1.【高性能计算】CANN并行张量计算新范式：pypto编程框架深度解析

一、项目简介

pypto（PyPTO，发音: pai p-t-o）是CANN推出的Parallel Tensor/Tile Operation（并行张量/分块操作）编程范式。作为一种创新的编程模型，pypto旨在简化NPU上的高性能并行计算开发，为开发者提供一种更直观、更高效的张量计算编程方式。

随着深度学习模型规模的不断增长，传统的逐元素计算方式已经无法满足性能需求。pypto通过引入分块（Tile）操作和并行计算的概念，让开发者能够以更自然的方式表达张量计算，同时充分发挥NPU硬件的并行计算能力。这种编程范式特别适合于矩阵运算、卷积操作等需要大量并行计算的场景。

相关链接：

• CANN组织链接：https://atomgit.com/cann
• pypto仓库链接：https://atomgit.com/cann/pypto

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二、核心功能与特性

2.1 PyPTO编程范式特点

特性	描述	优势
分块操作	将大张量自动分块处理	适应有限的Local Memory
并行计算	多核并行执行分块任务	充分利用AI Core资源
抽象层次高	隐藏底层硬件细节	降低编程复杂度
类型推导	自动推导张量形状和类型	减少样板代码
性能优化	内置性能优化策略	开箱即用的高性能

2.2 核心概念

1. Tile（分块）：将大张量切分为适合Local Memory的小块
2. Parallel（并行）：在多个AI Core上并行执行计算
3. Operation（操作）：定义在Tile上的计算操作
4. Reduction（归约）：跨Tile的数据聚合操作

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三、环境准备

3.1 系统要求

• 操作系统：Ubuntu 20.04/22.04
• 处理器：Atlas 300T/800T系列
• CANN版本：CANN 8.0.RC3及以上
• Python版本：3.8-3.10
• 编译器：GCC 9.0+ / Clang 12.0+

3.2 安装步骤

# 克隆pypto仓库
git clone https://atomgit.com/cann/pypto.git
cd pypto

# 安装Python依赖
pip install -r requirements.txt

# 编译安装
mkdir build && cd build
cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DCANN_INSTALL_PATH=/usr/local/Ascend \
    -DBUILD_PYTHON_BINDINGS=ON
make -j$(nproc)
make install

# 验证安装
python3 -c "import pypto; print('pypto installed successfully')"

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四、基础编程示例

4.1 简单的矩阵加法

import pypto as pt
import numpy as np

# 定义张量形状
M, N = 1024, 1024

# 创建输入张量
a = pt.Tensor((M, N), dtype=pt.float32)
b = pt.Tensor((M, N), dtype=pt.float32)
c = pt.Tensor((M, N), dtype=pt.float32)

# 填充数据
a_data = np.random.rand(M, N).astype(np.float32)
b_data = np.random.rand(M, N).astype(np.float32)

a.from_numpy(a_data)
b.from_numpy(b_data)

# 定义分块大小
tile_size = (64, 64)

# 使用pypto进行并行加法
@pt.pto_kernel
def matrix_add(a: pt.Tile, b: pt.Tile, c: pt.Tile):
    """
    矩阵加法核函数
    Args:
        a: 输入Tile A
        b: 输入Tile B
        c: 输出Tile C
    """
    # 遍历Tile中的每个元素
    for i in pt.range(a.shape[0]):
        for j in pt.range(a.shape[1]):
            c[i, j] = a[i, j] + b[i, j]

# 执行并行计算
pt.execute(matrix_add, a, b, c,
          tile_size=tile_size,
          parallel=True)

# 获取结果
result = c.to_numpy()
print("Addition completed. Result shape:", result.shape)

4.2 矩阵乘法

import pypto as pt
import numpy as np

# 定义矩阵维度
M, K, N = 512, 512, 512

# 创建输入张量
A = pt.Tensor((M, K), dtype=pt.float32)
B = pt.Tensor((K, N), dtype=pt.float32)
C = pt.Tensor((M, N), dtype=pt.float32)

