Extract predict/rollout's duplicated inference bootstrap into giant.checkpoint_io (issues.md Issue 5)
giant predict and giant rollout each carried a ~65-line, independently drifting copy of "load checkpoint -> validate -> resolve conditioning axes -> restore normalizers/vocab maps -> build models -> load weights", plus a third partial copy of _conditioning_axes in analysis/router_gating.py. A silent divergence there doesn't crash, it makes the two commands run different physics from the same checkpoint with no test coverage anywhere along that path. giant/checkpoint_io.py now holds the single implementation: load_for_inference() + an InferenceContext dataclass, raising CheckpointCompatibilityError (verbatim message text preserved) instead of calling typer directly, so it can be unit-tested and imported from non-Typer code. router_gating.py's load_router imports conditioning_axes from it lazily, keeping its "no torch at module scope" contract intact. Adds 17 direct unit tests for load_for_inference/conditioning_axes/stage_cfg plus CLI smoke tests confirming the error surfaces as typer.Exit(1) through predict and rollout — previously zero coverage on this path. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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"""Thin CLI smoke coverage for `giant rollout` (issues.md Issue 5) — confirms
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the CheckpointCompatibilityError raised by giant.checkpoint_io.load_for_inference
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surfaces as a clean typer.Exit(1) with the expected message, end-to-end
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through the CLI, not just at the giant.checkpoint_io unit level."""
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from __future__ import annotations
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import torch
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from typer.testing import CliRunner
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from giant.cli import app
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runner = CliRunner()
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def test_rollout_exits_1_on_checkpoint_missing_model_config(tmp_path):
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checkpoint = tmp_path / "bad.pt"
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torch.save({"sec_decoder": {}, "normalizer": {"sec_phys": {}}}, checkpoint)
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result = runner.invoke(
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app,
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[
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"rollout",
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"dummy.parquet",
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"--checkpoint",
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str(checkpoint),
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"--geometry",
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"dummy_geometry.pkl",
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],
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)
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assert result.exit_code == 1
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assert "checkpoint has no model_config" in result.output
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