File size: 9,323 Bytes
fcf4209 | 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 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | """Export the trained GLiNER2.5 multilingual boundary extraction stages to Core ML."""
import argparse
import json
import shutil
from pathlib import Path
import coremltools as ct
import numpy as np
import torch
from gliner2 import AutoExtractor, Schema
from huggingface_hub import snapshot_download
from convert_extraction_names import FEATURE_NAMES, SCORE_INPUT_NAMES
from extraction_export import ExtractionFeaturesExport, ExtractionScoreExport, coreml_trace_patches
from extraction_pool import select_candidates
from preprocessing import prepare_extraction
MODEL_ID = "fastino/gliner2.5-multi-v1"
MODEL_REVISION = "a221b77a8baf4a613b8f8652661d41fa10a5641e"
FIXTURE_TEXT = "Alice founded Acme in Toronto in 2020."
FIXTURE_SCHEMA = Schema().entities(["person", "organization", "location"])
def package_bytes(path: Path) -> int:
return sum(file.stat().st_size for file in path.rglob("*") if file.is_file())
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output-dir", default="build/extraction")
parser.add_argument("--length", type=int, default=128, help="Subword capacity")
parser.add_argument("--max-words", type=int, default=64)
parser.add_argument("--max-queries", type=int, default=8)
parser.add_argument("--precision", choices=["fp16", "fp32"], default="fp32")
args = parser.parse_args()
torch.set_num_threads(4)
source = snapshot_download(
MODEL_ID,
revision=MODEL_REVISION,
allow_patterns=[
"config.json",
"encoder_config/*",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
],
)
native = AutoExtractor.from_pretrained(source, map_location="cpu").eval()
arrays, batch = prepare_extraction(
native.processor, FIXTURE_TEXT, FIXTURE_SCHEMA, args.length, args.max_words, args.max_queries
)
tensors = tuple(torch.from_numpy(value) for value in arrays.values())
features_wrapper = ExtractionFeaturesExport(native).eval()
with torch.no_grad(), coreml_trace_patches():
features_reference = features_wrapper(*tensors)
traced_features = torch.jit.trace(features_wrapper, tensors, check_trace=False)
with torch.no_grad():
native_core = native._encode_core(batch)
valid_words = native_core["text_states"].shape[1]
valid_queries = native_core["query_states"].shape[1]
routing_error = max(
float((features_reference[0][:, :valid_words] - native_core["text_states"]).abs().max()),
float((features_reference[1][:, :valid_queries] - native_core["query_states"]).abs().max()),
)
if routing_error > 1e-4:
raise RuntimeError(f"Traced routing differs from native: {routing_error}")
precision = ct.precision.FLOAT16 if args.precision == "fp16" else ct.precision.FLOAT32
input_names = tuple(arrays)
features_model = ct.convert(
traced_features,
convert_to="mlprogram",
minimum_deployment_target=ct.target.iOS17,
compute_precision=precision,
compute_units=ct.ComputeUnit.CPU_ONLY,
inputs=[ct.TensorType(name=name, shape=arrays[name].shape, dtype=arrays[name].dtype) for name in input_names],
outputs=[ct.TensorType(name=name, dtype=np.float32) for name in FEATURE_NAMES],
)
features_model.short_description = "GLiNER2.5 multilingual trained boundary extraction features"
features_model.author = "Fastino (original); Fluid Inference (Core ML conversion)"
features_model.license = "Apache-2.0"
features_model.user_defined_metadata.update(
{
"source_model": MODEL_ID,
"source_revision": MODEL_REVISION,
"stage": "extraction features and trained boundary heads",
"subword_capacity": str(args.length),
"word_capacity": str(args.max_words),
"query_capacity": str(args.max_queries),
}
)
out = Path(args.output_dir)
out.mkdir(parents=True, exist_ok=True)
shutil.copy2(Path(source) / "config.json", out / "config.json")
tokenizer_dir = out / "tokenizer"
tokenizer_dir.mkdir(exist_ok=True)
for name in ("tokenizer.json", "tokenizer_config.json"):
shutil.copy2(Path(source) / name, tokenizer_dir / name)
suffix = f"{args.precision}_L{args.length}_W{args.max_words}_Q{args.max_queries}"
features_path = out / f"gliner2_multi_extraction_features_{suffix}.mlpackage"
