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model/lora.py — LoRA (Low-Rank Adaptation) for EVAFRILL-Mo hybrid models.
Injects trainable low-rank adapters into:
- Attention layers: qkv_proj, out_proj
- Mamba-2 layers: in_proj, out_proj
Usage:
model = LLM.from_pretrained(checkpoint)
apply_lora(model, rank=32, alpha=64)
# Only LoRA params are trainable; base model is frozen
# After training, merge LoRA weights back:
merge_lora(model)
# Or save/load LoRA weights separately:
save_lora(model, path)
load_lora(model, path)
"""
from __future__ import annotations
import math
from pathlib import Path
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .attention import MultiHeadAttention
from .mamba_block import Mamba2Block
class LoRALinear(nn.Module):
"""LoRA adapter wrapping an existing nn.Linear layer.
Computes: output = original_linear(x) + (alpha/rank) * x @ A^T @ B^T
where A: (rank, in_features), B: (out_features, rank)
"""
def __init__(
self,
original: nn.Linear,
rank: int = 32,
alpha: float = 64.0,
dropout: float = 0.0,
) -> None:
super().__init__()
self.original = original
self.rank = rank
self.alpha = alpha
self.scaling = alpha / rank
in_features = original.in_features
out_features = original.out_features
# A: down-projection (in_features → rank)
# Create on same device/dtype as original weights
_dev = original.weight.device
_dt = original.weight.dtype
self.lora_A = nn.Parameter(torch.empty(rank, in_features, device=_dev, dtype=_dt))
# B: up-projection (rank → out_features)
self.lora_B = nn.Parameter(torch.zeros(out_features, rank, device=_dev, dtype=_dt))
# Initialize A with kaiming uniform, B with zeros
nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
# B is already zeros → initial LoRA output is zero
self.dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
# Freeze original weights
original.weight.requires_grad = False
if original.bias is not None:
original.bias.requires_grad = False
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Original forward
result = self.original(x)
# LoRA path: x → dropout → A → B → scale
lora_out = self.dropout(x)
lora_out = F.linear(lora_out, self.lora_A) # (..., rank)
lora_out = F.linear(lora_out, self.lora_B) # (..., out_features)
return result + lora_out * self.scaling
def merge_weights(self) -> None:
"""Merge LoRA weights into the original linear layer permanently."""
with torch.no_grad():
# W' = W + scaling * B @ A
self.original.weight.add_(
(self.lora_B @ self.lora_A) * self.scaling
)
@property
def weight(self) -> torch.Tensor:
"""Access original weight for compatibility."""
return self.original.weight
@property
def bias(self) -> Optional[torch.Tensor]:
return self.original.bias
def apply_lora(
model: nn.Module,
rank: int = 32,
alpha: float = 64.0,
dropout: float = 0.0,
target_modules: Optional[list[str]] = None,
) -> int:
"""Apply LoRA adapters to a model, freeze base weights.
Args:
model: The LLM model (raw, not DDP-wrapped).
rank: LoRA rank (default 32).
alpha: LoRA scaling factor (default 64).
dropout: Dropout on LoRA path (default 0).
target_modules: List of module attribute names to adapt.
Default: ["qkv_proj", "out_proj", "in_proj"]
(covers both Attention and Mamba layers).
Returns:
Number of LoRA parameters added.
"""
if target_modules is None:
target_modules = ["qkv_proj", "out_proj", "in_proj"]
# First, freeze ALL parameters
for param in model.parameters():
param.requires_grad = False
lora_count = 0
total_lora_params = 0
for name, module in model.named_modules():
# Check Attention layers
if isinstance(module, MultiHeadAttention):
for attr in target_modules:
if hasattr(module, attr):
original = getattr(module, attr)
if isinstance(original, nn.Linear):
lora_layer = LoRALinear(original, rank=rank, alpha=alpha, dropout=dropout)
setattr(module, attr, lora_layer)
params = rank * original.in_features + original.out_features * rank
total_lora_params += params
lora_count += 1
# Check Mamba layers
elif isinstance(module, Mamba2Block):
for attr in target_modules:
if hasattr(module, attr):
original = getattr(module, attr)
if isinstance(original, nn.Linear):
lora_layer = LoRALinear(original, rank=rank, alpha=alpha, dropout=dropout)
setattr(module, attr, lora_layer)
params = rank * original.in_features + original.out_features * rank
total_lora_params += params
lora_count += 1
print(f"[LoRA] Applied {lora_count} adapters, {total_lora_params:,} trainable params "
f"(rank={rank}, alpha={alpha})")
return total_lora_params
def merge_lora(model: nn.Module) -> int:
"""Merge all LoRA weights back into base model and remove LoRA layers.
Returns:
Number of LoRA layers merged.
"""
merged = 0
for name, module in model.named_modules():
for attr_name in list(vars(module).keys()):
# Check nn.Module children
pass
if isinstance(module, (MultiHeadAttention, Mamba2Block)):
for attr in ["qkv_proj", "out_proj", "in_proj"]:
if hasattr(module, attr):
layer = getattr(module, attr)
if isinstance(layer, LoRALinear):
layer.merge_weights()
setattr(module, attr, layer.original)
merged += 1
# Unfreeze all parameters after merging
for param in model.parameters():
param.requires_grad = True
print(f"[LoRA] Merged {merged} adapters back into base model")
return merged
def get_lora_params(model: nn.Module) -> list[nn.Parameter]:
"""Get all LoRA trainable parameters."""
params = []
for module in model.modules():
if isinstance(module, LoRALinear):
params.append(module.lora_A)
params.append(module.lora_B)
return params
def save_lora(model: nn.Module, path: str | Path) -> Path:
"""Save only the LoRA adapter weights."""
path = Path(path)
path.mkdir(parents=True, exist_ok=True)
lora_state = {}
for name, module in model.named_modules():
if isinstance(module, LoRALinear):
lora_state[f"{name}.lora_A"] = module.lora_A.data.cpu()
lora_state[f"{name}.lora_B"] = module.lora_B.data.cpu()
save_path = path / "lora_weights.pt"
torch.save(lora_state, save_path)
n_params = sum(v.numel() for v in lora_state.values())
size_mb = save_path.stat().st_size / 1e6
print(f"[LoRA] Saved {len(lora_state)} tensors ({n_params:,} params, {size_mb:.1f} MB) → {save_path}")
return save_path
def load_lora(model: nn.Module, path: str | Path) -> int:
"""Load LoRA adapter weights. LoRA layers must already be applied."""
path = Path(path)
lora_file = path / "lora_weights.pt" if path.is_dir() else path
lora_state = torch.load(lora_file, map_location="cpu", weights_only=True)
loaded = 0
for name, module in model.named_modules():
if isinstance(module, LoRALinear):
a_key = f"{name}.lora_A"
b_key = f"{name}.lora_B"
if a_key in lora_state and b_key in lora_state:
module.lora_A.data.copy_(lora_state[a_key])
module.lora_B.data.copy_(lora_state[b_key])
loaded += 1
print(f"[LoRA] Loaded {loaded} adapter weight pairs from {lora_file}")
return loaded
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