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"output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'cnlps-caiccaic'...\n", + "remote: Enumerating objects: 73, done.\u001b[K\n", + "remote: Counting objects: 100% (73/73), done.\u001b[K\n", + "remote: Compressing objects: 100% (56/56), done.\u001b[K\n", + "remote: Total 73 (delta 32), reused 41 (delta 11), pack-reused 0\u001b[K\n", + "Unpacking objects: 100% (73/73), 1.89 MiB | 4.48 MiB/s, done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/kubapok/cnlps-caiccaic.git" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -Uq datasets transformers peft bitsandbytes loralib accelerate" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1qS0p25llt37", + "outputId": "e77b4672-c85f-4a5c-9e98-7de4070267a0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Create dataset" + ], + "metadata": { + "id": "rDDe5DJCy2r_" + } + }, + { + "cell_type": "code", + "source": [ + "!cat cnlps-caiccaic/train/in.tsv cnlps-caiccaic/dev-A/in.tsv > in.tsv\n", + "!cat cnlps-caiccaic/train/expected.tsv cnlps-caiccaic/dev-A/expected.tsv > expected.tsv" + ], + "metadata": { + "id": "OxO5rSflnWkT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import csv\n", + "from datasets import Dataset, DatasetDict\n", + "\n", + "with open('in.tsv', encoding='utf-8') as train_f_in, open('expected.tsv', encoding='utf-8') as train_f_exp:\n", + " train_list_in = list(csv.reader(train_f_in, delimiter='\\t'))\n", + " train_list_exp = train_f_exp.readlines()\n", + "\n", + "with open('cnlps-caiccaic/test-A/in.tsv', encoding='utf-8') as test_f_in:\n", + " test_list_in = list(csv.reader(test_f_in, delimiter='\\t'))\n", + "\n", + "train_data = Dataset.from_list([{'text': f'{in_[3]} language: {in_[1]}', 'intent': exp.strip().replace('}', ']').replace('{', '[').replace('\\t', '|')} for in_, exp in zip(train_list_in, train_list_exp)])\n", + "test_data = Dataset.from_list([{'text': f'{in_[3]} language: {in_[1]}', 'intent': ''} for in_ in test_list_in])\n", + "dataset = DatasetDict({'train': train_data, 'test': test_data})" + ], + "metadata": { + "id": "mOM2JAw4npqM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "dataset['train'][600]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bYmEQ35Rvjr-", + "outputId": "23d6c786-d03a-4301-da8d-d50480af3888" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'text': \"don't alert me when an event in my calendar in location kenner begins language: en-US\",\n", + " 'intent': \"Calendar|NotNotifyOnEventInLocation|['location': 'kenner']\"}" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Define training components" + ], + "metadata": { + "id": "GeSaTKE0y4_d" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "\n", + "model_name = 'google/flan-t5-large'\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, device_map='auto')" + ], + "metadata": { + "id": "qt0MadQYlO_l", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241, + "referenced_widgets": [ + 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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=12874, training_loss=0.06059208474804456, metrics={'train_runtime': 12136.5406, 'train_samples_per_second': 4.243, 'train_steps_per_second': 1.061, 'total_flos': 2.330193322605773e+16, 'train_loss': 0.06059208474804456, 'epoch': 1.0})" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Save the model" + ], + "metadata": { + "id": "mDOXSmts1J-G" + } + }, + { + "cell_type": "code", + "source": [ + "trainer.model.save_pretrained('results_v2')\n", + "tokenizer.save_pretrained('results_v2')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 202 + }, + "id": "RN9rhtHw1KC9", + "outputId": "690e6430-1476-45ef-da7f-b418063229b0" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpeft_model_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'results_v2'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'results_v2'\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 3\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'results_v2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!rm -r /content/drive/MyDrive/caiccaic; mkdir /content/drive/MyDrive/caiccaic\n", + "!cp -r results_v2 /content/drive/MyDrive/caiccaic" + ], + "metadata": { + "id": "FPUZs3SZPQ4z" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# from google.colab import