init

transformers/tests/models/pixtral/test_modeling_pixtral.py

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+# Copyright 2024 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 Pixtral model."""
+
+import unittest
+
+from transformers import (
+    PixtralVisionConfig,
+    PixtralVisionModel,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    require_torch,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+
+
+if is_torch_available():
+    import torch
+
+
+class PixtralVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        projection_dim=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.projection_dim = projection_dim
+        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 Pixtral, the seq length equals the number of patches * batch_size because the patches are flattened
+        self.seq_length = (image_size // patch_size) ** 2 * batch_size
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        image_sizes = torch.tensor(
+            [[self.image_size, self.image_size]] * self.batch_size, dtype=torch.long, device=torch_device
+        )
+        config = self.get_config()
+
+        return config, pixel_values, image_sizes
+
+    def get_config(self):
+        return PixtralVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            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 prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, image_sizes = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values, "image_sizes": image_sizes}
+        return config, inputs_dict
+
+
+@require_torch
+class PixtralVisionModelModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Model tester for `PixtralVisionModel`.
+    """
+
+    all_model_classes = (PixtralVisionModel,) if is_torch_available() else ()
+    additional_model_inputs = ["image_sizes"]
+    test_pruning = False
+    test_head_masking = False
+    test_torchscript = False
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = PixtralVisionModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=PixtralVisionConfig, 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))