fix arange and add global unittest (#7576)

oneflow/user/ops/arange_op.cpp

@@ -29,7 +29,7 @@ namespace oneflow {
     int64_t integer_start = ctx->Attr<int64_t>("integer_start");
     int64_t integer_limit = ctx->Attr<int64_t>("integer_limit");
     // CHECK when limit > start, delta > 0; limit < start, delta < 0;
-    CHECK_GT_OR_RETURN((integer_limit - integer_start) / integer_delta, static_cast<int64_t>(0))
+    CHECK_GE_OR_RETURN((integer_limit - integer_start) / integer_delta, static_cast<int64_t>(0))
         << "RuntimeError: upper bound and larger bound inconsistent with step sign";
     range_elem_cnt = std::ceil(static_cast<double>(integer_limit - integer_start) / integer_delta);
   } else {

python/oneflow/test/modules/test_consistent_arange.py

@@ -0,0 +1,71 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import numpy as np
+import unittest
+
+import oneflow as flow
+import oneflow.unittest
+
+from oneflow.test_utils.automated_test_util import *
+
+
+@autotest(n=1, auto_backward=False, check_graph=False)
+def _test_arange_with_random_data(test_case, placement, sbp):
+    start = random(0, 10).to(int).value()
+    end = start + random(0, 10).to(int).value()
+    step = random(1, max(2, end - start)).to(int).value()
+    start = start * 8
+    end = end * 8
+    x = torch.arange(start=start, end=end, step=step)
+    x.oneflow = flow.arange(
+        start=start, end=end, step=step, placement=placement, sbp=sbp
+    )
+    return x
+
+
+@autotest(n=1, auto_backward=True, check_graph=False)
+def _test_arange_with_float_delta(test_case, placement, sbp):
+    start = random(0, 10).to(int).value()
+    end = start + random(0, 10).to(int).value()
+    step = random(1, max(2, end - start)).to(float).value()
+    start = start * 8
+    end = end * 8
+    x = torch.arange(start=start, end=end, step=step, requires_grad=True)
+    x.oneflow = flow.arange(
+        start=start,
+        end=end,
+        step=step,
+        placement=placement,
+        sbp=sbp,
+        requires_grad=True,
+    )
+    return x
+
+
+class TestArange(flow.unittest.TestCase):
+    @globaltest
+    def test_arange(test_case):
+        for placement in all_placement():
+            # arange does not support split and partial_sum currently.
+            for sbp in all_sbp(
+                placement, max_dim=1, except_split=True, except_partial_sum=True
+            ):
+                _test_arange_with_random_data(test_case, placement, sbp)
+                _test_arange_with_float_delta(test_case, placement, sbp)
+
+
+if __name__ == "__main__":
+    unittest.main()