week10_efficiency/benchmark.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ld5GgOmT2WE1"
      },
      "source": [
        "# Installing the Dependencies\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/yandexdataschool/nlp_course/blob/quantization/week10_quantization/benchmark.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2v6kSVkV2cSS"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%pip install transformers==4.35.0\n",
        "%pip install accelerate==0.24.1\n",
        "%pip install Ninja==1.11.1.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xtpmhiksi62J"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cBpuR3vpi62J"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "from typing import Callable\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "from torch import Tensor\n",
        "import torch.nn as nn\n",
        "\n",
        "from transformers.models.llama.modeling_llama import LlamaForCausalLM\n",
        "from transformers.models.llama.configuration_llama import LlamaConfig\n",
        "import accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vwq0TdaxtreI"
      },
      "source": [
        "# Quantization Reduces Memory Footprint\n",
        "\n",
        "In PyTorch, basic tensors are stored as continious arrays of densely packed values. That is, for a basic tensor, the amount of memory a tensor requires can be easily infered from it's size and data type.\n",
        "\n",
        "The function below demonstrates that the amount of allocated GPU memory is consistent with those parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUmdCunVtzLm",
        "outputId": "ee765124-2697-434b-c801-f1a6fb949dea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "float16: 16\n",
            "int8: 8\n"
          ]
        }
      ],
      "source": [
        "def measure_bits_per_coordinate(size: int, dtype: torch.dtype) -> float:\n",
        "    \"\"\"Measure tensor memory footprint by measuring allocated memory, creating a tensor and measuring allocated memory again.\n",
        "    Args:\n",
        "        size (int): size of the tensor to be allocated\n",
        "        dtype (torch.dtype): data type of the tensor to be allocated\n",
        "\n",
        "    Returns:\n",
        "        float: tensor memory footprint divided by it's size\n",
        "    \"\"\"\n",
        "    torch.cuda.empty_cache()\n",
        "    baseline = torch.cuda.memory_allocated() * 8\n",
        "    tensor = torch.empty((size,), dtype=dtype, device=\"cuda\")\n",
        "    allocated = torch.cuda.memory_allocated() * 8\n",
        "    return (allocated - baseline) / size\n",
        "\n",
        "print(f\"float16: {measure_bits_per_coordinate(4096, torch.float16):.0f}\")\n",
        "print(f\"int8: {measure_bits_per_coordinate(4096, torch.int8):.0f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyt60Laa5tXr"
      },
      "source": [
        "In this notebook, we'll be working with `LLaMA-7b` model, that contains 7 billion parameters. In `float16`, that would amount to about *14 Gb* of model weigts. The colab *GPU* can barely fit it, and it's not nearly enough to run it on long sequences.\n",
        "\n",
        "As we have seen above, storing the model weights in small integer data types can greatly decrease it's memory footprint. We'll refer to this as quantization. For the sake of simplicity, we'll focus on quantization of `nn.Linear` layers, that make up the majority of the model weights.\n",
        "\n",
        "The function below calculates the expected `LLaMA-7b` memory footprint if all the `nn.Linear` layers were converted to a lower-bits representation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2brg40V_62yY"
      },
      "outputs": [],
      "source": [
        "LLAMA_REPO = \"yahma/llama-7b-hf\"\n",
        "\n",
        "\n",
        "def measure_model_footrpint(model: nn.Module, bits: int) -> float:\n",
        "    \"\"\"\n",
        "    Args:\n",
        "        model (nn.Module): model to calculate the memory footprint of\n",
        "        bits (int): bits per coordinate for linear layers\n",
        "\n",
        "    Returns:\n",
        "        float: memory footprint in bits\n",
        "    \"\"\"\n",
        "    memory_footpring_bits = 0\n",
        "    if isinstance(model, nn.Linear):\n",
