def softmax_relu_pass():
x = AnyPattern()
softmax_pattern = IsPrimTypeOf(P.Softmax())
pattern = CallWith(softmax_pattern, inputs=[x])
relu_pattern = IsPrimTypeOf(P.ReLU(), should_replace=False)
target = CallWith(relu_pattern, inputs=[x])
return pattern, target
def softmax_make_tuple_pass():
x = AnyPattern()
softmax = P.Softmax()
pattern = CallWith(softmax, inputs=[x])
target = CallWith("make_tuple",
inputs=[pattern, new_para],
should_replace=False)
return pattern, target
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
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
def single_bn_pass():
"""
Sub a BN which does NOT take MatMul as inputs to ReLU6.
"""
matmul = IsPrimTypeOf("MatMul")
pattern_0 = IsNot(matmul)
softmax = P.Softmax()
pattern = CallWith(softmax, inputs=[pattern_0])
relu6 = P.ReLU6()
target = CallWith(relu6, inputs=[pattern_0], should_replace=False)
return pattern, target
def softmax_neg_pass():
x = AnyPattern()
softmax_pattern = IsPrimTypeOf(P.Softmax())
call_softmax = CallWith(softmax_pattern, inputs=[x])
relu_pattern = IsPrimTypeOf(P.ReLU())
call_relu = CallWith(relu_pattern, inputs=[x])
pattern = IsIn([call_softmax, call_relu])
neg_ops = IsPrimTypeOf(P.Neg(), should_replace=False)
target = CallWith(neg_ops, inputs=[pattern])
return pattern, target
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
def softmax_addn_pass():
x = AnyPattern()
softmax = P.Softmax()
pattern = CallWith(softmax, inputs=[x])
weight_tensor = Tensor(np.zeros([42]), mindspore.float16)
new_weight = NewTensor(weight_tensor)
addn_ops = P.AddN()
target = CallWith(addn_ops,
inputs=[x, new_weight],
should_replace=False)
return pattern, target
def softmax_addn_pass():
x = AnyPattern()
softmax = P.Softmax()
pattern = CallWith(softmax, inputs=[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 = CallWith(P.MatMul(),
inputs=[new_para_0, new_para_1],
should_replace=False)
target = CallWith("make_tuple",
inputs=[target_0],
should_replace=False)
return pattern, target