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transformers/tests/models/idefics/test_modeling_idefics.py

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# coding=utf-8
# Copyright 2023 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 Idefics model."""
import inspect
import unittest
import pytest
from parameterized import parameterized
from transformers import BitsAndBytesConfig, IdeficsConfig, is_torch_available, is_vision_available
from transformers.testing_utils import (
TestCasePlus,
require_bitsandbytes,
require_torch,
require_torch_sdpa,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import IdeficsForVisionText2Text, IdeficsModel, IdeficsProcessor
from transformers.models.idefics.configuration_idefics import IdeficsPerceiverConfig, IdeficsVisionConfig
if is_vision_available():
from PIL import Image
class IdeficsModelTester:
def __init__(
self,
parent,
batch_size=1,
seq_length=7,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
alpha_initializer="ones",
num_labels=3,
scope=None,
modality_type_vocab_size=2,
vision_embed_dim=32,
vision_patch_size=2,
vision_image_size=30,
vision_num_attention_heads=4,
vision_num_hidden_layers=5,
vision_intermediate_size=37,
perceiver_qk_layer_norms_perceiver=False,
perceiver_resampler_depth=2,
perceiver_resampler_head_dim=8,
perceiver_resampler_n_heads=2,
perceiver_resampler_n_latents=16,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.num_labels = num_labels
self.scope = scope
self.modality_type_vocab_size = modality_type_vocab_size
self.vision_embed_dim = vision_embed_dim
self.vision_patch_size = vision_patch_size
self.vision_image_size = vision_image_size
self.vision_num_attention_heads = vision_num_attention_heads
self.vision_num_hidden_layers = vision_num_hidden_layers
self.vision_intermediate_size = vision_intermediate_size
self.vision_config = IdeficsVisionConfig(
embed_dim=self.vision_embed_dim,
patch_size=self.vision_patch_size,
image_size=self.vision_image_size,
num_attention_heads=self.vision_num_attention_heads,
num_hidden_layers=self.vision_num_hidden_layers,
intermediate_size=self.vision_intermediate_size,
).to_dict()
self.perceiver_qk_layer_norms_perceiver = perceiver_qk_layer_norms_perceiver
self.perceiver_resampler_depth = perceiver_resampler_depth
self.perceiver_resampler_head_dim = perceiver_resampler_head_dim
self.perceiver_resampler_n_heads = perceiver_resampler_n_heads
self.perceiver_resampler_n_latents = perceiver_resampler_n_latents
self.perceiver_config = IdeficsPerceiverConfig(
qk_layer_norms_perceiver=self.perceiver_qk_layer_norms_perceiver,
resampler_depth=self.perceiver_resampler_depth,
resampler_head_dim=self.perceiver_resampler_head_dim,
resampler_n_heads=self.perceiver_resampler_n_heads,
resampler_n_latents=self.perceiver_resampler_n_latents,
)
# we set the expected sequence length (which is used in several tests)
# this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token
self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1
def prepare_config_and_inputs(self, num_images=1, interpolate_pos_encoding=False, image_expansion=0):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
pixel_values = floats_tensor(
[
self.batch_size,
num_images,
self.num_channels,
self.image_size + image_expansion,
self.image_size + image_expansion,
]
)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, num_images])
config = self.get_config()
return (config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding)
def prepare_config_and_inputs_gate_tests(self):
