diff --git a/mindspore/core/ir/tensor.cc b/mindspore/core/ir/tensor.cc
index cef257a739..59ccf4509f 100644
--- a/mindspore/core/ir/tensor.cc
+++ b/mindspore/core/ir/tensor.cc
@@ -54,6 +54,18 @@ static size_t SizeOf(const std::vector<int> &shape) {
   return std::accumulate(shape.begin(), shape.end(), size_t(1), std::multiplies<size_t>());
 }
 
+static std::string ShapeToString(const std::vector<int> &shape) {
+  std::string str = "[";
+  const size_t count = shape.size();
+  for (size_t i = 0; i < count; ++i) {
+    if (i > 0) {
+      str.append(", ");
+    }
+    str.append(std::to_string(shape[i]));
+  }
+  return str.append("]");
+}
+
 template <typename T, typename U>
 std::unique_ptr<T[]> NewData(const U *input, size_t size) {
   if (input == nullptr || size == 0) {
@@ -84,7 +96,10 @@ template <typename T>
 std::unique_ptr<T[]> CopyData(const std::vector<int> &shape, void *const data, TypeId data_type) {
   const size_t size = SizeOf(shape);
   switch (data_type) {
-    case kNumberTypeBool:
+    case kNumberTypeBool: {
+      auto buf = static_cast<bool *>(data);
+      return NewData<T>(buf, size);
+    }
     case kNumberTypeUInt8: {
       auto buf = static_cast<uint8_t *>(data);
       return NewData<T>(buf, size);
@@ -200,7 +215,7 @@ class TensorDataImpl : public TensorData {
 
   std::string ToString(const TypeId type, const std::vector<int> &shape) const override {
     constexpr auto valid =
-      std::is_same<T, Bool>::value || std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value ||
+      std::is_same<T, bool>::value || std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value ||
       std::is_same<T, int16_t>::value || std::is_same<T, int32_t>::value || std::is_same<T, int64_t>::value ||
       std::is_same<T, uint16_t>::value || std::is_same<T, uint32_t>::value || std::is_same<T, uint64_t>::value ||
       std::is_same<T, float16>::value || std::is_same<T, float>::value || std::is_same<T, double>::value;
@@ -214,27 +229,28 @@ class TensorDataImpl : public TensorData {
 
     std::ostringstream ss;
     if (data_size_ == 1 && ndim_ == 0) {  // Scalar
-      OutputDataString(ss, type, 0, 0, 1);
+      OutputDataString(ss, 0, 0, 1);
       return ss.str();
     }
     ssize_t cursor = 0;
-    SummaryStringRecursive(ss, type, shape, &cursor, 0);
+    SummaryStringRecursive(ss, shape, &cursor, 0);
     return ss.str();
   }
 
  private:
-  void OutputDataString(std::ostringstream &ss, const TypeId type, ssize_t cursor, ssize_t start, ssize_t end) const {
-    bool isScalar = ndim_ == 0 && end - start == 1;
-    int linefeedThreshold;
+  void OutputDataString(std::ostringstream &ss, ssize_t cursor, ssize_t start, ssize_t end) const {
+    const bool isScalar = ndim_ == 0 && end - start == 1;
     constexpr auto isFloat =
       std::is_same<T, float16>::value || std::is_same<T, float>::value || std::is_same<T, double>::value;
+    constexpr auto isBool = std::is_same<T, bool>::value;
+    constexpr int linefeedThreshold = isFloat ? kThreshold1DFloat : (isBool ? kThreshold1DBool : kThreshold1DInt);
     for (ssize_t i = start; i < end && (cursor + i) < static_cast<ssize_t>(data_size_); i++) {
       const auto value = data_[cursor + i];
       if constexpr (isFloat) {
         if (isScalar) {
           ss << value;
         } else {
-          if (std::is_same<T, float16>::value) {
+          if constexpr (std::is_same<T, float16>::value) {
             ss << std::setw(11) << std::setprecision(4) << std::setiosflags(std::ios::scientific | std::ios::right)
                << value;
           } else {
@@ -242,14 +258,12 @@ class TensorDataImpl : public TensorData {
                << value;
           }
         }
-        linefeedThreshold = kThreshold1DFloat;
-      } else if (type == kNumberTypeBool) {
+      } else if (std::is_same<T, bool>::value) {
         if (isScalar) {
-          ss << (value == 0 ? "False" : "True");
+          ss << (value ? "True" : "False");
         } else {
-          ss << std::setw(5) << std::setiosflags(std::ios::right) << (value == 0 ? "False" : "True");
+          ss << std::setw(5) << std::setiosflags(std::ios::right) << (value ? "True" : "False");
         }
-        linefeedThreshold = kThreshold1DBool;
       } else {
         constexpr auto isSigned = std::is_same<T, int8_t>::value || std::is_same<T, int16_t>::value ||
                                   std::is_same<T, int32_t>::value || std::is_same<T, int64_t>::value;
@@ -276,7 +290,6 @@ class TensorDataImpl : public TensorData {
         } else {
           ss << value;
         }
-        linefeedThreshold = kThreshold1DInt;
       }
       if (!isScalar && i != end - 1) {
         ss << ' ';
@@ -288,7 +301,7 @@ class TensorDataImpl : public TensorData {
     }
   }
 
