## What does this PR do?
- Upgrade CICD test containers to latest
- Enable torch 2.10 testing in CICD
## Testing
<!-- Mention how have you tested your change if applicable. -->
CI/CD in this PR should pass
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added support for mixed-precision gradient handling with FSDP2.
* **Documentation**
* Updated Linux installation guide with CUDA 13.x support and cupy
dependency guidance.
* **Chores**
* Updated CI/CD workflows and test infrastructure to support PyTorch
2.10 and CUDA 13.
* Updated container image versions and test environment configurations.
* Updated TensorRT-LLM version requirements.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** ? <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
new feature
**Overview:** ?
- This PR adds the sparse attention calibration algorithm
- Chunked prefill to support long ctx_len
- Separated calibration for prefill and decode
## Usage
<!-- You can potentially add a usage example below. -->
```python
import modelopt.torch.sparsity.attention_sparsity as mtsa
# Apply sparse attention with calibration
model = mtsa.sparsify(model, config=SKIP_SOFTMAX_CALIB)
# Print summary - now shows actual thresholds
mtsa.print_sparse_attention_summary(model)
# Output:
# Method: flash_skip_softmax, Threshold: Dynamic (λ=437.395926)
# Or llm_eval integration
# HuggingFace sparse attention example
python examples/llm_sparsity/attention_sparsity/hf_sa.py \
--pyt_ckpt_path Qwen/Qwen3-4B \
--sparse_attn skip_softmax_calib
```
# The calibration method
## Calibration Algorithm
- Implemented the Inverse Power model: scale_factor = k / (1 -
sparsity)^p
- Fit model parameters (k, p) per phase using scipy.optimize.curve_fit
- At inference: threshold = k / (1 - target_sparsity)^p / seqlen
## Why Choosing the Inverse Power model?
The inverse power model better fits the relationship between sparsity
ratio and threshold_scale_factor.
<img width="2388" height="1082" alt="sparsity_model_analysis"
src="https://github.com/user-attachments/assets/4dfb45d4-8c16-4f15-a878-c8e08a9b6128"
/>
## Runtime Flexibility
- Target sparsity can be changed at inference time without recalibration
- Users can adjust module._sparse_method_instance.target_sparse_ratio
dynamically
- Threshold automatically adapts to sequence length
## Testing
<!-- Mention how have you tested your change if applicable. -->
The calibration results for `Qwen/Qwen3-30B-A3B-Thinking-2507` are shown
below and are mostly consistent with the ground-truth numbers collected
from the kernel side.
```
Prefill Calibration Results:
Model: scale_factor = k / (1 - sparsity)^p
Fitted k: 1003.3990
Fitted p: 1.2589
R-squared: 0.827549
Scale factors for different target sparsities:
Target Scale Factor
---------- ---------------
50% 2401.35
70% 4568.26
80% 7610.98
90% 18214.70
95% 43591.65
```
## 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: Kai Xu <kaix@nvidia.com>
## What does this PR do?
**Type of change:** ? <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
**Overview:** ?
## 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?**: 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/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. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Added NVFP4StaticQuantizer for improved 4-bit quantization with
enhanced precision control
* Introduced NVFP4MSECalibrator with flexible candidate generation for
calibration optimization
* **Improvements**
* Optimized GPU kernels for Hopper+ graphics cards with better
performance
* Extended Triton support to broader GPU compatibility
* Enhanced backward compatibility for restoring previously quantized
models
* **Tests**
* Added comprehensive test coverage for new quantizers and calibration
methods
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: realAsma <akuriparambi@nvidia.com>
## What does this PR do?
**Type of change:** New feature and bug fix
**Overview:**
Support Latent MOE and Repeated MTP for NemotronH models
- Enable latent MOE modules during megatron import/export
- Fix KV cache quantization export: remove old
`qkv_layer.output_quantizer` export & replace with proper
`k/v_bmm_quantizer` logic
- Improvements to EP amax sync
- Support repeated MTP import/export for NemotronH models (only BF16
export for MTP for now)
## 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?**: 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/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. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Added support for grouped MLP and self-attention scaling operations in
model export workflows
* Enhanced model parallel training capabilities with improved component
mapping
* Expanded quantization configuration handling with dynamic module
exclusion across distributed ranks
* Improved support for additional transformer engine components
* **Refactor**
* Reorganized internal export and import logic for improved
maintainability and specialist model architecture support
<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: jenchen13 <jennifchen@nvidia.com>
Signed-off-by: Jennifer Chen <jennifchen@nvidia.com>
Signed-off-by: Jenny Chen <jennifchen@nvidia.com>
## What does this PR do?
**Type of change:** new tests
**Overview:** Add new test cases for the newly added checkpoints on
HuggingFace.
