mirror of
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517 lines
19 KiB
Plaintext
517 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "5f93b7d1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"===================================BUG REPORT===================================\n",
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"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
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"For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
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"================================================================================\n",
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"CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
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"CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
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"CUDA SETUP: Detected CUDA version 117\n",
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"CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
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]
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}
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],
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"source": [
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"from transformers import AutoModelForSeq2SeqLM\n",
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"from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType\n",
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"import torch\n",
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"from datasets import load_dataset\n",
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"import os\n",
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"\n",
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"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n",
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"from transformers import AutoTokenizer\n",
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"from torch.utils.data import DataLoader\n",
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"from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
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"from tqdm import tqdm\n",
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"from datasets import load_dataset\n",
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"\n",
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"device = \"cuda\"\n",
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"model_name_or_path = \"t5-large\"\n",
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"tokenizer_name_or_path = \"t5-large\"\n",
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"\n",
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"checkpoint_name = \"financial_sentiment_analysis_prefix_tuning_v1.pt\"\n",
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"text_column = \"sentence\"\n",
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"label_column = \"text_label\"\n",
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"max_length = 128\n",
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"lr = 1e-2\n",
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"num_epochs = 5\n",
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"batch_size = 8"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d0850ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"# creating model\n",
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"peft_config = PrefixTuningConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, num_virtual_tokens=20)\n",
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"\n",
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"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
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"model = get_peft_model(model, peft_config)\n",
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"model.print_trainable_parameters()\n",
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4ee2babf",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Found cached dataset financial_phrasebank (/home/sourab/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ec4be98991b84181bfa75f8846422b8b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "82a6bd694c4f4751a23c370ab51f01a4",
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"version_major": 2,
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"version_minor": 0
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "3844878631534468a1495e435563e4b0",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"{'sentence': 'Finnish elevators and escalators maker KONE Corporation said on Tuesday ( 18 March ) that it has received a major order from Sir Robert McAlpine to supply all elevators and escalators for the Watermark Place project in the City of London .',\n",
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" 'label': 2,\n",
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" 'text_label': 'positive'}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# loading dataset\n",
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"dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
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"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
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"dataset[\"validation\"] = dataset[\"test\"]\n",
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"del dataset[\"test\"]\n",
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"\n",
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"classes = dataset[\"train\"].features[\"label\"].names\n",
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"dataset = dataset.map(\n",
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" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
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" batched=True,\n",
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" num_proc=1,\n",
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")\n",
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"\n",
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"dataset[\"train\"][0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "adf9608c",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/sourab/transformers/src/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
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"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
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"- Be aware that you SHOULD NOT rely on t5-large automatically truncating your input to 512 when padding/encoding.\n",
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"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
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"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "4af8c12efb5643659573347509079f3a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset: 0%| | 0/3 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "86033b6257384584afd034075af808cb",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset: 0%| | 0/1 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# data preprocessing\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
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"\n",
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"\n",
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"def preprocess_function(examples):\n",
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" inputs = examples[text_column]\n",
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" targets = examples[label_column]\n",
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" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
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" labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
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" labels = labels[\"input_ids\"]\n",
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" labels[labels == tokenizer.pad_token_id] = -100\n",
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" model_inputs[\"labels\"] = labels\n",
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" return model_inputs\n",
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"\n",
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"\n",
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"processed_datasets = dataset.map(\n",
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" preprocess_function,\n",
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" batched=True,\n",
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" num_proc=1,\n",
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" remove_columns=dataset[\"train\"].column_names,\n",
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" load_from_cache_file=False,\n",
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" desc=\"Running tokenizer on dataset\",\n",
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")\n",
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"\n",
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"train_dataset = processed_datasets[\"train\"]\n",
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"eval_dataset = processed_datasets[\"validation\"]\n",
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"\n",
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"train_dataloader = DataLoader(\n",
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" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
