AndrewZhaoLuo commented on a change in pull request #7722:
URL: https://github.com/apache/tvm/pull/7722#discussion_r600860418
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File path: python/tvm/topi/cuda/scan.py
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@@ -514,10 +616,54 @@ def cumsum(data, axis=None, dtype=None, exclusive=None):
The result has the same size as data, and the same shape as data if
axis is not None.
If axis is None, the result is a 1-d array.
"""
- if axis is None:
- axis = 0
- data = reshape(data, (prod(data.shape),))
- axis = get_const_int(axis)
- if exclusive is not None and exclusive != 0:
- return exclusive_scan(data, axis, output_dtype=dtype,
binop=tvm.tir.generic.add)
- return inclusive_scan(data, axis, output_dtype=dtype,
binop=tvm.tir.generic.add)
+ return scanop(
+ data=data,
+ binop=tvm.tir.generic.add,
+ identity_value=0,
+ axis=axis,
+ dtype=dtype,
+ exclusive=exclusive,
+ )
+
+
+def cumprod(
+ data: tvm.te.Tensor,
+ axis: Optional[int] = None,
+ dtype: Optional[int] = None,
+ exclusive: Optional[bool] = None,
+):
+ """Numpy style cumprod op. Return the cumulative product of the elements
along a given axis.
+
+ Parameters
+ ----------
+ data : tvm.te.Tensor
+ The input data to the operator.
+
+ axis : int, optional
+ Axis along which the cumulative product is computed. The default
(None) is to compute
+ the cumproduct over the flattened array.
+
+ dtype : string, optional
+ Type of the returned array and of the accumulator in which the
elements are multiplied.
+ If dtype is not specified, it defaults to the dtype of data.
+
+ exclusive : bool, optional
+ If True, will return exclusive product in which the first element is
not
+ included. In other terms, if True, the j-th output element would be
+ the product of the first (j-1) elements. Otherwise, it would be the
product of
+ the first j elements.
Review comment:
Well we might as well keep cumprod's exclusive option since it has very
little implementation overhead. The only thing that is there to support it is
the `identity_value` fields which are pretty simple. I say we might as well
keep it at this point.
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