466 lines
19 KiB
Python
466 lines
19 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. 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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"""Testing suite for the PyTorch Janus model."""
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import tempfile
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import unittest
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import numpy as np
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from transformers import AutoProcessor, AutoTokenizer, JanusProcessor
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from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
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class JanusProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = JanusProcessor
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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special_image_tokens = {
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"image_token": "<image_placeholder>",
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"boi_token": "<begin_of_image>",
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"eoi_token": "<end_of_image>",
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}
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processor = self.processor_class.from_pretrained(
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"deepseek-community/Janus-Pro-1B",
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extra_special_tokens=special_image_tokens,
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**self.prepare_processor_dict(),
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)
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# Set the processor to use the default system prompt to False as it's used based on input modality.
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# Hence set to False to avoid any issues in the test irrespective of inputs.
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processor.use_default_system_prompt = False
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processor.save_pretrained(self.tmpdirname)
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def get_tokenizer(self, **kwargs):
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return AutoTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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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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def get_processor(self):
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return AutoProcessor.from_pretrained(self.tmpdirname)
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def test_chat_template_single(self):
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"""
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Tests that the chat template matches the original implementation when applied to a single message.
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"""
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processor = self.get_processor()
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if processor.chat_template is None:
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self.skipTest("Processor has no chat template")
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# Single image message
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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{"type": "image"},
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],
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},
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]
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]
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correct_prompt = ["<|User|>: What is shown in this image?\n<image_placeholder>\n\n<|Assistant|>:"]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompt, correct_prompt)
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# Single image message with capitalization
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messages = [
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[
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{
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"role": "User",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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{"type": "image"},
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],
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},
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]
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]
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correct_prompt = ["<|User|>: What is shown in this image?\n<image_placeholder>\n\n<|Assistant|>:"]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompt, correct_prompt)
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# Single image message with uppercase
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messages = [
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[
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{
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"role": "USER",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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{"type": "image"},
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],
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},
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]
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]
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correct_prompt = ["<|User|>: What is shown in this image?\n<image_placeholder>\n\n<|Assistant|>:"]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompt, correct_prompt)
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"""
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Warning: normally, the other models have a test comparing chat template+tokenization as two separate steps
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versus as a single step (i.e. processor.apply_chat_template(..., tokenize=True)). However, our processor has
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some extra steps other than simply applying prompt to tokenizer. These include prepending the default system
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prompts and, following the implementation from the Janus codebase, expanding the image token.
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"""
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# Checking the output dict keys
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out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
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self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
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# Now test the ability to return dict
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messages[0][0]["content"][1].update(
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{
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"type": "image",
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"url": url_to_local_path(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
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),
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}
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)
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out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
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self.assertTrue(self.images_input_name in out_dict)
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# should always have input_ids and attention_mask
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self.assertEqual(len(out_dict["input_ids"]), 1)
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self.assertEqual(len(out_dict["attention_mask"]), 1)
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self.assertEqual(len(out_dict[self.images_input_name]), 1)
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# Passing generation prompt explicitly
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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{"type": "image"},
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],
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},
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": ""},
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],
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},
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]
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]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=False)
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self.assertEqual(formatted_prompt, correct_prompt)
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# Single prompt with multiple images
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Compare this image"},
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{"type": "image"},
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{"type": "text", "text": "with this image"},
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{"type": "image"},
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],
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},
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]
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]
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correct_prompt = [
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"<|User|>: Compare this image\n<image_placeholder>\nwith this image\n<image_placeholder>\n\n<|Assistant|>:"
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]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompt, correct_prompt)
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# Multiple turns and multiple images
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Compare this image"},
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{"type": "image"},
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{"type": "text", "text": "with this image"},
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{"type": "image"},
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],
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},
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": "The first image is an equation, the second is a pie chart."},
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],
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},
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{
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"type": "text",
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"text": "What about this third image? To which of the previous to is it more similar?",
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},
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],
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},
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]
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]
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correct_prompt = [
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"<|User|>: Compare this image\n<image_placeholder>\nwith this image\n<image_placeholder>\n\n<|Assistant|>: The first image is an equation, the second is a pie chart.<｜end▁of▁sentence｜><|User|>: <image_placeholder>\nWhat about this third image? To which of the previous to is it more similar?\n\n<|Assistant|>:"
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]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompt, correct_prompt)
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def test_chat_template_batched(self):
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"""
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Tests that the chat template matches the original implementation when applied to a batch of messages.
