mirror of
https://github.com/huggingface/transformers.git
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747f361da1
* Add sdpa for Beit * Updates * [run-slow] beit * Update inference benchmarks * Update * Fix - add missed to super().forward() * Updates * Fix missing import
582 lines
26 KiB
Python
582 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2022 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 Data2VecVision model."""
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import inspect
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import tempfile
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import unittest
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import numpy as np
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from parameterized import parameterized
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from transformers import Data2VecVisionConfig
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from transformers.testing_utils import (
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require_torch,
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require_torch_multi_gpu,
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require_torch_sdpa,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import (
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cached_property,
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is_torch_available,
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is_torch_bf16_available_on_device,
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is_torch_fp16_available_on_device,
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is_vision_available,
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)
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, sdpa_kernel
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import (
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Data2VecVisionForImageClassification,
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Data2VecVisionForSemanticSegmentation,
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Data2VecVisionModel,
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)
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from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
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if is_vision_available():
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from PIL import Image
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from transformers import BeitImageProcessor
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class Data2VecVisionModelTester:
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def __init__(
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self,
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parent,
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vocab_size=100,
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batch_size=13,
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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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use_labels=True,
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hidden_size=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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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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type_sequence_label_size=10,
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initializer_range=0.02,
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num_labels=3,
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scope=None,
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out_indices=[0, 1, 2, 3],
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attn_implementation="eager",
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mask_ratio=0.5,
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):
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self.parent = parent
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self.vocab_size = 100
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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.use_labels = use_labels
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self.hidden_size = hidden_size
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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.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.scope = scope
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self.out_indices = out_indices
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self.num_labels = num_labels
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# in BeiT, 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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self.num_masks = int(mask_ratio * self.seq_length)
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self.attn_implementation = attn_implementation
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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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labels = None
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pixel_labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels, pixel_labels
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def get_config(self):
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return Data2VecVisionConfig(
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vocab_size=self.vocab_size,
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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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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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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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is_decoder=False,
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initializer_range=self.initializer_range,
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out_indices=self.out_indices,
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attn_implementation=self.attn_implementation,
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)
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def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
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model = Data2VecVisionModel(config=config)
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model.to(torch_device)
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model.eval()
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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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num_patches = (self.image_size // self.patch_size) ** 2
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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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def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.type_sequence_label_size
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model = Data2VecVisionForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.num_labels
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model = Data2VecVisionForSemanticSegmentation(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
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)
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result = model(pixel_values, labels=pixel_labels)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
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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, labels, pixel_labels = 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 Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (
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(Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"image-feature-extraction": Data2VecVisionModel,
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"image-classification": Data2VecVisionForImageClassification,
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"image-segmentation": Data2VecVisionForSemanticSegmentation,
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}
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if is_torch_available()
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else {}
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)
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = Data2VecVisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="Data2VecVision does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@require_torch_multi_gpu
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@unittest.skip(
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reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"
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)
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def test_multi_gpu_data_parallel_forward(self):
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pass
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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(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_image_segmentation(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)
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def test_training(self):
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if not self.model_tester.is_training:
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self.skipTest(reason="model_tester.is_training is set to False")
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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for model_class in self.all_model_classes:
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if model_class.__name__ in MODEL_MAPPING_NAMES.values():
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.model_tester.is_training:
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self.skipTest(reason="model_tester.is_training is set to False")
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config.use_cache = False
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config.return_dict = True
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for model_class in self.all_model_classes:
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if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
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continue
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# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
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# this can then be incorporated into _prepare_for_class in test_modeling_common.py
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elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
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batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
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inputs_dict["labels"] = torch.zeros(
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[self.model_tester.batch_size, height, width], device=torch_device
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).long()
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model = model_class(config)
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model.gradient_checkpointing_enable()
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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# we skip lambda parameters as these require special initial values
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# determined by config.layer_scale_init_value
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if "lambda" in name:
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continue
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if param.requires_grad:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None):
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# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
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super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
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def test_for_image_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "facebook/data2vec-vision-base-ft1k"
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model = Data2VecVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
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@require_torch_sdpa
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# Copied from tests.models.beit.test_modeling_beit.BeitModelTest.test_eager_matches_sdpa_inference with Beit->Data2VecVision
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def test_eager_matches_sdpa_inference(self, torch_dtype: str):
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# The common test modifies the num_hidden_layers to be 1. However, for Data2VecVision we want to
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# avoid that because the num_hidden_layers is generally assumed to be 4. Also, the code
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# related to attention masks in the original common tests is not required as the Data2VecVision
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# model does not handle attention masks. Furthermore, some extra code like modifying
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# the norm layers eps values for specialized configs and checking for the 'noise'
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# has been omitted to simply the test.
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if not self.has_attentions:
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self.skipTest(reason="Model architecture does not support attentions")
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if not self.all_model_classes[0]._supports_sdpa:
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self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
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if torch_dtype == "float16" and not is_torch_fp16_available_on_device(torch_device):
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self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
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if torch_dtype == "bfloat16" and not is_torch_bf16_available_on_device(torch_device):
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self.skipTest(
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f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
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)
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# Not sure whether it's fine to put torch.XXX in a decorator if torch is not available so hacking it here instead.
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if torch_dtype == "float16":
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torch_dtype = torch.float16
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elif torch_dtype == "bfloat16":
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torch_dtype = torch.bfloat16
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elif torch_dtype == "float32":
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torch_dtype = torch.float32
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atols = {
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("cpu", False, torch.float32): 1e-6,
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("cpu", False, torch.float16): 5e-3,
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("cpu", False, torch.bfloat16): 1e-2,
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("cpu", True, torch.float32): 1e-6,
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("cpu", True, torch.float16): 5e-3,
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("cpu", True, torch.bfloat16): 1e-2,
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("cuda", False, torch.float32): 1e-6,
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("cuda", False, torch.bfloat16): 1e-2,
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("cuda", False, torch.float16): 5e-3,
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("cuda", True, torch.float32): 1e-6,
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("cuda", True, torch.bfloat16): 1e-2,
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("cuda", True, torch.float16): 5e-3,
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}
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rtols = {
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("cpu", False, torch.float32): 1e-4,
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("cpu", False, torch.float16): 5e-3,
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("cpu", False, torch.bfloat16): 1e-2,
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("cpu", True, torch.float32): 1e-4,
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("cpu", True, torch.float16): 5e-3,
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("cpu", True, torch.bfloat16): 1e-2,
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("cuda", False, torch.float32): 1e-4,
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("cuda", False, torch.bfloat16): 1e-2,
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("cuda", False, torch.float16): 5e-3,
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("cuda", True, torch.float32): 1e-4,
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("cuda", True, torch.bfloat16): 3e-2,
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("cuda", True, torch.float16): 5e-3,
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}
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def get_mean_reldiff(failcase, x, ref, atol, rtol):
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return f"{failcase}: mean relative difference: {((x - ref).abs() / (ref.abs() + 1e-12)).mean():.3e}, torch atol = {atol}, torch rtol = {rtol}"
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.rms_norm_eps = 1.0
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config.layer_norm_eps = 1.0
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config.norm_eps = 1.0
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config.norm_epsilon = 1.0
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config.layer_norm_epsilon = 1.0
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_sdpa = model_class.from_pretrained(tmpdirname, torch_dtype=torch_dtype, use_mask_token=True)
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model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
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model_eager = model_class.from_pretrained(
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tmpdirname,
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torch_dtype=torch_dtype,
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attn_implementation="eager",
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use_mask_token=True,
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)
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model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
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# Another way to make sure norm layers have desired epsilon. (Some models don't set it from its config.)
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for x in model_eager.modules():
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if isinstance(x, (nn.LayerNorm, nn.GroupNorm)):
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x.eps = 1.0
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for x in model_sdpa.modules():
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if isinstance(x, (nn.LayerNorm, nn.GroupNorm)):
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x.eps = 1.0
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# We use these for loops instead of parameterized.expand just for the interest of avoiding loading/saving 16 times the model,
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# but it would be nicer to have an efficient way to use parameterized.expand
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fail_cases = []
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for padding_side in ["left", "right"]:
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for use_mask in [False, True]:
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for output_attentions in [True, False]:
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can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
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if not (self.has_attentions and can_output_attn) and output_attentions:
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continue
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# TODO: if we can also check with `batch_size=1` without being flaky?
