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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.
Testing
Windows CPU: Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. 1986 passed in the complete restacked graph-surgery and public-compatibility suite. Original save and initializer-conversion implementations are preserved. All hooks pass with the equivalent Linux mypy platform setting; commits are signed. The default Windows mypy invocation encounters the unchanged upstream os.sched_getaffinity attribute error. GPU execution, DLA compilation and the complete Linux suite were not run locally.
Added the optional --force-fp16-activations setting for DLA compatibility conversion. When enabled, supported internal computations use FP16 while model inputs, outputs, and operations requiring FP32 remain FP32. It is disabled by default, and unsupported patterns remain unchanged.
Eligible last-axis L2 normalization followed by static scaling uses a denominator floor of 2**-14; intermediate values may overflow or underflow, even with finite FP32 inputs.
Eligible terminal projections with more than 8,192 output channels are processed in smaller chunks.
Documentation
Updated DLA compatibility guidance for the new setting. --remove-all-qdq remains available.
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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, and rewrites qualifying 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
The CLI and transformation entry points gain the default-disabled option. When enabled, the pipeline applies the FP16 transforms, then runs symbolic shape inference and checks 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
The transform decomposes eligible last-axis L2 normalization followed by static multiplication. For qualifying quantizer models, it migrates supported operators and axes to standard opset 19. Tests cover normalization and migration 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
The transform validates FLOAT initializer payloads, identifies FP32 regions and schema-required FLOAT inputs, converts eligible weights and activations, and inserts boundary casts. Tests cover boundaries, control inputs, initializer handling, nested graphs, generated values, and validation failures.
Matching terminal projection chains are split into chunks of at most 8192 output channels. Tests cover supported layouts, quantized weights, ownership conditions, and output values.
The FP16 option is opt-in and the default pipeline is unchanged. When it is enabled, L2 normalization can silently produce zeros for activations with large magnitudes. The API and CLI help document this limitation, so validate accuracy on your target runtime before relying on FP16 mode.
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The title clearly identifies the main change: adding opt-in FP16 strong typing for DLA. It is concise and consistent with the new API, CLI flag, and transformation behavior.
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@modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py:
- Around line 457-463: In the Squeeze producer branch, redirect internal
FP16-eligible, non-cast consumers to a private FP16 copy of the public output.
Use cache.get_consumers and _fp32_control to identify eligible readers, add the
cast after the producer via after, and leave control readers and existing casts
unchanged.
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Review comments at @modelopt/onnx/graph_surgery/make_dla_compatible.py:
- Around line 304-310: Update the final validation after
_apply_force_fp16_activations to run shape inference with type checking enabled
and ONNX model checking with full_check enabled, so tensor type mismatches are
detected before the converted model is saved.
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@modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py:
- Around line 475-476: Update the _is_cast(producer, fp32) branch to preserve
the public FLOAT output while routing eligible internal consumers through a
shared FP16 cast, excluding consumers that require FP32 or are already FP16
casts. Add a regression test for a Cast output shared with Mul that runs full
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Minor · Functional Correctness · Quick win
Give internal readers of a public Cast-to-FLOAT output a private FP16 view.
The _is_cast(producer, fp32) branch skips the output without updating its internal consumers. This is the same gap the Squeeze branch fixed at lines 463-473.
How the failure happens:
A non-float tensor goes through Cast(to=FLOAT) into graph output y. Integer masks or indices are typical sources.
y also feeds a standard arithmetic node, for example Mul(y, w) with a fixed weight w.
_fp32_regions does not mark y as native. Cast is excluded from the generated-output rule, and the Cast output type allows HALF.
Line 369 keeps this Cast at FLOAT because its output is public.
w is converted to FLOAT16, and the value_info of Mul is retyped to FLOAT16.
Mul therefore receives one FLOAT input and one FLOAT16 input.
Result: The strict typed inference in _transform_make_dla_compatible raises. make_dla_compatible(..., force_fp16_activations=True) therefore rejects a valid model, and the only workaround is to turn the option off.
Add the same shared FP16 cast for eligible internal readers. Add a regression test with the graph Cast(int→FLOAT) → y, where y also feeds Mul(y, w). The test should run full checking and compare CPU output.
