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crates/training/rtx-transformers/src/architectures/gpt/generation.rs

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+//! Text generation utilities for GPT models
+
+use crate::Result;
+use crate::tensor_bridge::TensorBridge;
+use rtx_tensor::Tensor;
+use std::collections::HashMap;
+
+/// Position encoding type for GPT
+#[derive(Debug, Clone, Copy, PartialEq)]
+#[derive(Default)]
+pub enum PositionEncodingType {
+    /// Sinusoidal position encoding
+    Sinusoidal,
+    /// Learned position embeddings
+    #[default]
+    Learned,
+    /// Rotary position embeddings (`RoPE`)
+    Rotary,
+}
+
+
+/// Sampling strategy for text generation
+#[derive(Debug, Clone, Copy, PartialEq)]
+#[derive(Default)]
+pub enum SamplingStrategy {
+    /// Greedy decoding - always pick highest probability token
+    #[default]
+    Greedy,
+    /// Sample from top-k tokens
+    TopK(usize),
+    /// Sample from nucleus (top-p) of probability distribution
+    TopP(f32),
+    /// Temperature sampling with optional top-k/top-p
+    Temperature {
+        temperature: f32,
+        top_k: Option<usize>,
+        top_p: Option<f32>,
+    },
+}
+
+
+/// Configuration for text generation
+#[derive(Debug, Clone)]
+pub struct GenerationConfig {
+    /// Maximum length of generated sequence
+    pub max_length: usize,
+    /// Minimum length of generated sequence
+    pub min_length: usize,
+    /// Temperature for sampling
+    pub temperature: f32,
+    /// Top-k sampling parameter
+    pub top_k: Option<usize>,
+    /// Top-p (nucleus) sampling parameter
+    pub top_p: Option<f32>,
+    /// Repetition penalty
+    pub repetition_penalty: f32,
+    /// Length penalty
+    pub length_penalty: f32,
+    /// Number of beams for beam search
+    pub num_beams: usize,
+    /// Sampling strategy
+    pub sampling_strategy: SamplingStrategy,
+    /// Early stopping
+    pub early_stopping: bool,
+    /// Padding token ID
+    pub pad_token_id: Option<usize>,
+    /// End of sequence token ID
+    pub eos_token_id: Option<usize>,
+    /// Beginning of sequence token ID
+    pub bos_token_id: Option<usize>,
+}
+
+impl Default for GenerationConfig {
+    fn default() -> Self {
+        Self {
+            max_length: 1024,
+            min_length: 1,
+            temperature: 1.0,
+            top_k: None,
+            top_p: None,
+            repetition_penalty: 1.0,
+            length_penalty: 1.0,
+            num_beams: 1,
+            sampling_strategy: SamplingStrategy::Greedy,
+            early_stopping: false,
+            pad_token_id: None,
+            eos_token_id: None,
+            bos_token_id: None,
+        }
+    }
+}
+
+/// Text generator for GPT models
+pub struct TextGenerator {
+    /// Vocabulary to token ID mapping
+    pub vocab_to_id: HashMap<String, usize>,
+    /// Token ID to vocabulary mapping
+    pub id_to_vocab: HashMap<usize, String>,
+    /// Special tokens
+    pub special_tokens: HashMap<String, usize>,
+}
+
+impl TextGenerator {
+    /// Create a new text generator
+    #[must_use] 
+    pub fn new(
+        vocab_to_id: HashMap<String, usize>,
+        special_tokens: HashMap<String, usize>,
+    ) -> Self {
+        let id_to_vocab = vocab_to_id.iter().map(|(k, v)| (*v, k.clone())).collect();
+
+        Self {
+            vocab_to_id,
+            id_to_vocab,
+            special_tokens,
+        }
+    }
+
+    /// Tokenize text into token IDs
+    pub fn tokenize(&self, text: &str) -> Result<Vec<usize>> {
+        // Simple whitespace tokenization for now
+        let tokens: Vec<usize> = text
+            .split_whitespace()
+            .filter_map(|word| self.vocab_to_id.get(word).copied())
+            .collect();
+
+        Ok(tokens)
+    }
+
+    /// Decode token IDs back to text
+    pub fn decode(&self, token_ids: &[usize]) -> Result<String> {
+        let tokens: Vec<String> = token_ids
+            .iter()
+            .filter_map(|id| self.id_to_vocab.get(id).cloned())
+            .collect();
+
+        Ok(tokens.join(" "))
+    }
+
+    /// Generate text from a prompt
+    pub fn generate(
+        &self,
+        _model: &dyn GenerativeModel,
+        _prompt: &str,
+        _config: &GenerationConfig,
+    ) -> Result<String> {
+        // Implementation would go here
+        // For now, return a placeholder
+        Ok("Generated text would appear here".to_string())
+    }
+
+    /// Sample next token based on logits and strategy
+    pub fn sample_token(
+        &self,
+        logits: &Tensor,
+        strategy: &SamplingStrategy,
+        _past_tokens: &[usize],
+    ) -> Result<usize> {
+        match strategy {
+            SamplingStrategy::Greedy => {
+                // Get argmax
+                let token_id = logits.argmax(Some(-1), false)?;
+                let token_data = token_id.to_cpu()?;
+                Ok(token_data[0] as usize)
+            }
+            SamplingStrategy::TopK(k) => {
+                // Top-k sampling
+                let _top_k = *k;
+                // Implementation would go here
+                Ok(0)
+            }
+            SamplingStrategy::TopP(p) => {
+                // Nucleus sampling
+                let _top_p = *p;
+                // Implementation would go here
+                Ok(0)
+            }
+            SamplingStrategy::Temperature {
+                temperature,
+                top_k,
+                top_p,
+            } => {
+                // Temperature-based sampling with optional filtering
+                let _temp = *temperature;
+                let _tk = *top_k;
+                let _tp = *top_p;
+                // Implementation would go here
+                Ok(0)
+            }
+        }
+    }
+}
+
+/// Trait for models that can generate text
+pub trait GenerativeModel {
+    /// Forward pass for generation
+    fn generate_forward(&self, input_ids: &Tensor) -> Result<Tensor>;
+
+    /// Get vocabulary size
+    fn vocab_size(&self) -> usize;
+}