#include <numeric>
#include <ostream>
#include <variant>
#include <pybind11/iostream.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include "mlx/ops.h"
#include "mlx/utils.h"
#include "python/src/load.h"
#include "python/src/utils.h"
namespace py = pybind11;
using namespace py::literals;
using namespace mlx::core;
using Scalar = std::variant<int, double>;
Dtype scalar_to_dtype(Scalar scalar) {
if (std::holds_alternative<int>(scalar)) {
return int32;
} else {
return float32;
}
}
double scalar_to_double(Scalar s) {
if (std::holds_alternative<double>(s)) {
return std::get<double>(s);
} else {
return static_cast<double>(std::get<int>(s));
}
}
void init_ops(py::module_& m) {
py::options options;
options.disable_function_signatures();
m.def(
"reshape",
&reshape,
"a"_a,
py::pos_only(),
"shape"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
reshape(a: array, /, shape: List[int], *, stream: Union[None, Stream, Device] = None) -> array
Reshape an array while preserving the size.
Args:
a (array): Input array.
shape (tuple(int)): New shape.
stream (Stream, optional): Stream or device. Defaults to ```None```
in which case the default stream of the default device is used.
Returns:
array: The reshaped array.
)pbdoc");
m.def(
"flatten",
[](const array& a,
int start_axis,
int end_axis,
const StreamOrDevice& s) { return flatten(a, start_axis, end_axis); },
"a"_a,
py::pos_only(),
"start_axis"_a = 0,
"end_axis"_a = -1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
flatten(a: array, /, start_axis: int = 0, end_axis: int = -1, *, stream: Union[None, Stream, Device] = None) -> array
Flatten an array.
Args:
a (array): Input array.
start_axis (int, optional): The first dimension to flatten. Defaults to ``0``.
end_axis (int, optional): The last dimension to flatten. Defaults to ``-1``.
stream (Stream, optional): Stream or device. Defaults to ``None``
in which case the default stream of the default device is used.
Returns:
array: The flattened array.
)pbdoc");
m.def(
"squeeze",
[](const array& a, const IntOrVec& v, const StreamOrDevice& s) {
if (std::holds_alternative<std::monostate>(v)) {
return squeeze(a, s);
} else if (auto pv = std::get_if<int>(&v); pv) {
return squeeze(a, *pv, s);
} else {
return squeeze(a, std::get<std::vector<int>>(v), s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = none,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
squeeze(a: array, /, axis: Union[None, int, List[int]] = None, *, stream: Union[None, Stream, Device] = None) -> array
Remove length one axes from an array.
Args:
a (array): Input array.
axis (int or tuple(int), optional): Axes to remove. Defaults
to ```None``` in which case all size one axes are removed.
Returns:
array: The output array with size one axes removed.
)pbdoc");
m.def(
"expand_dims",
[](const array& a,
const std::variant<int, std::vector<int>>& v,
StreamOrDevice s) {
if (auto pv = std::get_if<int>(&v); pv) {
return expand_dims(a, *pv, s);
} else {
return expand_dims(a, std::get<std::vector<int>>(v), s);
}
},
"a"_a,
py::pos_only(),
"axis"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
expand_dims(a: array, /, axis: Union[int, List[int]], *, stream: Union[None, Stream, Device] = None) -> array
Add a size one dimension at the given axis.
Args:
a (array): Input array.
axes (int or tuple(int)): The index of the inserted dimensions.
Returns:
array: The array with inserted dimensions.
)pbdoc");
m.def(
"abs",
&mlx::core::abs,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
abs(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise absolute value.
Args:
a (array): Input array.
Returns:
array: The absolute value of ``a``.
)pbdoc");
m.def(
"sign",
&sign,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sign(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise sign.
Args:
a (array): Input array.
Returns:
array: The sign of ``a``.
)pbdoc");
m.def(
"negative",
&negative,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
negative(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise negation.
Args:
a (array): Input array.
Returns:
array: The negative of ``a``.
)pbdoc");
m.def(
"add",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return add(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
add(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise addition.
Add two arrays with numpy-style broadcasting semantics. Either or both input arrays
can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The sum of ``a`` and ``b``.
)pbdoc");
m.def(
"subtract",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return subtract(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
subtract(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise subtraction.
Subtract one array from another with numpy-style broadcasting semantics. Either or both
input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The difference ``a - b``.
)pbdoc");
m.def(
"multiply",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return multiply(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
multiply(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise multiplication.
Multiply two arrays with numpy-style broadcasting semantics. Either or both
input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The multiplication ``a * b``.
)pbdoc");
m.def(
"divide",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return divide(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
divide(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise division.
Divide two arrays with numpy-style broadcasting semantics. Either or both
input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The quotient ``a / b``.
)pbdoc");
m.def(
"divmod",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return divmod(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
divmod(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise quotient and remainder.
The fuction ``divmod(a, b)`` is equivalent to but faster than
``(a // b, a % b)``. The function uses numpy-style broadcasting
semantics. Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
tuple(array, array): The quotient ``a // b`` and remainder ``a % b``.
)pbdoc");
m.def(
"floor_divide",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return floor_divide(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
floor_divide(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise integer division.
If either array is a floating point type then it is equivalent to
calling :func:`floor` after :func:`divide`.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The quotient ``a // b``.
)pbdoc");
m.def(
"remainder",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return remainder(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
remainder(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise remainder of division.
Computes the remainder of dividing a with b with numpy-style
broadcasting semantics. Either or both input arrays can also be
scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The remainder of ``a // b``.
)pbdoc");
m.def(
"equal",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return equal(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
equal(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise equality.
Equality comparison on two arrays with numpy-style broadcasting semantics.
Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The element-wise comparison ``a == b``.
)pbdoc");
m.def(
"not_equal",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return not_equal(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
not_equal(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise not equal.
Not equal comparison on two arrays with numpy-style broadcasting semantics.
Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The element-wise comparison ``a != b``.
)pbdoc");
m.def(
"less",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return less(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
less(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise less than.
Strict less than on two arrays with numpy-style broadcasting semantics.
Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The element-wise comparison ``a < b``.
)pbdoc");
m.def(
"less_equal",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return less_equal(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
less_equal(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise less than or equal.
Less than or equal on two arrays with numpy-style broadcasting semantics.
Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The element-wise comparison ``a <= b``.
)pbdoc");
m.def(
"greater",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return greater(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
greater(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise greater than.
Strict greater than on two arrays with numpy-style broadcasting semantics.
Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The element-wise comparison ``a > b``.
)pbdoc");
m.def(
"greater_equal",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return greater_equal(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
greater_equal(a: Union[scalar, array], b: Union[scalar, array], stream: Union[None, Stream, Device] = None) -> array
Element-wise greater or equal.
Greater than or equal on two arrays with numpy-style broadcasting semantics.
Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The element-wise comparison ``a >= b``.
)pbdoc");
m.def(
"array_equal",
[](const ScalarOrArray& a_,
const ScalarOrArray& b_,
bool equal_nan,
StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return array_equal(a, b, equal_nan, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"equal_nan"_a = false,
"stream"_a = none,
R"pbdoc(
array_equal(a: Union[scalar, array], b: Union[scalar, array], equal_nan: bool = False, stream: Union[None, Stream, Device] = None) -> array
Array equality check.
Compare two arrays for equality. Returns ``True`` if and only if the arrays
have the same shape and their values are equal. The arrays need not have
the same type to be considered equal.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
equal_nan (bool): If ``True``, NaNs are treated as equal.
Defaults to ``False``.
Returns:
array: A scalar boolean array.
)pbdoc");
m.def(
"matmul",
&matmul,
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
matmul(a: array, b: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Matrix multiplication.
Perform the (possibly batched) matrix multiplication of two arrays. This function supports
broadcasting for arrays with more than two dimensions.
- If the first array is 1-D then a 1 is prepended to its shape to make it
a matrix. Similarly if the second array is 1-D then a 1 is appended to its
shape to make it a matrix. In either case the singleton dimension is removed
from the result.
- A batched matrix multiplication is performed if the arrays have more than
2 dimensions. The matrix dimensions for the matrix product are the last
two dimensions of each input.
- All but the last two dimensions of each input are broadcast with one another using
standard numpy-style broadcasting semantics.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The matrix product of ``a`` and ``b``.
)pbdoc");
m.def(
"square",
&square,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
square(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise square.
Args:
a (array): Input array.
Returns:
array: The square of ``a``.
)pbdoc");
m.def(
"sqrt",
&mlx::core::sqrt,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sqrt(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise square root.
Args:
a (array): Input array.
Returns:
array: The square root of ``a``.
)pbdoc");
m.def(
"rsqrt",
&rsqrt,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
rsqrt(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise reciprocal and square root.
Args:
a (array): Input array.
