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nicknytko / numml

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Test Issues #13

Open GregorySchwing opened 11 months ago

GregorySchwing commented 11 months ago

After fixing build issues with #12, two tests failed

root@51e1485d94b7:/TorchVision/numml# pytest numml/tests ============================================================= test session starts ============================================================== platform linux -- Python 3.10.12, pytest-7.4.2, pluggy-1.3.0 rootdir: /TorchVision/numml plugins: shard-0.1.2, hypothesis-5.35.1, rerunfailures-12.0, xdist-3.3.1, xdoctest-1.0.2, flakefinder-1.1.0 collected 33 items
Running 33 items in this shard

numml/tests/test_add.py ... [ 9%] numml/tests/test_misc.py . [ 12%] numml/tests/test_spdmm.py .FF.. [ 27%] numml/tests/test_spmm.py ....... [ 48%] numml/tests/test_spmv.py ...... [ 66%] numml/tests/test_sptrsv.py ...... [ 84%] numml/tests/test_transpose.py ..... [100%]

=================================================================== FAILURES =================================================================== __ test_random_small ___

def test_random_small():
    it = 10
    for i in range(it):
        Nc = random.randint(3, 10)
        X = torch.randn(A_N, Nc)
        X_c = X.to(gpu)
        print('X_c shape', X_c.shape)
        print(X_c)

        AX_d = A_d @ X
      assert(torch.allclose(AX_d, A@X))

E AssertionError: assert False E + where False = <built-in method allclose of type object at 0x7facd8b4f680>(tensor([[-8.0905e-01, -1.2791e+00, 2.1114e+00, -2.1343e+00, -5.8658e+00],\n [ 2.3513e+00, 1.4423e+00, -2.9569e...00, -1.0391e+00, -5.9623e-01, -4.8469e+00],\n [-2.3855e+00, -1.8825e+00, -4.6459e+00, -8.6935e-01, 3.7740e+00]]), (<16x16 sparse matrix tensor of type 'torch.float32'\n with 46 stored elements in Compressed Sparse Row format> @ tensor([[ 2.3843e-01, -1.6153e-01, 5.9662e-01, -1.7112e+00, -2.1326e+00],\n [ 1.2859e+00, 9.5606e-01, -9.1817e...02, -1.1889e+00, 5.0320e-02, -1.0626e+00],\n [-6.7612e-01, -9.1202e-01, -2.9174e+00, -4.0951e-01, 1.3557e+00]]))) E + where <built-in method allclose of type object at 0x7facd8b4f680> = torch.allclose

