//! Real-GPU smoke test for `BceLossPlan`. FW × 4 dtypes × Mean.

use baracuda_driver::{init, Context, Device, DeviceBuffer, Stream};
use baracuda_kernels::{
    contiguous_stride, BceLossArgs, BceLossDescriptor, BceLossPlan, ElementKind, LossReduction,
    PlanPreference, TensorMut, TensorRef, Workspace,
};
use half::{bf16, f16};

fn setup() -> (Context, Stream) {
    init().expect("driver init");
    let device = Device::get(0).expect("device 0");
    let ctx = Context::new(&device).expect("context");
    let stream = Stream::new(&ctx).expect("stream");
    (ctx, stream)
}

fn host_bce_mean_f64(pred: &[f64], target: &[f64]) -> f64 {
    let mut s = 0.0;
    for i in 0..pred.len() {
        let p = pred[i];
        let t = target[i];
        s += -(t * p.ln() + (1.0 - t) * (1.0 - p).ln());
    }
    s / (pred.len() as f64)
}

#[test]
#[ignore]
fn loss_bce_f32_mean() {
    let (ctx, stream) = setup();
    let shape = [4i32, 4];
    let numel = 16usize;
    // pred ∈ (0, 1)
    let host_p: Vec<f32> = (0..numel).map(|i| 0.2 + (i as f32) * 0.04).collect();
    let host_t: Vec<f32> = (0..numel).map(|i| if i % 2 == 0 { 1.0 } else { 0.0 }).collect();
    let expected = host_bce_mean_f64(
        &host_p.iter().map(|&v| v as f64).collect::<Vec<_>>(),
        &host_t.iter().map(|&v| v as f64).collect::<Vec<_>>(),
    ) as f32;

    let dev_p = DeviceBuffer::from_slice(&ctx, &host_p).unwrap();
    let dev_t = DeviceBuffer::from_slice(&ctx, &host_t).unwrap();
    let mut dev_y: DeviceBuffer<f32> = DeviceBuffer::zeros(&ctx, 1).unwrap();
    let mut dev_ws: DeviceBuffer<u8> = DeviceBuffer::zeros(&ctx, numel * 4).unwrap();

    let desc = BceLossDescriptor {
        input_shape: shape,
        reduction: LossReduction::Mean,
        element: ElementKind::F32,
    };
    let plan = BceLossPlan::<f32, 2>::select(&stream, &desc, PlanPreference::default()).unwrap();
    plan.run(
        &stream,
        Workspace::Borrowed(dev_ws.as_slice_mut()),
        BceLossArgs {
            pred: TensorRef { data: dev_p.as_slice(), shape, stride: contiguous_stride(shape) },
            target: TensorRef { data: dev_t.as_slice(), shape, stride: contiguous_stride(shape) },
            out: TensorMut { data: dev_y.as_slice_mut(), shape: [1, 1], stride: [1, 1] },
        },
    )
    .unwrap();
    stream.synchronize().unwrap();
    let mut got = [0f32; 1];
    dev_y.copy_to_host(&mut got).unwrap();
    let tol = expected.abs() * 32.0 * f32::EPSILON + 1e-5;
    assert!((got[0] - expected).abs() <= tol, "f32 BCE: got={} want={}", got[0], expected);
}

#[test]
#[ignore]
fn loss_bce_f64_mean() {
    let (ctx, stream) = setup();
    let shape = [3i32, 4];
    let numel = 12usize;
    let host_p: Vec<f64> = (0..numel).map(|i| 0.3 + (i as f64) * 0.05).collect();
    let host_t: Vec<f64> = (0..numel).map(|i| if i % 3 == 0 { 1.0 } else { 0.0 }).collect();
    let expected = host_bce_mean_f64(&host_p, &host_t);

    let dev_p = DeviceBuffer::from_slice(&ctx, &host_p).unwrap();
    let dev_t = DeviceBuffer::from_slice(&ctx, &host_t).unwrap();
    let mut dev_y: DeviceBuffer<f64> = DeviceBuffer::zeros(&ctx, 1).unwrap();
    let mut dev_ws: DeviceBuffer<u8> = DeviceBuffer::zeros(&ctx, numel * 8).unwrap();

    let desc = BceLossDescriptor {
        input_shape: shape,
        reduction: LossReduction::Mean,
        element: ElementKind::F64,
    };
    let plan = BceLossPlan::<f64, 2>::select(&stream, &desc, PlanPreference::default()).unwrap();
    plan.run(
        &stream,
        Workspace::Borrowed(dev_ws.as_slice_mut()),
        BceLossArgs {
            pred: TensorRef { data: dev_p.as_slice(), shape, stride: contiguous_stride(shape) },
            target: TensorRef { data: dev_t.as_slice(), shape, stride: contiguous_stride(shape) },
            out: TensorMut { data: dev_y.as_slice_mut(), shape: [1, 1], stride: [1, 1] },
        },
    )
    .unwrap();
    stream.synchronize().unwrap();
    let mut got = [0f64; 1];
    dev_y.copy_to_host(&mut got).unwrap();
    let tol = expected.abs() * 32.0 * f64::EPSILON + 1e-12;
    assert!((got[0] - expected).abs() <= tol);
}

