test_generation_logits_process.py
a0ebe5d8b65emaster

test_generation_logits_process.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 ids_tensor


			if is_torch_available():
			import torch
			import torch.nn.functional as F

			from transformers.generation_logits_process import (
			EncoderNoRepeatNGramLogitsProcessor,
			ForcedBOSTokenLogitsProcessor,
			ForcedEOSTokenLogitsProcessor,
			HammingDiversityLogitsProcessor,
			LogitsProcessorList,
			MinLengthLogitsProcessor,
			NoBadWordsLogitsProcessor,
			NoRepeatNGramLogitsProcessor,
			PrefixConstrainedLogitsProcessor,
			RepetitionPenaltyLogitsProcessor,
			TemperatureLogitsWarper,
			TopKLogitsWarper,
			TopPLogitsWarper,
			)


			@require_torch
			class LogitsProcessorTest(unittest.TestCase):
			def _get_uniform_logits(self, batch_size: int, length: int):
			scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length
			return scores

			def test_min_lenght_dist_processor(self):
			vocab_size = 20
			batch_size = 4
			eos_token_id = 0

			min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id)

			# check that min length is applied at length 5
			input_ids = ids_tensor((batch_size, 5), vocab_size=20)
			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores_before_min_length = min_dist_processor(input_ids, scores)
			self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")])

			# check that min length is not applied anymore at length 15
			input_ids = ids_tensor((batch_size, 15), vocab_size=20)
			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores_before_min_length = min_dist_processor(input_ids, scores)
			self.assertFalse(torch.isinf(scores_before_min_length).any())

			def test_temperature_dist_warper(self):
			input_ids = None
			length = 20

			scores = self._get_uniform_logits(batch_size=2, length=length)

			# tweak scores to not be uniform anymore
			scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch
			scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch

			# compute softmax
			probs = F.softmax(scores, dim=-1)

			temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5)
			temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3)

			warped_prob_sharp = F.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1)
			warped_prob_smooth = F.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1)

			# uniform distribution stays uniform
			self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3))
			self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3))

			# sharp peaks get higher, valleys get lower
			self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max())
			self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min())

			# smooth peaks get lower, valleys get higher
			self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max())
			self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min())

			def test_repetition_penalty_dist_process(self):
			input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long)
			vocab_size = 10

			scores = self._get_uniform_logits(batch_size=2, length=vocab_size)

			# give values special values
			scores[0, 0] = -(1 / vocab_size)
			scores[1, 5] = 4 / vocab_size

			rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)

			scores = rep_penalty_proc(input_ids, scores.clone())

			# check that values were correctly changed
			self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2)
			self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2)

			self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2)
			self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2)

			def test_top_k_dist_warper(self):
			input_ids = None
			vocab_size = 10
			batch_size = 2

			# create ramp distribution
			ramp_logits = (
			torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1)
			)
			ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size

			top_k_warp = TopKLogitsWarper(3)

			scores = top_k_warp(input_ids, ramp_logits)

			# check that correct tokens are filtered
			self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False])
			self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True])

			# check special cases
			length = 5

			logits = self._get_uniform_logits(batch_size=batch_size, length=length)
			top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3)

			scores = top_k_warp_safety_check(input_ids, logits)
			# uniform dist is not changed
			self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0])

			ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1)
			scores = top_k_warp_safety_check(input_ids, ramp_logits)

			# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
			self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2])

			def test_top_p_dist_warper(self):
			input_ids = None
			vocab_size = 10
			batch_size = 2

			# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
			dist = torch.log(
			torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float)
			)

			top_p_warp = TopPLogitsWarper(0.7)
			filtered_dist = torch.exp(top_p_warp(input_ids, dist))

			# dist should be filtered to keep min num values so that sum is >= 0.7
			# exp (-inf) => 0
			EXPECTED_FILTERED_DIST = torch.tensor(
			[[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float
			)
			self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3))

			# check edge cases with negative and extreme logits
			ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(
			batch_size, 1
			) - (vocab_size // 2)

			# make ramp_logits more extreme
			ramp_logits[1] = ramp_logits[1] * 100.0

			# make sure at least 2 tokens are kept
			top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0)
			filtered_dist = top_p_warp(input_ids, ramp_logits)

			# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
			self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2])

			def test_no_repeat_ngram_dist_processor(self):
			vocab_size = 3
			batch_size = 2

			input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
			scores = self._get_uniform_logits(batch_size, vocab_size)

			no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2)
			no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3)

			filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
			filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())

			# 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch
			self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]])

			# 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch
			self.assertListEqual(
			torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]]
			)

			def test_encoder_no_repeat_ngram_dist_processor(self):
			vocab_size = 3
			num_beams = 2
			batch_size = 1

			encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long)

			input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long)
			scores = self._get_uniform_logits(batch_size * num_beams, vocab_size)

			no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids)
			no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids)

			filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
			filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())

			# 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam
			self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]])

			# 3-gram would forbid 1st token at 1st beam and no token at 2nd beam
			self.assertListEqual(
			torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]]
			)

			# Batched input
			vocab_size = 3
			num_beams = 2
			batch_size = 2
			encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long)

			input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long)
			scores = self._get_uniform_logits(batch_size * num_beams, vocab_size)

			no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids)
			no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids)

			filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
			filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())

