[Typing][A-48,A-49,A-50][debug] Add type annotations for 3 optimizers (RAdam, RMSProp, Rprop)

python/paddle/hapi/model_summary.py

@@ -22,7 +22,7 @@
 from typing_extensions import TypedDict
 
 import paddle
-from paddle import nn
+from paddle import Tensor, nn
 from paddle.autograd import no_grad
 from paddle.static import InputSpec
 
@@ -35,17 +35,16 @@ class ModelSummary(TypedDict):
 
 
 def summary(
-    net: paddle.nn.Layer,
-    input_size: int
-    | tuple[int, ...]
-    | InputSpec
-    | list[tuple[int, ...] | InputSpec]
-    | None = None,
+    net: nn.Layer,
+    input_size: (
+        int
+        | tuple[int, ...]
+        | InputSpec
+        | list[tuple[int, ...] | InputSpec]
+        | None
+    ) = None,
     dtypes: str | Sequence[str] | None = None,
-    input: paddle.Tensor
-    | Sequence[paddle.Tensor]
-    | dict[str, paddle.Tensor]
-    | None = None,
+    input: Tensor | Sequence[Tensor] | dict[str, Tensor] | None = None,
 ) -> ModelSummary:
     """Prints a string summary of the network.
 

python/paddle/optimizer/adam.py

@@ -129,8 +129,10 @@ class Adam(Optimizer):
             >>> inp = paddle.rand([10,10], dtype="float32")
             >>> out = linear(inp)
             >>> loss = paddle.mean(out)
-            >>> adam = paddle.optimizer.Adam(learning_rate=0.1,
-            ...         parameters=linear.parameters())
+            >>> adam = paddle.optimizer.Adam(
+            ...     learning_rate=0.1,
+            ...     parameters=linear.parameters()
+            ... )
             >>> loss.backward()
             >>> adam.step()
             >>> adam.clear_grad()
@@ -147,11 +149,13 @@ class Adam(Optimizer):
             >>> loss = paddle.mean(out)
             >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
             >>> beta2 = paddle.to_tensor([0.99], dtype="float32")
-            >>> adam = paddle.optimizer.Adam(learning_rate=0.1,
-            ...         parameters=linear.parameters(),
-            ...         beta1=beta1,
-            ...         beta2=beta2,
-            ...         weight_decay=0.01)
+            >>> adam = paddle.optimizer.Adam(
+            ...     learning_rate=0.1,
+            ...     parameters=linear.parameters(),
+            ...     beta1=beta1,
+            ...     beta2=beta2,
+            ...     weight_decay=0.01
+            ... )
             >>> loss.backward()
             >>> adam.step()
             >>> adam.clear_grad()
@@ -174,12 +178,14 @@ class Adam(Optimizer):
             ...         'beta1': 0.8
             ...     }],
             ...     weight_decay=0.01,
-            ...     beta1=0.9)
+            ...     beta1=0.9
+            ... )
             >>> loss.backward()
             >>> adam.step()
             >>> adam.clear_grad()
 
     """
+
     type: str
     _moment1_acc_str = "moment1"
     _moment2_acc_str = "moment2"
@@ -192,9 +198,9 @@ def __init__(
         beta1: float | Tensor = 0.9,
         beta2: float | Tensor = 0.999,
         epsilon: float | Tensor = 1e-8,
-        parameters: Sequence[Tensor]
-        | Sequence[_AdamParameterConfig]
-        | None = None,
+        parameters: (
+            Sequence[Tensor] | Sequence[_AdamParameterConfig] | None
+        ) = None,
         weight_decay: float | WeightDecayRegularizer | None = None,
         grad_clip: GradientClipBase | None = None,
         lazy_mode: bool = False,
@@ -265,9 +271,11 @@ def _add_moments_pows(self, p):
             name=self._beta1_pow_acc_str,
             param=p,
             dtype=acc_dtype,
-            fill_value=0.9
-            if isinstance(self._beta1, (Variable, Value))
-            else self._beta1,
+            fill_value=(
+                0.9
+                if isinstance(self._beta1, (Variable, Value))
+                else self._beta1
+            ),
             shape=[1],
             type=core.VarDesc.VarType.LOD_TENSOR,
             device='cpu',
@@ -276,9 +284,11 @@ def _add_moments_pows(self, p):
             name=self._beta2_pow_acc_str,
             param=p,
             dtype=acc_dtype,
-            fill_value=0.999
-            if isinstance(self._beta2, (Variable, Value))
-            else self._beta2,
+            fill_value=(
+                0.999
+                if isinstance(self._beta2, (Variable, Value))
+                else self._beta2
+            ),
             shape=[1],
             type=core.VarDesc.VarType.LOD_TENSOR,
             device='cpu',

python/paddle/optimizer/adamax.py

@@ -117,15 +117,16 @@ class Adamax(Optimizer):
             >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
             >>> beta2 = paddle.to_tensor([0.99], dtype="float32")
 
-            >>> adam = paddle.optimizer.Adamax(learning_rate=0.1,
-            ...         parameters=linear.parameters(),
-            ...         beta1=beta1,
-            ...         beta2=beta2,
-            ...         weight_decay=0.01
+            >>> adamax = paddle.optimizer.Adamax(
+            ...     learning_rate=0.1,
+            ...     parameters=linear.parameters(),
+            ...     beta1=beta1,
+            ...     beta2=beta2,
+            ...     weight_decay=0.01
             ... )
             >>> out.backward()
-            >>> adam.step()
-            >>> adam.clear_grad()
+            >>> adamax.step()
+            >>> adamax.clear_grad()
 
 
             >>> # Note that the learning_rate of linear_2 is 0.01.
@@ -135,7 +136,7 @@ class Adamax(Optimizer):
             >>> out = linear_1(inp)
             >>> out = linear_2(out)
             >>> loss = paddle.mean(out)
-            >>> adam = paddle.optimizer.Adamax(
+            >>> adamax = paddle.optimizer.Adamax(
             ...     learning_rate=0.1,
             ...     parameters=[{  # type: ignore
             ...         'params': linear_1.parameters()
@@ -149,9 +150,10 @@ class Adamax(Optimizer):
             ...     beta1=0.9
             ... )
             >>> out.backward()
-            >>> adam.step()
-            >>> adam.clear_grad()
+            >>> adamax.step()
+            >>> adamax.clear_grad()
     """
+
     type: str
     _moment_acc_str = "moment"
     _inf_norm_acc_str = "inf_norm"
@@ -163,9 +165,9 @@ def __init__(
         beta1: float | Tensor = 0.9,
         beta2: float | Tensor = 0.999,
         epsilon: float | Tensor = 1e-8,
-        parameters: Sequence[Tensor]
-        | Sequence[_AdamaxParameterConfig]
-        | None = None,
+        parameters: (
+            Sequence[Tensor] | Sequence[_AdamaxParameterConfig] | None
+        ) = None,
         weight_decay: float | WeightDecayRegularizer | None = None,
         grad_clip: GradientClipBase | None = None,
         name: str | None = None,

python/paddle/optimizer/adamw.py

@@ -121,11 +121,12 @@ class AdamW(Optimizer):
             >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
             >>> beta2 = paddle.to_tensor([0.99], dtype="float32")
 
