7_GPU_and_Specialized_Hardware.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlc-ai/notebooks/blob/main/7_GPU_and_Specialized_Hardware.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mpn1ti5Urdsv"
      },
      "source": [
        "# Ep7: GPU and Hardware Acceleration, Part 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXysoqn-vZuF"
      },
      "source": [
        "## Install packages \n",
        "\n",
        "For this course, we will use some ongoing development in tvm, which is an open-source machine learning compilation framework. We provide the following command to install a packaged version for mlc course. This particular notebook depends on a CUDA11 environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xe3vClsD9jlq",
        "outputId": "b9c660d4-8bea-4921-c8b5-0ac61536ac21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[proxychains] config file found: /etc/proxychains.conf\n",
            "[proxychains] preloading /usr/lib/x86_64-linux-gnu/libproxychains.so.4\n",
            "[proxychains] DLL init: proxychains-ng 4.14\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in links: https://mlc.ai/wheels\n",
            "Requirement already satisfied: mlc-ai-nightly-cu117 in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (0.12.dev1940)\n",
            "Requirement already satisfied: attrs in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (23.1.0)\n",
            "Requirement already satisfied: cloudpickle in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (3.0.0)\n",
            "Requirement already satisfied: decorator in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (5.1.1)\n",
            "Requirement already satisfied: ml-dtypes in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (0.3.1)\n",
            "Requirement already satisfied: numpy in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (1.26.2)\n",
            "Requirement already satisfied: psutil in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (5.9.0)\n",
            "Requirement already satisfied: scipy in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (1.11.4)\n",
            "Requirement already satisfied: tornado in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (6.3.3)\n",
            "Requirement already satisfied: typing-extensions in /home/murez/miniconda3/envs/.mlc/lib/python3.11/site-packages (from mlc-ai-nightly-cu117) (4.9.0)\n"
          ]
        }
      ],
      "source": [
        "!proxychains python3 -m  pip install mlc-ai-nightly-cu117 -f https://mlc.ai/wheels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IH1GR0DjvUiM",
        "outputId": "aaab791a-7ba9-4f67-bd47-bded876bb89e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sun Dec 31 17:15:04 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA GeForce ...  Off  | 00000000:02:00.0 Off |                  N/A |\n",
            "| 27%   25C    P8    20W / 250W |      0MiB / 11264MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "|   1  NVIDIA GeForce ...  Off  | 00000000:03:00.0 Off |                  N/A |\n",
            "| 27%   24C    P8    17W / 250W |      0MiB / 11264MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "|   2  NVIDIA GeForce ...  Off  | 00000000:82:00.0 Off |                  N/A |\n",
            "| 27%   23C    P8    12W / 250W |      0MiB / 11264MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "|   3  NVIDIA GeForce ...  Off  | 00000000:83:00.0 Off |                  N/A |\n",
            "| 27%   23C    P8    21W / 250W |      0MiB / 11264MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-14C4skxIrJ"
      },
      "source": [
        "## Prelude\n",
        "\n",
        "In the past chapter, we discussed  MLC flows in CPU environments. This chapter will discuss how to bring some of the optimizations onto GPU. We are going to use CUDA terminology. However, the same set of concepts applies to other kinds of GPUs as well.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBIuE2jc1DaU"
      },
      "source": [
        "## Preparations\n",
        "\n",
        "To begin with, let us import the necessary dependencies.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "BVp0fHyRkYj6"
      },
      "outputs": [],
      "source": [
        "import tvm\n",
        "from tvm.ir.module import IRModule\n",
        "from tvm.script import tir as T, relax as R\n",
        "from tvm import relax\n",
        "import numpy as np\n",
        "\n",
        "# This is needed for deferring annotation parsing in TVMScript\n",
        "from __future__ import annotations "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbu5EAE00kBs"
      },
      "source": [
        "## GPU Architecture\n",
        "\n",
        "Let us begin by reviewing what a GPU architecture looks like. A typical GPU contains a collection of stream multi-processors, and each multi-processor has many cores. A GPU device is massively parallel and allows us to execute many tasks concurrently."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1K801uQgU0I3"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yt0tVBCtX9Wi"
      },
      "source": [
        "To program a GPU, we need to create a set of thread blocks, with each thread mapping to the cores and the thread block map to the stream multiprocessors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixU4dDosX2mt"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCSVwxo2fmP-"
      },
      "source": [
        "Let us start GPU programming using a vector add example. The following TensorIR program takes two vectors, A and B, performs element-wise add, and stores the result in C."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "D2vIuJ2i0mo3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ast>:3: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n",
            "<ast>:4: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n",
            "<ast>:5: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n"
          ]
        }
      ],
      "source": [
        "@tvm.script.ir_module\n",
        "class MyModuleVecAdd:\n",
        "    @T.prim_func\n",
        "    def main(A: T.Buffer[(1024,), \"float32\"], \n",
        "             B: T.Buffer[(1024,), \"float32\"], \n",
        "             C: T.Buffer[(1024,), \"float32\"]) -> None:\n",
        "        T.func_attr({\"global_symbol\": \"main\", \"tir.noalias\": True})\n",
        "        for i in T.grid(1024):\n",
        "            with T.block(\"C\"):\n",
        "                vi = T.axis.remap(\"S\", [i])\n",
        "                C[vi] = A[vi] + B[vi]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgknjqDSVBzJ"
      },
      "source": [
        "We first split loop `i` into two loops."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBko3swU1C4I",
        "outputId": "d25f72f9-d4d9-4c32-820c-e456f2ace26c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), C: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0, i_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">128</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C&quot;</span>):\n",
              "                vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1)\n",
              "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[vi], B[vi])\n",
              "                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C[vi])\n",
              "                C[vi] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[vi] <span style=\"color: #AA22FF; font-weight: bold\">+</span> B[vi]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sch = tvm.tir.Schedule(MyModuleVecAdd)\n",
        "block_C = sch.get_block(\"C\")\n",
        "i, = sch.get_loops(block=block_C)\n",
        "i0, i1 = sch.split(i, [None, 128])\n",