Returns:
array: One over the square root of ``a``.
)pbdoc");
m.def(
"reciprocal",
&reciprocal,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
reciprocal(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise reciprocal.
Args:
a (array): Input array.
Returns:
array: The reciprocal of ``a``.
)pbdoc");
m.def(
"logical_not",
[](const ScalarOrArray& a, StreamOrDevice s) {
return logical_not(to_array(a), s);
},
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
logical_not(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise logical not.
Args:
a (array): Input array or scalar.
Returns:
array: The boolean array containing the logical not of ``a``.
)pbdoc");
m.def(
"logical_and",
[](const ScalarOrArray& a, const ScalarOrArray& b, StreamOrDevice s) {
return logical_and(to_array(a), to_array(b), s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
logical_and(a: array, b: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise logical and.
Args:
a (array): First input array or scalar.
b (array): Second input array or scalar.
Returns:
array: The boolean array containing the logical and of ``a`` and ``b``.
)pbdoc");
m.def(
"logical_or",
[](const ScalarOrArray& a, const ScalarOrArray& b, StreamOrDevice s) {
return logical_or(to_array(a), to_array(b), s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
logical_or(a: array, b: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise logical or.
Args:
a (array): First input array or scalar.
b (array): Second input array or scalar.
Returns:
array: The boolean array containing the logical or of ``a`` and ``b``.
)pbdoc");
m.def(
"logaddexp",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return logaddexp(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
logaddexp(a: Union[scalar, array], b: Union[scalar, array], /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise log-add-exp.
This is a numerically stable log-add-exp of two arrays with numpy-style
broadcasting semantics. Either or both input arrays can also be scalars.
The computation is is a numerically stable version of ``log(exp(a) + exp(b))``.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The log-add-exp of ``a`` and ``b``.
)pbdoc");
m.def(
"exp",
&mlx::core::exp,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
exp(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise exponential.
Args:
a (array): Input array.
Returns:
array: The exponential of ``a``.
)pbdoc");
m.def(
"erf",
&mlx::core::erf,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
erf(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise error function.
.. math::
\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_0^t e^{-t^2} \, dx
Args:
a (array): Input array.
Returns:
array: The error function of ``a``.
)pbdoc");
m.def(
"erfinv",
&mlx::core::erfinv,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
erfinv(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse of :func:`erf`.
Args:
a (array): Input array.
Returns:
array: The inverse error function of ``a``.
)pbdoc");
m.def(
"sin",
&mlx::core::sin,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sin(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise sine.
Args:
a (array): Input array.
Returns:
array: The sine of ``a``.
)pbdoc");
m.def(
"cos",
&mlx::core::cos,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
cos(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise cosine.
Args:
a (array): Input array.
Returns:
array: The cosine of ``a``.
)pbdoc");
m.def(
"tan",
&mlx::core::tan,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
tan(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise tangent.
Args:
a (array): Input array.
Returns:
array: The tangent of ``a``.
)pbdoc");
m.def(
"arcsin",
&mlx::core::arcsin,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
arcsin(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse sine.
Args:
a (array): Input array.
Returns:
array: The inverse sine of ``a``.
)pbdoc");
m.def(
"arccos",
&mlx::core::arccos,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
arccos(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse cosine.
Args:
a (array): Input array.
Returns:
array: The inverse cosine of ``a``.
)pbdoc");
m.def(
"arctan",
&mlx::core::arctan,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
arctan(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse tangent.
Args:
a (array): Input array.
Returns:
array: The inverse tangent of ``a``.
)pbdoc");
m.def(
"sinh",
&mlx::core::sinh,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sinh(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise hyperbolic sine.
Args:
a (array): Input array.
Returns:
array: The hyperbolic sine of ``a``.
)pbdoc");
m.def(
"cosh",
&mlx::core::cosh,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
cosh(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise hyperbolic cosine.
Args:
a (array): Input array.
Returns:
array: The hyperbolic cosine of ``a``.
)pbdoc");
m.def(
"tanh",
&mlx::core::tanh,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
tanh(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise hyperbolic tangent.
Args:
a (array): Input array.
Returns:
array: The hyperbolic tangent of ``a``.
)pbdoc");
m.def(
"arcsinh",
&mlx::core::arcsinh,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
arcsinh(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse hyperbolic sine.
Args:
a (array): Input array.
Returns:
array: The inverse hyperbolic sine of ``a``.
)pbdoc");
m.def(
"arccosh",
&mlx::core::arccosh,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
arccosh(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse hyperbolic cosine.
Args:
a (array): Input array.
Returns:
array: The inverse hyperbolic cosine of ``a``.
)pbdoc");
m.def(
"arctanh",
&mlx::core::arctanh,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
arctanh(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise inverse hyperbolic tangent.
Args:
a (array): Input array.
Returns:
array: The inverse hyperbolic tangent of ``a``.
)pbdoc");
m.def(
"log",
&mlx::core::log,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
log(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise natural logarithm.
Args:
a (array): Input array.
Returns:
array: The natural logarithm of ``a``.
)pbdoc");
m.def(
"log2",
&mlx::core::log2,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
log2(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise base-2 logarithm.
Args:
a (array): Input array.
Returns:
array: The base-2 logarithm of ``a``.
)pbdoc");
m.def(
"log10",
&mlx::core::log10,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
log10(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise base-10 logarithm.
Args:
a (array): Input array.
Returns:
array: The base-10 logarithm of ``a``.
)pbdoc");
m.def(
"log1p",
&mlx::core::log1p,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
log1p(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise natural log of one plus the array.
Args:
a (array): Input array.
Returns:
array: The natural logarithm of one plus ``a``.
)pbdoc");
m.def(
"stop_gradient",
&stop_gradient,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
stop_gradient(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Stop gradients from being computed.
The operation is the identity but it prevents gradients from flowing
through the array.
Args:
a (array): Input array.
Returns:
array: The unchanged input ``a`` but without gradient flowing
through it.
)pbdoc");
m.def(
"sigmoid",
&sigmoid,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sigmoid(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise logistic sigmoid.
The logistic sigmoid function is:
.. math::
\mathrm{sigmoid}(x) = \frac{1}{1 + e^{-x}}
Args:
a (array): Input array.
Returns:
array: The logistic sigmoid of ``a``.
)pbdoc");
m.def(
"power",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return power(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
power(a: Union[scalar, array], b: Union[scalar, array], /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise power operation.
Raise the elements of a to the powers in elements of b with numpy-style
broadcasting semantics. Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: Bases of ``a`` raised to powers in ``b``.
)pbdoc");
m.def(
"arange",
[](Scalar stop, std::optional<Dtype> dtype_, StreamOrDevice s) {
Dtype dtype =
dtype_.has_value() ? dtype_.value() : scalar_to_dtype(stop);
return arange(0.0, scalar_to_double(stop), 1.0, dtype, s);
},
"stop"_a,
"dtype"_a = none,
"stream"_a = none);
m.def(
"arange",
[](Scalar start,
Scalar stop,
std::optional<Dtype> dtype_,
StreamOrDevice s) {
Dtype dtype = dtype_.has_value()
? dtype_.value()
: promote_types(scalar_to_dtype(start), scalar_to_dtype(stop));
return arange(
scalar_to_double(start), scalar_to_double(stop), dtype, s);
},
"start"_a,
"stop"_a,
"dtype"_a = none,
"stream"_a = none);
m.def(
"arange",
[](Scalar stop,
Scalar step,
std::optional<Dtype> dtype_,
StreamOrDevice s) {
Dtype dtype = dtype_.has_value()
? dtype_.value()
: promote_types(scalar_to_dtype(stop), scalar_to_dtype(step));
return arange(
0.0, scalar_to_double(stop), scalar_to_double(step), dtype, s);
},
"stop"_a,
"step"_a,
"dtype"_a = none,
"stream"_a = none);
m.def(
"arange",
[](Scalar start,
Scalar stop,
Scalar step,
std::optional<Dtype> dtype_,
StreamOrDevice s) {
Dtype dtype = dtype_.has_value()
? dtype_.value()
: promote_types(
scalar_to_dtype(start),
promote_types(scalar_to_dtype(stop), scalar_to_dtype(step)));
return arange(
scalar_to_double(start),
scalar_to_double(stop),
scalar_to_double(step),
dtype,
s);
},
"start"_a,
"stop"_a,
"step"_a,
"dtype"_a = none,
"stream"_a = none,
R"pbdoc(
arange(start, stop, step, dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
Generates ranges of numbers.
Generate numbers in the half-open interval ``[start, stop)`` in
increments of ``step``.
Args:
start (float or int, optional): Starting value which defaults to ``0``.
stop (float or int): Stopping value.
step (float or int, optional): Increment which defaults to ``1``.
dtype (Dtype, optional): Specifies the data type of the output.
