readded RM training removed during merge conflict in KTO

megatron/neox_arguments/neox_args.py

@@ -1087,6 +1087,13 @@ class NeoXArgsTraining(NeoXArgsTemplate):
     Weight for undesirable loss in KTO. Might help if you have unbalanced desirable and undesirable classes.
     """
 
+    z_loss: float = 0.0
+    """
+    Z-loss parameter, only implemented for RM training currently.
+    https://arxiv.org/pdf/2204.02311
+    https://arxiv.org/pdf/2309.10305
+    """
+
     kto_beta: float = 0.1
     """
     Beta value for KTO

megatron/training.py

@@ -87,7 +87,7 @@ def save_base_shapes(neox_args, base_shapes, use_cache):
     base_model = GPT2ModelPipe(
         neox_args=neox_args,
         num_tokentypes=0,
-        parallel_output=True,
+        parallel_output=True if neox_args.train_impl != "rm" else False,
         topology=mpu.get_topology(),
         use_cache=use_cache,
     )
@@ -111,7 +111,7 @@ def save_base_shapes(neox_args, base_shapes, use_cache):
     delta_model = GPT2ModelPipe(
         neox_args=neox_args,
         num_tokentypes=0,
-        parallel_output=True,
+        parallel_output=True if neox_args.train_impl != "rm" else False,
         topology=mpu.get_topology(),
         use_cache=use_cache,
     )
@@ -324,7 +324,7 @@ def get_batch(neox_args, data_iterator):
     # Items and their type.
     if neox_args.train_impl in ["normal", "kto"]:
         keys = ["text", "label"] if neox_args.train_label_data_paths else ["text"]
-    elif neox_args.train_impl == "dpo":
+    elif neox_args.train_impl in ["dpo", "rm"]:
         keys = (
             [["pos", "pos_label"], ["neg", "neg_label"]]
             if neox_args.pos_train_label_data_paths
@@ -575,6 +575,32 @@ def forward_step(
         else:
             moe_loss = 0.0
         loss = main_loss + moe_loss
+    elif neox_args.train_impl == "rm":
+        maybe_tuple = model((tokens, position_ids, attention_mask), neox_args=neox_args)
+        if type(maybe_tuple) is tuple:
+            outputs, _ = maybe_tuple
+        else:
+            outputs = maybe_tuple
+        pos, neg = torch.chunk(outputs, 2, 0)
+        pos_loss_mask, neg_loss_mask = torch.chunk(loss_mask, 2, 0)
+        # We assume that each pos, neg pair occur in the same order
+        # e.g. second nonzero pos is the corresponding second nonzero neg
+        # and that there are also an equal number of pos and neg in each sequence.
+        pos_indx = pos_loss_mask.nonzero()
+        neg_indx = neg_loss_mask.nonzero()
+        # indx[:, 0] is the batch index, indx[:, 1] is the token index, we only care about the token index.
+        pos_indx = pos_indx[:, 1].unsqueeze(1)
+        neg_indx = neg_indx[:, 1].unsqueeze(1)
+        pos = torch.gather(pos.squeeze(), dim=1, index=pos_indx)
+        neg = torch.gather(neg.squeeze(), dim=1, index=neg_indx)
+        with torch.no_grad():
+            metrics["pos_values"] = pos.clone().detach().mean()
+            metrics["neg_values"] = neg.clone().detach().mean()
+            metrics["margin"] = (pos - neg).clone().detach().mean()
+            metrics["accuracy"] = ((pos - neg) > 0).clone().detach().float().mean()
+        loss = (-F.logsigmoid(pos - neg).mean()) + (
+            (neox_args.z_loss * (pos**2 + neg**2)).mean()
+        )
     elif neox_args.train_impl == "dpo":
         # Based on https://github.com/eric-mitchell/direct-preference-optimization/blob/main/trainers.py#L90
         with torch.inference_mode():
@@ -786,7 +812,7 @@ def get_model(neox_args, use_cache=False):
         model = GPT2ModelPipe(
             neox_args=neox_args,
             num_tokentypes=0,
-            parallel_output=True,
+            parallel_output=True if neox_args.train_impl != "rm" else False,
             topology=mpu.get_topology(),
             use_cache=use_cache,
         )