ipex-llm/python/llm/example/GPU/Deepspeed-AutoTP-FastAPI/serving.py

#
# 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 os
import torch
import transformers
import time
import json
import argparse
import torch.distributed as dist

from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse

from pydantic import BaseModel
import uvicorn
from threading import Thread
from ipex_llm.transformers.streamer import BatchTextIteratorStreamer


import asyncio, uuid
from collections import deque
from typing import Dict, List, Optional

from transformers.utils import logging

logger = logging.get_logger(__name__)

from ipex_llm.utils import BenchmarkWrapper


def get_int_from_env(env_keys, default):
    """Returns the first positive env value found in the `env_keys` list or the default."""
    for e in env_keys:
        val = int(os.environ.get(e, -1))
        if val >= 0:
            return val
    return int(default)


global max_num_seqs
global max_num_batched_tokens

local_rank = get_int_from_env(["LOCAL_RANK", "PMI_RANK"], "0")
world_size = get_int_from_env(["WORLD_SIZE", "PMI_SIZE"], "1")
os.environ["RANK"] = str(local_rank)
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "29500")

global model, tokenizer


class PromptRequest(BaseModel):
    prompt: str
    n_predict: int = 32


rest_req_deque = deque(maxlen=128)
request_queue: asyncio.Queue = asyncio.Queue()
result_dict: Dict[str, str] = {}
streamer_dict = {}
empty_req = PromptRequest(prompt="", n_predict=0)


def load_model(model_path, low_bit):

    from ipex_llm import optimize_model

    import torch
    import time
    import argparse

    from transformers import (
        AutoModelForCausalLM,
    )  # export AutoModelForCausalLM from transformers so that deepspeed use it
    from transformers import LlamaTokenizer, AutoTokenizer
    import deepspeed
    from deepspeed.accelerator.cpu_accelerator import CPU_Accelerator
    from deepspeed.accelerator import set_accelerator, get_accelerator
    from intel_extension_for_deepspeed import XPU_Accelerator

    # First use CPU as accelerator
    # Convert to deepspeed model and apply IPEX-LLM optimization on CPU to decrease GPU memory usage
    current_accel = CPU_Accelerator()
    set_accelerator(current_accel)
    global model, tokenizer
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        device_map={"": "cpu"},
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        trust_remote_code=True,
        use_cache=True,
    )

    model = deepspeed.init_inference(
        model,
        tensor_parallel={"tp_size": world_size},
        dtype=torch.bfloat16,
        replace_method="auto",
    )

    # Use IPEX-LLM `optimize_model` to convert the model into optimized low bit format
    # Convert the rest of the model into float16 to reduce allreduce traffic
    model = optimize_model(model.module.to(f"cpu"), low_bit=low_bit).to(torch.float16)

    # Next, use XPU as accelerator to speed up inference
    current_accel = XPU_Accelerator()
    set_accelerator(current_accel)

    # Move model back to xpu
    model = model.to(f"xpu:{local_rank}")
    model = BenchmarkWrapper(model)

    # Modify backend related settings
    if world_size > 1:
        get_accelerator().set_device(local_rank)
    dist_backend = get_accelerator().communication_backend_name()
    import deepspeed.comm.comm

    deepspeed.comm.comm.cdb = None
    from deepspeed.comm.comm import init_distributed

    init_distributed()

    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        model_path, trust_remote_code=True, padding_side="left"
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token


async def generate_stream_gate(prompt: List[str], n_predict=32, request_ids=[]):
    while prompt[-1] == "":
        prompt = prompt[:-1]
    if isinstance(n_predict, list):
        n_predict = max(n_predict)

    inputs = tokenizer(prompt, return_tensors="pt", padding=True)
    input_ids = inputs.input_ids.to(f"xpu:{local_rank}")
    attention_mask = inputs.attention_mask.to(f"xpu:{local_rank}")

    for request_id in request_ids:
        if request_id not in streamer_dict:
            streamer_dict[request_id] = asyncio.Queue()

    streamer = BatchTextIteratorStreamer(
        tokenizer=tokenizer,
        timeout=600,
        skip_prompt=True,
        skip_special_tokens=True,
        batch_size=len(prompt),
    )

    generated_kwargs = dict(
        max_new_tokens=n_predict,
        min_new_tokens=n_predict,
        streamer=streamer,
        attention_mask=attention_mask,
        do_sample=False,
    )

    def model_generate():
        model.generate(input_ids, **generated_kwargs)
        torch.xpu.empty_cache()
        torch.xpu.synchronize()

    t1 = Thread(target=model_generate)
    t1.start()

    stopped = False

    async def put_item(queue, item):
        await queue.put(item)

    for i in range(n_predict):
        tasks = []
        try:
            output_token = next(streamer)
        except StopIteration:
            stopped = True
        for index, request_id in enumerate(request_ids):
            task = asyncio.create_task(
                put_item(
                    streamer_dict[request_id],
                    (0 if stopped else n_predict - 1 - i, output_token[index]),
                )
            )
            tasks.append(task)
        await asyncio.gather(*tasks)
        if stopped:
            break


app = FastAPI()


async def stream_generator(token_queue, request_id):
    index = 0
    while True:
        if not token_queue.empty():
            remain, token = await token_queue.get()
            response = {
                "index": index,
                "message": {"role": "assistant", "content": token},
                "finish_reason": None,
            }
            yield json.dumps(response) + "\n"
            index = index + 1
            if remain == 0:
                response = {
                    "index": index,
                    "message": {"role": "assistant", "content": None},
                    "finish_reason": "length",
                }
                yield json.dumps(response) + "\n"
                break
        else:
            await asyncio.sleep(0)
    streamer_dict.pop(request_id, None)


async def generator(token_queue, request_id):
    while True:
        if not token_queue.empty():
            remain, token = await token_queue.get()
            yield token
            if remain == 0:
                break
        else:
            await asyncio.sleep(0)
    streamer_dict.pop(request_id, None)


