add float64 to Addn gpu

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

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2021 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.
@@ -18,6 +18,9 @@
 
 namespace mindspore {
 namespace kernel {
+MS_REG_GPU_KERNEL_ONE(
+  AddN, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+  AddNGpuFwdKernel, double)
 MS_REG_GPU_KERNEL_ONE(
   AddN, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   AddNGpuFwdKernel, float)

mindspore/ccsrc/backend/kernel_compiler/gpu/math/addn_gpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2021 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.
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ADDN_GPU_KERNEL_H_
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MATH_ADDN_GPU_KERNEL_H_
-#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ADDN_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MATH_ADDN_GPU_KERNEL_H_
 
 #include <memory>
 #include <vector>
@@ -63,11 +63,18 @@ class AddNGpuFwdKernel : public GpuKernel {
     }
     const float alpha = 1;
     const float beta = 0;
+    const double dalpha = static_cast<double>(1.0f);
+    const double dbeta = static_cast<double>(0.0f);
     for (size_t i = 0; i < num_input_; i++) {
       T *input_addr = GetDeviceAddress<T>(inputs, i);
       if (cudnn_data_type_ == CUDNN_DATA_INT32) {
         ElewiseArith(outputs[0]->size / sizeof(T), BROADCAST_TYPE_ADD, input_addr, work_addr, work_addr,
                      reinterpret_cast<cudaStream_t>(stream_ptr));
+      } else if (cudnn_data_type_ == CUDNN_DATA_DOUBLE) {
+        CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
+                                    cudnnAddTensor(cudnn_handle_, &dalpha, input_descriptor_, input_addr,
+                                                   &(i > 0 ? dalpha : dbeta), input_descriptor_, work_addr),
+                                    "cudnnAddTensor failed");
       } else {
         CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
                                     cudnnAddTensor(cudnn_handle_, &alpha, input_descriptor_, input_addr,
@@ -169,4 +176,4 @@ class AddNGpuFwdKernel : public GpuKernel {
 }  // namespace kernel
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ADDN_GPU_KERNEL_H_
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MATH_ADDN_GPU_KERNEL_H_

tests/st/ops/gpu/test_addn_op.py

@@ -1,4 +1,4 @@
-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2019-2021 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.
@@ -55,3 +55,42 @@ def test_net():
                        [96., 99., 102., 105.]]]]
 
     assert (output.asnumpy() == expect_result).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_net_float64():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
+    y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
+    z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
+    add = Net()
+    output = add(Tensor(x), Tensor(y), Tensor(z))
+    expect_result = np.array([[[[0., 3., 6., 9.],
+                                [12., 15., 18., 21.],
+                                [24., 27., 30., 33.]],
+                               [[36., 39., 42., 45.],
+                                [48., 51., 54., 57.],
+                                [60., 63., 66., 69.]],
+                               [[72., 75., 78., 81.],
+                                [84., 87., 90., 93.],
+                                [96., 99., 102., 105.]]]]).astype(np.float64)
+    assert (output.asnumpy() == expect_result).all()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
+    y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
+    z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float64)
+    add = Net()
+    output = add(Tensor(x), Tensor(y), Tensor(z))
+    expect_result = np.array([[[[0., 3., 6., 9.],
+                                [12., 15., 18., 21.],
+                                [24., 27., 30., 33.]],
+                               [[36., 39., 42., 45.],
+                                [48., 51., 54., 57.],
+                                [60., 63., 66., 69.]],
+                               [[72., 75., 78., 81.],
+                                [84., 87., 90., 93.],
+                                [96., 99., 102., 105.]]]]).astype(np.float64)
+    assert (output.asnumpy() == expect_result).all()