Update graph_decoder/diffusion_model.py

graph_decoder/diffusion_model.py

@@ -19,347 +19,348 @@ class GraphDiT(nn.Module):
         model_dtype,
     ):
         super().__init__()
+        pass
         
-        dm_cfg, data_info = utils.load_config(model_config_path, data_info_path)
-
-        input_dims = data_info.input_dims
-        output_dims = data_info.output_dims
-        nodes_dist = data_info.nodes_dist
-        active_index = data_info.active_index
-
-        self.model_config = dm_cfg
-        self.data_info = data_info
-        self.T = dm_cfg.diffusion_steps
-        self.Xdim = input_dims["X"]
-        self.Edim = input_dims["E"]
-        self.ydim = input_dims["y"]
-        self.Xdim_output = output_dims["X"]
-        self.Edim_output = output_dims["E"]
-        self.ydim_output = output_dims["y"]
-        self.node_dist = nodes_dist
-        self.active_index = active_index
-        self.max_n_nodes = data_info.max_n_nodes
-        self.atom_decoder = data_info.atom_decoder
-        self.hidden_size = dm_cfg.hidden_size
-        self.mol_visualizer = MolecularVisualization(self.atom_decoder)
-
-        self.denoiser = Transformer(
-            max_n_nodes=self.max_n_nodes,
-            hidden_size=dm_cfg.hidden_size,
-            depth=dm_cfg.depth,
-            num_heads=dm_cfg.num_heads,
-            mlp_ratio=dm_cfg.mlp_ratio,
-            drop_condition=dm_cfg.drop_condition,
-            Xdim=self.Xdim,
-            Edim=self.Edim,
-            ydim=self.ydim,
-        )
-
-        self.model_dtype = model_dtype
-        self.noise_schedule = utils.PredefinedNoiseScheduleDiscrete(
-            dm_cfg.diffusion_noise_schedule, timesteps=dm_cfg.diffusion_steps
-        )
-        x_marginals = data_info.node_types.to(self.model_dtype) / torch.sum(
-            data_info.node_types.to(self.model_dtype)
-        )
-        e_marginals = data_info.edge_types.to(self.model_dtype) / torch.sum(
-            data_info.edge_types.to(self.model_dtype)
-        )
-        x_marginals = x_marginals / x_marginals.sum()
-        e_marginals = e_marginals / e_marginals.sum()
-
-        xe_conditions = data_info.transition_E.to(self.model_dtype)
-        xe_conditions = xe_conditions[self.active_index][:, self.active_index]
-
-        xe_conditions = xe_conditions.sum(dim=1)
-        ex_conditions = xe_conditions.t()
-        xe_conditions = xe_conditions / xe_conditions.sum(dim=-1, keepdim=True)
-        ex_conditions = ex_conditions / ex_conditions.sum(dim=-1, keepdim=True)
-
-        self.transition_model = utils.MarginalTransition(
-            x_marginals=x_marginals,
-            e_marginals=e_marginals,
-            xe_conditions=xe_conditions,
-            ex_conditions=ex_conditions,
-            y_classes=self.ydim_output,
-            n_nodes=self.max_n_nodes,
-        )
-        self.limit_dist = utils.PlaceHolder(X=x_marginals, E=e_marginals, y=None)
-
-    def init_model(self, model_dir):
-        model_file = os.path.join(model_dir, 'model.pt')
-        if os.path.exists(model_file):
-            self.denoiser.load_state_dict(torch.load(model_file, map_location='cpu', weights_only=True))
-        else:
-            raise FileNotFoundError(f"Model file not found: {model_file}")
-
-    def disable_grads(self):
-        self.denoiser.disable_grads()
+    #     dm_cfg, data_info = utils.load_config(model_config_path, data_info_path)
+
+    #     input_dims = data_info.input_dims
+    #     output_dims = data_info.output_dims
+    #     nodes_dist = data_info.nodes_dist
+    #     active_index = data_info.active_index
+
+    #     self.model_config = dm_cfg
+    #     self.data_info = data_info
+    #     self.T = dm_cfg.diffusion_steps
+    #     self.Xdim = input_dims["X"]
+    #     self.Edim = input_dims["E"]
+    #     self.ydim = input_dims["y"]
+    #     self.Xdim_output = output_dims["X"]
+    #     self.Edim_output = output_dims["E"]
+    #     self.ydim_output = output_dims["y"]
+    #     self.node_dist = nodes_dist
