wehub-resource-sync
388 lines
16 KiB
Python
388 lines
16 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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 copy 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 random
|
|
import unittest
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
|
|
|
|
def ids_tensor(shape, vocab_size, rng=None, name=None):
|
|
# Creates a random int32 tensor of the shape within the vocab size
|
|
if rng is None:
|
|
rng = random.Random()
|
|
|
|
total_dims = 1
|
|
for dim in shape:
|
|
total_dims *= dim
|
|
|
|
values = []
|
|
for _ in range(total_dims):
|
|
values.append(rng.randint(0, vocab_size - 1))
|
|
|
|
return paddle.to_tensor(data=values).reshape(shape)
|
|
|
|
|
|
from paddlenlp.generation.logits_process import (
|
|
ForcedBOSTokenLogitsProcessor,
|
|
ForcedEOSTokenLogitsProcessor,
|
|
HammingDiversityLogitsProcessor,
|
|
LogitsProcessorList,
|
|
MinLengthLogitsProcessor,
|
|
NoBadWordsLogitsProcessor,
|
|
NoRepeatNGramLogitsProcessor,
|
|
PrefixConstrainedLogitsProcessor,
|
|
RepetitionPenaltyLogitsProcessor,
|
|
SequenceBiasLogitsProcessor,
|
|
TemperatureLogitsWarper,
|
|
TopKProcess,
|
|
TopPProcess,
|
|
)
|
|
|
|
|
|
class LogitsProcessorTest(unittest.TestCase):
|
|
def _get_uniform_logits(self, batch_size: int, length: int):
|
|
scores = paddle.ones((batch_size, length)) / length
|
|
return scores
|
|
|
|
def test_min_length_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 * [paddle.finfo(scores.dtype).min])
|
|
|
|
# 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((scores_before_min_length == paddle.finfo(scores.dtype).min).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 = nn.functional.softmax(scores, axis=-1)
|
|
|
|
temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5)
|
|
temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3)
|
|
|
|
warped_prob_sharp = nn.functional.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), axis=-1)
|
|
warped_prob_smooth = nn.functional.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), axis=-1)
|
|
|
|
# uniform distribution stays uniform
|
|
self.assertTrue(paddle.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3))
|
|
self.assertTrue(paddle.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 = paddle.to_tensor([[0, 1], [5, 0]])
|
|
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):
|
|
vocab_size = 10
|
|
batch_size = 2
|
|
|
|
# create ramp distribution
|
|
ramp_logits = paddle.arange(vocab_size).unsqueeze(0).tile((batch_size, 1))
|
|
ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size
|
|
ramp_logits = ramp_logits.astype("float32")
|
|
scores = TopKProcess(ramp_logits, 3, 1)
|
|
|
|
# check that correct tokens are filtered
|
|
self.assertListEqual((scores[0] == 0.0).tolist(), 7 * [True] + 3 * [False])
|
|
self.assertListEqual((scores[1] == 0.0).tolist(), 2 * [True] + 3 * [False] + 5 * [True])
|
|
|
|
# check special cases
|
|
length = 5
|
|
|
|
logits = self._get_uniform_logits(batch_size=batch_size, length=length)
|
|
scores = TopKProcess(logits, top_k=1, min_tokens_to_keep=3)
|
|
# uniform dist is not changed
|
|
self.assertListEqual((scores == 0.0).sum(axis=-1).tolist(), [0, 0])
|
|
|
|
ramp_logits = paddle.arange(length).unsqueeze(0).tile((batch_size, 1))
|
|
ramp_logits = ramp_logits.astype("float32")
|
|
scores = TopKProcess(ramp_logits, top_k=1, min_tokens_to_keep=3)
|
|
|
|
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
|
|
self.assertListEqual((scores == 0.0).sum(axis=-1).tolist(), [2, 2])
|
|
|
|
def test_top_p_dist_warper(self):
|
|
vocab_size = 10
|
|
batch_size = 2
|
|
|
|
# create distribution and take log (inverse to Softmax as taken in TopPProcess)
|
|
# dist = paddle.log(paddle.to_tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))
|
|
dist = paddle.to_tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]])
|
|
|
|
# filtered_dist = paddle.exp(TopPProcess(dist, 0.80001, 1))
|
|
filtered_dist = TopPProcess(dist, 0.79999, 1)
|
|
|
|
EXPECTED_FILTERED_DIST = paddle.to_tensor([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
|
|
self.assertTrue(paddle.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3))
|
|
|
|
# check edge cases with negative and extreme logits
|
|
ramp_logits = paddle.arange(vocab_size).unsqueeze(0).tile((batch_size, 1)) - (vocab_size // 2)
|
|
ramp_logits = ramp_logits.astype("float32")
|
|
# make ramp_logits more extreme
|
|
ramp_logits[1] = ramp_logits[1] * 10.0
|
|
sft_ramp_logits = paddle.nn.functional.softmax(ramp_logits, axis=-1)
|
|
# make sure at least 2 tokens are kept
|
|
filtered_dist = TopPProcess(sft_ramp_logits, 0.9, min_tokens_to_keep=2)