        "sch.mod.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DAXMG9m3nqF"
      },
      "source": [
        "### GPU Thread Blocks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XA2FvECSYWVP"
      },
      "source": [
        "Then we bind the iterators to the GPU thread blocks. Each thread is parameterized by two indices -- `threadIdx.x` and `blockIdx.x`. In practice, we can have multiple dimensional thread indices, but we keep them simple as one dimension."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qApYOsF9hFn9"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4giP-eG-1bJX",
        "outputId": "1673704a-5906-4ab6-8047-f44cde6ca956"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), C: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">8</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> i_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">128</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C&quot;</span>):\n",
              "                    vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1)\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[vi], B[vi])\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C[vi])\n",
              "                    C[vi] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[vi] <span style=\"color: #AA22FF; font-weight: bold\">+</span> B[vi]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sch.bind(i0, \"blockIdx.x\")\n",
        "sch.bind(i1, \"threadIdx.x\")\n",
        "sch.mod.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9lEEl4-omtG"
      },
      "source": [
        "### Build and Run the TensorIR Function on GPU\n",
        "\n",
        "We can build and test out the resulting function on the GPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hL1tGI-3DWf",
        "outputId": "ba0f5406-7ea8-4b6a-843f-bbf626ee4226"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.38077658 0.7886894  0.3509124  ... 0.3029991  0.8775514  0.23504174]\n",
            "[0.4527853  0.13592191 0.9008402  ... 0.9807693  0.08548537 0.2596029 ]\n",
            "[0.8335619  0.9246113  1.2517526  ... 1.2837684  0.9630368  0.49464464]\n"
          ]
        }
      ],
      "source": [
        "rt_mod = tvm.build(sch.mod, target=\"cuda\")\n",
        "\n",
        "A_np = np.random.uniform(size=(1024,)).astype(\"float32\")\n",
        "B_np = np.random.uniform(size=(1024,)).astype(\"float32\")\n",
        "A_nd = tvm.nd.array(A_np, tvm.cuda(0))\n",
        "B_nd = tvm.nd.array(B_np, tvm.cuda(0))\n",
        "C_nd = tvm.nd.array(np.zeros((1024,), dtype=\"float32\"), tvm.cuda(0))\n",
        "\n",
        "rt_mod[\"main\"](A_nd, B_nd, C_nd)\n",
        "print(A_nd)\n",
        "print(B_nd)\n",
        "print(C_nd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "NJjgpGtx3b4m"
      },
      "outputs": [],
      "source": [
        "np.testing.assert_allclose(C_nd.numpy(), A_np + B_np)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IspVTqVR3tBM"
      },
      "source": [
        "## Window Sum Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8_oVnfQj6CX"
      },
      "source": [
        "Now, let us move forward to another example -- window sum. This program can be viewed as a basic version of \"convolution\" with a predefined weight `[1,1,1]`. We are taking sliding over the input and add three neighboring values together."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sbpk4tTHYfCx"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "j_TJgnwBBNyq"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ast>:3: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n",
            "<ast>:4: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n"
          ]
        }
      ],
      "source": [
        "@tvm.script.ir_module\n",
        "class MyModuleWindowSum:\n",
        "    @T.prim_func\n",
        "    def main(A: T.Buffer[(1027,), \"float32\"], \n",
        "             B: T.Buffer[(1024,), \"float32\"]) -> None:\n",
        "        T.func_attr({\"global_symbol\": \"main\", \"tir.noalias\": True})\n",
        "        for i in T.grid(1024):\n",
        "            with T.block(\"C\"):\n",
        "                vi = T.axis.remap(\"S\", [i])\n",
        "                B[vi] = A[vi] + A[vi + 1] + A[vi + 2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MNiC6Aczj9PG"
      },
      "source": [
        "First, we can bind the loop to GPU threads."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sInzhG_cCUA-",
        "outputId": "0812ad60-f8ed-4f33-818e-5df4cdbf0202"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1027</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">8</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> i_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">128</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C&quot;</span>):\n",
              "                    vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1)\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[vi:vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">3</span>])\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(B[vi])\n",
              "                    B[vi] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[vi] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A[vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">1</span>] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A[vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">2</span>]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sch = tvm.tir.Schedule(MyModuleWindowSum)\n",
        "nthread = 128\n",
        "block_C = sch.get_block(\"C\")\n",
        "i,  = sch.get_loops(block=block_C)\n",
        "i0, i1 = sch.split(i, [None, nthread])\n",
        "sch.bind(i0, \"blockIdx.x\")\n",
        "sch.bind(i1, \"threadIdx.x\")\n",
        "sch.mod.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dR5UPy_ml-9F"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YBIc5hWl2sW"
      },
      "source": [
        "Importantly, in this case, there are reuse opportunities. Remember that each GPU thread block contains shared memory that all threads can access within the block. We use `cache_read` to add an intermediate stage that caches segments (in green below) onto the shared memory. After the caching is finished, the threads can then read from the shared memory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JotZy40CYiei"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0W6HSycCbxP",
        "outputId": "cb2abd3c-0cc6-4f77-df34-ff853e812092"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1027</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        A_shared <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1027</span>,), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;shared&quot;</span>)\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">8</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> i_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">128</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">for</span> ax0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">130</span>):\n",
              "                    <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;A_shared&quot;</span>):\n",
              "                        v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1027</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0)\n",
              "                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v0])\n",
              "                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(A_shared[v0])\n",
              "                        A_shared[v0] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v0]\n",
