Allow dtype input argument in np.sum

docs/source/reference/numpysupported.rst

@@ -210,13 +210,21 @@ The following methods of Numpy arrays are supported:
 * :meth:`~numpy.ndarray.repeat` (no axis argument)
 * :meth:`~numpy.ndarray.reshape` (only the 1-argument form)
 * :meth:`~numpy.ndarray.sort` (without arguments)
-* :meth:`~numpy.ndarray.sum` (with or without the ``axis`` argument. 
-  ``axis`` only supports ``integer`` values)
+* :meth:`~numpy.ndarray.sum` (with or without the ``axis`` and/or ``dtype`` arguments.)
 
+  * ``axis`` only supports ``integer`` values.
   * If the ``axis`` argument is a compile-time constant, all valid values are supported.
     An out-of-range value will result in a ``LoweringError`` at compile-time.
   * If the ``axis`` argument is not a compile-time constant, only values from 0 to 3 are supported.
     An out-of-range value will result in a runtime exception.
+  * All numeric ``dtypes`` are supported as ``dtype`` parameter. ``timedelta`` arrays can
+    be used as input arrays but ``timedelta`` is not supported as ``dtype`` parameter.
+  * When ``dtype`` is given, it determines the type of the internal accumulator. When it is not,
+    the selection is made automatically based on the input array's ``dtype``, mostly following the same
+    rules as NumPy. However, on 64-bit Windows, Numba uses a 64-bit accumulator for integer inputs
+    (``int64`` for ``int32`` inputs and ``uint64`` for ``uint32`` inputs), while NumPy would use a 32-bit
+    accumulator in those cases.
+
 
 * :meth:`~numpy.ndarray.transpose`
 * :meth:`~numpy.ndarray.view` (only the 1-argument form)

numba/targets/arraymath.py

@@ -177,50 +177,20 @@ def _array_sum_axis_nop(arr, v):
     return arr
 
 
-@lower_builtin(np.sum, types.Array, types.intp)
-@lower_builtin(np.sum, types.Array, types.IntegerLiteral)
-@lower_builtin("array.sum", types.Array, types.intp)
-@lower_builtin("array.sum", types.Array, types.IntegerLiteral)
-def array_sum_axis(context, builder, sig, args):
-    """
-    The third parameter to gen_index_tuple that generates the indexing
-    tuples has to be a const so we can't just pass "axis" through since
-    that isn't const.  We can check for specific values and have
-    different instances that do take consts.  Supporting axis summation
-    only up to the fourth dimension for now.
-    """
-    # typing/arraydecl.py:sum_expand defines the return type for sum with axis.
-    # It is one dimension less than the input array.
+def gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero):
+    def inner(arr, axis):
+        """
+        function that performs sums over one specific axis
 
-    retty = sig.return_type
-    zero = getattr(retty, 'dtype', retty)(0)
-    # if the return is scalar in type then "take" the 0th element of the
-    # 0d array accumulator as the return value
-    if getattr(retty, 'ndim', None) is None:
-        op = np.take
-    else:
-        op = _array_sum_axis_nop
-    [ty_array, ty_axis] = sig.args
-    is_axis_const = False
-    const_axis_val = 0
-    if isinstance(ty_axis, types.Literal):
-        # this special-cases for constant axis
-        const_axis_val = ty_axis.literal_value
-        # fix negative axis
-        if const_axis_val < 0:
-            const_axis_val = ty_array.ndim + const_axis_val
-        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
-            raise ValueError("'axis' entry is out of bounds")
+        The third parameter to gen_index_tuple that generates the indexing
+        tuples has to be a const so we can't just pass "axis" through since
+        that isn't const.  We can check for specific values and have
+        different instances that do take consts.  Supporting axis summation
+        only up to the fourth dimension for now.
 
