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"""Defines vmap function."""
from mindspore.ops.composite import _Vmap
from mindspore._c_expression import VmapGeneralPreprocess_, VmapGeneralRulePyAdapter_
__all__ = ['vmap']
vmap_instance = _Vmap()
[文档]def vmap(fn, in_axes=0, out_axes=0):
r"""
Vectorizing map (vmap) is a kind of higher-order function to map `fn` along the parameter axes.
Vmap is pioneered by Jax and it removes the restriction of batch dimension on the operator, and provides a
more convenient and unified operator expression. Moreover, it allows users to composite with other functional
modules such as :func:`mindspore.grad`, to improve the development efficiency, please refer to the
`Automatic Vectorization (Vmap) <https://www.mindspore.cn/tutorials/experts/en/r2.2/vmap/vmap.html>`_ tutorial
for more detail. In addition, the vectorizing map does not execute loops outside the function, but sinks loops
into the primitive operations of the function for better performance. When combined with `Graph Kernel Fusion`,
operational efficiency would be further improved.
.. warning::
This is an experimental API that is subject to change or deletion.
Note:
1. The power of vmap comes from the implementation of VmapRules of primitives. Although we have designed a
generalized rule for user custom operators, we can not guarantee that it works well for all operators,
unknown exceptions may occur, please be aware the risk of use.
2. When calling the random number generation methods within the scope of vmap, the same random number is
generated among vector functions each time. If you expect each vector branch to use different random numbers,
you need to generate batch random numbers externally in advance and then transfer them to vmap.
Args:
fn (Union[Cell, Function, CellList]): Function to be mapped along the parameter axes, which takes at least one
argument and returns one or more Tensors or the type of data supported by the MindSpore Tensor. When it is
a CellList, the model ensembling scenario, please make sure that the structure of each cell is the same
and the number of cells is consistent with the sizes of the mapped axes (`axis_size`).
in_axes (Union[int, list, tuple]): Specifies which dimensions (axes) of the inputs should be mapped over.
If `in_axes` is an integer, all arguments of `fn` are mapped over according to this axis index. If `in_axes`
is a tuple or list, which only composed of integers or Nones and the length should equal to the number of
positional arguments to `fn`, indicates which axis to map for each corresponding positional argument.
Note that, axis integers must be in range :math:`[-ndim, ndim)` for each argument, where `ndim` is the
number of dimensions of the corresponding argument. None means not mapping along any axis. Also the
mapping axis index of the `in_axes` must have at least one positional parameter not None. The sizes of
the mapped axes (`axis_size`) for all arguments must be equal. Default: ``0`` .
out_axes (Union[int, list, tuple]): Specifies where the mapped dimensions (axes) should appear in the
outputs. If `out_axes` is an integer, all outputs of `fn` are specified according to this axis. If
`out_axes` is a tuple or list, which only composed of integers or Nones. And its length also should be equal
to the number of outputs of `fn`. Note that, axis integers must be in range :math:`[-ndim, ndim)` for each
output, where `ndim` is the dimension of the output of the `vmap`-mapped function. All outputs with a
non-None mapped axis must specify a non-None `out_axes`, and if outputs with None mapped axis specifies
a non-None `out_axes`, the result broadcasts across the mapped axis. Default: ``0`` .
Returns:
Function, returns the Vectorized/Batched version function of `fn`. The arguments and outputs of this function
correspond to those of `fn`, but it adds an extra batch dimension at positions specified by `in_axes` and
`out_axes`.
Raises:
RuntimeError: If base elements in `in_axes` or `out_axes` are not a None or an integer.
If the all base elements in `in_axes` or `out_axes` are None.
If `in_axes` is not single integer, and the length of `in_axes` is not equal to the arguments sizes.
If `out_axes` is not single integer, and the length of `out_axes` is not equal to the outputs sizes.
If the `axis_size` of each arguments in the scope of `vmap` are not equal.
If the axis in `in_axes` or `out_axes` is out of bounds.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor
>>> from mindspore import vmap
>>> def test_vmap(x, y, z): # ([a],[a],[a]) -> [a]
... return x + y + z
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]).astype(np.float32)) # [b, a]
>>> y = Tensor(np.array([[-3, -2, -1], [3, 2, 1]]).astype(np.float32)) # [a, b]
>>> z = Tensor(np.array([0, 3]).astype(np.float32)) # [a]
>>> output = vmap(test_vmap, in_axes=(0, 1, None), out_axes=1)(x, y, z) # ([b, a],[a, b],[a]) -> [a, b]
>>> print(output)
[[-2 1 4]
[ 8 9 10]]
"""
return vmap_instance(fn, in_axes, out_axes)
class _VmapGeneralPreprocess(VmapGeneralPreprocess_):
"""
General preprocessing of VmapRules. If the source axes of all inputs are `None`,
means that vectorization is not performed, taking out the original input and call
the primitive directly.
"""
def __init__(self):
VmapGeneralPreprocess_.__init__(self, "VmapGeneralPreprocess")
class _VmapGeneralRule(VmapGeneralRulePyAdapter_):
"""
General rule python adapter is a adapter for general rule in c++. Some operators can
implement loop-stack method in their vmaprule by calling this adapter.
"""
def __init__(self, prim, axis_size):
VmapGeneralRulePyAdapter_.__init__(self, 'vmapgeneralrule', prim, axis_size)