def test_softmax_relu_sigmoid():
"""
Use python pass to transform from Softmax(x) to ReLU(Sigmoid(x)).
NOTE:
Sigmoid pattern only exists in the target.
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_relu_pass():
x = Any()
softmax_pattern = Prim(P.Softmax())
pattern = Call(softmax_pattern, [x])
sigmoid_pattern = Prim(P.Sigmoid())
call_sigmoid = Call(sigmoid_pattern, [x])
relu_pattern = Prim(P.ReLU())
target = Call(relu_pattern, [call_sigmoid])
return pattern, target
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(3)
unregiste_pass(softmax_relu_pass)
assert "ReLU" in transformed_repr
assert "Sigmoid" in transformed_repr
assert "Softmax" 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_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_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_1():
"""
Test IsNot pattern which expresses the IsNot semantics.
Case: IsNot pattern matches with the graph
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def single_bn_pass():
"""
Sub a BN which does NOT take MatMul as inputs to ReLU6.
"""
matmul = Prim("MatMul")
pattern_0 = NoneOf(matmul)
softmax = P.Softmax()
pattern = Call(softmax, [pattern_0])
relu6 = P.ReLU6()
target = Call(relu6, [pattern_0])
return pattern, target
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(5)
unregiste_pass(single_bn_pass)
assert "ReLU6" 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_isin_pattern_1():
"""
Test IsIn. IsIn is used as nested inputs for the target in this case.
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_neg_pass():
x = Any()
softmax_pattern = Prim(P.Softmax())
call_softmax = Call(softmax_pattern, [x])
relu_pattern = Prim(P.ReLU())
call_relu = Call(relu_pattern, [x])
pattern = OneOf([call_softmax, call_relu])
neg_ops = Prim(P.Neg())
target = Call(neg_ops, [pattern])
return pattern, target
transformed_repr = get_func_graph(softmax_model,
inputs).get_return().expanded_str(4)
unregiste_pass(softmax_neg_pass)
assert "Neg" in transformed_repr
assert "Softmax" in transformed_repr
def test_isin_pattern_0():
"""
Test IsIn pattern which expresses the IsIn/OneOf semantics.
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_relu_pass():
x = Any()
softmax_pattern = Prim(P.Softmax())
call_softmax = Call(softmax_pattern, [x])
relu_pattern = Prim(P.ReLU())
call_relu = Call(relu_pattern, [x])
pattern = OneOf([call_softmax, call_relu])
relu6_pattern = Prim(P.ReLU6())
target = Call(relu6_pattern, [x])
return pattern, target
transformed_repr = get_func_graph(softmax_model,
inputs).get_return().expanded_str(2)
unregiste_pass(softmax_relu_pass)
assert "ReLU6" in transformed_repr
assert "Softmax" not in transformed_repr
def test_imm_pattern():
"""
Test NewParameter pattern in the target
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_addn_pass():
x = AnyPattern()
softmax = P.Softmax()
pattern = CallWith(softmax, inputs=[x])
imm = Imm(0)
target_0 = CallWith("make_tuple",
inputs=[pattern],
should_replace=False)
target = CallWith("tuple_getitem",
inputs=[target_0, imm],
should_replace=False)
return pattern, target
transformed_repr = get_func_graph(softmax_model,
inputs).get_return().expanded_str(5)
print(transformed_repr)
unregiste_pass(softmax_addn_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)
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 = IsPrimTypeOf("Conv2D")
conv2d = CallWith(conv2d_prim)
pattern_0 = IsNot(conv2d)
pattern = CallWith(P.BatchNorm(), inputs=[pattern_0])
target = CallWith(P.ReLU6(), inputs=[pattern_0])
return pattern, target
@registe_pass(run_only_once=True)
def bn_pass():
"""
Sub a BN to Softmax.
"""
bn = P.BatchNorm()
pattern = CallWith(bn)
softmax = P.Softmax()
target = CallWith(softmax, should_replace=False)
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)
def test_prim():
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_relu_pass():
x = Any()
sigmoid_softmax_pattern = Prim([P.Sigmoid(), P.Softmax()])
pattern = Call(sigmoid_softmax_pattern, [x])
target = Call(P.ReLU(), [x])
return pattern, target
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(3)
unregiste_pass(softmax_relu_pass)
assert "ReLU" in transformed_repr
assert "Softmax" not in transformed_repr
def test_softmax_relu():
"""
Use python pass to transform from Softmax to ReLU.
"""
inputs = Tensor(np.ones([42]), mindspore.float16)
softmax_model = nn.Softmax()
@registe_pass(run_only_once=True)
def softmax_relu_pass():
x = Any()
pattern = Call(P.Softmax(), [x])
target = Call(P.ReLU(), [x])
return pattern, target
transformed_repr = get_func_graph(softmax_model, inputs).get_return().expanded_str(2)
unregiste_pass(softmax_relu_pass)
assert "ReLU" in transformed_repr
assert "Softmax" not in transformed_repr
