anyrenwei
5 years ago
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RELEASE.md
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| # Release 1.1.1 | |||
| ## Major Features and Improvements | |||
| * Enable Tensor core when processing GEMM operators in AKG by using poly to create the schedule needed by tensor core pass automatically; | |||
| * Implemented an akg mma lib with inlined ptx codes instead of wmma interface of cuda; | |||
| * Enable one-dimensional mapping to optimize memory promotion. | |||
| ## Bugfixes | |||
| * Fix Segmentation fault in Mapping OuterBand in mindspore (!321). | |||
| * Fix bugs for memory promotion issues (!306). | |||
| * Fix bugs during gen tuning space for scalar ops (!326). | |||
| ## Contributors | |||
| Thanks goes to these wonderful people: | |||
| chengyun, chendeshi, chenlei_autodiff, gengzhen, hanhuifeng, lvwenyuan, lishanni513, hujiahui8, polyhedral, shiliang, wYann, xixixian, xxxxxxw, xuhui, xiaruijie, yangsijia, yiyanzhi, zhangzhaochuang, zhengzuohe | |||
| Contributions of any kind are welcome! | |||
| # Release 1.1.0 | |||
| ## Major Features and Improvements | |||
| * GPU operators improvements | |||
| * Propose a new strategy to handle the reduction operators: The reduce axises would be detected and rescheduled as a seperated band in the schedule tree and then mapping to blocks, then it will call the akg_reduce_lib which using atomic operation to do reduction in the codegen pass. The experimental results show that AKG improves the execution performance relative to cudnn in the large shape cases; | |||
| * Optimize the auto-tiling algorithms which can improve the performance of reduction operators dramatically in most scenarios. | |||
| * Support AutoTuning for composite operators on GPU; | |||
| * Refactor composite framework to enable optimization in DSL level; | |||
| * Enhance CSE to support eliminating redundant vmadd on Ascend; | |||
| * Update scipy to 1.5.3. | |||
| ## Bugfixes | |||
| * TensorAdd support FRACTAL_NZ and DefaultFormat(!228). | |||
| * GPU fix cast: fp32 -> uint8(!216). | |||
| * bugfix: Fix bug in opt_broadcast(!272). | |||
| * fix vadds for int32(!250). | |||
| ## Contributors | |||
| Thanks goes to these wonderful people: | |||
| chengyun, chendeshi, chenlei_autodiff, gaoxiong, gengzhen, guanxiaowei, hanhuifeng, laekov, luoyin, lvwenyuan, liuchang, lishanni513, lingyunli63, polyhedral, shiliang, wYann, wangrao124, xiaruijie, xixixian, xuhui, 要术甲杰, yiyanzhi_akane, yangshuo, yangsijia, zhangzhaochuang, zhengzuohe, zhangrenwei, zengzitao | |||
| Contributions of any kind are welcome! | |||
| # Release 1.0.0 | |||
| ## Major Features and Improvements | |||
| * GPU Support | |||
| * AKG now can generate gpu cuda kernel with no-schedule by using polyhedral techniques, which will create initial schedule, tile outerBands, map with blocks and threads and memory promotion automatically in the AutoPoly pass. | |||
| * Some primitive and fused operators(most are element-wise operators and reduce operators) were added, as well as corresponding testcases. | |||
| * Schedule-templates enhancement | |||
| * Optimize the TVM original schedule-templates to get better performance in some reduce cases. | |||
| * Support fusing multi-outputs into one kernel for element-wise operators. | |||
| * Davinci Enhancement | |||
| * Eliminate unnecessary broadcast by transforming the element-wise computation, such as `D[i, j] = A[i] + B[i, j] + C[i]` -> `D[i, j] = A[i] + C[i] + B[i, j]`, which satisfies commutative law and associative law. | |||
| * Enhance the pass to_three_address to match more cases for vmadd. | |||
| ## Bugfixes | |||
| * fix a bug that random test case segment_max failed(!127). | |||
| * fix the permisson denied error of rewriting meta_file with same name(!147). | |||
| * fix warning for unsupported gpu built-in ops(!148). | |||
| ## Contributors | |||
| Thanks goes to these wonderful people: | |||
| baita, ConnZhai, gengzhen, guanxiaowei, hanhuifeng, hujiahui8, laekov, lvwenyuan, lishanni513, lingyunli63, polyhedral, wYann, wangrao124, xixixian, xuhui, 要术甲杰, yiyanzhi_akane, yangsijia, zhengzuohe, zhangrenwei, zengzitao | |||
