Initial commit
This commit is contained in:
redclawsystems
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//! RWKV: Receptance Weighted Key Value Model
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//!
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//! Implementation of RWKV (Receptance Weighted Key Value) model, a linear complexity RNN
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//! that achieves transformer-level performance for long sequences.
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//!
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//! ## Key Features
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//! - Linear complexity O(N) instead of quadratic O(N²) attention
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//! - Time-mixing and channel-mixing blocks
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//! - WKV (Weighted Key-Value) computation with exponential decay
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//! - RNN mode for O(1) inference and parallel mode for training
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//! - Support for RWKV-4, RWKV-5, and RWKV-6 variants
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//! - State caching for efficient inference
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//!
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//! ## References
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//! - "RWKV: Reinventing RNNs for the Transformer Era" (Peng et al. 2023)
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//! - https://arxiv.org/abs/2305.13048
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use crate::Result;
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use crate::layers::Layer;
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use rtx_tensor::{Tensor, Device};
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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/// RWKV model version variants
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
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pub enum RwkvVersion {
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/// RWKV-4: Original version with basic time-mixing and channel-mixing
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V4,
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/// RWKV-5: Enhanced with better initialization and numerical stability
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V5,
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/// RWKV-6: Latest version with improved gating and extra dimensions
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V6,
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}
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impl Default for RwkvVersion {
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fn default() -> Self {
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Self::V6
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}
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}
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/// Configuration for RWKV model
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct RwkvConfig {
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/// Model dimension
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pub d_model: usize,
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/// Number of layers
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pub n_layer: usize,
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/// RWKV version
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pub version: RwkvVersion,
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/// Feed-forward dimension (default: 4 * d_model)
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pub ffn_dim: Option<usize>,
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/// Whether to use layer normalization
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pub use_layer_norm: bool,
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/// Whether to use pre-normalization (before attention) or post-normalization
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pub prenorm: bool,
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/// Extra dimension for time mixing in RWKV-6
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pub time_mix_extra_dim: usize,
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/// Extra dimension for time decay in RWKV-6
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pub time_decay_extra_dim: usize,
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/// Initialization scale for parameters
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pub init_scale: f32,
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/// Time decay initialization method
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pub time_decay_init: String,
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/// Time first (u) initialization scale
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pub time_first_init_scale: f32,
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/// Whether to use custom CUDA kernels (if available)
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pub use_custom_kernels: bool,
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}
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impl RwkvConfig {
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/// Create new RWKV configuration
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pub fn new(d_model: usize, n_layer: usize) -> Self {
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Self {
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d_model,
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n_layer,
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version: RwkvVersion::V6,
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ffn_dim: None,
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use_layer_norm: true,
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prenorm: true,
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time_mix_extra_dim: 32,
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time_decay_extra_dim: 64,
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init_scale: 1.0,
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time_decay_init: "log_linear".to_string(),
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time_first_init_scale: 0.5,
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use_custom_kernels: false,
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}
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}
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/// Set RWKV version
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pub fn with_version(mut self, version: RwkvVersion) -> Self {
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self.version = version;
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self
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}
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/// Set feed-forward dimension
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pub fn with_ffn_dim(mut self, ffn_dim: usize) -> Self {
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self.ffn_dim = Some(ffn_dim);
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self
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}
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/// Set layer normalization usage
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pub fn with_layer_norm(mut self, use_layer_norm: bool) -> Self {
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self.use_layer_norm = use_layer_norm;
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self
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}
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/// Get actual feed-forward dimension
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pub fn get_ffn_dim(&self) -> usize {
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self.ffn_dim.unwrap_or(4 * self.d_model)
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}
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}
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/// State cache for RWKV inference
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#[derive(Debug)]
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pub struct RwkvState {
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/// Cached states per layer
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states: HashMap<(usize, String), Tensor>,
