add codeshell example (#9743)
This commit is contained in:
Yishuo Wang
GitHub
parent
daf536fb2d
commit
be13b162fe
2 changed files with 326 additions and 0 deletions
|
|
@ -0,0 +1,38 @@
|
|||
# CodeShell
|
||||
|
||||
In this directory, you'll find how to use this codeshell server with vscode codeshell extension.
|
||||
|
||||
## 0. Extra Environment Preparations
|
||||
|
||||
Suppose you have already configured GPU environment, you will need some extra preparation
|
||||
|
||||
1. install extra requirements
|
||||
```
|
||||
pip install uvicorn fastapi sse_starlette
|
||||
```
|
||||
|
||||
2. search `codeshell` in vscode extension market, then install `CodeShell VSCode Extension` extension
|
||||
|
||||
3. change extension settings:
|
||||
- change `Code Shell: Run Env For LLMs` to `GPU with TGI toolkit`
|
||||
- disable `Code Shell: Auto Trigger Completion` (use `Alt + \` to trigger completion manually)
|
||||
|
||||
4. download WisdomShell/CodeShell-7B-Chat (don't use CodeShell-7B)
|
||||
|
||||
## 1. How to use this server
|
||||
|
||||
```
|
||||
python server.py [--option value]
|
||||
```
|
||||
|
||||
1. `--checkpoint-path <path>`: path to huggingface model checkpoint
|
||||
2. `--device xpu`: enable GPU or not
|
||||
3. `--multi-turn`: enable multi turn conversation or just support single turn conversation
|
||||
4. `--cpu-embedding`: move Embedding layer to CPU or not
|
||||
5. `--max-context <number>`: Clip the context length in Code Completion, it won't affect other features, set it to 99999 to disable it
|
||||
|
||||
## 2. Note
|
||||
|
||||
In my test, if use vscode remote connection to connect to a remote machine, then install extension and running this server on that remote machine, all extension features expect for Code Completion can be used.
|
||||
|
||||
If don't use remote conection, then install extension and running this server on local machine, Code Completion can also be used.
|
||||
|
|
@ -0,0 +1,288 @@
|
|||
#
|
||||
# Copyright 2016 The BigDL Authors.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
import time
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
from argparse import ArgumentParser
|
||||
from typing import Dict, List
|
||||
from threading import Thread
|
||||
import torch
|
||||
import uvicorn
|
||||
from fastapi import FastAPI
|
||||
from fastapi.responses import JSONResponse
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from pydantic import BaseModel
|
||||
from transformers import AutoTokenizer
|
||||
# from transformers import AutoModelForCausalLM, AutoModel
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
|
||||
from transformers.generation import GenerationConfig, TextIteratorStreamer
|
||||
from transformers import StoppingCriteriaList, StoppingCriteria
|
||||
from sse_starlette.sse import EventSourceResponse
|
||||
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
class GenerationParameters(BaseModel):
|
||||
max_new_tokens: int
|
||||
temperature: float
|
||||
repetition_penalty: float
|
||||
top_p: float
|
||||
do_sample: bool
|
||||
stop: List[str]
|
||||
|
||||
|
||||
class GenerationRequest(BaseModel):
|
||||
inputs: str
|
||||
parameters: GenerationParameters
|
||||
|
||||
|
||||
class StopWordsCriteria(StoppingCriteria):
|
||||
"""Custom `StoppingCriteria` which checks if all generated functions in the batch are completed."""
|
||||
def __init__(self, input_length, stop_words, tokenizer):
|
||||
self.input_length = input_length
|
||||
self.stop_words = stop_words
|
||||
self.stop_words += ["|<end|", "|end>|"]
|
||||
self.tokenizer = tokenizer
|
||||
|
||||
def __call__(self, input_ids, scores, **kwargs):
|
||||
"""Returns true if all generated sequences contain any of the end-of-function strings."""
