mTk-AdversarialQA_en-SberQuAD_ru-1B / priming_objective.py

README & training scripts

32980f3 about 1 year ago

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	import logging
	import random
	from typing import Iterable, Union, Dict, List, Optional

	import torch
	from adaptor.objectives.seq2seq import Sequence2Sequence
	from transformers import BatchEncoding

	logger = logging.getLogger()

	priming_formats = {
	"QA": {"cs": "Otázka: %s Kontext: %s Odpověď:",
	"en": "Question: %s Context: %s Answer:",
	"ru": "Вопрос: %s Контекст: %s Отвечать:"}}


	class Priming(Sequence2Sequence):

	def __init__(self, *args,
	train_question_categories: Iterable[str],
	max_eval_samples: int,
	val_question_categories: Optional[Iterable[str]] = None,
	min_num_demonstrations: int = 2,
	max_num_demonstrations: int = 5,
	demos_infer_batch_size: int = 32,
	demos_selection_strategy: str = "hard",
	difficulty_sample: int = 64,
	max_input_length: int = 8000,
	**kwargs):
	super().__init__(args, *kwargs)

	self.train_question_categories = list(train_question_categories)
	self.val_question_categories = list(val_question_categories) if val_question_categories is not None else None

	self.min_num_demonstrations = min_num_demonstrations
	self.max_num_demonstrations = max_num_demonstrations
	self.demos_infer_batch_size = demos_infer_batch_size
	self.demos_selection_strategy = demos_selection_strategy
	self.difficulty_sample = difficulty_sample
	self.max_input_length = max_input_length
	self.max_eval_samples = max_eval_samples

	def _construct_qa_prompt(self, question: str, context: str) -> str:
	return priming_formats["QA"][self.source_lang_id] % (question, context)

	def _construct_demonstration(self, prompt: str, answer: str) -> str:
	return "%s %s " % (prompt, answer)

	def _construct_primed_prompt(self, primed_demonstrations: List[str], prompt: str) -> str:
	return " ".join(primed_demonstrations) + " " + prompt

	def forced_generation_score(self, input_texts: List[str], forced_output: str) -> torch.FloatTensor:
	inputs = self.tokenizer(input_texts, return_tensors="pt", padding="longest", truncation=True)
	inputs = inputs.to(self.compatible_head_model.device)

	with self.tokenizer.as_target_tokenizer():
	output_ids = self.tokenizer(forced_output, return_tensors="pt", padding="longest",
	truncation=True).input_ids.to(self.compatible_head_model.device)
	forced_outputs = self.compatible_head_model.prepare_decoder_input_ids_from_labels(output_ids)
	forced_outputs = forced_outputs.to(self.compatible_head_model.device)

	outputs = self.compatible_head_model(**inputs,
	decoder_input_ids=forced_outputs.expand(inputs.input_ids.shape[0], -1))
	output_log_probs = outputs.logits.log_softmax(-1)
	forced_output_logits = torch.gather(output_log_probs, -1,
	output_ids.expand(inputs.input_ids.shape[0], -1).unsqueeze(-1))
	forced_output_log_score = forced_output_logits.sum((-1, -2))
	# we do not need to normalize, as all the targets are the same <=> same length
	return forced_output_log_score.double().exp()

	def _pick_most_difficult_demo(self,
	selected_demos: List[str],
	next_demo_cands: List[str],
	predict_prompt: str,
	predicted_answer: str) -> int:
	with torch.no_grad():
	difficulties = torch.empty(0, device=self.compatible_head_model.device, dtype=torch.float)

	for batch_offset in range(0, len(next_demo_cands), self.demos_infer_batch_size):
	next_demo_cands_batch = next_demo_cands[batch_offset: batch_offset + self.demos_infer_batch_size]

	primed_prompts = [self._construct_primed_prompt(selected_demos + [demo], predict_prompt)
	for demo in next_demo_cands_batch]
	cands_difficulty = self.forced_generation_score(primed_prompts, predicted_answer)

	difficulties = torch.hstack((difficulties, cands_difficulty))

	assert difficulties.argmin() < len(next_demo_cands)

	return difficulties.argmin()

	def _get_inputs_iterator(self, split: str) -> Iterable[Union[BatchEncoding, Dict[str, torch.Tensor]]]:
	"""
	Creates a default iterator over encodings with aligned input and output texts.
	:param split: Data split. `train` or `eval`.
	:return: Iterator of model input encodings.
	"""
	# we materialize all samples in memory, so that we can heuristically pick the combinations
	questions, contexts, answers = (list(it) for it in self._per_split_iterators(split))
	question_categories = self.train_question_categories if split == "train" else self.val_question_categories

	assert len(questions) == len(contexts) == len(answers) == len(question_categories), \
	"Given numbers of questions, contexts and answers do not match."

