save,load:优化

doc/design.md

@@ -0,0 +1,21 @@
+# deepx默认原则
+
+## 一.DeepxIR
+
+### 1.deepIR结构
+```
+deepIR{
+    Meta{
+        int id
+        string author
+    } meta
+    string name 
+    []Param args
+    []Param returns
+}
+```
+
+excuter执行deepxIR的规则
+
++ excuter执行deepxIR时，不得修改args中的tensor
++ 但deepIR不限制args和returns中的Param同名，这样可以实现类似inplace的操作

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

@@ -6,36 +6,43 @@
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| vecset |  none  | vecset(vector<any> value)->(vector<any> name) | shape = [3  4  5] | vecset(vector<any> value)->(vector<any> name) |
-| argset |  none  | argset(var<any> value)->(var<any> name) | var argname = argvalue | argset(var<any> value)->(var<any> name) |
+| vecset |  none  | vecset(vector<any> value)->(vector<any> name) | [3  4  5]->shape | vecset(vector<any> value)->(vector<any> name) |
+| argset |  none  | argset(var<any> value)->(var<any> name) | argvalue->argname | argset(var<any> value)->(var<any> name) |
 
 ### tensorlife
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| renametensor |  none  | renametensor(tensor<any> t, var<string> new_name)->() | rename T1 to T2 | renametensor(tensor<any> t, var<string> new_name)->() |
+| renametensor |  none  | renametensor(var<string> new_name)->(tensor<any> t) | rename(newname)->T1 | renametensor(var<string> new_name)->(tensor<any> t) |
 | 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) |
-| deltensor |  none  | deltensor(tensor<any> t)->() | del T1 | deltensor(tensor<any> t)->() |
-| copytensor |  none  | copytensor(tensor<any> src, tensor<any> dst)->() | T2.data = T1.data | copytensor(tensor<any> src, tensor<any> dst)->() |
+| deltensor |  none  | deltensor()->(tensor<any> t) | del->T1 | deltensor()->(tensor<any> t) |
+| copytensor |  none  | copytensor(tensor<any> src)->(tensor<any> dst) | T2.data = T1.data | copytensor(tensor<any> src)->(tensor<any> dst) |
 
 ### io
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
+| loadtensordata |  none  | loadtensordata(var<string> path)->(tensor<any> t) | loadtensordata(path)->tensor | loadtensordata(var<string> path)->(tensor<any> t) |
 | save |  none  | save(tensor<any> t, var<string> path)->() | save(T1,path) | save(tensor<any> t, var<string> path)->() |
 | print | miaobyte | print(tensor<any> t)->() | print(T1) | print(tensor<any> t)->() |
 | print | miaobyte | print(tensor<any> t, var<string> format)->() | print(T1) | print(tensor<any> t, var<string> format)->() |
 | load |  none  | load(var<string> path)->() | load(path) | load(var<string> path)->() |
 
+### matmul
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| 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) |
+
 ### init
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| normal | miaobyte | normal(tensor<any> t, var<any> mean, var<any> stddev, var<int32> seed)->() | normal(T1,mean,stddev,seed) | normal(tensor<any> t, var<any> mean, var<any> stddev, var<int32> seed)->() |
-| 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)->() |
-| 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)->() |
-| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1) | constant(tensor<any> t, var<any> value)->() |
+| normal | miaobyte | normal(var<any> mean, var<any> stddev, var<int32> seed)->(tensor<any> t) | normal(mean,stddev,seed)->T1 | normal(var<any> mean, var<any> stddev, var<int32> seed)->(tensor<any> t) |
+| uniform | miaobyte | uniform(var<any> low, var<any> high, var<int32> seed)->(tensor<any> t) | uniform(low,high,seed)->T1 | uniform(var<any> low, var<any> high, var<int32> seed)->(tensor<any> t) |
+| arange | miaobyte | arange(var<any> start, var<any> step)->(tensor<any> t) | arange(start,step)->T1 | arange(var<any> start, var<any> step)->(tensor<any> t) |
+| constant | miaobyte | constant(var<any> value)->(tensor<any> t) | constant(value)->T1 | constant(var<any> value)->(tensor<any> t) |
 
 ### elementwise
 
@@ -64,20 +71,14 @@
 | 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) |
 | 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) |
 | 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) |
+| 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) |
+| 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) |
 | equal | miaobyte | equal(tensor<any> A, tensor<any> B, var<float64> epsilon)->(tensor<bool> mask) | mask=compare(T1, T2) | equal(tensor<any> A, tensor<any> B, var<float64> epsilon)->(tensor<bool> mask) |
 | 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) |
 | invert | miaobyte | invert(tensor<int64|int32|int16|int8> A)->(tensor<int64|int32|int16|int8> C) | T3=~T1 | invert(tensor<int64|int32|int16|int8> A)->(tensor<int64|int32|int16|int8> 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) |
-| 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) |
-
-### matmul
-
-| Operation | Author | Func Def | Math Formula | IR Instruction |
-|-----------|--------|------------|--------------|----------------|
-| 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) |
 