# Create a list of configs and inputs, to test 2 things:
# 1. For the same image, the output should be different when image_attention_mask is filled with 0s vs filled with 1s.
# 2. For 2 different images, the output should be the same when image_attention_mask is filled with 0s.
interpolate_pos_encoding = False
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
pixel_values = floats_tensor(
[
self.batch_size,
1,
self.num_channels,
self.image_size,
self.image_size,
]
)
pixel_values_list = [
pixel_values.clone(),
pixel_values.clone(),
pixel_values.clone().fill_(0.6),
pixel_values.clone().fill_(0.3),
]
attention_mask = None
if self.use_input_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, 1])
image_attention_mask_list = [
image_attention_mask.clone().fill_(0),
image_attention_mask.clone().fill_(1),
image_attention_mask.clone().fill_(0),
image_attention_mask.clone().fill_(0),
]
config = self.get_config()
inputs_list = []
for pixel_values, image_attention_mask in zip(pixel_values_list, image_attention_mask_list):
inputs_list.append(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"image_attention_mask": image_attention_mask,
"interpolate_pos_encoding": interpolate_pos_encoding,
}
)
inputs_w_same_img = inputs_list[:2]
inputs_w_0_img_attn = inputs_list[2:]
return config, inputs_w_same_img, inputs_w_0_img_attn
def get_config(self):
return IdeficsConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
alpha_initializer=self.alpha_initializer,
num_labels=self.num_labels,
modality_type_vocab_size=self.modality_type_vocab_size,
vision_config=self.vision_config,
)
def create_and_check_model(
self,
config,
input_ids,
input_mask,
pixel_values,
image_attention_mask,
interpolate_pos_encoding,
):
model = IdeficsModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
pixel_values=pixel_values,
image_attention_mask=image_attention_mask,
interpolate_pos_encoding=interpolate_pos_encoding,
)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, input_ids.shape[1], self.hidden_size)
)
def create_and_check_model_gen(
self,
config,
input_ids,
input_mask,
pixel_values,
image_attention_mask,
interpolate_pos_encoding,
):
model = IdeficsForVisionText2Text(config)
model.to(torch_device)
model.eval()
model.generate(
input_ids,
attention_mask=input_mask,
pixel_values=pixel_values,
image_attention_mask=image_attention_mask,
interpolate_pos_encoding=interpolate_pos_encoding,
max_length=self.seq_length + 2,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
pixel_values,
image_attention_mask,
interpolate_pos_encoding,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"pixel_values": pixel_values,
"image_attention_mask": image_attention_mask,
"interpolate_pos_encoding": interpolate_pos_encoding,
}
return config, inputs_dict
def prepare_pixel_values(self):
return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
@require_torch_sdpa
@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
def test_eager_matches_sdpa_inference(self, torch_dtype: str):
self.skipTest(reason="Idefics has a hard requirement on SDPA, skipping this test")
@require_torch_sdpa
@slow
@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
def test_eager_matches_sdpa_generate(self):
self.skipTest(reason="Idefics has a hard requirement on SDPA, skipping this test")
@require_torch
class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (IdeficsModel, IdeficsForVisionText2Text) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": IdeficsModel, "image-text-to-text": IdeficsForVisionText2Text}
if is_torch_available()
else {}
)
test_pruning = False
test_headmasking = False
test_torchscript = False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
# XXX: IdeficsForVisionText2TextTest has no MODEL_FOR group yet, but it should be the same
# as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing
# as super won't do it
if return_labels:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
@require_torch_sdpa
@unittest.skip("Idefics requires both text and image inputs which is currently not done in this test.")
def test_eager_matches_sdpa_inference(self):
pass
def test_model_outputs_equivalence(self):
try:
orig = self.all_model_classes
# IdeficsModel.forward doesn't have labels input arg - only IdeficsForVisionText2Text does
self.all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else ()
super().test_model_outputs_equivalence()
finally:
self.all_model_classes = orig
def setUp(self):
self.model_tester = IdeficsModelTester(self)
self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model_single_image(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=1, interpolate_pos_encoding=False, image_expansion=0
)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_multiple_images(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=2, interpolate_pos_encoding=False, image_expansion=0
)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_image_pos_embeddings_interpolation_single_image(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=1, interpolate_pos_encoding=True, image_expansion=2
)
