autograd:分离，life算子，func验证

doc/excuter/op-mem-cuda/list.md

@@ -6,43 +6,45 @@
 |-----------|--------|------------|--------------|----------------|
 | reducemax | miaobyte | reducemax(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) | B = reducemax(A, axis=[1 2], keepdims=false) | reducemax(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) |
 | broadcastTo | miaobyte | broadcastTo(tensor<any> A, vector<int32> new_shape)->(tensor<any> B) | T2 = T1.broadcastTo(new_shape=[4,3,2]) | broadcastTo(tensor<any> A, vector<int32> new_shape)->(tensor<any> B) |
-| concat | miaobyte | concat(listtensor<any> tensors, var<int32> axis)->(tensor<any> result) | Tresult = concat([T1, T2...], axis=3) | concat(listtensor<any> tensors, var<int32> axis)->(tensor<any> result) |
+| concat | miaobyte | concat(listtensor<any> tensors, var<int32> dim)->(tensor<any> result) | Tresult = concat([T1, T2...], axis=3) | concat(listtensor<any> tensors, var<int32> dim)->(tensor<any> result) |
 | transpose | miaobyte | transpose(tensor<any> A, vector<int32> dim_order)->(tensor<any> C) | T2 = T1.transpose(dimorder=[1,0]) | transpose(tensor<any> A, vector<int32> dim_order)->(tensor<any> C) |
 | reshape | miaobyte | reshape(tensor<any> A, vector<int32> shape)->(tensor<any> B) | T1.reshape(shape)->T2 | reshape(tensor<any> A, vector<int32> shape)->(tensor<any> B) |
 | matmul | cublas | matmul(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1 @ T2 | matmul(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | comparescalar | miaobyte | comparescalar(tensor<any> A, var<any> scalar)->(tensor<int8> mask) | mask=compare(T1, scalar) | comparescalar(tensor<any> A, var<any> scalar)->(tensor<int8> mask) |
-| add | cublas | add(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1+T2 | add(tensor<any> a, tensor<any> b)->(tensor<any> c) |
-| add | miaobyte | add(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1+T2 | add(tensor<any> a, tensor<any> b)->(tensor<any> c) |
+| compare | miaobyte | compare(tensor<any> A, tensor<any> B)->(tensor<int8> mask) | mask=compare(T1, T2) | compare(tensor<any> A, tensor<any> B)->(tensor<int8> mask) |
 | uniform | miaobyte | uniform(tensor<any> t, var<any> low, var<any> high, var<int32> seed)->() | uniform(T1,low,high,seed) | uniform(tensor<any> t, var<any> low, var<any> high, var<int32> seed)->() |
 | addscalar | miaobyte | addscalar(tensor<any> A, var<any> b)->(tensor<any> C) | T3=T1+scalar | addscalar(tensor<any> A, var<any> b)->(tensor<any> C) |
 | log | miaobyte | log(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) | T3=log(T1) | log(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) |
 | arange | miaobyte | arange(tensor<any> t, var<any> start, var<any> step)->() | arange(T1,start,step) | arange(tensor<any> t, var<any> start, var<any> step)->() |
 | divscalar | miaobyte | divscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=scalar/T1 | divscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
 | sin | miaobyte | sin(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) | T3=sin(T1) | sin(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) |
 | tan | miaobyte | tan(tensor<float64|float32> A)->(tensor<float64|float32> C) | T3=tan(T1) | tan(tensor<float64|float32> A)->(tensor<float64|float32> C) |
+| add | cublas | add(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1+T2 | add(tensor<any> a, tensor<any> b)->(tensor<any> c) |
+| add | miaobyte | add(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1+T2 | add(tensor<any> a, tensor<any> b)->(tensor<any> c) |
+| copytensor |  none  | copytensor(tensor<any> src, tensor<any> dst)->() | T2.data = T1.data | copytensor(tensor<any> src, tensor<any> dst)->() |
+| prod | miaobyte | prod(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) | B = prod(A, axis=[1 2], keepdims=false) | prod(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) |
+| min | miaobyte | min(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=min(T1, T2) | min(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | print | miaobyte | print(tensor<any> )->() | print(T1) | print(tensor<any> )->() |
 | print | miaobyte | print(tensor<any> , var<string> )->() | print(T1) | print(tensor<any> , var<string> )->() |
 | newtensor |  none  | newtensor(vector<int32> shape)->(tensor<any> tensor1) | T1 = zeros(shape) | newtensor(vector<int32> shape)->(tensor<any> tensor1) |
 | newtensor |  none  | newtensor(var<string> shape)->(tensor<any> tensor1) | T1 = zeros(shape) | newtensor(var<string> shape)->(tensor<any> tensor1) |
