repeat (#69)

* repeat:ompsimd 实现完成，待验证

* repeat:ompsimd 实现完成，待验证

* repeat:cuda 实现完成，完成验证

Author: miaobyte

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

@@ -2,105 +2,106 @@
 
 本页面由 `excuter/op-mem-cuda 生成，请勿手动修改 
 
-### arg
+### matmul
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| vecset |  none  | [3  4  5]->shape | vecset(vector<any>:value)->(vector<any>:name) |
-| argset |  none  | argvalue->argname | argset(var<any>:value)->(var<any>:name) |
+| matmul | cublas | T3=T1 @ T2 | matmul(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
 
-### tensorlife
+### init
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| renametensor |  none  | rename(newname)->T1 | renametensor(var<string>:new_name)->(tensor<any>:t) |
-| newtensor |  none  | T1 = zeros(shape) | newtensor(vector<int32>:shape)->(tensor<any>:tensor1) |
-| newtensor |  none  | T1 = zeros(shape) | newtensor(var<string>:shape)->(tensor<any>:tensor1) |
-| deltensor |  none  | del->T1 | deltensor()->(tensor<any>:t) |
-| copytensor |  none  | T2.data = T1.data | copytensor(tensor<any>:src)->(tensor<any>:dst) |
+| constant | miaobyte | constant(value)->T1 | constant(var<any>:value)->(tensor<any>:t) |
+| arange | miaobyte | arange(start,step)->T1 | arange(var<any>:start, var<any>:step)->(tensor<any>:t) |
+| uniform | miaobyte | uniform(low,high,seed)->T1 | uniform(var<any>:low, var<any>:high, var<int32>:seed)->(tensor<any>:t) |
+| dropout | miaobyte | dropout(p,seed)->A | dropout(var<float32>:p, var<int32>:seed)->(tensor<any>:A) |
+| normal | miaobyte | normal(mean,stddev,seed)->T1 | normal(var<any>:mean, var<any>:stddev, var<int32>:seed)->(tensor<any>:t) |
 
 ### io
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| loadtensordata |  none  | loadtensordata(path)->tensor | loadtensordata(var<string>:path)->(tensor<any>:t) |
-| save |  none  | save(T1,path) | save(tensor<any>:t, var<string>:path)->() |
+| load |  none  | load(path) | load(var<string>:path)->() |
 | print | miaobyte | print(T1) | print(tensor<any>:t)->() |
 | print | miaobyte | print(T1) | print(tensor<any>:t, var<string>:format)->() |
-| load |  none  | load(path) | load(var<string>:path)->() |
+| save |  none  | save(T1,path) | save(tensor<any>:t, var<string>:path)->() |
+| loadtensordata |  none  | loadtensordata(path)->tensor | loadtensordata(var<string>:path)->(tensor<any>:t) |
 
-### matmul
+### arg
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| matmul | cublas | T3=T1 @ T2 | matmul(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
+| argset |  none  | argvalue->argname | argset(var<any>:value)->(var<any>:name) |
+| vecset |  none  | [3  4  5]->shape | vecset(vector<any>:value)->(vector<any>:name) |
 
-### init
+### tensorlife
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| normal | miaobyte | normal(mean,stddev,seed)->T1 | normal(var<any>:mean, var<any>:stddev, var<int32>:seed)->(tensor<any>:t) |
-| dropout | miaobyte | dropout(p,seed)->A | dropout(var<float32>:p, var<int32>:seed)->(tensor<any>:A) |
-| uniform | miaobyte | uniform(low,high,seed)->T1 | uniform(var<any>:low, var<any>:high, var<int32>:seed)->(tensor<any>:t) |
-| arange | miaobyte | arange(start,step)->T1 | arange(var<any>:start, var<any>:step)->(tensor<any>:t) |
-| constant | miaobyte | constant(value)->T1 | constant(var<any>:value)->(tensor<any>:t) |
+| copytensor |  none  | T2.data = T1.data | copytensor(tensor<any>:src)->(tensor<any>:dst) |
+| newtensor |  none  | T1 = zeros(shape) | newtensor(vector<int32>:shape)->(tensor<any>:tensor1) |
+| newtensor |  none  | T1 = zeros(shape) | newtensor(var<string>:shape)->(tensor<any>:tensor1) |
+| renametensor |  none  | rename(newname)->T1 | renametensor(var<string>:new_name)->(tensor<any>:t) |
+| deltensor |  none  | del->T1 | deltensor()->(tensor<any>:t) |
 