if features_path.exists():
shutil.rmtree(features_path)
features_model.save(str(features_path))
print(f"Saved {features_path}", flush=True)
head = native.boundary_head
pooled = select_candidates(
features_reference[8],
features_reference[9],
features_reference[3].bool(),
tensors[5].bool(),
features_reference[4],
features_reference[5],
boundary_top_k=head.shared_pool_builder.pool_boundary_top_k,
pool_size=head.shared_pool_builder.pool_size,
min_pool_per_query=head.shared_pool_builder.min_pool_per_query,
)
score_tensors = (
features_reference[0],
tensors[3],
features_reference[1],
tensors[5],
features_reference[2],
features_reference[4],
features_reference[5],
features_reference[6],
features_reference[7],
pooled.indices.int(),
pooled.mask.float(),
pooled.compat_logits,
)
scorer_wrapper = ExtractionScoreExport(native).eval()
with torch.no_grad():
scores_reference = scorer_wrapper(*score_tensors)
traced_scores = torch.jit.trace(scorer_wrapper, score_tensors, check_trace=False)
scorer_model = ct.convert(
traced_scores,
convert_to="mlprogram",
minimum_deployment_target=ct.target.iOS17,
compute_precision=precision,
compute_units=ct.ComputeUnit.CPU_ONLY,
inputs=[
ct.TensorType(
name=name, shape=tuple(value.shape), dtype=np.int32 if name == "candidate_indices" else np.float32
)
for name, value in zip(SCORE_INPUT_NAMES, score_tensors)
],
outputs=[
ct.TensorType(name="pair_logits", dtype=np.float32),
ct.TensorType(name="candidate_states", dtype=np.float32),
],
)
scorer_model.short_description = "GLiNER2.5 multilingual trained shared-pool extraction scorer"
scorer_model.author = "Fastino (original); Fluid Inference (Core ML conversion)"
scorer_model.license = "Apache-2.0"
scorer_model.user_defined_metadata.update(
{
"source_model": MODEL_ID,
"source_revision": MODEL_REVISION,
"stage": "trained extraction candidate scorer",
"candidate_capacity": str(head.shared_pool_builder.pool_size),
}
)
scorer_path = out / f"gliner2_multi_extraction_scorer_{suffix}.mlpackage"
if scorer_path.exists():
shutil.rmtree(scorer_path)
scorer_model.save(str(scorer_path))
print(f"Saved {scorer_path}", flush=True)
# The first runtime check uses the same selected real fixture as the trace.
runtime_features = ct.models.MLModel(str(features_path), compute_units=ct.ComputeUnit.CPU_ONLY)
predicted_features = runtime_features.predict(arrays)
errors = {
name: float(np.max(np.abs(np.asarray(predicted_features[name]) - reference.detach().numpy())))
for name, reference in zip(FEATURE_NAMES, features_reference)
}
if any(not np.isfinite(value) for value in errors.values()):
raise RuntimeError("Extraction features contain non-finite values")
runtime_scorer = ct.models.MLModel(str(scorer_path), compute_units=ct.ComputeUnit.CPU_ONLY)
score_arrays = {
name: value.detach().numpy().astype(np.int32 if name == "candidate_indices" else np.float32)
for name, value in zip(SCORE_INPUT_NAMES, score_tensors)
}
predicted_scores = runtime_scorer.predict(score_arrays)
errors["pair_logits"] = float(np.max(np.abs(predicted_scores["pair_logits"] - scores_reference[0].numpy())))
errors["candidate_states"] = float(
np.max(np.abs(predicted_scores["candidate_states"] - scores_reference[1].numpy()))
)
if any(not np.isfinite(value) for value in errors.values()):
raise RuntimeError("Extraction scorer contains non-finite values")
report = {
"source_model": MODEL_ID,
"source_revision": MODEL_REVISION,
"precision": args.precision,
"fixture": FIXTURE_TEXT,
"shape": {
"subwords": args.length,
"words": args.max_words,
"queries": args.max_queries,
"candidates": head.shared_pool_builder.pool_size,
},
"routing_max_absolute_error": routing_error,
"runtime_max_absolute_errors": errors,
"packages": {
"features": {"path": str(features_path), "bytes": package_bytes(features_path)},
"scorer": {"path": str(scorer_path), "bytes": package_bytes(scorer_path)},
},
"coremltools": ct.__version__,
"torch": torch.__version__,
}
(out / f"conversion-{suffix}.json").write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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