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Add private TopK index-chain conversion, integer Unsqueeze consumer preparation and a standalone InstanceNormalization rank helper. Part 15/17 depends on part 14; merge the series from the bottom upward. Public registration follows in part 16.
TopK rewrites only a private, standard operator chain with unused values and an exactly proven Reshape/Tile layout. It preserves public/captured results, intentional floating cast rounding, runtime initializer defaults and unsupported schemas. Planning validates all candidate arities before mutation. The replacement uses allocated controls and names, updates index types and shapes, and inserts support nodes without copying unrelated payloads.
Unsqueeze preserves integer data and structural consumers, uses ONNX schema type bindings for consumers already requiring FLOAT, and leaves explicit casts and opaque operators intact. FLOAT repair creates a private view while retaining the original integer owner.
The standalone InstanceNormalization helper preserves input dtypes, attributes and native result rank. It validates unsupported ranks and adapter schemas before mutation, allocates names across existing owners, and declares generated legacy initializer inputs. Zero-sized inputs remain native. This helper stays separate from the ordered pipeline's existing boundary-normalization stage.
The optional runtime test import is bound once at module scope with pytest.importorskip.
Standalone InstanceNormalization validates known rank through the shared metadata-rank helper before checking concrete rewrite dimensions. Unsupported symbolic ranks refuse during preflight, preserving earlier eligible nodes and caller state.
InstanceNormalization parity coverage combines default and nontrivial affine parameters in one parametrized test. Concrete/symbolic late-rank refusal coverage retains the transactional guarantee.
The registered stage selects the prepared four-dimensional TopK layout explicitly.
Cast preparation protects the first integer index Cast only when the registered TopK stage can consume the complete current private chain. Both stages share one read-only eligibility planner. TopK also handles private INT32/INT64 Casts inserted by GatherElements preparation; floating-rounding paths remain native. Three registered-pipeline cases fail without protection, and two integer-tail regressions fail with the original single-Cast matcher. Seven registration/ownership/runtime-control cases and the floating-tail control retain existing behavior. All 68 TopK/Cast tests pass with checked CPU values. A single Windows CPU sample rewrites 512 chains with 128 MiB of retained payloads in 0.144686 seconds and +9.5000 MiB sampled peak RSS; tensor identities, bytes and runtime outputs are preserved (2 ms polling).
The real pipeline module is now loaded once at module scope with importlib. This follows the import convention and remains compatible with the public function re-export. The registered-pipeline coverage is unchanged; all 39 TopK tests pass.
Testing
Windows CPU: 1932 passed in the complete restacked graph-surgery and compatibility suite with Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. The public DLA API uses direct ONNX saving; existing initializer conversion and unrelated surgeries are preserved. Commits are signed and all commit hooks pass with the equivalent Linux mypy platform setting. GPU execution, DLA compilation and the complete Linux suite were not run locally.
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The DLA graph-surgery changes add transforms for rank-3 InstanceNormalization, eligible integer Unsqueeze consumers, and eligible TopK index chains. The compatibility pipeline registers Unsqueeze and TopK, and the FP32 cast stage can preserve planned TopK indices.
Eligible standard consumers of integer Unsqueeze outputs are redirected to private FLOAT Cast outputs. Tests check redirected consumers and unchanged cases.
TopK index-chain rewrite and pipeline integration modelopt/onnx/graph_surgery/dla_transforms/dla_topk.py, modelopt/onnx/graph_surgery/dla_transforms/dla_cast_to_fp32.py, modelopt/onnx/graph_surgery/make_dla_compatible.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_topk.py
Eligible TopK index chains are rewritten to use Gather. The rewrite removes intermediate Reshape, Tile, and optional Cast nodes. The pipeline registers Unsqueeze before Greater and TopK after shape inference. The FP32 cast stage can preserve planned TopK indices. Tests check rewrite conditions, pipeline options, output equivalence, and unchanged cases.
Some eligible TopK chains remain in their original form rather than receiving the DLA rewrite. Correct the early Cast protection before merging this compatibility change.
