deepseek-ai/DeepGEMM

先看结论

DeepGEMM: clean and efficient BLAS kernel library on GPUDeepGEMM DeepGEMM is a unified, high-performance tensor core kernel library that brings together the key computation primitives of modern large language models — GEMMs (FP8, FP4, BF16), fused MoE with overlapped communication (Mega MoE), MQA scoring for the lightning indexer, HyperConnection (HC), and more — into a single, cohesive CUDA codebase. All kernels are compiled at runtime through DeepJIT, requiring no CUDA compilation during installation. DeepGEMM leverages some concepts from CUTLASS and CuTe, but avoids heavy reliance on their templates or algebras. The library is designed for simplicity, with only a limited number of core kernel functions, making 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 2026.09.30 DeepGEMM Ascend is available! Check DeepGEMM-Ascend for more details Add more optimizations, including locality domain features, check #462 for more details 2026.09.10: Sparse Indexer, Mega Gate, Mega mHC, DeepJIT, MoE and Indexer optimizations and more. Please see #432 for more details. 2026.04.16: Mega MoE, FP8xFP4 GEMM, FP4 Indexer, PDL, faster JIT compilation and more. Please see #304 for more details. For Mega MoE benchmarks, refer to #316. 2025.09.28: DeepGEMM now supports scoring kernels (weighted ReLU MQA logits) for the lightning indexer for DeepSeek v3.2. Please see #200 for more details. 2025.07.20: DeepGEMM now supports both SM90/SM100, and has a full refactor with a low-CPU-overhead JIT CPP module. As NVCC 12.9 will automatically do the FFMA interleaving, all post optimizations will be no longer supported. Please see #112 for more details. 2025.05.14: DeepGEMM now offers weight gradient kernels for dense and MoE backward! See #95 for details. 2025.04.18: DeepGEMM now achieves up to 1550 TFLOPS on H800! See #74, #78, #81, #86 and 340d988 for details. Quick start Requirements NVIDIA SM90 or SM100 architecture GPU Python 3.8 or higher Compilers and standard libraries with C++20 <format> support CUDA Toolkit 12.9 or higher PyTorch 2.3 or higher CUTLASS 4.0 or higher (could be cloned by Git submodule) Development # Submodule must be cloned git clone --recursive git@github.com:deepseek-ai/DeepGEMM.git cd DeepGEMM # Link some essential includes and build the C++ extension cat develop.sh ./develop.sh 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: SM90 requires scaling factors in FP32 format. SM100 requires scaling factors in packed UE8M0 format, which packs 4 UE8M0 into a single torch.int. 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_fp4_mqa_logits as an example. Its main inputs are: q, a (q_data, q_sf) tuple; SM100 accepts MXFP4/MXFP8 data with packed UE8M0 scales kv, a (kv_data, kv_sf) tuple with shape [seq_len_kv, head_dim] logically weights, tensor with shape [seq_len, num_heads] (BF16 on SM100) cu_seq_len_k_start and cu_seq_len_k_end, int tensor with shape [seq_len] max_seqlen_k, the maximum valid KV span of any query row The output is compressed to [seq_len, max_seqlen_k]; row i stores its valid KV span starting at column zero. 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 corresponding compressed logit 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_fp4_paged_mqa_logits, please refer to tests/test_attention.py. Mega MoE Mega MoE fuses and overlaps EP dispatch, linear 1 and linear 2 (FP8xFP4 or FP8xFP8), SwiGLU, and EP combine into a single mega-kernel, overlapping NVLink communication and tensor core computation. It requires multi-process launch with symmetric memory. Usage: # Allocate symmetric memory buffer # NOTES: requires PyTorch >= 2.9 buffer = deep_gemm.get_symm_buffer_for_mega_moe( group, num_experts, num_max_tokens_per_rank, num_topk, hidden, intermediate_hidden, mma_type='fp8xfp4', # Use 'fp8xfp8' for FP8 routed-expert weights ) # Transform weights (FP4 or FP8 with UE8M0 SF) into the required layout transformed_l1, transformed_l2 = deep_gemm.transform_weights_for_mega_moe(l1_weights, l2_weights) # (Optional) Localize weights into locality domains transformed_l1 = (deep_gemm.localize(transformed_l1[0]), transformed_l1[1]) transformed_l2 = (deep_gemm.localize(transformed_l2[0]), transformed_l2[1]) deep_gemm.destroy_localizer() # Copy