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utils.py
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utils.py
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import os
from pathlib import Path
import torch
from transformers import DataCollatorForSeq2Seq
import re
import numpy as np
def get_datacollator(tokenizer, model, data_args, training_args):
label_pad_token_id = (
-100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
)
data_collator = DataCollatorForSeq2Seq(
tokenizer,
model=model,
label_pad_token_id=label_pad_token_id,
pad_to_multiple_of=8 if training_args.fp16 else None,
)
return data_collator
def pad_list(sequences, pad_token_id):
max_len = max([len(x) for x in sequences])
for i, input_ids in enumerate(sequences):
sequences[i] = input_ids + [pad_token_id] * (max_len - len(input_ids))
sequences = torch.LongTensor(sequences)
return sequences
def load_templates(template_dir, template_args, mode="e2e"):
if not isinstance(template_dir, Path):
template_dir = Path(template_dir)
if not template_args.eval_typed_templates:
raise NotImplemented
else:
num_types = len([d for d in os.listdir(template_dir) if d.startswith("type")])
all_templates = dict()
type_combination_indices = dict()
if template_args.eval_specific_type > -1:
type_list = [template_args.eval_specific_type]
else:
type_list = list(range(num_types))
for type_i in type_list:
if mode == "e2e":
dict_key = torch.load(
template_dir / f"type_{type_i}" / "field_combination.pt"
)
else:
dict_key = type_i
type_combination_indices[dict_key] = type_i
if template_args.local_search_prune_topk is None:
all_templates[dict_key] = torch.load(
template_dir / f"type_{type_i}" / "templates.pt"
)
else:
templates = torch.load(template_dir / f"type_{type_i}" / "templates.pt")
token_scores = torch.load(
template_dir / f"type_{type_i}" / "token_scores.pt"
)
score_stats = [
(
s.sum(dim=-1)[0]
/ s.lt(0).float().sum(dim=-1).pow(template_args.length_penalty)
)
.mean()
.item()
for s in token_scores
]
rank_ids = np.argsort(score_stats)
all_templates[dict_key] = []
for i in range(len(rank_ids)):
try:
template = templates[rank_ids[-(i + 1)]]
if len(template) > 3:
all_templates[dict_key].append(template)
if (
len(all_templates[dict_key])
== template_args.local_search_prune_topk
):
break
except IndexError:
continue
return all_templates, type_combination_indices
def exact_match_template(
target_text,
raw_field_dict,
field_max_lens,
tokenizer,
no_space_tokenizer,
mode="e2e",
):
"""
Implements Delexicalization for generating templates.
"""
tokenized_raw_field_dict = {
k: [
tokenizer.encode(
tok if mode == "e2e" else tok.lower(), add_special_tokens=False
)
for tok in tok_list
]
for k, tok_list in raw_field_dict.items()
}
if mode == "synthbio":
for k, tok_list in raw_field_dict.items():
for tok in tok_list:
tokenized_raw_field_dict[k].append(
no_space_tokenizer.encode(tok.lower(), add_special_tokens=False)
)
to_replace = []
max_field_len = max(
[
max([len(c) for c in content_list])
for content_list in tokenized_raw_field_dict.values()
]
)
cand_ngrams = {}
for n in range(1, max_field_len + 3):
cand_ngrams[n] = []
for i in range(0, len(target_text) + 1 - n):
cand_ngrams[n].append((i, target_text[i : i + n]))
for field_name, field_contents in tokenized_raw_field_dict.items():
for field_content in field_contents:
field_cands = cand_ngrams[len(field_content)]
for cand_i, cand_ngram in field_cands:
if tuple(cand_ngram) == tuple(field_content):
to_replace.append(
(
cand_i,
len(cand_ngram),
field_name,
tokenizer.decode(cand_ngram),
)
)
to_replace = sorted(to_replace, key=lambda x: x[1], reverse=True)
for i in range(len(to_replace)):
if to_replace[i] is None:
continue
replace_start, replace_len, field_name, _ = to_replace[i]
field_max_len = field_max_lens[field_name]
field_non_terminals = tokenizer.encode(
