Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_bug_for_lstmp

benchmark/IntelOptimizedPaddle.md

-# Benchmark
-
-Machine:
-
-- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
-- Laptop: TBD
-
-System: CentOS release 6.3 (Final), Docker 1.12.1.
-
-PaddlePaddle:
-- paddlepaddle/paddle:0.11.0 (for MKLML and MKL-DNN)
-  - MKL-DNN tag v0.11
-  - MKLML 2018.0.1.20171007
-- paddlepaddle/paddle:0.11.0-openblas (for OpenBLAS)
-  - OpenBLAS v0.2.20
-	 
-On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
-
-## Benchmark Model
-
-### Server
-
-#### Training
-Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
-Pay attetion that the speed below includes forward, backward and parameter update time. So we can not directly compare the data with the benchmark of caffe `time` [command](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/caffe/image/run.sh#L9), which only contain forward and backward. The updating time of parameter would become very heavy when the weight size are large, especially on alexnet.
-
-Input image size - 3 * 224 * 224, Time: images/second
-
-- VGG-19
-
-| BatchSize    | 64    | 128  | 256     |
-|--------------|-------| -----| --------|
-| OpenBLAS     | 7.80  | 9.00  | 10.80  | 
-| MKLML        | 12.12 | 13.70 | 16.18  |
-| MKL-DNN      | 28.46 | 29.83 | 30.44  |
-
-<img src="figs/vgg-cpu-train.png" width="500">
-
- - ResNet-50
-
-| BatchSize    | 64    | 128   | 256    |
-|--------------|-------| ------| -------|
-| OpenBLAS     | 25.22 | 25.68 | 27.12  | 
-| MKLML        | 32.52 | 31.89 | 33.12  |
-| MKL-DNN      | 81.69 | 82.35 | 84.08  |
-
-<img src="figs/resnet-cpu-train.png" width="500">
-
- - GoogLeNet
-
-| BatchSize    | 64    | 128   | 256    |
-|--------------|-------| ------| -------|
-| OpenBLAS     | 89.52 | 96.97 | 108.25 | 
-| MKLML        | 128.46| 137.89| 158.63 |
-| MKL-DNN      | 250.46| 264.83| 269.50 |
-
-<img src="figs/googlenet-cpu-train.png" width="500">
-
-- AlexNet
-
-| BatchSize    | 64     | 128    | 256    |
-|--------------|--------| ------ | -------|
-| OpenBLAS     | 45.62  | 72.79  | 107.22 | 
-| MKLML        | 66.37  | 105.60 | 144.04 |
-| MKL-DNN      | 399.00 | 498.94 | 626.53 | 
-
-<img src="figs/alexnet-cpu-train.png" width="500">
-
-#### Inference
-Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
-- VGG-19
-
-| BatchSize | 1     | 2     | 4     | 8     | 16    |
-|-----------|-------|-------|-------|-------|-------|
-| OpenBLAS  | 1.10  | 1.96  | 3.62  | 3.63  | 2.25  |
-| MKLML     | 5.58  | 9.80  | 15.15 | 21.21 | 28.67 |
-| MKL-DNN   | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
-
-<img src="figs/vgg-cpu-infer.png" width="500">
-
-- ResNet-50
-
-| BatchSize | 1     | 2      | 4      | 8      | 16     |
-|-----------|-------|--------|--------|--------|--------|
-| OpenBLAS  | 3.31  | 6.72   | 11.59  | 13.17  | 9.27   |
-| MKLML     | 6.33  | 12.02  | 22.88  | 40.53  | 63.09  |
-| MKL-DNN   | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
-
-<img src="figs/resnet-cpu-infer.png" width="500">
-
-- GoogLeNet
-
-| BatchSize | 1      | 2      | 4      | 8      | 16     |
-|-----------|--------|--------|--------|--------|--------|
-| OpenBLAS  | 12.06  | 23.56  | 34.48  | 36.45  | 23.12  |
-| MKLML     | 22.74  | 41.56  | 81.22  | 133.47 | 210.53 |
-| MKL-DNN   | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
-
-<img src="figs/googlenet-cpu-infer.png" width="500">
-
-- AlexNet
-
-| BatchSize | 1      | 2      | 4      | 8      | 16     |
-|-----------|--------|--------|--------|--------|--------|
-| OpenBLAS  | 3.53   | 6.23   | 15.04  | 26.06  | 31.62  |
-| MKLML     | 21.32  | 36.55  | 73.06  | 131.15 | 192.77 |
-| MKL-DNN   | 442.91 | 656.41 | 719.10 | 847.68 | 850.51 |
-
-<img src="figs/alexnet-cpu-infer.png" width="500">
-
-### Laptop
-TBD

benchmark/README.md

-# Benchmark
-
-Machine: 
-
-- CPU: 12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz
-- GPU: Tesla K40m
-- cuDNN: v5.1
-- system: Docker 1.12.1, all platforms are tested in docker environment.
-
-Platforms: 
-
-- PaddlePaddle: paddledev/paddle:gpu-devel-v0.9.0a0 
-- Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu 
-- Caffe: kaixhin/cuda-caffe
-
-Several convolutional neural networks and recurrent neural networks are used to test.
-
-## Image
-
-### Benchmark Model
-
-AlexNet, GoogleNet and a small network used in Caffe.
-
-- [AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet): but the group size is one.
-
-- [GoogleNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet): but remove loss1 and loss2 when testing benchmark.
-
-- [SmallNet](https://github.com/BVLC/caffe/blob/master/examples/cifar10/cifar10\_quick\_train\_test.prototxt)
-
-
-### Single-GPU
-
-- AlexNet:  input - 3 * 227 * 227,  Time: ms/batch
-
-| BatchSize    | 64  | 128  | 256   | 512  |
-|--------------|-----| -----| ------| -----|
-| PaddlePaddle | 195 | 334  | 602   | 1629 |
-| TensorFlow   | 223 | 364  | 645   | 1235 |
-| Caffe        | 324 | 627  | 1232  | 2513 |
- 
-**Notation**
-
-All platforms use cuDNN-v5.1. We see that caffe is slower in this experiment, because its workspace limit size of cuDNN-conv interface is 8 * 1024 * 1024, which is smaller in PaddlePaddle and TensorFlow. Note that Caffe will be faster if increasing the workspace limit size.
- 
-- GoogletNet:  input - 3 * 224 * 224, Time: ms/batch
-
-
-| BatchSize    | 64    |   128  | 256     |
-|--------------|-------| -------| --------|
-| PaddlePaddle | 613   | 1149   | 2348    |
-| TensorFlow   | 644   | 1176   | 2219    |
-| Caffe        | 694   | 1364   | out of memory   |
-
-- SmallNet: input - 3 * 32 * 32, Time ms/batch
-
-| BatchSize    | 64     |   128    | 256     | 512     |
-|--------------|--------| -------- | --------|---------|
-| PaddlePaddle | 10.463 | 18.184   | 33.113  |  63.039 |
-| TensorFlow   | 9     | 15       | 28      | 59       |
-| Caffe        | 9.373  | 16.6606  | 31.4797 | 59.719  |
-
-**Notation**
-
-All the single-GPU experiments in caffe use `caffe time` to calculate elapsed time, which does not include parameter updating time. However, both PaddlePaddle and TensorFlow experiments contain the parameter updating time. As compared with the total time, this part is relatively little on single machine, we can ignore it.
-
-In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN.
-
-### Multi-GPU: 4 GPUs
-
-- AlexNet,  ms / batch
-
-| total-BatchSize | 128 * 4  | 256 * 4    |
-|------------------|----------| -----------|
-| PaddlePaddle     | 347      | 622        |
-| TensorFlow       | 377      | 675        |
-| Caffe            | 1229     | 2435       |
-
-For example, if `total-BatchSize = 128 * 4`, the speedup ratio is calculated by 
-
-```
-  time_at_1gpu_batch_128 * 4 / time_at_4gpu_total_batch_512 
-= (334 * 4)/347 
-= 3.85
-``` 
-
-<img src="figs/alexnet-4gpu.png" width="420">
-
-
-- GoogleNet, ms / batch
-
-| total-BatchSize  | 128 * 4      |  256 * 4    |
-|-------------------|--------------| ----------- |
-| PaddlePaddle      | 1178         | 2367        |
-| TensorFlow        | 1210         | 2292        |
-| Caffe             | 2007         | out of memory  |
-
-<img src="figs/googlenet-4gpu.png" width="420">
-
-
-## RNN
-We use lstm network for text classfication to test benchmark.
-
-### Dataset
--  [IMDB](http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl)
-- Sequence length is 100. In fact, PaddlePaddle supports training with variable-length sequence, but TensorFlow needs to pad. Thus, we also pad sequence length to 100 in PaddlePaddle in order to compare.
-- Dictionary size=30000 
-- Peephole connection is used in `lstmemory` by default in PaddlePaddle. It is also configured in TensorFlow.
-
-### Single-GPU
-
-#### LSTM in Text Classification
-
-Testing `2 lstm layer + fc` network with different hidden size and batch size.
-  
-- Batch size = 64, ms / batch
- 
-| hidden_size  | 256   | 512    |  1280   |
-|--------------|-------| -------| --------|
-| PaddlePaddle | 83    | 184    | 641     |
-| TensorFlow   | 175   | 280    | 818     |
-
-- Batch size = 128, ms / batch
- 
-| hidden_size  | 256    | 512    |  1280   |
-|--------------|------- | -------| --------|
-| PaddlePaddle | 110    | 261    | 1007    |
-| TensorFlow   | 181    | 361    | 1237    |
-
-
-- Batch size = 256, ms / batch
- 
-| hidden_size  | 256   | 512    |  1280   |
-|--------------|-------| -------| --------|
-| PaddlePaddle | 170   | 414    | 1655    |
-| TensorFlow   | 238   | 536    | 1905    |
-
-<img src="figs/rnn_lstm_cls.png" width="600">
-
-#### Seq2Seq
-
-The benchmark of sequence-to-sequence network will be added later.
- 
-
-### Multi GPU: 4 GPUs
-
-#### LSTM in Text Classification
-
-- hidden_size = 256, ms / batch
- 
-| batch_size   | 256    |  512    |
-|--------------| -------| --------|
-| PaddlePaddle | 90     | 118     |
-| TensorFlow   | 226    | 118     |
-
-
-- hidden_size = 512, ms / batch
- 
-| batch_size   | 256    |  512    |
-|--------------| -------| --------|
-| PaddlePaddle | 189    | 268     |
-| TensorFlow   | 297    | 383     |
-
-
-<img src="figs/rnn_lstm_4gpus.png" width="420">
-
-#### Seq2Seq
-
-The benchmark of sequence-to-sequence network will be added later.

benchmark/fluid/Dockerfile

@@ -15,9 +15,6 @@ RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s
 RUN pip install -U pip
 RUN pip install -U kubernetes paddlepaddle
 
-RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
-RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
-RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.imikolov.fetch()" | python'
 RUN pip uninstall -y paddlepaddle && mkdir /workspace
 
 ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin

benchmark/paddle/image/alexnet.py

-#   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.
-
-from paddle.trainer_config_helpers import *
-
-height = 227
-width = 227
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 128)
-gp = get_config_arg('layer_num', int, 1)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
-    'height': height,
-    'width': width,
-    'color': True,
-    'num_class': num_class,
-    'is_infer': is_infer,
-    'num_samples': num_samples
-}
-define_py_data_sources2(
-    "train.list" if not is_infer else None,
-    "test.list" if is_infer else None,
-    module="provider",
-    obj="process",
-    args=args)
-
-settings(
-    batch_size=batch_size,
-    learning_rate=0.01 / batch_size,
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * batch_size))
-
-# conv1
-net = data_layer('data', size=height * width * 3)
-net = img_conv_layer(
-    input=net,
-    filter_size=11,
-    num_channels=3,
-    num_filters=96,
-    stride=4,
-    padding=1)
-net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
-net = img_pool_layer(input=net, pool_size=3, stride=2)
-
-# conv2
-net = img_conv_layer(
-    input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
-net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
-net = img_pool_layer(input=net, pool_size=3, stride=2)
-
-# conv3
-net = img_conv_layer(
-    input=net, filter_size=3, num_filters=384, stride=1, padding=1)
-# conv4
-net = img_conv_layer(
-    input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
-
-# conv5
-net = img_conv_layer(
-    input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
-net = img_pool_layer(input=net, pool_size=3, stride=2)
-
-net = fc_layer(
-    input=net,
-    size=4096,
-    act=ReluActivation(),
-    layer_attr=ExtraAttr(drop_rate=0.5))
-net = fc_layer(
-    input=net,
-    size=4096,
-    act=ReluActivation(),
-    layer_attr=ExtraAttr(drop_rate=0.5))
-net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
-
-if is_infer:
-    outputs(net)
-else:
-    lab = data_layer('label', num_class)
-    loss = cross_entropy(input=net, label=lab)
-    outputs(loss)

benchmark/paddle/image/googlenet.py

-#!/usr/bin/env python
-from paddle.trainer_config_helpers import *
-
-height = 224
-width = 224
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 128)
-use_gpu = get_config_arg('use_gpu', bool, True)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
-    'height': height,
-    'width': width,
-    'color': True,
-    'num_class': num_class,
-    'is_infer': is_infer,
-    'num_samples': num_samples
-}
-define_py_data_sources2(
-    "train.list" if not is_infer else None,
-    "test.list" if is_infer else None,
-    module="provider",
-    obj="process",
-    args=args)
-
-settings(
-    batch_size=batch_size,
-    learning_rate=0.01 / batch_size,
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * batch_size))
-
-conv_projection = conv_projection if use_gpu else img_conv_layer
-
-def inception2(name, input, channels, \
-    filter1,
-    filter3R, filter3,
-    filter5R, filter5,
-    proj):
-
-    conv1 = name + '_1'
-    conv3r = name + '_3r'
-    conv3 = name + '_3'
-    conv5r = name + '_5r'
-    conv5 = name + '_5'
-    maxpool = name + '_max'
-    convproj = name + '_proj'
-
-    cov1 = img_conv_layer(
-        name=conv1,
-        input=input,
-        filter_size=1,
-        num_channels=channels,
-        num_filters=filter1,
-        stride=1,
-        padding=0)
-
-    cov3r = img_conv_layer(
-        name=conv3r,
-        input=input,
-        filter_size=1,
-        num_channels=channels,
-        num_filters=filter3R,
-        stride=1,
-        padding=0)
-    cov3 = img_conv_layer(
-        name=conv3,
-        input=cov3r,
-        filter_size=3,
-        num_filters=filter3,
-        stride=1,
-        padding=1)
-
-    cov5r = img_conv_layer(
-        name=conv5r,
-        input=input,
-        filter_size=1,
-        num_channels=channels,
-        num_filters=filter5R,
-        stride=1,
-        padding=0)
-    cov5 = img_conv_layer(
-        name=conv5,
-        input=cov5r,
-        filter_size=5,
-        num_filters=filter5,
-        stride=1,
-        padding=2)
-
-    pool1 = img_pool_layer(
-        name=maxpool,
-        input=input,
-        pool_size=3,
-        num_channels=channels,
-        stride=1,
-        padding=1)
-    covprj = img_conv_layer(
-        name=convproj,
-        input=pool1,
-        filter_size=1,
-        num_filters=proj,
-        stride=1,
-        padding=0)
-
-    cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj])
-    return cat
-
-def inception(name, input, channels, \
-    filter1,
-    filter3R, filter3,
-    filter5R, filter5,
-    proj):
-
-    cov1 = conv_projection(
-        input=input,
-        filter_size=1,
-        num_channels=channels,
-        num_filters=filter1,
-        stride=1,
-        padding=0)
-
-    cov3r = img_conv_layer(
-        name=name + '_3r',
-        input=input,
-        filter_size=1,
-        num_channels=channels,
-        num_filters=filter3R,
-        stride=1,
-        padding=0)
-    cov3 = conv_projection(
-        input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1)
-
-    cov5r = img_conv_layer(
-        name=name + '_5r',
-        input=input,
-        filter_size=1,
-        num_channels=channels,
-        num_filters=filter5R,
-        stride=1,
-        padding=0)
-    cov5 = conv_projection(
-        input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2)
-
-    pool1 = img_pool_layer(
-        name=name + '_max',
-        input=input,
-        pool_size=3,
-        num_channels=channels,
-        stride=1,
-        padding=1)
-    covprj = conv_projection(
-        input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0)
-
-    cat = concat_layer(
-        name=name,
-        input=[cov1, cov3, cov5, covprj],
-        bias_attr=True if use_gpu else False,
-        act=ReluActivation())
-    return cat
-
-
-data = data_layer(name="input", size=3 * height * width)
-
-# stage 1
-conv1 = img_conv_layer(
-    name="conv1",
-    input=data,
-    filter_size=7,
-    num_channels=3,
-    num_filters=64,
-    stride=2,
-    padding=3)
-pool1 = img_pool_layer(
-    name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2)
-
-# stage 2
-conv2_1 = img_conv_layer(
-    name="conv2_1",
-    input=pool1,
-    filter_size=1,
-    num_filters=64,
-    stride=1,
-    padding=0)
-conv2_2 = img_conv_layer(
-    name="conv2_2",
-    input=conv2_1,
-    filter_size=3,
-    num_filters=192,
-    stride=1,
-    padding=1)
-pool2 = img_pool_layer(
-    name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
-
-# stage 3
-ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
-ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
-pool3 = img_pool_layer(
-    name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
-
-# stage 4
-ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
-ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
-ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
-ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
-ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
-pool4 = img_pool_layer(
-    name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
-
-# stage 5
-ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
-ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
-pool5 = img_pool_layer(
-    name="pool5",
-    input=ince5b,
-    num_channels=1024,
-    pool_size=7,
-    stride=7,
-    pool_type=AvgPooling())
-
-# We remove loss1 and loss2 for all system when testing benchmark
-# output 1
-# pool_o1 = img_pool_layer(name="pool_o1", input=ince4a, num_channels=512, pool_size=5, stride=3, pool_type=AvgPooling())
-# conv_o1 = img_conv_layer(name="conv_o1", input=pool_o1, filter_size=1, num_filters=128, stride=1, padding=0)
-# fc_o1 = fc_layer(name="fc_o1", input=conv_o1, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
-# out1 = fc_layer(name="output1", input=fc_o1,  size=1000, act=SoftmaxActivation())
-# loss1 = cross_entropy(name='loss1', input=out1, label=lab, coeff=0.3) 
-
-# output 2
-#pool_o2 = img_pool_layer(name="pool_o2", input=ince4d, num_channels=528, pool_size=5, stride=3, pool_type=AvgPooling())
-#conv_o2 = img_conv_layer(name="conv_o2", input=pool_o2, filter_size=1, num_filters=128, stride=1, padding=0)
-#fc_o2 = fc_layer(name="fc_o2", input=conv_o2, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
-#out2 = fc_layer(name="output2", input=fc_o2, size=1000, act=SoftmaxActivation())
-#loss2 = cross_entropy(name='loss2', input=out2, label=lab, coeff=0.3) 
-
-# output 3
-dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
-out3 = fc_layer(
-    name="output3", input=dropout, size=1000, act=SoftmaxActivation())
-
-if is_infer:
-    outputs(out3)
-else:
-    lab = data_layer(name="label", size=num_class)
-    loss3 = cross_entropy(name='loss3', input=out3, label=lab)
-    outputs(loss3)

benchmark/paddle/image/plotlog.py

-# Copyright (c) 2016 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 sys
-import argparse
-import matplotlib.pyplot as plt
-
-
-def parse_args():
-    parser = argparse.ArgumentParser('Parse Log')
-    parser.add_argument(
-        '--file_path', '-f', type=str, help='the path of the log file')
-    parser.add_argument(
-        '--sample_rate',
-        '-s',
-        type=float,
-        default=1.0,
-        help='the rate to take samples from log')
-    parser.add_argument(
-        '--log_period', '-p', type=int, default=1, help='the period of log')
-
-    args = parser.parse_args()
-    return args
-
-
-def parse_file(file_name):
-    loss = []
-    error = []
-    with open(file_name) as f:
-        for i, line in enumerate(f):
-            line = line.strip()
-            if not line.startswith('pass'):
-                continue
-            line_split = line.split(' ')
-            if len(line_split) != 5:
-                continue
-
-            loss_str = line_split[2][:-1]
-            cur_loss = float(loss_str.split('=')[-1])
-            loss.append(cur_loss)
-
-            err_str = line_split[3][:-1]
-            cur_err = float(err_str.split('=')[-1])
-            error.append(cur_err)
-
-    accuracy = [1.0 - err for err in error]
-
-    return loss, accuracy
-
-
-def sample(metric, sample_rate):
-    interval = int(1.0 / sample_rate)
-    if interval > len(metric):
-        return metric[:1]
-
-    num = len(metric) / interval
-    idx = [interval * i for i in range(num)]
-    metric_sample = [metric[id] for id in idx]
-    return metric_sample
-
-
-def plot_metric(metric,
-                batch_id,
-                graph_title,
-                line_style='b-',
-                line_label='y',
-                line_num=1):
-    plt.figure()
-    plt.title(graph_title)
-    if line_num == 1:
-        plt.plot(batch_id, metric, line_style, label=line_label)
-    else:
-        for i in range(line_num):
-            plt.plot(batch_id, metric[i], line_style[i], label=line_label[i])
-    plt.xlabel('batch')
-    plt.ylabel(graph_title)
-    plt.legend()
-    plt.savefig(graph_title + '.jpg')
-    plt.close()
-
-
-def main():
-    args = parse_args()
-    assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
-
-    loss, accuracy = parse_file(args.file_path)
-    batch = [args.log_period * i for i in range(len(loss))]
-
-    batch_sample = sample(batch, args.sample_rate)
-    loss_sample = sample(loss, args.sample_rate)
-    accuracy_sample = sample(accuracy, args.sample_rate)
-
-    plot_metric(loss_sample, batch_sample, 'loss', line_label='loss')
-    plot_metric(
-        accuracy_sample,
-        batch_sample,
-        'accuracy',
-        line_style='g-',
-        line_label='accuracy')
-
-
-if __name__ == '__main__':
-    main()

benchmark/paddle/image/resnet.py

-#!/usr/bin/env python
-from paddle.trainer_config_helpers import *
-
-height = 224
-width = 224
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 64)
-layer_num = get_config_arg("layer_num", int, 50)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
-    'height': height,
-    'width': width,
-    'color': True,
-    'num_class': num_class,
-    'is_infer': is_infer,
-    'num_samples': num_samples
-}
-define_py_data_sources2(
-    "train.list" if not is_infer else None,
-    "test.list" if is_infer else None,
-    module="provider",
-    obj="process",
-    args=args)
-
-settings(
-    batch_size=batch_size,
-    learning_rate=0.01 / batch_size,
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * batch_size))
-
-
-#######################Network Configuration #############
-def conv_bn_layer(name,
-                  input,
-                  filter_size,
-                  num_filters,
-                  stride,
-                  padding,
-                  channels=None,
-                  active_type=ReluActivation()):
-    """
-    A wrapper for conv layer with batch normalization layers.
-    Note:
-    conv layer has no activation.
-    """
-
-    tmp = img_conv_layer(
-        name=name + "_conv",
-        input=input,
-        filter_size=filter_size,
-        num_channels=channels,
-        num_filters=num_filters,
-        stride=stride,
-        padding=padding,
-        act=LinearActivation(),
-        bias_attr=False)
-    return batch_norm_layer(
-        name=name + "_bn",
-        input=tmp,
-        act=active_type,
-        use_global_stats=is_infer)
-
-
-def bottleneck_block(name, input, num_filters1, num_filters2):
-    """
-    A wrapper for bottlenect building block in ResNet.
-    Last conv_bn_layer has no activation.
-    Addto layer has activation of relu.
-    """
-    last_name = conv_bn_layer(
-        name=name + '_branch2a',
-        input=input,
-        filter_size=1,
-        num_filters=num_filters1,
-        stride=1,
-        padding=0)
-    last_name = conv_bn_layer(
-        name=name + '_branch2b',
-        input=last_name,
-        filter_size=3,
-        num_filters=num_filters1,
-        stride=1,
-        padding=1)
-    last_name = conv_bn_layer(
-        name=name + '_branch2c',
-        input=last_name,
-        filter_size=1,
-        num_filters=num_filters2,
-        stride=1,
-        padding=0,
-        active_type=LinearActivation())
-
-    return addto_layer(
-        name=name + "_addto", input=[input, last_name], act=ReluActivation())
-
-
-def mid_projection(name, input, num_filters1, num_filters2, stride=2):
-    """
-    A wrapper for middile projection in ResNet.
-    projection shortcuts are used for increasing dimensions,
-    and other shortcuts are identity
-    branch1: projection shortcuts are used for increasing
-    dimensions, has no activation.
-    branch2x: bottleneck building block, shortcuts are identity.
-    """
-    # stride = 2
-    branch1 = conv_bn_layer(
-        name=name + '_branch1',
-        input=input,
-        filter_size=1,
-        num_filters=num_filters2,
-        stride=stride,
-        padding=0,
-        active_type=LinearActivation())
-
-    last_name = conv_bn_layer(
-        name=name + '_branch2a',
-        input=input,
-        filter_size=1,
-        num_filters=num_filters1,
-        stride=stride,
-        padding=0)
-    last_name = conv_bn_layer(
-        name=name + '_branch2b',
-        input=last_name,
-        filter_size=3,
-        num_filters=num_filters1,
-        stride=1,
-        padding=1)
-
-    last_name = conv_bn_layer(
-        name=name + '_branch2c',
-        input=last_name,
-        filter_size=1,
-        num_filters=num_filters2,
-        stride=1,
-        padding=0,
-        active_type=LinearActivation())
-
-    return addto_layer(
-        name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
-
-
-img = data_layer(name='image', size=height * width * 3)
-
-
-def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
-    """
-    A wrapper for 50,101,152 layers of ResNet.
-    res2_num: number of blocks stacked in conv2_x
-    res3_num: number of blocks stacked in conv3_x
-    res4_num: number of blocks stacked in conv4_x
-    res5_num: number of blocks stacked in conv5_x
-    """
-    # For ImageNet
-    # conv1: 112x112
-    tmp = conv_bn_layer(
-        "conv1",
-        input=img,
-        filter_size=7,
-        channels=3,
-        num_filters=64,
-        stride=2,
-        padding=3)
-    tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
-
-    # conv2_x: 56x56
-    tmp = mid_projection(
-        name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
-    for i in xrange(2, res2_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
-
-    # conv3_x: 28x28
-    tmp = mid_projection(
-        name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
-    for i in xrange(2, res3_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res3_" + str(i),
-            input=tmp,
-            num_filters1=128,
-            num_filters2=512)
-
-    # conv4_x: 14x14
-    tmp = mid_projection(
-        name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
-    for i in xrange(2, res4_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res4_" + str(i),
-            input=tmp,
-            num_filters1=256,
-            num_filters2=1024)
-
-    # conv5_x: 7x7
-    tmp = mid_projection(
-        name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
-    for i in xrange(2, res5_num + 1, 1):
-        tmp = bottleneck_block(
-            name="res5_" + str(i),
-            input=tmp,
-            num_filters1=512,
-            num_filters2=2048)
-
-    tmp = img_pool_layer(
-        name='avgpool',
-        input=tmp,
-        pool_size=7,
-        stride=1,
-        pool_type=AvgPooling())
-
-    return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
-
-
-if layer_num == 50:
-    resnet = deep_res_net(3, 4, 6, 3)
-elif layer_num == 101:
-    resnet = deep_res_net(3, 4, 23, 3)
-elif layer_num == 152:
-    resnet = deep_res_net(3, 8, 36, 3)
-else:
-    print("Wrong layer number.")
-
-if is_infer:
-    outputs(resnet)
-else:
-    lbl = data_layer(name="label", size=num_class)
-    loss = cross_entropy(name='loss', input=resnet, label=lbl)
-    outputs(loss)

benchmark/paddle/image/run.sh

-#!/bin/bash
-
-set -e
-
-function train() {
-  cfg=$1
-  thread=$2
-  bz=$3
-  args="batch_size=$3"
-  prefix=$4
-  paddle train --job=time \
-    --config=$cfg \
-    --use_gpu=True \
-    --trainer_count=$thread \
-    --log_period=10 \
-    --test_period=100 \
-    --config_args=$args \
-    > logs/$prefix-${thread}gpu-$bz.log 2>&1 
-}
-
-if [ ! -d "train.list" ]; then
-  echo " " > train.list
-fi
-if [ ! -d "logs" ]; then
-  mkdir logs
-fi
-
-#========single-gpu=========#
-# alexnet
-train alexnet.py 1 64 alexnet
-train alexnet.py 1 128 alexnet
-train alexnet.py 1 256 alexnet
-train alexnet.py 1 512 alexnet
-
-# googlenet
-train googlenet.py 1 64 googlenet
-train googlenet.py 1 128 googlenet
-train googlenet.py 1 256 googlenet
-
-# smallnet
-train smallnet_mnist_cifar.py 1 64 smallnet
-train smallnet_mnist_cifar.py 1 128 smallnet
-train smallnet_mnist_cifar.py 1 256 smallnet
-train smallnet_mnist_cifar.py 1 512 smallnet
-
-
-############################
-#========multi-gpus=========#
-train alexnet.py 4 512 alexnet
-train alexnet.py 4 1024 alexnet
-
-train googlenet.py 4 512 googlenet 
-train googlenet.py 4 1024 googlenet

benchmark/paddle/image/run_mkl_infer.sh

-#!/bin/bash
-
-set -e
-
-function clock_to_seconds() {
-  hours=`echo $1 | awk -F ':' '{print $1}'`
-  mins=`echo $1 | awk -F ':' '{print $2}'`
-  secs=`echo $1 | awk -F ':' '{print $3}'`
-  echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
-}
-
-function infer() {
-  unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
-  topology=$1
-  layer_num=$2
-  bs=$3
-  use_mkldnn=$4
-  if [ $4 == "True" ]; then
-    thread=1
-    log="logs/infer-${topology}-${layer_num}-mkldnn-${bs}.log"
-  elif [ $4 == "False" ]; then
-    thread=`nproc`
-    if [ $thread -gt $bs ]; then
-      thread=$bs
-    fi
-    log="logs/infer-${topology}-${layer_num}-${thread}mklml-${bs}.log"
-  else
-    echo "Wrong input $4, use True or False."
-    exit 0
-  fi
-
-  models_in="models/${topology}-${layer_num}/pass-00000/"
-  if [ ! -d $models_in ]; then
-    echo "Training model ${topology}_${layer_num}"
-    paddle train --job=train \
-      --config="${topology}.py" \
-      --use_mkldnn=True \
-      --use_gpu=False \
-      --trainer_count=1 \
-      --num_passes=1 \
-      --save_dir="models/${topology}-${layer_num}" \
-      --config_args="batch_size=128,layer_num=${layer_num},num_samples=256" \
-      > /dev/null 2>&1
-    echo "Done"
-  fi
-  log_period=$((256 / bs))
-  paddle train --job=test \
-    --config="${topology}.py" \
-    --use_mkldnn=$use_mkldnn \
-    --use_gpu=False \
-    --trainer_count=$thread \
-    --log_period=$log_period \
-    --config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True" \
-    --init_model_path=$models_in \
-    2>&1 | tee ${log}
-
-  # calculate the last 5 logs period time of 1280 samples,
-  # the time before are burning time.
-  start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
-  end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
-  start_sec=`clock_to_seconds $start`
-  end_sec=`clock_to_seconds $end`
-  fps=`awk 'BEGIN{printf "%.2f",(1280 / ('$end_sec' - '$start_sec'))}'`
-  echo "Last 1280 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
-  echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
-  echo " " > train.list
-fi
-if [ ! -f "test.list" ]; then
-  echo " " > test.list
-fi
-if [ ! -d "logs" ]; then
-  mkdir logs
-fi
-if [ ! -d "models" ]; then
-  mkdir -p models
-fi
-
-# inference benchmark
-for use_mkldnn in True False; do
-  for batchsize in 1 2 4 8 16; do
-    infer vgg 19 $batchsize $use_mkldnn
-    infer resnet 50 $batchsize $use_mkldnn
-    infer googlenet v1 $batchsize $use_mkldnn
-    infer alexnet 2 $batchsize $use_mkldnn
-  done
-done

benchmark/paddle/image/run_mkl_train.sh

-#!/bin/bash
-
-set -e
-
-function train() {
-  unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
-  topology=$1
-  layer_num=$2
-  bs=$3
-  use_mkldnn=$4
-  if [ $4 == "True" ]; then
-    thread=1
-    log="logs/train-${topology}-${layer_num}-mkldnn-${bs}.log"
-  elif [ $4 == "False" ]; then
-    thread=`nproc`
-    # each trainer_count use only 1 core to avoid conflict
-    log="logs/train-${topology}-${layer_num}-${thread}mklml-${bs}.log"
-  else
-    echo "Wrong input $4, use True or False."
-    exit 0
-  fi
-  args="batch_size=${bs},layer_num=${layer_num}"
-  config="${topology}.py"
-  paddle train --job=time \
-    --config=$config \
-    --use_mkldnn=$use_mkldnn \
-    --use_gpu=False \
-    --trainer_count=$thread \
-    --log_period=10 \
-    --test_period=100 \
-    --config_args=$args \
-    2>&1 | tee ${log} 
-
-  avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
-  fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
-  echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
-  echo " " > train.list
-fi
-if [ ! -d "logs" ]; then
-  mkdir logs
-fi
-
-# training benchmark
-for use_mkldnn in True False; do
-  for batchsize in 64 128 256; do
-    train vgg 19 $batchsize $use_mkldnn
-    train resnet 50 $batchsize $use_mkldnn
-    train googlenet v1 $batchsize $use_mkldnn
-    train alexnet 2 $batchsize $use_mkldnn
-  done
-done

benchmark/paddle/image/run_openblas_infer.sh

-#!/bin/bash
-
-set -e
-
-function clock_to_seconds() {
-  hours=`echo $1 | awk -F ':' '{print $1}'`
-  mins=`echo $1 | awk -F ':' '{print $2}'`
-  secs=`echo $1 | awk -F ':' '{print $3}'`
-  echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
-}
-
-function infer() {
-  export OPENBLAS_MAIN_FREE=1
-  topology=$1
-  layer_num=$2
-  bs=$3
-  trainers=`nproc`
-  if [ $trainers -gt $bs ]; then
-    trainers=$bs
-  fi
-  log="logs/infer-${topology}-${layer_num}-${trainers}openblas-${bs}.log"
-  threads=$((`nproc` / trainers))
-  if [ $threads -eq 0 ]; then
-    threads=1
-  fi
-  export OPENBLAS_NUM_THREADS=$threads
-
-  models_in="models/${topology}-${layer_num}/pass-00000/"
-  if [ ! -d $models_in ]; then
-    echo "./run_mkl_infer.sh to save the model first"
-    exit 0
-  fi
-  log_period=$((32 / bs))
-  paddle train --job=test \
-    --config="${topology}.py" \
-    --use_mkldnn=False \
-    --use_gpu=False \
-    --trainer_count=$trainers \
-    --log_period=$log_period \
-    --config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True,num_samples=256" \
-    --init_model_path=$models_in \
-    2>&1 | tee ${log}
-
-  # calculate the last 5 logs period time of 160(=32*5) samples,
-  # the time before are burning time.
-  start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
-  end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
-  start_sec=`clock_to_seconds $start`
-  end_sec=`clock_to_seconds $end`
-  fps=`awk 'BEGIN{printf "%.2f",(160 / ('$end_sec' - '$start_sec'))}'`
-  echo "Last 160 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
-  echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
-  echo " " > train.list
-fi
-if [ ! -f "test.list" ]; then
-  echo " " > test.list
-fi
-if [ ! -d "logs" ]; then
-  mkdir logs
-fi
-
-# inference benchmark
-for batchsize in 1 2 4 8 16; do
-  infer vgg 19 $batchsize
-  infer resnet 50 $batchsize 
-  infer googlenet v1 $batchsize
-  infer alexnet 2 $batchsize
-done

benchmark/paddle/image/run_openblas_train.sh

-#!/bin/bash
-
-set -e
-
-function train() {
-  export OPENBLAS_NUM_THREADS=1
-  topology=$1
-  layer_num=$2
-  bs=$3
-  thread=`nproc`
-  # each trainer_count use only 1 core to avoid conflict
-  log="logs/train-${topology}-${layer_num}-${thread}openblas-${bs}.log"
-  args="batch_size=${bs},layer_num=${layer_num}"
-  config="${topology}.py"
-  paddle train --job=time \
-    --config=$config \
-    --use_mkldnn=False \
-    --use_gpu=False \
-    --trainer_count=$thread \
-    --log_period=3 \
-    --test_period=30 \
-    --config_args=$args \
-    2>&1 | tee ${log} 
-
-  avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
-  fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
-  echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
-  echo " " > train.list
-fi
-if [ ! -d "logs" ]; then
-  mkdir logs
-fi
-
-# training benchmark
-for batchsize in 64 128 256; do
-  train vgg 19 $batchsize
-  train resnet 50 $batchsize
-  train googlenet v1 $batchsize
-  train alexnet 2 $batchsize
-done

benchmark/paddle/image/smallnet_mnist_cifar.py

-#!/usr/bin/env python
-
-from paddle.trainer_config_helpers import *
-
-height = 32
-width = 32
-num_class = 10
-
-batch_size = get_config_arg('batch_size', int, 128)
-
-args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
-define_py_data_sources2(
-    "train.list", None, module="provider", obj="process", args=args)
-
-settings(
-    batch_size=batch_size,
-    learning_rate=0.01 / batch_size,
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * batch_size))
-
-# conv1
-net = data_layer('data', size=height * width * 3)
-net = img_conv_layer(
-    input=net,
-    filter_size=5,
-    num_channels=3,
-    num_filters=32,
-    stride=1,
-    padding=2)
-net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)
-
-# conv2
-net = img_conv_layer(
-    input=net, filter_size=5, num_filters=32, stride=1, padding=2)
-net = img_pool_layer(
-    input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
-
-# conv3
-net = img_conv_layer(
-    input=net, filter_size=3, num_filters=64, stride=1, padding=1)
-net = img_pool_layer(
-    input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
-
-net = fc_layer(input=net, size=64, act=ReluActivation())
-net = fc_layer(input=net, size=10, act=SoftmaxActivation())
-
-lab = data_layer('label', num_class)
-loss = classification_cost(input=net, label=lab)
-outputs(loss)

benchmark/paddle/image/vgg.py

-#!/usr/bin/env python
-from paddle.trainer_config_helpers import *
-
-height = 224
-width = 224
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 64)
-layer_num = get_config_arg('layer_num', int, 19)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
-    'height': height,
-    'width': width,
-    'color': True,
-    'num_class': num_class,
-    'is_infer': is_infer,
-    'num_samples': num_samples
-}
-define_py_data_sources2(
-    "train.list" if not is_infer else None,
-    "test.list" if is_infer else None,
-    module="provider",
-    obj="process",
-    args=args)
-
-settings(
-    batch_size=batch_size,
-    learning_rate=0.001 / batch_size,
-    learning_method=MomentumOptimizer(0.9),
-    regularization=L2Regularization(0.0005 * batch_size))
-
-img = data_layer(name='image', size=height * width * 3)
-
-
-def vgg_network(vgg_num=3):
-    tmp = img_conv_group(
-        input=img,
-        num_channels=3,
-        conv_padding=1,
-        conv_num_filter=[64, 64],
-        conv_filter_size=3,
-        conv_act=ReluActivation(),
-        pool_size=2,
-        pool_stride=2,
-        pool_type=MaxPooling())
-
-    tmp = img_conv_group(
-        input=tmp,
-        conv_num_filter=[128, 128],
-        conv_padding=1,
-        conv_filter_size=3,
-        conv_act=ReluActivation(),
-        pool_stride=2,
-        pool_type=MaxPooling(),
-        pool_size=2)
-
-    channels = []
-    for i in range(vgg_num):
-        channels.append(256)
-    tmp = img_conv_group(
-        input=tmp,
-        conv_num_filter=channels,
-        conv_padding=1,
-        conv_filter_size=3,
-        conv_act=ReluActivation(),
-        pool_stride=2,
-        pool_type=MaxPooling(),
-        pool_size=2)
-    channels = []
-    for i in range(vgg_num):
-        channels.append(512)
-    tmp = img_conv_group(
-        input=tmp,
-        conv_num_filter=channels,
-        conv_padding=1,
-        conv_filter_size=3,
-        conv_act=ReluActivation(),
-        pool_stride=2,
-        pool_type=MaxPooling(),
-        pool_size=2)
-    tmp = img_conv_group(
-        input=tmp,
-        conv_num_filter=channels,
-        conv_padding=1,
-        conv_filter_size=3,
-        conv_act=ReluActivation(),
-        pool_stride=2,
-        pool_type=MaxPooling(),
-        pool_size=2)
-
-    tmp = fc_layer(
-        input=tmp,
-        size=4096,
-        act=ReluActivation(),
-        layer_attr=ExtraAttr(drop_rate=0.5))
-
-    tmp = fc_layer(
-        input=tmp,
-        size=4096,
-        act=ReluActivation(),
-        layer_attr=ExtraAttr(drop_rate=0.5))
-
-    return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
-
-
-if layer_num == 16:
-    vgg = vgg_network(3)
-elif layer_num == 19:
-    vgg = vgg_network(4)
-else:
-    print("Wrong layer number.")
-
-if is_infer:
-    outputs(vgg)
-else:
-    lab = data_layer('label', num_class)
-    loss = cross_entropy(input=vgg, label=lab)
-    outputs(loss)

benchmark/paddle/rnn/provider.py

-#   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 io, os
-import random
-import numpy as np
-import six.moves.cPickle as pickle
-from paddle.trainer.PyDataProvider2 import *
-
-
-def remove_unk(x, n_words):
-    return [[1 if w >= n_words else w for w in sen] for sen in x]
-
-
-# ==============================================================
-#  tensorflow uses fixed length, but PaddlePaddle can process
-#  variable-length. Padding is used in benchmark in order to
-#  compare with other platform. 
-# ==============================================================
-def pad_sequences(sequences,
-                  maxlen=None,
-                  dtype='int32',
-                  padding='post',
-                  truncating='post',
-                  value=0.):
-    lengths = [len(s) for s in sequences]
-
-    nb_samples = len(sequences)
-    if maxlen is None:
-        maxlen = np.max(lengths)
-
-    x = (np.ones((nb_samples, maxlen)) * value).astype(dtype)
-    for idx, s in enumerate(sequences):
-        if len(s) == 0:
-            continue  # empty list was found
-        if truncating == 'pre':
-            trunc = s[-maxlen:]
-        elif truncating == 'post':
-            trunc = s[:maxlen]
-        else:
-            raise ValueError("Truncating type '%s' not understood" % padding)
-
-        if padding == 'post':
-            x[idx, :len(trunc)] = trunc
-        elif padding == 'pre':
-            x[idx, -len(trunc):] = trunc
-        else:
-            raise ValueError("Padding type '%s' not understood" % padding)
-    return x
-
-
-def initHook(settings, vocab_size, pad_seq, maxlen, **kwargs):
-    settings.vocab_size = vocab_size
-    settings.pad_seq = pad_seq
-    settings.maxlen = maxlen
-    settings.input_types = [
-        integer_value_sequence(vocab_size), integer_value(2)
-    ]
-
-
-@provider(
-    init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file):
-    f = open(file, 'rb')
-    train_set = pickle.load(f)
-    f.close()
-    x, y = train_set
-
-    # remove unk, namely remove the words out of dictionary
-    x = remove_unk(x, settings.vocab_size)
-    if settings.pad_seq:
-        x = pad_sequences(x, maxlen=settings.maxlen, value=0.)
-
-    for i in range(len(y)):
-        yield map(int, x[i]), int(y[i])

benchmark/paddle/rnn/rnn.py

-#!/usr/bin/env python
-
-from paddle.trainer_config_helpers import *
-import imdb
-
-num_class = 2
-vocab_size = 30000
-fixedlen = 100
-batch_size = get_config_arg('batch_size', int, 128)
-lstm_num = get_config_arg('lstm_num', int, 1)
-hidden_size = get_config_arg('hidden_size', int, 128)
-# whether to pad sequence into fixed length
-pad_seq = get_config_arg('pad_seq', bool, True)
-imdb.create_data('imdb.pkl')
-
-args = {'vocab_size': vocab_size, 'pad_seq': pad_seq, 'maxlen': fixedlen}
-define_py_data_sources2(
-    "train.list", None, module="provider", obj="process", args=args)
-
-settings(
-    batch_size=batch_size,
-    learning_rate=2e-3,
-    learning_method=AdamOptimizer(),
-    regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
-
-net = data_layer('data', size=vocab_size)
-net = embedding_layer(input=net, size=128)
-
-for i in xrange(lstm_num):
-    net = simple_lstm(input=net, size=hidden_size)
-
-net = last_seq(input=net)
-net = fc_layer(input=net, size=2, act=SoftmaxActivation())
-
-lab = data_layer('label', num_class)
-loss = classification_cost(input=net, label=lab)
-outputs(loss)

benchmark/paddle/rnn/run.sh

-#!/bin/bash
-
-set -e
-
-function train() {
-  cfg=$1
-  thread=$2
-  args="lstm_num=${3},seq_pad=${4},hidden_size=${5},batch_size=${6}"
-  paddle train --job=time \
-    --config=$cfg \
-    --use_gpu=1 \
-    --trainer_count=$thread \
-    --log_period=10 \
-    --test_period=100 \
-    --num_passes=1 \
-    --feed_data=1 \
-    --config_args=$args \
-    >logs/rnn-pad${4}-${thread}gpu-lstm${3}-batch${6}-hid${5}.log 2>&1
-}
-
-if [ ! -d "logs" ]; then
-  mkdir logs
-fi
-
-## padding, single gpu
-#-----config--gpu--lstm_num--padding--hidden_size--batch_size
-## lstm_num=2, batch_size=64
-train rnn.py 1 2 1 256 64 
-train rnn.py 1 2 1 512 64 
-train rnn.py 1 2 1 1280 64 
-
-## lstm_num=2, batch_size=128
-train rnn.py 1 2 1 256 128 
-train rnn.py 1 2 1 512 128 
-train rnn.py 1 2 1 1280 128 
-
-## lstm_num=4, batch_size=256
-train rnn.py 1 2 1 256 256 
-train rnn.py 1 2 1 512 256 
-train rnn.py 1 2 1 1280 256 
-
-
-#==================multi gpus=====================#
-# hidden_size=256, lstm_num=2, different batch size
-train rnn.py 4 2 1 256 128 
-train rnn.py 4 2 1 256 256 
-train rnn.py 4 2 1 256 512 
-
-# hidden_size=512, lstm_num=4, different batch size
-train rnn.py 4 2 1 512 128 
-train rnn.py 4 2 1 512 256 
-train rnn.py 4 2 1 512 512 

benchmark/tensorflow/machine_translation.py

@@ -35,8 +35,6 @@ import os
 import argparse
 import time
 
-import paddle.v2 as paddle
-
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--embedding_dim",

benchmark/tensorflow/mnist.py

@@ -21,7 +21,6 @@ import time
 import numpy as np
 
 import tensorflow as tf
-import paddle.v2 as paddle
 
 DTYPE = tf.float32
 

benchmark/tensorflow/resnet.py

@@ -27,7 +27,6 @@ import argparse
 import time
 import numpy as np
 
-import paddle.v2 as paddle
 import tensorflow as tf
 
 DTYPE = tf.float32

benchmark/tensorflow/stacked_dynamic_lstm.py

@@ -21,8 +21,6 @@ import argparse
 import time
 import tensorflow as tf
 
-import paddle.v2 as paddle
-
 
 def parse_args():
     parser = argparse.ArgumentParser("LSTM model benchmark.")

benchmark/tensorflow/vgg.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 """VGG16 benchmark in TensorFlow"""
 import tensorflow as tf
-import paddle.v2 as paddle
 import numpy as np
 import argparse
 import time

cmake/external/protobuf.cmake

@@ -203,7 +203,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
     ENDIF()
 
     SET(PROTOBUF_REPO "https://github.com/google/protobuf.git")
-    SET(PROTOBUF_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546")
+    SET(PROTOBUF_TAG "v3.6.1")
 
     ExternalProject_Add(
         ${TARGET_NAME}
@@ -231,7 +231,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
     )
 ENDFUNCTION()
 
-SET(PROTOBUF_VERSION 3.1)
+SET(PROTOBUF_VERSION 3.6.1)
 
 IF(NOT PROTOBUF_FOUND)
     build_protobuf(extern_protobuf FALSE)

cmake/external/python.cmake

@@ -74,8 +74,8 @@ IF(PYTHONINTERP_FOUND)
     find_python_module(wheel REQUIRED)
     find_python_module(google.protobuf REQUIRED)
     FIND_PACKAGE(NumPy REQUIRED)
-    IF(${PY_GOOGLE.PROTOBUF_VERSION} AND ${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0")
-        MESSAGE(FATAL_ERROR "Found Python Protobuf ${PY_GOOGLE.PROTOBUF_VERSION} < 3.0.0, "
+    IF(${PY_GOOGLE.PROTOBUF_VERSION} AND ${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.6.1")
+        MESSAGE(FATAL_ERROR "Found Python Protobuf ${PY_GOOGLE.PROTOBUF_VERSION} < 3.6.1, "
         "please use pip to upgrade protobuf. pip install -U protobuf")
     ENDIF()
 ENDIF(PYTHONINTERP_FOUND)

cmake/flags.cmake

@@ -147,6 +147,7 @@ set(GPU_COMMON_FLAGS
     -Wno-error=unused-function  # Warnings in Numpy Header.
     -Wno-error=array-bounds # Warnings in Eigen::array
 )
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m64")
 endif(NOT WIN32)
 
 if (APPLE)

paddle/fluid/API.spec

@@ -8,13 +8,13 @@ paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None
 paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
-paddle.fluid.name_scope ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.program_guard ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.name_scope ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
 paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.scope_guard ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
@@ -66,7 +66,7 @@ paddle.fluid.initializer.XavierInitializer.__init__ ArgSpec(args=['self', 'unifo
 paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
 paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
 paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
@@ -229,7 +229,7 @@ paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes',
 paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
 paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True))
 paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.Preprocessor.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.layers.Preprocessor.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Preprocessor.outputs ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
 paddle.fluid.layers.load ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,))
@@ -261,7 +261,7 @@ paddle.fluid.layers.increment ArgSpec(args=['x', 'value', 'in_place'], varargs=N
 paddle.fluid.layers.array_write ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.create_array ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.less_than ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords='ignored', defaults=(None, None))
-paddle.fluid.layers.equal ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords='ignored', defaults=(None,))
+paddle.fluid.layers.equal ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.array_read ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.array_length ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.IfElse.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
@@ -270,7 +270,7 @@ paddle.fluid.layers.IfElse.input ArgSpec(args=['self', 'x'], varargs=None, keywo
 paddle.fluid.layers.IfElse.output ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
 paddle.fluid.layers.IfElse.true_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.DynamicRNN.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.layers.DynamicRNN.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.memory ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32'))
 paddle.fluid.layers.DynamicRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
@@ -346,12 +346,12 @@ paddle.fluid.contrib.StateCell.set_state ArgSpec(args=['self', 'state_name', 'st
 paddle.fluid.contrib.StateCell.state_updater ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.StateCell.update_states ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.TrainingDecoder.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None))
-paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
@@ -456,7 +456,7 @@ paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', '
 paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None))
-paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
@@ -473,11 +473,11 @@ paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_
 paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
 paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
 paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None  2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None  3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None  4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None  5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None  6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None  7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None  8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None  9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None  10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None  11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None  12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None  13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None  14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None  15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None  16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPlace) -> None  17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None  18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None  19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None  20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None  21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None  22. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None  23. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None  24. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPinnedPlace) -> None
-paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
-paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
+paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, lod: List[List[int]]) -> None
+paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, recursive_sequence_lengths: List[List[int]]) -> None
 paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core.Tensor) -> List[int]
 paddle.fluid.LoDTensorArray.__init__ __init__(self: paddle.fluid.core.LoDTensorArray) -> None
-paddle.fluid.LoDTensorArray.append append(self: paddle.fluid.core.LoDTensorArray, arg0: paddle.fluid.core.LoDTensor) -> None
+paddle.fluid.LoDTensorArray.append append(self: paddle.fluid.core.LoDTensorArray, tensor: paddle.fluid.core.LoDTensor) -> None
 paddle.fluid.CPUPlace.__init__ __init__(self: paddle.fluid.core.CPUPlace) -> None
 paddle.fluid.CUDAPlace.__init__ __init__(self: paddle.fluid.core.CUDAPlace, arg0: int) -> None
 paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core.CUDAPinnedPlace) -> None
@@ -491,14 +491,14 @@ paddle.fluid.clip.ErrorClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'],
 paddle.fluid.clip.GradientClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.clip.GradientClipByNorm.__init__ ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.clip.GradientClipByGlobalNorm.__init__ ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',))
-paddle.fluid.profiler.cuda_profiler ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.profiler.cuda_profiler ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.profiler.reset_profiler ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.profiler.profiler ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.profiler.profiler ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
 paddle.fluid.profiler.start_profiler ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.profiler.stop_profiler ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
 paddle.fluid.unique_name.generate ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.unique_name.guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.unique_name.guard ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
 paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
 paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope

paddle/fluid/framework/async_executor.cc

@@ -244,6 +244,7 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   auto& block = main_program.Block(0);
   for (auto var_name : fetch_var_names) {
     auto var_desc = block.FindVar(var_name);
+    PADDLE_ENFORCE_NOT_NULL(var_desc, "%s is not found.", var_name);
     auto shapes = var_desc->GetShape();
     PADDLE_ENFORCE(shapes[shapes.size() - 1] == 1,
                    "var %s: Fetched var has wrong shape, "

paddle/fluid/framework/details/CMakeLists.txt

@@ -50,12 +50,15 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
 cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
 cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
 
-cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper)
+if(WITH_GPU)
+cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper gpu_info)
+else()
+cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper cpu_info)
+endif()
+
 cc_library(memory_optimize_pass SRCS memory_optimize_pass.cc DEPS memory_optimize_helper pass)
 cc_library(inplace_op_pass SRCS inplace_op_pass.cc DEPS memory_optimize_pass op_info)
 cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper)
-cc_library(memory_early_delete_pass SRCS memory_early_delete_pass.cc DEPS memory_optimize_pass computation_op_handle scale_loss_grad_op_handle rpc_op_handle
-        all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
 cc_library(reference_count_pass_helper SRCS reference_count_pass_helper.cc DEPS garbage_collector computation_op_handle)
 cc_library(eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper)
 cc_library(eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass)
@@ -67,13 +70,11 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
         scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
 
-set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass inplace_op_pass)
+set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass inplace_op_pass)
 if (WITH_GPU)
   list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
 endif()
-cc_test(memory_optimize_helper_test SRCS memory_optimize_helper_test.cc memory_optimize_helper.cc DEPS framework_proto graph)
-cc_test(memory_optimize_pass_test SRCS memory_optimize_pass_test.cc memory_optimize_pass.cc memory_optimize_helper.cc DEPS framework_proto graph graph_helper op_registry pass)
-
+cc_test(memory_optimize_helper_test SRCS memory_optimize_helper_test.cc memory_optimize_helper.cc DEPS framework_proto graph graph_helper op_registry)
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
 
 cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope

paddle/fluid/framework/details/build_strategy.cc

@@ -206,8 +206,6 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
           new std::vector<OpDesc *>(main_program.Block(0).AllOps());
       graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
                                               all_op_descs);  // take ownership
-      graph->Set<GraphNodePool>(kGraphNodePool,
-                                new GraphNodePool);  // take ownership
 
       pass->Erase(kAllOpDescs);
       pass->SetNotOwned<const std::vector<OpDesc *>>(kAllOpDescs, all_op_descs);
@@ -242,7 +240,9 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
         continue;
       }
     }
+    VLOG(3) << "Start Apply Pass " << pass->Type();
     graph = pass->Apply(std::move(graph));
+    VLOG(3) << "Finish Apply Pass " << pass->Type();
   }
   return graph;
 }

paddle/fluid/framework/details/build_strategy.h

@@ -77,9 +77,6 @@ struct BuildStrategy {
   bool fuse_relu_depthwise_conv_{false};
 
   bool memory_optimize_{false};
-
-  bool memory_early_delete_{false};
-
   // TODO(dzhwinter):
   // make enable_inplace, memory_optimize_
   // memory_early_delete_ true by default

paddle/fluid/framework/details/computation_op_handle.h

@@ -26,7 +26,7 @@
 namespace paddle {
 namespace framework {
 namespace details {
-struct ComputationOpHandle : public OpHandleBase {
+class ComputationOpHandle : public OpHandleBase {
  public:
   ComputationOpHandle(ir::Node *node, Scope *scope, platform::Place place,
                       size_t scope_idx);

paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc

@@ -34,8 +34,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
            ->Var(details::kLocalExecScopeName)
            ->GetMutable<Scope*>() = &local_scope;
       for (size_t j = 0; j < input_scope_idxes.size(); ++j) {
-        local_scope.Var("out_var" + j);
-        if (i == j) local_scope.Var("in_var" + j);
+        local_scope.Var("out_var" + std::to_string(j));
+        if (i == j) local_scope.Var("in_var" + std::to_string(j));
       }
       param_scopes_.emplace_back(&local_scope);
     }
@@ -62,20 +62,21 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
 
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
       // add input var handle
-      nodes_.emplace_back(
-          ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable));
-      VarHandle* in_var_handle =
-          new VarHandle(nodes_.back().get(), 1, input_scope_idxes[i],
-                        "in_var" + i, place_list_[input_scope_idxes[i]]);
+      nodes_.emplace_back(ir::CreateNodeForTest("in_node" + std::to_string(i),
+                                                ir::Node::Type::kVariable));
+      VarHandle* in_var_handle = new VarHandle(
+          nodes_.back().get(), 1, input_scope_idxes[i],
+          "in_var" + std::to_string(i), place_list_[input_scope_idxes[i]]);
       vars_.emplace_back(in_var_handle);
       op_handle_->AddInput(in_var_handle);
 
       // add output var handle
       for (size_t j = 0; j < place_list_.size(); ++j) {
-        nodes_.emplace_back(
-            ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable));
-        VarHandle* out_var_handle = new VarHandle(
-            nodes_.back().get(), 2, j, "out_var" + i, place_list_[j]);
+        nodes_.emplace_back(ir::CreateNodeForTest(
+            "out_node" + std::to_string(i), ir::Node::Type::kVariable));
+        VarHandle* out_var_handle =
+            new VarHandle(nodes_.back().get(), 2, j,
+                          "out_var" + std::to_string(i), place_list_[j]);
         vars_.emplace_back(out_var_handle);
         op_handle_->AddOutput(out_var_handle);
       }
@@ -86,7 +87,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
     std::vector<std::vector<float>> send_vec;
     f::LoD lod{{0, 10, 20}};
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
-      const std::string varname("in_var" + i);
+      const std::string varname("in_var" + std::to_string(i));
       float val_scalar = static_cast<float>(i);
       send_vec.push_back(
           InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
@@ -96,7 +97,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
 
     WaitAll();
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
-      const std::string& varname("out_var" + i);
+      const std::string& varname("out_var" + std::to_string(i));
       for (size_t j = 0; j < place_list_.size(); ++j) {
         LoDTensorEqual(varname, send_vec[i], lod, param_scopes_[j]);
       }
@@ -109,7 +110,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
                               2, 4, 6, 3, 1, 1, 1,  1, 3, 7};
     int height = static_cast<int>(kDims[0] * 2);
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
-      const std::string varname("in_var" + i);
+      const std::string varname("in_var" + std::to_string(i));
       float val_scalar = static_cast<float>(i);
       send_vector.push_back(InitSelectedRows(varname, input_scope_idxes[i],
                                              rows, height, val_scalar));
@@ -119,7 +120,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
 
     WaitAll();
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
-      const std::string& varname("out_var" + i);
+      const std::string& varname("out_var" + std::to_string(i));
       for (size_t j = 0; j < place_list_.size(); ++j) {
         SelectedRowsEqual(varname, input_scope_idxes[i], send_vector[i], rows,
                           height);

paddle/fluid/framework/details/inplace_op_pass.cc

@@ -49,7 +49,7 @@ DEFINE_bool(
     "If this option turns on, only these op in whitelist can be inplaced."
     "If it turns off, all of the running op can be candidate of inplaced op."
     "Such as scale, elementwise_add"
-    "By default, it's turned on");
+    "By default, it's turned off");
 
 DECLARE_string(memory_optimize_debug);
 
@@ -171,16 +171,15 @@ void InplacePass::InplaceModifyDesc(const std::string& var,
   }
 }
 
-const SSANodePair InplacePass::TryInplaceModifyVar(const std::string& var,
-                                                   const std::string& cache_var,
-                                                   const size_t& idx,
+const NodeSwapQueue InplacePass::TryInplaceModifyVar(
+    const std::string& var, const std::string& cache_var, const size_t& idx,
     ir::Graph* graph) const {
   PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
                  var_nodes_[var].at(0)->Var() != nullptr);
   std::unique_ptr<VarDesc> var_desc(new VarDesc(*var_nodes_[var].at(0)->Var()));
   var_desc->SetName(cache_var);
 
-  SSANodePair swap_nodes;
+  NodeSwapQueue swap_nodes;
 
   for (size_t i = idx; i < view_.AllOps().size(); ++i) {
     auto* op = view_.AllOps()[i];
@@ -230,7 +229,7 @@ const SSANodePair InplacePass::TryInplaceModifyVar(const std::string& var,
   return swap_nodes;
 }
 
-void InplacePass::CommitModify(const SSANodePair& swap_nodes,
+void InplacePass::CommitModify(const NodeSwapQueue& swap_nodes,
                                ir::Graph* graph) const {
   for (auto& pair : swap_nodes) {
     auto *node = pair.first, *cache_node = pair.second;
@@ -245,7 +244,7 @@ void InplacePass::CommitModify(const SSANodePair& swap_nodes,
   }
 }
 
-void InplacePass::WithdrawModify(const SSANodePair& nodes,
+void InplacePass::WithdrawModify(const NodeSwapQueue& nodes,
                                  ir::Graph* graph) const {
   for (auto& pair : nodes) {
     auto *node = pair.first, *cache_node = pair.second;

paddle/fluid/framework/details/inplace_op_pass.h

@@ -56,7 +56,8 @@ class GraphView {
   std::map<ir::Node*, std::unordered_set<ir::Node*>> adj_list_;
 };
 
-typedef std::vector<std::pair<ir::Node*, ir::Node*>> SSANodePair;
+// swap pairs in sequence
+typedef std::vector<std::pair<ir::Node*, ir::Node*>> NodeSwapQueue;
 class InplacePass : public ir::Pass {
  public:
   InplacePass();
@@ -68,14 +69,14 @@ class InplacePass : public ir::Pass {
   void InitSSAGraphNodes() const;
 
  private:
-  const SSANodePair TryInplaceModifyVar(const std::string& var,
+  const NodeSwapQueue TryInplaceModifyVar(const std::string& var,
                                           const std::string& cache_var,
                                           const size_t& idx,
                                           ir::Graph* graph) const;
 
-  void CommitModify(const SSANodePair&, ir::Graph* graph) const;
+  void CommitModify(const NodeSwapQueue&, ir::Graph* graph) const;
 
-  void WithdrawModify(const SSANodePair& nodes, ir::Graph* graph) const;
+  void WithdrawModify(const NodeSwapQueue& nodes, ir::Graph* graph) const;
 
   void InplaceModifyDesc(const std::string& in_var, const std::string& out_var,
                          const size_t& idx) const;

paddle/fluid/framework/details/memory_early_delete_pass.cc

-// 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.
-
-#include "paddle/fluid/framework/details/memory_early_delete_pass.h"
-#include <queue>
-#include <string>
-#include <vector>
-#include "paddle/fluid/framework/details/memory_optimize_helper.h"
-#include "paddle/fluid/framework/details/multi_devices_helper.h"
-#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
-#include "paddle/fluid/framework/ir/graph_helper.h"
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-static ComputationOpHandle* FindNextComputationOpHandle(VarHandle* var_in) {
-  std::queue<VarHandleBase*> queue;
-  queue.push(var_in);
-  do {
-    auto* var = queue.front();
-    queue.pop();
-    for (auto* op : var->PendingOps()) {
-      auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
-      if (compute_op != nullptr && compute_op->GetPlace() == var_in->place()) {
-        return compute_op;
-      }
-      for (auto* out_var : op->Outputs()) {
-        queue.push(out_var);
-      }
-    }
-  } while (!queue.empty());
-  return nullptr;
-}
-
-std::unique_ptr<ir::Graph> MemoryEarlyDeletePass::ApplyImpl(
-    std::unique_ptr<ir::Graph> graph) const {
-  auto& graph_pool = Get<GraphNodePool>(kGraphNodePool);
-  auto& gcs = Get<GarbageCollectorMap>(kGarbageCollector);
-
-  std::unordered_map<std::string, std::unordered_set<OpDesc*>> unlived_vars;
-  unlived_vars.reserve(graph_pool.size());
-  for (auto& pair : graph_pool) {
-    unlived_vars.insert(std::make_pair(pair.first, pair.second));
-  }
-
-  auto compare_and_insert_early_delete_op = [&](
-      OpHandleBase* op, const std::vector<VarHandleBase*>& vars) {
-    if (unlived_vars.empty()) return;
-    // unlived vars can be deleted after the last used op has finished.
-    auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
-    const auto& places = Get<std::vector<platform::Place>>(kAllPlaces);
-    for (auto& var : vars) {
-      auto* var_handle = dynamic_cast<VarHandle*>(var);
-      auto var_name = var->Node()->Name();
-      auto& var_place = var_handle->place();
-      if (unlived_vars.count(var_name) == 0) continue;
-      if (!unlived_vars[var_name].empty()) {
-        if (compute_op != nullptr &&
-            unlived_vars[var_name].count(compute_op->Node()->Op()) != 0) {
-          unlived_vars[var_name].erase(compute_op->Node()->Op());
-        }
-        continue;
-      }
-
-      if (var_handle == nullptr || !var_handle->Node()->IsVar() ||
-          var_handle->Node()->IsCtrlVar())
-        continue;
-
-      // shameless copyed from reference count pass.
-      if (compute_op == nullptr) {
-        // use next computation op scope
-        compute_op = FindNextComputationOpHandle(var_handle);
-      }
-      auto* early_delete_node =
-          graph->CreateEmptyNode("early_delete", ir::Node::Type::kOperation);
-      GarbageCollector* gc = gcs.at(places[compute_op->GetScopeIdx()]).get();
-      auto* early_delete_handle = new EarlyDeleteOpHandle(
-          early_delete_node, compute_op->GetScope(), var_place, {var_name}, gc);
-      if (compute_op->Outputs().empty()) {
-        auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
-        compute_op->AddOutput(dep_var);
-        graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
-      }
-      early_delete_handle->AddInput(compute_op->Outputs().front());
-      VLOG(5) << "Add early delete op " << var_name << " to Operator"
-              << compute_op->Name();
-    }
-  };
-
-  auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
-  for (auto& op : all_ops) {
-    compare_and_insert_early_delete_op(op, op->Inputs());
-    compare_and_insert_early_delete_op(op, op->Outputs());
-  }
-  return graph;
-}
-
-}  // namespace details
-}  // namespace framework
-}  // namespace paddle
-
-REGISTER_PASS(memory_early_delete_pass,
-              paddle::framework::details::MemoryEarlyDeletePass)
-    .RequireGraphAttr(paddle::framework::details::kGraphNodePool)
-    .RequireGraphAttr(paddle::framework::details::kGarbageCollector);

paddle/fluid/framework/details/memory_early_delete_pass.h

-// 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.
-
-#pragma once
-#include "paddle/fluid/framework/details/early_delete_op_handle.h"
-#include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/pass.h"
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-class MemoryEarlyDeletePass : public ir::Pass {
- protected:
-  std::unique_ptr<ir::Graph> ApplyImpl(
-      std::unique_ptr<ir::Graph> graph) const override;
-};
-
-}  // namespace details
-}  // namespace framework
-}  // namespace paddle

paddle/fluid/framework/details/memory_optimize_helper.cc

@@ -13,17 +13,114 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include <algorithm>
+#include <deque>
 #include <functional>
-#include <iostream>
+#include <iterator>
 #include <numeric>
 #include <sstream>
 #include <string>
+#include "paddle/fluid/framework/var_desc.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/gpu_info.h"
+#endif  // PADDLE_WITH_CUDA
 
 namespace paddle {
 namespace framework {
 namespace details {
+using paddle::framework::VarDesc;
+
+std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
+  PADDLE_ENFORCE(graph.Has(kAllOpDescs),
+                 "Graph has no attribute of kAllOpDescs.");
+  // 1. get op desc order
+  auto& op_descs = graph.Get<const std::vector<OpDesc*>>(kAllOpDescs);
+
+  // 2. topology sort order
+  auto nodes = graph.Nodes();
+  std::deque<ir::Node*> ops;
+  FilterVariables(nodes, [&](ir::Node* op) {
+    if (op->IsOp() && op->Op() != nullptr) {
+      ops.emplace_back(op);
+    }
+  });
+  std::unordered_map<ir::Node*, size_t> op_deps;
+  std::list<ir::Node*> ready_ops;
+  std::unordered_map<ir::Node*, std::unordered_set<ir::Node*>> pending_ops;
+
+  for (auto* op : ops) {
+    std::unordered_set<ir::Node*> preceding_op;
+    for (auto* in : op->inputs) {
+      if (in->inputs.empty()) continue;
+      PADDLE_ENFORCE(in->inputs.size() == 1 && in->inputs[0]->IsOp());
+      preceding_op.emplace(in->inputs[0]);
+      pending_ops[in->inputs[0]].emplace(op);
+    }
+    op_deps[op] = preceding_op.size();
+    if (preceding_op.empty()) {
+      ready_ops.emplace_back(op);
+    }
+  }
+
+  // 3. generated op list based desc order and the topology order
+  std::vector<ir::Node*> ret;
+  std::list<OpDesc*> op_descs_list(op_descs.begin(), op_descs.end());
+
+  auto update_by_found_node = [&](ir::Node* found_node) {
+    for (auto* pending_op : pending_ops[found_node]) {
+      if (--op_deps[pending_op] == 0) {
+        ready_ops.emplace_back(pending_op);
+      }
+    }
+    ready_ops.remove(found_node);
+    ret.emplace_back(found_node);
+  };
+
+  while (!ready_ops.empty()) {
+    bool all_of_ready_op_unmatched = true;
+    for (auto it = op_descs_list.begin(); it != op_descs_list.end();) {
+      auto op_desc = *it;
+      ir::Node* found_node = nullptr;
+      for (auto* op : ready_ops) {
+        if (IsSameDesc(op->Op(), op_desc)) {
+          found_node = op;
+          break;
+        }
+      }
+
+      // 3.1 op desc deleted by other pass
+      if (found_node == nullptr) {
+        ++it;
+        continue;
+      } else {
+        all_of_ready_op_unmatched = false;
+        it = op_descs_list.erase(it);
+      }
+      update_by_found_node(found_node);
+    }
 
-size_t NodeSizeInBytes(const VarDesc& node) {
+    // 3.2 op descs are added by other pass
+    // preceding op non empty means some new op descs are
+    // created, but not contained in return node list.
+    // these new op desc may depend on each other.
+    std::list<ir::Node*> prev_ready_ops(ready_ops);
+    if (all_of_ready_op_unmatched) {
+      for (auto op : prev_ready_ops) {
+        update_by_found_node(op);
+      }
+    }
+  }
+
+  PADDLE_ENFORCE(std::all_of(
+      op_deps.begin(), op_deps.end(),
+      [&](const std::pair<ir::Node*, size_t>& p) { return p.second == 0; }));
+
+  return ret;
+}
+
+size_t NodeSize(const VarDesc& node) {
   auto shape = node.GetShape();
   int size =
       std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
@@ -31,9 +128,9 @@ size_t NodeSizeInBytes(const VarDesc& node) {
   return type_size * std::abs(size);
 }
 
-size_t NodeSizeInBytes(ir::Node* n) {
+size_t NodeSize(ir::Node* n) {
   auto* desc = FindVarDescInBlock(n);
-  return NodeSizeInBytes(*desc);
+  return NodeSize(*desc);
 }
 
 std::string DebugStringImpl(VarDesc* var) {
@@ -59,7 +156,6 @@ std::string DebugStringImpl(VarDesc* var) {
 std::string DebugString(ir::Node* var) {
   return DebugStringImpl(FindVarDescInBlock(var));
 }
-// return DebugString(var->Var()); }
 
 // NOTE(dzh): based ir node, if a large node has been reused
 // by a small size node, then next time it appear in pool, it will
@@ -76,22 +172,26 @@ struct NodeComparator {
   bool operator()(ir::Node* lhs, ir::Node* rhs) const {
     auto* lhs_desc = FindVarDescInBlock(lhs);
     auto* rhs_desc = FindVarDescInBlock(rhs);
+    // match data type
+    if (lhs_desc->GetDataType() != rhs_desc->GetDataType()) {
+      return false;
+    }
+    // match shape
     auto lhs_shape = lhs_desc->GetShape();
     auto rhs_shape = rhs_desc->GetShape();
     if ((lhs_shape[0] == -1 && rhs_shape[0] == -1) ||
         (lhs_shape[0] != -1 && rhs_shape[0] != -1)) {
-      return NodeSizeInBytes(lhs) <= NodeSizeInBytes(rhs);
+      return NodeSize(lhs) <= NodeSize(rhs);
     } else {
       return false;
     }
   }
 };
 
-void OrderedNodeList::Insert(ir::Node* var, ir::Node* op) {
+void OrderedSet::Insert(ir::Node* var) {
   PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
-  PADDLE_ENFORCE(op->IsOp());
   if (mark_table_.count(var->Name()) != 0) {
-    mark_table_[var->Name()]->second.insert(op);
+    mark_table_[var->Name()]->emplace_back(var);
     return;
   }
 
@@ -99,14 +199,15 @@ void OrderedNodeList::Insert(ir::Node* var, ir::Node* op) {
   auto var_shape = var_desc->GetShape();
   int batch_size = static_cast<int>(var_shape[0]);
 
-  NodeComparator compare_node;
+  NodeComparator functor;
   Iter it = nodes_.begin();
   while (it != nodes_.end()) {
-    auto* cache_desc = FindVarDescInBlock(it->first);
+    auto& prev = it->front();
+    auto* cache_desc = FindVarDescInBlock(prev);
     int cache_batch_size = cache_desc->GetShape()[0];
     if ((cache_batch_size == -1 && batch_size == -1) ||
         (cache_batch_size != -1 && batch_size != -1)) {
-      if (compare_node(it->first, var)) {
+      if (functor(prev, var)) {
         ++it;
       } else {
         break;
@@ -118,62 +219,127 @@ void OrderedNodeList::Insert(ir::Node* var, ir::Node* op) {
     }
   }
 
-  it =
-      nodes_.insert(it, std::make_pair(var, std::unordered_set<ir::Node*>{op}));
+  it = nodes_.insert(it, {var});
   mark_table_[var->Name()] = it;
 }
 
-int OrderedNodeList::GetIndex(ir::Node* var) {
+int OrderedSet::GetNodeIndexInPool(ir::Node* var) {
   return std::distance(nodes_.begin(), mark_table_[var->Name()]);
 }
 
-ir::Node* OrderedNodeList::NodeMatch(ir::Node* var) const {
+ir::Node* OrderedSet::FindBestFitNode(ir::Node* var) const {
   ir::Node* found_node = nullptr;
-  NodeComparator compare_node;
+  NodeComparator functor;
 
   for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
-    if (compare_node(var, it->first)) {
-      found_node = it->first;
+    auto& candidate = it->front();
+    if (functor(var, candidate)) {
+      found_node = candidate;
       break;
     }
   }
   return found_node;
 }
 
-void OrderedNodeList::Erase(ir::Node* var) { Erase(var->Name()); }
+ir::Node* OrderedSet::FindNextBestFitNode(ir::Node* var, ir::Node* prev) const {
+  ir::Node* found_node = nullptr;
+  NodeComparator functor;
+  auto it =
+      std::find_if(nodes_.begin(), nodes_.end(), [&](const NodeVector& v) {
+        if (v.front() == prev)
+          return true;
+        else
+          return false;
+      });
+  PADDLE_ENFORCE(it != nodes_.end(), "Not found previous in node list!");
+  for (it = std::next(it); it != nodes_.end(); ++it) {
+    auto& candidate = it->front();
+    if (functor(var, candidate)) {
+      found_node = candidate;
+      break;
+    }
+  }
+  return found_node;
+}
 
-void OrderedNodeList::Erase(const std::string& var) {
+bool OrderedSet::Has(ir::Node* var) const {
+  if (mark_table_.count(var->Name())) {
+    auto& node_in_samename = mark_table_.at(var->Name());
+    auto iter =
+        std::find_if(node_in_samename->begin(), node_in_samename->end(),
+                     [&](ir::Node* n) { return n->Name() == var->Name(); });
+    return iter != node_in_samename->end();
+  }
+  return false;
+}
+
+void OrderedSet::Erase(const std::string& var) {
   PADDLE_ENFORCE(mark_table_.count(var));
   nodes_.erase(mark_table_[var]);
   mark_table_.erase(var);
 }
 
-std::string OrderedNodeList::ToString() const {
+void OrderedSet::Erase(ir::Node* var) {
+  PADDLE_ENFORCE(var != nullptr);
+  Erase(var->Name());
+}
+
+std::string OrderedSet::ToString() const {
   std::stringstream ss;
   for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
-    ss << DebugString(it->first) << " ";
+    for (auto& node : *it) {
+      ss << DebugString(node) << " ";
+    }
   }
   return ss.str();
 }
 
 bool NodeCanReused(ir::Node* node) {
+  // valid the node is a var node
   if (node == nullptr || !node->IsVar() || node->IsCtrlVar()) return false;
-  // auto* desc = node->Var();
-  bool flag = NodeCanReused(*node->Var());
+
+  bool flag = true;
+  // op output force generated in cpu, can not be reused.
   for (auto* op : node->inputs) {
     if (op->Op()->HasAttr("force_cpu")) {
-      // op output force generated in cpu, can not be reused.
       flag &= framework::AttrReader(op->Op()->GetAttrMap())
                   .Get<bool>("force_cpu") == 0;
     }
   }
+  // var desc validation.
+  flag &= NodeCanReused(*node->Var());
   return flag;
 }
 
+int MinChunkSize() {
+  int size{0};
+#ifdef PADDLE_WITH_CUDA
+  size = platform::GpuMinChunkSize();
+#else
+  size = platform::CpuMinChunkSize();
+#endif  // PADDLE_WITH_CUDA
+  return size;
+}
+
 bool NodeCanReused(const VarDesc& node) {
   auto type = node.GetType();
-  if (node.Persistable() || type != proto::VarType::LOD_TENSOR ||
-      node.GetShape().empty()) {
+  // only these types holds bulk of gpu memory
+  if (!(type == proto::VarType::LOD_TENSOR ||
+        type == proto::VarType::SELECTED_ROWS ||
+        type == proto::VarType::LOD_TENSOR_ARRAY)) {
+    return false;
+  }
+  // persistable variable is parameter
+  if (node.Persistable()) {
+    return false;
+  }
+  // shape < min_chunk_size is meaningless.
+  // further more, fetched loss always has size = 1
+  // which should not be reused.
+  auto shape = node.GetShape();
+  int size = std::abs(
+      std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()));
+  if (shape.empty() || size < MinChunkSize()) {
     return false;
   }
   // vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
@@ -193,6 +359,176 @@ bool OpHasSubBlock(OpDesc* desc) {
   return false;
 }
 
+ControlFlowGraph::ControlFlowGraph(const ir::Graph& graph) {
+  ops_ = SortOpLikeDescOrder(graph);
+  ConnectNodes();
+}
+
+void ControlFlowGraph::BuildCFGGraph() {
+  // FIXME(dzh): same effect with ConnectNodes, but use the control
+  // link to build dependency graph, it goes wrong in transformer.
+  for (ir::Node* op : ops_) {
+    for (auto& input_var : op->inputs) {
+      if (!input_var->inputs.empty()) {
+        PADDLE_ENFORCE(
+            input_var->inputs.size() == 1 && input_var->inputs[0]->IsOp(),
+            "Preceding Op Node of Var Node must be unique");
+        auto* pred_op = input_var->inputs[0];
+        if (pred_op->Op() != nullptr) {
+          predecessors_[op].insert(pred_op);
+          successors_[pred_op].insert(op);
+        }
+      }
+      if (input_var->IsVar() && !input_var->IsCtrlVar()) {
+        uses_[op].insert(input_var->Name());
+      }
+    }
+    for (auto& output_var : op->outputs) {
+      // output var may be used by many op
+      for (auto* succ_op : output_var->outputs) {
+        if (succ_op->Op() != nullptr) {
+          successors_[op].insert(succ_op);
+          predecessors_[succ_op].insert(op);
+        }
+      }
+      if (output_var->IsVar() && !output_var->IsCtrlVar()) {
+        defs_[op].insert(output_var->Name());
+      }
+    }
+  }
+}
+
+void ControlFlowGraph::ConnectNodes() {
+  for (size_t i = 0; i < ops_.size(); ++i) {
+    auto& op = ops_[i];
+    try {
+      auto& next_op = ops_.at(i + 1);
+      successors_[op].insert(next_op);
+      predecessors_[next_op].insert(op);
+    } catch (...) {
+      // do nothing
+    }
+
+    FilterVariables(op->inputs,
+                    [&](ir::Node* var) { uses_[op].emplace(var->Name()); });
+
+    FilterVariables(op->outputs,
+                    [&](ir::Node* var) { defs_[op].emplace(var->Name()); });
+  }
+}
+
+void ControlFlowGraph::LiveVariableAnalysis() {
+  // NOTE(dzh): variable liveless analysis (a.k.a reversed_ops algorithm)
+  // compute the liveness of for each variable though reversed_ops algorithm.
+  // It iterates the operators from end to begin, compute the live in/live out
+  // variable set for each op, then the diff between in/out will be used for
+  // the variable reuse. For detail refer to
+  // http://www.cs.cornell.edu/courses/cs4120/2013fa/lectures/lec26-fa13.pdf
+  std::list<ir::Node*> work_list(ops_.rbegin(), ops_.rend());
+  while (!work_list.empty()) {
+    ir::Node* op = work_list.front();
+    work_list.pop_front();
+    // get the live_in calculated before. Empty if first.
+    auto prev_live_in = std::move(live_in_[op]);
+    for (auto& s : successors_[op]) {
+      for (auto& var : live_in_[s]) {
+        live_out_[op].insert(var);
+      }
+    }
+    for (auto& var : uses_[op]) {
+      live_in_[op].insert(var);
+    }
+    for (auto& var : live_out_[op]) {
+      live_in_[op].insert(var);
+    }
+    for (auto& var : defs_[op]) {
+      live_in_[op].erase(var);
+    }
+
+    // If the live_in is not changed, then the liveness analysis of
+    // predecessors is completed.
+    //
+    // Otherwise, recalculate the predecessors liveness
+    if (live_in_[op] != prev_live_in) {
+      for (auto& pre : predecessors_[op]) {
+        work_list.push_back(pre);
+      }
+    }
+  }
+}
+
+void ControlFlowGraph::RenameVarInCFGGraph(const std::string& old_node,
+                                           const std::string& new_node,
+                                           int begin_idx) {
+  // update graph from begin idx to the end
+  for (size_t i = begin_idx; i != ops_.size(); ++i) {
+    auto* op = ops_[i];
+    if (uses_[op].find(old_node) != uses_[op].end()) {
+      uses_[op].erase(old_node);
+      uses_[op].insert(new_node);
+    }
+    if (defs_[op].find(old_node) != defs_[op].end()) {
+      defs_[op].erase(old_node);
+      defs_[op].insert(new_node);
+    }
+    if (live_in_[op].find(old_node) != live_in_[op].end()) {
+      live_in_[op].erase(old_node);
+      live_in_[op].insert(new_node);
+    }
+    if (live_out_[op].find(old_node) != live_out_[op].end()) {
+      live_out_[op].erase(old_node);
+      live_out_[op].insert(new_node);
+    }
+  }
+}
+
+const std::set<std::string> ControlFlowGraph::LiveIn(ir::Node* op) const {
+  auto it = live_in_.find(op);
+  PADDLE_ENFORCE(
+      it != live_in_.end(),
+      string::Sprintf("Expect %s in live_in, but Not Found.", op->Name()));
+  return it->second;
+}
+
+const std::set<std::string> ControlFlowGraph::LiveOut(ir::Node* op) const {
+  auto it = live_out_.find(op);
+  PADDLE_ENFORCE(
+      it != live_out_.end(),
+      string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
+  return it->second;
+}
+
+const std::set<std::string> ControlFlowGraph::Use(ir::Node* op) const {
+  auto it = uses_.find(op);
+  PADDLE_ENFORCE(
+      it != uses_.end(),
+      string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
+  return it->second;
+}
+
+const std::vector<ir::Node*> ControlFlowGraph::Ops() const { return ops_; }
+
+std::vector<ir::Node*>& ControlFlowGraph::Ops() { return ops_; }
+
+ir::Node* ControlFlowGraph::GetNodeByName(const std::string& name,
+                                          ir::Node* op) const {
+  // in ssa-graph, different version nodes have same name,
+  // this function get the latest version var before target op
+  // It may return nullptr, such as data node.
+  ir::Node* found_node = nullptr;
+  for (auto* node : ops_) {
+    if (node == op) break;
+    for (auto& output : node->outputs) {
+      PADDLE_ENFORCE((output != nullptr && output->IsVar()),
+                     "Output is empty!");
+      if (output->Var() && output->Name() == name) {
+        found_node = output;
+      }
+    }
+  }
+  return found_node;
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/memory_optimize_helper.h

@@ -17,6 +17,8 @@
 #include <iostream>
 #include <iterator>
 #include <list>
+#include <map>
+#include <set>
 #include <string>
 #include <utility>
 #include <vector>
@@ -27,41 +29,43 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-constexpr char kFetchedVars[] = "fetched_vars";
-constexpr char kGraphNodePool[] = "graph_node_pool";
+constexpr char kAllOpDescs[] = "all_op_descs";
 
-// NOTE(dzh): Variable and the operators use the var.
-// for early delete pass.
-// Because analysis var pass build base on ir::Node, which maybe released
-// or modified between passes, so we use OpDesc* to mark ops.
-using GraphNodePool = std::vector<
-    std::pair<std::string /*var node*/, std::unordered_set<OpDesc*> /* ops */>>;
+std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
 
-// NOTE(dzh): by default, it sort node in ascend order(by node bytes size).
-// in fluid, -1 means the batch_size is determined in runtime.
-// the node batch_size equal -1 always ranking in the front than the node not.
+// NOTE(dzh): A ordered set for node reuse in memory optimize.
+// the orderedset sort node in ascend order(by node bytes size).
+// in fluid, -1 means the batch_size, which is determined in runtime.
+// So the reuse happens between nodes who's batch_size both are -1
+// simultaneously or not.
+//
+// sort rule:
+// rule 0 : smaller node ranking in front.
+// rule 1 : batch_size equal -1 ranking in the front than the node not.
+//
 // For example,
 // node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
-// O(1) insert, delete
-class OrderedNodeList {
- public:
-  using NodePair = std::pair<ir::Node*, std::unordered_set<ir::Node*>>;
-  using Iter = typename std::list<NodePair>::iterator;
-  using ConstIter = typename std::list<NodePair>::const_iterator;
 
-  void Insert(ir::Node* var, ir::Node* op);
+class OrderedSet {
+ public:
+  // nodes with same name exists in pool.
+  using NodeVector = std::vector<ir::Node*>;
+  using Iter = typename std::list<NodeVector>::iterator;
+  using ConstIter = typename std::list<NodeVector>::const_iterator;
 
+  void Insert(ir::Node* var);
   void Erase(ir::Node* var);
-
   void Erase(const std::string& var);
-
-  bool Has(ir::Node* var) { return mark_table_.count(var->Name()); }
-
-  bool Has(const std::string& var) { return mark_table_.count(var); }
-
-  ir::Node* NodeMatch(ir::Node* var) const;
+  bool Has(ir::Node* var) const;
+  void Clear() {
+    mark_table_.clear();
+    nodes_.clear();
+  }
+  // find the bestfit shape node block with var.
+  ir::Node* FindBestFitNode(ir::Node* var) const;
+  ir::Node* FindNextBestFitNode(ir::Node* var, ir::Node* prev) const;
   // map store non-const iterator, can not promise const
-  int GetIndex(ir::Node* var);
+  int GetNodeIndexInPool(ir::Node* var);
   // pool all node to string
   std::string ToString() const;
 
@@ -69,18 +73,54 @@ class OrderedNodeList {
   Iter end() { return nodes_.end(); }
   ConstIter begin() const { return nodes_.begin(); }
   ConstIter end() const { return nodes_.end(); }
-  size_t size() const { return nodes_.size(); }
 
-  void Clear() {
-    mark_table_.clear();
-    nodes_.clear();
-  }
+  size_t size() const { return nodes_.size(); }
 
  private:
   // for searching.
   std::unordered_map<std::string, Iter> mark_table_;
-  // node swap pairs. var -> ops dep var
-  std::list<NodePair> nodes_;
+  // node pool
+  std::list<NodeVector> nodes_;
+};
+
+class ControlFlowGraph {
+ public:
+  ControlFlowGraph() = default;
+  // IR Graph
+  explicit ControlFlowGraph(const ir::Graph& graph);
+
+  void LiveVariableAnalysis();
+
+  void RenameVarInCFGGraph(const std::string& old_node,
+                           const std::string& new_node, int begin_idx);
+
+  const std::set<std::string> LiveIn(ir::Node* op) const;
+  const std::set<std::string> LiveOut(ir::Node* op) const;
+  const std::set<std::string> Use(ir::Node* op) const;
+  const std::vector<ir::Node*> Ops() const;
+  std::vector<ir::Node*>& Ops();
+
+  // for ssa-graph nodes
+  ir::Node* GetNodeByName(const std::string& name, ir::Node* op) const;
+
+ private:
+  void BuildCFGGraph();
+  void ConnectNodes();
+
+  using NodeListMap = std::unordered_map<ir::Node*, std::set<ir::Node*>>;
+  using VarSetMap = std::map<ir::Node*, std::set<std::string>>;
+  // successors ops use the output variables.
+  NodeListMap successors_;
+  // predecessors ops generated input variables.
+  NodeListMap predecessors_;
+  // variables lived before run current op.
+  VarSetMap live_in_;
+  // variables lived after run current op.
+  VarSetMap live_out_;
+  VarSetMap uses_;  // op inputs
+  VarSetMap defs_;  // op outputs
+
+  std::vector<ir::Node*> ops_;  // op sequence by topology sort
 };
 
 // valid a tensor can be reuse or not
@@ -93,15 +133,24 @@ bool NodeCanReused(const VarDesc& node);
 bool OpHasSubBlock(OpDesc* desc);
 
 // node memory size in bytes
-size_t NodeSizeInBytes(ir::Node* n);
+size_t NodeSize(ir::Node* n);
 
 // node memory size in bytes
-size_t NodeSizeInBytes(const VarDesc&);
+size_t NodeSize(const VarDesc&);
 
 std::string DebugString(ir::Node* var);
 
+// NOTE(dzhwinter)
+// after node reuse, the replaced node shape is
+// different with its VarDesc. So need to find the
+// correct VarDesc in Block.
 VarDesc* FindVarDescInBlock(ir::Node* n);
 
+static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
+  return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
+         op1->Outputs() == op2->Outputs();
+}
+
 template <typename Container, typename Callback>
 class FilterVariableImpl {
  public:

paddle/fluid/framework/details/memory_optimize_helper_test.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/details/memory_optimize_helper.h"
 #include <algorithm>
 #include <iostream>
+#include <iterator>
 #include <memory>
 #include <sstream>
 #include <string>
@@ -22,13 +23,19 @@
 #include <vector>
 #include "glog/logging.h"
 #include "gtest/gtest.h"
+#include "paddle/fluid/framework/details/graph_test_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
 
 namespace paddle {
 namespace framework {
 namespace details {
 
-TEST(OrderedNodeList, Normal) {
-  OrderedNodeList pool;
+TEST(OrderedSet, Normal) {
+  OrderedSet pool;
   std::vector<std::unique_ptr<ir::Node>> nodes;
 
   // clang-format off
@@ -56,8 +63,15 @@ TEST(OrderedNodeList, Normal) {
     nodes.emplace_back(std::move(node));
   }
 
+  // Insert
   for (auto& node : nodes) {
-    pool.Insert(node.get(), op.get());
+    pool.Insert(node.get());
+  }
+
+  // Has/size
+  ASSERT_EQ(pool.size(), shapes.size());
+  for (auto& node : nodes) {
+    ASSERT_TRUE(pool.Has(node.get()));
   }
 
   // assert its order and interface.
@@ -66,14 +80,14 @@ TEST(OrderedNodeList, Normal) {
   std::cout << pool.ToString() << std::endl;
 
   ASSERT_EQ(pool.size(), static_cast<size_t>(COUNT - 1));
-  ASSERT_EQ(pool.GetIndex(nodes.back().get()), 0);
+  ASSERT_EQ(pool.GetNodeIndexInPool(nodes.back().get()), 0);
 
   {
     auto v1 = block_desc->Var("11");
     v1->SetShape({-1, 256, 56, 56});
     std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v1);
     node1->inputs.emplace_back(op.get());
-    auto* cache = pool.NodeMatch(node1.get());
+    auto* cache = pool.FindBestFitNode(node1.get());
     ASSERT_EQ(cache, nullptr);
   }
   {
@@ -81,16 +95,447 @@ TEST(OrderedNodeList, Normal) {
     v2->SetShape({-1, 2, 5});
     std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v2);
     node1->inputs.emplace_back(op.get());
-    auto* cache = pool.NodeMatch(node1.get());
-    ASSERT_EQ(pool.GetIndex(cache), 2);  // match 6:[-1,2,5]
+    auto* cache = pool.FindBestFitNode(node1.get());
+    ASSERT_EQ(pool.GetNodeIndexInPool(cache), 2);  // match 6:[-1,2,5]
   }
   {
     auto v3 = block_desc->Var("13");
     v3->SetShape({2, 5});
     std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v3);
     node1->inputs.emplace_back(op.get());
-    auto* cache = pool.NodeMatch(node1.get());
-    ASSERT_EQ(pool.GetIndex(cache), 5);  // match  4:[5,2]
+    auto* cache = pool.FindBestFitNode(node1.get());
+    ASSERT_EQ(pool.GetNodeIndexInPool(cache), 5);  // match  4:[5,2]
+  }
+}
+
+TEST(OrderedSet, FindBestFitNode) {
+  OrderedSet pool;
+  std::vector<std::unique_ptr<ir::Node>> nodes;
+  ProgramDesc prog;
+  BlockDesc* block_desc = prog.MutableBlock(0);
+  auto* op_desc = block_desc->AppendOp();
+  op_desc->SetType("dummy");
+  std::unique_ptr<ir::Node> op = ir::CreateNodeForTest(op_desc);
+
+  {
+    auto desc = block_desc->Var("a");
+    desc->SetShape({128, 128});
+    std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
+    node->inputs.emplace_back(op.get());
+    nodes.emplace_back(std::move(node));
+  }
+  {
+    auto desc = block_desc->Var("b");
+    desc->SetShape({128, 129});
+    std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
+    node->inputs.emplace_back(op.get());
+    nodes.emplace_back(std::move(node));
+  }
+  {
+    auto desc = block_desc->Var("c");
+    desc->SetShape({128, 128});
+    std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
+    node->inputs.emplace_back(op.get());
+    nodes.emplace_back(std::move(node));
+  }
+
+  for (auto& node : nodes) {
+    pool.Insert(node.get());
+  }
+
+  // FindNextBestFitNode
+  auto* n = nodes[0].get();
+  auto* cache = pool.FindBestFitNode(n);
+  PADDLE_ENFORCE(cache->Name() == "a");
+  cache = pool.FindNextBestFitNode(n, cache);
+  PADDLE_ENFORCE(cache->Name() == "c");
+  cache = pool.FindNextBestFitNode(n, cache);
+  PADDLE_ENFORCE(cache->Name() == "b");
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_OPERATOR(sum, paddle::framework::DummyOp,
+                  paddle::framework::SumOpMaker,
+                  paddle::framework::DummyVarTypeInference);
+REGISTER_OPERATOR(assign, paddle::framework::DummyOp,
+                  paddle::framework::AssignOpMaker,
+                  paddle::framework::DummyVarTypeInference);
+REGISTER_OPERATOR(dummy, paddle::framework::DummyOp,
+                  paddle::framework::SumOpMaker,
+                  paddle::framework::DummyVarTypeInference);
+/*
+  https://en.wikipedia.org/wiki/Live_variable_analysis
+  Create a customed classical dependency graph, left row is the instruction
+  number.
+  1. a = 1
+  2. b = a
+  3. c = a
+  4. d = b + c
+  5. e = d
+
+  a--------+
+  |        |
+  b        c
+  |        |
+  d--------+
+  |
+  e
+  Then analysis these variable's liveness range
+ */
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+inline static ProgramDesc FillProgramDesc() {
+  ProgramDesc prog;
+  prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("d")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("e")->SetType(proto::VarType::LOD_TENSOR);
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("assign");
+    op->SetInput("X", {"a"});
+    op->SetOutput("Out", {"b"});
+  }
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("assign");
+    op->SetInput("X", {"a"});
+    op->SetOutput("Out", {"c"});
+  }
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("sum");
+    op->SetInput("X", {"b", "c"});
+    op->SetOutput("Out", {"d"});
+  }
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("assign");
+    op->SetInput("X", {"d"});
+    op->SetOutput("Out", {"e"});
+  }
+  return prog;
+}
+
+TEST(CFGGraph, IRGraph) {
+  // prepare ir graph
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  ControlFlowGraph cfg(graph);
+  cfg.LiveVariableAnalysis();
+
+  // test assign op
+  ASSERT_TRUE((std::set<std::string>{"a"} == cfg.LiveIn(cfg.Ops()[0])));
+  ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveOut(cfg.Ops()[0])));
+
+  // test assign op
+  ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveIn(cfg.Ops()[1])));
+  ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveOut(cfg.Ops()[1])));
+
+  // test sum op
+  ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveIn(cfg.Ops()[2])));
+  ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveOut(cfg.Ops()[2])));
+
+  // test assign op
+  ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveIn(cfg.Ops()[3])));
+  ASSERT_TRUE((std::set<std::string>{} == cfg.LiveOut(cfg.Ops()[3])));
+}
+
+// 1. normal test
+TEST(SortOpLikeDescOrder, NormalTest) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto nodes = SortOpLikeDescOrder(graph);
+  auto op_descs = prog.Block(0).AllOps();
+  for (size_t i = 0; i < nodes.size(); ++i) {
+    auto node = nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 2. remove some op_desc
+TEST(SortOpLikeDescOrder, RemoveOpDesc) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+  auto nodes = graph.Nodes();
+  auto op_descs = prog.Block(0).AllOps();
+  ir::Node* found_node = nullptr;
+  for (auto node : nodes) {
+    if (node->IsOp() && node->outputs.back()->Name() == "e") {
+      found_node = node;
+      break;
+    }
+  }
+  PADDLE_ENFORCE(found_node != nullptr);
+  for (auto it = op_descs.begin(); it != op_descs.end();) {
+    if (IsSameDesc(*it, found_node->Op())) {
+      it = op_descs.erase(it);
+    } else {
+      ++it;
+    }
+  }
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  ir::Node* e = find_node_in_graph("e");
+  ir::Node* d = find_node_in_graph("d");
+  std::remove(d->outputs.begin(), d->outputs.end(), found_node);
+  graph.RemoveNode(found_node);
+  graph.RemoveNode(e);
+
+  // other node keeps the same order
+  auto remain_nodes = SortOpLikeDescOrder(graph);
+  for (size_t i = 0; i < remain_nodes.size(); ++i) {
+    auto node = remain_nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 3. add some op_desc
+TEST(SortOpLikeDescOrder, AddOpDesc) {
+  auto prog = FillProgramDesc();
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  ir::Graph graph(prog);
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  // cached desc different with real one
+  // mimic the intermidiete pass modify the programdesc.
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto op_descs = prog.Block(0).AllOps();
+
+  auto op = prog.MutableBlock(0)->AppendOp();
+  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
+  op->SetType("sum");
+  op->SetInput("X", {"b", "c"});
+  op->SetOutput("Out", {"d1"});
+  ir::Node* node = graph.CreateOpNode(op);
+  ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
+  ir::Node* b = find_node_in_graph("b");
+  ir::Node* c = find_node_in_graph("c");
+  node->outputs.emplace_back(d1);
+  node->inputs.emplace_back(b);
+  node->inputs.emplace_back(c);
+  d1->inputs.emplace_back(node);
+  b->outputs.emplace_back(node);
+  c->outputs.emplace_back(node);
+  op_descs.insert(op_descs.begin() + 4, op);
+
+  auto nodes = SortOpLikeDescOrder(graph);
+
+  for (size_t i = 0; i < nodes.size(); ++i) {
+    auto node = nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 4. add and delete some op_desc
+TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  // remove sum node
+  auto op_descs = prog.Block(0).AllOps();
+  ir::Node* found_node = nullptr;
+  auto nodes = graph.Nodes();
+  for (auto node : nodes) {
+    if (node->Name() == "sum") {
+      found_node = node;
+      break;
+    }
+  }
+  PADDLE_ENFORCE(found_node != nullptr);
+  for (auto it = op_descs.begin(); it != op_descs.end();) {
+    if (IsSameDesc(*it, found_node->Op())) {
+      it = op_descs.erase(it);
+    } else {
+      ++it;
+    }
+  }
+  {
+    ir::Node* d = find_node_in_graph("d");
+    ir::Node* c = find_node_in_graph("c");
+    ir::Node* e = find_node_in_graph("e");
+    std::remove(d->outputs.begin(), d->outputs.end(), found_node);
+    std::remove(c->outputs.begin(), c->outputs.end(), found_node);
+    ir::Node* pending_op = found_node->outputs[0]->outputs[0];
+    graph.RemoveNode(e);
+    graph.RemoveNode(pending_op);
+    graph.RemoveNode(found_node);
+  }
+
+  // add node
+  auto op = prog.MutableBlock(0)->AppendOp();
+  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
+  op->SetType("sum");
+  op->SetInput("X", {"b", "c"});
+  op->SetOutput("Out", {"d1"});
+  {
+    ir::Node* node = graph.CreateOpNode(op);
+    ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
+    ir::Node* b = find_node_in_graph("b");
+    ir::Node* c = find_node_in_graph("c");
+    node->outputs.emplace_back(d1);
+    node->inputs.emplace_back(b);
+    node->inputs.emplace_back(c);
+    b->outputs.emplace_back(node);
+    c->outputs.emplace_back(node);
+  }
+  op_descs.insert(op_descs.begin() + 2, op);
+
+  // check the order
+  auto mynodes = SortOpLikeDescOrder(graph);
+  for (size_t i = 0; i < mynodes.size(); ++i) {
+    auto node = mynodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 5. add and replace some op_desc inplace.
+TEST(SortOpLikeDescOrder, AddAndReplaceOpDescInplace) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  auto op_descs = prog.Block(0).AllOps();
+  // add node
+  auto op = prog.MutableBlock(0)->AppendOp();
+  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
+  op->SetType("sum");
+  op->SetInput("X", {"b", "c"});
+  op->SetOutput("Out", {"d1"});
+  {
+    ir::Node* node = graph.CreateOpNode(op);
+    ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
+    ir::Node* b = find_node_in_graph("b");
+    ir::Node* c = find_node_in_graph("c");
+    node->outputs.emplace_back(d1);
+    node->inputs.emplace_back(b);
+    node->inputs.emplace_back(c);
+    d1->inputs.emplace_back(node);
+    b->outputs.emplace_back(node);
+    c->outputs.emplace_back(node);
+  }
+
+  op_descs.emplace_back(op);
+
+  // replace op_desc inplace
+  auto nodes = graph.Nodes();
+  ir::Node* found_node = nullptr;
+  for (auto node : nodes) {
+    if (node->IsOp() && node->Op() && node->Name() == "assign") {
+      if (node->outputs.size() == 1 && node->outputs[0]->Name() == "e") {
+        found_node = node;
+        break;
+      }
+    }
+  }
+  {
+    ir::Node* d = find_node_in_graph("d");
+    ir::Node* e = find_node_in_graph("e");
+    std::remove(d->outputs.begin(), d->outputs.end(), found_node);
+    std::remove(e->inputs.begin(), e->inputs.end(), found_node);
+    graph.RemoveNode(found_node);
+  }
+  op_descs.erase(op_descs.begin() + 3);
+
+  auto replace_op = prog.MutableBlock(0)->AppendOp();
+  replace_op->SetType("sum");
+  replace_op->SetInput("X", {"d", "d1"});
+  replace_op->SetOutput("Out", {"e"});
+  {
+    ir::Node* sum2 = graph.CreateOpNode(replace_op);
+    ir::Node* e = find_node_in_graph("e");
+    ir::Node* d = find_node_in_graph("d");
+    ir::Node* d1 = find_node_in_graph("d1");
+    sum2->inputs.emplace_back(d);
+    sum2->inputs.emplace_back(d1);
+    sum2->outputs.emplace_back(e);
+    e->inputs.emplace_back(sum2);
+    d->outputs.emplace_back(sum2);
+    d1->outputs.emplace_back(sum2);
+  }
+
+  op_descs.emplace_back(replace_op);
+  // compare op order
+  auto graph_nodes = SortOpLikeDescOrder(graph);
+  for (size_t i = 0; i < graph_nodes.size(); ++i) {
+    auto node = graph_nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
   }
 }
 

paddle/fluid/framework/details/memory_optimize_pass.cc

@@ -43,11 +43,6 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
-  return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
-         op1->Outputs() == op2->Outputs();
-}
-
 std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
   auto nodes = graph->Nodes();
@@ -74,11 +69,20 @@ std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
     }
 
     for (auto& var : op->outputs) {
-      if (!NodeCanReused(var) || cfg_->Use(op).count(var->Name()) == 0 ||
-          skip_set_.count(var->Name()))
+      if (var->IsVar() && !var->IsCtrlVar() && skip_set_.count(var->Name())) {
+        VLOG(3) << "Skip set contains variable of " << var->Name()
+                << "disable reuse on it. skipped";
         continue;
-      ir::Node* cache = pool_.NodeMatch(var);
-
+      }
+      if (NodeCanReused(var) && cfg_->Use(op).count(var->Name()) == 0) {
+        ir::Node* cache = pool_.FindBestFitNode(var);
+        while (cache != nullptr && var->Name() == cache->Name()) {
+          VLOG(3) << "The same cache variable is cascade reused. "
+                  << cache->Name() << " is re-filled to the pool after "
+                  << "the reused op is finished. Current op can not "
+                  << "replace it again. Skip this candidate.";
+          cache = pool_.FindNextBestFitNode(var, cache);
+        }
         if (var->Name() == FLAGS_memory_optimize_debug) {
           VLOG(3) << "start match var " << DebugString(var) << " of op "
                   << op->Name();
@@ -87,69 +91,45 @@ std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
                   << ((cache == nullptr) ? "False" : "True");
         }
 
-      if (cache == nullptr) continue;
-      if (var->Name() == cache->Name()) {
-        VLOG(3) << "The same cache variable is cascade reused." << var->Name()
-                << " is re-filled to the pool after"
-                << "the reused op is finished. Current op can not "
-                << "replace it again. Skip this candidate.";
-        continue;
-
-        int node_idx_in_pool = pool_.GetIndex(cache);
+        if (cache != nullptr) {
+          int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
           VLOG(3) << string::Sprintf(
               "!!! %s,  %s => %s, cache idx %d, pool size %d",
               std::to_string(reuse_id++), DebugString(var), DebugString(cache),
               node_idx_in_pool, static_cast<int>(pool_.size()));
-        // update CFG Graph on the fly.
-        // reused var maybe re-fill into the pool
-        cfg_->RenameVarInCFGGraph(var->Name(), cache->Name(), idx);
-        // NOTE(dzhwinter): we need to both update the ProgramDesc
-        // and IR Graph. because op_desc/var_desc is used in CreateOp,
-        // CreateVar when running happens. But IR Graph
-        // define the dependence relationship between nodes.
-        RenameVarInGraphDesc(var->Name(), cache->Name(), idx);
-        RenameVarInGraphNode(var->Name(), cache->Name(), idx, graph.get());
+          // NOTE(dzhwinter): update the ProgramDesc/IR Graph
+          // and the CFG Graph on the fly.
+          //
+          // IR Graph define the dependence relationship between nodes.
+          //
+          // ProgramDesc defines the input/output vars. Its used in
+          // CreateOp, CreateVar when running happens.
+          //
+          // CFG Graph store the liveness information, when reuse happens
+          // we also need to update the variable liveness.
+          const std::string var_name = var->Name();
+          const std::string cache_name = cache->Name();
 
-        pool_.Erase(cache);
+          cfg_->RenameVarInCFGGraph(var_name, cache_name, idx);
+          RenameVarInGraphDesc(var_name, cache_name, idx);
+          RenameVarInGraphNode(var_name, cache_name, idx, graph.get());
+          pool_.Erase(cache_name);
+        }
+      }
     }
     // fill the pool
-      std::unordered_set<std::string> unlived_vars;
     for (auto var : cfg_->LiveIn(op)) {
       if (cfg_->LiveOut(op).count(var) == 0) {
-          unlived_vars.emplace(var);
-        }
-      }
-      for (auto var : unlived_vars) {
-        ir::Node* var_node = cfg_->GetNodeFromVarName(var, op);
+        ir::Node* var_node = cfg_->GetNodeByName(var, op);
+        if (var_node == nullptr || var_node->IsCtrlVar()) continue;
         if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
-          pool_.Insert(var_node, op);
+          pool_.Insert(var_node);
         }
       }
     }
   }
   graph->ResolveHazard(var_nodes_);
 
-  // For early delete pass. use GraphNodePool load the unlived vars.
-  // 1. find all deps op for each unlived var in memory pool.
-  for (auto& op : graph->Nodes()) {
-    for (auto& var : op->inputs) {
-      if (pool_.Has(var)) {
-        pool_.Insert(var, op);
-      }
-    }
-  }
-  // 2. convert ir node based memory pool to graph node
-  // because Node* maybe released bettwen passes.
-  auto& graph_pool = graph->Get<GraphNodePool>(kGraphNodePool);
-  for (auto it = pool_.begin(); it != pool_.end(); ++it) {
-    std::unordered_set<OpDesc*> descs;
-    for (auto& op : it->second) {
-      PADDLE_ENFORCE(op->IsOp());
-      descs.insert(op->Op());
-    }
-    graph_pool.push_back(std::make_pair(it->first->Name(), descs));
-  }
-
   return graph;
 }
 
@@ -198,12 +178,12 @@ void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
     PADDLE_ENFORCE(sub_op != nullptr);
     for (auto* var : sub_op->outputs) {
       if (NodeCanReused(var)) {
-        ir::Node* cache = pool_.NodeMatch(var);
+        ir::Node* cache = pool_.FindBestFitNode(var);
         if (cache != nullptr) {
           if (var->Var()->GetDataType() != cache->Var()->GetDataType()) {
             continue;
           }
-          int node_idx_in_pool = pool_.GetIndex(cache);
+          int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
           VLOG(3) << string::Sprintf(
               "!!! %s,  %s => %s, cache idx %d, pool size %d",
               std::to_string(sub_reuse_id++), DebugString(var),
@@ -297,8 +277,7 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
     // redirect the input to the latest version of cache_var
     for (auto* node : op->inputs) {
       if (node->Name() == var) {
-        ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
-        var_nodes_[cache_var].emplace_back(cache_node);
+        ir::Node* cache_node = var_nodes_[cache_var].back();
 
         // swap node to cache_node
         cache_node->outputs.insert(cache_node->outputs.end(),
@@ -307,11 +286,15 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
         auto* prev_op = node->inputs[0];
         std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), node,
                      cache_node);
-        cache_node->inputs.emplace_back(prev_op);
         for (auto* next_op : node->outputs) {
           std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
                        cache_node);
         }
+
+        // erase unused node
+        auto& nodes = var_nodes_.at(var);
+        nodes.erase(std::remove(nodes.begin(), nodes.end(), node), nodes.end());
+        graph->RemoveNode(node);
       }
     }
 
@@ -331,271 +314,14 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
           std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
                        cache_node);
         }
-      }
-    }
-  }
 
-  // release node of unused var in graph
-  for (auto* node : var_nodes_[var]) {
+        // erase unused node
+        auto& nodes = var_nodes_.at(var);
+        nodes.erase(std::remove(nodes.begin(), nodes.end(), node), nodes.end());
         graph->RemoveNode(node);
       }
-  var_nodes_.at(var).clear();
-}
-
-std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
-  PADDLE_ENFORCE(graph.Has(kAllOpDescs),
-                 "Graph has no attribute of kAllOpDescs.");
-  // 1. get op desc order
-  auto& op_descs = graph.Get<const std::vector<OpDesc*>>(kAllOpDescs);
-
-  // 2. topology sort order
-  auto nodes = graph.Nodes();
-  std::deque<ir::Node*> ops;
-  FilterVariables(nodes, [&](ir::Node* op) {
-    if (op->IsOp() && op->Op() != nullptr) {
-      ops.emplace_back(op);
-    }
-  });
-  std::unordered_map<ir::Node*, size_t> op_deps;
-  std::list<ir::Node*> ready_ops;
-  std::unordered_map<ir::Node*, std::unordered_set<ir::Node*>> pending_ops;
-
-  for (auto* op : ops) {
-    std::unordered_set<ir::Node*> preceding_op;
-    for (auto* in : op->inputs) {
-      if (in->inputs.empty()) continue;
-      PADDLE_ENFORCE(in->inputs.size() == 1 && in->inputs[0]->IsOp());
-      preceding_op.emplace(in->inputs[0]);
-      pending_ops[in->inputs[0]].emplace(op);
-    }
-    op_deps[op] = preceding_op.size();
-    if (preceding_op.empty()) {
-      ready_ops.emplace_back(op);
-    }
-  }
-
-  // 3. generated op list based desc order and the topology order
-  std::vector<ir::Node*> ret;
-  std::list<OpDesc*> op_descs_list(op_descs.begin(), op_descs.end());
-
-  auto update_by_found_node = [&](ir::Node* found_node) {
-    for (auto* pending_op : pending_ops[found_node]) {
-      if (--op_deps[pending_op] == 0) {
-        ready_ops.emplace_back(pending_op);
-      }
-    }
-    ready_ops.remove(found_node);
-    ret.emplace_back(found_node);
-  };
-
-  while (!ready_ops.empty()) {
-    bool all_of_ready_op_unmatched = true;
-    for (auto it = op_descs_list.begin(); it != op_descs_list.end();) {
-      auto op_desc = *it;
-      ir::Node* found_node = nullptr;
-      for (auto* op : ready_ops) {
-        if (IsSameDesc(op->Op(), op_desc)) {
-          found_node = op;
-          break;
-        }
-      }
-
-      // 3.1 op desc deleted by other pass
-      if (found_node == nullptr) {
-        ++it;
-        continue;
-      } else {
-        all_of_ready_op_unmatched = false;
-        it = op_descs_list.erase(it);
-      }
-      update_by_found_node(found_node);
-    }
-
-    // 3.2 op descs are added by other pass
-    // preceding op non empty means some new op descs are
-    // created, but not contained in return node list.
-    // these new op desc may depend on each other.
-    std::list<ir::Node*> prev_ready_ops(ready_ops);
-    if (all_of_ready_op_unmatched) {
-      for (auto op : prev_ready_ops) {
-        update_by_found_node(op);
-      }
-    }
-  }
-
-  PADDLE_ENFORCE(std::all_of(
-      op_deps.begin(), op_deps.end(),
-      [&](const std::pair<ir::Node*, size_t>& p) { return p.second == 0; }));
-
-  return ret;
-}
-
-ControlFlowGraph::ControlFlowGraph(const ir::Graph& graph) {
-  ops_ = SortOpLikeDescOrder(graph);
-  ConnectNodes();
-}
-
-void ControlFlowGraph::BuildCFGGraph() {
-  // FIXME(dzh): same effect with ConnectNodes, but use the control
-  // link to build dependency graph, it goes wrong in transformer.
-  for (ir::Node* op : ops_) {
-    for (auto& input_var : op->inputs) {
-      if (!input_var->inputs.empty()) {
-        PADDLE_ENFORCE(
-            input_var->inputs.size() == 1 && input_var->inputs[0]->IsOp(),
-            "Preceding Op Node of Var Node must be unique");
-        auto* pred_op = input_var->inputs[0];
-        if (pred_op->Op() != nullptr) {
-          predecessors_[op].insert(pred_op);
-          successors_[pred_op].insert(op);
-        }
-      }
-      if (input_var->IsVar() && !input_var->IsCtrlVar()) {
-        uses_[op].insert(input_var->Name());
-      }
-    }
-    for (auto& output_var : op->outputs) {
-      // output var may be used by many op
-      for (auto* succ_op : output_var->outputs) {
-        if (succ_op->Op() != nullptr) {
-          successors_[op].insert(succ_op);
-          predecessors_[succ_op].insert(op);
-        }
-      }
-      if (output_var->IsVar() && !output_var->IsCtrlVar()) {
-        defs_[op].insert(output_var->Name());
-      }
-    }
-  }
-}
-
-void ControlFlowGraph::ConnectNodes() {
-  for (size_t i = 0; i < ops_.size(); ++i) {
-    auto& op = ops_[i];
-    try {
-      auto& next_op = ops_.at(i + 1);
-      successors_[op].insert(next_op);
-      predecessors_[next_op].insert(op);
-    } catch (...) {
-      // do nothing
-    }
-
-    FilterVariables(op->inputs,
-                    [&](ir::Node* var) { uses_[op].emplace(var->Name()); });
-
-    FilterVariables(op->outputs,
-                    [&](ir::Node* var) { defs_[op].emplace(var->Name()); });
-  }
-}
-
-void ControlFlowGraph::LiveVariableAnalysis() {
-  // NOTE(dzh): variable liveless analysis (a.k.a reversed_ops algorithm)
-  // compute the liveness of for each variable though reversed_ops algorithm.
-  // It iterates the operators from end to begin, compute the live in/live out
-  // variable set for each op, then the diff between in/out will be used for
-  // the variable reuse. For detail refer to
-  // http://www.cs.cornell.edu/courses/cs4120/2013fa/lectures/lec26-fa13.pdf
-  std::list<ir::Node*> work_list(ops_.rbegin(), ops_.rend());
-  while (!work_list.empty()) {
-    ir::Node* op = work_list.front();
-    work_list.pop_front();
-    // get the live_in calculated before. Empty if first.
-    auto prev_live_in = std::move(live_in_[op]);
-    for (auto& s : successors_[op]) {
-      for (auto& var : live_in_[s]) {
-        live_out_[op].insert(var);
-      }
-    }
-    for (auto& var : uses_[op]) {
-      live_in_[op].insert(var);
-    }
-    for (auto& var : live_out_[op]) {
-      live_in_[op].insert(var);
-    }
-    for (auto& var : defs_[op]) {
-      live_in_[op].erase(var);
-    }
-
-    // If the live_in is not changed, then the liveness analysis of
-    // predecessors is completed.
-    //
-    // Otherwise, recalculate the predecessors liveness
-    if (live_in_[op] != prev_live_in) {
-      for (auto& pre : predecessors_[op]) {
-        work_list.push_back(pre);
-      }
-    }
-  }
-}
-
-void ControlFlowGraph::RenameVarInCFGGraph(const std::string& old_node,
-                                           const std::string& new_node,
-                                           int begin_idx) {
-  // update graph from begin idx to the end
-  for (size_t i = begin_idx; i != ops_.size(); ++i) {
-    auto* op = ops_[i];
-    if (uses_[op].find(old_node) != uses_[op].end()) {
-      uses_[op].erase(old_node);
-      uses_[op].insert(new_node);
-    }
-    if (defs_[op].find(old_node) != defs_[op].end()) {
-      defs_[op].erase(old_node);
-      defs_[op].insert(new_node);
-    }
-    if (live_in_[op].find(old_node) != live_in_[op].end()) {
-      live_in_[op].erase(old_node);
-      live_in_[op].insert(new_node);
-    }
-    if (live_out_[op].find(old_node) != live_out_[op].end()) {
-      live_out_[op].erase(old_node);
-      live_out_[op].insert(new_node);
-    }
-  }
-}
-
-const std::set<std::string> ControlFlowGraph::LiveIn(ir::Node* op) const {
-  auto it = live_in_.find(op);
-  PADDLE_ENFORCE(
-      it != live_in_.end(),
-      string::Sprintf("Expect %s in live_in, but Not Found.", op->Name()));
-  return it->second;
-}
-
-const std::set<std::string> ControlFlowGraph::LiveOut(ir::Node* op) const {
-  auto it = live_out_.find(op);
-  PADDLE_ENFORCE(
-      it != live_out_.end(),
-      string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
-  return it->second;
-}
-
-const std::set<std::string> ControlFlowGraph::Use(ir::Node* op) const {
-  auto it = uses_.find(op);
-  PADDLE_ENFORCE(
-      it != uses_.end(),
-      string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
-  return it->second;
-}
-
-const std::vector<ir::Node*> ControlFlowGraph::Ops() const { return ops_; }
-
-std::vector<ir::Node*>& ControlFlowGraph::Ops() { return ops_; }
-
-ir::Node* ControlFlowGraph::GetNodeFromVarName(const std::string& name,
-                                               ir::Node* op) const {
-  // in ssa-graph, different version nodes have same name,
-  // this function get the latest version var before target op
-  // It may return nullptr, such as data node.
-  ir::Node* found_node = nullptr;
-  for (auto* node : ops_) {
-    if (node == op) break;
-    for (auto& output : node->outputs) {
-      if (output->Name() == name) {
-        found_node = output;
-      }
     }
   }
-  return found_node;
 }
 
 }  // namespace details
@@ -604,5 +330,4 @@ ir::Node* ControlFlowGraph::GetNodeFromVarName(const std::string& name,
 
 REGISTER_PASS(memory_optimize_pass,
               paddle::framework::details::MemoryOptimizePass)
-    .RequireGraphAttr(paddle::framework::details::kGraphNodePool)
     .RequireGraphAttr(paddle::framework::details::kAllOpDescs);

paddle/fluid/framework/details/memory_optimize_pass.h

@@ -32,20 +32,15 @@
 namespace paddle {
 namespace framework {
 namespace details {
-constexpr char kAllOpDescs[] = "all_op_descs";
-
-std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
-
-class ControlFlowGraph;
 
 class MemoryOptimizePass : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override;
-
- private:
   // fill the variable map(var_nodes) by version.
   void InitSSAGraphNodes() const;
+
+ private:
   // update program descs
   void RenameVarInGraphDesc(const std::string& var,
                             const std::string& cache_var, size_t idx) const;
@@ -62,7 +57,7 @@ class MemoryOptimizePass : public ir::Pass {
 
  private:
   // Reuse Node Pool, Owned.
-  mutable OrderedNodeList pool_;
+  mutable OrderedSet pool_;
   // controlflow Graph
   mutable std::unique_ptr<ControlFlowGraph> cfg_;
   // skip set
@@ -71,45 +66,6 @@ class MemoryOptimizePass : public ir::Pass {
   mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
 };
 
-class ControlFlowGraph {
- public:
-  ControlFlowGraph() = default;
-  // For IR Graph in parallelexecutor
-  explicit ControlFlowGraph(const ir::Graph& graph);
-
-  void LiveVariableAnalysis();
-
-  void RenameVarInCFGGraph(const std::string& old_node,
-                           const std::string& new_node, int begin_idx);
-
-  const std::set<std::string> LiveIn(ir::Node* op) const;
-  const std::set<std::string> LiveOut(ir::Node* op) const;
-  const std::set<std::string> Use(ir::Node* op) const;
-  const std::vector<ir::Node*> Ops() const;
-  std::vector<ir::Node*>& Ops();
-
-  // for ssa-graph nodes
-  ir::Node* GetNodeFromVarName(const std::string& name, ir::Node* op) const;
-
- private:
-  void BuildCFGGraph();
-  void ConnectNodes();
-  using NodeListMap = std::unordered_map<ir::Node*, std::set<ir::Node*>>;
-  using VarSetMap = std::map<ir::Node*, std::set<std::string>>;
-  // successors ops use the output variables.
-  NodeListMap successors_;
-  // predecessors ops generated input variables.
-  NodeListMap predecessors_;
-  // variables lived before run current op.
-  VarSetMap live_in_;
-  // variables lived after run current op.
-  VarSetMap live_out_;
-  VarSetMap uses_;  // op inputs
-  VarSetMap defs_;  // op outputs
-
-  std::vector<ir::Node*> ops_;  // op sequence by topology sort
-};
-
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/memory_optimize_pass_test.cc

-// 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.
-
-#include "paddle/fluid/framework/details/memory_optimize_pass.h"
-#include <algorithm>
-#include <iostream>
-#include <iterator>
-#include "glog/logging.h"
-#include "gtest/gtest.h"
-#include "paddle/fluid/framework/details/graph_test_base.h"
-#include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/graph_helper.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/framework/program_desc.h"
-
-REGISTER_OPERATOR(sum, paddle::framework::DummyOp,
-                  paddle::framework::SumOpMaker,
-                  paddle::framework::DummyVarTypeInference);
-REGISTER_OPERATOR(assign, paddle::framework::DummyOp,
-                  paddle::framework::AssignOpMaker,
-                  paddle::framework::DummyVarTypeInference);
-REGISTER_OPERATOR(dummy, paddle::framework::DummyOp,
-                  paddle::framework::SumOpMaker,
-                  paddle::framework::DummyVarTypeInference);
-/*
-  https://en.wikipedia.org/wiki/Live_variable_analysis
-  Create a customed classical dependency graph, left row is the instruction
-  number.
-  1. a = 1
-  2. b = a
-  3. c = a
-  4. d = b + c
-  5. e = d
-
-  a--------+
-  |        |
-  b        c
-  |        |
-  d--------+
-  |
-  e
-  Then analysis these variable's liveness range
- */
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
-  return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
-         op1->Outputs() == op2->Outputs();
-}
-
-inline static ProgramDesc FillProgramDesc() {
-  ProgramDesc prog;
-  prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
-  prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
-  prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
-  prog.MutableBlock(0)->Var("d")->SetType(proto::VarType::LOD_TENSOR);
-  prog.MutableBlock(0)->Var("e")->SetType(proto::VarType::LOD_TENSOR);
-  {
-    auto* op = prog.MutableBlock(0)->AppendOp();
-    op->SetType("assign");
-    op->SetInput("X", {"a"});
-    op->SetOutput("Out", {"b"});
-  }
-  {
-    auto* op = prog.MutableBlock(0)->AppendOp();
-    op->SetType("assign");
-    op->SetInput("X", {"a"});
-    op->SetOutput("Out", {"c"});
-  }
-  {
-    auto* op = prog.MutableBlock(0)->AppendOp();
-    op->SetType("sum");
-    op->SetInput("X", {"b", "c"});
-    op->SetOutput("Out", {"d"});
-  }
-  {
-    auto* op = prog.MutableBlock(0)->AppendOp();
-    op->SetType("assign");
-    op->SetInput("X", {"d"});
-    op->SetOutput("Out", {"e"});
-  }
-  return prog;
-}
-
-TEST(CFGGraph, IRGraph) {
-  // prepare ir graph
-  auto prog = FillProgramDesc();
-  ir::Graph graph(prog);
-  const std::vector<OpDesc*>* all_op_descs =
-      new std::vector<OpDesc*>(prog.Block(0).AllOps());
-  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
-
-  ControlFlowGraph cfg(graph);
-  cfg.LiveVariableAnalysis();
-
-  // test assign op
-  ASSERT_TRUE((std::set<std::string>{"a"} == cfg.LiveIn(cfg.Ops()[0])));
-  ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveOut(cfg.Ops()[0])));
-
-  // test assign op
-  ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveIn(cfg.Ops()[1])));
-  ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveOut(cfg.Ops()[1])));
-
-  // test sum op
-  ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveIn(cfg.Ops()[2])));
-  ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveOut(cfg.Ops()[2])));
-
-  // test assign op
-  ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveIn(cfg.Ops()[3])));
-  ASSERT_TRUE((std::set<std::string>{} == cfg.LiveOut(cfg.Ops()[3])));
-}
-
-// 1. normal test
-TEST(SortOpLikeDescOrder, NormalTest) {
-  auto prog = FillProgramDesc();
-  ir::Graph graph(prog);
-  const std::vector<OpDesc*>* all_op_descs =
-      new std::vector<OpDesc*>(prog.Block(0).AllOps());
-  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
-
-  auto nodes = SortOpLikeDescOrder(graph);
-  auto op_descs = prog.Block(0).AllOps();
-  for (size_t i = 0; i < nodes.size(); ++i) {
-    auto node = nodes[i];
-    auto op_desc = op_descs[i];
-    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
-  }
-}
-
-// 2. remove some op_desc
-TEST(SortOpLikeDescOrder, RemoveOpDesc) {
-  auto prog = FillProgramDesc();
-  ir::Graph graph(prog);
-  const std::vector<OpDesc*>* all_op_descs =
-      new std::vector<OpDesc*>(prog.Block(0).AllOps());
-  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
-  auto nodes = graph.Nodes();
-  auto op_descs = prog.Block(0).AllOps();
-  ir::Node* found_node = nullptr;
-  for (auto node : nodes) {
-    if (node->IsOp() && node->outputs.back()->Name() == "e") {
-      found_node = node;
-      break;
-    }
-  }
-  PADDLE_ENFORCE(found_node != nullptr);
-  for (auto it = op_descs.begin(); it != op_descs.end();) {
-    if (IsSameDesc(*it, found_node->Op())) {
-      it = op_descs.erase(it);
-    } else {
-      ++it;
-    }
-  }
-
-  auto find_node_in_graph = [&](std::string s) {
-    ir::Node* ret = nullptr;
-    for (auto n : graph.Nodes()) {
-      if (n->Name() == s) {
-        ret = n;
-        break;
-      }
-    }
-    PADDLE_ENFORCE(ret != nullptr);
-    return ret;
-  };
-
-  ir::Node* e = find_node_in_graph("e");
-  ir::Node* d = find_node_in_graph("d");
-  std::remove(d->outputs.begin(), d->outputs.end(), found_node);
-  graph.RemoveNode(found_node);
-  graph.RemoveNode(e);
-
-  // other node keeps the same order
-  auto remain_nodes = SortOpLikeDescOrder(graph);
-  for (size_t i = 0; i < remain_nodes.size(); ++i) {
-    auto node = remain_nodes[i];
-    auto op_desc = op_descs[i];
-    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
-  }
-}
-
-// 3. add some op_desc
-TEST(SortOpLikeDescOrder, AddOpDesc) {
-  auto prog = FillProgramDesc();
-  const std::vector<OpDesc*>* all_op_descs =
-      new std::vector<OpDesc*>(prog.Block(0).AllOps());
-  ir::Graph graph(prog);
-
-  auto find_node_in_graph = [&](std::string s) {
-    ir::Node* ret = nullptr;
-    for (auto n : graph.Nodes()) {
-      if (n->Name() == s) {
-        ret = n;
-        break;
-      }
-    }
-    PADDLE_ENFORCE(ret != nullptr);
-    return ret;
-  };
-
-  // cached desc different with real one
-  // mimic the intermidiete pass modify the programdesc.
-  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
-
-  auto op_descs = prog.Block(0).AllOps();
-
-  auto op = prog.MutableBlock(0)->AppendOp();
-  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
-  op->SetType("sum");
-  op->SetInput("X", {"b", "c"});
-  op->SetOutput("Out", {"d1"});
-  ir::Node* node = graph.CreateOpNode(op);
-  ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
-  ir::Node* b = find_node_in_graph("b");
-  ir::Node* c = find_node_in_graph("c");
-  node->outputs.emplace_back(d1);
-  node->inputs.emplace_back(b);
-  node->inputs.emplace_back(c);
-  d1->inputs.emplace_back(node);
-  b->outputs.emplace_back(node);
-  c->outputs.emplace_back(node);
-  op_descs.insert(op_descs.begin() + 4, op);
-
-  auto nodes = SortOpLikeDescOrder(graph);
-
-  for (size_t i = 0; i < nodes.size(); ++i) {
-    auto node = nodes[i];
-    auto op_desc = op_descs[i];
-    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
-  }
-}
-
-// 4. add and delete some op_desc
-TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
-  auto prog = FillProgramDesc();
-  ir::Graph graph(prog);
-  const std::vector<OpDesc*>* all_op_descs =
-      new std::vector<OpDesc*>(prog.Block(0).AllOps());
-  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
-
-  auto find_node_in_graph = [&](std::string s) {
-    ir::Node* ret = nullptr;
-    for (auto n : graph.Nodes()) {
-      if (n->Name() == s) {
-        ret = n;
-        break;
-      }
-    }
-    PADDLE_ENFORCE(ret != nullptr);
-    return ret;
-  };
-
-  // remove sum node
-  auto op_descs = prog.Block(0).AllOps();
-  ir::Node* found_node = nullptr;
-  auto nodes = graph.Nodes();
-  for (auto node : nodes) {
-    if (node->Name() == "sum") {
-      found_node = node;
-      break;
-    }
-  }
-  PADDLE_ENFORCE(found_node != nullptr);
-  for (auto it = op_descs.begin(); it != op_descs.end();) {
-    if (IsSameDesc(*it, found_node->Op())) {
-      it = op_descs.erase(it);
-    } else {
-      ++it;
-    }
-  }
-  {
-    ir::Node* d = find_node_in_graph("d");
-    ir::Node* c = find_node_in_graph("c");
-    ir::Node* e = find_node_in_graph("e");
-    std::remove(d->outputs.begin(), d->outputs.end(), found_node);
-    std::remove(c->outputs.begin(), c->outputs.end(), found_node);
-    ir::Node* pending_op = found_node->outputs[0]->outputs[0];
-    graph.RemoveNode(e);
-    graph.RemoveNode(pending_op);
-    graph.RemoveNode(found_node);
-  }
-
-  // add node
-  auto op = prog.MutableBlock(0)->AppendOp();
-  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
-  op->SetType("sum");
-  op->SetInput("X", {"b", "c"});
-  op->SetOutput("Out", {"d1"});
-  {
-    ir::Node* node = graph.CreateOpNode(op);
-    ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
-    ir::Node* b = find_node_in_graph("b");
-    ir::Node* c = find_node_in_graph("c");
-    node->outputs.emplace_back(d1);
-    node->inputs.emplace_back(b);
-    node->inputs.emplace_back(c);
-    b->outputs.emplace_back(node);
-    c->outputs.emplace_back(node);
-  }
-  op_descs.insert(op_descs.begin() + 2, op);
-
-  // check the order
-  auto mynodes = SortOpLikeDescOrder(graph);
-  for (size_t i = 0; i < mynodes.size(); ++i) {
-    auto node = mynodes[i];
-    auto op_desc = op_descs[i];
-    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
-  }
-}
-
-// 5. add and replace some op_desc inplace.
-TEST(SortOpLikeDescOrder, AddAndReplaceOpDescInplace) {
-  auto prog = FillProgramDesc();
-  ir::Graph graph(prog);
-  const std::vector<OpDesc*>* all_op_descs =
-      new std::vector<OpDesc*>(prog.Block(0).AllOps());
-  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
-
-  auto find_node_in_graph = [&](std::string s) {
-    ir::Node* ret = nullptr;
-    for (auto n : graph.Nodes()) {
-      if (n->Name() == s) {
-        ret = n;
-        break;
-      }
-    }
-    PADDLE_ENFORCE(ret != nullptr);
-    return ret;
-  };
-
-  auto op_descs = prog.Block(0).AllOps();
-  // add node
-  auto op = prog.MutableBlock(0)->AppendOp();
-  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
-  op->SetType("sum");
-  op->SetInput("X", {"b", "c"});
-  op->SetOutput("Out", {"d1"});
-  {
-    ir::Node* node = graph.CreateOpNode(op);
-    ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
-    ir::Node* b = find_node_in_graph("b");
-    ir::Node* c = find_node_in_graph("c");
-    node->outputs.emplace_back(d1);
-    node->inputs.emplace_back(b);
-    node->inputs.emplace_back(c);
-    d1->inputs.emplace_back(node);
-    b->outputs.emplace_back(node);
-    c->outputs.emplace_back(node);
-  }
-
-  op_descs.emplace_back(op);
-
-  // replace op_desc inplace
-  auto nodes = graph.Nodes();
-  ir::Node* found_node = nullptr;
-  for (auto node : nodes) {
-    if (node->IsOp() && node->Op() && node->Name() == "assign") {
-      if (node->outputs.size() == 1 && node->outputs[0]->Name() == "e") {
-        found_node = node;
-        break;
-      }
-    }
-  }
-  {
-    ir::Node* d = find_node_in_graph("d");
-    ir::Node* e = find_node_in_graph("e");
-    std::remove(d->outputs.begin(), d->outputs.end(), found_node);
-    std::remove(e->inputs.begin(), e->inputs.end(), found_node);
-    graph.RemoveNode(found_node);
-  }
-  op_descs.erase(op_descs.begin() + 3);
-
-  auto replace_op = prog.MutableBlock(0)->AppendOp();
-  replace_op->SetType("sum");
-  replace_op->SetInput("X", {"d", "d1"});
-  replace_op->SetOutput("Out", {"e"});
-  {
-    ir::Node* sum2 = graph.CreateOpNode(replace_op);
-    ir::Node* e = find_node_in_graph("e");
-    ir::Node* d = find_node_in_graph("d");
-    ir::Node* d1 = find_node_in_graph("d1");
-    sum2->inputs.emplace_back(d);
-    sum2->inputs.emplace_back(d1);
-    sum2->outputs.emplace_back(e);
-    e->inputs.emplace_back(sum2);
-    d->outputs.emplace_back(sum2);
-    d1->outputs.emplace_back(sum2);
-  }
-
-  op_descs.emplace_back(replace_op);
-  // compare op order
-  auto graph_nodes = SortOpLikeDescOrder(graph);
-  for (size_t i = 0; i < graph_nodes.size(); ++i) {
-    auto node = graph_nodes[i];
-    auto op_desc = op_descs[i];
-    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
-  }
-}
-
-}  // namespace details
-}  // namespace framework
-}  // namespace paddle

paddle/fluid/framework/details/parallel_ssa_graph_executor.cc

@@ -65,7 +65,7 @@ FeedFetchList ParallelSSAGraphExecutor::Run(
     if (pool_) {
       run_futures.emplace_back(pool_->enqueue(std::move(call)));
     } else {
-      fetch_data.emplace_back(std::move(call()));
+      fetch_data.emplace_back(call());
     }
   }
 
@@ -74,7 +74,7 @@ FeedFetchList ParallelSSAGraphExecutor::Run(
       if (exception_holder_.IsCaught()) {
         f.wait();
       } else {
-        fetch_data.emplace_back(std::move(f.get()));
+        fetch_data.emplace_back(f.get());
       }
     }
   }

paddle/fluid/framework/details/sequential_execution_pass.cc

@@ -17,6 +17,7 @@
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
 #include "paddle/fluid/framework/op_proto_maker.h"
 
 namespace paddle {

paddle/fluid/framework/details/sequential_execution_pass.h

@@ -21,8 +21,6 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-constexpr char kAllOpDescs[] = "all_op_descs";
-
 class SequentialExecutionPass : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(

paddle/fluid/framework/feed_fetch_method.cc

@@ -44,6 +44,7 @@ LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
   // Since we want to fetch LodTensor from a variable, the variable must
   // be created alreadly.
   Variable* g_fetch_value = scope.FindVar(var_name);
+  PADDLE_ENFORCE_NOT_NULL(g_fetch_value, "%s is not found.", var_name);
   PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
                  "Only %s can be invoked by GetFetchVariable",
                  typeid(FeedFetchList).name());

paddle/fluid/framework/inplace_op_inference.h

@@ -69,7 +69,7 @@ class InplaceInToOut : public InplaceOpInference {
   bool TryInplaceInputOutput(const VarDesc& in, const VarDesc& out) const {
     return in.Name() != out.Name() && details::NodeCanReused(in) &&
            details::NodeCanReused(out) &&
-           details::NodeSizeInBytes(out) <= details::NodeSizeInBytes(in);
+           details::NodeSize(out) <= details::NodeSize(in);
   }
 };
 

paddle/fluid/framework/inplace_op_inference_test.cc

@@ -179,11 +179,11 @@ TEST(InferInplace, SingleOpInplaceInToOut) {
   op->SetOutput("Out", {"test2_out"});
 
   prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
-  prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64});
+  prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
   prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
   prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
   prog.MutableBlock(0)->Var("test2_out");
-  prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16, 128, 128});
 
   auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
   auto in_to_outs = infer_inplace(*op, op->Block());
@@ -201,11 +201,11 @@ TEST(InferInplace, SingleGradOpInplaceInToOut) {
   op->SetOutput(GradVarName("X"), {"test2_a", "test2_b", "test2_c"});
 
   prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
-  prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16, 1024, 1024});
   prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
   prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
   prog.MutableBlock(0)->Var("test2_out");
-  prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16, 1024, 1024});
 
   auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
   auto in_to_outs = infer_inplace(*op, op->Block());
@@ -233,12 +233,12 @@ TEST(InferInplace, MultiOutInplaceInToOut) {
   prog.MutableBlock(0)->Var("o0");
   prog.MutableBlock(0)->Var("y0");
   prog.MutableBlock(0)->Var("z0");
-  prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
 
   auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
   auto in_to_outs = infer_inplace(*op, op->Block());
@@ -267,12 +267,12 @@ TEST(InferInplace, MultiGradInplaceInToOut) {
   prog.MutableBlock(0)->Var("o0");
   prog.MutableBlock(0)->Var("y0");
   prog.MutableBlock(0)->Var("z0");
-  prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
-  prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
+  prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
+  prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
 
   auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
   auto in_to_outs = infer_inplace(*op, op->Block());

paddle/fluid/framework/ir/graph.cc

@@ -76,7 +76,7 @@ std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
       var->inputs.push_back(node);
     }
   }
-  return std::move(var_nodes);
+  return var_nodes;
 }
 
 void Graph::ResolveHazard(

paddle/fluid/framework/ir/graph.h

@@ -142,7 +142,7 @@ class Graph {
     // TODO(panyx0718): control var name should be really unique.
     const std::string name = string::Sprintf(
         "%s@%llu", static_cast<const char *>(ir::Node::kControlDepVarName),
-        node_set_.size());
+        num_node_created_);
     auto *x = AddNode(new ir::Node(name, ir::Node::Type::kVariable));
     x->SetId(num_node_created_++);
     return x;

paddle/fluid/framework/ir/identity_scale_op_clean_pass.cc

@@ -38,9 +38,13 @@ std::unique_ptr<ir::Graph> IdentityScaleOpCleanPass::ApplyImpl(
                       ->assert_is_op("scale")
                       ->assert_op_attr<float>("scale", 1.)
                       ->assert_op_attr<float>("bias", 0.);
-  auto scale_out = detector.mutable_pattern()
+  auto scale_out =
+      detector.mutable_pattern()
           ->NewNode("scale_out")
-                       ->assert_is_op_output("scale");
+          ->assert_is_op_output("scale")
+          // scale's output var should has only one consumer, or it can't be
+          // removed.
+          ->assert_more([](Node* x) { return x->outputs.size() == 1UL; });
 
   pre_op->LinksTo({scale_in});
   scale_op->LinksFrom({scale_in}).LinksTo({scale_out});

paddle/fluid/framework/ir/infer_clean_graph_pass.cc

@@ -37,6 +37,7 @@ class InferCleanGraphPass : public FusePassBase {
     std::unordered_set<const Node*> invalid_nodes;
     int valid_op = 0;
     for (auto* node : graph->Nodes()) {
+      PADDLE_ENFORCE_NOT_NULL(node);
       if (is_valid_node(node)) {
         invalid_nodes.insert(node);
       } else if (node->IsOp()) {

paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc

@@ -164,7 +164,7 @@ ProgramDesc BuildProgramDesc(int num_inputs_of_concat) {
   };
   std::vector<std::string> concat_inputs;
   for (int i = 0; i < num_inputs_of_concat; ++i) {
-    std::string prefix = "seqpool_op_" + i;
+    std::string prefix = "seqpool_op_" + std::to_string(i);
     new_var(prefix + "in");
     new_var(prefix + "out");
     new_var(prefix + "out_unused");

paddle/fluid/framework/operator.cc

@@ -188,14 +188,14 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
     VLOG(3) << place << " " << DebugStringEx(&scope);
   } catch (platform::EnforceNotMet exception) {
     if (Attrs().count("sub_block") != 0) {
-      throw exception;
+      throw;
     }
 
     auto& callstack = Attr<std::vector<std::string>>(
         OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
 
     if (callstack.empty()) {
-      throw exception;
+      throw;
     }
     std::ostringstream sout;
     sout << "Invoke operator " << Type() << " error.\n";
@@ -206,7 +206,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
     sout << "C++ Callstacks: \n";
     sout << exception.err_str_;
     exception.err_str_ = sout.str();
-    throw exception;
+    throw;
   } catch (...) {
     std::rethrow_exception(std::current_exception());
   }
@@ -589,7 +589,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
  public:
   RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
                            const RuntimeContext& ctx)
-      : op_(op), scope_(scope), ctx_(ctx) {}
+      : op_(op), ctx_(ctx) {}
 
   bool HasInput(const std::string& name) const override {
     // has only one input
@@ -881,7 +881,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
   }
 
   const OperatorBase& op_;
-  const Scope& scope_;
   const RuntimeContext& ctx_;
 };
 
@@ -990,10 +989,13 @@ void OperatorWithKernel::TransferInplaceVarsBack(
     const Scope& transfer_scope) const {
   for (auto& var_name : inplace_vars) {
     VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
+    auto* origin_var = scope.FindVar(var_name);
+    PADDLE_ENFORCE_NOT_NULL(origin_var, "The var[%s] should not be nullptr.",
+                            var_name);
     auto* original_tensor =
-        GetMutableLoDTensorOrSelectedRowsValueFromVar(scope.FindVar(var_name));
+        GetMutableLoDTensorOrSelectedRowsValueFromVar(origin_var);
     auto* var = transfer_scope.FindVar(var_name);
-    PADDLE_ENFORCE(var != nullptr, "The var[%s] should not be nullptr",
+    PADDLE_ENFORCE_NOT_NULL(var, "The var[%s] should not be nullptr.",
                             var_name);
     auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var);
     original_tensor->ShareDataWith(*transformed_tensor);

paddle/fluid/framework/operator.h

@@ -222,12 +222,7 @@ class ExecutionContext {
     if (it == ctx_.inputs.end()) {
       return {};
     }
-    std::vector<const Variable*> res;
-    res.reserve(it->second.size());
-    std::transform(it->second.begin(), it->second.end(),
-                   std::back_inserter(res),
-                   [this](Variable* var) { return var; });
-    return res;
+    return {it->second.begin(), it->second.end()};
   }
 
   std::vector<Variable*> MultiOutputVar(const std::string& name) const {

paddle/fluid/framework/parallel_executor.cc

@@ -171,14 +171,6 @@ std::unique_ptr<ir::Graph> ParallelExecutorPrivate::PrepareGCAndRefCnts(
     eager_deletion_pass->SetNotOwned(details::kAllPlaces, &places_);
     graph = eager_deletion_pass->Apply(std::move(graph));
     VLOG(10) << "EagerDeletionPass Applied";
-
-    if (build_strategy_.memory_early_delete_) {
-      auto early_delete_pass =
-          ir::PassRegistry::Instance().Get("memory_early_delete_pass");
-      early_delete_pass->SetNotOwned(details::kGarbageCollector, &gcs_);
-      graph = early_delete_pass->Apply(std::move(graph));
-    }
-    VLOG(10) << "MemoryEarlyDeletePass Applied.";
   }
 
   return graph;
@@ -288,6 +280,8 @@ ParallelExecutor::ParallelExecutor(
   graphs.push_back(std::move(graph));
 #endif
   auto max_memory_size = GetEagerDeletionThreshold();
+  VLOG(10) << "Eager Deletion Threshold "
+           << static_cast<float>(max_memory_size) / (1 << 30);
   if (max_memory_size >= 0) {
     for (size_t i = 0; i < graphs.size(); ++i) {
       graphs[i] = member_->PrepareGCAndRefCnts(
@@ -506,6 +500,5 @@ ParallelExecutor::~ParallelExecutor() {
 }  // namespace framework
 }  // namespace paddle
 
-USE_PASS(memory_early_delete_pass);
 USE_PASS(reference_count_pass);
 USE_PASS(eager_deletion_pass);

paddle/fluid/framework/scope.cc

@@ -22,11 +22,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/string/printf.h"
 
-DEFINE_bool(benchmark, false,
-            "Doing memory benchmark. It will make deleting scope synchronized, "
-            "and add some memory usage logs."
-            "Default cuda is asynchronous device, set to True will"
-            "force op run in synchronous mode.");
+DECLARE_bool(benchmark);
 
 DEFINE_bool(
     eager_delete_scope, true,

paddle/fluid/imperative/layer.cc

@@ -207,7 +207,7 @@ framework::LoDTensor& VarBase::GradValue() {
 
 std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
   if (grad_op_descs_.empty() && backward_id_ <= 0) {
-    LOG(WARNING) << "op with no grad: " << op_desc_->Type();
+    VLOG(3) << "op with no grad: " << op_desc_->Type();
     return {};
   }
 

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -101,7 +101,7 @@ std::unique_ptr<Graph> IRPassManager::Apply(std::unique_ptr<Graph> graph) {
     }
     graph = pass->Apply(std::move(graph));
   }
-  return std::move(graph);
+  return graph;
 }
 
 framework::proto::ProgramDesc IRPassManager::AcquireProgram(

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc

@@ -460,77 +460,6 @@ inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
   return node.inputs.size() == n;
 }
 
-NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
-  PADDLE_ENFORCE(!source.empty(),
-                 "Start points of topological sorting should not be empty!");
-  // CHECK all the inputs' in-degree is 0
-  for (auto *node : source) {
-    PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
-  }
-
-  std::unordered_set<Node *> visited;
-  std::unordered_set<Node *> to_visit{source.begin(), source.end()};
-
-  std::vector<Node *> inlink_visited;
-  while (!to_visit.empty()) {
-    std::vector<Node *> queue(to_visit.begin(), to_visit.end());
-    for (auto *p : queue) {
-      if (Agent(p).deleted()) {
-        visited.insert(p);
-        to_visit.erase(p);
-      }
-
-      inlink_visited.clear();
-
-      std::copy_if(p->inputs.begin(), p->inputs.end(),
-                   std::back_inserter(inlink_visited),
-                   [&](Node *x) -> bool { return visited.count(x) != 0; });
-
-      if (inlink_visited.size() == p->inputs.size()) {
-        sorted_.push_back(p);
-        for (auto *_ : p->outputs) {
-          if (!visited.count(_)) {
-            to_visit.insert(_);
-          }
-        }
-
-        to_visit.erase(p);
-        visited.insert(p);
-      }
-    }
-  }
-}
-
-NodesTSIterator::NodesTSIterator(const NodesTSIterator &other)
-    : sorted_(other.sorted_), cursor_(other.cursor_) {}
-
-Node &NodesTSIterator::operator*() {
-  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
-  return *sorted_[cursor_];
-}
-
-NodesTSIterator &NodesTSIterator::operator++() {
-  if (++cursor_ >= sorted_.size()) {
-    sorted_.clear();
-    cursor_ = 0;
-  }
-  return *this;
-}
-NodesTSIterator &NodesTSIterator::operator=(const NodesTSIterator &other) {
-  cursor_ = other.cursor_;
-  sorted_ = other.sorted_;
-  return *this;
-}
-
-bool NodesTSIterator::operator==(const NodesTSIterator &other) {
-  return sorted_ == other.sorted_ && cursor_ == other.cursor_;
-}
-
-Node *NodesTSIterator::operator->() {
-  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
-  return sorted_[cursor_];
-}
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h

@@ -30,6 +30,7 @@ namespace inference {
 namespace analysis {
 
 using framework::ir::Graph;
+using framework::ir::NodesTSIterator;
 
 const char kIsFunctionNode[] = "__is_function_node__";
 const char kFunctionNodeSubGraph[] = "__function_node_sub_graph__";
@@ -132,32 +133,6 @@ struct Agent {
   framework::ir::Node *x_;
 };
 
-// Topological sorting iterator on nodes.
-struct NodesTSIterator
-    : public std::iterator<std::forward_iterator_tag, framework::ir::Node *> {
-  NodesTSIterator() = default;
-  explicit NodesTSIterator(const std::vector<framework::ir::Node *> &source);
-  NodesTSIterator(NodesTSIterator &&other)
-      : sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
-    other.cursor_ = 0;
-  }
-  NodesTSIterator(const NodesTSIterator &other);
-
-  framework::ir::Node &operator*();
-  NodesTSIterator &operator++();
-  // TODO(Superjomn) current implementation just compare the first
-  // element, need to compare the graph and all the elements in the queue and
-  // set.
-  NodesTSIterator &operator=(const NodesTSIterator &other);
-  bool operator==(const NodesTSIterator &other);
-  bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
-  framework::ir::Node *operator->();
-
- private:
-  std::vector<framework::ir::Node *> sorted_;
-  size_t cursor_{0};
-};
-
 // The nodes those have no input will be treated as start points.
 static std::vector<framework::ir::Node *> ExtractStartPoints(const Graph &g) {
   std::vector<framework::ir::Node *> result;

paddle/fluid/inference/api/analysis_predictor.cc

@@ -421,7 +421,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
   if (!dynamic_cast<AnalysisPredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;
   }
-  return std::move(predictor);
+  return predictor;
 }
 
 void AnalysisPredictor::PrepareFeedFetch() {

paddle/fluid/inference/api/paddle_api.h

@@ -16,6 +16,12 @@
 /*! \file paddle_api.h
  */
 
+/*! \mainpage Paddle Inference APIs
+ * \section intro_sec Introduction
+ * The Paddle inference library aims to offer an high performance inference SDK
+ * for Paddle users.
+ */
+
 #include <cassert>
 #include <memory>
 #include <string>
@@ -34,26 +40,49 @@ enum PaddleDType {
 };
 
 /**
- *\brief Memory menager for PaddleTensor.
+ * \brief Memory manager for `PaddleTensor`.
  *
- *The PaddleBuf holds a buffer for data input or output. The memory can be
- *allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
- *should be reused for better performance.
+ * The PaddleBuf holds a buffer for data input or output. The memory can be
+ * allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
+ * should be reused for better performance.
  *
- *For user allocated memory, the following API can be used:
- *- PaddleBuf(void* data, size_t length) to set an external memory by
- *specifying
- *  the memory address and length.
- *- Reset(void* data, size_t length) to reset the PaddleBuf with an external
+ * For user allocated memory, the following API can be used:
+ * - PaddleBuf(void* data, size_t length) to set an external memory by
+ * specifying the memory address and length.
+ * - Reset(void* data, size_t length) to reset the PaddleBuf with an external
  *memory.
- *ATTENTION, for user allocated memory, deallocation should be done by users
+ * ATTENTION, for user allocated memory, deallocation should be done by users
  *externally after the program finished. The PaddleBuf won't do any allocation
  *or deallocation.
  *
- *To have the PaddleBuf allocate and manage the memory:
- *- PaddleBuf(size_t length) will allocate a memory of size `length`.
- *- Resize(size_t length) resize the memory to no less than `length`, ATTENTION
+ * To have the PaddleBuf allocate and manage the memory:
+ * - PaddleBuf(size_t length) will allocate a memory of size `length`.
+ * - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
  *  if the allocated memory is larger than `length`, nothing will done.
+ *
+ * Usage:
+ *
+ * Let PaddleBuf manage the memory internally.
+ * \code{cpp}
+ * const int num_elements = 128;
+ * PaddleBuf buf(num_elements * sizeof(float));
+ * \endcode
+ *
+ * Or
+ * \code{cpp}
+ * PaddleBuf buf;
+ * buf.Resize(num_elements * sizeof(float));
+ * \endcode
+ * Works the exactly the same.
+ *
+ * One can also make the `PaddleBuf` use the external memory.
+ * \code{cpp}
+ * PaddleBuf buf;
+ * void* external_memory = new float[num_elements];
+ * buf.Reset(external_memory, num_elements*sizeof(float));
+ * ...
+ * delete[] external_memory; // manage the memory lifetime outside.
+ * \endcode
  */
 class PaddleBuf {
  public:
@@ -78,7 +107,7 @@ class PaddleBuf {
   /** Tell whether the buffer is empty.
    */
   bool empty() const { return length_ == 0; }
-  /** Get the memory address.
+  /** Get the data's memory address.
    */
   void* data() const { return data_; }
   /** Get the memory length.
@@ -110,7 +139,8 @@ struct PaddleTensor {
 };
 
 enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
-/** Tensor without copy, currently only supports AnalysisPredictor.
+
+/** Tensor without copy, currently only supports `AnalysisPredictor`.
  */
 class ZeroCopyTensor {
  public:
@@ -269,9 +299,11 @@ struct NativeConfig : public PaddlePredictor::Config {
  *
  * Usage:
  *
+ * \code{.cpp}
  * NativeConfig config;
  * ... // change the configs.
  * auto native_predictor = CreatePaddlePredictor(config);
+ * \endcode
  *
  * FOR EXTENSION DEVELOPER:
  * Different predictors are designated by config type. Similar configs can be

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -66,8 +66,54 @@ void GpuPassStrategy::EnableMKLDNN() {
   LOG(ERROR) << "GPU not support MKLDNN yet";
 }
 
+GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
+  passes_.assign({
+    "infer_clean_graph_pass",                        //
+        "identity_scale_op_clean_pass",              //
+        "conv_affine_channel_fuse_pass",             //
+        "conv_eltwiseadd_affine_channel_fuse_pass",  //
+        "conv_bn_fuse_pass",                         //
+#if CUDNN_VERSION >= 7100  // To run conv_fusion, the version of cudnn must be
+                           // guaranteed at least v7
+        "conv_elementwise_add_act_fuse_pass",   //
+        "conv_elementwise_add2_act_fuse_pass",  //
+        "conv_elementwise_add_fuse_pass",       //
+#endif
+  });
+
+  for (int i = 6; i >= 3; i--) {
+    passes_.push_back("transpose_flatten" + std::to_string(i) +
+                      "_concat_fuse_pass");
+  }
+  use_gpu_ = true;
+}
+
 void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
   analysis_passes_.push_back(pass);
 }
 
+CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
+  // NOTE the large fusions should be located in the front, so that they will
+  // not be damaged by smaller ones.
+  passes_.assign({
+      "infer_clean_graph_pass",         //
+      "attention_lstm_fuse_pass",       //
+      "seqpool_concat_fuse_pass",       //
+      "seqconv_eltadd_relu_fuse_pass",  //
+      // "embedding_fc_lstm_fuse_pass", //
+      "fc_lstm_fuse_pass",             //
+      "mul_lstm_fuse_pass",            //
+      "fc_gru_fuse_pass",              //
+      "mul_gru_fuse_pass",             //
+      "seq_concat_fc_fuse_pass",       //
+      "fc_fuse_pass",                  //
+      "repeated_fc_relu_fuse_pass",    //
+      "squared_mat_sub_fuse_pass",     //
+      "conv_bn_fuse_pass",             //
+      "conv_eltwiseadd_bn_fuse_pass",  //
+      "is_test_pass",                  //
+      "identity_scale_op_clean_pass",  //
+  });
+  use_gpu_ = false;
+}
 }  // namespace paddle

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -97,30 +97,7 @@ class PassStrategy : public PaddlePassBuilder {
  */
 class CpuPassStrategy : public PassStrategy {
  public:
-  CpuPassStrategy() : PassStrategy({}) {
-    // NOTE the large fusions should be located in the front, so that they will
-    // not be damaged by smaller ones.
-    passes_.assign({
-        "infer_clean_graph_pass",         //
-        "attention_lstm_fuse_pass",       //
-        "seqpool_concat_fuse_pass",       //
-        "seqconv_eltadd_relu_fuse_pass",  //
-        // "embedding_fc_lstm_fuse_pass", //
-        "fc_lstm_fuse_pass",             //
-        "mul_lstm_fuse_pass",            //
-        "fc_gru_fuse_pass",              //
-        "mul_gru_fuse_pass",             //
-        "seq_concat_fc_fuse_pass",       //
-        "fc_fuse_pass",                  //
-        "repeated_fc_relu_fuse_pass",    //
-        "squared_mat_sub_fuse_pass",     //
-        "conv_bn_fuse_pass",             //
-        "conv_eltwiseadd_bn_fuse_pass",  //
-        "is_test_pass",                  //
-        "identity_scale_op_clean_pass",  //
-    });
-    use_gpu_ = false;
-  }
+  CpuPassStrategy();
 
   explicit CpuPassStrategy(const CpuPassStrategy &other)
       : PassStrategy(other.AllPasses()) {}
@@ -153,27 +130,7 @@ class CpuPassStrategy : public PassStrategy {
  */
 class GpuPassStrategy : public PassStrategy {
  public:
-  GpuPassStrategy() : PassStrategy({}) {
-    passes_.assign({
-      "infer_clean_graph_pass",                        //
-          "identity_scale_op_clean_pass",              //
-          "conv_affine_channel_fuse_pass",             //
-          "conv_eltwiseadd_affine_channel_fuse_pass",  //
-          "conv_bn_fuse_pass",                         //
-#if CUDNN_VERSION >= 7100  // To run conv_fusion, the version of cudnn must be
-                           // guaranteed at least v7
-          "conv_elementwise_add_act_fuse_pass",   //
-          "conv_elementwise_add2_act_fuse_pass",  //
-          "conv_elementwise_add_fuse_pass",       //
-#endif
-    });
-
-    for (int i = 6; i >= 3; i--) {
-      passes_.push_back("transpose_flatten" + std::to_string(i) +
-                        "_concat_fuse_pass");
-    }
-    use_gpu_ = true;
-  }
+  GpuPassStrategy();
 
   explicit GpuPassStrategy(const GpuPassStrategy &other)
       : PassStrategy(other.AllPasses()) {

paddle/fluid/memory/allocation/allocator_facade.cc

@@ -83,7 +83,7 @@ class ChunkedAllocator : public Allocator {
         VLOG(1) << "Create AutoIncrementAllocator with chunk_size "
                 << max_chunk_size_ << " and capacity " << capacity;
         default_allocator_ = std::make_shared<AutoIncrementAllocator>(
-            [this] { return std::move(CreateAllocatorWithChunk()); }, capacity);
+            [this] { return CreateAllocatorWithChunk(); }, capacity);
       }
     }
 

paddle/fluid/memory/allocation/best_fit_allocator.cc

@@ -111,6 +111,8 @@ size_t BestFitAllocator::NumFreeChunks() const {
 }
 void BestFitAllocator::Free(Allocation* allocation) {
   auto* bf_allocation = dynamic_cast<BestFitAllocation*>(allocation);
+  PADDLE_ENFORCE_NOT_NULL(bf_allocation,
+                          "The input allocation is not BestFitAllocation.");
   auto chunk_it = bf_allocation->ChunkIterator();
   PADDLE_ENFORCE(!chunk_it->is_free);
   chunk_it->is_free = true;

paddle/fluid/memory/allocation/legacy_allocator.cc

@@ -36,6 +36,7 @@ DEFINE_bool(init_allocated_mem, false,
             "that initializing the allocated memory with a small value "
             "during unit testing.");
 DECLARE_double(fraction_of_gpu_memory_to_use);
+DECLARE_bool(benchmark);
 
 namespace paddle {
 namespace memory {
@@ -198,7 +199,7 @@ void *Alloc<platform::CUDAPlace>(const platform::CUDAPlace &place,
                << string::HumanReadableSize(Used<platform::CUDAPlace>(place));
     platform::SetDeviceId(cur_dev);
   } else {
-    if (VLOG_IS_ON(3)) {
+    if (FLAGS_benchmark) {
       allocation::GPUMemMonitor.Add(place.device, size);
     }
     if (FLAGS_init_allocated_mem) {
@@ -216,7 +217,7 @@ void Free<platform::CUDAPlace>(const platform::CUDAPlace &place, void *p,
                                size_t size) {
 #ifdef PADDLE_WITH_CUDA
   GetGPUBuddyAllocator(place.device)->Free(p);
-  if (VLOG_IS_ON(3)) {
+  if (FLAGS_benchmark) {
     allocation::GPUMemMonitor.Minus(place.device, size);
   }
 #else

paddle/fluid/operators/activation_op.cc

@@ -37,7 +37,7 @@ using paddle::framework::Tensor;
           "(bool, default false) Set to true for inference only, false " \
           "for training. Some layers may run faster when this is true.") \
           .SetDefault(false);                                            \
-      AddComment(#OP_COMMENT);                                           \
+      AddComment(OP_COMMENT);                                            \
     }                                                                    \
   }
 
@@ -124,7 +124,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
 UNUSED constexpr char SigmoidDoc[] = R"DOC(
 Sigmoid Activation Operator
 
-$$out = \frac{1}{1 + e^{-x}}$$
+$$out = \\frac{1}{1 + e^{-x}}$$
 
 )DOC";
 
@@ -187,14 +187,14 @@ $out = |x|$
 UNUSED constexpr char CeilDoc[] = R"DOC(
 Ceil Activation Operator.
 
-$out = ceil(x)$
+$out = \left \lceil x \right \rceil$
 
 )DOC";
 
 UNUSED constexpr char FloorDoc[] = R"DOC(
 Floor Activation Operator.
 
-$out = floor(x)$
+$out = \left \lfloor x \right \rfloor$
 
 )DOC";
 
@@ -252,7 +252,7 @@ $out = \ln(1 + e^{x})$
 UNUSED constexpr char SoftsignDoc[] = R"DOC(
 Softsign Activation Operator.
 
-$$out = \frac{x}{1 + |x|}$$
+$$out = \\frac{x}{1 + \|x\|}$$
 
 )DOC";
 

paddle/fluid/operators/controlflow/compare_op.cc

@@ -51,6 +51,11 @@ class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
                                   comment.type));
     AddInput("Y", string::Sprintf("the right hand operand of %s operator",
                                   comment.type));
+    AddAttr<int>(
+        "axis",
+        "The start dimension index for broadcasting Y onto X. [default -1]")
+        .SetDefault(-1)
+        .EqualGreaterThan(-1);
     AddAttr<bool>("force_cpu",
                   "Force fill output variable to cpu "
                   "memory. Otherwise, fill output variable to the running "
@@ -64,11 +69,6 @@ N-dim tensor. X and Y could be any type.  The each element of the Out tensor is
 calculated by $%s$
 )DOC",
                                comment.equation));
-    AddAttr<int>(
-        "axis",
-        "The start dimension index for broadcasting Y onto X. [default -1]")
-        .SetDefault(-1)
-        .EqualGreaterThan(-1);
   }
 };
 

paddle/fluid/operators/conv_op.cc

@@ -222,7 +222,7 @@ void Conv2DOpMaker::Make() {
       .SetDefault(4096);
   AddAttr<bool>("exhaustive_search",
                 "(bool, default false) cuDNN has many algorithm to calculation "
-                "convolution, whether enable exhaustive search ",
+                "convolution, whether enable exhaustive search "
                 "for cuDNN convolution or not, defalut is False.")
       .SetDefault(false);
   AddComment(R"DOC(
@@ -341,7 +341,7 @@ void Conv3DOpMaker::Make() {
       .SetDefault(4096);
   AddAttr<bool>("exhaustive_search",
                 "(bool, default false) cuDNN has many algorithm to calculation "
-                "convolution, whether enable exhaustive search ",
+                "convolution, whether enable exhaustive search "
                 "for cuDNN convolution or not, defalut is False.")
       .SetDefault(false);
   AddComment(R"DOC(

paddle/fluid/operators/detection/density_prior_box_op.h

@@ -72,7 +72,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for
 #endif
-    for (int i = 0; i < fixed_ratios.size(); i++) {
+    for (size_t i = 0; i < fixed_ratios.size(); i++) {
       sqrt_fixed_ratios.push_back(sqrt(fixed_ratios[i]));
     }
 
@@ -115,11 +115,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
       }
     }
     if (clip) {
-      platform::Transform<platform::CPUDeviceContext> trans;
-      ClipFunctor<T> clip_func;
-      trans(ctx.template device_context<platform::CPUDeviceContext>(),
-            boxes->data<T>(), boxes->data<T>() + boxes->numel(),
-            boxes->data<T>(), clip_func);
+      T* dt = boxes->data<T>();
+      std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
+        return std::min<T>(std::max<T>(v, 0.), 1.);
+      });
     }
     framework::Tensor var_t;
     var_t.mutable_data<T>(
@@ -141,7 +140,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
 #pragma omp parallel for collapse(2)
 #endif
     for (int i = 0; i < box_num; ++i) {
-      for (int j = 0; j < variances.size(); ++j) {
+      for (size_t j = 0; j < variances.size(); ++j) {
         e_vars(i, j) = variances[j];
       }
     }

paddle/fluid/operators/detection/prior_box_op.h

@@ -46,13 +46,6 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
   }
 }
 
-template <typename T>
-struct ClipFunctor {
-  HOSTDEVICE inline T operator()(T in) const {
-    return std::min<T>(std::max<T>(in, 0.), 1.);
-  }
-};
-
 template <typename T>
 class PriorBoxOpKernel : public framework::OpKernel<T> {
  public:
@@ -101,31 +94,30 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
     boxes->mutable_data<T>(ctx.GetPlace());
     vars->mutable_data<T>(ctx.GetPlace());
 
-    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
+    T* b_t = boxes->data<T>();
     for (int h = 0; h < feature_height; ++h) {
       for (int w = 0; w < feature_width; ++w) {
         T center_x = (w + offset) * step_width;
         T center_y = (h + offset) * step_height;
         T box_width, box_height;
-        int idx = 0;
         for (size_t s = 0; s < min_sizes.size(); ++s) {
           auto min_size = min_sizes[s];
           if (min_max_aspect_ratios_order) {
             box_width = box_height = min_size / 2.;
-            e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-            e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-            e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-            e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
-            idx++;
+            b_t[0] = (center_x - box_width) / img_width;
+            b_t[1] = (center_y - box_height) / img_height;
+            b_t[2] = (center_x + box_width) / img_width;
+            b_t[3] = (center_y + box_height) / img_height;
+            b_t += 4;
             if (max_sizes.size() > 0) {
               auto max_size = max_sizes[s];
               // square prior with size sqrt(minSize * maxSize)
               box_width = box_height = sqrt(min_size * max_size) / 2.;
-              e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-              e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-              e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-              e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
-              idx++;
+              b_t[0] = (center_x - box_width) / img_width;
+              b_t[1] = (center_y - box_height) / img_height;
+              b_t[2] = (center_x + box_width) / img_width;
+              b_t[3] = (center_y + box_height) / img_height;
+              b_t += 4;
             }
             // priors with different aspect ratios
             for (size_t r = 0; r < aspect_ratios.size(); ++r) {
@@ -135,11 +127,11 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
               }
               box_width = min_size * sqrt(ar) / 2.;
               box_height = min_size / sqrt(ar) / 2.;
-              e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-              e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-              e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-              e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
-              idx++;
+              b_t[0] = (center_x - box_width) / img_width;
+              b_t[1] = (center_y - box_height) / img_height;
+              b_t[2] = (center_x + box_width) / img_width;
+              b_t[3] = (center_y + box_height) / img_height;
+              b_t += 4;
             }
           } else {
             // priors with different aspect ratios
@@ -147,21 +139,21 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
               float ar = aspect_ratios[r];
               box_width = min_size * sqrt(ar) / 2.;
               box_height = min_size / sqrt(ar) / 2.;
-              e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-              e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-              e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-              e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
-              idx++;
+              b_t[0] = (center_x - box_width) / img_width;
+              b_t[1] = (center_y - box_height) / img_height;
+              b_t[2] = (center_x + box_width) / img_width;
+              b_t[3] = (center_y + box_height) / img_height;
+              b_t += 4;
             }
             if (max_sizes.size() > 0) {
               auto max_size = max_sizes[s];
               // square prior with size sqrt(minSize * maxSize)
               box_width = box_height = sqrt(min_size * max_size) / 2.;
-              e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-              e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-              e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-              e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
-              idx++;
+              b_t[0] = (center_x - box_width) / img_width;
+              b_t[1] = (center_y - box_height) / img_height;
+              b_t[2] = (center_x + box_width) / img_width;
+              b_t[3] = (center_y + box_height) / img_height;
+              b_t += 4;
             }
           }
         }
@@ -169,11 +161,10 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
     }
 
     if (clip) {
-      platform::Transform<platform::CPUDeviceContext> trans;
-      ClipFunctor<T> clip_func;
-      trans(ctx.template device_context<platform::CPUDeviceContext>(),
-            boxes->data<T>(), boxes->data<T>() + boxes->numel(),
-            boxes->data<T>(), clip_func);
+      T* dt = boxes->data<T>();
+      std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
+        return std::min<T>(std::max<T>(v, 0.), 1.);
+      });
     }
 
     framework::Tensor var_t;

paddle/fluid/operators/expand_op.cc

@@ -146,7 +146,11 @@ REGISTER_OPERATOR(expand, ops::ExpandOp, ops::ExpandOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(expand_grad, ops::ExpandGradOp);
 REGISTER_OP_CPU_KERNEL(
-    expand, ops::ExpandKernel<paddle::platform::CPUDeviceContext, float>);
+    expand, ops::ExpandKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ExpandKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::ExpandKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ExpandKernel<paddle::platform::CPUDeviceContext, bool>);
 REGISTER_OP_CPU_KERNEL(
     expand_grad,
-    ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/expand_op.cu

@@ -15,7 +15,11 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
-    expand, ops::ExpandKernel<paddle::platform::CUDADeviceContext, float>);
+    expand, ops::ExpandKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ExpandKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ExpandKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ExpandKernel<paddle::platform::CUDADeviceContext, bool>);
 REGISTER_OP_CUDA_KERNEL(
     expand_grad,
-    ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/fake_quantize_op.cc

@@ -21,26 +21,17 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVectorArrayMap =
-    Eigen::TensorMap<Eigen::Tensor<T, 1, MajorType, IndexType>>;
-
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using ConstEigenVectorArrayMap =
-    Eigen::TensorMap<const Eigen::Tensor<T, 1, MajorType, IndexType>>;
+template <typename T>
+struct Compare {
+ public:
+  bool operator()(const T a, const T b) { return (std::abs(a) < std::abs(b)); }
+};
 
 template <typename T>
 struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& ctx, const T* in,
                   const int num, T* out) {
-    Eigen::DSizes<Eigen::DenseIndex, 1> idim(num);
-    Eigen::DSizes<Eigen::DenseIndex, 1> odim(1);
-    Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>> in_e(in, idim);
-    Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> out_e(out, odim);
-
-    out_e = in_e.abs().maximum();
+    *out = *(std::max_element(in + 0, in + num, Compare<T>()));
   }
 };
 

paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/jit/kernels.h"
 #include "paddle/fluid/operators/math/blas.h"
 
 namespace paddle {
@@ -37,32 +38,24 @@ struct EmbeddingVSumFunctor {
                   const LoDTensor *table_t, const LoDTensor *ids_t,
                   LoDTensor *output_t) {
     auto *table = table_t->data<T>();
-    int64_t row_number = table_t->dims()[0];
-    int64_t row_width = table_t->dims()[1];
-    int64_t last_dim = output_t->dims()[1];
+    int64_t table_height = table_t->dims()[0];
+    int64_t table_width = table_t->dims()[1];
+    int64_t out_width = output_t->dims()[1];
     const int64_t *ids = ids_t->data<int64_t>();
     auto ids_lod = ids_t->lod()[0];
-    int64_t ids_count = ids_t->numel() / ids_lod.back();
-
+    int64_t idx_width = ids_t->numel() / ids_lod.back();
     auto *output = output_t->mutable_data<T>(context.GetPlace());
 
-    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
-    for (int64_t i = 0; i != ids_lod.size() - 1; ++i) {
-      size_t begin = ids_lod[i] * ids_count;
-      for (int64_t j = 0; j != ids_count; ++j) {
-        PADDLE_ENFORCE_LT(ids[begin], row_number);
-        PADDLE_ENFORCE_GE(ids[begin], 0, "ids %d", i);
-        blas.VCOPY(row_width, table + ids[begin + j] * row_width,
-                   output + i * last_dim + j * row_width);
-      }
+    PADDLE_ENFORCE_LE(table_width * idx_width, out_width);
 
-      for (int64_t r = (ids_lod[i] + 1) * ids_count;
-           r < ids_lod[i + 1] * ids_count; ++r) {
-        PADDLE_ENFORCE_LT(ids[r], row_number);
-        PADDLE_ENFORCE_GE(ids[r], 0, "ids %d", i);
-        blas.AXPY(row_width, 1., table + ids[r] * row_width,
-                  output + i * last_dim + (r % ids_count) * row_width);
-      }
+    jit::emb_seq_pool_attr_t attr(table_height, table_width, 0, idx_width,
+                                  out_width, jit::SeqPoolType::kSum);
+    for (int64_t i = 0; i != ids_lod.size() - 1; ++i) {
+      attr.index_height = ids_lod[i + 1] - ids_lod[i];
+      auto emb_seqpool = jit::Get<jit::kEmbSeqPool, jit::EmbSeqPoolTuples<T>,
+                                  platform::CPUPlace>(attr);
+      emb_seqpool(table, ids + ids_lod[i] * idx_width, output + i * out_width,
+                  &attr);
     }
   }
 };

paddle/fluid/operators/group_norm_op.cc

@@ -170,13 +170,48 @@ class GroupNormGradMaker : public framework::SingleGradOpDescMaker {
   }
 };
 
+class GroupNormInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    return {{"X", "Y"}};
+  }
+};
+
+class GroupNormGradInplaceInToOut : public framework::InplaceInToOut {
+ public:
+  using InplaceInToOut::InplaceInToOut;
+
+ protected:
+  std::unordered_map<std::string, std::string> Apply(
+      const framework::OpDesc &op_desc,
+      framework::BlockDesc *block) const override {
+    return {{framework::GradVarName("Y"), framework::GradVarName("X")}};
+  }
+};
+
+class GroupNormOpInferVarType
+    : public framework::PassInDtypeAndVarTypeToOutput {
+ protected:
+  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
+      const override {
+    return {{"X", /*->*/ "Y"}};
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
-                  ops::GroupNormGradMaker);
-REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp);
+                  ops::GroupNormOpInferVarType, ops::GroupNormGradMaker,
+                  ops::GroupNormInplaceInToOut);
+REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp,
+                  ops::GroupNormGradInplaceInToOut);
 REGISTER_OP_CPU_KERNEL(
     group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
     ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/jit/benchmark.cc

@@ -301,6 +301,37 @@ void BenchSeqPoolKernel() {
   }
 }
 
+template <jit::KernelType KT, typename T, typename PlaceType>
+void BenchEmbSeqPoolKernel() {
+  std::vector<jit::SeqPoolType> pool_types = {jit::SeqPoolType::kSum};
+  int64_t tbl_h = 1e4;
+  for (int tbl_w : {10, 16, 256}) {
+    Tensor table;
+    table.Resize({tbl_h, tbl_w});
+    RandomVec<T>(tbl_h * tbl_w, table.mutable_data<T>(PlaceType()), -2.f, 2.f);
+    const T* table_data = table.data<T>();
+    for (auto type : pool_types) {
+      for (int idx_w : {1, 2, 10, 16}) {
+        for (int idx_h : {1, 2, 9, 13, 16}) {
+          int64_t out_w = tbl_w * idx_w;
+          jit::emb_seq_pool_attr_t attr(tbl_h, tbl_w, idx_h, idx_w, out_w,
+                                        type);
+          Tensor idx, out;
+          idx.Resize({idx_h, idx_w});
+          out.Resize({out_w});
+          RandomVec<int64_t>(idx_h * idx_w,
+                             idx.mutable_data<int64_t>(PlaceType()), 0,
+                             tbl_h - 1);
+          const int64_t* idx_data = idx.data<int64_t>();
+          T* o_data = out.mutable_data<T>(PlaceType());
+          BenchAllImpls<KT, jit::EmbSeqPoolTuples<T>, PlaceType>(
+              attr, table_data, idx_data, o_data, &attr);
+        }
+      }
+    }
+  }
+}
+
 template <jit::KernelType KT, typename T, typename PlaceType>
 void BenchMatMulKernel() {
   for (int m : {1, 2, 3, 4}) {
@@ -339,6 +370,71 @@ void BenchSoftmaxKernel() {
   }
 }
 
+template <jit::KernelType KT, typename T, typename PlaceType>
+void BenchLayerNormKernel() {
+  const T epsilon = 9.99999975e-06;
+  for (int n : {1, 2, 10}) {
+    for (int x_dim_0 : {1, 9, 17, 50}) {
+      int left = n * x_dim_0;
+      for (int x_dim_1 : TestSizes()) {
+        int right = x_dim_1;
+        int sz = left * right;
+        Tensor x, mean, var, scale, bias, out;
+        x.Resize({n, x_dim_0, x_dim_1});
+        out.Resize({n, x_dim_0, x_dim_1});
+        mean.Resize({n, x_dim_0});
+        var.Resize({n, x_dim_0});
+        scale.Resize({x_dim_1});
+        bias.Resize({x_dim_1});
+
+        RandomVec<T>(sz, x.mutable_data<T>(PlaceType()), -2.f, 2.f);
+        RandomVec<T>(left, mean.mutable_data<T>(PlaceType()), -2.f, 2.f);
+        RandomVec<T>(left, var.mutable_data<T>(PlaceType()), -2.f, 2.f);
+        RandomVec<T>(right, scale.mutable_data<T>(PlaceType()), -2.f, 2.f);
+        RandomVec<T>(right, bias.mutable_data<T>(PlaceType()), -2.f, 2.f);
+
+        const T* scale_data = scale.data<T>();
+        const T* bias_data = bias.data<T>();
+        T* x_data = x.data<T>();
+        T* mean_data = mean.data<T>();
+        T* var_data = var.data<T>();
+        T* out_data = out.mutable_data<T>(PlaceType());
+
+        BenchAllImpls<KT, jit::LayerNormTuples<T>, PlaceType>(
+            right, x_data, out_data, mean_data, var_data, scale_data, bias_data,
+            left, epsilon, right);
+      }
+    }
+  }
+}
+
+template <jit::KernelType KT, typename T, typename PlaceType>
+void BenchCRFDecodingKernel() {
+  constexpr int state_trans_base_idx = 2;
+  for (int seq_len : {1, 11, 17, 50}) {
+    for (int tag_num : TestSizes()) {
+      int x_sz = seq_len * tag_num;
+      int w_sz = (tag_num + state_trans_base_idx) * tag_num;
+      Tensor x, w, alpha, track;
+      x.Resize({seq_len, tag_num});
+      w.Resize({tag_num + state_trans_base_idx, tag_num});
+      alpha.Resize({seq_len, tag_num});
+      track.Resize({seq_len, tag_num});
+
+      RandomVec<T>(x_sz, x.mutable_data<T>(PlaceType()), -2.f, 2.f);
+      RandomVec<T>(w_sz, w.mutable_data<T>(PlaceType()), -2.f, 2.f);
+
+      const T* x_data = x.data<T>();
+      const T* w_data = w.data<T>();
+      T* alpha_data = alpha.mutable_data<T>(PlaceType());
+      int* track_data = track.mutable_data<int>(PlaceType());
+
+      BenchAllImpls<KT, jit::CRFDecodingTuples<T>, PlaceType>(
+          tag_num, seq_len, x_data, w_data, alpha_data, track_data, tag_num);
+    }
+  }
+}
+
 using T = float;
 using CPUPlace = paddle::platform::CPUPlace;
 
@@ -376,12 +472,27 @@ BENCH_FP32_CPU(kGRUHtPart2) { BenchGRUKernel<jit::kGRUHtPart2, T, CPUPlace>(); }
 // seq pool function
 BENCH_FP32_CPU(kSeqPool) { BenchSeqPoolKernel<jit::kSeqPool, T, CPUPlace>(); }
 
+// embedding seq pool function
+BENCH_FP32_CPU(kEmbSeqPool) {
+  BenchEmbSeqPoolKernel<jit::kEmbSeqPool, T, CPUPlace>();
+}
+
 // matmul
 BENCH_FP32_CPU(kMatMul) { BenchMatMulKernel<jit::kMatMul, T, CPUPlace>(); }
 
 // softmax
 BENCH_FP32_CPU(kSoftmax) { BenchSoftmaxKernel<jit::kSoftmax, T, CPUPlace>(); }
 
+// layernorm
+BENCH_FP32_CPU(kLayerNorm) {
+  BenchLayerNormKernel<jit::kLayerNorm, T, CPUPlace>();
+}
+
+// crfdecoding
+BENCH_FP32_CPU(kCRFDecoding) {
+  BenchCRFDecodingKernel<jit::kCRFDecoding, T, CPUPlace>();
+}
+
 // Benchmark all jit kernels including jitcode, mkl and refer.
 // To use this tool, run command: ./benchmark [options...]
 // Options:

paddle/fluid/operators/jit/gen/CMakeLists.txt

@@ -31,3 +31,4 @@ USE_JITKERNEL_GEN(kNCHW16CMulNC)
 USE_JITKERNEL_GEN(kSeqPool)
 USE_JITKERNEL_GEN(kHMax)
 USE_JITKERNEL_GEN(kHSum)
+USE_JITKERNEL_GEN(kEmbSeqPool)

paddle/fluid/operators/jit/gen/act.h

@@ -63,7 +63,6 @@ class VActFunc : public JitCode {
  public:
   explicit VActFunc(size_t code_size, void* code_ptr)
       : JitCode(code_size, code_ptr) {}
-  virtual const char* name() const = 0;
   virtual void genCode() = 0;
 
  protected:
@@ -269,7 +268,7 @@ class VActJitCode : public VActFunc {
     this->genCode();
   }
 
-  const char* name() const override {
+  std::string name() const override {
     std::string base = "VActJitCode";
     switch (type_) {
       case operand_type::RELU:
@@ -293,7 +292,7 @@ class VActJitCode : public VActFunc {
       default:
         break;
     }
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/blas.h

@@ -41,7 +41,7 @@ class VXXJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "VXXJitCode";
     if (scalar_index_ == 1) {
       base += "_Scalar";
@@ -62,7 +62,7 @@ class VXXJitCode : public JitCode {
     }
     base += (with_relu_ ? "_Relu" : "");
     base += "_D" + std::to_string(num_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/embseqpool.cc

+/* 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. */
+
+#include "paddle/fluid/operators/jit/gen/embseqpool.h"
+#include <stddef.h>  // offsetof
+#include <vector>
+#include "paddle/fluid/operators/jit/gen/act.h"  // for exp_float_consts ones
+#include "paddle/fluid/operators/jit/registry.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+void EmbSeqPoolJitCode::genCode() {
+  preCode();
+  constexpr int block = YMM_FLOAT_BLOCK;
+  constexpr int max_num_regs = 8;
+  const int num_block = tbl_w_ / block;
+  const int num_groups = num_block / max_num_regs;
+  const size_t block_size = sizeof(float) * block;
+  std::vector<int> groups(num_groups, max_num_regs);
+  int rest_num_regs = num_block % max_num_regs;
+  if (rest_num_regs > 0) {
+    groups.push_back(rest_num_regs);
+  }
+
+  // protect param_dst
+  mov(reg_ptr_param_dst, param_dst);
+  mov(reg_idx_width_in_byte,
+      qword[param_attr + offsetof(emb_seq_pool_attr_t, index_width)]);
+  mov(reg_idx_height,
+      qword[param_attr + offsetof(emb_seq_pool_attr_t, index_height)]);
+  mov(rax, sizeof(int64_t));
+  mul(reg_idx_width_in_byte);
+  mov(reg_idx_width_in_byte, rax);
+  const size_t tbl_width_in_byte = sizeof(float) * tbl_w_;
+  int acc_num_regs = 0;
+  for (int num_regs : groups) {
+    Label l_next_idx_w, l_next_idx_h, l_save_now;
+    xor_(reg_idx_w_i_in_byte, reg_idx_w_i_in_byte);
+    mov(reg_ptr_dst_i, reg_ptr_param_dst);
+    add(reg_ptr_dst_i, acc_num_regs * block_size);
+
+    L(l_next_idx_w);
+    {
+      // h == 0
+      mov(reg_ptr_idx_i, param_idx);
+      add(reg_ptr_idx_i, reg_idx_w_i_in_byte);
+      mov(reg_idx, qword[reg_ptr_idx_i]);
+      mov(rax, tbl_width_in_byte);
+      mul(reg_idx);
+      mov(reg_ptr_tbl_i, rax);        // reg is offset now
+      add(reg_ptr_tbl_i, param_tbl);  // reg is ptr_i now
+      size_t w_offset = 0;
+      for (int reg_i = 0; reg_i < num_regs; ++reg_i) {
+        vmovups(ymm_t(reg_i + num_regs), ptr[reg_ptr_tbl_i + w_offset]);
+        w_offset += block_size;
+      }
+      add(reg_ptr_idx_i, reg_idx_width_in_byte);
+
+      // end condition of idx h
+      mov(reg_idx_h_end, reg_idx_height);
+      mov(rax, reg_idx_width_in_byte);
+      mul(reg_idx_h_end);
+      mov(reg_idx_h_end, rax);
+      add(reg_idx_h_end, reg_idx_w_i_in_byte);
+      add(reg_idx_h_end, param_idx);
+
+      cmp(reg_ptr_idx_i, reg_idx_h_end);
+      jge(l_save_now, T_NEAR);
+      L(l_next_idx_h);
+      {
+        mov(reg_idx, qword[reg_ptr_idx_i]);
+        mov(reg_ptr_tbl_i, reg_idx);
+        mov(rax, tbl_width_in_byte);
+        mul(reg_idx);
+        mov(reg_ptr_tbl_i, rax);
+        add(reg_ptr_tbl_i, param_tbl);
+        size_t w_offset = 0;
+        for (int reg_i = 0; reg_i < num_regs; ++reg_i) {
+          vmovups(ymm_t(reg_i), ptr[reg_ptr_tbl_i + w_offset]);
+          vaddps(ymm_t(reg_i + num_regs), ymm_t(reg_i + num_regs),
+                 ymm_t(reg_i));
+          w_offset += block_size;
+        }
+        add(reg_ptr_idx_i, reg_idx_width_in_byte);
+        cmp(reg_ptr_idx_i, reg_idx_h_end);
+        jl(l_next_idx_h, T_NEAR);
+      }  // end of idx h
+      L(l_save_now);
+      // avg or sqrt here, if needed
+      w_offset = 0;
+      for (int reg_i = 0; reg_i < num_regs; ++reg_i) {
+        vmovups(ptr[reg_ptr_dst_i + w_offset], ymm_t(reg_i + num_regs));
+        w_offset += block_size;
+      }
+      add(reg_ptr_dst_i, tbl_width_in_byte);
+      add(reg_idx_w_i_in_byte, sizeof(int64_t));
+      cmp(reg_idx_w_i_in_byte, reg_idx_width_in_byte);
+      jl(l_next_idx_w, T_NEAR);
+    }  // end of idx w
+
+    acc_num_regs += num_regs;
+    add(param_tbl, num_regs * block_size);  // do not use acc_num_regs
+  }                                         // end of groups
+  postCode();
+}
+
+class EmbSeqPoolCreator : public JitCodeCreator<emb_seq_pool_attr_t> {
+ public:
+  bool UseMe(const emb_seq_pool_attr_t& attr) const override {
+    return platform::MayIUse(platform::avx) &&
+           attr.table_width % YMM_FLOAT_BLOCK == 0;
+  }
+  size_t CodeSize(const emb_seq_pool_attr_t& attr) const override {
+    return 96 + (attr.table_width / YMM_FLOAT_BLOCK) * 96 * 8;
+  }
+  std::unique_ptr<GenBase> CreateJitCode(
+      const emb_seq_pool_attr_t& attr) const override {
+    PADDLE_ENFORCE_GT(attr.table_height, 0);
+    PADDLE_ENFORCE_GT(attr.table_width, 0);
+    PADDLE_ENFORCE_GT(attr.index_height, 0);
+    PADDLE_ENFORCE_GT(attr.index_width, 0);
+    PADDLE_ENFORCE_GT(attr.out_width, 0);
+    return make_unique<EmbSeqPoolJitCode>(attr, CodeSize(attr));
+  }
+};
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle
+
+namespace gen = paddle::operators::jit::gen;
+
+REGISTER_JITKERNEL_GEN(kEmbSeqPool, gen::EmbSeqPoolCreator);

paddle/fluid/operators/jit/gen/embseqpool.h

+/* 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. */
+
+#pragma once
+
+#include <string>
+#include "glog/logging.h"
+#include "paddle/fluid/operators/jit/gen/jitcode.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+namespace jit {
+namespace gen {
+
+class EmbSeqPoolJitCode : public JitCode {
+ public:
+  explicit EmbSeqPoolJitCode(const emb_seq_pool_attr_t& attr,
+                             size_t code_size = 256 * 1024,
+                             void* code_ptr = nullptr)
+      : JitCode(code_size, code_ptr),
+        tbl_w_(attr.table_width),
+        type_(attr.pool_type) {
+    if (type_ != SeqPoolType::kSum) {
+      LOG(FATAL) << "Only support sum pool yet ";
+    }
+    this->genCode();
+  }
+
+  std::string name() const override {
+    std::string base = "EmbSeqPoolJitCode";
+    if (type_ == SeqPoolType::kSum) {
+      base += "_Sum";
+    } else if (type_ == SeqPoolType::kAvg) {
+      base += "_Avg";
+    } else if (type_ == SeqPoolType::kSqrt) {
+      base += "_Sqrt";
+    }
+    base += ("_W" + std::to_string(tbl_w_));
+    return base;
+  }
+  void genCode() override;
+
+ private:
+  int tbl_w_;
+  SeqPoolType type_;
+  reg64_t param_tbl{abi_param1};
+  reg64_t param_idx{abi_param2};
+  reg64_t param_dst{abi_param3};
+  reg64_t param_attr{abi_param4};
+
+  reg64_t reg_tmp{rax};
+
+  reg64_t reg_idx_width_in_byte{r8};
+  reg64_t reg_idx_height{r9};
+
+  reg64_t reg_ptr_tbl_i{r10};
+  reg64_t reg_idx{r10};  // could use same of reg_ptr_tbl_i
+  reg64_t reg_ptr_idx_i{r11};
+  reg64_t reg_ptr_dst_i{r12};
+  reg64_t reg_ptr_param_dst{r13};  // rdx is used in mul so protect param_dst
+
+  reg64_t reg_idx_w_i_in_byte{r14};
+  reg64_t reg_idx_h_end{r15};
+};
+
+}  // namespace gen
+}  // namespace jit
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/jit/gen/gru.h

@@ -49,7 +49,7 @@ class GRUJitCode : public VActFunc {
     this->genCode();
   }
 
-  const char* name() const override {
+  std::string name() const override {
     std::string base = "GRUJitCode";
     if (id_ == 0) {
       base += "_H1";
@@ -81,7 +81,7 @@ class GRUJitCode : public VActFunc {
     };
     AddTypeStr(act_gate_);
     AddTypeStr(act_cand_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/hopv.h

@@ -35,14 +35,14 @@ class HOPVJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "VXXJitCode";
     if (type_ == operand_type::MAX) {
       base += "_MAX";
     } else {
       base += "_SUM";
     }
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/jitcode.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include <string>
 #include <type_traits>
 #include "paddle/fluid/operators/jit/gen_base.h"
 #include "paddle/fluid/platform/cpu_info.h"
@@ -59,7 +60,7 @@ typedef enum {
 } operand_type;
 
 #define DECLARE_JIT_CODE(codename) \
-  const char* name() const override { return #codename; }
+  std::string name() const override { return #codename; }
 
 class JitCode : public GenBase, public Xbyak::CodeGenerator {
  public:
@@ -68,7 +69,6 @@ class JitCode : public GenBase, public Xbyak::CodeGenerator {
             (code_size % 4096 != 0 ? (code_size / 4096 + 1) * 4096 : code_size),
             code_ptr) {}
 
-  virtual const char* name() const = 0;
   virtual void genCode() = 0;
 
   size_t getSize() const override { return CodeGenerator::getSize(); }

paddle/fluid/operators/jit/gen/lstm.h

@@ -53,7 +53,7 @@ class LSTMJitCode : public VActFunc {
     this->genCode();
   }
 
-  const char* name() const override {
+  std::string name() const override {
     std::string base = "LSTMJitCode";
     if (use_peephole_) {
       base += "_Peephole";
@@ -85,7 +85,7 @@ class LSTMJitCode : public VActFunc {
     AddTypeStr(act_gate_);
     AddTypeStr(act_cand_);
     AddTypeStr(act_cell_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/matmul.h

@@ -36,11 +36,11 @@ class MatMulJitCode : public JitCode {
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "MatMulJitCode";
     base = base + "_M" + std::to_string(m_) + "_N" + std::to_string(n_) + "_K" +
            std::to_string(k_);
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen/seqpool.h

@@ -32,13 +32,13 @@ class SeqPoolJitCode : public JitCode {
       : JitCode(code_size, code_ptr), w_(attr.w), type_(attr.type) {
     if (!(type_ == SeqPoolType::kSum || type_ == SeqPoolType::kAvg ||
           type_ == SeqPoolType::kSqrt)) {
-      LOG(FATAL) << "Only support sum pool yet ";
+      LOG(FATAL) << "Only supported pool type: sum, avg and sqrt.";
     }
     fp_h_[0] = 1.f;
     this->genCode();
   }
 
-  virtual const char* name() const {
+  std::string name() const override {
     std::string base = "SeqPoolJitCode";
     if (type_ == SeqPoolType::kSum) {
       base += "_Sum";
@@ -48,7 +48,7 @@ class SeqPoolJitCode : public JitCode {
       base += "_Sqrt";
     }
     base += ("_W" + std::to_string(w_));
-    return base.c_str();
+    return base;
   }
   void genCode() override;
 

paddle/fluid/operators/jit/gen_base.cc

@@ -17,7 +17,13 @@
 #include <iostream>
 #include <sstream>
 #include <vector>
+#include "paddle/fluid/memory/allocation/cpu_allocator.h"  // for posix_memalign
 #include "paddle/fluid/platform/cpu_info.h"
+#include "paddle/fluid/platform/enforce.h"
+
+#ifndef _WIN32
+#define posix_memalign_free free
+#endif
 
 DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file");
 
@@ -40,6 +46,17 @@ void GenBase::dumpCode(const unsigned char* code) const {
   }
 }
 
+void* GenBase::operator new(size_t size) {
+  void* ptr;
+  constexpr size_t alignment = 32ul;
+  PADDLE_ENFORCE_EQ(posix_memalign(&ptr, alignment, size), 0,
+                    "GenBase Alloc %ld error!", size);
+  PADDLE_ENFORCE(ptr, "Fail to allocate GenBase CPU memory: size = %d .", size);
+  return ptr;
+}
+
+void GenBase::operator delete(void* ptr) { posix_memalign_free(ptr); }
+
 std::vector<int> packed_groups(int n, int k, int* block_out, int* rest_out) {
   int block;
   int max_num_regs;

paddle/fluid/operators/jit/gen_base.h

@@ -16,6 +16,7 @@
 
 #include <gflags/gflags.h>
 #include <memory>  // for unique_ptr
+#include <string>
 #include <vector>
 #include "paddle/fluid/operators/jit/kernel_base.h"
 
@@ -28,7 +29,7 @@ namespace jit {
 class GenBase : public Kernel {
  public:
   virtual ~GenBase() = default;
-  virtual const char* name() const = 0;
+  virtual std::string name() const = 0;
   virtual size_t getSize() const = 0;
   virtual const unsigned char* getCodeInternal() = 0;
   template <typename Func>
@@ -42,6 +43,11 @@ class GenBase : public Kernel {
     return reinterpret_cast<Func>(const_cast<unsigned char*>(code));
   }
 
+  void* operator new(size_t size);
+  void operator delete(void* ptr);
+  void* operator new[](size_t size) { return operator new(size); }
+  void operator delete[](void* ptr) { operator delete(ptr); }
+
  protected:
   void dumpCode(const unsigned char* code) const;
 };

paddle/fluid/operators/jit/helper.cc

@@ -54,6 +54,7 @@ const char* to_string(KernelType kt) {
     ONE_CASE(kHMax);
     ONE_CASE(kHSum);
     ONE_CASE(kSoftmax);
+    ONE_CASE(kEmbSeqPool);
     default:
       PADDLE_THROW("Not support type: %d, or forget to add it.", kt);
       return "NOT JITKernel";

paddle/fluid/operators/jit/helper.h

@@ -172,6 +172,15 @@ inline std::ostream& operator<<(std::ostream& os, const seq_pool_attr_t& attr) {
   return os;
 }
 
+inline std::ostream& operator<<(std::ostream& os,
+                                const emb_seq_pool_attr_t& attr) {
+  os << "table_height[" << attr.table_height << "],table_width["
+     << attr.table_width << "],index_height[" << attr.index_height
+     << "],index_width[" << attr.index_width << "],output_width["
+     << attr.out_width << "],pool_type[" << to_string(attr.pool_type) << "]";
+  return os;
+}
+
 inline std::ostream& operator<<(std::ostream& os, const matmul_attr_t& attr) {
   os << "M[" << attr.m << "],N[" << attr.n << "],K[" << attr.k << "]";
   return os;

paddle/fluid/operators/jit/kernel_base.h

@@ -13,6 +13,7 @@
  * limitations under the License. */
 
 #pragma once
+#include <cstdint>
 #include "paddle/fluid/operators/jit/macro.h"
 #include "paddle/fluid/platform/macros.h"
 
@@ -20,34 +21,35 @@ namespace paddle {
 namespace operators {
 namespace jit {
 
-// TODO(TJ): reorder by alphabet
 typedef enum {
   kNone = 0,
-  kVMul = 1,
-  kVAdd = 2,
-  kVAddRelu,
-  kVSub,
-  kVScal,
-  kVAddBias,
-  kVRelu,
-  kVIdentity,
-  kVSquare,
-  kVExp,
-  kVSigmoid,
-  kVTanh,
-  kLSTMCtHt,
-  kLSTMC1H1,
+  // sort by alphabet
+  kCRFDecoding = 1,
+  kEmbSeqPool = 2,
   kGRUH1,
   kGRUHtPart1,
   kGRUHtPart2,
-  kCRFDecoding,
+  kHSum,  // horizontal max
+  kHMax,  // horizontal sum
+  kLSTMCtHt,
+  kLSTMC1H1,
   kLayerNorm,
+  kMatMul,
   kNCHW16CMulNC,
   kSeqPool,
-  kMatMul,
-  kHSum,  // horizontal max
-  kHMax,  // horizontal sum
   kSoftmax,
+  kVAdd,
+  kVAddBias,
+  kVAddRelu,
+  kVExp,
+  kVIdentity,
+  kVMul,
+  kVRelu,
+  kVScal,
+  kVSigmoid,
+  kVSquare,
+  kVSub,
+  kVTanh,
 } KernelType;
 
 typedef enum {
@@ -145,6 +147,32 @@ struct SeqPoolTuples {
   typedef void (*func_type)(const T*, T*, const seq_pool_attr_t*);
 };
 
+typedef struct emb_seq_pool_attr_s {
+  int64_t table_height, table_width;
+  int64_t index_height, index_width;
+  int64_t out_width;
+  SeqPoolType pool_type;
+  emb_seq_pool_attr_s() = default;
+  explicit emb_seq_pool_attr_s(int64_t tbl_height, int64_t tbl_width,
+                               int64_t idx_height, int64_t idx_width,
+                               int64_t output_width,
+                               SeqPoolType seqpool_type = SeqPoolType::kSum)
+      : table_height(tbl_height),
+        table_width(tbl_width),
+        index_height(idx_height),
+        index_width(idx_width),
+        out_width(output_width),
+        pool_type(seqpool_type) {}
+} emb_seq_pool_attr_t;
+
+template <typename T>
+struct EmbSeqPoolTuples {
+  typedef T data_type;
+  typedef emb_seq_pool_attr_t attr_type;
+  typedef void (*func_type)(const T*, const int64_t*, T*,
+                            const emb_seq_pool_attr_t*);
+};
+
 typedef struct matmul_attr_s {
   int m, n, k;
   void* packed_weight{nullptr};

paddle/fluid/operators/jit/kernel_key.cc

@@ -56,6 +56,11 @@ size_t JitCodeKey<matmul_attr_t>(const matmul_attr_t& attr) {
   return (key << shift * 2) + ((static_cast<size_t>(attr.n)) << shift) + attr.k;
 }
 
+template <>
+size_t JitCodeKey<emb_seq_pool_attr_t>(const emb_seq_pool_attr_t& attr) {
+  return attr.table_width;
+}
+
 }  // namespace jit
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/jit/more/mkl/CMakeLists.txt

@@ -13,3 +13,4 @@ USE_JITKERNEL_MORE(kVSigmoid, mkl)
 USE_JITKERNEL_MORE(kVTanh, mkl)
 USE_JITKERNEL_MORE(kSeqPool, mkl)
 USE_JITKERNEL_MORE(kSoftmax, mkl)
+USE_JITKERNEL_MORE(kEmbSeqPool, mkl)

paddle/fluid/operators/jit/more/mkl/mkl.cc

@@ -174,6 +174,16 @@ bool SeqPoolKernel<double>::UseMe(const seq_pool_attr_t& attr) const {
   return true;
 }
 
+template <>
+bool EmbSeqPoolKernel<float>::UseMe(const emb_seq_pool_attr_t& attr) const {
+  return true;
+}
+
+template <>
+bool EmbSeqPoolKernel<double>::UseMe(const emb_seq_pool_attr_t& attr) const {
+  return true;
+}
+
 template <>
 bool MatMulKernel<float>::UseMe(const matmul_attr_t& attr) const {
   return platform::MayIUse(platform::avx);
@@ -227,6 +237,7 @@ REGISTER_MKL_KERNEL(kVSquare, VSquare);
 REGISTER_MKL_KERNEL(kVSigmoid, VSigmoid);
 REGISTER_MKL_KERNEL(kVTanh, VTanh);
 REGISTER_MKL_KERNEL(kSeqPool, SeqPool);
+REGISTER_MKL_KERNEL(kEmbSeqPool, EmbSeqPool);
 REGISTER_MKL_KERNEL(kSoftmax, Softmax);
 
 #undef REGISTER_MKL_KERNEL

paddle/fluid/operators/jit/more/mkl/mkl.h

@@ -18,6 +18,7 @@
 #include <type_traits>
 #include <vector>
 #include "paddle/fluid/operators/jit/kernel_base.h"
+#include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
 namespace operators {
@@ -91,6 +92,32 @@ void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
   }
 }
 
+template <typename T>
+void EmbSeqPool(const T* table, const int64_t* idx, T* out,
+                const emb_seq_pool_attr_t* attr) {
+  PADDLE_ENFORCE_EQ(attr->table_width * attr->index_width, attr->out_width);
+  auto check_idx_value_valid = [&](int64_t i) {
+    PADDLE_ENFORCE_LT(idx[i], attr->table_height, "idx value: %d, i: %d",
+                      idx[i], i);
+    PADDLE_ENFORCE_GE(idx[i], 0, "idx value: %d, i: %d", idx[i], i);
+  };
+
+  for (int64_t w = 0; w != attr->index_width; ++w) {
+    check_idx_value_valid(w);
+    VCopy<T>(table + idx[w] * attr->table_width, out + w * attr->table_width,
+             attr->table_width);
+  }
+
+  for (int64_t h = 1; h < attr->index_height; ++h) {
+    for (int64_t w = 0; w < attr->index_width; ++w) {
+      int64_t i = h * attr->index_width + w;
+      check_idx_value_valid(i);
+      VAXPY<T>(static_cast<T>(1), table + idx[i] * attr->table_width,
+               out + w * attr->table_width, attr->table_width);
+    }
+  }
+}
+
 template <typename T>
 void ASum(const T* x, T* res, int n);
 
@@ -142,6 +169,8 @@ DECLARE_MKL_KERNEL(VSquare, XYNTuples);
 
 DECLARE_MKL_KERNEL(SeqPool, SeqPoolTuples);
 
+DECLARE_MKL_KERNEL(EmbSeqPool, EmbSeqPoolTuples);
+
 DECLARE_MKL_KERNEL(Softmax, SoftmaxTuples);
 
 #undef DECLARE_MKL_KERNEL

paddle/fluid/operators/jit/refer/CMakeLists.txt

@@ -32,3 +32,4 @@ USE_JITKERNEL_REFER(kVSquare)
 USE_JITKERNEL_REFER(kHSum)
 USE_JITKERNEL_REFER(kHMax)
 USE_JITKERNEL_REFER(kSoftmax)
+USE_JITKERNEL_REFER(kEmbSeqPool)

paddle/fluid/operators/jit/refer/refer.cc

@@ -57,4 +57,6 @@ REGISTER_REFER_KERNEL(kHSum, HSum);
 
 REGISTER_REFER_KERNEL(kSoftmax, Softmax);
 
+REGISTER_REFER_KERNEL(kEmbSeqPool, EmbSeqPool);
+
 #undef REGISTER_REFER_KERNEL

paddle/fluid/operators/jit/refer/refer.h

@@ -16,6 +16,7 @@
 
 #include <cmath>
 #include <limits>
+#include <string>
 #include "paddle/fluid/operators/jit/helper.h"
 #include "paddle/fluid/operators/jit/kernel_base.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -414,6 +415,37 @@ void Softmax(const T* x, T* y, int n, int bs = 1) {
   }
 }
 
+// embedding seq pool
+// table is a matrix with (tbl_h, tbl_w)
+// idx is a matrix with (idx_h, idx_w)
+// output is a vector with length tbl_w * idx_w
+template <typename T>
+void EmbSeqPool(const T* table, const int64_t* idx, T* out,
+                const emb_seq_pool_attr_t* attr) {
+  PADDLE_ENFORCE_EQ(attr->table_width * attr->index_width, attr->out_width);
+
+  auto check_idx_value_valid = [&](int64_t i) {
+    PADDLE_ENFORCE_LT(idx[i], attr->table_height, "idx value: %d, i: %d",
+                      idx[i], i);
+    PADDLE_ENFORCE_GE(idx[i], 0, "idx value: %d, i: %d", idx[i], i);
+  };
+
+  for (int64_t w = 0; w != attr->index_width; ++w) {
+    check_idx_value_valid(w);
+    std::memcpy(out + w * attr->table_width, table + idx[w] * attr->table_width,
+                attr->table_width * sizeof(T));
+  }
+
+  for (int64_t h = 1; h < attr->index_height; ++h) {
+    for (int64_t w = 0; w < attr->index_width; ++w) {
+      int64_t i = h * attr->index_width + w;
+      check_idx_value_valid(i);
+      VAdd(table + idx[i] * attr->table_width, out + w * attr->table_width,
+           out + w * attr->table_width, attr->table_width);
+    }
+  }
+}
+
 #define DECLARE_REFER_KERNEL(name, tuples)             \
   template <typename T>                                \
   class name##Kernel : public ReferKernel<tuples<T>> { \
@@ -462,6 +494,8 @@ DECLARE_REFER_KERNEL(HSum, XRNTuples);
 
 DECLARE_REFER_KERNEL(Softmax, SoftmaxTuples);
 
+DECLARE_REFER_KERNEL(EmbSeqPool, EmbSeqPoolTuples);
+
 #undef DECLARE_REFER_KERNEL
 
 }  // namespace refer

paddle/fluid/operators/jit/test.cc

@@ -270,6 +270,32 @@ struct TestFuncWithRefer<jit::SeqPoolTuples<T>, std::vector<T>, std::vector<T>,
   }
 };
 
+template <typename T>
+struct TestFuncWithRefer<jit::EmbSeqPoolTuples<T>, std::vector<T>,
+                         std::vector<int64_t>, std::vector<T>,
+                         typename jit::EmbSeqPoolTuples<T>::attr_type> {
+  void operator()(const typename jit::EmbSeqPoolTuples<T>::func_type tgt,
+                  const std::vector<T>& table, const std::vector<int64_t>& idx,
+                  const std::vector<T>& oref,
+                  const typename jit::EmbSeqPoolTuples<T>::attr_type& attr) {
+    EXPECT_TRUE(tgt != nullptr);
+    EXPECT_EQ(table.size(),
+              static_cast<size_t>(attr.table_height * attr.table_width));
+    EXPECT_EQ(idx.size(),
+              static_cast<size_t>(attr.index_height * attr.index_width));
+    EXPECT_EQ(oref.size(),
+              static_cast<size_t>(attr.table_width * attr.index_width));
+    const T* table_data = table.data();
+    const int64_t* idx_data = idx.data();
+    const T* oref_data = oref.data();
+    int o_w = oref.size();
+    std::vector<T> out(o_w);
+    T* o_data = out.data();
+    tgt(table_data, idx_data, o_data, &attr);
+    ExpectEQ<T>(o_data, oref_data, o_w);
+  }
+};
+
 template <typename T>
 struct TestFuncWithRefer<jit::MatMulTuples<T>, std::vector<T>, std::vector<T>,
                          std::vector<T>,
@@ -292,6 +318,63 @@ struct TestFuncWithRefer<jit::MatMulTuples<T>, std::vector<T>, std::vector<T>,
   }
 };
 
+template <typename T>
+struct TestFuncWithRefer<jit::LayerNormTuples<T>, std::vector<T>,
+                         std::vector<T>, std::vector<T>, std::vector<T>,
+                         std::vector<T>, std::vector<T>, int, float, int> {
+  void operator()(const typename jit::LayerNormTuples<T>::func_type tgt,
+                  std::vector<T>& x, std::vector<T>& outref,  // NOLINT
+                  std::vector<T>& mean, std::vector<T>& var,  // NOLINT
+                  const std::vector<T>& scale, const std::vector<T>& bias,
+                  int left, const float epsilon, int right) {
+    EXPECT_TRUE(tgt != nullptr);
+    EXPECT_EQ(x.size(), static_cast<size_t>(left * right));
+    EXPECT_EQ(outref.size(), static_cast<size_t>(left * right));
+    EXPECT_EQ(mean.size(), static_cast<size_t>(left));
+    EXPECT_EQ(var.size(), static_cast<size_t>(left));
+    EXPECT_EQ(scale.size(), static_cast<size_t>(right));
+    EXPECT_EQ(bias.size(), static_cast<size_t>(right));
+    std::vector<T> outtgt(outref.size());
+    const T* scale_data = scale.data();
+    const T* bias_data = bias.data();
+    T* x_data = x.data();
+    T* mean_data = mean.data();
+    T* var_data = var.data();
+    T* outref_data = outref.data();
+    T* outtgt_data = outtgt.data();
+
+    tgt(x_data, outtgt_data, mean_data, var_data, scale_data, bias_data, left,
+        epsilon, right);
+    ExpectEQ<T>(outtgt_data, outref_data, left * right);
+  }
+};
+
+template <typename T>
+struct TestFuncWithRefer<jit::CRFDecodingTuples<T>, int, std::vector<T>,
+                         std::vector<T>, std::vector<T>, std::vector<int>,
+                         int> {
+  void operator()(const typename jit::CRFDecodingTuples<T>::func_type tgt,
+                  const int seq_len, const std::vector<T>& x,
+                  const std::vector<T>& w, std::vector<T>& alpharef,  // NOLINT
+                  std::vector<int>& trackref, int tag_num) {          // NOLINT
+    constexpr int state_trans_base_idx = 2;
+    EXPECT_TRUE(tgt != nullptr);
+    EXPECT_EQ(x.size(), static_cast<size_t>(seq_len * tag_num));
+    EXPECT_EQ(w.size(),
+              static_cast<size_t>((tag_num + state_trans_base_idx) * tag_num));
+    EXPECT_EQ(alpharef.size(), static_cast<size_t>(seq_len * tag_num));
+    EXPECT_EQ(trackref.size(), static_cast<size_t>(seq_len * tag_num));
+    std::vector<T> alphatgt(alpharef.size());
+    std::vector<int> tracktgt(trackref.size());
+
+    memcpy(trackref.data(), tracktgt.data(), tag_num * sizeof(int));
+    tgt(seq_len, (const T*)x.data(), (const T*)w.data(), alphatgt.data(),
+        tracktgt.data(), tag_num);
+    ExpectEQ<T>(alpharef.data(), alphatgt.data(), seq_len * tag_num);
+    ExpectEQ<int>(trackref.data(), tracktgt.data(), seq_len * tag_num);
+  }
+};
+
 template <jit::KernelType KT, typename KernelTuples, typename PlaceType,
           typename... Args>
 void TestAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
@@ -587,6 +670,40 @@ void TestSoftmaxKernel() {
   }
 }
 
+template <jit::KernelType KT, typename T, typename PlaceType>
+void TestEmbSeqPoolKernel() {
+  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
+  int64_t tbl_h = 1e4;
+  std::vector<jit::SeqPoolType> pool_types = {
+      jit::SeqPoolType::kSum};  // only support sum yet
+  for (int tbl_w : TestSizes()) {
+    std::vector<T> table(tbl_h * tbl_w);
+    RandomVec<T>(tbl_h * tbl_w, table.data(), -2.f, 2.f);
+    const T* table_data = table.data();
+    for (auto type : pool_types) {
+      for (int idx_w : {1, 2, 10, 16}) {
+        for (int idx_h : {1, 2, 9, 13, 16}) {
+          auto ref = jit::GetRefer<KT, jit::EmbSeqPoolTuples<T>>();
+          EXPECT_TRUE(ref != nullptr);
+          std::vector<int64_t> idx(idx_h * idx_w);
+          RandomVec<int64_t>(idx_h * idx_w, idx.data(), 0, tbl_h - 1);
+          int64_t out_w = tbl_w * idx_w;
+          std::vector<T> oref(out_w);
+          const int64_t* idx_data = idx.data();
+          T* o_data = oref.data();
+          jit::emb_seq_pool_attr_t attr(tbl_h, tbl_w, idx_h, idx_w, out_w,
+                                        type);
+          ref(table_data, idx_data, o_data, &attr);
+
+          TestAllImpls<KT, jit::EmbSeqPoolTuples<T>, PlaceType, std::vector<T>,
+                       std::vector<int64_t>, std::vector<T>>(attr, table, idx,
+                                                             oref, attr);
+        }
+      }
+    }
+  }
+}
+
 template <jit::KernelType KT, typename T, typename PlaceType>
 void TestNCHW16CMulNCKernel() {
   VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
@@ -640,6 +757,71 @@ void TestNCHW16CMulNCKernel() {
   }
 }
 
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void TestLayerNormKernel() {
+  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
+  const T epsilon = 9.99999975e-06;
+  for (int n : {1, 2, 10}) {
+    for (int x_dim_0 : {1, 9, 17, 50}) {
+      int left = n * x_dim_0;
+      for (int x_dim_1 : TestSizes()) {
+        int right = x_dim_1;
+        auto ref = jit::GetRefer<KT, jit::LayerNormTuples<T>>();
+        EXPECT_TRUE(ref != nullptr);
+        int sz = left * right;
+        std::vector<T> x(sz), mean(left), var(left), scale(right), bias(right),
+            outref(sz);
+        RandomVec<T>(sz, x.data(), -2.f, 2.f);
+        RandomVec<T>(left, mean.data(), -2.f, 2.f);
+        RandomVec<T>(left, var.data(), -2.f, 2.f);
+        RandomVec<T>(right, scale.data(), -2.f, 2.f);
+        RandomVec<T>(right, bias.data(), -2.f, 2.f);
+
+        const T* scale_data = scale.data();
+        const T* bias_data = bias.data();
+        T* x_data = x.data();
+        T* mean_data = mean.data();
+        T* var_data = var.data();
+        T* outref_data = outref.data();
+
+        ref(x_data, outref_data, mean_data, var_data, scale_data, bias_data,
+            left, epsilon, right);
+
+        TestAllImpls<KT, jit::LayerNormTuples<T>, PlaceType, std::vector<T>,
+                     std::vector<T>, std::vector<T>, std::vector<T>,
+                     std::vector<T>, std::vector<T>, int, float>(
+            right, x, outref, mean, var, scale, bias, left, epsilon, right);
+      }
+    }
+  }
+}
+
+template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
+void TestCRFDecodingKernel() {
+  VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
+  constexpr int state_trans_base_idx = 2;
+  for (int seq_len : {1, 11, 17, 50}) {
+    for (int tag_num : TestSizes()) {
+      auto ref = jit::GetRefer<KT, jit::CRFDecodingTuples<T>>();
+      EXPECT_TRUE(ref != nullptr);
+      int x_sz = seq_len * tag_num;
+      int w_sz = (tag_num + state_trans_base_idx) * tag_num;
+      std::vector<T> x(x_sz), w(w_sz), alpharef(x_sz);
+      std::vector<int> trackref(x_sz);
+      RandomVec<T>(x_sz, x.data(), -2.f, 2.f);
+      RandomVec<T>(w_sz, w.data(), -2.f, 2.f);
+
+      ref(seq_len, (const T*)x.data(), (const T*)w.data(), alpharef.data(),
+          trackref.data(), tag_num);
+
+      TestAllImpls<KT, jit::CRFDecodingTuples<T>, PlaceType, int,
+                   std::vector<T>, std::vector<T>, std::vector<T>,
+                   std::vector<int>, int>(tag_num, seq_len, x, w, alpharef,
+                                          trackref, tag_num);
+    }
+  }
+}
+
 // XYZNTuple
 TEST(JITKernel, kVMul) {
   TestXYZNKernel<jit::kVMul, float, CPUPlace>();
@@ -756,12 +938,26 @@ TEST(JITKernel, kSoftmax) {
   TestSoftmaxKernel<jit::kSoftmax, double, CPUPlace>();
 }
 
+TEST(JITKernel, kEmbSeqPool) {
+  TestEmbSeqPoolKernel<jit::kEmbSeqPool, float, CPUPlace>();
+  TestEmbSeqPoolKernel<jit::kEmbSeqPool, double, CPUPlace>();
+}
+
 TEST(JITKernel, kNCHW16CMulNC) {
   TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, float, CPUPlace>();
   TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, double, CPUPlace>();
 }
 
-// TODO(yihua/TJ): add crf decoding and layer norm unit tests
+TEST(JITKernel, kLayerNorm) {
+  TestLayerNormKernel<jit::kLayerNorm, float, paddle::platform::CPUPlace>();
+  TestLayerNormKernel<jit::kLayerNorm, double, paddle::platform::CPUPlace>();
+}
+
+TEST(JITKernel, kCRFDecoding) {
+  TestCRFDecodingKernel<jit::kCRFDecoding, float, paddle::platform::CPUPlace>();
+  TestCRFDecodingKernel<jit::kCRFDecoding, double,
+                        paddle::platform::CPUPlace>();
+}
 
 TEST(JITKernel, pool) {
   // TODO(TJ): add some test

paddle/fluid/operators/load_combine_op.cc

@@ -64,7 +64,7 @@ class LoadCombineOp : public framework::OperatorBase {
       auto *tensor = out_var->GetMutable<framework::LoDTensor>();
 
       // Error checking
-      PADDLE_ENFORCE(static_cast<bool>(buffer), "Cannot read more");
+      PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
 
       // Get data from fin to tensor
       DeserializeFromStream(*buffer, tensor, dev_ctx);
@@ -90,6 +90,10 @@ class LoadCombineOp : public framework::OperatorBase {
         tensor->ShareDataWith(fp16_tensor);
       }
     }
+    buffer->peek();
+    PADDLE_ENFORCE(buffer->eof(),
+                   "You are not allowed to load partial data via "
+                   "load_combine_op, use load_op instead.");
   }
 };
 

paddle/fluid/operators/lookup_table_op.h

@@ -129,6 +129,7 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
           "must be either LoDTensor or SelectedRows");
     }
 
+    int64_t padding_idx = context.Attr<int64_t>("padding_idx");
     bool is_sparse = context.Attr<bool>("is_sparse");
     // Since paddings are not trainable and fixed in forward, the gradient of
     // paddings makes no sense and we don't deal with it in backward.
@@ -187,6 +188,10 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       memset(d_table_data, 0, d_table->numel() * sizeof(T));
 
       for (int64_t i = 0; i < ids->numel(); ++i) {
+        if (padding_idx != kNoPadding && ids_data[i] == padding_idx) {
+          // the gradient of padding_idx should be 0, already done by memset, so
+          // do nothing.
+        } else {
           PADDLE_ENFORCE_LT(ids_data[i], N);
           PADDLE_ENFORCE_GE(ids_data[i], 0);
           for (int j = 0; j < D; ++j) {
@@ -195,6 +200,7 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
         }
       }
     }
+  }
 };
 
 }  // namespace operators

paddle/fluid/operators/lstm_op.h

@@ -312,6 +312,10 @@ class LSTMGradKernel : public framework::OpKernel<T> {
         lstm_grad.prev_state_grad = c0_g ? ordered_c0_g.data<T>() : nullptr;
       }
 
+      // lstm_value.output_value not used in bp, set to nullptr
+      // lstm_grad.state_active_grad not used in bp, set to nullptr
+      lstm_value.output_value = nullptr;
+      lstm_grad.state_active_grad = nullptr;
       int cur_batch_size = bend - bstart;
       T cell_clip = 0.0;
       math::LstmUnitGradFunctor<DeviceContext, T>::compute(

paddle/fluid/operators/lstmp_op.h

@@ -454,6 +454,11 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       }
 
       int cur_batch_size = bend - bstart;
+      // lstmp_value.output_value not used in bp, set to null
+      // lstmp_grad.state_active_grad not used in bp, set to null
+      lstmp_value.output_value = nullptr;
+      lstmp_grad.state_active_grad = nullptr;
+
       math::LstmUnitGradFunctor<DeviceContext, T>::compute(
           device_ctx, lstmp_value, lstmp_grad, frame_size, cur_batch_size,
           cell_clip, gate_act, cell_act, cand_act);

paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc

@@ -282,7 +282,7 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
                                ? mkldnn::inner_product_backward_weights::desc(
                                      src, diff_weights, bias, diff_dst)
                                : mkldnn::inner_product_backward_weights::desc(
-                                     src, diff_weights, bias, diff_dst);
+                                     src, diff_weights, diff_dst);
 
     return mkldnn::inner_product_backward_weights::primitive_desc(
         bwd_weight_desc, engine, pd);

paddle/fluid/operators/ngraph/ngraph_bridge.cc

@@ -34,6 +34,10 @@ std::map<std::string,
         {"accuracy", NG_OPS::BuildAccuracyNode},
         {"conv2d", NG_OPS::BuildConv2dNode},
         {"conv2d_grad", NG_OPS::BuildConv2dGradNode},
+        {"batch_norm", NG_OPS::BuildBatchNormNode},
+        {"batch_norm_grad", NG_OPS::BuildBatchNormGradNode},
+        {"cross_entropy", NG_OPS::BuildCrossEntropyNode},
+        {"cross_entropy_grad", NG_OPS::BuildCrossEntropyGradNode},
         {"elementwise_add", NG_OPS::BuildElementwiseAddNode},
         {"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
         {"fill_constant", NG_OPS::BuildFillConstantNode},
@@ -46,8 +50,12 @@ std::map<std::string,
         {"softmax", NG_OPS::BuildSoftmaxNode},
         {"softmax_grad", NG_OPS::BuildSoftmaxGradNode},
         {"scale", NG_OPS::BuildScaleNode},
+        {"sigmoid", NG_OPS::BuildUnaryNode<ngraph::op::Sigmoid>},
+        {"sum", NG_OPS::BuildSumNode},
         {"relu", NG_OPS::BuildUnaryNode<ngraph::op::Relu>},
+        {"relu_grad", NG_OPS::BuildReluGradNode},
         {"tanh", NG_OPS::BuildUnaryNode<ngraph::op::Tanh>},
+        {"tanh_grad", NG_OPS::BuildTanhGradNode},
         {"top_k", NG_OPS::BuildTopKNode}};
 
 void NgraphBridge::BuildNgNode(

paddle/fluid/operators/ngraph/ngraph_ops.h

@@ -22,8 +22,11 @@ limitations under the License. */
 #pragma once
 
 #include "ops/accuracy_op.h"
+#include "ops/activation_op.h"
+#include "ops/batch_norm_op.h"
 #include "ops/binary_unary_op.h"
 #include "ops/conv2d_op.h"
+#include "ops/cross_entropy_op.h"
 #include "ops/elementwise_add_op.h"
 #include "ops/fill_constant_op.h"
 #include "ops/mean_op.h"
@@ -31,4 +34,5 @@ limitations under the License. */
 #include "ops/pool2d_op.h"
 #include "ops/scale_op.h"
 #include "ops/softmax_op.h"
+#include "ops/sum_op.h"
 #include "ops/top_k_op.h"

paddle/fluid/operators/ngraph/ops/activation_op.h

+/*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. */
+
+#pragma once
+
+#include <string>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildReluGradNode(
+    const std::shared_ptr<framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto out = platform::GetInputNode(op, "Out", ngb_node_map);
+  auto dout = platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
+  auto relu_grad = std::make_shared<ngraph::op::ReluBackprop>(out, dout);
+  platform::SetOutputNode(op, "X@GRAD", relu_grad, ngb_node_map);
+}
+
+void BuildTanhGradNode(
+    const std::shared_ptr<framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto out = platform::GetInputNode(op, "Out", ngb_node_map);
+  auto dout = platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
+  auto shape = out->get_shape();
+  auto node_const =
+      ngraph::op::Constant::create(ngraph::element::f32, shape, {1});
+  auto result = dout * (node_const - out * out);
+  platform::SetOutputNode(op, "X@GRAD", result, ngb_node_map);
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/ngraph/ops/batch_norm_op.h

+/*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. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildBatchNormNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  auto& data_layout = op_attrs.Get<std::string>("data_layout");
+
+  auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
+  auto mean = paddle::platform::GetInputNode(op, "Mean", ngb_node_map);
+  auto variance = paddle::platform::GetInputNode(op, "Variance", ngb_node_map);
+  auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+
+  const bool is_test = op_attrs.Get<bool>("is_test");
+  const float epsilon = op_attrs.Get<float>("epsilon");
+  const float momentum = op_attrs.Get<float>("momentum");
+
+  PADDLE_ENFORCE(
+      data_layout == "NHWC" || data_layout == "NCHW" || data_layout == "NC",
+      "The BatchNorm operator only supports NHWC/NCHW/NC data format");
+
+  if (data_layout == "NHWC") {
+    x = paddle::platform::Nhwc2Nchw(x);
+  }
+
+  std::shared_ptr<ngraph::Node> mean_out, saved_mean, saved_variance,
+      variance_out, y;
+
+  if (!is_test) {
+    auto BN = std::make_shared<ngraph::op::BatchNormTraining>(epsilon, scale,
+                                                              bias, x);
+    y = std::make_shared<ngraph::op::GetOutputElement>(BN, 0);
+    saved_mean = std::make_shared<ngraph::op::GetOutputElement>(BN, 1);
+    saved_variance = std::make_shared<ngraph::op::GetOutputElement>(BN, 2);
+
+    mean_out = std::make_shared<ngraph::op::Add>(
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            momentum, mean),
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            1. - momentum, saved_mean));
+    variance_out = std::make_shared<ngraph::op::Add>(
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            momentum, variance),
+        paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+            1. - momentum, saved_variance));
+
+    if (data_layout == "NHWC") {
+      y = paddle::platform::Nchw2Nhwc(y);
+    }
+
+    paddle::platform::SetOutputNode(op, "MeanOut", mean_out, ngb_node_map);
+    paddle::platform::SetOutputNode(op, "VarianceOut", variance_out,
+                                    ngb_node_map);
+    paddle::platform::SetOutputNode(op, "SavedMean", saved_mean, ngb_node_map);
+    paddle::platform::SetOutputNode(op, "SavedVariance", saved_variance,
+                                    ngb_node_map);
+    paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
+  } else {
+    y = std::make_shared<ngraph::op::BatchNormInference>(epsilon, scale, bias,
+                                                         x, mean, variance);
+    paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
+  }
+}
+
+void BuildBatchNormGradNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  auto& data_layout = op_attrs.Get<std::string>("data_layout");
+
+  auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
+  auto saved_mean =
+      paddle::platform::GetInputNode(op, "SavedMean", ngb_node_map);
+  auto saved_variance =
+      paddle::platform::GetInputNode(op, "SavedVariance", ngb_node_map);
+  auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
+  auto x_shape = x->get_shape();
+  auto dy_shape = dy->get_shape();
+
+  PADDLE_ENFORCE(x_shape.size() == 2 || x_shape.size() == 4,
+                 "BN grap input size needs to be 2 or 4");
+  PADDLE_ENFORCE_EQ(x_shape.size(), dy_shape.size(),
+                    "BN grap input and delta size needs to be equal");
+  PADDLE_ENFORCE(
+      data_layout == "NHWC" || data_layout == "NCHW" || data_layout == "NC",
+      "The BatchNorm operator only supports NHWC/NCHW/NC data format");
+
+  if (x_shape.size() == 2) {
+    x = std::make_shared<ngraph::op::Reshape>(
+        x, ngraph::AxisVector{0, 1},
+        ngraph::Shape{x_shape.at(0), x_shape.at(1), 1, 1});
+    dy = std::make_shared<ngraph::op::Reshape>(
+        dy, ngraph::AxisVector{0, 1},
+        ngraph::Shape{dy_shape.at(0), dy_shape.at(1), 1, 1});
+  }
+
+  if (data_layout == "NHWC") {
+    x = paddle::platform::Nhwc2Nchw(dy);
+    dy = paddle::platform::Nhwc2Nchw(dy);
+  }
+  const float epsilon = op_attrs.Get<float>("epsilon");
+
+  auto bn_bprop = std::make_shared<ngraph::op::BatchNormTrainingBackprop>(
+      epsilon, scale, bias, x, saved_mean, saved_variance, dy);
+
+  std::shared_ptr<ngraph::Node> dx =
+      std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 0);
+  auto dscale = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 1);
+  auto dbias = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 2);
+  paddle::platform::SetOutputNode(op, "Bias@GRAD", dbias, ngb_node_map);
+  paddle::platform::SetOutputNode(op, "Scale@GRAD", dscale, ngb_node_map);
+  if (x_shape.size() == 2) {
+    paddle::platform::SetOutputNode(
+        op, "X@GRAD", paddle::platform::NgReshaper(dx, x_shape), ngb_node_map);
+  } else {
+    if (data_layout == "NHWC") {
+      dx = paddle::platform::Nchw2Nhwc(dx);
+    }
+    paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+  }
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/ngraph/ops/cross_entropy_op.h

+/*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. */
+
+#pragma once
+
+#include <functional>
+#include <string>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildCrossEntropyNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
+  auto label_shape = label->get_shape();
+  auto x_shape = x->get_shape();
+  auto label_rank = label_shape.size();
+  auto x_rank = x_shape.size();
+  std::shared_ptr<ngraph::Node> x_2d = x, label_2d = label;
+  auto label_2d_shape = label_shape, x_2d_shape = x_shape;
+
+  if (label_rank > 2) {
+    label_2d_shape = paddle::platform::FlattenTo2d(label_shape, label_rank - 1);
+    label_2d = paddle::platform::NgReshaper(label, label_2d_shape);
+  }
+  if (x_rank > 2) {
+    x_2d_shape = paddle::platform::FlattenTo2d(x_shape, x_rank - 1);
+    x_2d = paddle::platform::NgReshaper(x, x_2d_shape);
+  }
+
+  auto batch_size = x_2d_shape.at(0);
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  const bool is_soft_label = op_attrs.Get<bool>("soft_label");
+
+  std::shared_ptr<ngraph::Node> node_1_hot = label_2d;
+  if (!is_soft_label) {
+    auto label_1d = paddle::platform::NgReshaper(
+        label_2d, ngraph::Shape{label_2d_shape.at(0)});
+    node_1_hot = std::make_shared<ngraph::op::OneHot>(label_1d, x_2d_shape, 1);
+  }
+  if (x->get_element_type() != node_1_hot->get_element_type()) {
+    node_1_hot = std::make_shared<ngraph::op::Convert>(node_1_hot,
+                                                       x->get_element_type());
+  }
+
+  auto node_log = std::make_shared<ngraph::op::Log>(x_2d);
+  auto high_clip = ngraph::op::Constant::create(node_log->get_element_type(),
+                                                node_log->get_shape(), {1e20});
+  auto low_clip = ngraph::op::Constant::create(node_log->get_element_type(),
+                                               node_log->get_shape(), {-1e20});
+  auto node_min = std::make_shared<ngraph::op::Minimum>(node_log, high_clip);
+  auto node_max = std::make_shared<ngraph::op::Maximum>(node_min, low_clip);
+  auto node_mul = node_1_hot * node_log;
+  auto node_sum =
+      std::make_shared<ngraph::op::Sum>(node_mul, ngraph::AxisSet{1});
+  auto node_neg = std::make_shared<ngraph::op::Negative>(node_sum);
+  auto xe =
+      paddle::platform::NgReshaper(node_neg, ngraph::Shape{batch_size, 1});
+
+  if (!is_soft_label) {
+    auto ignore_index = op_attrs.Get<int>("ignore_index");
+    auto ignore_node = ngraph::op::Constant::create(
+        label->get_element_type(), label_2d_shape, {ignore_index});
+    auto not_equal_node =
+        std::make_shared<ngraph::op::NotEqual>(label_2d, ignore_node);
+    auto mask = std::make_shared<ngraph::op::Convert>(not_equal_node,
+                                                      xe->get_element_type());
+    xe = xe * mask;
+  }
+
+  paddle::platform::SetOutputNode(op, "Y", xe, ngb_node_map);
+}
+
+void BuildCrossEntropyGradNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  const bool is_soft_label = op_attrs.Get<bool>("soft_label");
+
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
+  auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
+  auto x_shape = x->get_shape();
+  auto rank = x_shape.size();
+
+  std::shared_ptr<ngraph::Node> mask;
+  if (!is_soft_label) {
+    auto label_shape = label->get_shape();
+    label_shape.pop_back();
+    label = paddle::platform::NgReshaper(label, label_shape);
+
+    auto ignore_index = op_attrs.Get<int>("ignore_index");
+    auto ignore_node = ngraph::op::Constant::create(
+        label->get_element_type(), label_shape, {ignore_index});
+    auto not_equal_node =
+        std::make_shared<ngraph::op::NotEqual>(label, ignore_node);
+    mask = std::make_shared<ngraph::op::Convert>(not_equal_node,
+                                                 x->get_element_type());
+    mask = std::make_shared<ngraph::op::Broadcast>(mask, x_shape,
+                                                   ngraph::AxisSet{rank - 1});
+
+    label = std::make_shared<ngraph::op::OneHot>(label, x_shape, rank - 1);
+  }
+
+  auto dy_shape = dy->get_shape();
+  dy_shape.pop_back();
+  auto dy_reshape = paddle::platform::NgReshaper(dy, dy_shape);
+  auto dy_bcast = std::make_shared<ngraph::op::Broadcast>(
+      dy_reshape, x_shape, ngraph::AxisSet{rank - 1});
+  if (x->get_element_type() != label->get_element_type()) {
+    label = std::make_shared<ngraph::op::Convert>(label, x->get_element_type());
+  }
+
+  auto xe_grad = -label * dy_bcast / x;
+
+  if (!is_soft_label) {
+    xe_grad = xe_grad * mask;
+  }
+
+  paddle::platform::SetOutputNode(op, "X@GRAD", xe_grad, ngb_node_map);
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/ngraph/ops/fill_constant_op.h

@@ -46,8 +46,6 @@ void BuildFillConstantNode(
     ng_dtype = ngraph::element::i64;
   } else if (data_type == paddle::framework::proto::VarType::INT32) {
     ng_dtype = ngraph::element::i32;
-  } else if (data_type == paddle::framework::proto::VarType::BOOL) {
-    ng_dtype = ngraph::element::boolean;
   } else {
     PADDLE_THROW("unsupported data type: %s", data_type);
   }

paddle/fluid/operators/ngraph/ops/sum_op.h

+/*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. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildSumNode(
+    const std::shared_ptr<framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  std::vector<std::string> op_inputs;
+  for (auto& var_name_item : op->Inputs()) {
+    for (auto& var_name : var_name_item.second) {
+      op_inputs.push_back(var_name);
+      if (ngb_node_map->find(var_name) == ngb_node_map->end()) {
+        PADDLE_THROW("op % input varname %s is not found in var_node_map",
+                     op->Type(), var_name);
+      }
+    }
+  }
+  std::shared_ptr<ngraph::Node>& sum = ngb_node_map->at(op_inputs[0]);
+  for (size_t k = 1; k < op_inputs.size(); ++k) {
+    std::shared_ptr<ngraph::Node>& nodek = ngb_node_map->at(op_inputs[k]);
+    if (nodek->get_element_type() != sum->get_element_type()) {
+      nodek =
+          std::make_shared<ngraph::op::Convert>(nodek, sum->get_element_type());
+    }
+    sum = sum + nodek;
+  }
+  platform::SetOutputNode(op, "Out", sum, ngb_node_map);
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/random_crop_op.h

@@ -121,7 +121,7 @@ struct RandomCropFunctor {
   HOSTDEVICE void operator()(size_t ins_idx) {
     typename Random<DeviceContext>::Engine engine(seed_);
     engine.discard(ins_idx * (rank_ - num_batchsize_dims_));
-    size_t offsets[9];
+    size_t offsets[9] = {};
     for (int i = num_batchsize_dims_; i < rank_; ++i) {
       typename Random<DeviceContext>::template UniformIntDist<size_t> dist(
           0, x_dims_[i] - out_dims_[i]);

paddle/fluid/operators/row_conv_op.cc

@@ -109,23 +109,23 @@ from future subsequences in a computationally efficient manner to improve
 unidirectional recurrent neural networks. The row convolution operator is 
 different from the 1D sequence convolution, and is computed as follows:
 
-Given an input sequence $in$ of length $t$ and input dimension $d$, 
-and a filter ($W$) of size $context \times d$, 
+Given an input sequence $X$ of length $t$ and input dimension $D$, 
+and a filter ($W$) of size $context \times D$,
 the output sequence is convolved as:
 
 $$
-out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
+out_{i} = \\sum_{j=i}^{i + context - 1} X_{j} \\cdot W_{j-i}
 $$
 
 In the above equation:
 
 * $Out_{i}$: The i-th row of output variable with shape [1, D].
 
-* $\\tau$: Future context size.
+* $context$: Future context size.
 
 * $X_{j}$: The j-th row of input variable with shape [1, D].
 
-* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D].
+* $W_{j-i}$: The (j-i)-th row of parameters with shape [1, D].
 
 More details about row_conv please refer to
 the design document

paddle/fluid/operators/shape_op.cc

@@ -35,14 +35,15 @@ class ShapeOp : public framework::OperatorWithKernel {
 class ShapeOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("Input", "(Tensor), The input tensor.");
-    AddOutput("Out",
-              "(Tensor), The shape of input tensor, the data type of the shape"
+    AddInput("Input", "(LoDTensor), The input tensor.");
+    AddOutput(
+        "Out",
+        "(LoDTensor), The shape of input tensor, the data type of the shape"
         " is int32_t, will be on the same device with the input Tensor.");
     AddComment(R"DOC(
-Shape Operator
+Shape Operator.
 
-Get the shape of input tensor. Only support CPU input Tensor now.
+Return the shape of the input.
 )DOC");
   }
 };

paddle/fluid/platform/enforce.h

@@ -234,8 +234,10 @@ inline void throw_on_error(ncclResult_t stat, const std::string& msg) {
 #endif  // PADDLE_WITH_CUDA
 
 #define PADDLE_THROW(...)                                            \
+  do {                                                               \
     throw ::paddle::platform::EnforceNotMet(                         \
-      ::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__)
+        ::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__); \
+  } while (0)
 
 #define PADDLE_ENFORCE(COND, ...)                                         \
   do {                                                                    \
@@ -274,19 +276,21 @@ inline void throw_on_error(ncclResult_t stat, const std::string& msg) {
   do {                                                      \
     if (UNLIKELY(nullptr == (__VAL))) {                     \
       PADDLE_THROW(#__VAL " should not be null\n%s",        \
-                   paddle::string::Sprintf("" __VA_ARGS__)); \
+                   ::paddle::string::Sprintf(__VA_ARGS__)); \
     }                                                       \
   } while (0)
 
 #define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...)  \
   do {                                                                  \
-    if (UNLIKELY(!((__VAL0)__CMP(__VAL1)))) {                           \
+    auto __cond1__ = (__VAL0);                                          \
+    auto __cond2__ = (__VAL1);                                          \
+    if (UNLIKELY(!((__cond1__)__CMP(__cond2__)))) {                     \
       PADDLE_THROW("Enforce failed. Expected %s " #__CMP                \
                    " %s, but received %s:%s " #__INV_CMP " %s:%s.\n%s", \
                    #__VAL0, #__VAL1, #__VAL0,                           \
-                   paddle::string::to_string(__VAL0), #__VAL1,          \
-                   paddle::string::to_string(__VAL1),                   \
-                   paddle::string::Sprintf("" __VA_ARGS__));            \
+                   ::paddle::string::to_string(__cond1__), #__VAL1,     \
+                   ::paddle::string::to_string(__cond2__),              \
+                   ::paddle::string::Sprintf(__VA_ARGS__));             \
     }                                                                   \
   } while (0)
 

paddle/fluid/platform/ngraph_helper.h

@@ -23,6 +23,26 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 
+std::shared_ptr<ngraph::Node> Nhwc2Nchw(std::shared_ptr<ngraph::Node> in) {
+  auto in_shape = in->get_shape();
+  in_shape[0] = in->get_shape()[0];
+  in_shape[1] = in->get_shape()[3];
+  in_shape[2] = in->get_shape()[1];
+  in_shape[3] = in->get_shape()[2];
+  ngraph::AxisVector axis_vec = {0, 3, 1, 2};
+  return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
+}
+
+std::shared_ptr<ngraph::Node> Nchw2Nhwc(std::shared_ptr<ngraph::Node> in) {
+  auto in_shape = in->get_shape();
+  in_shape[0] = in->get_shape()[0];
+  in_shape[1] = in->get_shape()[2];
+  in_shape[2] = in->get_shape()[3];
+  in_shape[3] = in->get_shape()[1];
+  ngraph::AxisVector axis_vec = {0, 2, 3, 1};
+  return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
+}
+
 ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
   auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
                             std::multiplies<size_t>());

paddle/fluid/platform/place.cc

@@ -14,6 +14,12 @@ limitations under the License. */
 
 #include "paddle/fluid/platform/place.h"
 
+DEFINE_bool(benchmark, false,
+            "Doing memory benchmark. It will make deleting scope synchronized, "
+            "and add some memory usage logs."
+            "Default cuda is asynchronous device, set to True will"
+            "force op run in synchronous mode.");
+
 namespace paddle {
 namespace platform {
 

paddle/fluid/pybind/inference_api.cc

@@ -74,12 +74,12 @@ void BindPaddleBuf(py::module *m) {
       .def(py::init([](std::vector<float> &data) {
         auto buf = PaddleBuf(data.size() * sizeof(float));
         std::memcpy(buf.data(), static_cast<void *>(data.data()), buf.length());
-        return std::move(buf);
+        return buf;
       }))
       .def(py::init([](std::vector<int64_t> &data) {
         auto buf = PaddleBuf(data.size() * sizeof(int64_t));
         std::memcpy(buf.data(), static_cast<void *>(data.data()), buf.length());
-        return std::move(buf);
+        return buf;
       }))
       .def("resize", &PaddleBuf::Resize)
       .def("reset",

paddle/fluid/pybind/ir.cc

@@ -13,10 +13,12 @@
 // limitations under the License.
 