self.model_tester.create_and_check_model(*config_and_inputs)
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=1, interpolate_pos_encoding=True, image_expansion=0
)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_image_pos_embeddings_interpolation_multiple_images(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=2, interpolate_pos_encoding=True, image_expansion=2
)
self.model_tester.create_and_check_model(*config_and_inputs)
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=2, interpolate_pos_encoding=True, image_expansion=0
)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_generate_with_image_pos_embeddings_interpolation_single_image(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=1, interpolate_pos_encoding=True, image_expansion=2
)
self.model_tester.create_and_check_model_gen(*config_and_inputs)
def test_generate_with_image_pos_embeddings_interpolation_multiple_images(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
num_images=2, interpolate_pos_encoding=True, image_expansion=2
)
self.model_tester.create_and_check_model_gen(*config_and_inputs)
def test_cross_attention_gates(self):
config, inputs_w_same_img, inputs_w_0_img_attn = self.model_tester.prepare_config_and_inputs_gate_tests()
model = IdeficsModel(config=config).to(torch_device)
model.eval()
test_1_results = []
for inputs in inputs_w_same_img:
with torch.no_grad():
last_hidden_states = model(**inputs).last_hidden_state
last_hidden_states = model(**inputs).last_hidden_state
test_1_results.append(last_hidden_states)
self.assertNotEqual(test_1_results[0].sum().item(), test_1_results[1].sum().item())
test_2_results = []
for inputs in inputs_w_0_img_attn:
with torch.no_grad():
last_hidden_states = model(**inputs).last_hidden_state
test_2_results.append(last_hidden_states)
self.assertEqual(test_2_results[0].sum().item(), test_2_results[1].sum().item())
def test_training(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
for model_class in self.all_model_classes:
# IdeficsModel does not support training, users should use
# IdeficsForVisionText2Text for this purpose
if model_class == IdeficsModel:
self.skipTest(reason="IdeficsModel does not support training")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
for model_class in self.all_model_classes:
# IdeficsModel does not support training, users should use
# IdeficsForVisionText2Text for this purpose
if model_class == IdeficsModel:
self.skipTest(reason="IdeficsModel does not support training")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.use_cache = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.gradient_checkpointing_enable()
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""")
def test_retain_grad_hidden_states_attentions(self):
return
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
self.assertTrue(attentions[0] is None)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
# IDEFICS does not support outputting attention score becuase it uses SDPA under the hood
self.assertTrue(self_attentions[0] is None)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
@slow
def test_model_from_pretrained(self):
model_name = "HuggingFaceM4/idefics-9b"
model = IdeficsModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip("Idefics has a hard requirement on SDPA")
def test_sdpa_can_dispatch_non_composite_models(self):
pass
@require_torch
class IdeficsForVisionText2TextTest(IdeficsModelTest, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else ()
def setUp(self):
self.model_tester = IdeficsModelTester(
self,
modality_type_vocab_size=3,
)
self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37)
@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
@require_torch_sdpa
@unittest.skip("Idefics requires both text and image inputs which is currently not done in this test.")
def test_eager_matches_sdpa_inference(self, torch_dtype):
pass
@pytest.mark.generate
def test_left_padding_compatibility(self):
"""Overwrite because IDEFICS needs image attention mask to be also padded"""
# NOTE: left-padding results in small numerical differences. This is expected.
# See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
def _prepare_model_kwargs(input_ids, attention_mask, image_attention_mask, signature):
model_kwargs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"image_attention_mask": image_attention_mask,
}
if "position_ids" in signature:
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
if "cache_position" in signature:
cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
model_kwargs["cache_position"] = cache_position
return model_kwargs
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.prepare_config_and_inputs_for_generate()
input_ids = inputs_dict.pop("input_ids")
attention_mask = inputs_dict.pop("attention_mask")
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
image_attention_mask = inputs_dict.pop("image_attention_mask", None)
model = model_class(config).to(torch_device).eval()
signature = inspect.signature(model.forward).parameters.keys()
