mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-26 07:22:54 +08:00
Wenhao XieandLeiWang1999
d764dca84b
* [Enhancement] Add compile_flags parameter to JIT kernel and adapter classes for improved compilation control * lint fix * upd * lint fix * fix typo * update typing * update the use case of compile flags * ci fix * fix * Fix CI workflow to correctly activate virtual environment from shared cache directory * use local cache * fix * fix * fix --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
import tilelang
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import tilelang.language as T
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# @tilelang.jit(compile_flags=["-O3", "--use_fast_math", "--expt-relaxed-constexpr"])
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def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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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), 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_K, block_N), 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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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[ko * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local)
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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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M = 1024
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N = 1024
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K = 1024
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block_M = 128
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block_N = 128
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block_K = 32
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func = matmul(M, N, K, block_M, block_N, block_K)
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jit_kernel = tilelang.compile(
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func, out_idx=[2], target="cuda", compile_flags="-O3 --use_fast_math --expt-relaxed-constexpr")
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# or jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda", compile_flags=["-O3", "--use_fast_math", "--expt-relaxed-constexpr"])
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# or jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda", compile_flags=["-O3 --use_fast_math --expt-relaxed-constexpr"])
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import torch
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a = torch.randn(M, K, device="cuda", dtype=torch.float16)
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b = torch.randn(K, N, device="cuda", dtype=torch.float16)
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c = jit_kernel(a, b)
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print(c)
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ref_c = a @ b
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torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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print("Kernel output matches PyTorch reference.")
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