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llama : move end-user examples to tools directory (#13249)
* llama : move end-user examples to tools directory --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
This commit is contained in:
Diego Devesa
280
tools/llava/glmedge-convert-image-encoder-to-gguf.py
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280
tools/llava/glmedge-convert-image-encoder-to-gguf.py
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import argparse
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import os
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import json
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import re
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import torch
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import numpy as np
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from gguf import *
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TEXT = "clip.text"
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VISION = "clip.vision"
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from transformers import SiglipVisionModel, SiglipVisionConfig
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def k(raw_key: str, arch: str) -> str:
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return raw_key.format(arch=arch)
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def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
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if name in (
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"logit_scale",
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"text_model.embeddings.position_ids",
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"vision_model.embeddings.position_ids",
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):
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return True
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if name in (
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"vision_model.head.probe",
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"vision_model.head.attention.in_proj_weight",
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"vision_model.head.attention.in_proj_bias",
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"vision_model.head.attention.out_proj.weight",
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"vision_model.head.attention.out_proj.bias",
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"vision_model.head.layernorm.weight",
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"vision_model.head.layernorm.bias",
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"vision_model.head.mlp.fc1.weight",
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"vision_model.head.mlp.fc1.bias",
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"vision_model.head.mlp.fc2.weight",
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"vision_model.head.mlp.fc2.bias"
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):
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return True
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if name.startswith("v") and not has_vision:
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return True
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if name.startswith("t") and not has_text:
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return True
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return False
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def get_tensor_name(name: str) -> str:
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if "projection" in name:
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return name
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if "mm_projector" in name:
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name = name.replace("model.mm_projector", "mm")
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name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
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name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
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return name
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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")
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1))
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+ list(range(ord("¡"), ord("¬") + 1))
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+ list(range(ord("®"), ord("ÿ") + 1))
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)
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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ap = argparse.ArgumentParser()
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ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
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ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
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ap.add_argument("--text-only", action="store_true", required=False,
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help="Save a text-only model. It can't be used to encode images")
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ap.add_argument("--vision-only", action="store_true", required=False,
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help="Save a vision-only model. It can't be used to encode texts")
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ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
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help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
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ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
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help="The clip model is from openclip (for ViT-SO400M type))")
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ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
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ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
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ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
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# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
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# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
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default_image_mean = [0.5, 0.5, 0.5]
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default_image_std = [0.5, 0.5, 0.5]
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ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
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ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
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# with proper
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args = ap.parse_args()
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if args.text_only and args.vision_only:
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print("--text-only and --image-only arguments cannot be specified at the same time.")
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exit(1)
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if args.use_f32:
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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.")
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# output in the same directory as the model if output_dir is None
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dir_model = args.model_dir
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if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
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vocab = None
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tokens = None
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else:
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with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
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vocab = json.load(f)
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tokens = [key for key in vocab]
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with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
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config = json.load(f)
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if args.clip_model_is_vision:
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v_hparams = config
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t_hparams = None
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else:
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v_hparams = config["vision_config"]
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t_hparams = None
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# possible data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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#
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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ftype = 1
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if args.use_f32:
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ftype = 0
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vision_config = SiglipVisionConfig(**v_hparams)
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model = SiglipVisionModel(vision_config)
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model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
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fname_middle = None
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has_text_encoder = False
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has_vision_encoder = True
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has_glm_projector = True
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if args.text_only:
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fname_middle = "text-"
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has_vision_encoder = False
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elif args.llava_projector is not None:
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fname_middle = "mmproj-"
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has_text_encoder = False
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has_glm_projector = True
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elif args.vision_only:
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fname_middle = "vision-"
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has_text_encoder = False
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else:
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fname_middle = ""
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output_dir = args.output_dir if args.output_dir is not None else dir_model
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os.makedirs(output_dir, exist_ok=True)
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output_prefix = os.path.basename(output_dir).replace("ggml_", "")
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fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
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fout = GGUFWriter(path=fname_out, arch="clip")
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fout.add_bool("clip.has_text_encoder", has_text_encoder)
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fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
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fout.add_bool("clip.has_glm_projector", has_glm_projector)
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fout.add_file_type(ftype)
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model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
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fout.add_name(model_name)
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if has_glm_projector:
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fout.add_description("image encoder for glm4v")
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fout.add_string("clip.projector_type", "adapter")
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else:
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fout.add_description("two-tower CLIP model")
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if has_text_encoder:
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assert t_hparams is not None
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assert tokens is not None
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# text_model hparams
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fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
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fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
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fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
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fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
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fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
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fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
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fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
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fout.add_token_list(tokens)
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if has_vision_encoder:
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# vision_model hparams
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fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
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fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
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fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
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fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
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fout.add_uint32("clip.vision.projection_dim", 0)
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fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
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fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
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fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
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image_mean = args.image_mean if args.image_mean is not None else default_image_mean
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image_std = args.image_std if args.image_std is not None else default_image_std
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fout.add_array("clip.vision.image_mean", image_mean)
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fout.add_array("clip.vision.image_std", image_std)
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fout.add_bool("clip.use_gelu", True)
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if has_glm_projector:
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# model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
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projector = torch.load(args.llava_projector)
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for name, data in projector.items():
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name = get_tensor_name(name)
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# pw and dw conv ndim==4
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if data.ndim == 2 or data.ndim == 4:
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data = data.squeeze().numpy().astype(np.float16)
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else:
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data = data.squeeze().numpy().astype(np.float32)
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if name.startswith("vision."):
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name=name.replace("vision.","")
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fout.add_tensor(name, data)
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print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
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# print(f"Projector {name} tensors added\n")
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state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
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for name, data in state_dict.items():
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if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
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# we don't need this
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print(f"skipping parameter: {name}")
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continue
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name = get_tensor_name(name)
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data = data.squeeze().numpy()
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n_dims = len(data.shape)
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype_cur = 0
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if n_dims == 4:
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print(f"tensor {name} is always saved in f16")
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data = data.astype(np.float16)
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ftype_cur = 1
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elif ftype == 1:
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if name[-7:] == ".weight" and n_dims == 2:
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# print(" Converting to float16")
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data = data.astype(np.float16)
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ftype_cur = 1
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else:
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# print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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else:
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if data.dtype != np.float32:
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# print(" Converting to float32")
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data = data.astype(np.float32)
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ftype_cur = 0
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print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
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# print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
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fout.add_tensor(name, data)
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fout.write_header_to_file()
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fout.write_kv_data_to_file()
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fout.write_tensors_to_file()
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fout.close()
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print("Done. Output file: " + fname_out)
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