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Lei Wang
9c95a42dfb
* Remove redundant `map_torch_type` in favor of `T.dtype.as_torch()` `map_torch_type` was a standalone function that converted dtype strings or TVM DataType objects to `torch.dtype`. This was redundant since `T.dtype.as_torch()` (and `T.dtype(x).as_torch()`) already provides the same functionality with better handling (HIP/CUDA awareness, proper dtype lookup tables, etc.). Changes: - Remove `map_torch_type` from `tilelang/utils/tensor.py` and its re-export - Make `T.dtype()` idempotent for existing dtype objects (pass-through) - Add backward-compat for `"e4m3fnuz_float8"` TVM internal name in `as_torch()` - Replace all usages across production code, tests, and examples - Rebuild Cython extension to reflect the import change * Refactor output and accumulation data types in GEMM FP8 example Updated the `out_dtype` and `accum_dtype` variables in the `example_tilelang_gemm_fp8_intrinsic.py` file to use `T.float32` instead of string literals. This change enhances type consistency and aligns with recent updates in type handling.