diff --git a/lib/Conversion/TorchToMhlo/BasicOp.cpp b/lib/Conversion/TorchToMhlo/BasicOp.cpp
index 1c33234b2..c3586b978 100644
--- a/lib/Conversion/TorchToMhlo/BasicOp.cpp
+++ b/lib/Conversion/TorchToMhlo/BasicOp.cpp
@@ -22,6 +22,7 @@
 #include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
 #include "torch-mlir/Dialect/Torch/Utils/Utils.h"
 #include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
+#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
 #include <iostream>
 #include <numeric>
 
@@ -628,6 +629,35 @@ LogicalResult ConvertAtenOp<AtenReluOp>::matchAndRewrite(
 
 } // namespace
 
+// Convert a Aten::GELU to HLO
+// Gelu(x) = x * 1/2 * [1 + erf(x/(sqrt(2)))]
+namespace {
+template <>
+LogicalResult ConvertAtenOp<AtenGeluOp>::matchAndRewrite(
+    AtenGeluOp op,
+    OpAdaptor adaptor,
+    ConversionPatternRewriter& rewriter) const {
+  Location loc = op.getLoc();
+  Value input = adaptor.self();
+  auto inputTy = input.getType().template dyn_cast<RankedTensorType>();
+  if (!inputTy) {
+    return op.emitError("only ranked tensor type is supported.");
+  }
+
+  Value one = chlo::getConstantLike(rewriter, loc, 1.0, input);
+  Value two = chlo::getConstantLike(rewriter, loc, 2.0, input);
+  Value half = chlo::getConstantLike(rewriter, loc, 0.5, input);
+  auto rsqrtTwo = rewriter.create<mlir::mhlo::RsqrtOp>(loc, two);
+  auto erfElement = rewriter.create<mhlo::MulOp>(loc, input, rsqrtTwo);
+  auto erf = rewriter.create<mlir::chlo::ErfOp>(loc, erfElement);
+  auto erfAdd = rewriter.create<mhlo::AddOp>(loc, erf, one);
+  auto halfMul = rewriter.create<mhlo::MulOp>(loc, erfAdd, half);
+  rewriter.replaceOpWithNewOp<mhlo::MulOp>(op, input, halfMul);
+  return success();
+}
+} // namespace
+
+
 // AtenErfOp
 namespace {
 template <>
@@ -984,6 +1014,7 @@ void mlir::torch::torch_to_mhlo::populateBasicOpPatternsAndLegality(
   INSERT_ATENOP_PATTERN(AtenContiguousOp);
 
   INSERT_ATENOP_PATTERN(AtenReluOp);
+  INSERT_ATENOP_PATTERN(AtenGeluOp);
   INSERT_ATENOP_PATTERN(AtenErfOp);
 
   INSERT_ATENOP_PATTERN(AtenBatchNormOp);
diff --git a/lib/Conversion/TorchToMhlo/PopulatePatterns.h b/lib/Conversion/TorchToMhlo/PopulatePatterns.h
index 35bca4019..75cca68bd 100644
--- a/lib/Conversion/TorchToMhlo/PopulatePatterns.h
+++ b/lib/Conversion/TorchToMhlo/PopulatePatterns.h
@@ -25,6 +25,7 @@ void populateViewLikeOpPatternsAndLegality(TypeConverter &typeConverter,
 void populateGatherOpPatternsAndLegality(TypeConverter &typeConverter,
                                          RewritePatternSet &patterns,
                                          ConversionTarget &target);
+
 } // namespace torch_to_mhlo
 } // namespace torch
 } // namespace mlir
diff --git a/lib/Conversion/TorchToMhlo/TorchToMhlo.cpp b/lib/Conversion/TorchToMhlo/TorchToMhlo.cpp
index 1907a10c5..db66c0549 100644
--- a/lib/Conversion/TorchToMhlo/TorchToMhlo.cpp
+++ b/lib/Conversion/TorchToMhlo/TorchToMhlo.cpp
@@ -23,6 +23,7 @@
 #include "torch-mlir/Dialect/Torch/Utils/Utils.h"
 #include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
 #include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.h"
+#include "mlir-hlo/Dialect/mhlo/IR/chlo_ops.h"
 
 using namespace mlir;
 using namespace mlir::torch;
diff --git a/test/Conversion/TorchToMhlo/basic.mlir b/test/Conversion/TorchToMhlo/basic.mlir
index e11e40fce..ee7da07e1 100644
--- a/test/Conversion/TorchToMhlo/basic.mlir
+++ b/test/Conversion/TorchToMhlo/basic.mlir
@@ -1,41 +1,5 @@
 // RUN: torch-mlir-opt <%s -convert-torch-to-mhlo -split-input-file -verify-diagnostics | FileCheck %s
 
-// CHECK-LABEL:   func.func @torch.aten.tanh$basic(
-// CHECK-SAME:                                %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_2:.*]] = mhlo.tanh %[[VAL_1]] : tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.tanh$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.log$basic(
-// CHECK-SAME:                                %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_2:.*]] = mhlo.log %[[VAL_1]] : tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.log$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.log %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.exp$basic(
-// CHECK-SAME:                                %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_2:.*]] = mhlo.exponential %[[VAL_1]] : tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.exp$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.exp %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
 
 // -----
 
@@ -52,18 +16,6 @@ func.func @torch.aten.clone$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vt
   return %0 : !torch.vtensor<[?,?],f32>
 }
 
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.neg$basic(
-// CHECK-SAME:                              %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_2:.*]] = mhlo.negate %[[VAL_1]] : tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.neg$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
 
 // -----
 
@@ -87,329 +39,6 @@ func.func @torch.vtensor.literal$signed() -> !torch.vtensor<[2],si64> {
   return %0 : !torch.vtensor<[2],si64>
 }
 
