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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
Add opt-in force_fp16_activations to the public DLA API and CLI, retaining FP32 public boundaries and the default FP32 pipeline. Part 17/17 depends on part 16; merge from the bottom upward. This self-contained FP16 feature remains together under the approved source-size exception.
Use ONNX type bindings to select supported FP16 operations and retain FP32 controls, generated values, structured outputs, nested captures and opaque functions where required. Preserve runtime-overridable initializer defaults. Eligible fixed FLOAT weights use the existing ModelOpt initializer converter; malformed or unavailable weights fail validation before mutation. Supported contrib quantizers and activation retain their original domains and imports. Standard quantizers upgrade only supported imports, migrating reduction axes in nested graphs when needed.
Scaled L2 normalization and terminal projection splitting retain native contracts, ownership and generated-name safety. Projection conversion requires fixed private weights and controls, validates supported layouts and groups, and splits eligible projections at the 8192-channel limit. Batch replacements preserve unrelated Constant payload objects. Legacy generated initializer declarations remain valid.
Terminal projection splitting additionally requires the Conv result shape [1, channels, 1, 1]; nontrivial batch summation stays in the original chain. Public option documentation attaches QDQ mutual exclusion to the correct argument.
FP16 L2 decomposition prevalidates collapsed axes with the shared normalization mapping before generating ReduceSum. A removed normalization axis refuses consistently with the native FP32 route, before earlier eligible normalization branches can be mutated.
The public conversion contract assumes finite FP32 inputs. Operations and controls requiring FP32 retain it.
Eligible scaled last-axis L2 decomposition uses axis -1 for non-five-dimensional inputs, so generated ReduceSum follows the last runtime dimension after leading-rank promotion. Five-dimensional inputs keep the shared collapse mapping and prevalidation.
When FP16 quantizers require opset 19, legacy equal Split nodes receive num_outputs, including nested graphs. Empty optional split-input placeholders are removed when adding that attribute so ONNX Runtime recognizes the absent input. Explicit sizes and existing num_outputs remain unchanged. Six legacy equal-split cases fail before the fix; twelve no-Split, explicit-size and modern-schema controls pass. All 101 FP16 tests pass with graph checking and CPU output comparisons.
The public API and CLI help now document the existing FP16 L2 arithmetic limitation. Finite FP32 inputs can overflow squared terms or their sum, and small squares can underflow; the denominator retains its existing 2**-14 floor. Users should validate on the target runtime and leave FP16 mode disabled when the intermediates need a wider range. Numerical rescaling is intentionally deferred to preserve reference graph and weight parity. Four strict-FP16 counterexamples reproduce the limitation; the normalization algorithm is unchanged.
Removed test_excluded_initializers_are_restored_when_conversion_fails because payload validation rejects its fixture before converter rollback is reached. The existing malformed-late-weight test retains the stronger whole-model unchanged-state assertion. The FP16 normalization override also forwards the registered rank context to the shared rank stage; eight existing low-rank execution cases fail without that forwarding and eight FP32 controls remain valid. All 101 FP16 tests pass. The arithmetic recipe and the documented numerical limitation are unchanged.
Internal FP16-eligible readers of a public Squeeze output now share a private FP16 cast. The public output stays FP32; native controls and existing FP16 Cast readers keep their original inputs. Two arithmetic execution cases fail without the fix; two native-control cases remain valid. All 101 FP16 tests pass. The normalization recipe and its documented limitation are unchanged. A Windows CPU sample with 512 readers and a retained 64 MiB payload uses one shared public-output cast, takes 0.013661 seconds and adds 2.1367 MiB sampled peak RSS. Payload bytes/identity, graph checking and CPU values are verified. Single sample with 2 ms polling.
