From a8342d073de789f69fda1a2c34d846c73e909623 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 18 Oct 2016 12:50:31 +0800 Subject: [PATCH 001/347] Add benchmark config and document --- benchmark/README.md | 168 ++ benchmark/caffe/image/alexnet.prototxt | 347 +++ benchmark/caffe/image/googlenet.prototxt | 2334 +++++++++++++++++ benchmark/caffe/image/run.sh | 30 + benchmark/caffe/image/run_multi.sh | 24 + .../caffe/image/smallnet_mnist_cifar.prototxt | 198 ++ benchmark/caffe/image/solver.prototxt | 10 + benchmark/figs/alexnet-4gpu.png | Bin 0 -> 81990 bytes benchmark/figs/googlenet-4gpu.png | Bin 0 -> 82569 bytes benchmark/figs/rnn_lstm_4gpus.png | Bin 0 -> 73243 bytes benchmark/figs/rnn_lstm_cls.png | Bin 0 -> 117634 bytes benchmark/paddle/image/alexnet.py | 57 + benchmark/paddle/image/googlenet.py | 147 ++ benchmark/paddle/image/provider.py | 24 + benchmark/paddle/image/run.sh | 54 + benchmark/paddle/image/run_multi.sh | 42 + .../paddle/image/smallnet_mnist_cifar.py | 47 + benchmark/paddle/rnn/imdb.py | 42 + benchmark/paddle/rnn/provider.py | 64 + benchmark/paddle/rnn/rnn.py | 42 + benchmark/paddle/rnn/run.sh | 38 + benchmark/paddle/rnn/run_multi.sh | 34 + benchmark/tensorflow/image/alexnet.py | 260 ++ .../tensorflow/image/alexnet_multi_gpu.py | 335 +++ benchmark/tensorflow/image/googlenet.py | 282 ++ .../tensorflow/image/googlenet_multi_gpu.py | 381 +++ benchmark/tensorflow/image/run.sh | 28 + benchmark/tensorflow/image/run_multi.sh | 22 + .../tensorflow/image/smallnet_mnist_cifar.py | 273 ++ benchmark/tensorflow/rnn/README.md | 5 + benchmark/tensorflow/rnn/reader.py | 90 + benchmark/tensorflow/rnn/rnn.py | 201 ++ benchmark/tensorflow/rnn/rnn_multi_gpu.py | 306 +++ benchmark/tensorflow/rnn/run.sh | 29 + benchmark/tensorflow/rnn/run_multi.sh | 28 + 35 files changed, 5942 insertions(+) create mode 100644 benchmark/README.md create mode 100644 benchmark/caffe/image/alexnet.prototxt create mode 100644 benchmark/caffe/image/googlenet.prototxt create mode 100755 benchmark/caffe/image/run.sh create mode 100755 benchmark/caffe/image/run_multi.sh create mode 100644 benchmark/caffe/image/smallnet_mnist_cifar.prototxt create mode 100644 benchmark/caffe/image/solver.prototxt create mode 100644 benchmark/figs/alexnet-4gpu.png create mode 100644 benchmark/figs/googlenet-4gpu.png create mode 100644 benchmark/figs/rnn_lstm_4gpus.png create mode 100644 benchmark/figs/rnn_lstm_cls.png create mode 100644 benchmark/paddle/image/alexnet.py create mode 100644 benchmark/paddle/image/googlenet.py create mode 100644 benchmark/paddle/image/provider.py create mode 100755 benchmark/paddle/image/run.sh create mode 100755 benchmark/paddle/image/run_multi.sh create mode 100644 benchmark/paddle/image/smallnet_mnist_cifar.py create mode 100755 benchmark/paddle/rnn/imdb.py create mode 100644 benchmark/paddle/rnn/provider.py create mode 100755 benchmark/paddle/rnn/rnn.py create mode 100755 benchmark/paddle/rnn/run.sh create mode 100755 benchmark/paddle/rnn/run_multi.sh create mode 100644 benchmark/tensorflow/image/alexnet.py create mode 100644 benchmark/tensorflow/image/alexnet_multi_gpu.py create mode 100644 benchmark/tensorflow/image/googlenet.py create mode 100644 benchmark/tensorflow/image/googlenet_multi_gpu.py create mode 100755 benchmark/tensorflow/image/run.sh create mode 100755 benchmark/tensorflow/image/run_multi.sh create mode 100644 benchmark/tensorflow/image/smallnet_mnist_cifar.py create mode 100644 benchmark/tensorflow/rnn/README.md create mode 100755 benchmark/tensorflow/rnn/reader.py create mode 100755 benchmark/tensorflow/rnn/rnn.py create mode 100755 benchmark/tensorflow/rnn/rnn_multi_gpu.py create mode 100755 benchmark/tensorflow/rnn/run.sh create mode 100755 benchmark/tensorflow/rnn/run_multi.sh diff --git a/benchmark/README.md b/benchmark/README.md new file mode 100644 index 00000000000..8d2cf5737dd --- /dev/null +++ b/benchmark/README.md @@ -0,0 +1,168 @@ +# 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 platform are tested in docker environment. + +Platform: + +- PaddlePaddle: +- Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu +- Caffe: + +Several convolutional neural networks and recurrent neural network are used to test. + +## Image + +### Benchmark Model + +AlexNet, GooleNet and a small network which refer the config of cifar10 in Caffe are used. + +- [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) + + +### Singe-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. You might see that caffe is slower, because the workspace limit size is 8 * 1024 * 1024 in Caffe's cuDnn-conv interface. This size is larger in PaddlePaddle and TensorFlow. 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 tests in caffe use `caffe time` to execute, which is not including the parameter updating process. But the time in PaddlePaddle and TensorFlow contains it. + +In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN. + +### Multi-GPU: 4 GPUs + +- AlexNet, ms / batch + +| totoal-BatchSize | 128 * 4 | 256 * 4 | +|------------------|----------| -----------| +| PaddlePaddle | 347 | 622 | +| TensorFlow | 377 | 675 | +| Caffe | 1229 | 2435 | + +For example, if `totoal-BatchSize = 128 * 4`, the speed is calculated by + +``` + time_at_1gpu_batch_128 * 4 / time_at_4gpu_total_batch_512 += (334 * 4)/347 += 3.85 +``` + + + + +- GooleNet, ms / batch + +| totoal-BatchSize | 128 * 4 | 256 * 4 | +|-------------------|--------------| ----------- | +| PaddlePaddle | 1178 | 2367 | +| TensorFlow | 1210 | 2292 | +| Caffe | 2007 | out of memory | + + + + +## RNN +We use lstm network for text classfication to test benchmark. + +### Dataset +- [IMDB](http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl) +- Sequence legth=100, in fact, PaddlePaddle support training with variable-length sequence. But TensorFlow need to pad, in order to compare, we also pad sequence length to 100 in PaddlePaddle. +- 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 network for different hidden size, batch size with `2 lstm layer + fc` network. + +- 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 | + + + +#### Seq2Seq + +The benchmark of sequence-to-sequence network will be add 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 | + + + + +#### Seq2Seq + +The benchmark of sequence-to-sequence network will be add later. diff --git a/benchmark/caffe/image/alexnet.prototxt b/benchmark/caffe/image/alexnet.prototxt new file mode 100644 index 00000000000..aca184ddaf2 --- /dev/null +++ b/benchmark/caffe/image/alexnet.prototxt @@ -0,0 +1,347 @@ +name: "alexnet" +input: "data" +input_dim: 64 +input_dim: 3 +input_dim: 227 +input_dim: 227 +input: "label" +input_dim: 64 +input_dim: 1 +input_dim: 1 +input_dim: 1 +force_backward: true +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 96 + kernel_size: 11 + stride: 4 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "norm1" + type: "LRN" + bottom: "conv1" + top: "norm1" + lrn_param { + local_size: 5 + alpha: 0.0001 + beta: 0.75 + } +} +layer { + name: "pool1" + type: "Pooling" + bottom: "norm1" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "conv2" + type: "Convolution" + bottom: "pool1" + top: "conv2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 2 + kernel_size: 5 + group: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0.1 + } + } +} +layer { + name: "relu2" + type: "ReLU" + bottom: "conv2" + top: "conv2" +} +layer { + name: "norm2" + type: "LRN" + bottom: "conv2" + top: "norm2" + lrn_param { + local_size: 5 + alpha: 0.0001 + beta: 0.75 + } +} +layer { + name: "pool2" + type: "Pooling" + bottom: "norm2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "conv3" + type: "Convolution" + bottom: "pool2" + top: "conv3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 384 + pad: 1 + kernel_size: 3 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3" + type: "ReLU" + bottom: "conv3" + top: "conv3" +} +layer { + name: "conv4" + type: "Convolution" + bottom: "conv3" + top: "conv4" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 384 + pad: 1 + kernel_size: 3 + group: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0.1 + } + } +} +layer { + name: "relu4" + type: "ReLU" + bottom: "conv4" + top: "conv4" +} +layer { + name: "conv5" + type: "Convolution" + bottom: "conv4" + top: "conv5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + group: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0.1 + } + } +} +layer { + name: "relu5" + type: "ReLU" + bottom: "conv5" + top: "conv5" +} +layer { + name: "pool5" + type: "Pooling" + bottom: "conv5" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + weight_filler { + type: "gaussian" + std: 0.005 + } + bias_filler { + type: "constant" + value: 0.1 + } + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + weight_filler { + type: "gaussian" + std: 0.005 + } + bias_filler { + type: "constant" + value: 0.1 + } + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc8" + type: "InnerProduct" + bottom: "fc7" + top: "fc8" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 1000 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc8" + bottom: "label" + top: "loss" +} diff --git a/benchmark/caffe/image/googlenet.prototxt b/benchmark/caffe/image/googlenet.prototxt new file mode 100644 index 00000000000..c5f3b4fe3ef --- /dev/null +++ b/benchmark/caffe/image/googlenet.prototxt @@ -0,0 +1,2334 @@ +name: "googlenet" +input: "data" +input_dim: 128 +input_dim: 3 +input_dim: 224 +input_dim: 224 +input: "label" +input_dim: 128 +input_dim: 1 +input_dim: 1 +input_dim: 1 +layer { + name: "conv1/7x7_s2" + type: "Convolution" + bottom: "data" + top: "conv1/7x7_s2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "conv1/relu_7x7" + type: "ReLU" + bottom: "conv1/7x7_s2" + top: "conv1/7x7_s2" +} +layer { + name: "pool1/3x3_s2" + type: "Pooling" + bottom: "conv1/7x7_s2" + top: "pool1/3x3_s2" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +#layer { +# name: "pool1/norm1" +# type: "LRN" +# bottom: "pool1/3x3_s2" +# top: "pool1/norm1" +# lrn_param { +# local_size: 5 +# alpha: 0.0001 +# beta: 0.75 +# } +#} +layer { + name: "conv2/3x3_reduce" + type: "Convolution" +# bottom: "pool1/norm1" + bottom: "pool1/3x3_s2" + top: "conv2/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "conv2/relu_3x3_reduce" + type: "ReLU" + bottom: "conv2/3x3_reduce" + top: "conv2/3x3_reduce" +} +layer { + name: "conv2/3x3" + type: "Convolution" + bottom: "conv2/3x3_reduce" + top: "conv2/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 192 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "conv2/relu_3x3" + type: "ReLU" + bottom: "conv2/3x3" + top: "conv2/3x3" +} +#layer { +# name: "conv2/norm2" +# type: "LRN" +# bottom: "conv2/3x3" +# top: "conv2/norm2" +# lrn_param { +# local_size: 5 +# alpha: 0.0001 +# beta: 0.75 +# } +#} +layer { + name: "pool2/3x3_s2" + type: "Pooling" +# bottom: "conv2/norm2" + bottom: "conv2/3x3" + top: "pool2/3x3_s2" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "inception_3a/1x1" + type: "Convolution" + bottom: "pool2/3x3_s2" + top: "inception_3a/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3a/relu_1x1" + type: "ReLU" + bottom: "inception_3a/1x1" + top: "inception_3a/1x1" +} +layer { + name: "inception_3a/3x3_reduce" + type: "Convolution" + bottom: "pool2/3x3_s2" + top: "inception_3a/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 96 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3a/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_3a/3x3_reduce" + top: "inception_3a/3x3_reduce" +} +layer { + name: "inception_3a/3x3" + type: "Convolution" + bottom: "inception_3a/3x3_reduce" + top: "inception_3a/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3a/relu_3x3" + type: "ReLU" + bottom: "inception_3a/3x3" + top: "inception_3a/3x3" +} +layer { + name: "inception_3a/5x5_reduce" + type: "Convolution" + bottom: "pool2/3x3_s2" + top: "inception_3a/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3a/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_3a/5x5_reduce" + top: "inception_3a/5x5_reduce" +} +layer { + name: "inception_3a/5x5" + type: "Convolution" + bottom: "inception_3a/5x5_reduce" + top: "inception_3a/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3a/relu_5x5" + type: "ReLU" + bottom: "inception_3a/5x5" + top: "inception_3a/5x5" +} +layer { + name: "inception_3a/pool" + type: "Pooling" + bottom: "pool2/3x3_s2" + top: "inception_3a/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_3a/pool_proj" + type: "Convolution" + bottom: "inception_3a/pool" + top: "inception_3a/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 32 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3a/relu_pool_proj" + type: "ReLU" + bottom: "inception_3a/pool_proj" + top: "inception_3a/pool_proj" +} +layer { + name: "inception_3a/output" + type: "Concat" + bottom: "inception_3a/1x1" + bottom: "inception_3a/3x3" + bottom: "inception_3a/5x5" + bottom: "inception_3a/pool_proj" + top: "inception_3a/output" +} +layer { + name: "inception_3b/1x1" + type: "Convolution" + bottom: "inception_3a/output" + top: "inception_3b/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3b/relu_1x1" + type: "ReLU" + bottom: "inception_3b/1x1" + top: "inception_3b/1x1" +} +layer { + name: "inception_3b/3x3_reduce" + type: "Convolution" + bottom: "inception_3a/output" + top: "inception_3b/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3b/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_3b/3x3_reduce" + top: "inception_3b/3x3_reduce" +} +layer { + name: "inception_3b/3x3" + type: "Convolution" + bottom: "inception_3b/3x3_reduce" + top: "inception_3b/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 192 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3b/relu_3x3" + type: "ReLU" + bottom: "inception_3b/3x3" + top: "inception_3b/3x3" +} +layer { + name: "inception_3b/5x5_reduce" + type: "Convolution" + bottom: "inception_3a/output" + top: "inception_3b/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 32 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3b/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_3b/5x5_reduce" + top: "inception_3b/5x5_reduce" +} +layer { + name: "inception_3b/5x5" + type: "Convolution" + bottom: "inception_3b/5x5_reduce" + top: "inception_3b/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 96 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3b/relu_5x5" + type: "ReLU" + bottom: "inception_3b/5x5" + top: "inception_3b/5x5" +} +layer { + name: "inception_3b/pool" + type: "Pooling" + bottom: "inception_3a/output" + top: "inception_3b/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_3b/pool_proj" + type: "Convolution" + bottom: "inception_3b/pool" + top: "inception_3b/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_3b/relu_pool_proj" + type: "ReLU" + bottom: "inception_3b/pool_proj" + top: "inception_3b/pool_proj" +} +layer { + name: "inception_3b/output" + type: "Concat" + bottom: "inception_3b/1x1" + bottom: "inception_3b/3x3" + bottom: "inception_3b/5x5" + bottom: "inception_3b/pool_proj" + top: "inception_3b/output" +} +layer { + name: "pool3/3x3_s2" + type: "Pooling" + bottom: "inception_3b/output" + top: "pool3/3x3_s2" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "inception_4a/1x1" + type: "Convolution" + bottom: "pool3/3x3_s2" + top: "inception_4a/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 192 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4a/relu_1x1" + type: "ReLU" + bottom: "inception_4a/1x1" + top: "inception_4a/1x1" +} +layer { + name: "inception_4a/3x3_reduce" + type: "Convolution" + bottom: "pool3/3x3_s2" + top: "inception_4a/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 96 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4a/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_4a/3x3_reduce" + top: "inception_4a/3x3_reduce" +} +layer { + name: "inception_4a/3x3" + type: "Convolution" + bottom: "inception_4a/3x3_reduce" + top: "inception_4a/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 208 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4a/relu_3x3" + type: "ReLU" + bottom: "inception_4a/3x3" + top: "inception_4a/3x3" +} +layer { + name: "inception_4a/5x5_reduce" + type: "Convolution" + bottom: "pool3/3x3_s2" + top: "inception_4a/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4a/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_4a/5x5_reduce" + top: "inception_4a/5x5_reduce" +} +layer { + name: "inception_4a/5x5" + type: "Convolution" + bottom: "inception_4a/5x5_reduce" + top: "inception_4a/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 48 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4a/relu_5x5" + type: "ReLU" + bottom: "inception_4a/5x5" + top: "inception_4a/5x5" +} +layer { + name: "inception_4a/pool" + type: "Pooling" + bottom: "pool3/3x3_s2" + top: "inception_4a/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_4a/pool_proj" + type: "Convolution" + bottom: "inception_4a/pool" + top: "inception_4a/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4a/relu_pool_proj" + type: "ReLU" + bottom: "inception_4a/pool_proj" + top: "inception_4a/pool_proj" +} +layer { + name: "inception_4a/output" + type: "Concat" + bottom: "inception_4a/1x1" + bottom: "inception_4a/3x3" + bottom: "inception_4a/5x5" + bottom: "inception_4a/pool_proj" + top: "inception_4a/output" +} +#layer { +# name: "loss1/ave_pool" +# type: "Pooling" +# bottom: "inception_4a/output" +# top: "loss1/ave_pool" +# pooling_param { +# pool: AVE +# kernel_size: 5 +# stride: 3 +# } +#} +#layer { +# name: "loss1/conv" +# type: "Convolution" +# bottom: "loss1/ave_pool" +# top: "loss1/conv" +# param { +# lr_mult: 1 +# decay_mult: 1 +# } +# param { +# lr_mult: 2 +# decay_mult: 0 +# } +# convolution_param { +# num_output: 128 +# kernel_size: 1 +# weight_filler { +# type: "xavier" +# } +# bias_filler { +# type: "constant" +# value: 0.2 +# } +# } +#} +#layer { +# name: "loss1/relu_conv" +# type: "ReLU" +# bottom: "loss1/conv" +# top: "loss1/conv" +#} +#layer { +# name: "loss1/fc" +# type: "InnerProduct" +# bottom: "loss1/conv" +# top: "loss1/fc" +# param { +# lr_mult: 1 +# decay_mult: 1 +# } +# param { +# lr_mult: 2 +# decay_mult: 0 +# } +# inner_product_param { +# num_output: 1024 +# weight_filler { +# type: "xavier" +# } +# bias_filler { +# type: "constant" +# value: 0.2 +# } +# } +#} +#layer { +# name: "loss1/relu_fc" +# type: "ReLU" +# bottom: "loss1/fc" +# top: "loss1/fc" +#} +#layer { +# name: "loss1/drop_fc" +# type: "Dropout" +# bottom: "loss1/fc" +# top: "loss1/fc" +# dropout_param { +# dropout_ratio: 0.7 +# } +#} +#layer { +# name: "loss1/classifier" +# type: "InnerProduct" +# bottom: "loss1/fc" +# top: "loss1/classifier" +# param { +# lr_mult: 1 +# decay_mult: 1 +# } +# param { +# lr_mult: 2 +# decay_mult: 0 +# } +# inner_product_param { +# num_output: 1000 +# weight_filler { +# type: "xavier" +# } +# bias_filler { +# type: "constant" +# value: 0 +# } +# } +#} +#layer { +# name: "loss1/loss" +# type: "SoftmaxWithLoss" +# bottom: "loss1/classifier" +# bottom: "label" +# top: "loss1/loss1" +# loss_weight: 0.3 +#} +layer { + name: "inception_4b/1x1" + type: "Convolution" + bottom: "inception_4a/output" + top: "inception_4b/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 160 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4b/relu_1x1" + type: "ReLU" + bottom: "inception_4b/1x1" + top: "inception_4b/1x1" +} +layer { + name: "inception_4b/3x3_reduce" + type: "Convolution" + bottom: "inception_4a/output" + top: "inception_4b/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 112 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4b/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_4b/3x3_reduce" + top: "inception_4b/3x3_reduce" +} +layer { + name: "inception_4b/3x3" + type: "Convolution" + bottom: "inception_4b/3x3_reduce" + top: "inception_4b/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 224 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4b/relu_3x3" + type: "ReLU" + bottom: "inception_4b/3x3" + top: "inception_4b/3x3" +} +layer { + name: "inception_4b/5x5_reduce" + type: "Convolution" + bottom: "inception_4a/output" + top: "inception_4b/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4b/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_4b/5x5_reduce" + top: "inception_4b/5x5_reduce" +} +layer { + name: "inception_4b/5x5" + type: "Convolution" + bottom: "inception_4b/5x5_reduce" + top: "inception_4b/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4b/relu_5x5" + type: "ReLU" + bottom: "inception_4b/5x5" + top: "inception_4b/5x5" +} +layer { + name: "inception_4b/pool" + type: "Pooling" + bottom: "inception_4a/output" + top: "inception_4b/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_4b/pool_proj" + type: "Convolution" + bottom: "inception_4b/pool" + top: "inception_4b/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4b/relu_pool_proj" + type: "ReLU" + bottom: "inception_4b/pool_proj" + top: "inception_4b/pool_proj" +} +layer { + name: "inception_4b/output" + type: "Concat" + bottom: "inception_4b/1x1" + bottom: "inception_4b/3x3" + bottom: "inception_4b/5x5" + bottom: "inception_4b/pool_proj" + top: "inception_4b/output" +} +layer { + name: "inception_4c/1x1" + type: "Convolution" + bottom: "inception_4b/output" + top: "inception_4c/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4c/relu_1x1" + type: "ReLU" + bottom: "inception_4c/1x1" + top: "inception_4c/1x1" +} +layer { + name: "inception_4c/3x3_reduce" + type: "Convolution" + bottom: "inception_4b/output" + top: "inception_4c/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4c/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_4c/3x3_reduce" + top: "inception_4c/3x3_reduce" +} +layer { + name: "inception_4c/3x3" + type: "Convolution" + bottom: "inception_4c/3x3_reduce" + top: "inception_4c/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4c/relu_3x3" + type: "ReLU" + bottom: "inception_4c/3x3" + top: "inception_4c/3x3" +} +layer { + name: "inception_4c/5x5_reduce" + type: "Convolution" + bottom: "inception_4b/output" + top: "inception_4c/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4c/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_4c/5x5_reduce" + top: "inception_4c/5x5_reduce" +} +layer { + name: "inception_4c/5x5" + type: "Convolution" + bottom: "inception_4c/5x5_reduce" + top: "inception_4c/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4c/relu_5x5" + type: "ReLU" + bottom: "inception_4c/5x5" + top: "inception_4c/5x5" +} +layer { + name: "inception_4c/pool" + type: "Pooling" + bottom: "inception_4b/output" + top: "inception_4c/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_4c/pool_proj" + type: "Convolution" + bottom: "inception_4c/pool" + top: "inception_4c/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4c/relu_pool_proj" + type: "ReLU" + bottom: "inception_4c/pool_proj" + top: "inception_4c/pool_proj" +} +layer { + name: "inception_4c/output" + type: "Concat" + bottom: "inception_4c/1x1" + bottom: "inception_4c/3x3" + bottom: "inception_4c/5x5" + bottom: "inception_4c/pool_proj" + top: "inception_4c/output" +} +layer { + name: "inception_4d/1x1" + type: "Convolution" + bottom: "inception_4c/output" + top: "inception_4d/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 112 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4d/relu_1x1" + type: "ReLU" + bottom: "inception_4d/1x1" + top: "inception_4d/1x1" +} +layer { + name: "inception_4d/3x3_reduce" + type: "Convolution" + bottom: "inception_4c/output" + top: "inception_4d/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 144 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4d/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_4d/3x3_reduce" + top: "inception_4d/3x3_reduce" +} +layer { + name: "inception_4d/3x3" + type: "Convolution" + bottom: "inception_4d/3x3_reduce" + top: "inception_4d/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 288 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4d/relu_3x3" + type: "ReLU" + bottom: "inception_4d/3x3" + top: "inception_4d/3x3" +} +layer { + name: "inception_4d/5x5_reduce" + type: "Convolution" + bottom: "inception_4c/output" + top: "inception_4d/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 32 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4d/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_4d/5x5_reduce" + top: "inception_4d/5x5_reduce" +} +layer { + name: "inception_4d/5x5" + type: "Convolution" + bottom: "inception_4d/5x5_reduce" + top: "inception_4d/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4d/relu_5x5" + type: "ReLU" + bottom: "inception_4d/5x5" + top: "inception_4d/5x5" +} +layer { + name: "inception_4d/pool" + type: "Pooling" + bottom: "inception_4c/output" + top: "inception_4d/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_4d/pool_proj" + type: "Convolution" + bottom: "inception_4d/pool" + top: "inception_4d/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4d/relu_pool_proj" + type: "ReLU" + bottom: "inception_4d/pool_proj" + top: "inception_4d/pool_proj" +} +layer { + name: "inception_4d/output" + type: "Concat" + bottom: "inception_4d/1x1" + bottom: "inception_4d/3x3" + bottom: "inception_4d/5x5" + bottom: "inception_4d/pool_proj" + top: "inception_4d/output" +} +#layer { +# name: "loss2/ave_pool" +# type: "Pooling" +# bottom: "inception_4d/output" +# top: "loss2/ave_pool" +# pooling_param { +# pool: AVE +# kernel_size: 5 +# stride: 3 +# } +#} +#layer { +# name: "loss2/conv" +# type: "Convolution" +# bottom: "loss2/ave_pool" +# top: "loss2/conv" +# param { +# lr_mult: 1 +# decay_mult: 1 +# } +# param { +# lr_mult: 2 +# decay_mult: 0 +# } +# convolution_param { +# num_output: 128 +# kernel_size: 1 +# weight_filler { +# type: "xavier" +# } +# bias_filler { +# type: "constant" +# value: 0.2 +# } +# } +#} +#layer { +# name: "loss2/relu_conv" +# type: "ReLU" +# bottom: "loss2/conv" +# top: "loss2/conv" +#} +#layer { +# name: "loss2/fc" +# type: "InnerProduct" +# bottom: "loss2/conv" +# top: "loss2/fc" +# param { +# lr_mult: 1 +# decay_mult: 1 +# } +# param { +# lr_mult: 2 +# decay_mult: 0 +# } +# inner_product_param { +# num_output: 1024 +# weight_filler { +# type: "xavier" +# } +# bias_filler { +# type: "constant" +# value: 0.2 +# } +# } +#} +#layer { +# name: "loss2/relu_fc" +# type: "ReLU" +# bottom: "loss2/fc" +# top: "loss2/fc" +#} +#layer { +# name: "loss2/drop_fc" +# type: "Dropout" +# bottom: "loss2/fc" +# top: "loss2/fc" +# dropout_param { +# dropout_ratio: 0.7 +# } +#} +#layer { +# name: "loss2/classifier" +# type: "InnerProduct" +# bottom: "loss2/fc" +# top: "loss2/classifier" +# param { +# lr_mult: 1 +# decay_mult: 1 +# } +# param { +# lr_mult: 2 +# decay_mult: 0 +# } +# inner_product_param { +# num_output: 1000 +# weight_filler { +# type: "xavier" +# } +# bias_filler { +# type: "constant" +# value: 0 +# } +# } +#} +#layer { +# name: "loss2/loss" +# type: "SoftmaxWithLoss" +# bottom: "loss2/classifier" +# bottom: "label" +# top: "loss2/loss1" +# loss_weight: 0.3 +#} +layer { + name: "inception_4e/1x1" + type: "Convolution" + bottom: "inception_4d/output" + top: "inception_4e/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4e/relu_1x1" + type: "ReLU" + bottom: "inception_4e/1x1" + top: "inception_4e/1x1" +} +layer { + name: "inception_4e/3x3_reduce" + type: "Convolution" + bottom: "inception_4d/output" + top: "inception_4e/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 160 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4e/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_4e/3x3_reduce" + top: "inception_4e/3x3_reduce" +} +layer { + name: "inception_4e/3x3" + type: "Convolution" + bottom: "inception_4e/3x3_reduce" + top: "inception_4e/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 320 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4e/relu_3x3" + type: "ReLU" + bottom: "inception_4e/3x3" + top: "inception_4e/3x3" +} +layer { + name: "inception_4e/5x5_reduce" + type: "Convolution" + bottom: "inception_4d/output" + top: "inception_4e/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 32 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4e/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_4e/5x5_reduce" + top: "inception_4e/5x5_reduce" +} +layer { + name: "inception_4e/5x5" + type: "Convolution" + bottom: "inception_4e/5x5_reduce" + top: "inception_4e/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4e/relu_5x5" + type: "ReLU" + bottom: "inception_4e/5x5" + top: "inception_4e/5x5" +} +layer { + name: "inception_4e/pool" + type: "Pooling" + bottom: "inception_4d/output" + top: "inception_4e/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_4e/pool_proj" + type: "Convolution" + bottom: "inception_4e/pool" + top: "inception_4e/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_4e/relu_pool_proj" + type: "ReLU" + bottom: "inception_4e/pool_proj" + top: "inception_4e/pool_proj" +} +layer { + name: "inception_4e/output" + type: "Concat" + bottom: "inception_4e/1x1" + bottom: "inception_4e/3x3" + bottom: "inception_4e/5x5" + bottom: "inception_4e/pool_proj" + top: "inception_4e/output" +} +layer { + name: "pool4/3x3_s2" + type: "Pooling" + bottom: "inception_4e/output" + top: "pool4/3x3_s2" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "inception_5a/1x1" + type: "Convolution" + bottom: "pool4/3x3_s2" + top: "inception_5a/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5a/relu_1x1" + type: "ReLU" + bottom: "inception_5a/1x1" + top: "inception_5a/1x1" +} +layer { + name: "inception_5a/3x3_reduce" + type: "Convolution" + bottom: "pool4/3x3_s2" + top: "inception_5a/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 160 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5a/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_5a/3x3_reduce" + top: "inception_5a/3x3_reduce" +} +layer { + name: "inception_5a/3x3" + type: "Convolution" + bottom: "inception_5a/3x3_reduce" + top: "inception_5a/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 320 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5a/relu_3x3" + type: "ReLU" + bottom: "inception_5a/3x3" + top: "inception_5a/3x3" +} +layer { + name: "inception_5a/5x5_reduce" + type: "Convolution" + bottom: "pool4/3x3_s2" + top: "inception_5a/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 32 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5a/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_5a/5x5_reduce" + top: "inception_5a/5x5_reduce" +} +layer { + name: "inception_5a/5x5" + type: "Convolution" + bottom: "inception_5a/5x5_reduce" + top: "inception_5a/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5a/relu_5x5" + type: "ReLU" + bottom: "inception_5a/5x5" + top: "inception_5a/5x5" +} +layer { + name: "inception_5a/pool" + type: "Pooling" + bottom: "pool4/3x3_s2" + top: "inception_5a/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_5a/pool_proj" + type: "Convolution" + bottom: "inception_5a/pool" + top: "inception_5a/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5a/relu_pool_proj" + type: "ReLU" + bottom: "inception_5a/pool_proj" + top: "inception_5a/pool_proj" +} +layer { + name: "inception_5a/output" + type: "Concat" + bottom: "inception_5a/1x1" + bottom: "inception_5a/3x3" + bottom: "inception_5a/5x5" + bottom: "inception_5a/pool_proj" + top: "inception_5a/output" +} +layer { + name: "inception_5b/1x1" + type: "Convolution" + bottom: "inception_5a/output" + top: "inception_5b/1x1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 384 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5b/relu_1x1" + type: "ReLU" + bottom: "inception_5b/1x1" + top: "inception_5b/1x1" +} +layer { + name: "inception_5b/3x3_reduce" + type: "Convolution" + bottom: "inception_5a/output" + top: "inception_5b/3x3_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 192 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5b/relu_3x3_reduce" + type: "ReLU" + bottom: "inception_5b/3x3_reduce" + top: "inception_5b/3x3_reduce" +} +layer { + name: "inception_5b/3x3" + type: "Convolution" + bottom: "inception_5b/3x3_reduce" + top: "inception_5b/3x3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 384 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5b/relu_3x3" + type: "ReLU" + bottom: "inception_5b/3x3" + top: "inception_5b/3x3" +} +layer { + name: "inception_5b/5x5_reduce" + type: "Convolution" + bottom: "inception_5a/output" + top: "inception_5b/5x5_reduce" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 48 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5b/relu_5x5_reduce" + type: "ReLU" + bottom: "inception_5b/5x5_reduce" + top: "inception_5b/5x5_reduce" +} +layer { + name: "inception_5b/5x5" + type: "Convolution" + bottom: "inception_5b/5x5_reduce" + top: "inception_5b/5x5" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 2 + kernel_size: 5 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5b/relu_5x5" + type: "ReLU" + bottom: "inception_5b/5x5" + top: "inception_5b/5x5" +} +layer { + name: "inception_5b/pool" + type: "Pooling" + bottom: "inception_5a/output" + top: "inception_5b/pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "inception_5b/pool_proj" + type: "Convolution" + bottom: "inception_5b/pool" + top: "inception_5b/pool_proj" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.2 + } + } +} +layer { + name: "inception_5b/relu_pool_proj" + type: "ReLU" + bottom: "inception_5b/pool_proj" + top: "inception_5b/pool_proj" +} +layer { + name: "inception_5b/output" + type: "Concat" + bottom: "inception_5b/1x1" + bottom: "inception_5b/3x3" + bottom: "inception_5b/5x5" + bottom: "inception_5b/pool_proj" + top: "inception_5b/output" +} +layer { + name: "pool5/7x7_s1" + type: "Pooling" + bottom: "inception_5b/output" + top: "pool5/7x7_s1" + pooling_param { + pool: AVE + kernel_size: 7 + stride: 1 + } +} +layer { + name: "pool5/drop_7x7_s1" + type: "Dropout" + bottom: "pool5/7x7_s1" + top: "pool5/7x7_s1" + dropout_param { + dropout_ratio: 0.4 + } +} +layer { + name: "loss3/classifier" + type: "InnerProduct" + bottom: "pool5/7x7_s1" + top: "loss3/classifier" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "loss3/loss3" + type: "SoftmaxWithLoss" + bottom: "loss3/classifier" + bottom: "label" + top: "loss3/loss3" + loss_weight: 1 +} diff --git a/benchmark/caffe/image/run.sh b/benchmark/caffe/image/run.sh new file mode 100755 index 00000000000..aa9ac20ca5c --- /dev/null +++ b/benchmark/caffe/image/run.sh @@ -0,0 +1,30 @@ +set -e + +function test() { + cfg=$1 + batch=$2 + prefix=$3 + sed -i "/input: \"data\"/{n;s/^input_dim.*/input_dim: $batch/g}" $cfg + sed -i "/input: \"label\"/{n;s/^input_dim.*/input_dim: $batch/g}" $cfg + caffe time --model=$cfg --iterations=50 --gpu 0 > logs/$prefix-1gpu-batch${batch}.log 2>&1 +} + +if [ ! -d "logs" ]; then + mkdir logs +fi + +# alexnet +test alexnet.prototxt 64 alexnet +test alexnet.prototxt 128 alexnet +test alexnet.prototxt 256 alexnet +test alexnet.prototxt 512 alexnet + +# googlenet +test googlenet.prototxt 64 googlenet +test googlenet.prototxt 128 googlenet + +# small net +test smallnet_mnist_cifar.prototxt 64 smallnet +test smallnet_mnist_cifar.prototxt 128 smallnet +test smallnet_mnist_cifar.prototxt 256 smallnet +test smallnet_mnist_cifar.prototxt 512 smallnet diff --git a/benchmark/caffe/image/run_multi.sh b/benchmark/caffe/image/run_multi.sh new file mode 100755 index 00000000000..f72b062c11c --- /dev/null +++ b/benchmark/caffe/image/run_multi.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -e + +function test() { + cfg=$1 + batch=$2 + prefix=$3 + batch_per_gpu=`expr ${batch} / 4` + sed -i "/input: \"data\"/{n;s/^input_dim.*/input_dim: ${batch_per_gpu}/g}" $cfg + sed -i "/input: \"label\"/{n;s/^input_dim.*/input_dim: ${batch_per_gpu}/g}" $cfg + sed -i "1c\net : \"${cfg}\"" solver.prototxt + caffe train --solver=solver.prototxt -gpu all > logs/${prefix}-4gpu-batch${batch}.log 2>&1 +} + +if [ ! -d "logs" ]; then + mkdir logs +fi + +# alexnet +test alexnet.prototxt 512 alexnet +test alexnet.prototxt 1024 alexnet + +# googlnet +test googlenet.prototxt 512 googlenet diff --git a/benchmark/caffe/image/smallnet_mnist_cifar.prototxt b/benchmark/caffe/image/smallnet_mnist_cifar.prototxt new file mode 100644 index 00000000000..3cb0e32bbfb --- /dev/null +++ b/benchmark/caffe/image/smallnet_mnist_cifar.prototxt @@ -0,0 +1,198 @@ +name: "mnist/cifar" +input: "data" +input_dim: 128 +input_dim: 3 +input_dim: 32 +input_dim: 32 +input: "label" +input_dim: 128 +input_dim: 1 +input_dim: 1 +input_dim: 1 +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.0001 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "relu1" + type: "ReLU" + bottom: "pool1" + top: "pool1" +} +layer { + name: "conv2" + type: "Convolution" + bottom: "pool1" + top: "conv2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu2" + type: "ReLU" + bottom: "conv2" + top: "conv2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2" + top: "pool2" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 2 + } +} +layer { + name: "conv3" + type: "Convolution" + bottom: "pool2" + top: "conv3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 64 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "relu3" + type: "ReLU" + bottom: "conv3" + top: "conv3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3" + top: "pool3" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 2 + } +} +layer { + name: "ip1" + type: "InnerProduct" + bottom: "pool3" + top: "ip1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 64 + weight_filler { + type: "gaussian" + std: 0.1 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "ip2" + type: "InnerProduct" + bottom: "ip1" + top: "ip2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 10 + weight_filler { + type: "gaussian" + std: 0.1 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "accuracy" + type: "Accuracy" + bottom: "ip2" + bottom: "label" + top: "accuracy" + include { + phase: TEST + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "ip2" + bottom: "label" + top: "loss" +} diff --git a/benchmark/caffe/image/solver.prototxt b/benchmark/caffe/image/solver.prototxt new 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zsQRnkV3NOf>Y2%oGV5W&L}w0FP!Q>u=BGP6`93RKqRP7EwaM#urloG=SsaFw`tEaP zON!dhH=ei^UJ)D8x!