[Typing] 修复 shard_optimizer 以及示例中的类型标注错误 (#67529)

* [Fix] typing * [Fix] typing and ex run timeout
PaddlePaddle · Aug 21, 2024 · 6e805a2 · 6e805a2
1 parent 710797d
commit 6e805a2
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Showing 7 changed files with 23 additions and 21 deletions.
diff --git a/python/paddle/audio/datasets/esc50.py b/python/paddle/audio/datasets/esc50.py
@@ -64,10 +64,11 @@ class ESC50(AudioClassificationDataset):
 
         .. code-block:: python
 
+            >>> # doctest: +TIMEOUT(60)
             >>> import paddle
 
             >>> mode = 'dev'
-            >>> esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
+            >>> esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,  # type: ignore[arg-type]
             ...                                         feat_type='raw')
             >>> for idx in range(5):
             ...     audio, label = esc50_dataset[idx]
@@ -80,7 +81,7 @@ class ESC50(AudioClassificationDataset):
             [220500] 36
             [220500] 19
 
-            >>> esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
+            >>> esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,  # type: ignore[arg-type]
             ...                                         feat_type='mfcc',
             ...                                         n_mfcc=40)
             >>> for idx in range(5):

diff --git a/python/paddle/audio/datasets/tess.py b/python/paddle/audio/datasets/tess.py
@@ -53,18 +53,19 @@ class TESS(AudioClassificationDataset):
 
         .. code-block:: python
 
+            >>> # doctest: +TIMEOUT(60)
             >>> import paddle
 
             >>> mode = 'dev'
-            >>> tess_dataset = paddle.audio.datasets.TESS(mode=mode,
+            >>> tess_dataset = paddle.audio.datasets.TESS(mode=mode,  # type: ignore[arg-type]
             ...                                         feat_type='raw')
             >>> for idx in range(5):
             ...     audio, label = tess_dataset[idx]
             ...     # do something with audio, label
             ...     print(audio.shape, label)
             ...     # [audio_data_length] , label_id
 
-            >>> tess_dataset = paddle.audio.datasets.TESS(mode=mode,
+            >>> tess_dataset = paddle.audio.datasets.TESS(mode=mode,  # type: ignore[arg-type]
             ...                                         feat_type='mfcc',
             ...                                         n_mfcc=40)
             >>> for idx in range(5):

diff --git a/python/paddle/distributed/auto_parallel/api.py b/python/paddle/distributed/auto_parallel/api.py
@@ -1447,7 +1447,7 @@ def __call__(self, key: str, param: Tensor, accumulator: Tensor) -> Tensor:
 
 def shard_optimizer(
     optimizer: Optimizer,
-    shard_fn: Callable[[str, Layer, ProcessMesh], None] | None = None,
+    shard_fn: Callable[[str, Tensor, Tensor], Tensor] | None = None,
 ) -> _ShardOptimizer:
     """
 

diff --git a/python/paddle/distributed/communication/stream/all_to_all.py b/python/paddle/distributed/communication/stream/all_to_all.py
@@ -319,7 +319,7 @@ def alltoall_single(
             >>> else:
             ...     data = paddle.to_tensor([2, 3])
             >>> task = dist.stream.alltoall_single(output, data, sync_op=False)
-            >>> task.wait()  # type: ignore[union-attr]
+            >>> task.wait()
             >>> out = output.numpy()
             >>> print(out)
             >>> # [0, 2] (2 GPUs, out for rank 0)
@@ -339,7 +339,7 @@ def alltoall_single(
             ...                                 out_split_sizes,
             ...                                 in_split_sizes,
             ...                                 sync_op=False)
-            >>> task.wait()  # type: ignore[union-attr]
+            >>> task.wait()
             >>> out = output.numpy()
             >>> print(out)
             >>> # [[0., 0.], [1., 1.]]                     (2 GPUs, out for rank 0)

diff --git a/python/paddle/geometric/sampling/neighbors.py b/python/paddle/geometric/sampling/neighbors.py
@@ -133,7 +133,7 @@ def sample_neighbors(
             >>> row = paddle.to_tensor(row, dtype="int64")
             >>> colptr = paddle.to_tensor(colptr, dtype="int64")
             >>> nodes = paddle.to_tensor(nodes, dtype="int64")
-            >>> out_neighbors, out_count = paddle.geometric.sample_neighbors(row, colptr, nodes, sample_size=sample_size)
+            >>> out_neighbors, out_count = paddle.geometric.sample_neighbors(row, colptr, nodes, sample_size=sample_size, return_eids=False)
 
     """
 

diff --git a/python/paddle/io/dataloader/dataset.py b/python/paddle/io/dataloader/dataset.py
@@ -65,7 +65,7 @@ class Dataset(Generic[_T]):
             >>> from paddle.io import Dataset
 
             >>> # define a random dataset
-            >>> class RandomDataset(Dataset):
+            >>> class RandomDataset(Dataset):  # type: ignore[type-arg]
             ...     def __init__(self, num_samples):
             ...         self.num_samples = num_samples
             ...
@@ -438,7 +438,7 @@ class ChainDataset(IterableDataset[Any]):
 
 
             >>> # define a random dataset
-            >>> class RandomDataset(IterableDataset):
+            >>> class RandomDataset(IterableDataset):  # type: ignore[type-arg]
             ...     def __init__(self, num_samples):
             ...         self.num_samples = num_samples
             ...

diff --git a/python/paddle/vision/transforms/transforms.py b/python/paddle/vision/transforms/transforms.py
@@ -387,7 +387,7 @@ class ToTensor(BaseTransform[_InputT, "Tensor"]):
             >>> img_arr = ((paddle.rand((4, 5, 3)) * 255.).astype('uint8')).numpy()
             >>> fake_img = Image.fromarray(img_arr)
             >>> transform = T.ToTensor()
-            >>> tensor = transform(fake_img)
+            >>> tensor = transform(fake_img)  # type: ignore[call-overload]
             >>> print(tensor.shape)
             [3, 4, 5]
             >>> print(tensor.dtype)
@@ -530,7 +530,7 @@ class RandomResizedCrop(BaseTransform[_InputT, _RetT]):
 
             >>> transform = RandomResizedCrop(224)
             >>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (224, 224)
 
@@ -736,7 +736,7 @@ class CenterCrop(BaseTransform[_InputT, _RetT]):
 
             >>> transform = CenterCrop(224)
             >>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (224, 224)
 
@@ -985,7 +985,7 @@ class Transpose(BaseTransform[_InputT, _RetT]):
 
             >>> transform = Transpose()
             >>> fake_img = Image.fromarray((np.random.rand(300, 320, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.shape)
             (3, 300, 320)
 
@@ -1089,7 +1089,7 @@ class ContrastTransform(BaseTransform[_InputT, _RetT]):
 
             >>> transform = ContrastTransform(0.4)
             >>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (224, 224)
 
@@ -1138,7 +1138,7 @@ class SaturationTransform(BaseTransform[_InputT, _RetT]):
 
             >>> transform = SaturationTransform(0.4)
             >>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (224, 224)
     """
@@ -1184,7 +1184,7 @@ class HueTransform(BaseTransform[_InputT, _RetT]):
 
             >>> transform = HueTransform(0.4)
             >>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (224, 224)
 
@@ -1239,7 +1239,7 @@ class ColorJitter(BaseTransform[_InputT, _RetT]):
 
             >>> transform = ColorJitter(0.4, 0.4, 0.4, 0.4)
             >>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (224, 224)
 
@@ -1480,7 +1480,7 @@ class Pad(BaseTransform[_InputT, _RetT]):
 
             >>> transform = Pad(2)
             >>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (228, 228)
     """
@@ -1778,7 +1778,7 @@ class RandomRotation(BaseTransform[_InputT, _RetT]):
 
             >>> transform = RandomRotation(90)
             >>> fake_img = Image.fromarray((np.random.rand(200, 150, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(fake_img.size)
             (150, 200)
     """
@@ -2012,7 +2012,7 @@ class Grayscale(BaseTransform[_InputT, _RetT]):
 
             >>> transform = Grayscale()
             >>> fake_img = Image.fromarray((np.random.rand(224, 224, 3) * 255.).astype(np.uint8))
-            >>> fake_img = transform(fake_img)
+            >>> fake_img = transform(fake_img)  # type: ignore[call-overload]
             >>> print(np.array(fake_img).shape)
             (224, 224)
     """