Check negative index for attributes of Slice-1 (#3810)

* check negative value
Signed-off-by: NChun-Wei Chen <jacky82226@gmail.com>

* only perform rank inference
Signed-off-by: NChun-Wei Chen <jacky82226@gmail.com>

* resolve warning
Signed-off-by: NChun-Wei Chen <jacky82226@gmail.com>

* fix warning
Signed-off-by: NChun-Wei Chen <jacky82226@gmail.com>

* starts + ends
Signed-off-by: NChun-Wei Chen <jacky82226@gmail.com>

* change if; any_of; update comments
Signed-off-by: NChun-Wei Chen <jacky82226@gmail.com>
Co-authored-by: NAshwini Khade <askhade@microsoft.com>

onnx/defs/tensor/old.cc

@@ -3192,8 +3192,8 @@ ONNX_OPERATOR_SET_SCHEMA(
               starts.size() != ends.size()) {
             fail_shape_inference(
                 "Incorrect or missing attribute value for starts and ends");
-            ;
           }
+
           std::vector<int64_t> axes;
           if (!getRepeatedAttribute(ctx, "axes", axes)) {
             for (int i = 0; (size_t)i < starts.size(); ++i) {
@@ -3201,12 +3201,28 @@ ONNX_OPERATOR_SET_SCHEMA(
             }
           } else if (axes.size() != starts.size()) {
             fail_shape_inference("Attribute axes has incorrect length");
-            ;
           } else if (!std::is_sorted(axes.begin(), axes.end())) {
             // TODO support shape inference for unsorted axes
             return;
           }
 
+          auto is_negative = [](int64_t index) {
+            return index < 0;
+          };
+          if (std::any_of(starts.begin(), starts.end(), is_negative) ||
+              std::any_of(ends.begin(), ends.end(), is_negative) ||
+              std::any_of(axes.begin(), axes.end(), is_negative)) {
+            // Negative axes were not explicitly discussed in the spec before opset-10.
+            // Hence, they are officially not part of the spec, but some models/runtimes may use them.
+            // So we perform simple rank inference in this case.
+            for (size_t i = 0; (int64_t)i <
+                ctx.getInputType(0)->tensor_type().shape().dim_size();
+                ++i) {
+              ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape()->add_dim();
+            }
+            return;
+          }
+
           ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
 
           for (size_t i = 0, j = 0; (int64_t)i <