[cherry-pick][oneDNN]Extended adaptive pooling support for oneDNN pool kernel (#30993)

paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc

@@ -28,6 +28,270 @@ using mkldnn::reorder;
 using mkldnn::stream;
 using platform::to_void_cast;
 
+template <typename T>
+class PoolingMKLDNNHandler
+    : public platform::MKLDNNHandlerT<T, mkldnn::pooling_forward,
+                                      mkldnn::pooling_backward> {
+ public:
+  PoolingMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
+                       const platform::MKLDNNDeviceContext& dev_ctx,
+                       platform::Place cpu_place, const Tensor* input,
+                       Tensor* output, const std::string& unique_name)
+      : platform::MKLDNNHandlerT<T, mkldnn::pooling_forward,
+                                 mkldnn::pooling_backward>(
+            dev_ctx, dev_ctx.GetEngine(), cpu_place,
+            platform::CreateKey(dev_ctx, framework::vectorize(input->dims()),
+                                framework::ToMKLDNNDataType(input->type()),
+                                unique_name)) {
+    if (!this->isCached()) {
+      PADDLE_ENFORCE_EQ(input->layout(), DataLayout::kMKLDNN,
+                        platform::errors::InvalidArgument(
+                            "Wrong layout set for Input tensor."));
+      PADDLE_ENFORCE_NE(input->format(), MKLDNNMemoryFormat::undef,
+                        platform::errors::InvalidArgument(
+                            "Wrong format set for Input tensor."));
+
+      const std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+
+      std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
+      std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
+
+      std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
+      std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
+
+      std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
+      std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
+
+      const bool global_pooling = ctx.Attr<bool>("global_pooling");
+      const std::string padding_algorithm =
+          ctx.Attr<std::string>("padding_algorithm");
+
+      // Only 2D pooling is supported now
+      PADDLE_ENFORCE_EQ(
+          ksize.size(), 2,
+          platform::errors::InvalidArgument(
+              "The ksize must be 2D, i.e. 2D pooling, but received %dD.",
+              ksize.size()));
+      PADDLE_ENFORCE_EQ(
+          pooling_type == "max" || pooling_type == "avg", true,
+          platform::errors::InvalidArgument(
+              "The pooling_type must be 'max' or 'avg', but received %s.",
+              pooling_type));
+      PADDLE_ENFORCE_EQ(
+          input->dims().size(), 4,
+          platform::errors::InvalidArgument(
+              "Input dim must be with 4, i.e. NCHW, but received %d.",
+              input->dims().size()));
+
+      const auto input_dims = input->dims();
+      framework::DDim data_dims =
+          framework::slice_ddim(input_dims, 2, input_dims.size());
+
+      if (global_pooling) {
+        operators::UpdateKsize(&ksize, data_dims);
+      }
+
+      operators::UpdatePadding(&paddings, global_pooling, 0, padding_algorithm,
+                               data_dims, strides, ksize);
+
+      const auto src_tz = paddle::framework::vectorize(input->dims());
+      const auto dst_tz = paddle::framework::vectorize(output->dims());
+
+      const auto is_test = ctx.Attr<bool>("is_test");
+
+      const auto dt = framework::ToMKLDNNDataType(input->type());
+      const auto fmt = input->format();
+
+      const auto exclude_padding = ctx.Attr<bool>("exclusive");
+
+      const auto src_md = mkldnn::memory::desc(src_tz, dt, fmt);
+      /* create memory descriptor for pooling without specified format
+       * ('any') which lets a primitive (pooling in this case) choose
+       * the memory format preferred for best performance
+       */
+
+      const auto dst_md =
+          platform::MKLDNNMemDesc(dst_tz, dt, MKLDNNMemoryFormat::any);
+
+      auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
