triton-kernel

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Write optimized Triton GPU kernels for deep learning operations. Covers the full spectrum from basic vector ops to Flash Attention, persistent matmul, fused normalization, quantized GEMM, and memory-efficient patterns.

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Writing Optimized Triton GPU Kernels

Targets: Triton >= 2.1, any GPU with tl.dot support (SM70+/CDNA2+)

Core Patterns (always apply)

Kernel structure: Use @triton.jit decorator. Get block ID with tl.program_id(axis). Compute element offsets with tl.arange(0, BLOCK_SIZE). Build mask = offsets < n_elements for all loads/stores.

Block sizes: Strongly prefer powers of two (required for tl.arange; non-power-of-two may work but can reduce performance). Declare as tl.constexpr parameters. Use @triton.autotune to sweep BLOCK_SIZE_M/N/K configs per hardware.

Memory hierarchy: Keep intermediates in SRAM via block-level reductions (tl.sum, tl.max) before writing to global memory. Fuse multiple pointwise ops into one kernel to avoid DRAM round-trips.

Matmul: Use tl.dot(a, b) for tensor core operations. Always accumulate in tl.float32 when inputs are FP16. For L2 cache locality, use grouped tile ordering via group_id = pid // GROUP_SIZE.

Grid launching: Size grid dynamically: grid = lambda meta: (triton.cdiv(n, meta['BLOCK_SIZE']),).

Masking: ALWAYS mask boundary loads/stores: tl.load(ptr + offs, mask=offs < dim, other=0.0). Missing masks corrupt memory silently.

Benchmarking: Use triton.testing.Benchmark with x_names, x_vals, line_arg, line_vals to compare against PyTorch baselines.

Quick Reference Examples

Fused row-wise softmax — verified, based on official Triton tutorial:

@triton.jit
def fused_softmax(x_ptr, out_ptr, cols, BLOCK: tl.constexpr):
    row = tl.program_id(0)
    offs = tl.arange(0, BLOCK)
    mask = offs < cols
    x = tl.load(x_ptr + row * cols + offs, mask=mask, other=-1e9)
    x_max = tl.max(x, axis=0)
    ex = tl.exp(x - x_max)
    out = ex / tl.sum(ex, axis=0)
    tl.store(out_ptr + row * cols + offs, out, mask=mask)

Seed-based dropout — verified, based on official Triton tutorial:

@triton.jit
def dropout(x_ptr, out_ptr, seed, p, n, BLOCK: tl.constexpr):
    offs = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK)
    mask = offs < n
    x = tl.load(x_ptr + offs, mask=mask)
    r = tl.rand(seed, offs)  # Philox PRNG, deterministic
    keep = r > p
    tl.store(out_ptr + offs, x * keep / (1.0 - p), mask=mask)

Performance Bottleneck Quick-Reference

When optimizing an existing kernel, classify the bottleneck first (profile with ncu):

Bottleneck	Diagnosis	Fix
Memory-bound	DRAM throughput > 60% of peak, compute < 30%	PID swizzle, TMA, fuse ops to reduce loads
Compute-bound	Tensor core utilization > 60%, DRAM < 40%	Persistent kernels, increase `num_stages`, warp specialization
Underutilized	Both < 60%, high stall metrics	Reduce register pressure, increase `num_warps`, autotune

See triton-gpu-kernel-optimization.md for specific NCU metric names and detailed strategies.

Specialized Topics

Read these files for detailed guidance when the task involves these areas:

Task	File to read
Flash Attention / fused self-attention	`triton-flash-attention-v2.md`
Persistent kernels, warp specialization, TMA	`triton-persistent-warp-matmul.md`
LayerNorm, RMSNorm, GroupNorm (fwd + bwd)	`triton-fused-normalizations.md`
FP4/FP8 quantized matmul, block scaling	`triton-quantized-block-scaled-gemm.md`
Kernel fusion, Philox dropout, recomputation	`triton-memory-efficient-patterns.md`
General tiled GEMM, autotune, benchmarking	`triton-gpu-kernel-optimization.md`
Fusing normalization/gating/residual into attention or matmul epilogue	`triton-fused-epilogue-kernels.md`
Sequential stateful processing (LRU routing, mutable register state)	`triton-sequential-stateful-blocks.md`
Launcher tile selection, num_stages/num_warps heuristics	`triton-dynamic-launcher-tiling.md`

When to read specialized files: Only read the relevant file when the user's task specifically involves that topic. The core patterns above are sufficient for basic kernels (vector ops, elementwise fusion, simple reductions).

Other references

triton-opt.md: For general optimization techniques while writing triton kernels.