PyTorch bindings for CUTLASS grouped GEMM. Contribute to mvpatel2000/grouped_gemm development by creating an account on GitHub.
permute row_id_map官方解释:the mapping table for the row indices of the input activations before and aftergrouped_gemm.ops.permute // source_row_id multiply with num_topKsource_row_id=[0,1,2,3,4,0,1,2,3,4]// sorted_row id: store row idx after sorting and before permute opsorted...
目前的主流做法就是我上述说的那样,进行truncated或padding,然后使用Batched GEMM来进行计算。除了NLP之外,三维物体的点云同样也是不一定都是具有相同的点数,有的点会多一些,有的势必会少一些,这样就比较难stack/concat到一起进行批量训练了。 怎么实现Grouped GEMM 那么有没有办法,可以充分利用显存(不进行padding)、...
Motivation #3323 Grouped Gemm kernel added in Cublas 12.5 is useful. It can be applied to MoE EP layer/Lora layer for acceleration. Modifications Add cublas_grouped_gemm in sgl-kernel library, an...
【Grouped GEMM for MoE:用于MoE模型训练中分组GEMM的PyTorch工具箱,支持高效的矩阵运算和优化】'fanshiqing/grouped_gemm' GitHub: github.com/fanshiqing/grouped_gemm #PyTorch# #CUTLASS# #分组GEMM# #MoE模型# û收藏 8 评论 ñ14 评论 o p 同时转发到我的微博 按热度 按...
Add profiler for mk-nk-mn fp16 ggemm multi d splitk d14aaa5 aosewskiclosed thisSep 26, 2024 CollaboratorAuthor Sign up for freeto join this conversation on GitHub. Already have an account?Sign in to comment zjing14Awaiting requested review from zjing14 ...
今天介绍一个在SGLang中针对DeepSeek V3模型中的https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/layers/moe/topk.py#L99-L149部分的biased_grouped_topk函数的kernel优化,在DeepSeek V3端到端测试中吞吐提升5%以上。这个函数用于DeepSeek V3/R1模型中的MOE层,用于计算每个token的专家选择概率...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [WIP] Initial implementation of Grouped Gemm API · pytorch/pytorch@b98af95
Pull Request resolved: pytorch#148531 Approved by: https://github.com/drisspg addUtilForLinuxBuild(pytorch/pytorch#148375) 1 parent b98af95 commit 53a1a02 File tree aten/src/ATen/native cuda Blas.cpp RowwiseScaledMM.cu ScaledGroupMM.cu ScaledGroupMM.h cutlass_utils.cuh native_functions.yaml...
The documentation form_grouped_gemm_fp8_fp8_bf16_nt_contiguousstates that passing a value of -1 inm_indiceswill skip that block of 128 entries for the calculation. However, this does not seem to be the case - there does not seem to be any code that does this, and passing -1 in fact...