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https://github.com/tile-ai/tilelang.git
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92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
from tilelang import tvm as tvm
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import tilelang as tl
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import tilelang.language as T
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import tilelang.testing
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def modify(
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with_B: bool = False,
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with_bias: bool = False,
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):
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@T.prim_func
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def main(
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A: T.Tensor((64, 64)),
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B: T.Tensor((64, 64)),
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C: T.Tensor((64, 64)),
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D: T.Tensor((64, 64)),
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bias: T.Tensor((64, 64)),
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):
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if with_B:
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if with_bias:
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T.gemm(A, bias, D)
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T.gemm(A, B, D)
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else:
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with T.block():
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A_shared = T.alloc_shared((64, 64), dtype="float32")
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C_shared = T.alloc_shared((64, 64), dtype="float32")
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D_shared = T.alloc_shared((64, 64), dtype="float32")
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T.copy(A, A_shared)
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T.copy(C, C_shared)
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T.gemm(A_shared, C_shared, D_shared)
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T.copy(D_shared, D)
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return main
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def test_modify(with_B=False, with_bias=False):
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tester = modify(with_B=with_B, with_bias=with_bias)
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mod = tvm.IRModule({tester.attrs["global_symbol"]: tester})
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mod2 = tl.transform.Simplify()(mod)
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assert mod != mod2
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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.handle,
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b: T.handle,
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c: T.handle,
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):
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A = T.match_buffer(a, (M, K), dtype=dtype)
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B = T.match_buffer(b, (K, N), dtype=dtype)
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C = T.match_buffer(c, (M, N), dtype=accum_dtype)
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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 k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
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T.copy(A[by * block_M, k * block_K], A_shared)
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T.copy(B[k * 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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def test_matmul():
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func = matmul(1024, 1024, 1024, 128, 128, 32)
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mod = tvm.IRModule({func.attrs["global_symbol"]: func})
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mod = tl.transform.Simplify()(mod)
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kernel = tl.compile(mod["main"], out_idx=[2])
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import torch
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a = torch.randn(1024, 1024, dtype=torch.float16).cuda().half()
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b = torch.randn(1024, 1024, dtype=torch.float16).cuda().half()
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c = kernel(a, b)
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ref_c = a @ b
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ref_c = ref_c.float()
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torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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# Get CUDA Source
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print(kernel.get_kernel_source())
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if __name__ == "__main__":
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tilelang.testing.main()
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