rustorch 0.6.29

//! Quantization-Aware Training (QAT) support
//! 量子化認識学習（QAT）サポート

use super::calibration::Observer;
use super::schemes::{
    AsymmetricQuantization, QuantizationParams, QuantizationScheme, SymmetricQuantization,
};
use crate::autograd::Variable;
use crate::error::{RusTorchError, RusTorchResult};
use crate::nn::Module;
use crate::tensor::Tensor;
use ndarray::ArrayD;
use num_traits::Float;
use std::marker::PhantomData;
use std::sync::{Arc, Mutex};

/// Trait for modules that support quantization-aware training
/// 量子化認識学習をサポートするモジュールのトレイト
pub trait QATModule<T: Float> {
    /// Enable QAT mode (fake quantization during training)
    /// QATモードを有効化（学習中の偽量子化）
    fn enable_qat(&mut self);

    /// Disable QAT mode (normal training)
    /// QATモードを無効化（通常学習）
    fn disable_qat(&mut self);

    /// Check if QAT is enabled
    /// QATが有効かチェック
    fn is_qat_enabled(&self) -> bool;

    /// Get quantization parameters
    /// 量子化パラメータを取得
    fn get_quantization_params(&self) -> Option<(f32, i32)>;

    /// Set quantization parameters
    /// 量子化パラメータを設定
    fn set_quantization_params(&mut self, scale: f32, zero_point: i32);
}

/// Fake quantization operation for training
/// 学習用偽量子化演算
#[derive(Clone)]
pub struct FakeQuantize<T: Float> {
    /// Quantization scale
    /// 量子化スケール
    pub scale: f32,
    /// Zero point for asymmetric quantization
    /// 非対称量子化のゼロポイント
    pub zero_point: i32,
    /// Quantization scheme
    /// 量子化スキーム
    pub scheme: QuantizationScheme,
    /// Enable/disable fake quantization
    /// 偽量子化の有効/無効
    pub enabled: bool,
    /// Quantization bit width
    /// 量子化ビット幅
    pub bits: u8,
    /// Observer for calibration during QAT
    /// QAT中のキャリブレーション用観測器
    pub observer: Option<Arc<Mutex<dyn Observer<T>>>>,
    /// Phantom data for type parameter
    /// 型パラメータ用のファントムデータ
    _phantom: PhantomData<T>,
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> FakeQuantize<T> {
    /// Create a new fake quantization module
    /// 新しい偽量子化モジュールを作成
    pub fn new(scheme: QuantizationScheme, bits: u8) -> Self {
        let (qmin, qmax) = match bits {
            8 => (-128i32, 127i32),
            4 => (-8i32, 7i32),
            16 => (-32768i32, 32767i32),
            _ => (-128i32, 127i32), // Default to INT8
        };

        Self {
            scale: 1.0,
            zero_point: 0,
            scheme,
            enabled: true,
            bits,
            observer: None,
            _phantom: PhantomData,
        }
    }

    /// Create with observer for dynamic calibration
    /// 動的キャリブレーション用観測器付きで作成
    pub fn with_observer(
        scheme: QuantizationScheme,
        bits: u8,
        observer: Arc<Mutex<dyn Observer<T>>>,
    ) -> Self {
        let mut fake_quant = Self::new(scheme, bits);
        fake_quant.observer = Some(observer);
        fake_quant
    }

    /// Apply fake quantization to tensor
    /// テンソルに偽量子化を適用
    pub fn forward(&self, input: &Variable<T>) -> RusTorchResult<Variable<T>> {
        if !self.enabled {
            return Ok(input.clone());
        }

        // Access tensor data
        let input_tensor = input.data();
        let input_guard = input_tensor.read().unwrap();

        // Update observer if available
        // 利用可能な場合は観測器を更新
        if let Some(observer) = &self.observer {
            let mut obs = observer.lock().unwrap();
            obs.observe(&input_guard.data);

            // Update quantization parameters from observer
            // 観測器から量子化パラメータを更新
            if let Ok((new_scale, new_zero_point)) = obs.get_quantization_params(self.scheme) {
                // In a mutable version, we would update self.scale and self.zero_point
                // 可変バージョンでは、self.scaleとself.zero_pointを更新
            }
        }

