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triton-cuda-error-fix
triton-cuda常见错误及修复方法,用于代码生成时避免同类问题
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triton-cuda常见错误及修复方法,用于代码生成时避免同类问题
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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矩阵乘法矩阵乘法 A[M, K] @ B[K, N] = C[M, N]中,大K维度矩阵乘法(K>>M,N)优化:针对M/N较小但K极大(如M=N=256,K=131072)的场景,Split-K切分K维度并行化、Workspace+Reduce替代全局同步,实现显著性能提升
Triton Ascend hard API restrictions and forbidden syntax. MUST-follow rules that apply to every kernel: forbidden control flow (return/break/continue/lambda/while), tensor slice/index restrictions, scalar conversion rules, BLOCK_SIZE upper bound. Violating any of these produces a compile or runtime error on Ascend.
Triton Ascend 性能优化通用策略: BLOCK_SIZE 选择 (1024-2048 for elementwise, must be <65536), grid configuration (use VEC_CORE_NUM / CUBE_CORE_NUM, 2D/3D grid for matmul / conv / reduce, 1D grid + inner loop for elementwise / pointwise), 256B alignment for memory transfers, autotune block-size patterns, fp16 / fp32 precision conversion. Bind via keywords like matmul, elementwise, reduce, block_size, grid, autotune, alignment, fp16, fp32, tile, interleaved-loop, cube-core, vec-core.
通过 adaptive_search 或 evolve 搜索式 workflow 生成优化算子。 后台 silent mode 执行,轮询监控进度。
适用于归约(reduce)类算子和含归约子步骤的复合算子(如归一化)的优化指南。典型算子包括:sum, mean, max, min, prod, argmax, argmin, cumsum, cumprod, softmax, logsoftmax, layernorm, rmsnorm, groupnorm, instancenorm, batchnorm, l1norm, l2norm, frobeniusnorm, var, std, average_pooling, sum_pooling 等。特别重要:当归约维度不是最后一维(如 dim=1 归约 shape=[B,F,D1,D2]),需要正确处理多维索引和两阶段归约。包含 PyTorch normalized_shape 多轴归一化语义说明。不适用于纯逐元素运算或矩阵乘法。如果算子是损失函数(先逐元素计算再全局归约),应选择 elementwise-reduce-fused 指南。
CPU C++ 算子核心概念、标准结构模式、KernelBench 代码规范和内嵌扩展方法
| name | triton-cuda-error-fix |
| description | triton-cuda常见错误及修复方法,用于代码生成时避免同类问题 |
| category | fix |
| version | 1.0.0 |
| metadata | {"source":"error_fix","case_type":"fix","backend":"cuda","dsl":"triton_cuda"} |
AttributeError: module 'triton.language' has no attribute 'tanh'tl.extra.cuda.libdevice 模块调用tl.math.xxx 或 tl.xxx# 错误:直接使用 tl.tanh
result = 0.5 * x * (1.0 + tl.tanh(inner))
# 正确:通过 libdevice 调用
result = 0.5 * x * (1.0 + tl.extra.cuda.libdevice.tanh(inner))