mindspore.ops.ms_kernel
- mindspore.ops.ms_kernel(fn=None, reg_info=None, compile_attrs=None)[源代码]
用于MindSpore Hybrid DSL函数书写的装饰器。 给用MindSpore Hybrid DSL书写的函数加上此装饰器后,它可以用作一个普通的Python函数。 与此同时,他可以用于自定义算子
mindspore.ops.Custom
的输入,其对应的 func_type 可以设置为 hybrid 或者 pyfunc 。 使用 hybrid 类型的mindspore.ops.Custom
自定义算子可以自动推导数据类型和形状。参数:
fn (Function) - 将被作为自定义算子运行的Python函数。默认值:None。
reg_info (tuple[str, dict]) - 包含算子注册信息的dict或json字符串。默认值:None。
compile_attrs (Dict) - 算子编译信息。默认值:None。
返回:
Function,如果 fn 不是None,那么返回一个用Hybrid DSL写的可执行函数;如果 fn 是None,则返回一个装饰器,该装饰器只有 fn 一个参数。
- 支持平台:
Ascend
GPU
CPU
样例:
>>> import numpy as np >>> from mindspore import ops, Tensor >>> from mindspore.ops import ms_kernel, DataType, CustomRegOp ... >>> # Create a dict for the compile flags. >>> attrs = { ... "test1": True, ... "test2": "good", ... "test3": 12, ... } >>> # Create the reg info json string. >>> op_gpu_info = CustomRegOp() \ ... .input(0, "a") \ ... .input(0, "b") \ ... .output(0, "y") \ ... .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None) \ ... .target("GPU") \ ... .get_op_info() >>> >>> # Create inputs for the custom op. >>> input_x = np.ones([4, 4]).astype(np.float32) >>> input_y = np.ones([4, 4]).astype(np.float32) ... >>> # Write a Hybrid DSL function through the decorator @ms_kernel. >>> # We can also pass the compile attrs and the reg info through the decorator. >>> @ms_kernel(reg_info=op_gpu_info, compile_attrs=attrs) ... def outer_product(a, b): ... c = output_tensor(a.shape, a.dtype) ... ... with block_realize(c): ... 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 ... >>> # We can use the function directly as a python function. >>> # In this case, the inputs should be numpy arrays. >>> result = outer_product(input_x, input_y) ... >>> # Create a custom op with mode "hybrid" (default value) by the Hybrid DSL function. >>> # In this case, we will enjoy the automatic dtype/shape infer for free. >>> # The inputs should be mindspore tensors. >>> test_op_hybrid = ops.Custom(outer_product) >>> output = test_op_hybrid(Tensor(input_x), Tensor(input_y))