Revert "Added weekly report"

This reverts commit 146717f4.

-# -*- coding: utf-8 -*-
-import argparse
-import os
-import glob
-
-import gluonnlp as nlp
-import torch
-from torch.utils.data import DataLoader, Dataset
-from gluonnlp.data import SentencepieceTokenizer
-from kogpt2.pytorch_kogpt2 import get_pytorch_kogpt2_model
-from kogpt2.utils import get_tokenizer
-from tqdm import tqdm
-from util.data_loader import ArticleDataset, ToTensor
-
-if __name__ == "__main__":
-    ctx='cuda' if torch.cuda.is_available() else 'cpu'
-    device=torch.device(ctx)
-    tokenizer_path = get_tokenizer(cachedir='/code/model')
-    model, vocab = get_pytorch_kogpt2_model(ctx=ctx,cachedir='/code/model')
-    tokenizer = SentencepieceTokenizer(tokenizer_path,  num_best=0, alpha=0)
-    num_workers=0
-    padding_id=vocab[vocab.padding_token]
-
-    transform=ToTensor(tokenizer,vocab)
-    print("Preparing dataloader...")
-    trainset=DataLoader(ArticleDataset('/dataset',label='train', transform=transform),batch_size=64, num_workers=0,shuffle=True)
-    validset=DataLoader(ArticleDataset('/dataset',label='valid', transform=transform),batch_size=64, num_workers=0)
-    #testset=DataLoader(ArticleDataset('/dataset',label='test', transform=transform),batch_size=128, num_workers=4)
-    print("Prepared dataloader.")
-    epoches=200
-    checkpoint_epoch=0
-    learning_rate = 3e-5
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-    
-    save_path='/model/save'
-    saves=glob.glob(save_path+'*.state')
-    if len(saves)>0:
-        last_save=max(saves,key=os.path.getmtime)
-        checkpoint = torch.load(last_save)
-        print(f"Loading save from {last_save}")
-        model.load_state_dict(checkpoint['model_state_dict'])
-        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-        checkpoint_epoch = checkpoint['epoch']
-        loss = checkpoint['loss']
-    else:
-        print("No save exists.")
-
-
-    model.to(device)
-    model.train()
-
-    last_valid_loss=float('infinity')
-    for epoch in tqdm(range(checkpoint_epoch,epoches)):
-        train_loss_list=[]
-        valid_loss_list=[]
-        for data in tqdm(trainset):
-            optimizer.zero_grad()
-            data = data.to(ctx)
-            label = torch.where(data!=padding_id, data, torch.ones_like(data)*-100)
-            mask = torch.where(data!=padding_id,torch.ones_like(data),torch.zeros_like(data))
-            output=model(data, labels=label, attention_mask=mask)
-            loss, logits=output[0], output[1]
-            #loss = loss.to(ctx)
-            loss.backward()
-            optimizer.step()
-            train_loss_list.append(loss.item())
-        with torch.no_grad():
-            
-            for v_data in tqdm(validset):
-                v_data = v_data.to(ctx)
-                v_label = torch.where(data!=padding_id, v_data, torch.ones_like(v_data)*-100)
-                v_mask = torch.where(v_data!=padding_id,torch.ones_like(v_data),torch.zeros_like(v_data))
-                v_output=model(v_data,labels=v_label, attention_mask=v_mask)
-                v_loss, v_logits=v_output[0], v_output[1]
-                valid_loss_list.append(v_loss.item())
-        valid_loss=sum(valid_loss_list)/len(valid_loss_list)
-        print(f"epoch: {epoch} train loss: {sum(train_loss_list)/len(train_loss_list)} valid loss: {valid_loss}")
-        if valid_loss>last_valid_loss or (epoch%10==9):
-            try:
-                    torch.save({
-                            'epoch': epoch,
-                            'train_no': i,
-                            'model_state_dict': model.state_dict(),
-                            'optimizer_state_dict': optimizer.state_dict(),
-                            'loss': loss
-                        }, f"{save_path}KoGPT2_checkpoint_{ctx}{i}.state")
-            except Exception as e:
-                print(e)
-        last_valid_loss=valid_loss
-        if epoch==checkpoint_epoch: # Must run entire epoch first with num_worker=0 to fully cache dataset.
-            trainset.dataset.set_use_cache(True)
-            trainset.num_workers=num_workers
-            validset.dataset.set_use_cache(True)
-            validset.num_workers=num_workers
-            
-
-            

-과학
-100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3361/3361 [00:16<00:00, 197.95it/s]
-count[256]: 30451/215067 (%)
-count[512]: 137611/215067 (%)
-count[768]: 185856/215067 (%)
-count[1024]: 205300/215067 (%) --더 이상은 모델 한계로 불가능
-count[1280]: 211386/215067 (%)
-count[1536]: 213877/215067 (%)
-count[1792]: 214932/215067 (%)
-전체
-100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 53825/53825 [04:19<00:00, 207.39it/s]
-count[256]: 421097/3444755 (12.2%)
-count[512]: 2110517/3444755 (61.2%)
-count[768]: 2927091/3444755 (84.9%)
-count[1024]: 3242747/3444755 (94.1%) --더 이상은 모델 한계로 불가능
-count[1280]: 3355523/3444755 (97.4%)
-count[1536]: 3410390/3444755 (99.0%)
-count[1792]: 3437609/3444755 (99.7%)