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[16/17] DLA public integration and reshape cleanup - #2968
Availability on main: Parts 3?15 add internal implementation pieces. The public FP32 DLA API and make-dla-compatible CLI 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
Expose the ordered DLA conversion through make_dla_compatible, the graph-surgery registry and the make-dla-compatible CLI command. Part 16/17 depends on part 15; merge the series from the bottom upward. The public API loads external weights, runs the logged sequence, validates the result and saves inline or external output directly with onnx.save.
The final reshape cleanup collapses private fixed-control chains while preserving native ranks required by consumers, public/captured owners and runtime controls. Only identical input/output shapes bypass the chain; exposed identities retain their public names. Generated replacements use allocated names and batch graph updates preserve unrelated Constant objects.
The public documentation states the finite-FP32 branch-tensor assumption for zero-branch Where arithmetic lowering. Integration tests protect the established pass order, dispatch contract, boundary shapes, numerical outputs and existing public surgeries.
Final reshape cleanup distinguishes fixed legacy initializer declarations from modern runtime defaults. Collapsed legacy chains declare their new target inputs through the existing shared helper. Modern overridable targets retain their native chain.
Integration safety coverage verifies the native Gemm fallback introduced in part 8. Unsupported Conv layouts retain valid Gemm broadcasting and attributes with byte-identical serialization and exact reference outputs.
Public reshape-cleanup coverage directly requires nonempty, unique generated Identity names while retaining checked CPU output assertions.
Unused initializer cleanup removes obsolete input declarations together with fixed initializers for legacy IR versions. Modern overridable defaults remain initializers and inputs, preserving their runtime interface. Final reshape cleanup is tested together with the actual unused-initializer stage.
--qdq-quantized-dtypes selects removal dtypes and is mutually exclusive with --remove-all-qdq. Omission retains the existing UINT16/INT16 selection. --no-external-data requests inline output; --external-data-name selects the sidecar name.
Reshape chains follow their data producers, so independent interleaved branches collapse. Public/captured owners, shared readers and shape-control edges remain boundaries. Interleaved prefixes that are already identity views are bypassed while retaining the final target and attributes, including inferred dimensions. Four interleaving regressions fail before producer-based grouping; two target-preservation regressions fail without identity-prefix handling. All 18 cleanup tests pass with checked CPU values. A sample with 512 interleaved chains and a retained 64 MiB payload collapses 1,024 Reshapes to 512 in 0.052331 seconds with +1.4180 MiB sampled peak RSS; payload bytes and identity are preserved. Single Windows CPU sample, 2 ms polling.
Removed the redundant mock-only registry-dispatch test and its unused import. The existing real make-dla-compatible dispatcher test retains keyword forwarding and returned-model checks. All 36 focused safety and real-dispatch checks pass.
Name modelopt.onnx.graph_surgery.make_dla_compatible and make-dla-compatible in the two-sentence feature entry. The public API and rendered CLI help also describe possible zero-sign changes under the existing finite-FP32 Where lowering. No runtime arithmetic or eligibility changed.
The public API now saves directly through onnx.save, matching existing graph surgeries. Inline output, the default sidecar name and a custom sidecar name are covered by three actual save/reload and CPU execution cases, including two large tensor payloads with a trailing-0x1A pattern. The Windows ONNX dependency fix is inherited from main.
The public API leaves an omitted external-data location to ONNX 1.23.2, which chooses a unique sidecar name. Saving remains a direct onnx.save call; explicit relative filenames retain their existing behavior. Four repeated-save regressions preserve old sidecars and unrelated files across working-directory and destination collisions, reload both distinct payloads, pass full checker validation and execute exactly on CPU. The duplicate legacy initializer-input pruning block is removed and the existing discarded-name set is reused. All 127 focused API and cleanup cases pass.
The existing legacy reshape-cleanup integration test now verifies that original caller-declared input descriptors and initializer defaults remain byte-identical after cleanup, matching the default-preservation fix introduced in part 5. Legacy generated controls still receive their required declarations, and modern runtime defaults retain their native chain. Both IR3 and IR9 cases pass with exact CPU outputs.
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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Walkthrough
The pull request adds reshape-chain cleanup to the ordered DLA compatibility pipeline. It exposes a file-based API and CLI command with external-data and Q/DQ options. Tests cover transform order, graph rewrites, output equivalence, dispatch, and model saving.
Adds cleanup for eligible reshape chains. The transform skips chains with unsupported targets or invalid endpoint shapes. It bypasses, replaces, or combines eligible nodes. Tests compare outputs and cover graph outputs, subgraph captures, and legacy initializer handling.