+| minscalar | miaobyte | minscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=min(T1, scalar) | minscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
+| rdivscalar | miaobyte | rdivscalar(var<any> scalar, tensor<any> A)->(tensor<any> C) | T3=scalar/T1 | rdivscalar(var<any> scalar, tensor<any> A)->(tensor<any> C) |
+| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1) | constant(tensor<any> t, var<any> value)->() |
+| powscalar | miaobyte | powscalar(tensor<float64|float32> A, var<float64|float32> scalar)->(tensor<float64|float32> C) | T3=pow(T1, scalar) | powscalar(tensor<float64|float32> A, var<float64|float32> scalar)->(tensor<float64|float32> C) |
 | vecset |  none  | vecset(vector<any> value)->(vector<any> name) | shape = [3  4  5] | vecset(vector<any> value)->(vector<any> name) |
 | reducemin | miaobyte | reducemin(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) | B = reducemin(A, axis=[1 2], keepdims=false) | reducemin(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) |
 | subscalar | miaobyte | subscalar(tensor<any> A, var<any> b)->(tensor<any> C) | T3=T1-scalar | subscalar(tensor<any> A, var<any> b)->(tensor<any> C) |
 | sqrt | miaobyte | sqrt(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) | T3=sqrt(T1) | sqrt(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) |
+| sub | miaobyte | sub(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1-T2 | sub(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | sum | miaobyte | sum(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) | B = sum(A, axis=[1 2], keepdims=false) | sum(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) |
 | argset |  none  | argset(var<any> value)->(var<any> name) | var argname = argvalue | argset(var<any> value)->(var<any> name) |
-| sub | miaobyte | sub(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1-T2 | sub(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | mulscalar | miaobyte | mulscalar(tensor<any> A, var<any> b)->(tensor<any> C) | T3=T1*scalar | mulscalar(tensor<any> A, var<any> b)->(tensor<any> C) |
 | div | miaobyte | div(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1/T2 | div(tensor<any> A, tensor<any> B)->(tensor<any> C) |
-| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1) | constant(tensor<any> t, var<any> value)->() |
-| powscalar | miaobyte | powscalar(tensor<float64|float32> A, var<float64|float32> scalar)->(tensor<float64|float32> C) | T3=pow(T1, scalar) | powscalar(tensor<float64|float32> A, var<float64|float32> scalar)->(tensor<float64|float32> C) |
 | max | miaobyte | max(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=max(T1, T2) | max(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | pow | miaobyte | pow(tensor<float64|float32> A, tensor<float64|float32> B)->(tensor<float64|float32> C) | T3=pow(T1, T2) | pow(tensor<float64|float32> A, tensor<float64|float32> B)->(tensor<float64|float32> C) |
 | maxscalar | miaobyte | maxscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=max(T1, scalar) | maxscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
 | mul | miaobyte | mul(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1*T2 | mul(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | exp | miaobyte | exp(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) | T3=exp(T1) | exp(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) |
-| rdivscalar | miaobyte | rdivscalar(var<any> scalar, tensor<any> A)->(tensor<any> C) | T3=scalar/T1 | rdivscalar(var<any> scalar, tensor<any> A)->(tensor<any> C) |
-| minscalar | miaobyte | minscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=min(T1, scalar) | minscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
+| deltensor |  none  | deltensor(tensor<any> t)->() | del T1 | deltensor(tensor<any> t)->() |
 | cos | miaobyte | cos(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) | T3=cos(T1) | cos(tensor<float64|float32|float16|bfloat16> A)->(tensor<float64|float32|float16|bfloat16> C) |
-| prod | miaobyte | prod(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) | B = prod(A, axis=[1 2], keepdims=false) | prod(tensor<any> A, vector<int32> dims, var<bool> keepdims)->(tensor<any> B) |
-| min | miaobyte | min(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=min(T1, T2) | min(tensor<any> A, tensor<any> B)->(tensor<any> C) |
-| compare | miaobyte | compare(tensor<any> A, tensor<any> B)->(tensor<int8> mask) | mask=compare(T1, T2) | compare(tensor<any> A, tensor<any> B)->(tensor<int8> mask) |

