!22585 Add grad implementation of AdaptiveAvgPool2D

Merge pull request !22585 from zuochuanyong/adaptive_avgpool2d_grad

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/adaptive_avg_pool2d_grad_impl.cu

@@ -0,0 +1,181 @@
+/**
+ * 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.
+ */
+
+#include "backend/kernel_compiler/gpu/cuda_impl/adaptive_avg_pool2d_grad_impl.cuh"
+
+__device__ inline uint start_index(uint a, uint b, uint c) {
+  return floorf(__uint2float_rn(a * c) / __uint2float_rn(b));
+}
+
+__device__ inline uint end_index(uint a, uint b, uint c) {
+  return ceilf(__uint2float_rn((a + 1) * c) / __uint2float_rn(b));
+}
+
+template <typename T>
+__global__ void AdaptiveAvgPool2DGradKernel(const uint size, const uint input_height, const uint input_width,
+                                            const uint output_height, const uint output_width, T *input_data,
+                                            T *output_data) {
+  for (uint c = blockIdx.x * blockDim.x + threadIdx.x; c < size; c += gridDim.x * blockDim.x) {
+    T *input_ptr = input_data + c * input_height * input_width;
+    T *output_ptr = output_data + c * output_height * output_width;
+
+    size_t output_size = output_height * output_width;
+    for (size_t i = 0; i < output_size; i++) {
+      output_ptr[i] = 0;
+    }
+
+    for (uint ih = 0; ih < input_height; ih++) {
+      uint oh0 = start_index(ih, input_height, output_height);
+      uint oh1 = end_index(ih, input_height, output_height);
+      uint kh = oh1 - oh0;
+
+      for (uint iw = 0; iw < input_width; iw++) {
+        uint ow0 = start_index(iw, input_width, output_width);
+        uint ow1 = end_index(iw, input_width, output_width);
+        uint kw = ow1 - ow0;
+
+        T delta = input_ptr[ih * input_width + iw] / (kh * kw);
+        for (uint oh = oh0; oh < oh1; oh++) {
+          for (uint ow = ow0; ow < ow1; ow++) {
+            output_ptr[oh * output_width + ow] += delta;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <>
+__global__ void AdaptiveAvgPool2DGradKernel(const uint size, const uint input_height, const uint input_width,
+                                            const uint output_height, const uint output_width, float *input_data,
+                                            float *output_data) {
+  for (uint c = blockIdx.x * blockDim.x + threadIdx.x; c < size; c += gridDim.x * blockDim.x) {
+    float *input_ptr = input_data + c * input_height * input_width;
+    float *output_ptr = output_data + c * output_height * output_width;
+
+    size_t output_size = output_height * output_width;
+    for (size_t i = 0; i < output_size; i++) {
+      output_ptr[i] = 0;
+    }
+
+    for (uint ih = 0; ih < input_height; ih++) {
+      uint oh0 = start_index(ih, input_height, output_height);
+      uint oh1 = end_index(ih, input_height, output_height);
+      uint kh = oh1 - oh0;
+
+      for (uint iw = 0; iw < input_width; iw++) {
+        uint ow0 = start_index(iw, input_width, output_width);
+        uint ow1 = end_index(iw, input_width, output_width);
+        uint kw = ow1 - ow0;
+
+        float delta = input_ptr[ih * input_width + iw] / __uint2float_rn(kh * kw);
+        for (uint oh = oh0; oh < oh1; oh++) {
+          for (uint ow = ow0; ow < ow1; ow++) {
+            output_ptr[oh * output_width + ow] += delta;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <>
+__global__ void AdaptiveAvgPool2DGradKernel(const uint size, const uint input_height, const uint input_width,
+                                            const uint output_height, const uint output_width, half *input_data,
+                                            half *output_data) {
+  for (uint c = blockIdx.x * blockDim.x + threadIdx.x; c < size; c += gridDim.x * blockDim.x) {
+    half *input_ptr = input_data + c * input_height * input_width;
