triton-ascend-examples-mindspore

MindSpore + Triton Ascend 示例代码

MindSpore vs PyTorch 差异

特性	PyTorch	MindSpore
基类	torch.nn.Module	mindspore.nn.Cell
前向函数	forward	construct
张量创建	torch.empty	mindspore.ops.zeros 或 numpy
设备	device='cuda'/'npu'	自动管理或 context.set_context
数据类型	torch.float16	mindspore.float16

示例列表

1. Vector Add（向量加法）

MindSpore 实现:

import mindspore as ms
from mindspore import nn
import triton
import triton.language as tl

@triton.jit
def vector_add_kernel(a_ptr, b_ptr, c_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    
    a = tl.load(a_ptr + offsets, mask=mask)
    b = tl.load(b_ptr + offsets, mask=mask)
    c = a + b
    
    tl.store(c_ptr + offsets, c, mask=mask)

class ModelNew(nn.Cell):
    def __init__(self):
        super().__init__()
    
    def construct(self, a, b):
        # 注意：使用 numpy 创建输出张量
        import numpy as np
        c = ms.Tensor(np.empty_like(a.asnumpy()), dtype=a.dtype)
        
        n_elements = a.size
        grid = (triton.cdiv(n_elements, BLOCK_SIZE),)
        vector_add_kernel[grid](a, b, c, n_elements, BLOCK_SIZE=1024)
        return c

2. MatMul（矩阵乘法）

关键差异:

class ModelNew(nn.Cell):
    def __init__(self):
        super().__init__()
    
    def construct(self, x0, x1):  # 注意：使用 construct 而非 forward
        B, C = x0.shape
        C2, D = x1.shape
        assert C == C2, f"矩阵维度不匹配: {C} != {C2}"
        
        # MindSpore 张量创建
        import numpy as np
        output = ms.Tensor(np.empty((B, D), dtype=np.float32))
        
        matmul_kernel[1, 1, 1](output, x0, x1, 1, B, C, D)
        return output

3. Layer Norm（层归一化）

MindSpore 特有处理:

class ModelNew(nn.Cell):
    def __init__(self, normalized_shape, eps=1e-5):
        super().__init__()
        self.eps = eps
        self.normalized_shape = normalized_shape
        
        # MindSpore 参数初始化
        ms.set_seed(0)  # 注意：使用 ms.set_seed 而非 torch.manual_seed
        self.weight = ms.Parameter(ms.ops.ones(normalized_shape, ms.float32))
        self.bias = ms.Parameter(ms.ops.zeros(normalized_shape, ms.float32))
    
    def construct(self, x):
        M, N = x.shape
        import numpy as np
        output = ms.Tensor(np.empty_like(x.asnumpy()), dtype=x.dtype)
        
        grid = (M,)
        layernorm_kernel[grid](
            x, output, self.weight, self.bias,
            N, self.eps, BLOCK_SIZE=triton.next_power_of_2(N)
        )
        return output