# 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 GLM-4.6V model.""" import copy import unittest from transformers import ( AutoProcessor, Glm46VConfig, Glm46VForConditionalGeneration, Glm46VModel, is_torch_available, ) from transformers.testing_utils import ( Expectations, cleanup, require_deterministic_for_xpu, 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, ) if is_torch_available(): import torch class Glm46VVisionText2TextModelTester: def __init__( self, parent, batch_size=3, seq_length=7, num_channels=3, ignore_index=-100, image_size=112, video_start_token_id=3, video_end_token_id=4, image_start_token_id=5, image_end_token_id=6, image_token_id=7, video_token_id=8, is_training=True, text_config={ "vocab_size": 99, "hidden_size": 16, "intermediate_size": 22, "num_hidden_layers": 2, "num_attention_heads": 2, "num_key_value_heads": 1, "output_channels": 64, "hidden_act": "silu", "max_position_embeddings": 512, "rope_parameters": {"type": "default", "mrope_section": [2, 1, 1]}, "rope_theta": 10000, "tie_word_embeddings": True, "bos_token_id": 0, "eos_token_id": 0, "pad_token_id": 0, }, vision_config={ "depth": 2, "hidden_act": "silu", "hidden_size": 48, "out_hidden_size": 16, "intermediate_size": 22, "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.video_start_token_id = video_start_token_id self.video_end_token_id = video_end_token_id self.image_start_token_id = image_start_token_id self.image_end_token_id = image_end_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.hidden_size = text_config["hidden_size"] self.num_hidden_layers = text_config["num_hidden_layers"] self.num_attention_heads = text_config["num_attention_heads"] self.vocab_size = text_config["vocab_size"] self.num_image_tokens = 64 self.seq_length = seq_length + self.num_image_tokens def get_config(self): return Glm46VConfig( text_config=self.text_config, vision_config=self.vision_config, image_token_id=self.image_token_id, video_token_id=self.video_token_id, video_start_token_id=self.video_start_token_id, video_end_token_id=self.video_end_token_id, image_start_token_id=self.image_start_token_id, image_end_token_id=self.image_end_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[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.video_start_token_id] = self.pad_token_id input_ids[input_ids == self.image_start_token_id] = self.pad_token_id input_ids[input_ids == self.video_end_token_id] = self.pad_token_id input_ids[input_ids == self.image_end_token_id] = self.pad_token_id input_ids[:, 0] = self.image_start_token_id input_ids[:, 1 : 1 + self.num_image_tokens] = self.image_token_id input_ids[:, 1 + self.num_image_tokens] = self.image_end_token_id patch_size = config.vision_config.patch_size patches_per_side = self.image_size // patch_size inputs_dict = { "pixel_values": pixel_values, "image_grid_thw": torch.tensor( [[1, patches_per_side, patches_per_side]] * self.batch_size, device=torch_device ), "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class Glm46VModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (Glm46VModel, Glm46VForConditionalGeneration) if is_torch_available() else () model_split_percents = [0.7, 0.9] # model too big to split at 0.5 _is_composite = True def setUp(self): self.model_tester = Glm46VVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Glm46VConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() # GLM4V has images shaped as (bs*patch_len, dim) so we can't slice to batches in generate def prepare_config_and_inputs_for_generate(self, batch_size=2): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # We don't want a few model inputs in our model input dictionary for generation tests input_keys_to_ignore = [ # we don't want to mask attention heads # we don't want encoder-decoder models to start from filled decoder ids "decoder_input_ids", "decoder_attention_mask", # we'll set cache use in each test differently "use_cache", # Ignore labels if it is in the input dict "labels", # model-specific exceptions should overload/overwrite this function ] # The diff from the general `prepare_config_and_inputs_for_generate` lies here patch_size = config.vision_config.patch_size filtered_image_length = batch_size * (self.model_tester.image_size**2) // (patch_size**2) filtered_inputs_dict = { k: v[:batch_size, ...] if isinstance(v, torch.Tensor) else v for k, v in inputs_dict.items() if k not in input_keys_to_ignore } filtered_inputs_dict["pixel_values"] = inputs_dict["pixel_values"][:filtered_image_length] # It is important set `eos_token_id` to `None` to avoid early stopping (would break for length-based checks) text_gen_config = config.get_text_config(decoder=True) if text_gen_config.eos_token_id is not None and text_gen_config.pad_token_id is None: text_gen_config.pad_token_id = ( text_gen_config.eos_token_id if isinstance(text_gen_config.eos_token_id, int) else text_gen_config.eos_token_id[0] ) text_gen_config.eos_token_id = None text_gen_config.forced_eos_token_id = None return config, filtered_inputs_dict @unittest.skip(reason="No available kernels - not supported") def test_sdpa_can_dispatch_on_flash(self): pass @unittest.skip(reason="Size mismatch") def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip("Error with compilation") def test_generate_from_inputs_embeds_with_static_cache(self): pass def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] del inputs["pixel_values"] del inputs["image_grid_thw"] wte = model.get_input_embeddings() inputs["inputs_embeds"] = wte(input_ids) with torch.no_grad(): model(**inputs)[0] def test_inputs_embeds_matches_input_ids(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class) input_ids = inputs["input_ids"] del inputs["input_ids"] del inputs["pixel_values"] del inputs["image_grid_thw"] inputs_embeds = model.get_input_embeddings()(input_ids) with torch.no_grad(): out_ids = model(input_ids=input_ids, **inputs)[0] out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0] torch.testing.assert_close(out_embeds, out_ids) @require_torch class Glm46VIntegrationTest(unittest.TestCase): def setUp(self): cleanup(torch_device, gc_collect=True) self.processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking") self.message = [ { "role": "user", "content": [ { "type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg", }, {"type": "text", "text": "What kind of dog is this?"}, ], } ] self.message2 = [ { "role": "user", "content": [ { "type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png", }, {"type": "text", "text": "What kind of dog is this?"