# ######################################################################## # Copyright (C) 2025 Advanced Micro Devices, Inc. All rights Reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # ######################################################################## import numpy as np import scipy.sparse as sp from scipy.sparse import coo_matrix from numpy.random import default_rng from scipy.io import mmwrite import subprocess import os import pathlib rng = default_rng(42) def samePatternPerRow(m, n, col_per_row): row = np.repeat(np.arange(0, m), len(col_per_row)) col = np.tile(col_per_row, m) data = np.ones((len(row),)) return coo_matrix((data, (row, col)), shape=(m, n)) def consecutiveCols(m, n, nnz_per_row, first_col): if first_col + nnz_per_row >= n: col_per_row = np.arange(n-nnz_per_row, n) else: col_per_row = np.arange(first_col, first_col+nnz_per_row) return samePatternPerRow(m, n, col_per_row) def sameRandomRow(m, n, nnz_per_row): col_per_row = np.sort(rng.choice(n, size=nnz_per_row, replace=False)) return samePatternPerRow(m, n, col_per_row) def sameNnzPerRow(m, n, nnz_per_row): row = np.repeat(np.arange(0, m), nnz_per_row) col = np.zeros((len(row),), dtype=int) for i in range(0, m): col[i*nnz_per_row:(i+1)*nnz_per_row] = np.sort(rng.choice(n, size=nnz_per_row, replace=False)) data = np.ones((len(row),)) return coo_matrix((data, (row, col)), shape=(m, n)) def fullyRandom(m, n, nnz): oneDRand = np.sort(rng.choice(m*n, size=nnz, replace=False)) row = oneDRand/n col = oneDRand%n data = np.ones((nnz,)) return coo_matrix((data, (row, col)), shape=(m, n)) def writeToFile(filename, A): mmwrite(filename, A, comment='\n Matrix generated with sparseMatrixGenerator.\n', precision=6) def convert(filename, A, cleanFile=True): current_work_dir = os.getcwd() os.chdir(pathlib.Path(__file__).parent.resolve()) fileformat = ".mtx" writeToFile(filename + fileformat, A) subprocess.run("./convert "+ filename + fileformat + " " + filename + ".csr", shell=True, check=True) if cleanFile: os.remove(filename + fileformat) else: os.rename(str(pathlib.Path(__file__).parent.resolve()) + "/" + filename + fileformat, str(current_work_dir) + "/" + filename + fileformat) os.rename(str(pathlib.Path(__file__).parent.resolve()) + "/" + filename + ".csr", str(current_work_dir) + "/" + filename + ".csr") os.chdir(current_work_dir)