mindspore.ops.Custom

class mindspore.ops.Custom(func, bprop=None, out_dtype=None, func_type='hybrid', out_shape=None, reg_info=None)[源代码]

Custom 算子是MindSpore自定义算子的统一接口。用户可以利用该接口自行定义MindSpore内置算子库尚未包含的算子。根据输入函数的不用，你可以创建多个自定义算子，并且把它们用在神经网络中。关于自定义算子的详细说明和介绍，包括参数的正确书写，见教程。

Warning

这是一个实验性接口，后续可能删除或修改。

Note

不同自定义算子的函数类型（func_type）支持的平台类型不同。每种类型支持的平台如下：

“hybrid”: [“Ascend”, “GPU”, “CPU”].
“akg”: [“Ascend”, “GPU”, “CPU”].
“tbe”: [“Ascend”].
“aot”: [“GPU”, “CPU”].
“pyfunc”: [“CPU”].
“julia”: [“CPU”].
“aicpu”: [“Ascend”].

参数：

func (Union[function, str]) - 自定义算子的函数表达。
out_shape (Union[function, list, tuple]) - 自定义算子的输入的形状或者输出形状的推导函数。默认值：None。
out_dtype (Union[function, mindspore.dtype, tuple[mindspore.dtype]]) - 自定义算子的输入的数据类型或者输出数据类型的推导函数。默认值：None。
func_type (str) - 自定义算子的函数类型，必须是[“hybrid”, “akg”, “tbe”, “aot”, “pyfunc”, “julia”, “aicpu”]中之一。默认值：”hybrid”。
bprop (function) - 自定义算子的反向函数。默认值：None。
reg_info (Union[str, dict, list, tuple]) - 自定义算子的算子注册信息。默认值：None。

输入：

input (Union(tuple, list)) - 输入要计算的Tensor。

输出：

Tensor。自定义算子的计算结果。

异常：

TypeError - 如果输入 func 不合法，或者 func 对应的注册信息类型不对。
ValueError - func_type 的值不在列表内。
ValueError - 算子注册信息不合法，包括支持平台不匹配，算子输入和属性与函数不匹配。

支持平台：: Ascend GPU CPU

样例：

>>> import mindspore.ops as ops
>>> import numpy as np
>>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, ms_kernel
>>> from mindspore.common import dtype as mstype
>>> from mindspore.nn import Cell
>>> input_x = Tensor(np.ones([16, 16]).astype(np.float32))
>>> input_y = Tensor(np.ones([16, 16]).astype(np.float32))
>>>
>>> # Example, func_type = "hybrid"
>>> # This is the default func_type in Custom,
>>> # and both out_shape and out_dtype can be None(default value).
>>> # In this case, the input func must be a function written in the Hybrid DSL
>>> # and decorated by @ms_kernel.
>>> @ms_kernel
... def outer_product_script(a, b):
...     c = output_tensor(a.shape, a.dtype)
...     for i0 in range(a.shape[0]):
...         for i1 in range(b.shape[1]):
...             c[i0, i1] = 0.0
...             for i2 in range(a.shape[1]):
...                 c[i0, i1] = c[i0, i1] + (a[i0, i2] * b[i2, i1])
...     return c
>>>
>>> test_op_hybrid = ops.Custom(outer_product_script)
>>> output = test_op_hybrid(input_x, input_y)
>>> # the result will be a 16 * 16 tensor with all elements 16
>>> print(output.shape)
(16, 16)
>>> # Example, func_type = "akg"
>>> def outer_product(a_1, b_1):
...     d = output_tensor(a_1.shape, a_1.dtype)
...     for i0 in range(a_1.shape[0]):
...         for i1 in range(b_1.shape[1]):
...             d[i0, i1] = 0.0
...             for i2 in range(a.shape[1]):
...                 d[i0, i1] = d[i0, i1] + (a_1[i0, i2] * b_1[i2, i1])
...     return d
>>>
>>> # Example, func_type = "tbe"
>>> square_with_bias_op_info = CustomRegOp() \
...     .fusion_type("OPAQUE") \
...     .attr("bias", "required", "float") \
...     .input(0, "x") \
...     .output(0, "y") \
...     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
...     .dtype_format(DataType.F16_Default, DataType.F16_Default) \
...     .target("Ascend") \
...     .get_op_info()
>>>
>>> @custom_info_register(square_with_bias_op_info)
... def square_with_bias(input_x, output_y, bias=0.0, kernel_name="square_with_bias"):
...     import te.lang.cce
...     from te import tvm
...     from topi.cce import util
...
...     shape = input_x.get("shape")
...     dtype = input_x.get("dtype").lower()
...
...     shape = util.shape_refine(shape)
...     data = tvm.placeholder(shape, name="data", dtype=dtype)
...
...     with tvm.target.cce():
...         res0 = te.lang.cce.vmul(data, data)
...         res = te.lang.cce.vadds(res0, bias)
...         sch = te.lang.cce.auto_schedule(res)
...
...     config = {"print_ir": False,
...               "name": kernel_name,
...               "tensor_list": [data, res]}
...
...     te.lang.cce.cce_build_code(sch, config)
>>>
>>> def test_tbe():
...     square_with_bias = ops.Custom(square_with_bias, out_shape=lambda x, _: x, \
...                                   out_dtype=lambda x, _: x, func_type="tbe")
...     res = self.square_with_bias(input_x, 1.0)
...     return res
>>>
>>> # Example, func_type = "aicpu"
>>> resize_bilinear_op_info = CustomRegOp("ResizeBilinear") \
...     .fusion_type("OPAQUE") \
...     .input(0, "input", "required") \
...     .output(1, "output", "required") \
...     .attr("align_corners", "required", "bool") \
...     .attr("cust_aicpu", "optional", "str", "aicpu_kernels") \
...     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
...     .dtype_format(DataType.F16_Default, DataType.F32_Default) \
...     .target("Ascend") \
...     .get_op_info()
>>>
>>> @custom_info_register(resize_bilinear_op_info)
... def resize_bilinear_aicpu():
...     return
>>>
>>> def test_aicpu(x):
...     resize_bilinear_op = ops.Custom(resize_bilinear_aicpu, out_shape=[1, 1, 9, 9], \
...                                     out_dtype=mstype.float32, func_type="aicpu")
...     res = resize_bilinear_op(x, True, "aicpu_kernels")
...     return res
>>>
>>> # Example, func_type = "aot"
>>> def test_aot(x, y, out_shapes, out_types):
...     program = ops.Custom("./reorganize.so:CustomReorganize", out_shapes, out_types, "aot")
...     out = program(x, y)
...     return out
>>>
>>> # Example, func_type = "pyfunc"
>>> def func_multi_output(x1, x2):
...     return (x1 + x2), (x1 - x2)
>>>
>>> test_pyfunc = ops.Custom(func_multi_output, lambda x, _: (x, x), lambda x, _: (x, x), "pyfunc")
>>> output = test_pyfunc(input_x, input_y)
>>>
>>> # Example, func_type = "julia"
>>> # julia code:
>>> # add.jl
>>> # module Add
>>> # function add(x, y, z)
>>> #   z .= x + y
>>> #   return z
>>> # end
>>> # end
>>> def test_julia(x, y, out_shapes, out_types):
...     program = ops.Custom("./add.jl:Add:add", out_shapes, out_types, "julia")
...     out = program(x, y)
...     return out