Remove reduntant numpy output in Example code (1/3), test=document_fix (#48678)

python/paddle/distributed/communication/stream/all_reduce.py

@@ -106,7 +106,7 @@ def all_reduce(
                 data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
             task = dist.stream.all_reduce(data, sync_op=False)
             task.wait()
-            out = data.numpy()
+            out = data
             # [[5, 7, 9], [5, 7, 9]]
     """
     if _warn_cur_rank_not_in_group(group):

python/paddle/fft.py

@@ -530,26 +530,27 @@ def fftn(x, s=None, axes=None, norm="backward", name=None):
             x = paddle.meshgrid(arr, arr, arr)[1]
 
             fftn_xp = paddle.fft.fftn(x, axes=(1, 2))
-            print(fftn_xp.numpy())
-            # [[[24.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+8.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.-8.j  0.+0.j  0.+0.j  0.-0.j]]
-
-            #  [[24.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+8.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.-8.j  0.+0.j  0.+0.j  0.-0.j]]
-
-            #  [[24.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+8.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.-8.j  0.+0.j  0.+0.j  0.-0.j]]
-
-            #  [[24.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+8.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.+0.j  0.+0.j  0.+0.j  0.-0.j]
-            #   [-8.-8.j  0.+0.j  0.+0.j  0.-0.j]]]
+            print(fftn_xp)
+            # Tensor(shape=[4, 4, 4], dtype=complex128, place=Place(gpu:0), stop_gradient=True,
+            #        [[[(24+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+8j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8-8j),  0j    ,  0j    ,  -0j   ]],
+
+            #         [[(24+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+8j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8-8j),  0j    ,  0j    ,  -0j   ]],
+
+            #         [[(24+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+8j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8-8j),  0j    ,  0j    ,  -0j   ]],
+
+            #         [[(24+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+8j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8+0j),  0j    ,  0j    ,  -0j   ],
+            #          [(-8-8j),  0j    ,  0j    ,  -0j   ]]])
     """
     if is_integer(x) or is_floating_point(x):
         return fftn_r2c(

python/paddle/incubate/nn/layer/fused_transformer.py

@@ -533,8 +533,8 @@ class FusedFeedForward(Layer):
             fused_feedforward_layer = FusedFeedForward(8, 8)
             x = paddle.rand((1, 8, 8))
             out = fused_feedforward_layer(x)
-            print(out.numpy().shape)
-            # (1, 8, 8)
+            print(out.shape)
+            # [1, 8, 8]
     """
 
     def __init__(

python/paddle/nn/functional/activation.py

@@ -1677,11 +1677,12 @@ def glu(x, axis=-1, name=None):
 
             x = paddle.to_tensor(
                 [[-0.22014759, -1.76358426,  0.80566144,  0.04241343],
-                 [-1.94900405, -1.89956081,  0.17134808, -1.11280477]]
+                    [-1.94900405, -1.89956081,  0.17134808, -1.11280477]]
             )
-            print(F.glu(x).numpy())
-            # array([[-0.15216254, -0.9004892 ],
-            #        [-1.0577879 , -0.46985325]], dtype=float32)
+            print(F.glu(x))
+            # Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [[-0.15216254, -0.90048921],
+            #         [-1.05778778, -0.46985325]])
 
     """
     check_variable_and_dtype(

python/paddle/nn/functional/conv.py

@@ -657,10 +657,9 @@ def conv2d(
           w_var = paddle.randn((6, 3, 3, 3), dtype='float32')
 
           y_var = F.conv2d(x_var, w_var)
-          y_np = y_var.numpy()
 
-          print(y_np.shape)
-          # (2, 6, 6, 6)
+          print(y_var.shape)
+          # [2, 6, 6, 6]
     """
     # entry checks
     if data_format not in ["NCHW", "NHWC"]:
@@ -1234,10 +1233,9 @@ def conv2d_transpose(
           w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
 
           y_var = F.conv2d_transpose(x_var, w_var)
-          y_np = y_var.numpy()
 
-          print(y_np.shape)
-          # (2, 6, 10, 10)
+          print(y_var.shape)
+          # [2, 6, 10, 10]
     """
 
     if data_format not in ['NCHW', 'NHWC']:
@@ -1523,10 +1521,9 @@ def conv3d(
             w_var = paddle.randn((6, 3, 3, 3, 3), dtype='float32')
 
             y_var = F.conv3d(x_var, w_var)
-            y_np = y_var.numpy()
 
-            print(y_np.shape)
-            # (2, 6, 6, 6, 6)
+            print(y_var.shape)
+            # [2, 6, 6, 6, 6]
     """
     # entry check
     if data_format not in ["NCDHW", "NDHWC"]:
@@ -1738,10 +1735,9 @@ def conv3d_transpose(
           w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
 
           y_var = F.conv3d_transpose(x_var, w_var)
-          y_np = y_var.numpy()
 
-          print(y_np.shape)
-          # (2, 6, 10, 10, 10)
+          print(y_var.shape)
+          # [2, 6, 10, 10, 10]
     """
     # entry checks
     if data_format not in ["NCDHW", "NDHWC"]:

python/paddle/nn/functional/distance.py

@@ -63,7 +63,9 @@ def pairwise_distance(x, y, p=2.0, epsilon=1e-6, keepdim=False, name=None):
             x = paddle.to_tensor([[1., 3.], [3., 5.]], dtype=paddle.float64)
             y = paddle.to_tensor([[5., 6.], [7., 8.]], dtype=paddle.float64)
             distance = paddle.nn.functional.pairwise_distance(x, y)
-            print(distance.numpy()) # [5. 5.]
+            print(distance)
+    #       Tensor(shape=[2], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+    #              [4.99999860, 4.99999860])
 
     """
     check_type(p, 'porder', (float, int), 'PairwiseDistance')

python/paddle/nn/functional/extension.py

@@ -215,10 +215,11 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
             lengths = paddle.to_tensor([10, 9, 8])
             mask = paddle.nn.functional.sequence_mask(lengths)
 
-            print(mask.numpy())
-            # [[1 1 1 1 1 1 1 1 1 1]
-            #  [1 1 1 1 1 1 1 1 1 0]
-            #  [1 1 1 1 1 1 1 1 0 0]]
+            print(mask)
+            # Tensor(shape=[3, 10], dtype=int64, place=Place(gpu:0), stop_gradient=True,
+            #        [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+            #         [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+            #         [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
 
     """
 

python/paddle/nn/functional/loss.py

@@ -1353,17 +1353,20 @@ def l1_loss(input, label, reduction='mean', name=None):
             label = paddle.to_tensor([[1.7, 1], [0.4, 0.5]])
 
             l1_loss = paddle.nn.functional.l1_loss(input, label)
-            print(l1_loss.numpy())
-            # [0.35]
+            print(l1_loss)
+            # Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [0.34999999])
 
             l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='none')
-            print(l1_loss.numpy())
-            # [[0.20000005 0.19999999]
-            # [0.2        0.79999995]]
+            print(l1_loss)
+            # Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [[0.20000005, 0.19999999],
+            #         [0.20000000, 0.79999995]])
 
             l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='sum')
-            print(l1_loss.numpy())
-            # [1.4]
+            print(l1_loss)
+            # Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [1.39999998])
 
     """
     if reduction not in ['sum', 'mean', 'none']:
@@ -2530,9 +2533,11 @@ def cross_entropy(
             cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                 weight=weight, reduction=reduction)
             dy_ret = cross_entropy_loss(
-                                       input,
-                                       label)
-            print(dy_ret.numpy()) #[5.41993642]
+                                        input,
+                                        label)
+            print(dy_ret)
+            # Tensor(shape=[1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [5.34043430])
 
         .. code-block:: python
 
@@ -2550,13 +2555,15 @@ def cross_entropy(
             labels = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0)
             labels /= paddle.sum(labels, axis=axis, keepdim=True)
             paddle_loss_mean = paddle.nn.functional.cross_entropy(
-                                                                  logits,
-                                                                  labels,
-                                                                  soft_label=True,
-                                                                  axis=axis,
-                                                                  weight=weight,
-                                                                  reduction=reduction)
-            print(paddle_loss_mean.numpy()) #[1.12908343]
+                                                                    logits,
+                                                                    labels,
+                                                                    soft_label=True,
+                                                                    axis=axis,
+                                                                    weight=weight,
+                                                                    reduction=reduction)
+            print(paddle_loss_mean)
+            # Tensor(shape=[1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [1.11043464])
 
     """
 

python/paddle/nn/functional/vision.py

@@ -368,9 +368,8 @@ def pixel_shuffle(x, upscale_factor, data_format="NCHW", name=None):
 
             x = paddle.randn(shape=[2,9,4,4])
             out_var = F.pixel_shuffle(x, 3)
-            out = out_var.numpy()
-            print(out.shape)
-            # (2, 1, 12, 12)
+            print(out_var.shape)
+            # [2, 1, 12, 12]
     """
     if not isinstance(upscale_factor, int):
         raise TypeError("upscale factor must be int type")

python/paddle/nn/initializer/constant.py

@@ -32,11 +32,13 @@ class Constant(ConstantInitializer):
 
             data = paddle.rand([30, 10, 2], dtype='float32')
             linear = nn.Linear(2,
-                               4,
-                               weight_attr=nn.initializer.Constant(value=2.0))
+                                4,
+                                weight_attr=nn.initializer.Constant(value=2.0))
             res = linear(data)
-            print(linear.weight.numpy())
-            #result is [[2. 2. 2. 2.],[2. 2. 2. 2.]]
+            print(linear.weight)
+            # Tensor(shape=[2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=False,
+            #        [[2., 2., 2., 2.],
+            #         [2., 2., 2., 2.]])
 
     """
 

python/paddle/nn/layer/conv.py

@@ -668,9 +668,8 @@ class Conv2D(_ConvNd):
 
           conv = nn.Conv2D(4, 6, (3, 3))
           y_var = conv(x_var)
-          y_np = y_var.numpy()
-          print(y_np.shape)
-          # (2, 6, 6, 6)
+          print(y_var.shape)
+          # [2, 6, 6, 6]
     """
 
     def __init__(
@@ -841,9 +840,8 @@ class Conv2DTranspose(_ConvNd):
 
           conv = nn.Conv2DTranspose(4, 6, (3, 3))
           y_var = conv(x_var)
-          y_np = y_var.numpy()
-          print(y_np.shape)
-          # (2, 6, 10, 10)
+          print(y_var.shape)
+          # [2, 6, 10, 10]
     """
 
     def __init__(
@@ -999,9 +997,8 @@ class Conv3D(_ConvNd):
 
           conv = nn.Conv3D(4, 6, (3, 3, 3))
           y_var = conv(x_var)
-          y_np = y_var.numpy()
-          print(y_np.shape)
-          # (2, 6, 6, 6, 6)
+          print(y_var.shape)
+          # [2, 6, 6, 6, 6]
     """
 
     def __init__(
@@ -1181,9 +1178,8 @@ class Conv3DTranspose(_ConvNd):
 
           conv = nn.Conv3DTranspose(4, 6, (3, 3, 3))
           y_var = conv(x_var)
-          y_np = y_var.numpy()
-          print(y_np.shape)
-          # (2, 6, 10, 10, 10)
+          print(y_var.shape)
+          # [2, 6, 10, 10, 10]
     """
 
     def __init__(

python/paddle/nn/layer/distance.py

@@ -56,7 +56,9 @@ class PairwiseDistance(Layer):
             y = paddle.to_tensor([[5., 6.], [7., 8.]], dtype=paddle.float64)
             dist = paddle.nn.PairwiseDistance()
             distance = dist(x, y)
-            print(distance.numpy()) # [5. 5.]
+            print(distance)
+            # Tensor(shape=[2], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [4.99999860, 4.99999860])
 
     """
 

python/paddle/nn/layer/loss.py

@@ -102,7 +102,9 @@ class BCEWithLogitsLoss(Layer):
             label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32")
             bce_logit_loss = paddle.nn.BCEWithLogitsLoss()
             output = bce_logit_loss(logit, label)
-            print(output.numpy())  # [0.45618808]
+            print(output)
+            # Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [0.45618814])
 
     """
 
@@ -319,9 +321,11 @@ class CrossEntropyLoss(Layer):
             cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                 weight=weight, reduction=reduction)
             dy_ret = cross_entropy_loss(
-                                       input,
-                                       label)
-            print(dy_ret.numpy()) #[5.41993642]
+                                        input,
+                                        label)
+            print(dy_ret)
+            # Tensor(shape=[1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [5.34043430])
 
         .. code-block:: python
 
@@ -339,13 +343,15 @@ class CrossEntropyLoss(Layer):
             labels = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0)
             labels /= paddle.sum(labels, axis=axis, keepdim=True)
             paddle_loss_mean = paddle.nn.functional.cross_entropy(
-                                                                  logits,
-                                                                  labels,
-                                                                  soft_label=True,
-                                                                  axis=axis,
-                                                                  weight=weight,
-                                                                  reduction=reduction)
-            print(paddle_loss_mean.numpy()) #[1.12908343]
+                                                                    logits,
+                                                                    labels,
+                                                                    soft_label=True,
+                                                                    axis=axis,
+                                                                    weight=weight,
+                                                                    reduction=reduction)
+            print(paddle_loss_mean)
+            # Tensor(shape=[1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
+            #        [1.11043464])
 
     """
 
@@ -635,19 +641,22 @@ class L1Loss(Layer):
 
             l1_loss = paddle.nn.L1Loss()
             output = l1_loss(input, label)
-            print(output.numpy())
-            # [0.35]
+            print(output)
+            # Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [0.34999999])
 
             l1_loss = paddle.nn.L1Loss(reduction='sum')
             output = l1_loss(input, label)
-            print(output.numpy())
-            # [1.4]
+            print(output)
+            # Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [1.39999998])
 
             l1_loss = paddle.nn.L1Loss(reduction='none')
             output = l1_loss(input, label)
             print(output)
-            # [[0.20000005 0.19999999]
-            # [0.2        0.79999995]]
+            # Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            #        [[0.20000005, 0.19999999],
+            #         [0.20000000, 0.79999995]])
 
     """
 

python/paddle/nn/layer/vision.py

@@ -52,10 +52,9 @@ class PixelShuffle(Layer):
 
             x = paddle.randn(shape=[2,9,4,4])
             pixel_shuffle = nn.PixelShuffle(3)
-            out_var = pixel_shuffle(x)
-            out = out_var.numpy()
+            out = pixel_shuffle(x)
             print(out.shape)
-            # (2, 1, 12, 12)
+            # [2, 1, 12, 12]
 
     """
 

python/paddle/nn/quant/quant_layers.py

@@ -628,10 +628,8 @@ class QuantizedConv2DTranspose(Layer):
           conv_quantized = QuantizedConv2DTranspose(conv)
           y_quantized = conv_quantized(x_var)
           y_var = conv(x_var)
-          y_quantized_np = y_quantized.numpy()
-          y_np = y_var.numpy()
-          print(y_np.shape, y_quantized_np.shape)
-          # (2, 6, 10, 10), (2, 6, 10, 10)
+          print(y_var.shape, y_quantized.shape)
+          # [2, 6, 10, 10], [2, 6, 10, 10]
 
     """
 

python/paddle/tensor/manipulation.py

@@ -2453,7 +2453,10 @@ def unique(
 
             x = paddle.to_tensor([2, 3, 3, 1, 5, 3])
             unique = paddle.unique(x)
-            np_unique = unique.numpy() # [1 2 3 5]
+            print(unique)
+            # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
+            #        [1, 2, 3, 5])
+
             _, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True)
             print(indices)
             # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,

python/paddle/text/datasets/conll05.py

@@ -88,7 +88,7 @@ class Conll05st(Dataset):
 
                 model = SimpleNet()
                 pred_idx, mark, label= model(pred_idx, mark, label)
-                print(pred_idx.numpy(), mark.numpy(), label.numpy())
+                print(pred_idx, mark, label)
 
     """
 

python/paddle/text/datasets/imdb.py

@@ -67,7 +67,7 @@ class Imdb(Dataset):
 
                 model = SimpleNet()
                 image, label = model(doc, label)
-                print(doc.numpy().shape, label.numpy().shape)
+                print(doc.shape, label.shape)
 
     """
 

python/paddle/text/datasets/imikolov.py

@@ -67,7 +67,7 @@ class Imikolov(Dataset):
 
                 model = SimpleNet()
                 src, trg = model(src, trg)
-                print(src.numpy().shape, trg.numpy().shape)
+                print(src.shape, trg.shape)
 
     """
 

python/paddle/text/datasets/movielens.py

@@ -134,7 +134,7 @@ class Movielens(Dataset):
 
                 model = SimpleNet()
                 category, title, rating = model(category, title, rating)
-                print(category.numpy().shape, title.numpy().shape, rating.numpy().shape)
+                print(category.shape, title.shape, rating.shape)
 
     """