}, ], } ] def tearDown(self): cleanup(torch_device, gc_collect=True) @slow def test_small_model_integration_test(self): model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype="auto", device_map="auto" ) inputs = self.processor.apply_chat_template( self.message, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt" ) expected_input_ids = [151331, 151333, 151336, 198, 151339, 151343, 151343, 151343, 151343, 151343, 151343, 151343, 151343, 151343, 151343, 151343, 151343] # fmt: skip assert expected_input_ids == inputs.input_ids[0].tolist()[:17] expected_pixel_slice = torch.tensor( [ [-0.0988, -0.0842, -0.0842], [-0.5660, -0.5514, -0.4200], [-0.0259, -0.0259, -0.0259], [-0.1280, -0.0988, -0.2010], [-0.4638, -0.5806, -0.6974], [-1.2083, -1.2229, -1.2083], ], 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) # This model on the hub has `do_sample=True`. torch.manual_seed(42) output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat. Specifically" self.assertEqual( self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_batch(self): model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype="auto", device_map="auto" ) batch_messages = [self.message] * 2 inputs = self.processor.apply_chat_template( batch_messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt" ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat. Specifically", "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture has a stocky body, thick fur, and a face that's" ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow def test_small_model_integration_test_with_video(self): processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking", max_image_size={"longest_edge": 50176}) model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype=torch.float16, device_map="auto" ) questions = ["Describe this video."] video_urls = ["https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"] messages = [ [ { "role": "user", "content": [ { "type": "video", "video": video_url, }, {"type": "text", "text": question}, ], } ] for question, video_url in zip(questions, video_urls) ] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", padding=True ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = ["\n012345Describe this video.\nGot it, let's analyze the video. First, the scene is an indoor tennis court. There are two players: one in a white shirt"] # fmt: skip self.assertEqual( processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow @require_deterministic_for_xpu def test_small_model_integration_test_expand(self): model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype="auto", device_map="auto" ) inputs = self.processor.apply_chat_template( self.message, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt" ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) output = model.generate(**inputs, max_new_tokens=30, do_sample=False, num_beams=2, num_return_sequences=2) # fmt: off EXPECTED_DECODED_TEXTS = Expectations( { (None, None): ["\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat. Specifically", "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat, specifically" ], ("xpu", None): ["\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture is not a dog; it's a cat. Specifically, it looks", "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture is not a dog; it's a cat, specifically a Pallas" ], } ) # fmt: on EXPECTED_DECODED_TEXT = EXPECTED_DECODED_TEXTS.get_expectation() decoded_text = self.processor.batch_decode(output, skip_special_tokens=True) self.assertEqual(decoded_text, EXPECTED_DECODED_TEXT) @slow def test_small_model_integration_test_batch_wo_image(self): model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype="auto", device_map="auto" ) message_wo_image = [ {"role": "user", "content": [{"type": "text", "text": "Who are you?"}]}, ] batched_messages = [self.message, message_wo_image] inputs = self.processor.apply_chat_template( batched_messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", padding=True, ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat. Specifically", "\nWho are you?\nGot it, let's look at the user's question: \"Who are you?\" This is a common question when someone is just starting a conversation" ] # 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 = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype="auto", device_map="auto" ) batched_messages = [self.message, self.message2] inputs = self.processor.apply_chat_template( batched_messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", padding=True, ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat. Specifically", "\nWhat kind of dog is this?\nGot it, let's look at the image. Wait, the animals here are cats, not dogs. The question is about a dog, but", ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow @require_flash_attn @require_torch_accelerator def test_small_model_integration_test_batch_flashatt2(self): model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto", ) batched_messages = [self.message, self.message2] inputs = self.processor.apply_chat_template( batched_messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", padding=True, ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog. Wait, it's a cat,", "\nWhat kind of dog is this?\nGot it, let's look at the image. Wait, the animals here are cats, not dogs. The question is about a dog, but" ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow @require_flash_attn @require_torch_accelerator def test_small_model_integration_test_batch_wo_image_flashatt2(self): model = Glm46VForConditionalGeneration.from_pretrained( "THUDM/GLM-4.1V-9B-Thinking", dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto", ) message_wo_image = [ {"role": "user", "content": [{"type": "text", "text": "Who are you?"}]}, ] batched_messages = [self.message, message_wo_image] inputs = self.processor.apply_chat_template( batched_messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", padding=True, ).to(torch_device) # This model on the hub has `do_sample=True`. torch.manual_seed(42) # it should not matter whether two images are the same size or not output = model.generate(**inputs, max_new_tokens=30) EXPECTED_DECODED_TEXT = [ "\nWhat kind of dog is this?\nGot it, let's look at the image. The animal in the picture doesn't look like a dog; it's actually a cat. Specifically", "\nWho are you?\nGot it, let's look at the user's question: \"Who are you?\" This is a common question when someone is just starting a conversation" ] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, )