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pull-request/2240
14
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 jingyu-mlandClaude Opus 4.8
|
6b4ad85849 |
Qwen-Image diffusers PTQ: FP8 / NVFP4 / NVFP4-SVDQuant HF checkpoints (#1706)
### What does this PR do? Type of change: New feature Adds **Qwen-Image** (`Qwen/Qwen-Image`, `QwenImageTransformer2DModel`) to the diffusers quantization example and exports HuggingFace checkpoints in three precisions — **FP8**, **NVFP4**, and **NVFP4 + SVDQuant** — through the unified HF export. - Registers `--model qwen-image` (lazy diffusers import; no `trust_remote_code`). - Transformer-block-range recipe: quantizes only the linears under `transformer_blocks`, keeping the **first 2 / last 2** blocks (and everything outside `transformer_blocks`) in original precision. Applied **before** calibration so SVDQuant never mutates the excluded blocks. Expressed with the top-level `enable` `QuantizerCfgEntry` field (disable-all → re-enable `transformer_blocks` → disable first/last-N). - SVDQuant export (AWQ-style): promotes quantizer-owned tensors to clean module-level safetensors keys at export time — `weight_quantizer.svdquant_lora_a/b → <module>.svdquant_lora_a/b` and `input_quantizer._pre_quant_scale → <module>.pre_quant_scale` — with a documented `NVFP4_SVD` `quantization_config` (`group_size`, `has_zero_point: false`, `pre_quant_scale: true`, `lora_rank`). **Core SVDQuant quantization code (`modelopt/torch/quantization`) is unchanged.** - Shared export-path change — **intentionally global** (applies to all diffusers exports — SDXL / Flux / Wan, not just Qwen; the full export suite was verified green on GB200): `hide_quantizers_from_state_dict` now strips quantizer state from *all* modules (not just quant-linears) so calibrated norm-layer input quantizers no longer leak `input_quantizer._amax`. (An earlier `max_shard_size` workaround was dropped after merging `main`: #1794 makes the ComfyUI layerwise-metadata post-processing a no-op unless explicitly opted in, so a default sharded export no longer hits the unsupported-sharded path.) ### Usage ```bash python examples/diffusers/quantization/quantize.py \ --model qwen-image --override-model-path <Qwen-Image> --model-dtype BFloat16 \ --format fp4 --quant-algo svdquant --lowrank 32 \ --calib-size 64 --n-steps 20 \ --hf-ckpt-dir <out> # FP8: --format fp8 --quant-algo max # NVFP4: --format fp4 --quant-algo max ``` ### Testing - Focused unit + example tests pass on GB200 (sm_100): block-range recipe, `NVFP4_SVD` config schema, SVDQuant forward/fold (LoRA stays on `weight_quantizer`), Qwen dummy-input / strict-QKV-fusion / promotion, pipeline loading, and the diffusers HF-export test for Qwen FP8 / NVFP4 / SVDQuant. - Full `tests/examples/diffusers/test_export_diffusers_hf_ckpt.py` is green (SDXL, Flux, Qwen, Wan2.2) — confirms the shared export changes do not regress other models. - End-to-end on the real `Qwen/Qwen-Image` (~20B): all three formats export valid HF checkpoints — only `transformer_blocks` 2..57 quantized, nothing outside, no quantizer/`_amax` leak, correct `weight_scale`(`_2`)/`input_scale`, promoted SVDQuant keys (rank-consistent shapes), and the expected `quantization_config`. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ <!-- live-model LoRA storage unchanged; existing exports 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?: ❌ <!-- new-example feature; add a CHANGELOG.rst entry if required --> - Did you get Claude approval on this PR?: ❌ <!-- run /claude review --> ### Additional Information All changes are confined to the diffusers example (`examples/diffusers/quantization`) plus the shared export path (`modelopt/torch/export`); the core quantization library is untouched. ### Follow-up (next step): fused-QKV SVDQuant for sglang / Nunchaku This export keeps attention `q/k/v` (and `add_q/k/v_proj`) as **separate** projections — the diffusers-native layout. That matches sglang's bf16 / FP8 / plain-NVFP4 paths (which also keep QKV separate) and ModelOpt/TRT-LLM consumers, so those load 1:1. sglang's **NVFP4-SVDQuant (Nunchaku)** path, however, builds a **fused** `to_qkv` with a *single* fused rank-r LoRA in Nunchaku-native format (`proj_down`/`proj_up`, `smooth_factor`, `wscales`/`wtscale`). Our per-projection tensors (`svdquant_lora_a/b` + `pre_quant_scale`; three independent rank-r decompositions) are not directly loadable there — and cannot be fused at load time, because the fp16 weight residual needed to derive a single fused rank-r is not preserved after export. **Planned next step:** an opt-in fused-QKV SVDQuant export mode that fuses q/k/v **before** SVDQuant calibration (yielding one rank-r over the fused weight) and emits a Nunchaku-compatible layout, enabling lower-latency fused-QKV inference in sglang. Tracked as a separate follow-up. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added Qwen-Image (`QWEN_IMAGE`) model quantization and Diffusers export support * Added NVFP4_SVD (SVDQuant) export configuration support * Added transformer block-range quantization recipes (exclude first/last blocks) * **Bug Fixes** * Improved missing-pipeline error messaging for Qwen-Image * Prevented quantizer-related tensor/buffer leakage by promoting and cleaning quantizer outputs during export * **Tests** * Added Qwen-Image HF checkpoint export tests and offline fixtures * Added unit coverage for SVDQuant promotion/clean state-dict keys <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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|
jingyu-ml
