---

name: triton-ascend-example-relu description: "The full Triton Ascend example of the ReLU element by element operator. The standard mode for demonstrating vector 's element by element operation: 1D split, mask border processing, staggering. The code structure of this example can be consulted when generating the elementwise type operator." category: example version: "1.0.0" metadata: backend: ascend dsl: triton_ascend hardware: "Atlas A2, Atlas A3" operator_type: "elementwise" framework: torch

ReLU - Triton Ascend

import torch
import triton
import triton.language as tl


@triton.jit
def relu_kernel(
    x_ptr, y_ptr,
    n_elements,
    BLOCK_SIZE: tl.constexpr, CORE_NUM: tl.constexpr,
):
    pid = tl.program_id(0)
    num_blocks = tl.cdiv(n_elements, BLOCK_SIZE)
    for block_id in range(pid, num_blocks, CORE_NUM):
        offsets = block_id * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
        mask = offsets < n_elements
        x = tl.load(x_ptr + offsets, mask=mask, other=0.0)
        y = tl.maximum(x, 0.0)
        tl.store(y_ptr + offsets, y, 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):
        if not x.is_contiguous():
            x = x.contiguous()
        y = torch.empty_like(x)
        n_elements = x.numel()
        BLOCK_SIZE = 1024
        grid = (self.VEC_CORE_NUM,)
        relu_kernel[grid](x, y, n_elements, BLOCK_SIZE=BLOCK_SIZE, CORE_NUM=self.VEC_CORE_NUM)
        return y