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data_utils.py
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data_utils.py
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#(c) 2021 NCSOFT Corporation & Korea University. All rights reserved.
import logging
from collections import defaultdict
from itertools import chain
import torch
from torch.utils.data import DataLoader, TensorDataset
from python_tf_idf.tfidf import TfIdf
from utils_focus import get_dataset_only_train_dev, get_dataset_only_test
SPECIAL_TOKENS = ["<machine>", "<human>", "<persona>", "<knowledge>"]
ATTR_TO_SPECIAL_TOKEN = {'additional_special_tokens': ['<machine>', '<human>', '<persona>', '<knowledge>']}
BART_MODEL_INPUTS = ["input_ids", "decoder_input_ids", "lm_labels", "token_type_ids", "mc_token_ids",
"persona_candidates", "persona_can_idx", "persona_grounding",
"knowledge_candidates", "knowledge_can_idx", "knowledge_grounding", "reply"]
GPT2_MODEL_INPUTS = ["input_ids", "lm_labels", "token_type_ids", "mc_token_ids",
"persona_candidates", "persona_can_idx", "persona_grounding",
"knowledge_candidates", "knowledge_can_idx", "knowledge_grounding", "reply"]
GPT2_CTXT_MODEL_INPUTS = ["input_ids", "input_eos", "lm_labels", "token_type_ids", "mc_token_ids",
"persona_candidates", "persona_can_idx", "persona_grounding",
"knowledge_candidates", "knowledge_can_idx", "knowledge_grounding",
"tot_knowledge", "tot_knowledge_token_ids", "tot_knowledge_eos", "reply", "dialog", "dialog_tti"]
BART_CTXT_MODEL_INPUTS = ["input_ids", "input_eos", "decoder_input_ids", "lm_labels", "token_type_ids", "mc_token_ids",
"persona_candidates", "persona_can_idx", "persona_grounding",
"knowledge_candidates", "knowledge_can_idx", "knowledge_grounding",
"tot_knowledge", "tot_knowledge_eos", "reply", "dialog"]
BART_PADDED_INPUTS = ["decoder_input_ids", "lm_labels", "token_type_ids"]
GPT2_PADDED_INPUTS = ["input_ids", "lm_labels", "token_type_ids"]
logger = logging.getLogger(__file__)
num_persona = 5
num_knowledge = 10
def pad_dataset_gpt2_inctxt(dataset, padding=0):
""" Pad the dataset. This could be optimized by defining a Dataset class and padding at the batch level, but this is simpler. """
max_l = max(len(x) for x in dataset["input_ids"])
max_l_reply = max(len(x) for x in dataset["reply"])
# print([x for x in dataset])
# exit()
############### to delete examples with too long knowledge candidates ######################
remove_list = list()
persona_nonlist = list()
#print("knowledge candidates: ", dataset["knowledge_candidates"])
for idx_1, x in enumerate(dataset["knowledge_candidates"]):
for idx_2, i in enumerate(x):
if type(i) != list:
remove_list.append(idx_1)
elif len(i) > 500:
dataset["knowledge_candidates"][idx_1][idx_2] = i[:500]
for idx_1, x in enumerate(dataset["persona_candidates"]):
#print("? ", len(x))
if len(x) != num_persona or type(x) != list:
remove_list.append(idx_1)
persona_nonlist.append(idx_1)
for idx_2, i in enumerate(x):
if len(i) > 500 or type(i) != list:
remove_list.append(idx_1)
for idx_1, x in enumerate(dataset["tot_knowledge"]):
for idx_2, i in enumerate(x):
if type(i) != list:
remove_list.append(idx_1)
elif len(i) > 200:
dataset["tot_knowledge"][idx_1][idx_2] = i[:200]
remove_list = list(set(remove_list))
print("remove list: ", len(remove_list))
if len(remove_list) != 0:
new_dataset = defaultdict(list)
for input in GPT2_MODEL_INPUTS:
for i, element in enumerate(dataset[input]):
if i in remove_list:
continue
else:
if input == 'persona_candidates':
assert len(element) == num_persona
new_dataset[input].append(element)
else:
new_dataset = dataset
max_l_knowledge_cans = max([len(i) for x in new_dataset["knowledge_candidates"] for i in x])
max_l_tot_knowledge = max([len(i) for x in new_dataset["tot_knowledge"] for i in x])
max_l_persona_cans = max([len(i) for x in new_dataset["persona_candidates"] for i in x])
max_l_dialog = max(len(x) for x in new_dataset["dialog"])
for name in GPT2_PADDED_INPUTS:
new_dataset[name] = [x + [padding if name != "lm_labels" else -100] * (max_l - len(x)) for x in
new_dataset[name]]
new_dataset["reply"] = [x + [padding if name != "lm_labels" else -100] * (max_l_reply - len(x)) for x in
new_dataset["reply"]]
knowledge_list = list()
for i, knowledges in enumerate(new_dataset["knowledge_candidates"]):
candidates_list = list()
for candidates in knowledges:
padded_candidate = candidates + [padding] * (max_l_knowledge_cans - len(candidates))
candidates_list.append(padded_candidate)
knowledge_list.append(candidates_list)
new_dataset["knowledge_candidates"] = knowledge_list
persona_list = list()
for i, personas in enumerate(new_dataset["persona_candidates"]):
candidates_list = list()
for candidates in personas:
padded_candidate = candidates + [padding] * (max_l_persona_cans - len(candidates))
candidates_list.append(padded_candidate)
persona_list.append(candidates_list)
new_dataset["persona_candidates"] = persona_list
tot_knowledge_list = list()
for i, tot_kn in enumerate(new_dataset["tot_knowledge"]):
candidates_list = list()
for candidates in tot_kn:
padded_candidate = candidates + [padding] * (max_l_tot_knowledge - len(candidates))
candidates_list.append(padded_candidate)
tot_knowledge_list.append(candidates_list)
new_dataset["tot_knowledge"] = tot_knowledge_list
tot_knowledge_token_ids_list = list()
for i, tot_kn_ids in enumerate(new_dataset["tot_knowledge_token_ids"]):
candidates_list = list()
for candidates in tot_kn_ids:
padded_candidate = candidates + [padding] * (max_l_tot_knowledge - len(candidates))
candidates_list.append(padded_candidate)
tot_knowledge_token_ids_list.append(candidates_list)
new_dataset["tot_knowledge_token_ids"] = tot_knowledge_token_ids_list
new_dataset["dialog"] = [x + [padding] * (max_l_dialog - len(x)) for x in new_dataset["dialog"]]
new_dataset["dialog_tti"] = [x + [padding] * (max_l_dialog - len(x)) for x in new_dataset["dialog_tti"]]
#print("dial: ", new_dataset["dialog"][0], "dial_tti", new_dataset["dialog_tti"][0])
return new_dataset
def pad_dataset_bart_inctxt(dataset, padding=1):
""" Pad the dataset. This could be optimized by defining a Dataset class and padding at the batch level, but this is simpler. """
max_enc_l = max(len(x) for x in dataset["input_ids"])
max_l = max(len(x) for x in dataset["decoder_input_ids"])
max_l_reply = max(len(x) for x in dataset["reply"])
###############to delete examples with too long knowledge candidates######################
remove_list = list()
persona_nonlist = list()
for idx_1, x in enumerate(dataset["knowledge_candidates"]):
for idx_2, i in enumerate(x):
if len(i) > 500 or type(i) != list:
print("knowledge", len(i), type(i))
remove_list.append(idx_1)
for idx_1, x in enumerate(dataset["persona_candidates"]):
if len(x) != num_persona or type(x) != list:
remove_list.append(idx_1)
persona_nonlist.append(idx_1)
for idx_2, i in enumerate(x):
if len(i) > 500 or type(i) != list:
remove_list.append(idx_1)
for idx_1, x in enumerate(dataset["tot_knowledge"]):
for idx_2, i in enumerate(x):
if type(i) != list:
remove_list.append(idx_1)
elif len(i) > 200:
dataset["tot_knowledge"][idx_1][idx_2] = i[:200]
remove_list = list(set(remove_list))
print("remove list: ", len(remove_list))
if len(remove_list) != 0:
new_dataset = defaultdict(list)
for input in BART_MODEL_INPUTS:
for i, element in enumerate(dataset[input]):
if i in remove_list:
continue
else:
if input == 'persona_candidates':
assert len(element) == num_persona
new_dataset[input].append(element)
else:
new_dataset = dataset
max_l_knowledge_cans = max([len(i) for x in new_dataset["knowledge_candidates"] for i in x])
max_l_tot_knowledge = max([len(i) for x in new_dataset["tot_knowledge"] for i in x])
max_l_persona_cans = max([len(i) for x in new_dataset["persona_candidates"] for i in x])
max_l_dialog = max(len(x) for x in new_dataset["dialog"])
for name in BART_PADDED_INPUTS:
new_dataset[name] = [x + [padding if name != "lm_labels" else -100] * (max_l - len(x)) for x in new_dataset[name]]
