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miaobyte merged 3 commits into from
Apr 19, 2025
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doc:tensor list&normal初始化 #25

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7a343f0
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。
miaobyte Apr 19, 2025
c7d9ad1
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。
miaobyte Apr 19, 2025
e9c0411
doc:tensor list
miaobyte Apr 19, 2025
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88 changes: 66 additions & 22 deletions doc/excuter/op-mem-cuda/list.md
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Expand Up @@ -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) |
Expand All @@ -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) |

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