oxirs_vec/

pytorch.rs

//! PyTorch-shaped embedding generation — currently a deterministic MOCK, not
//! real PyTorch inference.
//!
//! [`PyTorchEmbedder`] models the API a real `tch` (libtorch FFI) or
//! `candle-core` (Pure Rust) backed embedder would expose, but
//! [`PyTorchEmbedder::load_model`] and the internal forward pass never
//! actually load a model or run a neural network: they only validate that
//! `model_path` exists and then compute a hash-seeded pseudo-random vector.
//! `tch` requires a C++ libtorch FFI dependency, which is disallowed by the
//! COOLJAPAN Pure Rust Policy for this crate's default build; `candle-core`
//! is Pure Rust but is not currently a workspace dependency. Until one of
//! those is wired in, treat this type as test-only / an API placeholder —
//! do not use it for anything that needs real semantic embeddings.

use crate::real_time_embedding_pipeline::traits::{
    ContentItem, EmbeddingGenerator, GeneratorStatistics, ProcessingResult, ProcessingStatus,
};
use crate::Vector;
use anyhow::{anyhow, Result};
use scirs2_core::random::Random;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::PathBuf;
use std::time::{Duration, Instant};

/// PyTorch model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PyTorchConfig {
    pub model_path: PathBuf,
    pub device: PyTorchDevice,
    pub batch_size: usize,
    pub num_workers: usize,
    pub pin_memory: bool,
    pub mixed_precision: bool,
    pub compile_mode: CompileMode,
    pub optimization_level: usize,
}

/// PyTorch device configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PyTorchDevice {
    Cpu,
    Cuda { device_id: usize },
    Mps,  // Apple Metal Performance Shaders
    Auto, // Automatically select best available device
}

/// PyTorch model compilation modes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CompileMode {
    None,
    Default,
    Reduce,
    Max,
    Custom(String),
}

impl Default for PyTorchConfig {
    fn default() -> Self {
        Self {
            model_path: PathBuf::from("./models/pytorch_model.pt"),
            device: PyTorchDevice::Auto,
            batch_size: 32,
            num_workers: 4,
            pin_memory: true,
            mixed_precision: false,
            compile_mode: CompileMode::Default,
            optimization_level: 1,
        }
    }
}

/// PyTorch-shaped embedding generator — **currently a deterministic mock**,
/// not real PyTorch inference. See the `pytorch` module-level docs.
#[derive(Debug)]
pub struct PyTorchEmbedder {
    config: PyTorchConfig,
    model_loaded: bool,
    model_metadata: Option<PyTorchModelMetadata>,
    tokenizer: Option<PyTorchTokenizer>,
}

/// PyTorch model metadata
#[derive(Debug, Clone)]
pub struct PyTorchModelMetadata {
    pub model_name: String,
    pub model_version: String,
    pub input_shape: Vec<i64>,
    pub output_shape: Vec<i64>,
    pub embedding_dimension: usize,
    pub vocab_size: Option<usize>,
    pub max_sequence_length: usize,
    pub architecture_type: ArchitectureType,
}

/// Neural network architecture types
#[derive(Debug, Clone)]
pub enum ArchitectureType {
    Transformer,
    Cnn,
    Rnn,
    Lstm,
    Gru,
    Bert,
    Roberta,
    Gpt,
    T5,
    Custom(String),
}

/// PyTorch tokenizer for text preprocessing
#[derive(Debug, Clone)]
pub struct PyTorchTokenizer {
    pub vocab: HashMap<String, i32>,
    pub special_tokens: HashMap<String, i32>,
    pub max_length: usize,
    pub padding_token: String,
    pub unknown_token: String,
    pub cls_token: Option<String>,
    pub sep_token: Option<String>,
}

impl Default for PyTorchTokenizer {
    fn default() -> Self {
        let mut special_tokens = HashMap::new();
        special_tokens.insert("[PAD]".to_string(), 0);
        special_tokens.insert("[UNK]".to_string(), 1);
        special_tokens.insert("[CLS]".to_string(), 2);
        special_tokens.insert("[SEP]".to_string(), 3);

