Fix input argument indexing error (#69)

* Reorganize main function.

* Follow review comments.

* Emit constants are globals in Krnl and LLVM dialects.

* Fixes. Emit error before return in shape inference.

* Fix description.

* Fix emitted error message.

* Fix index name.

src/Builder/FrontendDialectTransformer.cpp

@@ -434,10 +434,17 @@ private:
     module_.push_back(entryPoint);
 
     // Map graph inputs to entry block arguments.
-    for (int i = 0; i < graph.input().size(); ++i)
+    // Counter of un-initialized tensors. This counter is used to index the
+    // entry block arguments.
+    int entryBlockArgIdx = 0;
+    for (int i = 0; i < graph.input().size(); ++i) {
       if (!initializedTensors.ContainKey(
-              legalize_name(graph.input()[i].name())))
+              legalize_name(graph.input()[i].name()))) {
-        ImportInputTensorSymbol(graph.input()[i], entryBlock.getArguments()[i]);
+        ImportInputTensorSymbol(
+            graph.input()[i], entryBlock.getArguments()[entryBlockArgIdx]);
+        entryBlockArgIdx++;
+      }
+    }
 
     // Create a NoneTyped constant to be used for optional operation inputs
     // which are not used.

src/Dialect/ONNX/ONNXOps.cpp

@@ -514,7 +514,7 @@ bool ONNXAbsOp::inferShapes() {
 bool ONNXAddOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>()) {
-    emitError("ONNXAddOp inferShapes failed");
+    emitError("Input tensor(s) not ranked");
     return false;
   }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
@@ -529,8 +529,10 @@ bool ONNXAddOp::inferShapes() {
 /// shape inference interface.
 bool ONNXMulOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
@@ -543,8 +545,10 @@ bool ONNXMulOp::inferShapes() {
 /// shape inference interface.
 bool ONNXDivOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
@@ -557,8 +561,10 @@ bool ONNXDivOp::inferShapes() {
 /// shape inference interface.
 bool ONNXSubOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
@@ -571,8 +577,10 @@ bool ONNXSubOp::inferShapes() {
 /// shape inference interface.
 bool ONNXAndOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
@@ -585,8 +593,10 @@ bool ONNXAndOp::inferShapes() {
 /// shape inference interface.
 bool ONNXOrOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
@@ -599,8 +609,10 @@ bool ONNXOrOp::inferShapes() {
 /// shape inference interface.
 bool ONNXXorOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
-      !getOperand(1).getType().isa<RankedTensorType>())
+      !getOperand(1).getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
   auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
   getResult().setType(getBroadcastedType(lhsTy, rhsTy));
@@ -615,9 +627,11 @@ bool ONNXXorOp::inferShapes() {
 /// shape inference interface.
 bool ONNXSumOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
-    if (!getOperand(i).getType().cast<RankedTensorType>())
+    if (!getOperand(i).getType().cast<RankedTensorType>()) {
+      emitError("Input tensor(s) not ranked");
       return false;
     }
+  }
   Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
     Type nextTy = getOperand(i).getType().cast<RankedTensorType>();
@@ -633,9 +647,11 @@ bool ONNXSumOp::inferShapes() {
 /// shape inference interface.
 bool ONNXMaxOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
-    if (!getOperand(i).getType().cast<RankedTensorType>())
+    if (!getOperand(i).getType().cast<RankedTensorType>()) {
+      emitError("Input tensor(s) not ranked");
       return false;
     }
+  }
   Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
     Type nextTy = getOperand(i).getType().cast<RankedTensorType>();
@@ -651,9 +667,11 @@ bool ONNXMaxOp::inferShapes() {
 /// shape inference interface.
 bool ONNXMinOp::inferShapes() {
   for (int i = 0; i < getNumOperands(); ++i) {
-    if (!getOperand(i).getType().cast<RankedTensorType>())
+    if (!getOperand(i).getType().cast<RankedTensorType>()) {
+      emitError("Input tensor(s) not ranked");
       return false;
     }
+  }
   Type resultTy = getOperand(0).getType().cast<RankedTensorType>();
   for (int i = 1; i < getNumOperands(); ++i) {
     Type nextTy = getOperand(i).getType().cast<RankedTensorType>();
@@ -679,8 +697,10 @@ bool ONNXIdentityOp::inferShapes() {
 bool ONNXMatMulOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!A().getType().isa<RankedTensorType>() ||
-      !B().getType().isa<RankedTensorType>())
+      !B().getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
 
   auto lhsTy = A().getType().cast<RankedTensorType>();
   auto rhsTy = B().getType().cast<RankedTensorType>();
@@ -819,8 +839,10 @@ bool ONNXGemmOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!A().getType().isa<RankedTensorType>() ||
       !B().getType().isa<RankedTensorType>() ||
-      (hasBias && !C().getType().isa<RankedTensorType>()))
+      (hasBias && !C().getType().isa<RankedTensorType>())) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
   auto lhsTy = A().getType().cast<RankedTensorType>();
   auto rhsTy = B().getType().cast<RankedTensorType>();
 
@@ -862,8 +884,10 @@ bool ONNXBatchNormalizationTestModeOp::inferShapes() {
       !scale().getType().isa<RankedTensorType>() ||
       !B().getType().isa<RankedTensorType>() ||
       !mean().getType().isa<RankedTensorType>() ||
-      !var().getType().isa<RankedTensorType>())
+      !var().getType().isa<RankedTensorType>()) {
+    emitError("Input tensor(s) not ranked");
     return false;
+  }
 
   auto inputTensorTy = X().getType().cast<RankedTensorType>();
   auto scaleTensorTy = scale().getType().cast<RankedTensorType>();
@@ -915,8 +939,15 @@ bool ONNXBatchNormalizationTestModeOp::inferShapes() {
 
 bool ONNXReshapeOp::inferShapes() {
   // Cannot infer shape if no shape tensor is specified.
-  if (!shape().getType().isa<RankedTensorType>())
+  if (!data().getType().isa<RankedTensorType>()) {
+    emitError("Input data tensor not ranked");
+    return false;
+  }
+
+  if (!shape().getType().isa<RankedTensorType>()) {
     emitError("Shape tensor not ranked");
+    return false;
+  }
 
   auto inputTensorTy = data().getType().cast<RankedTensorType>();
   auto shapeTensorTy = shape().getType().cast<RankedTensorType>();
@@ -991,8 +1022,10 @@ bool ONNXReshapeOp::inferShapes() {
 
 bool ONNXTransposeOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!data().getType().isa<RankedTensorType>())
+  if (!data().getType().isa<RankedTensorType>()) {
+    emitError("Input tensor not ranked");
     return false;
+  }
 
   // Naive transposition which handles the default case of
   // reversing the shape of the tensor (similar to numpy.transpose).
@@ -1019,7 +1052,7 @@ bool ONNXTransposeOp::inferShapes() {
 
 bool ONNXReduceMaxOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
-    emitError("Shape tensor not ranked");
+    emitError("Input tensor not ranked");
     return false;
   }
 
@@ -1034,7 +1067,7 @@ bool ONNXReduceMaxOp::inferShapes() {
 
 bool ONNXReduceMinOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
-    emitError("Shape tensor not ranked");
+    emitError("Input tensor not ranked");
     return false;
   }
 
@@ -1049,7 +1082,7 @@ bool ONNXReduceMinOp::inferShapes() {
 
 bool ONNXReduceProdOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
-    emitError("Shape tensor not ranked");
+    emitError("Input tensor not ranked");
     return false;
   }
 
@@ -1064,7 +1097,7 @@ bool ONNXReduceProdOp::inferShapes() {
 
 bool ONNXReduceSumOp::inferShapes() {
   if (!getOperand().getType().isa<RankedTensorType>()) {
-    emitError("Shape tensor not ranked");
+    emitError("Input tensor not ranked");
     return false;
   }
 
@@ -1097,8 +1130,10 @@ bool ONNXConvOp::inferShapes() {
   // Cannot infer shape if no shape exists.
   if (!X().getType().isa<RankedTensorType>() ||
       !W().getType().isa<RankedTensorType>() ||
-      (hasBias && !B().getType().isa<RankedTensorType>()))
+      (hasBias && !B().getType().isa<RankedTensorType>())) {
+    emitError("Input tensor not ranked");
     return false;
+  }
 
   auto xTy = X().getType().cast<RankedTensorType>();
   auto xShape = xTy.getShape();
@@ -1210,8 +1245,10 @@ bool ONNXConvOp::inferShapes() {
 
 bool ONNXAveragePoolOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!X().getType().isa<RankedTensorType>())
+  if (!X().getType().isa<RankedTensorType>()) {
+    emitError("Input tensor not ranked");
     return false;
+  }
 
   // Get shape of input.
   auto xTy = X().getType().cast<RankedTensorType>();
@@ -1255,8 +1292,10 @@ bool ONNXAveragePoolOp::inferShapes() {
 
 bool ONNXMaxPoolSingleOutOp::inferShapes() {
   // Cannot infer shape if no shape exists.
-  if (!X().getType().isa<RankedTensorType>())
+  if (!X().getType().isa<RankedTensorType>()) {
+    emitError("Input tensor not ranked");
     return false;
+  }
 
   // Get shape of input.
   auto xTy = X().getType().cast<RankedTensorType>();
@@ -1364,8 +1403,10 @@ void ONNXPadConstantValuePadOp::build(Builder *builder, OperationState &state,
 // Unsqueeze
 
 bool ONNXUnsqueezeOp::inferShapes() {
-  if (!data().getType().isa<RankedTensorType>())
+  if (!data().getType().isa<RankedTensorType>()) {
+    emitError("Input tensor not ranked");
     return false;
+  }
 
   auto operandTy = data().getType().cast<RankedTensorType>();
   int inRank = operandTy.getRank();

src/MainUtils.hpp

@@ -38,6 +38,7 @@
 #include "mlir/Transforms/Passes.h"
 
 enum EmissionTargetType {
+  EmitONNXBasic,
   EmitONNXIR,
   EmitMLIR,
   EmitLLVMIR,

src/Transform/ONNX/ShapeInferencePass.cpp

@@ -37,7 +37,7 @@ public:
         if (auto shape_op = dyn_cast<ShapeInference>(op)) {
           if (!shape_op.inferShapes()) {
             op->emitError("unable to infer shape of operation without shape "
-                          "inference interface");
+                          "inference method");
             return signalPassFailure();
           }
         } else {

src/main.cpp

@@ -23,6 +23,9 @@ int main(int argc, char *argv[]) {
   llvm::cl::opt<EmissionTargetType> emissionTarget(
       llvm::cl::desc("Choose target to emit:"),
       llvm::cl::values(
+          clEnumVal(EmitONNXBasic,
+                    "Ingest ONNX and emit the basic ONNX operations without"
+                    "inferred shapes."),
           clEnumVal(EmitONNXIR,
                     "Ingest ONNX and emit corresponding ONNX dialect."),
           clEnumVal(EmitMLIR,
@@ -41,6 +44,7 @@ int main(int argc, char *argv[]) {
   processInputFile(inputFilename, emissionTarget, context, module);
 
   mlir::PassManager pm(&context);
+  if (emissionTarget >= EmitONNXIR)
     addONNXToMLIRPasses(pm);
 
   if (emissionTarget >= EmitMLIR) {