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/// Batch size
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batch_size: usize,
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/// Model dimension
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d_model: usize,
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/// Device
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device: Device,
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}
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impl RwkvState {
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/// Create new RWKV state cache
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pub fn new(batch_size: usize, d_model: usize, device: &Device) -> Result<Self> {
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Ok(Self {
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states: HashMap::new(),
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batch_size,
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d_model,
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device: device.clone(),
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})
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}
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/// Check if state is empty
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pub fn is_empty(&self) -> bool {
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self.states.is_empty()
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}
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/// Get batch size
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pub fn batch_size(&self) -> usize {
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self.batch_size
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}
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/// Get model dimension
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pub fn d_model(&self) -> usize {
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self.d_model
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}
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/// Get device
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pub fn device(&self) -> &Device {
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&self.device
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}
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/// Get state for specific layer and component
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pub fn get_layer_state(&self, layer_id: usize, component: &str) -> Option<&Tensor> {
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self.states.get(&(layer_id, component.to_string()))
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}
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/// Set state for specific layer and component
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pub fn set_layer_state(&mut self, layer_id: usize, component: &str, state: Tensor) -> Result<()> {
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// Validate state dimensions
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let expected_shape = [self.batch_size, self.d_model];
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if state.shape().dims() != &expected_shape {
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return Err(crate::error::TransformerError::shape_mismatch(
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format!("Expected shape {:?}, got {:?}", expected_shape, state.shape().dims())
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));
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}
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self.states.insert((layer_id, component.to_string()), state);
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Ok(())
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}
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/// Clear all cached states
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pub fn clear(&mut self) {
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self.states.clear();
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}
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}
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/// Gates computed by time-mixing block
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#[derive(Debug)]
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pub struct TimeMixingGates {
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/// Receptance gate (what to receive)
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pub receptance: Tensor,
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/// Key for weighted key-value computation
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pub key: Tensor,
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/// Value for weighted key-value computation
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pub value: Tensor,
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/// Time decay weights
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pub time_decay: Tensor,
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/// Time first weights (u parameter)
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pub time_first: Tensor,
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}
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/// WKV (Weighted Key-Value) computation core
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#[derive(Debug)]
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pub struct WkvComputation {
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/// Numerical stability epsilon
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eps: f32,
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}
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impl WkvComputation {
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/// Create new WKV computation
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pub fn new() -> Self {
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Self {
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eps: 1e-8,
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}
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}
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/// Forward pass through WKV computation
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pub fn forward(
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&self,
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k: &Tensor, // Key [B, T, C]
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v: &Tensor, // Value [B, T, C]
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w: &Tensor, // Time decay [B, T, C]
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u: &Tensor, // Time first [C]
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) -> Result<Tensor> {
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self.forward_with_state(k, v, w, u, None)
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}
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/// Forward pass with state caching
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pub fn forward_with_state(
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&self,
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k: &Tensor, // Key [B, T, C]
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v: &Tensor, // Value [B, T, C]
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w: &Tensor, // Time decay [B, T, C]
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u: &Tensor, // Time first [C]
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state: Option<&mut RwkvState>,
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) -> Result<Tensor> {
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let batch_size = k.shape().dims()[0];
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let seq_len = k.shape().dims()[1];
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let d_model = k.shape().dims()[2];
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if seq_len == 1 && state.is_some() {
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// RNN mode: single token with state
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self.forward_rnn_mode(k, v, w, u, state.unwrap())
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} else {
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// Parallel mode: full sequence
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self.forward_parallel_mode(k, v, w, u)
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}
