File size: 10,425 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | """Export GLiNER2.5 multilingual trained record assignment and anchorless heads."""
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 gliner2.training.trainer import ExtractorCollator
from huggingface_hub import snapshot_download
from extraction_export import ExtractionRecordAnchorlessExport, ExtractionRecordAssignmentExport
MODEL_ID = "fastino/gliner2.5-multi-v1"
MODEL_REVISION = "a221b77a8baf4a613b8f8652661d41fa10a5641e"
FIXTURE_TEXT = "Alice works at Acme. Bob works at Beta."
def record_fixture(native, mode):
schema = Schema()
builder = schema.structure("employment", mode=mode, anchor="person" if mode == "natural" else None)
builder.field("person", dtype="str")
builder.field("company", dtype="str")
batch = ExtractorCollator(native.processor, is_training=False, max_len=None, architecture="boundary")(
[(FIXTURE_TEXT, schema.build())]
)
with torch.no_grad():
core = native._encode_core(batch)
candidates = native.boundary_head(
core["text_states"], core["text_mask"], core["query_states"], core["query_mask"]
).candidates
spec = next(iter(batch.record_specs[0].values()))
group = native.record_decoder.forward_group(spec, core["query_states"][0], candidates, 0)
field_states = [
candidates.candidate_states[0, query_id][candidates.valid_mask[0, query_id]]
for query_id in group.field_query_ids
]
queries = core["query_states"][0][group.field_query_ids]
return group, spec, field_states, queries
def pad_first(value, size: int):
if value.shape[0] > size:
raise ValueError(f"Real record fixture exceeds bucket capacity {size}")
result = value.new_zeros((size, *value.shape[1:]))
result[: value.shape[0]] = value
return result
def package_bytes(path):
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("--precision", choices=["fp16", "fp32"], default="fp32")
parser.add_argument("--max-fields", type=int, default=8)
parser.add_argument("--max-candidates", type=int, default=192)
parser.add_argument("--max-instances", type=int, default=1536)
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()
head = native.record_decoder
if args.max_instances < args.max_fields * args.max_candidates:
raise ValueError("Instance bucket must hold all latent field candidates")
group, spec, field_states, queries = record_fixture(native, "natural")
anchor_index = group.field_query_ids.index(spec.anchor_query_id)
instances = field_states[anchor_index]
hidden = instances.shape[-1]
field_candidates = torch.zeros(args.max_fields, args.max_candidates, hidden)
for field_index, states in enumerate(field_states):
if states.shape[0] > args.max_candidates:
raise ValueError("Record candidate count exceeds bucket")
field_candidates[field_index, : states.shape[0]] = states
assignment_args = (pad_first(instances, args.max_instances), pad_first(queries, args.max_fields), field_candidates)
assignment_wrapper = ExtractionRecordAssignmentExport(native).eval()
with torch.no_grad():
assignment_reference = assignment_wrapper(*assignment_args)
for field_index, expected in enumerate(group.assign_logits):
actual = assignment_reference[0][: instances.shape[0], field_index, : expected.shape[1]]
if not torch.allclose(actual, expected, atol=1e-4):
raise RuntimeError("Record assignment wrapper differs from native")
assignment_trace = torch.jit.trace(assignment_wrapper, assignment_args, check_trace=False)
precision = ct.precision.FLOAT16 if args.precision == "fp16" else ct.precision.FLOAT32
assignment_model = ct.convert(
assignment_trace,
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.float32)
for name, value in zip(("instance_states", "field_queries", "field_candidate_states"), assignment_args)
],
outputs=[
ct.TensorType(name="assignment_logits", dtype=np.float32),
ct.TensorType(name="object_logits", dtype=np.float32),
ct.TensorType(name="latent_seed_logits", dtype=np.float32),
],
)
assignment_model.short_description = "GLiNER2.5 multilingual trained record assignment and object heads"
assignment_model.author = "Fastino (original); Fluid Inference (Core ML conversion)"
assignment_model.license = "Apache-2.0"
assignment_model.user_defined_metadata.update(
{
"source_model": MODEL_ID,
"source_revision": MODEL_REVISION,
"stage": "trained record assignment, object and latent seed heads",
