forked from EngineX-Cambricon/enginex-mlu370-vllm
62 lines
2.6 KiB
Python
62 lines
2.6 KiB
Python
import torch
|
|
import torch_mlu
|
|
import torch_mlu_ops as tmo
|
|
from common import *
|
|
import argparse
|
|
from tabulate import tabulate
|
|
import os
|
|
import random
|
|
|
|
params_dict = {"dynamic": [True],
|
|
"token_num": [1, 72, 490, 512, 525, 1024, 4096, 8192, 32768],
|
|
"hidden_size": [8192, 1024],
|
|
"input_dtype": [torch.bfloat16]}
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--repeat_times', type=int, default=10, help='repeat times for testing')
|
|
parser.add_argument('--csv', action='store_true', help='write the report data to csv')
|
|
parser.add_argument('-o', type=str, help='specify the output folder name under --csv mode')
|
|
|
|
args = parser.parse_args()
|
|
device = 'mlu'
|
|
titles = ["dynamic", "token_num", "hidden_size", "input_dytpe", "hardware_time(us)", "e2e_latency(us)", "IO efficiency"]
|
|
contents = []
|
|
params_list = product(params_dict["dynamic"], params_dict["token_num"], params_dict["hidden_size"], params_dict["input_dtype"])
|
|
bd = get_band_width()
|
|
for param in params_list:
|
|
dynamic, token_num, hidden_size, dtype = param[0], param[1], param[2], param[3]
|
|
input_shape = (token_num, hidden_size)
|
|
if dtype == torch.bfloat16 and not torch_mlu.mlu.is_bf16_supported():
|
|
dtype = torch.half
|
|
input = torch.randn(input_shape).to(device).to(dtype)
|
|
scale = torch.randn(input_shape[-1]).to(device).to(torch.float32)
|
|
zero = None
|
|
if dynamic:
|
|
hardware_time, e2e_time = benchmark_forward(tmo.per_token_smooth_quantize,
|
|
input,
|
|
scale,
|
|
zero,
|
|
None,
|
|
repeats=args.repeat_times)
|
|
else:
|
|
hardware_time, e2e_time = benchmark_forward(tmo.quantize,
|
|
input,
|
|
scale,
|
|
zero,
|
|
repeats=args.repeat_times)
|
|
io_bytes = (input.element_size() + 1) * input.nelement() + scale.element_size() * scale.nelement()
|
|
io_eff = io_bytes / hardware_time / bd
|
|
content = [f"{dynamic}", f"{token_num}", f"{hidden_size}", f"{dtype}", f"{hardware_time}", f"{e2e_time}", f"{io_eff}"]
|
|
contents.append(content)
|
|
table = [titles] + contents
|
|
print(tabulate(table, headers="firstrow", tablefmt="grid"))
|
|
|
|
if args.csv:
|
|
current_file_path = __file__
|
|
_, file_name = os.path.split(current_file_path)
|
|
save_to_csv(table, args.o, file_name)
|
|
|
|
if __name__=="__main__":
|
|
main()
|