t10_t1_llama2.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M8VW1uy8ZzGL"
      },
      "outputs": [],
      "source": [
        "!pip install datasets\n",
        "!pip install evaluate\n",
        "!pip install peft\n",
        "!pip install wandb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, Dataset, concatenate_datasets, DatasetDict\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, get_scheduler, DataCollatorWithPadding, Trainer, TrainingArguments\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.optim import AdamW\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from tqdm.auto import tqdm\n",
        "import evaluate\n",
        "import pandas as pd\n",
        "from peft import get_peft_model, LoraConfig, TaskType\n",
        "import numpy as np\n",
        "from collections import Counter\n",
        "import gc"
      ],
      "metadata": {
        "id": "X_tiMTMTyBz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameters\n",
        "lr = 1e-4\n",
        "num_epochs = 10 # 4 (or 7 epochs seems good)\n",
        "batch_size = 16\n",
        "llama_checkpoint = \"meta-llama/Llama-2-7b-hf\"\n",
        "# MAX_LEN = 512  # mandotory for roberta optional for others\n"
      ],
      "metadata": {
        "id": "GJAKHANH-epX"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "llama_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    pretrained_model_name_or_path=llama_checkpoint,\n",
        "    num_labels=8,\n",
        "    # device_map=\"auto\", #should try without this\n",
        "    offload_folder=\"offload\",# try without this\n",
        "    trust_remote_code=True\n",
        ")\n",
        "#For Llama 2, we have to add the padding token id as it is not defined by default.\n",
        "llama_model.config.pad_token_id = llama_model.config.eos_token_id\n"
      ],
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              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
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        {
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              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
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        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of LlamaForSequenceClassification were not initialized from the model checkpoint at meta-llama/Llama-2-7b-hf and are newly initialized: ['score.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
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        "llama_tokenizer = AutoTokenizer.from_pretrained(llama_checkpoint, add_prefix_spac=True)\n",
        "llama_tokenizer.pad_token_id = llama_tokenizer.eos_token_id\n",
        "llama_tokenizer.pad_token = llama_tokenizer.eos_token\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "train_data_files = {\n",
        "    \"train\": \"MaSaC_train_erc_task1.json\",\n",
        "    \"validation\": \"MaSaC_validation_erc_task1.json\"\n",
        "}\n",
        "\n",
        "train_dataset = load_dataset(\"json\", data_files=train_data_files)\n",
        "train_dataset = train_dataset.remove_columns(\"episode\")\n",
        "\n",
        "labels = ['anger', 'neutral', 'contempt', 'sadness', 'fear', 'disgust', 'joy', 'surprise']\n",
        "label_encoder = LabelEncoder()\n",
        "label_encoder.fit(labels)\n",
        "\n",
        "combined_dataset = {}\n",
        "for dataset_type in train_dataset:\n",
        "    for record in train_dataset[dataset_type]:\n",
        "        encoded_label = label_encoder.transform(record[\"emotions\"])\n",
        "        record[\"emotions\"] = encoded_label\n",
        "        if combined_dataset.get(dataset_type) is None:\n",
        "            combined_dataset[dataset_type] = Dataset.from_dict(record)\n",
        "        else:\n",
        "            combined_dataset[dataset_type] = concatenate_datasets([combined_dataset[dataset_type], Dataset.from_dict(record)])\n",
        "\n",
        "dataset_dict = DatasetDict(combined_dataset)\n",
        "\n",
        "\n",
        "dataset_dict['train'].to_pandas().info()\n",
        "dataset_dict['validation'].to_pandas().info()\n",
        "\n",
        "max_char = dataset_dict['train'].to_pandas()['utterances'].str.len().max()\n",
        "max_words = dataset_dict['train'].to_pandas()['utterances'].str.split().str.len().max()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBiqfnYi-lXW",
        "outputId": "6d8d9e65-58ea-43c8-bd7e-ec9e3eb138b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8506 entries, 0 to 8505\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   emotions    8506 non-null   float64\n",
            " 1   speakers    8506 non-null   object \n",
            " 2   utterances  8506 non-null   object \n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 199.5+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1354 entries, 0 to 1353\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   emotions    1354 non-null   float64\n",
