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miaobyte merged 3 commits into from
Jun 2, 2025
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repeat #69

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c28e243
repeat:ompsimd 实现完成，待验证
miaobyte Jun 1, 2025
19adfc8
repeat:ompsimd 实现完成，待验证
miaobyte Jun 1, 2025
d39e7fe
repeat:cuda 实现完成，完成验证
miaobyte Jun 2, 2025
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125 changes: 63 additions & 62 deletions doc/excuter/op-mem-cuda/list.md
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Expand Up @@ -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) |

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