torch_compile_Intro.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!nvcc --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLQs-Fp51CVV",
        "outputId": "c324a3c1-2b23-4bb3-f976-3d0a63fc0d04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyWNqgZx1R9D",
        "outputId": "2b068a98-c416-4dd0-faa9-b85faf503597"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/\n",
            "Requirement already satisfied: triton-nightly in /usr/local/lib/python3.10/dist-packages (3.0.0.post20240716052845)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from triton-nightly) (3.16.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mOgJHFEHzNFI"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of GPUs: {torch.cuda.device_count()}\")\n",
        "print([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])\n",
        "\n",
        "torch_ver = [int(x) for x in torch.__version__.split(\".\")[:2]]\n",
        "assert torch_ver >= [2, 0], \"Requires PyTorch >= 2.0\"\n",
        "print(\"PyTorch Version:\", torch.__version__)"
      ],
      "metadata": {
        "id": "DsFti93h04wD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37059bdf-9583-47fb-cd5c-8c5a84c9b731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of GPUs: 1\n",
            "['Tesla T4']\n",
            "PyTorch Version: 2.5.0+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Helper function to measure the execution time for a given function"
      ],
      "metadata": {
        "id": "RRAomMO_gUG1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "def timed(fn, n_test: int, dtype: torch.dtype) -> tuple:\n",
        "    \"\"\"\n",
        "    Measure the execution time for a given function.\n",
        "\n",
        "    Args:\n",
        "    - fn (function): The function to be timed.\n",
        "    - n_test (int): Number of times the function is executed to get the average time.\n",
        "    - dtype (torch.dtype): Data type for PyTorch tensors.\n",
        "\n",
        "    Returns:\n",
        "    - tuple: A tuple containing the average execution time (in milliseconds) and the output of the function.\n",
        "    \"\"\"\n",
        "    with torch.no_grad(), torch.autocast(device_type='cuda', dtype=dtype):\n",
        "        dt_loop_sum = []\n",
        "        for _ in range(n_test):\n",
        "            torch.cuda.synchronize()\n",
        "            start = time.time()\n",
        "            output = fn()\n",
        "            torch.cuda.synchronize()\n",
        "            end = time.time()\n",
        "            dt_loop_sum.append(end-start)\n",
        "        dt_test = sum(dt_loop_sum) / len(dt_loop_sum)\n",
        "\n",
        "    return dt_test * 1000, output"
      ],
      "metadata": {
        "id": "EPLOqyLG0yg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Now let's look at the list of backends available"
      ],
      "metadata": {
        "id": "bnI18OX5gwDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.compiler.list_backends()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yI3-zXYGfdRM",
        "outputId": "c52bafec-1ff8-4532-abf2-6bd5f924009e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['cudagraphs', 'inductor', 'onnxrt', 'openxla', 'tvm']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------------------------------------------------------\n",
        "\n"
      ],
      "metadata": {
        "id": "fuQ0it5EpmID"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's begin with a simple Resnet Model"
      ],
      "metadata": {
        "id": "JEXpI-Dtg9KC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download an example image from the pytorch website\n",
        "import urllib\n",
        "import matplotlib.pyplot as plt\n",
        "url, filename = (\"https://github.com/pytorch/hub/raw/master/images/dog.jpg\", \"dog.jpg\")\n",
        "try: urllib.URLopener().retrieve(url, filename)\n",
        "except: urllib.request.urlretrieve(url, filename)\n",
        "\n",
        "from PIL import Image\n",
        "input_image = Image.open(filename)\n",
        "plt.imshow(input_image)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "T6RjKdMZglWX",
        "outputId": "0bbdb9b9-ed2c-4669-90ce-51984d86d689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Some preprocessing\n",
        "\n",
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# create the image preprocessor\n",
        "preprocess = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])\n",
        "input_tensor = preprocess(input_image)\n",
        "input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
        "\n",
        "# load the resnet152 model\n",
        "model = torch.hub.load('pytorch/vision:v0.17.2', 'resnet152', pretrained=True)\n",
        "model.eval()\n",
        "\n",
        "# move the input and model to GPU for speed if available\n",
        "if torch.cuda.is_available():\n",
        "    input_batch = input_batch.to('cuda')\n",
        "    model.to('cuda')\n",
        "with torch.no_grad():\n",
        "    output = model(input_batch)\n",
        "\n",
        "# Tensor of shape 1000, with confidence scores over ImageNet's 1000 classes\n",
        "print(output.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExLUT6szo4o3",
        "outputId": "eb64c955-2174-4e37-83c4-1edb2d7c5667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/pytorch/vision/zipball/v0.17.2\" to /root/.cache/torch/hub/v0.17.2.zip\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet152_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet152_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet152-394f9c45.pth\" to /root/.cache/torch/hub/checkpoints/resnet152-394f9c45.pth\n",
            "100%|██████████| 230M/230M [00:01<00:00, 159MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 1000])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Helper function to get the topk labels based on probabilities\n",
        "def print_topk_labels(output, k):\n",
        "    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.\n",
        "    probabilities = torch.nn.functional.softmax(output[0], dim=0)\n",
        "    # Read the categories\n",
        "    with open(\"imagenet_classes.txt\", \"r\") as f:\n",
        "        categories = [s.strip() for s in f.readlines()]\n",
        "    # Show top categories per image\n",
        "    topk_prob, topk_catid = torch.topk(probabilities, k)\n",