new_dataset["input_ids"] = [x + [padding if name != "lm_labels" else -100] * (max_enc_l - len(x)) for x in new_dataset["input_ids"]]
new_dataset["reply"] = [x + [padding if name != "lm_labels" else -100] * (max_l_reply - len(x)) for x in new_dataset["reply"]]
knowledge_list = list()
for i, knowledges in enumerate(new_dataset["knowledge_candidates"]):
candidates_list = list()
for candidates in knowledges:
padded_candidate = candidates + [padding] * (max_l_knowledge_cans - len(candidates))
candidates_list.append(padded_candidate)
knowledge_list.append(candidates_list)
new_dataset["knowledge_candidates"] = knowledge_list
persona_list = list()
for i, personas in enumerate(new_dataset["persona_candidates"]):
candidates_list = list()
for candidates in personas:
padded_candidate = candidates + [padding] * (max_l_persona_cans - len(candidates))
candidates_list.append(padded_candidate)
persona_list.append(candidates_list)
new_dataset["persona_candidates"] = persona_list
tot_knowledge_list = list()
for i, tot_kn in enumerate(new_dataset["tot_knowledge"]):
candidates_list = list()
for candidates in tot_kn:
padded_candidate = candidates + [padding] * (max_l_tot_knowledge - len(candidates))
candidates_list.append(padded_candidate)
tot_knowledge_list.append(candidates_list)
new_dataset["tot_knowledge"] = tot_knowledge_list
tot_knowledge_token_ids_list = list()
for i, tot_kn_ids in enumerate(new_dataset["tot_knowledge_token_ids"]):
candidates_list = list()
for candidates in tot_kn_ids:
padded_candidate = candidates + [padding] * (max_l_tot_knowledge - len(candidates))
candidates_list.append(padded_candidate)
tot_knowledge_token_ids_list.append(candidates_list)
new_dataset["tot_knowledge_token_ids"] = tot_knowledge_token_ids_list
new_dataset["dialog"] = [x + [padding] * (max_l_dialog - len(x)) for x in new_dataset["dialog"]]
new_dataset["dialog_tti"] = [x + [padding] * (max_l_dialog - len(x)) for x in new_dataset["dialog_tti"]]
return new_dataset
def add_special_tokens_(model, tokenizer):
""" Add special tokens to the tokenizer and the model if they have not already been added. """
orig_num_tokens = len(tokenizer.encoder)
if type(tokenizer).__name__ == 'GPT2Tokenizer':
ATTR_TO_SPECIAL_TOKEN['pad_token'] = '<pad>'
print('<pad> token added!')
tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN) # doesn't add if they are already there
num_added_tokens = len(SPECIAL_TOKENS)
print("orig num", orig_num_tokens, "num_added", num_added_tokens) #50265, 4
if num_added_tokens > 0:
model.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)
def choose_knowledge(knowledge, question):
table = TfIdf()
for i, paragraph in enumerate(knowledge):
table.add_document(i, paragraph)
results = table.similarities(question)
results = sorted(results, key=lambda x: x[1], reverse=True)
result_idx = [i[0] for i in results[:5]]
chosen_knowledge = [knowledge[ri] for ri in result_idx]
return chosen_knowledge
def build_input_from_segments_bart_inctxt(persona, knowledge, history, persona_cans, persona_grounding, knowledge_cans, knowledge_answer_idx, ID, tokenizer, lm_labels=False, testset=False, inference=False, with_eos=True):
""" Build a sequence of input from 3 segments: persona, history and last reply. """
bos = tokenizer.bos_token_id
eos = tokenizer.eos_token_id
machine_st, human_st, persona_st, knowledge_st = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS)
#machine: 50265 human: 50266 persona: 50267 knowledge: 50268 padding: 1 bos: 0 eos: 2
history, reply = history[:-1], history[-1]
history = [[human_st if i % 2 == 0 else machine_st] + s for i, s in enumerate(history)]
reply = reply
reply_tti = [machine_st] * (len(reply)+2)
if inference == False:
if len(knowledge) > 1:
chosen_knowledge = choose_knowledge(knowledge, history[-1])
else:
chosen_knowledge = knowledge[0:5]
else:
chosen_knowledge = knowledge_cans[knowledge_answer_idx]
paragraphs = []
for para in chosen_knowledge:
#for para in knowledge:
if len(para) > 100:
short_para = para[:100]
else:
short_para = para
paragraphs.append(short_para)
if testset == False:
if len(history) == 1:
enc_sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + history
dec_sequence = [bos] + reply + ([eos] if with_eos else [])
dialog = [[bos]] + history
else:
enc_sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + [list(chain(*history))]
dec_sequence = [bos] + reply + ([eos] if with_eos else [])
dialog = [[bos]] + [list(chain(*history))]
else:
if len(history) == 1:
enc_sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + history
dec_sequence = [bos] + [machine_st]
reply_tti = [machine_st]
dialog = [[bos]] + history
else:
enc_sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + [list(chain(*history))]
dec_sequence = [bos] + [machine_st]
reply_tti = [machine_st]
dialog = [[bos]] + [list(chain(*history))]
max_tot_k_token_ids = max([len(i) for i in paragraphs])
instance = {}
instance["input_ids"] = list(chain(*enc_sequence))
instance["input_eos"] = len(list(chain(*enc_sequence))) - 1
instance["dialog"] = list(chain(*dialog))
instance["decoder_input_ids"] = dec_sequence
instance["token_type_ids"] = reply_tti
if lm_labels:
if len(dec_sequence) > 1:
instance["lm_labels"] = [-100] + dec_sequence[1:]
else:
instance["lm_labels"] = [-100]
instance["persona_candidates"] = [[bos] + can + [persona_st] + [eos] for can in persona_cans]
instance["persona_can_idx"] = [len(can)-1 for can in instance["persona_candidates"]]
instance["persona_grounding"] = persona_grounding
instance["knowledge_candidates"] = [[bos] + can[:100] + [knowledge_st] + [eos] if len(can) > 100 else [bos] + can + [knowledge_st] + [eos] for can in knowledge_cans]
instance["knowledge_can_idx"] = [len(can)-1 for can in instance["knowledge_candidates"]]
instance["knowledge_grounding"] = knowledge_answer_idx
instance["mc_token_ids"] = 0
instance["dialog_ID"] = ID
instance["reply"] = reply[1:]
instance['tot_knowledge'] = paragraphs
instance["tot_knowledge_token_ids"] = [[knowledge_st] * max_tot_k_token_ids + [tokenizer.pad_token_id] * (100 - max_tot_k_token_ids) for _ in range(5)]
instance['tot_knowledge_eos'] = [len(p)-1 for p in paragraphs]
assert len(instance["decoder_input_ids"]) == len(instance["lm_labels"])
return instance
def build_input_from_segments_gpt2_inctxt(persona, knowledge, history, persona_cans, persona_grounding, knowledge_cans, knowledge_answer_idx, ID, tokenizer, lm_labels=False, testset=False, inference=False, with_eos=True):
""" Build a sequence of input from 3 segments: persona, history and last reply. """
machine_st, human_st, persona_st, knowledge_st = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS)
# machine: 50257
# human: 50258
# persona: 50259
# knowledge: 50260
# padding: 50261
# bos: 50256
# eos: 50256
bos = tokenizer.bos_token_id
eos = tokenizer.eos_token_id
history, reply = history[:-1], history[-1]
history = [[human_st if i % 2 == 0 else machine_st] + s for i, s in enumerate(history)]
history_tti = [[sent[0]] * len(sent) for sent in history]
reply = [machine_st] + reply
reply_tti = [machine_st] * (len(reply)+1)
if inference == False:
if len(knowledge) > 1:
chosen_knowledge = choose_knowledge(knowledge, history[-1])
else:
chosen_knowledge = knowledge[0:5]
else:
chosen_knowledge = knowledge_cans[knowledge_answer_idx]
paragraphs = []
for para in chosen_knowledge:
#for para in knowledge:
if len(para) > 100:
short_para = para[:100]
else:
short_para = para
paragraphs.append(short_para)
if testset == False:
if len(history) == 1:
sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + history + [reply + ([eos] if with_eos else [])]
dialog = [[bos]] + history + [reply + ([eos] if with_eos else [])]
else:
sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + [list(chain(*history))] + [reply + ([eos] if with_eos else [])]
dialog = [[bos]] + [list(chain(*history))] + [reply + ([eos] if with_eos else [])]