+    #     self.active_index = active_index
+    #     self.max_n_nodes = data_info.max_n_nodes
+    #     self.atom_decoder = data_info.atom_decoder
+    #     self.hidden_size = dm_cfg.hidden_size
+    #     self.mol_visualizer = MolecularVisualization(self.atom_decoder)
+
+    #     self.denoiser = Transformer(
+    #         max_n_nodes=self.max_n_nodes,
+    #         hidden_size=dm_cfg.hidden_size,
+    #         depth=dm_cfg.depth,
+    #         num_heads=dm_cfg.num_heads,
+    #         mlp_ratio=dm_cfg.mlp_ratio,
+    #         drop_condition=dm_cfg.drop_condition,
+    #         Xdim=self.Xdim,
+    #         Edim=self.Edim,
+    #         ydim=self.ydim,
+    #     )
+
+    #     self.model_dtype = model_dtype
+    #     self.noise_schedule = utils.PredefinedNoiseScheduleDiscrete(
+    #         dm_cfg.diffusion_noise_schedule, timesteps=dm_cfg.diffusion_steps
+    #     )
+    #     x_marginals = data_info.node_types.to(self.model_dtype) / torch.sum(
+    #         data_info.node_types.to(self.model_dtype)
+    #     )
+    #     e_marginals = data_info.edge_types.to(self.model_dtype) / torch.sum(
+    #         data_info.edge_types.to(self.model_dtype)
+    #     )
+    #     x_marginals = x_marginals / x_marginals.sum()
+    #     e_marginals = e_marginals / e_marginals.sum()
+
+    #     xe_conditions = data_info.transition_E.to(self.model_dtype)
+    #     xe_conditions = xe_conditions[self.active_index][:, self.active_index]
+
+    #     xe_conditions = xe_conditions.sum(dim=1)
+    #     ex_conditions = xe_conditions.t()
+    #     xe_conditions = xe_conditions / xe_conditions.sum(dim=-1, keepdim=True)
+    #     ex_conditions = ex_conditions / ex_conditions.sum(dim=-1, keepdim=True)
+
+    #     self.transition_model = utils.MarginalTransition(
+    #         x_marginals=x_marginals,
+    #         e_marginals=e_marginals,
+    #         xe_conditions=xe_conditions,
+    #         ex_conditions=ex_conditions,
+    #         y_classes=self.ydim_output,
+    #         n_nodes=self.max_n_nodes,
+    #     )
+    #     self.limit_dist = utils.PlaceHolder(X=x_marginals, E=e_marginals, y=None)
+
+    # def init_model(self, model_dir):
+    #     model_file = os.path.join(model_dir, 'model.pt')
+    #     if os.path.exists(model_file):
+    #         self.denoiser.load_state_dict(torch.load(model_file, map_location='cpu', weights_only=True))
+    #     else:
+    #         raise FileNotFoundError(f"Model file not found: {model_file}")
+
+    # def disable_grads(self):
+    #     self.denoiser.disable_grads()
     
-    def forward(
-        self, x, edge_index, edge_attr, graph_batch, properties, no_label_index
-    ):
-        raise ValueError('Not Implement')
-
-    def _forward(self, noisy_data, unconditioned=False):
-        noisy_x, noisy_e, properties = (
-            noisy_data["X_t"].to(self.model_dtype),
-            noisy_data["E_t"].to(self.model_dtype),
-            noisy_data["y_t"].to(self.model_dtype).clone(),
-        )
-        node_mask, timestep = (
-            noisy_data["node_mask"],
-            noisy_data["t"],
-        )
+    # def forward(
+    #     self, x, edge_index, edge_attr, graph_batch, properties, no_label_index
+    # ):
+    #     raise ValueError('Not Implement')
+
+    # def _forward(self, noisy_data, unconditioned=False):
+    #     noisy_x, noisy_e, properties = (
+    #         noisy_data["X_t"].to(self.model_dtype),
+    #         noisy_data["E_t"].to(self.model_dtype),
+    #         noisy_data["y_t"].to(self.model_dtype).clone(),
+    #     )
+    #     node_mask, timestep = (
+    #         noisy_data["node_mask"],
+    #         noisy_data["t"],
+    #     )
         
-        pred = self.denoiser(
-            noisy_x,
-            noisy_e,
-            node_mask,
-            properties,
-            timestep,
-            unconditioned=unconditioned,
-        )
-        return pred
-
-    def apply_noise(self, X, E, y, node_mask):
-        """Sample noise and apply it to the data."""
