Guropi solver fail without any clear reason

I'm implementing a guropi solver for a quadratic binary integer programming problem, and it just fail without any reason. My problem involve finding the mind of xHx, with x is a binary vector of size 378, H is a matrix of size 378x378. The matrix H is a matrix with positive diagonal, every value off-diagonal = 0. Below is my minimum code and the link to google drive contains matrices of constraint. Please help me find out what is the reason gurobi fail. 

Link: https://drive.google.com/drive/folders/19XhlpmuZ4RpTnkK25YbkyUZZt8F5TGoo?usp=drive_link

import numpy as np
import torch
import cvxpy as cp
file_name = "all_constraint_optimization.pt"
all_constraint = torch.load(file_name)

diagonal_line = torch.tensor([1.2525e+03, 9.7295e+02, 4.2490e+02, 1.1673e+02, 6.3829e+01, 8.1240e+00,
        9.8926e-01, 7.8628e+02, 2.9606e+02, 5.4056e+01, 1.6693e+01, 3.1802e+00,
        1.5863e+00, 4.0531e-01, 4.2466e+02, 1.2126e+02, 3.9779e+01, 1.5050e+01,
        1.2652e+00, 1.0762e+00, 4.8066e-01, 4.2026e+02, 9.4895e+01, 1.4343e+01,
        5.7718e+00, 1.1434e+00, 2.3202e-01, 1.6105e-01, 2.0308e+03, 3.1875e+02,
        6.3297e+01, 1.5572e+01, 3.5865e+00, 1.0065e+00, 2.5226e-01, 3.5530e+01,
        1.4042e+01, 2.4167e+00, 6.4756e-01, 2.3935e-01, 4.9158e-02, 8.6918e-02,
        7.4883e+02, 8.3016e+01, 1.5530e+02, 8.8465e+00, 1.4316e+01, 7.5778e-01,
        2.6828e-01, 8.8426e+01, 1.8615e+01, 7.1063e+00, 2.5850e+00, 2.5981e-01,
        1.7596e-01, 4.7290e-02, 1.3084e+02, 1.2487e+01, 4.1722e+00, 8.2224e-01,
        4.5021e-01, 1.8046e-02, 1.9475e-02, 2.2598e+02, 2.9899e+01, 9.2085e+00,
        2.0253e+00, 6.2246e-01, 1.8812e-01, 4.1639e-02, 2.7707e+02, 3.9004e+01,
        9.1397e+00, 1.8779e+00, 5.5470e-01, 1.1793e-01, 0.0000e+00, 5.4764e+02,
        1.2506e+02, 1.5378e+01, 5.2141e+00, 1.7108e+00, 4.5536e-01, 1.1366e-01,
        2.4324e+02, 1.7591e+01, 5.2223e+00, 1.2038e+00, 7.0344e-01, 6.3978e-02,
        5.6973e-02, 2.6071e+02, 6.8975e+01, 1.5866e+01, 5.5680e+00, 1.4551e+00,
        4.3885e-01, 2.6352e-01, 3.0838e+02, 7.7244e+01, 1.9178e+01, 4.6728e+00,
        1.2892e+00, 6.2781e-01, 1.2679e-01, 4.3042e+01, 1.1116e+01, 3.5652e+00,
        3.7917e-01, 9.7780e-02, 6.6145e-02, 1.4888e-02, 2.0841e+02, 4.6140e+01,
        1.3249e+01, 2.6195e+00, 7.4985e-01, 2.3551e-01, 8.2446e-02, 1.2883e+02,
        2.4882e+01, 7.2592e+00, 1.5471e+00, 4.2695e-01, 1.2665e-01, 4.3976e-02,
        4.9041e+01, 1.2128e+01, 2.1507e+00, 6.2128e-01, 1.7721e-01, 4.2942e-02,
        1.5930e-02, 2.2093e+02, 1.3655e+01, 3.6789e+00, 9.2673e-01, 2.7743e-01,
        8.0983e-02, 4.1457e-02, 5.2817e+01, 1.2991e+01, 5.0801e+00, 8.8538e-01,
        9.4146e-01, 0.0000e+00, 4.0798e-02, 5.5224e+01, 8.7991e+00, 2.9960e+00,
        7.4703e-01, 6.0489e-02, 1.0952e-01, 0.0000e+00, 1.2666e+02, 1.1303e+01,
        2.7783e+00, 6.4297e-01, 2.5927e-01, 2.1052e-03, 0.0000e+00, 4.9272e+01,
        1.1429e+01, 2.6665e+00, 3.5228e-01, 8.9500e-02, 1.1621e-01, 2.3529e-02,
        6.3330e+02, 9.6525e+01, 2.9642e+01, 5.6173e+00, 2.0470e+00, 6.3282e-01,
        1.1075e-01, 3.1552e+02, 2.6665e+01, 6.9573e+00, 1.6338e+00, 5.9670e-01,
        2.0818e-01, 3.1426e-02, 3.2761e+02, 5.1484e+01, 1.5981e+01, 4.2796e+00,
