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[10/17] DLA model boundary promotion - #2962
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| Layer / File(s) | Summary |
|---|---|
Promote non-4D model inputs modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.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. Tests cover symbolic shapes, opaque operators, axes, and unsupported adapters. |
Restore model output ranks 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 output transform restores known ranks below 4 and checks cases where conversion cannot be applied. Shape recovery recognizes generated graph-output Squeeze boundaries. Tests cover output shapes, captured values, deferred conversion, and unsupported ranks. |
Repair intermediary adapters 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. Tests check rank-changing adapters and downstream consumers. |
Adapt InstanceNormalization inputs modelopt/onnx/graph_surgery/dla_transforms/dla_model_boundaries.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_model_boundaries.py |
Rank-3 InstanceNormalization inputs receive a trailing singleton dimension, with qualifying downstream axis, permutation, and reshape updates. Tests cover output restoration and normalization consumers. |
Register transforms and validate rank-conversion stages modelopt/onnx/graph_surgery/make_dla_compatible.py |
The DLA pipeline registers the transforms and checks configured stages for rank-conversion support. After specified validation errors, it validates a shape-inferred copy after earlier configured stages. |
Priority:
Estimated code review effort: 4 (Complex) | ~60 minutes
Change: Feature
Sequence Diagram(s)
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
Possibly related PRs
- NVIDIA/Model-Optimizer#2831: Adds graph utilities that this change imports and uses for DLA boundary promotion and validation.
Suggested reviewers: cjluo-nv, hthadicherla
Merge Risk: 
3daa8
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This change adds internal DLA boundary-promotion stages, and public registration comes in a later part. The earlier concerns about normalization consumers and preflight shape handling are addressed in the current code. No actionable merge-blocking risk remains after review.
Pre-merge checks: 
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly identifies the main change: DLA model boundary promotion. It is concise and directly related to the added input/output boundary transforms and rank handling. Docstring Coverage Docstring coverage is 98.28% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentati… Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns PASS. The PR changes three modelopt Python files and one test file; no examples, pyproject.toml, or requirements files changed. The added package code contains no torch.load, unsafe numpy.load/allow_p… Full details: Docstring Coverage
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Docstring coverage is 98.28% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 58 functions across 4 files.
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PASS. The PR changes three modelopt Python files and one test file; no examples, pyproject.toml, or requirements files changed. The added package code contains no torch.load, unsafe numpy.load/allow_pickle, trust_remote_code=True, eval/exec, or # nosec patterns. No new dependency was added.
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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.pymodelopt/onnx/graph_surgery/make_dla_compatible.pytests/unit/onnx/graph_surgery/dla_transforms/test_dla_model_boundaries.py
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Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
…youts Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
Signed-off-by: mgohil <[email protected]>
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What does this PR do?
Availability on
main: Parts 3?15 add internal implementation pieces. The public FP32 DLA API andmake-dla-compatibleCLI become available only after Part 16 (#2968) and its dependencies merge. Optional FP16 strong typing becomes available after Part 17 (#2969) merges.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.
Normalization now has a read-only planning mode used by pipeline preflight. Concat widening requires every operand to receive a compatible normalization bridge, and unsupported consumers refuse before caller mutation. Five real standalone/pipeline cases fail before the fix; three supported controls remain valid. The focused boundary file passes 274 cases with six skips for stages introduced by later parts. One Windows CPU sample plans 512 normalization branches with 64 MiB of retained payloads in 0.005862 seconds and +1.2578 MiB sampled peak RSS, preserving graph/payload bytes and tensor identity (2 ms polling).
Early boundary restoration now proves when a multi-input broadcast remains native instead of appending an invalid Squeeze. Four rank-two/rank-three cases fail before the fix; four input-port controls remain valid. All 282 boundary cases pass with six skips for later stages. Part 12 replaces this early proof with its existing shared positive-layout proof. A Windows CPU sample plans 512 branches with 64 MiB retained payloads in 0.003220 seconds and +0.5352 MiB sampled peak RSS, preserving graph and initializer bytes/identity (2 ms polling).
Output preflight now checks scalar outputs, Squeeze controls and restoration rank before any boundary mutation. Exposed runtime Squeeze axes raise a shared preflight error directly, avoiding a prerequisite-probe model copy. Six refusal cases preserve caller bytes; the existing missing-metadata control still passes. A synthetic Windows CPU sample checks 512 branches with 64 MiB retained payloads in 0.004992 seconds and +0.4688 MiB sampled peak RSS, preserving graph/payload bytes and tensor identity (2 ms polling).
Normalization preflight now routes unknown intermediate input shapes through the existing copied prerequisite probe, so an unsupported later consumer refuses before changing caller bytes. Original graph inputs, initializers and known shapes retain their existing route. The missing-metadata regression fails before the fix; supported direct and intermediate normalization cases also execute correctly. All 289 focused boundary cases pass with six skips for later stages; twelve final normalization execution/refusal controls pass. The fallback costs 0.246717 seconds and 195.0117 MiB sampled additional peak RSS for 512 branches with a retained 64 MiB payload, preserving caller bytes and payload ownership (Windows CPU, 2 ms polling). This is the existing full-model probe used only when prerequisite inference is needed. Known symbolic ranks retain native behavior; the guard distinguishes them from missing shape metadata. A redundant concrete batch-size case is replaced with symbolic-batch coverage without increasing the case count.
The missing-intermediate-rank refusal now requests prerequisite inference only during the initial preflight. The copied-model probe accepts sequence elements whose rank legitimately remains unknown after inference. A real SequenceAt/InstanceNormalization pipeline regression fails before the fix, while unsupported known-layout refusals remain intact. All 290 focused module cases pass, with six skips.
Testing
The transformations have been thoroughly tested on 22 models.
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-09-dla-lstm-decomposition. Review this self-contained boundary 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.
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