def main(args):
key_dimensions = ['3', '4', '5']
lambs = ['0.001', '0.0001', '0.00001', '0.000001']
learning_rates = ['0.00005', '0.0001', '0.0005']
mode_dimensions = ['1', '3']
ranks = ['60', '80', '100']
pattern = "parameters:\n" \
" models: MMP\n" \
" similarity: Cosine\n" \
" alpha: [0, 0.1, 1]\n" \
" batch_size: [32]\n" \
" corruption: [0.2, 0.3, 0.4]\n" \
" epoch: [300]\n" \
" iteration: [10]\n" \
" key_dimension: [{0}]\n" \
" lambda: [{1}]\n" \
" learning_rate: [{2}]\n" \
" mode_dimension: [{3}]\n" \
" rank: [{4}]\n" \
" root: [1.0]\n" \
" topK: [5, 10, 15, 20, 50]\n" \
" metric: [R-Precision, NDCG, Precision, Recall]"
i = 1
for key_dimension in key_dimensions:
for lamb in lambs:
for learning_rate in learning_rates:
for mode_dimension in mode_dimensions:
for rank in ranks:
content = pattern.format(key_dimension, lamb, learning_rate, mode_dimension, rank)
write_file('config/'+args.path+'/', 'mmp-part'+str(i)+'.yml', content, exe=False)
i += 1
def main(args):
bash_path = load_yaml('config/global.yml', key='path')['bashes']
config_path = 'config/{}'.format(args.dataset_name)
yaml_files = get_file_names(config_path, extension='.yml')
project_path = "~/DeepCritiquingForRecSys"
pattern = "#!/usr/bin/env bash\n" \
"source {0}\n" \
"cd {1}\n" \
"python tune_parameters.py --data_dir {2} --save_path {3}/{4}.csv --parameters config/{3}/{4}.yml\n"
for setting in yaml_files:
name, extension = os.path.splitext(setting)
content = pattern.format(args.virtualenv_path, project_path,
args.data_dir, args.dataset_name, name)
write_file(bash_path + args.dataset_name,
args.dataset_name + '-' + name + '.sh',
content,
exe=True)
bash_files = sorted(
get_file_names(bash_path + args.dataset_name, extension='.sh'))
commands = []
command_pattern = 'sbatch --nodes=1 --time={0}:00:00 --mem={1} --cpus=4 '
if args.gpu:
command_pattern = command_pattern + '--gres=gpu:1 '
command_pattern = command_pattern + '{2}'
for bash in bash_files:
commands.append(
command_pattern.format(args.max_time, args.memory, bash))
content = "\n".join(commands)
write_file(bash_path + args.dataset_name,
'run_' + args.dataset_name + '.sh', content)
def main(args):
bash_path = load_yaml('config/global.yml', key='path')['bashes']
yaml_files = get_file_names('config', extension='.yml')
#project_path = os.path.dirname(os.path.abspath(__file__))
project_path = "~/IF-VAE-Recommendation"
pattern = "#!/usr/bin/env bash\n" \
"source {0}\n" \
"cd {1}\n" \
"python tune_parameters.py -d {2} -n {3}/{4}.csv -y config/{4}.yml\n"
for setting in yaml_files:
name, extension = os.path.splitext(setting)
content = pattern.format(args.virtualenv_path, project_path,
args.data_path, args.problem, name)
write_file(bash_path + args.problem,
args.problem + '-' + name + '.sh',
content,
exe=True)
bash_files = sorted(
get_file_names(bash_path + args.problem, extension='.sh'))
commands = []
command_pattern = 'sbatch --nodes=1 --time={0}:00:00 --mem={1} --cpus=4 '
if args.gpu:
command_pattern = command_pattern + '--gres=gpu:1 '
command_pattern = command_pattern + '{2}'
for bash in bash_files:
commands.append(
command_pattern.format(args.max_time, args.memory, bash))
content = "\n".join(commands)
write_file(bash_path + args.problem, 'run_' + args.problem + '.sh',
content)
if line != '' and line != '</doc>':
doc += line + '\n'
logging.info(f'Cut WIKI to sentence')
knowledge_collect = []
collect_num = 0
for p in tqdm(passage):
k = p.split('\n')
topic = k[0]
article = ' '.join(k[1:])
sentences = sent_tokenize(article, language=language)
for s in sentences:
knowledge = topic + sep + s
knowledge_collect.append(knowledge)
if len(knowledge_collect) == size:
write_file(knowledge_collect,
f'{output_path}/{collect_num}.txt')
knowledge_collect = []
collect_num += 1
write_file(knowledge_collect, f'{output_path}/{collect_num}.txt')
logging.info(f'Index WIKI in Solr')
knowledge = []
for i in range(1000):
if os.path.exists(f'{output_path}/{i}.txt'):
logging.info('Load file', f'{output_path}/{i}.txt', len(knowledge))
knowledge.extend([
line[:-1]
for line in open(f'{output_path}/{i}.txt', encoding='utf-8')
])
def main():
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')
parser = argparse.ArgumentParser()
parser.add_argument('-dialog', type=str)
