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https://github.com/huggingface/transformers.git
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32eca7197a
* update * apply suggestion * fix tests for main branch * remove unused logger * add special tokens in tests * nit * fix more tests * fix test * pg also
109 lines
4.7 KiB
Python
109 lines
4.7 KiB
Python
# Copyright 2021 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 json
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import shutil
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import tempfile
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import unittest
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from transformers import AutoProcessor, AutoTokenizer, LlamaTokenizerFast, LlavaProcessor
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from transformers.testing_utils import require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import CLIPImageProcessor
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if is_torch_available:
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pass
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@require_vision
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class LlavaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = LlavaProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = CLIPImageProcessor(do_center_crop=False)
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tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b")
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tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
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processor_kwargs = cls.prepare_processor_dict()
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processor = LlavaProcessor(image_processor, tokenizer, **processor_kwargs)
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processor.save_pretrained(cls.tmpdirname)
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cls.image_token = processor.image_token
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def get_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdirname, ignore_errors=True)
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@staticmethod
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def prepare_processor_dict():
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return {
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"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
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"patch_size": 128,
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"vision_feature_select_strategy": "default"
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} # fmt: skip
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def test_chat_template_is_saved(self):
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processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
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processor_dict_loaded = json.loads(processor_loaded.to_json_string())
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# chat templates aren't serialized to json in processors
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self.assertFalse("chat_template" in processor_dict_loaded.keys())
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# they have to be saved as separate file and loaded back from that file
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# so we check if the same template is loaded
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processor_dict = self.prepare_processor_dict()
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self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
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def test_can_load_various_tokenizers(self):
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for checkpoint in ["Intel/llava-gemma-2b", "llava-hf/llava-1.5-7b-hf"]:
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processor = LlavaProcessor.from_pretrained(checkpoint)
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
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def test_special_mm_token_truncation(self):
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"""Tests that special vision tokens do not get truncated when `truncation=True` is set."""
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processor = LlavaProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
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input_str = self.prepare_text_inputs(batch_size=2, modality="image")
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image_input = self.prepare_image_inputs(batch_size=2)
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_ = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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truncation=None,
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padding=True,
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)
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with self.assertRaises(ValueError):
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_ = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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truncation=True,
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padding=True,
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max_length=5,
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)
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