If unspecified will default to ``float32`` if any of ``start``,
``stop``, or ``step`` are ``float``. Otherwise will default to
``int32``.
Returns:
array: The range of values.
Note:
Following the Numpy convention the actual increment used to
generate numbers is ``dtype(start + step) - dtype(start)``.
This can lead to unexpected results for example if `start + step`
is a fractional value and the `dtype` is integral.
)pbdoc");
m.def(
"linspace",
[](Scalar start,
Scalar stop,
int num,
std::optional<Dtype> dtype,
StreamOrDevice s) {
return linspace(
scalar_to_double(start),
scalar_to_double(stop),
num,
dtype.value_or(float32),
s);
},
"start"_a,
"stop"_a,
"num"_a = 50,
"dtype"_a = std::optional{float32},
"stream"_a = none,
R"pbdoc(
linspace(start, stop, num: Optional[int] = 50, dtype: Optional[Dtype] = float32, stream: Union[None, Stream, Device] = None) -> array
Generate ``num`` evenly spaced numbers over interval ``[start, stop]``.
Args:
start (scalar): Starting value.
stop (scalar): Stopping value.
num (int, optional): Number of samples, defaults to ``50``.
dtype (Dtype, optional): Specifies the data type of the output,
default to ``float32``.
Returns:
array: The range of values.
)pbdoc");
m.def(
"take",
[](const array& a,
const array& indices,
const std::optional<int>& axis,
StreamOrDevice s) {
if (axis.has_value()) {
return take(a, indices, axis.value(), s);
} else {
return take(a, indices, s);
}
},
"a"_a,
py::pos_only(),
"indices"_a,
"axis"_a = std::nullopt,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
take(a: array, /, indices: array, axis: Optional[int] = None, *, stream: Union[None, Stream, Device] = None) -> array
Take elements along an axis.
The elements are taken from ``indices`` along the specified axis.
If the axis is not specified the array is treated as a flattened
1-D array prior to performing the take.
As an example, if the ``axis=1`` this is equivalent to ``a[:, indices, ...]``.
Args:
a (array): Input array.
indices (array): Input array with integral type.
axis (int, optional): Axis along which to perform the take. If unspecified
the array is treated as a flattened 1-D vector.
Returns:
array: The indexed values of ``a``.
)pbdoc");
m.def(
"take_along_axis",
[](const array& a,
const array& indices,
const std::optional<int>& axis,
StreamOrDevice s) {
if (axis.has_value()) {
return take_along_axis(a, indices, axis.value(), s);
} else {
return take_along_axis(reshape(a, {-1}, s), indices, 0, s);
}
},
"a"_a,
py::pos_only(),
"indices"_a,
"axis"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
take_along_axis(a: array, /, indices: array, axis: Optional[int] = None, *, stream: Union[None, Stream, Device] = None) -> array
Take values along an axis at the specified indices.
Args:
a (array): Input array.
indices (array): Indices array. These should be broadcastable with
the input array excluding the `axis` dimension.
axis (int or None): Axis in the input to take the values from. If
``axis == None`` the array is flattened to 1D prior to the indexing
operation.
Returns:
array: The output array with the specified shape and values.
)pbdoc");
m.def(
"full",
[](const std::variant<int, std::vector<int>>& shape,
const ScalarOrArray& vals,
std::optional<Dtype> dtype,
StreamOrDevice s) {
if (auto pv = std::get_if<int>(&shape); pv) {
return full({*pv}, to_array(vals, dtype), s);
} else {
return full(
std::get<std::vector<int>>(shape), to_array(vals, dtype), s);
}
},
"shape"_a,
"vals"_a,
"dtype"_a = std::nullopt,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
full(shape: Union[int, List[int]], vals: Union[scalar, array], dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
Construct an array with the given value.
Constructs an array of size ``shape`` filled with ``vals``. If ``vals``
is an :obj:`array` it must be broadcastable to the given ``shape``.
Args:
shape (int or list(int)): The shape of the output array.
vals (float or int or array): Values to fill the array with.
dtype (Dtype, optional): Data type of the output array. If
unspecified the output type is inferred from ``vals``.
Returns:
array: The output array with the specified shape and values.
)pbdoc");
m.def(
"zeros",
[](const std::variant<int, std::vector<int>>& shape,
std::optional<Dtype> dtype,
StreamOrDevice s) {
auto t = dtype.value_or(float32);
if (auto pv = std::get_if<int>(&shape); pv) {
return zeros({*pv}, t, s);
} else {
return zeros(std::get<std::vector<int>>(shape), t, s);
}
},
"shape"_a,
"dtype"_a = std::optional{float32},
py::kw_only(),
"stream"_a = none,
R"pbdoc(
zeros(shape: Union[int, List[int]], dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
Construct an array of zeros.
Args:
shape (int or list(int)): The shape of the output array.
dtype (Dtype, optional): Data type of the output array. If
unspecified the output type defaults to ``float32``.
Returns:
array: The array of zeros with the specified shape.
)pbdoc");
m.def(
"zeros_like",
&zeros_like,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
zeros_like(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
An array of zeros like the input.
Args:
a (array): The input to take the shape and type from.
Returns:
array: The output array filled with zeros.
)pbdoc");
m.def(
"ones",
[](const std::variant<int, std::vector<int>>& shape,
std::optional<Dtype> dtype,
StreamOrDevice s) {
auto t = dtype.value_or(float32);
if (auto pv = std::get_if<int>(&shape); pv) {
return ones({*pv}, t, s);
} else {
return ones(std::get<std::vector<int>>(shape), t, s);
}
},
"shape"_a,
"dtype"_a = std::optional{float32},
py::kw_only(),
"stream"_a = none,
R"pbdoc(
ones(shape: Union[int, List[int]], dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
Construct an array of ones.
Args:
shape (int or list(int)): The shape of the output array.
dtype (Dtype, optional): Data type of the output array. If
unspecified the output type defaults to ``float32``.
Returns:
array: The array of ones with the specified shape.
)pbdoc");
m.def(
"ones_like",
&ones_like,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
ones_like(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
An array of ones like the input.
Args:
a (array): The input to take the shape and type from.
Returns:
array: The output array filled with ones.
)pbdoc");
m.def(
"eye",
[](int n,
std::optional<int> m,
int k,
std::optional<Dtype> dtype,
StreamOrDevice s) {
return eye(n, m.value_or(n), k, dtype.value_or(float32), s);
},
"n"_a,
"m"_a = py::none(),
"k"_a = 0,
"dtype"_a = std::optional{float32},
py::kw_only(),
"stream"_a = none,
R"pbdoc(
eye(n: int, m: Optional[int] = None, k: int = 0, dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
Create an identity matrix or a general diagonal matrix.
Args:
n (int): The number of rows in the output.
m (int, optional): The number of columns in the output. Defaults to n.
k (int, optional): Index of the diagonal. Defaults to 0 (main diagonal).
dtype (Dtype, optional): Data type of the output array. Defaults to float32.
stream (Stream, optional): Stream or device. Defaults to None.
Returns:
array: An array where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one.
)pbdoc");
m.def(
"identity",
[](int n, std::optional<Dtype> dtype, StreamOrDevice s) {
return identity(n, dtype.value_or(float32), s);
},
"n"_a,
"dtype"_a = std::optional{float32},
py::kw_only(),
"stream"_a = none,
R"pbdoc(
identity(n: int, dtype: Optional[Dtype] = float32, *, stream: Union[None, Stream, Device] = None) -> array
Create a square identity matrix.
Args:
n (int): The number of rows and columns in the output.
dtype (Dtype, optional): Data type of the output array. Defaults to float32.
stream (Stream, optional): Stream or device. Defaults to None.
Returns:
array: An identity matrix of size n x n.
)pbdoc");
m.def(
"tri",
[](int n,
std::optional<int> m,
int k,
std::optional<Dtype> type,
StreamOrDevice s) {
return tri(n, m.value_or(n), k, type.value_or(float32), s);
},
"n"_a,
"m"_a = none,
"k"_a = 0,
"dtype"_a = std::optional{float32},
py::kw_only(),
"stream"_a = none,
R"pbdoc(
tri(n: int, m: int, k: int, dtype: Optional[Dtype] = None, *, stream: Union[None, Stream, Device] = None) -> array
An array with ones at and below the given diagonal and zeros elsewhere.
Args:
n (int): The number of rows in the output.
m (int, optional): The number of cols in the output. Defaults to ``None``.
k (int, optional): The diagonal of the 2-D array. Defaults to ``0``.
dtype (Dtype, optional): Data type of the output array. Defaults to ``float32``.
stream (Stream, optional): Stream or device. Defaults to ``None``.
