!25457 add broadcast GPU float64 registration

Merge pull request !25457 from zhujingxuan/master

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

@@ -27,6 +27,10 @@ MS_REG_GPU_KERNEL_ONE(
   Minimum,
   KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
   BroadcastOpGpuKernel, double)
+MS_REG_GPU_KERNEL_ONE(
+  Maximum,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+  BroadcastOpGpuKernel, double)
 MS_REG_GPU_KERNEL_ONE(
   Less, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeBool),
   BroadcastOpGpuKernel, double)

tests/st/ops/gpu/test_broadcast_op.py

@@ -297,6 +297,66 @@ def test_broadcast_diff_dims():
     assert np.allclose(output_ms.asnumpy(), output_np)
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_broadcast_diff_dims_float64():
+    """
+    Feature: ALL To ALL
+    Description: test cases for broadcast operations execpted for DivNoNan
+    Expectation: the result match numpy results
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+
+    np.random.seed(42)
+    x1_np = np.random.rand(2).astype(np.float32)
+    x2_np = np.random.rand(2, 1).astype(np.float32)
+
+    output_ms = P.Minimum()(Tensor(x1_np), Tensor(x2_np))
+    output_np = np.minimum(x1_np, x2_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Maximum()(Tensor(x1_np), Tensor(x2_np))
+    output_np = np.maximum(x1_np, x2_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Greater()(Tensor(x1_np), Tensor(x2_np))
+    output_np = x1_np > x2_np
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Less()(Tensor(x1_np), Tensor(x2_np))
+    output_np = x1_np < x2_np
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Pow()(Tensor(x1_np), Tensor(x2_np))
+    output_np = np.power(x1_np, x2_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.RealDiv()(Tensor(x1_np), Tensor(x2_np))
+    output_np = x1_np / x2_np
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Mul()(Tensor(x1_np), Tensor(x2_np))
+    output_np = x1_np * x2_np
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Sub()(Tensor(x1_np), Tensor(x2_np))
+    output_np = x1_np - x2_np
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Mod()(Tensor(x1_np), Tensor(x2_np))
+    output_np = np.fmod(x1_np, x2_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.FloorMod()(Tensor(x1_np), Tensor(x2_np))
+    output_np = np.mod(x1_np, x2_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+    output_ms = P.Atan2()(Tensor(x1_np), Tensor(x2_np))
+    output_np = np.arctan2(x1_np, x2_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard