{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "UUXnh11hA75x"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##### Copyright 2023 The IREE Authors"
      ],
      "metadata": {
        "id": "UUXnh11hA75x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Licensed under the Apache License v2.0 with LLVM Exceptions.\n",
        "# See https://llvm.org/LICENSE.txt for license information.\n",
        "# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception"
      ],
      "metadata": {
        "cellView": "form",
        "id": "FqsvmKpjBJO2"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/1/10/PyTorch_logo_icon.svg/640px-PyTorch_logo_icon.svg.png\" height=\"20px\"> PyTorch Just-in-time (JIT) workflows using <img src=\"https://raw.githubusercontent.com/openxla/iree/main/docs/website/docs/assets/images/ghost.svg\" height=\"20px\"> IREE\n",
        "\n",
        "This notebook shows how to use [SHARK-Turbine](https://github.com/nod-ai/SHARK-Turbine) for eager execution within a PyTorch session using [IREE](https://github.com/openxla/iree) and [torch-mlir](https://github.com/llvm/torch-mlir) under the covers."
      ],
      "metadata": {
        "id": "38UDc27KBPD1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "jbcW5jMLK8gK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "#@title Uninstall existing packages\n",
        "#   This avoids some warnings when installing specific PyTorch packages below.\n",
        "!python -m pip uninstall -y fastai torchaudio torchdata torchtext torchvision"
      ],
      "metadata": {
        "id": "KsPubQSvCbXd",
        "cellView": "form"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install Pytorch 2.3.0 (prerelease)\n",
        "!python -m pip install --pre --index-url https://download.pytorch.org/whl/test/cpu --upgrade torch==2.3.0"
      ],
      "metadata": {
        "id": "KHbDmehBWuDW",
        "outputId": "c2af25cd-58c9-4757-bdda-1124f9f2aa88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://download.pytorch.org/whl/test/cpu\n",
            "Collecting torch==2.3.0\n",
            "  Downloading https://download.pytorch.org/whl/test/cpu/torch-2.3.0%2Bcpu-cp310-cp310-linux_x86_64.whl (190.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.4/190.4 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.3.0) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.3.0) (1.3.0)\n",
            "Installing collected packages: torch\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.2.1+cu121\n",
            "    Uninstalling torch-2.2.1+cu121:\n",
            "      Successfully uninstalled torch-2.2.1+cu121\n",
            "Successfully installed torch-2.3.0+cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iJFDHbsAzo4",
        "outputId": "13397b7e-42cd-4f14-d6e8-a21fa2d1f524"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting iree-turbine\n",
            "  Downloading iree_turbine-2.3.0rc20240410-py3-none-any.whl (150 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m150.4/150.4 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from iree-turbine) (1.25.2)\n",
            "Collecting iree-compiler>=20240410.859 (from iree-turbine)\n",
            "  Downloading iree_compiler-20240410.859-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (64.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.4/64.4 MB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting iree-runtime>=20240410.859 (from iree-turbine)\n",
            "  Downloading iree_runtime-20240410.859-cp310-cp310-manylinux_2_28_x86_64.whl (7.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from iree-turbine) (2.3.0+cpu)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from iree-compiler>=20240410.859->iree-turbine) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->iree-turbine) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->iree-turbine) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->iree-turbine) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->iree-turbine) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->iree-turbine) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->iree-turbine) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2.1.0->iree-turbine) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2.1.0->iree-turbine) (1.3.0)\n",
