57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
from data_stat import Stat
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import torch, scipy
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import numpy as np
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import argparse
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import time
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import json
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import sys, os
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parser = argparse.ArgumentParser()
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parser.add_argument('matrix_file', help='the input matrix (.mtx) file')
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parser.add_argument('iterations', type=int, help='the number of iterations of multiplication to perform')
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args = parser.parse_args()
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device = 'cpu'
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matrix = scipy.io.mmread(args.matrix_file)
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matrix = torch.sparse_coo_tensor(
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np.vstack((matrix.row, matrix.col)),
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matrix.data, matrix.shape,
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device=device
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).to_sparse_csr().type(torch.float)
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vector = torch.rand(matrix.shape[1], device=device)
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print(matrix, file=sys.stderr)
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print(vector, file=sys.stderr)
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start = time.time()
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for i in range(0, args.iterations):
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torch.mv(matrix, vector)
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#torch.sparse.mm(matrix, vector.unsqueeze(-1)).squeeze(-1)
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#print(i)
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end = time.time()
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result = dict()
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result[Stat.MATRIX_FILE.name] = os.path.splitext(os.path.basename(args.matrix_file))[0]
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print(f"Matrix: {result[Stat.MATRIX_FILE.name]}", file=sys.stderr)
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result[Stat.MATRIX_SHAPE.name] = matrix.shape
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print(f"Shape: {result[Stat.MATRIX_SHAPE.name]}", file=sys.stderr)
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result[Stat.MATRIX_SIZE.name] = matrix.shape[0] * matrix.shape[1]
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print(f"Size: {result[Stat.MATRIX_SIZE.name]}", file=sys.stderr)
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result[Stat.MATRIX_NNZ.name] = matrix.values().shape[0]
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print(f"NNZ: {result[Stat.MATRIX_NNZ.name]}", file=sys.stderr)
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result[Stat.MATRIX_DENSITY.name] = matrix.values().shape[0] / (matrix.shape[0] * matrix.shape[1])
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print(f"Density: {result[Stat.MATRIX_DENSITY.name]}", file=sys.stderr)
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result[Stat.TIME_S.name] = end - start
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print(f"Time: {result[Stat.TIME_S.name]} seconds", file=sys.stderr)
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print(json.dumps(result))
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