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780 lines
30 KiB
Python
780 lines
30 KiB
Python
# Copyright 2025 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 Siglip2 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 pytest import mark
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from transformers import Siglip2Config, Siglip2TextConfig, Siglip2VisionConfig
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from transformers.testing_utils import (
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Expectations,
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is_flaky,
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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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TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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require_torch_sdpa,
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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 Siglip2ForImageClassification, Siglip2Model, Siglip2TextModel, Siglip2VisionModel
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if is_vision_available():
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from PIL import Image, ImageDraw
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from transformers import Siglip2Processor
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class Siglip2ModelTesterMixin(ModelTesterMixin):
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@require_torch_sdpa
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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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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_flash_attn
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@require_torch_gpu
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@mark.flash_attn_test
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@slow
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def test_flash_attn_2_inference_equivalence(self):
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dtype = torch.float16
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for model_class in self.all_model_classes:
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if not model_class._supports_flash_attn_2:
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self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
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# Prepare inputs
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if "pixel_values" in inputs_dict:
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inputs_dict["pixel_values"] = inputs_dict["pixel_values"].to(dtype)
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# Separate masks
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attention_masks = {}
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if "attention_mask" in inputs_dict:
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# attention_masks["attention_mask"] = inputs_dict.pop("attention_mask")
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inputs_dict["attention_mask"] = None
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if "pixel_attention_mask" in inputs_dict:
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attention_masks["pixel_attention_mask"] = inputs_dict.pop("pixel_attention_mask")
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inputs_dict["pixel_attention_mask"] = None
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# Save and load model with flash attention 2 and eager attentions
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = model_class(config)
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model.save_pretrained(tmp_dir)
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model = model_class.from_pretrained(tmp_dir, torch_dtype=dtype)
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model_fa = model_class.from_pretrained(
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tmp_dir, torch_dtype=dtype, attn_implementation="flash_attention_2"
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)
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model_fa.to(torch_device)
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model.to(torch_device)
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# Run forward pass without attention masks
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with torch.no_grad():
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outputs = model(**inputs_dict, output_hidden_states=True)
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outputs_fa = model_fa(**inputs_dict, output_hidden_states=True)
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# Choose which key to compare
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key = [k for k in ["logits", "logits_per_image", "last_hidden_state"] if k in outputs][0]
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torch.testing.assert_close(outputs[key], outputs_fa[key], atol=4e-2, rtol=4e-2)
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# Run forward pass with attention masks
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inputs_dict.update(attention_masks)
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with torch.no_grad():
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outputs = model(**inputs_dict, output_hidden_states=True)
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outputs_fa = model_fa(**inputs_dict, output_hidden_states=True)
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output_tensor = outputs[key]
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output_tensor_fa = outputs_fa[key]
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# Mask out padded tokens, they are different for SDPA and Flash Attention 2
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if key == "last_hidden_state" and "pixel_attention_mask" in inputs_dict:
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output_tensor = output_tensor * inputs_dict["pixel_attention_mask"][..., None]
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output_tensor_fa = output_tensor_fa * inputs_dict["pixel_attention_mask"][..., None]
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elif key == "last_hidden_state" and inputs_dict.get("attention_mask", None) is not None:
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output_tensor = output_tensor * inputs_dict["attention_mask"][..., None]
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output_tensor_fa = output_tensor_fa * inputs_dict["attention_mask"][..., None]