runtime\n", + "# runtime.unassign()" + ], + "metadata": { + "id": "em0VFt0rz3uJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Inference" + ], + "metadata": { + "id": "ntt7iUnd8Wyc" + } + }, + { + "cell_type": "code", + "source": [ + "!cp -r /content/drive/MyDrive/results_v2 ." + ], + "metadata": { + "id": "muGPGGT5m4Wx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n", + "import torch\n", + "\n", + "model = AutoModelForSeq2SeqLM.from_pretrained('results_v2', device_map={'':0})\n", + "tokenizer = AutoTokenizer.from_pretrained('results_v2')\n", + "model.eval()" + ], + "metadata": { + "id": "hTshD-E_mv4p", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "30da8e10-c8fb-4d01-d558-07e8cb02d693" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "T5ForConditionalGeneration(\n", + " (shared): Embedding(32128, 1024)\n", + " (encoder): T5Stack(\n", + " (embed_tokens): Embedding(32128, 1024)\n", + " (block): ModuleList(\n", + " (0): T5Block(\n", + " (layer): ModuleList(\n", + " (0): T5LayerSelfAttention(\n", + " (SelfAttention): T5Attention(\n", + " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (relative_attention_bias): Embedding(32, 16)\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (1): T5LayerFF(\n", + " (DenseReluDense): T5DenseGatedActDense(\n", + " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (act): NewGELUActivation()\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (1-23): 23 x T5Block(\n", + " (layer): ModuleList(\n", + " (0): T5LayerSelfAttention(\n", + " (SelfAttention): T5Attention(\n", + " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (1): T5LayerFF(\n", + " (DenseReluDense): T5DenseGatedActDense(\n", + " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (act): NewGELUActivation()\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (final_layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (decoder): T5Stack(\n", + " (embed_tokens): Embedding(32128, 1024)\n", + " (block): ModuleList(\n", + " (0): T5Block(\n", + " (layer): ModuleList(\n", + " (0): T5LayerSelfAttention(\n", + " (SelfAttention): T5Attention(\n", + " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (relative_attention_bias): Embedding(32, 16)\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (1): T5LayerCrossAttention(\n", + " (EncDecAttention): T5Attention(\n", + " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (2): T5LayerFF(\n", + " (DenseReluDense): T5DenseGatedActDense(\n", + " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (act): NewGELUActivation()\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (1-23): 23 x T5Block(\n", + " (layer): ModuleList(\n", + " (0): T5LayerSelfAttention(\n", + " (SelfAttention): T5Attention(\n", + " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (1): T5LayerCrossAttention(\n", + " (EncDecAttention): T5Attention(\n", + " (q): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (k): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (v): Linear(in_features=1024, out_features=1024, bias=False)\n", + " (o): Linear(in_features=1024, out_features=1024, bias=False)\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (2): T5LayerFF(\n", + " (DenseReluDense): T5DenseGatedActDense(\n", + " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n", + " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (act): NewGELUActivation()\n", + " )\n", + " (layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (final_layer_norm): T5LayerNorm()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (lm_head): Linear(in_features=1024, out_features=32128, bias=False)\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from datasets import load_from_disk\n", + "from tqdm import tqdm\n", + "\n", + "def evaluate_peft_model(sample):\n", + " outputs = model.generate(input_ids=torch.tensor(sample['input_ids']).unsqueeze(0).cuda(), max_new_tokens=512) \n", + " prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)\n", + " return prediction\n", + "\n", + "test_dataset = tokenized_dataset['test']\n", + "\n", + "predictions, references = [], []\n", + "i = 0\n", + "for sample in tqdm(test_dataset):\n", + " p = evaluate_peft_model(sample)\n", + " if i % 100 == 0:\n", + " print('\\n', p)\n", + " predictions.append(p)\n", + " i += 1" + ], + "metadata": { + "id": "4t0Epw1x8YOG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a93e8654-603c-4353-bef6-04118302cf36" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\r 0%| | 0/10358 [00:00