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