!4251 adding type support for gpu kernels for EfficientNet

Merge pull request !4251 from Peilin/efficientnet

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/transpose_gpu_kernel.cc

@@ -21,5 +21,7 @@ MS_REG_GPU_KERNEL_ONE(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat32).A
                       TransposeGpuFwdKernel, float)
 MS_REG_GPU_KERNEL_ONE(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
                       TransposeGpuFwdKernel, half)
+MS_REG_GPU_KERNEL_ONE(Transpose, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                      TransposeGpuFwdKernel, int)
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/broadcast_grad_impl.cu

@@ -15,6 +15,7 @@
  */
 
 #include "backend/kernel_compiler/gpu/cuda_impl/broadcast_grad_impl.cuh"
+#include "backend/kernel_compiler/gpu/cuda_impl/util.cuh"
 #include "runtime/device/gpu/cuda_common.h"
 
 template <typename T>
@@ -22,9 +23,9 @@ struct MinimumGradFunc {
   __device__ __forceinline__ void operator()(const T &x1, const T &x2, const bool &grad_x1, const bool &grad_x2,
                                              const T &dy, T *dx1, T *dx2) {
     if (grad_x1 && x1 < x2) {
-      atomicAdd(dx1, dy);
+      ms_atomic_add(dx1, dy);
     } else if (grad_x2 && x1 >= x2) {
-      atomicAdd(dx2, dy);
+      ms_atomic_add(dx2, dy);
     }
   }
 };
@@ -34,9 +35,9 @@ struct MaximumGradFunc {
   __device__ __forceinline__ void operator()(const T &x1, const T &x2, const bool &grad_x1, const bool &grad_x2,
                                              const T &dy, T *dx1, T *dx2) {
     if (grad_x1 && x1 > x2) {
-      atomicAdd(dx1, dy);
+      ms_atomic_add(dx1, dy);
     } else if (grad_x2 && x1 <= x2) {
-      atomicAdd(dx2, dy);
+      ms_atomic_add(dx2, dy);
     }
   }
 };
@@ -117,6 +118,9 @@ template void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &
                               cudaStream_t stream);
 template void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op,
                               const int *x1, const int *x2, const int *dy, int *dx1, int *dx2, cudaStream_t stream);
+template void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op,
+                              const half *x1, const half *x2, const half *dy, half *dx1, half *dx2,
+                              cudaStream_t stream);
 template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
                             const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
                             const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const float *x1,
@@ -125,3 +129,7 @@ template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const i
                             const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
                             const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const int *x1,
                             const int *x2, const int *dy, int *dx1, int *dx2, cudaStream_t stream);
+template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
+                            const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
+                            const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const half *x1,
+                            const half *x2, const half *dy, half *dx1, half *dx2, cudaStream_t stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/transpose_impl.cu

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2020 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.
@@ -15,8 +15,10 @@
  */
 
 #include <cuda_runtime.h>
+
 #include "transpose_impl.cuh"
 #include "runtime/device/gpu/cuda_common.h"
+
 template <typename T>
 __global__ void Transpose(const int size, const T* input, const int* input_shape, const int* input_axis,
                           const int shape_size, T* output) {
@@ -63,3 +65,5 @@ template void CalTranspose<float>(const int size, const float* input, const int*
                                   const int shape_size, float* output, cudaStream_t cuda_stream);
 template void CalTranspose<half>(const int size, const half* input, const int* input_shape, const int* input_axis,
                                  const int shape_size, half* output, cudaStream_t cuda_stream);
+template void CalTranspose<int>(const int size, const int* input, const int* input_shape, const int* input_axis,
+                                 const int shape_size, int* output, cudaStream_t cuda_stream);

