# coding=utf-8 # 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 InternVL model.""" import unittest from io import BytesIO import pytest import requests from transformers import ( AutoProcessor, BitsAndBytesConfig, InternVLConfig, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, cleanup, require_av, require_bitsandbytes, require_deterministic_for_xpu, require_torch, require_torch_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 InternVLForConditionalGeneration, InternVLModel if is_vision_available(): from PIL import Image class InternVLVisionText2TextModelTester: def __init__( self, parent, batch_size=3, seq_length=7, image_seq_length=64, vision_feature_layer=-1, ignore_index=-100, image_token_id=1, num_channels=3, image_size=64, model_type="internvl", is_training=True, text_config={ "model_type": "qwen2", "vocab_size": 99, "hidden_size": 128, "intermediate_size": 37, "num_hidden_layers": 2, "num_attention_heads": 4, "num_key_value_heads": 2, "output_channels": 64, "hidden_act": "silu", "max_position_embeddings": 512, "rope_theta": 10000, "mlp_ratio": 4, "tie_word_embeddings": True, "bos_token_id": 3, "eos_token_id": 4, "pad_token_id": 5, }, vision_config={ "hidden_size": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 128, "image_size": 64, "patch_size": 4, "num_channels": 3, "hidden_act": "quick_gelu", "use_absolute_position_embeddings": True, }, ): 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.model_type = model_type self.text_config = text_config self.vision_config = vision_config self.batch_size = batch_size self.vision_feature_layer = vision_feature_layer self.is_training = is_training self.image_seq_length = image_seq_length self.num_channels = num_channels self.image_size = image_size self.seq_length = seq_length + 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 InternVLConfig( text_config=self.text_config, vision_config=self.vision_config, model_type=self.model_type, image_token_id=self.image_token_id, image_seq_length=self.image_seq_length, vision_feature_layer=self.vision_feature_layer, ) 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 def create_and_check_model_fp16_forward(self, config, input_ids, pixel_values, attention_mask): model = InternVLForConditionalGeneration(config=config) model.to(torch_device) model.half() model.eval() logits = model( input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values.to(torch.bfloat16), return_dict=True, )["logits"] self.parent.assertFalse(torch.isnan(logits).any().item()) def create_and_check_model_fp16_autocast_forward(self, config, input_ids, pixel_values, attention_mask): config.dtype = torch.float16 model = InternVLForConditionalGeneration(config=config) model.to(torch_device) model.eval() with torch.autocast(device_type=torch_device, dtype=torch.float16): logits = model( input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values.to(torch.bfloat16), return_dict=True, )["logits"] self.parent.assertFalse(torch.isnan(logits).any().item()) @require_torch class InternVLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (InternVLForConditionalGeneration, InternVLModel) if is_torch_available() else () all_generative_model_classes = (InternVLForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "image-text-to-text": InternVLForConditionalGeneration, } if is_torch_available() else {} ) def setUp(self): self.model_tester = InternVLVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=InternVLConfig, has_text_modality=False) @unittest.skip( reason="Failing with `torch._inductor.exc.InductorError: RuntimeError: No valid triton configs. OutOfMemoryError: out of resource: triton_tem_fused_0 Required: 147456 Hardware limit:101376 Reducing block sizes or `num_stages` may help.`" ) def test_flex_attention_with_grads(self): pass def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Compile not yet supported because in LLava models") @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): pass @unittest.skip("FlashAttention only support fp16 and bf16 data type") def test_flash_attn_2_fp32_ln(self): pass @slow @require_torch_accelerator class InternVLQwen2IntegrationTest(unittest.TestCase): def setUp(self): self.small_model_checkpoint = "OpenGVLab/InternVL3-1B-hf" self.medium_model_checkpoint = "OpenGVLab/InternVL3-2B-hf" cleanup(torch_device, gc_collect=True) def tearDown(self): cleanup(torch_device, gc_collect=True) @require_deterministic_for_xpu def test_qwen2_small_model_integration_generate(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = ( "<|im_start|>user\n\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n" ) inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16) with torch.no_grad(): generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) # fmt: off expected_outputs = Expectations( { (None, None): "The image shows two cats lying on a pink surface, which appears to be a bed or couch.", ("xpu", 3): "The image shows two cats lying on a pink blanket. The cat on the left is a tabby", } ) # fmt: on expected_output = expected_outputs.get_expectation() self.assertEqual(decoded_output, expected_output) def test_qwen2_small_model_integration_forward(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = ( "<|im_start|>user\n\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n" ) inputs = processor(images=image, text=prompt, 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_all = Expectations( { ("xpu", 