|
||||
texts = [ self.tokenizer.decode(ids[self.input_length:]) for ids in input_ids ]
|
||||
dones = [ any(stop_word in text for stop_word in self.stop_words) for text in texts ]
|
||||
return all(dones)
|
||||
|
||||
|
||||
@app.post("/generate")
|
||||
async def generate(request: GenerationRequest):
|
||||
global model, tokenizer, device, max_context
|
||||
|
||||
if device == 'xpu':
|
||||
torch.xpu.empty_cache()
|
||||
|
||||
prompt = request.inputs
|
||||
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
||||
input_length = len(input_ids[0])
|
||||
|
||||
if input_length > max_context:
|
||||
tokens = list(input_ids[0])
|
||||
prefix_index = tokens.index(70001) # fim_prefix
|
||||
middle_index = tokens.index(70002) # fim_middle
|
||||
suffix_index = tokens.index(70003) # fim_suffix
|
||||
|
||||
prefix_tokens = tokens[prefix_index+1:suffix_index]
|
||||
suffix_tokens = tokens[suffix_index+1:middle_index]
|
||||
prefix_len = suffix_index - prefix_index - 1
|
||||
suffix_len = middle_index - suffix_index - 1
|
||||
|
||||
if prefix_len + suffix_len > max_context:
|
||||
new_prefix_len = max_context * prefix_len // (prefix_len + suffix_len)
|
||||
new_suffix_len = max_context * suffix_len // (prefix_len + suffix_len)
|
||||
new_prefix_tokens = prefix_tokens[-new_prefix_len:]
|
||||
new_suffix_tokens = suffix_tokens[:new_suffix_len]
|
||||
|
||||
input_ids = torch.tensor(
|
||||
tokens[:prefix_index+1] +
|
||||
new_prefix_tokens +
|
||||
tokens[suffix_index:suffix_index+1] +
|
||||
new_suffix_tokens +
|
||||
tokens[middle_index:]
|
||||
).reshape(1, -1)
|
||||
input_length = len(input_ids[0])
|
||||
prompt = tokenizer.decode(input_ids[0])
|
||||
|
||||
input_ids = input_ids.to(device)
|
||||
|
||||
stopping_criteria = StoppingCriteriaList(
|
||||
[ StopWordsCriteria(input_length, request.parameters.stop, tokenizer) ]
|
||||
)
|
||||
|
||||
generation_kwargs = dict(stopping_criteria=stopping_criteria,
|
||||
max_new_tokens=request.parameters.max_new_tokens,
|
||||
temperature=request.parameters.temperature,
|
||||
repetition_penalty=request.parameters.repetition_penalty,
|
||||
top_p=request.parameters.top_p,
|
||||
do_sample=request.parameters.do_sample)
|
||||
|
||||
print('-'*80)
|
||||
print('input prompt:', prompt)
|
||||
print('input length:', input_length)
|
||||
print('-'*80)
|
||||
|
||||
output_ids = model.generate(input_ids, **generation_kwargs)
|
||||
output_text = tokenizer.decode(output_ids[0])
|
||||
|
||||
return JSONResponse({
|
||||
"generated_text": output_text[len(prompt):]
|
||||
})
|
||||
|
||||
|
||||
@app.post("/generate_stream")
|
||||
async def generate_stream(request: GenerationRequest):
|
||||
global model, tokenizer, device, multi_turn
|
||||
|
||||
if device == 'xpu':
|
||||
torch.xpu.empty_cache()
|
||||
|
||||
prompt = request.inputs
|
||||
|
||||
if multi_turn:
|
||||
prompt = prompt
|
||||
else:
|
||||
# extract the last turn input
|
||||
human_ins = "## human"
|
||||
first_ins = prompt.find(human_ins)
|
||||
last_ins = prompt.rfind(human_ins)
|
||||
prompt = prompt[:first_ins] + prompt[last_ins:]
|
||||
|
||||
input_ids = tokenizer(prompt, return_tensors="pt")
|
||||
input_length = len(input_ids['input_ids'][0])
|
||||
input_ids = input_ids.to(device)
|
||||
|
||||
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
|
||||
stopping_criteria = StoppingCriteriaList(
|
||||
[ StopWordsCriteria(input_length, request.parameters.stop, tokenizer) ]
|
||||
)
|
||||
|
||||
max_batch = 1024
