# 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 Gemma3 model.""" import logging import tempfile import unittest import pytest from parameterized import parameterized from transformers import ( AutoModelForCausalLM, AutoTokenizer, Gemma3Config, Gemma3TextConfig, is_torch_available, ) from transformers.testing_utils import ( Expectations, cleanup, is_flash_attn_2_available, require_deterministic_for_xpu, require_flash_attn, require_read_token, require_torch, require_torch_accelerator, require_torch_large_accelerator, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...models.gemma.test_modeling_gemma import GemmaModelTester from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import ( Gemma3ForCausalLM, Gemma3ForConditionalGeneration, Gemma3Model, Gemma3Processor, Gemma3TextModel, ) from transformers.pytorch_utils import is_torch_greater_or_equal class Gemma3ModelTester(GemmaModelTester): if is_torch_available(): config_class = Gemma3TextConfig model_class = Gemma3TextModel for_causal_lm_class = Gemma3ForCausalLM @require_torch class Gemma3ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (Gemma3TextModel, Gemma3ForCausalLM) if is_torch_available() else () all_generative_model_classes = (Gemma3ForCausalLM,) if is_torch_available() else () test_headmasking = False test_pruning = False _is_stateful = True model_split_percents = [0.5, 0.6] def setUp(self): self.model_tester = Gemma3ModelTester(self) self.config_tester = ConfigTester(self, config_class=Gemma3Config, hidden_size=37) @unittest.skip("Failing because of unique cache (HybridCache)") def test_model_outputs_equivalence(self, **kwargs): pass @parameterized.expand([("random",), ("same",)]) @pytest.mark.generate @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding") def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type): pass @pytest.mark.generate @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_sample(self): pass @unittest.skip("Gemma3 has HybridCache which is not compatible with dola decoding") def test_dola_decoding_sample(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support continue from past kv") def test_generate_continue_from_past_key_values(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support low_memory generation") def test_beam_search_low_memory(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_dict_outputs_use_cache(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_low_memory(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_with_static_cache(self): pass @unittest.skip("Gemma3 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("Gemma3 has HybridCache which auto-compiles. Compile and FA2 don't work together.") def test_eager_matches_fa2_generate(self): pass @unittest.skip( reason="HybridCache can't be gathered because it is not iterable. Adding a simple iter and dumping `distributed_iterator`" " as in Dynamic Cache doesn't work. NOTE: @gante all cache objects would need better compatibility with multi gpu setting" ) def test_multi_gpu_data_parallel_forward(self): pass class Gemma3Vision2TextModelTester: def __init__( self, parent, mm_tokens_per_image=2, image_token_index=4, boi_token_index=5, eoi_token_index=6, seq_length=25, is_training=True, vision_config={ "use_labels": True, "image_size": 20, "patch_size": 5, "num_channels": 3, "is_training": True, "hidden_size": 32, "num_key_value_heads": 1, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 37, "dropout": 0.1, "attention_dropout": 0.1, "initializer_range": 0.02, }, use_cache=False, ): self.parent = parent # `image_token_index` is set to 0 to pass "resize_embeddings" test, do not modify self.mm_tokens_per_image = mm_tokens_per_image self.image_token_index = image_token_index self.boi_token_index = boi_token_index self.eoi_token_index = eoi_token_index self.llm_tester = Gemma3ModelTester(self.parent) self.text_config = self.llm_tester.get_config() self.vision_config = vision_config self.seq_length = seq_length self.pad_token_id = self.text_config.pad_token_id self.num_hidden_layers = self.text_config.num_hidden_layers self.vocab_size = self.text_config.vocab_size self.hidden_size = self.text_config.hidden_size self.num_attention_heads = self.text_config.num_attention_heads self.is_training = is_training self.batch_size = 3 self.num_channels = vision_config["num_channels"] self.image_size = vision_config["image_size"] self.encoder_seq_length = seq_length self.use_cache = use_cache def get_config(self): return Gemma3Config( text_config=self.text_config, vision_config=self.vision_config, image_token_index=self.image_token_index, boi_token_index=self.boi_token_index, eoi_token_index=self.eoi_token_index, mm_tokens_per_image=self.mm_tokens_per_image, ) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [ self.batch_size, self.vision_config["num_channels"], 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 - 1) + 1 attention_mask = input_ids.ne(self.pad_token_id).to(torch_device) # set the 3 first tokens to be image, and ensure that no other tokens are image tokens # do not change this unless you modified image size or patch size input_ids[input_ids == config.image_token_index] = self.pad_token_id input_ids[:, :1] = config.image_token_index token_type_ids = torch.zeros_like(input_ids) token_type_ids[input_ids == config.image_token_index] = 1 inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } return config, inputs_dict @require_torch class Gemma3Vision2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( Gemma3Model, Gemma3ForConditionalGeneration, ) if is_torch_available() else () ) all_generative_model_classes = (Gemma3ForConditionalGeneration,) if is_torch_available() else () test_headmasking = False test_pruning = False test_missing_keys = False _is_stateful = True model_split_percents = [0.5, 0.6] # MP works but offload doesn't work when the SigLIP MultiheadAttention is offloaded # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"] # in the dispatch_model function test_cpu_offload = False test_disk_offload_safetensors = False test_disk_offload_bin = False def setUp(self): self.model_tester = Gemma3Vision2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Gemma3Config, hidden_size=37) @unittest.skip(reason="SiglipVisionModel (vision backbone) does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipVisionModel (vision backbone) does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipVisionModel (vision backbone) does not support standalone training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip( reason="HybridCache can't be gathered because it is not iterable. Adding a simple iter and dumping `distributed_iterator`" " as in Dynamic Cache doesn't work. NOTE: @gante all cache objects would need better compatibility with multi gpu setting" ) def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip("Failing because of unique cache (HybridCache)") def test_model_outputs_equivalence(self, **kwargs): pass @parameterized.expand([("random",), ("same",)]) @pytest.mark.generate @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding") def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type): pass @pytest.mark.generate @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_sample(self): pass @unittest.skip("Gemma3 has HybridCache which is not compatible with dola decoding") def test_dola_decoding_sample(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support continue from past kv") def test_generate_continue_from_past_key_values(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support low_memory generation") def test_beam_search_low_memory(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_dict_outputs_use_cache(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_low_memory(self): pass @unittest.skip("Gemma3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_with_static_cache(self): pass @unittest.skip("Gemma3 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("Gemma3 has HybridCache which auto-compiles. Compile and FA2 don't work together.") def test_eager_matches_fa2_generate(self): pass @unittest.skip( reason="Siglip (vision backbone) uses the same initialization scheme as the Flax original implementation" ) def test_initialization(self): pass def test_automodelforcausallm(self): """ Regression test for #36741/#36917 -- make sure `AutoModelForCausalLM` works with a Gemma3 config, i.e. that `AutoModelForCausalLM.from_pretrained` pulls the text config before loading the model """ config = self.model_tester.get_config() model = Gemma3ForConditionalGeneration(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) for_causal_lm = AutoModelForCausalLM.from_pretrained(tmp_dir) self.assertIsInstance(for_causal_lm, Gemma3ForConditionalGeneration) @slow @require_torch_accelerator @require_read_token class Gemma3IntegrationTest(unittest.TestCase): def setUp(self): self.processor = Gemma3Processor.from_pretrained("google/gemma-3-4b-it", padding_side="left") url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png" self.messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, { "role": "user", "content": [ {"type": "image", "url": url}, {"type": "text", "text": "What is shown in this image?"}, ], }, ] def tearDown(self): cleanup(torch_device, gc_collect=True) @require_deterministic_for_xpu def test_model_4b_bf16(self): model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(torch_device) inputs = self.processor.apply_chat_template( self.messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True, ).to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_TEXTS = Expectations( { ("xpu", 3): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach with turquoise water in the background. It looks like a lovely,'], ("cuda", 7): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach with turquoise water in the background. It looks like a lovely,'], ("cuda", 8): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach next to a turquoise ocean. It looks like a very sunny and'], ("rocm", (9, 5)): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach with turquoise water and a distant coastline in the background. It looks'], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(output_text, EXPECTED_TEXT) @require_torch_large_accelerator @require_deterministic_for_xpu def test_model_4b_batch(self): model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(torch_device) messages_2 = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, { "role": "user", "content": [ { "type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png", }, {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "Are these images identical?"}, ], }, ] inputs = self.processor.apply_chat_template( [self.messages, messages_2], tokenize=True, return_dict=True, return_tensors="pt", padding=True, add_generation_prompt=True, ).to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_TEXTS = Expectations( { ("xpu", 3): [ 'user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach next to a turquoise ocean. It looks like a very sunny and', 'user\nYou are a helpful assistant.\n\n\n\n\n\n\n\n\n\nAre these images identical?