533 lines
24 KiB
Python
533 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Idefics2 model."""
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import copy
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import gc
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import unittest
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from io import BytesIO
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import requests
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from transformers import (
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AutoProcessor,
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Idefics2Config,
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Idefics2ForConditionalGeneration,
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Idefics2Model,
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is_torch_available,
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is_vision_available,
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)
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from transformers.testing_utils import require_bitsandbytes, require_torch, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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else:
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is_torch_greater_or_equal_than_2_0 = False
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if is_vision_available():
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from PIL import Image
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class Idefics2VisionText2TextModelTester:
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def __init__(
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self,
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parent,
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is_training=True,
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batch_size=2,
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num_images=2,
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seq_length=10,
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vision_config={
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"image_size": 12,
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"patch_size": 12,
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"num_channels": 3,
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"hidden_size": 32,
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"num_hidden_layers": 2,
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"num_attention_heads": 4,
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"intermediate_size": 32,
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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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perceiver_config={
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"hidden_act": "silu",
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"resampler_n_latents": 2,
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"resampler_depth": 2,
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"resampler_n_heads": 2,
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"num_key_value_heads": 1,
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"resampler_head_dim": 12,
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"attention_dropout": 0.0,
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},
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text_config={
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"vocab_size": 100,
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"hidden_size": 64,
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"intermediate_size": 56,
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"num_hidden_layers": 3,
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"num_attention_heads": 2,
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"num_key_value_heads": 2,
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"hidden_act": "silu",
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"max_position_embeddings": 256,
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"initializer_range": 0.02,
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"rms_norm_eps": 1e-6,
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"pad_token_id": 0, # None in the original configuration_mistral, we set it to the unk_token_id
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"bos_token_id": 1,
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"eos_token_id": 2,
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"image_token_id": 32_001,
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"tie_word_embeddings": False,
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"rope_theta": 10000.0,
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"sliding_window": 32,
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"attention_dropout": 0.0,
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},
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use_cache=False,
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tie_word_embeddings=False,
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image_token_id=99,
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):
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self.parent = parent
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self.is_training = is_training
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self.batch_size = batch_size
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self.num_images = num_images
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self.num_channels = 3
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self.seq_length = seq_length
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self.use_cache = use_cache
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self.image_token_id = image_token_id
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self.tie_word_embeddings = tie_word_embeddings
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# Hack - add properties here so use common tests
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self.vocab_size = text_config["vocab_size"]
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self.num_hidden_layers = text_config["num_hidden_layers"]
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self.num_attention_heads = text_config["num_attention_heads"]
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self.hidden_size = text_config["hidden_size"]
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self.vision_config = vision_config
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self.perceiver_config = perceiver_config
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self.text_config = text_config
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def get_config(self):
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return Idefics2Config(
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use_cache=self.use_cache,
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image_token_id=self.image_token_id,
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tie_word_embeddings=self.tie_word_embeddings,
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vision_config=self.vision_config,
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perceiver_config=self.perceiver_config,
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text_config=self.text_config,
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vocab_size=self.vocab_size,
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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.num_images,
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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.text_config.vocab_size - 2) + 1
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# For simplicity just set the last n tokens to the image token
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n_image_tokens_per_batch = self.num_images * self.perceiver_config["resampler_n_latents"]
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input_ids[:, -n_image_tokens_per_batch:] = self.image_token_id
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attention_mask = input_ids.ne(1).to(torch_device)
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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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}
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return config, inputs_dict
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@require_torch
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class Idefics2ModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Model tester for `Idefics2`.
