Notebooks/TFLite/TFLiteExperiments.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/bhadreshpsavani/UnderstandingNLP/blob/master/TFLiteExperiments.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8sEOhm13Y0U"
      },
      "source": [
        "## Import\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBwvrw8CbgRh",
        "outputId": "a52bd780-c9a1-4fcf-d769-d68e8ffaddb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!pip install -q transformers"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.3MB 8.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 51.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.9MB 53.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 52.5MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6Jxf6Jcet5H"
      },
      "source": [
        "## Get Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plL0El0HcW_w",
        "outputId": "85943d27-cec6-4412-96e5-156fc9f46ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification\n",
        "import tensorflow as tf\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "qkVBsvu243wd",
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            "875895b413e5470a8539d93a4c374856"
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      },
      "source": [
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', return_dict=True)\n",
        "inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n",
        "inputs[\"labels\"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1"
      ],
      "execution_count": 3,
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              "model_id": "78a86cd1b2334981854a64e4a7aa6e1e",
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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        {
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            "\n"
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        },
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        {
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          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['activation_13', 'vocab_layer_norm', 'vocab_transform', 'vocab_projector']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier', 'dropout_19', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
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      "metadata": {
        "id": "Lhieplx15s-J",
        "outputId": "43213143-41a1-4e26-e3a9-929b24aeb0cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "inputs"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(1, 8), dtype=int32, numpy=\n",
              "array([[  101,  7592,  1010,  2026,  3899,  2003, 10140,   102]],\n",
              "      dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(1, 8), dtype=int32, numpy=array([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)>, 'labels': <tf.Tensor: shape=(1, 1), dtype=int32, numpy=array([[1]], dtype=int32)>}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mmx6qZ_5rTz",
        "outputId": "9d742912-9b7c-4eca-d1d8-7dbe076d4a3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "%%time\n",
        "outputs = model(inputs)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 123 ms, sys: 7.22 ms, total: 131 ms\n",
            "Wall time: 131 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mclNjkwFTQh"
      },
      "source": [
        "loss = outputs.loss\n",
        "logits = outputs.logits"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BWbzSF75xuh",
        "outputId": "1410ca01-3686-4b91-a042-c505e938b461",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "logits"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0.00194698, 0.05400003]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeLSlDJr_BCs",
        "outputId": "30c3c0a5-f227-4f2c-e876-3fa1c0ae5a16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "input_spec = tf.TensorSpec([1, 8], tf.int32)\n",
        "# model._set_inputs(input_spec, training=False) # for tf < 2.2\n",
        "model._saved_model_inputs_spec = None # for tf > 2.2\n",
        "model._set_save_spec(input_spec) # for tf > 2.2\n",
        "input_spec"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorSpec(shape=(1, 8), dtype=tf.int32, name=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzJEEqBP1vQx"
      },
      "source": [
        "model.inputs"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oj_ESLx7-ITF"
      },
      "source": [
        "model.save_weights('./tensorflow_distilbert/checkpoint')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qd6BB7_sAvKG"
      },
      "source": [
        "# TensorFlow Lite:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7s4qb2WC6od7"
      },
      "source": [
        "## With Normal Converstion:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jij_P2GhcZsc"
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "\n",
        "# For normal conversion:\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNme-dKhclC6",
        "outputId": "8f0ad5b1-e9a5-427f-894e-879b2f14db37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "tflite_model = converter.convert()\n",
        "open(\"distilbert.tflite\", \"wb\").write(tflite_model)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd328096c88>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272af5f8>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272bbb70>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272db198>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327428780>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327435d68>, because it is not built.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: /tmp/tmpf73k0p19/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:absl:Using experimental converter: If you encountered a problem please file a bug. You can opt-out by setting experimental_new_converter=False\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "266480408"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUEGsdCQcB0d"
      },
      "source": [
        "# Load the TFLite model and allocate tensors.\n",
        "interpreter = tf.lite.Interpreter(model_path=\"distilbert.tflite\")\n",
        "interpreter.allocate_tensors()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "801mpMuPccoL"
      },
      "source": [
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ahgS-Zcc9DA",
