[processor] clean up mulitmodal tests (#37362)
* clkea up mulitmodal processor tests * fixup * fix tests * fix one last test * forgot
This commit is contained in:
Raushan Turganbay
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GitHub
GitHub
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3c39c07939
commit
a563999a02
@@ -19,7 +19,7 @@ import unittest
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import requests
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from transformers import PixtralProcessor
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from transformers.testing_utils import require_read_token, require_vision
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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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@@ -34,7 +34,6 @@ if is_vision_available():
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@require_vision
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@require_read_token
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class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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"""This tests Pixtral processor with the new `spatial_merge_size` argument in Mistral3."""
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@@ -49,30 +48,37 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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cls.url_2 = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
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cls.image_2 = Image.open(requests.get(cls.url_2, stream=True).raw)
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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cls.tmpdirname = tempfile.mkdtemp()
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cls.addClassCleanup(lambda tempdir=cls.tmpdirname: shutil.rmtree(tempdir))
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processor_kwargs = cls.prepare_processor_dict()
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processor = PixtralProcessor.from_pretrained(
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"hf-internal-testing/Mistral-Small-3.1-24B-Instruct-2503-only-processor"
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"hf-internal-testing/Mistral-Small-3.1-24B-Instruct-2503-only-processor", **processor_kwargs
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)
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processor.save_pretrained(self.tmpdirname)
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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_processor(self):
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return self.processor_class.from_pretrained(self.tmpdirname)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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@staticmethod
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def prepare_processor_dict():
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return {
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"chat_template": "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\\nYour knowledge base was last updated on 2023-10-01. The current date is \" + today + \".\\n\\nWhen you're not sure about some information, you say that you don't have the information and don't make up anything.\\nIf the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \\\"What are some good restaurants around me?\\\" => \\\"Where are you?\\\" or \\\"When is the next flight to Tokyo\\\" => \\\"Where do you travel from?\\\")\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n {%- else %} \n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- set loop_messages = messages[1:] %}\n {%- endif %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for block in message['content'] %}\n {%- if block['type'] == 'text' %}\n {{- block['text'] }}\n {%- elif block['type'] == 'image' or block['type'] == 'image_url' %}\n {{- '[IMG]' }}\n {%- else %}\n {{- raise_exception('Only text and image blocks are supported in message content!') }}\n {%- endif %}\n {%- endfor %}\n {{- '[/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'system' %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- elif message['role'] == 'assistant' %}\n {%- if message['content'] is string %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- message['content'][0]['text'] + eos_token }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}",
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"patch_size": 128,
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} # fmt: skip
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def test_image_token_filling(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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# Important to check with non square image
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image = torch.randint(0, 2, (3, 500, 316))
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expected_image_tokens = 198
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expected_image_tokens = 4
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image_token_index = 10
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messages = [
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{
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"role": "system",
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"content": "",
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"content": [{"type": "text", "text": "You are a helpful assistant."}],
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},
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{
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"role": "user",
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@@ -104,14 +110,14 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertTrue(len(inputs_image["input_ids"]) == 1)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 36]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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@@ -121,36 +127,36 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertTrue(len(inputs_url["input_ids"]) == 1)
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self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 36]))
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# fmt: off
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input_ids = inputs_url["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Test passing inputs as a single list
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inputs_image = processor(text=prompt_string, images=[self.image_0], return_tensors="pt")
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 36]))
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# fmt: off
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self.assertEqual(
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inputs_image["input_ids"][0].tolist(),
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Test as nested single list
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inputs_image = processor(text=prompt_string, images=[[self.image_0]], return_tensors="pt")
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 36]))
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# fmt: off
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self.assertEqual(
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inputs_image["input_ids"][0].tolist(),
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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@@ -168,14 +174,14 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertTrue(len(inputs_image["input_ids"]) == 1)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 36]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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@@ -185,25 +191,25 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertTrue(len(inputs_url["input_ids"]) == 1)
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self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 36]))
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# fmt: off
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input_ids = inputs_url["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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# Test passing in as a nested list
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inputs_url = processor(text=prompt_string, images=[[self.image_0, self.image_1]], return_tensors="pt")
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 36]))
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# fmt: off
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self.assertEqual(
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inputs_url["input_ids"][0].tolist(),
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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@@ -226,14 +232,14 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertTrue(len(inputs_image["input_ids"]) == 2)
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self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 36, 36]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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@@ -243,14 +249,14 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertTrue(len(inputs_url["input_ids"]) == 2)
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self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
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self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 36, 36]))
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# fmt: off
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input_ids = inputs_url["input_ids"]
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self.assertEqual(
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input_ids[0].tolist(),
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# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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@@ -258,12 +264,12 @@ class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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inputs_image = processor(
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text=prompt_string, images=[self.image_0, self.image_1, self.image_2], return_tensors="pt", padding=True
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)
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
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self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 36, 36]))
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# fmt: off
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self.assertEqual(
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inputs_image["input_ids"][0].tolist(),
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[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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[1, 21510, 1058, 1032, 10, 10, 10, 12, 10, 10, 10, 13, 10, 10, 10, 12, 10, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
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)
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# fmt: on
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