# Copyright 2024 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-VL model.""" import copy import gc import tempfile import unittest import pytest import requests from transformers import ( AutoProcessor, Qwen2VLConfig, Qwen2VLForConditionalGeneration, Qwen2VLModel, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, backend_empty_cache, require_flash_attn, 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 if is_vision_available(): from PIL import Image class Qwen2VLVisionText2TextModelTester: def __init__( self, parent, batch_size=3, seq_length=7, num_channels=3, ignore_index=-100, image_size=14, text_config={ "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, "rope_parameters": {"type": "mrope", "mrope_section": [2, 1, 1]}, }, vision_start_token_id=3, image_token_id=4, video_token_id=5, is_training=True, vision_config={ "depth": 2, "embed_dim": 32, "hidden_act": "quick_gelu", "hidden_size": 32, "mlp_ratio": 4, "num_heads": 4, "patch_size": 14, "spatial_merge_size": 1, "temporal_patch_size": 2, }, ): 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.num_hidden_layers = text_config["num_hidden_layers"] self.num_attention_heads = text_config["num_attention_heads"] self.hidden_size = text_config["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.text_config = text_config self.vision_config = vision_config self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.is_training = is_training self.vocab_size = text_config["vocab_size"] self.num_image_tokens = 32 self.seq_length = seq_length + self.num_image_tokens def get_config(self): return Qwen2VLConfig( 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 attention_mask[:, -1] = 0 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 Qwen2VLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Model tester for `Qwen2VLForConditionalGeneration`. """ all_model_classes = ( ( Qwen2VLModel, Qwen2VLForConditionalGeneration, ) if is_torch_available() else () ) pipeline_model_mapping = { "image-text-to-text": Qwen2VLForConditionalGeneration, "any-to-any": Qwen2VLForConditionalGeneration, } _is_composite = True def setUp(self): self.model_tester = Qwen2VLVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Qwen2VLConfig, 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 prompt 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() curr_input_dict = copy.deepcopy(input_dict) _ = model(**curr_input_dict) # successful forward with no modifications # 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_forward_with_rope_deltas_cached(self): """ Tests that Qwen2-VL computes new rope deltas every forward pass with new set of inputs. Rope deltas are cached when we generate and re-used for decoding phase, byt are not reset automatically after generation ends. See https://github.com/huggingface/transformers/pull/36013 for more """ config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: model = model_class(config).to(torch_device) # Generate and make sure rope_deltas are not `None` self.assertTrue(model.model.rope_deltas is None) generation_output = model.generate( **input_dict, max_new_tokens=4, return_dict_in_generate=True, output_logits=True ) self.assertTrue(model.model.rope_deltas is not None) # Now if we try to do forward pass, we should get new rope logits, because cache is not passed forward_output = model(**input_dict) torch.testing.assert_close( generation_output.logits[0], forward_output.logits[:, -1, :], rtol=1e-4, atol=1e-4 ) 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, } ) # We need to do simple forward without cache in roder to trigger packed SDPA/FLEX/EAGER path 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 Qwen2VL 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 def test_enable_input_require_grads_with_gradient_checkpointing(self): if not self.model_tester.is_training: self.skipTest(reason="ModelTester not in training mode") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True}) model.enable_input_require_grads() model.train() for parameter in model.parameters(): parameter.requires_grad = False vision_module = None if hasattr(model, "visual"): vision_module = model.visual elif hasattr(model, "model") and hasattr(model.model, "visual"): vision_module = model.model.visual if vision_module is None: continue target_linear = vision_module.blocks[0].attn.qkv target_linear.weight.requires_grad = True if target_linear.bias is not None: target_linear.bias.requires_grad = True inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) outputs = model(**inputs) if hasattr(outputs, "loss") and outputs.loss is not None: loss = outputs.loss else: logits = outputs.logits if hasattr(outputs, "logits") else outputs[0] loss = logits.sum() loss.backward() self.assertIsNotNone( target_linear.weight.grad, f"qkv weights should receive gradients when enable_input_require_grads is used with gradient checkpointing. Model: {model_class.__name__}", ) self.assertGreater( target_linear.weight.grad.abs().sum().item(), 0, f"qkv weights should have non-zero gradients when enable_input_require_grads is used with gradient checkpointing. Model: {model_class.__name__}", ) @require_torch class Qwen2VLIntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") self.messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "What kind of dog is this?"}, ], } ] url = "https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/demo_small.jpg" self.image = Image.open(requests.get(url, stream=True).raw) def tearDown(self): gc.collect() backend_empty_cache(torch_device) @slow def test_small_model_integration_test(self): model = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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 assert 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", ) assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=3e-3) # 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 intelligent nature, making them popular choices" self.assertEqual( self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_batch(self): model = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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 intelligent nature, making them popular choices', '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 intelligent nature, making them popular choices', ] # 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 = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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 intelligent nature, making them popular choices', '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 intelligent nature, making them popular choices', '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 intelligent nature, making them popular choices', ] # 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 = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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 intelligent nature, making them popular choices', 'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am a large language model created by Alibaba Cloud. I am called Qwen.' ] # 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 = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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) DECODED_TEXT = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_DECODED_TEXTS = Expectations( { ("xpu", 3): [ '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 intelligent nature, making them popular choices', '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 intelligent nature, making them popular choices', ], ("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 intelligent nature, making them popular choices', '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 intelligent nature, making them popular pets', ], ("cuda", 8): [ '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 intelligent nature, making them popular choices', '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 intelligent nature, making them popular choices' ], } ) # fmt: skip EXPECTED_DECODED_TEXT = EXPECTED_DECODED_TEXTS.get_expectation() self.assertEqual(DECODED_TEXT, EXPECTED_DECODED_TEXT) @slow @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test def test_small_model_integration_test_batch_flashatt2(self): model = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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 = [ "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 intelligent nature, making them popular choices", "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 intelligent nature, making them popular choices", ] self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), 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 = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-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) 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 intelligent nature, making them popular choices', 'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am a large language model created by Alibaba Cloud. I am called Qwen.' ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, )