{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🔬 BERT Compression POC — Full Walkthrough\n",
    "\n",
    "This notebook walks through every step of the compression pipeline:\n",
    "\n",
    "1. Load a pretrained DistilBERT model\n",
    "2. Measure its baseline performance (size, speed, accuracy)\n",
    "3. Apply INT8 Dynamic Quantization\n",
    "4. Measure quantized performance\n",
    "5. Compare results side by side\n",
    "\n",
    "**Model:** `distilbert-base-uncased-finetuned-sst-2-english`  \n",
    "**Task:** Sentiment Analysis (Positive / Negative)  \n",
    "**Dataset:** SST-2 (Stanford Sentiment Treebank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0 — Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.insert(0, '../src')\n",
    "\n",
    "import torch\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "print(f'PyTorch version : {torch.__version__}')\n",
    "print(f'CUDA available  : {torch.cuda.is_available()}')\n",
    "print(f'Device          : {\"GPU\" if torch.cuda.is_available() else \"CPU\"}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 — Load the Baseline Model\n",
    "\n",
    "We use `distilbert-base-uncased-finetuned-sst-2-english` — a DistilBERT model already fine-tuned on SST-2 sentiment data by HuggingFace. This is our **FP32 baseline** — full 32-bit floating point precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from load_model import load_tokenizer, load_baseline_model, get_model_size_mb, count_parameters\n",
    "\n",
    "tokenizer = load_tokenizer()\n",
    "baseline_model = load_baseline_model()\n",
    "\n",
    "baseline_size = get_model_size_mb(model=baseline_model)\n",
    "baseline_params = count_parameters(baseline_model)\n",
    "\n",
    "print(f'\\nBaseline Model Summary')\n",
    "print(f'  Parameters : {baseline_params:,}')\n",
    "print(f'  Size (est.): {baseline_size} MB')\n",
    "print(f'  Precision  : FP32 (32-bit float)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 — Test the Baseline on a Few Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evaluate import predict_single\n",
    "\n",
    "test_sentences = [\n",
    "    \"This movie was absolutely fantastic, I loved every minute of it.\",\n",
    "    \"The food was terrible and the service was even worse.\",\n",
    "    \"An average experience, nothing special but nothing terrible either.\",\n",
    "    \"One of the best books I have ever read in my entire life.\",\n",
    "    \"Complete waste of time and money. Would not recommend.\"\n",
    "]\n",
    "\n",
    "print(f'{'Text':<55} {'Label':<12} {'Conf':>6} {'Time':>8}')\n",
    "print('-' * 85)\n",
    "for text in test_sentences:\n",
    "    label, conf, ms = predict_single(baseline_model, tokenizer, text)\n",
    "    short_text = text[:52] + '...' if len(text) > 52 else text\n",
    "    print(f'{short_text:<55} {label:<12} {conf:>6.1%} {ms:>7.1f}ms')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 — Evaluate Baseline on SST-2 (200 samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from evaluate import evaluate_model\n",
    "\n",
    "print('Evaluating baseline model on 200 SST-2 samples...')\n",
    "baseline_results = evaluate_model(baseline_model, tokenizer, num_samples=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 — Apply INT8 Dynamic Quantization\n",
    "\n",
    "**What is happening here?**\n",
    "\n",
    "PyTorch's `quantize_dynamic` converts all `nn.Linear` layers from FP32 to INT8. \n",
    "- Weights are permanently converted to 8-bit integers\n",
    "- Activations are quantized dynamically at runtime\n",
    "- No retraining needed — this is post-training quantization (PTQ)\n",
    "\n",
    "3 lines of code. That's it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from quantize import apply_dynamic_quantization, save_quantized_model\n",
    "\n",
    "# Apply quantization\n",
    "quantized_model = apply_dynamic_quantization(baseline_model)\n",
    "\n",
    "# Save to disk\n",
    "quantized_size_disk = save_quantized_model(quantized_model)\n",
    "\n",
    "print(f'\\nQuantization complete!')\n",
    "print(f'  Baseline size : {baseline_size} MB')\n",
    "print(f'  Quantized size: {quantized_size_disk} MB')\n",
    "print(f'  Reduction     : {round((1 - quantized_size_disk/baseline_size)*100, 1)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 — Evaluate Quantized Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Evaluating quantized model on 200 SST-2 samples...')\n",
    "quantized_results = evaluate_model(quantized_model, tokenizer, num_samples=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 — Side-by-Side Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "speedup = round(baseline_results['avg_inference_ms'] / quantized_results['avg_inference_ms'], 2)\n",
    "size_reduction = round(\n",
    "    ((baseline_results['model_size_mb'] - quantized_results['model_size_mb'])\n",
    "     / baseline_results['model_size_mb']) * 100, 1\n",
    ")\n",
    "accuracy_drop = round(\n",
    "    baseline_results['accuracy_pct'] - quantized_results['accuracy_pct'], 2\n",
    ")\n",
    "\n",
    "comparison = {\n",
    "    'Metric': ['Accuracy (%)', 'Avg Inference Time (ms)', 'P95 Inference Time (ms)', 'Model Size (MB)'],\n",
    "    'Baseline (FP32)': [\n",
    "        f\"{baseline_results['accuracy_pct']}%\",\n",
    "        f\"{baseline_results['avg_inference_ms']} ms\",\n",
    "        f\"{baseline_results['p95_inference_ms']} ms\",\n",
    "        f\"{baseline_results['model_size_mb']} MB\"\n",
    "    ],\n",
    "    'Quantized (INT8)': [\n",
    "        f\"{quantized_results['accuracy_pct']}%\",\n",
    "        f\"{quantized_results['avg_inference_ms']} ms\",\n",
    "        f\"{quantized_results['p95_inference_ms']} ms\",\n",
    "        f\"{quantized_results['model_size_mb']} MB\"\n",
    "    ],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(comparison)\n",
    "df = df.set_index('Metric')\n",
    "print(df.to_string())\n",
    "\n",
    "print(f'\\n--- Summary ---')\n",
    "print(f'Speedup Factor  : {speedup}x faster')\n",
    "print(f'Size Reduction  : {size_reduction}% smaller')\n",
    "print(f'Accuracy Cost   : -{accuracy_drop}% drop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 — Inference on Same Sentences (Baseline vs Quantized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'{'Text':<45} {'Baseline':>12} {'Quantized':>12} {'Match':>6}')\n",
    "print('-' * 80)\n",
    "\n",
    "for text in test_sentences:\n",
    "    b_label, b_conf, b_ms = predict_single(baseline_model, tokenizer, text)\n",
    "    q_label, q_conf, q_ms = predict_single(quantized_model, tokenizer, text)\n",
    "    match = '✓' if b_label == q_label else '✗'\n",
    "    short = text[:42] + '...' if len(text) > 42 else text\n",
    "    print(f'{short:<45} {b_label:>12} {q_label:>12} {match:>6}')\n",
    "\n",
    "print('\\n✓ = both models agree | ✗ = predictions differ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Dynamic INT8 quantization applied to DistilBERT demonstrates that:\n",
    "\n",
    "- **~75% model size reduction** with no architectural changes\n",
    "- **~3x inference speedup** on CPU\n",
    "- **Less than 1% accuracy drop** on SST-2 sentiment classification\n",
    "\n",
    "This is post-training quantization — no retraining, no labelled data required. Applied in under 30 seconds.\n",
    "\n",
    "For production edge deployment, this can be further combined with **structured pruning** and **knowledge distillation** to push the compression ratio even further while maintaining acceptable accuracy."
   ]
  }
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