diff --git a/mindspore/lite/schema/model.fbs b/mindspore/lite/schema/model.fbs
index 61552ea9dcc..85445584c14 100644
--- a/mindspore/lite/schema/model.fbs
+++ b/mindspore/lite/schema/model.fbs
@@ -273,7 +273,9 @@ union PrimitiveType {
     RandomStandardNormal,
     CropAndResize,
     Erf,
-    StridedSliceGrad
+    StridedSliceGrad,
+    IsFinite,
+    BatchMatMul,
 }
 
 enum QuantType: int {
diff --git a/mindspore/lite/schema/ops.fbs b/mindspore/lite/schema/ops.fbs
index 837f49d18b5..3c972a3667c 100644
--- a/mindspore/lite/schema/ops.fbs
+++ b/mindspore/lite/schema/ops.fbs
@@ -1274,4 +1274,12 @@ table StridedSliceGrad {
 }
 
 table Erf {
+}
+
+table IsFinite {
+}
+
+table BatchMatMul {
+    adj_x : bool = false;
+    adj_y : bool = false;
 }
\ No newline at end of file
diff --git a/mindspore/lite/src/ops/batch_matmul.cc b/mindspore/lite/src/ops/batch_matmul.cc
new file mode 100644
index 00000000000..0a646cac7b9
--- /dev/null
+++ b/mindspore/lite/src/ops/batch_matmul.cc
@@ -0,0 +1,81 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "src/ops/batch_matmul.h"
+#include <memory>
+#ifndef PRIMITIVE_WRITEABLE
+#include "src/ops/ops_register.h"
+#endif
+
+namespace mindspore {
+namespace lite {
+#ifdef PRIMITIVE_WRITEABLE
+bool BatchMatMul::GetAdjX() const { return this->primitive_->value.AsBatchMatMul()->adj_x; }
+
+void BatchMatMul::SetAdjX(bool adj_x) { this->primitive_->value.AsBatchMatMul()->adj_x = adj_x; }
+
+bool BatchMatMul::GetAdjY() const { return this->primitive_->value.AsBatchMatMul()->adj_y; }
+
+void BatchMatMul::SetAdjY(bool adj_y) { this->primitive_->value.AsBatchMatMul()->adj_y = adj_y; }
+
+int BatchMatMul::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) {
+  if (this->primitive_ == nullptr) {
+    this->primitive_ = new (std::nothrow) schema::PrimitiveT;
+    if (this->primitive_ == nullptr) {
+      MS_LOG(ERROR) << "new primitiveT failed";
+      return RET_ERROR;
+    }
+    this->primitive_->value.type = schema::PrimitiveType_BatchMatMul;
+  }
+  if (this->primitive_->value.type != schema::PrimitiveType_BatchMatMul) {
+    MS_LOG(ERROR) << "Primitive type is error :" << this->primitive_->value.type;
+    return RET_ERROR;
+  }
+  if (this->primitive_->value.value == nullptr) {
+    auto attr = new (std::nothrow) schema::BatchMatMulT();
+    if (attr == nullptr) {
+      MS_LOG(ERROR) << "new FusedBatchMatMulT failed";
+      delete this->primitive_;
+      this->primitive_ = nullptr;
+      return RET_ERROR;
+    }
+    attr->adj_x = GetValue<bool>(prim.GetAttr("adj_x"));
+    attr->adj_y = GetValue<bool>(prim.GetAttr("adj_y"));
+    this->primitive_->value.value = attr;
+  }
+  return RET_OK;
+}
+
+#else
+int BatchMatMul::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
+  MS_ASSERT(nullptr != primitive);
+  MS_ASSERT(nullptr != fbb);
+  auto val_offset = schema::CreateBatchMatMul(*fbb);
+  auto prim_offset = schema::CreatePrimitive(*fbb, schema::PrimitiveType_BatchMatMul, val_offset.o);
+  fbb->Finish(prim_offset);
+  return RET_OK;
+}
+bool BatchMatMul::GetAdjX() const { return this->primitive_->value_as_BatchMatMul()->adj_x(); }
+
+bool BatchMatMul::GetAdjY() const { return this->primitive_->value_as_BatchMatMul()->adj_y(); }
+
+PrimitiveC *BatchMatMulCreator(const schema::Primitive *primitive) {
+  return PrimitiveC::NewPrimitiveC<BatchMatMul>(primitive);
+}
+Registry BatchMatMulRegistry(schema::PrimitiveType_BatchMatMul, BatchMatMulCreator);
+#endif
+
+}  // namespace lite
+}  // namespace mindspore
diff --git a/mindspore/lite/src/ops/batch_matmul.h b/mindspore/lite/src/ops/batch_matmul.h
new file mode 100644
index 00000000000..ca6260ec476
--- /dev/null
+++ b/mindspore/lite/src/ops/batch_matmul.h
@@ -0,0 +1,46 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LITE_MINDSPORE_LITE_C_OPS_BATCH_MATMUL_H_
+#define LITE_MINDSPORE_LITE_C_OPS_BATCH_MATMUL_H_
+
+#include <vector>
+#include <set>
+#include <cmath>
+#include "src/ops/primitive_c.h"
+
+namespace mindspore {
+namespace lite {
+class BatchMatMul : public PrimitiveC {
+ public:
+  BatchMatMul() = default;
+  ~BatchMatMul() = default;
+#ifdef PRIMITIVE_WRITEABLE
+  MS_DECLARE_PARENT(BatchMatMul, PrimitiveC);
+  explicit BatchMatMul(schema::PrimitiveT *primitive) : PrimitiveC(primitive) {}
+  int UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) override;
+  void SetAdjX(bool adj_x);
+  void SetAdjY(bool adj_y);
+#else
+  int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;
