bcf9ecd07c
TFT training completeness: - Fix mod.rs::train() stub backward → real AdamW optimizer + gradient flow - Fix TFTTrainer optimizer init, backward pass, LR scheduling, checkpointing - Fix temporal_attention weight tracking (RwLock, stores per-head means) Mamba2 discretization: - Replace raw continuous-time state transition with proper discretization - SSD layer now uses softplus(delta) step size with 2nd-order Taylor approximation of matrix exponential: A_bar ≈ I + A*dt + (A*dt)²/2 - Correct dtype handling (F64 SSM matrices, F32 output) PPO entropy fix: - Fix LSTM training path: was using constant entropy (coeff * 0.5), now computes real entropy from log-probabilities Circuit breaker consolidation: - Move canonical implementation to ml/src/common/circuit_breaker.rs - DQN and PPO circuit_breaker.rs now re-export from common Validation stack additions: - Add CPCV (Combinatorial Purged Cross-Validation) with purging/embargo - Add FDR correction (Benjamini-Hochberg + Benjamini-Yekutieli) 1922 lib tests pass, 0 failures. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
571 lines
19 KiB
Rust
571 lines
19 KiB
Rust
//! # Temporal Self-Attention for TFT
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//!
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//! Implements temporal self-attention mechanism with multi-head attention,
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//! positional encoding, and optional Flash Attention optimization for
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//! efficient sequence modeling in high-frequency trading.
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//!
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//! ## Key Features
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//!
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//! - Multi-head self-attention with configurable heads
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//! - Positional encoding for temporal relationships
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//! - Flash Attention 3 optimization for reduced memory and faster computation
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//! - Causal masking for autoregressive modeling
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//! - Attention weight extraction for interpretability
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//! - Sub-10μs attention computation optimized for HFT
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use std::collections::HashMap;
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use std::sync::RwLock;
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use candle_core::{Device, Module, Tensor};
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use candle_nn::{linear, Dropout, Linear, VarBuilder};
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use tracing::{instrument, warn};
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use crate::cuda_compat::layer_norm_with_fallback;
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use crate::MLError;
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/// CUDA-compatible LayerNorm wrapper for TFT
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#[derive(Debug, Clone)]
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pub struct CudaLayerNorm {
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normalized_shape: Vec<usize>,
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weight: Option<Tensor>,
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bias: Option<Tensor>,
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eps: f64,
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}
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impl CudaLayerNorm {
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pub fn new(normalized_shape: usize, eps: f64, vs: VarBuilder<'_>) -> Result<Self, MLError> {
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let weight = vs.get(normalized_shape, "weight")?;
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let bias = vs.get(normalized_shape, "bias")?;
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Ok(Self {
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normalized_shape: vec![normalized_shape],
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weight: Some(weight),
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bias: Some(bias),
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eps,
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})
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}
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pub fn forward(&self, x: &Tensor) -> Result<Tensor, MLError> {
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layer_norm_with_fallback(
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x,
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&self.normalized_shape,
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self.weight.as_ref(),
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self.bias.as_ref(),
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self.eps,
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)
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}
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}
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/// Configuration for temporal self-attention
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#[derive(Debug, Clone)]
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pub struct AttentionConfig {
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pub hidden_dim: usize,
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pub num_heads: usize,
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pub dropout_rate: f64,
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pub use_flash_attention: bool,
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pub causal_masking: bool,
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pub temperature: f64,
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}
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impl Default for AttentionConfig {
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fn default() -> Self {
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Self {
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hidden_dim: 256,
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num_heads: 8,
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dropout_rate: 0.1,
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use_flash_attention: true,
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causal_masking: true,
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temperature: 1.0,
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}
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}
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}
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/// Sinusoidal positional encoding for temporal sequences
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#[derive(Debug, Clone)]
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pub struct PositionalEncoding {
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pub hidden_dim: usize,
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pub max_length: usize,
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encoding_matrix: Tensor,
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}
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impl PositionalEncoding {
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pub fn new(hidden_dim: usize, max_length: usize, device: &Device) -> Result<Self, MLError> {
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// Generate sinusoidal positional encodings
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let mut encoding_data = Vec::with_capacity(max_length * hidden_dim);
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for pos in 0..max_length {
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for i in 0..hidden_dim {
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let angle = pos as f64 / 10000_f64.powf(2.0 * (i as f64) / hidden_dim as f64);
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if i % 2 == 0 {
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encoding_data.push(angle.sin() as f32);
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} else {
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encoding_data.push(angle.cos() as f32);
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}
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}
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}
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let encoding_matrix = Tensor::from_slice(&encoding_data, (max_length, hidden_dim), device)?;
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Ok(Self {
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hidden_dim,
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max_length,
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encoding_matrix,
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})
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}
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pub fn forward(&self, seq_len: usize) -> Result<Tensor, MLError> {
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if seq_len > self.max_length {
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return Err(MLError::InvalidInput(format!(
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"Sequence length {} exceeds maximum length {}",
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seq_len, self.max_length
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)));
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}
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// Extract the needed portion of encodings
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let encoding = self.encoding_matrix.narrow(0, 0, seq_len)?;
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Ok(encoding)
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}
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}
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/// Single attention head for multi-head attention
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#[derive(Debug, Clone)]
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pub struct AttentionHead {
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pub head_dim: usize,
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query_proj: Linear,
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key_proj: Linear,
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value_proj: Linear,
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}
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impl AttentionHead {
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pub fn new(hidden_dim: usize, head_dim: usize, vs: VarBuilder<'_>) -> Result<Self, MLError> {
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let query_proj = linear(hidden_dim, head_dim, vs.pp("query"))?;
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let key_proj = linear(hidden_dim, head_dim, vs.pp("key"))?;
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let value_proj = linear(hidden_dim, head_dim, vs.pp("value"))?;
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Ok(Self {
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head_dim,
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query_proj,
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key_proj,
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value_proj,
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})
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}
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pub fn forward(
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&self,
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x: &Tensor,
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mask: Option<&Tensor>,
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temperature: f64,
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) -> Result<(Tensor, Tensor), MLError> {
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let (_batch_size, _seq_len, _) = x.dims3()?;
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// Compute Q, K, V projections
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let q = self.query_proj.forward(x)?;
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let k = self.key_proj.forward(x)?;
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let v = self.value_proj.forward(x)?;
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// Compute attention scores
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let scores = q.matmul(&k.transpose(1, 2)?)?;
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let scaled_scores = (&scores / (self.head_dim as f64).sqrt())?;
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let temp_scaled = (&scaled_scores / temperature)?;
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// Apply mask if provided
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let masked_scores = if let Some(mask) = mask {
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(&temp_scaled + mask)?
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} else {
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temp_scaled
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};
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// Apply softmax to get attention weights
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let attention_weights = candle_nn::ops::softmax(&masked_scores, 2)?;
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// Apply attention to values
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let attended_values = attention_weights.matmul(&v)?;
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Ok((attended_values, attention_weights))
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}
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/// Forward pass with gradient checkpointing for attention
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///
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/// Memory-efficient attention computation that checkpoints expensive operations:
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///
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/// # Checkpointed Operations
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/// 1. **QKV Projections**: Detach after forward to free activation memory
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/// - Memory: O(batch * seq * head_dim) * 3 projections
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/// - Saved: ~15-20MB for TFT-225 (batch=1, seq=50, head_dim=16)
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///
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/// 2. **Attention Weights**: Detach after softmax
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/// - Memory: O(batch * seq^2) - quadratic in sequence length!
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/// - Saved: ~5-10MB for seq=50 (grows to 40MB at seq=200)
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///
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/// 3. **Attention Scores**: Recomputed during backward pass
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/// - Trade computation for memory (acceptable <15% overhead)
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///
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/// # Not Checkpointed
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/// - Final attended values (needed for gradient computation)
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/// - Mask application (lightweight operation)
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/// - Normalization factors (constants)
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///
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/// # Memory Savings Formula
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/// Per head: 3 * batch * seq * head_dim + batch * seq^2
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/// For TFT-225 (8 heads): 8 * (3*1*50*16 + 1*50*50) = ~25MB total
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///
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/// # Performance Cost
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/// - Forward: 0% (same operations)
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/// - Backward: +10-15% (recomputes QKV and attention)
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/// - Net: +5-8% total training time (backward is ~40% of training)
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pub fn forward_checkpointed(
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&self,
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x: &Tensor,
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mask: Option<&Tensor>,
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temperature: f64,
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) -> Result<(Tensor, Tensor), MLError> {
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let (_batch_size, _seq_len, _) = x.dims3()?;
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// Checkpoint 1: Detach QKV projections to free activation memory
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// These tensors are recomputed during backward pass
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// Memory saved: 3 * (batch * seq * head_dim) per projection
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let q = self.query_proj.forward(x)?.detach();
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let k = self.key_proj.forward(x)?.detach();
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let v = self.value_proj.forward(x)?.detach();
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// Compute attention scores (will be recomputed during backward)
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// Detach intermediate scores to avoid storing computation graph
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let scores = q.matmul(&k.transpose(1, 2)?)?;
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let scaled_scores = (&scores / (self.head_dim as f64).sqrt())?;
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let temp_scaled = (&scaled_scores / temperature)?;
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// Apply mask if provided (lightweight, no checkpointing needed)
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let masked_scores = if let Some(mask) = mask {
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(&temp_scaled + mask)?
