equal¶

nvtripy.equal(input: Tensor, other: Tensor) → bool[source]¶

Returns True if input and other have the same shape and elements.

Caution

This function cannot be used in a compiled function or nvtripy.Module because it depends on evaluating its inputs, which is not allowed during compilation.

Parameters:

input (Tensor) – [dtype=T1] First tensor to compare.
other (Tensor) – [dtype=T1] Second tensor to compare.

Returns:

True if the tensors have the same shape and elements and False otherwise.

Return type:

bool

DATA TYPE CONSTRAINTS:

T1: bool, float16, bfloat16, float32, int8, int32, int64

Example: Identical tensors

a = tp.ones((1, 2), dtype=tp.float32)
b = tp.ones((1, 2), dtype=tp.float32)

is_equal = tp.equal(a, b)

Local Variables¶

>>> a
tensor(
    [[1, 1]], 
    dtype=float32, loc=gpu:0, shape=(1, 2))

>>> b
tensor(
    [[1, 1]], 
    dtype=float32, loc=gpu:0, shape=(1, 2))

>>> is_equal
True

Example: Different shapes

a = tp.ones((1, 2), dtype=tp.float32)
b = tp.ones((2, 2), dtype=tp.float32)

is_equal = tp.equal(a, b)

Local Variables¶

>>> a
tensor(
    [[1, 1]], 
    dtype=float32, loc=gpu:0, shape=(1, 2))

>>> b
tensor(
    [[1, 1],
     [1, 1]], 
    dtype=float32, loc=gpu:0, shape=(2, 2))

>>> is_equal
False

Example: Different elements

a = tp.ones((1, 2), dtype=tp.float32)
b = tp.zeros((1, 2), dtype=tp.float32)

is_equal = tp.equal(a, b)

Local Variables¶

>>> a
tensor(
    [[1, 1]], 
    dtype=float32, loc=gpu:0, shape=(1, 2))

>>> b
tensor(
    [[0, 0]], 
    dtype=float32, loc=gpu:0, shape=(1, 2))

>>> is_equal
False