Remove and reorganize the alias of APIs

paddle/fluid/pybind/imperative.cc

@@ -712,7 +712,7 @@ void BindImperative(py::module *m_ptr) {
                     tmp.stop_gradient=False
                     inputs.append(tmp)
                 ret = paddle.sums(inputs2)
-                loss = paddle.reduce_sum(ret)
+                loss = paddle.fluid.layers.reduce_sum(ret)
                 loss.backward()
                 print("Before clear_gradient {}".format(loss.grad))
                 loss.clear_gradient()

python/paddle/__init__.py

@@ -59,10 +59,9 @@
 from .tensor.attribute import rank  #DEFINE_ALIAS
 from .tensor.attribute import shape  #DEFINE_ALIAS
 from .tensor.creation import to_tensor  #DEFINE_ALIAS
-from .tensor.creation import crop_tensor  #DEFINE_ALIAS
 from .tensor.creation import diag  #DEFINE_ALIAS
 from .tensor.creation import eye  #DEFINE_ALIAS
-from .tensor.creation import fill_constant  #DEFINE_ALIAS
+# from .tensor.creation import fill_constant  #DEFINE_ALIAS
 # from .tensor.creation import get_tensor_from_selected_rows        #DEFINE_ALIAS
 from .tensor.creation import linspace  #DEFINE_ALIAS
 from .tensor.creation import ones  #DEFINE_ALIAS
@@ -103,8 +102,8 @@
 from .tensor.logic import logical_or  #DEFINE_ALIAS
 from .tensor.logic import logical_xor  #DEFINE_ALIAS
 from .tensor.logic import not_equal  #DEFINE_ALIAS
-from .tensor.logic import reduce_all  #DEFINE_ALIAS
-from .tensor.logic import reduce_any  #DEFINE_ALIAS
+# from .tensor.logic import reduce_all  #DEFINE_ALIAS
+# from .tensor.logic import reduce_any  #DEFINE_ALIAS
 from .tensor.logic import allclose  #DEFINE_ALIAS
 from .tensor.logic import equal_all  #DEFINE_ALIAS
 # from .tensor.logic import isnan        #DEFINE_ALIAS
@@ -145,23 +144,23 @@
 from .tensor.math import cos  #DEFINE_ALIAS
 from .tensor.math import cosh  #DEFINE_ALIAS
 from .tensor.math import cumsum  #DEFINE_ALIAS
-from .tensor.math import elementwise_add  #DEFINE_ALIAS
-from .tensor.math import elementwise_div  #DEFINE_ALIAS
-from .tensor.math import elementwise_floordiv  #DEFINE_ALIAS
-from .tensor.math import elementwise_mod  #DEFINE_ALIAS
-from .tensor.math import elementwise_pow  #DEFINE_ALIAS
-from .tensor.math import elementwise_sub  #DEFINE_ALIAS
+# from .tensor.math import elementwise_add  #DEFINE_ALIAS
+# from .tensor.math import elementwise_div  #DEFINE_ALIAS
+# from .tensor.math import elementwise_floordiv  #DEFINE_ALIAS
+# from .tensor.math import elementwise_mod  #DEFINE_ALIAS
+# from .tensor.math import elementwise_pow  #DEFINE_ALIAS
+# from .tensor.math import elementwise_sub  #DEFINE_ALIAS
 from .tensor.math import exp  #DEFINE_ALIAS
 from .tensor.math import floor  #DEFINE_ALIAS
 from .tensor.math import increment  #DEFINE_ALIAS
