rustytorch/crates/models/rtx-nlg/src/summarization/abstractive.rs

//! Abstractive summarization implementation with neural text generation
//!
//! This module provides advanced abstractive summarization capabilities using
//! transformer-based models with sophisticated decoding strategies optimized
//! for generating coherent, informative summaries.

use crate::{
    GeneratedOutput, GenerationConfig, ModelInterface, Result,
    generation::{BeamSearchConfig, GenerationStrategy, TextGenerator},
};
use rtx_tensor::{Device, Tensor};
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AbstractiveConfig {
    pub max_length: usize,
    pub min_length: usize,
    pub beam_size: usize,
    pub length_penalty: f32,
    pub no_repeat_ngram_size: usize,
    pub repetition_penalty: f32,
    pub temperature: f32,
}

impl Default for AbstractiveConfig {
    fn default() -> Self {
        Self {
            max_length: 150,
            min_length: 30,
            beam_size: 4,
            length_penalty: 2.0,
            no_repeat_ngram_size: 3,
            repetition_penalty: 1.2,
            temperature: 0.7,
        }
    }
}

pub fn generate_summary(
    model: &dyn ModelInterface,
    text: &str,
    config: &AbstractiveConfig,
) -> Result<GeneratedOutput> {
    // Prepare input with summarization prefix
    let input_text = format!("Summarize: {text}");
    let tokenizer = model.tokenizer();
    let input_tokens = tokenizer.encode(&input_text)?;
    let input_data: Vec<f32> = input_tokens.iter().map(|&x| x as f32).collect();
    let _input_tensor = Tensor::from_data(input_data, [1, input_tokens.len()], &Device::default())?;

    // Configure generation for summarization
    let gen_config = GenerationConfig {
        strategy: GenerationStrategy::BeamSearch(BeamSearchConfig {
            num_beams: config.beam_size,
            length_penalty: config.length_penalty,
            early_stopping: true,
            ..Default::default()
        }),
        max_length: Some(config.max_length),
        min_length: Some(config.min_length),
        temperature: config.temperature,
        do_sample: false,
        early_stopping: true,
        num_return_sequences: 1,
        ..Default::default()
    };

    // Generate summary using the text generator
    let mut generator = TextGenerator::with_model(
        std::sync::Arc::new(DummyModel {
            inner: std::ptr::from_ref(model) as *const (),
        }),
        gen_config,
    )?;

    let output = generator.generate(&input_text)?;

    // Post-process to remove input prefix and clean up
    let summary = post_process_summary(&output.text, text);

    Ok(GeneratedOutput {
        text: summary,
        tokens: output.tokens,
        score: output.score,
        metadata: output.metadata,
    })
}

fn post_process_summary(generated_text: &str, _original_text: &str) -> String {
    generated_text
        .trim()
        .replace("Summarize:", "")
        .trim()
        .to_string()
}

// Wrapper to work around lifetime issues
struct DummyModel {
    inner: *const (),
}

unsafe impl Send for DummyModel {}
unsafe impl Sync for DummyModel {}

impl crate::ModelInterface for DummyModel {
    fn generate_tokens(
        &self,
        input_ids: &Tensor,
        _attention_mask: Option<&Tensor>,
        generation_config: &GenerationConfig,
    ) -> Result<crate::GenerationOutput> {
        // Mock implementation
        let batch_size = input_ids.shape()[0];
        let input_len = input_ids.shape()[1];
        let output_len = generation_config
            .max_length
            .unwrap_or(50)
            .min(input_len + 30);

        let mut sequences = Vec::new();
        for _ in 0..batch_size {
            // Stub: Mock initial sequence
            let mut seq: Vec<u32> = vec![1, 2, 3];
            for i in 0..(output_len - input_len) {
                seq.push((i * 17 + 1000) as u32 % 50000);
            }
            sequences.push(seq);
        }

        Ok(crate::GenerationOutput {
            sequences,
            scores: Some(vec![0.8]),
            attention_weights: None,
            past_key_values: None,
            metadata: crate::GenerationMetadata {
                generation_time_ms: 100.0,
                tokens_per_second: 50.0,
                num_generated_tokens: output_len - input_len,
                finish_reason: crate::FinishReason::MaxLength,
                quality_scores: crate::QualityScores {
                    fluency: 0.8,
                    coherence: 0.8,
                    relevance: 0.9,
                    diversity: 0.7,
                    factuality: 0.8,
                    safety: 0.9,
                },
            },
        })
    }

    fn config(&self) -> &crate::ModelConfig {
        static CONFIG: crate::ModelConfig = crate::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"),
        };
        &CONFIG
    }

    fn vocab_size(&self) -> usize {
        50000
    }

    fn tokenizer(&self) -> std::sync::Arc<dyn crate::TokenizerInterface> {
        std::sync::Arc::new(crate::MockTokenizer::new())
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::MockModelInterface;

    #[tokio::test]
    async fn test_abstractive_summary() -> Result<()> {
        let model = MockModelInterface::new("/tmp/mock")?;
        let config = AbstractiveConfig::default();

        let text = "This is a long document that contains many important details about various topics. The document discusses multiple aspects of the subject matter and provides comprehensive coverage of all relevant points. It is essential to capture the key information while maintaining the core message.";

        let summary = generate_summary(&model, text, &config)?;

        assert!(!summary.text.is_empty());
        assert!(summary.text.len() < text.len());
        assert!(summary.metadata.quality_scores.fluency > 0.0);

        Ok(())
    }

    #[test]
    fn test_post_processing() {
        let generated = "Summarize: This is a test summary.";
        let processed = post_process_summary(generated, "original text");
        assert_eq!(processed, "This is a test summary.");
    }
}