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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) | |