PaddlePaddle
diff --git a/‎python/paddle/jit/api.py
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-69Lines changed: 0 additions & 69 deletions b/‎python/paddle/jit/api.py
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-69Lines changed: 0 additions & 69 deletions
diff --git a/‎python/paddle/jit/dy2static/decorator_transformer.py
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-1Lines changed: 0 additions & 1 deletion b/‎python/paddle/jit/dy2static/decorator_transformer.py
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diff --git a/‎python/paddle/nn/functional/conv.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎python/paddle/nn/functional/conv.py
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+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎test/dygraph_to_static/test_ast_util.py
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+11-12Lines changed: 11 additions & 12 deletions b/‎test/dygraph_to_static/test_ast_util.py
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+11-12Lines changed: 11 additions & 12 deletions
@@ -66,7 +66,6 @@
     ProgramTranslator,
     StaticFunction,
     SymbolicStaticFunction,
-    convert_to_static,
     unwrap_decorators,
 )
 from .translated_layer import (
@@ -114,74 +113,6 @@ def extract_vars(inputs, err_tag='inputs'):
     return result_list
 
 
-def _dygraph_to_static_func_(dygraph_func):
-    """
-    Converts imperative dygraph APIs into declarative function APIs. Decorator
-    @dygraph_to_static_func only converts imperative dygraph APIs into
-    declarative net-building APIs, which means it doesn't return immediate
-    digital result as imperative mode. Users should handle Program and Executor
-    by themselves.
-
-    Note:
-    This decorator is NOT our recommended way to transform imperative function
-    to declarative function. We will remove this decorator after we finalize
-    cleaning up code.
-
-    Args:
-        dygraph_func (callable): callable imperative function.
-
-    Returns:
-        Callable: converting imperative dygraph APIs into declarative
-        net-building APIs.
-
-    Examples:
-        .. code-block:: python
-
-            >>> # doctest: +SKIP('`paddle.jit.dygraph_to_static_func` can not run in xdoctest')
-            >>> import paddle
-            >>> from paddle.jit.api import dygraph_to_static_func
-
-            >>> @dygraph_to_static_func
-            ... def func(x):
-            ...     if paddle.mean(x) < 0:
-            ...         x_v = x - 1
-            ...     else:
-            ...         x_v = x + 1
-            ...
-            ...     return x_v
-            ...
-            >>> paddle.enable_static()
-            >>> x = paddle.full(shape=[3, 3], fill_value=0, dtype='float64')
-
-            >>> x_v = func(x)
-            >>> exe = paddle.static.Executor(paddle.CPUPlace())
-            >>> out = exe.run(fetch_list=[x_v])
-            >>> print(out[0])
-            [[1. 1. 1.]
-             [1. 1. 1.]
-             [1. 1. 1.]]
-
-    """
-
-    # TODO: remove this decorator after we finalize training API
-    def __impl__(*args, **kwargs):
-        program_translator = ProgramTranslator()
-        if in_dynamic_mode() or not program_translator.enable_to_static:
-            logging_utils.warn(
-                "The decorator 'dygraph_to_static_func' doesn't work in "
-                "dygraph mode or set 'paddle.jit.enable_to_static' to False. "
-                "We will just return dygraph output."
-            )
-            return dygraph_func(*args, **kwargs)
-        static_func = convert_to_static(dygraph_func)
-        return static_func(*args, **kwargs)
-
-    return __impl__
-
-
-dygraph_to_static_func = wrap_decorator(_dygraph_to_static_func_)
-
-
 def copy_decorator_attrs(original_func, decorated_obj):
     """
     Copies some necessary attributes from original function into decorated function.
 
@@ -26,7 +26,6 @@
 IGNORE_NAMES = [
     'declarative',
     'to_static',
-    'dygraph_to_static_func',
     'wraps',
     'staticmethod',
     'classmethod',
 
@@ -1725,7 +1725,7 @@ def conv3d_transpose(
     op_type = 'conv3d_transpose'
     data_format_ = "NHWC" if channel_last else "NCHW"
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         pre_bias = _C_ops.conv3d_transpose(
             x,
             weight,
 
@@ -19,6 +19,7 @@
 import numpy as np
 from dygraph_to_static_utils import (
     Dy2StTestBase,
+    static_guard,
     test_ast_only,
     test_legacy_and_pir,
 )
@@ -59,34 +60,32 @@ def func(x, y):
     @test_ast_only
     @test_legacy_and_pir
     def test_ast2func_dygraph(self):
-        paddle.disable_static()
         funcs = [dyfunc_with_if_else, dyfunc_with_if_else2, nested_if_else]
         x_data = np.random.random([10, 16]).astype('float32')
         for func in funcs:
-            x_v = base.dygraph.to_variable(x_data)
+            x_v = paddle.to_tensor(x_data)
             true_ret = func(x_v).numpy()
             test_ret = self._ast2func(func)(x_v).numpy()
             self.assertTrue((true_ret == test_ret).all())
 
     @test_ast_only
     @test_legacy_and_pir
     def test_ast2func_static(self):
-        paddle.enable_static()
-
         def func(x):
             y = F.relu(x)
             loss = paddle.mean(y)
             return loss
 
         x_data = np.random.random([10, 16]).astype('float32')
-        main_program = base.Program()
-        with base.program_guard(main_program):
-            x_v = paddle.assign(x_data)
-            true_ret = func(x_v)
-            test_ret = self._ast2func(func)(x_v)
-            exe = base.Executor(paddle.CPUPlace())
-            ret = exe.run(main_program, fetch_list=[true_ret, test_ret])
-            self.assertTrue((ret[0] == ret[1]).all())
+        with static_guard():
+            main_program = paddle.static.Program()
+            with base.program_guard(main_program):
+                x_v = paddle.assign(x_data)
+                true_ret = func(x_v)
+                test_ret = self._ast2func(func)(x_v)
+                exe = paddle.static.Executor(paddle.CPUPlace())
+                ret = exe.run(main_program, fetch_list=[true_ret, test_ret])
+                self.assertTrue((ret[0] == ret[1]).all())
 
     @test_ast_only
     @test_legacy_and_pir