diff --git a/lib/Graph/TensorLayout.cpp b/lib/Graph/TensorLayout.cpp
index 3da145fd78..7d29a74783 100644
--- a/lib/Graph/TensorLayout.cpp
+++ b/lib/Graph/TensorLayout.cpp
@@ -699,6 +699,8 @@ static bool acceptsAnyInputLayout(const glow::Node *node) {
   case Kinded::Kind::BatchNormalizationNodeKind:
   case Kinded::Kind::BatchNormalizationGradNodeKind:
   case Kinded::Kind::PadNodeKind:
+  case Kinded::Kind::NonZeroNodeKind:
+  case Kinded::Kind::IntLookupTableNodeKind:
   case Kinded::Kind::ReshapeNodeKind:
   case Kinded::Kind::MeanVarNormalizationNodeKind:
   case Kinded::Kind::MatMulNodeKind:
diff --git a/lib/Optimizer/GraphOptimizer/GraphOptimizer.cpp b/lib/Optimizer/GraphOptimizer/GraphOptimizer.cpp
index 8947208f32..acc4ef8a0b 100644
--- a/lib/Optimizer/GraphOptimizer/GraphOptimizer.cpp
+++ b/lib/Optimizer/GraphOptimizer/GraphOptimizer.cpp
@@ -1143,37 +1143,122 @@ bool SinkReshapes::run(Function *F, const CompilationContext &cctx) {
     auto *node = &N;
 
     // Sink Reshape below eltwise nodes.
-    if (!node->isDataParallel() || node->hasSideEffects()) {
-      continue;
-    }
+    if (node->isDataParallel() && !node->hasSideEffects()) {
+      // Unary eltwise nodes.
+      if (node->getNumInputs() == 1 && node->getNumResults() == 1) {
+        DCHECK(node->getNthResult(0).getType()->isFusedQuantizedType() ||
+               node->getNthInput(0).getType()->isFusedQuantizedType() ||
+               node->getNthResult(0).dims().equals(node->getNthInput(0).dims()))
+            << "SinkReshapes: not an element-wise node: " << node->toString();
+
+        auto *RS = dyn_cast<ReshapeNode>(node->getNthInput(0));
+        if (!RS) {
+          continue;
+        }
 
-    // Unary eltwise nodes.
-    if (node->getNumInputs() != 1 || node->getNumResults() != 1) {
-      continue;
-    }
+        // Create new eltwise node.
+        auto in = RS->getInput();
+        auto out = node->getNthResult(0);
+        auto newTy =
+            F->getParent()->uniqueTypeWithNewShape(out.getType(), in.dims());
+        auto *newN = F->addNode(node->clone());
+        newN->setNthInput(0, in);
+        newN->setTypeUnsafe(0, newTy);
+        newN->setPredicate(node->getPredicate());
+
+        // Create new Reshape.
+        auto *newRS = F->createReshape(RS->getName(), newN,
+                                       RS->getResult().getType()->dims());
+        newRS->setPredicate(node->getPredicate());
+        out.replaceAllUsesOfWith(newRS->getResult());
 
-    auto *RS = dyn_cast<ReshapeNode>(node->getNthInput(0));
-    if (!RS) {
-      continue;
-    }
+        changed = true;
+      }
+
+      // Binary eltwise nodes.
+      if (node->getNumInputs() == 2 && node->getNumResults() == 1) {
+        DCHECK(node->getNthResult(0).getType()->isFusedQuantizedType() ||
+               node->getNthInput(0).getType()->isFusedQuantizedType() ||
+               node->getNthResult(0).dims().equals(node->getNthInput(0).dims()))
+            << "SinkReshapes: not an element-wise node: " << node->toString();
+        DCHECK(node->getNthResult(0).getType()->isFusedQuantizedType() ||
+               node->getNthInput(1).getType()->isFusedQuantizedType() ||
+               node->getNthResult(0).dims().equals(node->getNthInput(1).dims()))
+            << "SinkReshapes: not an element-wise node: " << node->toString();
+
+        // At least one of the inputs must be a Reshape.
+        // If both inputs are Reshapes, they must have the same dimensions.
+        auto *LRN = dyn_cast<ReshapeNode>(node->getNthInput(0));
+        auto *RRN = dyn_cast<ReshapeNode>(node->getNthInput(1));
+        if (!LRN && !RRN) {
+          continue;
+        }
+        if (LRN && RRN &&
+            !LRN->getResult().dims().equals(RRN->getResult().dims())) {
+          continue;
+        }
 