              "                <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C&quot;</span>):\n",
              "                    vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1)\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A_shared[vi:vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">3</span>])\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(B[vi])\n",
              "                    B[vi] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A_shared[vi] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A_shared[vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">1</span>] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A_shared[vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">2</span>]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "A_shared = sch.cache_read(block_C, read_buffer_index=0, storage_scope=\"shared\")\n",
        "sch.compute_at(A_shared, i1)\n",
        "sch.mod.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrguFDF9nksn"
      },
      "source": [
        "Because the memory is shared across threads, we need to resplit the loop and bind the inner iterator of the fetching process onto the thread indices. This technique is called **cooperative fetching**, where multiple threads work together to bring the data onto the shared memory. The following reading process can be different. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_d0Hd-pGsIs",
        "outputId": "38e59c53-e33c-4507-cdee-ba13f2b3ce09"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1027</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>,), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        A_shared <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1027</span>,), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;shared&quot;</span>)\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">8</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> i_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">128</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">for</span> ax0_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">2</span>):\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> ax0_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">128</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;A_shared&quot;</span>):\n",
              "                            v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1027</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_1))\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>where(ax0_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_1 <span style=\"color: #AA22FF; font-weight: bold\">&lt;</span> <span style=\"color: #008000\">130</span>)\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v0])\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(A_shared[v0])\n",
              "                            A_shared[v0] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v0]\n",
              "                <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C&quot;</span>):\n",
              "                    vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1)\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A_shared[vi:vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">3</span>])\n",
              "                    T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(B[vi])\n",
              "                    B[vi] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A_shared[vi] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A_shared[vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">1</span>] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A_shared[vi <span style=\"color: #AA22FF; font-weight: bold\">+</span> <span style=\"color: #008000\">2</span>]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sch.get_loops(A_shared)[-1]\n",
        "ax0, ax1 = sch.split(ax, [None, nthread])\n",
        "sch.bind(ax1, \"threadIdx.x\")\n",
        "sch.mod.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_df2KCDovQc"
      },
      "source": [
        "We can inspect the corresponding low-level code (in CUDA). The generated code contains two parts: \n",
        "- A host part that calls into the GPU driver\n",
        "- A cuda kernel that runs the corresponding computation.\n",
        "\n",
        "We can print out the cuda kernel using the following code. We still need both the host and kernel code to run the program, so it is only a quick way to inspect what the final code generation result.\n",
        "\n",
        "Notably, the build process automatically compacts the shared memory stage to use a minimum region used within the thread block."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ngiuLMSHWM5",
        "outputId": "258b6c4e-daae-4de6-dd94-536a4af56418"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "#if (((__CUDACC_VER_MAJOR__ == 11) && (__CUDACC_VER_MINOR__ >= 4)) || \\\n",
            "     (__CUDACC_VER_MAJOR__ > 11))\n",
            "#define TVM_ENABLE_L2_PREFETCH 1\n",
            "#else\n",
            "#define TVM_ENABLE_L2_PREFETCH 0\n",
            "#endif\n",
            "\n",
            "#ifdef _WIN32\n",
            "  using uint = unsigned int;\n",
            "  using uchar = unsigned char;\n",
            "  using ushort = unsigned short;\n",
            "  using int64_t = long long;\n",
            "  using uint64_t = unsigned long long;\n",
            "#else\n",
            "  #define uint unsigned int\n",
            "  #define uchar unsigned char\n",
            "  #define ushort unsigned short\n",
            "  #define int64_t long long\n",
            "  #define uint64_t unsigned long long\n",
            "#endif\n",
            "extern \"C\" __global__ void __launch_bounds__(128) main_kernel(float* __restrict__ A, float* __restrict__ B);\n",
            "extern \"C\" __global__ void __launch_bounds__(128) main_kernel(float* __restrict__ A, float* __restrict__ B) {\n",
            "  __shared__ float A_shared[130];\n",
            "  for (int ax0_0 = 0; ax0_0 < 2; ++ax0_0) {\n",
            "    if (((ax0_0 * 64) + (((int)threadIdx.x) >> 1)) < 65) {\n",
            "      A_shared[((ax0_0 * 128) + ((int)threadIdx.x))] = A[(((((int)blockIdx.x) * 128) + (ax0_0 * 128)) + ((int)threadIdx.x))];\n",
            "    }\n",
            "  }\n",
            "  __syncthreads();\n",
            "  B[((((int)blockIdx.x) * 128) + ((int)threadIdx.x))] = ((A_shared[((int)threadIdx.x)] + A_shared[(((int)threadIdx.x) + 1)]) + A_shared[(((int)threadIdx.x) + 2)]);\n",
            "}\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "rt_mod = tvm.build(sch.mod, target=\"cuda\")\n",
        "print(rt_mod.imported_modules[0].get_source())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uW087bCZbwnX"
      },
      "source": [
        "### Build Code for Other GPU Platforms\n",
        "\n",
        "A MLC process usually support targeting multiple kinds of hardware platforms, we can generate opencl code(which is another kind of GPU programming model) by changing the target parameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-l21FKVb1ra",
        "outputId": "c61c5f4b-de82-4587-b6fa-bafebfa56627"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "// Function: main_kernel\n",
            "__kernel void main_kernel(__global float* restrict A, __global float* restrict B);\n",
            "__kernel void main_kernel(__global float* restrict A, __global float* restrict B) {\n",
            "  __local float A_shared[130];\n",
            "  for (int ax0_0 = 0; ax0_0 < 2; ++ax0_0) {\n",
            "    if (((ax0_0 * 64) + ((convert_int(get_local_id(0))) >> 1)) < 65) {\n",