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for batch_size in [7]:
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dummy_input = inputs_dict[model.main_input_name]
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if dummy_input.dtype in [torch.float32, torch.bfloat16, torch.float16]:
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dummy_input = dummy_input.to(torch_dtype)
|
|
|
|
dummy_input = dummy_input[:batch_size]
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|
for enable_kernels in [False, True]:
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|
failcase = f"padding_side={padding_side}, use_mask={use_mask}, enable_kernels={enable_kernels}"
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|
processed_inputs = {
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|
model.main_input_name: dummy_input,
|
|
"output_hidden_states": True,
|
|
}
|
|
|
|
if (
|
|
self.has_attentions
|
|
and "output_attentions" in inspect.signature(model_sdpa.forward).parameters
|
|
):
|
|
processed_inputs["output_attentions"] = output_attentions
|
|
|
|
if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
|
|
dummy_mask = torch.ones((self.model_tester.num_masks,))
|
|
mask_length = self.model_tester.seq_length - 1 - dummy_mask.size(0)
|
|
dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
|
|
dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
|
|
processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
with sdpa_kernel(
|
|
enable_flash=enable_kernels,
|
|
enable_math=True,
|
|
enable_mem_efficient=enable_kernels,
|
|
):
|
|
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
|
outputs_eager = model_eager(**prepared_inputs)
|
|
outputs_sdpa = model_sdpa(**prepared_inputs)
|
|
|
|
logits_eager = outputs_eager.hidden_states[-1]
|
|
logits_sdpa = outputs_sdpa.hidden_states[-1]
|
|
if torch_device in ["cpu", "cuda"]:
|
|
atol = atols[torch_device, enable_kernels, torch_dtype]
|
|
rtol = rtols[torch_device, enable_kernels, torch_dtype]
|
|
elif torch_device == "xpu":
|
|
# As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
|
|
# which is implemented on PyTorch level using aten operators and is
|
|
# device agnostic with respect to implementation of each aten operator.
|
|
atol = atols["cuda", False, torch_dtype]
|
|
rtol = rtols["cuda", False, torch_dtype]
|
|
else:
|
|
atol = 1e-7
|
|
rtol = 1e-4
|
|
|
|
# Masked tokens output slightly deviates - we don't mind that.
|
|
if use_mask:
|
|
_logits_sdpa = torch.zeros_like(input=logits_sdpa)
|
|
_logits_eager = torch.zeros_like(input=logits_eager)
|
|
|
|
_logits_sdpa[:-1] = logits_sdpa[:-1]
|
|
_logits_eager[:-1] = logits_eager[:-1]
|
|
|
|
if padding_side == "left":
|
|
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
|
|
_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
|
|
|
|
elif padding_side == "right":
|
|
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
|
|
_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
|
|
|
|
logits_sdpa = _logits_sdpa
|
|
logits_eager = _logits_eager
|
|
|
|
results = [
|
|
torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
|
|
for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
|
|
]
|
|
# If 80% batch elements have matched results, it's fine
|
|
if np.mean(results) < 0.8:
|
|
fail_cases.append(
|
|
get_mean_reldiff(failcase, logits_sdpa, logits_eager, atol, rtol)
|
|
)
|
|
|
|
self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases))
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class Data2VecVisionModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return (
|
|
BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None
|
|
)
|
|
|
|
@slow
|
|
def test_inference_image_classification_head_imagenet_1k(self):
|
|
model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to(
|
|
torch_device
|
|
)
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 1000))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]]
|
|
self.assertEqual(logits[0].topk(2).indices.cpu().tolist(), expected_top2)
|
|
|
|
@slow
|
|
def test_inference_interpolate_pos_encoding(self):
|
|
model_name = "facebook/data2vec-vision-base-ft1k"
|
|
model = Data2VecVisionModel.from_pretrained(model_name, **{"use_absolute_position_embeddings": True}).to(
|
|
torch_device
|
|
)
|
|
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
processor = BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k")
|
|
inputs = processor(images=image, return_tensors="pt", size={"height": 480, "width": 480})
|
|
pixel_values = inputs.pixel_values.to(torch_device)
|
|
|
|
# with interpolate_pos_encoding being False an exception should be raised with higher resolution
|
|
# images than what the model supports.
|
|
self.assertFalse(processor.do_center_crop)
|
|
with torch.no_grad():
|
|
with self.assertRaises(ValueError, msg="doesn't match model"):
|
|
model(pixel_values, interpolate_pos_encoding=False)
|
|
|
|
# with interpolate_pos_encoding being True the model should process the higher resolution image
|
|
# successfully and produce the expected output.
|
|
with torch.no_grad():
|
|
outputs = model(pixel_values, interpolate_pos_encoding=True)
|
|
|
|
expected_shape = torch.Size((1, 1801, 768))
|
|
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
|