# Copyright 2025 The Qwen Team and 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 Qwen2.5-VL model.""" import copy import tempfile import unittest import pytest import requests from transformers import ( AutoProcessor, Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLModel, is_torch_available, is_vision_available, ) from transformers.image_utils import load_image from transformers.testing_utils import ( Expectations, cleanup, require_cv2, require_flash_attn, require_torch, require_torch_accelerator, slow, torch_device, ) from transformers.utils import is_cv2_available from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, ) from ...test_processing_common import url_to_local_path if is_cv2_available(): import cv2 if is_torch_available(): import torch if is_vision_available(): from PIL import Image class Qwen2_5_VLVisionText2TextModelTester: def __init__( self, parent, batch_size=3, seq_length=7, num_channels=3, ignore_index=-100, image_size=14, bos_token_id=0, eos_token_id=1, pad_token_id=2, hidden_act="silu", hidden_size=32, vocab_size=99, intermediate_size=37, max_position_embeddings=512, max_window_layers=3, num_attention_heads=4, num_hidden_layers=2, num_key_value_heads=2, rope_theta=10000, tie_word_embeddings=True, is_training=True, vision_config=None, rope_parameters=None, vision_start_token_id=3, image_token_id=4, video_token_id=5, ): self.parent = parent self.ignore_index = ignore_index self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_size = hidden_size self.vision_start_token_id = vision_start_token_id self.image_token_id = image_token_id self.video_token_id = video_token_id self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.is_training = is_training self.vocab_size = vocab_size self.num_image_tokens = 32 self.seq_length = seq_length + self.num_image_tokens # Default vision config is None to avoid a mutable default argument if vision_config is None: vision_config = { "depth": 2, "in_chans": 3, "hidden_act": "silu", "intermediate_size": 32, "out_hidden_size": 32, "hidden_size": 32, "num_heads": 4, "patch_size": 14, "spatial_patch_size": 14, "spatial_merge_size": 1, "temporal_patch_size": 2, } self.vision_config = vision_config self.text_config = { "bos_token_id": bos_token_id, "eos_token_id": eos_token_id, "pad_token_id": pad_token_id, "hidden_act": hidden_act, "hidden_size": hidden_size, "intermediate_size": intermediate_size, "max_position_embeddings": max_position_embeddings, "max_window_layers": max_window_layers, "num_attention_heads": num_attention_heads, "num_hidden_layers": num_hidden_layers, "num_key_value_heads": num_key_value_heads, "rope_theta": rope_theta, "tie_word_embeddings": tie_word_embeddings, "vocab_size": vocab_size, "rope_parameters": {"type": "mrope", "mrope_section": [2, 1, 1]}, } def get_config(self): return Qwen2_5_VLConfig( text_config=self.text_config, vision_config=self.vision_config, vision_start_token_id=self.vision_start_token_id, image_token_id=self.image_token_id, video_token_id=self.video_token_id, ) def prepare_config_and_inputs(self): config = self.get_config() patch_size = config.vision_config.patch_size temporal_patch_size = config.vision_config.temporal_patch_size pixel_values = floats_tensor( [ self.batch_size * (self.image_size**2) // (patch_size**2), self.num_channels * (patch_size**2) * temporal_patch_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[:, -1] = self.pad_token_id input_ids[input_ids == self.video_token_id] = self.pad_token_id input_ids[input_ids == self.image_token_id] = self.pad_token_id input_ids[input_ids == self.vision_start_token_id] = self.pad_token_id input_ids[:, self.num_image_tokens] = self.image_token_id input_ids[:, self.num_image_tokens - 1] = self.vision_start_token_id inputs_dict = { "pixel_values": pixel_values, "image_grid_thw": torch.tensor([[1, 1, 1]] * self.batch_size, device=torch_device), "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class Qwen2_5_VLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): """ Model tester for `Qwen2_5_VLForConditionalGeneration`. """ all_model_classes = ( ( Qwen2_5_VLModel, Qwen2_5_VLForConditionalGeneration, ) if is_torch_available() else () ) def setUp(self): self.model_tester = Qwen2_5_VLVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Qwen2_5_VLConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() def test_mismatching_num_image_tokens(self): """ Tests that VLMs through an error with explicit message saying what is wrong when number of images don't match number of image tokens in the text. Also we need to test multi-image cases when one prompr has multiple image tokens. """ config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config).to(torch_device) model.eval() _ = model(**input_dict) # successful forward with no modifications curr_input_dict = copy.deepcopy(input_dict) # remove one image but leave the image token in text patch_size = config.vision_config.patch_size one_img_length = (self.model_tester.image_size**2) // (patch_size**2) curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-one_img_length:, ...] curr_input_dict["image_grid_thw"] = curr_input_dict["image_grid_thw"][-1:, ...] with self.assertRaises(ValueError): _ = model(**curr_input_dict) # simulate multi-image case by concatenating inputs where each has exactly one image/image-token input_ids = curr_input_dict["input_ids"][:1] pixel_values = curr_input_dict["pixel_values"][:one_img_length] image_grid_thw = curr_input_dict["image_grid_thw"][:1] input_ids = torch.cat([input_ids, input_ids], dim=0) # one image and two image tokens raise an error with self.assertRaises(ValueError): _ = model( input_ids=input_ids, pixel_values=pixel_values, image_grid_thw=image_grid_thw, ) # two images and two image tokens don't raise an error pixel_values = torch.cat([pixel_values, pixel_values], dim=0) image_grid_thw = torch.cat([image_grid_thw, image_grid_thw], dim=0) _ = model( input_ids=input_ids, pixel_values=pixel_values, image_grid_thw=image_grid_thw, ) def test_video_forward(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() B = self.model_tester.batch_size C = config.vision_config.in_chans T = config.vision_config.temporal_patch_size P = config.vision_config.patch_size input_ids = ids_tensor([B, self.model_tester.seq_length], self.model_tester.vocab_size) F = 4 patch_H = self.model_tester.image_size // P patch_W = self.model_tester.image_size // P patch_T = F // T patches_per_video = patch_T * patch_H * patch_W pixel_values_videos = floats_tensor( [ # first dim: batch_size * num_patches B * patches_per_video, # second dim: in_channels * temporal_patch_size * patch_size^2 C * T * (P**2), ] ) video_grid_thw = torch.tensor([[patch_T, patch_H, patch_W]] * B) # sanity check assert pixel_values_videos.shape[0] == video_grid_thw.prod(dim=1).sum().item() # Insert video token sequence input_ids[:, -1] = self.model_tester.pad_token_id input_ids[input_ids == self.model_tester.video_token_id] = self.model_tester.pad_token_id input_ids[input_ids == self.model_tester.image_token_id] = self.model_tester.pad_token_id input_ids[input_ids == self.model_tester.vision_start_token_id] = self.model_tester.pad_token_id input_ids[:, self.model_tester.num_image_tokens] = self.model_tester.video_token_id insertion_point = self.model_tester.num_image_tokens assert (B * patches_per_video) + insertion_point <= self.model_tester.seq_length for b in range(B): input_ids[b, insertion_point - 1] = self.model_tester.vision_start_token_id input_ids[b, insertion_point : insertion_point + patches_per_video] = self.model_tester.video_token_id for model_class in self.all_model_classes: second_per_grid_ts = torch.tensor([1.0] * B, device=torch_device) model = model_class(config).to(torch_device) outputs = model( input_ids=input_ids, pixel_values_videos=pixel_values_videos, video_grid_thw=video_grid_thw, second_per_grid_ts=second_per_grid_ts, ) self.assertIsNotNone(outputs) def attention_mask_padding_matches_padding_free_with_position_ids( self, attn_implementation: str, fa_kwargs: bool = False ): max_new_tokens = 30 for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) if 0 in inputs_dict["attention_mask"][:, -1]: inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1) dummy_attention_mask = inputs_dict["attention_mask"] inputs_dict["input_ids"][~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id model = ( model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation=attn_implementation, ) .to(torch_device) .eval() ) # flatten padfree_inputs_dict = { "pixel_values": inputs_dict["pixel_values"], "image_grid_thw": inputs_dict["image_grid_thw"], "input_ids": inputs_dict["input_ids"][dummy_attention_mask.bool()].unsqueeze(0), } # add position_ids vision_position_ids, deltas = model.model.get_rope_index( input_ids=inputs_dict["input_ids"], image_grid_thw=inputs_dict["image_grid_thw"], attention_mask=inputs_dict["attention_mask"], ) # [3, bs, padded-seq-len] vision_padfree_positions = vision_position_ids[:, dummy_attention_mask.bool()].view( 3, -1 ) # [3, bs*padfree-len] text_padfree_positions = torch.cat( [torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()] ) # [1, bs*padfree-len] text_padfree_positions = text_padfree_positions.long().unsqueeze(0).to(torch_device) padfree_inputs_dict["position_ids"] = torch.cat([text_padfree_positions, vision_padfree_positions])[ :, None, : ] if