[GitHub] [tvm] mbrookhart commented on a change in pull request #7722: [Topi, Relay] Add cumprod

[GitBox <gi...@apache.org>]

mbrookhart commented on a change in pull request #7722:
URL: https://github.com/apache/tvm/pull/7722#discussion_r600830424



##########
File path: python/tvm/topi/cuda/scan.py
##########
@@ -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:
       I don't think numpy has an exclusive option? Do we need this? https://numpy.org/doc/stable/reference/generated/numpy.cumprod.html




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