Metadata-Version: 2.4
Name: nvidia-cudnn-frontend
Version: 1.25.0
Summary: NVIDIA cuDNN Frontend — Python and C++ Graph API with SOTA attention (SDPA / Flash Attention), MoE grouped GEMM fusions, and FP8/MXFP8 kernels for Hopper and Blackwell GPUs.
License: MIT
Project-URL: Homepage, https://github.com/NVIDIA/cudnn-frontend
Project-URL: Documentation, https://docs.nvidia.com/deeplearning/cudnn/frontend/latest/
Project-URL: Blog, https://nvidia.github.io/cudnn-frontend/
Project-URL: Repository, https://github.com/NVIDIA/cudnn-frontend
Project-URL: Bug Tracker, https://github.com/NVIDIA/cudnn-frontend/issues
Project-URL: Release Notes, https://github.com/NVIDIA/cudnn-frontend/releases
Keywords: cudnn,cuda,gpu,nvidia,deep-learning,attention,sdpa,flash-attention,transformer,moe,mixture-of-experts,grouped-gemm,fp8,mxfp8,blackwell,hopper,pytorch,kernel,graph-api
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Provides-Extra: cutedsl
Requires-Dist: nvidia-cutlass-dsl[cu13]==4.5.0; extra == "cutedsl"
Requires-Dist: cuda-python; extra == "cutedsl"
Requires-Dist: torch; extra == "cutedsl"
Requires-Dist: apache-tvm-ffi; extra == "cutedsl"
Requires-Dist: torch-c-dlpack-ext; extra == "cutedsl"
Dynamic: license-file


# cuDNN Frontend (FE)

[![PyPI version](https://img.shields.io/pypi/v/nvidia-cudnn-frontend.svg)](https://pypi.org/project/nvidia-cudnn-frontend/)
[![PyPI downloads](https://img.shields.io/pypi/dm/nvidia-cudnn-frontend.svg)](https://pypi.org/project/nvidia-cudnn-frontend/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Docs](https://img.shields.io/badge/docs-nvidia.github.io-blue.svg)](https://nvidia.github.io/cudnn-frontend/)

**cuDNN Frontend** is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels — scaled dot-product attention (**SDPA / Flash Attention**), grouped GEMM fusions for **Mixture-of-Experts (MoE)** training, fused normalization + activation, and more.

It provides a **header-only C++ API** and a **Python interface** (with native PyTorch integration) to the cuDNN Graph API, targeting NVIDIA **Hopper** (H100/H200) and **Blackwell** (B200/GB200/GB300) GPUs across FP16, BF16, FP8, and **MXFP8** precision.

**Links:** [Documentation](https://docs.nvidia.com/deeplearning/cudnn/frontend/latest/) · [Blog & Deep Dives](https://nvidia.github.io/cudnn-frontend/) · [PyPI](https://pypi.org/project/nvidia-cudnn-frontend/) · [Release Notes](https://github.com/NVIDIA/cudnn-frontend/releases) · [Samples](samples/)

## 🚀 Latest news:

We will begin open-sourcing kernels based on customer needs, with the goal to educate developers and enable them to customize as needed.

We are now shipping **OSS kernels**, allowing you to inspect, modify, and contribute to the core logic. Check out our latest implementations:

*   **[GEMM + Amax](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/gemm_amax):** Optimized FP8 matrix multiplication with absolute maximum calculation.
*   **[GEMM + SwiGLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/gemm_swiglu):** High-performance implementation of the SwiGLU activation fused with GEMM.
*   **[GEMM + sReLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/gemm_srelu):** High-performance implementation of squared-ReLU fused with GEMM.
*   **[GEMM + dsReLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/gemm_dsrelu):** High-performance implementation of dsquared-ReLU fused with GEMM.
*   **[Grouped GEMM + GLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_glu):** Unified grouped GEMM GLU API supporting dense and discrete MoE weight layouts.
*   **[Grouped GEMM + GLU + Hadamard](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_glu_hadamard):** Dense grouped GEMM GLU forward fusion with a fused Hadamard transform and per-expert AMAX reduction.
*   **[Grouped GEMM + dGLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_dglu):** Unified grouped GEMM dGLU backward API supporting dense and discrete MoE weight layouts.
*   **[Grouped GEMM + SwiGLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_swiglu):** SwiGLU activation fused with Grouped GEMM.
*   **[Grouped GEMM + dSwiglu](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_dswiglu):** dSwiglu activation fused with Grouped GEMM.
*   **[Grouped GEMM + sReLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_srelu):** Contiguous grouped squared-ReLU GEMM for MoE workloads.
*   **[Grouped GEMM + dsReLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_dsrelu):** Contiguous grouped dsquared-ReLU GEMM for MoE workloads.
*   **[Discrete Grouped GEMM + SwiGLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/discrete_grouped_gemm/discrete_grouped_gemm_swiglu):** Per-expert-pointer SwiGLU grouped GEMM for MoE workloads without weight packing.
*   **[Discrete Grouped GEMM + dSwiGLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/discrete_grouped_gemm/discrete_grouped_gemm_dswiglu):** Per-expert-pointer dSwiGLU backward grouped GEMM for MoE workloads without weight packing.
*   **[Grouped GEMM + Quant](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_quant):** Legacy dense-only grouped GEMM quant API for MoE FC2/dFC1 workloads.
*   **[Grouped GEMM + Quant (Unified)](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_quant):** Unified grouped GEMM quant API with per-row gating for MoE FC2/dFC1 workloads.
*   **[Grouped GEMM + Wgrad](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/grouped_gemm/grouped_gemm_wgrad):** Unified grouped GEMM weight-gradient API supporting dense and discrete output layouts for MoE workloads.
*   **[NSA](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/native_sparse_attention/):** Native Sparse attention as described in the Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention.
*   **[SDPA Backward: SM100, D=256](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/sdpa):** SDPA Backward pass for D=256 on SM100.
*   **[cudnn SDPA Fprop](https://github.com/NVIDIA/cudnn-frontend/tree/main/include/cudnn_frontend/generated/sdpa):** Open sourcing the Hopper and Blackwell fprop kernels with stats.
*   **[Fused RMSNorm + SiLU](https://github.com/NVIDIA/cudnn-frontend/tree/main/include/cudnn_frontend/generated/rms_norm_silu):** Implementation of a fused kernel of RMS normalization followed by SiLU (Swish) activation.
*   **[SDPA PyTorch Op](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/experimental/ops):** PyTorch custom operator for cuDNN-accelerated Scaled Dot-Product Attention with autograd and `torch.compile` support.
*   **[DSA](https://github.com/NVIDIA/cudnn-frontend/tree/main/python/cudnn/deepseek_sparse_attention):** DSA/CSA kernels for DSv4 and DSv3.2 for fprop and bprop.

## Tech talks

* See our latest talk on GPU-Mode
  
   ▶ [Watch on YouTube](https://www.youtube.com/watch?v=kxP-vp1dgFY)

## 🔥🔥🔥  SOTA Attention Kernels from cudnn backend

#### Llama 3.1 style Forward and Bprop with causal masking (GB300)

  Llama 3.1 SDPA Benchmark on GB300 (only cuDNN)

#### Deepseek v3 style Forward and Bprop with causal masking (GB300)

  DSv3 SDPA Benchmark on GB300 (only cuDNN)

## Key Features

*   **Unified Graph API:** Create reusable, persistent `cudnn_frontend::graph::Graph` objects to describe complex subgraphs.
*   **Ease of Use:** Simplified C++ and Python bindings (via `pybind11`) that abstract away the boilerplate of the backend API.
*   **Performance:** Built-in autotuning and support for the latest NVIDIA GPU architectures.

## Installation

### 🐍 Python

The easiest way to get started is via pip:

```bash
pip install nvidia-cudnn-frontend
```

**Requirements:**
*   Python 3.9+
*   NVIDIA driver and CUDA Toolkit
*   NVIDIA cuDNN (minimum 8.5.0)

### ⚙️ C++ (Header Only)

Since the C++ API is header-only, integration is seamless. Simply include the header in your compilation unit:

```cpp
#include <cudnn_frontend.h>
```

Ensure your include path points to the `include/` directory of this repository.

