Move train.py to train_lstm.py and add new transformers training code
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train.py
92
train.py
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@ -1,97 +1,21 @@
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from argparse import ArgumentParser
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from dataset import Dataset
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from model import Model
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from predict import predict
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from datasets import load_dataset
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from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer
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parser = ArgumentParser()
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parser.add_argument('-d', '--device', default='cpu',
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help='device to train with')
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parser.add_argument('-i', '--input', default='data',
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help='training data input file')
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parser.add_argument('-o', '--output', default='model.pt',
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help='trained model output file')
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parser.add_argument('-e', '--epochs', default=10, type=int,
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help='number of epochs to train for')
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parser.add_argument('-s', '--seq-size', default=32, type=int,
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help='sequence size')
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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help='size of each training batch')
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parser.add_argument('-m', '--embedding-dim', default=64, type=int,
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help='size of the embedding')
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parser.add_argument('-l', '--lstm-size', default=256, type=int,
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help='size of the LSTM hidden state')
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parser.add_argument('-a', '--layers', default=3, type=int,
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help='number of LSTM layers')
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parser.add_argument('-r', '--dropout', default=0.2, type=int,
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help='how much dropout to apply')
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parser.add_argument('-n', '--max-norm', default=5, type=int,
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help='maximum norm for gradient clipping')
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args = parser.parse_args()
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# Prepare dataloader
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dataset = Dataset(args.input, args.seq_size)
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dataloader = DataLoader(dataset, args.batch_size)
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print(len(dataloader))
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raw_dataset = load_dataset('text', data_files={'train': args.input}, keep_linebreaks=True)
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tokenizer = AutoTokenizer.from_pretrained('distilgpt2')
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tokenized_dataset = raw_dataset.map(lambda examples : tokenizer(examples['text']), batched=True, remove_columns=raw_dataset["train"].column_names)
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# Prepare model
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device = torch.device(args.device)
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model = Model(dataset, args.embedding_dim, args.lstm_size,
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args.layers, args.dropout).to(device)
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print(model)
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model = AutoModelForCausalLM.from_pretrained('distilgpt2')
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loss_fn = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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for t in range(args.epochs):
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state_h, state_c = model.zero_state(args.batch_size)
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state_h = state_h.to(device)
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state_c = state_c.to(device)
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iteration = 0
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for batch, (X, y) in enumerate(dataloader):
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iteration += 1
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model.train()
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optimizer.zero_grad()
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X = X.to(device)
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y = y.to(device)
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# Compute prediction error
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logits, (state_h, state_c) = model(X, (state_h, state_c))
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loss = loss_fn(logits.transpose(1, 2), y)
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loss_value = loss.item()
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# Backpropogation
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loss.backward()
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state_h = state_h.detach()
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state_c = state_c.detach()
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_ = torch.nn.utils.clip_grad_norm_(
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model.parameters(), args.max_norm)
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optimizer.step()
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if iteration % 1 == 0:
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print('Epoch: {}/{}'.format(t, args.epochs),
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'Iteration: {}'.format(iteration),
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'Loss: {}'.format(loss_value))
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if iteration % 10 == 0:
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print(' '.join(predict(args.device, dataset, model, 'i am')))
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torch.save(model, args.output)
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trainer = Trainer(model=model, train_dataset=tokenized_dataset['train'], tokenizer=tokenizer)
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trainer.train()
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97
train_lstm.py
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97
train_lstm.py
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@ -0,0 +1,97 @@
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from argparse import ArgumentParser
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from dataset import Dataset
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from model import Model
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from predict import predict
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parser = ArgumentParser()
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parser.add_argument('-d', '--device', default='cpu',
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help='device to train with')
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parser.add_argument('-i', '--input', default='data',
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help='training data input file')
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parser.add_argument('-o', '--output', default='model.pt',
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help='trained model output file')
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parser.add_argument('-e', '--epochs', default=10, type=int,
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help='number of epochs to train for')
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parser.add_argument('-s', '--seq-size', default=32, type=int,
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help='sequence size')
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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help='size of each training batch')
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parser.add_argument('-m', '--embedding-dim', default=64, type=int,
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help='size of the embedding')
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parser.add_argument('-l', '--lstm-size', default=256, type=int,
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help='size of the LSTM hidden state')
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parser.add_argument('-a', '--layers', default=3, type=int,
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help='number of LSTM layers')
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parser.add_argument('-r', '--dropout', default=0.2, type=int,
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help='how much dropout to apply')
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parser.add_argument('-n', '--max-norm', default=5, type=int,
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help='maximum norm for gradient clipping')
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args = parser.parse_args()
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# Prepare dataloader
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dataset = Dataset(args.input, args.seq_size)
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dataloader = DataLoader(dataset, args.batch_size)
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print(len(dataloader))
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# Prepare model
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device = torch.device(args.device)
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model = Model(dataset, args.embedding_dim, args.lstm_size,
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args.layers, args.dropout).to(device)
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print(model)
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loss_fn = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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for t in range(args.epochs):
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state_h, state_c = model.zero_state(args.batch_size)
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state_h = state_h.to(device)
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state_c = state_c.to(device)
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iteration = 0
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for batch, (X, y) in enumerate(dataloader):
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iteration += 1
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model.train()
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optimizer.zero_grad()
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X = X.to(device)
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y = y.to(device)
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# Compute prediction error
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logits, (state_h, state_c) = model(X, (state_h, state_c))
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loss = loss_fn(logits.transpose(1, 2), y)
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loss_value = loss.item()
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# Backpropogation
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loss.backward()
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state_h = state_h.detach()
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state_c = state_c.detach()
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_ = torch.nn.utils.clip_grad_norm_(
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model.parameters(), args.max_norm)
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optimizer.step()
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if iteration % 1 == 0:
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print('Epoch: {}/{}'.format(t, args.epochs),
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'Iteration: {}'.format(iteration),
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'Loss: {}'.format(loss_value))
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if iteration % 10 == 0:
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print(' '.join(predict(args.device, dataset, model, 'i am')))
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torch.save(model, args.output)
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