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“d2f8befaab08a6986d44207b417b3d27e453a854”上不存在“lite/fpga/KD/fpga_cv.hpp”
未验证 提交 802be98b 编写于 作者: L LoneRanger 提交者： GitHub
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操作

relocate paddle/fluid/contrib/layers (#52820) 

* relocate metri_op.py

* reloacte nn.py

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* fix variable->tensor and fix __all__

* fix ctr_metric_bundle and sparse_embedding

* fix bug of function init

* fix bug of importing sparse_embedding and ctr_metric_bundle

* fix bug

* Update __init__.py
上级 cbfd43e4
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Showing with 731 addition and 761 deletion
paddle/fluid/operators/tdm_child_op.h
浏览文件 @ 802be98b
......@@ -57,7 +57,7 @@ void TDMChildInner(const framework::ExecutionContext &context,
input_data[input_ids],
node_nums,
platform::errors::InvalidArgument(
"input id of OP(fluid.contrib.layers.tdm_child) "
"input id of OP(paddle.incubate.layers.tdm_child) "
"expected >= 0 and < %ld, but got %ld. Please check input "
"value.",
node_nums,
......@@ -66,7 +66,7 @@ void TDMChildInner(const framework::ExecutionContext &context,
0,
input_data[input_ids],
platform::errors::InvalidArgument(
"input id of OP(fluid.contrib.layers.tdm_child) "
"input id of OP(paddle.incubate.layers.tdm_child) "
"expected >= 0 and < %ld, but got %ld. Please check input "
"value.",
node_nums,
......
python/paddle/fluid/contrib/__init__.py
浏览文件 @ 802be98b
......@@ -22,8 +22,7 @@ from . import extend_optimizer
from .extend_optimizer import *
from . import model_stat
from .model_stat import *
from . import layers
from .layers import *
from . import optimizer
from .optimizer import *
......@@ -32,5 +31,4 @@ __all__ = []
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
__all__ += extend_optimizer.__all__
__all__ += layers.__all__
__all__ += optimizer.__all__
python/paddle/fluid/contrib/layers/metric_op.py 已删除 100755 → 0
浏览文件 @ cbfd43e4
# Copyright (c) 2019 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.
"""
Contrib layers just related to metric.
"""
import paddle
import warnings
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.framework import Variable
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layers import tensor
import paddle
__all__ = ['ctr_metric_bundle']
def ctr_metric_bundle(input, label, ins_tag_weight=None):
"""
ctr related metric layer
This function help compute the ctr related metrics: RMSE, MAE, predicted_ctr, q_value.
To compute the final values of these metrics, we should do following computations using
total instance number:
MAE = local_abserr / instance number
RMSE = sqrt(local_sqrerr / instance number)
predicted_ctr = local_prob / instance number
q = local_q / instance number
Note that if you are doing distribute job, you should all reduce these metrics and instance
number first
Args:
input(Tensor): A floating-point 2D Tensor, values are in the range
[0, 1]. Each row is sorted in descending order. This
input should be the output of topk. Typically, this
Tensor indicates the probability of each label.
label(Tensor): A 2D int Tensor indicating the label of the training
data. The height is batch size and width is always 1.
ins_tag_weight(Tensor): A 2D int Tensor indicating the ins_tag_weight of the training
data. 1 means real data, 0 means fake data.
A LoDTensor or Tensor with type float32,float64.
Returns:
local_sqrerr(Tensor): Local sum of squared error
local_abserr(Tensor): Local sum of abs error
local_prob(Tensor): Local sum of predicted ctr
local_q(Tensor): Local sum of q value
Examples 1:
.. code-block:: python
import paddle
paddle.enable_static()
data = paddle.static.data(name="data", shape=[32, 32], dtype="float32")
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32")
predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(input=data, size=1))
auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label)
Examples 2:
.. code-block:: python
import paddle
paddle.enable_static()
data = paddle.static.data(name="data", shape=[32, 32], dtype="float32")
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32")
predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(input=data, size=1))
ins_tag_weight = paddle.static.data(name='ins_tag', shape=[-1,16], lod_level=0, dtype='int64')
auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label, ins_tag_weight=ins_tag_weight)
"""
if ins_tag_weight is None:
ins_tag_weight = paddle.tensor.fill_constant(
shape=[1, 1], dtype="float32", value=1.0
)
assert input.shape == label.shape
helper = LayerHelper("ctr_metric_bundle", **locals())
local_abserr = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_sqrerr = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_prob = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_q = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_pos_num = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_ins_num = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
tmp_res_elesub = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1]
)
tmp_res_sigmoid = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1]
)
tmp_ones = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1]
)
batch_prob = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_abserr = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_sqrerr = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_q = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_pos_num = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_ins_num = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
for var in [
local_abserr,
batch_abserr,
local_sqrerr,
batch_sqrerr,
local_prob,
batch_prob,
local_q,
batch_q,
batch_pos_num,
batch_ins_num,
local_pos_num,
local_ins_num,
]:
helper.set_variable_initializer(
var,
paddle.nn.initializer.ConstantInitializer(
value=0.0, force_cpu=True
),
)
helper.append_op(
type="elementwise_sub",
inputs={"X": [input], "Y": [label]},
outputs={"Out": [tmp_res_elesub]},
)
helper.append_op(
type="squared_l2_norm",
inputs={"X": [tmp_res_elesub]},
outputs={"Out": [batch_sqrerr]},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_sqrerr], "Y": [local_sqrerr]},
outputs={"Out": [local_sqrerr]},
)
helper.append_op(
type="l1_norm",
inputs={"X": [tmp_res_elesub]},
outputs={"Out": [batch_abserr]},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_abserr], "Y": [local_abserr]},
outputs={"Out": [local_abserr]},
)
helper.append_op(
type="reduce_sum", inputs={"X": [input]}, outputs={"Out": [batch_prob]}
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_prob], "Y": [local_prob]},
outputs={"Out": [local_prob]},
)
helper.append_op(
type="sigmoid",
inputs={"X": [input]},
outputs={"Out": [tmp_res_sigmoid]},
)
helper.append_op(
type="reduce_sum",
inputs={"X": [tmp_res_sigmoid]},
outputs={"Out": [batch_q]},
)
helper.append_op(
type="reduce_sum",
inputs={"X": [label]},
outputs={"Out": [batch_pos_num]},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_pos_num], "Y": [local_pos_num]},
outputs={"Out": [local_pos_num]},
)
helper.append_op(
type='fill_constant_batch_size_like',
inputs={"Input": label},
outputs={'Out': [tmp_ones]},
attrs={
'shape': [-1, 1],
'dtype': tmp_ones.dtype,
'value': float(1.0),
},
)
helper.append_op(
type="reduce_sum",
inputs={"X": [tmp_ones]},
outputs={"Out": [batch_ins_num]},
)
# if data is fake, return 0
inputs_slice = {'Input': ins_tag_weight}
attrs = {'axes': [0]}
attrs['starts'] = [0]
attrs['ends'] = [1]
helper.append_op(
type="slice",
inputs=inputs_slice,
attrs=attrs,
outputs={"Out": ins_tag_weight},
)
axis = helper.kwargs.get('axis', 0)
helper.append_op(
type="elementwise_mul",
inputs={"X": [batch_ins_num], "Y": [ins_tag_weight]},
outputs={"Out": [batch_ins_num]},
attrs={'axis': axis},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_ins_num], "Y": [local_ins_num]},
outputs={"Out": [local_ins_num]},
)
helper.append_op(
type="elementwise_mul",
inputs={"X": [batch_q], "Y": [ins_tag_weight]},
outputs={"Out": [batch_q]},
attrs={'axis': axis},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_q], "Y": [local_q]},
outputs={"Out": [local_q]},
)
return (
local_sqrerr,
local_abserr,
local_prob,
local_q,
local_pos_num,
local_ins_num,
)
python/paddle/fluid/layers/nn.py
浏览文件 @ 802be98b
......@@ -197,7 +197,7 @@ def embedding(
if is_distributed:
is_distributed = False
warnings.warn(
"is_distributed is go out of use, `fluid.contrib.layers.sparse_embedding` is your needed"
"is_distributed is go out of use, `paddle.static.nn.sparse_embedding` is your needed"
)
remote_prefetch = True if is_sparse else False
......@@ -227,128 +227,6 @@ def embedding(
return tmp
def _pull_gpups_sparse(
input, size, dtype='float32', is_distributed=False, is_sparse=False
):
r"""
**Pull GpuPS Sparse Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
GpuPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable, which
contains the IDs information.
size(int|list of int): The embedding size parameter of each input, which indicates the size of
each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now.
Returns:
Variable|list of Variable: The tensor variable storing the embeddings of the \
supplied inputs, whose size are indicated by size respectively.
Examples:
.. code-block:: python
import paddle.fluid as fluid
slots = []
data_1 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
slots.append(data_1)
data_2 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
slots.append(data_2)
embs = fluid.layers.pull_gpups_sparse(input=slots, size=[11, 35])
"""
helper = LayerHelper('pull_gpups_sparse', **locals())
if dtype != 'float32':
raise ValueError(
"GpuPS only support float type embedding now, and your type is: "
+ dtype
)
helper.input_dtype()
inputs = helper.multiple_input()
outs = [
helper.create_variable_for_type_inference(dtype)
for i in range(len(inputs))
]
w = helper.create_parameter(
attr=helper.param_attr, shape=[size[0]], dtype=dtype, is_bias=False
)
helper.append_op(
type='pull_gpups_sparse',
inputs={'Ids': inputs, 'W': w},
outputs={'Out': outs},
attrs={
'size': size,
'is_distributed': is_distributed,
'is_sparse': is_sparse,
},
)
if len(outs) == 1:
return outs[0]
return outs
def _pull_box_sparse(
input, size, dtype='float32', is_distributed=False, is_sparse=False
):
r"""
**Pull Box Sparse Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
BoxPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable, which
contains the IDs information.
size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now.
Returns:
Variable|list of Variable: The tensor variable storing the embeddings of the \
supplied inputs.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
emb = fluid.layers.pull_box_sparse(input=data, size=[11])
"""
helper = LayerHelper('pull_box_sparse', **locals())
if dtype != 'float32':
raise ValueError(
"BoxPS only support float type embedding now, and your type is: "
+ dtype
)
helper.input_dtype()
inputs = helper.multiple_input()
outs = [
helper.create_variable_for_type_inference(dtype)
for i in range(len(inputs))
]
w = helper.create_parameter(
attr=helper.param_attr, shape=[size], dtype=dtype, is_bias=False
)
helper.append_op(
type='pull_box_sparse',
inputs={'Ids': inputs, 'W': w},
outputs={'Out': outs},
attrs={
'size': size,
'is_distributed': is_distributed,
'is_sparse': is_sparse,
},
)
if len(outs) == 1:
return outs[0]
return outs
def autoincreased_step_counter(counter_name=None, begin=1, step=1):
"""
:api_attr: Static Graph
......
python/paddle/fluid/tests/unittests/dist_fleet_sparse_embedding_ctr.py
浏览文件 @ 802be98b
......@@ -96,7 +96,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
entry = paddle.distributed.ShowClickEntry("show", "click")
dnn_layer_dims = [128, 64, 32]
dnn_embedding = fluid.contrib.layers.sparse_embedding(
dnn_embedding = paddle.static.nn.sparse_embedding(
input=dnn_data,
size=[dnn_input_dim, dnn_layer_dims[0]],
is_test=inference,
......@@ -120,7 +120,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
dnn_out = fc
# build lr model
lr_embbding = fluid.contrib.layers.sparse_embedding(
lr_embbding = paddle.static.nn.sparse_embedding(
input=lr_data,
size=[lr_input_dim, 1],
is_test=inference,
......
python/paddle/fluid/tests/unittests/test_bilateral_slice_op.py
浏览文件 @ 802be98b
......@@ -19,6 +19,7 @@ import numpy as np
from eager_op_test import OpTest, paddle_static_guard
import paddle
from paddle.incubate.layers.nn import bilateral_slice
class Gsz:
......@@ -202,7 +203,7 @@ class TestBilateralSliceApi(unittest.TestCase):
grid = paddle.static.data(
name='grid', shape=[None, None, 8, 5, 3], dtype='float32'
)
paddle.fluid.contrib.layers.bilateral_slice(x, guide, grid, False)
bilateral_slice(x, guide, grid, False)
if not paddle.fluid.is_compiled_with_cuda():
return
......@@ -212,7 +213,7 @@ class TestBilateralSliceApi(unittest.TestCase):
guide1 = paddle.rand([3, 50, 30])
grid1 = paddle.rand([3, 2, 2, 5, 3])
paddle.fluid.contrib.bilateral_slice(x1, guide1, grid1, False)
bilateral_slice(x1, guide1, grid1, False)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_boxps.py
浏览文件 @ 802be98b
......@@ -18,7 +18,7 @@ import paddle
from paddle import fluid
from paddle.distributed.transpiler import collective
from paddle.fluid import core
from paddle.fluid.layers.nn import _pull_box_sparse
from paddle.incubate.layers.nn import _pull_box_sparse
class TestTranspile(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_dist_fleet_minimize.py
浏览文件 @ 802be98b
......@@ -73,7 +73,7 @@ class TestPSMinimize(unittest.TestCase):
name="1", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -105,7 +105,7 @@ class TestPSMinimize(unittest.TestCase):
name="2", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -136,7 +136,7 @@ class TestPSMinimize(unittest.TestCase):
name="3", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps11.py
浏览文件 @ 802be98b
......@@ -73,7 +73,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="1", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -105,7 +105,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="2", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -136,7 +136,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="3", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps12.py
浏览文件 @ 802be98b
......@@ -76,7 +76,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="1", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -108,7 +108,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="2", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -139,7 +139,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="3", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps13.py
浏览文件 @ 802be98b
......@@ -77,7 +77,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -109,7 +109,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -140,7 +140,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps2.py
浏览文件 @ 802be98b
......@@ -76,7 +76,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -109,7 +109,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -140,7 +140,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps4.py
浏览文件 @ 802be98b
......@@ -73,7 +73,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -105,7 +105,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -136,7 +136,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_ps6.py
浏览文件 @ 802be98b
......@@ -73,7 +73,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -105,7 +105,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -136,7 +136,7 @@ class TestPSPassWithBow(unittest.TestCase):
name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_dist_fleet_sparse_embedding_ctr.py
浏览文件 @ 802be98b
......@@ -214,7 +214,7 @@ class TestDistMnistAsync2x2WithGauss(TestFleetBase):
init = paddle.nn.initializer.Uniform()
dnn_layer_dims = [128, 64, 32]
dnn_embedding = fluid.contrib.layers.sparse_embedding(
dnn_embedding = paddle.static.nn.sparse_embedding(
input=dnn_data,
size=[dnn_input_dim, dnn_layer_dims[0]],
is_test=inference,
......@@ -239,7 +239,7 @@ class TestDistMnistAsync2x2WithGauss(TestFleetBase):
dnn_out = fc
# build lr model
lr_embbding = fluid.contrib.layers.sparse_embedding(
lr_embbding = paddle.static.nn.sparse_embedding(
input=lr_data,
size=[lr_input_dim, 1],
is_test=inference,
......
python/paddle/fluid/tests/unittests/test_dist_fleet_spmt.py
浏览文件 @ 802be98b
......@@ -71,7 +71,7 @@ class TestSPMT(unittest.TestCase):
name="1", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
q_emb = fluid.contrib.layers.sparse_embedding(
q_emb = paddle.static.nn.sparse_embedding(
input=q,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -103,7 +103,7 @@ class TestSPMT(unittest.TestCase):
name="2", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
pt_emb = fluid.contrib.layers.sparse_embedding(
pt_emb = paddle.static.nn.sparse_embedding(
input=pt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......@@ -134,7 +134,7 @@ class TestSPMT(unittest.TestCase):
name="3", shape=[-1, 1], dtype="int64", lod_level=1
)
# embedding
nt_emb = fluid.contrib.layers.sparse_embedding(
nt_emb = paddle.static.nn.sparse_embedding(
input=nt,
size=[dict_dim, emb_dim],
param_attr=fluid.ParamAttr(
......
python/paddle/fluid/tests/unittests/test_fleet_pyramid_hash.py
浏览文件 @ 802be98b
......@@ -23,6 +23,7 @@ from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler im
from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
StrategyFactory,
)
from paddle.incubate.layers.nn import search_pyramid_hash
class TestPyramidHashOpApi(unittest.TestCase):
......@@ -33,7 +34,7 @@ class TestPyramidHashOpApi(unittest.TestCase):
x = paddle.static.data(
name='x', shape=x_shape, dtype='int32', lod_level=1
)
hash_embd = fluid.contrib.layers.search_pyramid_hash(
hash_embd = search_pyramid_hash(
input=x,
num_emb=embed_dim,
space_len=num_voc * embed_dim,
......
python/paddle/fluid/tests/unittests/test_fuse_bn_add_act_pass.py
浏览文件 @ 802be98b
......@@ -97,7 +97,7 @@ class TestFusedBnAddActAPI(unittest.TestCase):
act=None,
data_layout='NHWC',
)
fused_bn_add_act = fluid.contrib.layers.fused_bn_add_act(
fused_bn_add_act = paddle.incubate.layers.nn.fused_bn_add_act(
conv1_2,
bn,
param_attr=self.bn_param_attr2,
......
python/paddle/fluid/tests/unittests/test_fused_emb_seq_pool_op.py
浏览文件 @ 802be98b
......@@ -20,6 +20,7 @@ from eager_op_test import OpTest, paddle_static_guard, skip_check_grad_ci
import paddle
import paddle.version as ver
from paddle.incubate.layers.nn import fused_embedding_seq_pool
@skip_check_grad_ci(
......@@ -114,7 +115,7 @@ class TestFusedEmbeddingSeqPoolApi(unittest.TestCase):
name='word', shape=[-1, 1], dtype='int64', lod_level=1
)
padding_idx = np.random.randint(1, 10)
out = fluid.contrib.fused_embedding_seq_pool(
out = fused_embedding_seq_pool(
input=data_t,
size=[dict_size, 32],
param_attr='w',
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 802be98b
......@@ -26,6 +26,13 @@ from paddle import fluid
from paddle.fluid import core, layers, nets
from paddle.fluid.dygraph import base, to_variable
from paddle.fluid.framework import Program, default_main_program, program_guard
from paddle.incubate.layers.nn import (
batch_fc,
partial_concat,
partial_sum,
rank_attention,
shuffle_batch,
)
from paddle.tensor import random
......@@ -2145,9 +2152,9 @@ class TestBook(LayerTest):
x = paddle.static.data(
name='X', shape=[-1, 4, 50], dtype='float32', lod_level=0
)
out1 = fluid.contrib.layers.shuffle_batch(x)
out1 = shuffle_batch(x)
default_main_program().random_seed = 1000
out2 = fluid.contrib.layers.shuffle_batch(x)
out2 = shuffle_batch(x)
self.assertIsNotNone(out1)
self.assertIsNotNone(out2)
return out1
......@@ -2156,9 +2163,7 @@ class TestBook(LayerTest):
with self.static_graph():
x = paddle.static.data(name="x", shape=[None, 3], dtype="float32")
y = paddle.static.data(name="y", shape=[None, 3], dtype="float32")
sum = fluid.contrib.layers.partial_sum(
[x, y], start_index=0, length=2
)
sum = partial_sum([x, y], start_index=0, length=2)
return sum
def test_batch_fc(self):
......@@ -2166,7 +2171,7 @@ class TestBook(LayerTest):
input = paddle.static.data(
name="input", shape=[16, 2, 3], dtype="float32"
)
out = fluid.contrib.layers.batch_fc(
out = batch_fc(
input=input,
param_size=[16, 3, 10],
param_attr=fluid.ParamAttr(
......@@ -2192,7 +2197,7 @@ class TestBook(LayerTest):
rank_offset = paddle.static.data(
name="rank_offset", shape=[None, 7], dtype="int32"
)
out = fluid.contrib.layers.rank_attention(
out = rank_attention(
input=input,
rank_offset=rank_offset,
rank_param_shape=[18, 3],
......@@ -2263,12 +2268,8 @@ class TestBook(LayerTest):
with self.static_graph():
x = paddle.static.data(name="x", shape=[None, 3], dtype="float32")
y = paddle.static.data(name="y", shape=[None, 3], dtype="float32")
concat1 = fluid.contrib.layers.partial_concat(
[x, y], start_index=0, length=2
)
concat2 = fluid.contrib.layers.partial_concat(
x, start_index=0, length=-1
)
concat1 = partial_concat([x, y], start_index=0, length=2)
concat2 = partial_concat(x, start_index=0, length=-1)
return concat1, concat2
def test_addmm(self):
......
python/paddle/fluid/tests/unittests/test_pow2_decay_with_linear_warmup_op.py
浏览文件 @ 802be98b
......@@ -15,7 +15,7 @@
import unittest
import paddle
from paddle.fluid.contrib.layers.nn import pow2_decay_with_linear_warmup
from paddle.incubate.layers.nn import pow2_decay_with_linear_warmup
from paddle.optimizer.lr import LinearWarmup, PolynomialDecay
......
python/paddle/fluid/tests/unittests/test_pull_gpups_sparse_op.py
浏览文件 @ 802be98b
......@@ -18,7 +18,7 @@ import numpy as np
import paddle
from paddle import fluid
from paddle.fluid.layers.nn import _pull_gpups_sparse
from paddle.incubate.layers import _pull_gpups_sparse
paddle.enable_static()
......
python/paddle/fluid/tests/unittests/test_pyramid_hash_op.py
浏览文件 @ 802be98b
......@@ -18,6 +18,7 @@ import numpy as np
import paddle
from paddle import fluid
from paddle.incubate.layers.nn import search_pyramid_hash
class TestPyramidHashOpApi(unittest.TestCase):
......@@ -28,7 +29,7 @@ class TestPyramidHashOpApi(unittest.TestCase):
x = paddle.static.data(
name='x', shape=x_shape, dtype='int32', lod_level=1
)
hash_embd = fluid.contrib.search_pyramid_hash(
hash_embd = search_pyramid_hash(
input=x,
num_emb=embed_dim,
space_len=num_voc * embed_dim,
......
python/paddle/fluid/tests/unittests/test_tdm_child_op.py
浏览文件 @ 802be98b
......@@ -19,6 +19,7 @@ from eager_op_test import OpTest, paddle_static_guard
import paddle
from paddle import fluid
from paddle.incubate.layers.nn import tdm_child
def create_tdm_tree():
......@@ -147,7 +148,7 @@ class TestTDMChildShape(unittest.TestCase):
tdm_tree_info = create_tdm_tree()
tree_info_np = np.array(tdm_tree_info).astype('int32')
child, leaf_mask = fluid.contrib.layers.tdm_child(
child, leaf_mask = tdm_child(
x=x,
node_nums=26,
child_nums=2,
......
python/paddle/fluid/tests/unittests/test_tdm_sampler_op.py
浏览文件 @ 802be98b
......@@ -20,6 +20,7 @@ from eager_op_test import OpTest, paddle_static_guard
import paddle
from paddle import fluid
from paddle.fluid import core
from paddle.incubate.layers.nn import tdm_sampler
def create_tdm_travel():
......@@ -284,7 +285,7 @@ class TestTDMSamplerShape(unittest.TestCase):
neg_samples_num_list = [1, 2, 3, 4]
leaf_node_num = 13
sample, label, mask = fluid.contrib.layers.tdm_sampler(
sample, label, mask = tdm_sampler(
x,
neg_samples_num_list,
layer_node_num_list,
......
python/paddle/fluid/tests/unittests/test_while_op.py
浏览文件 @ 802be98b
......@@ -21,6 +21,7 @@ from paddle import fluid
from paddle.fluid import core
from paddle.fluid.backward import append_backward
from paddle.fluid.executor import Executor
from paddle.incubate.layers.nn import shuffle_batch
paddle.enable_static()
......@@ -145,7 +146,7 @@ class TestIgnoreVarNameInWhile(unittest.TestCase):
def body_func(i, ten, batch_info, origin_seq):
print(batch_info)
batch_info = fluid.contrib.layers.shuffle_batch(batch_info)
batch_info = shuffle_batch(batch_info)
print(batch_info)
i = i + 1
return [i, ten, batch_info, origin_seq]
......
python/paddle/incubate/__init__.py
浏览文件 @ 802be98b
......@@ -35,6 +35,7 @@ from . import autotune # noqa: F401
from . import nn # noqa: F401
from . import asp # noqa: F401
from . import multiprocessing # noqa: F401
from . import layers
from .nn.loss import identity_loss
......
python/paddle/incubate/distributed/fleet/fleet_util.py
浏览文件 @ 802be98b
......@@ -1387,7 +1387,7 @@ class FleetUtil:
label=label, curve='ROC',\
num_thresholds=4096)
local_sqrerr, local_abserr, local_prob, local_q, local_pos_ins,\
local_total_ins = fluid.contrib.layers.ctr_metric_bundle(\
local_total_ins = paddle.static.ctr_metric_bundle(\
similarity_norm, label)
"""
......@@ -1587,7 +1587,7 @@ class FleetUtil:
label=label, curve='ROC',\
num_thresholds=4096)
local_sqrerr, local_abserr, local_prob, local_q, local_pos_ins, \
local_total_ins = fluid.contrib.layers.ctr_metric_bundle(\
local_total_ins = paddle.static.ctr_metric_bundle(\
similarity_norm, label)
"""
......
python/paddle/fluid/contrib/layers/__init__.py → python/paddle/incubate/layers/__init__.py
浏览文件 @ 802be98b
......@@ -13,11 +13,25 @@
# limitations under the License.
from . import nn
from .nn import *
from . import metric_op
from .metric_op import *
from .nn import (
fused_embedding_seq_pool,
fused_seqpool_cvm,
multiclass_nms2,
search_pyramid_hash,
shuffle_batch,
partial_concat,
partial_sum,
tdm_child,
tdm_sampler,
rank_attention,
batch_fc,
_pull_box_extended_sparse,
bilateral_slice,
correlation,
fused_bn_add_act,
pow2_decay_with_linear_warmup,
_pull_gpups_sparse,
_pull_box_sparse,
)
__all__ = []
__all__ += nn.__all__
__all__ += metric_op.__all__
python/paddle/fluid/contrib/layers/nn.py → python/paddle/incubate/layers/nn.py
浏览文件 @ 802be98b
......@@ -12,50 +12,26 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contrib layers just related to the neural network.
incubate layers just related to the neural network.
"""
import os
import warnings
import inspect
import numpy as np
import paddle
from paddle.fluid.layer_helper import LayerHelper
from ... import unique_name
from paddle import _legacy_C_ops
from paddle.fluid import core, unique_name
from paddle.fluid.data_feeder import (
check_variable_and_dtype,
check_type,
check_dtype,
convert_dtype,
check_type,
check_variable_and_dtype,
)
from paddle.fluid import core
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.framework import Variable, convert_np_dtype_to_dtype_
import paddle
import warnings
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.param_attr import ParamAttr
__all__ = [
'fused_embedding_seq_pool',
'multiclass_nms2',
'search_pyramid_hash',
'shuffle_batch',
'partial_concat',
'sparse_embedding',
'partial_sum',
'tdm_child',
'rank_attention',
'tdm_sampler',
'batch_fc',
'_pull_box_extended_sparse',
'bilateral_slice',
'correlation',
'fused_bn_add_act',
'fused_seqpool_cvm',
]
__all__ = []
def fused_embedding_seq_pool(
......@@ -73,7 +49,7 @@ def fused_embedding_seq_pool(
This layer is the fusion of lookup table and sequence_pool.
Args:
input (Variable): Input is a Tensor<int64> Variable, which contains the IDs' information.
input (Tensor): Input is a Tensor<int64> , which contains the IDs' information.
The value of the input IDs should satisfy :math:`0<= id < size[0]`.
size (tuple|list): The shape of the lookup_table parameter. It should
have two elements which indicate the size of the dictionary of
......@@ -91,7 +67,7 @@ def fused_embedding_seq_pool(
dtype (np.dtype|core.VarDesc.VarType|str): The dtype refers to the data type of output
tensor. It can be float32, float_16, int etc.
Returns:
The sequence pooling variable which is a Tensor.
The Tensor of sequence pooling.
Examples:
.. code-block:: python
import numpy as np
......@@ -103,7 +79,7 @@ def fused_embedding_seq_pool(
data_t = paddle.static.data(
name='word', shape=[-1, 1], dtype='int64', lod_level=1)
padding_idx = np.random.randint(1, 10)
out = fluid.contrib.fused_embedding_seq_pool(
out = paddle.incubate.layers.fused_embedding_seq_pool(
input=data_t,
size=[dict_size, 32],
param_attr='w',
......@@ -146,15 +122,15 @@ def fused_seqpool_cvm(
**Note:** The Op only receives List of LoDTensor as input, only support SUM pooling now.
Args:
input(Variable|list of Variable): Input is List of LoDTensor.
input(Tensor): Input is List of LoDTensor.
pool_type(str): pooling type, only support SUM pooling now.
cvm(Variable): cvm Variable.
cvm(Tensor): cvm Tensor.
pad_value(float, optional): padding value of sequence pool. Default: 0.0.
use_cvm(bool, optional): use cvm or not. Default: True.
cvm_offset(int, optional): cvm offset. Default: 2, which means cvm contains show, click.
Returns:
Variable|list of Variable: The tensor variable storing sequence pool and cvm
Tensor : The tensor storing sequence pool and cvm
of input.
Examples:
......@@ -167,14 +143,14 @@ def fused_seqpool_cvm(
data = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=1)
data2 = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=1)
inputs = [data, data2]
embs = fluid.layers.nn._pull_box_sparse(input=inputs, size=11, is_distributed=True, is_sparse=True)
embs = paddle.incubate.layers.nn._pull_box_sparse(input=inputs, size=11, is_distributed=True, is_sparse=True)
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64", lod_level=1)
ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1)
show_clk = paddle.cast(paddle.concat([ones, label], axis=1), dtype='float32')
show_clk.stop_gradient = True
cvms = fluid.contrib.layers.fused_seqpool_cvm(embs, 'sum', show_clk)
cvms = paddle.incubate.layers.fused_seqpool_cvm(embs, 'sum', show_clk)
"""
......@@ -243,7 +219,7 @@ def multiclass_nms2(
per image if keep_top_k is larger than -1.
Args:
bboxes (Variable): Two types of bboxes are supported:
bboxes (Tensor): Two types of bboxes are supported:
1. (Tensor) A 3-D Tensor with shape
[N, M, 4 or 8 16 24 32] represents the
predicted locations of M bounding bboxes,
......@@ -253,7 +229,7 @@ def multiclass_nms2(
2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
M is the number of bounding boxes, C is the
class number
scores (Variable): Two types of scores are supported:
scores (Tensor): Two types of scores are supported:
1. (Tensor) A 3-D Tensor with shape [N, C, M]
represents the predicted confidence predictions.
N is the batch size, C is the class number, M is
......@@ -283,8 +259,8 @@ def multiclass_nms2(
name(str): Name of the multiclass nms op. Default: None.
Returns:
A tuple with two Variables: (Out, Index) if return_index is True,
otherwise, a tuple with one Variable(Out) is returned.
A tuple with two dimensions of the tensor: (Out, Index) if return_index is True,
otherwise, a tuple with one dimension of the tensor(Out) is returned.
Out: A 2-D LoDTensor with shape [No, 6] represents the detections.
Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
or A 2-D LoDTensor with shape [No, 10] represents the detections.
......@@ -311,7 +287,7 @@ def multiclass_nms2(
dtype='float32', lod_level=1)
scores = paddle.static.data(name='scores', shape=[-1, 81],
dtype='float32', lod_level=1)
out, index = fluid.contrib.layers.multiclass_nms2(bboxes=boxes,
out, index = paddle.incubate.layers.multiclass_nms2(bboxes=boxes,
scores=scores,
background_label=0,
score_threshold=0.5,
......@@ -371,7 +347,7 @@ def search_pyramid_hash(
**Pyramid hash embedding**
Args:
input (Variable): LoDTensor<int32> Variable contained the IDs' information.
input (Tensor): LoDTensor<int32> Tensor contained the IDs' information.
num_emb (int): The embedding size of output.
space_len (int): The length of pyramid hash embedding space.
pyramid_layer (int): The number of pyramid layers. It should be greater than 2.
......@@ -396,9 +372,9 @@ def search_pyramid_hash(
Used in Distribute Transpiler to create a trainer/server program.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
dtype(str): The data type of output variable, float32.
dtype(str): The data type of output Tensor, float32.
Returns:
Variable: LoDTensor of pyramid hash embedding.
Tensor: LoDTensor of pyramid hash embedding.
"""
helper = LayerHelper('search_pyramid_hash', **locals())
......@@ -438,7 +414,7 @@ def search_pyramid_hash(
for param in distribute_update_vars:
if param not in special_name_list:
raise ValueError(
"Pyramid Hash layer didn't have parameter {}".format(param)
f"Pyramid Hash layer didn't have parameter {param}"
)
distribute_update_vars_str = ",".join(distribute_update_vars)
......@@ -491,12 +467,12 @@ def shuffle_batch(x, seed=None):
Out.dims = [4, 2]
Args:
x (Variable): The input variable. The input variable is a N-D LoDTensor with type int, float32 or float64.
seed (None|int|Variable): The start up seed. If set, seed will be set as the start up seed of shuffle engine.
x (Tensor): The input Tensor. The input Tensor is a N-D LoDTensor with type int, float32 or float64.
seed (None|int|Tensor): The start up seed. If set, seed will be set as the start up seed of shuffle engine.
If not set(Default), start up seed of shuffle engine will be generated randomly.
Returns:
Variables: The shuffled LoDTensor with the same shape and lod as input.
Tensor: The shuffled LoDTensor with the same shape and lod as input.
Examples:
......@@ -506,7 +482,7 @@ def shuffle_batch(x, seed=None):
import paddle
paddle.enable_static()
x = paddle.static.data(name="x", shape=[-1, 4])
out = fluid.contrib.layers.shuffle_batch(x)
out = paddle.incubate.layers.shuffle_batch(x)
"""
helper = LayerHelper('shuffle_batch', **locals())
......@@ -537,7 +513,7 @@ def partial_concat(input, start_index=0, length=-1):
"""
**Partial Concat**
This OP concatenates the inputs according to the start index and length. This
OP exists in contrib, which means that it is not shown to the public.
OP exists in incubate layers, which means that it is not shown to the public.
Only 2-D Tensor or LodTensor input is supported. Slice and concat can only be
performed along the second dimension.
......@@ -563,14 +539,14 @@ def partial_concat(input, start_index=0, length=-1):
length(int32): The length of each instance for partial concatenation. Default is -1.
Negative values for all elements after start_index.
Returns:
Variable: A Tensor with the same data type as input's.
Tensor: A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
x = paddle.randn(name="x", shape=[1,3], dtype="float32")
y = paddle.randn(name="y", shape=[1,3], dtype="float32")
concat = fluid.contrib.layers.partial_concat(
concat = paddle.incubate.layers.partial_concat(
[x, y], start_index=0, length=2)
"""
if not isinstance(input, list):
......@@ -605,7 +581,7 @@ def partial_sum(input, start_index=0, length=-1):
"""
**PartialSum**
This Op can sum the vars by specifying the initial position(start_index) and length(length).
This Op exists in contrib, which means that it is not shown to the public.
This Op exists in incubate layers, which means that it is not shown to the public.
Only 2-D Tensor or LodTensor input is supported. Slice and concat can only be
performed along the second dimension.
.. code-block:: text
......@@ -624,10 +600,9 @@ def partial_sum(input, start_index=0, length=-1):
input(list): List of input Tensors with data type float32, float64, int32,
int64.
Returns:
Variable: A Tensor with the same data type as input's.
Tensor: A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle.fluid.layers as layers
import paddle.fluid as fluid
import numpy as np
import paddle
......@@ -635,7 +610,7 @@ def partial_sum(input, start_index=0, length=-1):
x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
y = paddle.static.data(name="y", shape=[2, 3], dtype="float32")
sum = fluid.contrib.layers.partial_sum([x,y], start_index=0, length=2)
sum = paddle.incubate.layers.partial_sum([x,y], start_index=0, length=2)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
xx = np.array([1,2,3,4,5,6]).reshape((2,3)).astype("float32")
......@@ -662,203 +637,6 @@ def partial_sum(input, start_index=0, length=-1):
return out
def sparse_embedding(
input,
size,
padding_idx=None,
is_test=False,
entry=None,
table_class="MemorySparseTable",
param_attr=None,
dtype='float32',
slot=None,
):
r"""
:api_attr: Static Graph
The OP is used as the operator of the Embedding Lookup layer in the large-scale
sparse training of the parameter server mode, instead of using the paddle.nn.functional.embedding.
The operator is used to lookup embeddings vector of ids provided by :attr:`input` .
It automatically constructs a 2D embedding matrix based on the input :attr:`size`
(vocab_size, emb_size) and :attr:`dtype` .
The shape of output Tensor is generated by appending an emb_size dimension to the
last dimension of the input Tensor shape.
**Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` , otherwise
the program will throw an exception and exit.
.. code-block:: text
Case 1:
input is a Tensor. padding_idx = -1
input.data = [[1, 3], [2, 4], [4, 127]]
input.shape = [3, 2]
Given size = [128, 16]
output is a Tensor:
out.shape = [3, 2, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452],
[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745],
[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.945345345, 0.435394634, ..., 0.435345365],
[0.0, 0.0, ..., 0.0 ]]] # padding data
The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127
It will pad all-zero data when ids is 127.
Case 2:
input is a LoDTensor with 1-level LoD. padding_idx = 0
input.lod = [[2, 3]]
input.data = [[1], [3], [2], [4], [0]]
input.shape = [5, 1]
Given size = [128, 16]
output is a LoDTensor:
out.lod = [[2, 3]]
out.shape = [5, 1, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452]],
[[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745]],
[[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.0, 0.0, ..., 0.0 ]]] # padding data
It will pad all-zero data when ids is 0.
Args:
input(Variable): A Tensor or LoDTensor with type int64, which contains the id
information. The value of the input id should satisfy :math:`0<= id < size[0]` .
size(tuple|list): The shape of lookup table parameter (vocab_size, emb_size). It
should have two elements which indicates the size of the dictionary of embeddings
and the size of each embedding vector respectively. The initial parameter size
is 0 in the large-scale sparse scenario, which will gradually expand with the
training. So if vocab_size is temporarily useless, its value can be any integer.
The emb_size is the dimensional configuration of the word embedding weight parameter.
padding_idx(int|long|None, optional): padding_idx needs to be in the interval [-vocab_size, vocab_size).
If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever
lookup encounters :math:`padding\_idx` in id. And the padding data will not be updated
while training. If set None, it makes no efe mfect to output. Default: None.
is_test(bool, optional): Training or prediction mode. In prediction mode (is_test=False),
the output is not initialized and created, and it is filled with 0 and returned. Default: False.
entry(str, optional): Entry config with parameter server whose value is ProbabilityEntry,
CountFilterEntry or None. Default: None.
table_class(str, optional): The type of the sparse table. The value can be CommonSparseTable
or SSDSparseTable. The default is CommonSparseTable.
param_attr(ParamAttr, optional): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. In addition, user-defined or pre-trained word
vectors can be loaded with the :attr:`param_attr` parameter. The local word vector needs
to be transformed into numpy format, and the shape of local word vector should be consistent
with :attr:`size` .
dtype(str): It refers to the data type of output Tensor. It must be float32 or
float64. Default: float32.
Returns:
Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` .
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
sparse_feature_dim = 1024
embedding_size = 64
# Only when the feature appear more than 10 times or more will be participated in the training.
entry = paddle.distributed.CountFilterEntry(10)
input = paddle.static.data(name='ins', shape=[1], dtype='int64')
emb = paddle.static.nn.sparse_embedding(
input=input,
size=[sparse_feature_dim, embedding_size],
is_test=False,
entry=entry,
param_attr=paddle.ParamAttr(name="SparseFeatFactors",
initializer=paddle.nn.initializer.Uniform()))
"""
helper = LayerHelper('sparse_embedding', **locals())
check_variable_and_dtype(
input, 'input', ['int64'], 'fluid.contrib.layers.sparse_embedding'
)
check_dtype(
dtype,
'dtype',
['float32', 'float64'],
'paddle.static.nn.sparse_embedding',
)
if input.size == 0:
raise ValueError("input size should not be 0")
w = helper.create_parameter(
attr=helper.param_attr,
shape=size,
type=core.VarDesc.VarType.SELECTED_ROWS,
dtype=dtype,
is_bias=False,
)
tmp = helper.create_variable_for_type_inference(dtype)
padding_idx = (
-1
if padding_idx is None
else padding_idx
if padding_idx >= 0
else (size[0] + padding_idx)
)
if table_class not in [
"CommonSparseTable",
"SSDSparseTable",
"MemorySparseTable",
]:
raise ValueError(
"table_class must be in [CommonSparseTable, SSDSparseTable, MemorySparseTable]"
)
entry_str = "none"
if entry is not None:
if entry.__class__.__name__ not in [
"ProbabilityEntry",
"CountFilterEntry",
"ShowClickEntry",
]:
raise ValueError(
"entry must be instance in [paddle.distributed.ProbabilityEntry, paddle.distributed.CountFilterEntry, paddle.distributed.ShowClickEntry]"
)
entry_str = entry._to_attr()
if slot is None:
slot = 0
helper.append_op(
type='lookup_table',
inputs={'Ids': input, 'W': w},
outputs={'Out': tmp},
attrs={
'padding_idx': padding_idx,
'is_sparse': True,
'is_distributed': True,
'remote_prefetch': True,
'is_test': is_test,
'entry': entry_str,
'table_class': table_class,
'slot': slot,
},
)
return tmp
def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
"""
**Tdm Child**
......@@ -878,7 +656,7 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
leaf_mask = [[1, 1],
[0, 0]]
Args:
x(Variable): Variable contained the node_id information, dtype support int32/int64.
x(Tensor): Tensor contained the node_id information, dtype support int32/int64.
node_nums(int): Number of total nodes.
child_nums(int): Maximum number of child nodes per node.
param_attr(ParamAttr): To specify the tdm-tree-info parameter property. Default: None, which means the
......@@ -893,7 +671,7 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
dtype(str): The data type of output child and leaf_mask, support int32/int64.
Returns:
tuple: A tuple including input node's child(Variable) and leaf_mask(Variable).
tuple: A tuple including input node's child(Tensor) and leaf_mask(Tensor).
If child is a leaf node, leaf_mask equal ot 1, otherwise equal to 0.
Examples:
......@@ -910,7 +688,7 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
tree_info_np = np.reshape(tree_info_np, (7,5))
node_nums = 7
child_nums = 2
child, leaf_mask = fluid.contrib.layers.tdm_child(x, node_nums, child_nums,
child, leaf_mask = paddle.incubate.layers.tdm_child(x, node_nums, child_nums,
param_attr=fluid.ParamAttr(
initializer=paddle.nn.initializer.Assign(
tree_info_np)))
......@@ -922,7 +700,7 @@ def tdm_child(x, node_nums, child_nums, param_attr=None, dtype='int32'):
"""
helper = LayerHelper("tdm_child", **locals())
check_dtype(
dtype, 'dtype', ['int32', 'int64'], 'fluid.contrib.layers.tdm_child'
dtype, 'dtype', ['int32', 'int64'], 'paddle.incubate.layers.tdm_child'
)
c_dtype = convert_np_dtype_to_dtype_(dtype)
tree_info = helper.create_parameter(
......@@ -981,7 +759,7 @@ def tdm_sampler(
mask = [[1, 1], [1, 1], [1, 1], [1, 1]]
Args:
x (Variable): Variable contained the item_id(corresponding to leaf node) information, dtype support int32/int64.
x (Tensor): Tensor contained the item_id(corresponding to leaf node) information, dtype support int32/int64.
neg_samples_num_list (list(int)): Number of negative samples per layer.
layer_node_num_list (list(int)): Number of nodes per layer, must has same shape with neg_samples_num_list.
leaf_node_num (int): Number of leaf nodes.
......@@ -1003,7 +781,7 @@ def tdm_sampler(
and if it is a negative sample, it is 0. If the tree is unbalanced, in order to ensure the consistency of the
sampling result shape, the padding sample's mask = 0, the real sample's mask value = 1.
If output_list = True, the result will organize into list format specified by layer information.
Output variable have same type with tdm-travel and tdm-layer parameter(tree_dtype).
Output Tensor have same type with tdm-travel and tdm-layer parameter(tree_dtype).
Examples:
.. code-block:: python
......@@ -1022,7 +800,7 @@ def tdm_sampler(
travel_array = np.array(travel_list)
layer_array = np.array(layer_list_flat)
sample, label, mask = fluid.contrib.layers.tdm_sampler(
sample, label, mask = paddle.incubate.layers.tdm_sampler(
x,
neg_samples_num_list,
layer_node_num_list,
......@@ -1051,10 +829,10 @@ def tdm_sampler(
tree_dtype,
'tree_dtype',
['int32', 'int64'],
'fluid.contrib.layers.tdm_sampler',
'paddle.incubate.layers.tdm_sampler',
)
check_dtype(
dtype, 'dtype', ['int32', 'int64'], 'fluid.contrib.layers.tdm_sampler'
dtype, 'dtype', ['int32', 'int64'], 'paddle.incubate.layers.tdm_sampler'
)
c_dtype = convert_np_dtype_to_dtype_(dtype)
......@@ -1189,7 +967,7 @@ def rank_attention(
This Op can calculate rank attention between input and rank_param, and
rank_param gives the organization of data. Notice: It currently supports
GPU device.
This Op exists in contrib, which means that it is not shown to the public.
This Op exists in incubate layers, which means that it is not shown to the public.
Args:
input: Tensor with data type float32, float64.
rank_offset: Tensor with data type int32.
......@@ -1197,7 +975,7 @@ def rank_attention(
rank_param_attr: Attribute initializer of rank_param.
max_rank: The max rank of input's ranks.
Returns:
Variable: A Tensor with the same data type as input's.
Tensor: A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle.fluid as fluid
......@@ -1206,7 +984,7 @@ def rank_attention(
input = paddle.static.data(name="input", shape=[None, 2], dtype="float32")
rank_offset = paddle.static.data(name="rank_offset", shape=[None, 7], dtype="int32")
out = fluid.contrib.layers.rank_attention(input=input,
out = paddle.incubate.layers.rank_attention(input=input,
rank_offset=rank_offset,
rank_param_shape=[18,3],
rank_param_attr=
......@@ -1248,7 +1026,7 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
This Op can calculate BatchFC. This is similar to matmul op,
except that the bias and relu activation layers are added.
Notice: It currently supports GPU device.
This Op exists in contrib, which means that it is not shown to the public.
This Op exists in incubate layers, which means that it is not shown to the public.
Args:
input: Tensor with data type float32, float64.
param_size: The size of w.
......@@ -1258,7 +1036,7 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
act: Activation to be applied to the output of this layer.
Returns:
Variable: A Tensor with the same data type as input's.
Tensor: A Tensor with the same data type as input's.
Examples:
.. code-block:: python
import paddle.fluid as fluid
......@@ -1267,7 +1045,7 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
paddle.enable_static()
input = paddle.static.data(name="input", shape=[16, 2, 3], dtype="float32")
out = fluid.contrib.layers.batch_fc(input=input,
out = paddle.incubate.layers.batch_fc(input=input,
param_size=[16, 3, 10],
param_attr=
paddle.ParamAttr(learning_rate=1.0,
......@@ -1312,7 +1090,7 @@ def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'):
BoxPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable, which
input(Tensor): Input is a Tensor<int64>, which
contains the IDs information.
size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively.
......@@ -1321,13 +1099,13 @@ def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'):
dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now.
Returns:
Variable|list of Variable: The tensor variable storing the embeddings of the \
Tensor: The tensor storing the embeddings of the \
supplied inputs.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = paddle.static.data(name='sequence', shape=[-1, 1], dtype='int64', lod_level=1)
emb, emb_ex = fluid.contrib.layers._pull_box_extended_sparse(input=data, size=8, extend_size=128)
emb, emb_ex = paddle.incubate.layers._pull_box_extended_sparse(input=data, size=8, extend_size=128)
"""
helper = LayerHelper('pull_box_extended_sparse', **locals())
helper.input_dtype()
......@@ -1361,13 +1139,13 @@ def bilateral_slice(x, guide, grid, has_offset, name=None):
For more information of bilateral slicing, please refer to Deep Bilateral Learning for Real-Time Image Enhancement <https://groups.csail.mit.edu/graphics/hdrnet/data/hdrnet.pdf>_
Args:
x(Variable): The input tensor, which is a 4-D tensor with shape
x(Tensor): The input tensor, which is a 4-D tensor with shape
[N, C, H, W], N is the batch size, C is the channel
number, H and W is the feature height and width.
The data type is float32 and float64.
guide(Variable): Input grid tensor of shape [N, H, W]. The
guide(Tensor): Input grid tensor of shape [N, H, W]. The
data type is float32 and float64.
grid(Variable): Input grid tensor of shape [N, C, D, H, W]. The
grid(Tensor): Input grid tensor of shape [N, C, D, H, W]. The
data type is float32 and float64.
has_offset(bool): Whether to slice with affine offset.
name(str, optional): For detailed information, please refer
......@@ -1375,7 +1153,7 @@ def bilateral_slice(x, guide, grid, has_offset, name=None):
None by default.
Returns:
Variable: Output of shape [N, C, H, W]. The data type is same as input tensor.
Tensor: Output of shape [N, C, H, W]. The data type is same as input tensor.
Examples:
......@@ -1390,15 +1168,15 @@ def bilateral_slice(x, guide, grid, has_offset, name=None):
grid = paddle.randn(name='grid', shape=[1, 12, 8, 10, 6], dtype='float32')
# without offset
output = fluid.contrib.bilateral_slice(x, guide, grid, has_offset=False)
output = paddle.incubate.layers.bilateral_slice(x, guide, grid, has_offset=False)
# has offset
output = fluid.contrib.bilateral_slice(x, guide, grid, has_offset=True)
output = paddle.incubate.layers.bilateral_slice(x, guide, grid, has_offset=True)
"""
if paddle.fluid._non_static_mode():
attrs = ('has_offset', has_offset)
return getattr(_legacy_C_ops, "bilateral_slice")(x, grid, guide, *attrs)
return _legacy_C_ops.bilateral_slice(x, grid, guide, *attrs)
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'bilateral_slice')
check_variable_and_dtype(
......@@ -1463,7 +1241,7 @@ def correlation(
dtype="float32")
out = fluid.contrib.correlation(
out = paddle.incubate.layers.correlation(
x1,
x2,
pad_size=4,
......@@ -1489,7 +1267,7 @@ def correlation(
"corr_type_multiply",
corr_type_multiply,
)
output = getattr(_legacy_C_ops, "correlation")(x, y, *attrs)
output = _legacy_C_ops.correlation(x, y, *attrs)
else:
helper = LayerHelper("correlation", **locals())
output = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -1593,7 +1371,7 @@ def fused_bn_add_act(
input=conv1_1,
act=None,
data_layout='NHWC')
fused_bn_add_act = fluid.contrib.layers.fused_bn_add_act(conv1_2, bn)
fused_bn_add_act = paddle.incubate.layers.fused_bn_add_act(conv1_2, bn)
prediction = paddle.static.nn.fc(x=fused_bn_add_act, size=10, activation='softmax')
loss = paddle.nn.functional.cross_entropy(
input=prediction, label=y,
......@@ -1757,3 +1535,125 @@ def pow2_decay_with_linear_warmup(
},
)
return lr
def _pull_gpups_sparse(
input, size, dtype='float32', is_distributed=False, is_sparse=False
):
r"""
**Pull GpuPS Sparse Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
GpuPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
Args:
input(Tensor): Input is a Tensor<int64>, which
contains the IDs information.
size(int|list of int): The embedding size parameter of each input, which indicates the size of
each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now.
Returns:
Tensor: The tensor storing the embeddings of the \
supplied inputs, whose size are indicated by size respectively.
Examples:
.. code-block:: python
import paddle.incubate as incubate
slots = []
data_1 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
slots.append(data_1)
data_2 = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
slots.append(data_2)
embs = incubate.layers.pull_gpups_sparse(input=slots, size=[11, 35])
"""
helper = LayerHelper('pull_gpups_sparse', **locals())
if dtype != 'float32':
raise ValueError(
"GpuPS only support float type embedding now, and your type is: "
+ dtype
)
helper.input_dtype()
inputs = helper.multiple_input()
outs = [
helper.create_variable_for_type_inference(dtype)
for i in range(len(inputs))
]
w = helper.create_parameter(
attr=helper.param_attr, shape=[size[0]], dtype=dtype, is_bias=False
)
helper.append_op(
type='pull_gpups_sparse',
inputs={'Ids': inputs, 'W': w},
outputs={'Out': outs},
attrs={
'size': size,
'is_distributed': is_distributed,
'is_sparse': is_sparse,
},
)
if len(outs) == 1:
return outs[0]
return outs
def _pull_box_sparse(
input, size, dtype='float32', is_distributed=False, is_sparse=False
):
r"""
**Pull Box Sparse Layer**
This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
BoxPS lookup table. The result of this lookup is the embedding of each ID in the
:attr:`input`.
Args:
input(Tensor): Input is a Tensor<int64>, which
contains the IDs information.
size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now.
Returns:
Tensor: The tensor storing the embeddings of the \
supplied inputs.
Examples:
.. code-block:: python
import paddle.incubate as incubate
data = paddle.static.data(name='sequence', shape=[-1,1], dtype='int64', lod_level=1)
emb = incubate.layers.pull_box_sparse(input=data, size=[11])
"""
helper = LayerHelper('pull_box_sparse', **locals())
if dtype != 'float32':
raise ValueError(
"BoxPS only support float type embedding now, and your type is: "
+ dtype
)
helper.input_dtype()
inputs = helper.multiple_input()
outs = [
helper.create_variable_for_type_inference(dtype)
for i in range(len(inputs))
]
w = helper.create_parameter(
attr=helper.param_attr, shape=[size], dtype=dtype, is_bias=False
)
helper.append_op(
type='pull_box_sparse',
inputs={'Ids': inputs, 'W': w},
outputs={'Out': outs},
attrs={
'size': size,
'is_distributed': is_distributed,
'is_sparse': is_sparse,
},
)
if len(outs) == 1:
return outs[0]
return outs
python/paddle/static/__init__.py
浏览文件 @ 802be98b
......@@ -71,12 +71,12 @@ from ..fluid.optimizer import Optimizer # noqa: F401
from ..fluid.optimizer import Adam # noqa: F401
from ..fluid.optimizer import ExponentialMovingAverage # noqa: F401
from ..fluid.contrib.layers import ctr_metric_bundle # noqa: F401
from ..fluid.layers import exponential_decay # noqa: F401
from ..fluid.layers import learning_rate_scheduler # noqa: F401
from .nn.metric import auc # noqa: F401
from .nn.metric import accuracy # noqa: F401
from .nn.metric import ctr_metric_bundle # noqa: F401
__all__ = [ # noqa
'append_backward',
......
python/paddle/static/nn/__init__.py
浏览文件 @ 802be98b
......@@ -39,7 +39,7 @@ from .common import layer_norm # noqa: F401
from .common import embedding # noqa: F401
from ...fluid.contrib.layers import sparse_embedding # noqa: F401
from .common import sparse_embedding # noqa: F401
from ...fluid.layers import StaticRNN # noqa: F401
from .sequence_lod import sequence_conv # noqa: F401
......
python/paddle/static/nn/common.py
浏览文件 @ 802be98b
......@@ -3810,3 +3810,200 @@ def embedding(
},
)
return tmp
def sparse_embedding(
input,
size,
padding_idx=None,
is_test=False,
entry=None,
table_class="MemorySparseTable",
param_attr=None,
dtype='float32',
slot=None,
):
r"""
:api_attr: Static Graph
The OP is used as the operator of the Embedding Lookup layer in the large-scale
sparse training of the parameter server mode, instead of using the paddle.nn.functional.embedding.
The operator is used to lookup embeddings vector of ids provided by :attr:`input` .
It automatically constructs a 2D embedding matrix based on the input :attr:`size`
(vocab_size, emb_size) and :attr:`dtype` .
The shape of output Tensor is generated by appending an emb_size dimension to the
last dimension of the input Tensor shape.
**Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` , otherwise
the program will throw an exception and exit.
.. code-block:: text
Case 1:
input is a Tensor. padding_idx = -1
input.data = [[1, 3], [2, 4], [4, 127]]
input.shape = [3, 2]
Given size = [128, 16]
output is a Tensor:
out.shape = [3, 2, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452],
[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745],
[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.945345345, 0.435394634, ..., 0.435345365],
[0.0, 0.0, ..., 0.0 ]]] # padding data
The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127
It will pad all-zero data when ids is 127.
Case 2:
input is a LoDTensor with 1-level LoD. padding_idx = 0
input.lod = [[2, 3]]
input.data = [[1], [3], [2], [4], [0]]
input.shape = [5, 1]
Given size = [128, 16]
output is a LoDTensor:
out.lod = [[2, 3]]
out.shape = [5, 1, 16]
out.data = [[[0.129435295, 0.244512452, ..., 0.436322452]],
[[0.345421456, 0.524563927, ..., 0.144534654]],
[[0.345249859, 0.124939536, ..., 0.194353745]],
[[0.945345345, 0.435394634, ..., 0.435345365]],
[[0.0, 0.0, ..., 0.0 ]]] # padding data
It will pad all-zero data when ids is 0.
Args:
input(Tensor): A Tensor or LoDTensor with type int64, which contains the id
information. The value of the input id should satisfy :math:`0<= id < size[0]` .
size(tuple|list): The shape of lookup table parameter (vocab_size, emb_size). It
should have two elements which indicates the size of the dictionary of embeddings
and the size of each embedding vector respectively. The initial parameter size
is 0 in the large-scale sparse scenario, which will gradually expand with the
training. So if vocab_size is temporarily useless, its value can be any integer.
The emb_size is the dimensional configuration of the word embedding weight parameter.
padding_idx(int|long|None, optional): padding_idx needs to be in the interval [-vocab_size, vocab_size).
If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever
lookup encounters :math:`padding\_idx` in id. And the padding data will not be updated
while training. If set None, it makes no efe mfect to output. Default: None.
is_test(bool, optional): Training or prediction mode. In prediction mode (is_test=False),
the output is not initialized and created, and it is filled with 0 and returned. Default: False.
entry(str, optional): Entry config with parameter server whose value is ProbabilityEntry,
CountFilterEntry or None. Default: None.
table_class(str, optional): The type of the sparse table. The value can be CommonSparseTable
or SSDSparseTable. The default is CommonSparseTable.
param_attr(ParamAttr, optional): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. In addition, user-defined or pre-trained word
vectors can be loaded with the :attr:`param_attr` parameter. The local word vector needs
to be transformed into numpy format, and the shape of local word vector should be consistent
with :attr:`size` .
dtype(str): It refers to the data type of output Tensor. It must be float32 or
float64. Default: float32.
Returns:
Tensor: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` .
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
sparse_feature_dim = 1024
embedding_size = 64
# Only when the feature appear more than 10 times or more will be participated in the training.
entry = paddle.distributed.CountFilterEntry(10)
input = paddle.static.data(name='ins', shape=[1], dtype='int64')
emb = paddle.static.nn.sparse_embedding(
input=input,
size=[sparse_feature_dim, embedding_size],
is_test=False,
entry=entry,
param_attr=paddle.ParamAttr(name="SparseFeatFactors",
initializer=paddle.nn.initializer.Uniform()))
"""
helper = LayerHelper('sparse_embedding', **locals())
check_variable_and_dtype(
input, 'input', ['int64'], 'paddle.incubate.layers.sparse_embedding'
)
check_dtype(
dtype,
'dtype',
['float32', 'float64'],
'paddle.static.nn.sparse_embedding',
)
if input.size == 0:
raise ValueError("input size should not be 0")
w = helper.create_parameter(
attr=helper.param_attr,
shape=size,
type=core.VarDesc.VarType.SELECTED_ROWS,
dtype=dtype,
is_bias=False,
)
tmp = helper.create_variable_for_type_inference(dtype)
padding_idx = (
-1
if padding_idx is None
else padding_idx
if padding_idx >= 0
else (size[0] + padding_idx)
)
if table_class not in [
"CommonSparseTable",
"SSDSparseTable",
"MemorySparseTable",
]:
raise ValueError(
"table_class must be in [CommonSparseTable, SSDSparseTable, MemorySparseTable]"
)
entry_str = "none"
if entry is not None:
if entry.__class__.__name__ not in [
"ProbabilityEntry",
"CountFilterEntry",
"ShowClickEntry",
]:
raise ValueError(
"entry must be instance in [paddle.distributed.ProbabilityEntry, paddle.distributed.CountFilterEntry, paddle.distributed.ShowClickEntry]"
)
entry_str = entry._to_attr()
if slot is None:
slot = 0
helper.append_op(
type='lookup_table',
inputs={'Ids': input, 'W': w},
outputs={'Out': tmp},
attrs={
'padding_idx': padding_idx,
'is_sparse': True,
'is_distributed': True,
'remote_prefetch': True,
'is_test': is_test,
'entry': entry_str,
'table_class': table_class,
'slot': slot,
},
)
return tmp
python/paddle/static/nn/metric.py
浏览文件 @ 802be98b
......@@ -317,3 +317,253 @@ def auc(
batch_auc_out,
[batch_stat_pos, batch_stat_neg, stat_pos, stat_neg],
)
def ctr_metric_bundle(input, label, ins_tag_weight=None):
"""
ctr related metric layer
This function help compute the ctr related metrics: RMSE, MAE, predicted_ctr, q_value.
To compute the final values of these metrics, we should do following computations using
total instance number:
MAE = local_abserr / instance number
RMSE = sqrt(local_sqrerr / instance number)
predicted_ctr = local_prob / instance number
q = local_q / instance number
Note that if you are doing distribute job, you should all reduce these metrics and instance
number first
Args:
input(Tensor): A floating-point 2D Tensor, values are in the range
[0, 1]. Each row is sorted in descending order. This
input should be the output of topk. Typically, this
Tensor indicates the probability of each label.
label(Tensor): A 2D int Tensor indicating the label of the training
data. The height is batch size and width is always 1.
ins_tag_weight(Tensor): A 2D int Tensor indicating the ins_tag_weight of the training
data. 1 means real data, 0 means fake data.
A LoDTensor or Tensor with type float32,float64.
Returns:
local_sqrerr(Tensor): Local sum of squared error
local_abserr(Tensor): Local sum of abs error
local_prob(Tensor): Local sum of predicted ctr
local_q(Tensor): Local sum of q value
Examples 1:
.. code-block:: python
import paddle
paddle.enable_static()
data = paddle.static.data(name="data", shape=[32, 32], dtype="float32")
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32")
predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(input=data, size=1))
auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label)
Examples 2:
.. code-block:: python
import paddle
paddle.enable_static()
data = paddle.static.data(name="data", shape=[32, 32], dtype="float32")
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32")
predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(input=data, size=1))
ins_tag_weight = paddle.static.data(name='ins_tag', shape=[-1,16], lod_level=0, dtype='int64')
auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label, ins_tag_weight=ins_tag_weight)
"""
if ins_tag_weight is None:
ins_tag_weight = paddle.tensor.fill_constant(
shape=[1, 1], dtype="float32", value=1.0
)
assert input.shape == label.shape
helper = LayerHelper("ctr_metric_bundle", **locals())
local_abserr = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_sqrerr = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_prob = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_q = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_pos_num = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
local_ins_num = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1]
)
tmp_res_elesub = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1]
)
tmp_res_sigmoid = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1]
)
tmp_ones = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1]
)
batch_prob = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_abserr = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_sqrerr = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_q = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_pos_num = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
batch_ins_num = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1]
)
for var in [
local_abserr,
batch_abserr,
local_sqrerr,
batch_sqrerr,
local_prob,
batch_prob,
local_q,
batch_q,
batch_pos_num,
batch_ins_num,
local_pos_num,
local_ins_num,
]:
helper.set_variable_initializer(
var,
paddle.nn.initializer.ConstantInitializer(
value=0.0, force_cpu=True
),
)
helper.append_op(
type="elementwise_sub",
inputs={"X": [input], "Y": [label]},
outputs={"Out": [tmp_res_elesub]},
)
helper.append_op(
type="squared_l2_norm",
inputs={"X": [tmp_res_elesub]},
outputs={"Out": [batch_sqrerr]},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_sqrerr], "Y": [local_sqrerr]},
outputs={"Out": [local_sqrerr]},
)
helper.append_op(
type="l1_norm",
inputs={"X": [tmp_res_elesub]},
outputs={"Out": [batch_abserr]},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_abserr], "Y": [local_abserr]},
outputs={"Out": [local_abserr]},
)
helper.append_op(
type="reduce_sum", inputs={"X": [input]}, outputs={"Out": [batch_prob]}
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_prob], "Y": [local_prob]},
outputs={"Out": [local_prob]},
)
helper.append_op(
type="sigmoid",
inputs={"X": [input]},
outputs={"Out": [tmp_res_sigmoid]},
)
helper.append_op(
type="reduce_sum",
inputs={"X": [tmp_res_sigmoid]},
outputs={"Out": [batch_q]},
)
helper.append_op(
type="reduce_sum",
inputs={"X": [label]},
outputs={"Out": [batch_pos_num]},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_pos_num], "Y": [local_pos_num]},
outputs={"Out": [local_pos_num]},
)
helper.append_op(
type='fill_constant_batch_size_like',
inputs={"Input": label},
outputs={'Out': [tmp_ones]},
attrs={
'shape': [-1, 1],
'dtype': tmp_ones.dtype,
'value': float(1.0),
},
)
helper.append_op(
type="reduce_sum",
inputs={"X": [tmp_ones]},
outputs={"Out": [batch_ins_num]},
)
# if data is fake, return 0
inputs_slice = {'Input': ins_tag_weight}
attrs = {'axes': [0]}
attrs['starts'] = [0]
attrs['ends'] = [1]
helper.append_op(
type="slice",
inputs=inputs_slice,
attrs=attrs,
outputs={"Out": ins_tag_weight},
)
axis = helper.kwargs.get('axis', 0)
helper.append_op(
type="elementwise_mul",
inputs={"X": [batch_ins_num], "Y": [ins_tag_weight]},
outputs={"Out": [batch_ins_num]},
attrs={'axis': axis},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_ins_num], "Y": [local_ins_num]},
outputs={"Out": [local_ins_num]},
)
helper.append_op(
type="elementwise_mul",
inputs={"X": [batch_q], "Y": [ins_tag_weight]},
outputs={"Out": [batch_q]},
attrs={'axis': axis},
)
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_q], "Y": [local_q]},
outputs={"Out": [local_q]},
)
return (
local_sqrerr,
local_abserr,
local_prob,
local_q,
local_pos_num,
local_ins_num,
)
python/setup.py.in
浏览文件 @ 802be98b
......@@ -448,7 +448,6 @@ packages=['paddle',
'paddle.fluid.dataloader',
'paddle.fluid.contrib',
'paddle.fluid.contrib.extend_optimizer',
'paddle.fluid.contrib.layers',
'paddle.fluid.incubate',
'paddle.incubate.distributed.fleet',
'paddle.fluid.incubate.checkpoint',
......@@ -483,6 +482,7 @@ packages=['paddle',
'paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler',
'paddle.incubate.distributed.fleet.parameter_server.pslib',
'paddle.incubate.distributed.fleet.parameter_server.ir',
'paddle.incubate.layers',
'paddle.quantization',
'paddle.quantization.quanters',
'paddle.quantization.observers',
......
setup.py
浏览文件 @ 802be98b
......@@ -1424,7 +1424,6 @@ def get_setup_parameters():
'paddle.fluid.dataloader',
'paddle.fluid.contrib',
'paddle.fluid.contrib.extend_optimizer',
'paddle.fluid.contrib.layers',
'paddle.fluid.incubate',
'paddle.incubate.distributed.fleet',
'paddle.fluid.incubate.checkpoint',
......@@ -1459,6 +1458,7 @@ def get_setup_parameters():
'paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler',
'paddle.incubate.distributed.fleet.parameter_server.ir',
'paddle.incubate.distributed.fleet.parameter_server.pslib',
'paddle.incubate.layers',
'paddle.quantization',
'paddle.quantization.quanters',
'paddle.quantization.observers',
......
test/contrib/test_correlation.py
浏览文件 @ 802be98b
......@@ -114,7 +114,7 @@ class TestCorrelationOp(unittest.TestCase):
stride2=1,
)
out = fluid.contrib.correlation(
out = paddle.incubate.layers.correlation(
x1,
x2,
pad_size=4,
......@@ -142,7 +142,7 @@ class Net(paddle.nn.Layer):
super().__init__(name_scope)
def forward(self, x1, x2):
y = fluid.contrib.correlation(
y = paddle.incubate.layers.correlation(
x1,
x2,
pad_size=4,
......
test/legacy_test/test_detection.py
浏览文件 @ 802be98b
......@@ -171,10 +171,10 @@ class TestMulticlassNMS2(unittest.TestCase):
scores = paddle.static.data(
name='scores', shape=[-1, 10], dtype='float32'
)
output = fluid.contrib.multiclass_nms2(
output = paddle.incubate.layers.multiclass_nms2(
bboxes, scores, 0.3, 400, 200, 0.7
)
output2, index = fluid.contrib.multiclass_nms2(
output2, index = paddle.incubate.layers.multiclass_nms2(
bboxes, scores, 0.3, 400, 200, 0.7, return_index=True
)
self.assertIsNotNone(output)
......
test/ps/ps_dnn_model.py
浏览文件 @ 802be98b
......@@ -75,7 +75,7 @@ class DNNLayer(nn.Layer):
sparse_embs = []
for s_input in sparse_inputs:
if self.sync_mode == "gpubox":
emb = paddle.fluid.contrib.sparse_embedding(
emb = paddle.static.nn.sparse_embedding(
input=s_input,
size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=paddle.ParamAttr(name="embedding"),
......
test/xpu/test_pow2_decay_with_linear_warmup_op_xpu.py
浏览文件 @ 802be98b
......@@ -20,7 +20,7 @@ sys.path.append('../../python/paddle/fluid/tests/unittests')
from get_test_cover_info import record_op_test
import paddle
from paddle.fluid.contrib.layers.nn import pow2_decay_with_linear_warmup
from paddle.incubate.layers.nn import pow2_decay_with_linear_warmup
from paddle.optimizer.lr import LinearWarmup, PolynomialDecay
......
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