mindspore.ms_function

mindspore.ms_function(fn=None, input_signature=None, hash_args=None, jit_config=None)

将Python函数编译为一张可调用的MindSpore图。

MindSpore可以在运行时对图进行优化。

参数：

• fn (Function) - 要编译成图的Python函数。默认值：None。

• input_signature (Tensor) - 用于表示输入参数的Tensor。Tensor的shape和dtype将作为函数的输入shape和dtype。默认值：None。

• hash_args (Union[Object, List or Tuple of Objects]) - fn 里面用到的自由变量，比如外部函数或类对象，再次调用时若 hash_args 出现变化会触发重新编译。默认值：None。

• jit_config (JitConfig) - 编译时所使用的JitConfig配置项，详细可参考 mindspore.JitConfig。默认值：None。

Note

• 如果指定了 input_signature ，则 fn 的每个输入都必须是Tensor。并且 fn 的输入参数将不会接受 **kwargs 参数。

返回：

函数，如果 fn 不是None，则返回一个已经将输入 fn 编译成图的可执行函数；如果 fn 为None，则返回一个装饰器。当这个装饰器使用单个 fn 参数进行调用时，等价于 fn 不是None的场景。

支持平台：

Ascend GPU CPU

样例：

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> from mindspore import ms_function
...
>>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32))
>>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32))
...
>>> # create a callable MindSpore graph by calling ms_function
>>> def tensor_add(x, y):
...     z = x + y
...     return z
...
>>> tensor_add_graph = ms_function(fn=tensor_add)
>>> out = tensor_add_graph(x, y)
...
>>> # create a callable MindSpore graph through decorator @ms_function
>>> @ms_function
... def tensor_add_with_dec(x, y):
...     z = x + y
...     return z
...
>>> out = tensor_add_with_dec(x, y)
...
>>> # create a callable MindSpore graph through decorator @ms_function with input_signature parameter
>>> @ms_function(input_signature=(Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)),
...                               Tensor(np.ones([1, 1, 3, 3]).astype(np.float32))))
... def tensor_add_with_sig(x, y):
...     z = x + y
...     return z
...
>>> out = tensor_add_with_sig(x, y)
...
... # Set hash_args as fn, otherwise cache of compiled `closure_fn` will not be reused.
... # While fn differs during calling again, recompilation will be triggered.
>>> def func(x):
...     return ops.exp(x)
...
>>> def closure_fn(x, fn):
...     @ms_function(hash_args=fn)
...     def inner_fn(a):
...         return fn(a)
...     return inner_fn(x)
...
>>> inputs = Tensor(np.ones([10, 10, 10]).astype(np.float32))
>>> for i in range(10):
...     closure_fn(inputs, func)