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torch.testing.assert_close(output_tensor, output_tensor_fa, atol=4e-2, rtol=4e-2)
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# Check with inference + dropout
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model.train()
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_ = model_fa(**inputs_dict, output_hidden_states=True)
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@unittest.skip(reason="Siglip2 has default right padding (tested in test_flash_attn_2_inference_equivalence)")
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def test_flash_attn_2_inference_equivalence_right_padding(self):
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pass
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@unittest.skip(reason="SDPA can't dispatch on flash with not None `attention_mask`")
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def test_sdpa_can_dispatch_on_flash(self):
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pass
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class Siglip2VisionModelTester:
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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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num_patches=16,
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image_num_patches=24,
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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=64,
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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.num_patches = num_patches
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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.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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self.seq_length = image_num_patches
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor(
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[self.batch_size, self.seq_length, self.num_channels * self.patch_size * self.patch_size]
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)
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pixel_attention_mask = torch.zeros(self.batch_size, self.seq_length, device=torch_device, dtype=torch.long)
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spatial_shapes = [
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(height, width)
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for height in range(1, self.seq_length)
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for width in range(1, self.seq_length)
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if height * width <= self.seq_length
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] * self.batch_size
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spatial_shapes = spatial_shapes[: self.batch_size]
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spatial_shapes = torch.tensor(spatial_shapes, device=torch_device, dtype=torch.long)
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for i, (height, width) in enumerate(spatial_shapes):
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pixel_attention_mask[i, : height * width] = 1
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config = self.get_config()
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return config, pixel_values, pixel_attention_mask, spatial_shapes
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def get_config(self):
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return Siglip2VisionConfig(
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num_patches=self.num_patches,
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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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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, pixel_attention_mask, spatial_shapes):
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model = Siglip2VisionModel(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, pixel_attention_mask, spatial_shapes)
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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, pixel_values, pixel_attention_mask, spatial_shapes = self.prepare_config_and_inputs()
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inputs_dict = {
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"pixel_values": pixel_values,
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"pixel_attention_mask": pixel_attention_mask,
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"spatial_shapes": spatial_shapes,
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}
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return config, inputs_dict
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@require_torch
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class Siglip2VisionModelTest(Siglip2ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP2 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 = (Siglip2VisionModel,) if is_torch_available() else ()
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additional_model_inputs = ["pixel_attention_mask", "spatial_shapes"]
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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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# MP works but offload doesn't work when the MultiheadAttention is offloaded
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# TODO: One potential solution would be to add to set preload_module_classes = ["Siglip2MultiheadAttentionPoolingHead"]
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# in the dispatch_model function
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test_cpu_offload = False
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test_disk_offload_safetensors = False
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test_disk_offload_bin = False
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def setUp(self):
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self.model_tester = Siglip2VisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=Siglip2VisionConfig, 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="SIGLIP2 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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@unittest.skip(reason="Siglip2VisionModel does not support standalone training")
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def test_training(self):
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pass
