hw_05.ipynb

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      "source": [
        "from data.multiwoz_loader import create_utterances_dataset, BucketBatchSampler, tokenizer, collator\n",
        "from datasets import load_dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import GPT2LMHeadModel\n",
        "from transformers import AdamW\n",
        "from transformers import get_scheduler\n",
        "import torch\n",
        "from tqdm.auto import tqdm\n",
        "import re\n",
        "import json\n",
        "\n",
        "from model import fit"
      ]
    },
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      ],
      "source": [
        "raw_datasets = load_dataset(\"multi_woz_v22\", ignore_verifications=True)\n",
        "\n",
        "utterances_dataset_train = create_utterances_dataset(raw_datasets)\n",
        "utterances_dataset_valid = create_utterances_dataset(raw_datasets,split = \"validation\")\n",
        "\n",
        "BATCH_SIZE = 10\n",
        "\n",
        "bucket_batch_sampler_train = BucketBatchSampler(BATCH_SIZE, utterances_dataset_train)\n",
        "bucket_batch_sampler_valid = BucketBatchSampler(BATCH_SIZE, utterances_dataset_valid)\n",
        "\n",
        "train_dataloader = DataLoader(utterances_dataset_train, batch_sampler = bucket_batch_sampler_train, shuffle=False, drop_last=False, collate_fn=collator)\n",
        "eval_dataloader = DataLoader(utterances_dataset_valid, batch_sampler = bucket_batch_sampler_valid, shuffle=False, drop_last=False, collate_fn=collator)"
      ]
    },
    {
      "cell_type": "code",
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        {
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            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50261, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50261, bias=False)\n",
              ")"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
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        },
        "id": "sWSFvQumnZYm",
        "outputId": "5bd563ef-96d5-4a82-dfcb-d1e10b6fd240"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "c401d32efdf647bc83b48362ba96eb0d",
              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation loss 1.2420\n",
            "Validation loss 1.0715\n",
            "Validation loss 1.5776\n",
            "Validation loss 1.1670\n",
            "Validation loss 1.0882\n"
          ]
        }
      ],
      "source": [
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "num_epochs = 5\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps\n",
        ")\n",
        "\n",
        "fit(num_epochs, model, optimizer, train_dataloader, eval_dataloader, optimizer, lr_scheduler, num_training_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-LoLebdKptfO"
      },
      "outputs": [],
      "source": [
        "torch.save(model, \"5-epoch-run-0-corrected\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6vbpJHbqQao"
      },
      "source": [
        "Decoding"
      ]
    },
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              "model_id": "345ef98333c3479fad6987bc10d45b82",
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      ],
      "source": [
        "utterances_dataset_test = create_utterances_dataset(raw_datasets,split = \"test\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "pej-Hs4HqOYc"
      },
      "outputs": [],
      "source": [
        "bucket_batch_sampler_test = BucketBatchSampler(BATCH_SIZE, utterances_dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "A0qJqCJOqYUj"
      },
      "outputs": [],
      "source": [
        "test_dataloader = DataLoader(utterances_dataset_test, batch_sampler = bucket_batch_sampler_test, shuffle=False, drop_last=False, collate_fn=collator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Yl559OD1qb2f"
      },
      "outputs": [],
      "source": [
        "from belief_parser import BeliefParser\n",
        "\n",
        "bp = BeliefParser(['hotel :', 'restaurant :', 'attraction :', 'train :', 'taxi :', 'police :', 'hospital :'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "nelhOIHcqcfl"
      },
      "outputs": [],
      "source": [
        "from database.database import MultiWOZDatabase\n",
        "db = MultiWOZDatabase()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yv4XDChatGPI"
