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train.py
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train.py
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import json
import os
import time
import sys
import numpy as np
import torch
import wandb
import pandas as pd
from transformers import get_scheduler, AdamW
from tqdm.auto import tqdm
from torch.utils.data import DataLoader
from dataloader import get_input_output_ques_gen_seq, get_input_output_qna_seq, read_jsonl, GSMDataset
from model import DialogueGenerator
from rewards import bleu_reward_estimation, correct_ques_num_reward_estimation, qa_reward_estimation
from util import initialize_config, deterministic_behaviour, initialise_tokenizer
class Run:
def __init__(self, config_name, deterministic=True):
self.config = initialize_config(config_name)
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if deterministic:
deterministic_behaviour()
def train(self):
conf = self.config
model_path = os.path.join(conf['PRETRAINED_MODEL_PREFIX_PATH'], conf['MODEL_NAME'])
tokenizer = initialise_tokenizer(model_path)
if conf["MODEL_IMPORT_PATH"]:
model_path = conf["MODEL_IMPORT_PATH"]
print(f"Loading model from: {model_path}")
model = DialogueGenerator(model_name=model_path, tokenizer=tokenizer, max_decode_len=100)
model.to(self.device)
data_points_indices = []
if conf['TASK'] == "question-answering":
in_seq, out_seq = get_input_output_qna_seq(conf['SPLIT'])
elif conf['TASK'] == "question-generation":
in_seq, out_seq, data_points_indices = get_input_output_ques_gen_seq(conf['ITERATIVE'], conf['SPLIT'],
conf['PLANNING'], conf['REWARD'])
train_dset = GSMDataset(tokenizer, in_seq, out_seq)
batch_size = int(conf["BATCH_SIZE"])
train_loader = DataLoader(train_dset, batch_size=batch_size, shuffle=True, drop_last=True)
optim = AdamW(model.parameters(), lr=conf["LEARNING_RATE"])
num_training_steps = int(conf["EPOCHS"]) * len(train_loader)
lr_scheduler = get_scheduler(
conf['LR_SCHEDULER'],
optimizer=optim,
num_warmup_steps=0,
num_training_steps=num_training_steps,
)
optim.zero_grad()
rl_epochs = int(int(conf["EPOCHS"]) - (float(conf["RL_EPOCHS"]) * int(conf["EPOCHS"])))
# Add timestamp to the path to not override model exports
export_path = os.path.join(conf['EXPORT_PREFIX_PATH'], conf['MODEL_CKPT_PATH'] + str(time.time_ns()))
if not os.path.isdir(export_path):
os.makedirs(export_path)
with open(os.path.join(export_path, 'config.json'), 'w') as file:
json.dump(conf, file)
wandb.init(project=conf['TASK'], entity="", config=conf)
wandb.config.model_path = model_path
wandb.config.rl_epochs = rl_epochs
wandb.config.export_path = export_path
wandb.run.name = os.environ.get("RUN_NAME", f'{conf["MODEL_NAME"]}-{conf["PLANNING"]}')
output_dict = {}
table = wandb.Table(data=pd.DataFrame({"input": in_seq[:5],
"output": out_seq[:5]}))
wandb.log({'training_datasample': table})
epoch_results_table = wandb.Table(columns=["epoch", "prediction"])
max_valid_score = 0.0
model.train()
pbar = tqdm(range(num_training_steps))
for epoch in range(int(conf["EPOCHS"])):
# Train loop
model.train()
for batch in train_loader:
optim.zero_grad()
for k, v in batch.items():
if k != "raw_output" and k != "raw_input":
batch[k] = v.to(self.device)
mle_loss = model(batch["inpt"], batch["att_mask"], batch["lbl"])
train_loss = mle_loss
# RL loop
if epoch >= rl_epochs:
# Compute RL loss
train_gathered_logprobs, train_indicator_matrix, decoded_result_list = model.rl_sampling(
batch["inpt"], batch["att_mask"], top_p=conf["TOP_P"], return_seq=int(conf["RETURN_SEQ"]),
temperature=conf["TEMPERATURE"], num_beams=conf["NUM_BEAMS"])
if 'fluency' in conf["REWARD"].lower() or 'combined' in conf["REWARD"].lower():
# measure reward for question generation and correct number of questions
question_generation_reward = bleu_reward_estimation(batch["raw_output"], decoded_result_list,
int(conf["RETURN_SEQ"]))
assert len(question_generation_reward) == batch_size * int(conf["RETURN_SEQ"])
if 'number' in conf["REWARD"].lower() or 'combined' in conf["REWARD"].lower():