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@unittest.skip(reason="Siglip2VisionModel does not support standalone training")
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="Siglip2VisionModel does not support standalone training")
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(reason="Siglip2VisionModel does not support standalone training")
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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="Siglip2 uses the same initialization scheme as the Flax original implementation")
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def test_initialization(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "google/siglip2-base-patch16-naflex"
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model = Siglip2VisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
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@require_torch_sdpa
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@is_flaky()
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def test_eager_matches_sdpa_inference(self, *args):
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# adding only flaky decorator here and call the parent test method
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return getattr(ModelTesterMixin, self._testMethodName)(self)
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class Siglip2TextModelTester:
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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=64,
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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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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.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.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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self.initializer_range = initializer_range
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self.scope = scope
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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 Siglip2TextConfig(
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vocab_size=self.vocab_size,
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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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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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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = Siglip2TextModel(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 Siglip2TextModelTest(Siglip2ModelTesterMixin, unittest.TestCase):
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all_model_classes = (Siglip2TextModel,) if is_torch_available() else ()
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fx_compatible = False
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test_resize_embeddings = False
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test_pruning = False
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test_head_masking = False
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model_split_percents = [0.5, 0.8, 0.9]
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def setUp(self):
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self.model_tester = Siglip2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Siglip2TextConfig, 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(reason="Siglip2TextModel does not support standalone training")
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def test_training(self):
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pass
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@unittest.skip(reason="Siglip2TextModel does not support standalone training")
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2TextModel does not support standalone training")
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2TextModel does not support standalone training")
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2 does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
|
|
def test_initialization(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "google/siglip2-base-patch16-naflex"
|
|
model = Siglip2TextModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class Siglip2ModelTester:
|
|
def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
|
|
if text_kwargs is None:
|
|
text_kwargs = {}
|
|
if vision_kwargs is None:
|
|
vision_kwargs = {}
|
|
|
|
self.parent = parent
|
|
self.text_model_tester = Siglip2TextModelTester(parent, **text_kwargs)
|
|
self.vision_model_tester = Siglip2VisionModelTester(parent, **vision_kwargs)
|
|
self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
|
|
self.is_training = is_training
|
|
|
|
def prepare_config_and_inputs(self):
|
|
text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
|
|
vision_config, pixel_values, pixel_attention_mask, spatial_shapes = (
|
|
self.vision_model_tester.prepare_config_and_inputs()
|
|
)
|
|
|
|
config = self.get_config()
|
|
|
|
return config, input_ids, attention_mask, pixel_values, pixel_attention_mask, spatial_shapes
|
|
|
|
def get_config(self):
|
|
return Siglip2Config.from_text_vision_configs(
|
|
self.text_model_tester.get_config(),
|
|
self.vision_model_tester.get_config(),
|
|
)
|
|
|
|
def create_and_check_model(
|
|
self, config, input_ids, attention_mask, pixel_values, pixel_attention_mask, spatial_shapes
|
|
):
|
|
model = Siglip2Model(config).to(torch_device).eval()
|
|
with torch.no_grad():
|
|
result = model(input_ids, pixel_values, pixel_attention_mask, spatial_shapes, attention_mask)
|
|
self.parent.assertEqual(
|
|
result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
|
|
)
|
|
self.parent.assertEqual(
|
|
result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
|
|
)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, input_ids, attention_mask, pixel_values, pixel_attention_mask, spatial_shapes = config_and_inputs
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"pixel_values": pixel_values,
|
|
"pixel_attention_mask": pixel_attention_mask,
|
|
"spatial_shapes": spatial_shapes,
|
|
"attention_mask": attention_mask,
|
|
"position_ids": None,
|
|
"return_loss": False,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class Siglip2ModelTest(Siglip2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (Siglip2Model,) if is_torch_available() else ()
|
|
pipeline_model_mapping = {"feature-extraction": Siglip2Model} if is_torch_available() else {}
|
|
additional_model_inputs = [
|
|
"pixel_values",
