From 33c9892af58b7b161f2a532935dcccff8c8048c6 Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Thu, 30 Nov 2023 23:11:14 +0100 Subject: [PATCH] llava : ShareGPT4V compatibility (vision encoder only loading) (#4172) * ShareGPT4 compatibility (vision encoder only loading) Load only a CLIP vision encoder (as supplied by ShareGPT finetunes) Corrects the argument parsing for --img_mean and --img_std (which were previously not parsed but attempted to access) Defines defaults for img_mean and img_std which are equal to the llava 1.5 CLIP encoder, so you do not have to provide them * Update convert-image-encoder-to-gguf.py --- .../llava/convert-image-encoder-to-gguf.py | 52 +++++++++++++------ 1 file changed, 37 insertions(+), 15 deletions(-) diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py index 2f5eef199..729aaef8f 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert-image-encoder-to-gguf.py @@ -5,7 +5,7 @@ import json 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" @@ -78,11 +78,19 @@ ap.add_argument("--text-only", action="store_true", required=False, 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() @@ -96,15 +104,22 @@ if args.use_f32: # 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 @@ -117,9 +132,12 @@ ftype = 1 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 @@ -128,13 +146,13 @@ has_llava_projector = False 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 = "" @@ -182,8 +200,12 @@ if has_vision_encoder: 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)