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Idefics: enable generation tests (#34062)

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* add idefics

* conflicts after merging main

* enable tests but need to fix some

* fix tests

* no print

* fix/skip some slow tests

* continue not skip

* rebasing broken smth, this is the fix
This commit is contained in:
Raushan Turganbay
2024-10-15 11:17:14 +02:00
committed by GitHub
parent dd4216b766
commit 23874f5948
10 changed files with 406 additions and 96 deletions
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188
tests/models/idefics/test_modeling_idefics.py
View File

@@ -14,8 +14,10 @@
# 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
@@ -31,6 +33,7 @@ from transformers.testing_utils import (
)
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
@@ -318,6 +321,12 @@ class IdeficsModelTester:
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")
@unittest.skipIf(not is_torch_greater_or_equal_than_2_0, reason="pytorch 2.0 or higher is required")
@require_torch
@@ -580,8 +589,9 @@ class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
@unittest.skipIf(not is_torch_greater_or_equal_than_2_0, reason="pytorch 2.0 or higher is required")
@require_torch
class IdeficsForVisionText2TextTest(IdeficsModelTest, unittest.TestCase):
class IdeficsForVisionText2TextTest(IdeficsModelTest, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else ()
all_generative_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else ()
def setUp(self):
self.model_tester = IdeficsModelTester(
@@ -590,6 +600,182 @@ class IdeficsForVisionText2TextTest(IdeficsModelTest, unittest.TestCase):
)
self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37)
@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
self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, 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)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
"""
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_compile_fullgraph(self):
pass
@unittest.skip(reason="We only test the model that takes in multiple images")
def test_model(self):
pass

70
tests/models/idefics2/test_modeling_idefics2.py
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@@ -19,6 +19,7 @@ import gc
import unittest
from io import BytesIO
import pytest
import requests
from transformers import (
@@ -96,7 +97,7 @@ class Idefics2VisionText2TextModelTester:
"pad_token_id": 0, # None in the original configuration_mistral, we set it to the unk_token_id
"bos_token_id": 1,
"eos_token_id": 2,
"image_token_id": 32_001,
"image_token_id": 99,
"tie_word_embeddings": False,
"rope_theta": 10000.0,
"sliding_window": 32,
@@ -334,6 +335,7 @@ class Idefics2ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTest
"""
all_model_classes = (Idefics2ForConditionalGeneration,) if is_torch_available() else ()
all_generative_model_classes = (Idefics2ForConditionalGeneration,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_resize_embeddings = True
@@ -356,6 +358,72 @@ class Idefics2ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTest
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
def test_generate_from_inputs_embeds_decoder_only(self):
# overwrite because IDEFICS needs ids and embeds at the input to be not None
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.prepare_config_and_inputs_for_generate()
# Ignore:
# a) eos (to always output 20 tokens) and pad (so we don't try to infer the attn mask from the input_ids,
# which would cause a mismatch),
config.pad_token_id = config.eos_token_id = -1
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
input_ids = inputs_dict.pop("input_ids")
# Traditional way of generating text
outputs_from_ids = model.generate(
input_ids, max_new_tokens=5, return_dict_in_generate=True, output_scores=True
)
self.assertEqual(outputs_from_ids.sequences.shape, (input_ids.shape[0], input_ids.shape[1] + 5))
# Same thing, but from input embeddings (`input_ids` is passed so the prompt is present in the output)
inputs_embeds = model.get_input_embeddings()(input_ids)
outputs_from_embeds = model.generate(
input_ids,
inputs_embeds=inputs_embeds,
max_new_tokens=5,
return_dict_in_generate=True,
output_scores=True,
)
self.assertListEqual(outputs_from_ids.sequences.tolist(), outputs_from_embeds.sequences.tolist())
# But if we pass different inputs_embeds, we should get different outputs (the output text may be the
# same, but the logits will almost surely be different)
random_embeds = torch.rand_like(inputs_embeds)
outputs_from_rand_embeds = model.generate(
input_ids,
inputs_embeds=random_embeds,
max_new_tokens=5,
return_dict_in_generate=True,
output_scores=True,
)
for i in range(len(outputs_from_rand_embeds.scores)):
self.assertFalse(torch.allclose(outputs_from_embeds.scores[i], outputs_from_rand_embeds.scores[i]))
# 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()

68
tests/models/idefics3/test_modeling_idefics3.py
View File

@@ -19,6 +19,7 @@ import gc
import unittest
from io import BytesIO
import pytest
import requests
from transformers import (
@@ -321,6 +322,7 @@ class Idefics3ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTest
"""
all_model_classes = (Idefics3ForConditionalGeneration,) if is_torch_available() else ()
all_generative_model_classes = (Idefics3ForConditionalGeneration,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_resize_embeddings = True
@@ -343,6 +345,72 @@ class Idefics3ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTest
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
def test_generate_from_inputs_embeds_decoder_only(self):
# overwrite because IDEFICS needs ids and embeds at the input to be not None
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.prepare_config_and_inputs_for_generate()
# Ignore:
# a) eos (to always output 20 tokens) and pad (so we don't try to infer the attn mask from the input_ids,
# which would cause a mismatch),
config.pad_token_id = config.eos_token_id = -1
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
input_ids = inputs_dict.pop("input_ids")
# Traditional way of generating text
outputs_from_ids = model.generate(
input_ids, max_new_tokens=5, return_dict_in_generate=True, output_scores=True
)
self.assertEqual(outputs_from_ids.sequences.shape, (input_ids.shape[0], input_ids.shape[1] + 5))
# Same thing, but from input embeddings (`input_ids` is passed so the prompt is present in the output)
inputs_embeds = model.get_input_embeddings()(input_ids)
outputs_from_embeds = model.generate(
input_ids,
inputs_embeds=inputs_embeds,
max_new_tokens=5,
return_dict_in_generate=True,
output_scores=True,
)
self.assertListEqual(outputs_from_ids.sequences.tolist(), outputs_from_embeds.sequences.tolist())
# But if we pass different inputs_embeds, we should get different outputs (the output text may be the
# same, but the logits will almost surely be different)
random_embeds = torch.rand_like(inputs_embeds)
outputs_from_rand_embeds = model.generate(
input_ids,
inputs_embeds=random_embeds,
max_new_tokens=5,
return_dict_in_generate=True,
output_scores=True,
)
for i in range(len(outputs_from_rand_embeds.scores)):
self.assertFalse(torch.allclose(outputs_from_embeds.scores[i], outputs_from_rand_embeds.scores[i]))
# 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()