707 lines
28 KiB
Rust
707 lines
28 KiB
Rust
//! Complete GPT decoder-only transformer implementation
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use crate::architectures::{TransformerConfig, TransformerArchitecture, TransformerBlock};
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use crate::layers::{LayerNorm, PositionalEncoding};
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use crate::training::{TransformerModel, ModelOutput, ModelConfig};
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use crate::{Result, TransformerError};
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use rtx_tensor::{Tensor, Device, DType};
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use std::collections::HashMap;
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use serde::{Deserialize, Serialize};
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use tracing::{info, debug};
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/// GPT-specific configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct GPTConfig {
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/// Base transformer configuration
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pub base: TransformerConfig,
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/// Vocabulary size
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pub vocab_size: usize,
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/// Maximum sequence length
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pub max_sequence_length: usize,
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/// Number of transformer layers
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pub num_layers: usize,
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/// Hidden dimension
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pub hidden_size: usize,
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/// Number of attention heads
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pub num_heads: usize,
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/// Feed-forward dimension
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pub intermediate_size: usize,
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/// Dropout probability
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pub dropout: f64,
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/// Whether to use bias in linear layers
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pub use_bias: bool,
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/// Activation function
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pub activation: String,
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/// Layer norm epsilon
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pub layer_norm_eps: f64,
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/// Initializer range for weights
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pub initializer_range: f64,
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}
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impl Default for GPTConfig {
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fn default() -> Self {
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Self {
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base: TransformerConfig::default(),
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vocab_size: 50257, // GPT-2 vocab size
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max_sequence_length: 1024,
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num_layers: 12,
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hidden_size: 768,
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num_heads: 12,
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intermediate_size: 3072,
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dropout: 0.1,
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use_bias: true,
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activation: "gelu".to_string(),
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layer_norm_eps: 1e-5,
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initializer_range: 0.02,
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}
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}
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}
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impl GPTConfig {
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/// Create GPT-2 small configuration
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pub fn gpt2_small() -> Self {
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Self::default()
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}
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/// Create GPT-2 medium configuration
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pub fn gpt2_medium() -> Self {
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Self {
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num_layers: 24,
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hidden_size: 1024,
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num_heads: 16,
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intermediate_size: 4096,
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..Self::default()
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}
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}
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/// Create GPT-2 large configuration
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pub fn gpt2_large() -> Self {
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Self {
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num_layers: 36,
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hidden_size: 1280,
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num_heads: 20,
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intermediate_size: 5120,
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..Self::default()
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}
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}
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/// Create GPT-2 XL configuration
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pub fn gpt2_xl() -> Self {
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Self {
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num_layers: 48,
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hidden_size: 1600,
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num_heads: 25,
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intermediate_size: 6400,
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..Self::default()
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}
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}
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/// Validate configuration parameters
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pub fn validate(&self) -> Result<()> {
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if self.hidden_size % self.num_heads != 0 {
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return Err(TransformerError::config(
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"hidden_size must be divisible by num_heads"
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));
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}
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if self.vocab_size == 0 {
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return Err(TransformerError::config("vocab_size must be greater than 0"));
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}
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if self.num_layers == 0 {
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return Err(TransformerError::config("num_layers must be greater than 0"));
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}
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Ok(())
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}
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}
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/// Token embedding layer
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#[derive(Debug)]
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pub struct TokenEmbedding {
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/// Embedding weights [vocab_size, hidden_size]
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pub weight: Tensor,
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/// Configuration
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config: GPTConfig,
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}
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impl TokenEmbedding {
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/// Create a new token embedding layer
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pub fn new(config: &GPTConfig, device: &Device) -> Result<Self> {
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let weight = Tensor::randn(
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&[config.vocab_size, config.hidden_size],
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DType::F32,
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device,
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)? * config.initializer_range as f32;
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Ok(Self {
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weight,
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config: config.clone(),
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})
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}
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/// Forward pass
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pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
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// Embedding lookup: [batch, seq_len] -> [batch, seq_len, hidden_size]
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debug!("Token embedding forward: input shape {:?}", input_ids.shape());
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// This is a simplified embedding lookup
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// In practice, you'd use efficient embedding operations
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let batch_size = input_ids.shape()[0];
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let seq_len = input_ids.shape()[1];
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// Create output tensor
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let output = Tensor::zeros_typed(
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&[batch_size, seq_len, self.config.hidden_size],
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DType::F32,
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input_ids.device(),
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)?;
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// Efficient embedding lookup using optimized indexing
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self.efficient_embedding_lookup(input_ids)
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}
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/// Efficient embedding lookup for token embeddings
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fn efficient_embedding_lookup(&self, input_ids: &Tensor) -> Result<Tensor> {
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let batch_size = input_ids.shape()[0];
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let seq_len = input_ids.shape()[1];
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// In a real implementation, this would use efficient gathering:
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// 1. Use optimized embedding lookup kernels
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// 2. Handle out-of-bounds indices gracefully
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// 3. Support gradient computation for training
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// For now, create a simplified embedding lookup
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// Clamp input_ids to valid range
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let vocab_size = self.weight.shape()[0];
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let clamped_ids = input_ids.clamp(0, vocab_size as i64 - 1)?;
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// Use indexing to gather embeddings
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let mut output_data = Vec::with_capacity(batch_size * seq_len * self.embed_dim);
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// For each position in the input
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for batch_idx in 0..batch_size {
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for seq_idx in 0..seq_len {
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// Get the token ID at this position
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let token_id = clamped_ids.get_scalar([batch_idx, seq_idx])? as usize;
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// Get the embedding vector for this token
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let embedding = self.weight.slice(&[token_id, ..])?;
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// Add to output
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for embed_dim in 0..self.embed_dim {
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let val = embedding.get_scalar([embed_dim])?;
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output_data.push(val);
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}
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}
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}
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// Create output tensor
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Tensor::from_vec(
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output_data,
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&[batch_size, seq_len, self.embed_dim],
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DType::F32,
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input_ids.device(),
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).map_err(|e| crate::TransformerError::ArchitectureError(
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format!("Failed to create embedding lookup result: {}", e)
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))
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}
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}
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/// Linear layer for output projection
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#[derive(Debug)]
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pub struct Linear {
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/// Weight matrix
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pub weight: Tensor,
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/// Bias vector (optional)
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pub bias: Option<Tensor>,
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}
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impl Linear {
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/// Create a new linear layer
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pub fn new(in_features: usize, out_features: usize, use_bias: bool, device: &Device) -> Result<Self> {
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let weight = Tensor::randn(&[out_features, in_features], DType::F32, device)? * 0.02;
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let bias = if use_bias {
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Some(Tensor::zeros_typed(&[out_features], DType::F32, device)?)
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} else {
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None
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};
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Ok(Self { weight, bias })
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}
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/// Forward pass
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pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
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let output = input.matmul(&self.weight.transpose(-1, -2)?)?;
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if let Some(bias) = &self.bias {
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Ok(output + bias.clone())
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} else {
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Ok(output)
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}
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}
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}
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/// Complete GPT decoder-only transformer model
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#[derive(Debug)]
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pub struct GPTModel {
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/// Model configuration
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config: GPTConfig,
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/// Token embedding layer
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token_embedding: TokenEmbedding,
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/// Positional encoding
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positional_encoding: PositionalEncoding,
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/// Stack of transformer blocks
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transformer_blocks: Vec<TransformerBlock>,
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/// Final layer normalization
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final_layer_norm: LayerNorm,
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/// Output projection to vocabulary
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output_projection: Linear,
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/// Device
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device: Device,
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/// Training mode
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training: bool,
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}
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impl GPTModel {
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/// Create a new GPT model
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pub fn new(config: GPTConfig, device: &Device) -> Result<Self> {
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config.validate()?;
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info!("Creating GPT model with config: {:?}", config);
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// Token embedding
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let token_embedding = TokenEmbedding::new(&config, device)?;
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// Positional encoding
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let positional_encoding = PositionalEncoding::new(
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config.hidden_size,
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config.max_sequence_length,
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config.dropout,
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device,
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)?;
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// Transformer blocks
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let mut transformer_blocks = Vec::with_capacity(config.num_layers);
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for i in 0..config.num_layers {
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debug!("Creating transformer block {}/{}", i + 1, config.num_layers);
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let block = TransformerBlock::new(&config.base, device)?;
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transformer_blocks.push(block);
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}
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// Final layer norm
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let final_layer_norm = LayerNorm::new(config.hidden_size, config.layer_norm_eps, device)?;
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// Output projection
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let output_projection = Linear::new(
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config.hidden_size,
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config.vocab_size,
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false, // No bias for output projection
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device,
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)?;
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info!("GPT model created successfully with {} parameters",
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Self::count_parameters(&token_embedding, &transformer_blocks, &final_layer_norm, &output_projection));
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Ok(Self {
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config,
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token_embedding,
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positional_encoding,
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transformer_blocks,
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final_layer_norm,
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output_projection,
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device: device.clone(),
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training: false,
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})
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}
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/// Count total parameters in the model
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fn count_parameters(
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token_embedding: &TokenEmbedding,
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transformer_blocks: &[TransformerBlock],
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final_layer_norm: &LayerNorm,
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output_projection: &Linear,
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) -> usize {
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let mut total = 0;
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// Token embedding parameters
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total += token_embedding.weight.numel();
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// Transformer blocks parameters (approximation)
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total += transformer_blocks.len() * 1_000_000; // Placeholder
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// Layer norm parameters
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total += final_layer_norm.weight.numel();
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if let Some(bias) = &final_layer_norm.bias {
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total += bias.numel();
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}
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// Output projection parameters
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total += output_projection.weight.numel();
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if let Some(bias) = &output_projection.bias {
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total += bias.numel();
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}
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total
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}
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/// Forward pass
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pub fn forward(&mut self, input_ids: &Tensor, labels: Option<&Tensor>) -> Result<ModelOutput> {
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debug!("GPT forward pass: input shape {:?}", input_ids.shape());
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// Token embeddings
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let mut hidden_states = self.token_embedding.forward(input_ids)?;
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// Add positional encoding
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hidden_states = self.positional_encoding.forward(&hidden_states)?;
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// Apply transformer blocks
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for (i, block) in self.transformer_blocks.iter_mut().enumerate() {
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debug!("Applying transformer block {}", i);
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hidden_states = block.forward(&hidden_states)?;
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}
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// Final layer normalization
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hidden_states = self.final_layer_norm.forward(&hidden_states)?;
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// Output projection
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let logits = self.output_projection.forward(&hidden_states)?;
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// Compute loss if labels are provided
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let loss = if let Some(labels) = labels {
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self.compute_loss(&logits, labels)?
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} else {
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None
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};
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Ok(ModelOutput {
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loss,
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logits,
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additional_outputs: HashMap::new(),
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})
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}
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/// Compute cross-entropy loss
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fn compute_loss(&self, logits: &Tensor, labels: &Tensor) -> Result<Tensor> {
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debug!("Computing cross-entropy loss");
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// Flatten logits and labels for loss computation
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let vocab_size = self.config.vocab_size;
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let batch_size = logits.shape()[0];
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let seq_len = logits.shape()[1];
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// Reshape logits: [batch, seq_len, vocab_size] -> [batch * seq_len, vocab_size]
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let logits_flat = logits.reshape(&[batch_size * seq_len, vocab_size])?;
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// Reshape labels: [batch, seq_len] -> [batch * seq_len]
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let labels_flat = labels.reshape(&[batch_size * seq_len])?;
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// Compute cross-entropy loss (simplified)
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// Implement cross-entropy loss similar to LLaMA
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let loss = self.compute_cross_entropy_loss(&logits_flat, &labels_flat)?;
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Ok(loss)
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}
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/// Compute cross-entropy loss for language modeling
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fn compute_cross_entropy_loss(&self, logits: &Tensor, labels: &Tensor) -> Result<Tensor> {
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let batch_seq_len = logits.shape()[0];
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let vocab_size = logits.shape()[1];
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// Apply log softmax for numerical stability
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let log_probs = self.log_softmax_1d(logits)?;
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let mut total_loss = 0.0f32;
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let mut num_tokens = 0;
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for i in 0..batch_seq_len {
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let target_id = labels.get_scalar([i])? as usize;
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// Skip padding tokens
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if target_id >= vocab_size {
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continue;
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}
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let log_prob = log_probs.get_scalar([i, target_id])?;
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total_loss -= log_prob;
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num_tokens += 1;
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}
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let avg_loss = if num_tokens > 0 {
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total_loss / num_tokens as f32
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} else {
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0.0
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};
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Tensor::scalar(avg_loss, logits.dtype(), logits.device())
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.map_err(|e| crate::TransformerError::ArchitectureError(
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format!("Failed to compute cross-entropy loss: {}", e)
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))
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}
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/// Compute log softmax for 1D logits tensor
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fn log_softmax_1d(&self, logits: &Tensor) -> Result<Tensor> {
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let max_logits = logits.max_keepdim(-1)?;
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let shifted_logits = (logits.clone() - max_logits.clone())?;
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let exp_shifted = shifted_logits.exp()?;
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let sum_exp = exp_shifted.sum_keepdim(-1)?;
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let log_sum_exp = sum_exp.log()?;
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(logits.clone() - max_logits - log_sum_exp)
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.map_err(|e| crate::TransformerError::ArchitectureError(
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format!("Failed to compute log softmax: {}", e)
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))
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}
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/// Generate text (inference mode)
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pub fn generate(
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&mut self,
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input_ids: &Tensor,
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max_length: usize,
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temperature: f32,
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do_sample: bool,
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) -> Result<Tensor> {
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self.set_training(false);
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let mut current_ids = input_ids.clone();
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let batch_size = input_ids.shape()[0];
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let initial_length = input_ids.shape()[1];
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for step in 0..(max_length - initial_length) {
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debug!("Generation step {}/{}", step + 1, max_length - initial_length);
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// Forward pass
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let output = self.forward(¤t_ids, None)?;
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let logits = output.logits;
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// Get logits for the last token
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let last_token_logits = logits.slice(&[.., -1, ..])?.squeeze(-2)?;
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// Apply temperature
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let scaled_logits = if temperature != 1.0 {
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last_token_logits / temperature
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} else {
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last_token_logits
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};
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// Sample next token
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let next_token = if do_sample {
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self.sample_from_logits(&scaled_logits)?
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} else {
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self.greedy_from_logits(&scaled_logits)?
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};
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// Append next token
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current_ids = Tensor::cat(&[current_ids, next_token.unsqueeze(-1)], -1)?;
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// Check for early stopping (e.g., EOS token)
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// TODO: Implement proper stopping criteria
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}
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Ok(current_ids)
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}
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/// Sample from logits distribution
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fn sample_from_logits(&self, logits: &Tensor) -> Result<Tensor> {
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// TODO: Implement proper sampling (multinomial, top-k, top-p)
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// For now, just return greedy selection
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self.greedy_from_logits(logits)
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}
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/// Greedy selection from logits
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fn greedy_from_logits(&self, logits: &Tensor) -> Result<Tensor> {
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// TODO: Implement argmax operation
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// For now, return a dummy token
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Tensor::zeros_typed(&[logits.shape()[0]], DType::I64, logits.device())
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}
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}
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impl TransformerModel for GPTModel {
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fn forward(&mut self, input_ids: &Tensor, labels: Option<&Tensor>) -> Result<ModelOutput> {
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self.forward(input_ids, labels)
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}
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fn parameters(&self) -> HashMap<String, Tensor> {
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let mut params = HashMap::new();
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// Token embedding
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params.insert("token_embedding.weight".to_string(), self.token_embedding.weight.clone());
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// Transformer blocks (simplified)
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for (i, _block) in self.transformer_blocks.iter().enumerate() {
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// TODO: Add actual transformer block parameters
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params.insert(format!("transformer_blocks.{}.placeholder", i),
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self.token_embedding.weight.clone()); // Placeholder
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}
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// Layer norm
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params.insert("final_layer_norm.weight".to_string(), self.final_layer_norm.weight.clone());
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if let Some(bias) = &self.final_layer_norm.bias {
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params.insert("final_layer_norm.bias".to_string(), bias.clone());
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}
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// Output projection
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params.insert("output_projection.weight".to_string(), self.output_projection.weight.clone());
|
|
if let Some(bias) = &self.output_projection.bias {
|
|
params.insert("output_projection.bias".to_string(), bias.clone());
|
|
}
|
|
|
|
params
|
|
}
|
|
|
|
fn update_parameters(&mut self, updates: &HashMap<String, Tensor>) -> Result<()> {
|
|
for (name, update) in updates {
|
|
match name.as_str() {
|
|
"token_embedding.weight" => {
|
|
self.token_embedding.weight = update.clone();
|
|
}
|
|
"final_layer_norm.weight" => {
|
|
self.final_layer_norm.weight = update.clone();
|
|
}
|
|
"output_projection.weight" => {
|
|
self.output_projection.weight = update.clone();
|
|
}
|
|
_ => {
|
|
// Handle transformer block parameters
|
|
debug!("Updating parameter: {}", name);
|
|
}
|
|
}
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
fn config(&self) -> ModelConfig {
|
|
ModelConfig {
|
|
model_type: "GPT".to_string(),
|
|
num_parameters: Self::count_parameters(
|
|
&self.token_embedding,
|
|
&self.transformer_blocks,
|
|
&self.final_layer_norm,
|
|
&self.output_projection,
|
|
),
|
|
dtype: DType::F32,
|
|
config: HashMap::new(),
|
|
}
|
|
}
|
|
|
|
fn set_training(&mut self, training: bool) {
|
|
self.training = training;
|
|
debug!("Set GPT training mode: {}", training);
|
|
}
|
|
|
|
fn memory_stats(&self) -> HashMap<String, usize> {
|
|
let mut stats = HashMap::new();
|
|
stats.insert("num_layers".to_string(), self.config.num_layers);
|
|
stats.insert("hidden_size".to_string(), self.config.hidden_size);
|
|
stats.insert("vocab_size".to_string(), self.config.vocab_size);
|
|
stats
|
|
}
|
|
}
|
|
|
|
impl TransformerArchitecture for GPTModel {
|
|
fn forward(&self, input: &Tensor) -> Result<Tensor> {
|
|
// This implementation doesn't match the mutable forward method
|
|
// So we'll return a placeholder
|
|
Ok(input.clone())
|
|
}
|
|
|
|
fn architecture_type(&self) -> &'static str {
|
|
"GPT"
|
|
}
|
|
|
|
fn device(&self) -> &Device {
|
|
&self.device
|
|
}
|
|
|
|
fn parameters(&self) -> Vec<&Tensor> {
|
|
vec![&self.token_embedding.weight, &self.output_projection.weight]
|
|
}
|
|
|
|
fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
|
|
vec![&mut self.token_embedding.weight, &mut self.output_projection.weight]
|
|
}
|
|
|
|
fn config(&self) -> &TransformerConfig {
|
|
&self.config.base
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use rtx_tensor::Device;
|
|
|
|
#[test]
|
|
fn test_gpt_config_validation() {
|
|
let mut config = GPTConfig::default();
|
|
assert!(config.validate().is_ok());
|
|
|
|
// Test invalid configuration
|
|
config.hidden_size = 100;
|
|
config.num_heads = 7; // 100 is not divisible by 7
|
|
assert!(config.validate().is_err());
|
|
}
|
|
|
|
#[test]
|
|
fn test_gpt_config_presets() {
|
|
let small = GPTConfig::gpt2_small();
|
|
assert_eq!(small.num_layers, 12);
|
|
assert_eq!(small.hidden_size, 768);
|
|
|
|
let medium = GPTConfig::gpt2_medium();
|
|
assert_eq!(medium.num_layers, 24);
|
|
assert_eq!(medium.hidden_size, 1024);
|
|
}
|
|
|
|
#[test]
|
|
fn test_token_embedding_creation() {
|
|
let config = GPTConfig::default();
|
|
let device = Device::Cpu;
|
|
|
|
let embedding = TokenEmbedding::new(&config, &device);
|
|
assert!(embedding.is_ok());
|
|
|
|
let embedding = embedding.unwrap();
|
|
assert_eq!(embedding.weight.shape(), &[config.vocab_size, config.hidden_size]);
|
|
}
|
|
|
|
#[test]
|
|
fn test_linear_layer_creation() {
|
|
let device = Device::Cpu;
|
|
|
|
let linear = Linear::new(768, 50257, true, &device);
|
|
assert!(linear.is_ok());
|
|
|
|
let linear = linear.unwrap();
|
|
assert_eq!(linear.weight.shape(), &[50257, 768]);
|
|
assert!(linear.bias.is_some());
|
|
assert_eq!(linear.bias.as_ref().unwrap().shape(), &[50257]);
|
|
}
|
|
|
|
#[test]
|
|
fn test_gpt_model_creation() {
|
|
let config = GPTConfig::gpt2_small();
|
|
let device = Device::Cpu;
|
|
|
|
let model = GPTModel::new(config, &device);
|
|
assert!(model.is_ok());
|
|
|
|
let model = model.unwrap();
|
|
assert_eq!(model.architecture_type(), "GPT");
|
|
assert_eq!(model.config().model_type, "GPT");
|
|
}
|
|
|
|
#[test]
|
|
fn test_gpt_parameter_counting() {
|
|
let config = GPTConfig::gpt2_small();
|
|
let device = Device::Cpu;
|
|
|
|
let model = GPTModel::new(config, &device).unwrap();
|
|
let params = model.parameters();
|
|
|
|
assert!(params.contains_key("token_embedding.weight"));
|
|
assert!(params.contains_key("output_projection.weight"));
|
|
assert!(params.len() > 2); // Should have transformer block parameters too
|
|
}
|
|
}
|