## Usage
pytest test_deploy.py --run-release
```python
None
```
## Testing
None
## 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.
- **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/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No
## Additional Information
None
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added support for NVFP4 model variants across multiple model families
(DeepSeek, Llama, Qwen, and others).
* **Improvements**
* Enhanced backend availability detection to automatically identify and
manage supported deployment backends at runtime.
* **Tests**
* Improved test infrastructure for better reproducibility and backend
compatibility handling.
<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: noeyy-mino <174223378+noeyy-mino@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. -->
**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>
## What does this PR do?
**Type of change:** New feature
- So far users we didnt have the NAS step from the Minitron paper so
users had to manually find a pruned architecture which fits that params
constraints and for that they have to tweak different combinations of
width and depth pruning
- This PR adds a simplified version of the NAS from paper. We first find
all candidate subnets that fit the user's params constraint and sort
them by parameter count.
- [Paper] Then we pick top K candidates, do distillation for ~2B tokens
and then select the one with best score (LM Loss / MMLU / other metric
we care about). Note that the one among these top K with highest params
is often not the best pruned model
- [ModelOpt] Then we pick top K candidates, and select the one with best
score (LM Loss / MMLU / other metric we care about). While doing KD
gives better indication on which one to pick, skipping it makes the
pruning much faster, much less compute intense, and finish everything in
single prune API instead of first exporting top K models, doing KD and
eval for all K models separately. We do print a Note in pruning step to
let users know this so they can do KD if they want slightly better
pruned model.
- Further full KD is still needed as usual
- We also restrict the search space choices (e.g. `hidden_size` multiple
of 256, `ffn_hidden_size` multiple of 512) to make the process
efficient. Users can configure this if they want to.
## Usage
<!-- You can potentially add a usage example below. -->
Pruning API is same as before:
```python
import modelopt.torch.prune as mtp
mtp.prune(
model,
mode="mcore_minitron",
constraints=constraints,
dummy_input=None, # Not used
config=config,
)
```
1. Manual Pruning (Existing):
```python
constraints = {"export_config": {"hidden_size: 3072", "ffn_hidden_size": 9216}}
config = {"forward_loop": forward_loop, "checkpoint": "/path/to/cache/pruning/scores.pth"}
mtp.prune(...)
```
2. NAS-based Auto Pruning (New):
```python
constraints = {"params": 6e9}. # prune to 6B params
config = {"forward_loop": forward_loop, "checkpoint": "/path/to/cache/pruning/scores.pth"}
# define the score_func to maximize (e.g MMLU, negative val loss, etc.)
from modelopt.torch.utils.plugins.megatron_mmlu import megatron_mmlu
def score_func(m):
return megatron_mmlu(m, tokenizer, percentage=0.05) # 5% sampled data for faster eval
config["score_func"] = score_func
# overwrite search space choices (showing defaults):
config["max_width_pruning"] = 0.4
config["max_depth_pruning"] = 0.2
config["hparams_to_skip"] = [] # can be used to disable pruning some hparams e.g. ["num_attention_heads"]
config["top_k"] = 10 # might be better to use 20 at the cost of longer time to prune
mtp.prune(...)
```
To configure search space (shows defaults):
```python
ss_config = mtp.mcore_minitron.get_mcore_minitron_config(
hidden_size_divisor=256,
ffn_hidden_size_divisor=512,
mamba_head_dim_divisor=8,
num_moe_experts_divisor=8,
num_layers_divisor=2,
)
mtp.prune(model, mode=[("mcore_minitron", ss_config)], ....)
