tokenizer.decode(ids[self.input_length:]) for ids in input_ids ]
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dones = [ any(stop_word in text for stop_word in self.stop_words) for text in texts ]
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return all(dones)
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@app.post("/generate")
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async def generate(request: GenerationRequest):
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global model, tokenizer, device, max_context
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if device == 'xpu':
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torch.xpu.empty_cache()
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prompt = request.inputs
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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input_length = len(input_ids[0])
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if input_length > max_context:
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tokens = list(input_ids[0])
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prefix_index = tokens.index(70001) # fim_prefix
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middle_index = tokens.index(70002) # fim_middle
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suffix_index = tokens.index(70003) # fim_suffix
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prefix_tokens = tokens[prefix_index+1:suffix_index]
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suffix_tokens = tokens[suffix_index+1:middle_index]
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prefix_len = suffix_index - prefix_index - 1
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suffix_len = middle_index - suffix_index - 1
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if prefix_len + suffix_len > max_context:
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new_prefix_len = max_context * prefix_len // (prefix_len + suffix_len)
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new_suffix_len = max_context * suffix_len // (prefix_len + suffix_len)
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new_prefix_tokens = prefix_tokens[-new_prefix_len:]
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new_suffix_tokens = suffix_tokens[:new_suffix_len]
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input_ids = torch.tensor(
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tokens[:prefix_index+1] +
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new_prefix_tokens +
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tokens[suffix_index:suffix_index+1] +
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new_suffix_tokens +
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tokens[middle_index:]
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).reshape(1, -1)
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input_length = len(input_ids[0])
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prompt = tokenizer.decode(input_ids[0])
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input_ids = input_ids.to(device)
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stopping_criteria = StoppingCriteriaList(
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[ StopWordsCriteria(input_length, request.parameters.stop, tokenizer) ]
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)
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generation_kwargs = dict(stopping_criteria=stopping_criteria,
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max_new_tokens=request.parameters.max_new_tokens,
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temperature=request.parameters.temperature,
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repetition_penalty=request.parameters.repetition_penalty,
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top_p=request.parameters.top_p,
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do_sample=request.parameters.do_sample)
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print('-'*80)
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print('input prompt:', prompt)
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print('input length:', input_length)
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print('-'*80)
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output_ids = model.generate(input_ids, **generation_kwargs)
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output_text = tokenizer.decode(output_ids[0])
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return JSONResponse({
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"generated_text": output_text[len(prompt):]
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})
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@app.post("/generate_stream")
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async def generate_stream(request: GenerationRequest):
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global model, tokenizer, device, multi_turn
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if device == 'xpu':
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torch.xpu.empty_cache()
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prompt = request.inputs
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if multi_turn:
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prompt = prompt
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else:
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# extract the last turn input
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human_ins = "## human"
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first_ins = prompt.find(human_ins)
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last_ins = prompt.rfind(human_ins)
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prompt = prompt[:first_ins] + prompt[last_ins:]
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input_ids = tokenizer(prompt, return_tensors="pt")
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input_length = len(input_ids['input_ids'][0])
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input_ids = input_ids.to(device)
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
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stopping_criteria = StoppingCriteriaList(
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[ StopWordsCriteria(input_length, request.parameters.stop, tokenizer) ]
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)
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max_batch = 1024
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if input_length <= max_batch:
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past_key_values = None
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else:
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with torch.inference_mode():
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past_key_values = None
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for start_pos in range(0, input_length - 1, max_batch):
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end_pos = min(start_pos + max_batch, input_length - 1)
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output = model.forward(input_ids['input_ids'][:, start_pos:end_pos],
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past_key_values=past_key_values)
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past_key_values = output.past_key_values
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generation_kwargs = dict(input_ids,
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past_key_values=past_key_values,
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streamer=streamer,
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stopping_criteria=stopping_criteria,
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max_new_tokens=request.parameters.max_new_tokens,
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temperature=request.parameters.temperature,
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repetition_penalty=request.parameters.repetition_penalty,
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top_p=request.parameters.top_p,
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do_sample=request.parameters.do_sample)
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print('-'*80)
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print('input prompt:', prompt)
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print('input length:', input_length)
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print('-'*80)
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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def create_response(streamer):
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for word in tqdm(streamer, "Generating Tokens", unit="token"):
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yield json.dumps({
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"token": {
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"id": 0,
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"text": word,
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},
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})
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return EventSourceResponse(create_response(streamer), media_type="text/event-stream")
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def _get_args():
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parser = ArgumentParser()
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parser.add_argument(
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"-c",
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"--checkpoint-path",
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type=str,
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default="CodeShell-7B-Chat",
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help="Checkpoint name or path, default to %(default)r",
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)
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parser.add_argument(
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"--device",
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type=str,
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default="cpu",
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help="Device name."
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)
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parser.add_argument(
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"--server-port",
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type=int,
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default=8080,
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help="Demo server port."
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)
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parser.add_argument(
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"--server-name",
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type=str,
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default="127.0.0.1",
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help="Demo server name. Default: 127.0.0.1, which is only visible from the local computer."
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" If you want other computers to access your server, use 0.0.0.0 instead.",
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)
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parser.add_argument(
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"--multi-turn",
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action="store_true",
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help="Enable multi-turn chat",
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)
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parser.add_argument(
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"--cpu-embedding",
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action="store_true",
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help="Move Embedding layer to CPU"
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)
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parser.add_argument(
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"--max-context",
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type=int,
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default=300,
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help="Max context length when using code completion",
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)
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = _get_args()
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tokenizer = AutoTokenizer.from_pretrained(
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args.checkpoint_path,
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trust_remote_code=True,
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)
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model = AutoModelForCausalLM.from_pretrained(
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args.checkpoint_path,
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trust_remote_code=True,
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load_in_4bit=True,
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cpu_embedding=args.cpu_embedding
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).eval()
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device = args.device
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multi_turn = args.multi_turn
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max_context = args.max_context
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model = model.to(device)
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model.generation_config = GenerationConfig.from_pretrained(
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args.checkpoint_path,
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trust_remote_code=True,
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resume_download=True,
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)
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uvicorn.run(app, host=args.server_name, port=args.server_port, workers=1)
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