# 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 SmolVLM model.""" import copy import unittest from io import BytesIO import pytest import requests from parameterized import parameterized from transformers import ( AutoProcessor, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, cleanup, is_flaky, require_torch, 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 ( GenerationConfig, SmolVLMConfig, SmolVLMForConditionalGeneration, SmolVLMModel, ) if is_vision_available(): from PIL import Image class SmolVLMVisionText2TextModelTester: def __init__( self, parent, is_training=True, batch_size=2, scale_factor=2, num_images=2, vision_config={ "image_size": 16, "patch_size": 4, "hidden_size": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 32, "dropout": 0.1, "attention_dropout": 0.1, "initializer_range": 0.02, }, text_config={ "vocab_size": 100, "hidden_size": 64, "intermediate_size": 56, "num_hidden_layers": 2, "num_attention_heads": 2, "num_key_value_heads": 2, "hidden_act": "silu", "max_position_embeddings": 256, "initializer_range": 0.02, "rms_norm_eps": 1e-6, "pad_token_id": 2, "bos_token_id": 0, "eos_token_id": 1, "image_token_id": 57, "tie_word_embeddings": False, "rope_theta": 10000.0, "sliding_window": 32, "attention_dropout": 0.0, }, use_cache=False, tie_word_embeddings=False, image_token_id=57, ): self.parent = parent self.is_training = is_training self.batch_size = batch_size self.num_images = num_images self.scale_factor = scale_factor self.seq_length = ( int(((vision_config["image_size"] // vision_config["patch_size"]) ** 2) / (self.scale_factor**2)) * self.num_images ) self.use_cache = use_cache self.image_token_id = image_token_id self.tie_word_embeddings = tie_word_embeddings # Hack - add properties here so use common tests self.vocab_size = text_config["vocab_size"] 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_config = vision_config self.text_config = text_config def get_config(self): return SmolVLMConfig( use_cache=self.use_cache, image_token_id=self.image_token_id, tie_word_embeddings=self.tie_word_embeddings, vision_config=self.vision_config, text_config=self.text_config, vocab_size=self.vocab_size, scale_factor=self.scale_factor, ) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [ self.batch_size, self.num_images, 3, # SmolVLMImageProcessor always generates RGB pixel values self.vision_config["image_size"], self.vision_config["image_size"], ] ) config = self.get_config() 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], config.text_config.vocab_size - 2) + 1 # For simplicity just set the last n tokens to the image token n_image_tokens_per_batch = self.seq_length input_ids[:, -n_image_tokens_per_batch:] = self.image_token_id attention_mask = input_ids.ne(1).to(torch_device) inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class SmolVLMModelTest(ModelTesterMixin, unittest.TestCase): """ Model tester for `SmolVLM`. """ all_model_classes = (SmolVLMModel,) if is_torch_available() else () test_resize_embeddings = True def setUp(self): self.model_tester = SmolVLMVisionText2TextModelTester(self) self.config_tester = ConfigTester( self, config_class=SmolVLMConfig, has_text_modality=False, common_properties=["image_token_id"] ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Model does not support padding right") def test_flash_attn_2_inference_padding_right(self): pass @unittest.skip(reason="Compile not yet supported in SmolVLM models") @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): pass @unittest.skip(reason="Compile not yet supported in SmolVLM models") def test_sdpa_can_dispatch_on_flash(self): pass # We need to override as we need to prepare such that the image token is the last token def test_resize_tokens_embeddings(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.text_config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Ignore copy # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.seq_length model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.image_token_id # make sure that decoder_input_ids are resized as well if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1) self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size) self.assertTrue(model.config.text_config.vocab_size, model.vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) # Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size target_dimension = 128 model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0], target_dimension) with self.assertRaisesRegex( ValueError, "Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer", ): model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3) # We need to override as we need to prepare such that the image token is the last token def test_resize_embeddings_untied(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.seq_length model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.image_token_id # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @require_torch class