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5029894662
mlx/mlx/ops.cpp
Anton Belov 5029894662
[Issue #1187] Add nan_to_num function initial attempt (#1247) 
* initial attempt, working with wrong types

* not compiling; mx.float16 and mx.bfloat16 tests added

* fix nan to num

* nit

---------

Co-authored-by: Awni Hannun <awni@apple.com>
2024-07-25 09:57:37 -07:00

4448 lines
130 KiB
C++
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// Copyright © 2023-2024 Apple Inc.
#include <algorithm>
#include <climits>
#include <cmath>
#include <numeric>
#include <set>
#include <sstream>
#include "mlx/ops.h"
#include "mlx/primitives.h"
#include "mlx/transforms.h"
#include "mlx/utils.h"
namespace mlx::core {
namespace {
std::pair<std::vector<int>, std::vector<int>> compute_reduce_shape(
const std::vector<int>& axes,
const std::vector<int>& shape) {
std::set<int> axes_set;
auto ndim = shape.size();
for (auto ax : axes) {
int ax_ = (ax < 0) ? ax + ndim : ax;
if (ax_ < 0 || ax_ >= ndim) {
std::ostringstream msg;
msg << "Invalid axis " << ax << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
axes_set.insert(ax_);
}
if (axes_set.size() != axes.size()) {
throw std::invalid_argument("Duplicate axes detected in reduction.");
}
std::vector<int> out_shape;
for (int i = 0; i < ndim; ++i) {
if (axes_set.count(i) == 0) {
out_shape.push_back(shape[i]);
} else {
out_shape.push_back(1);
}
}
std::vector<int> sorted_axes(axes_set.begin(), axes_set.end());
return {out_shape, sorted_axes};
}
Dtype at_least_float(const Dtype& d) {
return issubdtype(d, inexact) ? d : promote_types(d, float32);
}
array indices_or_default(
std::optional<array> indices,
const array& x,
StreamOrDevice s) {
if (indices.has_value()) {
return indices.value();
}
std::vector<int> shape(x.shape().begin(), x.shape().end() - 2);
int total =
std::reduce(shape.begin(), shape.end(), 1, std::multiplies<int>());
return reshape(arange(total, uint32, s), shape, s);
}
std::pair<int, int> extract_quantized_matmul_dims(
std::string_view tag,
const array& x,
const array& w,
const array& scales,
const array& biases,
bool transpose,
int group_size,
int bits) {
if (w.dtype() != uint32) {
std::ostringstream msg;
msg << "[" << tag << "] The weight matrix should be uint32 "
<< "but received " << w.dtype();
throw std::invalid_argument(msg.str());
}
if (scales.shape() != biases.shape()) {
std::ostringstream msg;
msg << "[" << tag << "] Scales and biases should have the same shape. "
<< "Received scales with shape " << scales.shape()
<< " and biases with " << biases.shape();
throw std::invalid_argument(msg.str());
}
if (!std::equal(
w.shape().begin(), w.shape().end() - 2, scales.shape().begin())) {
std::ostringstream msg;
msg << "[" << tag
<< "] Weight, scales and biases should have the same batch shape. "
<< "Received weight with shape " << w.shape() << ", scales with "
<< scales.shape() << " and biases with " << biases.shape();
throw std::invalid_argument(msg.str());
}
if (w.shape(-1) * 32 / bits != scales.shape(-1) * group_size) {
std::ostringstream msg;
msg << "[" << tag << "] The shapes of the weight and scales are "
<< "incompatible based on bits and group_size. w.shape() == "
<< w.shape() << " and scales.shape() == " << scales.shape()
<< " with group_size=" << group_size << " and bits=" << bits;
throw std::invalid_argument(msg.str());
}
int x_inner_dims = x.shape(-1);
// Calculate the expanded w's dims
int w_inner_dims = (transpose) ? w.shape(-1) * 32 / bits : w.shape(-2);
int w_outer_dims = (transpose) ? w.shape(-2) : w.shape(-1) * 32 / bits;
if (w_inner_dims != x_inner_dims) {
std::ostringstream msg;
msg << "[" << tag << "] Last dimension of first input with "
<< "shape (..., " << x_inner_dims << ") does not match "
<< "the expanded quantized matrix (" << w_inner_dims << ", "
<< w_outer_dims << ") computed from shape " << w.shape()
<< " with group_size=" << group_size << ", bits=" << bits
<< " and transpose=" << std::boolalpha << transpose;
throw std::invalid_argument(msg.str());
}
return {w_inner_dims, w_outer_dims};
}
} // namespace
array arange(
double start,
double stop,
double step,
Dtype dtype,
StreamOrDevice s /* = {} */) {
if (dtype == bool_) {
std::ostringstream msg;
msg << bool_ << " not supported for arange.";
throw std::invalid_argument(msg.str());
}
if (std::isnan(start) || std::isnan(step) || std::isnan(stop)) {
throw std::invalid_argument("[arange] Cannot compute length.");
}
if (std::isinf(start) || std::isinf(stop)) {
throw std::invalid_argument("[arange] Cannot compute length.");
}
// Check if start and stop specify a valid range because if not, we have to
// return an empty array
if (std::isinf(step) &&
(step > 0 && start < stop || step < 0 && start > stop)) {
return array({start}, dtype);
}
double real_size = std::ceil((stop - start) / step);
if (real_size > INT_MAX) {
throw std::invalid_argument("[arange] Maximum size exceeded.");
}
int size = std::max(static_cast<int>(real_size), 0);
return array(
{size},
dtype,
std::make_shared<Arange>(to_stream(s), start, stop, step),
{});
}
array arange(
double start,
double stop,
double step,
StreamOrDevice s /* = {} */) {
return arange(start, stop, step, float32, to_stream(s));
}
array arange(
double start,
double stop,
Dtype dtype,
StreamOrDevice s /* = {} */) {
return arange(start, stop, 1.0, dtype, to_stream(s));
}
array arange(double start, double stop, StreamOrDevice s /* = {} */) {
return arange(start, stop, 1.0, float32, to_stream(s));
}
array arange(double stop, Dtype dtype, StreamOrDevice s /* = {} */) {
return arange(0.0, stop, 1.0, dtype, to_stream(s));
}
array arange(double stop, StreamOrDevice s /* = {} */) {
return arange(0.0, stop, 1.0, float32, to_stream(s));
}
array arange(int start, int stop, int step, StreamOrDevice s /* = {} */) {
return arange(
static_cast<double>(start),
static_cast<double>(stop),
static_cast<double>(step),
int32,
to_stream(s));
}
array arange(int start, int stop, StreamOrDevice s /* = {} */) {
return arange(
static_cast<double>(start),
static_cast<double>(stop),
1.0,
int32,
to_stream(s));
}
array arange(int stop, StreamOrDevice s /* = {} */) {
return arange(0.0, static_cast<double>(stop), 1.0, int32, to_stream(s));
}
array linspace(
double start,
double stop,
int num /* = 50 */,
Dtype dtype /* = float32 */,
StreamOrDevice s /* = {} */) {
if (num < 0) {
std::ostringstream msg;
msg << "[linspace] number of samples, " << num << ", must be non-negative.";
throw std::invalid_argument(msg.str());
}
if (num == 1) {
return astype(array({start}), dtype, to_stream(s));
}
array sequence = arange(0, num, float32, to_stream(s));
float step = (stop - start) / (num - 1);
return astype(
add(multiply(sequence, array(step), to_stream(s)),
array(start),
to_stream(s)),
dtype,
to_stream(s));
}
array astype(array a, Dtype dtype, StreamOrDevice s /* = {} */) {
if (dtype == a.dtype()) {
return std::move(a);
}
auto copied_shape = a.shape(); // |a| will be moved
return array(
std::move(copied_shape),
dtype,
std::make_shared<AsType>(to_stream(s), dtype),
{std::move(a)});
}
array as_strided(
array a,
std::vector<int> shape,
std::vector<size_t> strides,
size_t offset,
StreamOrDevice s /* = {} */) {
auto copied_shape = shape; // |shape| will be moved
auto dtype = a.dtype(); // |a| will be moved
return array(
std::move(copied_shape),
dtype,
std::make_shared<AsStrided>(
to_stream(s), std::move(shape), std::move(strides), offset),
// Force the input array to be contiguous.
{reshape(std::move(a), {-1}, s)});
}
array copy(array a, StreamOrDevice s /* = {} */) {
auto copied_shape = a.shape(); // |a| will be moved
auto dtype = a.dtype();
return array(
std::move(copied_shape),
dtype,
std::make_shared<Copy>(to_stream(s)),
{std::move(a)});
}
array full(
std::vector<int> shape,
array vals,
Dtype dtype,
StreamOrDevice s /* = {} */) {
if (std::any_of(shape.begin(), shape.end(), [](int i) { return i < 0; })) {
throw std::invalid_argument("[full] Negative dimensions not allowed.");
}
auto copied_shape = shape; // |shape| will be moved
return array(
std::move(copied_shape),
dtype,
std::make_shared<Full>(to_stream(s)),
{broadcast_to(astype(std::move(vals), dtype, s), std::move(shape), s)});
}
array full(std::vector<int> shape, array vals, StreamOrDevice s /* = {} */) {
auto dtype = vals.dtype(); // |vals| will be moved
return full(std::move(shape), std::move(vals), dtype, to_stream(s));
}
array zeros(
const std::vector<int>& shape,
Dtype dtype,
StreamOrDevice s /* = {} */) {
return full(shape, array(0, dtype), to_stream(s));
}
array zeros_like(const array& a, StreamOrDevice s /* = {} */) {
return zeros(a.shape(), a.dtype(), to_stream(s));
}
array ones(
const std::vector<int>& shape,
Dtype dtype,
StreamOrDevice s /* = {} */) {
return full(shape, array(1, dtype), to_stream(s));
}
array ones_like(const array& a, StreamOrDevice s /* = {} */) {
return ones(a.shape(), a.dtype(), to_stream(s));
}
array eye(int n, int m, int k, Dtype dtype, StreamOrDevice s /* = {} */) {
if (n <= 0 || m <= 0) {
throw std::invalid_argument("[eye] N and M must be positive integers.");
}
array result = zeros({n, m}, dtype, s);
if (k >= m || -k >= n) {
return result;
}
int diagonal_length = k >= 0 ? std::min(n, m - k) : std::min(n + k, m);
std::vector<array> indices;
auto s1 = std::max(0, -k);
auto s2 = std::max(0, k);
indices.push_back(arange(s1, diagonal_length + s1, int32, s));
indices.push_back(arange(s2, diagonal_length + s2, int32, s));
array ones_array = ones({diagonal_length, 1, 1}, dtype, s);
return scatter(result, indices, ones_array, {0, 1}, s);
}
array identity(int n, Dtype dtype, StreamOrDevice s /* = {} */) {
return eye(n, n, 0, dtype, s);
}
array tri(int n, int m, int k, Dtype type, StreamOrDevice s /* = {} */) {
auto l = expand_dims(arange(n, s), 1, s);
auto r = expand_dims(arange(-k, m - k, s), 0, s);
return astype(greater_equal(l, r, s), type, s);
}
array tril(array x, int k /* = 0 */, StreamOrDevice s /* = {} */) {
if (x.ndim() < 2) {
throw std::invalid_argument("[tril] array must be at least 2-D");
}
auto mask = tri(x.shape(-2), x.shape(-1), k, x.dtype(), s);
return where(mask, x, zeros_like(x, s), s);
}
array triu(array x, int k /* = 0 */, StreamOrDevice s /* = {} */) {
if (x.ndim() < 2) {
throw std::invalid_argument("[triu] array must be at least 2-D");
}
auto mask = tri(x.shape(-2), x.shape(-1), k - 1, x.dtype(), s);
return where(mask, zeros_like(x, s), x, s);
}
array reshape(
const array& a,
std::vector<int> shape,
StreamOrDevice s /* = {} */) {
if (a.shape() == shape) {
return a;
}
size_t size = 1;
int infer_idx = -1;
for (int i = 0; i < shape.size(); ++i) {
if (shape[i] == -1) {
if (infer_idx >= 0) {
throw std::invalid_argument(
"[reshape] Reshape can only infer one dimension.");
}
infer_idx = i;
} else {
size *= shape[i];
}
}
// Infer the shape
if (size > 0) {
auto q_and_r = std::ldiv(a.size(), size);
if (infer_idx >= 0) {
shape[infer_idx] = q_and_r.quot;
size *= q_and_r.quot;
}
} else if (infer_idx >= 0) {
throw std::invalid_argument(
"[reshape] Cannot infer the shape of an empty array");
}
// Check that the reshaping is valid
if (a.size() != size) {
std::ostringstream msg;
msg << "[reshape] Cannot reshape array of size " << a.size()
<< " into shape " << shape << ".";
throw std::invalid_argument(msg.str());
}
auto p = std::make_shared<Reshape>(to_stream(s), shape);
return array(std::move(shape), a.dtype(), std::move(p), {a});
}
array flatten(
const array& a,
int start_axis,
int end_axis /* = -1 */,
StreamOrDevice s /* = {} */) {
auto ndim = static_cast<int>(a.ndim());
auto start_ax = start_axis + (start_axis < 0 ? ndim : 0);
auto end_ax = end_axis + (end_axis < 0 ? ndim : 0);
start_ax = std::max(0, start_ax);
end_ax = std::min(ndim - 1, end_ax);
if (a.ndim() == 0) {
return reshape(a, {1}, s);
}
if (end_ax < start_ax) {
throw std::invalid_argument(
"[flatten] start_axis must be less than or equal to end_axis");
}
if (start_ax >= ndim) {
std::ostringstream msg;
msg << "[flatten] Invalid start_axis " << start_axis << " for array with "
<< ndim << " dimensions.";
throw std::invalid_argument(msg.str());
}
if (end_ax < 0) {
std::ostringstream msg;
msg << "[flatten] Invalid end_axis " << end_axis << " for array with "
<< ndim << " dimensions.";
throw std::invalid_argument(msg.str());
}
if (start_ax == end_ax) {
return a;
}
std::vector<int> new_shape(a.shape().begin(), a.shape().begin() + start_ax);
new_shape.push_back(-1);
new_shape.insert(
new_shape.end(), a.shape().begin() + end_ax + 1, a.shape().end());
return reshape(a, new_shape, s);
}
array flatten(const array& a, StreamOrDevice s /* = {} */) {
return flatten(a, 0, a.ndim() - 1, s);
}
array hadamard_transform(
const array& a,
std::optional<float> scale_ /* = std::nullopt */,
StreamOrDevice s /* = {} */) {
// Default to an orthonormal Hadamard matrix scaled by 1/sqrt(N)
float scale = scale_.has_value() ? *scale_ : 1.0f / std::sqrt(a.shape(-1));
auto dtype = issubdtype(a.dtype(), floating) ? a.dtype() : float32;
return array(