"".join([f"<{field_name}-{i}>" for i in range(field_max_len)]),
add_special_tokens=False,
)
target_text = (
target_text[:replace_start]
+ field_non_terminals
+ target_text[replace_start + replace_len :]
)
for j in range(i + 1, len(to_replace)):
if to_replace[j] is None:
continue
(
later_replace_start,
later_replace_len,
later_field_name,
later_ngram,
) = to_replace[j]
if later_replace_start < replace_start:
if later_replace_start + later_replace_len > replace_start:
to_replace[j] = None
elif (
later_replace_start >= replace_start
and later_replace_start < replace_start + replace_len
):
to_replace[j] = None
else:
to_replace[j] = (
later_replace_start - replace_len + field_max_len,
later_replace_len,
later_field_name,
later_ngram,
)
return [2, 0] + target_text + [2]
def decode_and_clean_template(template, tokenizer, field_to_maxlen_dict):
clean_template = tokenizer.decode(template)
clean_template = re.sub("<(s|/s|pad)>", "", clean_template)
tag_set = {
re.sub(r"[<>\-\d+]", "", w)
for w in re.findall(r"<[a-zA-Z_\s]+-\d+>", clean_template)
} # detect all the tags that look like <{some characters}_{digit}>
for tag in tag_set:
maxlen_tag = field_to_maxlen_dict[tag]
clean_template = re.sub(
re.compile("(?!>)" + f"<{tag}-\d>" * maxlen_tag),
f" [{tag.upper()}]",
clean_template,
)
clean_template = clean_template.replace(" ", " ").strip()
return clean_template
def compute_logprobs(model, input_ids, output_ids, pad_token_id, batch_size=16):
output_logprobs = []
for batch_start in range(0, output_ids.size(0), batch_size):
batch_input_ids = input_ids[batch_start : batch_start + batch_size].to(
model.device
)
batch_output_ids = output_ids[batch_start : batch_start + batch_size].to(
model.device
)
batch_outputs = model(
input_ids=batch_input_ids,
attention_mask=batch_input_ids.ne(pad_token_id),
decoder_input_ids=batch_output_ids,
return_dict=True,
)
batch_output_logprobs = (
batch_outputs.logits[:, :-1]
.log_softmax(dim=-1)
.gather(index=batch_output_ids[:, 1:].unsqueeze(-1).cuda(), dim=-1)
.squeeze(-1)
)
batch_output_logprobs = batch_output_logprobs * (
batch_output_ids[:, 1:].cuda().ne(pad_token_id)
)
output_logprobs.append(batch_output_logprobs)
del batch_outputs
output_logprobs = torch.cat(output_logprobs)
return output_logprobs
def get_field_name(field_id, tokenizer):
return re.sub(r"[<>\-\d+]", "", tokenizer._convert_id_to_token(field_id))
def align_and_chunk_logprobs(template, tokenizer, template_alignments, output_logprobs):
if not isinstance(template, list):
template = template.cpu().tolist()
num_non_pad = torch.tensor(template).ne(tokenizer.pad_token_id).sum().item()
all_aligned_logprobs = []
for i in range(output_logprobs.size(0)):
aligned_logprobs = []
for j in range(num_non_pad):
s, e = template_alignments[i][j : j + 2]
if s == 0 and e == 1:
aligned_logprobs.append(-1)
else:
aligned_logprobs.append(output_logprobs[i, s - 1 : e - 1].sum().item())
all_aligned_logprobs.append(aligned_logprobs)
all_aligned_logprobs = torch.tensor(all_aligned_logprobs)
template_chunk_indices = []
template_curr = 0
while template_curr < num_non_pad:
template_chunk_indices.append(template_curr)
token_id = template[template_curr]
if token_id not in tokenizer.all_field_tokens:
template_curr += 1
else:
field = get_field_name(token_id, tokenizer)
while (
token_id in tokenizer.all_field_tokens
and get_field_name(token_id, tokenizer) == field
):
template_curr += 1
token_id = template[template_curr]
template_chunk_indices.append(template_curr)
all_chunked_logprobs = []
for i, chunk_start in enumerate(template_chunk_indices[:-1]):
chunk_end = template_chunk_indices[i + 1]
all_chunked_logprobs.append(
all_aligned_logprobs[:, chunk_start:chunk_end].sum(dim=-1)
)
all_chunked_logprobs = torch.stack(all_chunked_logprobs, dim=-1)
return all_aligned_logprobs, all_chunked_logprobs, template_chunk_indices