-
-        # Sample a timestep t.
-        # When evaluating, the loss for t=0 is computed separately
-        lowest_t = 0 if self.training else 1
-        t_int = torch.randint(
-            lowest_t, self.T + 1, size=(X.size(0), 1), device=X.device
-        ).to(
-            self.model_dtype
-        )  # (bs, 1)
-        s_int = t_int - 1
-
-        t_float = t_int / self.T
-        s_float = s_int / self.T
-
-        # beta_t and alpha_s_bar are used for denoising/loss computation
-        beta_t = self.noise_schedule(t_normalized=t_float)  # (bs, 1)
-        alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s_float)  # (bs, 1)
-        alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t_float)  # (bs, 1)
-
-        Qtb = self.transition_model.get_Qt_bar(
-            alpha_t_bar, X.device
-        )  # (bs, dx_in, dx_out), (bs, de_in, de_out)
-
-        bs, n, d = X.shape
-        X_all = torch.cat([X, E.reshape(bs, n, -1)], dim=-1)
-        prob_all = X_all @ Qtb.X
-        probX = prob_all[:, :, : self.Xdim_output]
-        probE = prob_all[:, :, self.Xdim_output :].reshape(bs, n, n, -1)
-
-        sampled_t = utils.sample_discrete_features(
-            probX=probX, probE=probE, node_mask=node_mask
-        )
-
-        X_t = F.one_hot(sampled_t.X, num_classes=self.Xdim_output)
-        E_t = F.one_hot(sampled_t.E, num_classes=self.Edim_output)
-        assert (X.shape == X_t.shape) and (E.shape == E_t.shape)
-
-        y_t = y
-        z_t = utils.PlaceHolder(X=X_t, E=E_t, y=y_t).type_as(X_t).mask(node_mask)
-
-        noisy_data = {
-            "t_int": t_int,
-            "t": t_float,
-            "beta_t": beta_t,
-            "alpha_s_bar": alpha_s_bar,
-            "alpha_t_bar": alpha_t_bar,
-            "X_t": z_t.X,
-            "E_t": z_t.E,
-            "y_t": z_t.y,
-            "node_mask": node_mask,
-        }
-        return noisy_data
-
-    @torch.no_grad()
-    def generate(
-        self,
-        properties,
-        guide_scale=1.,
-        num_nodes=None,
-        number_chain_steps=50,
-    ):
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        properties = [float('nan') if x is None else x for x in properties]
-        properties = torch.tensor(properties, dtype=torch.float).reshape(1, -1).to(device)
-        batch_size = properties.size(0)
-        assert batch_size == 1
-        if num_nodes is None:
-            num_nodes = self.node_dist.sample_n(batch_size, device)
-        else:
-            num_nodes = torch.LongTensor([num_nodes]).to(device)
-
-        arange = (
-            torch.arange(self.max_n_nodes, device=device)
-            .unsqueeze(0)
-            .expand(batch_size, -1)
-        )
-        node_mask = arange < num_nodes.unsqueeze(1)
-
-        z_T = utils.sample_discrete_feature_noise(
-            limit_dist=self.limit_dist, node_mask=node_mask
-        )
-        X, E = z_T.X, z_T.E
-
-        assert (E == torch.transpose(E, 1, 2)).all()
-
-        if number_chain_steps > 0:
-            chain_X_size = torch.Size((number_chain_steps, X.size(1)))
-            chain_E_size = torch.Size((number_chain_steps, E.size(1), E.size(2)))
-            chain_X = torch.zeros(chain_X_size)
-            chain_E = torch.zeros(chain_E_size)
-
-        # Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
-        y = properties
-        for s_int in reversed(range(0, self.T)):
-            s_array = s_int * torch.ones((batch_size, 1)).type_as(y)
-            t_array = s_array + 1
-            s_norm = s_array / self.T
-            t_norm = t_array / self.T
-
-            # Sample z_s
-            sampled_s, discrete_sampled_s = self.sample_p_zs_given_zt(
-                s_norm, t_norm, X, E, y, node_mask, guide_scale, device
-            )
-            X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
+    #     pred = self.denoiser(
+    #         noisy_x,
+    #         noisy_e,
+    #         node_mask,
+    #         properties,
+    #         timestep,
+    #         unconditioned=unconditioned,
+    #     )
+    #     return pred
+
+    # def apply_noise(self, X, E, y, node_mask):
+    #     """Sample noise and apply it to the data."""
+
+    #     # Sample a timestep t.
+    #     # When evaluating, the loss for t=0 is computed separately
+    #     lowest_t = 0 if self.training else 1
+    #     t_int = torch.randint(
+    #         lowest_t, self.T + 1, size=(X.size(0), 1), device=X.device
+    #     ).to(
+    #         self.model_dtype
+    #     )  # (bs, 1)
+    #     s_int = t_int - 1
+
+    #     t_float = t_int / self.T
+    #     s_float = s_int / self.T
+
+    #     # beta_t and alpha_s_bar are used for denoising/loss computation
+    #     beta_t = self.noise_schedule(t_normalized=t_float)  # (bs, 1)
+    #     alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s_float)  # (bs, 1)
+    #     alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t_float)  # (bs, 1)
+
+    #     Qtb = self.transition_model.get_Qt_bar(
+    #         alpha_t_bar, X.device
+    #     )  # (bs, dx_in, dx_out), (bs, de_in, de_out)
+
+    #     bs, n, d = X.shape
+    #     X_all = torch.cat([X, E.reshape(bs, n, -1)], dim=-1)
+    #     prob_all = X_all @ Qtb.X
+    #     probX = prob_all[:, :, : self.Xdim_output]
+    #     probE = prob_all[:, :, self.Xdim_output :].reshape(bs, n, n, -1)
+
+    #     sampled_t = utils.sample_discrete_features(
+    #         probX=probX, probE=probE, node_mask=node_mask
+    #     )
+
+    #     X_t = F.one_hot(sampled_t.X, num_classes=self.Xdim_output)
+    #     E_t = F.one_hot(sampled_t.E, num_classes=self.Edim_output)
+    #     assert (X.shape == X_t.shape) and (E.shape == E_t.shape)
+
+    #     y_t = y
+    #     z_t = utils.PlaceHolder(X=X_t, E=E_t, y=y_t).type_as(X_t).mask(node_mask)
+
+    #     noisy_data = {
+    #         "t_int": t_int,
+    #         "t": t_float,
+    #         "beta_t": beta_t,
+    #         "alpha_s_bar": alpha_s_bar,
+    #         "alpha_t_bar": alpha_t_bar,
+    #         "X_t": z_t.X,
+    #         "E_t": z_t.E,
+    #         "y_t": z_t.y,
+    #         "node_mask": node_mask,
+    #     }
+    #     return noisy_data
+
+    # @torch.no_grad()
+    # def generate(
+    #     self,
+    #     properties,
+    #     guide_scale=1.,
+    #     num_nodes=None,
+    #     number_chain_steps=50,
+    # ):
+    #     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    #     properties = [float('nan') if x is None else x for x in properties]
+    #     properties = torch.tensor(properties, dtype=torch.float).reshape(1, -1).to(device)
+    #     batch_size = properties.size(0)
+    #     assert batch_size == 1
+    #     if num_nodes is None:
+    #         num_nodes = self.node_dist.sample_n(batch_size, device)
+    #     else:
+    #         num_nodes = torch.LongTensor([num_nodes]).to(device)
+
+    #     arange = (
+    #         torch.arange(self.max_n_nodes, device=device)
+    #         .unsqueeze(0)
+    #         .expand(batch_size, -1)
+    #     )
+    #     node_mask = arange < num_nodes.unsqueeze(1)
+
+    #     z_T = utils.sample_discrete_feature_noise(
+    #         limit_dist=self.limit_dist, node_mask=node_mask
+    #     )
+    #     X, E = z_T.X, z_T.E
+
+    #     assert (E == torch.transpose(E, 1, 2)).all()
+
+    #     if number_chain_steps > 0:
+    #         chain_X_size = torch.Size((number_chain_steps, X.size(1)))
+    #         chain_E_size = torch.Size((number_chain_steps, E.size(1), E.size(2)))
+    #         chain_X = torch.zeros(chain_X_size)
+    #         chain_E = torch.zeros(chain_E_size)
+
+    #     # Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
+    #     y = properties
+    #     for s_int in reversed(range(0, self.T)):
+    #         s_array = s_int * torch.ones((batch_size, 1)).type_as(y)
+    #         t_array = s_array + 1
+    #         s_norm = s_array / self.T
