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62c7ea0201
transformers/tests/models/idefics3/test_modeling_idefics3.py
Joao Gante 62c7ea0201
CI: avoid human error, automatically infer generative models (#33212) 
* tmp commit

* move tests to the right class

* remove ALL all_generative_model_classes = ...

* skip tf roberta

* skip InstructBlipForConditionalGenerationDecoderOnlyTest

* videollava

* reduce diff

* reduce diff

* remove  on vlms

* fix a few more

* manual rebase bits

* more manual rebase

* remove all manual generative model class test entries

* fix up to ernie

* a few more removals

* handle remaining cases

* recurrent gemma

* it's better here

* make fixup

* tf idefics is broken

* tf bert + generate is broken

* don't touch tf :()

* don't touch tf :(

* make fixup

* better comments for test skips

* revert tf changes

* remove empty line removal

* one more

* missing one
2025-02-13 16:27:11 +01:00

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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Idefics3 model."""
import copy
import unittest
from io import BytesIO
import pytest
import requests
from transformers import (
AutoProcessor,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import (
cleanup,
require_bitsandbytes,
require_torch,
require_torch_sdpa,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
import torch
from transformers import (
Idefics3Config,
Idefics3ForConditionalGeneration,
Idefics3Model,
)
if is_vision_available():
from PIL import Image
class Idefics3VisionText2TextModelTester:
def __init__(
self,
parent,
is_training=True,
batch_size=2,
scale_factor=2,
num_images=2,
vision_config={
"image_size": 16,
"patch_size": 4,
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 32,
"dropout": 0.1,
"attention_dropout": 0.1,
"initializer_range": 0.02,
},
text_config={
"vocab_size": 100,
"hidden_size": 64,
"intermediate_size": 56,
"num_hidden_layers": 3,
"num_attention_heads": 2,
"num_key_value_heads": 2,
"hidden_act": "silu",
"max_position_embeddings": 256,
"initializer_range": 0.02,
"rms_norm_eps": 1e-6,
"pad_token_id": 2,
"bos_token_id": 0,
"eos_token_id": 1,
"image_token_id": 57,
"tie_word_embeddings": False,
"rope_theta": 10000.0,
"sliding_window": 32,
"attention_dropout": 0.0,
},
use_cache=False,
tie_word_embeddings=False,
image_token_id=57,
):
self.parent = parent
self.is_training = is_training
self.batch_size = batch_size
self.num_images = num_images
self.scale_factor = scale_factor
self.seq_length = (
int(((vision_config["image_size"] // vision_config["patch_size"]) ** 2) / (self.scale_factor**2))
* self.num_images
)
self.use_cache = use_cache
self.image_token_id = image_token_id
self.tie_word_embeddings = tie_word_embeddings
# Hack - add properties here so use common tests
self.vocab_size = text_config["vocab_size"]
self.num_hidden_layers = text_config["num_hidden_layers"]
self.num_attention_heads = text_config["num_attention_heads"]
self.hidden_size = text_config["hidden_size"]
self.vision_config = vision_config
self.text_config = text_config
def get_config(self):
return Idefics3Config(
use_cache=self.use_cache,
image_token_id=self.image_token_id,
tie_word_embeddings=self.tie_word_embeddings,
vision_config=self.vision_config,
text_config=self.text_config,
vocab_size=self.vocab_size,
scale_factor=self.scale_factor,
)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor(
[
self.batch_size,
self.num_images,
3, # Idefics3ImageProcessor always generates RGB pixel values
self.vision_config["image_size"],
self.vision_config["image_size"],
]
)
config = self.get_config()
return config, pixel_values
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 1
# For simplicity just set the last n tokens to the image token
n_image_tokens_per_batch = self.seq_length
input_ids[:, -n_image_tokens_per_batch:] = self.image_token_id
attention_mask = input_ids.ne(1).to(torch_device)
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class Idefics3ModelTest(ModelTesterMixin, unittest.TestCase):
"""
Model tester for `Idefics3`.
"""
all_model_classes = (Idefics3Model,) if is_torch_available() else ()
fx_compatible = False
test_torchscript = False
test_pruning = False
test_resize_embeddings = True
test_head_masking = False
def setUp(self):
self.model_tester = Idefics3VisionText2TextModelTester(self)
self.config_tester = ConfigTester(
self, config_class=Idefics3Config, has_text_modality=False, common_properties=["image_token_id"]
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="input_embeds cannot be passed in without input_ids")
def test_inputs_embeds():
pass
@unittest.skip(reason="input_embeds cannot be passed in without input_ids")
def test_inputs_embeds_matches_input_ids(self):
pass
@unittest.skip(reason="Model does not support padding right")
def test_flash_attn_2_inference_padding_right(self):
pass
# We need to override as we need to prepare such that the image token is the last token
def test_resize_tokens_embeddings(self):
(original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.text_config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 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_embed = 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
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Ignore copy
# 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.seq_length
model.image_token_id = model_vocab_size - 15 - 1
inputs_dict["input_ids"][:, -n_images:] = model.image_token_id
# make sure that decoder_input_ids are resized as well
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
model_vocab_size = config.text_config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size)
model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0] // 64, 0)
self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size)
