Add one unit test for empty input

sdpython · sdpython · Jun 10, 2023 · Jun 5, 2023 · Jun 5, 2023 · Jun 5, 2023
commit cb11e55e305145bd109570e28d69c11d858be33a
diff --git a/_unittests/ut_npx/test_npx.py b/_unittests/ut_npx/test_npx.py
@@ -2492,7 +2492,18 @@ def test_numpy_all(self):
         got = ref.run(None, {"A": data})
         self.assertEqualArray(y, got[0])
 
+    def test_numpy_all_empty(self):
+        data = np.zeros((0, 1), dtype=np.bool_)
+        y = np.all(data)
+
+        f = all_inline(Input("A"), axis=1)
+        self.assertIsInstance(f, Var)
+        onx = f.to_onnx(constraints={"A": Bool[None]})
+        ref = ReferenceEvaluator(onx)
+        got = ref.run(None, {"A": data})
+        self.assertEqualArray(y, got[0])
+
 
 if __name__ == "__main__":
-    TestNpx().test_identity()
+    TestNpx().test_numpy_all_empty()
     unittest.main(verbosity=2)
diff --git a/onnx_array_api/npx/npx_functions.py b/onnx_array_api/npx/npx_functions.py
@@ -48,7 +48,15 @@ def all(
     axis: Optional[TensorType[ElemType.int64, "I"]] = None,
     keepdims: ParType[int] = 0,
 ) -> TensorType[ElemType.bool_, "T"]:
-    "See :func:`numpy.all`."
+    """
+    See :func:`numpy.all`.
+    If input x is empty, the answer is True.
+    """
+    # size = var(x, op="Size")
+    # empty = var(size, cst(np.array(0, dtype=np.int64)), op="Equal")
+
+    # z = make_tensor_value_info("Z", TensorProto.BOOL, [1])
+    # g1 = make_graph([make_node("Constant", [], ["Z"], value_bool=[True])], [], [z])
 
     xi = var(x, op="Cast", to=TensorProto.INT64)
 

diff --git a/onnx_array_api/npx/npx_types.py b/onnx_array_api/npx/npx_types.py
@@ -1,7 +1,7 @@
 from typing import Any, Tuple, Union
 
 import numpy as np
-from onnx import AttributeProto
+from onnx import AttributeProto, TensorProto
 from onnx.helper import np_dtype_to_tensor_dtype, tensor_dtype_to_np_dtype
 
 
@@ -49,10 +49,12 @@ def __eq__(self, dt: "DType") -> bool:
         "Compares two types."
         if dt.__class__ is DType:
             return self.code_ == dt.code_
-        if isinstance(dt, int):
-            raise TypeError(f"dt must be DType not {type(dt)}.")
-        if isinstance(dt, str):
+        if isinstance(dt, (int, bool, str)):
             return False
+        if dt is str:
+            return self.code_ == TensorProto.STRING
+        if dt is bool:
+            return self.code_ == TensorProto.BOOL
         if dt in ElemType.numpy_map:
             dti = ElemType.numpy_map[dt]
             return self.code_ == dti.code_