Refactor image processor phi4 (#36976)

* refactor image processor phi4

* nits fast image proc

* add image tests phi4

* Fix image processing tests

* update integration tests

* remove revision and add comment in integration tests

tests/models/phi4_multimodal/test_image_processing_phi4_multimodal.py

@@ -0,0 +1,307 @@
+# coding=utf-8
+# Copyright 2021 HuggingFace Inc.
+#
+# 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 inspect
+import math
+import unittest
+import warnings
+
+import numpy as np
+from packaging import version
+
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    if is_torchvision_available():
+        from transformers import Phi4MultimodalImageProcessorFast
+
+
+class Phi4MultimodalImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=100,
+        min_resolution=30,
+        max_resolution=400,
+        dynamic_hd=36,
+        do_resize=True,
+        size=None,
+        patch_size=14,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        do_convert_rgb=True,
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 100, "width": 100}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.dynamic_hd = dynamic_hd
+        self.do_resize = do_resize
+        self.size = size
+        self.patch_size = patch_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "patch_size": self.patch_size,
+            "dynamic_hd": self.dynamic_hd,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_image_shape(self, images):
+        max_num_patches = 0
+        for image in images:
+            if isinstance(image, Image.Image):
+                width, height = image.size
+            elif isinstance(image, np.ndarray):
+                height, width = image.shape[:2]
+            elif isinstance(image, torch.Tensor):
+                height, width = image.shape[-2:]
+            w_crop_num = math.ceil(width / float(self.size["width"]))
+            h_crop_num = math.ceil(height / float(self.size["height"]))
+            num_patches = min(w_crop_num * h_crop_num + 1, self.dynamic_hd)
+            max_num_patches = max(max_num_patches, num_patches)
+        num_patches = max_num_patches
+        return num_patches, self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class Phi4MultimodalImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    fast_image_processing_class = Phi4MultimodalImageProcessorFast if is_torchvision_available() else None
+    test_slow_image_processor = False
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Phi4MultimodalImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"height": 100, "width": 100})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    @unittest.skip(reason="Phi4MultimodalImageProcessorFast doesn't treat 4 channel PIL and numpy consistently yet")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_cast_dtype_device(self):
+        for image_processing_class in self.image_processor_list:
+            if self.test_cast_dtype is not None:
+                # Initialize image_processor
+                image_processor = image_processing_class(**self.image_processor_dict)
+
+                # create random PyTorch tensors
+                image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+                encoding = image_processor(image_inputs, return_tensors="pt")
+                # for layoutLM compatibility
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.float32)
+
+                encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.float16)
+
+                encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.bfloat16)
+
+                with self.assertRaises(TypeError):
+                    _ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
+
+                # Try with text + image feature
+                encoding = image_processor(image_inputs, return_tensors="pt")
+                encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
+                encoding = encoding.to(torch.float16)
+
+                self.assertEqual(encoding.image_pixel_values.device, torch.device("cpu"))
+                self.assertEqual(encoding.image_pixel_values.dtype, torch.float16)
+                self.assertEqual(encoding.input_ids.dtype, torch.long)
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").image_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+            encoded_images = image_processing(image_inputs, return_tensors="pt").image_pixel_values
+            self.assertEqual(
+                tuple(encoded_images.shape),
+                (self.image_processor_tester.batch_size, *expected_output_image_shape),
+            )
+
+    def test_image_processor_preprocess_arguments(self):
+        is_tested = False
+
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class(**self.image_processor_dict)
+
+            # validation done by _valid_processor_keys attribute
+            if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
+                preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
+                preprocess_parameter_names.remove("self")
+                preprocess_parameter_names.sort()
+                valid_processor_keys = image_processor._valid_processor_keys
+                valid_processor_keys.sort()
+                self.assertEqual(preprocess_parameter_names, valid_processor_keys)
+                is_tested = True
+
+            # validation done by @filter_out_non_signature_kwargs decorator
+            if hasattr(image_processor.preprocess, "_filter_out_non_signature_kwargs"):
+                if hasattr(self.image_processor_tester, "prepare_image_inputs"):
+                    inputs = self.image_processor_tester.prepare_image_inputs()
+                elif hasattr(self.image_processor_tester, "prepare_video_inputs"):
+                    inputs = self.image_processor_tester.prepare_video_inputs()
+                else:
+                    self.skipTest(reason="No valid input preparation method found")
+
+                with warnings.catch_warnings(record=True) as raised_warnings:
+                    warnings.simplefilter("always")
+                    image_processor(inputs, extra_argument=True)
+
+                messages = " ".join([str(w.message) for w in raised_warnings])
+                self.assertGreaterEqual(len(raised_warnings), 1)
+                self.assertIn("extra_argument", messages)
+                is_tested = True
+
+        if not is_tested:
+            self.skipTest(reason="No validation found for `preprocess` method")
+
+    @slow
+    def test_can_compile_fast_image_processor(self):
+        if self.fast_image_processing_class is None:
+            self.skipTest("Skipping compilation test as fast image processor is not defined")
+        if version.parse(torch.__version__) < version.parse("2.3"):
+            self.skipTest(reason="This test requires torch >= 2.3 to run.")
+
+        torch.compiler.reset()
+        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
+        image_processor = self.fast_image_processing_class(**self.image_processor_dict)
+        output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")
+
+        image_processor = torch.compile(image_processor, mode="reduce-overhead")
+        output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
+
+        torch.testing.assert_close(
+            output_eager.image_pixel_values, output_compiled.image_pixel_values, rtol=1e-4, atol=1e-4
+        )

tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py

@@ -31,12 +31,7 @@ from transformers import (
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import (
-    require_soundfile,
-    require_torch,
-    slow,
-    torch_device,
-)
+from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
 from transformers.utils import is_soundfile_available
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -285,6 +280,8 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
     audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/f2641_0_throatclearing.wav"
 
     def setUp(self):
+        # Currently, the Phi-4 checkpoint on the hub is not working with the latest Phi-4 code, so the slow integration tests
+        # won't pass without using the correct revision (refs/pr/70)
         self.processor = AutoProcessor.from_pretrained(self.checkpoint_path)
         self.generation_config = GenerationConfig(max_new_tokens=20, do_sample=False)
         self.user_token = "<|user|>"
@@ -325,7 +322,7 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
             self.checkpoint_path, torch_dtype=torch.float16, device_map=torch_device
         )
 
-        prompt = f"{self.user_token}<|image_1|>What is shown in this image?{self.end_token}{self.assistant_token}"
+        prompt = f"{self.user_token}<|image|>What is shown in this image?{self.end_token}{self.assistant_token}"
         inputs = self.processor(prompt, images=self.image, return_tensors="pt").to(torch_device)
 
         output = model.generate(
@@ -349,7 +346,7 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
         for i in range(1, 5):
             url = f"https://image.slidesharecdn.com/azureintroduction-191206101932/75/Introduction-to-Microsoft-Azure-Cloud-{i}-2048.jpg"
             images.append(Image.open(requests.get(url, stream=True).raw))
-            placeholder += f"<|image_{i}|>"
+            placeholder += "<|image|>"
 
         prompt = f"{self.user_token}{placeholder}Summarize the deck of slides.{self.end_token}{self.assistant_token}"
         inputs = self.processor(prompt, images, return_tensors="pt").to(torch_device)
@@ -371,8 +368,8 @@ class Phi4MultimodalIntegrationTest(unittest.TestCase):
             self.checkpoint_path, torch_dtype=torch.float16, device_map=torch_device
         )
 
-        prompt = f"{self.user_token}<|audio_1|>What is happening in this audio?{self.end_token}{self.assistant_token}"
-        inputs = self.processor(prompt, audios=self.audio, sampling_rate=self.sampling_rate, return_tensors="pt").to(
+        prompt = f"{self.user_token}<|audio|>What is happening in this audio?{self.end_token}{self.assistant_token}"
+        inputs = self.processor(prompt, audio=self.audio, sampling_rate=self.sampling_rate, return_tensors="pt").to(
             torch_device
         )