            "      A_shared[((ax0_0 * 128) + (convert_int(get_local_id(0))))] = A[((((convert_int(get_group_id(0))) * 128) + (ax0_0 * 128)) + (convert_int(get_local_id(0))))];\n",
            "    }\n",
            "  }\n",
            "  barrier(CLK_LOCAL_MEM_FENCE);\n",
            "  B[(((convert_int(get_group_id(0))) * 128) + (convert_int(get_local_id(0))))] = ((A_shared[(convert_int(get_local_id(0)))] + A_shared[((convert_int(get_local_id(0))) + 1)]) + A_shared[((convert_int(get_local_id(0))) + 2)]);\n",
            "}\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "rt_mod = tvm.build(sch.mod, target=\"opencl\")\n",
        "print(rt_mod.imported_modules[0].get_source())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yH4IMSMvF9o"
      },
      "source": [
        "## Matrix Multiplication\n",
        "\n",
        "Let us now get to something slightly more complicated and try out optimizing matrix multiplication on GPU. We will go over two common techniques for GPU performance optimization.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "XwRUCQBtxNh3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ast>:3: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n",
            "<ast>:4: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n",
            "<ast>:5: DeprecationWarning: T.Buffer[...] is deprecated, use T.Buffer(...) instead\n"
          ]
        }
      ],
      "source": [
        "@tvm.script.ir_module\n",
        "class MyModuleMatmul:\n",
        "    @T.prim_func\n",
        "    def main(A: T.Buffer[(1024, 1024), \"float32\"], \n",
        "             B: T.Buffer[(1024, 1024), \"float32\"], \n",
        "             C: T.Buffer[(1024, 1024), \"float32\"]) -> None:\n",
        "        T.func_attr({\"global_symbol\": \"main\", \"tir.noalias\": True})\n",
        "        for i, j, k in T.grid(1024, 1024, 1024):\n",
        "            with T.block(\"C\"):\n",
        "                vi, vj, vk = T.axis.remap(\"SSR\", [i, j, k])\n",
        "                with T.init():\n",
        "                    C[vi, vj] = 0.0\n",
        "                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWx5swIIqrJJ"
      },
      "source": [
        "### Local Blocking\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdECOWAIZ64k"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2gq6CqTrOdQ"
      },
      "source": [
        "To increase overall memory reuse. We can tile the loops. In particular, we introduce local tiles such that we only need to load stripe of data from A and B once, then use them to perform a `V * V` matrix multiplication result.\n",
        "\n",
        "This local tiling helps to reduce the memory pressure, as each element in the stripe is reused `V` times."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8TSH22_KGEc",
        "outputId": "02965ff7-3884-41e0-9e4b-6d7c25742670"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), C: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        C_local <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;local&quot;</span>)\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">16</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.y&quot;</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> j_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">16</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">for</span> i_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">8</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.y&quot;</span>):\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> j_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">8</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> i_2_init, j_2_init <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_init&quot;</span>):\n",
              "                                vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_init)\n",
              "                                vj <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_2_init)\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads()\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C_local[vi, vj])\n",
              "                                C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0</span>)\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> k_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">256</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">for</span> k_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>unroll(<span style=\"color: #008000\">4</span>):\n",
              "                                <span style=\"color: #008000; font-weight: bold\">for</span> i_2, j_2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>):\n",
              "                                    <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_update&quot;</span>):\n",
              "                                        vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2)\n",
              "                                        vj <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_2)\n",
              "                                        vk <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>reduce(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> k_1)\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(C_local[vi, vj], A[vi, vk], B[vk, vj])\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C_local[vi, vj])\n",
              "                                        C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">=</span> C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A[vi, vk] <span style=\"color: #AA22FF; font-weight: bold\">*</span> B[vk, vj]\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_local&quot;</span>):\n",
              "                                v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0)\n",
              "                                v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax1)\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(C_local[v0, v1])\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C[v0, v1])\n",
              "                                C[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> C_local[v0, v1]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def blocking(sch, \n",
        "             tile_local_y, \n",
        "             tile_local_x, \n",
        "             tile_block_y, \n",
        "             tile_block_x,\n",
        "             tile_k):\n",
        "    block_C = sch.get_block(\"C\")\n",
        "    C_local = sch.cache_write(block_C, 0, \"local\")\n",
        "\n",
        "    i, j, k = sch.get_loops(block=block_C)\n",
        "\n",
        "    i0, i1, i2 = sch.split(loop=i, factors=[None, tile_block_y, tile_local_y])\n",
        "    j0, j1, j2 = sch.split(loop=j, factors=[None, tile_block_x, tile_local_x])\n",
        "    k0, k1 = sch.split(loop=k, factors=[None, tile_k])\n",
        "    sch.unroll(k1)\n",
        "    sch.reorder(i0, j0, i1, j1, k0, k1, i2, j2)\n",
        "    sch.reverse_compute_at(C_local, j1)\n",
        "\n",
        "    sch.bind(i0, \"blockIdx.y\")\n",
        "    sch.bind(j0, \"blockIdx.x\")\n",
        "\n",
        "    sch.bind(i1, \"threadIdx.y\")\n",
        "    sch.bind(j1, \"threadIdx.x\")\n",
        "    sch.decompose_reduction(block_C, k0)\n",
        "\n",
        "    return sch\n",
        "\n",
        "sch = tvm.tir.Schedule(MyModuleMatmul)\n",
        "sch = blocking(sch, 8, 8, 8, 8, 4)\n",
        "sch.mod.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qu-JY7JUI4us",
        "outputId": "64d8a8f6-0c74-4c76-a345-825a741550ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GEMM-Blocking: 3458.863210 GFLOPS\n"
          ]
        }
      ],
      "source": [
        "rt_mod = tvm.build(sch.mod, target=\"cuda\")\n",