Final FP16 validation now runs ONNX inference with check_type=True and strict_mode=True, then structural checking. These match the strict typed-inference settings used by full_check, without repeating inference on a copied model. Both public-API regressions fail before the fix and now reject malformed Resize scale types before creating or replacing output files; all 91 FP16 tests pass. For 512 readers and a retained 64 MiB payload, one sequential Windows CPU comparison takes 0.655895 seconds/+261.6914 MiB sampled peak RSS before and 0.670173 seconds/+261.5703 MiB after; complete graph hashes and CPU values match. Repeating full_check instead needs +325.3555 MiB in the same sample. Single samples, 2 ms polling; GPU execution was not measured. The FP16 arithmetic module is unchanged.
Public Cast-to-FLOAT outputs now share the existing Squeeze boundary handling: arithmetic readers receive a shared private FP16 view, while the public FLOAT value, explicit Cast readers and FP32 control inputs retain their requested precision. Six INT32/INT64 regressions fail strict typed checking before the fix; four control cases retain existing behavior. All 101 FP16 tests pass, including the actual save/reload API, ReferenceEvaluator rounding checks and ORT CPU execution. ORT CPU promotes FP16 arithmetic, so rounding-sensitive values are checked with ReferenceEvaluator and representable values with ORT. One Windows CPU sample rewrites 512 readers with a retained 64 MiB payload in 0.013800 seconds and +2.1953 MiB sampled peak RSS (2 ms polling); payload bytes and identity are preserved, a single private view is shared, and every CPU output is checked. GPU execution was not measured. L2 arithmetic is unchanged.
The latest test-only cleanup removes 233 redundant parameter combinations while retaining all original test functions and assertion bodies, all original parameter-factor pairs, and selected critical triples. Existing CPU helpers evaluate all outputs in one inference and use one worker for small fixtures. Production transformation code is unchanged.
Follow-up review and quality cleanup binds the optional ONNX Runtime module at collection time and removes its redundant function-local import. Four optional symbolic-inference imports no longer carry unnecessary test-only type-ignore comments, keeping formatting stable under branch Ruff 0.15.20 and Code Quality Ruff 0.17.3. All 315 affected-module cases and both lint/format checks pass; production code and test assertions are unchanged.
Testing
The transformations have been thoroughly tested on 22 models.
Windows CPU: 1,816 passed with one existing numerical-overflow warning. Under identical line/branch coverage instrumentation, runtime decreased from 380.88 seconds (2,049 cases) to 304.66 seconds (1,816 cases), a 20.0% reduction. Exact coverage is unchanged: 6,008 source lines and 2,822 branches across 34 files, with no previously covered line or branch lost. Native runtime modules were preloaded before enabling Windows coverage tracing.
The full validation includes graph-surgery, GQA and DQ-transpose compatibility tests, using Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. All commit hooks pass and the test-only commit is signed and signed off. GPU execution, DLA compilation and the complete Linux suite were not run locally.
Added an optional --force-fp16-activations setting for DLA-compatible models. It enables FP16 internal computation while preserving FP32 model inputs and outputs, along with operations that require FP32.
Eligible large output projections are split into smaller chunks for compatibility.
Documentation
Documented potential overflow or underflow in FP16 L2 normalization, including with finite inputs, and the normalization denominator floor.
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Walkthrough
Adds an optional --force-fp16-activations flag to DLA graph surgery. When enabled, the pipeline converts eligible internal activations and weights to FP16, retains FP32 boundaries and required FP32 regions, rewrites eligible L2 normalization, and splits matching terminal projections.
Changes: FP16 DLA Activation Conversion
Layer / File(s)
Summary
Opt-in pipeline and CLI wiring modelopt/onnx/graph_surgery/__main__.py, modelopt/onnx/graph_surgery/make_dla_compatible.py, CHANGELOG.rst
Adds the default-disabled option to the CLI and transformation entry points. When enabled, the pipeline applies the FP16 transforms, performs shape inference, and validates the model.
Normalization rewrite and opset handling modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_force_fp16_activations.py
Decomposes eligible last-axis L2 normalization and migrates supported quantizer models to standard opset 19. Tests cover normalization, axis mapping, nested graphs, and unsupported cases.
FP16 conversion and FP32 preservation modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_force_fp16_activations.py
Validates FLOAT payloads, preserves required FP32 regions, converts eligible weights and activations, and inserts boundary casts. Tests cover controls, initializers, nested graphs, generated values, naming, and validation.
Splits matching terminal projection chains into chunks of at most 8192 output channels. Tests cover layouts, quantized weights, ownership conditions, legacy inputs, and output values.
Graph-surgery test coverage and runtime helpers tests/unit/onnx/graph_surgery/dla_transforms/*
Updates ONNX Runtime helpers to use one intra-op thread and fetch all graph outputs in one call. Replaces selected Cartesian test parameterizations with explicit cases.
The new opt-in FP16 mode can silently return zeroed L2-normalization outputs when activations are large. This limitation is documented, and the default FP32 pipeline is unaffected. It is mergeable with owner awareness. Users should validate numerics on their target runtime and leave the flag off when intermediates need a wider range.
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The title clearly and concisely describes the main change: an opt-in FP16 strong-typing mode for DLA.
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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 59 functions across 20 files. (1 skipped: 1 unsupported.)
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PASS. The authoritative PR diff adds no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, external-input eval()/exec(), or # nosec comments. The changed CLI only forwards the existing configurable args.trust_remote_code value. No dependency manifest changes or example Python changes are present.
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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 opt-in
force_fp16_activationsto the public DLA API and CLI, retaining FP32 public boundaries and the default FP32 pipeline. Part 17/17 depends on part 16; merge from the bottom upward. This self-contained FP16 feature remains together under the approved source-size exception.Use ONNX type bindings to select supported FP16 operations and retain FP32 controls, generated values, structured outputs, nested captures and opaque functions where required. Preserve runtime-overridable initializer defaults. Eligible fixed FLOAT weights use the existing ModelOpt initializer converter; malformed or unavailable weights fail validation before mutation. Supported contrib quantizers and activation retain their original domains and imports. Standard quantizers upgrade only supported imports, migrating reduction axes in nested graphs when needed.
Scaled L2 normalization and terminal projection splitting retain native contracts, ownership and generated-name safety. Projection conversion requires fixed private weights and controls, validates supported layouts and groups, and splits eligible projections at the 8192-channel limit. Batch replacements preserve unrelated Constant payload objects. Legacy generated initializer declarations remain valid.
Terminal projection splitting additionally requires the Conv result shape
[1, channels, 1, 1]; nontrivial batch summation stays in the original chain. Public option documentation attaches QDQ mutual exclusion to the correct argument.FP16 L2 decomposition prevalidates collapsed axes with the shared normalization mapping before generating ReduceSum. A removed normalization axis refuses consistently with the native FP32 route, before earlier eligible normalization branches can be mutated.
Usage
The public conversion contract assumes finite FP32 inputs. Operations and controls requiring FP32 retain it.
Eligible scaled last-axis L2 decomposition uses axis -1 for non-five-dimensional inputs, so generated ReduceSum follows the last runtime dimension after leading-rank promotion. Five-dimensional inputs keep the shared collapse mapping and prevalidation.
When FP16 quantizers require opset 19, legacy equal Split nodes receive num_outputs, including nested graphs. Empty optional split-input placeholders are removed when adding that attribute so ONNX Runtime recognizes the absent input. Explicit sizes and existing num_outputs remain unchanged. Six legacy equal-split cases fail before the fix; twelve no-Split, explicit-size and modern-schema controls pass. All 101 FP16 tests pass with graph checking and CPU output comparisons.
The public API and CLI help now document the existing FP16 L2 arithmetic limitation. Finite FP32 inputs can overflow squared terms or their sum, and small squares can underflow; the denominator retains its existing 2**-14 floor. Users should validate on the target runtime and leave FP16 mode disabled when the intermediates need a wider range. Numerical rescaling is intentionally deferred to preserve reference graph and weight parity. Four strict-FP16 counterexamples reproduce the limitation; the normalization algorithm is unchanged.
Removed test_excluded_initializers_are_restored_when_conversion_fails because payload validation rejects its fixture before converter rollback is reached. The existing malformed-late-weight test retains the stronger whole-model unchanged-state assertion. The FP16 normalization override also forwards the registered rank context to the shared rank stage; eight existing low-rank execution cases fail without that forwarding and eight FP32 controls remain valid. All 101 FP16 tests pass. The arithmetic recipe and the documented numerical limitation are unchanged.
Internal FP16-eligible readers of a public Squeeze output now share a private FP16 cast. The public output stays FP32; native controls and existing FP16 Cast readers keep their original inputs. Two arithmetic execution cases fail without the fix; two native-control cases remain valid. All 101 FP16 tests pass. The normalization recipe and its documented limitation are unchanged. A Windows CPU sample with 512 readers and a retained 64 MiB payload uses one shared public-output cast, takes 0.013661 seconds and adds 2.1367 MiB sampled peak RSS. Payload bytes/identity, graph checking and CPU values are verified. Single sample with 2 ms polling.
Final FP16 validation now runs ONNX inference with check_type=True and strict_mode=True, then structural checking. These match the strict typed-inference settings used by full_check, without repeating inference on a copied model. Both public-API regressions fail before the fix and now reject malformed Resize scale types before creating or replacing output files; all 91 FP16 tests pass. For 512 readers and a retained 64 MiB payload, one sequential Windows CPU comparison takes 0.655895 seconds/+261.6914 MiB sampled peak RSS before and 0.670173 seconds/+261.5703 MiB after; complete graph hashes and CPU values match. Repeating full_check instead needs +325.3555 MiB in the same sample. Single samples, 2 ms polling; GPU execution was not measured. The FP16 arithmetic module is unchanged.
Public Cast-to-FLOAT outputs now share the existing Squeeze boundary handling: arithmetic readers receive a shared private FP16 view, while the public FLOAT value, explicit Cast readers and FP32 control inputs retain their requested precision. Six INT32/INT64 regressions fail strict typed checking before the fix; four control cases retain existing behavior. All 101 FP16 tests pass, including the actual save/reload API, ReferenceEvaluator rounding checks and ORT CPU execution. ORT CPU promotes FP16 arithmetic, so rounding-sensitive values are checked with ReferenceEvaluator and representable values with ORT. One Windows CPU sample rewrites 512 readers with a retained 64 MiB payload in 0.013800 seconds and +2.1953 MiB sampled peak RSS (2 ms polling); payload bytes and identity are preserved, a single private view is shared, and every CPU output is checked. GPU execution was not measured. L2 arithmetic is unchanged.
The latest test-only cleanup removes 233 redundant parameter combinations while retaining all original test functions and assertion bodies, all original parameter-factor pairs, and selected critical triples. Existing CPU helpers evaluate all outputs in one inference and use one worker for small fixtures. Production transformation code is unchanged.
Follow-up review and quality cleanup binds the optional ONNX Runtime module at collection time and removes its redundant function-local import. Four optional symbolic-inference imports no longer carry unnecessary test-only type-ignore comments, keeping formatting stable under branch Ruff 0.15.20 and Code Quality Ruff 0.17.3. All 315 affected-module cases and both lint/format checks pass; production code and test assertions are unchanged.
Testing
The transformations have been thoroughly tested on 22 models.
Windows CPU: 1,816 passed with one existing numerical-overflow warning. Under identical line/branch coverage instrumentation, runtime decreased from 380.88 seconds (2,049 cases) to 304.66 seconds (1,816 cases), a 20.0% reduction. Exact coverage is unchanged: 6,008 source lines and 2,822 branches across 34 files, with no previously covered line or branch lost. Native runtime modules were preloaded before enabling Windows coverage tracing.
The full validation includes graph-surgery, GQA and DQ-transpose compatibility tests, using Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. All commit hooks pass and the test-only commit is signed and signed off. 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-16-dla-public-integration-and-reshape-cleanup.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
--force-fp16-activationssetting for DLA-compatible models. It enables FP16 internal computation while preserving FP32 model inputs and outputs, along with operations that require FP32.--remove-all-qdqremains available.