|
||||
if input_length <= max_batch:
|
||||
past_key_values = None
|
||||
else:
|
||||
with torch.inference_mode():
|
||||
past_key_values = None
|
||||
for start_pos in range(0, input_length - 1, max_batch):
|
||||
end_pos = min(start_pos + max_batch, input_length - 1)
|
||||
output = model.forward(input_ids['input_ids'][:, start_pos:end_pos],
|
||||
past_key_values=past_key_values)
|
||||
past_key_values = output.past_key_values
|
||||
|
||||
generation_kwargs = dict(input_ids,
|
||||
past_key_values=past_key_values,
|
||||
streamer=streamer,
|
||||
stopping_criteria=stopping_criteria,
|
||||
max_new_tokens=request.parameters.max_new_tokens,
|
||||
temperature=request.parameters.temperature,
|
||||
repetition_penalty=request.parameters.repetition_penalty,
|
||||
top_p=request.parameters.top_p,
|
||||
do_sample=request.parameters.do_sample)
|
||||
|
||||
print('-'*80)
|
||||
print('input prompt:', prompt)
|
||||
print('input length:', input_length)
|
||||
print('-'*80)
|
||||
|
||||
thread = Thread(target=model.generate, kwargs=generation_kwargs)
|
||||
thread.start()
|
||||
|
||||
def create_response(streamer):
|
||||
for word in tqdm(streamer, "Generating Tokens", unit="token"):
|
||||
yield json.dumps({
|
||||
"token": {
|
||||
"id": 0,
|
||||
"text": word,
|
||||
},
|
||||
})
|
||||
|
||||
return EventSourceResponse(create_response(streamer), media_type="text/event-stream")
|
||||
|
||||
|
||||
def _get_args():
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument(
|
||||
"-c",
|
||||
"--checkpoint-path",
|
||||
type=str,
|
||||
default="CodeShell-7B-Chat",
|
||||
help="Checkpoint name or path, default to %(default)r",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cpu",
|
||||
help="Device name."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--server-port",
|
||||
type=int,
|
||||
default=8080,
|
||||
help="Demo server port."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--server-name",
|
||||
type=str,
|
||||
default="127.0.0.1",
|
||||
help="Demo server name. Default: 127.0.0.1, which is only visible from the local computer."
|
||||
" If you want other computers to access your server, use 0.0.0.0 instead.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--multi-turn",
|
||||
action="store_true",
|
||||
help="Enable multi-turn chat",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cpu-embedding",
|
||||
action="store_true",
|
||||
help="Move Embedding layer to CPU"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max-context",
|
||||
type=int,
|
||||
default=300,
|
||||
help="Max context length when using code completion",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = _get_args()
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
args.checkpoint_path,
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
args.checkpoint_path,
|
||||
trust_remote_code=True,
|
||||
load_in_4bit=True,
|
||||
cpu_embedding=args.cpu_embedding
|
||||
).eval()
|
||||
|
||||
device = args.device
|
||||
multi_turn = args.multi_turn
|
||||
max_context = args.max_context
|
||||
|
||||
if device == 'xpu':
|
||||
import intel_extension_for_pytorch as ipex
|
||||
|
||||
model = model.to(device)
|
||||
|
||||
model.generation_config = GenerationConfig.from_pretrained(
|
||||
args.checkpoint_path,
|
||||
trust_remote_code=True,
|
||||
resume_download=True,
|
||||
)
|
||||
|
||||
uvicorn.run(app, host=args.server_name, port=args.server_port, workers=1)
|
||||
Loading…
Reference in a new issue