llama.cpp/examples/llava/convert_image_encoder_to_gguf.py

import argparse
import os
import json
import re

import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel, SiglipVisionModel

TEXT = "clip.text"
VISION = "clip.vision"


def k(raw_key: str, arch: str) -> str:
    return raw_key.format(arch=arch)


def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
    if name in (
        "logit_scale",
        "text_model.embeddings.position_ids",
        "vision_model.embeddings.position_ids",
    ):
        return True

    if has_llava and name in ["visual_projection.weight", "vision_model.post_layernorm.weight", "vision_model.post_layernorm.bias"]:
        return True

    if name.startswith("v") and not has_vision:
        return True

    if name.startswith("t") and not has_text:
        return True

    return False


def get_tensor_name(name: str) -> str:
    # Standardize the transformers llava next keys for
    # image newline / mm projector with the classes in haotian-liu LLaVA
    if name == "image_newline":
        return "model.image_newline"
    if name.startswith("multi_modal_projector"):
        name = name.replace("multi_modal_projector", "mm")
        if "linear_1" in name:
            name = name.replace("linear_1", "0")
        if "linear_2" in name:
            name = name.replace("linear_2", "2")
        return name

    if "projection" in name:
        return name
    if "mm_projector" in name:
        name = name.replace("model.mm_projector", "mm")
        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
        return name

    return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")


def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a significant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = (
        list(range(ord("!"), ord("~") + 1))
        + list(range(ord("¡"), ord("¬") + 1))
        + list(range(ord("®"), ord("ÿ") + 1))
    )
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
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)")

# Selectable visual encoders that are compatible with this script
encoder_group = ap.add_mutually_exclusive_group()
encoder_group.add_argument("--clip-model-is-openclip", action="store_true", required=False,
                help="The clip model is from openclip (for ViT-SO400M type))")
encoder_group.add_argument("--clip-model-is-siglip", action="store_true", required=False,
                help="the visual encoder is Siglip.")

ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
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
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
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()


if args.text_only and args.vision_only:
    print("--text-only and --image-only arguments cannot be specified at the same time.")
    exit(1)

if args.use_f32:
    print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")

# output in the same directory as the model if output_dir is None
dir_model = args.model_dir

if (
    args.clip_model_is_vision or
    not os.path.exists(dir_model + "/vocab.json") or
    args.clip_model_is_openclip or
    args.clip_model_is_siglip
):
    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)
    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
#   ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]

ftype = 1
if args.use_f32:
    ftype = 0

if args.clip_model_is_siglip:
    model = SiglipVisionModel.from_pretrained(dir_model)
    processor = None
elif args.clip_model_is_vision or args.clip_model_is_openclip:
    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
has_vision_encoder = True
has_llava_projector = False
if args.text_only:
    fname_middle = "text-"
    has_vision_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 = ""

output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")

fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_llava_projector", has_llava_projector)
fout.add_file_type(ftype)
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
fout.add_name(model_name)
if args.text_only:
    fout.add_description("text-only CLIP model")
elif args.vision_only and not has_llava_projector:
    fout.add_description("vision-only CLIP model")
elif has_llava_projector:
    fout.add_description("image encoder for LLaVA")
    # add projector type
    fout.add_string("clip.projector_type", args.projector_type)
else:
    fout.add_description("two-tower CLIP model")

if has_text_encoder:
    assert t_hparams is not None
    assert tokens is not None
    if args.clip_model_is_siglip:
        text_projection_dim = 0
    else:
        text_projection_dim = t_hparams.get("projection_dim", config["projection_dim"])
    # text_model hparams
    fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
    fout.add_uint32("clip.text.projection_dim", text_projection_dim)
    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
    fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
    fout.add_token_list(tokens)



def get_non_negative_vision_feature_layers(v_hparams):
    """
    Determine the vision feature layer(s) for the llava model, which are indices into the
    hidden states of the visual encoder. Note that the hidden states array generally takes the
    form:

        [<emb input>, <output of enc block 0>, ... <output of enc block num_hidden_layers>]

    so feature indices should be offset as n+1 to get the output of encoder block n.
    We convert all vision feature layers to non-negative so that -1 can be used in
    the model as an unset value. If no vision feature layer is found, we leave it unset.
    """
    num_hidden_layers = v_hparams["num_hidden_layers"]
    to_non_negative = lambda layer_idx: layer_idx  if layer_idx >= 0 else num_hidden_layers + layer_idx + 1
    feature_layers_key = None
    # Key used for llava models in transformers
    if "vision_feature_layer" in config:
        feature_layers_key = "vision_feature_layer"
    # Key used for llava models in the original format
    elif "mm_vision_select_layer" in config:
        feature_layers_key = "mm_vision_select_layer"
    if feature_layers_key is not None:
        feature_layers = config[feature_layers_key]
        if isinstance(feature_layers, int):
            feature_layers = [feature_layers]
        return [to_non_negative(feature_layer) for feature_layer in feature_layers]

