rustytorch/crates/training/rtx-compress/src/lib.rs

pub mod arrow_integration;
pub mod checkpoint;
pub mod distillation;
pub mod error;
pub mod kv_cache;
pub mod lora;
pub mod pipeline;
pub mod pruning;
pub mod quantization;

pub use error::{CompressionError, QuantizationError, Result};
pub use kv_cache::{CompressedKVCache, KVCacheConfig};
pub use pipeline::{CompressionPipeline, CompressionPipelineConfig};

// Main compression storage system
use rtx_tensor::Tensor;
use std::collections::HashMap;

#[derive(Debug, Clone, Copy)]
pub enum CompressionLevel {
    UltraFast,
    Fast,
    Balanced,
    High,
    Low,
    Medium,
}

#[derive(Debug, Clone, Copy)]
pub enum AccessPattern {
    VeryHigh,
    High,
    Medium,
    Low,
    VeryLow,
}

#[derive(Debug, Clone)]
pub struct CompressionConfig {
    pub default_compression_ratio: f64,
    pub quality_threshold: f64,
    pub memory_limit_mb: usize,
    pub adaptive_compression: bool,
}

#[derive(Debug, Clone)]
pub struct StorageStatistics {
    pub total_compressed_size_mb: usize,
    pub average_compression_ratio: f64,
    pub memory_utilization: f64,
    pub cache_hit_rate: f64,
    pub eviction_count: usize,
}

#[derive(Debug)]
pub struct TensorStats {
    pub compressed_size: usize,
    pub original_size: usize,
    pub compression_ratio: f64,
    pub access_count: usize,
}

// Placeholder implementations for main storage system
pub struct CompressedStorage {
    config: CompressionConfig,
    tensors: HashMap<String, Vec<u8>>,
    stats: StorageStatistics,
}

impl CompressedStorage {
    pub fn new(config: CompressionConfig) -> Self {
        Self {
            config,
            tensors: HashMap::new(),
            stats: StorageStatistics {
                total_compressed_size_mb: 0,
                average_compression_ratio: 1.0,
                memory_utilization: 0.0,
                cache_hit_rate: 1.0,
                eviction_count: 0,
            },
        }
    }

    pub fn store_tensor(
        &mut self,
        name: &str,
        tensor: &Tensor,
        level: CompressionLevel,
    ) -> Result<()> {
        // Placeholder implementation
        let compressed_data = self.compress_tensor(tensor, level)?;
        self.tensors.insert(name.to_string(), compressed_data);
        Ok(())
    }

    pub fn store_tensor_with_hint(
        &mut self,
        name: &str,
        tensor: &Tensor,
        access_pattern: AccessPattern,
    ) -> Result<()> {
        let level = match access_pattern {
            AccessPattern::VeryHigh => CompressionLevel::Low,
            AccessPattern::High => CompressionLevel::Fast,
            AccessPattern::Medium => CompressionLevel::Balanced,
            AccessPattern::Low => CompressionLevel::High,
            AccessPattern::VeryLow => CompressionLevel::High,
        };
        self.store_tensor(name, tensor, level)
    }

    pub fn store_tensor_with_level(
        &mut self,
        name: &str,
        tensor: &Tensor,
        level: CompressionLevel,
    ) -> Result<()> {
        self.store_tensor(name, tensor, level)
    }

    pub fn load_tensor(&self, name: &str) -> Result<Tensor> {
        let compressed_data = self
            .tensors
            .get(name)
            .ok_or_else(|| CompressionError::CompressionFailed("Tensor not found".to_string()))?;
        self.decompress_tensor(compressed_data)
    }

    pub fn get_statistics(&self) -> StorageStatistics {
        self.stats.clone()
    }

    pub fn get_tensor_stats(&self, name: &str) -> Result<TensorStats> {
        if !self.tensors.contains_key(name) {
            return Err(CompressionError::CompressionFailed(
                "Tensor not found".to_string(),
            ));
        }

        Ok(TensorStats {
            compressed_size: self.tensors.get(name).unwrap().len(),
            original_size: 1024,    // Placeholder
            compression_ratio: 3.0, // Placeholder
            access_count: 1,
        })
    }

    pub fn enable_load_monitoring(&mut self, _enable: bool) {
        // Placeholder
    }

    pub fn clone(&self) -> Self {
        Self {
            config: self.config.clone(),
            tensors: self.tensors.clone(),
            stats: StorageStatistics {
                total_compressed_size_mb: self.stats.total_compressed_size_mb,
                average_compression_ratio: self.stats.average_compression_ratio,
                memory_utilization: self.stats.memory_utilization,
                cache_hit_rate: self.stats.cache_hit_rate,
                eviction_count: self.stats.eviction_count,
            },
        }
    }

    fn compress_tensor(&self, tensor: &Tensor, _level: CompressionLevel) -> Result<Vec<u8>> {
        // Placeholder compression - just serialize shape info
        let shape = tensor.shape();
        let mut data = Vec::new();
        data.extend_from_slice(&shape.dims().len().to_le_bytes());
        for &dim in shape.dims() {
            data.extend_from_slice(&dim.to_le_bytes());
        }
        Ok(data)
    }

    fn decompress_tensor(&self, compressed_data: &[u8]) -> Result<Tensor> {
        // Placeholder decompression - create zeros tensor with stored shape
        let mut offset = 0;
        let shape_len = usize::from_le_bytes(
            compressed_data[offset..offset + 8]
                .try_into()
                .map_err(|_| {
                    CompressionError::DecompressionFailed("Invalid shape length".to_string())
                })?,
        );
        offset += 8;

        let mut shape = Vec::new();
        for _ in 0..shape_len {
            let dim =
                usize::from_le_bytes(compressed_data[offset..offset + 8].try_into().map_err(
                    |_| CompressionError::DecompressionFailed("Invalid dimension".to_string()),
                )?);
            shape.push(dim);
            offset += 8;
        }

        Tensor::zeros(shape.as_slice(), &rtx_tensor::Device::try_default()?)
            .map_err(CompressionError::Tensor)
    }
}