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.addscalar$basic(
-// CHECK-SAME:                                         %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int9 = torch.constant.int 9
-// CHECK:           %int1 = torch.constant.int 1
-// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_add %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.addscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int9 = torch.constant.int 9
-  %int1 = torch.constant.int 1
-  %0 = torch.aten.add.Scalar %arg0, %int9, %int1 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.addscalar$alpha(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int9 = torch.constant.int 9
-// CHECK:           %int2 = torch.constant.int 2
-// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_3:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_multiply %[[VAL_2]], %[[VAL_3]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
-// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_add %[[VAL_1]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.addscalar$alpha(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int9 = torch.constant.int 9
-  %int2 = torch.constant.int 2
-  %0 = torch.aten.add.Scalar %arg0, %int9, %int2 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.addtensor$basic(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int1 = torch.constant.int 1
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_add %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.addtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int1 = torch.constant.int 1
-  %0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.addtensor$alpha(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int2 = torch.constant.int 2
-// CHECK:           %[[VAL_4:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_multiply %[[VAL_3]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_6:.*]] = chlo.broadcast_add %[[VAL_2]], %[[VAL_5]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.addtensor$alpha(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int2 = torch.constant.int 2
-  %0 = torch.aten.add.Tensor %arg0, %arg1, %int2 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.addtensor$promote(
-// CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor<[?,?],si32>, 
-// CHECK-SAME:                                           %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
-// CHECK:           %int1 = torch.constant.int 1
-// CHECK:           %[[VAL_4:.*]] = mhlo.convert(%[[VAL_2]]) : (tensor<?x?xi32>) -> tensor<?x?xi64>
-// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_add %[[VAL_4]], %[[VAL_3]] : (tensor<?x?xi64>, tensor<?x?xi64>) -> tensor<?x?xi64>
-// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
-// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],si64>
-func.func @torch.aten.addtensor$promote(%arg0: !torch.vtensor<[?,?],si32>, %arg1: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
-  %int1 = torch.constant.int 1
-  %0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si64>, !torch.int -> !torch.vtensor<[?,?],si64>
-  return %0 : !torch.vtensor<[?,?],si64>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.subscalar$basic(
-// CHECK-SAME:                                         %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int9 = torch.constant.int 9
-// CHECK:           %int1 = torch.constant.int 1
-// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_subtract %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.subscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int9 = torch.constant.int 9
-  %int1 = torch.constant.int 1
-  %0 = torch.aten.sub.Scalar %arg0, %int9, %int1 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.subscalar$alpha(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int9 = torch.constant.int 9
-// CHECK:           %int2 = torch.constant.int 2
-// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_3:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_multiply %[[VAL_2]], %[[VAL_3]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
-// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_subtract %[[VAL_1]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.subscalar$alpha(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int9 = torch.constant.int 9
-  %int2 = torch.constant.int 2
-  %0 = torch.aten.sub.Scalar %arg0, %int9, %int2 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.subtensor$basic(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int1 = torch.constant.int 1
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_subtract %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.subtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int1 = torch.constant.int 1
-  %0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.subtensor$alpha(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int2 = torch.constant.int 2
-// CHECK:           %[[VAL_4:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_multiply %[[VAL_3]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_6:.*]] = chlo.broadcast_subtract %[[VAL_2]], %[[VAL_5]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.subtensor$alpha(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int2 = torch.constant.int 2
-  %0 = torch.aten.sub.Tensor %arg0, %arg1, %int2 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.subtensor$promote(
-// CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor<[?,?],si32>, 
-// CHECK-SAME:                                           %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
-// CHECK:           %int1 = torch.constant.int 1
-// CHECK:           %[[VAL_4:.*]] = mhlo.convert(%[[VAL_2]]) : (tensor<?x?xi32>) -> tensor<?x?xi64>
-// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_subtract %[[VAL_4]], %[[VAL_3]] : (tensor<?x?xi64>, tensor<?x?xi64>) -> tensor<?x?xi64>
-// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
-// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],si64>
-func.func @torch.aten.subtensor$promote(%arg0: !torch.vtensor<[?,?],si32>, %arg1: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
-  %int1 = torch.constant.int 1
-  %0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si64>, !torch.int -> !torch.vtensor<[?,?],si64>
-  return %0 : !torch.vtensor<[?,?],si64>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.mulscalar$basic(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int9 = torch.constant.int 9
-// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_multiply %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.mulscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int9 = torch.constant.int 9
-  %0 = torch.aten.mul.Scalar %arg0, %int9 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.multensor$basic(
-// CHECK-SAME:                                          %[[VLA_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                          %[[VLA_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VLA_2:.*]] = torch_c.to_builtin_tensor %[[VLA_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VLA_3:.*]] = torch_c.to_builtin_tensor %[[VLA_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VLA_4:.*]] = chlo.broadcast_multiply %[[VLA_2]], %[[VLA_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VLA_5:.*]] = torch_c.from_builtin_tensor %[[VLA_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VLA_5]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.multensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.mul.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.divscalar$basic(
-// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int9 = torch.constant.int 9
-// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
-// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_divide %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.divscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %int9 = torch.constant.int 9
-  %0 = torch.aten.div.Scalar %arg0, %int9 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.divtensor$basic(
-// CHECK-SAME:                                          %[[VLA_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                          %[[VLA_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VLA_2:.*]] = torch_c.to_builtin_tensor %[[VLA_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VLA_3:.*]] = torch_c.to_builtin_tensor %[[VLA_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VLA_4:.*]] = chlo.broadcast_divide %[[VLA_2]], %[[VLA_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VLA_5:.*]] = torch_c.from_builtin_tensor %[[VLA_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VLA_5]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.divtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.gt.scalar(
-// CHECK-SAME:                                   %arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
-// CHECK:           %0 = torch_c.to_builtin_tensor %arg0 : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %int3 = torch.constant.int 3
-// CHECK:           %1 = mhlo.constant dense<3.000000e+00> : tensor<f32>
-// CHECK:           %2 = chlo.broadcast_compare %0, %1 {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1>
-// CHECK:           %3 = torch_c.from_builtin_tensor %2 : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
-// CHECK:           return %3 : !torch.vtensor<[?,?],i1>
-func.func @torch.aten.gt.scalar(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
-  %int3 = torch.constant.int 3
-  %0 = torch.aten.gt.Scalar %arg0, %int3 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],i1>
-  return %0 : !torch.vtensor<[?,?],i1>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.gt.tensor(
-// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
-// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
-// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
-func.func @torch.aten.gt.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-  %0 = torch.aten.gt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
-  return %0 : !torch.vtensor<[?,?],i1>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.lt.tensor(
-// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction LT>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
-// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
-// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
-func.func @torch.aten.lt.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-  %0 = torch.aten.lt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
-  return %0 : !torch.vtensor<[?,?],i1>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.eq.tensor(
-// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction EQ>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
-// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
-// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
-func.func @torch.aten.eq.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-  %0 = torch.aten.eq.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
-  return %0 : !torch.vtensor<[?,?],i1>
-}
-
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.ne.tensor(
-// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
-// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
-// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction NE>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
-// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
-// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
-func.func @torch.aten.ne.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
-  %0 = torch.aten.ne.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
-  return %0 : !torch.vtensor<[?,?],i1>
-}
-
 // -----
 