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"""
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all_model_classes = (Idefics2Model,) 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 = Idefics2VisionText2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Idefics2Config, has_text_modality=False)
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@unittest.skip("input_embeds cannot be passed in without input_ids")
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def test_inputs_embeds():
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pass
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@unittest.skip("input_embeds cannot be passed in without input_ids")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip("Model does not support padding right")
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def test_flash_attn_2_generate_padding_right(self):
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pass
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@unittest.skip("Model does not support padding right")
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def test_flash_attn_2_inference_padding_right(self):
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pass
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# We need to override as we need to prepare such that the image token is the last token
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def test_resize_tokens_embeddings(self):
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(original_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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config = copy.deepcopy(original_config)
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model = model_class(config)
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model.to(torch_device)
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if self.model_tester.is_training is False:
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model.eval()
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model_vocab_size = config.text_config.vocab_size
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# Retrieve the embeddings and clone theme
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model_embed = model.resize_token_embeddings(model_vocab_size)
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cloned_embeddings = model_embed.weight.clone()
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# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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model_embed = model.resize_token_embeddings(model_vocab_size + 10)
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self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
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# Check that it actually resizes the embeddings matrix
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self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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model_embed = model.resize_token_embeddings(model_vocab_size - 15)
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self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
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# Check that it actually resizes the embeddings matrix
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self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
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# Ignore copy
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token
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inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2)
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n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"]
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model.image_token_id = model_vocab_size - 15 - 1
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inputs_dict["input_ids"][:, -n_images:] = model.image_token_id
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# make sure that decoder_input_ids are resized as well
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if "decoder_input_ids" in inputs_dict:
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inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that adding and removing tokens has not modified the first part of the embedding matrix.
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models_equal = True
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for p1, p2 in zip(cloned_embeddings, model_embed.weight):
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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config = copy.deepcopy(original_config)
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model = model_class(config)
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model.to(torch_device)
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model_vocab_size = config.text_config.vocab_size
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model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
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self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size)
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model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
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self.assertTrue(model_embed.weight.shape[0] // 64, 0)
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self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size)
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self.assertTrue(model.config.text_config.vocab_size, model.vocab_size)
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model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64)
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self.assertTrue(model_embed.weight.shape[0] // 64, 0)
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# Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size
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target_dimension = 128
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model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64)
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self.assertTrue(model_embed.weight.shape[0], target_dimension)
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with self.assertRaisesRegex(
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ValueError,
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"Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer",
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):
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model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
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# We need to override as we need to prepare such that the image token is the last token
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def test_resize_embeddings_untied(self):
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(original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
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original_config.tie_word_embeddings = False
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for model_class in self.all_model_classes:
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config = copy.deepcopy(original_config)
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model = model_class(config).to(torch_device)
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# if no output embeddings -> leave test
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if model.get_output_embeddings() is None:
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continue
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# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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model_vocab_size = config.text_config.vocab_size
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model.resize_token_embeddings(model_vocab_size + 10)
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self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
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output_embeds = model.get_output_embeddings()
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self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
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# Check bias if present
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if output_embeds.bias is not None:
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self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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model.resize_token_embeddings(model_vocab_size - 15)
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self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
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# Check that it actually resizes the embeddings matrix
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output_embeds = model.get_output_embeddings()
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self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
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# Check bias if present
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if output_embeds.bias is not None:
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self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token
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inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2)
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n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"]
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model.image_token_id = model_vocab_size - 15 - 1
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inputs_dict["input_ids"][:, -n_images:] = model.image_token_id
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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@require_torch
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class Idefics2ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTesterMixin, unittest.TestCase):
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"""
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Model tester for `Idefics2ForConditionalGeneration`.
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"""
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all_model_classes = (Idefics2ForConditionalGeneration,) 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 = True
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test_head_masking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = Idefics2VisionText2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Idefics2Config, has_text_modality=False)
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@unittest.skip("input_embeds cannot be passed in without input_ids")
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def test_inputs_embeds():
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pass
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@unittest.skip("Model does not support padding right")
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def test_flash_attn_2_generate_padding_right(self):
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pass
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@unittest.skip("Model does not support padding right")
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def test_flash_attn_2_inference_padding_right(self):
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pass
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# We need to override as we need to prepare such that the image token is the last token
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def test_resize_tokens_embeddings(self):
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(original_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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config = copy.deepcopy(original_config)
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model = model_class(config)
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model.to(torch_device)
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model_vocab_size = config.text_config.vocab_size
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# Retrieve the embeddings and clone theme
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model_embed = model.resize_token_embeddings(model_vocab_size)
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cloned_embeddings = model_embed.weight.clone()
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# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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model_embed = model.resize_token_embeddings(model_vocab_size + 10)
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self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
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# Check that it actually resizes the embeddings matrix
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self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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model_embed = model.resize_token_embeddings(model_vocab_size - 15)
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self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
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# Check that it actually resizes the embeddings matrix
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self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token
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inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2)
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n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"]
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model.model.image_token_id = model_vocab_size - 15 - 1
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inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id
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model(**self._prepare_for_class(inputs_dict, model_class))
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# Check that adding and removing tokens has not modified the first part of the embedding matrix.