 ### reduce
 

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

@@ -6,36 +6,44 @@
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| vecset |  none  | vecset(vector<any> value)->(vector<any> name) | shape = [3  4  5] | vecset(vector<any> value)->(vector<any> name) |
-| argset |  none  | argset(var<any> value)->(var<any> name) | var argname = argvalue | argset(var<any> value)->(var<any> name) |
+| vecset |  none  | vecset(vector<any> value)->(vector<any> name) | [3  4  5]->shape | vecset(vector<any> value)->(vector<any> name) |
+| argset |  none  | argset(var<any> value)->(var<any> name) | argvalue->argname | argset(var<any> value)->(var<any> name) |
 
 ### tensorlife
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| renametensor |  none  | renametensor(tensor<any> t, var<string> new_name)->() | rename T1 to T2 | renametensor(tensor<any> t, var<string> new_name)->() |
-| newtensor |  none  | newtensor(vector<int32> shape)->(tensor<any> tensor1) | T1 =Tensor(shape=[...]) | newtensor(vector<int32> shape)->(tensor<any> tensor1) |
+| renametensor |  none  | renametensor(var<string> new_name)->(tensor<any> t) | rename(newname)->T1 | renametensor(var<string> new_name)->(tensor<any> t) |
+| newtensor |  none  | newtensor(vector<int32> shape)->(tensor<any> t) | T1 =Tensor(shape=[...]) | newtensor(vector<int32> shape)->(tensor<any> t) |
 | 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)->() |
-| copytensor |  none  | copytensor(tensor<any> src, tensor<any> dst)->() | T2.data = T1.data | copytensor(tensor<any> src, tensor<any> dst)->() |
+| deltensor |  none  | deltensor()->(tensor<any> t) | del->T1 | deltensor()->(tensor<any> t) |
+| copytensor |  none  | copytensor(tensor<any> src)->(tensor<any> dst) | T1.data->T2.data | copytensor(tensor<any> src)->(tensor<any> dst) |
 
 ### io
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
+| loadtensordata |  none  | loadtensordata(var<string> path)->(tensor<any> t) | loadtensordata(path)->tensor.data | loadtensordata(var<string> path)->(tensor<any> t) |
 | save |  none  | save(tensor<any> t, var<string> path)->() | save(T1,path) | save(tensor<any> t, var<string> path)->() |
 | print | miaobyte | print(tensor<any> t)->() | print(T1) | print(tensor<any> t)->() |
 | print | miaobyte | print(tensor<any> t, var<string> format)->() | print(T1) | print(tensor<any> t, var<string> format)->() |
 | load |  none  | load(var<string> path)->() | mem.load(path) | load(var<string> path)->() |
 
+### matmul
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| 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) |
+
 ### init
 
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| normal | miaobyte | normal(tensor<any> t, var<any> mean, var<any> std, var<int32> seed)->() | normal(T1,mean,stddev,seed) | normal(tensor<any> t, var<any> mean, var<any> std, var<int32> seed)->() |
-| 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)->() |
-| 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)->() |
-| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1,value) | constant(tensor<any> t, var<any> value)->() |
+| normal | miaobyte | normal(var<any> mean, var<any> std, var<int32> seed)->(tensor<any> t) | normal(mean,stddev,seed)->T1 | normal(var<any> mean, var<any> std, var<int32> seed)->(tensor<any> t) |
+| uniform | miaobyte | uniform(var<any> low, var<any> high, var<int32> seed)->(tensor<any> t) | uniform(low,high,seed)->T1 | uniform(var<any> low, var<any> high, var<int32> seed)->(tensor<any> t) |
+| arange | miaobyte | arange(var<any> start, var<any> step)->(tensor<any> t) | arange(start,step)->T1 | arange(var<any> start, var<any> step)->(tensor<any> t) |
+| constant | miaobyte | constant(var<any> value)->(tensor<any> t) | constant(value)->T1 | constant(var<any> value)->(tensor<any> t) |
 
