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Add a standalone DLA cleanup pass for eligible input promotion, Squeeze/Unsqueeze replacement, reshape-chain cleanup, output adapters, constant folding, lossless Cast-chain cleanup, and unused initializer removal.
Rank promotion now follows known elementwise paths and preserves rank-sensitive consumers, including consumers behind other elementwise nodes. A shared input can still be promoted on a safe branch while another branch retains its original rank. When promotion reaches a graph output, an opset-compatible Squeeze restores the declared output rank while interior consumers keep their 4D tensors. The combined pass retains the original output descriptors through later cleanup stages. Analysis is computed lazily once per stage and stops propagating restrictions when broadcasting already fixes the output rank at 4D. Gather and GQA boundary exceptions are retained. PRelu slopes and Clip control inputs retain their original ranks to respect their broadcasting constraints.
Generated tensor, initializer and node names use the existing graph cache's name allocator. Graph-output adapters preserve declared ranks and values; non-squeezable output reshapes reach exactly 4D and squeeze every prepended axis. Shape fallback does not guess dimensions for unresolved explicit Squeeze axes; omitted axes retain their unit-dimension-removal fallback. The fallback honors legacy Squeeze axes attributes and normalizes negative Unsqueeze axes against the output rank. Generated Squeeze/Unsqueeze nodes use axes attributes below opset 13 and axes inputs on newer models, based on the actual default-domain import. The empty-domain import takes precedence; an ai.onnx import is used only when the empty-domain import is absent. Identity output Reshapes are retained to avoid the remove-all-unit-dimensions behavior of empty-axes Squeeze in ORT. Unsqueeze axes controls are excluded from data-input promotion. Lossless Cast-chain cleanup preserves all final Cast attributes, including float8 saturation and rounding, and leaves custom-domain operations untouched. Shape-operation matching and rank-safety analysis likewise keep custom or overloaded operations opaque, even when they reuse standard operation names. Subgraph-captured values remain defined across noop removal, Reshape/Cast chain collapse and canonical Unsqueeze cleanup. Capture collection reuses the existing scope-aware helper and also handles a name used as both a direct control input and a lexical capture. Test inference failures are surfaced, and all introduced functions have docstrings.
Part 3/17 is based directly on main; part 2 was withdrawn after removal of the custom save wrapper. The remaining PR numbers are retained. This remains a draft.
Source size: 1040 added lines. This is the existing self-contained cleanup slice; splitting its cooperating stages would require a separate implementation refactor.
Usage
frommodelopt.onnx.graph_surgery.dla_transforms.dla_graph_cleanupimport_apply_graph_cleanup# model is an onnx.ModelProto with a supported input pattern.model=_apply_graph_cleanup(model)
This is an internal pass. Public pipeline integration follows in part 16; operation-specific rank conversions may still be needed for a model.
Constant folding passes the input IR version directly to graphsurgeon instead of changing ONNX process-wide state. Three real-export regression cases fail on the global-state mutation before the fix and now preserve IR 8/9/10 and the global constant. All 109 cleanup tests pass.
Cleanup captures inferred native output ranks before promotion and restores those ranks even when the original output descriptor omits its shape. The original descriptors remain intact. Four previously failing cases and four declared-shape controls cover ranks two and three at opsets 12 and 17; checker and CPU results pass. A synthetic Windows CPU sample plans 512 branches with 64 MiB retained payloads in 0.005732 seconds and +0.9492 MiB sampled peak RSS, preserving payload bytes and tensor identity (2 ms polling).
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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Walkthrough
Adds an ONNX graph-cleanup pipeline for eligible tensor-shape rewrites, reshape simplification, constant folding, unused-initializer removal, and Cast-chain collapsing. Tests cover individual transforms and combined pipeline behavior.
Changes: DLA graph cleanup
Layer / File(s)
Summary
Input and intermediary shape rewrites modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py
Canonicalizes eligible rank-1–3 graph inputs and replaces eligible intermediary Squeeze and Unsqueeze nodes with Reshape nodes. Tests cover shape handling, rank-sensitive consumers, captured values, and protected GQA paths.
Reshape simplification and graph outputs modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py
Removes known no-op Reshape nodes, collapses eligible reshape chains, and rewrites eligible graph-output Reshape nodes. Tests cover reshape chains, output rewrites, and captured values.
Constant folding and initializer removal modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py
Adds constant folding through onnx-graphsurgeon when available and removes unused initializers. Tests cover folding, initializer retention, and IR-version preservation.
