triton_forward.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install triton\n",
        "!pip install torch"
      ],
      "metadata": {
        "id": "PCcpCakAkZkB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "import triton\n",
        "import triton.language as tl\n",
        "\n",
        "\n",
        "@triton.jit\n",
        "def forward_kernel(input_ptr, input_low_ptr, input_range_ptr, output_ptr, levels: int, n_elements:int, BLOCK_SIZE: tl.constexpr):\n",
        "    pid = tl.program_id(axis=0)\n",
        "    block_start = pid * BLOCK_SIZE\n",
        "    offsets = block_start + tl.arange(0, BLOCK_SIZE)\n",
        "    mask = offsets < n_elements\n",
        "\n",
        "    input = tl.load(input_ptr + offsets, mask=mask)\n",
        "    input_low = tl.load(input_low_ptr + offsets, mask=mask)\n",
        "    input_range = tl.load(input_range_ptr + offsets, mask=mask)\n",
        "\n",
        "    scale = (levels - 1) / input_range\n",
        "    zero_point = -1 * input_low * scale\n",
        "\n",
        "    input_max = input_low + input_range\n",
        "    output = tl.clamp(input, input_low, input_max)\n",
        "    output -= input_low\n",
        "    output *= scale\n",
        "    output -= zero_point\n",
        "    output = output / scale\n",
        "    tl.store(output_ptr + offsets, output, mask=mask)\n",
        "\n",
        "\n",
        "def forward_triton(\n",
        "    input_: torch.Tensor, input_low: torch.Tensor, input_range: torch.Tensor, levels: int\n",
        ") -> torch.Tensor:\n",
        "    output = torch.empty_like(input_)\n",
        "    n_elements = output.numel()\n",
        "    BLOCK_SIZE = n_elements\n",
        "    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )\n",
        "    forward_kernel[grid](input_, input_low, input_range, output, levels, n_elements, BLOCK_SIZE=1024)\n",
        "    return output\n",
        "\n",
        "\n",
        "def forward(\n",
        "    input_: torch.Tensor, input_low: torch.Tensor, input_range: torch.Tensor, levels: int\n",
        ") -> torch.Tensor:\n",
        "    scale = (levels - 1) / input_range\n",
        "    output = input_.clip(min=input_low, max=input_low + input_range)\n",
        "    zero_point = (-input_low * scale)\n",
        "    output -= input_low\n",
        "    output *= scale\n",
        "    output -= zero_point\n",
        "    output = output / scale\n",
        "    return output\n"
      ],
      "metadata": {
        "id": "QUCduYYIJas7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html\n",
        "\n",
        "@triton.testing.perf_report(\n",
        "    triton.testing.Benchmark(\n",
        "        x_names=['size'],\n",
        "        x_vals=[2**i for i in range(12, 28, 1)],\n",
        "        x_log=True,\n",
        "        line_arg='provider',\n",
        "        line_vals=['triton', 'torch'],\n",
        "        line_names=['Triton', 'Torch'],\n",
        "        styles=[('blue', '-'), ('green', '-')],\n",
        "        ylabel='GB/s',\n",
        "        plot_name='vector-add-performance',\n",
        "        args={},\n",
        "    ))\n",
        "def benchmark(size, provider):\n",
        "    input = torch.rand(size, device='cuda', dtype=torch.float32)\n",
        "    input_low = torch.rand(size, device='cuda', dtype=torch.float32)\n",
        "    input_range = torch.rand(size, device='cuda', dtype=torch.float32)\n",
        "    levels = 10\n",
        "    quantiles = [0.5, 0.2, 0.8]\n",
        "    if provider == 'torch':\n",
        "        ms, min_ms, max_ms = triton.testing.do_bench(lambda: forward(input, input_low, input_range, levels), quantiles=quantiles)\n",
        "    if provider == 'triton':\n",
        "        ms, min_ms, max_ms = triton.testing.do_bench(lambda: forward_triton(input, input_low, input_range, levels), quantiles=quantiles)\n",
        "    gbps = lambda ms: 3 * input.numel() * input.element_size() * 1e-9 / (ms * 1e-3)\n",
        "    return gbps(ms), gbps(max_ms), gbps(min_ms)\n",
        "\n",
        "\n",
        "benchmark.run(print_data=True, show_plots=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "HcF3s6AdkmeT",
        "outputId": "14983bee-4c3e-4223-a2c1-83434595934b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vector-add-performance:\n",
            "           size      Triton      Torch\n",
            "0        4096.0    6.360248   1.714286\n",
            "1        8192.0   16.297081   4.704441\n",
            "2       16384.0   36.035190   8.904348\n",
            "3       32768.0   57.420561  15.999999\n",
            "4       65536.0   85.930071  27.428571\n",
            "5      131072.0  114.707117  45.176470\n",
            "6      262144.0  139.636363  41.269522\n",
            "7      524288.0  161.684218  31.997394\n",
            "8     1048576.0  170.666661  24.674698\n",
            "9     2097152.0  177.204154  25.283950\n",
            "10    4194304.0  180.705879  25.764382\n",
            "11    8388608.0  182.292371  25.883096\n",
            "12   16777216.0  184.211171  26.054598\n",
            "13   33554432.0  185.871033  26.072252\n",
            "14   67108864.0  186.579351  26.114782\n",
            "15  134217728.0  187.047049  26.189520\n"
          ]
        }
      ]
    }
  ]
}