{
  "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 Ahead-of-time (AOT) export 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 export from a PyTorch session to [IREE](https://github.com/openxla/iree), leveraging [torch-mlir](https://github.com/llvm/torch-mlir) under the covers.\n",
        "\n",
        "SHARK-Turbine contains both a \"simple\" AOT exporter and an underlying advanced\n",
        "API for complicated models and full feature availability. This notebook shows\n",
        "some of the features available in the \"advanced\" toolkit."
      ],
      "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"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4iJFDHbsAzo4",
        "outputId": "642f4878-b5df-4499-c682-0cace5af016c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "google.colab.output.setIframeHeight(0, true, {maxHeight: 300})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting shark-turbine\n",
            "  Downloading shark-turbine-0.9.1.dev3.tar.gz (60 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/60.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m51.2/60.2 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.2/60.2 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shark-turbine) (1.23.5)\n",
            "Collecting iree-compiler>=20231004.665 (from shark-turbine)\n",
            "  Downloading iree_compiler-20231004.665-cp310-cp310-manylinux_2_28_x86_64.whl (57.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.2/57.2 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting iree-runtime>=20231004.665 (from shark-turbine)\n",
            "  Downloading iree_runtime-20231004.665-cp310-cp310-manylinux_2_28_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m91.3 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 shark-turbine) (2.1.0+cu118)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from iree-compiler>=20231004.665->shark-turbine) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (3.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (2.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2.1.0->shark-turbine) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2.1.0->shark-turbine) (1.3.0)\n",
            "Building wheels for collected packages: shark-turbine\n",
            "  Building wheel for shark-turbine (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shark-turbine: filename=shark_turbine-0.9.1.dev3-py3-none-any.whl size=70102 sha256=d4633a862e3a4815488be7a3b339b3aa927f1fd5637720b8e63a64ef31e1dd8f\n",
            "  Stored in directory: /root/.cache/pip/wheels/e9/78/0f/88c9d8224ef1550fe00b18a014eab5121f26264e2261f31926\n",
            "Successfully built shark-turbine\n",
            "Installing collected packages: iree-runtime, iree-compiler, shark-turbine\n",
            "Successfully installed iree-compiler-20231004.665 iree-runtime-20231004.665 shark-turbine-0.9.1.dev3\n"
          ]
        }
      ],
      "source": [
        "#@title Install SHARK-Turbine\n",
        "\n",
        "# Limit cell height.\n",
        "from IPython.display import Javascript\n",
        "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
        "\n",
        "!python -m pip install shark-turbine"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Report version information\n",
        "!echo \"Installed SHARK-Turbine, $(python -m pip show shark_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": "13d71d90-5f42-4e72-e85d-1d8137e1afda"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installed SHARK-Turbine, Version: 0.9.1.dev3\n",
            "\n",
            "Installed IREE, compiler version information:\n",
            "IREE (https://openxla.github.io/iree):\n",
            "  IREE compiler version 20231004.665 @ bb51f6f1a1b4ee619fb09a7396f449dadb211447\n",
            "  LLVM version 18.0.0git\n",
            "  Optimized build\n",
            "\n",
            "Installed PyTorch, version: 2.1.0+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Advanced AOT toolkit examples\n",
        "\n",
        "1. Define a PyTorch program using `torch.nn.Module`\n",
        "2. Define the API and properties of that program by using `aot.CompiledModule`\n",
        "3. Export the program using `aot.export()`\n",
        "4. Compile to a deployable artifact\n",
        "  * a: By staying within a Python session\n",
