Dynamic ONNX Importer (apache#6351)

* Change onnx importer to use dynamic upsampling3d (apache#3)

fix pylint

* Refactor ONNX frontend to be dynamic

Make OneHot dynamic

Support BatchMatMul with dynamically shaped inputs

fix dynamic broadcast

Add null checks to broadcast_to rel functions

fail more isolated broadcast_to test

use StructuralEqual instead of pointer comparisions in dynamic_to_static pass

add an optional weight freeze argument to onnx importer

convert onnx resize to dynamic op

add dynamic expand to onnx importer

add a shape_func for power

fix BERTSquad, lint

handle onnx graph initializer parameters more intelligently

* Dynamic ONNX importer: Upsampling and Pad (apache#2)

fix lint

fix Call reference

fix a type issue with expand

fix a bad test refactor

respond to review comments, fix batch matmul tests

* black format

* fix batch matmul test

* add dynamic strided slice to the onnx importer

* fix clip importer

* fix qnn tutorial

* fix bad merge, respond to review comments

* add a simple dynamic model test

* Add dynamic-shaped autopadding to convolution and pooling ops

* fix dynamic issues in a few ops

* fix pylint

* disable tests onnxrt doesn't support

* fix pytorch test

* respond to review comments

* add documentation about partially supporting dynamic shapes

Co-authored-by: Lily Orth-Smith <lorthsmith@octoml.ai>

src/relay/op/nn/convolution.h

@@ -369,7 +369,6 @@ bool Conv3DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
           << "Conv3D: shape of weight is inconsistent with channels, "
           << " channels=" << param->channels << " wshape=" << wshape;
     }
-
     if (!dshape_ncdhw[1].as<tir::AnyNode>() && !wshape[1].as<tir::AnyNode>()) {
       CHECK(reporter->AssertEQ(indexdiv(dshape_ncdhw[1], param->groups), wshape[1]));
     }