## What does this PR do?
**Type of change:** new tests
**Overview:** Add new checkpoints test cases.
## Usage
pytest test_deploy.py --run-release
```python
# Add a code snippet demonstrating how to use this
```
## Testing
N//A
## Before your PR is "*Ready for review*"
- **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
- **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/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No
## Additional Information
N/A
---------
Signed-off-by: noeyy-mino <174223378+noeyy-mino@users.noreply.github.com>
## What does this PR do?
**Type of change:** Bug fix <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
**Overview:** Current context manager for FSDP2 aware weight update only
works for modules with bias=False. Updated the code to handle modules
with bias=True
## 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. -->
`accelerate launch --config_file ./fsdp2.yaml --machine_rank=0
--num_machines=1 --num_processes=4 --main_process_ip=10.126.7.122
--main_process_port=6000
--fsdp_transformer_layer_cls_to_wrap=Qwen2DecoderLayer
./multinode_ptq.py --pyt_ckpt_path Qwen/Qwen2-7B-Instruct --qformat fp8
--kv_cache_qformat fp8 --batch_size 24 --calib_size 64 --export_path
B200-Qwen2-7B-Instruct-fp8-kvcache-fp8 --trust_remote_code`
`python /app/tensorrt_llm/examples/llm-api/quickstart_advanced.py
--model_dir B200-Qwen2-7B-Instruct-fp8-kvcache-fp8 --enable_attention_dp
--tp_size 1 --moe_ep_size 1 --kv_cache_fraction 0.6
--disable_kv_cache_reuse --max_batch_size 8 --max_num_tokens 1024
--trust_remote_code`
## 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 <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: N/A
- **Did you add or update any necessary documentation?**: N/A
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
? <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
NVBug [5711927]
---------
Signed-off-by: Suguna Velury <178320438+sugunav14@users.noreply.github.com>
## What does this PR do?
**Type of change:** Bug fix
**Overview:**
`BertSDPASelfAttention` quantization was failing
Issue: Decorators (e.g., @deprecate_kwarg in BERT) wrap methods and
change their free variables. The modified AST needs __class__ for
super(), but the decorated wrapper has different freevars (e.g.,
'additional_message', 'func').
Fix: Search the decorator's closure to find the actual undecorated
method with matching free variables, then use its globals and closure
for bytecode reconstruction.
## Usage
```
cd examples/chained_optimizations
bash scripts/1_prune.sh
bash scripts/2_int8_quantize.sh
```
## Testing
Added unit test: test_kv_quant_bert
## 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
- **Did you write any new necessary tests?**: Yes
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
NA
## Additional Information
<!-- E.g. related issue. -->
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
## What does this PR do?
**Type of change:**
Bug fix
[aq_Qwen3-235B-A22B-Thinking-2507_scores.html](https://github.com/user-attachments/files/23915284/aq_Qwen3-235B-A22B-Thinking-2507_scores.html)
**Overview:**
For Qwen3 MoE, sensitivity should be measured at `layer.x.mlp` not
`layer.x.mlp.experts` (`layer.x.mlp.experts` module forward is never
called. Hence sensitivity was not correctly estimated previously).
After this PR, Qwen3 MoE sensitivity is correctly estimated. (See
attached file)
## 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/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
<!-- E.g. related issue. -->
---------
Signed-off-by: realAsma <akuriparambi@nvidia.com>
## What does this PR do?
**Type of change:** Bug Fix
## Testing
<!-- Mention how have you tested your change if applicable. -->
- [x] Tests run locally in docker
- [x] Tests enabled in github per-PR CICD
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
- CI tests for min transformers version (4.48) started failing since new
peft 1.18 was released yesterday blocking all new PRs from merging
- Transformers v5.0 is right around the corner so we should not
recommend too older version
- Set new minimum transformers to 4.53 instead of 4.48
- nemo:25.07+ and trtllm:1.0.0+ docker containers have transformers
4.53+
- ModelOpt will still likely work for transformers 4.48-4.52 for now but
going forward will not be tested in CICD
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** new tests
**Overview:**
Add vLLM/SGLang/TRT LLM deployment tests for published checkpoints on
HF.
Add e2e test case for gpt-oss
## Usage
```python
# pytest test_deploy.py -k "vllm"
```
## Testing
## Before your PR is "*Ready for review*"
- **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
- **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/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No
## Additional Information
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Tests**
* Added ModelDeployer and ModelDeployerList test utilities with
COMMON_PROMPTS to run multi-backend deployment and inference scenarios.
* Introduced an end-to-end GPT-OSS QAT pipeline test (SFT → QAT → MXFP4
conversion) with optional deployment/benchmarking.
* Added a broad parametric LLM deployment test suite covering many
models/backends with readable test IDs.
* Added automatic Hugging Face cache cleanup after tests to keep runs
isolated.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: neoyy-mino <174223378+noeyy-mino@users.noreply.github.com>
Signed-off-by: noeyy-mino <174223378+noeyy-mino@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
## What does this PR do?
**Type of change:** Improve existing feature <!-- Use one of the
following: Bug fix, new feature, new example, new tests, documentation.
-->
**Overview:** GPT-OSS model has Yarn RoPE which adds additional
nn.Embedding modules that need to be enabled in DynamicModule for
Minitron pruning
## Testing
<!-- Mention how have you tested your change if applicable. -->
- gpt-oss-20b pruned using M-LM pruning example and conf scripts.
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** New feature <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->
- Support pruning `num_moe_experts`, `moe_ffn_hidden_size`, and
`moe_shared_expert_intermediate_size` in `mcore_minitron` pruning
## 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/TensorRT-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?**: Yes
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Added Mixture of Experts (MoE) pruning support with new configurable
dimensions for expert count and intermediate sizes
* Extended NAS architecture search capabilities to include MoE model
parameters
* **Documentation**
* Updated support matrix and pruning documentation for MoE-compatible
models
* Clarified available pruning dimensions and parameters for MoE
architectures
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
noeyy-mino