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models_equal = True
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for p1, p2 in zip(cloned_embeddings, model_embed.weight):
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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config = copy.deepcopy(original_config)
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model = model_class(config)
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model.to(torch_device)
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model_vocab_size = config.text_config.vocab_size
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model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
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self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size)
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model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
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self.assertTrue(model_embed.weight.shape[0] // 64, 0)
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self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size)
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self.assertTrue(model.config.text_config.vocab_size, model.vocab_size)
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model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64)
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self.assertTrue(model_embed.weight.shape[0] // 64, 0)
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# Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size
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target_dimension = 128
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model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64)
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self.assertTrue(model_embed.weight.shape[0], target_dimension)
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with self.assertRaisesRegex(
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ValueError,
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"Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer",
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):
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model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
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# We need to override as we need to prepare such that the image token is the last token
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def test_resize_embeddings_untied(self):
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(original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
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original_config.tie_word_embeddings = False
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for model_class in self.all_model_classes:
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config = copy.deepcopy(original_config)
|
|
model = model_class(config).to(torch_device)
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_vocab_size = config.text_config.vocab_size
|
|
model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
|
|
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
# Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token
|
|
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2)
|
|
n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"]
|
|
model.model.image_token_id = model_vocab_size - 15 - 1
|
|
inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id
|
|
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
|
|
@require_torch
|
|
class Idefics2ForConditionalGenerationIntegrationTest(unittest.TestCase):
|
|
def setUp(self):
|
|
self.processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b-base")
|
|
self.image1 = Image.open(
|
|
BytesIO(
|
|
requests.get(
|
|
"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
|
|
).content
|
|
)
|
|
)
|
|
self.image2 = Image.open(
|
|
BytesIO(requests.get("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg").content)
|
|
)
|
|
self.image3 = Image.open(
|
|
BytesIO(
|
|
requests.get(
|
|
"https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"
|
|
).content
|
|
)
|
|
)
|
|
|
|
def tearDown(self):
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
@slow
|
|
def test_integration_test(self):
|
|
model = Idefics2ForConditionalGeneration.from_pretrained(
|
|
"HuggingFaceM4/idefics2-8b-base",
|
|
torch_dtype=torch.bfloat16,
|
|
device_map="auto",
|
|
)
|
|
model.to(torch_device)
|
|
|
|
# Create inputs
|
|
text = "<image>In this image, we see"
|
|
images = self.image1
|
|
inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True)
|
|
inputs.to(torch_device)
|
|
|
|
generated_ids = model.generate(**inputs, max_new_tokens=10)
|
|
generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
# Batch affects generated text. Single batch output: ['In this image, we see the Statue of Liberty in the foreground and']
|
|
expected_generated_text = "In this image, we see the Statue of Liberty, the New York City"
|
|
self.assertEqual(generated_texts[0], expected_generated_text)
|
|
|
|
@slow
|
|
@require_bitsandbytes
|
|
def test_integration_test_4bit(self):
|
|
# Let' s make sure we test the preprocessing to replace what is used
|
|
model = Idefics2ForConditionalGeneration.from_pretrained(
|
|
"HuggingFaceM4/idefics2-8b-base", load_in_4bit=True, device_map="auto"
|
|
)
|
|
|
|
# Create pixel inputs
|
|
text = ["<image>In this image, we see", "bla, bla <image><image>"]
|
|
images = [[self.image1], [self.image2, self.image3]]
|
|
inputs = self.processor(text=text, images=images, padding=True, return_tensors="pt")
|
|
|
|
generated_ids = model.generate(**inputs, max_new_tokens=10)
|
|
generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
expected_generated_text = "In this image, we see the Statue of Liberty, the Hudson River,"
|
|
self.assertEqual(generated_texts[0], expected_generated_text)
|