mirror of
https://github.com/ggerganov/llama.cpp.git
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3fd62a6b1c
* py : type-check all Python scripts with Pyright * server-tests : use trailing slash in openai base_url * server-tests : add more type annotations * server-tests : strip "chat" from base_url in oai_chat_completions * server-tests : model metadata is a dict * ci : disable pip cache in type-check workflow The cache is not shared between branches, and it's 250MB in size, so it would become quite a big part of the 10GB cache limit of the repo. * py : fix new type errors from master branch * tests : fix test-tokenizer-random.py Apparently, gcc applies optimisations even when pre-processing, which confuses pycparser. * ci : only show warnings and errors in python type-check The "information" level otherwise has entries from 'examples/pydantic_models_to_grammar.py', which could be confusing for someone trying to figure out what failed, considering that these messages can safely be ignored even though they look like errors.
92 lines
3.1 KiB
Python
Executable File
92 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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from __future__ import annotations
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import uuid
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import hashlib
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import logging
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import argparse
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import os
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import sys
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from pathlib import Path
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from tqdm import tqdm
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# Necessary to load the local gguf package
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if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from gguf import GGUFReader # noqa: E402
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logger = logging.getLogger("gguf-hash")
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# UUID_NAMESPACE_LLAMA_CPP = uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp')
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UUID_NAMESPACE_LLAMA_CPP = uuid.UUID('ef001206-dadc-5f6d-a15f-3359e577d4e5')
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# For more information about what field.parts and field.data represent,
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# please see the comments in the modify_gguf.py example.
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def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
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sha1 = hashlib.sha1()
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uuidv5_sha1 = hashlib.sha1()
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uuidv5_sha1.update(UUID_NAMESPACE_LLAMA_CPP.bytes)
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# Total Weight Calculation For Progress Bar
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total_weights = 0
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for n, tensor in enumerate(reader.tensors, 1):
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# We don't need these
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if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
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continue
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# Calculate Tensor Volume
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sum_weights_in_tensor = 1
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for dim in tensor.shape:
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sum_weights_in_tensor *= dim
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total_weights += sum_weights_in_tensor
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# Hash Progress Bar
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bar = tqdm(desc="Hashing", total=total_weights, unit="weights", unit_scale=True, disable=disable_progress_bar)
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# Hashing Process
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for n, tensor in enumerate(reader.tensors, 1):
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# We don't need these
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if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
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continue
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# Progressbar
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sum_weights_in_tensor = 1
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for dim in tensor.shape:
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sum_weights_in_tensor *= dim
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bar.update(sum_weights_in_tensor)
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sha1_layer = hashlib.sha1()
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sha1_layer.update(tensor.data.data)
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sha1.update(tensor.data.data)
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uuidv5_sha1.update(tensor.data.data)
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print("sha1 {0} {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
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# Flush Hash Progress Bar
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bar.close()
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# Display Hash Output
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print("sha1 {0} {1}".format(sha1.hexdigest(), filename)) # noqa: NP100
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print("UUIDv5 {0} {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100
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def main() -> None:
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parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
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parser.add_argument("model", type=str, help="GGUF format model filename")
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parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
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parser.add_argument("--progressbar", action="store_true", help="enable progressbar")
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args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
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logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
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reader = GGUFReader(args.model, 'r')
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gguf_hash(reader, args.model, not args.progressbar)
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if __name__ == '__main__':
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main()
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