Skip to content

[Canonical LoRA] fix: forward under expert layers #1628

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
yaoyu-33 merged 1 commit into from
Dec 8, 2025
Merged

[Canonical LoRA] fix: forward under expert layers #1628

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 6 additions & 6 deletions src/megatron/bridge/peft/canonical_lora.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@

import logging
from dataclasses import dataclass, field
from typing import List, Literal, Optional, Tuple
from typing import Any, List, Literal, Optional, Tuple

import torch
from megatron.core.dist_checkpointing.mapping import ShardedStateDict
Expand Down Expand Up @@ -71,9 +71,9 @@ class LoRALinearSplitQKV(AdapterWrapper):
class to provide a specific implementation of the forward method.
"""

def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
# pylint: disable=C0115,C0116
linear_output, bias, layernorm_output = self.base_linear_forward(x)
linear_output, bias, layernorm_output = self.base_linear_forward(x, *args, **kwargs)
query = self.adapter.adapter_q(layernorm_output)
key = self.adapter.adapter_k(layernorm_output)
value = self.adapter.adapter_v(layernorm_output)
Expand All @@ -97,12 +97,12 @@ class LoRALinearSplitFC1UpGate(AdapterWrapper):
class to provide a specific implementation of the forward method.
"""

def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
# pylint: disable=C0115,C0116
linear_output, bias, layernorm_output = self.base_linear_forward(x)
linear_output, bias, layernorm_output = self.base_linear_forward(x, *args, **kwargs)
adapter_output_gate = self.adapter.adapter_gate(layernorm_output)
adapter_output_up = self.adapter.adapter_up(layernorm_output)
adapter_output = torch.cat([adapter_output_gate, adapter_output_up], dim=2)
adapter_output = torch.cat([adapter_output_gate, adapter_output_up], dim=-1)
return linear_output + adapter_output, bias


Expand Down