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[tests] resolve skipped HF models: 2nd batch (#21526)

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* resolve skipped HF models: 2nd batch

* commented out models with no info and less than 10 downloads

* Revert "commented out models with no info and less than 10 downloads"

This reverts commit a55861ed69.

* replace with self.image

* resolve todo

* Update tests/model_hub_tests/torch_tests/test_hf_transformers.py

Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>

* Update tests/model_hub_tests/torch_tests/test_hf_transformers.py

---------

Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
This commit is contained in:
Pavel Esir 2023-12-11 12:12:12 +01:00 committed by GitHub
parent f49f84a4b1
commit 948fc265b4
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 93 additions and 5 deletions
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10
tests/model_hub_tests/torch_tests/hf_transformers_models
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@ -65,7 +65,7 @@ EleutherAI/pythia-6.9b,gpt_neox
facebook/bart-large-mnli,bart
facebook/convnextv2-tiny-22k-384,convnextv2
facebook/detr-resnet-50,detr
facebook/dinov2-base,dinov2,skip,Load problem
facebook/dinov2-base,dinov2,xfail,Tracing error: Please check correctness of provided example_input (but eval was correct)
facebook/dpr-question_encoder-single-nq-base,dpr
facebook/encodec_24khz,encodec,xfail,Unsupported op aten::lstm
facebook/esm2_t6_8M_UR50D,esm
@ -168,7 +168,7 @@ HJHGJGHHG/GAU-Base-Full,gau,skip,Load problem
huggingface/autoformer-tourism-monthly,autoformer,skip,Load problem
huggingface/informer-tourism-monthly,informer,skip,Load problem
huggingface/time-series-transformer-tourism-monthly,time_series_transformer,skip,Load problem
HuggingFaceM4/tiny-random-idefics,idefics,skip,Load problem
HuggingFaceM4/tiny-random-idefics,idefics,xfail,tracing error: Please check correctness of provided example_input (eval was correct but trace failed with incommatible tuples and tensors)
HuggingFaceM4/tiny-random-vllama-clip,vllama,skip,Load problem
HuggingFaceM4/tiny-random-vopt-clip,vopt,skip,Load problem
HuiHuang/gpt3-damo-base-zh,gpt3,skip,Load problem
@ -243,12 +243,12 @@ microsoft/conditional-detr-resnet-50,conditional_detr
microsoft/deberta-base,deberta
microsoft/git-large-coco,git,skip,Load problem
microsoft/layoutlm-base-uncased,layoutlm
microsoft/layoutlmv2-base-uncased,layoutlmv2,skip,Load problem
microsoft/layoutlmv2-base-uncased,layoutlmv2,xfail,Tracing error: Please check correctness of provided example_input (but eval was correct)
microsoft/layoutlmv3-base,layoutlmv3
microsoft/markuplm-base,markuplm
microsoft/resnet-50,resnet
microsoft/speecht5_hifigan,hifigan,skip,Load problem
microsoft/speecht5_tts,speecht5,skip,Load problem
microsoft/speecht5_tts,speecht5,xfail,Tracing error: hangs with no error (probably because of infinite while inside generate)
microsoft/swinv2-tiny-patch4-window8-256,swinv2
microsoft/table-transformer-detection,table-transformer
microsoft/wavlm-large,wavlm,skip,Load problem
@ -317,7 +317,7 @@ sahasrarjn/interbert,BERT,skip,Load problem
saibo/genkalm-medium-gpt2,genkalm,skip,Load problem
SajjadAyoubi/clip-fa-vision,clip_vision_model
Salesforce/blip2-flan-t5-xl,blip-2,skip,Load problem
Salesforce/blip-image-captioning-large,blip,skip,Load problem
Salesforce/blip-image-captioning-large,blip
Salesforce/instructblip-vicuna-7b,instructblip,skip,Load problem
SamLowe/roberta-base-go_emotions,roberta
sanchit-gandhi/enhanced_direct_s2st_en_to_es,speech-to-speech,skip,Load problem

88
tests/model_hub_tests/torch_tests/test_hf_transformers.py
View File

@ -154,6 +154,94 @@ class TestTransformersModel(TestTorchConvertModel):
model = VIT_GPT2_Model(model)
example = (encoded_input.pixel_values,)
elif 'idefics' in mi.tags:
from transformers import IdeficsForVisionText2Text, AutoProcessor
model = IdeficsForVisionText2Text.from_pretrained(name)
processor = AutoProcessor.from_pretrained(name)
prompts = [[
"User: What is in this image?",
"https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG",
"<end_of_utterance>",
"\nAssistant: This picture depicts Idefix, the dog of Obelix in Asterix and Obelix. Idefix is running on the ground.<end_of_utterance>",
"\nUser:",
"https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052",
"And who is that?<end_of_utterance>",
"\nAssistant:",
]]
inputs = processor(prompts, add_end_of_utterance_token=False, return_tensors="pt")
exit_condition = processor.tokenizer("<end_of_utterance>", add_special_tokens=False).input_ids
bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
example = dict(inputs)
example.update({
'eos_token_id': exit_condition,
'bad_words_ids': bad_words_ids,
})
class Decorator(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, input_ids, attention_mask, pixel_values, image_attention_mask, eos_token_id, bad_words_ids):
return self.model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
pixel_values=pixel_values,
image_attention_mask=image_attention_mask,
eos_token_id=eos_token_id,
bad_words_ids=bad_words_ids,
max_length=100
)
model = Decorator(model)
elif 'blip' in mi.tags and 'text2text-generation' in mi.tags:
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained(name)
model = BlipForConditionalGeneration.from_pretrained(name)
text = "a photography of"
inputs = processor(self.image, text, return_tensors="pt")
class DecoratorForBlipForConditional(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, pixel_values, input_ids, attention_mask):
return self.model.generate(pixel_values, input_ids, attention_mask)
model = DecoratorForBlipForConditional(model)
example = dict(inputs)
elif 'speecht5' in mi.tags:
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
processor = SpeechT5Processor.from_pretrained(name)
model = SpeechT5ForTextToSpeech.from_pretrained(name)
inputs = processor(text="Hello, my dog is cute.", return_tensors="pt")
# load xvector containing speaker's voice characteristics from a dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
example = {'input_ids': inputs["input_ids"], 'speaker_embeddings': speaker_embeddings}
class DecoratorModelForSeq2SeqLM(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, input_ids, speaker_embeddings):
return self.model.generate_speech(input_ids=input_ids, speaker_embeddings=speaker_embeddings) #, vocoder=vocoder)
model = DecoratorModelForSeq2SeqLM(model)
elif 'layoutlmv2' in mi.tags:
from transformers import LayoutLMv2Processor
processor = LayoutLMv2Processor.from_pretrained(name)
question = "What's the content of this image?"
encoding = processor(self.image, question, max_length=512, truncation=True, return_tensors="pt")
example = dict(encoding)
elif 'pix2struct' in mi.tags:
from transformers import AutoProcessor, Pix2StructForConditionalGeneration
model = Pix2StructForConditionalGeneration.from_pretrained(name, **model_kwargs)