        "dev = tvm.cuda(0)\n",
        "A_np = np.random.uniform(size=(1024, 1024)).astype(\"float32\")\n",
        "B_np = np.random.uniform(size=(1024, 1024)).astype(\"float32\")\n",
        "A_nd = tvm.nd.array(A_np, dev)\n",
        "B_nd = tvm.nd.array(B_np, dev)\n",
        "C_nd = tvm.nd.array(np.zeros((1024, 1024), dtype=\"float32\"), dev)\n",
        "\n",
        "num_flop = 2 * 1024 * 1024 * 1024\n",
        "evaluator = rt_mod.time_evaluator(\"main\", dev, number=10)\n",
        "\n",
        "\n",
        "print(\"GEMM-Blocking: %f GFLOPS\" % (num_flop / evaluator(A_nd, B_nd, C_nd).mean / 1e9))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQhlKMMIbgqq"
      },
      "source": [
        "## Shared Memory Blocking\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pK7d8BY8aFUz"
      },
      "source": [
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8CP3jKIrvs_"
      },
      "source": [
        "Our first attempt did not consider the neighboring threads which sit in the same GPU thread block, and we can load the data they commonly need into a piece of shared memory.\n",
        "\n",
        "The following transformation does that."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCiSsNtjxTsq",
        "outputId": "9df52e4f-d45b-4ce2-a1df-042e1def162d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), C: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        C_local <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;local&quot;</span>)\n",
              "        A_shared <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;shared&quot;</span>)\n",
              "        B_shared <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;shared&quot;</span>)\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">16</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.y&quot;</span>):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> j_0 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">16</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">for</span> i_1_j_1_fused <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">64</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> i_2_init, j_2_init <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_init&quot;</span>):\n",
              "                            vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1_j_1_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_init)\n",
              "                            vj <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1_j_1_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_2_init)\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads()\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C_local[vi, vj])\n",
              "                            C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0</span>)\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> k_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">128</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">2</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">64</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                                <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>vectorized(<span style=\"color: #008000\">4</span>):\n",
              "                                    <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;A_shared&quot;</span>):\n",
              "                                        v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">8</span>)\n",
              "                                        v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">8</span>)\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v0, v1])\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(A_shared[v0, v1])\n",
              "                                        A_shared[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v0, v1]\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">2</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">64</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                                <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>vectorized(<span style=\"color: #008000\">4</span>):\n",
              "                                    <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;B_shared&quot;</span>):\n",
              "                                        v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">64</span>)\n",
              "                                        v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">64</span>)\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(B[v0, v1])\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(B_shared[v0, v1])\n",
              "                                        B_shared[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> B[v0, v1]\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> k_1, i_2, j_2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_update&quot;</span>):\n",
              "                                vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1_j_1_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2)\n",
              "                                vj <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1_j_1_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_2)\n",
              "                                vk <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>reduce(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> k_1)\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(C_local[vi, vj], A_shared[vi, vk], B_shared[vk, vj])\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C_local[vi, vj])\n",
              "                                C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">=</span> C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A_shared[vi, vk] <span style=\"color: #AA22FF; font-weight: bold\">*</span> B_shared[vk, vj]\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">8</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_local&quot;</span>):\n",
              "                            v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1_j_1_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0)\n",
              "                            v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, j_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_1_j_1_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax1)\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(C_local[v0, v1])\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C[v0, v1])\n",
              "                            C[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> C_local[v0, v1]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def cache_read_and_coop_fetch(sch, block, nthread, read_idx, read_loc):\n",
        "    read_cache = sch.cache_read(block=block, read_buffer_index=read_idx, storage_scope=\"shared\")\n",
        "    sch.compute_at(block=read_cache, loop=read_loc)\n",
        "    # vectorized cooperative fetch\n",
        "    inner0, inner1 = sch.get_loops(block=read_cache)[-2:]\n",
        "    inner = sch.fuse(inner0, inner1)\n",
        "    _, tx, vec = sch.split(loop=inner, factors=[None, nthread, 4])\n",
        "    sch.vectorize(vec)\n",
        "    sch.bind(tx, \"threadIdx.x\")\n",
        "\n",
        "\n",
        "def blocking_with_shared(\n",
        "    sch, \n",
        "    tile_local_y, \n",
        "    tile_local_x, \n",
        "    tile_block_y, \n",
        "    tile_block_x,\n",
        "    tile_k):\n",
        "    block_C = sch.get_block(\"C\")\n",
        "    C_local = sch.cache_write(block_C, 0, \"local\")\n",
        "\n",
