mirror of
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675 lines
26 KiB
Python
675 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2021 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 AIMv2 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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import requests
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from pytest import mark
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from transformers import AIMv2Config, AIMv2TextConfig, AIMv2VisionConfig
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from transformers.testing_utils import (
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require_flash_attn,
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require_torch,
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require_torch_gpu,
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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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is_torch_available,
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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 (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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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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AIMv2Model,
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AIMv2TextModel,
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AIMv2VisionModel,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import AutoImageProcessor, AutoProcessor
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class AIMv2VisionModelTester:
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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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):
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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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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches
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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 AIMv2VisionConfig(
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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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)
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def create_and_check_model(self, config, pixel_values):
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model = AIMv2VisionModel(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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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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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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class AIMv2ModelTesterMixin(ModelTesterMixin):
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"""
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Subclass of ModelTesterMixin with methods specific to testing AIMv2 models.
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The SDPA equivalence test is overridden here because AIMv2 models may have test/vision/text+vision inputs,
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different output logits, and are not supposed to be used or tested with padding_side="left".
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"""
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def test_sdpa_can_dispatch_composite_models(self):
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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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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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# Load the model with SDPA
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model_sdpa = model_class.from_pretrained(tmpdirname)
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# Load model with eager attention
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model_eager = model_class.from_pretrained(
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tmpdirname,
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attn_implementation="eager",
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)
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model_eager = model_eager.eval().to(torch_device)
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if hasattr(model_sdpa, "vision_model"):
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self.assertTrue(model_sdpa.vision_model.config._attn_implementation == "sdpa")
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self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager")
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if hasattr(model_sdpa, "text_model"):
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self.assertTrue(model_sdpa.text_model.config._attn_implementation == "sdpa")
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self.assertTrue(model_eager.text_model.config._attn_implementation == "eager")
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self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
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self.assertTrue(model_eager.config._attn_implementation == "eager")
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@require_torch
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class AIMv2VisionModelTest(AIMv2ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as AIMv2 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 = (AIMv2VisionModel,) if is_torch_available() else ()
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fx_compatible = False
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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 = AIMv2VisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=AIMv2VisionConfig, 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="AIMv2 does not use inputs_embeds")
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def test_inputs_embeds(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_forward_signature(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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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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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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class AIMv2TextModelTester:
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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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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_labels=True,
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vocab_size=99,
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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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max_position_embeddings=512,
