mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +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.
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import torch
|
|
import tilelang.testing
|
|
import tilelang.language as T
|
|
|
|
|
|
def calc_diff(x, y):
|
|
x, y = x.double(), y.double()
|
|
denominator = (x * x + y * y).sum()
|
|
sim = 2 * (x * y).sum() / denominator
|
|
return 1 - sim
|
|
|
|
|
|
def matmul_nt(M, N, K, bM, bN, bK, in_dtype, out_dtype, accum_dtype):
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor((M, K), in_dtype),
|
|
B: T.Tensor((N, K), in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, bN), T.ceildiv(M, bM), threads=128) as (bx, by):
|
|
A_shared = T.alloc_shared((bM, bK), in_dtype)
|
|
B_shared = T.alloc_shared((bN, bK), in_dtype)
|
|
C_local = T.alloc_fragment((bM, bN), accum_dtype)
|
|
|
|
T.clear(C_local)
|
|
for k in T.Pipelined(T.ceildiv(K, bK), num_stages=3):
|
|
T.copy(A[by * bM, k * bK], A_shared)
|
|
T.copy(B[bx * bN, k * bK], B_shared)
|
|
T.gemm(A_shared, B_shared, C_local, transpose_B=True)
|
|
|
|
T.copy(C_local, C[by * bM, bx * bN])
|
|
|
|
return main
|
|
|
|
|
|
def assert_matmul_correctness(M, N, K, block_M, block_N, block_K, in_dtype, out_dtype, accum_dtype):
|
|
func = matmul_nt(M, N, K, block_M, block_N, block_K, in_dtype, out_dtype, accum_dtype)
|
|
kernel = tilelang.compile(func, out_idx=-1)
|
|
|
|
A = torch.randn(M, K).to(T.dtype(in_dtype).as_torch()).cuda()
|
|
B = torch.randn(N, K).to(T.dtype(in_dtype).as_torch()).cuda()
|
|
|
|
C = kernel(A, B)
|
|
|
|
ref_c = torch.matmul(A.to(T.dtype(accum_dtype).as_torch()), B.T.to(T.dtype(accum_dtype).as_torch())).to(T.dtype(out_dtype).as_torch())
|
|
print(C)
|
|
print(ref_c)
|
|
diff = calc_diff(C, ref_c)
|
|
print(f"diff: {diff}")
|
|
assert diff < 1e-3
|
|
|
|
|
|
@tilelang.testing.requires_cuda
|
|
@tilelang.testing.requires_cuda_compute_version(9)
|
|
def test_assert_matmul():
|
|
assert_matmul_correctness(1024, 1024, 1024, 128, 128, 64, T.float8_e4m3fn, T.float32, T.float32)
|
|
assert_matmul_correctness(1024, 1024, 1024, 128, 128, 64, T.float8_e5m2, T.float32, T.float32)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|