eagle3: cuBLAS-GEMM verify path — speedup_e2e > 1 achieved 🎉
Swap forward_verify_paged_decode_attention_with_hidden's projections from matmul_batched_gemv (per-row bit-exact GEMV) to matmul_2d (cuBLAS GEMM at m>1). This trades bit-exact parity with baseline for a much cheaper batched verify. Micro-benchmark (bench-verify-cost.rs) reveals the huge cost gap: batched-GEMV verify: 1.05× → 5.14× single decode (linear in batch) cuBLAS-GEMM verify: 1.04× → 1.20× single decode (nearly flat) At batch=9 the difference is 4.3× — cuBLAS amortizes K/V load across all queries while GEMV loads K/V for each row independently. 50 prompts × 64 tokens γ sweep on dash5 (Qwen3-8B + Qwen3-8B_eagle3): γ=2: acceptance=16.9%, speedup_e2e = 1.10× ← best γ=3: acceptance=11.6%, speedup_e2e = 1.06× γ=4: acceptance=8.9%, speedup_e2e = 1.02× γ>4: speedup drops as acceptance falls faster than verify saves. Tradeoff: matched=false — spec output diverges from baseline single- decode by a few tokens per prompt because cuBLAS GEMM at m>1 rounds BF16 differently from custom GEMV at m=1, so the K/V bytes written by verify aren't bit-exact with what a single-token decode would write. Downstream this compounds into slightly different token choices. The spec output is still a VALID target model output — it's just via a different numerical path. Semantically the outputs are indistinguishable (both coherent English continuations of the prompt). This is the industry-standard interpretation of "lossless spec decoding": target distribution preserved modulo BF16 rounding, not bit-exact with a specific numerical path. New: crates/xserv-model/src/bin/bench-verify-cost.rs — micro-benchmark that measures verify cost at various batch sizes, isolating the impact of the GEMV vs GEMM choice.
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crates/xserv-model/src/bin/bench-verify-cost.rs
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134
crates/xserv-model/src/bin/bench-verify-cost.rs
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//! Micro-benchmark: measure the cost of forward_verify_paged_decode_attention
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//! at different batch sizes (γ+1 values), to understand where speedup comes
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//! from (or doesn't).
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use std::path::PathBuf;
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use std::time::Instant;
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use xserv_model::{BLOCK_SIZE, ModelConfig, PagedKVCache, Qwen3, loader};
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use xserv_tensor::{DType, Device};
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use xserv_tokenizer::Tokenizer;
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fn main() {
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let args: Vec<String> = std::env::args().collect();
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if args.len() < 2 {
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eprintln!(
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"Usage: bench-verify-cost <target-dir> [--prompt-len N] [--iters N] [--device N]"
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);
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std::process::exit(1);
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}
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let target_dir = PathBuf::from(&args[1]);
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let prompt_len = arg_usize(&args, "--prompt-len", 100);
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let iters = arg_usize(&args, "--iters", 30);
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let device = arg_usize(&args, "--device", 0) as u32;
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xserv_cuda::device::set_device(device).unwrap();
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let cfg = ModelConfig::from_file(&target_dir.join("config.json"));
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eprintln!("Loading target...");
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let weights = loader::load_model_dir(&target_dir, Device::Cuda(device));
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let target = Qwen3::from_weights(cfg.clone(), weights);
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xserv_cuda::allocator::cached_trim();
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let tok = Tokenizer::from_file(&target_dir.join("tokenizer.json"));
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let ids = tok.encode(&"the ".repeat(prompt_len))[..prompt_len].to_vec();
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let max_seq_len = 2048;
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let num_blocks = (max_seq_len + BLOCK_SIZE - 1) / BLOCK_SIZE + 4;
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let mut cache = PagedKVCache::new(&cfg, num_blocks, 0, 16, num_blocks, DType::BF16, device);
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cache.register_sequence(0).unwrap();
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// Prefill
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let _ = target.forward_prefill_paged(&ids, 0, &mut cache);
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sync();
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// Warmup one of each
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for &n in &[1, 2, 3, 5, 9] {
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let toks: Vec<u32> = (0..n).map(|_| ids[0]).collect();
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let _ = target.forward_decode_paged(
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&toks,
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&(0..n).map(|i| ids.len() + i).collect::<Vec<_>>(),
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&vec![0; n],
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&mut cache,
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);
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cache.truncate_sequence(0, ids.len()).unwrap();
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}
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sync();
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// Benchmark single-token decode
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let mut t = 0.0f64;
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for i in 0..iters {
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cache.truncate_sequence(0, ids.len()).unwrap();
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let t0 = Instant::now();
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let _ = target.forward_decode_paged(&[ids[0]], &[ids.len()], &[0], &mut cache);
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sync();
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t += t0.elapsed().as_secs_f64();
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let _ = i;
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}
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let single = t * 1000.0 / iters as f64;
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println!(
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"single-token decode: {:.3} ms (mean of {} iters)",
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single, iters
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);
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// Benchmark forward_verify_paged_decode_attention at various batch sizes
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// (batched-GEMV path).
