vbatts/llama.cpp
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llava : support v1.6 (#5267)

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* 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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John 2024-02-14 08:38:35 +01:00 • committed by GitHub
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commit aa23412989
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10 changed files with 1229 additions and 205 deletions
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37
convert.py
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@ -1173,7 +1173,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
for (name, tensor) in model.items()}
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
@ -1199,7 +1199,11 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
for name, lazy_tensor in model.items():
tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
if name_new is None:
raise Exception(f"Unexpected tensor name: {name}")
if skip_unknown:
print(f"Unexpected tensor name: {name} - skipping")
continue
else:
raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
if tensor_type in should_skip:
print(f"skipping tensor {name_new}")
@ -1377,19 +1381,20 @@ def main(args_in: list[str] | None = None) -> None:
output_choices.append("q8_0")
vocab_types = ["spm", "bpe", "hfft"]
parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None)
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY)
parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine")
parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None)
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY)
parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine")
parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
args = parser.parse_args(args_in)
if args.awq_path:
@ -1461,7 +1466,7 @@ def main(args_in: list[str] | None = None) -> None:
print(f"Special vocab info: {special_vocab}")
model = model_plus.model
model = convert_model_names(model, params)
model = convert_model_names(model, params, args.skip_unknown)
ftype = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, ftype)
outfile = args.outfile or default_outfile(model_plus.paths, ftype)