[REFACTOR/PASS] Formalize argument bind and match util #214
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ZihengJiang
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Jul 4, 2017
rohanmukh
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Jul 16, 2021
…#214) * Add shape function for mlas_matmul * Fix lint * Add dynamic shape checking for mlas AlterOpLayout * Add testing for dynamic shape checking * Fix dense alterOpLayout
ylc
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Sep 28, 2021
…#214) * Add shape function for mlas_matmul * Fix lint * Add dynamic shape checking for mlas AlterOpLayout * Add testing for dynamic shape checking * Fix dense alterOpLayout
vinx13
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Mar 9, 2022
Liam-Sturge
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Feb 3, 2023
This patch undoes the change that was put in place to prevent the build and installation of NNPACK from failing due to a renaming of the default branch to main by the NNPACK external dependency cpuinfo. See apache#13871 The issue has been fixed at the source by PR apache#214, so the change to `ubuntu_install_nnpack.sh` is no longer required: Maratyszcza/NNPACK#214
driazati
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Feb 7, 2023
This patch undoes the change that was put in place to prevent the build and installation of NNPACK from failing due to a renaming of the default branch to main by the NNPACK external dependency cpuinfo. See #13871 The issue has been fixed at the source by PR #214 which is now merged in to NNPACK, so the change to `ubuntu_install_nnpack.sh` is no longer required: Maratyszcza/NNPACK#214
LeiWang1999
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Nov 8, 2024
…e#214) * Refactor tilelang dequantize module and add matmul_blocked_weight_only function * remove un-implemented code. * Implement BaseScheduler to wrap some related items. * lint fix * test skip * Refactor tilelang dequantize module and add matmul_blocked_weight_only function * test fix * hardware tuning demo * remove debug related items. * imlement tuner and cache fix * lint fix * test case fix. * Adapt Tuning Space generation with Roller * lint fix * Refactor select_scheduler function for fine-grained interface The select_scheduler function in the dense/__init__.py module has been refactored to use a fine-grained interface. This change provides more flexibility and enables the implementation of high-performance kernels. Update MatmulScheduler class in matmul_tensorcore.py The MatmulScheduler class in the matmul_tensorcore.py module has been updated to calculate the number of threads based on the block size and warp size. This ensures optimal GPU warp configuration for NVIDIA GPUs. Improve test_general_matmul_tilelang_kernel.py The test_general_matmul_tilelang_kernel.py module has been improved to include additional test cases and assertions for correctness. * Refactor select_scheduler function for fine-grained interface * Refactor NotImplementedError message in BaseTLHint class * Update submodule reference in 3rdparty/tvm * Refactor matmul_finetune function to use topk=20 for hardware-aware finetuning * Refactor submodule reference in 3rdparty/tvm * lint fix * Refactor test_general_matmul_tilelang_impl.py and test_tilelang_gemm.py * Refactor MatmulConfig to enable weight propagation on supported devices * Refactor test_general_matmul_tilelang_impl.py and test_general_matmul_tilelang_kernel.py to use centered random values for input tensors * test fix * test fix * Refactor flash attention tests to use centered random values for input tensors * Refactor flash attention tests to use centered random values for input tensors * Refactor flash attention tests to skip test if flash_attn is not installed * lint fix * test fix * test fix * test fix * Refactor quantization module imports * lint fix * Update yapf version in requirements-dev.txt and requirements-test.txt * Refactor shared memory to global memory storage in MatmulFineGrainScheduler * test fix * format * test fix * Refactor tensorcore policy to use list comprehension for readability * lint fix
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add arg_binder.h for argument binding utilities