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[Bugfix] Fix LLaVA-NeXT feature size precision error (for real) #11772

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merged 2 commits into from
Jan 7, 2025
Merged

[Bugfix] Fix LLaVA-NeXT feature size precision error (for real) #11772

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6f1f27b
Fix LLaVA-NeXT feature size calculation (for real)
DarkLight1337 Jan 6, 2025
204edd4
Avoid type ignore
DarkLight1337 Jan 6, 2025
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3 changes: 2 additions & 1 deletion tests/models/decoder_only/vision_language/processing/test_llava_next.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,8 @@ def processor_for_llava_next():

@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
@pytest.mark.parametrize("image_size", [(1669, 2560), (2560, 1669), (183, 488),
(488, 183), (198, 176), (176, 198)])
(488, 183), (198, 176), (176, 198),
(161, 184), (184, 161)])
@pytest.mark.parametrize("num_imgs", [1, 2])
def test_processor_prompt_replacements(
processor_for_llava_next,
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3 changes: 2 additions & 1 deletion tests/models/decoder_only/vision_language/processing/test_llava_onevision.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,8 @@ def processor_for_llava_onevision():
@pytest.mark.parametrize("model_id",
["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"])
@pytest.mark.parametrize("image_size", [(1669, 2560), (2560, 1669), (183, 488),
(488, 183), (198, 176), (176, 198)])
(488, 183), (198, 176), (176, 198),
(161, 184), (184, 161)])
@pytest.mark.parametrize("num_imgs", [1, 2])
def test_processor_prompt_replacements(
processor_for_llava_onevision,
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39 changes: 19 additions & 20 deletions vllm/model_executor/models/llava_next.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,30 +121,29 @@ def _get_num_unpadded_features(
num_patch_height: int,
num_patch_width: int,
) -> tuple[int, int]:
current_height = npatches * num_patch_height
current_width = npatches * num_patch_width

# NOTE: Use float32 to remain consistent with HF output
original_aspect_ratio = np.array(original_width / original_height,
dtype=np.float32)
current_aspect_ratio = np.array(current_width / current_height,
dtype=np.float32)
current_height_f = np.float32(npatches * num_patch_height)
current_width_f = np.float32(npatches * num_patch_width)

original_width_f = np.float32(original_width)
original_height_f = np.float32(original_height)

original_aspect_ratio = original_width_f / original_height_f
current_aspect_ratio = current_width_f / current_height_f

if original_aspect_ratio > current_aspect_ratio:
scale_factor = np.array(current_width / original_width,
dtype=np.float32)
new_height = int(original_height * scale_factor)
padding = (current_height - new_height) // 2
current_height -= 2 * padding
scale_factor = current_width_f / original_width_f
new_height = int(original_height_f * scale_factor)
padding = (current_height_f - new_height) // 2
current_height_f -= 2 * padding
else:
scale_factor = np.array(current_height / original_height,
dtype=np.float32)
new_width = int(original_width * scale_factor)
padding = (current_width - new_width) // 2
current_width -= 2 * padding

unpadded_features = current_height * current_width
newline_features = current_height
scale_factor = current_height_f / original_height_f
new_width = int(original_width_f * scale_factor)
padding = (current_width_f - new_width) // 2
current_width_f -= 2 * padding

unpadded_features = int(current_height_f * current_width_f)
newline_features = int(current_height_f)

return (unpadded_features, newline_features)

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47 changes: 24 additions & 23 deletions vllm/model_executor/models/llava_onevision.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,36 +104,37 @@ def _get_num_unpadded_features(
num_patch_height: int,
num_patch_width: int,
) -> tuple[int, int]:
current_height = npatches * num_patch_height
current_width = npatches * num_patch_width

# NOTE: Use float32 to remain consistent with HF output
original_aspect_ratio = np.array(original_width / original_height,
dtype=np.float32)
current_aspect_ratio = np.array(current_width / current_height,
dtype=np.float32)
current_height_f = np.float32(npatches * num_patch_height)
current_width_f = np.float32(npatches * num_patch_width)

original_width_f = np.float32(original_width)
original_height_f = np.float32(original_height)

original_aspect_ratio = original_width_f / original_height_f
current_aspect_ratio = current_width_f / current_height_f

if original_aspect_ratio > current_aspect_ratio:
scale_factor = np.array(current_width / original_width,
dtype=np.float32)
new_height = int(original_height * scale_factor)
padding = (current_height - new_height) // 2
current_height -= 2 * padding
scale_factor = current_width_f / original_width_f
new_height = int(original_height_f * scale_factor)
padding = (current_height_f - new_height) // 2
current_height_f -= 2 * padding
else:
scale_factor = np.array(current_height / original_height,
dtype=np.float32)
new_width = int(original_width * scale_factor)
padding = (current_width - new_width) // 2
current_width -= 2 * padding
scale_factor = current_height_f / original_height_f
new_width = int(original_width_f * scale_factor)
padding = (current_width_f - new_width) // 2
current_width_f -= 2 * padding

unpadded_features = current_height * current_width
newline_features = current_height
unpadded_features = int(current_height_f * current_width_f)
newline_features = int(current_height_f)

ratio = math.sqrt(current_height * current_width / (9 * npatches**2))
ratio = math.sqrt(current_height_f * current_width_f /
(9 * npatches**2))
if ratio > 1.1:
unpadded_features = int(current_height // ratio) * int(
current_width // ratio)
newline_features = int(current_height // ratio)
height_factor = int(current_height_f // ratio)
width_factor = int(current_width_f // ratio)
unpadded_features = height_factor * width_factor
newline_features = height_factor

return (unpadded_features, newline_features)

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