        "    for i in range(topk_prob.size(0)):\n",
        "        print(categories[topk_catid[i]], topk_prob[i].item())\n",
        "\n",
        "# Download the Imagenet labels\n",
        "!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wx9cSFYbpInX",
        "outputId": "fce6afec-feac-4e03-ad7e-be55677d5d76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-10-24 03:50:17--  https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10472 (10K) [text/plain]\n",
            "Saving to: ‘imagenet_classes.txt’\n",
            "\n",
            "\rimagenet_classes.tx   0%[                    ]       0  --.-KB/s               \rimagenet_classes.tx 100%[===================>]  10.23K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2024-10-24 03:50:17 (19.7 MB/s) - ‘imagenet_classes.txt’ saved [10472/10472]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## So, what are the topk Labels?\n",
        "print_topk_labels(output, 5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0g3xfjupYUd",
        "outputId": "e88c71e8-7901-4980-eab9-84cfbd5dc861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Samoyed 0.7907484173774719\n",
            "Pomeranian 0.08977653086185455\n",
            "white wolf 0.03610274940729141\n",
            "keeshond 0.026814348995685577\n",
            "Arctic fox 0.022788120433688164\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T9S5gfEUpe2u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performance Evaluation in Eager Mode"
      ],
      "metadata": {
        "id": "XNG4JBqqpgyy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_warmup = 10\n",
        "n_test = 20\n",
        "dtype = torch.bfloat16\n",
        "inference_time=[]\n",
        "mode=[]\n",
        "\n",
        "# In general, it's always recommended to warm-up the GPUs with some iterations\n",
        "# before benchmarking the application\n",
        "t_warmup, _ = timed(lambda:model(input_batch), n_warmup, dtype)\n",
        "t_test, output = timed(lambda:model(input_batch), n_test, dtype)\n",
        "print(f\"Average inference time for resnet152(warmup): dt_test={t_warmup} ms\")\n",
        "print(f\"Average inference time for resnet152(test): dt_test={t_test} ms\")\n",
        "print_topk_labels(output, 5)\n",
        "inference_time.append(t_test)\n",
        "mode.append(\"eager\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RR52te5IpkFY",
        "outputId": "c094611f-6c9e-4060-933f-7d253aafc3bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average inference time for resnet152(warmup): dt_test=74.45104122161865 ms\n",
            "Average inference time for resnet152(test): dt_test=35.335707664489746 ms\n",
            "Samoyed 0.796875\n",
            "Pomeranian 0.083984375\n",
            "white wolf 0.037353515625\n",
            "keeshond 0.025634765625\n",
            "Arctic fox 0.021240234375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We run the GPU for 10 iterations followed by 20 iterations."
      ],
      "metadata": {
        "id": "3SbBoBGKqRM0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performance Evaluation in `torch.compile` Mode\n",
        "\n",
        "### Default Mode"
      ],
      "metadata": {
        "id": "y863K47hqno3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# need to reset to make sure there's no compiled remains\n",
        "torch._dynamo.reset()\n",
        "model_opt1 = torch.compile(model, fullgraph=True)\n",
        "t_compilation, _ = timed(lambda:model_opt1(input_batch), 1, dtype)\n",
        "t_warmup, _ = timed(lambda:model_opt1(input_batch), n_warmup, dtype)\n",
        "t_test, output = timed(lambda:model_opt1(input_batch), n_test, dtype)\n",
        "print(f\"Compilation time: dt_compilation={t_compilation} ms\")\n",
        "print(f\"Average inference time for compiled resnet152(warmup): dt_test={t_warmup} ms\")\n",
        "print(f\"Average inference time for compiled resnet152(test): dt_test={t_test} ms\")\n",
        "print_topk_labels(output, 5)\n",
        "inference_time.append(t_test)\n",
        "mode.append(\"default\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s53yERIAqP6b",
        "outputId": "c20ae6ed-5d80-443c-9f49-4442b4ae400e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compilation time: dt_compilation=45972.96953201294 ms\n",
            "Average inference time for compiled resnet152(warmup): dt_test=40.27864933013916 ms\n",
            "Average inference time for compiled resnet152(test): dt_test=31.566286087036133 ms\n",
            "Samoyed 0.796875\n",
            "Pomeranian 0.083984375\n",
            "white wolf 0.037353515625\n",
            "keeshond 0.025634765625\n",
            "Arctic fox 0.021240234375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "COAN1__Tr-No"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reduce-Overhead Mode\n",
        "\n",
        "Leverages CUDA graphs to reduce the launch overheads of kernels.\n",
        "Every Kernel Launch imposes a significant overhead on the overall\n",
        "execution time of the model. By compiling them to CUDA Graphs, we\n",
        "can reduce that overhead"
      ],
      "metadata": {
        "id": "qx0EWRm7sQ2n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch._dynamo.reset()\n",
        "model_opt2 = torch.compile(model, mode=\"reduce-overhead\", fullgraph=True)\n",
        "t_compilation, _ = timed(lambda:model_opt2(input_batch), 1, dtype)\n",
        "t_warmup, _ = timed(lambda:model_opt2(input_batch), n_warmup, dtype)\n",
        "t_test, output = timed(lambda:model_opt2(input_batch), n_test, dtype)\n",
        "print(f\"Compilation time: dt_compilation={t_compilation} ms\")\n",
        "print(f\"Average inference time for compiled resnet152(warmup): dt_test={t_warmup} ms\")\n",
        "print(f\"Average inference time for compiled resnet152(test): dt_test={t_test} ms\")\n",
        "print_topk_labels(output, 5)\n",
        "inference_time.append(t_test)\n",
        "mode.append(\"reduce-overhead\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKh4iZGCsTVQ",
        "outputId": "7474c739-2544-4933-9f08-6e5293575605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compilation time: dt_compilation=40412.38236427307 ms\n",
            "Average inference time for compiled resnet152(warmup): dt_test=40.248775482177734 ms\n",
            "Average inference time for compiled resnet152(test): dt_test=28.899741172790527 ms\n",
