File size: 2,940 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 | """Real-checkpoint parity for trained extraction graph wrappers."""
from pathlib import Path
import torch
from gliner2 import AutoExtractor, Schema
from gliner2.training.trainer import ExtractorCollator
from extraction_export import ExtractionFeaturesExport, ExtractionScoreExport, coreml_trace_patches
from extraction_pool import select_candidates
from preprocessing import prepare_extraction
SOURCE = (
Path.home()
/ ".cache/huggingface/hub/models--fastino--gliner2.5-multi-v1/snapshots/a221b77a8baf4a613b8f8652661d41fa10a5641e"
)
def test_export_wrappers_match_native_entity_scores():
torch.set_num_threads(4)
native = AutoExtractor.from_pretrained(str(SOURCE), map_location="cpu").eval()
text = "Alice founded Acme in Toronto in 2020."
schema = Schema().entities(["person", "organization", "location"])
batch = ExtractorCollator(native.processor, is_training=False, max_len=128, architecture=native.architecture)(
[(text, schema.build())]
)
arrays, _ = prepare_extraction(native.processor, text, schema, 128, 64, 8)
arguments = tuple(torch.from_numpy(value) for value in arrays.values())
with torch.no_grad():
core = native._encode_core(batch)
expected = native.boundary_head(
core["text_states"], core["text_mask"], core["query_states"], core["query_mask"]
)
with coreml_trace_patches():
features = ExtractionFeaturesExport(native).eval()(*arguments)
assert torch.allclose(features[0][:, : core["text_states"].shape[1]], core["text_states"], atol=1e-5)
assert torch.allclose(features[1][:, : core["query_states"].shape[1]], core["query_states"], atol=1e-5)
pool = select_candidates(
features[8],
features[9],
features[3].bool(),
arguments[5].bool(),
features[4],
features[5],
boundary_top_k=native.boundary_head.shared_pool_builder.pool_boundary_top_k,
pool_size=native.boundary_head.shared_pool_builder.pool_size,
min_pool_per_query=native.boundary_head.shared_pool_builder.min_pool_per_query,
)
assert torch.equal(
pool.indices.unsqueeze(1).expand_as(expected.candidates.indices), expected.candidates.indices
)
scores, candidate_states = ExtractionScoreExport(native).eval()(
features[0],
arguments[3],
features[1],
arguments[5],
features[2],
features[4],
features[5],
features[6],
features[7],
pool.indices.int(),
pool.mask.float(),
pool.compat_logits,
)
assert torch.allclose(scores[:, : core["query_states"].shape[1]], expected.candidates.pair_logits, atol=1e-4)
assert torch.allclose(candidate_states.unsqueeze(1), expected.candidates.candidate_states[:, :1], atol=1e-4)
|