Model-Optimizer

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Author	SHA1	Message	Date
Keval MorabiaandClaude Opus 4.8	0081473861	Speed up slow unit/gpu/example tests (#1616 ) ### What does this PR do? Type of change: test infrastructure / test speedups + CI stabilization Make the test suite faster, `tests/unit` hermetic, and the CI lanes stable, without losing coverage. Most changes are mechanical test/infra edits; the buckets below cover the diff broadly. Unit tests — hermetic (no HF Hub): toy local datasets/configs + the local tiny tokenizer (with a checked-in chat template) replace Hub assets; `tests/unit/conftest.py` enforces offline mode. Genuinely-HF tests moved to `tests/gpu` (e.g. the new `tests/gpu/torch/utils/test_dataset_utils.py`). `CONTRIBUTING.md` documents the hermetic-unit-test expectation. Unit-test speedups (no coverage loss):* speculative (disable CPU torch.compile), calibrator (fewer histogram bins), ONNX conv/dynamo (smaller shapes + representative subset), Ruler/sparse-attention (local tokenizer), data-parallel autoquant (world size 4→2). Shared `tiny_tokenizer` fixture. The distributed test helper now uses a private `spawn` context instead of mutating the global start method (avoids cross-test contamination). Rarely-used autonas/fastnas tests: heavy parametrize cases marked `@pytest.mark.manual`, one representative kept per test (fastnas preferred); lighter sibling tests still cover core behavior. The legacy FSDP1 NAS distributed test is also dropped: FastNAS/AutoNAS aren't used with either FSDP1 or FSDP2, and FSDP1 is superseded by the newer FSDP2 API — so we keep a single FSDP2 case as a sanity check and drop FSDP1, leaving the suite leaner. gpu_megatron: deduplicate distributed worker pools by world_size within a module (saves a redundant pool spin-up in multi-pool files; module-scoped, no cross-module reuse). Example tests: reduce per-test work via args that default to current behavior (tests pass the fast values) — torch_onnx TRT optimization level, diffusers calibration/inference steps, eagle `sample_size`, megatron_bridge iters/calib, llm_sparsity data slice, export safetensors-structure `calib_size`. Also enable the recently added `gpt-oss` example tests in CI. Per-test timeouts: `pytest-timeout` with a default per-directory timeout (60s unit / 300s gpu+example) enforced in `tests/conftest.py` (`timeout_func_only` in `pyproject.toml`), so a new test cannot silently exceed the budget — an unmapped test dir crashes collection. A few inherently slow tests carry explicit higher per-test overrides (CUDA-compile, autotune, dflash). CUDA kernel pre-compilation: a dedicated `tests/gpu/_extensions` test JIT-builds the conv3d implicit-GEMM kernel up front (collected before the functional tests in the same process) so the one-time build cost no longer lands on — and time out — the first functional test that uses it. Mirrored into the `llm_ptq`/`vlm_ptq` example lanes. Test relocation & optional-dependency guards: vLLM sparsity plugin test moved to `tests/gpu_vllm` (drops the in-test `importorskip`); diffusers-dependent unit test guarded with `importorskip("diffusers")` for partial-install lanes; `gpt_oss` example test dir renamed to `gpt-oss` to match the CI matrix. Diffusers test models: shared model-path constants in `tests/_test_utils/examples/models.py` consolidated/renamed and point at tiny `hf-internal-testing` test pipes (SDXL/SD3/FLUX) so cachify/quantize/export tests run on toy weights; `local_id`s normalized. Shared dataset utils: `examples/llm_sparsity/.../hf_pts.py` now uses `get_dataset_dataloader` (drops the bespoke cnn_dailymail-only `get_calib_dataloader`; supports any registered/HF/JSONL dataset, includes attention_mask); `data_prep.py` gains `--max_samples`. CI workflows: container image bumps (pytorch 26.04→26.05, TRT-LLM rc16→rc17) and tightened lane timeouts (unit 30→15 min, gpu lanes trimmed, onnx example lane 45 min). Imports at top of file: in-function imports across the test suite are moved to module top per the coding guideline, conservatively — optional deps stay guarded (in-function or behind a module-level `importorskip`) in `tests/unit` since the partial-install lane runs without them, and build/hardware-availability imports (apex, triton, megatron/transformer_engine, tensorrt_llm) plus `_test_utils` lazy guards are left in place. Kernel warning filters: the repeated `filterwarnings` blanket-ignore in six `tests/gpu/torch/kernels/` modules is consolidated into a scoped hook in `tests/gpu/torch/kernels/conftest.py` (kernel tests only — the rest of the suite keeps surfacing warnings). Eagle example speedups: `torch.compile` (eagle recipe default) added ~2 min to every eagle training test; it's now disabled in the eagle example tests except one smoke (`test_llama_eagle3[1-False]`), and the downstream resume / AR-validate / export tests point at the compile-free checkpoint. Measured: `test_ar_validate` 139s→17s, offline training 142s→22s, streaming 140s→23s — the compile path is still smoke-tested once. Example lanes install editable (`-e`): so example scripts launched as subprocesses resolve `modelopt` to the same source path as the test process and reuse the pre-compiled CUDA-extension cache instead of recompiling (~2 min/test); verified in the TRT-LLM container. Tiny test tokenizer: `get_tiny_tokenizer` defaults to left padding (what decoder-LM calibration expects) and ships a terse generation-tagged chat template — replacing a verbose ChatML one that inflated tokenized length on the 128-vocab tokenizer and broke the offline-PTQ example tests' `max-seq-len` filter. Restored Hub-download coverage:** the live (ungated) HF dataset round-trips exercising `get_dataset_samples`' download branch now live in `tests/gpu/torch/utils/test_dataset_utils.py` (they had been dropped from the hermetic unit file without a counterpart). Individual file changes not explicitly called out above fall under this general test/CI cleanup. ### Testing Unit + the touched gpu_megatron files validated locally; example/GPU lanes validated in CI. ### Before your PR is "Ready for review" - Is this change backward compatible?: ✅ (tests + example CLI args default to prior behavior) - If you copied code from any other sources or added a new PIP dependency: N/A - Did you write any new necessary tests?: N/A (optimizes/relocates existing tests) - Did you update Changelog?: N/A - Did you get Claude approval on this PR?: ❌ (pending) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## Release Notes * Chores * Updated container image versions for PyTorch (26.04→26.05), TensorRT-LLM (1.3.0rc16→1.3.0rc17), and ONNX/TensorRT (26.04→26.05). * Tests * Enhanced test isolation: unit tests now run hermetically without HuggingFace Hub access. * Optimized test runtime via smaller model/dataset parameters and parallel test caching. * Added CUDA extension availability tests and extended dataset utility coverage. * Documentation * Updated testing guidelines in `CONTRIBUTING.md` to emphasize offline test design. * Chores * Added pytest timeout configuration and improved CI/CD workflow efficiency with editable installs. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-04 17:13:10 +00:00
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>	2026-04-19 12:20:14 +05:30
jingyu-ml	2905cb0f2e	Updated the diffusion config issue and more test cases (#937 ) ## What does this PR do? Type of change: new tests, Bug fix <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> Overview: - Fixed the INT8 config issue - Add HF checkpoint export test coverage 1. The `--hf-ckpt-dir` export path had zero test coverage. This MR adds tests at two levels: 2. Unit tests (tests/unit/torch/export/test_export_diffusers.py): - Extended test_export_diffusers_real_quantized to parametrize over INT8, INT8 SmoothQuant, FP8, and FP4 configs - (previously only FP8). This gives 3 models x 4 configs = 12 test cases. 3. GPU integration tests (tests/gpu/torch/export/test_export_diffusers_hf_ckpt.py) - New file testing the full quantize.py --hf-ckpt-dir pipeline via subprocess with 4 combos: - SDXL INT8 smoothquant min-mean (the exact scenario that triggered the bug) - Flux INT8 smoothquant min-mean - SDXL FP8 - Flux FP4 ## Usage <!-- You can potentially add a usage example below. --> ```python # Add a code snippet demonstrating how to use this ``` ## 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?: Yes - 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 * Tests * Added test coverage for exporting Diffusers models with Hugging Face checkpoints across multiple quantization formats (INT8, FP8, FP4) * Extended quantization export testing to validate multiple configuration scenarios * Chores * Refined INT8 quantization configuration with improved calibrator support for convolution layers <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com>	2026-02-28 23:54:02 +05:30

Author

SHA1

Message

Date

Keval MorabiaandClaude Opus 4.8

0081473861

Speed up slow unit/gpu/example tests (#1616 )

### What does this PR do?

Type of change: test infrastructure / test speedups + CI stabilization

Make the test suite faster, `tests/unit` hermetic, and the CI lanes
stable, without losing coverage. Most changes are mechanical test/infra
edits; the buckets below cover the diff broadly.

**Unit tests — hermetic (no HF Hub):** toy local datasets/configs + the
local tiny tokenizer (with a checked-in chat template) replace Hub
assets; `tests/unit/conftest.py` enforces offline mode. Genuinely-HF
tests moved to `tests/gpu*` (e.g. the new
`tests/gpu/torch/utils/test_dataset_utils.py`). `CONTRIBUTING.md`
documents the hermetic-unit-test expectation.

**Unit-test speedups (no coverage loss):** speculative (disable CPU
torch.compile), calibrator (fewer histogram bins), ONNX conv/dynamo
(smaller shapes + representative subset), Ruler/sparse-attention (local
tokenizer), data-parallel autoquant (world size 4→2). Shared
`tiny_tokenizer` fixture. The distributed test helper now uses a private
`spawn` context instead of mutating the global start method (avoids
cross-test contamination).

**Rarely-used autonas/fastnas tests:** heavy parametrize cases marked
`@pytest.mark.manual`, one representative kept per test (fastnas
preferred); lighter sibling tests still cover core behavior. The legacy
FSDP1 NAS distributed test is also dropped: FastNAS/AutoNAS aren't used
with either FSDP1 or FSDP2, and FSDP1 is superseded by the newer FSDP2
API — so we keep a single FSDP2 case as a sanity check and drop FSDP1,
leaving the suite leaner.

**gpu_megatron:** deduplicate distributed worker pools by world_size
within a module (saves a redundant pool spin-up in multi-pool files;
module-scoped, no cross-module reuse).

**Example tests:** reduce per-test work via args that default to current
behavior (tests pass the fast values) — torch_onnx TRT optimization
level, diffusers calibration/inference steps, eagle `sample_size`,
megatron_bridge iters/calib, llm_sparsity data slice, export
safetensors-structure `calib_size`. Also enable the recently added
`gpt-oss` example tests in CI.

**Per-test timeouts:** `pytest-timeout` with a default per-directory
timeout (60s unit / 300s gpu+example) enforced in `tests/conftest.py`
(`timeout_func_only` in `pyproject.toml`), so a new test cannot silently
exceed the budget — an unmapped test dir crashes collection. A few
inherently slow tests carry explicit higher per-test overrides
(CUDA-compile, autotune, dflash).

**CUDA kernel pre-compilation:** a dedicated `tests/gpu/_extensions`
test JIT-builds the conv3d implicit-GEMM kernel up front (collected
before the functional tests in the same process) so the one-time build
cost no longer lands on — and time out — the first functional test that
uses it. Mirrored into the `llm_ptq`/`vlm_ptq` example lanes.

**Test relocation & optional-dependency guards:** vLLM sparsity plugin
test moved to `tests/gpu_vllm` (drops the in-test `importorskip`);
diffusers-dependent unit test guarded with `importorskip("diffusers")`
for partial-install lanes; `gpt_oss` example test dir renamed to
`gpt-oss` to match the CI matrix.

**Diffusers test models:** shared model-path constants in
`tests/_test_utils/examples/models.py` consolidated/renamed and point at
tiny `hf-internal-testing` test pipes (SDXL/SD3/FLUX) so
cachify/quantize/export tests run on toy weights; `local_id`s
normalized.

**Shared dataset utils:** `examples/llm_sparsity/.../hf_pts.py` now uses
`get_dataset_dataloader` (drops the bespoke cnn_dailymail-only
`get_calib_dataloader`; supports any registered/HF/JSONL dataset,
includes attention_mask); `data_prep.py` gains `--max_samples`.

**CI workflows:** container image bumps (pytorch 26.04→26.05, TRT-LLM
rc16→rc17) and tightened lane timeouts (unit 30→15 min, gpu lanes
trimmed, onnx example lane 45 min).

**Imports at top of file:** in-function imports across the test suite
are moved to module top per the coding guideline, conservatively —
optional deps stay guarded (in-function or behind a module-level
`importorskip`) in `tests/unit` since the partial-install lane runs
without them, and build/hardware-availability imports (apex, triton,
megatron/transformer_engine, tensorrt_llm) plus `_test_utils` lazy
guards are left in place.

