### Install PEER-pytorch

Source: https://github.com/lucidrains/peer-pytorch/blob/main/README.md

Install the PEER-pytorch library using pip. This command should be run in your terminal.

```bash
pip install PEER-pytorch
```

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

### PEER Block Usage Example

Source: https://github.com/lucidrains/peer-pytorch/blob/main/README.md

Demonstrates how to initialize and use the PEER block in PyTorch. Ensure the model and input tensors are on the CUDA-enabled GPU.

```python
import torch
from PEER_pytorch import PEER

peer = PEER(
    dim = 512,
    heads = 8,                   # tested up to 32 - (hk = heads * num_experts_per_head (16))
    num_experts = 1_000_000,     # he chose 1 million
    num_experts_per_head = 16,   # he settled on 16, but was 32 in PKM paper
    dim_key = 128,
    pre_rmsnorm = True
).cuda()

x = torch.randn(2, 1024, 512).cuda()

out = peer(x) + x

assert x.shape == out.shape
```

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

### Initialize and use a standard PEER block

Source: https://context7.com/lucidrains/peer-pytorch/llms.txt

Configures a PEER module with 1 million experts and demonstrates a forward pass with residual connection and sparse memory fine-tuning.

```python
import torch
from PEER_pytorch import PEER

# Create a PEER block with 1 million experts
peer = PEER(
    dim = 512,                    # Input/output dimension
    heads = 8,                    # Number of retrieval heads (hk = heads * num_experts_per_head)
    num_experts = 1_000_000,      # Total number of experts (must be a perfect square)
    num_experts_per_head = 16,    # Experts retrieved per head
    dim_key = 128,                # Dimension of product keys
    activation = torch.nn.GELU,   # Activation function (default: GELU)
    pre_rmsnorm = True,           # Apply RMSNorm before processing
    non_competing_scores = True,  # Use ReLU instead of softmax (recommended)
    separate_embed_per_head = False,  # Separate embeddings per head to reduce redundancy
    dropout = 0.1                 # Dropout rate
).cuda()

# Forward pass with residual connection
x = torch.randn(2, 1024, 512).cuda()  # (batch, sequence, dim)
out = peer(x) + x  # PEER output + residual

assert out.shape == (2, 1024, 512)

# Sparse memory fine-tuning (continual learning)
# Only train specific expert neurons while freezing others
train_mem_mask = torch.zeros(1_000_000, dtype=torch.bool).cuda()
train_mem_mask[:10000] = True  # Only train first 10k experts

out_sparse = peer(x, train_mem_mask=train_mem_mask)
```

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

### Initialize and Use Standalone Product Key Retrieval

Source: https://context7.com/lucidrains/peer-pytorch/llms.txt

Configures the PK module for nearest-neighbor lookup and performs a forward pass to retrieve scores and indices.

```python
import torch
from PEER_pytorch import PK

# Create product key retrieval module
pk = PK(
    dim = 512,              # Input dimension
    heads = 8,              # Number of parallel retrieval heads
    dim_key = 256,          # Dimension of each product key component
    num_keys = 1000,        # Number of keys per product component
    product_keys = 2,       # Number of product key components (total space = num_keys^product_keys)
    product_key_topk = 16,  # Top-k per product key component
    final_topk = 16         # Final top-k after combining product keys
)

# Forward pass - returns scores and indices
x = torch.randn(2, 1024, 512)  # (batch, sequence, dim)
scores, indices = pk(x)

print(f"Scores shape: {scores.shape}")   # (2, 1024, 8, 16)
print(f"Indices shape: {indices.shape}") # (2, 1024, 8, 16)

# With softmax normalization on scores
scores_softmax, indices = pk(x, softmax_scores=True)

# Maximum index space
print(f"Max index value: {pk.max_index}")  # 1000^2 = 1_000_000

# Using 3 product keys for even larger index space
pk_large = PK(
    dim = 512,
    num_keys = 100,
    product_keys = 3,  # Total space = 100^3 = 1_000_000
    final_topk = 10
)

scores, indices = pk_large(x)
print(f"Large PK max index: {pk_large.max_index}")  # 1_000_000
```

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

### Initialize and use a PEERLora block

Source: https://context7.com/lucidrains/peer-pytorch/llms.txt

Combines PEER retrieval with LoRA adapters for parameter-efficient fine-tuning.

```python
import torch
from PEER_pytorch import PEERLora

# Create a PEERLora block
peer_lora = PEERLora(
    dim = 512,                    # Input/output dimension
    expansion_factor = 2.0,       # Feedforward expansion factor
    num_experts = 1_000_000,      # Number of LoRA expert pairs
    heads = 4,                    # Number of retrieval heads
    num_experts_per_head = 4,     # LoRA k dimension = heads * num_experts_per_head (16)
    dim_key = 256,                # Dimension of product keys
    activation = torch.nn.GELU,   # Activation function
    pre_rmsnorm = True,           # Apply RMSNorm preprocessing
    non_competing_scores = True,  # Use non-competing ReLU activation
    dropout = 0.1                 # Dropout rate
).cuda()

# Forward pass
x = torch.randn(2, 1024, 512).cuda()  # (batch, sequence, dim)
out = peer_lora(x) + x  # PEERLora output + residual

assert out.shape == (2, 1024, 512)

# Check the effective LoRA rank
print(f"LoRA k dimension: {peer_lora.lora_k}")  # heads * num_experts_per_head = 16
```

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

### Implement Product Key Memory Attention

Source: https://context7.com/lucidrains/peer-pytorch/llms.txt

Configures PKAttention for large-scale key-value retrieval, supporting both causal and non-causal modes with optional masking.

```python
import torch
from PEER_pytorch import PKAttention

# Create PKAttention module
pk_attn = PKAttention(
    dim = 256,                    # Input/output dimension
    causal = True,                # Use causal masking for autoregressive models
    heads = 8,                    # Number of attention heads
    num_key_values = 1_000_000,   # Number of key-value pairs (must be perfect square)
    key_value_pk_topk = 16,       # Top-k key-values retrieved per position
    dim_key = 128,                # Dimension of product keys
    product_keys = 2,             # Number of product key components
    pre_rmsnorm = True,           # Apply RMSNorm preprocessing
    dropout = 0.1                 # Attention dropout
)

# Causal attention (autoregressive)
x = torch.randn(2, 512, 256)  # (batch, sequence, dim)
out = pk_attn(x) + x

assert out.shape == (2, 512, 256)

# Non-causal attention with optional mask
pk_attn_bidirectional = PKAttention(
    dim = 256,
    causal = False,
    heads = 8,
    num_key_values = 10_000,
    pre_rmsnorm = True
)

# With padding mask (True = attend, False = ignore)
mask = torch.ones(2, 512, dtype=torch.bool)
mask[:, 400:] = False  # Mask out positions after 400

out_masked = pk_attn_bidirectional(x, mask=mask)
```

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

### Process sequences with ChunkedPEER

Source: https://context7.com/lucidrains/peer-pytorch/llms.txt

Wraps a PEER module to enable gradient checkpointing and memory-efficient processing of long sequences.

```python
import torch
from PEER_pytorch import PEER, ChunkedPEER

# Create base PEER block
peer = PEER(
    dim = 512,
    heads = 8,
    num_experts = 1_000_000,
    num_experts_per_head = 16,
    pre_rmsnorm = True
).cuda()

# Wrap with chunking for memory efficiency
chunked_peer = ChunkedPEER(
    peer = peer,
    seq_chunk_size = 128  # Process 128 tokens at a time
)

# Forward pass with long sequence
x = torch.randn(1, 4096, 512).cuda().requires_grad_()
out = chunked_peer(x) + x

# Gradient checkpointing is automatically applied during training
out.sum().backward()

print(f"Processed sequence length: {out.shape[1]}")  # 4096
```

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

### Integrate PEER into a Transformer Block

Source: https://context7.com/lucidrains/peer-pytorch/llms.txt

Combines standard multi-head attention with a chunked PEER feedforward layer to create a memory-efficient transformer block.

```python
import torch
import torch.nn as nn
from PEER_pytorch import PEER, ChunkedPEER

class PEERTransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, num_experts=1_000_000, chunk_size=128):
        super().__init__()

        # Standard multi-head attention
        self.attn = nn.MultiheadAttention(dim, num_heads, batch_first=True)
        self.attn_norm = nn.LayerNorm(dim)

        # PEER feedforward with chunking for memory efficiency
        peer = PEER(
            dim = dim,
            heads = 8,
            num_experts = num_experts,
            num_experts_per_head = 16,
            pre_rmsnorm = True,
            non_competing_scores = True,
            dropout = 0.1
        )
        self.ff = ChunkedPEER(peer, seq_chunk_size=chunk_size)
        self.ff_norm = nn.LayerNorm(dim)

    def forward(self, x, attn_mask=None):
        # Attention with residual
        x_norm = self.attn_norm(x)
        attn_out, _ = self.attn(x_norm, x_norm, x_norm, attn_mask=attn_mask)
        x = x + attn_out

        # PEER feedforward with residual
        x_norm = self.ff_norm(x)
        x = x + self.ff(x_norm)

        return x

# Create transformer block
block = PEERTransformerBlock(
    dim = 512,
    num_heads = 8,
    num_experts = 1_000_000,
    chunk_size = 128
).cuda()

# Forward pass
x = torch.randn(4, 2048, 512).cuda()
out = block(x)

print(f"Input shape: {x.shape}")   # (4, 2048, 512)
print(f"Output shape: {out.shape}") # (4, 2048, 512)
```

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