 from .tensor.math import log  #DEFINE_ALIAS
 from .tensor.math import multiplex  #DEFINE_ALIAS
 from .tensor.math import pow  #DEFINE_ALIAS
 from .tensor.math import reciprocal  #DEFINE_ALIAS
-from .tensor.math import reduce_max  #DEFINE_ALIAS
-from .tensor.math import reduce_min  #DEFINE_ALIAS
-from .tensor.math import reduce_prod  #DEFINE_ALIAS
-from .tensor.math import reduce_sum  #DEFINE_ALIAS
+# from .tensor.math import reduce_max  #DEFINE_ALIAS
+# from .tensor.math import reduce_min  #DEFINE_ALIAS
+# from .tensor.math import reduce_prod  #DEFINE_ALIAS
+# from .tensor.math import reduce_sum  #DEFINE_ALIAS
 from .tensor.math import round  #DEFINE_ALIAS
 from .tensor.math import rsqrt  #DEFINE_ALIAS
 from .tensor.math import scale  #DEFINE_ALIAS
@@ -174,7 +173,7 @@
 from .tensor.math import sum  #DEFINE_ALIAS
 from .tensor.math import sums  #DEFINE_ALIAS
 from .tensor.math import tanh  #DEFINE_ALIAS
-from .tensor.math import elementwise_sum  #DEFINE_ALIAS
+# from .tensor.math import elementwise_sum  #DEFINE_ALIAS
 from .tensor.math import max  #DEFINE_ALIAS
 from .tensor.math import maximum  #DEFINE_ALIAS
 from .tensor.math import min  #DEFINE_ALIAS
@@ -192,7 +191,7 @@
 from .tensor.math import inverse  #DEFINE_ALIAS
 from .tensor.math import log1p  #DEFINE_ALIAS
 from .tensor.math import erf  #DEFINE_ALIAS
-from .tensor.math import addcmul  #DEFINE_ALIAS
+# from .tensor.math import addcmul  #DEFINE_ALIAS
 from .tensor.math import addmm  #DEFINE_ALIAS
 from .tensor.math import clip  #DEFINE_ALIAS
 from .tensor.math import trace  #DEFINE_ALIAS
@@ -212,8 +211,8 @@
 from .tensor.search import argmax  #DEFINE_ALIAS
 from .tensor.search import argmin  #DEFINE_ALIAS
 from .tensor.search import argsort  #DEFINE_ALIAS
-from .tensor.search import has_inf  #DEFINE_ALIAS
-from .tensor.search import has_nan  #DEFINE_ALIAS
+# from .tensor.search import has_inf  #DEFINE_ALIAS
+# from .tensor.search import has_nan  #DEFINE_ALIAS
 from .tensor.search import masked_select  #DEFINE_ALIAS
 from .tensor.search import topk  #DEFINE_ALIAS
 from .tensor.search import where  #DEFINE_ALIAS
@@ -223,36 +222,35 @@
 from .framework.random import manual_seed  #DEFINE_ALIAS
 from .framework.random import get_cuda_rng_state  #DEFINE_ALIAS
 from .framework.random import set_cuda_rng_state  #DEFINE_ALIAS
-from .framework import Variable  #DEFINE_ALIAS
 from .framework import ParamAttr  #DEFINE_ALIAS
-from .framework import create_global_var  #DEFINE_ALIAS
+# from .framework import create_global_var  #DEFINE_ALIAS
 from .framework import create_parameter  #DEFINE_ALIAS
 from .framework import CPUPlace  #DEFINE_ALIAS
 from .framework import CUDAPlace  #DEFINE_ALIAS
 from .framework import CUDAPinnedPlace  #DEFINE_ALIAS
 
 from .framework import grad  #DEFINE_ALIAS
 from .framework import no_grad  #DEFINE_ALIAS
-from .framework import save  #DEFINE_ALIAS
-from .framework import load  #DEFINE_ALIAS
+# from .framework import save  #DEFINE_ALIAS
+# from .framework import load  #DEFINE_ALIAS
 from .framework import DataParallel  #DEFINE_ALIAS
 
-from .framework import NoamDecay  #DEFINE_ALIAS
-from .framework import PiecewiseDecay  #DEFINE_ALIAS
-from .framework import NaturalExpDecay  #DEFINE_ALIAS
-from .framework import ExponentialDecay  #DEFINE_ALIAS
-from .framework import InverseTimeDecay  #DEFINE_ALIAS
-from .framework import PolynomialDecay  #DEFINE_ALIAS
-from .framework import CosineDecay  #DEFINE_ALIAS
+# from .framework import NoamDecay  #DEFINE_ALIAS
+# from .framework import PiecewiseDecay  #DEFINE_ALIAS
+# from .framework import NaturalExpDecay  #DEFINE_ALIAS
+# from .framework import ExponentialDecay  #DEFINE_ALIAS
+# from .framework import InverseTimeDecay  #DEFINE_ALIAS
+# from .framework import PolynomialDecay  #DEFINE_ALIAS
+# from .framework import CosineDecay  #DEFINE_ALIAS
 from .framework import set_default_dtype  #DEFINE_ALIAS
 from .framework import get_default_dtype  #DEFINE_ALIAS
 
 from .tensor.search import index_sample  #DEFINE_ALIAS
 from .tensor.stat import mean  #DEFINE_ALIAS
-from .tensor.stat import reduce_mean  #DEFINE_ALIAS
+# from .tensor.stat import reduce_mean  #DEFINE_ALIAS
 from .tensor.stat import std  #DEFINE_ALIAS
 from .tensor.stat import var  #DEFINE_ALIAS
-from .fluid.data import data
+# from .fluid.data import data
 from .tensor.stat import numel  #DEFINE_ALIAS
 from .device import get_cudnn_version
 from .device import set_device
@@ -268,6 +266,8 @@
 from .fluid.dygraph.base import disable_dygraph as enable_static  #DEFINE_ALIAS
 from .fluid.framework import in_dygraph_mode as in_dynamic_mode  #DEFINE_ALIAS
 from .fluid.dygraph.base import no_grad_ as no_grad  #DEFINE_ALIAS
+from .fluid.layers import crop_tensor as crop  #DEFINE_ALIAS
+
 
 from . import jit
 from . import static

python/paddle/amp/grad_scaler.py

@@ -56,7 +56,7 @@ class GradScaler(AmpScaler):
         data = paddle.rand([10, 3, 32, 32])
         with paddle.amp.auto_cast():
             conv = model(data)
-            loss = paddle.reduce_mean(conv) 
+            loss = paddle.fluid.layers.reduce_mean(conv) 
             scaled = scaler.scale(loss)  # scale the loss 
             scaled.backward()            # do backward
             scaler.minimize(optimizer, scaled)  # update parameters     
@@ -96,7 +96,7 @@ def scale(self, var):
             data = paddle.rand([10, 3, 32, 32])
             with paddle.amp.auto_cast():
                 conv = model(data)
-                loss = paddle.reduce_mean(conv) 
+                loss = paddle.fluid.layers.reduce_mean(conv) 
                 scaled = scaler.scale(loss)  # scale the loss 
                 scaled.backward()            # do backward
                 scaler.minimize(optimizer, scaled)  # update parameters  
@@ -128,7 +128,7 @@ def minimize(self, optimizer, *args, **kwargs):
             data = paddle.rand([10, 3, 32, 32])
             with paddle.amp.auto_cast():
                 conv = model(data)
-                loss = paddle.reduce_mean(conv) 
+                loss = paddle.fluid.layers.reduce_mean(conv) 
                 scaled = scaler.scale(loss)  # scale the loss 
                 scaled.backward()            # do backward
                 scaler.minimize(optimizer, scaled)  # update parameters  

python/paddle/distributed/collective.py

@@ -439,7 +439,7 @@ def barrier(group=0):
             paddle.distributed.barrier()
     """
     op_type = 'barrier'
-    temp = paddle.fill_constant([1], dtype="int32", value="1")
+    temp = fill_constant([1], dtype="int32", value="1")
     if in_dygraph_mode():
         return core.ops.barrier(temp, temp, 'ring_id', group)
     if not isinstance(group, int):

python/paddle/distribution.py

@@ -25,9 +25,9 @@
 from .fluid.layers import tensor
 from .fluid.layers import ops
 from .fluid.layers import nn
+from .fluid.layers import elementwise_mul, elementwise_div, elementwise_add, elementwise_sub
 from .fluid import core
 from .fluid.framework import in_dygraph_mode
-from .tensor.math import elementwise_mul, elementwise_div, elementwise_add, elementwise_sub
 from .tensor import arange, gather_nd, concat, multinomial
 import math
 import numpy as np

python/paddle/fluid/dygraph/base.py

@@ -480,7 +480,7 @@ def test_dygraph_grad(create_graph):
             paddle.disable_static()
 
             def test_dygraph_grad(grad_outputs=None):
-                x = paddle.fill_constant(shape=[1], value=2.0, dtype='float32')
+                x = paddle.fluid.layers.fill_constant(shape=[1], value=2.0, dtype='float32')
                 x.stop_gradient = False
 
                 y1 = x * x
@@ -503,7 +503,7 @@ def test_dygraph_grad(grad_outputs=None):
 
                 return dx.numpy()
 
-            grad_value = paddle.fill_constant(shape=[1], value=4.0, dtype='float32')
+            grad_value = paddle.fluid.layers.fill_constant(shape=[1], value=4.0, dtype='float32')
 
             # dy1 = [1], dy2 = [1]
             print(test_dygraph_grad(None)) # [7.]
@@ -515,7 +515,7 @@ def test_dygraph_grad(grad_outputs=None):
             print(test_dygraph_grad([grad_value, None])) # [19.]
 
             # dy1 = [3], dy2 = [4]
-            grad_y1 = paddle.fill_constant(shape=[1], value=3.0, dtype='float32')
+            grad_y1 = paddle.fluid.layers.fill_constant(shape=[1], value=3.0, dtype='float32')
             print(test_dygraph_grad([grad_y1, grad_value])) # [24.]
 	'''
 

python/paddle/fluid/dygraph/dygraph_to_static/variable_trans_func.py

@@ -87,7 +87,7 @@ def create_static_variable_gast_node(name):
 
 
 def create_fill_constant_node(name, value):
-    func_code = "{} = paddle.fill_constant(shape=[1], ".format(name)
+    func_code = "{} = paddle.fluid.layers.fill_constant(shape=[1], ".format(name)
     if isinstance(value, bool):
         func_code += "dtype='bool', value={})".format(value)
         return gast.parse(func_code).body[0]

python/paddle/fluid/dygraph/layers.py

@@ -1100,7 +1100,7 @@ def state_dict(self,
                 emb = paddle.nn.Embedding(10, 10)
 
                 state_dict = emb.state_dict()
-                paddle.save( state_dict, "paddle_dy.pdparams")
+                paddle.framework.io.save( state_dict, "paddle_dy.pdparams")
 
         '''
 
@@ -1148,8 +1148,8 @@ def set_state_dict(self,
                 emb = paddle.nn.Embedding(10, 10)
 
                 state_dict = emb.state_dict()
-                paddle.save(state_dict, "paddle_dy.pdparams")
-                para_state_dict = paddle.load("paddle_dy.pdparams")
+                paddle.framework.io.save(state_dict, "paddle_dy.pdparams")
+                para_state_dict = paddle.framework.io.load("paddle_dy.pdparams")
                 emb.set_state_dict(para_state_dict)
 
         '''

python/paddle/fluid/dygraph/nn.py

@@ -702,9 +702,6 @@ def forward(self, input):
 
 class Pool2D(layers.Layer):
     """
-    :alias_main: paddle.nn.Pool2D
-	:alias: paddle.nn.Pool2D,paddle.nn.layer.Pool2D,paddle.nn.layer.common.Pool2D
-	:old_api: paddle.fluid.dygraph.Pool2D
 
     This interface is used to construct a callable object of the ``Pool2D`` class.
     For more details, refer to code examples.
@@ -2354,9 +2351,6 @@ def forward(self, input):
 
 class BilinearTensorProduct(layers.Layer):
     """
-    :alias_main: paddle.nn.BilinearTensorProduct
-	:alias: paddle.nn.BilinearTensorProduct,paddle.nn.layer.BilinearTensorProduct,paddle.nn.layer.common.BilinearTensorProduct
-	:old_api: paddle.fluid.dygraph.BilinearTensorProduct
 
     **Add Bilinear Tensor Product Layer**
 

python/paddle/fluid/dygraph/parallel.py

@@ -509,9 +509,9 @@ def set_state_dict(self,
                 emb = fluid.dygraph.DataParallel(emb, strategy)
 
                 state_dict = emb.state_dict()
-                paddle.save(state_dict, "paddle_dy.pdparams")
+                paddle.framework.io.save(state_dict, "paddle_dy.pdparams")
 
-                para_state_dict = paddle.load("paddle_dy.pdparams")
+                para_state_dict = paddle.framework.io.load("paddle_dy.pdparams")
 
                 emb.set_state_dict(para_state_dict)
 

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -163,7 +163,7 @@ def backward(self, retain_graph=False):
                     tmp.stop_gradient=False
                     inputs.append(tmp)
                 ret = paddle.sums(inputs)
-                loss = paddle.reduce_sum(ret)
+                loss = paddle.fluid.layers.reduce_sum(ret)
                 loss.backward()
 
         """

python/paddle/fluid/framework.py

@@ -533,7 +533,7 @@ def name_scope(prefix=None):
           import paddle
           paddle.enable_static()
           with paddle.static.name_scope("s1"):
-             a = paddle.data(name='data', shape=[None, 1], dtype='int32')
+             a = paddle.fluid.data(name='data', shape=[None, 1], dtype='int32')
              b = a + 1
              with paddle.static.name_scope("s2"):
                 c = b * 1
@@ -1183,7 +1183,7 @@ def backward(self, retain_graph=False):
                     tmp.stop_gradient=False
                     inputs.append(tmp)
                 ret = paddle.sums(inputs)
-                loss = paddle.reduce_sum(ret)
+                loss = paddle.fluid.layers.reduce_sum(ret)
                 loss.backward()
 
         """
@@ -5345,8 +5345,8 @@ def default_startup_program():
             main_program = paddle.static.Program()
             startup_program = paddle.static.Program()
             with paddle.static.program_guard(main_program=main_program, startup_program=startup_program):
-                x = paddle.data(name="x", shape=[-1, 784], dtype='float32')
-                y = paddle.data(name="y", shape=[-1, 1], dtype='int32')
+                x = paddle.fluid.data(name="x", shape=[-1, 784], dtype='float32')
+                y = paddle.fluid.data(name="y", shape=[-1, 1], dtype='int32')
                 z = paddle.static.nn.fc(name="fc", input=x, size=10, act="relu")
 
                 print("main program is: {}".format(paddle.static.default_main_program()))
@@ -5360,7 +5360,7 @@ def default_main_program():
     This API can be used to get ``default main program`` which store the 
     descriptions of Ops and tensors.
 
-    For example ``z = paddle.elementwise_add(x, y)`` will create a new ``elementwise_add`` 
+    For example ``z = paddle.fluid.layers.elementwise_add(x, y)`` will create a new ``elementwise_add`` 
     Op and a new ``z`` tensor, and they will be recorded in ``default main program`` . 
 
     The ``default main program`` is the default value for ``Program`` parameter in 
@@ -5379,15 +5379,15 @@ def default_main_program():
 
             paddle.enable_static()
             # Sample Network:
-            data = paddle.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
-            label = paddle.data(name='label', shape=[None, 1], dtype='int64')
+            data = paddle.fluid.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
+            label = paddle.fluid.data(name='label', shape=[None, 1], dtype='int64')
 
             conv1 = paddle.static.nn.conv2d(data, 4, 5, 1, act=None)
             bn1 = paddle.static.nn.batch_norm(conv1, act='relu')
-            pool1 = paddle.nn.functional.pool2d(bn1, 2, 'max', 2)
+            pool1 = paddle.fluid.layers.pool2d(bn1, 2, 'max', 2)
             conv2 = paddle.static.nn.conv2d(pool1, 16, 5, 1, act=None)
             bn2 = paddle.static.nn.batch_norm(conv2, act='relu')
-            pool2 = paddle.nn.functional.pool2d(bn2, 2, 'max', 2)
+            pool2 = paddle.fluid.layers.pool2d(bn2, 2, 'max', 2)
 
             fc1 = paddle.static.nn.fc(pool2, size=50, act='relu')
             fc2 = paddle.static.nn.fc(fc1, size=102, act='softmax')