[circle-mlir/dialect] Enable TransposeOp IR

This will enable TransposeOp IR.

ONE-DCO-1.0-Signed-off-by: SaeHie Park <saehie.park@gmail.com>

Author: seanshpark

circle-mlir/circle-mlir/lib/dialect/mlir/CircleOps.td

@@ -418,4 +418,41 @@ def CIR_NoValueOp : Op<CIR_Dialect, "no_value", [ConstantLike, Pure]> {
   }];
 }
 
+def CIR_TransposeOp : CIR_Op<"transpose", [
+    Pure,
+    DeclareOpInterfaceMethods<CIR_ShapeInferenceOpInterface>,
+    CIR_OperandHasRankAtMost<0, 5>,
+    CIR_OperandHasRank<1, 1>,
+    PredOpTrait<"input and output must have same element type", CIR_TCresVTEtIsSameAsOp<0, 0>>/*,
+    SameOperandsAndResultsScale*/]> {
+  let summary = "Transpose operator";
+
+  let description = [{
+    Returns the Transpose of x
+  }];
+
+  let arguments = (ins
+    CIR_TensorOf<[I32, F32, I8, UI8, /*QI8, QUI8, CIR_Quint8,*/ I1, I64/*, QI16*/]>:$input,
+    CIR_TensorOf<[I32]>:$perm
+  );
+
+  let results = (outs
+    CIR_TensorOf<[I32, F32, I8, UI8, /*QI8, QUI8, CIR_Quint8,*/ I1, I64/*, QI16*/]>:$output
+  );
+
+  let hasVerifier = 1;
+
+  let hasFolder = 1;
+
+  let builders = [
+    OpBuilder<(ins "Value":$input, "Value":$perm),
+    [{ BuildTransposeOp(&$_builder, $_state, input, perm); }]>
+  ];
+
+  let extraClassDeclaration = [{
+    // Quantized axes are verified in the Verify function.
+    bool RequiredSameQuantizedAxes() { return false; }
+  }];
+}
+
 #endif // CIRCLE_OPS

circle-mlir/circle-mlir/lib/dialect/src/CircleDialect.cpp

@@ -446,6 +446,7 @@ void ConstBytesAttr::print(mlir::AsmPrinter &printer) const
 #include "ops/ConstOp.h"
 #include "ops/CustomOp.h"
 #include "ops/NoValueOp.h"
+#include "ops/TransposeOp.h"
 
 #include "mlir/CircleOpsDialect.cc.inc"
 #include "mlir/CircleOpsEnums.cc.inc"

circle-mlir/circle-mlir/lib/dialect/src/ShapeInference.cpp

@@ -149,5 +149,57 @@ void CustomOp::inferShapes()
   }
 }
 
+//===----------------------------------------------------------------------===//
+// TransposeOp
+//===----------------------------------------------------------------------===//
+
+void TransposeOp::inferShapes()
+{
+  TransposeOp op = *this;
+  auto output_type = op.getOutput().getType().cast<ShapedType>();
+  if (output_type.hasStaticShape())
+    return;
+
+  auto input_type = op.getInput().getType().cast<ShapedType>();
+  auto perm_type = op.getPerm().getType().cast<ShapedType>();
+
+  if (input_type.hasStaticShape() && perm_type.hasStaticShape())
+  {
+    if (perm_type.getNumElements() != input_type.getRank())
+    {
+      return;
+    }
+  }
+
+  mlir::DenseIntElementsAttr perm;
+  if (!matchPattern(op.getPerm(), m_Constant(&perm)))
+  {
+    return;
+  }
+
+  llvm::SmallVector<int64_t, 4> perm_list;
+  for (const auto &perm_element : perm.getValues<APInt>())
+  {
+    const int64_t val = perm_element.getSExtValue();
+    perm_list.push_back(val);
+  }
+
+  // Get transposed shape and set it to the output type
+  if (input_type.hasStaticShape() && !output_type.hasStaticShape())
+  {
+    llvm::SmallVector<int64_t, 4> transposed_shape;
+    for (int64_t axis : perm_list)
+    {
+      transposed_shape.push_back(input_type.getDimSize(axis));
+    }
+
+    dumpShape<TransposeOp>(op, transposed_shape);
+
+    auto inferred_type =
+      mlir::Circle::GetTypeFromTensorShape(transposed_shape, input_type.getElementType());
+    getResult().setType(inferred_type);
+  }
+}
+
 } // namespace Circle
 } // namespace mlir

circle-mlir/circle-mlir/lib/dialect/src/ops/TransposeOp.h

@@ -0,0 +1,204 @@
+/*
+ * Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// from tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
+
+#ifndef __CIRCLE_MLIR_DIALECT_OPS_TRANSPOSE_OP_H__
+#define __CIRCLE_MLIR_DIALECT_OPS_TRANSPOSE_OP_H__
+
+#include "circle-mlir/dialect/CircleDialect.h"
+
+namespace mlir
+{
+namespace Circle
+{
+
+//===----------------------------------------------------------------------===//
+// TransposeOp
+//===----------------------------------------------------------------------===//
+
+namespace
+{
+
+// Computes the permutation of a constant `input_tensor` according to `perm`.
+// The function recursively traverses the dimensions of the output tensor in
+// a row-major order and writes the value in the output tensor into
+// `new_values`.
+void ComputePermutation(ElementsAttr input_tensor, ArrayRef<int32_t> perm,
+                        ArrayRef<int64_t> output_shape, int num_dimensions, int output_axis,
+                        std::vector<uint64_t> *input_indices, std::vector<Attribute> *new_values)
+{
+  // Refer to the implementation of `Transpose` function in
+  // tensorflow/lite/kernels/internal/reference/reference_ops.h
+  assert(output_axis < num_dimensions);
+  const int input_axis = perm[output_axis];
+  for (int i = 0; i < output_shape[output_axis]; ++i)
+  {
+    // Update the input indices on `input_axis`.
+    input_indices->at(input_axis) = i;
+    // Write the value from `input_tensor` if it is the last axis or
+    // recurse into the next axis.
+    const bool is_last_axis = output_axis == num_dimensions - 1;
+    if (is_last_axis)
+    {
+      new_values->push_back(input_tensor.getValues<Attribute>()[*input_indices]);
+    }
+    else
+    {
+      ComputePermutation(input_tensor, perm, output_shape, num_dimensions, output_axis + 1,
+                         input_indices, new_values);
+    }
+  }
+}
+
+} // namespace
+
+OpFoldResult TransposeOp::fold(FoldAdaptor adaptor)
+{
+  auto operands = adaptor.getOperands();
+  assert(operands.size() == 2);
+  auto input_tensor = operands[0].dyn_cast_or_null<ElementsAttr>();
+  auto perm_tensor = operands[1].dyn_cast_or_null<ElementsAttr>();
+  if (!input_tensor || !perm_tensor)
+    return nullptr;
+
+  // Do not try to fold elements attr of a quant type because
+  // DenseElementsAttr does not support it.
+  if (!getType().cast<ShapedType>().getElementType().isSignlessIntOrFloat())
+    return nullptr;
+
+  assert(perm_tensor.getShapedType().getRank() == 1);
+  const int num_dimensions = input_tensor.getShapedType().getRank();
+  assert(perm_tensor.getShapedType().getNumElements() == num_dimensions);
+
+  ArrayRef<int64_t> input_shape = input_tensor.getShapedType().getShape();
+  auto output_type = getType().cast<ShapedType>();
+
+  SmallVector<int32_t, 4> perm;
+  SmallVector<int64_t, 4> output_shape;
+  for (int i = 0; i < num_dimensions; ++i)
+  {
+    perm.push_back(perm_tensor.getValues<IntegerAttr>()[i].getInt());
+    output_shape.push_back(input_shape[perm[i]]);
+
+    // Check that the derived output shape matches the static shape.
+    assert(!output_type.hasStaticShape() || output_type.getShape()[i] == output_shape[i]);
+  }
+
+  std::vector<Attribute> new_values;
+  new_values.reserve(input_tensor.getShapedType().getNumElements());
+  std::vector<uint64_t> input_indices(num_dimensions);
+  ComputePermutation(input_tensor, perm, output_shape, num_dimensions,
+                     /*output_axis=*/0, &input_indices, &new_values);
+  auto result_type =
+    mlir::Circle::GetTypeFromTensorShape(output_shape, output_type.getElementType());
+  return DenseElementsAttr::get(result_type, new_values);
+}
+
+mlir::LogicalResult TransposeOp::verify()
+{
+  TransposeOp op = *this;
+  auto input_type = op.getInput().getType().cast<ShapedType>();
+  auto perm_type = op.getPerm().getType().cast<ShapedType>();
+  auto output_type = op.getOutput().getType().cast<ShapedType>();
+  if (input_type.hasStaticShape() && perm_type.hasStaticShape())
+  {
+    if (perm_type.getNumElements() != input_type.getRank())
+    {
+      return op.emitOpError("perm tensor elements size is not equal to input tensor rank");
+    }
+  }
+
+  mlir::DenseIntElementsAttr perm;
+  if (!matchPattern(op.getPerm(), m_Constant(&perm)))
+  {
+    return success();
+  }
+
+  int index = 0;
+  llvm::SmallVector<int64_t, 4> axes;
+  for (const auto &axis_int : perm.getValues<APInt>())
+  {
+    const int64_t axis = axis_int.getSExtValue();
+    if (axis < 0 || (input_type.hasRank() && axis >= input_type.getRank()))
+    {
+      return op.emitOpError(llvm::formatv("perm[{0}] must be in [0, rank)", index));
+    }
+    if (std::count(axes.begin(), axes.end(), axis) > 0)
+    {
+      return op.emitOpError(llvm::formatv("perm[{0}] cannot have duplicated axis", index));
+    }
+    axes.push_back(axis);
+    index++;
+  }
+
+  if (input_type.hasStaticShape() && output_type.hasStaticShape())
+  {
+    llvm::SmallVector<int64_t, 4> transposed_shape;
+    for (int64_t axis : axes)
+    {
+      transposed_shape.push_back(input_type.getDimSize(axis));
+    }
+    auto expected_output_type =
+      mlir::Circle::GetTypeFromTensorShape(transposed_shape, input_type.getElementType());
+    if (failed(mlir::verifyCompatibleShape(output_type, expected_output_type)))
+    {
+      return op.emitOpError(
+        llvm::formatv("expect output type {0}, got {1}", expected_output_type, output_type));
+    }
+  }
+
+  // TODO enable quantization
+
+  return success();
+}
+
+static void BuildTransposeOp(OpBuilder *builder, OperationState &result, Value input, Value perm)
+{
+  // Output size is only known if input is ranked and perm is a constant.
+  auto input_type = input.getType().cast<TensorType>();
+  mlir::DenseIntElementsAttr perm_const;
+  if (!input_type.hasRank() || !matchPattern(perm, m_Constant(&perm_const)) || perm_const.empty())
+  {
+    TransposeOp::build(*builder, result, UnrankedTensorType::get(input_type.getElementType()),
+                       input, perm);
+    return;
+  }
+
+  const auto perm_value_it = perm_const.value_begin<APInt>();
+
+  const ArrayRef<int64_t> input_shape = input_type.getShape();
+  SmallVector<int64_t, 4> output_shape(input_shape.size());
+
+  for (int i = 0; i < output_shape.size(); ++i)
+  {
+    const APInt perm_val = perm_value_it[i];
+    output_shape[i] = input_shape[perm_val.getSExtValue()];
+  }
+
+  auto element_type = input_type.getElementType();
+
+  // TODO enable quantization
+
+  TransposeOp::build(*builder, result,
+                     mlir::Circle::GetTypeFromTensorShape(output_shape, element_type), input, perm);
+}
+
+} // namespace Circle
+} // namespace mlir
+
+#endif // __CIRCLE_MLIR_DIALECT_OPS_TRANSPOSE_OP_H__