transformers/tests/models/gemma3/test_modeling_gemma3.py

# 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,
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"""Testing suite for the PyTorch Gemma3 model."""

import unittest

from parameterized import parameterized

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    Gemma3Config,
    Gemma3TextConfig,
    is_torch_available,
)
from transformers.testing_utils import (
    cleanup,
    require_torch,
    require_torch_gpu,
    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,
        Gemma3Processor,
        Gemma3TextModel,
    )


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",)])
    @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

    @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 and doesn't support StaticCache. Though it could, it shouldn't support.")
    def test_generate_continue_from_inputs_embeds(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 doesnt 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=1,
        boi_token_index=2,
        eoi_token_index=3,
        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 = (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 doesnt 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",)])
    @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

    @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(
        reason="Siglip (vision backbone) uses the same initialization scheme as the Flax original implementation"
    )
    def test_initialization(self):
        pass

    @unittest.skip(
        reason="Siglip has no FLEX attention, and we don't have a proper way to set/test attn in VLMs. TODO @raushan"
    )
    def test_flex_attention_with_grads(self):
        pass


@slow
@require_torch_gpu
# @require_read_token
class Gemma3IntegrationTest(unittest.TestCase):
    def setUp(self):
        self.processor = Gemma3Processor.from_pretrained("gg-hf-g/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)

    def test_model_4b_bf16(self):
        model_id = "gg-hf-g/gemma-3-4b-it"

        model = Gemma3ForConditionalGeneration.from_pretrained(
            model_id, low_cpu_mem_usage=True, 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 = ['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 cow standing on a sandy beach with clear blue water and a blue sky in the background. It looks like']  # fmt: skip
        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_4b_batch(self):
        model_id = "gg-hf-g/gemma-3-4b-it"

        model = Gemma3ForConditionalGeneration.from_pretrained(
            model_id, low_cpu_mem_usage=True, 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 = [
            '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 cow standing on a sandy beach with clear turquoise water and a blue sky in the background. It looks like',
            "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. \n\nHere's a breakdown of the differences:\n\n*   **Image 1:** Shows a cow"
        ]  # fmt: skip
        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_4b_crops(self):
        model_id = "gg-hf-g/gemma-3-4b-it"

        model = Gemma3ForConditionalGeneration.from_pretrained(
            model_id, low_cpu_mem_usage=True, 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 = ["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\nDescribe this image in detail.\nmodel\nHere's a detailed description of the image:\n\n**Overall Impression:**\n\nThe image is a close-up shot of a garden scene featuring several"]  # fmt: skip
        self.assertEqual(len(inputs["pixel_values"]), EXPECTED_NUM_IMAGES)
        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_4b_multiimage(self):
        model_id = "gg-hf-g/gemma-3-4b-it"

        model = Gemma3ForConditionalGeneration.from_pretrained(
            model_id, low_cpu_mem_usage=True, 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 = ["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**Overall Scene:**\n\nIt looks like a street scene in a vibrant,"]  # fmt: skip
        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_1b_text_only(self):
        model_id = "gg-hf-g/gemma-3-1b-it"

        model = Gemma3ForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, 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 = ['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
        self.assertEqual(output_text, EXPECTED_TEXTS)

    # TODO: raushan FA2 generates gibberish for no reason, check later
    # @require_flash_attn
    # @require_torch_gpu
    # @mark.flash_attn_test
    # def test_model_4b_flash_attn(self):
    #     model_id = "gg-hf-g/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 = ['user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nPlease look out that you are what Grammy and Vi- ||.xfairesr--ith alerts themselves are||ِّ\n\n**General Note:**']  # fmt: skip
    #     self.assertEqual(output_text, EXPECTED_TEXTS)

    @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 = "gg-hf-g/gemma-3-1b-it"

        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)[:, 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)