            "Samoyed 0.796875\n",
            "Pomeranian 0.083984375\n",
            "white wolf 0.037353515625\n",
            "keeshond 0.025634765625\n",
            "Arctic fox 0.021240234375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Gains from using CUDA-Graphs depend on the characteristics of the underlying kernels. If the kernels have very short runtime, the use of CUDA Graphs can significantly reduce overall launch overhead, resulting in higher performance.***"
      ],
      "metadata": {
        "id": "vNBQ3WaatB-e"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uuQ0m34Wsi5w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performance Evaluation in `max-autotune` mode\n",
        "\n",
        "Here, the Triton-based matrix multiplications start coming into the picture. This overall takes the most amount of time but in most of the cases, also reduces the overall execution significantly.\n",
        "\n",
        "*Note: In this case you don't see any benefits at all - see the `warning`- as it states, this mode is effective on only larger GPUs.*"
      ],
      "metadata": {
        "id": "VIQHs4X-tgTI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch._dynamo.reset()\n",
        "model_opt3 = torch.compile(model, mode=\"max-autotune\", fullgraph=True)\n",
        "t_compilation, _ = timed(lambda:model_opt3(input_batch), 1, dtype)\n",
        "t_warmup, _ = timed(lambda:model_opt3(input_batch), n_warmup, dtype)\n",
        "t_test, output = timed(lambda:model_opt3(input_batch), n_test, dtype)\n",
        "print(f\"Compilation time: dt_compilation={t_compilation} ms\")\n",
        "print(f\"Average inference time for compiled resnet152(warmup): dt_test={t_warmup} ms\")\n",
        "print(f\"Average inference time for compiled resnet152(test): dt_test={t_test} ms\")\n",
        "print_topk_labels(output, 5)\n",
        "inference_time.append(t_test)\n",
        "mode.append(\"max-autotune\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z3GHIx2-tkoD",
        "outputId": "9221ac69-b88b-4ae8-8822-d3b1325ef0d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "W1024 04:09:27.830000 689 torch/_inductor/utils.py:1048] [0/0] Not enough SMs to use max_autotune_gemm mode\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py:1604: UserWarning: Tesla T4 does not support bfloat16 compilation natively, skipping\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compilation time: dt_compilation=42090.479373931885 ms\n",
            "Average inference time for compiled resnet152(warmup): dt_test=46.245551109313965 ms\n",
            "Average inference time for compiled resnet152(test): dt_test=33.144569396972656 ms\n",
            "Samoyed 0.796875\n",
            "Pomeranian 0.083984375\n",
            "white wolf 0.037353515625\n",
            "keeshond 0.025634765625\n",
            "Arctic fox 0.021240234375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Triton kernels autonomously optimize both matrix multiplication and convolution operations. On a compatible GPU, this process would take an extremely long time compared to the other modes.\n",
        "\n",
        "However, the benefits would be where there are a lot of matrix multiplications or convolution operations to optimize"
      ],
      "metadata": {
        "id": "jpIil766ujbM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "beuXKkk2tsrb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A Plot for Comparison"
      ],
      "metadata": {
        "id": "_ZQRU8ewu1LC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(mode, inference_time)\n",
        "print(inference_time)\n",
        "print(mode)\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('mode')\n",
        "plt.ylabel('Inference time (ms)')\n",
        "plt.title('ResNet-152')\n",
        "\n",
        "# Displaying the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "uliG1PP7u3hl",
        "outputId": "a24d91d3-88a5-4696-f354-b34f39ae050e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[35.335707664489746, 31.566286087036133, 28.899741172790527, 33.144569396972656]\n",
            "['eager', 'default', 'reduce-overhead', 'max-autotune']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oe0fU2MLu4uW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------------------------------------------------------"
      ],
      "metadata": {
        "id": "sDZFhY1svA-x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## FX Graphs and TorchInductor"
      ],
      "metadata": {
        "id": "ACwkBRLAY95H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Disable dynamic shapes for the purposes of this example\n",
        "@torch.compile(dynamic=False)\n",
        "def fn(x, y):\n",
        "  x *= x\n",
        "  x /= y\n",
        "  return x + 1\n",
        "\n",
        "# Set the logs to observe different artifacts of the compilation process\n",
        "# in this case setting the guards kwarg to True will print the relevant guards\n",
        "torch._logging.set_logs(guards=True)\n",
        "fn(torch.ones(2, 2), torch.ones(2, 2))\n",
        "\n",
        "# Other useful options:\n",
        "# guards - display generated guards\n",
        "# graph - display captured graph\n",
        "# output_code - display the generated output code from inductor\n",
        "# bytecode - display the rewritten bytecode for the function you are compiling\n",
        "# recompiles - print which guard failed if the function has been compiled previously\n",
        "torch._logging.set_logs(recompiles=True, guards=True)\n",
        "fn(torch.ones(4, 2), torch.ones(4, 2))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uoz_mZ8vDFW",
        "outputId": "18a0ca31-8046-431f-8cf6-6d8425fd9030"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "V1024 07:23:10.498000 630 torch/_dynamo/guards.py:2314] [0/0] [__guards] GUARDS:\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] \n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] TREE_GUARD_MANAGER:\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] +- RootGuardManager\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | +- DEFAULT_DEVICE: utils_device.CURRENT_DEVICE == None                           # _dynamo/output_graph.py:471 in init_ambient_guards\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | +- GLOBAL_STATE: ___check_global_state()\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | +- TORCH_FUNCTION_MODE_STACK: ___check_torch_function_mode_stack()\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | +- GuardManager: source=L['x'], accessed_by=DictGetItemGuardAccessor(x)\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | | +- TENSOR_MATCH: check_tensor(L['x'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[2, 2], stride=[2, 1])  # x *= x  # <ipython-input-3-bd6fc784ece8>:6 in fn\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | | +- NO_HASATTR: hasattr(L['x'], '_dynamo_dynamic_indices') == False           # x *= x  # <ipython-input-3-bd6fc784ece8>:6 in fn\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | | +- NO_TENSOR_ALIASING: check_no_aliasing(L['x'], L['y'])\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | +- GuardManager: source=L['y'], accessed_by=DictGetItemGuardAccessor(y)\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | | +- TENSOR_MATCH: check_tensor(L['y'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[2, 2], stride=[2, 1])  # x /= y  # <ipython-input-3-bd6fc784ece8>:7 in fn\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | | +- NO_HASATTR: hasattr(L['y'], '_dynamo_dynamic_indices') == False           # x /= y  # <ipython-input-3-bd6fc784ece8>:7 in fn\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] | | +- NO_TENSOR_ALIASING\n",
            "V1024 07:23:10.502000 630 torch/_dynamo/guards.py:2280] [0/0] [__guards] \n",
            "V1024 07:23:10.517000 630 torch/_dynamo/guards.py:2813] [0/1] [__recompiles] Recompiling function fn in <ipython-input-3-bd6fc784ece8>:4\n",
            "V1024 07:23:10.517000 630 torch/_dynamo/guards.py:2813] [0/1] [__recompiles]     triggered by the following guard failure(s):\n",
            "V1024 07:23:10.517000 630 torch/_dynamo/guards.py:2813] [0/1] [__recompiles]     - 0/0: tensor 'L['x']' size mismatch at index 0. expected 2, actual 4\n",
            "V1024 07:23:13.791000 630 torch/_dynamo/guards.py:2314] [0/1] [__guards] GUARDS:\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] \n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] TREE_GUARD_MANAGER:\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] +- RootGuardManager\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | +- DEFAULT_DEVICE: utils_device.CURRENT_DEVICE == None                           # _dynamo/output_graph.py:471 in init_ambient_guards\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | +- GLOBAL_STATE: ___check_global_state()\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | +- TORCH_FUNCTION_MODE_STACK: ___check_torch_function_mode_stack()\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | +- GuardManager: source=L['x'], accessed_by=DictGetItemGuardAccessor(x)\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | | +- TENSOR_MATCH: check_tensor(L['x'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[4, 2], stride=[2, 1])  # x *= x  # <ipython-input-3-bd6fc784ece8>:6 in fn\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | | +- NO_HASATTR: hasattr(L['x'], '_dynamo_dynamic_indices') == False           # x *= x  # <ipython-input-3-bd6fc784ece8>:6 in fn\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | | +- NO_TENSOR_ALIASING: check_no_aliasing(L['x'], L['y'])\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | +- GuardManager: source=L['y'], accessed_by=DictGetItemGuardAccessor(y)\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | | +- TENSOR_MATCH: check_tensor(L['y'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[4, 2], stride=[2, 1])  # x /= y  # <ipython-input-3-bd6fc784ece8>:7 in fn\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | | +- NO_HASATTR: hasattr(L['y'], '_dynamo_dynamic_indices') == False           # x /= y  # <ipython-input-3-bd6fc784ece8>:7 in fn\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] | | +- NO_TENSOR_ALIASING\n",
            "V1024 07:23:13.797000 630 torch/_dynamo/guards.py:2280] [0/1] [__guards] \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2., 2.],\n",
              "        [2., 2.],\n",
              "        [2., 2.],\n",
              "        [2., 2.]])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Handling special instances like data-dependent control flow\n",
        "\"\"\"Graph break on data-dependent control flow\n",
        "\n",
        "In this example, the program conditions on data within a tensor. This is not\n",
        "tracable into one graph because of the data dependent control flow.\n",
        "So, whenever such condition is encountered, the graph is broken,\n",
        "data extracted from the tensor, and tracing continues afterward.\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "import torch\n",
        "\n",
        "@torch.compile()\n",
        "def data_dep(x, y):\n",
        "  if x.item() == 1: # <--- graph break here, extracting data from a tensor.\n",
        "    y += 2\n",
        "    x += 3\n",
        "  else:\n",
        "    y += 3\n",
        "\n",
        "  return y\n",
        "\n",
        "torch._logging.set_logs(graph_breaks=True, recompiles=True, bytecode=True)\n",
        "data_dep(torch.ones(1), torch.ones(5, 5))\n",
        "data_dep(torch.zeros(1), torch.ones(5, 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SllVDLjaZ275",
        "outputId": "694e6590-f850-4e37-aca9-55d7b4bcdf3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] ORIGINAL BYTECODE data_dep <ipython-input-5-3d12de43402a> line 13 \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]  15           0 LOAD_FAST                0 (x)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]               2 LOAD_METHOD              0 (item)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]               4 CALL_METHOD              0\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]               6 LOAD_CONST               1 (1)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]               8 COMPARE_OP               2 (==)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              10 POP_JUMP_IF_FALSE       16 (to 32)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]  16          12 LOAD_FAST                1 (y)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              14 LOAD_CONST               2 (2)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              16 INPLACE_ADD\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              18 STORE_FAST               1 (y)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]  17          20 LOAD_FAST                0 (x)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              22 LOAD_CONST               3 (3)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              24 INPLACE_ADD\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              26 STORE_FAST               0 (x)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]  21          28 LOAD_FAST                1 (y)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              30 RETURN_VALUE\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]  19     >>   32 LOAD_FAST                1 (y)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              34 LOAD_CONST               3 (3)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              36 INPLACE_ADD\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              38 STORE_FAST               1 (y)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]  21          40 LOAD_FAST                1 (y)\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode]              42 RETURN_VALUE\n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.380000 630 torch/_dynamo/convert_frame.py:683] [3/0] [__bytecode] \n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks] Graph break: from user code at:\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"<ipython-input-5-3d12de43402a>\", line 15, in data_dep\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     if x.item() == 1: # <--- graph break here, extracting data from a tensor.\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks] Traceback (most recent call last):\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py\", line 582, in wrapper\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     return inner_fn(self, inst)\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py\", line 1602, in CALL_FUNCTION\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     self.call_function(fn, args, {})\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py\", line 830, in call_function\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     self.push(fn.call_function(self, args, kwargs))  # type: ignore[arg-type]\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/misc.py\", line 1024, in call_function\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     return self.obj.call_method(tx, self.name, args, kwargs)\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/tensor.py\", line 527, in call_method\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     result = handler_method(*args, **kwargs)\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/tensor.py\", line 769, in method_item\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     unimplemented(\"Tensor.item\")\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]   File \"/usr/local/lib/python3.10/dist-packages/torch/_dynamo/exc.py\", line 297, in unimplemented\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks]     raise Unsupported(msg, case_name=case_name)\n",
            "V1024 07:25:16.388000 630 torch/_dynamo/symbolic_convert.py:617] [3/0] [__graph_breaks] torch._dynamo.exc.Unsupported: Tensor.item\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode] MODIFIED BYTECODE data_dep <ipython-input-5-3d12de43402a> line 13 \n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]  13           0 LOAD_FAST                0 (x)\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]               2 LOAD_ATTR                0 (item)\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode] \n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]  15           4 CALL_FUNCTION            0\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]               6 LOAD_GLOBAL              1 (__resume_at_6_13)\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]               8 ROT_TWO\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]              10 LOAD_FAST                0 (x)\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]              12 LOAD_FAST                1 (y)\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]              14 CALL_FUNCTION            3\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode]              16 RETURN_VALUE\n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode] \n",
            "V1024 07:25:16.397000 630 torch/_dynamo/convert_frame.py:683] [3/0_1] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] ORIGINAL BYTECODE torch_dynamo_resume_in_data_dep_at_15 <ipython-input-5-3d12de43402a> line 15 \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  15           0 LOAD_FAST                0 (___stack0)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               2 JUMP_ABSOLUTE            5 (to 10)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               4 LOAD_FAST                1 (x)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               6 LOAD_ATTR                0 (item)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               8 CALL_FUNCTION            0\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]         >>   10 LOAD_CONST               1 (1)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              12 COMPARE_OP               2 (==)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              14 POP_JUMP_IF_FALSE       18 (to 36)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  16          16 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              18 LOAD_CONST               2 (2)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              20 INPLACE_ADD\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              22 STORE_FAST               2 (y)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  17          24 LOAD_FAST                1 (x)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              26 LOAD_CONST               3 (3)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              28 INPLACE_ADD\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              30 STORE_FAST               1 (x)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  21          32 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              34 RETURN_VALUE\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  19     >>   36 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              38 LOAD_CONST               3 (3)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              40 INPLACE_ADD\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              42 STORE_FAST               2 (y)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  21          44 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              46 RETURN_VALUE\n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.406000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] MODIFIED BYTECODE torch_dynamo_resume_in_data_dep_at_15 <ipython-input-5-3d12de43402a> line 15 \n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]  15           0 LOAD_GLOBAL              1 (__compiled_fn_15)\n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               2 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               4 LOAD_FAST                1 (x)\n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               6 CALL_FUNCTION            2\n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]               8 UNPACK_SEQUENCE          1\n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode]              10 RETURN_VALUE\n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.478000 630 torch/_dynamo/convert_frame.py:683] [4/0] [__bytecode] \n",
            "V1024 07:25:16.488000 630 torch/_dynamo/guards.py:2813] [4/1] [__recompiles] Recompiling function torch_dynamo_resume_in_data_dep_at_15 in <ipython-input-5-3d12de43402a>:15\n",
            "V1024 07:25:16.488000 630 torch/_dynamo/guards.py:2813] [4/1] [__recompiles]     triggered by the following guard failure(s):\n",
            "V1024 07:25:16.488000 630 torch/_dynamo/guards.py:2813] [4/1] [__recompiles]     - 4/0: L['___stack0'] == 1.0                                       \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] ORIGINAL BYTECODE torch_dynamo_resume_in_data_dep_at_15 <ipython-input-5-3d12de43402a> line 15 \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  15           0 LOAD_FAST                0 (___stack0)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               2 JUMP_ABSOLUTE            5 (to 10)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               4 LOAD_FAST                1 (x)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               6 LOAD_ATTR                0 (item)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               8 CALL_FUNCTION            0\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]         >>   10 LOAD_CONST               1 (1)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              12 COMPARE_OP               2 (==)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              14 POP_JUMP_IF_FALSE       18 (to 36)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  16          16 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              18 LOAD_CONST               2 (2)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              20 INPLACE_ADD\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              22 STORE_FAST               2 (y)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  17          24 LOAD_FAST                1 (x)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              26 LOAD_CONST               3 (3)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              28 INPLACE_ADD\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              30 STORE_FAST               1 (x)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  21          32 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              34 RETURN_VALUE\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  19     >>   36 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              38 LOAD_CONST               3 (3)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              40 INPLACE_ADD\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              42 STORE_FAST               2 (y)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  21          44 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]              46 RETURN_VALUE\n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.494000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] MODIFIED BYTECODE torch_dynamo_resume_in_data_dep_at_15 <ipython-input-5-3d12de43402a> line 15 \n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]  15           0 LOAD_GLOBAL              1 (__compiled_fn_17)\n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               2 LOAD_FAST                2 (y)\n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               4 CALL_FUNCTION            1\n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               6 UNPACK_SEQUENCE          1\n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode]               8 RETURN_VALUE\n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n",
            "V1024 07:25:16.571000 630 torch/_dynamo/convert_frame.py:683] [4/1] [__bytecode] \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[4., 4., 4., 4., 4.],\n",
              "        [4., 4., 4., 4., 4.],\n",
              "        [4., 4., 4., 4., 4.],\n",
              "        [4., 4., 4., 4., 4.],\n",
              "        [4., 4., 4., 4., 4.]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "1fkIbS0RaaoC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"Dynamic Shapes\n",
        "\n",
        "Dynamic shapes automatically get enabled if the shapes of the inputs change\n",
        "during a recompilation. In the output code the shapes values are passed to\n",
        "the kernel to take into account this dynamism.\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "import torch\n",
        "\n",
        "@torch.compile()\n",
        "def fn(x, y):\n",
        "  return x * y + 10\n",
        "\n",
        "torch._logging.set_logs(guards=True)\n",
        "fn(torch.ones(2, 2), torch.ones(2, 2))\n",
        "torch._logging.set_logs(guards=True, recompiles=True, output_code=True)\n",
        "fn(torch.ones(4, 2), torch.ones(4, 2))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tu3bD0nfamPB",
        "outputId": "aae50a3f-d3f5-4f67-97c6-5851641c2d82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "V1024 07:25:52.630000 630 torch/_dynamo/guards.py:2314] [5/0] [__guards] GUARDS:\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] \n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] TREE_GUARD_MANAGER:\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] +- RootGuardManager\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | +- DEFAULT_DEVICE: utils_device.CURRENT_DEVICE == None                           # _dynamo/output_graph.py:471 in init_ambient_guards\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | +- GLOBAL_STATE: ___check_global_state()\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | +- TORCH_FUNCTION_MODE_STACK: ___check_torch_function_mode_stack()\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | +- GuardManager: source=L['x'], accessed_by=DictGetItemGuardAccessor(x)\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | | +- TENSOR_MATCH: check_tensor(L['x'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[2, 2], stride=[2, 1])  # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | | +- NO_HASATTR: hasattr(L['x'], '_dynamo_dynamic_indices') == False           # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | | +- NO_TENSOR_ALIASING: check_no_aliasing(L['x'], L['y'])\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | +- GuardManager: source=L['y'], accessed_by=DictGetItemGuardAccessor(y)\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | | +- TENSOR_MATCH: check_tensor(L['y'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[2, 2], stride=[2, 1])  # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | | +- NO_HASATTR: hasattr(L['y'], '_dynamo_dynamic_indices') == False           # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] | | +- NO_TENSOR_ALIASING\n",
            "V1024 07:25:52.632000 630 torch/_dynamo/guards.py:2280] [5/0] [__guards] \n",
            "V1024 07:25:52.644000 630 torch/_dynamo/guards.py:2813] [5/1] [__recompiles] Recompiling function fn in <ipython-input-6-21aa7e7c386d>:11\n",
            "V1024 07:25:52.644000 630 torch/_dynamo/guards.py:2813] [5/1] [__recompiles]     triggered by the following guard failure(s):\n",
            "V1024 07:25:52.644000 630 torch/_dynamo/guards.py:2813] [5/1] [__recompiles]     - 5/0: tensor 'L['x']' size mismatch at index 0. expected 2, actual 4\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] Output code: \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] # AOT ID: ['7_inference']\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from ctypes import c_void_p, c_long, c_int\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] import torch\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] import math\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] import random\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] import os\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] import tempfile\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from math import inf, nan\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch._inductor.hooks import run_intermediate_hooks\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch._inductor.utils import maybe_profile\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch._inductor.codegen.memory_planning import _align as align\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch import device, empty_strided\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch._inductor.async_compile import AsyncCompile\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch._inductor.select_algorithm import extern_kernels\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] from torch._inductor.codegen.multi_kernel import MultiKernelCall\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] aten = torch.ops.aten\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] inductor_ops = torch.ops.inductor\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] _quantized = torch.ops._quantized\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] assert_size_stride = torch._C._dynamo.guards.assert_size_stride\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] alloc_from_pool = torch.ops.inductor._alloc_from_pool\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] async_compile = AsyncCompile()\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] cpp_fused_add_mul_0 = async_compile.cpp_pybinding(['const float*', 'const float*', 'float*', 'const int64_t'], '''\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] #include \"/tmp/torchinductor_root/vu/cvuvp4i7roujum4xemrfwnb3t4c5t3r3mihr4b7iegh6tcqvdg43.h\"\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] extern \"C\"  void kernel(const float* in_ptr0,\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]                        const float* in_ptr1,\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]                        float* out_ptr0,\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]                        const int64_t ks0)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] {\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     {\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]         for(int64_t x0=static_cast<int64_t>(0L); x0<static_cast<int64_t>(8L*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))); x0+=static_cast<int64_t>(8L))\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]         {\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + static_cast<int64_t>(x0), static_cast<int64_t>(8));\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + static_cast<int64_t>(x0), static_cast<int64_t>(8));\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp2 = tmp0 * tmp1;\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp3 = static_cast<float>(10.0);\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp4 = at::vec::Vectorized<float>(tmp3);\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp5 = tmp2 + tmp4;\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             tmp5.store(out_ptr0 + static_cast<int64_t>(x0));\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]         }\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]         for(int64_t x0=static_cast<int64_t>(8L*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))); x0<static_cast<int64_t>(2L*ks0); x0+=(static_cast<int64_t>(((-8L)*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))) + (2L*ks0)) == 0 ? 