doc/excuter/op-mem-ompsimd/list.md

@@ -8,39 +8,41 @@
 | broadcastTo | miaobyte | broadcastTo(tensor<any> A, vector<int32> new_shape)->(tensor<any> B) | T2 = T1.broadcastTo(new_shape=[4,3,2]) | broadcastTo(tensor<any> A, vector<int32> new_shape)->(tensor<any> B) |
 | concat | miaobyte | concat(listtensor<any> tensors, var<int32> dim)->(tensor<any> result) | Tresult = concat([T1, T2...], axis=3) | concat(listtensor<any> tensors, var<int32> dim)->(tensor<any> result) |
 | transpose | miaobyte | transpose(tensor<any> A, vector<int32> dim_order)->(tensor<any> C) | T1.transpose(dimorder=[1,0])->T2 | transpose(tensor<any> A, vector<int32> dim_order)->(tensor<any> C) |
-| add | cblas | add(tensor<float64|float32> a, tensor<float64|float32> b)->(tensor<float64|float32> c) | T3=T1+T2 | add(tensor<float64|float32> a, tensor<float64|float32> b)->(tensor<float64|float32> c) |
-| add | miaobyte | add(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1+T2 | add(tensor<any> a, tensor<any> b)->(tensor<any> c) |
+| reshape | miaobyte | reshape(tensor<any> A, vector<int32> shape)->(tensor<any> B) | T1.reshape(shape)->T2 | reshape(tensor<any> A, vector<int32> shape)->(tensor<any> B) |
+| matmul | cblas | matmul(tensor<float64|float32> A, tensor<float64|float32> B)->(tensor<float64|float32> C) | T3=T1 @ T2 | matmul(tensor<float64|float32> A, tensor<float64|float32> B)->(tensor<float64|float32> C) |
+| matmul | miaobyte | matmul(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1 @ T2 | matmul(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | comparescalar | miaobyte | comparescalar(tensor<any> A, var<any> scalar)->(tensor<float32> mask) | mask=compare(T1,scalar) | comparescalar(tensor<any> A, var<any> scalar)->(tensor<float32> mask) |
 | uniform | miaobyte | uniform(tensor<any> t, var<any> low, var<any> high, var<int32> seed)->() | uniform(T1,low,high,seed) | uniform(tensor<any> t, var<any> low, var<any> high, var<int32> seed)->() |
 | addscalar | miaobyte | addscalar(tensor<any> a, var<any> scalar)->(tensor<any> c) | T3=T1+scalar | addscalar(tensor<any> a, var<any> scalar)->(tensor<any> c) |
 | log | miaobyte | log(tensor<any> A)->(tensor<any> C) | T3=log(T1) | log(tensor<any> A)->(tensor<any> C) |
-| reshape | miaobyte | reshape(tensor<any> A, vector<int32> shape)->(tensor<any> B) | T1.reshape(shape)->T2 | reshape(tensor<any> A, vector<int32> shape)->(tensor<any> B) |
 | arange | miaobyte | arange(tensor<any> t, var<any> start, var<any> step)->() | arange(T1,start,step) | arange(tensor<any> t, var<any> start, var<any> step)->() |
 | divscalar | miaobyte | divscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=T1/scalar | divscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
+| add | cblas | add(tensor<float64|float32> a, tensor<float64|float32> b)->(tensor<float64|float32> c) | T3=T1+T2 | add(tensor<float64|float32> a, tensor<float64|float32> b)->(tensor<float64|float32> c) |
+| add | miaobyte | add(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1+T2 | add(tensor<any> a, tensor<any> b)->(tensor<any> c) |
+| copytensor |  none  | copytensor(tensor<any> src, tensor<any> dst)->() | T2.data = T1.data | copytensor(tensor<any> src, tensor<any> dst)->() |
+| prod | miaobyte | prod(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) | B = prod(A, axis=[1 2], keepdims=false) | prod(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) |
+| min | miaobyte | min(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=min(T1,T2) | min(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | print | miaobyte | print(tensor<any> )->() | print(T1) | print(tensor<any> )->() |
 | print | miaobyte | print(tensor<any> , var<string> )->() | print(T1) | print(tensor<any> , var<string> )->() |
 | newtensor |  none  | newtensor(vector<int32> shape)->(tensor<any> tensor1) | T1 =Tensor(shape=[...]) | newtensor(vector<int32> shape)->(tensor<any> tensor1) |
-| newtensor |  none  | newtensor(var<string> shape)->(tensor<any> tensor1) | T1 =Tensor(shape=[...]) | newtensor(var<string> shape)->(tensor<any> tensor1) |