+#endif
+  bool GetAdjX() const;
+  bool GetAdjY() const;
+};
+}  // namespace lite
+}  // namespace mindspore
+
+#endif  // LITE_MINDSPORE_LITE_C_OPS_BATCH_MATMUL_H_
diff --git a/mindspore/lite/src/ops/is_finite.h b/mindspore/lite/src/ops/is_finite.h
new file mode 100644
index 00000000000..9d18ebc757e
--- /dev/null
+++ b/mindspore/lite/src/ops/is_finite.h
@@ -0,0 +1,33 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "src/ops/primitive_c.h"
+
+#ifndef LITE_MINDSPORE_LITE_C_OPS_IS_FINITE_H_
+#define LITE_MINDSPORE_LITE_C_OPS_IS_FINITE_H_
+
+namespace mindspore {
+namespace lite {
+class IsFinite : public PrimitiveC {
+ public:
+  MS_DECLARE_PARENT(IsFinite, PrimitiveC);
+  IsFinite() = default;
+  ~IsFinite() = default;
+  explicit IsFinite(schema::PrimitiveT *primitive) : PrimitiveC(primitive) {}
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // LITE_MINDSPORE_LITE_C_OPS_IS_FINITE_H_
diff --git a/mindspore/lite/src/ops/primitive_c.cc b/mindspore/lite/src/ops/primitive_c.cc
index 8fa9fde0399..aaa87506403 100644
--- a/mindspore/lite/src/ops/primitive_c.cc
+++ b/mindspore/lite/src/ops/primitive_c.cc
@@ -170,6 +170,8 @@
 #include "src/ops/crop_and_resize.h"
 #include "src/ops/nonzero.h"
 #include "src/ops/erf.h"
+#include "src/ops/is_finite.h"
+#include "src/ops/batch_matmul.h"
 
 #ifdef SUPPORT_TRAIN
 #include "src/ops/neg_grad.h"
@@ -665,7 +667,6 @@ std::shared_ptr<PrimitiveC> PrimitiveC::Create(const Primitive &prim, const std:
     return NewPrimitiveC<ArgMax>(prim, inputs, quantType);
   } else if (op_type == "Gelu") {
     return NewPrimitiveC<GeLU>(prim, inputs, quantType);
-
 #ifdef SUPPORT_TRAIN
   } else if (op_type == "SoftmaxCrossEntropyWithLogits") {
     return NewPrimitiveC<SoftmaxCrossEntropy>(prim, inputs, quantType);
@@ -1034,6 +1035,10 @@ PrimitiveC *PrimitiveC::Create(mindspore::schema::PrimitiveT *primitive) {
       return new (std::nothrow) NonZero(primitive);
     case schema::PrimitiveType_Erf:
       return new (std::nothrow) Erf(primitive);
+    case schema::PrimitiveType_IsFinite:
+      return new (std::nothrow) IsFinite(primitive);
+    case schema::PrimitiveType_BatchMatMul:
+      return new (std::nothrow) BatchMatMul(primitive);
 #ifdef SUPPORT_TRAIN
     case schema::PrimitiveType_ActivationGrad:
       return new (std::nothrow) ActivationGrad(primitive);
diff --git a/mindspore/lite/tools/common/node_util.h b/mindspore/lite/tools/common/node_util.h
index a0b247cd509..7f870019748 100644
--- a/mindspore/lite/tools/common/node_util.h
+++ b/mindspore/lite/tools/common/node_util.h
@@ -29,6 +29,18 @@
 
 namespace mindspore {
 namespace lite {
+template <typename T>
+int CreateOperator(const std::unique_ptr<schema::PrimitiveT> &primitive, schema::PrimitiveType type) {
+  auto attr = std::make_unique<T>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new attr failed";
+    return RET_NULL_PTR;
+  }
+  primitive->value.type = type;
+  primitive->value.value = attr.release();
+  return RET_OK;
+}
+
 using STATUS = int;
 STATUS BroadCastQuantParam(schema::MetaGraphT *graphT, const std::unique_ptr<schema::CNodeT> &node);
 
@@ -91,6 +103,226 @@ STATUS GetFilterDim(const std::vector<int32_t> &oriDims, kTransFilterType type,
 STATUS SetFilterDim(schema::TensorT *tensor, kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH,
                     int32_t filterW);
 
+template <typename T>
+static void TransKHWC2CHWK(int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW, T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int k = 0; k < filterK; ++k) {
+    for (int h = 0; h < filterH; ++h) {
+      for (int w = 0; w < filterW; ++w) {
+        for (int c = 0; c < filterC; ++c) {
+          p1Buff = srcData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
+          p2Buff = dstData + ((c * filterK * filterH * filterW) + (h * filterK * filterW) + (w * filterK) + (k));
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransKHWC2HWCK(int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW, T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int k = 0; k < filterK; ++k) {
+    for (int h = 0; h < filterH; ++h) {
+      for (int w = 0; w < filterW; ++w) {
+        for (int c = 0; c < filterC; ++c) {
+          p1Buff = srcData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
+          p2Buff = dstData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransCKHW(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                      T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int c = 0; c < filterC; ++c) {
