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        "<a href=\"https://colab.research.google.com/github/abhilash1910/Accelerated_Deep_Learning/blob/master/Torch_JIT_Tracing_BERT_Large.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n"
      ],
      "metadata": {
        "id": "ffJPlFyLa77U"
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      "cell_type": "code",
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        "outputId": "e5bd8539-066e-4465-acae-16f602ca873b"
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          "text": [
            "Sat Jun 18 06:07:19 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
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      "cell_type": "code",
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        "!nvcc --version"
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        "id": "NjFv6-uXbGd5",
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            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
            "Cuda compilation tools, release 11.1, V11.1.105\n",
            "Build cuda_11.1.TC455_06.29190527_0\n"
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    {
      "cell_type": "markdown",
      "source": [
        "BERT large loading from HF standard template"
      ],
      "metadata": {
        "id": "IFjDkQV2pHUB"
      }
    },
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-large-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "#Standard BERT dummy from HF\n",
        "from transformers import BertModel, BertTokenizer, BertConfig\n",
        "import torch\n",
        "\n",
        "enc = BertTokenizer.from_pretrained(\"bert-large-uncased\")\n",
        "\n",
        "# Tokenizing input text\n",
        "text = \"[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]\"\n",
        "tokenized_text = enc.tokenize(text)\n",
        "\n",
        "# Masking one of the input tokens\n",
        "masked_index = 8\n",
        "tokenized_text[masked_index] = \"[MASK]\"\n",
        "indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)\n",
        "segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
        "\n",
        "# Creating a dummy input\n",
        "tokens_tensor = torch.tensor([indexed_tokens])\n",
        "segments_tensors = torch.tensor([segments_ids])\n",
        "dummy_input = [tokens_tensor, segments_tensors]\n",
        "\n",
        "# Initializing the model with the torchscript flag\n",
        "# Flag set to True even though it is not necessary as this model does not have an LM Head.\n",
        "config = BertConfig(\n",
        "    vocab_size_or_config_json_file=32000,\n",
        "    hidden_size=768,\n",
        "    num_hidden_layers=12,\n",
        "    num_attention_heads=12,\n",
        "    intermediate_size=3072,\n",
        "    torchscript=True,\n",
        ")\n",
        "\n",
        "# Instantiating the model\n",
        "model = BertModel(config)\n",
        "\n",
        "# The model needs to be in evaluation mode\n",
        "model.eval()\n",
        "\n",
        "# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag\n",
        "model = BertModel.from_pretrained(\"bert-large-uncased\", torchscript=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert model to Torch JIT "
      ],
      "metadata": {
        "id": "_O3fdsRApLn9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the trace\n",
        "model.to(\"cpu\")\n",
        "traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])\n",
        "torch.jit.save(traced_model, \"traced_bert.pt\")"
      ],
      "metadata": {
        "id": "R6VoKcXWcT4-"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "traced_model"
      ],
      "metadata": {
        "id": "nN3BY-dXc0cJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install timer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZYEF9Y4mQ4S",
        "outputId": "ad30f8b0-1309-4940-e475-93822d48976a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting timer\n",
            "  Downloading timer-0.2.2-py3-none-any.whl (7.8 kB)\n",
            "Installing collected packages: timer\n",
            "Successfully installed timer-0.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Export weights to CUDA device for both Native BERT large and Jit BERT large"
      ],
      "metadata": {
        "id": "nkxqsYWppPYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# setup=\"native_gpu = model.cuda()\"\n",
        "# tokens_tensor_gpu = tokens_tensor.cuda()\n",
        "# segments_tensors_gpu = segments_tensors.cuda()\n",
        "# stmt=\"native_gpu(tokens_tensor_gpu,segments_tensors_gpu)\"\n",
        "# timeit.repeat( stmt, setup, number=1, repeat=10, globals=globals())\n",
        "from timer import timer\n",
        "native_gpu = model.cuda()\n",
        "tokens_tensor_gpu = tokens_tensor.cuda()\n",
        "segments_tensors_gpu = segments_tensors.cuda()\n",
        "timer_list=[]\n",
        "with timer() as t:\n",
        "  for _ in range(100):\n",
        "    native_gpu(tokens_tensor_gpu,segments_tensors_gpu)\n",
        "    timer_list.append(t.elapse)\n",
        "    print(f\"time taken GPU:{t.elapse}\")\n",
        "print(f\"Mean Time on GPU {np.mean(timer_list)}\")\n",
        "native_gpu_mean,native_gpu_std=np.mean(timer_list),np.std(timer_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRaJMon7lcO7",
        "outputId": "0366150c-8bdf-47a3-e549-859b97b7f60c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time taken GPU:0.023450054000022647\n",
            "time taken GPU:0.04704280000032668\n",
            "time taken GPU:0.06928185700007816\n",
            "time taken GPU:0.0923203239999566\n",
            "time taken GPU:0.1140824590002012\n",
            "time taken GPU:0.1347084190001624\n",
            "time taken GPU:0.15618186900019282\n",
            "time taken GPU:0.17665664000014658\n",
            "time taken GPU:0.19668734500010032\n",
            "time taken GPU:0.217795851000119\n",
            "time taken GPU:0.2420310310003515\n",
            "time taken GPU:0.2633946930000093\n",
            "time taken GPU:0.28492573400035326\n",
            "time taken GPU:0.3061101140001483\n",
            "time taken GPU:0.32774900100002924\n",
            "time taken GPU:0.3510819390003235\n",
            "time taken GPU:0.3723013900003025\n",
            "time taken GPU:0.3940775040000517\n",
            "time taken GPU:0.4161324050000985\n",
            "time taken GPU:0.43655806600008873\n",
            "time taken GPU:0.4608281620003254\n",
            "time taken GPU:0.4825603920003232\n",
            "time taken GPU:0.503770361000079\n",
            "time taken GPU:0.5241934350001429\n",
            "time taken GPU:0.5457109960002526\n",
            "time taken GPU:0.5660439319999568\n",
            "time taken GPU:0.5874045570003545\n",
            "time taken GPU:0.6077456860002712\n",
            "time taken GPU:0.628186598999946\n",
            "time taken GPU:0.65211108099993\n",
            "time taken GPU:0.6761413190001804\n",
            "time taken GPU:0.7011619130003055\n",
            "time taken GPU:0.7242457110000942\n",
            "time taken GPU:0.7460158710000542\n",
            "time taken GPU:0.7680960030002097\n",
            "time taken GPU:0.7897900840002876\n",
            "time taken GPU:0.8104567170003065\n",
            "time taken GPU:0.8319691970000349\n",
            "time taken GPU:0.852259801999935\n",
            "time taken GPU:0.8730717840003308\n",
            "time taken GPU:0.8979290560000663\n",
            "time taken GPU:0.9185822320000625\n",
            "time taken GPU:0.9407927810002548\n",
            "time taken GPU:0.9619530120003219\n",
            "time taken GPU:0.9829637709999588\n",
            "time taken GPU:1.00629994000019\n",
            "time taken GPU:1.0286102620002566\n",
            "time taken GPU:1.0502469430002748\n",
            "time taken GPU:1.0740056449999429\n",
            "time taken GPU:1.095653762999973\n",
            "time taken GPU:1.121027762000267\n",
            "time taken GPU:1.1421620380001514\n",
            "time taken GPU:1.165670214000329\n",
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            "time taken GPU:1.2072964369999681\n",
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            "time taken GPU:1.250261652000063\n",
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            "time taken GPU:1.3661120450001363\n",
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            "time taken GPU:1.4368748090000736\n",
            "time taken GPU:1.457672908999939\n",
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            "time taken GPU:1.5238564510000288\n",
            "time taken GPU:1.5505216770002335\n",
            "time taken GPU:1.572061197000039\n",
            "time taken GPU:1.5933455580002374\n",
            "time taken GPU:1.6140756080003484\n",
            "time taken GPU:1.634248709000076\n",
            "time taken GPU:1.6547687780002889\n",
            "time taken GPU:1.6763754379999227\n",
            "time taken GPU:1.697683379999944\n",
            "time taken GPU:1.7271344210003008\n",
            "time taken GPU:1.748510505000013\n",
            "time taken GPU:1.7738388430002487\n",
            "time taken GPU:1.7955631630002244\n",
            "time taken GPU:1.828148259000045\n",
            "time taken GPU:1.8545726219999779\n",
            "time taken GPU:1.8751099370001612\n",
            "time taken GPU:1.8958337590001975\n",
            "time taken GPU:1.9175416900002347\n",
            "time taken GPU:1.9385876820001613\n",
            "time taken GPU:1.9593756900003427\n",
            "time taken GPU:1.9827492419999544\n",
            "time taken GPU:2.0038584420003644\n",
            "time taken GPU:2.0248577950001163\n",
            "time taken GPU:2.045879609000167\n",
            "time taken GPU:2.069856482999967\n",
            "time taken GPU:2.0920577470001263\n",
            "time taken GPU:2.113810163999915\n",
            "time taken GPU:2.1352688340002715\n",
            "time taken GPU:2.156555500000195\n",
            "time taken GPU:2.1772669199999655\n",
            "time taken GPU:2.2019279720002487\n",
            "time taken GPU:2.224316593000367\n",
            "Mean Time on GPU 1.1154597627001568\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "traced_model_gpu = torch.jit.trace(traced_model.cuda(), [tokens_tensor.cuda(), segments_tensors.cuda()])\n",
        "with timer() as t:\n",
        "  for _ in range(100):\n",
        "    traced_model_gpu(tokens_tensor_gpu,segments_tensors_gpu)\n",
        "    timer_list.append(t.elapse)\n",
        "    print(f\"time taken traced model GPU:{t.elapse}\")\n",
        "print(f\"Mean Time Traced Model on GPU {np.mean(timer_list)}\")\n",
        "traced_model_gpu_mean,traced_model_gpu_std=np.mean(timer_list),np.std(timer_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7u_g9MZnn_R4",
        "outputId": "22b9c9b7-840b-46e9-f396-fa26321f401b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/jit/_trace.py:736: UserWarning: The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is.\n",
            "  \"The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is.\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time taken traced model GPU:0.015598941000007471\n",
            "time taken traced model GPU:0.03234528599978148\n",
            "time taken traced model GPU:0.049356176999936\n",
            "time taken traced model GPU:0.0689895269997578\n",
            "time taken traced model GPU:0.08206378800014136\n",
            "time taken traced model GPU:0.09437793200004307\n",
            "time taken traced model GPU:0.10664279499997065\n",
            "time taken traced model GPU:0.11957215999973414\n",
            "time taken traced model GPU:0.13262462100010453\n",
            "time taken traced model GPU:0.1462258430001384\n",
            "time taken traced model GPU:0.15910816399991745\n",
            "time taken traced model GPU:0.17157254999983707\n",
            "time taken traced model GPU:0.1840195589998075\n",
            "time taken traced model GPU:0.19643460999986928\n",
            "time taken traced model GPU:0.21171923300016715\n",
            "time taken traced model GPU:0.22663808800007246\n",
            "time taken traced model GPU:0.23871973399991475\n",
            "time taken traced model GPU:0.2512184019997221\n",
            "time taken traced model GPU:0.2650144320000436\n",
            "time taken traced model GPU:0.2787801629997375\n",
            "time taken traced model GPU:0.29274012699988816\n",
            "time taken traced model GPU:0.30698593999977675\n",
            "time taken traced model GPU:0.3198607770000308\n",
            "time taken traced model GPU:0.33325420099981784\n",
            "time taken traced model GPU:0.346701074000066\n",
            "time taken traced model GPU:0.3604038309999851\n",
            "time taken traced model GPU:0.3757934310001474\n",
            "time taken traced model GPU:0.3876799340000616\n",
            "time taken traced model GPU:0.3999460780000845\n",
            "time taken traced model GPU:0.41177017200016053\n",
            "time taken traced model GPU:0.4239079290000518\n",
            "time taken traced model GPU:0.4391781119998086\n",
            "time taken traced model GPU:0.45162165800002185\n",
            "time taken traced model GPU:0.46406142199975875\n",
            "time taken traced model GPU:0.47638768700016954\n",
            "time taken traced model GPU:0.490304758000093\n",
            "time taken traced model GPU:0.5035495729998729\n",
            "time taken traced model GPU:0.5156694890001745\n",
            "time taken traced model GPU:0.5287257639997733\n",
            "time taken traced model GPU:0.5414645880000535\n",
            "time taken traced model GPU:0.5539038110000547\n",
            "time taken traced model GPU:0.5667270139997527\n",
            "time taken traced model GPU:0.5795371770000202\n",
            "time taken traced model GPU:0.5918551479999223\n",
            "time taken traced model GPU:0.6040737439998338\n",
            "time taken traced model GPU:0.6174692959998538\n",
            "time taken traced model GPU:0.6353421959997831\n",
            "time taken traced model GPU:0.6522892009998031\n",
            "time taken traced model GPU:0.668994938999731\n",
            "time taken traced model GPU:0.6809683480000785\n",
            "time taken traced model GPU:0.6931874140000218\n",
            "time taken traced model GPU:0.7063519049997922\n",
            "time taken traced model GPU:0.7182991069998934\n",
            "time taken traced model GPU:0.7303164820000347\n",
            "time taken traced model GPU:0.7427975420000621\n",
            "time taken traced model GPU:0.7544730240001627\n",
            "time taken traced model GPU:0.7709627369999907\n",
            "time taken traced model GPU:0.7831330489998436\n",
            "time taken traced model GPU:0.7966214180000861\n",
            "time taken traced model GPU:0.8090530680001393\n",
            "time taken traced model GPU:0.8216006869997727\n",
            "time taken traced model GPU:0.8336204600000201\n",
            "time taken traced model GPU:0.8456674210001438\n",
            "time taken traced model GPU:0.8611223799998697\n",
            "time taken traced model GPU:0.8752726439997787\n",
            "time taken traced model GPU:0.8886354010001014\n",
            "time taken traced model GPU:0.9012188470001092\n",
            "time taken traced model GPU:0.9136560679999093\n",
            "time taken traced model GPU:0.9258382439998059\n",
            "time taken traced model GPU:0.938141658999939\n",
            "time taken traced model GPU:0.9508245759998317\n",
            "time taken traced model GPU:0.9640214579999338\n",
            "time taken traced model GPU:0.9770286980001401\n",
            "time taken traced model GPU:0.9893514179998419\n",
            "time taken traced model GPU:1.002330149000045\n",
            "time taken traced model GPU:1.0147033299999748\n",
            "time taken traced model GPU:1.0299206740000955\n",
            "time taken traced model GPU:1.0435583369999222\n",
            "time taken traced model GPU:1.0562550089998695\n",
            "time taken traced model GPU:1.0735406630001307\n",
            "time taken traced model GPU:1.0917025059998196\n",
            "time taken traced model GPU:1.1044838510001682\n",
            "time taken traced model GPU:1.116425063999941\n",
            "time taken traced model GPU:1.128132347999781\n",
            "time taken traced model GPU:1.1402053599999817\n",
            "time taken traced model GPU:1.1530366500001037\n",
            "time taken traced model GPU:1.169338142000015\n",
            "time taken traced model GPU:1.1815174389998901\n",
            "time taken traced model GPU:1.1940271600001324\n",
            "time taken traced model GPU:1.2065255899997283\n",
            "time taken traced model GPU:1.2198207540000112\n",
            "time taken traced model GPU:1.2328693409999687\n",
            "time taken traced model GPU:1.244703974999993\n",
            "time taken traced model GPU:1.2570654309997735\n",
            "time taken traced model GPU:1.2693428940001468\n",
            "time taken traced model GPU:1.284874119000051\n",
            "time taken traced model GPU:1.2995812809999734\n",
            "time taken traced model GPU:1.3122220899999775\n",
            "time taken traced model GPU:1.3255543159998524\n",
            "time taken traced model GPU:1.3376452050001717\n",
            "Mean Time Traced Model on GPU 0.8988925651850537\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estimate mean and std for 100 runs .Differences pronounced in more runs"
      ],
      "metadata": {
        "id": "hTjAn6OWpUrZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "def plot(means, stds, labels, fig_name):\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.bar(np.arange(len(means)), means, yerr=stds,\n",
        "           align='center', alpha=0.5, ecolor='red', capsize=10, width=0.6)\n",
        "    ax.set_ylabel('Torch JIT trace (Second)')\n",
        "    ax.set_xticks(np.arange(len(means)))\n",
        "    ax.set_xticklabels(labels)\n",
        "    ax.yaxis.grid(True)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    plt.savefig(fig_name)\n",
        "    plt.close(fig)\n",
        "\n",
        "\n",
        "plot([traced_model_gpu_mean,native_gpu_mean],\n",
        "     [traced_model_gpu_std, native_gpu_std],\n",
        "     ['Trace Jit BERT GPU', 'Native BERT GPU'],\n",
        "     'trace_vs_bert_gpu.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "Yd8Ps4P-owWX",
        "outputId": "4859b793-b0b8-4776-f12f-15d7c5e3f817"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "native_model_cpu=native_gpu.to(\"cpu\")\n",
        "traced_model_cpu=traced_model_gpu.to(\"cpu\")\n",
        "timer_list=[]\n",
        "with timer() as t:\n",
        "  for _ in range(100):\n",
        "    traced_model_cpu(tokens_tensor,segments_tensors)\n",
        "    timer_list.append(t.elapse)\n",
        "    #print(f\"time taken traced model CPU:{t.elapse}\")\n",
        "print(f\"Mean Time Traced Model on CPU {np.mean(timer_list)}\")\n",
        "traced_model_cpu_mean,traced_model_cpu_std=np.mean(timer_list),np.std(timer_list)\n",
        "timer_list=[]\n",
        "with timer() as t:\n",
        "  for _ in range(100):\n",
        "    native_model_cpu(tokens_tensor,segments_tensors)\n",
        "    timer_list.append(t.elapse)\n",
        "    #print(f\"time taken traced model CPU:{t.elapse}\")\n",
        "print(f\"Mean Time Native Model on CPU {np.mean(timer_list)}\")\n",
        "native_cpu_mean,native_cpu_std=np.mean(timer_list),np.std(timer_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmoX_rh-pdUR",
        "outputId": "ef81921c-f67f-4c0c-dffa-697aca51036d"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Time Traced Model on CPU 17.975117748420452\n",
            "Mean Time Native Model on CPU 18.122856914840305\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plot([traced_model_cpu_mean,native_cpu_mean],\n",
        "     [traced_model_cpu_std, native_cpu_std],\n",
        "     ['Trace Jit BERT CPU', 'Native BERT CPU'],\n",
        "     'trace_vs_bert_cpu.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "t7VL-7ihqbpK",
        "outputId": "a9625d97-0de4-4756-c557-6381db18e972"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import numpy as np\n",
        "# import timeit\n",
        "# setup=\"\"\"loaded_model = torch.jit.load(\"traced_bert.pt\")\"\"\"\n",
        "# stmt=\"loaded_model.eval()\"\n",
        "# run_time_traced=timeit.repeat( stmt, setup, number=1, repeat=10, globals=globals())\n",
        "# trace_mean, trace_std = np.mean(run_time_traced), np.std(run_time_traced)\n",
        "# run_time_traced\n",
        "# torch.save(model,'orig_model.pt')\n",
        "# setup=\"\"\"loaded_model_o = torch.load(\"orig_model.pt\")\"\"\"\n",
        "# stmt=\"loaded_model_o.eval()\"\n",
        "# run_time_orig=timeit.repeat( stmt, setup, number=1, repeat=10, globals=globals())\n",
        "# rn_mean, rn_std = np.mean(run_time_orig), np.std(run_time_orig)\n",
        "# run_time_orig\n",
        "# overhead_tracing= abs(trace_mean/rn_mean)-1\n",
        "# print(f\"{overhead_tracing}% time wasted in  io loading with jit \")"
      ],
      "metadata": {
        "id": "poJ0jQ7Ac-NC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}