+    #         t_norm = t_array / self.T
+
+    #         # Sample z_s
+    #         sampled_s, discrete_sampled_s = self.sample_p_zs_given_zt(
+    #             s_norm, t_norm, X, E, y, node_mask, guide_scale, device
+    #         )
+    #         X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
             
-            if number_chain_steps > 0:
-                # Save the first keep_chain graphs
-                write_index = (s_int * number_chain_steps) // self.T
-                chain_X[write_index] = discrete_sampled_s.X[:1]
-                chain_E[write_index] = discrete_sampled_s.E[:1]
-
-        # Sample
-        sampled_s = sampled_s.mask(node_mask, collapse=True)
-        X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
-
-        molecule_list = []
-        n = num_nodes[0]
-        atom_types = X[0, :n].cpu()
-        edge_types = E[0, :n, :n].cpu()
-        molecule_list.append([atom_types, edge_types])
-        smiles = graph_to_smiles(molecule_list, self.atom_decoder)[0]
-
-        # Visualize Chains
-        if number_chain_steps > 0:
-            final_X_chain = X[:1]
-            final_E_chain = E[:1]
-
-            chain_X[0] = final_X_chain                  # Overwrite last frame with the resulting X, E
-            chain_E[0] = final_E_chain
-
-            chain_X = utils.reverse_tensor(chain_X)
-            chain_E = utils.reverse_tensor(chain_E)
-
-            # Repeat last frame to see final sample better
-            chain_X = torch.cat([chain_X, chain_X[-1:].repeat(10, 1)], dim=0)
-            chain_E = torch.cat([chain_E, chain_E[-1:].repeat(10, 1, 1)], dim=0)
-            mol_img_list = self.mol_visualizer.visualize_chain(chain_X.numpy(), chain_E.numpy())
-        else:
-            mol_img_list = []
-
-        return smiles, mol_img_list
-
-    def check_valid(self, smiles):
-        return check_valid(smiles)
+    #         if number_chain_steps > 0:
+    #             # Save the first keep_chain graphs
+    #             write_index = (s_int * number_chain_steps) // self.T
+    #             chain_X[write_index] = discrete_sampled_s.X[:1]
+    #             chain_E[write_index] = discrete_sampled_s.E[:1]
+
+    #     # Sample
+    #     sampled_s = sampled_s.mask(node_mask, collapse=True)
+    #     X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
+
+    #     molecule_list = []
+    #     n = num_nodes[0]
+    #     atom_types = X[0, :n].cpu()
+    #     edge_types = E[0, :n, :n].cpu()
+    #     molecule_list.append([atom_types, edge_types])
+    #     smiles = graph_to_smiles(molecule_list, self.atom_decoder)[0]
+
+    #     # Visualize Chains
+    #     if number_chain_steps > 0:
+    #         final_X_chain = X[:1]
+    #         final_E_chain = E[:1]
+
+    #         chain_X[0] = final_X_chain                  # Overwrite last frame with the resulting X, E
+    #         chain_E[0] = final_E_chain
+
+    #         chain_X = utils.reverse_tensor(chain_X)
+    #         chain_E = utils.reverse_tensor(chain_E)
+
+    #         # Repeat last frame to see final sample better
+    #         chain_X = torch.cat([chain_X, chain_X[-1:].repeat(10, 1)], dim=0)
+    #         chain_E = torch.cat([chain_E, chain_E[-1:].repeat(10, 1, 1)], dim=0)
+    #         mol_img_list = self.mol_visualizer.visualize_chain(chain_X.numpy(), chain_E.numpy())
+    #     else:
+    #         mol_img_list = []
+
+    #     return smiles, mol_img_list
+
+    # def check_valid(self, smiles):
+    #     return check_valid(smiles)
     
-    def sample_p_zs_given_zt(
-        self, s, t, X_t, E_t, properties, node_mask, guide_scale, device
-    ):
-        """Samples from zs ~ p(zs | zt). Only used during sampling.