Returns:
array: Array with its lower triangle filled with ones and zeros elsewhere
)pbdoc");
m.def(
"tril",
&tril,
"x"_a,
"k"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
tril(x: array, k: int, *, stream: Union[None, Stream, Device] = None) -> array
Zeros the array above the given diagonal.
Args:
x (array): input array.
k (int, optional): The diagonal of the 2-D array. Defaults to ``0``.
stream (Stream, optional): Stream or device. Defaults to ``None``.
Returns:
array: Array zeroed above the given diagonal
)pbdoc");
m.def(
"triu",
&triu,
"x"_a,
"k"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
triu(x: array, k: int, *, stream: Union[None, Stream, Device] = None) -> array
Zeros the array below the given diagonal.
Args:
x (array): input array.
k (int, optional): The diagonal of the 2-D array. Defaults to ``0``.
stream (Stream, optional): Stream or device. Defaults to ``None``.
Returns:
array: Array zeroed below the given diagonal
)pbdoc");
m.def(
"allclose",
&allclose,
"a"_a,
"b"_a,
py::pos_only(),
"rtol"_a = 1e-5,
"atol"_a = 1e-8,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
allclose(a: array, b: array, /, rtol: float = 1e-05, atol: float = 1e-08, *, stream: Union[None, Stream, Device] = None) -> array
Approximate comparison of two arrays.
The arrays are considered equal if:
.. code-block::
all(abs(a - b) <= (atol + rtol * abs(b)))
Note unlike :func:`array_equal`, this function supports numpy-style
broadcasting.
Args:
a (array): Input array.
b (array): Input array.
rtol (float): Relative tolerance.
atol (float): Absolute tolerance.
Returns:
array: The boolean output scalar indicating if the arrays are close.
)pbdoc");
m.def(
"all",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return all(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
all(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
An `and` reduction over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"any",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return any(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
any(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
An `or` reduction over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"minimum",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return minimum(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
minimum(a: Union[scalar, array], b: Union[scalar, array], /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise minimum.
Take the element-wise min of two arrays with numpy-style broadcasting
semantics. Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The min of ``a`` and ``b``.
)pbdoc");
m.def(
"maximum",
[](const ScalarOrArray& a_, const ScalarOrArray& b_, StreamOrDevice s) {
auto [a, b] = to_arrays(a_, b_);
return maximum(a, b, s);
},
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
maximum(a: Union[scalar, array], b: Union[scalar, array], /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise maximum.
Take the element-wise max of two arrays with numpy-style broadcasting
semantics. Either or both input arrays can also be scalars.
Args:
a (array): Input array or scalar.
b (array): Input array or scalar.
Returns:
array: The max of ``a`` and ``b``.
)pbdoc");
m.def(
"floor",
&mlx::core::floor,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
floor(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise floor.
Args:
a (array): Input array.
Returns:
array: The floor of ``a``.
)pbdoc");
m.def(
"ceil",
&mlx::core::ceil,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
ceil(a: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Element-wise ceil.
Args:
a (array): Input array.
Returns:
array: The ceil of ``a``.
)pbdoc");
m.def(
"isnan",
&mlx::core::isnan,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
isnan(a: array, stream: Union[None, Stream, Device] = None) -> array
Return a boolean array indicating which elements are NaN.
Args:
a (array): Input array.
Returns:
array: The boolean array indicating which elements are NaN.
)pbdoc");
m.def(
"isinf",
&mlx::core::isinf,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
isinf(a: array, stream: Union[None, Stream, Device] = None) -> array
Return a boolean array indicating which elements are +/- inifnity.
Args:
a (array): Input array.
Returns:
array: The boolean array indicating which elements are +/- infinity.
)pbdoc");
m.def(
"isposinf",
&isposinf,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
isposinf(a: array, stream: Union[None, Stream, Device] = None) -> array
Return a boolean array indicating which elements are positive infinity.
Args:
a (array): Input array.
stream (Union[None, Stream, Device]): Optional stream or device.
Returns:
array: The boolean array indicating which elements are positive infinity.
)pbdoc");
m.def(
"isneginf",
&isneginf,
"a"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
isneginf(a: array, stream: Union[None, Stream, Device] = None) -> array
Return a boolean array indicating which elements are negative infinity.
Args:
a (array): Input array.
stream (Union[None, Stream, Device]): Optional stream or device.
Returns:
array: The boolean array indicating which elements are negative infinity.
)pbdoc");
m.def(
"moveaxis",
&moveaxis,
"a"_a,
py::pos_only(),
"source"_a,
"destination"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
moveaxis(a: array, /, source: int, destination: int, *, stream: Union[None, Stream, Device] = None) -> array
Move an axis to a new position.
Args:
a (array): Input array.
source (int): Specifies the source axis.
destination (int): Specifies the destination axis.
Returns:
array: The array with the axis moved.
)pbdoc");
m.def(
"swapaxes",
&swapaxes,
"a"_a,
py::pos_only(),
"axis1"_a,
"axis2"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
swapaxes(a: array, /, axis1 : int, axis2: int, *, stream: Union[None, Stream, Device] = None) -> array
Swap two axes of an array.
Args:
a (array): Input array.
axis1 (int): Specifies the first axis.
axis2 (int): Specifies the second axis.
Returns:
array: The array with swapped axes.
)pbdoc");
m.def(
"transpose",
[](const array& a,
const std::optional<std::vector<int>>& axes,
StreamOrDevice s) {
if (axes.has_value()) {
return transpose(a, get_reduce_axes(axes.value(), a.ndim()), s);
} else {
return transpose(a, s);
}
},
"a"_a,
py::pos_only(),
"axes"_a = std::nullopt,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
transpose(a: array, /, axes: Optional[List[int]] = None, *, stream: Union[None, Stream, Device] = None) -> array
Transpose the dimensions of the array.
Args:
a (array): Input array.
axes (list(int), optional): Specifies the source axis for each axis
in the new array. The default is to reverse the axes.
Returns:
array: The transposed array.
)pbdoc");
m.def(
"sum",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return sum(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"array"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sum(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
Sum reduce the array over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"prod",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return prod(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
prod(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
An product reduction over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"min",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return min(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
min(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
An `min` reduction over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"max",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return max(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
max(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
An `max` reduction over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"logsumexp",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return logsumexp(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
logsumexp(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
A `log-sum-exp` reduction over the given axes.
The log-sum-exp reduction is a numerically stable version of:
.. code-block::
log(sum(exp(a), axis))
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the corresponding axes reduced.
)pbdoc");
m.def(
"mean",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
StreamOrDevice s) {
return mean(a, get_reduce_axes(axis, a.ndim()), keepdims, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
mean(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
Compute the mean(s) over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array of means.
)pbdoc");
m.def(
"var",
[](const array& a,
const IntOrVec& axis,
bool keepdims,
int ddof,
StreamOrDevice s) {
return var(a, get_reduce_axes(axis, a.ndim()), keepdims, ddof, s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
"keepdims"_a = false,
"ddof"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
var(a: array, /, axis: Union[None, int, List[int]] = None, keepdims: bool = False, ddof: int = 0, *, stream: Union[None, Stream, Device] = None) -> array
Compute the variance(s) over the given axes.
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or
axes to reduce over. If unspecified this defaults
to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
ddof (int, optional): The divisor to compute the variance
is ``N - ddof``, defaults to 0.
Returns:
array: The output array of variances.
)pbdoc");
m.def(
"split",
[](const array& a,
const std::variant<int, std::vector<int>>& indices_or_sections,
int axis,
StreamOrDevice s) {
if (auto pv = std::get_if<int>(&indices_or_sections); pv) {
return split(a, *pv, axis, s);
} else {
return split(
a, std::get<std::vector<int>>(indices_or_sections), axis, s);
}
},
"a"_a,
py::pos_only(),
"indices_or_sections"_a,
"axis"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
split(a: array, /, indices_or_sections: Union[int, List[int]], axis: int = 0, *, stream: Union[None, Stream, Device] = None) -> array
Split an array along a given axis.
Args:
a (array): Input array.
indices_or_sections (int or list(int)): If ``indices_or_sections``
is an integer the array is split into that many sections of equal
size. An error is raised if this is not possible. If ``indices_or_sections``
is a list, the list contains the indices of the start of each subarray
along the given axis.
axis (int, optional): Axis to split along, defaults to `0`.
Returns:
list(array): A list of split arrays.
)pbdoc");
m.def(
"argmin",
[](const array& a,
std::optional<int> axis,
bool keepdims,
StreamOrDevice s) {
if (axis) {
return argmin(a, *axis, keepdims, s);
} else {
return argmin(a, keepdims, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = std::nullopt,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
argmin(a: array, /, axis: Union[None, int] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
Indices of the minimum values along the axis.
Args:
a (array): Input array.
axis (int, optional): Optional axis to reduce over. If unspecified
this defaults to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the indices of the minimum values.