persona_tti = [persona_st] * (len(list(chain(*persona))) + 2) # bos, eos
tti = persona_tti + list(chain(*history_tti)) + reply_tti
dialog_tti = [history_tti[0][0]] + list(chain(*history_tti)) + reply_tti
else:
if len(history) == 1:
sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + history + [[machine_st]]
dialog = [[bos]] + history + [[machine_st]]
else:
sequence = [[bos]] + [[persona_st] + list(chain(*persona))] + [list(chain(*history))] + [[machine_st]]
dialog = [[bos]] + [list(chain(*history))] + [[machine_st]]
persona_tti = [persona_st] * (len(list(chain(*persona))) + 2)
tti = persona_tti + list(chain(*history_tti)) + [machine_st]
dialog_tti = [history_tti[0][0]] + list(chain(*history_tti)) + [machine_st]
mc_token_ids = list(chain(*sequence))
mc_list = [x for x, y in enumerate(mc_token_ids) if y == machine_st]
max_tot_k_token_ids = max([len(i) for i in paragraphs])
instance = {}
instance["input_ids"] = list(chain(*sequence))
instance["input_eos"] = len(tti)-1
instance["token_type_ids"] = tti
instance["dialog"] = list(chain(*dialog))
instance["dialog_tti"] = dialog_tti
if lm_labels:
instance["lm_labels"] = ([-100] * sum(len(s) for s in dialog[:-1])) + [-100] + dialog[-1][1:]
instance["persona_candidates"] = [can + [persona_st] + [eos] for can in persona_cans]
instance["persona_can_idx"] = [len(can)-1 for can in instance["persona_candidates"]]
instance["persona_grounding"] = persona_grounding
instance["knowledge_candidates"] = [[bos] + can[:100] + [knowledge_st] + [eos] if len(can) > 100 else [bos] + can + [knowledge_st] + [eos] for can in knowledge_cans]
instance["knowledge_can_idx"] = [len(can)-1 for can in instance["knowledge_candidates"]]
instance["knowledge_grounding"] = knowledge_answer_idx
instance['tot_knowledge'] = paragraphs
instance["mc_token_ids"] = mc_list[-1]
instance["dialog_ID"] = ID
instance["reply"] = reply[1:]
instance["tot_knowledge_token_ids"] = [[knowledge_st] * max_tot_k_token_ids + [tokenizer.pad_token_id] * (100 - max_tot_k_token_ids) for _ in range(5)]
instance['tot_knowledge_eos'] = [len(p)-1 for p in paragraphs]
assert len(instance["input_ids"]) == len(instance["token_type_ids"])
assert len(instance["dialog"]) == len(instance["lm_labels"]) == len(instance["dialog_tti"])
return instance
def get_data_loaders(args, tokenizer, generation=False):
""" Prepare the dataset for training and evaluation """
plan = get_dataset_only_train_dev(tokenizer, args.train_dataset_path, args.train_dataset_cache, args.dev_dataset_path, args.dev_dataset_cache)
model_name = args.model_name
logger.info("Build inputs and labels")
datasets = {"train": defaultdict(list), "valid": defaultdict(list)}
for dataset_name, dataset in plan.items():
print(dataset_name, len(dataset))
if generation == True:
testset = True
else:
testset = False
for dialog in dataset:
ID = dialog["dialogID"]
persona = dialog['persona']
knowledge = dialog['knowledge']
utterance = dialog['utterance']
for i, utt in enumerate(utterance):
history = utt['dialog'][-(2*args.max_history):]
persona_cans = utt['persona_candidates']
persona_grouding = utt['persona_grounding']
knowledge_cans = utt['knowledge_candidates']
knowledge_answer_idx = utt['knowledge_answer_index']
if model_name == 'GPT2' or model_name == 'transformer-decoder':
instance = build_input_from_segments_gpt2_inctxt(persona, knowledge, history, persona_cans, persona_grouding,
knowledge_cans, knowledge_answer_idx, ID, tokenizer,
lm_labels=True, testset=testset, inference=args.inference)
elif model_name == 'BART' or model_name == 'transformer-encdec':
instance = build_input_from_segments_bart_inctxt(persona, knowledge, history, persona_cans, persona_grouding,
knowledge_cans, knowledge_answer_idx, ID, tokenizer,
lm_labels=True, testset=testset, inference=args.inference)
for input_name, input_array in instance.items():
datasets[dataset_name][input_name].append(input_array)
logger.info("Pad inputs and convert to Tensor")
