tile op support 0D input for xpu (#53237)

paddle/phi/kernels/xpu/tile_kernel.cc

@@ -31,13 +31,15 @@ void TileKernel(const Context& dev_ctx,
                 DenseTensor* out) {
   using XPUType = typename XPUTypeTrait<T>::Type;
   auto rank = x.dims().size();
-  PADDLE_ENFORCE_GE(
-      rank,
-      1,
-      errors::InvalidArgument(
-          "The rank of the input 'x' for tile op must be a positive "
-          "integer, but the value received is %d.",
-          rank));
+  std::vector<int64_t> repeat_times = repeat_times_arr.GetData();
+  int repeat_times_size = repeat_times.size();
+  rank = std::max(rank, repeat_times_size);
+  PADDLE_ENFORCE_GE(rank,
+                    0,
+                    errors::InvalidArgument(
+                        "The rank of the input 'x' for tile op must be a >=0 "
+                        "integer, but the value received is %d.",
+                        rank));
   PADDLE_ENFORCE_LE(
       rank,
       MAX_RANK_SUPPORTED,
@@ -46,14 +48,12 @@ void TileKernel(const Context& dev_ctx,
           "must be less than or equal to %d, but the value received is %d.",
           MAX_RANK_SUPPORTED,
           rank));
-  std::vector<int64_t> repeat_times = repeat_times_arr.GetData();
-  int repeat_times_size = repeat_times.size();
   PADDLE_ENFORCE_GE(
       repeat_times_size,
-      1,
+      0,
       errors::InvalidArgument(
           "The number of elements of the input 'repeat_times' for tile "
-          "op must be positive, but the value received is %d.",
+          "op must be >=0, but the value received is %d.",
           repeat_times_size));
   PADDLE_ENFORCE_LE(
       repeat_times_size,
@@ -102,20 +102,15 @@ void TileKernel(const Context& dev_ctx,
   dev_ctx.template Alloc<T>(out);
 
   std::vector<int64_t> temp(repeat_times.size(), 1);
-  if (repeat_times == temp) {
+  if (rank == 0 || repeat_times == temp) {
     out->Resize(x.dims());
     dev_ctx.template Alloc<T>(out);
-    if (std::is_same<T, double>::value) {
-      int r = xpu::copy(dev_ctx.x_context(),
-                        reinterpret_cast<const int8_t*>(x.data<double>()),
-                        reinterpret_cast<int8_t*>(out->data<double>()),
-                        8 * x.numel());
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
-    } else {
-      int r = xpu::copy(
-          dev_ctx.x_context(), x.data<T>(), out->data<T>(), x.numel());
-      PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
-    }
+    int64_t count = x.numel() * sizeof(T);
+    int r = xpu::copy(dev_ctx.x_context(),
+                      reinterpret_cast<const int8_t*>(x.data<T>()),
+                      reinterpret_cast<int8_t*>(out->data<T>()),
+                      count);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
     return;
   }
 

test/xpu/test_tile_op_xpu.py

@@ -90,6 +90,21 @@ class XPUTestTileOpRank1(XPUOpTestWrapper):
             self.ori_shape = (2, 4, 5, 7)
             self.repeat_times = (3, 2, 1, 2)
 
+    class TestTileOpRank_ZeroDim1(TestTileOpRank1):
+        def init_data(self):
+            self.ori_shape = []
+            self.repeat_times = []
+
+    class TestTileOpRank_ZeroDim2(TestTileOpRank1):
+        def init_data(self):
+            self.ori_shape = []
+            self.repeat_times = [2]
+
+    class TestTileOpRank_ZeroDim3(TestTileOpRank1):
+        def init_data(self):
+            self.ori_shape = []
+            self.repeat_times = [2, 3]
+
 
 # Situation 2: repeat_times is a list (with tensor)
 class XPUTestTileOpRank1_tensor_attr(XPUOpTestWrapper):
@@ -209,5 +224,36 @@ class TestTileAPI(unittest.TestCase):
             assert np.array_equal(out_3.numpy(), np.tile(np_x, (2, 3)))
 
 
+class TestTileAPI_ZeroDim(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+
+        x = paddle.rand([])
+        x.stop_gradient = False
+
+        out = paddle.tile(x, [])
+        out.retain_grads()
+        out.backward()
+        self.assertEqual(out.shape, [])
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.grad.shape, [])
+
+        out = paddle.tile(x, [3])
+        out.retain_grads()
+        out.backward()
+        self.assertEqual(out.shape, [3])
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.grad.shape, [3])
+
+        out = paddle.tile(x, [2, 3])
+        out.retain_grads()
+        out.backward()
+        self.assertEqual(out.shape, [2, 3])
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.grad.shape, [2, 3])
+
+        paddle.enable_static()
+
+
 if __name__ == "__main__":
     unittest.main()