另一种比较折中的做法就是做padding或者truncated,比如我们知道矩阵大小最大不超过100,那么我们就可以把所有的矩阵给填充零补充到100使得他们的尺寸都相同,如果有个矩阵超过了100,那么我们也没办法,只能把矩阵想办法截断缩放到100那么大,然后使用Batched GEMM进行计算。对于第一种方法,可能会比较耗时,因为python的for循...
permute 操作 是完成 row_id_map 映射表格的计算及 重新组合 output matrix 。而 row_id_map 存储的是 经 topK 扩展后的 source_row_id 中每个元素 到 按专家分组的 sorted_row_id 之间每个元素的映射关系。 unpermute the row order in permuted matrix issorted_row_id,各个expert 完成对应的 gemm 计算...
test_m_grouped_gemm_contiguous() File "/home/bnell/DeepGEMM/tests/test_bug.py", line 67, in test_m_grouped_gemm_contiguous diff = calc_diff(out, ref_out) File "/home/bnell/DeepGEMM/deep_gemm/utils.py", line 142, in calc_diff denominator = (x * x + y * y).sum() Runt...
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...
cutlass/gemm/group_array_problem_shape.hpp" #include "cutlassgemm/collective/collective_builder.hpp" #include "cutlassepilogue/collective/collective_builder.hpp #include "cutlass/gemm/device/gemm_universal_adapterh" #include "cutlass/gemm/kernel/gemm_universal.hpp" # "cutlass/util...
【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...
File "grouped_gemm.py", line 30, in <module> result = gmm.npu_gmm(x.npu(), weight.npu(), bias=None, group_list=group_list, group_type=group_type) File "/tmp/MindSpeed_eef859a70d/mindspeed/ops/gmm.py", line 75, in npu_gmm ...
}elseif(kGemmType==GemmType::GroupedMasked) {returncurr_group_idx*shape_dim+block_idx*block_size; } } However,kIgnoreGroupedForGroupedContiguousseems to be false at the relevant call sites in practice. 1
config.moe_grouped_gemm: 89 + self.local_experts = torch.nn.ModuleList() 90 + for _ in range(self.num_local_experts): 91 + expert = MLP(self.config, submodules, is_expert=True) 92 + self.local_experts.append(expert) 93 + else: 94 + self.expert_parallel = config.expert...