correct_ques_num_reward = correct_ques_num_reward_estimation(batch["raw_output"],
decoded_result_list,
int(conf["RETURN_SEQ"]))
assert len(correct_ques_num_reward) == batch_size * int(conf["RETURN_SEQ"])
if 'qa' in conf["REWARD"].lower() or 'combined' in conf["REWARD"].lower():
qa_reward = qa_reward_estimation(batch["raw_input"], decoded_result_list,
int(conf["RETURN_SEQ"]), conf["QA_MODEL_PATH"],
export_path, epoch,
partial_reward=bool(conf["QA_PARTIAL_REWARD"]))
assert len(qa_reward) == batch_size * int(conf["RETURN_SEQ"])
# Combination
if 'fluency' in conf["REWARD"].lower():
# multiply by hyperparameter
total_reward = question_generation_reward
if 'number' in conf["REWARD"].lower():
total_reward = correct_ques_num_reward
if 'qa' in conf["REWARD"].lower():
total_reward = qa_reward
if 'combined' in conf["REWARD"].lower():
total_reward = [reward_a + reward_b + reward_c for reward_a, reward_b, reward_c in
zip(question_generation_reward, correct_ques_num_reward, qa_reward)]
train_reward = torch.FloatTensor(total_reward).type(train_indicator_matrix.type()).unsqueeze(-1)
assert train_reward.size() == torch.Size([batch_size * int(conf["RETURN_SEQ"]), 1])
train_sample_logprobs = train_gathered_logprobs * train_indicator_matrix
train_RL_term = train_reward * train_sample_logprobs
train_RL_loss = (-1 * torch.sum(train_RL_term)) / torch.sum(train_indicator_matrix)
alpha, beta = 0.5, 0.5
train_loss = alpha * mle_loss + beta * train_RL_loss
wandb.log({"rl_loss": train_RL_loss, "mle_loss": mle_loss})
wandb.log({"loss": train_loss})
train_loss.backward()
if conf["USE_GRADIENT_CLIPPING"] != "":
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optim.step()
lr_scheduler.step()
pbar.update(1)
# Validation set metrics
if epoch % int(conf["VALID_EVERY_EPOCH"]) == 0:
model.eval()
valid_in_seq, valid_out_seq, _ = get_input_output_ques_gen_seq(conf['ITERATIVE'], 'valid',
conf['PLANNING'],
conf["REWARD"].lower())
with torch.no_grad():
print(f"({epoch}) Starting validation evaluation...")
predicted_out = []
for in_sample in valid_in_seq:
encoded_sent = tokenizer(in_sample, return_tensors='pt').to(self.device)
input_ids, attention_mask = encoded_sent.input_ids, encoded_sent.attention_mask
decoded_out = model.generate(src_input=input_ids, src_mask=attention_mask)
predicted_out.append(' '.join(decoded_out))
valid_bleu_list = bleu_reward_estimation(valid_out_seq, predicted_out, 1)
valid_question_count_list = correct_ques_num_reward_estimation(valid_out_seq, predicted_out, 1)
mean_valid_bleu = np.array(valid_bleu_list).mean()
mean_valid_question_count = np.array(valid_question_count_list).mean()
print(f"Valid bleu: {mean_valid_bleu}, valid question count: {mean_valid_question_count}")
wandb.log({"valid_bleu": mean_valid_bleu, "valid_question_count": mean_valid_question_count})
if mean_valid_bleu > max_valid_score:
print(f"Saving best model with valid BLEU score {mean_valid_bleu}")
model.model.save_pretrained(os.path.join(export_path, "best_valid"))
max_valid_score = mean_valid_bleu
# Print generated samples for a training example after every epoch to see the progress.
test_examples = read_jsonl("test")
qn = test_examples[2]["question"]
encoded_sent = tokenizer(qn, return_tensors='pt').to(self.device)
input_ids, attention_mask = encoded_sent.input_ids, encoded_sent.attention_mask
output_dict[epoch] = ' '.join(model.generate(src_input=input_ids, src_mask=attention_mask))
epoch_results_table.add_data(epoch, output_dict[epoch])
model.model.save_pretrained(os.path.join(export_path, "final"))
wandb.log({'after_batch_prediction': epoch_results_table})
# save intermediate results in a csv file
with open(f"{export_path}/intermediate_results.csv", 'w') as f:
for key in output_dict.keys():
f.write("%s,%s\n" % (key, output_dict[key]))
if data_points_indices:
with open(f"{export_path}/train_indices.txt", 'w') as file:
file.write('\n'.join([str(x) for x in data_points_indices]))
print("Finish")
return export_path
if __name__ == "__main__":
config_name = sys.argv[1]
runner = Run(config_name)
runner.train()