        "    i, j, k = sch.get_loops(block=block_C)\n",
        "\n",
        "    i0, i1, i2 = sch.split(loop=i, factors=[None, tile_block_y, tile_local_y])\n",
        "    j0, j1, j2 = sch.split(loop=j, factors=[None, tile_block_x, tile_local_x])\n",
        "    k0, k1 = sch.split(loop=k, factors=[None, tile_k])\n",
        "\n",
        "    sch.reorder(i0, j0, i1, j1, k0, k1, i2, j2)\n",
        "    sch.reverse_compute_at(C_local, j1)\n",
        "\n",
        "    sch.bind(i0, \"blockIdx.y\")\n",
        "    sch.bind(j0, \"blockIdx.x\")\n",
        "\n",
        "    tx = sch.fuse(i1, j1)\n",
        "    sch.bind(tx, \"threadIdx.x\")\n",
        "    nthread = tile_block_y * tile_block_x\n",
        "    cache_read_and_coop_fetch(sch, block_C, nthread, 0, k0)\n",
        "    cache_read_and_coop_fetch(sch, block_C, nthread, 1, k0)    \n",
        "    sch.decompose_reduction(block_C, k0)\n",
        "\n",
        "    return sch\n",
        "\n",
        "sch = tvm.tir.Schedule(MyModuleMatmul)\n",
        "sch = blocking_with_shared(sch, 8, 8, 8, 8, 8)\n",
        "sch.mod.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emNWFAwexXWH",
        "outputId": "10a3760d-9b43-4d83-b339-53e022bd1894"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GEMM-Blocking: 5260.098604 GFLOPS\n"
          ]
        }
      ],
      "source": [
        "rt_mod = tvm.build(sch.mod, target=\"cuda\")\n",
        "dev = tvm.cuda(0)\n",
        "evaluator = rt_mod.time_evaluator(\"main\", dev, number=10)\n",
        "\n",
        "print(\"GEMM-Blocking: %f GFLOPS\" % (num_flop / evaluator(A_nd, B_nd, C_nd).mean / 1e9))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aC9IAGGb7xw"
      },
      "source": [
        "## Leveraging Automatic Program Optimization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhrY8D8UsE3O"
      },
      "source": [
        "So far, we have been manually writing transformations to optimize the TensorIR program on GPU. We can leverage the automatic program optimization framework to tune the same program. The following code does that, we only set a small number here, and it can take a few min to finish. \n",
        "And for all targets can be found @[tvm/src/target/tag.cc](https://github.com/apache/tvm/blob/main/src/target/tag.cc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANaU_GsMcAP1",
        "outputId": "b0f1ae6a-7f2b-4b63-b268-2750fefbb883"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-12-31 17:15:08 [INFO] Logging directory: ./tune_tmp/logs\n",
            "2023-12-31 17:15:18 [INFO] LocalBuilder: max_workers = 28\n",
            "2023-12-31 17:15:19 [INFO] LocalRunner: max_workers = 1\n",
            "2023-12-31 17:15:19 [INFO] [task_scheduler.cc:159] Initializing Task #0: \"main\"\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>FLOP</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Speed (GFLOPS)</th>\n",
              "      <th>Latency (us)</th>\n",
              "      <th>Weighted Latency (us)</th>\n",
              "      <th>Trials</th>\n",
              "      <th>Done</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>main</td>\n",
              "      <td>2147483648</td>\n",
              "      <td>1</td>\n",
              "      <td>N/A</td>\n",
              "      <td>N/A</td>\n",
              "      <td>N/A</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Name          FLOP    Weight    Speed (GFLOPS)    Latency (us)   \\\n",
              "0   main    2147483648         1               N/A             N/A    \n",
              "\n",
              "    Weighted Latency (us)    Trials    Done   \n",
              "0                     N/A         0           "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2023-12-31 17:15:19 [DEBUG] [task_scheduler.cc:318] \n",
            " ID | Name |       FLOP | Weight | Speed (GFLOPS) | Latency (us) | Weighted Latency (us) | Trials | Done \n",
            "---------------------------------------------------------------------------------------------------------\n",
            "  0 | main | 2147483648 |      1 |            N/A |          N/A |                   N/A |      0 |      \n",
            "---------------------------------------------------------------------------------------------------------\n",
            "Total trials: 0\n",
            "Total latency (us): 0\n",
            "\n",
            "\n",
            "Total trials: 0\n",
            "Total latency (us): 0\n",
            "\n",
            "2023-12-31 17:15:19 [INFO] [task_scheduler.cc:180] TaskScheduler picks Task #0: \"main\"\n",
            "2023-12-31 17:15:27 [INFO] [task_scheduler.cc:193] Sending 64 sample(s) to builder\n",
            "2023-12-31 17:16:00 [INFO] [task_scheduler.cc:195] Sending 64 sample(s) to runner\n",
            "2023-12-31 17:16:16 [DEBUG] XGB iter   0: tr-p-rmse: 0.743130\ttr-a-peak@32: 0.733501\ttr-rmse: 0.464791\ttr-rmse: 0.464791\n",
            "2023-12-31 17:16:16 [DEBUG] XGB iter  25: tr-p-rmse: 0.086273\ttr-a-peak@32: 1.000000\ttr-rmse: 0.482775\ttr-rmse: 0.482775\n",
            "2023-12-31 17:16:16 [DEBUG] XGB iter  50: tr-p-rmse: 0.086273\ttr-a-peak@32: 1.000000\ttr-rmse: 0.482775\ttr-rmse: 0.482775\n",
            "2023-12-31 17:16:16 [DEBUG] XGB stopped. Best iteration: [14] tr-p-rmse:0.08627\ttr-a-peak@32:1.00000\ttr-rmse:0.48277\ttr-rmse:0.48277 \n",
            "2023-12-31 17:16:16 [INFO] [task_scheduler.cc:237] [Updated] Task #0: \"main\"\n"
          ]
        },
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>FLOP</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Speed (GFLOPS)</th>\n",
              "      <th>Latency (us)</th>\n",
              "      <th>Weighted Latency (us)</th>\n",
              "      <th>Trials</th>\n",
              "      <th>Done</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>main</td>\n",
              "      <td>2147483648</td>\n",
              "      <td>1</td>\n",
              "      <td>9189.0587</td>\n",
              "      <td>233.7001</td>\n",
              "      <td>233.7001</td>\n",
              "      <td>64</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "    Name          FLOP    Weight    Speed (GFLOPS)    Latency (us)   \\\n",
              "0   main    2147483648         1         9189.0587        233.7001    \n",
              "\n",
              "    Weighted Latency (us)    Trials    Done   \n",
              "0                233.7001        64           "
            ]
          },
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        },
        {
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          "output_type": "stream",
          "text": [
            "2023-12-31 17:16:16 [DEBUG] [task_scheduler.cc:318] \n",
            " ID | Name |       FLOP | Weight | Speed (GFLOPS) | Latency (us) | Weighted Latency (us) | Trials | Done \n",
            "---------------------------------------------------------------------------------------------------------\n",
            "  0 | main | 2147483648 |      1 |      9189.0587 |     233.7001 |              233.7001 |     64 |      \n",
            "---------------------------------------------------------------------------------------------------------\n",
            "Total trials: 64\n",
            "Total latency (us): 233.7\n",
            "\n",
            "\n",
            "Total trials: 64\n",
            "Total latency (us): 233.7\n",
            "\n",
            "2023-12-31 17:16:16 [INFO] [task_scheduler.cc:260] Task #0 has finished. Remaining task(s): 0\n"
          ]
        },
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>FLOP</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Speed (GFLOPS)</th>\n",
              "      <th>Latency (us)</th>\n",