+| newtensor |  none  | newtensor(var<string> shape)->(tensor<any> t) | T1 =Tensor(shape=[...]) | newtensor(var<string> shape)->(tensor<any> t) |
+| deltensor |  none  | deltensor(tensor<any> t)->() | del T1 | deltensor(tensor<any> t)->() |
+| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1,value) | constant(tensor<any> t, var<any> value)->() |
+| powscalar | miaobyte | powscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=T1^scalar | powscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
 | vecset |  none  | vecset(vector<any> value)->(vector<any> name) | shape = [3  4  5] | vecset(vector<any> value)->(vector<any> name) |
 | reducemin | miaobyte | reducemin(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) | B = reducemin(A, axis=[1 2], keepdims=false) | reducemin(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) |
 | subscalar | miaobyte | subscalar(tensor<any> a, var<any> scalar)->(tensor<any> c) | T3=T1-scalar | subscalar(tensor<any> a, var<any> scalar)->(tensor<any> c) |
 | sqrt | miaobyte | sqrt(tensor<any> A)->(tensor<any> C) | T3=sqrt(T1) | sqrt(tensor<any> A)->(tensor<any> C) |
+| sub | miaobyte | sub(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1-T2 | sub(tensor<any> a, tensor<any> b)->(tensor<any> c) |
 | sum | miaobyte | sum(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) | B = sum(A, axis=[1 2], keepdims=false) | sum(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) |
 | argset |  none  | argset(var<any> value)->(var<any> name) | var argname = argvalue | argset(var<any> value)->(var<any> name) |
-| sub | miaobyte | sub(tensor<any> a, tensor<any> b)->(tensor<any> c) | T3=T1-T2 | sub(tensor<any> a, tensor<any> b)->(tensor<any> c) |
 | mulscalar | miaobyte | mulscalar(tensor<any> A, var<any> b)->(tensor<any> C) | T3=T1*scalar | mulscalar(tensor<any> A, var<any> b)->(tensor<any> C) |
 | div | miaobyte | div(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1/T2 | div(tensor<any> A, tensor<any> B)->(tensor<any> C) |
-| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1,value) | constant(tensor<any> t, var<any> value)->() |
-| powscalar | miaobyte | powscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=T1^scalar | powscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
 | max | miaobyte | max(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=max(T1,T2) | max(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | pow | miaobyte | pow(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1^T2 | pow(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | maxscalar | miaobyte | maxscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=max(T1,scalar) | maxscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
 | mul | miaobyte | mul(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1*T2 | mul(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | exp | miaobyte | exp(tensor<any> A)->(tensor<any> C) | T3=exp(T1) | exp(tensor<any> A)->(tensor<any> C) |
 | rdivscalar | miaobyte | rdivscalar(var<any> scalar, tensor<any> A)->(tensor<any> C) | T3=scalar/T1 | rdivscalar(var<any> scalar, tensor<any> A)->(tensor<any> C) |
 | minscalar | miaobyte | minscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) | T3=min(T1,scalar) | minscalar(tensor<any> A, var<any> scalar)->(tensor<any> C) |
-| prod | miaobyte | prod(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) | B = prod(A, axis=[1 2], keepdims=false) | prod(tensor<any> A, vector<int32> axis, var<bool> keepdims)->(tensor<any> B) |
-| min | miaobyte | min(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=min(T1,T2) | min(tensor<any> A, tensor<any> B)->(tensor<any> C) |
 | compare | miaobyte | compare(tensor<any> A, tensor<any> B)->(tensor<float32> mask) | mask=compare(T1,T2) | compare(tensor<any> A, tensor<any> B)->(tensor<float32> mask) |
-| matmul | cblas | matmul(tensor<float64|float32> A, tensor<float64|float32> B)->(tensor<float64|float32> C) | T3=T1 @ T2 | matmul(tensor<float64|float32> A, tensor<float64|float32> B)->(tensor<float64|float32> C) |
-| matmul | miaobyte | matmul(tensor<any> A, tensor<any> B)->(tensor<any> C) | T3=T1 @ T2 | matmul(tensor<any> A, tensor<any> B)->(tensor<any> C) |

