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qdq_to_dq always wrote the converted weight as INT8 and clipped it to
[-128, 127]. The DequantizeLinear stays in the graph with its original zero
point, so for an unsigned zero point the result is a graph whose weight and zero
point disagree on type, and whose values above 127 have been clipped away.
onnx.checker accepts that model, so nothing fails until load:
Type Error: Type parameter (T1) of Optype (DequantizeLinear) bound to
different types (tensor(int8) and tensor(uint8))
Derive the weight type and the clip range from the zero point instead. The
module already models unsigned quantization elsewhere -- onnx_bit_dtype_unsigned_map
and get_tensor_dtype() hand back UINT8 for a quantizer that has a zero point --
so insert_dq_nodes could produce weights that qdq_to_dq could not round-trip.
A zero point of any other integer type now raises instead of being silently
written as INT8.
Signed weights are unchanged: the INT8 path converts bit-identically.
Signed-off-by: SID <99672439+SID-6921@users.noreply.github.com>
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: preserving the converted weight type to match its zero point in ONNX quantization. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns PASS. The pull request changes only CHANGELOG.rst, modelopt/onnx/quantization/qdq_utils.py, and its ONNX unit test. The added production code maps ONNX INT8/UINT8 zero points to NumPy dtypes and clips…
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Review comments at @CHANGELOG.rst:
- Line 97: Update the changelog entry for --dq_only to state that ONNX exports
with unsigned-quantized weights now load correctly, and remove the explanation
of INT8 storage, zero points, and clipping.
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AGENTS.md keeps root-cause analysis and implementation detail out of CHANGELOG.rst; those belong in the PR description. Signed-off-by: SID <99672439+SID-6921@users.noreply.github.com>
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…in qdq_to_dq (#2554) ### What does this PR do? Type of change: Bug fix > Rewritten 2026-10-02 to describe what the branch actually does. The first version described a composed-permutation approach that review replaced; that machinery is gone. `qdq_to_dq()` assumed `DequantizeLinear` feeds `Conv`/`Gemm`/`MatMul` directly, tolerating only a `Cast` in between. A weight that reaches `MatMul` transposed — as in MaxViT's attention blocks — put `Transpose` in front of `_convert_weight`, which has no entry for it: ``` RuntimeError: Failed to convert node .../qkv/weight_quantizer/QuantizeLinear: Unsupported op_type for real weight quantization: Transpose ``` **Two changes, and the second is the one that matters.** **1. Trace `Cast`/`Transpose` to reach the real consumer.** That walk now exists only to find the node whose op type decides whether this conversion supports the weight at all. **2. Take the quantization axis from the Q/DQ pair, not from the consumer.** After conversion the `DequantizeLinear` stays in the graph with its input rewired to the weight initializer, so *its* axis is what indexes the stored weight — regardless of any `Transpose` downstream. The conversion follows that axis, defaulting to ONNX's `axis = 1` when the attribute is omitted. No permutation is composed and there is no consumer-axis fallback. That also fixes a case that is broken on `main` today and has nothing to do with `Transpose`. Measured end to end under onnxruntime, square weight into `MatMul`: | Q/DQ axis | Transpose | on `main` | this PR | |---|---|---|---| | 0 | no | **silently wrong** (max diff 4.62) | correct | | 0 | yes | raises `Unsupported op_type` | correct | | 1 | no | correct | correct | | 1 | yes | n/a (raised before this PR) | correct | A wrong axis on a square weight still satisfies the scale-length check, so nothing raises and the model simply computes something else. **Two smaller consequences of making the Q/DQ pair authoritative:** - A **single-element scale** covers the whole weight, so it broadcasts and no axis applies to it. It no longer borrows the consumer's axis, which only worked when that dimension happened to be 1 — a `(1, 32)` single-output projection failed before. A scalar scale converts as well, which is also the subject of #2613. - A **per-axis Q/DQ pair that disagrees** on the axis now raises instead of silently following the DQ and dropping the Q. Effective axes are compared, so declaring `axis` on only one side is not a disagreement. With nothing left reading the per-op axis values, `axis_map` became the set of supported consumers it was really being used as. ### Testing `TestQdqToDqTranspose`, 10 cases, each failing on `main`: - `test_conversion_matches_qdq` — 5 combinations of shape x axis x transpose x declared/omitted axis, comparing onnxruntime output before and after conversion - `test_dq_transpose_without_perm_is_traced` — ONNX lets `Transpose` omit `perm` - `test_one_element_scale_broadcasts_through_transpose` — `(1, 32)` and `(32, 1)` - `test_one_element_scale_is_not_per_axis` — single-output `Conv` - `test_disagreeing_qdq_axes_raise` On the model from #2442: a quantized `maxvit_tiny_rw_256` exported with `dq_only=True` raises on `main` and exports here, and the resulting DQ-only graph is **bit-identical to the QDQ graph under onnxruntime — max abs diff `0.0e+00`** across 457 quantizers. `tests/unit/onnx` is 659 passed locally with 11 failures that also occur on pristine `main` in this environment (int4 paths needing cupy, three `TestColumnMajorTransformation` tests needing MSVC build tools). `ruff check` reports the same 3 pre-existing `PLR0917` findings before and after. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ — a correctly emitted per-channel Q/DQ pair already declares the axis this conversion now reads, so well-formed graphs convert bit-identically; only graphs where the two disagreed change behaviour - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ - Did you get Claude approval on this PR?: N/A ### Additional Information Fixes #2442. #2613 edits `_convert_weight` against the lines this PR restructures, and #2567 touches the same function lower down. Happy to rebase in whatever order suits you. --------- Signed-off-by: SID <99672439+SID-6921@users.noreply.github.com> Signed-off-by: Siddhardha Nanda <99672439+SID-6921@users.noreply.github.com> Co-authored-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com>