## Building from Source

If you want to build the Python bindings from source or run the C++ samples:

**1. Dependencies**
*   `python-dev` (e.g., `apt-get install python-dev`)
*   Dependencies listed in `requirements.txt` (`pip install -r requirements.txt`)

**2. Python Source Build**
```bash
pip install -v git+https://github.com/NVIDIA/cudnn-frontend.git
```
*Environment variables `CUDAToolkit_ROOT` and `CUDNN_PATH` can be used to override default paths.*

**3. C++ Samples Build**
```bash
mkdir build && cd build
cmake -DCUDNN_PATH=/path/to/cudnn -DCUDAToolkit_ROOT=/path/to/cuda ../
cmake --build . -j16
./bin/samples
```

## Documentation & Examples

*   **Developer Guide:** [Official NVIDIA Documentation (latest)](https://docs.nvidia.com/deeplearning/cudnn/frontend/latest/)
*   **Blog & Deep Dives:** [nvidia.github.io/cudnn-frontend](https://nvidia.github.io/cudnn-frontend/) — release notes, installation guides, and technical deep-dives (MXFP8 attention, FP8 scale layouts, etc.)
*   **C++ Samples:** See [`samples/cpp`](samples/cpp) for end-to-end examples covering convolution, matmul, SDPA / Flash Attention, normalization, and more.
*   **Python Samples:** See [`samples/python`](samples/python) for Jupyter notebooks and PyTorch integration patterns.
*   **OSS Kernels:** See [`python/cudnn/`](python/cudnn/) for source of SDPA, grouped GEMM + SwiGLU/GLU, RMSNorm + SiLU, Native Sparse Attention, and other open-sourced kernels.
*   **PyTorch Custom Ops:** See [`python/cudnn/experimental/ops`](python/cudnn/experimental/ops) for `torch.compile`-compatible wrappers around cuDNN kernels.

## 🤝 Contributing

We strictly welcome contributions! Whether you are fixing a bug, improving documentation, or optimizing one of our new OSS kernels, your help makes cuDNN better for everyone.

1.  Check the [Contribution Guide](CONTRIBUTING.md) for details.
2.  Fork the repo and create your branch.
3.  Submit a Pull Request.

## Debugging

To view the execution flow and debug issues, you can enable logging via environment variables:

```bash
# Log to stdout
export CUDNN_FRONTEND_LOG_INFO=1
export CUDNN_FRONTEND_LOG_FILE=stdout

# Log to a file
export CUDNN_FRONTEND_LOG_INFO=1
export CUDNN_FRONTEND_LOG_FILE=execution_log.txt
```

**Logging Levels:**
- `CUDNN_FRONTEND_LOG_INFO=0`: No logging
- `CUDNN_FRONTEND_LOG_INFO=1`: Full logging with tensor dumps
- `CUDNN_FRONTEND_LOG_INFO=10`: Basic logging (safe for CUDA graph capture)

Alternatively, you can control logging programmatically via `cudnn_frontend::isLoggingEnabled()`.

### Overriding the CUDA runtime library

When the frontend is built with dynamic loading enabled, it locates the CUDA runtime
(`libcudart.so.*`) at runtime by searching for the supported major versions. In some
environments (for example, containers such as GKE where the TCPXO NCCL plugin mounts a
different `libcudart` major version from the host) multiple versions of `libcudart` may be
visible on the library search path, and the automatic detection aborts with a
`Multiple libcudart libraries found` error.

To resolve this, set the `CUDNN_FRONTEND_CUDART_LIB_NAME` environment variable to the
library name (or full path) that should be loaded. This bypasses the automatic detection:

```bash
export CUDNN_FRONTEND_CUDART_LIB_NAME=libcudart.so.13
# or an absolute path
export CUDNN_FRONTEND_CUDART_LIB_NAME=/usr/local/cuda/lib64/libcudart.so.13
```

## License

This project is licensed under the [MIT License](LICENSE).