	prompts = [self._construct_qa_prompt(q, c) for q, c in zip(questions, contexts)]

	features_batch = []
	cat_index = {cat: [i for i, sample_cat in enumerate(question_categories) if cat == sample_cat]
	for cat in set(question_categories)}

	retrieved_samples = 0

	for idx, sample_category in enumerate(question_categories):
	if not cat_index[sample_category]:
	logger.warning("No samples within the category %s", sample_category)
	continue

	pred_prompt, pred_answer = prompts[idx], answers[idx]

	picked_demonstrations = []

	# a number of demonstrations is in the specified range
	expected_num_demonstrations = random.randint(self.min_num_demonstrations, self.max_num_demonstrations)

	while len(picked_demonstrations) < expected_num_demonstrations:
	if sum(map(len, picked_demonstrations)) > self.max_input_length:
	logger.warning("Skipping too long prompt.")
	break
	if self.demos_selection_strategy == "hard":
	# pick the most difficult examples out of a sample
	# we do not need to worry for picking up the predicted sample among demonstrations in hard strategy
	if len(cat_index[sample_category]) <= 1:
	# we can not construct informative demonstrations for categories of a single item
	break

	samples_idx = random.choices(cat_index[sample_category], k=self.difficulty_sample)
	cand_demonstrations = [self._construct_demonstration(prompts[i], answers[i]) for i in samples_idx]
	selected_index = self._pick_most_difficult_demo(picked_demonstrations, cand_demonstrations,
	pred_prompt, pred_answer)
	picked_demonstrations.append(cand_demonstrations[selected_index])
	elif self.demos_selection_strategy == "informative":
	if len(cat_index[sample_category]) <= 1:
	# we can not construct informative demonstrations for categories of a single item
	break
	selected_cat_index = random.randint(1, len(cat_index[sample_category])-1)
	selected_index = cat_index[sample_category][selected_cat_index]
	if selected_index == idx:
	# we do not want to expose the predicted sample in demonstrations
	selected_index = cat_index[sample_category][selected_cat_index-1]
	picked_demonstration = self._construct_demonstration(prompts[selected_index],
	answers[selected_index])
	picked_demonstrations.append(picked_demonstration)
	elif self.demos_selection_strategy == "random":
	# evaluation: do not infer samples' difficulty, pick randomly
	selected_index = random.randint(1, len(prompts)-1)
	if selected_index == idx:
	# we do not want to expose the predicted sample in demonstrations
	selected_index -= 1
	picked_demonstration = self._construct_demonstration(prompts[selected_index],
	answers[selected_index])
	picked_demonstrations.append(picked_demonstration)
	else:
	raise ValueError("Unknown demon selection strategy: '%s'" % self.demos_selection_strategy)
	if len(picked_demonstrations) != expected_num_demonstrations:
	# we omit examples with none or only one demonstration in the category
	continue

	# encode a yielded batch
	primed_prompt = self._construct_primed_prompt(picked_demonstrations, pred_prompt)

	primed_prompt_encoding = self.tokenizer(primed_prompt, truncation=True)
	label_encoding = self.tokenizer(pred_answer, truncation=True)

	features_batch.append({"input_ids": primed_prompt_encoding.input_ids,
	"attention_mask": primed_prompt_encoding.attention_mask,
	"labels": label_encoding.input_ids})
	if len(features_batch) == self.batch_size:
	yield self.collator(features_batch)
	features_batch = []

	retrieved_samples += 1
	if split == "eval" and retrieved_samples >= self.max_eval_samples:
	# custom evaluation break - we need all samples in set to match categories,
	# but do not want to iterate them all
	break

	if features_batch:
	# yield last nonempty residual batch
	yield self.collator(features_batch)

	def _compute_loss(self,
	lm_logit_outputs: torch.FloatTensor,
	labels: torch.LongTensor,
	inputs: Optional[Union[BatchEncoding, Dict[str, torch.Tensor]]] = None) -> torch.FloatTensor:
	# customization for mt5 model, with incorrectly-set tokenizer.vocab_size
	# This should be fixed in upcoming release of adaptor (>=0.1.5)
	loss_fct = torch.nn.CrossEntropyLoss()
	lm_loss = loss_fct(lm_logit_outputs.flatten(end_dim=1), labels.flatten())

	return lm_loss