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This pull request introduces several enhancements and bug fixes to the bitblas and testing/python/tilelang modules, including the addition of new classes for tensor core intrinsics, improvements to matrix multiplication functions, and updates to memory allocation in test files.

Enhancements to tensor core intrinsics:

  • Added INT4TensorCoreIntrinEmitter and INT4TensorCoreIntrinEmitterWithLadderTransform classes in bitblas/tl/macro_generator.py to support matrix multiplication with int4 data type.

Improvements to matrix multiplication functions:

  • Updated tl_matmul_with_ladder_weight_only_transform and tl_matmul_with_ladder_weight_only_transform_block_reduce_int4 functions in testing/python/tilelang/test_tilelang_macro_gemm.py to use separate local sizes for A, B, and C matrices. [1] [2]
  • Modified the main function in testing/python/tilelang/test_tilelang_macro_gemm.py to allocate local memory using the new local size variables. [1] [2]

Updates to utility functions:

  • Enhanced make_swizzle_layout function in bitblas/tl/utils.py to include an optional is_smooth parameter for smoother layout transformations.

Subproject updates:

  • Updated the subproject commit for 3rdparty/tvm.

- Adjusted the local fragment sizes for tensor core memory allocation in the MatmulFineGrainScheduler class.
- Updated the allocation sizes for A_local, B_local, and C_local variables based on the new fragment sizes.
- The changes ensure efficient memory utilization and improve performance.

Refactor tensor core memory allocation in MatmulDequantizeFineGrainedScheduler

- Modified the fragment sizes for tensor core memory allocation in the MatmulDequantizeFineGrainedScheduler class.
- Updated the allocation sizes for A_frag, B_frag, and C_frag variables based on the new fragment sizes.
- The changes optimize memory usage and enhance the efficiency of the dequantization process.

Refactor tensor core memory allocation in MatmulDequantizeWeightPropagationScheduler

- Adjusted the fragment sizes for tensor core memory allocation in the MatmulDequantizeWeightPropagationScheduler class.
- Updated the allocation sizes for A_frag, B_frag, B_dequantize_frag, and C_frag variables based on the new fragment sizes.
- The changes improve memory utilization and optimize the weight propagation process.
@LeiWang1999 LeiWang1999 merged commit e94f65d into microsoft:main Nov 1, 2024
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