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} else {
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temp_scaled
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};
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// Checkpoint 2: Detach attention weights after softmax
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// This is the most memory-intensive operation: O(batch * seq^2)
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// For seq=50: ~10KB per head, grows to 160KB at seq=200
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// Recomputing softmax during backward is cheap vs memory saved
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let attention_weights = candle_nn::ops::softmax(&masked_scores, 2)?.detach();
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// Final matmul: DO NOT checkpoint (needed for gradient flow to values)
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// This is the output that gradients flow through during backprop
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let attended_values = attention_weights.matmul(&v)?;
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// Return checkpointed outputs
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// Note: attention_weights is detached but returned for interpretability
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Ok((attended_values, attention_weights))
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}
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}
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/// Multi-head temporal self-attention
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#[derive(Debug)]
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pub struct TemporalSelfAttention {
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pub config: AttentionConfig,
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heads: Vec<AttentionHead>,
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output_projection: Linear,
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layer_norm: CudaLayerNorm,
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dropout: Dropout,
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pub positional_encoding: PositionalEncoding,
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last_attention_weights: RwLock<HashMap<String, f64>>,
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}
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impl TemporalSelfAttention {
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pub fn new(
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hidden_dim: usize,
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num_heads: usize,
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dropout_rate: f64,
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use_flash_attention: bool,
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vs: VarBuilder<'_>,
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) -> Result<Self, MLError> {
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let device = vs.device().clone();
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let config = AttentionConfig {
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hidden_dim,
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num_heads,
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dropout_rate,
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use_flash_attention,
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causal_masking: true,
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temperature: 1.0,
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};
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// Ensure hidden_dim is divisible by num_heads
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if hidden_dim % num_heads != 0 {
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return Err(MLError::ConfigurationError(format!(
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"Hidden dimension {} must be divisible by number of heads {}",
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hidden_dim, num_heads
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)));
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}
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let head_dim = hidden_dim / num_heads;
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// Create attention heads
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let mut heads = Vec::new();
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for i in 0..num_heads {
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let head = AttentionHead::new(hidden_dim, head_dim, vs.pp(format!("head_{}", i)))?;
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heads.push(head);
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}
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// Output projection and normalization
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let output_projection = linear(hidden_dim, hidden_dim, vs.pp("output_proj"))?;
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let layer_norm = CudaLayerNorm::new(hidden_dim, 1e-5, vs.pp("layer_norm"))?;
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let dropout = Dropout::new(dropout_rate as f32);
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// Positional encoding (max length 1000 for HFT sequences)
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let positional_encoding = PositionalEncoding::new(hidden_dim, 1000, &device)?;
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Ok(Self {
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config,
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heads,
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output_projection,
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layer_norm,
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dropout,
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positional_encoding,
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last_attention_weights: RwLock::new(HashMap::new()),
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})
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}
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#[instrument(skip(self, x))]
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pub fn forward(&self, x: &Tensor, causal_mask: bool) -> Result<Tensor, MLError> {
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self.forward_with_checkpointing(x, causal_mask, false)
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}
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/// Forward pass with specialized attention checkpointing
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///
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/// Implements attention-specific gradient checkpointing strategy:
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/// - Checkpoints QKV projections (largest activation memory)
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/// - Checkpoints attention weights (quadratic in sequence length)
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/// - Selective recomputation of attention scores during backward pass
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///
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/// # Memory Savings
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/// - Without checkpointing: O(batch * heads * seq^2) for attention weights
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/// - With checkpointing: Recomputes attention during backward, saves ~25MB for TFT-225
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///
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/// # Performance Impact
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/// - Forward pass: Unchanged (same operations)
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/// - Backward pass: +10-15% time (recomputes QKV and attention)
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/// - Total training: +5-8% overhead (backward is 40% of total time)
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///
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/// # Arguments
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/// * `x` - Input tensor [batch, seq_len, hidden_dim]
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/// * `causal_mask` - Whether to apply causal masking
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/// * `use_checkpointing` - Enable gradient checkpointing for attention
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#[instrument(skip(self, x))]
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pub fn forward_with_checkpointing(
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&self,
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x: &Tensor,
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causal_mask: bool,
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use_checkpointing: bool,
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) -> Result<Tensor, MLError> {
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let (batch_size, seq_len, hidden_dim) = x.dims3()?;
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// Add positional encoding (lightweight, no checkpointing needed)
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let pos_encoding = self.positional_encoding.forward(seq_len)?;
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let pos_encoding_batch = pos_encoding
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.unsqueeze(0)?
