diff --git a/core/conversion/evaluators/BUILD b/core/conversion/evaluators/BUILD
index cbc5318342..038408771d 100644
--- a/core/conversion/evaluators/BUILD
+++ b/core/conversion/evaluators/BUILD
@@ -16,7 +16,9 @@ cc_library(
         "NodeEvaluatorRegistry.cpp",
         "prim.cpp",
         "aten.cpp",
-        "eval_macros.h"
+        "eval_macros.h",
+        "eval_util.h",
+        "eval_util.cpp"
     ],
     deps = [
         "//core/util:prelude",
diff --git a/core/conversion/evaluators/eval_util.cpp b/core/conversion/evaluators/eval_util.cpp
new file mode 100644
index 0000000000..486585f805
--- /dev/null
+++ b/core/conversion/evaluators/eval_util.cpp
@@ -0,0 +1,105 @@
+#include "ATen/core/ivalue.h"
+#include "ATen/core/List.h"
+#include "core/util/prelude.h"
+#include "ATen/core/functional.h"
+
+namespace trtorch {
+namespace core {
+namespace conversion {
+namespace evaluators {
+
+//TODO: Switch back to PyTorch canonical implimentation
+c10::optional<torch::jit::IValue> toIValue(const torch::jit::Value* v) {
+  if (v->node()->kind() != torch::jit::prim::Constant || v->type()->cast<c10::FunctionType>()) {
+    return c10::nullopt;
+  }
+  const torch::jit::Node* node = v->node();
+  const c10::TypePtr& type = v->type();
+  if (type->isSubtypeOf(c10::TensorType::get())) {
+    return node->t(c10::attr::value);
+  } else if (type->isSubtypeOf(c10::BoolType::get())) {
+    return (bool)node->i(c10::attr::value);
+  } else if (
+    type->isSubtypeOf(c10::NumberType::get()) &&
+     node->kindOf(c10::attr::value) == torch::jit::AttributeKind::i) {
+    return node->i(c10::attr::value);
+  } else if (
+    type->isSubtypeOf(c10::NumberType::get()) &&
+    node->kindOf(c10::attr::value) == torch::jit::AttributeKind::f) {
+    return node->f(c10::attr::value);
+  } else if (type->isSubtypeOf(c10::ListType::ofInts())) {
+    try {
+      const auto& is = node->is(c10::attr::value);
+      return is;
+    } catch (const std::exception& ex) {
+      const auto& ival = node->ival(c10::attr::value);
+      return ival;
+    }
+  } else if (type->isSubtypeOf(c10::ListType::ofFloats())) {
+    try {
+      const auto& fs = node->fs(c10::attr::value);
+      return fs;
+    } catch (const std::exception& ex) {
+      const auto& ival = node->ival(c10::attr::value);
+      return ival;
+    }
+  } else if (type->isSubtypeOf(c10::ListType::ofBools())) {
+    const auto bs = c10::fmap<bool>(node->is(c10::attr::value));
+    return bs;
+  } else if (type->isSubtypeOf(c10::ListType::ofTensors())) {
+    try {
+      const auto& ts = node->ts(c10::attr::value);
+      return ts;
+    } catch (const std::exception& ex) {
+      const auto& ival = node->ival(c10::attr::value);
+      return ival;
+    }
+  } else if (type->isSubtypeOf(c10::ListType::ofStrings())) {
+    try {
+      const auto& ss = node->ss(c10::attr::value);
+      auto vals = c10::impl::GenericList(c10::StringType::get());
+      for (const auto& str : ss) {
+        vals.push_back(str);
+      }
+      return vals;
+    } catch (const std::exception& ex) {
+      const auto& ival = node->ival(c10::attr::value);
+      return ival;
+    }
+  } else if (
+      type->cast<c10::ListType>() &&
+      node->kindOf(c10::attr::value) == torch::jit::AttributeKind::ival) {
+    const auto& list = node->ival(c10::attr::value);
+    TRTORCH_ASSERT(list.isList(), "Is not a list");
+    return list;
+  } else if (
+      type->cast<c10::DictType>() &&
+      node->kindOf(c10::attr::value) == torch::jit::AttributeKind::ival) {
+    const auto& dict = node->ival(c10::attr::value);
+    TRTORCH_ASSERT(dict.isGenericDict(), "Is not a dict");
+    return dict;
+  } else if (
+      type->cast<c10::TupleType>() &&
+      node->kindOf(c10::attr::value) == torch::jit::AttributeKind::ival) {
+    const auto& tup = node->ival(c10::attr::value);
+    TRTORCH_ASSERT(tup.isTuple(), "Is not a tuple");
+    return tup;
+  } else if (type == c10::StringType::get()) {
+    const auto& s = node->s(c10::attr::value);
+    return s;
+  } else if (type == c10::DeviceObjType::get()) {
+    auto d = c10::Device(node->s(c10::attr::value));
+    return d;
+  } else if (node->mustBeNone()) {
+    return torch::jit::IValue();
+  } else {
+    std::stringstream ss;
+    ss << "constant literal not supported for: " << type->str();
+    throw std::runtime_error(ss.str());
+  }
+}
+
+} // namespace evaluators
+} // namespace conversion
+} // namespace core
+} // namespace trtorch
diff --git a/core/conversion/evaluators/eval_util.h b/core/conversion/evaluators/eval_util.h
new file mode 100644
index 0000000000..1e31ddfe46
--- /dev/null
+++ b/core/conversion/evaluators/eval_util.h
@@ -0,0 +1,15 @@
+#pragma once
+
+#include "torch/csrc/jit/ir/ir.h"
+
+namespace trtorch {
+namespace core {
+namespace conversion {
+namespace evaluators {
+
+c10::optional<torch::jit::IValue> toIValue(const torch::jit::Value* v);
+
+} // namespace evaluators
+} // namespace conversion
+} // namespace core
+} // namespace trtorch
\ No newline at end of file
diff --git a/core/conversion/evaluators/prim.cpp b/core/conversion/evaluators/prim.cpp
index d40a33f4e7..385f6e0345 100644
--- a/core/conversion/evaluators/prim.cpp
+++ b/core/conversion/evaluators/prim.cpp
@@ -1,7 +1,7 @@
 #include <limits>
 
 #include "torch/csrc/jit/ir/ir.h"
-#include "torch/csrc/jit/ir/constants.h"
+//#include "torch/csrc/jit/ir/constants.h"
 #include "ATen/core/functional.h"
 #include "ATen/core/ivalue.h"
 #include "ATen/core/List.h"
@@ -11,6 +11,7 @@
 
 #include "core/conversion/evaluators/evaluators.h"
 #include "core/conversion/evaluators/eval_macros.h"
+#include "core/conversion/evaluators/eval_util.h"
 
 namespace trtorch {
 namespace core {
@@ -25,7 +26,7 @@ auto prim_registrations = RegisterNodeEvaluators()
             if (n->output()->type()->kind() == at::FunctionType::Kind) {
                 return {};
             }
-            return torch::jit::toIValue(n->output());
+            return evaluators::toIValue(n->output());
         }
     }).evaluator({
         torch::jit::prim::NumToTensor,