[fix] split nanmedian fluid deps (#43135)

paddle/phi/kernels/cpu/nanmedian_grad_kernel.cc

@@ -13,9 +13,11 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/nanmedian_grad_kernel.h"
+
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/impl/nanmedian_grad_kernel_impl.h"
 
 namespace phi {
 

paddle/phi/kernels/cpu/nanmedian_kernel.cc

@@ -13,8 +13,10 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/nanmedian_kernel.h"
+
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/impl/nanmedian_kernel_impl.h"
 #include "paddle/phi/kernels/top_k_kernel.h"
 
 namespace phi {

paddle/phi/kernels/gpu/nanmedian_grad_kernel.cu

@@ -12,13 +12,15 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/phi/kernels/nanmedian_grad_kernel.h"
+
 #include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
 #include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/core/tensor_meta.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
-#include "paddle/phi/kernels/nanmedian_grad_kernel.h"
+#include "paddle/phi/kernels/impl/nanmedian_grad_kernel_impl.h"
 
 namespace phi {
 

paddle/phi/kernels/gpu/nanmedian_kernel.cu

@@ -12,13 +12,15 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/phi/kernels/nanmedian_kernel.h"
+
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
 #include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/full_kernel.h"
-#include "paddle/phi/kernels/nanmedian_kernel.h"
+#include "paddle/phi/kernels/impl/nanmedian_kernel_impl.h"
 #include "paddle/phi/kernels/top_k_kernel.h"
 
 namespace phi {

paddle/phi/kernels/impl/nanmedian_grad_kernel_impl.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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.
+
+#pragma once
+
+#include "paddle/phi/kernels/nanmedian_grad_kernel.h"
+
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void PostprocessMedianGradKernel(const Context& dev_ctx,
+                                 DenseTensor* input,
+                                 const IntArray& raw_axes,
+                                 DenseTensor* x) {
+  auto input_dim = input->dims();
+  auto rank = input_dim.size();
+
+  std::vector<int64_t> axes = raw_axes.GetData();
+  int64_t axes_size = static_cast<int>(axes.size());
+  for (int64_t i = 0; i < axes_size; i++) {
+    if (axes[i] < 0) {
+      axes[i] += rank;
+    }
+  }
+
+  std::vector<int> trans_back;
+  std::vector<int> reshape_back;
+  trans_back.reserve(rank);
+  trans_back.resize(rank);
+
+  int offset = 0;
+  for (int64_t i = 0; i < rank; i++) {
+    if (std::find(axes.begin(), axes.end(), i) == axes.end()) {
+      reshape_back.push_back(input_dim[i]);
+      trans_back[i] = offset;
+      offset += 1;
+    }
+  }
+
+  for (int64_t i = 0; i < rank; i++) {
+    if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
+      trans_back[i] = offset;
+      reshape_back.push_back(input_dim[i]);
+      offset += 1;
+    }
+  }
+
+  input->Resize(make_ddim(reshape_back));
+  funcs::TransCompute<Context, T>(
+      static_cast<int>(trans_back.size()), dev_ctx, *input, x, trans_back);
+}
+
+}  // namespace phi

paddle/phi/kernels/impl/nanmedian_kernel_impl.h

+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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.
+
+#pragma once
+
+#include "paddle/phi/kernels/nanmedian_kernel.h"
+
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void PreprocessMedianKernel(const Context& dev_ctx,
+                            const DenseTensor& input,
+                            const IntArray& raw_axes,
+                            DenseTensor* x) {
+  auto input_dim = input.dims();
+  auto rank = input_dim.size();
+  std::vector<int> perm;
+  std::vector<int64_t> reshape;
+
+  std::vector<int64_t> axes = raw_axes.GetData();
+  int64_t axes_size = static_cast<int>(axes.size());
+  for (int64_t i = 0; i < axes_size; i++) {
+    if (axes[i] < 0) {
+      axes[i] += rank;
+    }
+  }
+
+  for (int64_t i = 0; i < rank; i++) {
+    if (std::find(axes.begin(), axes.end(), i) == axes.end()) {
+      perm.push_back(i);
+      reshape.push_back(input_dim[i]);
+    }
+  }
+
+  int64_t post_numel = 1;
+  for (int64_t i = 0; i < rank; i++) {
+    if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
+      perm.push_back(i);
+      post_numel *= input_dim[i];
+    }
+  }
+  reshape.push_back(post_numel);
+
+  DDim trans_dim(input_dim);
+  int ndims = perm.size();
+  for (int i = 0; i < ndims; i++) {
+    trans_dim[i] = input_dim[perm[i]];
+  }
+  x->Resize(trans_dim);
+  dev_ctx.template Alloc<T>(x);
+  funcs::TransCompute<Context, T>(ndims, dev_ctx, input, x, perm);
+
+  x->Resize(make_ddim(reshape));
+}
+
+}  // namespace phi

paddle/phi/kernels/nanmedian_grad_kernel.h

@@ -13,55 +13,12 @@
 // limitations under the License.
 
