accelerate the cuda concat op, avoid many times copy (#8585)

* "try enhance concat op"

* "enhance the concat operator"

paddle/fluid/operators/concat_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <utility>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/strided_memcpy.h"
@@ -34,12 +35,46 @@ class ConcatKernel : public framework::OpKernel<T> {
     auto out_stride = framework::stride_numel(out->dims());
 
     size_t output_offset = 0;
-    for (auto* in : ins) {
-      auto in_stride = framework::stride_numel(in->dims());
-      StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
-                                  out->data<T>() + output_offset, out_stride,
-                                  in->data<T>(), in_stride, in_stride[axis]);
-      output_offset += in_stride[axis];
+
+    // If axis >=1, copy to out immediately need to call many times
+    // of cuda memcpy. Copy the input to cpu and do the stride copy,
+    // then copy to gpu output.
+
+    if (platform::is_gpu_place(place) && axis >= 1) {
+      platform::CPUPlace copy_place;
+      auto& cpu_ctx = *platform::DeviceContextPool::Instance().Get(copy_place);
+      framework::Tensor cpu_out;
+      cpu_out.Resize(out->dims());
+      cpu_out.mutable_data<T>(copy_place);
+      auto& dev_ctx = ctx.device_context();
+      std::vector<std::unique_ptr<framework::Tensor>> cpu_ins;
+      for (auto* in : ins) {
+        std::unique_ptr<framework::Tensor> cpu_in(new framework::Tensor);
+        framework::TensorCopy(*in, copy_place, dev_ctx, cpu_in.get());
+        cpu_ins.emplace_back(std::move(cpu_in));
+      }
+      // TODO(dzhwinter): overlap copy and compute stream
+      // https://devblogs.nvidia.com/how-overlap-data-transfers-cuda-cc/
+      dev_ctx.Wait();
+
+      for (auto& in : cpu_ins) {
+        auto& cpu_in = *in.get();
+        auto in_stride = framework::stride_numel(cpu_in.dims());
+
+        StridedNumelCopyWithAxis<T>(
+            cpu_ctx, axis, cpu_out.data<T>() + output_offset, out_stride,
+            cpu_in.data<T>(), in_stride, in_stride[axis]);
+        output_offset += in_stride[axis];
+      }
+      framework::TensorCopy(cpu_out, place, dev_ctx, out);
+    } else {
+      for (auto* in : ins) {
+        auto in_stride = framework::stride_numel(in->dims());
+        StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
+                                    out->data<T>() + output_offset, out_stride,
+                                    in->data<T>(), in_stride, in_stride[axis]);
+        output_offset += in_stride[axis];
+      }
     }
   }
 };