use realizar::gguf::{MappedGGUFModel, OwnedQKVWeights, OwnedQuantizedModel};
use realizar::quantize::{dequantize_q4_k, fused_q4k_parallel_matvec, fused_q6k_parallel_matvec};
use realizar::rms_norm;
const GGUF_TYPE_Q4_K: u32 = 12;
const GGUF_TYPE_Q6_K: u32 = 14;
fn l2_norm(v: &[f32]) -> f32 {
(v.iter().map(|x| x * x).sum::<f32>()).sqrt()
}
fn fused_matmul(input: &[f32], data: &[u8], qtype: u32, in_dim: usize, out_dim: usize) -> Vec<f32> {
match qtype {
GGUF_TYPE_Q4_K => fused_q4k_parallel_matvec(data, input, in_dim, out_dim).expect("test"),
GGUF_TYPE_Q6_K => fused_q6k_parallel_matvec(data, input, in_dim, out_dim).expect("test"),
_ => panic!("Unsupported qtype: {}", qtype),
}
}
fn reference_matvec(weight: &[f32], input: &[f32], in_dim: usize, out_dim: usize) -> Vec<f32> {
let mut output = vec![0.0f32; out_dim];
for o in 0..out_dim {
let mut sum = 0.0f32;
for i in 0..in_dim {
sum += weight[o * in_dim + i] * input[i];
}
output[o] = sum;
}
output
}
fn main() {
let path = "/tmp/parity-bench/tinyllama-1.1b-q4_k_m.gguf";
let mapped = MappedGGUFModel::from_path(path).expect("Failed");
let model = OwnedQuantizedModel::from_mapped(&mapped).expect("test");
let hidden_dim = model.config().hidden_dim;
let eps = model.config().eps;
println!("=== O Weight Debug ===\n");
let token_id = 450u32;
let start = token_id as usize * hidden_dim;
let embedding: Vec<f32> = model.token_embedding()[start..start + hidden_dim].to_vec();
let layer = &model.layers()[0];
let normed = rms_norm(&embedding, &layer.attn_norm_weight, eps);
let OwnedQKVWeights::Separate { v: v_weight, .. } = &layer.qkv_weight else {
panic!("Expected separate")
};
let v = fused_matmul(
&normed,
&v_weight.data,
v_weight.qtype,
v_weight.in_dim,
v_weight.out_dim,
);
println!("V L2: {:.4}", l2_norm(&v));
let head_dim = hidden_dim / model.config().num_heads;
let group_size = model.config().num_heads / model.config().num_kv_heads;
let mut attn_out = Vec::with_capacity(hidden_dim);
for h in 0..model.config().num_heads {
let kv_head = h / group_size;
let start = kv_head * head_dim;
attn_out.extend_from_slice(&v[start..start + head_dim]);
}
println!("Attn out (expanded V) L2: {:.4}", l2_norm(&attn_out));
println!(
"Attn out first 20: {:?}",
&attn_out[0..20]
.iter()
.map(|x| format!("{:.8}", x))
.collect::<Vec<_>>()
);
let o_weight = &layer.attn_output_weight;
println!("\nO weight:");
println!(
" in_dim: {}, out_dim: {}",
o_weight.in_dim, o_weight.out_dim
);
println!(" qtype: {} (12=Q4_K)", o_weight.qtype);
println!(" data.len: {}", o_weight.data.len());
let o_dequant = dequantize_q4_k(&o_weight.data).expect("Failed to dequantize");
println!(
"\n Dequantized length: {} (expected {})",
o_dequant.len(),
o_weight.in_dim * o_weight.out_dim
);
println!(" Dequantized weight L2: {:.4}", l2_norm(&o_dequant));
println!(
"\n Row 0 (output 0) first 10: {:?}",
&o_dequant[0..10]
.iter()
.map(|x| format!("{:.8}", x))
.collect::<Vec<_>>()
);
let o_fused = fused_matmul(
&attn_out,
&o_weight.data,
o_weight.qtype,
o_weight.in_dim,
o_weight.out_dim,
);
println!("\nFused O output:");
println!(" L2: {:.4}", l2_norm(&o_fused));
println!(
" First 20: {:?}",
&o_fused[0..20]
.iter()
.map(|x| format!("{:.8}", x))
.collect::<Vec<_>>()
);
let o_ref = reference_matvec(&o_dequant, &attn_out, o_weight.in_dim, o_weight.out_dim);
println!("\nReference O output:");
println!(" L2: {:.4}", l2_norm(&o_ref));
println!(
" First 20: {:?}",
&o_ref[0..20]
.iter()
.map(|x| format!("{:.8}", x))
.collect::<Vec<_>>()
);
let diff_l2: f32 = o_fused
.iter()
.zip(o_ref.iter())
.map(|(a, b)| (a - b).powi(2))
.sum::<f32>()
.sqrt();
println!("\nL2 of difference (fused vs ref): {:.6}", diff_l2);
println!("\n=== HuggingFace Expected ===");
println!(" Attn proj L2: 0.2398");
println!(" Attn proj first 20: [0.00681, -0.00189, -0.00124, 0.00091, 0.00394, 0.00364, -0.00053, 0.00256, 0.00461, -0.00617, 0.00248, 0.00356, 0.00630, 0.00274, -0.00106, 0.00973, 0.00246, -0.00693, 0.00429, -0.00681]");
println!("\n=== Compare attn_out with HF ===");
println!("HF attn_out L2: 0.5596");
println!("HF attn_out first 20: [-0.00183, 0.00309, -0.00220, -0.00116, 0.00321, 0.00355, -0.00293, -0.00264, 0.00235, 0.00369, -0.00130, 0.00071, -0.01569, -0.00151, 0.00180, 0.00215, -0.00024, 0.00109, 0.00251, -0.00277]");
println!("\nOurs attn_out L2: {:.4}", l2_norm(&attn_out));
println!(
"Ours attn_out first 20: {:?}",
&attn_out[0..20]
.iter()
.map(|x| format!("{:.6}", x))
.collect::<Vec<_>>()
);
fn reference_matvec_colmajor(
weight: &[f32],
input: &[f32],
in_dim: usize,
out_dim: usize,
) -> Vec<f32> {
let mut output = vec![0.0f32; out_dim];
for i in 0..in_dim {
for o in 0..out_dim {
output[o] += weight[i * out_dim + o] * input[i];
}
}
output
}
let o_colmajor =
reference_matvec_colmajor(&o_dequant, &attn_out, o_weight.in_dim, o_weight.out_dim);
println!("\nCol-major O output:");
println!(" L2: {:.4}", l2_norm(&o_colmajor));
println!(
" First 20: {:?}",
&o_colmajor[0..20]
.iter()
.map(|x| format!("{:.8}", x))
.collect::<Vec<_>>()
);
}