mirror of
https://github.com/huggingface/transformers.git
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f8b88866f5
* VLMs can work with embeds now * update more models * fix tests * fix copies * fixup * fix * style * unskip tests * fix copies * fix tests * style * omni modality models * qwen models had extra indentation * fix some other tests * fix copies * fix test last time * unrelated changes revert * we can't rely only on embeds * delete file * de-flake mistral3 * fix qwen models * fix style * fix tests * fix copies * deflake the test * modular reverted by fixes, fix again * flaky test, overwritten * fix copies * style
310 lines
11 KiB
Python
310 lines
11 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch ColPali model."""
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import gc
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import unittest
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from typing import ClassVar
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import torch
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from datasets import load_dataset
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from tests.test_configuration_common import ConfigTester
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from tests.test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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from transformers import (
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is_torch_available,
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)
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from transformers.models.colpali.configuration_colpali import ColPaliConfig
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from transformers.models.colpali.modeling_colpali import ColPaliForRetrieval, ColPaliForRetrievalOutput
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from transformers.models.colpali.processing_colpali import ColPaliProcessor
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from transformers.testing_utils import (
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backend_empty_cache,
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require_torch,
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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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if is_torch_available():
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import torch
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class ColPaliForRetrievalModelTester:
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def __init__(
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self,
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parent,
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ignore_index=-100,
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image_token_index=0,
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projector_hidden_act="gelu",
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seq_length=25,
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vision_feature_select_strategy="default",
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vision_feature_layer=-1,
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projection_dim=32,
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text_config={
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"model_type": "gemma",
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"seq_length": 128,
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"is_training": True,
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"use_token_type_ids": False,
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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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"num_hidden_layers": 2,
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"num_attention_heads": 4,
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"num_key_value_heads": 1,
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"head_dim": 8,
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"intermediate_size": 37,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_dropout_prob": 0.1,
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"attention_probs_dropout_prob": 0.1,
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"max_position_embeddings": 512,
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"type_vocab_size": 16,
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"type_sequence_label_size": 2,
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"initializer_range": 0.02,
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"num_labels": 3,
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"num_choices": 4,
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"pad_token_id": 1,
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},
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is_training=False,
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vision_config={
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"use_labels": True,
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"image_size": 20,
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"patch_size": 5,
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"num_image_tokens": 4,
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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_key_value_heads": 1,
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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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},
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use_cache=False,
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embedding_dim=128,
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):
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self.parent = parent
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self.ignore_index = ignore_index
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# `image_token_index` is set to 0 to pass "resize_embeddings" test, do not modify
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self.image_token_index = image_token_index
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self.projector_hidden_act = projector_hidden_act
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self.vision_feature_select_strategy = vision_feature_select_strategy
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self.vision_feature_layer = vision_feature_layer
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self.text_config = text_config
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self.vision_config = vision_config
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self.seq_length = seq_length
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self.projection_dim = projection_dim
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self.pad_token_id = text_config["pad_token_id"]
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self.num_hidden_layers = text_config["num_hidden_layers"]
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self.vocab_size = text_config["vocab_size"]
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self.hidden_size = text_config["hidden_size"]
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self.num_attention_heads = text_config["num_attention_heads"]
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self.is_training = is_training
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self.batch_size = 3
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self.num_channels = vision_config["num_channels"]
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self.image_size = vision_config["image_size"]
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self.encoder_seq_length = seq_length
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self.use_cache = use_cache
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self.embedding_dim = embedding_dim
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self.vlm_config = {
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"model_type": "paligemma",
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"text_config": self.text_config,
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"vision_config": self.vision_config,
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"ignore_index": self.ignore_index,
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"image_token_index": self.image_token_index,
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"projector_hidden_act": self.projector_hidden_act,
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"projection_dim": self.projection_dim,
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"vision_feature_select_strategy": self.vision_feature_select_strategy,
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"vision_feature_layer": self.vision_feature_layer,
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}
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def get_config(self):
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return ColPaliConfig(
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vlm_config=self.vlm_config,
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embedding_dim=self.embedding_dim,
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)
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor(
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[
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self.batch_size,
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self.vision_config["num_channels"],
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self.vision_config["image_size"],
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self.vision_config["image_size"],
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]
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)
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config = self.get_config()
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return config, pixel_values
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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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input_ids = ids_tensor([self.batch_size, self.seq_length], config.vlm_config.text_config.vocab_size - 1) + 1
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attention_mask = input_ids.ne(1).to(torch_device)
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# set the 16 first tokens to be image, and ensure that no other tokens are image tokens
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# do not change this unless you modified image size or patch size
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input_ids[input_ids == config.vlm_config.image_token_index] = self.pad_token_id
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input_ids[:, :16] = config.vlm_config.image_token_index
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inputs_dict = {
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"pixel_values": pixel_values,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels": input_ids,
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}
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return config, inputs_dict
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@require_torch
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class ColPaliForRetrievalModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Model tester for `ColPaliForRetrieval`.
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"""
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all_model_classes = (ColPaliForRetrieval,) if is_torch_available() else ()
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fx_compatible = False
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test_torchscript = False
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test_pruning = False
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test_resize_embeddings = True
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test_head_masking = False
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def setUp(self):
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self.model_tester = ColPaliForRetrievalModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ColPaliConfig, has_text_modality=False)
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@slow
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@require_vision
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def test_colpali_forward_inputs(self):
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config, inputs_dict = 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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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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with torch.no_grad():
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outputs = model(**inputs, return_dict=True)
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self.assertIsInstance(outputs, ColPaliForRetrievalOutput)
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@unittest.skip(
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reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(
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reason="From PaliGemma: Some undefined behavior encountered with test versions of this model. Skip for now."
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)
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def test_model_parallelism(self):
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pass
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@unittest.skip(
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reason="PaliGemma's SigLip encoder uses the same initialization scheme as the Flax original implementation"
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)
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def test_initialization(self):
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pass
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# TODO extend valid outputs to include this test @Molbap
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@unittest.skip(reason="PaliGemma has currently one output format.")
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def test_model_outputs_equivalence(self):
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pass
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@unittest.skip(reason="Pass because ColPali requires `attention_mask is not None`")
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def test_sdpa_can_dispatch_on_flash(self):
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pass
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@unittest.skip(reason="Pass because ColPali requires `attention_mask is not None`")
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def test_sdpa_can_compile_dynamic(self):
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pass
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@require_torch
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class ColPaliModelIntegrationTest(unittest.TestCase):
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model_name: ClassVar[str] = "vidore/colpali-v1.2-hf"
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def setUp(self):
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self.processor = ColPaliProcessor.from_pretrained(self.model_name)
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def tearDown(self):
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gc.collect()
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backend_empty_cache(torch_device)
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@slow
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def test_model_integration_test(self):
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"""
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Test if the model is able to retrieve the correct pages for a small and easy dataset.
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"""
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model = ColPaliForRetrieval.from_pretrained(
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self.model_name,
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torch_dtype=torch.bfloat16,
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device_map=torch_device,
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).eval()
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# Load the test dataset
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ds = load_dataset("hf-internal-testing/document-visual-retrieval-test", split="test")
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# Preprocess the examples
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batch_images = self.processor(images=ds["image"]).to(torch_device)
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batch_queries = self.processor(text=ds["query"]).to(torch_device)
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# Run inference
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with torch.inference_mode():
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image_embeddings = model(**batch_images).embeddings
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query_embeddings = model(**batch_queries).embeddings
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# Compute retrieval scores
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scores = self.processor.score_retrieval(
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query_embeddings=query_embeddings,
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passage_embeddings=image_embeddings,
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) # (num_queries, num_passages)
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assert scores.ndim == 2, f"Expected 2D tensor, got {scores.ndim}"
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assert scores.shape == (len(ds), len(ds)), f"Expected shape {(len(ds), len(ds))}, got {scores.shape}"
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# Check if the maximum scores per row are in the diagonal of the matrix score
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self.assertTrue((scores.argmax(axis=1) == torch.arange(len(ds), device=scores.device)).all())
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# Further validation: fine-grained check, with a hardcoded score from the original implementation
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expected_scores = torch.tensor(
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[
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[15.5625, 6.5938, 14.4375],
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[12.2500, 16.2500, 11.0000],
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[15.0625, 11.7500, 21.0000],
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],
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dtype=scores.dtype,
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)
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assert torch.allclose(scores, expected_scores, atol=1), f"Expected scores {expected_scores}, got {scores}"
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