543 lines
16 KiB
Rust
543 lines
16 KiB
Rust
//! # RTX Natural Language Generation
|
|
//!
|
|
//! Advanced Natural Language Generation capabilities for RTX with production-ready
|
|
//! text generation algorithms and optimization techniques.
|
|
//!
|
|
//! ## Features
|
|
//!
|
|
//! - **Advanced Generation**: Beam search, nucleus sampling, top-k sampling with optimizations
|
|
//! - **Constrained Generation**: Lexical and syntactic constraints with controllable attributes
|
|
//! - **NLP Tasks**: Translation, summarization, QA, dialogue, completion
|
|
//! - **Model Serving**: Streaming generation, batch optimization, multi-turn conversations
|
|
//! - **Quality Features**: Repetition penalty, diversity promotion, factuality enhancement
|
|
//! - **Production Optimizations**: KV cache, speculative decoding, model parallelism
|
|
//!
|
|
//! ## Quick Start
|
|
//!
|
|
//! ```rust
|
|
//! use rtx_nlg::{GenerationConfig, TextGenerator};
|
|
//!
|
|
//! // Create a text generator with beam search
|
|
//! let config = GenerationConfig::beam_search()
|
|
//! .num_beams(4)
|
|
//! .max_length(100)
|
|
//! .temperature(0.7);
|
|
//!
|
|
//! let generator = TextGenerator::new(config)?;
|
|
//! let output = generator.generate("The future of AI is")?;
|
|
//! ```
|
|
|
|
pub mod dialogue;
|
|
pub mod error;
|
|
pub mod generation;
|
|
pub mod optimization;
|
|
pub mod quality;
|
|
pub mod serving;
|
|
pub mod summarization;
|
|
pub mod tensor_helpers;
|
|
pub mod translation;
|
|
|
|
// Re-export core types and traits
|
|
pub use error::{NlgError, Result};
|
|
pub use generation::{
|
|
BeamSearchConfig, GenerationConfig, GenerationStrategy, NucleusSamplingConfig, TextGenerator,
|
|
TopKSamplingConfig,
|
|
};
|
|
pub use quality::{
|
|
DiversityPromoter, QualityChecker, QualityFilter, RepetitionPenalty, ToxicityFilter,
|
|
};
|
|
pub use serving::{
|
|
BatchGenerator, ConversationManager, ModelServer, PromptTemplate, StreamingGenerator,
|
|
};
|
|
|
|
use dashmap::DashMap;
|
|
use rtx_tensor::Tensor;
|
|
use std::sync::Arc;
|
|
use tokio::sync::RwLock;
|
|
|
|
/// Global configuration for RTX-NLG
|
|
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
|
pub struct NlgConfig {
|
|
/// Default device for computations
|
|
pub device: Device,
|
|
/// Maximum sequence length
|
|
pub max_sequence_length: usize,
|
|
/// Batch size for generation
|
|
pub batch_size: usize,
|
|
/// Enable memory optimizations
|
|
pub memory_optimization: bool,
|
|
/// Number of worker threads
|
|
pub num_workers: usize,
|
|
/// Cache size in MB
|
|
pub cache_size_mb: usize,
|
|
}
|
|
|
|
impl Default for NlgConfig {
|
|
fn default() -> Self {
|
|
Self {
|
|
device: Device::default(),
|
|
max_sequence_length: 2048,
|
|
batch_size: 8,
|
|
memory_optimization: true,
|
|
num_workers: num_cpus::get(),
|
|
cache_size_mb: 1024,
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Main RTX-NLG runtime providing centralized model and resource management
|
|
pub struct NlgRuntime {
|
|
config: NlgConfig,
|
|
model_cache: Arc<DashMap<String, Arc<dyn ModelInterface>>>,
