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