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[14/17] DLA boolean and comparison rewrites - #2966
Add ordered Where, Not and Greater preparation to the internal DLA sequence. Part 14/17 depends on part 13; merge the series from the bottom upward. The public entry point follows in part 16.
Greater converts operands only where FLOAT conversion preserves comparison ordering. INT32/INT64 comparisons require a fixed threshold strictly inside the exact-integer range; other cases remain native. Private conversions preserve initializer defaults and shared owners. The stage also completes comparisons after a preceding FLOAT data adapter. Cast insertion reuses the existing batch helper and cached conversions.
Not lowers eligible standard Cast/Not chains with provably binary source values to FLOAT Clip/Sub output. Public or captured Cast owners, captured Not outputs and boolean consumer aliases stay native. Fixed binary checks use bounded chunks and retain only their boolean proof.
Where lowers fixed zero-branch selections under the explicitly retained finite-FP32 arithmetic assumption. NaN/Inf branch values are outside this supported input contract; the rewrite does not attempt to prove runtime finiteness. It preserves broadcasting and runtime condition defaults, declines overridable zero branches, and keeps original initializer owners. Zero checks retain only their result; static masks are inverted in place and serialized directly into their final initializer.
All three stages exclude opaque local functions and overloaded operators, allocate generated node/value/control names through the shared allocator, and declare generated legacy initializer inputs.
This slice extends the internal sequence; public registration follows in part 16.
Not retains its native BOOL output when a downstream Cast targets STRING, preserving the portable boolean string representation. Numeric and BOOL Cast consumers retain the existing rewrite. Eight new cases execute on ONNX ReferenceEvaluator and ONNX Runtime; the two STRING cases fail without the guard. All 20 Not tests pass.
Public Not outputs now retain their native BOOL contract, including outputs also read by numeric Cast nodes. Three public-output cases fail without the ownership guard. Existing lowering fixtures use private BOOL intermediates with numeric Cast readers, retaining coverage of the arithmetic rewrite and legacy control declarations. All 23 Not tests pass.
Document the signed-zero limitation of existing arithmetic Where lowering. Finite FP32 branch values can still produce a different zero sign after masked multiplication; a downstream reciprocal can expose that sign difference. The 32-case ReferenceEvaluator/ORT diagnostic covers both zero signs, branch positions and fixed/runtime conditions, reproducing 16 sign differences. Runtime arithmetic and eligibility are unchanged.
Where eligibility now uses the shared current-layout proof with materialized initializer metadata in its private cache, covering prepared Transpose and unary paths without a global inference pass or changes to existing caller metadata. Six cases fail against the original implementation and two initializer controls remain valid; all 23 Where tests pass. One Windows CPU sample lowers 512 selections with 64 MiB retained payloads in 0.030404 seconds and +1.6836 MiB sampled peak RSS (2 ms polling), preserving payload identity/bytes and checked CPU values. The documented finite-FP32 and signed-zero assumptions remain unchanged.
Fixed BOOL conditions now reuse FLOAT masks by condition and branch polarity. Direct and inverted masks stay distinct, and the original BOOL initializer is preserved. The regression fails before the fix and now passes exact ReferenceEvaluator and CPU execution. All 24 Where cases pass. For 16 readers of a 4 MiB condition, generated FLOAT payload falls from 256 MiB to 32 MiB; conversion time measured 0.1024 to 0.0151 seconds and sampled extra peak RSS 256.16 to 36.25 MiB on Windows CPU.
Testing
Windows CPU: 2049 passed in 360.69 seconds across 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.
Expanded DLA-compatible ONNX model handling for Greater, Not, and Where operations, allowing more models to pass through the DLA transformation pipeline.
Added support for eligible numeric comparisons and conditional selections while preserving model outputs in supported cases.
Tests
Added CPU-based checks comparing transformed model results with ONNX Runtime across a range of input types, shapes, and graph configurations.
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Walkthrough
Adds Greater, Not, and Where graph rewrites to DLA compatibility processing. Registers them in the transform pipeline and adds ONNX Runtime parity tests for rewritten and unchanged models.
The previously identified string-conversion and duplicate-mask concerns are addressed. No actionable merge-blocking risk remains after normal checks.
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The title clearly identifies the main change: DLA rewrites for boolean and comparison operations, including Where, Not, and Greater transforms.
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Cache the FLOAT mask for each fixed BOOL condition.
Each eligible Where node that reads the same fixed BOOL initializer, with the zero branch in the same position, adds a new FLOAT initializer with a full payload copy. The new initializer has a new name. A FLOAT copy uses four times the bytes of the BOOL source. A shared static mask, such as an attention mask used by many layers, can therefore grow the model by one FLOAT copy per Where node.
_apply_greater avoids this with its converted cache. Apply the same pattern here. Key the cache by (condition, zero_idx == 0), because the inverted mask differs from the direct mask.
♻️ Proposed change
if condition_type == TensorProto.BOOL:
- cond_raw = cache.get_init_array(condition)- if cond_raw is None:- continue- cond_arr = cond_raw.astype(np.float32)- if zero_idx == 0:- # Where(cond, 0, y) → y * (1 - cond_float)- np.subtract(1.0, cond_arr, out=cond_arr)- float_name = cache.unique_name(f"{condition}_float32")- graph.initializer.add(- name=float_name,- data_type=TensorProto.FLOAT,- dims=cond_arr.shape,- raw_data=cond_arr.astype("<f4", copy=False).tobytes(),- )- mul_inputs = [non_zero_name, float_name]+ mask_key = (condition, zero_idx == 0)+ if mask_key not in static_masks:+ cond_raw = cache.get_init_array(condition)+ if cond_raw is None:+ continue+ cond_arr = cond_raw.astype(np.float32)+ if zero_idx == 0:+ # Where(cond, 0, y) → y * (1 - cond_float)+ np.subtract(1.0, cond_arr, out=cond_arr)+ float_name = cache.unique_name(f"{condition}_float32")+ graph.initializer.add(+ name=float_name,+ data_type=TensorProto.FLOAT,+ dims=cond_arr.shape,+ raw_data=cond_arr.astype("<f4", copy=False).tobytes(),+ )+ static_masks[mask_key] = float_name+ mul_inputs = [non_zero_name, static_masks[mask_key]]
Also add static_masks: dict[tuple[str, bool], str] = {} next to zeros = {} at line 70.
