rwkv-architecture

name	rwkv-architecture
description	RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
version	1.0.0
author	Orchestra Research
license	MIT
tags	["RWKV","Model Architecture","RNN","Transformer Hybrid","Linear Complexity","Infinite Context","Efficient Inference","Linux Foundation","Alternative Architecture"]
dependencies	["rwkv","torch","transformers"]

RWKV - Receptance Weighted Key Value

Quick start

RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).

Installation:

# Install PyTorch
pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121

# Install dependencies
pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade

# Install RWKV
pip install rwkv

Basic usage (GPT mode + RNN mode):

import os
from rwkv.model import RWKV

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1'  # Use CUDA kernel for speed

# Load model
model = RWKV(
    model='/path/to/RWKV-4-Pile-1B5-20220903-8040',
    strategy='cuda fp16'
)

# GPT mode (parallel processing)
out, state = model.forward([187, 510, 1563, 310, 247], None)
print(out.detach().cpu().numpy())  # Logits

# RNN mode (sequential processing, same result)
out, state = model.forward([187, 510], None)  # First 2 tokens
out, state = model.forward([1563], state)      # Next token
out, state = model.forward([310, 247], state)  # Last tokens
print(out.detach().cpu().numpy())  # Same logits as above!

Common workflows

Workflow 1: Text generation (streaming)

Efficient token-by-token generation:

from rwkv.model import RWKV
from rwkv.utils import PIPELINE

model = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')
pipeline = PIPELINE(model, "20B_tokenizer.json")

# Initial prompt
prompt = "The future of AI is"
state = None

# Generate token by token
for token in prompt:
    out, state = pipeline.model.forward(pipeline.encode(token), state)

# Continue generation
for _ in range(100):
    out, state = pipeline.model.forward(None, state)
    token = pipeline.sample_logits(out)
    print(pipeline.decode(token), end='', flush=True)

Key advantage: Constant memory per token (no growing KV cache)

Workflow 2: Long context processing (infinite context)

Process million-token sequences:

model = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')

# Process very long document
state = None
long_document = load_document()  # e.g., 1M tokens

# Stream through entire document
for chunk in chunks(long_document, chunk_size=1024):
    out, state = model.forward(chunk, state)

# State now contains information from entire 1M token document
# Memory usage: O(1) (constant, not O(n)!)

Workflow 3: Fine-tuning RWKV

Standard fine-tuning workflow:

# Training script
import pytorch_lightning as pl
from rwkv.model import RWKV
from rwkv.trainer import RWKVTrainer

# Configure model
config = {
    'n_layer': 24,
    'n_embd': 1024,
    'vocab_size': 50277,
    'ctx_len': 1024
}

# Setup trainer
trainer = pl.Trainer(
    accelerator='gpu',
    devices=8,
    precision='bf16',
    strategy='deepspeed_stage_2',
    max_epochs=1
)

# Train
model = RWKV(config)
trainer.fit(model, train_dataloader)

Workflow 4: RWKV vs Transformer comparison

Memory comparison (1M token sequence):

# Transformer (GPT)
# Memory: O(n²) for attention
# KV cache: 1M × hidden_dim × n_layers × 2 (keys + values)
# Example: 1M × 4096 × 24 × 2 = ~400GB (impractical!)

# RWKV
# Memory: O(1) per token
# State: hidden_dim × n_layers = 4096 × 24 = ~400KB
# 1,000,000× more efficient!

Speed comparison (inference):

# Transformer: O(n) per token (quadratic overall)
# First token: 1 computation
# Second token: 2 computations
# ...
# 1000th token: 1000 computations

# RWKV: O(1) per token (linear overall)
# Every token: 1 computation
# 1000th token: 1 computation (same as first!)

When to use vs alternatives

Use RWKV when:

• Need very long context (100K+ tokens)
• Want constant memory usage
• Building streaming applications
• Need RNN efficiency with Transformer performance
• Memory-constrained deployment

Key advantages:

• Linear time: O(n) vs O(n²) for Transformers
• No KV cache: Constant memory per token
• Infinite context: No fixed window limit
• Parallelizable training: Like GPT
• Sequential inference: Like RNN

Use alternatives instead:

• Transformers: Need absolute best performance, have compute
• Mamba: Want state-space models
• RetNet: Need retention mechanism
• Hyena: Want convolution-based approach

Common issues

Issue: Out of memory during training

Use gradient checkpointing and DeepSpeed:

trainer = pl.Trainer(
    strategy='deepspeed_stage_3',  # Full ZeRO-3
    precision='bf16'
)

Issue: Slow inference

Enable CUDA kernel:

os.environ["RWKV_CUDA_ON"] = '1'

Issue: Model not loading

Check model path and strategy:

model = RWKV(
    model='/absolute/path/to/model.pth',
    strategy='cuda fp16'  # Or 'cpu fp32' for CPU
)

Issue: State management in RNN mode

Always pass state between forward calls:

# WRONG: State lost
out1, _ = model.forward(tokens1, None)
out2, _ = model.forward(tokens2, None)  # No context from tokens1!

# CORRECT: State preserved
out1, state = model.forward(tokens1, None)
out2, state = model.forward(tokens2, state)  # Has context from tokens1

Advanced topics

Time-mixing and channel-mixing: See references/architecture-details.md for WKV operation, time-decay mechanism, and receptance gates.

State management: See references/state-management.md for att_x_prev, att_kv, ffn_x_prev states, and numerical stability considerations.

RWKV-7 improvements: See references/rwkv7.md for latest architectural improvements (March 2025) and multimodal capabilities.

Hardware requirements

• GPU: NVIDIA (CUDA 11.6+) or CPU
• VRAM (FP16):
  • 169M model: 1GB
  • 430M model: 2GB
  • 1.5B model: 4GB
  • 3B model: 8GB
  • 7B model: 16GB
  • 14B model: 32GB
• Inference: O(1) memory per token
• Training: Parallelizable like GPT

Performance (vs Transformers):

• Speed: Similar training, faster inference
• Memory: 1000× less for long sequences
• Scaling: Linear vs quadratic

Resources

• Paper (RWKV): https://arxiv.org/abs/2305.13048 (May 2023)
• Paper (RWKV-7): https://arxiv.org/abs/2503.14456 (March 2025)
• GitHub: https://github.com/BlinkDL/RWKV-LM ⭐ 12,000+
• Docs: https://wiki.rwkv.com/
• Models: https://huggingface.co/BlinkDL
• Linux Foundation AI: Official project
• Production: Microsoft Windows, Office integration, NeMo support