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Add the separately ordered model-input/output boundary, intermediary-adapter and InstanceNormalization layout stages. Part 10/17 depends on part 9; merge from the bottom upward. Public registration follows in part 16.
Boundary planning preserves public and captured aliases, runtime controls, legacy initializer declarations and untouched payload owners. Output restoration requires a known four-dimensional producer or an explicit later rank-conversion stage. Preflight checks the actual registered prerequisite and rank-conversion stages before changing caller state.
Generic promotion and output planning check operator domain, overload and model-local identity. Opaque functions retain native interfaces; explicitly supported runtime schemas retain their existing route. Softmax and LogSoftmax use imported schema defaults and remap positive axes before leading dimensions are inserted, with the existing marker preventing a later duplicate update.
Normalization uses a trailing spatial singleton to preserve channels. Public Squeeze adapters follow unary paths and remove that trailing dimension. Binary branches receive a shared private leading-layout view, preserving broadcasting and shared public normalized values. Node replacement retains unrelated payload objects.
Input promotion now requires either a layout-safe operation or an explicitly registered rank/axis preparation stage. Ineligible Gemm operations retain native inputs, outputs and weight layouts, including unary output tails. Softmax, LogSoftmax and LayerNormalization preserve their imported defaults and native positive axes.
When native rank guards cannot establish support, preflight runs the actual earlier stages on a copy, including Constant conversion and shape inference, and then checks the remaining operators. Unsupported cases leave the caller unchanged. Normal Constant controls remain with their registered prerequisite stage; successful probes are released before normal transformation starts.
Scalar-index Gather requires an explicitly registered rank-conversion stage before public-output restoration. Preflight reads index rank from fixed controls or tensor metadata, including symbolic vector lengths, and rechecks Constant-produced controls after prerequisite preparation on a copy. Shape outputs and their unary tails retain their native vector rank, including a one-element result.
This slice extends the internal sequence; the public entry point follows in part 16.
Normalization consumer planning now prepares leading-layout views and imported axes/permutations for supported readers, and native three-dimensional views for Shape, opaque functions and native Conv1D. Consecutive normalization stages retain independent channel-preserving adapters. Allocated output aliases recover native shape without adding contradictory tensor metadata. Unsupported readers refuse before mutation.
Deferred public-output restoration verifies that a low-rank Reshape has an actual registered Non4DReshape stage before inserting Squeeze. Native rank-one unary paths remain native. Producer-rank preflight checks availability before mutation without requiring intermediate Squeeze metadata or triggering a redundant model copy.
Intermediary adapter cleanup preflights every affected reader before rewiring any node. It follows shared rank-preserving paths and refuses readers without a registered axis or permutation stage. Native value-info names remain available to those later stages. Fixed Reshape targets, scalar QDQ, negative-axis LayerNormalization and contrib Gelu retain their supported behavior. Eighteen unchanged-state regressions fail before the guard; six registered-reader execution cases and six safe-normalization execution cases verify the supported paths.
Adapter preflight stops at a private rank-restoring adapter only when its known four-dimensional output matches the actual planned bypass layout. Planning computes the same bypass-versus-Reshape decision that mutation uses, and the reader cache includes that layout. Four matching round-trip cases failed before the fix; nine execution/refusal cases cover direct and unary paths, mismatched four-dimensional layouts and a five-dimensional bypass hidden behind a four-dimensional output annotation.
Testing
Windows CPU: Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. 1840 passed across the complete restacked graph-surgery and public-compatibility suite on main 35922f1. All 51 focused execution/refusal checks for this batch also pass on the actual final package. Original save and initializer-conversion implementations are preserved. Required hooks pass with Linux CI typing semantics; commits are signed. The default Windows mypy invocation encounters the unchanged upstream os.sched_getaffinity attribute error; the temporary local platform setting retains every other repository typing option. GPU execution, DLA compilation and the complete Linux suite were not run locally; GitHub CI runs separately.
Shared-path planning sample with 256 unary nodes, 128 readers and a retained 64 MiB payload: 0.002671 seconds and +0.0312 MiB sampled peak RSS. Payload bytes/identity and checked CPU outputs are preserved. Single Windows CPU sample, with 2 ms polling that can miss brief allocations.
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Walkthrough
The DLA pipeline now promotes eligible non-4D inputs, restores supported output ranks, and adapts intermediary shapes and rank-3 InstanceNormalization inputs. It also validates whether configured stages support required rank conversions.
