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!42017 [ST][MS][OPS] Update accumulate_n Functional API and ST. 

Merge pull request !42017 from alashkari/update_accumulate_n
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i-robot 2022-09-15 11:13:40 +00:00 committed by Gitee
parent 5056784673 8479a9c0f0
commit 466cb6c7ee
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2 changed files with 9 additions and 11 deletions
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18
mindspore/python/mindspore/ops/function/math_func.py
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@ -5777,20 +5777,18 @@ def remainder(x, y):
return out
def accumulate_n(*x):
def accumulate_n(x):
r"""
Computes accumulation of all input tensors element-wise.
AccumulateNV2 is similar to AddN, but there is a significant difference
among them: AccumulateNV2 will not wait for all of its inputs to be ready
before summing. That is to say, AccumulateNV2 is able to save
memory when inputs are ready at different time since the minimum temporary
storage is proportional to the output size rather than the input size.
AccumulateNV2 is similar to AddN, but there is a significant difference among them: AccumulateNV2 will not wait
for all of its inputs to be ready before summing. That is to say, AccumulateNV2 is able to save memory when inputs
are ready at different time since the minimum temporary storage is proportional to the output size rather than the
input size.
Args:
- **x** (Union(tuple[Tensor], list[Tensor])) - The input tuple or list
is made up of multiple tensors whose dtype is number to be added together.
Each element of tuple or list should have the same shape.
x (Union(tuple[Tensor], list[Tensor])): The input tuple or list is made up of multiple tensors whose dtype is
number to be added together. Each element of tuple or list should have the same shape.
Returns:
Tensor, has the same shape and dtype as each entry of the `x`.
@ -5805,7 +5803,7 @@ def accumulate_n(*x):
Examples:
>>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> y = Tensor(np.array([4, 5, 6]), mindspore.float32)
>>> output = ops.accumulate_n(x, y, x, y)
>>> output = ops.accumulate_n([x, y, x, y])
>>> print(output)
[10. 14. 18.]
"""

2
tests/st/ops/ascend/test_accumulate_nv2.py
View File

@ -26,7 +26,7 @@ def accumulate_n_forward_functional(nptype):
input_x = Tensor(np.array([1, 2, 3]).astype(nptype))
input_y = Tensor(np.array([4, 5, 6]).astype(nptype))
output = F.accumulate_n(input_x, input_y, input_x, input_y)
output = F.accumulate_n([input_x, input_y, input_x, input_y])
expected = np.array([10., 14., 18.])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)