masahi commented on a change in pull request #6978:
URL: https://github.com/apache/tvm/pull/6978#discussion_r544676738
##########
File path: python/tvm/topi/cuda/sort.py
##########
@@ -316,6 +316,89 @@ def argsort_nms_thrust(data, valid_count, axis=-1,
is_ascend=1, dtype="float32")
return out[1]
+def sort(data, axis=-1, is_ascend=1):
+ """Performs sorting along the given axis and returns an array of
+ sorted values with teh same shape as the input data.
+
+ Parameters
+ ----------
+ data: tvm.te.Tensor
+ The input array.
+
+ axis : int, optional
+ Axis long which to sort the input tensor.
+
+ is_ascend : boolean, optional
+ Whether to sort in ascending or descending order.
+
+ Returns
+ -------
+ out : tvm.te.Tensor
+ The output of this function.
+ """
+ dtype = "float32"
+ value_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "value_buf",
data_alignment=8)
+ indices_buf = tvm.tir.decl_buffer(data.shape, dtype, "out_buf",
data_alignment=8)
+ out = te.extern(
+ [data.shape, data.shape],
+ [data],
+ lambda ins, outs: sort_ir(ins[0], outs[0], axis, is_ascend,
indices_out=outs[1]),
+ out_buffers=[value_buf, indices_buf],
+ name="sort_gpu",
+ tag="sort_gpu",
+ )[0]
+ return out
+
+
+def sort_thrust(data, axis=-1, is_ascend=1):
+ """Performs sorting along the given axis and returns an array of indicies
+ having same shape as an input array that index data in sorted order.
+
+ Parameters
+ ----------
+ data: tvm.te.Tensor
+ The input array.
+
+ axis : int, optional
+ Axis long which to sort the input tensor.
+
+ is_ascend : boolean, optional
+ Whether to sort in ascending or descending order.
+
+ Returns
+ -------
+ out : tvm.te.Tensor
+ The output of this function.
+ """
+ dtype = "float32"
+
+ ndim = len(data.shape)
+ axis = ndim + axis if axis < 0 else axis
+
+ if axis != ndim - 1:
+ # Prepare for sorting along axis -1.
+ axes = swap(list(range(ndim)), axis)
+ data = transpose(data, axes)
+
+ value_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "value_buf",
data_alignment=8)
+ indices_buf = tvm.tir.decl_buffer(data.shape, dtype, "out_buf",
data_alignment=8)
+ out = te.extern(
+ [data.shape, data.shape],
+ [data],
+ lambda ins, outs: tvm.tir.call_packed(
+ "tvm.contrib.thrust.sort", ins[0], outs[0], outs[1], is_ascend
+ ),
+ out_buffers=[value_buf, indices_buf],
Review comment:
Yeah that would be nice
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