```
## Testing
**Qwen3-8B -> 6B (~2 hours on 8xA5000)**
```python
0.4350 score -> {'num_layers': 34, 'hidden_size': 3328, 'ffn_hidden_size': 11264}
BEST 0.5705 score -> {'num_layers': 30, 'hidden_size': 3584, 'ffn_hidden_size': 11776}
0.4051 score -> {'num_layers': 36, 'hidden_size': 3840, 'ffn_hidden_size': 8192}
0.4593 score -> {'num_layers': 36, 'hidden_size': 3584, 'ffn_hidden_size': 9216}
0.2737 score -> {'num_layers': 36, 'hidden_size': 3072, 'ffn_hidden_size': 11776}
0.5556 score -> {'num_layers': 32, 'hidden_size': 3584, 'ffn_hidden_size': 10752}
0.3198 score -> {'num_layers': 28, 'hidden_size': 4096, 'ffn_hidden_size': 10240}
0.4119 score -> {'num_layers': 36, 'hidden_size': 4096, 'ffn_hidden_size': 7168}
0.3808 score -> {'num_layers': 36, 'hidden_size': 3328, 'ffn_hidden_size': 10240}
0.4783 score -> {'num_layers': 34, 'hidden_size': 3840, 'ffn_hidden_size': 8704}
```
**Nemotron-Nano-9B-v2 -> 7B (~2.5 hours on 8xA5000)**
```python
0.2629 score -> {'num_layers': 54, 'hidden_size': 4352, 'mamba_num_heads': 88, 'mamba_head_dim': 72, 'ffn_hidden_size': 15360}
0.2778 score -> {'num_layers': 50, 'hidden_size': 4480, 'mamba_num_heads': 128, 'mamba_head_dim': 56, 'ffn_hidden_size': 15680}
0.5041 score -> {'num_layers': 56, 'hidden_size': 4096, 'mamba_num_heads': 96, 'mamba_head_dim': 80, 'ffn_hidden_size': 14336}
BEST 0.6043 score -> {'num_layers': 48, 'hidden_size': 4352, 'mamba_num_heads': 120, 'mamba_head_dim': 80, 'ffn_hidden_size': 13824}
0.0772 score -> {'num_layers': 56, 'hidden_size': 4352, 'mamba_num_heads': 112, 'mamba_head_dim': 80, 'ffn_hidden_size': 10240}
0.3550 score -> {'num_layers': 50, 'hidden_size': 4480, 'mamba_num_heads': 112, 'mamba_head_dim': 64, 'ffn_hidden_size': 15680}
0.1016 score -> {'num_layers': 56, 'hidden_size': 4352, 'mamba_num_heads': 120, 'mamba_head_dim': 72, 'ffn_hidden_size': 10752}
0.5461 score -> {'num_layers': 46, 'hidden_size': 4480, 'mamba_num_heads': 128, 'mamba_head_dim': 72, 'ffn_hidden_size': 14848}
0.1992 score -> {'num_layers': 54, 'hidden_size': 4480, 'mamba_num_heads': 80, 'mamba_head_dim': 80, 'ffn_hidden_size': 14336}
0.5881 score -> {'num_layers': 48, 'hidden_size': 4480, 'mamba_num_heads': 112, 'mamba_head_dim': 80, 'ffn_hidden_size': 13824}
```
## 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.
- **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/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
OMNIML-3043
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Added NAS-based Auto Pruning for Minitron models as an alternative to
manual pruning using parameter constraints
* Introduced parameter counting capabilities for architecture search
* **Documentation**
* Expanded pruning guides with detailed examples and workflows for both
manual and automatic pruning approaches
* Updated configuration documentation with granular divisor parameters
* **Improvements**
* Enhanced parameter counting support for models with dynamic or
mixture-of-experts modules
<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: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** Bug fix
**Overview:** This PR fixes an input type mismatch in Resize layers when
being converted to FP16.
## Usage
```python
$ python -m modelopt.onnx.autocast --onnx_path=$MODEL_NAME.onnx
```
## Testing
Added unittest.
## 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
- **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
## Additional Information
This issue is also fixed by using the standalone type inference logic
from https://github.com/NVIDIA/Model-Optimizer/pull/719.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **Improvements**
* Enhanced the graph sanitization process to automatically duplicate
shared constants during optimization, ensuring improved model handling
and consistency.
* **Tests**
* Added test coverage for mixed precision conversion of Conv-Resize
model architectures.
<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: gcunhase <4861122+gcunhase@users.noreply.github.com>
## What does this PR do?
**Type of change:** Fix MCore KV Cache Quantization: Amax Device
Placement Bug; Code clean up; Distributed Sync of KVCache Quantizer
params; unittest expansion to hybrid models
**Overview:** Fixes bugs preventing MCore KV Cache quantization from
working during checkpoint restore.
### Bug Chain
**Bug 1:** `is_enabled = self.weight_quantizer.is_enabled if
hasattr(self, "weight_quantizer") else False`
No `weight_quantizer` for KV-cache-only quant → `is_enabled=False` →
metadata not saved → `modelopt_post_restore()` never called. *(Thanks to
@jenchen13 )*
**Bug 2:** After fixing Bug 1, `_amax` restored on CPU (via
`_reset_pytorch_state_from_metadata`). Fallback
`_calibrate_quantizers()` never called because `_amax` exists.
**Bug 3:** Even if called, `_calibrate_quantizers()` fails —
`core_attention` has no parameters → can't determine device/dtype.
