[GitHub] [incubator-tvm] tqchen commented on a change in pull request #5289: add tensorflow cumsum

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

tqchen commented on a change in pull request #5289: add tensorflow cumsum
URL: https://github.com/apache/incubator-tvm/pull/5289#discussion_r407139910
 
 

 ##########
 File path: topi/include/topi/transform.h
 ##########
 @@ -1105,6 +1105,88 @@ inline tvm::te::Tensor matmul(const tvm::te::Tensor& A,
   return tvm::te::compute(output_shape, l, name, tag);
 }
 
+/**
+ * Compute the cumulative sum of the tensor `A` along `axis`.
+ *
+ *  By default, this operation performs an inclusive cumsum, which means that the first
+ *  element of the input is identical to the first element of the output:
+ *
+ *  ```python
+ *  cumsum([a, b, c])  # [a, a + b, a + b + c]
+ *  ```
+ *
+ *  By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
+ *  instead:
+ *
+ *  ```python
+ *  cumsum([a, b, c], exclusive=True)  # [0, a, a + b]
+ *  ```
+ *
+ *  By setting the `reverse` kwarg to `True`, the cumsum is performed in the
+ *  opposite direction:
+ *
+ *  ```python
+ *  cumsum([a, b, c], reverse=True)  # [a + b + c, b + c, c]
+ *  ```
+ *
+ *  The `reverse` and `exclusive` kwargs can also be combined:
+ *
+ *  ```python
+ *  cumsum([a, b, c], exclusive=True, reverse=True)  # [b + c, c, 0]
+ *  ```
+ *
+ * @param A             Input tensor
+ * @param axis          Must be in the range `[-rank(x), rank(x))`
+ * @param exclusive     Perform exclusive cumsum
+ * @param reverse       Performed in the opposite direction
+ * @param name          The name of the operation
+ * @param tag           The tag to mark the operation
+ * @return              A Tensor whose op member is the cumsum operation
+ */
+inline tvm::te::Tensor cumsum(const tvm::te::Tensor& A,
+                              int axis,
+                              bool exclusive = false,
+                              bool reverse = false,
+                              std::string name = "T_cumsum",
+                              std::string tag = kCumsum) {
+    int totalSize = static_cast<int>(A->shape.size());
+    if (axis < 0) {
+        axis = totalSize + axis;
+    }
+    auto maxLength = A->shape[axis];
+    auto l = [&](const Array<Var>& input_indices) {
 
 Review comment:
   This way of implementing cumsum would cost additional compute, since we are doing reduction in parallel without reuse, can we use scan instead? https://tvm.apache.org/docs/tutorials/language/scan.html

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