[5565357] Fix SDXL NVFP4 export and performance - #2336

Type of change: Bug fix

Adds a compact SDXL and SDXL-Turbo mixed-precision FP4 recipe:

- block-16 NVFP4 for non-QKV Linear/GEMM layers;
- FP8 for Conv2d layers;
- high-precision Q/K/V projection Linears to preserve TensorRT
horizontal fusion;
- optional FP8 MHA quantization.

For SDXL FP4 export, Conv2d quantizers export directly through the
shared FP8 custom-op path. The previous `generate_fp8_scales` plus
`convert_zp_fp8` INT8 zero-point workaround is removed. The graph then
uses the existing FP8 Q/DQ normalization and `NVFP4QuantExporter`
lowering, with opset 23 for FLOAT4 support. Flux FP8 export also saves
the graph returned by its RoPE weight conversion.

This PR also changes shared exporter behavior:

- `_fp8_quantize` refreshes ONNX shape/type inference after applying the
custom FP8 operator's uint8 output metadata, affecting all FP8 ONNX
exports through this symbolic.
- `_quantized_sdpa` derives `disable_fp8_mha` from the live Q/K/V
quantizer state instead of a restored private module flag.

Other model recipe configurations remain unchanged.

```bash
python quantize.py \
    --model sdxl-1.0 \
    --model-dtype Half \
    --trt-high-precision-dtype Half \
    --format fp4 \
    --block-size 16 \
    --batch-size 2 \
    --calib-size 128 \
    --n-steps 20 \
    --quantized-torch-ckpt-save-path ./sdxl-fp4 \
    --onnx-dir ./onnx-sdxl-fp4
```

- CPU-only focused and generic NVFP4 exporter tests: 44 passed in 4.35
seconds.
- Focused Flux returned-graph save test: 1 passed.
- Required Linux unit CI at `034fe23ec` passed with the `all` dependency
set, including `tests/unit/examples/test_diffusers_fp4.py`.
- Latest changed-file pre-commit checks: all passed.
- TensorRT 10.14 on a B200 GPU:
  - 302 native block-scaled NVFP4 GEMM tactics;
  - 38 native FP8 Conv tactics;
  - no FP4 Q/K/V projections;
  - all 11 FP16 Q/K/V projection-fusion groups preserved;
- three alternating batch-2 profiles measured 18.614 ms FP4 versus
20.028 ms FP16 median UNet latency, a 7.06% reduction.
- FP8 SDXL/SD3 ONNX-to-TensorRT end-to-end runs were not executed
because they require explicit approval. The existing end-to-end test
matrix now includes SD3 FP8 alongside SDXL FP8.

Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).

Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).

- Is this change backward compatible?: ✅ — no public API or CLI flags
change; the shared changes preserve the intended FP8 export and
attention behavior.
- 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)?:
✅ — the shared NVFP4 opset, FP8 shape-inference, and Diffusers
attention-policy changes are recorded under bug fixes.
- Did you get Claude approval on this PR?: N/A

Tracking: [5565357]

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

- **New Features**
- Added SDXL support for mixed NVFP4/FP8 quantization, including
convolution and softmax handling.
- Added an SDXL quantization preset for streamlined post-training
quantization workflows.
- Expanded FP4 ONNX export support to Flux and SDXL, with improved
FP4/FP8 graph processing and export reliability.
- Added automatic quantization policy and format restoration from
checkpoints.

- **Documentation**
- Documented SDXL layer behavior, optional FP8 attention quantization,
and Blackwell/TensorRT requirements for FP4 and FP8 deployment.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

> 🤖 _Generated by Codex (AI agent)._

---------

Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Co-authored-by: Codex <codex@openai.com>
Signed-off-by: Chad Voegele <cvoegele@nvidia.com>

Type of change: Bug fix

Adds a compact SDXL and SDXL-Turbo mixed-precision FP4 recipe:

- block-16 NVFP4 for non-QKV Linear/GEMM layers;
- FP8 for Conv2d layers;
- high-precision Q/K/V projection Linears to preserve TensorRT
horizontal fusion;
- optional FP8 MHA quantization.

For SDXL FP4 export, Conv2d quantizers export directly through the
shared FP8 custom-op path. The previous `generate_fp8_scales` plus
`convert_zp_fp8` INT8 zero-point workaround is removed. The graph then
uses the existing FP8 Q/DQ normalization and `NVFP4QuantExporter`
lowering, with opset 23 for FLOAT4 support. Flux FP8 export also saves
the graph returned by its RoPE weight conversion.

