mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
119 lines
3.6 KiB
Python
Executable File
119 lines
3.6 KiB
Python
Executable File
import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('file', nargs='+')
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args = parser.parse_args()
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df = None
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#for jsonl_file in args.file:
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# # Read JSONL file into DataFrame
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# df_part = pd.read_json(jsonl_file, lines=True)
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# df_part['label'] = jsonl_file
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# if df is None:
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# df = df_part
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# else:
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# df = pd.concat([df, df_part])
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#
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for md_file in args.file:
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# Read markdown table file into DataFrame
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df_part = pd.read_csv(md_file, sep=r'\s*\|\s*', engine='python',
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header=0, skiprows=[1])
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# Clean up columns (remove empty columns from markdown formatting)
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df_part = df_part.iloc[:, 1:-1]
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df_part.columns = [col.strip() for col in df_part.columns]
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# Rename columns to match expected names
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df_part = df_part.rename(columns={
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'N_KV': 'n_kv',
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'S_PP t/s': 'speed_pp',
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'S_TG t/s': 'speed_tg'
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})
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# Convert to numeric types
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df_part['n_kv'] = pd.to_numeric(df_part['n_kv'])
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df_part['speed_pp'] = pd.to_numeric(df_part['speed_pp'])
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df_part['speed_tg'] = pd.to_numeric(df_part['speed_tg'])
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# Add label and append to main DataFrame
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df_part['label'] = md_file
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df = pd.concat([df, df_part]) if df is not None else df_part
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# Group by label and n_kv, calculate mean and std for both speed metrics
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df_grouped = df.groupby(['label', 'n_kv']).agg({
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'speed_pp': ['mean', 'std'],
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'speed_tg': ['mean', 'std']
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}).reset_index()
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# Flatten multi-index columns
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df_grouped.columns = ['label', 'n_kv', 'speed_pp_mean', 'speed_pp_std',
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'speed_tg_mean', 'speed_tg_std']
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# Replace NaN with 0 (std for a single sample is NaN)
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df_grouped['speed_pp_std'] = df_grouped['speed_pp_std'].fillna(0)
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df_grouped['speed_tg_std'] = df_grouped['speed_tg_std'].fillna(0)
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# Prepare ticks values for X axis (prune for readability)
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x_ticks = df['n_kv'].unique()
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while len(x_ticks) > 16:
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x_ticks = x_ticks[::2]
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# Get unique labels and color map
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labels = df_grouped['label'].unique()
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colors = plt.cm.rainbow(np.linspace(0, 1, len(labels)))
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# Create prompt processing plot
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plt.figure(figsize=(10, 6))
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ax1 = plt.gca()
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plt.grid()
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ax1.set_xticks(x_ticks)
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# Plot each label's data
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for label, color in zip(labels, colors):
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label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
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pp = ax1.errorbar(label_data['n_kv'], label_data['speed_pp_mean'],
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yerr=label_data['speed_pp_std'], color=color,
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marker='o', linestyle='-', label=label)
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# Add labels and title
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ax1.set_xlabel('Context Length (tokens)')
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ax1.set_ylabel('Prompt Processing Rate (t/s)')
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plt.title('Prompt Processing Performance Comparison')
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ax1.legend(loc='upper right')
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# Adjust layout and save
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plt.tight_layout()
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plt.savefig('performance_comparison_pp.png', bbox_inches='tight')
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plt.close()
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# Create token generation plot
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plt.figure(figsize=(10, 6))
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ax1 = plt.gca()
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plt.grid()
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ax1.set_xticks(x_ticks)
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# Plot each model's data
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for label, color in zip(labels, colors):
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label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
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tg = ax1.errorbar(label_data['n_kv'], label_data['speed_tg_mean'],
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yerr=label_data['speed_tg_std'], color=color,
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marker='s', linestyle='-', label=label)
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# Add labels and title
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ax1.set_xlabel('Context Length (n_kv)')
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ax1.set_ylabel('Token Generation Rate (t/s)')
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plt.title('Token Generation Performance Comparison')
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ax1.legend(loc='upper right')
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# Adjust layout and save
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plt.tight_layout()
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plt.savefig('performance_comparison_tg.png', bbox_inches='tight')
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plt.close()
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