tests/models/mlcd/test_modeling_mlcd.py - main - githubmirror/transformers

tests/models/mlcd/test_modeling_mlcd.py
209 строк7 KB
Ilyas Moutawwakil
Make Transformers more torch-exportable and dynamo-friendly (#42317)
22 янв 2026, 12:07
22 янв 2026, 12:07eff263c
# 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 MLCD model."""

import unittest

import requests
from PIL import Image

from transformers import (
    AutoProcessor,
    MLCDVisionConfig,
    MLCDVisionModel,
    is_torch_available,
)
from transformers.testing_utils import (
    require_torch,
    slow,
    torch_device,
)

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
    import torch


class MLCDVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

        # in MLCD, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches + 1

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return MLCDVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values):
        model = MLCDVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class MLCDVisionModelTest(ModelTesterMixin, unittest.TestCase):
    """
    Model tester for `MLCDVisionModel`.
    """

    all_model_classes = (MLCDVisionModel,) if is_torch_available() else ()

    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = MLCDVisionModelTester(self)
        self.config_tester = ConfigTester(self, config_class=MLCDVisionConfig, has_text_modality=False)

    def test_model_get_set_embeddings(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, torch.nn.Linear))

    @unittest.skip(
        reason="MLCD passes position embeddings as tuples in its vision encoder, which breaks reentrant GC."
    )
    def test_enable_input_require_grads_with_gradient_checkpointing(self):
        pass


@require_torch
class MLCDVisionModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "DeepGlint-AI/mlcd-vit-bigG-patch14-448"
        model = MLCDVisionModel.from_pretrained(model_name, attn_implementation="eager").to(torch_device)
        processor = AutoProcessor.from_pretrained(model_name)

        # process single image
        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        image = Image.open(requests.get(url, stream=True).raw)
        inputs = processor(images=image, return_tensors="pt")

        # move inputs to the same device as the model
        inputs = {k: v.to(torch_device) for k, v in inputs.items()}

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs, output_attentions=True)

        last_hidden_state = outputs.last_hidden_state
        last_attention = outputs.attentions[-1]

        # verify the shapes of last_hidden_state and last_attention
        self.assertEqual(
            last_hidden_state.shape,
            torch.Size([1, 1025, 1664]),
        )
        self.assertEqual(
            last_attention.shape,
            torch.Size([1, 16, 1025, 1025]),
        )

        # verify the values of last_hidden_state and last_attention
        # fmt: off
        expected_partial_5x5_last_hidden_state = torch.tensor(
            [
                [-0.8976, -0.1173, 0.4770, 0.4768, -0.5785],
                [0.2828, -2.6036, 0.4997, 0.5538, -1.0822],
                [0.3285, -0.3092, -0.4157, -0.1794, -0.7793],
                [-1.5005, -1.0548, -1.2262, 0.2269, -0.9054],
                [0.2317, -0.8372, -0.9653, -0.3017, 0.0871],
            ]
        ).to(torch_device)

        expected_partial_5x5_last_attention = torch.tensor(
            [
                [2.0956e-01, 6.4854e-05, 1.5120e-03, 2.6588e-05, 3.0168e-03],
                [1.6095e-04, 2.0924e-03, 4.7327e-04, 1.8991e-03, 5.4792e-04],
                [5.8087e-04, 1.1215e-03, 1.3588e-03, 1.1862e-03, 1.2509e-03],
                [9.5609e-05, 1.6015e-03, 2.8401e-04, 3.2878e-03, 2.0171e-04],
                [6.2485e-04, 1.1751e-03, 1.4737e-03, 8.2471e-04, 2.6918e-03],
            ]
        ).to(torch_device)
        # fmt: on

        torch.testing.assert_close(
            last_hidden_state[0, :5, :5], expected_partial_5x5_last_hidden_state, rtol=1e-3, atol=1e-3
        )
        torch.testing.assert_close(
            last_attention[0, 0, :5, :5], expected_partial_5x5_last_attention, rtol=1e-4, atol=1e-4
        )