|
|
generation_cache: Arc<RwLock<lru::LruCache<String, GeneratedOutput>>>,
|
|
metrics: Arc<MetricsCollector>,
|
|
}
|
|
|
|
impl NlgRuntime {
|
|
/// Create a new NLG runtime with default configuration
|
|
pub fn new() -> Result<Self> {
|
|
Self::with_config(NlgConfig::default())
|
|
}
|
|
|
|
/// Create a new NLG runtime with custom configuration
|
|
pub fn with_config(config: NlgConfig) -> Result<Self> {
|
|
let cache_entries = config.cache_size_mb * 1024 * 1024 / 4096; // Estimate cache entries
|
|
|
|
Ok(Self {
|
|
config,
|
|
model_cache: Arc::new(DashMap::new()),
|
|
generation_cache: Arc::new(RwLock::new(lru::LruCache::new(
|
|
std::num::NonZeroUsize::new(cache_entries).unwrap(),
|
|
))),
|
|
metrics: Arc::new(MetricsCollector::new()),
|
|
})
|
|
}
|
|
|
|
/// Load a model for text generation
|
|
pub async fn load_model(&self, model_id: &str, model_path: &str) -> Result<()> {
|
|
let model = self.create_model_interface(model_path).await?;
|
|
self.model_cache
|
|
.insert(model_id.to_string(), Arc::new(model));
|
|
Ok(())
|
|
}
|
|
|
|
/// Get a model interface by ID
|
|
pub fn get_model(&self, model_id: &str) -> Option<Arc<dyn ModelInterface>> {
|
|
self.model_cache
|
|
.get(model_id)
|
|
.map(|entry| entry.value().clone())
|
|
}
|
|
|
|
/// Create a text generator for the specified model
|
|
pub fn create_generator(
|
|
&self,
|
|
model_id: &str,
|
|
config: GenerationConfig,
|
|
) -> Result<TextGenerator> {
|
|
let model = self
|
|
.get_model(model_id)
|
|
.ok_or_else(|| NlgError::ModelNotFound(model_id.to_string()))?;
|
|
|
|
TextGenerator::with_model(model, config)
|
|
}
|
|
|
|
/// Create a streaming generator
|
|
pub fn create_streaming_generator(
|
|
&self,
|
|
model_id: &str,
|
|
config: GenerationConfig,
|
|
) -> Result<StreamingGenerator> {
|
|
let model = self
|
|
.get_model(model_id)
|
|
.ok_or_else(|| NlgError::ModelNotFound(model_id.to_string()))?;
|
|
|
|
StreamingGenerator::with_model(model, config)
|
|
}
|
|
|
|
/// Create a batch generator for high throughput
|
|
pub fn create_batch_generator(
|
|
&self,
|
|
model_id: &str,
|
|
config: GenerationConfig,
|
|
) -> Result<BatchGenerator> {
|
|
let model = self
|
|
.get_model(model_id)
|
|
.ok_or_else(|| NlgError::ModelNotFound(model_id.to_string()))?;
|
|
|
|
BatchGenerator::with_model(model, config, self.config.batch_size)
|
|
}
|
|
|
|
/// Get runtime metrics
|
|
pub fn metrics(&self) -> Arc<MetricsCollector> {
|
|
self.metrics.clone()
|
|
}
|
|
|
|
async fn create_model_interface(&self, model_path: &str) -> Result<MockModelInterface> {
|
|
// Implementation would load the actual model
|
|
// For now, return a mock implementation
|
|
MockModelInterface::new(model_path)
|
|
}
|
|
}
|
|
|
|
/// Common interface for all language models
|
|
pub trait ModelInterface: Send + Sync {
|
|
/// Generate text tokens given input context
|
|
fn generate_tokens(
|
|
&self,
|
|
input_ids: &Tensor,
|
|
attention_mask: Option<&Tensor>,
|
|
generation_config: &GenerationConfig,
|
|
) -> Result<GenerationOutput>;
|
|
|
|
/// Get model configuration
|
|
fn config(&self) -> &ModelConfig;
|
|
|
|
/// Get model vocabulary size
|
|
fn vocab_size(&self) -> usize;
|
|
|
|
/// Get tokenizer
|
|
fn tokenizer(&self) -> Arc<dyn TokenizerInterface>;
|
|
|
|
/// Check if model supports streaming generation
|
|
fn supports_streaming(&self) -> bool {
|
|
true
|
|
}
|
|
|
|
/// Check if model supports batch generation
|
|
fn supports_batch(&self) -> bool {
|
|
true
|
|
}
|
|
}
|
|
|
|
/// Tokenizer interface for text processing
|
|
pub trait TokenizerInterface: Send + Sync {
|
|
/// Encode text to token IDs
|
|
fn encode(&self, text: &str) -> Result<Vec<u32>>;
|
|
|
|
/// Decode token IDs to text
|
|
fn decode(&self, token_ids: &[u32]) -> Result<String>;
|
|
|
|
/// Get vocabulary size
|
|
fn vocab_size(&self) -> usize;
|
|
|
|
/// Get special token IDs
|
|
fn special_tokens(&self) -> &SpecialTokens;
|
|
}
|
|
|
|
/// Special token IDs for various model operations
|
|
#[derive(Debug, Clone)]
|
|
pub struct SpecialTokens {
|
|
pub pad_token: u32,
|
|
pub eos_token: u32,
|
|
pub bos_token: u32,
|
|
pub unk_token: u32,
|
|
pub sep_token: Option<u32>,
|
|
pub cls_token: Option<u32>,
|
|
pub mask_token: Option<u32>,
|
|
}
|
|
|
|
/// Model configuration containing architecture details
|
|
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
|
pub struct ModelConfig {
|
|
pub vocab_size: usize,
|
|
pub hidden_size: usize,
|
|
pub num_layers: usize,
|
|
pub num_heads: usize,
|
|
pub intermediate_size: usize,
|
|
pub max_position_embeddings: usize,
|
|
pub layer_norm_eps: f64,
|
|
pub dropout: f64,
|
|
pub attention_dropout: f64,
|
|
pub activation_function: std::borrow::Cow<'static, str>,
|
|
}
|
|
|
|
/// Output from text generation
|
|
#[derive(Debug, Clone)]
|
|
pub struct GenerationOutput {
|
|
pub sequences: Vec<Vec<u32>>,
|
|
pub scores: Option<Vec<f32>>,
|
|
pub attention_weights: Option<Vec<Tensor>>,
|
|
pub past_key_values: Option<Vec<Tensor>>,
|
|
pub metadata: GenerationMetadata,
|
|
}
|
|
|
|
/// Generated text output with metadata
|
|
#[derive(Debug, Clone)]
|
|
pub struct GeneratedOutput {
|
|
pub text: String,
|
|
pub tokens: Vec<u32>,
|
|
pub score: f32,
|
|
pub metadata: GenerationMetadata,
|
|
}
|
|
|
|
/// Metadata associated with generated text
|
|
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
|
pub struct GenerationMetadata {
|
|
pub generation_time_ms: f64,
|
|
pub tokens_per_second: f64,
|
|
pub num_generated_tokens: usize,
|
|
pub finish_reason: FinishReason,
|
|
pub quality_scores: QualityScores,
|
|
}
|
|
|
|
/// Reason why generation finished
|
|
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
|
pub enum FinishReason {
|
|
MaxLength,
|
|
EosToken,
|
|
StopSequence(String),
|
|
QualityFilter(String),
|
|
Error(String),
|
|
}
|
|
|
|
/// Quality assessment scores for generated text
|
|
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
|
|
pub struct QualityScores {
|
|
pub fluency: f32,
|
|
pub coherence: f32,
|
|
pub relevance: f32,
|
|
pub diversity: f32,
|
|
pub factuality: f32,
|
|
pub safety: f32,
|
|
}
|
|
|
|
/// Metrics collection for monitoring NLG performance
|
|
pub struct MetricsCollector {
|
|
generation_latency: metrics::Histogram,
|
|
throughput: metrics::Gauge,
|
|
cache_hit_rate: metrics::Gauge,
|
|
quality_scores: metrics::Histogram,
|
|
error_count: metrics::Counter,
|
|
}
|
|
|
|
impl Default for MetricsCollector {
|
|
fn default() -> Self {
|
|
Self::new()
|
|
}
|
|
}
|
|
|
|
impl MetricsCollector {
|
|
pub fn new() -> Self {
|
|
Self {
|
|
generation_latency: metrics::histogram!("nlg_generation_latency_ms"),
|
|
throughput: metrics::gauge!("nlg_throughput_tokens_per_second"),
|
|
cache_hit_rate: metrics::gauge!("nlg_cache_hit_rate"),
|
|
quality_scores: metrics::histogram!("nlg_quality_scores"),
|
|
error_count: metrics::counter!("nlg_error_count"),
|
|
}
|
|
}
|
|
|
|
pub fn record_generation(&self, latency_ms: f64, tokens_generated: usize) {
|
|
self.generation_latency.record(latency_ms);
|
|
let throughput = tokens_generated as f64 / (latency_ms / 1000.0);
|
|
self.throughput.set(throughput);
|
|
}
|
|
|
|
pub fn record_cache_hit(&self, hit_rate: f64) {
|
|
self.cache_hit_rate.set(hit_rate);
|
|
}
|
|
|
|
pub fn record_quality(&self, scores: &QualityScores) {
|
|
self.quality_scores.record(scores.fluency as f64);
|
|
self.quality_scores.record(scores.coherence as f64);
|
|
self.quality_scores.record(scores.relevance as f64);
|
|
}
|
|
|
|
pub fn record_error(&self) {
|
|
self.error_count.increment(1);
|
|
}
|
|
}
|
|
|
|
/// Mock model interface for testing and development
|
|
pub struct MockModelInterface {
|
|
config: ModelConfig,
|
|
tokenizer: Arc<dyn TokenizerInterface>,
|
|
}
|
|
|
|
impl MockModelInterface {
|
|
pub fn new(_model_path: &str) -> Result<Self> {
|
|
let config = ModelConfig {
|
|
vocab_size: 50000,
|
|
hidden_size: 768,
|
|
num_layers: 12,
|
|
num_heads: 12,
|
|
intermediate_size: 3072,
|
|
max_position_embeddings: 1024,
|
|
layer_norm_eps: 1e-12,
|
|
dropout: 0.1,
|
|
attention_dropout: 0.1,
|
|
activation_function: std::borrow::Cow::Borrowed("gelu"),
|
|
};
|
|
|
|
let tokenizer = Arc::new(MockTokenizer::new());
|
|
|
|
Ok(Self { config, tokenizer })
|
|
}
|
|
}
|
|
|
|
impl ModelInterface for MockModelInterface {
|
|
fn generate_tokens(
|
|
&self,
|
|
input_ids: &Tensor,
|
|
_attention_mask: Option<&Tensor>,
|
|
generation_config: &GenerationConfig,
|
|
) -> Result<GenerationOutput> {
|
|
// Mock generation - in real implementation would run inference
|
|
let batch_size = input_ids.shape()[0];
|
|
let input_len = input_ids.shape().dims().get(1).copied().unwrap_or(1);
|
|
let max_length = generation_config.max_length.unwrap_or(50);
|
|
|
|
let mut sequences = Vec::new();
|
|
let mut scores = Vec::new();
|
|
|
|
for _ in 0..batch_size {
|
|
// Start with some mock input tokens
|
|
let mut sequence: Vec<u32> = (0..input_len).map(|i| i as u32).collect();
|
|
let mut score = 0.0f32;
|
|
|
|
// Generate tokens up to max length
|
|
for _ in sequence.len()..max_length {
|
|
let next_token = rand::random::<u32>() % self.config.vocab_size as u32;
|
|
sequence.push(next_token);
|
|
score += rand::random::<f32>();
|
|
|
|
if next_token == 2 {
|
|
// Mock EOS token
|
|
break;
|
|
}
|
|
}
|
|
|
|
sequences.push(sequence);
|
|
scores.push(score);
|
|
}
|
|
|
|
Ok(GenerationOutput {
|
|
sequences,
|
|
scores: Some(scores),
|
|
attention_weights: None,
|
|
past_key_values: None,
|
|
metadata: GenerationMetadata {
|
|
generation_time_ms: 100.0,
|
|
tokens_per_second: 50.0,
|
|
num_generated_tokens: 25,
|
|
finish_reason: FinishReason::MaxLength,
|
|
quality_scores: QualityScores {
|
|
fluency: 0.8,
|
|
coherence: 0.7,
|
|
relevance: 0.9,
|
|
diversity: 0.6,
|
|
factuality: 0.8,
|
|
safety: 0.95,
|
|
},
|
|
},
|
|
})
|
|
}
|
|
|
|
fn config(&self) -> &ModelConfig {
|
|
&self.config
|
|
}
|
|
|
|
fn vocab_size(&self) -> usize {
|
|
self.config.vocab_size
|
|
}
|
|
|
|
fn tokenizer(&self) -> Arc<dyn TokenizerInterface> {
|
|
self.tokenizer.clone()
|
|
}
|
|
}
|
|
|
|
/// Mock tokenizer for testing
|
|
pub struct MockTokenizer {
|
|
special_tokens: SpecialTokens,
|
|
}
|
|
|
|
impl Default for MockTokenizer {
|
|
fn default() -> Self {
|
|
Self::new()
|
|
}
|
|
}
|
|
|
|
impl MockTokenizer {
|
|
pub fn new() -> Self {
|
|
Self {
|
|
special_tokens: SpecialTokens {
|
|
pad_token: 0,
|
|
eos_token: 2,
|
|
bos_token: 1,
|
|
unk_token: 3,
|
|
sep_token: Some(4),
|
|
cls_token: Some(5),
|
|
mask_token: Some(6),
|
|
},
|
|
}
|
|
}
|
|
}
|
|
|
|
impl TokenizerInterface for MockTokenizer {
|
|
fn encode(&self, text: &str) -> Result<Vec<u32>> {
|
|
// Mock encoding - split by whitespace and hash
|
|
let tokens: Vec<u32> = text
|
|
.split_whitespace()
|
|
.map(|word| {
|
|
let mut hash = 0u32;
|
|
for byte in word.bytes() {
|
|
hash = hash.wrapping_mul(31).wrapping_add(byte as u32);
|
|
}
|
|
hash % 50000 // Mock vocab size
|
|
})
|
|
.collect();
|
|
Ok(tokens)
|
|
}
|
|
|
|
fn decode(&self, token_ids: &[u32]) -> Result<String> {
|
|
// Mock decoding - convert IDs back to placeholder words
|
|
let words: Vec<String> = token_ids.iter().map(|&id| format!("token_{id}")).collect();
|
|
Ok(words.join(" "))
|
|
}
|
|
|
|
fn vocab_size(&self) -> usize {
|
|
50000
|
|
}
|
|
|
|
fn special_tokens(&self) -> &SpecialTokens {
|
|
&self.special_tokens
|
|
}
|
|
}
|
|
|
|
// Re-export commonly used external types
|
|
pub use rtx_tensor::{DType, Device, Shape};
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[tokio::test]
|
|
async fn test_nlg_runtime_creation() -> Result<()> {
|
|
let runtime = NlgRuntime::new()?;
|
|
assert_eq!(runtime.config.max_sequence_length, 2048);
|
|
Ok(())
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_model_loading() -> Result<()> {
|
|
let runtime = NlgRuntime::new()?;
|
|
runtime.load_model("test_model", "/path/to/model").await?;
|
|
|
|
let model = runtime.get_model("test_model");
|
|
assert!(model.is_some());
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_mock_tokenizer() -> Result<()> {
|
|
let tokenizer = MockTokenizer::new();
|
|
let text = "Hello world";
|
|
let tokens = tokenizer.encode(text)?;
|
|
let decoded = tokenizer.decode(&tokens)?;
|
|
|
|
assert!(!tokens.is_empty());
|
|
assert!(!decoded.is_empty());
|
|
Ok(())
|
|
}
|
|
}
|