[fsdp] feat: upcast MoE routing to FP32 for better accuracy

verl/models/transformers/npu_patch.py

@@ -53,6 +53,67 @@ def apply_rotary_pos_emb_npu(q, k, cos, sin, position_ids=None, unsqueeze_dim=1)
     return q_embed.to(q.dtype), k_embed.to(k.dtype)
 
 
+class RouterGatingLinearFunction(torch.autograd.Function):
+    """
+    Copied from Megatron-LM megatron/core/transformer/moe/moe_utils.py
+    Autograd function for router gating linear.
+    """
+
+    @staticmethod
+    def forward(
+        ctx,
+        inp: torch.Tensor,
+        weight: torch.Tensor,
+        router_dtype: torch.dtype,
+    ) -> torch.Tensor:
+        """
+        Forward pass of the RouterGatingLinearFunction function.
+
+        Args:
+            inp (torch.Tensor): The input tensor.
+            weight (torch.Tensor): The weight tensor.
+            router_dtype (torch.dtype): The router dtype.
+
+        Returns:
+            torch.Tensor: The output tensor.
+        """
+        ctx.save_for_backward(inp, weight)
+        ctx.router_dtype = router_dtype
+        ctx.input_dtype = inp.dtype
+        ctx.weight_dtype = weight.dtype
+        inp_shape = inp.shape
+        inp = inp.view(-1, inp_shape[-1])
+
+        output = torch.mm(inp.to(router_dtype), weight.to(router_dtype).t())
+
+        output = output.view(*inp_shape[:-1], -1)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, None]:
+        """
+        Backward pass of the RouterGatingLinearFunction function.
+
+        Args:
+            grad_output (torch.Tensor): The gradient output.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], None]:
+                The gradient input, gradient weight, gradient bias, and None.
+        """
+        inp, weight = ctx.saved_tensors
+        inp_shape = inp.shape
+        grad_shape = grad_output.shape
+        inp = inp.view(-1, inp_shape[-1])
+        grad_output = grad_output.view(-1, grad_shape[-1])
+
+        grad_input = torch.mm(grad_output, weight.to(ctx.router_dtype)).to(ctx.input_dtype)
+        grad_weight = torch.mm(grad_output.t(), inp.to(ctx.router_dtype)).to(ctx.weight_dtype)
+
+        grad_input = grad_input.view(*inp_shape)
+        return grad_input, grad_weight, None
+
+
 class NPUGmmFunction(torch.autograd.Function):
     @staticmethod
     def forward(ctx, x, weight, group_list, group_list_type=1):
@@ -111,7 +172,7 @@ def qwen3_moe_sparse_moe_block_forward_npu(self, hidden_states: torch.Tensor) ->
     hidden_dim = hidden_states.shape[-1]
     hidden_states = hidden_states.view(-1, hidden_dim)
     # router_logits: (batch * sequence_length, n_experts)
-    router_logits = self.gate(hidden_states)
+    router_logits = RouterGatingLinearFunction.apply(hidden_states, self.gate.weight, torch.float32)
 
     routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
     routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
@@ -220,7 +281,7 @@ def __init__(self, config):
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         batch_size = hidden_states.shape[0]
         hidden_states = hidden_states.reshape(-1, self.hidden_size)
-        router_logits = self.gate(hidden_states)
+        router_logits = RouterGatingLinearFunction.apply(hidden_states, self.gate.weight, torch.float32)
         routing_weights = torch.nn.functional.softmax(router_logits, dim=-1, dtype=torch.float)
         routing_weights, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)
         routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True)