diff --git a/test/cpp/jit/test_gpu.cpp b/test/cpp/jit/test_gpu.cpp
index dadce1dc99e6..7ca697c50020 100644
--- a/test/cpp/jit/test_gpu.cpp
+++ b/test/cpp/jit/test_gpu.cpp
@@ -1277,15 +1277,13 @@ int ceilDiv_(int a, int b) {
 
 void testGPU_FusionAdvancedComputeAt() {
   // Case 1
-  /*
-   * tv1 = tv0 * 0.5
-   * tv2 = tv1 * -1
-   * tv3 = tv1 + 3
-   * tv4 = tv1 * 2
-   * tv5 = tv3 + tv2
-   * tv6 = tv5 + tv4
-   * tv7 = tv1 + tv4
-   */
+  // tv1 = tv0 * 0.5
+  // tv2 = tv1 * -1
+  // tv3 = tv1 + 3
+  // tv4 = tv1 * 2
+  // tv5 = tv3 + tv2
+  // tv6 = tv5 + tv4
+  // tv7 = tv1 + tv4
   {
     Fusion fusion;
     FusionGuard fg(&fusion);
@@ -1355,14 +1353,12 @@ void testGPU_FusionAdvancedComputeAt() {
   }
 
   // Case 2
-  /*
-   * tv1 = tv0 * -1
-   * tv2 = tv0 + 3
-   * tv3 = tv0 * 2
-   * tv4 = tv2 + tv1
-   * tv5 = tv4 + tv3
-   * tv6 = tv5 + tv3
-   */
+  // tv1 = tv0 * -1
+  // tv2 = tv0 + 3
+  // tv3 = tv0 * 2
+  // tv4 = tv2 + tv1
+  // tv5 = tv4 + tv3
+  // tv6 = tv5 + tv3
   {
     Fusion fusion;
     FusionGuard fg(&fusion);
@@ -1550,6 +1546,44 @@ void testGPU_FusionAdvancedComputeAt() {
 
     TORCH_CHECK(at::allclose(outputs[0], t6), actual_kernel.str());
   }
+
+  // Case 5
+  // tv2 = tv0 + 2.0
+  // tv3 = tv1 * tv2
+  {
+    Fusion fusion;
+    FusionGuard fg(&fusion);
+
+    // Set up your input tensor views
+    TensorView* tv0 = makeDummyTensor(2);
+    fusion.addInput(tv0);
+    TensorView* tv1 = makeDummyTensor(2);
+    fusion.addInput(tv1);
+    TensorView* tv2 = add(tv0, new Float(2.0));
+    TensorView* tv3 = mul(tv1, tv2);
+    fusion.addOutput(tv3);
+
+    tv3->merge(0);
+    tv3->split(-1, 8);
+    tv3->split(-1, 4);
+
+    tv2->computeAt(tv3, 1);
+    tv2->split(-1, 4); // Kernel will break without this split
+    tv3->axis(0)->parallelize(ParallelType::BIDx);
+
+    auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
+    at::Tensor t0 = at::randn({63, 65}, options);
+    at::Tensor t1 = at::rand_like(t0, options);
+
+    auto t2 = t0.add(2.0);
+    auto t3 = t1.mul(t2);
+
+    torch::jit::fuser::cuda::FusionExecutor fe;
+    fe.compileFusion(&fusion);
+    auto outputs = fe.runFusion({t0, t1});
+
+    TORCH_CHECK(at::allclose(outputs[0], t3));
+  }
 }
 
 void testGPU_FusionScalarInputs() {
@@ -2577,7 +2611,6 @@ void testGPU_FusionReduction4() {
   TensorView* tv2 = tv1->rFactor({-3});
 
   tv0->computeAt(tv1, 1);
-
   tv1->axis(0)->parallelize(ParallelType::BIDy);
 
   for (auto* val : fusion.vals()) {
@@ -2600,7 +2633,10 @@ void testGPU_FusionReduction4() {
   fe.runFusion({input}, {cg_output});
 
   auto aten_output = input.sum({1});
-  TORCH_CHECK(aten_output.allclose(cg_output));
+  TORCH_CHECK(
+      aten_output.allclose(cg_output, 1e-5, 1e-7),
+      "Error of: ",
+      aten_output.sub(cg_output).abs().max());
 }
 
 void testGPU_FusionReduction5() {
diff --git a/torch/csrc/jit/codegen/cuda/compute_at.cpp b/torch/csrc/jit/codegen/cuda/compute_at.cpp
index 55df035e2fb4..1b434916178b 100644
--- a/torch/csrc/jit/codegen/cuda/compute_at.cpp
+++ b/torch/csrc/jit/codegen/cuda/compute_at.cpp
@@ -220,9 +220,8 @@ unsigned int ComputeAt::forwardComputeAt_impl(
   }
 
   consumer_entry.setPassPosition(replay.second);
-  if ((consumer_entry.shouldSetComputeAt(replay.second) &&
-       consumer != consumer_) ||
-      (consumer == consumer_ && replay.second >= consumer_position_)) {
+  if (consumer_entry.shouldSetComputeAt(replay.second) &&
+      consumer != consumer_) {
     consumer_entry.setComputeAtDomain(consumer->domain());
   }
 
diff --git a/torch/csrc/jit/codegen/cuda/kernel_resource_strings.h b/torch/csrc/jit/codegen/cuda/kernel_resource_strings.h
index c88caaee63a2..cdc41ddab51c 100644
--- a/torch/csrc/jit/codegen/cuda/kernel_resource_strings.h
+++ b/torch/csrc/jit/codegen/cuda/kernel_resource_strings.h
@@ -146,7 +146,7 @@ __device__  __inline__ float uniform(unsigned int x) {
 
 // Helper functions for Operations
 static auto code_helper_funcs = R"(
-__device__ int ceilDiv(const int a, const int b) {
+__device__ constexpr int ceilDiv(const int a, const int b) {
   return (a + b - 1) / b;
 }
 __device__ float clamp(const float x, const float minv, const float maxv) {