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

@@ -34,6 +34,22 @@ MS_REG_GPU_KERNEL_ONE(MaximumGrad,
                         .AddOutputAttr(kNumberTypeFloat32)
                         .AddOutputAttr(kNumberTypeFloat32),
                       BroadcastOpGradGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(MinimumGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16),
+                      BroadcastOpGradGpuKernel, half)
+MS_REG_GPU_KERNEL_ONE(MaximumGrad,
+                      KernelAttr()
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddInputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16)
+                        .AddOutputAttr(kNumberTypeFloat16),
+                      BroadcastOpGradGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(MinimumGrad,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeInt32)

tests/st/ops/gpu/test_transpose_op.py

@@ -28,25 +28,25 @@ context.set_context(device_target='GPU')
 
 
 class Transpose(nn.Cell):
-    def __init__(self):
+    def __init__(self, nptype):
         super(Transpose, self).__init__()
         self.transpose = P.Transpose()
 
-        self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(np.float32)), [5, 6]),
+        self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(nptype)), [5, 6]),
                               name='x_2D')
         self.perm_2D = (1, 0)
 
-        self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(np.float32)), [2, 2, 4]),
+        self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(nptype)), [2, 2, 4]),
                               name='x_3D')
         self.perm_3D = (1, 0, 2)
 
         self.x_4D = Parameter(
-            initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(np.float32)), [2, 3, 4, 5]),
+            initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(nptype)), [2, 3, 4, 5]),
             name='x_4D')
         self.perm_4D = (0, 1, 2, 3)
 
         self.x_5D = Parameter(
-            initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(np.float32)),
+            initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(nptype)),
                         [1, 2, 3, 4, 5]), name='x_5D')
         self.perm_5D = (1, 0, 3, 4, 2)
 
@@ -56,11 +56,8 @@ class Transpose(nn.Cell):
                 self.transpose(self.x_4D, self.perm_4D), self.transpose(self.x_5D, self.perm_5D))
 
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
-def test_transpose():
-    transpose = Transpose()
+def transpose1(nptype):
+    transpose = Transpose(nptype)
     output = transpose()
 
     expect0 = np.array([[[0, 6, 12, 18, 24],
@@ -68,11 +65,11 @@ def test_transpose():
                          [2, 8, 14, 20, 26],
                          [3, 9, 15, 21, 27],
                          [4, 10, 16, 22, 28],
-                         [5, 11, 17, 23, 29]]]).astype(np.float32)
+                         [5, 11, 17, 23, 29]]]).astype(nptype)
     expect1 = np.array([[[[0, 1, 2, 3],
                           [8, 9, 10, 11]],
                          [[4, 5, 6, 7],
-                          [12, 13, 14, 15]]]]).astype(np.float32)
+                          [12, 13, 14, 15]]]]).astype(nptype)
     expect2 = np.array([[[[[0, 1, 2, 3, 4],
                            [5, 6, 7, 8, 9],
                            [10, 11, 12, 13, 14],
@@ -97,7 +94,7 @@ def test_transpose():
                           [[100, 101, 102, 103, 104],
                            [105, 106, 107, 108, 109],
                            [110, 111, 112, 113, 114],
-                           [115, 116, 117, 118, 119]]]]]).astype(np.float32)
+                           [115, 116, 117, 118, 119]]]]]).astype(nptype)
     expect3 = np.array([[[[[[0, 20, 40],
                             [1, 21, 41],
                             [2, 22, 42],
@@ -138,8 +135,26 @@ def test_transpose():
                             [76, 96, 116],
                             [77, 97, 117],
                             [78, 98, 118],
-                            [79, 99, 119]]]]]]).astype(np.float32)
+                            [79, 99, 119]]]]]]).astype(nptype)
     assert (output[0].asnumpy() == expect0).all()
     assert (output[1].asnumpy() == expect1).all()
     assert (output[2].asnumpy() == expect2).all()
     assert (output[3].asnumpy() == expect3).all()
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_transpose_float32():
+    transpose1(np.float32)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_transpose_float16():
+    transpose1(np.float16)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_transpose_int32():
+    transpose1(np.int32)