Add the default shape inference for the transposition operation.

src/dialect/onnx/gen_doc.py

@@ -267,7 +267,7 @@ def gen_schema(schema) :
                         'Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor',
                         'Sum', 'Max', 'Min', 'MatMul', 'Gemm', 'LeakyRelu',
                         'Elu', 'Selu', 'HardSigmoid', 'Reshape', 'Reciprocal',
-                        'Identity', 'Cos', 'Log']
+                        'Identity', 'Cos', 'Log', 'Transpose']
     CanonicalList=['Add', 'Identity']
     line_indent = '  '
 

src/dialect/onnx/onnx_ops.cpp

@@ -396,6 +396,24 @@ void ONNXReshapeOp::inferShapes() {
       RankedTensorType::get(dims, inputTensorTy.getElementType()));
 }
 
+//===----------------------------------------------------------------------===//
+
+// Transpose
+
+void ONNXTransposeOp::inferShapes() {
+  // Cannot infer shape if no shape exists.
+  if (!getOperand()->getType().isa<RankedTensorType>())
+    emitError("Shape tensor not ranked.");
+
+  // Naive transposition which handles the default case of
+  // reversing the shape of the tensor (similar to numpy.transpose).
+  // TODO: Once attributes are supported we can handle the case where the
+  // transposition uses a permutation vector to interchange the axes.
+  auto arrayTy = getOperand()->getType().cast<RankedTensorType>();
+  SmallVector<int64_t, 2> dims(llvm::reverse(arrayTy.getShape()));
+  getResult()->setType(RankedTensorType::get(dims, arrayTy.getElementType()));
+}
+
 //===----------------------------------------------------------------------===//
 // TableGen'd op method definitions
 //===----------------------------------------------------------------------===//

src/dialect/onnx/onnxop.inc

@@ -3089,7 +3089,7 @@ def ONNXTopKOp:ONNX_Op<"TopK",
 }
 
 def ONNXTransposeOp:ONNX_Op<"Transpose", 
-    [NoSideEffect]> {
+    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let summary = "ONNX Transpose operation";
   let description = [{
     "Transpose the input tensor similar to numpy.transpose. For example, when"

src/pass/shape_inference_pass.cpp

@@ -115,7 +115,8 @@ public:
         op->getName().getStringRef() != "onnx.MatMul" &&
         op->getName().getStringRef() != "onnx.Gemm" &&
         op->getName().getStringRef() != "onnx.FullGemm" &&
-        op->getName().getStringRef() != "onnx.Reshape")
+        op->getName().getStringRef() != "onnx.Reshape" &&
+        op->getName().getStringRef() != "onnx.Transpose")
       return false;
     return llvm::any_of(op->getResultTypes(), [](Type result_type) {
       return !result_type.isa<RankedTensorType>();

test/mlir/onnx/onnx_shape_inference.mlir

@@ -0,0 +1,12 @@
+// RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
+
+/// Test the default behavior of transpose when no information for the
+/// permutation of the axes is provided.
+func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_default_transpose
+// CHECK: [[RES:%.+]] = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<32x1x5x5xf32>
+// CHECK: return [[RES]] : tensor<32x1x5x5xf32>