try to get the original perf

README.md

@@ -1,14 +1,34 @@
 Graph Diffusion Transformer for Multi-Conditional Molecular Generation
 ================================================================
 
+## Initial Setup
+
+Please download NASBench201 dataset(NAS-Bench-201-v1_1-096897.pth) from
+https://drive.google.com/file/d/16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_/view
+
+and put it in the `/path/to/repo/graph_dit` folder.
+
+## Running the code
+
+start command:
+``` bash
+python main.py --config-name=config.yaml \
+model.ensure_connected=True \
+dataset.task_name='nasbench201' \
+dataset.guidance_target='regression'
+```
+
+This repository contains the code for the paper "Inverse Molecular Design with Multi-Conditional Diffusion Guidance" by Gang Liu, Jiaxin Xu, Tengfei Luo, and Meng Jiang.
+
+
 Paper: https://arxiv.org/abs/2401.13858
 
-This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
+<!-- This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
 
 <div style="display: flex;" markdown="1">
       <img src="asset/reverse.png" style="width: 45%;" alt="Description of the first image">
       <img src="asset/arch.png" style="width: 45%;" alt="Description of the second image">
-</div>
+</div> -->
 
 
 ## Requirements

graph_dit/diffusion_model.py

@@ -195,15 +195,18 @@ class Graph_DiT(pl.LightningModule):
         # print("Size of the input features Xdim {}, Edim {}, ydim {}".format(self.Xdim, self.Edim, self.ydim))
 
     def on_train_epoch_start(self) -> None:
-        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
-            print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
+        if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
+            # print("Starting train epoch {}/{}...".format(self.current_epoch, self.trainer.max_epochs))
+            print("Starting train epoch {}/{}...".format(self.current_epoch, self.cfg.train.n_epochs))
         self.start_epoch_time = time.time()
         self.train_loss.reset()
         self.train_metrics.reset()
 
     def on_train_epoch_end(self) -> None:
 
-        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        if self.current_epoch / self.cfg.train.n_epochs in [0.25, 0.5, 0.75, 1.0]:
             log = True
         else:
             log = False
@@ -239,8 +242,8 @@ class Graph_DiT(pl.LightningModule):
         
                    self.val_X_logp.compute(), self.val_E_logp.compute()]
         
-        if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
+        # if self.current_epoch / self.trainer.max_epochs in [0.25, 0.5, 0.75, 1.0]:
-            print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
+        print(f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
                 f"Val Edge type KL: {metrics[2] :.2f}", 'Val loss: %.2f \t Best :  %.2f\n' % (metrics[0], self.best_val_nll))
         with open("validation-metrics.csv", "a") as f:
             # save the metrics as csv file
@@ -286,7 +289,7 @@ class Graph_DiT(pl.LightningModule):
                 samples.extend(self.sample_batch(batch_id=ident, batch_size=to_generate, y=batch_y,
                                                 save_final=to_save,
                                                 keep_chain=chains_save,
-                                                number_chain_steps=self.number_chain_steps))
+                                                number_chain_steps=self.number_chain_steps)[0])
                 ident += to_generate
                 start_index += to_generate
 
@@ -360,7 +363,7 @@ class Graph_DiT(pl.LightningModule):
             batch_y = torch.ones(to_generate, self.ydim_output, device=self.device)
 
             cur_sample = self.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
-                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)
+                                            keep_chain=chains_save, number_chain_steps=self.number_chain_steps)[0]
             samples = samples + cur_sample
             
             all_ys.append(batch_y)
@@ -601,6 +604,9 @@ class Graph_DiT(pl.LightningModule):
 
         assert (E == torch.transpose(E, 1, 2)).all()
 
+        total_log_probs = torch.zeros([self.cfg.general.final_model_samples_to_generate,10], device=self.device)
+        # total_log_probs = torch.zeros([self.cfg.general.samples_to_generate,10], device=self.device)
+
         # Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
         for s_int in reversed(range(0, self.T)):
             s_array = s_int * torch.ones((batch_size, 1)).type_as(y)
@@ -609,21 +615,24 @@ class Graph_DiT(pl.LightningModule):
             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)
+            sampled_s, discrete_sampled_s, log_probs= self.sample_p_zs_given_zt(s_norm, t_norm, X, E, y, node_mask)
             X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
+            print(f'sampled_s.X shape: {sampled_s.X.shape}, sampled_s.E shape: {sampled_s.E.shape}')
+            print(f'log_probs shape: {log_probs.shape}')
+            total_log_probs += log_probs
 
         # Sample
         sampled_s = sampled_s.mask(node_mask, collapse=True)
         X, E, y = sampled_s.X, sampled_s.E, sampled_s.y
         
-        molecule_list = []
+        graph_list = []
         for i in range(batch_size):
             n = n_nodes[i]
-            atom_types = X[i, :n].cpu()
+            node_types = X[i, :n].cpu()
             edge_types = E[i, :n, :n].cpu()
-            molecule_list.append([atom_types, edge_types])
+            graph_list.append([node_types, edge_types])
         
-        return molecule_list
+        return graph_list, total_log_probs
 
     def sample_p_zs_given_zt(self, s, t, X_t, E_t, y_t, node_mask):
         """Samples from zs ~ p(zs | zt). Only used during sampling.
@@ -635,6 +644,7 @@ class Graph_DiT(pl.LightningModule):
 
         # Neural net predictions
         noisy_data = {'X_t': X_t, 'E_t': E_t, 'y_t': y_t, 't': t, 'node_mask': node_mask}
+        print(f"sample p zs given zt X_t shape: {X_t.shape}, E_t shape: {E_t.shape}, y_t shape: {y_t.shape}, node_mask shape: {node_mask.shape}")
         
         def get_prob(noisy_data, unconditioned=False):
             pred = self.forward(noisy_data, unconditioned=unconditioned)
@@ -674,7 +684,19 @@ class Graph_DiT(pl.LightningModule):
         # with condition = P_t(G_{t-1} |G_t, C)
         # with condition = P_t(A_{t-1} |A_t, y)
         prob_X, prob_E, pred = get_prob(noisy_data)
+        print(f'prob_X shape: {prob_X.shape}, prob_E shape: {prob_E.shape}')
+        print(f'X_t shape: {X_t.shape}, E_t shape: {E_t.shape}, y_t shape: {y_t.shape}')
+        print(f'X_t: {X_t}')
+        log_prob_X = torch.log(torch.gather(prob_X, -1, X_t.long()).squeeze(-1))  # bs, n
+        log_prob_E = torch.log(torch.gather(prob_E, -1, E_t.long()).squeeze(-1))  # bs, n, n
 
+        # Sum the log_prob across dimensions for total log_prob
+        log_prob_X = log_prob_X.sum(dim=-1)
+        log_prob_E = log_prob_E.sum(dim=(1, 2))
+        print(f'log_prob_X shape: {log_prob_X.shape}, log_prob_E shape: {log_prob_E.shape}')
+        # log_probs = log_prob_E + log_prob_X
+        log_probs = torch.cat([log_prob_X, log_prob_E], dim=-1)  # (batch_size, 2)
+        print(f'log_probs shape: {log_probs.shape}')
         ### Guidance
         if self.guidance_target is not None and self.guide_scale is not None and self.guide_scale != 1:
             uncon_prob_X, uncon_prob_E, pred = get_prob(noisy_data, unconditioned=True)
@@ -810,4 +832,4 @@ class Graph_DiT(pl.LightningModule):
         out_one_hot = utils.PlaceHolder(X=X_s, E=E_s, y=y_t)
         out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=y_t)
 
-        return out_one_hot.mask(node_mask).type_as(y_t), out_discrete.mask(node_mask, collapse=True).type_as(y_t)
+        return out_one_hot.mask(node_mask).type_as(y_t), out_discrete.mask(node_mask, collapse=True).type_as(y_t), log_probs

graph_dit/main.py

@@ -1,4 +1,5 @@
 # These imports are tricky because they use c++, do not move them
+from tqdm import tqdm
 import os, shutil
 import warnings
 
