concrete/docs/user/tutorial/NUMPY_SUPPORT.md

Numpy Support

In this section, we list the operations which are supported currently in the Concrete Framework. Please have a look to numpy documentation to know what these operations are about.

Unary operations

List of supported unary functions:

• absolute
• arccos
• arccosh
• arcsin
• arcsinh
• arctan
• arctanh
• cbrt
• ceil
• cos
• cosh
• deg2rad
• degrees
• exp
• exp2
• expm1
• fabs
• floor
• isfinite
• isinf
• isnan
• log
• log10
• log1p
• log2
• logical_not
• negative
• positive
• rad2deg
• radians
• reciprocal
• rint
• sign
• signbit
• sin
• sinh
• spacing
• sqrt
• square
• tan
• tanh
• trunc

Binary operations

List of supported binary functions if one of the two operators is a constant scalar:

• arctan2
• bitwise_and
• bitwise_or
• bitwise_xor
• copysign
• equal
• float_power
• floor_divide
• fmax
• fmin
• fmod
• gcd
• greater
• greater_equal
• heaviside
• hypot
• lcm
• ldexp
• left_shift
• less
• less_equal
• logaddexp
• logaddexp2
• logical_and
• logical_or
• logical_xor
• maximum
• minimum
• nextafter
• not_equal
• power
• remainder
• right_shift
• true_divide

Indexing

Indexing is described in this section.

Other machine-learning-related operators

We support (sometimes, with limits) some other operators:

• dot: one of the operators must be non-encrypted
• clip: the minimum and maximum values must be constant
• transpose
• ravel
• reshape: the shapes must be constant
• flatten
• matmul: one of the two matrices must be non-encrypted. Only 2D matrix multiplication is supported for now

Operators which are not numpy-restricted

The framework also gives support for:

• shifts, i.e., x op y for op in [<<, >>, ]: if one of x or y is a constant
• boolean test operations, i.e., x op y for op in [<, <=, ==, !=, >, >=]: if one of x or y is a constant
• boolean operators, i.e., x op y for op in [&, ^, |]: if one of x or y is a constant
• powers, i.e., x ** y: if one of x or y is a constant
• modulo, i.e., x % y: if one of x or y is a constant
• invert, i.e., ~x
• true div, i.e., x / y: if one of x or y is a constant
• floor div, i.e., x // y: if one of x or y is a constant

There is support for astype as well, e.g. x.astype(numpy.int32). This allows to control which data type to use for computations. In the context of FHE going back to integers may allow to fuse floating point operations together, see this tutorial to see how to work with floating point values.

FIXME(Umut): speak about `shape`