mlx/mlx/linalg.cpp
Gabrijel Boduljak 6b0d30bb85
linalg.norm (#187)
* implemented vector_norm in cpp

added linalg to mlx

* implemented vector_norm python binding

* renamed vector_norm to norm, implemented norm without provided ord

* completed the implementation of the norm

* added tests

* removed unused import in linalg.cpp

* updated python bindings

* added some tests for python bindings

* handling inf, -inf as numpy does, more extensive tests of compatibility with numpy

* added better docs and examples

* refactored mlx.linalg.norm bindings

* reused existing util for implementation of linalg.norm

* more tests

* fixed a bug with no ord and axis provided

* removed unused imports

* some style and API consistency updates to linalg norm

* remove unused includes

* fix python tests

* fixed a bug with frobenius norm of a complex-valued matrix

* complex for vector too

---------

Co-authored-by: Awni Hannun <awni@apple.com>
2023-12-26 19:42:04 -08:00

176 lines
5.0 KiB
C++

// Copyright © 2023 Apple Inc.
#include <numeric>
#include <ostream>
#include <vector>
#include "mlx/dtype.h"
#include "mlx/linalg.h"
namespace mlx::core::linalg {
Dtype at_least_float(const Dtype& d) {
return is_floating_point(d) ? d : promote_types(d, float32);
}
inline array l2_norm(
const array& a,
const std::vector<int>& axis,
bool keepdims,
StreamOrDevice s) {
if (is_complex(a.dtype())) {
return sqrt(sum(abs(a, s) * abs(a, s), axis, keepdims, s), s);
} else {
return sqrt(sum(square(a, s), axis, keepdims, s), s);
}
}
inline array vector_norm(
const array& a,
const double ord,
const std::vector<int>& axis,
bool keepdims,
StreamOrDevice s) {
auto dtype = at_least_float(a.dtype());
if (ord == 0.0) {
return astype(sum(not_equal(a, array(0), s), axis, keepdims, s), dtype, s);
} else if (ord == 1.0) {
return astype(sum(abs(a, s), axis, keepdims, s), dtype, s);
} else if (ord == 2.0) {
return l2_norm(a, axis, keepdims, s);
} else if (ord == std::numeric_limits<double>::infinity()) {
return astype(max(abs(a, s), axis, keepdims, s), dtype, s);
} else if (ord == -std::numeric_limits<double>::infinity()) {
return astype(min(abs(a, s), axis, keepdims, s), dtype, s);
} else {
return power(
sum(power(abs(a, s), array(ord, dtype), s), axis, keepdims, s),
array(1.0 / ord, dtype),
s);
}
}
inline array matrix_norm(
const array& a,
const double ord,
const std::vector<int>& axis,
bool keepdims,
StreamOrDevice s) {
auto dtype = at_least_float(a.dtype());
auto row_axis = axis[0];
auto col_axis = axis[1];
if (ord == -1.0) {
col_axis -= (!keepdims && col_axis > row_axis && col_axis > 0);
return astype(
min(sum(abs(a, s), row_axis, keepdims, s), col_axis, keepdims, s),
dtype,
s);
} else if (ord == 1.0) {
col_axis -= (!keepdims && col_axis > row_axis && col_axis > 0);
return astype(
max(sum(abs(a, s), row_axis, keepdims, s), col_axis, keepdims, s),
dtype,
s);
} else if (ord == std::numeric_limits<double>::infinity()) {
row_axis -= (!keepdims && row_axis > col_axis && row_axis > 0);
return astype(
max(sum(abs(a, s), col_axis, keepdims, s), row_axis, keepdims, s),
dtype,
s);
} else if (ord == -std::numeric_limits<double>::infinity()) {
row_axis -= (!keepdims && row_axis > col_axis && row_axis > 0);
return astype(
min(sum(abs(a, s), col_axis, keepdims, s), row_axis, keepdims, s),
dtype,
s);
} else if (ord == 2.0 || ord == -2.0) {
throw std::runtime_error(
"[linalg::norm] Singular value norms are not implemented.");
} else {
std::ostringstream msg;
msg << "[linalg::norm] Invalid ord " << ord << " for matrix norm.";
throw std::invalid_argument(msg.str());
}
}
inline array matrix_norm(
const array& a,
const std::string& ord,
const std::vector<int>& axis,
bool keepdims,
StreamOrDevice s) {
if (ord == "f" || ord == "fro") {
return l2_norm(a, axis, keepdims, s);
} else if (ord == "nuc") {
throw std::runtime_error(
"[linalg::norm] Nuclear norm not yet implemented.");
} else {
std::ostringstream msg;
msg << "[linalg::norm] Invalid ord value '" << ord << "' for matrix norm.";
throw std::invalid_argument(msg.str());
}
}
array norm(
const array& a,
const std::optional<std::vector<int>>& axis /* = std::nullopt */,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
if (!axis) {
return norm(flatten(a, s), std::vector<int>{0}, keepdims, s);
}
if (axis.value().size() > 2) {
throw std::invalid_argument(
"[linalg::norm] Received too many axes for norm.");
}
return l2_norm(a, axis.value(), keepdims, s);
}
array norm(
const array& a,
const double ord,
const std::optional<std::vector<int>>& axis /* = std::nullopt */,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
std::vector<int> ax;
if (!axis) {
ax.resize(a.ndim());
std::iota(ax.begin(), ax.end(), 0);
} else {
ax = axis.value();
}
if (ax.size() == 1) {
return vector_norm(a, ord, ax, keepdims, s);
} else if (ax.size() == 2) {
return matrix_norm(a, ord, ax, keepdims, s);
} else {
throw std::invalid_argument(
"[linalg::norm] Received too many axes for norm.");
}
}
array norm(
const array& a,
const std::string& ord,
const std::optional<std::vector<int>>& axis /* = std::nullopt */,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
std::vector<int> ax;
if (!axis) {
ax.resize(a.ndim());
std::iota(ax.begin(), ax.end(), 0);
} else {
ax = axis.value();
}
if (ax.size() != 2) {
std::ostringstream msg;
msg << "[linalg::norm] Norm '" << ord << "' only supported for matrices,"
<< " but received " << ax.size() << " axis/axes.";
throw std::invalid_argument(msg.str());
}
return matrix_norm(a, ord, ax, keepdims, s);
}
} // namespace mlx::core::linalg