**Kernel warning filters:** the repeated `filterwarnings` blanket-ignore
in six `tests/gpu/torch/kernels/**` modules is consolidated into a
scoped hook in `tests/gpu/torch/kernels/conftest.py` (kernel tests only
— the rest of the suite keeps surfacing warnings).

**Eagle example speedups:** `torch.compile` (eagle recipe default) added
~2 min to every eagle training test; it's now disabled in the eagle
example tests except one smoke (`test_llama_eagle3[1-False]`), and the
downstream resume / AR-validate / export tests point at the compile-free
checkpoint. Measured: `test_ar_validate` 139s→17s, offline training
142s→22s, streaming 140s→23s — the compile path is still smoke-tested
once.

**Example lanes install editable (`-e`):** so example scripts launched
as subprocesses resolve `modelopt` to the same source path as the test
process and reuse the pre-compiled CUDA-extension cache instead of
recompiling (~2 min/test); verified in the TRT-LLM container.

**Tiny test tokenizer:** `get_tiny_tokenizer` defaults to left padding
(what decoder-LM calibration expects) and ships a terse
generation-tagged chat template — replacing a verbose ChatML one that
inflated tokenized length on the 128-vocab tokenizer and broke the
offline-PTQ example tests' `max-seq-len` filter.

**Restored Hub-download coverage:** the live (ungated) HF dataset
round-trips exercising `get_dataset_samples`' download branch now live
in `tests/gpu/torch/utils/test_dataset_utils.py` (they had been dropped
from the hermetic unit file without a counterpart).

Individual file changes not explicitly called out above fall under this
general test/CI cleanup.

### Testing

Unit + the touched gpu_megatron files validated locally; example/GPU
lanes validated in CI.

### Before your PR is "*Ready for review*"

- Is this change backward compatible?: ✅ (tests + example CLI args
default to prior behavior)
- If you copied code from any other sources or added a new PIP
dependency: N/A
- Did you write any new necessary tests?: N/A (optimizes/relocates
existing tests)
- Did you update Changelog?: N/A
- Did you get Claude approval on this PR?: ❌ (pending)

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

## Release Notes

* **Chores**
* Updated container image versions for PyTorch (26.04→26.05),
TensorRT-LLM (1.3.0rc16→1.3.0rc17), and ONNX/TensorRT (26.04→26.05).

* **Tests**
* Enhanced test isolation: unit tests now run hermetically without
HuggingFace Hub access.
* Optimized test runtime via smaller model/dataset parameters and
parallel test caching.
* Added CUDA extension availability tests and extended dataset utility
coverage.

* **Documentation**
* Updated testing guidelines in `CONTRIBUTING.md` to emphasize offline
test design.

* **Chores**
* Added pytest timeout configuration and improved CI/CD workflow
efficiency with editable installs.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-06-04 17:13:10 +00:00

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>

2026-04-19 12:20:14 +05:30

jingyu-ml

2905cb0f2e

Updated the diffusion config issue and more test cases (#937 )

## What does this PR do?

**Type of change:** new tests, Bug fix <!-- Use one of the following:
Bug fix, new feature, new example, new tests, documentation. -->

**Overview:** 

- **Fixed the INT8 config issue**

- **Add HF checkpoint export test coverage**

1. The `--hf-ckpt-dir` export path had zero test coverage. This MR adds
tests at two levels:
2. Unit tests (tests/unit/torch/export/test_export_diffusers.py):
- Extended test_export_diffusers_real_quantized to parametrize over
INT8, INT8 SmoothQuant, FP8, and FP4 configs
- (previously only FP8). This gives 3 models x 4 configs = 12 test
cases.

3. GPU integration tests
(tests/gpu/torch/export/test_export_diffusers_hf_ckpt.py)
- New file testing the full quantize.py --hf-ckpt-dir pipeline via
subprocess with 4 combos:
- SDXL INT8 smoothquant min-mean (the exact scenario that triggered the
bug)
    - Flux INT8 smoothquant min-mean
    - SDXL FP8
    - Flux FP4

## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## 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?**: Yes
- **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

* **Tests**
* Added test coverage for exporting Diffusers models with Hugging Face
checkpoints across multiple quantization formats (INT8, FP8, FP4)
* Extended quantization export testing to validate multiple
configuration scenarios

* **Chores**
* Refined INT8 quantization configuration with improved calibrator
support for convolution layers

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>

2026-02-28 23:54:02 +05:30