def initialize_document_embedding(int_flag=True, w2v=300, file_path=''):
device = 'cuda'
# Dict: word token -> ID.
if not int_flag:
emb_dict = data.load_dict(DIC_PATH=DIC_PATH)
else:
emb_dict = data.load_dict(DIC_PATH=DIC_PATH_INT)
ordered_docID_doc_list = data.get_ordered_docID_document(
ORDERED_QID_QUESTION_DICT)
docID_dict, doc_list = data.get_docID_indices(ordered_docID_doc_list)
# Index -> qid.
rev_docID_dict = {id: doc for doc, id in docID_dict.items()}
net = retriever_module.RetrieverModel(emb_size=w2v,
dict_size=len(docID_dict),
EMBED_FLAG=False,
device='cuda').to('cuda')
net.cuda()
net.zero_grad()
# temp_param_dict = get_net_parameter(net)
# Get trained wording embeddings.
path = file_path
net1 = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE,
LSTM_FLAG=True,
ATT_FLAG=False,
EMBED_FLAG=False).to(device)
net1.load_state_dict(torch.load(path))
doc_embedding_list = get_document_embedding(doc_list, emb_dict, net1)
# Add padding vector.
doc_embedding_list.append([0.0] * model.EMBEDDING_DIM)
doc_embedding_tensor = torch.tensor(doc_embedding_list).cuda()
net.document_emb.weight.data = doc_embedding_tensor.clone().detach()
# temp_param_dict1 = get_net_parameter(net)
MAP_for_queries = 1.0
epoch = 0
isExists = os.path.exists(SAVES_DIR)
if not isExists:
os.makedirs(SAVES_DIR)
# os.makedirs(SAVES_DIR, exist_ok=True)
# torch.save(net.state_dict(), os.path.join(SAVES_DIR, "initial_epoch_%03d_%.3f.dat" % (epoch, MAP_for_queries)))
torch.save(
net.state_dict(),
os.path.join(SAVES_DIR,
"initial_epoch_%03d_%.3f.dat" % (epoch, MAP_for_queries)))
def initialize_document_embedding():
device = 'cuda'
# Dict: word token -> ID.
emb_dict = data.load_dict(DIC_PATH=DIC_PATH)
ordered_docID_doc_list = data.get_ordered_docID_document(
ORDERED_QID_QUESTION_DICT)
docID_dict, doc_list = data.get_docID_indices(ordered_docID_doc_list)
# Index -> qid.
rev_docID_dict = {id: doc for doc, id in docID_dict.items()}
net = retriever_module.RetrieverModel(emb_size=50,
dict_size=len(docID_dict),
EMBED_FLAG=False,
device='cuda').to('cuda')
net.cuda()
net.zero_grad()
# temp_param_dict = get_net_parameter(net)
# Get trained wording embeddings.
path = '../data/saves/maml_batch8_att=0_newdata2k_1storder_1task/epoch_002_0.394_0.796.dat'
net1 = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE,
LSTM_FLAG=True,
ATT_FLAG=False,
EMBED_FLAG=False).to(device)
net1.load_state_dict(torch.load(path))
doc_embedding_list = get_document_embedding(doc_list, emb_dict, net1)
# Add padding vector.
doc_embedding_list.append([0.0] * model.EMBEDDING_DIM)
doc_embedding_tensor = torch.tensor(doc_embedding_list).cuda()
net.document_emb.weight.data = doc_embedding_tensor.clone().detach()
# temp_param_dict1 = get_net_parameter(net)
MAP_for_queries = 1.0
epoch = 0
os.makedirs(SAVES_DIR, exist_ok=True)
# torch.save(net.state_dict(), os.path.join(SAVES_DIR, "initial_epoch_%03d_%.3f.dat" % (epoch, MAP_for_queries)))
torch.save(
net.state_dict(),
os.path.join(SAVES_DIR,
"epoch_%03d_%.3f.dat" % (epoch, MAP_for_queries)))
def retriever_training(epochs,
RETRIEVER_EMBED_FLAG=True,
query_embedding=True):
''' One instance of the retriever training samples:
query_index = [800000, 0, 2, 100000, 400000, 600000]
document_range = [(700000, 944000),
(1, 10),
(10, 300000),
(10, 300000),
(300000, 500000),
(500000, 700000)]
positive_document_list =
[[700001-700000, 700002-700000, 900000-700000, 910000-700000, 944000-2-700000],
[2, 3],
[13009-10, 34555-10, 234-10, 6789-10, 300000-1-10],
[11-10, 16-10, 111111-10, 222222-10, 222223-10],
[320000-300000, 330000-300000, 340000-300000, 350000-300000, 360000-300000],
[600007-500000, 610007-500000, 620007-500000, 630007-500000, 690007-500000]]'''
retriever_path = '../data/saves/retriever/initial_epoch_000_1.000.dat'
device = 'cuda'
learning_rate = 0.01
docID_dict, _ = data.get_docID_indices(
data.get_ordered_docID_document(ORDERED_QID_QUESTION_DICT))
# Index -> qid.
rev_docID_dict = {id: doc for doc, id in docID_dict.items()}
training_samples = data.load_json(TRAINING_SAMPLE_DICT)
net = retriever_module.RetrieverModel(emb_size=50,
dict_size=len(docID_dict),
EMBED_FLAG=RETRIEVER_EMBED_FLAG,
device=device).to(device)
net.load_state_dict(torch.load(retriever_path))
net.zero_grad()
# temp_param_dict = get_net_parameter(net)
# retriever_optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=learning_rate)
# retriever_optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=learning_rate, eps=1e-3)
retriever_optimizer = adabound.AdaBound(filter(lambda p: p.requires_grad,
net.parameters()),
lr=1e-3,
final_lr=0.1)
# temp_param_dict = get_net_parameter(net)
emb_dict = None
net1 = None
qid_question_pair = {}
if query_embedding:
emb_dict = data.load_dict(DIC_PATH=DIC_PATH)
# Get trained wording embeddings.
path = RETRIEVER_PATH
net1 = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE,
LSTM_FLAG=True,
ATT_FLAG=False,
EMBED_FLAG=False).to(device)
net1.load_state_dict(torch.load(path))
qid_question_pair = data.get_qid_question_pairs(
ORDERED_QID_QUESTION_DICT)
max_value = MAX_MAP
MAP_for_queries = MAX_MAP
for i in range(epochs):
print('Epoch %d is training......' % (i))
# count= 0
for key, value in training_samples.items():
retriever_optimizer.zero_grad()
net.zero_grad()
if query_embedding:
if key in qid_question_pair:
question_tokens = qid_question_pair[key]
else:
print("ERROR! NO SUCH QUESTION: %s!" % (str(key)))
continue
query_tensor = data.get_question_embedding(
question_tokens, emb_dict, net1)
else:
query_tensor = torch.tensor(net.pack_input(
value['query_index']).tolist(),
requires_grad=False).cuda()
document_range = (value['document_range'][0],
value['document_range'][1])
logsoftmax_output = net(query_tensor, document_range)[0]
logsoftmax_output = logsoftmax_output.cuda()
positive_document_list = [
k - value['document_range'][0]
for k in value['positive_document_list']
]
possitive_logsoftmax_output = torch.stack(
[logsoftmax_output[k] for k in positive_document_list])
loss_policy_v = -possitive_logsoftmax_output.mean()
loss_policy_v = loss_policy_v.cuda()
loss_policy_v.backward()
retriever_optimizer.step()
# temp_param_dict = get_net_parameter(net)
# if count%100==0:
# print(' Epoch %d, %d samples have been trained.' %(i, count))
# count+=1
# Record trained parameters.
if i % 1 == 0:
MAP_list = []
for j in range(int(len(training_samples) / 40)):
random.seed(datetime.now())
key, value = random.choice(list(training_samples.items()))
if query_embedding:
question_tokens = qid_question_pair[key]
query_tensor = data.get_question_embedding(
question_tokens, emb_dict, net1)
else:
query_tensor = torch.tensor(net.pack_input(
value['query_index']).tolist(),
requires_grad=False).cuda()
document_range = (value['document_range'][0],
value['document_range'][1])
logsoftmax_output = net(query_tensor, document_range)[0]
order = net.calculate_rank(logsoftmax_output.tolist())
positive_document_list = [
k - value['document_range'][0]
for k in value['positive_document_list']
]
orders = [order[k] for k in positive_document_list]
MAP = mean(orders)
MAP_list.append(MAP)
MAP_for_queries = mean(MAP_list)
print('------------------------------------------------------')
print('Epoch %d, MAP_for_queries: %f' % (i, MAP_for_queries))
print('------------------------------------------------------')
if MAP_for_queries < max_value:
max_value = MAP_for_queries
if MAP_for_queries < 500:
output_str = "AdaBound"
if RETRIEVER_EMBED_FLAG:
output_str += "_DocEmbed"
if query_embedding:
output_str += "_QueryEmbed"
torch.save(
net.state_dict(),
os.path.join(
SAVES_DIR, output_str + "_epoch_%03d_%.3f.dat" %
(i, MAP_for_queries)))
print('Save the state_dict: %s' %
(str(i) + ' ' + str(MAP_for_queries)))
if MAP_for_queries < 10:
break