add float64 support to reducemax grad

fix ci

mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.cc

@@ -35,6 +35,10 @@ MS_REG_GPU_KERNEL_ONE(
   AbsGrad,
   KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
   BroadcastOpGpuKernel, double)
+MS_REG_GPU_KERNEL_ONE(
+  RealDiv,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+  BroadcastOpGpuKernel, double)
 
 // fp32
 MS_REG_GPU_KERNEL_ONE(

tests/st/ops/gpu/test_reduce_max_grad_op.py

@@ -0,0 +1,59 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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 mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+
+def test_reduce_sum_grad():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.op = P.ReduceMax()
+
+        def construct(self, x1, x2):
+            return self.op(x1, x2)
+
+
+    class GradNet(nn.Cell):
+        def __init__(self, network):
+            super(GradNet, self).__init__()
+            self.grad = C.GradOperation(get_all=True, sens_param=True)
+            self.network = network
+
+        def construct(self, x1, x2, dy):
+            return self.grad(self.network)(x1, x2, dy)
+
+    net = Net()
+    grad_net = GradNet(net)
+    x1 = Tensor(np.array([[1, 2], [5, 4], [9, 16]]).astype(np.float32))
+    x2 = 1
+    dy = Tensor(np.array([2, 10, 1]).astype(np.float32))
+    out = grad_net(x1, x2, dy)
+    expected = np.array([[0, 2], [10, 0], [0, 1]])
+    np.testing.assert_allclose(out[0].asnumpy(), expected, rtol=1e-6)
+
+    x1 = Tensor(np.array([[9, 2], [4, 5], [1, 16]]).astype(np.float32))
+    x2 = 0
+    dy = Tensor(np.array([10, 11]).astype(np.float32))
+    out = grad_net(x1, 0, dy)
+    expected = np.array([[10, 0], [0, 0], [0, 11]])
+    np.testing.assert_allclose(out[0].asnumpy(), expected, rtol=1e-6)