def test_extend_inplace(self): mySymbolicMatricesList1 = TypedListType( TensorType(aesara.config.floatX, (False, False)))() mySymbolicMatricesList2 = TypedListType( TensorType(aesara.config.floatX, (False, False)))() z = Extend()(mySymbolicMatricesList1, mySymbolicMatricesList2) m = aesara.compile.mode.get_default_mode().including( "typed_list_inplace_opt") f = aesara.function( [ In(mySymbolicMatricesList1, borrow=True, mutable=True), mySymbolicMatricesList2, ], z, mode=m, ) assert f.maker.fgraph.toposort()[0].op.inplace x = random_ranged(-1000, 1000, [100, 101]) y = random_ranged(-1000, 1000, [100, 101]) assert np.array_equal(f([x], [y]), [x, y])
def test_insert_inplace(self): mySymbolicMatricesList = TypedListType( TensorType(aesara.config.floatX, (False, False)))() mySymbolicIndex = scalar(dtype="int64") mySymbolicMatrix = matrix() z = Insert()(mySymbolicMatricesList, mySymbolicIndex, mySymbolicMatrix) m = aesara.compile.mode.get_default_mode().including( "typed_list_inplace_opt") f = aesara.function( [ In(mySymbolicMatricesList, borrow=True, mutable=True), mySymbolicIndex, mySymbolicMatrix, ], z, accept_inplace=True, mode=m, ) assert f.maker.fgraph.toposort()[0].op.inplace x = random_ranged(-1000, 1000, [100, 101]) y = random_ranged(-1000, 1000, [100, 101]) assert np.array_equal(f([x], np.asarray(1, dtype="int64"), y), [x, y])
def test_filter_sanity_check(self): # Simple test on typed list type filter myType = TypedListType(TensorType(aesara.config.floatX, (False, False))) x = random_ranged(-1000, 1000, [100, 100]) assert np.array_equal(myType.filter([x]), [x])
def test_load_alot(self): myType = TypedListType(TensorType(aesara.config.floatX, (False, False))) x = random_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(TensorType(aesara.config.floatX, (False, False))) myType = TypedListType(myNestedType) x = random_ranged(-1000, 1000, [100, 100]) assert np.array_equal(myType.filter([[x]]), [[x]])
mode=mode_no_scipy, ) 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, ) TestErfinvBroadcast = makeBroadcastTester( op=at.erfinv, expected=expected_erfinv, good={ "normal": [random_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, ) TestErfcinvBroadcast = makeBroadcastTester( op=at.erfcinv, expected=expected_erfcinv, good={ "normal": [random_ranged(0.001, 1.9, (2, 3))], "empty": [np.asarray([], dtype=config.floatX)], }, grad=_grad_broadcast_unary_0_2_no_complex,
mode=mode_no_scipy, ) 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, ) TestErfinvBroadcast = makeBroadcastTester( op=aet.erfinv, expected=expected_erfinv, good={ "normal": [random_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, ) TestErfcinvBroadcast = makeBroadcastTester( op=aet.erfcinv, expected=expected_erfcinv, good={ "normal": [random_ranged(0.001, 1.9, (2, 3))], "empty": [np.asarray([], dtype=config.floatX)], }, grad=_grad_broadcast_unary_0_2_no_complex,