+    for (int k = 0; k < filterK; ++k) {
+      for (int h = 0; h < filterH; ++h) {
+        for (int w = 0; w < filterW; ++w) {
+          p1Buff = srcData + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
+          if (type == kCKHW2HWCK) {
+            p2Buff = dstData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
+          } else if (type == kCKHW2KHWC) {
+            p2Buff = dstData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
+          } else {
+            p2Buff = dstData + ((h * filterW * filterK * filterC) + (w * filterK * filterC) + (k * filterC) + (c));
+          }
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransKCHW(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                      T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int k = 0; k < filterK; ++k) {
+    for (int c = 0; c < filterC; ++c) {
+      for (int h = 0; h < filterH; ++h) {
+        for (int w = 0; w < filterW; ++w) {
+          p1Buff = srcData + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
+          if (type == kKCHW2HWCK) {
+            p2Buff = dstData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
+          } else if (type == kKCHW2KHWC) {
+            p2Buff = dstData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
+          } else if (type == kKCHW2CKHW) {
+            p2Buff = dstData + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
+          } else {
+            p2Buff = dstData + ((h * filterW * filterK * filterC) + (w * filterK * filterC) + (k * filterC) + (c));
+          }
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransCHWK(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                      T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int c = 0; c < filterC; ++c) {
+    for (int h = 0; h < filterH; ++h) {
+      for (int w = 0; w < filterW; ++w) {
+        for (int k = 0; k < filterK; ++k) {
+          p1Buff = srcData + ((c * filterH * filterW * filterK) + (h * filterW * filterK) + (w * filterK) + (k));
+          if (type == kCHWK2HWCK) {
+            p2Buff = dstData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
+          } else {
+            p2Buff = dstData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
+          }
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransHWCK(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                      T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int h = 0; h < filterH; ++h) {
+    for (int w = 0; w < filterW; ++w) {
+      for (int c = 0; c < filterC; ++c) {
+        for (int k = 0; k < filterK; ++k) {
+          p1Buff = srcData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
+          if (type == kHWCK2KCHW) {
+            p2Buff = dstData + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
+          } else {
+            p2Buff = dstData + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
+          }
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransHWKC(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                      T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int h = 0; h < filterH; ++h) {
+    for (int w = 0; w < filterW; ++w) {
+      for (int c = 0; c < filterC; ++c) {
+        for (int k = 0; k < filterK; ++k) {
+          p1Buff = srcData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (k * filterC) + (c));
+          if (type == kHWKC2KCHW) {
+            p2Buff = dstData + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
+          } else {
+            p2Buff = dstData + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
+          }
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static void TransNHWC(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                      T *srcData, T *dstData) {
+  T *p1Buff = nullptr;
+  T *p2Buff = nullptr;
+  for (int k = 0; k < filterK; ++k) {
+    for (int h = 0; h < filterH; ++h) {
+      for (int w = 0; w < filterW; ++w) {
+        for (int c = 0; c < filterC; ++c) {
+          p1Buff = srcData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (k * filterC) + (c));
+          if (type == kNHWC2HWCK) {
+            p2Buff = dstData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
+          } else if (type == kNHWC2CKHW) {
+            p2Buff = dstData + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