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"""
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processor = self.get_processor()
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if processor.chat_template is None:
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self.skipTest("Processor has no chat template")
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# Test 1: Simple single image per message batch
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batched_messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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{"type": "image"},
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],
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},
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],
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is shown in this image?"},
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{"type": "image"},
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],
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},
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],
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]
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correct_prompts = [
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"<|User|>: What is shown in this image?\n<image_placeholder>\n\n<|Assistant|>:",
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"<|User|>: What is shown in this image?\n<image_placeholder>\n\n<|Assistant|>:",
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]
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formatted_prompts = processor.apply_chat_template(batched_messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompts, correct_prompts)
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# Similarly to the single case, no test for chat template+tokenization as two separate steps versus as a single step
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# Checking the output dict keys
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out_dict = processor.apply_chat_template(
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batched_messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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padding=True,
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)
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self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
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# Verify image inputs are included in the output dict
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batched_messages[0][0]["content"][1].update(
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{
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"type": "image",
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"url": url_to_local_path(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
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),
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}
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)
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batched_messages[1][0]["content"][1].update(
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{"type": "image", "url": url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")}
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)
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out_dict = processor.apply_chat_template(
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batched_messages, add_generation_prompt=True, tokenize=True, return_dict=True, padding=True
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)
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self.assertTrue(self.images_input_name in out_dict)
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self.assertEqual(len(out_dict["input_ids"]), 2) # Batch size for text
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self.assertEqual(len(out_dict["attention_mask"]), 2) # Batch size for attention mask
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self.assertEqual(len(out_dict[self.images_input_name]), 2) # Batch size for images
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# Test 2: Two images per message batch with different prompts
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batched_messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Compare this image"},
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{"type": "image"},
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{"type": "text", "text": "with this image"},
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{"type": "image"},
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],
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},
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],
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[
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "Describe how the previous image compares to the following"},
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{"type": "image"},
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],
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},
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],
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]
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correct_prompts = [
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"<|User|>: Compare this image\n<image_placeholder>\nwith this image\n<image_placeholder>\n\n<|Assistant|>:",
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"<|User|>: <image_placeholder>\nDescribe how the previous image compares to the following\n<image_placeholder>\n\n<|Assistant|>:",
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]
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formatted_prompts = processor.apply_chat_template(batched_messages, add_generation_prompt=True)
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self.assertEqual(formatted_prompts, correct_prompts)
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# Test 3: Multi-turn conversations with multiple images
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batched_messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Compare this image"},
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{"type": "image"},
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{"type": "text", "text": "with this image"},
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{"type": "image"},
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],
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},
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": "The first image is an equation, the second is a pie chart."},
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],
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},
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{
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"type": "text",
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"text": "What about this third image? To which of the previous to is it more similar?",
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},
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],
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},
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],
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[
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{
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"role": "user",
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"content": [
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{"type": "image"},
|
|||
|
|
{"type": "text", "text": "Describe how the previous image compares to the following"},
|
|||
|
|
{"type": "image"},
|
|||
|
|
],
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"role": "assistant",
|
|||
|
|
"content": [
|
|||
|
|
{"type": "text", "text": "The first image is a formula, the second is a plot."},
|
|||
|
|
],
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"role": "user",
|
|||
|
|
"content": [
|
|||
|
|
{"type": "text", "text": "Which of them is closer to the following?"},
|
|||
|
|
{"type": "image"},
|
|||
|
|
],
|
|||
|
|
},
|
|||
|
|
],
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
correct_prompts = [
|
|||
|
|
"<|User|>: Compare this image\n<image_placeholder>\nwith this image\n<image_placeholder>\n\n<|Assistant|>: The first image is an equation, the second is a pie chart.<｜end▁of▁sentence｜><|User|>: <image_placeholder>\nWhat about this third image? To which of the previous to is it more similar?\n\n<|Assistant|>:",
|
|||
|
|
"<|User|>: <image_placeholder>\nDescribe how the previous image compares to the following\n<image_placeholder>\n\n<|Assistant|>: The first image is a formula, the second is a plot.<｜end▁of▁sentence｜><|User|>: Which of them is closer to the following?\n<image_placeholder>\n\n<|Assistant|>:",
|
|||
|
|
]
|
|||
|
|
formatted_prompts = processor.apply_chat_template(batched_messages, add_generation_prompt=True)
|
|||
|
|
self.assertEqual(formatted_prompts, correct_prompts)
|
|||
|
|
|
|||
|
|
def test_chat_template_accepts_processing_kwargs(self):
|
|||
|
|
"""Tests that the chat template correctly handles additional processing arguments."""
