tests / test_generation_beam_search.py

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Team Inc.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a clone of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	import unittest

	from transformers import is_torch_available
	from transformers.testing_utils import require_torch, torch_device

	from .test_modeling_common import floats_tensor, ids_tensor


	if is_torch_available():
	import torch

	from transformers.generation_beam_search import BeamHypotheses, BeamSearchScorer


	class BeamSearchTester:
	def __init__(
	self,
	parent,
	batch_size=3,
	sequence_length=10,
	vocab_size=99,
	pad_token_id=0,
	max_length=20,
	num_beams=4,
	length_penalty=2.0,
	do_early_stopping=True,
	num_beam_hyps_to_keep=2,
	):
	self.parent = parent
	self.batch_size = batch_size
	self.sequence_length = sequence_length
	self.vocab_size = vocab_size
	self.pad_token_id = pad_token_id
	self.max_length = max_length
	self.num_beams = num_beams
	self.length_penalty = length_penalty
	self.do_early_stopping = do_early_stopping
	self.num_beam_hyps_to_keep = num_beam_hyps_to_keep

	# cannot be randomely generated
	self.eos_token_id = vocab_size + 1

	def prepare_beam_scorer(self, **kwargs):
	return BeamSearchScorer(
	batch_size=kwargs.get("batch_size", self.batch_size),
	num_beams=kwargs.get("num_beams", self.num_beams),
	device=torch_device,
	length_penalty=kwargs.get("length_penalty", self.length_penalty),
	do_early_stopping=kwargs.get("do_early_stopping", self.do_early_stopping),
	num_beam_hyps_to_keep=kwargs.get("num_beam_hyps_to_keep", self.num_beam_hyps_to_keep),
	)

	def prepare_inputs(self):
	input_ids = ids_tensor((self.batch_size * self.num_beams, self.sequence_length), self.vocab_size)
	next_tokens = ids_tensor((self.batch_size, 2 * self.num_beams), self.vocab_size).to(torch_device)
	next_indices = ids_tensor((self.batch_size, 2 * self.num_beams), self.num_beams).to(torch_device)
	next_scores, _ = (-floats_tensor((self.batch_size, 2 * self.num_beams)).to(torch_device)).sort(descending=True)
	return (input_ids, next_tokens, next_indices, next_scores)

	def check_beam_hypotheses(self, input_ids, *args):
	# check that correct number of beam hypotheses is set in beam scorer
	beam_scorer = self.prepare_beam_scorer(do_early_stopping=True)
	beam_hyp = beam_scorer._beam_hyps[0]

	self.parent.assertEqual(len(beam_scorer._beam_hyps), self.batch_size)

	# check correct type
	self.parent.assertTrue(isinstance(beam_hyp, BeamHypotheses))

	# check that num_beams is correctly set
	self.parent.assertEqual(beam_hyp.num_beams, self.num_beams)

	# check for early stopping deactivated
	for beam_idx in range(self.num_beams):
	beam_hyp.add(input_ids[beam_idx], -10.0)

	# if early stopping True -> score does not matter
	self.parent.assertTrue(beam_hyp.is_done(-10.0, 5))

	# re-init
	beam_scorer = self.prepare_beam_scorer(do_early_stopping=False)
	beam_hyp = beam_scorer._beam_hyps[0]

	# add `num_beams + 1` beams to change `worst_score`
	for beam_idx in range(self.num_beams + 1):
	beam_hyp.add(input_ids[beam_idx], -10.0 + float(beam_idx))

	# -10.0 is removed => -9.0 is worst score
	self.parent.assertAlmostEqual(beam_hyp.worst_score, -9.0 / (self.sequence_length ** beam_hyp.length_penalty))

	# -5.0 is better than worst score => should not be finished
	self.parent.assertFalse(beam_hyp.is_done(-5.0, self.sequence_length))

	# -20.0 is worse than worst score => should be finished
	self.parent.assertTrue(beam_hyp.is_done(-20.0, self.sequence_length))

	def check_beam_scorer_update(self, input_ids, next_tokens, next_indices, next_scores):
	# check too many eos tokens
	beam_scorer = self.prepare_beam_scorer()

	tokens = next_tokens.clone()
	tokens[0, :] = self.eos_token_id

	with self.parent.assertRaises(ValueError):
	beam_scorer.process(input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id)

	# check all batches are done
	beam_scorer = self.prepare_beam_scorer()

	tokens = next_tokens.clone()
	tokens[:, : self.num_beams] = self.eos_token_id
	beam_scorer.process(input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id)
	# beam scorer should be done
	self.parent.assertTrue(beam_scorer.is_done)

	# check
	beam_scorer = self.prepare_beam_scorer()

	tokens = next_tokens.clone()
	tokens[:, 1] = self.eos_token_id
	beam_outputs = beam_scorer.process(
	input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id
	)
	output_scores = beam_outputs["next_beam_scores"]
	output_tokens = beam_outputs["next_beam_tokens"]
	output_indices = beam_outputs["next_beam_indices"]

	def cut_expected_tensor(tensor):
	return torch.cat([tensor[:, :1], tensor[:, 2 : self.num_beams + 1]], dim=1).flatten()

	# check all outptus
	# cut out id of eos token and take best `num_beams` outputs
	expected_output_tokens = cut_expected_tensor(tokens)
	expected_output_scores = cut_expected_tensor(next_scores)

	# add num_beams * batch_idx
	expected_output_indices = (
	cut_expected_tensor(next_indices)
	+ (torch.arange(self.num_beams * self.batch_size, device=torch_device) // self.num_beams) * self.num_beams
	)

	self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
	self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())
	self.parent.assertTrue(torch.allclose(expected_output_scores, output_scores, atol=1e-3))

	# make sure ids of eos token are correctly saved in beam_hyps of beam scorer
	for batch_idx in range(self.batch_size):
	correct_idx = batch_idx * self.num_beams + next_indices[batch_idx, 1]
	self.parent.assertListEqual(
	input_ids[correct_idx].tolist(), beam_scorer._beam_hyps[batch_idx].beams[0][-1].tolist()
	)

	def check_beam_scores_finalize(self, input_ids, next_tokens, next_indices, next_scores):
	# max_length should be only one more than current input_ids to check that eos is correctly appended
	max_length = self.sequence_length + 1
	beam_scorer = self.prepare_beam_scorer(num_beam_hyps_to_keep=1, length_penalty=1.0, do_early_stopping=False)

	# update beams and append to input_ids
	tokens = next_tokens.clone()
	# first batch, first output has to finish with eos token id since scores are correctly sorted
	tokens[0, 0] = self.eos_token_id
	# make sure corresponding score is as good as possible to surely be picked first
	next_scores[0, 0] = 0.0
	beam_outputs = beam_scorer.process(
	input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id
	)
	output_scores = beam_outputs["next_beam_scores"]
	output_tokens = beam_outputs["next_beam_tokens"]
	output_indices = beam_outputs["next_beam_indices"]

	input_ids = torch.cat([input_ids[output_indices, :], output_tokens.unsqueeze(-1)], dim=-1)

	# finalize
	sequence_output = beam_scorer.finalize(
	input_ids,
	output_scores,
	output_tokens,
	output_indices,
	pad_token_id=self.pad_token_id,
	eos_token_id=self.eos_token_id,
	max_length=max_length,
	)

	sequences = sequence_output["sequences"]
	sequence_scores = sequence_output["sequence_scores"]

	# since `num_beam_hyps_to_keep` = 1 => only return `batch_size` x `max_length`
	self.parent.assertListEqual(list(sequences.shape), [self.batch_size, max_length])
	self.parent.assertListEqual(list(sequence_scores.shape), [self.batch_size])

	# check sequence_scores
	self.parent.assertFalse((sequence_scores > 0).any().item())

	# first batch has to finish with eos_token
	self.parent.assertEqual(sequences[0, -1].item(), self.eos_token_id)

	# other batches cannot finish with eos token
	self.parent.assertNotEqual(sequences[1, -1].item(), self.eos_token_id)
	self.parent.assertNotEqual(sequences[2, -1].item(), self.eos_token_id)

	# now test that if `num_beam_hyps_to_keep` is 3 => all beams are returned
	beam_scorer.num_beam_hyps_to_keep = self.num_beams
	sequence_output = beam_scorer.finalize(
	input_ids,
	output_scores,
	output_tokens,
	output_indices,
	pad_token_id=self.pad_token_id,
	eos_token_id=self.eos_token_id,
	max_length=max_length,
	)
	sequences = sequence_output["sequences"]
	sequence_scores = sequence_output["sequence_scores"]

	self.parent.assertListEqual(list(sequences.shape), [self.num_beams * self.batch_size, max_length])
	self.parent.assertListEqual(list(sequence_scores.shape), [self.num_beams * self.batch_size])


	@require_torch
	class BeamSearchTest(unittest.TestCase):
	def setUp(self):
	self.beam_search_tester = BeamSearchTester(self)

	def test_beam_hypotheses(self):
	inputs = self.beam_search_tester.prepare_inputs()
	self.beam_search_tester.check_beam_hypotheses(*inputs)

	def test_beam_scorer_update(self):
	inputs = self.beam_search_tester.prepare_inputs()
	self.beam_search_tester.check_beam_scorer_update(*inputs)

	def test_beam_scorer_finalize(self):
	inputs = self.beam_search_tester.prepare_inputs()
	self.beam_search_tester.check_beam_scores_finalize(*inputs)