l0n0lacl 1.0.1

用于调用ascendc编写的算子

1 功能描述

由于在ascendc算子开发过程中运行算子比较复杂，为了简化算子的运行，将运行算子变成可以用python直接调用的函数。所以编写了此代码。

2 安装

pip install l0n0lacl

3 运行算子实例

3.1 先切换到cann环境,比如我的环境是:

source /home/HwHiAiUser/Ascend/ascend-toolkit/set_env.sh

3.2 先安装我们编写的算子

bash custom_opp_xxx_aarch64.run

3.3 创建算子运行器

from l0n0lacl import *
ascendc_gelu = OpRunner("Gelu", op_path_prefix='customize')

3.4 调用算子

3.4.1 先看调用传参顺序

在算子工程编译后，会有代码生成，在算子工程目录: ${算子目录}/build_out/autogen/aclnn_xxx.h中可以找到aclnnXXXGetWorkspaceSize函数。以Gelu为例：

__attribute__((visibility("default")))
aclnnStatus aclnnGeluGetWorkspaceSize(
    const aclTensor *x,
    const aclTensor *out,
    uint64_t *workspaceSize,
    aclOpExecutor **executor);

可以看到参数为 x, out, workspaceSize, executor。其中 workspaceSize, executor不需要管。

• aclTensor*对应numpy.ndarray
• 其他参考: ctypes类型

3.4.2 调用算子

import torch
from l0n0lacl import *
ascendc_gelu = OpRunner("Gelu", op_path_prefix='customize')
target_dtype = torch.float
x = torch.empty(shape, dtype=target_dtype).uniform_(-1, 1)
y = torch.empty(shape, dtype=target_dtype).zero_()
out = ascendc_gelu(x.numpy(), y.numpy()).to_cpu()
print(out)

4. api参考

4.1 AclNDTensor

class AclNDTensor:
    def __init__(self, np_array: np.ndarray):
        pass
    def to_cpu(self):
        pass

numpy ndarray与ascend nd tensor间的桥梁

4.1.1 __init__

• np_array: numpy的tensor

4.1.2 to_cpu

将运算结果从npu拷贝到cpu

4.2 OpRunner

class OpRunner:
    def __init__(self, name, op_path_prefix='customize', op_path=None, device_id=0) -> None:
        pass
    def __call__(self, *args, outCout=1, argtypes=None, stream=None) -> Union[AclNDTensor, List[AclNDTensor]]:
        pass
    def sync_stream(self)->None:
        pass

4.2.1 __init__

• name:算子名称，
• op_path_prefix: 算子工程中CMakePresets.json文件中vender_name的值。默认是customize,可以不传

"vendor_name": {
    "type": "STRING",
    "value": "customize"
},

• op_path: 算子libcust_opapi.so库的绝对位置。不传。
• device_id: 设备ID。默认0

4.2.2 __call__

• args: 表示传给aclnnXXXGetWorkspaceSize除了workspaceSize, executor的参数
• outCout : 表示算子的输出个数。如果输出个数为1,返回一个AclNDTensor。如果输出个数大于1,返回List[AclNDTensor]
• argtypes: 表示aclnnXXXGetWorkspaceSize的参数ctypes参数类型，对于特别复杂的算子，如果发现调用异常，可以手动指定类型。 比如(仅用于举例，其实可以不传，自动推导就可运行。但是当发现运行异常的情况下，可以自己指定)，对于:

__attribute__((visibility("default")))
aclnnStatus aclnnCumsumGetWorkspaceSize(
    const aclTensor *x,
    const aclTensor *axis,
    bool exclusiveOptional,
    bool reverseOptional,
    const aclTensor *out,
    uint64_t *workspaceSize,
    aclOpExecutor **executor);

import ctypes
from l0n0lacl import *
ascendc_cumsum = OpRunner("Cumsum")
target_dtype = np.float32
data_range = (-10, 10)
shape = [100, 3, 2304]
axis_py = 1
exclusive = True
reverse = False
x = np.random.uniform(*data_range, shape).astype(target_dtype)
axis = np.array([axis_py]).astype(np.int32)
golden: np.ndarray = tf.cumsum(x, axis_py, exclusive, reverse, argtypes=[
    ctypes.c_void_p, # x
    ctypes.c_void_p, # axis
    ctypes.c_bool,   # exclusiveOptional
    ctypes.c_bool,   # reverseOptional
    ctypes.c_void_p, # out
    ctypes.c_void_p, # workspaceSize
    ctypes.c_void_p, # executor
]).numpy()
y = np.ones_like(golden, golden.dtype) * 123
ascendc_cumsum(x, axis, exclusive, reverse,  y).to_cpu()
print(y)

• stream 如果是多stream的情况下，可以自己指定stream: 例如:

import numpy as np
from l0n0lacl import *
ascendc_gelu = OpRunner("Gelu", op_path_prefix='customize')
target_dtype = np.float32
shape = [10, 10]
x = np.random.uniform(-10, 10, shape).astype(target_dtype)
y = np.zeros_like(x, dtype=target_dtype)
with AclStream(0) as stream:
    out = ascendc_gelu(x, y, stream=stream).to_cpu()
print(out)

4.2.3 sync_stream

用于同步stream

4.3 verify_result

参考自：https://gitee.com/ascend/samples/blob/master/operator/AddCustomSample/KernelLaunch/AddKernelInvocationNeo/scripts/verify_result.py

def verify_result(real_result:numpy.ndarray, golden:numpy.ndarray):
    pass

判断精度是否符合 float16: 千分之一 float32: 万分之一 int16,int32,int8: 0

4.4 AclArray

class AclArray:
    def __init__(self, np_array: np.ndarray):
        pass

实例：

__attribute__((visibility("default")))
aclnnStatus aclnnEyeGetWorkspaceSize(
    aclTensor *yRef,
    int64_t numRows,
    int64_t numColumnsOptional,
    const aclIntArray *batchShapeOptional,
    int64_t dtypeOptional,
    uint64_t *workspaceSize,
    aclOpExecutor **executor);

import tensorflow as tf
from l0n0lacl import *
ascendc_fn = OpRunner("Eye")
for i, target_dtype in enumerate([np.float16, np.float32]):
    numRows = 2
    numColumnsOptional = 3
    batchShapeOptional = 0
    dtypeOptional = 0
    shape = [numRows * numColumnsOptional]
    for value_range in [(-1, 1), (1, 10), (-1000, 1000)]:
        y = np.zeros(shape, dtype=target_dtype)
        batchShape = AclArray(np.array([1, 2, 3], dtype=np.int64))
        output = ascendc_fn(y, numRows, numColumnsOptional, batchShape, 0, outCout=5)
        output[0].to_cpu()
        golden = tf.eye(numRows, numColumnsOptional)
        print(y)
        print(golden)
        print(value_range)
        verify_result(y, golden.numpy().reshape(shape))