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memory-llm-ffn-decoupling Decouple feed-forward networks from self-attention by training FFNs on context-free token embeddings instead of residual streams. Enables pre-computation of FFN outputs as static lookup tables for inference efficiency and improved interpretability.
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Basierend auf der SOC-Berufsklassifikation
name memory-llm-ffn-decoupling title MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers version 0.0.2 engine skillxiv-v0.0.2-claude-opus-4.6 license MIT url https://arxiv.org/abs/2602.00398 keywords ["FFN Architecture","Memory Mechanism","Interpretability","Static Lookup","Transformer Components"] description Decouple feed-forward networks from self-attention by training FFNs on context-free token embeddings instead of residual streams. Enables pre-computation of FFN outputs as static lookup tables for inference efficiency and improved interpretability.
MemoryLLM: Decoupled Feed-Forward Memory
Standard Transformers intertwine attention outputs with FFN inputs through the residual stream, making each token's FFN output context-dependent. MemoryLLM decouples this by training FFNs independently on token-indexed embeddings rather than context-aware representations. This transforms FFNs into interpretable lookup tables that can be pre-computed and stored on-device, enabling faster inference and clearer understanding of what FFNs learn.
The key insight is that FFN parameters can be viewed as a retrieval table where tokens are keys and hidden states are values. By separating FFN training from attention, we get both efficiency and interpretability.
Core Concept
MemoryLLM operates on architectural decoupling:
Standard Transformer : Attention → FFN, where FFN sees attention-modified residualsMemoryLLM : Attention and FFN work independently; FFN receives only token embeddings (context-free)
This enables:
Static Lookup : FFN outputs pre-computed and cachedInterpretability : FFN behavior per-token is visibleFlexibility : FFN outputs can be selectively disabled or modified
Architecture Overview
Independent FFN Training : FFNs trained only on token embeddings, not residual streamsToken-Key-Value Framework : Think of FFN as (up_proj: key, down_proj: value)Parallel Processing : Attention and FFN execute independentlyStatic Lookup Layer : FFN outputs cached as token-indexed lookup tablesFlexible Integration : Optionally use context-aware variant for better performance
Implementation
Step 1: Design Context-Free FFN
Create FFN that operates only on token identity, not context.
class MemoryFFN (nn.Module):
def __init__ (self, hidden_dim: int , intermediate_dim: int ,
vocab_size: int ):
"""
FFN operating on token embeddings (context-free).
Args:
hidden_dim: Hidden dimension
intermediate_dim: Intermediate expansion dimension
vocab_size: Vocabulary size for static lookup
"""
super
self
True
self
self
True
self
None
self
def
forward
self, token_embeddings: torch.Tensor
"""
Apply FFN to token embeddings (context-free).
Args:
token_embeddings: [batch, seq_len, hidden_dim]
Returns:
ffn_output: [batch, seq_len, hidden_dim]
"""
self
self
self
return
def
precompute_static_lookup
self, embedding_layer: nn.Module
"""
Pre-compute FFN outputs for all vocabulary tokens.
Args:
embedding_layer: Model's token embedding layer
"""
self
with
self
self
self
'_static_lookup_buffer'
self
def
lookup_static
self, token_ids: torch.Tensor
"""Retrieve pre-computed FFN outputs (for inference)."""
if
self
is
None
return
None
return
self
Step 2: Create Context-Aware Variant (Optional) For better performance, support hybrid mode mixing context-free and context-aware.
class FlexMemoryFFN (nn.Module):
def __init__ (self, hidden_dim: int , intermediate_dim: int ,
vocab_size: int , context_weight: float = 0.5 ):
"""
Flexible FFN: mix context-free and context-aware components.
Args:
hidden_dim: Hidden dimension
intermediate_dim: Intermediate expansion
vocab_size: Vocabulary size
context_weight: Balance between context-free (0) and context-aware (1)
"""
super ().__init__()
self .context_weight = context_weight
self .memory_ffn = MemoryFFN(hidden_dim, intermediate_dim, vocab_size)
if context_weight > 0 :
self .context_up = nn.Linear(hidden_dim, intermediate_dim // 2 , bias=True )
self .context_down = nn.Linear(intermediate_dim // 2 , hidden_dim, bias=True )
self .activation = nn.GELU()
def forward (self, token_embeddings: torch.Tensor,
context_residuals: torch.Tensor ) -> torch.Tensor:
"""
Combine context-free and context-aware outputs.
Args:
token_embeddings: [batch, seq_len, hidden_dim]
context_residuals: [batch, seq_len, hidden_dim] (from attention)
Returns:
ffn_output: [batch, seq_len, hidden_dim]
"""
context_free_out = self .memory_ffn(token_embeddings)
if self .context_weight == 0 :
return context_free_out
context_aware_out = self .context_up(context_residuals)
context_aware_out = self .activation(context_aware_out)
context_aware_out = self .context_down(context_aware_out)
output = ((1 - self .context_weight) * context_free_out +
self .context_weight * context_aware_out)
return output
Step 3: Integrate into Transformer Layer Modify standard transformer layer to use decoupled FFN.
class MemoryTransformerLayer (nn.Module):
def __init__ (self, hidden_dim: int , num_heads: int ,
intermediate_dim: int , vocab_size: int ):
"""
Transformer layer with decoupled memory FFN.
