mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
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.
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
import tilelang
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import tilelang.language as T
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# add decorator @tilelang.jit if you want to return a torch function
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# @tilelang.jit
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def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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@T.prim_func
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def main(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((N, K), dtype),
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C: T.Tensor((M, N), dtype),
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):
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_N, block_K), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=0):
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# Copy tile of A
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# This is a sugar syntax for parallelized copy
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.clear(A_shared)
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# Demonstrate parallelized copy from global to shared for B
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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# Perform a tile-level GEMM on the shared buffers
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# Currently we dispatch to the cute/hip on Nvidia/AMD GPUs
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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# Copy result back to global memory
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def run_matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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program = matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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kernel = tilelang.compile(program, out_idx=[2])
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import torch
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a = torch.randn((M, K), dtype=dtype.as_torch()).cuda()
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b = torch.randn((N, K), dtype=dtype.as_torch()).cuda()
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c = kernel(a, b)
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assert torch.allclose(c, torch.zeros_like(c))
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def test_matmul():
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run_matmul(1024, 1024, 1024, 128, 128, 32)
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if __name__ == "__main__":
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test_matmul()
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