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Prepend bos token to Blip generations (#29642)

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* prepend "bos" to blip generation

* minor changes

* Update src/transformers/models/blip_2/modeling_blip_2.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/instructblip/modeling_instructblip.py

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

* add generation tester mixin

---------

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Raushan Turganbay
2024-03-21 21:33:18 +05:00
committed by GitHub
parent ee38fc31fb
commit b469ebc5cf
4 changed files with 39 additions and 13 deletions
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8
tests/models/blip_2/test_modeling_blip_2.py
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@@ -32,6 +32,7 @@ from transformers.testing_utils import (
)
from transformers.utils import is_torch_available, is_vision_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
@@ -434,7 +435,7 @@ class Blip2ForConditionalGenerationDecoderOnlyModelTester:
@require_torch
class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
fx_compatible = False
test_head_masking = False
@@ -683,7 +684,7 @@ class Blip2ModelTester:
@require_torch
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
pipeline_model_mapping = (
{
@@ -869,7 +870,8 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
# max_length for BLIP includes prompt length from now on, use max_new_tokens
predictions = model.generate(**inputs, max_new_tokens=11)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output