fixup! [Feature] Add Multi-Token Prediction (MTP) module implementation

xtuner/v1/model/moe/moe.py

@@ -695,7 +695,7 @@ def _forward(
             for idx, (mtp_hidden, mtp_ctx) in enumerate(zip(mtp_outputs, mtp_loss_ctx_list)):
                 shifted_tensor = mtp_ctx.loss_kwargs.shifted_labels
                 mtp_ctx.loss_kwargs.shifted_labels = roll_packed_tensor(
-                    shifted_tensor, seq_ctx.cu_seq_lens_k, -idx - 1, dim=-1
+                    shifted_tensor, seq_ctx.cu_seq_lens_k, -idx - 1, dim=-1, fill_value=-100
                 )
 
                 mtp_hidden_states, mtp_router_results, mtp_router_weights = mtp_hidden

xtuner/v1/module/mtp/utils.py

@@ -10,6 +10,7 @@ def roll_packed_tensor(
     cu_seq_lens: torch.IntTensor,
     shifts: int = -1,
     dim: int = -1,
+    fill_value: float | int = 0,
 ) -> torch.Tensor:
     """Roll a packed tensor along the specified dimension.
 
@@ -24,9 +25,12 @@ def roll_packed_tensor(
             Only negative shifts are supported.
         dim (int): Dimension along which to roll. The ``cu_seq_lens`` boundaries
             are applied on this dimension. Default is -1 (last dimension).
+        fill_value (float | int): Value used to fill boundary positions after rolling.
+            Defaults to 0. Use the loss ignore index (e.g., -100) when rolling label
+            tensors to ensure boundary positions are excluded from loss computation.
 
     Returns:
-        torch.Tensor: Rolled tensor with boundary positions zeroed.
+        torch.Tensor: Rolled tensor with boundary positions filled with ``fill_value``.
 
     Example:
         For packed sequences [1,2,3] and [4,5,6] with shifts=-1, dim=-1:
@@ -39,7 +43,7 @@ def roll_packed_tensor(
         >>> tensor = torch.arange(12).reshape(1, 6, 2)
         >>> cu_seq_lens = torch.tensor([0, 3, 6], dtype=torch.int32)
         >>> rolled = roll_packed_tensor(tensor, cu_seq_lens, shifts=-1, dim=-2)
-        >>> rolled[0, 2]  # tensor([0, 0])  (boundary zeroed)
+        >>> rolled[0, 2]  # tensor([0, 0])  (boundary filled with fill_value=0)
     """
     assert shifts <= 0, "Only negative shift is supported"
 
@@ -57,13 +61,13 @@ def roll_packed_tensor(
         seq_slice = tensor.narrow(dim, start_idx, end_idx - start_idx)  # type: ignore[arg-type]
         rolled_seq = torch.roll(seq_slice, shifts=shifts, dims=dim)
 
-        # Zero out the last |shifts| positions along dim to avoid information
+        # Fill the last |shifts| positions along dim to avoid information
         # leakage across sequences.  For shifts=-1 the last 1 position is
-        # zeroed; for shifts=-2 the last 2 positions are zeroed, etc.
-        zero_len = -shifts
-        zero_start = (end_idx - start_idx) - zero_len
-        zero_slice = rolled_seq.narrow(dim, zero_start, zero_len)  # type: ignore[arg-type]
-        zero_slice.zero_()
+        # filled; for shifts=-2 the last 2 positions are filled, etc.
+        fill_len = -shifts
+        fill_start = (end_idx - start_idx) - fill_len
+        fill_slice = rolled_seq.narrow(dim, fill_start, fill_len)  # type: ignore[arg-type]
+        fill_slice.fill_(fill_value)
 
         # Write back to the rolled tensor
         rolled_tensor.narrow(dim, start_idx, end_idx - start_idx).copy_(rolled_seq)  # type: ignore[arg-type]