mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-31 11:39:52 +00:00
46bf73a37f
* examples : add new sweep-bench benchmark * Change documentation to reference ik_llama.cpp * Made it compile with ik_llama * Fix JSONL output --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
101 lines
2.8 KiB
Python
Executable File
101 lines
2.8 KiB
Python
Executable File
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('file', nargs='+')
|
|
args = parser.parse_args()
|
|
|
|
df = None
|
|
|
|
for jsonl_file in args.file:
|
|
# Read JSONL file into DataFrame
|
|
df_part = pd.read_json(jsonl_file, lines=True)
|
|
df_part['label'] = jsonl_file
|
|
if df is None:
|
|
df = df_part
|
|
else:
|
|
df = pd.concat([df, df_part])
|
|
|
|
# Group by model and n_kv, calculate mean and std for both speed metrics
|
|
df_grouped = df.groupby(['label', 'n_kv']).agg({
|
|
'speed_pp': ['mean', 'std'],
|
|
'speed_tg': ['mean', 'std']
|
|
}).reset_index()
|
|
|
|
# Flatten multi-index columns
|
|
df_grouped.columns = ['label', 'n_kv', 'speed_pp_mean', 'speed_pp_std',
|
|
'speed_tg_mean', 'speed_tg_std']
|
|
|
|
# Replace NaN with 0 (std for a single sample is NaN)
|
|
|
|
df_grouped['speed_pp_std'] = df_grouped['speed_pp_std'].fillna(0)
|
|
df_grouped['speed_tg_std'] = df_grouped['speed_tg_std'].fillna(0)
|
|
|
|
# Prepare ticks values for X axis (prune for readability)
|
|
x_ticks = df['n_kv'].unique()
|
|
while len(x_ticks) > 16:
|
|
x_ticks = x_ticks[::2]
|
|
|
|
# Get unique labels and color map
|
|
labels = df_grouped['label'].unique()
|
|
colors = plt.cm.rainbow(np.linspace(0, 1, len(labels)))
|
|
|
|
# Create prompt processing plot
|
|
plt.figure(figsize=(10, 6))
|
|
ax1 = plt.gca()
|
|
|
|
plt.grid()
|
|
|
|
ax1.set_xticks(x_ticks)
|
|
|
|
# Plot each label's data
|
|
for label, color in zip(labels, colors):
|
|
label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
|
|
|
|
# Plot prompt processing
|
|
pp = ax1.errorbar(label_data['n_kv'], label_data['speed_pp_mean'],
|
|
yerr=label_data['speed_pp_std'], color=color,
|
|
marker='o', linestyle='-', label=label)
|
|
|
|
# Add labels and title
|
|
ax1.set_xlabel('Context Length (tokens)')
|
|
ax1.set_ylabel('Prompt Processing Rate (t/s)')
|
|
plt.title('Prompt Processing Performance Comparison')
|
|
|
|
ax1.legend(loc='upper right')
|
|
|
|
# Adjust layout and save
|
|
plt.tight_layout()
|
|
plt.savefig('performance_comparison_pp.png', bbox_inches='tight')
|
|
plt.close()
|
|
|
|
# Create token generation plot
|
|
plt.figure(figsize=(10, 6))
|
|
ax1 = plt.gca()
|
|
|
|
plt.grid()
|
|
ax1.set_xticks(x_ticks)
|
|
|
|
# Plot each model's data
|
|
for label, color in zip(labels, colors):
|
|
label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
|
|
|
|
# Plot token generation
|
|
tg = ax1.errorbar(label_data['n_kv'], label_data['speed_tg_mean'],
|
|
yerr=label_data['speed_tg_std'], color=color,
|
|
marker='s', linestyle='-', label=label)
|
|
|
|
# Add labels and title
|
|
ax1.set_xlabel('Context Length (n_kv)')
|
|
ax1.set_ylabel('Token Generation Rate (t/s)')
|
|
plt.title('Token Generation Performance Comparison')
|
|
|
|
ax1.legend(loc='upper right')
|
|
|
|
# Adjust layout and save
|
|
plt.tight_layout()
|
|
plt.savefig('performance_comparison_tg.png', bbox_inches='tight')
|
|
plt.close()
|