@@ -144,10 +145,32 @@ def main(cfg: DictConfig):
     else:
         trainer.test(model, datamodule=datamodule, ckpt_path=cfg.general.test_only)
 
+from accelerate import Accelerator
+from accelerate.utils import set_seed, ProjectConfiguration
+
 @hydra.main(
     version_base="1.1", config_path="../configs", config_name="config"
 )
 def test(cfg: DictConfig):
+    os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
+    accelerator_config = ProjectConfiguration(
+        project_dir=os.path.join(cfg.general.log_dir, cfg.general.name),
+        automatic_checkpoint_naming=True,
+        total_limit=cfg.general.number_checkpoint_limit,
+    )
+    accelerator = Accelerator(
+        mixed_precision='no',
+        project_config=accelerator_config,
+        # gradient_accumulation_steps=cfg.train.gradient_accumulation_steps * cfg.train.n_epochs,
+        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps, 
+    )
+
+    # Debug: 确认可用设备
+    print(f"Available GPUs: {torch.cuda.device_count()}")
+    print(f"Using device: {accelerator.device}")
+
+    set_seed(cfg.train.seed, device_specific=True)
+
     datamodule = dataset.DataModule(cfg)
     datamodule.prepare_data()
     dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg, dataset=datamodule.dataset)
@@ -169,40 +192,216 @@ def test(cfg: DictConfig):
         "visualization_tools": visulization_tools,
     }
 
+    # Debug: 确认可用设备
+    print(f"Available GPUs: {torch.cuda.device_count()}")
+    print(f"Using device: {accelerator.device}")
+
     if cfg.general.test_only:
         cfg, _ = get_resume(cfg, model_kwargs)
         os.chdir(cfg.general.test_only.split("checkpoints")[0])
     elif cfg.general.resume is not None:
         cfg, _ = get_resume_adaptive(cfg, model_kwargs)
         os.chdir(cfg.general.resume.split("checkpoints")[0])
-    # os.environ["CUDA_VISIBLE_DEVICES"] = cfg.general.gpu_number
     model = Graph_DiT(cfg=cfg, **model_kwargs)
-    trainer = Trainer(
+    graph_dit_model = model
-        gradient_clip_val=cfg.train.clip_grad,
-        # accelerator="cpu",
-        accelerator="gpu"
-        if torch.cuda.is_available() and cfg.general.gpus > 0
-        else "cpu",
-        devices=[cfg.general.gpu_number]
-        if torch.cuda.is_available() and cfg.general.gpus > 0
-        else None,
-        max_epochs=cfg.train.n_epochs,
-        enable_checkpointing=False,
-        check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
-        val_check_interval=cfg.train.val_check_interval,
-        strategy="ddp" if cfg.general.gpus > 1 else "auto",
-        enable_progress_bar=cfg.general.enable_progress_bar,
-        callbacks=[],
-        reload_dataloaders_every_n_epochs=0,
-        logger=[],
-    )
 
-    if not cfg.general.test_only:
+    inference_dtype = torch.float32
-        print("start testing fit method")
+    graph_dit_model.to(accelerator.device, dtype=inference_dtype)
-        trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
+
-        if cfg.general.save_model:
+
-            trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
+    # optional: freeze the model
-        trainer.test(model, datamodule=datamodule)
+    # graph_dit_model.model.requires_grad_(True)
+
+    import torch.nn.functional as F
+    optimizer = graph_dit_model.configure_optimizers()
+    train_dataloader = accelerator.prepare(datamodule.train_dataloader())
+    optimizer, graph_dit_model = accelerator.prepare(optimizer, graph_dit_model)
+    # start training
+    for epoch in range(cfg.train.n_epochs):
+        graph_dit_model.train()  # 设置模型为训练模式
+        print(f"Epoch {epoch}", end="\n")
+        graph_dit_model.on_train_epoch_start()
+        for data in train_dataloader:  # 从数据加载器中获取一个批次的数据
+            # data.to(accelerator.device)
+            # data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
+            # data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
+            # dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
+            # dense_data = dense_data.mask(node_mask)
+            # X, E = dense_data.X, dense_data.E
+            # noisy_data = graph_dit_model.apply_noise(X, E, data.y, node_mask)
+            # pred = graph_dit_model.forward(noisy_data)
+            # loss = graph_dit_model.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
+            #                     true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
+            #                     log=epoch % graph_dit_model.log_every_steps == 0)
+            # # print(f'training loss: {loss}, epoch: {self.current_epoch}, batch: {i}\n, pred type: {type(pred)}, pred.X shape: {type(pred.X)}, {pred.X.shape}, pred.E shape: {type(pred.E)}, {pred.E.shape}')
+            # graph_dit_model.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
+            #                 log=epoch % graph_dit_model.log_every_steps == 0)
+            # graph_dit_model.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
+            # print(f"training loss: {loss}")
+            # with open("training-loss.csv", "a") as f:
+            #     f.write(f"{loss}, {epoch}\n")
+            loss = graph_dit_model.training_step(data, epoch)
+            loss = loss['loss']
+
+            accelerator.backward(loss)
+            optimizer.step()
+            optimizer.zero_grad()
+            # return {'loss': loss}
+        graph_dit_model.on_train_epoch_end()
+        if epoch % cfg.train.check_val_every_n_epoch == 0:
+            print(f'print validation loss')
+            graph_dit_model.eval()
+            graph_dit_model.on_validation_epoch_start()
+            graph_dit_model.validation_step(data, epoch)
+            graph_dit_model.on_validation_epoch_end()
+    
+    # start testing
+    print("start testing")
+    graph_dit_model.eval()
+    test_dataloader = accelerator.prepare(datamodule.test_dataloader())
+    graph_dit_model.on_test_epoch_start()
+    for data in test_dataloader:
+        nll = graph_dit_model.test_step(data, epoch)
+        # data_x = F.one_hot(data.x, num_classes=12).float()[:, graph_dit_model.active_index]
+        # data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
+
+        # dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, graph_dit_model.max_n_nodes)
+        # dense_data = dense_data.mask(node_mask)
+        # noisy_data = graph_dit_model.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
+        # pred = graph_dit_model.forward(noisy_data)
+        # nll = graph_dit_model.compute_val_loss(pred, noisy_data, dense_data.X, dense_data.E, data.y, node_mask, test=True)
+        # graph_dit_model.test_y_collection.append(data.y)
+        print(f'test loss: {nll}')
+    
+    graph_dit_model.on_test_epoch_end()
+
+    # start sampling
+
+    # samples_left_to_generate = cfg.general.final_model_samples_to_generate
+    # samples_left_to_save = cfg.general.final_model_samples_to_save
+    # chains_left_to_save = cfg.general.final_model_chains_to_save
+
+    # samples, all_ys, batch_id = [], [], 0
+    # samples_with_log_probs = []
+    # test_y_collection = torch.cat(graph_dit_model.test_y_collection, dim=0)
+    # num_examples = test_y_collection.size(0)
+    # if cfg.general.final_model_samples_to_generate > num_examples:
+    #     ratio = cfg.general.final_model_samples_to_generate // num_examples
+    #     test_y_collection = test_y_collection.repeat(ratio+1, 1)
+    #     num_examples = test_y_collection.size(0)
+    
+    # Normal reward function
+    # from nas_201_api import NASBench201API as API
+    # api = API('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth')
+    # def graph_reward_fn(graphs, true_graphs=None, device=None, reward_model='swap'):
+    #     rewards = []
+    #     if reward_model == 'swap':
+    #         import csv
+    #         with open('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/swap_results.csv', 'r') as f:
+    #             reader = csv.reader(f)
+    #             header = next(reader)
+    #             data = [row for row in reader]
+    #             swap_scores = [float(row[0]) for row in data]
+    #             for graph in graphs:
+    #                 node_tensor = graph[0]
+    #                 node = node_tensor.cpu().numpy().tolist()
+
+    #                 def nodes_to_arch_str(nodes):
+    #                     num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
+    #                     nodes_str = [num_to_op[node] for node in nodes]
+    #                     arch_str = '|' + nodes_str[1] + '~0|+' + \
+    #                             '|' + nodes_str[2] + '~0|' + nodes_str[3] + '~1|+' +\
+    #                             '|' + nodes_str[4] + '~0|' + nodes_str[5] + '~1|' + nodes_str[6] + '~2|' 
+    #                     return arch_str
+                    
+    #                 arch_str = nodes_to_arch_str(node)
+    #                 reward = swap_scores[api.query_index_by_arch(arch_str)]
+    #                 rewards.append(reward)
+                
+    #     # for graph in graphs:
+    #     #     reward = 1.0
+    #     #     rewards.append(reward)
+    #     return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
+    # old_log_probs = None
+    # while samples_left_to_generate > 0:
+    #     print(f'samples left to generate: {samples_left_to_generate}/'
+    #         f'{cfg.general.final_model_samples_to_generate}', end='', flush=True)
+    #     bs = 1 * cfg.train.batch_size
+    #     to_generate = min(samples_left_to_generate, bs)
+    #     to_save = min(samples_left_to_save, bs)
+    #     chains_save = min(chains_left_to_save, bs)
+    #     # batch_y = test_y_collection[batch_id : batch_id + to_generate]
+    #     batch_y = torch.ones(to_generate, graph_dit_model.ydim_output, device=graph_dit_model.device)
+
+    #     cur_sample, log_probs = graph_dit_model.sample_batch(batch_id, to_generate, batch_y, save_final=to_save,
+    #                                     keep_chain=chains_save, number_chain_steps=graph_dit_model.number_chain_steps)
+    #     log_probs = torch.sum(log_probs, dim=-1).unsqueeze(1)
+    #     samples = samples + cur_sample
+    #     reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
+    #     advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6)
+    #     print(f'reward: {reward.shape}, advantages: {advantages.shape}, log_probs: {log_probs.shape}, cur_sample: {len(cur_sample)}')
+    #     if old_log_probs is None:
+    #         old_log_probs = log_probs.clone()
+    #     ratio = torch.exp(log_probs - old_log_probs)
+    #     unclipped_loss = -advantages * ratio
+    #     clipped_loss = -advantages * torch.clamp(ratio, 1.0 - cfg.ppo.clip_param, 1.0 + cfg.ppo.clip_param)
+    #     loss = torch.mean(torch.max(unclipped_loss, clipped_loss))
+    #     accelerator.backward(loss)
+    #     optimizer.step()
+    #     optimizer.zero_grad()
+
+
+    #     samples_with_log_probs.append((cur_sample, log_probs, reward))
+        
+    #     all_ys.append(batch_y)
+    #     batch_id += to_generate
+
+    #     samples_left_to_save -= to_save
+    #     samples_left_to_generate -= to_generate
+    #     chains_left_to_save -= chains_save
+        
+    # print(f"final Computing sampling metrics...")
+    # graph_dit_model.sampling_metrics.reset()
+    # graph_dit_model.sampling_metrics(samples, all_ys, graph_dit_model.name, graph_dit_model.current_epoch, graph_dit_model.val_counter, test=True)
+    # graph_dit_model.sampling_metrics.reset()
+    # print(f"Done.")
+
+    # # save samples
+    # print("Samples:")
+    # print(samples)
+
+    # ========================
+    
+
+    
+
+
+    # trainer = Trainer(
+    #     gradient_clip_val=cfg.train.clip_grad,
+    #     # accelerator="cpu",
+    #     accelerator="gpu"
+    #     if torch.cuda.is_available() and cfg.general.gpus > 0
+    #     else "cpu",
+    #     devices=[cfg.general.gpu_number]
+    #     if torch.cuda.is_available() and cfg.general.gpus > 0
+    #     else None,
+    #     max_epochs=cfg.train.n_epochs,
+    #     enable_checkpointing=False,
+    #     check_val_every_n_epoch=cfg.train.check_val_every_n_epoch,
+    #     val_check_interval=cfg.train.val_check_interval,
+    #     strategy="ddp" if cfg.general.gpus > 1 else "auto",
+    #     enable_progress_bar=cfg.general.enable_progress_bar,
+    #     callbacks=[],
+    #     reload_dataloaders_every_n_epochs=0,
+    #     logger=[],
+    # )
+
+    # if not cfg.general.test_only:
+    #     print("start testing fit method")
+    #     trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume)
+    #     if cfg.general.save_model:
+    #         trainer.save_checkpoint(f"checkpoints/{cfg.general.name}/last.ckpt")
+    #     trainer.test(model, datamodule=datamodule)
 
