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* @license Apache-2.0
*
* Copyright (c) 2025 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var ndarray2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
var normalizeIndices = require( '@stdlib/ndarray/base/to-unique-normalized-indices' );
var indicesComplement = require( '@stdlib/array/base/indices-complement' );
var takeIndexed2 = require( '@stdlib/array/base/take-indexed2' );
var takeIndexed = require( '@stdlib/array/base/take-indexed' );
var iterationOrder = require( '@stdlib/ndarray/base/iteration-order' );
var strides2order = require( '@stdlib/ndarray/base/strides2order' );
var numel = require( '@stdlib/ndarray/base/numel' );
var join = require( '@stdlib/array/base/join' );
var format = require( '@stdlib/string/format' );
var initializeViews = require( './initialize_array_views.js' );
var strategy = require( './strategy.js' );
var defaults = require( './defaults.js' );
var validate = require( './validate.js' );
var blockedunary2d = require( './2d_blocked.js' );
var blockedunary3d = require( './3d_blocked.js' );
var blockedunary4d = require( './4d_blocked.js' );
var blockedunary5d = require( './5d_blocked.js' );
var blockedunary6d = require( './6d_blocked.js' );
var blockedunary7d = require( './7d_blocked.js' );
var blockedunary8d = require( './8d_blocked.js' );
var blockedunary9d = require( './9d_blocked.js' );
var blockedunary10d = require( './10d_blocked.js' );
var unary0d = require( './0d.js' );
var unary1d = require( './1d.js' );
var unary2d = require( './2d.js' );
var unary3d = require( './3d.js' );
var unary4d = require( './4d.js' );
var unary5d = require( './5d.js' );
var unary6d = require( './6d.js' );
var unary7d = require( './7d.js' );
var unary8d = require( './8d.js' );
var unary9d = require( './9d.js' );
var unary10d = require( './10d.js' );
var unarynd = require( './nd.js' );
// VARIABLES //
var UNARY = [
unary0d,
unary1d,
unary2d,
unary3d,
unary4d,
unary5d,
unary6d,
unary7d,
unary8d,
unary9d,
unary10d
];
var BLOCKED_UNARY = [
blockedunary2d, // 0
blockedunary3d,
blockedunary4d,
blockedunary5d,
blockedunary6d,
blockedunary7d,
blockedunary8d,
blockedunary9d,
blockedunary10d // 8
];
var MAX_DIMS = UNARY.length - 1;
// MAIN //
/**
* Returns a function for applying a one-dimensional strided array function to a list of specified dimensions in an input ndarray and assigning results to a provided output ndarray.
*
* @private
* @param {Options} [options] - function options
* @param {boolean} [options.strictTraversalOrder=false] - boolean specifying whether to require that element traversal match the memory layout of an input ndarray
* @throws {TypeError} options argument must be an object
* @throws {TypeError} must provide valid options
* @returns {Function} function for applying a strided array function
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
* var gcusum = require( '@stdlib/blas/ext/base/gcusum' ).ndarray;
*
* function wrapper( arrays ) {
* var x = arrays[ 0 ];
* var y = arrays[ 1 ];
* var s = arrays[ 2 ];
* return gcusum( numelDimension( x, 0 ), ndarraylike2scalar( s ), getData( x ), getStride( x, 0 ), getOffset( x ), getData( y ), getStride( y, 0 ), getOffset( y ) );
* }
*
* // Create data buffers:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
* var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 1, 3, 2, 2 ];
* var ysh = [ 1, 3, 2, 2 ];
*
* // Define the array strides:
* var sx = [ 12, 4, 2, 1 ];
* var sy = [ 12, 4, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
*
* // Create an input ndarray-like object:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Create an ndarray-like object for the initial sum:
* var initial = {
* 'dtype': 'float64',
* 'data': new Float64Array( [ 0.0 ] ),
* 'shape': [ 1, 3 ],
* 'strides': [ 0, 0 ],
* 'offset': 0,
* 'order': 'row-major'
* };
*
* // Create an output ndarray-like object:
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Apply strided function:
* var f = factory();
* f( wrapper, [ x, y, initial ], [ 2, 3 ] );
*
* var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
* // returns [ [ [ [ 1.0, 3.0 ], [ 6.0, 10.0 ] ], [ [ 5.0, 11.0 ], [ 18.0, 26.0 ] ], [ [ 9.0, 19.0 ], [ 30.0, 42.0 ] ] ] ]
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
* var gcusum = require( '@stdlib/blas/ext/base/gcusum' ).ndarray;
*
* function wrapper( arrays ) {
* var x = arrays[ 0 ];
* var y = arrays[ 1 ];
* var s = arrays[ 2 ];
* return gcusum( numelDimension( x, 0 ), ndarraylike2scalar( s ), getData( x ), getStride( x, 0 ), getOffset( x ), getData( y ), getStride( y, 0 ), getOffset( y ) );
* }
*
* // Create data buffers:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
* var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 1, 3, 2, 2 ];
* var ysh = [ 1, 3, 2, 2 ];
*
* // Define the array strides:
* var sx = [ 12, 4, 2, 1 ];
* var sy = [ 12, 4, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
*
* // Create an input ndarray-like object:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Create an ndarray-like object for the initial sum:
* var initial = {
* 'dtype': 'float64',
* 'data': new Float64Array( [ 0.0 ] ),
* 'shape': [],
* 'strides': [ 0 ],
* 'offset': 0,
* 'order': 'row-major'
* };
*
* // Create an output ndarray-like object:
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Apply strided function:
* var f = factory();
* f( wrapper, [ x, y, initial ], [ 0, 1, 2, 3 ] );
*
* var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
* // returns [ [ [ [ 1.0, 3.0 ], [ 6.0, 10.0 ] ], [ [ 15.0, 21.0 ], [ 28.0, 36.0 ] ], [ [ 45.0, 55.0 ], [ 66.0, 78.0 ] ] ] ]
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
* var gcusum = require( '@stdlib/blas/ext/base/gcusum' ).ndarray;
*
* function wrapper( arrays ) {
* var x = arrays[ 0 ];
* var y = arrays[ 1 ];
* var s = arrays[ 2 ];
* return gcusum( numelDimension( x, 0 ), ndarraylike2scalar( s ), getData( x ), getStride( x, 0 ), getOffset( x ), getData( y ), getStride( y, 0 ), getOffset( y ) );
* }
*
* // Create data buffers:
* var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
* var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 3, 2, 2 ];
* var ysh = [ 3, 2, 2 ];
*
* // Define the array strides:
* var sx = [ 4, 2, 1 ];
* var sy = [ 4, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
*
* // Create an input ndarray-like object:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
*
* // Create an ndarray-like object for the initial sum:
* var initial = {
* 'dtype': 'float64',
* 'data': new Float64Array( [ 0.0 ] ),
* 'shape': [ 3, 2, 2 ],
* 'strides': [ 0, 0, 0 ],
* 'offset': 0,
* 'order': 'row-major'
* };
*
* // Create an output ndarray-like object:
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Apply strided function:
* var f = factory();
* f( wrapper, [ x, y, initial ], [] );
*
* var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
* // returns [ [ [ 1.0, 2.0 ], [ 3.0, 4.0 ] ], [ [ 5.0, 6.0 ], [ 7.0, 8.0 ] ], [ [ 9.0, 10.0 ], [ 11.0, 12.0 ] ] ]
*/
function factory( options ) {
var OPTS;
var err;
OPTS = defaults();
if ( arguments.length ) {
err = validate( OPTS, options );
if ( err ) {
throw err;
}
}
return unaryStrided1d;
/**
* Applies a one-dimensional strided array function to a list of specified dimensions in an input ndarray and assigns results to a provided output ndarray.
