test case to sum twice in different order (#12253)

* test case to sum twice in different order

fixed by #12251

* try metal

.github/workflows/test.yml

@@ -559,6 +559,21 @@ jobs:
     - name: Test ONNX
       run: CL=1 RANGEIFY=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
 
+  testrangeifymacos:
+    name: MacOS (rangeify)
+    runs-on: macos-14
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: metal
+        deps: testing
+    - name: Test METAL=1 RANGEIFY=1
+      run: METAL=1 RANGEIFY=1 python -m pytest -n=auto test/test_ops.py --durations=20
+
   testdevectorize:
     name: Linux (devectorize)
     runs-on: ubuntu-24.04

test/test_ops.py

@@ -2,7 +2,7 @@ import time, math, unittest, functools, platform, warnings
 import numpy as np
 from typing import List, Callable
 import torch
-from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, CPU_LLVM, AMD_LLVM, RANGEIFY
+from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, CPU_LLVM, AMD_LLVM, RANGEIFY, OSX
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
@@ -312,6 +312,12 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: torch.nn.functional.pad(torch.ones(256,256), pad=(0,64,0,0)).sum(axis=1),
                        lambda: Tensor.ones(256,256).pad(((0,0), (0,64))).sum(axis=1), forward_only=True)
 
+  @unittest.skipUnless(OSX or Device.DEFAULT=="CPU", "TODO fail on some devices")
+  def test_sum_twice(self):
+    helper_test_op([(4, 4, 4)], lambda x: x.sum((0, 1)).sum())
+    helper_test_op([(4, 4, 4)], lambda x: x.sum((0, 2)).sum())
+    helper_test_op([(4, 4, 4)], lambda x: x.sum((1, 2)).sum())
+
   # this is more complex and won't fold for a while
   def test_sum_cat_collapse(self):
     helper_test_op([], lambda: torch.cat([torch.ones(256,256), torch.zeros(256,64)], dim=1).sum(axis=1),