        1.0948e+00, 4.0624e-01, 1.7532e-01, 1.0524e+02, 2.9626e+01, 8.9242e+00,
        2.1962e+00, 5.1428e-01, 1.2574e-01, 2.1217e-02, 7.5012e+01, 1.7928e+01,
        4.1177e+00, 1.4196e+00, 3.0308e-01, 2.2731e-01, 2.4876e-03, 1.7973e+02,
        2.6119e+01, 6.9838e+00, 1.9823e+00, 6.6638e-01, 2.0605e-01, 8.5401e-02,
        1.0472e+02, 2.7505e+01, 8.9784e+00, 2.0148e+00, 7.8067e-01, 1.8138e-01,
        5.2795e-02, 1.1375e+02, 1.9636e+01, 6.0499e+00, 2.0797e+00, 4.5743e-01,
        1.8445e-01, 2.4181e-02, 2.3472e+02, 1.5098e+01, 3.8942e+00, 1.4666e+00,
        5.0298e-01, 9.8779e-02, 3.2563e-02, 6.9781e+01, 1.7444e+01, 5.7184e+00,
        1.6514e+00, 3.9180e-01, 1.1670e-01, 2.7425e-02, 9.4496e+01, 1.2985e+01,
        3.4539e+00, 1.6764e+00, 3.5944e-01, 7.3787e-02, 6.0665e-02, 2.3470e+02,
        1.5281e+01, 3.2893e+00, 1.4370e+00, 3.6739e-01, 1.2644e-01, 4.9863e-02,
        5.3887e+01, 1.2165e+01, 3.9335e+00, 1.3971e+00, 3.2666e-01, 1.2689e-01,
        6.4329e-02, 7.7040e+01, 1.5940e+01, 4.8654e+00, 1.9191e+00, 8.1373e-01,
        2.6671e-01, 1.0220e-01, 1.0536e+02, 1.5339e+01, 3.9978e+00, 6.6039e-01,
        4.9615e-01, 5.9513e-02, 0.0000e+00, 6.2157e+01, 1.3058e+01, 3.6886e+00,
        7.3468e-01, 2.4646e-01, 9.3328e-02, 6.2606e-02, 1.9602e+02, 1.9988e+01,
        7.5848e+00, 1.6497e+00, 6.4702e-01, 1.3914e-01, 1.1279e-01, 1.6915e+02,
        1.6311e+01, 4.3886e+00, 1.6788e+00, 2.3090e-01, 1.5589e-01, 6.5742e-02,
        8.0428e+01, 1.5120e+01, 3.8383e+00, 1.3950e+00, 6.7361e-01, 8.7440e-02,
        6.2232e-02, 3.4076e+02, 7.4679e+01, 2.6462e+01, 9.7130e+00, 2.3537e+00,
        7.5897e-01, 1.7328e-01, 4.5610e+02, 3.2187e+01, 7.4400e+00, 4.5974e+00,
        1.4114e-01, 3.7285e-01, 2.4908e-01, 5.0092e+02, 6.2055e+01, 1.6063e+01,
        6.0775e+00, 2.3364e+00, 4.5719e-01, 0.0000e+00, 2.1216e+02, 2.3072e+01,
        6.1835e+00, 1.6485e+00, 5.8806e-01, 1.6575e-01, 5.4512e-02, 7.1253e+02,
        4.0884e+01, 1.5118e+01, 4.1171e+00, 1.2515e+00, 3.8431e-01, 9.7005e-02,
        6.9450e+02, 5.7954e+01, 1.4690e+01, 2.4038e+00, 1.7780e+00, 0.0000e+00,
        3.0989e-01, 1.2033e+03, 3.1471e+02, 1.5894e+02, 4.5085e+01, 2.7082e+00,
        4.0673e+00, 4.7928e-01, 4.1922e+02, 1.6758e+02, 6.0466e+01, 1.5106e+01,
        1.3847e+01, 1.0273e+00, 1.0029e+00, 4.4204e+03, 4.2799e+02, 1.9486e+01,
        5.1759e+00, 1.0851e+00, 3.5613e-01, 2.0448e-01, 6.5255e+02, 1.8346e+02,
        3.2312e+01, 8.9642e+00, 1.9113e+00, 5.5657e-01, 1.3129e-01, 8.8452e+03,
        2.0199e+02, 2.6627e+01, 7.7998e+00, 2.0748e+00, 4.9833e-01, 1.2194e-01],
       dtype=torch.float64)
G_final = torch.diag(diagonal_line)
x = cp.Variable(G_final.size()[0],integer=True)
param_size_matrix = all_constraint["param_size_matrix"]
max_cost = all_constraint["max_cost"]
condition_layer_matrix = all_constraint["condition_layer_matrix"]
param_size_matrix = all_constraint["param_size_matrix"]
layer_condition_output = all_constraint["layer_condition_output"]
constraints = [param_size_matrix @ x <= max_cost,
                   condition_layer_matrix @ x == layer_condition_output,
                   x >= 0,
                   x <= 1]

prob = cp.Problem(cp.Minimize((1 / 2) * cp.quad_form(x, cp.psd_wrap(G_final))),
                      constraints
                      )
prob.solve(solver=cp.GUROBI,verbose=True)