Gahow Wang
d5532ef209
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>