              "      <th>Weighted Latency (us)</th>\n",
              "      <th>Trials</th>\n",
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              "  <tbody>\n",
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              "      <td>main</td>\n",
              "      <td>2147483648</td>\n",
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              "      <td>9189.0587</td>\n",
              "      <td>233.7001</td>\n",
              "      <td>233.7001</td>\n",
              "      <td>64</td>\n",
              "      <td>Y</td>\n",
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              "</table>\n",
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            "text/plain": [
              "    Name          FLOP    Weight    Speed (GFLOPS)    Latency (us)   \\\n",
              "0   main    2147483648         1         9189.0587        233.7001    \n",
              "\n",
              "    Weighted Latency (us)    Trials    Done   \n",
              "0                233.7001        64       Y   "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Total trials: 64\n",
            "Total latency (us): 233.7\n",
            "\n",
            "2023-12-31 17:16:16 [DEBUG] [task_scheduler.cc:318] \n",
            " ID | Name |       FLOP | Weight | Speed (GFLOPS) | Latency (us) | Weighted Latency (us) | Trials | Done \n",
            "---------------------------------------------------------------------------------------------------------\n",
            "  0 | main | 2147483648 |      1 |      9189.0587 |     233.7001 |              233.7001 |     64 |    Y \n",
            "---------------------------------------------------------------------------------------------------------\n",
            "Total trials: 64\n",
            "Total latency (us): 233.7\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from tvm import meta_schedule as ms\n",
        "\n",
        "database = ms.tune_tir(\n",
        "    mod=MyModuleMatmul,\n",
        "    target=\"nvidia/geforce-rtx-2080-ti\",\n",
        "    max_trials_global=64,\n",
        "    num_trials_per_iter=64,\n",
        "    work_dir=\"./tune_tmp\",\n",
        "    # task_name=\"main\"\n",
        ")\n",
        "sch_tuned = ms.tir_integration.compile_tir(database, MyModuleMatmul, \"nvidia/geforce-rtx-2080-ti\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "MD_C5bkrer6c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div class=\"highlight\" style=\"background: \"><pre style=\"line-height: 125%;\"><span></span><span style=\"color: #007979; font-style: italic\"># from tvm.script import ir as I</span>\n",
              "<span style=\"color: #007979; font-style: italic\"># from tvm.script import tir as T</span>\n",
              "\n",
              "<span style=\"color: #AA22FF\">@I</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>ir_module\n",
              "<span style=\"color: #008000; font-weight: bold\">class</span> <span style=\"color: #0000FF; font-weight: bold\">Module</span>:\n",
              "    <span style=\"color: #AA22FF\">@T</span><span style=\"color: #AA22FF; font-weight: bold\">.</span>prim_func\n",
              "    <span style=\"color: #008000; font-weight: bold\">def</span> <span style=\"color: #0000FF\">main</span>(A: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), B: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>), C: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>Buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;float32&quot;</span>)):\n",
              "        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>func_attr({<span style=\"color: #BA2121\">&quot;tir.noalias&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>bool(<span style=\"color: #008000; font-weight: bold\">True</span>)})\n",
              "        <span style=\"color: #007979; font-style: italic\"># with T.block(&quot;root&quot;):</span>\n",
              "        C_local <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;local&quot;</span>)\n",
              "        A_shared <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;shared&quot;</span>)\n",
              "        B_shared <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>alloc_buffer((<span style=\"color: #008000\">1024</span>, <span style=\"color: #008000\">1024</span>), scope<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;shared&quot;</span>)\n",
              "        <span style=\"color: #008000; font-weight: bold\">for</span> i_0_j_0_fused <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">64</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;blockIdx.x&quot;</span>, annotations<span style=\"color: #AA22FF; font-weight: bold\">=</span>{<span style=\"color: #BA2121\">&quot;pragma_auto_unroll_max_step&quot;</span>: <span style=\"color: #008000\">64</span>, <span style=\"color: #BA2121\">&quot;pragma_unroll_explicit&quot;</span>: <span style=\"color: #008000\">1</span>}):\n",
              "            <span style=\"color: #008000; font-weight: bold\">for</span> i_1_j_1_fused <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">1</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;vthread.x&quot;</span>):\n",
              "                <span style=\"color: #008000; font-weight: bold\">for</span> i_2_j_2_fused <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">256</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> i_3_init, j_3_init, i_4_init, j_4_init <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">1</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_init&quot;</span>):\n",
              "                            vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_j_2_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">32</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_3_init <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_4_init)\n",
              "                            vj <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_j_2_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_3_init <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_4_init)\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads()\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C_local[vi, vj])\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block_attr({<span style=\"color: #BA2121\">&quot;meta_schedule.thread_extent_high_inclusive&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;meta_schedule.thread_extent_low_inclusive&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), <span style=\"color: #BA2121\">&quot;meta_schedule.tiling_structure&quot;</span>: <span style=\"color: #BA2121\">&quot;SSSRRSRS&quot;</span>})\n",
              "                            C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>float32(<span style=\"color: #008000\">0</span>)\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> k_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">128</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">1</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">256</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                                <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>vectorized(<span style=\"color: #008000\">2</span>):\n",
              "                                    <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;A_shared&quot;</span>):\n",
              "                                        v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">512</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">8</span>)\n",
              "                                        v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">512</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">8</span>)\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(A[v0, v1])\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(A_shared[v0, v1])\n",
