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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 AutoModelForCausalLM, GenerationConfig | |
| from trl.models.modeling_base import GeometricMixtureWrapper, create_reference_model | |
| class TestGeometricMixtureWrapper(unittest.TestCase): | |
| def setUp(self): | |
| model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5" | |
| self.model = AutoModelForCausalLM.from_pretrained(model_id) | |
| self.ref_model = create_reference_model(self.model) | |
| self.generation_config = GenerationConfig.from_pretrained(model_id) | |
| self.mixture_coef = 0.5 | |
| self.wrapper = GeometricMixtureWrapper( | |
| self.model, self.ref_model, self.generation_config, mixture_coef=self.mixture_coef | |
| ) | |
| def test_forward(self): | |
| input_ids = torch.tensor([[1, 2, 3, 4, 5]]) | |
| attention_mask = torch.ones_like(input_ids) | |
| output = self.wrapper(input_ids=input_ids, attention_mask=attention_mask) | |
| self.assertIsNotNone(output) | |
| self.assertTrue(hasattr(output, "logits")) | |
| self.assertEqual(output.logits.shape, (1, 5, self.model.config.vocab_size)) | |
| def test_mixture_coefficient(self): | |
| input_ids = torch.tensor([[1, 2, 3, 4, 5]]) | |
| attention_mask = torch.ones_like(input_ids) | |
| with torch.no_grad(): | |
| model_output = self.model(input_ids=input_ids, attention_mask=attention_mask) | |
| ref_model_output = self.ref_model(input_ids=input_ids, attention_mask=attention_mask) | |
| wrapper_output = self.wrapper(input_ids=input_ids, attention_mask=attention_mask) | |
| expected_logits = torch.nn.functional.log_softmax( | |
| self.mixture_coef * ref_model_output.logits + (1 - self.mixture_coef) * model_output.logits, dim=-1 | |
| ) | |
| self.assertTrue(torch.allclose(wrapper_output.logits, expected_logits, atol=1e-5)) | |
| def test_prepare_inputs_for_generation(self): | |
| input_ids = torch.tensor([[1, 2, 3, 4, 5]]) | |
| attention_mask = torch.ones_like(input_ids) | |
| inputs = self.wrapper.prepare_inputs_for_generation(input_ids, attention_mask=attention_mask, use_cache=True) | |
| self.assertIn("input_ids", inputs) | |
| self.assertIn("attention_mask", inputs) | |
| self.assertFalse(inputs.get("use_cache", False)) | |