Transform order and rewrite validation modelopt/onnx/graph_surgery/make_dla_compatible.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_logged_pipeline.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_make_compatible.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_rewrite_safety.py
Schedules reshape cleanup after TopK and before unused-initializer removal. Tests check the 36-stage transform order and compare outputs for transformed graphs across operator and control-flow cases.
Exposes the file-based API and CLI command. The API loads, transforms, and saves models. The CLI accepts external-data settings and mutually exclusive Q/DQ options. Tests cover dispatch, option behavior, model validation, and saved tensor data.
sequenceDiagram
participant CLI
participant run_graph_surgery
participant dla_make_dla_compatible
participant DLA_TRANSFORMS
participant save_onnx_model
CLI->>run_graph_surgery: Dispatch command with paths and options
run_graph_surgery->>dla_make_dla_compatible: Forward paths and options
dla_make_dla_compatible->>DLA_TRANSFORMS: Transform loaded model
dla_make_dla_compatible->>save_onnx_model: Save transformed model
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Merge Risk: Minimal · previous review a4f76
The previously identified reshape-cleanup risks are addressed, and no further merge-blocking issue was established. Merge after normal checks.
Pre-merge checks: 6
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The title "[16/17] DLA public integration and reshape cleanup" names the two main parts of the changeset: the public make_dla_compatible API with the make-dla-compatible CLI and registry dispatch, and…
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Docstring coverage is 100.00% 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. Documentat…
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Security Anti-Patterns
PASS. Review of the authoritative PR diff and SECURITY.md found no introduced prohibited pattern: no unsafe torch.load, numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, exte…
Full details: Title check
Explanation
The title "[16/17] DLA public integration and reshape cleanup" names the two main parts of the changeset: the public make_dla_compatible API with the make-dla-compatible CLI and registry dispatch, and the reshape cleanup pass (RemoveReshapes and related unused-initializer changes). The series prefix "[16/17]" is a ordering marker and does not describe the change itself, and "DLA" is a domain term that a teammate may not know without context. The title is specific enough to identify the primary change, so it passes.
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Docstring coverage is 100.00% 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 31 functions across 9 files. (1 skipped: 1 unsupported.)
Full details: Security Anti-Patterns
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PASS. Review of the authoritative PR diff and SECURITY.md found no introduced prohibited pattern: no unsafe torch.load, numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, external-input eval()/exec(), or # nosec. The diff also adds no pyproject.toml or requirements.txt dependencies.
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853-853: Trivial · Maintainability & Code Quality · Low value
Remove the duplicate legacy-input pruning in _remove_unused_initializers.
For ir_version <= 3, the code now removes graph inputs for discarded initializers twice. The pre-existing block at lines 855-859 builds removed_names, which is identical to the new discarded, and deletes the matching inputs. The new block at lines 862-865 then repeats that work, so it does nothing.
Keep one implementation. The diff below removes the new copy. After that, discarded replaces the removed_names set at line 856. Line 856 is unchanged context, so edit it as well.
♻️ Proposed consolidation
- if model.ir_version <= 3:- inputs = [value for value in graph.input if value.name not in discarded]- graph.ClearField("input")- graph.input.extend(inputs)
if model.ir_version <= 3:
- removed_names = {init.name for init in graph.initializer if init.name not in used}
for index in reversed(range(len(graph.input))):
- if graph.input[index].name in removed_names:+ if graph.input[index].name in discarded:
del graph.input[index]
Also applies to: 862-865
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Review comment at
@modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py at line 853:
In `_remove_unused_initializers`, keep a single legacy-input pruning path for
`ir_version <= 3`: reuse the existing `discarded` set in the input-removal loop
and remove the duplicate block that filters and rebuilds `graph.input`.
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Inline comments:
Review comments at @modelopt/onnx/graph_surgery/make_dla_compatible.py:
- Around line 353-363: Update the default sidecar-name handling in
dla_make_dla_compatible to check for collisions both at the relative name and
under dirname(output_path) before calling onnx.save. Generate a different
relative name when either path exists, without deleting or overwriting the
existing sidecar.
---
Nitpick comments:
Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py:
- Line 853: In `_remove_unused_initializers`, keep a single legacy-input pruning
path for `ir_version <= 3`: reuse the existing `discarded` set in the
input-removal loop and remove the duplicate block that filters and rebuilds
`graph.input`.
