scripts/plot_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7943b934",
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd /home/ubuntu/Qwen-Image-Edit-Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5de64216",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07aff18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qwenimage.experiment import ExperimentConfig\n",
    "from qwenimage.experiments.experiments_qwen import ExperimentRegistry\n",
    "\n",
    "\n",
    "# experiment_names = ExperimentRegistry.keys()\n",
    "experiment_names = [\n",
    "\n",
    "    \"qwen_base\",\n",
    "    \"qwen_fa3\",\n",
    "    \"qwen_aot\",\n",
    "    \"qwen_fuse_aot\",\n",
    "    \"qwen_fa3_aot\",\n",
    "\n",
    "    \"qwen_fbcache_052\",\n",
    "    \"qwen_fbcache_053\",\n",
    "    \"qwen_fbcache_054\",\n",
    "    \"qwen_fbcache_055\",\n",
    "\n",
    "    \"qwen_fa3_aot_fp8\",\n",
    "    \"qwen_fa3_aot_fp8_fuse\",\n",
    "\n",
    "    \"qwen_base_3step\",\n",
    "    \"qwen_base_2step\",\n",
    "    \"qwen_fa3_aot_int8_fuse_3step\",\n",
    "    \"qwen_fa3_aot_int8_fuse_2step\",\n",
    "\n",
    "    \"qwen_lightning_lora\",\n",
    "    \"qwen_lightning_lora_2step\",\n",
    "    \"qwen_lightning_lora_3step\",\n",
    "    \"qwen_lightning_fa3_aot_int8_fuse_3step\",\n",
    "    \"qwen_lightning_fa3_aot_int8_fuse_2step\",\n",
    "    \"qwen_lightning_fa3_aot_fp8_fuse\",\n",
    "\n",
    "]\n",
    "\n",
    "report_dir = ExperimentConfig().report_dir\n",
    "\n",
    "all_results = []\n",
    "for name in experiment_names:\n",
    "    csv_path = report_dir / f\"{name}.csv\"\n",
    "    \n",
    "    df = pd.read_csv(csv_path, index_col=0)\n",
    "    df['experiment'] = name\n",
    "    all_results.append(df)\n",
    "    print(f\"Loaded results for {name}: {len(df)} rows\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53cb7629",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bb86726",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df = pd.concat(all_results, ignore_index=True)\n",
    "print(f\"{combined_df.shape=}\")\n",
    "print(f\"{combined_df.columns.tolist()=}\")\n",
    "combined_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fcd8c1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fac9587f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile_targets = [\"loop\", \"run_once\"]\n",
    "profile_targets = {\n",
    "    \"loop\": \"equal\",\n",
    "    \"run_once\": \"contain\"\n",
    "}\n",
    "\n",
    "for target, match_strategy in profile_targets.items():\n",
    "    if match_strategy == \"equal\":\n",
    "        plot_data = combined_df[combined_df['name'] == target].copy()\n",
    "    elif match_strategy == \"contain\":\n",
    "        plot_data = combined_df[combined_df['name'].str.contains(target, case=False, na=False)].copy()\n",
    "    else:\n",
    "        raise ValueError()\n",
    "    plot_data = plot_data.sort_values('mean', ascending=False)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    x_pos = range(len(plot_data))\n",
    "    max_time = plot_data['mean'].max()\n",
    "\n",
    "    bars = ax.bar(x_pos, plot_data['mean'], yerr=plot_data['std'], \n",
    "                    capsize=12, alpha=0.7, edgecolor='black')\n",
    "    ax.set_xlabel('Optimization Type', fontsize=12, fontweight='bold')\n",
    "    ax.set_ylabel('Time (s)', fontsize=12, fontweight='bold')\n",
    "    ax.set_title(f'Optimizations Comparison Over: {target}', \n",
    "                    fontsize=14, fontweight='bold')\n",
    "    ax.set_xticks(x_pos)\n",
    "    ax.set_xticklabels([row['experiment'] for _, row in plot_data.iterrows()], \n",
    "                        rotation=45, ha='right', fontsize=12)\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "\n",
    "    for i, (idx, row) in enumerate(plot_data.iterrows()):        \n",
    "        ax.text(i - 0.2, row['mean'] + 0.01, f\"{row['mean']:.3f}s\", \n",
    "                ha='center', va='bottom', fontsize=12)\n",
    "        \n",
    "        pct_decrease = ((max_time - row['mean']) / max_time) * 100\n",
    "        ax.text(i + 0.2, row['mean'] + 0.01, f\"(-{pct_decrease:.1f}%)\", \n",
    "                ha='center', va='bottom', fontsize=12, color='green')\n",
    "    \n",
    "    \n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plot_path = report_dir / f'{target}_performance_comparison.png'\n",
    "    plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eec1ec7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_targets = [\"Preprocessing\", \"Encode Prompt\", \"Prep gen\", \"loop\", \"pre decode\", \"vae.decode\", \"post process\", \"offload\"]\n",
    "\n",
    "\n",
    "stack_data = combined_df[combined_df['name'].isin(stack_targets)].copy()\n",
    "pivot_data = stack_data.pivot(index='experiment', columns='name', values='mean')\n",
    "pivot_data = pivot_data[stack_targets]\n",
    "pivot_data['total'] = pivot_data.sum(axis=1)\n",
    "pivot_data = pivot_data.sort_values('total', ascending=False)\n",
    "pivot_data = pivot_data.drop('total', axis=1)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "pivot_data.plot(kind='bar', stacked=True, ax=ax, \n",
    "                colormap='viridis', edgecolor='black', capsize=12, alpha=0.7, width=0.8)\n",
    "\n",
    "\n",
    "ax.set_xlabel('Optimization Type', fontsize=12, fontweight='bold')\n",
    "ax.set_ylabel('Time (s)', fontsize=12, fontweight='bold')\n",
    "ax.set_title('Pipeline Time Breakdown by Optimization', \n",
    "             fontsize=14, fontweight='bold')\n",
    "ax.set_xticklabels(pivot_data.index, rotation=45, ha='right', fontsize=12)\n",
    "ax.legend(title='Pipeline Stage', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=10)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "max_time = pivot_data.sum(axis=1).max()\n",
    "for i, (idx, row) in enumerate(pivot_data.iterrows()):\n",
    "    total = row.sum()\n",
    "    \n",
    "    ax.text(i - 0.2, total + 0.01, f'{total:.3f}s', \n",
    "            ha='center', va='bottom', fontsize=12, fontweight='bold')\n",
    "    \n",
    "    pct_decrease = ((max_time - total) / max_time) * 100\n",
    "    ax.text(i + 0.2, total + 0.01, f'(-{pct_decrease:.1f}%)', \n",
    "            ha='center', va='bottom', fontsize=12, color='green', fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plot_path = report_dir / 'stacked_time_breakdown.png'\n",
    "plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db8bf253",
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd23a11b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b386ec5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c2c15ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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