Documentation

vcmpsel

Description

Compares lhs with rhs element-wise based on operation. The operations specified by operation are selected from eq, ne, lt, gt, le, and ge, which indicate ==, !=, <, >, <=, and >=, respectively. If the expression is true, the value of slhs is returned. Otherwise, the value of srhs is returned.

The following describes the operations by using expressions. a indicates an element in lhs, b indicates an element in rhs, c indicates an element in slhs, d indicates an element in srhs, and res indicates an element of the result tensor. The expression is as follows:
  • lt: res = c (a < b) or d (a >= b)
  • gt: res = c (a > b) or d (a <= b)
  • le: res = c (a <= b) or d (a > b)
  • ge: res = c (a >= b) or d (a < b)
  • eq: res = c (a == b) or d (a != b)
  • ne: res = c (a != b) or d (a == b)
  • If rhs is None, the elements in lhs are compared with the floating-point number 2.0.
  • If slhs is None, the value of lhs is returned when the expression is true.
  • If srhs is None and rhs is a tensor, the value of rhs is returned when the expression is not true.

If srhs is None and rhs is a scalar, the floating-point number 0.0 is returned when the expression is not true.

Prototype

vcmpsel(lhs,rhs=None,operation='lt', slhs=None, srhs=None)

Parameters

  • lhs: a tvm.tensor for the left operand.
  • rhs: a tvm.tensor or scalar for the right operand. Defaults to None.
  • slhs: a tvm.tensor or scalar for the value returned when the comparison expression is true. Defaults to None.
  • srhs: a tvm.tensor or scalar for the value returned when the comparison expression is not true. Defaults to None.
  • operation: operation type selected from eq, ne, lt, gt, ge, or le. Defaults to lt.
  • The data types of all parameters must be the same.

    Atlas 200/300/500 Inference Product: supports float16.

    Atlas Training Series Product: supports float16 and float32.

Returns

res_tensor: a tvm.tensor for the result tensor

Restrictions

None

Applicability

Atlas 200/300/500 Inference Product

Atlas Training Series Product

Example

from tbe import tvm
from tbe import dsl
shape = (1024,1024)
input_dtype = "float16"
data1 = tvm.placeholder(shape, name="data1", dtype=input_dtype)
data2 = tvm.placeholder(shape, name="data2", dtype=input_dtype) 
data3 = tvm.placeholder(shape, name="data3", dtype=input_dtype)
data4 = tvm.placeholder(shape, name="data4", dtype=input_dtype)
res = dsl.vcmpsel(data1, data2, 'gt', data3, data4)
Parent topic: Math Compute APIs