DeepGEMM

DeepGEMM is a library designed for clean and efficient General Matrix Multiplications (GEMMs). It supports FP8 and BF16 (working in progress) for both normal and Mix-of-Experts (MoE) grouped scenarios. Written in CUDA, the library has no kernel compilation need during installation, by compiling all kernels at runtime using a lightweight Just-In-Time (JIT) module.

DeepGEMM leverages some concepts from CUTLASS and CuTe, it avoids heavy reliance on their templates or algebras. Instead, the library is designed for simplicity, with only a limited number of core kernel functions. This makes it a clean and accessible resource for learning NVIDIA GPU kernel optimization techniques.

Despite its lightweight design, DeepGEMM's performance matches or exceeds expert-tuned libraries across various matrix shapes.

News

Roadmap

Quick start

Requirements

Development

# Submodule must be cloned
git clone --recursive [email protected]:deepseek-ai/DeepGEMM.git cd DeepGEMM
# Link some essential includes and build the CPP JIT module cat develop.sh ./develop.sh
# Test all GEMM implements python tests/test_layout.py python tests/test_attention.py python tests/test_core.py

Installation

cat install.sh
./install.sh

Then, import deep_gemm in your Python project, and enjoy!

Interfaces

Notices

This library provides optimized GEMM kernels for NVIDIA GPUs with a naming convention: D = C + A @ B. The input shape layout is NT (non-transposed A, transposed B). While the SM90 implementation supports only the NT memory layout (row-major, col-major), the SM100 implementation supports all memory layouts (NT, TN, NN, TT). For example, fp8_gemm_nt will do a D = C + A @ B.T

For both architectures, the LHS scaling factor is required to have a TMA-aligned and transposed layout. And the data format for the scaling factor of SM90 and SM100 is different:

Please note that operations like input transposition or FP8 casting must be handled separately by the user, please implement or fuse them into prior kernels independently. While the library provides some simple PyTorch utility functions, these may result in slower performance, but our primary focus is on optimizing the GEMM kernels themselves.

Normal dense GEMMs (non-grouped)

To perform a basic non-grouped FP8 GEMM, call the fp8_gemm_{nt, nn, tn, tt} function. For more details, please refer to the function documentation.

Grouped GEMMs (contiguous layout)

Unlike traditional grouped GEMMs in CUTLASS, DeepGEMM groups only the M-axis, while N and K must remain fixed. This design is tailored for scenarios where experts in an MoE model share the same shape. For training forward passes or inference prefilling, where each expert may process a varying number of tokens, we concatenate these tokens into a single tensor, referred to as the "contiguous" layout. Note that each expert segment must be aligned to the GEMM M block size (get_mk_alignment_for_contiguous_layout()). For more information, please refer to the m_grouped_fp8_gemm_{nt, nn}_contiguous function documentation.

We also provide a K-axis-grouped API for MoE weight backward (with M and N must remain fixed), please refer to k_grouped_fp8_gemm_tn_contiguous for more information.

Grouped GEMMs (masked layout)

During the inference decoding phase, when CUDA graph is enabled and the CPU is unaware of the number of tokens each expert receives, we support masked grouped GEMMs. By providing a mask tensor, the kernel computes only the valid portions.

Use m_grouped_fp8_gemm_nt_masked for this purpose and consult the relevant documentation. An example usage is to use the output of low-latency kernels from DeepEP as input.

V3.2 MQA kernels for the indexer

The kernel family has two versions, non-paged (for prefilling) and paged (for decoding). Take the non-paged version fp8_mqa_logits as an example. It has 6 inputs:

The output tensor is shaped as [seq_len, seq_len_kv], indicating token-to-token logits. For each token i in q, it will iterate all tokens j from [cu_seq_len_k_start[i], cu_seq_len_k_end[i]), and calculate the logit out[i, j] as:

kv_j = kv[0][j, :] * kv[1][j].unsqueeze(1)  # [head_dim]
out_ij = q[i, :, :] @ kv_j  # [num_heads] out_ij = out_ij.relu() * weights[i, :]  # [num_heads] out_ij = out_ij.sum()  # Scalar

For more details and the paged version fp8_paged_mqa_logits, please refer to tests/test_attention.py.

Utilities

The library provides some utility functions besides the above kernels:

The library also provides some environment variables, which may be useful:

For additional examples and details, please refer to the test code or review the corresponding Python documentation.

Acknowledgement

DeepGEMM is inspired by the CUTLASS project. Thanks and respect to the developers!

License

This code repository is released under the MIT License.