llava : support v1.6 (#5267)
* Create llava-survery-v2.py * Update convert-image-encoder-to-gguf.py * Update convert-image-encoder-to-gguf.py * Rename llava-survery-v2.py to llava-surgery-v2.py * Update convert-image-encoder-to-gguf.py will now search for projector * Update convert-image-encoder-to-gguf.py whoops * Update llava-surgery-v2.py * Clip: Bugfix for normalization (it did not loat the 3 std and mean values) Clip: bicubic resize function Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6) Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final convert-image-encoder: fixed image-grid flattening * whitespace corrections * ws * Tensors are now properly permuted. Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference. * ws * added verbose_prompt support into cli added stopwords for llava-1.6 into cli * moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed * ws * convert : skip unknown tensors (need for LLaVA) * llava : update readme * llava : fix compile warnings * llava : style * convert : add --skip-unknown CLI arg * server : remove clip structs * bugfix for non llava-1.6 It should now work with llava-1.5 as well * clip : minor code rearrange * llava : update readme a bit --------- Co-authored-by: John <cmt-nct@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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167
examples/llava/llava-surgery-v2.py
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examples/llava/llava-surgery-v2.py
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import argparse
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import glob
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import os
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import torch
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from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file
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# Function to determine if file is a SafeTensor file
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def is_safetensor_file(file_path):
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return file_path.endswith('.safetensors')
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# Unified loading function
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def load_model(file_path):
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if is_safetensor_file(file_path):
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tensors = {}
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with safe_open(file_path, framework="pt", device="cpu") as f:
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for key in f.keys():
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tensors[key] = f.get_tensor(key).clone()
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# output shape
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print(f"{key} : {tensors[key].shape}")
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return tensors, 'safetensor'
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else:
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return torch.load(file_path, map_location=torch.device('cpu')), 'pytorch'
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# Unified saving function
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def save_model(model, file_path, file_type):
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if file_type == 'safetensor':
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# safe_save(model, file_path)
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save_file(model, file_path)
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else:
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torch.save(model, file_path)
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# Adapted function to clean vision tower from checkpoint
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def clean_vision_tower_from_checkpoint(checkpoint_path):
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checkpoint, file_type = load_model(checkpoint_path)
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# file_type = 'pytorch'
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model_path = os.path.dirname(checkpoint_path)
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print(f"Searching for vision tower tensors in {checkpoint_path}")
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clip_tensors = [k for k, v in checkpoint.items() if (k.startswith("model.vision_tower") or k.startswith("vit."))]
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if len(clip_tensors) > 0:
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print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}")
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# Adapted for file type
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clip_path = os.path.join(model_path, "llava.clip")
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if os.path.exists(clip_path):
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print(f"Loading existing llava.clip from {clip_path}")
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existing_clip, _ = load_model(clip_path)
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else:
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print(f"Creating new llava.clip at {clip_path}")
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existing_clip = {}
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# Update existing_clip with new tensors, avoid duplicates
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for name in clip_tensors:
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simple_name = name[name.index('vision_model.'):] if 'vision_model.' in name else name
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print(f"Adding {simple_name} to llava.clip")
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if simple_name not in existing_clip:
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existing_clip[simple_name] = checkpoint[name]
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# Save the updated clip tensors back to llava.clip
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save_model(existing_clip, clip_path, 'pytorch')
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# Remove the tensors from the original checkpoint
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for name in clip_tensors:
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del checkpoint[name]
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# Save the updated checkpoint
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checkpoint_path = checkpoint_path
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save_model(checkpoint, checkpoint_path, file_type)
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return True
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return False
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def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector):
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newline_checkpoint_path = None
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projector_checkpoint_path = None
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for path in checkpoint_paths:
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checkpoint, _ = load_model(path)
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if newline_criteria(checkpoint) and newline_checkpoint_path is None:
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newline_checkpoint_path = path
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if projector(checkpoint):
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projector_checkpoint_path = path
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return newline_checkpoint_path, projector_checkpoint_path
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def newline_criteria(checkpoint):
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return any(k.startswith("model.image_newline") for k in checkpoint.keys())
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def proj_criteria(checkpoint):
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return any(k.startswith("model.mm_projector") or k.startswith("vision_proj.") for k in checkpoint.keys())
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# Command-line interface setup
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ap = argparse.ArgumentParser()
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ap.add_argument("-m", "--model", required=True, help="Path to LLaVA v1.5+ model")
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ap.add_argument("-C", "--clean-vision-tower", action="store_true", help="Remove any vision tower from the model files")
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args = ap.parse_args()
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if args.clean_vision_tower:
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# Generalized to handle both PyTorch and SafeTensors models
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model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
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# checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and path.startswith('pytorch')) or (path.endswith('.safetensors') and path.startswith('model'))]
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checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
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for projector_checkpoint_path in checkpoint_paths:
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print(f"Cleaning {projector_checkpoint_path}")
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if not clean_vision_tower_from_checkpoint(projector_checkpoint_path):
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print(f"No vision tower found in {projector_checkpoint_path}")
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# we break once none is found, so far all models append them at the end
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# break
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print("Done! All vision tower tensors are removed from the model files and stored in llava.clip file.")
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# Now we look for the projector in the last checkpoint
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model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
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checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
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# last_checkpoint_path = checkpoint_paths[0]
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# first_checkpoint_path = checkpoint_paths[-1]
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newline_checkpoint_path, projector_checkpoint_path = find_relevant_checkpoints(checkpoint_paths, newline_criteria, proj_criteria)
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print(f"Taking projector from {projector_checkpoint_path}")
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first_mm_tensors = []
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first_checkpoint = None
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if newline_checkpoint_path is not None:
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print(f"Taking newline from {newline_checkpoint_path}")
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first_checkpoint, file_type = load_model(newline_checkpoint_path)
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first_mm_tensors = [k for k, v in first_checkpoint.items() if k.startswith("model.image_newline")]
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# Load the checkpoint
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mm_tensors = []
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last_checkpoint = None
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if projector_checkpoint_path is not None:
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last_checkpoint, file_type = load_model(projector_checkpoint_path)
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mm_tensors = [k for k, v in last_checkpoint.items() if k.startswith("model.mm_projector") or k.startswith("vision_proj.")]
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if len(mm_tensors) == 0:
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if last_checkpoint is not None:
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for k, v in last_checkpoint.items():
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print(k)
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print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.")
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print("No tensors found. Is this a LLaVA model?")
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exit()
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print(f"Found {len(mm_tensors)} tensors to extract.")
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print(f"Found additional {len(first_mm_tensors)} tensors to extract.")
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# projector = {name: checkpoint.[name].float() for name in mm_tensors}
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projector = {}
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for name in mm_tensors:
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projector[name] = last_checkpoint[name].float()
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for name in first_mm_tensors:
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projector[name] = first_checkpoint[name].float()
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if len(projector) > 0:
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save_model(projector, f"{args.model}/llava.projector", 'pytorch')
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for name in mm_tensors:
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del last_checkpoint[name]
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for name in first_mm_tensors:
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del first_checkpoint[name]
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if len(mm_tensors) > 0:
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save_model(last_checkpoint, projector_checkpoint_path, file_type)
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if len(first_mm_tensors) > 0:
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save_model(first_checkpoint, newline_checkpoint_path, file_type)
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print("Done!")
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print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
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print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")
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