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giant/tests/test_cli_predict.py
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v0.3.0 post-implementation audit: resolve all 9 tracked discrepancies
Works through docs/v0.3.0-followups.md item by item, closing the gap
between the design doc and the shipped v0.3.0-stage2-autoregressive code:

1. validate.py: 7-tuple batch unpacking, sample_stage1/sample_stage2
   dispatch, stage-2 particle-type-class marginal.
2. Stage-prefixed --stage1-*/--stage2-* CLI flags for train/new-run.
3. Thread stage2_model.k_max through loader/transforms/dataset/pipeline/
   train instead of the hardcoded K_MAX constant.
4. Mixed conditioning.particle.type / conditioning.material.type support
   end-to-end (data pipeline + dwarf warm-cache).
5. conditioning.share_stages = true: one shared ConditionEncoder instance
   across both stages.
6. stage2_model.generator = "ddpm" formally deferred into design doc §11.2
   (was silently unimplemented).
7. giant predict/rollout: implement conditioning.*.type = "onehot" via the
   checkpoint's saved pdg_topn_map/mat_topn_map.
8. network.py's checkpoint-path model_config migration now fails loudly on
   non-zero legacy expert_hidden_dim/expert_n_blocks, matching config.py's
   TOML-load path (§4.2).
9. validate_config now rejects stage2_model.n_sec.mode = "truth" for a
   rollout-capable checkpoint (§9).

Also cleared all pre-existing `ty check` noise (44 -> 0 diagnostics),
mostly a test-helper dict-unpack pattern that made every unrelated
constructor keyword look like a type error.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-08-07 16:12:58 +02:00

159 lines
4.7 KiB
Python

import uuid
import yaml
from giant.cli import (
_CEPH_PREDICTIONS,
_resolve_prediction_output,
_write_prediction_ref,
)
# ---------------------------------------------------------------------------
# _resolve_prediction_output
# ---------------------------------------------------------------------------
def test_non_ceph_path_goes_to_data_parent(tmp_path):
data = tmp_path / "pools" / "pbwo4" / "full.manifest"
out, dataset_path, pred_uuid = _resolve_prediction_output(data, None)
assert out.parent == data.parent
assert out.name == f"{pred_uuid}.parquet"
assert dataset_path == data.resolve()
def test_ceph_path_goes_to_central_store(tmp_path, monkeypatch):
# Patch resolve() so /ceph/... exists on any machine running the tests.
ceph_data = _CEPH_PREDICTIONS.parent / "pools" / "pbwo4" / "full.manifest"
monkeypatch.setattr(
"giant.cli.Path.resolve",
lambda self: ceph_data if self == ceph_data else self.absolute(),
)
out, _, pred_uuid = _resolve_prediction_output(ceph_data, None)
assert out.parent == _CEPH_PREDICTIONS
assert out.name == f"{pred_uuid}.parquet"
def test_explicit_out_is_used_as_is(tmp_path):
data = tmp_path / "data.parquet"
explicit = tmp_path / "my_output.parquet"
out, _, _ = _resolve_prediction_output(data, explicit)
assert out == explicit
def test_uuid_is_valid(tmp_path):
data = tmp_path / "data.parquet"
_, _, pred_uuid = _resolve_prediction_output(data, None)
parsed = uuid.UUID(pred_uuid)
assert parsed.version == 4
def test_each_call_produces_a_distinct_uuid(tmp_path):
data = tmp_path / "data.parquet"
_, _, uuid1 = _resolve_prediction_output(data, None)
_, _, uuid2 = _resolve_prediction_output(data, None)
assert uuid1 != uuid2
def test_dataset_path_is_resolved(tmp_path):
data = tmp_path / "data.parquet"
_, dataset_path, _ = _resolve_prediction_output(data, None)
assert dataset_path.is_absolute()
# ---------------------------------------------------------------------------
# _write_prediction_ref
# ---------------------------------------------------------------------------
def test_ref_file_created_in_checkpoint_dir(tmp_path):
ckpt_dir = tmp_path / "checkpoints" / "run1"
ckpt_dir.mkdir(parents=True)
checkpoint = ckpt_dir / "best.pt"
checkpoint.touch()
out = tmp_path / "predictions" / "abc.parquet"
dataset = tmp_path / "pools" / "pbwo4" / "full.manifest"
pred_uuid = str(uuid.uuid4())
ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset)
assert ref_path == ckpt_dir / f"{pred_uuid}.yaml"
assert ref_path.exists()
def test_ref_yaml_contains_expected_fields(tmp_path):
ckpt_dir = tmp_path / "checkpoints"
ckpt_dir.mkdir()
checkpoint = ckpt_dir / "best.pt"
checkpoint.touch()
out = tmp_path / "pred.parquet"
dataset = tmp_path / "full.manifest"
pred_uuid = str(uuid.uuid4())
ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset)
data = yaml.safe_load(ref_path.read_text())
assert data["prediction_id"] == pred_uuid
assert data["output"] == str(out)
assert data["dataset"] == str(dataset)
assert data["checkpoint"] == str(checkpoint.resolve())
assert "timestamp" in data
assert "comment" not in data
def test_ref_yaml_includes_comment_when_provided(tmp_path):
ckpt_dir = tmp_path / "checkpoints"
ckpt_dir.mkdir()
checkpoint = ckpt_dir / "best.pt"
checkpoint.touch()
out = tmp_path / "pred.parquet"
dataset = tmp_path / "full.manifest"
pred_uuid = str(uuid.uuid4())
ref_path = _write_prediction_ref(
checkpoint, pred_uuid, out, dataset, comment="baseline sweep run 3"
)
data = yaml.safe_load(ref_path.read_text())
assert data["comment"] == "baseline sweep run 3"
def test_ref_timestamp_is_iso_format(tmp_path):
from datetime import datetime
ckpt_dir = tmp_path / "checkpoints"
ckpt_dir.mkdir()
checkpoint = ckpt_dir / "best.pt"
checkpoint.touch()
pred_uuid = str(uuid.uuid4())
ref_path = _write_prediction_ref(
checkpoint, pred_uuid, tmp_path / "p.parquet", tmp_path / "d"
)
data = yaml.safe_load(ref_path.read_text())
# Must parse without error and be timezone-aware (UTC).
ts = datetime.fromisoformat(data["timestamp"])
assert ts.tzinfo is not None
def test_ref_checkpoint_path_is_absolute(tmp_path):
ckpt_dir = tmp_path / "checkpoints"
ckpt_dir.mkdir()
checkpoint = ckpt_dir / "best.pt"
checkpoint.touch()
pred_uuid = str(uuid.uuid4())
ref_path = _write_prediction_ref(
checkpoint, pred_uuid, tmp_path / "p.parquet", tmp_path / "d"
)
data = yaml.safe_load(ref_path.read_text())
assert data["checkpoint"].startswith("/")