Standard Multi-Head Attention . . . . . . . . . . . . . . . . 6 2.1.2 Low-Rank Key-Value Joint Compression . . . . . . . . . . . . . . . . . . . 7 2.1.3 Decoupled Rotary Position Embedding . . . . . . of both worlds, we introduce MLA, an attention mechanism equipped with low-rank key-value joint compression. Empirically, MLA achieves superior performance compared with MHA, and meanwhile significantly innovative archi- tectures. For attention, we design MLA, which utilizes low-rank key-value joint compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference

TOPI Schedule Primitives & Optimizations Symbols NNVM & Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer Schedule explorer Cost model [direct]) def conv2d_pvr(cfg, data, kernel, strides, padding, dilation, layout, out_dtype): #Describe algorithm with tensor expression language'; #Return the out operation w How to compute. @autotvm.register_

● Simple and easy to implement 👍 ● One op per subgraph results in overhead 👎 (working on an algorithm to merge annotated ops) Graph-level annotation ● High flexibility and allow multiple ops in a Affiliates. All rights reserved. ● Send PRs to the upstream ● Improve graph partitioning ● An algorithm to merge supported operators Next Steps Target Device Relay IR Graph Annotation with Your Annotator