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389 lines
16 KiB
Python
389 lines
16 KiB
Python
# Copyright 2024 HuggingFace Inc.
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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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import numpy as np
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from transformers import MllamaProcessor
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import 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 PIL import Image
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@require_torch
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@require_vision
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class MllamaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = MllamaProcessor
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@classmethod
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def setUpClass(cls):
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cls.checkpoint = "hf-internal-testing/mllama-11b"
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processor = MllamaProcessor.from_pretrained(cls.checkpoint)
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cls.image1 = Image.new("RGB", (224, 220))
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cls.image2 = Image.new("RGB", (512, 128))
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cls.image_token = processor.image_token
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cls.image_token_id = processor.image_token_id
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cls.pad_token_id = processor.tokenizer.pad_token_id
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cls.bos_token = processor.bos_token
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cls.bos_token_id = processor.tokenizer.bos_token_id
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cls.tmpdirname = tempfile.mkdtemp()
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processor.save_pretrained(cls.tmpdirname)
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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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def prepare_processor_dict(self):
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return {"chat_template": "{% for message in messages %}{% if loop.index0 == 0 %}{{ bos_token }}{% endif %}{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' }}{% if message['content'] is string %}{{ message['content'] }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' %}{{ '<|image|>' }}{% elif content['type'] == 'text' %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{ '<|eot_id|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"} # 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_apply_chat_template(self):
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# Message contains content which a mix of lists with images and image urls and string
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messages = [
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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": "image"},
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{"type": "text", "text": "What do these images show?"},
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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 shows the statue of Liberty in New York."},
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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": "And who is that?"},
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],
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},
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]
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processor = MllamaProcessor.from_pretrained(self.tmpdirname)
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rendered = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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expected_rendered = (
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"<|begin_of_text|>"
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"<|start_header_id|>user<|end_header_id|>\n\n"
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"<|image|><|image|>What do these images show?"
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"<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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"The first image shows the statue of Liberty in New York."
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"<|eot_id|>"
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"<|start_header_id|>user<|end_header_id|>\n\n"
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"And who is that?"
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"<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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self.assertEqual(rendered, expected_rendered)
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messages = [
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{
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"role": "system",
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"content": [
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{"type": "text", "text": "This is a test sentence."},
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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": "This is a response."},
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],
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},
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]
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input_ids = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
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expected_ids = [
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[
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128000, # <|begin_of_text|>
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128006, # <|start_header_id|>
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9125, # "system"
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128007, # <|end_of_header|>
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271, # "\n\n"
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2028,
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374,
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264,
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1296,
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11914,
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13, # "This is a test sentence."
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128009, # <|eot_id|>
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128006, # <|start_header_id|>
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882, # "user"
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128007, # <|end_of_header|>
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271, # "\n\n"
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2028,
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374,
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264,
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2077,
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13, # "This is a response.",
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128009, # <|eot_id|>
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128006, # <|start_header_id|>
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78191, # "assistant"
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128007, # <|end_of_header|>
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271, # "\n\n"
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]
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]
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self.assertEqual(input_ids, expected_ids)
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# test image in multiple locations
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Describe this image in two sentences"},
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{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
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{"type": "text", "text": " Test sentence "},
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{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
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{"type": "text", "text": "ok\n"},
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],
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}
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]
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rendered = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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expected_rendered = (
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"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
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"Describe this image in two sentences<|image|> Test sentence <|image|>ok\n<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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self.assertEqual(rendered, expected_rendered)
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input_ids = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
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# fmt: off
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expected_ids = [[
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128000, 128006, 882, 128007, 271, 75885, 420, 2217, 304, 1403, 23719, 128256,
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3475, 11914, 262, 128256, 564, 198, 128009, 128006, 78191, 128007, 271,
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]]
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# fmt: on
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self.assertEqual(input_ids, expected_ids)
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# text format for content
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messages_list = [
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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 this image in two sentences"},
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],
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}
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]
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messages_str = [
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{
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"role": "user",
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"content": "<|image|>Describe this image in two sentences",
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}
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]
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rendered_list = processor.apply_chat_template(messages_list, add_generation_prompt=True, tokenize=False)
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rendered_str = processor.apply_chat_template(messages_str, add_generation_prompt=True, tokenize=False)
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self.assertEqual(rendered_list, rendered_str)
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def test_process_interleaved_images_prompts_image_splitting(self):
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processor = MllamaProcessor.from_pretrained(self.tmpdirname)
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# Test that a single image is processed correctly
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inputs = processor(images=self.image2, size={"width": 224, "height": 224})
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self.assertEqual(inputs["pixel_values"].shape, (1, 1, 4, 3, 224, 224))
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# Test that text is processed correctly
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text = "<|begin_of_text|>This is a test sentence.<|end_of_text|>"
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inputs = processor(text=text)
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expected_ids = [128000, 2028, 374, 264, 1296, 11914, 13, 128001]
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self.assertEqual(inputs["input_ids"][0], expected_ids)
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self.assertEqual(inputs["attention_mask"][0], [1] * len(expected_ids))
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self.assertEqual(inputs.get("cross_attention_mask"), None)
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# Test a single sample with image and text
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image_str = "<|image|>"
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text_str = "This is a test sentence."
