RWKV-7 "Goose" Architecture Analysis — A Linear-Time Model Surpassing Transformers

# Standard Attention: O(n^2) time, O(n) memory
# output = softmax(Q @ K^T / sqrt(d)) @ V

# Linear Attention: O(n) time, O(d^2) memory (constant)
# output = (Q @ (K^T @ V)) -- reordering matrix multiplication

# RWKV-7: O(n) time, O(d^2) memory (constant)
# + Dynamic State Evolution maximizes expressiveness

# TC0 limitation example: state tracking problem
# Input: "A is in room1. A moves to room2. B is in room1. Where is A?"
# TC0 models are theoretically incapable of tracking such state changes

import torch
import torch.nn as nn

class RWKV7_TimeMix(nn.Module):
    """RWKV-7 Core: Dynamic State Evolution"""

    def __init__(self, d_model, n_heads):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.d_head = d_model // n_heads

        # Input-dependent parameter generators
        self.W_r = nn.Linear(d_model, d_model)  # Receptance
        self.W_k = nn.Linear(d_model, d_model)  # Key
        self.W_v = nn.Linear(d_model, d_model)  # Value
        self.W_a = nn.Linear(d_model, d_model)  # State transition
        self.W_g = nn.Linear(d_model, d_model)  # Gate

        # Dynamic decay parameters
        self.w_decay = nn.Parameter(torch.randn(n_heads, self.d_head))

        # State evolution matrix learnable parameters
        self.A_log = nn.Parameter(torch.randn(n_heads, self.d_head, self.d_head))

    def forward(self, x, state=None):
        B, T, C = x.shape
        H = self.n_heads
        D = self.d_head

        r = self.W_r(x).view(B, T, H, D)
        k = self.W_k(x).view(B, T, H, D)
        v = self.W_v(x).view(B, T, H, D)
        a = torch.sigmoid(self.W_a(x).view(B, T, H, D))
        g = torch.sigmoid(self.W_g(x).view(B, T, H, D))

        if state is None:
            state = torch.zeros(B, H, D, D, device=x.device)

        outputs = []
        for t in range(T):
            # Dynamic State Evolution: the core innovation
            # Previous: state = decay * state + k^T @ v (fixed decay)
            # RWKV-7: state = A(x_t) @ state + k^T @ v (dynamic transition matrix)

            # Input-dependent transition matrix
            A_t = self._compute_transition(a[:, t], state)

            # State update
            kv = torch.einsum('bhd,bhe->bhde', k[:, t], v[:, t])
            state = A_t * state + kv

            # Output computation
            out = torch.einsum('bhd,bhde->bhe', r[:, t], state)
            out = out * g[:, t]
            outputs.append(out)

        output = torch.stack(outputs, dim=1).view(B, T, C)
        return output, state

    def _compute_transition(self, a_t, state):
        """Dynamically generate transition matrix based on input"""
        # a_t: [B, H, D] - transition parameters derived from current input
        # This is the key to how RWKV-7 surpasses TC0
        decay = torch.exp(-torch.exp(self.w_decay))
        A = decay.unsqueeze(-1) * torch.eye(
            self.d_head, device=a_t.device
        ).unsqueeze(0)

        # Input-dependent correction
        A = A + torch.einsum('bhd,bhe->bhde', a_t, a_t) * \
            torch.exp(self.A_log).unsqueeze(0)

        return A

# RWKV-6 (previous): state_{t+1} = diag(w) * state_t + k_t * v_t^T
# -> Diagonal matrix multiplication: each dimension decays independently
# -> Within TC0: cannot track state with finite depth

# RWKV-7 (new): state_{t+1} = A(x_t) * state_t + k_t * v_t^T
# -> A(x_t) is an input-dependent transition matrix (non-diagonal)
# -> Cross-dimension interaction possible: can solve state tracking problems
# -> Expressiveness beyond TC0

class RWKV7Block(nn.Module):
    """RWKV-7 basic block"""

    def __init__(self, d_model, n_heads):
        super().__init__()
        self.ln1 = nn.LayerNorm(d_model)
        self.ln2 = nn.LayerNorm(d_model)
        self.time_mix = RWKV7_TimeMix(d_model, n_heads)
        self.channel_mix = RWKV7_ChannelMix(d_model)

    def forward(self, x, state=None):
        # Time Mixing (inter-token interaction)
        h, state = self.time_mix(self.ln1(x), state)
        x = x + h

        # Channel Mixing (serves as FFN)
        x = x + self.channel_mix(self.ln2(x))

        return x, state


class RWKV7_ChannelMix(nn.Module):
    """Channel mixing (SwiGLU variant)"""

    def __init__(self, d_model, expand=3.5):
        super().__init__()
        hidden = int(d_model * expand)
        self.W_key = nn.Linear(d_model, hidden)
        self.W_value = nn.Linear(hidden, d_model)
        self.W_gate = nn.Linear(d_model, hidden)

    def forward(self, x):
        k = self.W_key(x)
        v = self.W_value(torch.relu(k) ** 2)  # squared ReLU
        g = torch.sigmoid(self.W_gate(x))
        return v * g

class RWKV7_Inference:
    """Operates in RNN mode during inference: O(d^2) computation per token"""

    def __init__(self, model):
        self.model = model
        self.states = None  # [n_layers, n_heads, d_head, d_head]

    def generate_token(self, token_id):
        x = self.model.embed(token_id)

        for i, block in enumerate(self.model.blocks):
            x, self.states[i] = block(x.unsqueeze(0).unsqueeze(0),
                                       self.states[i])
            x = x.squeeze(0).squeeze(0)

        logits = self.model.head(self.model.ln_out(x))
        return logits

    # Memory usage: constant regardless of sequence length!
    # Transformer: KV Cache uses O(n * d) memory
    # RWKV-7: State uses O(d^2) fixed memory

# RWKV-7 excels particularly in multilingual performance
# Trained on 100+ languages, achieving SOTA-level results for non-English languages

# On Korean/Japanese/Chinese benchmarks,
# outperforms same-size Transformers
# -> Efficient multilingual tokenization + long context handling

# Inference cost comparison by sequence length
#
# Sequence Length | Transformer | RWKV-7
# 1K             | 1x          | 0.8x
# 4K             | 4x          | 0.8x
# 16K            | 16x         | 0.8x
# 64K            | 64x         | 0.8x
# 1M             | OOM         | 0.8x
#
# RWKV-7 maintains constant cost regardless of sequence length!

# pip install rwkv torch transformers

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

# Load model
model = RWKV(
    model='/path/to/RWKV-7-World-3B',
    strategy='cuda fp16'  # GPU FP16
)

pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

# Text generation
context = "The advantages of Kubernetes VPA are"
result = pipeline.generate(
    context,
    token_count=200,
    temperature=0.8,
    top_p=0.9
)
print(result)

# RWKV-7 vLLM serving (experimental support)
pip install vllm>=0.6.0

python -m vllm.entrypoints.openai.api_server \
  --model RWKV/rwkv-7-world-3b \
  --tokenizer RWKV/rwkv-7-world-3b \
  --dtype float16 \
  --max-model-len 32768

# After downloading the RWKV-7 GGUF model
ollama create rwkv7 -f Modelfile

# Modelfile example:
# FROM rwkv-7-world-3b-q4_k_m.gguf
# PARAMETER temperature 0.7
# PARAMETER num_ctx 32768

ollama run rwkv7

# Scenarios where RWKV-7 excels:
# 1. Very long context processing (100K+ tokens)
# 2. Edge device inference (constant memory)
# 3. Multilingual services (Korean/Japanese/Chinese, etc.)
# 4. Real-time streaming (constant time per token)

# Scenarios where Transformers are still better:
# 1. When maximum performance is needed (especially in English)
# 2. Short contexts (under 1K)
# 3. Leveraging existing ecosystem/tools