 // CHECK-LABEL:   func.func @torch.prim.NumToTensor.Scalar$basic() -> !torch.vtensor<[],si64> {
@@ -451,24 +80,6 @@ func.func @torch.aten.reciprocal(%arg0: !torch.vtensor<[?,?,?],f32>) -> !torch.v
   return %0 : !torch.vtensor<[?,?,?],f32>
 }
 
-// -----
-
-// CHECK-LABEL:   func.func @torch.aten.permute$basic(
-// CHECK-SAME:                                   %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[64,4],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
-// CHECK:           %[[VAL_2:.*]] = torch.constant.int 0
-// CHECK:           %[[VAL_3:.*]] = torch.constant.int 1
-// CHECK:           %[[VAL_4:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_2]] : (!torch.int, !torch.int) -> !torch.list<int>
-// CHECK:           %[[VAL_5:.*]] = "mhlo.transpose"(%[[VAL_1]]) {permutation = dense<[1, 0]> : tensor<2xi64>} : (tensor<4x64xf32>) -> tensor<64x4xf32>
-// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<64x4xf32> -> !torch.vtensor<[64,4],f32>
-// CHECK:           return %[[VAL_6]] : !torch.vtensor<[64,4],f32>
-func.func @torch.aten.permute$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[64,4],f32> {
-  %int0 = torch.constant.int 0
-  %int1 = torch.constant.int 1
-  %0 = torch.prim.ListConstruct %int1, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
-  %1 = torch.aten.permute %arg0, %0 : !torch.vtensor<[4,64],f32>, !torch.list<int> -> !torch.vtensor<[64,4],f32>
-  return %1 : !torch.vtensor<[64,4],f32>
-}
 
 // -----
 
@@ -518,20 +129,6 @@ func.func @torch.aten.broadcast_to$dynamic_implicit(%arg0: !torch.vtensor<[?,?],
 