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text = image_str + text_str
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inputs = processor(
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text=text,
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images=self.image1,
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size={"width": 128, "height": 128},
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)
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expected_ids = [self.image_token_id, self.bos_token_id] + [2028, 374, 264, 1296, 11914, 13]
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self.assertEqual(inputs["pixel_values"].shape, (1, 1, 4, 3, 128, 128))
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self.assertEqual(inputs["input_ids"][0], expected_ids)
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self.assertEqual(inputs["attention_mask"][0], [1] * len(expected_ids))
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cross_attention_mask = inputs["cross_attention_mask"]
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self.assertEqual(cross_attention_mask.shape, (1, 8, 1, 4))
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self.assertTrue(
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np.all(cross_attention_mask == 1), f"Cross attention mask is not all ones: {cross_attention_mask}"
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)
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# Test batch
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text = [
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"<|image|>This is a test sentence.",
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"This is a test sentence.<|image|><|image|>This is a test sentence.",
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]
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# fmt: off
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expected_ids = [
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[self.image_token_id, self.bos_token_id, 2028, 374, 264, 1296, 11914, 13],
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[self.bos_token_id, 2028, 374, 264, 1296, 11914, 13, self.image_token_id, self.image_token_id, 2028, 374, 264, 1296, 11914, 13],
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]
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# fmt: onn
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images = [[self.image1], [self.image1, self.image2]]
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inputs = processor(text=text, images=images, padding=True, size={"width": 256, "height": 256})
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self.assertEqual(inputs["pixel_values"].shape, (2, 2, 4, 3, 256, 256))
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for input_ids_i, attention_mask_i, expected_ids_i in zip(inputs["input_ids"], inputs["attention_mask"], expected_ids):
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pad_ids = [id for id, m in zip(input_ids_i, attention_mask_i) if m == 0]
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input_ids = [id for id, m in zip(input_ids_i, attention_mask_i) if m == 1]
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self.assertEqual(input_ids, expected_ids_i)
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self.assertEqual(pad_ids, [self.pad_token_id] * len(pad_ids))
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cross_attention_mask = inputs["cross_attention_mask"]
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self.assertEqual(cross_attention_mask.shape, (2, 15, 2, 4))
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# Check that only first tile of first sample is attended to all text tokens
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first_sample_mask = cross_attention_mask[0].copy()
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first_image_first_tile_attention = first_sample_mask[:, :1, :1] # text tokens, images, tiles
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self.assertTrue(np.all(first_image_first_tile_attention == 1), f"Cross attention mask is not all ones: {first_image_first_tile_attention}")
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# zero out first tile of first image
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first_image_first_tile_attention[:, :1, :1] = 0
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self.assertTrue(np.all(first_image_first_tile_attention == 0), f"Cross attention mask is not all zeros: {first_image_first_tile_attention}")
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# second sample
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second_sample_mask = cross_attention_mask[1].copy()
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first_image_first_tile_attention = second_sample_mask[7:, :1, :1] # text tokens, images, tiles
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self.assertTrue(np.all(first_image_first_tile_attention == 1), f"Cross attention mask is not all ones: {first_image_first_tile_attention}")
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second_image_two_tiles_attention = second_sample_mask[8:, 1:2, :2] # text tokens, images, tiles
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self.assertTrue(np.all(second_image_two_tiles_attention == 1), f"Cross attention mask is not all ones: {second_image_two_tiles_attention}")
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# zero out both images masks
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second_sample_mask[7:, :1, :1] = 0
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second_sample_mask[8:, 1:2, :2] = 0
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self.assertTrue(np.all(second_sample_mask == 0), f"Cross attention mask is not all zeros: {second_sample_mask}")
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def test_process_interleaved_images_prompts_image_error(self):
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text = [
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"This is a test sentence.",
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"In this other sentence we try some good things",
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]
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processor = MllamaProcessor.from_pretrained(self.tmpdirname)
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inputs = processor(text=text, images=None, padding=True)
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self.assertIsNotNone(inputs["input_ids"])
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text = [
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"This is a test sentence.<|image|>",
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"In this other sentence we try some good things",
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]
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with self.assertRaises(ValueError):
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processor(text=text, images=None, padding=True)
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images = [[self.image1], []]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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text = [
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"This is a test sentence.<|image|>",
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"In this other sentence we try some good things<|image|>",
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]
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with self.assertRaises(ValueError):
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processor(text=text, images=None, padding=True)
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text = [
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"This is a test sentence.<|image|>",
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"In this other sentence we try some good things<|image|>",
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]
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images = [[self.image1], [self.image2]]
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inputs = processor(text=text, images=images, padding=True)
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images = [[self.image1, self.image2], []]
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with self.assertRaises(ValueError):
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processor(text=text, images=None, padding=True)
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# see https://github.com/huggingface/transformers/pull/35934
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images = [self.image1, self.image2]
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with self.assertRaises(ValueError):
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processor(text=text, images=None, padding=True)
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def test_unstructured_kwargs_batched(self):
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# Overridden because Mllama expects images in nested format. For 2 images it can't infer
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# the correct nesting, so we better throw an error
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor_kwargs = self.prepare_processor_dict()
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processor = self.processor_class(**processor_components, **processor_kwargs)
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self.skip_processor_without_typed_kwargs(processor)
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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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image_input = [[image_input[0]], [image_input[1]]]
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inputs = 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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do_rescale=True,
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rescale_factor=-1,
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padding="longest",
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max_length=76,
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)
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self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)
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self.assertTrue(
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len(inputs[self.text_input_name][0]) == len(inputs[self.text_input_name][1])
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and len(inputs[self.text_input_name][1]) < 76
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)
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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 = self.get_processor()
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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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image_input = [[image_input[0]], [image_input[1]]]
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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=3,
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)
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