^hj{!1Z}2oamJwOw6T&>L$j)3p(Nrh%XKPM3HtjuY$)ik@?# z)~FhVc`EMYG(R-4Is}#GO6U$j}HJ|wrnCF_NpP|;#(Ea~6NHPK;hOw253Z}2! z>njJVEDP<#+$C5nxJBYS#qY!|t*y*gbm`phec?$_yNiz&Fcr6tlR0J>LR}bZUoRv@ z@JTJ1*S~4!SEe*QpeATwL6`>*gW)aw52X z;$S2WIX0ymY^^ZQj>+DVf@_6$rZ??H!+OfS(3Pv?`Z%%m?%1)_5@*uKbj_HhboYf( z1347xyMT(Xw?3mHqc!lur01uG3-&(j&cAWNPyX>chu-2Zk2@Y4=OtAtx{m*D(Sv4_ zHRSPjJN`6*)ZN)I0V-B5UXujnSQjCdBvoWW(&&UlyED>tlYChl+ajy zIom{@L7dXGe&Og9NgLN2fweh2cx{ye9%Wn2SDxtm%S{eKD!!8_pViX{1CsgXH|pMH zZN`)ZWtPD6`wTKF z^2hvt+!8EmKm&7d+~Z%urhn{M+D6&BvxX|NyFrIv?1!uV@%jIKj2>72XGyM`|F6Bo z!ejd?5r=tLUtfQ@?oKj2kNmL@;Lcc64am&Q+~`b*iHuxBDJz#xPWP3n> train.txt + done + fi + + if [ ! -d "train.list" ]; then + echo "train.txt" > train.list + fi +} + +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 \ + --cudnn_dir=/home/dangqingqing/tools/cudnn-5.1/lib64 \ + > logs/$prefix-${thread}gpu-$bz.log 2>&1 +} + +gen_file +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 diff --git a/benchmark/paddle/image/run_multi.sh b/benchmark/paddle/image/run_multi.sh new file mode 100755 index 00000000000..c506668fe0b --- /dev/null +++ b/benchmark/paddle/image/run_multi.sh @@ -0,0 +1,42 @@ +set -e + +function gen_file() { + if [ ! -d "train.txt" ]; then + for ((i=1;i<=1024;i++)) + do + echo "train/n09246464/n09246464_38735.jpeg 972" >> train.txt + done + fi + + if [ ! -d "train.list" ]; then + echo "train.txt" > train.list + fi +} + +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 +} + +gen_file +if [ ! -d "logs" ]; then + mkdir logs +fi + +#========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 diff --git a/benchmark/paddle/image/smallnet_mnist_cifar.py b/benchmark/paddle/image/smallnet_mnist_cifar.py new file mode 100644 index 00000000000..78dba880d29 --- /dev/null +++ b/benchmark/paddle/image/smallnet_mnist_cifar.py @@ -0,0 +1,47 @@ +#!/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) diff --git a/benchmark/paddle/rnn/imdb.py b/benchmark/paddle/rnn/imdb.py new file mode 100755 index 00000000000..93e1686854b --- /dev/null +++ b/benchmark/paddle/rnn/imdb.py @@ -0,0 +1,42 @@ +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') + +if __name__ == "__main__": + main() diff --git a/benchmark/paddle/rnn/provider.py b/benchmark/paddle/rnn/provider.py new file mode 100644 index 00000000000..90d3fee6760 --- /dev/null +++ b/benchmark/paddle/rnn/provider.py @@ -0,0 +1,64 @@ +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]) diff --git a/benchmark/paddle/rnn/rnn.py b/benchmark/paddle/rnn/rnn.py new file mode 100755 index 00000000000..fc8221b1126 --- /dev/null +++ b/benchmark/paddle/rnn/rnn.py @@ -0,0 +1,42 @@ +#!/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) diff --git a/benchmark/paddle/rnn/run.sh b/benchmark/paddle/rnn/run.sh new file mode 100755 index 00000000000..92c6e0b4b42 --- /dev/null +++ b/benchmark/paddle/rnn/run.sh @@ -0,0 +1,38 @@ +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 diff --git a/benchmark/paddle/rnn/run_multi.sh b/benchmark/paddle/rnn/run_multi.sh new file mode 100755 index 00000000000..50ee469bcd9 --- /dev/null +++ b/benchmark/paddle/rnn/run_multi.sh @@ -0,0 +1,34 @@ +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${3}-hid${5}-batch${6}.log 2>&1 +} + + +if [ ! -d "logs" ]; then + mkdir logs +fi + +#-----config--gpu--lstm_num--padding--hidden_size--batch_size +#==================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 diff --git a/benchmark/tensorflow/image/alexnet.py b/benchmark/tensorflow/image/alexnet.py new file mode 100644 index 00000000000..57b7ef6c323 --- /dev/null +++ b/benchmark/tensorflow/image/alexnet.py @@ -0,0 +1,260 @@ +from six.moves import xrange # pylint: disable=redefined-builtin +from datetime import datetime +import math +import time + +import tensorflow.python.platform +import tensorflow as tf + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 128, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_boolean('forward_only', False, + """Only run the forward pass.""") +tf.app.flags.DEFINE_boolean('forward_backward_only', False, + """Only run the forward-forward pass.""") +tf.app.flags.DEFINE_string('data_format', 'NCHW', + """The data format for Convnet operations. + Can be either NHWC or NCHW. + """) +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") + +def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.0005): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w',[kH, kW, nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + if wd is not None and wd > 0: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + if FLAGS.data_format == 'NCHW': + strides = [1, 1, dH, dW] + else: + strides = [1, dH, dW, 1] + conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType, + data_format=FLAGS.data_format) + + biases = tf.get_variable(name=name + '_b', shape=[nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32) + + bias = tf.reshape( + tf.nn.bias_add(conv, biases, data_format=FLAGS.data_format), + conv.get_shape()) + + conv1 = tf.nn.relu(bias, name=scope) + return conv1 + +def _affine(name, inpOp, nIn, nOut, wd=0.0005, act=True, drop=None): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w', [nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + if wd is not None and wd > 0: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + biases = tf.get_variable(name + '_b', [nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32,trainable=True) + + affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ + tf.matmul(inpOp, kernel) + biases + + output = tf.nn.dropout(affine1, drop) if drop else affine1 + + return output + +def _mpool(name, inpOp, kH, kW, dH, dW): + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.max_pool(inpOp, + ksize=ksize, + strides=strides, + padding='VALID', + data_format=FLAGS.data_format, + name=name) + +def _norm(name, l_input, lsize=4): + return tf.nn.lrn(l_input, lsize, bias=1.0, + alpha=0.001 / 9.0, + beta=0.75, name=name) + + + +def loss(logits, labels): + labels = tf.cast(labels, tf.int64) + cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( + logits, labels, name='cross_entropy_per_example') + cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') + tf.add_to_collection('losses', cross_entropy_mean) + + # The total loss is defined as the cross entropy loss plus all of the weight + # decay terms (L2 loss). + return tf.add_n(tf.get_collection('losses'), name='total_loss') + +def get_incoming_shape(incoming): + """ Returns the incoming data shape """ + if isinstance(incoming, tf.Tensor): + return incoming.get_shape().as_list() + elif type(incoming) in [np.array, list, tuple]: + return np.shape(incoming) + else: + raise Exception("Invalid incoming layer.") + +def inference(images): + conv1 = _conv ('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID') + pool1 = _mpool('pool1', conv1, 3, 3, 2, 2) + norm1 = _norm ('norm1', pool1, lsize=5) + conv2 = _conv ('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME') + pool2 = _mpool('pool2', conv2, 3, 3, 2, 2) + norm2 = _norm ('norm2', pool2, lsize=5) + conv3 = _conv ('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME') + conv4 = _conv ('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME') + conv5 = _conv ('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME') + pool5 = _mpool('pool5', conv5, 3, 3, 2, 2) + resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6]) + affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096, 0.5) + affn2 = _affine('fc7', affn1, 4096, 4096, 0.5) + affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc + + return affn3 + + +def time_tensorflow_run(session, target, info_string): + num_steps_burn_in = 10 + total_duration = 0.0 + total_duration_squared = 0.0 + if not isinstance(target, list): + target = [target] + target_op = tf.group(*target) + for i in xrange(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + _ = session.run(target_op) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + print ('%s: step %d, duration = %.3f' % + (datetime.now(), i - num_steps_burn_in, duration)) + total_duration += duration + total_duration_squared += duration * duration + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), info_string, FLAGS.num_batches, mn, sd)) + +def _add_loss_summaries(total_loss): + """ + Generates moving average for all losses and associated summaries for + visualizing the performance of the network. + + Args: + total_loss: Total loss from loss(). + Returns: + loss_averages_op: op for generating moving averages of losses. + """ + # Compute the moving average of all individual losses and the total loss. + loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') + losses = tf.get_collection('losses') + loss_averages_op = loss_averages.apply(losses + [total_loss]) + + # Attach a scalar summary to all individual losses and the total loss; do the + # same for the averaged version of the losses. + for l in losses + [total_loss]: + # Name each loss as '(raw)' and name the moving average version of the loss + # as the original loss name. + tf.scalar_summary(l.op.name +' (raw)', l) + tf.scalar_summary(l.op.name, loss_averages.average(l)) + + return loss_averages_op + + + +def run_benchmark(): + with tf.Graph().as_default(): + with tf.device('/gpu:0'): + # Generate some dummy images. + image_size = 224 + # Note that our padding definition is slightly different the cuda-convnet. + # In order to force the model to start with the same activations sizes, + # we add 3 to the image_size and employ VALID padding above. + if FLAGS.data_format == 'NCHW': + image_shape = [FLAGS.batch_size, 3, image_size + 3, image_size + 3] + else: + image_shape = [FLAGS.batch_size, image_size + 3, image_size + 3, 3] + images = tf.get_variable('image', image_shape, + initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32), + dtype=tf.float32, + trainable=False) + + labels = tf.get_variable('label', [FLAGS.batch_size], + initializer=tf.constant_initializer(1), + dtype=tf.int32, + trainable=False) + + # Build a Graph that computes the logits predictions from the + # inference model. + last_layer = inference(images) + + objective = loss(last_layer, labels) + # Compute the gradient with respect to all the parameters. + + # Compute gradients. + # opt = tf.train.GradientDescentOptimizer(0.001) + opt = tf.train.MomentumOptimizer(0.001, 0.9) + grads = opt.compute_gradients(objective) + global_step = tf.get_variable('global_step', [], + initializer=tf.constant_initializer(0.0, dtype=tf.float32), + trainable=False, dtype=tf.float32) + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Track the moving averages of all trainable variables. + variable_averages = tf.train.ExponentialMovingAverage( + 0.9, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + # Build an initialization operation. + init = tf.initialize_all_variables() + + # Start running operations on the Graph. + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + + run_forward = True + run_forward_backward = True + if FLAGS.forward_only and FLAGS.forward_backward_only: + raise ValueError("Cannot specify --forward_only and " + "--forward_backward_only at the same time.") + if FLAGS.forward_only: + run_forward_backward = False + elif FLAGS.forward_backward_only: + run_forward = False + + if run_forward: + time_tensorflow_run(sess, last_layer, "Forward") + + if run_forward_backward: + with tf.control_dependencies([apply_gradient_op, variables_averages_op]): + train_op = tf.no_op(name='train') + time_tensorflow_run(sess, [train_op, objective], "Forward-backward") + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() diff --git a/benchmark/tensorflow/image/alexnet_multi_gpu.py b/benchmark/tensorflow/image/alexnet_multi_gpu.py new file mode 100644 index 00000000000..949ad77f3b8 --- /dev/null +++ b/benchmark/tensorflow/image/alexnet_multi_gpu.py @@ -0,0 +1,335 @@ +from six.moves import xrange # pylint: disable=redefined-builtin +from datetime import datetime +import math +import re +import time + +import tensorflow.python.platform +import tensorflow as tf + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 64, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_string('data_format', 'NCHW', + """The data format for Convnet operations. + Can be either NHWC or NCHW. + """) + +tf.app.flags.DEFINE_string('train_dir', '/train_model', + """Directory where to write event logs """ + """and checkpoint.""") +tf.app.flags.DEFINE_integer('num_gpus', 4, + """How many GPUs to use.""") +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") + +NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN=50000 +NUM_EPOCHS_PER_DECAY=50 +INITIAL_LEARNING_RATE = 0.1 +LEARNING_RATE_DECAY_FACTOR = 0.1 +TOWER_NAME = 'tower' + + +def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w',[kH, kW, nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + if wd is not None: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + if FLAGS.data_format == 'NCHW': + strides = [1, 1, dH, dW] + else: + strides = [1, dH, dW, 1] + conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType, + data_format=FLAGS.data_format) + + biases = tf.get_variable(name=name + '_b', shape=[nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32) + + bias = tf.reshape( + tf.nn.bias_add(conv, biases, data_format=FLAGS.data_format), + conv.get_shape()) + + conv1 = tf.nn.relu(bias, name=scope) + return conv1 + +def _affine(name, inpOp, nIn, nOut, wd=0.005, act=True): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w', [nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + if wd is not None: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + biases = tf.get_variable(name + '_b', [nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32,trainable=True) + + affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ + tf.matmul(inpOp, kernel) + biases + + return affine1 + +def _mpool(name, inpOp, kH, kW, dH, dW): + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.max_pool(inpOp, + ksize=ksize, + strides=strides, + padding='VALID', + data_format=FLAGS.data_format, + name=name) + +def _norm(name, l_input, lsize=4): + return tf.nn.lrn(l_input, lsize, bias=1.0, + alpha=0.001 / 9.0, + beta=0.75, name=name) + +def loss(logits, labels): + labels = tf.cast(labels, tf.int64) + cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( + logits, labels, name='cross_entropy_per_example') + cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') + tf.add_to_collection('losses', cross_entropy_mean) + + # The total loss is defined as the cross entropy loss plus all of the weight + # decay terms (L2 loss). + return tf.add_n(tf.get_collection('losses'), name='total_loss') + + +def get_incoming_shape(incoming): + """ Returns the incoming data shape """ + if isinstance(incoming, tf.Tensor): + return incoming.get_shape().as_list() + elif type(incoming) in [np.array, list, tuple]: + return np.shape(incoming) + else: + raise Exception("Invalid incoming layer.") + +def inference(images): + conv1 = _conv ('conv1', images, 3, 96, 11, 11, 4, 4, 'VALID') + pool1 = _mpool('pool1', conv1, 3, 3, 2, 2) + norm1 = _norm ('norm1', pool1, lsize=5) + conv2 = _conv ('conv2', norm1, 96, 256, 5, 5, 1, 1, 'SAME') + pool2 = _mpool('pool2', conv2, 3, 3, 2, 2) + norm2 = _norm ('norm2', pool2, lsize=5) + conv3 = _conv ('conv3', norm2, 256, 384, 3, 3, 1, 1, 'SAME') + conv4 = _conv ('conv4', conv3, 384, 384, 3, 3, 1, 1, 'SAME') + conv5 = _conv ('conv5', conv4, 384, 256, 3, 3, 1, 1, 'SAME') + pool5 = _mpool('pool5', conv5, 3, 3, 2, 2) + resh1 = tf.reshape(pool5, [-1, 256 * 6 * 6]) + affn1 = _affine('fc6', resh1, 256 * 6 * 6, 4096) + affn2 = _affine('fc7', affn1, 4096, 4096) + affn3 = _affine('fc8', affn2, 4096, 1000, wd=None, act=False) # last fc + + return affn3 + +def tower_loss(scope): + """Calculate the total loss on a single tower running the model. + Args: + scope: unique prefix string identifying the tower, e.g. 'tower_0' + Returns: + Tensor of shape [] containing the total loss for a batch of data + """ + image_size = 224 + if FLAGS.data_format == 'NCHW': + image_shape = [FLAGS.batch_size, 3, image_size + 3, image_size + 3] + else: + image_shape = [FLAGS.batch_size, image_size + 3, image_size + 3, 3] + images = tf.get_variable('image', image_shape, + initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32), + dtype=tf.float32, + trainable=False) + + labels = tf.get_variable('label', [FLAGS.batch_size], + initializer=tf.constant_initializer(1), + dtype=tf.int32, + trainable=False) + + # Build a Graph that computes the logits predictions from the + # inference model. + last_layer = inference(images) + + # Build the portion of the Graph calculating the losses. Note that we will + # assemble the total_loss using a custom function below. + _ = loss(last_layer, labels) + + # Assemble all of the losses for the current tower only. + losses = tf.get_collection('losses', scope) + + # Calculate the total loss for the current tower. + total_loss = tf.add_n(losses, name='total_loss') + + # Compute the moving average of all individual losses and the total loss. + loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') + loss_averages_op = loss_averages.apply(losses + [total_loss]) + + # Attach a scalar summary to all individual losses and the total loss; do the + # same for the averaged version of the losses. + for l in losses + [total_loss]: + # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training + # session. This helps the clarity of presentation on tensorboard. + loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name) + # Name each loss as '(raw)' and name the moving average version of the loss + # as the original loss name. + tf.scalar_summary(loss_name +' (raw)', l) + tf.scalar_summary(loss_name, loss_averages.average(l)) + + with tf.control_dependencies([loss_averages_op]): + total_loss = tf.identity(total_loss) + return total_loss + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(0, grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + +def time_tensorflow_run(session, target): + num_steps_burn_in = 50 + total_duration = 0.0 + total_duration_squared = 0.0 + for i in xrange(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + _, loss_value = session.run(target) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus + examples_per_sec = num_examples_per_step / duration + sec_per_batch = duration + + format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f ' + 'sec/batch batch_size = %d)') + print (format_str % + (datetime.now(), i - num_steps_burn_in, + loss_value, duration, sec_per_batch, num_examples_per_step)) + + total_duration += duration + total_duration_squared += duration * duration + + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), FLAGS.num_batches, mn, sd)) + +def run_benchmark(): + with tf.Graph().as_default(), tf.device('/cpu:0'): + # Create a variable to count the number of train() calls. This equals the + # number of batches processed * FLAGS.num_gpus. + global_step = tf.get_variable( + 'global_step', [], + initializer=tf.constant_initializer(0), trainable=False) + + # Calculate the learning rate schedule. + num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / + FLAGS.batch_size) + decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) + + # Decay the learning rate exponentially based on the number of steps. + lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, + global_step, + decay_steps, + LEARNING_RATE_DECAY_FACTOR, + staircase=True) + + # Create an optimizer that performs gradient descent. + # opt = tf.train.GradientDescentOptimizer(lr) + opt = tf.train.MomentumOptimizer(lr, 0.9) + + # Calculate the gradients for each model tower. + tower_grads = [] + for i in xrange(FLAGS.num_gpus): + with tf.device('/gpu:%d' % i): + with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope: + # Calculate the loss for one tower of the model. This function + # constructs the entire model but shares the variables across + # all towers. + loss = tower_loss(scope) + + # Reuse variables for the next tower. + tf.get_variable_scope().reuse_variables() + + # Retain the summaries from the final tower. + summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope) + + # Calculate the gradients for the batch of data on this tower. + grads = opt.compute_gradients(loss) + + # Keep track of the gradients across all towers. + tower_grads.append(grads) + + # We must calculate the mean of each gradient. Note that this is the + # synchronization point across all towers. + grads = average_gradients(tower_grads) + + # Apply the gradients to adjust the shared variables. + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Group all updates to into a single train op. + train_op = tf.group(apply_gradient_op) + + # Build an initialization operation. + init = tf.initialize_all_variables() + + # Start running operations on the Graph. allow_soft_placement must be set to + # True to build towers on GPU, as some of the ops do not have GPU + # implementations. + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + time_tensorflow_run(sess, [train_op, loss]) + + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() diff --git a/benchmark/tensorflow/image/googlenet.py b/benchmark/tensorflow/image/googlenet.py new file mode 100644 index 00000000000..097a8997b78 --- /dev/null +++ b/benchmark/tensorflow/image/googlenet.py @@ -0,0 +1,282 @@ +from six.moves import xrange +from datetime import datetime +import math +import time + +import tensorflow.python.platform +import tensorflow as tf + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 128, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_boolean('forward_only', False, + """Only run the forward pass.""") +tf.app.flags.DEFINE_boolean('forward_backward_only', False, + """Only run the forward-forward pass.""") +tf.app.flags.DEFINE_string('data_format', 'NCHW', + """The data format for Convnet operations. + Can be either NHWC or NCHW. + """) +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") + +parameters = [] + +conv_counter = 1 +pool_counter = 1 +affine_counter = 1 + +def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd = 0.0005): + global conv_counter + global parameters + name = 'conv' + str(conv_counter) + conv_counter += 1 + with tf.name_scope(name) as scope: + kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut], + dtype=tf.float32, + stddev=1e-1), name='weights') + + if wd is not None and wd > 0: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + if FLAGS.data_format == 'NCHW': + strides = [1, 1, dH, dW] + else: + strides = [1, dH, dW, 1] + conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType, + data_format=FLAGS.data_format) + biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32), + trainable=True, name='biases') + bias = tf.reshape(tf.nn.bias_add(conv, biases, + data_format=FLAGS.data_format), + conv.get_shape()) + conv1 = tf.nn.relu(bias, name=scope) + parameters += [kernel, biases] + return conv1 + +def _affine(inpOp, nIn, nOut, act=True, wd = 0.0005): + global affine_counter + global parameters + name = 'affine' + str(affine_counter) + affine_counter += 1 + with tf.name_scope(name) as scope: + kernel = tf.Variable(tf.truncated_normal([nIn, nOut], + dtype=tf.float32, + stddev=1e-1), name='weights') + + if wd is not None and wd > 0: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32), + trainable=True, name='biases') + affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else tf.matmul(inpOp, kernel) + biases + parameters += [kernel, biases] + return affine1 + +def _mpool(inpOp, kH, kW, dH, dW, padding): + global pool_counter + global parameters + name = 'pool' + str(pool_counter) + pool_counter += 1 + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.max_pool(inpOp, + ksize=ksize, + strides=strides, + padding=padding, + data_format=FLAGS.data_format, + name=name) + +def _apool(inpOp, kH, kW, dH, dW, padding): + global pool_counter + global parameters + name = 'pool' + str(pool_counter) + pool_counter += 1 + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.avg_pool(inpOp, + ksize=ksize, + strides=strides, + padding=padding, + data_format=FLAGS.data_format, + name=name) + +def _inception(inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2): + conv1 = _conv(inp, inSize, o1s, 1, 1, 1, 1, 'VALID') + + conv3_ = _conv(inp, inSize, o2s1, 1, 1, 1, 1, 'VALID') + conv3 = _conv(conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME') + + conv5_ = _conv(inp, inSize, o3s1, 1, 1, 1, 1, 'VALID') + conv5 = _conv(conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME') + + pool_ = _mpool(inp, o4s1, o4s1, 1, 1, 'SAME') + pool = _conv(pool_, inSize, o4s2, 1, 1, 1, 1, 'VALID') + + if FLAGS.data_format == 'NCHW': + channel_dim = 1 + else: + channel_dim = 3 + incept = tf.concat(channel_dim, [conv1, conv3, conv5, pool]) + return incept + + +def loss(logits, labels): + batch_size = tf.size(labels) + labels = tf.expand_dims(labels, 1) + indices = tf.expand_dims(tf.range(0, batch_size, 1), 1) + concated = tf.concat(1, [indices, labels]) + onehot_labels = tf.sparse_to_dense( + concated, tf.pack([batch_size, 1000]), 1.0, 0.0) + cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, + onehot_labels, + name='xentropy') + loss = tf.reduce_mean(cross_entropy, name='xentropy_mean') + return loss + +def inference(images): + # stage 1 + conv1 = _conv (images, 3, 64, 7, 7, 2, 2, 'SAME') + pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME') + # stage 2 + conv2 = _conv (pool1, 64, 64, 1, 1, 1, 1, 'VALID') + conv3 = _conv (conv2, 64, 192, 3, 3, 1, 1, 'SAME') + pool3 = _mpool(conv3, 3, 3, 2, 2, 'SAME') + + # stage 3 + incept3a = _inception(pool3, 192, 64, 96, 128, 16, 32, 3, 32) + incept3b = _inception(incept3a, 256, 128, 128, 192, 32, 96, 3, 64) + pool4 = _mpool(incept3b, 3, 3, 2, 2, 'SAME') + + # stage 4 + incept4a = _inception(pool4, 480, 192, 96, 208, 16, 48, 3, 64) + incept4b = _inception(incept4a, 512, 160, 112, 224, 24, 64, 3, 64) + incept4c = _inception(incept4b, 512, 128, 128, 256, 24, 64, 3, 64) + incept4d = _inception(incept4c, 512, 112, 144, 288, 32, 64, 3, 64) + incept4e = _inception(incept4d, 528, 256, 160, 320, 32, 128, 3, 128) + pool5 = _mpool(incept4e, 3, 3, 2, 2, 'SAME') + + # stage 5 + incept5a = _inception(pool5, 832, 256, 160, 320, 32, 128, 3, 128) + incept5b = _inception(incept5a, 832, 384, 192, 384, 48, 128, 3, 128) + pool6 = _apool(incept5b, 7, 7, 1, 1, 'VALID') + + # output 1 + resh1 = tf.reshape(pool6, [-1, 1024]) + drop = tf.nn.dropout(resh1, 0.4) + affn1 = _affine(resh1, 1024, 1000, act=False) + + return affn1 + + +def time_tensorflow_run(session, target, info_string): + num_steps_burn_in = 10 + total_duration = 0.0 + total_duration_squared = 0.0 + if not isinstance(target, list): + target = [target] + target_op = tf.group(*target) + for i in range(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + _ = session.run(target_op) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + print ('%s: step %d, duration = %.3f' % + (datetime.now(), i - num_steps_burn_in, duration)) + total_duration += duration + total_duration_squared += duration * duration + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), info_string, FLAGS.num_batches, mn, sd)) + +def run_benchmark(): + global parameters + with tf.Graph().as_default(): + # Generate some dummy images. + image_size = 224 + if FLAGS.data_format == 'NCHW': + image_shape = [FLAGS.batch_size, 3, image_size, image_size] + else: + image_shape = [FLAGS.batch_size, image_size, image_size, 3] + + images = tf.get_variable('image', image_shape, + initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32), + dtype=tf.float32, + trainable=False) + + labels = tf.get_variable('label', [FLAGS.batch_size], + initializer=tf.constant_initializer(1), + dtype=tf.int32, + trainable=False) + + # Build a Graph that computes the logits predictions from the + # inference model. + last_layer = inference(images) + + objective = loss(last_layer, labels) + + # Compute gradients. + # opt = tf.train.GradientDescentOptimizer(0.001) + opt = tf.train.MomentumOptimizer(0.001, 0.9) + grads = opt.compute_gradients(objective) + global_step = tf.get_variable('global_step', [], + initializer=tf.constant_initializer(0.0, dtype=tf.float32), + trainable=False, dtype=tf.float32) + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Track the moving averages of all trainable variables. + variable_averages = tf.train.ExponentialMovingAverage( + 0.9, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + # Build an initialization operation. + init = tf.initialize_all_variables() + + # Start running operations on the Graph. + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + + run_forward = True + run_forward_backward = True + if FLAGS.forward_only and FLAGS.forward_backward_only: + raise ValueError("Cannot specify --forward_only and " + "--forward_backward_only at the same time.") + if FLAGS.forward_only: + run_forward_backward = False + elif FLAGS.forward_backward_only: + run_forward = False + + if run_forward: + # Run the forward benchmark. + time_tensorflow_run(sess, last_layer, "Forward") + + if run_forward_backward: + with tf.control_dependencies([apply_gradient_op, variables_averages_op]): + train_op = tf.no_op(name='train') + time_tensorflow_run(sess, [train_op, objective], "Forward-backward") + + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() diff --git a/benchmark/tensorflow/image/googlenet_multi_gpu.py b/benchmark/tensorflow/image/googlenet_multi_gpu.py new file mode 100644 index 00000000000..e22a6b6253e --- /dev/null +++ b/benchmark/tensorflow/image/googlenet_multi_gpu.py @@ -0,0 +1,381 @@ +from six.moves import xrange # pylint: disable=redefined-builtin +from datetime import datetime +import math +import re +import time + +import tensorflow.python.platform +import tensorflow as tf + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 64, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_string('data_format', 'NCHW', + """The data format for Convnet operations. + Can be either NHWC or NCHW. + """) + +tf.app.flags.DEFINE_string('train_dir', '/train_model', + """Directory where to write event logs """ + """and checkpoint.""") +tf.app.flags.DEFINE_integer('num_gpus', 4, + """How many GPUs to use.""") +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") + +NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN=50000 +NUM_EPOCHS_PER_DECAY=50 +INITIAL_LEARNING_RATE = 0.1 +LEARNING_RATE_DECAY_FACTOR = 0.1 +TOWER_NAME = 'tower' + + +def _conv(name, inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w',[kH, kW, nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + if wd is not None: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + if FLAGS.data_format == 'NCHW': + strides = [1, 1, dH, dW] + else: + strides = [1, dH, dW, 1] + conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType, + data_format=FLAGS.data_format) + + biases = tf.get_variable(name=name + '_b', shape=[nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32) + + bias = tf.reshape( + tf.nn.bias_add(conv, biases, data_format=FLAGS.data_format), + conv.get_shape()) + + conv1 = tf.nn.relu(bias, name=scope) + return conv1 + +def _affine(name, inpOp, nIn, nOut, wd=0.005, act=True): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w', [nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + if wd is not None: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + biases = tf.get_variable(name + '_b', [nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32,trainable=True) + + affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ + tf.matmul(inpOp, kernel) + biases + + return affine1 + +def _mpool(name, inpOp, kH, kW, dH, dW, padding): + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.max_pool(inpOp, + ksize=ksize, + strides=strides, + padding=padding, + data_format=FLAGS.data_format, + name=name) + +def _apool(name, inpOp, kH, kW, dH, dW, padding): + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.avg_pool(inpOp, + ksize=ksize, + strides=strides, + padding=padding, + data_format=FLAGS.data_format, + name=name) + +def loss(logits, labels): + labels = tf.cast(labels, tf.int64) + cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( + logits, labels, name='cross_entropy_per_example') + cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') + tf.add_to_collection('losses', cross_entropy_mean) + + # The total loss is defined as the cross entropy loss plus all of the weight + # decay terms (L2 loss). + return tf.add_n(tf.get_collection('losses'), name='total_loss') + + +def get_incoming_shape(incoming): + """ Returns the incoming data shape """ + if isinstance(incoming, tf.Tensor): + return incoming.get_shape().as_list() + elif type(incoming) in [np.array, list, tuple]: + return np.shape(incoming) + else: + raise Exception("Invalid incoming layer.") + + +def _inception(name, inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2): + conv1 = _conv(name + '_1' , inp, inSize, o1s, 1, 1, 1, 1, 'VALID') + + conv3_ = _conv(name + '_3r', inp, inSize, o2s1, 1, 1, 1, 1, 'VALID') + conv3 = _conv(name + '_3', conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME') + + conv5_ = _conv(name + '_5r', inp, inSize, o3s1, 1, 1, 1, 1, 'VALID') + conv5 = _conv(name + '5', conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME') + + pool_ = _mpool(name + 'pool', inp, o4s1, o4s1, 1, 1, 'SAME') + pool = _conv(name + 'proj', pool_, inSize, o4s2, 1, 1, 1, 1, 'VALID') + + if FLAGS.data_format == 'NCHW': + channel_dim = 1 + else: + channel_dim = 3 + incept = tf.concat(channel_dim, [conv1, conv3, conv5, pool]) + return incept + + +def inference(images): + # stage 1 + conv1 = _conv ('conv1', images, 3, 64, 7, 7, 2, 2, 'SAME') + pool1 = _mpool('pool1', conv1, 3, 3, 2, 2, 'SAME') + + # stage 2 + conv2 = _conv ('conv2', pool1, 64, 64, 1, 1, 1, 1, 'VALID') + conv3 = _conv ('conv3', conv2, 64, 192, 3, 3, 1, 1, 'SAME') + pool3 = _mpool('pool3', conv3, 3, 3, 2, 2, 'SAME') + + # stage 3 + incept3a = _inception('ince3a', pool3, 192, 64, 96, 128, 16, 32, 3, 32) + incept3b = _inception('ince3b', incept3a, 256, 128, 128, 192, 32, 96, 3, 64) + pool4 = _mpool('pool4', incept3b, 3, 3, 2, 2, 'SAME') + + # stage 4 + incept4a = _inception('ince4a', pool4, 480, 192, 96, 208, 16, 48, 3, 64) + incept4b = _inception('ince4b', incept4a, 512, 160, 112, 224, 24, 64, 3, 64) + incept4c = _inception('ince4c', incept4b, 512, 128, 128, 256, 24, 64, 3, 64) + incept4d = _inception('ince4d', incept4c, 512, 112, 144, 288, 32, 64, 3, 64) + incept4e = _inception('ince4e', incept4d, 528, 256, 160, 320, 32, 128, 3, 128) + pool5 = _mpool('pool5', incept4e, 3, 3, 2, 2, 'SAME') + + # stage 5 + incept5a = _inception('ince5a', pool5, 832, 256, 160, 320, 32, 128, 3, 128) + incept5b = _inception('ince5b', incept5a, 832, 384, 192, 384, 48, 128, 3, 128) + pool6 = _apool('pool6', incept5b, 7, 7, 1, 1, 'VALID') + + # output 1 + resh1 = tf.reshape(pool6, [-1, 1024]) + drop = tf.nn.dropout(resh1, 0.4) + affn1 = _affine('fc_out', resh1, 1024, 1000, act=False) + + return affn1 + +def tower_loss(scope): + """Calculate the total loss on a single tower running the model. + Args: + scope: unique prefix string identifying the tower, e.g. 