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Change unittest to use flattened buffer. #18

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Nov 13, 2021
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Change unittest to use flattened buffer. #18

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40 changes: 19 additions & 21 deletions tests/python/sparsetir/test_tir_sparse_correctness.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,11 +22,7 @@


@T.prim_func
def csrmm(a: T.handle, b: T.handle, c: T.handle, indptr: T.handle, indices: T.handle) -> None:
n = T.var("int32")
m = T.var("int32")
k = T.var("int32")
nnz = T.var("int32")
def csrmm(a: T.handle, b: T.handle, c: T.handle, indptr: T.handle, indices: T.handle, m: T.int32, n: T.int32, k: T.int32, nnz: T.int32) -> None:
I = T.dense_fixed(m)
J = T.sparse_variable((n, m + 1, nnz), (indptr, indices), "int32")
K = T.dense_fixed(k)
Expand All @@ -40,26 +36,22 @@ def csrmm(a: T.handle, b: T.handle, c: T.handle, indptr: T.handle, indices: T.ha


@T.prim_func
def csrmm_tir(a: T.handle, b: T.handle, c: T.handle, indptr: T.handle, indices: T.handle) -> None:
def csrmm_tir(a: T.handle, b: T.handle, c: T.handle, indptr: T.handle, indices: T.handle, M: T.int32, N: T.int32, K: T.int32, nnz: T.int32) -> None:
T.func_attr({"global_symbol": "main", "tir.noalias": True})
n = T.var("int32")
m = T.var("int32")
k = T.var("int32")
nnz = T.var("int32")
A_data = T.match_buffer(a, (nnz,), "float32")
B = T.match_buffer(b, (n, k), "float32")
C = T.match_buffer(c, (m, k), "float32")
A_indptr = T.match_buffer(indptr, (m + 1,), "int32")
B = T.match_buffer(b, (N * K,), "float32")
C = T.match_buffer(c, (M * K,), "float32")
A_indptr = T.match_buffer(indptr, (M + 1,), "int32")
A_indices = T.match_buffer(indices, (nnz,), "int32")
for i, k in T.grid(m, k):
for i, k in T.grid(M, K):
with T.block("spmm_outer"):
vi, vk = T.axis.remap("SS", [i, k])
with T.init():
C[vi, vk] = 0.
C[vi * K + vk] = 0.
for j in T.serial(0, A_indptr[vi + 1] - A_indptr[vi]):
with T.block("spmm_inner"):
vj = T.axis.R(n, j + A_indptr[vi])
C[vi, vk] = C[vi, vk] + A_data[vj] * B[A_indices[vj], vk]
vj = T.axis.R(N, j + A_indptr[vi])
C[vi * K + vk] = C[vi * K + vk] + A_data[vj] * B[A_indices[vj] * K + vk]


def test_csrmm():
Expand All @@ -70,7 +62,12 @@ def test_csrmm():
y = np.zeros((4096, 256)).astype("float32")

# specialize function
sch = tir.Schedule(csrmm_tir)
_, _, _, _, _, m, n, k, nnz = csrmm_tir.params
sch = tir.Schedule(
csrmm_tir.specialize(
{m: 4096, n: 4096, k: 256, nnz: A.nnz}
)
)
blk_outer = sch.get_block("spmm_outer")
i, k = sch.get_loops(blk_outer)
sch.bind(i, "blockIdx.x")
Expand All @@ -80,14 +77,15 @@ def test_csrmm():
A_indptr = tvm.nd.array(A.indptr.astype("int32"), device=tvm.cuda(0))
A_indices = tvm.nd.array(A.indices.astype("int32"), device=tvm.cuda(0))
A_data = tvm.nd.array(A.data.astype("float32"), device=tvm.cuda(0))
X_nd = tvm.nd.array(x, device=tvm.cuda(0))
Y_nd = tvm.nd.array(y, device=tvm.cuda(0))
X_nd = tvm.nd.array(x.reshape(-1), device=tvm.cuda(0))
Y_nd = tvm.nd.array(y.reshape(-1), device=tvm.cuda(0))

# build function
f = tvm.build(sch.mod, target='cuda')
f(A_data, X_nd, Y_nd, A_indptr, A_indices)

assert np.allclose(y_ground_truth, Y_nd.numpy())
# assertion
assert np.allclose(y_ground_truth.reshape(-1), Y_nd.numpy())


if __name__ == "__main__":
Expand Down