cleanup to use diffusion prior trainer

train_diffusion_prior.py

@@ -6,22 +6,24 @@ import numpy as np
 
 import torch
 from torch import nn
-from torch.cuda.amp import autocast, GradScaler
 
 from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork
-from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model, print_ribbon
-from dalle2_pytorch.optimizer import get_optimizer
+from dalle2_pytorch.train import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model, print_ribbon
 from dalle2_pytorch.trackers import ConsoleTracker, WandbTracker
 
 from embedding_reader import EmbeddingReader
 
 from tqdm import tqdm
 
+# constants
+
 NUM_TEST_EMBEDDINGS = 100 # for cosine similarity reporting during training
 REPORT_METRICS_EVERY = 100 # for cosine similarity and other metric reporting during training
 
 tracker = WandbTracker()
 
+# functions
+
 def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_type,phase="Validation"):
     model.eval()
     with torch.no_grad():
@@ -126,7 +128,8 @@ def train(image_embed_dim,
           dropout=0.05,
           amp=False):
 
-    # DiffusionPriorNetwork 
+    # diffusion prior network
+
     prior_network = DiffusionPriorNetwork( 
         dim = image_embed_dim,
         depth = dpn_depth,
@@ -134,9 +137,11 @@ def train(image_embed_dim,
         heads = dpn_heads,
         attn_dropout = dropout,
         ff_dropout = dropout,
-            normformer = dp_normformer).to(device)
+        normformer = dp_normformer
+    )
+    
+    # diffusion prior with text embeddings and image embeddings pre-computed
 
-    # DiffusionPrior with text embeddings and image embeddings pre-computed
     diffusion_prior = DiffusionPrior( 
         net = prior_network,
         clip = clip,
@@ -144,29 +149,43 @@ def train(image_embed_dim,
         timesteps = dp_timesteps,
         cond_drop_prob = dp_cond_drop_prob,
         loss_type = dp_loss_type,
-            condition_on_text_encodings = dp_condition_on_text_encodings).to(device)
+        condition_on_text_encodings = dp_condition_on_text_encodings
+    )
 
     # Load pre-trained model from DPRIOR_PATH
+
     if RESUME:
-        diffusion_prior=load_diffusion_model(DPRIOR_PATH,device)   
-        wandb.init( entity=wandb_entity, project=wandb_project, config=config) 
+        diffusion_prior = load_diffusion_model(DPRIOR_PATH, device)
+
+        # TODO, optimizer and scaler needs to be loaded as well
+
+        tracker.init(entity = wandb_entity, project = wandb_project, config = config)
+
+    # diffusion prior trainer
+
+    trainer = DiffusionPriorTrainer(
+        diffusion_prior = diffusion_prior,
+        lr = learning_rate,
+        wd = weight_decay,
+        max_grad_norm = max_grad_norm,
+        amp = amp,
+    ).to(device)
 
     # Create save_path if it doesn't exist
+
     if not os.path.exists(save_path):
         os.makedirs(save_path)
 
     # Get image and text embeddings from the servers
+
     print_ribbon("Downloading embeddings - image and text")
     image_reader = EmbeddingReader(embeddings_folder=image_embed_url, file_format="npy")
     text_reader  = EmbeddingReader(embeddings_folder=text_embed_url, file_format="npy")
     num_data_points = text_reader.count
 
     ### Training code ###
-    scaler = GradScaler(enabled=amp)
-    optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
-    epochs = num_epochs
 
-    step = 0
+    epochs = num_epochs
     t = time.time()
 
     train_set_size = int(train_percent*num_data_points)
@@ -178,18 +197,17 @@ def train(image_embed_dim,
         for emb_images,emb_text in zip(image_reader(batch_size=batch_size, start=0, end=train_set_size),
                 text_reader(batch_size=batch_size, start=0, end=train_set_size)):
 
-            diffusion_prior.train()
+            trainer.train()
             
             emb_images_tensor = torch.tensor(emb_images[0]).to(device)
             emb_text_tensor = torch.tensor(emb_text[0]).to(device)
 
-            with autocast(enabled=amp):
-                loss = diffusion_prior(text_embed = emb_text_tensor,image_embed = emb_images_tensor)
-                scaler.scale(loss).backward()
+            loss = trainer(text_embed = emb_text_tensor,image_embed = emb_images_tensor)
 
             # Samples per second
-            step+=1
+
             samples_per_sec = batch_size*step/(time.time()-t)
+
             # Save checkpoint every save_interval minutes
             if(int(time.time()-t) >= 60*save_interval):
                 t = time.time()
@@ -197,8 +215,8 @@ def train(image_embed_dim,
                 save_diffusion_model(
                     save_path,
                     diffusion_prior,
-                    optimizer,
-                    scaler,
+                    trainer.optimizer,
+                    trainer.scaler,
                     config,
                     image_embed_dim)
 
@@ -227,17 +245,12 @@ def train(image_embed_dim,
                         dp_loss_type,
                         phase="Validation")
 
-            scaler.unscale_(optimizer)
-            nn.utils.clip_grad_norm_(diffusion_prior.parameters(), max_grad_norm)
-
-            scaler.step(optimizer)
-            scaler.update()
-            optimizer.zero_grad()
+            trainer.update()
 
     ### Test run ###
     test_set_size = int(test_percent*train_set_size) 
-    start=train_set_size+val_set_size
-    end=num_data_points
+    start = train_set_size+val_set_size
+    end = num_data_points
     eval_model(diffusion_prior,device,image_reader,text_reader,start,end,batch_size,dp_loss_type,phase="Test")
 
 def main():
@@ -303,8 +316,11 @@ def main():
         })
 
     RESUME = False
+
     # Check if DPRIOR_PATH exists(saved model path)
+
     DPRIOR_PATH = args.pretrained_model_path
+
     if(DPRIOR_PATH is not None):
         RESUME = True
     else: