More ERROR_IF supports

- Also delay tensor allocation after operator being validated
  ERROR_IF can be caught first before 0 or negative dimension set the graph_status to UNPREDICTABLE
- Rescale, Argmax, FullyConnected, Matmul, Pad, Reshape, Slice, Transpose, Clamp, Concat, Equal, Greater, GreaterEqual, Table

Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: I4e1b3e5794fe195ce1a37e28443ae584645a3b91

1 files changed, 6 insertions, 14 deletions

diff --git a/reference_model/src/ops/ewise_binary.cc b/reference_model/src/ops/ewise_binary.cc
index 023158c..6808604 100644
--- a/reference_model/src/ops/ewise_binary.cc
+++ b/reference_model/src/ops/ewise_binary.cc
@@ -60,26 +60,16 @@ int BinaryNodeBase<Rank, InDtype, OutDtype>::checkTensorAttributes()
         return 1;
     }
 
-    // In some ops, only rank of input and output tensor needs to match
-    if (nodeType == Op_MUL || nodeType == Op_GREATER || nodeType == Op_EQUAL || nodeType == Op_GREATER_EQUAL)
-    {
-        if (inputs[0]->matchRank(*outputs[0]))
-        {
-            std::string err =
-                "Binary operators " + std::string(EnumNamesOp()[nodeType]) + " input and output rank must match";
-            printNodeValidationError(err.c_str());
-            return 1;
-        }
-    }
-    // Otherwise both rand/type of input and output must match
-    else if (inputs[0]->matchRankType(*outputs[0]))
+    if (inputs[0]->matchRank(*outputs[0]))
     {
         std::string err =
-            "Binary operators " + std::string(EnumNamesOp()[nodeType]) + " input and output rank and type must match";
+            "Binary operators " + std::string(EnumNamesOp()[nodeType]) + " input and output rank must match";
         printNodeValidationError(err.c_str());
         return 1;
     }
 
+    ERROR_IF(outputs[0]->getDtype() != OutDtype, "Binary operator type doesn't match");
+
     a      = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
     b      = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]);
     result = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
@@ -532,6 +522,7 @@ int OpTable<Rank, InDtype>::checkTensorAttributes()
             printNodeValidationError("OpTable: Table must be INT8[256] if input is INT8");
             return 1;
         }
+        ERROR_IF(outputs[0]->getDtype() != DType_INT8, "OpTable: output tensor must be INT8");
     }
     else if (inputs[0]->getDtype() == DType_INT16)
     {
@@ -540,6 +531,7 @@ int OpTable<Rank, InDtype>::checkTensorAttributes()
             printNodeValidationError("OpTable: Table must be INT16[513] if input is INT16");
             return 1;
         }
+        ERROR_IF(outputs[0]->getDtype() != DType_INT32, "OpTable: output tensor must be INT32");
     }
 
     in    = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);