Pruning_Analysis.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bhadreshpsavani/UnderstandingNLP/blob/master/Pruning_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhYJHhbmHrJb"
      },
      "source": [
        "## Prune Bert Model Comparision with Normal Bert Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "!pip install -q transformers datasets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.9MB 21.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 194kB 55.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.2MB 49.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 53.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 245kB 50.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 61.3MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "outputId": "48f1cde3-b110-424b-d918-ce03b5eb6ed4"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\r\n",
        "import torch\r\n",
        "from datasets import load_metric, load_dataset\r\n",
        "from tqdm.notebook import tqdm\r\n",
        "device =  'cuda' if torch.cuda.is_available() else 'cpu'\r\n",
        "device"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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        "tokenizer = AutoTokenizer.from_pretrained(\"huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad\")\r\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(\"huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad\")"
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        "bert_tokenizer = AutoTokenizer.from_pretrained(\"csarron/bert-base-uncased-squad-v1\")\r\n",
        "bert_model = AutoModelForQuestionAnswering.from_pretrained(\"csarron/bert-base-uncased-squad-v1\")"
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      "source": [
        "def get_result(predictions, references, dataset='squad_v2'):\r\n",
        "  squad_metric = load_metric(dataset)\r\n",
        "  score = squad_metric.compute(predictions=predictions, references=references)\r\n",
        "  return score\r\n",
        "\r\n",
        "def get_validation_data(dataset='squad_v2'):\r\n",
        "  datasets = load_dataset(dataset)\r\n",
        "  valid_dataset = datasets['validation']\r\n",
        "  return valid_dataset\r\n",
        "\r\n",
        "def get_infernece(valid_dataset, model, tokenizer, device):\r\n",
        "  predictions=[]\r\n",
        "  references=[]\r\n",
        "  model.to(device)\r\n",
        "  for example in tqdm(valid_dataset):\r\n",
        "    inputs = tokenizer(example['question'], example['context'], return_tensors=\"pt\", truncation=True)\r\n",
        "    inputs.to(device)\r\n",
        "    output = model(**inputs)\r\n",
        "    start_index = torch.argmax(output['start_logits'])\r\n",
        "    end_index = torch.argmax(output['end_logits'])\r\n",
        "    ans_ids = inputs['input_ids'][0][start_index :end_index+1]\r\n",
        "    answer = tokenizer.decode(ans_ids)\r\n",
        "    answer = tokenizer.clean_up_tokenization(answer).strip()\r\n",
        "    no_answer_probability = 1 if len(example['answers']['answer_start'])!=0 else 0\r\n",
        "    \"\"\"\r\n",
        "    predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\r\n",
        "    references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\r\n",
        "    \"\"\"\r\n",
        "    pred = {'prediction_text':answer, 'id': example['id']}\r\n",
        "    ref = {'answers': example['answers'] , 'id': example['id']}\r\n",
        "    predictions.append(pred)\r\n",
        "    references.append(ref)\r\n",
        "  return predictions, references"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqzxqfDrKFuF"
      },
      "source": [
        "def print_results(model, tokenizer, device, dataset_name='squad'):\r\n",
        "  validation_dataset = get_validation_data(dataset_name)\r\n",
        "  predictions, references = get_infernece(validation_dataset, model, tokenizer, device)\r\n",
        "  score = get_result(predictions, references, dataset_name)\r\n",
        "  return score"
      ],
      "execution_count": 7,
      "outputs": []
    },
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        "id": "3HXbAuuyKI7k",
        "outputId": "35a02409-f29d-4815-a83e-b6b5f7e43ca6"
      },
      "source": [
        "## Inference for bert Model on Colab GPU\r\n",
        "## Note: This script for inference is not 100% perfect \r\n",
        "# It does not include sliding window and post processing\r\n",
        "print_results(bert_model, bert_tokenizer, device)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "0bb8550763c9432dbbf5a7809badf481",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1907.0, 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": "2880dac6997a4b04ae2ee65ae0669c2a",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=955.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading and preparing dataset squad/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to /root/.cache/huggingface/datasets/squad/plain_text/1.0.0/0fd9e01360d229a22adfe0ab7e2dd2adc6e2b3d6d3db03636a51235947d4c6e9...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b4f804498b6d4c44a5741eaa0015b83f",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=8116577.0, style=ProgressStyle(descript…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "597962817ede4c92844dd2e9e4fb2f3c",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1054280.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "567e9dfd8ca54cc9b70782afa0de3061",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab63038c90e44b0d94ae738e43967c8d",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset squad downloaded and prepared to /root/.cache/huggingface/datasets/squad/plain_text/1.0.0/0fd9e01360d229a22adfe0ab7e2dd2adc6e2b3d6d3db03636a51235947d4c6e9. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73bba986c8a94e79a9e54375c6a2fa44",
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              "HBox(children=(FloatProgress(value=0.0, max=10570.0), HTML(value='')))"
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4516a2ee77b54b1e992f0466d7f74b7b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1726.0, 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": "4e7f62bd71a2415c841734256e46e397",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1132.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "{'exact_match': 72.07190160832545, 'f1': 81.26854601807015}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100,
          "referenced_widgets": [
            "6e01a8b025b54c84bb179622f75d16f4",
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        "id": "hYjeVJLrMCjD",
        "outputId": "d0a18bd4-05cd-421c-c36c-daa4257fff47"
      },
      "source": [
        "## Inference for prone bert Model on Colab GPU\r\n",
        "print_results(model, tokenizer, device)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/0fd9e01360d229a22adfe0ab7e2dd2adc6e2b3d6d3db03636a51235947d4c6e9)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e01a8b025b54c84bb179622f75d16f4",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=10570.0), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "{'exact_match': 67.18070009460737, 'f1': 77.45808864399302}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zo-ZkR2xLpz1"
      },
      "source": [
        "## Performance on Colab GPU:\r\n",
        "\r\n",
        "SQUAD v1 Dataset:\r\n",
        "\r\n",
        "| Model | EM | F1 | Inference Time (10570 examples)| Throughput | Model Size |\r\n",
        "| --- | --- | --- | --- | --- | --- |\r\n",
        "| Bert | 72.07 | 81.26 | 3mins | 58.72 examples/sec | 417.7mb | \r\n",
        "| PruneBert | 67.18 | 77.46 | 2mins 47 secs| 63.29 exmaples/sec | 417.7mb | "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BL6w2_uzKVHc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}