        "  * b: By outputting MLIR and continuing using native tools\n",
        "\n",
        "Useful documentation:\n",
        "\n",
        "* [IREE PyTorch guide](https://iree.dev/guides/ml-frameworks/pytorch/)\n",
        "* [PyTorch Modules](https://pytorch.org/docs/stable/notes/modules.html) (`nn.Module`) as building blocks for stateful computation\n",
        "* IREE compiler and runtime [Python bindings](https://www.iree.dev/reference/bindings/python/)"
      ],
      "metadata": {
        "id": "1Mi3YR75LBxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 1. Define a program using `torch.nn.Module`\n",
        "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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 2. Define the API and properties of that program by using aot.CompiledModule\n",
        "\n",
        "import shark_turbine.aot as aot\n",
        "\n",
        "example_weight = torch.randn(4, 3)\n",
        "example_bias = torch.randn(3)\n",
        "\n",
        "class CompiledLinearModule(aot.CompiledModule):\n",
        "  params = aot.export_parameters(linear_module, mutable=True)\n",
        "  compute = aot.jittable(linear_module.forward)\n",
        "\n",
        "  def main(self, x=aot.AbstractTensor(4)):\n",
        "    return self.compute(x)\n",
        "\n",
        "  def get_weight(self):\n",
        "    return self.params[\"weight\"]\n",
        "\n",
        "  def set_weight(self, weight=aot.abstractify(example_weight)):\n",
        "    self.params[\"weight\"] = weight\n",
        "\n",
        "  def get_bias(self):\n",
        "    return self.params[\"bias\"]\n",
        "\n",
        "  def set_bias(self, bias=aot.abstractify(example_bias)):\n",
        "    self.params[\"bias\"] = bias"
      ],
      "metadata": {
        "id": "Ua3tNtUIozoa"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 3. Export the program using `aot.export()`\n",
        "\n",
        "example_arg = torch.randn(4)\n",
        "export_output = aot.export(CompiledLinearModule, example_arg)"
      ],
      "metadata": {
        "id": "eK2fWVfiSQ8f"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 4a. Compile fully to a deployable artifact, in our existing Python session\n",
        "\n",
        "# Staying in Python gives the API a chance to reuse memory, improving\n",
        "# performance when compiling large programs.\n",
        "\n",
        "compiled_binary = export_output.compile(save_to=None)\n",
        "\n",
        "# Use the IREE runtime API to test the compiled program.\n",
        "import numpy as np\n",
        "import iree.runtime as ireert\n",
        "\n",
        "config = ireert.Config(\"local-task\")\n",
        "vm_module = ireert.load_vm_module(\n",
        "    ireert.VmModule.wrap_buffer(config.vm_instance, compiled_binary.map_memory()),\n",
        "    config,\n",
        ")\n",
        "\n",
        "input = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)\n",
        "result = vm_module.main(input)\n",
        "print(result.to_host())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMRNdFdos900",
        "outputId": "465d47e1-45a5-4f88-bcf9-33ceb5d417e7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1.4178504 -1.2343317 -7.4767947]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 4b. Output MLIR then continue from Python or native tools later\n",
        "\n",
        "# Leaving Python allows for file system checkpointing and grants access to\n",
        "# native development workflows.\n",
        "\n",
        "mlir_file_path = \"/tmp/linear_module_pytorch.mlirbc\"\n",
        "vmfb_file_path = \"/tmp/linear_module_pytorch_llvmcpu.vmfb\"\n",
        "\n",
        "export_output.print_readable()\n",
        "export_output.save_mlir(mlir_file_path)\n",
        "\n",
        "!iree-compile --iree-input-type=torch --iree-hal-target-backends=llvm-cpu {mlir_file_path} -o {vmfb_file_path}\n",
        "!iree-run-module --module={vmfb_file_path} --device=local-task --input=\"4xf32=[1.0, 2.0, 3.0, 4.0]\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AdkXY8VNL2-",
        "outputId": "52995990-0d11-46f3-b538-98a0f1e94473"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "module @compiled_linear {\n",
            "  util.global private mutable @_params.weight {inlining_policy = #util.inline.never} = dense<[[1.54099607, -0.293428898, -2.17878938], [0.568431258, -1.08452237, -1.39859545], [0.403346837, 0.838026344, -0.719257593], [-0.403343529, -0.596635341, 0.182036489]]> : tensor<4x3xf32>\n",
