vyre-libs 0.6.3

//! Fused `linear_silu` constructor  -  Linear + SiLU activation in one
//! GPU dispatch.
//!
//! ROADMAP H5  -  GEMM + bias + activation fusion. Companion to
//! `linear_relu`; computes `out[i] = silu(sum_k x[k] * w[k, i] + b[i])`
//! where `silu(z) = z / (1 + exp(-z))`.
//!
//! Without this fused variant, the same effect requires two
//! dispatches (linear, then silu) with an intermediate buffer
//! materialising the linear output to global memory only to be
//! re-read by silu. The fused variant keeps the matmul accumulator
//! in registers through the activation, halving the global memory
//! traffic.
//!
//! Soundness: numerically equivalent to `linear` followed by `silu`
//! because the activation is element-wise and depends only on the
//! per-output-row accumulator value.

use vyre::ir::{DataType, Program};

use super::fused_activation::linear_fused_activation;
use crate::nn::activation::silu::silu_expr;

const OP_ID: &str = "vyre-libs::nn::linear_silu";

/// Build a Program that computes `out[i] = silu(sum_k x[k] * w[k, i] + b[i])`.
///
/// Fused variant of `linear` followed by SiLU activation.
///
/// # Errors
/// Returns `Err` when `in_dim == 0` or `out_dim == 0`.
pub fn linear_silu(
    x: &str,
    w: &str,
    b: &str,
    out: &str,
    in_dim: u32,
    out_dim: u32,
) -> Result<Program, String> {
    linear_fused_activation(
        "linear_silu",
        OP_ID,
        x,
        w,
        b,
        out,
        in_dim,
        out_dim,
        silu_expr,
    )
}

inventory::submit! {
    crate::harness::OpEntry {
        id: OP_ID,
        build: || {
            linear_silu("x", "w", "b", "out", 4, 4).unwrap_or_else(|error| {
                crate::builder::invalid_output_program(
                    OP_ID,
                    "out",
                    DataType::F32,
                    error,
                )
            })
        },
        test_inputs: Some(|| {
            let f32_bytes = vyre_primitives::wire::pack_f32_slice;
            let x = f32_bytes(&(0..4).map(|i| i as f32).collect::<Vec<_>>());
            let w = f32_bytes(&(0..16).map(|i| i as f32).collect::<Vec<_>>());
            let bias = f32_bytes(&[0.0, 0.0, 0.0, 0.0]);
            vec![vec![x, w, bias]]
        }),
        expected_output: Some(|| {
            // linear: x=[0,1,2,3], w[k,i] = k*4+i, b=[0,0,0,0]
            // out[i] = sum_k x[k] * w[k, i]
            //        = 0*i + 1*(4+i) + 2*(8+i) + 3*(12+i)
            //        = (4 + 8*2 + 12*3) + (1 + 2 + 3) * i
            //        = (4 + 16 + 36) + 6 * i
            //        = 56 + 6*i
            // Then silu(z) = z / (1 + exp(-z))
            let acc: Vec<f32> = (0..4).map(|i| 56.0 + 6.0 * i as f32).collect();
            let silu: Vec<f32> = acc.iter().map(|z| z / (1.0 + (-z).exp())).collect();
            let bytes = vyre_primitives::wire::pack_f32_slice(&silu);
            vec![vec![bytes]]
        }),
        category: Some("nn"),
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::test_support::byte_pack::f32_bytes;
    use vyre_reference::value::Value;

    fn decode(bytes: &[u8]) -> Vec<f32> {
        vyre_primitives::wire::decode_f32_le_bytes_all(bytes)
    }

    fn silu_scalar(z: f32) -> f32 {
        z / (1.0 + (-z).exp())
    }

    /// `linear_silu` matches `linear` followed by element-wise silu
    /// when both are evaluated through the reference interpreter.
    #[test]
    fn linear_silu_matches_linear_plus_silu_reference() {
        let in_dim = 4u32;
        let out_dim = 4u32;
        let x: Vec<f32> = (0..in_dim).map(|i| i as f32).collect();
        let w: Vec<f32> = (0..in_dim * out_dim).map(|i| i as f32 * 0.1).collect();
        let bias = vec![0.5, -0.25, 1.0, 0.0];
        let prog = linear_silu("x", "w", "b", "out", in_dim, out_dim).expect("Fix: build");
        let outputs = vyre_reference::reference_eval(
            &prog,
            &[
                Value::from(f32_bytes(&x)),
                Value::from(f32_bytes(&w)),
                Value::from(f32_bytes(&bias)),
                Value::from(vec![0u8; (out_dim as usize) * 4]),
            ],
        )
        .expect("Fix: linear_silu must execute in the reference interpreter.");
        let actual = decode(&outputs[0].to_bytes());
        let expected: Vec<f32> = (0..out_dim as usize)
            .map(|i| {
                let acc = bias[i]
                    + (0..in_dim as usize)
                        .map(|k| x[k] * w[k * out_dim as usize + i])
                        .sum::<f32>();
                silu_scalar(acc)
            })
            .collect();
        for (a, e) in actual.iter().zip(expected.iter()) {
            assert!((a - e).abs() <= 1.0e-5, "{a} != {e}");
        }
    }

    /// `linear_silu(0, _)` rejects the empty reduction.
    #[test]
    fn linear_silu_rejects_empty_in_dim() {
        let err =
            linear_silu("x", "w", "b", "out", 0, 4).expect_err("Fix: empty reduction must error");
        assert!(err.contains("in_dim=0"));
    }

    /// `linear_silu(_, 0)` rejects empty output.
    #[test]
    fn linear_silu_rejects_empty_out_dim() {
        let err =
            linear_silu("x", "w", "b", "out", 4, 0).expect_err("Fix: empty output must error");
        assert!(err.contains("out_dim=0"));
    }

    #[test]
    fn linear_silu_reuses_standalone_tiny_flush_semantics() {
        let subnormal = f32::from_bits(1);
        let prog = linear_silu("x", "w", "b", "out", 1, 1).expect("Fix: build linear_silu");
        let outputs = vyre_reference::reference_eval(
            &prog,
            &[
                Value::from(f32_bytes(&[0.0])),
                Value::from(f32_bytes(&[0.0])),
                Value::from(f32_bytes(&[subnormal])),
                Value::from(vec![0u8; 4]),
            ],
        )
        .expect("Fix: linear_silu must execute with subnormal bias");
        let actual = decode(&outputs[0].to_bytes());
        assert_eq!(
            actual[0].to_bits(),
            0.0f32.to_bits(),
            "linear_silu must use the same flush_tiny SiLU semantics as standalone silu"
        );
    }
}