a.shape(),
dtype,
std::make_shared<Hadamard>(to_stream(s), scale),
{astype(a, dtype, s)});
}
array squeeze(
const array& a,
const std::vector<int>& axes,
StreamOrDevice s /* = {} */) {
std::set<int> unique_axes;
for (auto ax : axes) {
ax = ax < 0 ? ax + a.ndim() : ax;
if (ax < 0 || ax >= a.ndim()) {
std::ostringstream msg;
msg << "[squeeze] Invalid axes " << ax << " for array with " << a.ndim()
<< " dimensions.";
throw std::invalid_argument(msg.str());
}
if (a.shape(ax) != 1) {
std::ostringstream msg;
msg << "[squeeze] Cannot squeeze axis " << ax << " with size "
<< a.shape(ax) << " which is not equal to 1.";
throw std::invalid_argument(msg.str());
}
unique_axes.insert(ax);
}
if (unique_axes.size() != axes.size()) {
throw std::invalid_argument("[squeeze] Received duplicate axes.");
}
std::vector<int> sorted_axes(unique_axes.begin(), unique_axes.end());
std::vector<int> shape;
for (int i = 0, j = 0; i < a.ndim(); ++i) {
if (j < sorted_axes.size() && i == sorted_axes[j]) {
j++;
} else {
shape.push_back(a.shape(i));
}
}
return reshape(a, shape, s);
}
array squeeze(const array& a, StreamOrDevice s /* = {} */) {
std::vector<int> axes;
for (int i = 0; i < a.ndim(); ++i) {
if (a.shape(i) == 1) {
axes.push_back(i);
}
}
return squeeze(a, axes, s);
}
array expand_dims(const array& a, int axis, StreamOrDevice s /* = {} */) {
int out_dim = a.ndim() + 1;
int ax = axis < 0 ? axis + out_dim : axis;
if (ax < 0 || ax >= out_dim) {
std::ostringstream msg;
msg << "[expand_dims] Invalid axis " << axis << " for output array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
auto shape = a.shape();
shape.insert(shape.begin() + ax, 1);
return reshape(a, std::move(shape), s);
}
array expand_dims(
const array& a,
const std::vector<int>& axes,
StreamOrDevice s /* = {} */) {
{ // Check for repeats
std::set<int> unique_axes(axes.begin(), axes.end());
if (unique_axes.size() != axes.size()) {
throw std::invalid_argument("[expand_dims] Received duplicate axes.");
}
}
int out_ndim = axes.size() + a.ndim();
std::vector<int> canonical_axes = axes;
for (auto& ax : canonical_axes) {
ax = ax < 0 ? ax + out_ndim : ax;
if (ax < 0 || ax >= out_ndim) {
std::ostringstream msg;
msg << "[expand_dims] Invalid axis " << ax << " for output array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
}
// Check for repeats again
std::set<int> unique_axes(canonical_axes.begin(), canonical_axes.end());
if (unique_axes.size() != axes.size()) {
throw std::invalid_argument("[expand_dims] Received duplicate axes.");
}
std::vector<int> sorted_axes(unique_axes.begin(), unique_axes.end());
auto out_shape = a.shape();
for (int i = 0; i < sorted_axes.size(); ++i) {
out_shape.insert(out_shape.begin() + sorted_axes[i], 1);
}
return reshape(a, std::move(out_shape), s);
}
// Slice helper
namespace {
inline auto normalize_slice(
const std::vector<int>& shape,
std::vector<int>& start,
std::vector<int>& stop,
std::vector<int>& strides) {
std::vector<int> out_shape(shape.size());
bool has_neg_strides = false;
for (int i = 0; i < shape.size(); ++i) {
// Following numpy docs
// Negative i and j are interpreted as n + i and n + j where n is
// the number of elements in the corresponding dimension. Negative
// k makes stepping go towards smaller indices
auto n = shape[i];
auto s = start[i];
s = s < 0 ? s + n : s;
auto e = stop[i];
e = e < 0 ? e + n : e;
// Note: -ve strides require start >= stop
if (strides[i] < 0) {
has_neg_strides = true;
// Clamp to bounds
auto st = std::min(s, n - 1);
auto ed = std::max(-1, e);
start[i] = st;
stop[i] = ed > st ? st : ed;
auto str = -strides[i];
out_shape[i] = (start[i] - stop[i] + str - 1) / str;
} else {
// Clamp to bounds
auto st = std::max(0, std::min(s, n));
auto ed = std::max(0, std::min(e, n));
start[i] = st;
stop[i] = ed < st ? st : ed;
out_shape[i] = (stop[i] - start[i] + strides[i] - 1) / strides[i];
}
}
return std::make_pair(has_neg_strides, out_shape);
}
} // namespace
array slice(
const array& a,
std::vector<int> start,
std::vector<int> stop,
std::vector<int> strides,
StreamOrDevice s /* = {} */) {
if (start.size() != a.ndim() || stop.size() != a.ndim() ||
strides.size() != a.ndim()) {
std::ostringstream msg;
msg << "[slice] Invalid number of indices or strides for "
<< "array with dimension " << a.ndim() << ".";
throw std::invalid_argument(msg.str());
}
auto [has_neg_strides, out_shape] =
normalize_slice(a.shape(), start, stop, strides);
if (!has_neg_strides && out_shape == a.shape()) {
return a;
}
return array(
out_shape,
a.dtype(),
std::make_shared<Slice>(
to_stream(s), std::move(start), std::move(stop), std::move(strides)),
{a});
}
array slice(
const array& a,
const std::vector<int>& start,
const std::vector<int>& stop,
StreamOrDevice s /* = {} */) {
return slice(a, start, stop, std::vector<int>(a.ndim(), 1), to_stream(s));
}
/** Update a slice from the source array */
array slice_update(
const array& src,
const array& update,
std::vector<int> start,
std::vector<int> stop,
std::vector<int> strides,
StreamOrDevice s /* = {} */) {
// Check dimensions
if (start.size() != src.ndim() || stop.size() != src.ndim() ||
strides.size() != src.ndim()) {
std::ostringstream msg;
msg << "[slice] Invalid number of indices or strides for "
<< "array with dimension " << src.ndim() << ".";
throw std::invalid_argument(msg.str());
}
// Process slice dimensions
auto [has_neg_strides, upd_shape] =
normalize_slice(src.shape(), start, stop, strides);
// Broadcast update shape to slice shape
auto update_broadcasted = broadcast_to(update, upd_shape, s);
// If the entire src is the slice, just return the update
if (!has_neg_strides && upd_shape == src.shape()) {
return astype(update_broadcasted, src.dtype(), s);
}
return array(
src.shape(),
src.dtype(),
std::make_shared<SliceUpdate>(
to_stream(s), std::move(start), std::move(stop), std::move(strides)),
{src, update_broadcasted});
}
/** Update a slice from the source array with stride 1 in each dimension */
array slice_update(
const array& src,
const array& update,
std::vector<int> start,
std::vector<int> stop,
StreamOrDevice s /* = {} */) {
auto strides = std::vector<int>(src.ndim(), 1);
return slice_update(
src, update, std::move(start), std::move(stop), std::move(strides), s);
}
std::vector<array> split(
const array& a,
const std::vector<int>& indices,
int axis,
StreamOrDevice s /* = {} */) {
auto ax = axis < 0 ? axis + a.ndim() : axis;
if (ax < 0 || ax >= a.ndim()) {
std::ostringstream msg;
msg << "Invalid axis (" << axis << ") passed to split"
<< " for array with shape " << a.shape() << ".";
throw std::invalid_argument(msg.str());
}
if (indices.empty()) {
return {a};
}
if (indices.size() < 10 &&
std::is_sorted(indices.begin(), indices.end(), std::less<>{}) &&
indices[0] > 0 && indices.back() < a.shape(ax)) {
std::vector<Dtype> dtypes(indices.size() + 1, a.dtype());
std::vector<std::vector<int>> shapes(indices.size() + 1, a.shape());
shapes[0][ax] = indices[0];
for (int i = 1; i < indices.size(); i++) {
shapes[i][ax] = indices[i] - indices[i - 1];
}
shapes.back()[ax] = a.shape(ax) - indices.back();
return array::make_arrays(
std::move(shapes),
dtypes,
std::make_shared<Split>(to_stream(s), indices, ax),
{a});
}
std::vector<array> res;
auto out_shape = a.shape();
auto start_indices = std::vector<int>(a.ndim(), 0);
auto stop_indices = a.shape();
for (int i = 0; i < indices.size() + 1; ++i) {
stop_indices[ax] = i < indices.size() ? indices[i] : a.shape(ax);
res.push_back(slice(a, start_indices, stop_indices, to_stream(s)));
start_indices[ax] = stop_indices[ax];
}
return res;
}
std::vector<array> split(
const array& a,
const std::vector<int>& indices,
StreamOrDevice s /* = {} */) {
return split(a, indices, 0, s);
}
std::vector<array>
split(const array& a, int num_splits, int axis, StreamOrDevice s /* = {} */) {
auto ax = axis < 0 ? axis + a.ndim() : axis;
if (ax < 0 || ax >= a.ndim()) {
std::ostringstream msg;
msg << "Invalid axis " << axis << " passed to split"
<< " for array with shape " << a.shape() << ".";
throw std::invalid_argument(msg.str());
}
auto q_and_r = std::ldiv(a.shape(axis), num_splits);
if (q_and_r.rem) {
std::ostringstream msg;
msg << "Array split does not result in sub arrays with equal size:"
<< " attempting " << num_splits << " splits along axis " << axis
<< " for shape " << a.shape() << ".";
throw std::invalid_argument(msg.str());
}
auto split_size = q_and_r.quot;
std::vector<int> indices(num_splits - 1);
for (int i = 0; i < indices.size(); ++i) {
indices[i] = (i + 1) * split_size;
}
return split(a, indices, axis, s);
}
std::vector<array>
split(const array& a, int num_splits, StreamOrDevice s /* = {} */) {
return split(a, num_splits, 0, to_stream(s));
}
std::vector<array> meshgrid(
const std::vector<array>& arrays,
bool sparse /* = false */,
std::string indexing /* = "xy" */,
StreamOrDevice s /* = {} */) {
if (indexing != "xy" && indexing != "ij") {
throw std::invalid_argument(
"[meshgrid] Invalid indexing value. Valid values are 'xy' and 'ij'.");
}
auto ndim = arrays.size();
std::vector<array> outputs;
for (int i = 0; i < ndim; ++i) {
std::vector<int> shape(ndim, 1);
shape[i] = -1;
outputs.push_back(reshape(arrays[i], std::move(shape), s));
}
if (indexing == "xy" and ndim > 1) {
std::vector<int> shape(ndim, 1);
shape[1] = arrays[0].size();
outputs[0] = reshape(arrays[0], shape, s);
shape[1] = 1;
shape[0] = arrays[1].size();
outputs[1] = reshape(arrays[1], std::move(shape), s);
}
if (!sparse) {
outputs = broadcast_arrays(outputs, s);
}
return outputs;
}
array clip(
const array& a,
const std::optional<array>& a_min,
const std::optional<array>& a_max,
StreamOrDevice s /* = {} */) {
if (!a_min.has_value() && !a_max.has_value()) {
throw std::invalid_argument("At most one of a_min and a_max may be None");
}
array result = astype(a, a.dtype(), s);
if (a_min.has_value()) {
result = maximum(result, a_min.value(), s);
}
if (a_max.has_value()) {
result = minimum(result, a_max.value(), s);
}
return result;
}
array concatenate(
const std::vector<array>& arrays,
int axis,
StreamOrDevice s /* = {} */) {
if (arrays.size() == 0) {
throw std::invalid_argument(
"[concatenate] No arrays provided for concatenation");
}
// Normalize the given axis
auto ax = axis < 0 ? axis + arrays[0].ndim() : axis;
if (ax < 0 || ax >= arrays[0].ndim()) {
std::ostringstream msg;
msg << "[concatenate] Invalid axis (" << axis << ") passed to concatenate"
<< " for array with shape " << arrays[0].shape() << ".";
throw std::invalid_argument(msg.str());
}
auto throw_invalid_shapes = [&]() {
std::ostringstream msg;
msg << "[concatenate] All the input array dimensions must match exactly "
<< "except for the concatenation axis. However, the provided shapes are ";
for (auto& a : arrays) {
msg << a.shape() << ", ";
}
msg << "and the concatenation axis is " << axis << ".";
throw std::invalid_argument(msg.str());
};
std::vector<int> shape = arrays[0].shape();
shape[ax] = 0;
// Make the output shape and validate that all arrays have the same shape
// except for the concatenation axis.
for (auto& a : arrays) {
if (a.ndim() != shape.size()) {
std::ostringstream msg;
msg << "[concatenate] All the input arrays must have the same number of "
<< "dimensions. However, got arrays with dimensions " << shape.size()
<< " and " << a.ndim() << ".";
throw std::invalid_argument(msg.str());
}
for (int i = 0; i < a.ndim(); i++) {
if (i == ax) {
continue;
}
if (a.shape(i) != shape[i]) {
throw_invalid_shapes();
}
}
shape[ax] += a.shape(ax);
}
// Promote all the arrays to the same type
auto dtype = result_type(arrays);
return array(
std::move(shape),
dtype,
std::make_shared<Concatenate>(to_stream(s), ax),
std::move(arrays));
}
array concatenate(
const std::vector<array>& arrays,
StreamOrDevice s /* = {} */) {
std::vector<array> flat_inputs;
for (auto& a : arrays) {
flat_inputs.push_back(reshape(a, {-1}, s));
}
return concatenate(flat_inputs, 0, s);
}
/** Stack arrays along a new axis */
array stack(
const std::vector<array>& arrays,
int axis,
StreamOrDevice s /* = {} */) {
if (arrays.empty()) {
throw std::invalid_argument("No arrays provided for stacking");
}
if (!is_same_shape(arrays)) {
throw std::invalid_argument("All arrays must have the same shape");
}
int normalized_axis = normalize_axis(axis, arrays[0].ndim() + 1);
std::vector<array> new_arrays;
new_arrays.reserve(arrays.size());
for (auto& a : arrays) {
new_arrays.emplace_back(expand_dims(a, normalized_axis, s));
}
return concatenate(new_arrays, axis, s);
}
array stack(const std::vector<array>& arrays, StreamOrDevice s /* = {} */) {
return stack(arrays, 0, s);
}
/** array repeat with axis */
array repeat(const array& arr, int repeats, int axis, StreamOrDevice s) {
axis = normalize_axis(axis, arr.ndim());
if (repeats < 0) {
throw std::invalid_argument(
"[repeat] Number of repeats cannot be negative");
}
if (repeats == 0) {
return array({}, arr.dtype());
}
if (repeats == 1) {
return arr;
}
// Broadcast to (S_1, S_2, ..., S_axis, repeats, S_axis+1, ...)
