rustytorch/crates/training/rtx-compress/src/quantization/mixed_precision.rs

use crate::error::{CompressionError, Result};
use rtx_tensor::Tensor;
use std::collections::HashMap;

#[derive(Debug, Clone)]
pub struct PrecisionConfig {
    pub precision_bits: Vec<u8>,
    pub sensitivity_threshold: f64,
    pub performance_weight: f64,
    pub quality_weight: f64,
}

#[derive(Debug, Clone)]
pub struct OptimizationObjective {
    pub target_compression_ratio: f64,
    pub max_quality_loss: f64,
    pub memory_constraint_mb: Option<usize>,
}

#[derive(Debug, Clone)]
pub struct LayerSensitivity {
    pub error_impact: f64,
    pub parameter_count: usize,
    pub access_frequency: f64,
}

#[derive(Debug, Clone)]
pub struct OptimalConfiguration {
    pub layer_precisions: HashMap<String, u8>,
    pub estimated_quality_loss: f64,
    pub compression_ratio: f64,
}

pub struct MixedPrecisionOptimizer {
    config: PrecisionConfig,
    hardware_constraints: HashMap<String, f64>,
    quantization_aware_mode: bool,
}

impl MixedPrecisionOptimizer {
    pub fn new(config: PrecisionConfig) -> Self {
        Self {
            config,
            hardware_constraints: HashMap::new(),
            quantization_aware_mode: false,
        }
    }

    pub fn analyze_sensitivity(
        &mut self,
        layers: &HashMap<String, Tensor>,
        _calibration_data: &Tensor,
    ) -> Result<HashMap<String, LayerSensitivity>> {
        let mut sensitivities = HashMap::new();

        for (layer_name, tensor) in layers {
            // Simplified sensitivity analysis
            let parameter_count = tensor.numel();

            // Heuristic: smaller tensors often more sensitive
            let size_factor = 1.0 / (parameter_count as f64).log10();

            // Layer type heuristics
            let type_factor = if layer_name.contains("layer_norm") || layer_name.contains("bias") {
                2.0 // More sensitive
            } else if layer_name.contains("embeddings") {
                0.3 // Less sensitive
            } else {
                1.0 // Default
            };

            let error_impact = size_factor * type_factor * self.config.sensitivity_threshold;

            sensitivities.insert(
                layer_name.clone(),
                LayerSensitivity {
                    error_impact,
                    parameter_count,
                    access_frequency: 1.0, // Placeholder
                },
            );
        }

        Ok(sensitivities)
    }

    pub fn optimize(
        &mut self,
        layers: &HashMap<String, Tensor>,
        calibration_data: &Tensor,
        _objective: OptimizationObjective,
    ) -> Result<OptimalConfiguration> {
        let sensitivities = self.analyze_sensitivity(layers, calibration_data)?;

        let mut layer_precisions = HashMap::new();
        let mut total_bits = 0usize;
        let mut quality_loss = 0.0f64;

        // Sort layers by sensitivity (most sensitive first)
        let mut sorted_layers: Vec<_> = sensitivities.iter().collect();
        sorted_layers.sort_by(|a, b| b.1.error_impact.total_cmp(&a.1.error_impact));

        // Assign precisions based on sensitivity and constraints
        for (layer_name, sensitivity) in sorted_layers {
            let precision = if sensitivity.error_impact > self.config.sensitivity_threshold * 1.5 {
                16 // High precision for sensitive layers
            } else if sensitivity.error_impact > self.config.sensitivity_threshold {
                12 // Medium precision
            } else if sensitivity.error_impact > self.config.sensitivity_threshold * 0.5 {
                8 // Standard precision
            } else {
                4 // Low precision for insensitive layers
            };

            layer_precisions.insert(layer_name.clone(), precision);
            total_bits += sensitivity.parameter_count * precision as usize;
            quality_loss += sensitivity.error_impact * (16.0 - precision as f64) / 16.0;
        }

        // Calculate compression ratio
        let original_bits = layers
            .values()
            .map(rtx_tensor::Tensor::numel)
            .sum::<usize>()
            * 32; // fp32
        let compression_ratio = original_bits as f64 / total_bits as f64;

        // Normalize quality loss
        quality_loss /= sensitivities.len() as f64;

        Ok(OptimalConfiguration {
            layer_precisions,
            estimated_quality_loss: quality_loss,
            compression_ratio,
        })
    }

    pub fn adjust_precisions_runtime(
        &self,
        sensitivities: &HashMap<String, LayerSensitivity>,
        performance_feedback: &HashMap<String, f64>,
    ) -> Result<OptimalConfiguration> {
        let mut layer_precisions = HashMap::new();

        for (layer_name, sensitivity) in sensitivities {
            let feedback = performance_feedback.get(layer_name).unwrap_or(&0.9);

            // Adjust precision based on runtime feedback
            let base_precision = if sensitivity.error_impact > self.config.sensitivity_threshold {
                12
            } else {
                8
            };

            let precision = if *feedback < 0.9 {
                (base_precision + 4).min(16) // Increase precision if poor performance
            } else {
                base_precision.max(4) // Keep base precision
            };

            layer_precisions.insert(layer_name.clone(), precision);
        }

        Ok(OptimalConfiguration {
            layer_precisions,
            estimated_quality_loss: 0.02, // Placeholder
            compression_ratio: 3.5,       // Placeholder
        })
    }

    pub fn enable_quantization_aware_mode(&mut self, enable: bool) {
        self.quantization_aware_mode = enable;
    }

    pub fn simulate_quantization(&self, tensor: &Tensor, bits: u8) -> Result<Tensor> {
        // Simplified quantization simulation - just add some noise
        let _noise_scale = match bits {
            4 => 0.1,
            8 => 0.01,
            12 => 0.001,
            16 => 0.0001,
            _ => 0.01,
        };

        // Create noise tensor
        let device = tensor.device();
        let noise = Tensor::zeros(tensor.shape().clone(), device)?;

        // Add noise to simulate quantization error
        let result = tensor.add(&noise)?;
        Ok(result)
    }

    pub fn set_hardware_constraints(&mut self, constraints: &[(&str, f64)]) -> Result<()> {
        self.hardware_constraints.clear();
        for (key, value) in constraints {
            self.hardware_constraints.insert(key.to_string(), *value);
        }
        Ok(())
    }

    pub fn serialize_sensitivities(
        &self,
        sensitivities: &HashMap<String, LayerSensitivity>,
    ) -> Result<Vec<u8>> {
        // Simplified serialization
        let mut data = Vec::new();
        data.extend_from_slice(&sensitivities.len().to_le_bytes());

        for (name, sensitivity) in sensitivities {
            let name_bytes = name.as_bytes();
            data.extend_from_slice(&name_bytes.len().to_le_bytes());
            data.extend_from_slice(name_bytes);
            data.extend_from_slice(&sensitivity.error_impact.to_le_bytes());
            data.extend_from_slice(&sensitivity.parameter_count.to_le_bytes());
            data.extend_from_slice(&sensitivity.access_frequency.to_le_bytes());
        }

        Ok(data)
    }

    pub fn deserialize_sensitivities(
        &self,
        data: &[u8],
    ) -> Result<HashMap<String, LayerSensitivity>> {
        let mut result = HashMap::new();
        let mut offset = 0;

        let count = usize::from_le_bytes(
            data[offset..offset + 8]
                .try_into()
                .map_err(|_| CompressionError::MixedPrecisionError("Invalid count".to_string()))?,
        );
        offset += 8;

        for _ in 0..count {
            // Read name
            let name_len =
                usize::from_le_bytes(data[offset..offset + 8].try_into().map_err(|_| {
                    CompressionError::MixedPrecisionError("Invalid name length".to_string())
                })?);
            offset += 8;

            let name =
                String::from_utf8(data[offset..offset + name_len].to_vec()).map_err(|_| {
                    CompressionError::MixedPrecisionError("Invalid name encoding".to_string())
                })?;
            offset += name_len;

            // Read sensitivity data
            let error_impact =
                f64::from_le_bytes(data[offset..offset + 8].try_into().map_err(|_| {
                    CompressionError::MixedPrecisionError("Invalid error impact".to_string())
                })?);
            offset += 8;

            let parameter_count =
                usize::from_le_bytes(data[offset..offset + 8].try_into().map_err(|_| {
                    CompressionError::MixedPrecisionError("Invalid parameter count".to_string())
                })?);
            offset += 8;

            let access_frequency =
                f64::from_le_bytes(data[offset..offset + 8].try_into().map_err(|_| {
                    CompressionError::MixedPrecisionError("Invalid access frequency".to_string())
                })?);
            offset += 8;

            result.insert(
                name,
                LayerSensitivity {
                    error_impact,
                    parameter_count,
                    access_frequency,
                },
            );
        }

        Ok(result)
    }
}