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60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import tilelang
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang.language as T
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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=0):
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T.copy(A[by * block_M, ko * block_K], A_shared, coalesced_width=2)
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T.copy(B[ko * block_K, bx * block_N], B_shared, coalesced_width=2)
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for i, j, k in T.grid(block_M, block_N, block_K):
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C_local[i, j] += A_shared[i, k] * B_shared[k, j]
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T.copy(C_local, C[by * block_M, bx * block_N], coalesced_width=2)
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return main
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def assert_gemm_codegen(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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dtype="float16",
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accum_dtype="float",
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):
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func = matmul(M, N, K, block_M, block_N, block_K, dtype=dtype, accum_dtype=accum_dtype)
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# Because the current pass context have been polluted by previous testing.
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with tvm.transform.PassContext(), tvm.target.Target("webgpu"):
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artifact = tilelang.lower(func, target="webgpu")
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src_code = artifact.kernel_source
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assert src_code is not None
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def test_gemm_codegen():
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assert_gemm_codegen(1024, 1024, 1024, 16, 16, 16)
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if __name__ == "__main__":
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tilelang.testing.main()
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