)pbdoc");
m.def(
"argmax",
[](const array& a,
std::optional<int> axis,
bool keepdims,
StreamOrDevice s) {
if (axis) {
return argmax(a, *axis, keepdims, s);
} else {
return argmax(a, keepdims, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = std::nullopt,
"keepdims"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
argmax(a: array, /, axis: Union[None, int] = None, keepdims: bool = False, *, stream: Union[None, Stream, Device] = None) -> array
Indices of the maximum values along the axis.
Args:
a (array): Input array.
axis (int, optional): Optional axis to reduce over. If unspecified
this defaults to reducing over the entire array.
keepdims (bool, optional): Keep reduced axes as
singleton dimensions, defaults to `False`.
Returns:
array: The output array with the indices of the maximum values.
)pbdoc");
m.def(
"sort",
[](const array& a, std::optional<int> axis, StreamOrDevice s) {
if (axis) {
return sort(a, *axis, s);
} else {
return sort(a, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = -1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
sort(a: array, /, axis: Union[None, int] = -1, *, stream: Union[None, Stream, Device] = None) -> array
Returns a sorted copy of the array.
Args:
a (array): Input array.
axis (int or None, optional): Optional axis to sort over.
If ``None``, this sorts over the flattened array.
If unspecified, it defaults to -1 (sorting over the last axis).
Returns:
array: The sorted array.
)pbdoc");
m.def(
"argsort",
[](const array& a, std::optional<int> axis, StreamOrDevice s) {
if (axis) {
return argsort(a, *axis, s);
} else {
return argsort(a, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = -1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
argsort(a: array, /, axis: Union[None, int] = -1, *, stream: Union[None, Stream, Device] = None) -> array
Returns the indices that sort the array.
Args:
a (array): Input array.
axis (int or None, optional): Optional axis to sort over.
If ``None``, this sorts over the flattened array.
If unspecified, it defaults to -1 (sorting over the last axis).
Returns:
array: The indices that sort the input array.
)pbdoc");
m.def(
"partition",
[](const array& a, int kth, std::optional<int> axis, StreamOrDevice s) {
if (axis) {
return partition(a, kth, *axis, s);
} else {
return partition(a, kth, s);
}
},
"a"_a,
py::pos_only(),
"kth"_a,
"axis"_a = -1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
partition(a: array, /, kth: int, axis: Union[None, int] = -1, *, stream: Union[None, Stream, Device] = None) -> array
Returns a partitioned copy of the array such that the smaller ``kth``
elements are first.
The ordering of the elements in partitions is undefined.
Args:
a (array): Input array.
kth (int): Element at the ``kth`` index will be in its sorted
position in the output. All elements before the kth index will
be less or equal to the ``kth`` element and all elements after
will be greater or equal to the ``kth`` element in the output.
axis (int or None, optional): Optional axis to partition over.
If ``None``, this partitions over the flattened array.
If unspecified, it defaults to ``-1``.
Returns:
array: The partitioned array.
)pbdoc");
m.def(
"argpartition",
[](const array& a, int kth, std::optional<int> axis, StreamOrDevice s) {
if (axis) {
return argpartition(a, kth, *axis, s);
} else {
return argpartition(a, kth, s);
}
},
"a"_a,
py::pos_only(),
"kth"_a,
"axis"_a = -1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
argpartition(a: array, /, kth: int, axis: Union[None, int] = -1, *, stream: Union[None, Stream, Device] = None) -> array
Returns the indices that partition the array.
The ordering of the elements within a partition in given by the indices
is undefined.
Args:
a (array): Input array.
kth (int): Element index at the ``kth`` position in the output will
give the sorted position. All indices before the ``kth`` position
will be of elements less or equal to the element at the ``kth``
index and all indices after will be of elements greater or equal
to the element at the ``kth`` index.
axis (int or None, optional): Optional axis to partition over.
If ``None``, this partitions over the flattened array.
If unspecified, it defaults to ``-1``.
Returns:
array: The indices that partition the input array.
)pbdoc");
m.def(
"topk",
[](const array& a, int k, std::optional<int> axis, StreamOrDevice s) {
if (axis) {
return topk(a, k, *axis, s);
} else {
return topk(a, k, s);
}
},
"a"_a,
py::pos_only(),
"k"_a,
"axis"_a = -1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
topk(a: array, /, k: int, axis: Union[None, int] = -1, *, stream: Union[None, Stream, Device] = None) -> array
Returns the ``k`` largest elements from the input along a given axis.
The elements will not necessarily be in sorted order.
Args:
a (array): Input array.
k (int): ``k`` top elements to be returned
axis (int or None, optional): Optional axis to select over.
If ``None``, this selects the top ``k`` elements over the
flattened array. If unspecified, it defaults to ``-1``.
Returns:
array: The top ``k`` elements from the input.
)pbdoc");
m.def(
"broadcast_to",
[](const ScalarOrArray& a,
const std::vector<int>& shape,
StreamOrDevice s) { return broadcast_to(to_array(a), shape, s); },
"a"_a,
py::pos_only(),
"shape"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
broadcast_to(a: Union[scalar, array], /, shape: List[int], *, stream: Union[None, Stream, Device] = None) -> array
Broadcast an array to the given shape.
The broadcasting semantics are the same as Numpy.
Args:
a (array): Input array.
shape (list(int)): The shape to broadcast to.
Returns:
array: The output array with the new shape.
)pbdoc");
m.def(
"softmax",
[](const array& a, const IntOrVec& axis, StreamOrDevice s) {
return softmax(a, get_reduce_axes(axis, a.ndim()), s);
},
"a"_a,
py::pos_only(),
"axis"_a = none,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
softmax(a: array, /, axis: Union[None, int, List[int]] = None, *, stream: Union[None, Stream, Device] = None) -> array
Perform the softmax along the given axis.
This operation is a numerically stable version of:
.. code-block::
exp(a) / sum(exp(a), axis, keepdims=True)
Args:
a (array): Input array.
axis (int or list(int), optional): Optional axis or axes to compute
the softmax over. If unspecified this performs the softmax over
the full array.
Returns:
array: The output of the softmax.
)pbdoc");
m.def(
"concatenate",
[](const std::vector<array>& arrays,
std::optional<int> axis,
StreamOrDevice s) {
if (axis) {
return concatenate(arrays, *axis, s);
} else {
return concatenate(arrays, s);
}
},
"arrays"_a,
py::pos_only(),
"axis"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
concatenate(arrays: List[array], axis: Optional[int] = 0, *, stream: Union[None, Stream, Device] = None) -> array
Concatenate the arrays along the given axis.
Args:
arrays (list(array)): Input :obj:`list` or :obj:`tuple` of arrays.
axis (int, optional): Optional axis to concatenate along. If
unspecified defaults to ``0``.
Returns:
array: The concatenated array.
)pbdoc");
m.def(
"stack",
[](const std::vector<array>& arrays,
std::optional<int> axis,
StreamOrDevice s) {
if (axis.has_value()) {
return stack(arrays, axis.value(), s);
} else {
return stack(arrays, s);
}
},
"arrays"_a,
py::pos_only(),
"axis"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
stack(arrays: List[array], axis: Optional[int] = 0, *, stream: Union[None, Stream, Device] = None) -> array
Stacks the arrays along a new axis.
Args:
arrays (list(array)): A list of arrays to stack.
axis (int, optional): The axis in the result array along which the
input arrays are stacked. Defaults to ``0``.
stream (Stream, optional): Stream or device. Defaults to ``None``.
Returns:
array: The resulting stacked array.
)pbdoc");
m.def(
"repeat",
[](const array& array,
int repeats,
std::optional<int> axis,
StreamOrDevice s) {
if (axis.has_value()) {
return repeat(array, repeats, axis.value(), s);
} else {
return repeat(array, repeats, s);
}
},
"array"_a,
py::pos_only(),
"repeats"_a,
"axis"_a = none,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
repeat(array: array, repeats: int, axis: Optional[int] = None, *, stream: Union[None, Stream, Device] = None) -> array
Repeat an array along a specified axis.
Args:
array (array): Input array.
repeats (int): The number of repetitions for each element.
axis (int, optional): The axis in which to repeat the array along. If
unspecified it uses the flattened array of the input and repeats
along axis 0.
stream (Stream, optional): Stream or device. Defaults to ``None``.
Returns:
array: The resulting repeated array.
)pbdoc");
m.def(
"clip",
[](const array& a,
const std::optional<ScalarOrArray>& min,
const std::optional<ScalarOrArray>& max,
StreamOrDevice s) {
std::optional<array> min_ = std::nullopt;
std::optional<array> max_ = std::nullopt;
if (min) {
min_ = to_array(min.value());
}
if (max) {
max_ = to_array(max.value());
}
return clip(a, min_, max_, s);
},
"a"_a,
py::pos_only(),
"a_min"_a,
"a_max"_a,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
clip(a: array, /, a_min: Union[scalar, array, None], a_max: Union[scalar, array, None], *, stream: Union[None, Stream, Device] = None) -> array
Clip the values of the array between the given minimum and maximum.
