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PolarSparsity

2505.14884

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Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity

Polar Sparsity is a framework for efficient sparse inferencing in large language models (LLMs), leveraging custom Triton kernels and learned routers for selective activation of MLP neurons and attention heads. This repository provides tools for data collection, router training, benchmarking, and end-to-end sparse generation.

Code: https://github.com/susavlsh10/Polar-Sparsity

⚠️ Requirements

Python 3.8+
PyTorch (tested on >=1.13)
Transformers (tested on >=4.30)
See environment.yml for all dependencies.

Note: Some scripts may require additional dependencies (e.g., matplotlib, pandas).

🗂️ Model Indices

The following table lists common model indices used in --model_index (see also HybridTensor/utils/activations.py):

Index	Model Name
5	facebook/opt-6.7b
8	facebook/opt-66b
11	meta-llama/Llama-2-7b-hf
15	meta-llama/Llama-3.1-70B

📦 Repository Structure

Router Data Collection & Training
- Data Collection: HybridTensor/routers/datacollection/data_collection.py
- MLP Router Training: HybridTensor/routers/mlp/main_mlp.py
- MHA Router Training: HybridTensor/routers/mha/main_att.py
Benchmarks
- Evaluation: HybridTensor/benchmarks/model_eval.py
Kernel Implementations
- Triton Kernels: HybridTensor/triton/
- Example Runners: run_sparse_mlp.py, run_sparse_attn.py, run_sparse_transformer_block.py
Sparse Generation
- End-to-End Sparse Generation: model_sparse_generation.py

🚀 Getting Started

1. Environment Setup

Install dependencies (see environment.yml for details).

conda env create -f environment.yml

For Triton kernels, install the latest nightly build:
pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly

2. Router Data Collection

To collect router data for a specific model, you can use:

bash

python -m HybridTensor.routers.datacollection.data_collection \
    --model_index 5 \
    --batch_size 8 \ 
    --device_map auto \
    --data_dir <PATH_TO_ACTIVATION_DATA> \
    --max_samples 400000 \
    --model_family <opt/llama> \
    --mlp_activation True \
    --attn_norm True

Argument explanations:

--model_index: Index of the model to use (see HybridTensor/utils/activations.py for available indices).
--batch_size: Number of samples per batch during data collection, adjust to configure GPU memory usage.
--data_dir: Directory to save the collected activation data.
--model_family: Model family (e.g., opt, llama).
--mlp_activation: Set to True to collect MLP activation data. Only for sparse MLP models.
--attn_norm: Set to True to collect attention norm data.

3. Router Training and Optimizations

MLP Router:

To run the MLP router training use the following scripts

For a single layer:

bash

python -m HybridTensor.routers.mlp.main_mlp \
    --model_index <MODEL_INDEX> \
    --L <LAYER_NUMBER> \
    --data_dir <PATH_TO_ACTIVATION_DATA> \
    --ckpt_dir <PATH_TO_SAVE_CHECKPOINTS> \
    --gpu <GPU_ID>

For all layers, edit the `HybridTensor/routers/mlp/train_mlp_routers.sh' file with the number of GPUs available, model index, total number of layers, data_dir and ckpt_dir.

./HybridTensor/routers/mlp/train_mlp_routers.sh

MHA Router:

To run the attention router training use the following scripts

For a single layer:

bash

python -m HybridTensor.routers.mha.main_att \
    --model_index <MODEL_INDEX> \
    --L <LAYER_NUMBER> \
    --k <TOPK_VALUE> \
    --data_dir <PATH_TO_ACTIVATION_DATA> \
    --ckpt_dir <PATH_TO_SAVE_CHECKPOINTS>

For all layers, edit the `HybridTensor/routers/mha/train_mha_routers_topk.sh' file with the number of GPUs available, model index, total number of layers, data_dir and ckpt_dir.

./HybridTensor/routers/mha/train_mha_routers_topk.sh

To optimize the MLP layers for ReLU model with our dynamic layer wise top-k algorithm, you can use:

bash

python -m HybridTensor.routers.mlp.mlp_router_optim_fast --model_index <MODEL_INDEX> --batch_size <BATCH_SIZE_INFERENCE> --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> --act_data_dir <PATH_TO_ACTIVATION_DATA>

--batch_size: batch size to optimize for inference

4. Model Evaluation

You can evaluate your models on various benchmarks using the HybridTensor/benchmarks/model_eval.py script. Below are example commands and explanations for the main arguments. These scripts use huggingface implementations with masking for easy benchmarking. These do not use the optimized kernels for efficient inference.