+
+      const bool ceil_mode = ctx.Attr<bool>("ceil_mode");
+
+      if (ceil_mode) {
+        CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides,
+                          mkldnn_paddings[1]);
+      }
+
+      ComputeAdaptivePoolParameters(ctx, src_tz, &ksize, &strides);
+
+      this->AcquireForwardPrimitiveDescriptor(
+          is_test ? mkldnn::prop_kind::forward_inference
+                  : mkldnn::prop_kind::forward_training,
+          pooling_type == "max"
+              ? mkldnn::algorithm::pooling_max
+              : (exclude_padding
+                     ? mkldnn::algorithm::pooling_avg_exclude_padding
+                     : mkldnn::algorithm::pooling_avg_include_padding),
+          src_md, dst_md, strides, ksize, mkldnn_paddings[0],
+          mkldnn_paddings[1]);
+    }
+  }
+
+  PoolingMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
+                       const platform::MKLDNNDeviceContext& dev_ctx,
+                       platform::Place cpu_place, const Tensor* in_x,
+                       const Tensor* out_grad, Tensor* in_x_grad,
+                       const std::string& unique_name)
+      : platform::MKLDNNHandlerT<T, mkldnn::pooling_forward,
+                                 mkldnn::pooling_backward>(
+            dev_ctx, dev_ctx.GetEngine(), cpu_place,
+            platform::CreateKey(dev_ctx, framework::vectorize(in_x->dims()),
+                                framework::ToMKLDNNDataType(in_x->type()),
+                                unique_name)) {
+    if (!this->isBwdCached()) {
+      PADDLE_ENFORCE_EQ(in_x->layout(), DataLayout::kMKLDNN,
+                        platform::errors::InvalidArgument(
+                            "Wrong layout set for Input tensor"));
+      PADDLE_ENFORCE_NE(in_x->format(), MKLDNNMemoryFormat::undef,
+                        platform::errors::InvalidArgument(
+                            "Wrong format set for Input tensor"));
+
+      PADDLE_ENFORCE_EQ(out_grad->layout(), DataLayout::kMKLDNN,
+                        platform::errors::InvalidArgument(
+                            "Wrong layout set for Input output_grad tensor"));
+      PADDLE_ENFORCE_NE(out_grad->format(), MKLDNNMemoryFormat::undef,
+                        platform::errors::InvalidArgument(
+                            "Wrong format set for Input output_grad tensor"));
+
+      PADDLE_ENFORCE_EQ(
+          ctx.Attr<bool>("is_test"), false,
+          platform::errors::InvalidArgument(
+              "is_test attribute should be set to False in training phase."));
+
+      std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+
+      std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
+      std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
+
+      std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
+      std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
+
+      std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
+      std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
+
+      bool global_pooling = ctx.Attr<bool>("global_pooling");
+      std::string padding_algorithm =
+          ctx.Attr<std::string>("padding_algorithm");
+
+      auto in_x_dims = in_x->dims();
+      framework::DDim data_dims =
+          framework::slice_ddim(in_x_dims, 2, in_x_dims.size());
+
+      if (global_pooling) {
+        operators::UpdateKsize(&ksize, data_dims);
+      }
+
+      operators::UpdatePadding(&paddings, global_pooling, 0, padding_algorithm,
+                               data_dims, strides, ksize);
+
+      auto src_tz = paddle::framework::vectorize<int64_t>(in_x->dims());
+      auto diff_src_tz =
+          paddle::framework::vectorize<int64_t>(in_x_grad->dims());
+      auto diff_dst_tz =
+          paddle::framework::vectorize<int64_t>(out_grad->dims());
+
+      auto diff_dst_md = mkldnn::memory::desc(
+          diff_dst_tz, platform::MKLDNNGetDataType<T>(), out_grad->format());
+      auto diff_src_md =
+          mkldnn::memory::desc(diff_src_tz, platform::MKLDNNGetDataType<T>(),
