triton-ascend-example-layernorm

name	triton-ascend-example-layernorm
description	LayerNorm 归约算子的完整 Triton Ascend 实现示例。展示两阶段归约模式（统计量计算 → 归一化输出）、标量累加器、分块遍历等技巧。当生成 reduce/normalize 类算子时可参考此示例的代码结构。
category	example
version	1.0.0
metadata	{"backend":"ascend","dsl":"triton_ascend","hardware":"Atlas A2, Atlas A3","operator_type":"reduce","framework":"torch"}

LayerNorm — Triton Ascend 实现示例

import torch
import triton
import triton.language as tl


@triton.jit
def layernorm_kernel(
    X_ptr, Y_ptr,
    batch_size: tl.constexpr, feature_size: tl.constexpr,
    eps: tl.constexpr,
    BLOCK_SIZE: tl.constexpr, CORE_NUM: tl.constexpr,
):
    core_id = tl.program_id(0)
    for batch_idx in range(core_id, batch_size, CORE_NUM):
        batch_offset = batch_idx * feature_size

        # Phase 1: compute mean & variance
        mean_acc = 0.0
        var_acc = 0.0
        for i in range(0, feature_size, BLOCK_SIZE):
            offsets = batch_offset + i + tl.arange(0, BLOCK_SIZE)
            mask = offsets < batch_offset + feature_size
            x = tl.load(X_ptr + offsets, mask=mask, other=0.0)
            mean_acc += tl.sum(x, axis=0)
            var_acc += tl.sum(x * x, axis=0)

        mean_val = mean_acc / feature_size
        std_val = tl.sqrt(var_acc / feature_size - mean_val * mean_val + eps)

        # Phase 2: normalize
        for i in range(0, feature_size, BLOCK_SIZE):
            offsets = batch_offset + i + tl.arange(0, BLOCK_SIZE)
            mask = offsets < batch_offset + feature_size
            x = tl.load(X_ptr + offsets, mask=mask, other=0.0)
            tl.store(Y_ptr + offsets, (x - mean_val) / std_val, mask=mask)


class ModelNew(torch.nn.Module):
    def __init__(self):
        super().__init__()
        try:
            import torch
            import triton
            device = torch.npu.current_device()
            properties = triton.runtime.driver.active.utils.get_device_properties(device)
            self.VEC_CORE_NUM = properties.get("num_vectorcore", 40)
        except:
            self.VEC_CORE_NUM = 40

    def forward(self, x):
        shape = x.shape
        batch_size = shape[0]
        feature_size = 1
        for s in shape[1:]:
            feature_size *= s
        if not x.is_contiguous():
            x = x.contiguous()
        y = torch.empty_like(x)
        BLOCK_SIZE = 1024
        grid = (self.VEC_CORE_NUM,)
        layernorm_kernel[grid](x, y, batch_size, feature_size, 1e-5,
                               BLOCK_SIZE=BLOCK_SIZE, CORE_NUM=self.VEC_CORE_NUM)
        return y