+    half *output_ptr = output_data + c * output_height * output_width;
+
+    size_t output_size = output_height * output_width;
+    for (size_t i = 0; i < output_size; i++) {
+      output_ptr[i] = 0;
+    }
+
+    for (uint ih = 0; ih < input_height; ih++) {
+      uint oh0 = start_index(ih, input_height, output_height);
+      uint oh1 = end_index(ih, input_height, output_height);
+      uint kh = oh1 - oh0;
+
+      for (uint iw = 0; iw < input_width; iw++) {
+        uint ow0 = start_index(iw, input_width, output_width);
+        uint ow1 = end_index(iw, input_width, output_width);
+        uint kw = ow1 - ow0;
+
+        half delta = input_ptr[ih * input_width + iw] / __uint2half_rn(kh * kw);
+        for (uint oh = oh0; oh < oh1; oh++) {
+          for (uint ow = ow0; ow < ow1; ow++) {
+            output_ptr[oh * output_width + ow] += delta;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <>
+__global__ void AdaptiveAvgPool2DGradKernel(const uint size, const uint input_height, const uint input_width,
+                                            const uint output_height, const uint output_width, double *input_data,
+                                            double *output_data) {
+  for (uint c = blockIdx.x * blockDim.x + threadIdx.x; c < size; c += gridDim.x * blockDim.x) {
+    double *input_ptr = input_data + c * input_height * input_width;
+    double *output_ptr = output_data + c * output_height * output_width;
+
+    size_t output_size = output_height * output_width;
+    for (size_t i = 0; i < output_size; i++) {
+      output_ptr[i] = 0;
+    }
+
+    for (uint ih = 0; ih < input_height; ih++) {
+      uint oh0 = start_index(ih, input_height, output_height);
+      uint oh1 = end_index(ih, input_height, output_height);
+      uint kh = oh1 - oh0;
+
+      for (uint iw = 0; iw < input_width; iw++) {
+        uint ow0 = start_index(iw, input_width, output_width);
+        uint ow1 = end_index(iw, input_width, output_width);
+        uint kw = ow1 - ow0;
+
+        double delta = input_ptr[ih * input_width + iw] / __uint2double_rn(kh * kw);
+        for (uint oh = oh0; oh < oh1; oh++) {
+          for (uint ow = ow0; ow < ow1; ow++) {
+            output_ptr[oh * output_width + ow] += delta;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+void ApplyAdaptiveAvgPool2DGrad(const uint size, const uint input_height, const uint input_width,
+                                const uint output_height, const uint output_width, T *input_data,
+                                T *output_data, cudaStream_t cuda_stream) {
+  AdaptiveAvgPool2DGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(
+    size, input_height, input_width, output_height, output_width, input_data, output_data);
+}
+
+template void ApplyAdaptiveAvgPool2DGrad<float>(const uint size, const uint input_height, const uint input_width,
+                                                const uint output_height, const uint output_width, float *input_data,
+                                                float *output_data, cudaStream_t cuda_stream);
+
+template void ApplyAdaptiveAvgPool2DGrad<half>(const uint size, const uint input_height, const uint input_width,
+                                               const uint output_height, const uint output_width, half *input_data,
+                                               half *output_data, cudaStream_t cuda_stream);
+
+template void ApplyAdaptiveAvgPool2DGrad<double>(const uint size, const uint input_height, const uint input_width,
+                                                 const uint output_height, const uint output_width, double *input_data,
+                                                 double *output_data, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/adaptive_avg_pool2d_grad_impl.cuh