# 填充数据
A_data = np.random.rand(M, K).astype(np.float32)
B_data = np.random.rand(K, N).astype(np.float32)

A.from_numpy(A_data)
B.from_numpy(B_data)

# 定义分块矩阵乘法
@pt.pto_kernel
def matmul_tile(A_tile: pt.Tile, B_tile: pt.Tile,
                C_tile: pt.Tile, K_inner: int):
    """
    分块矩阵乘法
    C_tile = A_tile x B_tile
    """
    # 定义累加器
    acc = pt.zeros((A_tile.shape[0], B_tile.shape[1]), dtype=pt.float32)

    # K维循环
    for k in pt.range(K_inner):
        # 计算部分积
        for i in pt.range(A_tile.shape[0]):
            for j in pt.range(B_tile.shape[1]):
                acc[i, j] += A_tile[i, k] * B_tile[k, j]

    # 写回结果
    for i in pt.range(C_tile.shape[0]):
        for j in pt.range(C_tile.shape[1]):
            C_tile[i, j] = acc[i, j]

# 设置分块参数
tile_m, tile_k, tile_n = 64, 64, 64
K_blocks = (K + tile_k - 1) // tile_k

# 执行分块矩阵乘法
for kb in range(K_blocks):
    k_start = kb * tile_k
    k_end = min(k_start + tile_k, K)
    k_size = k_end - k_start

    # 提取K维分块
    A_block = A[:, k_start:k_end]
    B_block = B[k_start:k_end, :]

    # 执行计算
    pt.execute(matmul_tile, A_block, B_block, C,
              tile_size=(tile_m, tile_n),
              kwargs={'K_inner': k_size},
              accumulate=(kb > 0))  # 第一次不清零，后续累加

result = C.to_numpy()

# 验证结果
expected = np.dot(A_data, B_data)
print("Matrix multiplication error:", np.max(np.abs(result - expected)))

4.3 卷积操作

import pypto as pt
import numpy as np

# 定义卷积参数
batch_size = 8
in_channels = 64
out_channels = 128
height, width = 32, 32
kernel_size = 3
padding = 1
stride = 1

# 创建输入和权重张量
input_tensor = pt.Tensor((batch_size, in_channels, height, width), dtype=pt.float32)
weight = pt.Tensor((out_channels, in_channels, kernel_size, kernel_size), dtype=pt.float32)
output = pt.Tensor((batch_size, out_channels, height, width), dtype=pt.float32)

# 填充随机数据
input_data = np.random.randn(batch_size, in_channels, height, width).astype(np.float32)
weight_data = np.random.randn(out_channels, in_channels, kernel_size, kernel_size).astype(np.float32) * 0.1

input_tensor.from_numpy(input_data)
weight.from_numpy(weight_data)

# 定义2D卷积核函数
@pt.pto_kernel
def conv2d_tile(input_tile: pt.Tile, weight_tile: pt.Tile,
                output_tile: pt.Tile, params: dict):
    """
    2D卷积操作
    """
    batch, in_ch, h, w = input_tile.shape
    out_ch = weight_tile.shape[0]
    kernel_size = weight_tile.shape[2]

    # 对每个输出通道
    for oc in pt.range(out_ch):
        # 对每个batch
        for b in pt.range(batch):
            # 对每个输出位置
            for oh in pt.range(h):
                for ow in pt.range(w):
                    acc = 0.0
                    # 对每个输入通道
                    for ic in pt.range(in_ch):
                        # 对每个kernel位置
                        for kh in pt.range(kernel_size):
                            for kw in pt.range(kernel_size):
                                ih = oh + kh - params['padding']
                                iw = ow + kw - params['padding']

                                # 边界检查
                                if 0 <= ih < h and 0 <= iw < w:
                                    acc += input_tile[b, ic, ih, iw] * weight_tile[oc, ic, kh, kw]

                    output_tile[b, oc, oh, ow] = acc

# 设置卷积参数
conv_params = {
    'kernel_size': kernel_size,
    'padding': padding,
    'stride': stride
}

# 设置分块大小
tile_batch = 2
tile_out_ch = 32
tile_h = 16
tile_w = 16

# 执行卷积
pt.execute(conv2d_tile, input_tensor, weight, output,
          tile_size=(tile_batch, tile_out_ch, tile_h, tile_w),
          kwargs={'params': conv_params},
          parallel=True)

result = output.to_numpy()
print("Convolution output shape:", result.shape)

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五、高级特性示例

5.1 Reduce操作（归约）

import pypto as pt
import numpy as np

# 创建输入张量
input_tensor = pt.Tensor((1024, 1024), dtype=pt.float32)
sum_result = pt.Tensor((1,), dtype=pt.float32)
max_result = pt.Tensor((1,), dtype=pt.float32)

input_data = np.random.rand(1024, 1024).astype(np.float32)
input_tensor.from_numpy(input_data)

# 定义归约核函数
@pt.pto_kernel
def reduce_ops(input_tile: pt.Tile, sum_tile: pt.Tile,
               max_tile: pt.Tile, op_type: str):
    """
    归约操作：求和或求最大值
    """
    if op_type == 'sum':
        acc = 0.0
        for i in pt.range(input_tile.shape[0]):
            for j in pt.range(input_tile.shape[1]):
                acc += input_tile[i, j]
        sum_tile[0] = acc

    elif op_type == 'max':
        acc = -float('inf')
        for i in pt.range(input_tile.shape[0]):
            for j in pt.range(input_tile.shape[1]):
                val = input_tile[i, j]
                if val > acc:
                    acc = val
        max_tile[0] = acc

# 执行求和归约
pt.execute(reduce_ops, input_tensor, sum_result, max_result,
          tile_size=(256, 256),
          kwargs={'op_type': 'sum'},
          reduce_op='sum')

# 执行求最大值归约
pt.execute(reduce_ops, input_tensor, sum_result, max_result,
          tile_size=(256, 256),
          kwargs={'op_type': 'max'},
          reduce_op='max')

sum_val = sum_result.to_numpy()[0]
max_val = max_result.to_numpy()[0]

print(f"Sum: {sum_val}, Max: {max_val}")
print(f"Expected Sum: {np.sum(input_data)}, Expected Max: {np.max(input_data)}")

5.2 逐元素操作

import pypto as pt
import numpy as np

# 创建输入张量
input_tensor = pt.Tensor((512, 512), dtype=pt.float32)
relu_output = pt.Tensor((512, 512), dtype=pt.float32)
sigmoid_output = pt.Tensor((512, 512), dtype=pt.float32)

input_data = np.random.randn(512, 512).astype(np.float32)
input_tensor.from_numpy(input_data)

# 定义ReLU激活函数
@pt.pto_kernel
def relu_kernel(input_tile: pt.Tile, output_tile: pt.Tile):
    """ReLU激活函数：max(0, x)"""
    for i in pt.range(input_tile.shape[0]):
        for j in pt.range(input_tile.shape[1]):
            val = input_tile[i, j]
            output_tile[i, j] = pt.max(0.0, val)

# 定义Sigmoid激活函数
@pt.pto_kernel
def sigmoid_kernel(input_tile: pt.Tile, output_tile: pt.Tile):
    """Sigmoid激活函数：1 / (1 + exp(-x))"""
    for i in pt.range(input_tile.shape[0]):
        for j in pt.range(input_tile.shape[1]):
            val = input_tile[i, j]
            output_tile[i, j] = 1.0 / (1.0 + pt.exp(-val))

# 执行ReLU
pt.execute(relu_kernel, input_tensor, relu_output,
          tile_size=(128, 128), parallel=True)

# 执行Sigmoid
pt.execute(sigmoid_kernel, input_tensor, sigmoid_output,
          tile_size=(128, 128), parallel=True)

relu_result = relu_output.to_numpy()
sigmoid_result = sigmoid_output.to_numpy()

print("ReLU completed, min:", relu_result.min(), "max:", relu_result.max())
print("Sigmoid completed, min:", sigmoid_result.min(), "max:", sigmoid_result.max())