Cast chains and cleanup orchestration modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py
Collapses eligible consecutive Cast nodes and runs the cleanup pipeline, including restoration of original graph-output declarations. Tests cover Cast-chain conditions, attributes, custom-domain operations, and combined cleanup.
sequenceDiagram
participant Cleanup as Cleanup pipeline
participant ShapePasses as Shape and reshape transforms
participant Fold as Constant folding
participant CastPass as Cast-chain cleanup
participant Initializers as Initializer cleanup
Cleanup->>ShapePasses: Replace eligible nodes, promote inputs, and restore output declarations
Cleanup->>ShapePasses: Simplify reshapes and rewrite eligible graph outputs
Cleanup->>Fold: Fold eligible constants
Cleanup->>CastPass: Collapse eligible Cast chains
Cleanup->>Initializers: Remove unused initializers
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Possibly related PRs
NVIDIA/Model-Optimizer#2831: Adds graph-mutation and shape-inference helpers that this cleanup pipeline imports and uses.
NVIDIA/Model-Optimizer#2939: Adds shared ONNX model-saving support in the preceding DLA-transforms implementation slice.
Suggested reviewers:vishalpandya1990, ajrasane
Merge Risk: Minimal · up to b5ea6
The cleanup pass restores each graph output to its original declared rank after promoting interior tensors to 4D. This PR is not wired into the public pipeline until a later part of the stack, which limits exposure. No unresolved merge-blocking concern remains from this review.
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The PR changes only modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py and its test file. Added code contains no torch.load(..., weights_only=False), pickle-enabled NumPy load, hardco…
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The title "[3/17] Standalone DLA graph cleanup" matches the main change. The PR adds dla_graph_cleanup.py, a standalone cleanup pass covering input-rank promotion, Squeeze/Unsqueeze replacement, Reshape-chain collapsing, output adapters, constant folding, Cast-chain cleanup, and unused-initializer removal, plus a matching test suite. The "[3/17]" prefix is a series marker. It does not describe the change, but it does not make the title misleading. The title is short and specific, so it passes.
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Add assertions to test_unsqueeze_after_gqa_4d_output_not_skipped or remove it.
The test has no assertions. The docstring says it verifies that the GQA skip rule does not fire. The inline comment admits that the 5-D output path skips the node anyway. As written, the test only checks that the transform does not raise an exception.
To test the stated behavior, use an Unsqueeze output that is at most 4-D. Then assert that the node was replaced by a Reshape. Otherwise delete the test.
As per path instructions: "checked-in tests should be lean and document expected behavior, protect against regressions".
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Review comment at
@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py around
lines 1019 - 1071:
Update test_unsqueeze_after_gqa_4d_output_not_skipped so the Unsqueeze output is
at most 4-D and assert that _replace_sq_unsq replaces the Unsqueeze with a
Reshape, verifying the GQA-specific skip rule does not apply; otherwise remove
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Inline comments:
Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py:
- Around line 633-641: Update the fallback Reshape logic so its target always
has rank 4: use the existing pad4d helper for out_shape and set the squeeze axes
to all prepended dimensions, preserving output values. Update
test_graph_output_reshape_not_squeezable_padded_to_4d to expect [1,1,6,4].
- Line 184: Update _canonicalize_inputs to avoid redirecting rank-1/2/3 inputs
to prepended-4-D tensors for consumers whose semantics depend on rank, including
Transpose, axis-based operators, and MatMul, unless their attributes and shape
handling are adjusted to preserve behavior. Apply the same guard to Squeeze and
Unsqueeze replacements, and preserve the existing Gather and GroupQueryAttention
exceptions.
- Around line 345-350: Update the generated initializer names in the
Reshape-shape creation paths to use cache.unique_name, with a nonempty node-name
fallback, so they are unique against existing graph tensors and other generated
initializers. Apply this to rs_name, the initializer created for each chain
tail, and the initializer created from c_node; preserve each path’s existing
name suffix and shape behavior.
Review comments at
@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py:
- Line 60: Move the onnxruntime and SymbolicShapeInference imports to module
scope immediately after the existing pytest.importorskip call, then remove their
local imports from _run_ort and _prepare_model. Update _prepare_model’s broad
exception handling to catch only failures the helper is intended to tolerate, so
other shape-inference failures are not silently discarded.
