fix maximum minimum backward bug (#7519)

* fix maximum minimum backward bug

* refine

* refine

* auto format by CI

* refine

Co-authored-by: oneflow-ci-bot <ci-bot@oneflow.org>
Co-authored-by: oneflow-ci-bot <69100618+oneflow-ci-bot@users.noreply.github.com>
Co-authored-by: Houjiang Chen <chenhoujiangcug@gmail.com>

oneflow/user/kernels/elementwise_maximum_minimum_kernel.h

@@ -32,6 +32,9 @@ struct MaximumGradFunctor {
     T dy_val = 0;
     if (x > y) {
       dx_val = dz;
+    } else if (x == y) {
+      dx_val = dz / 2;
+      dy_val = dz / 2;
     } else {
       dy_val = dz;
     }
@@ -52,6 +55,9 @@ struct MinimumGradFunctor {
     T dy_val = 0;
     if (x < y) {
       dx_val = dz;
+    } else if (x == y) {
+      dx_val = dz / 2;
+      dy_val = dz / 2;
     } else {
       dy_val = dz;
     }

python/oneflow/test/modules/test_consistent_maximum_minimum.py

@@ -0,0 +1,109 @@
+"""
+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 unittest
+
+import oneflow as flow
+import oneflow.unittest
+import torch as torch_original
+from packaging import version
+from oneflow.test_utils.automated_test_util import *
+
+
+@autotest(
+    n=5,
+    auto_backward=(
+        version.parse(torch_original.__version__) >= version.parse("1.10.2")
+    ),
+    check_graph=False,
+)
+def _test_broadcast_maximum(test_case, placement, x_sbp, y_sbp):
+    x = random_tensor(ndim=5, dim0=8, dim1=8, dim2=8, dim3=1, dim4=8).to_global(
+        placement, x_sbp
+    )
+    y = random_tensor(ndim=5, dim0=8, dim1=8, dim2=1, dim3=8, dim4=1).to_global(
+        placement, y_sbp
+    )
+    z = torch.maximum(x, y)
+    return z
+
+
+@autotest(
+    n=5,
+    auto_backward=(
+        version.parse(torch_original.__version__) >= version.parse("1.10.2")
+    ),
+    check_graph=False,
+)
+def _test_broadcast_minimum(test_case, placement, x_sbp, y_sbp):
+    x = random_tensor(ndim=5, dim0=8, dim1=8, dim2=8, dim3=1, dim4=8).to_global(
+        placement, x_sbp
+    )
+    y = random_tensor(ndim=5, dim0=8, dim1=8, dim2=1, dim3=8, dim4=1).to_global(
+        placement, y_sbp
+    )
+    z = torch.minimum(x, y)
+    return z
+
+
+@autotest(
+    n=5,
+    auto_backward=(
+        version.parse(torch_original.__version__) >= version.parse("1.10.2")
+    ),
+    check_graph=False,
+)
+def _test_maximum_with_same_input(test_case, placement, sbp):
+    x = random_tensor(ndim=4, dim0=8, dim1=8, dim2=8, dim3=8).to_global(placement, sbp)
+    y = x.detach().clone()
+    y.requires_grad = True
+    z = torch.maximum(x, y)
+    return z
+
+
+@autotest(
+    n=5,
+    auto_backward=(
+        version.parse(torch_original.__version__) >= version.parse("1.10.2")
+    ),
+    check_graph=False,
+)
+def _test_minimum_with_same_input(test_case, placement, sbp):
+    x = random_tensor(ndim=4, dim0=8, dim1=8, dim2=8, dim3=8).to_global(placement, sbp)
+    y = x.detach().clone()
+    y.requires_grad = True
+    z = torch.minimum(x, y)
+    return z
+
+
+class TestMaximumMinimumOps(flow.unittest.TestCase):
+    @globaltest
+    def test_maximum_minimum_with_same_input(test_case):
+        for placement in all_placement():
+            for sbp in all_sbp(placement, max_dim=2):
+                _test_maximum_with_same_input(test_case, placement, sbp)
+                _test_minimum_with_same_input(test_case, placement, sbp)
+
+    @globaltest
+    def test_broadcast_maximum_minimum(test_case):
+        for placement in all_placement():
+            for x_sbp in all_sbp(placement, valid_split_axis=[0, 1, 2, 4]):
+                for y_sbp in all_sbp(placement, valid_split_axis=[0, 1, 3]):
+                    _test_broadcast_maximum(test_case, placement, x_sbp, y_sbp)
+                    _test_broadcast_minimum(test_case, placement, x_sbp, y_sbp)
+
+
+if __name__ == "__main__":
+    unittest.main()