fix: dependency order of inserted long input casts

core/lowering/lowering.cpp

@@ -31,6 +31,8 @@ int AutocastLongInputs(
     ir::TypeMap input_type_map,
     std::string target_device_name) {
   int num_autocasts = 0;
+  auto old_insert_point = g->insertPoint();
+  g->setInsertPoint(g->nodes().front());
   // For each graph input, determine if it can be autocasted
   for (size_t i = 0; i < g->inputs().size(); i++) {
     auto input = g->inputs()[i];
@@ -71,7 +73,7 @@ int AutocastLongInputs(
       auto cast_node = g->create(torch::jit::aten::to, {input, cuda, const_type, const_false, const_false, none_val});
 
       // Replace all uses of the original tensor with that of the casted tensor
-      g->prependNode(cast_node);
+      g->insertNode(cast_node);
       input->replaceAllUsesAfterNodeWith(cast_node, cast_node->outputs()[0]);
 
       // Mark the cast node to run in PyTorch for ease of casting
@@ -80,7 +82,7 @@ int AutocastLongInputs(
       num_autocasts++;
     }
   }
-
+  g->setInsertPoint(old_insert_point);
   LOG_GRAPH("Inserted " << num_autocasts << " autocasts");
 
   if (num_autocasts > 0) {

tests/core/lowering/BUILD

@@ -27,6 +27,10 @@ cc_test(
     }),
 )
 
+lowering_test(
+    name = "test_autocast_long_inputs",
+)
+
 lowering_test(
     name = "test_conv_pass",
 )

tests/core/lowering/test_autocast_long_inputs.cpp

@@ -0,0 +1,36 @@
+#include <string>
+#include "core/compiler.h"
+#include "core/lowering/passes/passes.h"
+#include "gtest/gtest.h"
+#include "tests/util/util.h"
+#include "torch/csrc/jit/ir/irparser.h"
+#include "torch/csrc/jit/ir/subgraph_matcher.h"
+
+TEST(LoweringPasses, AutocastLongInputs) {
+  std::string source_graph = R"IR(
+    graph(%long_0 : Tensor, %long_1 : Tensor):
+      %res  : Tensor = aten::add(%long_0, %long_1)
+      return (%res))IR";
+  std::string target_graph = R"IR(
+    graph(%long_0 : Tensor, %long_1 : Tensor):
+      %3 : bool = prim::Constant[value=0]()
+      %4 : Device = prim::Constant[value="cuda:0"]()
+      %5 : NoneType = prim::Constant()
+      %6 : int = prim::Constant[value=4]()
+      %7 : Tensor = aten::to[to_compile=0](%long_0, %4, %6, %3, %3, %5)
+      %8 : int = prim::Constant[value=4]()
+      %9 : Tensor = aten::to[to_compile=0](%long_1, %4, %8, %3, %3, %5)
+      %2 : Tensor = aten::add(%7, %9)
+      return (%2))IR";
+
+  auto sg = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(source_graph, &*sg);
+  std::unordered_map<const torch::jit::Value*, c10::optional<at::ScalarType>> type_map;
+  type_map[sg->inputs()[0]] = at::kLong;
+  type_map[sg->inputs()[1]] = at::kLong;
+  torch_tensorrt::core::lowering::AutocastLongInputs(sg, type_map, "cuda:0");
+  auto tg = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(target_graph, &*tg);
+  ASSERT_TRUE(sg->nodes().front()->kind() == torch::jit::prim::Constant); // confirm constants are added before casts
+  ASSERT_TRUE(!torch::jit::findPatternMatches(*tg, *sg).empty());
+}