3): torch.tensor([11.9922, 14.7188, 14.3125, 10.6719, 6.9297], dtype=torch.float16), ("cuda", 7): torch.tensor([11.9531, 14.7031, 14.2734, 10.6562, 6.9219], dtype=torch.float16), ("cuda", 8): torch.tensor([11.9609, 14.7188, 14.2734, 10.6484, 6.9141], dtype=torch.float16), } ) # fmt: skip expected_logits = expected_logits_all.get_expectation() 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)}", ) @require_deterministic_for_xpu def test_qwen2_small_model_integration_generate_text_only(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) prompt = "<|im_start|>user\nWrite a haiku<|im_end|>\n<|im_start|>assistant\n" inputs = processor(text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16) with torch.no_grad(): generate_ids = model.generate(**inputs, max_new_tokens=200, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_outputs = Expectations( { ("xpu", 3): "Whispers of dawn,\nSilent whispers of night,\nPeace in the stillness.", ("cuda", 7): 'Whispers of dawn,\nSilent whispers of night,\nPeace in the stillness.', ("cuda", 8): 'Whispers of dawn,\nSilent whispers of night,\nPeace in the stillness.', } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual(decoded_output, expected_output) def test_qwen2_small_model_integration_generate_chat_template(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) 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=20, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "The image shows two cats lying on a pink surface, which appears to be a bed or couch." self.assertEqual(decoded_output, expected_output) @require_deterministic_for_xpu def test_qwen2_small_model_integration_batched_generate(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) # Prepare inputs prompt = [ "<|im_start|>user\n\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n", "<|im_start|>user\n\nDescribe this image<|im_end|>\n<|im_start|>assistant\n", ] image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw) image2 = Image.open( requests.get( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg", stream=True, ).raw ) inputs = processor(text=prompt, images=[[image1], [image2]], padding=True, return_tensors="pt").to( torch_device, dtype=torch.float16 ) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) # Check first output decoded_output = processor.decode(output[0], skip_special_tokens=True) expected_output = "user\n\nWrite a haiku for this image\nassistant\nSilky lake, \nWooden pier, \nNature's peace." # fmt: skip self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) # Check second output decoded_output = processor.decode(output[1], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of', ("cuda", 7): 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of', } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) def test_qwen2_small_model_integration_batched_generate_multi_image(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) # Prepare inputs prompt = [ "<|im_start|>user\n\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n", "<|im_start|>user\n\nWhat are the differences between these two images?<|im_end|>\n<|im_start|>assistant\n", ] image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw) image2 = Image.open( BytesIO( requests.get( "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" ).content ) ) image3 = Image.open( BytesIO( requests.get( "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg" ).content ) ) inputs = processor(text=prompt, images=[[image1], [image2, image3]], padding=True, return_tensors="pt").to( torch_device, dtype=torch.float16 ) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) # Check first output decoded_output = processor.decode(output[0], 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_output = "user\n\nWrite a haiku for this image\nassistant\nSilky lake, \nWooden pier, \nNature's peace." # fmt: skip self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) # Check second output decoded_output = processor.decode(output[1], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): "user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Foreground", ("cuda", 7): "user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Foreground", } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) @require_av @require_bitsandbytes def test_qwen2_medium_model_integration_video(self): processor = AutoProcessor.from_pretrained(self.medium_model_checkpoint) quantization_config = BitsAndBytesConfig(load_in_4bit=True) model = InternVLForConditionalGeneration.from_pretrained( self.medium_model_checkpoint, quantization_config=quantization_config ) # Prepare inputs messages = [ { "role": "user", "content": [ { "type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4", }, {"type": "text", "text": "What type of shot is the man performing?"}, ], } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", num_frames=8, ).to(torch_device, dtype=torch.float16) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): "The man is performing a volley.", ("cuda", 7): "The man is performing a forehand shot.", ("rocm", (9, 5)): "The man is performing a volley shot.", } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) @require_av @require_deterministic_for_xpu def test_qwen2_small_model_integration_interleaved_images_videos(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, dtype=torch.float16, device_map=torch_device ) messages = [ [ { "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": "What are the differences between these two images?"