def test_expansion2():
sdfg = expansion2.to_sdfg()
graph = sdfg.nodes()[0]
kwargs = {
'A': np.random.rand(M.get(), N.get()).astype(np.float64),
'B': np.random.rand(M.get(), O.get()).astype(np.float64),
'out1': np.ndarray((M.get(), N.get()), dtype=np.float64),
'out2': np.ndarray((M.get(), O.get()), dtype=np.float64),
'N': N,
'M': M,
'O': O
}
run(sdfg, graph, kwargs)
out1 = kwargs['out1'].copy()
out2 = kwargs['out2'].copy()
kwargs['out1'].fill(0)
kwargs['out2'].fill(0)
expand_maps(sdfg, graph)
run(sdfg, graph, kwargs)
out3 = kwargs['out1'].copy()
out4 = kwargs['out2'].copy()
assert np.linalg.norm(out1) > 0.01
assert np.allclose(out1, out3)
assert np.allclose(out2, out4)
def test_p3(in_transient, out_transient):
sdfg = reduction_test_3.to_sdfg()
sdfg.apply_strict_transformations()
state = sdfg.nodes()[0]
A = np.random.rand(M.get(), N.get()).astype(np.float64)
B = np.random.rand(M.get(), N.get()).astype(np.float64)
C1 = np.zeros([N.get()], dtype=np.float64)
C2 = np.zeros([N.get()], dtype=np.float64)
C3 = np.zeros([N.get()], dtype=np.float64)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C1, N=N, M=M)
del csdfg
expand_reduce(sdfg,
state,
create_in_transient=in_transient,
create_out_transient=out_transient)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C2, N=N, M=M)
del csdfg
expand_maps(sdfg, state)
fusion(sdfg, state)
csdfg = sdfg.compile()
csdfg(A=A, B=B, C=C3, N=N, M=M)
del csdfg
assert np.linalg.norm(C1) > 0.01
assert np.allclose(C1, C2)
assert np.allclose(C1, C3)
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_2fuse():
sdfg = softmax.to_sdfg()
sdfg._name = 'softmax_2part'
sdfg.apply_strict_transformations()
X_in = np.random.rand(H.get(), B.get(), SN.get(),
SM.get()).astype(np.float32)
csdfg = sdfg.compile()
res1 = csdfg(X_in=X_in, H=H, B=B, SN=SN, SM=SM)
subgraph = get_partition(sdfg, sdfg.nodes()[0])
expand_reduce(sdfg, sdfg.nodes()[0], subgraph)
expand_maps(sdfg, sdfg.nodes()[0], subgraph)
fusion(sdfg, sdfg.nodes()[0], subgraph)
csdfg = sdfg.compile()
res2 = csdfg(X_in=X_in, H=H, B=B, SN=SN, SM=SM)
assert np.allclose(res1, res2)
print("PASS")
return
def test_1fuse():
sdfg = softmax.to_sdfg()
sdfg._name = 'softmax_fused'
sdfg.apply_strict_transformations()
X_in = np.random.rand(H.get(), B.get(), SN.get(),
SM.get()).astype(np.float32)
csdfg = sdfg.compile()
res1 = csdfg(X_in=X_in, H=H, B=B, SN=SN, SM=SM)
expand_reduce(sdfg, sdfg.nodes()[0])
expand_maps(sdfg, sdfg.nodes()[0])
fusion(sdfg, sdfg.nodes()[0])
#sdfg.specialize({'SM':SM})
csdfg = sdfg.compile()
res2 = csdfg(X_in=X_in, H=H, B=B, SN=SN, SM=SM)
print(np.linalg.norm(res1))
print(np.linalg.norm(res2))
assert np.allclose(res1, res2)
print("PASS")
return
def test_1fuse():
sdfg = softmax.to_sdfg()
sdfg.name = 'softmax_fused'
sdfg.simplify()
X_in = np.random.rand(H.get(), B.get(), SN.get(), SM.get()).astype(np.float32)
csdfg = sdfg.compile()
res1 = csdfg(X_in=X_in, H=H, B=B, SN=SN, SM=SM)
del csdfg
expand_reduce(sdfg, sdfg.nodes()[0])
expand_maps(sdfg, sdfg.nodes()[0])
fusion(sdfg, sdfg.nodes()[0])
csdfg = sdfg.compile()
res2 = csdfg(X_in=X_in, H=H, B=B, SN=SN, SM=SM)
del csdfg
print(np.linalg.norm(res1))
print(np.linalg.norm(res2))
assert np.allclose(res1, res2)
print("PASS")
return
def test_qualitatively(sdfg, graph):
expand_reduce(sdfg, graph)
expand_maps(sdfg, graph)
fusion(sdfg, graph)
sdfg.validate()
print("PASS")
