Merge branch 'nv-legate:branch-24.03' into enh-ext-rng

cmake/versions.json

@@ -5,7 +5,7 @@
       "git_url" : "https://github.com/nv-legate/legate.core.git",
       "git_shallow": false,
       "always_download": false,
-      "git_tag" : "b84e86e9e3518d7102cdf531b5b733dc09c0e8d9"
+      "git_tag" : "0f509a007f36609d2b0bd536d8e5c54f2391b444"
     }
   }
 }

cunumeric/_ufunc/comparison.py

@@ -73,13 +73,15 @@
     "logical_and",
     BinaryOpCode.LOGICAL_AND,
     relation_types_of(all_dtypes),
+    red_code=UnaryRedCode.ALL,
 )
 
 logical_or = create_binary_ufunc(
     "Compute the truth value of x1 OR x2 element-wise.",
     "logical_or",
     BinaryOpCode.LOGICAL_OR,
     relation_types_of(all_dtypes),
+    red_code=UnaryRedCode.ANY,
 )
 
 logical_xor = create_binary_ufunc(

cunumeric/_ufunc/ufunc.py

@@ -753,6 +753,16 @@ def reduce(
                 f"reduction for {self} is not yet implemented"
             )
 
+        if self._op_code in [
+            BinaryOpCode.LOGICAL_AND,
+            BinaryOpCode.LOGICAL_OR,
+        ]:
+            res_dtype = bool
+            if dtype is not None:
+                raise TypeError("Cannot set dtype on a logical reduction")
+        else:
+            res_dtype = None
+
         # NumPy seems to be using None as the default axis value for scalars
         if array.ndim == 0 and axis == 0:
             axis = None
@@ -767,6 +777,7 @@ def reduce(
             keepdims=keepdims,
             initial=initial,
             where=where,
+            res_dtype=res_dtype,
         )
 
 

cunumeric/array.py

@@ -2257,6 +2257,20 @@ def clip(
         Multiple GPUs, Multiple CPUs
 
         """
+        min = (
+            min
+            if min is not None
+            else np.iinfo(self.dtype).min
+            if self.dtype.kind == "i"
+            else -np.inf
+        )
+        max = (
+            max
+            if max is not None
+            else np.iinfo(self.dtype).max
+            if self.dtype.kind == "i"
+            else np.inf
+        )
         args = (
             np.array(min, dtype=self.dtype),
             np.array(max, dtype=self.dtype),