### The Fix
1. Remove `is_enabled` check entirely — disabled modules may still need
metadata restore. Explicitly skip `output_layer` from extra state
callbacks (never quantized)
2. Set `dtype`/`device` on `core_attention` from parent Attention
module, `modelopt_post_restore()` calls `self.to(device, dtype)`
3. Remove dead `_calibrate_quantizers()` code (will bring back similar
logic for KV cache affine quantization)
### Previous Unit Test Was Wrong
`model_test` was `mtq.quantize()`'d, not `mto.restore()`'d. Never tested
actual restore path.
### Additional Fixes
- Amax sync across DP/TP for KV cache quantizers
- `flash_decode` auto-disabled
### Code Cleanup
Removed ~100 lines of dead code.
## Testing
1. MCore KV Cache QAD with Nano V3 + Context Parallel works
2. Unit tests: hybrid models, KV+GEMM configs, correct restore workflow,
backward pass validation
## Before your PR is "*Ready for review*"
- **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?**: Yes
---------
Signed-off-by: realAsma <akuriparambi@nvidia.com>
Co-authored-by: Asma Thekkumpate <akuriparambi@cw-dfw-cs-001-vscode-02.cm.cluster>
## What does this PR do?
**Type of change:** Add TRT LLM/vLLM/SGLang functional test cases for
newly added checkpoints on HF
**Overview:**
1.Since the speculative draft model only supports loading from a local
path, we should set the MODELOPT_LOCAL_MODEL_ROOT environment variable.
If we don't set it, these test cases will be skipped.
2. Newly added checkpoints:
- nvidia/gpt-oss-120b-Eagle3-short-context
- nvidia/gpt-oss-120b-Eagle3-throughput
- nvidia/EAGLE3-NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8
## Usage
```python
pytest tests/examples/llm_ptq/test_deploy.py --run-release
```
## Testing
Run release testing
## Before your PR is "*Ready for review*"
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.
- **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/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
**Overview:** Ops with 0-dim scalar constants were being forced to have
shape of 1 instead of 0. This PR fixes that issue.
## Usage
```python
$ python -m modelopt.onnx.autocast --onnx_path=$MODEL_NAME.onnx
```
## Testing
Added unittest.
## 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
- **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
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
## What does this PR do?
**Type of change:** ? <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
New feature
**Overview:** ?
This PR provides a sparse attention support in ModelOpt for applying
attention sparsity through skip softmax method, enabling inference
speedups for LLMs.
Key Features:
- Skip softmax support
- Sparse attention config
- Extensible method registry for future sparse attention algorithms
- HuggingFace Transformers integration
- Phase-aware thresholds (separate prefill/decode)
[Design
doc](https://docs.google.com/document/d/1OgmTAKkoD4ZSWYXel-FeaQqmI5PtyNhQ4dEuhGiZAQQ/edit?tab=t.0#heading=h.dyp44woziy9x)
## Usage
<!-- You can potentially add a usage example below. -->
```python
import torch
import modelopt.torch.sparsity.attention_sparsity as mts
from transformers import AutoModelForCausalLM
# Load model (must use eager attention for softmax patching)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
attn_implementation="eager", # Required!
torch_dtype=torch.bfloat16,
)
# Use pre-defined configuration
from modelopt.torch.sparsity.attention_sparsity import SKIP_SOFTMAX_DEFAULT
model = mts.sparsify(model, SKIP_SOFTMAX_DEFAULT)
```
## Testing
<!-- Mention how have you tested your change if applicable. -->
### Unit Test
```bash
pytest tests/unit/torch/sparsity/attention_sparsity -v
pytest tests/gpu/torch/sparsity/attention_sparsity -v
pytest tests/examples/llm_sparsity/attention_sparsity -v
```
ALL PASSED.
### Accuracy
Benchmark: MMLU
Model: Qwen/Qwen3-4B
Cmd: python mmlu.py --model_name causal --model_path Qwen/Qwen3-4B
--sparse_cfg SKIP_SOFTMAX_DEFAULT
| | MMLU |
|----------------------|-------|
| BF16 | 69.96 |
| SKIP_SOFTMAX_DEFAULT | 69.86 |
## 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: Kai Xu <kaix@nvidia.com>
iCo-authored-by: Asma Kuriparambil Thekkumpate <akuriparambi@nvidia.com>
## What does this PR do?
**Type of change:** New Feature
**Overview:**
This MR adds support for quantizing TE Ops in megatron, specifically
TERowParallelLinear, TEColumnParallelLinear and
TELayerNormColumnParallelLinear.
## Usage
It can be used by enabling TE spec in megatron
## Testing
Added unit tests for testing functionality
`test_homogeneous_sharded_state_dict_te_spec`
`test_convert_mcore_te_gpt_model`
`test_quantize_forward_backward`
## 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)?**:
Yes
## Additional Information
<!-- E.g. related issue. -->
---------
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
## 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>
Keval Morabia