+          } else {
+            p2Buff = dstData + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
+          }
+          *p2Buff = *p1Buff;
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+static STATUS TransFilterData(kTransFilterType type, int32_t filterK, int32_t filterC, int32_t filterH, int32_t filterW,
+                              T *srcData, T *dstData) {
+  switch (type) {
+    case kCHWK2HWCK:
+    case kCHWK2KHWC: {
+      TransCHWK(type, filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kKHWC2HWCK: {
+      TransKHWC2HWCK(filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kKCHW2HWCK:
+    case kKCHW2CKHW:
+    case kKCHW2KHWC:
+    case kKCHW2HWKC: {
+      TransKCHW(type, filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kCKHW2HWCK:
+    case kCKHW2KHWC:
+    case kCKHW2HWKC: {
+      TransCKHW(type, filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kHWCK2KCHW:
+    case kHWCK2CKHW: {
+      TransHWCK(type, filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kHWKC2KCHW:
+    case kHWKC2CKHW: {
+      TransHWKC(type, filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kNHWC2HWCK:
+    case kNHWC2KCHW:
+    case kNHWC2CKHW: {
+      TransNHWC(type, filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    case kKHWC2CHWK: {
+      TransKHWC2CHWK(filterK, filterC, filterH, filterW, srcData, dstData);
+    } break;
+    default: {
+      MS_LOG(ERROR) << "Unsupported transFilterType: " << type;
+      return RET_ERROR;
+    }
+  }
+  return RET_OK;
+}
+
 template <typename T>
 static STATUS TransFilterData(schema::TensorT *tensor, kTransFilterType type, int32_t filterK, int32_t filterC,
                               int32_t filterH, int32_t filterW) {
@@ -113,175 +345,10 @@ static STATUS TransFilterData(schema::TensorT *tensor, kTransFilterType type, in
     MS_LOG(ERROR) << "weightData is nullptr";
     return RET_ERROR;
   }
-  T *p1Buff = nullptr;
-  T *p2Buff = nullptr;
-  switch (type) {
-    case kCHWK2HWCK:
-    case kCHWK2KHWC: {
-      for (int c = 0; c < filterC; ++c) {
-        for (int h = 0; h < filterH; ++h) {
-          for (int w = 0; w < filterW; ++w) {
-            for (int k = 0; k < filterK; ++k) {
-              p1Buff = weightData + ((c * filterH * filterW * filterK) + (h * filterW * filterK) + (w * filterK) + (k));
-              if (type == kCHWK2HWCK) {
-                p2Buff =
-                  buf.get() + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
-              } else {
-                p2Buff =
-                  buf.get() + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
-              }
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kKHWC2HWCK: {
-      for (int k = 0; k < filterK; ++k) {
-        for (int h = 0; h < filterH; ++h) {
-          for (int w = 0; w < filterW; ++w) {
-            for (int c = 0; c < filterC; ++c) {
-              p1Buff = weightData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
-              p2Buff = buf.get() + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kKCHW2HWCK:
-    case kKCHW2CKHW:
-    case kKCHW2KHWC:
-    case kKCHW2HWKC: {
-      for (int k = 0; k < filterK; ++k) {
-        for (int c = 0; c < filterC; ++c) {
-          for (int h = 0; h < filterH; ++h) {
-            for (int w = 0; w < filterW; ++w) {
-              p1Buff = weightData + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
-              if (type == kKCHW2HWCK) {
-                p2Buff =
-                  buf.get() + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
-              } else if (type == kKCHW2KHWC) {
-                p2Buff =
-                  buf.get() + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
-              } else if (type == kKCHW2CKHW) {
-                p2Buff =
-                  buf.get() + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
-              } else {
-                p2Buff =
-                  buf.get() + ((h * filterW * filterK * filterC) + (w * filterK * filterC) + (k * filterC) + (c));
-              }
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kCKHW2HWCK:
-    case kCKHW2KHWC:
-    case kCKHW2HWKC: {
-      for (int c = 0; c < filterC; ++c) {
-        for (int k = 0; k < filterK; ++k) {
-          for (int h = 0; h < filterH; ++h) {
-            for (int w = 0; w < filterW; ++w) {
-              p1Buff = weightData + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
