[MLIR][ONNX] Add OnnxToTorch support for matmul ops (#2629)

This commit adds the OnnxToTorch support for Matmul.

Author: wu-s-john

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -26,4 +26,17 @@ using namespace mlir::torch::onnx_c;
 // results in a lot of ONNX test cases that all reduce to the exact same
 // thing here, so we simplify.
 void mlir::torch::onnx_c::populateDefaultDomainGtoP(
-    OnnxCustomOpConversionPattern &patterns) {}
+    OnnxCustomOpConversionPattern &patterns) {
+
+  patterns.onOp("MatMul", 13,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value lhs, rhs;
+                  if (binder.tensorOperands(lhs, rhs) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
+                  rewriter.replaceOpWithNewOp<Torch::AtenMatmulOp>(
+                      binder.op, resultType, lhs, rhs);
+                  return success();
+                });
+}

test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir

@@ -0,0 +1,26 @@
+// RUN: torch-mlir-opt <%s -convert-torch-onnx-to-torch | 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
+// of tests in this file, whereas the others tend to be more bespoke.
+
+// CHECK-LABEL: @test_matmul_2d
+func.func @test_matmul_2d(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4,3],f32>) -> !torch.vtensor<[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<[3,4],f32>, !torch.vtensor<[4,3],f32> -> !torch.vtensor<[3,3],f32>
+  %0 = torch.operator "onnx.MatMul"(%arg0, %arg1) : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[4,3],f32>) -> !torch.vtensor<[3,3],f32>
+  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>
+  %0 = torch.operator "onnx.MatMul"(%arg0, %arg1) : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,4,3],f32>) -> !torch.vtensor<[2,3,3],f32>
+  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>
+  %0 = torch.operator "onnx.MatMul"(%arg0, %arg1) : (!torch.vtensor<[1,2,3,4],f32>, !torch.vtensor<[1,2,4,3],f32>) -> !torch.vtensor<[1,2,3,3],f32>
+  return %0 : !torch.vtensor<[1,2,3,3],f32>
+}