-  void SummaryStringRecursive(std::ostringstream &ss, const TypeId type, const std::vector<int> &shape, ssize_t *cursor,
+  void SummaryStringRecursive(std::ostringstream &ss, const std::vector<int> &shape, ssize_t *cursor,
                               ssize_t depth) const {
     if (depth >= static_cast<ssize_t>(ndim_)) {
       return;
@@ -297,11 +310,11 @@ class TensorDataImpl : public TensorData {
     if (depth == static_cast<ssize_t>(ndim_) - 1) {  // Bottom dimension
       ssize_t num = shape[depth];
       if (num > kThreshold && ndim_ > 1) {
-        OutputDataString(ss, type, *cursor, 0, kThreshold / 2);
+        OutputDataString(ss, *cursor, 0, kThreshold / 2);
         ss << ' ' << kEllipsis << ' ';
-        OutputDataString(ss, type, *cursor, num - kThreshold / 2, num);
+        OutputDataString(ss, *cursor, num - kThreshold / 2, num);
       } else {
-        OutputDataString(ss, type, *cursor, 0, num);
+        OutputDataString(ss, *cursor, 0, num);
       }
       *cursor += num;
     } else {  // Middle dimension
@@ -312,7 +325,7 @@ class TensorDataImpl : public TensorData {
           ss << '\n';
           ss << std::setw(depth + 1) << ' ';  // Add the indent.
         }
-        SummaryStringRecursive(ss, type, shape, cursor, depth + 1);
+        SummaryStringRecursive(ss, shape, cursor, depth + 1);
       }
       // Handle the ignored part.
       if (num > kThreshold) {
@@ -334,7 +347,7 @@ class TensorDataImpl : public TensorData {
         for (ssize_t i = num - kThreshold / 2; i < num; i++) {
           ss << '\n';
           ss << std::setw(depth + 1) << ' ';  // Add the indent.
-          SummaryStringRecursive(ss, type, shape, cursor, depth + 1);
+          SummaryStringRecursive(ss, shape, cursor, depth + 1);
         }
       }
     }
@@ -350,6 +363,7 @@ template <typename... Args>
 TensorDataPtr MakeTensorData(TypeId data_type, const std::vector<int> &shape, const Args... args) {
   switch (data_type) {
     case kNumberTypeBool:
+      return std::make_shared<TensorDataImpl<bool>>(shape, args...);
     case kNumberTypeUInt8:
       return std::make_shared<TensorDataImpl<uint8_t>>(shape, args...);
     case kNumberTypeInt8:
@@ -466,31 +480,35 @@ std::string Tensor::GetShapeAndDataTypeInfo() const {
 }
 
 std::string Tensor::ToString() const {
-  const int small_tensor_size = 30;
+  constexpr int small_tensor_size = 30;
   std::ostringstream buf;
+  auto dtype = Dtype();
+  MS_EXCEPTION_IF_NULL(dtype);
   data_sync();
-  buf << "Tensor shape:[" << shape() << "]" << this->Dtype()->ToString();
-  // only print small tensor
+  buf << "Tensor(shape=" << ShapeToString(shape_) << ", dtype=" << dtype->ToString() << ",\n";
   if (DataSize() < small_tensor_size) {
-    buf << ", value:" << data().ToString(data_type_, shape());
+    // Only print data for small tensor.
+    buf << data().ToString(data_type_, shape_) << ')';
+  } else {
+    buf << "[...])";
   }
   return buf.str();
 }
 
 std::string Tensor::ToStringRepr() const {
   std::ostringstream buf;
-  auto type_ptr = this->Dtype();
-  MS_EXCEPTION_IF_NULL(type_ptr);
+  auto dtype = Dtype();
+  MS_EXCEPTION_IF_NULL(dtype);
   data_sync();
-  buf << "Tensor shape:[" << shape() << "]" << type_ptr->ToString();
-  buf << "\nvalue:" << data().ToString(data_type_, shape());
+  buf << "Tensor(shape=" << ShapeToString(shape_) << ", dtype=" << dtype->ToString() << ",\n"
+      << data().ToString(data_type_, shape_) << ')';
   return buf.str();
 }
 
 void Tensor::data_sync() const {
   if (device_sync_ != nullptr) {
     if (!device_sync_->SyncDeviceToHost(shape(), static_cast<size_t>(data().nbytes()), data_type(), data_c())) {
-      MS_LOG(EXCEPTION) << "SyncDeviceToHost when asnumpy.";
+      MS_LOG(EXCEPTION) << "SyncDeviceToHost failed.";
     }
   }
 }