mirror of
https://github.com/zama-ai/concrete.git
synced 2026-02-09 03:55:04 -05:00
138 lines
3.5 KiB
Markdown
138 lines
3.5 KiB
Markdown
# Numpy Support
|
|
|
|
In this section, we list the operations which are supported currently in the **Concrete Framework**. Please have a look to numpy [documentation](https://numpy.org/doc/stable/user/index.html) to know what these operations are about.
|
|
|
|
## Unary operations
|
|
|
|
<!--- gen_supported_ufuncs.py: inject supported operations [BEGIN] -->
|
|
<!--- do not edit, auto generated part by `python3 gen_supported_ufuncs.py` in docker -->
|
|
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
|
|
<!--- gen_supported_ufuncs.py: inject supported operations [END] -->
|
|
|
|
# Shapes
|
|
|
|
Our encrypted tensors have shapes just like numpy arrays.
|
|
We determine the shapes of the inputs from the inputset, and we infer the shapes of the intermediate values from the function that is being compiled.
|
|
|
|
You can access the shape of a tensor by accessing the `shape` property, just like in numpy.
|
|
Here is an example:
|
|
```python
|
|
def function_to_compile(x):
|
|
return x.reshape((x.shape[0], -1))
|
|
```
|
|
|
|
One important aspect of our library is that, scalars are tensors of shape `()`.
|
|
This is transparent to you, as a user, but it's something to keep in mind, especialy if you are accessing the `shape` property in the functions that you are compiling.
|
|
This schema is used by numpy and pytorch as well.
|
|
|
|
## Indexing
|
|
|
|
Indexing is described in [this section](../tutorial/indexing.md).
|
|
|
|
## 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](../tutorial/working_with_floating_points.md) to see how to work with floating point values.
|