[GitHub] [incubator-tvm] lixiaoquan commented on a change in pull request #6024: [Relay][TF] Make StridedSlice support dynamic input and constant attrs

[GitBox]

lixiaoquan commented on a change in pull request #6024:
URL: https://github.com/apache/incubator-tvm/pull/6024#discussion_r469071731



##
File path: python/tvm/relay/frontend/tensorflow.py
##
@@ -1458,6 +1458,15 @@ def _impl(inputs, attr, params, mod):
 
 return ret
 
+def _dyn():
+for d in data_shape:
+if not isinstance(d, int):
+return True
+return False
+
+if _dyn():

Review comment:
   Because mask transformation needs a concrete shape which doesn't work 
for dynamic shape input. I think there is more to do to handle mask in dynamic 
case but I'm afraid it can't support all mask transformation without concrete 
shape.





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[GitHub] [incubator-tvm] lixiaoquan commented on a change in pull request #6024: [Relay][TF] Make StridedSlice support dynamic input and constant attrs

[GitBox]

lixiaoquan commented on a change in pull request #6024:
URL: https://github.com/apache/incubator-tvm/pull/6024#discussion_r453138358



##
File path: src/relay/op/tensor/transform.cc
##
@@ -2146,7 +2146,18 @@ Array StridedSliceCompute(const Attrs& 
attrs, const Array();
   CHECK(param != nullptr);
-  if (param->begin && param->end && param->strides) {
+
+  bool dyn = false;
+  for (auto& v : out_type.as()->shape) {
+if (const tir::VarNode* var_node = v.as()) {
+  if (var_node->name_hint == "any_dim") {
+dyn = true;
+break;
+  }
+}
+  }
+
+  if (param->begin && param->end && param->strides && !dyn) {

Review comment:
   topi::strided_slice requires static shape because it will get value from 
each dim. Should we change this requirement or just use DynamicStrideSlice?
   ```
   diff --git a/topi/include/topi/transform.h b/topi/include/topi/transform.h
   index b5fc02ae7..329a62ce7 100644
   --- a/topi/include/topi/transform.h
   +++ b/topi/include/topi/transform.h
   @@ -610,6 +610,7 @@ inline Tensor strided_slice(const Tensor& x, const 
Array& begin, const
  for (size_t i = 0; i < src_tensor_dim; ++i) {
int64_t begin_range = stride_vec[i] < 0 ? -1 : 0;
int64_t dim_i = GetConstInt(x->shape[i]);
   +LOG(INFO) << dim_i; // it is -1 for modified case
int64_t end_range = stride_vec[i] < 0 ? dim_i - 1 : dim_i;
// transform negative indices to positive value, clips on the correct 
range
auto index_canonicalization = [dim_i, begin_range, end_range](int64_t 
index) {
   @@ -635,6 +636,8 @@ inline Tensor strided_slice(const Tensor& x, const 
Array& begin, const
out_shape.push_back(slice_size);
  }

   +//  LOG(INFO) << out_shape;
   +
  return compute(
  out_shape,
  [&](const Array& indices) {
   ```





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