def test_insert_inplace(self):
mySymbolicMatricesList = TypedListType(
tt.TensorType(theano.config.floatX, (False, False)))()
mySymbolicIndex = tt.scalar(dtype="int64")
mySymbolicMatrix = tt.matrix()
z = Insert()(mySymbolicMatricesList, mySymbolicIndex, mySymbolicMatrix)
m = theano.compile.mode.get_default_mode().including(
"typed_list_inplace_opt")
f = theano.function(
[
In(mySymbolicMatricesList, borrow=True, mutable=True),
mySymbolicIndex,
mySymbolicMatrix,
],
z,
accept_inplace=True,
mode=m,
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x], np.asarray(1, dtype="int64"), y), [x, y])
def test_extend_inplace(self):
mySymbolicMatricesList1 = TypedListType(
tt.TensorType(theano.config.floatX, (False, False)))()
mySymbolicMatricesList2 = TypedListType(
tt.TensorType(theano.config.floatX, (False, False)))()
z = Extend()(mySymbolicMatricesList1, mySymbolicMatricesList2)
m = theano.compile.mode.get_default_mode().including(
"typed_list_inplace_opt")
f = theano.function(
[
In(mySymbolicMatricesList1, borrow=True, mutable=True),
mySymbolicMatricesList2,
],
z,
mode=m,
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x], [y]), [x, y])
def test_filter_sanity_check(self):
# Simple test on typed list type filter
myType = TypedListType(
tt.TensorType(theano.config.floatX, (False, False)))
x = rand_ranged(-1000, 1000, [100, 100])
assert np.array_equal(myType.filter([x]), [x])
def test_load_alot(self):
myType = TypedListType(
tt.TensorType(theano.config.floatX, (False, False)))
x = rand_ranged(-1000, 1000, [10, 10])
testList = []
for i in range(10000):
testList.append(x)
assert np.array_equal(myType.filter(testList), testList)
def test_basic_nested_list(self):
# Testing nested list with one level of depth
myNestedType = TypedListType(
tt.TensorType(theano.config.floatX, (False, False)))
myType = TypedListType(myNestedType)
x = rand_ranged(-1000, 1000, [100, 100])
assert np.array_equal(myType.filter([[x]]), [[x]])
def test_reverse_inplace(self):
mySymbolicMatricesList = TypedListType(
tt.TensorType(aesara.config.floatX, (False, False)))()
z = Reverse()(mySymbolicMatricesList)
m = aesara.compile.mode.get_default_mode().including(
"typed_list_inplace_opt")
f = aesara.function(
[In(mySymbolicMatricesList, borrow=True, mutable=True)],
z,
accept_inplace=True,
mode=m,
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x, y]), [y, x])
)
TestErfcxInplaceBroadcast = makeBroadcastTester(
op=inplace.erfcx_inplace,
expected=expected_erfcx,
good=_good_broadcast_unary_normal_float_no_complex_small_neg_range,
eps=2e-10,
mode=mode_no_scipy,
inplace=True,
skip=skip_scipy,
)
TestErfinvBroadcast = makeBroadcastTester(
op=aet.erfinv,
expected=expected_erfinv,
good={
"normal": [rand_ranged(-0.9, 0.9, (2, 3))],
"empty": [np.asarray([], dtype=config.floatX)],
},
grad=_grad_broadcast_unary_abs1_no_complex,
eps=2e-10,
mode=mode_no_scipy,
skip=skip_scipy,
)
TestErfcinvBroadcast = makeBroadcastTester(
op=aet.erfcinv,
expected=expected_erfcinv,
good={
"normal": [rand_ranged(0.001, 1.9, (2, 3))],
"empty": [np.asarray([], dtype=config.floatX)],
},
)
TestErfcxInplaceBroadcast = makeBroadcastTester(
op=inplace.erfcx_inplace,
expected=expected_erfcx,
good=_good_broadcast_unary_normal_float_no_complex_small_neg_range,
eps=2e-10,
mode=mode_no_scipy,
inplace=True,
skip=skip_scipy,
)
TestErfinvBroadcast = makeBroadcastTester(
op=aet.erfinv,
expected=expected_erfinv,
good={
"normal": [rand_ranged(-0.9, 0.9, (2, 3))],
"empty": [np.asarray([], dtype=config.floatX)],
},
grad=_grad_broadcast_unary_abs1_no_complex,
eps=2e-10,
mode=mode_no_scipy,
skip=skip_scipy,
)
TestErfcinvBroadcast = makeBroadcastTester(
op=aet.erfcinv,
expected=expected_erfcinv,
good={
"normal": [rand_ranged(0.001, 1.9, (2, 3))],
"empty": [np.asarray([], dtype=config.floatX)],
},