@@ -0,0 +1,26 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAPTIVE_AVGPOOL2D_GRAD_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAPTIVE_AVGPOOL2D_GRAD_IMPL_H_
+
+#include "runtime/device/gpu/cuda_common.h"
+template <typename T>
+void ApplyAdaptiveAvgPool2DGrad(const uint size, const uint input_height, const uint input_width,
+                                const uint output_height, const uint output_width, T *input_data,
+                                T *output_data, cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAPTIVE_AVGPOOL2D_GRAD_IMPL_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adaptive_avg_pool2d_grad_gpu_kernel.cc

@@ -0,0 +1,34 @@
+/**
+ * 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.
+ */
+
+#include "backend/kernel_compiler/gpu/nn/adaptive_avg_pool2d_grad_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(
+  AdaptiveAvgPool2DGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  AdaptiveAvgPool2DGradKernel, half)
+MS_REG_GPU_KERNEL_ONE(
+  AdaptiveAvgPool2DGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  AdaptiveAvgPool2DGradKernel, float)
+MS_REG_GPU_KERNEL_ONE(
+  AdaptiveAvgPool2DGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+  AdaptiveAvgPool2DGradKernel, double)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adaptive_avg_pool2d_grad_gpu_kernel.h

@@ -0,0 +1,120 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_ADAPTIVEAVGPOOL2D_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_ADAPTIVEAVGPOOL2D_GPU_KERNEL_H_
+
+#include <vector>
+#include <string>
+#include <algorithm>
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/adaptive_avg_pool2d_grad_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+constexpr size_t kAdaptiveAvgPool2dGradInputNum = 2;
+constexpr size_t kAdaptiveAvgPool2dGradMinRank = 2;
+
+template <typename T>
+class AdaptiveAvgPool2DGradKernel : public GpuKernel {
+ public:
+  AdaptiveAvgPool2DGradKernel()
+      : input_size_(0),
+        output_size_(0),
+        input_height_(0),
+        input_width_(0),
+        output_height_(0),
+        output_width_(0),
+        size_(0) {}
+  ~AdaptiveAvgPool2DGradKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    T *dy_addr = GetDeviceAddress<T>(inputs, 1);
+    T *dx_addr = GetDeviceAddress<T>(outputs, 0);
+
+    ApplyAdaptiveAvgPool2DGrad(size_, input_height_, input_width_, output_height_, output_width_, dy_addr, dx_addr,
+                               reinterpret_cast<cudaStream_t>(stream_ptr));
+
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != kAdaptiveAvgPool2dGradInputNum) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but AdaptiveAvgPool2DGrad needs "
+                    << kAdaptiveAvgPool2dGradInputNum << " inputs.";
+      return false;
+    }
+
+    input_size_ = sizeof(T);
+    output_size_ = sizeof(T);
+
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);  // dy
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);         // dx
+
+    auto input_rank = input_shape.size();
+    auto output_rank = output_shape.size();
+    if (input_rank < kAdaptiveAvgPool2dGradMinRank || output_rank < kAdaptiveAvgPool2dGradMinRank) {
+      MS_LOG(ERROR) << "The input or output should have rank at least 2.";
+      return false;
+    }
+    input_height_ = static_cast<uint>(input_shape[input_rank - 2]);
+    input_width_ = static_cast<uint>(input_shape[input_rank - 1]);
+    size_ = static_cast<uint>(input_rank == (kAdaptiveAvgPool2dGradMinRank + 1) ? input_shape[0]
+                                                                                : input_shape[0] * input_shape[1]);
+    for (uint i = 0; i < input_rank; i++) {
+      input_size_ *= input_shape[i];
+    }
+
+    output_height_ = static_cast<uint>(output_shape[output_rank - 2]);
+    output_width_ = static_cast<uint>(output_shape[output_rank - 1]);
+    for (size_t i = 0; i < output_shape.size(); i++) {
+      output_size_ *= output_shape[i];
+    }
+
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    output_size_list_.push_back(output_size_);
+  }
+
+ private:
+  size_t input_size_;
+  size_t output_size_;
+  uint input_height_;
+  uint input_width_;
+  uint output_height_;
+  uint output_width_;
+  uint size_;
+
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_ADAPTIVEAVGPOOL2D_GPU_KERNEL_H_

mindspore/ops/_grad/grad_nn_ops.py

@@ -318,6 +318,17 @@ def get_bprop_avg_pool_grad(self):
     return bprop
 
 
+@bprop_getters.register(P.AdaptiveAvgPool2D)
+def get_bprop_adaptive_avg_pool2d_grad(self):
+    """Grad definition for `AdaptiveAvgPool2D` operation."""
+    adaptive_avgpool_grad = G.AdaptiveAvgPool2DGrad()
+
+    def bprop(x, out, dout):
+        dx = adaptive_avgpool_grad(x, dout)
+        return (dx,)
+
+    return bprop
+
 @bprop_getters.register(P.AvgPool3D)
 def get_bprop_avg_pool_3d_grad(self):
     """Grad definition for `AvgPool3D` operation."""

