excuter/op-mem-ompsimd 支持算子列表

本页面由 excuter/op-mem-ompsimd/src/deepx/op/opfactory.hpp 生成，请勿手动修改

Operation	Data Types	Math Formula	IR Instruction
sum	float32, float64	T2 = sum(T1, dims=[1,2])	sum@float32 T1 1 2 -> T2
matmul	float32, float64	T3 = T1 @ T2	matmul@float32 T1 T2 -> T3
concat	float32, float64	T3 = concat([T1, T2], axis=3)	concat@float32 T1 T2 3 -> T3
pow_scalar	float32, float64	T2 = T1 ^ 2.0	pow_scalar@float32 T1 2.0 -> T2
pow	float32, float64	T3 = T1 ^ T2	pow@float32 T1 T2 -> T3
max_scalar	float32, float64	T2 = max(T1, 0.0)	max_scalar@float32 T1 0.0 -> T2
exp	float32, float64	T2 = exp(T1)	exp@float32 T1 -> T2
min_scalar	float32, float64	B= min(A, 1.0)	min_scalar@float32 A 1.0 -> B
sqrt	float32, float64	T2 = sqrt(T1)	sqrt@float32 T1 -> T2
div	float32, float64	T3 = T1 / T2	div@float32 T1 T2 -> T3
mul	float32, float64	T3 = T1 * T2	mul@float32 T1 T2 -> T3
newtensor	float32, float64, int16, int32, int64, int8	T1 = zeros(shape)	newtensor@float32 shape -> T1
print	any		print@any ->
min	float32, float64	C = min(A,B)	min@float32 A B -> C
copytensor	float32, float64, int16, int32, int64, int8	T2 = T1.copy()	copytensor@float32 T1 -> T2
clonetensor	float32, float64, int16, int32, int64, int8	T2 = T1.clone()	clonetensor@float32 T1 -> T2
arange	float32, float64	arange(start=0.0, step=1.0,T1)	arange@float32 0.0 1.0 -> T1
argset	float32, float64, int32	shape = [3, 4, 5]	argset@int32 3 4 5 -> shape
sub	float32, float64	T3 = T1 - T2	sub@int32 T1 T2 -> T3
mul_scalar	float32, float64	T2 = T1 * 2.0	mul_scalar@float32 T1 2.0 -> T2
uniform	float32, float64	uniform(-1.0, 1.0,T1)	uniform@float32 -1.0 1.0 -> T1
add	float32, float64	T3 = T1 + T2	add@int32 T1 T2 -> T3
max	float32, float64	T3 = max(T1,T2)	max@float32 T1 -> T2
constant	float32, float64	T1 = full(shape, 0.0)	constant@float32 0.0 -> T1
rdiv_scalar	float32, float64	T3 =1 / T2	rdiv_scalar@float32 1 T2 -> T3
add_scalar	float32, float64	T2 = T1 + 1.0	add_scalar@float32 T1 1.0 -> T2
transpose	any	T2 = transpose(T1, dimorder=[1,0])	transpose@float32 T1 1 0 -> T2
div_scalar	float32, float64	T2 = T1 / 2.0	div_scalar@float32 T1 2.0 -> T2
reshape	any	T2 = reshape(T1, [2,3,4])	reshape@float32 T1 2 3 4 -> T2