feat: add 5d blocked kernel

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Author: kgryte

lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d-to-struct/lib/5d_blocked.js

@@ -0,0 +1,363 @@
+/**
+* @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.
+*/
+
+/* eslint-disable max-depth */
+
+'use strict';
+
+// MODULES //
+
+var loopOrder = require( '@stdlib/ndarray/base/unary-loop-interchange-order' );
+var blockSize = require( '@stdlib/ndarray/base/unary-tiling-block-size' );
+var takeIndexed = require( '@stdlib/array/base/take-indexed' );
+var copyIndexed = require( '@stdlib/array/base/copy-indexed' );
+var zeros = require( '@stdlib/array/base/zeros' );
+var incrementOffsets = require( './increment_offsets.js' );
+var setViewOffsets = require( './set_view_offsets.js' );
+var offsets = require( './offsets.js' );
+
+
+// MAIN //
+
+/**
+* Performs a reduction over an input ndarray and assigns results to a provided output ndarray via loop blocking.
+*
+* @private
+* @param {Function} fcn - wrapper for a one-dimensional strided array reduction function
+* @param {Array<Object>} arrays - ndarrays
+* @param {Array<Object>} views - initialized ndarray-like objects representing sub-array views
+* @param {IntegerArray} strides - loop dimension strides for the input ndarray
+* @param {Function} strategy - input ndarray reshape strategy
+* @param {Options} opts - function options
+* @returns {void}
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
+* var Float64Results = require( '@stdlib/stats/base/ztest/one-sample/results/float64' );
+* var structFactory = require( '@stdlib/array/struct-factory' );
+* var ztest = require( '@stdlib/stats/base/ndarray/ztest' );
+*
+* var ResultsArray = structFactory( Float64Results );
+*
+* // 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 ResultsArray( 3 );
+*
+* // Define the array shapes:
+* var xsh = [ 1, 1, 1, 1, 3, 2, 2 ];
+* var ysh = [ 1, 1, 1, 1, 3 ];
+*
+* // Define the array strides:
+* var sx = [ 12, 12, 12, 12, 4, 2, 1 ];
+* var sy = [ 3, 3, 3, 3, 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 output ndarray-like object:
+* var y = {
+*     'dtype': Float64Results,
+*     'data': ybuf,
+*     'shape': ysh,
+*     'strides': sy,
+*     'offset': oy,
+*     'order': 'row-major'
+* };
+*
+* // Create additional parameter ndarray-like objects:
+* var alternative = {
+*    'dtype': 'generic',
+*    'data': [ 'two-sided' ],
+*    'shape': ysh,
+*    'strides': [ 0, 0, 0, 0, 0 ],
+*    'offset': 0,
+*    'order': 'row-major'
+};
+* var alpha = {
+*     'dtype': 'float64',
+*     'data': [ 0.05 ],
+*     'shape': ysh,
+*     'strides': [ 0, 0, 0, 0, 0 ],
+*     'offset': 0,
+*     'order': 'row-major'
+};
+* var mu = {
+*     'dtype': 'float64',
+*     'data': [ 0.0 ],
+*     'shape': ysh,
+*     'strides': [ 0, 0, 0, 0, 0 ],
+*     'offset': 0,
+*     'order': 'row-major'
+};
+* var sigma = {
+*     'dtype': 'float64',
+*     'data': [ 1.0 ],
+*     'shape': ysh,
+*     'strides': [ 0, 0, 0, 0, 0 ],
+*     'offset': 0,
+*     'order': 'row-major'
+* };
+*
+* // Initialize ndarray-like objects representing sub-array views:
+* var views = [
+*     {
+*         'dtype': x.dtype,
+*         'data': x.data,
+*         'shape': [ 2, 2 ],
+*         'strides': [ 2, 1 ],
+*         'offset': x.offset,
+*         'order': x.order
+*     },
+*     {
+*         'dtype': y.dtype,
+*         'data': y.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': y.offset,
+*         'order': y.order
+*     },
+*     {
+*         'dtype': alternative.dtype,
+*         'data': alternative.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': alternative.offset,
+*         'order': alternative.order
+*     },
+*     {
+*         'dtype': alpha.dtype,
+*         'data': alpha.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': alpha.offset,
+*         'order': alpha.order
+*     },
+*     {
+*         'dtype': mu.dtype,
+*         'data': mu.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': mu.offset,
+*         'order': mu.order
+*     },
+*     {
+*         'dtype': sigma.dtype,
+*         'data': sigma.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': sigma.offset,
+*         'order': sigma.order
+*     }
+* ];
+*
+* // Define a reshape strategy:
+* function strategy( x ) {
+*     return {
+*         'dtype': x.dtype,
+*         'data': x.data,
+*         'shape': [ 4 ],
+*         'strides': [ 1 ],
+*         'offset': x.offset,
+*         'order': x.order
+*     };
+* }
+*
+* // Perform a reduction:
+* blockedunary5d( ztest, [ x, y, alternative, alpha, mu, sigma ], views, [ 12, 12, 12, 12, 4 ], strategy, {} );
+*
+* var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
+* // returns [ [ [ [ [ <Float64Results>, <Float64Results>, <Float64Results> ] ] ] ] ]
+*/
+function blockedunary5d( fcn, arrays, views, strides, strategy, opts ) { // eslint-disable-line max-statements
+	var bsize;
+	var dv0;
+	var dv1;
+	var dv2;
+	var dv3;
+	var dv4;
+	var ov1;
+	var ov2;
+	var ov3;
+	var ov4;
+	var sh;
+	var s0;
+	var s1;
+	var s2;
+	var s3;
+	var s4;
+	var sv;
+	var ov;
+	var iv;
+	var i0;
+	var i1;
+	var i2;
+	var i3;
+	var i4;
+	var j0;
+	var j1;
+	var j2;
+	var j3;
+	var j4;
+	var N;
+	var x;
+	var y;
+	var v;
+	var o;
+	var k;
+
+	// Note on variable naming convention: S#, dv#, i#, j# where # corresponds to the loop number, with `0` being the innermost loop...
+
+	N = arrays.length;
+	x = arrays[ 0 ];
+	y = arrays[ 1 ];
+
+	// Resolve the loop interchange order:
+	o = loopOrder( y.shape, strides, y.strides );
+	sh = o.sh;
+	sv = [ o.sx, o.sy ];
+	for ( k = 2; k < N; k++ ) {
+		sv.push( takeIndexed( arrays[k].strides, o.idx ) );
+	}
+	// Determine the block size:
+	bsize = blockSize( x.dtype, y.dtype );
+
+	// Resolve a list of pointers to the first indexed elements in the respective ndarrays:
+	ov = offsets( arrays );
+
+	// Cache offset increments for the innermost loop...
+	dv0 = [];
+	for ( k = 0; k < N; k++ ) {
+		dv0.push( sv[k][0] );
+	}
+	// Initialize loop variables...
+	ov1 = zeros( N );
+	ov2 = zeros( N );
+	ov3 = zeros( N );
+	ov4 = zeros( N );
+	dv1 = zeros( N );
+	dv2 = zeros( N );
+	dv3 = zeros( N );
+	dv4 = zeros( N );
+	iv = zeros( N );
+
+	// Shallow copy the list of views to an internal array so that we can update with reshaped views without impacting the original list of views:
+	v = copyIndexed( views );
+
+	// Iterate over blocks...
+	for ( j4 = sh[4]; j4 > 0; ) {
+		if ( j4 < bsize ) {
+			s4 = j4;
+			j4 = 0;
+		} else {
+			s4 = bsize;
+			j4 -= bsize;
+		}
+		for ( k = 0; k < N; k++ ) {
+			ov4[ k ] = ov[k] + ( j4*sv[k][4] );
+		}
+		for ( j3 = sh[3]; j3 > 0; ) {
+			if ( j3 < bsize ) {
+				s3 = j3;
+				j3 = 0;
+			} else {
+				s3 = bsize;
+				j3 -= bsize;
+			}
+			for ( k = 0; k < N; k++ ) {
+				dv4[ k ] = sv[k][4] - ( s3*sv[k][3] );
+				ov3[ k ] = ov4[k] + ( j3*sv[k][3] );
+			}
+			for ( j2 = sh[2]; j2 > 0; ) {
+				if ( j2 < bsize ) {
+					s2 = j2;
+					j2 = 0;
+				} else {
+					s2 = bsize;
+					j2 -= bsize;
+				}
+				for ( k = 0; k < N; k++ ) {
+					dv3[ k ] = sv[k][3] - ( s2*sv[k][2] );
+					ov2[ k ] = ov3[k] + ( j2*sv[k][2] );
+				}
+				for ( j1 = sh[1]; j1 > 0; ) {
+					if ( j1 < bsize ) {
+						s1 = j1;
+						j1 = 0;
+					} else {
+						s1 = bsize;
+						j1 -= bsize;
+					}
+					for ( k = 0; k < N; k++ ) {
+						dv2[ k ] = sv[k][2] - ( s1*sv[k][1] );
+						ov1[ k ] = ov2[k] + ( j1*sv[k][1] );
+					}
+					for ( j0 = sh[0]; j0 > 0; ) {
+						if ( j0 < bsize ) {
+							s0 = j0;
+							j0 = 0;
+						} else {
+							s0 = bsize;
+							j0 -= bsize;
+						}
+						// Compute index offsets and loop offset increments for the first ndarray elements in the current block...
+						for ( k = 0; k < N; k++ ) {
+							iv[ k ] = ov1[k] + ( j0*sv[k][0] );
+							dv1[ k ] = sv[k][1] - ( s0*sv[k][0] );
+						}
+						// Iterate over the non-reduced ndarray dimensions...
+						for ( i4 = 0; i4 < s4; i4++ ) {
+							for ( i3 = 0; i3 < s3; i3++ ) {
+								for ( i2 = 0; i2 < s2; i2++ ) {
+									for ( i1 = 0; i1 < s1; i1++ ) {
+										for ( i0 = 0; i0 < s0; i0++ ) {
+											setViewOffsets( views, iv );
+											v[ 0 ] = strategy( views[ 0 ] );
+											fcn( v, opts );
+											incrementOffsets( iv, dv0 );
+										}
+										incrementOffsets( iv, dv1 );
+									}
+									incrementOffsets( iv, dv2 );
+								}
+								incrementOffsets( iv, dv3 );
+							}
+							incrementOffsets( iv, dv4 );
+						}
+					}
+				}
+			}
+		}
+	}
+}
+
+
+// EXPORTS //
+
+module.exports = blockedunary5d;