Update error messages for NegativeLogLikelihoodLoss inference function (

#6021) ### Description Add the invalid shape information in errors message of NegativeLogLikelihoodLoss's inference function. ### Motivation and Context Better errors Signed-off-by: Justin Chu <justinchu@microsoft.com>
onnx · Mar 18, 2024 · 17dbae7 · 17dbae7
1 parent 9cc907f
commit 17dbae7
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Showing 2 changed files with 28 additions and 10 deletions.
diff --git a/onnx/defs/math/defs.cc b/onnx/defs/math/defs.cc
@@ -2519,10 +2519,14 @@ ONNX_OPERATOR_SET_SCHEMA(
             const int target_rank = static_cast<int>(target_shape.dim_size());
 
             if (input_rank < 2) {
-              fail_shape_inference("Input rank must be >= 2.")
+              fail_shape_inference("Input rank must be >= 2. input_rank=", input_rank);
             }
             if (target_rank != input_rank - 1) {
-              fail_shape_inference("Target rank must be 1 less than the input rank.");
+              fail_shape_inference(
+                  "Target rank must be 1 less than the input rank. input_rank=",
+                  input_rank,
+                  ", target_rank=",
+                  target_rank);
             }
 
             // match input dimensions (N, C, d1, ..., dk) with target
@@ -2532,13 +2536,18 @@ ONNX_OPERATOR_SET_SCHEMA(
               const auto target_dim = target_shape.dim(dim);
               if (input_dim.has_dim_value() && target_dim.has_dim_value() &&
                   input_dim.dim_value() != target_dim.dim_value())
-                fail_shape_inference("Input and target dimension value mismatch.");
+                fail_shape_inference(
+                    "Input and target dimension value mismatch. input_dim_value=",
+                    input_dim.dim_value(),
+                    " target_dim_value=",
+                    target_dim.dim_value());
             }
 
             if (ctx.getNumInputs() == 3 && hasInputShape(ctx, 2)) {
               const TensorShapeProto& weight_shape = ctx.getInputType(2)->tensor_type().shape();
-              if (weight_shape.dim_size() != 1) {
-                fail_shape_inference("Weight rank must be 1.");
+              const auto weight_rank = weight_shape.dim_size();
+              if (weight_rank != 1) {
+                fail_shape_inference("Weight rank must be 1. weight_rank=", weight_rank);
               }
             }
 

diff --git a/onnx/defs/math/old.cc b/onnx/defs/math/old.cc
@@ -1294,10 +1294,14 @@ ONNX_OPERATOR_SET_SCHEMA(
             const int target_rank = static_cast<int>(target_shape.dim_size());
 
             if (input_rank < 2) {
-              fail_shape_inference("Input rank must be >= 2.");
+              fail_shape_inference("Input rank must be >= 2. input_rank=", input_rank);
             }
             if (target_rank != input_rank - 1) {
-              fail_shape_inference("Target rank must be 1 less than the input rank.");
+              fail_shape_inference(
+                  "Target rank must be 1 less than the input rank. input_rank=",
+                  input_rank,
+                  ", target_rank=",
+                  target_rank);
             }
 
             // match input dimensions (N, C, d1, ..., dk) with target
@@ -1307,13 +1311,18 @@ ONNX_OPERATOR_SET_SCHEMA(
               const auto target_dim = target_shape.dim(dim);
               if (input_dim.has_dim_value() && target_dim.has_dim_value() &&
                   input_dim.dim_value() != target_dim.dim_value())
-                fail_shape_inference("Input and target dimension value mismatch.");
+                fail_shape_inference(
+                    "Input and target dimension value mismatch. input_dim_value=",
+                    input_dim.dim_value(),
+                    " target_dim_value=",
+                    target_dim.dim_value());
             }
 
             if (ctx.getNumInputs() == 3 && hasInputShape(ctx, 2)) {
               const TensorShapeProto& weight_shape = ctx.getInputType(2)->tensor_type().shape();
-              if (weight_shape.dim_size() != 1) {
-                fail_shape_inference("Weight rank must be 1.");
+              const auto weight_rank = weight_shape.dim_size();
+              if (weight_rank != 1) {
+                fail_shape_inference("Weight rank must be 1. weight_rank=", weight_rank);
               }
             }