      },
      "outputs": [],
      "source": [
        "for batch in tqdm(test_dataloader):\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    batch_attention_mask = batch['context_mask'] + batch['belief_mask'] + batch['database_mask'] + batch['utterance_mask']\n",
        "    batch_ids = batch['labels']\n",
        "    dialog_state_targets = batch_ids * batch['belief_mask']\n",
        "    utterance_targets = batch_ids * batch['utterance_mask']\n",
        "\n",
        "    utterance_targets[batch['utterance_mask'] == 0] = -100\n",
        "    dialog_state_targets[batch['belief_mask'] == 0] = -100\n",
        "    for i in range(batch_ids.shape[0]):\n",
        "        input = {'input_ids':batch['labels'][i,:][batch['context_mask'][i,:]]}\n",
        "        input['input_ids'] = input['input_ids'].unsqueeze(0)\n",
        "        output_ids = model.generate(**input, eos_token_id = tokenizer.encode(['}}'])[0], max_length = 200)\n",
        "        belief_state = tokenizer.batch_decode(output_ids[:,input['input_ids'].shape[1]:])\n",
        "        parsed_belief_state = bp(belief_state[0])\n",
        "        db_results = {}\n",
        "        for domain,constraints in parsed_belief_state.items():\n",
        "            db_results[domain] = len(db.query(domain, constraints = constraints))\n",
        "\n",
        "        db_result_encoded = ''.join(('<|database|> ', re.sub('[\\[\\]{}\"]', '', json.dumps(db_results)), '<|endoftext|> '))\n",
        "\n",
        "        new_prompt = torch.cat((output_ids.squeeze(), tokenizer(db_result_encoded, return_tensors='pt')['input_ids'].squeeze().to(device)))\n",
        "        output_ids = model.generate(new_prompt.unsqueeze(0), max_length = 400)\n",
        "\n",
        "        system_utterance = tokenizer.batch_decode(output_ids[:,len(new_prompt):])\n",
        "\n",
        "        with open('multiwoz_outputs.txt', 'a') as f:\n",
        "            f.write(f'{belief_state[0]}\\t{system_utterance[0]}\\n')\n"
      ]
    },
    {
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        "colab": {
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      "outputs": [],
      "source": [
        "for batch in tqdm(test_dataloader):\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    batch_attention_mask = batch['context_mask'] + batch['belief_mask'] + batch['database_mask'] + batch['utterance_mask']\n",
        "    batch_ids = batch['labels']\n",
        "    dialog_state_targets = batch_ids * batch['belief_mask']\n",
        "    utterance_targets = batch_ids * batch['utterance_mask']\n",
        "\n",
        "    utterance_targets[batch['utterance_mask'] == 0] = -100\n",
        "    dialog_state_targets[batch['belief_mask'] == 0] = -100\n",
        "    for i in range(batch_ids.shape[0]):\n",
        "        input = {'input_ids':batch['labels'][i,:][batch['context_mask'][i,:]]}\n",
        "        input['input_ids'] = input['input_ids'].unsqueeze(0)\n",
        "        output_ids = model.generate(**input, eos_token_id = tokenizer.encode(['}}'])[0], max_length = 200)\n",
        "        belief_state = tokenizer.batch_decode(output_ids[:,input['input_ids'].shape[1]:])\n",
        "        parsed_belief_state = bp(belief_state[0])\n",
        "        db_results = {}\n",
        "        for domain,constraints in parsed_belief_state.items():\n",
        "            db_results[domain] = len(db.query(domain, constraints = constraints))\n",
        "\n",
        "        db_result_encoded = ''.join(('<|database|> ', re.sub('[\\[\\]{}\"]', '', json.dumps(db_results)), '<|endoftext|> '))\n",
        "\n",
        "        new_prompt = torch.cat((output_ids.squeeze(), tokenizer(db_result_encoded, return_tensors='pt')['input_ids'].squeeze().to(device)))\n",
        "        output_ids = model.generate(new_prompt.unsqueeze(0), max_length = 400)\n",
        "\n",
        "        system_utterance = tokenizer.batch_decode(output_ids[:,len(new_prompt):])\n",
        "\n",
        "\n",
        "\n",
        "        with open('multiwoz_outputs.txt', 'a') as f:\n",
        "            f.write(f'{belief_state[0]}\\t{system_utterance[0]}\\n')\n"
      ]
    },
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