'tower_0' + Returns: + Tensor of shape [] containing the total loss for a batch of data + """ + image_size = 224 + if FLAGS.data_format == 'NCHW': + image_shape = [FLAGS.batch_size, 3, image_size, image_size] + else: + image_shape = [FLAGS.batch_size, image_size, image_size, 3] + images = tf.get_variable('image', image_shape, + initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32), + dtype=tf.float32, + trainable=False) + + labels = tf.get_variable('label', [FLAGS.batch_size], + initializer=tf.constant_initializer(1), + dtype=tf.int32, + trainable=False) + + # Build a Graph that computes the logits predictions from the + # inference model. + last_layer = inference(images) + + # Build the portion of the Graph calculating the losses. Note that we will + # assemble the total_loss using a custom function below. + _ = loss(last_layer, labels) + + # Assemble all of the losses for the current tower only. + losses = tf.get_collection('losses', scope) + + # Calculate the total loss for the current tower. + total_loss = tf.add_n(losses, name='total_loss') + + # Compute the moving average of all individual losses and the total loss. + loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') + loss_averages_op = loss_averages.apply(losses + [total_loss]) + + # Attach a scalar summary to all individual losses and the total loss; do the + # same for the averaged version of the losses. + for l in losses + [total_loss]: + # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training + # session. This helps the clarity of presentation on tensorboard. + loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name) + # Name each loss as '(raw)' and name the moving average version of the loss + # as the original loss name. + tf.scalar_summary(loss_name +' (raw)', l) + tf.scalar_summary(loss_name, loss_averages.average(l)) + + with tf.control_dependencies([loss_averages_op]): + total_loss = tf.identity(total_loss) + return total_loss + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(0, grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + +def time_tensorflow_run(session, target): + num_steps_burn_in = 50 + total_duration = 0.0 + total_duration_squared = 0.0 + for i in xrange(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + _, loss_value = session.run(target) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus + examples_per_sec = num_examples_per_step / duration + sec_per_batch = duration + + format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f ' + 'sec/batch batch_size = %d)') + print (format_str % + (datetime.now(), i - num_steps_burn_in, + loss_value, duration, sec_per_batch, num_examples_per_step)) + + total_duration += duration + total_duration_squared += duration * duration + + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), FLAGS.num_batches, mn, sd)) + +def run_benchmark(): + with tf.Graph().as_default(), tf.device('/cpu:0'): + # Create a variable to count the number of train() calls. This equals the + # number of batches processed * FLAGS.num_gpus. + global_step = tf.get_variable( + 'global_step', [], + initializer=tf.constant_initializer(0), trainable=False) + + # Calculate the learning rate schedule. + num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / + FLAGS.batch_size) + decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) + + # Decay the learning rate exponentially based on the number of steps. + lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, + global_step, + decay_steps, + LEARNING_RATE_DECAY_FACTOR, + staircase=True) + + # Create an optimizer that performs gradient descent. + opt = tf.train.MomentumOptimizer(lr, 0.9) + + # Calculate the gradients for each model tower. + tower_grads = [] + for i in xrange(FLAGS.num_gpus): + with tf.device('/gpu:%d' % i): + with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope: + # Calculate the loss for one tower of the model. This function + # constructs the entire model but shares the variables across + # all towers. + loss = tower_loss(scope) + + # Reuse variables for the next tower. + tf.get_variable_scope().reuse_variables() + + # Retain the summaries from the final tower. + summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope) + + # Calculate the gradients for the batch of data on this tower. + grads = opt.compute_gradients(loss) + + # Keep track of the gradients across all towers. + tower_grads.append(grads) + + # We must calculate the mean of each gradient. Note that this is the + # synchronization point across all towers. + grads = average_gradients(tower_grads) + + # Apply the gradients to adjust the shared variables. + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Group all updates to into a single train op. + train_op = tf.group(apply_gradient_op) + + # Build an initialization operation. + init = tf.initialize_all_variables() + + # Start running operations on the Graph. allow_soft_placement must be set to + # True to build towers on GPU, as some of the ops do not have GPU + # implementations. + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + time_tensorflow_run(sess, [train_op, loss]) + + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() diff --git a/benchmark/tensorflow/image/run.sh b/benchmark/tensorflow/image/run.sh new file mode 100755 index 00000000000..eade36beb9d --- /dev/null +++ b/benchmark/tensorflow/image/run.sh @@ -0,0 +1,28 @@ +set -e + +function test() { + cfg=$1 + batch_size=$2 + prefix=$3 + python $cfg --batch_size=$batch_size > logs/${prefix}-1gpu-${batch_size}.log 2>&1 +} + +if [ ! -d "logs" ]; then + mkdir logs +fi + +# alexnet +test alexnet.py 64 alexnet +test alexnet.py 128 alexnet +test alexnet.py 256 alexnet +test alexnet.py 512 alexnet + +# googlenet +test googlenet.py 64 googlenet +test googlenet.py 128 googlenet + +# smallnet +test smallnet_mnist_cifar.py 64 smallnet +test smallnet_mnist_cifar.py 128 smallnet +test smallnet_mnist_cifar.py 256 smallnet +test smallnet_mnist_cifar.py 512 smallnet diff --git a/benchmark/tensorflow/image/run_multi.sh b/benchmark/tensorflow/image/run_multi.sh new file mode 100755 index 00000000000..69faa433174 --- /dev/null +++ b/benchmark/tensorflow/image/run_multi.sh @@ -0,0 +1,22 @@ +set -e + +function test() { + cfg=$1 + num_gpu=$2 + batch_size=$3 + batch_per_gpu=`expr ${batch_size} / ${num_gpu}` + prefix=$4 + python $cfg --num_gpus=$num_gpu --batch_size=${batch_per_gpu} > logs/${prefix}-4gpu-${batch_size}.log 2>&1 +} + +if [ ! -d "logs" ]; then + mkdir logs +fi + +# alexnet +test alexnet_multi_gpu.py 4 512 alexnet +test alexnet_multi_gpu.py 4 1024 alexnet + +# googlenet +test googlenet_multi_gpu.py 4 512 alexnet +test googlenet_multi_gpu.py 4 1024 alexnet diff --git a/benchmark/tensorflow/image/smallnet_mnist_cifar.py b/benchmark/tensorflow/image/smallnet_mnist_cifar.py new file mode 100644 index 00000000000..b539d1bed06 --- /dev/null +++ b/benchmark/tensorflow/image/smallnet_mnist_cifar.py @@ -0,0 +1,273 @@ +from six.moves import xrange # pylint: disable=redefined-builtin +from datetime import datetime +import math +import time + +import tensorflow.python.platform +import tensorflow as tf + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 128, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_boolean('forward_only', False, + """Only run the forward pass.""") +tf.app.flags.DEFINE_boolean('forward_backward_only', False, + """Only run the forward-forward pass.""") +tf.app.flags.DEFINE_string('data_format', 'NCHW', + """The data format for Convnet operations. + Can be either NHWC or NCHW. + """) +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") + +parameters = [] + +conv_counter = 1 +pool_counter = 1 +affine_counter = 1 + +def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, wd=0.005, act=True): + global conv_counter + global parameters + name = 'conv' + str(conv_counter) + conv_counter += 1 + with tf.name_scope(name) as scope: + kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut], + dtype=tf.float32, + stddev=1e-1), name='weights') + + if wd is not None: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + if FLAGS.data_format == 'NCHW': + strides = [1, 1, dH, dW] + else: + strides = [1, dH, dW, 1] + conv = tf.nn.conv2d(inpOp, kernel, strides, padding=padType, + data_format=FLAGS.data_format) + biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32), + trainable=True, name='biases') + bias = tf.reshape(tf.nn.bias_add(conv, biases, + data_format=FLAGS.data_format), + conv.get_shape()) + + conv1 = tf.nn.relu(bias, name=scope) if act else bias + + parameters += [kernel, biases] + + return conv1 + +def _affine(inpOp, nIn, nOut, wd=None, act=True): + global affine_counter + global parameters + name = 'affine' + str(affine_counter) + affine_counter += 1 + with tf.name_scope(name) as scope: + kernel = tf.Variable(tf.truncated_normal([nIn, nOut], + dtype=tf.float32, + stddev=1e-1), name='weights') + + if wd is not None: + weight_decay = tf.mul(tf.nn.l2_loss(kernel), wd, name='weight_loss') + tf.add_to_collection('losses', weight_decay) + + biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32), + trainable=True, name='biases') + + affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else tf.matmul(inpOp, kernel) + biases + + parameters += [kernel, biases] + + return affine1 + +def _mpool(inpOp, kH, kW, dH, dW, padding): + global pool_counter + global parameters + name = 'pool' + str(pool_counter) + pool_counter += 1 + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.max_pool(inpOp, + ksize=ksize, + strides=strides, + padding=padding, + data_format=FLAGS.data_format, + name=name) + + +def _apool(inpOp, kH, kW, dH, dW, padding): + global pool_counter + global parameters + name = 'pool' + str(pool_counter) + pool_counter += 1 + if FLAGS.data_format == 'NCHW': + ksize = [1, 1, kH, kW] + strides = [1, 1, dH, dW] + else: + ksize = [1, kH, kW, 1] + strides = [1, dH, dW, 1] + return tf.nn.avg_pool(inpOp, + ksize=ksize, + strides=strides, + padding=padding, + data_format=FLAGS.data_format, + name=name) + +def _norm(name, l_input, lsize=4): + return tf.nn.lrn(l_input, lsize, bias=1.0, + alpha=0.001 / 9.0, + beta=0.75, name=name) + +def loss(logits, labels): + batch_size = tf.size(labels) + labels = tf.expand_dims(labels, 1) + indices = tf.expand_dims(tf.range(0, batch_size, 1), 1) + concated = tf.concat(1, [indices, labels]) + onehot_labels = tf.sparse_to_dense( + concated, tf.pack([batch_size, 10]), 1.0, 0.0) + cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, + onehot_labels, + name='xentropy') + loss = tf.reduce_mean(cross_entropy, name='xentropy_mean') + return loss + +def get_incoming_shape(incoming): + """ Returns the incoming data shape """ + if isinstance(incoming, tf.Tensor): + return incoming.get_shape().as_list() + elif type(incoming) in [np.array, list, tuple]: + return np.shape(incoming) + else: + raise Exception("Invalid incoming layer.") + +def inference(images): + conv1 = _conv (images, 3, 32, 5, 5, 1, 1, 'SAME') + pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME') + conv2 = _conv (pool1, 32, 32, 5, 5, 1, 1, 'SAME') + pool2 = _apool(conv2, 3, 3, 2, 2, 'SAME') + conv3 = _conv (pool2, 32, 64, 5, 5, 1, 1, 'SAME') + pool3 = _apool(conv3, 3, 3, 2, 2, 'SAME') + resh1 = tf.reshape(pool3, [-1, 64 * 4 * 4]) + affn1 = _affine(resh1, 64 * 4 * 4, 64) + affn2 = _affine(affn1, 64, 10, act=False) + + print ('conv1:', get_incoming_shape(conv1)) + print ('pool1:', get_incoming_shape(pool1)) + print ('conv2:', get_incoming_shape(conv2)) + print ('pool2:', get_incoming_shape(pool2)) + print ('conv3:', get_incoming_shape(conv3)) + print ('pool3:', get_incoming_shape(pool3)) + + return affn2 + + +def time_tensorflow_run(session, target, info_string): + num_steps_burn_in = 10 + total_duration = 0.0 + total_duration_squared = 0.0 + if not isinstance(target, list): + target = [target] + target_op = tf.group(*target) + for i in xrange(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + _ = session.run(target_op) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + print ('%s: step %d, duration = %.3f' % + (datetime.now(), i - num_steps_burn_in, duration)) + total_duration += duration + total_duration_squared += duration * duration + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), info_string, FLAGS.num_batches, mn, sd)) + +def run_benchmark(): + global parameters + with tf.Graph().as_default(): + # Generate some dummy images. + image_size = 32 + # Note that our padding definition is slightly different the cuda-convnet. + # In order to force the model to start with the same activations sizes, + # we add 3 to the image_size and employ VALID padding above. + if FLAGS.data_format == 'NCHW': + image_shape = [FLAGS.batch_size, 3, image_size, image_size] + else: + image_shape = [FLAGS.batch_size, image_size, image_size, 3] + + images = tf.get_variable('image', image_shape, + initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32), + dtype=tf.float32, + trainable=False) + + labels = tf.get_variable('label', [FLAGS.batch_size], + initializer=tf.constant_initializer(1), + dtype=tf.int32, + trainable=False) + + # Build a Graph that computes the logits predictions from the + # inference model. + last_layer = inference(images) + + objective = loss(last_layer, labels) + + # Compute gradients. + # opt = tf.train.GradientDescentOptimizer(0.001) + opt = tf.train.MomentumOptimizer(0.001, 0.9) + grads = opt.compute_gradients(objective) + global_step = tf.get_variable('global_step', [], + initializer=tf.constant_initializer(0.0, dtype=tf.float32), + trainable=False, dtype=tf.float32) + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Track the moving averages of all trainable variables. + variable_averages = tf.train.ExponentialMovingAverage( + 0.9, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + + # Build an initialization operation. + init = tf.initialize_all_variables() + + # Start running operations on the Graph. + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + + run_forward = True + run_forward_backward = True + if FLAGS.forward_only and FLAGS.forward_backward_only: + raise ValueError("Cannot specify --forward_only and " + "--forward_backward_only at the same time.") + if FLAGS.forward_only: + run_forward_backward = False + elif FLAGS.forward_backward_only: + run_forward = False + + if run_forward: + # Run the forward benchmark. + time_tensorflow_run(sess, last_layer, "Forward") + + if run_forward_backward: + with tf.control_dependencies([apply_gradient_op, variables_averages_op]): + train_op = tf.no_op(name='train') + time_tensorflow_run(sess, [train_op, objective], "Forward-backward") + + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() diff --git a/benchmark/tensorflow/rnn/README.md b/benchmark/tensorflow/rnn/README.md new file mode 100644 index 00000000000..b5314d54460 --- /dev/null +++ b/benchmark/tensorflow/rnn/README.md @@ -0,0 +1,5 @@ +You also should install tflearn: + +```bash +pip install tflearn +``` diff --git a/benchmark/tensorflow/rnn/reader.py b/benchmark/tensorflow/rnn/reader.py new file mode 100755 index 00000000000..0d8308046ed --- /dev/null +++ b/benchmark/tensorflow/rnn/reader.py @@ -0,0 +1,90 @@ +import os.path +import io +import numpy as np +import tensorflow as tf + +# tflearn +import tflearn +from tflearn.data_utils import to_categorical, pad_sequences +from tflearn.datasets import imdb + + +FLAGS = tf.app.flags.FLAGS + +class DataSet(object): + def __init__(self, data, labels): + assert data.shape[0] == labels.shape[0], ( + 'data.shape: %s labels.shape: %s' % (data.shape, + labels.shape)) + self._num_examples = data.shape[0] + + self._data = data + self._labels = labels + self._epochs_completed = 0 + self._index_in_epoch = 0 + + @property + def data(self): + return self._data + + @property + def labels(self): + return self._labels + + @property + def num_examples(self): + return self._num_examples + + @property + def epochs_completed(self): + return self._epochs_completed + + def next_batch(self, batch_size): + assert batch_size <= self._num_examples + + start = self._index_in_epoch + self._index_in_epoch += batch_size + if self._index_in_epoch > self._num_examples: + # Finished epoch + self._epochs_completed += 1 + # Shuffle the data + perm = np.arange(self._num_examples) + np.random.shuffle(perm) + self._data = self._data[perm] + self._labels = self._labels[perm] + # Start next epoch + start = 0 + self._index_in_epoch = batch_size + + end = self._index_in_epoch + + return self._data[start:end], self._labels[start:end] + + +def create_datasets(file_path, vocab_size=30000, val_fraction=0.0): + + # IMDB Dataset loading + train, test, _ = imdb.load_data(path=file_path, n_words=vocab_size, + valid_portion=val_fraction, sort_by_len=False) + trainX, trainY = train + testX, testY = test + + # Data preprocessing + # Sequence padding + trainX = pad_sequences(trainX, maxlen=FLAGS.max_len, value=0.) + testX = pad_sequences(testX, maxlen=FLAGS.max_len, value=0.) + # Converting labels to binary vectors + trainY = to_categorical(trainY, nb_classes=2) + testY = to_categorical(testY, nb_classes=2) + + train_dataset = DataSet(trainX, trainY) + + return train_dataset + + +def main(): + create_datasets('imdb.pkl') + + +if __name__ == "__main__": + main() diff --git a/benchmark/tensorflow/rnn/rnn.py b/benchmark/tensorflow/rnn/rnn.py new file mode 100755 index 00000000000..5377187f391 --- /dev/null +++ b/benchmark/tensorflow/rnn/rnn.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python +from six.moves import xrange # pylint: disable=redefined-builtin +import math +import time +import numpy as np +from datetime import datetime + +import reader +import tensorflow as tf +from tensorflow.python.ops import rnn + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 128, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('num_layers', 1, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('max_len', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_boolean('forward_only', False, + """Only run the forward pass.""") +tf.app.flags.DEFINE_boolean('forward_backward_only', False, + """Only run the forward-forward pass.""") +tf.app.flags.DEFINE_integer('hidden_size', 128, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('emb_size', 128, + """Number of batches to run.""") +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") + +VOCAB_SIZE=30000 +NUM_CLASS=2 + +def get_feed_dict(x_data, y_data=None): + feed_dict = {} + + if y_data is not None: + feed_dict[y_input] = y_data + + for i in xrange(x_data.shape[0]): + feed_dict[x_input[i]] = x_data[i, :, :] + + return feed_dict + +def get_incoming_shape(incoming): + """ Returns the incoming data shape """ + if isinstance(incoming, tf.Tensor): + return incoming.get_shape().as_list() + elif type(incoming) in [np.array, list, tuple]: + return np.shape(incoming) + else: + raise Exception("Invalid incoming layer.") + + +# Note input * W is done in LSTMCell, +# which is different from PaddlePaddle +def single_lstm(name, incoming, n_units, use_peepholes=True, + return_seq=False, return_state=False): + with tf.name_scope(name) as scope: + cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes) + output, _cell_state = rnn.rnn(cell, incoming, dtype=tf.float32) + out = output if return_seq else output[-1] + return (out, _cell_state) if return_state else out + +def lstm(name, incoming, n_units, use_peepholes=True, + return_seq=False, return_state=False, num_layers=1): + with tf.name_scope(name) as scope: + lstm_cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes) + cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * num_layers) + initial_state = cell.zero_state(FLAGS.batch_size, dtype=tf.float32) + if not isinstance(incoming, list): + # if the input is embeding, the Tensor shape : [None, time_step, emb_size] + incoming = [tf.squeeze(input_, [1]) + for input_ in tf.split(1, FLAGS.max_len, incoming)] + outputs, state = tf.nn.rnn(cell, incoming, initial_state=initial_state, + dtype=tf.float32) + out = outputs if return_seq else outputs[-1] + return (out, _cell_state) if return_state else out + + +def embedding(name, incoming, vocab_size, emb_size): + with tf.name_scope(name) as scope: + #with tf.device("/cpu:0"): + embedding = tf.get_variable( + name+'_emb', [vocab_size, emb_size], dtype=tf.float32) + out = tf.nn.embedding_lookup(embedding, incoming) + return out + +def fc(name, inpOp, nIn, nOut, act=True): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w', [nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + biases = tf.get_variable(name + '_b', [nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32,trainable=True) + + net = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ + tf.matmul(inpOp, kernel) + biases + + return net + +def inference(seq): + net = embedding('emb', seq, VOCAB_SIZE, FLAGS.emb_size) + print "emb:", get_incoming_shape(net) + net = lstm('lstm', net, FLAGS.hidden_size, num_layers=FLAGS.num_layers) + print "lstm:", get_incoming_shape(net) + net = fc('fc1', net, FLAGS.hidden_size, 2) + return net + +def loss(logits, labels): + # one label index for one sample + labels = tf.cast(labels, tf.float32) + cross_entropy = tf.nn.softmax_cross_entropy_with_logits( + logits, labels, name='cross_entropy_per_example') + cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') + tf.add_to_collection('losses', cross_entropy_mean) + return tf.add_n(tf.get_collection('losses'), name='total_loss') + + +def time_tensorflow_run(session, target, x_input, y_input, info_string): + num_steps_burn_in = 50 + total_duration = 0.0 + total_duration_squared = 0.0 + if not isinstance(target, list): + target = [target] + target_op = tf.group(*target) + train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE) + for i in xrange(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + data, label = train_dataset.next_batch(FLAGS.batch_size) + _ = session.run(target_op, feed_dict={x_input:data, y_input:label}) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + print ('%s: step %d, duration = %.3f' % + (datetime.now(), i - num_steps_burn_in, duration)) + total_duration += duration + total_duration_squared += duration * duration + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), info_string, FLAGS.num_batches, mn, sd)) + + +def run_benchmark(): + with tf.Graph().as_default(): + global_step=0 + with tf.device('/cpu:0'): + global_step = tf.Variable(0, trainable=False) + with tf.device('/gpu:0'): + #x_input = tf.placeholder(tf.int32, [None, FLAGS.max_len], name="x_input") + #y_input = tf.placeholder(tf.int32, [None, NUM_CLASS], name="y_input") + x_input = tf.placeholder(tf.int32, [FLAGS.batch_size, FLAGS.max_len], name="x_input") + y_input = tf.placeholder(tf.int32, [FLAGS.batch_size, NUM_CLASS], name="y_input") + # Generate some dummy sequnce. + + + last_layer = inference(x_input) + + objective = loss(last_layer, y_input) + opt = tf.train.AdamOptimizer(0.001) + grads = opt.compute_gradients(objective) + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + init = tf.initialize_all_variables() + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + + run_forward = True + run_forward_backward = True + if FLAGS.forward_only and FLAGS.forward_backward_only: + raise ValueError("Cannot specify --forward_only and " + "--forward_backward_only at the same time.") + if FLAGS.forward_only: + run_forward_backward = False + elif FLAGS.forward_backward_only: + run_forward = False + + if run_forward: + time_tensorflow_run(sess, last_layer, x_input, y_input, "Forward") + + if run_forward_backward: + with tf.control_dependencies([apply_gradient_op]): + train_op = tf.no_op(name='train') + time_tensorflow_run(sess, [train_op, objective], x_input, y_input, "Forward-backward") + + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() + diff --git a/benchmark/tensorflow/rnn/rnn_multi_gpu.py b/benchmark/tensorflow/rnn/rnn_multi_gpu.py new file mode 100755 index 00000000000..97ba5d4c296 --- /dev/null +++ b/benchmark/tensorflow/rnn/rnn_multi_gpu.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python +from six.moves import xrange # pylint: disable=redefined-builtin +import re +import math +import time +import numpy as np +from datetime import datetime + +import reader +import tensorflow as tf +from tensorflow.python.ops import rnn + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_integer('batch_size', 64, + """Batch size.""") +tf.app.flags.DEFINE_integer('num_batches', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('num_layers', 1, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('max_len', 100, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('hidden_size', 128, + """Number of batches to run.""") +tf.app.flags.DEFINE_integer('emb_size', 64, + """Number of batches to run.""") +tf.app.flags.DEFINE_boolean('log_device_placement', False, + """Whether to log device placement.""") +tf.app.flags.DEFINE_integer('num_gpus', 4, + """How many GPUs to use.""") + +VOCAB_SIZE=30000 +NUM_CLASS=2 + + +NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN=50000 +NUM_EPOCHS_PER_DECAY=50 +INITIAL_LEARNING_RATE = 0.1 +LEARNING_RATE_DECAY_FACTOR = 0.1 +TOWER_NAME = 'tower' + +train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE) + +def get_incoming_shape(incoming): + """ Returns the incoming data shape """ + if isinstance(incoming, tf.Tensor): + return incoming.get_shape().as_list() + elif type(incoming) in [np.array, list, tuple]: + return np.shape(incoming) + else: + raise Exception("Invalid incoming layer.") + + +# Note input * W is done in LSTMCell, +# which is different from PaddlePaddle +def single_lstm(name, incoming, n_units, use_peepholes=True, + return_seq=False, return_state=False): + with tf.name_scope(name) as scope: + cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes) + output, _cell_state = rnn.rnn(cell, incoming, dtype=tf.float32) + out = output if return_seq else output[-1] + return (out, _cell_state) if return_state else out + + +def lstm(name, incoming, n_units, use_peepholes=True, + return_seq=False, return_state=False, num_layers=1): + with tf.name_scope(name) as scope: + lstm_cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes) + cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * num_layers) + initial_state = cell.zero_state(FLAGS.batch_size, dtype=tf.float32) + if not isinstance(incoming, list): + # if the input is embeding, the Tensor shape : [None, time_step, emb_size] + incoming = [tf.squeeze(input_, [1]) + for input_ in tf.split(1, FLAGS.max_len, incoming)] + outputs, state = tf.nn.rnn(cell, incoming, initial_state=initial_state, + dtype=tf.float32) + out = outputs if return_seq else outputs[-1] + return (out, _cell_state) if return_state else out + + +def embedding(name, incoming, vocab_size, emb_size): + with tf.name_scope(name) as scope: + #with tf.device("/cpu:0"): + embedding = tf.get_variable( + name+'_emb', [vocab_size, emb_size], dtype=tf.float32) + out = tf.nn.embedding_lookup(embedding, incoming) + return out + + +def fc(name, inpOp, nIn, nOut, act=True): + with tf.name_scope(name) as scope: + kernel = tf.get_variable(name + '_w', [nIn, nOut], + initializer=tf.truncated_normal_initializer(stddev=0.01, dtype=tf.float32), + dtype=tf.float32) + + biases = tf.get_variable(name + '_b', [nOut], + initializer=tf.constant_initializer(value=0.0, dtype=tf.float32), + dtype=tf.float32,trainable=True) + + net = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \ + tf.matmul(inpOp, kernel) + biases + + return net + + +def inference(seq): + net = embedding('emb', seq, VOCAB_SIZE, FLAGS.emb_size) + print "emb:", get_incoming_shape(net) + net = lstm('lstm', net, FLAGS.hidden_size, num_layers=FLAGS.num_layers) + print "lstm:", get_incoming_shape(net) + net = fc('fc1', net, FLAGS.hidden_size, 2) + return net + + +def loss(logits, labels): + # one label index for one sample + #labels = tf.cast(labels, tf.int64) + # cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( + # logits, labels, name='cross_entropy_per_example') + labels = tf.cast(labels, tf.float32) + cross_entropy = tf.nn.softmax_cross_entropy_with_logits( + logits, labels, name='cross_entropy_per_example') + cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') + tf.add_to_collection('losses', cross_entropy_mean) + return tf.add_n(tf.get_collection('losses'), name='total_loss') + + +def tower_loss(scope): + """Calculate the total loss on a single tower running the model. + Args: + scope: unique prefix string identifying the tower, e.g. 'tower_0' + Returns: + Tensor of shape [] containing the total loss for a batch of data + """ + data, label = train_dataset.next_batch(FLAGS.batch_size) + + # Build a Graph that computes the logits predictions from the + # inference model. + last_layer = inference(data) + + # Build the portion of the Graph calculating the losses. Note that we will + # assemble the total_loss using a custom function below. + #_ = loss(last_layer, label) + _ = loss(last_layer, label) + + # Assemble all of the losses for the current tower only. + losses = tf.get_collection('losses', scope) + + # Calculate the total loss for the current tower. + total_loss = tf.add_n(losses, name='total_loss') + + # Compute the moving average of all individual losses and the total loss. + loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') + loss_averages_op = loss_averages.apply(losses + [total_loss]) + + # Attach a scalar summary to all individual losses and the total loss; do the + # same for the averaged version of the losses. + for l in losses + [total_loss]: + # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training + # session. This helps the clarity of presentation on tensorboard. + loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name) + # Name each loss as '(raw)' and name the moving average version of the loss + # as the original loss name. + tf.scalar_summary(loss_name +' (raw)', l) + #tf.scalar_summary(loss_name, loss_averages.average(l)) + + with tf.control_dependencies([loss_averages_op]): + total_loss = tf.identity(total_loss) + return total_loss + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(0, grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + +def time_tensorflow_run(session, target): + num_steps_burn_in = 80 + total_duration = 0.0 + total_duration_squared = 0.0 + for i in xrange(FLAGS.num_batches + num_steps_burn_in): + start_time = time.time() + _ = session.run(target, feed_dict={x_input:data, y_input:label}) + _, loss_value = session.run(target) + duration = time.time() - start_time + if i > num_steps_burn_in: + if not i % 10: + num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus + examples_per_sec = num_examples_per_step / duration + # sec_per_batch = duration / FLAGS.num_gpus + sec_per_batch = duration + + format_str = ('%s: step %d, loss= %.2f (%.1f examples/sec; %.3f ' + 'sec/batch batch_size= %d)') + print (format_str % + (datetime.now(), i - num_steps_burn_in, + loss_value, duration, sec_per_batch, num_examples_per_step)) + + total_duration += duration + total_duration_squared += duration * duration + + mn = total_duration / FLAGS.num_batches + vr = total_duration_squared / FLAGS.num_batches - mn * mn + sd = math.sqrt(vr) + print ('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' % + (datetime.now(), FLAGS.num_batches, mn, sd)) + +def run_benchmark(): + with tf.Graph().as_default(), tf.device('/cpu:0'): + # Create a variable to count the number of train() calls. This equals the + # number of batches processed * FLAGS.num_gpus. + global_step = tf.get_variable( + 'global_step', [], + initializer=tf.constant_initializer(0), trainable=False) + + # Calculate the learning rate schedule. + num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / + FLAGS.batch_size) + decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) + + # Create an optimizer that performs gradient descent. + opt = tf.train.AdamOptimizer(0.001) + + #train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE) + + # Calculate the gradients for each model tower. + tower_grads = [] + for i in xrange(FLAGS.num_gpus): + with tf.device('/gpu:%d' % i): + with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope: + # Calculate the loss for one tower of the model. This function + # constructs the entire model but shares the variables across + # all towers. + loss = tower_loss(scope) + + # Reuse variables for the next tower. + tf.get_variable_scope().reuse_variables() + + # Retain the summaries from the final tower. + # summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope) + + # Calculate the gradients for the batch of data on this tower. + grads = opt.compute_gradients(loss) + + # Keep track of the gradients across all towers. + tower_grads.append(grads) + + # We must calculate the mean of each gradient. Note that this is the + # synchronization point across all towers. + grads = average_gradients(tower_grads) + + # Apply the gradients to adjust the shared variables. + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + # Group all updates to into a single train op. + train_op = tf.group(apply_gradient_op) + + # Build an initialization operation. + init = tf.initialize_all_variables() + + # Start running operations on the Graph. allow_soft_placement must be set to + # True to build towers on GPU, as some of the ops do not have GPU + # implementations. + sess = tf.Session(config=tf.ConfigProto( + allow_soft_placement=True, + log_device_placement=FLAGS.log_device_placement)) + sess.run(init) + time_tensorflow_run(sess, [train_op, loss]) + + +def main(_): + run_benchmark() + + +if __name__ == '__main__': + tf.app.run() diff --git a/benchmark/tensorflow/rnn/run.sh b/benchmark/tensorflow/rnn/run.sh new file mode 100755 index 00000000000..bb4c69cb95f --- /dev/null +++ b/benchmark/tensorflow/rnn/run.sh @@ -0,0 +1,29 @@ +set -e + +function test() { + lstm_num=$1 + batch_size=$2 + hid_size=$3 + prefix=$4 + python rnn.py --num_layers=${lstm_num} --batch_size=$batch_size \ + --hidden_size=${hid_size} \ + --forward_backward_only=1 \ + > logs/1gpu-${lstm_num}lstm-batch${batch_size}-hid${hid_size}.log 2>&1 +} + +if [ ! -d "logs" ]; then + mkdir logs +fi + +#--lstm_num--batch_size--hidden_size--# +test 2 64 256 +test 2 64 512 +test 2 64 1280 + +test 2 128 256 +test 2 128 512 +test 2 128 1280 + +test 2 256 256 +test 2 256 512 +test 2 256 1280 diff --git a/benchmark/tensorflow/rnn/run_multi.sh b/benchmark/tensorflow/rnn/run_multi.sh new file mode 100755 index 00000000000..f7f52e01e38 --- /dev/null +++ b/benchmark/tensorflow/rnn/run_multi.sh @@ -0,0 +1,28 @@ +set -e + +function test() { + num_gpu=$1 + lstm_num=$2 + hid_size=$3 + batch_per_gpu=`expr ${batch_size} / ${num_gpu}` + batch_size=$4 + python rnn_multi_gpu.py --num_layers=${lstm_num} --batch_size=$batch_per_gpu \ + --num_gpus=${num_gpu} \ + --hidden_size=${hid_size} \ + --forward_backward_only=1 \ + > logs/${num_gpu}gpu-${lstm_num}lstm-hid${hid_size}-batch${batch_size}.log 2>&1 +} + +if [ ! -d "logs" ]; then + mkdir logs +fi + +#--num_gpus--lstm_num--hiddne_size--batch_size--# +test 4 2 256 128 +test 4 2 256 256 +test 4 2 256 512 + +test 4 2 512 128 +test 4 2 512 256 +test 4 2 512 512 + -- GitLab From c6a0298e2a582d22144a229664c0b99d13b3330d Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 17:24:21 +0800 Subject: [PATCH 002/347] create PR to polish test_period meaning --- paddle/trainer/Trainer.cpp | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp index 7fc48dd1fbe..32c4bad239e 100644 --- a/paddle/trainer/Trainer.cpp +++ b/paddle/trainer/Trainer.cpp @@ -46,6 +46,18 @@ P_DEFINE_int32(test_period, 0, " If not 0, test log_period batches." " If 0, test on all test data"); +P_DEFINE_int32(test_batches_while_training, 0, + "Run test every so many train batches." + " 0 for testing after each pass." + " If not 0, test log_period batches." + " If 0, test on all test data"); + +P_DEFINE_int32(test_batches_while_end, 0, + "Run test every so many train batches." + " 0 for testing after each pass." + " If not 0, test log_period batches." + " If 0, test on all test data"); + P_DEFINE_bool(local, true, "Train in local mode or not"); P_DEFINE_bool( -- GitLab From 0a0c55d2289d3d474014590758ec3143decf2ba2 Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 19:27:29 +0800 Subject: [PATCH 003/347] more friendly test options --- paddle/trainer/Tester.cpp | 18 ++++++++---- paddle/trainer/Tester.h | 2 +- paddle/trainer/TesterConfig.h | 11 +++++-- paddle/trainer/Trainer.cpp | 54 ++++++++++++++++++++--------------- 4 files changed, 53 insertions(+), 32 deletions(-) diff --git a/paddle/trainer/Tester.cpp b/paddle/trainer/Tester.cpp index d3b88019faa..f57e09d40af 100644 --- a/paddle/trainer/Tester.cpp +++ b/paddle/trainer/Tester.cpp @@ -90,13 +90,20 @@ void Tester::testOneDataBatch( testContext_.numSamples += dataBatch.getSize(); } -void Tester::testOnePeriod() { +void Tester::testOnePeriod(bool finishPass) { DataBatch dataBatch; int64_t batchSize = config_->getOptConfig().batch_size(); + bool testAllData = - intconfig_->testPeriod == 0 || intconfig_->testAllDataInOnePeriod; - int batches = - testAllData ? std::numeric_limits::max() : intconfig_->testPeriod; + (!finishPass && !intconfig_->testBatchesWhileTraining) || + (finishPass && !intconfig_->testBatchesWhileEnd); + int batches; + if (testAllData) { + batches = std::numeric_limits::max(); + } else { + batches = finishPass ? + intconfig_->testBatchesWhileEnd : intconfig_->testBatchesWhileTraining; + } std::vector outArgs; @@ -108,7 +115,8 @@ void Tester::testOnePeriod() { if (intconfig_->prevBatchState) { gradientMachine_->resetState(); } - if (testAllData) { + if ((!finishPass && !intconfig_->testBatchesWhileTraining) || + (finishPass && !intconfig_->testBatchesWhileEnd)) { break; } else { num = testDataProvider_->getNextBatch(batchSize, &dataBatch); diff --git a/paddle/trainer/Tester.h b/paddle/trainer/Tester.h index 671ffc5220e..21e11422aa4 100644 --- a/paddle/trainer/Tester.h +++ b/paddle/trainer/Tester.h @@ -67,7 +67,7 @@ public: * It is convenience to test small set of data when test data set is large and * is training at same time. */ - void testOnePeriod(); + void testOnePeriod(bool finishPass = true); void startTestPeriod(); void finishTestPeriod(); void testOneDataBatch(const DataBatch& dataBatch, diff --git a/paddle/trainer/TesterConfig.h b/paddle/trainer/TesterConfig.h index d5e644ce612..b7b550dec7c 100644 --- a/paddle/trainer/TesterConfig.h +++ b/paddle/trainer/TesterConfig.h @@ -38,12 +38,17 @@ struct TesterConfig { /** * indicate test period */ - int testPeriod; + int testPeriodWhileTraining; /** - * indicate whether testing data in one period + * indicate how many batches are used for testing under training */ - bool testAllDataInOnePeriod; + bool testBatchesWhileTraining; + + /** + * indicate how many batches are used for testing at pass end + */ + bool testBatchesWhileEnd; /** * indicate whether to save previous batch state diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp index 32c4bad239e..477813b4748 100644 --- a/paddle/trainer/Trainer.cpp +++ b/paddle/trainer/Trainer.cpp @@ -40,31 +40,28 @@ limitations under the License. */ #include "TrainerConfigHelper.h" P_DEFINE_string(config, "", "Trainer config file"); -P_DEFINE_int32(test_period, 0, - "Run test every so many train batches." - " 0 for testing after each pass." - " If not 0, test log_period batches." - " If 0, test on all test data"); -P_DEFINE_int32(test_batches_while_training, 0, +P_DEFINE_int32(test_period, 0, + "This option was deprecated, use test_period_while_training " + " instead. "); +P_DEFINE_int32(test_period_while_training, 0, "Run test every so many train batches." - " 0 for testing after each pass." " If not 0, test log_period batches." + " If 0, test nothing."); +P_DEFINE_int32(test_batches_while_training, 1000, + "test test_batches_while_training batches if test_period != 0." " If 0, test on all test data"); - P_DEFINE_int32(test_batches_while_end, 0, - "Run test every so many train batches." - " 0 for testing after each pass." - " If not 0, test log_period batches." - " If 0, test on all test data"); + "test test_batches_while_end batches at pass end." + " Always run test at pass end." + " If not 0, test test_batches_while_end batches." + " If 0, test on all test data."); +P_DEFINE_bool(test_all_data_in_one_period, false, + "This option was deprecated, use test_batches_while_training " + "and test_batches_while_end instead"); P_DEFINE_bool(local, true, "Train in local mode or not"); -P_DEFINE_bool( - test_all_data_in_one_period, false, - "true will test all data in one test peroid." - "Otherwise test (batch_size * log_peroid) data in one test period."); - P_DEFINE_int32(average_test_period, 0, "Do test on average parameter every so" " many batches. MUST be devided by FLAGS_log_period." @@ -469,9 +466,9 @@ void Trainer::trainOneDataBatch(DataBatch& dataBatch) { FOR_TIMING(globalStat.reset()); } - if (testDataProvider_ && FLAGS_test_period > 0 && - trainPassContext_.batchId % FLAGS_test_period == 0) { - tester_->testOnePeriod(); + if (testDataProvider_ && FLAGS_test_period_while_training > 0 && + trainPassContext_.batchId % FLAGS_test_period_while_training == 0) { + tester_->testOnePeriod(false); } if (FLAGS_saving_period_by_batches > 0 && @@ -480,7 +477,7 @@ void Trainer::trainOneDataBatch(DataBatch& dataBatch) { 0 == FLAGS_trainer_id) { trainerInternal_.getParameterUpdater()->catchUpWith(); if (testDataProvider_) { - tester_->testOnePeriod(); + tester_->testOnePeriod(false); } paramUtil_->saveParametersOnePass( trainPassContext_.passId, trainPassContext_.passInnerId); @@ -636,8 +633,19 @@ void Trainer::test() { std::unique_ptr Trainer::createTesterConfig() { TesterConfig* conf = new TesterConfig; - conf->testPeriod = FLAGS_test_period; - conf->testAllDataInOnePeriod = FLAGS_test_all_data_in_one_period; + if (FLAGS_test_period) { + LOG(WARNING) + << "--test_period was deprecated, use --test_period_while_training" + << "--test_batches_while_training --test_batches_while_end instead."; + } + if (FLAGS_test_all_data_in_one_period) { + LOG(WARNING) + << "--test_all_data_in_one_period was deprecated, use" + << " --test_batches_while_training and --test_batches_while_end instead"; + } + conf->testPeriodWhileTraining = FLAGS_test_period_while_training; + conf->testBatchesWhileTraining = FLAGS_test_batches_while_training; + conf->testBatchesWhileEnd = FLAGS_test_batches_while_end; conf->prevBatchState = FLAGS_prev_batch_state; conf->logPeriod = FLAGS_log_period; conf->loadsaveParametersInPserver = FLAGS_loadsave_parameters_in_pserver; -- GitLab From 0feecbd13c9cf700e80fe4dbce84c9b724cf248e Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 19:50:01 +0800 Subject: [PATCH 004/347] modify on docs --- doc/ui/cmd_argument/argument_outline.md | 11 ++++++++--- doc/ui/cmd_argument/detail_introduction.md | 14 +++++++++----- doc/ui/cmd_argument/use_case.md | 7 ++++--- paddle/trainer/Trainer.cpp | 3 ++- 4 files changed, 23 insertions(+), 12 deletions(-) diff --git a/doc/ui/cmd_argument/argument_outline.md b/doc/ui/cmd_argument/argument_outline.md index d6cc2c6ed7c..bafa5dfef2c 100644 --- a/doc/ui/cmd_argument/argument_outline.md +++ b/doc/ui/cmd_argument/argument_outline.md @@ -68,7 +68,7 @@ It looks like there are a lot of arguments. However, most of them are for develo -test_period +test_period_while_training √√ @@ -143,8 +143,13 @@ It looks like there are a lot of arguments. However, most of them are for develo -testing during trainingtest_all_data_in_one_period -√√ +testing during trainingtest_batches_while_training +√√√< + + + +testing during trainingtest_batches_while_end +√√√< diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/ui/cmd_argument/detail_introduction.md index 07608e5edf7..0628289dbd6 100644 --- a/doc/ui/cmd_argument/detail_introduction.md +++ b/doc/ui/cmd_argument/detail_introduction.md @@ -109,9 +109,9 @@ - Load parameter from this pass to test. - type: int32 (default: -1). -* `--test_period` - - Run testing every test_period train batches. If not set, run testing each pass. - - type: int32 (default: 1000). +* `--test_period_while_training` + - Run test every so many train batches. If not 0, test log_period batches. If 0, test nothing. + - type: int32 (default: 0). * `--test_wait` - Whether to wait for parameter per pass if not exist. If set test_data_path in submitting environment of cluster, it will launch one process to perfom testing, so we need to set test_wait=1. Note that in the cluster submitting environment, this argument has been set True by default. @@ -121,8 +121,12 @@ - File that saves the model list when testing. It was set automatically when using cluster submitting environment after setting model_path. - type: string (default: "", null). -* `--test_all_data_in_one_period` - - This argument is usually used in testing period during traning. If true, all data will be tested in one test period. Otherwise (batch_size * log_peroid) data will be tested. +* `--test_batches_while_training` + - Test test_batches_while_training batches if test_batches_while_training != 0. If 0, test on all test data. + - type: bool (default: 1000). + +* `--test_batches_while_end` + - Test test_batches_while_end batches if test_batches_while_end != 0. If 0, test on all test data. - type: bool (default: 0). * `--predict_output_dir` diff --git a/doc/ui/cmd_argument/use_case.md b/doc/ui/cmd_argument/use_case.md index a6bfba29af4..b243560106d 100644 --- a/doc/ui/cmd_argument/use_case.md +++ b/doc/ui/cmd_argument/use_case.md @@ -10,9 +10,10 @@ paddle train \ --config=network_config \ --save_dir=output \ --trainer_count=COUNT \ #(default:1) - --test_period=M \ #(default:1000） - --test_all_data_in_one_period=true \ #(default:false) - --num_passes=N \ #(defalut:100） + --test_period_while_training=M \ #(default:0) + --test_batches_while_training=BATCHES \#(default:1000) + --test_batches_while_end=BATCHES \ #(default:0) + --num_passes=N \ #(defalut:100) --log_period=K \ #(default:100) --dot_period=1000 \ #(default:1) #[--show_parameter_stats_period=100] \ #(default:0) diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp index 477813b4748..107fa240cf3 100644 --- a/paddle/trainer/Trainer.cpp +++ b/paddle/trainer/Trainer.cpp @@ -49,7 +49,8 @@ P_DEFINE_int32(test_period_while_training, 0, " If not 0, test log_period batches." " If 0, test nothing."); P_DEFINE_int32(test_batches_while_training, 1000, - "test test_batches_while_training batches if test_period != 0." + "test test_batches_while_training batches if " + "test_batches_while_training != 0." " If 0, test on all test data"); P_DEFINE_int32(test_batches_while_end, 0, "test test_batches_while_end batches at pass end." -- GitLab From c509908b748c98d3a46d70ac044edd0cbb297002 Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 21:12:31 +0800 Subject: [PATCH 005/347] follow comments --- doc/ui/cmd_argument/detail_introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/ui/cmd_argument/detail_introduction.md index 0628289dbd6..1ff4bdbaa3a 100644 --- a/doc/ui/cmd_argument/detail_introduction.md +++ b/doc/ui/cmd_argument/detail_introduction.md @@ -110,7 +110,7 @@ - type: int32 (default: -1). * `--test_period_while_training` - - Run test every so many train batches. If not 0, test log_period batches. If 0, test nothing. + - Run test every test_period_while_training batches while doing training. If not 0, test log_period batches. If 0, test nothing. - type: int32 (default: 0). * `--test_wait` -- GitLab From dcf06bff04b6486817f83d160288c515763ea0b4 Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 21:34:43 +0800 Subject: [PATCH 006/347] follow comments --- doc/ui/cmd_argument/detail_introduction.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/ui/cmd_argument/detail_introduction.md index 1ff4bdbaa3a..40c6f5d2d87 100644 --- a/doc/ui/cmd_argument/detail_introduction.md +++ b/doc/ui/cmd_argument/detail_introduction.md @@ -110,7 +110,7 @@ - type: int32 (default: -1). * `--test_period_while_training` - - Run test every test_period_while_training batches while doing training. If not 0, test log_period batches. If 0, test nothing. + - Run test every test_period_while_training batches while doing training. If not 0, test log_period batches, if 0 test nothing. - type: int32 (default: 0). * `--test_wait` @@ -122,11 +122,11 @@ - type: string (default: "", null). * `--test_batches_while_training` - - Test test_batches_while_training batches if test_batches_while_training != 0. If 0, test on all test data. + - Test test_batches_while_training batches if test_batches_while_training != 0 while doing training. If 0, test on all test data. - type: bool (default: 1000). * `--test_batches_while_end` - - Test test_batches_while_end batches if test_batches_while_end != 0. If 0, test on all test data. + - Test test_batches_while_end batches if test_batches_while_end != 0 at pass end. If 0, test on all test data. - type: bool (default: 0). * `--predict_output_dir` -- GitLab From 453e6ba598906b58cc74d854bb0170bf800675fa Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 21:35:36 +0800 Subject: [PATCH 007/347] follow comments --- doc/ui/cmd_argument/detail_introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/ui/cmd_argument/detail_introduction.md index 40c6f5d2d87..af97e04d541 100644 --- a/doc/ui/cmd_argument/detail_introduction.md +++ b/doc/ui/cmd_argument/detail_introduction.md @@ -110,7 +110,7 @@ - type: int32 (default: -1). * `--test_period_while_training` - - Run test every test_period_while_training batches while doing training. If not 0, test log_period batches, if 0 test nothing. + - Run test every test_period_while_training batches while doing training. If not 0, test log_period batches, if 0, test nothing. - type: int32 (default: 0). * `--test_wait` -- GitLab From b62c80f1561efe07156ef99c1e57fb3eeb24267d Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 9 Nov 2016 21:42:28 +0800 Subject: [PATCH 008/347] qfg --- doc/ui/cmd_argument/detail_introduction.md | 2 +- paddle/trainer/Trainer.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/ui/cmd_argument/detail_introduction.md index af97e04d541..1f7e406a53e 100644 --- a/doc/ui/cmd_argument/detail_introduction.md +++ b/doc/ui/cmd_argument/detail_introduction.md @@ -110,7 +110,7 @@ - type: int32 (default: -1). * `--test_period_while_training` - - Run test every test_period_while_training batches while doing training. If not 0, test log_period batches, if 0, test nothing. + - Run test every test_period_while_training batches while doing training. If not 0, test test_batches_while_training batches, if 0, test nothing. - type: int32 (default: 0). * `--test_wait` diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp index 107fa240cf3..507a080cc4f 100644 --- a/paddle/trainer/Trainer.cpp +++ b/paddle/trainer/Trainer.cpp @@ -45,8 +45,8 @@ P_DEFINE_int32(test_period, 0, "This option was deprecated, use test_period_while_training " " instead. "); P_DEFINE_int32(test_period_while_training, 0, - "Run test every so many train batches." - " If not 0, test log_period batches." + "Run test every test_period_while_training batches." + " If not 0, test test_batches_while_training batches." " If 0, test nothing."); P_DEFINE_int32(test_batches_while_training, 1000, "test test_batches_while_training batches if " -- GitLab From 9ec91b6ecaea86a00336fa338bed6349dabef5da Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 14 Nov 2016 17:47:50 +0800 Subject: [PATCH 009/347] Revise build docs --- doc/build/build_from_source.md | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/doc/build/build_from_source.md b/doc/build/build_from_source.md index b8f26f431eb..efdda298288 100644 --- a/doc/build/build_from_source.md +++ b/doc/build/build_from_source.md @@ -95,7 +95,7 @@ As a simple example, consider the following: ```bash # necessary sudo apt-get update - sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python python-pip libpython-dev m4 libprotobuf-dev protobuf-compiler python-protobuf python-numpy git + sudo apt-get install -y g++ make cmake swig build-essential libatlas-base-dev python python-pip libpython-dev m4 libprotobuf-dev protobuf-compiler python-protobuf python-numpy git # optional sudo apt-get install libgoogle-glog-dev sudo apt-get install libgflags-dev @@ -149,15 +149,15 @@ If still not found, you can manually set it based on CMake error information fro As a simple example, consider the following: -- **Only CPU** +- **Only CPU with swig** ```bash - cmake .. -DWITH_GPU=OFF + cmake .. -DWITH_GPU=OFF -DWITH_SWIG_PY=ON ``` -- **GPU** +- **GPU with swig** ```bash - cmake .. -DWITH_GPU=ON + cmake .. -DWITH_GPU=ON -DWITH_SWIG_PY=ON ``` - **GPU with doc and swig** @@ -170,15 +170,13 @@ Finally, you can build PaddlePaddle: ```bash # you can add build option here, such as: -cmake .. -DWITH_GPU=ON -DCMAKE_INSTALL_PREFIX= +cmake .. -DWITH_GPU=ON -DCMAKE_INSTALL_PREFIX= -DWITH_SWIG_PY=ON # please use sudo make install, if you want to install PaddlePaddle into the system make -j `nproc` && make install # set PaddlePaddle installation path in ~/.bashrc export PATH=/bin:$PATH ``` -**Note:** - If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed. Otherwise, PaddlePaddle will automatically install python dependencies at first time when user run paddle commands, such as `paddle version`, `paddle train`. -- GitLab From 20f853ba8c71a64c676517d9ec5e18b256ef04e6 Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 14 Nov 2016 17:50:03 +0800 Subject: [PATCH 010/347] Travis ci does not build when only docs changed --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ffe3bc193b4..d39d93f7050 100644 --- a/.travis.yml +++ b/.travis.yml @@ -42,7 +42,7 @@ addons: before_install: - | if [ ${JOB} == "BUILD_AND_TEST" ]; then - if ! git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)' + if ! git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)|(\.rst$)' then echo "Only markdown docs were updated, stopping build process." exit -- GitLab From d7d14ce35051477a37b339eb2047580b248e62da Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 14 Nov 2016 18:04:41 +0800 Subject: [PATCH 011/347] Change png/jpg files do not trigger Travis ci --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index d39d93f7050..effcf907696 100644 --- a/.travis.yml +++ b/.travis.yml @@ -42,7 +42,7 @@ addons: before_install: - | if [ ${JOB} == "BUILD_AND_TEST" ]; then - if ! git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)|(\.rst$)' + if ! git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)|(\.rst$)|(\.jpg$)|(\.png$)' then echo "Only markdown docs were updated, stopping build process." exit -- GitLab From 76a41f31471a4bba66110f0ca2bc8921d0d70004 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 15 Nov 2016 16:55:20 +0800 Subject: [PATCH 012/347] Keep libcudart.so link in binary --- CMakeLists.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d7e7e49e9a0..b1fdae3b5c0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -115,7 +115,6 @@ else() endif(WITH_AVX) if(WITH_DSO) - set(CUDA_LIBRARIES "") add_definitions(-DPADDLE_USE_DSO) endif(WITH_DSO) -- GitLab From 2e9ea1cece1274ccffc69d6be703fbff4a71eb9a Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 15 Nov 2016 16:56:19 +0800 Subject: [PATCH 013/347] Add Gpu profiler interface --- paddle/cuda/include/hl_cuda.h | 10 ++++++++++ paddle/cuda/include/stub/hl_cuda_stub.h | 4 ++++ paddle/cuda/src/hl_cuda_device.cc | 13 +++++++++++-- 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/paddle/cuda/include/hl_cuda.h b/paddle/cuda/include/hl_cuda.h index 3196db67f61..d763658c937 100644 --- a/paddle/cuda/include/hl_cuda.h +++ b/paddle/cuda/include/hl_cuda.h @@ -335,4 +335,14 @@ extern bool hl_cuda_event_is_ready(hl_event_t event); */ extern void hl_device_synchronize(); +/** + * @brief gpu profiler start + */ +extern void hl_profiler_start(); + +/** + * @brief gpu profiler stop + */ +extern void hl_profiler_end(); + #endif // HL_CUDA_H_ diff --git a/paddle/cuda/include/stub/hl_cuda_stub.h b/paddle/cuda/include/stub/hl_cuda_stub.h index 675ac03b0e1..fa7904421d0 100644 --- a/paddle/cuda/include/stub/hl_cuda_stub.h +++ b/paddle/cuda/include/stub/hl_cuda_stub.h @@ -93,4 +93,8 @@ inline bool hl_cuda_event_is_ready(hl_event_t event) { return true; } inline void hl_device_synchronize() {} +inline void hl_profiler_start() {} + +inline void hl_profiler_end() {} + #endif // HL_CUDA_STUB_H_ diff --git a/paddle/cuda/src/hl_cuda_device.cc b/paddle/cuda/src/hl_cuda_device.cc index 3ea2c91bd5a..0f45462adc7 100644 --- a/paddle/cuda/src/hl_cuda_device.cc +++ b/paddle/cuda/src/hl_cuda_device.cc @@ -17,6 +17,7 @@ limitations under the License. */ #include #include #include +#include #include #include "hl_cuda.h" #include "hl_cuda.ph" @@ -133,8 +134,9 @@ void* cudart_dso_handle = nullptr; __macro(cudaGetLastError) \ __macro(cudaFuncSetCacheConfig) \ __macro(cudaRuntimeGetVersion) \ - __macro(cudaGetErrorString) - + __macro(cudaGetErrorString) \ + __macro(cudaProfilerStart) \ + __macro(cudaProfilerStop) CUDA_ROUTINE_EACH(DYNAMIC_LOAD_CUDART_WRAP) #undef CUDA_ROUNTINE_EACH @@ -756,3 +758,10 @@ bool hl_cuda_event_is_ready(hl_event_t event) { } return true; } + +void hl_profiler_start() { + CHECK_CUDA(dynload::cudaProfilerStart()); +} +void hl_profiler_end() { + CHECK_CUDA(dynload::cudaProfilerStop()); +} -- GitLab From e8c0fb9e143b2cdf619cc921d7afbd5339a6ca14 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 15 Nov 2016 17:22:22 +0800 Subject: [PATCH 014/347] Add GPU Profiler unit test --- paddle/math/tests/CMakeLists.txt | 1 + paddle/math/tests/test_GpuProfiler.cpp | 123 +++++++++++++++++++++++++ 2 files changed, 124 insertions(+) create mode 100644 paddle/math/tests/test_GpuProfiler.cpp diff --git a/paddle/math/tests/CMakeLists.txt b/paddle/math/tests/CMakeLists.txt index 247be983ba3..33d4478b4d3 100644 --- a/paddle/math/tests/CMakeLists.txt +++ b/paddle/math/tests/CMakeLists.txt @@ -14,3 +14,4 @@ add_simple_unittest(test_perturbation) add_simple_unittest(test_CpuGpuVector) add_simple_unittest(test_Allocator) add_simple_unittest(test_FPException) +add_simple_unittest(test_GpuProfiler) \ No newline at end of file diff --git a/paddle/math/tests/test_GpuProfiler.cpp b/paddle/math/tests/test_GpuProfiler.cpp new file mode 100644 index 00000000000..7645447789c --- /dev/null +++ b/paddle/math/tests/test_GpuProfiler.cpp @@ -0,0 +1,123 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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. */ + +#ifndef PADDLE_ONLY_CPU + +#include "paddle/utils/Util.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/SparseMatrix.h" +#include +#include "paddle/gserver/tests/TestUtil.h" +#include "paddle/utils/Stat.h" +#include "hl_cuda.h" + +using namespace paddle; // NOLINT +using namespace std; // NOLINT + +void MatrixCheckErr(const Matrix& matrix1, const Matrix& matrix2) { + CHECK(matrix1.getHeight() == matrix2.getHeight()); + CHECK(matrix1.getWidth() == matrix2.getWidth()); +#ifndef PADDLE_TYPE_DOUBLE + real err = 1e-3; +#else + real err = 1e-10; +#endif + + int height = matrix1.getHeight(); + int width = matrix1.getWidth(); + const real* data1 = matrix1.getData(); + const real* data2 = matrix2.getData(); + int count = 0; + for (int i = 0; i < height; i++) { + for (int j = 0; j < width; j++) { + real a = data1[i * width + j]; + real b = data2[i * width + j]; + if (fabs(a - b) > err) { + if ((fabsf(a - b) / fabsf(a)) > (err / 10.0f)) { + count++; + } + } + } + } + EXPECT_EQ(count, 0) << "There are " << count << " different element."; +} + +void testBilinearFwdBwd(int numSamples, int imgSizeH, int imgSizeW, + int channels) { + int inWidth = imgSizeH * imgSizeW * channels; + int outWidth = 2 * imgSizeH * 2 * imgSizeW * channels; + real ratioH = 0.5; + real ratioW = 0.5; + + // forward + MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false); + MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true); + + MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true); + MatrixPtr targetCheck = CpuMatrix::create(numSamples, outWidth, false, false); + + input->randomizeUniform(); + inputGpu->copyFrom(*input); + + target->bilinearForward(*input, imgSizeH, imgSizeW, + 2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW); + targetGpu->bilinearForward(*inputGpu, imgSizeH, imgSizeW, + 2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW); + + // check + targetCheck->copyFrom(*targetGpu); + MatrixCheckErr(*target, *targetCheck); + + // backward + MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false); + MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true); + + MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr targetGpuGrad = GpuMatrix::create(numSamples, outWidth, false, + true); + MatrixPtr targetCheckGrad = + CpuMatrix::create(numSamples, inWidth, false, false); + + inputGrad->randomizeUniform(); + targetGrad->randomizeUniform(); + inputGpuGrad->copyFrom(*inputGrad); + targetGpuGrad->copyFrom(*targetGrad); + + inputGrad->bilinearBackward(*targetGrad, 2 * imgSizeH, 2 * imgSizeW, + imgSizeH, imgSizeW, channels, ratioH, ratioW); + inputGpuGrad->bilinearBackward(*targetGpuGrad, 2 * imgSizeH, 2 * imgSizeW, + imgSizeH, imgSizeW, channels, ratioH, ratioW); + + // check + targetCheckGrad->copyFrom(*inputGpuGrad); + MatrixCheckErr(*inputGrad, *targetCheckGrad); +} + +TEST(Profiler, BilinearFwdBwd) { + hl_profiler_start(); + auto numSamples = 10; + auto channels = 16; + auto imgSize = 64; + testBilinearFwdBwd(numSamples, imgSize, imgSize, channels); + hl_profiler_end(); +} + +int main(int argc, char** argv) { + testing::InitGoogleTest(&argc, argv); + initMain(argc, argv); + return RUN_ALL_TESTS(); +} + +#endif /* PADDLE_ONLY_CPU */ -- GitLab From 23bce472ea35b128d629e43a6f5d99356e22c547 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 15 Nov 2016 17:55:54 +0800 Subject: [PATCH 015/347] Add gpu profiling docs --- doc/index.md | 1 + doc/optimization/gpu_profiling.rst | 77 +++++++++++++++++++++++++++++ doc/optimization/index.rst | 7 +++ doc/optimization/nvprof.png | Bin 0 -> 487751 bytes doc/optimization/nvvp1.png | Bin 0 -> 426047 bytes 5 files changed, 85 insertions(+) create mode 100644 doc/optimization/gpu_profiling.rst create mode 100644 doc/optimization/index.rst create mode 100644 doc/optimization/nvprof.png create mode 100644 doc/optimization/nvvp1.png diff --git a/doc/index.md b/doc/index.md index a4dffb0405a..9c5ebba9bed 100644 --- a/doc/index.md +++ b/doc/index.md @@ -17,6 +17,7 @@ Development Guide * [Layer Documents](layer.md) * [Writing New Layers](dev/new_layer/index.rst) * [Source Code Documents](source/index.md) +* [GPU Profiling Documents](optimization/index.rst) Algorithm Tutorial ------------------ diff --git a/doc/optimization/gpu_profiling.rst b/doc/optimization/gpu_profiling.rst new file mode 100644 index 00000000000..583c2d6caee --- /dev/null +++ b/doc/optimization/gpu_profiling.rst @@ -0,0 +1,77 @@ +GPU Profiling +============= + +This tutorial will guide you step-by-step through how to conduct profiling and performance tuning using :code:`nvprof` and :code:`nvvp`. + +- What is profiling? +- Why we need profiling? +- How to do profiling? +- Profile tools +- Hands-on Tutorial + +What's profiling? +================= +In software engineering, profiling is a form of dynamic program analysis that measures the space (memory) or time +complexity of a program, the usage of particular instructions, or the frequency and duration of function calls. +Most commonly, profiling information serves to aid program optimization. + +Briefly, profiler is used to measure application performance. Program analysis tools are extremely important for +understanding program behavior. Simple profiling can tell you that how long does an operation take? For advanced +profiling, it can interpret why does an operation take a long time? + +Why we need profiling? +====================== +Since training deep neural network typically take a very long time to get over, performance is gradually becoming +the most important thing in deep learning field. The first step to improve performance is to understand what parts +are slow. No point in improving performance of a region which doesn’t take much time! + + +How to do profiling? +==================== +To achieve maximum performance, there are five steps you can take to reach your goals. + +- Profile the code +- Find the slow parts +- Work out why they’re slow +- Make them fast +- Profile the code again + +Usually, processor has two key performance limits include float point throughput and +memory throughput. For GPU, it also need more parallelism to fulfill its potential. +This is why they can be so fast. + +Profiler Tools +============== +For general GPU profiling, a bunch of tools are provided from both NVIDIA and third party. + +:code:`nvprof` is Nvidia profiler and :code:`nvvp` is (GUI based) Nvidia visual profiler. +In this tutorial, we will focus on nvprof and nvvp. + +:code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate +above profilers. + +.. code-block:: c++ + + TEST(Profiler, BilinearFwdBwd) { + hl_profiler_start(); + auto numSamples = 10; + auto channels = 16; + auto imgSize = 64; + testBilinearFwdBwd(numSamples, imgSize, imgSize, channels); + hl_profiler_end(); + } + +:code:`hl_profiler_start` and :code:`hl_profiler_end` can be used to profile only regions of interest +in PaddlePaddle. They are wrapper functions of :code:`cudaProfilerStart` and :code:`cudaProfilerStop` +respectively to avoid program crashes when CPU version of PaddlePaddle invokes them. + +Hands-on Approach +================= + +.. image:: nvprof.png + :align: center + :scale: 30% + +.. image:: nvvp1.png + :align: center + :scale: 30% \ No newline at end of file diff --git a/doc/optimization/index.rst b/doc/optimization/index.rst new file mode 100644 index 00000000000..c9e87e0778d --- /dev/null +++ b/doc/optimization/index.rst @@ -0,0 +1,7 @@ +Performance Tuning +================== + +.. toctree:: + :maxdepth: 3 + + gpu_profiling.rst diff --git a/doc/optimization/nvprof.png b/doc/optimization/nvprof.png new file mode 100644 index 0000000000000000000000000000000000000000..5931a9b7dc43e6438c9c2105020f59eb3367f0d9 GIT binary patch literal 487751 zcmeFYcUTn9(=R$3$T?>g$r1%5BSDEGB1n=bl5>V73kpb10)hg9ARzXsC95die`03iSXhygf+6(9f+$O0%s*#5z{e2C!u5y{( 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zJXx9ang{7sTB2?Z2jnI8UGk2jiF@hbu0<~tTLe@;-t2Gb9~CAP*NnT#?ON=LFV}eo zduqpO*qcpvzM50BNQVL9Uwg~YZ;|5UI77gS_ZwT?q0`xeAkpd#Y0w#@*;jQuQHv9` zRX0M1Qol&Ob{;B0;GuQTTz+|l?2iR0{0}dIjoT8{D0kyf_@mXD<)*kP`)~ffFd)Y` z@xiYV*dQW}$oV%FoQpL(X01aKXq!b4@6%(nIt9u%zKcwbzj3_KjWnDHOrd+#<* ziV)ZzP2-&1$e{}vgpPUEv0gf Date: Wed, 16 Nov 2016 00:13:17 +0800 Subject: [PATCH 017/347] Replace md to rst for doc index --- doc/index.md | 24 ------------------------ doc/index.rst | 1 + 2 files changed, 1 insertion(+), 24 deletions(-) delete mode 100644 doc/index.md diff --git a/doc/index.md b/doc/index.md deleted file mode 100644 index 9c5ebba9bed..00000000000 --- a/doc/index.md +++ /dev/null @@ -1,24 +0,0 @@ -PaddlePaddle Documentation -========================== - -User Guide ----------- -* [Introduction](introduction/index.md) -* [Quick Start](demo/quick_start/index_en.md) -* [Build and Installation](build/index.rst) -* [Contribute Code](build/contribute_to_paddle.md) -* [User Interface](ui/index.md) -* [Model Config Interface](ui/api/trainer_config_helpers/index.md) -* [Example and Demo](demo/index.md) -* [Cluster Train](cluster/index.md) - -Development Guide ------------------ -* [Layer Documents](layer.md) -* [Writing New Layers](dev/new_layer/index.rst) -* [Source Code Documents](source/index.md) -* [GPU Profiling Documents](optimization/index.rst) - -Algorithm Tutorial ------------------- -* [RNN Configuration](algorithm/rnn/rnn.rst) diff --git a/doc/index.rst b/doc/index.rst index 668ad75a902..76fb7a3ace8 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -8,3 +8,4 @@ PaddlePaddle Documentation user_guide.rst dev/index.rst algorithm/index.rst + optimization/index.rst -- GitLab From 496d64ebdb3f0c81f4684b9f48fdef90f9d94547 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 16 Nov 2016 17:53:31 +0800 Subject: [PATCH 018/347] Support rectangle input for CNN --- paddle/gserver/layers/BatchNormBaseLayer.cpp | 13 +- paddle/gserver/layers/BatchNormBaseLayer.h | 5 +- paddle/gserver/layers/BilinearInterpLayer.cpp | 6 +- paddle/gserver/layers/ConvBaseLayer.cpp | 15 +- paddle/gserver/layers/ConvOperator.cpp | 13 +- paddle/gserver/layers/ConvProjection.cpp | 2 +- paddle/gserver/layers/DataLayer.cpp | 4 +- paddle/gserver/layers/ExpandConvBaseLayer.cpp | 30 +-- paddle/gserver/layers/MaxOutLayer.cpp | 6 +- paddle/gserver/layers/NormLayer.cpp | 3 + paddle/gserver/layers/NormLayer.h | 2 +- paddle/gserver/layers/NormProjectionLayer.cpp | 2 +- .../layers/SpatialPyramidPoolLayer.cpp | 13 +- paddle/gserver/tests/img_pool_a.conf | 2 - paddle/gserver/tests/test_LayerGrad.cpp | 63 +++-- paddle/parameter/Argument.cpp | 2 + paddle/trainer/tests/test_config.conf | 1 - proto/ModelConfig.proto.m4 | 40 +-- python/paddle/trainer/config_parser.py | 238 +++++++++--------- .../paddle/trainer_config_helpers/layers.py | 26 +- .../configs/protostr/img_layers.protostr | 13 + .../protostr/img_trans_layers.protostr | 11 + .../configs/protostr/projections.protostr | 2 + .../protostr/test_bilinear_interp.protostr | 26 +- .../configs/protostr/test_maxout.protostr | 56 +++-- .../configs/protostr/test_spp_layer.protostr | 12 +- .../tests/configs/test_bilinear_interp.py | 2 +- .../tests/configs/test_maxout.py | 13 +- .../tests/configs/test_spp_layer.py | 8 +- 29 files changed, 360 insertions(+), 269 deletions(-) diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp index 8052b35ec69..7bf4c1fd5ec 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.cpp +++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp @@ -61,15 +61,10 @@ bool BatchNormBaseLayer::init(const LayerMap& layerMap, void BatchNormBaseLayer::calFeatureMapSize() { const ImageConfig& conf = config_.inputs(0).image_conf(); - if (inputLayers_[0]->getOutput().getFrameHeight() == 0 && - inputLayers_[0]->getOutput().getFrameWidth() == 0) { - imgSize_ = conf.img_size(); - imageH_ = imgSize_; - imageW_ = imgSize_; - } else { - imageH_ = inputLayers_[0]->getOutput().getFrameHeight(); - imageW_ = inputLayers_[0]->getOutput().getFrameWidth(); - } + imageH_ = inputLayers_[0]->getOutput().getFrameHeight(); + imageW_ = inputLayers_[0]->getOutput().getFrameWidth(); + if (imageH_ == 0) imageH_ = conf.img_size_y(); + if (imageW_ == 0) imageW_ = conf.img_size(); imgPixels_ = imageH_ * imageW_; getOutput().setFrameHeight(imageH_); getOutput().setFrameWidth(imageW_); diff --git a/paddle/gserver/layers/BatchNormBaseLayer.h b/paddle/gserver/layers/BatchNormBaseLayer.h index 2302d1a8e0b..4ea493b5f52 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.h +++ b/paddle/gserver/layers/BatchNormBaseLayer.h @@ -78,9 +78,8 @@ protected: MatrixPtr savedMean_; MatrixPtr savedInvVar_; - /// Height or width of input image feature, now height is equal to width. - /// imgSize is 1 if the input is fully-connected layer. - int imgSize_; + /// Height or width of input image feature. + /// Both of them are 1 if the input is fully-connected layer. int imageH_; int imageW_; /// Height * Width. diff --git a/paddle/gserver/layers/BilinearInterpLayer.cpp b/paddle/gserver/layers/BilinearInterpLayer.cpp index ac5f87be7af..64d3046b565 100644 --- a/paddle/gserver/layers/BilinearInterpLayer.cpp +++ b/paddle/gserver/layers/BilinearInterpLayer.cpp @@ -26,15 +26,15 @@ size_t BilinearInterpLayer::getSize() { const BilinearInterpConfig& conf = config_.inputs(0).bilinear_interp_conf(); if (inImgH_ == 0) { - inImgH_ = conf.img_size_y(); + inImgH_ = conf.image_conf().img_size_y(); } if (inImgW_ == 0) { - inImgW_ = conf.img_size_x(); + inImgW_ = conf.image_conf().img_size(); } outImgH_ = conf.out_size_y(); outImgW_ = conf.out_size_x(); - numChannels_ = conf.num_channels(); + numChannels_ = conf.image_conf().channels(); CHECK(outImgH_ > 0 && outImgW_ > 0); CHECK(inImgH_ > 0 && inImgW_ > 0); diff --git a/paddle/gserver/layers/ConvBaseLayer.cpp b/paddle/gserver/layers/ConvBaseLayer.cpp index 6bc3b3b8017..8f358a5e411 100644 --- a/paddle/gserver/layers/ConvBaseLayer.cpp +++ b/paddle/gserver/layers/ConvBaseLayer.cpp @@ -37,11 +37,13 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, filterSizeY_.push_back(conf.filter_size_y()); filterPixels_.push_back(filterSize_.back() * filterSizeY_.back()); channels_.push_back(conf.channels()); - imgSizeH_.push_back(conf.img_size()); + imgSizeH_.push_back(conf.has_img_size_y() ? conf.img_size_y() : + conf.img_size()); imgSizeW_.push_back(conf.img_size()); groups_.push_back(conf.groups()); filterChannels_.push_back(conf.filter_channels()); - outputH_.push_back(conf.output_x()); + outputH_.push_back(conf.has_output_y() ? conf.output_y() : + conf.output_x()); outputW_.push_back(conf.output_x()); } @@ -90,11 +92,12 @@ size_t ConvBaseLayer::calOutputSize() { for (size_t i = 0; i < inputLayers_.size(); i++) { inH.push_back(inputLayers_[i]->getOutput().getFrameHeight()); inW.push_back(inputLayers_[i]->getOutput().getFrameWidth()); + const ConvConfig& conf = config_.inputs(i).conv_conf(); if (isDeconv_) { if (inH[i] == 0) - inH[i] = config_.inputs(i).conv_conf().output_x(); + inH[i] = conf.has_output_y() ? conf.output_y() : conf.output_x(); if (inW[i] == 0) - inW[i] = config_.inputs(i).conv_conf().output_x(); + inW[i] = conf.output_x(); outH.push_back( imageSize(inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_)); @@ -103,9 +106,9 @@ size_t ConvBaseLayer::calOutputSize() { caffeMode_)); } else { if (inH[i] == 0) - inH[i] = config_.inputs(i).conv_conf().img_size(); + inH[i] = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); if (inW[i] == 0) - inW[i] = config_.inputs(i).conv_conf().img_size(); + inW[i] = conf.img_size(); outH.push_back( outputSize(inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_)); diff --git a/paddle/gserver/layers/ConvOperator.cpp b/paddle/gserver/layers/ConvOperator.cpp index 2d9c892fe59..7830efab1d0 100644 --- a/paddle/gserver/layers/ConvOperator.cpp +++ b/paddle/gserver/layers/ConvOperator.cpp @@ -93,9 +93,9 @@ private: bool caffeMode_; int inputOffset_, outputOffset_, weightOffset_; int numFilters_; - int padding_, stride_, filterSize_, channels_, imgSize_; + int padding_, stride_, filterSize_, channels_, imgSize_, imgSizeY_; int paddingY_, strideY_, filterSizeY_; - int imgPixels_, filterPixels_, filterChannels_, outputX_, outputs_; + int imgPixels_, filterPixels_, filterChannels_, outputX_, outputY_, outputs_; /// Following member variables are same with CudnnConvLayer. /// There is no explanation here. @@ -144,7 +144,7 @@ void ConvOperator::allocConvWorkSpace(size_t maxWorkSpace) { void ConvOperator::reshape(int batchSize) { imageH_ = ins_[0]->getFrameHeight(); imageW_ = ins_[0]->getFrameWidth(); - if (imageH_ == 0) imageH_ = imgSize_; + if (imageH_ == 0) imageH_ = imgSizeY_; if (imageW_ == 0) imageW_ = imgSize_; outputH_ = outputSize(imageH_, filterSizeY_, paddingY_, strideY_, caffeMode_); outputW_ = outputSize(imageW_, filterSize_, padding_, stride_, caffeMode_); @@ -176,7 +176,10 @@ void ConvOperator::computeConvSizes() { hl_create_tensor_descriptor(&inputDesc_); int outputX = outputSize(imgSize_, filterSize_, padding_, stride_, caffeMode_); + int outputY = + outputSize(imgSizeY_, filterSizeY_, paddingY_, strideY_, caffeMode_); CHECK_EQ(outputX, outputX_); + CHECK_EQ(outputY, outputY_); hl_create_tensor_descriptor(&outputDesc_); hl_create_convolution_descriptor(&convDesc_, inputDesc_, filterDesc_, paddingY_, padding_, strideY_, stride_); @@ -208,10 +211,12 @@ void ConvOperator::getConvParams() { filterPixels_ = filterSize_ * filterSizeY_; channels_ = conf.channels(); imgSize_ = conf.img_size(); - imgPixels_ = imgSize_ * imgSize_; + imgSizeY_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); + imgPixels_ = imgSize_ * imgSizeY_; CHECK_EQ(conf.groups(), 1U); filterChannels_ = conf.filter_channels(); outputX_ = conf.output_x(); + outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x(); outputs_ = outputX_ * outputX_; } diff --git a/paddle/gserver/layers/ConvProjection.cpp b/paddle/gserver/layers/ConvProjection.cpp index d1ce53fe263..161bbad4f5c 100644 --- a/paddle/gserver/layers/ConvProjection.cpp +++ b/paddle/gserver/layers/ConvProjection.cpp @@ -47,7 +47,7 @@ void ConvProjection::getConvParams() { filterH_ = conf.filter_size_y(); filterW_ = conf.filter_size(); - configImgH_ = conf.img_size(); + configImgH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); configImgW_ = conf.img_size(); channels_ = conf.channels(); diff --git a/paddle/gserver/layers/DataLayer.cpp b/paddle/gserver/layers/DataLayer.cpp index 79b9181e694..b83d4f44b0c 100644 --- a/paddle/gserver/layers/DataLayer.cpp +++ b/paddle/gserver/layers/DataLayer.cpp @@ -48,8 +48,8 @@ void DataLayer::copyDataToOutput(Argument& output) { output.ids->copyFrom(*data_.ids); } } - output.setFrameHeight(data_.getFrameHeight()); - output.setFrameWidth(data_.getFrameWidth()); + output.setFrameHeight(config_.height()); + output.setFrameWidth(config_.width()); output.cpuSequenceDims = data_.cpuSequenceDims; output.sequenceStartPositions = data_.sequenceStartPositions; output.subSequenceStartPositions = data_.subSequenceStartPositions; diff --git a/paddle/gserver/layers/ExpandConvBaseLayer.cpp b/paddle/gserver/layers/ExpandConvBaseLayer.cpp index 0bab0ca764f..953c9d78416 100644 --- a/paddle/gserver/layers/ExpandConvBaseLayer.cpp +++ b/paddle/gserver/layers/ExpandConvBaseLayer.cpp @@ -30,17 +30,19 @@ bool ExpandConvBaseLayer::init(const LayerMap &layerMap, * meaning as in conv, we need to swap channels_ and numFilters here for * convTrans, and in other functions too. * */ - int channel; - int numFilters; + /* Initialize the projection */ for (auto &inputConfig : config_.inputs()) { const ConvConfig &conf = inputConfig.conv_conf(); - numFilters = isDeconv_ ? conf.channels() : numFilters_; + int numFilters = isDeconv_ ? conf.channels() : numFilters_; subM_.push_back(numFilters / conf.groups()); - subN_.push_back(conf.output_x() * conf.output_x()); - channel = isDeconv_ ? numFilters_ : conf.channels(); - subK_.push_back(channel * conf.filter_size() * conf.filter_size() / - conf.groups()); + subN_.push_back(conf.output_x() * + (conf.has_output_y() ? conf.output_y() : conf.output_x())); + int channel = isDeconv_ ? numFilters_ : conf.channels(); + subK_.push_back( + channel * conf.filter_size() * + (conf.has_filter_size_y() ? conf.filter_size_y() : conf.filter_size()) / + conf.groups()); /* Consistent caffe mode for multiple input */ caffeMode_ = conf.caffe_mode(); } @@ -107,9 +109,9 @@ void ExpandConvBaseLayer::expandOneFrame(MatrixPtr image, size_t startIdx, imgData, 1, imgSizeH_[inIdx] * imgSizeW_[inIdx] * channel, false, useGpu_); expandInput_->convExpand(*imageTmp, imgSizeH_[inIdx], imgSizeW_[inIdx], - channel, filterSize_[inIdx], - filterSize_[inIdx], stride_[inIdx], stride_[inIdx], - padding_[inIdx], padding_[inIdx], + channel, filterSizeY_[inIdx], + filterSize_[inIdx], strideY_[inIdx], stride_[inIdx], + paddingY_[inIdx], padding_[inIdx], outputH_[inIdx], outputW_[inIdx]); imageTmp->clear(); } @@ -188,10 +190,10 @@ void ExpandConvBaseLayer::bpropActs(MatrixPtr out, MatrixPtr image, imgSizeH_[inpIdx] * imgSizeW_[inpIdx] * channel, false, useGpu_); vTmp->convShrink(*oneGradTmp, imgSizeH_[inpIdx], imgSizeW_[inpIdx], - channel, filterSize_[inpIdx], - filterSize_[inpIdx], stride_[inpIdx], stride_[inpIdx], - padding_[inpIdx], padding_[inpIdx], - outputH_[inpIdx], outputW_[inpIdx], 1.0f, 1.0f); + channel, filterSizeY_[inpIdx], + filterSize_[inpIdx], strideY_[inpIdx], stride_[inpIdx], + paddingY_[inpIdx], padding_[inpIdx], outputH_[inpIdx], + outputW_[inpIdx], 1.0f, 1.0f); vTmp->clear(); oneGradTmp->clear(); diff --git a/paddle/gserver/layers/MaxOutLayer.cpp b/paddle/gserver/layers/MaxOutLayer.cpp index a3de069bf7a..b7f1b980413 100644 --- a/paddle/gserver/layers/MaxOutLayer.cpp +++ b/paddle/gserver/layers/MaxOutLayer.cpp @@ -25,10 +25,10 @@ size_t MaxOutLayer::getSize() { imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight(); imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth(); if (imgSizeH_ == 0) { - imgSizeH_ = maxoutConf.img_size_y(); + imgSizeH_ = maxoutConf.image_conf().img_size_y(); } if (imgSizeW_ == 0) { - imgSizeW_ = maxoutConf.img_size_x(); + imgSizeW_ = maxoutConf.image_conf().img_size(); } featLen_ = imgSizeH_ * imgSizeW_; @@ -50,7 +50,7 @@ bool MaxOutLayer::init(const LayerMap& layerMap, const MaxOutConfig& conf = config_.inputs(0).maxout_conf(); groups_ = conf.groups(); - channels_ = conf.channels(); + channels_ = conf.image_conf().channels(); CHECK_EQ(channels_ % groups_, 0UL); outputChannels_ = channels_ / groups_; diff --git a/paddle/gserver/layers/NormLayer.cpp b/paddle/gserver/layers/NormLayer.cpp index ad8b92d2ff7..b02a542a51e 100644 --- a/paddle/gserver/layers/NormLayer.cpp +++ b/paddle/gserver/layers/NormLayer.cpp @@ -49,6 +49,9 @@ bool ResponseNormLayer::init(const LayerMap& layerMap, outputX_ = conf.output_x(); imgSize_ = conf.img_size(); denoms_ = NULL; + + outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x(); + imgSizeY_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); return true; } diff --git a/paddle/gserver/layers/NormLayer.h b/paddle/gserver/layers/NormLayer.h index 2b05be6fcb4..9e4acffd1fd 100644 --- a/paddle/gserver/layers/NormLayer.h +++ b/paddle/gserver/layers/NormLayer.h @@ -50,7 +50,7 @@ public: */ class ResponseNormLayer : public NormLayer { protected: - size_t channels_, size_, outputX_, imgSize_; + size_t channels_, size_, outputX_, imgSize_, outputY_, imgSizeY_; float scale_, pow_; MatrixPtr denoms_; diff --git a/paddle/gserver/layers/NormProjectionLayer.cpp b/paddle/gserver/layers/NormProjectionLayer.cpp index eab6e904ee9..e33b985facd 100644 --- a/paddle/gserver/layers/NormProjectionLayer.cpp +++ b/paddle/gserver/layers/NormProjectionLayer.cpp @@ -24,7 +24,7 @@ size_t CMRProjectionNormLayer::getSize() { imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight(); imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth(); if (imgSizeH_ == 0) { - imgSizeH_ = imgSize_; + imgSizeH_ = imgSizeY_; } if (imgSizeW_ == 0) { imgSizeW_ = imgSize_; diff --git a/paddle/gserver/layers/SpatialPyramidPoolLayer.cpp b/paddle/gserver/layers/SpatialPyramidPoolLayer.cpp index 2fcfc8e1ae6..2675f954012 100644 --- a/paddle/gserver/layers/SpatialPyramidPoolLayer.cpp +++ b/paddle/gserver/layers/SpatialPyramidPoolLayer.cpp @@ -56,14 +56,14 @@ ProjectionConfig SpatialPyramidPoolLayer::getConfig(size_t imgSizeW, size_t SpatialPyramidPoolLayer::getSize() { CHECK_EQ(inputLayers_.size(), 1UL); size_t layerSize = 0; - const SppConfig& sppConf = config_.inputs(0).spp_conf(); + const ImageConfig& conf = config_.inputs(0).spp_conf().image_conf(); imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight(); imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth(); if (imgSizeH_ == 0) { - imgSizeH_ = sppConf.has_img_size_y() ? sppConf.img_size_y() : imgSizeW_; + imgSizeH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); } if (imgSizeW_ == 0) { - imgSizeW_ = sppConf.img_size(); + imgSizeW_ = conf.img_size(); } size_t outputH = 1; @@ -82,9 +82,10 @@ bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap, pyramidHeight_ = sppConf.pyramid_height(); poolType_ = sppConf.pool_type(); - channels_ = sppConf.channels(); - imgSizeW_ = sppConf.img_size(); - imgSizeH_ = sppConf.has_img_size_y() ? sppConf.img_size_y() : imgSizeW_; + const ImageConfig& imageConf = sppConf.image_conf(); + channels_ = imageConf.channels(); + imgSizeW_ = imageConf.img_size(); + imgSizeH_ = imageConf.has_img_size_y() ? imageConf.img_size_y() : imgSizeW_; poolProjections_.reserve(pyramidHeight_); projCol_.reserve(pyramidHeight_); projOutput_.resize(pyramidHeight_); diff --git a/paddle/gserver/tests/img_pool_a.conf b/paddle/gserver/tests/img_pool_a.conf index 5938e761120..9bd046b533d 100644 --- a/paddle/gserver/tests/img_pool_a.conf +++ b/paddle/gserver/tests/img_pool_a.conf @@ -28,7 +28,6 @@ maxpool = img_pool_layer(input=conv, stride_y=2, padding=1, padding_y=2, - img_width=16, pool_type=MaxPooling(), ) avgpool = img_pool_layer(input=conv, @@ -39,7 +38,6 @@ avgpool = img_pool_layer(input=conv, stride_y=2, padding=1, padding_y=2, - img_width=16, pool_type=AvgPooling(), ) diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index a79dfe39c9b..e8398510990 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -194,9 +194,10 @@ TEST(Layer, BilinearInterpLayer) { LayerInputConfig* input = config.layerConfig.add_inputs(); BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf(); - bilinear->set_img_size_x(32); - bilinear->set_img_size_y(32); - bilinear->set_num_channels(4); + ImageConfig* image = bilinear->mutable_image_conf(); + image->set_img_size(32); + image->set_img_size_y(32); + image->set_channels(4); for (auto useGpu : {false, true}) { for (auto outSize : {32, 64}) { @@ -314,7 +315,7 @@ void testConvLayer(const string& type, bool trans, bool useGpu) { config.layerConfig.set_partial_sum(1); config.layerConfig.set_shared_biases(true); - config.inputDefs.push_back({INPUT_DATA, "layer_0", 768, 288}); + config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); LayerInputConfig* input = config.layerConfig.add_inputs(); ConvConfig* conv = input->mutable_conv_conf(); conv->set_filter_size(2); @@ -327,10 +328,14 @@ void testConvLayer(const string& type, bool trans, bool useGpu) { conv->set_groups(1); conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_img_size(16); + conv->set_img_size_y(8); conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(), conv->padding(), conv->stride(), /* caffeMode */ true)); - config.layerConfig.set_size(conv->output_x() * conv->output_x() * + conv->set_output_y(outputSize(conv->img_size_y(), conv->filter_size_y(), + conv->padding_y(), conv->stride_y(), + /* caffeMode */ true)); + config.layerConfig.set_size(conv->output_x() * conv->output_y() * config.layerConfig.num_filters()); testLayerGrad(config, "conv", 100, trans, useGpu); @@ -427,10 +432,11 @@ TEST(Layer, maxoutLayer) { config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0}); LayerInputConfig* input = config.layerConfig.add_inputs(); MaxOutConfig* maxout = input->mutable_maxout_conf(); + ImageConfig* image = maxout->mutable_image_conf(); - maxout->set_img_size_x(32); - maxout->set_img_size_y(32); - maxout->set_channels(4); + image->set_img_size(32); + image->set_img_size_y(32); + image->set_channels(4); maxout->set_groups(2); for (auto useGpu : {false, true}) { @@ -902,7 +908,7 @@ void testNormLayer(const string& normType, bool trans, bool useGpu) { config.layerConfig.set_type("norm"); config.layerConfig.set_active_type("relu"); - config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0}); + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0}); LayerInputConfig* input = config.layerConfig.add_inputs(); NormConfig* norm = input->mutable_norm_conf(); norm->set_norm_type(normType); @@ -912,7 +918,9 @@ void testNormLayer(const string& normType, bool trans, bool useGpu) { norm->set_pow(0.75); norm->set_blocked(0); norm->set_img_size(14); + norm->set_img_size_y(7); norm->set_output_x(norm->img_size()); + norm->set_output_y(norm->img_size_y()); if (norm->norm_type() == "cmrnorm" || norm->norm_type() == "cmrnorm-projection") { norm->set_scale(norm->scale() / norm->size()); @@ -920,7 +928,7 @@ void testNormLayer(const string& normType, bool trans, bool useGpu) { norm->set_scale(norm->scale() / (norm->size() * norm->size())); } - config.layerConfig.set_size(norm->output_x() * norm->output_x() * + config.layerConfig.set_size(norm->output_x() * norm->output_y() * norm->channels()); config.biasSize = 0; @@ -1018,11 +1026,12 @@ void testSppLayer(const string& poolType, const int pyramidHeight, bool trans, SppConfig* sppConfig = input->mutable_spp_conf(); sppConfig->set_pool_type(poolType); sppConfig->set_pyramid_height(pyramidHeight); - sppConfig->set_channels(16); - sppConfig->set_img_size(10); - sppConfig->set_img_size_y(20); + ImageConfig* imageConfig = sppConfig->mutable_image_conf(); + imageConfig->set_channels(16); + imageConfig->set_img_size(10); + imageConfig->set_img_size_y(20); int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1); - config.layerConfig.set_size(outputSize * sppConfig->channels()); + config.layerConfig.set_size(outputSize * imageConfig->channels()); testLayerGrad(config, "spp", 100, trans, useGpu); } @@ -1328,12 +1337,13 @@ void testBatchNormLayer(const string& type, bool trans, bool useGpu) { TestConfig config; const int CHANNELS = 10; const int IMG_SIZE = 16; + const int IMG_SIZE_Y = 8; + size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y; config.layerConfig.set_type(type); - config.layerConfig.set_size(CHANNELS * IMG_SIZE * IMG_SIZE); + config.layerConfig.set_size(size); config.layerConfig.set_active_type("sigmoid"); config.biasSize = CHANNELS; - config.inputDefs.push_back({INPUT_DATA, "layer_0", - /* dim= */ IMG_SIZE * IMG_SIZE * CHANNELS, + config.inputDefs.push_back({INPUT_DATA, "layer_0", /* dim= */ size, /* paraSize= */ CHANNELS}); config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS}); @@ -1348,6 +1358,7 @@ void testBatchNormLayer(const string& type, bool trans, bool useGpu) { ImageConfig* img_conf = input->mutable_image_conf(); img_conf->set_channels(CHANNELS); img_conf->set_img_size(IMG_SIZE); + img_conf->set_img_size_y(IMG_SIZE_Y); testLayerGrad(config, "batch_norm", 64, /* trans= */ trans, useGpu, /* useWeight */ true); @@ -1370,6 +1381,7 @@ TEST(Operator, conv) { const int FILTER_SIZE_Y = 3; const int CHANNELS = 3; const int IMAGE_SIZE = 16; + const int IMAGE_SIZE_Y = 8; OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); operatorConf.set_type("conv"); ConvConfig* conv = operatorConf.mutable_conv_conf(); @@ -1384,17 +1396,18 @@ TEST(Operator, conv) { conv->set_groups(1); conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_img_size(IMAGE_SIZE); - int output_x = - outputSize(conv->img_size(), conv->filter_size(), conv->padding(), - conv->stride(), /* caffeMode */ true); - conv->set_output_x(output_x); - config.layerConfig.set_size(output_x * output_x * - config.layerConfig.num_filters()); - config.layerConfig.set_size(conv->output_x() * conv->output_x() * + conv->set_img_size_y(IMAGE_SIZE_Y); + conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(), + conv->padding(), conv->stride(), + /* caffeMode */ true)); + conv->set_output_y(outputSize(conv->img_size_y(), conv->filter_size_y(), + conv->padding_y(), conv->stride_y(), + /* caffeMode */ true)); + config.layerConfig.set_size(conv->output_x() * conv->output_y() * NUM_FILTERS); config.inputDefs.push_back( - {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE * CHANNELS, 0}); + {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0}); config.inputDefs.push_back( {INPUT_DATA, "layer_1", FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS, 0}); diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 42c74661d2b..2d5cd29aed7 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -203,6 +203,8 @@ void Argument::resizeAndCopyFrom(const Argument& src, bool useGpu, } resizeAndCopy(udp, src.udp, useGpu, stream); resizeAndCopy(strs, src.strs, useGpu, stream); + frameWidth = src.frameWidth; + frameHeight = src.frameHeight; } int32_t Argument::resizeAndCopyFrom(const Argument& src, int32_t startSeq, diff --git a/paddle/trainer/tests/test_config.conf b/paddle/trainer/tests/test_config.conf index 664e18cb986..2a4548896ff 100644 --- a/paddle/trainer/tests/test_config.conf +++ b/paddle/trainer/tests/test_config.conf @@ -59,7 +59,6 @@ pool = img_pool_layer(input=fc2, padding_y=2, stride=2, stride_y=3, - img_width=3, pool_type=CudnnAvgPooling()) concat = concat_layer(input=[fc3, fc4]) diff --git a/proto/ModelConfig.proto.m4 b/proto/ModelConfig.proto.m4 index aea77248cba..3c35075a92e 100644 --- a/proto/ModelConfig.proto.m4 +++ b/proto/ModelConfig.proto.m4 @@ -76,6 +76,12 @@ message ConvConfig { required uint32 filter_size_y = 10; required uint32 padding_y = 11; required uint32 stride_y = 12; + + // if not set, use output_x + optional uint32 output_y = 13 [default = 0]; + + // if not set, use img_size + optional uint32 img_size_y = 14 [default = 0]; } message PoolConfig { @@ -121,11 +127,9 @@ message PoolConfig { } message SppConfig { - required string pool_type = 1; - required uint32 pyramid_height = 2; - required uint32 channels = 3; - required uint32 img_size = 4; - optional uint32 img_size_y = 5; + required ImageConfig image_conf = 1; + required string pool_type = 2; + required uint32 pyramid_height = 3; } message NormConfig { @@ -155,6 +159,12 @@ message NormConfig { // fixed window: shared a fixed window for each value // sliding window: have a different window for each value optional bool blocked = 8; + + // if not set, use output_x + optional uint32 output_y = 9 [default = 0]; + + // if not set, use img_size + optional uint32 img_size_y = 10 [default = 0]; } message BlockExpandConfig { @@ -179,12 +189,8 @@ message BlockExpandConfig { } message MaxOutConfig { - required uint32 channels = 1; + required ImageConfig image_conf = 1; required uint32 groups = 2; - - // The size of input feature map. - required uint32 img_size_x = 3; - required uint32 img_size_y = 4; } message ProjectionConfig { @@ -225,12 +231,10 @@ message OperatorConfig { message BilinearInterpConfig { // The size of input feature map. - optional uint32 img_size_x = 1; - optional uint32 img_size_y = 2; + required ImageConfig image_conf = 1; // The size of output feature map. - required uint32 out_size_x = 3; - required uint32 out_size_y = 4; - required uint32 num_channels = 5; + required uint32 out_size_x = 2; + required uint32 out_size_y = 3; } message ImageConfig { @@ -240,6 +244,7 @@ message ImageConfig { // The size of input feature map. required uint32 img_size = 8; + required uint32 img_size_y = 9; } message LayerInputConfig { @@ -412,7 +417,10 @@ sinclude(`ModelConfigLayer.proto.m4') // string type is used for flexibility: different types can be converted // to string and reinterpreted in the user's own layer implementation. optional string user_arg = 49; - + + // to indicate rectangle image data + optional uint64 height = 50; + optional uint64 width = 51; } message EvaluatorConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index dbe2f3b2927..a7ad40e4837 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -138,7 +138,14 @@ def init_config_environment( g_root_submodel=None, g_submodel_map={}, g_submodel_stack=[], - g_add_submodel_suffix=False, ): + g_add_submodel_suffix=False, + + # Whether current layer needs to pass the image height and width. + # Default value is true, but if it encounters recurrent_layer_group, + # it will be false. The reason is that image is converted to be sequence, + # image height will be sequence length, and image width will be feature + # length of each timestep. + g_pass_height_width=True, ): for k, v in locals().iteritems(): globals()[k] = copy.deepcopy(v) @@ -592,6 +599,7 @@ class DotMulProjection(Projection): def calc_parameter_dims(self, input_size, output_size): return [1, output_size] + # ScalingProjection @config_class class ScalingProjection(Projection): @@ -685,9 +693,9 @@ class ConvProjection(Projection): parse_conv(conv_conf, input_layer_name, self.proj_conf.conv_conf, num_filters) - # TODO: support rectangle input - self.proj_conf.output_size = (self.proj_conf.conv_conf.output_x - **2) * num_filters + self.proj_conf.output_size = self.proj_conf.conv_conf.output_x * \ + self.proj_conf.conv_conf.output_y * \ + num_filters def calc_output_size(self, input_layer_config): return self.proj_conf.output_size @@ -762,8 +770,9 @@ class ConvOperator(Operator): parse_conv(conv_conf, MakeLayerNameInSubmodel(input_layer_names[0]), self.operator_conf.conv_conf, num_filters) - self.operator_conf.output_size = (self.operator_conf.conv_conf.output_x - **2) * num_filters + self.operator_conf.output_size = self.operator_conf.conv_conf.output_x * \ + self.operator_conf.conv_conf.output_y * \ + num_filters config_assert(len(input_layer_names) == 2, "Conv is binary operator") @@ -798,14 +807,12 @@ class Conv(Cfg): config_assert(output_x <= 0) -# please refer to the comments in proto/ModelConfig.proto @config_class class BilinearInterp(Cfg): - def __init__(self, out_size_x=None, out_size_y=None, num_channels=None): + def __init__(self, out_size_x=None, out_size_y=None, channels=None): self.add_keys(locals()) -# please refer to the comments in proto/ModelConfig.proto @config_class class Pool(Cfg): def __init__(self, @@ -813,7 +820,6 @@ class Pool(Cfg): channels, size_x, size_y=None, - img_width=None, start=None, stride=None, stride_y=None, @@ -822,14 +828,12 @@ class Pool(Cfg): self.add_keys(locals()) -# please refer to the comments in proto/ModelConfig.proto @config_class class SpatialPyramidPool(Cfg): - def __init__(self, pool_type, pyramid_height, channels, img_width=None): + def __init__(self, pool_type, pyramid_height, channels): self.add_keys(locals()) -# please refer to the comments in proto/ModelConfig.proto @config_class class Norm(Cfg): def __init__(self, @@ -844,7 +848,6 @@ class Norm(Cfg): self.add_keys(locals()) -# please refer to the comments in proto/ModelConfig.proto @config_class class Image(Cfg): def __init__(self, channels, img_size=None): @@ -1051,18 +1054,8 @@ def TestData(data_config, async_load_data=None): g_config.test_data_config.async_load_data = async_load_data -def parse_bilinear(bilinear, input_layer_name, bilinear_conf): - bilinear_conf.out_size_x = bilinear.out_size_x - bilinear_conf.out_size_y = bilinear.out_size_y - bilinear_conf.num_channels = bilinear.num_channels - - -''' -caffe_mode: compute the output size using floor instead of ceil, - which is consistent of caffe and CuDNN's convention. -''' - - +#caffe_mode: compute the output size using floor instead of ceil, +# which is consistent of caffe and CuDNN's convention. def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode): output = (2 * padding + img_size - filter_size) / float(stride) if caffe_mode: @@ -1071,20 +1064,34 @@ def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode): return 1 + int(math.ceil(output)) -''' -calcualte image_size based on output_size for convolution. -It is the reverse function of cnn_output_size -''' - - +#calcualte image_size based on output_size for convolution. +#It is the reverse function of cnn_output_size def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode): - if caffe_mode: - img_size = (output_size - 1) * stride + filter_size - 2 * padding - else: - img_size = (output_size - 2) * stride + filter_size - 2 * padding + 1 + img_size = (output_size - 1) * stride + filter_size - 2 * padding + if not caffe_mode: + img_size = img_size + 1 return img_size +def set_img_size(input_layer_name, channels): + input = g_layer_map[input_layer_name] + img_pixels = input.size / channels + img_size = input.width if input.width > 0 else int(img_pixels**0.5) + img_size_y = input.height if input.height > 0 else int(img_pixels / + img_size) + config_assert( + img_size * img_size_y == img_pixels, + "Input layer %s: Incorrect input image size %d * %d for input image pixels %d" + % (input_layer_name, img_size, img_size_y, img_pixels)) + return img_size, img_size_y + + +def parse_bilinear(bilinear, input_layer_name, bilinear_conf): + parse_image(bilinear, input_layer_name, bilinear_conf.image_conf) + bilinear_conf.out_size_x = bilinear.out_size_x + bilinear_conf.out_size_y = bilinear.out_size_y + + def parse_pool(pool, input_layer_name, pool_conf): pool_conf.pool_type = pool.pool_type config_assert(pool.pool_type in [ @@ -1100,14 +1107,8 @@ def parse_pool(pool, input_layer_name, pool_conf): pool_conf.size_y = default(pool.size_y, pool_conf.size_x) pool_conf.stride_y = default(pool.stride_y, pool_conf.stride) - img_pixels = g_layer_map[input_layer_name].size / pool.channels - # the img_width may be removed, - # and it can be calculated automatically later. - pool_conf.img_size = default(pool.img_width, int(img_pixels**0.5)) - pool_conf.img_size_y = img_pixels / pool_conf.img_size - config_assert(pool_conf.img_size * pool_conf.img_size_y == img_pixels, - "Incorrect input image size %d for input image pixels %d" % - (pool_conf.img_size, img_pixels)) + pool_conf.img_size, pool_conf.img_size_y = \ + set_img_size(input_layer_name, pool.channels) config_assert(not pool.start, "start is deprecated in pooling.") @@ -1123,29 +1124,18 @@ def parse_pool(pool, input_layer_name, pool_conf): def parse_spp(spp, input_layer_name, spp_conf): + parse_image(spp, input_layer_name, spp_conf.image_conf) spp_conf.pool_type = spp.pool_type config_assert(spp.pool_type in ['max-projection', 'avg-projection'], "pool-type %s is not in " "['max-projection', 'avg-projection']" % spp.pool_type) spp_conf.pyramid_height = spp.pyramid_height - spp_conf.channels = spp.channels - - img_pixels = g_layer_map[input_layer_name].size / spp_conf.channels - - spp_conf.img_size = default(spp.img_width, int(img_pixels**0.5)) - spp_conf.img_size_y = img_pixels / spp_conf.img_size - config_assert(spp_conf.img_size * spp_conf.img_size_y == img_pixels, - "Incorrect input image size %d for input image pixels %d" % - (spp_conf.img_size, img_pixels)) def parse_image(image, input_layer_name, image_conf): image_conf.channels = image.channels - image_pixels = g_layer_map[input_layer_name].size / image_conf.channels - image_conf.img_size = int(image_pixels**0.5) - config_assert((image_conf.img_size**2) == image_pixels, - "Incorrect input image size %d for input image pixels %d" % - (image_conf.img_size, image_pixels)) + image_conf.img_size, image_conf.img_size_y = \ + set_img_size(input_layer_name, image_conf.channels) def parse_norm(norm, input_layer_name, norm_conf): @@ -1159,24 +1149,18 @@ def parse_norm(norm, input_layer_name, norm_conf): norm_conf.pow = norm.pow norm_conf.blocked = norm.blocked - img_pixels = g_layer_map[input_layer_name].size / norm.channels - norm_conf.img_size = int(img_pixels**0.5) - config_assert((norm_conf.img_size**2) == img_pixels, - "Incorrect input image size %d for input image pixels %d" % - (norm_conf.img_size, img_pixels)) + norm_conf.img_size, norm_conf.img_size_y = \ + set_img_size(input_layer_name, norm.channels) norm_conf.output_x = norm_conf.img_size + norm_conf.output_y = norm_conf.img_size_y if norm.norm_type in ['cmrnorm-projection']: norm_conf.scale /= norm.size else: norm_conf.scale /= norm.size**2 -''' -caffe_mode: compute the output size using floor instead of ceil, - which is consistent of caffe and CuDNN's convention. -''' - - +#caffe_mode: compute the output size using floor instead of ceil, +# which is consistent of caffe and CuDNN's convention. def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False): conv_conf.filter_size = conv.filter_size conv_conf.filter_size_y = conv.filter_size_y @@ -1190,33 +1174,24 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False): if not trans: conv_conf.filter_channels = conv.channels / conv.groups - - img_pixels = g_layer_map[input_layer_name].size / conv.channels - print('channels=%d size=%d' % (conv.channels, - g_layer_map[input_layer_name].size)) - conv_conf.img_size = int(img_pixels**0.5) - config_assert((conv_conf.img_size**2) == img_pixels, ( - "Input layer %s: Incorrect input image size %d for input " + - "image pixels %d") % - (input_layer_name, conv_conf.img_size, img_pixels)) - + conv_conf.img_size, conv_conf.img_size_y = \ + set_img_size(input_layer_name, conv.channels) conv_conf.output_x = cnn_output_size( conv_conf.img_size, conv_conf.filter_size, conv_conf.padding, conv_conf.stride, conv_conf.caffe_mode) + conv_conf.output_y = cnn_output_size( + conv_conf.img_size_y, conv_conf.filter_size_y, conv_conf.padding_y, + conv_conf.stride_y, conv_conf.caffe_mode) else: conv_conf.filter_channels = num_filters / conv.groups - - outputSize = g_layer_map[input_layer_name].size / conv.channels - print('channels=%d size=%d' % (conv.channels, - g_layer_map[input_layer_name].size)) - conv_conf.output_x = int(outputSize**0.5) - config_assert((conv_conf.output_x**2) == outputSize, ( - "Input layer %s: Incorrect input image size %d for input " + - "image pixels %d") % - (input_layer_name, conv_conf.output_x, outputSize)) + conv_conf.output_x, conv_conf.output_y = \ + set_img_size(input_layer_name, conv.channels) conv_conf.img_size = cnn_image_size( conv_conf.output_x, conv_conf.filter_size, conv_conf.padding, conv_conf.stride, conv_conf.caffe_mode) + conv_conf.img_size_y = cnn_output_size( + conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y, + conv_conf.stride_y, conv_conf.caffe_mode) def parse_block_expand(block_expand, input_layer_name, block_expand_conf): @@ -1245,10 +1220,8 @@ def parse_block_expand(block_expand, input_layer_name, block_expand_conf): def parse_maxout(maxout, input_layer_name, maxout_conf): - maxout_conf.channels = maxout.channels + parse_image(maxout, input_layer_name, maxout_conf.image_conf) maxout_conf.groups = maxout.groups - maxout_conf.img_size_x = maxout.img_size_x - maxout_conf.img_size_y = maxout.img_size_y # Define an evaluator @@ -1375,6 +1348,12 @@ class LayerBase(object): g_current_submodel.layer_names.append(self.config.name) + if self.config.type != 'data' and g_pass_height_width: + height = self.get_input_layer(0).height + width = self.get_input_layer(0).width + if height and width: + self.set_layer_height_width(height, width) + def get_input_layer(self, input_index): return g_layer_map[self.config.inputs[input_index].input_layer_name] @@ -1492,6 +1471,23 @@ class LayerBase(object): 'Different inputs result in' + 'different layer size at layer %s' % self.config.name) + def set_layer_height_width(self, height, width): + self.config.height = height + self.config.width = width + + def set_cnn_layer(self, + input_layer_name, + height, + width, + channels, + is_print=True): + size = height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(height, width) + if is_print: + print("output for %s: c = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, height, width, size)) + @config_layer('multi_class_cross_entropy_with_selfnorm') class MultiClassCrossEntropySelfNormCostLayer(LayerBase): @@ -1581,9 +1577,11 @@ class PrintLayer(LayerBase): @config_layer('data') class DataLayer(LayerBase): - def __init__(self, name, size, device=None): + def __init__(self, name, size, height=None, width=None, device=None): super(DataLayer, self).__init__( name, 'data', size, inputs=[], device=device) + if height and width: + self.set_layer_height_width(height, width) ''' @@ -1682,14 +1680,13 @@ class ConvLayerBase(LayerBase): for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) - parse_conv(self.inputs[input_index].conv, input_layer.name, - self.config.inputs[input_index].conv_conf, num_filters) conv_conf = self.config.inputs[input_index].conv_conf + parse_conv(self.inputs[input_index].conv, input_layer.name, + conv_conf, num_filters) psize = self.calc_parameter_size(conv_conf) - print("output size for %s is %d " % (name, conv_conf.output_x)) self.create_input_parameter(input_index, psize) - self.set_layer_size( - (conv_conf.output_x**2) * self.config.num_filters) + self.set_cnn_layer(name, conv_conf.output_y, conv_conf.output_x, + self.config.num_filters) psize = self.config.size if shared_biases: @@ -1776,10 +1773,11 @@ class NormLayer(LayerBase): name, 'norm', 0, inputs=inputs, device=device) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) - parse_norm(self.inputs[input_index].norm, input_layer.name, - self.config.inputs[input_index].norm_conf) norm_conf = self.config.inputs[input_index].norm_conf - self.set_layer_size((norm_conf.output_x**2) * norm_conf.channels) + parse_norm(self.inputs[input_index].norm, input_layer.name, + norm_conf) + self.set_cnn_layer(name, norm_conf.output_y, norm_conf.output_x, + norm_conf.channels, False) @config_layer('pool') @@ -1789,13 +1787,11 @@ class PoolLayer(LayerBase): name, 'pool', 0, inputs=inputs, device=device) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) - parse_pool(self.inputs[input_index].pool, input_layer.name, - self.config.inputs[input_index].pool_conf) pool_conf = self.config.inputs[input_index].pool_conf - print("output size for %s is %d*%d " % (name, pool_conf.output_y, - pool_conf.output_x)) - self.set_layer_size( - (pool_conf.output_x * pool_conf.output_y) * pool_conf.channels) + parse_pool(self.inputs[input_index].pool, input_layer.name, + pool_conf) + self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x, + pool_conf.channels) @config_layer('spp') @@ -1805,12 +1801,10 @@ class SpatialPyramidPoolLayer(LayerBase): name, 'spp', 0, inputs=inputs, device=device) for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) - parse_spp(self.inputs[input_index].spp, input_layer.name, - self.config.inputs[input_index].spp_conf) spp_conf = self.config.inputs[input_index].spp_conf - output_size = (pow(4, spp_conf.pyramid_height) - 1) / (4 - 1) - print("output size for %s is %d " % (name, output_size)) - self.set_layer_size(output_size * spp_conf.channels) + parse_spp(self.inputs[input_index].spp, input_layer.name, spp_conf) + output_x = (pow(4, spp_conf.pyramid_height) - 1) / (4 - 1) + self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels) @config_layer('batch_norm') @@ -1872,10 +1866,10 @@ class BatchNormLayer(LayerBase): self.config.moving_average_fraction = moving_average_fraction input_layer = self.get_input_layer(0) - parse_image(self.inputs[0].image, input_layer.name, - self.config.inputs[0].image_conf) image_conf = self.config.inputs[0].image_conf - self.set_layer_size((image_conf.img_size**2) * image_conf.channels) + parse_image(self.inputs[0].image, input_layer.name, image_conf) + self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, + image_conf.channels) psize = self.calc_parameter_size(image_conf) dims = [1, psize] @@ -1933,11 +1927,12 @@ class MaxOutLayer(LayerBase): super(MaxOutLayer, self).__init__( name, 'maxout', 0, inputs=inputs, **xargs) input_layer = self.get_input_layer(0) - parse_maxout(self.inputs[0].maxout, input_layer.name, - self.config.inputs[0].maxout_conf) maxout_conf = self.config.inputs[0].maxout_conf + parse_maxout(self.inputs[0].maxout, input_layer.name, maxout_conf) self.set_layer_size(g_layer_map[input_layer.name].size / maxout_conf.groups) + self.set_layer_height_width(g_layer_map[input_layer.name].height, + g_layer_map[input_layer.name].width) # key: cost type @@ -2517,11 +2512,10 @@ class BilinearInterpLayer(LayerBase): super(BilinearInterpLayer, self).__init__( name, 'bilinear_interp', 0, inputs=inputs, **xargs) input_layer = self.get_input_layer(0) - parse_bilinear(self.inputs[0].bilinear_interp, input_layer.name, - self.config.inputs[0].bilinear_interp_conf) - conf = self.inputs[0].bilinear_interp - self.set_layer_size(conf.out_size_x * conf.out_size_y * - conf.num_channels) + conf = self.config.inputs[0].bilinear_interp_conf + parse_bilinear(self.inputs[0].bilinear_interp, input_layer.name, conf) + self.set_cnn_layer(name, conf.out_size_y, conf.out_size_x, + conf.image_conf.channels) @config_layer('sum_to_one_norm') @@ -2994,6 +2988,8 @@ class CTCLayer(LayerBase): @config_layer('recurrent_layer_group') class RecurrentLayerGroup(LayerBase): def __init__(self, name, device=None): + global g_pass_height_width + g_pass_height_width = False super(RecurrentLayerGroup, self).__init__( name, 'recurrent_layer_group', 0, inputs=[], device=device) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index d984e843204..fbb28e6caf6 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -763,7 +763,7 @@ def mixed_layer(size=0, @layer_support() -def data_layer(name, size, layer_attr=None): +def data_layer(name, size, height=None, width=None, layer_attr=None): """ Define DataLayer For NeuralNetwork. @@ -778,6 +778,10 @@ def data_layer(name, size, layer_attr=None): :type name: basestring :param size: Size of this data layer. :type size: int + :param height: Height of this data layer, used for image + :type size: int|None + :param width: Width of this data layer, used for image + :type size: int|None :param layer_attr: Extra Layer Attribute. :type layer_attr: ExtraLayerAttribute. :return: LayerOutput object. @@ -787,6 +791,8 @@ def data_layer(name, size, layer_attr=None): type=LayerType.DATA, name=name, size=size, + height=height, + width=width, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput(name, LayerType.DATA, size=size) @@ -1480,7 +1486,7 @@ def bilinear_interp_layer(input, bilinear_interp=BilinearInterp( out_size_x=out_size_x, out_size_y=out_size_y, - num_channels=num_channels)), + channels=num_channels)), type=LayerType.BILINEAR_INTERP_LAYER, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( @@ -1908,8 +1914,7 @@ def img_pool_layer(input, layer_attr=None, pool_size_y=None, stride_y=None, - padding_y=None, - img_width=None): + padding_y=None): """ Image pooling Layer. @@ -1940,9 +1945,6 @@ def img_pool_layer(input, :type stride_y: int|None :param layer_attr: Extra Layer attribute. :type layer_attr: ExtraLayerAttribute - :param img_width: the width of input feature map. If it is None, the input feature - map should be square. - :type img_width: int|None :return: LayerOutput object. :rtype: LayerOutput """ @@ -1978,8 +1980,7 @@ def img_pool_layer(input, padding=padding, size_y=pool_size_y, stride_y=stride_y, - padding_y=padding_y, - img_width=img_width)) + padding_y=padding_y)) ], **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( @@ -1997,7 +1998,6 @@ def spp_layer(input, num_channels=None, pool_type=None, pyramid_height=None, - img_width=None, layer_attr=None): """ Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. @@ -2014,9 +2014,6 @@ def spp_layer(input, :type scale: BasePoolingType :param pyramid_height: pyramid height. :type pyramid_height: int - :param img_width: the width of input feature map. If it is None, the input feature - map should be square. - :type img_width: int|None :param layer_attr: Extra Layer Attribute. :type layer_attr: ExtraLayerAttribute :return: LayerOutput object. @@ -2043,8 +2040,7 @@ def spp_layer(input, spp=SpatialPyramidPool( pool_type=type_name, channels=num_channels, - pyramid_height=pyramid_height, - img_width=img_width)), + pyramid_height=pyramid_height)), **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( name, diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr index 1f262af2112..1a577b8d9b1 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr @@ -26,11 +26,15 @@ layers { filter_size_y: 32 padding_y: 1 stride_y: 1 + output_y: 227 + img_size_y: 256 } } bias_parameter_name: "___conv_0__.wbias" num_filters: 64 shared_biases: true + height: 227 + width: 227 } layers { name: "__batch_norm_0__" @@ -43,6 +47,7 @@ layers { image_conf { channels: 64 img_size: 227 + img_size_y: 227 } } inputs { @@ -55,6 +60,8 @@ layers { } bias_parameter_name: "___batch_norm_0__.wbias" moving_average_fraction: 0.9 + height: 227 + width: 227 } layers { name: "__crmnorm_0__" @@ -72,8 +79,12 @@ layers { output_x: 227 img_size: 227 blocked: false + output_y: 227 + img_size_y: 227 } } + height: 227 + width: 227 } layers { name: "__pool_0__" @@ -97,6 +108,8 @@ layers { padding_y: 0 } } + height: 196 + width: 196 } parameters { name: "___conv_0__.w0" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr index 38346354080..ac1e2adff5d 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr @@ -26,6 +26,8 @@ layers { filter_size_y: 32 padding_y: 1 stride_y: 1 + output_y: 227 + img_size_y: 198 } } bias_parameter_name: "___conv_0__.wbias" @@ -43,6 +45,7 @@ layers { image_conf { channels: 64 img_size: 256 + img_size_y: 256 } } inputs { @@ -55,6 +58,8 @@ layers { } bias_parameter_name: "___batch_norm_0__.wbias" moving_average_fraction: 0.9 + height: 256 + width: 256 } layers { name: "__crmnorm_0__" @@ -72,8 +77,12 @@ layers { output_x: 256 img_size: 256 blocked: false + output_y: 256 + img_size_y: 256 } } + height: 256 + width: 256 } layers { name: "__pool_0__" @@ -97,6 +106,8 @@ layers { padding_y: 0 } } + height: 225 + width: 225 } parameters { name: "___conv_0__.w0" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr index 2b3951c2424..2943ab130bd 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr @@ -177,6 +177,8 @@ layers { filter_size_y: 3 padding_y: 0 stride_y: 1 + output_y: 30 + img_size_y: 32 } num_filters: 64 } diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bilinear_interp.