        // Apply fake quantization with straight-through estimator
        // ストレートスルー推定器を使用した偽量子化を適用
        let quantized_data = self.fake_quantize_tensor(&input_guard.data)?;

        Ok(Variable::new(
            Tensor::from_ndarray(quantized_data),
            input.requires_grad(),
        ))
    }

    /// Fake quantize a tensor (simulate quantization without actually quantizing)
    /// テンソルの偽量子化（実際に量子化せずに量子化をシミュレート）
    fn fake_quantize_tensor(&self, data: &ArrayD<T>) -> RusTorchResult<ArrayD<T>> {
        let (qmin, qmax) = self.get_quantization_range();

        let quantized_data = data.mapv(|val| {
            // Convert to f32 for quantization math
            let val_f32 = val.to_f32().unwrap_or(0.0);

            // Quantize: round((x - zero_point) / scale)
            let quantized_int =
                ((val_f32 / self.scale).round() as i32 + self.zero_point).clamp(qmin, qmax);

            // Dequantize back to floating point (straight-through for gradients)
            let dequantized_f32 = (quantized_int - self.zero_point) as f32 * self.scale;

            // Convert back to T
            T::from_f32(dequantized_f32).unwrap_or(val)
        });

        Ok(quantized_data)
    }

    /// Get quantization range based on bit width
    /// ビット幅に基づいて量子化範囲を取得
    fn get_quantization_range(&self) -> (i32, i32) {
        match self.bits {
            8 => (-128, 127),
            4 => (-8, 7),
            16 => (-32768, 32767),
            _ => (-128, 127),
        }
    }

    /// Calibrate quantization parameters from data
    /// データから量子化パラメータをキャリブレート
    pub fn calibrate(&mut self, data: &ArrayD<T>) -> RusTorchResult<()> {
        let (scale, zero_point) = match self.scheme {
            QuantizationScheme::Symmetric => SymmetricQuantization::compute_params(data)?,
            QuantizationScheme::Asymmetric => AsymmetricQuantization::compute_params(data)?,
            _ => {
                // For per-channel schemes, use asymmetric for simplicity
                AsymmetricQuantization::compute_params(data)?
            }
        };

        self.scale = scale;
        self.zero_point = zero_point;
        Ok(())
    }
}

/// Shared QAT configuration and fake quantization components
/// 共有QAT設定と偽量子化コンポーネント
#[derive(Clone)]
pub struct QATConfig<T: Float> {
    /// Weight fake quantization
    /// 重み用偽量子化
    pub weight_fake_quant: FakeQuantize<T>,
    /// Activation fake quantization
    /// 活性化用偽量子化
    pub activation_fake_quant: FakeQuantize<T>,
    /// QAT enabled flag
    /// QAT有効フラグ
    pub enabled: bool,
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> QATConfig<T> {
    /// Create default QAT configuration
    /// デフォルトQAT設定を作成
    pub fn new() -> Self {
        Self {
            weight_fake_quant: FakeQuantize::new(QuantizationScheme::Symmetric, 8),
            activation_fake_quant: FakeQuantize::new(QuantizationScheme::Asymmetric, 8),
            enabled: true,
        }
    }

    /// Apply fake quantization to input and weights
    /// 入力と重みに偽量子化を適用
    pub fn apply_quantization(
        &self,
        input: &Variable<T>,
        weight: &Variable<T>,
    ) -> RusTorchResult<(Variable<T>, Variable<T>)> {
        let quantized_input = if self.enabled {
            self.activation_fake_quant.forward(input)?
        } else {
            input.clone()
        };

        let quantized_weight = if self.enabled {
            self.weight_fake_quant.forward(weight)?
        } else {
            weight.clone()
        };

        Ok((quantized_input, quantized_weight))
    }
}

/// QAT-enabled Linear layer
/// QAT対応線形層
pub struct QATLinear<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> {
    /// Weight tensor
    /// 重みテンソル
    pub weight: Variable<T>,
    /// Bias tensor (optional)
    /// バイアステンソル（オプション）
    pub bias: Option<Variable<T>>,
    /// QAT configuration
    /// QAT設定
    pub qat_config: QATConfig<T>,
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> QATLinear<T> {
    /// Create a new QAT linear layer
    /// 新しいQAT線形層を作成
    pub fn new(in_features: usize, out_features: usize) -> Self {
        // Initialize weights with small random values
        // 小さなランダム値で重みを初期化
        let weight_data = ArrayD::from_shape_fn(
            vec![out_features, in_features],
            |_| T::from_f32(0.01).unwrap_or_else(T::zero), // Small initialization
        );
        let weight = Variable::new(Tensor::from_ndarray(weight_data), true);

        // Initialize bias to zero
        // バイアスをゼロで初期化
        let bias_data = ArrayD::zeros(vec![out_features]);
        let bias = Some(Variable::new(Tensor::from_ndarray(bias_data), true));

        Self {
            weight,
            bias,
            qat_config: QATConfig::new(),
        }
    }

    /// Forward pass with QAT
    /// QAT付きフォワードパス
    pub fn forward(&self, input: &Variable<T>) -> RusTorchResult<Variable<T>> {
        // Use QAT config to apply quantization
        // QAT設定を使用して量子化を適用
        let (quantized_input, quantized_weight) =
            self.qat_config.apply_quantization(input, &self.weight)?;

        // Perform linear transformation: output = input @ weight.T + bias
        // 線形変換を実行：output = input @ weight.T + bias
        let output = self.linear_forward(&quantized_input, &quantized_weight)?;

        Ok(output)
    }

    /// Perform actual linear computation
    /// 実際の線形計算を実行
    fn linear_forward(
        &self,
        input: &Variable<T>,
        weight: &Variable<T>,
    ) -> RusTorchResult<Variable<T>> {
        // Simplified linear operation - in full implementation would use proper matrix multiplication
        // 簡略化線形演算 - 完全実装では適切な行列乗算を使用

        let input_tensor = input.data();
        let input_guard = input_tensor.read().unwrap();
        let input_shape = input_guard.shape();

        let weight_tensor = weight.data();
        let weight_guard = weight_tensor.read().unwrap();
        let weight_shape = weight_guard.shape();

        if input_shape.len() < 2 || weight_shape.len() != 2 {
            return Err(RusTorchError::TensorOp {
                message: "Invalid shapes for linear layer".to_string(),
                source: None,
            });
        }

        let batch_size = input_shape[0];
        let in_features = input_shape[input_shape.len() - 1];
        let out_features = weight_shape[0];

        if weight_shape[1] != in_features {
            return Err(RusTorchError::ShapeMismatch {
                expected: vec![in_features],
                actual: vec![weight_shape[1]],
            });
        }

        // Create output tensor
        // 出力テンソルを作成
        let mut output_data = ArrayD::zeros(vec![batch_size, out_features]);

        // Simplified matrix multiplication
        // 簡略化行列乗算
        for b in 0..batch_size {
            for o in 0..out_features {
                let mut sum = T::zero();
                for i in 0..in_features {
                    sum = sum + input_guard.data[[b, i]] * weight_guard.data[[o, i]];
                }

                // Add bias if present
                // バイアスが存在する場合は加算
                if let Some(ref bias) = self.bias {
                    let bias_tensor = bias.data();
                    let bias_guard = bias_tensor.read().unwrap();
                    sum = sum + bias_guard.data[o];
                }

                output_data[[b, o]] = sum;
            }
        }

        Ok(Variable::new(
            Tensor::from_ndarray(output_data),
            input.requires_grad(),
        ))
    }
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> QATModule<T>
    for QATLinear<T>
{
    fn enable_qat(&mut self) {
        self.qat_config.enabled = true;
        self.qat_config.weight_fake_quant.enabled = true;
        self.qat_config.activation_fake_quant.enabled = true;
    }

    fn disable_qat(&mut self) {
        self.qat_config.enabled = false;
        self.qat_config.weight_fake_quant.enabled = false;
        self.qat_config.activation_fake_quant.enabled = false;
    }

    fn is_qat_enabled(&self) -> bool {
        self.qat_config.enabled
    }

    fn get_quantization_params(&self) -> Option<(f32, i32)> {
        if self.qat_config.enabled {
            Some((
                self.qat_config.weight_fake_quant.scale,
                self.qat_config.weight_fake_quant.zero_point,
            ))
        } else {
            None
        }
    }

    fn set_quantization_params(&mut self, scale: f32, zero_point: i32) {
        self.qat_config.weight_fake_quant.scale = scale;
        self.qat_config.weight_fake_quant.zero_point = zero_point;
    }
}

/// QAT-enabled Conv2D layer (simplified)
/// QAT対応Conv2D層（簡略化）
pub struct QATConv2d<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> {
    /// Convolution weight
    /// 畳み込み重み
    pub weight: Variable<T>,
    /// Bias (optional)
    /// バイアス（オプション）
    pub bias: Option<Variable<T>>,
    /// QAT configuration
    /// QAT設定
    pub qat_config: QATConfig<T>,
    /// Convolution parameters
    /// 畳み込みパラメータ
    pub stride: (usize, usize),
    pub padding: (usize, usize),
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> QATConv2d<T> {
    /// Create a new QAT Conv2D layer
    /// 新しいQAT Conv2D層を作成
    pub fn new(
        in_channels: usize,
        out_channels: usize,
        kernel_size: (usize, usize),
        stride: (usize, usize),
        padding: (usize, usize),
    ) -> Self {
        let weight_data = ArrayD::from_shape_fn(
            vec![out_channels, in_channels, kernel_size.0, kernel_size.1],
            |_| T::from_f32(0.01).unwrap_or_else(T::zero),
        );
        let weight = Variable::new(Tensor::from_ndarray(weight_data), true);

        let bias_data = ArrayD::zeros(vec![out_channels]);
        let bias = Some(Variable::new(Tensor::from_ndarray(bias_data), true));

        Self {
            weight,
            bias,
            qat_config: QATConfig::new(),
            stride,
            padding,
        }
    }

    /// Forward pass (simplified - placeholder implementation)
    /// フォワードパス（簡略化 - プレースホルダー実装）
    pub fn forward(&self, input: &Variable<T>) -> RusTorchResult<Variable<T>> {
        // Use QAT config to apply quantization
        // QAT設定を使用して量子化を適用
        let (quantized_input, quantized_weight) =
            self.qat_config.apply_quantization(input, &self.weight)?;

        // Simplified convolution - in practice would implement proper conv2d
        // 簡略化畳み込み - 実際には適切なconv2dを実装
        let input_tensor = quantized_input.data();
        let input_guard = input_tensor.read().unwrap();
        let output_data = input_guard.data.clone(); // Placeholder

        Ok(Variable::new(
            Tensor::from_ndarray(output_data),
            input.requires_grad(),
        ))
    }
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> QATModule<T>
    for QATConv2d<T>
{
    fn enable_qat(&mut self) {
        self.qat_config.enabled = true;
        self.qat_config.weight_fake_quant.enabled = true;
        self.qat_config.activation_fake_quant.enabled = true;
    }

    fn disable_qat(&mut self) {
        self.qat_config.enabled = false;
        self.qat_config.weight_fake_quant.enabled = false;
        self.qat_config.activation_fake_quant.enabled = false;
    }

    fn is_qat_enabled(&self) -> bool {
        self.qat_config.enabled
    }

    fn get_quantization_params(&self) -> Option<(f32, i32)> {
        if self.qat_config.enabled {
            Some((
                self.qat_config.weight_fake_quant.scale,
                self.qat_config.weight_fake_quant.zero_point,
            ))
        } else {
            None
        }
    }

    fn set_quantization_params(&mut self, scale: f32, zero_point: i32) {
        self.qat_config.weight_fake_quant.scale = scale;
        self.qat_config.weight_fake_quant.zero_point = zero_point;
    }
}

/// QAT training utilities
/// QAT学習ユーティリティ
pub struct QATTrainer<T: Float> {
    /// Learning rate for QAT fine-tuning
    /// QATファインチューニング用学習率
    pub learning_rate: T,
    /// Number of calibration steps before starting QAT
    /// QAT開始前のキャリブレーション段階数
    pub calibration_steps: usize,
    /// Current step
    /// 現在のステップ
    pub current_step: usize,
}

impl<T: Float + Send + Sync + ndarray::ScalarOperand + num_traits::FromPrimitive> QATTrainer<T> {
    /// Create a new QAT trainer
    /// 新しいQATトレーナーを作成
    pub fn new(learning_rate: T, calibration_steps: usize) -> Self {
        Self {
            learning_rate,
            calibration_steps,
            current_step: 0,
        }
    }

    /// Training step with QAT
    /// QAT付き学習ステップ
    pub fn train_step<M: QATModule<T>>(
        &mut self,
        model: &mut M,
        _input: &Variable<T>,
        _target: &Variable<T>,
    ) -> RusTorchResult<T> {
        // Enable QAT after calibration period
        // キャリブレーション期間後にQATを有効化
        if self.current_step >= self.calibration_steps {
            model.enable_qat();
        } else {
            model.disable_qat();
        }

        self.current_step += 1;

        // In a full implementation, would compute loss and update parameters
        // 完全実装では、損失を計算してパラメータを更新
        Ok(T::zero()) // Placeholder loss
    }

    /// Prepare model for quantized deployment
    /// 量子化デプロイメント用のモデル準備
    pub fn prepare_for_deployment<M: QATModule<T>>(&self, model: &mut M) {
        model.disable_qat();
        // In practice, would convert fake quantization to real quantization
        // 実際には、偽量子化を実量子化に変換
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::tensor::Tensor;
    use ndarray::Array2;

    #[test]
    fn test_fake_quantize() {
        let fake_quant = FakeQuantize::<f32>::new(QuantizationScheme::Symmetric, 8);

        let data = Array2::from_shape_vec((2, 2), vec![1.0f32, 2.0, 3.0, 4.0])
            .unwrap()
            .into_dyn();
        let input = Variable::new(Tensor::from_ndarray(data), false);

        let output = fake_quant.forward(&input).unwrap();
        let output_tensor = output.data();
        let output_guard = output_tensor.read().unwrap();
        assert_eq!(output_guard.shape(), &[2, 2]);
    }

    #[test]
    fn test_qat_linear() {
        let mut linear = QATLinear::<f32>::new(3, 2);

        let input_data = Array2::from_shape_vec((1, 3), vec![1.0f32, 2.0, 3.0])
            .unwrap()
            .into_dyn();
        let input = Variable::new(Tensor::from_ndarray(input_data), false);

        assert!(linear.is_qat_enabled());

        let output = linear.forward(&input).unwrap();
        let output_tensor = output.data();
        let output_guard = output_tensor.read().unwrap();
        assert_eq!(output_guard.shape(), &[1, 2]);

        // Test disabling QAT
        linear.disable_qat();
        assert!(!linear.is_qat_enabled());
    }

    #[test]
    fn test_qat_conv2d() {
        let mut conv = QATConv2d::<f32>::new(3, 16, (3, 3), (1, 1), (1, 1));

        assert!(conv.is_qat_enabled());

        conv.set_quantization_params(0.1, 0);
        assert_eq!(conv.get_quantization_params(), Some((0.1, 0)));
    }

    #[test]
    fn test_qat_trainer() {
        let mut trainer = QATTrainer::new(0.001f32, 100);
        let mut linear = QATLinear::<f32>::new(2, 1);

        let input_data = Array2::from_shape_vec((1, 2), vec![1.0f32, 2.0])
            .unwrap()
            .into_dyn();
        let input = Variable::new(Tensor::from_ndarray(input_data), false);

        let target_data = Array2::from_shape_vec((1, 1), vec![3.0f32])
            .unwrap()
            .into_dyn();
        let target = Variable::new(Tensor::from_ndarray(target_data), false);

        // Initially should disable QAT (calibration phase)
        trainer.train_step(&mut linear, &input, &target).unwrap();
        assert!(!linear.is_qat_enabled());

        // After calibration steps, should enable QAT
        trainer.current_step = 101;
        trainer.train_step(&mut linear, &input, &target).unwrap();
        assert!(linear.is_qat_enabled());
    }
}