 // -----
 
-// CHECK-LABEL:   func.func @torch.aten.relu(
-// CHECK-SAME:                               %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
-// CHECK:           %[[VAL_2:.*]] = "chlo.constant_like"(%[[VAL_1]]) {value = 0.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
-// CHECK:           %[[VAL_3:.*]] = mhlo.maximum %[[VAL_1]], %[[VAL_2]] : tensor<?x?xf32>
-// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
-// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
-func.func @torch.aten.relu(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
-  %0 = torch.aten.relu %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
-  return %0 : !torch.vtensor<[?,?],f32>
-}
-
-// -----
-
 // CHECK-LABEL:   func.func @torch.aten.batch_norm$training(
 // CHECK-SAME:                                              %[[VAL_0:.*]]: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
 // CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,3,?,?],f32> -> tensor<?x3x?x?xf32>
diff --git a/test/Conversion/TorchToMhlo/elementwise.mlir b/test/Conversion/TorchToMhlo/elementwise.mlir
new file mode 100644
index 000000000..643c44b11
--- /dev/null
+++ b/test/Conversion/TorchToMhlo/elementwise.mlir
@@ -0,0 +1,431 @@
+// RUN: torch-mlir-opt <%s -convert-torch-to-mhlo -split-input-file -verify-diagnostics | FileCheck %s
+
+// -----
+
+// CHECK-LABEL:  func.func @torch.aten.gelu(
+// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:         %[[T1:.*]] = "chlo.constant_like"(%[[T0]]) {value = 1.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:         %[[T2:.*]] = "chlo.constant_like"(%[[T0]]) {value = 2.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:         %[[T3:.*]] = "chlo.constant_like"(%[[T0]]) {value = 5.000000e-01 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:         %[[T4:.*]] = mhlo.rsqrt %[[T2]] : tensor<?x?xf32>
+// CHECK:         %[[T5:.*]] = mhlo.multiply %[[T0]], %[[T4]] : tensor<?x?xf32>
+// CHECK:         %[[T6:.*]] = chlo.erf %[[T5]] : tensor<?x?xf32> -> tensor<?x?xf32>
+// CHECK:         %[[T7:.*]] = mhlo.add %[[T6]], %[[T1]] : tensor<?x?xf32>
+// CHECK:         %[[T8:.*]] = mhlo.multiply %[[T7]], %[[T3]] : tensor<?x?xf32>
+// CHECK:         %[[T9:.*]] = mhlo.multiply %[[T0]], %[[T8]] : tensor<?x?xf32>
+// CHECK:         %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:         return %[[T10]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.gelu(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+    %str = torch.constant.str "none"
+    %0 = torch.aten.gelu %arg0, %str : !torch.vtensor<[?,?],f32>, !torch.str -> !torch.vtensor<[?,?],f32>
+    return %0 : !torch.vtensor<[?,?],f32>
+}
+
+
+// CHECK-LABEL:   func.func @torch.aten.tanh$basic(
+// CHECK-SAME:                                %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_2:.*]] = mhlo.tanh %[[VAL_1]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.tanh$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.log$basic(
+// CHECK-SAME:                                %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_2:.*]] = mhlo.log %[[VAL_1]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.log$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.log %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.exp$basic(
+// CHECK-SAME:                                %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_2:.*]] = mhlo.exponential %[[VAL_1]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.exp$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.exp %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.neg$basic(
+// CHECK-SAME:                              %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_2:.*]] = mhlo.negate %[[VAL_1]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.neg$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.addscalar$basic(
+// CHECK-SAME:                                         %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int9 = torch.constant.int 9
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_add %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.addscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int9 = torch.constant.int 9
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.add.Scalar %arg0, %int9, %int1 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.addscalar$alpha(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int9 = torch.constant.int 9
+// CHECK:           %int2 = torch.constant.int 2
+// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_3:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_multiply %[[VAL_2]], %[[VAL_3]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
+// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_add %[[VAL_1]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.addscalar$alpha(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int9 = torch.constant.int 9
+  %int2 = torch.constant.int 2
+  %0 = torch.aten.add.Scalar %arg0, %int9, %int2 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.addtensor$basic(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_add %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.addtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.addtensor$alpha(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int2 = torch.constant.int 2
+// CHECK:           %[[VAL_4:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_multiply %[[VAL_3]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_6:.*]] = chlo.broadcast_add %[[VAL_2]], %[[VAL_5]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.addtensor$alpha(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int2 = torch.constant.int 2