-        if last_step, return the graph prediction as well"""
-        bs, n, _ = X_t.shape
-        beta_t = self.noise_schedule(t_normalized=t)  # (bs, 1)
-        alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s)
-        alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t)
-
-        # Neural net predictions
-        noisy_data = {
-            "X_t": X_t,
-            "E_t": E_t,
-            "y_t": properties,
-            "t": t,
-            "node_mask": node_mask,
-        }
-
-        def get_prob(noisy_data, unconditioned=False):
-            pred = self._forward(noisy_data, unconditioned=unconditioned)
-
-            # Normalize predictions
-            pred_X = F.softmax(pred.X, dim=-1)  # bs, n, d0
-            pred_E = F.softmax(pred.E, dim=-1)  # bs, n, n, d0
-
-            # Retrieve transitions matrix
-            Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, device)
-            Qsb = self.transition_model.get_Qt_bar(alpha_s_bar, device)
-            Qt = self.transition_model.get_Qt(beta_t, device)
-
-            Xt_all = torch.cat([X_t, E_t.reshape(bs, n, -1)], dim=-1)
-            predX_all = torch.cat([pred_X, pred_E.reshape(bs, n, -1)], dim=-1)
-
-            unnormalized_probX_all = utils.reverse_diffusion(
-                predX_0=predX_all, X_t=Xt_all, Qt=Qt.X, Qsb=Qsb.X, Qtb=Qtb.X
-            )
-
-            unnormalized_prob_X = unnormalized_probX_all[:, :, : self.Xdim_output]
-            unnormalized_prob_E = unnormalized_probX_all[
-                :, :, self.Xdim_output :
-            ].reshape(bs, n * n, -1)
-
-            unnormalized_prob_X[torch.sum(unnormalized_prob_X, dim=-1) == 0] = 1e-5
-            unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5
-
-            prob_X = unnormalized_prob_X / torch.sum(
-                unnormalized_prob_X, dim=-1, keepdim=True
-            )  # bs, n, d_t-1
-            prob_E = unnormalized_prob_E / torch.sum(
-                unnormalized_prob_E, dim=-1, keepdim=True
-            )  # bs, n, d_t-1
-            prob_E = prob_E.reshape(bs, n, n, pred_E.shape[-1])
-
-            return prob_X, prob_E
-
-        prob_X, prob_E = get_prob(noisy_data)
-
-        ### Guidance
-        if guide_scale != 1:
-            uncon_prob_X, uncon_prob_E = get_prob(
-                noisy_data, unconditioned=True
-            )
-            prob_X = (
-                uncon_prob_X
-                * (prob_X / uncon_prob_X.clamp_min(1e-5)) ** guide_scale
-            )
-            prob_E = (
-                uncon_prob_E
-                * (prob_E / uncon_prob_E.clamp_min(1e-5)) ** guide_scale
-            )
-            prob_X = prob_X / prob_X.sum(dim=-1, keepdim=True).clamp_min(1e-5)
-            prob_E = prob_E / prob_E.sum(dim=-1, keepdim=True).clamp_min(1e-5)
-
-        # assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-3).all()
-        # assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-3).all()
-
-        sampled_s = utils.sample_discrete_features(
-            prob_X, prob_E, node_mask=node_mask, step=s[0, 0].item()
-        )
-
-        X_s = F.one_hot(sampled_s.X, num_classes=self.Xdim_output).to(self.model_dtype)
-        E_s = F.one_hot(sampled_s.E, num_classes=self.Edim_output).to(self.model_dtype)
-
-        assert (E_s == torch.transpose(E_s, 1, 2)).all()
-        assert (X_t.shape == X_s.shape) and (E_t.shape == E_s.shape)
-
-        out_one_hot = utils.PlaceHolder(X=X_s, E=E_s, y=properties)
-        out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=properties)
-
-        return out_one_hot.mask(node_mask).type_as(properties), out_discrete.mask(
-            node_mask, collapse=True
-        ).type_as(properties)
+    # def sample_p_zs_given_zt(
+    #     self, s, t, X_t, E_t, properties, node_mask, guide_scale, device
+    # ):
+    #     """Samples from zs ~ p(zs | zt). Only used during sampling.
