move fluid.layers.create_global_var to static.create_global_var

python/paddle/distributed/fleet/meta_optimizers/dgc_optimizer.py

@@ -23,9 +23,9 @@
 from paddle.common_ops_import import LayerHelper
 from paddle.fluid.clip import GradientClipByNorm, append_gradient_clip_ops
 from paddle.fluid.dygraph import base as imperative_base
-from paddle.fluid.layers import tensor
 from paddle.fluid.optimizer import Momentum, Optimizer
 from paddle.framework import core
+from paddle.static import create_global_var
 
 
 class DGCMomentumOptimizer(Optimizer):
@@ -217,7 +217,7 @@ def _append_dgc_ops(self, param_and_grads):
         )
 
         # rampup begin step var for all_reduce_op_handle
-        self._rampup_begin_step_var = tensor.create_global_var(
+        self._rampup_begin_step_var = create_global_var(
             shape=[1],
             dtype=core.VarDesc.VarType.FP32,
             persistable=True,
@@ -237,7 +237,7 @@ def _append_dgc_ops(self, param_and_grads):
 
             v_var = self._add_accumulator(self._v_velocity_acc_str, param_var)
 
-            k_var = tensor.create_global_var(
+            k_var = create_global_var(
                 shape=[1],
                 dtype=param_var.dtype,
                 persistable=True,
@@ -246,7 +246,7 @@ def _append_dgc_ops(self, param_and_grads):
                 force_cpu=True,
             )
 
-            encoded_var = tensor.create_global_var(
+            encoded_var = create_global_var(
                 shape=[1],
                 dtype=param_var.dtype,
                 persistable=True,
@@ -255,7 +255,7 @@ def _append_dgc_ops(self, param_and_grads):
                 force_cpu=False,
             )
 
-            gather_var = tensor.create_global_var(
+            gather_var = create_global_var(
                 shape=[1],
                 dtype=param_var.dtype,
                 persistable=True,

python/paddle/distributed/fleet/metrics/metric.py

@@ -40,7 +40,7 @@ def sum(input, scope=None, util=None):
           # in model.py
           input = fluid.layers.cast(some_input, dtype='float32')
           cnt = paddle.sum(input)
-          global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
+          global_cnt = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp = fluid.layers.elementwise_add(cnt, global_cnt)
           fluid.layers.assign(tmp, global_cnt)
 
@@ -80,7 +80,7 @@ def max(input, scope=None, util=None):
           # in model.py
           input = fluid.layers.cast(some_input, dtype='float32')
           cnt = paddle.sum(input)
-          global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
+          global_cnt = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp = paddle.maximum(cnt, global_cnt)
           fluid.layers.assign(tmp, global_cnt)
 
@@ -120,7 +120,7 @@ def min(input, scope=None, util=None):
           # in model.py
           input = fluid.layers.cast(some_input, dtype='float32')
           cnt = paddle.sum(input)
-          global_cnt = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
+          global_cnt = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp = fluid.layers.elementwise_min(cnt, global_cnt)
           fluid.layers.assign(tmp, global_cnt)
 
@@ -391,15 +391,15 @@ def acc(correct, total, scope=None, util=None):
         .. code-block:: python
 
           # in model.py
-          correct = fluid.layers.create_global_var(dtype='float32', shape=[1], value=0)
-          total = fluid.layers.create_global_var(dtype='float32', shape=[1], value=0)
+          correct = paddle.static.create_global_var(dtype='float32', shape=[1], value=0)
+          total = paddle.static.create_global_var(dtype='float32', shape=[1], value=0)
           acc = fluid.layers.acc(predict, label, k=1, correct=correct, total=total)
 
-          global_correct = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
+          global_correct = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp1 = fluid.layers.elementwise_min(correct, global_correct)
           fluid.layers.assign(tmp1, global_correct)
 
-          global_total = fluid.layers.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
+          global_total = paddle.static.create_global_var(persistable=True, dtype='float32', shape=[1], value=0)
           tmp2 = fluid.layers.elementwise_min(total, global_total)
           fluid.layers.assign(tmp2, global_total)
 

python/paddle/distributed/passes/auto_parallel_gradient_merge.py

@@ -64,7 +64,7 @@ def _remove_and_get_optimizer_op(main_program, dist_context):
 def _get_gm_cond_var(main_program, k_steps, dist_context):
     main_block = main_program.global_block()
     # Add const var
-    k_step_var = layers.create_global_var(
+    k_step_var = paddle.static.create_global_var(
         name="gradient_merge_k",
         shape=[1],
         value=int(k_steps),
@@ -74,7 +74,7 @@ def _get_gm_cond_var(main_program, k_steps, dist_context):
     )
     set_var_dist_attr(dist_context, k_step_var, [-1], world_process_group.ranks)
 
-    zero_var = layers.create_global_var(
+    zero_var = paddle.static.create_global_var(
         name="gradient_merge_zero",
         shape=[1],
         value=int(0),
@@ -85,7 +85,7 @@ def _get_gm_cond_var(main_program, k_steps, dist_context):
     set_var_dist_attr(dist_context, zero_var, [-1], world_process_group.ranks)
 
     # Add step var & cond var
-    step_var = layers.create_global_var(
+    step_var = paddle.static.create_global_var(
         name="gradient_merge_step",
         shape=[1],
         value=int(0),

python/paddle/fluid/contrib/mixed_precision/bf16/decorator.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import paddle
 from paddle.fluid import (
     core,
     default_main_program,
@@ -68,7 +69,7 @@ def _init_amp_var(self):
         if isinstance(self._optimizer._learning_rate, float):
             self._optimizer._learning_rate_map[
                 default_main_program()
-            ] = layers.create_global_var(
+            ] = paddle.static.create_global_var(
                 name=unique_name.generate("learning_rate"),
                 shape=[1],
                 value=float(self._optimizer._learning_rate),

python/paddle/fluid/contrib/mixed_precision/decorator.py

@@ -122,7 +122,7 @@ def _supports_check_nan_inf(self):
         return getattr(self._optimizer, "_supports_check_nan_inf", False)
 
     def _init_amp_var(self):
-        self._loss_scaling = layers.create_global_var(
+        self._loss_scaling = paddle.static.create_global_var(
             name=unique_name.generate("loss_scaling"),
             shape=[1],
             value=self._init_loss_scaling,
@@ -131,14 +131,14 @@ def _init_amp_var(self):
         )
 
         if self._use_dynamic_loss_scaling:
-            self._num_good_steps = layers.create_global_var(
+            self._num_good_steps = paddle.static.create_global_var(
                 name=unique_name.generate("num_good_steps"),
                 shape=[1],
                 value=0,
                 dtype='int32',
                 persistable=True,
             )
-            self._num_bad_steps = layers.create_global_var(
+            self._num_bad_steps = paddle.static.create_global_var(
                 name=unique_name.generate("num_bad_steps"),
                 shape=[1],
                 value=0,
@@ -151,7 +151,7 @@ def _init_amp_var(self):
         if isinstance(self._optimizer._learning_rate, float):
             self._optimizer._learning_rate_map[
                 default_main_program()
-            ] = layers.create_global_var(
+            ] = paddle.static.create_global_var(
                 name=unique_name.generate("learning_rate"),
                 shape=[1],
                 value=float(self._optimizer._learning_rate),

python/paddle/fluid/contrib/optimizer.py

@@ -143,7 +143,7 @@ def _create_master_weight(self, param):
 
         var_name = param.name + "_fp32_master"
         var_name = unique_name.generate(var_name)
-        var = layers.create_global_var(
+        var = paddle.static.create_global_var(
             name=var_name,
             shape=param.shape,
             value=0,

python/paddle/fluid/dygraph/learning_rate_scheduler.py

@@ -68,7 +68,7 @@ def create_lr_var(self, lr):
         """
         from .. import layers
 
-        lr = layers.create_global_var(
+        lr = paddle.static.create_global_var(
             name=unique_name.generate("learning_rate"),
             shape=[1],
             value=float(lr),

python/paddle/fluid/layers/control_flow.py

@@ -2368,9 +2368,10 @@ class Switch:
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
 
-            lr = fluid.layers.create_global_var(
+            lr = paddle.static.create_global_var(
                 shape=[1],
                 value=0.0,
                 dtype='float32',

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -420,7 +420,7 @@ def piecewise_decay(boundaries, values):
         else:
             global_step = _decay_step_counter()
 
-            lr = tensor.create_global_var(
+            lr = paddle.static.create_global_var(
                 shape=[1],
                 value=0.0,
                 dtype='float32',
@@ -575,7 +575,7 @@ def linear_lr_warmup(learning_rate, warmup_steps, start_lr, end_lr):
             )
             return lr
         else:
-            lr = tensor.create_global_var(
+            lr = paddle.static.create_global_var(
                 shape=[1],
                 value=0.0,
                 dtype=dtype,

python/paddle/fluid/layers/tensor.py

@@ -12,25 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import numpy
 import warnings
 
 from ..layer_helper import LayerHelper
-from ..param_attr import ParamAttr
-from ..initializer import Initializer
 from ..framework import (
     _current_expected_place,
     convert_np_dtype_to_dtype_,
     _non_static_mode,
     _varbase_creator,
-    device_guard,
     _in_legacy_dygraph,
     in_dygraph_mode,
-    _get_paddle_place,
 )
 from ..framework import Variable
-from ..initializer import Constant
 from ..core import VarDesc
 from .. import core
 from .layer_function_generator import templatedoc
@@ -47,7 +41,6 @@
 from paddle import _C_ops, _legacy_C_ops
 
 __all__ = [
-    'create_global_var',
     'cast',
     'tensor_array_to_tensor',
     'concat',
@@ -61,86 +54,6 @@
 ]
 
 
-def create_global_var(
-    shape, value, dtype, persistable=False, force_cpu=False, name=None
-):
-    """
-    This function creates a new tensor variable with value in the global block(block 0).
-
-    Parameters:
-        shape (list[int]|tuple[int]): Shape of the variable
-        value (float): The value of the variable. The new created
-                      variable will be filled with it.
-        dtype (str): Data type of the variable
-        persistable (bool, optional): If this variable is persistable.
-                           Default: False
-        force_cpu (bool, optional): Force this variable to be on CPU.
-                         Default: False
-        name (str, optional): For detailed information, please refer to
-           :ref:`api_guide_Name` . Usually name is no need to set and None by default.
-
-    Returns:
-        Variable: The created Variable
-
-    Examples:
-        .. code-block:: python
-
-            import paddle
-            paddle.enable_static()
-            var = paddle.static.create_global_var(shape=[2,3], value=1.0, dtype='float32',
-                                           persistable=True, force_cpu=True, name='new_var')
-    """
-    check_type(
-        shape, 'shape', (list, tuple, numpy.ndarray), 'create_global_var'
-    )
-    for item in shape:
-        check_type(
-            item,
-            'item of shape',
-            (
-                int,
-                numpy.uint8,
-                numpy.int8,
-                numpy.int16,
-                numpy.int32,
-                numpy.int64,
-            ),
-            'create_global_var',
-        )
-
-    check_dtype(
-        dtype,
-        'dtype',
-        [
-            'bool',
-            'float16',
-            'float32',
-            'float64',
-            'int8',
-            'int16',
-            'int32',
-            'int64',
-            'uint8',
-            'uint16',
-        ],
-        'create_global_var',
-    )
-
-    helper = LayerHelper("global_var", **locals())
-    var = helper.create_global_variable(
-        dtype=dtype,
-        shape=shape,
-        persistable=persistable,
-        name=name,
-        stop_gradient=True,
-    )
-    helper.set_variable_initializer(
-        var, initializer=Constant(value=float(value), force_cpu=force_cpu)
-    )
-
-    return var
-
-
 def cast(x, dtype):
     """