Pixtral: vectorize patch embeddings and enable tests (#35122)
* initial POC * - batch mix feature * fix tests * fix tests * make style * do not skip and instead fix tests * update * return back the test * correct text with the correct ckpt
This commit is contained in:
Raushan Turganbay
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GitHub
GitHub
parent
8bc4c89ee9
commit
9725e5be2f
@@ -14,7 +14,6 @@
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import shutil
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import tempfile
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import unittest
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from typing import Optional
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import requests
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import torch
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@@ -28,7 +27,7 @@ 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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from transformers import AutoTokenizer, PixtralImageProcessor, PixtralProcessor
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from transformers import PixtralProcessor
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@require_vision
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@@ -46,20 +45,15 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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# FIXME - just load the processor directly from the checkpoint
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/pixtral-12b")
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image_processor = PixtralImageProcessor()
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processor = PixtralProcessor(tokenizer=tokenizer, image_processor=image_processor)
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processor = PixtralProcessor.from_pretrained("mistral-community/pixtral-12b")
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processor.save_pretrained(self.tmpdirname)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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@unittest.skip("No chat template was set for this model (yet)")
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def test_chat_template(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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expected_prompt = "USER: [IMG]\nWhat is shown in this image? ASSISTANT:"
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expected_prompt = "<s>[INST][IMG]What is shown in this image?[/INST]"
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messages = [
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{
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@@ -73,13 +67,12 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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self.assertEqual(expected_prompt, formatted_prompt)
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@unittest.skip("No chat template was set for this model (yet)")
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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 = 1526
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image_token_index = 32000
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expected_image_tokens = 640
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image_token_index = 10
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messages = [
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{
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@@ -111,11 +104,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_image)
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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"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], 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, 32, 32]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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@@ -131,11 +121,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_url)
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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_url["pixel_values"], list)
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self.assertTrue(len(inputs_url["pixel_values"]) == 1)
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self.assertIsInstance(inputs_url["pixel_values"][0], list)
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self.assertTrue(len(inputs_url["pixel_values"][0]) == 1)
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self.assertIsInstance(inputs_url["pixel_values"][0][0], 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, 32, 32]))
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# fmt: off
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input_ids = inputs_url["input_ids"]
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@@ -146,6 +133,28 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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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, 32, 32]))
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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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[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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)
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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, 32, 32]))
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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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[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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)
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# fmt: on
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def test_processor_with_multiple_images_single_list(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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prompt_string = "USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:"
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@@ -159,11 +168,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_image)
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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"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 1)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], 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, 32, 32]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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@@ -179,11 +185,9 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_url)
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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_url["pixel_values"], list)
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self.assertTrue(len(inputs_url["pixel_values"]) == 1)
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self.assertIsInstance(inputs_url["pixel_values"][0], list)
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self.assertTrue(len(inputs_url["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_url["pixel_values"][0][0], 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, 32, 32]))
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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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@@ -193,6 +197,17 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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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, 32, 32]))
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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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[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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)
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# fmt: on
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def test_processor_with_multiple_images_multiple_lists(self):
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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prompt_string = [
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@@ -211,11 +226,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_image)
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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"], list)
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self.assertTrue(len(inputs_image["pixel_values"]) == 2)
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self.assertIsInstance(inputs_image["pixel_values"][0], list)
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self.assertTrue(len(inputs_image["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_image["pixel_values"][0][0], 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, 32, 32]))
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# fmt: off
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input_ids = inputs_image["input_ids"]
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@@ -231,11 +243,8 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_url)
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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_url["pixel_values"], list)
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self.assertTrue(len(inputs_url["pixel_values"]) == 2)
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self.assertIsInstance(inputs_url["pixel_values"][0], list)
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self.assertTrue(len(inputs_url["pixel_values"][0]) == 2)
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self.assertIsInstance(inputs_url["pixel_values"][0][0], 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, 32, 32]))
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# fmt: off
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input_ids = inputs_url["input_ids"]
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@@ -246,6 +255,19 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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)
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# fmt: on
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# Test passing as a single flat list
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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, 32, 32]))
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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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[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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)
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# fmt: on
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def test_processor_returns_full_length_batches(self):
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# to avoid https://github.com/huggingface/transformers/issues/34204
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processor = self.processor_class.from_pretrained(self.tmpdirname)
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@@ -264,13 +286,3 @@ class PixtralProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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self.assertIn("input_ids", inputs_image)
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self.assertTrue(len(inputs_image["input_ids"]) == 5)
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self.assertTrue(len(inputs_image["pixel_values"]) == 5)
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# Override as PixtralProcessor needs nested images to work properly with batched inputs
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@require_vision
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def prepare_image_inputs(self, batch_size: Optional[int] = None):
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"""This function prepares a list of PIL images for testing"""
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if batch_size is None:
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return super().prepare_image_inputs()
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if batch_size < 1:
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raise ValueError("batch_size must be greater than 0")
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return [[super().prepare_image_inputs()]] * batch_size
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