# Determine if we have explicitly specified vision feature layers in our config
feature_layers = get_non_negative_vision_feature_layers(v_hparams)

if has_vision_encoder:
    # Siglip does not have a visual projector; set projection dim to 0
    if args.clip_model_is_siglip:
        visual_projection_dim = 0
    else:
        visual_projection_dim = v_hparams.get("projection_dim", config["projection_dim"])

    # set vision_model hparams
    fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
    fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
    fout.add_uint32("clip.vision.projection_dim", visual_projection_dim)
    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
    if feature_layers:
        block_count = max(feature_layers)
    else:
        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_grid_pinpoints": [
                            #         [
                            #         336,
                            #         672
                            #         ],
                            #         [
                            #         672,
                            #         336
                            #         ],
                            #         [
                            #         672,
                            #         672
                            #         ],
                            #         [
                            #         1008,
                            #         336
                            #         ],
                            #         [
                            #         336,
                            #         1008
                            #         ]
                            #     ],
                            #     Flattened:
                            #     [
                            #         336, 672,
                            #         672, 336,
                            #         672, 672,
                            #         1008, 336,
                            #         336, 1008
                            #     ]
                            #  *
                            #  */
    if "image_grid_pinpoints" in v_hparams:
        # flatten it
        image_grid_pinpoints = []
        for pinpoint in v_hparams["image_grid_pinpoints"]:
            for p in pinpoint:
                image_grid_pinpoints.append(p)
        fout.add_array("clip.vision.image_grid_pinpoints", image_grid_pinpoints)
    if "image_crop_resolution" in v_hparams:
        fout.add_uint32("clip.vision.image_crop_resolution", v_hparams["image_crop_resolution"])
    if "image_aspect_ratio" in v_hparams:
        fout.add_string("clip.vision.image_aspect_ratio", v_hparams["image_aspect_ratio"])
    if "image_split_resolution" in v_hparams:
        fout.add_uint32("clip.vision.image_split_resolution", v_hparams["image_split_resolution"])
    if "mm_patch_merge_type" in v_hparams:
        fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"])
    if "mm_projector_type" in v_hparams:
        fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"])
    if feature_layers:
        fout.add_array("clip.vision.feature_layer", feature_layers)

    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  # pyright: ignore[reportAttributeAccessIssue]
        image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std  # pyright: ignore[reportAttributeAccessIssue]
    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)

use_gelu = v_hparams["hidden_act"] == "gelu"
fout.add_bool("clip.use_gelu", use_gelu)


if has_llava_projector:
    # By default, we drop the last layer for llava projector
    # models unless we have explicitly set vision feature layers
    if feature_layers is None:
        model.vision_model.encoder.layers.pop(-1)
    else:
        model.vision_model.encoder.layers = model.vision_model.encoder.layers[:max(feature_layers)]

    projector = torch.load(args.llava_projector)
    for name, data in projector.items():
        name = get_tensor_name(name)
        # pw and dw conv ndim==4
        if data.ndim == 2 or data.ndim == 4:
            data = data.squeeze().numpy().astype(np.float16)
        else:
            data = data.squeeze().numpy().astype(np.float32)

        fout.add_tensor(name, data)

    print("Projector tensors added\n")

state_dict = model.state_dict()
for name, data in state_dict.items():
    if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
        # we don't need this
        print(f"skipping parameter: {name}")
        continue

    name = get_tensor_name(name)
    data = data.squeeze().numpy()

    n_dims = len(data.shape)

    # ftype == 0 -> float32, ftype == 1 -> float16
    ftype_cur = 0
    if n_dims == 4:
        print(f"tensor {name} is always saved in f16")
        data = data.astype(np.float16)
        ftype_cur = 1
    elif ftype == 1:
        if name[-7:] == ".weight" and n_dims == 2:
            print("  Converting to float16")
            data = data.astype(np.float16)
            ftype_cur = 1
        else:
            print("  Converting to float32")
            data = data.astype(np.float32)
            ftype_cur = 0
    else:
        if data.dtype != np.float32:
            print("  Converting to float32")
            data = data.astype(np.float32)
            ftype_cur = 0

    print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
    fout.add_tensor(name, data)


fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()

print("Done. Output file: " + fname_out)