🐛 Proposed fix
--- "a/modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py"+++ "b/modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py"@@ -472,8 +472,19 @@
for consumer, port in internal:
consumer.input[port] = view
continue
- if _is_cast(producer, fp32):- continue+ if _is_cast(producer, fp32):+ internal = [+ (consumer, port)+ for consumer, port in cache.get_consumers(value.name)+ if not _fp32_control(consumer, port, native_nodes, fp32_inputs)+ and not _is_cast(consumer, fp16)+ ]+ if internal:+ view = cache.unique_name(value.name + "_fp16")+ after.setdefault(id(producer), []).append(_cast(value.name, view, fp16, cache))+ for consumer, port in internal:+ consumer.input[port] = view+ continue
tensor = cache.get_init(value.name) if producer is None else None
if producer is None and (tensor is None or tensor.data_type != fp16):
continue
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only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at
@modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py around
lines 475 - 476:
Update the _is_cast(producer, fp32) branch to preserve the public FLOAT output
while routing eligible internal consumers through a shared FP16 cast, excluding
consumers that require FP32 or are already FP16 casts. Add a regression test for
a Cast output shared with Mul that runs full checking and compares CPU output.
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Addressed in 570a58b. 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.
Validation on the restacked head: 1986 passed; hooks pass and commits are signed.
#!/bin/bash
cat AGENTS.md SECURITY.md
sed -n '453,482p' modelopt/onnx/graph_surgery/dla_transforms/dla_force_fp16_activations.py
sed -n '1035,1205p' tests/unit/onnx/graph_surgery/dla_transforms/test_dla_force_fp16_activations.py
Length of output: 24317
🏁 Script executed:
#!/bin/bash
rg -n -A 100 '^## .*Coding Standards|^## .*coding standards|^### Test design principles' CONTRIBUTING.md
Length of output: 7482
🏁 Script executed:
#!/bin/bash
sed -n '1,175p' CONTRIBUTING.md
Length of output: 12299
@mgohil-png I verified the fix in 570a58b06b. Public Cast-to-FLOAT outputs now share the Squeeze boundary handling. Eligible arithmetic readers use one private FP16 view. Public FLOAT outputs, explicit Cast readers, and FP32 control inputs retain their precision.
The regression tests cover INT32/INT64 inputs, full checking, rounding-sensitive comparisons, CPU execution, and public API save/reload. This addresses the finding. I inspected the code and tests but did not rerun them.
AGENTS.md prohibits agent thread resolution, so I will leave closure to a human reviewer.
🧠 Learnings used
Learnt from: CR
Repo: NVIDIA/Model-Optimizer PR: 0
File: AGENTS.md:0-0
Timestamp: 2026-10-20T14:44:36.749Z
Learning: Source excerpt:
# Agent Instructions for ModelOpt
## Pre-push review pass
- **Evidence.** Each fix has a test that fails without it; check by reverting.
A test for a hang fails fast instead of hanging CI. Run the paths CI skips
(multi-GPU, GPU-only) when the change touches them, and state in the PR what
you ran and what you did not.
Learnt from: CR
Repo: NVIDIA/Model-Optimizer PR: 0
File: AGENTS.md:0-0
Timestamp: 2026-10-20T14:44:36.749Z
Learning: Source excerpt:
# Agent Instructions for ModelOpt
## Pre-push review pass
- **Performance and memory, especially on GPU.** Measure peak memory and time
of changed heavy paths at realistic sizes (e.g. with
`torch.cuda.max_memory_allocated`) instead of reasoning about them. Look for
full-size temporaries (float32 copies of narrower tensors, int64
intermediates, whole-tensor work a chunked loop could bound), expensive work a
cache or earlier result already holds, and host syncs or device-to-host copies
on hot paths.
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What does this PR do?
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.
Testing
Windows CPU: Python 3.11.18, ONNX 1.23.2 and ONNX Runtime 1.22.0. 1986 passed in the complete restacked graph-surgery and public-compatibility suite. Original save and initializer-conversion implementations are preserved. All hooks pass with the equivalent Linux mypy platform setting; commits are signed. The default Windows mypy invocation encounters the unchanged upstream os.sched_getaffinity attribute error. GPU execution, DLA compilation and the complete Linux suite were not run locally.
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Additional Information
Base branch:
dla/review-16-dla-public-integration-and-reshape-cleanup.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 by CodeRabbit
--force-fp16-activationssetting for DLA compatibility conversion. When enabled, supported internal computations use FP16 while model inputs, outputs, and operations requiring FP32 remain FP32. It is disabled by default, and unsupported patterns remain unchanged.2**-14; intermediate values may overflow or underflow, even with finite FP32 inputs.--remove-all-qdqremains available.