            " 1   speakers    1354 non-null   object \n",
            " 2   utterances  1354 non-null   object \n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 31.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "code above common for all 3 models"
      ],
      "metadata": {
        "id": "Jq8vNq1l-7e7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_counts = Counter(dataset_dict[\"train\"][\"emotions\"])\n",
        "num_classes = 8\n",
        "num_samples = len(dataset_dict[\"train\"])\n",
        "class_weights = [num_samples / (num_classes * label_counts[i]) for i in range(num_classes)]"
      ],
      "metadata": {
        "id": "2zH_u-OANHZA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "col_to_delete = [\"speakers\", \"utterances\"]\n",
        "\n",
        "def llama_preporcessing_function(exampmle):\n",
        "    return llama_tokenizer(exampmle[\"utterances\"], truncation=True)\n",
        "\n",
        "llama_tokenized_datasets = dataset_dict.map(llama_preporcessing_function, batched=True, remove_columns=col_to_delete)\n",
        "llama_tokenized_datasets = llama_tokenized_datasets.rename_column(\"emotions\", \"label\") # check the label val for each model individually\n",
        "llama_tokenized_datasets.set_format(\"torch\")\n",
        "\n",
        "llama_data_collator = DataCollatorWithPadding(tokenizer=llama_tokenizer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119,
          "referenced_widgets": [
            "233913ef7ece44b7851cd6755efe060d",
            "5f6ad1fbf3e143c786009ea13f5cdae7",
            "c71c15e98e204ecba7789a5fc3c29180",
            "f76cfd157bcc43a69fba6458c6ab4449",
            "c2d9063d3b134653996a8286e6e51593",
            "dfce5de1071c41e4a24f80928e48b979",
            "bbd7dd1fbdd444b9bb14b03163988be5",
            "50cb7269bf91449e9862d9c09dbf6b61",
            "3d6e68bfd9724c05b1752a5bcaf5fe0e",
            "561036822cbb4d1caa88e5aeafc695d7",
            "4be007abc7f240e4a5a0c6d386e3aa24",
            "fa5720f73695444c8735cbb03867fc28",
            "bc4f29e033b44122afd54d2fe6fecbec",
            "45d2f32c46d74df8a508e6ecf415d026",
            "fce8951436b64cc9abf52846a883da8c",
            "129ffce22415417594dcec194ee01ae6",
            "441b3e6f94c44ee78abe08655a10bbb5",
            "f3b124b978ac4452aea8367ea96c9410",
            "4d62c17274d040c8833a9ad52075edb9",
            "36161dc0f3e94e0aa669211c47b70491",
            "23acc65d6cda476fad83be8dc0f242da",
            "1244b18324134adab122ab29dc0dc071"
          ]
        },
        "id": "f_6Rkmzy-eje",
        "outputId": "63437d6f-5944-4e89-9349-e4d4fc290cd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/8506 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "233913ef7ece44b7851cd6755efe060d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1354 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa5720f73695444c8735cbb03867fc28"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lora setup for llama classifier\n",
        "llama_peft_config = LoraConfig(\n",
        "    task_type=TaskType.SEQ_CLS, r=16, lora_alpha=16, lora_dropout=0.5, bias=\"none\",\n",
        "    target_modules=[\n",
        "        \"q_proj\",\n",
        "        \"v_proj\"\n",
        "    ]\n",
        ")\n",
        "llama_model = get_peft_model(llama_model, llama_peft_config)\n",
        "llama_model.print_trainable_parameters()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWqLjOUz-ebu",
        "outputId": "5e2135c4-8988-49a8-8d13-0d6dd9b1b7bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 8,421,376 || all params: 6,615,797,760 || trainable%: 0.1272919201206054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    precision_metric = evaluate.load(\"precision\")\n",
        "    recall_metric = evaluate.load(\"recall\")\n",
        "    f1_metric = evaluate.load(\"f1\")\n",
        "    accuracy_metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    precision = precision_metric.compute(predictions=predictions, references=labels, average='weighted')[\"precision\"]\n",
        "    recall = recall_metric.compute(predictions=predictions, references=labels, average='weighted')[\"recall\"]\n",
        "    f1 = f1_metric.compute(predictions=predictions, references=labels, average='weighted')[\"f1\"]\n",
        "    accuracy = accuracy_metric.compute(predictions=predictions, references=labels)[\"accuracy\"]\n",
        "    return {\"precision\": precision, \"recall\": recall, \"f1-score\": f1, 'accuracy': accuracy}\n"
      ],
      "metadata": {
        "id": "F7xasFV8-eYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5VVGs6dZxfn",
        "outputId": "4849192d-4955-4a49-c1bb-2b585f8c3bb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "91"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
