parallel supports more elementary-wise operators

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -90,6 +90,7 @@ REGISTER(TensorAddInfo);
 REGISTER(BiasAddInfo);
 REGISTER(MulInfo);
 REGISTER(DivInfo);
+REGISTER(ModInfo);
 REGISTER(RealDivInfo);
 REGISTER(PowInfo);
 REGISTER(ExpInfo);
@@ -117,15 +118,56 @@ REGISTER(MaximumInfo);
 REGISTER(MinimumInfo);
 REGISTER(CastInfo);
 REGISTER(GreaterInfo);
+REGISTER(GreaterEqualInfo);
+REGISTER(LessEqualInfo);
+REGISTER(LessInfo);
+REGISTER(ApproximateEqualInfo);
 REGISTER(SparseSoftmaxCrossEntropyWithLogitsInfo);
 REGISTER(AssignSubInfo);
+REGISTER(FloorModInfo);
+REGISTER(AssignInfo);
+REGISTER(AssignAddInfo);
+REGISTER(Atan2Info);
+REGISTER(DivNoNanInfo);
+REGISTER(LogicalAndInfo);
+REGISTER(LogicalOrInfo);
+REGISTER(EluInfo);
 REGISTER(ReLUInfo);
+REGISTER(ReLU6Info);
+REGISTER(ReLUV2Info);
+REGISTER(SoftplusInfo);
+REGISTER(SoftsignInfo);
 REGISTER(GatherV2Info);
 REGISTER(SparseGatherV2Info);
 REGISTER(SqrtInfo);
 REGISTER(SigmoidInfo);
 REGISTER(GetNextInfo);
 REGISTER(NegInfo);
+REGISTER(AbsInfo);
+REGISTER(AcoshInfo);
+REGISTER(AsinInfo);
+REGISTER(AsinhInfo);
+REGISTER(AtanInfo);
+REGISTER(AtanhInfo);
+REGISTER(CeilInfo);
+REGISTER(CoshInfo);
+REGISTER(Expm1Info);
+REGISTER(Log1pInfo);
+REGISTER(SinInfo);
+REGISTER(SinhInfo);
+REGISTER(TanInfo);
+REGISTER(RsqrtInfo);
+REGISTER(InvInfo);
+REGISTER(ReciprocalInfo);
+REGISTER(RoundInfo);
+REGISTER(FloorInfo);
+REGISTER(SignInfo);
+REGISTER(ErfInfo);
+REGISTER(ErfcInfo);
+REGISTER(ZerosLikeInfo);
+REGISTER(OnesLikeInfo);
+REGISTER(BesselI0eInfo);
+REGISTER(BesselI1eInfo);
 REGISTER(BatchMatMulInfo);
 REGISTER(ExpandDimsInfo);
 REGISTER(SqueezeInfo);

mindspore/ccsrc/frontend/parallel/ops_info/activation_info.h

@@ -131,6 +131,13 @@ class LogSoftmaxInfo : public Softmax {
   ~LogSoftmaxInfo() override = default;
 };
 
+class EluInfo : public ActivationOther {
+ public:
+  EluInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~EluInfo() override = default;
+};
+
 class ReLUInfo : public ActivationOther {
  public:
   ReLUInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
@@ -139,6 +146,38 @@ class ReLUInfo : public ActivationOther {
   ~ReLUInfo() override = default;
 };
 
+class ReLU6Info : public ActivationOther {
+ public:
+  ReLU6Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ReLU6Info() override = default;
+};
+
+class ReLUV2Info : public ActivationOther {
+ public:
+  ReLUV2Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+             const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ReLUV2Info() override = default;
+};
+
+class SoftsignInfo : public ActivationOther {
+ public:
+  SoftsignInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+               const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SoftsignInfo() override = default;
+};
+
+class SoftplusInfo : public ActivationOther {
+ public:
+  SoftplusInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+               const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SoftplusInfo() override = default;
+};
+
 class CastInfo : public ActivationOther {
  public:
   CastInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,

mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.h

@@ -82,6 +82,13 @@ class DivInfo : public ArithmeticBase {
   ~DivInfo() override = default;
 };
 
