icemelon9 commented on a change in pull request #5459:
URL: https://github.com/apache/incubator-tvm/pull/5459#discussion_r425950082
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -47,6 +47,66 @@ namespace tvm {
namespace relay {
using tir::IntImmNode;
+int64_t* ToVector(const runtime::NDArray& array) {
+ size_t len = array.Shape().front();
+ int64_t* rel_vec = new int64_t[len];
Review comment:
Also consider move this function and [similar
one](https://github.com/apache/incubator-tvm/blob/master/src/relay/op/tensor/transform.cc#L450)
to some util.h so that others can reuse.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -944,7 +1005,21 @@ bool FullRel(const Array<Type>& types, int num_inputs,
const Attrs& attrs,
CHECK_EQ(fill_value->shape.size(), 0)
<< "Fill value should be a scalar but has dimension " <<
fill_value->shape.size() << ".";
- reporter->Assign(types[1], TensorType(param->shape, out_dtype));
+ const IntImmNode* shape_shape = fill_shape->shape[0].as<IntImmNode>();
+ CHECK(shape_shape) << "Parameter shape must have static shape";
+
+ std::vector<IndexExpr> oshape;
+ if (param->shape) {
+ const Array<Integer>& cshape_array = param->shape.value();
+ for (size_t i = 0; i < cshape_array.size(); ++i) {
+ oshape.push_back(cshape_array[i]);
+ }
Review comment:
I think you can merge line 1013-1016 to `ToVector` function since all
the use cases need `Array`.
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -47,6 +47,66 @@ namespace tvm {
namespace relay {
using tir::IntImmNode;
+int64_t* ToVector(const runtime::NDArray& array) {
+ size_t len = array.Shape().front();
+ int64_t* rel_vec = new int64_t[len];
Review comment:
Please don't use raw pointer. it will cause memory leak.
##########
File path: topi/python/topi/sort.py
##########
@@ -133,7 +133,10 @@ def topk(data, k=1, axis=-1, ret_type="both",
is_ascend=False, dtype="int64"):
assert ret_type in ["both", "values", "indices"]
data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "data_buf",
data_alignment=8)
out_shape = list(get_const_tuple(data.shape))
- if k >= 1:
+ kvar = tvm.te.var("k")
Review comment:
```suggestion
kvar = tvm.te.size_var("k")
```
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