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Fix Blip-2 CI (#21595)

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* use fp16

* use fp16

* use fp16

* use fp16

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-02-13 16:44:27 +01:00
committed by GitHub
parent fd5320bb57
commit edc1e734bf
1 changed files with 10 additions and 6 deletions
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16
tests/models/blip_2/test_modeling_blip_2.py
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@@ -768,11 +768,13 @@ def prepare_img():
class Blip2ModelIntegrationTest(unittest.TestCase):
def test_inference_opt(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b").to(torch_device)
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
).to(torch_device)
# prepare image
image = prepare_img()
inputs = processor(images=image, return_tensors="pt").to(torch_device)
inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
@@ -783,7 +785,7 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
# image and context
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
@@ -797,11 +799,13 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
def test_inference_t5(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl").to(torch_device)
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
).to(torch_device)
# prepare image
image = prepare_img()
inputs = processor(images=image, return_tensors="pt").to(torch_device)
inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
@@ -812,7 +816,7 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
# image and context
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()