}, ], }, ], [ { "role": "user", "content": [ { "type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4", }, {"type": "text", "text": "What type of shot is the man performing?"}, ], }, ], [ { "role": "user", "content": [ { "type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg", }, {"type": "text", "text": "Write a haiku for this image"}, ], } ], ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", padding=True, num_frames=8, ).to(torch_device, dtype=torch.float16) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) decoded_output = processor.decode(output[0], 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): "user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image:**\n - The Statue of Liberty is prominently featured on an", ("cuda", 7): 'user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image:**\n - The Statue of Liberty is prominently featured on an', } ) # 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], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot", ("cuda", 7): 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot', } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) # Check third output decoded_output = processor.decode(output[2], skip_special_tokens=True) expected_output = ( "user\n\nWrite a haiku for this image\nassistant\nSilky lake, \nWooden pier, \nNature's peace." ) self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) @slow @require_torch_accelerator class InternVLLlamaIntegrationTest(unittest.TestCase): def setUp(self): self.small_model_checkpoint = "OpenGVLab/InternVL2_5-2B-MPO-hf" self.medium_model_checkpoint = "OpenGVLab/InternVL2_5-8B-MPO-hf" cleanup(torch_device, gc_collect=True) def tearDown(self): cleanup(torch_device, gc_collect=True) def test_llama_small_model_integration_generate(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = ( "<|im_start|>user\n\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n" ) inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16) with torch.no_grad(): generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "The image shows two cats sleeping on a pink couch. They are lying side by side, with their" self.assertEqual(decoded_output, expected_output) def test_llama_small_model_integration_forward(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = ( "<|im_start|>user\n\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n" ) inputs = processor(images=image, text=prompt, 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_all = Expectations( { ("xpu", 3): [-9.8828, -0.4954, 1.4561, -10.3438, -10.3438], ("cuda", 7): [-9.8750, -0.4861, 1.4648, -10.3359, -10.3359], ("cuda", 8): [-9.8906, -0.4995, 1.4473, -10.3359, -10.3438], ("rocm", (9, 4)): [ -9.8828, -0.5005, 1.4697, -10.3438, -10.3438], ("rocm", (9, 5)): [ -9.8906, -0.4976, 1.4502, -10.3359, -10.3438], } ) # fmt: skip expected_logits = torch.tensor(expected_logits_all.get_expectation(), dtype=torch.float16) # The original implementation and the transformers implementation do not match exactly, hence the higher tolerance. # The difference is likely due to the different implementations of the attention mechanism (different order of operations) # between the transformers Llama model and the original InternLM model. # The difference has almost no effect on the output tokens, but it does affect the logits a lot more. self.assertTrue( torch.allclose(actual_logits, expected_logits, atol=1e-3), f"Actual logits: {actual_logits}" f"\nExpected logits: {expected_logits}" f"\nDifference: {torch.abs(actual_logits - expected_logits)}", ) def test_llama_small_model_integration_generate_text_only(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) prompt = "<|im_start|>user\nWrite a haiku<|im_end|>\n<|im_start|>assistant\n" inputs = processor(text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16) with torch.no_grad(): generate_ids = model.generate(**inputs, max_new_tokens=200, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_outputs = Expectations( { ("xpu", 3): "Autumn leaves fall,\nNature's breath, a season's sigh,\nSilent woods awake.", ("cuda", 7): "Autumn leaves fall,\nNature's breath, a gentle sigh,\nSilent whispers.", ("cuda", 8): "Autumn leaves fall,\nNature's breath, a silent sigh,\nWinter's chill approaches.", } ) expected_output = expected_outputs.get_expectation() self.assertEqual(decoded_output, expected_output) def test_llama_small_model_integration_generate_chat_template(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) 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=20, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) expected_output = "The image shows two cats sleeping on a pink couch. They are lying side by side, with their" self.assertEqual(decoded_output, expected_output) def test_llama_small_model_integration_batched_generate(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) # Prepare inputs prompt = [ "<|im_start|>user\n\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n", "<|im_start|>user\n\nDescribe this image<|im_end|>\n<|im_start|>assistant\n", ] image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw) image2 = Image.open( requests.get( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg", stream=True, ).raw ) inputs = processor(text=prompt, images=[[image1], [image2]], padding=True, return_tensors="pt").to( torch_device, dtype=torch.float16 ) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) # Check first output decoded_output = processor.decode(output[0], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.", ("cuda", 7): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.', ("cuda", 8): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.', } ) # 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], skip_special_tokens=True) expected_output = "user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese gate in the background, adorned with red and gold colors and Chinese characters" self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) def test_llama_small_model_integration_batched_generate_multi_image(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16 ) # Prepare inputs prompt = [ "<|im_start|>user\n\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n", "<|im_start|>user\n\nWhat are the difference between these two images?<|im_end|>\n<|im_start|>assistant\n", ] image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw) image2 = Image.open( BytesIO( requests.get( "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" ).content ) ) image3 = Image.open( BytesIO( requests.get( "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg" ).content ) ) inputs = processor(text=prompt, images=[[image1], [image2, image3]], padding=True, return_tensors="pt").to( torch_device, dtype=torch.float16 ) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) # Check first output decoded_output = processor.decode(output[0], 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_output = "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors." self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) # Check second output decoded_output = processor.decode(output[1], skip_special_tokens=True) expected_output = "user\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After closely examining the images again, I can see that there are several differences" self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) @require_av @require_bitsandbytes def test_llama_medium_model_integration_video(self): processor = AutoProcessor.from_pretrained(self.medium_model_checkpoint) quantization_config = BitsAndBytesConfig(load_in_4bit=True) model = InternVLForConditionalGeneration.from_pretrained( self.medium_model_checkpoint, quantization_config=quantization_config ) # Prepare inputs messages = [ { "role": "user", "content": [ { "type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4", }, {"type": "text", "text": "What type of shot is the man performing?"}, ], } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", num_frames=8, ).to(torch_device, dtype=torch.float16) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True) expected_output = "The man is performing a forehand shot." self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) @require_av def test_llama_small_model_integration_interleaved_images_videos(self): processor = AutoProcessor.from_pretrained(self.small_model_checkpoint) model = InternVLForConditionalGeneration.from_pretrained( self.small_model_checkpoint, dtype=torch.float16, device_map=torch_device ) messages = [ [ { "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": "What are the difference between these two images?"}, ], }, ], [ { "role": "user", "content": [ { "type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4", }, {"type": "text", "text": "What type of shot is the man performing?"}, ], }, ], [ { "role": "user", "content": [ { "type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg", }, {"type": "text", "text": "Write a haiku for this image"}, ], } ], ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", padding=True, num_frames=8, ).to(torch_device, dtype=torch.float16) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) decoded_output = processor.decode(output[0], 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): "user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **", ("cuda", 7): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **', ("cuda", 8): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After re-examining the images, I can see that there are no', ("rocm", (9, 4)): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **', ("rocm", (9, 5)): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After re-examining the images, I can see that there are no', } ) # 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], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common stroke in tennis where the player swings the racket across their", ("cuda", 7): 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common stroke in tennis where the player swings the racket across their', ("cuda", 8): 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common stroke in tennis where the player swings the racket across their', } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) # Check third output decoded_output = processor.decode(output[2], skip_special_tokens=True) expected_outputs = Expectations( { ("xpu", 3): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.", ("cuda", 7): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.', ("cuda", 8): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.', } ) # fmt: skip expected_output = expected_outputs.get_expectation() self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", )