225 lines
8.3 KiB
Python
225 lines
8.3 KiB
Python
import torch
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from torch.nn import functional as F
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import numpy as np
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from utils import PlaceHolder
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def sum_except_batch(x):
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return x.reshape(x.size(0), -1).sum(dim=-1)
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def assert_correctly_masked(variable, node_mask):
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assert (variable * (1 - node_mask.long())).abs().max().item() < 1e-4, \
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'Variables not masked properly.'
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def cosine_beta_schedule_discrete(timesteps, s=0.008):
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""" Cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ. """
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steps = timesteps + 2
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x = np.linspace(0, steps, steps)
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alphas_cumprod = np.cos(0.5 * np.pi * ((x / steps) + s) / (1 + s)) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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alphas = (alphas_cumprod[1:] / alphas_cumprod[:-1])
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betas = 1 - alphas
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return betas.squeeze()
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def custom_beta_schedule_discrete(timesteps, average_num_nodes=30, s=0.008):
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""" Cosine schedule as proposed in https://openreview.net/forum?id=-NEXDKk8gZ. """
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steps = timesteps + 2
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x = np.linspace(0, steps, steps)
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alphas_cumprod = np.cos(0.5 * np.pi * ((x / steps) + s) / (1 + s)) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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alphas = (alphas_cumprod[1:] / alphas_cumprod[:-1])
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betas = 1 - alphas
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assert timesteps >= 100
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p = 4 / 5 # 1 - 1 / num_edge_classes
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num_edges = average_num_nodes * (average_num_nodes - 1) / 2
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# First 100 steps: only a few updates per graph
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updates_per_graph = 1.2
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beta_first = updates_per_graph / (p * num_edges)
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betas[betas < beta_first] = beta_first
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return np.array(betas)
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def check_mask_correct(variables, node_mask):
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for i, variable in enumerate(variables):
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if len(variable) > 0:
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assert_correctly_masked(variable, node_mask)
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def check_tensor_same_size(*args):
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for i, arg in enumerate(args):
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if i == 0:
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continue
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assert args[0].size() == arg.size()
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def reverse_tensor(x):
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return x[torch.arange(x.size(0) - 1, -1, -1)]
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def sample_discrete_features(probX, probE, node_mask, step=None, add_nose=True):
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''' Sample features from multinomial distribution with given probabilities (probX, probE, proby)
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:param probX: bs, n, dx_out node features
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:param probE: bs, n, n, de_out edge features
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:param proby: bs, dy_out global features.
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'''
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bs, n, _ = probX.shape
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# Noise X
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# The masked rows should define probability distributions as well
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probX[~node_mask] = 1 / probX.shape[-1]
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# Flatten the probability tensor to sample with multinomial
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probX = probX.reshape(bs * n, -1) # (bs * n, dx_out)
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# Sample X
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probX = probX + 1e-12
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probX = probX / probX.sum(dim=-1, keepdim=True)
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X_t = probX.multinomial(1) # (bs * n, 1)
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X_t = X_t.reshape(bs, n) # (bs, n)
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# Noise E
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# The masked rows should define probability distributions as well
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inverse_edge_mask = ~(node_mask.unsqueeze(1) * node_mask.unsqueeze(2))
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diag_mask = torch.eye(n).unsqueeze(0).expand(bs, -1, -1)
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probE[inverse_edge_mask] = 1 / probE.shape[-1]
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probE[diag_mask.bool()] = 1 / probE.shape[-1]
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probE = probE.reshape(bs * n * n, -1) # (bs * n * n, de_out)
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probE = probE + 1e-12
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probE = probE / probE.sum(dim=-1, keepdim=True)
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# Sample E
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E_t = probE.multinomial(1).reshape(bs, n, n) # (bs, n, n)
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E_t = torch.triu(E_t, diagonal=1)
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E_t = (E_t + torch.transpose(E_t, 1, 2))
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return PlaceHolder(X=X_t, E=E_t, y=torch.zeros(bs, 0).type_as(X_t))
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def compute_batched_over0_posterior_distribution(X_t, Qt, Qsb, Qtb):
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""" M: X or E
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Compute xt @ Qt.T * x0 @ Qsb / x0 @ Qtb @ xt.T for each possible value of x0
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X_t: bs, n, dt or bs, n, n, dt
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Qt: bs, d_t-1, dt
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Qsb: bs, d0, d_t-1
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Qtb: bs, d0, dt.
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"""
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X_t = X_t.float()
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Qt_T = Qt.transpose(-1, -2).float() # bs, N, dt
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assert Qt.dim() == 3
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left_term = X_t @ Qt_T
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left_term = left_term.unsqueeze(dim=2) # bs, N, 1, d_t-1
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right_term = Qsb.unsqueeze(1)
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numerator = left_term * right_term # bs, N, d0, d_t-1
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denominator = Qtb @ X_t.transpose(-1, -2) # bs, d0, N
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denominator = denominator.transpose(-1, -2) # bs, N, d0
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denominator = denominator.unsqueeze(-1) # bs, N, d0, 1
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denominator[denominator == 0] = 1.
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return numerator / denominator
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def mask_distributions(true_X, true_E, pred_X, pred_E, node_mask):
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# Add a small value everywhere to avoid nans
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pred_X = pred_X.clamp_min(1e-18)
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pred_X = pred_X / torch.sum(pred_X, dim=-1, keepdim=True)
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pred_E = pred_E.clamp_min(1e-18)
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pred_E = pred_E / torch.sum(pred_E, dim=-1, keepdim=True)
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# Set masked rows to arbitrary distributions, so it doesn't contribute to loss
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row_X = torch.ones(true_X.size(-1), dtype=true_X.dtype, device=true_X.device)
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row_E = torch.zeros(true_E.size(-1), dtype=true_E.dtype, device=true_E.device).clamp_min(1e-18)
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row_E[0] = 1.
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diag_mask = ~torch.eye(node_mask.size(1), device=node_mask.device, dtype=torch.bool).unsqueeze(0)
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true_X[~node_mask] = row_X
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true_E[~(node_mask.unsqueeze(1) * node_mask.unsqueeze(2) * diag_mask), :] = row_E
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pred_X[~node_mask] = row_X
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pred_E[~(node_mask.unsqueeze(1) * node_mask.unsqueeze(2) * diag_mask), :] = row_E
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return true_X, true_E, pred_X, pred_E
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def posterior_distributions(X, X_t, Qt, Qsb, Qtb, X_dim):
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bs, n, d = X.shape
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X = X.float()
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Qt_X_T = torch.transpose(Qt.X, -2, -1).float() # (bs, d, d)
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left_term = X_t @ Qt_X_T # (bs, N, d)
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right_term = X @ Qsb.X # (bs, N, d)
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numerator = left_term * right_term # (bs, N, d)
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denominator = X @ Qtb.X # (bs, N, d) @ (bs, d, d) = (bs, N, d)
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denominator = denominator * X_t
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num_X = numerator[:, :, :X_dim]
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num_E = numerator[:, :, X_dim:].reshape(bs, n*n, -1)
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deno_X = denominator[:, :, :X_dim]
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deno_E = denominator[:, :, X_dim:].reshape(bs, n*n, -1)
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# denominator = (denominator * X_t).sum(dim=-1) # (bs, N, d) * (bs, N, d) + sum = (bs, N)
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denominator = denominator.unsqueeze(-1) # (bs, N, 1)
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deno_X = deno_X.sum(dim=-1).unsqueeze(-1)
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deno_E = deno_E.sum(dim=-1).unsqueeze(-1)
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deno_X[deno_X == 0.] = 1
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deno_E[deno_E == 0.] = 1
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prob_X = num_X / deno_X
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prob_E = num_E / deno_E
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prob_E = prob_E / prob_E.sum(dim=-1, keepdim=True)
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prob_X = prob_X / prob_X.sum(dim=-1, keepdim=True)
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return PlaceHolder(X=prob_X, E=prob_E, y=None)
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def sample_discrete_feature_noise(limit_dist, node_mask):
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""" Sample from the limit distribution of the diffusion process"""
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bs, n_max = node_mask.shape
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x_limit = limit_dist.X[None, None, :].expand(bs, n_max, -1)
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U_X = x_limit.flatten(end_dim=-2).multinomial(1).reshape(bs, n_max)
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U_X = F.one_hot(U_X.long(), num_classes=x_limit.shape[-1]).float()
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e_limit = limit_dist.E[None, None, None, :].expand(bs, n_max, n_max, -1)
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U_E = e_limit.flatten(end_dim=-2).multinomial(1).reshape(bs, n_max, n_max)
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U_E = F.one_hot(U_E.long(), num_classes=e_limit.shape[-1]).float()
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U_X = U_X.to(node_mask.device)
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U_E = U_E.to(node_mask.device)
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# Get upper triangular part of edge noise, without main diagonal
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upper_triangular_mask = torch.zeros_like(U_E)
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indices = torch.triu_indices(row=U_E.size(1), col=U_E.size(2), offset=1)
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upper_triangular_mask[:, indices[0], indices[1], :] = 1
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U_E = U_E * upper_triangular_mask
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U_E = (U_E + torch.transpose(U_E, 1, 2))
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assert (U_E == torch.transpose(U_E, 1, 2)).all()
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return PlaceHolder(X=U_X, E=U_E, y=None).mask(node_mask)
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def index_QE(X, q_e, n_bond=5):
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bs, n, n_atom = X.shape
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node_indices = X.argmax(-1) # (bs, n)
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exp_ind1 = node_indices[ :, :, None, None, None].expand(bs, n, n_atom, n_bond, n_bond)
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exp_ind2 = node_indices[ :, :, None, None, None].expand(bs, n, n, n_bond, n_bond)
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q_e = torch.gather(q_e, 1, exp_ind1)
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q_e = torch.gather(q_e, 2, exp_ind2) # (bs, n, n, n_bond, n_bond)
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node_mask = X.sum(-1) != 0
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no_edge = (~node_mask)[:, :, None] & (~node_mask)[:, None, :]
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q_e[no_edge] = torch.tensor([1, 0, 0, 0, 0]).type_as(q_e)
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return q_e
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