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