This PR also changes shared exporter behavior:

- `_fp8_quantize` refreshes ONNX shape/type inference after applying the
custom FP8 operator's uint8 output metadata, affecting all FP8 ONNX
exports through this symbolic.
- `_quantized_sdpa` derives `disable_fp8_mha` from the live Q/K/V
quantizer state instead of a restored private module flag.

Other model recipe configurations remain unchanged.

```bash
python quantize.py \
    --model sdxl-1.0 \
    --model-dtype Half \
    --trt-high-precision-dtype Half \
    --format fp4 \
    --block-size 16 \
    --batch-size 2 \
    --calib-size 128 \
    --n-steps 20 \
    --quantized-torch-ckpt-save-path ./sdxl-fp4 \
    --onnx-dir ./onnx-sdxl-fp4
```

- CPU-only focused and generic NVFP4 exporter tests: 44 passed in 4.35
seconds.
- Focused Flux returned-graph save test: 1 passed.
- Required Linux unit CI at `034fe23ec` passed with the `all` dependency
set, including `tests/unit/examples/test_diffusers_fp4.py`.
- Latest changed-file pre-commit checks: all passed.
- TensorRT 10.14 on a B200 GPU:
  - 302 native block-scaled NVFP4 GEMM tactics;
  - 38 native FP8 Conv tactics;
  - no FP4 Q/K/V projections;
  - all 11 FP16 Q/K/V projection-fusion groups preserved;
- three alternating batch-2 profiles measured 18.614 ms FP4 versus
20.028 ms FP16 median UNet latency, a 7.06% reduction.
- FP8 SDXL/SD3 ONNX-to-TensorRT end-to-end runs were not executed
because they require explicit approval. The existing end-to-end test
matrix now includes SD3 FP8 alongside SDXL FP8.

Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).

Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).

- Is this change backward compatible?: ✅ — no public API or CLI flags
change; the shared changes preserve the intended FP8 export and
attention behavior.
- 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)?:
✅ — the shared NVFP4 opset, FP8 shape-inference, and Diffusers
attention-policy changes are recorded under bug fixes.
- Did you get Claude approval on this PR?: N/A

Tracking: [5565357]

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

- **New Features**
- Added SDXL support for mixed NVFP4/FP8 quantization, including
convolution and softmax handling.
- Added an SDXL quantization preset for streamlined post-training
quantization workflows.
- Expanded FP4 ONNX export support to Flux and SDXL, with improved
FP4/FP8 graph processing and export reliability.
- Added automatic quantization policy and format restoration from
checkpoints.

- **Documentation**
- Documented SDXL layer behavior, optional FP8 attention quantization,
and Blackwell/TensorRT requirements for FP4 and FP8 deployment.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

> 🤖 _Generated by Codex (AI agent)._

---------

Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Co-authored-by: Codex <codex@openai.com>
Signed-off-by: Chad Voegele <cvoegele@nvidia.com>

#2438 #2402 #2434 (#2504)

## Cherry-picked PRs

- #2202
- #2317
- #2314
- #2403
- #2203
- #2413
- #2436
- #2451
- #2336
- #2438
- #2402
- #2434

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Added Step-3.7 PTQ recipes for routed experts and MLPs using NVFP4,
with FP8 KV-cache support.
  * Added SDXL FP4 quantization with FP8 convolution support.
  * Expanded FP4 ONNX export guidance to include Flux and SDXL.
  * Added BF16 compatibility for FP8 ONNX export.

* **Bug Fixes**
* Improved ONNX handling for large models, external data, metadata,
opsets, and output types.
  * Improved attention quantization and invalid dynamic-scale handling.
* Quantization now reports unmatched weight configurations, with an
override for valid pipeline-parallel workflows.

* **Documentation**
* Updated calibration, ONNX opset, CLI, Step-3.7, and TensorRT-LLM
deployment guidance.
* Updated deployment guidance to use unified Hugging Face exports with
the TensorRT-LLM PyTorch backend.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>
Signed-off-by: Chad Voegele <cvoegele@nvidia.com>
Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Signed-off-by: Noey Yang <174223378+noeyy-mino@users.noreply.github.com>
Signed-off-by: Ajinkya Rasane <ajinkyaashwin@gmail.com>
Signed-off-by: Yue <yueshen@nvidia.com>
Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
Co-authored-by: Zhiyu <zhiyuc@nvidia.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com>
Co-authored-by: Codex <noreply@openai.com>
Co-authored-by: Codex <codex@openai.com>
Co-authored-by: noeyy-mino <174223378+noeyy-mino@users.noreply.github.com>
Co-authored-by: Ajinkya Rasane <ajinkyaashwin@gmail.com>
Co-authored-by: yueshen2016 <39203804+yueshen2016@users.noreply.github.com>
Co-authored-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>