Merge branch 'main' into jcw/bump-c-17

.azure-pipelines/Linux-CI.yml

@@ -81,7 +81,7 @@ jobs:
   - script: |
       source venv/bin/activate
 
-      pytest -sv
+      pytest -sv --cov=onnx --cov-report=xml --cov-append --cov-branch --junit-xml pytest.xml
       if [ $? -ne 0 ]; then
         echo "pytest failed"
         exit 1
@@ -97,6 +97,14 @@ jobs:
 
     displayName: 'Run ONNX tests'
 
+  - script: |
+      curl -Os https://uploader.codecov.io/latest/linux/codecov
+      chmod +x codecov
+      ./codecov
+
+    continueOnError: true
+    displayName: 'Upload to codecov'
+
   - script: |
       source venv/bin/activate
       python onnx/backend/test/cmd_tools.py generate-data --clean

.reuse/dep5

@@ -13,6 +13,9 @@ Files: .azure-pipelines/*.yml
 COPYRIGHT: Copyright (c) ONNX Project Contributors
 License: Apache-2.0
 
+Files: codecov.yml
+COPYRIGHT: Copyright (c) ONNX Project Contributors
+License: Apache-2.0
 
 Files: .github/**/*.md .github/pull_request_template.md
 COPYRIGHT: Copyright (c) ONNX Project Contributors

README.md

@@ -43,12 +43,12 @@ ONNX is [widely supported](http://onnx.ai/supported-tools) and can be found in m
 
 # Contribute
 
-ONNX is a community project and the open governance model is described [here](community/readme.md). We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the [Special Interest Groups](community/sigs.md) and [Working Groups](community/working-groups.md) to shape the future of ONNX.
+ONNX is a community project and the open governance model is described [here](https://github.com/onnx/onnx/blob/main/community/readme.md). We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the [Special Interest Groups](https://github.com/onnx/onnx/blob/main/community/sigs.md) and [Working Groups](https://github.com/onnx/onnx/blob/main/community/working-groups.md) to shape the future of ONNX.
 
-Check out our [contribution guide](/CONTRIBUTING.md) to get started.
+Check out our [contribution guide](https://github.com/onnx/onnx/blob/main/CONTRIBUTING.md) to get started.
 
 If you think some operator should be added to ONNX specification, please read
-[this document](docs/AddNewOp.md).
+[this document](https://github.com/onnx/onnx/blob/main/docs/AddNewOp.md).
 
 # Community meetings
 
@@ -280,7 +280,7 @@ For full list refer to CMakeLists.txt
 * `USE_MSVC_STATIC_RUNTIME` should be 1 or 0, not ON or OFF. When set to 1 onnx links statically to runtime library.
 **Default**: `USE_MSVC_STATIC_RUNTIME=0`
 
-* `DEBUG` should be 0 or 1. When set to 1 onnx is built in debug mode. or debug versions of the dependencies, you need to open the [CMakeLists file](CMakeLists.txt) and append a letter `d` at the end of the package name lines. For example, `NAMES protobuf-lite` would become `NAMES protobuf-lited`.
+* `DEBUG` should be 0 or 1. When set to 1 onnx is built in debug mode. or debug versions of the dependencies, you need to open the [CMakeLists file](https://github.com/onnx/onnx/blob/main/CMakeLists.txt) and append a letter `d` at the end of the package name lines. For example, `NAMES protobuf-lite` would become `NAMES protobuf-lited`.
 **Default**: `Debug=0`
 
 ### CMake variables
@@ -321,7 +321,7 @@ pytest
 
 # Development
 
-Check out the [contributor guide](/CONTRIBUTING.md) for instructions.
+Check out the [contributor guide](https://github.com/onnx/onnx/blob/main/CONTRIBUTING.md) for instructions.
 
 # License
 

codecov.yml

@@ -0,0 +1,8 @@
+coverage:
+  status:
+    project:
+      default:
+        informational: true
+    patch:
+      default:
+        informational: true

docs/Operators.md

@@ -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/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);