### Install Fast Weight Attention

Source: https://github.com/lucidrains/fast-weight-attention/blob/master/README.md

Installation command for the library via pip.

```bash
$ pip install fast-weight-attention
```

--------------------------------

### Initialize and Use FastWeightAttention

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Initialize the FastWeightAttention module with various parameters for attention heads, learning rates, and gating. Demonstrates forward passes with and without memory updates, and inference-only usage.

```python
import torch
from fast_weight_attention import FastWeightAttention

# Initialize the fast weight attention module
mem = FastWeightAttention(
    dim=512,                      # Input/output dimension
    dim_head=64,                  # Dimension per attention head
    dim_value_head=None,          # Value head dimension (defaults to dim_head)
    heads=8,                      # Number of attention heads
    causal=True,                  # Use causal masking
    max_learning_rate=1e-2,       # Max per-token learning rate for SGD updates
    max_muon_learning_rate=1e-1,  # Max learning rate for Muon updates
    muon_update=True,             # Use Muon optimizer (Newton-Schulz orthogonalization)
    use_polar_express=False,      # Use polar decomposition instead of Newton-Schulz
    use_gates=True,               # Enable gating mechanism on outputs
    max_fast_weight_norm=None     # Optional weight norm clipping (e.g., 2.0)
)

tokens = torch.randn(1, 64, 512)  # (batch, seq_len, dim)

# Forward pass with memory updates
past_mem = None
retrieved, next_mem = mem(tokens, past_mem=past_mem, return_next_memories=True)
# Continue processing with accumulated memory
retrieved, next_mem = mem(tokens, past_mem=next_mem, return_next_memories=True)
retrieved, next_mem = mem(tokens, past_mem=next_mem, return_next_memories=True)

assert retrieved.shape == tokens.shape  # Output: (1, 64, 512)

# Inference only (no memory update)
retrieved = mem(tokens, return_next_memories=False)
```

--------------------------------

### Training Loop for MemorizingModel

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Initializes the model and optimizer, then performs a training loop with a pattern-repetition task. It uses cross-entropy loss and monitors accuracy on the second half of the sequence.

```python
model = MemorizingModel(num_tokens=8, dim=64, depth=1, chunk_size=4)
optim = Adam(model.parameters(), lr=3e-3)

for step in range(100):
    # Task: repeat pattern AB -> ABAB
    half = torch.randint(0, 8, (16, 8))  # (batch, half_len)
    seq = torch.cat((half, half), dim=-1)  # (batch, seq_len=16)
    labels = seq[:, 1:]

    preds, _ = model(seq, return_next_memories=True)
    preds = preds[:, :-1]

    loss = F.cross_entropy(
        rearrange(preds, 'b n d -> (b n) d'),
        rearrange(labels, 'b n -> (b n)')
    )

    loss.backward()
    optim.step()
    optim.zero_grad()

    if step % 20 == 0:
        with torch.no_grad():
            preds_class = preds.argmax(dim=-1)
            acc = (preds_class[:, 7:] == labels[:, 7:]).float().mean()
        print(f"Step {step}: loss={loss.item():.3f}, second_half_acc={acc.item():.3f}")
```

--------------------------------

### Simulate streaming with variable chunk sizes

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Demonstrates how to process tokens in chunks while maintaining memory continuity across calls.

```python
for start_idx in range(0, 32, 4):
    end_idx = min(start_idx + 4, 32)
    chunk = tokens[:, start_idx:end_idx, :]

    out_chunk, past_mem = manager(
        chunk,
        return_next_memories=True,
        past_mem=past_mem,
        detach_next_memories_every=None,  # Detach gradients every N chunks (None = never)
        ablate_mem=False                   # Set True to disable memory (for ablation studies)
    )

    if out_chunk is not None:
        outs.append(out_chunk)

out_streamed = torch.cat(outs, dim=1)
# Output matches batch processing
assert torch.allclose(out_all, out_streamed, atol=1e-4)
```