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bilinear_interp.protostr index 13d0d477eb5..9fae596f281 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bilinear_interp.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bilinear_interp.protostr @@ -26,11 +26,15 @@ layers { filter_size_y: 3 padding_y: 1 stride_y: 1 + output_y: 48 + img_size_y: 48 } } bias_parameter_name: "___conv_0__.wbias" num_filters: 16 shared_biases: true + height: 48 + width: 48 } layers { name: "__bilinear_interp_layer_0__" @@ -40,11 +44,17 @@ layers { inputs { input_layer_name: "__conv_0__" bilinear_interp_conf { + image_conf { + channels: 16 + img_size: 48 + img_size_y: 48 + } out_size_x: 64 out_size_y: 64 - num_channels: 16 } } + height: 64 + width: 64 } layers { name: "__pool_0__" @@ -55,19 +65,21 @@ layers { input_layer_name: "__bilinear_interp_layer_0__" pool_conf { pool_type: "max-projection" - channels: 4 + channels: 16 size_x: 2 stride: 2 - output_x: 64 - img_size: 128 + output_x: 32 + img_size: 64 padding: 0 size_y: 2 stride_y: 2 - output_y: 64 - img_size_y: 128 + output_y: 32 + img_size_y: 64 padding_y: 0 } } + height: 32 + width: 32 } layers { name: "__fc_layer_0__" @@ -78,6 +90,8 @@ layers { input_layer_name: "__pool_0__" input_parameter_name: "___fc_layer_0__.w0" } + height: 32 + width: 32 } parameters { name: "___conv_0__.w0" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_maxout.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_maxout.protostr index 1be2a7ceebf..c763a95f9d1 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_maxout.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_maxout.protostr @@ -4,6 +4,8 @@ layers { type: "data" size: 2304 active_type: "" + height: 48 + width: 48 } layers { name: "__conv_0__" @@ -26,11 +28,15 @@ layers { filter_size_y: 3 padding_y: 1 stride_y: 1 + output_y: 48 + img_size_y: 48 } } bias_parameter_name: "___conv_0__.wbias" num_filters: 16 shared_biases: true + height: 48 + width: 48 } layers { name: "__maxout_layer_0__" @@ -40,12 +46,16 @@ layers { inputs { input_layer_name: "__conv_0__" maxout_conf { - channels: 16 + image_conf { + channels: 16 + img_size: 48 + img_size_y: 48 + } groups: 2 - img_size_x: 0 - img_size_y: 0 } } + height: 48 + width: 48 } layers { name: "__pool_0__" @@ -69,48 +79,58 @@ layers { padding_y: 0 } } + height: 24 + width: 24 } layers { name: "__conv_1__" type: "exconv" - size: 18432 + size: 73728 active_type: "" inputs { input_layer_name: "__pool_0__" input_parameter_name: "___conv_1__.w0" conv_conf { filter_size: 3 - channels: 32 + channels: 8 stride: 1 padding: 1 groups: 1 - filter_channels: 32 - output_x: 12 - img_size: 12 + filter_channels: 8 + output_x: 24 + img_size: 24 caffe_mode: true filter_size_y: 3 padding_y: 1 stride_y: 1 + output_y: 24 + img_size_y: 24 } } bias_parameter_name: "___conv_1__.wbias" num_filters: 128 shared_biases: true + height: 24 + width: 24 } layers { name: "__maxout_layer_1__" type: "maxout" - size: 9216 + size: 18432 active_type: "" inputs { - input_layer_name: "__conv_0__" + input_layer_name: "__conv_1__" maxout_conf { - channels: 128 + image_conf { + channels: 128 + img_size: 24 + img_size_y: 24 + } groups: 4 - img_size_x: 0 - img_size_y: 0 } } + height: 24 + width: 24 } layers { name: "__block_expand_layer_0__" @@ -118,7 +138,7 @@ layers { size: 192 active_type: "" inputs { - input_layer_name: "__maxout_layer_0__" + input_layer_name: "__maxout_layer_1__" block_expand_conf { channels: 32 stride_x: 1 @@ -133,6 +153,8 @@ layers { img_size_y: 0 } } + height: 24 + width: 24 } layers { name: "__fc_layer_0__" @@ -143,6 +165,8 @@ layers { input_layer_name: "__block_expand_layer_0__" input_parameter_name: "___fc_layer_0__.w0" } + height: 24 + width: 24 } parameters { name: "___conv_0__.w0" @@ -164,9 +188,9 @@ parameters { } parameters { name: "___conv_1__.w0" - size: 36864 + size: 9216 initial_mean: 0.0 - initial_std: 0.0833333333333 + initial_std: 0.166666666667 initial_strategy: 0 initial_smart: false } diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_spp_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_spp_layer.protostr index 8b0a8f2146b..ca1b2d8cffd 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_spp_layer.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_spp_layer.protostr @@ -4,6 +4,8 @@ layers { type: "data" size: 3200 active_type: "" + height: 20 + width: 10 } layers { name: "__spp_0__" @@ -13,13 +15,17 @@ layers { inputs { input_layer_name: "data" spp_conf { + image_conf { + channels: 16 + img_size: 10 + img_size_y: 20 + } pool_type: "max-projection" pyramid_height: 2 - channels: 16 - img_size: 10 - img_size_y: 20 } } + height: 1 + width: 5 } input_layer_names: "data" output_layer_names: "__spp_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_bilinear_interp.py b/python/paddle/trainer_config_helpers/tests/configs/test_bilinear_interp.py index e15a55b412f..be83f4f83c5 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_bilinear_interp.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_bilinear_interp.py @@ -17,7 +17,7 @@ bilinear = bilinear_interp_layer(input=conv, out_size_x=64, out_size_y=64) pool = img_pool_layer( input=bilinear, - num_channels=4, + num_channels=16, pool_size=2, stride=2, pool_type=MaxPooling()) diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_maxout.py b/python/paddle/trainer_config_helpers/tests/configs/test_maxout.py index 081430d7160..eb14270baa0 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_maxout.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_maxout.py @@ -2,7 +2,7 @@ from paddle.trainer_config_helpers import * settings(batch_size=1000, learning_rate=1e-5) -data = data_layer(name='data', size=2304) +data = data_layer(name='data', size=2304, height=48, width=48) conv = img_conv_layer( input=data, @@ -21,16 +21,21 @@ pool = img_pool_layer( conv2 = img_conv_layer( input=pool, filter_size=3, - num_channels=32, + num_channels=8, num_filters=128, padding=1, act=LinearActivation(), bias_attr=True) -maxout2 = maxout_layer(input=conv, num_channels=128, groups=4) +maxout2 = maxout_layer(input=conv2, num_channels=128, groups=4) block = block_expand_layer( - input=maxout, num_channels=32, stride_x=1, stride_y=1, block_x=1, block_y=6) + input=maxout2, + num_channels=32, + stride_x=1, + stride_y=1, + block_x=1, + block_y=6) fc = fc_layer(input=block, size=384, bias_attr=False) diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_spp_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_spp_layer.py index e20ffb584e8..e0b0d0d3be2 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_spp_layer.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_spp_layer.py @@ -2,13 +2,9 @@ from paddle.trainer_config_helpers import * settings(batch_size=100, learning_rate=1e-5) -data = data_layer(name='data', size=3200) +data = data_layer(name='data', size=3200, height=20, width=10) spp = spp_layer( - input=data, - pyramid_height=2, - num_channels=16, - pool_type=MaxPooling(), - img_width=10) + input=data, pyramid_height=2, num_channels=16, pool_type=MaxPooling()) outputs(spp) -- GitLab From 2c84c1ecfbb90ed865a9cd82ac6c9a6d388014b3 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Nov 2016 14:18:55 +0800 Subject: [PATCH 019/347] Add profiler object and update docs --- CMakeLists.txt | 5 + doc/optimization/gpu_profiling.rst | 175 ++++++++++++++++++++----- paddle/cuda/src/hl_cuda_device.cc | 2 + paddle/math/tests/test_GpuProfiler.cpp | 14 +- paddle/utils/Stat.cpp | 1 + paddle/utils/Stat.h | 20 +++ 6 files changed, 180 insertions(+), 37 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index b1fdae3b5c0..8b62f297874 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -36,6 +36,7 @@ option(WITH_RDMA "Compile PaddlePaddle with rdma support" OFF) option(WITH_GLOG "Compile PaddlePaddle use glog, otherwise use a log implement internally" ${LIBGLOG_FOUND}) option(WITH_GFLAGS "Compile PaddlePaddle use gflags, otherwise use a flag implement internally" ${GFLAGS_FOUND}) option(WITH_TIMER "Compile PaddlePaddle use timer" OFF) +option(WITH_PROFILER "Compile PaddlePaddle use gpu profiler" OFF) option(WITH_TESTING "Compile and run unittest for PaddlePaddle" ${GTEST_FOUND}) option(WITH_DOC "Compile PaddlePaddle with documentation" OFF) option(WITH_SWIG_PY "Compile PaddlePaddle with py PaddlePaddle prediction api" ${SWIG_FOUND}) @@ -134,6 +135,10 @@ if(NOT WITH_TIMER) add_definitions(-DPADDLE_DISABLE_TIMER) endif(NOT WITH_TIMER) +if(NOT WITH_PROFILER) + add_definitions(-DPADDLE_DISABLE_PROFILER) +endif(NOT WITH_PROFILER) + if(WITH_AVX) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${AVX_FLAG}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${AVX_FLAG}") diff --git a/doc/optimization/gpu_profiling.rst b/doc/optimization/gpu_profiling.rst index efdf5552c35..37dc7dfe3b4 100644 --- a/doc/optimization/gpu_profiling.rst +++ b/doc/optimization/gpu_profiling.rst @@ -1,7 +1,7 @@ -GPU Profiling -============= +Profiling on PaddlePaddle +========================= -This tutorial will guide you step-by-step through how to conduct profiling and performance tuning using :code:`nvprof` and :code:`nvvp`. +This tutorial will guide you step-by-step through how to conduct profiling and performance tuning using built-in timer, **nvprof** and **nvvp**. - What is profiling? - Why we need profiling? @@ -45,73 +45,182 @@ Profiler Tools ============== For general GPU profiling, a bunch of tools are provided from both NVIDIA and third party. -:code:`nvprof` is Nvidia profiler and :code:`nvvp` is (GUI based) Nvidia visual profiler. +**nvprof** is Nvidia profiler and **nvvp** is (GUI based) Nvidia visual profiler. In this tutorial, we will focus on nvprof and nvvp. :code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate above profilers. -.. code-block:: c++ +.. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp + :language: c++ + :lines: 107-121 + :linenos: - TEST(Profiler, BilinearFwdBwd) { - hl_profiler_start(); - auto numSamples = 10; - auto channels = 16; - auto imgSize = 64; - testBilinearFwdBwd(numSamples, imgSize, imgSize, channels); - hl_profiler_end(); - } +The above code snippet includes two methods, you can use any of them to profile the regions of interest. -:code:`hl_profiler_start` and :code:`hl_profiler_end` can be used to profile only regions of interest -in PaddlePaddle. They are wrapper functions of :code:`cudaProfilerStart` and :code:`cudaProfilerStop` -respectively to avoid program crashes when CPU version of PaddlePaddle invokes them. +1. :code:`REGISTER_TIMER_INFO` is a built-in timer wrapper which can calculate the time overhead of both cpu functions and cuda kernels. +2. :code:`REGISTER_GPU_PROFILER` is a general purpose wrapper object of :code:`cudaProfilerStart` and :code:`cudaProfilerStop` to avoid +program crashes when CPU version of PaddlePaddle invokes them. + +You can find all the gory details about how to use both of them in the next session. Hands-on Approach ================= -To use this command line profiler :code:`nvprof`, you can simply issue the command: +Built-in Timer +-------------- -.. code-block:: bash +To enable built-in timer in PaddlePaddle, first you have to add :code:`REGISTER_TIMER_INFO` into the regions of you interest. +Then, all information could be stamped in the console via :code:`printStatus` or :code:`printAllStatus` function. +As a simple example, consider the following: + +1. Add :code:`REGISTER_TIMER_INFO` and :code:`printStatus` functions (see the emphasize-lines). + + .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp + :language: c++ + :lines: 107-121 + :emphasize-lines: 10-11,14 + :linenos: + +2. Configure cmake with **WITH_TIMER** and recompile PaddlePaddle. + + .. code-block:: bash + + cmake .. -DWITH_TIMER=ON + make + +3. Execute your code and observe the results (see the emphasize-lines). + + .. code-block:: bash + :emphasize-lines: 1,12-15 + + > ./paddle/math/tests/test_GpuProfiler + I1117 11:13:42.313065 2522362816 Util.cpp:155] commandline: ./paddle/math/tests/test_GpuProfiler + I1117 11:13:42.845065 2522362816 Util.cpp:130] Calling runInitFunctions + I1117 11:13:42.845208 2522362816 Util.cpp:143] Call runInitFunctions done. + [==========] Running 1 test from 1 test case. + [----------] Global test environment set-up. + [----------] 1 test from Profiler + [ RUN ] Profiler.BilinearFwdBwd + I1117 11:13:42.845310 2522362816 test_GpuProfiler.cpp:114] Enable GPU Profiler Stat: [testBilinearFwdBwd] "numSamples = 10, channels = 16, im + gSizeX = 64, imgSizeY = 64" + I1117 11:13:42.850154 2522362816 ThreadLocal.cpp:37] thread use undeterministic rand seed:20659751 + I1117 11:13:42.981501 2522362816 Stat.cpp:130] ======= StatSet: [GlobalStatInfo] status ====== + I1117 11:13:42.981539 2522362816 Stat.cpp:133] Stat=testBilinearFwdBwd total=136.141 avg=136.141 max=136.141 min=136.141 count=1 + I1117 11:13:42.981572 2522362816 Stat.cpp:141] ======= BarrierStatSet status ====== + I1117 11:13:42.981575 2522362816 Stat.cpp:154] -------------------------------------------------- + [ OK ] Profiler.BilinearFwdBwd (136 ms) + [----------] 1 test from Profiler (136 ms total) + + [----------] Global test environment tear-down + [==========] 1 test from 1 test case ran. (136 ms total) + [ PASSED ] 1 test. + +nvprof profiler +--------------- - nvprof ./paddle/math/tests/test_GpuProfiler +To use this command line profiler **nvprof**, you can simply issue the following command: + +1. Add :code:`REGISTER_GPU_PROFILER` function (see the emphasize-lines). + + .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp + :language: c++ + :lines: 107-121 + :emphasize-lines: 7-8 + :linenos: + +2. Configure cmake with **WITH_PROFILER** and recompile PaddlePaddle. + + .. code-block:: bash + + cmake .. -DWITH_PROFILER=ON + make + +3. Use Nvidia profiler **nvprof** to profile the binary. + + .. code-block:: bash + + nvprof ./paddle/math/tests/test_GpuProfiler Then, you can get the following profiling result: -.. image:: nvprof.png - :align: center - :scale: 30% +.. code-block:: bash -For visual profiler :code:`nvvp`, you can either import the output of :code:`nvprof –o ...` or + ==78544== Profiling application: ./paddle/math/tests/test_GpuProfiler + ==78544== Profiling result: + Time(%) Time Calls Avg Min Max Name + 27.60% 9.6305ms 5 1.9261ms 3.4560us 6.4035ms [CUDA memcpy HtoD] + 26.07% 9.0957ms 1 9.0957ms 9.0957ms 9.0957ms KeBilinearInterpBw + 23.78% 8.2977ms 1 8.2977ms 8.2977ms 8.2977ms KeBilinearInterpFw + 22.55% 7.8661ms 2 3.9330ms 1.5798ms 6.2863ms [CUDA memcpy DtoH] + + ==78544== API calls: + Time(%) Time Calls Avg Min Max Name + 46.85% 682.28ms 8 85.285ms 12.639us 682.03ms cudaStreamCreateWithFlags + 39.83% 580.00ms 4 145.00ms 302ns 550.27ms cudaFree + 9.82% 143.03ms 9 15.892ms 8.7090us 142.78ms cudaStreamCreate + 1.23% 17.983ms 7 2.5690ms 23.210us 6.4563ms cudaMemcpy + 1.23% 17.849ms 2 8.9247ms 8.4726ms 9.3768ms cudaStreamSynchronize + 0.66% 9.5969ms 7 1.3710ms 288.43us 2.4279ms cudaHostAlloc + 0.13% 1.9530ms 11 177.54us 7.6810us 591.06us cudaMalloc + 0.07% 1.0424ms 8 130.30us 1.6970us 453.72us cudaGetDevice + 0.04% 527.90us 40 13.197us 525ns 253.99us cudaEventCreateWithFlags + 0.03% 435.73us 348 1.2520us 124ns 42.704us cuDeviceGetAttribute + 0.03% 419.36us 1 419.36us 419.36us 419.36us cudaGetDeviceCount + 0.02% 260.75us 2 130.38us 129.32us 131.43us cudaGetDeviceProperties + 0.02% 222.32us 2 111.16us 106.94us 115.39us cudaLaunch + 0.01% 214.06us 4 53.514us 28.586us 77.655us cuDeviceGetName + 0.01% 115.45us 4 28.861us 9.8250us 44.526us cuDeviceTotalMem + 0.01% 83.988us 4 20.997us 578ns 77.760us cudaSetDevice + 0.00% 38.918us 1 38.918us 38.918us 38.918us cudaEventCreate + 0.00% 34.573us 31 1.1150us 279ns 12.784us cudaDeviceGetAttribute + 0.00% 17.767us 1 17.767us 17.767us 17.767us cudaProfilerStart + 0.00% 15.228us 2 7.6140us 3.5460us 11.682us cudaConfigureCall + 0.00% 14.536us 2 7.2680us 1.1490us 13.387us cudaGetLastError + 0.00% 8.6080us 26 331ns 173ns 783ns cudaSetupArgument + 0.00% 5.5470us 6 924ns 215ns 2.6780us cuDeviceGet + 0.00% 5.4090us 6 901ns 328ns 3.3320us cuDeviceGetCount + 0.00% 4.1770us 3 1.3920us 1.0630us 1.8300us cuDriverGetVersion + 0.00% 3.4650us 3 1.1550us 1.0810us 1.2680us cuInit + 0.00% 830ns 1 830ns 830ns 830ns cudaRuntimeGetVersion + + +nvvp profiler +------------- + +For visual profiler **nvvp**, you can either import the output of :code:`nvprof –o ...` or run application through GUI. +**Note: nvvp also support CPU profiling** (Click the box in nvvp to enable profile execution on CPU). + .. image:: nvvp1.png :align: center - :scale: 30% + :scale: 33% -From the perspective of kernel functions, :code:`nvvp` can even illustrate why does an operation take a long time? +From the perspective of kernel functions, **nvvp** can even illustrate why does an operation take a long time? As shown in the following figure, kernel's block usage, register usage and shared memory usage from :code:`nvvp` -allow us to fully utilize all warps on the GPU. +allow us to fully utilize all warps on the GPU. .. image:: nvvp2.png :align: center - :scale: 30% + :scale: 33% -From the perspective of application, :code:`nvvp` can give you some suggestions to address performance bottleneck. +From the perspective of application, **nvvp** can give you some suggestions to address performance bottleneck. For instance, some advice in data movement and compute utilization from the below figure can guide you to tune performance. .. image:: nvvp3.png :align: center - :scale: 30% + :scale: 33% .. image:: nvvp4.png :align: center - :scale: 30% + :scale: 33% Profiling tips ============== -- The :code:`nvprof` and :code:`nvvp` output is a very good place to start -- The timeline is a good place to go next +- The **nvprof** and **nvvp** output is a very good place to start. +- The timeline is a good place to go next. - Only dig deep into a kernel if it’s taking a significant amount of your time. - Where possible, try to match profiler output with theory. 1) For example, if I know I’m moving 1GB, and my kernel takes 10ms, I expect the profiler to report 100GB/s. @@ -119,7 +228,7 @@ Profiling tips - Know your hardware: If your GPU can do 6 TFLOPs, and you’re already doing 5.5 TFLOPs, you won’t go much faster! -Profiling is a key step in optimisation. Sometimes quite simple changes can lead to big improvements in performance. +Profiling is a key step in optimization. Sometimes quite simple changes can lead to big improvements in performance. Your mileage may vary! Reference diff --git a/paddle/cuda/src/hl_cuda_device.cc b/paddle/cuda/src/hl_cuda_device.cc index 0f45462adc7..aa1d184a3ee 100644 --- a/paddle/cuda/src/hl_cuda_device.cc +++ b/paddle/cuda/src/hl_cuda_device.cc @@ -762,6 +762,8 @@ bool hl_cuda_event_is_ready(hl_event_t event) { void hl_profiler_start() { CHECK_CUDA(dynload::cudaProfilerStart()); } + void hl_profiler_end() { CHECK_CUDA(dynload::cudaProfilerStop()); } + diff --git a/paddle/math/tests/test_GpuProfiler.cpp b/paddle/math/tests/test_GpuProfiler.cpp index 7645447789c..ea1bd2481e7 100644 --- a/paddle/math/tests/test_GpuProfiler.cpp +++ b/paddle/math/tests/test_GpuProfiler.cpp @@ -20,7 +20,6 @@ limitations under the License. */ #include #include "paddle/gserver/tests/TestUtil.h" #include "paddle/utils/Stat.h" -#include "hl_cuda.h" using namespace paddle; // NOLINT using namespace std; // NOLINT @@ -106,12 +105,19 @@ void testBilinearFwdBwd(int numSamples, int imgSizeH, int imgSizeW, } TEST(Profiler, BilinearFwdBwd) { - hl_profiler_start(); auto numSamples = 10; auto channels = 16; auto imgSize = 64; - testBilinearFwdBwd(numSamples, imgSize, imgSize, channels); - hl_profiler_end(); + { + // nvprof: GPU Proflier + REGISTER_GPU_PROFILER("testBilinearFwdBwd", + "numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64"); + // Paddle built-in timer + REGISTER_TIMER_INFO("testBilinearFwdBwd", + "numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64"); + testBilinearFwdBwd(numSamples, imgSize, imgSize, channels); + } + globalStat.printStatus("testBilinearFwdBwd"); } int main(int argc, char** argv) { diff --git a/paddle/utils/Stat.cpp b/paddle/utils/Stat.cpp index d7b20ca5eb2..733fc7f1a70 100644 --- a/paddle/utils/Stat.cpp +++ b/paddle/utils/Stat.cpp @@ -65,6 +65,7 @@ std::ostream& operator<<(std::ostream& outPut, const Stat& stat) { auto showStat = [&](const StatInfo* info, pid_t tid, bool isFirst = true) { uint64_t average = 0; if (info->count_ > 0) { + outPut << std::setfill(' ') << std::left; if (!isFirst) { outPut << std::setw(42) << " "; } diff --git a/paddle/utils/Stat.h b/paddle/utils/Stat.h index 00e5aaec2ba..8f65abb2d05 100644 --- a/paddle/utils/Stat.h +++ b/paddle/utils/Stat.h @@ -28,6 +28,7 @@ limitations under the License. */ #include "Locks.h" #include "ThreadLocal.h" #include "BarrierStat.h" +#include "hl_gpu.h" namespace paddle { @@ -280,4 +281,23 @@ inline StatSet& registerTimerArg2(uint64_t threshold = -1, #endif // DISABLE_TIMER +class GpuProfiler final { +public: + GpuProfiler() { hl_profiler_start(); } + ~GpuProfiler() { hl_profiler_end(); } +}; + +#ifdef PADDLE_DISABLE_PROFILER + +#define REGISTER_GPU_PROFILER(statName, ...) + +#else + +#define REGISTER_GPU_PROFILER(statName, ...) \ + LOG(INFO) << "Enable GPU Profiler Stat: [" \ + << statName << "] " << #__VA_ARGS__; \ + GpuProfiler __gpuProfiler; + +#endif // DISABLE_PROFILER + } // namespace paddle -- GitLab From 9670b9a1be2f2b1d668a0364549f8a240ec5009a Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Nov 2016 14:29:16 +0800 Subject: [PATCH 020/347] pre-commit hook check python style --- doc/optimization/gpu_profiling.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/optimization/gpu_profiling.rst b/doc/optimization/gpu_profiling.rst index 37dc7dfe3b4..013edb396e0 100644 --- a/doc/optimization/gpu_profiling.rst +++ b/doc/optimization/gpu_profiling.rst @@ -62,7 +62,7 @@ The above code snippet includes two methods, you can use any of them to profile 2. :code:`REGISTER_GPU_PROFILER` is a general purpose wrapper object of :code:`cudaProfilerStart` and :code:`cudaProfilerStop` to avoid program crashes when CPU version of PaddlePaddle invokes them. -You can find all the gory details about how to use both of them in the next session. +You can find more details about how to use both of them in the next session. Hands-on Approach ================= -- GitLab From 8a50f79686839de58cf1ccde7c29da76b6c02224 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Nov 2016 15:28:32 +0800 Subject: [PATCH 021/347] Convert markdown to rst for h-rnn --- doc_cn/algorithm/rnn/hierarchical-rnn.md | 403 -------------------- doc_cn/algorithm/rnn/hierarchical-rnn.rst | 440 ++++++++++++++++++++++ 2 files changed, 440 insertions(+), 403 deletions(-) delete mode 100644 doc_cn/algorithm/rnn/hierarchical-rnn.md create mode 100644 doc_cn/algorithm/rnn/hierarchical-rnn.rst diff --git a/doc_cn/algorithm/rnn/hierarchical-rnn.md b/doc_cn/algorithm/rnn/hierarchical-rnn.md deleted file mode 100644 index c184a34e85a..00000000000 --- a/doc_cn/algorithm/rnn/hierarchical-rnn.md +++ /dev/null @@ -1,403 +0,0 @@ -# 双层RNN配置与示例 - -我们在`paddle/gserver/tests/test_RecurrentGradientMachine`单测中，通过多组语义相同的单双层RNN配置，讲解如何使用双层RNN。 - -## 示例1：双进双出，subseq间无memory - -配置：单层RNN（`sequence_layer_group`）和双层RNN（`sequence_nest_layer_group`），语义完全相同。 - -### 读取双层序列的方法 - -首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）： - -- 单层序列的数据（`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。 - -```text -2 酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。 -2 很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 * -2 位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。 -2 交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。 -2 本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 . -2 这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店 -2 挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！ -2 HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。 -2 酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 * -2 挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格 -``` - -- 双层序列的数据（`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。 - -```text -2 酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。 -2 很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 * - -2 位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。 -2 交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。 -2 本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 . - -2 这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店 -2 挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！ - -2 HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。 -2 酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 * -2 挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格 -``` - -其次，我们看一下单双层序列的不同dataprovider（见`sequenceGen.py`）： - -- 单层序列的dataprovider如下： - - word_slot是integer_value_sequence类型，代表单层序列。 - - label是integer_value类型，代表一个向量。 - -```python -def hook(settings, dict_file, **kwargs): - settings.word_dict = dict_file - settings.input_types = [integer_value_sequence(len(settings.word_dict)), - integer_value(3)] - -@provider(init_hook=hook) -def process(settings, file_name): - with open(file_name, 'r') as fdata: - for line in fdata: - label, comment = line.strip().split('\t') - label = int(''.join(label.split())) - words = comment.split() - word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict] - yield word_slot, label -``` - -- 双层序列的dataprovider如下： - - word_slot是integer_value_sub_sequence类型，代表双层序列。 - - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。 - - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。 - -```python -def hook2(settings, dict_file, **kwargs): - settings.word_dict = dict_file - settings.input_types = [integer_value_sub_sequence(len(settings.word_dict)), - integer_value_sequence(3)] - -@provider(init_hook=hook2) -def process2(settings, file_name): - with open(file_name) as fdata: - label_list = [] - word_slot_list = [] - for line in fdata: - if (len(line)) > 1: - label,comment = line.strip().split('\t') - label = int(''.join(label.split())) - words = comment.split() - word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict] - label_list.append(label) - word_slot_list.append(word_slot) - else: - yield word_slot_list, label_list - label_list = [] - word_slot_list = [] -``` - -### 模型中的配置 - -首先，我们看一下单层序列的配置（见`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。 - -```python -settings(batch_size=5) - -data = data_layer(name="word", size=dict_dim) - -emb = embedding_layer(input=data, size=word_dim) - -# (lstm_input + lstm) is equal to lstmemory -with mixed_layer(size=hidden_dim*4) as lstm_input: - lstm_input += full_matrix_projection(input=emb) - -lstm = lstmemory_group(input=lstm_input, - size=hidden_dim, - act=TanhActivation(), - gate_act=SigmoidActivation(), - state_act=TanhActivation(), - lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50)) - -lstm_last = last_seq(input=lstm) - -with mixed_layer(size=label_dim, - act=SoftmaxActivation(), - bias_attr=True) as output: - output += full_matrix_projection(input=lstm_last) - -outputs(classification_cost(input=output, label=data_layer(name="label", size=1))) - -``` -其次，我们看一下语义相同的双层序列配置（见`sequence_nest_layer_group.conf`），并对其详细分析： - -- batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。 -- data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。 -- lstmemory: - - 单层序列过了一个mixed_layer和lstmemory_group。 - - 双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。 -- last_seq： - - 单层序列直接取了最后一个元素 - - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。 - - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。 - -```python -settings(batch_size=2) - -data = data_layer(name="word", size=dict_dim) - -emb_group = embedding_layer(input=data, size=word_dim) - -# (lstm_input + lstm) is equal to lstmemory -def lstm_group(lstm_group_input): - with mixed_layer(size=hidden_dim*4) as group_input: - group_input += full_matrix_projection(input=lstm_group_input) - - lstm_output = lstmemory_group(input=group_input, - name="lstm_group", - size=hidden_dim, - act=TanhActivation(), - gate_act=SigmoidActivation(), - state_act=TanhActivation(), - lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50)) - return lstm_output - -lstm_nest_group = recurrent_group(input=SubsequenceInput(emb_group), - step=lstm_group, - name="lstm_nest_group") -# hasSubseq ->(seqlastins) seq -lstm_last = last_seq(input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE) - -# seq ->(expand) hasSubseq -lstm_expand = expand_layer(input=lstm_last, expand_as=emb_group, expand_level=ExpandLevel.FROM_SEQUENCE) - -# hasSubseq ->(average) seq -lstm_average = pooling_layer(input=lstm_expand, - pooling_type=AvgPooling(), - agg_level=AggregateLevel.EACH_SEQUENCE) - -with mixed_layer(size=label_dim, - act=SoftmaxActivation(), - bias_attr=True) as output: - output += full_matrix_projection(input=lstm_average) - -outputs(classification_cost(input=output, label=data_layer(name="label", size=1))) -``` -## 示例2：双进双出，subseq间有memory - -配置：单层RNN（`sequence_rnn.conf`），双层RNN（`sequence_nest_rnn.conf`和`sequence_nest_rnn_readonly_memory.conf`），语义完全相同。 - -### 读取双层序列的方法 - -我们看一下单双层序列的不同数据组织形式和dataprovider（见`rnn_data_provider.py`） -```python -data = [ - [[[1, 3, 2], [4, 5, 2]], 0], - [[[0, 2], [2, 5], [0, 1, 2]], 1], -] - -@provider(input_types=[integer_value_sub_sequence(10), - integer_value(3)]) -def process_subseq(settings, file_name): - for d in data: - yield d - -@provider(input_types=[integer_value_sequence(10), - integer_value(3)]) -def process_seq(settings, file_name): - for d in data: - seq = [] -``` -- 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。 -- 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。 -- 单双层序列的label都分别是0和1 - -### 模型中的配置 - -我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 - -- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 - -```python -def step(y): - mem = memory(name="rnn_state", size=hidden_dim) - return fc_layer(input=[y, mem], - size=hidden_dim, - act=TanhActivation(), - bias_attr=True, - name="rnn_state") - -out = recurrent_group(step=step, input=emb) -``` -- 双层序列，外层memory是一个元素： - - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 - - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 - -```python -def outer_step(x): - outer_mem = memory(name="outer_rnn_state", size=hidden_dim) - def inner_step(y): - inner_mem = memory(name="inner_rnn_state", - size=hidden_dim, - boot_layer=outer_mem) - return fc_layer(input=[y, inner_mem], - size=hidden_dim, - act=TanhActivation(), - bias_attr=True, - name="inner_rnn_state") - - inner_rnn_output = recurrent_group( - step=inner_step, - input=x) - last = last_seq(input=inner_rnn_output, name="outer_rnn_state") - - return inner_rnn_output - -out = recurrent_group(step=outer_step, input=SubsequenceInput(emb)) -``` -- 双层序列，外层memory是单层序列： - - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 - - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 - -## 示例3：双进双出，输入不等长 - -**输入不等长**是指recurrent_group的多个输入在各时刻的长度可以不相等, 但需要指定一个和输出长度一致的input，用targetInlink表示。参考配置：单层RNN（`sequence_rnn_multi_unequalength_inputs.conf`），双层RNN（`sequence_nest_rnn_multi_unequalength_inputs.conf`） - -### 读取双层序列的方法 - -我们看一下单双层序列的数据组织形式和dataprovider（见`rnn_data_provider.py`） -```python -data2 = [ - [[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]] ,0], - [[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]], 1], -] - -@provider(input_types=[integer_value_sub_sequence(10), - integer_value_sub_sequence(10), - integer_value(2)], - should_shuffle=False) -def process_unequalength_subseq(settings, file_name): #双层RNN的dataprovider - for d in data2: - yield d - - -@provider(input_types=[integer_value_sequence(10), - integer_value_sequence(10), - integer_value(2)], - should_shuffle=False) -def process_unequalength_seq(settings, file_name): #单层RNN的dataprovider - for d in data2: - words1=reduce(lambda x,y: x+y, d[0]) - words2=reduce(lambda x,y: x+y, d[1]) - yield words1, words2, d[2] -``` - -data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 - -- 单层序列：两个样本分别为[[1, 2, 4, 5, 2], [5, 4, 1, 3, 1]] 和 [[0, 2, 2, 5, 0, 1, 2], [1, 5, 4, 2, 3, 6, 1]] -- 双层序列：两个样本分别为 - - **样本1**：[[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]]]。fea1和fea2都分别有2个子句，fea1=[[1, 2], [4, 5, 2]], fea2=[[5, 4, 1], [3, 1]] - - **样本2**：[[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]]]。fea1和fea2都分别有3个子句， fea1=[[0, 2], [2, 5], [0, 1, 2]], fea2=[[1, 5], [4], [2, 3, 6, 1]]。