      ],
      "metadata": {
        "id": "9pm0ORtLNpEt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# not that good at all, wasnt representative\n",
        "class WeightedCELossTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.pop(\"labels\")\n",
        "        # Get model's predictions\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get(\"logits\")\n",
        "        # Compute custom loss\n",
        "        loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor(class_weights, device=model.device, dtype=logits.dtype))\n",
        "        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
        "        return (loss, outputs) if return_outputs else loss\n"
      ],
      "metadata": {
        "id": "meP7KEt8Y4v1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# can make this custom loss function https://chat.openai.com/share/5c3b8a5b-7cbb-4bed-bcff-7470773cfc26\n",
        "class WeightedCEWithF1LossTrainer(Trainer):\n",
        "\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.pop(\"labels\")\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get(\"logits\")\n",
        "        # class_weights = torch.tensor(class_weights, device=model.device, dtype=logits.dtype)\n",
        "        loss = self.weighted_ce_f1_loss(labels, logits, torch.tensor(class_weights, device=model.device, dtype=logits.dtype))\n",
        "\n",
        "        return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "    @staticmethod\n",
        "    def weighted_ce_f1_loss(y_true, logits, epsilon=1e-7):\n",
        "        # Convert labels to one-hot encoding\n",
        "        y_true_one_hot = F.one_hot(y_true, num_classes=logits.size(-1)).float()\n",
        "\n",
        "        # Apply softmax to logits to get probabilities\n",
        "        y_pred = F.softmax(logits, dim=-1)\n",
        "\n",
        "        # Calculate Cross Entropy Loss without reduction\n",
        "        ce = F.cross_entropy(logits, y_true, reduction='none')\n",
        "        # Ensure ce is [batch_size, 1] for broadcasting\n",
        "        ce = ce.view(-1, 1)\n",
        "\n",
        "        # Calculate components of F1 score for each sample and each class\n",
        "        tp = (y_true_one_hot * y_pred)\n",
        "        fp = ((1 - y_true_one_hot) * y_pred)\n",
        "        fn = (y_true_one_hot * (1 - y_pred))\n",
        "\n",
        "        # Sum tp, fp, fn over all samples for each class\n",
        "        tp_sum = tp.sum(dim=0)\n",
        "        fp_sum = fp.sum(dim=0)\n",
        "        fn_sum = fn.sum(dim=0)\n",
        "\n",
        "        # Calculate Precision and Recall for each class\n",
        "        precision = tp_sum / (tp_sum + fp_sum + epsilon)\n",
        "        recall = tp_sum / (tp_sum + fn_sum + epsilon)\n",
        "\n",
        "        # Calculate F1 Score for each class\n",
        "        f1 = 2 * precision * recall / (precision + recall + epsilon)\n",
        "        f1 = torch.where(torch.isnan(f1), torch.zeros_like(f1), f1)\n",
        "\n",
        "        # Calculate mean F1 score across all classes\n",
        "        f1_mean = f1.mean()\n",
        "        # Ensure f1_mean is broadcastable with ce\n",
        "        f1_mean = f1_mean.view(1, -1)\n",
        "\n",
        "        # Calculate Weighted Loss\n",
        "        weighted_loss = ce * (1 - f1_mean)\n",
        "        return torch.mean(weighted_loss)\n"
      ],
      "metadata": {
        "id": "5d9iX0bP-eUh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class WeightedF1ApproxLossTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.pop(\"labels\")\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get(\"logits\")\n",
        "\n",
        "        # Cross-entropy part\n",
        "        ce_loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor(class_weights, device=model.device, dtype=logits.dtype))\n",
        "        ce_loss = ce_loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
        "\n",
        "        # F1-approximating part\n",
        "        # Softmax to get probabilities\n",
        "        probs = F.softmax(logits, dim=-1)\n",
        "        # One-hot encode labels\n",
        "        true_labels = F.one_hot(labels, num_classes=self.model.config.num_labels).to(dtype=probs.dtype)\n",
        "\n",
        "        # Calculate precision and recall per class\n",
        "        tp = torch.sum(probs * true_labels, axis=0)\n",
        "        fp = torch.sum(probs * (1 - true_labels), axis=0)\n",
        "        fn = torch.sum((1 - probs) * true_labels, axis=0)\n",
        "\n",
        "        precision = tp / (tp + fp + 1e-8)\n",
        "        recall = tp / (tp + fn + 1e-8)\n",
        "\n",
        "        # Use harmonic mean of precision and recall as a proxy for F1\n",