 ### elementwise
 
@@ -61,21 +69,14 @@
 | 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) |
 | sqrt | miaobyte | sqrt(tensor<any> A)->(tensor<any> C) | T3=sqrt(T1) | sqrt(tensor<any> A)->(tensor<any> C) |
 | 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) |
+| exp | miaobyte | exp(tensor<any> A)->(tensor<any> C) | T3=exp(T1) | exp(tensor<any> A)->(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) |
 | equal | miaobyte | equal(tensor<any> A, tensor<any> B)->(tensor<bool> mask) | mask=equal(T1,T2) | equal(tensor<any> A, tensor<any> B)->(tensor<bool> mask) |
 | 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) |
 | invert | miaobyte | invert(tensor<int64|int32|int16|int8> A)->(tensor<int64|int32|int16|int8> C) | T3=~T1 | invert(tensor<int64|int32|int16|int8> A)->(tensor<int64|int32|int16|int8> 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) |
-| 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) |
-
-### matmul
-
-| Operation | Author | Func Def | Math Formula | IR Instruction |
-|-----------|--------|------------|--------------|----------------|
-| 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) |
 
 ### reduce
 

excuter/cpp-common/src/deepx/shape.cpp

@@ -8,18 +8,19 @@
 #include "deepx/dtype.hpp"
 namespace deepx
 {
-
     Shape::Shape(const int *shape, int dim)
     {
         setshape(shape, dim);
     }
+    int Shape::dim() const{
+        return shape.size();
+    }
     int64_t Shape::bytes() const{
         return size * (precision_bits(dtype) / 8);
     }
     void Shape::setshape(const int *shape, int dim)
     {
         this->shape.resize(dim);
-        this->dim = dim;
         std::copy(shape, shape + dim, this->shape.begin());
         strides.resize(dim);
         strides[dim - 1] = 1;
@@ -57,10 +58,10 @@ namespace deepx
     void Shape::print() const
     {
         std::cout << "shape:[";
-        for (int i = 0; i < dim; ++i)
+        for (int i = 0; i < dim(); ++i)
         {
             std::cout << shape[i];
-            if (i < dim - 1)
+            if (i < dim() - 1)
                 std::cout << ", ";
         }
         std::cout << "]" << std::endl;
@@ -73,8 +74,8 @@ namespace deepx
         return idx;
     }
     std::vector<int> Shape::linearto(int idx_linear) const{
-        std::vector<int> indices(dim,0);
-        for(int i=0;i<dim;i++){
+        std::vector<int> indices(dim(),0);
+        for(int i=0;i<dim();i++){
             indices[i]=idx_linear/strides[i];
             idx_linear%=strides[i];
         }
@@ -84,7 +85,7 @@ namespace deepx
     std::string Shape::toYaml() const{
         YAML::Node node;
         node["dtype"] = precision_str(dtype);
-        node["dim"] = dim;
+        node["dim"] = dim();
         node["shape"] = shape;
         node["stride"] = strides;
         node["size"] = size;
@@ -93,9 +94,43 @@ namespace deepx
     void Shape::fromYaml(const std::string &yaml){
         YAML::Node node = YAML::Load(yaml);
         dtype = precision(node["dtype"].as<std::string>());
-        dim = node["dim"].as<int>();
         shape = node["shape"].as<std::vector<int>>();
         strides=node["stride"].as<std::vector<int>>();
         size=node["size"].as<int>();
+
+        //check
+        Shape checkedshape(shape);
+        if(checkedshape.shape!=shape){
+            throw std::runtime_error("Shape::fromYaml: shape mismatch");
+        }
+        if(checkedshape.strides!=strides){
+            throw std::runtime_error("Shape::fromYaml: strides mismatch");
+        }
+         if(checkedshape.size!=size){
+            throw std::runtime_error("Shape::fromYaml: size mismatch");
+        }
     }
+
+    void Shape::saveShape( const std::string &tensorPath) const{
+            std::string shapedata = toYaml();
+            std::ofstream shape_fs(tensorPath + ".shape", std::ios::binary);
+            shape_fs.write(shapedata.c_str(), shapedata.size());
+            shape_fs.close();
+        }
+
+    pair<std::string,Shape> Shape::loadShape(const std::string &path)   
+    {
+        std::string shapepath = path + ".shape";
+        std::ifstream shape_fs(shapepath, std::ios::binary);
+        if (!shape_fs.is_open())
+        {
+                throw std::runtime_error("Failed to open shape file: " + shapepath);
+            }
+            std::string shapedata((std::istreambuf_iterator<char>(shape_fs)), std::istreambuf_iterator<char>());
+            Shape shape;
+            shape.fromYaml(shapedata);
+            std::string filename = stdutil::filename(path);
+            std::string tensor_name = filename.substr(0, filename.find_last_of('.'));
+            return std::make_pair(tensor_name, shape);
+        }
 }