# no cache as some models require special cache classes to be init outside forward
model.generation_config.use_cache = False
# Without padding
model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, image_attention_mask, signature)
next_logits_wo_padding = model(**model_kwargs, **inputs_dict).logits[:, -1, :]
# With left-padding (length 32)
# can hardcode pad_token to be 0 as we'll do attn masking anyway
pad_token_id = (
config.get_text_config().pad_token_id if config.get_text_config().pad_token_id is not None else 0
)
pad_size = (input_ids.shape[0], 32)
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * pad_token_id
padded_input_ids = torch.cat((padding, input_ids), dim=1)
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
pad_size_img = (input_ids.shape[0], 32, image_attention_mask.shape[-1])
extra_img_mask = torch.zeros(pad_size_img, dtype=image_attention_mask.dtype, device=torch_device)
padded_image_attention_mask = torch.cat([extra_img_mask, image_attention_mask], dim=1)
model_kwargs = _prepare_model_kwargs(
padded_input_ids, padded_attention_mask, padded_image_attention_mask, signature
)
next_logits_with_padding = model(**model_kwargs, **inputs_dict).logits[:, -1, :]
# They should result in very similar logits
torch.testing.assert_close(next_logits_wo_padding, next_logits_with_padding, rtol=1e-5, atol=1e-5)
@pytest.mark.generate
def test_generate_continue_from_past_key_values(self):
"""Overwrite because IDEFICS needs image attention mask to be also processed"""
# Tests that we can continue generating from past key values, returned from a previous `generate` call
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
# Let's make it always:
# 1. use cache (for obvious reasons)
# 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
# would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
# continuation would force it to generate beyond an EOS token)
# 3. ignore `token_type_ids` for simplicity
# 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
# active by default on some models
# 5. ignore `encoder_no_repeat_ngram_size`, which is set by default in some encoder-decoder models. When
# we use their decoder as a stand-alone model, `encoder_no_repeat_ngram_size` actually prevents
# repetition exclusively from the prompt. This test relies on comparing one call vs 2 calls
# with cache, what is considered a prompt is different in the two cases.
model = model_class(config).to(torch_device)
model.eval()
model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
model.generation_config.forced_eos_token_id = None
model.generation_config.encoder_no_repeat_ngram_size = 0
model.generation_config.use_cache = True
# Traditional way of generating text, with `return_dict_in_generate` to return the past key values
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True)
# Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
# inputs may need to be tweaked across `generate` calls (like the attention mask).
outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True)
# Continue from the tokens generated above, preparing the inputs accordingly
inputs["past_key_values"] = outputs_cached.past_key_values
new_attention_len = outputs_cached.sequences.shape[-1]
inputs["input_ids"] = outputs_cached.sequences
if "attention_mask" in inputs:
inputs["attention_mask"] = torch.nn.functional.pad(
inputs["attention_mask"],
(0, new_attention_len - inputs["attention_mask"].shape[1]),
mode="constant",
value=1,
)
if "image_attention_mask" in inputs:
inputs["image_attention_mask"] = inputs["image_attention_mask"][:, -1:, :]
outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True)
# The two sets of generated text and past kv should be equal to each other
self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist())
for layer_idx in range(len(outputs_cached.past_key_values)):
for kv_idx in range(len(outputs_cached.past_key_values[layer_idx])):
self.assertTrue(
torch.allclose(
outputs.past_key_values[layer_idx][kv_idx],
outputs_cached.past_key_values[layer_idx][kv_idx],
)
)
@pytest.mark.generate
def test_generate_without_input_ids(self):
"""Overwrite because IDEFICS needs image attention mask to be also processed and requires image at input always."""
config, input_dict = self.prepare_config_and_inputs_for_generate()
pixel_values = input_dict["pixel_values"]
image_attention_mask = input_dict["image_attention_mask"][:, -1:, :]
# hack in case they are equal, otherwise the attn mask will be [0]
if config.bos_token_id == config.pad_token_id:
config.pad_token_id = None
for model_class in self.all_generative_model_classes:
model = model_class(config).to(torch_device)
model.eval()
output_ids_generate = model.generate(
pixel_values=pixel_values,
image_attention_mask=image_attention_mask,
do_sample=False,
max_new_tokens=self.max_new_tokens,
remove_invalid_values=True,
)
self.assertIsNotNone(output_ids_generate)
@pytest.mark.generate
def test_generate_continue_from_inputs_embeds(self):
"""Overwrite for IDEFICS: Ensure image attention mask is processed while continuing from `inputs_embeds`."""