        "        memory_footpring_bits = bits * model.weight.data.numel()\n",
        "    elif len(list(model.children())) == 0:\n",
        "        for p in model.parameters():\n",
        "            memory_footpring_bits += p.data.numel() * 16\n",
        "\n",
        "    for name, child in model.named_children():\n",
        "        memory_footpring_bits += measure_model_footrpint(child, bits)\n",
        "\n",
        "    return memory_footpring_bits\n",
        "\n",
        "\n",
        "with accelerate.init_empty_weights():\n",
        "    model = LlamaForCausalLM(LlamaConfig.from_pretrained(LLAMA_REPO))\n",
        "\n",
        "llama_size_fp16_gb = measure_model_footrpint(model, 16) * 2 ** (-33)\n",
        "llama_size_int8_gb = measure_model_footrpint(model, 8) * 2 ** (-33)\n",
        "llama_size_int4_gb = measure_model_footrpint(model, 4) * 2 ** (-33)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "F23B9Dp7Jtth",
        "outputId": "a1648671-8123-471a-883f-4dec6b03dd65"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dtypes = ['fp16', 'int8', 'int4']\n",
        "sizes = [llama_size_fp16_gb, llama_size_int8_gb, llama_size_int4_gb]\n",
        "bar_colors = ['red', 'green', 'green']\n",
        "\n",
        "plt.bar(dtypes, sizes, color=bar_colors)\n",
        "plt.ylabel(\"Model size, Gb\")\n",
        "plt.axhline(15.0, linestyle=\"--\", color=\"black\", label=\"Available VRAM\")\n",
        "plt.axhline(12.7, linestyle=\"--\", color=\"gray\", label=\"Available RAM\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzo-4YMYU9EC"
      },
      "source": [
        "As we can see, we can easily fit the quantized model into both RAM and VRAM."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwI0EjA_KIos"
      },
      "source": [
        "# Quantized Tensors are Faster to Multiply"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cp3JJfbZKopP"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def benchmark_multiplication(\n",
        "    batch_size: int,\n",
        "    input_size: int,\n",
        "    output_size: int,\n",
        "    n_repeats: int,\n",
        "    dtype: torch.dtype,\n",
        "    matmul_fn: Callable[[Tensor, Tensor], Tensor],\n",
        "    device:torch.device=\"cuda\"\n",
        ") -> float:\n",
        "    \"\"\"\n",
        "    Args:\n",
        "        batch_size (int): first tensor outer shape\n",
        "        input_size (int): first tensor inner shape = second tensor inner shape\n",
        "        output_size (int): second tensor outer shape\n",
        "        n_repeats (int): number of multiplications benchmarked\n",
        "        dtype (torch.dtype): data type of the tensors\n",
        "        matmul_fn (Callable[[Tensor, Tensor], Tensor]): a function performing the matrix multiplication\n",
        "        device (torch.device, optional): device to perform multiplication on. Defaults to \"cuda\".\n",
        "\n",
        "    Returns:\n",
        "        float: total time in seconds\n",
        "    \"\"\"\n",
        "    weight = torch.empty((output_size, input_size), dtype=dtype, device=device) # notice that output_size is first\n",
        "    input = torch.empty((batch_size, input_size), dtype=dtype, device=device)\n",
        "\n",
        "    # Preheat\n",
        "    # YOUR CODE HERE>>>>>>>>>\n",
        "    for i in range(n_repeats):\n",
        "        _ = matmul_fn(input, weight)\n",
        "    # <<<<<<<<<<<<<<<<<<<<<<<\n",
        "    torch.cuda.synchronize()\n",
        "\n",
        "    start = time.time()\n",
        "    # YOUR CODE HERE>>>>>>>>>\n",
        "    for i in range(n_repeats):\n",
        "        _ = matmul_fn(input, weight)\n",
        "    # <<<<<<<<<<<<<<<<<<<<<<<\n",
        "    torch.cuda.synchronize()\n",
        "    end = time.time()\n",
        "\n",
        "    return end - start\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z698nmMxKpLY"
      },
      "source": [
        "### Int-4 multiplication\n",
        "\n",
        "Torch doesn't navtively support Int4 multiplication. We'll have to write our own GPU multiplication kernels, using CUDA toolkit and [cutlass](https://github.com/NVIDIA/cutlass) - a dedicated linear algebra package for CUDA."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_zhNNCbKt8j"
      },
      "source": [
        "**Install CUDA 12.3 and cutlass**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%capture\n",