+class ModInfo : public ArithmeticBase {
+ public:
+  ModInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
+  ~ModInfo() override = default;
+};
+
 class RealDivInfo : public ArithmeticBase {
  public:
   RealDivInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
@@ -98,6 +105,14 @@ class FloorDivInfo : public ArithmeticBase {
   ~FloorDivInfo() override = default;
 };
 
+class FloorModInfo : public ArithmeticBase {
+ public:
+  FloorModInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+               const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
+  ~FloorModInfo() override = default;
+};
+
 class PowInfo : public ArithmeticBase {
  public:
   PowInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
@@ -105,14 +120,6 @@ class PowInfo : public ArithmeticBase {
   ~PowInfo() override = default;
 };
 
-class GreaterInfo : public ArithmeticBase {
- public:
-  GreaterInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
-              const PrimitiveAttrs &attrs)
-      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
-  ~GreaterInfo() override = default;
-};
-
 class AssignSubInfo : public ArithmeticBase {
  public:
   AssignSubInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
@@ -121,6 +128,22 @@ class AssignSubInfo : public ArithmeticBase {
   ~AssignSubInfo() override = default;
 };
 
+class AssignInfo : public ArithmeticBase {
+ public:
+  AssignInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+             const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~AssignInfo() override = default;
+};
+
+class AssignAddInfo : public ArithmeticBase {
+ public:
+  AssignAddInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~AssignAddInfo() override = default;
+};
+
 // All dimensions can be split arbitrarily, but the split method of Logits should be the same as that of label.
 class SigmoidCrossEntropyWithLogitsInfo : public ArithmeticBase {
  public:
@@ -129,6 +152,38 @@ class SigmoidCrossEntropyWithLogitsInfo : public ArithmeticBase {
       : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
   ~SigmoidCrossEntropyWithLogitsInfo() override = default;
 };
+
+class Atan2Info : public ArithmeticBase {
+ public:
+  Atan2Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~Atan2Info() override = default;
+};
+
+class DivNoNanInfo : public ArithmeticBase {
+ public:
+  DivNoNanInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+               const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
+  ~DivNoNanInfo() override = default;
+};
+
+class LogicalAndInfo : public ArithmeticBase {
+ public:
+  LogicalAndInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                 const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~LogicalAndInfo() override = default;
+};
+
+class LogicalOrInfo : public ArithmeticBase {
+ public:
+  LogicalOrInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~LogicalOrInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/frontend/parallel/ops_info/comparison_function_info.h

@@ -36,6 +36,14 @@ class EqualInfo : public ArithmeticBase {
   ~EqualInfo() override = default;
 };
 
+class ApproximateEqualInfo : public ArithmeticBase {
+ public:
+  ApproximateEqualInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                       const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~ApproximateEqualInfo() override = default;
+};
+
 class NotEqualInfo : public ArithmeticBase {
  public:
   NotEqualInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
@@ -59,6 +67,38 @@ class MinimumInfo : public ArithmeticBase {
       : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(true)) {}
   ~MinimumInfo() override = default;
 };
+
+class GreaterInfo : public ArithmeticBase {
+ public:
+  GreaterInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+              const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~GreaterInfo() override = default;
+};
+
+class GreaterEqualInfo : public ArithmeticBase {
+ public:
+  GreaterEqualInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                   const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~GreaterEqualInfo() override = default;
+};
+
+class LessInfo : public ArithmeticBase {
+ public:
+  LessInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~LessInfo() override = default;
+};
+
+class LessEqualInfo : public ArithmeticBase {
+ public:
+  LessEqualInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                const PrimitiveAttrs &attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<ArithmeticCost>(false)) {}
+  ~LessEqualInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/frontend/parallel/ops_info/elementary_function_info.h

