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data.py
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data.py
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import os
import csv
import json
import numpy as np
import torch
from util import prepro_sentence, prepro_sentence_pair, \
prepro_sentence_pair_single
def load_data(data_dir, task, k, seed, split):
data_dir = os.path.join(data_dir, "k-shot", task, "{}-{}".format(k, seed))
data = []
if os.path.exists(os.path.join(data_dir, "{}.tsv".format(split))):
with open(os.path.join(data_dir, "{}.tsv".format(split)), "r") as f:
for line in f:
data.append(line.strip().split("\t"))
if task=="CoLA":
data = [(sent, label) for _, label, _, sent in data]
elif task=="RTE":
data = [(json.dumps({
"text": p, "question": h[:-1] if h.endswith(".") else h
}), "1" if l=="entailment" else "0")
for _, p, h, l in data[1:]]
elif data[0]==["sentence", "label"]:
data = data[1:]
elif os.path.exists(os.path.join(data_dir, "{}.csv".format(split))):
with open(os.path.join(data_dir, "{}.csv".format(split)), "r") as f:
for label, text in csv.reader(f):
data.append((text, label))
else:
raise NotImplementedError(data_dir)
# all data should have (input, output) format
assert np.all([len(dp)==2 for dp in data])
return data
def prepare_data(tokenizer, train_data, test_data, max_length, max_length_per_example,
n_classes=2, templates=None, method_type="generative",
is_training=False, use_demonstrations=False,
ensemble=False, is_null=False):
if type(templates)==list:
transform = None
assert len(templates)==n_classes
else:
transform = templates
assert method_type in ["direct", "channel"]
bos_token_id = tokenizer.bos_token_id
eos_token_id = tokenizer.eos_token_id
'''
if method==direct, "sent prompt sent prompt ..."
- prompt should have space
- if demonstrations are used, 2nd sentneces to the input sentence should have space
if method==channel, "prompt sent prompt sent ..."
- input sent should have space
- if demonstrations are used, 2nd prompts to the input prompt should have space
'''
# For calibration method, following Zhao et al. 2021
if is_null:
assert test_data is None
assert method_type=="direct"
test_data = [("N/A", "0")]
prefixes_with_space = None
if transform is None:
templates = [template.strip() for template in templates]
if method_type=="direct":
templates = [" "+template for template in templates]
if use_demonstrations:
test_data = [(" "+sent, label) for sent, label in test_data]
elif method_type=="channel":
test_data = [(" "+sent, label) for sent, label in test_data]
if train_data is not None:
train_data = [(" "+sent, label) for sent, label in train_data]
prefixes_with_space = [tokenizer(" "+template)["input_ids"] for template in templates]
else:
raise NotImplementedError()
if transform is None:
test_inputs = [tokenizer(sent)["input_ids"] for sent, _ in test_data]
truncated = np.sum([len(inputs)>max_length_per_example-16 for inputs in test_inputs])
if truncated > 0:
test_inputs = [inputs[:max_length_per_example-16] for inputs in test_inputs]
print ("%d/%d truncated" % (truncated, len(test_inputs)))
prefixes = [tokenizer(template)["input_ids"] for template in templates]
idx = [idx for idx, _prefixes in enumerate(zip(*prefixes))
if not np.all([_prefixes[0]==_prefix for _prefix in _prefixes])][0]
else:
test_inputs = [transform(dp, tokenizer,
max_length_per_example-16,
groundtruth_only=is_training)
for dp in test_data]
if not is_training:
assert np.all([len(dp)==2 and
np.all([len(dpi)==n_classes for dpi in dp])
for dp in test_inputs])
if is_training:
assert not use_demonstrations
assert not ensemble
input_ids, attention_mask, token_type_ids = [], [], []
for test_input, dp in zip(test_inputs, test_data):
if transform is not None:
test_input, test_output = test_input
encoded = prepro_sentence_pair_single(
test_input, test_output, max_length, bos_token_id, eos_token_id
)
else:
prefix = prefixes[int(dp[1])]
if method_type=="channel":
encoded = prepro_sentence_pair_single(
prefix, test_input, max_length, bos_token_id, eos_token_id)
elif method_type=="direct":
encoded = prepro_sentence_pair_single(
test_input + prefix[:idx], prefix[idx:], max_length, bos_token_id, eos_token_id)
else:
raise NotImplementedError()
input_ids.append(encoded[0])
attention_mask.append(encoded[1])
token_type_ids.append(encoded[2])
return dict(input_ids=torch.LongTensor(input_ids),
attention_mask=torch.LongTensor(attention_mask),
token_type_ids=torch.LongTensor(token_type_ids))
if use_demonstrations:
if transform is not None:
raise NotImplementedError()
if ensemble:
return prepare_data_for_parallel(
tokenizer, train_data, test_data,
max_length, max_length_per_example,
method_type, n_classes,
test_inputs, prefixes, idx, prefixes_with_space,
bos_token_id, eos_token_id)
assert train_data is not None
demonstrations = []
np.random.shuffle(train_data)
for sent, label in train_data:
if len(demonstrations)>0:
if method_type=="direct":
sent = " " + sent
elif method_type=="channel":
prefixes = prefixes_with_space
if transform is None:
tokens = tokenizer(sent)["input_ids"][:max_length_per_example]
else:
tokens = transform(sent, tokenizer, max_length_per_example)
prefix = prefixes[(int(label))]
if method_type=="channel":
tokens = prefix + tokens
elif method_type=="direct":
tokens = tokens + prefix
else:
raise NotImplementedError()
demonstrations += tokens
if transform is None:
# check if idx is set well
for i in range(n_classes):
for j in range(i+1, n_classes):
assert prefixes[i][:idx]==prefixes[j][:idx]
assert prefixes[i][idx]!=prefixes[j][idx]
input_tensors = []
for i in range(n_classes):
if transform is None:
prefix = prefixes[i].copy()
if method_type=="channel":
if use_demonstrations:
prefix = demonstrations.copy() + prefix
tensor = prepro_sentence_pair([prefix], test_inputs, max_length,
bos_token_id, eos_token_id,
allow_truncation=use_demonstrations)
elif method_type=="direct":
if use_demonstrations:
prompt = [demonstrations.copy() + test_input + prefix[:idx] for test_input in test_inputs]
else:
prompt = [test_input + prefix[:idx] for test_input in test_inputs]
tensor = prepro_sentence_pair(prompt,
[prefix[idx:]], max_length,
bos_token_id, eos_token_id,
allow_truncation=use_demonstrations)
else:
raise NotImplementedError()
else:
input_ids, attention_mask, token_type_ids = [], [], []
for input_, output_ in test_inputs:
encoded = prepro_sentence_pair_single(
input_[i], output_[i], max_length,
bos_token_id,
None if is_generation else eos_token_id,
allow_truncation=False)
input_ids.append(encoded[0])
attention_mask.append(encoded[1])
token_type_ids.append(encoded[2])
tensor = dict(input_ids=torch.LongTensor(input_ids),
attention_mask=torch.LongTensor(attention_mask),
token_type_ids=torch.LongTensor(token_type_ids))
input_tensors.append(tensor)
return input_tensors
def prepare_data_for_parallel(tokenizer, train_data, test_data,
max_length, max_length_per_example,
method_type, n_classes,
test_inputs, prefixes, idx, prefixes_with_space,
bos_token_id, eos_token_id):
# get len(train_data) number of demonstrations
assert train_data is not None
demonstrations_list = []
np.random.shuffle(train_data)
for sent, label in train_data:
tokens = tokenizer(sent)["input_ids"][:max_length_per_example]
prefix = prefixes[(int(label))]
if method_type=="channel":
tokens = prefix + tokens
elif method_type=="direct":
tokens = tokens + prefix
else:
raise NotImplementedError()
demonstrations_list.append(tokens)
# check if idx is set well
for i in range(n_classes):
for j in range(i+1, n_classes):
assert prefixes[i][:idx]==prefixes[j][:idx]
assert prefixes[i][idx]!=prefixes[j][idx]
input_tensors = []
for i in range(n_classes):
if method_type=="channel":
prefix = prefixes_with_space[i].copy()
prompt = [demonstrations + prefix
for demonstrations in demonstrations_list]
tensor = prepro_sentence_pair(
prompt, test_inputs, max_length,
bos_token_id, eos_token_id,
allow_truncation=True)
elif method_type=="direct":
prefix = prefixes[i].copy()
prompt = [demonstrations.copy() + test_input + prefix[:idx]
for test_input in test_inputs
for demonstrations in demonstrations_list]
tensor = prepro_sentence_pair(prompt,
[prefix[idx:]], max_length,
bos_token_id, eos_token_id,
allow_truncation=True)
else:
raise NotImplementedError()
input_tensors.append(tensor)
return input_tensors