|
|
|
|
# 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).sum(axis=-1).tolist(), [3, 2])
|
|
|
|
def test_no_repeat_ngram_dist_processor(self):
|
|
vocab_size = 3
|
|
batch_size = 2
|
|
|
|
input_ids = paddle.to_tensor([[1, 1, 2, 1], [0, 1, 0, 1]])
|
|
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(
|
|
(filtered_scores_2_gram == paddle.finfo(scores.dtype).min).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(
|
|
(filtered_scores_3_gram == paddle.finfo(scores.dtype).min).tolist(),
|
|
[[False, False, False], [True, False, False]],
|
|
)
|
|
|
|
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)
|
|
no_repeat_proc = NoRepeatNGramLogitsProcessor(2)
|
|
|
|
# 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 = no_repeat_proc(input_ids, scores)
|
|
|
|
# with processor list
|
|
processor = LogitsProcessorList(
|
|
[
|
|
min_dist_proc,
|
|
temp_dist_warp,
|
|
rep_penalty_proc,
|
|
no_repeat_proc,
|
|
]
|
|
)
|
|
scores_comp = processor(input_ids, scores_comp)
|
|
|
|
# scores should be equal
|
|
self.assertTrue(paddle.allclose(scores, scores_comp, atol=1e-3))
|
|
|
|
# input_ids should never be changed
|
|
self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())
|
|
|
|
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 = paddle.to_tensor([0, 3, 1, 2])
|
|
|
|
diversity_logits_processor = HammingDiversityLogitsProcessor(
|
|
diversity_rate=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups
|
|
)
|
|
|
|
processed_scores = diversity_logits_processor(None, scores, current_tokens, 1)
|
|
|
|
self.assertTrue(
|
|
paddle.allclose(processed_scores[0], paddle.to_tensor([-0.7500, 0.2500, 0.2500, 0.2500]), atol=1e-3)
|
|
)
|
|
self.assertTrue(
|
|
paddle.allclose(processed_scores[1], paddle.to_tensor([0.2500, -0.7500, 0.2500, 0.2500]), atol=1e-3)
|
|
)
|
|
|
|
def test_forced_bos_token_logits_processor(self):
|
|
vocab_size = 20
|
|
batch_size = 4
|
|
bos_token_id = 0
|
|
|
|
logits_processor = ForcedBOSTokenLogitsProcessor(forced_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((scores[:, bos_token_id + 1 :] == paddle.finfo(scores.dtype).min).all())
|
|
self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id should 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((scores == paddle.finfo(scores.dtype).min).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, forced_eos_token_id=eos_token_id)
|
|
|
|
# check that all scores are -inf except the eos_token_id when max_length-1 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((scores[:, eos_token_id + 1 :] == paddle.finfo(scores.dtype).min).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-1 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((scores == paddle.finfo(scores.dtype).min).any())
|
|
|
|
def test_bias_dist_processor(self):
|
|
vocab_size = 5
|
|
batch_size = 2
|
|
|
|
input_ids = paddle.to_tensor([[0, 1, 3, 1], [0, 1, 0, 1]])
|
|
positive_bias = {(1,): 100.0, (4,): 100.0}
|
|
negative_bias = {(1, 0): -100.0, (0, 1, 2): -100.0, (1, 3, 1, 3): -100.0}
|
|
# biases the same termination twice, to ensure we can handle overlapping terminations (it won't have an effect
|
|
# on the test cases, though)
|
|
negative_bias.update({(1, 3, 1, 3, 1, 3): -100.0})
|
|
sequence_bias = {**positive_bias, **negative_bias}
|
|
|
|
# scores = 0 to facilitate checks
|
|
scores = paddle.zeros((batch_size, vocab_size))
|
|
|
|
bias_dist_proc = SequenceBiasLogitsProcessor(sequence_bias=sequence_bias)
|
|
filtered_scores = bias_dist_proc(input_ids, scores.clone())
|
|
|
|
# batch 1: positive bias: tokens (1, 4); negative bias: tokens (0, 3); neutral: tokens (2)
|
|
# batch 2: positive bias: tokens (1, 4); negative bias: tokens (0, 2); neutral: tokens (3)
|
|
self.assertListEqual(
|
|
filtered_scores.tolist(), [[-100.0, 100.0, 0.0, -100.0, 100.0], [-100.0, 100.0, -100.0, 0.0, 100.0]]
|
|
)
|
|
|
|
def test_no_bad_words_dist_processor(self):
|
|
vocab_size = 5
|
|
batch_size = 2
|
|
eos_token_id = 4
|
|
|
|
input_ids = paddle.to_tensor([[0, 1, 3, 1], [0, 1, 0, 1]])
|
|
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(
|
|
paddle.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(paddle.allclose(scores, filtered_scores, atol=1e-3).numpy())
|
|
|
|
def test_prefix_constrained_logits_processor(self):
|
|
vocab_size = 5
|
|
batch_size = 2
|
|
|
|
input_ids = paddle.to_tensor([[0, 1, 3, 1], [0, 1, 0, 1]])
|
|
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(
|
|
(filtered_scores == paddle.finfo(filtered_scores.dtype).min).tolist(),
|
|
[[False, False, True, True, True], [True, True, False, False, True]],
|
|
)
|