| Contributions of any kind are welcome! | |||
| # Release 0.7.0-beta | |||
| ## Major Features and Improvements | |||
| * Backend refactoring | |||
| * Rewrite instruction args calculation module in EmitInsn by implementing a new computing strategy based on axis spliting, which achieved improvement both on performance and code simplicity. | |||
| ## Bugfixes | |||
| * fix dump code error when running gpu operators and set env MS_AKG_DUMP_CODE=ON(!113). | |||
| ## Contributors | |||
| ## Release 1.2.0 | |||
| ### Bug fixes | |||
| * Fixed local memory promotion for large thread (2980!) | |||
| * Fixed reduce binding dimension issue on gpu platform (ff38!) | |||
| ## Release 1.2.0-rc1 | |||
| ### Major Features and Improvements | |||
| * [STABLE] Rebuild the AKG repository for providing a new way to support ascend backend by linking a static library contained all the ascend passes. (Ascend) | |||
| * [STABLE] Optimize the reduction add operation in ascend backend. (Ascend) | |||
| * [STABLE] Add support for tuning elemwise&&reduction operators. (GPU) | |||
| ### Bug fixes | |||
| * Fixed a problem that data prefetch cannot be enabled by attributes in DSL. | |||
| * Fixed bugs of autotiling algorithms (tiling too small, cannot adapted matmul+bias, etc.) in Ascend platform. | |||
| ### Contributors | |||
| Thanks goes to these wonderful people: | |||
| lvwenyuan, shiliang, xuhui, wYann | |||
| yangsijia, xxxxxxw, polyhedral, zhangrenwei, yiyanzhi, xixixian, hujiahui8, zhengzuohe, lishanni, zhangzhaochuang, xuhui, liuchao, gengzhen, xiaruijie, | |||
| chenlei_autodiff, lingyunli63, wYann, lvwenyuan, peiwenfang, hanhuifeng, gaoxiong, chengyun | |||
| Contributions of any kind are welcome! | |||
| # Release 0.6.0-beta | |||
| ## Major Features and Improvements | |||
| * AutoPoly refactor to support integrating multi-backend targets easily | |||
| * Employ a pass/passmgr framework to manage all the transformations of ISL schedule tree in which transformation such as InitialSchTree and tileOuterBand would be considered as a pass to schedule tree. | |||
| * Refactor some data structure of poly so that they can de-couple with Davinci chips. | |||
| * Backend refactoring | |||
| * Enhance min alignment analysis with more accurate propagate conditions. | |||
| * Finetune pragma using alignment information before EmitInsn pass. | |||
| * Simplify EmitInsn pass by unifying the emit method for different patterns. | |||
| * Change the way of using TVM | |||
| * Delete the repository ktvm and reference TVM directly in sourcecode(third_party/incubator-tvm). | |||
| * Enable GPU operators generation which was tailored in ktvm. | |||
| ## Bugfixes | |||
| * fix wrong hoist problem in multicore loop switch hoist pass(!87). | |||
| * fix scalar rearrange bug(!84). | |||
| * fix matmul tuning and support all space tuning(!73). | |||
| * fix variable broadcast_idx redefinition error when pragma dma_copy is replaced by opt_broadcast(!45). | |||
| * fix the bug in broadcast_rewrite(!22). | |||
| * fix bugs of multi-core processing(!33). | |||
| * fix a bug that extra pipe_barrier inserted in the loop(!30). | |||
| * fix inefficient auto tiling for axis with tail and remove duplicated check(!6). | |||
| ## Contributors | |||
| Thanks goes to these wonderful people: | |||
| brovensmile, chengyun, chenlei_autodiff, chengbin, ConnZhai, fuxiaoteng, gaoxiong, gengzhen, hanhuifeng, KasonChan, luoyin, lvwenyuan, peiwenfang, xuhui, yangsijia, wangzhuo325, wYann | |||
| Contributions of any kind are welcome! | |||
| # Release 0.5.0-beta | |||
| ## Major Features | |||
| * Support auto-schedule and code-generation on Ascend platform. | |||
| * Provide C++ APIs of basic operators used in MindSpore. | |||
| * Support Elementwise-Elementwise, Reduce-Elementwise fusion patterns in Bert. | |||
| * Support LambUpdateWithLR, LambNextMv, BatchMatmul optimazition for Bert. | |||
| ## Initial Version | |||
| * Upload the initial framework | |||
| * Basic support for Ascend910 platform | |||
| * Integration with GraphKernel | |||
| * Basic support for Ascend910 platform and gpu v100 | |||
| * Integration with GraphKernel fusion of MindSpore. | |||