+                               MKLDNNMemoryFormat::any);
+
+      auto mkldnn_paddings = platform::ToMkldnnPadding(paddings);
+      const bool ceil_mode = ctx.Attr<bool>("ceil_mode");
+
+      if (ceil_mode) {
+        CorrectOutputSize(src_tz, diff_dst_tz, ksize, paddings, strides,
+                          mkldnn_paddings[1]);
+      }
+      ComputeAdaptivePoolParameters(ctx, diff_src_tz, &ksize, &strides);
+
+      const auto exclude_padding = ctx.Attr<bool>("exclusive");
+      this->AcquireBackwardPrimitiveDescriptor(
+          pooling_type == "max"
+              ? mkldnn::algorithm::pooling_max
+              : (exclude_padding
+                     ? mkldnn::algorithm::pooling_avg_exclude_padding
+                     : mkldnn::algorithm::pooling_avg_include_padding),
+          diff_src_md, diff_dst_md, strides, ksize, mkldnn_paddings[0],
+          mkldnn_paddings[1]);
+    }
+  }
+
+  std::shared_ptr<mkldnn::memory> AcquireWorkspaceMemory(void) {
+    mkldnn::memory::desc workspace_md = this->fwd_pd_->workspace_desc();
+    // Pooling PD has to be passed to Grad op that
+    // may be executed by diffrent thread, hence
+    // for that one we use key that does not contain TID
+    auto local_key = this->key_common_ + "@workspace";
+    auto mem_p = std::static_pointer_cast<mkldnn::memory>(
+        this->dev_ctx_.GetBlob(local_key));
+    if (mem_p == nullptr) {
+      static std::mutex acquire_barrier;
+      std::lock_guard<std::mutex> block_threads_until_finish_this_job(
+          acquire_barrier);
+      mem_p = std::static_pointer_cast<mkldnn::memory>(
+          this->dev_ctx_.GetBlob(local_key));
+      if (mem_p == nullptr) {
+        mem_p = std::make_shared<mkldnn::memory>(workspace_md, this->engine_);
+        this->dev_ctx_.SetBlob(local_key, mem_p);
+      }
+    }
+    return mem_p;
+  }
+
+  static void ComputeAdaptivePoolParameters(
+      const paddle::framework::ExecutionContext& ctx,
+      const std::vector<int64_t>& src_tz, std::vector<int64_t>* ksize,
+      std::vector<int64_t>* strides) {
+    if (ctx.Attr<bool>("adaptive")) {
+      // https://github.com/oneapi-src/oneDNN/tree/bkocot/adaptive-pooling/rfcs/20200818-adaptive-pooling
+      auto IH = static_cast<double>(src_tz[src_tz.size() - 2]);
+      auto IW = static_cast<double>(src_tz[src_tz.size() - 1]);
+      auto OH = static_cast<double>(ksize->at(0));
+      auto OW = static_cast<double>(ksize->at(1));
+
+      strides->at(0) =
+          static_cast<int64_t>(floor((IH * 2.0) / OH) - floor(IH / OH));
+      strides->at(1) =
+          static_cast<int64_t>(floor((IW * 2.0) / OW) - floor(IW / OW));
+      ksize->at(0) =
+          static_cast<int64_t>(ceil((IH * 2.0) / OH) - floor(IH / OH));
+      ksize->at(1) =
+          static_cast<int64_t>(ceil((IW * 2.0) / OW) - floor(IW / OW));
+    }
+  }
+
+ private:
+  static inline int ComputeCeiledOutput(int input_size, int kernel_size,
+                                        int padding, int stride) {
+    return (input_size - kernel_size + 2 * padding) / stride + 1;
+  }
+
+  static inline void CorrectOutputSize(
+      const std::vector<int64_t>& src_tz, const std::vector<int64_t>& dst_tz,
+      const std::vector<int64_t>& kernel_size,
+      const std::vector<int64_t>& paddings, const std::vector<int64_t>& strides,
+      std::vector<int64_t>& right_bot_padding) {  // NOLINT
+    for (size_t i = 0; i < right_bot_padding.size(); i++) {
+      int desired_size = ComputeCeiledOutput(src_tz[i + 2], kernel_size[i],
+                                             paddings[i], strides[i]);
+      if (desired_size != dst_tz[i + 2]) {
+        right_bot_padding[i] += strides[i] - 1;
+      }
+    }
+  }
+};
+
 template <typename T>
 class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
@@ -37,14 +301,12 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
                           "Operator DNNL Pool must use CPUPlace"));
     auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
-    const auto& mkldnn_engine = dev_ctx.GetEngine();
 