    - - **注意**：每个样本中，各特征的子句数目需要相等。这里说的“双进双出，输入不等长”是指fea1在i时刻的输入的长度可以不等于fea2在i时刻的输入的长度。如对于第1个样本，时刻i=2, fea1[2]=[4, 5, 2]，fea2[2]=[3, 1]，3≠2。 -- 单双层序列中，两个样本的label都分别是0和1 - -### 模型中的配置 - -单层RNN（`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（`sequence_nest_rnn_multi_unequalength_inputs.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 - -- 单层序列： - - 过了一个简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全连接，功能与示例2中`sequence_rnn.conf`的`step`函数完全相同。这里，两个输入x1,x2分别通过calrnn返回最后时刻的状态。结果得到的encoder1_rep和encoder2_rep分别是单层序列，最后取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 - - 注意到这里recurrent_group输入的每个样本中，fea1和fea2的长度都分别相等，这并非偶然，而是因为recurrent_group要求输入为单层序列时，所有输入的长度都必须相等。 - -```python -def step(x1, x2): - def calrnn(y): - mem = memory(name = 'rnn_state_' + y.name, size = hidden_dim) - out = fc_layer(input = [y, mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'rnn_state_' + y.name) - return out - - encoder1 = calrnn(x1) - encoder2 = calrnn(x2) - return [encoder1, encoder2] - -encoder1_rep, encoder2_rep = recurrent_group( - name="stepout", - step=step, - input=[emb1, emb2]) - -encoder1_last = last_seq(input = encoder1_rep) -encoder1_expandlast = expand_layer(input = encoder1_last, - expand_as = encoder2_rep) -context = mixed_layer(input = [identity_projection(encoder1_expandlast), - identity_projection(encoder2_rep)], - size = hidden_dim) -``` -- 双层序列： - - 双层RNN中，对输入的两个特征分别求时序上的连续全连接(`inner_step1`和`inner_step2`分别处理fea1和fea2)，其功能与示例2中`sequence_nest_rnn.conf`的`outer_step`函数完全相同。不同之处是，此时输入`[SubsequenceInput(emb1), SubsequenceInput(emb2)]`在各时刻并不等长。 - - 函数`outer_step`中可以分别处理这两个特征，但我们需要用targetInlink指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 - - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 - -```python -def outer_step(x1, x2): - outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim) - outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim) - def inner_step1(y): - inner_mem = memory(name = 'inner_rnn_state_' + y.name, - size = hidden_dim, - boot_layer = outer_mem1) - out = fc_layer(input = [y, inner_mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'inner_rnn_state_' + y.name) - return out - - def inner_step2(y): - inner_mem = memory(name = 'inner_rnn_state_' + y.name, - size = hidden_dim, - boot_layer = outer_mem2) - out = fc_layer(input = [y, inner_mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'inner_rnn_state_' + y.name) - return out - - encoder1 = recurrent_group( - step = inner_step1, - name = 'inner1', - input = x1) - - encoder2 = recurrent_group( - step = inner_step2, - name = 'inner2', - input = x2) - - sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1') - sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2') - - encoder1_expand = expand_layer(input = sentence_last_state1, - expand_as = encoder2) - - return [encoder1_expand, encoder2] - -encoder1_rep, encoder2_rep = recurrent_group( - name="outer", - step=outer_step, - input=[SubsequenceInput(emb1), SubsequenceInput(emb2)], - targetInlink=emb2) - -encoder1_last = last_seq(input = encoder1_rep) -encoder1_expandlast = expand_layer(input = encoder1_last, - expand_as = encoder2_rep) -context = mixed_layer(input = [identity_projection(encoder1_expandlast), - identity_projection(encoder2_rep)], - size = hidden_dim) -``` - -## 示例4：beam_search的生成 - -TBD diff --git a/doc_cn/algorithm/rnn/hierarchical-rnn.rst b/doc_cn/algorithm/rnn/hierarchical-rnn.rst new file mode 100644 index 00000000000..7de54cc0b13 --- /dev/null +++ b/doc_cn/algorithm/rnn/hierarchical-rnn.rst @@ -0,0 +1,440 @@ +################# +双层RNN配置与示例 +################# + +我们在 :code:`paddle/gserver/tests/test_RecurrentGradientMachine` 单测中，通过多组语义相同的单双层RNN配置，讲解如何使用双层RNN。 + +示例1：双进双出，subseq间无memory +================================= + +配置：单层RNN（:code:`sequence_layer_group`）和双层RNN（:code:`sequence_nest_layer_group`），语义完全相同。 + +读取双层序列的方法 +------------------ + +首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）\: + +- 单层序列的数据（ :code:`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。 + +.. code-block:: text + + 2 酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。 + 2 很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 * + 2 位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。 + 2 交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。 + 2 本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 . + 2 这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店 + 2 挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！ + 2 HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。 + 2 酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 * + 2 挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格 + + +- 双层序列的数据（ :code:`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。 + +.. code-block:: text + + 2 酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。 + 2 很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 * + + 2 位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。 + 2 交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。 + 2 本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 . + + 2 这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店 + 2 挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！ + + 2 HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。 + 2 酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 * + 2 挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格 + +其次，我们看一下单双层序列的不同dataprovider（见 :code:`sequenceGen.py` ）： + +- 单层序列的dataprovider如下： + + - word_slot是integer_value_sequence类型，代表单层序列。 + - label是integer_value类型，代表一个向量。 + +.. code-block:: python + + def hook(settings, dict_file, **kwargs): + settings.word_dict = dict_file + settings.input_types = [integer_value_sequence(len(settings.word_dict)), + integer_value(3)] + + @provider(init_hook=hook) + def process(settings, file_name): + with open(file_name, 'r') as fdata: + for line in fdata: + label, comment = line.strip().split('\t') + label = int(''.join(label.split())) + words = comment.split() + word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict] + yield word_slot, label + +- 双层序列的dataprovider如下： + + - word_slot是integer_value_sub_sequence类型，代表双层序列。 + - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。 + - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。 + +.. code-block:: python + + def hook2(settings, dict_file, **kwargs): + settings.word_dict = dict_file + settings.input_types = [integer_value_sub_sequence(len(settings.word_dict)), + integer_value_sequence(3)] + + @provider(init_hook=hook2) + def process2(settings, file_name): + with open(file_name) as fdata: + label_list = [] + word_slot_list = [] + for line in fdata: + if (len(line)) > 1: + label,comment = line.strip().split('\t') + label = int(''.join(label.split())) + words = comment.split() + word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict] + label_list.append(label) + word_slot_list.append(word_slot) + else: + yield word_slot_list, label_list + label_list = [] + word_slot_list = [] + + +模型中的配置 +------------ + +首先，我们看一下单层序列的配置（见 :code:`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。 + +.. code-block:: python + + settings(batch_size=5) + + data = data_layer(name="word", size=dict_dim) + + emb = embedding_layer(input=data, size=word_dim) + + # (lstm_input + lstm) is equal to lstmemory + with mixed_layer(size=hidden_dim*4) as lstm_input: + lstm_input += full_matrix_projection(input=emb) + + lstm = lstmemory_group(input=lstm_input, + size=hidden_dim, + act=TanhActivation(), + gate_act=SigmoidActivation(), + state_act=TanhActivation(), + lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50)) + + lstm_last = last_seq(input=lstm) + + with mixed_layer(size=label_dim, + act=SoftmaxActivation(), + bias_attr=True) as output: + output += full_matrix_projection(input=lstm_last) + + outputs(classification_cost(input=output, label=data_layer(name="label", size=1))) + + +其次，我们看一下语义相同的双层序列配置（见 :code:`sequence_nest_layer_group.conf` ），并对其详细分析： + +- batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。 +- data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。 +- lstmemory\: + + - 单层序列过了一个mixed_layer和lstmemory_group。 + - 双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。 + +- last_seq\: + + - 单层序列直接取了最后一个元素 + - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。 + - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。 + +.. code-block:: python + + settings(batch_size=2) + + data = data_layer(name="word", size=dict_dim) + + emb_group = embedding_layer(input=data, size=word_dim) + + # (lstm_input + lstm) is equal to lstmemory + def lstm_group(lstm_group_input): + with mixed_layer(size=hidden_dim*4) as group_input: + group_input += full_matrix_projection(input=lstm_group_input) + + lstm_output = lstmemory_group(input=group_input, + name="lstm_group", + size=hidden_dim, + act=TanhActivation(), + gate_act=SigmoidActivation(), + state_act=TanhActivation(), + lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50)) + return lstm_output + + lstm_nest_group = recurrent_group(input=SubsequenceInput(emb_group), + step=lstm_group, + name="lstm_nest_group") + # hasSubseq ->(seqlastins) seq + lstm_last = last_seq(input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE) + + # seq ->(expand) hasSubseq + lstm_expand = expand_layer(input=lstm_last, expand_as=emb_group, expand_level=ExpandLevel.FROM_SEQUENCE) + + # hasSubseq ->(average) seq + lstm_average = pooling_layer(input=lstm_expand, + pooling_type=AvgPooling(), + agg_level=AggregateLevel.EACH_SEQUENCE) + + with mixed_layer(size=label_dim, + act=SoftmaxActivation(), + bias_attr=True) as output: + output += full_matrix_projection(input=lstm_average) + + outputs(classification_cost(input=output, label=data_layer(name="label", size=1))) + +示例2：双进双出，subseq间有memory +================================= + +配置：单层RNN（ :code:`sequence_rnn.conf` ），双层RNN（ :code:`sequence_nest_rnn.conf` 和 :code:`sequence_nest_rnn_readonly_memory.conf` ），语义完全相同。 + +读取双层序列的方法 +------------------ + +我们看一下单双层序列的不同数据组织形式和dataprovider（见 :code:`rnn_data_provider.py`） + +.. code-block:: python + + data = [ + [[[1, 3, 2], [4, 5, 2]], 0], + [[[0, 2], [2, 5], [0, 1, 2]], 1], + ] + + @provider(input_types=[integer_value_sub_sequence(10), + integer_value(3)]) + def process_subseq(settings, file_name): + for d in data: + yield d + + @provider(input_types=[integer_value_sequence(10), + integer_value(3)]) + def process_seq(settings, file_name): + for d in data: + seq = [] + +- 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。 +- 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。 +- 单双层序列的label都分别是0和1 + +模型中的配置 +------------ + +我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 + +- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 + +.. code-block:: python + + def step(y): + mem = memory(name="rnn_state", size=hidden_dim) + return fc_layer(input=[y, mem], + size=hidden_dim, + act=TanhActivation(), + bias_attr=True, + name="rnn_state") + + out = recurrent_group(step=step, input=emb) + +- 双层序列，外层memory是一个元素： + - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 + - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 + +.. code-block:: + + def outer_step(x): + outer_mem = memory(name="outer_rnn_state", size=hidden_dim) + def inner_step(y): + inner_mem = memory(name="inner_rnn_state", + size=hidden_dim, + boot_layer=outer_mem) + return fc_layer(input=[y, inner_mem], + size=hidden_dim, + act=TanhActivation(), + bias_attr=True, + name="inner_rnn_state") + + inner_rnn_output = recurrent_group( + step=inner_step, + input=x) + last = last_seq(input=inner_rnn_output, name="outer_rnn_state") + + return inner_rnn_output + + out = recurrent_group(step=outer_step, input=SubsequenceInput(emb)) + +- 双层序列，外层memory是单层序列： + - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 + - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 + +示例3：双进双出，输入不等长 +=========================== + +.. role:: red + +.. raw:: html + + + +**输入不等长** 是指recurrent_group的多个输入在各时刻的长度可以不相等, 但需要指定一个和输出长度一致的input，用 :red:`targetInlink` 表示。参考配置：单层RNN（:code:`sequence_rnn_multi_unequalength_inputs.conf`），双层RNN（:code:`sequence_nest_rnn_multi_unequalength_inputs.conf`） + +读取双层序列的方法 +------------------ + +我们看一下单双层序列的数据组织形式和dataprovider（见`rnn_data_provider.py`） + +.. code-block:: python + + data2 = [ + [[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]] ,0], + [[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]], 1], + ] + + @provider(input_types=[integer_value_sub_sequence(10), + integer_value_sub_sequence(10), + integer_value(2)], + should_shuffle=False) + def process_unequalength_subseq(settings, file_name): #双层RNN的dataprovider + for d in data2: + yield d + + + @provider(input_types=[integer_value_sequence(10), + integer_value_sequence(10), + integer_value(2)], + should_shuffle=False) + def process_unequalength_seq(settings, file_name): #单层RNN的dataprovider + for d in data2: + words1=reduce(lambda x,y: x+y, d[0]) + words2=reduce(lambda x,y: x+y, d[1]) + yield words1, words2, d[2] + +data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 + +- 单层序列：两个样本分别为[[1, 2, 4, 5, 2], [5, 4, 1, 3, 1]] 和 [[0, 2, 2, 5, 0, 1, 2], [1, 5, 4, 2, 3, 6, 1]] +- 双层序列：两个样本分别为 + + - **样本1**\：[[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]]]。fea1和fea2都分别有2个子句，fea1=[[1, 2], [4, 5, 2]], fea2=[[5, 4, 1], [3, 1]] + - **样本2**\：[[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]]]。fea1和fea2都分别有3个子句， fea1=[[0, 2], [2, 5], [0, 1, 2]], fea2=[[1, 5], [4], [2, 3, 6, 1]]。
    + - **注意**\：每个样本中，各特征的子句数目需要相等。这里说的“双进双出，输入不等长”是指fea1在i时刻的输入的长度可以不等于fea2在i时刻的输入的长度。如对于第1个样本，时刻i=2, fea1[2]=[4, 5, 2]，fea2[2]=[3, 1]，3≠2。 + +- 单双层序列中，两个样本的label都分别是0和1 + +模型中的配置 +------------ + +单层RNN（ :code:`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（ :code:`sequence_nest_rnn_multi_unequalength_inputs.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 + +- 单层序列\： + + - 过了一个简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全连接，功能与示例2中`sequence_rnn.conf`的`step`函数完全相同。这里，两个输入x1,x2分别通过calrnn返回最后时刻的状态。结果得到的encoder1_rep和encoder2_rep分别是单层序列，最后取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 + - 注意到这里recurrent_group输入的每个样本中，fea1和fea2的长度都分别相等，这并非偶然，而是因为recurrent_group要求输入为单层序列时，所有输入的长度都必须相等。 + +.. code-block:: python + + def step(x1, x2): + def calrnn(y): + mem = memory(name = 'rnn_state_' + y.name, size = hidden_dim) + out = fc_layer(input = [y, mem], + size = hidden_dim, + act = TanhActivation(), + bias_attr = True, + name = 'rnn_state_' + y.name) + return out + + encoder1 = calrnn(x1) + encoder2 = calrnn(x2) + return [encoder1, encoder2] + + encoder1_rep, encoder2_rep = recurrent_group( + name="stepout", + step=step, + input=[emb1, emb2]) + + encoder1_last = last_seq(input = encoder1_rep) + encoder1_expandlast = expand_layer(input = encoder1_last, + expand_as = encoder2_rep) + context = mixed_layer(input = [identity_projection(encoder1_expandlast), + identity_projection(encoder2_rep)], + size = hidden_dim) + +- 双层序列\： + + - 双层RNN中，对输入的两个特征分别求时序上的连续全连接(`inner_step1`和`inner_step2`分别处理fea1和fea2)，其功能与示例2中`sequence_nest_rnn.conf`的`outer_step`函数完全相同。不同之处是，此时输入`[SubsequenceInput(emb1), SubsequenceInput(emb2)]`在各时刻并不等长。 + - 函数`outer_step`中可以分别处理这两个特征，但我们需要用targetInlink指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 + - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 + +.. code-block:: python + + def outer_step(x1, x2): + outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim) + outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim) + def inner_step1(y): + inner_mem = memory(name = 'inner_rnn_state_' + y.name, + size = hidden_dim, + boot_layer = outer_mem1) + out = fc_layer(input = [y, inner_mem], + size = hidden_dim, + act = TanhActivation(), + bias_attr = True, + name = 'inner_rnn_state_' + y.name) + return out + + def inner_step2(y): + inner_mem = memory(name = 'inner_rnn_state_' + y.name, + size = hidden_dim, + boot_layer = outer_mem2) + out = fc_layer(input = [y, inner_mem], + size = hidden_dim, + act = TanhActivation(), + bias_attr = True, + name = 'inner_rnn_state_' + y.name) + return out + + encoder1 = recurrent_group( + step = inner_step1, + name = 'inner1', + input = x1) + + encoder2 = recurrent_group( + step = inner_step2, + name = 'inner2', + input = x2) + + sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1') + sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2') + + encoder1_expand = expand_layer(input = sentence_last_state1, + expand_as = encoder2) + + return [encoder1_expand, encoder2] + + encoder1_rep, encoder2_rep = recurrent_group( + name="outer", + step=outer_step, + input=[SubsequenceInput(emb1), SubsequenceInput(emb2)], + targetInlink=emb2) + + encoder1_last = last_seq(input = encoder1_rep) + encoder1_expandlast = expand_layer(input = encoder1_last, + expand_as = encoder2_rep) + context = mixed_layer(input = [identity_projection(encoder1_expandlast), + identity_projection(encoder2_rep)], + size = hidden_dim) + +示例4：beam_search的生成 +======================== + +TBD -- GitLab From f1955e2b2092d63f9acab07d8971ef2c5572170c Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Nov 2016 16:36:26 +0800 Subject: [PATCH 022/347] Remove copy & paste code. --- doc_cn/algorithm/rnn/hierarchical-rnn.rst | 337 +++------------------- 1 file changed, 38 insertions(+), 299 deletions(-) diff --git a/doc_cn/algorithm/rnn/hierarchical-rnn.rst b/doc_cn/algorithm/rnn/hierarchical-rnn.rst index 7de54cc0b13..7c81ce8c673 100644 --- a/doc_cn/algorithm/rnn/hierarchical-rnn.rst +++ b/doc_cn/algorithm/rnn/hierarchical-rnn.rst @@ -16,37 +16,14 @@ - 单层序列的数据（ :code:`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。 -.. code-block:: text - - 2 酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。 - 2 很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 * - 2 位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。 - 2 交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。 - 2 本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 . - 2 这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店 - 2 挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！ - 2 HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。 - 2 酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 * - 2 挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格 +.. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg + :language: text - 双层序列的数据（ :code:`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。 -.. code-block:: text - - 2 酒店 有 很 舒适 的 床垫 子 ， 床上用品 也 应该 是 一人 一 换 ， 感觉 很 利落 对 卫生 很 放心 呀 。 - 2 很 温馨 ， 也 挺 干净 的 * 地段 不错 ， 出来 就 有 全家 ， 离 地铁站 也 近 ， 交通 很方便 * 就是 都 不 给 刷牙 的 杯子 啊 ， 就 第一天 给 了 一次性杯子 * - - 2 位置 方便 ， 强烈推荐 ， 十一 出去玩 的 时候 选 的 ， 对面 就是 华润万家 ， 周围 吃饭 的 也 不少 。 - 2 交通便利 ， 吃 很 便利 ， 乾 浄 、 安静 ， 商务 房 有 电脑 、 上网 快 ， 价格 可以 ， 就 早餐 不 好吃 。 整体 是 不错 的 。 適 合 出差 來 住 。 - 2 本来 准备 住 两 晚 ， 第 2 天 一早 居然 停电 ， 且 无 通知 ， 只有 口头 道歉 。 总体来说 性价比 尚可 ， 房间 较 新 ， 还是 推荐 . - - 2 这个 酒店 去过 很多 次 了 ， 选择 的 主要原因 是 离 客户 最 便宜 相对 又 近 的 酒店 - 2 挺好 的 汉庭 ， 前台 服务 很 热情 ， 卫生 很 整洁 ， 房间 安静 ， 水温 适中 ， 挺好 ！ - - 2 HowardJohnson 的 品质 ， 服务 相当 好 的 一 家 五星级 。 房间 不错 、 泳池 不错 、 楼层 安排 很 合理 。 还有 就是 地理位置 ， 简直 一 流 。 就 在 天一阁 、 月湖 旁边 ， 离 天一广场 也 不远 。 下次 来 宁波 还会 住 。 - 2 酒店 很干净 ， 很安静 ， 很 温馨 ， 服务员 服务 好 ， 各方面 都 不错 * - 2 挺好 的 ， 就是 没 窗户 ， 不过 对 得 起 这 价格 +.. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest + :language: text 其次，我们看一下单双层序列的不同dataprovider（见 :code:`sequenceGen.py` ）： @@ -55,22 +32,9 @@ - word_slot是integer_value_sequence类型，代表单层序列。 - label是integer_value类型，代表一个向量。 -.. code-block:: python - - def hook(settings, dict_file, **kwargs): - settings.word_dict = dict_file - settings.input_types = [integer_value_sequence(len(settings.word_dict)), - integer_value(3)] - - @provider(init_hook=hook) - def process(settings, file_name): - with open(file_name, 'r') as fdata: - for line in fdata: - label, comment = line.strip().split('\t') - label = int(''.join(label.split())) - words = comment.split() - word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict] - yield word_slot, label +.. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py + :language: python + :lines: 21-39 - 双层序列的dataprovider如下： @@ -78,64 +42,18 @@ - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。 - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。 -.. code-block:: python - - def hook2(settings, dict_file, **kwargs): - settings.word_dict = dict_file - settings.input_types = [integer_value_sub_sequence(len(settings.word_dict)), - integer_value_sequence(3)] - - @provider(init_hook=hook2) - def process2(settings, file_name): - with open(file_name) as fdata: - label_list = [] - word_slot_list = [] - for line in fdata: - if (len(line)) > 1: - label,comment = line.strip().split('\t') - label = int(''.join(label.split())) - words = comment.split() - word_slot = [settings.word_dict[w] for w in words if w in settings.word_dict] - label_list.append(label) - word_slot_list.append(word_slot) - else: - yield word_slot_list, label_list - label_list = [] - word_slot_list = [] - +.. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py + :language: python + :lines: 42-71 模型中的配置 ------------ 首先，我们看一下单层序列的配置（见 :code:`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。 -.. code-block:: python - - settings(batch_size=5) - - data = data_layer(name="word", size=dict_dim) - - emb = embedding_layer(input=data, size=word_dim) - - # (lstm_input + lstm) is equal to lstmemory - with mixed_layer(size=hidden_dim*4) as lstm_input: - lstm_input += full_matrix_projection(input=emb) - - lstm = lstmemory_group(input=lstm_input, - size=hidden_dim, - act=TanhActivation(), - gate_act=SigmoidActivation(), - state_act=TanhActivation(), - lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50)) - - lstm_last = last_seq(input=lstm) - - with mixed_layer(size=label_dim, - act=SoftmaxActivation(), - bias_attr=True) as output: - output += full_matrix_projection(input=lstm_last) - - outputs(classification_cost(input=output, label=data_layer(name="label", size=1))) +.. literalinclude:: ../../../paddle/gserver/tests/sequence_layer_group.conf + :language: python + :lines: 38-63 其次，我们看一下语义相同的双层序列配置（见 :code:`sequence_nest_layer_group.conf` ），并对其详细分析： @@ -153,48 +71,9 @@ - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。 - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。 -.. code-block:: python - - settings(batch_size=2) - - data = data_layer(name="word", size=dict_dim) - - emb_group = embedding_layer(input=data, size=word_dim) - - # (lstm_input + lstm) is equal to lstmemory - def lstm_group(lstm_group_input): - with mixed_layer(size=hidden_dim*4) as group_input: - group_input += full_matrix_projection(input=lstm_group_input) - - lstm_output = lstmemory_group(input=group_input, - name="lstm_group", - size=hidden_dim, - act=TanhActivation(), - gate_act=SigmoidActivation(), - state_act=TanhActivation(), - lstm_layer_attr=ExtraLayerAttribute(error_clipping_threshold=50)) - return lstm_output - - lstm_nest_group = recurrent_group(input=SubsequenceInput(emb_group), - step=lstm_group, - name="lstm_nest_group") - # hasSubseq ->(seqlastins) seq - lstm_last = last_seq(input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE) - - # seq ->(expand) hasSubseq - lstm_expand = expand_layer(input=lstm_last, expand_as=emb_group, expand_level=ExpandLevel.FROM_SEQUENCE) - - # hasSubseq ->(average) seq - lstm_average = pooling_layer(input=lstm_expand, - pooling_type=AvgPooling(), - agg_level=AggregateLevel.EACH_SEQUENCE) - - with mixed_layer(size=label_dim, - act=SoftmaxActivation(), - bias_attr=True) as output: - output += full_matrix_projection(input=lstm_average) - - outputs(classification_cost(input=output, label=data_layer(name="label", size=1))) +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_layer_group.conf + :language: python + :lines: 38-84 示例2：双进双出，subseq间有memory ================================= @@ -206,24 +85,9 @@ 我们看一下单双层序列的不同数据组织形式和dataprovider（见 :code:`rnn_data_provider.py`） -.. code-block:: python - - data = [ - [[[1, 3, 2], [4, 5, 2]], 0], - [[[0, 2], [2, 5], [0, 1, 2]], 1], - ] - - @provider(input_types=[integer_value_sub_sequence(10), - integer_value(3)]) - def process_subseq(settings, file_name): - for d in data: - yield d - - @provider(input_types=[integer_value_sequence(10), - integer_value(3)]) - def process_seq(settings, file_name): - for d in data: - seq = [] +.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py + :language: python + :lines: 20-32 - 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。 - 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。 @@ -236,46 +100,21 @@ - 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 -.. code-block:: python - - def step(y): - mem = memory(name="rnn_state", size=hidden_dim) - return fc_layer(input=[y, mem], - size=hidden_dim, - act=TanhActivation(), - bias_attr=True, - name="rnn_state") - - out = recurrent_group(step=step, input=emb) +.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn.conf + :language: python + :lines: 36-48 - 双层序列，外层memory是一个元素： + - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 -.. code-block:: - - def outer_step(x): - outer_mem = memory(name="outer_rnn_state", size=hidden_dim) - def inner_step(y): - inner_mem = memory(name="inner_rnn_state", - size=hidden_dim, - boot_layer=outer_mem) - return fc_layer(input=[y, inner_mem], - size=hidden_dim, - act=TanhActivation(), - bias_attr=True, - name="inner_rnn_state") - - inner_rnn_output = recurrent_group( - step=inner_step, - input=x) - last = last_seq(input=inner_rnn_output, name="outer_rnn_state") - - return inner_rnn_output - - out = recurrent_group(step=outer_step, input=SubsequenceInput(emb)) +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf + :language: python + :lines: 39-66 - 双层序列，外层memory是单层序列： + - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 @@ -293,33 +132,11 @@ 读取双层序列的方法 ------------------ -我们看一下单双层序列的数据组织形式和dataprovider（见`rnn_data_provider.py`） - -.. code-block:: python - - data2 = [ - [[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]] ,0], - [[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]], 1], - ] - - @provider(input_types=[integer_value_sub_sequence(10), - integer_value_sub_sequence(10), - integer_value(2)], - should_shuffle=False) - def process_unequalength_subseq(settings, file_name): #双层RNN的dataprovider - for d in data2: - yield d - +我们看一下单双层序列的数据组织形式和dataprovider（见 :code:`rnn_data_provider.py` ） - @provider(input_types=[integer_value_sequence(10), - integer_value_sequence(10), - integer_value(2)], - should_shuffle=False) - def process_unequalength_seq(settings, file_name): #单层RNN的dataprovider - for d in data2: - words1=reduce(lambda x,y: x+y, d[0]) - words2=reduce(lambda x,y: x+y, d[1]) - yield words1, words2, d[2] +.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py + :language: python + :lines: 69-97 data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 @@ -335,40 +152,16 @@ data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 模型中的配置 ------------ -单层RNN（ :code:`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（ :code:`sequence_nest_rnn_multi_unequalength_inputs.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 +单层RNN（ :code:`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（ :code:`v.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 - 单层序列\： - 过了一个简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全连接，功能与示例2中`sequence_rnn.conf`的`step`函数完全相同。这里，两个输入x1,x2分别通过calrnn返回最后时刻的状态。结果得到的encoder1_rep和encoder2_rep分别是单层序列，最后取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 - 注意到这里recurrent_group输入的每个样本中，fea1和fea2的长度都分别相等，这并非偶然，而是因为recurrent_group要求输入为单层序列时，所有输入的长度都必须相等。 -.. code-block:: python - - def step(x1, x2): - def calrnn(y): - mem = memory(name = 'rnn_state_' + y.name, size = hidden_dim) - out = fc_layer(input = [y, mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'rnn_state_' + y.name) - return out - - encoder1 = calrnn(x1) - encoder2 = calrnn(x2) - return [encoder1, encoder2] - - encoder1_rep, encoder2_rep = recurrent_group( - name="stepout", - step=step, - input=[emb1, emb2]) - - encoder1_last = last_seq(input = encoder1_rep) - encoder1_expandlast = expand_layer(input = encoder1_last, - expand_as = encoder2_rep) - context = mixed_layer(input = [identity_projection(encoder1_expandlast), - identity_projection(encoder2_rep)], - size = hidden_dim) +.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf + :language: python + :lines: 41-58 - 双层序列\： @@ -376,63 +169,9 @@ data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 - 函数`outer_step`中可以分别处理这两个特征，但我们需要用targetInlink指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 -.. code-block:: python - - def outer_step(x1, x2): - outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim) - outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim) - def inner_step1(y): - inner_mem = memory(name = 'inner_rnn_state_' + y.name, - size = hidden_dim, - boot_layer = outer_mem1) - out = fc_layer(input = [y, inner_mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'inner_rnn_state_' + y.name) - return out - - def inner_step2(y): - inner_mem = memory(name = 'inner_rnn_state_' + y.name, - size = hidden_dim, - boot_layer = outer_mem2) - out = fc_layer(input = [y, inner_mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'inner_rnn_state_' + y.name) - return out - - encoder1 = recurrent_group( - step = inner_step1, - name = 'inner1', - input = x1) - - encoder2 = recurrent_group( - step = inner_step2, - name = 'inner2', - input = x2) - - sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1') - sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2') - - encoder1_expand = expand_layer(input = sentence_last_state1, - expand_as = encoder2) - - return [encoder1_expand, encoder2] - - encoder1_rep, encoder2_rep = recurrent_group( - name="outer", - step=outer_step, - input=[SubsequenceInput(emb1), SubsequenceInput(emb2)], - targetInlink=emb2) - - encoder1_last = last_seq(input = encoder1_rep) - encoder1_expandlast = expand_layer(input = encoder1_last, - expand_as = encoder2_rep) - context = mixed_layer(input = [identity_projection(encoder1_expandlast), - identity_projection(encoder2_rep)], - size = hidden_dim) +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf + :language: python + :lines: 41-89 示例4：beam_search的生成 ======================== -- GitLab From 4351084181570c7082857018ea5ad676fb1d7105 Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Mon, 31 Oct 2016 19:00:48 +0800 Subject: [PATCH 023/347] Add PaddlePaddle QuickStart Demo on Kubernetes --- .../build_and_install/paddle_on_kubernetes.md | 190 ++++++++++++++++++ 1 file changed, 190 insertions(+) create mode 100644 doc_cn/build_and_install/paddle_on_kubernetes.md diff --git a/doc_cn/build_and_install/paddle_on_kubernetes.md b/doc_cn/build_and_install/paddle_on_kubernetes.md new file mode 100644 index 00000000000..b7609c24c98 --- /dev/null +++ b/doc_cn/build_and_install/paddle_on_kubernetes.md @@ -0,0 +1,190 @@ +# Paddle On Kubernetes + +## 制作Docker镜像 + + 本文使用QucikStart的例子来作为镜像，详细的Paddle镜像请参考[Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html),使用paddledev/paddle:cpu-demo-latest作为基础镜像。 + +### 运行容器 + +``` +$ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest +``` +进入容器`/root/paddle/demo/quick_start/data`目录，使用`get_data.sh`下载数据 + +``` +$ root@fbd1f2bb71f4:~/paddle/demo/quick_start/data# ./get_data.sh + +Downloading Amazon Electronics reviews data... +--2016-10-31 01:33:43-- http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz +Resolving snap.stanford.edu (snap.stanford.edu)... 171.64.75.80 +Connecting to snap.stanford.edu (snap.stanford.edu)|171.64.75.80|:80... connected. +HTTP request sent, awaiting response... 200 OK +Length: 495854086 (473M) [application/x-gzip] +Saving to: 'reviews_Electronics_5.json.gz' + + 0% [ ] 874,279 64.7KB/s eta 2h 13m + +``` + +下载完数据后，修改`/root/paddle/demo/quick_start/train.sh`文件，内容如下（增加了一条cd命令） +``` +set -e +cd /root/paddle/demo/quick_start +cfg=trainer_config.lr.py +#cfg=trainer_config.emb.py +#cfg=trainer_config.cnn.py +#cfg=trainer_config.lstm.py +#cfg=trainer_config.bidi-lstm.py +#cfg=trainer_config.db-lstm.py +paddle train \ + --config=$cfg \ + --save_dir=./output \ + --trainer_count=4 \ + --log_period=20 \ + --num_passes=15 \ + --use_gpu=false \ + --show_parameter_stats_period=100 \ + --test_all_data_in_one_period=1 \ + 2>&1 | tee 'train.log' +``` + +### 提交镜像 + +下载完数据后，退出容器，使用`docker commit`命令创建新镜像。 + +``` +$ docker commit quick_start_data mypaddle/paddle:quickstart +``` + +## 使用 Kubernetes 进行训练 + +### 编写yaml文件 + +在训练时，输出结果可能会随着容器的消耗而被删除，需要在创建Job时，挂载卷。使用前面构造的镜像，可以创建一个Kubernetes Job，简单的yaml文件如下： + +``` +apiVersion: batch/v1 +kind: Job +metadata: + name: quickstart +spec: + parallelism: 1 + completions: 1 + template: + metadata: + name: quickstart + spec: + volumes: + - name: output + hostPath: + path: /home/work/paddle_output + containers: + - name: pi + image: mypaddle/paddle:quickstart + command: ["bin/bash", "-c", "/root/paddle/demo/quick_start/train.sh"] + volumeMounts: + - name: output + mountPath: /root/paddle/demo/quick_start/output + restartPolicy: Never +``` + +### 创建Paddle Job + +使用上文创建的yaml文件创建kubernetes job，命令为： + +``` +$ kubectl create -f paddle.yaml +``` + +查看job的详细情况： + +``` +$ kubectl get job +NAME DESIRED SUCCESSFUL AGE +quickstart 1 0 58s + +$ kubectl describe job quickstart +Name: quickstart +Namespace: default +Image(s): registry.baidu.com/public/paddle:cpu-demo-latest +Selector: controller-uid=f120da72-9f18-11e6-b363-448a5b355b84 +Parallelism: 1 +Completions: 1 +Start Time: Mon, 31 Oct 2016 11:20:16 +0800 +Labels: controller-uid=f120da72-9f18-11e6-b363-448a5b355b84,job-name=quickstart +Pods Statuses: 0 Running / 1 Succeeded / 0 Failed +Volumes: + output: + Type: HostPath (bare host directory volume) + Path: /home/work/paddle_output +Events: + FirstSeen LastSeen Count From SubobjectPath Type Reason Message + --------- -------- ----- ---- ------------- -------- ------ ------- + 1m 1m 1 {job-controller } Normal SuccessfulCreate Created pod: quickstart-fa0wx +``` + +### 查看训练结果 + +根据job对应的pod信息，可以查看此Pod运行的宿主机。 + +``` +kubectl describe pod quickstart-fa0wx +Name: quickstart-fa0wx +Namespace: default +Node: paddle-demo-let02/10.206.202.44 +Start Time: Mon, 31 Oct 2016 11:20:17 +0800 +Labels: controller-uid=f120da72-9f18-11e6-b363-448a5b355b84,job-name=quickstart +Status: Succeeded +IP: 10.0.0.9 +Controllers: Job/quickstart +Containers: + quickstart: + Container ID: docker://b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465 + Image: registry.baidu.com/public/paddle:cpu-demo-latest + Image ID: docker://18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750 + Port: + Command: + bin/bash + -c + /root/paddle/demo/quick_start/train.sh + QoS Tier: + cpu: BestEffort + memory: BestEffort + State: Terminated + Reason: Completed + Exit Code: 0 + Started: Mon, 31 Oct 2016 11:20:20 +0800 + Finished: Mon, 31 Oct 2016 11:21:46 +0800 + Ready: False + Restart Count: 0 + Environment Variables: +Conditions: + Type Status + Ready False +Volumes: + output: + Type: HostPath (bare host directory volume) + Path: /home/work/paddle_output +``` + +登录宿主机，查看结果。 + +``` +[root@paddle-demo-let02 paddle_output]# ll +total 60 +drwxr-xr-x 2 root root 4096 Oct 31 11:20 pass-00000 +drwxr-xr-x 2 root root 4096 Oct 31 11:20 pass-00001 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00002 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00003 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00004 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00005 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00006 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00007 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00008 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00009 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00010 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00011 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00012 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00013 +drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00014 +``` \ No newline at end of file -- GitLab From e2caa1f713a34960cad191d45e2a74a1ffefe4b7 Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Thu, 3 Nov 2016 14:04:03 +0800 Subject: [PATCH 024/347] Added some explanation to paddle_on_kubernetes.md --- .../build_and_install/paddle_on_kubernetes.md | 29 ++++++++++++++----- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/doc_cn/build_and_install/paddle_on_kubernetes.md b/doc_cn/build_and_install/paddle_on_kubernetes.md index b7609c24c98..612b43dbaad 100644 --- a/doc_cn/build_and_install/paddle_on_kubernetes.md +++ b/doc_cn/build_and_install/paddle_on_kubernetes.md @@ -1,14 +1,25 @@ # Paddle On Kubernetes +>在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。 + ## 制作Docker镜像 - 本文使用QucikStart的例子来作为镜像，详细的Paddle镜像请参考[Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html),使用paddledev/paddle:cpu-demo-latest作为基础镜像。 +在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式操作系统来存储训练数据。这样的话，一个分布式Paddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 +Paddle的Docker Image里。为此，我们需要制作一个包含训练数据的Paddle镜像。 +Paddle 的 [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html) +里介绍了用Paddle源码中的脚本下载训练数据的过程。 +而 `paddledev/paddle:cpu-demo-latest` 镜像里有 Paddle 源码与Demo，（ 请注意，默认的 +Paddle镜像 `paddledev/paddle:cpu-latest` 是不包括源码的, Paddle的各版本镜像可以参考 [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html) ），所以我们使用这个镜像来下载训练数据到Docker Container中，然后把这个包含了训练数据的container保存为一个新的镜像。 + ### 运行容器 ``` $ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest ``` + +### 下载数据 + 进入容器`/root/paddle/demo/quick_start/data`目录，使用`get_data.sh`下载数据 ``` @@ -22,10 +33,12 @@ HTTP request sent, awaiting response... 200 OK Length: 495854086 (473M) [application/x-gzip] Saving to: 'reviews_Electronics_5.json.gz' - 0% [ ] 874,279 64.7KB/s eta 2h 13m + 10% [=======> ] 874,279 64.7KB/s eta 2h 13m ``` +### 修改启动脚本 + 下载完数据后，修改`/root/paddle/demo/quick_start/train.sh`文件，内容如下（增加了一条cd命令） ``` set -e @@ -58,9 +71,11 @@ $ docker commit quick_start_data mypaddle/paddle:quickstart ## 使用 Kubernetes 进行训练 +>针对任务运行完成后容器自动退出的场景，Kubernetes有Job类型的资源来支持。下文就是用Job类型的资源来进行训练。 + ### 编写yaml文件 -在训练时，输出结果可能会随着容器的消耗而被删除，需要在创建Job时，挂载卷。使用前面构造的镜像，可以创建一个Kubernetes Job，简单的yaml文件如下： +在训练时，输出结果可能会随着容器的消耗而被删除，需要在创建容器前挂载卷以便我们保存训练结果。使用我们之前构造的镜像，可以创建一个 [Kubernetes Job](http://kubernetes.io/docs/user-guide/jobs/#what-is-a-job)，简单的yaml文件如下： ``` apiVersion: batch/v1 @@ -90,7 +105,7 @@ spec: ### 创建Paddle Job -使用上文创建的yaml文件创建kubernetes job，命令为： +使用上文创建的yaml文件创建Kubernetes Job，命令为： ``` $ kubectl create -f paddle.yaml @@ -125,7 +140,7 @@ Events: ### 查看训练结果 -根据job对应的pod信息，可以查看此Pod运行的宿主机。 +根据Job对应的Pod信息，可以查看此Pod运行的宿主机。 ``` kubectl describe pod quickstart-fa0wx @@ -167,7 +182,7 @@ Volumes: Path: /home/work/paddle_output ``` -登录宿主机，查看结果。 +我们还可以登录到宿主机上查看训练结果。 ``` [root@paddle-demo-let02 paddle_output]# ll @@ -187,4 +202,4 @@ drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00011 drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00012 drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00013 drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00014 -``` \ No newline at end of file +``` -- GitLab From 2aa8c6161275b8aea6905ff0c1666337d7fcfe70 Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Sat, 5 Nov 2016 23:07:51 +0800 Subject: [PATCH 025/347] add paddle cluster train on k8s --- .../cluster_train_on_kubernetes.md | 290 ++++++++++++++++++ 1 file changed, 290 insertions(+) create mode 100644 doc_cn/build_and_install/cluster_train_on_kubernetes.md diff --git a/doc_cn/build_and_install/cluster_train_on_kubernetes.md b/doc_cn/build_and_install/cluster_train_on_kubernetes.md new file mode 100644 index 00000000000..162b087a13e --- /dev/null +++ b/doc_cn/build_and_install/cluster_train_on_kubernetes.md @@ -0,0 +1,290 @@ + +# 使用Kubernetes进行分布式训练 + +>前一篇文章介绍了如何使用Kubernetes Job进行一次单机的Paddle训练。在这篇文档里，我们介绍如何使用 Kubernetes 进行Paddle的集群训练作业。 +>关于Paddle的分布式集群训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文在此基础上，利用了Kubernetes快速构建Paddle集群，进行分布式训练任务。 + +## 制作镜像 + +Paddle的集群训练需要有一个Paddle集群来实现，在本文中，我们使用Kubernetes来快速创建一个Paddle集群。我们使用 `paddledev/paddle:cpu-demo-latest` 镜像作为Paddle集群节点的运行环境，里面包含了 Paddle 运行所需要的相关依赖，同时，为了能将训练任务及配置统一分发到各个节点，需要使用到`sshd`以便使用`fabric`来操作。