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# Copyright 2020-2025 The HuggingFace Team. 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 unittest
import torch
from transformers import AutoTokenizer, GenerationConfig
from trl import AutoModelForCausalLMWithValueHead
from trl.core import LengthSampler
from trl.extras import BestOfNSampler
def queries_to_scores(list_of_strings):
return [torch.rand(1).item() for _ in list_of_strings]
class BestOfNSamplerTester(unittest.TestCase):
"""
Tests the BestOfNSampler class
"""
ref_model_name = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
output_length_sampler = LengthSampler(2, 6)
model = AutoModelForCausalLMWithValueHead.from_pretrained(ref_model_name)
tokenizer = AutoTokenizer.from_pretrained(ref_model_name)
tokenizer.pad_token = tokenizer.eos_token
output_length_sampler = LengthSampler(2, 6)
def test_different_input_types(self):
r"""
Tests if the different input types normalizer works
"""
generation_config = GenerationConfig(
min_length=-1,
top_k=0.0,
top_p=1.0,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id,
)
output_length_sampler = LengthSampler(2, 6)
best_of_n = BestOfNSampler(
self.model,
self.tokenizer,
queries_to_scores,
length_sampler=output_length_sampler,
generation_config=generation_config,
)
queries = ["hello world", "goodbye world"]
tokenized_queries = [self.tokenizer.encode(query) for query in queries]
various_queries_formats = [
(tokenized_queries[0], 1),
(tokenized_queries, 2),
(torch.tensor(tokenized_queries[1]), 1),
([torch.tensor(query) for query in tokenized_queries], 2),
]
for q, expected_length in various_queries_formats:
results = best_of_n.generate(q)
self.assertIsInstance(results, list)
self.assertEqual(len(results), expected_length)
def test_different_sample_sizes_and_n_candidates_values(self):
r"""
Tests different sample sizes and n_candidates values
"""
generation_config = GenerationConfig(
min_length=-1,
top_k=0.0,
top_p=1.0,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id,
)
output_length_sampler = LengthSampler(6, 10)
for sample_value, n_candidates_values, expected in [
(4, 2, 2),
(10, 3, 3),
(6, 4, 4),
]:
best_of_n = BestOfNSampler(
self.model,
self.tokenizer,
queries_to_scores,
length_sampler=output_length_sampler,
generation_config=generation_config,
sample_size=sample_value,
n_candidates=n_candidates_values,
)
queries = ["hello world", "troll the world"]
tokenized_queries = [self.tokenizer.encode(query) for query in queries]
results = best_of_n.generate(tokenized_queries)
for result in results:
self.assertEqual(len(result), expected)
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