[GitHub] [incubator-mxnet] bartekkuncer commented on a change in pull request #19896: [FEATURE] OneDNN adaptive pooling support was added to mxnet.

[GitBox <gi...@apache.org>]

bartekkuncer commented on a change in pull request #19896:
URL: https://github.com/apache/incubator-mxnet/pull/19896#discussion_r588225900



##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -197,6 +198,91 @@ num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
   }
 }
 
+#if MXNET_USE_MKLDNN == 1
+bool CanMKLDNNSupportAveragePooling(const NDArray &in_data,

Review comment:
       Please change the name to SupportMKLDNNAveragePooling or SupportMKLDNNAP to stay consistent with other implementations.
   

##########
File path: src/operator/nn/mkldnn/mkldnn_adaptive_pooling-inl.h
##########
@@ -0,0 +1,147 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file mkldnn_adaptive_pooling-inl.h
+ * \brief
+ * \author Mateusz Ozga
+*/
+#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_ADAPTIVE_POOLING_INL_H_
+#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_ADAPTIVE_POOLING_INL_H_
+
+#if MXNET_USE_MKLDNN == 1
+
+#include <mkldnn.hpp>
+#include <utility>
+#include "../../operator_common.h"
+#include "./mkldnn_base-inl.h"
+
+namespace mxnet {
+namespace op {
+
+class MKLDNNAdaptivePoolingFwd {
+ public:
+  MKLDNNAdaptivePoolingFwd(const mxnet::NDArray &input,
+                           const mxnet::NDArray &output,
+                           const mkldnn::memory::dims &kernel,
+                           const mkldnn::memory::dims &strides,
+                           const mkldnn::memory::dims &pad_l,
+                           const mkldnn::memory::dims &pad_r,
+                           const mkldnn::algorithm alg_kind,
+                           const bool with_workspace, const bool is_train)
+      : with_workspace_(with_workspace), fwd_(nullptr) {
+    Init(input, output, kernel, strides, pad_l, pad_r, is_train, alg_kind);
+  }
+  ~MKLDNNAdaptivePoolingFwd() = default;
+
+ public:
+  void Execute(const NDArray &input, const OpReqType req, const NDArray &output,
+               const NDArray *workspace);
+
+ private:
+  bool with_workspace_;
+  std::shared_ptr<mkldnn::pooling_forward::primitive_desc> fwd_pd_;
+  std::shared_ptr<mkldnn::pooling_forward> fwd_;
+
+ private:
+  void Init(const mxnet::NDArray &input, const mxnet::NDArray &output,
+            const mkldnn::memory::dims &kernel,
+            const mkldnn::memory::dims &strides,
+            const mkldnn::memory::dims &pad_l,
+            const mkldnn::memory::dims &pad_r, const bool is_train,
+            const mkldnn::algorithm alg_kind);
+};
+
+
+template <typename T = mkldnn::memory::dims>
+void updateAdaptivePaddingKernel(T *kernel, T *strides, T *pad_l, T *pad_r,
+                                 const NDArray &in_data,
+                                 const NDArray &out_data) {
+  const int IH = in_data.shape()[2];
+  const int IW = in_data.shape()[3];
+  const int OH = out_data.shape()[2];
+  const int OW = out_data.shape()[3];
+
+  strides->at(0) = floor((IH << 1) / OH) - floor(IH / OH);
+  strides->at(1) = floor((IW << 1) / OW) - floor(IW / OW);
+  kernel->at(0) = ceil((IH << 1) / OH) - floor(IH / OH);
+  kernel->at(1) = ceil((IW << 1) / OW) - floor(IW / OW);
+  pad_l->at(0) = (strides->at(0) * (OH - 1) + kernel->at(0) - IH) >> 1;
+  pad_l->at(1) = (strides->at(1) * (OW - 1) + kernel->at(1) - IW) >> 1;
+}
+
+template <typename T>
+MKLDNNAdaptivePoolingFwd &GetPoolingFwd(const T &param, const bool is_train,
+                                        const NDArray &input,
+                                        const NDArray &output) {
+  if (input.shape().ndim() != 4) {
+    LOG(FATAL) << "MKLDNN Adaptive Avg Pool 2d: Expect only 2D input";
+  }
+  typedef ParamOpSign<T> MKLDNNPoolingSignature;
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<MKLDNNPoolingSignature,
+                                         MKLDNNAdaptivePoolingFwd, OpHash>
+      pooling_fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<MKLDNNPoolingSignature,
+                                            MKLDNNAdaptivePoolingFwd, OpHash>
+      pooling_fwds;
+#endif
+  bool with_workspace = is_train && true;

Review comment:
       What does this do?




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