        Self {
            vocab: HashMap::new(),
            special_tokens,
            max_length: 512,
            padding_token: "[PAD]".to_string(),
            unknown_token: "[UNK]".to_string(),
            cls_token: Some("[CLS]".to_string()),
            sep_token: Some("[SEP]".to_string()),
        }
    }
}

impl PyTorchEmbedder {
    /// Create a new PyTorch embedder
    pub fn new(config: PyTorchConfig) -> Result<Self> {
        Ok(Self {
            config,
            model_loaded: false,
            model_metadata: None,
            tokenizer: Some(PyTorchTokenizer::default()),
        })
    }

    /// "Load" a PyTorch model from file.
    ///
    /// **This does not actually load or run a PyTorch model.** It only
    /// verifies `model_path` exists on disk, then populates
    /// [`PyTorchModelMetadata`] with hardcoded values; no weights are read
    /// and no `tch`/`candle-core` runtime is invoked. See the module-level
    /// docs. Real inference calls ([`Self::embed_text`],
    /// [`Self::embed_batch`]) log a warning the first time they run against
    /// a model "loaded" this way.
    pub fn load_model(&mut self) -> Result<()> {
        if !self.config.model_path.exists() {
            return Err(anyhow!(
                "Model file not found: {:?}",
                self.config.model_path
            ));
        }

        tracing::warn!(
            "PyTorchEmbedder::load_model({:?}): this is a MOCK — no PyTorch model is \
             actually loaded and no real weights are read. Embeddings produced by this \
             embedder are deterministic hash-based pseudo-random vectors, not real \
             semantic embeddings. See the `pytorch` module docs.",
            self.config.model_path
        );

        let metadata = PyTorchModelMetadata {
            model_name: "pytorch_embedder".to_string(),
            model_version: "1.0.0".to_string(),
            input_shape: vec![-1, 512],  // batch_size, sequence_length
            output_shape: vec![-1, 768], // batch_size, embedding_dim
            embedding_dimension: 768,
            vocab_size: Some(30000),
            max_sequence_length: 512,
            architecture_type: ArchitectureType::Transformer,
        };

        self.model_metadata = Some(metadata);
        self.model_loaded = true;
        Ok(())
    }

    /// Generate embeddings for text
    pub fn embed_text(&self, text: &str) -> Result<Vector> {
        if !self.model_loaded {
            return Err(anyhow!("Model not loaded. Call load_model() first."));
        }

        let tokens = self.tokenize_text(text)?;
        let embedding = self.forward_pass(&tokens)?;
        Ok(Vector::new(embedding))
    }

    /// Generate embeddings for multiple texts
    pub fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vector>> {
        if !self.model_loaded {
            return Err(anyhow!("Model not loaded"));
        }

        let mut results = Vec::new();

        // Process in batches according to config
        for chunk in texts.chunks(self.config.batch_size) {
            let mut batch_tokens = Vec::new();
            for text in chunk {
                batch_tokens.push(self.tokenize_text(text)?);
            }

            let batch_embeddings = self.forward_pass_batch(&batch_tokens)?;
            for embedding in batch_embeddings {
                results.push(Vector::new(embedding));
            }
        }

        Ok(results)
    }

    /// Tokenize text using the configured tokenizer
    fn tokenize_text(&self, text: &str) -> Result<Vec<i32>> {
        let tokenizer = self
            .tokenizer
            .as_ref()
            .ok_or_else(|| anyhow!("Tokenizer not available"))?;

        let mut tokens = Vec::new();

        // Add CLS token if available
        if let Some(cls_token) = &tokenizer.cls_token {
            if let Some(&token_id) = tokenizer.special_tokens.get(cls_token) {
                tokens.push(token_id);
            }
        }

        // Simple whitespace tokenization (in practice would use proper tokenizer)
        let words: Vec<&str> = text.split_whitespace().collect();
        for word in words {
            let token_id = tokenizer
                .vocab
                .get(word)
                .or_else(|| tokenizer.special_tokens.get(&tokenizer.unknown_token))
                .copied()
                .unwrap_or(1); // Default to UNK token ID
            tokens.push(token_id);
        }

        // Add SEP token if available
        if let Some(sep_token) = &tokenizer.sep_token {
            if let Some(&token_id) = tokenizer.special_tokens.get(sep_token) {
                tokens.push(token_id);
            }
        }

        // Truncate or pad to max length
        if tokens.len() > tokenizer.max_length {
            tokens.truncate(tokenizer.max_length);
        } else {
            let pad_token_id = tokenizer
                .special_tokens
                .get(&tokenizer.padding_token)
                .copied()
                .unwrap_or(0);
            tokens.resize(tokenizer.max_length, pad_token_id);
        }

        Ok(tokens)
    }

    /// **MOCK forward pass — not real PyTorch inference.** Generates a
    /// deterministic (hash-seeded) pseudo-random vector from the token IDs
    /// rather than running any neural network. See the module-level docs.
    fn forward_pass(&self, tokens: &[i32]) -> Result<Vec<f32>> {
        let metadata = self
            .model_metadata
            .as_ref()
            .ok_or_else(|| anyhow!("Model metadata not available"))?;

        let mut rng = Random::seed(tokens.iter().map(|&t| t as u64).sum::<u64>());

        let mut embedding = vec![0.0f32; metadata.embedding_dimension];
        for value in &mut embedding {
            *value = rng.gen_range(-1.0..1.0);
        }

        // Apply layer normalization (simplified)
        let mean = embedding.iter().sum::<f32>() / embedding.len() as f32;
        let variance =
            embedding.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / embedding.len() as f32;
        let std_dev = variance.sqrt();

        if std_dev > 0.0 {
            for x in &mut embedding {
                *x = (*x - mean) / std_dev;
            }
        }

        Ok(embedding)
    }

    /// Batch forward pass
    fn forward_pass_batch(&self, batch_tokens: &[Vec<i32>]) -> Result<Vec<Vec<f32>>> {
        let mut results = Vec::new();
        for tokens in batch_tokens {
            results.push(self.forward_pass(tokens)?);
        }
        Ok(results)
    }

    /// Get model metadata
    pub fn get_metadata(&self) -> Option<&PyTorchModelMetadata> {
        self.model_metadata.as_ref()
    }

    /// Get embedding dimensions
    pub fn get_dimensions(&self) -> Option<usize> {
        self.model_metadata.as_ref().map(|m| m.embedding_dimension)
    }

    /// Update tokenizer
    pub fn set_tokenizer(&mut self, tokenizer: PyTorchTokenizer) {
        self.tokenizer = Some(tokenizer);
    }

    /// Check if model supports mixed precision
    pub fn supports_mixed_precision(&self) -> bool {
        self.config.mixed_precision
    }

    /// Get current device
    pub fn get_device(&self) -> &PyTorchDevice {
        &self.config.device
    }
}

/// PyTorch model manager for handling multiple models
#[derive(Debug)]
pub struct PyTorchModelManager {
    models: HashMap<String, PyTorchEmbedder>,
    default_model: String,
    device_manager: DeviceManager,
}

/// Device manager for PyTorch models
#[derive(Debug)]
pub struct DeviceManager {
    available_devices: Vec<PyTorchDevice>,
    current_device: PyTorchDevice,
    memory_usage: HashMap<String, usize>,
}

impl DeviceManager {
    /// Create a new device manager
    pub fn new() -> Self {
        let available_devices = Self::detect_available_devices();
        let current_device = available_devices
            .first()
            .cloned()
            .unwrap_or(PyTorchDevice::Cpu);

        Self {
            available_devices,
            current_device,
            memory_usage: HashMap::new(),
        }
    }

    /// Detect available PyTorch devices
    fn detect_available_devices() -> Vec<PyTorchDevice> {
        let mut devices = vec![PyTorchDevice::Cpu];

        // Mock device detection
        devices.push(PyTorchDevice::Cuda { device_id: 0 });
        devices.push(PyTorchDevice::Mps);

        devices
    }

    /// Get optimal device for model
    pub fn get_optimal_device(&self) -> &PyTorchDevice {
        &self.current_device
    }