numml/tests/test_spdmm.py:43: AssertionError ------------------------------------------------------------- Captured stdout call ------------------------------------------------------------- X_c shape torch.Size([16, 7]) tensor([[-1.4404, 0.9939, -0.2333, -2.4501, -2.3983, -1.0288, -1.5292], [-1.1281, -1.9627, -1.0358, 1.0002, 1.2289, 0.0903, 0.7665], [-0.8329, 1.7655, -1.1728, -0.1793, -1.4545, 0.0141, -1.2997], [-1.7013, -0.9780, 0.5817, -1.3643, 1.5334, 0.6137, 0.5878], [ 0.4688, 0.1218, 0.8664, -1.0398, -0.2259, 2.2077, -0.3796], [-1.3176, -0.0595, 2.0575, 1.5325, -0.7838, -0.1799, 0.7953], [ 0.2436, -2.7121, 0.0796, -0.2203, 0.2659, 0.9575, -0.4840], [ 0.5368, 1.4393, -0.4030, -0.5211, -0.5826, -1.3434, -1.5291], [-0.0376, 0.3784, 1.1600, 1.3821, 1.3911, -1.4542, -1.5568], [ 1.1818, 0.9652, 1.0323, 0.5053, -0.0812, -1.3966, 0.6781], [-1.1266, -0.4934, 1.1943, 0.5738, -1.2757, 0.7131, 1.4122], [-0.6217, -0.2607, -1.1545, -0.5109, 1.4249, -0.0774, -1.4336], [ 0.0060, -1.3877, 0.3932, -0.8707, -0.5830, 0.7535, 0.9527], [ 0.1212, -0.0844, -0.8639, 0.1214, 0.6723, 1.7152, -0.2713], [-1.3067, 0.8132, 0.4800, -2.3894, 0.9714, 0.4336, -1.1133], [ 0.7836, 0.4225, 0.0109, -1.2518, -1.2735, -0.3628, -0.5556]], device='cuda:0') X_c shape torch.Size([16, 6]) tensor([[-1.6990, 0.4994, 0.5237, 0.5681, -0.4384, 1.2278], [-0.5838, 0.1167, 0.7284, 0.8329, 0.0827, -0.0480], [ 0.2050, 1.3393, -1.5127, -0.9296, -1.1537, 1.8951], [-0.7593, -0.6198, -1.6280, -1.8682, -1.8920, 0.1182], [ 1.7719, -0.4381, -1.1646, -1.5857, -2.2401, 1.6927], [ 1.2371, -0.7035, -0.5878, -0.5940, 1.0570, -0.1962], [-1.1086, -2.3059, 0.3850, -0.6142, 1.1206, 0.3247], [ 0.2323, -0.7529, -0.6670, -0.0404, 1.4693, -0.9065], [-1.6904, -0.4596, -0.3784, 2.1711, 0.8126, 2.7665], [-1.9220, 0.6336, -0.8892, -0.5160, 1.7726, 0.6870], [-1.9859, 0.8231, -1.2517, -0.6015, 0.6485, 0.5330], [-0.7359, -0.4978, -0.1557, 0.4194, -2.9327, -1.5208], [-0.6135, -0.6400, -1.1061, -1.6915, -0.3025, 0.9853], [-1.4310, 1.7253, -0.6249, -0.2569, 1.1259, 1.0723], [ 0.5355, -0.5477, -0.3024, -0.2601, 0.0102, -0.1565], [ 0.0215, -0.2547, -0.2166, -0.5851, 0.4905, 0.5024]], device='cuda:0') X_c shape torch.Size([16, 4]) tensor([[ 0.1297, -0.6206, -1.3438, -0.2378], [ 0.3542, -2.1727, -0.7693, 0.1832], [-2.0531, -0.2708, 0.4542, -0.7819], [-0.6752, -0.1414, 0.8351, -0.5958], [ 0.4637, -0.6149, 1.0920, -0.4849], [ 0.0196, 0.5081, -1.0010, -0.2156], [ 0.3359, 0.7699, -2.2112, -1.8252], [ 0.6903, 1.0862, -1.0192, 1.3107], [-0.2100, -1.2446, -0.4514, 0.6777], [ 0.8924, -1.0165, 0.4438, -0.0431], [ 1.4194, -0.1714, -0.0895, 0.0612], [ 0.1829, 0.6200, 0.8062, -0.8499], [-0.5414, -1.6655, 0.6044, 0.9376], [ 0.2957, 0.7887, 0.4509, 0.1071], [ 0.0645, -0.5269, -1.9421, -1.1922], [ 1.1843, -1.8400, -0.0630, -0.2972]], device='cuda:0') X_c shape torch.Size([16, 7]) tensor([[ 1.4878e+00, -4.0182e-01, 2.2026e+00, -5.4703e-01, -1.5376e+00, -1.0124e+00, -1.7798e-01], [ 3.1039e+00, 1.3340e+00, -6.4154e-01, 2.7985e+00, -1.0064e+00, -1.2514e-01, -8.9587e-01], [ 6.9080e-01, -5.0479e-03, -5.4363e-01, -1.3511e+00, 2.3356e-01, -4.4866e-01, -2.3332e+00], [ 3.6111e-01, -5.2257e-01, -8.4437e-01, 1.4156e+00, -2.1662e-01, -6.6325e-01, -1.4181e-01], [ 7.6379e-01, -1.5159e+00, 2.3365e-01, 2.8765e-01, 1.8937e+00, -1.7489e-01, -5.9196e-01], [ 6.6749e-01, 1.1846e+00, 5.3885e-01, 1.5514e+00, 1.6079e+00, 