# 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 GotOcr2 model.""" import unittest from parameterized import parameterized from transformers import ( AutoProcessor, AyaVisionConfig, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( cleanup, require_read_token, require_torch, require_torch_gpu, 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 ( AyaVisionForConditionalGeneration, ) if is_vision_available(): pass class AyaVisionVisionText2TextModelTester: def __init__( self, parent, batch_size=3, seq_length=7, vision_feature_layer=-1, downsample_factor=2, ignore_index=-100, bos_token_id=0, eos_token_id=0, pad_token_id=0, image_token_index=1, num_channels=3, image_size=64, model_type="aya_vision", is_training=True, text_config={ "model_type": "cohere2", "vocab_size": 99, "hidden_size": 128, "intermediate_size": 37, "num_hidden_layers": 4, "num_attention_heads": 4, "output_channels": 64, "hidden_act": "silu", "max_position_embeddings": 512, "tie_word_embeddings": True, "bos_token_id": 0, "eos_token_id": 0, "pad_token_id": 0, }, vision_config={ "model_type": "siglip_vision_model", "hidden_size": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 128, "image_size": 64, "patch_size": 8, "vision_use_head": False, }, ): 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.image_token_index = image_token_index 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.downsample_factor = downsample_factor self.is_training = is_training self.num_channels = num_channels self.image_size = image_size self.image_seq_length = (image_size // (vision_config["patch_size"] * downsample_factor)) ** 2 self.seq_length = seq_length + self.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 AyaVisionConfig( text_config=self.text_config, vision_config=self.vision_config, model_type=self.model_type, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, image_token_index=self.image_token_index, vision_feature_layer=self.vision_feature_layer, downsample_factor=self.downsample_factor, ) 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) print("attention_mask", attention_mask.shape) # input_ids[:, -1] = self.pad_token_id input_ids[input_ids == self.image_token_index] = self.pad_token_id input_ids[:, : self.image_seq_length] = self.image_token_index 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 = AyaVisionForConditionalGeneration(config=config) model.to(torch_device) model.half() model.eval() logits = model( input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values, 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.torch_dtype = torch.float16 model = AyaVisionForConditionalGeneration(config=config) model.to(torch_device) model.eval() with torch.autocast(device_type="cuda", dtype=torch.float16): logits = model( input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values, return_dict=True, )["logits"] self.parent.assertFalse(torch.isnan(logits).any().item()) @require_torch class AyaVisionModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (AyaVisionForConditionalGeneration,) if is_torch_available() else () all_generative_model_classes = (AyaVisionForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "image-text-to-text": AyaVisionForConditionalGeneration, } if is_torch_available() else {} ) fx_compatible = False test_pruning = False test_torchscript = False test_head_masking = False _is_composite = True def setUp(self): self.model_tester = AyaVisionVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=AyaVisionConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs 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 = self._prepare_for_class(inputs_dict, model_class) input_ids = inputs["input_ids"] del inputs["input_ids"] del inputs["pixel_values"] wte = model.get_input_embeddings() inputs["inputs_embeds"] = wte(input_ids) with torch.no_grad(): model(**inputs) # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs # while some other models require pixel_values to be present 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"] 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) @unittest.skip("Failing because of unique cache (HybridCache)") def test_model_outputs_equivalence(self, **kwargs): pass @unittest.skip("Cohere2's forcefully disables sdpa due to softcapping") def test_sdpa_can_dispatch_non_composite_models(self): pass @unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different") def test_eager_matches_sdpa_generate(self): pass @parameterized.expand([("random",), ("same",)]) @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding") def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_sample(self): pass @unittest.skip("Cohere2 has HybridCache which is not compatible with dola decoding") def test_dola_decoding_sample(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support continue from past kv") def test_generate_continue_from_past_key_values(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support low_memory generation") def test_beam_search_low_memory(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_dict_outputs_use_cache(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_low_memory(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_with_static_cache(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_from_inputs_embeds_with_static_cache(self): pass @unittest.skip("Cohere2 has HybridCache and doesn't support progressive generation using input embeds.") def test_generate_continue_from_inputs_embeds(self): pass @unittest.skip("Failing because of unique cache (HybridCache)") def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different") def test_sdpa_equivalence(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation") def test_initialization(self): pass @unittest.skip(reason="Compile not yet supported because in LLava models") 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 # todo: yoni - fix or improve the test @unittest.skip("Difference is slightly higher than the threshold") def test_batching_equivalence(self): pass @require_read_token @require_torch class AyaVisionIntegrationTest(unittest.TestCase): def setUp(self): self.model_checkpoint = "CohereForAI/aya-vision-8b" def tearDown(self): cleanup(torch_device, gc_collect=True) @slow @require_torch_gpu def test_small_model_integration_forward(self): processor = AutoProcessor.from_pretrained(self.model_checkpoint) model = AyaVisionForConditionalGeneration.from_pretrained( self.model_checkpoint, device_map=torch_device, torch_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) # Forward with torch.inference_mode(): output = model(**inputs) actual_logits = output.logits[0, -1, :5].cpu() print("actual_logits", actual_logits) expected_logits = torch.tensor([0.4109, 0.1532, 0.8018, 2.1328, 0.5483], dtype=torch.float16) 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)}", ) @slow @require_torch_gpu def test_small_model_integration_generate_text_only(self): processor = AutoProcessor.from_pretrained(self.model_checkpoint) model = AyaVisionForConditionalGeneration.from_pretrained( self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16 ) messages = [ { "role": "user", "content": [ {"type": "text", "text": "Write a haiku"}, ], } ] 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=25, do_sample=False) decoded_output = processor.decode( generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True ) print("decoded_output", decoded_output) expected_output = "Whispers on the breeze,\nLeaves dance under moonlit skies,\nNature's quiet song." self.assertEqual(decoded_output, expected_output) @slow @require_torch_gpu def test_small_model_integration_generate_chat_template(self): processor = AutoProcessor.from_pretrained(self.model_checkpoint) model = AyaVisionForConditionalGeneration.from_pretrained( self.model_checkpoint, device_map=torch_device, torch_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 ) print("decoded_output", decoded_output) expected_output = "The image depicts a cozy scene of two cats resting on a bright pink blanket. The cats," # fmt: skip self.assertEqual(decoded_output, expected_output) @slow @require_torch_gpu def test_small_model_integration_batched_generate(self): processor = AutoProcessor.from_pretrained(self.model_checkpoint) model = AyaVisionForConditionalGeneration.from_pretrained( self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16 ) # Prepare inputs messages = [ [ { "role": "user", "content": [ {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"}, {"type": "text", "text": "Write a haiku for this image"}, ], }, ], [ { "role": "user", "content": [ {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "Describe this image"}, ], }, ], ] inputs = processor.apply_chat_template( messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.float16) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) # Check first output decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True) print("decoded_output", decoded_output) expected_output = "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest scene." # 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, inputs["input_ids"].shape[1] :], skip_special_tokens=True) print("decoded_output", decoded_output) expected_output = 'This image captures a vibrant street scene in a bustling urban area, likely in an Asian city. The focal point is a' # fmt: skip self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) @slow @require_torch_gpu def test_small_model_integration_batched_generate_multi_image(self): processor = AutoProcessor.from_pretrained(self.model_checkpoint) model = AyaVisionForConditionalGeneration.from_pretrained( self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16 ) # Prepare inputs messages = [ [ { "role": "user", "content": [ {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"}, {"type": "text", "text": "Write a haiku for this image"}, ], }, ], [ { "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": "These images depict two different landmarks. Can you identify them?", }, ], }, ], ] inputs = processor.apply_chat_template( messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.float16) output = model.generate(**inputs, do_sample=False, max_new_tokens=25) # Check first output decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], 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 = "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest scene." # fmt: skip print("decoded_output", decoded_output) self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", ) # Check second output decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True) print("decoded_output", decoded_output) expected_output = "The first image showcases the Statue of Liberty, a colossal neoclassical sculpture on Liberty Island in New York Harbor. Standing at a" # fmt: skip self.assertEqual( decoded_output, expected_output, f"Decoded output: {decoded_output}\nExpected output: {expected_output}", )