---
Nitpick comments:
Review comments at
@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_graph_cleanup.py:
- Around line 1019-1071: Update test_unsqueeze_after_gqa_4d_output_not_skipped
so the Unsqueeze output is at most 4-D and assert that _replace_sq_unsq replaces
the Unsqueeze with a Reshape, verifying the GQA-specific skip rule does not
apply; otherwise remove the test.
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- Around line 675-682: Update the graph cleanup flow around `gs.export_onnx` to
explicitly set `model2.ir_version` to the input `model.ir_version` after export.
Remove the temporary global `onnx.IR_VERSION` mutation and its `try/finally`
restoration; keep the import, graph transformations, and export flow unchanged.
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Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py:
- Around line 951-952: Update _apply_graph_cleanup to retain the graph outputs
inferred by its first infer_shapes call and pass them to
_restore_promoted_graph_outputs for rank comparisons, so outputs declared
without shapes still have a reference rank. Keep the original_outputs
declarations for the final CopyFrom restoration.
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What does this PR do?
Type of change: new feature
Add a standalone DLA cleanup pass for eligible input promotion, Squeeze/Unsqueeze replacement, reshape-chain cleanup, output adapters, constant folding, lossless Cast-chain cleanup, and unused initializer removal.
Rank promotion now follows known elementwise paths and preserves rank-sensitive consumers, including consumers behind other elementwise nodes. A shared input can still be promoted on a safe branch while another branch retains its original rank. When promotion reaches a graph output, an opset-compatible Squeeze restores the declared output rank while interior consumers keep their 4D tensors. The combined pass retains the original output descriptors through later cleanup stages. Analysis is computed lazily once per stage and stops propagating restrictions when broadcasting already fixes the output rank at 4D. Gather and GQA boundary exceptions are retained. PRelu slopes and Clip control inputs retain their original ranks to respect their broadcasting constraints.
Generated tensor, initializer and node names use the existing graph cache's name allocator. Graph-output adapters preserve declared ranks and values; non-squeezable output reshapes reach exactly 4D and squeeze every prepended axis. Shape fallback does not guess dimensions for unresolved explicit Squeeze axes; omitted axes retain their unit-dimension-removal fallback. The fallback honors legacy Squeeze axes attributes and normalizes negative Unsqueeze axes against the output rank. Generated Squeeze/Unsqueeze nodes use axes attributes below opset 13 and axes inputs on newer models, based on the actual default-domain import. The empty-domain import takes precedence; an
ai.onnximport is used only when the empty-domain import is absent. Identity output Reshapes are retained to avoid the remove-all-unit-dimensions behavior of empty-axes Squeeze in ORT. Unsqueeze axes controls are excluded from data-input promotion. Lossless Cast-chain cleanup preserves all final Cast attributes, including float8 saturation and rounding, and leaves custom-domain operations untouched. Shape-operation matching and rank-safety analysis likewise keep custom or overloaded operations opaque, even when they reuse standard operation names. Subgraph-captured values remain defined across noop removal, Reshape/Cast chain collapse and canonical Unsqueeze cleanup. Capture collection reuses the existing scope-aware helper and also handles a name used as both a direct control input and a lexical capture. Test inference failures are surfaced, and all introduced functions have docstrings.Part 3/17 is based directly on main; part 2 was withdrawn after removal of the custom save wrapper. The remaining PR numbers are retained. This remains a draft.
Source size: 1040 added lines. This is the existing self-contained cleanup slice; splitting its cooperating stages would require a separate implementation refactor.
Usage
This is an internal pass. Public pipeline integration follows in part 16; operation-specific rank conversions may still be needed for a model.
Constant folding passes the input IR version directly to graphsurgeon instead of changing ONNX process-wide state. Three real-export regression cases fail on the global-state mutation before the fix and now preserve IR 8/9/10 and the global constant. All 109 cleanup tests pass.
Cleanup captures inferred native output ranks before promotion and restores those ranks even when the original output descriptor omits its shape. The original descriptors remain intact. Four previously failing cases and four declared-shape controls cover ranks two and three at opsets 12 and 17; checker and CPU results pass. A synthetic Windows CPU sample plans 512 branches with 64 MiB retained payloads in 0.005732 seconds and +0.9492 MiB sampled peak RSS, preserving payload bytes and tensor identity (2 ms polling).
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.
Before your PR is "Ready for review"
Additional Information
Base branch:
main. The next slice is PR4 #2956.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.