1.3231e+00, -6.3911e-01], [-5.4540e-02, 4.1947e-01, -1.6453e-01, 2.9159e+00, -8.3123e-01, -1.0161e+00, 7.0982e-01], [-4.6030e-01, 1.4809e+00, 1.4565e+00, -1.4879e+00, -1.6303e-02, 1.0789e+00, 4.5324e-01], [-6.6465e-01, -5.4234e-01, -8.2486e-01, 4.7613e-01, -4.2572e-01, 3.7492e-01, 1.8957e-01], [ 2.1584e-01, -1.6270e+00, -4.7006e-02, -2.4820e+00, -3.4601e-01, -1.2760e-01, -4.9856e-01], [-1.7065e+00, 4.8403e-01, 5.1602e-01, -9.9702e-01, 7.8479e-01, 1.3271e+00, -3.4873e-01], [ 1.2887e+00, -7.6163e-01, -5.9705e-01, -2.8159e-02, -1.8242e-01, 1.0442e+00, -3.6987e-01], [-3.2847e-01, 7.2676e-01, -1.1154e+00, -2.7572e-01, 1.0034e+00, -3.6583e-01, -6.5653e-01], [ 1.1268e+00, -2.1012e-01, 9.4497e-01, 1.4431e-03, 1.4943e-01, -2.2855e+00, 1.1309e+00], [ 8.3730e-01, 8.8567e-01, 2.4612e+00, 2.3620e-01, 3.0178e-01, 3.9953e-01, 7.6512e-01], [ 1.2079e+00, -7.4040e-01, -1.3897e-01, 1.8495e+00, -1.0368e+00, 6.5823e-01, 1.1544e+00]], device='cuda:0') X_c shape torch.Size([16, 9]) tensor([[ 1.1608e+00, 5.2404e-01, -9.3447e-01, 8.4869e-01, -1.6773e-01, 7.4402e-01, 3.3067e-01, 1.3007e+00, -9.4865e-02], [ 5.6259e-01, 5.4271e-01, 1.9070e+00, 9.6501e-01, -2.9819e-01, 1.2864e+00, 1.5435e+00, -6.1125e-01, -5.1652e-01], [-1.0947e+00, 3.3532e-02, 1.6763e+00, -1.9187e-01, 8.5340e-01, -2.9716e-01, -6.9661e-01, -1.4154e+00, 4.9596e-01], [-1.5290e+00, 4.0677e-01, 1.1593e+00, 6.7598e-01, 3.6662e-01, -7.1687e-01, -5.2561e-01, 9.8510e-01, -1.2586e+00], [-6.7048e-01, 3.8925e-01, 3.8757e-01, 3.9510e-01, -2.3837e-01, -3.9225e-01, -2.2230e-01, 3.3597e-01, 7.5240e-01], [-7.9070e-02, 4.8587e-01, -3.1904e-01, -1.8768e+00, -1.2998e-01, -1.8312e+00, 8.6905e-01, 1.4286e+00, 4.3833e-01], [-7.1978e-01, 4.6438e-01, 2.9762e+00, 6.1793e-01, 5.5364e-01, -7.6758e-01, -3.9675e-02, 2.0812e+00, -6.5035e-01], [ 1.4078e+00, 5.3098e-01, 1.7682e+00, 1.2678e+00, 2.0861e-01, 2.8300e+00, -4.1974e-01, -1.3530e+00, -2.2785e+00], [-1.8561e+00, -1.2427e-01, 9.4302e-01, 2.0212e+00, 5.6076e-01, -8.7159e-01, -2.2218e-01, -8.7846e-01, 9.1332e-01], [ 6.2230e-02, -2.7110e+00, -9.0537e-01, -7.0086e-01, -4.0505e-01, -1.0166e+00, 1.7999e+00, 9.1718e-01, 1.1454e+00], [ 1.9940e+00, -1.5937e+00, -1.3100e+00, -1.5429e-01, 1.4527e+00, -2.9646e-01, -6.5418e-01, -1.5161e+00, -5.7280e-01], [-2.1334e-01, -2.4639e+00, 2.1572e+00, 8.8547e-01, -9.6068e-01, -7.8268e-01, -3.3942e-01, -1.7174e-01, -2.7654e-01], [ 1.2832e-01, -5.5500e-01, 1.1688e-01, 7.3154e-01, -1.5545e+00, -6.0215e-01, -1.7040e-01, -4.1701e-02, 5.9348e-01], [ 1.0569e+00, -6.8000e-01, -1.0715e+00, -1.2657e+00, -6.0673e-01, 7.7856e-01, -2.0067e-03, 2.1625e-01, -3.8065e-01], [ 4.5311e-01, 7.3426e-01, -3.1287e+00, -4.6920e-01, -6.8173e-01, -5.4592e-01, -2.2556e+00, -1.1776e+00, -2.0990e-01], [ 6.1662e-01, 1.3936e+00, -7.9541e-01, 1.5003e+00, -3.3568e-01, 5.5757e-01, -6.6173e-01, 1.5195e+00, -1.0046e+00]], device='cuda:0') X_c shape torch.Size([16, 10]) tensor([[ 1.2644, 0.9153, -1.4336, 0.7042, 0.6132, 0.2158, 0.2887, 0.5955, -1.4903, 0.4799], [ 0.4747, -0.5038, 0.2039, 0.4999, 1.4249, -0.6385, -0.1321, 0.0411, -0.0987, 0.5058], [-0.2237, 1.0426, 1.3616, 2.1435, -0.2582, -0.1537, 0.6851, -0.1747, 2.4723, 0.8146], [ 1.1464, 0.1538, 0.2178, -2.1835, -0.5254, 0.5341, 0.0711, 1.0723, 1.5233, 0.8579], [-0.2565, -0.6761, 1.3049, -1.2890, -0.9296, -0.3167, -1.4590, -0.3813, 0.6864, 0.9067], [-0.0045, -0.6211, -1.3761, -1.8368, -0.7799, -1.4577, 