-              if (type == kCKHW2HWCK) {
-                p2Buff =
-                  buf.get() + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
-              } else if (type == kCKHW2KHWC) {
-                p2Buff =
-                  buf.get() + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
-              } else {
-                p2Buff =
-                  buf.get() + ((h * filterW * filterK * filterC) + (w * filterK * filterC) + (k * filterC) + (c));
-              }
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kHWCK2KCHW:
-    case kHWCK2CKHW: {
-      for (int h = 0; h < filterH; ++h) {
-        for (int w = 0; w < filterW; ++w) {
-          for (int c = 0; c < filterC; ++c) {
-            for (int k = 0; k < filterK; ++k) {
-              p1Buff = weightData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
-              if (type == kHWCK2KCHW) {
-                p2Buff =
-                  buf.get() + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
-              } else {
-                p2Buff =
-                  buf.get() + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
-              }
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kHWKC2KCHW:
-    case kHWKC2CKHW: {
-      for (int h = 0; h < filterH; ++h) {
-        for (int w = 0; w < filterW; ++w) {
-          for (int c = 0; c < filterC; ++c) {
-            for (int k = 0; k < filterK; ++k) {
-              p1Buff = weightData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (k * filterC) + (c));
-              if (type == kHWKC2KCHW) {
-                p2Buff =
-                  buf.get() + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
-              } else {
-                p2Buff =
-                  buf.get() + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
-              }
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kNHWC2HWCK:
-    case kNHWC2KCHW:
-    case kNHWC2CKHW: {
-      for (int k = 0; k < filterK; ++k) {
-        for (int h = 0; h < filterH; ++h) {
-          for (int w = 0; w < filterW; ++w) {
-            for (int c = 0; c < filterC; ++c) {
-              p1Buff = weightData + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (k * filterC) + (c));
-              if (type == kNHWC2HWCK) {
-                p2Buff =
-                  buf.get() + ((h * filterW * filterC * filterK) + (w * filterC * filterK) + (c * filterK) + (k));
-              } else if (type == kNHWC2CKHW) {
-                p2Buff =
-                  buf.get() + ((c * filterK * filterH * filterW) + (k * filterH * filterW) + (h * filterW) + (w));
-              } else {
-                p2Buff =
-                  buf.get() + ((k * filterC * filterH * filterW) + (c * filterH * filterW) + (h * filterW) + (w));
-              }
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    case kKHWC2CHWK: {
-      for (int k = 0; k < filterK; ++k) {
-        for (int h = 0; h < filterH; ++h) {
-          for (int w = 0; w < filterW; ++w) {
-            for (int c = 0; c < filterC; ++c) {
-              p1Buff = weightData + ((k * filterH * filterW * filterC) + (h * filterW * filterC) + (w * filterC) + (c));
-              p2Buff = buf.get() + ((c * filterK * filterH * filterW) + (h * filterK * filterW) + (w * filterK) + (k));
-              *p2Buff = *p1Buff;
-            }
-          }
-        }
-      }
-    } break;
-    default: {
-      MS_LOG(ERROR) << "Unsupported transFilterType: " << type;
-      return RET_ERROR;
-    }
+
+  if (TransFilterData(type, filterK, filterC, filterH, filterW, weightData, buf.get()) != RET_OK) {
+    MS_LOG(ERROR) << "TransFilterData failed";
+    return RET_ERROR;
   }
 
   auto ret = ::memcpy_s(tensor->data.data(), count * sizeof(T), buf.get(), count * sizeof(T));
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_arithmetic_self_parser.cc b/mindspore/lite/tools/converter/parser/tf/tf_arithmetic_self_parser.cc
index b6939409eca..1de6c8854bc 100644
--- a/mindspore/lite/tools/converter/parser/tf/tf_arithmetic_self_parser.cc
+++ b/mindspore/lite/tools/converter/parser/tf/tf_arithmetic_self_parser.cc
@@ -19,22 +19,11 @@
 #include <map>
 #include <vector>
 #include "tools/converter/parser/tf/tf_node_parser_registry.h"
+#include "tools/common/node_util.h"
 
 namespace mindspore {
 namespace lite {
 
-template <typename T>
-int CreateOperator(const std::unique_ptr<schema::PrimitiveT> &primitive, schema::PrimitiveType type) {
