Add Mistral3 (#36790)

* initial start * style and dummies * Create convert_mistral3_weights_to_hf.py * update * typo * typo * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * up * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * update * update * Update image_processing_mistral3.py * Update convert_mistral3_weights_to_hf.py * fix patch merger * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * up * update modular to fit * style * Update convert_mistral3_weights_to_hf.py * typo * Update modular_mistral3.py * simplify a lot all shape shenanigans * simplify * add working test processor * Add partially working common modeling tests * All tests working and remove mistral3 image processors * add docs and fixup * fix inference with image size >1540 * 🚨fix test image proc pixtral * Remove vision_feature_select_strategy * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * clean * fix test checkpoints * Update test_modeling_mistral3.py * Update test_modeling_mistral3.py * style * Use Pixtral processor * up * finish cleaning processor to use pixtral directly * Update __init__.py * Update processing_pixtral.py * doc * Update __init__.py * Update mistral3.md * Update _toctree.yml --------- Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co> Co-authored-by: yonigozlan <yoni.gozlan10@gmail.com>
2025-03-18 12:04:42 +01:00
parent bd92073692
commit e959530b8f
21 changed files with 2303 additions and 6 deletions
--- a/tests/models/mistral3/test_processor_mistral3.py
+++ b/tests/models/mistral3/test_processor_mistral3.py
@@ -0,0 +1,293 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+
+import requests
+
+from transformers import PixtralProcessor
+from transformers.testing_utils import require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+@require_vision
+class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    """This tests Pixtral processor with the new `spatial_merge_size` argument in Mistral3."""
+
+    processor_class = PixtralProcessor
+
+    @classmethod
+    def setUpClass(cls):
+        cls.url_0 = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        cls.image_0 = Image.open(requests.get(cls.url_0, stream=True).raw)
+        cls.url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        cls.image_1 = Image.open(requests.get(cls.url_1, stream=True).raw)
+        cls.url_2 = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
+        cls.image_2 = Image.open(requests.get(cls.url_2, stream=True).raw)
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        processor = PixtralProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+        processor.save_pretrained(self.tmpdirname)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def test_chat_template(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        expected_prompt = "<s>[INST][IMG]What is shown in this image?[/INST]"
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+        self.assertEqual(expected_prompt, formatted_prompt)
+
+    def test_image_token_filling(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 500, 316))
+        expected_image_tokens = 198
+        image_token_index = 10
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)
+
+    def test_processor_with_single_image(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        prompt_string = "USER: [IMG]\nWhat's the content of the image? ASSISTANT:"
+
+        # Make small for checking image token expansion
+        processor.image_processor.size = {"longest_edge": 30}
+        processor.patch_size = 6
+
+        # Test passing in an image
+        inputs_image = processor(text=prompt_string, images=self.image_0, return_tensors="pt")
+        self.assertIn("input_ids", inputs_image)
+        self.assertTrue(len(inputs_image["input_ids"]) == 1)
+        self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
+        self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
+
+        # fmt: off
+        input_ids = inputs_image["input_ids"]
+        self.assertEqual(
+            input_ids[0].tolist(),
+            # Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
+            [1, 21510,  1058,  1032,    10,    10,    12,    10,    10,    13,  1010, 7493,  1681,  1278,  4701,  1307,  1278,  3937,  1063,  1349,  4290, 16002, 41150,  1058]
+        )
+        # fmt: on
+
+        # Test passing in a url
+        inputs_url = processor(text=prompt_string, images=self.url_0, return_tensors="pt")
+        self.assertIn("input_ids", inputs_url)
+        self.assertTrue(len(inputs_url["input_ids"]) == 1)
+        self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
+        self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
+
+        # fmt: off
+        input_ids = inputs_url["input_ids"]
+        self.assertEqual(
+            input_ids[0].tolist(),
+            # Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
+            [1, 21510,  1058,  1032,    10,    10,    12,    10,    10,    13,  1010, 7493,  1681,  1278,  4701,  1307,  1278,  3937,  1063,  1349,  4290, 16002, 41150,  1058]
+        )
+        # fmt: on
+
+        # Test passing inputs as a single list
+        inputs_image = processor(text=prompt_string, images=[self.image_0], return_tensors="pt")
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
+
+        # fmt: off
+        self.assertEqual(
+            inputs_image["input_ids"][0].tolist(),
+            [1, 21510,  1058,  1032,    10,    10,    12,    10,    10,    13,  1010, 7493,  1681,  1278,  4701,  1307,  1278,  3937,  1063,  1349,  4290, 16002, 41150,  1058]
+        )
+        # fmt: on
+
+        # Test as nested single list
+        inputs_image = processor(text=prompt_string, images=[[self.image_0]], return_tensors="pt")
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
+
+        # fmt: off
+        self.assertEqual(
+            inputs_image["input_ids"][0].tolist(),
+            [1, 21510,  1058,  1032,    10,    10,    12,    10,    10,    13,  1010, 7493,  1681,  1278,  4701,  1307,  1278,  3937,  1063,  1349,  4290, 16002, 41150,  1058]
+        )
+        # fmt: on
+
+    def test_processor_with_multiple_images_single_list(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        prompt_string = "USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:"
+
+        # Make small for checking image token expansion
+        processor.image_processor.size = {"longest_edge": 30}
+        processor.patch_size = 6
+
+        # Test passing in an image
+        inputs_image = processor(text=prompt_string, images=[self.image_0, self.image_1], return_tensors="pt")
+        self.assertIn("input_ids", inputs_image)
+        self.assertTrue(len(inputs_image["input_ids"]) == 1)
+        self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
+        self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
+
+        # fmt: off
+        input_ids = inputs_image["input_ids"]
+        self.assertEqual(
+            input_ids[0].tolist(),
+            # 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:"]
+            [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]
+                    )
+        # fmt: on
+
+        # Test passing in a url
+        inputs_url = processor(text=prompt_string, images=[self.url_0, self.url_1], return_tensors="pt")
+        self.assertIn("input_ids", inputs_url)
+        self.assertTrue(len(inputs_url["input_ids"]) == 1)
+        self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
+        self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
+
+        # fmt: off
+        input_ids = inputs_url["input_ids"]
+        self.assertEqual(
+            input_ids[0].tolist(),
+            # 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:"]
+            [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]
+        )
+        # fmt: on
+
+        # Test passing in as a nested list
+        inputs_url = processor(text=prompt_string, images=[[self.image_0, self.image_1]], return_tensors="pt")
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
+
+        # fmt: off
+        self.assertEqual(
+            inputs_url["input_ids"][0].tolist(),
+            [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]
+        )
+        # fmt: on
+
+    def test_processor_with_multiple_images_multiple_lists(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        prompt_string = [
+            "USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:",
+            "USER: [IMG]\nWhat's the content of the image? ASSISTANT:",
+        ]
+        processor.tokenizer.pad_token = "</s>"
+        image_inputs = [[self.image_0, self.image_1], [self.image_2]]
+
+        # Make small for checking image token expansion
+        processor.image_processor.size = {"longest_edge": 30}
+        processor.patch_size = 6
+
+        # Test passing in an image
+        inputs_image = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
+        self.assertIn("input_ids", inputs_image)
+        self.assertTrue(len(inputs_image["input_ids"]) == 2)
+        self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
+        self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
+
+        # fmt: off
+        input_ids = inputs_image["input_ids"]
+        self.assertEqual(
+            input_ids[0].tolist(),
+            # 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:"]
+            [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]
+        )
+        # fmt: on
+
+        # Test passing in a url
+        inputs_url = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
+        self.assertIn("input_ids", inputs_url)
+        self.assertTrue(len(inputs_url["input_ids"]) == 2)
+        self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
+        self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
+
+        # fmt: off
+        input_ids = inputs_url["input_ids"]
+        self.assertEqual(
+            input_ids[0].tolist(),
+            # 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:"]
+            [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]
+        )
+        # fmt: on
+
+        # Test passing as a single flat list
+        inputs_image = processor(
+            text=prompt_string, images=[self.image_0, self.image_1, self.image_2], return_tensors="pt", padding=True
+        )
+        self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
+
+        # fmt: off
+        self.assertEqual(
+            inputs_image["input_ids"][0].tolist(),
+            [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]
+        )
+        # fmt: on
+
+    def test_processor_returns_full_length_batches(self):
+        # to avoid https://github.com/huggingface/transformers/issues/34204
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        prompt_string = [
+            "USER: [IMG]\nWhat's the content of the image? ASSISTANT:",
+        ] * 5
+        processor.tokenizer.pad_token = "</s>"
+        image_inputs = [[self.image_0]] * 5
+
+        # Make small for checking image token expansion
+        processor.image_processor.size = {"longest_edge": 30}
+        processor.patch_size = 6
+
+        # Test passing in an image
+        inputs_image = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
+        self.assertIn("input_ids", inputs_image)
+        self.assertTrue(len(inputs_image["input_ids"]) == 5)