 ### elementwise
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| switch | miaobyte | C=switch(tensors,cases) | switch(listtensor<any>:tensors, tensor<int32|bool>:cases)->(tensor<any>:result) |
-| greaterscalar | miaobyte | mask=compare(T1, scalar) | greaterscalar(tensor<any>:A, var<any>:scalar)->(tensor<bool>:mask) |
-| notequal | miaobyte | T1!=T2->mask | notequal(tensor<any>:A, tensor<any>:B, var<float32>:epsilon)->(tensor<bool>:mask) |
-| equalscalar | miaobyte | T1==scalar->mask | equalscalar(tensor<any>:A, var<any>:scalar, var<float32>:epsilon)->(tensor<bool>:mask) |
-| min | miaobyte | T3=min(T1, T2) | min(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
-| maxscalar | miaobyte | T3=max(T1, scalar) | maxscalar(tensor<any>:A, var<any>:scalar)->(tensor<any>:C) |
-| tan | miaobyte | T3=tan(T1) | tan(tensor<float64|float32>:A)->(tensor<float64|float32>:C) |
-| sin | miaobyte | T3=sin(T1) | sin(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
-| less | miaobyte | mask=compare(T1, T2) | less(tensor<any>:A, tensor<any>:B)->(tensor<bool>:mask) |
-| powscalar | miaobyte | T3=pow(T1, scalar) | powscalar(tensor<float64|float32>:A, var<float64|int32>:scalar)->(tensor<float64|float32>:C) |
-| rsubscalar | miaobyte | T3=scalar-T1 | rsubscalar(var<any>:scalar, tensor<any>:A)->(tensor<any>:C) |
-| divscalar | miaobyte | T3=scalar/T1 | divscalar(tensor<any>:A, var<any>:scalar)->(tensor<any>:C) |
-| log | miaobyte | T3=log(T1) | log(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
-| addscalar | miaobyte | T3=T1+scalar | addscalar(tensor<any>:A, var<any>:b)->(tensor<any>:C) |
+| pow | miaobyte | T3=pow(T1, T2) | pow(tensor<float64|float32>:A, tensor<float64|float32>:B)->(tensor<float64|float32>:C) |
+| max | miaobyte | T3=max(T1, T2) | max(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
+| mulscalar | miaobyte | T3=T1*scalar | mulscalar(tensor<any>:A, var<any>:b)->(tensor<any>:C) |
+| equal | miaobyte | T1==T2->mask | equal(tensor<any>:A, tensor<any>:B, var<float32>:epsilon)->(tensor<bool>:mask) |
+| mul | miaobyte | T3=T1*T2 | mul(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
+| exp | miaobyte | T3=exp(T1) | exp(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
+| subscalar | miaobyte | T3=T1-scalar | subscalar(tensor<any>:A, var<any>:b)->(tensor<any>:C) |
+| sqrt | miaobyte | T3=sqrt(T1) | sqrt(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
+| sub | miaobyte | T3=T1-T2 | sub(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
+| add | cublas | T3=T1+T2 | add(tensor<any>:a, tensor<any>:b)->(tensor<any>:c) |
+| add | miaobyte | T3=T1+T2 | add(tensor<any>:a, tensor<any>:b)->(tensor<any>:c) |
+| todtype |  none  | T3(dtypeA)->T1(dtypeB) | todtype(tensor<any>:a)->(tensor<any>:b) |
+| invert | miaobyte | T3=~T1 | invert(tensor<int64|int32|int16|int8|bool>:A)->(tensor<int64|int32|int16|int8|bool>:C) |
+| rdivscalar | miaobyte | T3=scalar/T1 | rdivscalar(var<any>:scalar, tensor<any>:A)->(tensor<any>:C) |
+| rpowscalar | miaobyte | T3=pow(scalar, T1) | rpowscalar(var<float32|int32>:scalar, tensor<float64|float32>:A)->(tensor<float64|float32>:C) |
+| cos | miaobyte | T3=cos(T1) | cos(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
 | greater | miaobyte | mask=compare(T1, T2) | greater(tensor<any>:A, tensor<any>:B)->(tensor<bool>:mask) |
+| addscalar | miaobyte | T3=T1+scalar | addscalar(tensor<any>:A, var<any>:b)->(tensor<any>:C) |
+| div | miaobyte | T3=T1/T2 | div(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
+| divscalar | miaobyte | T3=scalar/T1 | divscalar(tensor<any>:A, var<any>:scalar)->(tensor<any>:C) |
 | lessscalar | miaobyte | mask=compare(T1, scalar) | lessscalar(tensor<any>:A, var<any>:scalar)->(tensor<bool>:mask) |
-| cos | miaobyte | T3=cos(T1) | cos(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
+| less | miaobyte | mask=compare(T1, T2) | less(tensor<any>:A, tensor<any>:B)->(tensor<bool>:mask) |
 | notequalscalar | miaobyte | T1!=scalar->mask | notequalscalar(tensor<any>:A, var<any>:scalar, var<float32>:epsilon)->(tensor<bool>:mask) |
+| rsubscalar | miaobyte | T3=scalar-T1 | rsubscalar(var<any>:scalar, tensor<any>:A)->(tensor<any>:C) |
+| sin | miaobyte | T3=sin(T1) | sin(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
 | minscalar | miaobyte | T3=min(T1, scalar) | minscalar(tensor<any>:A, var<any>:scalar)->(tensor<any>:C) |
-| rpowscalar | miaobyte | T3=pow(scalar, T1) | rpowscalar(var<float32|int32>:scalar, tensor<float64|float32>:A)->(tensor<float64|float32>:C) |
-| rdivscalar | miaobyte | T3=scalar/T1 | rdivscalar(var<any>:scalar, tensor<any>:A)->(tensor<any>:C) |
-| todtype |  none  | T3(dtypeA)->T1(dtypeB) | todtype(tensor<any>:a)->(tensor<any>:b) |
-| add | cublas | T3=T1+T2 | add(tensor<any>:a, tensor<any>:b)->(tensor<any>:c) |
-| add | miaobyte | T3=T1+T2 | add(tensor<any>:a, tensor<any>:b)->(tensor<any>:c) |
-| sub | miaobyte | T3=T1-T2 | sub(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
-| sqrt | miaobyte | T3=sqrt(T1) | sqrt(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
-| subscalar | miaobyte | T3=T1-scalar | subscalar(tensor<any>:A, var<any>:b)->(tensor<any>:C) |
-| exp | miaobyte | T3=exp(T1) | exp(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
-| mul | miaobyte | T3=T1*T2 | mul(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
-| equal | miaobyte | T1==T2->mask | equal(tensor<any>:A, tensor<any>:B, var<float32>:epsilon)->(tensor<bool>:mask) |
-| mulscalar | miaobyte | T3=T1*scalar | mulscalar(tensor<any>:A, var<any>:b)->(tensor<any>:C) |
-| div | miaobyte | T3=T1/T2 | div(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
-| invert | miaobyte | T3=~T1 | invert(tensor<int64|int32|int16|int8|bool>:A)->(tensor<int64|int32|int16|int8|bool>:C) |
-| max | miaobyte | T3=max(T1, T2) | max(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
-| pow | miaobyte | T3=pow(T1, T2) | pow(tensor<float64|float32>:A, tensor<float64|float32>:B)->(tensor<float64|float32>:C) |
+| tan | miaobyte | T3=tan(T1) | tan(tensor<float64|float32>:A)->(tensor<float64|float32>:C) |
+| maxscalar | miaobyte | T3=max(T1, scalar) | maxscalar(tensor<any>:A, var<any>:scalar)->(tensor<any>:C) |
+| min | miaobyte | T3=min(T1, T2) | min(tensor<any>:A, tensor<any>:B)->(tensor<any>:C) |
+| log | miaobyte | T3=log(T1) | log(tensor<float64|float32|float16|bfloat16>:A)->(tensor<float64|float32|float16|bfloat16>:C) |
+| equalscalar | miaobyte | T1==scalar->mask | equalscalar(tensor<any>:A, var<any>:scalar, var<float32>:epsilon)->(tensor<bool>:mask) |
+| notequal | miaobyte | T1!=T2->mask | notequal(tensor<any>:A, tensor<any>:B, var<float32>:epsilon)->(tensor<bool>:mask) |
+| greaterscalar | miaobyte | mask=compare(T1, scalar) | greaterscalar(tensor<any>:A, var<any>:scalar)->(tensor<bool>:mask) |
+| switch | miaobyte | C=switch(tensors,cases) | switch(listtensor<any>:tensors, tensor<int32|bool>:cases)->(tensor<any>:result) |
+| powscalar | miaobyte | T3=pow(T1, scalar) | powscalar(tensor<float64|float32>:A, var<float64|int32>:scalar)->(tensor<float64|float32>:C) |
 
-### reduce
+### changeshape
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| prod | miaobyte | B = prod(A, axis=[1 2], keepdims=false) | prod(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
-| reducemax | miaobyte | B = reducemax(A, axis=[1 2], keepdims=false) | reducemax(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
-| sum | miaobyte | B = sum(A, axis=[1 2], keepdims=false) | sum(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
-| reducemin | miaobyte | B = reducemin(A, axis=[1 2], keepdims=false) | reducemin(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
+| reshape | miaobyte | T1.reshape(shape)->T2 | reshape(tensor<any>:A, vector<int32>:shape)->(tensor<any>:B) |
+| transpose | miaobyte | T2 = T1.transpose(dimorder=[1,0]) | transpose(tensor<any>:A, vector<int32>:dim_order)->(tensor<any>:C) |
+| concat | miaobyte | Tresult = concat([T1, T2...], axis=3) | concat(listtensor<any>:tensors, var<int32>:dim)->(tensor<any>:result) |
+| broadcastTo | miaobyte | T2 = T1.broadcastTo(new_shape=[4,3,2]) | broadcastTo(tensor<any>:A, vector<int32>:new_shape)->(tensor<any>:B) |
+| indexselect | miaobyte | T2 = T1.indexselect(index=[1,2], axis=1) | indexselect(tensor<any>:A, tensor<int64|int32>:indices, var<int32>:axis)->(tensor<any>:B) |
+| repeat | miaobyte | T2 = T1.repeat(repeats=[3 4 5]) | repeat(tensor<any>:A, vector<int32>:repeats)->(tensor<any>:B) |
 
