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import torchmetrics.classification
from torchmetrics.text import ROUGEScore
from model import build_transformer
from shakespeare_config import (get_config,
get_data_folder_path,
get_weights_file_path,
latest_weights_file_path,
current_directory,
causal_mask,
get_gpt2_tokenizer)
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import torchmetrics
import numpy as np
import warnings
import os
from pathlib import Path
def get_model(config):
model = build_transformer(vocab_size=config['vocab_size'],
seq_len=config['seq_len'],
d_model=config['d_model'])
return model
def get_batch(split, data_dir, block_size, batch_size, device='gpu', device_type='cuda'):
# We recreate np.memmap every batch to avoid a memory leak, as per
# https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122
if split == 'train':
data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
else:
data = np.memmap(os.path.join(data_dir, 'test.bin'), dtype=np.uint16, mode='r')
ix = torch.randint(len(data) - block_size, (batch_size,))
x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
# if device_type == 'cuda':
# # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
# x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
# else:
# x, y = x.to(device), y.to(device)
return x, y
def greedy_decode(model,
input,
mask,
tokenizer,
max_len,
device):
while True:
if input.size(1) == max_len:
break
out = model.decode(input, mask)
prob = model.project(out[:, -1])
_, next_word = torch.max(prob, dim=1)
input = torch.cat(
[input, torch.empty(1,1).type_as(input).fill_(next_word.item()).to(device)],
dim=1
)
if next_word == tokenizer.eos_token_id:
break
return input.squeeze(0)
def run_validation(model,
x,
y,
tokenizer,
max_len,
device,
print_msg,
global_step,
writer,
rouge:ROUGEScore):
model.eval()
source_texts = []
expected = []
predicted = []
with torch.no_grad():
decoder_input = x.to(device) # (b, seq)
mask = causal_mask(x.size(1)).to(device) #(b,1,1,seq)
# check that batch size is 1
assert decoder_input.size(0)==1, "batch size must be 1 for validation"
model_out = greedy_decode(model,
decoder_input,
mask,
tokenizer,
max_len,
device)
source_text = tokenizer.decode(x[0])
target_text = tokenizer.decode(y[0])
model_out_text = tokenizer.decode(model_out.detach().cpu().numpy())
source_texts.append(source_text)
expected.append(target_text)
predicted.append(model_out_text)
# Print the source, target and model output
print_msg('-'*100)
print_msg(f"{f'SOURCE: ':>12}{source_text}")
print_msg(f"{f'TARGET: ':>12}{target_text}")
print_msg(f"{f'PREDICTED: ':>12}{model_out_text}")
rouge_score = rouge(predicted, expected)
print_msg(f"{f'ROUGE-1 Score: ':>12}{rouge_score['rouge1_fmeasure'].item()}")
print_msg(f"{f'ROUGE-2 Score: ':>12}{rouge_score['rouge2_fmeasure'].item()}")
print_msg(f"{f'ROUGE-L Score: ':>12}{rouge_score['rougeL_fmeasure'].item()}")
print_msg('-'*100)
if writer:
writer.add_scalar('validation ROUGE/ROUGE-1', rouge_score["rouge1_fmeasure"].item(), global_step)
writer.add_scalar('validation ROUGE/ROUGE-2', rouge_score["rouge2_fmeasure"].item(), global_step)
writer.add_scalar('validation ROUGE/ROUGE-L', rouge_score["rougeL_fmeasure"].item(), global_step)
writer.add_scalar('validation ROUGE/ROUGE-L', rouge_score["rougeLsum_fmeasure"].item(), global_step)
writer.flush()
def train_model(config):
# define the device
device = "cuda" if torch.cuda.is_available() else "mps" if torch.has_ms or torch.backends.mps.is_available else "cpu"
print("Using device:", device)
if (device == 'cuda'):
print(f"Device name: {torch.cuda.get_device_name(device=device.index)}")
print(f"Device memory: {torch.cuda.get_device_properties(device.index).total_memory / 1024 ** 3} GB")
elif (device == 'cpu'):
print(f"device name: <mps>")
else:
print("It's cpu")
device = torch.device(device)
# make sure the weights folder exists
Path(f"{current_directory}/{config['datasource']}_{config['model_folder']}").mkdir(parents=True, exist_ok=True)
tokenizer = get_gpt2_tokenizer(config=config)
model = get_model(config).to(device)
# tensorboard
writer = SummaryWriter(f"{current_directory}/{config['experiment_name']}")
optimizer = torch.optim.Adam(model.parameters(),
lr=config['lr'],
eps=1e-9)
rouge:ROUGEScore = ROUGEScore()
# if the user specified a model to preload before training, load it
initial_epoch = 0
global_step = 0
preload = config['preload']
model_filename = (latest_weights_file_path(config) if preload == 'latest' else get_weights_file_path(config, preload) if preload else None)
if model_filename:
print(f'Preloading model {model_filename}')
state = torch.load(model_filename)
model.load_state_dict(state['model_state_dict'])
initial_epoch = state['epoch'] + 1
optimizer.load_state_dict(state['optimizer_state_dict'])
global_step = state['global_step']
else:
print('No model to preload, starting from scratch')
loss_fn = nn.CrossEntropyLoss(ignore_index=tokenizer.convert_tokens_to_ids('[PAD]'), label_smoothing=0.1).to(device)
for epoch in range(initial_epoch, config['num_epochs']):
torch.cuda.empty_cache()
model.train()
#batch_iterator = tqdm(train_dataloader, desc=f"Processing Epoch {epoch:02d}")
X, y = get_batch(split='train',
data_dir=get_data_folder_path(config=config),
block_size=config['seq_len'],
batch_size=config['batch_size'])
print(f'length of the batch: {len(X)}, type:{X.shape}')
decoder_input = X.to(device) # (b, seq_len)
decoder_mask = causal_mask(config['seq_len']).to(device) # (1, seq_len, seq_len)
# run the tensors through the encoder, decoder and the projection layer
decoder_output = model.decode(decoder_input, decoder_mask) # (b, seq, d_model)
proj_output = model.project(decoder_output) # (B, seq_len, vocab_size)
# compare the output with the label
label = y.to(device) #(b, seq_len)
# compute the loss using a simple cross entropy
loss = loss_fn(proj_output.view(-1, config['vocab_size']),
label.view(-1))
#batch_iterator.set_postfix({"loss": f"{loss.item():6.3f}"})
print(f"loss: {loss.item():6.3f}")
# log the loss
writer.add_scalar('train loss', loss.item(), global_step)
writer.flush()
# backpropagate the loss
loss.backward()
# update the weights
optimizer.step()
optimizer.zero_grad(set_to_none=True)
global_step += 1
# run validation at the end of every epoch
X_val, y_val = get_batch(split='val',
data_dir=get_data_folder_path(config=config),
block_size=config['seq_len'],
batch_size=1)
run_validation(model,
X_val,
y_val,
tokenizer,
config['seq_len'],
device,
lambda msg: print(msg),
global_step,
writer,
rouge)
if epoch%1000==0 or epoch >= (config['num_epochs']-1):
# save the model at the end of every epoch
model_filename = get_weights_file_path(config, f"{epoch:02d}")
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'global_step': global_step
}, model_filename)
if __name__ == '__main__':
warnings.filterwarnings("ignore")
config = get_config()
train_model(config) |