[xdoctest][task 171-180] reformat example code with google style in audio/* (#56159)

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

* [xdoctest][task 171-180] reformat example code with google style in audio/*

python/paddle/amp/auto_cast.py

@@ -786,48 +786,50 @@ def decorate(
 
         .. code-block:: python
 
-        # required: gpu
-        # Demo1: single model and optimizer:
-        import paddle
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> # Demo1: single model and optimizer:
+            >>> import paddle
+            >>> paddle.device.set_device('gpu')
 
-        model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
-        optimizer = paddle.optimizer.SGD(parameters=model.parameters())
+            >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
+            >>> optimizer = paddle.optimizer.SGD(parameters=model.parameters())
 
-        model, optimizer = paddle.amp.decorate(models=model, optimizers=optimizer, level='O2')
+            >>> model, optimizer = paddle.amp.decorate(models=model, optimizers=optimizer, level='O2')
 
-        data = paddle.rand([10, 3, 32, 32])
+            >>> data = paddle.rand([10, 3, 32, 32])
 
-        with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
-            output = model(data)
-            print(output.dtype) # FP16
+            >>> with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
+            ...     output = model(data)
+            ...     assert output.dtype == paddle.float16
 
-        # required: gpu
-        # Demo2: multi models and optimizers:
-        model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
-        optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> # Demo2: multi models and optimizers:
+            >>> model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
+            >>> optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
 
-        models, optimizers = paddle.amp.decorate(models=[model, model2], optimizers=[optimizer, optimizer2], level='O2')
+            >>> models, optimizers = paddle.amp.decorate(models=[model, model2], optimizers=[optimizer, optimizer2], level='O2')
 
-        data = paddle.rand([10, 3, 32, 32])
+            >>> data = paddle.rand([10, 3, 32, 32])
 
-        with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
-            output = models[0](data)
-            output2 = models[1](data)
-            print(output.dtype) # FP16
-            print(output2.dtype) # FP16
+            >>> with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
+            ...    output = models[0](data)
+            ...    output2 = models[1](data)
+            ...    assert output.dtype == paddle.float16
+            ...    assert output2.dtype == paddle.float16
 
-        # required: gpu
-        # Demo3: optimizers is None:
-        model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
-        optimizer3 = paddle.optimizer.Adam(parameters=model3.parameters())
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> # Demo3: optimizers is None:
+            >>> model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
+            >>> optimizer3 = paddle.optimizer.Adam(parameters=model3.parameters())
 
-        model = paddle.amp.decorate(models=model3, level='O2')
+            >>> model = paddle.amp.decorate(models=model3, level='O2')
 
-        data = paddle.rand([10, 3, 32, 32])
+            >>> data = paddle.rand([10, 3, 32, 32])
+
+            >>> with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
+            ...    output = model(data)
+            ...    assert output.dtype == paddle.float16
 
-        with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
-            output = model(data)
-            print(output.dtype) # FP16
     """
     return amp_decorate(
         models,

python/paddle/amp/debugging.py

@@ -610,23 +610,23 @@ def disable_tensor_checker():
 
         .. code-block:: python
 
-            import paddle
-
-            checker_config = paddle.amp.debugging.TensorCheckerConfig(enable=True, debug_mode=paddle.amp.debugging.DebugMode.CHECK_NAN_INF)
-            paddle.amp.debugging.enable_tensor_checker(checker_config)
-
-            x = paddle.to_tensor([1, 0, 3], place=paddle.CPUPlace(), dtype='float32', stop_gradient=False)
-            y = paddle.to_tensor([0.2, 0, 0.5], place=paddle.CPUPlace(), dtype='float32')
-            res = paddle.pow(x, y)
-            paddle.autograd.backward(res, retain_graph=True)
-            paddle.amp.debugging.disable_tensor_checker()
-            #[PRECISION] [ERROR] in [device=cpu, op=elementwise_pow_grad, tensor=, dtype=fp32], numel=3, num_nan=1, num_inf=0, num_zero=0, max=2.886751e-01, min=2.000000e-01, mean=-nan
-
-            # when DebugMode.CHECK_NAN_INF_AND_ABORT and stack_height_limit = 1
-            # Traceback (most recent call last):
-            #     res = paddle.pow(x, y)
-            #   File "/usr/local/lib/python3.8/dist-packages/paddle/tensor/math.py", line 447, in pow
-            #     return _C_ops.elementwise_pow(x, y)
+            >>> import paddle
+
+            >>> checker_config = paddle.amp.debugging.TensorCheckerConfig(enable=True, debug_mode=paddle.amp.debugging.DebugMode.CHECK_NAN_INF)
+            >>> paddle.amp.debugging.enable_tensor_checker(checker_config)
+
+            >>> x = paddle.to_tensor([1, 0, 3], place=paddle.CPUPlace(), dtype='float32', stop_gradient=False)
+            >>> y = paddle.to_tensor([0.2, 0, 0.5], place=paddle.CPUPlace(), dtype='float32')
+            >>> res = paddle.pow(x, y)
+            >>> paddle.autograd.backward(res, retain_graph=True)
+            >>> paddle.amp.debugging.disable_tensor_checker()
+            >>> #[PRECISION] [ERROR] in [device=cpu, op=elementwise_pow_grad, tensor=, dtype=fp32], numel=3, num_nan=1, num_inf=0, num_zero=0, max=2.886751e-01, min=2.000000e-01, mean=-nan
+
+            >>> # when DebugMode.CHECK_NAN_INF_AND_ABORT and stack_height_limit = 1
+            >>> # Traceback (most recent call last):
+            >>> #     res = paddle.pow(x, y)
+            >>> #   File "/usr/local/lib/python3.8/dist-packages/paddle/tensor/math.py", line 447, in pow
+            >>> #     return _C_ops.elementwise_pow(x, y)
 
     """
     paddle.set_flags({"FLAGS_check_nan_inf": 0})

python/paddle/amp/grad_scaler.py

@@ -1180,17 +1180,17 @@ class GradScaler(AmpScaler):
 
             .. code-block:: python
 
-                # required: gpu,xpu
-                import paddle
-
-                scaler = paddle.amp.GradScaler(enable=True,
-                                               init_loss_scaling=1024,
-                                               incr_ratio=2.0,
-                                               decr_ratio=0.5,
-                                               incr_every_n_steps=1000,
-                                               decr_every_n_nan_or_inf=2,
-                                               use_dynamic_loss_scaling=True)
-                scaler_state = scaler.state_dict()
-                scaler.load_state_dict(scaler_state)
+                >>> # doctest: +REQUIRES(env:GPU, env:XPU)
+                >>> import paddle
+
+                >>> scaler = paddle.amp.GradScaler(enable=True,
+                ...                               init_loss_scaling=1024,
+                ...                               incr_ratio=2.0,
+                ...                               decr_ratio=0.5,
+                ...                               incr_every_n_steps=1000,
+                ...                               decr_every_n_nan_or_inf=2,
+                ...                               use_dynamic_loss_scaling=True)
+                >>> scaler_state = scaler.state_dict()
+                >>> scaler.load_state_dict(scaler_state)
         """
         super().load_state_dict(state_dict)

python/paddle/device/__init__.py

@@ -875,23 +875,25 @@ class stream_guard:
 
 
 def synchronize(device=None):
-    '''
+    """
     Wait for the compute on the given device to finish.
     Parameters:
         device(str|paddle.CUDAPlace(n)|paddle.XPUPlace(n)|paddle.CustomPlace(n)): The device which want to wait for.  If device is None, the device is the current device. Default: None.
             It can be ``gpu``, ``gpu:x``, ``xpu``, ``xpu:x``, ``custom_device``, ``custom_device:x``, where ``custom_device`` is the name of CustomDevicec,
             where ``x`` is the index of the GPUs, XPUs. And it can be paddle.CUDAPlace(n) or paddle.XPUPlace(n) or paddle.CustomPlace(n).
     Examples:
+
         .. code-block:: python
-            # required: custom_device
-            import paddle
 
