菜单
Optimize fused cross-entropy loss kernels in Triton for NVIDIA and AMD GPUs. Covers fused log-softmax + NLL, online log-sum-exp, and large vocabulary handling. Use when writing or optimizing cross-entropy, label-smoothed CE, or similar loss kernels.
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基于 SOC 职业分类
Optimize FlashAttention-style fused attention kernels in Triton for NVIDIA and AMD GPUs. Covers online softmax, tiled QK/AV GEMM, causal masking, and memory-efficient attention. Use when writing or optimizing self-attention, cross-attention, or any QKV attention kernel.
Optimize dense matrix multiplication (GEMM) kernels in Triton for NVIDIA and AMD GPUs. Covers tiled blocking, L2 cache grouping, tensor core utilization, and dual-platform autotune. Use when writing or optimizing matmul, linear layers, or any GEMM-based kernel.
Optimize RMS Normalization kernels in Triton for NVIDIA and AMD GPUs. Covers row-parallel reduction, vectorized loads, warp shuffle patterns, and rsqrt usage. Use when writing or optimizing RMSNorm, LayerNorm, or similar normalization kernels.
Optimize Rotary Position Embedding (RoPE) kernels in Triton for NVIDIA and AMD GPUs. Covers interleaved/sequential layouts, sincos precomputation, and multi-row processing. Use when writing or optimizing RoPE, position embeddings, or rotary transformations.
Optimize fused softmax kernels in Triton for NVIDIA and AMD GPUs. Covers online max/sum reduction, multi-row processing, numerical stability, and warp-level patterns. Use when writing or optimizing softmax, log-softmax, or similar row-wise normalization kernels.
Chooses block/tile dimensions, warp counts, pipeline stages, and L2-aware block grouping for GPU kernels (Triton/CUDA/HIP). Use when autotuning matmul-like kernels, tuning occupancy, or aligning tile geometry with M/N/K and NVIDIA vs AMD thread models.
| name | cross-entropy-kernel |
| description | Optimize fused cross-entropy loss kernels in Triton for NVIDIA and AMD GPUs. Covers fused log-softmax + NLL, online log-sum-exp, and large vocabulary handling. Use when writing or optimizing cross-entropy, label-smoothed CE, or similar loss kernels. |
Fused cross-entropy: loss = -log_softmax(logits)[target] computed in a single kernel pass
per row, without materializing the full softmax output.
Cross-entropy is memory-bound for large vocabularies (30K-250K). The key metric is GBps (only need to read logits once, write one scalar per row).
Per-row computation:
m = max(logits)log_sum_exp = log(sum(exp(logits - m)))-(logits[target] - m - log_sum_exp)Only the target index value is needed -- no need to compute full softmax.
For vocab > 64K (e.g., LLaMA's 128K):
python skills/kernels/cross-entropy/test_cross_entropy.py
torch.nn.functional.cross_entropy(logits, targets)(batch * vocab * elem_size) / latency / 1e9