parser.add_argument('-dialog2', type=str)
parser.add_argument('-k', type=str)
parser.add_argument('-pool', type=str)
parser.add_argument('-m', type=int)
parser.add_argument('-save_path', type=str)
parser.add_argument('--bc_size', type=int, default=32)
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument('--pool_size', type=int, default=10)
parser.add_argument('--max_len', type=int, default=64)
parser.add_argument('--language', type=str, default='en')
parser.add_argument('--pt_path', type=str, default='none')
args = parser.parse_args()
dialog_path = args.dialog
dialog2_path = args.dialog2
knowledge_path = args.k
pool_path = args.pool
max_step = args.m
batch_size = args.bc_size
lr = args.lr
pool_size = args.pool_size
max_len = args.max_len
lang_code = mbart_lang_to_id[args.language]
save_path = args.save_path
language = args.language
if knowledge_path != 'redis':
knowledge = []
for i in range(200):
if os.path.exists(f'{knowledge_path}/{i}.pkl'):
knowledge.extend(read_pkl(f'{knowledge_path}/{i}.pkl'))
else:
knowledge = knowledge_path
knowledge_pool = read_pkl(pool_path)
dataset = DuoData(read_pkl(f'{dialog_path}/context.pkl'),
read_pkl(f'{dialog_path}/response.pkl'),
read_pkl(f'{dialog2_path}/context.pkl'),
read_pkl(f'{dialog2_path}/context.pkl'),
knowledge_pool, pool_size=pool_size, knowledge=knowledge, order=None,
max_len=max_len, lang_code=lang_code, curriculum=max_step)
test_dataset = CKGCTestData(args.language, pool=f'dataset/ckgc/{args.language}/pool.txt',
max_len=max_len, lang_code=lang_code)
tokenizer = get_tokenizer('mbart')
tokenizer.lang_code_to_id = mbart_lang_to_id
logging.info('Build generator')
generator = Generator()
if torch.cuda.is_available():
generator = generator.cuda()
optimizer = AdamW(generator.parameters(), lr)
pretrained_path = args.pt_path
if os.path.exists(pretrained_path):
logging.info(f'Load pretrained model from {pretrained_path}')
generator.load_state_dict({k.replace("module.", ""): v for k, v in torch.load(pretrained_path).items()})
cur_step = 0
while cur_step < max_step:
dataset.set_offset(cur_step)
logging.info(f'Training step {cur_step} / max step {max_step}')
# train_generator(generator, optimizer, dataset,
# pad_idx=1, batch_size=batch_size, step=10)
cur_step += 10 * batch_size
predict, true = test_generator(generator, test_dataset, language, tokenizer,
pad_idx=1, batch_size=batch_size, epoch=0, word_mask=None)
logging.info(eval_all(predict, true))
write_file(predict, f'{save_path}/predict/{cur_step}.txt')
torch.save(generator.state_dict(), f'{save_path}/generator/{cur_step}.pt')
def main():
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
parser = argparse.ArgumentParser()
parser.add_argument('-dialog', type=str)
parser.add_argument('-k', type=str)
parser.add_argument('-pool', type=str)
parser.add_argument('-save_path', type=str)
parser.add_argument('--bc_size', type=int, default=32)
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument('--pool_size', type=int, default=1)
parser.add_argument('--max_len', type=int, default=64)
parser.add_argument('--language', type=str, default='en')
parser.add_argument('--pt_path', type=str, default='none')
parser.add_argument('--dist', type=int, default=1)
args = parser.parse_args()
dialog_path = args.dialog
knowledge_path = args.k
pool_path = args.pool
batch_size = args.bc_size
lr = args.lr
pool_size = args.pool_size
max_len = args.max_len
lang_code = mbart_lang_to_id[args.language]
distributed = args.dist
save_path = args.save_path
language = args.language
if distributed:
dist_init()
local_rank = dist.get_rank() if distributed else 0
if knowledge_path != 'redis':
knowledge = []
for i in range(200):
if os.path.exists(f'{knowledge_path}/{i}.pkl'):
knowledge.extend(read_pkl(f'{knowledge_path}/{i}.pkl'))
else:
knowledge = knowledge_path
knowledge_pool = read_pkl(pool_path)
dataset = Data(read_pkl(f'{dialog_path}/context.pkl'),
read_pkl(f'{dialog_path}/response.pkl'),
knowledge_pool,
pool_size=pool_size,
knowledge=knowledge,
order=None,
max_len=max_len,
lang_code=lang_code)
test_dataset = CKGCTestData(args.language,
pool=f'dataset/ckgc/{args.language}/pool.txt',
max_len=max_len,
lang_code=lang_code)
tokenizer = get_tokenizer('mbart')