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")\n",
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"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "f733a3c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# optimizer and lr scheduler\n",
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"optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
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"lr_scheduler = get_linear_schedule_with_warmup(\n",
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" optimizer=optimizer,\n",
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" num_warmup_steps=0,\n",
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" num_training_steps=(len(train_dataloader) * num_epochs),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6b3a4090",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch=0: train_ppl=tensor(2760654.5000, device='cuda:0') train_epoch_loss=tensor(14.8310, device='cuda:0') eval_ppl=tensor(1.0124, device='cuda:0') eval_epoch_loss=tensor(0.0124, device='cuda:0')\n"
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"text": [
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"epoch=1: train_ppl=tensor(2.7329, device='cuda:0') train_epoch_loss=tensor(1.0054, device='cuda:0') eval_ppl=tensor(1.0081, device='cuda:0') eval_epoch_loss=tensor(0.0080, device='cuda:0')\n"
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"output_type": "stream",
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"text": [
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"epoch=2: train_ppl=tensor(2.1698, device='cuda:0') train_epoch_loss=tensor(0.7747, device='cuda:0') eval_ppl=tensor(1.0057, device='cuda:0') eval_epoch_loss=tensor(0.0057, device='cuda:0')\n"
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]
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"output_type": "stream",
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"text": [
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"epoch=3: train_ppl=tensor(2.0724, device='cuda:0') train_epoch_loss=tensor(0.7287, device='cuda:0') eval_ppl=tensor(1.0051, device='cuda:0') eval_epoch_loss=tensor(0.0051, device='cuda:0')\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|████████████████████████████████████████████████████████████████████████████████████████| 255/255 [01:02<00:00, 4.10it/s]\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch=4: train_ppl=tensor(1.7598, device='cuda:0') train_epoch_loss=tensor(0.5652, device='cuda:0') eval_ppl=tensor(1.0047, device='cuda:0') eval_epoch_loss=tensor(0.0047, device='cuda:0')\n"
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]
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}
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],
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"source": [
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"# training and evaluation\n",
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"model = model.to(device)\n",
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"\n",
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"for epoch in range(num_epochs):\n",
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" model.train()\n",
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" total_loss = 0\n",
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" for step, batch in enumerate(tqdm(train_dataloader)):\n",
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" batch = {k: v.to(device) for k, v in batch.items()}\n",
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" outputs = model(**batch)\n",
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" loss = outputs.loss\n",
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" total_loss += loss.detach().float()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" lr_scheduler.step()\n",
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" optimizer.zero_grad()\n",
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"\n",
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" model.eval()\n",
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" eval_loss = 0\n",
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" eval_preds = []\n",
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" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
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" batch = {k: v.to(device) for k, v in batch.items()}\n",
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" with torch.no_grad():\n",
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" outputs = model(**batch)\n",
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" loss = outputs.loss\n",
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" eval_loss += loss.detach().float()\n",
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" eval_preds.extend(\n",
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" tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
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" )\n",
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"\n",
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" eval_epoch_loss = eval_loss / len(eval_dataloader)\n",
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" eval_ppl = torch.exp(eval_epoch_loss)\n",
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" train_epoch_loss = total_loss / len(train_dataloader)\n",
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" train_ppl = torch.exp(train_epoch_loss)\n",
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" print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "6cafa67b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"accuracy=96.91629955947137 % on the evaluation dataset\n",
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"eval_preds[:10]=['negative', 'positive', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral']\n",
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"dataset['validation']['text_label'][:10]=['negative', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral']\n"
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]
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}
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],
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"source": [
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"# print accuracy\n",
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"correct = 0\n",
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"total = 0\n",
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"for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
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" if pred.strip() == true.strip():\n",
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" correct += 1\n",
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" total += 1\n",
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"accuracy = correct / total * 100\n",
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"print(f\"{accuracy=} % on the evaluation dataset\")\n",
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"print(f\"{eval_preds[:10]=}\")\n",
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"print(f\"{dataset['validation']['text_label'][:10]=}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a8de6005",
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"metadata": {},
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"outputs": [],
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"source": [
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"# saving model\n",
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"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
|
|
"model.save_pretrained(peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "bd20cd4c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"3,8M\tt5-large_PREFIX_TUNING_SEQ_2_SEQ_LM/adapter_model.bin\r\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ckpt = f\"{peft_model_id}/adapter_model.bin\"\n",
|
|
"!du -h $ckpt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "76c2fc29",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from peft import PeftModel, PeftConfig\n",
|
|
"\n",
|
|
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
|
|
"\n",
|
|
"config = PeftConfig.from_pretrained(peft_model_id)\n",
|
|
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
|
|
"model = PeftModel.from_pretrained(model, peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "d997f1cc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Acando AB ( ACANB SS ) fell 8.9 percent to 13.35 kronor , the lowest close since Dec. 11 .\n",
|
|
"{'input_ids': tensor([[ 4292, 232, 32, 3, 5359, 41, 3, 22029, 14972, 3,\n",
|
|
" 4256, 3, 61, 4728, 4848, 1298, 1093, 12, 8808, 2469,\n",
|
|
" 3, 22318, 29, 127, 3, 6, 8, 7402, 885, 437,\n",
|
|
" 4451, 5, 850, 3, 5, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
|
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
|
|
"tensor([[ 0, 2841, 1]])\n",
|
|
"['negative']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.eval()\n",
|
|
"i = 107\n",
|
|
"inputs = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"pt\")\n",
|
|
"print(dataset[\"validation\"][text_column][i])\n",
|
|
"print(inputs)\n",
|
|
"\n",
|
|
"with torch.no_grad():\n",
|
|
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
|
|
" print(outputs)\n",
|
|
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fb746c1e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.5"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|