diff --git a/api.py b/api.py new file mode 100644 index 0000000..c1204b8 --- /dev/null +++ b/api.py @@ -0,0 +1,48 @@ +import torch +import uvicorn +from fastapi import FastAPI +from pydantic import BaseModel +from transformers import MT5Tokenizer,AutoTokenizer, AutoModel ,T5ForConditionalGeneration +import warnings +import json +import random +import torch.nn.functional as F +#from ece import compute_ECE +from torch.utils.data import DataLoader +from functools import reduce + +warnings.filterwarnings("ignore") +DEVICE ='cpu' +model_dir = "C:/Users/david/Desktop/T5_JUPYTER/qa_model" +tokenizer_dir = "C:/Users/david/Desktop/T5_JUPYTER/qa_tokenizer" +MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE) +print("Model succesfully loaded!") +TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=True) +print("Tokenizer succesfully loaded!") +Q_LEN = 512 +TOKENIZER.add_tokens('') + + +print('model loaded') + +app = FastAPI() + +# BASE MODEL +class InputData(BaseModel): + context: str + question: str + +@app.post("/predict") +async def predict(input_data: InputData): + inputs = TOKENIZER(input_data.question, input_data.context, max_length=512, padding="max_length", truncation=True, add_special_tokens=True) + input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0) + attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0) + outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask, return_dict_in_generate=True,output_scores=True,max_length=512) + predicted_ids = outputs.sequences.numpy() + predicted_text = TOKENIZER.decode(predicted_ids[0], skip_special_tokens=True) + + return {'prediction':predicted_text} + + +if __name__ == "__main__": + uvicorn.run(app, host="127.0.0.1", port=8090) \ No newline at end of file diff --git a/aplication.py b/aplication.py new file mode 100644 index 0000000..95ee556 --- /dev/null +++ b/aplication.py @@ -0,0 +1,33 @@ +import requests +import json +import streamlit as st + +def predict(context,question): + url = 'http://localhost:8090/predict' + data = {'context': context,'question': question} + json_data = json.dumps(data) + headers = {'Content-type': 'application/json'} + response = requests.post(url, data=json_data, headers=headers) + result = response.json() + return result + +def main(): + st.title("T5 model inference") + + # Vytvoríme polia pre zadanie hodnôt + context = st.text_input("context:") + question = st.text_input("question:") + prediction = predict(context,question) + # Vytvoríme tlačidlo pre vykonanie akcie + if st.button("Execute"): + + st.json({ + 'context': context, + 'question': question, + 'prediciton':prediction + }) + + + +if __name__ == "__main__": + main() diff --git a/condadiplo.yaml b/condadiplo.yaml new file mode 100644 index 0000000..2a1c319 --- /dev/null +++ b/condadiplo.yaml @@ -0,0 +1,91 @@ +name: DIPLOcondaEnviroment +channels: + - defaults +dependencies: + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.01.10=haa95532_0 + - libffi=3.4.2=hd77b12b_6 + - openssl=1.1.1t=h2bbff1b_0 + - pip=23.0.1=py311haa95532_0 + - python=3.11.2=h966fe2a_0 + - setuptools=66.0.0=py311haa95532_0 + - sqlite=3.41.2=h2bbff1b_0 + - tk=8.6.12=h2bbff1b_0 + - vc=14.2=h21ff451_1 + - vs2015_runtime=14.27.29016=h5e58377_2 + - wheel=0.38.4=py311haa95532_0 + - xz=5.2.10=h8cc25b3_1 + - zlib=1.2.13=h8cc25b3_0 + - pip: + - altair==4.2.2 + - anyio==3.6.2 + - attrs==23.1.0 + - blinker==1.6.2 + - cachetools==5.3.0 + - certifi==2022.12.7 + - charset-normalizer==3.1.0 + - click==8.1.3 + - colorama==0.4.6 + - comtypes==1.1.14 + - decorator==5.1.1 + - entrypoints==0.4 + - fastapi==0.92.0 + - filelock==3.11.0 + - gitdb==4.0.10 + - gitpython==3.1.31 + - h11==0.14.0 + - huggingface-hub==0.12.1 + - idna==3.4 + - importlib-metadata==6.4.1 + - jinja2==3.1.2 + - jsonschema==4.17.3 + - markdown-it-py==2.2.0 + - markupsafe==2.1.2 + - mdurl==0.1.2 + - mouseinfo==0.1.3 + - mpmath==1.3.0 + - networkx==3.1 + - numpy==1.24.2 + - packaging==23.1 + - pandas==1.5.3 + - pillow==9.5.0 + - protobuf==3.20.3 + - pyarrow==11.0.0 + - pydantic==1.10.7 + - pydeck==0.8.1b0 + - pygments==2.15.0 + - pympler==1.0.1 + - pyperclip==1.8.2 + - pyrsistent==0.19.3 + - python-dateutil==2.8.2 + - pytz==2023.3 + - pytz-deprecation-shim==0.1.0.post0 + - pywin32==305 + - pywinauto==0.6.8 + - pyyaml==6.0 + - regex==2023.3.23 + - requests==2.28.2 + - rich==13.3.4 + - six==1.16.0 + - smmap==5.0.0 + - sniffio==1.3.0 + - starlette==0.25.0 + - streamlit==1.21.0 + - sympy==1.11.1 + - tokenizers==0.13.2 + - toml==0.10.2 + - toolz==0.12.0 + - torch==2.0.0 + - torchaudio==2.0.1 + - torchvision==0.15.1 + - tornado==6.2 + - tqdm==4.65.0 + - transformers==4.26.1 + - typing-extensions==4.5.0 + - tzdata==2023.3 + - tzlocal==4.3 + - urllib3==1.26.15 + - uvicorn==0.20.0 + - validators==0.20.0 + - watchdog==3.0.0 + - zipp==3.15.0 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..69054aa --- /dev/null +++ b/requirements.txt @@ -0,0 +1,72 @@ +altair==4.2.2 +anyio==3.6.2 +attrs==23.1.0 +blinker==1.6.2 +cachetools==5.3.0 +certifi==2022.12.7 +charset-normalizer==3.1.0 +click==8.1.3 +colorama==0.4.6 +comtypes==1.1.14 +decorator==5.1.1 +entrypoints==0.4 +fastapi==0.92.0 +filelock==3.11.0 +gitdb==4.0.10 +GitPython==3.1.31 +h11==0.14.0 +huggingface-hub==0.12.1 +idna==3.4 +importlib-metadata==6.4.1 +Jinja2==3.1.2 +jsonschema==4.17.3 +markdown-it-py==2.2.0 +MarkupSafe==2.1.2 +mdurl==0.1.2 +MouseInfo==0.1.3 +mpmath==1.3.0 +networkx==3.1 +numpy==1.24.2 +packaging==23.1 +pandas==1.5.3 +Pillow==9.5.0 +protobuf==3.20.3 +pyarrow==11.0.0 +pydantic==1.10.7 +pydeck==0.8.1b0 +Pygments==2.15.0 +Pympler==1.0.1 +pyperclip==1.8.2 +pyrsistent==0.19.3 +python-dateutil==2.8.2 +pytz==2023.3 +pytz-deprecation-shim==0.1.0.post0 +pywin32==305 +pywinauto==0.6.8 +PyYAML==6.0 +regex==2023.3.23 +requests==2.28.2 +rich==13.3.4 +six==1.16.0 +smmap==5.0.0 +sniffio==1.3.0 +starlette==0.25.0 +streamlit==1.21.0 +sympy==1.11.1 +tokenizers==0.13.2 +toml==0.10.2 +toolz==0.12.0 +torch==2.0.0 +torchaudio==2.0.1 +torchvision==0.15.1 +tornado==6.2 +tqdm==4.65.0 +transformers==4.26.1 +typing_extensions==4.5.0 +tzdata==2023.3 +tzlocal==4.3 +urllib3==1.26.15 +uvicorn==0.20.0 +validators==0.20.0 +watchdog==3.0.0 +zipp==3.15.0 diff --git a/train.py b/train.py new file mode 100644 index 0000000..2429684 --- /dev/null +++ b/train.py @@ -0,0 +1,175 @@ +import torch +import json +from tqdm import tqdm +import torch.nn as nn +from torch.optim import Adam +import nltk +import spacy +import string +import evaluate # Bleu +from torch.utils.data import