Dynamic ONNX Importer (#6351)

* Change onnx importer to use dynamic upsampling3d (#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 (#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>

Author: Matthew Brookhart

include/tvm/relay/transform.h

@@ -208,6 +208,17 @@ TVM_DLL Pass SimplifyInference();
  */
 TVM_DLL Pass FastMath();
 
+/*!
+ * \brief Find Dynamic ops and make them static
+ *
+ * Searches the graph for dynamic ops. If the dynamic inputs to those ops are constants, it replaces
+ * them with static ops and re-performs type inference and constant folding. The pass repeats
+ * itself until the graph stops changing or we run too many iterations.
+ *
+ * \return The pass.
+ */
+TVM_DLL Pass DynamicToStatic();
+
 /*!
  * \brief Infer the type of an expression.
  *

include/tvm/topi/broadcast.h

@@ -54,14 +54,19 @@ inline tvm::te::Tensor broadcast_to(const tvm::te::Tensor& t,
       << "\nvs\ninput: " << t;
   auto bh = detail::BroadcastShape(output_shape, t->shape);
   CHECK_EQ(output_shape.size(), bh.common_shape.size());
+  Array<PrimExpr> oshape;
   for (size_t i = 0; i < output_shape.size(); ++i) {
-    CHECK(topi::detail::EqualCheck(output_shape[i], bh.common_shape[i]));
+    if (output_shape[i].as<tir::IntImmNode>() == nullptr) {
+      oshape.push_back(output_shape[i]);
+    } else {
+      CHECK(topi::detail::EqualCheck(output_shape[i], bh.common_shape[i]));
+      oshape.push_back(bh.common_shape[i]);
+    }
   }
   auto l = [&](tvm::Array<tvm::tir::Var> ovars) {
     return t(detail::InputIndexFromBroadcast(ovars, t, bh.vars2, bh.all_vars));
   };
-  return tvm::te::compute(tvm::Array<tvm::PrimExpr>(bh.common_shape.begin(), bh.common_shape.end()),
-                          l, name, tag);
+  return tvm::te::compute(oshape, l, name, tag);
 }
 
 #define TOPI_DEFINE_BCAST_OP(Name, ComputeRule)                                                   \