Args:
hidden_dim: Model dimension
num_heads: Number of attention heads
intermediate_dim: FFN intermediate dimension
vocab_size: Vocabulary size
"""
super ().__init__()
self .self_attn = nn.MultiheadAttention(
hidden_dim,
num_heads,
batch_first=True
)
self .memory_ffn = MemoryFFN(hidden_dim, intermediate_dim, vocab_size)
self .norm1 = nn.LayerNorm(hidden_dim)
self .norm2 = nn.LayerNorm(hidden_dim)
def forward (self, hidden_states: torch.Tensor,
token_embeddings: torch.Tensor,
attention_mask: Optional [torch.Tensor] = None ) -> torch.Tensor:
"""
Forward with independent attention and memory FFN.
Args:
hidden_states: [batch, seq_len, hidden_dim] (from residual)
token_embeddings: [batch, seq_len, hidden_dim] (token identities)
attention_mask: Optional
Returns:
output: [batch, seq_len, hidden_dim]
"""
attn_input = self .norm1(hidden_states)
attn_output, _ = self .self_attn(attn_input, attn_input, attn_input,
attn_mask=attention_mask)
hidden_states = hidden_states + attn_output
ffn_input = self .norm2(hidden_states)
ffn_output = self .memory_ffn(token_embeddings)
output = hidden_states + ffn_output
return output
Step 4: Training with Decoupled FFN Modify training to properly supervise context-free FFN.
def train_memory_transformer (
model: nn.Module,
train_loader,
embedding_layer: nn.Module,
num_epochs: int = 10
):
"""
Train transformer with decoupled memory FFN.
Args:
model: Transformer model using MemoryFFN
train_loader: Training data
embedding_layer: Token embedding layer
num_epochs: Training epochs
"""
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4 )
for epoch in range (num_epochs):
total_loss = 0
for batch_idx, (input_ids, labels) in enumerate (train_loader):
token_embeddings = embedding_layer(input_ids)
logits = model(input_ids, token_embeddings)
loss = torch.nn.functional.cross_entropy(
logits.view(-1 , logits.shape[-1 ]),
labels.view(-1 )
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
avg_loss = total_loss / len (train_loader)
print (f"Epoch {epoch} : loss={avg_loss:.4 f} " )
for layer in model.layers:
if hasattr (layer, 'memory_ffn' ):
layer.memory_ffn.precompute_static_lookup(embedding_layer)
return model
Step 5: Inference with Static Lookups Use pre-computed FFN outputs for efficiency.
class MemoryTransformerInference (nn.Module):
def __init__ (self, model: nn.Module ):
"""
Inference wrapper using static FFN lookups.
Args:
model: Trained MemoryTransformer
"""
super ().__init__()
self .model = model
def forward (self, input_ids: torch.Tensor,
use_cache: bool = False ) -> torch.Tensor:
"""
Forward pass using pre-computed FFN lookups.
Args:
input_ids: [batch, seq_len]
use_cache: Whether to cache KV pairs
Returns:
logits: [batch, seq_len, vocab_size]
"""
batch_size, seq_len = input_ids.shape
hidden_states = self .model.embed_tokens(input_ids)
cache = None
if use_cache:
cache = []
for layer in self .model.layers:
attn_input = layer.norm1(hidden_states)
if cache:
attn_output, cache_entry = layer.self_attn(
attn_input, use_cache=True
)
cache.append(cache_entry)
else :
attn_output = layer.self_attn(attn_input)
hidden_states = hidden_states + attn_output
if hasattr (layer, 'memory_ffn' ):
ffn_lookup = layer.memory_ffn.lookup_static(input_ids)
if ffn_lookup is not None :
hidden_states = hidden_states + ffn_lookup
else :
ffn_output = layer.memory_ffn(
self .model.embed_tokens(input_ids)
)
hidden_states = hidden_states + ffn_output
logits = self .model.lm_head(hidden_states)
return logits
def generate (self, input_ids: torch.Tensor,
max_new_tokens: int = 128 ) -> torch.Tensor:
"""Generate using memory model."""
generated = input_ids.clone()
for _ in range (max_new_tokens):
logits = self .forward(generated)
next_token = torch.argmax(logits[:, -1 ], dim=-1 , keepdim=True )
generated = torch.cat([generated, next_token], dim=1 )
return generated
Practical Guidance
Scenarios where interpretability matters (understanding what FFNs learn)
On-device inference with memory constraints
Models where FFN computation is bottleneck
Research on separable components in language models
Maximum performance is critical (context-aware FFNs better)
Models with complex token interdependencies
Real-time systems where pre-computation overhead matters
Scenarios with very large vocabularies (static lookup memory)
Static lookup overhead: Pre-computing for 100K vocabulary requires substantial memory
Performance regression: Context-free FFN loses modeling capacity; may need larger hidden dims
Vocabulary mismatch: Re-generating static lookup if vocabulary changes
Training instability: Context-free FFN may need careful initialization
Hyperparameter Guidelines:
Parameter Range Tuning context_weight 0.0-1.0 0.0 = full memory; 0.5+ for better quality intermediate_dim hidden_dim × 2-4 Standard; larger for context-free compensation Pre-compute budget Model allows Static lookup must fit in available memory
Reference Key results: Improved interpretability with comparable performance on 250M-1B models. Static lookups enable efficient on-device inference. Flex-MemoryLLM variant bridges performance gap with standard Transformers.