-        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
-        axis_val = context.get_constant(ty_axis, const_axis_val)
-        # rewrite arguments
-        args = args[0], axis_val
-        # rewrite sig
-        sig = sig.replace(args=[ty_array, ty_axis])
-        is_axis_const = True
-
-    def array_sum_impl_axis(arr, axis):
+        typing/arraydecl.py:sum_expand defines the return type for sum with axis.
+        It is one dimension less than the input array.
+        """
         ndim = arr.ndim
 
         if not is_axis_const:
@@ -268,8 +238,107 @@ def array_sum_impl_axis(arr, axis):
                 elif axis == 3:
                     index_tuple4 = _gen_index_tuple(arr.shape, axis_index, 3)
                     result += arr[index_tuple4]
-
         return op(result, 0)
+    return inner
+
+
+@lower_builtin(np.sum, types.Array, types.intp, types.DTypeSpec)
+@lower_builtin(np.sum, types.Array, types.IntegerLiteral, types.DTypeSpec)
+@lower_builtin("array.sum", types.Array, types.intp, types.DTypeSpec)
+@lower_builtin("array.sum", types.Array, types.IntegerLiteral, types.DTypeSpec)
+def array_sum_axis_dtype(context, builder, sig, args):
+    retty = sig.return_type
+    zero = getattr(retty, 'dtype', retty)(0)
+    # if the return is scalar in type then "take" the 0th element of the
+    # 0d array accumulator as the return value
+    if getattr(retty, 'ndim', None) is None:
+        op = np.take
+    else:
+        op = _array_sum_axis_nop
+    [ty_array, ty_axis, ty_dtype] = sig.args
+    is_axis_const = False
+    const_axis_val = 0
+    if isinstance(ty_axis, types.Literal):
+        # this special-cases for constant axis
+        const_axis_val = ty_axis.literal_value
+        # fix negative axis
+        if const_axis_val < 0:
+            const_axis_val = ty_array.ndim + const_axis_val
+        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
+            raise ValueError("'axis' entry is out of bounds")
+
+        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
+        axis_val = context.get_constant(ty_axis, const_axis_val)
+        # rewrite arguments
+        args = args[0], axis_val, args[2]
+        # rewrite sig
+        sig = sig.replace(args=[ty_array, ty_axis, ty_dtype])
+        is_axis_const = True
+
+    gen_impl = gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero)
+    compiled = register_jitable(gen_impl)
+
+    def array_sum_impl_axis(arr, axis, dtype):
+        return compiled(arr, axis)
+
+    res = context.compile_internal(builder, array_sum_impl_axis, sig, args)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.sum, types.Array,  types.DTypeSpec)
+@lower_builtin("array.sum", types.Array, types.DTypeSpec)
+def array_sum_dtype(context, builder, sig, args):
+    zero = sig.return_type(0)
+
+    def array_sum_impl(arr, dtype):
+        c = zero
+        for v in np.nditer(arr):
+            c += v.item()
+        return c
+
+    res = context.compile_internal(builder, array_sum_impl, sig, args,
+                                   locals=dict(c=sig.return_type))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(np.sum, types.Array, types.intp)
+@lower_builtin(np.sum, types.Array, types.IntegerLiteral)
+@lower_builtin("array.sum", types.Array, types.intp)
+@lower_builtin("array.sum", types.Array, types.IntegerLiteral)
+def array_sum_axis(context, builder, sig, args):
+    retty = sig.return_type
+    zero = getattr(retty, 'dtype', retty)(0)
+    # if the return is scalar in type then "take" the 0th element of the
+    # 0d array accumulator as the return value
+    if getattr(retty, 'ndim', None) is None:
+        op = np.take
+    else:
+        op = _array_sum_axis_nop
+    [ty_array, ty_axis] = sig.args
+    is_axis_const = False
+    const_axis_val = 0
+    if isinstance(ty_axis, types.Literal):
+        # this special-cases for constant axis
+        const_axis_val = ty_axis.literal_value
+        # fix negative axis
+        if const_axis_val < 0:
+            const_axis_val = ty_array.ndim + const_axis_val
+        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
+            raise ValueError("'axis' entry is out of bounds")
+
+        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
+        axis_val = context.get_constant(ty_axis, const_axis_val)
+        # rewrite arguments
+        args = args[0], axis_val
+        # rewrite sig
+        sig = sig.replace(args=[ty_array, ty_axis])
+        is_axis_const = True
+
+    gen_impl = gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero)
+    compiled = register_jitable(gen_impl)
+
+    def array_sum_impl_axis(arr, axis):
+        return compiled(arr, axis)
 
     res = context.compile_internal(builder, array_sum_impl_axis, sig, args)
     return impl_ret_new_ref(context, builder, sig.return_type, res)