def test_argmax_pushdown():
x = matrix()
for sm in [softmax_graph, softmax_legacy]:
# test that the max_and_argmax is pushed down if the max is not used
out = max_and_argmax(sm(exp(tanh(sigmoid(x)))), axis=-1)[1]
fgraph = FunctionGraph([x], [out])
optdb.query(OPT_FAST_RUN).optimize(fgraph)
# print 'AFTER'
# for node in fgraph.toposort():
# print node.op
assert len(fgraph.toposort()) == 1
assert isinstance(fgraph.toposort()[0].op, Argmax)
assert check_stack_trace(fgraph, ops_to_check=Argmax)
x = matrix()
# test that the max_and_argmax is not pushed down if the max is used
out = max_and_argmax(sm(exp(tanh(sigmoid(x)))), axis=-1)[0]
fgraph = FunctionGraph([x], [out])
assert hasattr(fgraph.outputs[0].tag, "trace")
optdb.query(OPT_FAST_RUN).optimize(fgraph)
# print 'AFTER'
# for node in fgraph.toposort():
# print node.op
assert len(fgraph.toposort()) == 3
assert isinstance(fgraph.toposort()[0].op, Elemwise)
assert isinstance(fgraph.toposort()[1].op, Softmax)
assert isinstance(fgraph.toposort()[2].op, CAReduce)
assert isinstance(fgraph.toposort()[2].op.scalar_op,
aesara.scalar.ScalarMaximum)
def test_equal_computations():
a, b = iscalars(2)
with pytest.raises(ValueError):
equal_computations([a], [a, b])
assert equal_computations([a], [a])
assert equal_computations([at.as_tensor(1)], [at.as_tensor(1)])
assert not equal_computations([b], [a])
assert not equal_computations([at.as_tensor(1)], [at.as_tensor(2)])
assert equal_computations([2], [2])
assert equal_computations([np.r_[2, 1]], [np.r_[2, 1]])
assert equal_computations([np.r_[2, 1]], [at.as_tensor(np.r_[2, 1])])
assert equal_computations([at.as_tensor(np.r_[2, 1])], [np.r_[2, 1]])
assert not equal_computations([2], [a])
assert not equal_computations([np.r_[2, 1]], [a])
assert not equal_computations([a], [2])
assert not equal_computations([a], [np.r_[2, 1]])
assert equal_computations([NoneConst], [NoneConst])
m = matrix()
max_argmax1 = max_and_argmax(m)
max_argmax2 = max_and_argmax(m)
assert equal_computations(max_argmax1, max_argmax2)
def test_argmax_pushdown_bias():
x = matrix()
b = vector()
out = argmax(softmax_with_bias(x, b), axis=-1)
fgraph = FunctionGraph([x, b], [out])
optdb.query(OPT_FAST_RUN).optimize(fgraph)
types_to_check = (DimShuffle, Elemwise, Argmax)
assert len(fgraph.toposort()) == 3
for i, type in enumerate(types_to_check):
assert isinstance(fgraph.toposort()[i].op, type)
assert check_stack_trace(fgraph, ops_to_check=types_to_check)
x = matrix()
b = vector()
out = max_and_argmax(softmax_with_bias(x, b), axis=-1)[0]
fgraph = FunctionGraph([x, b], [out])
optdb.query(OPT_FAST_RUN).optimize(fgraph)
assert len(fgraph.toposort()) == 2
assert isinstance(fgraph.toposort()[0].op, SoftmaxWithBias)
assert isinstance(fgraph.toposort()[1].op, CAReduce)
assert isinstance(fgraph.toposort()[1].op.scalar_op,
aesara.scalar.ScalarMaximum)
assert check_stack_trace(fgraph, ops_to_check=(SoftmaxWithBias, CAReduce))
def test_optimization(self):
# If we use only the max output, we should replace this op with
# a faster one.
mode = aesara.compile.mode.get_default_mode().including(
"canonicalize", "fast_run")
for axis in [0, 1, -1]:
n = matrix()
f = function([n], max_and_argmax(n, axis)[0], mode=mode)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, CAReduce)
f = function([n], max_and_argmax(n, axis), mode=mode)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, MaxAndArgmax)