# Conflicts: # CHANGELOG.rst # tests/unit/onnx/quantization/test_qdq_utils.py
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### What does this PR do? Type of change: Bug fix `MFTLoss.forward` flattens both logit tensors to `(batch * positions, vocab)` and its docstring only assumes the class dimension is last, so leading dimensions are clearly meant to be allowed: ```python soft_log_probs = soft_log_probs.view(-1, soft_log_probs.size(-1)) # (new B, C) target_logits = target_logits.view(-1, target_logits.size(-1)) # (new B, C) soft_targets = self._prepare_corrected_distributions(target_logits, labels, ...) ``` The labels were passed through untouched, and `_prepare_corrected_distributions` rejects anything that is not 1-D: ``` ValueError: Logits must be a 2D tensor and labels must be a 1D tensor. ``` So the shapes a language model actually produces — `(batch, seq_len, vocab)` logits against `(batch, seq_len)` labels — cannot be used: | logits | labels | on `main` | this PR | |---|---|---|---| | `(2, 8, 50)` | `(2, 8)` | `ValueError` | converges, matches the flattened form exactly | | `(16, 50)` | `(16,)` | works | unchanged | That is the setting Minifinetuning (arXiv:2506.15702) is for, so in practice a caller had to flatten the labels themselves and nothing documented that. Flattening them alongside the logits fixes it, and the docstring now says what shape the labels are expected in. ### Testing `test_mft_loss_accepts_sequence_shaped_logits` drives `MFTLoss` at `(2, 8, 50)` / `(2, 8)` and asserts the result equals the pre-flattened `(16, 50)` / `(16,)` call. It fails on `main` with the `ValueError` above and passes here. Worth noting why this was not caught: the existing `test_distillation_model_mft` drives `MFTLoss` through a vision model, whose logits are already `(batch, classes)` and labels already `(batch,)`, so the flattening never does anything there. `tests/unit/torch/distill` is 30 passed, 1 skipped locally. The skip is `plugins/test_huggingface_kd.py`, which needs `transformers` and is not installed in my environment; it exercises `LogitsDistillationLoss`, which this change does not touch. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ — already-1-D labels reshape to themselves, so existing callers are unaffected - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ - Did you get Claude approval on this PR?: N/A ### Additional Information Unrelated to my open ONNX PRs (#2553, #2554, #2567, #2575, #2583) — no shared files. One thing I noticed next door and did not touch, in case it is of interest: `LogitsDistillationLoss` defaults to `reduction="mean"`, while `MFTLoss` in the same file defaults to `"batchmean"`. PyTorch warns on every call that `"mean"` is not the KL divergence value and that it will be changed to behave as `"batchmean"` in a future major release, so the default path is currently a factor of the vocabulary size away from the other two reductions and will shift silently when that lands. Changing a training default is your call rather than mine, so I have left it alone — happy to open a separate issue if useful. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Improved handling of sequence-shaped labels when calculating distillation loss, ensuring results match equivalent flattened inputs. * **Tests** * Added coverage verifying consistent loss results for sequence-shaped and flattened inputs. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Siddhardha Nanda <99672439+SID-6921@users.noreply.github.com> Co-authored-by: Asha Anoosheh <aanoosheh@nvidia.com>
Signed-off-by: Siddhardha Nanda <99672439+SID-6921@users.noreply.github.com> # Conflicts: # modelopt/onnx/quantization/qdq_utils.py
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Signed-off-by: Siddhardha Nanda <99672439+SID-6921@users.noreply.github.com> # Conflicts: # CHANGELOG.rst
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What does this PR do?
Type of change: Bug fix
qdq_to_dq()always writes the converted weight asint8and clips it to[-128, 127]. TheDequantizeLinearstays in the graph with its original zero point, so when that zero point is unsigned the result is a graph whose weight and zero point disagree on type, with every value above 127 clipped away.onnx.checkeraccepts the model, so nothing fails at conversion time:Measured on
main(23355ed), asymmetric per-channel UINT8 weights into aMatMul:mainzero_pointtypeonnx.checkerThis is reachable from the module's own behaviour rather than only from hand-written graphs:
onnx_bit_dtype_unsigned_mapandget_tensor_dtype(num_bits, has_zero_point)hand backUINT8for a quantizer that has a zero point, soinsert_dq_nodescan produce weights thatqdq_to_dqcannot round-trip.The weight type and the clip range now both come from the zero point, through one accessor so they cannot drift apart. A zero point of any other integer type raises instead of being silently written as
int8.Testing
TestQdqToDqWeightType::test_converted_weight_matches_zero_point_type, parametrized signed/unsigned, asserts the stored weight type and compares onnxruntime output before and after conversion.onnx.checkerdoes not catch the mismatch, so the test loads the converted model rather than only checking it.main, passes heretests/unit/onnxis 649 passed locally with 11 failures that also occur on pristinemainin this environment (int4 paths needing cupy, threeTestColumnMajorTransformationtests needing MSVC build tools).ruff checkreports the same 3 pre-existingPLR0917findings before and after.Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
Branched from
main, not stacked on #2554. Both touch_convert_weight, but in different regions (that PR changes axis selection; this one changes the stored type and clip range), so they should merge independently — flagging it in case you would rather they were ordered.Summary by CodeRabbit
Bug Fixes
--dq_onlywith unsigned quantized weights so they load in ONNX Runtime and produce outputs matching the source model.Tests