mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
d23aae2b8c
* update models * why rename * return attn weights when sdpa * fixes * fix attn implementation composite * fix moshi * add message * add typings * use explicitly all flags for each attn type * fix some tests * import what is needed * kosmos on main has ew attention already, yay * new models in main, run fixup * won't fix kosmos yet * fix-copies * clean up after rebasing * fix tests * style * dont cast attns to fp32 * did we update ruff? oke, let's just do what it asks * fix pixtral after rebase
130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Pixtral model."""
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import unittest
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from transformers import (
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PixtralVisionConfig,
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PixtralVisionModel,
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is_torch_available,
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)
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from transformers.testing_utils import (
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require_torch,
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torch_device,
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)
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor
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if is_torch_available():
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import torch
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class PixtralVisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=32,
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projection_dim=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=0.02,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.scope = scope
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# in Pixtral, the seq length equals the number of patches * batch_size because the patches are flattened
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self.seq_length = (image_size // patch_size) ** 2 * batch_size
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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image_sizes = torch.tensor(
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[[self.image_size, self.image_size]] * self.batch_size, dtype=torch.long, device=torch_device
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)
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config = self.get_config()
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return config, pixel_values, image_sizes
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def get_config(self):
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return PixtralVisionConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, image_sizes = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values, "image_sizes": image_sizes}
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return config, inputs_dict
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@require_torch
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class PixtralVisionModelModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Model tester for `PixtralVisionModel`.
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"""
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all_model_classes = (PixtralVisionModel,) if is_torch_available() else ()
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additional_model_inputs = ["image_sizes"]
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test_pruning = False
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test_head_masking = False
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test_torchscript = False
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = PixtralVisionModelTester(self)
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self.config_tester = ConfigTester(self, config_class=PixtralVisionConfig, has_text_modality=False)
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def test_model_get_set_embeddings(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
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