        "!wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin\n",
        "!sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600\n",
        "!wget https://developer.download.nvidia.com/compute/cuda/12.3.0/local_installers/cuda-repo-ubuntu2204-12-3-local_12.3.0-545.23.06-1_amd64.deb\n",
        "!sudo dpkg -i cuda-repo-ubuntu2204-12-3-local_12.3.0-545.23.06-1_amd64.deb\n",
        "!sudo cp /var/cuda-repo-ubuntu2204-12-3-local/cuda-*-keyring.gpg /usr/share/keyrings/\n",
        "!sudo apt-get update\n",
        "!sudo apt-get -y install cuda-toolkit-12-3\n",
        "!pip install --upgrade pip\n",
        "\n",
        "!git clone https://github.com/NVIDIA/cutlass.git\n",
        "!export CUDACXX=/usr/local/cuda/bin/nvcc\n",
        "!cd cutlass && mkdir build && cd build && cmake .. -DCUTLASS_NVCC_ARCHS=75"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxX6B2AaKt8m"
      },
      "source": [
        "**Loading custom CUDA kernels**\n",
        "\n",
        "Along with this jupyter notebook, two `CUDA` files are provided, implementing efficient matrix multiplication for `int4` and `int8`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "File ‘kernel.cpp’ already there; not retrieving.\n",
            "\n",
            "File ‘kernel.cu’ already there; not retrieving.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -nc https://raw.githubusercontent.com/yandexdataschool/nlp_course/2023/week10_quantization/kernel.cpp --no-check-certificate\n",
        "!wget -nc https://raw.githubusercontent.com/yandexdataschool/nlp_course/2023/week10_quantization/kernel.cu --no-check-certificate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8iQwPXkkKt8m"
      },
      "outputs": [],
      "source": [
        "from torch.utils.cpp_extension import load\n",
        "\n",
        "custom_kernel = load(name='custom_kernel', sources=['kernel.cpp', 'kernel.cu'], extra_include_paths=[r\"cutlass/include\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJwm7Y5OLfR8"
      },
      "source": [
        "Usage: `output = custom_kernel.int8_matmul(X, Y)` and `output = custom_kernel.int4_matmul(X, Y)` compute $XY^T$ (same as `torch.nn.functional.linear`).\n",
        "\n",
        "Notice that `int8_matmul` takes the normal `torch.int8` tensors, but int4_matmul expects `int4` values densely packed into `torch.uint8` tensors. Without goint into further detail, an `int4` matrix of size `A x B` would be represented by a `torch.uint8` tensor of shape `A x B//2`. Remember that when benchmarking the operations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0u7KUVLuMAN"
      },
      "source": [
        "### Benchmarking matmul"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiYxZHuyuR2b",
        "outputId": "7ef9618f-91be-4cb1-a14d-5b1354a47f57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fp16: 2.24 ms\n",
            "int8: 1.30 ms\n",
            "int4: 0.70 ms\n"
          ]
        }
      ],
      "source": [
        "n_repeats = 256\n",
        "batch_size = 1024\n",
        "input_size = output_size = 4096\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "# YOUR CODE HERE>>>>>>>>>\n",
        "fp16_ms = benchmark_multiplication(batch_size, input_size, output_size, n_repeats, torch.float16, nn.functional.linear) / n_repeats* 1000\n",
        "int8_ms = benchmark_multiplication(batch_size, input_size, output_size, n_repeats, torch.int8, custom_kernel.int8_matmul) / n_repeats * 1000\n",
        "int4_ms = benchmark_multiplication(batch_size, input_size // 2, output_size, n_repeats, torch.uint8, custom_kernel.int4_matmul) / n_repeats * 1000\n",
        "# <<<<<<<<<<<<<<<<<<<<<<<\n",
        "\n",
        "print(f\"fp16: {fp16_ms:.2f} ms\")\n",
        "print(f\"int8: {int8_ms:.2f} ms\")\n",
        "print(f\"int4: {int4_ms:.2f} ms\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Rby418z5HNS6",
        "outputId": "a5c70ebe-e5df-4ce4-e882-e516faf4736e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dtypes = ['fp16', 'int8', 'int4']\n",
        "times = [fp16_ms, int8_ms, int4_ms]\n",
        "bar_colors = ['red', 'green', 'green']\n",
        "\n",
        "plt.bar(dtypes, times, color=bar_colors)\n",
        "plt.ylabel(\"MatMul time, ms\")\n",
        "plt.show()"
      ]
    }
  ],
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