SmolVLMForConditionalGenerationModelTest( GenerationTesterMixin, ModelTesterMixin, PipelineTesterMixin, unittest.TestCase ): """ Model tester for `SmolVLMForConditionalGeneration`. """ all_model_classes = (SmolVLMForConditionalGeneration,) if is_torch_available() else () all_generative_model_classes = (SmolVLMForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "image-text-to-text": SmolVLMForConditionalGeneration, "any-to-any": SmolVLMForConditionalGeneration, } if is_torch_available() else () ) test_resize_embeddings = True def setUp(self): self.model_tester = SmolVLMVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=SmolVLMConfig, has_text_modality=False) @unittest.skip(reason="Model does not support padding right") def test_flash_attn_2_inference_padding_right(self): pass @pytest.mark.generate @is_flaky(description="TODO: check why flaky") def test_generate_methods_with_logits_to_keep(self): super().test_generate_methods_with_logits_to_keep() @unittest.skip def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Unsupported") def test_generate_with_static_cache(self): pass @unittest.skip(reason="Compile not yet supported in SmolVLM models") @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): pass @unittest.skip(reason="Compile not yet supported in SmolVLM models") def test_sdpa_can_dispatch_on_flash(self): pass @pytest.mark.generate @slow @unittest.skip( reason="SmolVLM doesn't support SDPA for all backbones, vision backbones has only eager/FA2 attention" ) def test_eager_matches_sdpa_generate(self): pass @parameterized.expand([("random",), ("same",)]) @pytest.mark.generate @unittest.skip(reason="Cache position is off by one leaving out image tokens, FIXME raushan") def test_assisted_decoding_matches_greedy_search(self, assistant_type): pass # We need to override as we need to prepare such that the image token is the last token def test_resize_tokens_embeddings(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.text_config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.seq_length model.model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1) self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size) self.assertTrue(model.config.text_config.vocab_size, model.vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) # Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size target_dimension = 128 model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0], target_dimension) with self.assertRaisesRegex( ValueError, "Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer", ): model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3) # We need to override as we need to prepare such that the image token is the last token def test_resize_embeddings_untied(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.seq_length model.model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @require_torch class SmolVLMForConditionalGenerationIntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct") self.image1 = Image.open( BytesIO( requests.get( "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" ).content ) ) self.video_messages = [ { "role": "user", "content": [ { "type": "video", "path": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/assisted-generation/gif_1_1080p.mov", }, {"type": "text", "text": "Describe this video in detail"}, ], }, ] def tearDown(self): cleanup(torch_device, gc_collect=True) @slow def test_integration_test(self): model = SmolVLMForConditionalGeneration.from_pretrained( "HuggingFaceTB/SmolVLM2-256M-Video-Instruct", dtype=torch.bfloat16, device_map="auto", ) # Create inputs text = "In this image, we see" images = self.image1 inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True) inputs.to(device=torch_device, dtype=torch.bfloat16) generated_ids = model.generate(**inputs, max_new_tokens=9) generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True) expected_generated_text = "\n\n\n\nIn this image, we see a view of the Statue of Liberty and the" self.assertEqual(generated_texts[0], expected_generated_text) @slow def test_integration_test_video(self): model = SmolVLMForConditionalGeneration.from_pretrained( "HuggingFaceTB/SmolVLM2-256M-Video-Instruct", dtype=torch.bfloat16, device_map="auto", ) # Create inputs inputs = self.processor.apply_chat_template( self.video_messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(device=torch_device, dtype=torch.bfloat16) generated_ids = model.generate(**inputs, max_new_tokens=20) generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True) expected_generated_text = Expectations( { (None, None): 'User: You are provided the following series of nine frames from a 0:00:09 [H:MM:SS] video.\n\nFrame from 00:00:\nFrame from 00:01:\nFrame from 00:02:\nFrame from 00:03:\nFrame from 00:04:\nFrame from 00:05:\nFrame from 00:06:\nFrame from 00:08:\nFrame from 00:09:\n\nDescribe this video in detail\nAssistant: The video depicts a large language model architecture, specifically a language model with a "quick brown" feature', ("cuda", (8, 0)): 'User: You are provided the following series of nine frames from a 0:00:09 [H:MM:SS] video.\n\nFrame from 00:00:\nFrame from 00:01:\nFrame from 00:02:\nFrame from 00:03:\nFrame from 00:04:\nFrame from 00:05:\nFrame from 00:06:\nFrame from 00:08:\nFrame from 00:09:\n\nDescribe this video in detail\nAssistant: The video showcases a large language model architecture, specifically a "Quick Brown" model, which is designed', ("cuda", (8, 6)): 'User: You are provided the following series of nine frames from a 0:00:09 [H:MM:SS] video.\n\nFrame from 00:00:\nFrame from 00:01:\nFrame from 00:02:\nFrame from 00:03:\nFrame from 00:04:\nFrame from 00:05:\nFrame from 00:06:\nFrame from 00:08:\nFrame from 00:09:\n\nDescribe this video in detail\nAssistant: The video showcases a large language model, specifically a neural network model, which is designed to learn and', ("rocm", (9, 4)): 'User: You are provided the following series of nine frames from a 0:00:09 [H:MM:SS] video.\n\nFrame from 00:00:\nFrame from 00:01:\nFrame from 00:02:\nFrame from 00:03:\nFrame from 00:04:\nFrame from 00:05:\nFrame from 00:06:\nFrame from 00:08:\nFrame from 00:09:\n\nDescribe this video in detail\nAssistant: The video depicts a large language model architecture, specifically a language model with a "quick brown" feature', ("rocm", None): 'User: You are provided the following series of nine frames from a 0:00:09 [H:MM:SS] video.\n\nFrame from 00:00:\nFrame from 00:01:\nFrame from 00:02:\nFrame from 00:03:\nFrame from 00:04:\nFrame from 00:05:\nFrame from 00:06:\nFrame from 00:08:\nFrame from 00:09:\n\nDescribe this video in detail\nAssistant: The video showcases a large language model architecture, specifically a "Quick Brown" model, which is designed', } ).get_expectation() # fmt: skip self.assertEqual(generated_texts[0], expected_generated_text) @slow def test_export_smolvlm_vision_encoder(self): from transformers import AutoConfig from transformers.integrations.executorch import TorchExportableModuleForVLM model_id = "HuggingFaceTB/SmolVLM2-256M-Video-Instruct" # NOTE: The attention_mask is prepared internally in the vision encoder, depending on whether flash attention is used or not # For ExecuTorch, flash attention is not supported, so the way of exporting vison encoder should be compatible with text-decoder config = AutoConfig.from_pretrained(model_id) config.text_config._flash_attn_2_enabled = False # Load model and extract vision encoder model = SmolVLMForConditionalGeneration.from_pretrained( model_id, dtype=torch.float32, config=config, ) exportable_module = TorchExportableModuleForVLM(model) exported_program = exportable_module.export_vision_encoder() self.assertIsInstance(exported_program, torch.export.ExportedProgram) @slow def test_export_smolvlm_connector(self): from transformers import AutoConfig from transformers.integrations.executorch import TorchExportableModuleForVLM model_id = "HuggingFaceTB/SmolVLM2-256M-Video-Instruct" # NOTE: The attention_mask is prepared internally in the vision encoder, depending on whether flash attention is used or not # For ExecuTorch, flash attention is not supported, so the way of exporting vison encoder should be compatible with text-decoder config = AutoConfig.from_pretrained(model_id) config.text_config._flash_attn_2_enabled = False # Load the model and extract the connector (multi-modal projector) model = SmolVLMForConditionalGeneration.from_pretrained( model_id, dtype=torch.float32, config=config, ) connector = model.model.connector connector.eval() exportable_module = TorchExportableModuleForVLM(model) exported_program = exportable_module.export_connector() self.assertIsInstance(exported_program, torch.export.ExportedProgram) @slow def test_export_smolvlm_text_decoder(self): from transformers import AutoConfig from transformers.integrations.executorch import TorchExportableModuleForVLM model_id = "HuggingFaceTB/SmolVLM2-256M-Video-Instruct" # NOTE: The attention_mask is prepared internally in the vision encoder, depending on whether flash attention is used or not # For ExecuTorch, flash attention is not supported, so the way of exporting vison encoder should be compatible with text-decoder config = AutoConfig.from_pretrained(model_id) config.text_config._flash_attn_2_enabled = False config.text_config.use_cache = True config.text_config.attn_implementation = "sdpa" generation_config = GenerationConfig( use_cache=True, cache_implementation="static", max_length=1234, cache_config={ "batch_size": 1, "max_cache_len": 1234, }, ) # Load the model and extract the text decoder model = SmolVLMForConditionalGeneration.from_pretrained( model_id, dtype=torch.float32, config=config, ) model.model.text_model.generation_config = generation_config text_decoder = model.model.text_model text_decoder.eval() exportable_module = TorchExportableModuleForVLM(model) exported_program = exportable_module.export_text_decoder() self.assertIsInstance(exported_program, torch.export.ExportedProgram)