//! # RustyTorch++ ROCm Backend
//!
//! AMD GPU backend implementation using HIP runtime.
//!
//! ## Features
//!
//! - **HIP Runtime**: AMD's CUDA-compatible API for GPU computing
//! - **rocBLAS**: Optimized matrix operations
//! - **MIOpen**: Deep learning primitives
//! - **Source Compatibility**: HIP kernels are ~95% CUDA-compatible
//!
//! ## Architecture
//!
//! ```text
//! RocmBackend
//! ├── RocmTensorPrimitive - HIP device memory
//! ├── RocmDevice - Device context and streams
//! └── Ops
//!     ├── Basic - Add, mul, etc. (HIP kernels)
//!     ├── GEMM - Matrix multiply (rocBLAS)
//!     └── Attention - Flash Attention (ported from CUDA)
//! ```
//!
//! ## Example
//!
//! ```rust,ignore
//! use rtx_backend_rocm::{RocmBackend, RocmDevice};
//! use rtx_backend::Backend;
//!
//! let device = RocmDevice::new(0)?;
//! let a = RocmBackend::zeros([1024, 1024], &device);
//! let b = RocmBackend::randn([1024, 1024], &device);
//! let c = RocmBackend::matmul(&a, &b);
//! ```

#![warn(missing_docs)]

mod device;
mod error;
/// HIP FFI bindings for low-level GPU access.
pub mod hip_ffi;
/// HIP kernel implementations for GPU operations.
#[cfg(feature = "hip-runtime")]
pub mod hip_kernels;
/// Operations module - re-exported for tests and direct access.
pub mod ops;
mod tensor;

pub use device::{RocmDevice, RocmDeviceInfo};
pub use error::{RocmBackendError, RocmBackendResult};
pub use tensor::{RocmBuffer, RocmTensorPrimitive};

use rtx_backend::{Backend, BoolU8};

/// ROCm backend for RustyTorch++.
///
/// This backend uses AMD GPUs via HIP runtime and provides:
/// - rocBLAS for matrix operations
/// - HIP kernels (CUDA source-compatible)
/// - Multi-GPU support
#[derive(Clone, Debug, Default)]
pub struct RocmBackend;

impl Backend for RocmBackend {
    type TensorPrimitive<const D: usize> = RocmTensorPrimitive<D>;
    type Device = RocmDevice;
    type FloatElem = f32;
    type IntElem = i32;
    type BoolElem = BoolU8;

    fn name() -> &'static str {
        "rocm"
    }

    fn seed(seed: u64) {
        ops::seed_rng(seed);
    }

    // ==================== Tensor Creation ====================

    fn zeros<const D: usize>(shape: [usize; D], device: &Self::Device) -> Self::TensorPrimitive<D> {
        ops::creation::zeros(shape, device)
    }

    fn ones<const D: usize>(shape: [usize; D], device: &Self::Device) -> Self::TensorPrimitive<D> {
        ops::creation::ones(shape, device)
    }

    fn full<const D: usize>(
        shape: [usize; D],
        fill_value: Self::FloatElem,
        device: &Self::Device,
    ) -> Self::TensorPrimitive<D> {
        ops::creation::full(shape, fill_value, device)
    }

    fn rand<const D: usize>(shape: [usize; D], device: &Self::Device) -> Self::TensorPrimitive<D> {
        ops::creation::rand(shape, device)
    }

    fn randn<const D: usize>(shape: [usize; D], device: &Self::Device) -> Self::TensorPrimitive<D> {
        ops::creation::randn(shape, device)
    }

    fn from_data<const D: usize>(
        data: &[Self::FloatElem],
        shape: [usize; D],
        device: &Self::Device,
    ) -> Self::TensorPrimitive<D> {
        ops::creation::from_data(data, shape, device)
    }

    // ==================== Basic Operations ====================

    fn add<const D: usize>(
        lhs: Self::TensorPrimitive<D>,
        rhs: Self::TensorPrimitive<D>,
    ) -> Self::TensorPrimitive<D> {
        ops::basic::add(&lhs, &rhs)
    }

    fn sub<const D: usize>(
        lhs: Self::TensorPrimitive<D>,
        rhs: Self::TensorPrimitive<D>,
    ) -> Self::TensorPrimitive<D> {
        ops::basic::sub(&lhs, &rhs)
    }

    fn mul<const D: usize>(
        lhs: Self::TensorPrimitive<D>,
        rhs: Self::TensorPrimitive<D>,
    ) -> Self::TensorPrimitive<D> {
        ops::basic::mul(&lhs, &rhs)
    }

    fn div<const D: usize>(
        lhs: Self::TensorPrimitive<D>,
        rhs: Self::TensorPrimitive<D>,
    ) -> Self::TensorPrimitive<D> {
        ops::basic::div(&lhs, &rhs)
    }

    fn matmul(
        lhs: Self::TensorPrimitive<2>,
        rhs: Self::TensorPrimitive<2>,
    ) -> Self::TensorPrimitive<2> {
        ops::gemm::matmul(&lhs, &rhs)
    }

    fn bmm(
        lhs: Self::TensorPrimitive<3>,
        rhs: Self::TensorPrimitive<3>,
    ) -> Self::TensorPrimitive<3> {
        ops::gemm::bmm(&lhs, &rhs)
    }

    // ==================== Unary Operations ====================

    fn neg<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::neg(&tensor)
    }

    fn exp<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::exp(&tensor)
    }

    fn log<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::log(&tensor)
    }

    fn sqrt<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::sqrt(&tensor)
    }

    fn abs<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::abs(&tensor)
    }

    fn sin<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::sin(&tensor)
    }

    fn cos<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::unary::cos(&tensor)
    }

    fn pow<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        exp: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::unary::pow(&tensor, exp)
    }

    fn clamp<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        min: Self::FloatElem,
        max: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::comparison::clamp(&tensor, Some(min), Some(max))
    }

    // ==================== Activation Functions ====================

    fn relu<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::activation::relu(&tensor)
    }

    fn sigmoid<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::activation::sigmoid(&tensor)
    }

    fn tanh<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::activation::tanh(&tensor)
    }

    // ==================== Reduction Operations ====================

    fn sum<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<1> {
        ops::reduction::sum(&tensor)
    }

    fn sum_dim<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        dim: usize,
    ) -> Self::TensorPrimitive<D> {
        ops::reduction::sum_dim(&tensor, dim)
    }

    fn mean<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<1> {
        ops::reduction::mean(&tensor)
    }

    fn mean_dim<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        dim: usize,
    ) -> Self::TensorPrimitive<D> {
        ops::reduction::mean_dim(&tensor, dim)
    }

    fn var<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<1> {
        ops::reduction::var(&tensor)
    }

    fn var_dim<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        dim: usize,
    ) -> Self::TensorPrimitive<D> {
        ops::reduction::var_dim(&tensor, dim)
    }

    fn max<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<1> {
        ops::reduction::max(&tensor)
    }

    fn min<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<1> {
        ops::reduction::min(&tensor)
    }

    // ==================== Shape Operations ====================

    fn shape<const D: usize>(tensor: &Self::TensorPrimitive<D>) -> [usize; D] {
        tensor.shape
    }

    fn reshape<const D1: usize, const D2: usize>(
        tensor: Self::TensorPrimitive<D1>,
        shape: [usize; D2],
    ) -> Self::TensorPrimitive<D2> {
        ops::shape::reshape(tensor, shape)
    }

    fn transpose<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::shape::transpose(&tensor)
    }

    fn swap_dims<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        dim1: usize,
        dim2: usize,
    ) -> Self::TensorPrimitive<D> {
        ops::shape::swap_dims(&tensor, dim1, dim2)
    }

    // ==================== LLM-Specific Operations ====================

    fn flash_attention(
        query: Self::TensorPrimitive<4>,
        key: Self::TensorPrimitive<4>,
        value: Self::TensorPrimitive<4>,
        mask: Option<&Self::TensorPrimitive<4>>,
        scale: Self::FloatElem,
        causal: bool,
    ) -> Self::TensorPrimitive<4> {
        ops::attention::flash_attention(&query, &key, &value, mask, scale, causal)
    }

    fn softmax<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        dim: usize,
    ) -> Self::TensorPrimitive<D> {
        ops::activation::softmax(&tensor, dim)
    }

    fn layer_norm<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        weight: &Self::TensorPrimitive<1>,
        bias: Option<&Self::TensorPrimitive<1>>,
        eps: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::normalization::layer_norm(&tensor, weight, bias, eps)
    }

    fn rms_norm<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        weight: &Self::TensorPrimitive<1>,
        eps: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::normalization::rms_norm(&tensor, weight, eps)
    }

    fn rope<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        cos: &Self::TensorPrimitive<2>,
        sin: &Self::TensorPrimitive<2>,
    ) -> Self::TensorPrimitive<D> {
        ops::attention::rope(&tensor, cos, sin)
    }

    fn gelu<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::activation::gelu(&tensor)
    }

    fn silu<const D: usize>(tensor: Self::TensorPrimitive<D>) -> Self::TensorPrimitive<D> {
        ops::activation::silu(&tensor)
    }

    fn leaky_relu<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        negative_slope: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::activation::leaky_relu(&tensor, negative_slope)
    }

    fn elu<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        alpha: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::activation::elu(&tensor, alpha)
    }

    // ==================== Comparison Operations ====================

    fn gt_scalar<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        value: Self::FloatElem,
    ) -> Self::TensorPrimitive<D> {
        ops::comparison::gt_scalar(&tensor, value)
    }

    // ==================== Convolution Operations ====================

    fn conv2d(
        input: Self::TensorPrimitive<4>,
        weight: &Self::TensorPrimitive<4>,
        bias: Option<&Self::TensorPrimitive<1>>,
        stride: [usize; 2],
        padding: [usize; 2],
        dilation: [usize; 2],
        groups: usize,
    ) -> Self::TensorPrimitive<4> {
        let config = ops::convolution::Conv2dConfig {
            kernel_size: (weight.shape[2], weight.shape[3]),
            stride: (stride[0], stride[1]),
            padding: (padding[0], padding[1]),
            dilation: (dilation[0], dilation[1]),
            groups,
        };
        ops::convolution::conv2d(&input, weight, bias, &config)
    }

    // ==================== Pooling Operations ====================

    fn max_pool2d(
        input: Self::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
    ) -> Self::TensorPrimitive<4> {
        let config = ops::pooling::Pool2dConfig {
            kernel_size: (kernel_size[0], kernel_size[1]),
            stride: (stride[0], stride[1]),
            padding: (padding[0], padding[1]),
            dilation: (1, 1),
            ceil_mode: false,
        };
        ops::pooling::max_pool2d(&input, &config)
    }

    fn avg_pool2d(
        input: Self::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        count_include_pad: bool,
    ) -> Self::TensorPrimitive<4> {
        let config = ops::pooling::Pool2dConfig {
            kernel_size: (kernel_size[0], kernel_size[1]),
            stride: (stride[0], stride[1]),
            padding: (padding[0], padding[1]),
            dilation: (1, 1),
            ceil_mode: false,
        };
        ops::pooling::avg_pool2d(&input, &config, count_include_pad)
    }

    // ==================== Device Management ====================

    fn device<const D: usize>(tensor: &Self::TensorPrimitive<D>) -> Self::Device {
        tensor.device.clone()
    }

    fn to_device<const D: usize>(
        tensor: Self::TensorPrimitive<D>,
        device: &Self::Device,
    ) -> Self::TensorPrimitive<D> {
        if tensor.device == *device {
            tensor
        } else {
            ops::device::copy_to_device(tensor, device)
        }
    }

    fn to_data<const D: usize>(tensor: &Self::TensorPrimitive<D>) -> Vec<Self::FloatElem> {
        ops::device::copy_to_host(tensor)
    }

    fn sync(device: &Self::Device) {
        let _ = device.synchronize();
    }
}

/// Check if ROCm is available on this system.
pub fn is_available() -> bool {
    #[cfg(feature = "hip-runtime")]
    {
        hip_ffi::HipRuntime::is_available()
    }
    #[cfg(not(feature = "hip-runtime"))]
    {
        device::is_available()
    }
}

/// Get the number of available AMD GPUs.
pub fn device_count() -> usize {
    hip_ffi::HipRuntime::device_count()
}

/// Check if HIP runtime feature is enabled.
pub fn has_hip_runtime() -> bool {
    cfg!(feature = "hip-runtime")
}

/// Check if rocBLAS feature is enabled.
pub fn has_rocblas() -> bool {
    cfg!(feature = "rocblas")
}

/// Check if MIOpen feature is enabled.
pub fn has_miopen() -> bool {
    cfg!(feature = "miopen")
}

/// Type alias for training with ROCm + autodiff.
pub type RocmTraining = RocmBackend;

/// Type alias for inference with ROCm (no autodiff overhead).
pub type RocmInference = RocmBackend;