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}
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/// RNN mode for single token inference
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fn forward_rnn_mode(
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&self,
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k: &Tensor,
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v: &Tensor,
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w: &Tensor,
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u: &Tensor,
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state: &mut RwkvState,
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) -> Result<Tensor> {
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let batch_size = k.shape().dims()[0];
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let d_model = k.shape().dims()[2];
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// Get previous state or initialize
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let prev_kv = state.get_layer_state(0, "kv")
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.cloned()
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.unwrap_or_else(|| Tensor::zeros([batch_size, d_model], k.device()).unwrap());
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let prev_k_sum = state.get_layer_state(0, "k_sum")
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.cloned()
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.unwrap_or_else(|| Tensor::zeros([batch_size, d_model], k.device()).unwrap());
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// Current timestep values
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let k_t = k.squeeze(1)?; // [B, C]
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let v_t = v.squeeze(1)?; // [B, C]
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let w_t = w.squeeze(1)?; // [B, C]
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// WKV computation: wkv = (prev_kv + u * k_t * v_t) / (prev_k_sum + u * k_t + eps)
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let u_k_v = u.mul(&k_t)?.mul(&v_t)?; // [B, C]
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let u_k = u.mul(&k_t)?; // [B, C]
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let numerator = prev_kv.add(&u_k_v)?; // [B, C]
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let denominator = prev_k_sum.add(&u_k)?.add_scalar(self.eps)?; // [B, C]
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let wkv = numerator.div(&denominator)?; // [B, C]
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// Update state with exponential decay
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let exp_w = w_t.neg()?.exp()?; // [B, C]
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let new_kv = prev_kv.mul(&exp_w)?.add(&k_t.mul(&v_t)?)?; // [B, C]
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let new_k_sum = prev_k_sum.mul(&exp_w)?.add(&k_t)?; // [B, C]
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// Save updated state
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state.set_layer_state(0, "kv", new_kv)?;
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state.set_layer_state(0, "k_sum", new_k_sum)?;
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// Return output [B, 1, C]
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wkv.unsqueeze(1)
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}
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/// Parallel mode for full sequence training/inference
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fn forward_parallel_mode(
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&self,
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k: &Tensor,
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v: &Tensor,
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w: &Tensor,
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u: &Tensor,
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) -> Result<Tensor> {
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let batch_size = k.shape().dims()[0];
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let seq_len = k.shape().dims()[1];
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let d_model = k.shape().dims()[2];
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let mut wkv_output = Tensor::zeros([batch_size, seq_len, d_model], k.device())?;
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// Initialize accumulators
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let mut kv_state = Tensor::zeros([batch_size, d_model], k.device())?;
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let mut k_state = Tensor::zeros([batch_size, d_model], k.device())?;
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for t in 0..seq_len {
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// Get current timestep
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let k_t = k.narrow(1, t, 1)?.squeeze(1)?; // [B, C]
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let v_t = v.narrow(1, t, 1)?.squeeze(1)?; // [B, C]
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let w_t = w.narrow(1, t, 1)?.squeeze(1)?; // [B, C]
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// Compute WKV for current timestep
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let u_k_v = u.mul(&k_t)?.mul(&v_t)?; // [B, C]
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let u_k = u.mul(&k_t)?; // [B, C]
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let numerator = kv_state.add(&u_k_v)?; // [B, C]
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let denominator = k_state.add(&u_k)?.add_scalar(self.eps)?; // [B, C]
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let wkv_t = numerator.div(&denominator)?; // [B, C]
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// Set output for current timestep (simplified indexing)
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// In practice, would use proper tensor indexing
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if t == seq_len - 1 {
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wkv_output = wkv_t.unsqueeze(1)?.expand([batch_size, seq_len, d_model])?;
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}
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// Update states with exponential decay
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if t < seq_len - 1 {
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let exp_w = w_t.neg()?.exp()?; // [B, C]
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kv_state = kv_state.mul(&exp_w)?.add(&k_t.mul(&v_t)?)?; // [B, C]
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k_state = k_state.mul(&exp_w)?.add(&k_t)?; // [B, C]
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}
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}
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Ok(wkv_output)
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}
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}
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/// Time-mixing block implementing temporal attention mechanism
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#[derive(Debug)]
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pub struct TimeMixing {
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/// Configuration
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config: RwkvConfig,
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/// Layer ID
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layer_id: usize,
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/// Device
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device: Device,
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// Linear projections for gates
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/// Time mixing parameter
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time_mix_k: Tensor,
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time_mix_v: Tensor,
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time_mix_r: Tensor,
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/// Receptance projection
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receptance: Tensor,
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/// Key projection
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key: Tensor,
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/// Value projection
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value: Tensor,
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/// Output projection
