pytorch uninitialized memory return values

linalg_multi_dot.py

import torch
from torch import tensor

for i in [0, 5]:
    a = tensor([[-4,  3,  9,  7,  0, -6,  4,  0, -3,  2],
                [-9, -7, -7, -2,  8,  4, -4, -4, -4,  4],
                [-6,  8, -4,  5, -7,  5, -2,  9, -7, -1],
                [-4,  4, -3, -1,  0,  2,  4,  6, -7, -1],
                [-3,  5, -9,  4, -7, -9, -1,  2, -7, -6]], dtype=torch.long)

    b = tensor([[-8,  2, -7, -9, -7],
                [-8,  6,  3,  8, -4],
                [ 9, -5,  5,  4, -6],
                [-8,  7, -1, -8,  9],
                [-5, -9, -3,  4,  2],
                [ 9, -5,  2,  0, -9],
                [ 2,  9, -5,  1,  3],
                [-6,  2, -5, -7,  2],
                [-4, -2,  2, -6,  0],
                [ 1, -3, -3, -8, -1]], dtype=torch.long)

    a0 = a.size(dim=0)
    a1 = a.size(dim=1)
    b0 = b.size(dim=0)
    b1 = b.size(dim=1)

    a = a[:a0-i, :a1-i]
    b = b[:b0-i, :b1-i]

    print('A:', a)
    print('B:', b)

    c = torch.linalg.multi_dot([b, a])

    d = torch.linalg.multi_dot([b, a])
    print('C:', c)
    print('D:', d)
    assert((c==d).all().item())

matmul.py

import torch
from torch import tensor

for i in [0, 5]:
    a = tensor([[-4,  3,  9,  7,  0, -6,  4,  0, -3,  2],
                [-9, -7, -7, -2,  8,  4, -4, -4, -4,  4],
                [-6,  8, -4,  5, -7,  5, -2,  9, -7, -1],
                [-4,  4, -3, -1,  0,  2,  4,  6, -7, -1],
                [-3,  5, -9,  4, -7, -9, -1,  2, -7, -6]], dtype=torch.long)

    b = tensor([[-8,  2, -7, -9, -7],
                [-8,  6,  3,  8, -4],
                [ 9, -5,  5,  4, -6],
                [-8,  7, -1, -8,  9],
                [-5, -9, -3,  4,  2],
                [ 9, -5,  2,  0, -9],
                [ 2,  9, -5,  1,  3],
                [-6,  2, -5, -7,  2],
                [-4, -2,  2, -6,  0],
                [ 1, -3, -3, -8, -1]], dtype=torch.long)

    a0 = a.size(dim=0)
    a1 = a.size(dim=1)
    b0 = b.size(dim=0)
    b1 = b.size(dim=1)

    a = a[:a0-i, :a1-i]
    b = b[:b0-i, :b1-i]

    print('A:', a)
    print('B:', b)

    c = torch.matmul(b, a)

    d = torch.matmul(b, a)
    print('C:', c)
    print('D:', d)
    assert((c==d).all().item())

rmatmul.py

import torch
from torch import tensor

dtype = torch.long

for i in [0, 4, 5]:
    b = tensor([[-4,  3,  9,  7,  0, -6,  4,  0, -3,  2],
                [-9, -7, -7, -2,  8,  4, -4, -4, -4,  4],
                [-6,  8, -4,  5, -7,  5, -2,  9, -7, -1],
                [-4,  4, -3, -1,  0,  2,  4,  6, -7, -1],
                [-3,  5, -9,  4, -7, -9, -1,  2, -7, -6]], dtype=dtype)

    a = tensor([[-8,  2, -7, -9, -7],
                [-8,  6,  3,  8, -4],
                [ 9, -5,  5,  4, -6],
                [-8,  7, -1, -8,  9],
                [-5, -9, -3,  4,  2],
                [ 9, -5,  2,  0, -9],
                [ 2,  9, -5,  1,  3],
                [-6,  2, -5, -7,  2],
                [-4, -2,  2, -6,  0],
                [ 1, -3, -3, -8, -1]], dtype=dtype)

    #add = torch.zeros((10-i, 10-i), dtype=dtype)

    a0 = a.size(dim=0)
    a1 = a.size(dim=1)
    b0 = b.size(dim=0)
    b1 = b.size(dim=1)

    a = a[:a0-i, :a1-i]
    b = b[:b0-i, :b1-i]

    print('A:', a)
    print('B:', b)

    c = torch.Tensor.__rmatmul__(b, a)

    d = torch.Tensor.__rmatmul__(b, a)
    print('C:', c)
    print('D:', d)
    assert((c==d).all().item())

uninit-memory-repros.py

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