| { |
| "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/overrides/.icons/iree/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" |
| }, |
| "execution_count": 2, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/", |
| "height": 300 |
| }, |
| "id": "4iJFDHbsAzo4", |
| "outputId": "6ed6f706-f701-47a6-f8b9-2d0141579f8d" |
| }, |
| "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[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.2/60.2 kB\u001b[0m \u001b[31m786.0 kB/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[31m14.2 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[31m60.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
| "\u001b[?25hCollecting torch>=2.1.0 (from shark-turbine)\n", |
| " Downloading torch-2.1.0-cp310-cp310-manylinux1_x86_64.whl (670.2 MB)\n", |
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| "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", |
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| "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->shark-turbine) (3.1)\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", |
| "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n", |
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| "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=2.1.0->shark-turbine)\n", |
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| "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n", |
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| "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n", |
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| "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n", |
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| "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n", |
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| "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n", |
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| "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n", |
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| "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", |
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| "\u001b[?25hCollecting nvidia-nccl-cu12==2.18.1 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl (209.8 MB)\n", |
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| "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", |
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| "\u001b[?25hCollecting triton==2.1.0 (from torch>=2.1.0->shark-turbine)\n", |
| " Downloading triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB)\n", |
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| "\u001b[?25hCollecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=2.1.0->shark-turbine)\n", |
| " Downloading nvidia_nvjitlink_cu12-12.2.140-py3-none-manylinux1_x86_64.whl (20.2 MB)\n", |
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| "\u001b[?25hRequirement 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=73e3b15d1dfbe2c9d718b6d9f08ba3ec8dc149061c13935ed97214fb6aa77ac7\n", |
| " Stored in directory: /root/.cache/pip/wheels/e9/78/0f/88c9d8224ef1550fe00b18a014eab5121f26264e2261f31926\n", |
| "Successfully built shark-turbine\n", |
| "Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, iree-runtime, iree-compiler, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, shark-turbine\n", |
| " Attempting uninstall: triton\n", |
| " Found existing installation: triton 2.0.0\n", |
| " Uninstalling triton-2.0.0:\n", |
| " Successfully uninstalled triton-2.0.0\n", |
| " Attempting uninstall: torch\n", |
| " Found existing installation: torch 2.0.1+cu118\n", |
| " Uninstalling torch-2.0.1+cu118:\n", |
| " Successfully uninstalled torch-2.0.1+cu118\n", |
| "Successfully installed iree-compiler-20231004.665 iree-runtime-20231004.665 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.2.140 nvidia-nvtx-cu12-12.1.105 shark-turbine-0.9.1.dev3 torch-2.1.0 triton-2.1.0\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": "1fa62bc3-6cba-4d7b-9ccf-d8ad024df53b" |
| }, |
| "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://iree.dev):\n", |
| " IREE compiler version 20231004.665 @ bb51f6f1a1b4ee619fb09a7396f449dadb211447\n", |
| " LLVM version 18.0.0git\n", |
| " Optimized build\n", |
| "\n", |
| "Installed PyTorch, version: 2.1.0+cu121\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": 5, |
| "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": "7696a60a-46d1-4d4b-a38b-901aa36530b5" |
| }, |
| "execution_count": 6, |
| "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": "c965bf26-5d23-4776-8cda-80ce8a307d28" |
| }, |
| "execution_count": 7, |
| "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", |
| " func.func @main(%arg0: tensor<4x3xf32>, %arg1: tensor<3xf32>, %arg2: tensor<4xf32>) -> (tensor<3xf32>, tensor<1x4xf32>) {\n", |
| " %cst = arith.constant 0.000000e+00 : f32\n", |
| " %expanded = tensor.expand_shape %arg2 [[0, 1]] : tensor<4xf32> into tensor<1x4xf32>\n", |
| " %0 = tensor.empty() : tensor<1x3xf32>\n", |
| " %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<1x3xf32>) -> tensor<1x3xf32>\n", |
| " %2 = linalg.matmul ins(%expanded, %arg0 : tensor<1x4xf32>, tensor<4x3xf32>) outs(%1 : tensor<1x3xf32>) -> tensor<1x3xf32>\n", |
| " %collapsed = tensor.collapse_shape %2 [[0, 1]] : tensor<1x3xf32> into tensor<3xf32>\n", |
| " %3 = tensor.empty() : tensor<3xf32>\n", |
| " %4 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = [\"parallel\"]} ins(%collapsed, %arg1 : tensor<3xf32>, tensor<3xf32>) outs(%3 : tensor<3xf32>) {\n", |
| " ^bb0(%in: f32, %in_0: f32, %out: f32):\n", |
| " %5 = arith.addf %in, %in_0 : f32\n", |
| " linalg.yield %5 : f32\n", |
| " } -> tensor<3xf32>\n", |
| " return %4, %expanded : tensor<3xf32>, tensor<1x4xf32>\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" |
| ] |
| }, |
| { |
| "output_type": "stream", |
| "name": "stderr", |
| "text": [ |
| "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py:1510: UserWarning: Your compiler for AOTAutograd is returning a function that doesn't take boxed arguments. Please wrap it with functorch.compile.make_boxed_func or handle the boxed arguments yourself. See https://github.com/pytorch/pytorch/pull/83137#issuecomment-1211320670 for rationale.\n", |
| " warnings.warn(\n" |
| ] |
| } |
| ] |
| } |
| ] |
| } |