mindspore/ops/bprop_mindir/ApproximateEqual_bprop.mindir

@@ -16,4 +16,4 @@ bprop.32:x*
 bprop.32:y*
 bprop.32:out*
 
bprop.32:dout2
-bprop.32:[CNode]35:3:€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
+bprop.32:[CNode]35:3:€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

mindspore/ops/bprop_mindir/Argmax_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.13:x*
 bprop.13:out*
 
bprop.13:dout2
-bprop.13:[CNode]15:2:€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
+bprop.13:[CNode]15:2:€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

mindspore/ops/bprop_mindir/Argmin_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.16:x*
 bprop.16:out*
 
bprop.16:dout2
-bprop.16:[CNode]18:2:€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mindspore/ops/bprop_mindir/AssignAdd_bprop.mindir

@@ -16,4 +16,4 @@ bprop.21:x*
 bprop.21:y*
 bprop.21:out*
 
bprop.21:dout2
-bprop.21:[CNode]24:3:€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mindspore/ops/bprop_mindir/AssignSub_bprop.mindir

@@ -16,4 +16,4 @@ bprop.25:x*
 bprop.25:y*
 bprop.25:out*
 
bprop.25:dout2
-bprop.25:[CNode]28:3:€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mindspore/ops/bprop_mindir/Assign_bprop.mindir

@@ -12,4 +12,4 @@ bprop.60:x*
 bprop.60:y*
 bprop.60:out*
 
bprop.60:dout2
-bprop.60:[CNode]62:2:€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mindspore/ops/bprop_mindir/BNTrainingReduce_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.67:x*
 bprop.67:out*
 
bprop.67:dout2
-bprop.67:[CNode]69:2:€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mindspore/ops/bprop_mindir/Broadcast_bprop.mindir

@@ -6,4 +6,4 @@ l
 bprop.19:x*
 bprop.19:out*
 
bprop.19:dout2
-bprop.19:[CNode]20:1:€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mindspore/ops/bprop_mindir/DropoutDoMask_bprop.mindir

@@ -17,4 +17,4 @@
 bprop.110:keep_prob*
 
bprop.110:out*
 bprop.110:dout2
-bprop.110:[CNode]114:4:€087787fe3abde92d74a97b5b9f48f23d8ccdd6de450a931c64f578b83dcb5c2f1bb10d35eb13b30786fa05cc950c1d1711dbc21a5dda0cf3c1670158c0627efe2366f7bd59ea5ec135e982de03b4f7cab6b61d833d046a6e13f78bdaf2fb2b224c332efad4a51b4773cb78093dd53a4ca850b2dc6cdd5f2ae47106b3fda77bb3565f906930f68ca2413e9ad958d105e129e717cd183b95d11d65a8b0b030fc0d7e635a08323207b4cb3f73fd8437b4d7ee28a7676a68f005a7749bd19e5ed4eca6c407ad6a3b57190d3702d6a45031d13b97bb6952735edf94fb36f73dbff6cdab258748286fc6d783abacce203dfc79d2fc31e23a427ce1f86e08777a687f71be985a048b98205beb531a0e96f3c9c3c36cb9a5fef472e532f1e8041d85d279c414b8c313aac4f85c6217fbbb7009dd079b2d5548f8b695a470a11cb8cc83e6f5e78f5b3c67f2e7bf339b250c3638aee952e1a073002e2834011401f3827260ffb378d62977d2a1338d9b64be24b7349347c19c65faf6ba72837f4df97ff84c
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mindspore/ops/bprop_mindir/DropoutGenMask_bprop.mindir

@@ -12,4 +12,4 @@
 bprop.50:keep_prob*
 bprop.50:out*
 
bprop.50:dout2
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mindspore/ops/bprop_mindir/Equal_bprop.mindir

