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tests/models/cohere2_vision/test_modeling_cohere2_vision.py

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+# Copyright 2025 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch GotOcr2 model."""
+
+import unittest
+
+from transformers import (
+    AutoProcessor,
+    Cohere2VisionConfig,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    get_device_properties,
+    require_deterministic_for_xpu,
+    require_torch,
+    require_torch_accelerator,
+    require_torch_large_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Cohere2VisionForConditionalGeneration,
+        Cohere2VisionModel,
+    )
+
+
+class Cohere2VisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        seq_length=7,
+        downsample_factor=2,
+        alignment_intermediate_size=32,
+        ignore_index=-100,
+        image_token_id=2,
+        num_channels=3,
+        image_size=64,
+        is_training=True,
+        text_config={
+            "model_type": "cohere2",
+            "vocab_size": 99,
+            "hidden_size": 128,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "output_channels": 64,
+            "hidden_act": "silu",
+            "max_position_embeddings": 512,
+            "tie_word_embeddings": True,
+            "bos_token_id": 0,
+            "eos_token_id": 0,
+            "pad_token_id": 0,
+        },
+        vision_config={
+            "model_type": "siglip_vision_model",
+            "hidden_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "intermediate_size": 128,
+            "image_size": 64,
+            "patch_size": 8,
+            "vision_use_head": False,
+        },
+    ):
+        self.parent = parent
+        self.ignore_index = ignore_index
+        self.bos_token_id = text_config["bos_token_id"]
+        self.eos_token_id = text_config["eos_token_id"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.image_token_id = image_token_id
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.batch_size = batch_size
+        self.downsample_factor = downsample_factor
+        self.alignment_intermediate_size = alignment_intermediate_size
+        self.is_training = is_training
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.image_seq_length = 16
+        self.seq_length = seq_length + self.image_seq_length
+
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.vocab_size = text_config["vocab_size"]
+        self.hidden_size = text_config["hidden_size"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+
+    def get_config(self):
+        return Cohere2VisionConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            image_token_id=self.image_token_id,
+            downsample_factor=self.downsample_factor,
+            alignment_intermediate_size=self.alignment_intermediate_size,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        input_ids[:, : self.image_seq_length] = self.image_token_id
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Cohere2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            Cohere2VisionModel,
+            Cohere2VisionForConditionalGeneration,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (Cohere2VisionForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "image-text-to-text": Cohere2VisionForConditionalGeneration,
+            "any-to-any": Cohere2VisionForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = Cohere2VisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Cohere2VisionConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+
+@require_torch
+class Cohere2IntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.model_checkpoint = "CohereLabs/command-a-vision-07-2025"
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def get_model(self, dummy=True):
+        device_type, major, _ = get_device_properties()
+        dtype = torch.float16
+
+        # too large to fit into A10
+        config = Cohere2VisionConfig.from_pretrained(self.model_checkpoint)
+        if dummy:
+            config.text_config.num_hidden_layers = 4
+            config.text_config.layer_types = config.text_config.layer_types[:4]
+
+        model = Cohere2VisionForConditionalGeneration.from_pretrained(
+            self.model_checkpoint,
+            config=config,
+            dtype=dtype,
+            device_map="auto",
+        )
+        return model
+
+    @slow
+    @require_torch_accelerator
+    def test_model_integration_forward(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model(dummy=False)
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        # Forward
+        with torch.inference_mode():
+            output = model(**inputs)
+
+        actual_logits = output.logits[0, -1, :5].cpu()
+
+        EXPECTED_LOGITS = Expectations(
+            {
+                ("xpu", 3): [2.4297, 1.6836, 1.8779, 2.1895, 1.9395],
+                # 4-bit
+                ("cuda", 7): [0.1097, 0.3481, 3.8340, 9.7969, 2.0488],
+                ("cuda", 8): [2.4277, 1.6875, 1.8789, 2.1875, 1.9375],
+            }
+        )  # fmt: skip
+        expected_logits = torch.tensor(EXPECTED_LOGITS.get_expectation(), dtype=torch.float16)
+
+        self.assertTrue(
+            torch.allclose(actual_logits, expected_logits, atol=0.1),
+            f"Actual logits: {actual_logits}"
+            f"\nExpected logits: {expected_logits}"
+            f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
+        )
+
+    @slow
+    @require_torch_accelerator
+    @require_deterministic_for_xpu
+    def test_model_integration_generate_text_only(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Write a haiku"},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=10, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>",
+                ("cuda", 8): "<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(decoded_output, expected_output)
+
+    @slow
+    @require_torch_accelerator
+    @require_deterministic_for_xpu
+    def test_model_integration_generate_chat_template(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=10, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+                ("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(decoded_output, expected_output)
+
+    @slow
+    @require_torch_accelerator
+    def test_model_integration_batched_generate(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model(dummy=False)
+        # Prepare inputs
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg",
+                        },
+                        {"type": "text", "text": "Describe this image"},
+                    ],
+                },
+            ],
+        ]
+        inputs = processor.apply_chat_template(
+            messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=torch.float16)
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=5)
+
+        # Check first output
+        decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): 'Dock stretches to calm',
+                ("cuda", 8): 'Dock stretches to calm',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): 'The image depicts a',
+                ("cuda", 8): 'The image depicts a',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    @slow
+    @require_torch_accelerator
+    @require_deterministic_for_xpu
+    def test_model_integration_batched_generate_multi_image(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        # Prepare inputs
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+                        },
+                        {
+                            "type": "image",
+                            "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+                        },
+                        {
+                            "type": "text",
+                            "text": "These images depict two different landmarks. Can you identify them?",
+                        },
+                    ],
+                },
+            ],
+        ]
+        inputs = processor.apply_chat_template(
+            messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=torch.float16)
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=10)
+
+        # Check first output
+        decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+                ("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+                ("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+
+@slow
+@require_torch_large_accelerator
+class Cohere2MoeVisionIntegrationTest(unittest.TestCase):
+    """Integration tests for Cohere2VisionForConditionalGeneration with the Command A+ Model."""
+
+    model_checkpoint = "/root/repos/moe/engines/command_a+_bf16"
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def get_model(self):
+        return Cohere2VisionForConditionalGeneration.from_pretrained(
+            self.model_checkpoint,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+        ).eval()
+
+    @slow
+    @require_torch_large_accelerator
+    def test_model_forward_vision(self):
+        """Forward pass with an image + text input; checks first token logit values."""
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=torch.bfloat16)
+
+        with torch.inference_mode():
+            output = model(**inputs)
+
+        actual_logits = output.logits[0, -1, :5].cpu().to(torch.float32)
+        expected_logits = torch.tensor([0.7383, 0.6172, 2.125, -67.5, -4.7813])
+        self.assertTrue(
+            torch.allclose(actual_logits, expected_logits, atol=0.1),
+            f"Actual logits: {actual_logits}\nExpected logits: {expected_logits}",
+        )
+
+    @slow
+    @require_torch_large_accelerator
+    def test_model_generate_vision(self):
+        """Image + text generation with the cohere2moe backbone."""
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=torch.bfloat16)
+
+        with torch.no_grad():
+            gen_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        decoded = processor.decode(gen_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+
+        expected = "<|START_THINKING|><|END_THINKING|><|START_TEXT|>The image shows two tabby cats sleeping on a bright pink blanket or couch. Both"
+        self.assertEqual(decoded, expected, f"Decoded: {decoded!r}")