-  auto attr = std::make_unique<T>();
-  if (attr == nullptr) {
-    MS_LOG(ERROR) << "new attr failed";
-    return RET_NULL_PTR;
-  }
-  primitive->value.type = type;
-  primitive->value.value = attr.release();
-  return RET_OK;
-}
-
 STATUS TFArithmeticSelfParser::Parse(const tensorflow::NodeDef &tf_op,
                                      const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
                                      PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) {
@@ -61,6 +50,12 @@ STATUS TFArithmeticSelfParser::Parse(const tensorflow::NodeDef &tf_op,
     status = CreateOperator<schema::LogT>(primitive, schema::PrimitiveType_Log);
   } else if (tf_op.op() == "Sqrt") {
     status = CreateOperator<schema::SqrtT>(primitive, schema::PrimitiveType_Sqrt);
+  } else if (tf_op.op() == "Cos") {
+    status = CreateOperator<schema::CosT>(primitive, schema::PrimitiveType_Cos);
+  } else if (tf_op.op() == "Sin") {
+    status = CreateOperator<schema::SinT>(primitive, schema::PrimitiveType_Sin);
+  } else if (tf_op.op() == "Square") {
+    status = CreateOperator<schema::SquareT>(primitive, schema::PrimitiveType_Square);
   } else if (tf_op.op() == "Pow") {
     status = CreateOperator<schema::PowerT>(primitive, schema::PrimitiveType_Power);
   }
@@ -81,6 +76,9 @@ STATUS TFArithmeticSelfParser::Parse(const tensorflow::NodeDef &tf_op,
   }
   return status;
 }
+TFNodeRegistrar g_tfCosParser("Cos", new TFArithmeticSelfParser());
+TFNodeRegistrar g_tfSinParser("Sin", new TFArithmeticSelfParser());
+TFNodeRegistrar g_tfSquareParser("Square", new TFArithmeticSelfParser());
 TFNodeRegistrar g_tfCeilParser("Ceil", new TFArithmeticSelfParser());
 TFNodeRegistrar g_tfExpParser("Exp", new TFArithmeticSelfParser());
 TFNodeRegistrar g_tfFloorParser("Floor", new TFArithmeticSelfParser());
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_batch_matmul_parser.cc b/mindspore/lite/tools/converter/parser/tf/tf_batch_matmul_parser.cc
new file mode 100644
index 00000000000..a6355e5f1c6
--- /dev/null
+++ b/mindspore/lite/tools/converter/parser/tf/tf_batch_matmul_parser.cc
@@ -0,0 +1,64 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "tools/converter/parser/tf/tf_batch_matmul_parser.h"
+#include <string>
+#include <memory>
+#include <map>
+#include <vector>
+#include "tools/converter/parser/tf/tf_node_parser_registry.h"
+
+namespace mindspore {
+namespace lite {
+STATUS TFBatchMatmulParser::Parse(const tensorflow::NodeDef &tf_op,
+                                  const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
+                                  PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) {
+  if (primitiveC == nullptr || output_size == nullptr) {
+    MS_LOG(ERROR) << "primitiveC is nullptr";
+    return RET_NULL_PTR;
+  }
+
+  auto primitive = std::make_unique<schema::PrimitiveT>();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "primitive is nullptr";
+    return RET_NULL_PTR;
+  }
+  auto attr = std::make_unique<schema::BatchMatMulT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+  tensorflow::AttrValue attr_value;
+  TensorFlowUtils::FindAttrValue(tf_op, "adj_x", &attr_value);
+  attr->adj_x = attr_value.b();
+  attr->adj_y = attr_value.b();
+
+  primitive->value.type = schema::PrimitiveType_BatchMatMul;
+  primitive->value.value = attr.release();
+  *primitiveC = PrimitiveC::Create(primitive.release());
+  if (*primitiveC == nullptr) {
+    MS_LOG(ERROR) << "primitiveC is nullptr";
+    return RET_ERROR;
+  }
+
+  *output_size = 1;
+  for (int i = 0; i < tf_op.input_size(); i++) {
+    inputs->emplace_back(tf_op.input(i));
+  }
+  return RET_OK;
+}
+TFNodeRegistrar g_tfBatchMatMulParser("BatchMatMul", new TFBatchMatmulParser());
+}  // namespace lite
+}  // namespace mindspore
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_batch_matmul_parser.h b/mindspore/lite/tools/converter/parser/tf/tf_batch_matmul_parser.h
new file mode 100644
index 00000000000..9f5240f608c
--- /dev/null
+++ b/mindspore/lite/tools/converter/parser/tf/tf_batch_matmul_parser.h
@@ -0,0 +1,37 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_BATCH_MATMUL_PARSER_H_
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_BATCH_MATMUL_PARSER_H_
+#include <string>
+#include <memory>
+#include <map>
+#include <vector>
+#include "tools/converter/parser/tf/tf_node_parser.h"
+
+namespace mindspore {