|
|||
|
|
# Get processor and skip if it doesn't have a chat template
|
|||
|
|
processor = self.get_processor()
|
|||
|
|
if processor.chat_template is None:
|
|||
|
|
self.skipTest("Processor has no chat template")
|
|||
|
|
|
|||
|
|
# Create a simple text message for testing
|
|||
|
|
messages = [
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
"role": "user",
|
|||
|
|
"content": [
|
|||
|
|
{"type": "text", "text": "What is shown in this image?"},
|
|||
|
|
],
|
|||
|
|
},
|
|||
|
|
]
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
# Test 1: Padding to max_length
|
|||
|
|
# PS: we have to override the parent max_length of 50 to 80 because the output is already 51 tokens
|
|||
|
|
formatted_prompt_tokenized = processor.apply_chat_template(
|
|||
|
|
messages,
|
|||
|
|
add_generation_prompt=True,
|
|||
|
|
tokenize=True,
|
|||
|
|
padding="max_length",
|
|||
|
|
max_length=80,
|
|||
|
|
)
|
|||
|
|
self.assertEqual(len(formatted_prompt_tokenized[0]), 80)
|
|||
|
|
|
|||
|
|
# Test 2: Truncation
|
|||
|
|
# Verify that the output is truncated to exactly 5 tokens
|
|||
|
|
formatted_prompt_tokenized = processor.apply_chat_template(
|
|||
|
|
messages,
|
|||
|
|
add_generation_prompt=True,
|
|||
|
|
tokenize=True,
|
|||
|
|
truncation=True,
|
|||
|
|
max_length=5,
|
|||
|
|
)
|
|||
|
|
self.assertEqual(len(formatted_prompt_tokenized[0]), 5)
|
|||
|
|
|
|||
|
|
# Test 3: Image processing kwargs
|
|||
|
|
# Add an image and test image processing parameters
|
|||
|
|
messages[0][0]["content"].append(
|
|||
|
|
{
|
|||
|
|
"type": "image",
|
|||
|
|
"url": url_to_local_path(
|
|||
|
|
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
|
|||
|
|
),
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
# Process with image rescaling and verify the pixel values are negative
|
|||
|
|
out_dict = processor.apply_chat_template(
|
|||
|
|
messages,
|
|||
|
|
add_generation_prompt=True,
|
|||
|
|
tokenize=True,
|
|||
|
|
return_dict=True,
|
|||
|
|
do_rescale=True,
|
|||
|
|
rescale_factor=-1,
|
|||
|
|
return_tensors="np",
|
|||
|
|
)
|
|||
|
|
self.assertLessEqual(out_dict[self.images_input_name][0][0].mean(), 0)
|
|||
|
|
|
|||
|
|
def test_processor_postprocess(self):
|
|||
|
|
processor_components = self.prepare_components()
|
|||
|
|
processor = self.processor_class(**processor_components)
|
|||
|
|
|
|||
|
|
input_str = "lower newer"
|
|||
|
|
orig_image_input = self.prepare_image_inputs()
|
|||
|
|
orig_image = np.array(orig_image_input).transpose(2, 0, 1)
|
|||
|
|
|
|||
|
|
inputs = processor(text=input_str, images=orig_image, do_resize=False, do_pad=False, return_tensors="np")
|
|||
|
|
normalized_image_input = inputs.pixel_values
|
|||
|
|
unnormalized_images = processor.postprocess(normalized_image_input, return_tensors="np")["pixel_values"]
|
|||
|
|
|
|||
|
|
# For an image where pixels go from 0 to 255 the diff can be 1 due to some numerical precision errors when scaling and unscaling
|
|||
|
|
self.assertTrue(np.abs(orig_image - unnormalized_images).max() >= 1)
|