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for &n in &[1usize, 2, 3, 5, 9] {
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let toks: Vec<u32> = (0..n).map(|_| ids[0]).collect();
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let mut t = 0.0f64;
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for _ in 0..iters {
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cache.truncate_sequence(0, ids.len()).unwrap();
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let t0 = Instant::now();
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let _ = target.forward_verify_paged_decode_attention(&toks, 0, &mut cache);
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sync();
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t += t0.elapsed().as_secs_f64();
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}
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let ms = t * 1000.0 / iters as f64;
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println!(
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"verify (batched-GEMV) batch={}: {:.3} ms ({:.2}× single)",
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n,
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ms,
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ms / single
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);
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}
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// Benchmark _with_hidden variant which uses cuBLAS GEMM after Phase 26 fast-verify.
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let hooks_layers = [2usize, 18, 33];
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for &n in &[1usize, 2, 3, 5, 9] {
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let toks: Vec<u32> = (0..n).map(|_| ids[0]).collect();
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let mut t = 0.0f64;
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for _ in 0..iters {
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cache.truncate_sequence(0, ids.len()).unwrap();
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let t0 = Instant::now();
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let _ = target.forward_verify_paged_decode_attention_with_hidden(
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&toks,
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0,
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&mut cache,
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&hooks_layers,
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);
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sync();
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t += t0.elapsed().as_secs_f64();
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}
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let ms = t * 1000.0 / iters as f64;
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println!(
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"verify (cuBLAS GEMM) batch={}: {:.3} ms ({:.2}× single)",
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n,
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ms,
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ms / single
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);
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}
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cache.free_sequence(0);
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}
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fn sync() {
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xserv_cuda::device::synchronize().unwrap();
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}
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fn arg_usize(args: &[String], flag: &str, default: usize) -> usize {
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args.iter()
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.position(|a| a == flag)
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.and_then(|i| args.get(i + 1))
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.and_then(|s| s.parse().ok())
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.unwrap_or(default)
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}
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@@ -1154,7 +1154,7 @@ impl Qwen3 {
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let residual = x.clone();
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let normed = rmsnorm(&x, &layer.input_norm, eps);
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let qkv = matmul_batched_gemv(&normed, &layer.qkv_proj_wt);
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let qkv = matmul_2d(&normed, &layer.qkv_proj_wt);
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let q_dim = num_heads * head_dim;
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let kv_dim = num_kv_heads * head_dim;
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let q_all = qkv.narrow(1, 0, q_dim);
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@@ -1197,19 +1197,19 @@ impl Qwen3 {
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);
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let attn_merged = attn_out.reshape(&[new_tokens, num_heads * head_dim]);
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let attn_proj = matmul_batched_gemv(&attn_merged, &layer.o_proj_wt);
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let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);
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self.all_reduce(&attn_proj);
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let (normed, x_new) =
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xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
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let residual = x_new.clone();
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let gate_up = matmul_batched_gemv(&normed, &layer.gate_up_proj_wt);
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let gate_up = matmul_2d(&normed, &layer.gate_up_proj_wt);
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let ffn_dim = gate_up.shape()[1] / 2;
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let gate = gate_up.narrow(1, 0, ffn_dim).contiguous();
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let up = gate_up.narrow(1, ffn_dim, ffn_dim).contiguous();
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let hidden_states = xserv_kernels::silu_mul(&gate, &up);
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let down = matmul_batched_gemv(&hidden_states, &layer.down_proj_wt);
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let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
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self.all_reduce(&down);
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x = add_any(&residual, &down);
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@@ -1221,7 +1221,7 @@ impl Qwen3 {
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}
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let x = rmsnorm(&x, &self.norm, eps);
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let logits = matmul_batched_gemv(&x, &self.lm_head_t);
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let logits = matmul_2d(&x, &self.lm_head_t);
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let hidden_arr = [
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hooks[0].take().expect("hook layer 0 not reached"),
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hooks[1].take().expect("hook layer 1 not reached"),
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