Author: Ronan Lamy <[email protected]>
Branch: fix-result-types
Changeset: r77336:9d3a820f8c4f
Date: 2015-05-15 04:48 +0100
http://bitbucket.org/pypy/pypy/changeset/9d3a820f8c4f/
Log: simplify loop.call2()
diff --git a/pypy/module/micronumpy/loop.py b/pypy/module/micronumpy/loop.py
--- a/pypy/module/micronumpy/loop.py
+++ b/pypy/module/micronumpy/loop.py
@@ -18,35 +18,7 @@
greens=['shapelen', 'func', 'calc_dtype', 'res_dtype'],
reds='auto')
-def call2(space, shape, func, calc_dtype, res_dtype, w_lhs, w_rhs, out):
- # handle array_priority
- # w_lhs and w_rhs could be of different ndarray subtypes. Numpy does:
- # 1. if __array_priorities__ are equal and one is an ndarray and the
- # other is a subtype, return a subtype
- # 2. elif rhs.__array_priority__ is higher, return the type of rhs
-
- w_ndarray = space.gettypefor(W_NDimArray)
- lhs_type = space.type(w_lhs)
- rhs_type = space.type(w_rhs)
- lhs_for_subtype = w_lhs
- rhs_for_subtype = w_rhs
- #it may be something like a FlatIter, which is not an ndarray
- if not space.is_true(space.issubtype(lhs_type, w_ndarray)):
- lhs_type = space.type(w_lhs.base)
- lhs_for_subtype = w_lhs.base
- if not space.is_true(space.issubtype(rhs_type, w_ndarray)):
- rhs_type = space.type(w_rhs.base)
- rhs_for_subtype = w_rhs.base
-
- w_highpriority = w_lhs
- highpriority_subtype = lhs_for_subtype
- if space.is_w(lhs_type, w_ndarray) and not space.is_w(rhs_type, w_ndarray):
- highpriority_subtype = rhs_for_subtype
- w_highpriority = w_rhs
- if support.is_rhs_priority_higher(space, w_lhs, w_rhs):
- highpriority_subtype = rhs_for_subtype
- w_highpriority = w_rhs
-
+def call2(space, shape, func, calc_dtype, w_lhs, w_rhs, out):
if w_lhs.get_size() == 1:
w_left = w_lhs.get_scalar_value().convert_to(space, calc_dtype)
left_iter = left_state = None
@@ -63,13 +35,9 @@
right_iter, right_state = w_rhs.create_iter(shape)
right_iter.track_index = False
- if out is None:
- w_ret = W_NDimArray.from_shape(space, shape, res_dtype,
- w_instance=highpriority_subtype)
- else:
- w_ret = out
- out_iter, out_state = w_ret.create_iter(shape)
+ out_iter, out_state = out.create_iter(shape)
shapelen = len(shape)
+ res_dtype = out.get_dtype()
while not out_iter.done(out_state):
call2_driver.jit_merge_point(shapelen=shapelen, func=func,
calc_dtype=calc_dtype,
res_dtype=res_dtype)
@@ -82,9 +50,7 @@
out_iter.setitem(out_state, func(calc_dtype, w_left,
w_right).convert_to(
space, res_dtype))
out_state = out_iter.next(out_state)
- if out is None:
- w_ret = space.call_method(w_highpriority, '__array_wrap__', w_ret)
- return w_ret
+ return out
call1_driver = jit.JitDriver(
name='numpy_call1',
diff --git a/pypy/module/micronumpy/ufuncs.py b/pypy/module/micronumpy/ufuncs.py
--- a/pypy/module/micronumpy/ufuncs.py
+++ b/pypy/module/micronumpy/ufuncs.py
@@ -45,6 +45,38 @@
'''
raise NotImplementedError()
+
+def array_priority(space, w_lhs, w_rhs):
+ # handle array_priority
+ # w_lhs and w_rhs could be of different ndarray subtypes. Numpy does:
+ # 1. if __array_priorities__ are equal and one is an ndarray and the
+ # other is a subtype, return a subtype
+ # 2. elif rhs.__array_priority__ is higher, return the type of rhs
+
+ w_ndarray = space.gettypefor(W_NDimArray)
+ lhs_type = space.type(w_lhs)
+ rhs_type = space.type(w_rhs)
+ lhs_for_subtype = w_lhs
+ rhs_for_subtype = w_rhs
+ #it may be something like a FlatIter, which is not an ndarray
+ if not space.is_true(space.issubtype(lhs_type, w_ndarray)):
+ lhs_type = space.type(w_lhs.base)
+ lhs_for_subtype = w_lhs.base
+ if not space.is_true(space.issubtype(rhs_type, w_ndarray)):
+ rhs_type = space.type(w_rhs.base)
+ rhs_for_subtype = w_rhs.base
+
+ w_highpriority = w_lhs
+ highpriority_subtype = lhs_for_subtype
+ if space.is_w(lhs_type, w_ndarray) and not space.is_w(rhs_type, w_ndarray):
+ highpriority_subtype = rhs_for_subtype
+ w_highpriority = w_rhs
+ if is_rhs_priority_higher(space, w_lhs, w_rhs):
+ highpriority_subtype = rhs_for_subtype
+ w_highpriority = w_rhs
+ return w_highpriority, highpriority_subtype
+
+
class W_Ufunc(W_Root):
_immutable_fields_ = [
"name", "promote_to_largest", "promote_to_float", "promote_bools",
"nin",
@@ -620,8 +652,17 @@
assert isinstance(w_rhs, W_NDimArray)
new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs)
new_shape = shape_agreement(space, new_shape, out,
broadcast_down=False)
- return loop.call2(space, new_shape, self.func, calc_dtype,
- res_dtype, w_lhs, w_rhs, out)
+ w_highpriority, out_subtype = array_priority(space, w_lhs, w_rhs)
+ if out is None:
+ w_ret = W_NDimArray.from_shape(space, new_shape, res_dtype,
+ w_instance=out_subtype)
+ else:
+ w_ret = out
+ w_ret = loop.call2(space, new_shape, self.func, calc_dtype,
+ w_lhs, w_rhs, w_ret)
+ if out is None:
+ w_ret = space.call_method(w_highpriority, '__array_wrap__', w_ret)
+ return w_ret
def call_scalar(self, space, w_lhs, w_rhs, in_dtype, out_dtype, out):
w_val = self.func(in_dtype,
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