 #include "paddle/fluid/pybind/ir.h"
+#include <algorithm>
 #include <string>
 #include <unordered_map>
 #include <unordered_set>
 #include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/framework/op_desc.h"
@@ -27,6 +29,10 @@ namespace py = pybind11;
 using paddle::framework::ir::Graph;
 using paddle::framework::ir::Node;
 using paddle::framework::ir::GraphSafeRemoveNodes;
+using paddle::framework::ir::HasCircle;
+using paddle::framework::ir::GraphNum;
+using paddle::framework::ir::TopologySortOperations;
+using paddle::framework::ir::BuildOperationAdjList;
 using paddle::framework::OpDesc;
 using paddle::framework::ProgramDesc;
 using paddle::framework::VarDesc;
@@ -36,6 +42,12 @@ namespace paddle {
 namespace pybind {
 void BindGraph(py::module *m) {
   m->def("graph_safe_remove_nodes", GraphSafeRemoveNodes);
+  m->def("has_circle", HasCircle);
+  m->def("graph_num", GraphNum);
+  m->def("topology_sort", TopologySortOperations,
+         return_value_policy::reference);
+  m->def("build_adjacency_list", BuildOperationAdjList,
+         return_value_policy::reference);
   py::class_<Graph, std::shared_ptr<Graph>>(
       *m, "Graph",
       "The graph is a Directed Acyclic Single Static Assignment Graph, see "
@@ -46,7 +58,6 @@ void BindGraph(py::module *m) {
       .def("get_float", &Graph::Get<float>)
       .def("get_double", &Graph::Get<double>)
       .def("get_string", &Graph::Get<std::string>)
-      .def("get_program", &Graph::Get<ProgramDesc>)
       .def("get_marked_nodes", &Graph::Get<std::unordered_set<const Node *>>)
       .def("set", [](Graph &self, const std::string &attr_name,
                      int attr) { return self.Set(attr_name, new int(attr)); })
@@ -63,11 +74,6 @@ void BindGraph(py::module *m) {
            [](Graph &self, const std::string &attr_name, double attr) {
              return self.Set(attr_name, new double(attr));
            })
-      .def("set",
-           [](Graph &self, const std::string &attr_name,
-              const ProgramDesc &attr) {
-             return self.Set(attr_name, new ProgramDesc(attr));
-           })
       .def("set",
            [](Graph &self, const std::string &attr_name,
               const std::unordered_set<const Node *> &attr) {
@@ -108,42 +114,42 @@ void BindNode(py::module *m) {
       .def("is_op", &Node::IsOp)
       .def("is_var", &Node::IsVar)
       .def("is_ctrl_var", &Node::IsCtrlVar)
+      .def("clear_inputs", [](Node &self) { self.inputs.clear(); })
       .def("inputs_remove",
            [](Node &self, int node_id) {
-             for (auto it = self.inputs.begin(); it != self.inputs.end();
-                  it++) {
-               if ((*it)->id() == node_id) {
-                 self.inputs.erase(it);
-               }
+             auto pos = std::find_if(
+                 self.inputs.begin(), self.inputs.end(),
+                 [&node_id](const Node *n) { return n->id() == node_id; });
+             if (pos != self.inputs.end()) {
+               self.inputs.erase(pos);
              }
            })
       .def("inputs_remove",
            [](Node &self, Node &node) {
-             for (auto it = self.inputs.begin(); it != self.inputs.end();
-                  it++) {
-               if (*it == &node) {
-                 self.inputs.erase(it);
-               }
+             auto pos =
+                 std::find(self.inputs.begin(), self.inputs.end(), &node);
+             if (pos != self.inputs.end()) {
+               self.inputs.erase(pos);
              }
            })
       .def("inputs_append",
            [](Node &self, Node &node) { self.inputs.push_back(&node); })
+      .def("clear_outputs", [](Node &self) { self.outputs.clear(); })
       .def("outputs_remove",
            [](Node &self, int node_id) {
-             for (auto it = self.outputs.begin(); it != self.outputs.end();
-                  it++) {
-               if ((*it)->id() == node_id) {
-                 self.outputs.erase(it);
-               }
+             auto pos = std::find_if(
+                 self.outputs.begin(), self.outputs.end(),
+                 [&node_id](const Node *n) { return n->id() == node_id; });
+             if (pos != self.outputs.end()) {
+               self.outputs.erase(pos);
              }
            })
       .def("outputs_remove",
            [](Node &self, Node &node) {
-             for (auto it = self.outputs.begin(); it != self.outputs.end();
-                  it++) {
-               if (*it == &node) {
-                 self.outputs.erase(it);
-               }
+             auto pos =
+                 std::find(self.outputs.begin(), self.outputs.end(), &node);
+             if (pos != self.outputs.end()) {
+               self.outputs.erase(pos);
              }
            })
       .def("outputs_append",

paddle/fluid/pybind/pybind.cc

@@ -295,6 +295,7 @@ PYBIND11_MODULE(core, m) {
       .def("_get_float_element", TensorGetElement<float>)
       .def("_set_double_element", TensorSetElement<double>)
       .def("_get_double_element", TensorGetElement<double>)
+      .def("_place", [](Tensor &self) { return self.place(); })
       .def("_dtype", [](Tensor &self) { return self.type(); });
 
   py::class_<LoDTensor, Tensor>(m, "LoDTensor", R"DOC(
@@ -372,7 +373,13 @@ PYBIND11_MODULE(core, m) {
              PADDLE_ENFORCE(CheckLoD(new_lod, vectorize(self.dims()).front()),
                             "the provided lod info is invalid");
              self.set_lod(new_lod);
-           })
+           },
+           py::arg("lod"), R"DOC(
+           Set LoD of the LoDTensor.
+
+           Args:
+               lod (List[List[int]]): the lod to be set.
+           )DOC")
       .def("set_recursive_sequence_lengths",
            [](LoDTensor &self, const std::vector<std::vector<size_t>>
                                    &recursive_sequence_lengths) {
@@ -388,7 +395,17 @@ PYBIND11_MODULE(core, m) {
                  CheckLoD(new_offset_lod, vectorize(self.dims()).front()),
                  "the provided recursive_sequence_lengths info is invalid");
              self.set_lod(new_offset_lod);
-           })
+           },
+           py::arg("recursive_sequence_lengths"), R"DOC(
+           Set LoD of the LoDTensor according to recursive sequence length.
+
+           For example, if recursive_sequence_lengths=[[2, 3]], meaning that
+           there are two sequences with length 2 and 3 respectively, the 
+           corresponding lod would be [[0, 2, 2+3]], i.e, [[0, 2, 5]].  
+
+           Args:
+                recursive_sequence_lengths (List[List[int]]): sequence lengths. 
+           )DOC")
       .def("lod",
            [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
              // output the offset-based lod info
@@ -397,7 +414,13 @@ PYBIND11_MODULE(core, m) {
              new_lod.reserve(lod.size());
              std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
              return new_lod;
-           })
+           },
+           R"DOC(
+           Return the LoD of the LoDTensor.
+
+           Returns:
+               out (List[List[int]]): the lod of the LoDTensor.
+           )DOC")
       // Set above comments of set_lod.
       .def("recursive_sequence_lengths",
            [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
@@ -407,12 +430,25 @@ PYBIND11_MODULE(core, m) {
              new_lod.reserve(lod.size());
              std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
              return new_lod;
-           })
-      .def("has_valid_recursive_sequence_lengths", [](LoDTensor &self) -> bool {
+           },
+           R"DOC(
+           Return the sequence length of the LoDTensor corresponding to LoD.
+
+           Returns:
+               out (List[List[int]): the sequence lengths. 
+           )DOC")
+      .def("has_valid_recursive_sequence_lengths",
+           [](LoDTensor &self) -> bool {
              // Check that the lod info is valid and match the outermost
              // dimension of the LoDTensor data
              return CheckLoD(self.lod(), vectorize(self.dims()).front());
-      });
+           },
+           R"DOC(
+           Check whether the lod of the LoDTensor is valid.
+
+           Returns:
+               out (bool): whether the lod is valid.
+           )DOC");
 
   py::class_<SelectedRows>(m, "SelectedRows")
       .def("__init__",
@@ -548,11 +584,45 @@ All parameter, weight, gradient are variables in Paddle.
            [](Scope &self, const std::string &name) -> Variable * {
              return self.Var(name);
            },
+           py::arg("name"),
+           R"DOC(
+           Find or create variable named :code:`name` in the current scope. 
+
+           If the variable named :code:`name` does not exist in the 
+           current scope, the variable would be created. Otherwise,
+           return the existing variable. 
+
+           Args:
+               name (str): the variable name.  
+          
+           Returns:
+               out (core.Variable): the found or created variable. 
+           )DOC",
+           py::return_value_policy::reference)
+      .def("find_var", &Scope::FindVar, py::arg("name"),
+           R"DOC(
+           Find variable named :code:`name` in the current scope or 
+           its parent scope. Return None if not found.
+        
+           Args:
+               name (str): the variable name.
+            
+           Returns:
+               out (core.Variable|None): the found variable or None.   
+           )DOC",
            py::return_value_policy::reference)
-      .def("find_var", &Scope::FindVar, py::return_value_policy::reference)
       .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
+           R"DOC(
+           Create a new sub-scope of the current scope.
+
+           Returns:
+               out (core._Scope): the created sub-scope.
+           )DOC",
            py::return_value_policy::reference)
-      .def("drop_kids", &Scope::DropKids);
+      .def("drop_kids", &Scope::DropKids,
+           R"DOC(
+           Delete all sub-scopes of the current scope.
+           )DOC");
 
   m.def("Scope",
         []() -> Scope * {
@@ -560,6 +630,12 @@ All parameter, weight, gradient are variables in Paddle.
           ScopePool::Instance().Insert(std::unique_ptr<Scope>(s));
           return s;
         },
+        R"DOC(
+        Create a new scope.
+        
+        Returns:
+            out (core._Scope): the created scope.
+        )DOC",
         py::return_value_policy::reference);
 
   //! @note: Be careful! PyBind will return std::string as an unicode, not
@@ -673,6 +749,12 @@ All parameter, weight, gradient are variables in Paddle.
 
   py::class_<platform::Place>(m, "Place")
       .def(py::init<>())
+      .def("is_gpu_place",
+           [](platform::Place &self) { return platform::is_gpu_place(self); })
+      .def("gpu_device_id",
+           [](platform::Place &self) {
+             return boost::get<platform::CUDAPlace>(self).device;
+           })
       .def("set_place",
            [](platform::Place &self, const platform::CPUPlace &cpu_place) {
              self = cpu_place;
@@ -782,11 +864,13 @@ All parameter, weight, gradient are variables in Paddle.
              self[i].ShareDataWith(t);
              self[i].set_lod(t.lod());
            })
-      .def("append", [](LoDTensorArray &self, const LoDTensor &t) {
+      .def("append",
+           [](LoDTensorArray &self, const LoDTensor &t) {
              self.emplace_back();
              self.back().ShareDataWith(t);
              self.back().set_lod(t.lod());
-      });
+           },
+           py::arg("tensor"), "Append a LoDensor to LoDTensorArray.");
 
   m.def("IsInplace",
         [](std::string op) -> bool { return operators::IsInplace(op); });
@@ -822,8 +906,7 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("disable_profiler", platform::DisableProfiler);
   m.def("is_profiler_enabled", platform::IsProfileEnabled);
   m.def("reset_profiler", platform::ResetProfiler);
-  m.def("get_pass", [](const py::bytes &binary_str) {
-    std::string pass_type(binary_str);
+  m.def("get_pass", [](const std::string &pass_type) {
     auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
     return std::shared_ptr<framework::ir::Pass>(std::move(pass));
   });
@@ -831,10 +914,9 @@ All parameter, weight, gradient are variables in Paddle.
   py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
   pass.def(py::init())
       .def("has", &ir::Pass::Has)
-      .def("set",
-           [](ir::Pass &self, const std::string &attr_name,
-              const ProgramDesc &attr) {
-             return self.Set(attr_name, new ProgramDesc(attr));
+      .def("set_not_owned",
+           [](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) {
+             self.SetNotOwned<ProgramDesc>(attr_name, &attr);
            })
       .def(
           "set",
@@ -843,7 +925,6 @@ All parameter, weight, gradient are variables in Paddle.
           })
       .def("set", [](ir::Pass &self, const std::string &name,
                      int val) { self.Set<const int>(name, new int(val)); })
-      .def("get_program", &ir::Pass::Get<ProgramDesc>)
       .def("type", &ir::Pass::Type)
       .def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
         std::unique_ptr<ir::Graph> origin_graph(graph.get());
@@ -1092,10 +1173,6 @@ All parameter, weight, gradient are variables in Paddle.
           "is_distribution",
           [](const BuildStrategy &self) { return self.is_distribution_; },
           [](BuildStrategy &self, bool b) { self.is_distribution_ = b; })
-      .def_property(
-          "memory_early_delete",
-          [](const BuildStrategy &self) { return self.memory_early_delete_; },
-          [](BuildStrategy &self, bool b) { self.memory_early_delete_ = b; })
       .def_property(
           "enable_inplace",
           [](const BuildStrategy &self) { return self.enable_inplace_; },

paddle/scripts/paddle_build.sh

@@ -88,6 +88,7 @@ function cmake_gen() {
             -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.5/include/python3.5m/
             -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5m.dylib"
                 WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
+                pip3.5 uninstall -y protobuf
                 pip3.5 install --user -r ${PADDLE_ROOT}/python/requirements.txt
             else
                 exit 1
@@ -101,6 +102,7 @@ function cmake_gen() {
             -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.6/include/python3.6m/
             -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/libpython3.6m.dylib"
                 WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
+                pip3.6 uninstall -y protobuf
                 pip3.6 install --user -r ${PADDLE_ROOT}/python/requirements.txt
             else
                 exit 1
@@ -114,6 +116,7 @@ function cmake_gen() {
             -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m/
             -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib"
                 WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
+                pip3.7 uninstall -y protobuf
                 pip3.7 install --user -r ${PADDLE_ROOT}/python/requirements.txt
             else
                 exit 1
@@ -128,31 +131,44 @@ function cmake_gen() {
                 PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/python/cp27-cp27m/bin/python
             -DPYTHON_INCLUDE_DIR:PATH=/opt/python/cp27-cp27m/include/python2.7
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-2.7.11-ucs2/lib/libpython2.7.so"
+                pip uninstall -y protobuf
+                pip install -r ${PADDLE_ROOT}/python/requirements.txt
             elif [ "$1" == "cp27-cp27mu" ]; then
                 export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs2/lib:}
                 export PATH=/opt/python/cp27-cp27mu/bin/:${PATH}
                 PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/python/cp27-cp27mu/bin/python
             -DPYTHON_INCLUDE_DIR:PATH=/opt/python/cp27-cp27mu/include/python2.7
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-2.7.11-ucs4/lib/libpython2.7.so"
+                pip uninstall -y protobuf
+                pip install -r ${PADDLE_ROOT}/python/requirements.txt
             elif [ "$1" == "cp35-cp35m" ]; then
                 export LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH}
                 export PATH=/opt/_internal/cpython-3.5.1/bin/:${PATH}
                 export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.5.1/bin/python3
             -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.5.1/include/python3.5m
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.5.1/lib/libpython3.so"
+                pip3.5 uninstall -y protobuf
+                pip3.5 install -r ${PADDLE_ROOT}/python/requirements.txt
             elif [ "$1" == "cp36-cp36m" ]; then
                 export LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH}
                 export PATH=/opt/_internal/cpython-3.6.0/bin/:${PATH}
                 export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.6.0/bin/python3
             -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.6.0/include/python3.6m
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.6.0/lib/libpython3.so"
+                pip3.6 uninstall -y protobuf
+                pip3.6 install -r ${PADDLE_ROOT}/python/requirements.txt
             elif [ "$1" == "cp37-cp37m" ]; then
                 export LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH}
                 export PATH=/opt/_internal/cpython-3.7.0/bin/:${PATH}
                 export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.7.0/bin/python3.7
             -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.7.0/include/python3.7m
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.7.0/lib/libpython3.so"
+                pip3.7 uninstall -y protobuf
+                pip3.7 install -r ${PADDLE_ROOT}/python/requirements.txt
            fi
+        else
+            pip uninstall -y protobuf
+            pip install -r ${PADDLE_ROOT}/python/requirements.txt
         fi
     fi
 

python/CMakeLists.txt

@@ -64,6 +64,7 @@ if (WITH_TESTING)
   add_subdirectory(paddle/dataset/tests)
   add_subdirectory(paddle/fluid/tests)
   add_subdirectory(paddle/fluid/contrib/tests)
+  add_subdirectory(paddle/fluid/contrib/slim/tests)
 endif()
 install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
     DESTINATION opt/paddle/share/wheels

python/paddle/__init__.py

@@ -25,4 +25,5 @@ import paddle.reader
 import paddle.dataset
 import paddle.batch
 import paddle.compat
+import paddle.distributed
 batch = batch.batch

python/paddle/distributed/__init__.py

+# 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.

tools/run_mp.py → python/paddle/distributed/launch.py

@@ -37,7 +37,7 @@ default_envs = {
 GPUS = 8
 
 
-def start_procs(gpus, cmd, log_dir):
+def start_procs(gpus, entrypoint, entrypoint_args, log_dir):
     procs = []
     log_fns = []
     os.system("mkdir -p %s" % log_dir)
@@ -73,12 +73,11 @@ def start_procs(gpus, cmd, log_dir):
             "PADDLE_TRAINER_ENDPOINTS": all_nodes_devices_endpoints
         })
 
-        print("starting process ", i, cmd, curr_env)
+        print("starting process ", i, entrypoint, entrypoint_args, curr_env)
         fn = open("%s/workerlog.%d" % (log_dir, i), "w")
         log_fns.append(fn)
-        procs.append(
-            subprocess.Popen(
-                cmd.strip().split(" "), stdout=fn, stderr=fn, env=curr_env))
+        cmd = [sys.executable, "-u", entrypoint] + entrypoint_args
+        procs.append(subprocess.Popen(cmd, stdout=fn, stderr=fn, env=curr_env))
 
     for i in range(gpus):
         try:
@@ -89,7 +88,8 @@ def start_procs(gpus, cmd, log_dir):
             pass
 
 
-def main():
+def parse_args():
+
     parser = argparse.ArgumentParser(
         description='''start paddle training using multi-process mode.
 NOTE: your train program ***must*** run as distributed nccl2 mode,
@@ -108,21 +108,27 @@ POD_IP (current node ip address, not needed for local training)
         type=int,
         default=8,
         help='start number of processes for every gpu')
-    parser.add_argument(
-        '--cmd',
-        type=str,
-        default="",
-        help='command to run for each process, e.g. python train.py --lr 0.1')
     parser.add_argument(
         '--log_dir',
         type=str,
         default="mylog",
         help='directory to put logs per process.')
-    args = parser.parse_args()
-    if args.cmd == "":
-        parser.print_help()
-        exit(0)
-    start_procs(args.gpus, args.cmd, args.log_dir)
+    parser.add_argument(
+        'entrypoint_script',
+        type=str,
+        help="The entrypoint script to be launched in parallel,"
+        "followed by all the arguments for each process,"
+        "e.g. train.py --lr 0.1")
+    parser.add_argument('entrypoint_args', nargs=argparse.REMAINDER)
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # launch multiple training process
+    start_procs(args.gpus, args.entrypoint_script, args.entrypoint_args,
+                args.log_dir)
 
 
 if __name__ == "__main__":

python/paddle/fluid/__init__.py

@@ -161,7 +161,6 @@ def __bootstrap__():
             'times_excess_than_required_tmp_allocation',
             'enable_inplace_whitelist'
         ]
-
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])
     core.init_glog(sys.argv[0])

python/paddle/fluid/compiler.py

@@ -177,7 +177,10 @@ class CompiledProgram(object):
 
         # FIXME(dzhwinter): enable_inplace should be after memory_optimize
         # if turn on python memory optimize, turn off the inplace_pass.
-        self._build_strategy.enable_inplace = False if self._program._is_mem_optimized else True
+        if self._build_strategy.memory_optimize is None:
+            self._build_strategy.memory_optimize = False if main._is_mem_optimized else True
+        if self._build_strategy.enable_inplace is None:
+            self._build_strategy.enable_inplace = False if main._is_mem_optimized else True
 
         if self._build_strategy.num_trainers > 1 and trainers_endpoints:
             assert self._build_strategy.num_trainers == len(

python/paddle/fluid/contrib/decoder/beam_search_decoder.py

@@ -22,7 +22,7 @@ This API is still under active development and may change drastically.
 
 from __future__ import print_function
 
-import contextlib
+from ...wrapped_decorator import signature_safe_contextmanager
 import numpy as np
 import six
 
@@ -419,7 +419,7 @@ class TrainingDecoder(object):
         self._state_cell = state_cell
         self._state_cell._enter_decoder(self)
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         """
         Define the behavior of the decoder for each RNN time step.
@@ -613,7 +613,7 @@ class BeamSearchDecoder(object):
         self._word_dim = word_dim
         self._input_var_dict = input_var_dict
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         """
         Define the behavior of the decoder for each RNN time step.

python/paddle/fluid/contrib/inferencer.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 
 from .. import core
 
@@ -105,7 +105,7 @@ class Inferencer(object):
 
         return results
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(main_program=self.inference_program):
             with executor.scope_guard(self.scope):

python/paddle/fluid/contrib/int8_inference/README.md

@@ -63,10 +63,10 @@ Notes:
 ## 4. How to reproduce the results
 * Small dataset
 ```bash
-python python/paddle/fluid/contrib/tests/test_calibration.py
+FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py
 ```
 
 * Full dataset
 ```bash
-DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
+FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
 ```

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -13,14 +13,19 @@
 # limitations under the License.
 
 import collections
+import numpy as np
+import six
+from ..... import compat as cpt
 from .... import core
 from ....framework import IrGraph
 from ....framework import Program
-from ....framework import Variable
 from ....initializer import Constant
 from .... import unique_name
 
-__all__ = ['QuantizationTransformPass']
+__all__ = [
+    'QuantizationTransformPass', 'QuantizationFreezePass', 'ConvertToInt8Pass',
+    'TransformForMobilePass'
+]
 
 
 class QuantizationTransformPass(object):
@@ -35,7 +40,13 @@ class QuantizationTransformPass(object):
         """
         Convert and rewrite the IrGraph according to weight and
         activation quantization type.
+
         Args:
+            scope(fluid.Scope): When activation use 'range_abs_max' as the quantize
+            type, this pass will create some new parameters. The scope is used to
+            initialize these new parameters.
+            program_exe(fluid.Executor): program_exe is used to initialize new
+            parameters described above.
             weight_bits (int): quantization bit number for weights,
                 the bias is not quantized.
             activation_bits (int): quantization bit number for activation.
@@ -49,6 +60,7 @@ class QuantizationTransformPass(object):
                 support 'abs_max'. The 'range_abs_max' usually is not used for
                 weight, since weights are fixed once the model is well trained.
             window_size (int): the window size for 'range_abs_max' quantization.
+
         Examples:
         .. code-block:: python
             # The original graph will be rewrite.
@@ -88,31 +100,35 @@ class QuantizationTransformPass(object):
         self._quantizable_grad_ops = [
             '%s_grad' % (op) for op in self._quantizable_ops
         ]
-        self._fake_quant_op_types = [
-            'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
-        ]
-        self._fake_dequant_op_types = ['fake_dequantize_max_abs']
         self._is_test = None
         self._global_step = None
 
     def apply(self, graph):
+        """
+        Quantize the graph for training process. According to weight and
+        activation quantization type, the graph will be added some fake
+        quantize operators and fake dequantize operators.
+
+        Args:
+            graph(IrGraph): the applied graph.
+        """
         assert isinstance(graph,
                           IrGraph), 'graph must be the instance of IrGraph.'
         self._need_initialized.clear()
         self._is_test = graph.is_test()
         # marked the variable which has been dequantized.
         dequantized_vars = collections.OrderedDict()
-        params = [p.name() for p in graph.all_parameters()]
+        persistable_vars = [p.name() for p in graph.all_persistable_vars()]
 
         def _transform_forward(graph, op):
             for var_node in op.inputs:
                 if var_node.name() in dequantized_vars:
                     dequant_var_node = dequantized_vars[var_node.name()]
                 else:
-                    quant_bits = self._weight_bits if var_node.name() in params \
+                    quant_bits = self._weight_bits if var_node.name() in persistable_vars \
                     else self._activation_bits
                     quant_type = self._weight_quantize_type if var_node.name() \
-                        in params else self._activation_quantize_type
+                        in persistable_vars else self._activation_quantize_type
                     quant_var_node, scale_var_node = self._insert_quant_op(
                         graph, var_node, quant_bits, quant_type)
                     dequant_var_node = self._insert_dequant_op(
@@ -150,9 +166,14 @@ class QuantizationTransformPass(object):
             assert self._program_exe is not None, \
             'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.'
             init_program = Program()
-            for var_desc, initializer in self._need_initialized.iteritems():
-                var = Variable(init_program.global_block())
-                var._set_desc(var_desc)
+            for var_desc, initializer in six.iteritems(self._need_initialized):
+                var = init_program.global_block().create_var(
+                    name=var_desc.name(),
+                    shape=var_desc.shape(),
+                    dtype=var_desc.dtype(),
+                    type=var_desc.type(),
+                    lod_level=var_desc.lod_level(),
+                    persistable=var_desc.persistable())
                 initializer(var, init_program.global_block())
             self._program_exe.run(program=init_program, scope=self._scope)
 
@@ -161,7 +182,7 @@ class QuantizationTransformPass(object):
     def _create_global_step(self, graph):
         if self._weight_quantize_type == 'range_abs_max' or \
                 self._activation_quantize_type == 'range_abs_max':
-            counter_name = '@STEP_COUNTER@'
+            counter_name = cpt.to_text('@STEP_COUNTER@')
             for node in graph.all_vars():
                 if node.name() == counter_name:
                     self._global_step = node
@@ -175,9 +196,14 @@ class QuantizationTransformPass(object):
                     Constant(value=0, force_cpu=True)
                 global_step_out = graph.create_var_node_from_desc(
                     global_step_in.var())
+                # The attribute of `op_role` is needed by ParallelExecutor.
                 increment_op = graph.create_op_node(
                     op_type='increment',
-                    attrs={'step': 1.0},
+                    attrs={
+                        'step': 1.0,
+                        'op_role':
+                        core.op_proto_and_checker_maker.OpRole.Forward
+                    },
                     inputs={'X': global_step_in},
                     outputs={'Out': global_step_out})
                 graph.link_to(global_step_in, increment_op)
@@ -212,7 +238,10 @@ class QuantizationTransformPass(object):
             var_dtype=var_node.var().dtype())
         quant_op_node = graph.create_op_node(
             op_type='fake_quantize_abs_max',
-            attrs={'bit_length': quant_bits},
+            attrs={
+                'bit_length': quant_bits,
+                'op_role': core.op_proto_and_checker_maker.OpRole.Forward
+            },
             inputs={'X': var_node},
             outputs={'Out': quant_var_node,
                      'OutScale': scale_var_node})
@@ -257,7 +286,8 @@ class QuantizationTransformPass(object):
         attrs = {
             'window_size': self._window_size,
             'bit_length': quant_bits,
-            'is_test': self._is_test
+            'is_test': self._is_test,
+            'op_role': core.op_proto_and_checker_maker.OpRole.Forward
         }
         quant_op_node = graph.create_op_node(
             op_type='fake_quantize_range_abs_max',
@@ -290,7 +320,10 @@ class QuantizationTransformPass(object):
         max_range = (1 << (quant_bits - 1)) - 1
         dequant_op_node = graph.create_op_node(
             op_type='fake_dequantize_max_abs',
-            attrs={'max_range': float(max_range)},
+            attrs={
+                'max_range': float(max_range),
+                'op_role': core.op_proto_and_checker_maker.OpRole.Forward
+            },
             inputs={'X': var_node,
                     'Scale': scale_var_node},
             outputs={'Out': dequant_var_node})
@@ -316,3 +349,330 @@ class QuantizationTransformPass(object):
         Return the scale name of quantized variable for the input `var_name`.
         """
         return "%s.scale" % (var_name)
+
+
+class QuantizationFreezePass(object):
+    """
+    The freeze pass is used to adjust the quantize operator order, for example:
+        1) `activation -> quant -> dequant -> conv2d` will be freezed into
+        `activation -> quant -> conv2d -> dequant`
+        2) `weight -> quant -> dequant -> conv2d` will be freezed into `weight -> conv2d`,
+        and weight will be sacled offline.
+
+    Args:
+        scope(fluid.Scope): scope is used to get the weight tensor values.
+        place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors.
+        weight_bits (int): quantization bit number for weights.
+        activation_bits (int): quantization bit number for activation.
+        weight_quantize_type (str): quantization type for weights, support 'abs_max'.
+        The 'range_abs_max' usually is not used for weight, since weights are fixed once the
+        model is well trained.
+    """
+
+    def __init__(self,
+                 scope,
+                 place,
+                 weight_bits=8,
+                 activation_bits=8,
+                 weight_quantize_type='abs_max'):
+        assert scope is not None, \
+            'The scope cannot be set None.'
+        assert place is not None, \
+            'The place cannot be set None.'
+        self._scope = scope
+        self._place = place
+        self._weight_bits = weight_bits
+        self._activation_bits = activation_bits
+        self._weight_quantize_type = weight_quantize_type
+        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+        self._fake_quant_op_names = [
+            'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
+        ]
+        self._fake_dequant_op_names = ['fake_dequantize_max_abs']
+        self._op_input_rename_map = collections.OrderedDict()
+        self._op_output_rename_map = collections.OrderedDict()
+        self._var_scale_map = collections.OrderedDict()
+
+    def apply(self, graph):
+        """
+        Adjust quantize/dequantize operators order for the inference process.
+
+        Args:
+            graph(IrGraph): the applied graph.
+        """
+        persistable_vars = [p.name() for p in graph.all_persistable_vars()]
+        ops = graph.all_ops()
+        for op_node in ops:
+            op_name = op_node.name()
+            if op_name in self._fake_quant_op_names:
+                input_arg_name = op_node.op().input('X')[0]
+                if input_arg_name in persistable_vars:
+                    if self._weight_quantize_type == 'abs_max':
+                        param = self._load_var(input_arg_name)
+                        scale_v = np.max(np.abs(param))
+                    else:
+                        scale_v = self._load_var(op_node.op().output('OutScale')
+                                                 [0])[0]
+                    self._var_scale_map[input_arg_name] = scale_v
+                else:
+                    scale_v = graph.var_node(op_node.op().output('OutScale')[0])
+                    self._var_scale_map[input_arg_name] = scale_v
+                if input_arg_name in persistable_vars:
+                    self._remove_fake_quant_and_dequant_op(graph, op_node)
+                    # quantize weight and restore
+                    param_v = self._load_var(input_arg_name)
+                    quantized_param_v = self._quant(param_v, scale_v,
+                                                    self._weight_bits)
+                    self._restore_var(input_arg_name, quantized_param_v)
+
+        ops = graph.all_ops()
+        for op_node in ops:
+            op_name = op_node.name()
+            if op_name in self._fake_dequant_op_names:
+                self._remove_fake_quant_and_dequant_op(graph, op_node)
+
+        ops = graph.all_ops()
+        for op_node in ops:
+            op_name = op_node.name()
+            if op_name in self._quantizable_ops:
+                self._insert_post_dequant_op(graph, op_node)
+
+        for op_node in ops:
+            # insert dequant_op after fc/conv, need to rename inputs of the followed ops
+            for var_node in op_node.inputs:
+                name = var_node.name()
+                if name in self._op_output_rename_map:
+                    old_in = graph.var_node(name)
+                    new_in = self._op_output_rename_map[name]
+                    graph.update_input_link(old_in, new_in, op_node)
+
+        # remove the unused var node in the graph
+        self._remove_unused_var_nodes(graph)
+        return graph
+
+    def _remove_fake_quant_and_dequant_op(self, graph, op_node):
+        k = op_node.op().output('Out')[0]
+        v = op_node.op().input('X')[0]
+        if v not in self._op_input_rename_map:
+            self._op_input_rename_map[k] = v
+        else:
+            self._op_input_rename_map[k] = self._op_input_rename_map[v]
+        graph.safe_remove_nodes(op_node)
+
+    def _insert_post_dequant_op(self, graph, op_node):
+        max_range = None
+        scale_var_node = None
+        persistable_vars = [p.name() for p in graph.all_persistable_vars()]
+        for var_node in op_node.inputs:
+            name = var_node.name()
+            if name in self._op_input_rename_map:
+                old_in = graph.var_node(name)
+                new_in = graph.var_node(self._op_input_rename_map[name])
+                new_in.clear_outputs()
+                graph.update_input_link(old_in, new_in, op_node)
+            original_var_name = self._original_var_name(name)
+            scale_v = self._var_scale_map[original_var_name]
+            if original_var_name in persistable_vars:
+                param_range = (1 << (self._weight_bits - 1)) - 1
+                act_range = (1 << (self._activation_bits - 1)) - 1
+                assert self._is_float(
+                    scale_v), 'The scale of parameter %s is not a float.' % (
+                        original_var_name)
+                max_range = param_range * act_range / scale_v
+            else:
+                assert isinstance(scale_v, core.Node)
+                scale_var_node = self._var_scale_map[original_var_name]
+
+        if len(op_node.outputs) != 1:
+            raise ValueError("Only support one output, but op %s has"
+                             " more than one output." % (op_node.name()))
+
+        output_var_node = op_node.outputs[0]
+        dequant_var_node = graph.create_var_node(
+            name=self._dequantized_var_name(output_var_node.name()),
+            var_type=output_var_node.var().type(),
+            shape=output_var_node.var().shape(),
+            var_dtype=output_var_node.var().dtype())
+        dequant_op_node = graph.create_op_node(
+            op_type='fake_dequantize_max_abs',
+            attrs={
+                'max_range': float(max_range),
+                'op_role': core.op_proto_and_checker_maker.OpRole.Forward
+            },
+            inputs={'X': output_var_node,
+                    'Scale': scale_var_node},
+            outputs={'Out': dequant_var_node})
+        graph.link_to(output_var_node, dequant_op_node)
+        graph.link_to(scale_var_node, dequant_op_node)
+        graph.link_to(dequant_op_node, dequant_var_node)
+        self._op_output_rename_map[output_var_node.name()] = dequant_var_node
+        return dequant_var_node
+
+    def _load_var(self, name):
+        return np.array(self._scope.find_var(name).get_tensor())
+
+    def _restore_var(self, name, array):
+        tensor = self._scope.find_var(name).get_tensor()
+        tensor.set(array, self._place)
+
+    def _remove_unused_var_nodes(self, graph):
+        all_used_vars = set()
+        ops = graph.all_ops()
+        for op_node in ops:
+            for input_node in op_node.inputs:
+                all_used_vars.add(input_node)
+            for output_node in op_node.outputs:
+                all_used_vars.add(output_node)
+
+        all_unused_vars = graph.all_vars() - all_used_vars
+        graph.safe_remove_nodes(all_unused_vars)
+
+    def _original_var_name(self, var_name):
+        """
+        Return the original variable name.
+        """
+        if var_name.endswith('.quantized.dequantized'):
+            return var_name[:-len('.quantized.dequantized')]
+        if var_name.endswith('.quantized'):
+            return var_name[:-len('.quantized')]
+        if var_name.endswith('.dequantized'):
+            return var_name[:-len('.dequantized')]
+        if var_name.endswith('.scale'):
+            return var_name[:-len('.scale')]
+        else:
+            return var_name
+
+    def _dequantized_var_name(self, var_name):
+        """
+        Return dequantized variable name for the input `var_name`.
+        """
+        return "%s.dequantized" % (var_name)
+
+    def _is_float(self, v):
+        return isinstance(v, float) or isinstance(v, np.float32) \
+            or isinstance(v, np.float64)
+
+    def _quant(self, x, scale, num_bits):
+        return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
+
+
+class ConvertToInt8Pass(object):
+    """
+    Convert the weights into int8_t type.
+
+    Args:
+        scope(fluid.Scope): scope is used to get the weight tensor values.
+        place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the
+        8bits weight tensors.
+    """
+
+    def __init__(self, scope, place):
+        assert scope is not None, \
+            'The scope cannot be set None.'
+        assert place is not None, \
+            'The place cannot be set None.'
+        self._scope = scope
+        self._place = place
+        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+
+    def apply(self, graph):
+        """
+        Convert weights' tpye of the graph. After that, the data type of the
+        graph weigths is int8_t.
+
+        Args:
+            graph(IrGraph): the applied graph.
+        """
+        persistable_vars = [p.name() for p in graph.all_persistable_vars()]
+        ops = graph.all_ops()
+        input_map = {}
+        for op_node in ops:
+            op_name = op_node.name()
+            if op_name in self._quantizable_ops:
+                for var_node in op_node.inputs:
+                    name = var_node.name()
+                    if name in persistable_vars:
+                        if name not in input_map:
+                            int8_var_node = self._convert_to_int8(graph,
+                                                                  var_node)
+                            input_map[name] = int8_var_node
+                        graph.update_input_link(var_node, input_map[name],
+                                                op_node)
+
+        # remove the unused var node in the graph
+        self._remove_unused_var_nodes(graph)
+        return graph
+
+    def _convert_to_int8(self, graph, var_node):
+        int8_var_node_name = var_node.name() + ".int8"
+        int8_var_node = graph.create_param_node(
+            name=cpt.to_text(int8_var_node_name),
+            var_type=var_node.var().type(),
+            shape=var_node.var().shape(),
+            var_dtype=core.VarDesc.VarType.INT8)
+        array = self._load_var(var_node.name())
+        self._scope.var(int8_var_node_name)
+        self._store_var(int8_var_node_name, array, np.int8)
+        return int8_var_node
+
+    def _load_var(self, name):
+        return np.array(self._scope.find_var(name).get_tensor())
+
+    def _store_var(self, name, array, dtype):
+        tensor = self._scope.find_var(name).get_tensor()
+        tensor.set(array.astype(dtype), self._place)
+
+    def _remove_unused_var_nodes(self, graph):
+        all_used_vars = set()
+        ops = graph.all_ops()
+        for op_node in ops:
+            for input_node in op_node.inputs:
+                all_used_vars.add(input_node)
+            for output_node in op_node.outputs:
+                all_used_vars.add(output_node)
+
+        all_unused_vars = graph.all_vars() - all_used_vars
+        graph.safe_remove_nodes(all_unused_vars)
+
+
+class TransformForMobilePass(object):
+    """
+    This pass is used to convert the freezed graph for paddle-mobile execution.
+    """
+
+    def __init__(self):
+        self._fake_quant_op_names = [
+            'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
+        ]
+        self._fake_dequant_op_names = ['fake_dequantize_max_abs']
+
+    def apply(self, graph):
+        """
+        Because paddle-mobile use `quantize` an `dequantize` as the names of
+        quantize operator and dequantize operator, the `apply` function just
+        realize this logic.
+
+        Args:
+            graph(IrGraph): the graph will be transformed.
+        """
+        ops = graph.all_ops()
+        for op_node in ops:
+            name = op_node.name()
+            if name in self._fake_quant_op_names:
+                op_node.op().set_type('quantize')
+                quant_node = graph.create_op_node_from_desc(op_node.op())
+                for input_node in op_node.inputs:
+                    graph.link_to(input_node, quant_node)
+                for output_node in op_node.outputs:
+                    graph.link_to(quant_node, output_node)
+                graph.safe_remove_nodes(op_node)
+            if name in self._fake_dequant_op_names:
+                op_node.op().set_type('dequantize')
+                dequant_node = graph.create_op_node_from_desc(op_node.op())
+                for input_node in op_node.inputs:
+                    graph.link_to(input_node, dequant_node)
+                for output_node in op_node.outputs:
+                    graph.link_to(dequant_node, output_node)
+                graph.safe_remove_nodes(op_node)
+
+        return graph

python/paddle/fluid/contrib/slim/tests/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+foreach(src ${TEST_OPS})
+    py_test(${src} SRCS ${src}.py)
+endforeach()

python/paddle/fluid/contrib/slim/unitest/configs/config.yaml → python/paddle/fluid/contrib/slim/tests/configs/config.yaml

 version: 1.0
-include: ["./unitest/configs/pruners.yaml", "./unitest/configs/pruners_0.yaml"]
+include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
 pruners:
     pruner_1:
         class: 'RatioPruner'

python/paddle/fluid/contrib/slim/unitest/test_factory.py → python/paddle/fluid/contrib/slim/tests/test_factory.py

@@ -18,7 +18,7 @@ import unittest
 
 class TestFactory(unittest.TestCase):
     def test_parse(self):
-        factory = ConfigFactory('./unitest/configs/config.yaml')
+        factory = ConfigFactory('./configs/config.yaml')
 
         pruner = factory.instance('pruner_1')
         self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)

python/paddle/fluid/contrib/slim/tests/test_graph.py

+#   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.
+
+from __future__ import print_function
+import unittest
+import paddle.fluid as fluid
+import six
+from paddle.fluid.framework import IrGraph
+from paddle.fluid import core
+
+
+def residual_block(num):
+    def conv_bn_layer(input,
+                      ch_out,
+                      filter_size,
+                      stride,
+                      padding,
+                      act='relu',
+                      bias_attr=False):
+        tmp = fluid.layers.conv2d(
+            input=input,
+            filter_size=filter_size,
+            num_filters=ch_out,
+            stride=stride,
+            padding=padding,
+            act=None,
+            bias_attr=bias_attr)
+        return fluid.layers.batch_norm(input=tmp, act=act)
+
+    data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    hidden = data
+    for _ in six.moves.xrange(num):
+        conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
+        short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
+        hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
+    fc = fluid.layers.fc(input=hidden, size=10)
+    loss = fluid.layers.cross_entropy(input=fc, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
+class TestGraph(unittest.TestCase):
+    def test_graph_functions(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            loss = residual_block(2)
+            opt = fluid.optimizer.Adam(learning_rate=0.001)
+            opt.minimize(loss)
+        graph = IrGraph(core.Graph(main.desc), for_test=False)
+        marked_nodes = set()
+        for op in graph.all_ops():
+            if op.name().find('conv2d') > -1:
+                marked_nodes.add(op)
+        graph.draw('.', 'residual', marked_nodes)
+        self.assertFalse(graph.has_circle())
+        self.assertEqual(graph.graph_num(), 1)
+        nodes = graph.topology_sort()
+        self.assertEqual(len(nodes), len(graph.all_ops()))
+        nodes_map = graph.build_adjacency_list()
+        self.assertEqual(len(nodes_map), len(graph.all_ops()))
+        nodes_num = len(graph.all_nodes())
+        graph.safe_remove_nodes(marked_nodes)
+        self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/contrib/slim/unitest/test_quantization_pass.py → python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py

@@ -17,9 +17,12 @@ import random
 import numpy as np
 import paddle.fluid as fluid
 import six
-from paddle.fluid.framework import Program
+import paddle
 from paddle.fluid.framework import IrGraph
 from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
+from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
+from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
 from paddle.fluid import core
 
 
@@ -65,6 +68,28 @@ def residual_block(num):
     return loss
 
 
+def conv_net(img, label):
+    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+        input=img,
+        filter_size=5,
+        num_filters=20,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+        input=conv_pool_1,
+        filter_size=5,
+        num_filters=50,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_loss = fluid.layers.mean(loss)
+    return avg_loss
+
+
 class TestQuantizationTransformPass(unittest.TestCase):
     def setUp(self):
         self.quantizable_op_and_inputs = {
@@ -171,5 +196,177 @@ class TestQuantizationTransformPass(unittest.TestCase):
         self.residual_block_quant('range_abs_max')
 
 
+class TestQuantizationFreezePass(unittest.TestCase):
+    def freeze_graph(self, use_cuda, seed, quant_type):
+        def build_program(main, startup, is_test):
+            main.random_seed = seed
+            startup.random_seed = seed
+            with fluid.unique_name.guard():
+                with fluid.program_guard(main, startup):
+                    img = fluid.layers.data(
+                        name='image', shape=[1, 28, 28], dtype='float32')
+                    label = fluid.layers.data(
+                        name='label', shape=[1], dtype='int64')
+                    loss = conv_net(img, label)
+                    if not is_test:
+                        opt = fluid.optimizer.Adam(learning_rate=0.001)
+                        opt.minimize(loss)
+            return [img, label], loss
+
+        random.seed(0)
+        np.random.seed(0)
+
+        main = fluid.Program()
+        startup = fluid.Program()
+        test_program = fluid.Program()
+        feeds, loss = build_program(main, startup, False)
+        build_program(test_program, startup, True)
+        test_program = test_program.clone(for_test=True)
+        main_graph = IrGraph(core.Graph(main.desc), for_test=False)
+        test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
+
+        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        scope = fluid.Scope()
+        with fluid.scope_guard(scope):
+            exe.run(startup)
+        transform_pass = QuantizationTransformPass(
+            scope=scope, program_exe=exe, activation_quantize_type=quant_type)
+        transform_pass.apply(main_graph)
+        transform_pass.apply(test_graph)
+        dev_name = '_gpu_' if use_cuda else '_cpu_'
+        marked_nodes = set()
+        for op in main_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
+        marked_nodes = set()
+        for op in test_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
+
+        quantized_main_program = main_graph.to_program()
+        quantized_test_program = test_graph.to_program()
+        iters = 5
+        batch_size = 8
+
+        #train_exe = fluid.ParallelExecutor(
+        #    main_program=quantized_main_program,
+        #    use_cuda=bool(use_cuda),
+        #    loss_name=loss.name,
+        #    scope=scope)
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.mnist.train(), buf_size=500),
+            batch_size=batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        feeder = fluid.DataFeeder(feed_list=feeds, place=place)
+        with fluid.scope_guard(scope):
+            for _ in range(iters):
+                data = next(train_reader())
+                loss_v = exe.run(program=quantized_main_program,
+                                 feed=feeder.feed(data),
+                                 fetch_list=[loss])
+                #loss_v = train_exe.run(feed=feeder.feed(data),
+                #                       fetch_list=[loss.name])
+                #print('{}: {}'.format('loss' + dev_name + quant_type, loss_v))
+
+        test_data = next(test_reader())
+        with fluid.program_guard(quantized_test_program):
+            w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
+                                             quantized_test_program)
+        # Testing
+        with fluid.scope_guard(scope):
+            test_loss1, w_quant = exe.run(program=quantized_test_program,
+                                          feed=feeder.feed(test_data),
+                                          fetch_list=[loss, w_var])
+
+        # Freeze graph for inference, but the weight of fc/conv is still float type.
+        freeze_pass = QuantizationFreezePass(scope=scope, place=place)
+        freeze_pass.apply(test_graph)
+        marked_nodes = set()
+        for op in test_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
+                        marked_nodes)
+
+        server_program = test_graph.to_program()
+        with fluid.scope_guard(scope):
+            test_loss2, = exe.run(program=server_program,
+                                  feed=feeder.feed(test_data),
+                                  fetch_list=[loss])
+        self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
+        #print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1))
+        #print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2))
+        w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
+        # Maybe failed, this is due to the calculation precision
+        # self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
+        #print('{}: {}'.format('w_freeze' + dev_name + quant_type,
+        #                      np.sum(w_freeze)))
+        #print('{}: {}'.format('w_quant' + dev_name + quant_type,
+        #                      np.sum(w_quant)))
+
+        # Convert parameter to 8-bit.
+        convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
+        convert_int8_pass.apply(test_graph)
+        marked_nodes = set()
+        for op in test_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes)
+        server_program_int8 = test_graph.to_program()
+        # Save the 8-bit parameter and model file.
+        with fluid.scope_guard(scope):
+            fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
+                                          ['image', 'label'], [loss], exe,
+                                          server_program_int8)
+            # Test whether the 8-bit parameter and model file can be loaded successfully.
+            [infer, feed, fetch] = fluid.io.load_inference_model(
+                'server_int8' + dev_name + quant_type, exe)
+        # Check the loaded 8-bit weight.
+        w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
+        self.assertEqual(w_8bit.dtype, np.int8)
+        self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
+        #print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit)))
+        #print('{}: {}'.format('w_freeze' + dev_name + quant_type,
+        #                      np.sum(w_freeze)))
+
+        mobile_pass = TransformForMobilePass()
+        mobile_pass.apply(test_graph)
+        marked_nodes = set()
+        for op in test_graph.all_ops():
+            if op.name().find('quantize') > -1:
+                marked_nodes.add(op)
+        test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
+                        marked_nodes)
+
+        mobile_program = test_graph.to_program()
+        with fluid.scope_guard(scope):
+            fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
+                                          ['image', 'label'], [loss], exe,
+                                          mobile_program)
+
+    def test_freeze_graph_cuda_dynamic(self):
+        if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
+                self.freeze_graph(True, seed=1, quant_type='abs_max')
+
+    def test_freeze_graph_cpu_dynamic(self):
+        with fluid.unique_name.guard():
+            self.freeze_graph(False, seed=2, quant_type='abs_max')
+
+    def test_freeze_graph_cuda_static(self):
+        if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
+                self.freeze_graph(True, seed=1, quant_type='range_abs_max')
+
+    def test_freeze_graph_cpu_static(self):
+        with fluid.unique_name.guard():
+            self.freeze_graph(False, seed=2, quant_type='range_abs_max')
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/contrib/tests/CMakeLists.txt

@@ -6,5 +6,9 @@ if(APPLE OR WIN32 OR NOT WITH_MKL)
 endif()
 
 foreach(src ${TEST_OPS})
+    if(src MATCHES "test_calibration")
+        py_test(${src} SRCS ${src}.py ENVS FLAGS_use_mkldnn=true)
+    else()
         py_test(${src} SRCS ${src}.py)
+    endif()
 endforeach()

python/paddle/fluid/contrib/tests/test_calibration.py

@@ -199,7 +199,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
 
     def run_program(self, model_path, generate_int8=False, algo='direct'):
         image_shape = [3, 224, 224]
-        os.environ['FLAGS_use_mkldnn'] = 'True'
 
         fluid.memory_optimize(fluid.default_main_program())
 
@@ -241,9 +240,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
             label = label.reshape([-1, 1])
             running_program = calibrator.sampling_program.clone(
             ) if generate_int8 else infer_program.clone()
-            for op in running_program.current_block().ops:
-                if op.has_attr("use_mkldnn"):
-                    op._set_attr("use_mkldnn", True)
 
             t1 = time.time()
             _, acc1, _ = exe.run(

python/paddle/fluid/contrib/tests/test_quantize_transpiler.py

@@ -204,9 +204,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
         build_program(test_program, startup, True)
         test_program = test_program.clone(for_test=True)
 