|
26ae8da517 |
[2/3] Implicit Gemm NVFP4 (#1227)
### What does this PR do? Type of change: new feature <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> - Add Conv3D implicit GEMM kernel with BF16 WMMA tensor cores and fused NVFP4 activation quantization for video diffusion VAE layers - Integrate into _QuantConv3d via QuantModuleRegistry — automatically dispatched when NVFP4 quantization is applied to nn.Conv3d - Move kernel from `experimental/conv/ to modelopt/torch/kernels/conv/`; move tests to `tests/gpu/torch/quantization/kernels/` ### Testing <!-- Mention how have you tested your change if applicable. --> - Added test cases to measure the difference between cuDNN and our CUDA implicit GEMM kernel - Added an NVFP4 fake quantization test using CUDA code ### Before your PR is "*Ready for review*" 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?: ✅ <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ <!--- Mandatory --> - Did you write any new necessary tests?: ✅ <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Per-backbone quantization/export in a single run with per-backbone checkpoints and backbone-aware quant filters * Configurable NVFP4 block-size via CLI/config; improved NVFP4 Conv3D inference path and Wan 2.2 quantization support * **Bug Fixes** * Video-model calibration now respects extra params and forces video decoding during calibration * **Documentation** * Added comprehensive Conv3D implicit‑GEMM kernel documentation; removed experimental Conv3D prototype docs/benchmark * **Tests** * New Wan 2.2 quantization/export tests and expanded Conv3D/FP4 kernel test coverage <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com> |
||
|
jingyu-ml
|
1070d895dc |
Flux2-Dev Quantization (#947)
## What does this PR do?
**Type of change:** new example <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->
**Overview:**
- Register Flux2Attention and Flux2ParallelSelfAttention in the
quantization plugin so bmm quantizers are patched (enables
--quantize-mha).
- Add Flux2-specific dummy input generation for HF checkpoint export.
- Guard check_conv_and_mha with hasattr for bmm quantizer attributes
## Usage
<!-- You can potentially add a usage example below. -->
```bash
python quantize.py \
--model flux2-dev \
--model-dtype BFloat16 \
--format fp4 --batch-size 2 --calib-size 1 \
--n-steps 20 --quantized-torch-ckpt-save-path ./flux2-dev-fp4.pt --collect-method default \
--hf-ckpt-dir ./flux2-dev-fp4
```
## Testing
<!-- Mention how have you tested your change if applicable. -->
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes<!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added Flux2-dev model support with Flux2-compatible dummy input
generation and default inference params (768×1024, guidance scale 4.0).
* **Refactor**
* Made attention quantization disabling more robust by iterating
available quantizers before disabling.
* **Infrastructure**
* Flux2 attention components are now optional and registered only when
present to avoid import issues.
* **Tests**
* Added Flux2 test helpers and coverage validating Flux2 dummy input
shapes.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
|
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|
jingyu-ml
|
812e8c60a2 |
Minor update on the LTX2 NVFP4 recipe (#1010)
### What does this PR do? Type of change: minor code change <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> 1. Update the default calibration dataset for LTX_VIDEO_DEV and LTX2 from Gustavosta/Stable-Diffusion-Prompts to nkp37/OpenVid-1M, which provides video-specific captions better suited for video model calibration. 2. update the default recipe for ltx2: first 3 and last 3 layers stays at higher precision. ### Usage ### Testing <!-- Mention how have you tested your change if applicable. --> ### Before your PR is "*Ready for review*" 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?: ✅ / ❌ / N/A <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A <!--- Mandatory --> - Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Chores** * Updated default dataset configuration for LTX-Video and LTX2 models. * Refined model filtering pattern for LTX-Video to support additional model components. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jingyu Xin <jingyux@nvidia.com> |
||
|
jingyu-ml
|
37d3f10cbd |
To support LTX2 ComfyUI format (#972)
### What does this PR do?
Type of change: Bug fix <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
<!-- Details about the change. -->
- Added a flag merged_base_safetensor_path to the example code so that
user can export the ComfyUI style ckpt.
