def test_quantitatively(sdfg, graph):
A = np.random.rand(N.get()).astype(np.float64)
B = np.random.rand(M.get()).astype(np.float64)
C = np.random.rand(O.get()).astype(np.float64)
out1_base = np.ndarray((N.get(), M.get()), np.float64)
out2_base = np.ndarray((1), np.float64)
out3_base = np.ndarray((N.get(), M.get(), O.get()), np.float64)
out1 = np.ndarray((N.get(), M.get()), np.float64)
out2 = np.ndarray((1), np.float64)
out3 = np.ndarray((N.get(), M.get(), O.get()), np.float64)
csdfg = sdfg.compile()
csdfg(A=A,
B=B,
C=C,
out1=out1_base,
out2=out2_base,
out3=out3_base,
N=N,
M=M,
O=O)
expand_reduce(sdfg, graph)
expand_maps(sdfg, graph)
subgraph = SubgraphView(graph, [node for node in graph.nodes()])
assert SubgraphFusion.match(sdfg, subgraph) == True
fusion(sdfg, graph)
sdfg.validate()
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C, out1=out1, out2=out2, out3=out3, N=N, M=M, O=O)
assert np.allclose(out1, out1_base)
assert np.allclose(out2, out2_base)
assert np.allclose(out3, out3_base)
print('PASS')
def test_quantitatively(sdfg):
graph = sdfg.nodes()[0]
A = np.random.rand(N.get()).astype(np.float64)
B = np.random.rand(N.get()).astype(np.float64)
C1 = np.random.rand(N.get()).astype(np.float64)
C2 = np.random.rand(N.get()).astype(np.float64)
D1 = np.random.rand(N.get()).astype(np.float64)
D2 = np.random.rand(N.get()).astype(np.float64)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C1, D=D1, N=N)
subgraph = SubgraphView(graph, [node for node in graph.nodes()])
expansion = MultiExpansion()
fusion = SubgraphFusion()
assert expansion.match(sdfg, subgraph) == True
expansion.apply(sdfg, subgraph)
assert fusion.match(sdfg, subgraph) == True
fusion.apply(sdfg, subgraph)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C2, D=D2, N=N)
assert np.allclose(C1, C2)
assert np.allclose(D1, D2)
def test_p1():
N.set(20)
M.set(30)
O.set(50)
P.set(40)
Q.set(42)
R.set(25)
sdfg = test_program.to_sdfg()
sdfg.apply_strict_transformations()
state = sdfg.nodes()[0]
A = np.random.rand(N.get()).astype(np.float64)
B = np.random.rand(M.get()).astype(np.float64)
C = np.random.rand(O.get()).astype(np.float64)
D = np.random.rand(M.get()).astype(np.float64)
E = np.random.rand(N.get()).astype(np.float64)
F = np.random.rand(P.get()).astype(np.float64)
G = np.random.rand(M.get()).astype(np.float64)
H = np.random.rand(P.get()).astype(np.float64)
I = np.random.rand(N.get()).astype(np.float64)
J = np.random.rand(R.get()).astype(np.float64)
X = np.random.rand(N.get()).astype(np.float64)
Y = np.random.rand(M.get()).astype(np.float64)
Z = np.random.rand(P.get()).astype(np.float64)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C, D=D, E=E, F=F, G=G, H=H, I=I, J=J, X=X, Y=Y, Z=Z,\
N=N, M=M, O=O, P=P, R=R,Q=Q)
subgraph = SubgraphView(state, [node for node in state.nodes()])
expansion = MultiExpansion()
fusion = SubgraphFusion()
assert MultiExpansion.match(sdfg, subgraph)
expansion.apply(sdfg, subgraph)
assert SubgraphFusion.match(sdfg, subgraph)
fusion.apply(sdfg, subgraph)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C, D=D, E=E, F=F, G=G, H=H, I=I, J=J, X=X, Y=Y, Z=Z,\
N=N, M=M, O=O, P=P, R=R,Q=Q)
print("PASS")