Changes: DLA model shape compatibility
Layer / File(s)
Summary
Promote non-4D model inputs modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.py, modelopt/onnx/graph_surgery/utils/graph_utils.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_model_boundaries.py
The transform promotes eligible rank-2 and rank-3 inputs, adjusts selected operator axes, and rewires consumers. Shape recovery recognizes generated graph-output Squeeze boundaries. Tests cover symbolic shapes, opaque operators, axes, and unsupported adapters.
Restore model output ranks modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.py, tests/unit/onnx/graph_surgery/make_dla_compatible.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_model_boundaries.py
The output transform restores known ranks below 4 and checks cases where output conversion cannot be applied. Tests cover output shapes, captured values, deferred conversion, and unsupported ranks.
Repair intermediary adapters and InstanceNormalization inputs modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_model_boundaries.py
Eligible Squeeze and Unsqueeze nodes are removed or replaced with Reshape nodes. Rank-3 InstanceNormalization inputs receive a trailing singleton dimension, with qualifying downstream updates.
Register transforms and validate rank-conversion stages modelopt/onnx/graph_surgery/make_dla_compatible.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_model_boundaries.py
The DLA pipeline registers the new transforms and checks configured stages for rank-conversion support. After specified validation errors, it validates a shape-inferred copy after earlier configured stages.
sequenceDiagram
participant DLAPipeline
participant BoundaryValidation
participant ShapeInference
participant ConfiguredTransforms
DLAPipeline->>BoundaryValidation: Check boundary contract and prepared operators
alt Validation raises ValueError or NotImplementedError
DLAPipeline->>ShapeInference: Infer shapes on a model copy
DLAPipeline->>ConfiguredTransforms: Apply configured stages before Non4dModelInputs
DLAPipeline->>BoundaryValidation: Check transformed copy
end
DLAPipeline->>ConfiguredTransforms: Apply configured stages to original model
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Possibly related PRs
NVIDIA/Model-Optimizer#2831: Adds GraphCache and graph-surgery helpers that this PR uses for shape and graph analysis.
Suggested labels:waiting for feedback
Suggested reviewers:cjluo-nv, hthadicherla
Merge Risk: Minimal · up to df253
The DLA boundary-promotion stages now either adapt consumers of a widened rank-3 InstanceNormalization output or refuse the model before changing it. The previously open concern is resolved, and no concrete merge-blocking risk remains. Final confidence rests on source inspection; the tests were not run here.
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The title "[10/17] DLA model boundary promotion" matches the PR's main change: adding separately ordered DLA model-input/output boundary promotion stages, the subject of the boundary transforms in dla_model_boundaries.py and make_dla_compatible.py. The "[10/17]" series prefix is a stack marker rather than a description, but the rest of the title names the primary change accurately. The PR also adds intermediary-adapter and InstanceNormalization stages, which the title does not mention, so it is partially broader than the title. Overall the title is related to the main change.
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Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.py:
- Around line 146-153: Always create the explicit squeeze-axes initializer in
the output-boundary transformation, including when shape has rank one. Use the
already computed axes so the appended Squeeze preserves the declared [1] output
shape instead of squeezing all singleton dimensions.
- Around line 92-98: Update the generated initializer names at all three reshape
and rotate-left call sites, including the one in _apply_non4d_model_inputs, to
derive them from the node’s output tensor name rather than its optional node
name. Preserve the existing suffixes so unnamed nodes with distinct outputs
receive unique initializer names.
- Around line 198-215: Update the Squeeze/Unsqueeze elimination condition so
nodes with different input and output ranks are retained; only bypass a node
when the shapes match exactly, rather than relying on the suffix check. Apply
this guard before rewiring `node.input` and `node.output` names or adding the
node to `removed`.
- Around line 234-259: Update _apply_fix_instancenorm_channels to find
graph-output Squeeze consumers of the promoted InstanceNormalization output and
change their axis from 0 to 3. Keep the trailing singleton dimension introduced
by the reshape so the output retains the channel axis.
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@modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.py:
- Around line 532-538: Update require_prepared_readers to stop traversal at a
standard Squeeze or Unsqueeze when its output is known to have rank 4 and match
the planned rank-4 bypass shape; do not stop based on rank 4 alone when the
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What does this PR do?
Type of change: new feature
Add the separately ordered model-input/output boundary, intermediary-adapter and InstanceNormalization layout stages. Part 10/17 depends on part 9; merge from the bottom upward. Public registration follows in part 16.