-        quant_transpiler = QuantizeTranspiler()
-        quant_transpiler.training_transpile(main)
-        quant_transpiler.training_transpile(test_program)
+        quant_type = 'range_abs_max'  # 'range_abs_max' or 'abs_max'
+        quant_transpiler = QuantizeTranspiler(
+            activation_quantize_type=quant_type)
+        quant_transpiler.training_transpile(main, startup)
+        quant_transpiler.training_transpile(test_program, startup)
 
         place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
         exe = fluid.Executor(place)

python/paddle/fluid/contrib/trainer.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 import os
 import errno
 import shutil
@@ -453,7 +453,7 @@ class Trainer(object):
             io.save_inference_model(param_path, feeded_var_names, target_vars,
                                     exe)
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(
                 main_program=self.train_program,

python/paddle/fluid/executor.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import os
 import multiprocessing
 import numpy as np
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 import six
 from .framework import Program, default_main_program, Variable
 from . import core
@@ -49,7 +49,7 @@ def _switch_scope(scope):
     return ex
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def scope_guard(scope):
     """
     Change the global/default scope instance by Python `with` statement. All

python/paddle/fluid/framework.py

@@ -16,7 +16,9 @@ from __future__ import print_function
 
 import collections
 from collections import defaultdict
+from collections import Iterable
 import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 import os
 import re
 import traceback
@@ -111,7 +113,7 @@ class NameScope(object):
 _name_scope = NameScope()
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def name_scope(prefix=None):
     """
     Generate hierarchical name prefix for the operators.
@@ -555,7 +557,8 @@ class OpProtoHolder(object):
         return {
             core.op_proto_and_checker_maker.kOpRoleAttrName(),
             core.op_proto_and_checker_maker.kOpRoleVarAttrName(),
-            core.op_proto_and_checker_maker.kOpNameScopeAttrName()
+            core.op_proto_and_checker_maker.kOpNameScopeAttrName(),
+            core.op_proto_and_checker_maker.kOpCreationCallstackAttrName()
         }
 
 
@@ -1529,12 +1532,16 @@ class Block(object):
 
 class IrGraph(object):
     """
-    IrGraph uses core.Graph as the delegation to accomplish the manipulation.
+    Python IrGraph. Beneath it is a core.Graph, which is used for
+    create a c++ Ir Pass Graph. An IrGraph is just a graph view of
+    a Program. In an IrGraph, both Variables and Operators are graph
+    nodes.
     """
 
     def __init__(self, graph, for_test=False):
         """
-        Construct the IrGraph using core.Graph.
+        Construct an IrGraph using core.Graph.
+
         Args:
             graph(core.Graph): C++ Graph.
             for_test(bool): True for the test graph and false for the train graph.
@@ -1545,23 +1552,81 @@ class IrGraph(object):
         self._for_test = for_test
 
     def is_test(self):
+        """
+        If the graph is used for testing, the function returns true. Otherwise, returns false.
+        """
         return self._for_test
 
-    def all_parameters(self):
-        param_nodes = set()
-        for node in self.graph.nodes():
-            if node.is_var() and node.var() is not None and node.var(
-            ).persistable():
-                param_nodes.add(node)
-        return param_nodes
+    def all_nodes(self):
+        """
+        Return all nodes included in the graph as a set.
+        """
+        return {node for node in self.graph.nodes()}
 
     def all_vars(self):
+        """
+        Return all variable nodes included in the graph as a set.
+        """
         return {node for node in self.graph.nodes() if node.is_var()}
 
+    def all_persistable_vars(self):
+        """
+        Return all persistable variable nodes included in the graph as a set.
+        """
+        persistable_nodes = set()
+        for node in self.graph.nodes():
+            if node.is_var() and node.var() is not None and node.var(
+            ).persistable():
+                persistable_nodes.add(node)
+        return persistable_nodes
+
     def all_ops(self):
+        """
+        Return all operator nodes included in the graph as a set.
+        """
         return {node for node in self.graph.nodes() if node.is_op()}
 
+    def var_node(self, name):
+        """
+        Get a variable node by name from the graph.
+
+        Args:
+            name(str): the name of the variable node.
+
+        Raises:
+            ValueError: The If input's type is not str, or this graph
+            doesn't have a variable with the giving name.
+
+        Returns:
+            core.Node: the variable node with the giving name.
+        """
+        if not isinstance(name, six.string_types):
+            raise TypeError(
+                "var require string as parameter, but get %s instead." %
+                (type(name)))
+        target_var_node = None
+        var_nodes = self.all_vars()
+        for var_node in var_nodes:
+            if var_node.name() == name:
+                target_var_node = var_node
+        if target_var_node is None:
+            raise ValueError("var_node %s not in this graph" % name)
+        return target_var_node
+
     def create_param_node(self, name, var_type, shape, var_dtype):
+        """
+        Create a persistable variable node in the graph. In IrGraph,
+        it can not distinguish between persistable variables and parameters.
+
+        Args:
+            name(str): the name of the persistable variable node.
+            vart_type(core.VarDesc.VarType): the type of the persistable variable node.
+            shape(list): the shape of the persistable variable node.
+            var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.
+
+        Returns:
+            core.Node: the created persistable variable node.
+        """
         var_desc = core.VarDesc(name)
         var_desc.set_type(var_type)
         var_desc.set_shape(shape)
@@ -1570,6 +1635,20 @@ class IrGraph(object):
         return self.graph.create_var_node(var_desc)
 
     def create_var_node(self, name, var_type, shape, var_dtype):
+        """
+        Create a variable node in the graph. The created variable node is
+        not persistable.
+
+        Args:
+            name(str): the name of the variable node.
+            vart_type(core.VarDesc.VarType): the type of the variable node.
+            shape(list): the shape of the variable node.
+            var_dtype(core.VarDesc.VarType): the data type of the variable node.
+
+        Returns:
+            core.Node: the created variable node.
+        """
+
         var_desc = core.VarDesc(name)
         var_desc.set_type(var_type)
         var_desc.set_shape(shape)
@@ -1577,19 +1656,41 @@ class IrGraph(object):
         return self.graph.create_var_node(var_desc)
 
     def create_var_node_from_desc(self, var_desc):
+        """
+        Create a variable node by using an existing VarDesc in the graph.
+        Depend on the giving VarDesc, the created variable node may be persistable.
+
+        Args:
+            var_desc(core.VarDesc): the giving variable description.
+
+        Returns:
+            core.Node: the created variable node.
+        """
         return self.graph.create_var_node(var_desc)
 
     def create_op_node(self, op_type, attrs, inputs, outputs):
+        """
+        Create a operator node in the graph.
+
+        Args:
+            op_type(str): the type of the operator node.
+            attrs(dict): the attributes of the operator node.
+            inputs(dict): the inputs of the operator node.
+            outputs(dict): the outpus of the operator node.
+
+        Returns:
+            core.Node: the created operator node.
+        """
         op_desc = core.OpDesc()
         op_desc.set_type(op_type)
-        for attr, value in attrs.iteritems():
+        for attr, value in six.iteritems(attrs):
             self._update_desc_attr(op_desc, attr, value)
-        for input_name, var_nodes in inputs.iteritems():
+        for input_name, var_nodes in six.iteritems(inputs):
             if not isinstance(var_nodes, list):
                 var_nodes = [var_nodes]
             op_desc.set_input(input_name,
                               [var_node.name() for var_node in var_nodes])
-        for output_name, var_nodes in outputs.iteritems():
+        for output_name, var_nodes in six.iteritems(outputs):
             if not isinstance(var_nodes, list):
                 var_nodes = [var_nodes]
             op_desc.set_output(output_name,
@@ -1597,11 +1698,29 @@ class IrGraph(object):
         return self.graph.create_op_node(op_desc)
 
     def create_op_node_from_desc(self, op_desc):
+        """
+        Create a operator node by using an existing OpDesc in the graph.
+
+        Args:
+            op_desc(core.VarDesc): the giving operator description.
+
+        Returns:
+            core.Node: the created operator node.
+        """
         return self.graph.create_op_node(op_desc)
 
     def update_input_link(self, old_input_node, new_input_node, op_node):
-        assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
-            op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
+        """
+        Update the input's link of a operator node.
+
+        Args:
+            old_input_node(core.Node): the old input node of the giving op_node.
+            new_input_node(core.Node): the new input node of the giving op_node.
+            op_node(core.Node): the operator node that is needed to update input's link.
+        """
+        assert old_input_node in self.graph.nodes() and new_input_node in \
+        self.graph.nodes() and op_node in self.graph.nodes(), \
+        'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
         old_input_node.outputs_remove(op_node)
         op_node.inputs_remove(old_input_node)
         new_input_node.outputs_append(op_node)
@@ -1609,17 +1728,85 @@ class IrGraph(object):
         op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
 
     def link_to(self, node_in, node_out):
+        """
+        Connect two nodes.
+
+        Args:
+            node_in(core.Node): the input node.
+            node_out(core.Node): the output node.
+        """
         assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
-            'Th two arguments must be in the graph nodes.'
+            'The two arguments(node_in&node_out) must be in the graph nodes.'
         node_in.outputs_append(node_out)
         node_out.inputs_append(node_in)
 
     def safe_remove_nodes(self, remove_nodes):
+        """
+        Remove nodes safely since links connected to these removed nodes are
+        also removed.
+
+        Args:
+            remove_nodes(set): the nodes prepared to be removed.
+        """
         if not isinstance(remove_nodes, set):
+            if isinstance(remove_nodes, Iterable):
                 remove_nodes = set(remove_nodes)
+            else:
+                remove_nodes = {remove_nodes}
         core.graph_safe_remove_nodes(self.graph, remove_nodes)
 
-    def draw(self, save_path, name, marked_nodes=None):
+    def has_circle(self):
+        """
+        Check if the graph has a circle.
+
+        Returns:
+            bool: True if the graph has a circle else False.
+        """
+        return core.has_circle(self.graph)
+
+    def graph_num(self):
+        """
+        Count the number of unconnected graphs in this graph.
+
+        Returns:
+            int: the number of unconnected graphs.
+        """
+        return core.graph_num(self.graph)
+
+    def topology_sort(self):
+        """
+        Perform the topology sort operation on the graph.
+
+        Notes: the `graph` cannot contain a circle.
+
+        Returns:
+            set(core.Node): nodes in topology order.
+        """
+        return core.topology_sort(self.graph)
+
+    def build_adjacency_list(self):
+        """
+        Build an adjacency list of operations for the `graph`.
+
+        Returns:
+            dict{core.Node: set(core.Node)}: the adjacency list.
+        """
+        return core.build_adjacency_list(self.graph)
+
+    def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
+        """
+        Draw the graph. If `dot` command is installed, the drawn graph
+        will be saved as pdf file type, otherwise dot file type is used.
+
+        Args:
+            save_path(str): the save path of drawn graph.
+            name(str): the name of drawn graph.
+            marked_nodes(set(core.Node)): nodes that are needed to be marked.
+            Default value is None.
+            remove_ctr_var(bool): If it is set True, all control variable nodes
+            in the graph will be removed. Default value is True.
+        """
+
         def _convert_to_pdf(dot_file_path):
             pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
             exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
@@ -1629,15 +1816,17 @@ class IrGraph(object):
                 print('The {} is saved as the dot filetype.'.format(
                     dot_file_path))
 
+        if remove_ctr_var:
             remove_ctr_vars = set()
-        ops_num = 0
             for node in self.graph.nodes():
                 if node.is_ctrl_var():
                     remove_ctr_vars.add(node)
-            elif node.is_op():
+            self.safe_remove_nodes(remove_ctr_vars)
+        ops_num = 0
+        for node in self.graph.nodes():
+            if node.is_op():
                 ops_num += 1
         print('Total ops num = {}.'.format(ops_num))
-        self.safe_remove_nodes(remove_ctr_vars)
         if marked_nodes is not None:
             if not isinstance(marked_nodes, set):
                 marked_nodes = set(marked_nodes)
@@ -1652,10 +1841,20 @@ class IrGraph(object):
         _convert_to_pdf(viz_dot_path)
 
     def to_program(self):
+        """
+        Convert the graph into a Program.
+
+        Notes: When the graph includes backward operator nodes, the
+        conversion process may be failed. Usually, this function is
+        only used to convert a test graph.
+
+        Returns:
+            Program: a program converted from the graph.
+        """
         convert_pass = core.get_pass('graph_to_program_pass')
-        convert_pass.set('program', Program().desc)
+        desc = core.ProgramDesc()
+        convert_pass.set_not_owned('program', desc)
         convert_pass.apply(self.graph)
-        desc = convert_pass.get_program('program')
         program = Program._construct_from_desc(desc)
         return program
 
@@ -1775,7 +1974,7 @@ class Program(object):
     def set_op_role_var(self, var_name):
         self._op_role_var = [var_name]
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _optimized_guard(self, param_and_grads):
         """
         A with guard to set :code:`Optimization` :code:`OpRole` and
@@ -1805,7 +2004,7 @@ class Program(object):
         self._op_role_var = tmp_var
         self._current_role = tmp_role
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def _lr_schedule_guard(self, is_with_opt=False):
         """
         A with guard to set :code:`LRSched` :code:`OpRole` and
@@ -2459,7 +2658,7 @@ def switch_startup_program(program):
     return prev_program
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def program_guard(main_program, startup_program=None):
     """
     Change the global main program and startup program with `with` statement.
@@ -2524,7 +2723,7 @@ def _get_var(name, program=None):
     return program.global_block().var(name)
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def _imperative_guard(tracer):
     global _imperative_tracer_
     tmp_trace = _imperative_tracer_
@@ -2535,7 +2734,7 @@ def _imperative_guard(tracer):
     _imperative_tracer_ = tmp_trace
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def _imperative_place_guard(place):
     global _imperative_current_expected_place_
     tmp_place = _imperative_current_expected_place_

python/paddle/fluid/imperative/base.py

@@ -11,7 +11,7 @@
 # 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 contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 import numpy as np
 
 from paddle.fluid import core
@@ -24,7 +24,7 @@ def enabled():
     return framework._in_imperative_mode()
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def guard(place=None):
     train = framework.Program()
     startup = framework.Program()

python/paddle/fluid/imperative/layers.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import collections
 import contextlib
 import sys
 import numpy as np
@@ -30,31 +31,45 @@ class Layer(core.Layer):
     def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
         self._built = False
         self._dtype = dtype
+        self._parameters = collections.OrderedDict()
+        self._sub_layers = collections.OrderedDict()
+
+    def parameters(self, include_sublayers=True):
+        """Returns a list of Parameters from current and sub-layers.
+
+        Args:
+            include_sublayers: If true, also include the parameters from
+            sublayers.
+
+        Returns a list of Parameters.
+        """
+        ret = [p for p in self._parameters.values()]
+        if include_sublayers:
+            for l in self._sub_layers.values():
+                for p in l.parameters(include_sublayers):
+                    ret.append(p)
+        return ret
 
-    def parameters(self):
-        params = []
-        for key in self.__dict__.keys():
-            value = self.__dict__[key]
-            if isinstance(value, framework.Parameter):
-                params.append(value)
-            elif isinstance(value, core.Layer):
-                params.extend(value.parameters())
-            elif isinstance(value, collections.Container):
-                if len(value) == 0:
-                    continue
-                if isinstance(value[0], framework.Parameter):
-                    params.extend(value)
-                elif isinstance(value[0], core.Layer):
-                    for v in value:
-                        params.extend(v.parameters())
-
-        return params
+    def sublayers(self, include_sublayers=True):
+        """Returns a list of sub layers.
+
+        Args:
+            include_sublayers: If true, also include the layers from sublayers.
+
+        Returns a list of sub layers.
+        """
+        ret = [l for l in self._sub_layers.values()]
+        if include_sublayers:
+            for l in self._sub_layers.values():
+                for sub_l in l.sublayers(include_sublayers):
+                    ret.append(sub_l)
+        return ret
 
     def clear_gradients(self):
         for p in self.parameters():
             p._clear_gradient()
 
-    def _build_once(self, inputs):
+    def _build_once(self, *args):
         pass
 
     def __call__(self, *inputs):
@@ -71,6 +86,66 @@ class Layer(core.Layer):
     def backward(self, *inputs):
         raise ValueError("Layer shouldn't implement backward")
 
+    def add_sublayer(self, name, sublayer):
+        """Adds a sub Layer instance.
+
+          Added sublayer can be access like self.name.
+
+        Args:
+            name: name of this sublayer.
+            sublayer: an instance of Layer.
+        Returns:
+            the sublayer passed in.
+        """
+        assert isinstance(sublayer, core.Layer)
+        self._sub_layers[name] = sublayer
+        return sublayer
+
+    def add_parameter(self, name, parameter):
+        """Adds a Parameter instance.
+
+          Added parameter can be access like self.name.
+
+        Args:
+            name: name of this sublayer.
+            parameter: an instance of Parameter.
+        Returns:
+            the parameter passed in.
+        """
+        assert isinstance(parameter, framework.Parameter)
+        self._parameters[name] = parameter
+        return parameter
+
+    def __getattr__(self, name):
+        if name in self._parameters:
+            return self._parameters[name]
+        elif name in self._sub_layers:
+            return self._sub_layers[name]
+
+    def __setattr__(self, name, value):
+        if isinstance(value, framework.Parameter):
+            params = self.__dict__.get('_parameters', None)
+            if params is None:
+                raise ValueError(
+                    "super(YourLayer, self).__init__() should be called first")
+            params[name] = value
+        elif isinstance(value, core.Layer):
+            layers = self.__dict__.get('_sub_layers', None)
+            if layers is None:
+                raise ValueError(
+                    "super(YourLayer, self).__init__() should be called first")
+            layers[name] = value
+        else:
+            object.__setattr__(self, name, value)
+
+    def __delattr__(self, name):
+        if name in self._parameters:
+            del self._parameters[name]
+        elif name in self._sub_layers:
+            del self._sub_layers[name]
+        else:
+            object.__delattr__(self, name)
+
 
 class PyLayer(core.PyLayer):
     """Layers composed of user-defined python codes."""

python/paddle/fluid/imperative/nn.py

@@ -225,9 +225,6 @@ class FC(layers.Layer):
             act=act,
             name=name)
 
-    def parameters(self):
-        return [self._w, self._b]
-
     def _build_once(self, input):
         input_shape = input.shape
         param_shape = [
@@ -478,9 +475,6 @@ class Embedding(layers.Layer):
             dtype=self._dtype,
             is_bias=False)
 
-    def parameters(self):
-        return [self._w]
-
     def forward(self, input):
         out = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(

python/paddle/fluid/initializer.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 from . import framework
 import numpy as np
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 from .core import VarDesc
 from . import unique_name
 
@@ -49,7 +49,7 @@ def force_init_on_cpu():
     return _force_init_on_cpu_
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def init_on_cpu():
     """
     Force the variable to be inited on CPU.

python/paddle/fluid/layer_helper.py

@@ -302,7 +302,8 @@ class LayerHelper(object):
         if default_initializer is None and attr.initializer is None:
             if isinstance(dtype, core.VarDesc.VarType):
                 if dtype != core.VarDesc.VarType.FP32 and \
-                    dtype != core.VarDesc.VarType.FP64:
+                    dtype != core.VarDesc.VarType.FP64 and \
+                    dtype != core.VarDesc.VarType.FP16:
                     raise TypeError(
                         "Can not create parameter with default initializer when dtype is not float type. Set default_initializer to fit the parameter dtype!"
                     )

python/paddle/fluid/layers/control_flow.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 
 from .layer_function_generator import autodoc, templatedoc
 from .tensor import assign, fill_constant
@@ -506,9 +506,9 @@ class While(object):
     while loop control flow.
 
     Args:
-        cond (Variable): condition used to compare.
+        cond(Variable): condition used to compare.
         is_test(bool): A flag indicating whether execution is in test phase.
-        name (str): The name of this layer.
+        name(str): The name of this layer.
 
     Examples:
           .. code-block:: python
@@ -589,7 +589,8 @@ class While(object):
 
 
 def lod_rank_table(x, level=0):
-    """LoD Rank Table Operator. Given an input variable **x** and a level number
+    """
+    LoD Rank Table Operator. Given an input variable **x** and a level number
     of LoD, this layer creates a LodRankTable object. A LoDRankTable object
     contains a list of bi-element tuples. Each tuple consists of an index and
     a length, both of which are int type. Refering to specified level of LoD,
@@ -883,10 +884,8 @@ def less_than(x, y, force_cpu=None, cond=None, **ignored):
     return cond
 
 
-def equal(x, y, cond=None, **ignored):
+def equal(x, y, cond=None):
     """
-    **equal**
-
     This layer returns the truth value of :math:`x == y` elementwise.
 
     Args:
@@ -1458,7 +1457,6 @@ class DynamicRNN(object):
 
         Returns:
             The current timestep in the input sequence.
-
         """
         self._assert_in_rnn_block_("step_input")
         if not isinstance(x, Variable):
@@ -1532,11 +1530,10 @@ class DynamicRNN(object):
             outputs={'Out': [x_reordered]})
         return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         """
-        The block for user to define operators in RNN. See the class docstring
-        for more details.
+        The block for user to define operators in RNN.
         """
         if self.status != DynamicRNN.BEFORE_RNN:
             raise ValueError("rnn.block() can only be invoke once")
@@ -1640,8 +1637,7 @@ class DynamicRNN(object):
             dtype(str|numpy.dtype): The data type of the initialized memory.
 
         Returns:
-            the memory variable.
-
+            The memory variable.
         """
         self._assert_in_rnn_block_('memory')
         self._init_zero_idx_()
@@ -1740,7 +1736,7 @@ class DynamicRNN(object):
 
     def output(self, *outputs):
         """
-        mark the RNN output variables.
+        Mark the RNN output variables.
 
         Args:
             outputs: The output variables.

python/paddle/fluid/layers/io.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
 import multiprocessing
 import os
 import six
@@ -56,7 +56,10 @@ def data(name,
 
     Args:
        name(str): The name/alias of the function
-       shape(list): Tuple declaring the shape.
+       shape(list): Tuple declaring the shape. If :code:`append_batch_size` is 
+                    True and there is no -1 inside :code:`shape`, it should be 
+                    considered as the shape of the each sample. Otherwise, it
+                    should be considered as the shape of the batched data.  
        append_batch_size(bool):
           1. If true, it prepends -1 to the shape.
             For example if shape=[1], the resulting shape is [-1, 1].
@@ -1116,7 +1119,7 @@ class Preprocessor(object):
     def _is_completed(self):
         return self.sub_block and self.source_var_names and self.sink_var_names
 
-    @contextlib.contextmanager
+    @signature_safe_contextmanager
     def block(self):
         self.status = Preprocessor.IN_SUB_BLOCK
         self.sub_block = self.main_prog._create_block()

python/paddle/fluid/layers/layer_function_generator.py

@@ -24,7 +24,7 @@ from ..framework import OpProtoHolder, Variable, core, convert_np_dtype_to_dtype
 from ..layer_helper import LayerHelper
 
 __all__ = [
-    'deprecated', 'generate_layer_fn', 'generate_layer_fn_noattr', 'autodoc',
+    'deprecated', 'generate_layer_fn', 'generate_activation_fn', 'autodoc',
     'templatedoc'
 ]
 
@@ -89,6 +89,9 @@ def _generate_doc_string_(op_proto, additional_args_lines=None):
         buf.write('\n')
 
     skip_attrs = OpProtoHolder.generated_op_attr_names()
+    # attr use_mkldnn and is_test also should not be visible to users.
+    skip_attrs.add("use_mkldnn")
+    skip_attrs.add("is_test")
 
     for each_attr in op_proto.attrs:
         if each_attr.name in skip_attrs:
@@ -226,7 +229,7 @@ def generate_layer_fn(op_type):
     return func
 
 
-def generate_layer_fn_noattr(op_type):
+def generate_activation_fn(op_type):
     """Register the Python layer for an Operator without Attribute.
 
     Args:
@@ -246,6 +249,7 @@ def generate_layer_fn_noattr(op_type):
 
     func.__name__ = op_type
     func.__doc__ = _generate_doc_string_(op_proto)
+
     return func
 
 

python/paddle/fluid/layers/nn.py

@@ -2961,6 +2961,7 @@ def batch_norm(input,
             "momentum": momentum,
             "epsilon": epsilon,
             "is_test": is_test,
+            "data_layout": data_layout,
             "use_mkldnn": False,
             "fuse_with_relu": fuse_with_relu,
             "use_global_stats": use_global_stats
@@ -3266,7 +3267,7 @@ def group_norm(input,
     # create output
     mean_out = helper.create_variable(dtype=dtype, stop_gradient=True)
     variance_out = helper.create_variable(dtype=dtype, stop_gradient=True)
-    group_norm_out = helper.create_variable(dtype)
+    group_norm_out = helper.create_variable(dtype=dtype)
 
     helper.append_op(
         type="group_norm",
@@ -5966,13 +5967,10 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
                                 than :attr:`shape`.
         act (str): The non-linear activation to be applied to the reshaped tensor
                    variable.
-        inplace(bool): Must use :attr:`False` if :attr:`x` is used in multiple
-                       operators. If this flag is set :attr:`True`, reuse input
-                       :attr:`x` to reshape, which will change the shape of
-                       tensor variable :attr:`x` and might cause errors when
-                       :attr:`x` is used in multiple operators. If :attr:`False`,
-                       preserve the shape :attr:`x` and create a new output tensor
-                       variable whose data is copied from input x but reshaped.
+        inplace(bool): If ``inplace`` is `True`, the input and output of ``layers.reshape``
+                       are the same variable, otherwise, the input and output of
+                       ``layers.reshape`` are different variables. Note that if :attr:`x`
+                       is more than one layer's input, ``inplace`` must be :attr:`False`.
         name (str): The name of this layer. It is optional.
 
     Returns:
@@ -8365,6 +8363,46 @@ def stack(x, axis=0):
     If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
     If :code:`axis` is None, it would be replaced with 0.
 
+    For Example:
+
+    .. code-block:: text
+
+        Case 1:
+          Input:
+            x[0].data = [ [1.0 , 2.0 ] ]
+            x[0].dims = [1, 2]
+            x[1].data = [ [3.0 , 4.0 ] ]
+            x[1].dims = [1, 2]
+            x[2].data = [ [5.0 , 6.0 ] ]
+            x[2].dims = [1, 2]
+
+          Attrs:
+            axis = 0
+
+          Output:
+            Out.data =[ [ [1.0, 2.0] ],
+                        [ [3.0, 4.0] ],
+                        [ [5.0, 6.0] ] ]
+            Out.dims = [3, 1, 2]
+
+        Case 2:
+          Given
+            x[0].data = [ [1.0 , 2.0 ] ]
+            x[0].dims = [1, 2]
+            x[1].data = [ [3.0 , 4.0 ] ]
+            x[1].dims = [1, 2]
+            x[2].data = [ [5.0 , 6.0 ] ]
+            x[2].dims = [1, 2]
+
+          Attrs:
+            axis = 1 or axis = -2
+
+          Output:
+            Out.data =[ [ [1.0, 2.0]
+                          [3.0, 4.0]
+                          [5.0, 6.0] ] ]
+            Out.dims = [1, 3, 2]
+
     Args:
         x (Variable|list(Variable)|tuple(Variable)): Input variables.
         axis (int|None): The axis along which all inputs are stacked.
@@ -8737,16 +8775,17 @@ def slice(input, axes, starts, ends):
     return out
 
 
-@templatedoc()
 def shape(input):
     """
-    ${comment}
+    **Shape Layer**
+
+    Get the shape of the input.
 
     Args:
-        input (Variable): ${input_comment}
+        input (Variable): The input variable.
 
     Returns:
-        out (Variable): ${out_comment}
+        Variable: The shape of the input variable.
 
     Examples:
         .. code-block:: python

python/paddle/fluid/layers/ops.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 import os
-from .layer_function_generator import generate_layer_fn, generate_layer_fn_noattr
+from .layer_function_generator import generate_layer_fn, generate_activation_fn
 from .. import core
 from ..framework import convert_np_dtype_to_dtype_
 
@@ -53,7 +53,7 @@ globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
 __all__ += __activations_noattr__
 
 for _OP in set(__activations_noattr__):
-    globals()[_OP] = generate_layer_fn_noattr(_OP)
+    globals()[_OP] = generate_activation_fn(_OP)
 
 __all__ += ["uniform_random"]
 

python/paddle/fluid/layers/tensor.py

@@ -567,7 +567,7 @@ def ones(shape, dtype, force_cpu=False):
     It also sets *stop_gradient* to True.
 
     Args:
-        shape(tuple|list|None): Shape of output tensor
+        shape(tuple|list): Shape of output tensor
         dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
 
     Returns:
@@ -578,6 +578,10 @@ def ones(shape, dtype, force_cpu=False):
 
           data = fluid.layers.ones(shape=[1], dtype='int64')
     """
+    assert isinstance(shape, list) or isinstance(
+        shape, tuple), "The shape's type should be list or tuple."
+    assert reduce(lambda x, y: x * y,
+                  shape) > 0, "The shape is invalid: %s." % (str(shape))
     return fill_constant(value=1.0, **locals())
 
 

python/paddle/fluid/optimizer.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from collections import defaultdict
-from contextlib import contextmanager
+from .wrapped_decorator import signature_safe_contextmanager
 
 from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
 from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
@@ -1368,9 +1368,9 @@ class FtrlOptimizer(Optimizer):
 
     Args:
         learning_rate (float|Variable): global learning rate.
-        l1 (float):
-        l2 (float):
-        lr_power (float):
+        l1 (float): L1 regularization strength.
+        l2 (float): L2 regularization strength.
+        lr_power (float): Learning Rate Power.
         regularization: A Regularizer, such as
                         fluid.regularizer.L2DecayRegularizer.
         name: A optional name prefix.
@@ -1610,7 +1610,7 @@ class ModelAverage(Optimizer):
             },
             stop_gradient=True)
 
-    @contextmanager
+    @signature_safe_contextmanager
     def apply(self, executor, need_restore=True):
         """Apply average values to parameters of current model.
         """

python/paddle/fluid/parallel_executor.py

@@ -148,6 +148,9 @@ class ParallelExecutor(object):
             else framework.default_main_program()
         # FIXME(dzhwinter): enable_inplace should be after memory_optimize
         # if turn on python memory optimize, turn off the inplace_pass.
+        if build_strategy.memory_optimize is None:
+            build_strategy.memory_optimize = False if main._is_mem_optimized else True
+        if build_strategy.enable_inplace is None:
             build_strategy.enable_inplace = False if main._is_mem_optimized else True
         scope = scope if scope is not None else executor.global_scope()
 

python/paddle/fluid/profiler.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from . import core
-from contextlib import contextmanager
+from .wrapped_decorator import signature_safe_contextmanager
 import os
 import six
 
@@ -35,7 +35,7 @@ NVPROF_CONFIG = [
 ]
 
 
-@contextmanager
+@signature_safe_contextmanager
 def cuda_profiler(output_file, output_mode=None, config=None):
     """The CUDA profiler.
     This fuctions is used to profile CUDA program by CUDA runtime application
@@ -217,7 +217,7 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
     core.disable_profiler(key_map[sorted_key], profile_path)
 
 
-@contextmanager
+@signature_safe_contextmanager
 def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
     """The profiler interface.
     Different from cuda_profiler, this profiler can be used to profile both CPU

python/paddle/fluid/recordio_writer.py

@@ -15,14 +15,14 @@
 from __future__ import print_function
 
 import os
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 from . import core
 __all__ = [
     'convert_reader_to_recordio_file', 'convert_reader_to_recordio_files'
 ]
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def create_recordio_writer(filename,
                            compressor=core.RecordIOWriter.Compressor.Snappy,
                            max_num_records=1000):

python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py

@@ -16,7 +16,6 @@ from __future__ import print_function
 
 import sys
 import paddle.fluid as fluid
-import paddle.v2 as paddle
 
 
 def load_vocab(filename):

python/paddle/fluid/tests/demo/pyreader.py

@@ -20,7 +20,6 @@ import six
 import paddle
 import paddle.dataset.mnist as mnist
 import paddle.fluid as fluid
-import paddle.v2
 
 
 def network(is_train):
@@ -72,7 +71,7 @@ def main():
         use_cuda=use_cuda, share_vars_from=trainer, main_program=test_prog)
 
     train_reader.decorate_paddle_reader(
-        paddle.v2.reader.shuffle(
+        paddle.reader.shuffle(
             paddle.batch(mnist.train(), 512), buf_size=8192))
 
     test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -77,6 +77,7 @@ list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
 list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
 list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
 list(REMOVE_ITEM TEST_OPS test_imperative_optimizer)
+list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
@@ -107,13 +108,17 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
 set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 450)
 py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
+if(NOT WIN32)
+py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer SERIAL)
+endif()
 if(NOT APPLE)
     py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
+    if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+        # change the timeout from 600 to 1200, because in debug mode, this test need more time.
+        set_tests_properties(test_image_classification_resnet PROPERTIES TIMEOUT 1200)
+    endif()
 endif()
-if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-    # change the timeout from 600 to 900, because in debug mode, this test need more time.
-    set_tests_properties(test_image_classification_resnet PROPERTIES TIMEOUT 900)
-endif()
+
 
 if (WITH_NGRAPH)
     add_subdirectory(ngraph)

python/paddle/fluid/tests/unittests/ngraph/test_accuracy_ngraph_op.py

@@ -15,39 +15,7 @@
 from __future__ import print_function
 
 import unittest
-import numpy as np
-from paddle.fluid.tests.unittests.op_test import OpTest
-
-
-class TestNGRAPHAccuracyOp(OpTest):
-    def setUp(self):
-        self.op_type = "accuracy"
-        self.dtype = np.float32
-        self.init_dtype()
-        n = 128
-        infer = np.random.random((n, 1)).astype(self.dtype)
-        indices = np.random.randint(0, 2, (n, 1))
-        label = np.random.randint(0, 2, (n, 1))
-        self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
-        num_correct = 0
-        for rowid in range(n):
-            for ele in indices[rowid]:
-                if ele == label[rowid]:
-                    num_correct += 1
-                    break
-        self.outputs = {
-            'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
-            'Correct': np.array([num_correct]).astype("int64"),
-            'Total': np.array([n]).astype("int64")
-        }
-        self._cpu_only = True
-
-    def init_dtype(self):
-        pass
-
-    def test_check_output(self):
-        self.check_output()
-
+from paddle.fluid.tests.unittests.test_accuracy_op import TestAccuracyOp
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py

@@ -18,17 +18,7 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.tests.unittests.op_test import OpTest
-from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh
-
-
-class TestNGRAPHReluDim2(TestRelu):
-    def setUp(self):
-        super(TestNGRAPHReluDim2, self).setUp()
-
-
-class TestNGRAPHTanhDim2(TestTanh):
-    def setUp(self):
-        super(TestNGRAPHTanhDim2, self).setUp()
+from paddle.fluid.tests.unittests.test_activation_op import TestSigmoid, TestRelu, TestTanh
 
 
 class TestNGRAPHReluDim4(TestRelu):

python/paddle/utils/dump_config.py → python/paddle/fluid/tests/unittests/ngraph/test_batch_norm_ngraph_op.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#   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.
@@ -12,34 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.trainer.config_parser import parse_config
-from paddle.proto import TrainerConfig_pb2
-import sys
+from __future__ import print_function
 
-__all__ = []
+import unittest
+from paddle.fluid.tests.unittests.test_batch_norm_op import TestBatchNormOpTraining, TestBatchNormOpInference
 
 if __name__ == '__main__':
-    whole_conf = False
-    binary = False
-    if len(sys.argv) == 2:
-        conf = parse_config(sys.argv[1], '')
-    elif len(sys.argv) == 3:
-        conf = parse_config(sys.argv[1], sys.argv[2])
-    elif len(sys.argv) == 4:
-        conf = parse_config(sys.argv[1], sys.argv[2])
-        if sys.argv[3] == '--whole':
-            whole_conf = True
-        elif sys.argv[3] == '--binary':
-            binary = True
-    else:
-        raise RuntimeError()
-
-    assert isinstance(conf, TrainerConfig_pb2.TrainerConfig)
-
-    if whole_conf:
-        print(conf)
-    else:
-        if binary:
-            sys.stdout.write(conf.model_config.SerializeToString())
-        else:
-            print(conf.model_config)
+    unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py

@@ -17,60 +17,5 @@ from __future__ import print_function
 import unittest
 from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1
 
-
-class TestNGRAPH(TestConv2dOp):
-    def setUp(self):
-        super(TestNGRAPH, self).setUp()
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        super(TestNGRAPH, self).init_kernel_type()
-
-
-class TestNGRAPHWithPad(TestWithPad):
-    def setUp(self):
-        super(TestNGRAPHWithPad, self).setUp()
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        super(TestNGRAPHWithPad, self).init_kernel_type()
-
-
-class TestNGRAPHWithStride(TestWithStride):
-    def setUp(self):
-        super(TestNGRAPHWithStride, self).setUp()
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        super(TestNGRAPHWithStride, self).init_kernel_type()
-
-
-class TestNGRAPHWithGroup(TestWithGroup):
-    def setUp(self):
-        super(TestNGRAPHWithGroup, self).setUp()
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        super(TestNGRAPHWithGroup, self).init_kernel_type()
-
-
-class TestNGRAPHWith1x1(TestWith1x1):
-    def setUp(self):
-        super(TestNGRAPHWith1x1, self).setUp()
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        super(TestNGRAPHWith1x1, self).init_kernel_type()
-
-
-class TestNGRAPHWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
-    def setUp(self):
-        super(TestNGRAPHWithInput1x1Filter1x1, self).setUp()
-        self._cpu_only = True
-
-    def init_kernel_type(self):
-        super(TestNGRAPHWithInput1x1Filter1x1, self).init_kernel_type()
-
-
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_cross_entropy_ngraph_op.py

+#   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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+from paddle.fluid.tests.unittests.op_test import OpTest, randomize_probability
+
+
+class TestCrossEntropyOp(OpTest):
+    """Test cross-entropy with discrete one-hot labels.
+    """
+
+    def setUp(self):
+        self.op_type = "cross_entropy"
+        self.soft_label = False
+        self.ignore_index = -100
+        self.dtype = np.float64
+        self.batch_size = 30
+        self.class_num = 10
+        self._cpu_only = True
+
+        self.init_dtype_type()
+        self.init_attr_type()
+        self.init_bs_class_num()
+        self.init_x()
+        self.init_label()
+        self.get_cross_entropy()
+
+        self.inputs = {"X": self.x, "Label": self.label}
+        self.outputs = {"Y": self.cross_entropy}
+        self.attrs = {
+            "soft_label": self.soft_label,
+            "ignore_index": self.ignore_index
+        }
+
+    def init_x(self):
+        self.x = randomize_probability(
+            self.batch_size, self.class_num, dtype=self.dtype)
+
+    def init_label(self):
+        self.label = np.random.randint(
+            0, self.class_num, (self.batch_size, 1), dtype="int64")
+
+    def get_cross_entropy(self):
+        self.cross_entropy = np.asmatrix(
+            [[-np.log(self.x[i][self.label[i][0]])]
+             for i in range(self.x.shape[0])],
+            dtype="float64")
+
+    def init_attr_type(self):
+        pass
+
+    def init_dtype_type(self):
+        pass
+
+    def init_bs_class_num(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp2(TestCrossEntropyOp):
+    """Test cross-entropy with vectorized soft labels.
+    """
+
+    def init_label(self):
+        self.label = np.random.uniform(
+            0.1, 1.0, [self.batch_size, self.class_num]).astype(self.dtype)
+        self.label /= self.label.sum(axis=1, keepdims=True)
+
+    def get_cross_entropy(self):
+        self.cross_entropy = (-self.label * np.log(self.x)).sum(
+            axis=1, keepdims=True).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.batch_size = 5
+        self.class_num = 37
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp3(TestCrossEntropyOp):
+    """Test cross-entropy with vectorized one-hot representation of labels.
+    """
+
+    def init_label(self):
+        self.label_index = np.random.randint(0, self.class_num,
+                                             (self.batch_size))
+        self.label = np.zeros(self.x.shape).astype(self.dtype)
+        self.label[np.arange(self.batch_size), self.label_index] = 1
+
+    def get_cross_entropy(self):
+        self.cross_entropy = np.asmatrix(
+            [[-np.log(self.x[i][self.label_index[i]])]
+             for i in range(self.x.shape[0])]).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.batch_size = 5
+        self.class_num = 17
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp4(TestCrossEntropyOp):
+    """Test high rank tensor cross-entropy with discrete one-hot labels.
+    """
+
+    def init_x(self):
+        self.shape = [10, 2, 4]
+        self.ins_num = np.prod(np.array(self.shape))
+        self.X_2d = randomize_probability(self.ins_num,
+                                          self.class_num).astype(self.dtype)
+        self.x = self.X_2d.reshape(self.shape + [self.class_num])
+
+    def init_label(self):
+        self.label_2d = np.random.randint(
+            0, self.class_num, (self.ins_num, 1), dtype="int64")
+        self.label = self.label_2d.reshape(self.shape + [1])
+
+    def get_cross_entropy(self):
+        cross_entropy_2d = np.asmatrix(
+            [[-np.log(self.X_2d[i][self.label_2d[i][0]])]
+             for i in range(self.X_2d.shape[0])]).astype(self.dtype)
+        self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
+                                                                [1])
+
+    def init_attr_type(self):
+        self.soft_label = False
+
+    def init_dtype_type(self):
+        self.dtype = np.float64
+
+    def init_bs_class_num(self):
+        self.class_num = 10
+
+
+class TestCrossEntropyOp5(TestCrossEntropyOp):
+    """Test high rank tensor cross-entropy with vectorized soft labels.
+    """
+
+    def init_x(self):
+        self.shape = [4, 3]
+        self.ins_num = np.prod(np.array(self.shape))
+        self.X_2d = randomize_probability(self.ins_num,
+                                          self.class_num).astype(self.dtype)
+        self.x = self.X_2d.reshape(self.shape + [self.class_num])
+
+    def init_label(self):
+        self.label_2d = np.random.uniform(
+            0.1, 1.0, [self.ins_num, self.class_num]).astype(self.dtype)
+        self.label_2d /= self.label_2d.sum(axis=1, keepdims=True)
+        self.label = self.label_2d.reshape(self.shape + [self.class_num])
+
+    def get_cross_entropy(self):
+        cross_entropy_2d = (-self.label_2d * np.log(self.X_2d)).sum(
+            axis=1, keepdims=True).astype(self.dtype)
+        self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
+                                                                [1])
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.class_num = 37
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp6(TestCrossEntropyOp):
+    """Test high rank tensor cross-entropy with vectorized one-hot representation of labels.
+    """
+
+    def init_x(self):
+        self.shape = [4, 3, 2]
+        self.ins_num = np.prod(np.array(self.shape))
+        self.X_2d = randomize_probability(self.ins_num,
+                                          self.class_num).astype(self.dtype)
+        self.x = self.X_2d.reshape(self.shape + [self.class_num])
+
+    def init_label(self):
+        self.label_index_2d = np.random.randint(
+            0, self.class_num, (self.ins_num), dtype="int64")
+        label_2d = np.zeros(self.X_2d.shape)
+        label_2d[np.arange(self.ins_num), self.label_index_2d] = 1
+        self.label = label_2d.reshape(self.shape + [self.class_num]).astype(
+            self.dtype)
+
+    def get_cross_entropy(self):
+        cross_entropy_2d = np.asmatrix(
+            [[-np.log(self.X_2d[i][self.label_index_2d[i]])]
+             for i in range(self.X_2d.shape[0])])
+        self.cross_entropy = np.array(cross_entropy_2d).reshape(
+            self.shape + [1]).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = True
+
+    def init_dtype_type(self):
+        self.dtype = np.float32
+
+    def init_bs_class_num(self):
+        self.class_num = 17
+
+    def test_check_grad(self):
+        self.check_grad(
+            ["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
+
+
+class TestCrossEntropyOp7(TestCrossEntropyOp):
+    """Test cross-entropy with ignore index.
+    """
+
+    def init_label(self):
+        self.label = np.random.randint(
+            0, self.class_num, (self.batch_size, 1), dtype="int64")
+
+    def get_cross_entropy(self):
+        self.cross_entropy = np.asmatrix(
+            [[-np.log(self.x[i][self.label[i][0]])]
+             if self.label[i][0] != self.ignore_index else [0]
+             for i in range(self.x.shape[0])]).astype(self.dtype)
+
+    def init_attr_type(self):
+        self.soft_label = False
+        self.ignore_index = 3
+
+    def init_dtype_type(self):
+        self.dtype = np.float64
+
+    def init_bs_class_num(self):
+        self.batch_size = 30
+        self.class_num = 10
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_elementwise_add_ngraph_op.py

@@ -13,18 +13,9 @@
 # limitations under the License.
 
 from __future__ import print_function
-import unittest
-from paddle.fluid.tests.unittests.test_elementwise_add_op import TestElementwiseAddOp
-
-
-class TestNGRAPHElementwiseAddOp(TestElementwiseAddOp):
-    def setUp(self):
-        super(TestNGRAPHElementwiseAddOp, self).setUp()
-        self._cpu_only = True
-
-    def init_input_output(self):
-        super(TestNGRAPHElementwiseAddOp, self).init_input_output()
 
+import unittest
+from paddle.fluid.tests.unittests.test_elementwise_add_op import TestElementwiseAddOp, TestElementwiseAddOp_broadcast_0
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_fill_constant_ngraph_op.py

@@ -13,24 +13,34 @@
 # limitations under the License.
 
 from __future__ import print_function
+
 import unittest
+import numpy as np
 from paddle.fluid.tests.unittests.test_fill_constant_op import TestFillConstantOp1, TestFillConstantOp2, TestFillConstantOpWithSelectedRows
 
 
-class TestNGRAPHFillConstantOp1(TestFillConstantOp1):
+class TestNGRAPHFillConstantFP64(TestFillConstantOp1):
     def setUp(self):
-        super(TestNGRAPHFillConstantOp1, self).setUp()
+        super(TestNGRAPHFillConstantFP64, self).setUp()
+
+        self.attrs = {'shape': [123, 92], 'value': 3.8, 'dtype': 6}
+        self.outputs = {'Out': np.full((123, 92), 3.8)}
 
 
-class TestNGRAPHFillConstantOp2(TestFillConstantOp2):
+class TestNGRAPHFillConstantINT32(TestFillConstantOp2):
     def setUp(self):
-        super(TestNGRAPHFillConstantOp2, self).setUp()
+        super(TestNGRAPHFillConstantINT32, self).setUp()
 
+        self.attrs = {'shape': [123, 92], 'dtype': 2}
+        self.outputs = {'Out': np.full((123, 92), 0)}
 
-class TestNGRAPHFillConstantOpWithSelectedRows(
-        TestFillConstantOpWithSelectedRows):
+
+class TestNGRAPHFillConstantINT64(TestFillConstantOp2):
     def setUp(self):
-        super(TestFillConstantOpWithSelectedRows, self).setUp()
+        super(TestNGRAPHFillConstantINT64, self).setUp()
+
+        self.attrs = {'shape': [123, 92], 'dtype': 3}
+        self.outputs = {'Out': np.full((123, 92), 0)}
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ngraph/test_mean_ngraph_op.py

@@ -16,12 +16,5 @@ from __future__ import print_function
 import unittest
 from paddle.fluid.tests.unittests.test_mean_op import TestMeanOp
 
-
-class TestNGRAPHMeanOp(TestMeanOp):
-    def setUp(self):
-        super(TestNGRAPHMeanOp, self).setUp()
-        self._cpu_only = True
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.py

@@ -15,39 +15,7 @@
 from __future__ import print_function
 
 import unittest
-import numpy as np
-from paddle.fluid.tests.unittests.op_test import OpTest
-
-
-class TestNGRAPHMulOp(OpTest):
-    def setUp(self):
-        self.op_type = "mul"
-        self.dtype = np.float32
-        self.init_dtype_type()
-        self.inputs = {
-            'X': np.random.random((2, 4)).astype(self.dtype),
-            'Y': np.random.random((4, 4)).astype(self.dtype)
-        }
-        self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
-        self._cpu_only = True
-
-    def init_dtype_type(self):
-        pass
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
-
-    def test_check_grad_ingore_x(self):
-        self.check_grad(
-            ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
-
-    def test_check_grad_ingore_y(self):
-        self.check_grad(
-            ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
-
+from paddle.fluid.tests.unittests.test_mul_op import TestMulOp, TestMulOp2
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py

@@ -14,61 +14,25 @@
 
 from __future__ import print_function
 
-from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
-
-
-class TestNGRAPHPool2D_Op(TestPool2D_Op):
-    def setUp(self):
-        super(TestNGRAPHPool2D_Op, self).setUp()
-        self._cpu_only = True
-
-    def init_test_case(self):
-        super(TestNGRAPHPool2D_Op, self).init_test_case()
-
-
-class TestNGRAPHCase1(TestCase1):
-    def setUp(self):
-        super(TestNGRAPHCase1, self).setUp()
-        self._cpu_only = True
-
-    def init_test_case(self):
-        super(TestNGRAPHCase1, self).init_test_case()
+import unittest
 
-
-class TestNGRAPHCase2(TestCase2):
-    def setUp(self):
-        super(TestNGRAPHCase2, self).setUp()
-        self._cpu_only = True
-
-    def init_test_case(self):
-        super(TestNGRAPHCase2, self).init_test_case()
-
-
-class TestNGRAPHCase3(TestCase3):
-    def setUp(self):
-        super(TestNGRAPHCase3, self).setUp()
-        self._cpu_only = True
-
-    def init_pool_type(self):
-        super(TestNGRAPHCase3, self).init_pool_type()
+from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
 
 
-class TestNGRAPHCase4(TestCase4):
+class TestNGRAPHCeilMode(TestCase1):
     def setUp(self):
-        super(TestNGRAPHCase4, self).setUp()
-        self._cpu_only = True
+        super(TestNGRAPHCeilMode, self).setUp()
 
-    def init_pool_type(self):
-        super(TestNGRAPHCase4, self).init_pool_type()
+    def init_ceil_mode(self):
+        self.ceil_mode = True
 
 
-class TestNGRAPHCase5(TestCase5):
+class TestNGRAPHAdaptive(TestCase1):
     def setUp(self):
-        super(TestNGRAPHCase5, self).setUp()
-        self._cpu_only = True
+        super(TestNGRAPHAdaptive, self).setUp()
 
-    def init_pool_type(self):
-        super(TestNGRAPHCase5, self).init_pool_type()
+    def init_adaptive(self):
+        self.adaptive = True
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/ngraph/test_scale_ngraph_op.py

@@ -15,24 +15,5 @@ from __future__ import print_function
 import unittest
 from paddle.fluid.tests.unittests.test_scale_op import TestScaleOp, TestScaleOpSelectedRows
 