+  %0 = torch.aten.add.Tensor %arg0, %arg1, %int2 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.addtensor$promote(
+// CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor<[?,?],si32>, 
+// CHECK-SAME:                                           %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %[[VAL_4:.*]] = mhlo.convert(%[[VAL_2]]) : (tensor<?x?xi32>) -> tensor<?x?xi64>
+// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_add %[[VAL_4]], %[[VAL_3]] : (tensor<?x?xi64>, tensor<?x?xi64>) -> tensor<?x?xi64>
+// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
+// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],si64>
+func.func @torch.aten.addtensor$promote(%arg0: !torch.vtensor<[?,?],si32>, %arg1: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si64>, !torch.int -> !torch.vtensor<[?,?],si64>
+  return %0 : !torch.vtensor<[?,?],si64>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.subscalar$basic(
+// CHECK-SAME:                                         %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int9 = torch.constant.int 9
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_subtract %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.subscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int9 = torch.constant.int 9
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.sub.Scalar %arg0, %int9, %int1 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.subscalar$alpha(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int9 = torch.constant.int 9
+// CHECK:           %int2 = torch.constant.int 2
+// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_3:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_multiply %[[VAL_2]], %[[VAL_3]] : (tensor<f32>, tensor<f32>) -> tensor<f32>
+// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_subtract %[[VAL_1]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.subscalar$alpha(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int9 = torch.constant.int 9
+  %int2 = torch.constant.int 2
+  %0 = torch.aten.sub.Scalar %arg0, %int9, %int2 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.subtensor$basic(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_subtract %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.subtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.subtensor$alpha(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                          %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int2 = torch.constant.int 2
+// CHECK:           %[[VAL_4:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_multiply %[[VAL_3]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_6:.*]] = chlo.broadcast_subtract %[[VAL_2]], %[[VAL_5]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.subtensor$alpha(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int2 = torch.constant.int 2
+  %0 = torch.aten.sub.Tensor %arg0, %arg1, %int2 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.subtensor$promote(
+// CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor<[?,?],si32>, 
+// CHECK-SAME:                                           %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
+// CHECK:           %int1 = torch.constant.int 1
+// CHECK:           %[[VAL_4:.*]] = mhlo.convert(%[[VAL_2]]) : (tensor<?x?xi32>) -> tensor<?x?xi64>
+// CHECK:           %[[VAL_5:.*]] = chlo.broadcast_subtract %[[VAL_4]], %[[VAL_3]] : (tensor<?x?xi64>, tensor<?x?xi64>) -> tensor<?x?xi64>
+// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xi64> -> !torch.vtensor<[?,?],si64>
+// CHECK:           return %[[VAL_6]] : !torch.vtensor<[?,?],si64>
+func.func @torch.aten.subtensor$promote(%arg0: !torch.vtensor<[?,?],si32>, %arg1: !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> {
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si64>, !torch.int -> !torch.vtensor<[?,?],si64>
+  return %0 : !torch.vtensor<[?,?],si64>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.mulscalar$basic(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int9 = torch.constant.int 9
+// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_multiply %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.mulscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int9 = torch.constant.int 9
+  %0 = torch.aten.mul.Scalar %arg0, %int9 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.multensor$basic(
+// CHECK-SAME:                                          %[[VLA_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                          %[[VLA_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VLA_2:.*]] = torch_c.to_builtin_tensor %[[VLA_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VLA_3:.*]] = torch_c.to_builtin_tensor %[[VLA_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VLA_4:.*]] = chlo.broadcast_multiply %[[VLA_2]], %[[VLA_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VLA_5:.*]] = torch_c.from_builtin_tensor %[[VLA_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VLA_5]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.multensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.mul.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.divscalar$basic(
+// CHECK-SAME:                                          %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int9 = torch.constant.int 9
+// CHECK:           %[[VAL_2:.*]] = mhlo.constant dense<9.000000e+00> : tensor<f32>
+// CHECK:           %[[VAL_3:.*]] = chlo.broadcast_divide %[[VAL_1]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.divscalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %int9 = torch.constant.int 9
+  %0 = torch.aten.div.Scalar %arg0, %int9 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.divtensor$basic(
+// CHECK-SAME:                                          %[[VLA_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                          %[[VLA_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VLA_2:.*]] = torch_c.to_builtin_tensor %[[VLA_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VLA_3:.*]] = torch_c.to_builtin_tensor %[[VLA_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VLA_4:.*]] = chlo.broadcast_divide %[[VLA_2]], %[[VLA_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VLA_5:.*]] = torch_c.from_builtin_tensor %[[VLA_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VLA_5]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.divtensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.gt.scalar(
+// CHECK-SAME:                                   %arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
+// CHECK:           %0 = torch_c.to_builtin_tensor %arg0 : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %int3 = torch.constant.int 3
+// CHECK:           %1 = mhlo.constant dense<3.000000e+00> : tensor<f32>
+// CHECK:           %2 = chlo.broadcast_compare %0, %1 {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xi1>
+// CHECK:           %3 = torch_c.from_builtin_tensor %2 : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
+// CHECK:           return %3 : !torch.vtensor<[?,?],i1>
+func.func @torch.aten.gt.scalar(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
+  %int3 = torch.constant.int 3
+  %0 = torch.aten.gt.Scalar %arg0, %int3 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],i1>
+  return %0 : !torch.vtensor<[?,?],i1>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.gt.tensor(
+// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
+// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
+// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
+func.func @torch.aten.gt.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+  %0 = torch.aten.gt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
+  return %0 : !torch.vtensor<[?,?],i1>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.lt.tensor(
+// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction LT>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
+// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
+// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
+func.func @torch.aten.lt.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+  %0 = torch.aten.lt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
+  return %0 : !torch.vtensor<[?,?],i1>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.eq.tensor(
+// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction EQ>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
+// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
+// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
+func.func @torch.aten.eq.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+  %0 = torch.aten.eq.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
+  return %0 : !torch.vtensor<[?,?],i1>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.ne.tensor(
+// CHECK-SAME:                                    %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>, 
+// CHECK-SAME:                                    %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+// CHECK:           %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
+// CHECK:           %[[VAL_4:.*]] = chlo.broadcast_compare %[[VAL_2]], %[[VAL_3]] {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction NE>} : (tensor<?x?xf32>, tensor<64xf32>) -> tensor<?x?xi1>
+// CHECK:           %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
+// CHECK:           return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
+func.func @torch.aten.ne.tensor(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?],i1> {
+  %0 = torch.aten.ne.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[?,?],i1>
+  return %0 : !torch.vtensor<[?,?],i1>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.permute$basic(
+// CHECK-SAME:                                   %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[64,4],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
+// CHECK:           %[[VAL_2:.*]] = torch.constant.int 0
+// CHECK:           %[[VAL_3:.*]] = torch.constant.int 1
+// CHECK:           %[[VAL_4:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_5:.*]] = "mhlo.transpose"(%[[VAL_1]]) {permutation = dense<[1, 0]> : tensor<2xi64>} : (tensor<4x64xf32>) -> tensor<64x4xf32>
+// CHECK:           %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<64x4xf32> -> !torch.vtensor<[64,4],f32>
+// CHECK:           return %[[VAL_6]] : !torch.vtensor<[64,4],f32>
+func.func @torch.aten.permute$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[64,4],f32> {
+  %int0 = torch.constant.int 0
+  %int1 = torch.constant.int 1
+  %0 = torch.prim.ListConstruct %int1, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
+  %1 = torch.aten.permute %arg0, %0 : !torch.vtensor<[4,64],f32>, !torch.list<int> -> !torch.vtensor<[64,4],f32>
+  return %1 : !torch.vtensor<[64,4],f32>
+}
+
+// -----
+
+// CHECK-LABEL:   func.func @torch.aten.relu(
+// CHECK-SAME:                               %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
+// CHECK:           %[[VAL_2:.*]] = "chlo.constant_like"(%[[VAL_1]]) {value = 0.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK:           %[[VAL_3:.*]] = mhlo.maximum %[[VAL_1]], %[[VAL_2]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
+// CHECK:           return %[[VAL_4]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.relu(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.aten.relu %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+  return %0 : !torch.vtensor<[?,?],f32>
+}