1.2145, -0.4969, 0.8128, -0.0879], [-0.2819, 1.1318, 0.5851, 1.5346, -0.5031, 1.0811, 1.4598, -0.2922, -1.0698, -0.6239], [ 1.5007, 0.4776, 0.5756, -0.1036, 0.3009, -1.4226, 0.1117, 0.0685, 1.4614, -0.6837], [ 0.2659, -0.1216, 0.4524, -0.8880, -0.0903, 0.0514, -0.3307, 1.3727, 0.4569, 0.1330], [-1.3085, 0.3482, 0.8168, 1.4885, -1.0427, 0.9636, -0.2865, -0.1337, 0.4671, -0.9516], [ 0.3257, 0.4804, 0.8782, -2.1633, 2.6182, -1.2561, 1.5588, 0.4776, -1.4705, -1.6446], [ 0.0566, 0.8201, -0.8358, 1.4604, -0.6825, 0.1172, -0.0529, -1.3245, 1.3474, 0.5735], [ 0.8320, -0.2725, -0.8190, 0.2139, -0.6147, 1.8202, 0.8190, -0.1298, 0.7880, 0.4006], [-0.2897, 0.3501, -0.0573, -1.8452, -1.0914, 0.7466, 1.7042, 0.8599, 0.2791, 0.3213], [-0.8833, 1.0887, -1.6795, 0.2387, 1.3289, 1.0220, -0.4792, -0.3981, -0.2759, -0.3885], [ 0.3195, 1.4533, -0.5149, -0.4989, 0.0384, 0.1927, 1.1188, -1.7946, 0.1168, 0.0319]], device='cuda:0') X_c shape torch.Size([16, 9]) tensor([[-0.1261, 1.1235, -1.0696, -1.1387, 0.2743, -0.5753, 0.5699, 0.5149, -1.5009], [-0.0163, 0.8872, 0.9338, 0.0028, -0.7828, -1.7374, 0.0482, 0.6348, -0.6524], [-2.3185, -1.5195, 1.6479, 0.4001, 1.1418, 1.3328, 0.3003, 1.3518, 0.4094], [-1.4756, -1.3459, -0.3559, 2.3344, 0.4912, -0.2207, -0.5194, 1.3649, -0.1092], [ 2.0059, -0.7026, -1.1630, 0.3063, -0.3568, -0.4022, -0.1154, -2.0210, -0.2269], [ 0.1390, 0.1577, -0.4332, 1.3558, 1.3769, 0.7643, 0.7959, 0.1422, 0.6731], [-1.1134, -1.7257, -0.6762, 0.2199, -0.7438, -1.0302, -1.3823, 1.9751, -0.2361], [-2.2063, 0.1420, -2.1714, 0.4063, 0.1646, 0.2098, 0.7066, -0.1464, -0.3691], [-0.2976, -2.1344, 0.8700, 0.5639, 1.2127, -1.7652, -1.5514, 1.2440, -0.0477], [ 1.1270, -2.5411, -2.1104, 0.6838, 0.5407, 0.3054, 0.0783, -0.6014, -1.3387], [-0.9373, 1.9748, -0.3357, 1.2090, -1.0583, -0.2297, -0.8720, 0.2073, 0.0692], [ 0.4263, -0.9635, 0.3399, -0.1582, 1.5743, -1.2042, -1.2970, 1.5115, -0.8326], [-1.3463, -1.0039, -1.3930, 1.9752, 0.1632, -1.1564, -1.1395, 1.0007, -0.0174], [-0.7713, 1.0199, -0.1383, 0.6839, -1.4275, -1.4894, 0.5242, 1.1992, -1.1920], [ 1.3805, 0.6728, -0.1094, 1.4066, 0.8237, 0.3745, -1.4426, 2.3637, -1.2894], [ 0.7094, -0.9870, -0.8011, -1.8140, -0.1459, -0.4534, -1.2952, -0.4278, 0.9144]], device='cuda:0') X_c shape torch.Size([16, 5]) tensor([[ 2.3843e-01, -1.6153e-01, 5.9662e-01, -1.7112e+00, -2.1326e+00], [ 1.2859e+00, 9.5606e-01, -9.1817e-01, -1.2881e+00, 1.6006e+00], [-1.7942e-02, 6.3140e-01, 5.2398e-01, -2.7630e-01, 1.5381e+00], [-5.1545e-01, 6.3432e-01, -6.3058e-01, 1.4679e-02, 1.4887e+00], [-1.0812e+00, -5.2346e-02, 2.2321e-01, 8.6249e-02, 7.4195e-01], [ 3.1881e-01, 5.8811e-01, 1.0562e+00, 1.3342e-01, 2.9426e-01], [-1.1309e+00, 2.4535e+00, -5.7642e-01, 2.8675e-01, -1.9409e-01], [-1.0309e+00, -5.4439e-01, -7.7303e-01, 3.2667e-02, 1.8129e+00], [-6.7964e-04, -8.9906e-01, 2.0343e+00, 3.6000e-01, -2.7563e-01], [-3.0619e-01, 1.5841e+00, 1.5015e+00, -8.4094e-01, 3.2306e-01], [-2.0362e-01, -5.3436e-01, 9.7085e-01, 9.9474e-01, -3.6796e-01], [-8.1160e-01, 2.9902e+00, 1.3559e-01, -5.7836e-01, -7.6623e-01], [ 1.2366e+00, 8.4008e-01, 3.7793e-01, 4.3765e-01, -3.7793e-01], [-9.6175e-01, -2.6040e-01, 1.5787e+00, 1.1064e+00, 1.3660e+00], [ 1.0333e+00, 5.8460e-02, -1.1889e+00, 5.0320e-02, -1.0626e+00], [-6.7612e-01, -9.1202e-01, -2.9174e+00, -4.0951e-01, 1.3557e+00]], device='cuda:0') __ test_random_large ___

