first commit

tests/models/lfm2_vl/test_modeling_lfm2_vl.py

@@ -0,0 +1,385 @@
+# 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 LFM2-VL model."""
+
+import math
+import unittest
+from io import BytesIO
+
+import pytest
+import requests
+
+from transformers import AutoProcessor, is_torch_available
+from transformers.models.lfm2_vl.modeling_lfm2_vl import Lfm2VlForConditionalGeneration
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_deterministic_for_xpu,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+from transformers.utils.import_utils import is_vision_available
+
+from ...causal_lm_tester import CausalLMModelTester
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_vision_available():
+    from PIL import Image
+
+if is_torch_available():
+    import torch
+
+    from transformers import Lfm2VlConfig, Lfm2VlForConditionalGeneration, Lfm2VlModel
+
+
+class Lfm2VlModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = Lfm2VlConfig
+        base_model_class = Lfm2VlModel
+        causal_lm_class = Lfm2VlForConditionalGeneration
+
+    def __init__(
+        self,
+        parent,
+        is_training=True,
+        batch_size=2,
+        scale_factor=2,
+        num_images=2,
+        vision_config={
+            "hidden_size": 32,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 2,
+            "num_channels": 3,
+            "num_patches": 16,
+            "patch_size": 4,
+            "hidden_act": "gelu_pytorch_tanh",
+            "layer_norm_eps": 1e-6,
+            "attention_dropout": 0.0,
+        },
+        text_config={
+            "vocab_size": 100,
+            "hidden_size": 32,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 2,
+            "max_position_embeddings": 100,
+            "pad_token_id": 0,
+            "bos_token_id": 1,
+            "eos_token_id": 2,
+            "tie_word_embeddings": True,
+            "rope_theta": 1000000.0,
+            "conv_bias": False,
+            "conv_L_cache": 3,
+            "block_multiple_of": 2,
+            "full_attn_idxs": [0],
+        },
+        image_token_id=4,
+        downsample_factor=4,
+        projector_hidden_size=32,
+    ):
+        super().__init__(parent)
+        self.vision_config = vision_config
+        self.text_config = text_config
+        self.image_token_id = image_token_id
+        self.is_training = is_training
+        self.batch_size = batch_size
+        self.scale_factor = scale_factor
+        self.num_images = num_images
+        self.downsample_factor = downsample_factor
+        self.projector_hidden_size = projector_hidden_size
+        self.image_seq_length = 4
+
+    def get_config(self):
+        return Lfm2VlConfig(
+            vision_config=self.vision_config,
+            text_config=self.text_config,
+            image_token_id=self.image_token_id,
+            downsample_factor=self.downsample_factor,
+            projector_hidden_size=self.projector_hidden_size,
+        )
+
+    def prepare_config_and_inputs(self):
+        # Create dummy pixel values: [num_images, num_patches, channels * patch_size^2]
+        patch_size = self.vision_config["patch_size"]
+        pixel_values = floats_tensor([self.num_images, 64, 3 * patch_size * patch_size])
+
+        # Spatial shapes: one (height_patches, width_patches) per image
+        patches = int(math.sqrt(64))
+        spatial_shapes = torch.tensor([[patches, patches]] * self.num_images, dtype=torch.long, device=torch_device)
+
+        # Pixel attention mask: mark all patches as valid (no padding)
+        pixel_attention_mask = torch.ones((self.num_images, 64), dtype=torch.long, device=torch_device)
+        config = self.get_config()
+        return config, pixel_values, spatial_shapes, pixel_attention_mask
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, spatial_shapes, pixel_attention_mask = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 1
+
+        # For simplicity just set the last n tokens to the image token
+        input_ids[input_ids == self.image_token_id] = self.text_config["pad_token_id"]
+        input_ids[:, : self.image_seq_length] = self.image_token_id
+
+        attention_mask = input_ids.ne(1).to(torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "spatial_shapes": spatial_shapes,
+            "pixel_attention_mask": pixel_attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Lfm2VlModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (Lfm2VlModel, Lfm2VlForConditionalGeneration) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Lfm2VlModel,
+            "text-generation": Lfm2VlForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    model_tester_class = Lfm2VlModelTester
+    _is_composite = True
+    test_torch_exportable = False
+
+    def setUp(self):
+        self.model_tester = Lfm2VlModelTester(self)
+        common_properties = ["image_token_id", "projector_hidden_size"]
+        self.config_tester = ConfigTester(
+            self, config_class=Lfm2VlConfig, has_text_modality=False, common_properties=common_properties
+        )
+
+    def _get_conv_state_shape(self, batch_size: int, config):
+        return (batch_size, config.hidden_size, config.conv_L_cache)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(
+        "Lfm2 backbone alternates between attention and conv layers, so attention are only returned for attention layers"
+    )
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip(
+        "Lfm2 backbone has a special cache format which is not compatible with compile as it has static address for conv cache"
+    )
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing(self):
+        super().test_training_gradient_checkpointing()
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        super().test_training_gradient_checkpointing_use_reentrant_false()
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        super().test_training_gradient_checkpointing_use_reentrant_true()
+
+
+@require_torch_accelerator
+@slow
+class Lfm2VlForConditionalGenerationIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-1.6B")
+        self.processor.tokenizer.padding_side = "left"
+        self.image = Image.open(
+            requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
+        )
+        self.image2 = Image.open(
+            BytesIO(
+                requests.get(
+                    "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+                ).content
+            )
+        )
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @require_deterministic_for_xpu
+    def test_integration_test(self):
+        model = Lfm2VlForConditionalGeneration.from_pretrained(
+            "LiquidAI/LFM2-VL-1.6B",
+            dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+        # Create inputs
+        text = "<image>In this image, we see"
+        images = self.image
+        inputs = self.processor(text=text, images=images, return_tensors="pt")
+        inputs.to(device=torch_device, dtype=torch.bfloat16)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        expected_generated_text = "In this image, we see two cats sleeping on a pink blanket. There are also two remote controls on the blanket.\n\n\n\n"
+        self.assertEqual(generated_texts[0], expected_generated_text)
+
+    @require_deterministic_for_xpu
+    def test_integration_test_high_resolution(self):
+        model = Lfm2VlForConditionalGeneration.from_pretrained(
+            "LiquidAI/LFM2-VL-1.6B",
+            dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+        # Create inputs
+        text = "<image>In this image, we see"
+        images = self.image2
+        inputs = self.processor(text=text, images=images, return_tensors="pt")
+        inputs.to(device=torch_device, dtype=torch.bfloat16)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        expected_generated_text = (
+            "In this image, we see the Statue of Liberty, standing tall on its pedestal. The statue is made of metal,"
+        )
+        self.assertEqual(generated_texts[0], expected_generated_text)
+
+    @require_deterministic_for_xpu
+    def test_integration_test_batched(self):
+        model = Lfm2VlForConditionalGeneration.from_pretrained(
+            "LiquidAI/LFM2-VL-450M",
+            dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+        # Create inputs
+        text = ["<image>In this image, we see", "<image>In this image, we see a cat"]
+        images = [[self.image2], [self.image]]
+        inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True)
+        inputs.to(device=torch_device, dtype=torch.bfloat16)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        expected_generated_text = [
+            "In this image, we see a panoramic view of the New York City skyline. The iconic Statics and the New York",
+            "In this image, we see a cat that is lying on its side on a cat bed.",
+        ]
+        self.assertListEqual(generated_texts, expected_generated_text)
+
+
+@require_torch_accelerator
+@slow
+class Lfm2_5VlForConditionalGenerationIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("LiquidAI/LFM2.5-VL-1.6B")
+        self.processor.tokenizer.padding_side = "left"
+        self.image = Image.open(
+            requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
+        )
+        self.image2 = Image.open(
+            BytesIO(
+                requests.get(
+                    "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+                ).content
+            )
+        )
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @require_deterministic_for_xpu
+    def test_integration_test(self):
+        model = Lfm2VlForConditionalGeneration.from_pretrained(
+            "LiquidAI/LFM2.5-VL-1.6B",
+            dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+        # Create inputs
+        text = "<image>In this image, we see"
+        images = self.image
+        inputs = self.processor(text=text, images=images, return_tensors="pt")
+        inputs.to(device=torch_device, dtype=torch.bfloat16)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        expected_generated_text = (
+            "In this image, we see two cats lying on a pink blanket. One cat is a tabby, and the other is a"
+        )
+        self.assertEqual(generated_texts[0], expected_generated_text)
+
+    @require_deterministic_for_xpu
+    def test_integration_test_high_resolution(self):
+        model = Lfm2VlForConditionalGeneration.from_pretrained(
+            "LiquidAI/LFM2.5-VL-1.6B",
+            dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+        # Create inputs
+        text = "<image>In this image, we see"
+        images = self.image2
+        inputs = self.processor(text=text, images=images, return_tensors="pt")
+        inputs.to(device=torch_device, dtype=torch.bfloat16)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        expected_generated_text = "In this image, we see the Statue of Liberty, an iconic symbol of freedom and democracy. It stands on Liberty Island in"
+        self.assertEqual(generated_texts[0], expected_generated_text)
+
+    @require_deterministic_for_xpu
+    def test_integration_test_batched(self):
+        model = Lfm2VlForConditionalGeneration.from_pretrained(
+            "LiquidAI/LFM2.5-VL-1.6B",
+            dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+        # Create inputs
+        text = ["<image>In this image, we see", "<image>In this image, we see"]
+        images = [[self.image2], [self.image]]
+        inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True)
+        inputs.to(device=torch_device, dtype=torch.bfloat16)
+
+        generated_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        expected_generated_text = Expectations(
+            {
+                (None, None): [
+                    "In this image, we see the Statue of Liberty, an iconic symbol of freedom and democracy. It stands on Liberty Island in",
+                    "In this image, we see two cats lying on a pink blanket. One cat is a tabby, and the other is a",
+                ],
+                ("xpu", 5): [
+                    "In this image, we see the Statue of Liberty, an iconic symbol of freedom and democracy. It stands tall on a small",
+                    "In this image, we see two cats lying on a pink blanket. One cat is a tabby, and the other is a",
+                ],
+            }
+        ).get_expectation()
+        self.assertListEqual(generated_texts, expected_generated_text)