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BLIPs clean-up (#35560)

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* blips clean up

* update processor

* readability

* fix processor length

* fix copies

* tmp

* update and fix copies

* why keep these, delete?

* fix test fetcher

* irrelevant comment

* fix tests

* fix tests

* fix copies
This commit is contained in:
Raushan Turganbay
2025-07-29 10:03:06 +02:00
committed by GitHub
parent 4f8f51be4e
commit 75794792ad
15 changed files with 301 additions and 592 deletions
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31
tests/models/instructblip/test_modeling_instructblip.py
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@@ -809,34 +809,3 @@ class InstructBlipModelIntegrationTest(unittest.TestCase):
predictions[0].tolist(), [0, 37, 1023, 753, 3, 9, 2335, 3823, 30, 8, 2608, 28, 3, 9, 1782, 5, 1]
)
self.assertEqual(generated_text, "The image features a woman sitting on the beach with a dog.")
def test_expansion_in_processing(self):
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")
model = InstructBlipForConditionalGeneration.from_pretrained(
"Salesforce/instructblip-flan-t5-xl",
torch_dtype=torch.bfloat16,
).to(torch_device)
image = prepare_img()
prompt = "What's in the image?"
# Make sure we will go the legacy path by setting these args to None
processor.num_query_tokens = None
model.config.image_token_index = None
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs, do_sample=False, max_new_tokens=15)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Add args to the config to trigger new logic when inputs are expanded in processing file
processor.num_query_tokens = model.config.num_query_tokens
processor.tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
model.config.image_token_index = len(processor.tokenizer) - 2
model.resize_token_embeddings(processor.tokenizer.vocab_size, pad_to_multiple_of=64)
# Generate again with new inputs
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions_expanded = model.generate(**inputs, do_sample=False, max_new_tokens=15)
generated_text_expanded = processor.batch_decode(predictions_expanded, skip_special_tokens=True)[0].strip()
self.assertTrue(generated_text_expanded == generated_text)