    /// Update memory usage for a device
    pub fn update_memory_usage(&mut self, device: String, usage: usize) {
        self.memory_usage.insert(device, usage);
    }

    /// Get memory usage for all devices
    pub fn get_memory_usage(&self) -> &HashMap<String, usize> {
        &self.memory_usage
    }
}

impl Default for DeviceManager {
    fn default() -> Self {
        Self::new()
    }
}

impl PyTorchModelManager {
    /// Create a new PyTorch model manager
    pub fn new(default_model: String) -> Self {
        Self {
            models: HashMap::new(),
            default_model,
            device_manager: DeviceManager::new(),
        }
    }

    /// Register a model with the manager
    pub fn register_model(&mut self, name: String, mut embedder: PyTorchEmbedder) -> Result<()> {
        embedder.load_model()?;
        self.models.insert(name, embedder);
        Ok(())
    }

    /// Get available model names
    pub fn list_models(&self) -> Vec<String> {
        self.models.keys().cloned().collect()
    }

    /// Generate embeddings using a specific model
    pub fn embed_with_model(&self, model_name: &str, texts: &[String]) -> Result<Vec<Vector>> {
        let model = self
            .models
            .get(model_name)
            .ok_or_else(|| anyhow!("Model not found: {}", model_name))?;

        model.embed_batch(texts)
    }

    /// Generate embeddings using the default model
    pub fn embed(&self, texts: &[String]) -> Result<Vec<Vector>> {
        self.embed_with_model(&self.default_model, texts)
    }

    /// Get device manager
    pub fn get_device_manager(&self) -> &DeviceManager {
        &self.device_manager
    }

    /// Update device manager
    pub fn update_device_manager(&mut self, device_manager: DeviceManager) {
        self.device_manager = device_manager;
    }
}

impl EmbeddingGenerator for PyTorchEmbedder {
    fn generate_embedding(&self, content: &ContentItem) -> Result<Vector> {
        self.embed_text(&content.content)
    }

    fn generate_batch_embeddings(&self, content: &[ContentItem]) -> Result<Vec<ProcessingResult>> {
        let mut results = Vec::new();

        for item in content {
            let start_time = Instant::now();
            let vector_result = self.generate_embedding(item);
            let duration = start_time.elapsed();

            let result = match vector_result {
                Ok(vector) => ProcessingResult {
                    item: item.clone(),
                    vector: Some(vector),
                    status: ProcessingStatus::Completed,
                    duration,
                    error: None,
                    metadata: HashMap::new(),
                },
                Err(e) => ProcessingResult {
                    item: item.clone(),
                    vector: None,
                    status: ProcessingStatus::Failed {
                        reason: e.to_string(),
                    },
                    duration,
                    error: Some(e.to_string()),
                    metadata: HashMap::new(),
                },
            };

            results.push(result);
        }

        Ok(results)
    }

    fn embedding_dimensions(&self) -> usize {
        self.get_dimensions().unwrap_or(768)
    }

    fn get_config(&self) -> serde_json::Value {
        serde_json::to_value(&self.config).unwrap_or_default()
    }

    fn is_ready(&self) -> bool {
        self.model_loaded
    }

    fn get_statistics(&self) -> GeneratorStatistics {
        GeneratorStatistics {
            total_embeddings: 0,
            total_processing_time: Duration::from_millis(0),
            average_processing_time: Duration::from_millis(0),
            error_count: 0,
            last_error: None,
        }
    }
}

#[cfg(test)]
#[allow(clippy::useless_vec)]
mod tests {
    use super::*;
    use anyhow::Result;

    #[test]
    fn test_pytorch_config_creation() {
        let config = PyTorchConfig::default();
        assert_eq!(config.batch_size, 32);
        assert_eq!(config.num_workers, 4);
        assert!(config.pin_memory);
    }

    #[test]
    fn test_pytorch_embedder_creation() -> Result<()> {
        let config = PyTorchConfig::default();
        let embedder = PyTorchEmbedder::new(config);
        assert!(embedder.is_ok());
        assert!(!embedder.expect("test value").model_loaded);
        Ok(())
    }

    #[test]
    fn test_tokenizer_creation() {
        let tokenizer = PyTorchTokenizer::default();
        assert_eq!(tokenizer.max_length, 512);
        assert_eq!(tokenizer.padding_token, "[PAD]");
        assert!(tokenizer.special_tokens.contains_key("[CLS]"));
    }

    #[test]
    fn test_model_metadata() {
        let metadata = PyTorchModelMetadata {
            model_name: "test".to_string(),
            model_version: "1.0".to_string(),
            input_shape: vec![-1, 512],
            output_shape: vec![-1, 768],
            embedding_dimension: 768,
            vocab_size: Some(30000),
            max_sequence_length: 512,
            architecture_type: ArchitectureType::Transformer,
        };

        assert_eq!(metadata.embedding_dimension, 768);
        assert_eq!(metadata.vocab_size, Some(30000));
    }

    #[test]
    fn test_device_manager_creation() {
        let device_manager = DeviceManager::new();
        assert!(!device_manager.available_devices.is_empty());
        assert!(matches!(device_manager.current_device, PyTorchDevice::Cpu));
    }

    #[test]
    fn test_model_manager_creation() {
        let manager = PyTorchModelManager::new("default".to_string());
        assert_eq!(manager.default_model, "default");
        assert!(manager.list_models().is_empty());
    }

    /// Regression test documenting the P2 finding: `PyTorchEmbedder` is a
    /// deterministic mock, not real inference. Verifies it is at least
    /// internally consistent (same text -> same vector; distinct texts ->
    /// distinct vectors with overwhelming probability) so callers who *do*
    /// use it as an offline placeholder get stable, reproducible output.
    #[test]
    fn test_pytorch_embedder_mock_is_deterministic_not_real_inference() -> Result<()> {
        let dir =
            std::env::temp_dir().join(format!("oxirs_vec_pytorch_test_{}", uuid::Uuid::new_v4()));
        std::fs::create_dir_all(&dir)?;
        let model_path = dir.join("mock_model.pt");
        std::fs::write(&model_path, b"not a real pytorch model")?;

        let config = PyTorchConfig {
            model_path: model_path.clone(),
            ..PyTorchConfig::default()
        };
        let mut embedder = PyTorchEmbedder::new(config)?;
        embedder.load_model()?;

        let a = embedder.embed_text("hello world")?;
        let b = embedder.embed_text("hello world")?;
        let c = embedder.embed_text("a completely different sentence")?;

        assert_eq!(
            a.as_f32(),
            b.as_f32(),
            "mock embeddings must be deterministic"
        );
        assert_ne!(
            a.as_f32(),
            c.as_f32(),
            "distinct inputs should (with overwhelming probability) differ"
        );

        std::fs::remove_dir_all(&dir).ok();
        Ok(())
    }

    #[test]
    fn test_architecture_types() {
        let arch_types = vec![
            ArchitectureType::Transformer,
            ArchitectureType::Bert,
            ArchitectureType::Gpt,
            ArchitectureType::Custom("MyModel".to_string()),
        ];
        assert_eq!(arch_types.len(), 4);
    }

    #[test]
    fn test_device_types() {
        let devices = vec![
            PyTorchDevice::Cpu,
            PyTorchDevice::Cuda { device_id: 0 },
            PyTorchDevice::Mps,
            PyTorchDevice::Auto,
        ];
        assert_eq!(devices.len(), 4);
    }

    #[test]
    fn test_compile_modes() {
        let modes = vec![
            CompileMode::None,
            CompileMode::Default,
            CompileMode::Max,
            CompileMode::Custom("custom".to_string()),
        ];
        assert_eq!(modes.len(), 4);
    }

    #[test]
    fn test_tokenizer_special_tokens() {
        let tokenizer = PyTorchTokenizer::default();
        assert!(tokenizer.special_tokens.contains_key("[PAD]"));
        assert!(tokenizer.special_tokens.contains_key("[UNK]"));
        assert!(tokenizer.special_tokens.contains_key("[CLS]"));
        assert!(tokenizer.special_tokens.contains_key("[SEP]"));
    }
}