File size: 3,341 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 | """Real-checkpoint parity of all three native record formation modes."""
from pathlib import Path
import torch
from gliner2 import AutoExtractor, Schema
from gliner2.training.trainer import ExtractorCollator
from extraction_export import ExtractionRecordAnchorlessExport, ExtractionRecordAssignmentExport
SOURCE = (
Path.home()
/ ".cache/huggingface/hub/models--fastino--gliner2.5-multi-v1/snapshots/a221b77a8baf4a613b8f8652661d41fa10a5641e"
)
def test_record_heads_match_native_natural_latent_and_anchorless():
torch.set_num_threads(4)
native = AutoExtractor.from_pretrained(str(SOURCE), map_location="cpu").eval()
text = "Alice works at Acme. Bob works at Beta."
for mode in ("natural", "latent", "anchorless"):
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")(
[(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 = []
for query_id in group.field_query_ids:
keep = candidates.valid_mask[0, query_id]
field_states.append(candidates.candidate_states[0, query_id][keep])
field_queries = core["query_states"][0][group.field_query_ids]
max_candidates = max(states.shape[0] for states in field_states)
padded_fields = torch.stack(
[
torch.nn.functional.pad(states, (0, 0, 0, max_candidates - states.shape[0]))
for states in field_states
]
)
if mode == "natural":
anchor_index = group.field_query_ids.index(spec.anchor_query_id)
instances = field_states[anchor_index]
elif mode == "latent":
instances = torch.cat(field_states, 0)
else:
instances = native.record_decoder._anchorless_states(field_states)
context = torch.cat(field_states, 0)
predicted = ExtractionRecordAnchorlessExport(native).eval()(context, torch.ones(context.shape[0]))
assert torch.allclose(predicted, instances, atol=1e-5)
assignment, object_scores, latent_scores = ExtractionRecordAssignmentExport(native).eval()(
instances, field_queries, padded_fields
)
for field_index, expected in enumerate(group.assign_logits):
assert torch.allclose(assignment[:, field_index, : expected.shape[1]], expected, atol=1e-5)
if mode == "anchorless":
assert torch.allclose(object_scores, group.object_logits, atol=1e-5)
if mode == "latent":
assert torch.allclose(latent_scores, group.object_logits, atol=1e-5)
|