std::vector<int> shape(arr.shape());
shape.insert(shape.begin() + axis + 1, repeats);
array out = expand_dims(arr, axis + 1, s);
out = broadcast_to(out, shape, s);
// Reshape back into a contiguous array where S_axis is now S_axis * repeats
shape.erase(shape.begin() + axis + 1);
shape[axis] *= repeats;
out = reshape(out, shape, s);
return out;
}
array repeat(const array& arr, int repeats, StreamOrDevice s) {
return repeat(flatten(arr, s), repeats, 0, s);
}
array tile(
const array& arr,
std::vector<int> reps,
StreamOrDevice s /* = {} */) {
auto shape = arr.shape();
if (reps.size() < shape.size()) {
reps.insert(reps.begin(), shape.size() - reps.size(), 1);
}
if (reps.size() > shape.size()) {
shape.insert(shape.begin(), reps.size() - shape.size(), 1);
}
std::vector<int> expand_shape;
std::vector<int> broad_shape;
std::vector<int> final_shape;
for (int i = 0; i < shape.size(); i++) {
if (reps[i] != 1) {
expand_shape.push_back(1);
broad_shape.push_back(reps[i]);
}
expand_shape.push_back(shape[i]);
broad_shape.push_back(shape[i]);
final_shape.push_back(reps[i] * shape[i]);
}
auto x = reshape(arr, expand_shape, s);
x = broadcast_to(x, broad_shape, s);
return reshape(x, final_shape, s);
}
/** Pad an array with a constant value */
array pad(
const array& a,
const std::vector<int>& axes,
const std::vector<int>& low_pad_size,
const std::vector<int>& high_pad_size,
const array& pad_value /*= array(0)*/,
StreamOrDevice s /* = {}*/) {
if (axes.size() != low_pad_size.size() ||
axes.size() != high_pad_size.size()) {
std::ostringstream msg;
msg << "Invalid number of padding sizes passed to pad "
<< "with axes of size " << axes.size();
throw std::invalid_argument(msg.str());
}
std::vector<int> out_shape = a.shape();
for (int i = 0; i < axes.size(); i++) {
if (low_pad_size[i] < 0) {
std::ostringstream msg;
msg << "Invalid low padding size (" << low_pad_size[i]
<< ") passed to pad" << " for axis " << i
<< ". Padding sizes must be non-negative";
throw std::invalid_argument(msg.str());
}
if (high_pad_size[i] < 0) {
std::ostringstream msg;
msg << "Invalid high padding size (" << high_pad_size[i]
<< ") passed to pad" << " for axis " << i
<< ". Padding sizes must be non-negative";
throw std::invalid_argument(msg.str());
}
auto ax = axes[i] < 0 ? a.ndim() + axes[i] : axes[i];
out_shape[ax] += low_pad_size[i] + high_pad_size[i];
}
return array(
out_shape,
a.dtype(),
std::make_shared<Pad>(to_stream(s), axes, low_pad_size, high_pad_size),
{a, astype(pad_value, a.dtype(), s)});
}
/** Pad an array with a constant value along all axes */
array pad(
const array& a,
const std::vector<std::pair<int, int>>& pad_width,
const array& pad_value /*= array(0)*/,
StreamOrDevice s /*= {}*/) {
std::vector<int> axes(a.ndim(), 0);
std::iota(axes.begin(), axes.end(), 0);
std::vector<int> lows;
std::vector<int> highs;
for (auto& pads : pad_width) {
lows.push_back(pads.first);
highs.push_back(pads.second);
}
return pad(a, axes, lows, highs, pad_value, s);
}
array pad(
const array& a,
const std::pair<int, int>& pad_width,
const array& pad_value /*= array(0)*/,
StreamOrDevice s /*= {}*/) {
return pad(
a, std::vector<std::pair<int, int>>(a.ndim(), pad_width), pad_value, s);
}
array pad(
const array& a,
int pad_width,
const array& pad_value /*= array(0)*/,
StreamOrDevice s /*= {}*/) {
return pad(
a,
std::vector<std::pair<int, int>>(a.ndim(), {pad_width, pad_width}),
pad_value,
s);
}
array moveaxis(
const array& a,
int source,
int destination,
StreamOrDevice s /* = {} */) {
auto check_ax = [&a](int ax) {
auto ndim = static_cast<int>(a.ndim());
if (ax < -ndim || ax >= ndim) {
std::ostringstream msg;
msg << "[moveaxis] Invalid axis " << ax << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
return ax < 0 ? ax + ndim : ax;
};
source = check_ax(source);
destination = check_ax(destination);
std::vector<int> reorder(a.ndim());
std::iota(reorder.begin(), reorder.end(), 0);
reorder.erase(reorder.begin() + source);
reorder.insert(reorder.begin() + destination, source);
return transpose(a, reorder, s);
}
array swapaxes(
const array& a,
int axis1,
int axis2,
StreamOrDevice s /* = {} */) {
auto check_ax = [&a](int ax) {
auto ndim = static_cast<int>(a.ndim());
if (ax < -ndim || ax >= ndim) {
std::ostringstream msg;
msg << "[swapaxes] Invalid axis " << ax << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
return ax < 0 ? ax + ndim : ax;
};
axis1 = check_ax(axis1);
axis2 = check_ax(axis2);
std::vector<int> reorder(a.ndim());
std::iota(reorder.begin(), reorder.end(), 0);
std::swap(reorder[axis1], reorder[axis2]);
return transpose(a, std::move(reorder), s);
}
array transpose(
const array& a,
std::vector<int> axes,
StreamOrDevice s /* = {} */) {
for (auto& ax : axes) {
ax = ax < 0 ? ax + a.ndim() : ax;
}
if (axes.size() != a.ndim()) {
std::ostringstream msg;
msg << "[transpose] Recived " << axes.size() << " axes for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
// Check in bounds and for duplicates
std::vector<int> shape(axes.size(), 0);
for (auto& ax : axes) {
if (ax < 0 || ax >= a.ndim()) {
std::ostringstream msg;
msg << "[transpose] Invalid axis (" << ax << ") for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
if (shape[ax] != 0) {
throw std::invalid_argument("[transpose] Repeat axes not allowed.");
}
shape[ax] = 1;
}
for (int i = 0; i < axes.size(); ++i) {
shape[i] = a.shape()[axes[i]];
}
return array(
std::move(shape),
a.dtype(),
std::make_shared<Transpose>(to_stream(s), std::move(axes)),
{a});
}
array transpose(const array& a, StreamOrDevice s /* = {} */) {
std::vector<int> axes(a.ndim());
std::iota(axes.rbegin(), axes.rend(), 0);
return transpose(a, std::move(axes), to_stream(s));
}
array broadcast_to(
const array& a,
const std::vector<int>& shape,
StreamOrDevice s /* = {} */) {
if (a.shape() == shape) {
return a;
}
// Make sure the shapes are broadcastable
auto bxshape = broadcast_shapes(a.shape(), shape);
if (bxshape != shape) {
std::ostringstream msg;
msg << "Cannot broadcast array of shape " << a.shape() << " into shape "
<< shape << ".";
throw std::invalid_argument(msg.str());
}
return array(
std::move(bxshape),
a.dtype(),
std::make_shared<Broadcast>(to_stream(s), shape),
{a});
}
std::vector<array>
broadcast_arrays(const array& a, const array& b, StreamOrDevice s /* = {} */) {
std::vector<int> shape = broadcast_shapes(a.shape(), b.shape());
return {broadcast_to(a, shape, s), broadcast_to(b, shape, s)};
}
std::vector<array> broadcast_arrays(
const std::vector<array>& inputs,
StreamOrDevice s /* = {} */) {
std::vector<int> shape{};
for (const auto& in : inputs) {
shape = broadcast_shapes(shape, in.shape());
}
std::vector<array> outputs;
for (const auto& in : inputs) {
outputs.push_back(broadcast_to(in, shape, s));
}
return outputs;
}
array equal(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape, bool_, std::make_shared<Equal>(to_stream(s)), std::move(inputs));
}
array not_equal(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
bool_,
std::make_shared<NotEqual>(to_stream(s)),
std::move(inputs));
}
array greater(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape, bool_, std::make_shared<Greater>(to_stream(s)), std::move(inputs));
}
array greater_equal(
const array& a,
const array& b,
StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
bool_,
std::make_shared<GreaterEqual>(to_stream(s)),
std::move(inputs));
}
array less(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape, bool_, std::make_shared<Less>(to_stream(s)), std::move(inputs));
}
array less_equal(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
bool_,
std::make_shared<LessEqual>(to_stream(s)),
std::move(inputs));
}
array array_equal(
const array& a,
const array& b,
bool equal_nan,
StreamOrDevice s /* = {} */) {
if (a.shape() != b.shape()) {
return array(false);
} else {
auto dtype = promote_types(a.dtype(), b.dtype());
equal_nan &= issubdtype(dtype, inexact);
return all(
array(
a.shape(),
bool_,
std::make_shared<Equal>(to_stream(s), equal_nan),
{astype(a, dtype, s), astype(b, dtype, s)}),
false,
s);
}
}
array isnan(const array& a, StreamOrDevice s /* = {} */) {
if (issubdtype(a.dtype(), integer) || a.dtype() == bool_) {
return full(a.shape(), false, bool_, s);
}
return not_equal(a, a, s);
}
array isinf(const array& a, StreamOrDevice s /* = {} */) {
return logical_or(isposinf(a, s), isneginf(a, s), s);
}
array isposinf(const array& a, StreamOrDevice s /* = {} */) {
if (issubdtype(a.dtype(), integer) || a.dtype() == bool_) {
return full(a.shape(), false, bool_, s);
}
return equal(a, array(std::numeric_limits<float>::infinity(), a.dtype()), s);
}
array isneginf(const array& a, StreamOrDevice s /* = {} */) {
if (issubdtype(a.dtype(), integer) || a.dtype() == bool_) {
return full(a.shape(), false, bool_, s);
}
return equal(a, array(-std::numeric_limits<float>::infinity(), a.dtype()), s);
}
array where(
const array& a,
const array& b,
const array& c,
StreamOrDevice s /* = {} */) {
auto condition = astype(a, bool_, s);
Dtype out_dtype = promote_types(b.dtype(), c.dtype());
auto inputs = broadcast_arrays(
{condition, astype(b, out_dtype, s), astype(c, out_dtype, s)}, s);
return array(
inputs[0].shape(),
out_dtype,
std::make_shared<Select>(to_stream(s)),
inputs);
}
array nan_to_num(
const array& a,
float nan /* = 0.0f */,
const std::optional<float>& posinf_ /* = std::nullopt */,
const std::optional<float>& neginf_ /* = std::nullopt */,
StreamOrDevice s /* = {} */) {
Dtype dtype = a.dtype();
if (!issubdtype(dtype, inexact)) {
return a;
}
auto type_to_max = [](const auto& dtype) -> float {
if (dtype == float32) {
return std::numeric_limits<float>::max();
} else if (dtype == bfloat16) {
return std::numeric_limits<bfloat16_t>::max();
} else if (dtype == float16) {
return std::numeric_limits<float16_t>::max();
} else {
std::ostringstream msg;
msg << "[nan_to_num] Does not yet support given type: " << dtype << ".";
throw std::invalid_argument(msg.str());
}
};
float posinf = posinf_ ? *posinf_ : type_to_max(dtype);
float neginf = neginf_ ? *neginf_ : -type_to_max(dtype);
auto out = where(isnan(a, s), array(nan, dtype), a, s);
out = where(isposinf(a, s), array(posinf, dtype), out, s);
out = where(isneginf(a, s), array(neginf, dtype), out, s);
return out;
}
array allclose(
const array& a,
const array& b,
double rtol /* = 1e-5 */,
double atol /* = 1e-8 */,
bool equal_nan /* = false */,
StreamOrDevice s /* = {}*/) {
return all(isclose(a, b, rtol, atol, equal_nan, s), s);
}
array isclose(
const array& a,
const array& b,
double rtol /* = 1e-5 */,
double atol /* = 1e-8 */,
bool equal_nan /* = false */,
StreamOrDevice s /* = {}*/) {
// |a - b| <= atol + rtol * |b|
auto rhs = add(array(atol), multiply(array(rtol), abs(b, s), s), s);
auto lhs = abs(subtract(a, b, s), s);
auto out = less_equal(lhs, rhs, s);
// Correct the result for infinite values.
auto any_inf = logical_or(isinf(a, s), isinf(b, s), s);
auto both_inf = logical_or(
logical_and(isposinf(a, s), isposinf(b, s), s),
logical_and(isneginf(a, s), isneginf(b, s), s),
s);
// Convert all elements where either value is infinite to False.
out = logical_and(out, logical_not(any_inf, s), s);
// Convert all the elements where both values are infinite and of the same
// sign to True.
out = logical_or(out, both_inf, s);
if (equal_nan) {
auto both_nan = logical_and(isnan(a, s), isnan(b, s), s);
out = logical_or(out, both_nan, s);
}
return out;
}
array all(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return all(a, axes, keepdims, s);
}
array all(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
if (axes.empty()) {
return astype(a, bool_, s);
}
auto [out_shape, sorted_axes] = compute_reduce_shape(axes, a.shape());
auto out = array(
out_shape,
bool_,
std::make_shared<Reduce>(to_stream(s), Reduce::And, sorted_axes),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array all(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return all(a, std::vector<int>{axis}, keepdims, s);
}
array any(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return any(a, axes, keepdims, s);
}
array any(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
if (axes.empty()) {
return astype(a, bool_, s);
}
auto [out_shape, sorted_axes] = compute_reduce_shape(axes, a.shape());
auto out = array(
out_shape,
bool_,
std::make_shared<Reduce>(to_stream(s), Reduce::Or, sorted_axes),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array any(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return any(a, std::vector<int>{axis}, keepdims, s);
}
array sum(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return sum(a, axes, keepdims, s);
}
array sum(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
if (axes.empty()) {
return a;
}
auto [out_shape, sorted_axes] = compute_reduce_shape(axes, a.shape());
auto out_type = a.dtype() == bool_ ? int32 : a.dtype();
auto out = array(
out_shape,
out_type,
std::make_shared<Reduce>(to_stream(s), Reduce::Sum, sorted_axes),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array sum(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return sum(a, std::vector<int>{axis}, keepdims, s);
}
array mean(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return mean(a, axes, keepdims, to_stream(s));
}
array mean(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
int ndim = a.ndim();
for (int axis : axes) {
if (axis < -ndim || axis >= ndim) {
std::ostringstream msg;
msg << "[mean] axis " << axis << " is out of bounds for array with "
<< ndim << " dimensions.";
throw std::invalid_argument(msg.str());
}
}
auto dtype = at_least_float(a.dtype());
auto normalizer = number_of_elements(a, axes, true, dtype, s);
return multiply(sum(a, axes, keepdims, s), normalizer, s);
}
array mean(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return mean(a, std::vector<int>{axis}, keepdims, to_stream(s));
}
array var(
const array& a,
bool keepdims,
int ddof /* = 0*/,
StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return var(a, axes, keepdims, ddof, to_stream(s));
}
array var(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
int ddof /* = 0*/,
StreamOrDevice s /* = {}*/) {
auto dtype = at_least_float(a.dtype());
auto mu2 = square(mean(a, axes, keepdims, s), s);
auto a2 = mean(square(a, s), axes, keepdims, s);
auto v = subtract(a2, mu2, s);
if (ddof != 0) {
auto nelements = number_of_elements(a, axes, false, dtype, s);
auto factor = divide(
nelements,
maximum(subtract(nelements, array(ddof, dtype), s), array(0, dtype), s),
s);
v = multiply(v, factor, s);
}
return v;
}
array var(
const array& a,
int axis,
bool keepdims /* = false */,
int ddof /* = 0*/,
StreamOrDevice s /* = {} */) {
return var(a, std::vector<int>{axis}, keepdims, ddof, to_stream(s));
}
array std(
const array& a,
bool keepdims,
int ddof /* = 0*/,
StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return std(a, axes, keepdims, ddof, to_stream(s));
}
array std(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
int ddof /* = 0*/,
StreamOrDevice s /* = {}*/) {
return sqrt(var(a, axes, keepdims, ddof, s), s);
}
array std(
const array& a,
int axis,
bool keepdims /* = false */,
int ddof /* = 0*/,
StreamOrDevice s /* = {} */) {
return std(a, std::vector<int>{axis}, keepdims, ddof, to_stream(s));
}
array prod(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return prod(a, axes, keepdims, s);
}
array prod(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
if (axes.empty()) {
return a;
}
auto [out_shape, sorted_axes] = compute_reduce_shape(axes, a.shape());
auto out = array(
out_shape,
a.dtype(),
std::make_shared<Reduce>(to_stream(s), Reduce::Prod, sorted_axes),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array prod(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return prod(a, std::vector<int>{axis}, keepdims, s);
}
array max(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return max(a, axes, keepdims, s);
}
array max(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
if (a.size() == 0) {
throw std::invalid_argument("[max] Cannot max reduce zero size array.");
}
if (axes.empty()) {
return a;
}
auto [out_shape, sorted_axes] = compute_reduce_shape(axes, a.shape());
auto out = array(
out_shape,
a.dtype(),
std::make_shared<Reduce>(to_stream(s), Reduce::Max, sorted_axes),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array max(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return max(a, std::vector<int>{axis}, keepdims, s);
}
array min(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return min(a, axes, keepdims, s);
}
array min(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
if (a.size() == 0) {
throw std::invalid_argument("[min] Cannot min reduce zero size array.");
}
if (axes.empty()) {
return a;
}
auto [out_shape, sorted_axes] = compute_reduce_shape(axes, a.shape());
auto out = array(
out_shape,
a.dtype(),
std::make_shared<Reduce>(to_stream(s), Reduce::Min, sorted_axes),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array min(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return min(a, std::vector<int>{axis}, keepdims, s);
}
array argmin(const array& a, bool keepdims, StreamOrDevice s /* = {} */) {
int size = a.size();
auto result = argmin(reshape(a, {size}, s), 0, true, s);
if (keepdims) {
result = reshape(result, std::vector<int>(a.shape().size(), 1), s);
} else {
result = squeeze(result, s);
}
return result;
}
array argmin(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
if (a.size() == 0) {
throw std::invalid_argument(
"[argmin] Cannot argmin reduce zero size array.");
}
auto [out_shape, sorted_axes] = compute_reduce_shape({axis}, a.shape());
auto out = array(
out_shape,
uint32,
std::make_shared<ArgReduce>(
to_stream(s), ArgReduce::ArgMin, sorted_axes[0]),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
array argmax(const array& a, bool keepdims, StreamOrDevice s /* = {} */) {
int size = a.size();
auto result = argmax(reshape(a, {size}, s), 0, true, s);
if (keepdims) {
result = reshape(result, std::vector<int>(a.shape().size(), 1), s);
} else {
result = squeeze(result, s);
}
return result;
}
array argmax(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
if (a.size() == 0) {
throw std::invalid_argument(
"[argmax] Cannot argmax reduce zero size array.");
}
auto [out_shape, sorted_axes] = compute_reduce_shape({axis}, a.shape());
auto out = array(
out_shape,
uint32,
std::make_shared<ArgReduce>(
to_stream(s), ArgReduce::ArgMax, sorted_axes[0]),
{a});
if (!keepdims) {
out = squeeze(out, sorted_axes, s);
}
return out;
}
/** Returns a sorted copy of the flattened array. */
array sort(const array& a, StreamOrDevice s /* = {} */) {
int size = a.size();
return sort(reshape(a, {size}, s), 0, s);
}
/** Returns a sorted copy of the array along a given axis. */
array sort(const array& a, int axis, StreamOrDevice s /* = {} */) {
// Check for valid axis
if (axis + static_cast<int>(a.ndim()) < 0 ||
axis >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[sort] Received invalid axis " << axis << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
return array(
a.shape(), a.dtype(), std::make_shared<Sort>(to_stream(s), axis), {a});
}
/** Returns indices that sort the flattened array. */
array argsort(const array& a, StreamOrDevice s /* = {} */) {
int size = a.size();
return argsort(reshape(a, {size}, s), 0, s);
}
/** Returns indices that sort the array along a given axis. */
array argsort(const array& a, int axis, StreamOrDevice s /* = {} */) {
// Check for valid axis
if (axis + static_cast<int>(a.ndim()) < 0 ||
axis >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[argsort] Received invalid axis " << axis << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
// TODO: Fix GPU kernel
if (a.shape(axis) >= (1u << 21) && to_stream(s).device.type == Device::gpu) {
std::ostringstream msg;
msg << "[argsort] GPU sort cannot handle sort axis of >= 2M elements,"
<< " got array with sort axis size " << a.shape(axis) << "."
<< " Please place this operation on the CPU instead.";
throw std::runtime_error(msg.str());
}
return array(
a.shape(), uint32, std::make_shared<ArgSort>(to_stream(s), axis), {a});
}
/**
* Returns a partitioned copy of the flattened array
* such that the smaller kth elements are first.
**/
array partition(const array& a, int kth, StreamOrDevice s /* = {} */) {
int size = a.size();
return partition(reshape(a, {size}, s), kth, 0, s);
}
/**
* Returns a partitioned copy of the array along a given axis
* such that the smaller kth elements are first.
**/
array partition(
const array& a,
int kth,
int axis,
StreamOrDevice s /* = {} */) {
// Check for valid axis
if (axis + static_cast<int>(a.ndim()) < 0 ||
axis >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[partition] Received invalid axis " << axis << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
int axis_ = axis < 0 ? axis + a.ndim() : axis;
int kth_ = kth < 0 ? kth + a.shape(axis) : kth;
if (kth_ < 0 || kth_ >= a.shape(axis_)) {
std::ostringstream msg;
msg << "[partition] Received invalid kth " << kth << "along axis " << axis
<< " for array with shape: " << a.shape();
throw std::invalid_argument(msg.str());
}
return array(
a.shape(),
a.dtype(),
std::make_shared<Partition>(to_stream(s), kth_, axis_),
{a});
}
/**
* Returns indices that partition the flattened array
* such that the smaller kth elements are first.
**/
array argpartition(const array& a, int kth, StreamOrDevice s /* = {} */) {
int size = a.size();
return argpartition(reshape(a, {size}, s), kth, 0, s);
}
/**
* Returns indices that partition the array along a given axis
* such that the smaller kth elements are first.
**/
array argpartition(
const array& a,
int kth,
int axis,
StreamOrDevice s /* = {} */) {
// Check for valid axis
if (axis + static_cast<int>(a.ndim()) < 0 ||
axis >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[argpartition] Received invalid axis " << axis << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
int axis_ = axis < 0 ? axis + a.ndim() : axis;
int kth_ = kth < 0 ? kth + a.shape(axis) : kth;
if (kth_ < 0 || kth_ >= a.shape(axis_)) {
std::ostringstream msg;
msg << "[argpartition] Received invalid kth " << kth << " along axis "
<< axis << " for array with shape: " << a.shape();
throw std::invalid_argument(msg.str());
}
return array(
a.shape(),
uint32,
std::make_shared<ArgPartition>(to_stream(s), kth_, axis_),
{a});
}
/** Returns topk elements of the flattened array. */
array topk(const array& a, int k, StreamOrDevice s /* = {}*/) {
int size = a.size();
return topk(reshape(a, {size}, s), k, 0, s);
}
/** Returns topk elements of the array along a given axis. */
array topk(const array& a, int k, int axis, StreamOrDevice s /* = {}*/) {
// Check for valid axis
int axis_ = axis < 0 ? axis + a.ndim() : axis;
if (axis_ < 0 || axis_ >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[topk] Received invalid axis " << axis << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
if (k < 0 || k > a.shape(axis_)) {
std::ostringstream msg;
msg << "[topk] Received invalid k=" << k << " along axis " << axis
<< " for array with shape: " << a.shape();
throw std::invalid_argument(msg.str());
}
// Return early if the whole input was requested.
if (k == a.shape(axis_)) {
return a;
}
array a_partitioned = partition(a, -k, axis_, s);
std::vector<int> slice_starts(a.ndim(), 0);
std::vector<int> slice_ends = a.shape();
slice_starts[axis_] = a.shape(axis_) - k;
return slice(a_partitioned, slice_starts, slice_ends, s);
}
array logsumexp(const array& a, bool keepdims, StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return logsumexp(a, axes, keepdims, s);
}
array logsumexp(
const array& a,
const std::vector<int>& axes,
bool keepdims /* = false */,
StreamOrDevice s /* = {}*/) {
auto maxval = stop_gradient(max(a, axes, true, s), s);
auto out = log(sum(exp(subtract(a, maxval, s), s), axes, keepdims, s), s);
out = add(out, reshape(maxval, out.shape(), s), s);
if (!keepdims) {
maxval = squeeze(maxval, axes, s);
}
return where(isinf(maxval, s), maxval, out, s);
}
array logsumexp(
const array& a,
int axis,
bool keepdims /* = false */,
StreamOrDevice s /* = {} */) {
return logsumexp(a, std::vector<int>{axis}, keepdims, s);
}
array abs(const array& a, StreamOrDevice s /* = {} */) {
auto out =
array(a.shape(), a.dtype(), std::make_shared<Abs>(to_stream(s)), {a});
if (a.dtype() == complex64) {
out = astype(out, float32, s);
}
return out;
}
array negative(const array& a, StreamOrDevice s /* = {} */) {
if (a.dtype() == bool_) {
auto msg = "[negative] Not supported for bool, use logical_not instead.";
throw std::invalid_argument(msg);
}
return array(
a.shape(), a.dtype(), std::make_shared<Negative>(to_stream(s)), {a});
}
array operator-(const array& a) {
return negative(a);
}
array sign(const array& a, StreamOrDevice s /* = {} */) {
return array(a.shape(), a.dtype(), std::make_shared<Sign>(to_stream(s)), {a});
}
array logical_not(const array& a, StreamOrDevice s /* = {} */) {
return array(
a.shape(),
bool_,
std::make_shared<LogicalNot>(to_stream(s)),
{astype(a, bool_, s)});
}
array logical_and(const array& a, const array& b, StreamOrDevice s /* = {} */) {
// Broadcast arrays to a common shape
auto inputs = broadcast_arrays(astype(a, bool_, s), astype(b, bool_, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
bool_,
std::make_shared<LogicalAnd>(to_stream(s)),
std::move(inputs));
}
array operator&&(const array& a, const array& b) {
return logical_and(a, b);
}
array logical_or(const array& a, const array& b, StreamOrDevice s /* = {} */) {
// Broadcast arrays to a common shape
auto inputs = broadcast_arrays(astype(a, bool_, s), astype(b, bool_, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
bool_,
std::make_shared<LogicalOr>(to_stream(s)),
std::move(inputs));
}
array operator||(const array& a, const array& b) {
return logical_or(a, b);
}
array reciprocal(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return divide(array(1.0f, dtype), a, to_stream(s));
}
array add(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto out_type = promote_types(a.dtype(), b.dtype());
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& shape = inputs[0].shape();
return array(
shape, out_type, std::make_shared<Add>(to_stream(s)), std::move(inputs));
}
array operator+(const array& a, const array& b) {
return add(a, b);
}
array subtract(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto out_type = promote_types(a.dtype(), b.dtype());
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
out_type,
std::make_shared<Subtract>(to_stream(s)),
std::move(inputs));
}
array operator-(const array& a, const array& b) {
return subtract(a, b);
}
array multiply(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto out_type = promote_types(a.dtype(), b.dtype());
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
out_type,
std::make_shared<Multiply>(to_stream(s)),
std::move(inputs));
}
array operator*(const array& a, const array& b) {
return multiply(a, b);
}
array divide(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(promote_types(a.dtype(), b.dtype()));
auto inputs =
broadcast_arrays(astype(a, dtype, s), astype(b, dtype, to_stream(s)), s);
auto& shape = inputs[0].shape();
return array(
shape, dtype, std::make_shared<Divide>(to_stream(s)), std::move(inputs));
}
array operator/(const array& a, const array& b) {
return divide(a, b);
}
array operator/(double a, const array& b) {
return divide(array(a), b);
}
array operator/(const array& a, double b) {
return divide(a, array(b));
}
array floor_divide(
const array& a,
const array& b,
StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
if (issubdtype(dtype, inexact)) {
return floor(divide(a, b, s), s);
}
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape, dtype, std::make_shared<Divide>(to_stream(s)), std::move(inputs));
}
array remainder(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
auto inputs =
broadcast_arrays(astype(a, dtype, s), astype(b, dtype, to_stream(s)), s);
auto& shape = inputs[0].shape();
return array(
shape,
dtype,
std::make_shared<Remainder>(to_stream(s)),
std::move(inputs));
}
array operator%(const array& a, const array& b) {
return remainder(a, b);
}
std::vector<array>
divmod(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
if (issubdtype(dtype, complexfloating)) {
throw std::invalid_argument("[divmod] Complex type not supported.");
}
auto inputs =
broadcast_arrays(astype(a, dtype, s), astype(b, dtype, to_stream(s)), s);
return array::make_arrays(
{inputs[0].shape(), inputs[0].shape()},
{inputs[0].dtype(), inputs[0].dtype()},
std::make_shared<DivMod>(to_stream(s)),
inputs);
}
array maximum(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto out_type = promote_types(a.dtype(), b.dtype());
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
out_type,
std::make_shared<Maximum>(to_stream(s)),
std::move(inputs));
}
array minimum(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto out_type = promote_types(a.dtype(), b.dtype());
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
out_type,
std::make_shared<Minimum>(to_stream(s)),
std::move(inputs));
}
array floor(const array& a, StreamOrDevice s /* = {} */) {
if (a.dtype() == complex64) {
throw std::invalid_argument("[floor] Not supported for complex64.");
}
return array(
a.shape(), a.dtype(), std::make_shared<Floor>(to_stream(s)), {a});
}
array ceil(const array& a, StreamOrDevice s /* = {} */) {
if (a.dtype() == complex64) {
throw std::invalid_argument("[floor] Not supported for complex64.");
}
return array(a.shape(), a.dtype(), std::make_shared<Ceil>(to_stream(s)), {a});
}
array square(const array& a, StreamOrDevice s /* = {} */) {
return array(
a.shape(), a.dtype(), std::make_shared<Square>(to_stream(s)), {a});
}
array exp(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Exp>(to_stream(s)), {input});
}
array expm1(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<Expm1>(to_stream(s)), {input});
}
array sin(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Sin>(to_stream(s)), {input});
}
array cos(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Cos>(to_stream(s)), {input});
}
array tan(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Tan>(to_stream(s)), {input});
}
array arcsin(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<ArcSin>(to_stream(s)), {input});
}
array arccos(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<ArcCos>(to_stream(s)), {input});
}
array arctan(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<ArcTan>(to_stream(s)), {input});
}
array arctan2(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(promote_types(a.dtype(), b.dtype()));
auto inputs = broadcast_arrays(astype(a, dtype, s), astype(b, dtype, s), s);
auto& shape = inputs[0].shape();
return array(
shape, dtype, std::make_shared<ArcTan2>(to_stream(s)), std::move(inputs));
}
array sinh(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Sinh>(to_stream(s)), {input});
}
array cosh(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Cosh>(to_stream(s)), {input});
}
array tanh(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(a.shape(), dtype, std::make_shared<Tanh>(to_stream(s)), {input});
}
array arcsinh(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<ArcSinh>(to_stream(s)), {input});
}
array arccosh(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<ArcCosh>(to_stream(s)), {input});
}
array arctanh(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<ArcTanh>(to_stream(s)), {input});
}
array degrees(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return multiply(a, array(180.0 / M_PI, dtype), s);
}
array radians(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return multiply(a, array(M_PI / 180.0, dtype), s);
}
array log(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(),
dtype,
std::make_shared<Log>(to_stream(s), Log::Base::e),
{input});
}
array log2(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(),
dtype,
std::make_shared<Log>(to_stream(s), Log::Base::two),
{input});
}
array log10(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(),
dtype,
std::make_shared<Log>(to_stream(s), Log::Base::ten),
{input});
}
array log1p(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<Log1p>(to_stream(s)), {input});
}
array logaddexp(const array& a, const array& b, StreamOrDevice s /* = {} */) {
// Make sure out type is floating point
auto out_type = at_least_float(promote_types(a.dtype(), b.dtype()));
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& shape = inputs[0].shape();
return array(
shape,
out_type,
std::make_shared<LogAddExp>(to_stream(s)),
std::move(inputs));
}
array sigmoid(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
auto input = astype(a, dtype, s);
return array(
a.shape(), dtype, std::make_shared<Sigmoid>(to_stream(s)), {input});
}
array erf(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return array(
a.shape(),
dtype,
std::make_shared<Erf>(to_stream(s)),
{astype(a, dtype, s)});
}
array erfinv(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return array(
a.shape(),
dtype,
std::make_shared<ErfInv>(to_stream(s)),
{astype(a, dtype, s)});
}
array stop_gradient(const array& a, StreamOrDevice s /* = {} */) {
return array(
a.shape(), a.dtype(), std::make_shared<StopGradient>(to_stream(s)), {a});
}
array round(const array& a, int decimals, StreamOrDevice s /* = {} */) {
if (decimals == 0) {
return array(
a.shape(), a.dtype(), std::make_shared<Round>(to_stream(s)), {a});
}
auto dtype = at_least_float(a.dtype());
float scale = std::pow(10, decimals);
auto result = multiply(a, array(scale, dtype), s);
result = round(result, 0, s);
result = multiply(result, array(1 / scale, dtype), s);
return astype(result, a.dtype(), s);
}
array matmul(
const array& in_a,
const array& in_b,
StreamOrDevice s /* = {} */) {
auto a = in_a;
auto b = in_b;
if (a.ndim() == 0 || b.ndim() == 0) {
throw std::invalid_argument(
"[matmul] Got 0 dimension input. Inputs must "
"have at least one dimension.");
}
if (a.ndim() == 1) {
// Insert a singleton dim in the beginning
a = reshape(a, {1, -1}, s);
}
if (b.ndim() == 1) {
// Insert a singleton dim at the end
b = reshape(b, {-1, 1}, s);
}
if (a.shape(-1) != b.shape(-2)) {
std::ostringstream msg;
msg << "[matmul] Last dimension of first input with shape " << a.shape()
<< " must match second to last dimension of"
<< " second input with shape " << b.shape() << ".";
throw std::invalid_argument(msg.str());
}
// Type promotion
auto out_type = promote_types(a.dtype(), b.dtype());
if (!issubdtype(out_type, floating)) {
std::ostringstream msg;
msg << "[matmul] Only real floating point types are supported but "
<< a.dtype() << " and " << b.dtype() << " were provided which results"
<< " in " << out_type << ", which is not a real floating point type.";
throw std::invalid_argument(msg.str());
}
if (a.dtype() != out_type) {
a = astype(a, out_type, s);
}
if (b.dtype() != out_type) {
b = astype(b, out_type, s);
}
// We can batch the multiplication by reshaping a
if (a.ndim() > 2 && b.ndim() == 2) {
std::vector<int> out_shape = a.shape();
a = reshape(a, {-1, out_shape.back()}, s);
out_shape.back() = b.shape(-1);
if (in_b.ndim() == 1) {
out_shape.pop_back();
}
auto out = array(
{a.shape(0), b.shape(1)},
out_type,
std::make_shared<Matmul>(to_stream(s)),
{a, b});
return reshape(out, out_shape, s);
}
if (a.ndim() > 2 || b.ndim() > 2) {
std::vector<int> bsx_a(a.shape().begin(), a.shape().end() - 2);
std::vector<int> bsx_b(b.shape().begin(), b.shape().end() - 2);
auto inner_shape = broadcast_shapes(bsx_a, bsx_b);
// Broadcast a
inner_shape.push_back(a.shape(-2));
inner_shape.push_back(a.shape(-1));
a = broadcast_to(a, inner_shape, s);
// Broadcast b
*(inner_shape.end() - 2) = b.shape(-2);
*(inner_shape.end() - 1) = b.shape(-1);
b = broadcast_to(b, inner_shape, s);
}
auto out_shape = a.shape();
out_shape.back() = b.shape(-1);
auto p = std::make_shared<Matmul>(to_stream(s));
// Remove the possibly inserted singleton dimensions
if (in_a.ndim() == 1 || in_b.ndim() == 1) {
auto out = array(out_shape, out_type, std::move(p), {a, b});
out_shape.erase(
out_shape.end() - ((in_a.ndim() == 1) ? 2 : 1),
out_shape.end() - ((in_b.ndim() == 1) ? 0 : 1));
return reshape(out, std::move(out_shape), s);
}
return array(std::move(out_shape), out_type, std::move(p), {a, b});
}
array gather(
const array& a,
const std::vector<array>& indices,
const std::vector<int>& axes,
const std::vector<int>& slice_sizes,
StreamOrDevice s /* = {} */) {
// Checks that indices, dimensions, and slice_sizes are all valid
if (indices.size() > a.ndim()) {
std::ostringstream msg;
msg << "[gather] Too many index arrays. Got " << indices.size()
<< " index arrays for input with " << a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
std::set dims(axes.begin(), axes.end());
if (dims.size() != axes.size()) {
throw std::invalid_argument("[gather] Repeat axes not allowed in gather.");
}
if (!dims.empty() && (*dims.begin() < 0 || *dims.rbegin() >= a.ndim())) {
throw std::invalid_argument("[gather] Axes don't match array dimensions.");
}
if (indices.size() != axes.size()) {
throw std::invalid_argument(
"[gather] Number of index arrays does not match number of axes.");
}
for (auto& x : indices) {
if (x.dtype() == bool_) {
throw("[Gather] Boolean indices not supported.");
}
}
if (slice_sizes.size() != a.ndim()) {
std::ostringstream msg;
msg << "[gather] Got slice_sizes with size " << slice_sizes.size()
<< " for array with " << a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
for (int i = 0; i < a.ndim(); ++i) {
if (slice_sizes[i] < 0 || slice_sizes[i] > a.shape(i)) {
std::ostringstream msg;
msg << "[gather] Slice sizes must be in [0, a.shape(i)]. Got "
<< slice_sizes << " for array with shape " << a.shape() << ".";
throw std::invalid_argument(msg.str());
}
}
// Promote indices to the same type
auto dtype = result_type(indices);
if (issubdtype(dtype, inexact)) {
throw std::invalid_argument(
"[gather] Got indices with invalid dtype. Indices must be integral.");
}
// Broadcast and cast indices if necessary
auto inputs = broadcast_arrays(indices);
for (auto& idx : inputs) {
idx = astype(idx, dtype, s);
}
std::vector<int> out_shape;
if (!inputs.empty()) {
out_shape = inputs[0].shape();
}
out_shape.insert(out_shape.end(), slice_sizes.begin(), slice_sizes.end());
inputs.insert(inputs.begin(), a);
return array(
out_shape,
a.dtype(),
std::make_shared<Gather>(to_stream(s), axes, slice_sizes),
inputs);
}
array take(
const array& a,
const array& indices,
int axis,
StreamOrDevice s /* = {} */) {
// Check for valid axis
if (axis + static_cast<int>(a.ndim()) < 0 ||
axis >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[take] Received invalid axis " << axis << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
// Check for valid take
if (a.size() == 0 && indices.size() != 0) {
throw std::invalid_argument(
"[take] Cannot do a non-empty take from an array with zero elements.");
}
// Handle negative axis
axis = axis < 0 ? a.ndim() + axis : axis;
// Make slice sizes to pass to gather
std::vector<int> slice_sizes = a.shape();
slice_sizes[axis] = indices.size() > 0 ? 1 : 0;
auto out = gather(a, indices, axis, slice_sizes, s);
// Transpose indices dimensions to axis dimension
if (axis != 0) {
std::vector<int> t_axes(out.ndim());
std::iota(t_axes.begin(), t_axes.begin() + axis, indices.ndim());
std::iota(t_axes.begin() + axis, t_axes.begin() + axis + indices.ndim(), 0);
std::iota(
t_axes.begin() + axis + indices.ndim(),
t_axes.end(),
indices.ndim() + axis);
out = transpose(out, t_axes, s);
}
// Squeeze the axis we take over
std::vector<int> out_shape = out.shape();
out_shape.erase(out_shape.begin() + indices.ndim() + axis);
return reshape(out, out_shape, s);
}
array take(const array& a, const array& indices, StreamOrDevice s /* = {} */) {
return take(reshape(a, {-1}, s), indices, 0, s);
}
array take_along_axis(
const array& a,
const array& indices,
int axis,
StreamOrDevice s /* = {} */) {
if (axis + a.ndim() < 0 || axis >= static_cast<int>(a.ndim())) {
std::ostringstream msg;
msg << "[take_along_axis] Received invalid axis " << " for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
if (indices.ndim() != a.ndim()) {
std::ostringstream msg;
msg << "[take_along_axis] Indices of dimension " << indices.ndim()
<< " does not match array of dimension " << a.ndim() << ".";
throw std::invalid_argument(msg.str());
}
// Allow negative axis
axis = axis < 0 ? a.ndim() + axis : axis;
std::vector<array> nd_indices;
std::vector<int> index_shape(a.ndim(), 1);
for (int i = 0; i < a.ndim(); ++i) {
if (i == axis) {
nd_indices.push_back(indices);
} else {
// Reshape so they can be broadcast
index_shape[i] = a.shape(i);
nd_indices.push_back(reshape(arange(a.shape(i), s), index_shape, s));
index_shape[i] = 1;
}
}
std::vector<int> dims(a.ndim());
std::iota(dims.begin(), dims.end(), 0);
std::vector<int> slice_sizes(a.ndim(), a.size() > 0);
auto out = gather(a, nd_indices, dims, slice_sizes, s);
// Squeeze out the slice shape
std::vector<int> out_shape(
out.shape().begin(), out.shape().begin() + a.ndim());
return reshape(out, out_shape, s);
}
/** Scatter updates to given indices */
array scatter(
const array& a,
const std::vector<array>& indices,
const array& updates,
const std::vector<int>& axes,
Scatter::ReduceType mode /*= Scatter::ReduceType::None*/,
StreamOrDevice s /*= {}*/) {
// Checks that indices, dimensions, and slice_sizes are all valid
if (indices.size() > a.ndim()) {
std::ostringstream msg;
msg << "[scatter] Too many index arrays. Got " << indices.size()
<< " index arrays for input with " << a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
for (auto& x : indices) {
if (x.dtype() == bool_) {
throw("[scatter] Boolean indices not supported.");
}
}
std::set dims(axes.begin(), axes.end());
if (dims.size() != axes.size()) {
throw std::invalid_argument(
"[scatter] Repeat axes not allowed in scatter.");
}
if (!dims.empty() && (*dims.begin() < 0 || *dims.rbegin() >= a.ndim())) {
throw std::invalid_argument("[scatter] Axes don't match array dimensions.");
}
if (indices.size() != axes.size()) {
throw std::invalid_argument(
"[scatter] Number of index arrays does not match number of axes.");
}
// Broadcast and cast indices if necessary
auto inputs = broadcast_arrays(indices);
std::vector<int> idx_shape;
if (!inputs.empty()) {
idx_shape = inputs[0].shape();
}
if (updates.ndim() != (a.ndim() + idx_shape.size())) {
std::ostringstream msg;
msg << "[scatter] Updates with " << updates.ndim()
<< " dimensions does not match the sum of the array and indices "
"dimensions "
<< a.ndim() + idx_shape.size() << ".";
throw std::invalid_argument(msg.str());
}
for (int i = 0; i < idx_shape.size(); ++i) {
if (updates.shape(i) != idx_shape[i]) {
std::ostringstream msg;
msg << "[scatter] Update shape " << updates.shape()
<< " is not valid for broadcasted index shape " << idx_shape << ".";
throw std::invalid_argument(msg.str());
}
}
for (int i = 0; i < a.ndim(); ++i) {
auto up_shape = updates.shape(i + idx_shape.size());
if (up_shape > a.shape(i)) {
std::ostringstream msg;
msg << "[scatter] Updates with shape " << updates.shape()
<< " are too large for array with shape " << a.shape() << ".";
throw std::invalid_argument(msg.str());
}
}
// Promote indices to the same type
auto dtype = result_type(indices);
if (issubdtype(dtype, inexact)) {
throw std::invalid_argument(
"[scatter] Got indices with invalid dtype. Indices must be integral.");
}
for (auto& idx : inputs) {
idx = astype(idx, dtype, s);
}
// TODO, remove when scatter supports 64-bit outputs
if (to_stream(s).device == Device::gpu && size_of(a.dtype()) == 8) {
std::ostringstream msg;
msg << "[scatter] GPU scatter does not yet support " << a.dtype()
<< " for the input or updates.";
throw std::invalid_argument(msg.str());
}
inputs.insert(inputs.begin(), a);
// TODO promote or cast?
inputs.push_back(astype(updates, a.dtype(), s));
return array(
a.shape(),
a.dtype(),
std::make_shared<Scatter>(to_stream(s), mode, axes),
inputs);
}
array scatter(
const array& a,
const std::vector<array>& indices,
const array& updates,
const std::vector<int>& axes,
StreamOrDevice s /*= {}*/) {
return scatter(a, indices, updates, axes, Scatter::None, s);
}
array scatter_add(
const array& a,
const std::vector<array>& indices,
const array& updates,
const std::vector<int>& axes,
StreamOrDevice s /*= {}*/) {
return scatter(a, indices, updates, axes, Scatter::Sum, s);
}
array scatter_prod(
const array& a,
const std::vector<array>& indices,
const array& updates,
const std::vector<int>& axes,
StreamOrDevice s /*= {}*/) {
return scatter(a, indices, updates, axes, Scatter::Prod, s);
}
array scatter_max(
const array& a,
const std::vector<array>& indices,
const array& updates,
const std::vector<int>& axes,
StreamOrDevice s /*= {}*/) {
return scatter(a, indices, updates, axes, Scatter::Max, s);
}
array scatter_min(
const array& a,
const std::vector<array>& indices,
const array& updates,
const std::vector<int>& axes,
StreamOrDevice s /*= {}*/) {
return scatter(a, indices, updates, axes, Scatter::Min, s);
}
array sqrt(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return array(
a.shape(),
dtype,
std::make_shared<Sqrt>(to_stream(s)),
{astype(a, dtype, s)});
}
array rsqrt(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = at_least_float(a.dtype());
return array(
a.shape(),
dtype,
std::make_shared<Sqrt>(to_stream(s), true),
{astype(a, dtype, s)});
}
array softmax(
const array& a,
const std::vector<int>& axes,
bool precise /* = false */,
StreamOrDevice s /* = {}*/) {
if (axes.size() == 1 && (a.ndim() == axes[0] + 1 || axes[0] == -1)) {
auto dtype = at_least_float(a.dtype());
return array(
a.shape(),
dtype,
std::make_shared<Softmax>(to_stream(s), precise),
{astype(a, dtype, s)});
} else {
auto in = a;
if (precise) {
in = astype(a, float32, s);
}
auto a_max = stop_gradient(max(in, axes, /*keepdims = */ true, s), s);
auto ex = exp(subtract(in, a_max, s), s);
return astype(
divide(ex, sum(ex, axes, /*keepdims = */ true, s), s), a.dtype(), s);
}
}
array softmax(
const array& a,
bool precise /* = false */,
StreamOrDevice s /* = {}*/) {
std::vector<int> axes(a.ndim());
std::iota(axes.begin(), axes.end(), 0);
return softmax(a, axes, precise, s);
}
array power(const array& a, const array& b, StreamOrDevice s /* = {} */) {
auto dtype = promote_types(a.dtype(), b.dtype());
std::vector<array> inputs = {astype(a, dtype, s), astype(b, dtype, s)};
if (a.shape() != b.shape()) {
inputs = broadcast_arrays(inputs, s);
}
return array(
inputs[0].shape(), dtype, std::make_shared<Power>(to_stream(s)), inputs);
}
array cumsum(
const array& a,
int axis,
bool reverse /* = false*/,
bool inclusive /* = true*/,
StreamOrDevice s /* = {}*/) {
int ndim = a.ndim();
if (axis >= ndim || axis < -ndim) {
std::ostringstream msg;
msg << "[cumsum] Axis " << axis << " is out of bounds for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
axis = (axis + a.ndim()) % a.ndim();
auto out_type = a.dtype() == bool_ ? int32 : a.dtype();
return array(
a.shape(),
out_type,
std::make_shared<Scan>(
to_stream(s), Scan::ReduceType::Sum, axis, reverse, inclusive),
{a});
}
array cumprod(
const array& a,
int axis,
bool reverse /* = false*/,
bool inclusive /* = true*/,
StreamOrDevice s /* = {}*/) {
int ndim = a.ndim();
if (axis >= ndim || axis < -ndim) {
std::ostringstream msg;
msg << "[cumprod] Axis " << axis << " is out of bounds for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
axis = (axis + a.ndim()) % a.ndim();
return array(
a.shape(),
a.dtype(),
std::make_shared<Scan>(
to_stream(s), Scan::ReduceType::Prod, axis, reverse, inclusive),
{a});
}
array cummax(
const array& a,
int axis,
bool reverse /* = false*/,
bool inclusive /* = true*/,
StreamOrDevice s /* = {}*/) {
int ndim = a.ndim();
if (axis >= ndim || axis < -ndim) {
std::ostringstream msg;
msg << "[cummax] Axis " << axis << " is out of bounds for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
axis = (axis + a.ndim()) % a.ndim();
return array(
a.shape(),
a.dtype(),
std::make_shared<Scan>(
to_stream(s), Scan::ReduceType::Max, axis, reverse, inclusive),
{a});
}
array cummin(
const array& a,
int axis,
bool reverse /* = false*/,
bool inclusive /* = true*/,
StreamOrDevice s /* = {}*/) {
int ndim = a.ndim();
if (axis >= ndim || axis < -ndim) {
std::ostringstream msg;
msg << "[cummin] Axis " << axis << " is out of bounds for array with "
<< a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
axis = (axis + a.ndim()) % a.ndim();
return array(
a.shape(),
a.dtype(),
std::make_shared<Scan>(
to_stream(s), Scan::ReduceType::Min, axis, reverse, inclusive),
{a});
}
/** Convolution operations */
namespace {
// Conv helpers
inline int conv_out_axis_size(int in_dim, int wt_dim, int stride, int padding) {
return ((in_dim + padding - wt_dim) / stride) + 1;
}
// Conv helpers
inline int dilate_size(int dim, int dil) {
return 1 + dil * (dim - 1);
}
inline std::vector<int> conv_out_shape(
const std::vector<int>& in_shape,
const std::vector<int>& wt_shape,
const std::vector<int>& strides,
const std::vector<int>& pads_lo,
const std::vector<int>& pads_hi,
const std::vector<int>& kernel_dilation,
const std::vector<int>& input_dilation) {
int N = in_shape[0];
int O = wt_shape[0];
std::vector<int> out_shape(in_shape.size());
int i = 0;
out_shape[i++] = N;
int spatial_dims = in_shape.size() - 2;
if (strides.size() != spatial_dims) {
std::ostringstream msg;
msg << "[conv] Invalid strides " << strides << " for " << spatial_dims
<< "D convolution.";
throw std::invalid_argument(msg.str());
}
if (pads_lo.size() != spatial_dims || pads_hi.size() != spatial_dims) {
std::ostringstream msg;
msg << "[conv] Invalid padding " << pads_lo << " | " << pads_hi << "for "
<< spatial_dims << "D convolution.";
throw std::invalid_argument(msg.str());
}
if (kernel_dilation.size() != spatial_dims) {
std::ostringstream msg;
msg << "[conv] Invalid kernel dilation " << kernel_dilation << "for "
<< spatial_dims << "D convolution.";
throw std::invalid_argument(msg.str());
}
if (input_dilation.size() != spatial_dims) {
std::ostringstream msg;
msg << "[conv] Invalid input dilation " << input_dilation << "for "
<< spatial_dims << "D convolution.";
throw std::invalid_argument(msg.str());
}
for (; i < in_shape.size() - 1; i++) {
if (kernel_dilation[i - 1] <= 0) {
std::ostringstream msg;
msg << "[conv] Kernel dilation sizes must be positive."
<< " Got kernel dilation " << kernel_dilation << ".";
throw std::invalid_argument(msg.str());
}
if (input_dilation[i - 1] <= 0) {
std::ostringstream msg;
msg << "[conv] Input dilation sizes must be positive."
<< " Got input dilation " << input_dilation << ".";
throw std::invalid_argument(msg.str());
}
if (pads_lo[i - 1] < 0 || pads_hi[i - 1] < 0) {
std::ostringstream msg;
msg << "[conv] Padding sizes must be non-negative." << " Got padding "
<< pads_lo << " | " << pads_hi << ".";
throw std::invalid_argument(msg.str());
}
if (strides[i - 1] <= 0) {
std::ostringstream msg;
msg << "[conv] Stride sizes must be positive." << " Got strides "
<< strides << ".";
throw std::invalid_argument(msg.str());
}
int kd = dilate_size(wt_shape[i], kernel_dilation[i - 1]);
int id = dilate_size(in_shape[i], input_dilation[i - 1]);
out_shape[i] = conv_out_axis_size(
id, kd, strides[i - 1], pads_lo[i - 1] + pads_hi[i - 1]);
if (out_shape[i] <= 0) {
std::ostringstream msg;
msg << "[conv] Spatial dimensions of input after padding "
<< " cannot be smaller than weight spatial dimensions."
<< " Got error at axis " << i << " for input with shape " << in_shape
<< ", padding low " << pads_lo << ", padding high " << pads_hi
<< ", and weight of shape " << wt_shape << ".";
throw std::invalid_argument(msg.str());
}
}
out_shape[i] = O;
return out_shape;
}
inline void
run_conv_checks(const array& in, const array& wt, int n_dim, int groups) {
if (!issubdtype(in.dtype(), floating)) {
std::ostringstream msg;
msg << "[conv] Invalid input array with type " << in.dtype() << "."
<< " Convolution currently only supports floating point types";
throw std::invalid_argument(msg.str());
}
if (in.ndim() != n_dim + 2) {
std::ostringstream msg;
msg << "[conv] Invalid input array with " << in.ndim() << " dimensions for "
<< n_dim << "D convolution." << " Expected an array with " << n_dim + 2
<< " dimensions following the format [N, ..., C_in].";
throw std::invalid_argument(msg.str());
}
if (wt.ndim() != n_dim + 2) {
std::ostringstream msg;
msg << "[conv] Invalid weight array with " << wt.ndim()
<< " dimensions for " << n_dim << "D convolution."
<< " Expected an array with " << n_dim + 2
<< " dimensions following the format [C_out, ..., C_in].";
throw std::invalid_argument(msg.str());
}
if (in.shape(n_dim + 1) % groups != 0) {
std::ostringstream msg;
msg << "[conv] The input channels must be divisible by the number"
<< " of groups. Got input with shape " << in.shape() << " and "
<< groups << " groups.";
throw std::invalid_argument(msg.str());
}
if (groups > 1 && wt.shape(0) % groups != 0) {
std::ostringstream msg;
msg << "[conv] If groups > 1, the output channels must be divisible by the number"
<< " of groups. Got " << wt.shape(0) << " output channels and "
<< groups << " groups.";
throw std::invalid_argument(msg.str());
}
if (in.shape(n_dim + 1) != (groups * wt.shape(n_dim + 1))) {
std::ostringstream msg;
if (groups == 1) {
msg << "[conv] Expect the input channels in the input"
<< " and weight array to match but got shapes -"
<< " input: " << in.shape() << " and weight: " << wt.shape();
} else {
msg << "Given groups=" << groups << " and weights of shape " << wt.shape()
<< ", expected to have " << (groups * wt.shape(n_dim + 1))
<< " input channels but got " << in.shape(n_dim + 1)
<< " input channels instead.";
}
throw std::invalid_argument(msg.str());
}
}
} // namespace
/** 1D convolution with a filter */
array conv1d(
const array& in_,
const array& wt_,
int stride /* = 1 */,
int padding /* = 0 */,
int dilation /* = 1 */,
int groups /* = 1 */,
StreamOrDevice s /* = {} */) {
return conv_general(
/* const array& input = */ in_,
/* const array& weight = */ wt_,
/* std::vector<int> stride = */ {stride},
/* std::vector<int> padding = */ {padding},
/* std::vector<int> kernel_dilation = */ {dilation},
/* std::vector<int> input_dilation = */ {1},
/* int groups = */ groups,
/* bool flip = */ false,
s);
}
/** 2D convolution with a filter */
array conv2d(
const array& in_,
const array& wt_,
const std::pair<int, int>& stride /* = {1, 1} */,
const std::pair<int, int>& padding /* = {0, 0} */,
const std::pair<int, int>& dilation /* = {1, 1} */,
int groups /* = 1 */,
StreamOrDevice s /* = {} */) {
return conv_general(
/* const array& input = */ in_,
/* const array& weight = */ wt_,
/* std::vector<int> stride = */ {stride.first, stride.second},
/* std::vector<int> padding = */ {padding.first, padding.second},
/* std::vector<int> kernel_dilation = */
{dilation.first, dilation.second},
/* std::vector<int> input_dilation = */ {1, 1},
/* int groups = */ groups,
/* bool flip = */ false,
s);
}
/** 3D convolution with a filter */
array conv3d(
const array& in_,
const array& wt_,
const std::tuple<int, int, int>& stride /* = {1, 1, 1} */,
const std::tuple<int, int, int>& padding /* = {0, 0, 0} */,
const std::tuple<int, int, int>& dilation /* = {1, 1, 1} */,
int groups /* = 1 */,
StreamOrDevice s /* = {} */) {
return conv_general(
/* const array& input = */ in_,
/* const array& weight = */ wt_,
/* std::vector<int> stride = */
{std::get<0>(stride), std::get<1>(stride), std::get<2>(stride)},
/* std::vector<int> padding = */
{std::get<0>(padding), std::get<1>(padding), std::get<2>(padding)},
/* std::vector<int> kernel_dilation = */
{std::get<0>(dilation), std::get<1>(dilation), std::get<2>(dilation)},
/* std::vector<int> input_dilation = */ {1, 1, 1},
/* int groups = */ groups,
/* bool flip = */ false,
s);
}
/** General convolution with a filter */
array conv_general(
array in,
array wt,
std::vector<int> stride /* = {} */,
std::vector<int> padding_lo /* = {} */,
std::vector<int> padding_hi /* = {} */,
std::vector<int> kernel_dilation /* = {} */,
std::vector<int> input_dilation /* = {} */,
int groups /* = 1 */,
bool flip /* = false */,
StreamOrDevice s /* = {} */) {
// Run checks
if (groups != 1 && in.ndim() != 3 && in.ndim() != 4) {
throw std::invalid_argument(
"[conv] Can only handle groups != 1 in 1D or 2D convolutions.");
}
int spatial_dims = in.ndim() - 2;
if (spatial_dims < 1 || spatial_dims > 3) {
throw std::invalid_argument(
"[conv] Only works for inputs with 1-3 spatial dimensions."
" The inputs must be in the format [N, ..., C_in]");
}
// Run checks
run_conv_checks(in, wt, spatial_dims, groups);
// Type promotion
auto out_type = promote_types(in.dtype(), wt.dtype());
in = astype(in, out_type, s);
wt = astype(wt, out_type, s);
if (stride.size() <= 1) {
int stride_int = stride.size() ? stride[0] : 1;
stride = std::vector<int>(spatial_dims, stride_int);
}
if (padding_lo.size() <= 1) {
int padding_int = padding_lo.size() ? padding_lo[0] : 0;
padding_lo = std::vector<int>(spatial_dims, padding_int);
}
if (padding_hi.size() <= 1) {
int padding_int = padding_hi.size() ? padding_hi[0] : 0;
padding_hi = std::vector<int>(spatial_dims, padding_int);
}
if (kernel_dilation.size() <= 1) {
int kernel_dilation_int = kernel_dilation.size() ? kernel_dilation[0] : 1;
kernel_dilation = std::vector<int>(spatial_dims, kernel_dilation_int);
}
if (input_dilation.size() <= 1) {
int input_dilation_int = input_dilation.size() ? input_dilation[0] : 1;
input_dilation = std::vector<int>(spatial_dims, input_dilation_int);
}
// Check for negative padding
bool has_neg_padding = false;
for (auto& pd : padding_lo) {
has_neg_padding |= (pd < 0);
}
for (auto& pd : padding_hi) {
has_neg_padding |= (pd < 0);
}
// Handle negative padding
if (has_neg_padding) {
std::vector<int> starts(in.ndim(), 0);
std::vector<int> stops = in.shape();
for (int i = 0; i < spatial_dims; i++) {
if (padding_lo[i] < 0) {
starts[i + 1] -= padding_lo[i];
padding_lo[i] = 0;
}
if (padding_hi[i] < 0) {
stops[i + 1] += padding_hi[i];
padding_hi[i] = 0;
}
}
in = slice(in, std::move(starts), std::move(stops), s);
}
// Get output shapes
std::vector<int> out_shape = conv_out_shape(
in.shape(),
wt.shape(),
stride,
padding_lo,
padding_hi,
kernel_dilation,
input_dilation);
return array(
out_shape,
in.dtype(),
std::make_shared<Convolution>(
to_stream(s),
stride,
padding_lo,
kernel_dilation,
input_dilation,
groups,
flip),
{in, wt});
}
array quantized_matmul(
const array& x,
const array& w,
const array& scales,
const array& biases,
bool transpose /* = true */,
int group_size /* = 64 */,
int bits /* = 4 */,
StreamOrDevice s /* = {} */) {
// Check and extract the quantized matrix shape against x
auto [w_inner_dims, w_outer_dims] = extract_quantized_matmul_dims(
"quantized_matmul", x, w, scales, biases, transpose, group_size, bits);
if (w.ndim() != 2) {
std::ostringstream msg;
msg << "[quantized_matmul] Batched quantized matmul is not supported for now "
<< "received w with shape " << w.shape();
throw std::invalid_argument(msg.str());
}
auto dtype = result_type(x, scales, biases);
if (!issubdtype(dtype, floating)) {
std::ostringstream msg;
msg << "[quantized_matmul] Only real floating types are supported but "
<< "the passed types where x.dtype() == " << x.dtype()
<< ", scales.dtype() == " << scales.dtype()
<< " and biases.dtype() == " << biases.dtype();
throw std::invalid_argument(msg.str());
}
auto out_shape = x.shape();
out_shape.back() = w_outer_dims;
return array(
std::move(out_shape),
dtype,
std::make_shared<QuantizedMatmul>(
to_stream(s), group_size, bits, transpose),
{astype(x, dtype, s),
w,
astype(scales, dtype, s),
astype(biases, dtype, s)});
}
std::tuple<array, array, array> quantize(
const array& w,
int group_size /* = 64 */,
int bits /* = 4 */,
StreamOrDevice s /* = {} */) {
if (group_size != 32 && group_size != 64 && group_size != 128) {
std::ostringstream msg;
msg << "[quantize] The requested group size " << group_size
<< " is not supported. The supported group sizes are 64 and 128.";
throw std::invalid_argument(msg.str());
}
if (bits != 2 && bits != 4 && bits != 8) {
std::ostringstream msg;
msg << "[quantize] The requested number of bits " << bits
<< " is not supported. The supported bits are 2, 4 and 8.";
throw std::invalid_argument(msg.str());
}
if (w.ndim() < 2) {
std::ostringstream msg;
msg << "[quantize] The matrix to be quantized must have at least 2 dimension "
<< "but it has only " << w.ndim() << ".";
throw std::invalid_argument(msg.str());
}
if ((w.shape(-1) % group_size) != 0) {
std::ostringstream msg;
msg << "[quantize] The last dimension of the matrix needs to be divisible by "
<< "the quantization group size " << group_size
<< ". However the provided " << " matrix has shape " << w.shape();
throw std::invalid_argument(msg.str());
}
// Compute some constants used for the quantization
array n_bins((1 << bits) - 1, w.dtype()); // 2**bits - 1
array eps(1e-7, w.dtype());
array zero(0, w.dtype());
int el_per_int = 32 / bits;
array shifts = power(array(2, uint32), arange(0, 32, bits, uint32, s), s);
shifts = reshape(shifts, {1, 1, -1}, s);
// Check that the w matrix will fill up a whole SIMD.
// This is an implementation detail which should be removed in the future but
// at least we bail out early which will result in a nice readable error.
//
// Hopefully nobody is quantizing matrices that small anyway.
if (w.shape(-1) < 32 * el_per_int) {
std::ostringstream msg;
msg << "[quantize] The feature dimension (2nd dimension of the matrix) is "
<< "too small for quantization. We support >=512 for 2 bits, "
<< ">= 256 for 4 bits and >= 128 for 8 bits. The provided matrix has "
<< "shape " << w.shape() << ".";
throw std::invalid_argument(msg.str());
}
// Prepare the shape for the outputs.
auto wshape = w.shape();
wshape.back() = -1;
// Compute scales and biases
array packed_w = reshape(w, {-1, w.shape(-1) / group_size, group_size}, s);
array w_max = max(packed_w, /* axis= */ -1, /* keepdims= */ true, s);
array w_min = min(packed_w, /* axis= */ -1, /* keepdims= */ true, s);
array mask = greater(abs(w_min, s), abs(w_max, s), s);
array scales = maximum(divide(subtract(w_max, w_min, s), n_bins, s), eps, s);
scales = where(mask, scales, negative(scales), s);
array edge = where(mask, w_min, w_max, s);
array q0 = round(divide(edge, scales, s), s);
scales = where(not_equal(q0, zero, s), divide(edge, q0, s), scales);
array biases = where(equal(q0, zero, s), zero, edge);
// Quantize and pack w
packed_w = astype(
clip(
round(divide(subtract(packed_w, biases, s), scales, s), s),
zero,
n_bins),
uint32);
packed_w = reshape(packed_w, {packed_w.shape(0), -1, el_per_int}, s);
packed_w = sum(
multiply(packed_w, shifts, s), /* axis= */ 2, /* keepdims= */ false, s);
return std::make_tuple(
reshape(packed_w, wshape, s),
reshape(scales, wshape, s),
reshape(biases, wshape, s));
}
array dequantize(
const array& w,
const array& scales,
const array& biases,
int group_size /* = 64 */,
int bits /* = 4 */,
StreamOrDevice s /* = {} */) {
if (bits <= 0) {
std::ostringstream msg;
msg << "[dequantize] Invalid value for bits: " << bits;
throw std::invalid_argument(msg.str());
}
if (group_size <= 0) {
std::ostringstream msg;
msg << "[dequantize] Invalid value for group_size: " << group_size;
throw std::invalid_argument(msg.str());
}
if (w.ndim() < 2 || scales.ndim() < 2 || biases.ndim() < 2) {
std::ostringstream msg;
msg << "[quantize] The matrix to be quantized must have at least 2 dimension "
<< "but it has only " << w.ndim() << ".";
throw std::invalid_argument(msg.str());
}
auto wshape = w.shape();
auto sshape = scales.shape();
auto bshape = biases.shape();
wshape.back() = -1;
sshape.back() = -1;
bshape.back() = -1;
if (wshape != sshape || wshape != bshape) {
throw std::invalid_argument(
"[dequantize] Shape of scales and biases does not match the matrix");
}
if (w.dtype() != uint32) {
throw std::invalid_argument(
"[dequantize] The matrix should be given as a uint32");
}
// Compute some constants for the dequantization
int el_per_int = 32 / bits;
if (w.shape(-1) * el_per_int != scales.shape(-1) * group_size) {
std::ostringstream msg;
msg << "[dequantize] Shape of scales and biases does not match the matrix "
<< "given the quantization parameters. Provided matrix of shape "
<< w.shape() << " and scales/biases of shape " << scales.shape()
<< " with group_size=" << group_size << " and bits=" << bits << ".";
throw std::invalid_argument(msg.str());
}
// Extract the pieces from the passed quantized matrix
std::vector<array> parts;
for (int start = 0; start < 32; start += bits) {
int shift_left = 32 - (start + bits);
int shift_right = shift_left + start;
parts.push_back(expand_dims(
right_shift(
left_shift(w, array(32 - (start + bits), uint32), s),
array(32 - bits, uint32),
s),
-1,
s));
}
array w_full = concatenate(parts, -1, s);
// Dequantize
wshape.push_back(group_size);
w_full = reshape(w_full, wshape, s);
w_full = multiply(w_full, expand_dims(scales, -1, s), s);
w_full = add(w_full, expand_dims(biases, -1, s), s);
w_full = reshape(w_full, sshape, s);
return w_full;
}
array gather_qmm(
const array& x,
const array& w,
const array& scales,
const array& biases,
std::optional<array> lhs_indices_ /* = std::nullopt */,
std::optional<array> rhs_indices_ /* = std::nullopt */,
bool transpose /* = true */,
int group_size /* = 64 */,
int bits /* = 4 */,
StreamOrDevice s /* = {} */) {
if (!lhs_indices_ && !rhs_indices_) {
return quantized_matmul(
x, w, scales, biases, transpose, group_size, bits, s);
}
auto [w_inner_dims, w_outer_dims] = extract_quantized_matmul_dims(
"gather_qmm", x, w, scales, biases, transpose, group_size, bits);
// Extract indices and broadcast them
array lhs_indices = indices_or_default(lhs_indices_, x, s);
array rhs_indices = indices_or_default(rhs_indices_, w, s);
auto out_bsx_shape =
broadcast_shapes(lhs_indices.shape(), rhs_indices.shape());
lhs_indices = broadcast_to(lhs_indices, out_bsx_shape, s);
rhs_indices = broadcast_to(rhs_indices, out_bsx_shape, s);
// Compute the full output shape
auto out_shape = out_bsx_shape;
out_shape.push_back(x.shape(-2));
out_shape.push_back(w_outer_dims);
// and output type
auto out_type = result_type(x, scales, biases);
auto out = array(
std::move(out_shape),
out_type,
std::make_shared<GatherQMM>(to_stream(s), group_size, bits, transpose),
{astype(x, out_type, s),
w,
astype(scales, out_type, s),
astype(biases, out_type, s),
lhs_indices,
rhs_indices});
return out;
}
array tensordot(
const array& a,
const array& b,
const int axis /* = 2 */,
StreamOrDevice s /* = {} */
) {
if (axis < 0) {
throw std::invalid_argument(
"[tensordot] axis must be greater or equal to 0.");
}
if (axis > std::min(a.ndim(), b.ndim())) {
throw std::invalid_argument(
"[tensordot] axis must be less than the number of dimensions of a and b.");
}
std::vector<int> adims;
std::vector<int> bdims;
for (int i = 0; i < axis; i++) {
bdims.emplace_back(i);
adims.emplace_back(i - axis);
}
return tensordot(a, b, {adims}, {bdims}, s);
}
array tensordot(
const array& a,
const array& b,
const std::vector<int>& axes_a,
const std::vector<int>& axes_b,
StreamOrDevice s /* = {} */) {
if (axes_a.size() != axes_b.size()) {
throw std::invalid_argument("[tensordot] axes must have the same size.");
}
int csize = 1;
auto x = a;
auto y = b;
for (int i = 0; i < axes_a.size(); i++) {
if (x.shape(axes_a.at(i)) == y.shape(axes_b.at(i))) {
csize *= x.shape(axes_a.at(i));
} else {
throw std::invalid_argument(
"[tensordot] a and b must have the same shape on the contracted axes.");
}
}
std::vector<bool> cdims1(x.ndim(), false);
std::vector<bool> cdims2(y.ndim(), false);
for (const auto n : axes_a) {
int n_ = (n < 0) ? n + x.ndim() : n;
cdims1[n_] = true;
}
for (const auto n : axes_b) {
int n_ = (n < 0) ? n + y.ndim() : n;
cdims2[n_] = true;
}
std::vector<int> t1;
std::vector<int> t2;
std::vector<int> rshape;
int size1 = 1;
int size2 = 1;
for (int i = 0; i < a.ndim(); i++) {
if (!cdims1[i]) {
t1.emplace_back(i);
size1 *= a.shape(i);
rshape.emplace_back(a.shape(i));
}
}
for (const auto x : axes_a) {
t1.emplace_back(x);
}
for (const auto x : axes_b) {
t2.emplace_back(x);
}
for (int i = 0; i < b.ndim(); i++) {
if (!cdims2[i]) {
t2.emplace_back(i);
size2 *= b.shape(i);
rshape.emplace_back(b.shape(i));
}
}
x = reshape(transpose(x, t1, s), {size1, csize}, s);
y = reshape(transpose(y, t2, s), {csize, size2}, s);
return reshape(matmul(x, y, s), rshape, s);
}
array outer(const array& a, const array& b, StreamOrDevice s /* = {} */) {
return multiply(
reshape(a, {static_cast<int>(a.size()), 1}, s), flatten(b, s), s);
}
array inner(const array& a, const array& b, StreamOrDevice s /* = {} */) {
if (a.ndim() == 0 || b.ndim() == 0) {
return multiply(a, b, s);
}
if (a.shape(-1) != b.shape(-1)) {
throw std::invalid_argument(
"[inner] a and b must have the same last dimension.");
}
return tensordot(a, b, {-1}, {-1}, s);
}
/** Compute D = beta * C + alpha * (A @ B) */
array addmm(
array c,
array a,
array b,
const float& alpha /* = 1.f */,
const float& beta /* = 1.f */,
StreamOrDevice s /* = {} */) {
int in_a_ndim = a.ndim();
int in_b_ndim = b.ndim();
if (a.ndim() == 0 || b.ndim() == 0) {
throw std::invalid_argument(
"[addmm] Got 0 dimension input. Inputs must "
"have at least one dimension.");
}
if (a.ndim() == 1) {
// Insert a singleton dim in the beginning
a = reshape(a, {1, -1}, s);
}
if (b.ndim() == 1) {
// Insert a singleton dim at the end
b = reshape(b, {-1, 1}, s);
}
if (a.shape(-1) != b.shape(-2)) {
std::ostringstream msg;
msg << "[addmm] Last dimension of first input with shape " << a.shape()
<< " must match second to last dimension of"
<< " second input with shape " << b.shape() << ".";
throw std::invalid_argument(msg.str());
}
// Type promotion
auto out_type = result_type(a, b, c);
if (!issubdtype(out_type, floating)) {
std::ostringstream msg;
msg << "[addmm] Only real floating point types are supported but "
<< c.dtype() << ", " << a.dtype() << " and " << b.dtype()
<< " were provided which results in " << out_type
<< ", which is not a real floating point type.";
throw std::invalid_argument(msg.str());
}
a = astype(a, out_type, s);
b = astype(b, out_type, s);
c = astype(c, out_type, s);
// We can batch the multiplication by reshaping a
if (a.ndim() > 2 && b.ndim() == 2 && c.ndim() <= 1) {
std::vector<int> out_shape = a.shape();
a = reshape(a, {-1, out_shape.back()}, s);
out_shape.back() = b.shape(-1);
if (in_b_ndim == 1) {
out_shape.pop_back();
}
c = broadcast_to(c, {a.shape(0), b.shape(1)}, s);
auto out = array(
{a.shape(0), b.shape(1)},
out_type,
std::make_shared<AddMM>(to_stream(s), alpha, beta),
{a, b, c});
return reshape(out, out_shape, s);
}
if (a.ndim() > 2 || b.ndim() > 2) {
std::vector<int> bsx_a(a.shape().begin(), a.shape().end() - 2);
std::vector<int> bsx_b(b.shape().begin(), b.shape().end() - 2);
auto inner_shape = broadcast_shapes(bsx_a, bsx_b);
// Broadcast a
inner_shape.push_back(a.shape(-2));
inner_shape.push_back(a.shape(-1));
a = broadcast_to(a, inner_shape, s);
// Broadcast b
*(inner_shape.end() - 2) = b.shape(-2);
*(inner_shape.end() - 1) = b.shape(-1);
b = broadcast_to(b, inner_shape, s);
}
auto out_shape = a.shape();
out_shape.back() = b.shape(-1);
auto out_shape_adjusted = out_shape;
if (in_a_ndim == 1 || in_b_ndim == 1) {
out_shape_adjusted.erase(
out_shape_adjusted.end() - ((in_a_ndim == 1) ? 2 : 1),
out_shape_adjusted.end() - ((in_b_ndim == 1) ? 0 : 1));
}
auto c_broadcast_shape = broadcast_shapes(c.shape(), out_shape_adjusted);
c = broadcast_to(c, c_broadcast_shape, s);
if (in_a_ndim == 1 || in_b_ndim == 1) {
auto c_reshape = c.shape();
if (in_b_ndim == 1) {
c_reshape.push_back(1);
}
if (in_a_ndim == 1) {
c_reshape.push_back(c_reshape.back());
c_reshape[c_reshape.size() - 2] = 1;
}
c = reshape(c, c_reshape, s);
}
auto out = array(
out_shape,
out_type,
std::make_shared<AddMM>(to_stream(s), alpha, beta),
{a, b, c});
// Remove the possibly inserted singleton dimensions
if (in_a_ndim == 1 || in_b_ndim == 1) {
out = reshape(out, out_shape_adjusted, s);
}
return out;
}
/** Compute matrix product with tile-level masking */
array block_masked_mm(
array a,
array b,
int block_size,
std::optional<array> mask_out /* = std::nullopt */,
std::optional<array> mask_lhs /* = std::nullopt */,
std::optional<array> mask_rhs /* = std::nullopt */,
StreamOrDevice s /* = {} */) {
// If no masks, just perform regular matmul
if (!mask_out && !mask_lhs && !mask_rhs) {
return matmul(a, b, s);
}
bool has_out_mask = mask_out.has_value();
bool has_operand_mask = mask_lhs.has_value() || mask_rhs.has_value();
// Check valid tile sizes
// TODO: Add support for 16x16 tile
if (block_size != 32 && block_size != 64) {
std::ostringstream msg;
msg << "[block_masked_mm] Only block_sizes 32, 64 are supported."
<< "Got block size " << block_size << ".";
throw std::invalid_argument(msg.str());
}
// Do shape checks for operands
int in_a_ndim = a.ndim();
int in_b_ndim = b.ndim();
if (a.ndim() == 0 || b.ndim() == 0) {
throw std::invalid_argument(
"[block_masked_mm] Got 0 dimension input. Inputs must "
"have at least one dimension.");
}
if (a.ndim() == 1) {
// Insert a singleton dim in the beginning
a = reshape(a, {1, -1}, s);
}
if (b.ndim() == 1) {
// Insert a singleton dim at the end
b = reshape(b, {-1, 1}, s);
}
if (a.shape(-1) != b.shape(-2)) {
std::ostringstream msg;
msg << "[block_masked_mm] Last dimension of first input with shape "
<< a.shape() << " must match second to last dimension of"
<< " second input with shape " << b.shape() << ".";
throw std::invalid_argument(msg.str());
}
// Type promotion
auto out_type = result_type(a, b);
if (!issubdtype(out_type, floating)) {
std::ostringstream msg;
msg << "[block_masked_mm] Only real floating point types are supported but "
<< a.dtype() << " and " << b.dtype()
<< " were provided which results in " << out_type
<< ", which is not a real floating point type.";
throw std::invalid_argument(msg.str());
}
a = astype(a, out_type, s);
b = astype(b, out_type, s);
// Handle broadcasting
std::vector<int> bsx_a(a.shape().begin(), a.shape().end() - 2);
std::vector<int> bsx_b(b.shape().begin(), b.shape().end() - 2);
auto bsx_shape = broadcast_shapes(bsx_a, bsx_b);
bsx_shape.push_back(1);
bsx_shape.push_back(1);
int nd = bsx_shape.size();
int M = a.shape(-2);
int N = b.shape(-1);
int K = a.shape(-1);
// Prepare A
bsx_shape[nd - 2] = M;
bsx_shape[nd - 1] = K;
a = broadcast_to(a, bsx_shape, s);
// Prepare B
bsx_shape[nd - 2] = K;
bsx_shape[nd - 1] = N;
b = broadcast_to(b, bsx_shape, s);
// Get output shape
auto out_shape = bsx_shape;
out_shape[nd - 2] = M;
out_shape[nd - 1] = N;
// Determine mask shape requirments
int tm = (M + block_size - 1) / block_size;
int tn = (N + block_size - 1) / block_size;
int tk = (K + block_size - 1) / block_size;
std::vector<array> inputs = {a, b};
// Broadcast and astype mask
auto broadcast_mask = [](array mask,
std::vector<int>& bs_shape,
int y,
int x,
Dtype mask_dtype,
StreamOrDevice s) {
int nd_bsx = bs_shape.size();
bs_shape[nd_bsx - 2] = y;
bs_shape[nd_bsx - 1] = x;
mask = astype(mask, mask_dtype, s);
return broadcast_to(mask, bs_shape, s);
};
// Out mask
if (mask_out.has_value()) {
array mask_out_p = mask_out.value_or(array({true}));
if (in_a_ndim == 1 || in_b_ndim == 1) {
std::vector<int> ex_dims;
if (in_a_ndim == 1)
ex_dims.push_back(-2);
if (in_b_ndim == 1)
ex_dims.push_back(-1);
mask_out_p = expand_dims(mask_out_p, ex_dims, s);
}
auto maskout_dtype = mask_out_p.dtype() == bool_ ? bool_ : out_type;
mask_out_p =
broadcast_mask(mask_out_p, bsx_shape, tm, tn, maskout_dtype, s);
inputs.push_back(mask_out_p);
}
// Operand masks
if (has_operand_mask) {
// Pull masks
array mask_lhs_p = mask_lhs.value_or(array({true}));
array mask_rhs_p = mask_rhs.value_or(array({true}));
auto mask_dtype =
(mask_lhs_p.dtype() == bool_ && mask_rhs_p.dtype() == bool_) ? bool_
: out_type;
// LHS mask
if (in_a_ndim == 1) {
mask_lhs_p = expand_dims(mask_lhs_p, -2, s);
}
mask_lhs_p = broadcast_mask(mask_lhs_p, bsx_shape, tm, tk, mask_dtype, s);
// RHS mask
if (in_b_ndim == 1) {
mask_rhs_p = expand_dims(mask_rhs_p, -1, s);
}
mask_rhs_p = broadcast_mask(mask_rhs_p, bsx_shape, tk, tn, mask_dtype, s);
inputs.push_back(mask_lhs_p);
inputs.push_back(mask_rhs_p);
}
// Caculate array
auto out = array(
out_shape,
out_type,
std::make_shared<BlockMaskedMM>(to_stream(s), block_size),
std::move(inputs));
// Remove the possibly inserted singleton dimensions
if (in_a_ndim == 1 || in_b_ndim == 1) {
out_shape.erase(
out_shape.end() - ((in_a_ndim == 1) ? 2 : 1),
out_shape.end() - ((in_b_ndim == 1) ? 0 : 1));
out = reshape(out, out_shape, s);
}
return out;
}
/** Compute matrix product with matrix-level gather */
array gather_mm(
array a,
array b,
std::optional<array> lhs_indices_ /* = std::nullopt */,
std::optional<array> rhs_indices_ /* = std::nullopt */,
StreamOrDevice s /* = {} */) {
// If no indices, fall back to full matmul
if (!lhs_indices_ && !rhs_indices_) {
return matmul(a, b, s);
}
// Do shape checks for operands
int in_a_ndim = a.ndim();
int in_b_ndim = b.ndim();
if (a.ndim() == 0 || b.ndim() == 0) {
throw std::invalid_argument(
"[gather_mm] Got 0 dimension input. Inputs must "
"have at least one dimension.");
}
if (a.ndim() == 1) {
// Insert a singleton dim in the beginning
a = reshape(a, {1, -1}, s);
}
if (b.ndim() == 1) {
// Insert a singleton dim at the end
b = reshape(b, {-1, 1}, s);
}
if (a.shape(-1) != b.shape(-2)) {
std::ostringstream msg;
msg << "[gather_mm] Last dimension of first input with shape " << a.shape()
<< " must match second to last dimension of"
<< " second input with shape " << b.shape() << ".";
throw std::invalid_argument(msg.str());
}
// Type promotion
auto out_type = result_type(a, b);
if (!issubdtype(out_type, floating)) {
std::ostringstream msg;
msg << "[gather_mm] Only real floating point types are supported but "
<< a.dtype() << " and " << b.dtype()
<< " were provided which results in " << out_type
<< ", which is not a real floating point type.";
throw std::invalid_argument(msg.str());
}
a = astype(a, out_type, s);
b = astype(b, out_type, s);
// Handle broadcasting
array lhs_indices = indices_or_default(lhs_indices_, a, s);
array rhs_indices = indices_or_default(rhs_indices_, b, s);
if (!issubdtype(lhs_indices.dtype(), integer)) {
throw std::invalid_argument(
"[gather_mm] Got lhs_indices with invalid dtype. Indices must be integral.");
}
if (!issubdtype(rhs_indices.dtype(), integer)) {
throw std::invalid_argument(
"[gather_mm] Got rhs_indices with invalid dtype. Indices must be integral.");
}
lhs_indices = astype(lhs_indices, uint32, s);
rhs_indices = astype(rhs_indices, uint32, s);
int M = a.shape(-2);
int N = b.shape(-1);
int K = a.shape(-1);
auto out_bsx_shape =
broadcast_shapes(lhs_indices.shape(), rhs_indices.shape());
lhs_indices = broadcast_to(lhs_indices, out_bsx_shape, s);
rhs_indices = broadcast_to(rhs_indices, out_bsx_shape, s);
auto out_shape = out_bsx_shape;
out_shape.push_back(M);
out_shape.push_back(N);
// Caculate array
auto out = array(
out_shape,
out_type,
std::make_shared<GatherMM>(to_stream(s)),
{a, b, lhs_indices, rhs_indices});
// Remove the possibly inserted singleton dimensions
if (in_a_ndim == 1 || in_b_ndim == 1) {
out_shape.erase(
out_shape.end() - ((in_a_ndim == 1) ? 2 : 1),
out_shape.end() - ((in_b_ndim == 1) ? 0 : 1));
out = reshape(out, out_shape, s);
}
return out;
}
array diagonal(
const array& a,
int offset /* = 0 */,
int axis1 /* = 0 */,
int axis2 /* = 1 */,
StreamOrDevice s /* = {} */
) {
int ndim = a.ndim();
if (ndim < 2) {
std::ostringstream msg;
msg << "[diagonal] Array must have at least two dimensions, but got "
<< ndim << " dimensions.";
throw std::invalid_argument(msg.str());
}
auto ax1 = (axis1 < 0) ? axis1 + ndim : axis1;
if (ax1 < 0 || ax1 >= ndim) {
std::ostringstream msg;
msg << "[diagonal] Invalid axis1 " << axis1 << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
auto ax2 = (axis2 < 0) ? axis2 + ndim : axis2;
if (ax2 < 0 || ax2 >= ndim) {
std::ostringstream msg;
msg << "[diagonal] Invalid axis2 " << axis2 << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
if (ax1 == ax2) {
throw std::invalid_argument(
"[diagonal] axis1 and axis2 cannot be the same axis");
}
auto off1 = std::max(-offset, 0);
auto off2 = std::max(offset, 0);
auto diag_size = std::min(a.shape(ax1) - off1, a.shape(ax2) - off2);
diag_size = std::max(diag_size, 0);
std::vector<array> indices = {
arange(off1, off1 + diag_size, s), arange(off2, off2 + diag_size, s)};
std::vector<int> slice_sizes = a.shape();
slice_sizes[ax1] = 1;
slice_sizes[ax2] = 1;
auto out = gather(a, indices, {ax1, ax2}, slice_sizes, s);
return moveaxis(squeeze(out, {ax1 + 1, ax2 + 1}, s), 0, -1, s);
}
array diag(const array& a, int k /* = 0 */, StreamOrDevice s /* = {} */) {
if (a.ndim() == 1) {
int a_size = a.size();
int n = a_size + std::abs(k);
auto res = zeros({n, n}, a.dtype(), s);
std::vector<array> indices;
auto s1 = std::max(0, -k);
auto s2 = std::max(0, k);
indices.push_back(arange(s1, a_size + s1, uint32, s));
indices.push_back(arange(s2, a_size + s2, uint32, s));
return scatter(res, indices, reshape(a, {a_size, 1, 1}, s), {0, 1}, s);
} else if (a.ndim() == 2) {
return diagonal(a, k, 0, 1, s);
} else {
std::ostringstream msg;
msg << "[diag] array must be 1-D or 2-D, got array with " << a.ndim()
<< " dimensions.";
throw std::invalid_argument(msg.str());
}
}
array trace(
const array& a,
int offset,
int axis1,
int axis2,
Dtype dtype,
StreamOrDevice s /* = {} */) {
int ndim = a.ndim();
if (ndim < 2) {
std::ostringstream msg;
msg << "[trace] Array must have at least two dimensions, but got " << ndim
<< " dimensions.";
throw std::invalid_argument(msg.str());
}
auto ax1 = (axis1 < 0) ? axis1 + ndim : axis1;
if (ax1 < 0 || ax1 >= ndim) {
std::ostringstream msg;
msg << "[trace] Invalid axis1 " << axis1 << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
auto ax2 = (axis2 < 0) ? axis2 + ndim : axis2;
if (ax2 < 0 || ax2 >= ndim) {
std::ostringstream msg;
msg << "[trace] Invalid axis2 " << axis2 << " for array with " << ndim
<< " dimensions.";
throw std::out_of_range(msg.str());
}
if (ax1 == ax2) {
throw std::invalid_argument(
"[trace] axis1 and axis2 cannot be the same axis");
}
return sum(
astype(diagonal(a, offset, axis1, axis2, s), dtype, s),
/* axis = */ -1,
/* keepdims = */ false,
s);
}
array trace(
const array& a,
int offset,
int axis1,
int axis2,
StreamOrDevice s /* = {} */) {
auto dtype = a.dtype();
return trace(a, offset, axis1, axis2, dtype, s);
}
array trace(const array& a, StreamOrDevice s /* = {} */) {
auto dtype = a.dtype();
return trace(a, 0, 0, 1, dtype, s);
}
std::vector<array> depends(
const std::vector<array>& inputs,
const std::vector<array>& dependencies) {
std::vector<array> all_inputs = inputs;
all_inputs.insert(all_inputs.end(), dependencies.begin(), dependencies.end());
// Compute the stream. Maybe do it in a smarter way at some point in the
// future.
Stream s = (inputs[0].has_primitive()) ? inputs[0].primitive().stream()
: to_stream({});
// Make the output info
std::vector<std::vector<int>> shapes;
std::vector<Dtype> dtypes;
for (const auto& in : inputs) {
shapes.emplace_back(in.shape());
dtypes.emplace_back(in.dtype());
}
return array::make_arrays(
std::move(shapes),
dtypes,
std::make_shared<Depends>(to_stream(s)),
all_inputs);
}
array atleast_1d(const array& a, StreamOrDevice s /* = {} */) {
if (a.ndim() == 0) {
return reshape(a, {1}, s);
}
return a;
}
std::vector<array> atleast_1d(
const std::vector<array>& arrays,
StreamOrDevice s /* = {} */) {
std::vector<array> out;
out.reserve(arrays.size());
for (const auto& a : arrays) {
out.push_back(atleast_1d(a, s));
}
return out;
}
array atleast_2d(const array& a, StreamOrDevice s /* = {} */) {
switch (a.ndim()) {
case 0:
return reshape(a, {1, 1}, s);
case 1:
return reshape(a, {1, static_cast<int>(a.size())}, s);
default:
return a;
}
}
std::vector<array> atleast_2d(
const std::vector<array>& arrays,
StreamOrDevice s /* = {} */) {
std::vector<array> out;
out.reserve(arrays.size());
for (const auto& a : arrays) {
out.push_back(atleast_2d(a, s));
}
return out;
}
array atleast_3d(const array& a, StreamOrDevice s /* = {} */) {
switch (a.ndim()) {
case 0:
return reshape(a, {1, 1, 1}, s);
case 1:
return reshape(a, {1, static_cast<int>(a.size()), 1}, s);
case 2:
return reshape(a, {a.shape(0), a.shape(1), 1}, s);
default:
return a;
}
}
std::vector<array> atleast_3d(
const std::vector<array>& arrays,
StreamOrDevice s /* = {} */) {
std::vector<array> out;
out.reserve(arrays.size());
for (const auto& a : arrays) {
out.push_back(atleast_3d(a, s));
}
return out;
}
array number_of_elements(
const array& a,
std::vector<int> axes,
bool inverted,
Dtype dtype /* = int32 */,
StreamOrDevice s /* = {} */) {
for (auto& ax : axes) {
int normal_axis = (ax + a.ndim()) % a.ndim();
if (normal_axis >= a.ndim() || normal_axis < 0) {
std::ostringstream msg;
msg << "[number_of_elements] Can't get the shape for axis " << ax
<< " from an array with " << a.ndim() << " dimensions.";
throw std::invalid_argument(msg.str());
}
ax = normal_axis;
}
return stop_gradient(array(
std::vector<int>{},
dtype,
std::make_shared<NumberOfElements>(
to_stream(s), std::move(axes), inverted, dtype),
{a}));
}
array conjugate(const array& a, StreamOrDevice s /* = {} */) {
// Mirror NumPy's behaviour for real input
if (a.dtype() != complex64) {
return a;
}
return array(
a.shape(), a.dtype(), std::make_shared<Conjugate>(to_stream(s)), {a});
}
array bitwise_impl(
const array& a,
const array& b,
BitwiseBinary::Op op,
const std::string& op_name,
const StreamOrDevice& s) {
auto out_type = promote_types(a.dtype(), b.dtype());
if (!(issubdtype(out_type, integer) || out_type == bool_)) {
std::ostringstream msg;
msg << "[" << op_name
<< "] Only allowed on integer or boolean types "
"but got types "
<< a.dtype() << " and " << b.dtype() << ".";
throw std::runtime_error(msg.str());
}
auto inputs =
broadcast_arrays(astype(a, out_type, s), astype(b, out_type, s), s);
auto& out_shape = inputs[0].shape();
return array(
out_shape,
out_type,
std::make_shared<BitwiseBinary>(to_stream(s), op),
std::move(inputs));
}
array bitwise_and(const array& a, const array& b, StreamOrDevice s /* = {} */) {
return bitwise_impl(a, b, BitwiseBinary::Op::And, "bitwise_and", s);
}
array operator&(const array& a, const array& b) {
return bitwise_and(a, b);
}
array bitwise_or(const array& a, const array& b, StreamOrDevice s /* = {} */) {
return bitwise_impl(a, b, BitwiseBinary::Op::Or, "bitwise_or", s);
}
array operator|(const array& a, const array& b) {
return bitwise_or(a, b);
}
array bitwise_xor(const array& a, const array& b, StreamOrDevice s /* = {} */) {
return bitwise_impl(a, b, BitwiseBinary::Op::Xor, "bitwise_xor", s);
}
array operator^(const array& a, const array& b) {
return bitwise_xor(a, b);
}
array left_shift(const array& a, const array& b, StreamOrDevice s /* = {} */) {
// Bit shift on bool always up-casts to uint8
auto t = promote_types(result_type(a, b), uint8);
return bitwise_impl(
astype(a, t, s),
astype(b, t, s),
BitwiseBinary::Op::LeftShift,
"left_shift",
s);
}
array operator<<(const array& a, const array& b) {
return left_shift(a, b);
}
array right_shift(const array& a, const array& b, StreamOrDevice s /* = {} */) {
// Bit shift on bool always up-casts to uint8
auto t = promote_types(result_type(a, b), uint8);
return bitwise_impl(
astype(a, t, s),
astype(b, t, s),
BitwiseBinary::Op::RightShift,
"right_shift",
s);
}
array operator>>(const array& a, const array& b) {
return right_shift(a, b);
}
array view(const array& a, const Dtype& dtype, StreamOrDevice s /* = {} */) {
if (a.dtype() == dtype) {
return a;
}
auto out_shape = a.shape();
auto ibytes = size_of(a.dtype());
auto obytes = size_of(dtype);
if (a.ndim() == 0 && ibytes != obytes) {
throw std::invalid_argument(
"[view] Changing the type of a scalar is only allowed"
" for types with the same size.");
} else {
if (ibytes < obytes) {
if (out_shape.back() % (obytes / ibytes) != 0) {
throw std::invalid_argument(
"[view] When viewing as a larger dtype, the size in bytes of the last"
" axis must be a multiple of the requested type size.");
}
out_shape.back() /= (obytes / ibytes);
} else {
// Type size ratios are always integers
out_shape.back() *= (ibytes / obytes);
}
}
return array(
out_shape, dtype, std::make_shared<View>(to_stream(s), dtype), {a});
}
} // namespace mlx::core
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