+    #     if last_step, return the graph prediction as well"""
+    #     bs, n, _ = X_t.shape
+    #     beta_t = self.noise_schedule(t_normalized=t)  # (bs, 1)
+    #     alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s)
+    #     alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t)
+
+    #     # Neural net predictions
+    #     noisy_data = {
+    #         "X_t": X_t,
+    #         "E_t": E_t,
+    #         "y_t": properties,
+    #         "t": t,
+    #         "node_mask": node_mask,
+    #     }
+
+    #     def get_prob(noisy_data, unconditioned=False):
+    #         pred = self._forward(noisy_data, unconditioned=unconditioned)
+
+    #         # Normalize predictions
+    #         pred_X = F.softmax(pred.X, dim=-1)  # bs, n, d0
+    #         pred_E = F.softmax(pred.E, dim=-1)  # bs, n, n, d0
+
+    #         # Retrieve transitions matrix
+    #         Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, device)
+    #         Qsb = self.transition_model.get_Qt_bar(alpha_s_bar, device)
+    #         Qt = self.transition_model.get_Qt(beta_t, device)
+
+    #         Xt_all = torch.cat([X_t, E_t.reshape(bs, n, -1)], dim=-1)
+    #         predX_all = torch.cat([pred_X, pred_E.reshape(bs, n, -1)], dim=-1)
+
+    #         unnormalized_probX_all = utils.reverse_diffusion(
+    #             predX_0=predX_all, X_t=Xt_all, Qt=Qt.X, Qsb=Qsb.X, Qtb=Qtb.X
+    #         )
+
+    #         unnormalized_prob_X = unnormalized_probX_all[:, :, : self.Xdim_output]
+    #         unnormalized_prob_E = unnormalized_probX_all[
+    #             :, :, self.Xdim_output :
+    #         ].reshape(bs, n * n, -1)
+
+    #         unnormalized_prob_X[torch.sum(unnormalized_prob_X, dim=-1) == 0] = 1e-5
+    #         unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5
+
+    #         prob_X = unnormalized_prob_X / torch.sum(
+    #             unnormalized_prob_X, dim=-1, keepdim=True
+    #         )  # bs, n, d_t-1
+    #         prob_E = unnormalized_prob_E / torch.sum(
+    #             unnormalized_prob_E, dim=-1, keepdim=True
+    #         )  # bs, n, d_t-1
+    #         prob_E = prob_E.reshape(bs, n, n, pred_E.shape[-1])
+
+    #         return prob_X, prob_E
+
+    #     prob_X, prob_E = get_prob(noisy_data)
+
+    #     ### Guidance
+    #     if guide_scale != 1:
+    #         uncon_prob_X, uncon_prob_E = get_prob(
+    #             noisy_data, unconditioned=True
+    #         )
+    #         prob_X = (
+    #             uncon_prob_X
+    #             * (prob_X / uncon_prob_X.clamp_min(1e-5)) ** guide_scale
+    #         )
+    #         prob_E = (
+    #             uncon_prob_E
+    #             * (prob_E / uncon_prob_E.clamp_min(1e-5)) ** guide_scale
+    #         )
+    #         prob_X = prob_X / prob_X.sum(dim=-1, keepdim=True).clamp_min(1e-5)
+    #         prob_E = prob_E / prob_E.sum(dim=-1, keepdim=True).clamp_min(1e-5)
+
+    #     # assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-3).all()
+    #     # assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-3).all()
+
+    #     sampled_s = utils.sample_discrete_features(
+    #         prob_X, prob_E, node_mask=node_mask, step=s[0, 0].item()
+    #     )
+
+    #     X_s = F.one_hot(sampled_s.X, num_classes=self.Xdim_output).to(self.model_dtype)
+    #     E_s = F.one_hot(sampled_s.E, num_classes=self.Edim_output).to(self.model_dtype)
+
+    #     assert (E_s == torch.transpose(E_s, 1, 2)).all()
+    #     assert (X_t.shape == X_s.shape) and (E_t.shape == E_s.shape)
+
+    #     out_one_hot = utils.PlaceHolder(X=X_s, E=E_s, y=properties)
+    #     out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=properties)
+
+    #     return out_one_hot.mask(node_mask).type_as(properties), out_discrete.mask(
+    #         node_mask, collapse=True
+    #     ).type_as(properties)