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Review comments at @modelopt/onnx/graph_surgery/make_dla_compatible.py:
- Line 149: Update the Cast-stage protection logic used by the rank-converted
TopK handler registered with _apply_topk so it protects the first Cast only when
the complete integer TopK → Cast → Reshape → Tile → Cast → GatherElements chain
is eligible for the DLA rewrite. Keep FLOAT-target Casts eligible for the
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@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_topk.py:
- Line 345: Move the import of modelopt.onnx.graph_surgery.make_dla_compatible
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@modelopt/onnx/graph_surgery/dla_transforms/dla_cast_to_fp32.py:
- Around line 58-63: Update _topk_plans with a read-only option to follow
eligible integer tail Casts even when rank_converted is false. At the pre-rank
Cast boundary, use this option to add those chains’ Cast outputs to blocked,
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What does this PR do?
Type of change: new feature
Add private TopK index-chain conversion, integer Unsqueeze consumer preparation and a standalone InstanceNormalization rank helper. Part 15/17 depends on part 14; merge the series from the bottom upward. Public registration follows in part 16.
TopK rewrites only a private, standard operator chain with unused values and an exactly proven Reshape/Tile layout. It preserves public/captured results, intentional floating cast rounding, runtime initializer defaults and unsupported schemas. Planning validates all candidate arities before mutation. The replacement uses allocated controls and names, updates index types and shapes, and inserts support nodes without copying unrelated payloads.
Unsqueeze preserves integer data and structural consumers, uses ONNX schema type bindings for consumers already requiring FLOAT, and leaves explicit casts and opaque operators intact. FLOAT repair creates a private view while retaining the original integer owner.
The standalone InstanceNormalization helper preserves input dtypes, attributes and native result rank. It validates unsupported ranks and adapter schemas before mutation, allocates names across existing owners, and declares generated legacy initializer inputs. Zero-sized inputs remain native. This helper stays separate from the ordered pipeline's existing boundary-normalization stage.
The optional runtime test import is bound once at module scope with
pytest.importorskip.Standalone InstanceNormalization validates known rank through the shared metadata-rank helper before checking concrete rewrite dimensions. Unsupported symbolic ranks refuse during preflight, preserving earlier eligible nodes and caller state.
InstanceNormalization parity coverage combines default and nontrivial affine parameters in one parametrized test. Concrete/symbolic late-rank refusal coverage retains the transactional guarantee.
Usage
The registered stage selects the prepared four-dimensional TopK layout explicitly.
Cast preparation protects the first integer index Cast only when the registered TopK stage can consume the complete current private chain. Both stages share one read-only eligibility planner. TopK also handles private INT32/INT64 Casts inserted by GatherElements preparation; floating-rounding paths remain native. Three registered-pipeline cases fail without protection, and two integer-tail regressions fail with the original single-Cast matcher. Seven registration/ownership/runtime-control cases and the floating-tail control retain existing behavior. All 68 TopK/Cast tests pass with checked CPU values. A single Windows CPU sample rewrites 512 chains with 128 MiB of retained payloads in 0.144686 seconds and +9.5000 MiB sampled peak RSS; tensor identities, bytes and runtime outputs are preserved (2 ms polling).
The real pipeline module is now loaded once at module scope with importlib. This follows the import convention and remains compatible with the public function re-export. The registered-pipeline coverage is unchanged; all 39 TopK tests pass.
Testing
Windows CPU: 1932 passed in the complete restacked graph-surgery and compatibility suite with Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. The public DLA API uses direct ONNX saving; existing initializer conversion and unrelated surgeries are preserved. Commits are signed and all commit hooks pass with the equivalent Linux mypy platform setting. GPU execution, DLA compilation and the complete Linux suite were not run locally.
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Additional Information
Base branch:
dla/review-14-dla-boolean-and-comparison-rewrites.Merge order: PR1 (merged); Part 2 (withdrawn); then Part 3/17 → Part 4/17 → Part 5/17 → Part 6/17 → Part 7/17 → Part 8/17 → Part 9/17 → Part 10/17 → Part 11/17 → Part 12/17 → Part 13/17 → Part 14/17 → Part 15/17 → Part 16/17 → Part 17/17.