inputs into the buffer before each call # You may fuse these into previous kernels buffer.x[:num_tokens].copy_(x_fp8) buffer.x_sf[:num_tokens].copy_(x_sf) buffer.topk_idx[:num_tokens].copy_(topk_idx) buffer.topk_weights[:num_tokens].copy_(topk_weights) # Run the fused mega MoE kernel y = torch.empty((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') deep_gemm.fp8_fp4_mega_moe(y, transformed_l1, transformed_l2, buffer) For the full example with multi-process setup and benchmarking, please refer to tests/test_mega_moe.py. Utilities The library provides some utility functions besides the above kernels: deep_gemm.set_num_sms / get_num_sms: set/get the maximum SM count to use deep_gemm.set_tc_util / get_tc_util: set/get an approximated tensor core utilization ratio deep_gemm.set_pdl / get_pdl: enable/disable Programmatic Dependent Launch (PDL) deep_gemm.use_deterministic_algorithms: enable/disable deterministic algorithms deep_gemm.set_mk_alignment_for_contiguous_layout / get_mk_alignment_for_contiguous_layout: set/get the group-level M/K alignment for contiguous layout deep_gemm.get_theoretical_mk_alignment_for_contiguous_layout: get the theoretical minimum M/K alignment deep_gemm.set_ignore_compile_dims: configure dimensions to ignore during JIT compilation deep_gemm.set_block_size_multiple_of: constrain block sizes to be multiples of a given value deep_gemm.transform_sf_into_required_layout: transform scaling factors into the required layout deep_gemm.get_tma_aligned_size: get the required TMA alignment size deep_gemm.get_mn_major_tma_aligned_tensor: get a MN-major TMA-aligned tensor deep_gemm.get_mn_major_tma_aligned_packed_ue8m0_tensor: get a MN-major TMA-aligned tensor (with packing FP32 into UE8M0) deep_gemm.get_k_grouped_mn_major_tma_aligned_packed_ue8m0_tensor: K-grouped GEMM packing kernel The library also provides some environment variables, which may be useful: Each DG_JIT_* variable falls back to the corresponding global DJ_JIT_* variable when unset. General DG_JIT_DEBUG: 0 or 1, enable JIT debugging features, including compiler command and PTXAS output, load-time reporting, line info, and PTX/SASS dumps; 0 by default DG_PRINT_CONFIGS: 0 or 1, print selected configs for each shape, 0 by default JIT cache DG_JIT_CACHE_DIR: string, cache directory (or a :-separated list of directories) for compiled kernels; lookup searches all paths front-to-back (first hit wins) and a cache miss compiles into the first path, $HOME/.dj by default Compiler selection DG_JIT_NVCC_COMPILER: string, NVCC compiler path; otherwise CUDA is found through CUDA_HOME, CUDA_PATH, which nvcc, then /usr/local/cuda DG_JIT_CPP_STANDARD: integer, C++ standard version, 20 by default Compiler output DG_JIT_PRINT_COMPILER_COMMAND: 0 or 1, print compilation commands, 0 by default DG_JIT_PTXAS_VERBOSE: 0 or 1, show detailed PTXAS output, 0 by default DG_JIT_CHECK_NO_SPILLS: 0 or 1, assert no register spills in compiled kernels, 0 by default DG_JIT_CHECK_NO_LOCAL_MEMORY: 0 or 1, assert no local memory usage in compiled kernels, 0 by default DG_JIT_PRINT_LOAD_TIME: 0 or 1, print kernel load time, 0 by default Debug and profiling DG_JIT_WITH_LINEINFO: 0 or 1, embed source line info for profiling tools, 0 by default DG_JIT_DUMP_ASM: 0 or 1, dump both PTX and SASS, 0 by default DG_JIT_DUMP_PTX: 0 or 1, dump PTX output, 0 by default DG_JIT_DUMP_SASS: 0 or 1, dump SASS output, 0 by default DG_COMM_KERNEL_DEBUG: 0 or 1, zero symmetric buffer before each Mega MoE call for debugging, 0 by default DG_USE_NVIDIA_TOOLS: 0 or 1, skip internal profiling when running under external NVIDIA tools, 0 by default Build options DG_SKIP_CUDA_BUILD: 0 or 1, skip CUDA extension build during installation, 0 by default DG_FORCE_BUILD: 0 or 1, force local build instead of downloading pre-built wheels, 0 by default 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. Citation @misc{deepgemm2025, title={DeepGEMM: clean and efficient BLAS kernel library on GPU}, author={Chenggang Zhao and Zhean Xu and Liang Zhao and Jiashi Li and Chenhao Xu and Anyi Xu and Shengyu Liu and Kexing Zhou and Kuai Yu}, year={2025}, publisher = {GitHub}, howpublished = {\url{https://github.com/deepseek-ai/DeepGEMM}}, }