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Add logged QDQ removal and Constant-to-initializer preprocessing to the private DLA pipeline, plus an independent QDQ rank wrapper. The orchestrator calls each stage in order. QDQ removal defaults to UINT16/INT16 storage and accepts explicit storage selections; Constant folding handles dense, scalar, repeated and sparse numeric values.
QDQ wrapping prepends unit dimensions, adjusts per-axis attributes, and restores the original output rank. It uses versioned Squeeze/Unsqueeze syntax, reserves generated names, and infers missing output types from quantization controls. Blocked quantization stays unchanged until its scales can be promoted safely. For IR versions through 3, a private DLA helper adds the required graph-input descriptors for initializer defaults created by Constant folding, QDQ axes and optional Clip bounds. The newer IR behavior is unchanged. Known ranks with symbolic or unnamed dimensions are padded while retaining their dimension metadata. Runtime-domain per-tensor QDQ keeps an absent axis; explicitly declared axes still shift. Four cases fail against the preceding head and now pass full checking and CPU execution. Custom functions and values captured by nested graphs retain their definitions. Optional input clipping uses the model's Clip schema and output dtype; unsupported clipping is rejected before graph changes.
Part 5/17, based on PR4 #2956. Merge from the bottom of the stack upward. This remains a draft; public integration follows in part 16. This preprocessing slice contains approximately 600 source lines; it retains the agreed 17-part series and includes the fixes required for these operations to stand alone.
Usage
importonnxfrommodelopt.onnx.graph_surgery.make_dla_compatibleimport_transform_make_dla_compatiblefrommodelopt.onnx.graph_surgery.dla_transforms.dla_handle_qdqimport_apply_handle_qdqmodel=onnx.load("model.onnx", load_external_data=True)
model=_transform_make_dla_compatible(model, qdq_quantized_dtypes=["uint16", "int16"])
# Independent rank wrapping is available for models with inferred data shapes.model=_apply_handle_qdq(model)
onnx.checker.check_model(model)
These transformations are not exhaustive; model-specific requirements may need additional passes.
Cleanup now removes legacy input declarations together with pruned initializer defaults. QDQ graph-output bypass rewires every occurrence in a repeated-input consumer. Three regressions fail before these fixes and three controls remain valid; all 155 constants, QDQ and cleanup cases pass.
Optional input clipping now refuses graph-input scale or zero-point controls before mutation, including initializer defaults that remain overridable. Four regressions cover both controls under IR3 and IR9; ReferenceEvaluator verifies runtime overrides, with additional ORT execution for IR9. Fixed-control clipping remains supported and the normal no-clip pipeline is unchanged. All 34 focused QDQ cases pass.
Unused-initializer cleanup preserves original caller-declared input/default pairs. The existing legacy-input helper marks only descriptors it generates, allowing cleanup to prune those descriptors together with unused generated initializers. The modern-default keepalive rule is available from this slice instead of waiting until part 16. Existing IR3/IR9 pipeline cases now verify byte-preservation of original defaults and descriptors alongside generated-control pruning; all 133 focused constants and cleanup cases pass.
Rank adapters now exclude model-local Q/DQ function identities, including unoverloaded contrib names. Type-changing Q/DQ removal allocates Cast names through the existing graph name cache, preserving reserved names and distinguishing unnamed pairs. Four regressions fail before these fixes; all 61 Q/DQ module cases pass with checker validation and real reference/CPU execution.
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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The change adds DLA graph transforms to remove eligible Q/DQ pairs, pad supported low-rank Q/DQ inputs, and convert supported ONNX Constant nodes to initializers. It registers Q/DQ removal and Constant conversion before existing DLA transforms and adds parity, legacy IR, and domain tests.
The transform removes eligible Q/DQ pairs, rewrites node inputs and graph outputs, and handles dtype differences and supported input clipping. Tests cover dtype selection, graph outputs, chains, fanout, scope captures, and unsupported cases.
Pad eligible Q/DQ input ranks modelopt/onnx/graph_surgery/dla_transforms/dla_handle_qdq.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_handle_qdq.py
The transform wraps eligible rank 1–3 Q/DQ data inputs with Unsqueeze and Squeeze nodes and adjusts applicable axes. Tests cover multiple ranks and opsets, blocked quantization, and unchanged 4-D nodes.
