add qwen3

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Chranos
2026-02-04 17:22:39 +08:00
parent d1c0f68ab4
commit 8511fe8530
1932 changed files with 300426 additions and 0 deletions

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import math
import pickle
import re
from collections import defaultdict
from typing import List
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from torch.utils.benchmark import Measurement as TMeasurement
from vllm.utils import FlexibleArgumentParser
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description='Benchmark the latency of processing a single batch of '
'requests till completion.')
parser.add_argument('filename', type=str)
args = parser.parse_args()
with open(args.filename, 'rb') as f:
data: List[TMeasurement] = pickle.load(f)
results = defaultdict(lambda: list())
for v in data:
result = re.search(r"MKN=\(\d+x(\d+x\d+)\)", v.task_spec.sub_label)
if result is not None:
KN = result.group(1)
else:
raise Exception("MKN not found")
result = re.search(r"MKN=\((\d+)x\d+x\d+\)", v.task_spec.sub_label)
if result is not None:
M = result.group(1)
else:
raise Exception("MKN not found")
kernel = v.task_spec.description
results[KN].append({
"kernel": kernel,
"batch_size": M,
"median": v.median
})
rows = int(math.ceil(len(results) / 2))
fig, axs = plt.subplots(rows, 2, figsize=(12, 5 * rows))
axs = axs.flatten()
for axs_idx, (shape, data) in enumerate(results.items()):
plt.sca(axs[axs_idx])
df = pd.DataFrame(data)
sns.lineplot(data=df,
x="batch_size",
y="median",
hue="kernel",
style="kernel",
markers=True,
dashes=False,
palette="Dark2")
plt.title(f"Shape: {shape}")
plt.ylabel("time (median, s)")
plt.tight_layout()
plt.savefig("graph_machete_bench.pdf")