├── .gitignore ├── README.md ├── cat.png ├── codegen ├── op_build.py └── relay_build.py ├── compile_tvm_in_docker.md ├── dataflow_controlflow ├── calculate_by_python.py └── calculate_by_tensorflow.py ├── mlir-ods └── MLIR ODS要点总结.md ├── optimize_gemm ├── README.md ├── auto_scheduler │ ├── gemm.json │ └── gemm.py ├── cpufp │ ├── LICENSE │ ├── README.md │ ├── build.sh │ ├── clean.sh │ ├── cpufp_kernel_x86.h │ ├── cpufp_kernel_x86_avx.s │ ├── cpufp_kernel_x86_avx512_vnni.s │ ├── cpufp_kernel_x86_avx512f.s │ ├── cpufp_kernel_x86_fma.s │ ├── cpufp_kernel_x86_sse.s │ ├── cpufp_x86.c │ ├── cpuid_x86.cpp │ ├── smtl.c │ └── smtl.h ├── how_to_optimize_gemm │ ├── MMult0.h │ ├── MMult1.h │ ├── MMult2.h │ ├── MMult_1x4_3.h │ ├── MMult_1x4_4.h │ ├── MMult_1x4_5.h │ ├── MMult_1x4_6.h │ ├── MMult_1x4_7.h │ ├── MMult_1x4_8.h │ ├── MMult_1x4_9.h │ ├── MMult_1x4x6.h │ ├── MMult_4x4_10.h │ ├── MMult_4x4_11.h │ ├── MMult_4x4_12.h │ ├── MMult_4x4_13.h │ ├── MMult_4x4_14.h │ ├── MMult_4x4_3.h │ ├── MMult_4x4_4.h │ ├── MMult_4x4_5.h │ ├── MMult_4x4_6.h │ ├── MMult_4x4_7.h │ ├── MMult_4x4_8.h │ ├── Makefile │ ├── README.md │ ├── dclock.h │ ├── matrix_multiply_origin.h │ ├── now.txt │ ├── plot_gflops.py │ └── test_matrix_multiply.cpp ├── optimize_matmul_in_gemm │ ├── tvm_autoschedule_tune.py │ ├── tvm_autotvm_tune.py │ ├── tvm_without_tune.py │ ├── tvm_without_tune_blocking_vectorize.py │ ├── tvm_without_tune_blocking_vectorize_reorder.py │ ├── tvm_without_tune_blocking_vectorize_reorder_pack.py │ ├── tvm_without_tune_blocking_vectorize_reorder_pack_write_cache.py │ ├── tvm_without_tune_default_schedule.py │ └── tvm_without_tune_only_blocking.py └── sgemm_kernel │ ├── build.sh │ ├── clean.sh │ ├── main.c │ ├── sgemm_kernel_x64_fma.S │ └── sgemm_kernel_x64_fma.c ├── paper_reading ├── Ansor Generating High-Performance Tensor Programs for Deep Learning.md ├── MLIR: A Complier Infrastructure for the End of Moore's Law.md ├── PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections.md └── Roller: Fast and Efficient Tensor Compilation for Deep Learning.md ├── pytorch_resnet18_export_onnx.py ├── relay ├── README.md ├── darknet_yolo.py ├── jetsonnano │ ├── README.md │ ├── deploy_model_on_jetsonnano.py │ ├── jetson_detect_video.py │ ├── server_cross_compile.py │ └── server_rpc_tune.py ├── sample_ir_module.py ├── simplenet.ipynb ├── simplenet.py ├── tune_relay_cuda.py └── use_pass_infra.py ├── scheduler ├── bind.py ├── cache_read.py ├── cache_write.py ├── compute_at.py ├── compute_inline.py ├── compute_root.py ├── create_group.py ├── fuse.py ├── parallel.py ├── pragma.py ├── prefetch.py ├── reorder.py ├── rfactor.py ├── set_scope.py ├── set_store_predicate.py ├── split.py ├── storage_align.py ├── tensor_expr_get_started.py ├── tensorize.py ├── tile.py ├── unroll.py └── vectorize.py ├── torchscript ├── loop_script_onnx.py ├── loop_trace_onnx.py ├── script.py └── trace.py ├── tvm_onnx_resnet18_inference.py └── 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