tokenizer.lang_code_to_id = mbart_lang_to_id
logging.info('Build generator')
generator = Generator()
if torch.cuda.is_available():
generator = generator.cuda()
if distributed:
generator = torch.nn.parallel.DistributedDataParallel(
generator,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True)
optimizer = AdamW(generator.parameters(), lr)
pretrained_path = args.pt_path
if os.path.exists(pretrained_path):
logging.info(f'Load pretrained model from {pretrained_path}')
if distributed:
dist.barrier()
map_location = {'cuda:%d' % 0: 'cuda:%d' % dist.get_rank()}
generator.load_state_dict(
torch.load(pretrained_path, map_location=map_location))
dist.barrier()
else:
generator.load_state_dict({
k.replace("module.", ""): v
for k, v in torch.load(pretrained_path).items()
})
for epoch in range(100):
if os.path.exists(f'{save_path}/generator/{epoch}.pt'):
if distributed:
dist.barrier()
map_location = {'cuda:%d' % 0: 'cuda:%d' % dist.get_rank()}
generator.load_state_dict(
torch.load(f'{save_path}/generator/{epoch}.pt',
map_location=map_location))
dist.barrier()
else:
generator.load_state_dict({
k.replace("module.", ""): v
for k, v in torch.load(save_path + f'_{epoch}.pt').items()
})
continue
if distributed:
dist.barrier()
logging.info(f'Training epoch {epoch}')
train_generator(generator,
optimizer,
dataset,
pad_idx=1,
batch_size=batch_size,
epoch=epoch,
distributed=distributed)
if distributed:
dist.barrier()
if local_rank == 0:
predict, true = test_generator(generator,
test_dataset,
language,
tokenizer,
pad_idx=1,
batch_size=batch_size,
epoch=epoch,
word_mask=None)
logging.info(eval_all(predict, true))
write_file(predict, f'{save_path}/predict/{epoch}.txt')
torch.save(generator.state_dict(),
f'{save_path}/generator/{epoch}.pt')
if distributed:
dist.barrier()
knowledge.append(ks)
if dohash(k.strip()) == dohash(
turn['selected_knowledge'].strip()):
pool = [knowledge2id[ks]] + pool
fg = 0
else:
pool.append(knowledge2id[ks])
if fg:
pool = [0] + pool
if 0 not in pool:
pool.append(0)
context.append(prefix)
response.append(turn['text'])
pools.append(pool)
prefix = prefix + ' </s> ' + turn['text']
write_file(context, f'dataset/ckgc/{lang}/context.txt')
write_file(response, f'dataset/ckgc/{lang}/response.txt')
write_file(knowledge, f'dataset/ckgc/{lang}/knowledge.txt')
write_file(pools, f'dataset/ckgc/{lang}/pool.txt')
# input('>>>>')
# for reddit-english
data = []
data.extend(
read_file(
'dataset/reddit_en/reddit_conversations.3turns.train.topical.txt'))
data.extend(
read_file('dataset/reddit_en/reddit_conversations.3turns.dev.topical.txt'))
data.extend(
read_file(
'dataset/reddit_en/reddit_conversations.3turns.test.topical.txt'))
def main():
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
parser = argparse.ArgumentParser()
parser.add_argument('-q', type=str)
parser.add_argument('-d', type=str)
parser.add_argument('-pool', type=str)
parser.add_argument('-save_path', type=str)
parser.add_argument('--bc_size', type=int, default=32)
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument('--pool_size', type=int, default=10)
parser.add_argument('--max_len', type=int, default=64)
parser.add_argument('--language', type=str, default='en')
parser.add_argument('--pt_path', type=str, default='none')
parser.add_argument('--dist', type=int, default=1)
args = parser.parse_args()
query_path = args.q
document_path = args.d
pool_path = args.pool
batch_size = args.bc_size
lr = args.lr
pool_size = args.pool_size
max_len = args.max_len
lang_code = mbart_lang_to_id[args.language]
distributed = args.dist
save_path = args.save_path
if distributed:
dist_init()
local_rank = dist.get_rank() if distributed else 0
logging.info(
f'Load query from {query_path} and document from {document_path}')
query = read_pkl(query_path)
if document_path != 'redis':
document = []
for i in range(200):
if os.path.exists(f'{document_path}/{i}.pkl'):
document.extend(read_pkl(f'{document_path}/{i}.pkl'))
else:
document = document_path
knowledge_pool = read_pkl(pool_path)
dataset = Data(query,
query,
knowledge_pool,
pool_size=pool_size,
knowledge=document,
order=None,
max_len=max_len,
lang_code=lang_code)
test_dataset = CKGCTestData(args.language,
pool=f'dataset/ckgc/{args.language}/pool.txt',
max_len=max_len,
lang_code=lang_code)
logging.info('Build retriever')
retriever = Retriever()
if torch.cuda.is_available():
retriever = retriever.cuda()
if distributed:
retriever = torch.nn.parallel.DistributedDataParallel(
retriever,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True)
optimizer = AdamW(retriever.parameters(), lr)
pretrained_path = args.pt_path
if os.path.exists(pretrained_path):
logging.info(f'Load pretrained model from {pretrained_path}')
if distributed:
dist.barrier()
map_location = {'cuda:%d' % 0: 'cuda:%d' % dist.get_rank()}
retriever.load_state_dict(
torch.load(pretrained_path, map_location=map_location))
dist.barrier()
else:
retriever.load_state_dict({
k.replace("module.", ""): v
for k, v in torch.load(pretrained_path).items()
})
for epoch in range(100):
if os.path.exists(f'{save_path}/retriever/{epoch}.pt'):
if distributed:
dist.barrier()
map_location = {'cuda:%d' % 0: 'cuda:%d' % dist.get_rank()}
retriever.load_state_dict(
torch.load(f'{save_path}/retriever/{epoch}.pt',
map_location=map_location))
dist.barrier()
else:
retriever.load_state_dict({
k.replace("module.", ""): v
for k, v in torch.load(save_path + f'_{epoch}.pt').items()
})
continue
if distributed:
dist.barrier()
logging.info(f'Training epoch {epoch}')
train_retriever(retriever,
optimizer,
dataset,
pad_idx=1,
batch_size=batch_size,
epoch=epoch,
distributed=distributed)
if distributed:
dist.barrier()
if local_rank == 0:
ranks = test_retriever(retriever,
test_dataset,
pad_idx=1,
batch_size=batch_size,
epoch=epoch)
write_file(ranks, f'{save_path}/ranks/{epoch}.txt')
torch.save(retriever.state_dict(),
f'{save_path}/retriever/{epoch}.pt')
if distributed:
dist.barrier()
def main():
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')
parser = argparse.ArgumentParser()
parser.add_argument('-q1', type=str)
parser.add_argument('-q2', type=str)
parser.add_argument('-d', type=str)
parser.add_argument('-pool', type=str)
parser.add_argument('-m', type=int)
parser.add_argument('-save_path', type=str)
parser.add_argument('--bc_size', type=int, default=32)
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument('--pool_size', type=int, default=10)
parser.add_argument('--max_len', type=int, default=64)
parser.add_argument('--language', type=str, default='en')
parser.add_argument('--pt_path', type=str, default='none')
args = parser.parse_args()
query1_path = args.q1
query2_path = args.q2
document_path = args.d
pool_path = args.pool
max_step = args.m
batch_size = args.bc_size
lr = args.lr
pool_size = args.pool_size
max_len = args.max_len
lang_code = mbart_lang_to_id[args.language]
save_path = args.save_path
logging.info(f'Load query from {query1_path}-{query2_path} and document from {document_path}')
query1 = read_pkl(query1_path)
query2 = read_pkl(query2_path)
if document_path != 'redis':
document = []
for i in range(200):
if os.path.exists(f'{document_path}/{i}.pkl'):
document.extend(read_pkl(f'{document_path}/{i}.pkl'))
else:
document = document_path
knowledge_pool = read_pkl(pool_path)
dataset = DuoData(query1, query1, query2, query2, knowledge_pool, pool_size=pool_size, knowledge=document,
order=None, max_len=max_len, lang_code=lang_code, curriculum=max_step)
test_dataset = CKGCTestData(args.language, pool=f'dataset/ckgc/{args.language}/pool.txt',
max_len=max_len, lang_code=lang_code)
logging.info('Build retriever')
retriever = Retriever()
if torch.cuda.is_available():
retriever = retriever.cuda()
optimizer = AdamW(retriever.parameters(), lr)
pretrained_path = args.pt_path
if os.path.exists(pretrained_path):
logging.info(f'Load pretrained model from {pretrained_path}')
retriever.load_state_dict({k.replace("module.", ""): v for k, v in torch.load(pretrained_path).items()})
cur_step = 0
while cur_step < max_step:
dataset.set_offset(cur_step)
logging.info(f'Training step {cur_step} / max step {max_step}')
train_retriever(retriever, optimizer, dataset, pad_idx=1, batch_size=batch_size, step=10)
cur_step += 10 * batch_size
ranks = test_retriever(retriever, test_dataset, pad_idx=1, batch_size=batch_size, epoch=0)
write_file(ranks, f'{save_path}/ranks/{cur_step}.txt')
torch.save(retriever.state_dict(), f'{save_path}/retriever/{cur_step}.pt')
