Chranos/enginex-mlu370-vllm
1
0
Fork 0
forked from EngineX-Cambricon/enginex-mlu370-vllm
Code Pull Requests Activity

add ops

Browse Source
This commit is contained in:
Chranos
2026-02-04 17:39:32 +08:00
parent 8511fe8530
commit 79dfc69789
299 changed files with 55927 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

93
torch_mlu_ops-v1.3.2/benchmarks/benchmark_moe_expand_input.py Normal file
View File

@@ -0,0 +1,93 @@
import torch
import torch_mlu
import torch_mlu_ops as tmo
from common import *
import argparse
from tabulate import tabulate
import os
import numpy as np
e2e_time_param_dict_list = [{"token_num": 1, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 16, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 32, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 64, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 128, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 512, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 1024, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 4096, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 8192, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]},
{"token_num": 32768, "hidden_size": 4096, "expert_num": 32, "topk": 5,
"start_expert_id": 0, "expert_size": 32, "input_dtype": [torch.int8, torch.float16, torch.bfloat16]}]
def gen_tensor(token_num, hidden_size, expert_num, topk, start_expert_id, expert_size, dtype):
input = torch.randn(token_num, hidden_size).to(dtype).to('mlu')
gather_idx = torch.randint(low=0, high=token_num, size=(token_num * topk,)).to(torch.int32).to('mlu')
cusum_token_count, _ = generate_token_count(expert_num, token_num * topk)
cusum_token_count = cusum_token_count.to('mlu')
use_all_experts = expert_num == expert_size
if use_all_experts:
cusum_token_count = None
real_token_count = token_num * topk
else:
real_token_count = cusum_token_count[start_expert_id+expert_size] - cusum_token_count[start_expert_id]
return input, gather_idx, cusum_token_count, real_token_count
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()
titles = ["token_num", "hidden_size", "expert_num", "topk", "start_expert_id", "expert_size", "input_dtype",
"hardware_time(us)", "e2e_latency(us)", "IO efficiency"]
contents = []
bd = get_band_width()
for params_dict in e2e_time_param_dict_list:
token_num = params_dict["token_num"]
hidden_size = params_dict["hidden_size"]
expert_num = params_dict["expert_num"]
topk = params_dict["topk"]
start_expert_id = params_dict["start_expert_id"]
expert_size = params_dict["expert_size"]
input_dtype_list = params_dict["input_dtype"]
for dtype in input_dtype_list:
if dtype == torch.bfloat16 and not torch_mlu.mlu.is_bf16_supported():
continue
input, gather_idx, cusum_token_count, real_token_count = \
gen_tensor(token_num, hidden_size, expert_num,topk, start_expert_id, expert_size, dtype)
hardware_time, e2e_time = benchmark_forward(tmo.moe_expand_input,
input,
gather_idx,
cusum_token_count,
start_expert_id,
expert_size,
repeats=args.repeat_times)
io_bytes = input.element_size() * input.nelement() + \
gather_idx.element_size() * gather_idx.nelement() + \
(cusum_token_count.element_size() * cusum_token_count.nelement() if cusum_token_count is not None else 0) + \
real_token_count * input.element_size()
io_eff = io_bytes / hardware_time / bd
content = [f"{token_num}", f"{hidden_size}", f"{expert_num}", f"{topk}", f"{start_expert_id}", f"{expert_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()