        "outputId": "48436aa1-677e-4fb2-b780-2063c3a1a0ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "input_details"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'dtype': numpy.int32,\n",
              "  'index': 0,\n",
              "  'name': 'args_0',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 8], dtype=int32),\n",
              "  'shape_signature': array([1, 8], dtype=int32),\n",
              "  'sparsity_parameters': {}}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsTSx58wcqXY",
        "outputId": "8d59a200-417a-44d8-e653-f575ea15b645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "output_details"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'dtype': numpy.float32,\n",
              "  'index': 638,\n",
              "  'name': 'Identity',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 2], dtype=int32),\n",
              "  'shape_signature': array([1, 2], dtype=int32),\n",
              "  'sparsity_parameters': {}}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDAEGBEnI9ak",
        "outputId": "f7c09bea-2813-4e64-81b3-731485708f7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "list(inputs['input_ids'].numpy()[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycp5rB-EciZu",
        "outputId": "f609178e-60d8-4e8f-c62d-50f7a972d6c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "%%time\n",
        "interpreter.set_tensor(input_details[0]['index'], inputs['input_ids'])\n",
        "interpreter.invoke()\n",
        "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
        "output_data"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 57.4 ms, sys: 3.41 ms, total: 60.8 ms\n",
            "Wall time: 48.3 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mwm49fZ6bZXT"
      },
      "source": [
        ""
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czFEpszS6wl6"
      },
      "source": [
        "## FP16 Quantization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHr49xzo6xRg",
        "outputId": "88207849-239e-4e10-9b07-f652d837bc2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Below Two methods makes models size 4 time smaller\n",
        "# For conversion with FP16 quantization:\n",
        "# supports CPUs, GPUs\n",
        "converter_16 = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "converter_16.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n",
        "converter_16.target_spec.supported_types = [tf.float16]\n",
        "converter_16.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter_16.experimental_new_converter = True\n",
        "\n",
        "tflite_model_fp16 = converter_16.convert()\n",
        "open(\"distilbert_fp16.tflite\", \"wb\").write(tflite_model_fp16)\n",
        "\n",
        "# Load the TFLite model and allocate tensors.\n",
        "interpreter_fp16 = tf.lite.Interpreter(model_path=\"distilbert_fp16.tflite\")\n",
        "interpreter_fp16.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors.\n",
        "input_details = interpreter_fp16.get_input_details()\n",
        "output_details = interpreter_fp16.get_output_details()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd328096c88>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd328096c88>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272af5f8>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272af5f8>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272bbb70>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272bbb70>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272db198>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272db198>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327428780>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327428780>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327435d68>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327435d68>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmp0x89xjfh/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmp0x89xjfh/assets\n",
            "INFO:absl:Using experimental converter: If you encountered a problem please file a bug. You can opt-out by setting experimental_new_converter=False\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzfUpaYBGEnv",
        "outputId": "3ff3dfaa-65aa-44ba-81fe-a3589610007f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "%%time\n",
        "interpreter_fp16.set_tensor(input_details[0]['index'], inputs['input_ids'])\n",
        "interpreter_fp16.invoke()\n",
        "output_data_fp16 = interpreter_fp16.get_tensor(output_details[0]['index'])\n",
        "output_data_fp16"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 187 ms, sys: 5.25 ms, total: 192 ms\n",
            "Wall time: 230 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0t-G8R5v66-K"
      },
      "source": [
        "## Hybrid Quantization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2T9Z0-Bl65QK",
        "outputId": "39b91a24-4f05-42ac-96b9-268dfe66e426",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# For conversion with hybrid quantization:\n",
        "# This only support CPU\n",
        "converter_hy = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "converter_hy.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n",
        "converter_hy.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
        "converter_hy.experimental_new_converter = True\n",
        "\n",
        "tflite_model_hy = converter_hy.convert()\n",
        "open(\"distilbert_hy.tflite\", \"wb\").write(tflite_model_hy)\n",
        "\n",
        "# Load the TFLite model and allocate tensors.\n",
        "interpreter_hy = tf.lite.Interpreter(model_path=\"distilbert_hy.tflite\")\n",
        "interpreter_hy.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors.\n",
        "input_details = interpreter_hy.get_input_details()\n",