Convert constants to initializers modelopt/onnx/graph_surgery/dla_transforms/dla_constants_to_initializers.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_constants_to_initializers.py
The transform converts supported Constant attributes to initializers. Tests cover dense and sparse values, conversion counts, legacy IR handling, and output parity.
Register DLA transforms and verify domains modelopt/onnx/graph_surgery/make_dla_compatible.py, modelopt/onnx/graph_surgery/dla_transforms/dla_graph_cleanup.py, tests/unit/onnx/graph_surgery/dla_transforms/test_dla_preprocessing_domains.py
The DLA transform list now starts with Q/DQ removal and Constant conversion. For IR versions 1–3, unused-initializer cleanup also removes matching graph inputs. Domain tests check that custom local functions with recognized operator names remain unchanged.
NVIDIA/Model-Optimizer#2831: Adds graph-surgery helpers imported by the new DLA Q/DQ and Constant transforms.
Merge Risk: Moderate · up to a1c83
Large sparse constants can exhaust preprocessing memory, and models with local Q/DQ-named functions can be changed incorrectly. Resolve these cases before merging unless their impact is explicitly accepted.
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_prepare_model and _run_ort import onnxruntime inside functions. The module already calls pytest.importorskip("onnxruntime", ...) at line 56. Bind its return value once at module scope. Import errors then appear at collection time, and the in-function imports are no longer needed. The SymbolicShapeInference import can use pytest.importorskip in the same way.
Also, except Exception: pass in _prepare_model hides shape-inference failures. If inference fails, the transform skips the node because the shape is unknown. The failure then surfaces only as a misleading assertion in _assert_wrapped. Log or re-raise the error.
♻️ Proposed change
-pytest.importorskip("onnxruntime", reason="onnxruntime required for ORT parity checks")+ort = pytest.importorskip("onnxruntime", reason="onnxruntime required for ORT parity checks")
def _run_ort(model: onnx.ModelProto, feeds: dict) -> dict:
- import onnxruntime as ort # type: ignore[import-untyped]-
_clamp_ir(model)
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@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_handle_qdq.py around
lines 78 - 97:
Bind the module-level onnxruntime import through pytest.importorskip and remove
the local import from _run_ort. In _prepare_model, stop silently swallowing
shape-inference failures; let them surface, and use pytest.importorskip for
SymbolicShapeInference if it is an optional dependency.
Line 60 already calls pytest.importorskip("onnxruntime"). The imports inside _prepare_model and _run_ort therefore have no optional-dependency or circular-import reason. Bind ort = pytest.importorskip(...) once and import SymbolicShapeInference at module scope.
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around lines 85 - 87:
Move the `onnxruntime` imports out of `_prepare_model` and `_run_ort` to module
scope; reuse the existing `pytest.importorskip("onnxruntime")` result as `ort`
and import `SymbolicShapeInference` at module scope so import failures surface
during test collection.
Line 60 already calls pytest.importorskip("onnxruntime"). The imports inside _prepare_model and _run_ort therefore have no optional-dependency or circular-import reason. Bind the module once with ort = pytest.importorskip("onnxruntime", ...) and use ort in _run_ort. Import SymbolicShapeInference at module scope after the skip guard. The module scope imports also surface import errors at collection time.
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@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_remove_qdq.py around
lines 87 - 89:
Move the `onnxruntime` and `SymbolicShapeInference` imports from
`_prepare_model` and `_run_ort` to module scope after the `pytest.importorskip`
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Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_constants_to_initializers.py:
- Around line 50-52: Update the sparse_value branch to densify
attr.sparse_tensor using its values, indices, and dims before creating the
output TensorProto; do not pass the SparseTensorProto directly to
numpy_helper.to_array. Keep sparse_value support and add a test covering
conversion of a sparse constant.
Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_handle_qdq.py:
- Line 56: Update the replacement lookup in the graph-node loop to use positions
from a single snapshot of graph.node: enumerate the snapshot and use node_pos as
the key in replacements instead of looking up id(node) through node_idx. Remove
the identity-based node_idx mapping.