@@ -63,6 +63,202 @@ class LogicalNotInfo : public ActivationOther {
       : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
   ~LogicalNotInfo() override = default;
 };
+
+class AbsInfo : public ActivationOther {
+ public:
+  AbsInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~AbsInfo() override = default;
+};
+
+class SignInfo : public ActivationOther {
+ public:
+  SignInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SignInfo() override = default;
+};
+
+class FloorInfo : public ActivationOther {
+ public:
+  FloorInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~FloorInfo() override = default;
+};
+
+class RoundInfo : public ActivationOther {
+ public:
+  RoundInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~RoundInfo() override = default;
+};
+
+class ReciprocalInfo : public ActivationOther {
+ public:
+  ReciprocalInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                 const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ReciprocalInfo() override = default;
+};
+
+class InvInfo : public ActivationOther {
+ public:
+  InvInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~InvInfo() override = default;
+};
+
+class RsqrtInfo : public ActivationOther {
+ public:
+  RsqrtInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~RsqrtInfo() override = default;
+};
+
+class TanInfo : public ActivationOther {
+ public:
+  TanInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~TanInfo() override = default;
+};
+
+class SinInfo : public ActivationOther {
+ public:
+  SinInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SinInfo() override = default;
+};
+
+class SinhInfo : public ActivationOther {
+ public:
+  SinhInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~SinhInfo() override = default;
+};
+
+class Log1pInfo : public ActivationOther {
+ public:
+  Log1pInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~Log1pInfo() override = default;
+};
+
+class Expm1Info : public ActivationOther {
+ public:
+  Expm1Info(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~Expm1Info() override = default;
+};
+
+class CoshInfo : public ActivationOther {
+ public:
+  CoshInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~CoshInfo() override = default;
+};
+
+class CeilInfo : public ActivationOther {
+ public:
+  CeilInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~CeilInfo() override = default;
+};
+
+class AtanhInfo : public ActivationOther {
+ public:
+  AtanhInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~AtanhInfo() override = default;
+};
+
+class AtanInfo : public ActivationOther {
+ public:
+  AtanInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~AtanInfo() override = default;
+};
+
+class AsinInfo : public ActivationOther {
+ public:
+  AsinInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~AsinInfo() override = default;
+};
+
+class AsinhInfo : public ActivationOther {
+ public:
+  AsinhInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~AsinhInfo() override = default;
+};
+
+class AcoshInfo : public ActivationOther {
+ public:
+  AcoshInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+            const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~AcoshInfo() override = default;
+};
+
+class ErfInfo : public ActivationOther {
+ public:
+  ErfInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ErfInfo() override = default;
+};
+
+class ErfcInfo : public ActivationOther {
+ public:
+  ErfcInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+           const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ErfcInfo() override = default;
+};
+
+class ZerosLikeInfo : public ActivationOther {
+ public:
+  ZerosLikeInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~ZerosLikeInfo() override = default;
+};
+
+class OnesLikeInfo : public ActivationOther {
+ public:
+  OnesLikeInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+               const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~OnesLikeInfo() override = default;
+};
+
+class BesselI0eInfo : public ActivationOther {
+ public:
+  BesselI0eInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~BesselI0eInfo() override = default;
+};
+
+class BesselI1eInfo : public ActivationOther {
+ public:
+  BesselI1eInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                const PrimitiveAttrs &attrs)
+      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
+  ~BesselI1eInfo() override = default;
+};
+
 }  // namespace parallel
 }  // namespace mindspore
 

tests/ut/python/parallel/test_arithmetic.py

@@ -122,17 +122,39 @@ def test_matmul_mul():
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
     compile_net(net, x, y, b)
 
-
+def test_matmul_mod():
-def test_matmul_div():
     class Net(nn.Cell):
         def __init__(self, strategy1, strategy2):
             super().__init__()
             self.matmul = P.MatMul().set_strategy(strategy1)
-            self.div = P.Div().set_strategy(strategy2)
+            self.mod = P.Mod().set_strategy(strategy2)
 
         def construct(self, x, y, b):
             out = self.matmul(x, y)
-            out = self.div(out, b)
+            out = self.mod(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+def test_matmul_floormod():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.floormod = P.FloorMod().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.floormod(out, b)
             return out
 
     context.set_auto_parallel_context(device_num=8, global_rank=0)
@@ -147,16 +169,122 @@ def test_matmul_div():
     compile_net(net, x, y, b)
 
 
-def test_matmul_greater():
+def test_matmul_atan2():
     class Net(nn.Cell):
         def __init__(self, strategy1, strategy2):
             super().__init__()
             self.matmul = P.MatMul().set_strategy(strategy1)
-            self.greater = P.Greater().set_strategy(strategy2)
+            self.atan2 = P.Atan2().set_strategy(strategy2)
 
         def construct(self, x, y, b):
             out = self.matmul(x, y)
-            out = self.greater(out, b)
+            out = self.atan2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_divNoNan():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.divNoNan = P.DivNoNan().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.divNoNan(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_logicaland():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.equal = P.Equal().set_strategy(strategy2)
+            self.notequal = P.NotEqual().set_strategy(strategy2)
+            self.logical = P.LogicalAnd().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out1 = self.equal(out, b)
+            out = self.matmul(x, y)
+            out2 = self.notequal(out, b)
+            out = self.logical(out1, out2)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_logicalor():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.equal = P.Equal().set_strategy(strategy2)
+            self.notequal = P.NotEqual().set_strategy(strategy2)
+            self.logical = P.LogicalOr().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out1 = self.equal(out, b)
+            out = self.matmul(x, y)
+            out2 = self.notequal(out, b)
+            out = self.logical(out1, out2)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_div():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.div = P.Div().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.div(out, b)
             return out
 
     context.set_auto_parallel_context(device_num=8, global_rank=0)
@@ -528,3 +656,97 @@ def test_assign_sub():
     net = SubGradWrap(SubNetWithLoss(Net()))
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     compile_sub_net(net, x)
+
+
+def test_assign_add():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.assign_sub = P.AssignAdd()
+            self.mul = P.Mul()
+            self.mul_weight = Parameter(Tensor(np.full([128, 32],
+                                                       0.5, dtype=np.float32)),
+                                        name="mul_weight")
+            self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
+                                                             1.1, dtype=np.float32)),
+                                              name="assignsub_weight")
+
+        def construct(self, x):
+            out = self.mul(x, self.mul_weight)
+            out = self.assign_sub(self.assignsub_weight, out)
+            return out
+
+    class SubNetWithLoss(nn.Cell):
+        def __init__(self, network):
+            super(SubNetWithLoss, self).__init__()
+            self.loss = VirtualLoss()
+            self.network = network
+
+        def construct(self, x):
+            predict = self.network(x,)
+            return self.loss(predict)
+
+    class SubGradWrap(nn.Cell):
+        def __init__(self, network):
+            super(SubGradWrap, self).__init__()
+            self.network = network
+
+        def construct(self, x):
+            return grad_all(self.network)(x)
+
+    def compile_sub_net(net, x):
+        net.set_auto_parallel()
+        _executor.compile(net, x)
+
+    context.set_auto_parallel_context(device_num=64, global_rank=15)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    net = SubGradWrap(SubNetWithLoss(Net()))
+    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    compile_sub_net(net, x)
+
+
+def test_assign():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.assign_sub = P.Assign()
+            self.mul = P.Mul()
+            self.mul_weight = Parameter(Tensor(np.full([128, 32],
+                                                       0.5, dtype=np.float32)),
+                                        name="mul_weight")
+            self.assignsub_weight = Parameter(Tensor(np.full([128, 32],
+                                                             1.1, dtype=np.float32)),
+                                              name="assignsub_weight")
+
+        def construct(self, x):
+            out = self.mul(x, self.mul_weight)
+            out = self.assign_sub(self.assignsub_weight, out)
+            return out
+
+    class SubNetWithLoss(nn.Cell):
+        def __init__(self, network):
+            super(SubNetWithLoss, self).__init__()
+            self.loss = VirtualLoss()
+            self.network = network
+
+        def construct(self, x):
+            predict = self.network(x,)
+            return self.loss(predict)
+
+    class SubGradWrap(nn.Cell):
+        def __init__(self, network):
+            super(SubGradWrap, self).__init__()
+            self.network = network
+
+        def construct(self, x):
+            return grad_all(self.network)(x)
+
+    def compile_sub_net(net, x):
+        net.set_auto_parallel()
+        _executor.compile(net, x)
+
+    context.set_auto_parallel_context(device_num=64, global_rank=15)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    net = SubGradWrap(SubNetWithLoss(Net()))
+    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
+    compile_sub_net(net, x)