Example usage:

bash

python -m HybridTensor.benchmarks.model_eval \
    --model_index <MODEL_INDEX> \
    --batch_size <BATCH_SIZE> \
    --mode <dense|sparse|sparse_attn> \
    --benchmark <all|BENCHMARK_NAME> \
    --attn_topk <TOPK_VALUE> \
    --attn_ckpt_dir <PATH_TO_ATTENTION_ROUTER_CHECKPOINTS> \
    --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> \
    --data_collection <True|False> \
    --device auto \
    --note <NOTE>

Additional argument explanations:

--batch_size: Batch size to use for evaluation.
--mode: Evaluation mode. Options are dense (standard), sparse (sparse MLP and/or attention using trained routers), or sparse_attn (sparse attention only using ground truth activations ,doesn't require routers).
--benchmark: Which benchmark(s) to run. Use all for the full suite or specify a single benchmark (e.g., mmlu).
--attn_topk: Top-k value for attention sparsity (e.g., 0.5 for 50% sparsity).
--attn_ckpt_dir: Directory containing attention router checkpoints.
--mlp_ckpt_dir: Directory containing MLP router checkpoints.
--data_collection: Set to True to enable data collection mode for threshold sweeps.
--device: Device ID to use (e.g., 0 for cuda:0).
--note: Optional note to append to the results filename.

Adjust the arguments as needed for your experiment or hardware setup.

5. Kernel Implementations

Triton Kernels: Custom kernels for selective MLP and attention are in HybridTensor/triton/.

Benchmark the speedup of the selective GEMM kernel (used for sparse MLPs):

bash

python -m HybridTensor.triton.gather_gemm_col \
    --batch_size <BATCH_SIZE> \
    --in_features <EMBEDDING_DIMENSION> \
    --index_size <TOTAL_ACTIVE_NEURONS>

--in_features: Model embedding dimension (e.g., 8192).
--index_size: Total number of active neurons selected by the router. Needs to be less than or equal to total neurons.

Benchmark the speedup for a sparse MLP layer:

bash

python run_sparse_mlp.py \
    --in_features <EMBEDDING_DIMENSION> \
    --batch_size <BATCH_SIZE> \
    --index_size <ACTIVE_NEURONS>

Benchmark the speedup for a sparse Multi-Head Attention (MHA) layer:

bash

python run_sparse_attn.py \
    --in_features <EMBEDDING_DIMENSION> \
    --batch_size <BATCH_SIZE> \
    --seq_len <SEQUENCE_LENGTH> \
    --attn_topk <TOPK_VALUE>

--attn_topk: Fraction of attention heads to keep active (e.g., 0.5 for 50%).

Use the following script before running autotune_configs.py

export TRITON_PRINT_AUTOTUNING="1"

For models with sparse MLP, use the HybridTensor/triton/heuristics/autotune_configs.py script to compile the kernels for different batch sizes and activation to speedup inference.

Benchmark the speedup for a full sparse transformer block with different batch sizes and sequence lengths:

bash

python run_sparse_transformer_block.py \
    --in_features <EMBEDDING_DIMENSION> \
    --batch_size <BATCH_SIZE> \
    --seq_len <SEQUENCE_LENGTH> \
    --index_size <ACTIVE_NEURONS> \
    --attn_topk <TOPK_VALUE>

Note:
The run_sparse_transformer_block.py script can also be used to simulate large-scale inferencing setups with large batch sizes and sequence lengths on a single GPU if multi-GPU system is not available, since only a single transformer layer is executed in this script.

6. Sparse Generation

Run end-to-end sparse generation using trained routers. This example shows how to build the sparse model for end-to-end generation using the optimized kernels and batched inference.

bash

python -m HybridTensor.benchmarks.generation.model_sparse_generation \
    --model_index <MODEL_INDEX> \
    --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> \
    --attn_ckpt_dir <PATH_TO_ATTENTION_ROUTER_CHECKPOINTS> \
    --batch_stats_dir <PATH_TO_BATCH_STATS> \
    --attn_topk <TOPK_VALUE>

--batch_stats_dir: used for sparse MLP models, path to the output from dynamic top-k optimization. Saved in configs/<model_name>

Citation

If you find our work helpful, please cite us:

bibtex

@misc{shrestha2025polarsparsityhighthroughput,
      title={Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity}, 
      author={Susav Shrestha and Brad Settlemyer and Nikoli Dryden and Narasimha Reddy},
      year={2025},
      eprint={2505.14884},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.14884}, 
}

Susav/PolarSparsity

Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity

⚠️ Requirements

🗂️ Model Indices

📦 Repository Structure

🚀 Getting Started

1. Environment Setup

2. Router Data Collection

3. Router Training and Optimizations

4. Model Evaluation

5. Kernel Implementations

6. Sparse Generation

Citation

Susav/
PolarSparsity