"field_capacity": str(args.max_fields),
"candidate_capacity": str(args.max_candidates),
"instance_capacity": str(args.max_instances),
}
)
out = Path(args.output_dir)
out.mkdir(parents=True, exist_ok=True)
suffix = f"{args.precision}_F{args.max_fields}_C{args.max_candidates}_I{args.max_instances}"
assignment_path = out / f"gliner2_multi_record_assignment_{suffix}.mlpackage"
if assignment_path.exists():
shutil.rmtree(assignment_path)
assignment_model.save(str(assignment_path))
assignment_runtime = ct.models.MLModel(str(assignment_path), compute_units=ct.ComputeUnit.CPU_ONLY)
assignment_prediction = assignment_runtime.predict(
{
name: value.numpy().astype(np.float32)
for name, value in zip(("instance_states", "field_queries", "field_candidate_states"), assignment_args)
}
)
assignment_errors = {
name: float(np.max(np.abs(assignment_prediction[name] - expected.numpy())))
for name, expected in zip(("assignment_logits", "object_logits", "latent_seed_logits"), assignment_reference)
}
_, _, anchorless_field_states, _ = record_fixture(native, "anchorless")
context = torch.cat(anchorless_field_states, 0)
context_size = args.max_fields * args.max_candidates
context_states = pad_first(context, context_size)
context_mask = torch.zeros(context_size, dtype=torch.float32)
context_mask[: context.shape[0]] = 1.0
anchorless_wrapper = ExtractionRecordAnchorlessExport(native).eval()
with torch.no_grad():
anchorless_reference = anchorless_wrapper(context_states, context_mask)
native_states = head._anchorless_states(anchorless_field_states)
wrapper_error = float((anchorless_reference - native_states).abs().max())
if wrapper_error > 1e-4:
raise RuntimeError(f"Anchorless wrapper differs from native: {wrapper_error}")
anchorless_trace = torch.jit.trace(anchorless_wrapper, (context_states, context_mask), check_trace=False)
anchorless_model = ct.convert(
anchorless_trace,
convert_to="mlprogram",
minimum_deployment_target=ct.target.iOS17,
compute_precision=precision,
compute_units=ct.ComputeUnit.CPU_ONLY,
inputs=[
ct.TensorType(name="context_states", shape=tuple(context_states.shape), dtype=np.float32),
ct.TensorType(name="context_mask", shape=tuple(context_mask.shape), dtype=np.float32),
],
outputs=[ct.TensorType(name="instance_states", dtype=np.float32)],
)
anchorless_model.short_description = "GLiNER2.5 multilingual trained anchorless record instance head"
anchorless_model.author = "Fastino (original); Fluid Inference (Core ML conversion)"
anchorless_model.license = "Apache-2.0"
anchorless_model.user_defined_metadata.update(
{
"source_model": MODEL_ID,
"source_revision": MODEL_REVISION,
"stage": "trained anchorless record instance head",
"context_capacity": str(context_size),
}
)
anchorless_path = out / f"gliner2_multi_record_anchorless_{suffix}.mlpackage"
if anchorless_path.exists():
shutil.rmtree(anchorless_path)
anchorless_model.save(str(anchorless_path))
anchorless_runtime = ct.models.MLModel(str(anchorless_path), compute_units=ct.ComputeUnit.CPU_ONLY)
anchorless_prediction = anchorless_runtime.predict(
{
"context_states": context_states.numpy().astype(np.float32),
"context_mask": context_mask.numpy().astype(np.float32),
}
)["instance_states"]
anchorless_error = float(np.max(np.abs(anchorless_prediction - anchorless_reference.numpy())))
if not all(np.isfinite(value) for value in (*assignment_errors.values(), anchorless_error)):
raise RuntimeError("Record head produced non-finite values")
report = {
"source_model": MODEL_ID,
"source_revision": MODEL_REVISION,
"precision": args.precision,
"fixture": FIXTURE_TEXT,
"shape": {"fields": args.max_fields, "candidates": args.max_candidates, "instances": args.max_instances},
"assignment_max_absolute_errors": assignment_errors,
"anchorless_wrapper_max_absolute_error": wrapper_error,
"anchorless_coreml_max_absolute_error": anchorless_error,
"packages": {
"assignment": {"path": str(assignment_path), "bytes": package_bytes(assignment_path)},
"anchorless": {"path": str(anchorless_path), "bytes": package_bytes(anchorless_path)},
},
"coremltools": ct.__version__,
"torch": torch.__version__,
}
(out / f"record-{suffix}.json").write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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