excuter/cpp-common/src/deepx/dtype.hpp

@@ -10,7 +10,7 @@
 
 namespace deepx
 {
-    using namespace  std;
+    using namespace std;
 
     template <typename T>
     T to(const std::string &textvalue)
@@ -226,15 +226,19 @@ namespace deepx
             return value != other.value;
         }
 
-        // 判断当前类型是否在other类型的精度范围内
-        bool in(const TypeDef &other) const
+        // 判断other是否在当前类型的精度范围内
+        bool match(const TypeDef &other) const
         {
             // 类型必须相同
-            if (parts.category != other.parts.category)
+            uint8_t this_cat = static_cast<uint8_t>(parts.category);
+            uint8_t other_cat = static_cast<uint8_t>(other.parts.category);
+            if ((this_cat & other_cat) != this_cat)
             {
                 return false;
             }
-            // other的精度必须包含当前精度（通过位与运算判断）
+
+            // 使用位操作检查precision
+            // 检查this的precision位是否都在other的precision中
             uint16_t this_prec = static_cast<uint16_t>(parts.precision);
             uint16_t other_prec = static_cast<uint16_t>(other.parts.precision);
             return (this_prec & other_prec) == this_prec;

excuter/cpp-common/src/deepx/tensorfunc/elementwise.hpp

@@ -323,6 +323,21 @@ namespace deepx::tensorfunc
     {
         comparescalarDispatcher<Author, T>::comparescalar(A, scalar, mask);
     }
+
+    // 判断两个张量是否相等，TODO
+    template <typename Author, typename T>
+    struct equalDispatcher
+    {
+        static bool equal(const Tensor<T> &A, const Tensor<T> &B, float epsilon=1e-6) = delete;
+    };
+
+    template <typename Author, typename T>
+    bool equal(const Tensor<T> &A, const Tensor<T> &B,float epsilon=1e-6)
+    {
+        return equalDispatcher<Author, T>::equal(A, B, epsilon);
+    }
+
+
 } // namespace deepx::tensorfunc
 
 #endif // DEEPX_TENSORFUNC_ELEMENTWISE_HPP

excuter/cpp-common/src/deepx/tensorfunc/tensorlife.hpp

@@ -0,0 +1,23 @@
+#ifndef DEEPX_TENSORFUNC_TENSORLIFE_HPP
+#define DEEPX_TENSORFUNC_TENSORLIFE_HPP
+
+#include "deepx/tensor.hpp"
+
+namespace deepx::tensorfunc
+{
+    //New
+    template < typename T>
+    Tensor<T> New(const std::vector<int> &shape);
+
+    template <typename T>
+    Tensor<T> New(const std::initializer_list<int> &shape){
+        std::vector<int> shape_vec(shape);
+        return New<T>(shape_vec);
+    }
+
+    //copy
+    template <typename T>
+    void copy(const Tensor<T> &src,Tensor<T> &dst);
+
+}
+#endif