Merge pull request #35 from clang-ykt/perm-transpose

Infer shape of transposition operations using the perm attribute

src/dialect/onnx/onnx_ops.cpp

@@ -13,6 +13,7 @@
 #include "mlir/IR/Function.h"
 #include "mlir/IR/IntegerSet.h"
 #include "mlir/IR/Matchers.h"
+#include "mlir/IR/Module.h"
 #include "mlir/IR/OpImplementation.h"
 #include "mlir/IR/PatternMatch.h"
 #include "llvm/ADT/SetVector.h"
@@ -407,13 +408,38 @@ void ONNXTransposeOp::inferShapes() {
 
   // 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()));
+  SmallVector<int64_t, 2> dims;
+
+  if (auto permutation = getAttrOfType<ArrayAttr>(
+          ONNXTransposeOp::getPermAttrName())) {
+    // Perform transposition according to perm attribute.
+    for (auto perm : permutation.getValue())
+      dims.emplace_back(arrayTy.getShape()[perm.cast<IntegerAttr>().getInt()]);
+  } else {
+    // Default
+    for (auto dim : llvm::reverse(arrayTy.getShape()))
+      dims.emplace_back(dim);
+  }
+
   getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
 }
 
+LogicalResult verify(ONNXTransposeOp op) {
+  auto module = op.getParentOfType<ModuleOp>();
+  if (!module)
+    op.emitError("Expected to belong to a module.");
+
+  if (auto permutation = op.getAttrOfType<ArrayAttr>(
+          ONNXTransposeOp::getPermAttrName())) {
+    for (auto perm : permutation.getValue())
+      if (perm.cast<IntegerAttr>().getInt() < 0)
+        op.emitError("Cannot tranpose, permuation contains negative index.");
+  }
+
+  return success();
+}
+
 //===----------------------------------------------------------------------===//
 // TableGen'd op method definitions
 //===----------------------------------------------------------------------===//

src/dialect/onnx/onnxop.inc

@@ -3098,6 +3098,12 @@ def ONNXTransposeOp:ONNX_Op<"Transpose",
   }];
   let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
+
+  let extraClassDeclaration = [{
+    static StringRef getPermAttrName() { return "perm"; }
+  }];
+
+  let verifier = [{ return ::verify(*this); }];
 }
 
 def ONNXUniqueOp:ONNX_Op<"Unique", 

test/mlir/onnx/onnx_shape_inference.mlir

@@ -10,3 +10,13 @@ func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
 // CHECK-LABEL: test_default_transpose
 // CHECK: [[RES:%.+]] = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<32x1x5x5xf32>
 // CHECK: return [[RES]] : tensor<32x1x5x5xf32>
+
+/// Test shape inference for transposition when perm attribute is specified.
+func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_transpose
+// CHECK: [[RES_ATTR:%.+]] = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<1x5x32x5xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>