-            paddle.set_device('custom_cpu')
-            paddle.device.synchronize()
-            paddle.device.synchronize("custom_cpu:0")
-            place = paddle.CustomPlace('custom_cpu', 0)
-            paddle.device.synchronize(place)
-    '''
+            >>> # doctest: +REQUIRES(env:CUSTOM_DEVICE)
+            >>> import paddle
+
+            >>> paddle.set_device('custom_cpu')
+            >>> paddle.device.synchronize()
+            >>> paddle.device.synchronize("custom_cpu:0")
+            >>> place = paddle.CustomPlace('custom_cpu', 0)
+            >>> paddle.device.synchronize(place)
+    """
 
     if device is None:
         place = paddle.framework._current_expected_place()

python/paddle/device/cuda/__init__.py

@@ -509,7 +509,7 @@ def get_device_name(device=None):
 
 
 def get_device_capability(device=None):
-    '''
+    """
     Return the major and minor revision numbers defining the device's compute capability which are got from CUDA function `cudaDeviceProp <https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__DEVICE.html#group__CUDART__DEVICE_1g1bf9d625a931d657e08db2b4391170f0>`_.
 
     Parameters:
@@ -522,16 +522,16 @@ def get_device_capability(device=None):
 
         .. code-block:: python
 
-            # required: gpu
+            >>> # doctest: +REQUIRES(env:GPU)
 
-            import paddle
+            >>> import paddle
+            >>> paddle.device.set_device('gpu')
+            >>> paddle.device.cuda.get_device_capability()
 
-            paddle.device.cuda.get_device_capability()
+            >>> paddle.device.cuda.get_device_capability(0)
 
-            paddle.device.cuda.get_device_capability(0)
+            >>> paddle.device.cuda.get_device_capability(paddle.CUDAPlace(0))
 
-            paddle.device.cuda.get_device_capability(paddle.CUDAPlace(0))
-
-    '''
+    """
     prop = get_device_properties(device)
     return prop.major, prop.minor

python/paddle/device/xpu/__init__.py

@@ -28,7 +28,7 @@ __all__ = [
     reason="synchronize in paddle.device.xpu will be removed in future",
 )
 def synchronize(device=None):
-    '''
+    """
     Wait for the compute on the given XPU device to finish.
 
     Parameters:
@@ -38,14 +38,14 @@ def synchronize(device=None):
     Examples:
         .. code-block:: python
 
-            # required: xpu
-            import paddle
+            >>> # doctest: +REQUIRES(env:XPU)
+            >>> import paddle
+            >>> paddle.device.set_device('xpu')
+            >>> paddle.device.xpu.synchronize()
+            >>> paddle.device.xpu.synchronize(0)
+            >>> paddle.device.xpu.synchronize(paddle.XPUPlace(0))
 
-            paddle.device.xpu.synchronize()
-            paddle.device.xpu.synchronize(0)
-            paddle.device.xpu.synchronize(paddle.XPUPlace(0))
-
-    '''
+    """
 
     device_id = -1
 

python/paddle/profiler/profiler.py

@@ -860,20 +860,21 @@ class Profiler:
 
         Examples:
             .. code-block:: python
-                :name: code-example8
 
-                # required: gpu
-                import paddle.profiler as profiler
-                prof = profiler.Profiler(
-                    targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
-                    scheduler = (3, 7),
-                    on_trace_ready = profiler.export_chrome_tracing('./log'))
-                prof.start()
-                for iter in range(10):
-                    #train()
-                    prof.step()
-                prof.stop()
-                prof.summary(sorted_by=profiler.SortedKeys.CPUTotal, op_detail=True, thread_sep=False, time_unit='ms')
+                >>> # doctest: +REQUIRES(env:GPU)
+                >>> import paddle
+                >>> paddle.device.set_device('gpu')
+                >>> import paddle.profiler as profiler
+                >>> prof = profiler.Profiler(
+                ...     targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.GPU],
+                ...     scheduler = (3, 7),
+                ...     on_trace_ready = profiler.export_chrome_tracing('./log'))
+                >>> prof.start()
+                >>> for iter in range(10):
+                ...     #train()
+                ...     prof.step()
+                >>> prof.stop()
+                >>> prof.summary(sorted_by=profiler.SortedKeys.CPUTotal, op_detail=True, thread_sep=False, time_unit='ms')
         """
         if isinstance(views, SummaryView):
             views = [views]

python/paddle/profiler/utils.py

@@ -116,7 +116,7 @@ class RecordEvent(ContextDecorator):
             self.event = _RecordEvent(self.name, self.event_type)
 
     def end(self):
-        r'''
+        r"""
         Record the time of ending.
 