self.assertTrue(model.config.text_config.vocab_size, model.vocab_size)
model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0] // 64, 0)
# Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size
target_dimension = 128
model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0], target_dimension)
with self.assertRaisesRegex(
ValueError,
"Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer",
):
model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
# We need to override as we need to prepare such that the image token is the last token
def test_resize_embeddings_untied(self):
(original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
original_config.tie_word_embeddings = False
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# 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.seq_length
model.image_token_id = model_vocab_size - 15 - 1
inputs_dict["input_ids"][:, -n_images:] = 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 Idefics3ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTesterMixin, unittest.TestCase):
"""
Model tester for `Idefics3ForConditionalGeneration`.
"""
all_model_classes = (Idefics3ForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = {"image-text-to-text": Idefics3ForConditionalGeneration} if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_resize_embeddings = True
test_head_masking = False
test_torchscript = False
def setUp(self):
self.model_tester = Idefics3VisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Idefics3Config, has_text_modality=False)
@unittest.skip(reason="input_embeds cannot be passed in without input_ids")
def test_inputs_embeds():
pass
@unittest.skip(reason="Model does not support padding right")
def test_flash_attn_2_inference_padding_right(self):
pass
@unittest.skip(reason="Contrastive search is not implemented for VLMs that do cross-attn")
def test_contrastive_generate(self):
pass
@unittest.skip(reason="Contrastive search is not implemented for VLMs that do cross-attn")
def test_contrastive_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="Contrastive search is not implemented for VLMs that do cross-attn")
def test_contrastive_generate_low_memory(self):
pass
@unittest.skip(
reason="Prompt lookup decoding needs a way to indicate `bad_word_ids` that should not be suggested as candidates"
)
def test_prompt_lookup_decoding_matches_greedy_search(self):
pass
@unittest.skip(reason=" FlashAttention only support fp16 and bf16 data type")
def test_flash_attn_2_fp32_ln(self):
pass
@pytest.mark.generate
@require_torch_sdpa
@slow
@unittest.skip(
reason="Idefics3 doesn't support SDPA for all backbones, vision backbones has only eager/FA2 attention"
)
def test_eager_matches_sdpa_generate(self):
pass
# We need to override as we need to prepare such that the image token is the last token
def test_resize_tokens_embeddings(self):
(original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
model_vocab_size = config.text_config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 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_embed = 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
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 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.seq_length
model.model.image_token_id = model_vocab_size - 15 - 1
inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
model_vocab_size = config.text_config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size)
model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0] // 64, 0)
self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size)
self.assertTrue(model.config.text_config.vocab_size, model.vocab_size)
model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0] // 64, 0)
# Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size
target_dimension = 128
model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64)
self.assertTrue(model_embed.weight.shape[0], target_dimension)
with self.assertRaisesRegex(
ValueError,
"Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer",
):
model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
# We need to override as we need to prepare such that the image token is the last token
def test_resize_embeddings_untied(self):
(original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
original_config.tie_word_embeddings = False
for model_class in self.all_model_classes:
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.seq_length
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 Idefics3ForConditionalGenerationIntegrationTest(unittest.TestCase):
def setUp(self):
self.processor = AutoProcessor.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3")
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):
cleanup(torch_device, gc_collect=True)
@slow
@unittest.skip("multi-gpu tests are disabled for now")
def test_integration_test(self):
model = Idefics3ForConditionalGeneration.from_pretrained(
"HuggingFaceM4/Idefics3-8B-Llama3",
torch_dtype=torch.bfloat16,
device_map="auto",
)
# 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)
expected_generated_text = "<image>In this image, we see the Statue of Liberty, which is located on Liberty"
self.assertEqual(generated_texts[0], expected_generated_text)
@slow
@require_bitsandbytes
@unittest.skip("multi-gpu tests are disabled for now")
def test_integration_test_4bit(self):
# Let' s make sure we test the preprocessing to replace what is used
model = Idefics3ForConditionalGeneration.from_pretrained(
"HuggingFaceM4/Idefics3-8B-Llama3",
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 = "<image>In this image, we see the Statue of Liberty, trees, buildings, water"
self.assertEqual(generated_texts[0], expected_generated_text)
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