        "        f1_proxy = 2 * (precision * recall) / (precision + recall + 1e-8)\n",
        "        f1_proxy_loss = 1 - torch.mean(f1_proxy)  # Loss is 1 - mean F1 proxy\n",
        "\n",
        "        # Combine losses\n",
        "        # You might need to tune the balance coefficient\n",
        "        balance_coefficient = 0.5\n",
        "        loss = (1 - balance_coefficient) * ce_loss + balance_coefficient * f1_proxy_loss\n",
        "\n",
        "        return (loss, outputs) if return_outputs else loss\n"
      ],
      "metadata": {
        "id": "N-E-hXtHl4TL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llama_model = llama_model.cuda()"
      ],
      "metadata": {
        "id": "uiOU7MF-iVcE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"llama-lora-token-classification\",\n",
        "    learning_rate=lr,\n",
        "    lr_scheduler_type= \"constant\",\n",
        "    warmup_ratio= 0.1,\n",
        "    max_grad_norm= 0.3,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=num_epochs,\n",
        "    weight_decay=0.001,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    report_to=\"wandb\",\n",
        "    fp16=True,\n",
        "    gradient_checkpointing=True,\n",
        ")\n"
      ],
      "metadata": {
        "id": "cN5q0_n-hvD-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llama_trainer = WeightedCEWithF1LossTrainer(\n",
        "    model=llama_model,\n",
        "    args=training_args,\n",
        "    train_dataset=llama_tokenized_datasets['train'],\n",
        "    eval_dataset=llama_tokenized_datasets[\"validation\"],\n",
        "    data_collator=llama_data_collator,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n"
      ],
      "metadata": {
        "id": "hHEQR17ghu_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lKan0LUi0rh",
        "outputId": "6029b38d-4209-45e3-fb50-093b43f807bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llama_trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "rsUkwX0BnEaP",
        "outputId": "a2460395-293a-4f8c-d0fa-b8dd951fb6f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "\"nll_loss_forward_no_reduce_cuda_kernel_index\" not implemented for 'Float'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-6b60589a4ecc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mllama_trainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1553\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1555\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1556\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1557\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1859\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1860\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2724\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2725\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2727\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-23-043e5c659226>\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"logits\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;31m# class_weights = torch.tensor(class_weights, device=model.device, dtype=logits.dtype)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweighted_ce_f1_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_outputs\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-23-043e5c659226>\u001b[0m in \u001b[0;36mweighted_ce_f1_loss\u001b[0;34m(y_true, logits, epsilon)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# Calculate Cross Entropy Loss without reduction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;31m# Ensure ce is [batch_size, 1] for broadcasting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mce\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m   3051\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3052\u001b[0m         \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3053\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_smoothing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: \"nll_loss_forward_no_reduce_cuda_kernel_index\" not implemented for 'Float'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eval"
      ],
      "metadata": {
        "id": "IM1fQVlqjegZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_files = {\n",
        "    \"test\": \"MaSaC_test_erc_task1.json\",\n",
        "}\n",
        "\n",
        "dataset = load_dataset(\"json\", data_files=test_data_files)\n",
        "dataset = dataset.remove_columns(\"episode\")\n",
        "\n",
        "combined_dataset = {}\n",
        "for dataset_type in dataset:\n",
        "    for record in dataset[dataset_type]:\n",
        "        if combined_dataset.get(dataset_type) is None:\n",
        "            combined_dataset[dataset_type] = Dataset.from_dict(record)\n",
        "        else:\n",
        "            combined_dataset[dataset_type] = concatenate_datasets([combined_dataset[dataset_type], Dataset.from_dict(record)])\n",
        "\n",
        "dataset_dict_test = DatasetDict(combined_dataset)\n",
        "print(dataset_dict_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "22c9cadbf7ad4145a46af5d52632e46d",
            "6f6f77db95ea45f5ab42d76fab715d83",
            "747ace8299a14cf58867dd312c250feb",
            "741107e8e2cb4942b0bd19681afc71dd",
            "e5a760d5fed24a3e8cb12c4e09a32fd0",
            "142c3e8765f4443197f15c828879f020",
            "f89039a73f8643f9bcbe2edc1ba3793c",
            "216055f147f94826ba5972e30314115e",
            "54a89f9e315040d282c49208b2792ff3",
            "0e9636f7b58b4a228f834d7c010117c2",
            "98264df01c4f49979a581c0034f9138d"
          ]
        },
        "id": "FqQhf5jujd9o",
        "outputId": "e2017b78-7497-4f0f-fe73-c7582c5e7ad1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22c9cadbf7ad4145a46af5d52632e46d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    test: Dataset({\n",
            "        features: ['utterances', 'speakers'],\n",
            "        num_rows: 1580\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llama_tokenized_datasets = dataset_dict_test.map(llama_preporcessing_function, batched=True, remove_columns=col_to_delete)\n",
        "llama_tokenized_datasets.set_format(\"torch\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bf2808c6c02642b4b87331eaa5fa3f73",
            "28b3af1655fa495192745058b7c4e5ff",
            "35d0468f64b24c0aa104aaf04dc7a3a6",
            "299ecc1a04494a9d9f29cf5a10c769bc",
            "671be9aeae404b91982b8509e644cecb",
            "c05242a2155e42d5851cd650dc102ed7",
            "f30dbc9737d54debbb0fae96ac6a5ad5",
            "d3567ff090fa4b8a8c87387243b10135",
            "45a4a7b34173459e996e34c774f7a89a",
            "80c1718a1b2247748ad7f364299c824d",
            "7abd9488232d4d2ead63807fd06a6113"
          ]
        },
        "id": "rfrgHuPyjd7C",
        "outputId": "3b65e4b4-ae4c-43a7-9b35-f98a8a13c867"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1580 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf2808c6c02642b4b87331eaa5fa3f73"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(llama_tokenized_datasets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lGkbslkssnM",
        "outputId": "945832d7-f4ae-433e-e63d-3488fd7ed35a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    test: Dataset({\n",
            "        features: ['input_ids', 'attention_mask'],\n",
            "        num_rows: 1580\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataloader = DataLoader(llama_tokenized_datasets[\"test\"], batch_size=15, collate_fn=llama_data_collator)\n"
      ],
      "metadata": {
        "id": "u3MwY52hjd4P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions = []"
      ],
      "metadata": {
        "id": "uMewQkxnrwAR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llama_model.eval()\n",
        "for batch in test_dataloader:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = llama_model(**batch)\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "    predictions = predictions.cpu().tolist()\n",
        "    decoded_label = label_encoder.inverse_transform(predictions).tolist()\n",
        "    emotions = emotions + decoded_label\n"
      ],
      "metadata": {
        "id": "6Y7IvCLshuyj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(emotions, len(emotions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hie8fVXK5LnR",
        "outputId": "c9793fd8-2ce3-4a66-82c2-8a2f46dcfec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['sadness', 'neutral', 'neutral', 'neutral', 'anger', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'fear', 'anger', 'neutral', 'neutral', 'sadness', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'anger', 'neutral', 'neutral', 'neutral', 'neutral', 'anger', 'neutral', 'anger', 'sadness', 'joy', 'neutral', 'joy', 'neutral', 'neutral', 'neutral', 'anger', 'anger', 'anger', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'surprise', 'joy', 'joy', 'joy', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'anger', 'joy', 'neutral', 'neutral', 'surprise', 'joy', 'neutral', 'neutral', 'neutral', 'joy', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'contempt', 'neutral', 'neutral', 'neutral', 'neutral', 'anger', 'anger', 'neutral', 'neutral', 'neutral', 'joy', 'joy', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'anger', 'neutral', 'neutral', 'neutral', 'neutral', 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = 'answer_t1_llama_0_43.txt'\n",
        "\n",
        "with open(file_path, 'w') as file:\n",
        "    for item in emotions:\n",
        "        file.write(item + '\\n')\n"
      ],
      "metadata": {
        "id": "jOlOVYtyhuvY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZJHgoP_Ky86m"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}