[MLIR][ONNX] Add OnnxToTorch support for Gelu (#2647)

This commit adds the OnnxToTorch support for Gelu op.

---------

Co-authored-by: Rob Suderman <suderman@google.com>

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -27,6 +27,26 @@ using namespace mlir::torch::onnx_c;
 // thing here, so we simplify.
 void mlir::torch::onnx_c::populateDefaultDomainGtoP(
     OnnxCustomOpConversionPattern &patterns) {
+
+  patterns.onOp(
+      "Gelu", 20, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Value operand;
+        Torch::ValueTensorType resultType;
+        std::string approximate;
+
+        if (binder.tensorOperand(operand) ||
+            binder.tensorResultType(resultType) ||
+            binder.customOpNameStringAttr(approximate, "approximate", "none"))
+          return failure();
+
+        Value vApproximate = rewriter.create<Torch::ConstantStrOp>(
+            binder.getLoc(), rewriter.getType<Torch::StringType>(),
+            rewriter.getStringAttr(approximate));
+
+        rewriter.replaceOpWithNewOp<Torch::AtenGeluOp>(binder.op, resultType,
+                                                       operand, vApproximate);
+        return success();
+      });
   patterns.onOp("MatMul", 13,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;

test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir

@@ -1,4 +1,4 @@
-// RUN: torch-mlir-opt <%s -convert-torch-onnx-to-torch | FileCheck %s
+// RUN: torch-mlir-opt <%s -convert-torch-onnx-to-torch --split-input-file | FileCheck %s
 // Generally, the test cases accumulated here come from running the importer
 // over all included backend tests that involve simple ops with no model
 // level constants. This is a pragmatic choice which lets us have a lot
@@ -131,6 +131,8 @@ func.func @test_matmul_2d(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtenso
   return %0 : !torch.vtensor<[3,3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: @test_matmul_3d
 func.func @test_matmul_3d(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vtensor<[2,4,3],f32>) -> !torch.vtensor<[2,3,3],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,4,3],f32> -> !torch.vtensor<[2,3,3],f32>
@@ -138,6 +140,8 @@ func.func @test_matmul_3d(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vten
   return %0 : !torch.vtensor<[2,3,3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: @test_matmul_4d
 func.func @test_matmul_4d(%arg0: !torch.vtensor<[1,2,3,4],f32>, %arg1: !torch.vtensor<[1,2,4,3],f32>) -> !torch.vtensor<[1,2,3,3],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[1,2,3,4],f32>, !torch.vtensor<[1,2,4,3],f32> -> !torch.vtensor<[1,2,3,3],f32>
@@ -145,6 +149,48 @@ func.func @test_matmul_4d(%arg0: !torch.vtensor<[1,2,3,4],f32>, %arg1: !torch.vt
   return %0 : !torch.vtensor<[1,2,3,3],f32>
 }
 
+// -----
+
+// CHECK-LABEL: @test_gelu_default_1
+func.func @test_gelu_default_1(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[STR1:.*]] = torch.constant.str "none"
+  // CHECK: torch.aten.gelu %arg0, %[[STR1]] : !torch.vtensor<[3],f32>, !torch.str -> !torch.vtensor<[3],f32>
+  %0 = torch.operator "onnx.Gelu"(%arg0) : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
+  return %0 : !torch.vtensor<[3],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_gelu_default_2
+func.func @test_gelu_default_2(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[STR1:.*]] = torch.constant.str "none"
+  // CHECK: torch.aten.gelu %arg0, %[[STR1]] : !torch.vtensor<[3,4,5],f32>, !torch.str -> !torch.vtensor<[3,4,5],f32>
+  %0 = torch.operator "onnx.Gelu"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
+  return %0 : !torch.vtensor<[3,4,5],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_gelu_tanh_1
+func.func @test_gelu_tanh_1(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[STR1:.*]] = torch.constant.str "tanh"
+  // CHECK: torch.aten.gelu %arg0, %[[STR1]] : !torch.vtensor<[3],f32>, !torch.str -> !torch.vtensor<[3],f32>
+  %0 = torch.operator "onnx.Gelu"(%arg0) {torch.onnx.approximate = "tanh"} : (!torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32>
+  return %0 : !torch.vtensor<[3],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_gelu_tanh_2
+func.func @test_gelu_tanh_2(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[STR1:.*]] = torch.constant.str "tanh"
+  // CHECK: torch.aten.gelu %arg0, %[[STR1]] : !torch.vtensor<[3,4,5],f32>, !torch.str -> !torch.vtensor<[3,4,5],f32>
+  %0 = torch.operator "onnx.Gelu"(%arg0) {torch.onnx.approximate = "tanh"} : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
+  return %0 : !torch.vtensor<[3,4,5],f32>
+}
+
+// -----
+
 // CHECK-LABEL: func.func @test_less_or_equal
 func.func @test_less_or_equal(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],i1> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 16 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4,5],f32>