### Usage
```bash
python quantize.py \
--model ltx-2 --format fp4 --batch-size 1 --calib-size 32 --n-steps 40 \
--extra-param checkpoint_path=./ltx-2-19b-dev-fp8.safetensors \
--extra-param distilled_lora_path=./ltx-2-19b-distilled-lora-384.safetensors \
--extra-param spatial_upsampler_path=./ltx-2-spatial-upscaler-x2-1.0.safetensors \
--extra-param gemma_root=./gemma-3-12b-it-qat-q4_0-unquantized \
--extra-param fp8transformer=true \
--quantized-torch-ckpt-save-path ./ltx-2-transformer.pt \
--hf-ckpt-dir ./LTX2-NVFP4/ \
--extra-param merged_base_safetensor_path=./ltx-2-19b-dev-fp8.safetensors
```
### Testing
<!-- Mention how have you tested your change if applicable. -->
### Before your PR is "*Ready for review*"
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`, using
`torch.load(..., weights_only=True)`, avoiding `pickle`, etc.).
- Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain
why. -->
- If you copied code from any other source, did you follow IP policy in
[CONTRIBUTING.md](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md#-copying-code-from-other-sources)?:
✅ / ❌ / N/A <!--- Mandatory -->
- Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory
for new features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes
or backward incompatible changes. -->
### Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Added new command-line parameters documentation for LTX-2 FP4
quantization examples (--hf-ckpt-dir and merged_base_safetensor_path
configuration options)
* **Improvements**
* Enhanced quantization pipeline to support conditional export behavior
based on model type
* Expanded LTX-Video model filtering patterns for more comprehensive
block detection
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
|
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|
jingyu-ml
|
10efcb65f6 |
[3.1/4] Diffusion Quantized ckpt export - WAN 2.2 14B (#855)
## What does this PR do?
**Type of change:** documentation <!-- Use one of the following: Bug
fix, new feature, new example, new tests, documentation. -->
**Overview:**
1. Added multi‑backbone support for quantization: --backbone now accepts
space- or comma-separated lists and resolves to a list of backbone
modules.
2. Introduced PipelineManager.iter_backbones() to iterate named backbone
modules and updated get_backbone() to return a single module or a
ModuleList for multi‑backbone.
3. Updated ExportManager to save/restore per‑backbone checkpoints when a
directory is provided, with {backbone_name}.pt files, and to create
target directories when missing.
4. Simplified save_checkpoint() calls to rely on the registered
pipeline_manager by default.
**Usage: **
```bash
python quantize.py --model wan2.2-t2v-14b --format fp4 --batch-size 1 --calib-size 32 \
--n-steps 30 --backbone transformer transformer_2 --model-dtype BFloat16 \
--quantized-torch-ckpt-save-path ./wan22_mo_ckpts \
--hf-ckpt-dir ./wan2.2-t2v-14b
```
Plans
- [x] [1/4] Add the basic functionalities to support limited image
models with NVFP4 + FP8, with some refactoring on the previous LLM code
and the diffusers example. PIC: @jingyu-ml
- [x] [2/4] Add support to more video gen models. PIC: @jingyu-ml
- [x] [3/4] Add test cases, refactor on the doc, and all related README.
PIC: @jingyu-ml
- [ ] [4/4] Add the final support to ComfyUI. PIC @jingyu-ml
## Testing
<!-- Mention how have you tested your change if applicable. -->
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: No <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Unified Hugging Face export support for diffusers pipelines and
components
* LTX-2 and Wan2.2 (T2V) support in diffusers quantization workflow
* Comprehensive ONNX export and TensorRT engine build documentation for
diffusion models
* **Documentation**
* Updated to clarify support for both transformers and diffusers models
in unified export API
* Expanded diffusers examples with LoRA fusion guidance and additional
model options (Flux, SD3, SDXL variants)
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
|
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jingyu-ml
|
2e43c80609 |
[2/4] Diffusion Quantized ckpt export (#810)
## What does this PR do? **Type of change:** New feature <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> **Overview:** This MR adds HuggingFace checkpoint export support for LTX‑2 by treating TI2VidTwoStagesPipeline as a diffusion-like pipeline, exporting only the stage‑1 transformer (with QKV-fusion-enabled dummy inputs) and falling back to writing model.safetensors when save_pretrained isn’t available. It also preserves the original forward in DynamicModule patching (_forward_pre_dm) so downstream callers can still invoke the pre-patched forward implementation. **Changes** 1. Added the calibration & quantization support of the LTX2, even with FP8 precision. 2. Preserve original forward before `DynamicModule` patching: when patching forward, we now stash the pre-patched implementation in `self._forward_pre_dm` (once) so downstream code can still call the original forward, then re-bind forward to the class implementation. This is needed for the LTX2 FP8 calibration. 3. Added LTX‑2 HF export path: `export_hf_checkpoint()` now also treats ltx_pipelines.ti2vid_two_stages.TI2VidTwoStagesPipeline as a “diffusion-like” object and routes it through _export_diffusers_checkpoint() (import guarded; no hard dependency). 4. Generalized component discovery: introduced get_diffusion_components() (aliasing the old get_diffusers_components) to support non-diffusers pipelines; for LTX‑2 it returns only stage_1_transformer. 5. Enabled QKV fusion for LTX‑2 backbone: added a model-aware dummy forward generator (generate_diffusion_dummy_forward_fn) that builds minimal LTX Modality inputs (including correct timesteps broadcasting) so shared-input hooks can run and fuse QKV when applicable. 6. Export fallback for non-save_pretrained modules: when a component lacks save_pretrained (LTX‑2 transformer), export now writes model.safetensors + minimal config.json instead of pytorch_model.bin. Plans - [x] [1/4] Add the basic functionalities to support limited image models with NVFP4 + FP8, with some refactoring on the previous LLM code and the diffusers example. PIC: @jingyu-ml - [x] [2/4] Add support to more video gen models. PIC: @jingyu-ml - [ ] [3/4] Add test cases, refactor on the doc, and all related README. PIC: @jingyu-ml - [ ] [4/4] Add the final support to ComfyUI. PIC @jingyu-ml ## Usage <!-- You can potentially add a usage example below. --> ```bash python quantize.py --model ltx-2 --format fp4 --batch-size 64 --calib-size 1 --n-steps 40 --extra-param checkpoint_path=/home/scratch.omniml_data_2/jingyux/models/LTX-2/ltx-2-19b-dev-fp8.safetensors --extra-param distilled_lora_path=/home/scratch.omniml_data_2/jingyux/models/LTX-2/ltx-2-19b-distilled-lora-384.safetensors --extra-param spatial_upsampler_path=/home/scratch.omniml_data_2/jingyux/models/LTX-2/ltx-2-spatial-upscaler-x2-1.0.safetensors --extra-param gemma_root=/home/scratch.omniml_data_2/jingyux/models/LTX-2/gemma-3-12b-it-qat-q4_0-unquantized --extra-param fp8transformer=true --hf-ckpt-dir ./ltx2-nvfp4 ``` ## Testing <!-- Mention how have you tested your change if applicable. --> ## Before your PR is "*Ready for review*" <!-- If you haven't finished some of the above items you can still open `Draft` PR. --> - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes <!--- If No, explain why. --> - **Did you write any new necessary tests?**:No - **Did you add or update any necessary documentation?**:No - **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:No <!--- Only for new features, API changes, critical bug fixes or bw breaking changes. --> ## Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## Release Notes * **New Features** * Added LTX-2 video model support with complete quantization and export pipeline integration * Introduced `--extra-param` CLI option for flexible model configuration and parameter passing * Enhanced export capabilities with broader diffusion model compatibility * **Chores** * Changed default model data type from Half to BFloat16 for improved numerical stability <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com> |
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jingyu-ml
|
668b8a19e8 |
[1/3] Diffusion ckpt export for NVFP4 & FP8 (#781)
## What does this PR do? **Type of change:** New feature <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> **Overview:** This PR adds support for exporting quantized diffusers models (DiT, Flux, SD3, UNet, etc.) to HuggingFace checkpoint format, enabling deployment to inference frameworks like SGLang, vLLM, and TensorRT-LLM. **Changes** New file: `diffusers_utils.py` - Dummy input generation for various diffusion models - Pipeline component extraction helpers - QKV projection detection and grouping - `hide_quantizers_from_state_dict()` context manager for clean saves Refactored: `unified_export_hf.py` - New `_fuse_qkv_linears_diffusion()` for QKV amax fusion - `_export_diffusers_checkpoint()` to export full pipelines (models + tokenizers + schedulers etc.) Plans - [x] [1/3] Add the basic functionalities to support limited image models with NVFP4 + FP8, with some refactoring on the previous LLM code and the diffusers example. PIC: @jingyu-ml - [ ] [2/3] Add support to more video gen modelsPIC: @jingyu-ml - [ ] [3/3] Add test cases, refactor on the doc, and all related README. PIC: @jingyu-ml ## Usage <!-- You can potentially add a usage example below. --> ``` mtq.quantize(pipe, quant_config, forward_call) export_hf_checkpoint(pipe, export_dir=hf_ckpt_dir) ``` ## Testing <!-- Mention how have you tested your change if applicable. --> ## Before your PR is "*Ready for review*" <!-- If you haven't finished some of the above items you can still open `Draft` PR. --> - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes <!--- If No, explain why. --> - **Did you write any new necessary tests?**:No - **Did you add or update any necessary documentation?**:No - **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:No <!--- Only for new features, API changes, critical bug fixes or bw breaking changes. --> ## Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## New Features * Added HuggingFace checkpoint export support for quantized diffusion models with configurable output directory * Introduced new `--hf-ckpt-dir` CLI argument for specifying checkpoint export destination * Extended export functionality to support selective component exports from diffusion pipelines * Enhanced quantized model export with improved component handling and multi-stage checkpoint generation <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com> |
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|
Shengliang Xu
|
8844a2b2bc |
Support attention quantization for diffusers >= 0.35.0 (#608)
## What does this PR do? **Type of change:** new feature **Overview:** ? Attention mechanism has changed from diffusers 0.35. Many model attentions are now subclass of a new Mixin class: AttentionModuleMixin, which is not a sub class of Attention To fix it, patch the mixin class by forcing to use native attention impl so the existing function monkey patch still work. ## Testing manual quant of Wan, Flux --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> |
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Shengliang Xu
|
e35db173b1 |
Support Wan2.2 t2v diffusers quantization (#556)
## What does this PR do? **Type of change:** new feature **Overview:** Support Wan2.2 t2v diffusers quantization 1. fix torch2.9 support 2. add Wan2.2 t2v diffusers pipeline quantization Main difference of the Wan2.2 pipeline comparing to exisiting pipelines is that there are 2 backbone models for denoising. For the quantization therefore we need to quantize both of them. However, it turns out our base library does not well support quantization of multiple models in the same time. Therefore, the change here just stick to quantize a single model each time, and then run the quantization multiple times. So, we need to allow users to pick which backbone to quantize, therefore adding a new argment for it 3. add a workaround for the exporting ONNX issue when we upgrade diffusers to >= 0.35.0. The issue lies is the exporting of the torch.nn.RMSNorm. Some pipelines in the diffusers > 0.35.0 use the torch version RMSNorm while before that they use the diffusers' own version of RMSNorm. It turns out they are directly replacable so the workaround is to simply replace the torch RMSNorm usages with diffusers RMSNorm. But we need to fix it properly soon by porting our ONNX export to be based on torch dynamo instead of torchscript. Issue reported from external user: https://github.com/NVIDIA/TensorRT-Model-Optimizer/issues/262 4. allow use of a prompts file, which is simply a text file with a list of prompts, one prompt each line 5. allow each component of a pipeline to have different dtype accuracy. added a new list stype command line arg --component-dtype for this. example: --component-dtype vae:Float 6. print the summary of the quantized model so users can capture issues from log ## Usage python quantize.py \ --model wan2.2-t2v-14b \ --format fp8 \ --batch-size 4 \ --calib-size 64 \ --n-steps 20 \ --backbone transformer \ --model-dtype BFloat16 \ --component-dtype vae:Float \ --trt-high-precision-dtype BFloat16 \ --quantized-torch-ckpt-save-path ./wan_transformer.pt \ --onnx-dir wan-transformer-onnx \ --prompts-file wan-prompts.txt ## Testing Tested SDXL_BASE, LTX_VIDEO_DEV, WAN22_T2V ## Before your PR is "*Ready for review*" <!-- If you haven't finished some of the above items you can still open `Draft` PR. --> - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes/No <!--- If No, explain why. --> - **Did you write any new necessary tests?**: Yes/No - **Did you add or update any necessary documentation?**: Yes/No - **Did you update [Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**: Yes/No <!--- Only for new features, API changes, critical bug fixes or bw breaking changes. --> ## Additional Information https://github.com/NVIDIA/TensorRT-Model-Optimizer/issues/262 --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> |
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Keval Morabia
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Push latest changes
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> |
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Keval Morabia
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7af33d29ce | Update for 0.31.0 release | ||
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Keval Morabia
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2017cd9063 | Update for 0.25.0 release | ||
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Keval Morabia
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73d6af785f | Update 0.23.0 - OSS release |