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ShareGPT4V compatibility (vision encoder only loading) #4172

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merged 2 commits into from
Nov 30, 2023
Merged

ShareGPT4V compatibility (vision encoder only loading) #4172

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ab8b6f1
ShareGPT4 compatibility (vision encoder only loading)
cmp-nct Nov 22, 2023
f16b338
Update convert-image-encoder-to-gguf.py
cmp-nct Nov 22, 2023
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52 changes: 37 additions & 15 deletions examples/llava/convert-image-encoder-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel

TEXT = "clip.text"
VISION = "clip.vision"
Expand Down Expand Up @@ -78,11 +78,19 @@ def bytes_to_unicode():
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)

# with proper
args = ap.parse_args()


Expand All @@ -96,15 +104,22 @@ def bytes_to_unicode():
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir


with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
if args.clip_model_is_vision:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]

with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
v_hparams = config["vision_config"]
t_hparams = config["text_config"]
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = config["text_config"]

# possible data types
# ftype == 0 -> float32
Expand All @@ -117,9 +132,12 @@ def bytes_to_unicode():
if args.use_f32:
ftype = 0


model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)
if args.clip_model_is_vision:
model = CLIPVisionModel.from_pretrained(dir_model)
processor = None
else:
model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)

fname_middle = None
has_text_encoder = True
Expand All @@ -128,13 +146,13 @@ def bytes_to_unicode():
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_llava_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""

Expand Down Expand Up @@ -182,8 +200,12 @@ def bytes_to_unicode():
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)

image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None else args.image_std
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
else:
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)

Expand Down