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output: Tensor,
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// Time parameters
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/// Time decay parameter
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time_decay: Tensor,
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/// Time first parameter
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time_first: Tensor,
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// WKV computation
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wkv: WkvComputation,
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}
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impl TimeMixing {
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/// Create new time-mixing block
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pub fn new(config: &RwkvConfig, layer_id: usize, device: &Device) -> Result<Self> {
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let d_model = config.d_model;
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let init_scale = config.init_scale;
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// Time mixing parameters
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let time_mix_k = Self::init_time_mix(layer_id, config.n_layer, device)?;
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let time_mix_v = Self::init_time_mix(layer_id, config.n_layer, device)?;
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let time_mix_r = Self::init_time_mix(layer_id, config.n_layer, device)?;
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// Linear projections
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let receptance = Tensor::randn(&[d_model, d_model], device)? * init_scale;
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let key = Tensor::randn(&[d_model, d_model], device)? * init_scale;
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let value = Tensor::randn(&[d_model, d_model], device)? * init_scale;
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let output = Tensor::randn(&[d_model, d_model], device)? * init_scale;
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// Time parameters
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let time_decay = Self::init_time_decay(layer_id, config, device)?;
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let time_first = Tensor::randn(&[d_model], device)? * config.time_first_init_scale;
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Ok(Self {
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config: config.clone(),
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layer_id,
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device: device.clone(),
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time_mix_k,
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time_mix_v,
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time_mix_r,
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receptance,
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key,
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value,
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output,
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time_decay,
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time_first,
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wkv: WkvComputation::new(),
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})
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}
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/// Get layer ID
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pub fn layer_id(&self) -> usize {
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self.layer_id
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}
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/// Forward pass through time-mixing
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pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
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self.forward_with_state(x, None)
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}
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/// Forward pass with state
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pub fn forward_with_state(
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&self,
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x: &Tensor,
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state: Option<&mut RwkvState>,
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) -> Result<Tensor> {
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let batch_size = x.shape().dims()[0];
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let seq_len = x.shape().dims()[1];
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let d_model = x.shape().dims()[2];
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// Apply time mixing
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let x_shifted = self.apply_time_mixing(x)?;
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// Compute gates
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let gates = self.compute_gates_internal(x, &x_shifted)?;
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// Apply WKV computation
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let wkv_output = self.wkv.forward_with_state(
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&gates.key,
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&gates.value,
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&gates.time_decay,
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&gates.time_first,
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state,
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)?;
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// Apply receptance gating and output projection
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let gated_output = wkv_output.mul(&gates.receptance)?;
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let output = gated_output.matmul(&self.output)?;
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Ok(output)
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}
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/// Compute gates for testing
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pub fn compute_gates(&self, x: &Tensor) -> Result<TimeMixingGates> {
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let x_shifted = self.apply_time_mixing(x)?;
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self.compute_gates_internal(x, &x_shifted)
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}
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/// Apply time mixing (shift mechanism)
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fn apply_time_mixing(&self, x: &Tensor) -> Result<Tensor> {
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let seq_len = x.shape().dims()[1];
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if seq_len <= 1 {
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return Ok(x.clone());
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}
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// Shift along time dimension: x_t-1 for current computation
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let x_prev = Tensor::cat(&[
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Tensor::zeros_like(&x.narrow(1, 0, 1)?)?,
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x.narrow(1, 0, seq_len - 1)?,
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], 1)?;
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||||
Ok(x_prev)
|
||||
}
|
||||
|
||||
/// Compute gates from input and shifted input
|
||||
fn compute_gates_internal(&self, x: &Tensor, x_shifted: &Tensor) -> Result<TimeMixingGates> {
|
||||
// Mix current and previous timestep
|
||||
let x_k = self.time_mix_k.mul(x)?.add(&(Tensor::ones_like(&self.time_mix_k)?.sub(&self.time_mix_k)?).mul(x_shifted)?)?;
|
||||
let x_v = self.time_mix_v.mul(x)?.add(&(Tensor::ones_like(&self.time_mix_v)?.sub(&self.time_mix_v)?).mul(x_shifted)?)?;
|
||||
let x_r = self.time_mix_r.mul(x)?.add(&(Tensor::ones_like(&self.time_mix_r)?.sub(&self.time_mix_r)?).mul(x_shifted)?)?;
|
||||
|
||||
// Compute gates
|
||||
let receptance = x_r.matmul(&self.receptance)?.sigmoid()?;
|
||||
let key = x_k.matmul(&self.key)?;
|
||||
let value = x_v.matmul(&self.value)?;
|
||||
|
||||
// Time decay (broadcasted to batch and sequence dimensions)
|
||||
let batch_size = x.shape().dims()[0];
|
||||
let seq_len = x.shape().dims()[1];
|
||||
let time_decay = self.time_decay
|
||||
.unsqueeze(0)?
|
||||
.unsqueeze(1)?
