def test_gen_new_parameter():
"""
Test gen_new_parameter
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
default_tensor = Tensor(np.ones((4, 4)), mindspore.float32)
new_para = NewParameter("Merlin", default_tensor)
set_renorm(False)
set_reopt(False)
gen_new_parameter(new_para)
@registe_pass(requires_grad=False, run_only_once=True)
def softmax_make_tuple_pass():
x = Any()
softmax = P.Softmax()
pattern = Call(softmax, [x])
target = Call("make_tuple", [pattern, new_para])
return pattern, target
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
assert "Merlin" in transformed_repr
unregiste_pass(softmax_make_tuple_pass)
cancel_new_parameter(new_para)
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
assert "Merlin" not in transformed_repr
def test_newparameter_pattern():
"""
Test NewParameter pattern in the target
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
set_renorm(False)
set_reopt(False)
@registe_pass(requires_grad=False, run_only_once=True)
def softmax_addn_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
default_tensor0 = Tensor(np.ones((4, 4)), mindspore.float32)
default_tensor1 = Tensor(np.ones((4, 4)), mindspore.float32)
new_para_0 = NewParameter("Merlin", default_tensor0)
new_para_1 = NewParameter("Arthur", default_tensor1)
target_0 = Call(P.MatMul(), [new_para_0, new_para_1])
target = Call("make_tuple", [target_0])
return pattern, target
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
unregiste_pass(softmax_addn_pass)
assert "MatMul" in transformed_repr
assert "make_tuple" in transformed_repr
assert "Softmax" not in transformed_repr
def test_newtensor_pattern():
"""
Test NewTensor pattern in the target
"""
set_renorm(False)
set_reopt(False)
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_addn_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
weight_tensor = Tensor(np.zeros([42]), mindspore.float16)
new_weight = NewTensor(weight_tensor)
target = Call(P.AddN(), [x, new_weight])
return pattern, target
transformed_repr = get_func_graph(softmax_model,
inputs).get_return().expanded_str(2)
unregiste_pass(softmax_addn_pass)
assert "AddN" in transformed_repr
assert "Softmax" not in transformed_repr
set_renorm(True)
def test_imm_target():
"""
Test NewParameter pattern in the target
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
set_renorm(False)
set_reopt(False)
@registe_pass(run_only_once=True)
def softmax_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
imm = Imm(0)
target_0 = Call("make_tuple", [pattern])
target = Call("tuple_getitem", [target_0, imm])
return pattern, target
transformed_repr = get_func_graph(softmax_model,
inputs).get_return().expanded_str(5)
unregiste_pass(softmax_pass)
assert "make_tuple" in transformed_repr
assert "tuple_getitem" in transformed_repr
assert "Softmax" in transformed_repr
def test_isnot_pattern_0():
"""
Test IsNot pattern which expresses the IsNot semantics.
Case: IsNot pass failed to match
"""
set_renorm(False)
set_reopt(False)
class ConvBN(nn.Cell):
def __init__(self):
super(ConvBN, self).__init__()
self.conv = P.Conv2D(32, 3)
self.conv_weight = Tensor(np.ones([32, 32, 3, 3]),
mindspore.float32)
self.scale = Tensor(np.ones([32]), mindspore.float32)
self.bias = Tensor(np.ones([32]), mindspore.float32)
self.mean = Tensor(np.ones([32]), mindspore.float32)
self.variance = Tensor(np.ones([32]), mindspore.float32)
self.bn = P.BatchNorm()
def construct(self, x):
x = self.conv(x, self.conv_weight)
x = self.bn(x, self.scale, self.bias, self.mean, self.variance)
return x
inputs = Tensor(np.random.normal(0, 1, (10, 32, 32, 32)),
mindspore.float32)
conv_bn_model = ConvBN()
@registe_pass(run_only_once=True)
def single_bn_pass():
"""
Sub a BN which does NOT take Conv as inputs to ReLU6.
"""
conv2d_prim = Prim("Conv2D")
conv2d = Call(conv2d_prim)
pattern_0 = NoneOf(conv2d)
pattern = Call(P.BatchNorm(), [pattern_0])
target = Call(P.ReLU6(), [pattern_0])
return pattern, target
@registe_pass(run_only_once=True)
def bn_pass():
"""
Sub a BN to Softmax.
"""
pattern = Call(P.BatchNorm())
target = Call(P.Softmax())
return pattern, target
transformed_repr = get_func_graph(conv_bn_model,
inputs).get_return().expanded_str(5)
unregiste_pass(single_bn_pass)
unregiste_pass(bn_pass)
assert "ReLU6" not in transformed_repr
assert "Softmax" in transformed_repr
set_renorm(True)