-    // Create new eltwise node.
-    auto in = RS->getInput();
-    auto out = node->getNthResult(0);
-    auto newTy =
-        F->getParent()->uniqueTypeWithNewShape(out.getType(), in.dims());
-    auto *newN = F->addNode(node->clone());
-    newN->setNthInput(0, in);
-    newN->setTypeUnsafe(0, newTy);
-    newN->setPredicate(node->getPredicate());
+        // Canonicalize node to simplify transformation implementation (make LHS
+        // always be the input with a Reshape).
+        bool swap = (LRN == nullptr);
+        auto nv = node->getNthInput(1);
+        if (swap) {
+          nv = node->getNthInput(0);
+          LRN = RRN;
+          RRN = nullptr;
+        }
 
-    // Create new Reshape.
-    auto *newRS = F->createReshape(RS->getName(), newN,
-                                   RS->getResult().getType()->dims());
-    newRS->setPredicate(node->getPredicate());
-    out.replaceAllUsesOfWith(newRS->getResult());
+        // RHS must be either a Reshape or a Constant (+ Quantize) or Splat.
+        auto *RQ = dyn_cast<QuantizeNode>(nv);
+        auto *RC = dyn_cast<Constant>(RQ ? RQ->getInput() : nv);
+        auto *RS = dyn_cast<SplatNode>(nv);
+        if (!RRN && !RC && !RS) {
+          continue;
+        }
 
-    changed = true;
+        // Create new Constant, Quantize or Splat, if needed.
+        NodeValue rhs;
+        if (RRN) {
+          rhs = RRN->getInput();
+        }
+        if (RC) {
+          auto ty = F->getParent()->uniqueTypeWithNewShape(
+              RC->getType(), LRN->getInput().dims());
+          auto *newC = F->getParent()->createConstant(ty, RC->getName());
+          newC->getPayloadMutable().copyRawFrom(&RC->getPayload());
+          rhs = newC->getOutput();
+        }
+        if (RQ) {
+          auto ty = F->getParent()->uniqueTypeWithNewShape(
+              RQ->getResult().getType(), LRN->getInput().dims());
+          rhs = F->createQuantize(RQ->getName(), rhs, ty);
+        }
+        if (RS) {
+          auto ty = F->getParent()->uniqueTypeWithNewShape(
+              RS->getResult().getType(), LRN->getInput().dims());
+          rhs = F->createSplat(RS->getName(), ty, RS->getValue());
+        }
+
+        // Create new eltwise node.
+        auto lhs = LRN->getInput();
+        auto out = node->getNthResult(0);
+        auto newTy =
+            F->getParent()->uniqueTypeWithNewShape(out.getType(), lhs.dims());
+        auto *newN = F->addNode(node->clone());
+        newN->setNthInput(0, swap ? rhs : lhs);
+        newN->setNthInput(1, swap ? lhs : rhs);
+        newN->setTypeUnsafe(0, newTy);
+        newN->setPredicate(node->getPredicate());
+
+        // Create new Reshape.
+        auto *newRN = F->createReshape(LRN->getName(), newN,
+                                       LRN->getResult().getType()->dims());
+        newRN->setPredicate(node->getPredicate());
+        out.replaceAllUsesOfWith(newRN->getResult());
+
+        changed = true;
+      }
+    }
   }
   return changed;
 }
diff --git a/tests/unittests/GraphOptzTest.cpp b/tests/unittests/GraphOptzTest.cpp
index 35d5614120..d1edd7fea8 100644
--- a/tests/unittests/GraphOptzTest.cpp
+++ b/tests/unittests/GraphOptzTest.cpp
@@ -2323,21 +2323,23 @@ TEST_F(GraphOptz, ReshapeAfterSplat) {
   const dim_t reshape[] = {1, 6000};
   Type t1(ElemKind::FloatTy, shape);
   Type t2(ElemKind::FloatTy, reshape);
-  Node *input = F_->getParent()->createPlaceholder(ElemKind::FloatTy, shape,
-                                                   "input", true);
+  Node *input1 = F_->getParent()->createPlaceholder(ElemKind::FloatTy, shape,
+                                                    "input1", true);
+  Node *input2 = F_->getParent()->createPlaceholder(ElemKind::FloatTy, reshape,
+                                                    "input2", true);
   auto *Z1 = F_->createSplat("zero1", &t1, 1.5);
-  auto *A1 = F_->createAdd("add1", Z1->getResult().getType(), input, Z1);
+  auto *A1 = F_->createAdd("add1", Z1->getResult().getType(), input1, Z1);
   auto *R1 = F_->createReshape("reshape1", Z1, reshape);
   // Z1 is used by R1 and A1.
   // The reshape optimization will thus NOT be able to remove this reshape node
   // (R1).
-  auto *R2 = F_->createReshape("reshape2", A1, reshape);
-  auto *A2 = F_->createAdd("add", R1->getResult().getType(), R1, R2);
+  F_->createSave("save", A1);
+  auto *A2 = F_->createAdd("add", R1->getResult().getType(), R1, input2);
   auto *Z2 = F_->createSplat("zero2", &t1, 2.5);
-  auto *R3 = F_->createReshape("reshape3", Z2, reshape);
-  // Z2 is only used by R3.
-  // The Z2,R3 nodes will be replaced by a new splat node with the shape of R3.
-  auto *A3 = F_->createAdd("add", A2->getResult().getType(), A2, R3);
+  auto *R2 = F_->createReshape("reshape3", Z2, reshape);
+  // Z2 is only used by R2.
+  // The Z2,R2 nodes will be replaced by a new splat node with the shape of R2.
+  auto *A3 = F_->createAdd("add", A2->getResult().getType(), A2, R2);
   auto *O = F_->createSave("ret", A3);
 