Dataset, DataLoader, RandomSampler +import pandas as pd +import numpy as np +import transformers +from sklearn.model_selection import train_test_split +import matplotlib.pyplot as plt +#from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration, T5TokenizerFast +from transformers import AutoTokenizer, T5ForConditionalGeneration +import warnings +warnings.filterwarnings("ignore") + +print("Imports succesfully done") + +DEVICE ='cuda:0' +#TOKENIZER = AutoTokenizer.from_pretrained("ApoTro/slovak-t5-small") +TOKENIZER=AutoTokenizer.from_pretrained('google/mt5-small') +#TOKENIZER=AutoTokenizer.from_pretrained('google/mt5-base') +TOKENIZER.add_tokens('') +#MODEL = T5ForConditionalGeneration.from_pretrained("ApoTro/slovak-t5-small").to(DEVICE) +MODEL = T5ForConditionalGeneration.from_pretrained("google/mt5-small").to(DEVICE) +#MODEL = T5ForConditionalGeneration.from_pretrained("google/mt5-base").to(DEVICE) + +#pridam token +MODEL.resize_token_embeddings(len(TOKENIZER)) +#lr = learning rate = 10-5 +OPTIMIZER = Adam(MODEL.parameters(), lr=0.00001) +Q_LEN = 256 # Question Length +T_LEN = 32 # Target Length +BATCH_SIZE = 4 #dávka dát +print("Model succesfully loaded") + +path_train = '/home/omasta/T5_JUPYTER/skquad-221017/train-v1.json' + +with open(path_train) as f: + data = json.load(f) + + +def prepare_data(data): + articles = [] + for article in data["data"]: + for paragraph in article["paragraphs"]: + for qa in paragraph["qas"]: + question = qa["question"] + + answer = qa["answers"][0]["text"] + #input_ = 'generuj_odpoved : ' + paragraph["context"] + ' ' + question + ' ' + + inputs = {"input": paragraph["context"]+''+question, "answer": answer} + #inputs = {'context': input_ ,'answer':answer} + + articles.append(inputs) + + return articles + +prepared_data = prepare_data(data) +print(prepared_data[0]) + +#Dataframe +data = pd.DataFrame(prepared_data) + + + +class QA_Dataset(Dataset): + def __init__(self, tokenizer, dataframe, q_len, t_len): + self.tokenizer = tokenizer + self.q_len = q_len + self.t_len = t_len + self.data = dataframe + self.input = self.data['input'] + #self.context = self.data["context"] + self.answer = self.data['answer'] + def __len__(self): + return len(self.questions) + + def __getitem__(self, idx): + input = self.input[idx] + answer = self.answer[idx] + + input_tokenized = self.tokenizer(input, max_length=self.q_len, padding="max_length", + truncation=True, pad_to_max_length=True, add_special_tokens=True) + answer_tokenized = self.tokenizer(answer, max_length=self.t_len, padding="max_length", + truncation=True, pad_to_max_length=True, add_special_tokens=True) + + labels = torch.tensor(answer_tokenized["input_ids"], dtype=torch.long) + labels[labels == 0] = -100 + + return { + "input_ids": torch.tensor(input_tokenized["input_ids"], dtype=torch.long), + "attention_mask": torch.tensor(input_tokenized["attention_mask"], dtype=torch.long), + "labels": labels, + "decoder_attention_mask": torch.tensor(answer_tokenized["attention_mask"], dtype=torch.long) + } + + + +##DATA LOADERS + +train_data, val_data = train_test_split(data, test_size=0.2, random_state=42) +train_sampler = RandomSampler(train_data.index) +val_sampler = RandomSampler(val_data.index) +qa_dataset = QA_Dataset(TOKENIZER, data, Q_LEN, T_LEN) + +train_loader = DataLoader(qa_dataset, batch_size=BATCH_SIZE, sampler=train_sampler) +val_loader = DataLoader(qa_dataset, batch_size=BATCH_SIZE, sampler=val_sampler) +print("Loaders working fine") + + + + +### TRAINING (46MINS ACCORDING THE V1_DATA) + + +train_loss = 0 +val_loss = 0 +train_batch_count = 0 +val_batch_count = 0 + +for epoch in range(4): + MODEL.train() + for batch in tqdm(train_loader, desc="Training batches"): + input_ids = batch["input_ids"].to(DEVICE) + attention_mask = batch["attention_mask"].to(DEVICE) + labels = batch["labels"].to(DEVICE) + decoder_attention_mask = batch["decoder_attention_mask"].to(DEVICE) + + outputs = MODEL( + input_ids=input_ids, + attention_mask=attention_mask, + labels=labels, + decoder_attention_mask=decoder_attention_mask + ) + + OPTIMIZER.zero_grad() + outputs.loss.backward() + OPTIMIZER.step() + train_loss += outputs.loss.item() + train_batch_count += 1 + + #Evaluation + MODEL.eval() + for batch in tqdm(val_loader, desc="Validation batches"): + input_ids = batch["input_ids"].to(DEVICE) + attention_mask = batch["attention_mask"].to(DEVICE) + labels = batch["labels"].to(DEVICE) + decoder_attention_mask = batch["decoder_attention_mask"].to(DEVICE) + + outputs = MODEL( + input_ids=input_ids, + attention_mask=attention_mask, + labels=labels, + decoder_attention_mask=decoder_attention_mask + ) + + OPTIMIZER.zero_grad() + outputs.loss.backward() + OPTIMIZER.step() + val_loss += outputs.loss.item() + val_batch_count += 1 + print(f"{epoch+1}/{2} -> Train loss: {train_loss / train_batch_count}\tValidation loss: {val_loss/val_batch_count}") + + +print("Training done succesfully") + +## SAVE FINE_TUNED MODEL +MODEL.save_pretrained("qa_model_mT5_small") +TOKENIZER.save_pretrained('qa_tokenizer_mT5_small') diff --git a/usecase.py b/usecase.py new file mode 100644 index 0000000..1a32a39 --- /dev/null +++ b/usecase.py @@ -0,0 +1,139 @@ +## IMPORT NESSESARY EQUIPMENTS +from transformers import T5ForConditionalGeneration, T5Tokenizer,AutoTokenizer +import torch +import evaluate # Bleu +import json +import random +import statistics +from sklearn.metrics import precision_score, recall_score, f1_score +## TURN WARNINGS OFF +import warnings +warnings.filterwarnings("ignore") +##13/03/23 added +from rouge import Rouge + +# Názov modelu +DEVICE ='cuda:0' + + +#T5 MODEL +#model_name = 'T5_SK_model' +#model_dir = "/home/omasta/T5_JUPYTER/qa_model" +#tokenizer_dir = "/home/omasta/T5_JUPYTER/qa_tokenizer" + +#mT5 SMALL MODEL +model_name = 'mT5_SMALL' +model_dir = '/home/omasta/T5_JUPYTER/qa_model_mT5_small' +tokenizer_dir = '/home/omasta/T5_JUPYTER/qa_tokenizer_mT5_small' + +#Načítanie modelu z adresára +MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE) +print("Model succesfully loaded!") +TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=True) +print("Tokenizer succesfully loaded!") +Q_LEN = 512 +TOKENIZER.add_tokens('') +MODEL.resize_token_embeddings(len(TOKENIZER)) + +def predict_answer(data, ref_answer=None,random=None): + predictions=[] + for i in data: + inputs = TOKENIZER(i['input'], max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True) + input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0) + attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0) + outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask) + predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True) + ref_answer = i['answer'].lower() + #print(ref_answer) + if ref_answer: + # Load the Bleu metric + bleu = evaluate.load("google_bleu") + #print('debug') + #precision = list(precision_score(ref_answer, predicted_answer)) + #recall = list(recall_score(ref_answer, predicted_answer)) + #f1 = list(f1_score(ref_answer, predicted_answer)) + score = bleu.compute(predictions=[predicted_answer], + references=[ref_answer]) + predictions.append({'prediction':predicted_answer,'ref_answer':ref_answer,'score':score['google_bleu']}) + return predictions + +def prepare_data(data): + articles = [] + for article in data["data"]: + for paragraph in article["paragraphs"]: + for qa in paragraph["qas"]: + question = qa["question"] + answer = qa["answers"][0]["text"] + inputs = {"input": paragraph["context"]+ "" + question, "answer": answer} + articles.append(inputs) + + return articles + +dev_data_path = '/home/omasta/T5_JUPYTER/skquad-221017/dev-v1.json' +with open(dev_data_path,'r') as f: + data=json.load(f) +#print('data imported') + +dev_data = prepare_data(data) + +#print('data prepared') +print(f'Number of dev samples {len(dev_data)}') +print(dev_data[0]) +bleu_score = [] +precisions=[] +f1_scores=[] +recall_scores=[] +rouge_1 = [] +rouge_2 = [] +#X = 150 +evaluate = predict_answer(dev_data) +rouge = Rouge() +for item in evaluate: + bleu_score.append(item['score']) + try: + #scores = rouge.get_scores(item['prediction'], item['ref_answer'], avg=True) + precision=precision_score(list(item['ref_answer']), list(item['prediction']),average='macro') + recall=recall_score(list(item['ref_answer']), list(item['prediction']),average='macro') + f1=f1_score(list(item['ref_answer']), list(item['prediction']),average='macro') + except ValueError: + precision=0 + recall=0 + f1=0 + precisions.append(precision) + f1_scores.append(f1) + recall_scores.append(recall) + + +def rouge_eval(dict_x): + rouge = Rouge() + rouge_scores=[] + for item in dict_x: + if item['prediction'] and item['ref_answer']: + rouge_score = rouge.get_scores(item['prediction'], item['ref_answer']) + rouge_scores.append(rouge_score) + else: + continue + return rouge_scores + + +print(f'VYHODNOTENIE VYSLEDKOV : ------------------------') +#print(evaluate) +#bleu_score_total = statistics.mean(bleu_score) +#recall_score_total= statistics.mean(recall_scores) +#f1_score_total = statistics.mean(f1_scores) +#precision_total = statistics.mean(precisions) +#print(f'Bleu_score of model {model_name} : ',bleu_score_total) +#print(f'Recall of model {model_name}: ',recall_score_total) +#print(f'F1 of model {model_name} : ', f1_score_total) +#print(f'Precision of model {model_name}: :',precision_total) +#print(rouge_eval(evaluate)) +print(f'{model_name} results') +rouge_scores = rouge_eval(evaluate) +rouge_values = [score[0]['rouge-1']['f'] for score in rouge_scores] +mean_rouge_score = statistics.mean(rouge_values) +print(f'Rouge:{mean_rouge_score}') + +rouge2_values = [score[0]['rouge-2']['f'] for score in rouge_scores] +mean_rouge_score =statistics.mean(rouge2_values) +print(f'Rouge-2:{mean_rouge_score}') +