If either ``a_min`` or ``a_max`` are ``None``, then corresponding edge
is ignored. At least one of ``a_min`` and ``a_max`` cannot be ``None``.
The input ``a`` and the limits must broadcast with one another.
Args:
a (array): Input array.
a_min (scalar or array or None): Minimum value to clip to.
a_max (scalar or array or None): Maximum value to clip to.
Returns:
array: The clipped array.
)pbdoc");
m.def(
"pad",
[](const array& a,
const std::variant<
int,
std::tuple<int>,
std::pair<int, int>,
std::vector<std::pair<int, int>>>& pad_width,
const ScalarOrArray& constant_value,
StreamOrDevice s) {
if (auto pv = std::get_if<int>(&pad_width); pv) {
return pad(a, *pv, to_array(constant_value), s);
} else if (auto pv = std::get_if<std::tuple<int>>(&pad_width); pv) {
return pad(a, std::get<0>(*pv), to_array(constant_value), s);
} else if (auto pv = std::get_if<std::pair<int, int>>(&pad_width); pv) {
return pad(a, *pv, to_array(constant_value), s);
} else {
auto v = std::get<std::vector<std::pair<int, int>>>(pad_width);
if (v.size() == 1) {
return pad(a, v[0], to_array(constant_value), s);
} else {
return pad(a, v, to_array(constant_value), s);
}
}
},
"a"_a,
py::pos_only(),
"pad_width"_a,
"constant_values"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
pad(a: array, pad_with: Union[int, Tuple[int], Tuple[int, int], List[Tuple[int, int]]], constant_values: Union[scalar, array] = 0, *, stream: Union[None, Stream, Device] = None) -> array
Pad an array with a constant value
Args:
a (array): Input array.
pad_width (int, tuple(int), tuple(int, int) or list(tuple(int, int))): Number of padded
values to add to the edges of each axis:``((before_1, after_1),
(before_2, after_2), ..., (before_N, after_N))``. If a single pair
of integers is passed then ``(before_i, after_i)`` are all the same.
If a single integer or tuple with a single integer is passed then
all axes are extended by the same number on each side.
constant_value (array or scalar, optional): Optional constant value
to pad the edges of the array with.
Returns:
array: The padded array.
)pbdoc");
m.def(
"as_strided",
[](const array& a,
std::optional<std::vector<int>> shape,
std::optional<std::vector<size_t>> strides,
size_t offset,
StreamOrDevice s) {
std::vector<int> a_shape = (shape) ? *shape : a.shape();
std::vector<size_t> a_strides;
if (strides) {
a_strides = *strides;
} else {
std::fill_n(std::back_inserter(a_strides), a_shape.size(), 1);
for (int i = a_shape.size() - 1; i > 0; i--) {
a_strides[i - 1] = a_shape[i] * a_strides[i];
}
}
return as_strided(a, a_shape, a_strides, offset, s);
},
"a"_a,
py::pos_only(),
"shape"_a = none,
"strides"_a = none,
"offset"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
as_strided(a: array, /, shape: Optional[List[int]] = None, strides: Optional[List[int]] = None, offset: int = 0, *, stream: Union[None, Stream, Device] = None) -> array
Create a view into the array with the given shape and strides.
The resulting array will always be as if the provided array was row
contiguous regardless of the provided arrays storage order and current
strides.
.. note::
Note that this function should be used with caution as it changes
the shape and strides of the array directly. This can lead to the
resulting array pointing to invalid memory locations which can
result into crashes.
Args:
a (array): Input array
shape (list(int), optional): The shape of the resulting array. If
None it defaults to ``a.shape()``.
strides (list(int), optional): The strides of the resulting array. If
None it defaults to the reverse exclusive cumulative product of
``a.shape()``.
offset (int): Skip that many elements from the beginning of the input
array.
Returns:
array: The output array which is the strided view of the input.
)pbdoc");
m.def(
"cumsum",
[](const array& a,
std::optional<int> axis,
bool reverse,
bool inclusive,
StreamOrDevice s) {
if (axis) {
return cumsum(a, *axis, reverse, inclusive, s);
} else {
return cumsum(reshape(a, {-1}, s), 0, reverse, inclusive, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = std::nullopt,
py::kw_only(),
"reverse"_a = false,
"inclusive"_a = true,
"stream"_a = none,
R"pbdoc(
cumsum(a: array, /, axis: Optional[int] = None, *, reverse: bool = False, inclusive: bool = True, stream: Union[None, Stream, Device] = None) -> array
Return the cumulative sum of the elements along the given axis.
Args:
a (array): Input array
axis (int, optional): Optional axis to compute the cumulative sum
over. If unspecified the cumulative sum of the flattened array is
returned.
reverse (bool): Perform the cumulative sum in reverse.
inclusive (bool): The i-th element of the output includes the i-th
element of the input.
)pbdoc");
m.def(
"cumprod",
[](const array& a,
std::optional<int> axis,
bool reverse,
bool inclusive,
StreamOrDevice s) {
if (axis) {
return cumprod(a, *axis, reverse, inclusive, s);
} else {
return cumprod(reshape(a, {-1}, s), 0, reverse, inclusive, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = std::nullopt,
py::kw_only(),
"reverse"_a = false,
"inclusive"_a = true,
"stream"_a = none,
R"pbdoc(
cumprod(a: array, /, axis: Optional[int] = None, *, reverse: bool = False, inclusive: bool = True, stream: Union[None, Stream, Device] = None) -> array
Return the cumulative product of the elements along the given axis.
Args:
a (array): Input array
axis (int, optional): Optional axis to compute the cumulative product
over. If unspecified the cumulative product of the flattened array is
returned.
reverse (bool): Perform the cumulative product in reverse.
inclusive (bool): The i-th element of the output includes the i-th
element of the input.
)pbdoc");
m.def(
"cummax",
[](const array& a,
std::optional<int> axis,
bool reverse,
bool inclusive,
StreamOrDevice s) {
if (axis) {
return cummax(a, *axis, reverse, inclusive, s);
} else {
return cummax(reshape(a, {-1}, s), 0, reverse, inclusive, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = std::nullopt,
py::kw_only(),
"reverse"_a = false,
"inclusive"_a = true,
"stream"_a = none,
R"pbdoc(
cummax(a: array, /, axis: Optional[int] = None, *, reverse: bool = False, inclusive: bool = True, stream: Union[None, Stream, Device] = None) -> array
Return the cumulative maximum of the elements along the given axis.
Args:
a (array): Input array
axis (int, optional): Optional axis to compute the cumulative maximum
over. If unspecified the cumulative maximum of the flattened array is
returned.
reverse (bool): Perform the cumulative maximum in reverse.
inclusive (bool): The i-th element of the output includes the i-th
element of the input.
)pbdoc");
m.def(
"cummin",
[](const array& a,
std::optional<int> axis,
bool reverse,
bool inclusive,
StreamOrDevice s) {
if (axis) {
return cummin(a, *axis, reverse, inclusive, s);
} else {
return cummin(reshape(a, {-1}, s), 0, reverse, inclusive, s);
}
},
"a"_a,
py::pos_only(),
"axis"_a = std::nullopt,
py::kw_only(),
"reverse"_a = false,
"inclusive"_a = true,
"stream"_a = none,
R"pbdoc(
cummin(a: array, /, axis: Optional[int] = None, *, reverse: bool = False, inclusive: bool = True, stream: Union[None, Stream, Device] = None) -> array
Return the cumulative minimum of the elements along the given axis.
Args:
a (array): Input array
axis (int, optional): Optional axis to compute the cumulative minimum
over. If unspecified the cumulative minimum of the flattened array is
returned.
reverse (bool): Perform the cumulative minimum in reverse.
inclusive (bool): The i-th element of the output includes the i-th
element of the input.
)pbdoc");
m.def(
"convolve",
[](const array& a,
const array& v,
const std::string& mode,
StreamOrDevice s) {
if (a.ndim() != 1 || v.ndim() != 1) {
throw std::invalid_argument("[convolve] Inputs must be 1D.");
}
array in = a.size() < v.size() ? v : a;
array wt = a.size() < v.size() ? a : v;
wt = slice(wt, {wt.shape(0) - 1}, {-wt.shape(0) - 1}, {-1}, s);
in = reshape(in, {1, -1, 1}, s);
wt = reshape(wt, {1, -1, 1}, s);
int padding = 0;
if (mode == "full") {
padding = wt.size() - 1;
} else if (mode == "valid") {
padding = 0;
} else if (mode == "same") {
if (wt.size() % 2) {
padding = wt.size() / 2;
} else { int pad_l = wt.size() / 2;
int pad_r = std::max(0, pad_l - 1);
in = pad(in, {{0, 0}, {pad_l, pad_r}, {0, 0}}, array(0), s);
}
} else {
throw std::invalid_argument("[convolve] Invalid mode.");
}
array out = conv1d(
in,
wt,
1,
padding,
1,
1,
s);
return reshape(out, {-1}, s);
},
"a"_a,
"v"_a,
py::pos_only(),
"mode"_a = "full",
py::kw_only(),
"stream"_a = none,
R"pbdoc(
convolve(a: array, v: array, /, mode: str = "full", *, stream: Union[None, Stream, Device] = None) -> array
The discrete convolution of 1D arrays.
If ``v`` is longer than ``a``, then they are swapped.
The conv filter is flipped following signal processing convention.
Args:
a (array): 1D Input array.
v (array): 1D Input array.
mode (str, optional): {'full', 'valid', 'same'}
Returns:
array: The convolved array.
)pbdoc");
m.def(
"conv1d",
&conv1d,
"input"_a,
"weight"_a,
py::pos_only(),
"stride"_a = 1,
"padding"_a = 0,
"dilation"_a = 1,
"groups"_a = 1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
conv1d(input: array, weight: array, /, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, *, stream: Union[None, Stream, Device] = None) -> array
1D convolution over an input with several channels
Note: Only the default ``groups=1`` is currently supported.
Args:
input (array): input array of shape (``N``, ``H``, ``C_in``)
weight (array): weight array of shape (``C_out``, ``H``, ``C_in``)
stride (int, optional): kernel stride. Default: ``1``.
padding (int, optional): input padding. Default: ``0``.
dilation (int, optional): kernel dilation. Default: ``1``.
groups (int, optional): input feature groups. Default: ``1``.
Returns:
array: The convolved array.
)pbdoc");
m.def(
"conv2d",
[](const array& input,
const array& weight,
const std::variant<int, std::pair<int, int>>& stride,
const std::variant<int, std::pair<int, int>>& padding,
const std::variant<int, std::pair<int, int>>& dilation,
int groups,
StreamOrDevice s) {
std::pair<int, int> stride_pair{1, 1};
std::pair<int, int> padding_pair{0, 0};
std::pair<int, int> dilation_pair{1, 1};
if (auto pv = std::get_if<int>(&stride); pv) {
stride_pair = std::pair<int, int>{*pv, *pv};
} else {
stride_pair = std::get<std::pair<int, int>>(stride);
}
if (auto pv = std::get_if<int>(&padding); pv) {
padding_pair = std::pair<int, int>{*pv, *pv};
} else {
padding_pair = std::get<std::pair<int, int>>(padding);
}
if (auto pv = std::get_if<int>(&dilation); pv) {
dilation_pair = std::pair<int, int>{*pv, *pv};
} else {
dilation_pair = std::get<std::pair<int, int>>(dilation);
}
return conv2d(
input, weight, stride_pair, padding_pair, dilation_pair, groups, s);
},
"input"_a,
"weight"_a,
py::pos_only(),
"stride"_a = 1,
"padding"_a = 0,
"dilation"_a = 1,
"groups"_a = 1,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
conv2d(input: array, weight: array, /, stride: Union[int, Tuple[int, int]] = 1, padding: Union[int, Tuple[int, int]] = 0, dilation: Union[int, Tuple[int, int]] = 1, groups: Union[int, Tuple[int, int]] = 1, *, stream: Union[None, Stream, Device] = None) -> array
2D convolution over an input with several channels
Note: Only the default ``groups=1`` is currently supported.
Args:
input (array): input array of shape ``(N, H, W, C_in)``
weight (array): weight array of shape ``(C_out, H, W, C_in)``
stride (int or tuple(int), optional): :obj:`tuple` of size 2 with
kernel strides. All spatial dimensions get the same stride if
only one number is specified. Default: ``1``.
padding (int or tuple(int), optional): :obj:`tuple` of size 2 with
symmetric input padding. All spatial dimensions get the same
padding if only one number is specified. Default: ``0``.
dilation (int or tuple(int), optional): :obj:`tuple` of size 2 with
kernel dilation. All spatial dimensions get the same dilation
if only one number is specified. Default: ``1``
groups (int, optional): input feature groups. Default: ``1``.
Returns:
array: The convolved array.
)pbdoc");
m.def(
"save",
&mlx_save_helper,
"file"_a,
"arr"_a,
R"pbdoc(
save(file: str, arr: array)
Save the array to a binary file in ``.npy`` format.
Args:
file (str): File to which the array is saved
arr (array): Array to be saved.
)pbdoc");
m.def(
"savez",
[](py::object file, py::args args, const py::kwargs& kwargs) {
mlx_savez_helper(file, args, kwargs, false);
},
"file"_a,
py::pos_only(),
py::kw_only(),
R"pbdoc(
savez(file: str, *args, **kwargs)
Save several arrays to a binary file in uncompressed ``.npz`` format.
.. code-block:: python
import mlx.core as mx
x = mx.ones((10, 10))
mx.savez("my_path.npz", x=x)
import mlx.nn as nn
from mlx.utils import tree_flatten
model = nn.TransformerEncoder(6, 128, 4)
flat_params = tree_flatten(model.parameters())
mx.savez("model.npz", **dict(flat_params))
Args:
file (file, str): Path to file to which the arrays are saved.
args (arrays): Arrays to be saved.
kwargs (arrays): Arrays to be saved. Each array will be saved
with the associated keyword as the output file name.
)pbdoc");
m.def(
"savez_compressed",
[](py::object file, py::args args, const py::kwargs& kwargs) {
mlx_savez_helper(file, args, kwargs, true);
},
"file"_a,
py::pos_only(),
py::kw_only(),
R"pbdoc(
savez_compressed(file: str, *args, **kwargs)
Save several arrays to a binary file in compressed ``.npz`` format.
Args:
file (file, str): Path to file to which the arrays are saved.
args (arrays): Arrays to be saved.
kwargs (arrays): Arrays to be saved. Each array will be saved
with the associated keyword as the output file name.
)pbdoc");
m.def(
"load",
&mlx_load_helper,
"file"_a,
py::pos_only(),
"format"_a = none,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
load(file: str, /, format: Optional[str] = None, *, stream: Union[None, Stream, Device] = None) -> Union[array, Dict[str, array]]
Load array(s) from a binary file.
The supported formats are ``.npy``, ``.npz``, ``.safetensors``, and ``.gguf``.
Args:
file (file, str): File in which the array is saved.
format (str, optional): Format of the file. If ``None``, the format
is inferred from the file extension. Supported formats: ``npy``,
``npz``, and ``safetensors``. Default: ``None``.
Returns:
result (array, dict):
A single array if loading from a ``.npy`` file or a dict mapping
names to arrays if loading from a ``.npz`` or ``.safetensors`` file.
Warning:
When loading unsupported quantization formats from GGUF, tensors will
automatically cast to ``mx.float16``
)pbdoc");
m.def(
"save_safetensors",
&mlx_save_safetensor_helper,
"file"_a,
"arrays"_a,
R"pbdoc(
save_safetensors(file: str, arrays: Dict[str, array])
Save array(s) to a binary file in ``.safetensors`` format.
See the `Safetensors documentation <https://huggingface.co/docs/safetensors/index>`_
for more information on the format.
Args:
file (file, str): File in which the array is saved.
arrays (dict(str, array)): The dictionary of names to arrays to be saved.
)pbdoc");
m.def(
"save_gguf",
&mlx_save_gguf_helper,
"file"_a,
"arrays"_a,
R"pbdoc(
save_gguf(file: str, arrays: Dict[str, array])
Save array(s) to a binary file in ``.gguf`` format.
See the `GGUF documentation <https://github.com/ggerganov/ggml/blob/master/docs/gguf.md>`_ for
more information on the format.
Args:
file (file, str): File in which the array is saved.
arrays (dict(str, array)): The dictionary of names to arrays to be saved.
)pbdoc");
m.def(
"where",
[](const ScalarOrArray& condition,
const ScalarOrArray& x_,
const ScalarOrArray& y_,
StreamOrDevice s) {
auto [x, y] = to_arrays(x_, y_);
return where(to_array(condition), x, y, s);
},
"condition"_a,
"x"_a,
"y"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
where(condition: Union[scalar, array], x: Union[scalar, array], y: Union[scalar, array], /, *, stream: Union[None, Stream, Device] = None) -> array
Select from ``x`` or ``y`` according to ``condition``.
The condition and input arrays must be the same shape or broadcastable
with each another.