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Addressed the duplicate legacy-pruning nitpick in 48677b5. The public API leaves an omitted external-data location to ONNX 1.23.2, which chooses a unique sidecar name. Saving remains a direct onnx.save call; explicit relative filenames retain their existing behavior. Four repeated-save regressions preserve old sidecars and unrelated files across working-directory and destination collisions, reload both distinct payloads, pass full checker validation and execute exactly on CPU. The duplicate legacy initializer-input pruning block is removed and the existing discarded-name set is reused. All 127 focused API and cleanup cases pass.
Final restacked validation: 2030 passed; hooks pass and commits are signed.
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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
Expose the ordered DLA conversion through
make_dla_compatible, the graph-surgery registry and themake-dla-compatibleCLI command. Part 16/17 depends on part 15; merge the series from the bottom upward. The public API loads external weights, runs the logged sequence, validates the result and saves inline or external output directly withonnx.save.The final reshape cleanup collapses private fixed-control chains while preserving native ranks required by consumers, public/captured owners and runtime controls. Only identical input/output shapes bypass the chain; exposed identities retain their public names. Generated replacements use allocated names and batch graph updates preserve unrelated Constant objects.
The public documentation states the finite-FP32 branch-tensor assumption for zero-branch Where arithmetic lowering. Integration tests protect the established pass order, dispatch contract, boundary shapes, numerical outputs and existing public surgeries.
Final reshape cleanup distinguishes fixed legacy initializer declarations from modern runtime defaults. Collapsed legacy chains declare their new target inputs through the existing shared helper. Modern overridable targets retain their native chain.
Integration safety coverage verifies the native Gemm fallback introduced in part 8. Unsupported Conv layouts retain valid Gemm broadcasting and attributes with byte-identical serialization and exact reference outputs.
Public reshape-cleanup coverage directly requires nonempty, unique generated Identity names while retaining checked CPU output assertions.
Unused initializer cleanup removes obsolete input declarations together with fixed initializers for legacy IR versions. Modern overridable defaults remain initializers and inputs, preserving their runtime interface. Final reshape cleanup is tested together with the actual unused-initializer stage.
Usage
python -m modelopt.onnx.graph_surgery make-dla-compatible \ --input input.onnx --output output.onnx --remove-all-qdq--qdq-quantized-dtypesselects removal dtypes and is mutually exclusive with--remove-all-qdq. Omission retains the existing UINT16/INT16 selection.--no-external-datarequests inline output;--external-data-nameselects the sidecar name.Reshape chains follow their data producers, so independent interleaved branches collapse. Public/captured owners, shared readers and shape-control edges remain boundaries. Interleaved prefixes that are already identity views are bypassed while retaining the final target and attributes, including inferred dimensions. Four interleaving regressions fail before producer-based grouping; two target-preservation regressions fail without identity-prefix handling. All 18 cleanup tests pass with checked CPU values. A sample with 512 interleaved chains and a retained 64 MiB payload collapses 1,024 Reshapes to 512 in 0.052331 seconds with +1.4180 MiB sampled peak RSS; payload bytes and identity are preserved. Single Windows CPU sample, 2 ms polling.
Removed the redundant mock-only registry-dispatch test and its unused import. The existing real make-dla-compatible dispatcher test retains keyword forwarding and returned-model checks. All 36 focused safety and real-dispatch checks pass.
Name modelopt.onnx.graph_surgery.make_dla_compatible and make-dla-compatible in the two-sentence feature entry. The public API and rendered CLI help also describe possible zero-sign changes under the existing finite-FP32 Where lowering. No runtime arithmetic or eligibility changed.
The public API now saves directly through
onnx.save, matching existing graph surgeries. Inline output, the default sidecar name and a custom sidecar name are covered by three actual save/reload and CPU execution cases, including two large tensor payloads with a trailing-0x1A pattern. The Windows ONNX dependency fix is inherited from main.The public API leaves an omitted external-data location to ONNX 1.23.2, which chooses a unique sidecar name. Saving remains a direct onnx.save call; explicit relative filenames retain their existing behavior. Four repeated-save regressions preserve old sidecars and unrelated files across working-directory and destination collisions, reload both distinct payloads, pass full checker validation and execute exactly on CPU. The duplicate legacy initializer-input pruning block is removed and the existing discarded-name set is reused. All 127 focused API and cleanup cases pass.
The existing legacy reshape-cleanup integration test now verifies that original caller-declared input descriptors and initializer defaults remain byte-identical after cleanup, matching the default-preservation fix introduced in part 5. Legacy generated controls still receive their required declarations, and modern runtime defaults retain their native chain. Both IR3 and IR9 cases pass with exact CPU outputs.
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.
Before your PR is "Ready for review"
Additional Information
Base branch:
dla/review-15-dla-topk-and-dtype-compatibility.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.