            "Installing collected packages: iree-runtime, iree-compiler, iree-turbine\n",
            "Successfully installed iree-compiler-20240410.859 iree-runtime-20240410.859 iree-turbine-2.3.0rc20240410\n"
          ]
        }
      ],
      "source": [
        "#@title Install iree-turbine\n",
        "!python -m pip install iree-turbine"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Report version information\n",
        "!echo \"Installed iree-turbine, $(python -m pip show iree_turbine | grep Version)\"\n",
        "\n",
        "!echo -e \"\\nInstalled IREE, compiler version information:\"\n",
        "!iree-compile --version\n",
        "\n",
        "import torch\n",
        "print(\"\\nInstalled PyTorch, version:\", torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkVLzRpcDnVL",
        "outputId": "230b113c-6800-45e2-ec93-eddefa439803"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installed iree-turbine, Version: 2.3.0rc20240410\n",
            "\n",
            "Installed IREE, compiler version information:\n",
            "IREE (https://iree.dev):\n",
            "  IREE compiler version 20240410.859 @ b4273a4bfc66ba6dd8f62f6483d74d42a7b936f1\n",
            "  LLVM version 19.0.0git\n",
            "  Optimized build\n",
            "\n",
            "Installed PyTorch, version: 2.3.0+cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sample JIT workflow\n",
        "\n",
        "1. Define a program using `torch.nn.Module`\n",
        "2. Run `torch.compile(module, backend=\"turbine_cpu\")`\n",
        "3. Use the resulting `OptimizedModule` as you would a regular `nn.Module`\n",
        "\n",
        "Useful documentation:\n",
        "\n",
        "* [PyTorch Modules](https://pytorch.org/docs/stable/notes/modules.html) (`nn.Module`) as building blocks for stateful computation\n",
        "* [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) as an interface to TorchDynamo and optimizing using backend compilers like Turbine"
      ],
      "metadata": {
        "id": "1Mi3YR75LBxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(0)\n",
        "\n",
        "class LinearModule(torch.nn.Module):\n",
        "  def __init__(self, in_features, out_features):\n",
        "    super().__init__()\n",
        "    self.weight = torch.nn.Parameter(torch.randn(in_features, out_features))\n",
        "    self.bias = torch.nn.Parameter(torch.randn(out_features))\n",
        "\n",
        "  def forward(self, input):\n",
        "    return (input @ self.weight) + self.bias\n",
        "\n",
        "linear_module = LinearModule(4, 3)"
      ],
      "metadata": {
        "id": "oPdjrmPZMNz6"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt_linear_module = torch.compile(linear_module, backend=\"turbine_cpu\")\n",
        "print(\"Compiled module using Turbine. New module type is\", type(opt_linear_module))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eK2fWVfiSQ8f",
        "outputId": "337f5077-22e3-4ff0-cde1-38b08ff5bea5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compiled module using Turbine. New module type is <class 'torch._dynamo.eval_frame.OptimizedModule'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "args = torch.randn(4)\n",
        "turbine_output = opt_linear_module(args)\n",
        "\n",
        "print(\"Weight:\", linear_module.weight)\n",
        "print(\"Bias:\", linear_module.bias)\n",
        "print(\"Args:\", args)\n",
        "print(\"Output:\", turbine_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AdkXY8VNL2-",
        "outputId": "0f19bdd4-15ff-43ce-b9a7-6fa1113d124c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "module {\n",
            "  func.func @main(%arg0: !torch.vtensor<[4,3],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[4],f32>) -> (!torch.vtensor<[3],f32>, !torch.vtensor<[1,4],f32>) {\n",
            "    %int0 = torch.constant.int 0\n",
            "    %0 = torch.aten.unsqueeze %arg2, %int0 : !torch.vtensor<[4],f32>, !torch.int -> !torch.vtensor<[1,4],f32>\n",