@@ -16,4 +16,4 @@ bprop.70:x*
 bprop.70:y*
 bprop.70:out*
 
bprop.70:dout2
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mindspore/ops/bprop_mindir/GreaterEqual_bprop.mindir

@@ -16,4 +16,4 @@ bprop.82:x*
 bprop.82:y*
 bprop.82:out*
 
bprop.82:dout2
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mindspore/ops/bprop_mindir/Greater_bprop.mindir

@@ -16,4 +16,4 @@ bprop.78:x*
 bprop.78:y*
 bprop.78:out*
 
bprop.78:dout2
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mindspore/ops/bprop_mindir/IOU_bprop.mindir

@@ -16,4 +16,4 @@ bprop.63:x*
 bprop.63:y*
 bprop.63:out*
 
bprop.63:dout2
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mindspore/ops/bprop_mindir/Identity_bprop.mindir

@@ -5,4 +5,4 @@ f
 	bprop.2:x*
 bprop.2:out*
 bprop.2:dout2
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mindspore/ops/bprop_mindir/IsFinite_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.29:x*
 bprop.29:out*
 
bprop.29:dout2
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mindspore/ops/bprop_mindir/LessEqual_bprop.mindir

@@ -16,4 +16,4 @@ bprop.90:x*
 bprop.90:y*
 bprop.90:out*
 
bprop.90:dout2
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mindspore/ops/bprop_mindir/Less_bprop.mindir

@@ -16,4 +16,4 @@ bprop.86:x*
 bprop.86:y*
 bprop.86:out*
 
bprop.86:dout2
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mindspore/ops/bprop_mindir/LinSpace_bprop.mindir

@@ -15,4 +15,4 @@
 bprop.45:num*
 bprop.45:out*
 
bprop.45:dout2
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mindspore/ops/bprop_mindir/LogicalAnd_bprop.mindir

@@ -16,4 +16,4 @@ bprop.94:x*
 bprop.94:y*
 bprop.94:out*
 
bprop.94:dout2
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mindspore/ops/bprop_mindir/LogicalNot_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.39:x*
 bprop.39:out*
 
bprop.39:dout2
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mindspore/ops/bprop_mindir/LogicalOr_bprop.mindir

@@ -16,4 +16,4 @@ bprop.98:x*
 bprop.98:y*
 bprop.98:out*
 
bprop.98:dout2
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mindspore/ops/bprop_mindir/NotEqual_bprop.mindir

@@ -16,4 +16,4 @@ bprop.74:x*
 bprop.74:y*
 bprop.74:out*
 
bprop.74:dout2
-bprop.74:[CNode]77:3:€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mindspore/ops/bprop_mindir/OneHot_bprop.mindir

@@ -19,4 +19,4 @@
 bprop.54:off_value*
 bprop.54:out*
 
bprop.54:dout2
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mindspore/ops/bprop_mindir/OnesLike_bprop.mindir

@@ -7,4 +7,4 @@ l
 	bprop.7:x*
 bprop.7:out*
 bprop.7:dout2
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mindspore/ops/bprop_mindir/Range_bprop.mindir

@@ -7,4 +7,4 @@ l
 	bprop.4:x*
 bprop.4:out*
 bprop.4:dout2
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mindspore/ops/bprop_mindir/ReLU_bprop.mindir

@@ -8,4 +8,4 @@ f
 	bprop.0:x*
 bprop.0:out*
 bprop.0:dout2
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mindspore/ops/bprop_mindir/ReduceAll_bprop.mindir

@@ -12,4 +12,4 @@
 bprop.102:axis*
 
bprop.102:out*
 bprop.102:dout2
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mindspore/ops/bprop_mindir/ReduceAny_bprop.mindir

@@ -12,4 +12,4 @@
 bprop.106:axis*
 
bprop.106:out*
 bprop.106:dout2
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mindspore/ops/bprop_mindir/Round_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.42:x*
 bprop.42:out*
 
bprop.42:dout2
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mindspore/ops/bprop_mindir/Sign_bprop.mindir