5.3 转置操作

import pypto as pt
import numpy as np

# 创建输入张量
input_tensor = pt.Tensor((256, 512), dtype=pt.float32)
output_tensor = pt.Tensor((512, 256), dtype=pt.float32)

input_data = np.random.rand(256, 512).astype(np.float32)
input_tensor.from_numpy(input_data)

# 定义转置核函数
@pt.pto_kernel
def transpose_kernel(input_tile: pt.Tile, output_tile: pt.Tile):
    """
    矩阵转置
    """
    in_rows, in_cols = input_tile.shape

    for i in pt.range(in_rows):
        for j in pt.range(in_cols):
            output_tile[j, i] = input_tile[i, j]

# 执行转置
pt.execute(transpose_kernel, input_tensor, output_tensor,
          tile_size=(64, 64), parallel=True)

result = output_tensor.to_numpy()
expected = input_data.T

print("Transpose error:", np.max(np.abs(result - expected)))

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六、性能优化技巧

6.1 自动分块优化

import pypto as pt

# 启用自动分块优化
pt_config = pt.PTOConfig(
    auto_tile=True,              # 自动计算最优分块大小
    tile_alignment=64,           # 分块对齐
    use_cache=True,              # 使用分块缓存
    parallel='auto'              # 自动选择并行策略
)

# 使用优化配置执行
@pt.pto_kernel(config=pt_config)
def optimized_kernel(input_tile: pt.Tile, output_tile: pt.Tile):
    """使用自动优化的核函数"""
    for i in pt.range(input_tile.shape[0]):
        for j in pt.range(input_tile.shape[1]):
            output_tile[i, j] = input_tile[i, j] * 2.0

# 执行
pt.execute(optimized_kernel, input_tensor, output_tensor)

6.2 融合操作

import pypto as pt

# 定义融合操作：Add + ReLU
@pt.pto_kernel
def fused_add_relu(a: pt.Tile, b: pt.Tile, c: pt.Tile):
    """
    融合加法和ReLU操作
    c = relu(a + b)
    """
    for i in pt.range(a.shape[0]):
        for j in pt.range(a.shape[1]):
            val = a[i, j] + b[i, j]
            c[i, j] = pt.max(0.0, val)

# 执行融合操作
pt.execute(fused_add_relu, tensor_a, tensor_b, output_c,
          tile_size=(128, 128),
          fuse_ops=True)  # 启用算子融合

6.3 流水线并行

import pypto as pt

# 定义流水线操作
pipeline = pt.Pipeline()

# 添加计算阶段
@pipeline.add_stage('load')
def load_data(input_tile: pt.Tile):
    """加载数据阶段"""
    return input_tile

@pipeline.add_stage('compute', depends_on=['load'])
def compute(data: pt.Tile):
    """计算阶段"""
    return data * 2.0

@pipeline.add_stage('store', depends_on=['compute'])
def store_data(result: pt.Tile, output_tile: pt.Tile):
    """存储数据阶段"""
    output_tile[:] = result

# 执行流水线
pipeline.execute(input_tensor, output_tensor,
                tile_size=(128, 128),
                num_stages=3)

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七、实际应用场景

7.1 图像处理流水线

import pypto as pt
import cv2
import numpy as np

# 读取图像
image = cv2.imread('input.jpg')
image_tensor = pt.Tensor.from_numpy(image.astype(np.float32) / 255.0)

# 定义图像处理流水线
@pt.pto_kernel
def rgb_to_gray(input_tile: pt.Tile, output_tile: pt.Tile):
    """RGB转灰度"""
    for i in pt.range(input_tile.shape[0]):
        for j in pt.range(input_tile.shape[1]):
            r = input_tile[i, j, 0]
            g = input_tile[i, j, 1]
            b = input_tile[i, j, 2]
            gray = 0.299 * r + 0.587 * g + 0.114 * b
            output_tile[i, j] = gray

