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Allow FP16 or other precision inference for Pipelines (#31342)

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* cast image features to model.dtype where needed to support FP16 or other precision in pipelines

* Update src/transformers/pipelines/image_feature_extraction.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Use .to instead

* Add FP16 pipeline support for zeroshot audio classification

* Remove unused torch imports

* Add docs on FP16 pipeline

* Remove unused import

* Add FP16 tests to pipeline mixin

* Add fp16 placeholder for mask_generation pipeline test

* Add FP16 tests for all pipelines

* Fix formatting

* Remove torch_dtype arg from is_pipeline_test_to_skip*

* Fix format

* trigger ci

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Billy Cao
2024-07-06 00:21:50 +08:00
committed by GitHub
parent e786844425
commit ac26260436
45 changed files with 354 additions and 79 deletions
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6
tests/pipelines/test_pipelines_visual_question_answering.py
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@@ -55,8 +55,10 @@ else:
class VisualQuestionAnsweringPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def get_test_pipeline(self, model, tokenizer, processor):
vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
def get_test_pipeline(self, model, tokenizer, processor, torch_dtype="float32"):
vqa_pipeline = pipeline(
"visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa", torch_dtype=torch_dtype
)
examples = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),