1 : static_cast<int64_t>(((-8L)*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))) + (2L*ks0))))\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]         {\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + static_cast<int64_t>(x0), static_cast<int64_t>(((-8L)*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))) + (2L*ks0)));\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + static_cast<int64_t>(x0), static_cast<int64_t>(((-8L)*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))) + (2L*ks0)));\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp2 = tmp0 * tmp1;\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp3 = static_cast<float>(10.0);\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp4 = at::vec::Vectorized<float>(tmp3);\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             auto tmp5 = tmp2 + tmp4;\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]             tmp5.store(out_ptr0 + static_cast<int64_t>(x0), static_cast<int64_t>(((-8L)*(c10::div_floor_integer(static_cast<int64_t>(ks0), static_cast<int64_t>(4L)))) + (2L*ks0)));\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]         }\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     }\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] }\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] ''')\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] async_compile.wait(globals())\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] del async_compile\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] def call(args):\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     arg0_1, arg1_1, arg2_1 = args\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     args.clear()\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     s0 = arg0_1\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     assert_size_stride(arg1_1, (s0, 2), (2, 1))\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     assert_size_stride(arg2_1, (s0, 2), (2, 1))\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     buf0 = empty_strided_cpu((s0, 2), (2, 1), torch.float32)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     cpp_fused_add_mul_0(arg1_1, arg2_1, buf0, s0)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     del arg1_1\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     del arg2_1\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     return (buf0, )\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] def benchmark_compiled_module(times=10, repeat=10):\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     from torch._dynamo.testing import rand_strided\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     from torch._inductor.utils import print_performance\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     arg0_1 = 4\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     arg1_1 = rand_strided((4, 2), (2, 1), device='cpu', dtype=torch.float32)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     arg2_1 = rand_strided((4, 2), (2, 1), device='cpu', dtype=torch.float32)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     fn = lambda: call([arg0_1, arg1_1, arg2_1])\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     return print_performance(fn, times=times, repeat=repeat)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] if __name__ == \"__main__\":\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     from torch._inductor.wrapper_benchmark import compiled_module_main\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code]     compiled_module_main('None', benchmark_compiled_module)\n",
            "V1024 07:25:52.740000 630 torch/_inductor/graph.py:1849] [5/1] [__output_code] \n",
            "I1024 07:25:55.023000 630 torch/_inductor/graph.py:1883] [5/1] [__output_code] Output code written to: /tmp/torchinductor_root/ao/caouh4qo2wiv76oharm2uxgizxxvdrqdn2e2poey74kvprjlku3c.py\n",
            "V1024 07:25:55.038000 630 torch/_dynamo/guards.py:2314] [5/1] [__guards] GUARDS:\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] \n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] TREE_GUARD_MANAGER:\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] +- RootGuardManager\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | +- DEFAULT_DEVICE: utils_device.CURRENT_DEVICE == None                           # _dynamo/output_graph.py:471 in init_ambient_guards\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | +- GLOBAL_STATE: ___check_global_state()\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | +- TORCH_FUNCTION_MODE_STACK: ___check_torch_function_mode_stack()\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | +- GuardManager: source=L['x'], accessed_by=DictGetItemGuardAccessor(x)\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | | +- TENSOR_MATCH: check_tensor(L['x'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[None, 2], stride=[2, 1])  # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | | +- NO_HASATTR: hasattr(L['x'], '_dynamo_dynamic_indices') == False           # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | | +- NO_TENSOR_ALIASING: check_no_aliasing(L['x'], L['y'])\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | +- GuardManager: source=L['y'], accessed_by=DictGetItemGuardAccessor(y)\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | | +- TENSOR_MATCH: check_tensor(L['y'], Tensor, DispatchKeySet(CPU, BackendSelect, ADInplaceOrView, AutogradCPU), torch.float32, device=None, requires_grad=False, size=[None, 2], stride=[2, 1])  # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | | +- NO_HASATTR: hasattr(L['y'], '_dynamo_dynamic_indices') == False           # return x * y + 10  # <ipython-input-6-21aa7e7c386d>:13 in fn\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] | | +- NO_TENSOR_ALIASING\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] +- LAMBDA_GUARD: L['y'].size()[0] == L['x'].size()[0]                          # _dynamo/output_graph.py:463 in init_ambient_guards\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] +- LAMBDA_GUARD: 2 <= L['x'].size()[0]                                         # _dynamo/output_graph.py:463 in init_ambient_guards\n",
            "V1024 07:25:55.040000 630 torch/_dynamo/guards.py:2280] [5/1] [__guards] \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[11., 11.],\n",
              "        [11., 11.],\n",
              "        [11., 11.],\n",
              "        [11., 11.]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qJH7sHxsatqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "49jlNnqi3zOE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}