prepare for 1.15.0rc2 (#5681)

VERSION_NUMBER

@@ -1 +1 @@
-1.15.0rc1
+1.15.0rc2

docs/Changelog.md

@@ -23972,7 +23972,7 @@ This version of the operator has been available since version 20 of the default
 
 <dl>
 <dt><tt>grid</tt> (differentiable) : T1</dt>
-<dd>output tensor of shape (N, C, H, W, 2) of 2D sample coordinates or (N, C, D, H, W, 3) of 3D sample coordinates.</dd>
+<dd>output tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.</dd>
 </dl>
 
 #### Type Constraints

docs/Operators.md

@@ -545,7 +545,7 @@ This version of the operator has been available since version 20 of the default
 
 <dl>
 <dt><tt>grid</tt> (differentiable) : T1</dt>
-<dd>output tensor of shape (N, C, H, W, 2) of 2D sample coordinates or (N, C, D, H, W, 3) of 3D sample coordinates.</dd>
+<dd>output tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.</dd>
 </dl>
 
 #### Type Constraints
@@ -23094,7 +23094,7 @@ expect(
     node,
     inputs=[data, axes],
     outputs=[reduced],
-    name="test_reduce_sum_empty_set",
+    name="test_reduce_sum_empty_set_non_reduced_axis_zero",
 )
 ```
 

docs/TestCoverage.md

@@ -15681,7 +15681,7 @@ expect(
     node,
     inputs=[data, axes],
     outputs=[reduced],
-    name="test_reduce_sum_empty_set",
+    name="test_reduce_sum_empty_set_non_reduced_axis_zero",
 )
 ```
 

onnx/backend/test/case/node/reducesum.py

@@ -242,5 +242,5 @@ def export_non_reduced_axis_zero() -> None:
             node,
             inputs=[data, axes],
             outputs=[reduced],
-            name="test_reduce_sum_empty_set",
+            name="test_reduce_sum_empty_set_non_reduced_axis_zero",
         )

onnx/checker.cc

@@ -190,11 +190,11 @@ void check_tensor(const TensorProto& tensor, const CheckerContext& ctx) {
         }
         std::string data_path = path_join(ctx.get_model_dir(), relative_path);
         // use stat64 to check whether the file exists
-#if defined(__APPLE__) || defined(__wasm__)
-        struct stat buffer; // APPLE does not have stat64
+#if defined(__APPLE__) || defined(__wasm__) || !defined(__GLIBC__)
+        struct stat buffer; // APPLE, wasm and non-glic stdlibs do not have stat64
         if (stat((data_path).c_str(), &buffer) != 0) {
 #else
-        struct stat64 buffer; // All POSIX except APPLE have stat64
+        struct stat64 buffer; // All POSIX under glibc except APPLE and wasm have stat64
         if (stat64((data_path).c_str(), &buffer) != 0) {
 #endif
           fail_check(

onnx/defs/tensor/defs.cc

@@ -2527,7 +2527,7 @@ ONNX_OPERATOR_SET_SCHEMA(
         .Output(
             0,
             "grid",
-            "output tensor of shape (N, C, H, W, 2) of 2D sample coordinates or (N, C, D, H, W, 3) of 3D sample coordinates.",
+            "output tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.",
             "T1",
             OpSchema::Single,
             true,

onnx/defs/traditionalml/defs.cc

@@ -382,28 +382,6 @@ ONNX_ML_OPERATOR_SET_SCHEMA(
             fail_shape_inference(
                 "At least one of values_tensor, values_strings, values_int64s, values_floats must be set.");
           }
-
-          int default_length, default_type;
-          std::tie(default_type, default_length) = getAttributeElementTypeAndLength(
-              ctx, {"default_tensor", "default_string", "default_int64", "default_float"});
-          if (default_type != TensorProto::UNDEFINED) {
-            if (value_type != default_type) {
-              fail_shape_inference(
-                  "The value type ",
-                  value_type,
-                  " and the default type ",
-                  default_type,
-                  " are different, which is not permitted for LabelEncoders.");
-            }
-
-            // Ensure default_tensor is a singleton if set
-            const AttributeProto* default_tensor = ctx.getAttribute("default_tensor");
-            if (default_tensor != nullptr &&
-                (default_tensor->t().dims_size() != 1 || default_tensor->t().dims(0) != 1)) {
-              fail_shape_inference("default_tensor must be a singleton if set.");
-            }
-          }
-
           if (value_length != key_length) {
             fail_shape_inference(
                 "The number of keys ",
@@ -413,6 +391,22 @@ ONNX_ML_OPERATOR_SET_SCHEMA(
                 " must be the same in the LabelEncoder.");
           }
 
+          auto default_attr = ctx.getAttribute("default_tensor");
+          if (nullptr != default_attr && default_attr->has_t() && default_attr->t().has_data_type() &&
+              default_attr->t().data_type() != TensorProto_DataType_UNDEFINED) {
+            auto default_tensor = default_attr->t();
+            if (default_tensor.data_type() != value_type) {
+              fail_shape_inference(
+                  "The default tensor type ",
+                  default_tensor.data_type(),
+                  " and the value type ",
+                  value_type,
+                  " must be the same in the LabelEncoder.");
+            }
+            if (1 != default_tensor.dims_size() || 1 != default_tensor.dims(0)) {
+              fail_shape_inference("The default tensor must be a singleton 1D tensor.");
+            }
+          }
           // Propagate shape from input type and assign output type based on value type
           ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(value_type);
           propagateShapeFromInputToOutput(ctx, 0, 0);