CUDA layer for the paged-KV + swap work:
- csrc: new paged_attention.cu plus updates across attention/gemm/norm/
activation/embedding/reduce kernels and common.cuh.
- xserv-kernels: new dispatch module and kernel-binding updates.
- xserv-cuda: cudaMallocHost/FreeHost bindings + PinnedBuffer (host swap
pool backing) and offset-aware D2H/H2D copies used to move KV blocks
between the GPU pool and pinned host memory.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three performance optimizations targeting decode throughput:
1. Decode Attention Kernel (csrc/attention/flash_attention.cu):
- Specialized kernel for Q_len=1 (decode step)
- 256 threads parallelize across KV sequence dimension
- Online softmax with block-level warp-shuffle reduction
- Replaces FA2 kernel which wasted 63/64 threads for decode
- flash_attention() auto-dispatches when q_len==1
2. Fused SiLU×Mul (csrc/activation/activations.cu):
- Single kernel: out = silu(gate) * up
- Saves 1 HBM read + 1 HBM write per FFN layer (N elements)
- Eliminates intermediate tensor allocation
3. Fused Add+RMSNorm (csrc/normalization/rmsnorm.cu):
- Single kernel: (normed, sum) = (rmsnorm(x+residual), x+residual)
- Saves 1 full HBM round-trip per attention block
- Eliminates separate add + rmsnorm kernel pair
Performance analysis:
- At current short sequences (max 79 tokens), these optimizations provide
marginal benefit because the bottleneck is cuBLAS GEMV overhead:
252 weight matrix reads × ~32MB each = 15.5 GB per decode step.
Theoretical minimum at 1.79 TB/s = 8.7ms, actual ~78ms (9x gap).
- The fused kernels and decode attention will show larger gains at
longer sequences where attention and element-wise ops dominate.
- Next optimization target: CUDA Graphs to eliminate kernel launch
overhead, or custom GEMV kernels to replace cuBLAS for M=1.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
