Handle error shape inference (#47)

* add return to inferShape

* ran clang-format

* minor changes according to review

* fix format

src/Dialect/ONNX/ONNXOps.cpp

@@ -151,9 +151,8 @@ static void processConvStrideParam(T *op, Optional<ArrayAttr> kernelShape) {
 // Support function that computes default values for pads.
 //
 template <class T>
-static void processConvPadParam(T *op,
+static void processConvPadParam(T *op, ArrayRef<int64_t> inputShape,
-    ArrayRef<int64_t> inputShape, Optional<ArrayAttr> kernelShape,
+    Optional<ArrayAttr> kernelShape, Optional<ArrayAttr> stridesOpt,
-    Optional<ArrayAttr> stridesOpt,
     Optional<ArrayAttr> dilationsOpt = llvm::None) {
   auto builder = mlir::Builder(op->getContext());
 
@@ -256,7 +255,7 @@ static void processConvTypeParams(T *op, Value inputOperand) {
   processConvDilationParam<T>(op, kernelShape);
   auto dilationsOpt = op->dilations();
 
- // Strides.
+  // Strides.
   processConvStrideParam<T>(op, kernelShape);
   auto stridesOpt = op->strides();
 
@@ -341,219 +340,271 @@ ONNXEntryPointOp ONNXEntryPointOp::create(mlir::Location location,
 // Exp
 /// Infer the output shape of the ONNXExpOp. This method is required by the
 /// shape inference interface.
-void ONNXExpOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXExpOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Tanh
 /// Infer the output shape of the ONNXTanhOp. This method is required by the
 /// shape inference interface.
-void ONNXTanhOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXTanhOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Sinh
 /// Infer the output shape of the ONNXSinhOp. This method is required by the
 /// shape inference interface.
-void ONNXSinhOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXSinhOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Cosh
 /// Infer the output shape of the ONNXCoshOp. This method is required by the
 /// shape inference interface.
-void ONNXCoshOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXCoshOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Cos
 /// Infer the output shape of the ONNXCosOp. This method is required by the
 /// shape inference interface.
-void ONNXCosOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXCosOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Log
 /// Infer the output shape of the ONNXLogOp. This method is required by the
 /// shape inference interface.
-void ONNXLogOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXLogOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // HardSigmoid
 /// Infer the output shape of the ONNXHardSigmoidOp. This method is required by
 /// the shape inference interface.
-void ONNXHardSigmoidOp::inferShapes() {
+bool ONNXHardSigmoidOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Sigmoid
 /// Infer the output shape of the ONNXSigmoidOp. This method is required by the
 /// shape inference interface.
-void ONNXSigmoidOp::inferShapes() {
+bool ONNXSigmoidOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Elu
 /// Infer the output shape of the ONNXEluOp. This method is required by the
 /// shape inference interface.
-void ONNXEluOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXEluOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Relu
 /// Infer the output shape of the ONNXReluOp. This method is required by the
 /// shape inference interface.
-void ONNXReluOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXReluOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // LeakyRelu
 /// Infer the output shape of the ONNXLeakyReluOp. This method is required by
 /// the shape inference interface.
-void ONNXLeakyReluOp::inferShapes() {
+bool ONNXLeakyReluOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Selu
 /// Infer the output shape of the ONNXSeluOp. This method is required by
 /// the shape inference interface.
-void ONNXSeluOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXSeluOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Reciprocal
 /// Infer the output shape of the ONNXReciprocalOp. This method is required by
 /// the shape inference interface.
-void ONNXReciprocalOp::inferShapes() {
+bool ONNXReciprocalOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Softmax
 /// Infer the output shape of the ONNXSoftmaxOp. This method is required by
 /// the shape inference interface.
-void ONNXSoftmaxOp::inferShapes() {
+bool ONNXSoftmaxOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Softplus
 /// Infer the output shape of the ONNXSoftplusOp. This method is required by
 /// the shape inference interface.
-void ONNXSoftplusOp::inferShapes() {
+bool ONNXSoftplusOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Softsign
 /// Infer the output shape of the ONNXSoftsignOp. This method is required by
 /// the shape inference interface.
-void ONNXSoftsignOp::inferShapes() {
+bool ONNXSoftsignOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Sqrt
 /// Infer the output shape of the ONNXSqrtOp. This method is required by
 /// the shape inference interface.
-void ONNXSqrtOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXSqrtOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Sign
 /// Infer the output shape of the ONNXSignOp. This method is required by
 /// the shape inference interface.
-void ONNXSignOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXSignOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Abs
 /// Infer the output shape of the ONNXAbsOp. This method is required by the
 /// shape inference interface.
-void ONNXAbsOp::inferShapes() { getResult().setType(getOperand().getType()); }
+bool ONNXAbsOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+  return true;
+}
 
 //===----------------------------------------------------------------------===//
 // Add
 /// Infer the output shape of the ONNXAddOp. This method is required by the
 /// shape inference interface.
-void ONNXAddOp::inferShapes() {
+bool ONNXAddOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
-    return;
+    emitError("ONNXAddOp inferShapes failed");
+    return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Mul
 /// Infer the output shape of the ONNXMulOp. This method is required by the
 /// shape inference interface.
-void ONNXMulOp::inferShapes() {
+bool ONNXMulOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
-    return;
+    return false;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Div
 /// Infer the output shape of the ONNXDivOp. This method is required by the
 /// shape inference interface.
-void ONNXDivOp::inferShapes() {
+bool ONNXDivOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
-    return;
+    return false;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Sub
 /// Infer the output shape of the ONNXSubOp. This method is required by the
 /// shape inference interface.
-void ONNXSubOp::inferShapes() {
+bool ONNXSubOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
-    return;
+    return false;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // And
 /// Infer the output shape of the ONNXAndOp. This method is required by the
 /// shape inference interface.
-void ONNXAndOp::inferShapes() {
+bool ONNXAndOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
-    return;
+    return false;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Or
 /// Infer the output shape of the ONNXOrOp. This method is required by the
 /// shape inference interface.
-void ONNXOrOp::inferShapes() {
+bool ONNXOrOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
-    return;
+    return false;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Xor
 /// Infer the output shape of the ONNXXorOp. This method is required by the
 /// shape inference interface.
-void ONNXXorOp::inferShapes() {
+bool ONNXXorOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
-    return;
+    return false;
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
@@ -562,10 +613,10 @@ void ONNXXorOp::inferShapes() {
 // Sum
 /// Infer the output shape of the ONNXSumOp. This method is required by the
 /// shape inference interface.
-void ONNXSumOp::inferShapes() {
+bool ONNXSumOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
     if (!getOperand(i).getType().cast<RankedTensorType>())
-      return;
+      return false;
   }
   Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
@@ -573,16 +624,17 @@ void ONNXSumOp::inferShapes() {
     resultTy = getBroadcastedType(resultTy, nextTy);
   }
   getResult().setType(resultTy);
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Max
 /// Infer the output shape of the ONNXMaxOp. This method is required by the
 /// shape inference interface.
-void ONNXMaxOp::inferShapes() {
+bool ONNXMaxOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
     if (!getOperand(i).getType().cast<RankedTensorType>())
-      return;
+      return false;
   }
   Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
@@ -590,16 +642,17 @@ void ONNXMaxOp::inferShapes() {
     resultTy = getBroadcastedType(resultTy, nextTy);
   }
   getResult().setType(resultTy);
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Min
 /// Infer the output shape of the ONNXMinOp. This method is required by the
 /// shape inference interface.
-void ONNXMinOp::inferShapes() {
+bool ONNXMinOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
     if (!getOperand(i).getType().cast<RankedTensorType>())
-      return;
+      return false;
   }
   Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
@@ -607,25 +660,27 @@ void ONNXMinOp::inferShapes() {
     resultTy = getBroadcastedType(resultTy, nextTy);
   }
   getResult().setType(resultTy);
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Identity
 /// Infer the output shape of the ONNXIdentityOp. This method is required by the
 /// shape inference interface.
-void ONNXIdentityOp::inferShapes() {
+bool ONNXIdentityOp::inferShapes() {
   getResult().setType(getOperand().getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // MatMul
 
-void ONNXMatMulOp::inferShapes() {
+bool ONNXMatMulOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!A().getType().isa<RankedTensorType>() ||
       !B().getType().isa<RankedTensorType>())
-    return;
+    return false;
 
   auto lhsTy = A().getType().cast<RankedTensorType>();
   auto rhsTy = B().getType().cast<RankedTensorType>();
@@ -752,19 +807,20 @@ void ONNXMatMulOp::inferShapes() {
   }
 
   getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // Gemm
 
-void ONNXGemmOp::inferShapes() {
+bool ONNXGemmOp::inferShapes() {
   bool hasBias = !C().getType().isa<NoneType>();
   // Cannot infer shape if no shape exists.
   if (!A().getType().isa<RankedTensorType>() ||
       !B().getType().isa<RankedTensorType>() ||
       (hasBias && !C().getType().isa<RankedTensorType>()))
-    return;
+    return false;
   auto lhsTy = A().getType().cast<RankedTensorType>();
   auto rhsTy = B().getType().cast<RankedTensorType>();
 
@@ -796,17 +852,18 @@ void ONNXGemmOp::inferShapes() {
   dims.emplace_back(M);
   dims.emplace_back(N);
   getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
+  return true;
 }
 
 /// BatchNormalizationTestMode
-void ONNXBatchNormalizationTestModeOp::inferShapes() {
+bool ONNXBatchNormalizationTestModeOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!X().getType().isa<RankedTensorType>() ||
       !scale().getType().isa<RankedTensorType>() ||
       !B().getType().isa<RankedTensorType>() ||
       !mean().getType().isa<RankedTensorType>() ||
       !var().getType().isa<RankedTensorType>())
-    return;
+    return false;
 
   auto inputTensorTy = X().getType().cast<RankedTensorType>();
   auto scaleTensorTy = scale().getType().cast<RankedTensorType>();
@@ -845,6 +902,7 @@ void ONNXBatchNormalizationTestModeOp::inferShapes() {
 
   // The output tensor of the same shape as the input.
   getResult().setType(X().getType());
+  return true;
 }
 
 // TODO:
@@ -855,7 +913,7 @@ void ONNXBatchNormalizationTestModeOp::inferShapes() {
 
 // Reshape
 
-void ONNXReshapeOp::inferShapes() {
+bool ONNXReshapeOp::inferShapes() {
   // Cannot infer shape if no shape tensor is specified.
   if (!shape().getType().isa<RankedTensorType>())
     emitError("Shape tensor not ranked");
@@ -875,7 +933,7 @@ void ONNXReshapeOp::inferShapes() {
 
   // Compute total number of elements.
   int64_t totalInputSize = 1;
-  for(auto inputDim : inputTensorTy.getShape())
+  for (auto inputDim : inputTensorTy.getShape())
     totalInputSize *= inputDim;
 
   // Check if second argument of ReshapeOp is a constant.
@@ -891,7 +949,7 @@ void ONNXReshapeOp::inferShapes() {
 
     // Get dims from valueAttribute.
     auto valueIt = valueAttribute.getValues<IntegerAttr>().begin();
-    for (int i=0; i<outputRank; ++i)
+    for (int i = 0; i < outputRank; ++i)
       dims[i] = (*valueIt++).cast<IntegerAttr>().getInt();
 
     if (valueIt != valueAttribute.getValues<IntegerAttr>().end())
@@ -900,7 +958,7 @@ void ONNXReshapeOp::inferShapes() {
     int64_t numberOfDynamicInputs = 0;
     int64_t totalKnownDimsSize = 1;
     int64_t dynamicValueIndex = -1;
-    for (int i=0; i<outputRank; ++i) {
+    for (int i = 0; i < outputRank; ++i) {
       // Set output dimension.
       if (dims[i] == 0)
         dims[i] = inputTensorTy.getShape()[i];
@@ -924,16 +982,17 @@ void ONNXReshapeOp::inferShapes() {
 
   getResult().setType(
       RankedTensorType::get(dims, inputTensorTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // Transpose
 
-void ONNXTransposeOp::inferShapes() {
+bool ONNXTransposeOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!data().getType().isa<RankedTensorType>())
-    return;
+    return false;
 
   // Naive transposition which handles the default case of
   // reversing the shape of the tensor (similar to numpy.transpose).
@@ -951,62 +1010,67 @@ void ONNXTransposeOp::inferShapes() {
   }
 
   getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // ReduceMax
 
-void ONNXReduceMaxOp::inferShapes() {
+bool ONNXReduceMaxOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
     emitError("Shape tensor not ranked");
-    return;
+    return false;
   }
 
   auto operandTy = getOperand().getType().cast<RankedTensorType>();
   getResult().setType(getReductionOutputType(operandTy, axes(), keepdims()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // ReduceMin
 
-void ONNXReduceMinOp::inferShapes() {
+bool ONNXReduceMinOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
     emitError("Shape tensor not ranked");
-    return;
+    return false;
   }
 
   auto operandTy = getOperand().getType().cast<RankedTensorType>();
   getResult().setType(getReductionOutputType(operandTy, axes(), keepdims()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // ReduceProd
 
-void ONNXReduceProdOp::inferShapes() {
+bool ONNXReduceProdOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
     emitError("Shape tensor not ranked");
-    return;
+    return false;
   }
 
   auto operandTy = getOperand().getType().cast<RankedTensorType>();
   getResult().setType(getReductionOutputType(operandTy, axes(), keepdims()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 
 // ReduceSum
 
-void ONNXReduceSumOp::inferShapes() {
+bool ONNXReduceSumOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
     emitError("Shape tensor not ranked");
-    return;
+    return false;
   }
 
   auto operandTy = getOperand().getType().cast<RankedTensorType>();
   getResult().setType(getReductionOutputType(operandTy, axes(), keepdims()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1022,7 +1086,7 @@ void ONNXReduceSumOp::inferShapes() {
 //   -  kernelShape: inferred from weight matrix if not defined by user;
 //   -  pads: set to proper value, 0 if not defined by user.
 
-void ONNXConvOp::inferShapes() {
+bool ONNXConvOp::inferShapes() {
   // Generic shape for data input X, weight tensor W, and optional bias B
   // X: (N x C x D1 x D2 ... x Dn)
   // W: (M x C/group x k1 x k2 x ... x kn)
@@ -1034,7 +1098,7 @@ void ONNXConvOp::inferShapes() {
   if (!X().getType().isa<RankedTensorType>() ||
       !W().getType().isa<RankedTensorType>() ||
       (hasBias && !B().getType().isa<RankedTensorType>()))
-    return;
+    return false;
 
   auto xTy = X().getType().cast<RankedTensorType>();
   auto xShape = xTy.getShape();
@@ -1043,12 +1107,16 @@ void ONNXConvOp::inferShapes() {
   auto builder = mlir::Builder(this->getContext());
 
   // Lowest supported convolution is a one dimensional convolution.
-  if (xShape.size() < 3)
+  if (xShape.size() < 3) {
     emitError("Data input shape must be at least (NxCxD1)");
+    return false;
+  }
 
   // Check that shape of weight and data have same length.
-  if (xShape.size() != weightShape.size())
+  if (xShape.size() != weightShape.size()) {
     emitError("Weight size not compatible with data size");
+    return false;
+  }
 
   // Group is a required attribute and should have default value of 1.
   int64_t group = ONNXConvOp::group().getSExtValue();
@@ -1059,19 +1127,25 @@ void ONNXConvOp::inferShapes() {
 
   // Check that the X.shape[1] == (W.shape[1] * group) == C condition holds.
   if (xShape[1] != -1 && weightShape[1] != -1 &&
-      xShape[1] != (weightShape[1] * group))
+      xShape[1] != (weightShape[1] * group)) {
     emitError("Channel dimension mismatch");
+    return false;
+  }
 
   // Check the size of bias.
   if (hasBias) {
     auto bTx = B().getType().cast<RankedTensorType>();
     auto bShape = bTx.getShape();
-    if (bShape.size() != 1)
+    if (bShape.size() != 1) {
       emitError("bias should be one dimensional");
-    if (bShape[0] != weightShape[0])
+      return false;
+    }
+    if (bShape[0] != weightShape[0]) {
       emitError("bias should have same dimensions as weight's first dimension");
+      return false;
+    }
   }
-  
+
   // Note: the value of the group attribut only impacts the way the
   // computation is carried out and not the actual output size.
 
@@ -1083,12 +1157,16 @@ void ONNXConvOp::inferShapes() {
   // argument.
   auto kernelShape = kernel_shape();
   if (kernelShape.hasValue()) {
-    if (ArrayAttrSize(kernelShape) != spatialRank)
+    if (ArrayAttrSize(kernelShape) != spatialRank) {
       emitError("kernel_shape length incompatible with spatial dimensions");
+      return false;
+    }
     // Have the right number of values, check them.
     for (int i = 0; i < spatialRank; ++i)
-      if (ArrayAttrIntVal(kernelShape, i) < 1)
+      if (ArrayAttrIntVal(kernelShape, i) < 1) {
         emitError("bad kernel_shape value");
+        return false;
+      }
   } else {
     // Deduce shape from weight input.
     SmallVector<int64_t, 2> defaultVals;
@@ -1119,6 +1197,7 @@ void ONNXConvOp::inferShapes() {
       &outputDims, xShape, kernelShape, padsOpt, stridesOpt, dilationsOpt);
 
   getResult().setType(RankedTensorType::get(outputDims, xTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1129,10 +1208,10 @@ void ONNXConvOp::inferShapes() {
 //   -  strides: set to 1 if not defined by user;
 //   -  pads: set to proper value, 0 if not defined by user.
 
-void ONNXAveragePoolOp::inferShapes() {
+bool ONNXAveragePoolOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!X().getType().isa<RankedTensorType>())
-    return;
+    return false;
 
   // Get shape of input.
   auto xTy = X().getType().cast<RankedTensorType>();
@@ -1163,6 +1242,7 @@ void ONNXAveragePoolOp::inferShapes() {
       llvm::None, ceilMode);
 
   getResult().setType(RankedTensorType::get(outputDims, xTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1173,10 +1253,10 @@ void ONNXAveragePoolOp::inferShapes() {
 //   -  dilations, strides: set to 1 if not defined by user;
 //   -  pads: set to proper value, 0 if not defined by user.
 
-void ONNXMaxPoolSingleOutOp::inferShapes() {
+bool ONNXMaxPoolSingleOutOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!X().getType().isa<RankedTensorType>())
-    return;
+    return false;
 
   // Get shape of input.
   auto xTy = X().getType().cast<RankedTensorType>();
@@ -1211,6 +1291,7 @@ void ONNXMaxPoolSingleOutOp::inferShapes() {
       dilationsOpt, ceilMode);
 
   getResult().setType(RankedTensorType::get(outputDims, xTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
@@ -1234,7 +1315,7 @@ static Type padShapeInferenceHelper(Value data, ArrayAttr padsOpt) {
       // Have to non-negative constant
       if (p1 < 0 || p2 < 0)
         return (Type)NULL;
-      if (outputShape[i] != -1) 
+      if (outputShape[i] != -1)
         outputShape[i] += p1 + p2;
     }
 
@@ -1246,24 +1327,26 @@ static Type padShapeInferenceHelper(Value data, ArrayAttr padsOpt) {
 
 // PadConstantPad
 
-void ONNXPadConstantPadOp::inferShapes() {
+bool ONNXPadConstantPadOp::inferShapes() {
   auto outputType = padShapeInferenceHelper(data(), pads());
   if (outputType) {
     getResult().setType(outputType);
+    return true;
   }
-  return;
+  return false;
 }
 
 //===----------------------------------------------------------------------===//
 
 // PadConstantValuePad
 
-void ONNXPadConstantValuePadOp::inferShapes() {
+bool ONNXPadConstantValuePadOp::inferShapes() {
   auto outputType = padShapeInferenceHelper(data(), pads());
   if (outputType) {
     getResult().setType(outputType);
-  } 
+    return true;
-  return;
+  }
+  return false;
 }
 
 void ONNXPadConstantValuePadOp::build(Builder *builder, OperationState &state,
@@ -1280,9 +1363,9 @@ void ONNXPadConstantValuePadOp::build(Builder *builder, OperationState &state,
 
 // Unsqueeze
 
-void ONNXUnsqueezeOp::inferShapes() {
+bool ONNXUnsqueezeOp::inferShapes() {
   if (!data().getType().isa<RankedTensorType>())
-    return;
+    return false;
 
   auto operandTy = data().getType().cast<RankedTensorType>();
   int inRank = operandTy.getRank();
@@ -1299,11 +1382,14 @@ void ONNXUnsqueezeOp::inferShapes() {
       assert(axis >= -outRank && axis <= outRank - 1);
       if (std::find(axes.begin(), axes.end(), axis) == axes.end())
         axes.emplace_back(axis);
-      else
+      else {
         emitError("Duplicated axes");
+        return false;
+      }
     }
   } else {
     emitError("Axes attribute is required");
+    return false;
   }
 
   SmallVector<int64_t, 4> dims;
@@ -1315,12 +1401,13 @@ void ONNXUnsqueezeOp::inferShapes() {
     }
   }
   getResult().setType(RankedTensorType::get(dims, operandTy.getElementType()));
+  return true;
 }
 
 //===----------------------------------------------------------------------===//
 // Constant
 
-void ONNXConstantOp::inferShapes() {
+bool ONNXConstantOp::inferShapes() {
   if ((sparse_value().hasValue() && value().hasValue()) ||
       (!sparse_value().hasValue() && !value().hasValue()))
     emitError("Require exactly one of the two attributes, either value or "
@@ -1332,6 +1419,7 @@ void ONNXConstantOp::inferShapes() {
   else
     valAttr = valueAttr().cast<DenseElementsAttr>();
   getResult().setType(valAttr.getType());
+  return true;
 }
 
 //===----------------------------------------------------------------------===//

src/Interface/ShapeInferenceInterface.td

@@ -25,7 +25,7 @@ def ShapeInferenceOpInterface : OpInterface<"ShapeInference"> {
 
   let methods = [
     InterfaceMethod<"Infer and set the output shape for the current operation.",
-                    "void", "inferShapes">
+                    "bool", "inferShapes">
   ];
 }
 

src/Transform/ONNX/ShapeInferencePass.cpp

@@ -35,7 +35,11 @@ public:
     f.walk([&](mlir::Operation *op) {
       if (returnsDynamicShape(op)) {
         if (auto shape_op = dyn_cast<ShapeInference>(op)) {
-          shape_op.inferShapes();
+          if (!shape_op.inferShapes()) {
+            op->emitError("unable to infer shape of operation without shape "
+                          "inference interface");
+            return signalPassFailure();
+          }
         } else {
           op->emitError("unable to infer shape of operation without shape "
                         "inference interface");