            "    %1 = torch.aten.mm %0, %arg0 : !torch.vtensor<[1,4],f32>, !torch.vtensor<[4,3],f32> -> !torch.vtensor<[1,3],f32>\n",
            "    %int0_0 = torch.constant.int 0\n",
            "    %2 = torch.aten.squeeze.dim %1, %int0_0 : !torch.vtensor<[1,3],f32>, !torch.int -> !torch.vtensor<[3],f32>\n",
            "    %int1 = torch.constant.int 1\n",
            "    %3 = torch.aten.add.Tensor %2, %arg1, %int1 : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>\n",
            "    return %3, %0 : !torch.vtensor<[3],f32>, !torch.vtensor<[1,4],f32>\n",
            "  }\n",
            "}\n",
            "\n",
            "#map = affine_map<(d0) -> (d0)>\n",
            "module {\n",
            "  util.func public @main$async(%arg0: !hal.buffer_view, %arg1: !hal.buffer_view, %arg2: !hal.buffer_view, %arg3: !hal.fence, %arg4: !hal.fence) -> (!hal.buffer_view, !hal.buffer_view) attributes {inlining_policy = #util.inline.never, iree.abi.model = \"coarse-fences\", iree.abi.stub} {\n",
            "    %cst = arith.constant 0.000000e+00 : f32\n",
            "    %0 = hal.tensor.import wait(%arg3) => %arg0 : !hal.buffer_view -> tensor<4x3xf32>\n",
            "    %1 = hal.tensor.import wait(%arg3) => %arg1 : !hal.buffer_view -> tensor<3xf32>\n",
            "    %2 = hal.tensor.import wait(%arg3) => %arg2 : !hal.buffer_view -> tensor<4xf32>\n",
            "    %expanded = tensor.expand_shape %2 [[0, 1]] : tensor<4xf32> into tensor<1x4xf32>\n",
            "    %3 = tensor.empty() : tensor<1x3xf32>\n",
            "    %4 = linalg.fill ins(%cst : f32) outs(%3 : tensor<1x3xf32>) -> tensor<1x3xf32>\n",
            "    %5 = linalg.matmul ins(%expanded, %0 : tensor<1x4xf32>, tensor<4x3xf32>) outs(%4 : tensor<1x3xf32>) -> tensor<1x3xf32>\n",
            "    %collapsed = tensor.collapse_shape %5 [[0, 1]] : tensor<1x3xf32> into tensor<3xf32>\n",
            "    %6 = tensor.empty() : tensor<3xf32>\n",
            "    %7 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = [\"parallel\"]} ins(%collapsed, %1 : tensor<3xf32>, tensor<3xf32>) outs(%6 : tensor<3xf32>) {\n",
            "    ^bb0(%in: f32, %in_0: f32, %out: f32):\n",
            "      %11 = arith.addf %in, %in_0 : f32\n",
            "      linalg.yield %11 : f32\n",
            "    } -> tensor<3xf32>\n",
            "    %8:2 = hal.tensor.barrier join(%7, %expanded : tensor<3xf32>, tensor<1x4xf32>) => %arg4 : !hal.fence\n",
            "    %9 = hal.tensor.export %8#0 : tensor<3xf32> -> !hal.buffer_view\n",
            "    %10 = hal.tensor.export %8#1 : tensor<1x4xf32> -> !hal.buffer_view\n",
            "    util.return %9, %10 : !hal.buffer_view, !hal.buffer_view\n",
            "  }\n",
            "  util.func public @main(%arg0: !hal.buffer_view, %arg1: !hal.buffer_view, %arg2: !hal.buffer_view) -> (!hal.buffer_view, !hal.buffer_view) attributes {iree.abi.stub} {\n",
            "    %c-1_i32 = arith.constant -1 : i32\n",
            "    %c0 = arith.constant 0 : index\n",
            "    %device_0 = hal.devices.get %c0 : !hal.device\n",
            "    %0 = util.null : !hal.fence\n",
            "    %fence = hal.fence.create device(%device_0 : !hal.device) flags(\"None\") : !hal.fence\n",
            "    %1:2 = util.call @main$async(%arg0, %arg1, %arg2, %0, %fence) : (!hal.buffer_view, !hal.buffer_view, !hal.buffer_view, !hal.fence, !hal.fence) -> (!hal.buffer_view, !hal.buffer_view)\n",
            "    %status = hal.fence.await until([%fence]) timeout_millis(%c-1_i32) : i32\n",
            "    util.return %1#0, %1#1 : !hal.buffer_view, !hal.buffer_view\n",
            "  }\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weight: Parameter containing:\n",
            "tensor([[ 1.5410, -0.2934, -2.1788],\n",
            "        [ 0.5684, -1.0845, -1.3986],\n",
            "        [ 0.4033,  0.8380, -0.7193],\n",
            "        [-0.4033, -0.5966,  0.1820]], requires_grad=True)\n",
            "Bias: Parameter containing:\n",
            "tensor([-0.8567,  1.1006, -1.0712], requires_grad=True)\n",
            "Args: tensor([ 0.1227, -0.5663,  0.3731, -0.8920])\n",
            "Output: tensor([-0.4792,  2.5237, -0.9772], grad_fn=<CompiledFunctionBackward>)\n"
          ]
        }
      ]
    }
  ]
}