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Review comment at @modelopt/onnx/graph_surgery/dla_transforms/dla_where.py
around lines 118 - 133:
Add a static FLOAT-mask cache to the Where transform and reuse converted
initializers for repeated BOOL conditions. Key entries by condition and whether
the mask is inverted (zero_idx == 0), so direct and inverted masks remain
distinct; use the cached initializer name in mul_inputs.
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Review comments at @modelopt/onnx/graph_surgery/dla_transforms/dla_where.py:
- Around line 118-133: Add a static FLOAT-mask cache to the Where transform and
reuse converted initializers for repeated BOOL conditions. Key entries by
condition and whether the mask is inverted (zero_idx == 0), so direct and
inverted masks remain distinct; use the cached initializer name in mul_inputs.
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Addressed in ec5f81e. Fixed BOOL conditions now reuse FLOAT masks by condition and branch polarity. Direct and inverted masks stay distinct, and the original BOOL initializer is preserved. The regression fails before the fix and now passes exact ReferenceEvaluator and CPU execution. All 24 Where cases pass. For 16 readers of a 4 MiB condition, generated FLOAT payload falls from 256 MiB to 32 MiB; conversion time measured 0.1024 to 0.0151 seconds and sampled extra peak RSS 256.16 to 36.25 MiB on Windows CPU.
Final restacked validation: 2049 passed; hooks pass and commits are signed.
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What does this PR do?
Type of change: new feature
Add ordered Where, Not and Greater preparation to the internal DLA sequence. Part 14/17 depends on part 13; merge the series from the bottom upward. The public entry point follows in part 16.
Greater converts operands only where FLOAT conversion preserves comparison ordering. INT32/INT64 comparisons require a fixed threshold strictly inside the exact-integer range; other cases remain native. Private conversions preserve initializer defaults and shared owners. The stage also completes comparisons after a preceding FLOAT data adapter. Cast insertion reuses the existing batch helper and cached conversions.
Not lowers eligible standard Cast/Not chains with provably binary source values to FLOAT Clip/Sub output. Public or captured Cast owners, captured Not outputs and boolean consumer aliases stay native. Fixed binary checks use bounded chunks and retain only their boolean proof.
Where lowers fixed zero-branch selections under the explicitly retained finite-FP32 arithmetic assumption. NaN/Inf branch values are outside this supported input contract; the rewrite does not attempt to prove runtime finiteness. It preserves broadcasting and runtime condition defaults, declines overridable zero branches, and keeps original initializer owners. Zero checks retain only their result; static masks are inverted in place and serialized directly into their final initializer.
All three stages exclude opaque local functions and overloaded operators, allocate generated node/value/control names through the shared allocator, and declare generated legacy initializer inputs.
Usage
This slice extends the internal sequence; public registration follows in part 16.
Not retains its native BOOL output when a downstream Cast targets STRING, preserving the portable boolean string representation. Numeric and BOOL Cast consumers retain the existing rewrite. Eight new cases execute on ONNX ReferenceEvaluator and ONNX Runtime; the two STRING cases fail without the guard. All 20 Not tests pass.
Public Not outputs now retain their native BOOL contract, including outputs also read by numeric Cast nodes. Three public-output cases fail without the ownership guard. Existing lowering fixtures use private BOOL intermediates with numeric Cast readers, retaining coverage of the arithmetic rewrite and legacy control declarations. All 23 Not tests pass.
Document the signed-zero limitation of existing arithmetic Where lowering. Finite FP32 branch values can still produce a different zero sign after masked multiplication; a downstream reciprocal can expose that sign difference. The 32-case ReferenceEvaluator/ORT diagnostic covers both zero signs, branch positions and fixed/runtime conditions, reproducing 16 sign differences. Runtime arithmetic and eligibility are unchanged.
Where eligibility now uses the shared current-layout proof with materialized initializer metadata in its private cache, covering prepared Transpose and unary paths without a global inference pass or changes to existing caller metadata. Six cases fail against the original implementation and two initializer controls remain valid; all 23 Where tests pass. One Windows CPU sample lowers 512 selections with 64 MiB retained payloads in 0.030404 seconds and +1.6836 MiB sampled peak RSS (2 ms polling), preserving payload identity/bytes and checked CPU values. The documented finite-FP32 and signed-zero assumptions remain unchanged.
Fixed BOOL conditions now reuse FLOAT masks by condition and branch polarity. Direct and inverted masks stay distinct, and the original BOOL initializer is preserved. The regression fails before the fix and now passes exact ReferenceEvaluator and CPU execution. All 24 Where cases pass. For 16 readers of a 4 MiB condition, generated FLOAT payload falls from 256 MiB to 32 MiB; conversion time measured 0.1024 to 0.0151 seconds and sampled extra peak RSS 256.16 to 36.25 MiB on Windows CPU.
Testing
Windows CPU: 2049 passed in 360.69 seconds across 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-13-standalone-operation-rank-conversion-compatibility. Review this slice against that branch.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.
Summary by CodeRabbit
Greater,Not, andWhereoperations, allowing more models to pass through the DLA transformation pipeline.