-            >>> opt = paddle.optimizer.AdamW(learning_rate=0.1,
-            ...         parameters=linear.parameters(),
-            ...         beta1=beta1,
-            ...         beta2=beta2,
-            ...         weight_decay=0.01
+            >>> opt = paddle.optimizer.AdamW(
+            ...     learning_rate=0.1,
+            ...     parameters=linear.parameters(),
+            ...     beta1=beta1,
+            ...     beta2=beta2,
+            ...     weight_decay=0.01
             ... )
             >>> loss.backward()
             >>> opt.step()
@@ -171,9 +172,9 @@ def __init__(
         beta1: float | Tensor = 0.9,
         beta2: float | Tensor = 0.999,
         epsilon: float | Tensor = 1e-8,
-        parameters: Sequence[Tensor]
-        | Sequence[_AdamParameterConfig]
-        | None = None,
+        parameters: (
+            Sequence[Tensor] | Sequence[_AdamParameterConfig] | None
+        ) = None,
         weight_decay: float | Tensor = 0.01,
         lr_ratio: Callable[[Tensor], float] | None = None,
         apply_decay_param_fun: Callable[[str], bool] | None = None,
@@ -383,9 +384,11 @@ def _add_moments_pows(self, p):
             name=self._beta1_pow_acc_str,
             param=p,
             dtype=acc_dtype,
-            fill_value=0.9
-            if isinstance(self._beta1, (Variable, Value))
-            else self._beta1,
+            fill_value=(
+                0.9
+                if isinstance(self._beta1, (Variable, Value))
+                else self._beta1
+            ),
             shape=[1],
             type=core.VarDesc.VarType.LOD_TENSOR,
             device='cpu',
@@ -394,9 +397,11 @@ def _add_moments_pows(self, p):
             name=self._beta2_pow_acc_str,
             param=p,
             dtype=acc_dtype,
-            fill_value=0.999
-            if isinstance(self._beta2, (Variable, Value))
-            else self._beta2,
+            fill_value=(
+                0.999
+                if isinstance(self._beta2, (Variable, Value))
+                else self._beta2
+            ),
             shape=[1],
             type=core.VarDesc.VarType.LOD_TENSOR,
             device='cpu',
@@ -538,9 +543,11 @@ def _append_optimize_op(self, block, param_and_grad):
                 "multi_precision": find_master,
                 "with_decay": with_decay,
                 "coeff": self._weight_decay,
-                "lr_ratio": 1.0
-                if self._lr_ratio is None
-                else self._lr_ratio(param_and_grad[0]),
+                "lr_ratio": (
+                    1.0
+                    if self._lr_ratio is None
+                    else self._lr_ratio(param_and_grad[0])
+                ),
             }
 
             if isinstance(self._beta1, Variable):

python/paddle/optimizer/asgd.py

@@ -94,7 +94,12 @@ class ASGD(Optimizer):
             >>> inp = paddle.to_tensor(inp)
             >>> out = linear(inp)
             >>> loss = paddle.mean(out)
-            >>> asgd = paddle.optimizer.ASGD(learning_rate=0.001, batch_num=10, parameters=linear.parameters(), weight_decay=0.01)
+            >>> asgd = paddle.optimizer.ASGD(
+            ...     learning_rate=0.001,
+            ...     batch_num=10,
+            ...     parameters=linear.parameters(),
+            ...     weight_decay=0.01
+            ... )
             >>> out.backward()
             >>> asgd.step()
             >>> asgd.clear_grad()

python/paddle/optimizer/lamb.py

@@ -111,12 +111,18 @@ class Lamb(Optimizer):
             >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
             >>> beta2 = paddle.to_tensor([0.85], dtype="float32")
             >>> lamb = paddle.optimizer.Lamb(
-            ...     learning_rate=0.002, beta1=beta1, beta2=beta2, parameters=linear.parameters(), lamb_weight_decay=0.01)
+            ...     learning_rate=0.002,
+            ...     beta1=beta1,
+            ...     beta2=beta2,
+            ...     parameters=linear.parameters(),
+            ...     lamb_weight_decay=0.01
+            ... )
             >>> back = out.backward()
             >>> lamb.step()
             >>> lamb.clear_grad()
 
     """
+
     _moment1_acc_str = "moment1"
     _moment2_acc_str = "moment2"
     _beta1_pow_acc_str = "beta1_pow_acc"
@@ -129,9 +135,9 @@ def __init__(
         beta1: float | Tensor = 0.9,
         beta2: float | Tensor = 0.999,
         epsilon: float | Tensor = 1e-6,
-        parameters: Sequence[Tensor]
-        | Sequence[_LambParameterConfig]
-        | None = None,
+        parameters: (
+            Sequence[Tensor] | Sequence[_LambParameterConfig] | None
+        ) = None,
         grad_clip: GradientClipBase | None = None,
         exclude_from_weight_decay_fn: Callable[[Tensor], bool] | None = None,
         multi_precision: bool = False,
@@ -211,9 +217,9 @@ def _add_moments_pows(self, p):
             name=self._beta1_pow_acc_str,
             param=p,
             dtype=acc_dtype,
-            fill_value=0.9
-            if isinstance(self._beta1, Variable)
-            else self._beta1,
+            fill_value=(
+                0.9 if isinstance(self._beta1, Variable) else self._beta1
+            ),
             shape=[1],
             type=core.VarDesc.VarType.LOD_TENSOR,
             device='cpu',
@@ -222,9 +228,9 @@ def _add_moments_pows(self, p):
             name=self._beta2_pow_acc_str,
             param=p,
             dtype=acc_dtype,
-            fill_value=0.999
-            if isinstance(self._beta2, Variable)
-            else self._beta2,
+            fill_value=(
+                0.999 if isinstance(self._beta2, Variable) else self._beta2
+            ),
             shape=[1],
             type=core.VarDesc.VarType.LOD_TENSOR,
             device='cpu',

python/paddle/optimizer/momentum.py

@@ -88,7 +88,11 @@ class Momentum(Optimizer):
             >>> loss = paddle.mean(out)
             >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
             >>> beta2 = paddle.to_tensor([0.99], dtype="float32")
-            >>> momentum = paddle.optimizer.Momentum(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
+            >>> momentum = paddle.optimizer.Momentum(
+            ...     learning_rate=0.1,
+            ...     parameters=linear.parameters(),
+            ...     weight_decay=0.01
+            ... )
             >>> back = out.backward()
             >>> momentum.step()
             >>> momentum.clear_grad()
@@ -117,6 +121,7 @@ class Momentum(Optimizer):
             >>> momentum.clear_grad()
 
     """
+
     _velocity_acc_str = "velocity"
 
     def __init__(

python/paddle/optimizer/nadam.py

@@ -109,8 +109,10 @@ class NAdam(Optimizer):
             >>> out = linear(inp)
             >>> loss = paddle.mean(out)
 
-            >>> nadam = paddle.optimizer.NAdam(learning_rate=0.1,
-            ...                     parameters=linear.parameters())
+            >>> nadam = paddle.optimizer.NAdam(
+            ...     learning_rate=0.1,
+            ...     parameters=linear.parameters()
+            ... )
             >>> out.backward()
             >>> nadam.step()
             >>> nadam.clear_grad()
@@ -124,7 +126,7 @@ class NAdam(Optimizer):
             >>> loss = paddle.mean(out)
             >>> opt = paddle.optimizer.NAdam(
             ...     learning_rate=0.1,
-            ...     parameters=[{ # type: ignore
+            ...     parameters=[{  # type: ignore
             ...         'params': linear_1.parameters()
             ...     }, {
             ...         'params': linear_2.parameters(),
@@ -154,9 +156,9 @@ def __init__(
         beta2: float | Tensor = 0.999,
         epsilon: float = 1.0e-8,
         momentum_decay: float = 0.004,
-        parameters: Sequence[Tensor]
-        | Sequence[_NAdamParameterConfig]
-        | None = None,
+        parameters: (
+            Sequence[Tensor] | Sequence[_NAdamParameterConfig] | None
+        ) = None,
         weight_decay: float | Tensor | None = None,
         grad_clip: GradientClipBase | None = None,
         name: str | None = None,