        "output_details = interpreter_hy.get_output_details()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd328096c88>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd328096c88>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272af5f8>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272af5f8>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272bbb70>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272bbb70>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272db198>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd3272db198>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327428780>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327428780>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327435d68>, because it is not built.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dropout object at 0x7fd327435d68>, because it is not built.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpee36el4u/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpee36el4u/assets\n",
            "INFO:absl:Using experimental converter: If you encountered a problem please file a bug. You can opt-out by setting experimental_new_converter=False\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dM1zWvqFGNIH",
        "outputId": "62876718-376a-4b32-cf6c-23b39d42e8b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "%%time\n",
        "interpreter_hy.set_tensor(input_details[0]['index'], inputs['input_ids'])\n",
        "interpreter_hy.invoke()\n",
        "output_data_hy = interpreter_hy.get_tensor(output_details[0]['index'])\n",
        "output_data_hy"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 35.4 ms, sys: 1.18 ms, total: 36.6 ms\n",
            "Wall time: 38.5 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZRh4hDV802v"
      },
      "source": [
        "## Observations:\n",
        "For DistilBert For Sequence Classification Model here is Our Observation\n",
        "\n",
        "|Method|Model Weight Size|Inference Time |\n",
        "|----- | ----| ----- |\n",
        "| Normal Tensorflow Weight saving | 255 Mb | 155ms |\n",
        "| Normal TFLite | 254 Mb| 70ms |\n",
        "| TFLite with FP16 Quantization | 127.28 Mb | 52ms|\n",
        "| TFLite With Hybrid Quantization | 63.97 Mb | 37 ms |\n",
        "\n",
        "* Support Fix Length Inputs\n",
        "* Only Support TensorFLow and Keras Model\n",
        "* TFlite might not support Complex Model input While Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mP50Ax3Oydeg"
      },
      "source": [
        "## Albert:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ttlfiQmyczs",
        "outputId": "bbcbe6c1-c83a-4457-ecaf-e80f7d89ec8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "d9aa19967bcc48398605b50fc09ce31d",
            "13d29bdac6364856878cb20f4f3b2f85",
            "0a5d64986265451cbb80716783be8d9d",
            "307c78a582fc42fe9a57f8571dec960d",
            "bc1b427d120741618f8c12bf3e6ee9ac",
            "f13ad22a09f14cc0aa963a393028f420",
            "ada29dee956f46779bec29660a8d540b",
            "afcfcc96eaeb48968a1100768d6c44f2",
            "2873ac71fa484679a00590beddd48e32",
            "ffd20090676b45febe63510a15804797",
            "09e35e5cdd8748e7b3a4a5d8461d21d8",
            "2135c779438b472a8142e13a61367625",
            "8accfd63cc8b45949080914ace61c770",
            "0a2a603ddad44ea39640569d088bd6d5",
            "2f3fbf3408ce48209df6320bd9ecb7e8",
            "61970687021d4f66b30989160b01f709",
            "ba7f581693f440a0a77c5ecc141d64db",
            "c55ead79079e4ada9e03ab36511e71b7",
            "ec32a242c08741f78a67e9eae350b77c",
            "2bfd08ef3928491fa326608a80bc1858",
            "19ba8900e46541479ba2b8e303079c5c",
            "9b9d11ce4a5b446e9d5c74d6bbbfb73a",
            "a4dedd77652248c0ad886cec2add1168",
            "57efff46f8b54cc5b8b5651e747842b4"
          ]
        }
      },
      "source": [
        "from transformers import AlbertTokenizer, TFAlbertForSequenceClassification\n",
        "\n",
        "tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')\n",
        "model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2', return_dict=True)\n",
        "\n",
        "inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n",
        "inputs[\"labels\"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1\n",
        "\n",
        "outputs = model(inputs)\n",
        "loss = outputs.loss\n",
        "logits = outputs.logits"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d9aa19967bcc48398605b50fc09ce31d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=760289.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2873ac71fa484679a00590beddd48e32",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=684.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba7f581693f440a0a77c5ecc141d64db",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=63048440.0, style=ProgressStyle(descrip…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at albert-base-v2 were not used when initializing TFAlbertForSequenceClassification: ['predictions']\n",
            "- This IS expected if you are initializing TFAlbertForSequenceClassification 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 TFAlbertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFAlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['dropout_24', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyemcFHdzP5S",
        "outputId": "c1ac3a58-50d6-4989-9b11-f7cc17fff2af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "inputs"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(1, 8), dtype=int32, numpy=\n",
              "array([[    2, 10975,    15,    51,  1952,    25, 10901,     3]],\n",
              "      dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(1, 8), dtype=int32, numpy=array([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(1, 8), dtype=int32, numpy=array([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)>}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wQ1YUbVy4Dg",
        "outputId": "ff884dfb-df40-470c-9843-d9b49a51489e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "input_spec = tf.TensorSpec([1, 8], tf.int32)\n",
        "# model._set_inputs(input_spec, training=False) # for tf < 2.2\n",
        "model._saved_model_inputs_spec = None # for tf > 2.2\n",
        "model._set_save_spec(input_spec) # for tf > 2.2\n",
        "input_spec"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorSpec(shape=(1, 8), dtype=tf.int32, name=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjVuLcjKzco4",
        "outputId": "73273cc2-1361-4b62-8b19-c6c232ca2e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "\n",
        "# For normal conversion:\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]\n",
        "\n",
