[tutorial] edited hands-on practices (#1899)

* Add handson to ColossalAI.

* Change names of handsons and edit sequence parallel example.

* Edit wrong folder name

* resolve conflict

* delete readme

examples/tutorial/opt/inference/opt_fastapi.py

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+import argparse
+import logging
+import random
+from typing import Optional
+
+import uvicorn
+from energonai import QueueFullError, launch_engine
+from energonai.model import opt_6B, opt_30B, opt_125M, opt_175B
+from fastapi import FastAPI, HTTPException, Request
+from pydantic import BaseModel, Field
+from transformers import GPT2Tokenizer
+
+from batch import BatchManagerForGeneration
+from cache import ListCache, MissCacheError
+
+
+class GenerationTaskReq(BaseModel):
+    max_tokens: int = Field(gt=0, le=256, example=64)
+    prompt: str = Field(
+        min_length=1, example='Question: Where were the 2004 Olympics held?\nAnswer: Athens, Greece\n\nQuestion: What is the longest river on the earth?\nAnswer:')
+    top_k: Optional[int] = Field(default=None, gt=0, example=50)
+    top_p: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.5)
+    temperature: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.7)
+
+
+app = FastAPI()
+
+
+@app.post('/generation')
+async def generate(data: GenerationTaskReq, request: Request):
+    logger.info(f'{request.client.host}:{request.client.port} - "{request.method} {request.url.path}" - {data}')
+    key = (data.prompt, data.max_tokens)
+    try:
+        if cache is None:
+            raise MissCacheError()
+        outputs = cache.get(key)
+        output = random.choice(outputs)
+        logger.info('Cache hit')
+    except MissCacheError:
+        inputs = tokenizer(data.prompt, truncation=True, max_length=512)
+        inputs['max_tokens'] = data.max_tokens
+        inputs['top_k'] = data.top_k
+        inputs['top_p'] = data.top_p
+        inputs['temperature'] = data.temperature
+        try:
+            uid = id(data)
+            engine.submit(uid, inputs)
+            output = await engine.wait(uid)
+            output = tokenizer.decode(output, skip_special_tokens=True)
+            if cache is not None:
+                cache.add(key, output)
+        except QueueFullError as e:
+            raise HTTPException(status_code=406, detail=e.args[0])
+
+    return {'text': output}
+
+
+@app.on_event("shutdown")
+async def shutdown(*_):
+    engine.shutdown()
+    server.should_exit = True
+    server.force_exit = True
+    await server.shutdown()
+
+
+def get_model_fn(model_name: str):
+    model_map = {
+        'opt-125m': opt_125M,
+        'opt-6.7b': opt_6B,
+        'opt-30b': opt_30B,
+        'opt-175b': opt_175B
+    }
+    return model_map[model_name]
+
+
+def print_args(args: argparse.Namespace):
+    print('\n==> Args:')
+    for k, v in args.__dict__.items():
+        print(f'{k} = {v}')
+
+
+FIXED_CACHE_KEYS = [
+    ('Question: What is the name of the largest continent on earth?\nAnswer: Asia\n\nQuestion: What is at the center of the solar system?\nAnswer:', 64),
+    ('A chat between a salesman and a student.\n\nSalesman: Hi boy, are you looking for a new phone?\nStudent: Yes, my phone is not functioning well.\nSalesman: What is your budget? \nStudent: I have received my scholarship so I am fine with any phone.\nSalesman: Great, then perhaps this latest flagship phone is just right for you.', 64),
+    ("English: I am happy today.\nChinese: 我今天很开心。\n\nEnglish: I am going to play basketball.\nChinese: 我一会去打篮球。\n\nEnglish: Let's celebrate our anniversary.\nChinese:", 64)
+]
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('model', choices=['opt-125m', 'opt-6.7b', 'opt-30b', 'opt-175b'])
+    parser.add_argument('--tp', type=int, default=1)
+    parser.add_argument('--master_host', default='localhost')
+    parser.add_argument('--master_port', type=int, default=19990)
+    parser.add_argument('--rpc_port', type=int, default=19980)
+    parser.add_argument('--max_batch_size', type=int, default=8)
+    parser.add_argument('--pipe_size', type=int, default=1)
+    parser.add_argument('--queue_size', type=int, default=0)
+    parser.add_argument('--http_host', default='0.0.0.0')
+    parser.add_argument('--http_port', type=int, default=7070)
+    parser.add_argument('--checkpoint', default=None)
+    parser.add_argument('--cache_size', type=int, default=0)
+    parser.add_argument('--cache_list_size', type=int, default=1)
+    args = parser.parse_args()
+    print_args(args)
+    model_kwargs = {}
+    if args.checkpoint is not None:
+        model_kwargs['checkpoint'] = args.checkpoint
+
+    logger = logging.getLogger(__name__)
+    tokenizer = GPT2Tokenizer.from_pretrained('facebook/opt-30b')
+    if args.cache_size > 0:
+        cache = ListCache(args.cache_size, args.cache_list_size, fixed_keys=FIXED_CACHE_KEYS)
+    else:
+        cache = None
+    engine = launch_engine(args.tp, 1, args.master_host, args.master_port, args.rpc_port, get_model_fn(args.model),
+                           batch_manager=BatchManagerForGeneration(max_batch_size=args.max_batch_size,
+                                                                   pad_token_id=tokenizer.pad_token_id),
+                           pipe_size=args.pipe_size,
+                           queue_size=args.queue_size,
+                           **model_kwargs)
+    config = uvicorn.Config(app, host=args.http_host, port=args.http_port)
+    server = uvicorn.Server(config=config)
+    server.run()