@@ -9,4 +9,4 @@ s
 bprop.36:x*
 bprop.36:out*
 
bprop.36:dout2
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mindspore/ops/bprop_mindir/ZerosLike_bprop.mindir

@@ -9,4 +9,4 @@ r
 bprop.10:x*
 bprop.10:out*
 
bprop.10:dout2
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mindspore/ops/operations/_grad_ops.py

@@ -843,6 +843,20 @@ class AvgPoolGrad(_PoolGrad):
         return x1_dtype
 
 
+class AdaptiveAvgPool2DGrad(PrimitiveWithInfer):
+    """Gradients of the adaptive avg pool 2D operation."""
+
+    @prim_attr_register
+    def __init__(self):
+        """Initialize AdaptiveAvgPool2DGrad"""
+
+    def infer_shape(self, x1_shape, grad_shape):
+        return x1_shape
+
+    def infer_dtype(self, x1_dtype, grad_dtype):
+        return x1_dtype
+
+
 class AvgPool3DGrad(Primitive):
     """Gradients of the avg pool3d operation."""
 

tests/st/ops/gpu/test_adaptive_avg_pool2d_op.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor, ops
 from mindspore.ops import operations as P
+from mindspore.ops.operations import _grad_ops as G
 from mindspore.common.api import ms_function
 
 context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
@@ -36,6 +37,16 @@ class Net(nn.Cell):
         return self.adaptive_avg_pool2d(x)
 
 
+class GradNet(nn.Cell):
+    def __init__(self):
+        super(GradNet, self).__init__()
+        self.adaptive_avg_pool2d_grad = G.AdaptiveAvgPool2DGrad()
+
+    @ms_function
+    def construct(self, x, dy):
+        return self.adaptive_avg_pool2d_grad(x, dy)
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -85,20 +96,80 @@ def test_net_value():
     assert output.asnumpy().shape == expect_shape
     assert (output.asnumpy() == expect_output).all
 
+    expect_dx = np.array([[[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]],
+                          [[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]],
+                          [[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]]])
+    grad_net = GradNet()
+    dx = grad_net(Tensor(x), output)
+    assert dx.asnumpy().shape == x.shape
+    assert (dx.asnumpy() == expect_dx).all
+
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
-def test_net_pynative():
+def test_net_graph_mode():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     x = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
                   [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
                   [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]])
     adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((2, 2))
-    output = adaptive_avg_pool_2d(Tensor(x))
+    output = adaptive_avg_pool_2d(Tensor(x, mindspore.float16))
     expect_shape = (3, 2, 2)
     expect_output = np.array([[[3.0, 4.0], [6.0, 7.0]],
                               [[3.0, 4.0], [6.0, 7.0]],
                               [[3.0, 4.0], [6.0, 7.0]]])
     assert output.asnumpy().shape == expect_shape
     assert (output.asnumpy() == expect_output).all
+
+    expect_dx = np.array([[[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]],
+                          [[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]],
+                          [[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]]])
+    grad_net = GradNet()
+    dx = grad_net(Tensor(x, mindspore.float16), output)
+    assert dx.asnumpy().shape == x.shape
+    assert (dx.asnumpy() == expect_dx).all
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_net_graph_mode_fp64():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    x = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
+                  [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
+                  [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]])
+    adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((2, 2))
+    output = adaptive_avg_pool_2d(Tensor(x, mindspore.float64))
+    expect_shape = (3, 2, 2)
+    expect_output = np.array([[[3.0, 4.0], [6.0, 7.0]],
+                              [[3.0, 4.0], [6.0, 7.0]],
+                              [[3.0, 4.0], [6.0, 7.0]]])
+    assert output.asnumpy().shape == expect_shape
+    assert (output.asnumpy() == expect_output).all
+
+    expect_dx = np.array([[[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]],
+                          [[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]],
+                          [[0.75, 1.75, 1.0],
+                           [2.25, 5.0, 2.75],
+                           [1.5, 3.25, 1.75]]])
+    grad_net = GradNet()
+    dx = grad_net(Tensor(x, mindspore.float64), output)
+    assert dx.asnumpy().shape == x.shape
+    assert (dx.asnumpy() == expect_dx).all