-
-class TestNGRAPHScaleOp(TestScaleOp):
-    def setUp(self):
-        super(TestNGRAPHScaleOp, self).setUp()
-        self._cpu_only = True
-
-    def init_dtype_type(self):
-        pass
-
-
-class TestNGRAPHScaleOpSelectedRows(TestScaleOpSelectedRows):
-    def setUp(self):
-        super(TestNGRAPHScaleOpSelectedRows, self).setUp()
-        self._cpu_only = True
-
-    def init_dtype_type(self):
-        pass
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_softmax_ngraph_op.py

@@ -16,11 +16,5 @@ from __future__ import print_function
 import unittest
 from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp
 
-
-class TestSoftmaxNGRAPHOp(TestSoftmaxOp):
-    def setUp(self):
-        super(TestSoftmaxNGRAPHOp, self).setUp()
-
-
 if __name__ == "__main__":
     unittest.main()

benchmark/paddle/rnn/imdb.py → python/paddle/fluid/tests/unittests/ngraph/test_sum_ngraph_op.py

@@ -11,50 +11,9 @@
 # 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.
-
 from __future__ import print_function
-import six.moves.cPickle as pickle
-import gzip
-import os
-import numpy
-
-
-def get_dataset_file(dataset, default_dataset, origin):
-    data_dir, data_file = os.path.split(dataset)
-    if (not os.path.isfile(dataset)) and data_file == default_dataset:
-        from six.moves import urllib
-        print('Downloading data from %s' % origin)
-        urllib.request.urlretrieve(origin, dataset)
-
-    return dataset
-
-
-def create_data(path="imdb.pkl"):
-
-    if (not os.path.isfile('imdb.train.pkl')):
-        path = get_dataset_file(
-            path, "imdb.pkl",
-            "http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl")
-
-        if path.endswith(".gz"):
-            f = gzip.open(path, 'rb')
-        else:
-            f = open(path, 'rb')
-
-        train_set = pickle.load(f)
-        test_set = pickle.load(f)
-        f.close()
-
-        pickle.dump(train_set, open('imdb.train.pkl', 'wb'))
-        pickle.dump(test_set, open('imdb.test.pkl', 'wb'))
-
-    if (not os.path.isfile('train.list')):
-        file('train.list', 'w').write('imdb.train.pkl\n')
-
-
-def main():
-    create_data('imdb.pkl')
-
+import unittest
+from paddle.fluid.tests.unittests.test_sum_op import TestSumOp, TestSelectedRowsSumOp, TestLoDTensorAndSelectedRowsOp
 
 if __name__ == "__main__":
-    main()
+    unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_top_k_ngraph_op.py

@@ -16,30 +16,5 @@ from __future__ import print_function
 import unittest
 from paddle.fluid.tests.unittests.test_top_k_op import TestTopkOp, TestTopkOp3d, TestTopkOp2, TestTopkOp3, TestTopkOp4
 
-
-class TestNGRAPHTopkOp(TestTopkOp):
-    def setUp(self):
-        super(TestNGRAPHTopkOp, self).setUp()
-        self._cpu_only = True
-
-
-class TestNGRAPHTopkOp2(TestTopkOp2):
-    def setUp(self):
-        super(TestNGRAPHTopkOp2, self).setUp()
-        self._cpu_only = True
-
-
-class TestNGRAPHTopkOp3(TestTopkOp3):
-    def setUp(self):
-        super(TestNGRAPHTopkOp3, self).setUp()
-        self._cpu_only = True
-
-
-class TestNGRAPHTopkOp4(TestTopkOp4):
-    def setUp(self):
-        super(TestNGRAPHTopkOp4, self).setUp()
-        self._cpu_only = True
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/op_test.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 
+import os
 import unittest
 import numpy as np
 import random
@@ -374,6 +375,9 @@ class OpTest(unittest.TestCase):
                 return []
         places = [fluid.CPUPlace()]
         cpu_only = self._cpu_only if hasattr(self, '_cpu_only') else False
+        use_ngraph = bool(os.getenv("FLAGS_use_ngraph", False))
+        if use_ngraph:
+            cpu_only = True
         if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type)\
            and not cpu_only:
             places.append(core.CUDAPlace(0))

python/paddle/fluid/tests/unittests/parallel_executor_test_base.py

@@ -79,7 +79,7 @@ class TestParallelExecutorBase(unittest.TestCase):
             if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
         build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
         build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
-        build_strategy.memory_optimize = use_ir_memory_optimize
+        build_strategy.memory_optimize = False if memory_opt else use_ir_memory_optimize
         # python memory optimization is conflict with inplace pass.
         # Use ir graph memory optimization after inplace pass is the correct way.
         build_strategy.enable_inplace = False if memory_opt else enable_inplace

python/paddle/fluid/tests/unittests/test_base_layer.py

+# 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 unittest
+import numpy as np
+
+import paddle.fluid as fluid
+from paddle.fluid.layer_helper import LayerHelper
+
+
+class L1(fluid.imperative.Layer):
+    def __init__(self):
+        super(L1, self).__init__()
+        self._helper = LayerHelper(
+            'MyLayer',
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.1)))
+
+        self.w1 = self._helper.create_parameter(
+            attr=self._helper.param_attr,
+            shape=[2, 2],
+            dtype='float32',
+            is_bias=False)
+        self.w2 = self._helper.create_parameter(
+            attr=self._helper.param_attr,
+            shape=[2, 2],
+            dtype='float32',
+            is_bias=False)
+
+    def forward(self):
+        return self.w1 + self.w2
+
+
+class L2(fluid.imperative.Layer):
+    def __init__(self):
+        super(L2, self).__init__()
+        self.layer1 = L1()
+        self.layer2 = L1()
+
+    def forward(self):
+        return self.layer1() + self.layer2()
+
+
+class L3(fluid.imperative.Layer):
+    def __init__(self):
+        super(L3, self).__init__()
+        self.layer1 = L2()
+        self.layer2 = L2()
+
+    def forward(self):
+        return self.layer1() + self.layer2()
+
+
+class TestBaseLayer(unittest.TestCase):
+    def test_one_level(self):
+        with fluid.imperative.guard():
+            l = L1()
+            ret = l()
+            self.assertEqual(l.w1.name, "MyLayer_0.w_0")
+            self.assertEqual(l.w2.name, "MyLayer_0.w_1")
+            self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
+
+    def test_three_level(self):
+        with fluid.imperative.guard():
+            l = L3()
+            ret = l()
+            self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -22,6 +22,9 @@ import six
 import unittest
 import numpy as np
 
+import gc
+gc.set_debug(gc.DEBUG_COLLECTABLE)
+
 import paddle.fluid as fluid
 
 
@@ -99,6 +102,12 @@ class TranspilerTest(unittest.TestCase):
         with fluid.unique_name.guard():
             with fluid.program_guard(main, startup):
                 self.transpiler_test_impl()
+        # NOTE: run gc.collect to eliminate pybind side objects to
+        # prevent random double-deallocate when inherited in python.
+        del self.transpiler
+        del main
+        del startup
+        gc.collect()
 
 
 class TestBasicModel(TranspilerTest):
@@ -797,6 +806,7 @@ class TestNCCL2Transpile(TranspilerTest):
             print([op.type for op in startup.global_block().ops])
             self.assertEqual(startup.global_block().ops[-1].type, "gen_nccl_id")
             self.assertIsNotNone(startup.global_block().vars.get("NCCLID"))
+            gc.collect()
         else:
             pass
 

python/paddle/fluid/tests/unittests/test_expand_op.py

@@ -109,5 +109,32 @@ class TestExpandOpRank4(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestExpandOpInteger(OpTest):
+    def setUp(self):
+        self.op_type = "expand"
+        self.inputs = {
+            'X': np.random.randint(
+                10, size=(2, 4, 5)).astype("int32")
+        }
+        self.attrs = {'expand_times': [2, 1, 4]}
+        output = np.tile(self.inputs['X'], (2, 1, 4))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestExpandOpBoolean(OpTest):
+    def setUp(self):
+        self.op_type = "expand"
+        self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
+        self.attrs = {'expand_times': [2, 1, 4]}
+        output = np.tile(self.inputs['X'], (2, 1, 4))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_fuse_elewise_add_act_pass.py

@@ -121,6 +121,8 @@ class TestMNIST(TestParallelExecutorBase):
                 regularization=fluid.regularizer.L2Decay(1e-6))
             return optimizer
 
+        # NOTE(dzh):
+        # need to make it compatible with elewise fuse act
         not_fuse_op_first_loss, not_fuse_op_last_loss = self.check_network_convergence(
             model,
             feed_dict={"image": img,
@@ -128,6 +130,7 @@ class TestMNIST(TestParallelExecutorBase):
             use_cuda=use_cuda,
             fuse_elewise_add_act_ops=False,
             memory_opt=False,
+            use_ir_memory_optimize=False,
             optimizer=_optimizer)
         fuse_op_first_loss, fuse_op_last_loss = self.check_network_convergence(
             model,
@@ -136,6 +139,7 @@ class TestMNIST(TestParallelExecutorBase):
             use_cuda=use_cuda,
             fuse_elewise_add_act_ops=True,
             memory_opt=False,
+            use_ir_memory_optimize=False,
             optimizer=_optimizer)
 
         for loss in zip(not_fuse_op_first_loss, fuse_op_first_loss):

python/paddle/fluid/tests/unittests/test_imperative.py

@@ -333,6 +333,18 @@ class TestImperative(unittest.TestCase):
         self.assertTrue(np.allclose(dy_out, static_out))
         self.assertTrue(np.allclose(dy_grad, static_grad))
 
+        params = mlp.parameters(True)
+        self.assertEqual("FC_0.w_0", params[0].name)
+        self.assertEqual("FC_0.b_0", params[1].name)
+        self.assertEqual("FC_1.w_0", params[2].name)
+        self.assertEqual("FC_1.b_0", params[3].name)
+        self.assertEqual(len(params), 4)
+
+        sublayers = mlp.sublayers(True)
+        self.assertEqual(mlp._fc1, sublayers[0])
+        self.assertEqual(mlp._fc2, sublayers[1])
+        self.assertEqual(len(sublayers), 2)
+
     def test_rnn(self):
         np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
                            [10.0, 11.0, 12.0]])

python/paddle/fluid/tests/unittests/test_imperative_gan.py

@@ -33,9 +33,6 @@ class Discriminator(fluid.imperative.Layer):
         self._fc1 = FC(size=32, act='elu', name="d_fc1")
         self._fc2 = FC(size=1, name="d_fc2")
 
-    def parameters(self):
-        return self._fc1.parameters() + self._fc2.parameters()
-
     def forward(self, inputs):
         x = self._fc1(inputs)
         return self._fc2(x)
@@ -48,10 +45,6 @@ class Generator(fluid.imperative.Layer):
         self._fc2 = FC(size=64, act='elu', name="g_fc2")
         self._fc3 = FC(size=1, name="g_fc3")
 
-    def parameters(self):
-        return self._fc1.parameters() + self._fc2.parameters(
-        ) + self._fc3.parameters()
-
     def forward(self, inputs):
         x = self._fc1(inputs)
         x = self._fc2(x)

python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py

@@ -75,16 +75,6 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
             self.hidden_array.append(pre_hidden)
             self.cell_array.append(pre_cell)
 
-    def parameters(self):
-        parameters = list()
-        for param in self.weight_1_arr:
-            parameters.append(param)
-        for param in self.weight_2_arr:
-            parameters.append(param)
-        for bias in self.bias_arr:
-            parameters.append(bias)
-        return parameters
-
     def forward(self, input_embedding, init_hidden=None, init_cell=None):
         res = []
         for index in range(self._num_steps):
@@ -177,12 +167,6 @@ class PtbModel(fluid.imperative.Layer):
     def _build_once(self, input, label, init_hidden, init_cell):
         pass
 
-    def parameters(self):
-        parameters = self.simple_lstm_rnn.parameters() + [
-            self.softmax_weight, self.softmax_bias
-        ] + self.embedding.parameters()
-        return parameters
-
     def forward(self, input, label, init_hidden, init_cell):
 
         init_h = fluid.layers.reshape(

python/paddle/fluid/tests/unittests/test_imperative_resnet.py

@@ -21,7 +21,6 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.optimizer import SGDOptimizer
 from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
 from paddle.fluid.imperative.base import to_variable
 from test_imperative_base import new_program_scope
@@ -173,11 +172,13 @@ class ResNet(fluid.imperative.Layer):
         for block in range(len(depth)):
             shortcut = False
             for i in range(depth[block]):
-                bottleneck_block = BottleneckBlock(
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
                         num_channels=num_channels,
                         num_filters=num_filters[block],
                         stride=2 if i == 0 and block != 0 else 1,
-                    shortcut=shortcut)
+                        shortcut=shortcut))
                 num_channels = bottleneck_block._num_channels_out
                 self.bottleneck_block_list.append(bottleneck_block)
                 shortcut = True
@@ -223,8 +224,7 @@ class TestImperativeResnet(unittest.TestCase):
                 batch_size=batch_size)
 
             dy_param_init_value = {}
-            for param in fluid.default_main_program().global_block(
-            ).all_parameters():
+            for param in resnet.parameters():
                 dy_param_init_value[param.name] = param._numpy()
 
             for batch_id, data in enumerate(train_reader()):
@@ -247,16 +247,14 @@ class TestImperativeResnet(unittest.TestCase):
                 dy_out = avg_loss._numpy()
 
                 if batch_id == 0:
-                    for param in fluid.default_main_program().global_block(
-                    ).all_parameters():
+                    for param in resnet.parameters():
                         if param.name not in dy_param_init_value:
                             dy_param_init_value[param.name] = param._numpy()
 
                 avg_loss._backward()
 
                 dy_grad_value = {}
-                for param in fluid.default_main_program().global_block(
-                ).all_parameters():
+                for param in resnet.parameters():
                     if not param.stop_gradient:
                         np_array = np.array(param._ivar._grad_ivar().value()
                                             .get_tensor())
@@ -267,8 +265,7 @@ class TestImperativeResnet(unittest.TestCase):
                 resnet.clear_gradients()
 
                 dy_param_value = {}
-                for param in fluid.default_main_program().global_block(
-                ).all_parameters():
+                for param in resnet.parameters():
                     dy_param_value[param.name] = param._numpy()
 
         with new_program_scope():
@@ -349,6 +346,7 @@ class TestImperativeResnet(unittest.TestCase):
         self.assertTrue(np.allclose(static_out, dy_out))
 
         self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
+
         for key, value in six.iteritems(static_param_init_value):
             self.assertTrue(np.allclose(value, dy_param_init_value[key]))
             self.assertTrue(np.isfinite(value.all()))

python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py

+# 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
+import unittest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+
+os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
+os.environ[
+    'RECORDIO_FILENAME'] = '/tmp/ir_memory_optimize_transformer.wmt16.recordio'
+
+from test_parallel_executor_transformer import TestTransformer
+from test_parallel_executor_transformer import transformer
+
+
+# NOTE(dzhwinter): test diferent strategy colisions.
+# open the eager delete tensor strategy by default.
+class TestTransformerWithIR(TestTransformer):
+    def test_main(self):
+        if core.is_compiled_with_cuda():
+            # check python transpiler
+            self.check_network_convergence(
+                transformer,
+                use_cuda=True,
+                memory_opt=True,
+                use_ir_memory_optimize=False)
+            # check IR memory optimize
+            self.check_network_convergence(
+                transformer,
+                use_cuda=True,
+                memory_opt=False,
+                use_ir_memory_optimize=True)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -355,6 +355,10 @@ class ControlFlowGraph(object):
                                                  is_forward).dtype()
                         cache_dtype = self._find_var(block_desc, cache_var,
                                                      is_forward).dtype()
+                        if x_dtype != cache_dtype:
+                            if PRINT_LOG:
+                                print("x_dtype and cache_dtype are different")
+                            continue
 
                         if not compare_shape(x_shape, cache_shape, level):
                             continue

python/paddle/fluid/unique_name.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 import collections
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
 import six
 import sys
 
@@ -68,7 +68,7 @@ def switch(new_generator=None):
     return old
 
 
-@contextlib.contextmanager
+@signature_safe_contextmanager
 def guard(new_generator=None):
     if isinstance(new_generator, six.string_types):
         new_generator = UniqueNameGenerator(new_generator)

benchmark/paddle/image/provider.py → python/paddle/fluid/wrapped_decorator.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -12,36 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import io, os
-import random
-import numpy as np
-from paddle.trainer.PyDataProvider2 import *
+import decorator
+import contextlib
 
+__all__ = ['wrap_decorator', 'signature_safe_contextmanager']
 
-def initHook(settings, height, width, color, num_class, **kwargs):
-    settings.height = height
-    settings.width = width
-    settings.color = color
-    settings.num_class = num_class
-    if settings.color:
-        settings.data_size = settings.height * settings.width * 3
-    else:
-        settings.data_size = settings.height * settings.width
-    settings.is_infer = kwargs.get('is_infer', False)
-    settings.num_samples = kwargs.get('num_samples', 2560)
-    if settings.is_infer:
-        settings.slots = [dense_vector(settings.data_size)]
-    else:
-        settings.slots = [dense_vector(settings.data_size), integer_value(1)]
 
+def wrap_decorator(decorator_func):
+    @decorator.decorator
+    def __impl__(func, *args, **kwargs):
+        wrapped_func = decorator_func(func)
+        return wrapped_func(*args, **kwargs)
 
-@provider(
-    init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file_list):
-    for i in xrange(settings.num_samples):
-        img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
-        if settings.is_infer:
-            yield img.astype('float32')
-        else:
-            lab = random.randint(0, settings.num_class - 1)
-            yield img.astype('float32'), int(lab)
+    return __impl__
+
+
+signature_safe_contextmanager = wrap_decorator(contextlib.contextmanager)

python/paddle/utils/dump_v2_config.py

-# Copyright (c) 2016 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 collections
-
-from paddle.trainer_config_helpers.layers import LayerOutput
-from paddle.v2.layer import parse_network
-from paddle.proto import TrainerConfig_pb2
-
-__all__ = ["dump_v2_config"]
-
-
-def dump_v2_config(topology, save_path, binary=False):
-    """ Dump the network topology to a specified file.
-
-    This function is only used to dump network defined by using PaddlePaddle V2
-    APIs. This function will NOT dump configurations related to PaddlePaddle
-    optimizer.
-
-    :param topology: The output layers (can be more than one layers given in a
-                     Python List or Tuple) of the entire network. Using the
-                     specified layers (if more than one layer is given) as root,
-                     traversing back to the data layer(s), all the layers
-                     connected to the specified output layers will be dumped.
-                     Layers not connceted to the specified will not be dumped.
-    :type topology: LayerOutput|List|Tuple
-    :param save_path: The path to save the dumped network topology.
-    :type save_path: str
-    :param binary: Whether to dump the serialized network topology or not.
-                   The default value is false. NOTE that, if you call this
-                   function to generate network topology for PaddlePaddle C-API,
-                   a serialized version of network topology is required. When
-                   using PaddlePaddle C-API, this flag MUST be set to True.
-    :type binary: bool
-    """
-
-    if isinstance(topology, LayerOutput):
-        topology = [topology]
-    elif isinstance(topology, collections.Sequence):
-        for out_layer in topology:
-            assert isinstance(out_layer, LayerOutput), (
-                "The type of each element in the parameter topology "
-                "should be LayerOutput.")
-    else:
-        raise RuntimeError("Error input type for parameter topology.")
-
-    model_str = parse_network(topology)
-    with open(save_path, "w") as fout:
-        if binary:
-            fout.write(model_str.SerializeToString())
-        else:
-            fout.write(str(model_str))

python/paddle/utils/image_multiproc.py

-#   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, sys
-import numpy as np
-from PIL import Image
-import six
-from six.moves import cStringIO as StringIO
-import multiprocessing
-import functools
-import itertools
-
-from paddle.utils.image_util import *
-from paddle.trainer.config_parser import logger
-
-try:
-    import cv2
-except ImportError:
-    logger.warning("OpenCV2 is not installed, using PIL to process")
-    cv2 = None
-
-__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
-
-
-class CvTransformer(ImageTransformer):
-    """
-    CvTransformer used python-opencv to process image.
-    """
-
-    def __init__(
-            self,
-            min_size=None,
-            crop_size=None,
-            transpose=(2, 0, 1),  # transpose to C * H * W
-            channel_swap=None,
-            mean=None,
-            is_train=True,
-            is_color=True):
-        ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
-        self.min_size = min_size
-        self.crop_size = crop_size
-        self.is_train = is_train
-
-    def resize(self, im, min_size):
-        row, col = im.shape[:2]
-        new_row, new_col = min_size, min_size
-        if row > col:
-            new_row = min_size * row / col
-        else:
-            new_col = min_size * col / row
-        im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
-        return im
-
-    def crop_and_flip(self, im):
-        """
-        Return cropped image.
-        The size of the cropped image is inner_size * inner_size.
-        im: (H x W x K) ndarrays
-        """
-        row, col = im.shape[:2]
-        start_h, start_w = 0, 0
-        if self.is_train:
-            start_h = np.random.randint(0, row - self.crop_size + 1)
-            start_w = np.random.randint(0, col - self.crop_size + 1)
-        else:
-            start_h = (row - self.crop_size) / 2
-            start_w = (col - self.crop_size) / 2
-        end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
-        if self.is_color:
-            im = im[start_h:end_h, start_w:end_w, :]
-        else:
-            im = im[start_h:end_h, start_w:end_w]
-        if (self.is_train) and (np.random.randint(2) == 0):
-            if self.is_color:
-                im = im[:, ::-1, :]
-            else:
-                im = im[:, ::-1]
-        return im
-
-    def transform(self, im):
-        im = self.resize(im, self.min_size)
-        im = self.crop_and_flip(im)
-        # transpose, swap channel, sub mean
-        im = im.astype('float32')
-        ImageTransformer.transformer(self, im)
-        return im
-
-    def load_image_from_string(self, data):
-        flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
-        im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
-        return im
-
-    def transform_from_string(self, data):
-        im = self.load_image_from_string(data)
-        return self.transform(im)
-
-    def load_image_from_file(self, file):
-        flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
-        im = cv2.imread(file, flag)
-        return im
-
-    def transform_from_file(self, file):
-        im = self.load_image_from_file(file)
-        return self.transform(im)
-
-
-class PILTransformer(ImageTransformer):
-    """
-    PILTransformer used PIL to process image.
-    """
-
-    def __init__(
-            self,
-            min_size=None,
-            crop_size=None,
-            transpose=(2, 0, 1),  # transpose to C * H * W
-            channel_swap=None,
-            mean=None,
-            is_train=True,
-            is_color=True):
-        ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
-        self.min_size = min_size
-        self.crop_size = crop_size
-        self.is_train = is_train
-
-    def resize(self, im, min_size):
-        row, col = im.size[:2]
-        new_row, new_col = min_size, min_size
-        if row > col:
-            new_row = min_size * row / col
-        else:
-            new_col = min_size * col / row
-        im = im.resize((new_row, new_col), Image.ANTIALIAS)
-        return im
-
-    def crop_and_flip(self, im):
-        """
-        Return cropped image.
-        The size of the cropped image is inner_size * inner_size.
-        """
-        row, col = im.size[:2]
-        start_h, start_w = 0, 0
-        if self.is_train:
-            start_h = np.random.randint(0, row - self.crop_size + 1)
-            start_w = np.random.randint(0, col - self.crop_size + 1)
-        else:
-            start_h = (row - self.crop_size) / 2
-            start_w = (col - self.crop_size) / 2
-        end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
-        im = im.crop((start_h, start_w, end_h, end_w))
-        if (self.is_train) and (np.random.randint(2) == 0):
-            im = im.transpose(Image.FLIP_LEFT_RIGHT)
-        return im
-
-    def transform(self, im):
-        im = self.resize(im, self.min_size)
-        im = self.crop_and_flip(im)
-        im = np.array(im, dtype=np.float32)  # convert to numpy.array
-        # transpose, swap channel, sub mean
-        ImageTransformer.transformer(self, im)
-        return im
-
-    def load_image_from_string(self, data):
-        im = Image.open(StringIO(data))
-        return im
-
-    def transform_from_string(self, data):
-        im = self.load_image_from_string(data)
-        return self.transform(im)
-
-    def load_image_from_file(self, file):
-        im = Image.open(file)
-        return im
-
-    def transform_from_file(self, file):
-        im = self.load_image_from_file(file)
-        return self.transform(im)
-
-
-def job(is_img_string, transformer, data_label_pack):
-    (data, label) = data_label_pack
-    if is_img_string:
-        return transformer.transform_from_string(data), label
-    else:
-        return transformer.transform_from_file(data), label
-
-
-class MultiProcessImageTransformer(object):
-    def __init__(self,
-                 procnum=10,
-                 resize_size=None,
-                 crop_size=None,
-                 transpose=(2, 0, 1),
-                 channel_swap=None,
-                 mean=None,
-                 is_train=True,
-                 is_color=True,
-                 is_img_string=True):
-        """
-        Processing image with multi-process. If it is used in PyDataProvider,
-        the simple usage for CNN is as follows:
-
-        .. code-block:: python
-
-            def hool(settings, is_train,  **kwargs):
-                settings.is_train = is_train
-                settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
-                settings.input_types = [
-                    dense_vector(3 * 224 * 224),
-                    integer_value(1)]
-                settings.transformer = MultiProcessImageTransformer(
-                    procnum=10,
-                    resize_size=256,
-                    crop_size=224,
-                    transpose=(2, 0, 1),
-                    mean=settings.mean_values,
-                    is_train=settings.is_train)
-
-
-            @provider(init_hook=hook, pool_size=20480)
-            def process(settings, file_list):
-                with open(file_list, 'r') as fdata:
-                    for line in fdata:
-                        data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
-                        data = data_dic['data']
-                        labels = data_dic['label']
-                        labels = np.array(labels, dtype=np.float32)
-                        for im, lab in settings.dp.run(data, labels):
-                            yield [im.astype('float32'), int(lab)]
-
-        :param procnum: processor number.
-        :type procnum: int
-        :param resize_size: the shorter edge size of image after resizing.
-        :type resize_size: int
-        :param crop_size: the croping size.
-        :type crop_size: int
-        :param transpose: the transpose order, Paddle only allow C * H * W order.
-        :type transpose: tuple or list
-        :param channel_swap: the channel swap order, RGB or BRG.
-        :type channel_swap: tuple or list
-        :param mean: the mean values of image, per-channel mean or element-wise mean.
-        :type mean: array, The dimension is 1 for per-channel mean.
-                    The dimension is 3 for element-wise mean.
-        :param is_train: training peroid or testing peroid.
-        :type is_train: bool.
-        :param is_color: the image is color or gray.
-        :type is_color: bool.
-        :param is_img_string: The input can be the file name of image or image string.
-        :type is_img_string: bool.
-        """
-
-        self.procnum = procnum
-        self.pool = multiprocessing.Pool(procnum)
-        self.is_img_string = is_img_string
-        if cv2 is not None:
-            self.transformer = CvTransformer(resize_size, crop_size, transpose,
-                                             channel_swap, mean, is_train,
-                                             is_color)
-        else:
-            self.transformer = PILTransformer(resize_size, crop_size, transpose,
-                                              channel_swap, mean, is_train,
-                                              is_color)
-
-    def run(self, data, label):
-        fun = functools.partial(job, self.is_img_string, self.transformer)
-        return self.pool.imap_unordered(
-            fun, six.moves.zip(data, label), chunksize=100 * self.procnum)

python/paddle/utils/make_model_diagram.py

-# Copyright (c) 2016 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.
-
-# Generate dot diagram file for the given paddle model config
-# The generated file can be viewed using Graphviz (http://graphviz.org)
-
-from __future__ import print_function
-
-import six
-import sys
-import traceback
-
-from paddle.trainer.config_parser import parse_config
-
-
-def make_layer_label(layer_config):
-    label = '%s type=%s' % (layer_config.name, layer_config.type)
-    if layer_config.reversed:
-        label += ' <=='
-
-    label2 = ''
-    if layer_config.active_type:
-        label2 += 'act=%s ' % layer_config.active_type
-    if layer_config.bias_parameter_name:
-        label2 += 'bias=%s ' % layer_config.bias_parameter_name
-
-    if label2:
-        label += '\l' + label2
-    return label
-
-
-def make_diagram(config_file, dot_file, config_arg_str):
-    config = parse_config(config_file, config_arg_str)
-    make_diagram_from_proto(config.model_config, dot_file)
-
-
-def make_diagram_from_proto(model_config, dot_file):
-    # print >> sys.stderr, config
-    name2id = {}
-    f = open(dot_file, 'w')
-    submodel_layers = set()
-
-    def make_link(link):
-        return 'l%s -> l%s;' % (name2id[link.layer_name],
-                                name2id[link.link_name])
-
-    def make_mem(mem):
-        s = ''
-        if mem.boot_layer_name:
-            s += 'l%s -> l%s;\n' % (name2id[mem.boot_layer_name],
-                                    name2id[mem.layer_name])
-        s += 'l%s -> l%s [style=dashed];' % (name2id[mem.layer_name],
-                                             name2id[mem.link_name])
-        return s
-
-    print('digraph graphname {', file=f)
-    print('node [width=0.375,height=0.25];', file=f)
-    for i in six.moves.xrange(len(model_config.layers)):
-        l = model_config.layers[i]
-        name2id[l.name] = i
-
-    i = 0
-    for sub_model in model_config.sub_models:
-        if sub_model.name == 'root':
-            continue
-        print('subgraph cluster_%s {' % i, file=f)
-        print('style=dashed;', file=f)
-        label = '%s ' % sub_model.name
-        if sub_model.reversed:
-            label += '<=='
-        print('label = "%s";' % label, file=f)
-        i += 1
-        submodel_layers.add(sub_model.name)
-        for layer_name in sub_model.layer_names:
-            submodel_layers.add(layer_name)
-            lid = name2id[layer_name]
-            layer_config = model_config.layers[lid]
-            label = make_layer_label(layer_config)
-            print('l%s [label="%s", shape=box];' % (lid, label), file=f)
-        print('}', file=f)
-
-    for i in six.moves.xrange(len(model_config.layers)):
-        l = model_config.layers[i]
-        if l.name not in submodel_layers:
-            label = make_layer_label(l)
-            print('l%s [label="%s", shape=box];' % (i, label), file=f)
-
-    for sub_model in model_config.sub_models:
-        if sub_model.name == 'root':
-            continue
-        for link in sub_model.in_links:
-            print(make_link(link), file=f)
-        for link in sub_model.out_links:
-            print(make_link(link), file=f)
-        for mem in sub_model.memories:
-            print(make_mem(mem), file=f)
-
-    for i in six.moves.xrange(len(model_config.layers)):
-        for l in model_config.layers[i].inputs:
-            print(
-                'l%s -> l%s [label="%s"];' % (name2id[l.input_layer_name], i,
-                                              l.input_parameter_name),
-                file=f)
-
-    print('}', file=f)
-    f.close()
-
-
-def usage():
-    print(
-        ("Usage: python show_model_diagram.py" +
-         " CONFIG_FILE DOT_FILE [config_str]"),
-        file=sys.stderr)
-    exit(1)
-
-
-if __name__ == '__main__':
-    if len(sys.argv) < 3 or len(sys.argv) > 4:
-        usage()
-
-    config_file = sys.argv[1]
-    dot_file = sys.argv[2]
-    config_arg_str = sys.argv[3] if len(sys.argv) == 4 else ''
-
-    try:
-        make_diagram(config_file, dot_file, config_arg_str)
-    except:
-        traceback.print_exc()
-        raise

python/paddle/utils/merge_model.py

-# Copyright (c) 2016 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 gzip
-import struct
-import os
-
-from paddle.trainer_config_helpers.layers import LayerOutput
-from paddle.v2.parameters import Parameters
-from paddle.proto import ModelConfig_pb2
-from paddle.v2.topology import Topology
-
-
-def merge_v2_model(net, param_file, output_file):
-    '''Merge the model config and parameters into one file.
-
-    The model configuration file describes the model structure which
-    ends with .py. The parameters file stores the parameters of the model
-    which ends with .tar.gz.
-
-    @param  net            The output layer of the network for inference.
-    @param  param_file     Path of the parameters (.tar.gz) which is stored by
-                           v2 api.
-    @param  output_file    Path of the merged file which will be generated.
-
-    Usage:
-
-        from paddle.utils.merge_model import merge_v2_model
-        # import your network configuration
-        from example_net import net_conf
-
-        net = net_conf(is_predict=True)
-        param_file = './param_pass_00000.tar.gz'
-        output_file = './output.paddle'
-
-        merge_v2_model(net, param_file, output_file)
-
-    '''
-
-    assert isinstance(net, LayerOutput), \
-            "The net should be the output of the network for inference"
-    assert os.path.exists(param_file), \
-            "The model parameters file %s does not exists " % (param_file)
-
-    model_proto = Topology(net).proto()
-    assert isinstance(model_proto, ModelConfig_pb2.ModelConfig)
-
-    with gzip.open(param_file) as f:
-        params = Parameters.from_tar(f)
-
-    if os.path.exists(output_file):
-        os.remove(output_file)
-
-    with open(output_file, 'w') as f:
-        param_names = [param.name for param in model_proto.parameters]
-        conf_str = model_proto.SerializeToString()
-        f.write(struct.pack('q', len(conf_str)))
-        f.write(conf_str)
-        for pname in param_names:
-            params.serialize(pname, f)
-
-    print('Generate  %s  success!' % (output_file))

python/paddle/utils/predefined_net.py

-# Copyright (c) 2016 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 numpy as np
-import six
-import os
-from paddle.trainer.config_parser import *
-from paddle.utils.preprocess_img import \
-    ImageClassificationDatasetCreater
-from paddle.trainer_config_helpers import *
-
-
-def image_data(data_dir,
-               processed_image_size,
-               overwrite=False,
-               color=True,
-               train_list="batches/train.list",
-               test_list="batches/test.list",
-               meta_file="batches/batches.meta",
-               use_jpeg=1):
-    """
-    Predefined image data provider for image classification.
-    train_list: a text file containing a list of training batches.
-    test_list: a text file containing a list of test batches.
-    processed_image_size: all the input images will be resized into this size.
-       If the image is not square. Then the shorter edge will be resized into
-       this size, and the aspect ratio is kept the same.
-    color: whether the images are color or gray.
-    meta_path: the path of the meta file that stores the mean image file and
-               other dataset information, such as the size of images,
-               the size of the mean image, the number of classes.
-    async_load_data: whether to load image data asynchronuously.
-    """
-    data_creator = ImageClassificationDatasetCreater(
-        data_dir, processed_image_size, color)
-    batch_data_dir = data_dir
-    train_list = os.path.join(batch_data_dir, train_list)
-    test_list = os.path.join(batch_data_dir, test_list)
-    meta_path = os.path.join(batch_data_dir, meta_file)
-    image_size = processed_image_size
-    conf = np.load(meta_path)
-    mean_image_size = conf["mean_image_size"]
-    is_color = conf["color"]
-    num_classes = conf["num_classes"]
-    color_string = "color" if is_color else "gray"
-
-    args = {
-        'meta': meta_path,
-        'mean_img_size': mean_image_size,
-        'img_size': image_size,
-        'num_classes': num_classes,
-        'use_jpeg': use_jpeg != 0,
-        'color': color_string
-    }
-
-    define_py_data_sources2(
-        train_list,
-        test_list,
-        module='image_provider',
-        obj='processData',
-        args=args)
-    return {
-        "image_size": image_size,
-        "num_classes": num_classes,
-        "is_color": is_color
-    }
-
-
-def get_extra_layer_attr(drop_rate):
-    if drop_rate == 0:
-        return None
-    else:
-        return ExtraLayerAttribute(drop_rate=drop_rate)
-
-
-def image_data_layers(image_size, num_classes, is_color=False,
-                      is_predict=False):
-    """
-    Data layers for image classification.
-    image_size: image size.
-    num_classes: num of classes.
-    is_color: whether the input images are color.
-    is_predict: whether the network is used for prediction.
-    """
-    num_image_channels = 3 if is_color else 1
-    data_input = data_layer("input",
-                            image_size * image_size * num_image_channels)
-    if is_predict:
-        return data_input, None, num_image_channels
-    else:
-        label_input = data_layer("label", 1)
-        return data_input, label_input, num_image_channels
-
-
-def simple_conv_net(data_conf, is_color=False):
-    """
-    A Wrapper for a simple network for MNIST digit recognition.
-    It contains two convolutional layers, one fully conencted layer, and
-    one softmax layer.
-    data_conf is a dictionary with the following keys:
-        image_size: image size.
-        num_classes: num of classes.
-        is_color: whether the input images are color.
-    """
-    for k, v in six.iteritems(data_conf):
-        globals()[k] = v
-    data_input, label_input, num_image_channels = \
-        image_data_layers(image_size, num_classes, is_color, is_predict)
-    filter_sizes = [5, 5]
-    num_channels = [32, 64]
-    strides = [1, 1]
-    fc_dims = [500]
-    conv_bn_pool1 = img_conv_bn_pool(
-        name="g1",
-        input=data_input,
-        filter_size=filter_sizes[0],
-        num_channel=num_image_channels,
-        num_filters=num_channels[0],
-        conv_stride=1,
-        conv_padding=0,
-        pool_size=3,
-        pool_stride=2,
-        act=ReluActivation())
-    conv_bn_pool2 = img_conv_bn_pool(
-        name="g2",
-        input=conv_bn_pool1,
-        filter_size=filter_sizes[1],
-        num_channel=num_channels[0],
-        num_filters=num_channels[1],
-        conv_stride=1,
-        conv_padding=0,
-        pool_size=3,
-        pool_stride=2,
-        act=ReluActivation())
-    fc3 = fc_layer(
-        name="fc3", input=conv_bn_pool2, dim=fc_dims[0], act=ReluActivation())
-    fc3_dropped = dropout_layer(name="fc3_dropped", input=fc3, dropout_rate=0.5)
-    output = fc_layer(
-        name="output",
-        input=fc3_dropped,
-        dim=fc_dims[0],
-        act=SoftmaxActivation())
-    if is_predict:
-        end_of_network(output)
-    else:
-        cost = classify(name="cost", input=output, label=label_input)
-        end_of_network(cost)
-
-
-def conv_layer_group(prefix_num,
-                     num_layers,
-                     input,
-                     input_channels,
-                     output_channels,
-                     drop_rates=[],
-                     strides=[],
-                     with_bn=[]):
-    """
-    A set of convolution layers, and batch normalization layers,
-    followed by one pooling layer.
-    It is utilized in VGG network for image classifcation.
-    prefix_num: the prefix number of the layer names.
-                For example, if prefix_num = 1, the first convolutioal layer's
-                name will be conv_1_1.
-    num_layers: number of the convolutional layers.
-    input: the name of the input layer.
-    input_channels: the number of channels of the input feature map.
-    output_channels: the number of channels of the output feature map.
-    drop_rates: the drop rates of the BN layers. It will be all zero by default.
-    strides: the stride of the convolution for the layers.
-             It will be all 1 by  default.
-    with_bn: whether to use Batch Normalization for Conv layers.
-             By default,  it is all false.
-    """
-    if len(drop_rates) == 0: drop_rates = [0] * num_layers
-    if len(strides) == 0: strides = [1] * num_layers
-    if len(with_bn) == 0: with_bn = [False] * num_layers
-    assert (len(drop_rates) == num_layers)
-    assert (len(strides) == num_layers)
-
-    for i in range(1, num_layers + 1):
-        if i == 1:
-            i_conv_in = input
-        else:
-            i_conv_in = group_output
-        i_channels_conv = input_channels if i == 1 else output_channels
-        conv_act = LinearActivation() if with_bn[i - 1] else ReluActivation()
-        conv_output = img_conv_layer(
-            name="conv%d_%d" % (prefix_num, i),
-            input=i_conv_in,
-            filter_size=3,
-            num_channels=i_channels_conv,
-            num_filters=output_channels,
-            stride=strides[i - 1],
-            padding=1,
-            act=conv_act)
-        if with_bn[i - 1]:
-            bn = batch_norm_layer(
-                name="conv%d_%d_bn" % (prefix_num, i),
-                input=conv_output,
-                num_channels=output_channels,
-                act=ReluActivation(),
-                layer_attr=get_extra_layer_attr(drop_rate=drop_rates[i - 1]))
-            group_output = bn
-        else:
-            group_output = conv_output
-    pool = img_pool_layer(
-        name="pool%d" % prefix_num,
-        input=group_output,
-        pool_size=2,
-        num_channels=output_channels,
-        stride=2)
-    return pool
-
-
-def vgg_conv_net(image_size,
-                 num_classes,
-                 num_layers,
-                 channels,
-                 strides,
-                 with_bn,
-                 fc_dims,
-                 drop_rates,
-                 drop_rates_fc=[],
-                 is_color=True,
-                 is_predict=False):
-    """
-    A Wrapper for a VGG network for image classification.
-    It is a set of convolutional groups followed by several fully
-    connected layers, and a cross-entropy classifiation loss.
-    The detailed architecture of the paper can be found here:
-      Very Deep Convolutional Networks for Large-Scale Visual Recognition
-      http://www.robots.ox.ac.uk/~vgg/research/very_deep/
-    image_size: image size.
-    num_classes: num of classes.
-    num_layers: the number of layers for all the convolution groups.
-    channels: the number of output filters for all the convolution groups.
-    with_bn: whether each layer of a convolution group is followed by a
-    batch normalization.
-    drop_rates: the dropout rates for all the convolutional layers.
-    fc_dims: the dimension for all the fully connected layers.
-    is_color: whether the input images are color.
-    """
-    data_input, label_input, num_image_channels = \
-        image_data_layers(image_size, num_classes, is_color, is_predict)
-    assert (len(num_layers) == len(channels))
-    assert (len(num_layers) == len(strides))
-    assert (len(num_layers) == len(with_bn))
-    num_fc_layers = len(fc_dims)
-    assert (num_fc_layers + 1 == len(drop_rates_fc))
-
-    for i in range(len(num_layers)):
-        input_layer = data_input if i == 0 else group_output
-        input_channels = 3 if i == 0 else channels[i - 1]
-        group_output = conv_layer_group(
-            prefix_num=i + 1,
-            num_layers=num_layers[i],
-            input=input_layer,
-            input_channels=input_channels,
-            output_channels=channels[i],
-            drop_rates=drop_rates[i],
-            strides=strides[i],
-            with_bn=with_bn[i])
-    conv_output_name = group_output
-    if drop_rates_fc[0] != 0.0:
-        dropped_pool_name = "pool_dropped"
-        conv_output_name = dropout_layer(
-            name=dropped_pool_name,
-            input=conv_output_name,
-            dropout_rate=drop_rates_fc[0])
-    for i in range(len(fc_dims)):
-        input_layer_name = conv_output_name if i == 0 else fc_output
-        active_type = LinearActivation() if i == len(
-            fc_dims) - 1 else ReluActivation()
-        drop_rate = 0.0 if i == len(fc_dims) - 1 else drop_rates_fc[i + 1]
-        fc_output = fc_layer(
-            name="fc%d" % (i + 1),
-            input=input_layer_name,
-            size=fc_dims[i],
-            act=active_type,
-            layer_attr=get_extra_layer_attr(drop_rate))
-    bn = batch_norm_layer(
-        name="fc_bn",
-        input=fc_output,
-        num_channels=fc_dims[len(fc_dims) - 1],
-        act=ReluActivation(),
-        layer_attr=get_extra_layer_attr(drop_rate=drop_rates_fc[-1]))
-    output = fc_layer(
-        name="output", input=bn, size=num_classes, act=SoftmaxActivation())
-    if is_predict:
-        outputs(output)
-    else:
-        cost = classification_cost(name="cost", input=output, label=label_input)
-        outputs(cost)
-
-
-def vgg16_conv_net(image_size, num_classes, is_color=True, is_predict=False):
-    """
-    A Wrapper for a 16 layers VGG network for image classification.
-    The detailed architecture of the paper can be found here:
-      Very Deep Convolutional Networks for Large-Scale Visual Recognition
-      http://www.robots.ox.ac.uk/~vgg/research/very_deep/
-    image_size: image size.
-    num_classes: num of classes.
-    is_color: whether the input images are color.
-    """
-    vgg_conv_net(image_size, num_classes,
-                 num_layers=[2, 2, 3, 3, 3],
-                 channels=[64, 128, 256, 512, 512],
-                 strides=[[], [], [], [], []],
-                 with_bn=[[False, True], [False, True], [False, False, True], \
-                          [False, False, True], [False, False, True]],
-                 drop_rates=[[]] * 5,
-                 drop_rates_fc=[0.0, 0.5, 0.5],
-                 fc_dims=[4096, 4096],
-                 is_predict=is_predict)
-
-
-def small_vgg(data_conf, is_predict=False):
-    """
-    A Wrapper for a small VGG network for CIFAR-10 image classification.
-    The detailed architecture of the paper can be found here:
-      92.45% on CIFAR-10 in Torch
-      http://torch.ch/blog/2015/07/30/cifar.html
-    Due to the constraints of CuDNN, it only has four convolutional groups
-    rather than five.
-    Thus, it only achieves 91.2% test accuracy and 98.1% training accuracy.
-    data_conf is a dictionary with the following keys:
-        image_size: image size.
-        num_classes: num of classes.
-        is_color: whether the input images are color.
-    """
-    for k, v in six.iteritems(data_conf):
-        globals()[k] = v
-    vgg_conv_net(image_size, num_classes,
-                 num_layers=[2, 2, 3, 3],
-                 channels=[64, 128, 256, 512],
-                 strides=[[], [], [], []],
-                 with_bn=[[True, True], [True, True], [True, True, True], \
-                          [True, True, True]],
-                 drop_rates=[[0.3, 0.0], [0.4, 0.0],
-                             [0.4, 0.4, 0.0], [0.4, 0.4, 0.0]],
-                 drop_rates_fc=[0.5, 0.5],
-                 fc_dims=[512],
-                 is_predict=is_predict)
-
-
-def training_settings(learning_rate=0.1,
-                      batch_size=128,
-                      algorithm="sgd",
-                      momentum=0.9,
-                      decay_rate=0.001):
-    """
-    Training settings.
-    learning_rate: learning rate of the training.
-    batch_size: the size of each training batch.
-    algorithm: training algorithm, can be
-       - sgd
-       - adagrad
-       - adadelta
-       - rmsprop
-    momentum: momentum of the training algorithm.
-    decay_rate: weight decay rate.
-    """
-    Settings(
-        algorithm=algorithm,
-        batch_size=batch_size,
-        learning_rate=learning_rate / float(batch_size))
-    default_momentum(momentum)
-    default_decay_rate(decay_rate * batch_size)

python/requirements.txt

 requests==2.9.2
 numpy>=1.12
-protobuf==3.1
+protobuf>=3.6
 recordio>=0.1.0
 matplotlib==2.2.3 # TODO: let python3 paddlepaddle package use latest matplotlib
 rarfile
@@ -11,3 +11,4 @@ graphviz
 six
 funcsigs
 pyyaml
+decorator

python/setup.py.in

@@ -100,6 +100,7 @@ packages=['paddle',
           'paddle.utils',
           'paddle.dataset',
           'paddle.reader',
+          'paddle.distributed',
           'paddle.fluid',
           'paddle.fluid.imperative',
           'paddle.fluid.proto',

tools/manylinux1/Dockerfile.x64

@@ -31,10 +31,10 @@ RUN wget --no-check-certificate -qO- https://storage.googleapis.com/golang/go1.8
 ENV GOROOT=/usr/local/go GOPATH=/root/gopath
 ENV PATH=${GOROOT}/bin:${GOPATH}/bin:${PATH}
 
-# protobuf 3.1.0
-RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf/releases/download/v3.1.0/protobuf-cpp-3.1.0.tar.gz && \
-    tar xzf protobuf-cpp-3.1.0.tar.gz && \
-    cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz
+# protobuf 3.6.1
+RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf/releases/download/v3.6.1/protobuf-cpp-3.6.1.tar.gz && \
+    tar xzf protobuf-cpp-3.6.1.tar.gz && \
+    cd protobuf-3.6.1 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.6.1.tar.gz
 
 RUN wget https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt -O /root/requirements.txt
 

tools/manylinux1/build_scripts/build.sh

@@ -17,7 +17,7 @@ OPENSSL_ROOT=openssl-1.1.0i
 OPENSSL_HASH=ebbfc844a8c8cc0ea5dc10b86c9ce97f401837f3fa08c17b2cdadc118253cf99
 EPEL_RPM_HASH=e5ed9ecf22d0c4279e92075a64c757ad2b38049bcf5c16c4f2b75d5f6860dc0d
 DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
-PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
+PATCHELF_HASH=f2aa40a6148cb3b0ca807a1bf836b081793e55ec9e5540a5356d800132be7e0a
 CURL_ROOT=curl-7.49.1
 CURL_HASH=eb63cec4bef692eab9db459033f409533e6d10e20942f4b060b32819e81885f1
 AUTOCONF_ROOT=autoconf-2.69
@@ -107,11 +107,11 @@ curl-config --features
 rm -rf /usr/local/ssl
 
 # Install patchelf (latest with unreleased bug fixes)
-curl -sLO http://nipy.bic.berkeley.edu/manylinux/patchelf-0.9njs2.tar.gz
-check_sha256sum patchelf-0.9njs2.tar.gz $PATCHELF_HASH
-tar -xzf patchelf-0.9njs2.tar.gz
-(cd patchelf-0.9njs2 && ./configure && make && make install)
-rm -rf patchelf-0.9njs2.tar.gz patchelf-0.9njs2
+curl -sLO https://nixos.org/releases/patchelf/patchelf-0.9/patchelf-0.9.tar.gz
+check_sha256sum patchelf-0.9.tar.gz $PATCHELF_HASH
+tar -xzf patchelf-0.9.tar.gz
+(cd patchelf-0.9 && ./configure && make && make install)
+rm -rf patchelf-0.9.tar.gz patchelf-0.9
 
 # Install latest pypi release of auditwheel
 LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib" $PY35_BIN/pip install auditwheel