|
|
"pixel_attention_mask",
|
|
"spatial_shapes",
|
|
]
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
# MP works but offload doesn't work when the MultiheadAttention is offloaded
|
|
# TODO: One potential solution would be to add to set preload_module_classes = ["Siglip2MultiheadAttentionPoolingHead"]
|
|
# in the dispatch_model function
|
|
test_cpu_offload = False
|
|
test_disk_offload_safetensors = False
|
|
test_disk_offload_bin = False
|
|
_is_composite = True
|
|
|
|
def setUp(self):
|
|
self.model_tester = Siglip2ModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=Siglip2Config, has_text_modality=False)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
@unittest.skip(reason="Hidden_states is tested in individual model tests")
|
|
def test_hidden_states_output(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2Model does not have input/output embeddings")
|
|
def test_model_get_set_embeddings(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
|
|
def test_initialization(self):
|
|
pass
|
|
|
|
def test_load_vision_text_config(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save Siglip2Config and check if we can load Siglip2VisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = Siglip2VisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save Siglip2Config and check if we can load Siglip2TextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = Siglip2TextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "google/siglip2-base-patch16-naflex"
|
|
model = Siglip2Model.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@mark.flash_attn_test
|
|
def test_flash_attn_2_inference_equivalence_right_padding(self):
|
|
self.skipTest("Siglip2 does not support right padding")
|
|
|
|
|
|
class Siglip2ForImageClassificationModelTester(Siglip2ModelTester):
|
|
def __init__(self, parent):
|
|
super().__init__(parent)
|
|
self.batch_size = self.vision_model_tester.batch_size
|
|
self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
|
|
self.hidden_size = self.vision_model_tester.hidden_size
|
|
self.seq_length = self.vision_model_tester.seq_length
|
|
|
|
def prepare_config_and_inputs(self):
|
|
_, pixel_values, pixel_attention_mask, spatial_shapes = self.vision_model_tester.prepare_config_and_inputs()
|
|
config = self.get_config()
|
|
|
|
return config, pixel_values, pixel_attention_mask, spatial_shapes
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, pixel_values, pixel_attention_mask, spatial_shapes = config_and_inputs
|
|
inputs_dict = {
|
|
"pixel_values": pixel_values,
|
|
"pixel_attention_mask": pixel_attention_mask,
|
|
"spatial_shapes": spatial_shapes,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class Siglip2ForImageClassificationModelTest(Siglip2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (Siglip2ForImageClassification,) if is_torch_available() else ()
|
|
pipeline_model_mapping = {"image-classification": Siglip2ForImageClassification} if is_torch_available() else {}
|
|
additional_model_inputs = ["pixel_values", "pixel_attention_mask", "spatial_shapes"]
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
# MP works but offload doesn't work when the MultiheadAttention is offloaded
|
|
# TODO: One potential solution would be to add to set preload_module_classes = ["Siglip2MultiheadAttentionPoolingHead"]
|
|
# in the dispatch_model function
|
|
test_cpu_offload = False
|
|
test_disk_offload_safetensors = False
|
|
test_disk_offload_bin = False
|
|
_is_composite = True
|
|
|
|
def setUp(self):
|
|
self.model_tester = Siglip2ForImageClassificationModelTester(self)
|
|
|
|
@unittest.skip(reason="Siglip2ForImageClassification does not support inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2ForImageClassification does not support inputs_embeds")
|
|
def test_model_get_set_embeddings(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet")
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet")
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet")
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
|
|
def test_initialization(self):
|
|
pass
|
|
|
|
|
|
# Draw a circle on an images with different aspect ratios
|
|
def prepare_images():
|
|
shapes = [(224, 224), (1024, 1024), (224, 1024)]
|
|
images = []
|
|
for height, width in shapes:
|
|
image = Image.new("RGB", (width, height), color="red")
|
|
draw = ImageDraw.Draw(image)
|
|
center_x = image.width // 2
|
|
center_y = image.height // 2
|
|
radius = min(center_x, center_y) // 8 * 7
|
|
draw.ellipse(
|
|
(center_x - radius, center_y - radius, center_x + radius, center_y + radius),
|
|
fill="blue",
|
|
outline="green",
|
|
width=image.width // 20,
|
|
)
|
|
images.append(image)
|
|
return images
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class Siglip2ModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "google/siglip2-base-patch16-naflex"
|
|
model = Siglip2Model.from_pretrained(model_name).to(torch_device)
|
|
processor = Siglip2Processor.from_pretrained(model_name)
|
|
|
|
images = prepare_images()
|
|
text = [
|
|
"circle",
|
|
"ellipsoid",
|
|
"blue circle on red background",
|
|
"blue circle with green border on red background",
|
|
"green circle on red background",
|
|
"a dog",
|
|
"a blue dog with a green border on a red background",
|
|
]
|
|
|
|
inputs = processor(text=text, images=images, return_tensors="pt")
|
|
inputs = inputs.to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
logits_per_image = outputs.logits_per_image
|
|
logits_per_text = outputs.logits_per_text
|
|
|
|
# verify the logits shape
|
|
self.assertEqual(
|
|
logits_per_image.shape,
|
|
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
|
)
|
|
self.assertEqual(
|
|
logits_per_text.shape,
|
|
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
|
)
|
|
|
|
# verify the logits values
|
|
# fmt: off
|
|
expected_logits_per_texts = Expectations({
|
|
("cuda", None): [
|
|
[ 1.0195, -0.0280, -1.4468], [ -4.5395, -6.2269, -1.5667], [ 4.1757, 5.0358, 3.5159],
|
|
[ 9.4264, 10.1879, 6.3353], [ 2.4409, 3.1058, 4.5491], [-12.3230, -13.7355, -13.4632],
|
|
[ 1.1520, 1.1687, -1.9647],
|
|
],
|
|
("rocm", (9, 5)): [
|
|
[ 1.0236, -0.0376, -1.4464], [ -4.5358, -6.2235, -1.5628], [ 4.1708, 5.0334, 3.5187],
|
|
[ 9.4241, 10.1828, 6.3366], [ 2.4371, 3.1062, 4.5530], [-12.3173, -13.7240, -13.4580],
|
|
[ 1.1502, 1.1716, -1.9623]
|
|
],
|
|
})
|
|
EXPECTED_LOGITS_PER_TEXT = torch.tensor(expected_logits_per_texts.get_expectation()).to(torch_device)
|
|
# fmt: on
|
|
|
|
torch.testing.assert_close(outputs.logits_per_text, EXPECTED_LOGITS_PER_TEXT, rtol=1e-3, atol=1e-3)
|