-### changeshape
+### reduce
 
 | Operation | Author |  Math Formula | IR Instruction |
 |-----------|--------|--------------|----------------|
-| indexselect | miaobyte | T2 = T1.indexselect(index=[1,2], axis=1) | indexselect(tensor<any>:A, tensor<int64|int32>:indices, var<int32>:axis)->(tensor<any>:B) |
-| broadcastTo | miaobyte | T2 = T1.broadcastTo(new_shape=[4,3,2]) | broadcastTo(tensor<any>:A, vector<int32>:new_shape)->(tensor<any>:B) |
-| concat | miaobyte | Tresult = concat([T1, T2...], axis=3) | concat(listtensor<any>:tensors, var<int32>:dim)->(tensor<any>:result) |
-| transpose | miaobyte | T2 = T1.transpose(dimorder=[1,0]) | transpose(tensor<any>:A, vector<int32>:dim_order)->(tensor<any>:C) |
-| reshape | miaobyte | T1.reshape(shape)->T2 | reshape(tensor<any>:A, vector<int32>:shape)->(tensor<any>:B) |
+| reducemin | miaobyte | B = reducemin(A, axis=[1 2], keepdims=false) | reducemin(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
+| sum | miaobyte | B = sum(A, axis=[1 2], keepdims=false) | sum(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
+| reducemax | miaobyte | B = reducemax(A, axis=[1 2], keepdims=false) | reducemax(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |
+| prod | miaobyte | B = prod(A, axis=[1 2], keepdims=false) | prod(tensor<any>:A, vector<int32>:dims, var<bool>:keepdims)->(tensor<any>:B) |