            "  util.global private mutable @_params.bias {inlining_policy = #util.inline.never} = dense<[-0.856674611, 1.10060418, -1.07118738]> : tensor<3xf32>\n",
            "  func.func @main(%arg0: tensor<4xf32>) -> tensor<3xf32> attributes {torch.args_schema = \"[1, {\\22type\\22: \\22builtins.tuple\\22, \\22context\\22: \\22null\\22, \\22children_spec\\22: [{\\22type\\22: \\22builtins.list\\22, \\22context\\22: \\22null\\22, \\22children_spec\\22: [{\\22type\\22: null, \\22context\\22: null, \\22children_spec\\22: []}]}, {\\22type\\22: \\22builtins.dict\\22, \\22context\\22: \\22[]\\22, \\22children_spec\\22: []}]}]\", torch.return_schema = \"[1, {\\22type\\22: null, \\22context\\22: null, \\22children_spec\\22: []}]\"} {\n",
            "    %0 = torch_c.from_builtin_tensor %arg0 : tensor<4xf32> -> !torch.vtensor<[4],f32>\n",
            "    %1 = call @forward(%0) : (!torch.vtensor<[4],f32>) -> !torch.vtensor<[3],f32>\n",
            "    %2 = torch_c.to_builtin_tensor %1 : !torch.vtensor<[3],f32> -> tensor<3xf32>\n",
            "    return %2 : tensor<3xf32>\n",
            "  }\n",
            "  func.func private @forward(%arg0: !torch.vtensor<[4],f32>) -> !torch.vtensor<[3],f32> {\n",
            "    %int0 = torch.constant.int 0\n",
            "    %0 = torch.aten.unsqueeze %arg0, %int0 : !torch.vtensor<[4],f32>, !torch.int -> !torch.vtensor<[1,4],f32>\n",
            "    %_params.weight = util.global.load @_params.weight : tensor<4x3xf32>\n",
            "    %1 = torch_c.from_builtin_tensor %_params.weight : tensor<4x3xf32> -> !torch.vtensor<[4,3],f32>\n",
            "    %2 = torch.aten.mm %0, %1 : !torch.vtensor<[1,4],f32>, !torch.vtensor<[4,3],f32> -> !torch.vtensor<[1,3],f32>\n",
            "    %int0_0 = torch.constant.int 0\n",
            "    %3 = torch.aten.squeeze.dim %2, %int0_0 : !torch.vtensor<[1,3],f32>, !torch.int -> !torch.vtensor<[3],f32>\n",
            "    %_params.bias = util.global.load @_params.bias : tensor<3xf32>\n",
            "    %4 = torch_c.from_builtin_tensor %_params.bias : tensor<3xf32> -> !torch.vtensor<[3],f32>\n",
            "    %int1 = torch.constant.int 1\n",
            "    %5 = torch.aten.add.Tensor %3, %4, %int1 : !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.int -> !torch.vtensor<[3],f32>\n",
            "    return %5 : !torch.vtensor<[3],f32>\n",
            "  }\n",
            "  func.func @get_weight() -> tensor<4x3xf32> attributes {torch.return_schema = \"[1, {\\22type\\22: null, \\22context\\22: null, \\22children_spec\\22: []}]\"} {\n",
            "    %_params.weight = util.global.load @_params.weight : tensor<4x3xf32>\n",
            "    return %_params.weight : tensor<4x3xf32>\n",
            "  }\n",
            "  func.func @set_weight(%arg0: tensor<4x3xf32>) attributes {torch.args_schema = \"[1, {\\22type\\22: \\22builtins.tuple\\22, \\22context\\22: \\22null\\22, \\22children_spec\\22: [{\\22type\\22: \\22builtins.list\\22, \\22context\\22: \\22null\\22, \\22children_spec\\22: [{\\22type\\22: null, \\22context\\22: null, \\22children_spec\\22: []}]}, {\\22type\\22: \\22builtins.dict\\22, \\22context\\22: \\22[]\\22, \\22children_spec\\22: []}]}]\"} {\n",
            "    util.global.store %arg0, @_params.weight : tensor<4x3xf32>\n",
            "    return\n",
            "  }\n",
            "  func.func @get_bias() -> tensor<3xf32> attributes {torch.return_schema = \"[1, {\\22type\\22: null, \\22context\\22: null, \\22children_spec\\22: []}]\"} {\n",
            "    %_params.bias = util.global.load @_params.bias : tensor<3xf32>\n",
            "    return %_params.bias : tensor<3xf32>\n",
            "  }\n",
            "  func.func @set_bias(%arg0: tensor<3xf32>) attributes {torch.args_schema = \"[1, {\\22type\\22: \\22builtins.tuple\\22, \\22context\\22: \\22null\\22, \\22children_spec\\22: [{\\22type\\22: \\22builtins.list\\22, \\22context\\22: \\22null\\22, \\22children_spec\\22: [{\\22type\\22: null, \\22context\\22: null, \\22children_spec\\22: []}]}, {\\22type\\22: \\22builtins.dict\\22, \\22context\\22: \\22[]\\22, \\22children_spec\\22: []}]}]\"} {\n",
            "    util.global.store %arg0, @_params.bias : tensor<3xf32>\n",
            "    return\n",
            "  }\n",
            "}\n"
          ]
        }
      ]
    }
  ]
}