Small optimizations for conv2d kernel subroutines. (#29188)

- Make sure that oneDNN memory descriptors are created only once at
first iteration.

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -290,13 +290,25 @@ class ConvMKLDNNHandlerT
   std::shared_ptr<mkldnn::memory> AcquireSrcMemoryWithReorder(
       const framework::Tensor* input) {
     const T* input_data = input->data<T>();
-    auto user_src_md = platform::MKLDNNMemDesc(
-        framework::vectorize(input->dims()), platform::MKLDNNGetDataType<T>(),
-        input->format());
+    const std::string user_key_suffix{"@src_mem_p_user"};
+    auto user_src_mem_p = this->AcquireMemory(user_key_suffix);
 
-    return this->AcquireMemoryWithReorder(
-        user_src_md, this->fwd_pd_->src_desc(), to_void_cast<T>(input_data),
-        "@src_mem_p");
+    if (!user_src_mem_p) {
+      auto user_src_md = platform::MKLDNNMemDesc(
+          framework::vectorize(input->dims()), platform::MKLDNNGetDataType<T>(),
+          input->format());
+      return this->AcquireMemoryWithReorder(
+          user_src_md, this->fwd_pd_->src_desc(), to_void_cast<T>(input_data),
+          "@src_mem_p");
+    } else {
+      const std::string target_key_suffix{"@src_mem_p_target"};
+      const auto target_src_mem_p = this->AcquireMemory(target_key_suffix);
+      user_src_mem_p->set_data_handle(to_void_cast<T>(input_data));
+      if (user_src_mem_p != target_src_mem_p) {
+        this->AcquireReorder(user_src_mem_p, target_src_mem_p, "@src_mem_p");
+      }
+      return target_src_mem_p;
+    }
   }
 
   std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryWithReorder(
@@ -324,14 +336,19 @@ class ConvMKLDNNHandlerT
 
   std::shared_ptr<mkldnn::memory> AcquireBiasMemoryWithReorder(
       const framework::Tensor* bias, const bool is_test) {
-    const K* bias_data = bias->data<K>();
-    auto user_bias_md = platform::MKLDNNMemDesc(
-        framework::vectorize(bias->dims()), platform::MKLDNNGetDataType<K>(),
-        MKLDNNMemoryFormat::x);
-
-    return this->AcquireMemoryWithReorder(
-        user_bias_md, this->fwd_pd_->bias_desc(), to_void_cast<K>(bias_data),
-        "@bias_mem_p", is_test);
+    auto bias_mem_p = this->AcquireMemory("@bias_mem_p_target");
+    if (is_test && bias_mem_p) {
+      return bias_mem_p;
+    } else {
+      const K* bias_data = bias->data<K>();
+      auto user_bias_md = platform::MKLDNNMemDesc(
+          framework::vectorize(bias->dims()), platform::MKLDNNGetDataType<K>(),
+          MKLDNNMemoryFormat::x);
+
+      return this->AcquireMemoryWithReorder(
+          user_bias_md, this->fwd_pd_->bias_desc(), to_void_cast<K>(bias_data),
+          "@bias_mem_p", is_test);
+    }
   }
 
   std::shared_ptr<mkldnn::memory> AcquireResidualMemory(
@@ -340,13 +357,19 @@ class ConvMKLDNNHandlerT
         residual_param->type() == framework::DataTypeTrait<T_out>::DataType()
             ? to_void_cast<T_out>(residual_param->data<T_out>())
             : to_void_cast<T>(residual_param->data<T>());
-    auto user_residual_md = platform::MKLDNNMemDesc(
-        framework::vectorize(residual_param->dims()),
-        framework::ToMKLDNNDataType(residual_param->type()),
-        residual_param->format());
+    auto residual_mem_p = this->AcquireMemory("@user_residual_data_mem_p");
+    if (residual_mem_p) {
+      residual_mem_p->set_data_handle(residual_data);
+      return residual_mem_p;
+    } else {
+      auto user_residual_md = platform::MKLDNNMemDesc(
+          framework::vectorize(residual_param->dims()),
+          framework::ToMKLDNNDataType(residual_param->type()),
+          residual_param->format());
 
-    return this->AcquireMemoryFromPrimitive(user_residual_md, residual_data,
-                                            "@user_residual_data_mem_p");
+      return this->AcquireMemoryFromPrimitive(user_residual_md, residual_data,
+                                              "@user_residual_data_mem_p");
+    }
   }
 
   std::shared_ptr<mkldnn::memory> AcquireDstMemoryWithResidual(