#[test]
#[ignore]
fn loss_bce_f16_mean() {
    let (ctx, stream) = setup();
    let shape = [4i32, 5];
    let numel = 20usize;
    let host_p_f32: Vec<f32> = (0..numel).map(|i| 0.25 + (i as f32) * 0.03).collect();
    let host_t_f32: Vec<f32> = (0..numel).map(|i| if i % 2 == 0 { 1.0 } else { 0.0 }).collect();
    let expected = host_bce_mean_f64(
        &host_p_f32.iter().map(|&v| v as f64).collect::<Vec<_>>(),
        &host_t_f32.iter().map(|&v| v as f64).collect::<Vec<_>>(),
    ) as f32;
    let host_p: Vec<f16> = host_p_f32.iter().map(|&v| f16::from_f32(v)).collect();
    let host_t: Vec<f16> = host_t_f32.iter().map(|&v| f16::from_f32(v)).collect();

    let dev_p = DeviceBuffer::from_slice(&ctx, &host_p).unwrap();
    let dev_t = DeviceBuffer::from_slice(&ctx, &host_t).unwrap();
    let mut dev_y: DeviceBuffer<f16> = DeviceBuffer::zeros(&ctx, 1).unwrap();
    let mut dev_ws: DeviceBuffer<u8> = DeviceBuffer::zeros(&ctx, numel * 2).unwrap();

    let desc = BceLossDescriptor {
        input_shape: shape,
        reduction: LossReduction::Mean,
        element: ElementKind::F16,
    };
    let plan = BceLossPlan::<f16, 2>::select(&stream, &desc, PlanPreference::default()).unwrap();
    plan.run(
        &stream,
        Workspace::Borrowed(dev_ws.as_slice_mut()),
        BceLossArgs {
            pred: TensorRef { data: dev_p.as_slice(), shape, stride: contiguous_stride(shape) },
            target: TensorRef { data: dev_t.as_slice(), shape, stride: contiguous_stride(shape) },
            out: TensorMut { data: dev_y.as_slice_mut(), shape: [1, 1], stride: [1, 1] },
        },
    )
    .unwrap();
    stream.synchronize().unwrap();
    let mut got = [f16::ZERO; 1];
    dev_y.copy_to_host(&mut got).unwrap();
    let g = got[0].to_f32();
    let tol = expected.abs() * 32.0 * 9.77e-4_f32 + 5e-3;
    assert!((g - expected).abs() <= tol, "f16 BCE: got={} want={}", g, expected);
}

#[test]
#[ignore]
fn loss_bce_bf16_mean() {
    let (ctx, stream) = setup();
    let shape = [4i32, 4];
    let numel = 16usize;
    let host_p_f32: Vec<f32> = (0..numel).map(|i| 0.3 + (i as f32) * 0.04).collect();
    let host_t_f32: Vec<f32> = (0..numel).map(|i| if i % 2 == 0 { 1.0 } else { 0.0 }).collect();
    let expected = host_bce_mean_f64(
        &host_p_f32.iter().map(|&v| v as f64).collect::<Vec<_>>(),
        &host_t_f32.iter().map(|&v| v as f64).collect::<Vec<_>>(),
    ) as f32;
    let host_p: Vec<bf16> = host_p_f32.iter().map(|&v| bf16::from_f32(v)).collect();
    let host_t: Vec<bf16> = host_t_f32.iter().map(|&v| bf16::from_f32(v)).collect();

    let dev_p = DeviceBuffer::from_slice(&ctx, &host_p).unwrap();
    let dev_t = DeviceBuffer::from_slice(&ctx, &host_t).unwrap();
    let mut dev_y: DeviceBuffer<bf16> = DeviceBuffer::zeros(&ctx, 1).unwrap();
    let mut dev_ws: DeviceBuffer<u8> = DeviceBuffer::zeros(&ctx, numel * 2).unwrap();

    let desc = BceLossDescriptor {
        input_shape: shape,
        reduction: LossReduction::Mean,
        element: ElementKind::Bf16,
    };
    let plan = BceLossPlan::<bf16, 2>::select(&stream, &desc, PlanPreference::default()).unwrap();
    plan.run(
        &stream,
        Workspace::Borrowed(dev_ws.as_slice_mut()),
        BceLossArgs {
            pred: TensorRef { data: dev_p.as_slice(), shape, stride: contiguous_stride(shape) },
            target: TensorRef { data: dev_t.as_slice(), shape, stride: contiguous_stride(shape) },
            out: TensorMut { data: dev_y.as_slice_mut(), shape: [1, 1], stride: [1, 1] },
        },
    )
    .unwrap();
    stream.synchronize().unwrap();
    let mut got = [bf16::ZERO; 1];
    dev_y.copy_to_host(&mut got).unwrap();
    let g = got[0].to_f32();
    let tol = expected.abs() * 32.0 * 7.81e-3_f32 + 3e-2;
    assert!((g - expected).abs() <= tol, "bf16 BCE: got={} want={}", g, expected);
}