         Examples:
@@ -132,7 +132,7 @@ class RecordEvent(ContextDecorator):
                 data2 = paddle.randn(shape=[3])
                 result = data1 * data2
                 record_event.end()
-        '''
+        """
         if self.event:
             self.event.end()
 
@@ -197,15 +197,17 @@ def wrap_optimizers():
 
 @contextmanager
 def _nvprof_range(iter_id, start, end, exit_after_prof=True):
-    '''
+    """
     A range profiler interface (not public yet).
     Examples:
         .. code-block:: python
-            model = Model()
-            for i in range(max_iter):
-                paddle.fluid.profiler._nvprof_range(i, 10, 20):
-                    out = model(in)
-    '''
+
+            >>> import paddle
+            >>> model = Model()
+            >>> for i in range(max_iter):
+            ...     with paddle.profiler.utils._nvprof_range(i, 10, 20):
+            ...         out = model(in)
+    """
     if start >= end:
         yield
         return

python/paddle/sparse/binary.py

@@ -193,25 +193,28 @@ def mv(x, vec, name=None):
 
         .. code-block:: python
 
-            # required: gpu
-            import paddle
-            paddle.seed(100)
-
-            # csr @ dense -> dense
-            crows = [0, 2, 3, 5]
-            cols = [1, 3, 2, 0, 1]
-            values = [1., 2., 3., 4., 5.]
-            dense_shape = [3, 4]
-            csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
-            # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
-            #        crows=[0, 2, 3, 5],
-            #        cols=[1, 3, 2, 0, 1],
-            #        values=[1., 2., 3., 4., 5.])
-            vec = paddle.randn([4])
-
-            out = paddle.sparse.mv(csr, vec)
-            # Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            #        [-3.85499096, -2.42975140, -1.75087738])
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> import paddle
+            >>> paddle.device.set_device('gpu')
+            >>> paddle.seed(100)
+
+            >>> # csr @ dense -> dense
+            >>> crows = [0, 2, 3, 5]
+            >>> cols = [1, 3, 2, 0, 1]
+            >>> values = [1., 2., 3., 4., 5.]
+            >>> dense_shape = [3, 4]
+            >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
+            >>> print(csr)
+            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
+                   crows=[0, 2, 3, 5],
+                   cols=[1, 3, 2, 0, 1],
+                   values=[1., 2., 3., 4., 5.])
+            >>> vec = paddle.randn([4])
+
+            >>> out = paddle.sparse.mv(csr, vec)
+            >>> print(out)
+            Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+                   [-3.85499096, -2.42975140, -1.75087738])
 
     """
     return _C_ops.sparse_mv(x, vec)

python/paddle/sparse/creation.py

@@ -219,18 +219,18 @@ def sparse_csr_tensor(
 
         .. code-block:: python
 
-        import paddle
-
-        crows = [0, 2, 3, 5]
-        cols = [1, 3, 2, 0, 1]
-        values = [1, 2, 3, 4, 5]
-        dense_shape = [3, 4]
-        csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
-        # print(csr)
-        # Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True,
-        #       crows=[0, 2, 3, 5],
-        #       cols=[1, 3, 2, 0, 1],
-        #       values=[1, 2, 3, 4, 5])
+            >>> import paddle
+
+            >>> crows = [0, 2, 3, 5]
+            >>> cols = [1, 3, 2, 0, 1]
+            >>> values = [1, 2, 3, 4, 5]
+            >>> dense_shape = [3, 4]
+            >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
+            >>> print(csr)
+            Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(cpu), stop_gradient=True,
+                  crows=[0, 2, 3, 5],
+                  cols=[1, 3, 2, 0, 1],
+                  values=[1, 2, 3, 4, 5])
     """
 
     place = _get_place(place)