			# 2gram
			# Batch 1
			# - Beam 1: tokens (1, 2) forbidden
			# - Beam 2: tokens (1) forbidden
			# Batch 2
			# - Beam 1: tokens (0, 2) forbidden
			# - Beam 2: tokens (1) forbidden
			self.assertListEqual(
			torch.isinf(filtered_scores_2_gram).tolist(),
			[[False, True, True], [False, True, False], [True, False, True], [False, True, False]],
			)

			# Batch 1
			# - Beam 1: tokens (1) forbidden
			# - Beam 2: tokens () forbidden
			# Batch 2
			# - Beam 1: tokens (2) forbidden
			# - Beam 2: tokens () forbidden
			self.assertListEqual(
			torch.isinf(filtered_scores_3_gram).tolist(),
			[[False, True, False], [False, False, False], [False, False, True], [False, False, False]],
			)

			def test_no_bad_words_dist_processor(self):
			vocab_size = 5
			batch_size = 2
			eos_token_id = 4

			input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
			bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]]
			scores = self._get_uniform_logits(batch_size, vocab_size)

			no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id)

			filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())

			# batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden
			# batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden
			# Note that 5th element cannot be forbidden as it is EOS token
			self.assertListEqual(
			torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]]
			)

			# check edge case
			no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id)
			filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
			self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3))

			def test_processor_list(self):
			batch_size = 4
			sequence_length = 10
			vocab_size = 15
			eos_token_id = 0

			# dummy input_ids and scores
			input_ids = ids_tensor((batch_size, sequence_length), vocab_size)
			input_ids_comp = input_ids.clone()

			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores_comp = scores.clone()

			# instantiate all dist processors
			min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id)
			temp_dist_warp = TemperatureLogitsWarper(temperature=0.5)
			rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
			top_k_warp = TopKLogitsWarper(3)
			top_p_warp = TopPLogitsWarper(0.8)
			no_repeat_proc = NoRepeatNGramLogitsProcessor(2)
			no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id)

			# no processor list
			scores = min_dist_proc(input_ids, scores)
			scores = temp_dist_warp(input_ids, scores)
			scores = rep_penalty_proc(input_ids, scores)
			scores = top_k_warp(input_ids, scores)
			scores = top_p_warp(input_ids, scores)
			scores = no_repeat_proc(input_ids, scores)
			scores = no_bad_words_dist_proc(input_ids, scores)

			# with processor list
			processor = LogitsProcessorList(
			[
			min_dist_proc,
			temp_dist_warp,
			rep_penalty_proc,
			top_k_warp,
			top_p_warp,
			no_repeat_proc,
			no_bad_words_dist_proc,
			]
			)
			scores_comp = processor(input_ids, scores_comp)

			# scores should be equal
			self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3))

			# input_ids should never be changed
			self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())

			def test_prefix_constrained_logits_processor(self):
			vocab_size = 5
			batch_size = 2

			input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
			scores = self._get_uniform_logits(batch_size, vocab_size)

			def prefix_allowed_tokens_fn(batch_id, inputs_ids):
			return [[0, 1], [2, 3]][batch_id]

			prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1)

			filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone())

			# batch 1: 1st, 2nd (0, 1) token are allowed
			# batch 2: 3rd, 4th (2, 3) token are allowed
			self.assertListEqual(
			torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]]
			)

			def test_hamming_diversity(self):
			vocab_size = 4
			num_beams = 2
			num_beam_groups = 2

			scores = self._get_uniform_logits(num_beams, vocab_size)
			# batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1
			# batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1
			current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long)

			diversity_logits_processor = HammingDiversityLogitsProcessor(
			diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups
			)

			processed_scores = diversity_logits_processor(None, scores, current_tokens, 1)

			self.assertTrue(
			torch.allclose(
			processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3
			)
			)
			self.assertTrue(
			torch.allclose(
			processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3
			)
			)

			def test_forced_bos_token_logits_processor(self):
			vocab_size = 20
			batch_size = 4
			bos_token_id = 0

			logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id)

			# check that all scores are -inf except the bos_token_id score
			input_ids = ids_tensor((batch_size, 1), vocab_size=20)
			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores = logits_processor(input_ids, scores)
			self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all())
			self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero

			# check that bos_token_id is not forced if current length is greater than 1
			input_ids = ids_tensor((batch_size, 4), vocab_size=20)
			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores = logits_processor(input_ids, scores)
			self.assertFalse(torch.isinf(scores).any())

			def test_forced_eos_token_logits_processor(self):
			vocab_size = 20
			batch_size = 4
			eos_token_id = 0
			max_length = 5

			logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id)

			# check that all scores are -inf except the eos_token_id when max_length is reached
			input_ids = ids_tensor((batch_size, 4), vocab_size=20)
			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores = logits_processor(input_ids, scores)
			self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all())
			self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero

			# check that eos_token_id is not forced if max_length is not reached
			input_ids = ids_tensor((batch_size, 3), vocab_size=20)
			scores = self._get_uniform_logits(batch_size, vocab_size)
			scores = logits_processor(input_ids, scores)
			self.assertFalse(torch.isinf(scores).any())

R11484/source_code/transformers/tests/test_generation_logits_process.pya0ebe5d8b65emaster

test_generation_logits_process.pyView Options

R11484/source_code/transformers/tests/test_generation_logits_process.py
a0ebe5d8b65emaster

test_generation_logits_process.py
View Options