     const Tensor* input = ctx.Input<Tensor>("X");
     Tensor* output = ctx.Output<Tensor>("Out");
 
-    platform::PoolingMKLDNNHandler<T> handler(ctx, dev_ctx, mkldnn_engine,
-                                              ctx.GetPlace(), input, output,
-                                              ctx.OutputName("Out"));
+    PoolingMKLDNNHandler<T> handler(ctx, dev_ctx, ctx.GetPlace(), input, output,
+                                    ctx.OutputName("Out"));
 
     auto src_memory = handler.AcquireSrcMemory(input);
     auto dst_memory = handler.AcquireDstMemory(output);
@@ -82,72 +344,11 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
     Tensor* in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
 
-    PADDLE_ENFORCE_EQ(
-        in_x->layout(), DataLayout::kMKLDNN,
-        platform::errors::InvalidArgument("Wrong layout set for Input tensor"));
-    PADDLE_ENFORCE_NE(
-        in_x->format(), MKLDNNMemoryFormat::undef,
-        platform::errors::InvalidArgument("Wrong format set for Input tensor"));
-
-    PADDLE_ENFORCE_EQ(out_grad->layout(), DataLayout::kMKLDNN,
-                      platform::errors::InvalidArgument(
-                          "Wrong layout set for Input output_grad tensor"));
-    PADDLE_ENFORCE_NE(out_grad->format(), MKLDNNMemoryFormat::undef,
-                      platform::errors::InvalidArgument(
-                          "Wrong format set for Input output_grad tensor"));
-
-    PADDLE_ENFORCE_EQ(
-        ctx.Attr<bool>("is_test"), false,
-        platform::errors::InvalidArgument(
-            "is_test attribute should be set to False in training phase."));
-
-    std::string pooling_type = ctx.Attr<std::string>("pooling_type");
-
-    std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
-    std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
-
-    std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
-    std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
-
-    std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
-    std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
-
-    bool global_pooling = ctx.Attr<bool>("global_pooling");
-    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
-
-    auto in_x_dims = in_x->dims();
-    framework::DDim data_dims =
-        framework::slice_ddim(in_x_dims, 2, in_x_dims.size());
-
-    if (global_pooling) {
-      UpdateKsize(&ksize, data_dims);
-    }
-
-    UpdatePadding(&paddings, global_pooling, 0, padding_algorithm, data_dims,
-                  strides, ksize);
-
-    platform::PoolingMKLDNNHandler<T>::ComputeAdaptivePoolParameters(
-        ctx, paddle::framework::vectorize(in_x->dims()), &ksize, &strides);
-
     auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
 
-    std::vector<mkldnn::primitive> pipeline;
-
-    auto diff_src_tz = paddle::framework::vectorize<int64_t>(in_x_grad->dims());
-    auto diff_dst_tz = paddle::framework::vectorize<int64_t>(out_grad->dims());
-
-    // Get an unique name from "argument" name of "Out" variable
-    // This name will be used as key when referring info from device context
-    const std::string key = platform::CreateKey(
-        dev_ctx, diff_src_tz, pooling_type, ksize, strides, paddings,
-        memory::data_type::f32, in_x->format(), ctx.InputName("Out"));
-
-    platform::PoolingMKLDNNHandler<T> handler(
-        diff_dst_tz, diff_src_tz, ksize, strides, paddings, pooling_type,
-        ctx.Attr<bool>("ceil_mode"), in_x->format(), out_grad->format(),
-        paddle::framework::ToMKLDNNDataType(out_grad->type()), dev_ctx,
-        ctx.GetPlace(), ctx.InputName("Out"), ctx.Attr<bool>("exclusive"));
+    PoolingMKLDNNHandler<T> handler(ctx, dev_ctx, ctx.GetPlace(), in_x,
+                                    out_grad, in_x_grad, ctx.InputName("Out"));
 
     auto diff_dst_memory = handler.AcquireDiffDstMemory(out_grad);
     auto diff_src_memory = handler.AcquireDiffSrcMemory(in_x_grad);
@@ -155,7 +356,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     auto pool_bwd_p = handler.AcquireBackwardPrimitive();
 
     mkldnn::stream astream(dev_ctx.GetEngine());
-    if (pooling_type == "max") {
+    if (ctx.Attr<std::string>("pooling_type") == "max") {
       // Max - pooling needs Workspace
       auto workspace_memory = handler.AcquireWorkspaceMemory();
       pool_bwd_p->execute(astream, {{MKLDNN_ARG_DIFF_SRC, *diff_src_memory},

paddle/fluid/operators/pool_op.cc

@@ -144,6 +144,35 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
   ctx->ShareLoD("X", "Out");
 }
 
+bool CanMKLDNNSupportPool(const framework::ExecutionContext& ctx) {
+  if (ctx.Attr<bool>("adaptive") == false) return true;
+  // (jczaja): oneDNN is supporting only unchangable in size pool window
+  auto src_tz = paddle::framework::vectorize(ctx.Input<Tensor>("X")->dims());
+  std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
+  // Fast but not exhustive check
+  if ((src_tz[src_tz.size() - 1] % ksize[1] == 0) &&
+      (src_tz[src_tz.size() - 2] % ksize[0] == 0))
+    return true;
+
+  // Exhustive check
+  auto IH = static_cast<double>(src_tz[src_tz.size() - 2]);
+  auto IW = static_cast<double>(src_tz[src_tz.size() - 1]);
+  auto OH = static_cast<double>(ksize[0]);
+  auto OW = static_cast<double>(ksize[1]);
+
+  auto SH = static_cast<int>(floor((IH * 2.0) / OH) - floor(IH / OH));
+  auto SW = static_cast<int>(floor((IW * 2.0) / OW) - floor(IW / OW));
+  auto KH = static_cast<int>(ceil((IH * 2.0) / OH) - floor(IH / OH));
+  auto KW = static_cast<int>(ceil((IW * 2.0) / OW) - floor(IW / OW));
+
+  auto PH = (SH * (static_cast<int>(OH) - 1) + KH - static_cast<int>(IH));
+  auto PW = (SW * (static_cast<int>(OW) - 1) + KW - static_cast<int>(IW));
+  // If there is additional padding needed then
+  // this is situation that oneDNN cannot comply with
+  // paddlepaddle reference implementation
+  return (PH == 0) && (PW == 0);
+}
+
 framework::OpKernelType PoolOp::GetExpectedKernelType(
     const framework::ExecutionContext& ctx) const {
   framework::LibraryType library_{framework::LibraryType::kPlain};
@@ -157,7 +186,7 @@ framework::OpKernelType PoolOp::GetExpectedKernelType(
 #endif
 #ifdef PADDLE_WITH_MKLDNN
   if (library_ == framework::LibraryType::kPlain &&
-      this->CanMKLDNNBeUsed(ctx)) {
+      this->CanMKLDNNBeUsed(ctx) && CanMKLDNNSupportPool(ctx)) {
     library_ = framework::LibraryType::kMKLDNN;
     layout_ = framework::DataLayout::kMKLDNN;
   }
@@ -213,7 +242,7 @@ framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
 #endif
 #ifdef PADDLE_WITH_MKLDNN
   if (library_ == framework::LibraryType::kPlain &&
-      this->CanMKLDNNBeUsed(ctx)) {
+      this->CanMKLDNNBeUsed(ctx) && CanMKLDNNSupportPool(ctx)) {
     library_ = framework::LibraryType::kMKLDNN;
     layout_ = framework::DataLayout::kMKLDNN;
   }

paddle/fluid/platform/mkldnn_reuse.h

@@ -120,6 +120,15 @@ class MKLDNNHandlerT {
     return (dev_ctx_.GetBlob(key_p) != nullptr);
   }
 
+  bool isBwdCached() {
+    const std::string key_pd = key_common_ + "@bwd_pd";
+    bwd_pd_ = std::static_pointer_cast<typename TBackward::primitive_desc>(
+        dev_ctx_.GetBlob(key_pd));
+
+    const std::string key_p = key_ + "@bwd_p";
+    return (dev_ctx_.GetBlob(key_p) != nullptr);
+  }
+
   // If your primitive descriptor requires attributes, pass them as a
   // first argument and paramters to descriptor constructor in the following
   // arguments. Otherwise, all arguments will be forwarded to descriptor
@@ -722,210 +731,6 @@ class LRNMKLDNNHandler
   }
 };
 
-template <typename T>
-class PoolingMKLDNNHandler : public MKLDNNHandlerT<T, mkldnn::pooling_forward,
-                                                   mkldnn::pooling_backward> {
- public:
-  PoolingMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
-                       const MKLDNNDeviceContext& dev_ctx,
-                       const mkldnn::engine mkldnn_engine,
-                       platform::Place cpu_place, const Tensor* input,
-                       Tensor* output, const std::string& unique_name)
-      : platform::MKLDNNHandlerT<T, mkldnn::pooling_forward,
-                                 mkldnn::pooling_backward>(
-            dev_ctx, dev_ctx.GetEngine(), cpu_place,
-            platform::CreateKey(dev_ctx, framework::vectorize(input->dims()),
-                                framework::ToMKLDNNDataType(input->type()),
-                                unique_name)) {
-    if (!this->isCached()) {
-      PADDLE_ENFORCE_EQ(input->layout(), DataLayout::kMKLDNN,
-                        platform::errors::InvalidArgument(
-                            "Wrong layout set for Input tensor."));
-      PADDLE_ENFORCE_NE(input->format(), MKLDNNMemoryFormat::undef,
-                        platform::errors::InvalidArgument(
-                            "Wrong format set for Input tensor."));
-
-      const std::string pooling_type = ctx.Attr<std::string>("pooling_type");
-
-      std::vector<int> ksize_temp = ctx.Attr<std::vector<int>>("ksize");
-      std::vector<int64_t> ksize(begin(ksize_temp), end(ksize_temp));
-
-      std::vector<int> strides_temp = ctx.Attr<std::vector<int>>("strides");
-      std::vector<int64_t> strides(begin(strides_temp), end(strides_temp));
-
-      std::vector<int> paddings_temp = ctx.Attr<std::vector<int>>("paddings");
-      std::vector<int64_t> paddings(begin(paddings_temp), end(paddings_temp));
-
-      const bool global_pooling = ctx.Attr<bool>("global_pooling");
-      const std::string padding_algorithm =
-          ctx.Attr<std::string>("padding_algorithm");
-
-      // Only 2D pooling is supported now
-      PADDLE_ENFORCE_EQ(
-          ksize.size(), 2,
-          platform::errors::InvalidArgument(
-              "The ksize must be 2D, i.e. 2D pooling, but received %dD.",
-              ksize.size()));
-      PADDLE_ENFORCE_EQ(
-          pooling_type == "max" || pooling_type == "avg", true,
-          platform::errors::InvalidArgument(
-              "The pooling_type must be 'max' or 'avg', but received %s.",
-              pooling_type));
-      PADDLE_ENFORCE_EQ(
-          input->dims().size(), 4,
-          platform::errors::InvalidArgument(
-              "Input dim must be with 4, i.e. NCHW, but received %d.",
-              input->dims().size()));
-
-      const auto input_dims = input->dims();
