inferno-ai 0.10.3

#![allow(dead_code, unused_imports, unused_variables)]
// Model quantization module for Inferno AI/ML platform
// Supports INT8, INT4, FP16 quantization for GGUF and ONNX models

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use tokio::fs;
use tokio::io::{AsyncReadExt, AsyncWriteExt};

/// Quantization precision types
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq)]
pub enum QuantizationType {
    FP32, // Full precision
    FP16, // Half precision
    INT8, // 8-bit integers
    INT4, // 4-bit integers
}

impl std::fmt::Display for QuantizationType {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            QuantizationType::FP32 => write!(f, "fp32"),
            QuantizationType::FP16 => write!(f, "fp16"),
            QuantizationType::INT8 => write!(f, "int8"),
            QuantizationType::INT4 => write!(f, "int4"),
        }
    }
}

/// Quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationConfig {
    pub enabled: bool,
    pub default_precision: QuantizationType,
    pub per_layer_precision: HashMap<String, QuantizationType>,
    pub calibration_dataset_size: usize,
    pub preserve_accuracy_threshold: f32,
    pub compression_ratio_target: f32,
    pub use_dynamic_quantization: bool,
    pub use_symmetric_quantization: bool,
}

impl Default for QuantizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            default_precision: QuantizationType::INT8,
            per_layer_precision: HashMap::new(),
            calibration_dataset_size: 1000,
            preserve_accuracy_threshold: 0.95,
            compression_ratio_target: 4.0,
            use_dynamic_quantization: true,
            use_symmetric_quantization: false,
        }
    }
}

/// Quantization metrics
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct QuantizationMetrics {
    pub accuracy_loss: f64,
    pub compression_ratio: f64,
    pub inference_speedup: f64,
    pub memory_reduction: f64,
    pub quantization_time: f64,
}

/// Model quantizer implementation
pub struct ModelQuantizer {
    config: QuantizationConfig,
    metrics: QuantizationMetrics,
    calibration_data: Vec<Vec<f32>>,
}

impl ModelQuantizer {
    /// Create new model quantizer
    pub async fn new(config: QuantizationConfig) -> Result<Self> {
        Ok(Self {
            config,
            metrics: QuantizationMetrics::default(),
            calibration_data: Vec::new(),
        })
    }

    /// Quantize a model to specified precision
    pub async fn quantize_model(
        &mut self,
        model_path: &str,
        target_format: &str,
    ) -> Result<String> {
        let start_time = std::time::Instant::now();

        tracing::info!(
            "Starting quantization of model: {} to {}",
            model_path,
            self.config.default_precision
        );

        let model_path = Path::new(model_path);
        let output_path = self.generate_output_path(model_path, target_format)?;

        // Determine model format and apply appropriate quantization
        match model_path.extension().and_then(|s| s.to_str()) {
            Some("gguf") => self.quantize_gguf_model(model_path, &output_path).await?,
            Some("onnx") => self.quantize_onnx_model(model_path, &output_path).await?,
            Some("pt") | Some("pth") => {
                self.quantize_pytorch_model(model_path, &output_path)
                    .await?
            }
            Some("safetensors") => {
                self.quantize_safetensors_model(model_path, &output_path)
                    .await?
            }
            _ => return Err(anyhow::anyhow!("Unsupported model format for quantization")),
        }

        // Update metrics
        self.metrics.quantization_time = start_time.elapsed().as_secs_f64();
        self.calculate_compression_metrics(model_path, &output_path)
            .await?;

        tracing::info!(
            "Quantization completed in {:.2}s, compression ratio: {:.2}x",
            self.metrics.quantization_time,
            self.metrics.compression_ratio
        );

        Ok(output_path.to_string_lossy().to_string())
    }

    /// Quantize GGUF model
    async fn quantize_gguf_model(&mut self, input_path: &Path, output_path: &Path) -> Result<()> {
        tracing::debug!("Quantizing GGUF model: {:?}", input_path);

        let mut input_file = fs::File::open(input_path).await?;
        let mut output_file = fs::File::create(output_path).await?;

        // Read GGUF header
        let mut header_buffer = vec![0u8; 12]; // GGUF magic + version + tensor count
        input_file.read_exact(&mut header_buffer).await?;

        // Verify GGUF magic
        if &header_buffer[0..4] != b"GGUF" {
            return Err(anyhow::anyhow!("Invalid GGUF file format"));
        }

        // Write header to output
        output_file.write_all(&header_buffer).await?;

        // Read and process tensors
        let tensor_count = u64::from_le_bytes([
            header_buffer[8],
            header_buffer[9],
            header_buffer[10],
            header_buffer[11],
            0,
            0,
            0,
            0,
        ]);

        tracing::debug!("Processing {} tensors for quantization", tensor_count);

        for i in 0..tensor_count {
            self.quantize_gguf_tensor(&mut input_file, &mut output_file, i)
                .await?;
        }

        Ok(())
    }

    /// Quantize individual GGUF tensor
    async fn quantize_gguf_tensor(
        &self,
        input: &mut fs::File,
        output: &mut fs::File,
        tensor_idx: u64,
    ) -> Result<()> {
        // Read tensor metadata
        let mut name_len_bytes = [0u8; 8];
        input.read_exact(&mut name_len_bytes).await?;
        let name_len = u64::from_le_bytes(name_len_bytes);

        let mut name_bytes = vec![0u8; name_len as usize];
        input.read_exact(&mut name_bytes).await?;
        let tensor_name = String::from_utf8(name_bytes)?;

        // Read tensor dimensions and type
        let mut dims_count_bytes = [0u8; 4];
        input.read_exact(&mut dims_count_bytes).await?;
        let dims_count = u32::from_le_bytes(dims_count_bytes);

        let mut dims = vec![0u64; dims_count as usize];
        for dim in dims.iter_mut() {
            let mut dim_bytes = [0u8; 8];
            input.read_exact(&mut dim_bytes).await?;
            *dim = u64::from_le_bytes(dim_bytes);
        }

        let mut type_bytes = [0u8; 4];
        input.read_exact(&mut type_bytes).await?;
        let tensor_type = u32::from_le_bytes(type_bytes);

        // Calculate tensor size
        let element_count: u64 = dims.iter().product();
        let element_size = self.get_element_size_from_type(tensor_type);
        let tensor_size = element_count * element_size as u64;

        tracing::debug!(
            "Quantizing tensor '{}' ({}x{} elements, type: {})",
            tensor_name,
            element_count,
            element_size,
            tensor_type
        );

        // Read tensor data
        let mut tensor_data = vec![0u8; tensor_size as usize];
        input.read_exact(&mut tensor_data).await?;

        // Apply quantization based on layer type and config
        let quantized_data = self
            .apply_quantization(&tensor_data, &tensor_name, tensor_type)
            .await?;

        // Write quantized tensor to output
        output
            .write_all(&u64::to_le_bytes(tensor_name.len() as u64))
            .await?;
        output.write_all(tensor_name.as_bytes()).await?;
        output.write_all(&u32::to_le_bytes(dims_count)).await?;

        for &dim in &dims {
            output.write_all(&u64::to_le_bytes(dim)).await?;
        }

        // Update tensor type if quantized
        let output_type = self.get_quantized_tensor_type(tensor_type);
        output.write_all(&u32::to_le_bytes(output_type)).await?;
        output.write_all(&quantized_data).await?;

        Ok(())
    }

    /// Quantize ONNX model
    async fn quantize_onnx_model(&mut self, input_path: &Path, output_path: &Path) -> Result<()> {
        tracing::debug!("Quantizing ONNX model: {:?}", input_path);

        // Read ONNX model
        let model_data = fs::read(input_path).await?;

        // Parse ONNX model structure (simplified)
        let quantized_data = self.quantize_onnx_data(model_data).await?;

        // Write quantized model
        fs::write(output_path, quantized_data).await?;

        Ok(())
    }

    /// Quantize PyTorch model
    async fn quantize_pytorch_model(
        &mut self,
        input_path: &Path,
        output_path: &Path,
    ) -> Result<()> {
        tracing::debug!("Quantizing PyTorch model: {:?}", input_path);

        // Read PyTorch model (simplified implementation)
        let model_data = fs::read(input_path).await?;
        let quantized_data = self.quantize_pytorch_data(model_data).await?;

        fs::write(output_path, quantized_data).await?;
        Ok(())
    }

    /// Quantize SafeTensors model
    async fn quantize_safetensors_model(
        &mut self,
        input_path: &Path,
        output_path: &Path,
    ) -> Result<()> {
        tracing::debug!("Quantizing SafeTensors model: {:?}", input_path);

        let model_data = fs::read(input_path).await?;
        let quantized_data = self.quantize_safetensors_data(model_data).await?;

        fs::write(output_path, quantized_data).await?;
        Ok(())
    }

    /// Apply quantization to tensor data
    async fn apply_quantization(
        &self,
        data: &[u8],
        tensor_name: &str,
        tensor_type: u32,
    ) -> Result<Vec<u8>> {
        // Get quantization precision for this layer
        let precision = self
            .config
            .per_layer_precision
            .get(tensor_name)
            .copied()
            .unwrap_or(self.config.default_precision);

        match precision {
            QuantizationType::FP32 => Ok(data.to_vec()),
            QuantizationType::FP16 => self.quantize_to_fp16(data, tensor_type).await,
            QuantizationType::INT8 => self.quantize_to_int8(data, tensor_type).await,
            QuantizationType::INT4 => self.quantize_to_int4(data, tensor_type).await,
        }
    }

    /// Quantize tensor data to FP16
    async fn quantize_to_fp16(&self, data: &[u8], tensor_type: u32) -> Result<Vec<u8>> {
        if tensor_type != 0 {
            // Assuming 0 is FP32
            return Ok(data.to_vec()); // Already quantized or not float
        }

        let mut quantized = Vec::with_capacity(data.len() / 2);

        // Convert FP32 to FP16
        for chunk in data.chunks_exact(4) {
            let fp32_bits = u32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
            let fp32_value = f32::from_bits(fp32_bits);

            // Simple FP16 conversion (could use half crate for proper implementation)
            let fp16_value = self.fp32_to_fp16(fp32_value);
            quantized.extend_from_slice(&fp16_value.to_le_bytes());
        }

        Ok(quantized)
    }

    /// Quantize tensor data to INT8
    async fn quantize_to_int8(&self, data: &[u8], tensor_type: u32) -> Result<Vec<u8>> {
        if tensor_type != 0 {
            // Not FP32
            return Ok(data.to_vec());
        }

        let mut quantized = Vec::with_capacity(data.len() / 4);

        // Convert FP32 to INT8 with calibration
        let (scale, zero_point) = self.calculate_quantization_params(data).await?;

        for chunk in data.chunks_exact(4) {
            let fp32_bits = u32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
            let fp32_value = f32::from_bits(fp32_bits);

            let quantized_value = self.quantize_fp32_to_int8(fp32_value, scale, zero_point);
            quantized.push(quantized_value as u8);
        }

        Ok(quantized)
    }

    /// Quantize tensor data to INT4
    async fn quantize_to_int4(&self, data: &[u8], tensor_type: u32) -> Result<Vec<u8>> {
        if tensor_type != 0 {
            // Not FP32
            return Ok(data.to_vec());
        }

        let mut quantized = Vec::with_capacity(data.len() / 8);

        // Convert FP32 to INT4 with calibration
        let (scale, zero_point) = self.calculate_quantization_params(data).await?;

        for chunk in data.chunks_exact(8) {
            // Process 2 FP32 values at once
            let fp32_1 =
                f32::from_bits(u32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]));
            let fp32_2 =
                f32::from_bits(u32::from_le_bytes([chunk[4], chunk[5], chunk[6], chunk[7]]));

            let q1 = self.quantize_fp32_to_int4(fp32_1, scale, zero_point);
            let q2 = self.quantize_fp32_to_int4(fp32_2, scale, zero_point);

            // Pack two 4-bit values into one byte
            let packed = ((q2 & 0x0F) << 4) | (q1 & 0x0F);
            quantized.push(packed);
        }

        Ok(quantized)
    }

    /// Calculate quantization parameters (scale and zero_point)
    async fn calculate_quantization_params(&self, data: &[u8]) -> Result<(f32, i32)> {
        let mut values = Vec::new();

        // Extract FP32 values
        for chunk in data.chunks_exact(4) {
            let fp32_bits = u32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
            let fp32_value = f32::from_bits(fp32_bits);
            values.push(fp32_value);
        }

        // Calculate min/max for symmetric or asymmetric quantization
        let min_val = values.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max_val = values.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

        let (scale, zero_point) = if self.config.use_symmetric_quantization {
            // Symmetric quantization
            let abs_max = max_val.abs().max(min_val.abs());
            let scale = abs_max / 127.0; // For INT8: [-128, 127]
            (scale, 0)
        } else {
            // Asymmetric quantization
            let scale = (max_val - min_val) / 255.0; // For INT8: [0, 255]
            let zero_point = (-min_val / scale).round() as i32;
            (scale, zero_point)
        };

        Ok((scale, zero_point))
    }

    /// Convert FP32 to FP16 (simplified)
    fn fp32_to_fp16(&self, value: f32) -> u16 {
        // Simplified FP16 conversion (use half crate for production)
        let bits = value.to_bits();
        let sign = (bits >> 31) & 0x1;
        let exp = ((bits >> 23) & 0xFF) as i32;
        let mantissa = bits & 0x7FFFFF;

        // Handle special cases and convert
        if exp == 0 {
            0 // Zero
        } else if exp == 0xFF {
            if mantissa == 0 {
                ((sign << 15) | 0x7C00) as u16 // Infinity
            } else {
                ((sign << 15) | 0x7C00 | (mantissa >> 13)) as u16 // NaN
            }
        } else {
            // Normal number
            let new_exp = exp - 127 + 15; // Rebias exponent
            if new_exp <= 0 {
                0 // Underflow to zero
            } else if new_exp >= 31 {
                ((sign << 15) | 0x7C00) as u16 // Overflow to infinity
            } else {
                let new_mantissa = mantissa >> 13;
                ((sign << 15) | ((new_exp as u32) << 10) | new_mantissa) as u16
            }
        }
    }

    /// Quantize FP32 to INT8
    fn quantize_fp32_to_int8(&self, value: f32, scale: f32, zero_point: i32) -> i8 {
        let quantized = (value / scale).round() as i32 + zero_point;
        quantized.clamp(-128, 127) as i8
    }

    /// Quantize FP32 to INT4
    fn quantize_fp32_to_int4(&self, value: f32, scale: f32, zero_point: i32) -> u8 {
        let quantized = (value / scale).round() as i32 + zero_point;
        quantized.clamp(0, 15) as u8
    }

    /// Generate output path for quantized model
    fn generate_output_path(&self, input_path: &Path, target_format: &str) -> Result<PathBuf> {
        let stem = input_path
            .file_stem()
            .ok_or_else(|| anyhow::anyhow!("Invalid input path"))?;

        let extension = if target_format.is_empty() {
            input_path
                .extension()
                .ok_or_else(|| anyhow::anyhow!("No file extension"))?
        } else {
            std::ffi::OsStr::new(target_format)
        };

        let quantized_name = format!(
            "{}_{}_{}",
            stem.to_string_lossy(),
            self.config.default_precision,
            "quantized"
        );

        let mut output_path = input_path.with_file_name(quantized_name);
        output_path.set_extension(extension);

        Ok(output_path)
    }

    /// Get element size from GGUF tensor type
    fn get_element_size_from_type(&self, tensor_type: u32) -> usize {
        match tensor_type {
            0 => 4, // FP32
            1 => 2, // FP16
            2 => 1, // INT8
            3 => 1, // INT4 (packed)
            _ => 4, // Default to FP32
        }
    }

    /// Get quantized tensor type
    fn get_quantized_tensor_type(&self, original_type: u32) -> u32 {
        match self.config.default_precision {
            QuantizationType::FP32 => 0,
            QuantizationType::FP16 => 1,
            QuantizationType::INT8 => 2,
            QuantizationType::INT4 => 3,
        }
    }

    /// Quantize ONNX data (simplified)
    async fn quantize_onnx_data(&self, data: Vec<u8>) -> Result<Vec<u8>> {
        // Simplified ONNX quantization - in practice, would use ONNX Runtime quantization tools
        tracing::debug!("Applying ONNX quantization (simplified)");
        Ok(data) // Return original for now
    }

    /// Quantize PyTorch data (simplified)
    async fn quantize_pytorch_data(&self, data: Vec<u8>) -> Result<Vec<u8>> {
        // Simplified PyTorch quantization - in practice, would use torch.quantization
        tracing::debug!("Applying PyTorch quantization (simplified)");
        Ok(data) // Return original for now
    }

    /// Quantize SafeTensors data (simplified)
    async fn quantize_safetensors_data(&self, data: Vec<u8>) -> Result<Vec<u8>> {
        // Simplified SafeTensors quantization
        tracing::debug!("Applying SafeTensors quantization (simplified)");
        Ok(data) // Return original for now
    }

    /// Calculate compression metrics
    async fn calculate_compression_metrics(
        &mut self,
        input_path: &Path,
        output_path: &Path,
    ) -> Result<()> {
        let input_size = fs::metadata(input_path).await?.len();
        let output_size = fs::metadata(output_path).await?.len();

        self.metrics.compression_ratio = input_size as f64 / output_size as f64;
        self.metrics.memory_reduction = 1.0 - (output_size as f64 / input_size as f64);

        // Estimate inference speedup based on quantization type
        self.metrics.inference_speedup = match self.config.default_precision {
            QuantizationType::FP32 => 1.0,
            QuantizationType::FP16 => 1.5,
            QuantizationType::INT8 => 2.5,
            QuantizationType::INT4 => 4.0,
        };

        // Estimate accuracy loss based on quantization type
        self.metrics.accuracy_loss = match self.config.default_precision {
            QuantizationType::FP32 => 0.0,
            QuantizationType::FP16 => 0.01,
            QuantizationType::INT8 => 0.05,
            QuantizationType::INT4 => 0.15,
        };

        Ok(())
    }

    /// Get current quantization metrics
    pub async fn get_metrics(&self) -> QuantizationMetrics {
        self.metrics.clone()
    }

    /// Benchmark quantization performance
    pub async fn benchmark(&self, model_path: &str, num_requests: usize) -> Result<f64> {
        tracing::info!("Benchmarking quantization with {} requests", num_requests);

        // Simulate quantization benchmark
        tokio::time::sleep(std::time::Duration::from_millis(50)).await;

        // Return performance multiplier based on quantization type
        Ok(match self.config.default_precision {
            QuantizationType::FP32 => 1.0,
            QuantizationType::FP16 => 1.5,
            QuantizationType::INT8 => 2.5,
            QuantizationType::INT4 => 4.0,
        })
    }
}

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

    #[tokio::test]
    async fn test_quantizer_creation() {
        let config = QuantizationConfig::default();
        let quantizer = ModelQuantizer::new(config).await;
        assert!(quantizer.is_ok());
    }

    #[test]
    fn test_quantization_type_display() {
        assert_eq!(QuantizationType::FP32.to_string(), "fp32");
        assert_eq!(QuantizationType::INT8.to_string(), "int8");
    }

    #[tokio::test]
    async fn test_quantization_params_calculation() {
        let config = QuantizationConfig::default();
        let quantizer = ModelQuantizer::new(config).await.unwrap();

        // Test data: [1.0, 2.0, 3.0, 4.0] as bytes
        let test_data = vec![
            0x00, 0x00, 0x80, 0x3F, // 1.0
            0x00, 0x00, 0x00, 0x40, // 2.0
            0x00, 0x00, 0x40, 0x40, // 3.0
            0x00, 0x00, 0x80, 0x40, // 4.0
        ];

        let (scale, zero_point) = quantizer
            .calculate_quantization_params(&test_data)
            .await
            .unwrap();
        assert!(scale > 0.0);
        assert!(zero_point >= -128 && zero_point <= 127);
    }
}