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https://github.com/tile-ai/tilelang.git
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Gongen-Ali
4e53b50750
* Fix some bugs on ci and open rocm ci test * fix pre-commit * fix build issue on hip * refactor on hip * pre-commit fix * fix test with pdl * fix pdl test
58 lines
2.0 KiB
Python
58 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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X_shared = A_shared[:block_M, :block_K]
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X_local = C_local[:block_M, :block_K]
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T.clear(X_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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aliased_offset = T.int32()
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T.let(aliased_offset, ko * block_K)
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T.copy(A[by * block_M, aliased_offset], X_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[:block_N, :block_K])
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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(X_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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kernel.run_once()
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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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