Args:
condition (array): The condition array.
x (array): The input selected from where condition is ``True``.
y (array): The input selected from where condition is ``False``.
Returns:
result (array): The output containing elements selected from ``x`` and ``y``.
)pbdoc");
m.def(
"round",
[](const array& a, int decimals, StreamOrDevice s) {
return round(a, decimals, s);
},
"a"_a,
py::pos_only(),
"decimals"_a = 0,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
round(a: array, /, decimals: int = 0, stream: Union[None, Stream, Device] = None) -> array
Round to the given number of decimals.
Basically performs:
.. code-block:: python
s = 10**decimals
x = round(x * s) / s
Args:
a (array): Input array
decimals (int): Number of decimal places to round to. (default: 0)
Returns:
result (array): An array of the same type as ``a`` rounded to the given number of decimals.
)pbdoc");
m.def(
"quantized_matmul",
&quantized_matmul,
"x"_a,
"w"_a,
py::pos_only(),
"scales"_a,
"biases"_a,
"transpose"_a = true,
"group_size"_a = 64,
"bits"_a = 4,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
quantized_matmul(x: array, w: array, /, scales: array, biases: array, transpose: bool = True, group_size: int = 64, bits: int = 4, *, stream: Union[None, Stream, Device] = None) -> array
Perform the matrix multiplication with the quantized matrix ``w``. The
quantization uses one floating point scale and bias per ``group_size`` of
elements. Each element in ``w`` takes ``bits`` bits and is packed in an
unsigned 32 bit integer.
Args:
x (array): Input array
w (array): Quantized matrix packed in unsigned integers
scales (array): The scales to use per ``group_size`` elements of ``w``
biases (array): The biases to use per ``group_size`` elements of ``w``
transpose (bool, optional): Defines whether to multiply with the
transposed ``w`` or not, namely whether we are performing
``x @ w.T`` or ``x @ w``. (default: ``True``)
group_size (int, optional): The size of the group in ``w`` that
shares a scale and bias. (default: ``64``)
bits (int, optional): The number of bits occupied by each element in
``w``. (default: ``4``)
Returns:
result (array): The result of the multiplication of ``x`` with ``w``.
)pbdoc");
m.def(
"quantize",
&quantize,
"w"_a,
py::pos_only(),
"group_size"_a = 64,
"bits"_a = 4,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
quantize(w: array, /, group_size: int = 64, bits : int = 4, *, stream: Union[None, Stream, Device] = None) -> Tuple[array, array, array]
Quantize the matrix ``w`` using ``bits`` bits per element.
Note, every ``group_size`` elements in a row of ``w`` are quantized
together. Hence, number of columns of ``w`` should be divisible by
``group_size``. In particular, the rows of ``w`` are divided into groups of
size ``group_size`` which are quantized together.
.. warning::
``quantize`` currently only supports 2D inputs with dimensions which are multiples of 32
Formally, for a group of :math:`g` consecutive elements :math:`w_1` to
:math:`w_g` in a row of ``w`` we compute the quantized representation
of each element :math:`\hat{w_i}` as follows
.. math::
\begin{aligned}
\alpha &= \max_i w_i \\
\beta &= \min_i w_i \\
s &= \frac{\alpha - \beta}{2^b - 1} \\
\hat{w_i} &= \textrm{round}\left( \frac{w_i - \beta}{s}\right).
\end{aligned}
After the above computation, :math:`\hat{w_i}` fits in :math:`b` bits
and is packed in an unsigned 32-bit integer from the lower to upper
bits. For instance, for 4-bit quantization we fit 8 elements in an
unsigned 32 bit integer where the 1st element occupies the 4 least
significant bits, the 2nd bits 4-7 etc.
In order to be able to dequantize the elements of ``w`` we also need to
save :math:`s` and :math:`\beta` which are the returned ``scales`` and
``biases`` respectively.
Args:
w (array): Matrix to be quantized
group_size (int, optional): The size of the group in ``w`` that shares a
scale and bias. (default: ``64``)
bits (int, optional): The number of bits occupied by each element of
``w`` in the returned quantized matrix. (default: ``4``)
Returns:
(tuple): A tuple containing
- w_q (array): The quantized version of ``w``
- scales (array): The scale to multiply each element with, namely :math:`s`
- biases (array): The biases to add to each element, namely :math:`\beta`
)pbdoc");
m.def(
"dequantize",
&dequantize,
"w"_a,
py::pos_only(),
"scales"_a,
"biases"_a,
"group_size"_a = 64,
"bits"_a = 4,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
dequantize(w: array, /, scales: array, biases: array, group_size: int = 64, bits: int = 4, *, stream: Union[None, Stream, Device] = None) -> array
Dequantize the matrix ``w`` using the provided ``scales`` and
``biases`` and the ``group_size`` and ``bits`` configuration.
Formally, given the notation in :func:`quantize`, we compute
:math:`w_i` from :math:`\hat{w_i}` and corresponding :math:`s` and
:math:`\beta` as follows
.. math::
w_i = s \hat{w_i} - \beta
Args:
w (array): Matrix to be quantized
scales (array): The scales to use per ``group_size`` elements of ``w``
biases (array): The biases to use per ``group_size`` elements of ``w``
group_size (int, optional): The size of the group in ``w`` that shares a
scale and bias. (default: ``64``)
bits (int, optional): The number of bits occupied by each element in
``w``. (default: ``4``)
Returns:
result (array): The dequantized version of ``w``
)pbdoc");
m.def(
"tensordot",
[](const array& a,
const array& b,
const std::variant<int, std::vector<std::vector<int>>>& dims,
StreamOrDevice s) {
if (auto pv = std::get_if<int>(&dims); pv) {
return tensordot(a, b, *pv, s);
} else {
auto x = std::get<std::vector<std::vector<int>>>(dims);
if (x.size() != 2) {
throw std::invalid_argument(
"[tensordot] dims must be a list of two lists.");
}
return tensordot(a, b, {x[0], x[1]}, s);
}
},
"a"_a,
"b"_a,
py::pos_only(),
"dims"_a = 2,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
tensordot(a: array, b: array, /, dims: Union[int, List[List[int]]] = 2, *, stream: Union[None, Stream, Device] = None) -> array
Compute the tensor dot product along the specified axes.
Args:
a (array): Input array
b (array): Input array
dims (int or list(list(int)), optional): The number of dimensions to
sum over. If an integer is provided, then sum over the last
``dims`` dimensions of ``a`` and the first ``dims`` dimensions of
``b``. If a list of lists is provided, then sum over the
corresponding dimensions of ``a`` and ``b``. (default: 2)
Returns:
result (array): The tensor dot product.
)pbdoc");
m.def(
"inner",
&inner,
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
inner(a: array, b: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes.
Args:
a (array): Input array
b (array): Input array
Returns:
result (array): The inner product.
)pbdoc");
m.def(
"outer",
&outer,
"a"_a,
"b"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
outer(a: array, b: array, /, *, stream: Union[None, Stream, Device] = None) -> array
Compute the outer product of two 1-D arrays, if the array's passed are not 1-D a flatten op will be run beforehand.
Args:
a (array): Input array
b (array): Input array
Returns:
result (array): The outer product.
)pbdoc");
m.def(
"tile",
[](const array& a, const IntOrVec& reps, StreamOrDevice s) {
if (auto pv = std::get_if<int>(&reps); pv) {
return tile(a, {*pv}, s);
} else {
return tile(a, std::get<std::vector<int>>(reps), s);
}
},
"a"_a,
"reps"_a,
py::pos_only(),
py::kw_only(),
"stream"_a = none,
R"pbdoc(
tile(a: array, reps: Union[int, List[int]], /, *, stream: Union[None, Stream, Device] = None) -> array
Construct an array by repeating ``a`` the number of times given by ``reps``.
Args:
a (array): Input array
reps (int or list(int)): The number of times to repeat ``a`` along each axis.
Returns:
result (array): The tiled array.
)pbdoc");
m.def(
"addmm",
&addmm,
"c"_a,
"a"_a,
"b"_a,
py::pos_only(),
"alpha"_a = 1.0f,
"beta"_a = 1.0f,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
addmm(c: array, a: array, b: array, /, alpha: float = 1.0, beta: float = 1.0, *, stream: Union[None, Stream, Device] = None) -> array
Matrix multiplication with addition and optional scaling.
Perform the (possibly batched) matrix multiplication of two arrays and add to the result
with optional scaling factors.
Args:
c (array): Input array or scalar.
a (array): Input array or scalar.
b (array): Input array or scalar.
alpha (float, optional): Scaling factor for the
matrix product of ``a`` and ``b`` (default: ``1``)
beta (float, optional): Scaling factor for ``c`` (default: ``1``)
Returns:
array: ``alpha * (a @ b) + beta * c``
)pbdoc");
}