   // Before optimization, we have 9 nodes in the graph.
@@ -2352,7 +2354,7 @@ TEST_F(GraphOptz, ReshapeAfterSplat) {
   // replace by a new splat node.
   EXPECT_EQ(F_->getNodes().size(), 8);
 
-  // The second input of A3 shoule be a splat node with a shape of R3.
+  // The second input of A3 shoule be a splat node with a shape of R2.
   auto *newA3 = llvm::dyn_cast<AddNode>(O->getInput());
   ASSERT_TRUE(newA3);
   auto *SN = llvm::dyn_cast<SplatNode>(newA3->getRHS());
@@ -2362,8 +2364,8 @@ TEST_F(GraphOptz, ReshapeAfterSplat) {
   // R1 should still be in the graph.
   EXPECT_TRUE(functionContainsNode(F_, R1));
 
-  // R3 and Z2 should not be in the graph any more.
-  EXPECT_FALSE(functionContainsNode(F_, R3));
+  // R2 and Z2 should not be in the graph any more.
+  EXPECT_FALSE(functionContainsNode(F_, R2));
   EXPECT_FALSE(functionContainsNode(F_, Z2));
 }
 
@@ -8043,6 +8045,62 @@ TEST_F(GraphOptz, SinkReshapeBelowUnaryEltwiseOps) {
   checkNumericalEquivalence(0.f);
 }
 
+TEST_F(GraphOptz, SinkReshapeBelowBinaryEltwiseOps) {
+  const dim_t dimsIn[] = {10, 10};
+  const dim_t dimsOut[] = {5, 5, 4};
+
+  // Prepare inputs.
+  auto *in1 = mod_.createPlaceholder(glow::ElemKind::Int8QTy, dimsIn, 0.12f, 0,
+                                     "in1", false);
+  auto *in2 = mod_.createPlaceholder(glow::ElemKind::Int8QTy, dimsIn, 0.17f, 0,
+                                     "in2", false);
+  auto *QCN =
+      mod_.createConstant(ElemKind::Int8QTy, dimsOut, 0.13f, 0, "quant_const");
+  auto *FCN = mod_.createConstant(ElemKind::FloatTy, dimsOut, "float_const");
+  auto qTy = mod_.uniqueType(ElemKind::Int8QTy, dimsOut, 0.15f, 0);
+  auto *QN = F_->createQuantize("quantize", FCN, qTy);
+  auto *SN = F_->createSplat("splat", qTy, 1.79f);
+  QCN->getHandle<int8_t>().randomize(-128, 127, mod_.getPRNG());
+  FCN->getHandle<float>().randomize(-1.f, 2.f, mod_.getPRNG());
+
+  // Test different combinations of Reshape, Constant, Quantize, Splat passed as
+  // LHS or RHS.
+  auto *RN1 = F_->createReshape("reshape", in1, dimsOut);
+  auto *RN2 = F_->createReshape("reshape", in2, dimsOut);
+  auto *AN = F_->createAdd("add", RN1, RN2);
+  auto *MLN = F_->createMul("mul", AN, QCN);
+  auto *MXN = F_->createMax("max", QN, MLN);
+  auto *SBN = F_->createSub("sub", MXN, SN);
+  auto *save = F_->createSave("ret", SBN);
+
+  optimizedF_ = optimizeFunctionForTest(F_);
+
+  auto *optSave =
+      llvm::dyn_cast<SaveNode>(optimizedF_->getNodeByName(save->getName()));
+  ASSERT_TRUE(optSave);
+  auto *optRN = llvm::dyn_cast<ReshapeNode>(optSave->getInput());
+  ASSERT_TRUE(optRN);
+  EXPECT_EQ(optRN->getResult().dims(), llvm::makeArrayRef(dimsOut));
+  auto *optSBN = llvm::dyn_cast<SubNode>(optRN->getInput());
+  ASSERT_TRUE(optSBN);
+  EXPECT_EQ(optSBN->getResult().dims(), llvm::makeArrayRef(dimsIn));
+  auto *optMXN = llvm::dyn_cast<MaxNode>(optSBN->getLHS());
+  ASSERT_TRUE(optMXN);
+  EXPECT_EQ(optMXN->getResult().dims(), llvm::makeArrayRef(dimsIn));
+  auto *optMLN = llvm::dyn_cast<MulNode>(optMXN->getRHS());
+  ASSERT_TRUE(optMLN);
+  EXPECT_EQ(optMLN->getResult().dims(), llvm::makeArrayRef(dimsIn));
+  auto *optAN = llvm::dyn_cast<AddNode>(optMLN->getLHS());
+  ASSERT_TRUE(optAN);
+  EXPECT_EQ(optAN->getResult().dims(), llvm::makeArrayRef(dimsIn));
+
+  bindings_.allocate(in1)->getHandle<int8_t>().randomize(-128, 127,
+                                                         mod_.getPRNG());
+  bindings_.allocate(in2)->getHandle<int8_t>().randomize(-128, 127,
+                                                         mod_.getPRNG());
+  checkNumericalEquivalence(0.f);
+}
+
 TEST_F(GraphOptz, OptConvertToDequantize) {
   auto *I =
       mod_.createPlaceholder(ElemKind::Int8QTy, {32, 64}, 0.2f, 1, "A", false);
diff --git a/tools/ClassGen/NodeGen.cpp b/tools/ClassGen/NodeGen.cpp
index def7b09b52..2bb0faa958 100644
--- a/tools/ClassGen/NodeGen.cpp
+++ b/tools/ClassGen/NodeGen.cpp
@@ -673,7 +673,6 @@ int main(int argc, char **argv) {
       .addResultFromCtorArg()
       .dataParallel()
       .setDocstring("Performs element-wise exponential to the Input.");
-  // clang-format on
 
   BB.newNode("Logit")
       .addInput("Input")
@@ -685,8 +684,8 @@ int main(int argc, char **argv) {
   BB.newNode("NonZero")
       .addInput("Cond")
       .addResultFromCtorArg()
-      .dataParallel()
       .setDocstring("Selects indices of the true elements in Cond");
+  // clang-format on
 
   BB.newNode("Select")
       .addInput("Cond")
@@ -1432,7 +1431,6 @@ int main(int argc, char **argv) {
       .addInput("Input")
       .addInput("Mapping")
       .addResultFromCtorArg()
-      .dataParallel()
       .setDocstring("Simple mapping between quantized numbers."
                     "This can be used as quantized sigmoid or tanh functions.");