SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

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

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

4
tests/pipelines/test_pipelines_text_classification.py
View File

@@ -179,8 +179,8 @@ class TextClassificationPipelineTests(unittest.TestCase):
outputs = text_classifier("Birds are a type of animal")
self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
def get_test_pipeline(self, model, tokenizer, processor):
text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
def get_test_pipeline(self, model, tokenizer, processor, torch_dtype="float32"):
text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, torch_dtype=torch_dtype)
return text_classifier, ["HuggingFace is in", "This is another test"]
def run_pipeline_test(self, text_classifier, _):