9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
139 lines
6 KiB
Python
139 lines
6 KiB
Python
#
|
|
# 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 deepspeed
|
|
|
|
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)
|
|
|
|
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")
|
|
|
|
|
|
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
|
|
from deepspeed.accelerator.cpu_accelerator import CPU_Accelerator
|
|
from deepspeed.accelerator import set_accelerator, get_accelerator
|
|
from intel_extension_for_deepspeed import XPU_Accelerator
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
|
|
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('--prompt', type=str, default="Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun",
|
|
help='Prompt to infer')
|
|
parser.add_argument('--n-predict', type=int, default=32,
|
|
help='Max tokens to predict')
|
|
parser.add_argument('--low-bit', type=str, default='sym_int4',
|
|
help='The quantization type the model will convert to.')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
low_bit = args.low_bit
|
|
|
|
# First use CPU as accelerator
|
|
# Convert to deepspeed model and apply bigdl-llm optimization on CPU to decrease GPU memory usage
|
|
current_accel = CPU_Accelerator()
|
|
set_accelerator(current_accel)
|
|
model = AutoModelForCausalLM.from_pretrained(args.repo_id_or_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,
|
|
mp_size=world_size,
|
|
dtype=torch.float16,
|
|
replace_method="auto",
|
|
)
|
|
|
|
# Use bigdl-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}')
|
|
|
|
# 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()
|
|
|
|
print(model)
|
|
|
|
# Load tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
prompt = args.prompt
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(f'xpu:{local_rank}')
|
|
# ipex model needs a warmup, then inference time can be accurate
|
|
output = model.generate(input_ids,
|
|
max_new_tokens=args.n_predict,
|
|
use_cache=True)
|
|
|
|
# start inference
|
|
st = time.time()
|
|
# if your selected model is capable of utilizing previous key/value attentions
|
|
# to enhance decoding speed, but has `"use_cache": false` in its model config,
|
|
# it is important to set `use_cache=True` explicitly in the `generate` function
|
|
# to obtain optimal performance with BigDL-LLM INT4 optimizations
|
|
output = model.generate(input_ids,
|
|
do_sample=False,
|
|
max_new_tokens=args.n_predict)
|
|
torch.xpu.synchronize()
|
|
end = time.time()
|
|
if local_rank == 0:
|
|
output = output.cpu()
|
|
actual_output_len = output.shape[1] - input_ids.shape[1]
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
avg_time = (end - st) / actual_output_len * 1000
|
|
print(f'Inference time of generating {actual_output_len} tokens: {end-st} s, average token latency is {avg_time} ms/token.')
|
|
print('-'*20, 'Prompt', '-'*20)
|
|
print(prompt)
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(output_str)
|
|
deepspeed.comm.destroy_process_group()
|
|
print("process group destroyed, exiting...")
|