Replace custom IOHW -> OIHW reorder with build-in oneDNN reorder (#37175)

* Use oneDNN reorder instead of custom one

* Fix whitespace typo

* Fix Code format error

* Incorporating feedback

* Remove unncessary reorder

* Support GIOHW format

* Fix code format error

paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc

@@ -68,7 +68,7 @@ class ConvTransposeMKLDNNHandlerT
     PADDLE_ENFORCE_EQ(
         filter->layout(), DataLayout::kMKLDNN,
         platform::errors::InvalidArgument(
-            "The filter tensor's laytout should be %d, but got %d.",
+            "The filter tensor's layout should be %d, but got %d.",
             DataLayout::kMKLDNN, filter->layout()));
     PADDLE_ENFORCE_NE(filter->format(), MKLDNNMemoryFormat::undef,
                       platform::errors::InvalidArgument(
@@ -227,34 +227,12 @@ class ConvTransposeMKLDNNHandlerT
 
     auto user_src_md = platform::MKLDNNMemDesc(
         weights_tz, platform::MKLDNNGetDataType<K>(),
-        (g == 1) ? filter->format() : MKLDNNMemoryFormat::goihw);
-
-    auto iohw_weights_tz = framework::vectorize(filter->dims());
-    // Custom Reorder from IOHW to OIHW
-    auto iohw2oihw_reorder =
-        [&iohw_weights_tz](const K* filter_data) -> std::shared_ptr<K> {
-      int o = iohw_weights_tz[1];
-      int c = iohw_weights_tz[0];
-      int h = iohw_weights_tz[2];
-      int w = iohw_weights_tz[3];
-      std::shared_ptr<K> reordered_filter_data(new K[o * c * h * w](),
-                                               std::default_delete<K[]>());
-      for (int i = 0; i < c; ++i) {
-        for (int j = 0; j < o; ++j) {
-          int in_offset = j * h * w + i * o * h * w;
-          int out_offset = j * c * h * w + i * h * w;
-          std::memcpy(&(reordered_filter_data.get())[out_offset],
-                      &filter_data[in_offset], h * w * sizeof(K));
-        }
-      }
-
-      return reordered_filter_data;
-    };
+        (g == 1) ? MKLDNNMemoryFormat::iohw : MKLDNNMemoryFormat::giohw);
 
     return this->template AcquireMemoryWithReorder<K>(
         dev_ctx, user_src_md, this->fwd_pd_->weights_desc(),
-        platform::to_void_cast<K>(filter_data), key, "@weights_mem_p", is_test_,
-        iohw2oihw_reorder);
+        platform::to_void_cast<K>(filter_data), key, "@weights_mem_p",
+        is_test_);
   }
 
   template <typename F = T>
@@ -263,7 +241,6 @@ class ConvTransposeMKLDNNHandlerT
       const mkldnn::memory::desc& user_md,
       const mkldnn::memory::desc& target_md, void* ptr, const std::string& key,
       const std::string& suffix, bool is_persistent = false,
-      std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {},
       const std::vector<float>& scale_data = {1.0f}, int mask = 0) {
     const auto target_key = key + suffix + "_target";
     const auto key_reorder_p = key + suffix + "reorder_p";
@@ -273,12 +250,6 @@ class ConvTransposeMKLDNNHandlerT
         std::static_pointer_cast<dnnl::memory>(dev_ctx.GetBlob(target_key));
 
     if (target_memory_p == nullptr) {
-      if (custom_reorder_func) {
-        auto reordered_data =
-            custom_reorder_func(reinterpret_cast<const F*>(ptr));
-        dev_ctx.SetBlob(key_reorder_p + "-custom_reorder", reordered_data);
-        ptr = reinterpret_cast<void*>(reordered_data.get());
-      }
       auto user_memory_p =
           std::make_shared<dnnl::memory>(user_md, this->engine_, ptr);
       if (user_md != target_md) {