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Paddle
未验证 提交 208f625b 编写于 作者: J JYChen 提交者： GitHub
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[Fluid Clean] remove apis in fluid.layers.ops (#47867) 

* remove apis in fluid.ops

* fix test_activation_nn_grad

* fix circle import error

* fix ops

* fix cos

* fix divide not inplace

* remove lazy-import part
上级 70589379
隐藏空白更改
内联 并排
Showing with 294 addition and 1551 deletion
python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py
浏览文件 @ 208f625b
......@@ -449,8 +449,8 @@ class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
communicate()
self._generate_avg_loss(main_block, loss, avg_loss)
next_local_steps = layers.cast(
layers.ceil(
layers.sqrt(
paddle.ceil(
paddle.sqrt(
lr_0
* avg_loss
/ (global_lr * loss_0)
......
python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py
浏览文件 @ 208f625b
......@@ -68,7 +68,7 @@ class GroupShardedClipGrad:
merge_grad = layers.get_tensor_from_selected_rows(
layers.merge_selected_rows(g)
)
square = layers.square(merge_grad)
square = paddle.square(merge_grad)
sum_square = layers.reduce_sum(square)
if p.dtype == paddle.float16:
......@@ -133,7 +133,7 @@ class GroupShardedClipGrad:
with device_guard(dev_id, "gpu"):
paddle.distributed.all_reduce(global_norm_var, group=self._group)
global_norm_var = layers.sqrt(global_norm_var)
global_norm_var = paddle.sqrt(global_norm_var)
max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
)
......
python/paddle/distributed/fleet/meta_parallel/sharding/sharding_utils.py
浏览文件 @ 208f625b
......@@ -69,7 +69,7 @@ class ShardingClipGrad:
merge_grad = layers.get_tensor_from_selected_rows(
layers.merge_selected_rows(g)
)
square = layers.square(merge_grad)
square = paddle.square(merge_grad)
sum_square = layers.reduce_sum(square)
if p.dtype == paddle.float16:
......@@ -131,7 +131,7 @@ class ShardingClipGrad:
with device_guard(dev_id, "gpu"):
paddle.distributed.all_reduce(global_norm_var, group=self._group)
global_norm_var = layers.sqrt(global_norm_var)
global_norm_var = paddle.sqrt(global_norm_var)
max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
)
......
python/paddle/distribution/categorical.py
浏览文件 @ 208f625b
......@@ -17,7 +17,7 @@ import paddle
from paddle.distribution import distribution
from paddle.fluid.data_feeder import check_type, convert_dtype
from paddle.fluid.framework import _non_static_mode
from paddle.fluid.layers import ops, tensor
from paddle.fluid.layers import tensor
from paddle.tensor import multinomial
......@@ -214,8 +214,8 @@ class Categorical(distribution.Distribution):
other_logits = other.logits - paddle.max(
other.logits, axis=-1, keepdim=True
)
e_logits = ops.exp(logits)
other_e_logits = ops.exp(other_logits)
e_logits = paddle.exp(logits)
other_e_logits = paddle.exp(other_logits)
z = paddle.sum(e_logits, axis=-1, keepdim=True)
other_z = paddle.sum(other_e_logits, axis=-1, keepdim=True)
prob = e_logits / z
......@@ -255,7 +255,7 @@ class Categorical(distribution.Distribution):
"""
name = self.name + '_entropy'
logits = self.logits - paddle.max(self.logits, axis=-1, keepdim=True)
e_logits = ops.exp(logits)
e_logits = paddle.exp(logits)
z = paddle.sum(e_logits, axis=-1, keepdim=True)
prob = e_logits / z
......
python/paddle/distribution/normal.py
浏览文件 @ 208f625b
......@@ -23,7 +23,6 @@ from paddle.fluid.layers import (
elementwise_div,
elementwise_sub,
nn,
ops,
tensor,
)
......@@ -288,7 +287,7 @@ class Normal(distribution.Distribution):
var = self.scale * self.scale
return elementwise_div(
ops.exp(
paddle.exp(
-1.0 * ((value - self.loc) * (value - self.loc)) / (2.0 * var)
),
(math.sqrt(2 * math.pi) * self.scale),
......
python/paddle/fluid/clip.py
浏览文件 @ 208f625b
......@@ -72,7 +72,7 @@ def _squared_l2_norm(x):
or x.dtype == core.VarDesc.VarType.FP16
or x.dtype == core.VarDesc.VarType.BF16
):
square = layers.square(x)
square = paddle.square(x)
sum_square = layers.reduce_sum(square)
return sum_square
......@@ -540,7 +540,7 @@ class ClipGradByGlobalNorm(ClipGradBase):
global_norm_var_fp64 = paddle.add_n(sum_square_list)
global_norm_var.append(global_norm_var_fp64)
global_norm_var = paddle.add_n(global_norm_var)
global_norm_var = layers.sqrt(global_norm_var)
global_norm_var = paddle.sqrt(global_norm_var)
max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
)
......@@ -648,7 +648,7 @@ class ClipGradByGlobalNorm(ClipGradBase):
if len(global_norm_var) > 1
else global_norm_var[0]
)
global_norm_var = layers.sqrt(x=global_norm_var)
global_norm_var = paddle.sqrt(x=global_norm_var)
max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
)
......@@ -727,7 +727,7 @@ class ClipGradByGlobalNorm(ClipGradBase):
group_scale_name = self.group_name + "_scale"
if group_scale_name not in self.context:
group_norm_var = layers.sums(input=self.context[self.group_name])
group_norm_var = layers.sqrt(x=group_norm_var)
group_norm_var = paddle.sqrt(x=group_norm_var)
clip_var = self.context[self.group_name + "_clip"]
group_scale_var = layers.elementwise_div(
x=clip_var,
......
python/paddle/fluid/contrib/layers/rnn_impl.py
浏览文件 @ 208f625b
......@@ -14,6 +14,7 @@
import copy
import paddle
from paddle.fluid import layers, unique_name
from paddle.fluid.dygraph import Layer
from paddle.fluid.dygraph.layer_object_helper import LayerObjectHelper
......@@ -95,8 +96,8 @@ class BasicGRUUnit(Layer):
self._hiden_size = hidden_size
self._param_attr = param_attr
self._bias_attr = bias_attr
self._gate_activation = gate_activation or layers.sigmoid
self._activation = activation or layers.tanh
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._dtype = dtype
def _build_once(self, input, pre_hidden):
......@@ -845,8 +846,8 @@ class BasicLSTMUnit(Layer):
self._hiden_size = hidden_size
self._param_attr = param_attr
self._bias_attr = bias_attr
self._gate_activation = gate_activation or layers.sigmoid
self._activation = activation or layers.tanh
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._forget_bias = layers.fill_constant(
[1], dtype=dtype, value=forget_bias
)
......@@ -879,10 +880,14 @@ class BasicLSTMUnit(Layer):
new_cell = layers.elementwise_add(
layers.elementwise_mul(
pre_cell,
layers.sigmoid(layers.elementwise_add(f, self._forget_bias)),
paddle.nn.functional.sigmoid(
layers.elementwise_add(f, self._forget_bias)
),
),
layers.elementwise_mul(
paddle.nn.functional.sigmoid(i), paddle.tanh(j)
),
layers.elementwise_mul(layers.sigmoid(i), layers.tanh(j)),
)
new_hidden = layers.tanh(new_cell) * layers.sigmoid(o)
new_hidden = paddle.tanh(new_cell) * paddle.nn.functional.sigmoid(o)
return new_hidden, new_cell
python/paddle/fluid/contrib/slim/quantization/adaround.py
浏览文件 @ 208f625b
......@@ -17,6 +17,7 @@ import time
import sys
import logging
import paddle
import paddle.fluid as fluid
from ....log_helper import get_logger
......@@ -41,7 +42,9 @@ ZETA = 1.1
def compute_soft_rounding(alpha_v):
return fluid.layers.clip(
fluid.layers.sigmoid(alpha_v) * (ZETA - GAMMA) + GAMMA, min=0, max=1
paddle.nn.functional.sigmoid(alpha_v) * (ZETA - GAMMA) + GAMMA,
min=0,
max=1,
)
......@@ -73,8 +76,7 @@ class AdaRoundLoss:
# calculate regularization term - which ensures parameter to converge to exactly zeros and ones
# at the end of optimization
reg_term = fluid.layers.reduce_sum(
-fluid.layers.pow(fluid.layers.abs(2 * h_v - 1), factor=beta)
+ 1
-fluid.layers.pow(paddle.abs(2 * h_v - 1), factor=beta) + 1
)
# calculate the rounding loss
......
python/paddle/fluid/contrib/tests/test_weight_decay_extend.py
浏览文件 @ 208f625b
......@@ -82,7 +82,7 @@ def bow_net(
input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim]
)
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
bow_tanh = paddle.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
......
python/paddle/fluid/dygraph/learning_rate_scheduler.py
浏览文件 @ 208f625b
......@@ -270,12 +270,10 @@ class NaturalExpDecay(LearningRateDecay):
self.staircase = staircase
def step(self):
from .. import layers
div_res = self.create_lr_var(self.step_num / self.decay_steps)
if self.staircase:
div_res = layers.floor(div_res)
decayed_lr = self.learning_rate * layers.exp(
div_res = paddle.floor(div_res)
decayed_lr = self.learning_rate * paddle.exp(
-1 * self.decay_rate * div_res
)
......@@ -356,11 +354,9 @@ class ExponentialDecay(LearningRateDecay):
self.staircase = staircase
def step(self):
from .. import layers
div_res = self.create_lr_var(self.step_num / self.decay_steps)
if self.staircase:
div_res = layers.floor(div_res)
div_res = paddle.floor(div_res)
decayed_lr = self.learning_rate * (self.decay_rate**div_res)
......@@ -437,11 +433,9 @@ class InverseTimeDecay(LearningRateDecay):
self.staircase = staircase
def step(self):
from .. import layers
div_res = self.create_lr_var(self.step_num / self.decay_steps)
if self.staircase:
div_res = layers.floor(div_res)
div_res = paddle.floor(div_res)
decayed_lr = self.learning_rate / (1 + self.decay_rate * div_res)
......@@ -524,12 +518,10 @@ class PolynomialDecay(LearningRateDecay):
self.cycle = cycle
def step(self):
from .. import layers
tmp_step_num = self.step_num
tmp_decay_steps = self.decay_steps
if self.cycle:
div_res = layers.ceil(
div_res = paddle.ceil(
self.create_lr_var(tmp_step_num / float(self.decay_steps))
)
......@@ -601,15 +593,13 @@ class CosineDecay(LearningRateDecay):
self.epochs = epochs
def step(self):
from .. import layers
cur_epoch = layers.floor(
cur_epoch = paddle.floor(
self.create_lr_var(self.step_num / self.step_each_epoch)
)
decayed_lr = (
self.learning_rate
* 0.5
* (layers.cos(cur_epoch * math.pi / self.epochs) + 1)
* (paddle.cos(cur_epoch * math.pi / self.epochs) + 1)
)
return decayed_lr
......
python/paddle/fluid/dygraph/rnn.py
浏览文件 @ 208f625b
......@@ -12,10 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from . import Layer
from ..layers import (
sigmoid,
tanh,
concat,
fill_constant,
matmul,
......@@ -139,8 +138,8 @@ class LSTMCell(Layer):
self._param_attr = param_attr
self._bias_attr = bias_attr
self._dtype = dtype
self._gate_activation = gate_activation or sigmoid
self._activation = activation or tanh
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._use_cudnn_impl = use_cudnn_impl
if self._use_cudnn_impl:
......@@ -254,7 +253,9 @@ class LSTMCell(Layer):
elementwise_add(f, self._forget_bias)
),
),
elementwise_mul(sigmoid(i), tanh(j)),
elementwise_mul(
paddle.nn.functional.sigmoid(i), paddle.tanh(j)
),
)
new_hidden = self._activation(new_cell) * self._gate_activation(o)
......@@ -357,8 +358,8 @@ class GRUCell(Layer):
self._param_attr = param_attr
self._bias_attr = bias_attr
self._dtype = dtype
self._gate_activation = gate_activation or sigmoid
self._activation = activation or tanh
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._use_cudnn_impl = use_cudnn_impl
if self._use_cudnn_impl:
......