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
print(inputs)
model = model_class(config).to(torch_device).eval()
model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
model.generation_config.forced_eos_token_id = None
model.generation_config.use_cache = True
input_ids = inputs.pop("input_ids")
input_embeds = model.get_input_embeddings()(input_ids)
generation_kwargs = {
"return_dict_in_generate": True,
"do_sample": False,
}
inputs["inputs_embeds"] = input_embeds
# Traditional way of generating text, with `return_dict_in_generate` to return the past key values
outputs = model.generate(**inputs, max_new_tokens=4, **generation_kwargs)
# Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
# inputs may need to be tweaked across `generate` calls (like the attention mask).
initial_output = model.generate(**inputs, max_new_tokens=3, **generation_kwargs)
inputs["past_key_values"] = initial_output.past_key_values
new_attention_len = input_ids.shape[1] + initial_output.sequences.shape[-1]
continued_embeds = torch.cat([input_embeds, model.get_input_embeddings()(initial_output.sequences)], dim=1)
inputs["inputs_embeds"] = continued_embeds
if "attention_mask" in inputs:
inputs["attention_mask"] = torch.nn.functional.pad(
inputs["attention_mask"],
(0, new_attention_len - inputs["attention_mask"].shape[1]),
mode="constant",
value=1,
)
if "image_attention_mask" in inputs:
inputs["image_attention_mask"] = inputs["image_attention_mask"][..., -1:, :]
cached_output = model.generate(**inputs, max_new_tokens=1, **generation_kwargs)
# Verify that the combined outputs match the full generation.
combined_output_sequences = torch.concat([initial_output.sequences, cached_output.sequences], axis=1)
self.assertListEqual(outputs.sequences.tolist(), combined_output_sequences.tolist())
for layer_idx in range(len(cached_output.past_key_values)):
for kv_idx in range(len(cached_output.past_key_values[layer_idx])):
self.assertTrue(
torch.allclose(
outputs.past_key_values[layer_idx][kv_idx],
cached_output.past_key_values[layer_idx][kv_idx],
)
)
def _check_attentions_for_generate(
self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
):
"""
Overwrite from generation tests because Idefics has only SDPA layers.
Do not skip because we still want generation tests to run. Rather we can remove checks for shape.
"""
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="We only test the model that takes in multiple images")
def test_custom_4d_attention_mask(self):
pass
@unittest.skip(reason="IDEFICS cannot compile due to dynamic control flow when checking inputs")
def test_generate_with_static_cache(self):
pass
@unittest.skip(reason="IDEFICS cannot compile due to dynamic control flow when checking inputs")
def test_generate_compile_model_forward(self):
pass
@unittest.skip(reason="We only test the model that takes in multiple images")
def test_model(self):
pass
@unittest.skip(reason="We only test the model that takes in multiple images")
def test_for_token_classification(self):
pass
@unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip("Idefics has a hard requirement on SDPA")
def test_sdpa_can_dispatch_non_composite_models(self):
pass
@require_torch
@require_vision
class IdeficsModelIntegrationTest(TestCasePlus):
@cached_property
def default_processor(self):
return (
IdeficsProcessor.from_pretrained("HuggingFaceM4/idefics-9b", revision="refs/pr/11")
if is_vision_available()
else None
)
@require_bitsandbytes
@slow
def test_inference_natural_language_visual_reasoning(self):
cat_image_path = self.tests_dir / "fixtures/tests_samples/COCO/000000039769.png"
cats_image_obj = Image.open(cat_image_path) # 2 cats
dogs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
prompts = [
[
"User:",
dogs_image_url,
"Describe this image.\nAssistant: An image of two dogs.\n",
"User:",
cats_image_obj,
"Describe this image.\nAssistant:",
],
[
"User:",
cats_image_obj,
"Describe this image.\nAssistant: An image of two kittens.\n",
"User:",
dogs_image_url,
"Describe this image.\nAssistant:",
],
]
# the CI gpu is small so using quantization to fit
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype="float16",
)
model = IdeficsForVisionText2Text.from_pretrained(
"HuggingFaceM4/idefics-9b", quantization_config=quantization_config, device_map="auto"
)
processor = self.default_processor
inputs = processor(text=prompts, return_tensors="pt", padding="longest").to(torch_device)
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
# keep for debugging
for i, t in enumerate(generated_text):
t = bytes(t, "utf-8").decode("unicode_escape")
print(f"{i}:\n{t}\n")
self.assertIn("image of two cats", generated_text[0])
self.assertIn("image of two dogs", generated_text[1])
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