@app.post("/generate/")
async def generate(prompt_request: PromptRequest):
    request_id = str(uuid.uuid4())
    await request_queue.put((request_id, prompt_request))
    while True:
        await asyncio.sleep(0)
        if request_id in streamer_dict:
            output_str = []
            token_queue = streamer_dict[request_id]
            async for item in generator(token_queue, request_id):
                output_str.append(item)

            return {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": "".join(output_str),
                },
                "finish_reason": "stop",
            }


@app.post("/generate_stream/")
async def generate_stream(prompt_request: PromptRequest):
    request_id = str(uuid.uuid4()) + "stream"
    await request_queue.put((request_id, prompt_request))
    while True:
        await asyncio.sleep(0)
        if request_id in streamer_dict:
            token_queue = streamer_dict[request_id]

            return StreamingResponse(
                stream_generator(token_queue, request_id), media_type="application/json"
            )


async def process_requests():
    while True:
        # Pause briefly to ensure stability in concurrency
        await asyncio.sleep(0.1)
        request_ids, prompt_requests = [], []
        cur_batched_tokens = 0

        if local_rank == 0:
            while rest_req_deque:
                request_id, rest_request = rest_req_deque.popleft()
                prompt = rest_request.prompt
                cur_prompt_len = tokenizer(
                    prompt_request.prompt, return_tensors="pt"
                ).input_ids.size(1)
                cur_batched_tokens += cur_prompt_len
                if cur_batched_tokens > max_num_batched_tokens:
                    cur_batched_tokens -= cur_prompt_len
                    rest_req_deque.appendleft((request_id, rest_request))
                    break
                request_ids.append(request_id)
                prompt_requests.append(rest_request)
                if len(prompt_requests) == max_num_seqs:
                    break

            for _ in range(max_num_seqs - len(prompt_requests)):
                if request_queue.empty():
                    break
                request_id, prompt_request = await request_queue.get()
                cur_prompt_len = tokenizer(
                    prompt_request.prompt, return_tensors="pt"
                ).input_ids.size(1)
                cur_batched_tokens += cur_prompt_len
                if cur_batched_tokens > max_num_batched_tokens:
                    cur_batched_tokens -= cur_prompt_len
                    rest_req_deque.appendleft((request_id, prompt_request))
                    break
                request_ids.append(request_id)
                prompt_requests.append(prompt_request)

        if local_rank == 0 and prompt_requests:
            object_list = prompt_requests
            if len(object_list) < max_num_seqs:
                object_list = object_list + [empty_req] * (
                    max_num_seqs - len(object_list)
                )
            logger.info(
                f"Running: {len(prompt_requests)}, Pending: {request_queue.qsize()}"
            )
            dist.broadcast_object_list(object_list, src=0)

            start_time = time.time()
            await generate_stream_gate(
                [req.prompt for req in object_list],
                [req.n_predict for req in object_list],
                request_ids,
            )

            generate_time = time.time() - start_time

            logger.info(
                f"First token latency: {model.first_cost}, next token latency: {model.rest_cost_mean}, generate time: {generate_time}"
            )


@app.on_event("startup")
async def startup_event():
    if local_rank == 0:
        asyncio.create_task(process_requests())


async def main():
    parser = argparse.ArgumentParser(
        description="Predict Tokens using fastapi by leveraging DeepSpeed-AutoTP"
    )
    parser.add_argument(
        "--repo-id-or-model-path",
        type=str,
        default="meta-llama/Llama-2-7b-chat-hf",
        help="The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf`, `meta-llama/Llama-2-13b-chat-hf` and `meta-llama/Llama-2-70b-chat-hf`) to be downloaded"
        ", or the path to the huggingface checkpoint folder",
    )
    parser.add_argument(
        "--low-bit",
        type=str,
        default="sym_int4",
        help="The quantization type the model will convert to.",
    )
    parser.add_argument(
        "--port",
        type=int,
        default=8000,
        help="The port number on which the server will run.",
    )
    parser.add_argument(
        "--max-num-batched-tokens",
        type=int,
        default=4096,
        help="Max tokens can be batched by this service.",
    )
    parser.add_argument(
        "--max-num-seqs",
        type=int,
        default=8,
        help="Max requests can be batched by this service.",
    )

    global max_num_seqs
    global max_num_batched_tokens

    args = parser.parse_args()
    model_path = args.repo_id_or_model_path
    low_bit = args.low_bit
    max_num_seqs = args.max_num_seqs
    max_num_batched_tokens = args.max_num_batched_tokens
    load_model(model_path, low_bit)

    config = uvicorn.Config(app=app, host="0.0.0.0", port=args.port)
    server = uvicorn.Server(config)

    if local_rank == 0:
        await server.serve()
    else:
        while True:
            object_list = [None] * max_num_seqs
            dist.broadcast_object_list(object_list, src=0)
            await generate_stream_gate(
                [req.prompt for req in object_list],
                [req.n_predict for req in object_list],
            )


if __name__ == "__main__":
    asyncio.run(main())