-      framework::DDim data_dims =
-          framework::slice_ddim(input_dims, 2, input_dims.size());
-
-      if (global_pooling) {
-        operators::UpdateKsize(&ksize, data_dims);
-      }
-
-      operators::UpdatePadding(&paddings, global_pooling, 0, padding_algorithm,
-                               data_dims, strides, ksize);
-
-      const auto src_tz = paddle::framework::vectorize(input->dims());
-      const auto dst_tz = paddle::framework::vectorize(output->dims());
-
-      const auto is_test = ctx.Attr<bool>("is_test");
-
-      const auto dt = framework::ToMKLDNNDataType(input->type());
-      const auto fmt = input->format();
-
-      const auto exclude_padding = ctx.Attr<bool>("exclusive");
-
-      const auto src_md = mkldnn::memory::desc(src_tz, dt, fmt);
-      /* create memory descriptor for pooling without specified format
-       * ('any') which lets a primitive (pooling in this case) choose
-       * the memory format preferred for best performance
-       */
-
-      const auto dst_md =
-          platform::MKLDNNMemDesc(dst_tz, dt, MKLDNNMemoryFormat::any);
-
-      auto mkldnn_paddings = ToMkldnnPadding(paddings);
-
-      const bool ceil_mode = ctx.Attr<bool>("ceil_mode");
-
-      if (ceil_mode) {
-        CorrectOutputSize(src_tz, dst_tz, ksize, paddings, strides,
-                          mkldnn_paddings[1]);
-      }
-
-      ComputeAdaptivePoolParameters(ctx, src_tz, &ksize, &strides);
-
-      this->AcquireForwardPrimitiveDescriptor(
-          is_test ? mkldnn::prop_kind::forward_inference
-                  : mkldnn::prop_kind::forward_training,
-          pooling_type == "max"
-              ? mkldnn::algorithm::pooling_max
-              : (exclude_padding
-                     ? mkldnn::algorithm::pooling_avg_exclude_padding
-                     : mkldnn::algorithm::pooling_avg_include_padding),
-          src_md, dst_md, strides, ksize, mkldnn_paddings[0],
-          mkldnn_paddings[1]);
-    }
-  }
-
-  PoolingMKLDNNHandler(
-      const std::vector<int64_t>& diff_dst_dims,
-      const std::vector<int64_t>& diff_src_dims,
-      const std::vector<int64_t>& ksize, const std::vector<int64_t>& strides,
-      const std::vector<int64_t>& paddings, const std::string& pooling_type,
-      bool ceil_mode, const MKLDNNMemoryFormat fmt,
-      const MKLDNNMemoryFormat diff_dst_fmt, mkldnn::memory::data_type dt,
-      const platform::MKLDNNDeviceContext& dev_ctx, platform::Place cpu_place,
-      const std::string& unique_name, bool exclude_padding)
-      : platform::MKLDNNHandlerT<T, mkldnn::pooling_forward,
-                                 mkldnn::pooling_backward>(
-            dev_ctx, dev_ctx.GetEngine(), cpu_place,
-            platform::CreateKey(dev_ctx, diff_src_dims, dt, unique_name)) {
-    auto diff_dst_md = mkldnn::memory::desc(
-        diff_dst_dims, platform::MKLDNNGetDataType<T>(), diff_dst_fmt);
-    auto diff_src_md =
-        mkldnn::memory::desc(diff_src_dims, platform::MKLDNNGetDataType<T>(),
-                             MKLDNNMemoryFormat::any);
-
-    auto mkldnn_paddings = ToMkldnnPadding(paddings);
-
-    this->AcquireBackwardPrimitiveDescriptor(
-        pooling_type == "max"
-            ? mkldnn::algorithm::pooling_max
-            : (exclude_padding
-                   ? mkldnn::algorithm::pooling_avg_exclude_padding
-                   : mkldnn::algorithm::pooling_avg_include_padding),