@pt.pto_kernel
def gaussian_blur(input_tile: pt.Tile, output_tile: pt.Tile, kernel: list):
    """高斯模糊"""
    kh, kw = 3, 3
    for i in pt.range(1, input_tile.shape[0] - 1):
        for j in pt.range(1, input_tile.shape[1] - 1):
            acc = 0.0
            for ki in pt.range(kh):
                for kj in pt.range(kw):
                    ni = i + ki - 1
                    nj = j + kj - 1
                    acc += input_tile[ni, nj] * kernel[ki * kw + kj]
            output_tile[i, j] = acc

# 创建输出张量
gray_tensor = pt.Tensor((image.shape[0], image.shape[1]), dtype=pt.float32)
blur_tensor = pt.Tensor((image.shape[0], image.shape[1]), dtype=pt.float32)

# 执行图像处理
pt.execute(rgb_to_gray, image_tensor, gray_tensor,
          tile_size=(256, 256), parallel=True)

# 高斯核
gaussian_kernel = [1/16, 2/16, 1/16,
                   2/16, 4/16, 2/16,
                   1/16, 2/16, 1/16]

pt.execute(gaussian_blur, gray_tensor, blur_tensor,
          tile_size=(128, 128),
          kwargs={'kernel': gaussian_kernel})

# 获取结果
result = blur_tensor.to_numpy()
cv2.imwrite('output.jpg', (result * 255).astype(np.uint8))

7.2 批处理推理

import pypto as pt

# 批处理矩阵乘法
def batch_matmul(A_list, B_list):
    """
    批量矩阵乘法
    Args:
        A_list: 矩阵A列表
        B_list: 矩阵B列表
    Returns:
        结果列表
    """
    batch_size = len(A_list)
    results = []

    # 创建批处理张量
    batch_A = pt.Tensor((batch_size, *A_list[0].shape), dtype=pt.float32)
    batch_B = pt.Tensor((batch_size, *B_list[0].shape), dtype=pt.float32)
    batch_C = pt.Tensor((batch_size, A_list[0].shape[0], B_list[0].shape[1]), dtype=pt.float32)

    # 填充数据
    for i, (A, B) in enumerate(zip(A_list, B_list)):
        batch_A[i].from_numpy(A)
        batch_B[i].from_numpy(B)

    # 执行批量矩阵乘法
    @pt.pto_kernel
    def batch_matmul_kernel(A_batch: pt.Tile, B_batch: pt.Tile, C_batch: pt.Tile):
        for b in pt.range(A_batch.shape[0]):
            for i in pt.range(A_batch.shape[1]):
                for j in pt.range(B_batch.shape[2]):
                    acc = 0.0
                    for k in pt.range(A_batch.shape[2]):
                        acc += A_batch[b, i, k] * B_batch[b, k, j]
                    C_batch[b, i, j] = acc

    pt.execute(batch_matmul_kernel, batch_A, batch_B, batch_C,
              tile_size=(4, 64, 64), parallel=True)

    # 获取结果
    for i in range(batch_size):
        results.append(batch_C[i].to_numpy())

    return results

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八、性能优化建议

1. 合理选择分块大小：根据Local Memory大小和数据局部性选择合适的分块
2. 利用向量化：使用pypto提供的向量化操作替代循环
3. 减少内存访问：尽量重用已加载的数据
4. 使用融合操作：将多个操作融合为一个核函数
5. 启用自动优化：利用pypto的自动优化功能

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九、总结

pypto作为CANN生态系统中的创新编程范式，为开发者提供了一种更直观、更高效的张量计算编程方式。通过分块操作和并行计算的抽象，pypto大大降低了NPU编程的复杂度，同时保持了接近底层编程的性能。本文通过丰富的示例代码展示了pypto的核心功能和高级特性，帮助开发者快速掌握这一强大的编程工具。

相关链接：

• CANN组织链接：https://atomgit.com/cann
• pypto仓库链接：https://atomgit.com/cann/pypto