d249a9e90e
Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from fractions import Fraction
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from transformers import LlavaNextProcessor
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from transformers.image_processing_utils import select_best_resolution
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class Granite4VisionProcessor(LlavaNextProcessor):
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"""Processor for Granite 4 Vision.
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Extends LlavaNextProcessor to account for the Window Q-Former
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downsampling when computing the number of image features.
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This processor is needed because the granite4_vision processor type
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is not yet in the transformers version pinned by vLLM.
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"""
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model_type = "granite4_vision"
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def __init__(
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self,
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image_processor=None,
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tokenizer=None,
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patch_size=None,
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vision_feature_select_strategy=None,
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chat_template=None,
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image_token="<image>",
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num_additional_image_tokens=0,
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downsample_rate=None,
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**kwargs,
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):
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super().__init__(
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image_processor=image_processor,
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tokenizer=tokenizer,
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patch_size=patch_size,
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vision_feature_select_strategy=vision_feature_select_strategy,
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chat_template=chat_template,
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image_token=image_token,
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num_additional_image_tokens=num_additional_image_tokens,
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)
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self.downsample_rate = downsample_rate
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def _get_number_of_features(
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self,
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orig_height: int,
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orig_width: int,
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height: int,
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width: int,
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) -> int:
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image_grid_pinpoints = self.image_processor.image_grid_pinpoints
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height_best_resolution, width_best_resolution = select_best_resolution(
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[orig_height, orig_width], image_grid_pinpoints
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)
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scale_height = height_best_resolution // height
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scale_width = width_best_resolution // width
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patches_height = height // self.patch_size
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patches_width = width // self.patch_size
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if self.downsample_rate is not None:
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ds_rate = Fraction(self.downsample_rate)
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patches_height = int(patches_height * ds_rate)
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patches_width = int(patches_width * ds_rate)
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unpadded_features, newline_features = self._get_unpadded_features(
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orig_height,
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orig_width,
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patches_height,
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patches_width,
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scale_height,
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scale_width,
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)
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base_features = (
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patches_height * patches_width + self.num_additional_image_tokens
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)
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return unpadded_features + newline_features + base_features
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