 if __name__ == "__main__":
     test()

graph_dit/models/conditions.py

@@ -76,6 +76,8 @@ class CategoricalEmbedder(nn.Module):
             embeddings = embeddings + noise
         return embeddings
     
+# 相似的condition cluster起来
+# size 
 class ClusterContinuousEmbedder(nn.Module):
     def __init__(self, input_size, hidden_size, dropout_prob):
         super().__init__()
@@ -108,6 +110,8 @@ class ClusterContinuousEmbedder(nn.Module):
         
         if drop_ids is not None:
             embeddings = torch.zeros((labels.shape[0], self.hidden_size), device=labels.device)
+            # print(labels[~drop_ids].shape)
+            # torch.Size([1200])
             embeddings[~drop_ids] = self.mlp(labels[~drop_ids])
             embeddings[drop_ids] += self.embedding_drop.weight[0]
         else:

graph_dit/models/transformer.py

@@ -17,20 +17,22 @@ class Denoiser(nn.Module):
         num_heads=16,
         mlp_ratio=4.0,
         drop_condition=0.1,
-        Xdim=118,
+        Xdim=7,
-        Edim=5,
+        Edim=2,
-        ydim=3,
+        ydim=1,
         task_type='regression',
     ):
         super().__init__()
+        print(f"Denoiser, xdim: {Xdim}, edim: {Edim}, ydim: {ydim}, hidden_size: {hidden_size}, depth: {depth}, num_heads: {num_heads}, mlp_ratio: {mlp_ratio}, drop_condition: {drop_condition}")
         self.num_heads = num_heads
         self.ydim = ydim
         self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
 
         self.t_embedder = TimestepEmbedder(hidden_size)
+        # 
         self.y_embedding_list = torch.nn.ModuleList()
 
-        self.y_embedding_list.append(ClusterContinuousEmbedder(2, hidden_size, drop_condition))
+        self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
         for i in range(ydim - 2):
             if task_type == 'regression':
                 self.y_embedding_list.append(ClusterContinuousEmbedder(1, hidden_size, drop_condition))
@@ -88,6 +90,8 @@ class Denoiser(nn.Module):
         
         # print("Denoiser Forward")
         # print(x.shape, e.shape, y.shape, t.shape, unconditioned)
+        # torch.Size([1200, 8, 7]) torch.Size([1200, 8, 8, 2]) torch.Size([1200, 2]) torch.Size([1200, 1]) False
+        # print(y)
         force_drop_id = torch.zeros_like(y.sum(-1))
         # drop the nan values
         force_drop_id[torch.isnan(y.sum(-1))] = 1
@@ -109,11 +113,12 @@ class Denoiser(nn.Module):
         c1 = self.t_embedder(t)
         # print("C1 after t_embedder")
         # print(c1.shape)
-        for i in range(1, self.ydim):
+        c2 = self.y_embedding_list[0](y[:,0].unsqueeze(-1), self.training, force_drop_id, t)
-            if i == 1:
+        # for i in range(1, self.ydim):
-                c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
+        #     if i == 1:
-            else:
+        #         c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
-                c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
+        #     else:
+                # c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
         # print("C2 after y_embedding_list")
         # print(c2.shape)
         # print("C1 + C2")