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[tests] resolve skipped HF tests: 1st batch (#21305)

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* initial

* some corrections for the first batch

* corrected classes for generate, corrected xfail

* leave only models with correct example input

* remove an obsolete line

* Update tests/model_hub_tests/torch_tests/test_hf_transformers.py

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

* update musicgen

* cleanup test_hf_transformers.py

* typo fix

* Update tests/model_hub_tests/torch_tests/test_hf_transformers.py

* Update tests/model_hub_tests/torch_tests/test_hf_transformers.py

* move to up: corrected xfail

* revert back accidentally deleted elif

* Update tests/model_hub_tests/torch_tests/test_hf_transformers.py

* 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 2023-11-30 15:37:41 +01:00 committed by GitHub
parent df03b0437a
commit d5d9fd11b3
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2 changed files with 44 additions and 3 deletions
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6
tests/model_hub_tests/torch_tests/hf_transformers_models
View File

@ -74,8 +74,8 @@ facebook/flava-image-codebook,flava_image_codebook,skip,Load problem
facebook/m2m100_418M,m2m_100
facebook/mask2former-swin-base-coco-panoptic,mask2former
facebook/maskformer-swin-base-coco,maskformer
facebook/mms-tts-eng,vits,skip,Load problem
facebook/musicgen-small,musicgen,skip,Load problem
facebook/mms-tts-eng,vits,xfail,Accuracy failed: results cannot be broadcasted
facebook/musicgen-small,musicgen
facebook/opt-125m,opt
facebook/rag-token-nq,rag,skip,Load problem
facebook/sam-vit-large,sam,xfail,No node with name original_sizes
@ -104,7 +104,7 @@ google/mobilebert-uncased,mobilebert
google/mobilenet_v1_0.75_192,mobilenet_v1
google/mt5-base,mt5
google/owlvit-base-patch32,owlvit
google/pix2struct-docvqa-base,pix2struct,skip,Load problem
google/pix2struct-docvqa-base,pix2struct
google/realm-orqa-nq-openqa,realm,skip,Load problem
google/reformer-crime-and-punishment,reformer,xfail,Tracing problem
google/tapas-large-finetuned-wtq,tapas

41
tests/model_hub_tests/torch_tests/test_hf_transformers.py
View File

@ -88,6 +88,26 @@ class TestTransformersModel(TestTorchConvertModel):
model = VIT_GPT2_Model(model)
example = (encoded_input.pixel_values,)
elif 'pix2struct' in mi.tags:
from transformers import AutoProcessor, Pix2StructForConditionalGeneration
model = Pix2StructForConditionalGeneration.from_pretrained(name)
processor = AutoProcessor.from_pretrained(name)
import requests
from PIL import Image
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt")
example = dict(inputs)
class DecoratorModelForSeq2SeqLM(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, flattened_patches, attention_mask):
return self.model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask)
model = DecoratorModelForSeq2SeqLM(model)
elif "mms-lid" in name:
# mms-lid model config does not have auto_model attribute, only direct loading available
from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
@ -149,6 +169,27 @@ class TestTransformersModel(TestTorchConvertModel):
0, 255, [16, 3, 224, 224]).to(torch.float32))
inputs = processor(video, return_tensors="pt")
example = dict(inputs)
elif 'text-to-speech' in mi.tags:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(name)
text = "some example text in the English language"
inputs = tokenizer(text, return_tensors="pt")
example = dict(inputs)
elif 'musicgen' in mi.tags:
from transformers import AutoProcessor, AutoModelForTextToWaveform
processor = AutoProcessor.from_pretrained(name)
model = AutoModelForTextToWaveform.from_pretrained(name, torchscript=True)
inputs = processor(
text=["80s pop track with bassy drums and synth"],
padding=True,
return_tensors="pt",
)
example = dict(inputs)
# works for facebook/musicgen-small
pad_token_id = model.generation_config.pad_token_id
example["decoder_input_ids"] = torch.ones(
(inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long) * pad_token_id
else:
try:
if auto_model == "AutoModelForCausalLM":