[xdoctest] reformat example code with google style in No.386-389 (#58206

)

* fix

* undo mkldnn

* fix

* fix sys msg

* fix another sys msg

---------

Co-authored-by: SigureMo <[email protected]>

python/paddle/nn/functional/flash_attention.py

@@ -200,10 +200,22 @@ def flash_attention(
 
             >>> import paddle
 
-            >>> paddle.seed(1)
+            >>> paddle.seed(2023)
             >>> q = paddle.rand((1, 128, 2, 16))
 
             >>> output = paddle.nn.functional.flash_attention.flash_attention(q, q, q, 0.9, False, False)
+            >>> print(output)
+            (Tensor(shape=[1, 128, 2, 16], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[[[0.34992966, 0.34456208, 0.45826620, ..., 0.39883569,
+                0.42132431, 0.39157745],
+               [0.76687670, 0.65837246, 0.69117945, ..., 0.82817286,
+                0.76690865, 0.71485823]],
+              ...,
+              [[0.71662450, 0.57275224, 0.57053083, ..., 0.48108247,
+                0.53336465, 0.54540104],
+               [0.59137970, 0.51350880, 0.50449550, ..., 0.38860250,
+                0.40526697, 0.60541755]]]]), None)
+
     """
     head_dim = query.shape[3]
     sdp_func_name = _select_sdp(head_dim)
@@ -357,11 +369,12 @@ def flash_attn_unpadded(
         .. code-block:: python
 
             >>> import paddle
-            >>> paddle.seed(1)
-            >>> q = paddle.rand((1, 128, 2, 16))
+            >>> paddle.seed(2023)
+            >>> q = paddle.rand((2, 128, 8, 16), dtype='float16')
+            >>> cu = paddle.arange(0, 384, 128, dtype='int32')
+            >>> qq = paddle.reshape(q, [256, 8, 16])
+            >>> output = paddle.nn.functional.flash_attention.flash_attn_unpadded(qq, qq, qq, cu, cu, 128, 128, 0.25, 0.0, False, False)
 
-            >>> output = paddle.nn.functional.flash_attention.flash_attn_unpadded(q, q, q, 0.9, False, False)
-            >>> print(output)
     """
     if in_dynamic_mode():
         (
@@ -478,7 +491,7 @@ def scaled_dot_product_attention(
     Examples:
         .. code-block:: python
 
-            >>> # doctest: +SKIP()
+            >>> # doctest: +SKIP('bfloat need V100 compile')
             >>> import paddle
             >>> q = paddle.rand((1, 128, 2, 16), dtype=paddle.bfloat16)
             >>> output = paddle.nn.functional.scaled_dot_product_attention(q, q, q, None, 0.9, False)

python/paddle/quantization/config.py

@@ -127,7 +127,7 @@ def add_layer_config(
                 >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
                 >>> q_config = QuantConfig(activation=None, weight=None)
                 >>> q_config.add_layer_config([model.fc], activation=quanter, weight=quanter)
-                >>> # doctest: +SKIP
+                >>> # doctest: +SKIP('random memory address')
                 >>> print(q_config)
                 Global config:
                 None
@@ -176,7 +176,7 @@ def add_name_config(
                 >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
                 >>> q_config = QuantConfig(activation=None, weight=None)
                 >>> q_config.add_name_config([model.fc.full_name()], activation=quanter, weight=quanter)
-                >>> # doctest: +SKIP
+                >>> # doctest: +SKIP('random memory address')
                 >>> print(q_config)
                 Global config:
                 None
@@ -226,7 +226,7 @@ def add_type_config(
                 >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
                 >>> q_config = QuantConfig(activation=None, weight=None)
                 >>> q_config.add_type_config([Linear], activation=quanter, weight=quanter)
-                >>> # doctest: +SKIP
+                >>> # doctest: +SKIP('random memory address')
                 >>> print(q_config)
                 Global config:
                 None

python/paddle/quantization/factory.py

@@ -83,7 +83,7 @@ def quanter(class_name):
     Examples:
         .. code-block:: python
 
-            >>> # doctest: +SKIP
+            >>> # doctest: +SKIP('need 2 file to run example')
             >>> # Given codes in ./customized_quanter.py
             >>> from paddle.quantization import quanter
             >>> from paddle.quantization import BaseQuanter

python/paddle/tensor/linalg.py

@@ -1958,14 +1958,12 @@ def slogdet(x, name=None):
 
             >>> import paddle
             >>> paddle.seed(2023)
-            >>> x =  paddle.randn([3,3,3])
+            >>> x = paddle.randn([3, 3, 3])
             >>> A = paddle.linalg.slogdet(x)
             >>> print(A)
-            >>> # doctest: +SKIP
             Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
             [[-1.        ,  1.        ,  1.        ],
              [ 0.25681755, -0.25061053, -0.10809582]])
-            >>> # doctest: -SKIP
 
     """
     if in_dynamic_mode():
@@ -2801,10 +2799,12 @@ def eigh(x, UPLO='L', name=None):
             property.  For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        - out_value(Tensor):  A Tensor with shape [*, N] and data type of float32 and float64.
-            The eigenvalues of eigh op.
-        - out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,
-            complex64 and complex128. The eigenvectors of eigh op.
+        2-element tuple containing
+
+        - out_value(Tensor): A Tensor with shape :math:`[*, N]` and data type of float32 and float64.
+          The eigenvalues of eigh op.
+        - out_vector(Tensor): A Tensor with shape :math:`[*, N, N]` and data type of float32, float64,
+          complex64 and complex128. The eigenvectors of eigh op.
 
     Examples:
         .. code-block:: python