add ipo, hinge and cpo loss to dpo trainer (#6788)

add ipo and hinge loss to dpo trainer

examples/research_projects/diffusion_dpo/train_diffusion_dpo.py

@@ -299,9 +299,15 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--beta_dpo",
         type=int,
-        default=5000,
+        default=2500,
         help="DPO KL Divergence penalty.",
     )
+    parser.add_argument(
+        "--loss_type",
+        type=str,
+        default="sigmoid",
+        help="DPO loss type. Can be one of 'sigmoid' (default), 'ipo', or 'cpo'",
+    )
     parser.add_argument(
         "--learning_rate",
         type=float,
@@ -858,12 +864,19 @@ def main(args):
                 accelerator.unwrap_model(unet).enable_adapters()
 
                 # Final loss.
-                scale_term = -0.5 * args.beta_dpo
-                inside_term = scale_term * (model_diff - ref_diff)
-                loss = -1 * F.logsigmoid(inside_term).mean()
+                logits = ref_diff - model_diff
+                if args.loss_type == "sigmoid":
+                    loss = -1 * F.logsigmoid(args.beta_dpo * logits).mean()
+                elif args.loss_type == "hinge":
+                    loss = torch.relu(1 - args.beta_dpo * logits).mean()
+                elif args.loss_type == "ipo":
+                    losses = (logits - 1 / (2 * args.beta)) ** 2
+                    loss = losses.mean()
+                else:
+                    raise ValueError(f"Unknown loss type {args.loss_type}")
 
-                implicit_acc = (inside_term > 0).sum().float() / inside_term.size(0)
-                implicit_acc += 0.5 * (inside_term == 0).sum().float() / inside_term.size(0)
+                implicit_acc = (logits > 0).sum().float() / logits.size(0)
+                implicit_acc += 0.5 * (logits == 0).sum().float() / logits.size(0)
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients: