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eval_e2e_templates.py
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eval_e2e_templates.py
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import os
import torch
from pathlib import Path
from transformers import (
BartForConditionalGeneration,
AutoTokenizer,
HfArgumentParser,
Seq2SeqTrainingArguments,
)
from train_seq2seq import ModelArguments, DataTrainingArguments
from template_args import TemplateArguments
import utils
import load_utils
from data import TabularData
from tqdm import tqdm, trange
import numpy as np
from field_transformation import _e2e_field_transformations_complex
parser = HfArgumentParser(
(
ModelArguments,
DataTrainingArguments,
Seq2SeqTrainingArguments,
TemplateArguments,
)
)
(
model_args,
data_args,
training_args,
template_args,
) = parser.parse_args_into_dataclasses()
model, tokenizer, no_space_tokenizer = load_utils.load_model_and_tokenizer(model_args)
model.init_for_template_search(tokenizer, None, False)
model.resize_token_embeddings(len(tokenizer))
model.cuda()
train_dataset = TabularData(
data_args.dataset_name,
"train",
tokenizer,
no_space_tokenizer,
data_args,
training_args,
template_args.field_tokens_cutoff,
)
# reload tokenizer because special tokens might be different across loading
# TODO: figure out why
tokenizer = AutoTokenizer.from_pretrained(training_args.output_dir)
eval_dataset = TabularData(
data_args.dataset_name,
"validation" if template_args.evaluation_split == "val" else "test",
tokenizer,
no_space_tokenizer,
data_args,
training_args,
template_args.field_tokens_cutoff,
)
# Load Template
template_dir = Path(training_args.output_dir)
all_templates_temp, type_combination_indices = utils.load_templates(
template_dir, template_args
)
from collections import defaultdict
import re
all_templates = defaultdict(list)
for k, v in all_templates_temp.items():
for template in v:
template_str = utils.decode_and_clean_template(
template, tokenizer, train_dataset.field_max_lens
)
field_combination = tuple(
sorted([f[1:-1] for f in re.findall(r"\[[a-zA-Z]+\]", template_str)])
) # detect all the tags that look like <{some characters}_{digit}>
all_templates[field_combination].append(template)
k = tuple(f.upper() for f in k)
all_templates[k].append(template)
# Evaluate Lexicalization and Validity
all_lexicalizations = set()
for v in _e2e_field_transformations_complex.values():
all_lexicalizations.update(v)
for v in train_dataset.field_possible_values.values():
all_lexicalizations.update(v)
lex_templates = set()
lex_in_templates = []
invalid_templates = set()
unique_template_strs = set()
for k, v in all_templates.items():
templates = []
seen_template_str = set()
for template in v:
template_str = utils.decode_and_clean_template(
template, tokenizer, train_dataset.field_max_lens
)
unique_template_strs.add(template_str)
if template_str in seen_template_str:
continue
is_clean = True
if "<" in template_str:
invalid_templates.add(template_str)
is_clean = False
for lex in all_lexicalizations:
if lex in template_str and lex != "restaurant":
is_clean = False
lex_templates.add(template_str)
lex_in_templates.append(lex)
if template_args.eval_postprocess and is_clean:
templates.append(template)
elif not template_args.eval_postprocess:
templates.append(template)
seen_template_str.add(template_str)
all_templates[k] = templates
template_count = len(unique_template_strs)
print(
f"Out of {template_count} unique templates",
f" {len(lex_templates)} {len(lex_templates) / template_count:.2%} contain lexicalization",
f" {len(invalid_templates)} {len(invalid_templates) / template_count:.2%} contain invalid field",
)
data_collator = utils.get_datacollator(tokenizer, model, data_args, training_args)
best_outputs = []
all_input_ids = []
all_sen_ids = []
all_sen_scores = []
all_templates_expanded = []
boundaries = [0]
count = 0
from itertools import combinations
for i, sample in tqdm(
enumerate(eval_dataset.processed_dataset),
total=len(eval_dataset.processed_dataset),
):
sample_field_combination = tuple(
[f.upper() for f in eval_dataset.indices_to_combination[i]]
)
if sample_field_combination in all_templates:
type_templates = all_templates[sample_field_combination]
else:
print("unseen: ", sample_field_combination)
type_templates = []
for backoff_len in range(1, len(sample_field_combination)):
for backoff_field_combination in combinations(
sample_field_combination, backoff_len
):
if backoff_field_combination in all_templates:
type_templates.extend(all_templates[backoff_field_combination])
if len(type_templates) == 0:
print("failed to backoff. searching over all templates")
for templates in all_templates.values():
type_templates.extend(templates)
if template_args.random_selection_inference:
import random
type_template_str = [utils.decode_and_clean_template(
t, tokenizer, train_dataset.field_max_lens
) for t in type_templates]
num_fields = [len(tuple(
sorted([f[1:-1] for f in re.findall(r"\[[a-zA-Z]+\]", t_str)])
)) for t_str in type_template_str]
num_max_fields = max(num_fields)
idx_with_max_num_fields = [i for i, n in enumerate(num_fields) if n == num_max_fields]
rand_template_i = random.choice(idx_with_max_num_fields)
type_templates = [type_templates[rand_template_i]]
eval_input_ids = [
torch.LongTensor(eval_dataset.processed_dataset[i]["input_ids"])
]
eval_field_dicts = [eval_dataset.field_dicts[i]]
fill_in_sens = [
model.fill_in_template(t, eval_input_ids, eval_field_dicts, verbose=False)[
0
]
.squeeze(0)
.tolist()
for t in type_templates
]
all_templates_expanded.extend(type_templates)
all_sen_ids.extend(fill_in_sens)
all_input_ids.extend(
[
eval_dataset.processed_dataset[i]["input_ids"]
for _ in range(len(fill_in_sens))
]
)
count += len(fill_in_sens)
boundaries.append(count)
for batch_start in trange(
0,
len(all_sen_ids),
template_args.inference_batch_size,
desc="computing template scores",
):
batch_output_ids = utils.pad_list(
all_sen_ids[batch_start : batch_start + template_args.inference_batch_size],
tokenizer.pad_token_id,
).cuda()
batch_input_ids = utils.pad_list(
all_input_ids[
batch_start : batch_start + template_args.inference_batch_size
],
tokenizer.pad_token_id,
).cuda()
output_logits = model(
input_ids=batch_input_ids, decoder_input_ids=batch_output_ids
).logits
output_logprobs = (
output_logits[:, :-1]
.log_softmax(dim=-1)
.gather(index=batch_output_ids[:, 1:].unsqueeze(-1), dim=-1)
.squeeze(-1)
)
output_logprobs = output_logprobs * (
batch_output_ids[:, 1:].ne(tokenizer.pad_token_id)
)
output_logprobs = output_logprobs.sum(dim=-1)
if template_args.length_penalty > 0:
num_non_pad = batch_output_ids[:, 1:].ne(tokenizer.pad_token_id).sum(dim=-1)
output_logprobs = output_logprobs / num_non_pad.float().pow(
template_args.length_penalty
)
all_sen_scores.extend(output_logprobs.tolist())
return_sens = []
return_templates = []
for i in range(len(eval_dataset.processed_dataset)):
sample_scores = all_sen_scores[boundaries[i] : boundaries[i + 1]]
best_template_i = np.argmax(sample_scores)
return_sens.append(
tokenizer.decode(
all_sen_ids[boundaries[i] + best_template_i], skip_special_tokens=True
)
)
return_templates.append(
utils.decode_and_clean_template(
all_templates_expanded[boundaries[i] + best_template_i],
tokenizer,
train_dataset.field_max_lens,
)
)
if template_args.local_search_prune_topk is not None:
template_dir = template_dir / f"prune{template_args.local_search_prune_topk}"
if template_args.random_selection_inference:
template_dir = template_dir / "random_inference"
if template_args.evaluation_split == "val":
split_name = "validation"
output_dir = template_dir / "dev_output"
elif template_args.evaluation_split == "test":
split_name = "test"
output_dir = template_dir / "test_output"
os.makedirs(output_dir, exist_ok=True)
with open(output_dir / "lex_stats.txt", "w") as f:
f.write(f"#. Templates: {template_count}\n")
f.write(f"#. Lex. Templates: {len(lex_templates)}\n")
f.write(f"%. Lex. Templates: {len(lex_templates)/template_count:.2%}\n")
file_name = (
"typed_best_outputs_postprocessed.txt"
if template_args.eval_postprocess
else "typed_best_outputs.txt"
)
with open(output_dir / file_name, "w") as f:
for t in return_sens:
f.write(t.strip() + "\n")
file_name = (
"typed_best_templates_postprocessed.txt"
if template_args.eval_postprocess
else "typed_best_templates.txt"
)
with open(output_dir / file_name, "w") as f:
for t in return_templates:
f.write(t.strip() + "\n")
file_name = "input_data.txt"
with open(output_dir / file_name, "w") as f:
for t in eval_dataset.raw_datasets[split_name]:
f.write(t["meaning_representation"].strip() + "\n")