        "tflite_model = converter.convert()\n",
        "open(\"albert.tflite\", \"wb\").write(tflite_model)\n",
        "\n",
        "# Load the TFLite model and allocate tensors.\n",
        "interpreter = tf.lite.Interpreter(model_path=\"albert.tflite\")\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:absl:Using experimental converter: If you encountered a problem please file a bug. You can opt-out by setting experimental_new_converter=False\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXapKmmOz-UD",
        "outputId": "9fa52f0d-53de-4924-b157-9b32170e7000",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "%%time\n",
        "interpreter.set_tensor(input_details[0]['index'], inputs['input_ids'])\n",
        "interpreter.invoke()\n",
        "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
        "print(output_data)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.24272975  0.44961914]]\n",
            "CPU times: user 104 ms, sys: 5.84 ms, total: 110 ms\n",
            "Wall time: 79.1 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_LTR2Bu0XC-"
      },
      "source": [
        "## Roberta:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIkOfncF0lXV",
        "outputId": "5c1ba9c0-a42e-4de2-87de-2f6b57859866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318,
          "referenced_widgets": [
            "b943d9f6c2e040c79cb4ce197c88391f",
            "2e559bbfc27b40b8b4d52ced4b21ab13",
            "de7c83acb38a44419dcb6845f17a186e",
            "c1879c2cace7457e8ee8eaaffe3bfc14",
            "3b890afa509047f4acd60ac225bfaf32",
            "7db12c3cfce64781893e3ab8e3aac118",
            "493da9cdbee3468895eb945bd0596638",
            "800f205708bc4857aa3ecc45447e7d6e",
            "ca386ce64775401cb91c1fa0209247e1",
            "964f419e6a46449e9976e740f9b8c91b",
            "288b02b30edb47048e31b40a5a408df2",
            "fe27d0fd653644ae9a66350e519521e5",
            "76604bfa45524df1b5ed4065a1d439e5",
            "e25fd3e0ea754c8bbcd4ee1ae3efab00",
            "22cc57025de041bb8ff3212cd54d4fa3",
            "0c8bed84222e41e2a068c1e89b3e5f25",
            "9fb6eade3d864d2d84d93e14757f40cd",
            "03deb9c4484b4b38b1785f5f1c74d391",
            "7c09f0c09a7b442da303548ee93595b4",
            "02ca10fb40f64ea5a8d17704d7d69779",
            "3f75800401064450aaf4a46e7a842041",
            "a32e5686931246ff9cdfad9fc236500a",
            "84cb93766d784f35b1bb76aa2ddc0c4c",
            "137eff4df8a74334967910b4339bee87",
            "2fb9f1a428264ab9b185965947deae7e",
            "efd87b845b9f4f399d7b165116e4c89b",
            "6632f9c1c5454197ab4d9e296531c17a",
            "4e0644c1378045f49a9853220a8998f0",
            "03317d29519f42a4b403158de1c77d4a",
            "c9ca378655c6400c82022f37ee4417fb",
            "7e8a426f3c3a4a04a10b18d8ceecff2a",
            "384514691210470b84cede8fb099d672"
          ]
        }
      },
      "source": [
        "from transformers import RobertaTokenizer, TFRobertaForSequenceClassification\n",
        "import tensorflow as tf\n",
        "\n",
        "tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
        "model = TFRobertaForSequenceClassification.from_pretrained('roberta-base', return_dict=True)\n",
        "\n",
        "inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"tf\")\n",
        "inputs[\"labels\"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1\n",
        "\n",
        "outputs = model(inputs)\n",
        "loss = outputs.loss\n",
        "logits = outputs.logits"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b943d9f6c2e040c79cb4ce197c88391f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca386ce64775401cb91c1fa0209247e1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9fb6eade3d864d2d84d93e14757f40cd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=481.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fb9f1a428264ab9b185965947deae7e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=657434796.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at roberta-base were not used when initializing TFRobertaForSequenceClassification: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaForSequenceClassification 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 TFRobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFRobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiSYlHVr2alV",
        "outputId": "96da3d81-cfb4-4365-c784-61eb1110fe58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "\n",
        "# For normal conversion:\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]\n",
        "\n",
        "tflite_model = converter.convert()\n",
        "open(\"roberta.tflite\", \"wb\").write(tflite_model)\n",
        "\n",
        "# Load the TFLite model and allocate tensors.\n",
        "interpreter = tf.lite.Interpreter(model_path=\"roberta.tflite\")\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: /tmp/tmpsybow1s6/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:absl:Using experimental converter: If you encountered a problem please file a bug. You can opt-out by setting experimental_new_converter=False\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MtShZz3q2e0P"
      },
      "source": [
        "%%time\n",
        "interpreter.set_tensor(input_details[0]['index'], inputs['input_ids'])\n",
        "interpreter.invoke()\n",
        "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
        "print(output_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7RrwUkGC5Kib"
      },
      "source": [
        "Reference:\n",
        "* https://discuss.huggingface.co/t/how-can-we-test-transformer-models-after-converting-it-to-tflite-format/1670/4\n",
        "* https://www.tensorflow.org/lite/guide/inference\n",
        "* https://github.com/huggingface/tflite-android-transformers/blob/master/models_generation/distilbert.py\n",
        "* https://www.tensorflow.org/lite/performance/post_training_quantization\n",
        "* https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_lite/tflite_c02_transfer_learning.ipynb\n",
        "* https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_lite/tflite_c04_exercise_convert_model_to_tflite_solution.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74im3jIRyb4F"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}