-        diff_src_md, diff_dst_md, strides, ksize, mkldnn_paddings[0],
-        mkldnn_paddings[1]);
-  }
-
-  std::shared_ptr<mkldnn::memory> AcquireWorkspaceMemory(void) {
-    mkldnn::memory::desc workspace_md = this->fwd_pd_->workspace_desc();
-    // Pooling PD has to be passed to Grad op that
-    // may be executed by diffrent thread, hence
-    // for that one we use key that does not contain TID
-    auto local_key = this->key_common_ + "@workspace";
-    auto mem_p = std::static_pointer_cast<mkldnn::memory>(
-        this->dev_ctx_.GetBlob(local_key));
-    if (mem_p == nullptr) {
-      static std::mutex acquire_barrier;
-      std::lock_guard<std::mutex> block_threads_until_finish_this_job(
-          acquire_barrier);
-      mem_p = std::static_pointer_cast<mkldnn::memory>(
-          this->dev_ctx_.GetBlob(local_key));
-      if (mem_p == nullptr) {
-        mem_p = std::make_shared<mkldnn::memory>(workspace_md, this->engine_);
-        this->dev_ctx_.SetBlob(local_key, mem_p);
-      }
-    }
-    return mem_p;
-  }
-
-  static void ComputeAdaptivePoolParameters(
-      const paddle::framework::ExecutionContext& ctx,
-      const std::vector<int64_t>& src_tz, std::vector<int64_t>* ksize,
-      std::vector<int64_t>* strides) {
-    if (ctx.Attr<bool>("adaptive")) {
-      // (jczaja): oneDNN is supporting only unchangable in size pool window
-      PADDLE_ENFORCE_EQ(
-          src_tz[src_tz.size() - 1] % ksize->at(1), 0,
-          platform::errors::Unimplemented(
-              "Input dim must be divisible by corressponding ksize dim."));
-      PADDLE_ENFORCE_EQ(
-          src_tz[src_tz.size() - 2] % ksize->at(0), 0,
-          platform::errors::Unimplemented(
-              "Input dim must be divisible by corressponding ksize dim."));
-      ksize->at(0) = src_tz[src_tz.size() - 2] / ksize->at(0);
-      ksize->at(1) = src_tz[src_tz.size() - 1] / ksize->at(1);
-      strides->at(0) = ksize->at(0);
-      strides->at(1) = ksize->at(1);
-    }
-  }
-
- private:
-  static inline int ComputeCeiledOutput(int input_size, int kernel_size,
-                                        int padding, int stride) {
-    return (input_size - kernel_size + 2 * padding) / stride + 1;
-  }
-
-  static inline void CorrectOutputSize(
-      const std::vector<int64_t>& src_tz, const std::vector<int64_t>& dst_tz,
-      const std::vector<int64_t>& kernel_size,
-      const std::vector<int64_t>& paddings, const std::vector<int64_t>& strides,
-      std::vector<int64_t>& right_bot_padding) {  // NOLINT
-    for (size_t i = 0; i < right_bot_padding.size(); i++) {
-      int desired_size = ComputeCeiledOutput(src_tz[i + 2], kernel_size[i],
-                                             paddings[i], strides[i]);
-      if (desired_size != dst_tz[i + 2]) {
-        right_bot_padding[i] += strides[i] - 1;
-      }
-    }
-  }
-};
-
 template <typename T>
 class TransposeMKLDNNHandler : public MKLDNNHandler {
  public:

python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py

@@ -92,6 +92,15 @@ class TestAvgPoolAdaptive2(TestAvgPoolAdaptive):
         self.shape = [2, 3, 6, 6]
 
 
+class TestAvgPoolAdaptive3(TestAvgPoolAdaptive):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+
+    def init_shape(self):
+        self.shape = [1, 3, 16, 16]
+
+
 class TestAsymPad(TestPool2D_Op):
     def init_test_case(self):
         self.ksize = [3, 3]