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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.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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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.max_position_embeddings = max_position_embeddings
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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config = self.get_config()
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return config, input_ids, input_mask
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def get_config(self):
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return AIMv2TextConfig(
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vocab_size=self.vocab_size,
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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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max_position_embeddings=self.max_position_embeddings,
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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = AIMv2TextModel(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(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class AIMv2TextModelTest(AIMv2ModelTesterMixin, unittest.TestCase):
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all_model_classes = (AIMv2TextModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_head_masking = False
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = AIMv2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=AIMv2TextConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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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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@unittest.skip
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def test_training(self):
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pass
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@unittest.skip
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="This model has no Loss")
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(reason="This model has no Loss")
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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="AIMv2 does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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class AIMv2ModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.text_model_tester = AIMv2TextModelTester(parent, **text_kwargs)
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self.vision_model_tester = AIMv2VisionModelTester(parent, **vision_kwargs)
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self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, attention_mask, pixel_values
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def get_config(self):
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return AIMv2Config.from_text_vision_configs(
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self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
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)
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def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
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model = AIMv2Model(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids, pixel_values, attention_mask)
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self.parent.assertEqual(
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result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
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)
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self.parent.assertEqual(
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result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
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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, input_ids, attention_mask, pixel_values = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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"return_loss": False,
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}
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return config, inputs_dict
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@require_torch
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class AIMv2ModelTest(AIMv2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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additional_model_inputs = ["pixel_values"]
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all_model_classes = (AIMv2Model,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": AIMv2Model, "image-feature-extraction": AIMv2VisionModel}
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if is_torch_available()
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else {}
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)
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fx_compatible = False
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = False
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_is_composite = True
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def setUp(self):
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self.model_tester = AIMv2ModelTester(self)
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common_properties = ["projection_dim", "logit_scale_init_value"]
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self.config_tester = ConfigTester(
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self, config_class=AIMv2Config, has_text_modality=False, common_properties=common_properties
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)
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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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print(config_and_inputs)
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self.model_tester.create_and_check_model(*config_and_inputs)
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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="Hidden_states is tested in individual model tests")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Retain_grad is tested in individual model tests")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="AIMv2Model does not have input/output embeddings")
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def test_model_get_set_embeddings(self):
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pass
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# Override as the `logit_scale` parameter initialization is different for AIMv2
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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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if param.requires_grad:
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# check if `logit_scale` is initialized as per the original implementation
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if name == "logit_scale":
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self.assertAlmostEqual(
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param.data.item(),
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np.log(1 / 0.07),
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delta=1e-3,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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else:
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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 test_load_vision_text_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# Save AIMv2Config and check if we can load AIMv2VisionConfig from it
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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vision_config = AIMv2VisionConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
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# Save AIMv2Config and check if we can load AIMv2TextConfig from it
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with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
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|
text_config = AIMv2TextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_inference_equivalence(self):
|
|
for model_class in self.all_model_classes:
|
|
if not model_class._supports_flash_attn_2:
|
|
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model_fa = model_class.from_pretrained(
|
|
tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
|
|
)
|
|
model_fa.to(torch_device)
|
|
|
|
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16)
|
|
model.to(torch_device)
|
|
|
|
dummy_pixel_values = inputs_dict["pixel_values"].to(torch.bfloat16)
|
|
dummy_input_ids = inputs_dict["input_ids"]
|
|
|
|
outputs = model(pixel_values=dummy_pixel_values, input_ids=dummy_input_ids, output_hidden_states=True)
|
|
outputs_fa = model_fa(
|
|
pixel_values=dummy_pixel_values, input_ids=dummy_input_ids, output_hidden_states=True
|
|
)
|
|
|
|
self.assertTrue(
|
|
torch.allclose(outputs.logits_per_image, outputs_fa.logits_per_image, atol=4e-2, rtol=4e-2),
|
|
f"Image logits max diff: {torch.max(torch.abs(outputs.logits_per_image - outputs_fa.logits_per_image))}",
|
|
)
|
|
self.assertTrue(
|
|
torch.allclose(outputs.logits_per_text, outputs_fa.logits_per_text, atol=4e-2, rtol=4e-2),
|
|
f"Text logits max diff: {torch.max(torch.abs(outputs.logits_per_text - outputs_fa.logits_per_text))}",
|
|
)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@mark.flash_attn_test
|
|
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
|
for model_class in self.all_model_classes:
|
|
if not model_class._supports_flash_attn_2:
|
|
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model_fa = model_class.from_pretrained(
|
|
tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
|
|
)
|
|
model_fa.to(torch_device)
|
|
|
|
model = model_class.from_pretrained(
|
|
tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="eager"
|
|
)
|
|
model.to(torch_device)
|
|
|
|
dummy_pixel_values = inputs_dict["pixel_values"].to(torch.bfloat16)
|
|
dummy_input_ids = inputs_dict["input_ids"]
|
|
dummy_pixel_mask = inputs_dict["attention_mask"]
|
|
|
|
# right padding
|
|
dummy_pixel_mask[:] = 1
|
|
dummy_pixel_mask[:, -1:] = 0
|
|
|
|
outputs = model(pixel_values=dummy_pixel_values, input_ids=dummy_input_ids, output_hidden_states=True)
|
|
outputs_fa = model_fa(
|
|
pixel_values=dummy_pixel_values, input_ids=dummy_input_ids, output_hidden_states=True
|
|
)
|
|
|
|
logits_per_image_eager = outputs.logits_per_image[:, :-1]
|
|
logits_per_text_eager = outputs.logits_per_text[:, :-1]
|
|
|
|
logits_per_image_sdpa = outputs_fa.logits_per_image[:, :-1]
|
|
logits_per_text_sdpa = outputs_fa.logits_per_text[:, :-1]
|
|
|
|
self.assertTrue(
|
|
torch.allclose(logits_per_image_eager, logits_per_image_sdpa, atol=4e-2, rtol=4e-2),
|
|
f"Image logits max diff: {torch.max(torch.abs(logits_per_image_eager - logits_per_image_sdpa))}",
|
|
)
|
|
self.assertTrue(
|
|
torch.allclose(logits_per_text_eager, logits_per_text_sdpa, atol=4e-2, rtol=4e-2),
|
|
f"Text logits max diff: {torch.max(torch.abs(logits_per_text_eager - logits_per_text_sdpa))}",
|
|
)
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class AIMv2ModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "yaswanthgali/aimv2-large-patch14-224-lit-HF"
|
|
model = AIMv2Model.from_pretrained(model_name, device_map="auto")
|
|
processor = AutoProcessor.from_pretrained(model_name)
|
|
|
|
image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
|
|
inputs = processor(
|
|
text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pt"
|
|
).to(model.device)
|
|
|
|
# Forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# Verify the logits
|
|
self.assertEqual(
|
|
outputs.logits_per_image.shape,
|
|
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
|
)
|
|
self.assertEqual(
|
|
outputs.logits_per_text.shape,
|
|
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
|
)
|
|
|
|
# handle device
|
|
expected_logits = torch.tensor([[34.2415, 24.6724]]).to(model.device)
|
|
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class AIMv2VisionModelIntegrationTests(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "yaswanthgali/aimv2-large-patch14-224-HF"
|
|
|
|
model = AIMv2VisionModel.from_pretrained(model_name, device_map="auto")
|
|
processor = AutoImageProcessor.from_pretrained(model_name)
|
|
|
|
image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
|
|
inputs = processor(image, return_tensors="pt").to(model.device)
|
|
|
|
with torch.no_grad():
|
|
output = model(**inputs)
|
|
|
|
# Verify logits shape
|
|
self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 256, 1024]))
|
|
|
|
# Verify logits slice
|
|
# fmt: off
|
|
expected_logits = torch.tensor(
|
|
[[ 0.0510, 0.0806, -0.0990, -0.0154],
|
|
[ 2.7850, -2.5143, -0.3320, 2.4196],
|
|
[ 2.8179, -2.4089, -0.2770, 2.3218],
|
|
[ 2.7641, -2.4114, -0.3684, 2.2998],
|
|
[ 2.7972, -2.3180, -0.4490, 2.2302],
|
|
[ 2.8584, -2.5322, -0.2302, 2.4936],
|
|
[-2.7849, 2.4121, 1.3670, -1.5514]]).to(model.device)
|
|
# fmt: on
|
|
|
|
output_slice = output.last_hidden_state.squeeze(0)[0:7, 0:4]
|
|
self.assertTrue(torch.allclose(output_slice, expected_logits, atol=1e-3))
|
|
|
|
@slow
|
|
def test_inference_for_native_resolution(self):
|
|
model_name = "yaswanthgali/aimv2-large-patch14-native-HF"
|
|
|
|
model = AIMv2VisionModel.from_pretrained(model_name, device_map="auto")
|
|
processor = AutoImageProcessor.from_pretrained(model_name)
|
|
|
|
image = image = Image.open(
|
|
requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
|
|
)
|
|
inputs = processor(image, return_tensors="pt").to(model.device)
|
|
|
|
with torch.no_grad():
|
|
output = model(**inputs)
|
|
|
|
# Verify logits shape
|
|
self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 1530, 1024]))
|
|
|
|
# Verify logits slice
|
|
# fmt: off
|
|
expected_logits = torch.tensor(
|
|
[[-1.3342, 0.3720, 0.0963, 0.4159],
|
|
[-1.5328, 0.4677, 0.0936, 0.4321],
|
|
[-0.3775, -0.2758, -0.0803, -0.5367],
|
|
[-1.3877, 0.5561, -1.9064, -1.1766],
|
|
[-0.5148, 0.0108, -0.4515, -0.6402],
|
|
[-0.3400, -0.1711, -0.1855, -0.4219],
|
|
[-1.2877, -0.0585, -0.1646, 0.7420]]).to(model.device)
|
|
# fmt: on
|
|
|
|
output_slice = output.last_hidden_state.squeeze(0)[0:7, 0:4]
|
|
self.assertTrue(torch.allclose(output_slice, expected_logits, atol=1e-3))
|