LLMs-from-scratch/ch04/02_performance-analysis/flops-analysis.ipynb

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    "<table style=\"width:100%\">\n",
    "<tr>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<font size=\"2\">\n",
    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
    "</font>\n",
    "</td>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
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    "## FLOPS Analysis"
   ]
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    "- FLOPs (Floating Point Operations Per Second) measure the computational complexity of neural network models by counting the number of floating-point operations executed\n",
    "-  High FLOPs indicate more intensive computation and energy consumption"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# pip install -r requirements-extra.txt"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thop version: 0.1.1-2209072238\n",
      "torch version: 2.2.2\n",
      "tiktoken version: 0.5.1\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "import matplotlib\n",
    "import torch\n",
    "\n",
    "print(\"thop version:\", version(\"thop\"))\n",
    "print(\"torch version:\", version(\"torch\"))"
   ]
  },
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     "text": [
      "gpt-small (124M)  : 5.1e+11 FLOPS\n",
      "gpt-medium (355M) : 1.4e+12 FLOPS\n",
      "gpt-large (774M)  : 3.2e+12 FLOPS\n",
      "gpt-xl (1558M)    : 6.4e+12 FLOPS\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from thop import profile\n",
    "\n",
    "from previous_chapters import GPTModel\n",
    "\n",
    "\n",
    "BASE_CONFIG = {\n",
    "    \"vocab_size\": 50257,     # Vocabulary size\n",
    "    \"context_length\": 1024,  # Context length\n",
    "    \"drop_rate\": 0.0,        # Dropout rate\n",
    "    \"qkv_bias\": True         # Query-key-value bias\n",
    "}\n",
    "\n",
    "model_configs = {\n",
    "    \"gpt-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
    "    \"gpt-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
    "    \"gpt-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
    "    \"gpt-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
    "}\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "input_tensor = torch.randint(0, 50257, (2, 1024)).to(device)\n",
    "\n",
    "for size in model_configs:\n",
    "    BASE_CONFIG.update(model_configs[size])\n",
    "    \n",
    "    model = GPTModel(BASE_CONFIG).bfloat16()\n",
    "    model.to(device)\n",
    "\n",
    "    # MACS = multiply-accumulate operations\n",
    "    # MACS are typically counted as two FLOPS (one multiply and one accumulate)\n",
    "    macs, params = profile(model, inputs=(input_tensor,), verbose=False)\n",
    "    flops = 2*macs\n",
    "    print(f\"{size:18}: {flops:.1e} FLOPS\")\n",
    "    \n",
    "    del model\n",
    "    torch.cuda.empty_cache()"
   ]
  }
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