fa_kwargs: cu_seq_lens = [0] + dummy_attention_mask.sum(1).tolist() cu_seq_lens = torch.tensor(cu_seq_lens, device=torch_device) max_length = cu_seq_lens.diff().max().item() padfree_inputs_dict.update( { "cu_seq_lens_q": cu_seq_lens.cumsum(-1).to(dtype=torch.int32), "cu_seq_lens_k": cu_seq_lens.cumsum(-1).to(dtype=torch.int32), "max_length_q": max_length, "max_length_k": max_length, } ) res_padded = model(**inputs_dict, use_cache=False) res_padfree = model(**padfree_inputs_dict, use_cache=False) logits_padded = res_padded.logits[inputs_dict["attention_mask"].bool()] logits_padfree = res_padfree.logits[0] # acceptable numerical instability tol = torch.finfo(torch.bfloat16).eps torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol) @unittest.skip(reason="Feedforward chunking is not yet supported") def test_feed_forward_chunking(self): pass @unittest.skip(reason="CPU offload is not yet supported") def test_cpu_offload(self): pass @unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.") def test_disk_offload_bin(self): pass @unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.") def test_disk_offload_safetensors(self): pass @unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.") def test_model_parallelism(self): pass @unittest.skip(reason="Compile not yet supported because in Qwen2_5_VL models") def test_sdpa_can_dispatch_on_flash(self): pass @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.") def test_multi_gpu_data_parallel_forward(self): pass @require_torch class Qwen2_5_VLIntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") self.messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "What kind of dog is this?"}, ], } ] img_url = url_to_local_path("https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/demo_small.jpg") self.image = load_image(img_url).convert("RGB") cleanup(torch_device, gc_collect=True) def tearDown(self): cleanup(torch_device, gc_collect=True) @slow def test_small_model_integration_test(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype="auto", device_map="auto" ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) inputs = self.processor(text=[text], images=[self.image], return_tensors="pt") expected_input_ids = [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655] # fmt: skip torch.testing.assert_close(expected_input_ids, inputs.input_ids[0].tolist()[:17]) expected_pixel_slice = torch.tensor( [ [0.8792, 0.8792, 0.9084], [1.1858, 1.1858, 1.2296], [1.2004, 1.2004, 1.2150], [1.4340, 1.4340, 1.4194], [1.3902, 1.4048, 1.4194], [1.5216, 1.5362, 1.5362], ], dtype=torch.float32, device="cpu", ) torch.testing.assert_close(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=5e-4, rtol=1e-5) # verify generation inputs = inputs.to(torch_device) output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in" self.assertEqual( self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_batch(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype="auto", device_map="auto" ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) inputs = self.processor(text=[text, text], images=[self.image, self.image], return_tensors="pt").to( torch_device ) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_expand(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype="auto", device_map="auto" ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) inputs = self.processor(text=[text], images=[self.image], return_tensors="pt").to(torch_device) output = model.generate(**inputs, max_new_tokens=30, num_return_sequences=3) EXPECTED_DECODED_TEXT = [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_batch_wo_image(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype="auto", device_map="auto" ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) messages2 = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ] text2 = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True) inputs = self.processor(text=[text, text2], images=[self.image], padding=True, return_tensors="pt").to( torch_device ) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\n addCriterion', ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_batch_different_resolutions(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype="auto", device_map="auto" ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) text2 = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) image2 = self.image.resize((224, 224)) inputs = self.processor( text=[text, text2], images=[self.image, image2], padding=True, return_tensors="pt", ).to(torch_device) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) expected_decoded_texts = Expectations( { (None, None): [ "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in", "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\n addCriterion\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and gentle nature, which is", ], ("cuda", (8, 6)): [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', ], ("rocm", None): [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\n addCriterion\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and gentle nature, which is', ], } ).get_expectation() # fmt: skip decoded_texts = self.processor.batch_decode(output, skip_special_tokens=True) for i, (expected, decoded) in enumerate(zip(expected_decoded_texts, decoded_texts)): self.assertEqual( decoded, expected, f"Decoded text {i}:\n{repr(decoded)}\ndoes not match expected decoded text:\n{repr(expected)}", ) @slow @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test def test_small_model_integration_test_batch_flashatt2(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto", ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) inputs = self.processor(text=[text, text], images=[self.image, self.image], return_tensors="pt").to( torch_device ) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) expected_decoded_text = Expectations({ ("cuda", None): "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in", ("rocm", (9, 4)): "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in", ("xpu", None): "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in", }).get_expectation() # fmt: skip # Since the test is to generate twice the same text, we just test twice against the expected decoded text decoded_texts = self.processor.batch_decode(output, skip_special_tokens=True) self.assertEqual(decoded_texts[0], expected_decoded_text) self.assertEqual(decoded_texts[1], expected_decoded_text) @slow @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test def test_small_model_integration_test_batch_wo_image_flashatt2(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto", ) text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) messages2 = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ] text2 = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True) inputs = self.processor(text=[text, text2], images=[self.image], padding=True, return_tensors="pt").to( torch_device ) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) # FIXME: The second decoded text in the CUDA expectation seems to be incorrect, it used to be the second text # on the ROCm expectation that was the correct one. Either model changed or code is buggy. EXPECTED_DECODED_TEXT = Expectations({ ("cuda", None): [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', "system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\n�\n\n addCriterion\nI'm sorry, but I don't understand your question. Could you please provide more context or clarify what you're asking", ], ("rocm", (9, 4)): [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', "system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am Qwen, a large language model created by Alibaba Cloud. I am designed to answer a wide range of questions and provide information on various topics", ], ("xpu", None): [ 'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and energetic nature, which is evident in', 'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\niclaim to be a large language model created by Alibaba Cloud. I am called Qwen.', ], }).get_expectation() # fmt: skip decoded_text = self.processor.batch_decode(output, skip_special_tokens=True) self.assertEqual(decoded_text, EXPECTED_DECODED_TEXT) @slow @require_cv2 def test_small_model_integration_test_with_video(self): model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", dtype="auto", device_map="auto" ) video_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4" messages2 = [ { "role": "user", "content": [ { "type": "video", }, {"type": "text", "text": "What is shown in this video?"}, ], } ] text = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True) with tempfile.NamedTemporaryFile(suffix=".mp4") as f: f.write(requests.get(video_url).content) f.flush() cap = cv2.VideoCapture(f.name) frames = [] while True: ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(Image.fromarray(frame_rgb).resize((224, 224), Image.BICUBIC)) cap.release() inputs = self.processor(text=[text], videos=[frames], return_tensors="pt").to(torch_device) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ 'system\nYou are a helpful assistant.\nuser\nWhat is shown in this video?\nassistant\nThe video shows an indoor tennis court with a person standing on one side, preparing to serve the ball. The individual is dressed in athletic attire, including', ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, )