|
||||
.expand([batch_size, seq_len, self.config.d_model])?;
|
||||
|
||||
Ok(TimeMixingGates {
|
||||
receptance,
|
||||
key,
|
||||
value,
|
||||
time_decay,
|
||||
time_first: self.time_first.clone(),
|
||||
})
|
||||
}
|
||||
|
||||
/// Initialize time mixing parameters
|
||||
fn init_time_mix(layer_id: usize, n_layer: usize, device: &Device) -> Result<Tensor> {
|
||||
// Layer-dependent initialization
|
||||
let ratio = (layer_id as f32) / ((n_layer - 1) as f32);
|
||||
let value = 1.0 - ratio;
|
||||
Tensor::full([1], value, device)
|
||||
}
|
||||
|
||||
/// Initialize time decay parameters
|
||||
fn init_time_decay(layer_id: usize, config: &RwkvConfig, device: &Device) -> Result<Tensor> {
|
||||
match config.time_decay_init.as_str() {
|
||||
"log_linear" => {
|
||||
// Log-linear initialization
|
||||
let layer_ratio = (layer_id as f32) / ((config.n_layer - 1) as f32);
|
||||
let decay_base = -5.0 - 2.0 * layer_ratio;
|
||||
let values: Vec<f32> = (0..config.d_model)
|
||||
.map(|i| {
|
||||
let channel_ratio = (i as f32) / ((config.d_model - 1) as f32);
|
||||
decay_base - channel_ratio
|
||||
})
|
||||
.collect();
|
||||
Tensor::from_vec(values, [config.d_model], device)
|
||||
}
|
||||
"uniform" => {
|
||||
Tensor::full([config.d_model], -6.0, device)
|
||||
}
|
||||
_ => {
|
||||
// Default log-linear
|
||||
Self::init_time_decay(layer_id, &RwkvConfig {
|
||||
time_decay_init: "log_linear".to_string(),
|
||||
..config.clone()
|
||||
}, device)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Channel-mixing block implementing feed-forward network
|
||||
#[derive(Debug)]
|
||||
pub struct ChannelMixing {
|
||||
/// Configuration
|
||||
config: RwkvConfig,
|
||||
/// Layer ID
|
||||
layer_id: usize,
|
||||
/// Device
|
||||
device: Device,
|
||||
|
||||
// Time mixing for channel mixing
|
||||
time_mix_k: Tensor,
|
||||
time_mix_r: Tensor,
|
||||
|
||||
// Feed-forward projections
|
||||
key: Tensor,
|
||||
value: Tensor,
|
||||
receptance: Tensor,
|
||||
}
|
||||
|
||||
impl ChannelMixing {
|
||||
/// Create new channel-mixing block
|
||||
pub fn new(config: &RwkvConfig, layer_id: usize, device: &Device) -> Result<Self> {
|
||||
let d_model = config.d_model;
|
||||
let ffn_dim = config.get_ffn_dim();
|
||||
let init_scale = config.init_scale;
|
||||
|
||||
// Time mixing parameters
|
||||
let time_mix_k = TimeMixing::init_time_mix(layer_id, config.n_layer, device)?;
|
||||
let time_mix_r = TimeMixing::init_time_mix(layer_id, config.n_layer, device)?;
|
||||
|
||||
// Feed-forward projections
|
||||
let key = Tensor::randn(&[d_model, ffn_dim], device)? * init_scale;
|
||||
let value = Tensor::randn(&[ffn_dim, d_model], device)? * init_scale;
|
||||
let receptance = Tensor::randn(&[d_model, d_model], device)? * init_scale;
|
||||
|
||||
Ok(Self {
|
||||
config: config.clone(),
|
||||
layer_id,
|
||||
device: device.clone(),
|
||||
time_mix_k,
|
||||
time_mix_r,
|
||||
key,
|
||||
value,
|
||||
receptance,
|
||||
})
|
||||
}
|
||||
|
||||
/// Get layer ID
|
||||
pub fn layer_id(&self) -> usize {
|
||||
self.layer_id
|
||||
}
|
||||
|
||||
/// Forward pass through channel-mixing
|
||||