---
Nitpick comments:
Review comments at
@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_constants_to_initializers.py:
- Around line 85-87: Move the `onnxruntime` imports out of `_prepare_model` and
`_run_ort` to module scope; reuse the existing
`pytest.importorskip("onnxruntime")` result as `ort` and import
`SymbolicShapeInference` at module scope so import failures surface during test
collection.
Review comments at
@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_handle_qdq.py:
- Around line 78-97: Bind the module-level onnxruntime import through
pytest.importorskip and remove the local import from _run_ort. In
_prepare_model, stop silently swallowing shape-inference failures; let them
surface, and use pytest.importorskip for SymbolicShapeInference if it is an
optional dependency.
Review comments at
@tests/unit/onnx/graph_surgery/dla_transforms/test_dla_remove_qdq.py:
- Around line 87-89: Move the `onnxruntime` and `SymbolicShapeInference` imports
from `_prepare_model` and `_run_ort` to module scope after the
`pytest.importorskip` guard; bind the skipped module as `ort` and use it in
`_run_ort` so import errors surface during collection.
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@modelopt/onnx/graph_surgery/dla_transforms/dla_constants_to_initializers.py:
- Around line 75-79: Update the graph-input handling around `init.name` so
`_remove_unused_initializers` cannot prune an initializer while leaving its
added input descriptor behind. Preserve the input–initializer pairing through
cleanup, or skip adding inputs for Constants that cleanup will discard.
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@modelopt/onnx/graph_surgery/dla_transforms/dla_remove_qdq.py:
- Line 300: Remove the break from the consumer.input rename loop so every
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- Around line 113-167: In _remove_qdq_from_model, refuse input-clip preservation
with NotImplementedError when the QuantizeLinear scale or DequantizeLinear
zero-point is a graph input, since calculate_clip_range would bake initializer
defaults into constant Clip bounds. Keep the existing transformation for pairs
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- Around line 64-66: Update the Q/DQ node guard to check `model.functions` for a
function matching the node’s domain, `op_type` name, and overload, and skip
wrapping matching calls. Preserve wrapping for built-in QuantizeLinear and
DequantizeLinear nodes that have no matching model-local function.
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@modelopt/onnx/graph_surgery/dla_transforms/dla_constants_to_initializers.py:
- Line 54: In the sparse Constant densification path, check the dense byte count
from sparse.dims and values.dtype before calling np.zeros; reuse an existing
allocation limit if available, and leave oversized Constants unchanged or
preserve them in sparse form.
Review comments at
@modelopt/onnx/graph_surgery/dla_transforms/dla_remove_qdq.py:
- Around line 75-76: Update the Q/DQ node filtering guard to exclude calls
matching model-local functions by domain, operator name, and overload, using
identities from model.functions before pairing or removing nodes. Add a
com.microsoft local-function case to the remove_qdq stage coverage in
test_dla_preprocessing_domains.py.
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Major · Stability & Availability · Quick win
Limit sparse densification before allocating the array.
A valid sparse Constant can contain few values but declare very large dimensions. For example, a float32 tensor with dimensions [100000, 100000] requires about 40 GB at this line. The allocation can exhaust memory and terminate preprocessing. Check the dense byte count before allocation, and leave oversized sparse Constants unchanged or preserve them in sparse form. (raw.githubusercontent.com)
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line 54:
In the sparse Constant densification path, check the dense byte count from
sparse.dims and values.dtype before calling np.zeros; reuse an existing
allocation limit if available, and leave oversized Constants unchanged or
preserve them in sparse form.
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Minor · Functional Correctness · Quick win
Exclude model-local functions from Q/DQ pairing, as dla_handle_qdq.py does.
The guard on Line 75 checks only the node domain and overload. It does not check model.functions. A model can define a local function named QuantizeLinear or DequantizeLinear in the com.microsoft domain with no overload. The pass then treats calls to that function as built-in Q/DQ nodes.
If such a function has a selected zero-point type, the pass removes the pair as an identity and rewires consumers. This silently changes the function's behavior. For example, a Q function whose body adds a constant would be dropped.
_apply_handle_qdq already excludes these identities with a local_functions set. This pass needs the same guard. The current domain test uses the local domain, which the Line 75 domain check already skips. The test therefore does not exercise this path.