python/paddle/fluid/layers/__init__.py
浏览文件 @ 208f625b
......@@ -12,8 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from . import ops
from .ops import *
from . import nn
from .nn import *
from . import io
......@@ -43,7 +41,6 @@ __all__ += nn.__all__
__all__ += io.__all__
__all__ += tensor.__all__
__all__ += control_flow.__all__
__all__ += ops.__all__
__all__ += device.__all__
__all__ += detection.__all__
__all__ += metric_op.__all__
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 208f625b
......@@ -14,7 +14,7 @@
from ..wrapped_decorator import signature_safe_contextmanager
from .layer_function_generator import autodoc, templatedoc
from .layer_function_generator import templatedoc
from .tensor import assign, cast, fill_constant
from .. import core
from ..framework import (
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 208f625b
......@@ -17,15 +17,13 @@ All layers just related to the detection neural network.
import paddle
from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from .layer_function_generator import templatedoc
from ..layer_helper import LayerHelper
from ..framework import Variable, _non_static_mode, static_only, in_dygraph_mode
from .. import core
from .loss import softmax_with_cross_entropy
from . import tensor
from . import nn
from . import ops
from ..data_feeder import check_variable_and_dtype, check_type, check_dtype
import math
import numpy as np
......
python/paddle/fluid/layers/distributions.py
浏览文件 @ 208f625b
......@@ -14,7 +14,6 @@
from . import control_flow
from . import tensor
from . import ops
from . import nn
import math
import numpy as np
......@@ -535,8 +534,8 @@ class Categorical(Distribution):
other_logits = other.logits - nn.reduce_max(
other.logits, dim=-1, keep_dim=True
)
e_logits = ops.exp(logits)
other_e_logits = ops.exp(other_logits)
e_logits = paddle.exp(logits)
other_e_logits = paddle.exp(other_logits)
z = nn.reduce_sum(e_logits, dim=-1, keep_dim=True)
other_z = nn.reduce_sum(other_e_logits, dim=-1, keep_dim=True)
prob = e_logits / z
......@@ -556,7 +555,7 @@ class Categorical(Distribution):
"""
logits = self.logits - nn.reduce_max(self.logits, dim=-1, keep_dim=True)
e_logits = ops.exp(logits)
e_logits = paddle.exp(logits)
z = nn.reduce_sum(e_logits, dim=-1, keep_dim=True)
prob = e_logits / z
entropy = -1.0 * nn.reduce_sum(
......
python/paddle/fluid/layers/io.py
浏览文件 @ 208f625b
......@@ -19,7 +19,6 @@ import threading
from ..data_feeder import DataFeeder
from .control_flow import BlockGuard
from .layer_function_generator import templatedoc
from .. import core
from ..executor import global_scope
from ..framework import (
......
python/paddle/fluid/layers/layer_function_generator.py 100755 → 100644
浏览文件 @ 208f625b
......@@ -45,14 +45,11 @@ __all__ = [
def _convert_(name):
"""
Formatting.
Args:
name: The name/alias
This function takes in a name and converts it to a standard format of
group1_group2. Where as per the regular expression, group1 can have
alphabets and numbers and group2 has capital alphabets.
"""
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
......@@ -80,10 +77,8 @@ def _generate_doc_string_(
):
"""
Generate docstring by OpProto
Args:
op_proto (framework_pb2.OpProto): a protobuf message typed OpProto
Returns:
str: the document string
"""
......@@ -148,13 +143,10 @@ def _generate_doc_string_(
def generate_layer_fn(op_type):
"""Register the Python layer for an Operator.
Args:
op_type: The name of the operator to be created.
This function takes in the operator type (sigmoid, mean , average etc) and
creates the operator functionality.
"""
op_proto = OpProtoHolder.instance().get_op_proto(op_type)
not_intermediate_outputs = [
......@@ -271,13 +263,10 @@ def generate_layer_fn(op_type):
def generate_activation_fn(op_type):
"""Register the Python layer for an Operator without Attribute.
Args:
op_type: The name of the operator to be created.
This function takes in the operator type (sigmoid, exp , tanh etc) and
creates the operator functionality.
"""
op_proto = OpProtoHolder.instance().get_op_proto(op_type)
......@@ -330,10 +319,8 @@ def generate_activation_fn(op_type):
def generate_inplace_fn(inplace_op_type):
"""Register the Python layer for an Inplace Operator without Attribute.
Args:
inplace_op_type: The name of the inplace operator to be created.
This function takes in the inplace operator type (exp_ , ceil_ etc) and
creates the operator functionality.
"""
......@@ -378,12 +365,10 @@ def templatedoc(op_type=None):
"""
Decorator of layer function. It will use the docstring from the layer
function as the template. The template arguments are:
* ${comment}: The operator comment written in CPP.
* ${{name}_comment}: The comment of ${name} written with AddAttr, AddOutput,
and AddInput. The ${name} is Python snake style. i.e., xxx_xxx.
* ${{name}_type}: The type of ${name}.
Returns:
Decorated function.
"""
......@@ -438,7 +423,6 @@ def templatedoc(op_type=None):
def add_sample_code(func, sample_code):
"""
Append sample code for dynamically generated functions.
Args:
func: The function of the function to be append sample code to.
sample_code: sample code session in rst format.
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ 208f625b
......@@ -26,7 +26,6 @@ import numbers
import paddle
from . import control_flow
from . import nn
from . import ops
from . import tensor
from ..framework import default_main_program, Parameter, unique_name, name_scope
from ..framework import Variable
......@@ -171,7 +170,7 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
div_res = paddle.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
return decayed_lr
......@@ -233,8 +232,8 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
div_res = paddle.floor(div_res)
decayed_lr = learning_rate * paddle.exp(-1 * decay_rate * div_res)
return decayed_lr
......@@ -293,7 +292,7 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
div_res = global_step / decay_steps
if staircase:
div_res = ops.floor(div_res)
div_res = paddle.floor(div_res)
decayed_lr = learning_rate / (1 + decay_rate * div_res)
......@@ -347,7 +346,7 @@ def polynomial_decay(
global_step = _decay_step_counter()
if cycle:
div_res = ops.ceil(global_step / decay_steps)
div_res = paddle.ceil(global_step / decay_steps)
zero_var = tensor.fill_constant(
shape=[1], dtype='float32', value=0.0
)
......@@ -497,11 +496,11 @@ def cosine_decay(learning_rate, step_each_epoch, epochs):
else:
global_step = _decay_step_counter()
cur_epoch = ops.floor(global_step / step_each_epoch)
cur_epoch = paddle.floor(global_step / step_each_epoch)
decayed_lr = (
learning_rate
* 0.5
* (ops.cos(cur_epoch * math.pi / epochs) + 1)
* (paddle.cos(cur_epoch * math.pi / epochs) + 1)
)
return decayed_lr
......
python/paddle/fluid/layers/loss.py
浏览文件 @ 208f625b
......@@ -1737,7 +1737,6 @@ def kldiv_loss(x, target, reduction='mean', name=None):
return loss
from .ops import square
from .control_flow import equal
......
python/paddle/fluid/layers/ops.py 已删除 100755 → 0
浏览文件 @ 70589379
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from .layer_function_generator import (
generate_layer_fn,
generate_activation_fn,
generate_inplace_fn,
add_sample_code,
)
from .. import core
from ..framework import convert_np_dtype_to_dtype_, Variable, in_dygraph_mode
from ..data_feeder import (
convert_dtype,
check_variable_and_dtype,
check_type,
check_dtype,
)
from paddle.utils import deprecated
from paddle import _C_ops, _legacy_C_ops
import paddle
__deprecated_func_name__ = {
'tanh_shrink': 'tanhshrink',
'logsigmoid': 'log_sigmoid',
}
__activations_noattr__ = [
'sigmoid',
'silu',
'logsigmoid',
'tanh_shrink',
'softsign',
'tanh',
]
__unary_func__ = [
'exp',
'expm1',
'atan',
'sqrt',
'rsqrt',
'abs',
'ceil',
'floor',
'cos',
'tan',
'acos',
'sin',
'sinh',
'asin',
'cosh',
'round',
'reciprocal',
'square',
'acosh',
'asinh',
'atanh',
'lgamma',
]
__inplace_unary_func__ = [
'exp_',
'sqrt_',
'rsqrt_',
'ceil_',
'floor_',
'round_',
'reciprocal_',
]
__all__ = [
'softplus',
'softshrink',
'hard_shrink',
'cumsum',
'thresholded_relu',
'gelu',
'erf',
]
for _OP in set(__all__):
globals()[_OP] = generate_layer_fn(_OP)
# It is a hot fix in some unittest using:
# fluid.layers.scale(x=x, scale=10.0, out=out_var)
# e.g.: test_program_code.py, test_dist_train.py
globals()['_scale'] = generate_layer_fn('scale')
globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
__all__ += __activations_noattr__
__all__ += __unary_func__
__all__ += __inplace_unary_func__
for _OP in set(__activations_noattr__):
_new_OP = _OP
if _OP in __deprecated_func_name__:
_new_OP = __deprecated_func_name__[_OP]
_func = generate_activation_fn(_OP)
_func = deprecated(
since="2.0.0", update_to="paddle.nn.functional.%s" % (_new_OP)
)(_func)
globals()[_OP] = _func
for _OP in set(__unary_func__):
_new_OP = _OP
if _OP in __deprecated_func_name__:
_new_OP = __deprecated_func_name__[_OP]
_func = generate_activation_fn(_OP)
_func = deprecated(since="2.0.0", update_to="paddle.%s" % (_new_OP))(_func)
globals()[_OP] = _func
for _OP in set(__inplace_unary_func__):
_new_OP = _OP
if _OP in __deprecated_func_name__:
_new_OP = __deprecated_func_name__[_OP]
_func = generate_inplace_fn(_OP)
_func = deprecated(since="2.0.0", update_to="paddle.%s" % (_new_OP))(_func)
globals()[_OP] = _func
add_sample_code(
globals()["sigmoid"],
r"""
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.sigmoid(x)
print(out)
# [0.40131234 0.450166 0.52497919 0.57444252]
""",
)
add_sample_code(
globals()["silu"],
r"""
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
out = F.silu(x)
print(out)
# [ 0.7310586 1.7615942 2.8577224, 3.9280552 ]
""",
)
add_sample_code(
globals()["logsigmoid"],
r"""
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.log_sigmoid(x)
print(out)
# [-0.91301525 -0.79813887 -0.64439666 -0.55435524]
""",
)
add_sample_code(
globals()["exp"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.exp(x)
print(out)
# [0.67032005 0.81873075 1.10517092 1.34985881]
""",
)
add_sample_code(
globals()["expm1"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.expm1(x)
print(out)
# [-0.32967997, -0.18126924, 0.10517092, 0.34985882]
""",
)
add_sample_code(
globals()["tanh"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.tanh(x)
print(out)
# [-0.37994896 -0.19737532 0.09966799 0.29131261]
""",
)
add_sample_code(
globals()["atan"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.atan(x)
print(out)
# [-0.38050638 -0.19739556 0.09966865 0.29145679]
""",
)
add_sample_code(
globals()["tanh_shrink"],
r"""
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.tanhshrink(x)
print(out)
# [-0.020051, -0.00262468, 0.000332005, 0.00868739]
""",
)
add_sample_code(
globals()["sqrt"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4])
out = paddle.sqrt(x)
print(out)
# [0.31622777 0.4472136 0.54772256 0.63245553]
""",
)
add_sample_code(
globals()["rsqrt"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4])
out = paddle.rsqrt(x)
print(out)
# [3.16227766 2.23606798 1.82574186 1.58113883]
""",
)
add_sample_code(
globals()["abs"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.abs(x)
print(out)
# [0.4 0.2 0.1 0.3]
""",
)
add_sample_code(
globals()["ceil"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.ceil(x)
print(out)
# [-0. -0. 1. 1.]
""",
)
add_sample_code(
globals()["floor"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.floor(x)
print(out)
# [-1. -1. 0. 0.]
""",
)
add_sample_code(
globals()["cos"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.cos(x)
print(out)
# [0.92106099 0.98006658 0.99500417 0.95533649]
""",
)
add_sample_code(
globals()["tan"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.tan(x)
print(out)
# [-0.42279324, -0.20271005, 0.10033467, 0.30933627]
""",
)
add_sample_code(
globals()["acos"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.acos(x)
print(out)
# [1.98231317 1.77215425 1.47062891 1.26610367]
""",
)
add_sample_code(
globals()["sin"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.sin(x)
print(out)
# [-0.38941834 -0.19866933 0.09983342 0.29552021]
""",
)
add_sample_code(
globals()["asin"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.asin(x)
print(out)
# [-0.41151685 -0.20135792 0.10016742 0.30469265]
""",
)
add_sample_code(
globals()["cosh"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.cosh(x)
print(out)
# [1.08107237 1.02006676 1.00500417 1.04533851]
""",
)
add_sample_code(
globals()["sinh"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.sinh(x)
print(out)
# [-0.41075233 -0.201336 0.10016675 0.30452029]
""",
)
add_sample_code(
globals()["asinh"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.asinh(x)
print(out)
# [-0.39003533, -0.19869010, 0.09983408, 0.29567307]
""",
)
add_sample_code(
globals()["acosh"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([1., 3., 4., 5.])