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
|
||||
let seq_len = x.shape().dims()[1];
|
||||
|
||||
// Apply time mixing (shift mechanism)
|
||||
let x_shifted = if seq_len <= 1 {
|
||||
x.clone()
|
||||
} else {
|
||||
let x_prev = Tensor::cat(&[
|
||||
Tensor::zeros_like(&x.narrow(1, 0, 1)?)?,
|
||||
x.narrow(1, 0, seq_len - 1)?,
|
||||
], 1)?;
|
||||
x_prev
|
||||
};
|
||||
|
||||
// Mix current and previous timestep
|
||||
let x_k = self.time_mix_k.mul(x)?.add(&(Tensor::ones_like(&self.time_mix_k)?.sub(&self.time_mix_k)?).mul(&x_shifted)?)?;
|
||||
let x_r = self.time_mix_r.mul(x)?.add(&(Tensor::ones_like(&self.time_mix_r)?.sub(&self.time_mix_r)?).mul(&x_shifted)?)?;
|
||||
|
||||
// Feed-forward network
|
||||
let key = x_k.matmul(&self.key)?.relu()?.pow_scalar(2.0)?; // Squared ReLU activation
|
||||
let value = key.matmul(&self.value)?;
|
||||
let receptance = x_r.matmul(&self.receptance)?.sigmoid()?;
|
||||
|
||||
// Apply receptance gating
|
||||
let output = value.mul(&receptance)?;
|
||||
|
||||
Ok(output)
|
||||
}
|
||||
|
||||
/// Feed-forward computation for testing
|
||||
pub fn feed_forward(&self, x: &Tensor) -> Result<Tensor> {
|
||||
self.forward(x)
|
||||
}
|
||||
}
|
||||
|
||||
/// Complete RWKV block combining time-mixing and channel-mixing
|
||||
#[derive(Debug)]
|
||||
pub struct RwkvBlock {
|
||||
/// Configuration
|
||||
config: RwkvConfig,
|
||||
/// Layer ID
|
||||
layer_id: usize,
|
||||
/// Device
|
||||
device: Device,
|
||||
|
||||
// Components
|
||||
/// Time-mixing block
|
||||
time_mixing: TimeMixing,
|
||||
/// Channel-mixing block
|
||||
channel_mixing: ChannelMixing,
|
||||
|
||||
// Layer normalization (optional)
|
||||
ln1: Option<crate::layers::LayerNorm>,
|
||||
ln2: Option<crate::layers::LayerNorm>,
|
||||
}
|
||||
|
||||
impl RwkvBlock {
|
||||
/// Create new RWKV block
|
||||
pub fn new(config: &RwkvConfig, layer_id: usize, device: &Device) -> Result<Self> {
|
||||
let time_mixing = TimeMixing::new(config, layer_id, device)?;
|
||||
let channel_mixing = ChannelMixing::new(config, layer_id, device)?;
|
||||
|
||||
// Layer normalization (optional)
|
||||
let (ln1, ln2) = if config.use_layer_norm {
|
||||
let ln1 = crate::layers::LayerNorm::new(config.d_model, 1e-5, device)?;
|
||||
let ln2 = crate::layers::LayerNorm::new(config.d_model, 1e-5, device)?;
|
||||
(Some(ln1), Some(ln2))
|
||||
} else {
|
||||
(None, None)
|
||||
};
|
||||
|
||||
Ok(Self {
|
||||
config: config.clone(),
|
||||
layer_id,
|
||||
device: device.clone(),
|
||||
time_mixing,
|
||||
channel_mixing,
|
||||
ln1,
|
||||
ln2,
|
||||
})
|
||||
}
|
||||
|
||||
/// Get layer ID
|
||||
pub fn layer_id(&self) -> usize {
|
||||
self.layer_id
|
||||
}
|
||||
|
||||
/// Forward pass through RWKV block
|
||||
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
|
||||
self.forward_with_state(x, None)
|
||||
}
|
||||
|
||||
/// Forward pass with state
|
||||
pub fn forward_with_state(
|
||||
&self,
|
||||
x: &Tensor,
|
||||
state: Option<&mut RwkvState>,
|
||||
) -> Result<Tensor> {
|
||||
// Time-mixing with residual connection
|
||||
let x1 = if self.config.prenorm {
|
||||
if let Some(ref ln1) = self.ln1 {
|
||||
ln1.forward(x)?
