Repository: huggingface/transformers
Pull request: Remove double soft-max in load-balancing loss. Fixes #39055 .

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Fixes #39055 


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Changed files:
filename: src/transformers/models/dbrx/modeling_dbrx.py
patch: @@ -124,7 +124,7 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
 
 
 def load_balancing_loss_func(
-    gate_logits: torch.Tensor,
+    gate_probabilities: torch.Tensor,
     num_experts: int,
     top_k: int,
     attention_mask: Optional[torch.Tensor],
@@ -150,14 +150,12 @@ def load_balancing_loss_func(
     Returns:
         The auxiliary loss.
     """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
+    if gate_probabilities is None or not isinstance(gate_probabilities, tuple):
         return torch.tensor(0.0)
 
-    if isinstance(gate_logits, tuple):
-        compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
-
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    if isinstance(gate_probabilities, tuple):
+        compute_device = gate_probabilities[0].device
+        routing_weights = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_probabilities], dim=0)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -171,7 +169,7 @@ def load_balancing_loss_func(
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
         batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = routing_weights.shape[0] // (batch_size * sequence_length)
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (