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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<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",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FLOPS Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 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",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install -r requirements-extra.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thop version: 0.1.1-2209072238\n",
      "torch version: 2.2.1+cu121\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "pkgs = [\n",
    "    \"thop\",\n",
    "    \"torch\",\n",
    "]\n",
    "for p in pkgs:\n",
    "    print(f\"{p} version: {version(p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;\n",
    "# Simple benchmark with fixed batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GerIdRMXd6g9",
    "outputId": "ccdd5c71-d221-4a84-f9bc-09557e77162d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
    "batch_size = 2\n",
    "input_tensor = torch.randint(0, 50257, (batch_size, 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;\n",
    "# Simple benchmark with automatic batch size finding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing gpt-small (124M)\n",
      "  Batch size 128: 3.2e+13 FLOPS\n",
      "  Batch size 160: 4.0e+13 FLOPS\n",
      "  Batch size 176: 4.5e+13 FLOPS\n",
      "  Batch size 184: 4.7e+13 FLOPS\n",
      "  Batch size 186: 4.7e+13 FLOPS\n",
      "\n",
      "Processing gpt-medium (355M)\n",
      "  Batch size 128: 9.3e+13 FLOPS\n",
      "  Batch size 136: 9.8e+13 FLOPS\n",
      "  Batch size 140: 1.0e+14 FLOPS\n",
      "  Batch size 142: 1.0e+14 FLOPS\n",
      "  Batch size 143: 1.0e+14 FLOPS\n",
      "\n",
      "Processing gpt-large (774M)\n",
      "  Batch size 128: 2.0e+14 FLOPS\n",
      "\n",
      "Processing gpt-xl (1558M)\n",
      "  Batch size 64: 2.0e+14 FLOPS\n",
      "  Batch size 96: 3.1e+14 FLOPS\n"
     ]
    }
   ],
   "source": [
    "for size in model_configs:\n",
    "    print(f\"\\nProcessing {size}\")\n",
    "    config = BASE_CONFIG.copy()\n",
    "    config.update(model_configs[size])\n",
    "\n",
    "    min_batch_size = 1\n",
    "    max_batch_size = None\n",
    "    max_possible_batch_size = 4096\n",
    "\n",
    "    while min_batch_size <= max_possible_batch_size:\n",
    "        batch_size = (min_batch_size + max_possible_batch_size) // 2\n",
    "        try:\n",
    "            input_tensor = torch.randint(\n",
    "                0, config[\"vocab_size\"],\n",
    "                (batch_size, config[\"context_length\"]),\n",
    "                device=device\n",
    "            )\n",
    "\n",
    "            model = GPTModel(config).bfloat16().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\"  Batch size {batch_size}: {flops:.1e} FLOPS\")\n",
    "\n",
    "            # If successful, try a larger batch size\n",
    "            min_batch_size = batch_size + 1\n",
    "            max_batch_size = batch_size\n",
    "\n",
    "            # Clean up\n",
    "            del model, input_tensor\n",
    "            torch.cuda.empty_cache()\n",
    "\n",
    "        except RuntimeError as e:\n",
    "            if \"out of memory\" in str(e):\n",
    "                # Try smaller batch size\n",
    "                max_possible_batch_size = batch_size - 1\n",
    "\n",
    "                # Clean up\n",
    "                try:\n",
    "                    del model, input_tensor\n",
    "                    torch.cuda.empty_cache()\n",
    "                except NameError:\n",
    "                    pass\n",
    "            else:\n",
    "                raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;\n",
    "# Benchmark with automatic batch size finding and Model FLOP Utilization (MFU)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Model FLOPs Utilization (MFU) explanation from the [PaLM paper](https://arxiv.org/abs/2204.02311)\n",
    "\n",
    "> We propose a new metric for efficiency that is implementation-independent and permits a cleaner comparison of system efficiency, called model FLOPs utilization (MFU). This is the ratio of the observed throughput (tokens-per-second) relative to the theoretical maximum throughput of a system operating at peak FLOPs. Crucially, the “theoretical maximum” throughput only accounts for the required operations to compute the forward+backward passes, and not rematerialization.\n",
    "\n",
    "\n",
    "$$\\text{MFU} = \\frac{\\text{Observed Tokens per Second}}{\\text{Theoretical Max Tokens per Second}}$$\n",
    "\n",
    "where \n",
    "\n",
    "$$\\text{Theoretical Max Tokens per Second} = \\frac{\\text{Max FLOPs per Second}}{\\text{Total FLOPs per Token}}$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\\text{Tokens per Second} = \\frac{\\text{Batch Size} \\times \\text{Sequence Length}}{\\text{Total Time}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Max flops per second provided by the GPU manufacturer\n",
    "\n",
    "flops_per_second = {\n",
    "    \"H100\": {\n",
    "        torch.float32: 60e12,  # 60 TFLOPs for FP32 on NVIDIA H100\n",
    "        torch.float16: 1.979e15,  # 1979 TFLOPs for FP16 on NVIDIA H100\n",
    "        torch.bfloat16: 1.979e15\n",
    "    },\n",
    "    \"L4\": {\n",
    "        torch.float32: 15e12,  # 15 TFLOPs for FP32 on NVIDIA L4\n",
    "        torch.float16: 30e12,   # 30 TFLOPs for FP16 on NVIDIA L4\n",
    "        torch.bfloat16: 30e12 \n",
    "    },\n",
    "    \"T4\": {\n",
    "        torch.float32: 8.1e12,  # 8.1 TFLOPs for FP32 on NVIDIA T4\n",
    "        torch.float16: 130e12,  # 130 TFLOPs for FP16 on NVIDIA T4\n",
    "        torch.bfloat16: 130e12\n",
    "    },\n",
    "    \"A10G\": {\n",
    "        torch.float32: 15.6e12,  # 15.6 TFLOPs for FP32 on NVIDIA A10G\n",
    "        torch.float16: 78e12,    # 78 TFLOPs for FP16 on NVIDIA A10G\n",
    "        torch.bfloat16: 78e12\n",
    "    },\n",
    "    \"A100\": {\n",
    "        torch.float32: 19.5e12,  # 19.5 TFLOPs for FP32 on NVIDIA A100\n",
    "        torch.float16: 1.248e15, # 1248 TFLOPs for FP16 on NVIDIA A100\n",
    "        torch.bfloat16: 1.248e15\n",
    "    },\n",
    "    \"H200\": {\n",
    "        torch.float32: 70e12,    # 70 TFLOPs for FP32 on NVIDIA H200\n",
    "        torch.float16: 1.2e15,   # Assuming 1200 TFLOPs for FP16 on NVIDIA H200\n",
    "        torch.bfloat16: 1.2e15\n",
    "    },\n",
    "    \"RTX_3080\": {\n",
    "        torch.float32: 29.8e12,  # 29.8 TFLOPs for FP32 on NVIDIA RTX 3080\n",
    "        torch.float16: 59.6e12,  # 59.6 TFLOPs for FP16 on NVIDIA RTX 3080\n",
    "        torch.bfloat16: 59.6e12\n",
    "    },\n",
    "    \"RTX_3090\": {\n",
    "        torch.float32: 35.6e12,  # 35.6 TFLOPs for FP32 on NVIDIA RTX 3090\n",
    "        torch.float16: 71.2e12,  # 71.2 TFLOPs for FP16 on NVIDIA RTX 3090\n",
    "        torch.bfloat16: 71.2e12\n",
    "    },\n",
    "    \"GTX_1080\": {\n",
    "        torch.float32: 8.9e12,   # 8.9 TFLOPs for FP32 on NVIDIA GTX 1080\n",
    "        torch.float16: 8.9e12,   # No dedicated FP16 performance; using FP32 value\n",
    "        torch.bfloat16: 8.9e12\n",
    "    },\n",
    "    \"GTX_1080Ti\": {\n",
    "        torch.float32: 11.3e12,  # 11.3 TFLOPs for FP32 on NVIDIA GTX 1080Ti\n",
    "        torch.float16: 11.3e12,  # No dedicated FP16 performance; using FP32 value\n",
    "        torch.bfloat16: 11.3e12\n",
    "    },\n",
    "    \"GTX_1660\": {\n",
    "        torch.float32: 5e12,     # 5 TFLOPs for FP32 on NVIDIA GTX 1660\n",
    "        torch.float16: 5e12,     # No dedicated FP16 performance; using FP32 value\n",
    "        torch.bfloat16: 5e12\n",
    "    },\n",
    "    \"GTX_1660Ti\": {\n",
    "        torch.float32: 5.5e12,   # 5.5 TFLOPs for FP32 on NVIDIA GTX 1660Ti\n",
    "        torch.float16: 5.5e12,   # No dedicated FP16 performance; using FP32 value\n",
    "        torch.bfloat16: 5.5e12\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU Model: L4\n",
      "\n",
      "Processing gpt-small (124M)\n",
      "  Batch size 8: Tokens/sec: 14488.21, MFU: 0.3580\n",
      "  Batch size 12: Tokens/sec: 15378.16, MFU: 0.3799\n",
      "\n",
      "Processing gpt-medium (355M)\n",
      "  Batch size 2: Tokens/sec: 6493.81, MFU: 0.4591\n",
      "  Batch size 3: Tokens/sec: 6328.82, MFU: 0.4474\n",
      "\n",
      "Processing gpt-large (774M)\n",
      "  Batch size 4: Tokens/sec: 3130.38, MFU: 0.4834\n",
      "\n",
      "Processing gpt-xl (1558M)\n",
      "  Batch size 2: Tokens/sec: 1896.17, MFU: 0.5897\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "def get_gpu_model(flops_per_second_dict):\n",
    "    device_name = torch.cuda.get_device_name(0)\n",
    "    for model in flops_per_second_dict.keys():\n",
    "        if model in device_name:\n",
    "            return model\n",
    "    return \"Unknown\"  # Default if no matching model is found\n",
    "\n",
    "\n",
    "gpu_model = get_gpu_model(flops_per_second)\n",
    "print(\"GPU Model:\", gpu_model)\n",
    "\n",
    "if gpu_model != \"Unknown\":\n",
    "\n",
    "    for size in model_configs:\n",
    "        print(f\"\\nProcessing {size}\")\n",
    "        config = BASE_CONFIG.copy()\n",
    "        config.update(model_configs[size])\n",
    "\n",
    "        min_batch_size = 1\n",
    "        max_batch_size = None\n",
    "        max_possible_batch_size = 4096\n",
    "\n",
    "        while min_batch_size <= max_possible_batch_size:\n",
    "            batch_size = (min_batch_size + max_possible_batch_size) // 2\n",
    "            try:\n",
    "                input_tensor = torch.randint(\n",
    "                    0, config[\"vocab_size\"],\n",
    "                    (batch_size, config[\"context_length\"]),\n",
    "                    device=device\n",
    "                )\n",
    "\n",
    "                model = GPTModel(config).bfloat16().to(device)\n",
    "                model.train()\n",
    "\n",
    "                # Start timing\n",
    "                torch.cuda.synchronize()\n",
    "                start_time = time.time()\n",
    "\n",
    "                # Forward & backward pass\n",
    "                output = model(input_tensor)\n",
    "                loss = output.sum()  # Compute a dummy loss \n",
    "                loss.backward()\n",
    "\n",
    "                # End timing\n",
    "                torch.cuda.synchronize()\n",
    "                end_time = time.time()\n",
    "\n",
    "                total_time_seconds = end_time - start_time\n",
    "\n",
    "                # Calculate FLOPs for forward pass\n",
    "                macs, params = profile(model, inputs=(input_tensor,), verbose=False)\n",
    "                flops_forward = 2 * macs  # Assuming one MAC equals two FLOPs\n",
    "\n",
    "                # Estimate FLOPs for backward pass (typically 2x forward FLOPs)\n",
    "                flops_backward = 2 * flops_forward\n",
    "\n",
    "                # Total FLOPs for forward + backward passes\n",
    "                total_flops = flops_forward + flops_backward  # Or total_flops = flops_forward * 3\n",
    "\n",
    "                data_type = next(model.parameters()).dtype\n",
    "                max_flops_per_second = flops_per_second[gpu_model].get(data_type, 0)\n",
    "\n",
    "                # Compute tokens per second\n",
    "                tokens_processed = batch_size * config[\"context_length\"]\n",
    "                tokens_per_second = tokens_processed / total_time_seconds\n",
    "\n",
    "                # Compute FLOPs per token\n",
    "                flops_per_token = total_flops / tokens_processed\n",
    "\n",
    "                # Compute theoretical max tokens per second\n",
    "                if flops_per_token > 0:\n",
    "                    theoretical_max_tokens_per_second = max_flops_per_second / flops_per_token\n",
    "                else:\n",
    "                    theoretical_max_tokens_per_second = 0  # Avoid division by zero\n",
    "\n",
    "                # Compute MFU\n",
    "                if theoretical_max_tokens_per_second > 0:\n",
    "                    mfu = tokens_per_second / theoretical_max_tokens_per_second\n",
    "                else:\n",
    "                    mfu = 0  # Avoid division by zero\n",
    "\n",
    "                print(f\"  Batch size {batch_size}: Tokens/sec: {tokens_per_second:.2f}, MFU: {mfu:.4f}\")\n",
    "\n",
    "                # If successful, try a larger batch size\n",
    "                min_batch_size = batch_size + 1\n",
    "                max_batch_size = batch_size\n",
    "\n",
    "                # Clean up\n",
    "                del model, input_tensor, output, loss\n",
    "                torch.cuda.empty_cache()\n",
    "\n",
    "            except RuntimeError as e:\n",
    "                if \"out of memory\" in str(e).lower():\n",
    "                    # Try smaller batch size\n",
    "                    max_possible_batch_size = batch_size - 1\n",
    "\n",
    "                    # Clean up\n",
    "                    try:\n",
    "                        del model, input_tensor\n",
    "                        torch.cuda.empty_cache()\n",
    "                    except NameError:\n",
    "                        pass\n",
    "                else:\n",
    "                    raise e\n",
    "\n",
    "else:\n",
    "    print(\"Unknown GPU model. Please update the flops_per_second dictionary with your GPU information.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that the batch sizes are smaller than previously because we also carry out the backward pass here, which is more memory-intensive"
   ]
  }
 ],
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