| 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. |
Cross-Entropy Kernel Optimization
Overview
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).
Core Technique
Fused Log-Softmax + NLL
Per-row computation:
- Load row of logits into FP32
- Find max:
m = max(logits)
- Compute
log_sum_exp = log(sum(exp(logits - m)))
- Loss =
-(logits[target] - m - log_sum_exp)
Only the target index value is needed -- no need to compute full softmax.
Large Vocabulary Handling
For vocab > 64K (e.g., LLaMA's 128K):
- BLOCK_SIZE = next_power_of_2(vocab) can be very large
- Consider chunked approach: process in BLOCK_SIZE chunks, accumulate online
Verification
python skills/kernels/cross-entropy/test_cross_entropy.py
- Correctness: compare vs
torch.nn.functional.cross_entropy(logits, targets)
- Performance: GBps =
(batch * vocab * elem_size) / latency / 1e9
Common Pitfalls
- Integer overflow in target indexing: target must be int64 for large vocabularies
- Missing FP32 accumulation: log-sum-exp in FP16 is numerically unstable
- Reduction to scalar: remember to average over batch dimension
References