tests/ut/python/parallel/test_comparison_function_info.py

@@ -98,6 +98,126 @@ def test_matmul_not_equal():
     compile_net(net, x, y, b)
 
 
+def test_matmul_approximateEqual():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.approximateEqual = P.ApproximateEqual(tolerance=0.5).set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.approximateEqual(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_greater():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.greater = P.Greater().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.greater(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_greaterEqual():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.greaterEqual = P.GreaterEqual().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.greaterEqual(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_less():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.less = P.Less().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.less(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_lessEqual():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.lessEqual = P.LessEqual().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.lessEqual(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
 def test_matmul_not_equal_repeated_calculation():
     class Net(nn.Cell):
         def __init__(self, strategy1, strategy2):

tests/ut/python/parallel/test_element_wise_function.py

@@ -129,6 +129,652 @@ def test_matmul_log():
     b = Tensor(np.ones([64, 64]), dtype=ms.float32)
     compile_net(net, x, y, b)
 
+def test_matmul_abs():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.abs = P.Abs().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.abs(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+def test_matmul_sign():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.sign = P.Sign().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.sign(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_floor():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.floor = P.Floor().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.floor(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+def test_matmul_round():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.round = P.Round().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.round(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_reciprocal():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.reciprocal = P.Reciprocal().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.reciprocal(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_inv():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.inv = P.Inv().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.inv(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_rsqrt():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.rsqrt = P.Rsqrt().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.rsqrt(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_tan():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.tan = P.Tan().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.tan(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_sin():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.sin = P.Sin().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.sin(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_sinh():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.sinh = P.Sinh().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.sinh(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_log1p():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.log1p = P.Log1p().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.log1p(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_expm1():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.expm1 = P.Expm1().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.expm1(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_cosh():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.cosh = P.Cosh().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.cosh(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+def test_matmul_erf():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.erf = P.Erf().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.erf(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(1, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(1, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(1, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_erfc():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.erfc = P.Erfc().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.erfc(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(1, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(1, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(1, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_zeroslike():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.zeroslike = P.ZerosLike().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.zeroslike(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(1, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(1, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(1, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_oneslike():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.oneslike = P.OnesLike().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.oneslike(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(1, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(1, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(1, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_BesselI0e():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.BesselI0e = P.BesselI0e().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.BesselI0e(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(1, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(1, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(1, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_BesselI1e():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.BesselI1e = P.BesselI1e().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.BesselI1e(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(1, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(1, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(1, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_ceil():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.Ceil = P.Ceil().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.Ceil(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_atan():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.atan = P.Atan().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.atan(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_Atanh():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.atanh = P.Atanh().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.atanh(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_asin():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.asin = P.Asin().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.asin(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_asinh():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.asinh = P.Asinh().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.asinh(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
+
+def test_matmul_acosh():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.acosh = P.Acosh().set_strategy(strategy2)
+            self.matmul2 = P.MatMul().set_strategy(strategy1)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.acosh(out)
+            out = self.matmul2(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2),)
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.random.uniform(-5, 5, size=(128, 32)), dtype=ms.float32)
+    y = Tensor(np.random.uniform(-5, 5, size=(32, 64)), dtype=ms.float32)
+    b = Tensor(np.random.uniform(-5, 5, size=(64, 64)), dtype=ms.float32)
+    compile_net(net, x, y, b)
+
 
 def test_matmul_logical_not():
     class Net(nn.Cell):