              "                                        A_shared[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> A[v0, v1]\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_0 <span style=\"color: #008000; font-weight: bold\">in</span> range(<span style=\"color: #008000\">4</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>thread_binding(<span style=\"color: #008000\">256</span>, thread<span style=\"color: #AA22FF; font-weight: bold\">=</span><span style=\"color: #BA2121\">&quot;threadIdx.x&quot;</span>):\n",
              "                                <span style=\"color: #008000; font-weight: bold\">for</span> ax0_ax1_fused_2 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>vectorized(<span style=\"color: #008000\">2</span>):\n",
              "                                    <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;B_shared&quot;</span>):\n",
              "                                        v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">512</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">256</span>)\n",
              "                                        v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> (ax0_ax1_fused_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">512</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0_ax1_fused_2) <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">256</span>)\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(B[v0, v1])\n",
              "                                        T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(B_shared[v0, v1])\n",
              "                                        B_shared[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> B[v0, v1]\n",
              "                        <span style=\"color: #008000; font-weight: bold\">for</span> k_1, i_3, j_3, k_2, i_4, j_4 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">2</span>, <span style=\"color: #008000\">4</span>, <span style=\"color: #008000\">8</span>, <span style=\"color: #008000\">1</span>):\n",
              "                            <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_update&quot;</span>):\n",
              "                                vi <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_j_2_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">32</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_3 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_4)\n",
              "                                vj <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_j_2_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_3 <span style=\"color: #AA22FF; font-weight: bold\">+</span> j_4)\n",
              "                                vk <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>reduce(<span style=\"color: #008000\">1024</span>, k_0 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">8</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> k_1 <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> k_2)\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(C_local[vi, vj], A_shared[vi, vk], B_shared[vk, vj])\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C_local[vi, vj])\n",
              "                                T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block_attr({<span style=\"color: #BA2121\">&quot;meta_schedule.thread_extent_high_inclusive&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">1024</span>), <span style=\"color: #BA2121\">&quot;meta_schedule.thread_extent_low_inclusive&quot;</span>: T<span style=\"color: #AA22FF; font-weight: bold\">.</span>int64(<span style=\"color: #008000\">32</span>), <span style=\"color: #BA2121\">&quot;meta_schedule.tiling_structure&quot;</span>: <span style=\"color: #BA2121\">&quot;SSSRRSRS&quot;</span>})\n",
              "                                C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">=</span> C_local[vi, vj] <span style=\"color: #AA22FF; font-weight: bold\">+</span> A_shared[vi, vk] <span style=\"color: #AA22FF; font-weight: bold\">*</span> B_shared[vk, vj]\n",
              "                    <span style=\"color: #008000; font-weight: bold\">for</span> ax0, ax1 <span style=\"color: #008000; font-weight: bold\">in</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>grid(<span style=\"color: #008000\">32</span>, <span style=\"color: #008000\">2</span>):\n",
              "                        <span style=\"color: #008000; font-weight: bold\">with</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>block(<span style=\"color: #BA2121\">&quot;C_local&quot;</span>):\n",
              "                            v0 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">64</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_j_2_fused <span style=\"color: #AA22FF; font-weight: bold\">//</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">32</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax0)\n",
              "                            v1 <span style=\"color: #AA22FF; font-weight: bold\">=</span> T<span style=\"color: #AA22FF; font-weight: bold\">.</span>axis<span style=\"color: #AA22FF; font-weight: bold\">.</span>spatial(<span style=\"color: #008000\">1024</span>, i_0_j_0_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">4</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">256</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> i_2_j_2_fused <span style=\"color: #AA22FF; font-weight: bold\">%</span> <span style=\"color: #008000\">128</span> <span style=\"color: #AA22FF; font-weight: bold\">*</span> <span style=\"color: #008000\">2</span> <span style=\"color: #AA22FF; font-weight: bold\">+</span> ax1)\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>reads(C_local[v0, v1])\n",
              "                            T<span style=\"color: #AA22FF; font-weight: bold\">.</span>writes(C[v0, v1])\n",
              "                            C[v0, v1] <span style=\"color: #AA22FF; font-weight: bold\">=</span> C_local[v0, v1]\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sch_tuned.mod.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "03Gk8fhRenjU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MetaSchedule: 9551.643754 GFLOPS\n"
          ]
        }
      ],
      "source": [
        "rt_mod = tvm.build(sch_tuned.mod, target=\"nvidia/geforce-rtx-2080-ti\")\n",
        "dev = tvm.cuda(0)\n",
        "evaluator = rt_mod.time_evaluator(\"main\", dev, number=10)\n",
        "\n",
        "print(\"MetaSchedule: %f GFLOPS\" % (num_flop / evaluator(A_nd, B_nd, C_nd).mean / 1e9))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZGWq5EjJ0BA"
      },
      "source": [
        "## Summary\n",
        "\n",
        "This chapter studies another axis of MLC -- how we can transform our program for hardware acceleration. The MLC process helps us to bridge the input models toward different GPU programming models and environments. We will visit more hardware specialization topics in the incoming chapter as well.\n",
        "\n",
        "- A typical GPU contains two-level hierarchy. Each thread is indexed by(in cuda terminology) `threadIdx.x` and `blockIdx.x`(there can be multiple dimension indices as well, but they can be fused to one.\n",
        "- Shared memory helps cache data commonly used across the threads within the same block.\n",
        "- Encourage memory reuse during GPU optimization.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "7 GPU and Specialized Hardware.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
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