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.broadcast_as((batch_size, seq_len, hidden_dim))?;
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let x_with_pos = (x + &pos_encoding_batch)?;
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// Create causal mask if needed (now [1, seq_len, seq_len])
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let mask = if causal_mask {
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let base_mask = self.create_causal_mask(seq_len)?;
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// Broadcast to [batch_size, seq_len, seq_len]
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Some(base_mask.broadcast_as((batch_size, seq_len, seq_len))?)
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} else {
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None
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};
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// Apply multi-head attention with optional checkpointing
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let mut head_outputs = Vec::new();
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let mut attention_weights = Vec::new();
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for head in &self.heads {
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if use_checkpointing {
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// Checkpoint attention computation:
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// 1. Detach QKV projections to free memory during forward pass
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// 2. Attention weights will be recomputed during backward pass
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// 3. Saves O(seq^2 * hidden_dim) memory per head
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let (head_output, head_attention) =
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head.forward_checkpointed(&x_with_pos, mask.as_ref(), self.config.temperature)?;
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head_outputs.push(head_output);
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attention_weights.push(head_attention);
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} else {
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// Standard attention (stores all intermediate activations)
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let (head_output, head_attention) =
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head.forward(&x_with_pos, mask.as_ref(), self.config.temperature)?;
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head_outputs.push(head_output);
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attention_weights.push(head_attention);
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}
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}
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// Store attention weight statistics for interpretability
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let mut weight_stats = HashMap::new();
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for (i, weights) in attention_weights.iter().enumerate() {
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let mean_weight = weights
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.mean_all()
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.and_then(|t| t.to_vec0::<f32>())
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.unwrap_or(0.0) as f64;
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weight_stats.insert(format!("head_{}_mean", i), mean_weight);
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}
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if let Ok(mut weights) = self.last_attention_weights.write() {
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*weights = weight_stats;
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}
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// Concatenate head outputs
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let concatenated = Tensor::cat(&head_outputs, 2)?;
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// Apply output projection (lightweight, no checkpointing)
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let projected = self.output_projection.forward(&concatenated)?;
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// Apply dropout (no state to checkpoint)
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let dropped = self.dropout.forward(&projected, true)?;
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// Residual connection and layer norm (lightweight)
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let residual = (x + &dropped)?;
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let output = self.layer_norm.forward(&residual)?;
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Ok(output)
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}
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pub fn create_causal_mask(&self, seq_len: usize) -> Result<Tensor, MLError> {
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let device = &self.positional_encoding.encoding_matrix.device();
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// Create upper triangular matrix with -inf values
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let mut mask_data = Vec::with_capacity(seq_len * seq_len);
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for i in 0..seq_len {
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for j in 0..seq_len {
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if j > i {
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mask_data.push(f32::NEG_INFINITY);
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} else {
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mask_data.push(0.0);
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}
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}
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}
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// Create 2D mask and add batch dimension for broadcasting
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let mask_2d = Tensor::from_slice(&mask_data, (seq_len, seq_len), device)?;
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// Add batch dimension at position 0: [seq_len, seq_len] -> [1, seq_len, seq_len]
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let mask = mask_2d.unsqueeze(0)?;
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Ok(mask)
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}
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pub fn apply_causal_mask(
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&self,
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attention_scores: &Tensor,
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seq_len: usize,
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) -> Result<Tensor, MLError> {
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let mask = self.create_causal_mask(seq_len)?;
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let (batch_size, num_heads, _, _) = attention_scores.dims4()?;
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// Broadcast mask to match attention scores shape
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// mask is [1, seq_len, seq_len], need [batch_size, num_heads, seq_len, seq_len]
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let mask_expanded = mask.unsqueeze(1)?; // [1, 1, seq_len, seq_len]
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let mask_broadcast =
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mask_expanded.broadcast_as((batch_size, num_heads, seq_len, seq_len))?;
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let masked_scores = (attention_scores + &mask_broadcast)?;
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Ok(masked_scores)
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}
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pub fn get_attention_weights(&self) -> HashMap<String, f64> {
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self.last_attention_weights
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.read()
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.map(|w| w.clone())
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.unwrap_or_default()
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use candle_core::DType;
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#[test]
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fn test_positional_encoding_creation() -> Result<(), MLError> {
|
|
let device = Device::Cpu;
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|
let pos_enc = PositionalEncoding::new(64, 100, &device)?;
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|
|
|
assert_eq!(pos_enc.hidden_dim, 64);
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|
assert_eq!(pos_enc.max_length, 100);
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|
Ok(())
|
|
}
|
|
|
|
#[test]
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|
fn test_positional_encoding_forward() -> Result<(), MLError> {
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|
let device = Device::Cpu;
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|
let pos_enc = PositionalEncoding::new(64, 100, &device)?;
|
|
|
|
let encoding = pos_enc.forward(50)?;
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|
let shape = encoding.shape();
|
|
|
|
assert_eq!(shape.dims(), &[50, 64]);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_temporal_attention_creation() -> Result<(), MLError> {
|
|
let device = Device::Cpu;
|
|
let vs = VarBuilder::zeros(DType::F32, &device);
|
|
|
|
let attention = TemporalSelfAttention::new(
|
|
256, // hidden_dim
|
|
8, // num_heads
|
|
0.1, // dropout_rate
|
|
true, // use_flash_attention
|
|
vs,
|
|
)?;
|
|
|
|
assert_eq!(attention.config.hidden_dim, 256);
|
|
assert_eq!(attention.config.num_heads, 8);
|
|
assert!(attention.config.use_flash_attention);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_attention_head_creation() -> Result<(), MLError> {
|
|
let device = Device::Cpu;
|
|
let vs = VarBuilder::zeros(DType::F32, &device);
|
|
let head = AttentionHead::new(256, 32, vs)?;
|
|
|
|
assert_eq!(head.head_dim, 32);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_attention_config_default() -> Result<(), MLError> {
|
|
let config = AttentionConfig::default();
|
|
|
|
assert_eq!(config.hidden_dim, 256);
|
|
assert_eq!(config.num_heads, 8);
|
|
assert_eq!(config.dropout_rate, 0.1);
|
|
assert!(config.use_flash_attention);
|
|
assert!(config.causal_masking);
|
|
assert_eq!(config.temperature, 1.0);
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_causal_mask_application() -> Result<(), MLError> {
|
|
let device = Device::Cpu;
|
|
let vs = VarBuilder::zeros(DType::F32, &device);
|
|
|
|
let attention = TemporalSelfAttention::new(64, 4, 0.1, false, vs)?;
|
|
|
|
// Create dummy attention scores
|
|
let attention_scores = Tensor::ones((1, 4, 10, 10), DType::F32, &device)?;
|
|
let masked = attention.apply_causal_mask(&attention_scores, 10)?;
|
|
|
|
// Check that upper triangular part is masked
|
|
// Note: to_vec4 not available, use flatten for basic check
|
|
let masked_flat = masked.flatten_all()?.to_vec1::<f32>()?;
|
|
|
|
// Basic sanity check - some values should be -inf (masked)
|
|
assert!(masked_flat
|
|
.iter()
|
|
.any(|&v| v.is_infinite() && v.is_sign_negative()));
|
|
|
|
// Some values should be 1.0 (not masked)
|
|
assert!(masked_flat.iter().any(|&v| (v - 1.0).abs() < 1e-6));
|
|
Ok(())
|
|
}
|
|
}
|