#include "core/registration.h" #include "moe_ops.h" TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) { // Apply topk softmax to the gating outputs. m.def( "topk_softmax(Tensor! topk_weights, Tensor! topk_indices, Tensor! " "token_expert_indices, Tensor gating_output) -> ()"); m.impl("topk_softmax", torch::kCUDA, &topk_softmax); // Calculate the result of moe by summing up the partial results // from all selected experts. m.def("moe_sum(Tensor! input, Tensor output) -> ()"); m.impl("moe_sum", torch::kCUDA, &moe_sum); // Aligning the number of tokens to be processed by each expert such // that it is divisible by the block size. m.def( "moe_align_block_size(Tensor topk_ids, int num_experts," " int block_size, Tensor! sorted_token_ids," " Tensor! experts_ids," " Tensor! num_tokens_post_pad) -> ()"); m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size); // temporarily adapted from // https://github.com/sgl-project/sglang/commit/ded9fcd09a43d5e7d5bb31a2bc3e9fc21bf65d2a m.def( "sgl_moe_align_block_size(Tensor topk_ids, int num_experts," " int block_size, Tensor! sorted_token_ids," " Tensor! experts_ids," " Tensor! num_tokens_post_pad) -> ()"); m.impl("sgl_moe_align_block_size", torch::kCUDA, &sgl_moe_align_block_size); #ifndef USE_ROCM m.def( "marlin_gemm_moe(Tensor! a, Tensor! b_q_weights, Tensor! sorted_ids, " "Tensor! topk_weights, Tensor! topk_ids, Tensor! b_scales, Tensor! " "b_zeros, Tensor! g_idx, Tensor! perm, Tensor! workspace, " "int b_q_type, SymInt size_m, " "SymInt size_n, SymInt size_k, bool is_k_full, int num_experts, int " "topk, " "int moe_block_size, bool replicate_input, bool apply_weights)" " -> Tensor"); // conditionally compiled so impl registration is in source file #endif } REGISTER_EXTENSION(TORCH_EXTENSION_NAME)