88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team and the Swiss AI Initiative. All rights reserved.
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#
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# This code is based on HuggingFace's LLaMA implementation in this library.
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# It has been modified from its original forms to accommodate minor architectural
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# differences compared to LLaMA used by the Swiss AI Initiative that trained the model.
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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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"""Testing suite for the PyTorch Apertus model."""
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import (
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require_read_token,
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require_torch,
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require_torch_accelerator,
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slow,
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)
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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if is_torch_available():
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from transformers import (
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ApertusConfig,
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ApertusForCausalLM,
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ApertusForTokenClassification,
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ApertusModel,
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)
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class ApertusModelTester(CausalLMModelTester):
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if is_torch_available():
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config_class = ApertusConfig
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base_model_class = ApertusModel
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causal_lm_class = ApertusForCausalLM
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token_class = ApertusForTokenClassification
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@require_torch
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class ApertusModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (
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(
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ApertusModel,
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ApertusForCausalLM,
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ApertusForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"feature-extraction": ApertusModel,
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"text-generation": ApertusForCausalLM,
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"token-classification": ApertusForTokenClassification,
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}
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if is_torch_available()
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else {}
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)
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test_headmasking = False
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test_pruning = False
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model_tester_class = ApertusModelTester
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# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
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# This is because we are hitting edge cases with the causal_mask buffer
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model_split_percents = [0.5, 0.7, 0.8]
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# used in `test_torch_compile_for_training`
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_torch_compile_train_cls = ApertusForCausalLM if is_torch_available() else None
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@require_torch_accelerator
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@require_read_token
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@slow
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class ApertusIntegrationTest(unittest.TestCase):
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pass
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