Instructions to use FluidInference/jeff-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiFormer
How to use FluidInference/jeff-coreml with GLiFormer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 2,488 Bytes
d00480b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | """Create targeted int8 Jeff Core ML variants without modifying the FP16 source."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import coremltools as ct
import coremltools.optimize.coreml as cto
def package_bytes(path: Path) -> int:
return sum(file.stat().st_size for file in path.rglob("*") if file.is_file())
def selected_constants(model, scheme: str) -> list[str]:
metadata = cto.get_weights_metadata(model, weight_threshold=2048)
selected = []
for name, weight in metadata.items():
if not weight.child_ops:
continue
consumer = weight.child_ops[0].op_type
embedding = consumer == "gather" and len(weight.val.shape) == 2
linear = consumer == "linear" and len(weight.val.shape) == 2
if (scheme == "e8" and embedding) or (scheme == "w8" and (embedding or linear)):
selected.append(name)
if not selected:
raise ValueError(f"no eligible constants found for {scheme}")
return selected
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--source", type=Path, default=Path("build/JeffDecision-L128-FP16.mlpackage"))
parser.add_argument("--scheme", choices=("e8", "w8"), required=True)
parser.add_argument("--output", type=Path)
args = parser.parse_args()
output = args.output or Path(f"build/JeffDecision-L128-{args.scheme.upper()}.mlpackage")
if output.exists():
parser.error(f"output already exists: {output}")
model = ct.models.MLModel(str(args.source), compute_units=ct.ComputeUnit.CPU_ONLY)
names = selected_constants(model, args.scheme)
config = cto.OpLinearQuantizerConfig(mode="linear_symmetric", dtype="int8", granularity="per_channel")
compressed = cto.linear_quantize_weights(
model, cto.OptimizationConfig(op_name_configs={name: config for name in names})
)
compressed.user_defined_metadata["precision"] = args.scheme
compressed.save(str(output))
report = {
"source": str(args.source.resolve()),
"source_bytes": package_bytes(args.source),
"output": str(output.resolve()),
"output_bytes": package_bytes(output),
"compressed_constants": len(names),
"scheme": args.scheme,
}
Path(f"build/quantize-{args.scheme}.json").write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2), flush=True)
if __name__ == "__main__":
main()
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