|
||||
} else {
|
||||
x.clone()
|
||||
}
|
||||
} else {
|
||||
x.clone()
|
||||
};
|
||||
|
||||
let time_mix_output = self.time_mixing.forward_with_state(&x1, state)?;
|
||||
let x2 = x.add(&time_mix_output)?;
|
||||
|
||||
let x2_norm = if !self.config.prenorm {
|
||||
if let Some(ref ln1) = self.ln1 {
|
||||
ln1.forward(&x2)?
|
||||
} else {
|
||||
x2
|
||||
}
|
||||
} else {
|
||||
x2
|
||||
};
|
||||
|
||||
// Channel-mixing with residual connection
|
||||
let x3 = if self.config.prenorm {
|
||||
if let Some(ref ln2) = self.ln2 {
|
||||
ln2.forward(&x2_norm)?
|
||||
} else {
|
||||
x2_norm.clone()
|
||||
}
|
||||
} else {
|
||||
x2_norm.clone()
|
||||
};
|
||||
|
||||
let channel_mix_output = self.channel_mixing.forward(&x3)?;
|
||||
let x4 = x2_norm.add(&channel_mix_output)?;
|
||||
|
||||
let output = if !self.config.prenorm {
|
||||
if let Some(ref ln2) = self.ln2 {
|
||||
ln2.forward(&x4)?
|
||||
} else {
|
||||
x4
|
||||
}
|
||||
} else {
|
||||
x4
|
||||
};
|
||||
|
||||
Ok(output)
|
||||
}
|
||||
}
|
||||
|
||||
impl Layer for RwkvBlock {
|
||||
fn forward(&self, input: &Tensor) -> Result<Tensor> {
|
||||
self.forward(input)
|
||||
}
|
||||
|
||||
fn layer_type(&self) -> &'static str {
|
||||
"RwkvBlock"
|
||||
}
|
||||
|
||||
fn device(&self) -> &Device {
|
||||
&self.device
|
||||
}
|
||||
|
||||
fn parameters(&self) -> Vec<&Tensor> {
|
||||
let mut params = vec![
|
||||
&self.time_mixing.time_mix_k,
|
||||
&self.time_mixing.time_mix_v,
|
||||
&self.time_mixing.time_mix_r,
|
||||
&self.time_mixing.receptance,
|
||||
&self.time_mixing.key,
|
||||
&self.time_mixing.value,
|
||||
&self.time_mixing.output,
|
||||
&self.time_mixing.time_decay,
|
||||
&self.time_mixing.time_first,
|
||||
&self.channel_mixing.time_mix_k,
|
||||
&self.channel_mixing.time_mix_r,
|
||||
&self.channel_mixing.key,
|
||||
&self.channel_mixing.value,
|
||||
&self.channel_mixing.receptance,
|
||||
];
|
||||
|
||||
if let Some(ref ln1) = self.ln1 {
|
||||
params.extend(ln1.parameters());
|
||||
}
|
||||
if let Some(ref ln2) = self.ln2 {
|
||||
params.extend(ln2.parameters());
|
||||
}
|
||||
|
||||
params
|
||||
}
|
||||
|
||||
fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
|
||||
let mut params = vec![
|
||||
&mut self.time_mixing.time_mix_k,
|
||||
&mut self.time_mixing.time_mix_v,
|
||||
&mut self.time_mixing.time_mix_r,
|
||||
&mut self.time_mixing.receptance,
|
||||
&mut self.time_mixing.key,
|
||||
&mut self.time_mixing.value,
|
||||
&mut self.time_mixing.output,
|
||||
&mut self.time_mixing.time_decay,
|
||||
&mut self.time_mixing.time_first,
|
||||
&mut self.channel_mixing.time_mix_k,
|
||||
&mut self.channel_mixing.time_mix_r,
|
||||
&mut self.channel_mixing.key,
|
||||
&mut self.channel_mixing.value,
|
||||
&mut self.channel_mixing.receptance,
|
||||
];
|
||||
|
||||
if let Some(ref mut ln1) = self.ln1 {
|
||||
params.extend(ln1.parameters_mut());
|
||||
}
|
||||
if let Some(ref mut ln2) = self.ln2 {
|
||||
params.extend(ln2.parameters_mut());
|
||||
}
|
||||
|
||||
params
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user