156 lines
No EOL
6.3 KiB
Python
156 lines
No EOL
6.3 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.
|
|
#
|
|
|
|
|
|
# this code is copied from llama2 example test, and added performance test
|
|
import torch
|
|
import time
|
|
|
|
from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
|
|
from transformers import AutoTokenizer
|
|
import numpy as np
|
|
from datetime import date
|
|
|
|
import os
|
|
current_dir = os.path.dirname(os.path.realpath(__file__))
|
|
benchmark_util_path = os.path.join(current_dir, '..')
|
|
import sys
|
|
sys.path.append(benchmark_util_path)
|
|
from benchmark_util import BenchmarkWrapper
|
|
from bigdl.llm.utils.common.log4Error import invalidInputError
|
|
|
|
results = []
|
|
|
|
|
|
def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3):
|
|
# TODO: make a parameter
|
|
if test_api == 'transformer_int4':
|
|
result = run_transformer_int4(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials)
|
|
elif test_api == 'native_int4':
|
|
run_native_int4(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials)
|
|
|
|
for in_out_pair in in_out_pairs:
|
|
results.append([repo_id,
|
|
np.mean(result[in_out_pair], axis=0)[0],
|
|
np.mean(result[in_out_pair], axis=0)[1],
|
|
np.mean(result[in_out_pair], axis=0)[2],
|
|
in_out_pair])
|
|
|
|
def get_model_path(repo_id, local_model_hub):
|
|
if local_model_hub:
|
|
repo_model_name = repo_id.split("/")[1]
|
|
return local_model_hub + os.path.sep + repo_model_name
|
|
else:
|
|
return repo_id
|
|
|
|
|
|
def run_native_int4(repo_id,
|
|
local_model_hub,
|
|
in_out_pairs,
|
|
warm_up,
|
|
num_trials):
|
|
model_path = get_model_path(repo_id, local_model_hub)
|
|
from bigdl.llm.transformers import BigdlNativeForCausalLM
|
|
from bigdl.llm import llm_convert
|
|
if "chatglm" in repo_id.lower():
|
|
family = "chatglm"
|
|
elif "llama" in repo_id.lower():
|
|
family = "llama"
|
|
else:
|
|
invalidInputError(False, "Model family unknown: " + repo_id)
|
|
|
|
bigdl_llm_path = llm_convert(model=model_path,
|
|
outfile="./", outtype='int4', model_family=family)
|
|
for in_out in in_out_pairs:
|
|
in_out_len = in_out.split("-")
|
|
in_len = int(in_out_len[0])
|
|
out_len = int(in_out_len[1])
|
|
input_str = open(f"prompt/{in_len}.txt", 'r').read()
|
|
# As different tokenizer has different encodings,
|
|
# slice the input_ids to ensure the prompt length is required length.
|
|
n_ctx = in_len + out_len if in_len + out_len > 512 else 512
|
|
for i in range(num_trials + warm_up):
|
|
model = BigdlNativeForCausalLM.from_pretrained(bigdl_llm_path, model_family=family, n_ctx=n_ctx)
|
|
input_ids = model.tokenize(input_str)
|
|
input_ids = input_ids[:in_len]
|
|
true_input = model.batch_decode(input_ids)
|
|
st = time.perf_counter()
|
|
output = model(true_input, max_tokens=out_len)
|
|
end = time.perf_counter()
|
|
print("model generate cost: " + str(end - st))
|
|
print(output)
|
|
|
|
os.remove(bigdl_llm_path)
|
|
|
|
|
|
def run_transformer_int4(repo_id,
|
|
local_model_hub,
|
|
in_out_pairs,
|
|
warm_up,
|
|
num_trials):
|
|
model_path = get_model_path(repo_id, local_model_hub)
|
|
# Load model in 4 bit,
|
|
# which convert the relevant layers in the model into INT4 format
|
|
st = time.perf_counter()
|
|
if repo_id in ['THUDM/chatglm-6b', 'THUDM/chatglm2-6b']:
|
|
model = AutoModel.from_pretrained(model_path, load_in_4bit=True, trust_remote_code=True, torch_dtype='auto')
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
else:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
|
end = time.perf_counter()
|
|
print(">> loading of model costs {}s".format(end - st))
|
|
|
|
model = BenchmarkWrapper(model)
|
|
|
|
result = {}
|
|
with torch.inference_mode():
|
|
for in_out in in_out_pairs:
|
|
in_out_len = in_out.split("-")
|
|
in_len = int(in_out_len[0])
|
|
out_len = int(in_out_len[1])
|
|
input_str = open(f"prompt/{in_len}.txt", 'r').read()
|
|
# As different tokenizer has different encodings,
|
|
# slice the input_ids to ensure the prompt length is required length.
|
|
input_ids = tokenizer.encode(input_str, return_tensors="pt")
|
|
input_ids = input_ids[:, :in_len]
|
|
true_str = tokenizer.batch_decode(input_ids)[0]
|
|
input_ids = tokenizer.encode(true_str, return_tensors="pt")
|
|
result[in_out] = []
|
|
for i in range(num_trials + warm_up):
|
|
st = time.perf_counter()
|
|
output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len)
|
|
end = time.perf_counter()
|
|
print("model generate cost: " + str(end - st))
|
|
output = tokenizer.batch_decode(output_ids)
|
|
print(output[0])
|
|
if i >= warm_up:
|
|
result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time])
|
|
return result
|
|
|
|
|
|
if __name__ == '__main__':
|
|
from omegaconf import OmegaConf
|
|
conf = OmegaConf.load(f'{current_dir}/config.yaml')
|
|
today = date.today()
|
|
|
|
import pandas as pd
|
|
for api in conf.test_api:
|
|
for model in conf.repo_id:
|
|
run_model(model, api, conf['in_out_pairs'], conf['local_model_hub'], conf['warm_up'], conf['num_trials'])
|
|
df = pd.DataFrame(results, columns=['model', '1st token avg latency (s)', '2+ avg latency (s/token)', 'encoder time (s)', 'input/output tokens'])
|
|
df.to_csv(f'{current_dir}/{api}-results-{today}.csv')
|
|
result = [] |