need to update the model

graph_dit/diffusion_model.py

@@ -239,7 +239,7 @@ 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} -- ",
                 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:

graph_dit/main.py

@@ -242,6 +242,12 @@ def test(cfg: DictConfig):
             optimizer.step()
             optimizer.zero_grad()
             # return {'loss': loss}
+        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")
@@ -281,6 +287,53 @@ def test(cfg: DictConfig):
             reward = 1.0
             rewards.append(reward)
         return torch.tensor(rewards, dtype=torch.float32, requires_grad=True).unsqueeze(0).to(device)
+    # 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)
+    #     samples = samples + cur_sample
+    #     reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
+
+    #     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)
+
+    # perm = torch.randperm(len(samples_with_log_probs), device=accelerator.device)
+    # samples, log_probs, rewards = samples_with_log_probs[perm]
+    # samples = list(samples)
+    # log_probs = list(log_probs)
+    # for i in range(len(log_probs)):
+    #     log_probs[i] = torch.sum(log_probs[i], dim=-1).unsqueeze(0)
+    # print(f'log_probs: {log_probs[:5]}')
+    # print(f'log_probs: {log_probs[0].shape}') # torch.Size([1])
+    # rewards = list(rewards)
+    # log_probs = torch.cat(log_probs, dim=0)
+    # print(f'log_probs: {log_probs.shape}') # torch.Size([1000, 1])
+    # old_log_probs = log_probs.clone()
+    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)
@@ -289,14 +342,34 @@ def test(cfg: DictConfig):
         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, old_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)
+        # 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)
+        with accelerator.accumulate(graph_dit_model):
             batch_y = torch.ones(to_generate, graph_dit_model.ydim_output, device=graph_dit_model.device)
+            new_samples, 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)
+            samples = samples + new_samples
+            reward = graph_reward_fn(new_samples, device=graph_dit_model.device)
+            advantages = (reward - torch.mean(reward)) / (torch.std(reward) + 1e-6)
+            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()
 
-        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)
-        samples = samples + cur_sample
-        reward = graph_reward_fn(cur_sample, device=graph_dit_model.device)
 
-        samples_with_log_probs.append((cur_sample, log_probs, reward))
+        samples_with_log_probs.append((new_samples, log_probs, reward))
         
         all_ys.append(batch_y)
         batch_id += to_generate
@@ -304,6 +377,7 @@ def test(cfg: DictConfig):
         samples_left_to_save -= to_save
         samples_left_to_generate -= to_generate
         chains_left_to_save -= chains_save
+        # break
         
     print(f"final Computing sampling metrics...")
     graph_dit_model.sampling_metrics.reset()
@@ -315,47 +389,46 @@ def test(cfg: DictConfig):
     print("Samples:")
     print(samples)
 
-    perm = torch.randperm(len(samples_with_log_probs), device=accelerator.device)
+    # perm = torch.randperm(len(samples_with_log_probs), device=accelerator.device)
-    samples, log_probs, rewards = samples_with_log_probs[perm]
+    # samples, log_probs, rewards = samples_with_log_probs[perm]
-    samples = list(samples)
+    # samples = list(samples)
-    log_probs = list(log_probs)
+    # log_probs = list(log_probs)
-    for i in range(len(log_probs)):
+    # for i in range(len(log_probs)):
-        log_probs[i] = torch.sum(log_probs[i], dim=-1).unsqueeze(0)
+    #     log_probs[i] = torch.sum(log_probs[i], dim=-1).unsqueeze(0)
-    print(f'log_probs: {log_probs[:5]}')
+    # print(f'log_probs: {log_probs[:5]}')
-    print(f'log_probs: {log_probs[0].shape}') # torch.Size([1])
+    # print(f'log_probs: {log_probs[0].shape}') # torch.Size([1])
-    rewards = list(rewards)
+    # rewards = list(rewards)
-    log_probs = torch.cat(log_probs, dim=0)
+    # log_probs = torch.cat(log_probs, dim=0)
-    print(f'log_probs: {log_probs.shape}') # torch.Size([1000, 1])
+    # print(f'log_probs: {log_probs.shape}') # torch.Size([1000, 1])
-    old_log_probs = log_probs.clone()
+    # old_log_probs = log_probs.clone()
+    # # multi metrics range
+    # # reward hacking hiking
+    # for inner_epoch in range(cfg.train.n_epochs):
+    #     # print(f'rewards: {rewards.shape}') # torch.Size([1000])
+    #     print(f'rewards: {rewards[:5]}')
+    #     print(f'len rewards: {len(rewards)}')
+    #     print(f'type rewards: {type(rewards)}')
+    #     if len(rewards) > 1 and isinstance(rewards, list):
+    #         rewards = torch.cat(rewards, dim=0)
+    #     elif len(rewards) == 1 and isinstance(rewards, list):
+    #         rewards = rewards[0]
+    #     # print(f'rewards: {rewards.shape}')
+    #     advantages = (rewards - torch.mean(rewards)) / (torch.std(rewards) + 1e-6)
+    #     print(f'advantages: {advantages.shape}')
+    #     with accelerator.accumulate(graph_dit_model):
+    #         ratio = torch.exp(log_probs - old_log_probs)
+    #         unclipped_loss = -advantages * ratio
+    #         # z-score normalization
+    #         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()
 
-    # multi metrics range
+    #     accelerator.log({"loss": loss.item(), "epoch": inner_epoch})
-    # reward hacking hiking
+    #     print(f"loss: {loss.item()}, epoch: {inner_epoch}")
-    for inner_epoch in range(cfg.train.n_epochs):
-        # print(f'rewards: {rewards.shape}') # torch.Size([1000])
-        print(f'rewards: {rewards[:5]}')
-        print(f'len rewards: {len(rewards)}')
-        print(f'type rewards: {type(rewards)}')
-        if len(rewards) > 1 and isinstance(rewards, list):
-            rewards = torch.cat(rewards, dim=0)
-        elif len(rewards) == 1 and isinstance(rewards, list):
-            rewards = rewards[0]
-        # print(f'rewards: {rewards.shape}')
-        advantages = (rewards - torch.mean(rewards)) / (torch.std(rewards) + 1e-6)
-        print(f'advantages: {advantages.shape}')
-        with accelerator.accumulate(graph_dit_model):
-            ratio = torch.exp(log_probs - old_log_probs)
-            unclipped_loss = -advantages * ratio
-            # z-score normalization
-            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()
-
-        accelerator.log({"loss": loss.item(), "epoch": inner_epoch})
-        print(f"loss: {loss.item()}, epoch: {inner_epoch}")
 
 
     # trainer = Trainer(