Proposed fix
--- "a/modelopt/onnx/graph_surgery/dla_transforms/dla_remove_qdq.py"+++ "b/modelopt/onnx/graph_surgery/dla_transforms/dla_remove_qdq.py"@@ -69,11 +69,14 @@
opset = versions.get("", versions.get("ai.onnx", 1))
unique_initializer: set[str] = set()
node_datatype_map: dict[str, int] = {}
captured = _subgraph_captures(graph)
+ local_functions = {(fn.domain, fn.name, fn.overload) for fn in model.functions}
for node in graph.node:
if node.domain not in {"", "ai.onnx", "com.microsoft"} or node.overload:
continue
+ if (node.domain, node.op_type, node.overload) in local_functions:+ continue
if node.op_type == "QuantizeLinear":
if node.input[0] in captured or node.output[0] in captured:
continue
Add a com.microsoft local-function case to test_dla_preprocessing_domains.py for the remove_qdq stage.
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Review comment at @modelopt/onnx/graph_surgery/dla_transforms/dla_remove_qdq.py
around lines 75 - 76:
Update the Q/DQ node filtering guard to exclude calls matching model-local
functions by domain, operator name, and overload, using identities from
model.functions before pairing or removing nodes. Add a com.microsoft
local-function case to the remove_qdq stage coverage in
test_dla_preprocessing_domains.py.
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What does this PR do?
Type of change: new feature
Add logged QDQ removal and Constant-to-initializer preprocessing to the private DLA pipeline, plus an independent QDQ rank wrapper. The orchestrator calls each stage in order. QDQ removal defaults to UINT16/INT16 storage and accepts explicit storage selections; Constant folding handles dense, scalar, repeated and sparse numeric values.
QDQ wrapping prepends unit dimensions, adjusts per-axis attributes, and restores the original output rank. It uses versioned Squeeze/Unsqueeze syntax, reserves generated names, and infers missing output types from quantization controls. Blocked quantization stays unchanged until its scales can be promoted safely. For IR versions through 3, a private DLA helper adds the required graph-input descriptors for initializer defaults created by Constant folding, QDQ axes and optional Clip bounds. The newer IR behavior is unchanged. Known ranks with symbolic or unnamed dimensions are padded while retaining their dimension metadata. Runtime-domain per-tensor QDQ keeps an absent axis; explicitly declared axes still shift. Four cases fail against the preceding head and now pass full checking and CPU execution. Custom functions and values captured by nested graphs retain their definitions. Optional input clipping uses the model's Clip schema and output dtype; unsupported clipping is rejected before graph changes.
Part 5/17, based on PR4 #2956. Merge from the bottom of the stack upward. This remains a draft; public integration follows in part 16. This preprocessing slice contains approximately 600 source lines; it retains the agreed 17-part series and includes the fixes required for these operations to stand alone.
Usage
These transformations are not exhaustive; model-specific requirements may need additional passes.
Cleanup now removes legacy input declarations together with pruned initializer defaults. QDQ graph-output bypass rewires every occurrence in a repeated-input consumer. Three regressions fail before these fixes and three controls remain valid; all 155 constants, QDQ and cleanup cases pass.
Optional input clipping now refuses graph-input scale or zero-point controls before mutation, including initializer defaults that remain overridable. Four regressions cover both controls under IR3 and IR9; ReferenceEvaluator verifies runtime overrides, with additional ORT execution for IR9. Fixed-control clipping remains supported and the normal no-clip pipeline is unchanged. All 34 focused QDQ cases pass.
Unused-initializer cleanup preserves original caller-declared input/default pairs. The existing legacy-input helper marks only descriptors it generates, allowing cleanup to prune those descriptors together with unused generated initializers. The modern-default keepalive rule is available from this slice instead of waiting until part 16. Existing IR3/IR9 pipeline cases now verify byte-preservation of original defaults and descriptors alongside generated-control pruning; all 133 focused constants and cleanup cases pass.
Rank adapters now exclude model-local Q/DQ function identities, including unoverloaded contrib names. Type-changing Q/DQ removal allocates Cast names through the existing graph name cache, preserving reserved names and distinguishing unnamed pairs. Four regressions fail before these fixes; all 61 Q/DQ module cases pass with checker validation and real reference/CPU execution.
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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Additional Information
Base branch:
dla/review-04-private-ordered-dla-scaffold-and-node-ordering. Next: PR6 #2958.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