*
* @private
* @param {Function} fcn - wrapper for a one-dimensional strided array function
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @param {IntegerArray} dims - list of dimensions to which to apply a strided array function
* @param {Options} [options] - function options
* @throws {Error} arrays must have the expected number of dimensions
* @throws {RangeError} dimension indices must not exceed input ndarray bounds
* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
* @throws {Error} must provide unique dimension indices
* @throws {Error} arrays must have the same loop dimension sizes
* @returns {void}
*/
function unaryStrided1d( fcn, arrays, dims, options ) { // eslint-disable-line max-statements
var strategyX;
var strategyY;
var views;
var ndims;
var ldims;
var opts;
var arr;
var tmp;
var len;
var shl;
var shc;
var shx;
var iox;
var ioy;
var scx;
var scy;
var slx;
var sly;
var ord;
var ns;
var d;
var s;
var N;
var M;
var K;
var x;
var y;
var i;
var j;
if ( arguments.length > 3 ) {
opts = options;
} else {
opts = {};
}
// Standardize ndarray meta data...
N = arrays.length;
arr = [];
for ( i = 0; i < N; i++ ) {
arr.push( ndarray2object( arrays[ i ] ) );
}
// Cache references to the input and output arrays:
x = arr[ 0 ];
y = arr[ 1 ];
// Resolve the number of input array dimensions:
shx = x.shape;
ndims = shx.length;
// Verify that we've been provided a list of unique dimension indices...
M = dims.length;
d = normalizeIndices( dims, ndims-1 );
if ( d === null ) {
throw new RangeError( format( 'invalid argument. Third argument contains an out-of-bounds dimension index. Value: [%s].', join( dims, ',' ) ) );
}
d.sort();
if ( d.length !== M ) {
throw new Error( format( 'invalid argument. Third argument must contain a list of unique dimension indices. Value: [%s].', join( dims, ',' ) ) );
}
// Check whether we've been provided a valid number of dimensions...
if ( M > ndims ) {
throw new RangeError( format( 'invalid argument. Number of specified dimensions cannot exceed the number of dimensions in the input array. Number of dimensions: %d. Value: [%s].', ndims, join( dims, ',' ) ) );
}
// Verify that provided ancillary ndarrays have the expected number of dimensions...
K = ndims - M;
for ( i = 2; i < N; i++ ) {
if ( arr[ i ].shape.length !== K ) {
throw new Error( format( 'invalid argument. Array arguments after the first two arrays must have the same number of loop dimensions. Input array shape: [%s]. Number of loop dimensions: %d. Array shape: [%s] (index: %d).', join( shx, ',' ), K, join( arr[ i ].shape, ',' ), i ) );
}
}
// Verify that the input and output arrays have the same shape...
if ( ndims !== y.shape.length ) {
throw new Error( 'invalid arguments. Input and output arrays must have the same shape.' );
}
for ( i = 0; i < ndims; i++ ) {
if ( shx[ i ] !== y.shape[ i ] ) {
throw new Error( 'invalid arguments. Input and output arrays must have the same shape.' );
}
}
// Resolve the loop dimensions and associated strides:
ldims = indicesComplement( shx.length, d );
tmp = takeIndexed2( shx, x.strides, ldims );
shl = tmp[ 0 ];
slx = tmp[ 1 ];
sly = takeIndexed( y.strides, ldims );
// Resolve the core dimensions and associated strides:
tmp = takeIndexed2( shx, x.strides, d );
shc = tmp[ 0 ];
scx = tmp[ 1 ];
scy = takeIndexed( y.strides, d );
// Verify that provided ancillary arrays have the same loop dimensions...
len = 1; // number of elements
ns = 0; // number of singleton dimensions
for ( i = 0; i < K; i++ ) {
s = shl[ i ];
for ( j = 2; j < N; j++ ) {
if ( s !== arr[ j ].shape[ i ] ) {
throw new Error( format( 'invalid argument. Loop dimensions must be consistent across all provided arrays. Input array shape: [%s]. Loop dimension indices: [%s]. Loop dimensions: [%s]. Array shape: [%s] (index: %d).', join( shx, ',' ), join( ldims, ',' ), join( shl, ',' ), join( arr[ j ].shape, ',' ), j ) );
}
}
// Note that, if one of the dimensions is `0`, the length will be `0`...
len *= s;
// Check whether the current dimension is a singleton dimension...
if ( s === 1 ) {
ns += 1;
}
}
// Check whether we were provided empty ndarrays...
if ( len === 0 || ( shc.length && numel( shc ) === 0 ) ) {
return;
}
// Initialize ndarray-like objects for representing sub-array views...
views = [
{
'dtype': x.dtype,
'data': x.data,
'shape': shc,
'strides': scx,
'offset': x.offset,
'order': x.order
},
{
'dtype': y.dtype,
'data': y.data,
'shape': shc,
'strides': scy,
'offset': y.offset,
'order': y.order
}
];
initializeViews( arr, views );
// Determine the strategy for marshaling data to and from sub-array views of the input and output arrays before and after performing an operation:
strategyX = strategy( views[ 0 ] );
strategyY = strategy( views[ 1 ] );
// Determine whether we can avoid iteration altogether...
if ( K === 0 ) {
return UNARY[ K ]( fcn, arr, strategyX, strategyY, opts );
}
// Determine whether we only have one loop dimension and can thus readily perform one-dimensional iteration...
if ( K === 1 ) {
return UNARY[ K ]( fcn, arr, views, shl, slx, sly, strategyX, strategyY, opts ); // eslint-disable-line max-len
}
// Determine whether the loop dimensions have only **one** non-singleton dimension (e.g., shape=[10,1,1,1]) so that we can treat loop iteration as being equivalent to one-dimensional iteration...
if ( ns === K-1 ) {
// Get the index of the non-singleton dimension...
for ( i = 0; i < K; i++ ) {
if ( shl[ i ] !== 1 ) {
break;
}
}
for ( j = 0; j < N; j++ ) {
arr[ j ].strides = [ arr[j].strides[i] ];
}
slx = [ slx[i] ];
sly = [ sly[i] ];
return UNARY[ 1 ]( fcn, arr, views, [ shl[i] ], slx, sly, strategyX, strategyY, opts ); // eslint-disable-line max-len
}
iox = iterationOrder( slx ); // +/-1
ioy = iterationOrder( sly ); // +/-1
// Determine whether we can avoid blocked iteration...
ord = strides2order( slx );
if ( iox !== 0 && ioy !== 0 && ord === strides2order( sly ) && K <= MAX_DIMS ) { // eslint-disable-line max-len
// So long as iteration for each respective array always moves in the same direction (i.e., no mixed sign strides) and the memory layouts are the same, we can leverage cache-optimal (i.e., normal) nested loops without resorting to blocked iteration...
return UNARY[ K ]( fcn, arr, views, shl, slx, sly, ord === 1, strategyX, strategyY, opts ); // eslint-disable-line max-len
}
// Check whether blocked iteration is prohibited due to a requirement that the order of element traversal match the memory layout of a provided input ndarray...
if ( OPTS.strictTraversalOrder && K <= MAX_DIMS ) {
// We have two choices here: (1) we could copy to contiguous memory or (2) we can perform normal nested loop iteration, even though this is not cache-optimal based on the assumption that, while this may hurt performance, for many cases (i.e., smaller ndarrays), this should be fine and likely better than performing a complete copy...
return UNARY[ K ]( fcn, arr, views, shl, slx, sly, ord === 1, strategyX, strategyY, opts ); // eslint-disable-line max-len
}
// At this point, we're either dealing with non-contiguous n-dimensional arrays, high dimensional n-dimensional arrays, and/or arrays having differing memory layouts, so our only hope is that we can still perform blocked iteration...
// Determine whether we can perform blocked iteration...
if ( K <= MAX_DIMS ) {
return BLOCKED_UNARY[ K-2 ]( fcn, arr, views, shl, slx, sly, strategyX, strategyY, opts ); // eslint-disable-line max-len
}
// Perform linear view iteration without regard for how data is stored in memory (i.e., take the slow path)...
unarynd( fcn, arr, views, shl, slx, sly, strategyX, strategyY, opts );
}
}
// EXPORTS //
module.exports = factory;
|