--------------------------------

### Initialize and accumulate memory states

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Creates memory state dictionaries and demonstrates accumulation across forward passes.

```python
import torch
from fast_weight_attention import FastWeightAttention

mem = FastWeightAttention(512, causal=True, heads=8)
tokens = torch.randn(2, 64, 512)  # batch_size=2

# Initialize memories for a batch
initial_memory = mem.init_memories(batch=2)
print(f"Memory keys: {list(initial_memory.keys())}")  # ['wq', 'wk', 'wv', 'wo', 'wg']
print(f"wq shape: {initial_memory['wq'].shape}")      # (2, 8, 512, 64) = (batch, heads, dim, dim_head)

# Process and accumulate memory
out1, mem1 = mem(tokens, past_mem=None, return_next_memories=True)
out2, mem2 = mem(tokens, past_mem=mem1, return_next_memories=True)

# Memory is accumulated across forward passes
# Each memory tensor: (batch, heads, dim_in, dim_out)
```

--------------------------------

### Initialize and Use FastWeightAttention

Source: https://github.com/lucidrains/fast-weight-attention/blob/master/README.md

Basic usage for initializing the attention module and performing iterative memory updates.

```python
import torch
from fast_weight_attention import FastWeightAttention

mem = FastWeightAttention(512, causal = True)

tokens = torch.randn(1, 64, 512)

past_mem = None

retrieved, next_mem = mem(tokens, past_mem = past_mem, return_next_memories = True)
retrieved, next_mem = mem(tokens, past_mem = next_mem, return_next_memories = True)
retrieved, next_mem = mem(tokens, past_mem = next_mem, return_next_memories = True)

assert retrieved.shape == tokens.shape

# you can then retrieve without fast weight updating

retrieved = mem(tokens, return_next_memories = False)
```

--------------------------------

### Initialize and Use ChunkManager

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Initialize a base attention module and wrap it with ChunkManager for chunked sequence processing. Demonstrates processing an entire sequence at once and preparing for streaming inference.

```python
import torch
from fast_weight_attention import FastWeightAttention, ChunkManager
from fast_weight_attention.chunk_manager import ChunkingState

# Create base attention module
net = FastWeightAttention(
    dim=16,
    dim_head=8,
    heads=2,
    causal=True,
    use_gates=True
)

# Wrap with chunk manager
manager = ChunkManager(
    net,
    chunk_size=8,              # Process 8 tokens per chunk
    use_forget_gate=True       # Enable learnable forget gate for memory decay
)

tokens = torch.randn(1, 32, 16)  # (batch, seq_len, dim)

# Process entire sequence at once (internally chunked)
out_all, mem_all = manager(tokens, return_next_memories=True)

# Streaming: process variable-sized chunks
outs = []
past_mem = None  # Will be ChunkingState after first call
```

--------------------------------

### Initialize and Use ChunkedFastWeightAttention

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Initialize the ChunkedFastWeightAttention module, which wraps FastWeightAttention with ChunkManager for processing long sequences in chunks. Demonstrates processing a long sequence that is automatically chunked.

```python
import torch
from fast_weight_attention import ChunkedFastWeightAttention

# Initialize chunked attention module
mem = ChunkedFastWeightAttention(
    512,                  # Input dimension
    causal=True,          # Causal attention masking
    chunk_size=64         # Process 64 tokens at a time
)

# Process a long sequence (automatically chunked)
tokens = torch.randn(1, 512, 512)  # (batch, long_seq_len, dim)

retrieved, next_mem = mem(tokens, return_next_memories=True)

assert retrieved.shape == tokens.shape  # Output: (1, 512, 512)
```

--------------------------------

### Implement a memorization task model

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

A complete model architecture using Fast Weight Attention and ChunkManager for sequence tasks.

```python
import torch
import torch.nn.functional as F
from torch import nn
from torch.optim import Adam
from einops import rearrange
from fast_weight_attention import FastWeightAttention, ChunkManager

class MemorizingModel(nn.Module):
    def __init__(self, num_tokens, dim, depth=1, chunk_size=4):
        super().__init__()
        self.embed = nn.Embedding(num_tokens, dim)

        self.layers = nn.ModuleList([
            nn.ModuleList([
                ChunkManager(
                    FastWeightAttention(
                        dim=dim,
                        dim_head=32,
                        dim_value_head=32,
                        heads=4,
                        causal=True,
                        muon_update=True,
                        use_polar_express=True,
                        max_learning_rate=1e-3,
                        use_gates=True
                    ),
                    chunk_size=chunk_size,
                    use_forget_gate=False
                ),
                nn.Linear(dim, dim)  # Simple feedforward
            ]) for _ in range(depth)
        ])

        self.head = nn.Linear(dim, num_tokens)

    def forward(self, x, past_mems=None, return_next_memories=False):
        h = self.embed(x)
        past_mems = past_mems or [None] * len(self.layers)
        next_mems = []

        for (attn, ff), past_mem in zip(self.layers, past_mems):
            if return_next_memories:
                attn_out, next_mem = attn(h, past_mem=past_mem, return_next_memories=True)
                next_mems.append(next_mem)
            else:
                attn_out = attn(h, past_mem=past_mem)

            h = h + attn_out
            h = h + ff(h)

        if return_next_memories:
            return self.head(h), next_mems
        return self.head(h)
```