out = paddle.acosh(x)
print(out)
# [0. , 1.76274729, 2.06343699, 2.29243159]
""",
)
add_sample_code(
globals()["atanh"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.atanh(x)
print(out)
# [-0.42364895, -0.20273256, 0.10033535, 0.30951962]
""",
)
add_sample_code(
globals()["round"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.5, -0.2, 0.6, 1.5])
out = paddle.round(x)
print(out)
# [-1. -0. 1. 2.]
""",
)
add_sample_code(
globals()["reciprocal"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.reciprocal(x)
print(out)
# [-2.5 -5. 10. 3.33333333]
""",
)
add_sample_code(
globals()["square"],
r"""
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.square(x)
print(out)
# [0.16 0.04 0.01 0.09]
""",
)
_softplus_ = generate_layer_fn('softplus')
def softplus(x, beta: float = 1.0, threshold: float = 20.0, name=None):
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'softplus')
locals_val = locals().copy()
kwargs = dict()
for name, val in locals_val.items():
if val is not None:
kwargs[name] = val
return _softplus_(**kwargs)
softplus.__doc__ = r"""
:alias_main: paddle.nn.functional.softplus
:alias: paddle.nn.functional.softplus, paddle.nn.functional.activation.softplus
:old_api: paddle.fluid.layers.softplus
:strong:`Softplus Activation Operator`
Equation:
.. math::
out = \\frac{1}{beta} * log(1 + e^{beta * x})
For numerical stability, the implementation reverts to the linear function when: beta * x > threshold.
Args:
x(Tensor): Input of Softplus op, Tensor, dtype: float32 or float64
beta(float, optional): The value of beta for softplus. Default is 1
threshold (float, optional): The value of threshold for softplus. Default is 20
name(str, optional): Name for the operation (optional, default is None)
Returns:
Variable: The output of Softplus op, Tensor, dtype: float32 or float64
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.softplus(x)
print(out)
# [0.513015, 0.598139, 0.744397, 0.854355]
"""
add_sample_code(
globals()["softsign"],
r"""
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.softsign(x)
print(out)
# [-0.285714, -0.166667, 0.0909091, 0.230769]
""",
)
_softshrink_ = generate_layer_fn('softshrink')
def softshrink(x, alpha=None):
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'softshrink'
)
locals_var = locals().copy()
kwargs = dict()
for name, val in locals_var.items():
if val is not None:
if name == 'alpha':
kwargs['lambda'] = val
else:
kwargs[name] = val
return _softshrink_(**kwargs)
softshrink.__doc__ = r"""
:alias_main: paddle.nn.functional.softshrink
:alias: paddle.nn.functional.softshrink,paddle.nn.functional.activation.softshrink
:old_api: paddle.fluid.layers.softshrink
:strong:`Softshrink Activation Operator`
.. math::
out = \\begin{cases}
x - \\alpha, \\text{if } x > \\alpha \\\\
x + \\alpha, \\text{if } x < -\\alpha \\\\
0, \\text{otherwise}
\\end{cases}
Args:
x: Input of Softshrink operator, an N-D Tensor, with data type float32, float64 or float16.
alpha (float): non-negative offset
Returns:
Output of Softshrink operator with the same type of input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.data(name="input", shape=[None, 784])
result = fluid.layers.softshrink(x=data, alpha=0.3)
"""
_hard_shrink_ = generate_layer_fn('hard_shrink')
@deprecated(since="2.0.0", update_to="paddle.nn.functional.hardshrink")
def hard_shrink(x, threshold=None):
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'hard_shrink'
)
locals_var = locals().copy()
kwargs = dict()
for name, val in locals_var.items():
if val is not None:
kwargs[name] = val
return _hard_shrink_(**kwargs)
hard_shrink.__doc__ = (
_hard_shrink_.__doc__
+ """
Examples:
>>> import paddle.fluid as fluid
>>> data = fluid.layers.data(name="input", shape=[784])
>>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""
)
_cum_sum_ = generate_layer_fn('cumsum')
@deprecated(
since="2.0.0",
update_to="paddle.cumsum",
reason="New APIs for Paddle 2.0 are coming.",
)
def cumsum(x, axis=None, exclusive=None, reverse=None):
check_type(x, 'x', (Variable), 'cumsum')
locals_var = locals().copy()
kwargs = dict()
for name, val in locals_var.items():
if val is not None:
kwargs[name] = val
return _cum_sum_(**kwargs)
cumsum.__doc__ = """
:alias_main: paddle.cumsum
:alias: paddle.cumsum,paddle.tensor.cumsum,paddle.tensor.math.cumsum
:old_api: paddle.fluid.layers.cumsum
The cumulative sum of the elements along a given axis. By default, the first element of the result is the same of the first element of the input. If exlusive is true, the first element of the result is 0.
Args:
x (Variable): Input of cumsum operator, the Tensor/LoDTensor needed to be cumsumed.
axis (int, optional): The dimension to accumulate along. -1 means the last dimension. Default is -1.
exclusive (bool, optional): Whether to perform exclusive cumsum. Default is False.
reverse (bool, optional): If true, the cumsum is performed in the reversed direction. Default is False.
Returns:
Variable(Tensor/LoDTensor): The result of cumsum operator, output of cumsum operator.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="input", shape=[32, 784])
result = fluid.layers.cumsum(data, axis=0)
"""
_thresholded_relu_ = generate_layer_fn('thresholded_relu')
def thresholded_relu(x, threshold=None):
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'thresholded_relu'
)
locals_var = locals().copy()
kwargs = dict()
for name, val in locals_var.items():
if val is not None:
kwargs[name] = val
return _thresholded_relu_(**kwargs)
thresholded_relu.__doc__ = r"""
:alias_main: paddle.nn.functional.thresholded_relu
:alias: paddle.nn.functional.thresholded_relu,paddle.nn.functional.activation.thresholded_relu
:old_api: paddle.fluid.layers.thresholded_relu
:strong:`Thresholded ReLU Activation Operator`
Equation:
.. math::
out = \\begin{cases}
x, &if x > threshold \\\\
0, &otherwise
\\end{cases}
Args:
x(Variable): The input of Thresholded ReLU op, Tensor or LoDTensor, dtype: float32 or float64.
threshold(float, optional): The threshold value. Note that if the arg `threshold` is not set, the threshold in the equation is 1.0.
Returns:
Variable: The output of Thresholded ReLU op, Tensor or LoDTensor, dtype: float32 or float64, the same as the input, shape: the same as the input.
Examples:
.. code-block:: python
# declarative mode
import numpy as np
from paddle import fluid
x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
y = fluid.layers.thresholded_relu(x, threshold=0.1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
start = fluid.default_startup_program()
main = fluid.default_main_program()
data = np.random.randn(2, 3).astype("float32")
exe.run(start)
y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
data
# array([[ 0.21134382, -1.1805999 , 0.32876605],
# [-1.2210793 , -0.7365624 , 1.0013918 ]], dtype=float32)
y_np
# array([[ 0.21134382, -0. , 0.32876605],
# [-0. , -0. , 1.0013918 ]], dtype=float32)
.. code-block:: python
# imperative mode
import numpy as np
from paddle import fluid
import paddle.fluid.dygraph as dg
data = np.random.randn(2, 3).astype("float32")
place = fluid.CPUPlace()
with dg.guard(place) as g:
x = dg.to_variable(data)
y = fluid.layers.thresholded_relu(x, threshold=0.1)
y_np = y.numpy()
data
# array([[ 0.21134382, -1.1805999 , 0.32876605],
# [-1.2210793 , -0.7365624 , 1.0013918 ]], dtype=float32)
y_np
# array([[ 0.21134382, -0. , 0.32876605],
# [-0. , -0. , 1.0013918 ]], dtype=float32)
"""
_gelu_ = generate_layer_fn('gelu')
@deprecated(since="2.0.0", update_to="paddle.nn.functional.gelu")
def gelu(x, approximate=False):
locals_var = locals().copy()
kwargs = dict()
for name, val in locals_var.items():
if val is not None:
kwargs[name] = val
return _gelu_(**kwargs)
gelu.__doc__ = r"""
:strong:`GeLU Activation Operator`
For more details, see [Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415).
Equation:
if approximate is True
.. math::
out = 0.5 * x * (1 + tanh(\\sqrt{\\frac{2}{\\pi}} * (x + 0.044715x^{3})))
else
.. math::
out = 0.5 * x * (1 + erf(\\frac{x}{\\sqrt{2}}))
Args:
x(Variable): The input of GeLU op, Tensor or LoDTensor, dtype: float32 or float64.
Returns:
Variable: The output of GeLU op, Tensor or LoDTensor, dtype: float32 or float64, the same as the input, shape: the same as the input.
Examples:
.. code-block:: python
# declarative mode
import numpy as np
from paddle import fluid
x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
y = fluid.layers.gelu(x)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
start = fluid.default_startup_program()
main = fluid.default_main_program()
data = np.random.randn(2, 3).astype("float32")
exe.run(start)
y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
data
# array([[ 0.87165993, -1.0541513 , -0.37214822],
# [ 0.15647964, 0.32496083, 0.33045998]], dtype=float32)
y_np
# array([[ 0.70456535, -0.15380788, -0.13207214],
# [ 0.08796856, 0.20387867, 0.2080159 ]], dtype=float32)
.. code-block:: python
# imperative mode
import numpy as np
from paddle import fluid
import paddle.fluid.dygraph as dg
data = np.random.randn(2, 3).astype("float32")
place = fluid.CPUPlace()
with dg.guard(place) as g:
x = dg.to_variable(data)
y = fluid.layers.gelu(x)
y_np = y.numpy()
data
# array([[ 0.87165993, -1.0541513 , -0.37214822],
# [ 0.15647964, 0.32496083, 0.33045998]], dtype=float32)
y_np
# array([[ 0.70456535, -0.15380788, -0.13207214],
# [ 0.08796856, 0.20387867, 0.2080159 ]], dtype=float32)
"""
_erf_ = generate_layer_fn('erf')
def erf(x, name=None):
if in_dygraph_mode():
return _C_ops.erf(x)
locals_var = locals().copy()
kwargs = dict()
for name, val in locals_var.items():
if val is not None:
kwargs[name] = val
return _erf_(**kwargs)
erf.__doc__ = r"""
:strong:`Erf Operator`
For more details, see [Error function](https://en.wikipedia.org/wiki/Error_function).
