Created
August 2, 2022 09:29
-
-
Save younesbelkada/813e2b753d7f56a52ab6e3838959f1a2 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import time | |
import torch | |
import numpy as np | |
import argparse | |
from transformers import pipeline | |
parser = argparse.ArgumentParser(description='Benchmark pipeline runtime for int8 models') | |
parser.add_argument('--batch_size', default=1, type=int, help='batch_size for experiments') | |
parser.add_argument('--nb_runs', default=10, type=int, help='number of times for repeating experiments') | |
parser.add_argument('--nb_gpus', default=7, type=int, help='number of GPUs to use') | |
parser.add_argument('--seq_length', default=20, type=int, help='maximum number of tokens to generate') | |
parser.add_argument('--max_memory', default="30GB", type=str, help='Maximum memory to use for each GPU') | |
parser.add_argument('--model', type=str) | |
args = parser.parse_args() | |
NB_RUNS = args.nb_runs | |
BATCH_SIZE=args.batch_size | |
def get_input(): | |
input_test = ["test" for _ in range(BATCH_SIZE)] | |
return input_test | |
def run_pipeline(): | |
total_time = [] | |
for _ in range(NB_RUNS): | |
start = time.perf_counter() | |
_ = pipe(input_test) | |
end = time.perf_counter() | |
torch.cuda.synchronize() | |
total_time.append(end-start) | |
return total_time | |
def get_gpus_max_memory(max_memory, n_gpus): | |
assert n_gpus <= torch.cuda.device_count(), "You are requesting more GPUs than available GPUs" | |
max_memory = {i: max_memory for i in range(n_gpus)} | |
return max_memory | |
input_test = get_input() | |
mapping_gpu_memory = get_gpus_max_memory(args.max_memory, args.nb_gpus) | |
pipe = pipeline(model=args.model, model_kwargs= {"device_map": "auto", "torch_dtype": torch.float16, "max_memory":mapping_gpu_memory}, max_new_tokens=args.seq_length, batch_size=args.batch_size, use_fast=False) | |
# Do a dummy run | |
_ = pipe(input_test) | |
total_time = run_pipeline() | |
print("Time elapsed: {} +- {}".format(np.mean(total_time), np.std(total_time))) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment