use std::ffi::c_void;
use xserv_tensor::{DType, Device, Tensor};

unsafe extern "C" {
    fn launch_gelu_f32(x: *const c_void, out: *mut c_void, n: i32, stream: *mut c_void);
    fn launch_gelu_bf16(x: *const c_void, out: *mut c_void, n: i32, stream: *mut c_void);
    fn launch_silu_f32(x: *const c_void, out: *mut c_void, n: i32, stream: *mut c_void);
    fn launch_silu_bf16(x: *const c_void, out: *mut c_void, n: i32, stream: *mut c_void);
    fn launch_scale_f32(
        x: *const c_void,
        out: *mut c_void,
        scale: f32,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_scale_bf16(
        x: *const c_void,
        out: *mut c_void,
        scale: f32,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_add_f32(
        a: *const c_void,
        b: *const c_void,
        out: *mut c_void,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_add_bf16(
        a: *const c_void,
        b: *const c_void,
        out: *mut c_void,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_mul_f32(
        a: *const c_void,
        b: *const c_void,
        out: *mut c_void,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_mul_bf16(
        a: *const c_void,
        b: *const c_void,
        out: *mut c_void,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_silu_mul_bf16(
        gate: *const c_void,
        up: *const c_void,
        out: *mut c_void,
        n: i32,
        stream: *mut c_void,
    );
    fn launch_gpt_oss_glu_bf16(
        gate_up: *const c_void,
        out: *mut c_void,
        n_elements: i32,
        alpha: f32,
        limit: f32,
        stream: *mut c_void,
    );
    fn launch_bias_add_2d_bf16(
        x: *const c_void,
        bias: *const c_void,
        out: *mut c_void,
        rows: i32,
        cols: i32,
        stream: *mut c_void,
    );
}

fn dispatch_unary(
    x: &Tensor,
    f32_fn: unsafe extern "C" fn(*const c_void, *mut c_void, i32, *mut c_void),
    bf16_fn: unsafe extern "C" fn(*const c_void, *mut c_void, i32, *mut c_void),
) -> Tensor {
    assert!(x.is_contiguous() && matches!(x.device(), Device::Cuda(_)));
    let out = Tensor::empty(x.shape(), x.dtype(), x.device());
    let n = x.numel();
    assert!(
        n <= i32::MAX as usize,
        "tensor too large for i32 kernel param ({n} elements)"
    );
    let n = n as i32;
    unsafe {
        match x.dtype() {
            DType::F32 => f32_fn(
                x.data_ptr() as _,
                out.data_ptr() as *mut c_void,
                n,
                xserv_cuda::current_stream_raw(),
            ),
            DType::BF16 => bf16_fn(
                x.data_ptr() as _,
                out.data_ptr() as *mut c_void,
                n,
                xserv_cuda::current_stream_raw(),
            ),
            _ => panic!("unsupported dtype"),
        }
    }
    out
}

fn dispatch_binary(
    a: &Tensor,
    b: &Tensor,
    f32_fn: unsafe extern "C" fn(*const c_void, *const c_void, *mut c_void, i32, *mut c_void),
    bf16_fn: unsafe extern "C" fn(*const c_void, *const c_void, *mut c_void, i32, *mut c_void),
) -> Tensor {
    assert_eq!(a.shape(), b.shape());
    assert!(a.is_contiguous() && b.is_contiguous());
    assert!(matches!(a.device(), Device::Cuda(_)));
    assert_eq!(a.dtype(), b.dtype());
    let out = Tensor::empty(a.shape(), a.dtype(), a.device());
    let n = a.numel();
    assert!(
        n <= i32::MAX as usize,
        "tensor too large for i32 kernel param ({n} elements)"
    );
    let n = n as i32;
    unsafe {
        match a.dtype() {
            DType::F32 => f32_fn(
                a.data_ptr() as _,
                b.data_ptr() as _,
                out.data_ptr() as *mut c_void,
                n,
                xserv_cuda::current_stream_raw(),
            ),
            DType::BF16 => bf16_fn(
                a.data_ptr() as _,
                b.data_ptr() as _,
                out.data_ptr() as *mut c_void,
                n,
                xserv_cuda::current_stream_raw(),
            ),
            _ => panic!("unsupported dtype"),
        }
    }
    out
}

pub fn gelu(x: &Tensor) -> Tensor {
    dispatch_unary(x, launch_gelu_f32, launch_gelu_bf16)
}
pub fn silu(x: &Tensor) -> Tensor {
    dispatch_unary(x, launch_silu_f32, launch_silu_bf16)
}

pub fn scale(x: &Tensor, scale_val: f32) -> Tensor {
    assert!(x.is_contiguous() && matches!(x.device(), Device::Cuda(_)));
    let out = Tensor::empty(x.shape(), x.dtype(), x.device());
    let n = x.numel();
    assert!(
        n <= i32::MAX as usize,
        "tensor too large for i32 kernel param ({n} elements)"
    );
    let n = n as i32;
    unsafe {
        match x.dtype() {
            DType::F32 => launch_scale_f32(
                x.data_ptr() as _,
                out.data_ptr() as *mut c_void,
                scale_val,
                n,
                xserv_cuda::current_stream_raw(),
            ),
            DType::BF16 => launch_scale_bf16(
                x.data_ptr() as _,
                out.data_ptr() as *mut c_void,
                scale_val,
                n,
                xserv_cuda::current_stream_raw(),
            ),
            _ => panic!("unsupported dtype for scale"),
        }
    }
    out
}

pub fn add(a: &Tensor, b: &Tensor) -> Tensor {
    dispatch_binary(a, b, launch_add_f32, launch_add_bf16)
}
pub fn mul(a: &Tensor, b: &Tensor) -> Tensor {
    dispatch_binary(a, b, launch_mul_f32, launch_mul_bf16)
}

/// Row-broadcast bias add: out[r, c] = x[r, c] + bias[c] (BF16 only).
pub fn bias_add_2d(x: &Tensor, bias: &Tensor) -> Tensor {
    assert_eq!(x.ndim(), 2);
    assert_eq!(bias.ndim(), 1);
    assert_eq!(x.dtype(), DType::BF16);
    assert_eq!(bias.dtype(), DType::BF16);
    assert!(x.is_contiguous() && bias.is_contiguous());
    assert!(matches!(x.device(), Device::Cuda(_)));
    let rows = x.shape()[0];
    let cols = x.shape()[1];
    assert_eq!(
        bias.shape()[0],
        cols,
        "bias size {} != cols {cols}",
        bias.shape()[0]
    );
    assert!(rows * cols <= i32::MAX as usize);
    let out = Tensor::empty(&[rows, cols], DType::BF16, x.device());
    unsafe {
        launch_bias_add_2d_bf16(
            x.data_ptr() as _,
            bias.data_ptr() as _,
            out.data_ptr() as *mut c_void,
            rows as i32,
            cols as i32,
            xserv_cuda::current_stream_raw(),
        );
    }
    out
}

/// Fused SiLU×Mul: out = silu(gate) * up (BF16 only)
/// Saves one HBM read + one HBM write compared to separate silu + mul.
pub fn silu_mul(gate: &Tensor, up: &Tensor) -> Tensor {
    assert_eq!(gate.shape(), up.shape());
    assert!(gate.is_contiguous() && up.is_contiguous());
    assert!(matches!(gate.device(), Device::Cuda(_)));
    assert_eq!(gate.dtype(), DType::BF16, "silu_mul requires BF16");
    let out = Tensor::empty(gate.shape(), gate.dtype(), gate.device());
    let n = gate.numel();
    assert!(
        n <= i32::MAX as usize,
        "tensor too large for i32 kernel param ({n} elements)"
    );
    let n = n as i32;
    unsafe {
        launch_silu_mul_bf16(
            gate.data_ptr() as *const c_void,
            up.data_ptr() as *const c_void,
            out.data_ptr() as *mut c_void,
            n,
            xserv_cuda::current_stream_raw(),
        );
    }
    out
}

/// gpt-oss fused GLU activation (BF16 only).
/// Input: gate_up [rows, 2*D] with interleaved columns (gate=even, up=odd).
/// Output: [rows, D]
/// Computes: gate.clamp(max=limit) * sigmoid(gate * alpha) * (up.clamp(-limit,limit) + 1)
pub fn gpt_oss_glu(gate_up: &Tensor, alpha: f32, limit: f32) -> Tensor {
    assert!(gate_up.is_contiguous());
    assert!(matches!(gate_up.device(), Device::Cuda(_)));
    assert_eq!(gate_up.dtype(), DType::BF16, "gpt_oss_glu requires BF16");
    assert_eq!(gate_up.ndim(), 2);
    let rows = gate_up.shape()[0];
    let cols = gate_up.shape()[1];
    assert_eq!(cols % 2, 0);
    let d = cols / 2;
    let out = Tensor::empty(&[rows, d], gate_up.dtype(), gate_up.device());
    let n_elements = (rows * d) as i32;
    unsafe {
        launch_gpt_oss_glu_bf16(
            gate_up.data_ptr() as *const c_void,
            out.data_ptr() as *mut c_void,
            n_elements,
            alpha,
            limit,
            xserv_cuda::current_stream_raw(),
        );
    }
    out
}