Fix the broadcast bug in the elementwise ops, cherry-pick from the develop

PR types: Bug fixes
PR changes:Ops
Describe:
Fix the broadcast bug in elementwise ops, when elementwise ops do not have fast gradient calculate way，use the default way to calculate.

paddle/fluid/operators/elementwise/elementwise_op_function.h

@@ -1040,22 +1040,21 @@ void CommonGradBroadcastCUDA(
   // fallback
   // to old fast path.
   // 2. if both x and y need broadcast, then do it one by one.
+  bool fast_broadcast = false;
   if (x_broadcast_pos.empty() && !y_broadcast_pos.empty()) {
     can_split_y = SplitDims(y_broadcast_pos, max_dim);
     if (can_split_y) {
       // only y need to do broadcast on h
       if (y_broadcast_pos[0] == 0) {
         FastBroadCastHeightCUDAF(y_broadcast_pos, true);
-      } else {
-        LOG(ERROR) << "Error, broadcast should not into w broadcast";
+        fast_broadcast = true;
       }
-      return;
     } else if (y_broadcast_pos.size() == 1 ||
                CheckContiguousDims(y_broadcast_pos)) {  // for only one dim and
                                                         // contiguous broadcast.
       // If cannot split,  which means input has 3 parts
       FastBroadCastAllCUDAF(y_broadcast_pos, max_dim, true);
-      return;
+      fast_broadcast = true;
     }
   } else if (y_broadcast_pos.empty() && !x_broadcast_pos.empty()) {
     // only x need broadcast
@@ -1063,49 +1062,53 @@ void CommonGradBroadcastCUDA(
     if (can_split_x) {
       if (x_broadcast_pos[0] == 0) {
         FastBroadCastHeightCUDAF(x_broadcast_pos, false);
-      } else {
-        // x need to do broadcast on w
-        LOG(ERROR) << "Error, broadcast should not into w broadcast";
+        fast_broadcast = true;
       }
-      return;
     } else if (x_broadcast_pos.size() == 1 ||
                CheckContiguousDims(x_broadcast_pos)) {
       FastBroadCastAllCUDAF(x_broadcast_pos, max_dim, false);
-      return;
+      fast_broadcast = true;
     }
   } else if (!x_broadcast_pos.empty() && !y_broadcast_pos.empty()) {
     // do x and y broadcast each.
     can_split_y = SplitDims(y_broadcast_pos, max_dim);
+    bool fast_broadcast_x = false;
+    bool fast_broadcast_y = false;
     if (can_split_y) {
       // begin at start.
       if (y_broadcast_pos[0] == 0) {
         FastCommonCUDAF(y_broadcast_pos, true);
-      } else {
-        // finish at end
-        LOG(ERROR) << "Error, broadcast should not into w broadcast";
+        fast_broadcast_y = true;
       }
     } else if (y_broadcast_pos.size() == 1) {
       FastBroadCastOneCUDAF(y_broadcast_pos, max_dim, false);
       can_split_y = true;
+      fast_broadcast_y = true;
     }
     can_split_x = SplitDims(x_broadcast_pos, max_dim);
     if (can_split_x) {
       if (x_broadcast_pos[0] == 0) {
         FastCommonCUDAF(x_broadcast_pos, false);
-      } else {
-        LOG(ERROR) << "Error, broadcast should not into w broadcast";
+        fast_broadcast_x = true;
       }
     } else if (x_broadcast_pos.size() == 1) {
       FastBroadCastOneCUDAF(x_broadcast_pos, max_dim, true);
       can_split_x = true;
+      fast_broadcast_x = true;
     }
     VLOG(3) << "CommonBroadcast can_split_y:" << can_split_y
             << " can_split_x:" << can_split_x;
     // if both x and y into fast path then return
-    if (can_split_y && can_split_x) return;
+    if (fast_broadcast_x && fast_broadcast_y) {
+      fast_broadcast = true;
+    }
+    if (can_split_y && can_split_x && fast_broadcast) return;
   }
 
   // Should remove memory copy, use reg instead.
+  if (fast_broadcast) {
+    return;
+  }
   int x_blocks = 0;
   int x_threads = 0;
   ComputeBroadcastKernelSize(x_dims_array, out_dims_array, &x_blocks,
@@ -1136,7 +1139,7 @@ void CommonGradBroadcastCUDA(
                                        1, std::multiplies<int>());
   int x_block_size = std::min(ELEMWISE_MAX_BLOCK_DIM, x_threads);
   int y_block_size = std::min(ELEMWISE_MAX_BLOCK_DIM, y_threads);
-  if (dx && !can_split_x) {
+  if (dx) {
     auto x_strides_order_tmp = memory::Alloc(ctx, bytes);
     int *x_strides_order_gpu =
         reinterpret_cast<int *>(x_strides_order_tmp->ptr());
@@ -1153,7 +1156,7 @@ void CommonGradBroadcastCUDA(
         x_strides_order_gpu, x_dims_order_gpu, x_data, y_data, out_data,
         dout_data, dx_data, out_size, max_dim, x_threads, dx_op);
   }
-  if (dy && !can_split_y) {
+  if (dy) {
     auto y_strides_order_tmp = memory::Alloc(ctx, bytes);
     int *y_strides_order_gpu =
         reinterpret_cast<int *>(y_strides_order_tmp->ptr());

python/paddle/fluid/tests/unittests/test_elementwise_add_op.py

@@ -263,6 +263,13 @@ class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
         self.out = self.x + self.y
 
 
+class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
+        self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
+        self.out = self.x + self.y
+
+
 class TestFP16ElementwiseAddOp_broadcast_6(TestFP16ElementwiseAddOp):
     def init_input_output(self):
         self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)