Equation:
.. math::
out = \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x}e^{- \\eta^{2}}d\\eta
Args:
x (Tensor): The input tensor, it's data type should be float32, float64.
Returns:
Tensor: The output of Erf op, dtype: float32 or float64, the same as the input, shape: the same as the input.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.erf(x)
print(out)
# [-0.42839236 -0.22270259 0.11246292 0.32862676]
"""
def lgamma(x, name=None):
r"""
Calculates the lgamma of the given input tensor, element-wise.
This operator performs elementwise lgamma for input $X$.
:math:`out = log\Gamma(x)`
Args:
x (Tensor): Input Tensor. Must be one of the following types: float32, float64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, the lgamma of the input Tensor, the shape and data type is the same with input.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = paddle.lgamma(x)
print(out)
# [1.31452441, 1.76149750, 2.25271273, 1.09579802]
"""
return paddle.Tensor.lgamma(x)
python/paddle/fluid/nets.py
浏览文件 @ 208f625b
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from . import layers
from .data_feeder import check_variable_and_dtype, convert_dtype
from ..utils import deprecated
......@@ -387,7 +388,7 @@ def glu(input, dim=-1):
input, 'input', ['float16', 'float32', 'float64'], "glu"
)
a, b = layers.split(input, num_or_sections=2, dim=dim)
act_b = layers.sigmoid(x=b)
act_b = paddle.nn.functional.sigmoid(x=b)
out = layers.elementwise_mul(x=a, y=act_b)
return out
......
python/paddle/fluid/optimizer.py
浏览文件 @ 208f625b
......@@ -48,7 +48,6 @@ from .clip import (
from .framework import program_guard
from .initializer import Constant
from .layer_helper import LayerHelper
from .layers import ops
from .dygraph import base as imperative_base
from .dygraph import no_grad
from .dygraph.learning_rate_scheduler import (
......@@ -4457,7 +4456,7 @@ class ModelAverage(Optimizer):
sum = layers.cast(
x=sum, dtype='float32' if self._dtype is None else self._dtype
)
ops._elementwise_div(x=sum, y=tmp, out=param)
paddle.assign(paddle.divide(sum, tmp), output=param)
def _add_average_restore_op(self, block, param_grad):
param = block._clone_variable(param_grad[0])
......
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @ 208f625b
......@@ -70,21 +70,21 @@ def dyn_rnn_lstm(
gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
return gate0 + gate1
forget_gate = fluid.layers.sigmoid(
forget_gate = paddle.nn.functional.sigmoid(
x=gate_common(word, prev_hidden, lstm_size)
)
input_gate = fluid.layers.sigmoid(
input_gate = paddle.nn.functional.sigmoid(
x=gate_common(word, prev_hidden, lstm_size)
)
output_gate = fluid.layers.sigmoid(
output_gate = paddle.nn.functional.sigmoid(
x=gate_common(word, prev_hidden, lstm_size)
)
cell_gate = fluid.layers.sigmoid(
cell_gate = paddle.nn.functional.sigmoid(
x=gate_common(word, prev_hidden, lstm_size)
)
cell = forget_gate * prev_cell + input_gate * cell_gate
hidden = output_gate * fluid.layers.tanh(x=cell)
hidden = output_gate * paddle.tanh(x=cell)
rnn.update_memory(prev_cell, cell)
rnn.update_memory(prev_hidden, hidden)
rnn.output(hidden)
......
python/paddle/fluid/tests/book/test_rnn_encoder_decoder.py
浏览文件 @ 208f625b
......@@ -70,10 +70,10 @@ def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
def linear(inputs):
return fluid.layers.fc(input=inputs, size=size, bias_attr=True)
forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t]))
forget_gate = paddle.nn.functional.sigmoid(x=linear([hidden_t_prev, x_t]))
input_gate = paddle.nn.functional.sigmoid(x=linear([hidden_t_prev, x_t]))
output_gate = paddle.nn.functional.sigmoid(x=linear([hidden_t_prev, x_t]))
cell_tilde = paddle.tanh(x=linear([hidden_t_prev, x_t]))
cell_t = fluid.layers.sums(
input=[
......@@ -83,7 +83,7 @@ def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
)
hidden_t = fluid.layers.elementwise_mul(
x=output_gate, y=fluid.layers.tanh(x=cell_t)
x=output_gate, y=paddle.tanh(x=cell_t)
)
return hidden_t, cell_t
......
python/paddle/fluid/tests/test_if_else_op.py
浏览文件 @ 208f625b
......@@ -175,12 +175,12 @@ class TestIfElse(unittest.TestCase):
ie = layers.IfElse(ifcond)
with ie.true_block():
true_target = ie.input(src)
true_target = fluid.layers.exp(true_target)
true_target = paddle.exp(true_target)
ie.output(true_target)
with ie.false_block():
false_target = ie.input(src)
false_target = fluid.layers.tanh(false_target)
false_target = paddle.tanh(false_target)
ie.output(false_target)
if_out = ie()
out = layers.reduce_sum(if_out[0])
......@@ -244,7 +244,7 @@ class TestIfElseError(unittest.TestCase):
ie = layers.IfElse(ifcond)
with ie.true_block():
true_target = ie.input(src)
true_target = fluid.layers.exp(true_target)
true_target = paddle.exp(true_target)
ie.output([])
......
python/paddle/fluid/tests/unittests/dist_fleet_simnet_bow.py
浏览文件 @ 208f625b
......@@ -130,7 +130,7 @@ def train_network(
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -157,7 +157,7 @@ def train_network(
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -181,7 +181,7 @@ def train_network(
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/dygraph_to_static/ifelse_simple_func.py
浏览文件 @ 208f625b
......@@ -61,7 +61,7 @@ def dyfunc_with_if_else2(x, col=100):
y = fluid.layers.relu(x)
else:
x_pow = fluid.layers.pow(x, 2)
y = fluid.layers.tanh(x_pow)
y = paddle.tanh(x_pow)
return y
......@@ -161,7 +161,7 @@ def nested_if_else(x_v):
tmp = y * w
y = fluid.layers.relu(tmp)
if paddle.mean(y).numpy()[0] < batch_size:
y = fluid.layers.abs(y)
y = paddle.abs(y)
else:
tmp = fluid.layers.fill_constant(
y.shape, dtype='float32', value=-1
......@@ -276,7 +276,7 @@ class NetWithControlFlowIf(fluid.dygraph.Layer):
self.constant_vars['w'] = fluid.layers.fill_constant(
[hidden_dim], dtype='float32', value=9
)
y = fluid.layers.abs(y)
y = paddle.abs(y)
else:
tmp = fluid.layers.fill_constant(
y.shape, dtype='float32', value=-1
......
python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py
浏览文件 @ 208f625b
......@@ -49,8 +49,8 @@ class BasicLSTMUnit(Layer):
self._hiden_size = hidden_size
self._param_attr = param_attr
self._bias_attr = bias_attr
self._gate_activation = gate_activation or layers.sigmoid
self._activation = activation or layers.tanh
self._gate_activation = gate_activation or paddle.nn.functional.sigmoid
self._activation = activation or paddle.tanh
self._forget_bias = forget_bias
self._dtype = dtype
self._input_size = input_size
......@@ -76,12 +76,14 @@ class BasicLSTMUnit(Layer):
i, j, f, o = layers.split(gate_input, num_or_sections=4, dim=-1)
new_cell = layers.elementwise_add(
layers.elementwise_mul(
pre_cell, layers.sigmoid(f + self._forget_bias)
pre_cell, paddle.nn.functional.sigmoid(f + self._forget_bias)
),
layers.elementwise_mul(
paddle.nn.functional.sigmoid(i), paddle.tanh(j)
),
layers.elementwise_mul(layers.sigmoid(i), layers.tanh(j)),
)
new_hidden = layers.tanh(new_cell) * layers.sigmoid(o)
new_hidden = paddle.tanh(new_cell) * paddle.nn.functional.sigmoid(o)
return new_hidden, new_cell
......
python/paddle/fluid/tests/unittests/dygraph_to_static/simnet_dygraph_model.py
浏览文件 @ 208f625b
......@@ -13,7 +13,6 @@
# limitations under the License.
import paddle
import paddle.fluid as fluid
import paddle.fluid.param_attr as attr
......@@ -232,7 +231,7 @@ class SoftsignLayer:
"""
operation
"""
softsign = fluid.layers.softsign(input)
softsign = paddle.nn.functional.softsign(input)
return softsign
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_cycle_gan.py
浏览文件 @ 208f625b
......@@ -89,28 +89,22 @@ class Cycle_Gan(fluid.dygraph.Layer):
cyc_A = self.build_generator_resnet_9blocks_b(fake_B)
cyc_B = self.build_generator_resnet_9blocks_a(fake_A)
diff_A = fluid.layers.abs(
fluid.layers.elementwise_sub(x=input_A, y=cyc_A)
)
diff_B = fluid.layers.abs(
fluid.layers.elementwise_sub(x=input_B, y=cyc_B)
)
diff_A = paddle.abs(fluid.layers.elementwise_sub(x=input_A, y=cyc_A))
diff_B = paddle.abs(fluid.layers.elementwise_sub(x=input_B, y=cyc_B))
cyc_A_loss = fluid.layers.reduce_mean(diff_A) * lambda_A
cyc_B_loss = fluid.layers.reduce_mean(diff_B) * lambda_B
cyc_loss = cyc_A_loss + cyc_B_loss
fake_rec_A = self.build_gen_discriminator_a(fake_B)
g_A_loss = fluid.layers.reduce_mean(fluid.layers.square(fake_rec_A - 1))
g_A_loss = paddle.mean(paddle.square(fake_rec_A - 1))
fake_rec_B = self.build_gen_discriminator_b(fake_A)
g_B_loss = fluid.layers.reduce_mean(fluid.layers.square(fake_rec_B - 1))
g_B_loss = paddle.mean(paddle.square(fake_rec_B - 1))
G = g_A_loss + g_B_loss
idt_A = self.build_generator_resnet_9blocks_a(input_B)
idt_loss_A = (
fluid.layers.reduce_mean(
fluid.layers.abs(
fluid.layers.elementwise_sub(x=input_B, y=idt_A)
)
paddle.abs(fluid.layers.elementwise_sub(x=input_B, y=idt_A))
)
* lambda_B
* lambda_identity
......@@ -119,9 +113,7 @@ class Cycle_Gan(fluid.dygraph.Layer):
idt_B = self.build_generator_resnet_9blocks_b(input_A)
idt_loss_B = (
fluid.layers.reduce_mean(
fluid.layers.abs(
fluid.layers.elementwise_sub(x=input_A, y=idt_B)
)
paddle.abs(fluid.layers.elementwise_sub(x=input_A, y=idt_B))
)
* lambda_A
* lambda_identity
......@@ -271,7 +263,7 @@ class build_generator_resnet_9blocks(fluid.dygraph.Layer):
y = self.deconv1(y)
y = fluid.layers.pad2d(y, [3, 3, 3, 3], mode="reflect")
y = self.conv3(y)
y = fluid.layers.tanh(y)
y = paddle.tanh(y)
return y
......@@ -647,8 +639,7 @@ def train(args, to_static):
data_B, fake_pool_B
)
d_loss_A = (
fluid.layers.square(fake_pool_rec_B)
+ fluid.layers.square(rec_B - 1)
paddle.square(fake_pool_rec_B) + paddle.square(rec_B - 1)
) / 2.0
d_loss_A = fluid.layers.reduce_mean(d_loss_A)
......@@ -661,8 +652,7 @@ def train(args, to_static):
data_A, fake_pool_A
)
d_loss_B = (
fluid.layers.square(fake_pool_rec_A)
+ fluid.layers.square(rec_A - 1)
paddle.square(fake_pool_rec_A) + paddle.square(rec_A - 1)
) / 2.0
d_loss_B = fluid.layers.reduce_mean(d_loss_B)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py
浏览文件 @ 208f625b
......@@ -99,10 +99,10 @@ class SimpleLSTMRNN(fluid.Layer):
i, j, f, o = fluid.layers.split(
gate_input, num_or_sections=4, dim=-1
)
c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
i
) * fluid.layers.tanh(j)
m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
hidden_array[k] = m
cell_array[k] = c
step_input = m
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py
浏览文件 @ 208f625b
......@@ -145,7 +145,7 @@ class BOW(fluid.dygraph.Layer):
emb = emb * mask_emb
emb = fluid.layers.reshape(emb, shape=[-1, self.seq_len, self.hid_dim])
bow_1 = fluid.layers.reduce_sum(emb, dim=1)
bow_1 = fluid.layers.tanh(bow_1)
bow_1 = paddle.tanh(bow_1)
fc_1 = self._fc1(bow_1)
fc_2 = self._fc2(fc_1)
prediction = self._fc_prediction(fc_2)
......@@ -197,7 +197,7 @@ class GRU(fluid.dygraph.Layer):
fc_1 = self._fc1(emb)
gru_hidden = self._gru(fc_1)
gru_hidden = fluid.layers.reduce_max(gru_hidden, dim=1)
tanh_1 = fluid.layers.tanh(gru_hidden)
tanh_1 = paddle.tanh(gru_hidden)
fc_2 = self._fc2(tanh_1)
prediction = self._fc_prediction(fc_2)
......@@ -253,8 +253,8 @@ class BiGRU(fluid.dygraph.Layer):
fc_1 = self._fc1(emb)
gru_forward = self._gru_forward(fc_1)
gru_backward = self._gru_backward(fc_1)
gru_forward_tanh = fluid.layers.tanh(gru_forward)
gru_backward_tanh = fluid.layers.tanh(gru_backward)
gru_forward_tanh = paddle.tanh(gru_forward)
gru_backward_tanh = paddle.tanh(gru_backward)
encoded_vector = fluid.layers.concat(
input=[gru_forward_tanh, gru_backward_tanh], axis=2
)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_word2vec.py
浏览文件 @ 208f625b
......@@ -18,6 +18,7 @@ import numpy as np
import paddle.fluid as fluid
import unittest
import paddle
from paddle.fluid.dygraph.nn import Embedding
from paddle.fluid.dygraph import ProgramTranslator
from paddle.fluid.dygraph import declarative
......@@ -260,7 +261,7 @@ class SkipGram(fluid.dygraph.Layer):
)
word_sim = fluid.layers.reduce_sum(word_sim, dim=-1)
pred = fluid.layers.sigmoid(word_sim)
pred = paddle.nn.functional.sigmoid(word_sim)
loss = fluid.layers.sigmoid_cross_entropy_with_logits(word_sim, label)
loss = fluid.layers.reduce_mean(loss)
......
python/paddle/fluid/tests/unittests/ipu/test_cumsum_op_ipu.py
浏览文件 @ 208f625b
......@@ -51,7 +51,7 @@ class TestBase(IPUOpTest):
x = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype="float32"
)
out = paddle.fluid.layers.cumsum(x, **self.attrs)
out = paddle.cumsum(x, **self.attrs)
self.fetch_list = [out.name]
def run_model(self, exec_mode):
......@@ -90,7 +90,7 @@ class TestCase4(TestBase):
x = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype="int32"
)
out = paddle.fluid.layers.cumsum(x, **self.attrs)
out = paddle.cumsum(x, **self.attrs)
self.fetch_list = [out.name]
......@@ -104,7 +104,7 @@ class TestCase5(TestBase):
x = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype="int64"
)
out = paddle.fluid.layers.cumsum(x, **self.attrs)
out = paddle.cumsum(x, **self.attrs)
self.fetch_list = [out.name]
......
python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py
浏览文件 @ 208f625b
......@@ -45,7 +45,7 @@ class TestBase(IPUOpTest):
x = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
)
out = paddle.fluid.layers.gelu(x, **self.attrs)
out = paddle.nn.functional.gelu(x, **self.attrs)
self.fetch_list = [out.name]
def run_model(self, exec_mode):
......
python/paddle/fluid/tests/unittests/ipu/test_unary_ops_ipu.py
浏览文件 @ 208f625b
......@@ -29,7 +29,7 @@ class TestBase(IPUOpTest):
self.set_feed_attr()
def set_test_op(self):
self.op = paddle.fluid.layers.abs
self.op = paddle.abs
self.op_attrs = {}
def set_data_feed(self):
......@@ -70,55 +70,55 @@ class TestAcos(TestBase):
self.atol = 1e-6
def set_test_op(self):
self.op = paddle.fluid.layers.acos
self.op = paddle.acos
self.op_attrs = {}
class TestAsin(TestAcos):
def set_test_op(self):
self.op = paddle.fluid.layers.asin
self.op = paddle.asin
self.op_attrs = {}
class TestSinh(TestAcos):
def set_test_op(self):
self.op = paddle.fluid.layers.sinh
self.op = paddle.sinh
self.op_attrs = {}
class TestAtan(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.atan
self.op = paddle.atan
self.op_attrs = {}
class TestCeil(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.ceil
self.op = paddle.ceil
self.op_attrs = {}
class TestCos(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.cos
self.op = paddle.cos
self.op_attrs = {}
class TestCosh(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.cosh
self.op = paddle.cosh
self.op_attrs = {}
class TestErf(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.erf
self.op = paddle.erf
self.op_attrs = {}
class TestExp(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.exp
self.op = paddle.exp
self.op_attrs = {}
......@@ -128,19 +128,19 @@ class TestFloor(TestBase):
return False
def set_test_op(self):
self.op = paddle.fluid.layers.floor
self.op = paddle.floor
self.op_attrs = {}
class TestLog(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.log
self.op = paddle.log
self.op_attrs = {}
class TestReciprocal(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.reciprocal
self.op = paddle.reciprocal
self.op_attrs = {}
......@@ -152,55 +152,55 @@ class TestRelu(TestBase):
class TestRound(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.round
self.op = paddle.round
self.op_attrs = {}
class TestSigmoid(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.sigmoid
self.op = paddle.nn.functional.sigmoid
self.op_attrs = {}
class TestSign(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.sign
self.op = paddle.sign
self.op_attrs = {}
class TestSin(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.sin
self.op = paddle.sin
self.op_attrs = {}
class TestSoftplus(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.softplus
self.op = paddle.nn.functional.softplus
self.op_attrs = {}
class TestSoftsign(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.softsign
self.op = paddle.nn.functional.softsign
self.op_attrs = {}
class TestSqrt(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.sqrt
self.op = paddle.sqrt
self.op_attrs = {}
class TestTan(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.tan
self.op = paddle.tan
self.op_attrs = {}
class TestTanh(TestBase):
def set_test_op(self):
self.op = paddle.fluid.layers.tanh
self.op = paddle.tanh
self.op_attrs = {}
......
python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_elt_act_fuse_pass.py
浏览文件 @ 208f625b
......@@ -75,7 +75,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_Tanh(
):
def set_params(self):
self.operand = fluid.layers.elementwise_add
self.act = fluid.layers.tanh
self.act = paddle.tanh
class ElementwiseActivationMkldnnFusePassTest_Add_LeakyRelu(
......@@ -108,7 +108,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_SQRT(
):
def set_params(self):
self.operand = fluid.layers.elementwise_add
self.act = fluid.layers.sqrt
self.act = paddle.sqrt
class ElementwiseActivationMkldnnFusePassTest_Add_ABS(
......@@ -116,7 +116,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_ABS(
):
def set_params(self):
self.operand = fluid.layers.elementwise_add
self.act = fluid.layers.abs
self.act = paddle.abs
class ElementwiseActivationMkldnnFusePassTest_Add_Clip(
......@@ -134,7 +134,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_Gelu(
):
def set_params(self):
self.operand = fluid.layers.elementwise_add
self.act = fluid.layers.gelu
self.act = paddle.nn.functional.gelu
class ElementwiseActivationMkldnnFusePassTest_Add_Gelu_Tanh(
......@@ -142,7 +142,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_Gelu_Tanh(
):
def set_params(self):
self.operand = fluid.layers.elementwise_add
self.act = fluid.layers.gelu
self.act = paddle.nn.functional.gelu
self.act_alpha = True
......@@ -159,7 +159,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_Sigmoid(
):
def set_params(self):
self.operand = fluid.layers.elementwise_add
self.act = fluid.layers.sigmoid
self.act = paddle.nn.functional.sigmoid
class ElementwiseActivationMkldnnFusePassTest_Sub_Relu(
......@@ -175,7 +175,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_Tanh(
):
def set_params(self):
self.operand = fluid.layers.elementwise_sub
self.act = fluid.layers.tanh
self.act = paddle.tanh
class ElementwiseActivationMkldnnFusePassTest_Sub_LeakyRelu(
......@@ -208,7 +208,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_ABS(
):
def set_params(self):
self.operand = fluid.layers.elementwise_sub
self.act = fluid.layers.abs
self.act = paddle.abs
class ElementwiseActivationMkldnnFusePassTest_Sub_Clip(
......@@ -226,7 +226,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_Gelu(
):
def set_params(self):
self.operand = fluid.layers.elementwise_sub
self.act = fluid.layers.gelu
self.act = paddle.nn.functional.gelu
class ElementwiseActivationMkldnnFusePassTest_Sub_Gelu_Tanh(
......@@ -234,7 +234,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_Gelu_Tanh(
):
def set_params(self):
self.operand = fluid.layers.elementwise_sub
self.act = fluid.layers.gelu
self.act = paddle.nn.functional.gelu
self.act_alpha = True
......@@ -251,7 +251,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_Sigmoid(
):
def set_params(self):
self.operand = fluid.layers.elementwise_sub
self.act = fluid.layers.sigmoid
self.act = paddle.nn.functional.sigmoid
class ElementwiseActivationMkldnnFusePassTest_Mul_Relu(
......@@ -267,7 +267,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_Tanh(
):
def set_params(self):
self.operand = fluid.layers.elementwise_mul
self.act = fluid.layers.tanh
self.act = paddle.tanh
class ElementwiseActivationMkldnnFusePassTest_Mul_LeakyRelu(
......@@ -300,7 +300,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_SQRT(
):
def set_params(self):
self.operand = fluid.layers.elementwise_mul
self.act = fluid.layers.sqrt
self.act = paddle.sqrt
class ElementwiseActivationMkldnnFusePassTest_Mul_ABS(
......@@ -308,7 +308,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_ABS(
):
def set_params(self):
self.operand = fluid.layers.elementwise_mul
self.act = fluid.layers.abs
self.act = paddle.abs
class ElementwiseActivationMkldnnFusePassTest_Mul_Clip(
......@@ -326,7 +326,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_Gelu(
):
def set_params(self):
self.operand = fluid.layers.elementwise_mul
self.act = fluid.layers.gelu
self.act = paddle.nn.functional.gelu
class ElementwiseActivationMkldnnFusePassTest_Mul_Gelu_Tanh(
......@@ -334,7 +334,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_Gelu_Tanh(
):
def set_params(self):
self.operand = fluid.layers.elementwise_mul
self.act = fluid.layers.gelu
self.act = paddle.nn.functional.gelu
self.act_alpha = True
......@@ -351,7 +351,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_Sigmoid(
):
def set_params(self):
self.operand = fluid.layers.elementwise_mul
self.act = fluid.layers.sigmoid
self.act = paddle.nn.functional.sigmoid
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py
浏览文件 @ 208f625b
......@@ -17,6 +17,7 @@ import shutil
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import PassVersionChecker
......@@ -81,7 +82,7 @@ class TensorRTSubgraphPassSoftMaxTest(TensorRTSubgraphPassActivationTest):
class TensorRTSubgraphPassSigmoidTest(TensorRTSubgraphPassActivationTest):
def append_act(self, x):
return fluid.layers.sigmoid(x)
return paddle.nn.functional.sigmoid(x)
class TensorRTSubgraphPassHardSwishTest(TensorRTSubgraphPassActivationTest):
......@@ -108,7 +109,7 @@ class TensorRTSubgraphPassClipTest(TensorRTSubgraphPassActivationTest):
class TensorRTSubgraphPassTanhTest(TensorRTSubgraphPassActivationTest):
def append_act(self, x):
return fluid.layers.tanh(x)
return paddle.tanh(x)
class TensorRTSubgraphPassSwishTest(TensorRTSubgraphPassActivationTest):
......@@ -303,7 +304,7 @@ class TensorRTSubgraphPassPreluFp16DynamicSerializeTest(
class TensorRTSubgraphPassGeluTest(TensorRTSubgraphPassActivationTest):
def append_act(self, x):
return fluid.layers.gelu(x)
return paddle.nn.functional.gelu(x)
class TensorRTSubgraphPassGeluDynamicTest(TensorRTSubgraphPassActivationTest):
......@@ -322,7 +323,7 @@ class TensorRTSubgraphPassGeluDynamicTest(TensorRTSubgraphPassActivationTest):
)
def append_act(self, x):
return fluid.layers.gelu(x)
return paddle.nn.functional.gelu(x)
class TensorRTSubgraphPassGeluFp16Test(TensorRTSubgraphPassActivationTest):
......@@ -333,7 +334,7 @@ class TensorRTSubgraphPassGeluFp16Test(TensorRTSubgraphPassActivationTest):
)
def append_act(self, x):
return fluid.layers.gelu(x)
return paddle.nn.functional.gelu(x)
class TensorRTSubgraphPassGeluFp16SerializeTest(
......@@ -346,7 +347,7 @@ class TensorRTSubgraphPassGeluFp16SerializeTest(
)
def append_act(self, x):
return fluid.layers.gelu(x)
return paddle.nn.functional.gelu(x)
class TensorRTSubgraphPassGeluFp16DynamicTest(
......@@ -367,7 +368,7 @@ class TensorRTSubgraphPassGeluFp16DynamicTest(
)
def append_act(self, x):
return fluid.layers.gelu(x)
return paddle.nn.functional.gelu(x)
class TensorRTSubgraphPassGeluFp16DynamicSerializeTest(
......@@ -388,7 +389,7 @@ class TensorRTSubgraphPassGeluFp16DynamicSerializeTest(
)
def append_act(self, x):
return fluid.layers.gelu(x)
return paddle.nn.functional.gelu(x)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py
浏览文件 @ 208f625b
......@@ -15,6 +15,7 @@
import unittest
import numpy as np
import paddle
from pass_test import PassTest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
......@@ -85,10 +86,14 @@ class FusionGroupPassComplicatedTest(FusionGroupPassTest):
one = layers.fill_constant(shape=[1], dtype=dtype, value=1.0)
tmp_0 = one * self.feed_vars[0]
# subgraph with 9 op nodes
tmp_1 = tmp_0 * layers.sigmoid(self.feed_vars[1]) + layers.sigmoid(
self.feed_vars[2]
) * layers.tanh(self.feed_vars[3])
tmp_2 = layers.tanh(tmp_1) + layers.sigmoid(self.feed_vars[4])
tmp_1 = tmp_0 * paddle.nn.functional.sigmoid(
self.feed_vars[1]
) + paddle.nn.functional.sigmoid(self.feed_vars[2]) * paddle.tanh(
self.feed_vars[3]
)
tmp_2 = paddle.tanh(tmp_1) + paddle.nn.functional.sigmoid(
self.feed_vars[4]
)
self.append_gradients(tmp_2)
......@@ -162,10 +167,10 @@ class FusionGroupPassSumTest(FusionGroupPassTest):
tmp_0 = layers.sum(
[self.feed_vars[0], self.feed_vars[1], self.feed_vars[2]]
)
tmp_1 = layers.sqrt(tmp_0)
tmp_1 = paddle.sqrt(tmp_0)
tmp_2 = layers.mul(tmp_0, self.feed_vars[3])
# subgraph with 2 op nodes
tmp_3 = layers.square(layers.sum([tmp_1, tmp_2]))
tmp_3 = paddle.square(layers.sum([tmp_1, tmp_2]))
self.append_gradients(tmp_3)
......
python/paddle/fluid/tests/unittests/mlu/sync_batch_norm_op_mlu.py
浏览文件 @ 208f625b
......@@ -97,7 +97,7 @@ class TestSyncBatchNormOpTraining(TestSyncBatchNormRunnerBase):
)
if self.bn_dtype == np.float16:
bn = fluid.layers.cast(bn, 'float32')
sigmoid = fluid.layers.sigmoid(bn)
sigmoid = paddle.nn.functional.sigmoid(bn)
out = fluid.layers.reduce_sum(sigmoid)
# if not sync_bn:
# out = out / core.get_mlu_device_count()
......
python/paddle/fluid/tests/unittests/mlu/test_gelu_op_mlu.py
浏览文件 @ 208f625b
......@@ -109,7 +109,7 @@ class TestGeluNet(unittest.TestCase):
c = paddle.multiply(a, b)
fc_1 = fluid.layers.fc(input=c, size=128)
fc_1_gelu = fluid.layers.gelu(fc_1)
fc_1_gelu = paddle.nn.functional.gelu(fc_1)
prediction = fluid.layers.fc(input=fc_1_gelu, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/npu/sync_batch_norm_op_npu.py
浏览文件 @ 208f625b
......@@ -99,7 +99,7 @@ class TestSyncBatchNormOpTraining(TestSyncBatchNormRunnerBase):
)
# if self.dtype == np.float16:
# bn = fluid.layers.cast(bn, 'float32')
sigmoid = fluid.layers.sigmoid(bn)
sigmoid = paddle.nn.functional.sigmoid(bn)
out = fluid.layers.reduce_sum(sigmoid)
# if not sync_bn:
# out = out / core.get_npu_device_count()
......
python/paddle/fluid/tests/unittests/npu/test_gelu_op_npu.py
浏览文件 @ 208f625b
......@@ -109,7 +109,7 @@ class TestGeluNet(unittest.TestCase):
c = paddle.multiply(a, b)
fc_1 = fluid.layers.fc(input=c, size=128)
fc_1_gelu = fluid.layers.gelu(fc_1)
fc_1_gelu = paddle.nn.functional.gelu(fc_1)
prediction = fluid.layers.fc(input=fc_1_gelu, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/simple_nets.py
浏览文件 @ 208f625b
......@@ -88,7 +88,7 @@ def bow_net(
input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim]
)
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
bow_tanh = paddle.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
......
python/paddle/fluid/tests/unittests/test_activation_nn_grad.py
浏览文件 @ 208f625b
......@@ -33,7 +33,7 @@ class TestSigmoidTripleGradCheck(unittest.TestCase):
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.sigmoid(x)
y = F.sigmoid(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
......@@ -51,7 +51,7 @@ class TestSigmoidTripleGradCheck(unittest.TestCase):
class TestSigmoidDoubleGradCheck(unittest.TestCase):
def sigmoid_wrapper(self, x):
return fluid.layers.sigmoid(x[0])
return F.sigmoid(x[0])
@prog_scope()
def func(self, place):
......@@ -60,7 +60,7 @@ class TestSigmoidDoubleGradCheck(unittest.TestCase):
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.sigmoid(x)
y = F.sigmoid(x)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.double_grad_check(
......@@ -92,7 +92,7 @@ class TestTanhTripleGradCheck(unittest.TestCase):
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.tanh(x)
y = paddle.tanh(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
......@@ -322,7 +322,7 @@ class TestSqrtDoubleGradCheck(unittest.TestCase):
x = layers.data('x', shape, False, dtype)
x.persistable = True
y = layers.sqrt(x)
y = paddle.sqrt(x)
x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)
gradient_checker.double_grad_check(
......@@ -354,7 +354,7 @@ class TestRsqrtDoubleGradCheck(unittest.TestCase):
x = layers.data('x', shape, False, dtype)
x.persistable = True
y = layers.rsqrt(x)
y = paddle.rsqrt(x)
x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)
gradient_checker.double_grad_check(
......@@ -386,7 +386,7 @@ class TestSquareDoubleGradCheck(unittest.TestCase):
x = layers.data('x', shape, False, dtype)
x.persistable = True
y = layers.square(x)
y = paddle.square(x)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
gradient_checker.double_grad_check(
......@@ -417,7 +417,7 @@ class TestAbsDoubleGradCheck(unittest.TestCase):
x = layers.data('x', shape, False, dtype)
x.persistable = True
y = layers.abs(x)
y = paddle.abs(x)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
# Because we set delta = 0.005 in calculating numeric gradient,
# if x is too small, the numeric gradient is inaccurate.
......@@ -449,7 +449,7 @@ class TestLogDoubleGradCheck(unittest.TestCase):
x = layers.data('x', shape, False, dtype)
x.persistable = True
y = layers.log(x)
y = paddle.log(x)
x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)
......@@ -608,7 +608,7 @@ class TestSinTripleGradCheck(unittest.TestCase):
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.sin(x)
y = paddle.sin(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
......@@ -733,7 +733,7 @@ class TestCosTripleGradCheck(unittest.TestCase):
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.cos(x)
y = paddle.cos(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
......
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @ 208f625b
......@@ -33,17 +33,17 @@ class TestSqrtOpError(unittest.TestCase):
with program_guard(Program(), Program()):
# The input type of sqrt op must be Variable or numpy.ndarray.
in1 = 1
self.assertRaises(TypeError, fluid.layers.sqrt, in1)
self.assertRaises(TypeError, paddle.sqrt, in1)
# The input dtype of sqrt op must be float16, float32, float64.
in2 = fluid.layers.data(
name='input2', shape=[12, 10], dtype="int32"
)
self.assertRaises(TypeError, fluid.layers.sqrt, in2)
self.assertRaises(TypeError, paddle.sqrt, in2)
in3 = fluid.layers.data(
name='input3', shape=[12, 10], dtype="float16"
)
fluid.layers.sqrt(x=in3)
paddle.sqrt(x=in3)
class TestActivation(OpTest):
......@@ -390,16 +390,6 @@ class TestLogSigmoidAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.fluid.data('X', [11, 17])
out = paddle.fluid.layers.logsigmoid(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = np.log(1 / (1 + np.exp(-self.x_np)))
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -488,16 +478,6 @@ class TestTanhAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', [10, 12], self.dtype)
out = fluid.layers.tanh(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = np.tanh(self.x_np)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -593,7 +573,7 @@ class TestSinhAPI(unittest.TestCase):
with fluid.dygraph.guard():
np_x = np.array([0.1])
x = fluid.dygraph.to_variable(np_x)
z = fluid.layers.sinh(x).numpy()
z = paddle.sinh(x).numpy()
z_expected = np.sinh(np_x)
np.testing.assert_allclose(z, z_expected, rtol=1e-05)
......@@ -610,7 +590,7 @@ class TestSinhAPI(unittest.TestCase):
dtype="float32",
)
pd_sinh_out = fluid.layers.sinh(data_x)
pd_sinh_out = paddle.sinh(data_x)
exe = fluid.Executor(place=fluid.CPUPlace())
exe.run(fluid.default_startup_program())
(np_sinh_res,) = exe.run(
......@@ -630,7 +610,7 @@ class TestSinhAPI(unittest.TestCase):
)
var = fluid.dygraph.to_variable(input_x)
var.stop_gradient = False
loss = fluid.layers.sinh(var)
loss = paddle.sinh(var)
loss.backward()
grad_var = var.gradient()
self.assertEqual(grad_var.shape, input_x.shape)
......@@ -640,13 +620,13 @@ class TestSinhOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.sinh, 1)
self.assertRaises(TypeError, paddle.sinh, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.sinh, x_int32)
self.assertRaises(TypeError, paddle.sinh, x_int32)
# support the input dtype is float16
x_fp16 = fluid.data(name='x_fp16', shape=[12, 10], dtype='float16')
fluid.layers.sinh(x_fp16)
paddle.sinh(x_fp16)
class TestCosh(TestActivation):
......@@ -678,7 +658,7 @@ class TestCoshAPI(unittest.TestCase):
with fluid.dygraph.guard():
np_x = np.array([0.1])
x = fluid.dygraph.to_variable(np_x)
z = fluid.layers.cosh(x).numpy()
z = paddle.cosh(x).numpy()
z_expected = np.cosh(np_x)
np.testing.assert_allclose(z, z_expected, rtol=1e-05)
......@@ -715,7 +695,7 @@ class TestCoshAPI(unittest.TestCase):
)
var = fluid.dygraph.to_variable(input_x)
var.stop_gradient = False
loss = fluid.layers.cosh(var)
loss = paddle.cosh(var)
loss.backward()
grad_var = var.gradient()
self.assertEqual(grad_var.shape, input_x.shape)
......@@ -725,13 +705,13 @@ class TestCoshOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.cosh, 1)
self.assertRaises(TypeError, paddle.cosh, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.cosh, x_int32)
self.assertRaises(TypeError, paddle.cosh, x_int32)
# support the input dtype is float16
x_fp16 = fluid.data(name='x_fp16', shape=[12, 10], dtype='float16')
fluid.layers.cosh(x_fp16)
paddle.cosh(x_fp16)
def ref_tanhshrink(x):
......@@ -798,16 +778,6 @@ class TestTanhshrinkAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.tanh_shrink(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_tanhshrink(self.x_np)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -914,16 +884,6 @@ class TestHardShrinkAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', [10, 12])
out = fluid.layers.hard_shrink(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_hardshrink(self.x_np, 0.5)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -1080,16 +1040,6 @@ class TestSoftshrinkAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softshrink(x, self.threshold)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_softshrink(self.x_np, self.threshold)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -1780,16 +1730,6 @@ class TestLeakyReluAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', [10, 12])
out = fluid.layers.leaky_relu(x, 0.01)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_leaky_relu(self.x_np)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -3120,16 +3060,6 @@ class TestSoftplusAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softplus(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_softplus(self.x_np)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -3215,16 +3145,6 @@ class TestSoftsignAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softsign(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_softsign(self.x_np)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -3314,16 +3234,6 @@ class TestThresholdedReluAPI(unittest.TestCase):
np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
paddle.enable_static()
def test_fluid_api(self):
paddle.enable_static()
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.thresholded_relu(x, self.threshold)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_thresholded_relu(self.x_np, self.threshold)
np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
def test_errors(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
......@@ -3660,45 +3570,6 @@ class TestMishAPI(unittest.TestCase):
F.mish(x_fp16)
# ------------------ Test Error Activation----------------------
def create_test_error_class(op_type):
class TestOpErrors(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
op = getattr(fluid.layers, op_type)
# The input dtype of op_type must be float32, float64.
in1 = fluid.layers.data(
name='input2', shape=[12, 10], dtype="int32"
)
in2 = fluid.layers.data(
name='input3', shape=[12, 10], dtype="int64"
)
self.assertRaises(TypeError, op, in1)
self.assertRaises(TypeError, op, in2)
cls_name = "{0}_{1}".format(op_type, "test_errors")
TestOpErrors.__name__ = cls_name
globals()[cls_name] = TestOpErrors
create_test_error_class('acos')
create_test_error_class('asin')
create_test_error_class('atan')
create_test_error_class('ceil')
create_test_error_class('cos')
create_test_error_class('floor')
create_test_error_class('reciprocal')
create_test_error_class('round')
create_test_error_class('rsqrt')
create_test_error_class('sin')
create_test_error_class('sqrt')
create_test_error_class('tanh')
create_test_error_class('tan')
create_test_error_class('acosh')
create_test_error_class('asinh')
create_test_error_class('atanh')
# ------------------ Test Cudnn Activation----------------------
def create_test_act_cudnn_class(parent, atol=1e-3, grad_atol=1e-3):
@unittest.skipIf(
......
python/paddle/fluid/tests/unittests/test_cumsum_op.py
浏览文件 @ 208f625b
......@@ -371,7 +371,7 @@ class BadInputTest(unittest.TestCase):
def test_bad_x():
data = [1, 2, 4]
result = fluid.layers.cumsum(data, axis=0)
result = paddle.cumsum(data, axis=0)
self.assertRaises(TypeError, test_bad_x)
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps.py
浏览文件 @ 208f625b
......@@ -87,7 +87,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -119,7 +119,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -150,7 +150,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps11.py
浏览文件 @ 208f625b
......@@ -83,7 +83,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -111,7 +111,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -138,7 +138,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps12.py
浏览文件 @ 208f625b
......@@ -86,7 +86,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -114,7 +114,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -141,7 +141,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps13.py
浏览文件 @ 208f625b
......@@ -89,7 +89,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -119,7 +119,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -148,7 +148,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps2.py
浏览文件 @ 208f625b
......@@ -88,7 +88,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
q_ss = fluid.layers.data_norm(input=q_ss)
# fc layer after conv
q_fc = fluid.layers.fc(
......@@ -119,7 +119,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -148,7 +148,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps3.py
浏览文件 @ 208f625b
......@@ -87,7 +87,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -119,7 +119,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -150,7 +150,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps4.py
浏览文件 @ 208f625b
......@@ -85,7 +85,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -115,7 +115,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -144,7 +144,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps5.py
浏览文件 @ 208f625b
......@@ -87,7 +87,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -119,7 +119,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -150,7 +150,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps6.py
浏览文件 @ 208f625b
......@@ -85,7 +85,7 @@ class TestPSPassWithBow(unittest.TestCase):
q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
# vsum
q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
q_ss = fluid.layers.softsign(q_sum)
q_ss = paddle.nn.functional.softsign(q_sum)
# fc layer after conv
q_fc = fluid.layers.fc(
input=q_ss,
......@@ -115,7 +115,7 @@ class TestPSPassWithBow(unittest.TestCase):
pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
# vsum
pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
pt_ss = fluid.layers.softsign(pt_sum)
pt_ss = paddle.nn.functional.softsign(pt_sum)
# fc layer
pt_fc = fluid.layers.fc(
input=pt_ss,
......@@ -144,7 +144,7 @@ class TestPSPassWithBow(unittest.TestCase):
nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
# vsum
nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
nt_ss = fluid.layers.softsign(nt_sum)
nt_ss = paddle.nn.functional.softsign(nt_sum)
# fc layer
nt_fc = fluid.layers.fc(
input=nt_ss,
......
python/paddle/fluid/tests/unittests/test_eager_deletion_gru_net.py
浏览文件 @ 208f625b
......@@ -38,7 +38,7 @@ def gru_net(
fc0 = fluid.layers.fc(input=emb, size=hid_dim * 3)
gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False)
gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max')
gru_max_tanh = fluid.layers.tanh(gru_max)
gru_max_tanh = paddle.tanh(gru_max)
fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh')
prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/test_eager_deletion_lstm_net.py
浏览文件 @ 208f625b
......@@ -40,7 +40,7 @@ def lstm_net(
input=fc0, size=hid_dim * 4, is_reverse=False
)
lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
lstm_max_tanh = fluid.layers.tanh(lstm_max)
lstm_max_tanh = paddle.tanh(lstm_max)
fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py
浏览文件 @ 208f625b
......@@ -191,10 +191,10 @@ def lm_model(
ends=[hidden_size * 4],
)
c = pre_cell * layers.sigmoid(f) + layers.sigmoid(
i
) * layers.tanh(j)
m = layers.tanh(c) * layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
rnn.update_memory(pre_hidden, m)
rnn.update_memory(pre_cell, c)
......@@ -299,10 +299,10 @@ def lm_model(
gate_input = layers.elementwise_add(gate_input, bias)
i, j, f, o = layers.split(gate_input, num_or_sections=4, dim=-1)
c = pre_cell * layers.sigmoid(f) + layers.sigmoid(
i
) * layers.tanh(j)
m = layers.tanh(c) * layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
hidden_array[k] = m
cell_array[k] = c
......
python/paddle/fluid/tests/unittests/test_eager_deletion_recurrent_op.py
浏览文件 @ 208f625b
......@@ -327,7 +327,9 @@ class EagerDeletionRecurrentOpTest2(EagerDeletionRecurrentOpTest1):
bias_attr=False,
)
h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))
h = paddle.nn.functional.sigmoid(
x=layers.elementwise_add(x=temp_l, y=temp_r)
)
rnn.update_memory(h_pre, h)
rnn.output(h)
......
python/paddle/fluid/tests/unittests/test_erf_op.py
浏览文件 @ 208f625b
......@@ -48,7 +48,7 @@ class TestErfLayer(unittest.TestCase):
y_ref = erf(x)
with dg.guard(place) as g:
x_var = dg.to_variable(x)
y_var = fluid.layers.erf(x_var)
y_var = paddle.erf(x_var)
y_test = y_var.numpy()
np.testing.assert_allclose(y_ref, y_test, rtol=1e-05)
......
python/paddle/fluid/tests/unittests/test_gelu_op.py
浏览文件 @ 208f625b
......@@ -45,7 +45,7 @@ class TestGeluOp(unittest.TestCase):
place = fluid.CPUPlace()
with dg.guard(place) as g:
x_var = dg.to_variable(x)
y_var = fluid.layers.gelu(x_var, approximate)
y_var = F.gelu(x_var, approximate)
y_test = y_var.numpy()
np.testing.assert_allclose(y_ref, y_test, rtol=1e-05, atol=1e-08)
......@@ -56,7 +56,7 @@ class TestGeluOp(unittest.TestCase):
place = fluid.CUDAPlace(0)
with dg.guard(place) as g:
x_var = dg.to_variable(x)
y_var = fluid.layers.gelu(x_var, approximate)
y_var = F.gelu(x_var, approximate)
y_test = y_var.numpy()
np.testing.assert_allclose(y_ref, y_test, rtol=1e-05, atol=1e-08)
......
python/paddle/fluid/tests/unittests/test_gradient_clip.py
浏览文件 @ 208f625b
......@@ -35,7 +35,7 @@ def bow_net(
input=data, is_sparse=True, size=[dict_dim, emb_dim]
)
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
bow_tanh = paddle.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
......
python/paddle/fluid/tests/unittests/test_imperative_basic.py
浏览文件 @ 208f625b
......@@ -914,7 +914,7 @@ class TestDygraphUtils(unittest.TestCase):
with fluid.dygraph.guard():
a = paddle.to_tensor(a_np)
res1 = func(a, act="sigmoid", use_mkldnn=True, use_cudnn=True)
res2 = fluid.layers.sigmoid(a)
res2 = paddle.nn.functional.sigmoid(a)
np.testing.assert_allclose(res1.numpy(), res2.numpy(), rtol=1e-05)
def test_append_activation_in_dygraph2(self):
......@@ -929,7 +929,7 @@ class TestDygraphUtils(unittest.TestCase):
with fluid.dygraph.guard():
a = paddle.to_tensor(a_np)
res1 = func(a, act="sigmoid", use_cudnn=True)
res2 = fluid.layers.sigmoid(a)
res2 = paddle.nn.functional.sigmoid(a)
np.testing.assert_array_equal(res1.numpy(), res2.numpy())
def test_append_activation_in_dygraph3(self):
......
python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py
浏览文件 @ 208f625b
......@@ -317,7 +317,7 @@ class SimpleAttention(fluid.dygraph.Layer):
concated = fluid.layers.elementwise_add(
encoder_proj, decoder_state_expand
)
concated = fluid.layers.tanh(x=concated)
concated = paddle.tanh(x=concated)
attention_weight = self.fc_2(concated)
weights_reshape = fluid.layers.reshape(
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ 208f625b
......@@ -115,10 +115,10 @@ class SimpleLSTMRNN(fluid.Layer):
i, j, f, o = fluid.layers.split(
gate_input, num_or_sections=4, dim=-1
)
c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
i
) * fluid.layers.tanh(j)
m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
self.hidden_array[k] = m
self.cell_array[k] = c
self._input = m
......
python/paddle/fluid/tests/unittests/test_imperative_save_load.py
浏览文件 @ 208f625b
......@@ -110,10 +110,10 @@ class SimpleLSTMRNN(fluid.Layer):
i, j, f, o = fluid.layers.split(
gate_input, num_or_sections=4, dim=-1
)
c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
i
) * fluid.layers.tanh(j)
m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
self.hidden_array[k] = m
self.cell_array[k] = c
self._input = m
......
python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py
浏览文件 @ 208f625b
......@@ -112,10 +112,10 @@ class SimpleLSTMRNN(fluid.Layer):
i, j, f, o = fluid.layers.split(
gate_input, num_or_sections=4, dim=-1
)
c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
i
) * fluid.layers.tanh(j)
m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
self.hidden_array[k] = m
self.cell_array[k] = c
self._input = m
......
python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py
浏览文件 @ 208f625b
......@@ -322,7 +322,7 @@ class Generator(fluid.dygraph.Layer):
res_block = self._res_block(conv0)
deconv = self._deconv(res_block)
conv1 = self._conv1(deconv)
out = fluid.layers.tanh(conv1)
out = paddle.tanh(conv1)
return out
......@@ -437,11 +437,9 @@ def gradient_penalty(f, real, fake, no_grad_set, cfg):
)
epsilon = 1e-16
norm = fluid.layers.sqrt(
fluid.layers.reduce_sum(fluid.layers.square(gradient), dim=1) + epsilon
)
norm = paddle.sqrt(paddle.sum(paddle.square(gradient), axis=1) + epsilon)
gp = fluid.layers.reduce_mean(fluid.layers.square(norm - 1.0))
gp = paddle.mean(paddle.square(norm - 1.0))
return gp
......@@ -451,7 +449,7 @@ def get_generator_loss(
fake_img = generator(image_real, label_trg)
rec_img = generator(fake_img, label_org)
g_loss_rec = fluid.layers.reduce_mean(
fluid.layers.abs(fluid.layers.elementwise_sub(image_real, rec_img))
paddle.abs(paddle.subtract(image_real, rec_img))
)
pred_fake, cls_fake = discriminator(fake_img)
......
python/paddle/fluid/tests/unittests/test_imperative_triple_grad.py
浏览文件 @ 208f625b
......@@ -182,7 +182,7 @@ class TestDygraphTripleGrad(TestCase):
numel = z_np.size
z.stop_gradient = False
out = fluid.layers.sigmoid(paddle.matmul(x, y) + z)
out = paddle.nn.functional.sigmoid(paddle.matmul(x, y) + z)
out_np = out.numpy()
(dx_actual,) = self.grad([out], [x], create_graph=True)
......@@ -278,7 +278,7 @@ class TestDygraphTripleGradBradcastCase(TestCase):
numel = z_np.size
z.stop_gradient = False
out = fluid.layers.sigmoid(paddle.matmul(x, y) + z)
out = paddle.nn.functional.sigmoid(paddle.matmul(x, y) + z)
out_np = out.numpy()
(dx_actual,) = self.grad([out], [x], create_graph=True)
......
python/paddle/fluid/tests/unittests/test_inplace_abn_op.py
浏览文件 @ 208f625b
......@@ -87,7 +87,7 @@ class TestInplaceANBOpTraining(unittest.TestCase):
# a new Variable for fetch
bn = bn * 1.0
sigmoid = fluid.layers.sigmoid(bn)
sigmoid = paddle.nn.functional.sigmoid(bn)
out = fluid.layers.reduce_sum(sigmoid)
if not only_forward:
sgd_opt = fluid.optimizer.SGD(learning_rate=0.0)
......
python/paddle/fluid/tests/unittests/test_ir_memory_optimize_nlp.py
浏览文件 @ 208f625b
......@@ -41,7 +41,7 @@ def lstm_net(
input=fc0, size=hid_dim * 4, is_reverse=False
)
lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
lstm_max_tanh = fluid.layers.tanh(lstm_max)
lstm_max_tanh = paddle.tanh(lstm_max)
fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 208f625b
......@@ -3680,126 +3680,6 @@ class TestBook(LayerTest):
)
return out
def make_sigmoid(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.sigmoid(input, name='sigmoid')
return out
def make_exp(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.exp(input, name='exp')
return out
def make_tanh(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.tanh(input, name='tanh')
return out
def make_tanh_shrink(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.tanh_shrink(input, name='tanh_shrink')
return out
def make_sqrt(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.sqrt(input, name='sqrt')
return out
def make_abs(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.abs(input, name='abs')
return out
def make_ceil(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.ceil(input, name='ceil')
return out
def make_floor(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.floor(input, name='floor')
return out
def make_cos(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.cos(input, name='cos')
return out
def make_sin(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.sin(input, name='sin')
return out
def make_round(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.round(input, name='round')
return out
def make_reciprocal(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.reciprocal(input, name='reciprocal')
return out
def make_square(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.square(input, name='square')
return out
def make_softplus(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.softplus(input, name='softplus')
return out
def make_softsign(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.softsign(input, name='softsign')
return out
def make_mish(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......@@ -3920,14 +3800,6 @@ class TestBook(LayerTest):
out = layers.scale(input, scale=scale_var)
return out
def make_softshrink(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
input = self._get_data(name="input", shape=[16], dtype="float32")
out = layers.softshrink(input, alpha=0.3)
return out
def make_iou_similarity(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......
python/paddle/fluid/tests/unittests/test_lgamma_op.py
浏览文件 @ 208f625b
......@@ -63,7 +63,7 @@ class TestLgammaOpApi(unittest.TestCase):
shape = (1, 4)
data = np.random.random(shape).astype(self.dtype) + 1
data_ = paddle.to_tensor(data)
out = paddle.fluid.layers.lgamma(data_)
out = paddle.lgamma(data_)
result = special.gammaln(data)
np.testing.assert_allclose(result, out.numpy(), rtol=1e-05)
paddle.enable_static()
......
python/paddle/fluid/tests/unittests/test_program_prune_backward.py
浏览文件 @ 208f625b
......@@ -50,7 +50,7 @@ def lstm_net(use_feed):
input=fc0, size=hid_dim * 4, is_reverse=False
)
lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
lstm_max_tanh = fluid.layers.tanh(lstm_max)
lstm_max_tanh = paddle.tanh(lstm_max)
fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/test_py_func_op.py
浏览文件 @ 208f625b
......@@ -81,7 +81,7 @@ def simple_fc_net(img, label, use_py_func_op):
),
)
if not use_py_func_op:
hidden = fluid.layers.tanh(hidden)
hidden = paddle.tanh(hidden)
else:
new_hidden = (
fluid.default_main_program()
......
python/paddle/fluid/tests/unittests/test_recurrent_op.py
浏览文件 @ 208f625b
......@@ -316,7 +316,9 @@ class RecurrentOpTest2(RecurrentOpTest1):
bias_attr=False,
)
h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))
h = paddle.nn.functional.sigmoid(
x=layers.elementwise_add(x=temp_l, y=temp_r)
)
rnn.update_memory(h_pre, h)
rnn.output(h)
......@@ -710,7 +712,9 @@ class RecurrentOpStopGradientTest(RecurrentOpTest1):
bias_attr=False,
)
h = layers.sigmoid(x=layers.elementwise_add(temp_l, temp_r))
h = paddle.nn.functional.sigmoid(
x=layers.elementwise_add(temp_l, temp_r)
)
rnn.update_memory(h_pre, h)
rnn.output(h)
......
python/paddle/fluid/tests/unittests/test_regularizer.py
浏览文件 @ 208f625b
......@@ -135,7 +135,7 @@ def bow_net(
input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim]
)
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
bow_tanh = paddle.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
......@@ -225,7 +225,7 @@ class TestRegularizer(unittest.TestCase):
param_list = fluid.default_main_program().block(0).all_parameters()
para_sum = []
for para in param_list:
para_mul = fluid.layers.square(x=para)
para_mul = paddle.square(x=para)
para_sum.append(fluid.layers.reduce_sum(input=para_mul))
avg_cost_l2 += fluid.layers.sums(para_sum) * 0.5
......
python/paddle/fluid/tests/unittests/test_regularizer_api.py
浏览文件 @ 208f625b
......@@ -41,7 +41,7 @@ def bow_net(
input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim]
)
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
bow_tanh = paddle.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
......@@ -133,7 +133,7 @@ class TestRegularizer(unittest.TestCase):
param_list = fluid.default_main_program().block(0).all_parameters()
para_sum = []
for para in param_list:
para_mul = fluid.layers.square(x=para)
para_mul = paddle.square(x=para)
para_sum.append(fluid.layers.reduce_sum(input=para_mul))
avg_cost_l2 += fluid.layers.sums(para_sum) * 0.5
......
python/paddle/fluid/tests/unittests/test_retain_graph.py
浏览文件 @ 208f625b
......@@ -30,7 +30,7 @@ class Generator(fluid.dygraph.Layer):
def forward(self, x):
x = self.conv1(x)
x = fluid.layers.tanh(x)
x = paddle.tanh(x)
return x
......
python/paddle/fluid/tests/unittests/test_static_save_load.py
浏览文件 @ 208f625b
......@@ -122,10 +122,10 @@ class SimpleLSTMRNN(fluid.Layer):
i, j, f, o = fluid.layers.split(
gate_input, num_or_sections=4, dim=-1
)
c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
i
) * fluid.layers.tanh(j)
m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
c = pre_cell * paddle.nn.functional.sigmoid(
f
) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
self.hidden_array[k] = m
self.cell_array[k] = c
self._input = m
......
python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py
浏览文件 @ 208f625b
......@@ -94,7 +94,7 @@ class TestSyncBatchNormOpTraining(unittest.TestCase):
bn = fluid.layers.cast(bn, 'float32')
else:
bn = fluid.layers.cast(bn, 'float64')
sigmoid = fluid.layers.sigmoid(bn)
sigmoid = paddle.nn.functional.sigmoid(bn)
out = fluid.layers.reduce_sum(sigmoid)
if not sync_bn:
out = out / core.get_cuda_device_count()
......
python/paddle/fluid/tests/unittests/test_weight_decay.py
浏览文件 @ 208f625b
......@@ -59,7 +59,7 @@ def bow_net(
input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim]
)
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
bow_tanh = paddle.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
......
python/paddle/fluid/tests/unittests/transformer_model.py
浏览文件 @ 208f625b
......@@ -15,6 +15,7 @@
from functools import partial
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
......@@ -156,7 +157,7 @@ def multi_head_attention(
# So, here define the softmax for temporary solution.
def __softmax(x, eps=1e-9):
exp_out = layers.exp(x=x)
exp_out = paddle.exp(x=x)
sum_out = layers.reduce_sum(exp_out, dim=-1, keep_dim=False)
return layers.elementwise_div(x=exp_out, y=sum_out, axis=0)
......
python/paddle/incubate/distributed/models/moe/grad_clip.py
浏览文件 @ 208f625b
......@@ -209,7 +209,7 @@ class ClipGradForMOEByGlobalNorm(ClipGradBase):
global_norm_var = global_norm_var_normal + global_norm_var_moe
params_and_grads = []
global_norm_var = layers.sqrt(global_norm_var)
global_norm_var = paddle.sqrt(global_norm_var)
max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
)
......
python/paddle/incubate/optimizer/modelaverage.py
浏览文件 @ 208f625b
......@@ -557,7 +557,7 @@ class ModelAverage(Optimizer):
sum = layers.cast(
x=sum, dtype='float32' if self._dtype is None else self._dtype
)
layers.ops._elementwise_div(x=sum, y=tmp, out=param)
paddle.tensor.ops._elementwise_div(x=sum, y=tmp, out=param)
def _add_average_restore_op(self, block, param):
param = block._clone_variable(param)
......
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