444409 gpt2-fine-tuning

This commit is contained in:
Marcin Kostrzewski 2023-06-29 20:24:48 +02:00
parent 9d17f9743d
commit eb63850925
3 changed files with 18166 additions and 17933 deletions

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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AutoConfig
# In[2]:
import lzma
def read_xz_file(fname):
with lzma.open(fname, mode='rt', encoding='utf-8') as f:
return [line.strip() for line in f.readlines()]
def read_file(fname):
with open(fname, mode='rt', encoding='utf-8') as f:
return [line.strip() for line in f.readlines()]
def get_contexts(input_text):
all_fields = input_text.replace(r'\n', ' ').split('\t')
return {'left': all_fields[6], 'right': all_fields[7]}
bos = '<|endoftext|>'
eos = '<|EOS|>'
def compose_sentences(raw_input, labels):
result = []
for input, label in zip(raw_input, labels):
context = get_contexts(input)
result.append(f'{bos} {context["left"]} {input} {eos}')
result.append(f'{bos} {input} {context["right"]} {eos}')
return result
# In[6]:
pad = '<|pad|>'
special_tokens_dict = {'eos_token': eos, 'bos_token': bos, 'pad_token': pad}
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
num_add_tokens = tokenizer.add_special_tokens(special_tokens_dict)
# In[4]:
class AmericaDataset(Dataset):
def __init__(self, tokenizer, data):
self.tokenizer = tokenizer
self.sentences = []
for entry in data:
self.sentences.append(
torch.tensor(self.tokenizer.encode(entry, padding=True))
)
def __len__(self):
return len(self.sentences)
def __getitem__(self, item):
return self.sentences[item]
# In[5]:
train_input_raw = read_xz_file('challenging-america-word-gap-prediction/train/in.tsv.xz')
train_labels = read_file('challenging-america-word-gap-prediction/train/expected.tsv')
train_sentences = compose_sentences(train_input_raw, train_labels)
train_dataset = AmericaDataset(tokenizer, train_sentences)
# In[11]:
config = AutoConfig.from_pretrained('distilgpt2', bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id, output_hidden_states=False, return_dict_in_generate=True)
model = GPT2LMHeadModel.from_pretrained('distilgpt2', config=config)
model.resize_token_embeddings(len(tokenizer))
device = torch.device('cuda')
model.to(device)
# In[8]:
def pack_tensor(new_tensor, packed_tensor, max_seq_len):
if packed_tensor is None:
return new_tensor, True, None
if new_tensor.size()[1] + packed_tensor.size()[1] > max_seq_len:
return packed_tensor, False, new_tensor
else:
packed_tensor = torch.cat([new_tensor, packed_tensor[:, 1:]], dim=1)
return packed_tensor, True, None
# In[9]:
import os
from transformers import AdamW, get_linear_schedule_with_warmup
from tqdm import tqdm
def train(
model,
dataset,
batch_size=16, epochs=5, lr=2e-5,
warmup_steps=200,
output_dir=".", output_prefix="gpt2",
save_model_on_epoch=False,
):
device = torch.device("cuda")
model = model.to(device)
model.train()
optimizer = AdamW(model.parameters(), lr=lr)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=warmup_steps, num_training_steps=-1
)
loss = 0
accumulating_batch_count = 0
input_tensor = None
dataloader = DataLoader(dataset, batch_size=1, shuffle=True)
for epoch in range(epochs):
print(f"Training epoch {epoch}")
print(loss)
for idx, entry in tqdm(enumerate(dataloader)):
(input_tensor, carry_on, remainder) = pack_tensor(entry, input_tensor, 1024)
if carry_on and idx != len(dataset) - 1:
continue
input_tensor = input_tensor.to(device)
outputs = model(input_tensor, labels=input_tensor)
loss = outputs[0]
loss.backward()
if (accumulating_batch_count % batch_size) == 0:
optimizer.step()
scheduler.step()
optimizer.zero_grad()
model.zero_grad()
accumulating_batch_count += 1
input_tensor = None
if save_model_on_epoch:
torch.save(
model.state_dict(),
os.path.join(output_dir, f"{output_prefix}-{epoch}.pt"),
)
return model
# In[12]:
model = train(model, train_dataset)
# In[3]:
dev_input_raw = read_xz_file('challenging-america-word-gap-prediction/dev-0/in.tsv.xz')
dev_input_contexts = [get_contexts(input_text) for input_text in dev_input_raw]
test_input_raw = read_xz_file('challenging-america-word-gap-prediction/test-A/in.tsv.xz')
test_input_contexts = [get_contexts(input_text) for input_text in test_input_raw]
# In[15]:
from tqdm import tqdm
tokenizer.truncation_side = 'left'
blacklist = ['ia', 'ix', 'io',
'ik'] # Te tokeny się prawie zawsze powtarzają, a nie są to żadne słowa w języku angielskim.
def predict_words(dataset):
preds = []
for entry in tqdm(dataset):
text = f"{entry['left']}"
src = tokenizer.encode(text, return_tensors="pt", truncation=True).to(device)
output = model.generate(src, max_length=len(src[0]) + 1, do_sample=True, top_k=0, temperature=0.8,
num_return_sequences=1, no_repeat_ngram_size=2, output_scores=True)
probs, idxs = torch.softmax(output.scores[0][-1], dim=0).topk(50)
current_output = ''
accumulated_probability = 0
for prob, token_id in zip(probs, idxs):
token = tokenizer.decode(token_id, skip_special_tokens=True).split(' ')[-1]
if not token.isalnum() or token in blacklist:
continue
prob_value = prob.item()
accumulated_probability += prob_value
current_output += f'{token.strip()}:{prob_value} '
current_output += f':{1 - accumulated_probability}'
preds.append(current_output)
return preds
# In[ ]:
dev_preds = predict_words(dev_input_contexts)
with open('challenging-america-word-gap-prediction/dev-0/out.tsv', 'w') as f:
f.writelines(line + '\n' for line in dev_preds)
# In[ ]:
test_preds = predict_words(test_input_contexts)
with open('challenging-america-word-gap-prediction/test-A/out.tsv', 'w') as f:
f.writelines(line + '\n' for line in test_preds)

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