\nmodel\nNo, these images are not identical. They depict very different scenes:\n\n* **Image 1** shows a cow standing on a beach.', ], ("cuda", 7): [], ("cuda", 8): [ 'user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach next to a turquoise ocean. It looks like a very sunny and', 'user\nYou are a helpful assistant.\n\n\n\n\n\n\n\n\n\nAre these images identical?\nmodel\nNo, these images are not identical. They depict very different scenes:\n\n* **Image 1** shows a cow standing on a beach.', ], ("rocm", (9, 5)): [ 'user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown and white cow standing on a sandy beach next to a turquoise ocean. There are some clouds in the blue', 'user\nYou are a helpful assistant.\n\n\n\n\n\n\n\n\n\nAre these images identical?\nmodel\nNo, these images are not identical. They depict very different scenes. \n\n* **Image 1** shows a cow standing on a beach', ], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(output_text, EXPECTED_TEXT) @require_torch_large_accelerator def test_model_4b_crops(self): model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(torch_device) crop_config = { "images_kwargs": { "do_pan_and_scan": True, "pan_and_scan_max_num_crops": 448, "pan_and_scan_min_crop_size": 32, "pan_and_scan_min_ratio_to_activate": 0.3, } } inputs = self.processor.apply_chat_template( self.messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True, **crop_config, ).to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_NUM_IMAGES = 3 # one for the origin image and two crops of images EXPECTED_TEXTS = Expectations( { ("xpu", 3): ['user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown cow standing on a sandy beach next to a turquoise ocean. There are clouds in the blue sky above.'], ("cuda", 7): [], ("cuda", 8): ['user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown cow standing on a sandy beach next to a turquoise ocean. There are clouds in the blue sky above.'], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(len(inputs["pixel_values"]), EXPECTED_NUM_IMAGES) self.assertEqual(output_text, EXPECTED_TEXT) @require_torch_large_accelerator @require_deterministic_for_xpu def test_model_4b_batch_crops(self): model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(torch_device) crop_config = { "images_kwargs": { "do_pan_and_scan": True, "pan_and_scan_max_num_crops": 448, "pan_and_scan_min_crop_size": 32, "pan_and_scan_min_ratio_to_activate": 0.3, } } messages_2 = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, { "role": "user", "content": [ { "type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png", }, {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "Are these images identical?"}, ], }, ] inputs = self.processor.apply_chat_template( [self.messages, messages_2], tokenize=True, return_dict=True, return_tensors="pt", padding=True, add_generation_prompt=True, **crop_config, ).to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_NUM_IMAGES = 9 # 3 * (one for the origin image and two crops of images) = 9 EXPECTED_TEXTS = Expectations( { ("xpu", 3): [ 'user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown cow standing on a sandy beach next to a turquoise ocean. There are clouds in the blue sky above.', 'user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nAre these images identical?\nmodel\nNo, the images are not identical. \n\nThe first image shows a cow on a beach, while the second image shows a street scene with a', ], ("cuda", 7): [], ("cuda", 8): [ 'user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown cow standing on a sandy beach next to a turquoise ocean. There are clouds in the blue sky above.', 'user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nAre these images identical?\nmodel\nNo, the images are not identical. \n\nThe first image shows a cow on a beach, while the second image shows a street scene with a', ], ("rocm", (9, 5)) : [ 'user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown cow standing on a sandy beach next to a turquoise ocean. There are clouds in the blue sky above.', 'user\nYou are a helpful assistant.\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nHere is the original image \n\n\n\n and here are some crops to help you see better \n\n\n\n \n\n\n\nAre these images identical?\nmodel\nNo, the images are not identical. \n\nThe first image shows a cow on a beach, while the second image shows a street scene with a', ], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(len(inputs["pixel_values"]), EXPECTED_NUM_IMAGES) self.assertEqual(output_text, EXPECTED_TEXT) @require_torch_large_accelerator def test_model_4b_multiimage(self): model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(torch_device) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, { "role": "user", "content": [ {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, {"type": "text", "text": "What do you see here?"}, ], }, ] inputs = self.processor.apply_chat_template( messages, tokenize=True, return_dict=True, return_tensors="pt", padding=True, add_generation_prompt=True, ).to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_TEXTS = Expectations( { ("xpu", 3): ["user\nYou are a helpful assistant.\n\n\n\n\n\nWhat do you see here?\nmodel\nOkay, let's break down what I see in this image!\n\nHere's a description of the scene:\n\n* **Chinese Arch"], ("cuda", 7): [], ("cuda", 8): ["user\nYou are a helpful assistant.\n\n\n\n\n\nWhat do you see here?\nmodel\nOkay, let's break down what I see in this image:\n\n**Main Features:**\n\n* **Chinese Archway:** The most prominent"], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(output_text, EXPECTED_TEXT) @require_deterministic_for_xpu def test_model_1b_text_only(self): model_id = "google/gemma-3-1b-it" model = Gemma3ForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left") inputs = tokenizer("Write a poem about Machine Learning.", return_tensors="pt").to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) EXPECTED_TEXTS = Expectations( { ("xpu", 3): ['Write a poem about Machine Learning.\n\n---\n\nThe data flows, a river deep,\nWith patterns hidden, secrets sleep.\nA neural net, a watchful eye,\nLearning'], ("cuda", 7): ['Write a poem about Machine Learning.\n\n---\n\nThe data flows, a silent stream,\nInto the neural net, a waking dream.\nAlgorithms hum, a coded grace,\n'], ("cuda", 8): ['Write a poem about Machine Learning.\n\n---\n\nThe data flows, a silent stream,\nInto the neural net, a waking dream.\nAlgorithms hum, a coded grace,\n'], ("rocm", (9, 5)): ['Write a poem about Machine Learning.\n\n---\n\nThe data flows, a river deep,\nWith patterns hidden, secrets sleep.\nA neural net, a watchful eye,\nLearning'], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(output_text, EXPECTED_TEXT) # TODO: raushan FA2 generates gibberish for no reason, check later @require_flash_attn @require_torch_large_accelerator @pytest.mark.flash_attn_test def test_model_4b_flash_attn(self): model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ).to(torch_device) inputs = self.processor.apply_chat_template( self.messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True, ).to(torch_device) output = model.generate(**inputs, max_new_tokens=30, do_sample=False) output_text = self.processor.batch_decode(output, skip_special_tokens=True) EXPECTED_TEXTS = Expectations( { ("xpu", 3): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown and white cow standing on a sandy beach with turquoise water and a distant island in the background. It looks like a sunny day'], ("cuda", 7): [], ("cuda", 8): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown and white cow standing on a sandy beach with turquoise water and a distant island in the background. It looks like a sunny day'], ("rocm", (9, 5)): ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nThe image shows a brown and white cow standing on a sandy beach with a turquoise ocean and a distant island in the background. It looks like a sunny'], } ) # fmt: skip EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.assertEqual(output_text, EXPECTED_TEXT) @parameterized.expand([("flash_attention_2",), ("sdpa",), ("eager",)]) def test_generation_beyond_sliding_window(self, attn_implementation: str): """Test that we can correctly generate beyond the sliding window. This is non trivial as we need to correctly slice the attention mask in all cases (because we use a HybridCache). Outputs for every attention functions should be coherent and identical. """ model_id = "google/gemma-3-1b-it" if attn_implementation == "flash_attention_2" and not is_flash_attn_2_available(): self.skipTest("FlashAttention2 is required for this test.") input_text = [ "This is a nice place. " * 800 + "I really enjoy the scenery,", # This is larger than 4096 tokens "A list of colors: red, blue", # This will almost all be padding tokens ] tokenizer = AutoTokenizer.from_pretrained(model_id, padding="left") inputs = tokenizer(input_text, padding=True, return_tensors="pt").to(torch_device) model = AutoModelForCausalLM.from_pretrained( model_id, attn_implementation=attn_implementation, torch_dtype=torch.float16 ).to(torch_device) # Make sure prefill is larger than sliding window input_size = inputs.input_ids.shape[-1] self.assertTrue(input_size > model.config.sliding_window) out = model.generate(**inputs, max_new_tokens=20, do_sample=False)[:, input_size:] output_text = tokenizer.batch_decode(out) EXPECTED_COMPLETIONS = [" and I'm going to take a walk.\n\nI really enjoy the scenery, and I'", ", green, yellow, orange, purple, brown, black, white, gray.\n\nI'"] # fmt: skip self.assertEqual(output_text, EXPECTED_COMPLETIONS) def test_export_text_only_with_hybrid_cache(self): if not is_torch_greater_or_equal("2.6.0"): self.skipTest(reason="This test requires torch >= 2.6 to run.") from transformers.integrations.executorch import TorchExportableModuleForDecoderOnlyLM model_id = "google/gemma-3-1b-it" model = AutoModelForCausalLM.from_pretrained(model_id) self.assertEqual(model.config.cache_implementation, "hybrid") # Export + HybridCache model.eval() exportable_module = TorchExportableModuleForDecoderOnlyLM(model) exported_program = exportable_module.export() logging.info(f"\nExported program: {exported_program}") # Test generation with the exported model prompt = "What is the capital of France?" max_new_tokens_to_generate = 20 # Generate text with the exported model tokenizer = AutoTokenizer.from_pretrained(model_id) export_generated_text = TorchExportableModuleForDecoderOnlyLM.generate( exported_program, tokenizer, prompt, max_new_tokens=max_new_tokens_to_generate ) logging.info(f"\nExport generated texts: '{export_generated_text}'") input_text = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): eager_outputs = model.generate( **input_text, max_new_tokens=max_new_tokens_to_generate, do_sample=False, # Use greedy decoding to match the exported model ) eager_generated_text = tokenizer.decode(eager_outputs[0], skip_special_tokens=True) logging.info(f"\nEager generated texts: '{eager_generated_text}'") self.assertEqual(export_generated_text, eager_generated_text)