doc:tensor list&normal初始化 (#25)

* normal:初始化。解了一天bug，最后发现init.hpp switch里漏写了break，导致normal调用后数据异常。

* normal:初始化。解了一天bug，最后发现init.hpp switch里漏写了break，导致normal调用后数据异常。

* doc:tensor list

Author: miaobyte

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

@@ -2,44 +2,63 @@
 
 本页面由 `excuter/op-mem-cuda 生成，请勿手动修改 
 
+### arg
+
 | Operation | Author | Func Def | Math Formula | IR Instruction |
 |-----------|--------|------------|--------------|----------------|
-| 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> 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) |
+| 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) |
+
+### io
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| print | miaobyte | print(tensor<any> )->() | print(T1) | print(tensor<any> )->() |
+| print | miaobyte | print(tensor<any> , var<string> )->() | print(T1) | print(tensor<any> , var<string> )->() |
+
+### tensorlife
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| copytensor |  none  | copytensor(tensor<any> src, tensor<any> dst)->() | T2.data = T1.data | copytensor(tensor<any> src, tensor<any> dst)->() |
+| 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)->() |
+
+### 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)->() |
+
+### elementwise
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| switch | miaobyte | switch(listtensor<any> tensors, tensor<int8> cases)->(tensor<any> result) | C=switch(tensors,cases) | switch(listtensor<any> tensors, tensor<int8> cases)->(tensor<any> result) |
+| greaterscalar | miaobyte | greaterscalar(tensor<any> A, var<any> scalar)->(tensor<bool> mask) | mask=compare(T1, scalar) | greaterscalar(tensor<any> A, var<any> scalar)->(tensor<bool> mask) |
 | equalscalar | miaobyte | equalscalar(tensor<any> A, var<any> scalar, var<float64> epsilon)->(tensor<bool> mask) | mask=compare(T1, scalar) | equalscalar(tensor<any> A, var<any> scalar, var<float64> epsilon)->(tensor<bool> mask) |
-| 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) |
 | 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) |
-| 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)->() |
-| 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) |
-| constant | miaobyte | constant(tensor<any> t, var<any> value)->() | constant(T1) | constant(tensor<any> t, var<any> value)->() |
+| greater | miaobyte | greater(tensor<any> A, tensor<any> B)->(tensor<bool> mask) | mask=compare(T1, T2) | greater(tensor<any> A, tensor<any> B)->(tensor<bool> mask) |
+| less | miaobyte | less(tensor<any> A, tensor<any> B)->(tensor<bool> mask) | mask=compare(T1, T2) | less(tensor<any> A, tensor<any> B)->(tensor<bool> mask) |
 | powscalar | miaobyte | powscalar(tensor<float64|float32> A, var<float64|int32> scalar)->(tensor<float64|float32> C) | T3=pow(T1, scalar) | powscalar(tensor<float64|float32> A, var<float64|int32> 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) |
 | 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) |
 | rpowscalar | miaobyte | rpowscalar(var<float64|int32> scalar, tensor<float64|float32> A)->(tensor<float64|float32> C) | T3=pow(scalar, T1) | rpowscalar(var<float64|int32> scalar, tensor<float64|float32> A)->(tensor<float64|float32> 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) |
+| 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) |
 | 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) |
@@ -48,5 +67,30 @@
 | 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) |
-| deltensor |  none  | deltensor(tensor<any> t)->() | del T1 | deltensor(tensor<any> t)->() |
+| lessscalar | miaobyte | lessscalar(tensor<any> A, var<any> scalar)->(tensor<bool> mask) | mask=compare(T1, scalar) | lessscalar(tensor<any> A, var<any> scalar)->(tensor<bool> mask) |
 | 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) |
+
+### 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) |
+
+### changeshape
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| 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) | 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) |
+
+### reduce
+
+| Operation | Author | Func Def | Math Formula | IR Instruction |
+|-----------|--------|------------|--------------|----------------|
+| 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) |
+| 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) |
+| 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) |
+| 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) |
+