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114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from transformers import AutoTokenizer, GenerationConfig
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from trl import AutoModelForCausalLMWithValueHead
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from trl.core import LengthSampler
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from trl.extras import BestOfNSampler
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from .testing_utils import TrlTestCase
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def queries_to_scores(list_of_strings):
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return [torch.rand(1).item() for _ in list_of_strings]
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class TestBestOfNSampler(TrlTestCase):
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"""
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Tests the BestOfNSampler class
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"""
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ref_model_name = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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output_length_sampler = LengthSampler(2, 6)
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model = AutoModelForCausalLMWithValueHead.from_pretrained(ref_model_name)
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tokenizer = AutoTokenizer.from_pretrained(ref_model_name)
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tokenizer.pad_token = tokenizer.eos_token
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output_length_sampler = LengthSampler(2, 6)
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def test_different_input_types(self):
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r"""
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Tests if the different input types normalizer works
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"""
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generation_config = GenerationConfig(
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min_length=-1,
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top_k=0.0,
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top_p=1.0,
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do_sample=True,
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pad_token_id=self.tokenizer.eos_token_id,
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)
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output_length_sampler = LengthSampler(2, 6)
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best_of_n = BestOfNSampler(
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self.model,
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self.tokenizer,
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queries_to_scores,
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length_sampler=output_length_sampler,
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generation_config=generation_config,
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)
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queries = ["hello world", "goodbye world"]
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tokenized_queries = [self.tokenizer.encode(query) for query in queries]
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various_queries_formats = [
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(tokenized_queries[0], 1),
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(tokenized_queries, 2),
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(torch.tensor(tokenized_queries[1]), 1),
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([torch.tensor(query) for query in tokenized_queries], 2),
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]
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for q, expected_length in various_queries_formats:
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results = best_of_n.generate(q)
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assert isinstance(results, list)
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assert len(results) == expected_length
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def test_different_sample_sizes_and_n_candidates_values(self):
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r"""
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Tests different sample sizes and n_candidates values
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"""
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generation_config = GenerationConfig(
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min_length=-1,
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top_k=0.0,
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top_p=1.0,
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do_sample=True,
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pad_token_id=self.tokenizer.eos_token_id,
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)
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output_length_sampler = LengthSampler(6, 10)
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for sample_value, n_candidates_values, expected in [
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(4, 2, 2),
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(10, 3, 3),
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(6, 4, 4),
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]:
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best_of_n = BestOfNSampler(
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self.model,
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self.tokenizer,
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queries_to_scores,
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length_sampler=output_length_sampler,
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generation_config=generation_config,
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sample_size=sample_value,
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n_candidates=n_candidates_values,
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)
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queries = ["hello world", "troll the world"]
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tokenized_queries = [self.tokenizer.encode(query) for query in queries]
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results = best_of_n.generate(tokenized_queries)
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for result in results:
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assert len(result) == expected
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