--------------------------------

### Reference Citations

Source: https://github.com/lucidrains/fast-weight-attention/blob/master/README.md

BibTeX citations for related research and methodologies.

```bibtex
@article{zhang2026loger,
    title   = {LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory},
    author  = {Zhang, Junyi and Herrmann, Charles and Hur, Junhwa and Sun, Chen and Yang, Ming-Hsuan and Cole, Forrester and Darrell, Trevor and Sun, Deqing},
    journal = {arXiv preprint arXiv:2603.03269},
    year    = {2026}
}
```

```bibtex
@misc{zhao2026fastweightproductkeymemory,
    title   = {Fast-weight Product Key Memory},
    author  = {Tianyu Zhao and Llion Jones},
    year    = {2026},
    eprint  = {2601.00671},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url     = {https://arxiv.org/abs/2601.00671},
}
```

```bibtex
@misc{jordan2024muon,
    author  = {Keller Jordan and Yuchen Jin and Vlado Boza and Jiacheng You and Franz Cesista and Laker Newhouse and Jeremy Bernstein},
    title   = {Muon: An optimizer for hidden layers in neural networks},
    year    = {2024},
    url     = {https://kellerjordan.github.io/posts/muon/}
```

```bibtex
@article{Yaghoubietal2026,
    author  = {Yaghoubi, Mohammad and Nieto-Posadas, Andres and Mosser, Coralie-Anne and Gisiger, Thomas and Wilson, Émmanuel and Williams, Sylvain and Brandon, Mark P.},
    title   = {Predictive coding of reward in the hippocampus},
    journal = {Nature},
    year    = {2026},
    doi     = {10.1038/s41586-025-09958-0}
```

```bibtex
@article{volchkov2026cliptogrok,
    title   = {Clip to Grok: Weight Norm Clipping for Accelerated Generalization},
    author  = {Volchkov, Vladimir and Rivlin, Aviad},
    year    = {2026},
    journal = {arXiv preprint},
    note    = {Implementation available at \url{https://github.com/NiftyliuS/cliptogrok}}
```

--------------------------------

### Process Sequences with ChunkedFastWeightAttention

Source: https://github.com/lucidrains/fast-weight-attention/blob/master/README.md

Usage for processing long sequences by segmenting them into chunks and maintaining memory state across them.

```python
import torch
from fast_weight_attention import ChunkedFastWeightAttention

mem = ChunkedFastWeightAttention(
    512,
    causal = True,
    chunk_size = 64   # process 64 tokens at a time, carrying fast weight memories across chunks
)

tokens = torch.randn(1, 512, 512)

retrieved, next_mem = mem(tokens, return_next_memories = True)

assert retrieved.shape == tokens.shape
```

--------------------------------

### Manage ChunkingState for streaming inference

Source: https://context7.com/lucidrains/fast-weight-attention/llms.txt

Encapsulates the state for chunked processing to enable pause/resume functionality.

```python
from fast_weight_attention.chunk_manager import ChunkingState

# ChunkingState fields:
state = ChunkingState(
    memory=None,           # Fast weight memory dict {wq, wk, wv, wo, wg}
    last_token=None,       # Last processed token tensor
    token_count=0,         # Total tokens processed
    boundary_state=None,   # State for chunk boundary handling
    buffer=None            # Incomplete chunk buffer for streaming
)

# Access state after processing
out, next_state = manager(tokens, return_next_memories=True)
print(f"Tokens processed: {next_state.token_count}")
print(f"Memory keys: {list(next_state.memory.keys()) if next_state.memory else None}")
print(f"Has buffer: {next_state.buffer is not None}")
```

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