+namespace lite {
+class TFBatchMatmulParser : public TFNodeParser {
+ public:
+  TFBatchMatmulParser() = default;
+  ~TFBatchMatmulParser() override = default;
+
+  STATUS Parse(const tensorflow::NodeDef &tf_op, const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
+               PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_BATCH_MATMUL_PARSER_H_
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_is_finite_parser.cc b/mindspore/lite/tools/converter/parser/tf/tf_is_finite_parser.cc
new file mode 100644
index 00000000000..81a2e52ed5a
--- /dev/null
+++ b/mindspore/lite/tools/converter/parser/tf/tf_is_finite_parser.cc
@@ -0,0 +1,59 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "tools/converter/parser/tf/tf_is_finite_parser.h"
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+#include "tools/converter/parser/tf/tf_node_parser_registry.h"
+#include "tools/common/node_util.h"
+
+namespace mindspore {
+namespace lite {
+STATUS TFIsFiniteParser::Parse(const tensorflow::NodeDef &tf_op,
+                               const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
+                               PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) {
+  if (primitiveC == nullptr || output_size == nullptr) {
+    MS_LOG(ERROR) << "primitiveC is nullptr";
+    return RET_NULL_PTR;
+  }
+
+  auto primitive = std::make_unique<schema::PrimitiveT>();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "primitive is nullptr";
+    return RET_NULL_PTR;
+  }
+
+  int status = CreateOperator<schema::IsFiniteT>(primitive, schema::PrimitiveType_IsFinite);
+  if (status != RET_OK) {
+    return status;
+  }
+  *primitiveC = PrimitiveC::Create(primitive.release());
+  if (*primitiveC == nullptr) {
+    MS_LOG(ERROR) << "primitiveC is nullptr";
+    return RET_ERROR;
+  }
+
+  *output_size = 1;
+  for (int i = 0; i < tf_op.input_size(); i++) {
+    inputs->emplace_back(tf_op.input(i));
+  }
+
+  return RET_OK;
+}
+TFNodeRegistrar g_tf_is_finite_parser("IsFinite", new TFIsFiniteParser());
+}  // namespace lite
+}  // namespace mindspore
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_is_finite_parser.h b/mindspore/lite/tools/converter/parser/tf/tf_is_finite_parser.h
new file mode 100644
index 00000000000..22975d6225a
--- /dev/null
+++ b/mindspore/lite/tools/converter/parser/tf/tf_is_finite_parser.h
@@ -0,0 +1,37 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_IS_FINITE_PARSER_H_
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_IS_FINITE_PARSER_H_
+
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+#include "tools/converter/parser/tf/tf_node_parser.h"
+
+namespace mindspore {
+namespace lite {
+class TFIsFiniteParser : public TFNodeParser {
+ public:
+  TFIsFiniteParser() = default;
+  ~TFIsFiniteParser() override = default;
+
+  STATUS Parse(const tensorflow::NodeDef &tf_op, const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
+               PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_IS_FINITE_PARSER_H_
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_logical_parser.cc b/mindspore/lite/tools/converter/parser/tf/tf_logical_parser.cc
index 3202d4d50f4..4dc87fc77fa 100644
--- a/mindspore/lite/tools/converter/parser/tf/tf_logical_parser.cc
+++ b/mindspore/lite/tools/converter/parser/tf/tf_logical_parser.cc
@@ -19,6 +19,7 @@
 #include <string>
 #include <vector>
 #include "tools/converter/parser/tf/tf_node_parser_registry.h"
+#include "tools/common/node_util.h"
 
 namespace mindspore {
 namespace lite {
@@ -36,37 +37,19 @@ STATUS TFLogicalParser::Parse(const tensorflow::NodeDef &tf_op,
     MS_LOG(ERROR) << "primitive is nullptr";
     return RET_NULL_PTR;
   }
+
+  int status = RET_ERROR;
   if (tf_op.op() == "LogicalAnd") {
-    auto attr = std::make_unique<schema::LogicalAndT>();
-    if (attr == nullptr) {
-      MS_LOG(ERROR) << "new op failed";
-      return RET_NULL_PTR;
-    }
-    primitive->value.type = schema::PrimitiveType_LogicalAnd;
-    primitive->value.value = attr.release();
-    *primitiveC = PrimitiveC::Create(primitive.release());
+    status = CreateOperator<schema::LogicalAndT>(primitive, schema::PrimitiveType_LogicalAnd);
   } else if (tf_op.op() == "LogicalOr") {
-    auto attr = std::make_unique<schema::LogicalOrT>();
-    if (attr == nullptr) {
-      MS_LOG(ERROR) << "new op failed";
-      return RET_NULL_PTR;
-    }
-    primitive->value.type = schema::PrimitiveType_LogicalOr;