def test_random_large():
    it = 5
    for i in range(it):
        Nc = random.randint(3, 6)
        X = torch.randn(AL_N, Nc)
        X_c = X.to(gpu)

        AX_d = AL_d @ X
      assert(torch.allclose(AX_d, AL@X))

E AssertionError: assert False E + where False = <built-in method allclose of type object at 0x7facd8b4f680>(tensor([[-3.1022, -0.1757, 0.2265, 2.6405, 2.6282],\n [ 6.8254, 1.6969, -2.2275, -5.1129, -1.2788],\n ...4.5011],\n [-0.8021, -0.4414, 1.2198, -0.4238, -4.7221],\n [ 3.6725, -0.1307, -2.1412, -0.1562, 3.5673]]), (<2048x2048 sparse matrix tensor of type 'torch.float32'\n with 6142 stored elements in Compressed Sparse Row format> @ tensor([[-0.4234, 0.3763, -0.2115, -0.0504, 1.1204],\n [ 2.2554, 0.9283, -0.6494, -2.7413, -0.3875],\n ...1.1468],\n [ 1.0123, -0.3853, 0.2250, -0.2176, -1.1945],\n [ 2.3424, -0.2580, -0.9581, -0.1869, 1.1864]]))) E + where <built-in method allclose of type object at 0x7facd8b4f680> = torch.allclose

numml/tests/test_spdmm.py:55: AssertionError =========================================================== short test summary info ============================================================ FAILED numml/tests/test_spdmm.py::test_random_small - AssertionError: assert False FAILED numml/tests/test_spdmm.py::test_random_large - AssertionError: assert False ======================================================== 2 failed, 31 passed in 12.38s ========================================================= root@51e1485d94b7:/TorchVision/numml#