tensor_datasets = {"train": [], "valid": []}
for dataset_name, dataset in datasets.items():
if model_name == 'GPT2' or model_name == 'transformer-decoder':
dataset = pad_dataset_gpt2_inctxt(dataset, padding=tokenizer.pad_token_id)
MODEL_INPUTS = GPT2_CTXT_MODEL_INPUTS
elif model_name == 'BART' or model_name == 'transformer-encdec':
dataset = pad_dataset_bart_inctxt(dataset, padding=tokenizer.pad_token_id)
MODEL_INPUTS = BART_CTXT_MODEL_INPUTS
for input_name in MODEL_INPUTS:
#print("tensor: ", input_name, "len: ", len(dataset[input_name]))
tensor = torch.tensor(dataset[input_name], device=args.device)
tensor_datasets[dataset_name].append(tensor)
logger.info("Build train and validation dataloaders")
train_dataset, valid_dataset = TensorDataset(*tensor_datasets["train"]), TensorDataset(*tensor_datasets["valid"])
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None
valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None
train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, shuffle=(not args.distributed))
valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.valid_batch_size, shuffle=False)
logger.info("Train dataset (Batch, Candidates, Seq length): {}".format(train_dataset.tensors[0].shape)) #[131438, 4, 280]
logger.info("Valid dataset (Batch, Candidates, Seq length): {}".format(valid_dataset.tensors[0].shape)) #[7801, 20, 184]
return train_loader, valid_loader, train_sampler, valid_sampler
def get_testdata_loaders(args, tokenizer, generation=True):
""" Prepare the dataset for training and evaluation """
plan = get_dataset_only_test(tokenizer, args.test_dataset_path, args.test_dataset_cache)
model_name = args.model_name
logger.info("Build inputs and labels")
datasets = {"test": defaultdict(list)}
for dataset_name, dataset in plan.items():
print(dataset_name, len(dataset))
if generation == True:
testset = True
else:
testset = False
for dialog in dataset:
ID = dialog["dialogID"]
persona = dialog['persona']
knowledge = dialog['knowledge']
utterance = dialog['utterance']
for i, utt in enumerate(utterance):
history = utt['dialog'][-(2*args.max_history):]
persona_cans = utt['persona_candidates']
persona_grouding = utt['persona_grounding']
knowledge_cans = utt['knowledge_candidates']
knowledge_answer_idx = utt['knowledge_answer_index']
if model_name == 'GPT2' or model_name == 'transformer-decoder':
instance = build_input_from_segments_gpt2_inctxt(persona, knowledge, history, persona_cans,
persona_grouding,
knowledge_cans, knowledge_answer_idx, ID,
tokenizer,
lm_labels=True, testset=testset,
inference=args.inference)
elif model_name == 'BART' or model_name == 'transformer-encdec':
instance = build_input_from_segments_bart_inctxt(persona, knowledge, history, persona_cans, persona_grouding,
knowledge_cans, knowledge_answer_idx, ID, tokenizer,
lm_labels=True, testset=testset)
for input_name, input_array in instance.items():
datasets[dataset_name][input_name].append(input_array)
logger.info("Pad inputs and convert to Tensor")
tensor_datasets = {"test": []}
for dataset_name, dataset in datasets.items():
if model_name == 'GPT2' or model_name == 'transformer-decoder':
dataset = pad_dataset_gpt2_inctxt(dataset, padding=tokenizer.pad_token_id)
MODEL_INPUTS = GPT2_CTXT_MODEL_INPUTS
elif model_name == 'BART' or model_name == 'transformer-encdec':
dataset = pad_dataset_bart_inctxt(dataset, padding=tokenizer.pad_token_id)
MODEL_INPUTS = BART_CTXT_MODEL_INPUTS
for input_name in MODEL_INPUTS:
tensor = torch.tensor(dataset[input_name], device=args.device)
tensor_datasets[dataset_name].append(tensor)
logger.info("Build train and validation dataloaders")
test_dataset = TensorDataset(*tensor_datasets["test"])
test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None
test_loader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.test_batch_size, shuffle=False)
logger.info("Test dataset (Batch, Candidates, Seq length): {}".format(test_dataset.tensors[0].shape))
return test_loader, test_sampler