mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-20 06:30:07 +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.
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
import torch
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang as tl
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import tilelang.language as T
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@tl.jit
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def tensor_null_test(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32, with_bias=False):
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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((K, N), dtype),
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C: T.Tensor((M, N), accum_dtype),
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Bias: T.Tensor((N), accum_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=3):
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# Copy tile of A
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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if with_bias:
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for i, j in T.Parallel(block_M, block_N):
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C_local[i, j] += Bias[bx * block_N + j]
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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_test(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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a = torch.randn(M, K, device="cuda", dtype=dtype.as_torch())
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b = torch.randn(N, K, device="cuda", dtype=dtype.as_torch())
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c = torch.zeros(M, N, device="cuda", dtype=accum_dtype.as_torch())
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kernel = tensor_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype, with_bias=False)
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kernel(a, b, c, None)
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def test_nullptr():
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run_test(1024, 1024, 1024, 128, 128, 32)
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if __name__ == "__main__":
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tilelang.testing.main()
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