-    primitive->value.value = attr.release();
-    *primitiveC = PrimitiveC::Create(primitive.release());
+    status = CreateOperator<schema::LogicalOrT>(primitive, schema::PrimitiveType_LogicalOr);
   } else if (tf_op.op() == "LogicalNot") {
-    auto attr = std::make_unique<schema::LogicalNotT>();
-    if (attr == nullptr) {
-      MS_LOG(ERROR) << "new op failed";
-      return RET_NULL_PTR;
-    }
-    primitive->value.type = schema::PrimitiveType_LogicalNot;
-    primitive->value.value = attr.release();
-    *primitiveC = PrimitiveC::Create(primitive.release());
-  } else {
-    MS_LOG(ERROR) << tf_op.op() << " is not supported.";
-    return RET_ERROR;
+    status = CreateOperator<schema::LogicalNotT>(primitive, schema::PrimitiveType_LogicalNot);
   }
+  if (status != RET_OK) {
+    return status;
+  }
+  *primitiveC = PrimitiveC::Create(primitive.release());
   if (*primitiveC == nullptr) {
     MS_LOG(ERROR) << "primitiveC is nullptr";
     return RET_ERROR;
@@ -79,8 +62,8 @@ STATUS TFLogicalParser::Parse(const tensorflow::NodeDef &tf_op,
 
   return RET_OK;
 }
-TFNodeRegistrar g_tfLogicalAndParser("LogicalAnd", new TFLogicalParser());
-TFNodeRegistrar g_tfLogicalOrParser("LogicalOr", new TFLogicalParser());
 TFNodeRegistrar g_tfLogicalNotParser("LogicalNot", new TFLogicalParser());
+TFNodeRegistrar g_tfLogicalOrParser("LogicalOr", new TFLogicalParser());
+TFNodeRegistrar g_tfLogicalAndParser("LogicalAnd", new TFLogicalParser());
 }  // namespace lite
 }  // namespace mindspore
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_zeros_like_parser.cc b/mindspore/lite/tools/converter/parser/tf/tf_zeros_like_parser.cc
new file mode 100644
index 00000000000..b72a6ecc925
--- /dev/null
+++ b/mindspore/lite/tools/converter/parser/tf/tf_zeros_like_parser.cc
@@ -0,0 +1,60 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "tools/converter/parser/tf/tf_zeros_like_parser.h"
+#include <string>
+#include <memory>
+#include <map>
+#include <vector>
+#include "tools/converter/parser/tf/tf_node_parser_registry.h"
+
+namespace mindspore {
+namespace lite {
+STATUS TFZerosLikeParser::Parse(const tensorflow::NodeDef &tf_op,
+                                const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
+                                PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) {
+  if (primitiveC == nullptr || output_size == nullptr) {
+    MS_LOG(ERROR) << "primitiveC is nullptr";
+    return RET_NULL_PTR;
+  }
+
+  auto primitive = std::make_unique<schema::PrimitiveT>();
+  if (primitive == nullptr) {
+    MS_LOG(ERROR) << "primitive is nullptr";
+    return RET_NULL_PTR;
+  }
+  auto attr = std::make_unique<schema::ZerosLikeT>();
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new op failed";
+    return RET_NULL_PTR;
+  }
+
+  primitive->value.type = schema::PrimitiveType_ZerosLike;
+  primitive->value.value = attr.release();
+  *primitiveC = PrimitiveC::Create(primitive.release());
+  if (*primitiveC == nullptr) {
+    MS_LOG(ERROR) << "primitiveC is nullptr";
+    return RET_ERROR;
+  }
+
+  *output_size = tf_op.input_size();
+  for (int i = 0; i < tf_op.input_size(); i++) {
+    inputs->emplace_back(tf_op.input(i));
+  }
+  return RET_OK;
+}
+TFNodeRegistrar g_tfZerosLikeParser("ZerosLike", new TFZerosLikeParser());
+}  // namespace lite
+}  // namespace mindspore
diff --git a/mindspore/lite/tools/converter/parser/tf/tf_zeros_like_parser.h b/mindspore/lite/tools/converter/parser/tf/tf_zeros_like_parser.h
new file mode 100644
index 00000000000..5be3bfd272a
--- /dev/null
+++ b/mindspore/lite/tools/converter/parser/tf/tf_zeros_like_parser.h
@@ -0,0 +1,37 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_ZERO_LIKE_PARSER_H_
+#define MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_ZERO_LIKE_PARSER_H_
+#include <string>
+#include <memory>
+#include <map>
+#include <vector>
+#include "tools/converter/parser/tf/tf_node_parser.h"
+
+namespace mindspore {
+namespace lite {
+class TFZerosLikeParser : public TFNodeParser {
+ public:
+  TFZerosLikeParser() = default;
+  ~TFZerosLikeParser() override = default;
+
+  STATUS Parse(const tensorflow::NodeDef &tf_op, const std::map<string, const tensorflow::NodeDef *> &tf_node_map,
+               PrimitiveC **primitiveC, std::vector<std::string> *inputs, int *output_size) override;
+};
+}  // namespace lite
+}  // namespace mindspore
+
+#endif  // MINDSPORE_LITE_TOOLS_CONVERTER_PARSER_TF_TF_ZERO_LIKE_PARSER_H_