 #pragma once
+
 #include "paddle/phi/common/int_array.h"
 #include "paddle/phi/core/dense_tensor.h"
-#include "paddle/phi/kernels/funcs/math_function.h"
 
 namespace phi {
 
-template <typename T, typename Context>
-void PostprocessMedianGradKernel(const Context& dev_ctx,
-                                 DenseTensor* input,
-                                 const IntArray& raw_axes,
-                                 DenseTensor* x) {
-  auto input_dim = input->dims();
-  auto rank = input_dim.size();
-
-  std::vector<int64_t> axes = raw_axes.GetData();
-  int64_t axes_size = static_cast<int>(axes.size());
-  for (int64_t i = 0; i < axes_size; i++) {
-    if (axes[i] < 0) {
-      axes[i] += rank;
-    }
-  }
-
-  std::vector<int> trans_back;
-  std::vector<int> reshape_back;
-  trans_back.reserve(rank);
-  trans_back.resize(rank);
-
-  int offset = 0;
-  for (int64_t i = 0; i < rank; i++) {
-    if (std::find(axes.begin(), axes.end(), i) == axes.end()) {
-      reshape_back.push_back(input_dim[i]);
-      trans_back[i] = offset;
-      offset += 1;
-    }
-  }
-
-  for (int64_t i = 0; i < rank; i++) {
-    if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
-      trans_back[i] = offset;
-      reshape_back.push_back(input_dim[i]);
-      offset += 1;
-    }
-  }
-
-  input->Resize(make_ddim(reshape_back));
-  funcs::TransCompute<Context, T>(
-      static_cast<int>(trans_back.size()), dev_ctx, *input, x, trans_back);
-}
-
 template <typename T, typename Context>
 void NanmedianGradKernel(const Context& dev_ctx,
                          const DenseTensor& x,

paddle/phi/kernels/nanmedian_kernel.h

@@ -13,58 +13,12 @@
 // limitations under the License.
 
 #pragma once
+
 #include "paddle/phi/common/int_array.h"
 #include "paddle/phi/core/dense_tensor.h"
-#include "paddle/phi/kernels/funcs/math_function.h"
 
 namespace phi {
 
-template <typename T, typename Context>
-void PreprocessMedianKernel(const Context& dev_ctx,
-                            const DenseTensor& input,
-                            const IntArray& raw_axes,
-                            DenseTensor* x) {
-  auto input_dim = input.dims();
-  auto rank = input_dim.size();
-  std::vector<int> perm;
-  std::vector<int64_t> reshape;
-
-  std::vector<int64_t> axes = raw_axes.GetData();
-  int64_t axes_size = static_cast<int>(axes.size());
-  for (int64_t i = 0; i < axes_size; i++) {
-    if (axes[i] < 0) {
-      axes[i] += rank;
-    }
-  }
-
-  for (int64_t i = 0; i < rank; i++) {
-    if (std::find(axes.begin(), axes.end(), i) == axes.end()) {
-      perm.push_back(i);
-      reshape.push_back(input_dim[i]);
-    }
-  }
-
-  int64_t post_numel = 1;
-  for (int64_t i = 0; i < rank; i++) {
-    if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
-      perm.push_back(i);
-      post_numel *= input_dim[i];
-    }
-  }
-  reshape.push_back(post_numel);
-
-  DDim trans_dim(input_dim);
-  int ndims = perm.size();
-  for (int i = 0; i < ndims; i++) {
-    trans_dim[i] = input_dim[perm[i]];
-  }
-  x->Resize(trans_dim);
-  dev_ctx.template Alloc<T>(x);
-  funcs::TransCompute<Context, T>(ndims, dev_ctx, input, x, perm);
-
-  x->Resize(make_ddim(reshape));
-}
-
 template <typename T, typename Context>
 void NanmedianKernel(const Context& dev_ctx,
                      const DenseTensor& x,