phase 15: Tensor::empty + CUDA Graph infra — 50.3 tok/s (140% of HF, 45% roofline)

Two optimizations:

1. Tensor::empty() — skip cudaMemset for output tensors
   All kernel wrappers that fully overwrite their output now use
   Tensor::empty() instead of Tensor::zeros(). Eliminates ~756
   cudaMemset calls per decode step (21 per layer × 36 layers).
   Improvement: 46.6 → 50.3 tok/s (+8%).

2. CUDA Graph infrastructure (for future use)
   Added FFI bindings (cudaStreamBeginCapture, cudaGraphInstantiate,
   cudaGraphLaunch) and RAII CudaGraph wrapper. Not yet used in the
   forward pass due to variable kv_len, but provides foundation for
   future graph-based decode optimization.

Ablation (dash5, RTX 5090, Qwen3-8B BF16, serial decode):

| Optimization | tok/s | vs HF | Roofline |
|-------------|-------|-------|----------|
| Phase 14 baseline | 12.9 | 36% | 12% |
| + Fused kernels | 13.2 | 37% | 12% |
| + Batched decode | 13.2 (serial) | 37% | 12% |
| + Custom GEMV | 46.6 | 130% | 42% |
| + Tensor::empty | 50.3 | 140% | 45% |

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-05-22 23:57:34 +08:00
parent e207523e21
commit d5532ef209
13 changed files with 170 additions and 20 deletions

View File

@@ -20,7 +20,7 @@ pub fn rmsnorm(x: &Tensor, gamma: &Tensor, eps: f32) -> Tensor {
assert_eq!(x.dtype(), gamma.dtype());
let rows = x.numel() / hidden_size;
let out = Tensor::zeros(x.shape(), x.dtype(), x.device());
let out = Tensor::empty(x.shape(), x.dtype(), x.device());
unsafe {
match x.dtype() {
@@ -54,8 +54,8 @@ pub fn add_rmsnorm(x: &Tensor, residual: &Tensor, gamma: &Tensor, eps: f32) -> (
assert_eq!(gamma.shape(), &[hidden_size]);
let rows = x.numel() / hidden_size;
let normed_out = Tensor::zeros(x.shape(), DType::BF16, x.device());
let sum_out = Tensor::zeros(x.shape(), DType::BF16, x.device());
let normed_out = Tensor::empty(x.shape(), DType::BF16, x.device());
let sum_out = Tensor::empty(x.shape(), DType::BF16, x.device());
unsafe {
launch_add_rmsnorm_bf16(