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https://github.com/huggingface/transformers.git
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241d79026f
* fix pixtral processor * test out full length batches + remove undue ValueError * fix up processing * fix tests * fix * last fixup * style * [run-slow] pixtral * [run-slow] pixtral * fix config key * skip torchscript tests * [run-slow] pixtral * add missing key * [run-slow] pixtral * fix docs * [run-slow] pixtral * fix wrong url for integration test * [run-slow] pixtral * pixtralVisionModel does not have a lm head * [run-slow] pixtral
256 lines
8.4 KiB
Python
256 lines
8.4 KiB
Python
# coding=utf-8
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# 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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is_vision_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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else:
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is_torch_greater_or_equal_than_2_0 = False
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if is_vision_available():
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pass
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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 ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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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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config = self.get_config()
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return config, pixel_values
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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 create_and_check_model(self, config, pixel_values):
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model = PixtralVisionModel(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(pixel_values)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_model_with_projection(self, config, pixel_values):
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model = PixtralVisionModel(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(pixel_values)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
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self.parent.assertEqual(result.image_embeds.shape, (self.batch_size, self.projection_dim))
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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 = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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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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test_pruning = False
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test_head_masking = False
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test_torchscript = 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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@unittest.skip("model does not support input embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip("model does not support input embeds")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="Compile not yet supported because in Pixtral models")
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def test_sdpa_can_compile_dynamic(self):
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pass
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@unittest.skip(reason="Compile not yet supported because in Pixtral models")
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def test_sdpa_can_dispatch_on_flash(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_attention_outputs(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_cpu_offload(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_batching_equivalence(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_disk_offload_bin(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_parallelism(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_outputs_equivalence(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_save_load(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_resize_tokens_embeddings(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_model_main_input_name(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_initialization(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_gradient_checkpointing_backward_compatibility(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_feed_forward_chunking(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_disk_offload_safetensors(self):
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pass
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@unittest.skip(reason="Not supported yet")
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def test_determinism(self):
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pass
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