Boundary planning preserves public and captured aliases, runtime controls, legacy initializer declarations and untouched payload owners. Output restoration requires a known four-dimensional producer or an explicit later rank-conversion stage. Preflight checks the actual registered prerequisite and rank-conversion stages before changing caller state.
Generic promotion and output planning check operator domain, overload and model-local identity. Opaque functions retain native interfaces; explicitly supported runtime schemas retain their existing route. Softmax and LogSoftmax use imported schema defaults and remap positive axes before leading dimensions are inserted, with the existing marker preventing a later duplicate update.
Normalization uses a trailing spatial singleton to preserve channels. Public Squeeze adapters follow unary paths and remove that trailing dimension. Binary branches receive a shared private leading-layout view, preserving broadcasting and shared public normalized values. Node replacement retains unrelated payload objects.
Input promotion now requires either a layout-safe operation or an explicitly registered rank/axis preparation stage. Ineligible Gemm operations retain native inputs, outputs and weight layouts, including unary output tails. Softmax, LogSoftmax and LayerNormalization preserve their imported defaults and native positive axes.
When native rank guards cannot establish support, preflight runs the actual earlier stages on a copy, including Constant conversion and shape inference, and then checks the remaining operators. Unsupported cases leave the caller unchanged. Normal Constant controls remain with their registered prerequisite stage; successful probes are released before normal transformation starts.
Scalar-index Gather requires an explicitly registered rank-conversion stage before public-output restoration. Preflight reads index rank from fixed controls or tensor metadata, including symbolic vector lengths, and rechecks Constant-produced controls after prerequisite preparation on a copy. Shape outputs and their unary tails retain their native vector rank, including a one-element result.
Usage
This slice extends the internal sequence; the public entry point follows in part 16.
Normalization consumer planning now prepares leading-layout views and imported axes/permutations for supported readers, and native three-dimensional views for Shape, opaque functions and native Conv1D. Consecutive normalization stages retain independent channel-preserving adapters. Allocated output aliases recover native shape without adding contradictory tensor metadata. Unsupported readers refuse before mutation.
Deferred public-output restoration verifies that a low-rank Reshape has an actual registered Non4DReshape stage before inserting Squeeze. Native rank-one unary paths remain native. Producer-rank preflight checks availability before mutation without requiring intermediate Squeeze metadata or triggering a redundant model copy.
Intermediary adapter cleanup preflights every affected reader before rewiring any node. It follows shared rank-preserving paths and refuses readers without a registered axis or permutation stage. Native value-info names remain available to those later stages. Fixed Reshape targets, scalar QDQ, negative-axis LayerNormalization and contrib Gelu retain their supported behavior. Eighteen unchanged-state regressions fail before the guard; six registered-reader execution cases and six safe-normalization execution cases verify the supported paths.
Adapter preflight stops at a private rank-restoring adapter only when its known four-dimensional output matches the actual planned bypass layout. Planning computes the same bypass-versus-Reshape decision that mutation uses, and the reader cache includes that layout. Four matching round-trip cases failed before the fix; nine execution/refusal cases cover direct and unary paths, mismatched four-dimensional layouts and a five-dimensional bypass hidden behind a four-dimensional output annotation.
Testing
Windows CPU: Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. 1840 passed across the complete restacked graph-surgery and public-compatibility suite on main 35922f1. All 51 focused execution/refusal checks for this batch also pass on the actual final package. Original save and initializer-conversion implementations are preserved. Required hooks pass with Linux CI typing semantics; commits are signed. The default Windows mypy invocation encounters the unchanged upstream os.sched_getaffinity attribute error; the temporary local platform setting retains every other repository typing option. GPU execution, DLA compilation and the complete Linux suite were not run locally; GitHub CI runs separately.
Shared-path planning sample with 256 unary nodes, 128 readers and a retained 64 MiB payload: 0.002671 seconds and +0.0312 MiB sampled peak RSS. Payload bytes/identity and checked CPU outputs are preserved. Single Windows CPU sample, with 2 ms polling that can miss brief allocations.
Before your PR is "Ready for review"
Additional Information
Base branch:
dla/review-09-dla-lstm-decomposition. Review this self-contained boundary slice against that branch.Merge order: PR1 (merged) → Part 2/17 → 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
Summary
InstanceNormalizationchannel layouts and intermediarySqueeze/Unsqueezepatterns.