lnigam 66d09faeed ggml : recurrent state rollback for ggml_ssm_scan (llama/26623)
* Initial changes for Recurrent state rollback for nemotron for cpu and cuda

* Removing CPU RS rollback. Will enable it in subsequent PRs

* addition of test case

* Removing assert and calling runtime API to check if op is supported

* removing extra API and updating the call sites for K

* replace static cuda detection to runtime fused_op api

* address review comments and fallback when SSM rollback not supprted

* Adding changes for supporting RS-rollback in CPU. Also added test-backend-ops for cpu and cuda

* removing memory manipulation as rs rollback is now supported in CPU

* removing the static probe which is not needed now

* correcting the format

* address review comments

* enabling test for all the backends, unsupported backends will fallback to CPU

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* choose different graph based on the result of fused_ssm_op is supported or not and also handled memory->n_rs_seq >1 case incase of op is not supported

* Support K > 1 in ssm_scan for all backends

* Fix CI Issues

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
2026-08-14 18:12:14 +03:00
2026-04-14 15:27:40 +03:00
2026-07-09 23:39:29 +03:00
2025-11-04 20:40:52 +02:00
2026-08-07 17:08:38 +03:00
2025-02-04 13:03:55 +02:00
2026-02-07 10:37:38 +02:00
2026-03-16 14:54:53 +02:00

ggml

Manifesto

Tensor library for machine learning

Note that this project is under active development.
Some of the development is currently happening in the llama.cpp and whisper.cpp repos

Features

  • Low-level cross-platform implementation
  • Integer quantization support
  • Broad hardware support
  • Automatic differentiation
  • ADAM and L-BFGS optimizers
  • No third-party dependencies
  • Zero memory allocations during runtime

Build

git clone https://github.com/ggml-org/ggml
cd ggml

# install python dependencies in a virtual environment
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# build the examples
mkdir build && cd build
cmake ..
cmake --build . --config Release -j 8

GPT inference (example)

# run the GPT-2 small 117M model
../examples/gpt-2/download-ggml-model.sh 117M
./bin/gpt-2-backend -m models/gpt-2-117M/ggml-model.bin -p "This is an example"

For more information, checkout the corresponding programs in the examples folder.

Resources

S
Description
ggml 是面向机器学习的张量库。|GitHub 镜像 15.2k · 🍴 1.8k
https://github.com/ggml-org/ggml Readme MIT 36 MiB
Languages
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Metal 2.4%
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Other 4.3%