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train_scst.py
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train_scst.py
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#!/usr/bin/env python3
import os
import random
import argparse
import logging
import numpy as np
from tensorboardX import SummaryWriter
from libbots import data, model, utils
import torch
import torch.optim as optim
import torch.nn.functional as F
import ptan
SAVES_DIR = "saves"
BATCH_SIZE = 16
LEARNING_RATE = 1e-4
MAX_EPOCHES = 10000
log = logging.getLogger("train")
def run_test(test_data, net, end_token, device="cpu"):
bleu_sum = 0.0
bleu_count = 0
for p1, p2 in test_data:
input_seq = model.pack_input(p1, net.emb, device)
enc = net.encode(input_seq)
_, tokens = net.decode_chain_argmax(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
ref_indices = [
indices[1:]
for indices in p2
]
bleu_sum += utils.calc_bleu_many(tokens, ref_indices)
bleu_count += 1
return bleu_sum / bleu_count
if __name__ == "__main__":
logging.basicConfig(format="%(asctime)-15s %(levelname)s %(message)s", level=logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("--data", required=True, help="Category to use for training. Empty string to train on full dataset")
parser.add_argument("--cuda", action='store_true', default=False, help="Enable cuda")
parser.add_argument("-n", "--name", required=True, help="Name of the run")
parser.add_argument("-l", "--load", required=True, help="Load model and continue in RL mode")
parser.add_argument("--samples", type=int, default=4, help="Count of samples in prob mode")
parser.add_argument("--disable-skip", default=False, action='store_true', help="Disable skipping of samples with high argmax BLEU")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
saves_path = os.path.join(SAVES_DIR, args.name)
os.makedirs(saves_path, exist_ok=True)
phrase_pairs, emb_dict = data.load_data(genre_filter=args.data)
log.info("Obtained %d phrase pairs with %d uniq words", len(phrase_pairs), len(emb_dict))
data.save_emb_dict(saves_path, emb_dict)
end_token = emb_dict[data.END_TOKEN]
train_data = data.encode_phrase_pairs(phrase_pairs, emb_dict)
rand = np.random.RandomState(data.SHUFFLE_SEED)
rand.shuffle(train_data)
train_data, test_data = data.split_train_test(train_data)
log.info("Training data converted, got %d samples", len(train_data))
train_data = data.group_train_data(train_data)
test_data = data.group_train_data(test_data)
log.info("Train set has %d phrases, test %d", len(train_data), len(test_data))
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE).to(device)
log.info("Model: %s", net)
writer = SummaryWriter(comment="-" + args.name)
net.load_state_dict(torch.load(args.load))
log.info("Model loaded from %s, continue training in RL mode...", args.load)
# BEGIN token
beg_token = torch.LongTensor([emb_dict[data.BEGIN_TOKEN]]).to(device)
with ptan.common.utils.TBMeanTracker(writer, batch_size=100) as tb_tracker:
optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE, eps=1e-3)
batch_idx = 0
best_bleu = None
for epoch in range(MAX_EPOCHES):
random.shuffle(train_data)
dial_shown = False
total_samples = 0
skipped_samples = 0
bleus_argmax = []
bleus_sample = []
for batch in data.iterate_batches(train_data, BATCH_SIZE):
batch_idx += 1
optimiser.zero_grad()
input_seq, input_batch, output_batch = model.pack_batch_no_out(batch, net.emb, device)
enc = net.encode(input_seq)
net_policies = []
net_actions = []
net_advantages = []
beg_embedding = net.emb(beg_token)
for idx, inp_idx in enumerate(input_batch):
total_samples += 1
ref_indices = [
indices[1:]
for indices in output_batch[idx]
]
item_enc = net.get_encoded_item(enc, idx)
r_argmax, actions = net.decode_chain_argmax(item_enc, beg_embedding, data.MAX_TOKENS,
stop_at_token=end_token)
argmax_bleu = utils.calc_bleu_many(actions, ref_indices)
bleus_argmax.append(argmax_bleu)
if not args.disable_skip and argmax_bleu > 0.99:
skipped_samples += 1
continue
if not dial_shown:
log.info("Input: %s", utils.untokenize(data.decode_words(inp_idx, rev_emb_dict)))
ref_words = [utils.untokenize(data.decode_words(ref, rev_emb_dict)) for ref in ref_indices]
log.info("Refer: %s", " ~~|~~ ".join(ref_words))
log.info("Argmax: %s, bleu=%.4f", utils.untokenize(data.decode_words(actions, rev_emb_dict)),
argmax_bleu)
for _ in range(args.samples):
r_sample, actions = net.decode_chain_sampling(item_enc, beg_embedding,
data.MAX_TOKENS, stop_at_token=end_token)
sample_bleu = utils.calc_bleu_many(actions, ref_indices)
if not dial_shown:
log.info("Sample: %s, bleu=%.4f", utils.untokenize(data.decode_words(actions, rev_emb_dict)),
sample_bleu)
net_policies.append(r_sample)
net_actions.extend(actions)
net_advantages.extend([sample_bleu - argmax_bleu] * len(actions))
bleus_sample.append(sample_bleu)
dial_shown = True
if not net_policies:
continue
policies_v = torch.cat(net_policies)
actions_t = torch.LongTensor(net_actions).to(device)
adv_v = torch.FloatTensor(net_advantages).to(device)
log_prob_v = F.log_softmax(policies_v, dim=1)
log_prob_actions_v = adv_v * log_prob_v[range(len(net_actions)), actions_t]
loss_policy_v = -log_prob_actions_v.mean()
loss_v = loss_policy_v
loss_v.backward()
optimiser.step()
tb_tracker.track("advantage", adv_v, batch_idx)
tb_tracker.track("loss_policy", loss_policy_v, batch_idx)
tb_tracker.track("loss_total", loss_v, batch_idx)
bleu_test = run_test(test_data, net, end_token, device)
bleu = np.mean(bleus_argmax)
writer.add_scalar("bleu_test", bleu_test, batch_idx)
writer.add_scalar("bleu_argmax", bleu, batch_idx)
writer.add_scalar("bleu_sample", np.mean(bleus_sample), batch_idx)
writer.add_scalar("skipped_samples", skipped_samples / total_samples, batch_idx)
writer.add_scalar("epoch", batch_idx, epoch)
log.info("Epoch %d, test BLEU: %.3f", epoch, bleu_test)
if best_bleu is None or best_bleu < bleu_test:
best_bleu = bleu_test
log.info("Best bleu updated: %.4f", bleu_test)
torch.save(net.state_dict(), os.path.join(saves_path, "bleu_%.3f_%02d.dat" % (bleu_test, epoch)))
if epoch % 10 == 0:
torch.save(net.state_dict(), os.path.join(saves_path, "epoch_%03d_%.3f_%.3f.dat" % (epoch, bleu, bleu_test)))
writer.close()