镜像的 Dockerfile 如下： + +``` +FROM paddledev/paddle:cpu-demo-latest + +RUN apt-get update +RUN apt-get install -y openssh-server +RUN mkdir /var/run/sshd +RUN echo 'root:root' | chpasswd + +RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config +RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config + +EXPOSE 22 + +CMD ["/usr/sbin/sshd", "-D"] +``` + +使用 `docker build` 构建镜像： + +``` +docker build -t mypaddle:paddle_demo_ssh . +``` + +## 准备工作空间 + +工作空间 [Job Workspace](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md#prepare-job-workspace) , 即一个包含了依赖库，训练，测试数据，模型配置文件的目录。参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)中的例子，我们也是用`demo/recommendation`作为本文的训练任务。此demo可直接从[Github Paddle源码](https://github.com/baidu/Paddle/tree/develop/demo/recommendation)中获取。 + +### 准备训练数据 + +在Paddle源码中，找到`demo/recommendation`文件夹，即为我们的Workspace, 在本文的环境中，路径为`/home/work/paddle-demo/Paddle/demo/recommendation` + +``` +[root@paddle-k8s-node0 recommendation]# tree +. +├── common_utils.py +├── data +│   ├── config_generator.py +│   ├── config.json +│   ├── meta_config.json +│   ├── meta_generator.py +│   ├── ml_data.sh +│   └── split.py +├── dataprovider.py +├── evaluate.sh +├── prediction.py +├── preprocess.sh +├── requirements.txt +├── run.sh +└── trainer_config.py + +1 directory, 14 files +``` + +运行`data/ml_data.sh`脚本，下载数据，然后运行`preprocess.sh`脚本进行预处理。 + +``` +[root@paddle-k8s-node0 recommendation]# data/ml_data.sh +++ dirname data/ml_data.sh ++ cd data ++ wget http://files.grouplens.org/datasets/movielens/ml-1m.zip +--2016-11-04 10:14:49-- http://files.grouplens.org/datasets/movielens/ml-1m.zip +Resolving files.grouplens.org (files.grouplens.org)... 128.101.34.146 +Connecting to files.grouplens.org (files.grouplens.org)|128.101.34.146|:80... connected. +HTTP request sent, awaiting response... 200 OK +Length: 5917549 (5.6M) [application/zip] +Saving to: ‘ml-1m.zip’ + +100%[==========================>] 5,917,549 50.6KB/s in 2m 29s + +2016-11-04 10:17:20 (38.8 KB/s) - ‘ml-1m.zip’ saved [5917549/5917549] + ++ unzip ml-1m.zip +Archive: ml-1m.zip + creating: ml-1m/ + inflating: ml-1m/movies.dat + inflating: ml-1m/ratings.dat + inflating: ml-1m/README + inflating: ml-1m/users.dat ++ rm ml-1m.zip + +[root@paddle-k8s-node0 recommendation]# ./preprocess.sh +generate meta config file +generate meta file +split train/test file +shuffle train file +``` + +### 修改集群训练配置 + +参考[Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)中的介绍，我们使用`paddle/scripts/cluster_train/`中的文件来作为分布式训练任务的配置和启动脚本。在`run.sh`文件中，填入我们的workspace和训练配置文件路径。 + +``` +#!/bin/sh +python paddle.py \ + --job_dispatch_package="/home/work/paddle-demo/Paddle/demo/recommendation" \ + --dot_period=10 \ + --ports_num_for_sparse=2 \ + --log_period=50 \ + --num_passes=10 \ + --trainer_count=4 \ + --saving_period=1 \ + --local=0 \ + --config=/home/work/paddle-demo/Paddle/demo/recommendation/trainer_config.py \ + --save_dir=./output \ + --use_gpu=0 +``` + +## 创建Paddle集群 + +创建Paddle集训需要编写创建Kubernetes资源的yaml文件，首先，创建一个Service,便于我们通过此Service来查找其对应的Paddle节点。 + +``` +apiVersion: v1 +kind: Service +metadata: + name: cluster-demo +spec: + selector: + app: cluster-demo + ports: + - name: default + protocol: TCP + port: 7164 + targetPort: 7164 +``` + +为了创建多个Paddle节点,我们使用Kubernetes ReplicationController资源来控制Paddle集群中的节点数量，Paddle节点之间需要开放相关的端口来互相通信。下面的例子中，我们开放了每个Paddle节点的7164-7167端口，例如，一个包含4个节点的Paddle集群的yaml文件如下： + +``` +apiVersion: v1 +kind: ReplicationController +metadata: + name: cluster-demo +spec: + replicas: 4 + selector: + app: cluster-demo + template: + metadata: + name: cluster-demo + labels: + app: cluster-demo + spec: + containers: + - name: cluster-demo + image: mypaddle:paddle_demo_ssh + ports: + - containerPort: 7164 + - containerPort: 7165 + - containerPort: 7166 + - containerPort: 7167 +``` + +然后我们可以通过`kubectl`工具来查看所创建的资源信息。 + +首先查看我们创建的Paddle Service，然后根据Service,查看所创建的Paddle节点的IP地址。 + +``` +[root@paddle-k8s-node0 cluster_train]# kubectl get svc +NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE +cluster-demo 11.1.1.77 7164/TCP 6h + +[root@paddle-k8s-node0 cluster_train]# kubectl get -o json endpoints cluster-demo | grep ip + "ip": "192.168.129.79", + "ip": "192.168.129.80", + "ip": "192.168.223.157", + "ip": "192.168.223.158", +``` + +## 开始集群训练 + +我们需要在`paddle/scripts/cluster_train/conf.py`文件中指定各个节点的IP地址以及开放的端口。根据上文创建的信息，`conf.py`文件修改如下： + +``` +HOSTS = [ + "root@192.168.129.79", + "root@192.168.129.80", + "root@192.168.223.157", + "root@192.168.223.158" + ] + +''' +workspace configuration +''' +#root dir for workspace, can be set as any director with real user account +ROOT_DIR = "/home/paddle" + + +''' +network configuration +''' +#pserver nics +PADDLE_NIC = "eth0" +#pserver port +PADDLE_PORT = 7164 +#pserver ports num +PADDLE_PORTS_NUM = 2 +#pserver sparse ports num +PADDLE_PORTS_NUM_FOR_SPARSE = 2 + +#environments setting for all processes in cluster job +LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib64" +``` +然后使用`run.sh`脚本开始训练，启动的打印如下： + +``` +[root@paddle-k8s-node0 cluster_train]# ./run.sh +[root@192.168.129.79] Executing task 'job_create_workspace' +...... +[root@192.168.129.80] Executing task 'job_create_workspace' +...... +[root@192.168.223.157] Executing task 'job_create_workspace' +...... +[root@192.168.223.158] Executing task 'job_create_workspace' +...... +[root@192.168.129.79] run: echo 0 > /home/paddle/JOB20161104171630/nodefile +[root@192.168.129.80] Executing task 'set_nodefile' +[root@192.168.129.80] run: echo 1 > /home/paddle/JOB20161104171630/nodefile +[root@192.168.223.157] Executing task 'set_nodefile' +[root@192.168.223.157] run: echo 2 > /home/paddle/JOB20161104171630/nodefile +[root@192.168.223.158] Executing task 'set_nodefile' +[root@192.168.223.158] run: echo 3 > /home/paddle/JOB20161104171630/nodefile +``` + +可以看到192.168.129.79，192.168.129.80，192.168.223.157，192.168.223.158分别为Paddle集群的Node 0-3. + +我们可以进入其中一个Paddle节点查看训练的日志。 + +``` +root@cluster-demo-fwwi5:/home/paddle/JOB20161104171700/log# less paddle_trainer.INFO +Log file created at: 2016/11/04 09:17:20 +Running on machine: cluster-demo-fwwi5 +Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg +I1104 09:17:20.346797 108 Util.cpp:155] commandline: /usr/local/bin/../opt/paddle/bin/paddle +_trainer --num_gradient_servers=4 --nics=eth0 --port=7164 --ports_num=2 --comment=paddle_proce +ss_by_paddle --pservers=192.168.129.79,192.168.129.80,192.168.223.157,192.168.223.158 --ports_ +num_for_sparse=2 --config=./trainer_config.py --trainer_count=4 --use_gpu=0 --num_passes=10 -- +save_dir=./output --log_period=50 --dot_period=10 --saving_period=1 --local=0 --trainer_id=1 + +root@cluster-demo-fwwi5:/home/paddle/JOB20161104171700/log# tailf paddle_trainer.INFO +...... +I1104 09:17:37.376471 150 ThreadLocal.cpp:37] thread use undeterministic rand seed:151 +I1104 09:18:54.159624 108 TrainerInternal.cpp:163] Batch=50 samples=80000 AvgCost=4.03478 CurrentCost=4.03478 Eval: CurrentEval: + +I1104 09:20:10.207902 108 TrainerInternal.cpp:163] Batch=100 samples=160000 AvgCost=3.75806 CurrentCost=3.48134 Eval: CurrentEval: +I1104 09:21:26.493571 108 TrainerInternal.cpp:163] Batch=150 samples=240000 AvgCost=3.64512 CurrentCost=3.41923 Eval: CurrentEval: + +``` + +最后，我们可以在Paddle集群的node0（192.168.129.79）上查看训练的输出结果。 + +``` +[root@paddle-k8s-node0 ~]# ssh root@192.168.129.79 +...... +root@cluster-demo-r65g0:/home/paddle/JOB20161104171700/output/pass-00000# ll +total 14876 +drwxr-xr-x. 2 root root 4096 Nov 4 09:40 ./ +drwxr-xr-x. 3 root root 23 Nov 4 09:40 ../ +-rw-r--r--. 1 root root 4046864 Nov 4 09:40 ___embedding_0__.w0 +-rw-r--r--. 1 root root 100368 Nov 4 09:40 ___embedding_1__.w0 +-rw-r--r--. 1 root root 6184976 Nov 4 09:40 ___embedding_2__.w0 +-rw-r--r--. 1 root root 2064 Nov 4 09:40 ___embedding_3__.w0 +-rw-r--r--. 1 root root 7184 Nov 4 09:40 ___embedding_4__.w0 +-rw-r--r--. 1 root root 21520 Nov 4 09:40 ___embedding_5__.w0 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 ___fc_layer_0__.w0 +-rw-r--r--. 1 root root 1040 Nov 4 09:40 ___fc_layer_0__.wbias +...... +...... +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w0 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w1 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w2 +-rw-r--r--. 1 root root 1040 Nov 4 09:40 _movie_fusion.wbias +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w0 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w1 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w2 +-rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w3 +-rw-r--r--. 1 root root 1040 Nov 4 09:40 _user_fusion.wbias +-rw-r--r--. 1 root root 169 Nov 4 09:40 done +-rw-r--r--. 1 root root 17 Nov 4 09:40 path.txt +-rw-r--r--. 1 root root 3495 Nov 4 09:40 trainer_config.py +``` \ No newline at end of file -- GitLab From 23b6dfd07a594f7b73f1329c4d5eae40238ee36b Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Tue, 8 Nov 2016 19:10:25 +0800 Subject: [PATCH 026/347] modify some words in paddle_on_kubernetes docs --- ...es.md => distributed_training_on_kubernetes.md} | 5 ++--- doc_cn/build_and_install/paddle_on_kubernetes.md | 14 +++++++------- 2 files changed, 9 insertions(+), 10 deletions(-) rename doc_cn/build_and_install/{cluster_train_on_kubernetes.md => distributed_training_on_kubernetes.md} (95%) diff --git a/doc_cn/build_and_install/cluster_train_on_kubernetes.md b/doc_cn/build_and_install/distributed_training_on_kubernetes.md similarity index 95% rename from doc_cn/build_and_install/cluster_train_on_kubernetes.md rename to doc_cn/build_and_install/distributed_training_on_kubernetes.md index 162b087a13e..d1769bb40aa 100644 --- a/doc_cn/build_and_install/cluster_train_on_kubernetes.md +++ b/doc_cn/build_and_install/distributed_training_on_kubernetes.md @@ -1,8 +1,7 @@ -# 使用Kubernetes进行分布式训练 +# Paddle on Kubernetes：分布式训练 ->前一篇文章介绍了如何使用Kubernetes Job进行一次单机的Paddle训练。在这篇文档里，我们介绍如何使用 Kubernetes 进行Paddle的集群训练作业。 ->关于Paddle的分布式集群训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文在此基础上，利用了Kubernetes快速构建Paddle集群，进行分布式训练任务。 +前一篇文章介绍了如何在Kubernetes集群上启动一个单机Paddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上启动分布式Paddle训练作业。关于Paddle的分布式集群训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文在此基础上，利用Kubernetes快速构建Paddle集群，进行分布式训练任务。 ## 制作镜像 diff --git a/doc_cn/build_and_install/paddle_on_kubernetes.md b/doc_cn/build_and_install/paddle_on_kubernetes.md index 612b43dbaad..f8c9f19a9fe 100644 --- a/doc_cn/build_and_install/paddle_on_kubernetes.md +++ b/doc_cn/build_and_install/paddle_on_kubernetes.md @@ -1,16 +1,16 @@ -# Paddle On Kubernetes +# Paddle On Kubernetes：单机训练 ->在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。 +在这篇文档里，我们介绍如何在 Kubernetes 集群上启动一个单机使用CPU的Paddle训练作业。在下一篇中，我们将介绍如何启动分布式训练作业。 ## 制作Docker镜像 -在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式操作系统来存储训练数据。这样的话，一个分布式Paddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 -Paddle的Docker Image里。为此，我们需要制作一个包含训练数据的Paddle镜像。 +在一个功能齐全的Kubernetes机群里，通常我们会安装Ceph等分布式文件系统来存储训练数据。这样的话，一个分布式Paddle训练任务中的每个进程都可以从Ceph读取数据。在这个例子里，我们只演示一个单机作业，所以可以简化对环境的要求，把训练数据直接放在 +Paddle的Docker image里。为此，我们需要制作一个包含训练数据的Paddle镜像。 Paddle 的 [Quick Start Tutorial](http://www.paddlepaddle.org/doc/demo/quick_start/index_en.html) 里介绍了用Paddle源码中的脚本下载训练数据的过程。 -而 `paddledev/paddle:cpu-demo-latest` 镜像里有 Paddle 源码与Demo，（ 请注意，默认的 -Paddle镜像 `paddledev/paddle:cpu-latest` 是不包括源码的, Paddle的各版本镜像可以参考 [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html) ），所以我们使用这个镜像来下载训练数据到Docker Container中，然后把这个包含了训练数据的container保存为一个新的镜像。 +而 `paddledev/paddle:cpu-demo-latest` 镜像里有 Paddle 源码与demo，（ 请注意，默认的 +Paddle镜像 `paddledev/paddle:cpu-latest` 是不包括源码的, Paddle的各版本镜像可以参考 [Docker installation guide](http://www.paddlepaddle.org/doc/build/docker_install.html) ），所以我们使用这个镜像来下载训练数据到Docker container中，然后把这个包含了训练数据的container保存为一个新的镜像。 ### 运行容器 @@ -63,7 +63,7 @@ paddle train \ ### 提交镜像 -下载完数据后，退出容器，使用`docker commit`命令创建新镜像。 +修改启动脚本后，退出容器，使用`docker commit`命令创建新镜像。 ``` $ docker commit quick_start_data mypaddle/paddle:quickstart -- GitLab From 212dcedc48a978c98b0bd6482cdc7e1f7773b48e Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Thu, 17 Nov 2016 17:16:04 +0800 Subject: [PATCH 027/347] rewrite distributed_training_on_k8s.md --- .../distributed_training_on_kubernetes.md | 289 ------------------ doc_cn/cluster/k8s/Dockerfile | 7 + .../k8s/distributed_training_on_kubernetes.md | 254 +++++++++++++++ doc_cn/cluster/k8s/job.yaml | 43 +++ doc_cn/cluster/k8s/start.sh | 19 ++ doc_cn/cluster/k8s/start_paddle.py | 159 ++++++++++ 6 files changed, 482 insertions(+), 289 deletions(-) delete mode 100644 doc_cn/build_and_install/distributed_training_on_kubernetes.md create mode 100644 doc_cn/cluster/k8s/Dockerfile create mode 100644 doc_cn/cluster/k8s/distributed_training_on_kubernetes.md create mode 100644 doc_cn/cluster/k8s/job.yaml create mode 100755 doc_cn/cluster/k8s/start.sh create mode 100755 doc_cn/cluster/k8s/start_paddle.py diff --git a/doc_cn/build_and_install/distributed_training_on_kubernetes.md b/doc_cn/build_and_install/distributed_training_on_kubernetes.md deleted file mode 100644 index d1769bb40aa..00000000000 --- a/doc_cn/build_and_install/distributed_training_on_kubernetes.md +++ /dev/null @@ -1,289 +0,0 @@ - -# Paddle on Kubernetes：分布式训练 - -前一篇文章介绍了如何在Kubernetes集群上启动一个单机Paddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上启动分布式Paddle训练作业。关于Paddle的分布式集群训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文在此基础上，利用Kubernetes快速构建Paddle集群，进行分布式训练任务。 - -## 制作镜像 - -Paddle的集群训练需要有一个Paddle集群来实现，在本文中，我们使用Kubernetes来快速创建一个Paddle集群。我们使用 `paddledev/paddle:cpu-demo-latest` 镜像作为Paddle集群节点的运行环境，里面包含了 Paddle 运行所需要的相关依赖，同时，为了能将训练任务及配置统一分发到各个节点，需要使用到`sshd`以便使用`fabric`来操作。镜像的 Dockerfile 如下： - -``` -FROM paddledev/paddle:cpu-demo-latest - -RUN apt-get update -RUN apt-get install -y openssh-server -RUN mkdir /var/run/sshd -RUN echo 'root:root' | chpasswd - -RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config -RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config - -EXPOSE 22 - -CMD ["/usr/sbin/sshd", "-D"] -``` - -使用 `docker build` 构建镜像： - -``` -docker build -t mypaddle:paddle_demo_ssh . -``` - -## 准备工作空间 - -工作空间 [Job Workspace](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md#prepare-job-workspace) , 即一个包含了依赖库，训练，测试数据，模型配置文件的目录。参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)中的例子，我们也是用`demo/recommendation`作为本文的训练任务。此demo可直接从[Github Paddle源码](https://github.com/baidu/Paddle/tree/develop/demo/recommendation)中获取。 - -### 准备训练数据 - -在Paddle源码中，找到`demo/recommendation`文件夹，即为我们的Workspace, 在本文的环境中，路径为`/home/work/paddle-demo/Paddle/demo/recommendation` - -``` -[root@paddle-k8s-node0 recommendation]# tree -. -├── common_utils.py -├── data -│   ├── config_generator.py -│   ├── config.json -│   ├── meta_config.json -│   ├── meta_generator.py -│   ├── ml_data.sh -│   └── split.py -├── dataprovider.py -├── evaluate.sh -├── prediction.py -├── preprocess.sh -├── requirements.txt -├── run.sh -└── trainer_config.py - -1 directory, 14 files -``` - -运行`data/ml_data.sh`脚本，下载数据，然后运行`preprocess.sh`脚本进行预处理。 - -``` -[root@paddle-k8s-node0 recommendation]# data/ml_data.sh -++ dirname data/ml_data.sh -+ cd data -+ wget http://files.grouplens.org/datasets/movielens/ml-1m.zip ---2016-11-04 10:14:49-- http://files.grouplens.org/datasets/movielens/ml-1m.zip -Resolving files.grouplens.org (files.grouplens.org)... 128.101.34.146 -Connecting to files.grouplens.org (files.grouplens.org)|128.101.34.146|:80... connected. -HTTP request sent, awaiting response... 200 OK -Length: 5917549 (5.6M) [application/zip] -Saving to: ‘ml-1m.zip’ - -100%[==========================>] 5,917,549 50.6KB/s in 2m 29s - -2016-11-04 10:17:20 (38.8 KB/s) - ‘ml-1m.zip’ saved [5917549/5917549] - -+ unzip ml-1m.zip -Archive: ml-1m.zip - creating: ml-1m/ - inflating: ml-1m/movies.dat - inflating: ml-1m/ratings.dat - inflating: ml-1m/README - inflating: ml-1m/users.dat -+ rm ml-1m.zip - -[root@paddle-k8s-node0 recommendation]# ./preprocess.sh -generate meta config file -generate meta file -split train/test file -shuffle train file -``` - -### 修改集群训练配置 - -参考[Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)中的介绍，我们使用`paddle/scripts/cluster_train/`中的文件来作为分布式训练任务的配置和启动脚本。在`run.sh`文件中，填入我们的workspace和训练配置文件路径。 - -``` -#!/bin/sh -python paddle.py \ - --job_dispatch_package="/home/work/paddle-demo/Paddle/demo/recommendation" \ - --dot_period=10 \ - --ports_num_for_sparse=2 \ - --log_period=50 \ - --num_passes=10 \ - --trainer_count=4 \ - --saving_period=1 \ - --local=0 \ - --config=/home/work/paddle-demo/Paddle/demo/recommendation/trainer_config.py \ - --save_dir=./output \ - --use_gpu=0 -``` - -## 创建Paddle集群 - -创建Paddle集训需要编写创建Kubernetes资源的yaml文件，首先，创建一个Service,便于我们通过此Service来查找其对应的Paddle节点。 - -``` -apiVersion: v1 -kind: Service -metadata: - name: cluster-demo -spec: - selector: - app: cluster-demo - ports: - - name: default - protocol: TCP - port: 7164 - targetPort: 7164 -``` - -为了创建多个Paddle节点,我们使用Kubernetes ReplicationController资源来控制Paddle集群中的节点数量，Paddle节点之间需要开放相关的端口来互相通信。下面的例子中，我们开放了每个Paddle节点的7164-7167端口，例如，一个包含4个节点的Paddle集群的yaml文件如下： - -``` -apiVersion: v1 -kind: ReplicationController -metadata: - name: cluster-demo -spec: - replicas: 4 - selector: - app: cluster-demo - template: - metadata: - name: cluster-demo - labels: - app: cluster-demo - spec: - containers: - - name: cluster-demo - image: mypaddle:paddle_demo_ssh - ports: - - containerPort: 7164 - - containerPort: 7165 - - containerPort: 7166 - - containerPort: 7167 -``` - -然后我们可以通过`kubectl`工具来查看所创建的资源信息。 - -首先查看我们创建的Paddle Service，然后根据Service,查看所创建的Paddle节点的IP地址。 - -``` -[root@paddle-k8s-node0 cluster_train]# kubectl get svc -NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE -cluster-demo 11.1.1.77 7164/TCP 6h - -[root@paddle-k8s-node0 cluster_train]# kubectl get -o json endpoints cluster-demo | grep ip - "ip": "192.168.129.79", - "ip": "192.168.129.80", - "ip": "192.168.223.157", - "ip": "192.168.223.158", -``` - -## 开始集群训练 - -我们需要在`paddle/scripts/cluster_train/conf.py`文件中指定各个节点的IP地址以及开放的端口。根据上文创建的信息，`conf.py`文件修改如下： - -``` -HOSTS = [ - "root@192.168.129.79", - "root@192.168.129.80", - "root@192.168.223.157", - "root@192.168.223.158" - ] - -''' -workspace configuration -''' -#root dir for workspace, can be set as any director with real user account -ROOT_DIR = "/home/paddle" - - -''' -network configuration -''' -#pserver nics -PADDLE_NIC = "eth0" -#pserver port -PADDLE_PORT = 7164 -#pserver ports num -PADDLE_PORTS_NUM = 2 -#pserver sparse ports num -PADDLE_PORTS_NUM_FOR_SPARSE = 2 - -#environments setting for all processes in cluster job -LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib64" -``` -然后使用`run.sh`脚本开始训练，启动的打印如下： - -``` -[root@paddle-k8s-node0 cluster_train]# ./run.sh -[root@192.168.129.79] Executing task 'job_create_workspace' -...... -[root@192.168.129.80] Executing task 'job_create_workspace' -...... -[root@192.168.223.157] Executing task 'job_create_workspace' -...... -[root@192.168.223.158] Executing task 'job_create_workspace' -...... -[root@192.168.129.79] run: echo 0 > /home/paddle/JOB20161104171630/nodefile -[root@192.168.129.80] Executing task 'set_nodefile' -[root@192.168.129.80] run: echo 1 > /home/paddle/JOB20161104171630/nodefile -[root@192.168.223.157] Executing task 'set_nodefile' -[root@192.168.223.157] run: echo 2 > /home/paddle/JOB20161104171630/nodefile -[root@192.168.223.158] Executing task 'set_nodefile' -[root@192.168.223.158] run: echo 3 > /home/paddle/JOB20161104171630/nodefile -``` - -可以看到192.168.129.79，192.168.129.80，192.168.223.157，192.168.223.158分别为Paddle集群的Node 0-3. - -我们可以进入其中一个Paddle节点查看训练的日志。 - -``` -root@cluster-demo-fwwi5:/home/paddle/JOB20161104171700/log# less paddle_trainer.INFO -Log file created at: 2016/11/04 09:17:20 -Running on machine: cluster-demo-fwwi5 -Log line format: [IWEF]mmdd hh:mm:ss.uuuuuu threadid file:line] msg -I1104 09:17:20.346797 108 Util.cpp:155] commandline: /usr/local/bin/../opt/paddle/bin/paddle -_trainer --num_gradient_servers=4 --nics=eth0 --port=7164 --ports_num=2 --comment=paddle_proce -ss_by_paddle --pservers=192.168.129.79,192.168.129.80,192.168.223.157,192.168.223.158 --ports_ -num_for_sparse=2 --config=./trainer_config.py --trainer_count=4 --use_gpu=0 --num_passes=10 -- -save_dir=./output --log_period=50 --dot_period=10 --saving_period=1 --local=0 --trainer_id=1 - -root@cluster-demo-fwwi5:/home/paddle/JOB20161104171700/log# tailf paddle_trainer.INFO -...... -I1104 09:17:37.376471 150 ThreadLocal.cpp:37] thread use undeterministic rand seed:151 -I1104 09:18:54.159624 108 TrainerInternal.cpp:163] Batch=50 samples=80000 AvgCost=4.03478 CurrentCost=4.03478 Eval: CurrentEval: - -I1104 09:20:10.207902 108 TrainerInternal.cpp:163] Batch=100 samples=160000 AvgCost=3.75806 CurrentCost=3.48134 Eval: CurrentEval: -I1104 09:21:26.493571 108 TrainerInternal.cpp:163] Batch=150 samples=240000 AvgCost=3.64512 CurrentCost=3.41923 Eval: CurrentEval: - -``` - -最后，我们可以在Paddle集群的node0（192.168.129.79）上查看训练的输出结果。 - -``` -[root@paddle-k8s-node0 ~]# ssh root@192.168.129.79 -...... -root@cluster-demo-r65g0:/home/paddle/JOB20161104171700/output/pass-00000# ll -total 14876 -drwxr-xr-x. 2 root root 4096 Nov 4 09:40 ./ -drwxr-xr-x. 3 root root 23 Nov 4 09:40 ../ --rw-r--r--. 1 root root 4046864 Nov 4 09:40 ___embedding_0__.w0 --rw-r--r--. 1 root root 100368 Nov 4 09:40 ___embedding_1__.w0 --rw-r--r--. 1 root root 6184976 Nov 4 09:40 ___embedding_2__.w0 --rw-r--r--. 1 root root 2064 Nov 4 09:40 ___embedding_3__.w0 --rw-r--r--. 1 root root 7184 Nov 4 09:40 ___embedding_4__.w0 --rw-r--r--. 1 root root 21520 Nov 4 09:40 ___embedding_5__.w0 --rw-r--r--. 1 root root 262160 Nov 4 09:40 ___fc_layer_0__.w0 --rw-r--r--. 1 root root 1040 Nov 4 09:40 ___fc_layer_0__.wbias -...... -...... --rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w0 --rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w1 --rw-r--r--. 1 root root 262160 Nov 4 09:40 _movie_fusion.w2 --rw-r--r--. 1 root root 1040 Nov 4 09:40 _movie_fusion.wbias --rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w0 --rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w1 --rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w2 --rw-r--r--. 1 root root 262160 Nov 4 09:40 _user_fusion.w3 --rw-r--r--. 1 root root 1040 Nov 4 09:40 _user_fusion.wbias --rw-r--r--. 1 root root 169 Nov 4 09:40 done --rw-r--r--. 1 root root 17 Nov 4 09:40 path.txt --rw-r--r--. 1 root root 3495 Nov 4 09:40 trainer_config.py -``` \ No newline at end of file diff --git a/doc_cn/cluster/k8s/Dockerfile b/doc_cn/cluster/k8s/Dockerfile new file mode 100644 index 00000000000..3a73606c614 --- /dev/null +++ b/doc_cn/cluster/k8s/Dockerfile @@ -0,0 +1,7 @@ +FROM paddledev/paddle:cpu-latest + +MAINTAINER zjsxzong89@gmail.com + +COPY start.sh /root/ +COPY start_paddle.py /root/ +CMD ["bash"," -c","/root/start.sh"] \ No newline at end of file diff --git a/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md b/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md new file mode 100644 index 00000000000..8e947f8d56a --- /dev/null +++ b/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md @@ -0,0 +1,254 @@ + +# Paddle on Kubernetes：分布式训练 + +前一篇文章介绍了如何在Kubernetes集群上启动一个单机Paddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式Paddle训练作业。关于Paddle的分布式训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文利用Kubernetes的调度功能与容器编排能力，快速构建Paddle容器集群，进行分布式训练任务。 + +## Kubernetes 基本概念 + +在介绍分布式训练之前，需要对Kubernetes(k8s)有一个基本的认识，下面先简要介绍一下本文用到的几个k8s概念。 + +### Node + +[`Node`](http://kubernetes.io/docs/admin/node/) 表示一个k8s集群中的一个工作节点，这个节点可以是物理机或者虚拟机，k8s集群就是由`node`节点与`master`节点组成的。每个node都安装有Docker,在本文的例子中，`Paadle`容器就在node上运行。 + +### Pod + +一个[`Pod`](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器，pod是k8s的最小调度单元，一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET,PID,IPC,UTS等Linux namespace,它们使用同一个IP地址，可以通过`localhost`互相通信。不同pod之间可以通过IP地址访问。 + +### Job + +[`Job`](http://kubernetes.io/docs/user-guide/jobs/) 可以翻译为作业，每个job可以设定pod成功完成的次数,一次作业会创建多个pod，当成功完成的pod个数达到预设值时，就表示job成功结束了。 + +### Volume + +[`Volume`](http://kubernetes.io/docs/user-guide/volumes/) 存储卷，是pod内的容器都可以访问的共享目录，也是容器与node之间共享文件的方式，因为容器内的文件都是暂时存在的，当容器因为各种原因被销毁时，其内部的文件也会随之消失。通过volume,就可以将这些文件持久化存储。k8s支持多种volume,例如`hostPath(宿主机目录)`，`gcePersistentDisk`，`awsElasticBlockStore`等。 + +### Namespace + +[`Namespaces`](http://kubernetes.io/docs/user-guide/volumes/) 命名空间，在k8s中创建的所有资源对象(例如上文的pod,job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要用来为不同的用户提供相对隔离的环境。本文只使用了`default`默认命名空间，读者可以不关心此概念。 + +## 整体方案 + +### 前提条件 + +首先，我们需要拥有一个k8s集群，在这个集群中所有node与pod都可以互相通信。关于k8s集群搭建，可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/)，在以后的文章中我们也会介绍AWS上搭建的方案。在本文的环境中，k8s集群中所有node都挂载了一个`mfs`(分布式文件系统)共享目录，我们通过这个目录来存放训练文件与最终输出的模型。在训练之前，用户将配置与训练数据切分好放在mfs目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。 + +### 使用 `Job` + +我们使用k8s中的job这个概念来代表一次分布式训练。`Job`表示一次性作业，在作业完成后，k8s会销毁job产生的容器并且释放相关资源。 + +在k8s中，可以通过编写一个 `yaml` 文件，来描述这个job，在这个文件中，主要包含了一些配置信息，例如Paddle节点的个数，`paddle pserver`开放的端口个数与端口号，`paddle`使用的网卡设备等，这些信息通过环境变量的形式传递给容器内的程序使用。 + +在一次分布式训练中，用户确定好本次训练需要的Paddle节点个数，将切分好的训练数据与配置文件上传到`mfs`共享目录中。然后编写这次训练的`job yaml`文件,提交给k8s集群创建并开始作业。 + +### 创建`Paddle`节点 + +当k8s master收到`job yaml`文件后，会解析相关字段，创建出多个pod(个数为Paddle节点数)，k8s会把这些pod调度到集群的node上运行。一个`pod`就代表一个`Paddle`节点，当pod被成功分配到一台物理/虚拟机上后，k8s会启动pod内的容器，这个容器会根据`job yaml`文件中的环境变量，启动`paddle pserver`与`paddle train`进程。 + +### 启动训练 + +在容器启动后，会通过脚本来启动这次分布式训练，我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的`trainer_id`，由于`Paddle`本身不提供类似服务发现的功能，所以在本文的启动脚本中，每个节点会根据`job name`向`k8s apiserver`查询这个`job`对应的所有`pod`信息(k8s默认会在每个容器的环境变量中写入`apiserver`的地址)。 + +根据这些pod信息，就可以通过某种方式，为每个pod分配一个唯一的`trainer_id`。本文把所有pod的IP地址进行排序，将顺序作为每个`Paddle`节点的`trainer_id`。启动脚本的工作流程大致如下： + + 1. 查询`k8s apiserver`获取pod信息，根据IP分配`trainer_id` + 1. 从`mfs`共享目录中拷贝训练文件到容器内 + 1. 根据环境变量，解析出`paddle pserver`与`paddle train`的启动参数，启动进程 + 1. 训练时，`Paddle`会自动将结果保存在`trainer_id`为0的节点上，将输出路径设置为`mfs`目录，保存输出的文件 + + +## 搭建过程 + +根据前文的描述，要在已有的k8s集群上进行`Paddle`的分布式训练，主要分为以下几个步骤： + +1. 制作`Paddle`镜像 +1. 将训练文件与切分好的数据上传到共享存储 +1. 编写本次训练的`job yaml`文件，创建`k8s job` +1. 训练结束后查看输出结果 + +下面就根据这几个步骤分别介绍。 + + + +### 制作镜像 + +`Paddle`镜像需要提供`paddle pserver`与`paddle train`进程的运行环境，用这个镜像创建的容器需要有以下两个功能： + +- 拷贝训练文件到容器内 + +- 生成`paddle pserver`与`paddle train`进程的启动参数，并且启动训练 + +因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含`Paddle`的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。镜像的`Dockerfile`如下： + +```Dockerfile +FROM paddledev/paddle:cpu-latest + +MAINTAINER zjsxzong89@gmail.com + +COPY start.sh /root/ +COPY start_paddle.py /root/ +CMD ["bash"," -c","/root/start.sh"] +``` + +[`start.sh`](start.sh)文件拷贝训练文件到容器内，然后执行[`start_paddle.py`](start_paddle.py)脚本启动训练，前文提到的获取其他节点IP地址，分配`trainer_id`等都在`start_paddle.py`脚本中完成。 + + +使用 `docker build` 构建镜像： + +```bash +docker build -t registry.baidu.com/public/paddle:mypaddle . +``` + +然后将构建成功的镜像上传到镜像仓库，注意本文中使用的`registry.baidu.com`是一个私有仓库，读者可以根据自己的情况部署私有仓库或者使用`Docker hub`。 + +```bash +docker push registry.baidu.com/public/paddle:mypaddle +``` + +### 上传训练文件 + +本文使用`Paddle`官方的`recommendation demo`作为这次训练的内容，我们将训练文件与数据放在一个`job name`命名的目录中，上传到`mfs`共享存储。完成后`mfs`上的文件内容大致如下： + +```bash +[root@paddle-k8s-node0 mfs]# tree -d +. +└── paddle-cluster-job + ├── data + │   ├── 0 + │   │ + │   ├── 1 + │   │ + │   └── 2 + ├── output + └── recommendation +``` + +目录中`paddle-cluster-job`是本次训练对应的`job name`，本次训练要求有3个`Paddle`节点，在`paddle-cluster-job/data`目录中存放切分好的数据，文件夹`0,1,2`分别代表3个节点的`trainer_id`。`recommendation`文件夹内存放训练文件，`output`文件夹存放训练结果与日志。 + +### 创建`job` + +`k8s`可以通过`yaml`文件来创建相关对象，然后可以使用命令行工具创建`job`。 + +`job yaml`文件描述了这次训练使用的Docker镜像,需要启动的节点个数以及 `paddle pserver`与 `paddle train`进程启动的必要参数，也描述了容器需要使用的存储卷挂载的情况。`yaml`文件中各个字段的具体含义，可以查看[`k8s官方文档`](http://kubernetes.io/docs/api-reference/batch/v1/definitions/#_v1_job)。例如，本次训练的`yaml`文件可以写成： + +```yaml +apiVersion: batch/v1 +kind: Job +metadata: + name: paddle-cluster-job +spec: + parallelism: 3 + completions: 3 + template: + metadata: + name: paddle-cluster-job + spec: + volumes: + - name: jobpath + hostPath: + path: /home/work/mfs + containers: + - name: trainer + image: registry.baidu.com/public/paddle:mypaddle + command: ["bin/bash", "-c", "/root/start.sh"] + env: + - name: JOB_NAME + value: paddle-cluster-job + - name: JOB_PATH + value: /home/jobpath + - name: JOB_NAMESPACE + value: default + - name: TRAIN_CONFIG_DIR + value: recommendation + - name: CONF_PADDLE_NIC + value: eth0 + - name: CONF_PADDLE_PORT + value: "7164" + - name: CONF_PADDLE_PORTS_NUM + value: "2" + - name: CONF_PADDLE_PORTS_NUM_SPARSE + value: "2" + - name: CONF_PADDLE_GRADIENT_NUM + value: "3" + volumeMounts: + - name: jobpath + mountPath: /home/jobpath + restartPolicy: Never +``` + +文件中，`metadata`下的`name`表示这个`job`的名字。`parallelism,completions`字段表示这个`job`会同时开启3个`Paddle`节点，成功训练且退出的`pod`数目为3时，这个`job`才算成功结束。然后申明一个存储卷`jobpath`，代表宿主机目录`/home/work/mfs`，在对容器的描述`containers`字段中，将此目录挂载为容器的`/home/jobpath`目录，这样容器的`/home/jobpath`目录就成为了共享存储，放在这个目录里的文件其实是保存到了`mfs`上。 + +`env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内。 + +`JOB_PATH`表示共享存储挂载的路径，`JOB_NAME`表示job名字，`TRAIN_CONFIG_DIR`表示本次训练文件所在目录，这三个变量组合就可以找到本次训练需要的文件路径。 + +`CONF_PADDLE_NIC`表示`paddle pserver`进程需要的`--nics`参数，即网卡名 + +`CONF_PADDLE_PORT`表示`paddle pserver`的`--port`参数，`CONF_PADDLE_PORTS_NUM`则表示稠密更新的端口数量，也就是`--ports_num`参数。 + +`CONF_PADDLE_PORTS_NUM_SPARSE`表示稀疏更新的端口数量，也就是`--ports_num_for_sparse`参数。 + +`CONF_PADDLE_GRADIENT_NUM`表示训练节点数量，即`--num_gradient_servers`参数 + +编写完`yaml`文件后，可以使用k8s的命令行工具创建`job`. + +```bash +kubectl create -f job.yaml +``` + +创建成功后，k8s就会创建3个`pod`作为`Paddle`节点然后拉取镜像，启动容器开始训练。 + + +### 查看输出 + +在训练过程中，可以在共享存储上查看输出的日志和模型，例如`output`目录下就存放了输出结果。注意`node_0`，`node_1`，`node_2`这几个目录表示`Paddle`节点与`trainer_id`,并不是k8s中的`node`概念。 + +```bash +[root@paddle-k8s-node0 output]# tree -d +. +├── node_0 +│   ├── server.log +│   └── train.log +├── node_1 +│   ├── server.log +│   └── train.log +├── node_2 +...... +├── pass-00002 +│   ├── done +│   ├── ___embedding_0__.w0 +│   ├── ___embedding_1__.w0 +...... +``` + +我们可以通过日志查看容器训练的情况，例如： + +```bash +[root@paddle-k8s-node0 node_0]# cat train.log +I1116 09:10:17.123121 50 Util.cpp:155] commandline: + /usr/local/bin/../opt/paddle/bin/paddle_trainer + --nics=eth0 --port=7164 + --ports_num=2 --comment=paddle_process_by_paddle + --pservers=192.168.129.66,192.168.223.143,192.168.129.71 + --ports_num_for_sparse=2 --config=./trainer_config.py + --trainer_count=4 --num_passes=10 --use_gpu=0 + --log_period=50 --dot_period=10 --saving_period=1 + --local=0 --trainer_id=0 + --save_dir=/home/jobpath/paddle-cluster-job/output +I1116 09:10:17.123440 50 Util.cpp:130] Calling runInitFunctions +I1116 09:10:17.123764 50 Util.cpp:143] Call runInitFunctions done. +[WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config. +[INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating] +[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__regression_cost_0__] +I1116 09:10:17.392917 50 Trainer.cpp:170] trainer mode: Normal +I1116 09:10:17.613910 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process +I1116 09:10:17.680917 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process +I1116 09:10:17.681543 50 GradientMachine.cpp:134] Initing parameters.. +I1116 09:10:18.012390 50 GradientMachine.cpp:141] Init parameters done. +I1116 09:10:18.018641 50 ParameterClient2.cpp:122] pserver 0 192.168.129.66:7164 +I1116 09:10:18.018950 50 ParameterClient2.cpp:122] pserver 1 192.168.129.66:7165 +I1116 09:10:18.019069 50 ParameterClient2.cpp:122] pserver 2 192.168.223.143:7164 +I1116 09:10:18.019492 50 ParameterClient2.cpp:122] pserver 3 192.168.223.143:7165 +I1116 09:10:18.019716 50 ParameterClient2.cpp:122] pserver 4 192.168.129.71:7164 +I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7165 +``` \ No newline at end of file diff --git a/doc_cn/cluster/k8s/job.yaml b/doc_cn/cluster/k8s/job.yaml new file mode 100644 index 00000000000..1e0ac464b2e --- /dev/null +++ b/doc_cn/cluster/k8s/job.yaml @@ -0,0 +1,43 @@ +apiVersion: batch/v1 +kind: Job +metadata: + name: paddle-cluster-job +spec: + parallelism: 3 + completions: 3 + template: + metadata: + name: paddle-cluster-job + spec: + volumes: + - name: jobpath + hostPath: + path: /home/work/paddle_output + containers: + - name: trainer + image: registry.baidu.com/public/paddle:mypaddle + command: ["bin/bash", "-c", "/root/start.sh"] + env: + - name: JOB_NAME + value: paddle-cluster-job + - name: JOB_PATH + value: /home/jobpath + - name: JOB_NAMESPACE + value: default + - name: TRAIN_CONFIG_DIR + value: recommendation + - name: CONF_PADDLE_NIC + value: eth0 + - name: CONF_PADDLE_PORT + value: "7164" + - name: CONF_PADDLE_PORTS_NUM + value: "2" + - name: CONF_PADDLE_PORTS_NUM_SPARSE + value: "2" + - name: CONF_PADDLE_GRADIENT_NUM + value: "3" + volumeMounts: + - name: jobpath + mountPath: /home/jobpath + restartPolicy: Never + \ No newline at end of file diff --git a/doc_cn/cluster/k8s/start.sh b/doc_cn/cluster/k8s/start.sh new file mode 100755 index 00000000000..b3a1334174a --- /dev/null +++ b/doc_cn/cluster/k8s/start.sh @@ -0,0 +1,19 @@ +#!/bin/sh +set -eu + +jobconfig=${JOB_PATH}"/"${JOB_NAME}"/"${TRAIN_CONFIG_DIR} +cd /root +cp -rf $jobconfig . +cd $TRAIN_CONFIG_DIR + + +python /root/start_paddle.py \ + --dot_period=10 \ + --ports_num_for_sparse=$CONF_PADDLE_PORTS_NUM \ + --log_period=50 \ + --num_passes=10 \ + --trainer_count=4 \ + --saving_period=1 \ + --local=0 \ + --config=./trainer_config.py \ + --use_gpu=0 diff --git a/doc_cn/cluster/k8s/start_paddle.py b/doc_cn/cluster/k8s/start_paddle.py new file mode 100755 index 00000000000..bc0112a77fb --- /dev/null +++ b/doc_cn/cluster/k8s/start_paddle.py @@ -0,0 +1,159 @@ +#!/usr/bin/python +# Copyright (c) 2016 Baidu, Inc. 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 requests +import time +import socket +import os +import argparse + + +# configuration for cluster +API = "/api/v1/namespaces/" +JOBSELECTOR = "labelSelector=job-name=" +JOB_PATH = os.getenv("JOB_PATH") + "/" + os.getenv("JOB_NAME") +JOB_PATH_DATA = JOB_PATH + "/data" +JOB_PATH_OUTPUT = JOB_PATH + "/output" +JOBNAME = os.getenv("JOB_NAME") +NAMESPACE = os.getenv("JOB_NAMESPACE") +PADDLE_NIC = os.getenv("CONF_PADDLE_NIC") +PADDLE_PORT = os.getenv("CONF_PADDLE_PORT") +PADDLE_PORTS_NUM = os.getenv("CONF_PADDLE_PORTS_NUM") +PADDLE_PORTS_NUM_SPARSE = os.getenv("CONF_PADDLE_PORTS_NUM_SPARSE") +PADDLE_SERVER_NUM = os.getenv("CONF_PADDLE_GRADIENT_NUM") + + +def refine_unknown_args(cmd_args): + ''' + refine unknown parameters to handle some special parameters + ''' + new_args = [] + for arg in cmd_args: + if arg.startswith("--") and arg.find("=") != -1: + equal_pos = arg.find("=") # find first = pos + arglist = list(arg) + arglist[equal_pos] = " " + arg = "".join(arglist) + arg = arg.lstrip("-") + new_args += arg.split(" ") + elif arg.startswith("--") and arg.find("=") == -1: + arg = arg.lstrip("-") + new_args.append(arg) + else: + new_args.append(arg) + return new_args + + +def isPodAllRunning(podlist): + ''' + check all pod is running + ''' + require = len(podlist["items"]) + running = 0 + for pod in podlist["items"]: + if pod["status"]["phase"] == "Running": + running += 1 + if require == running: + return True + return False + + +def getPodList(): + ''' + get all container status of the job + ''' + apiserver = "https://" + \ + os.getenv("KUBERNETES_SERVICE_HOST") + ":" + \ + os.getenv("KUBERNETES_SERVICE_PORT_HTTPS") + + pod = API + NAMESPACE + "/pods?" + job = JOBNAME + return requests.get(apiserver + pod + JOBSELECTOR + job, + verify=False).json() + + +def getIdMap(podlist): + ''' + generate tainer_id by ip + ''' + ips = [] + for pod in podlist["items"]: + ips.append(pod["status"]["podIP"]) + ips.sort() + idMap = {} + for i in range(len(ips)): + idMap[ips[i]] = i + return idMap + + +def startPaddle(idMap={}, train_args_dict=None): + ''' + start paddle pserver and trainer + ''' + program = 'paddle train' + args = " --nics=" + PADDLE_NIC + args += " --port=" + str(PADDLE_PORT) + args += " --ports_num=" + str(PADDLE_PORTS_NUM) + args += " --comment=" + "paddle_process_by_paddle" + ip_string = "" + for ip in idMap.keys(): + ip_string += (ip + ",") + ip_string = ip_string.rstrip(",") + args += " --pservers=" + ip_string + args_ext = "" + for key, value in train_args_dict.items(): + args_ext += (' --' + key + '=' + value) + localIP = socket.gethostbyname(socket.gethostname()) + trainerId = idMap[localIP] + args += " " + args_ext + " --trainer_id=" + \ + str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT + logDir = JOB_PATH_OUTPUT + "/node_" + str(trainerId) + if not os.path.exists(JOB_PATH_OUTPUT): + os.makedirs(JOB_PATH_OUTPUT) + os.mkdir(logDir) + copyCommand = 'cp -rf ' + JOB_PATH_DATA + \ + "/" + str(trainerId) + " ./data" + os.system(copyCommand) + startPserver = 'nohup paddle pserver' + \ + " --port=" + str(PADDLE_PORT) + \ + " --ports_num=" + str(PADDLE_PORTS_NUM) + \ + " --ports_num_for_sparse=" + str(PADDLE_PORTS_NUM_SPARSE) + \ + " --nics=" + PADDLE_NIC + \ + " --comment=" + "paddle_process_by_paddle" + \ + " --num_gradient_servers=" + str(PADDLE_SERVER_NUM) +\ + " > " + logDir + "/server.log 2>&1 &" + print startPserver + os.system(startPserver) + # wait until pservers completely start + time.sleep(10) + startTrainer = program + args + " > " + \ + logDir + "/train.log 2>&1 < /dev/null" + print startTrainer + os.system(startTrainer) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(prog="start_paddle.py", + description='simple tool for k8s') + args, train_args_list = parser.parse_known_args() + train_args = refine_unknown_args(train_args_list) + train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) + podlist = getPodList() + # need to wait until all pods are running + while not isPodAllRunning(podlist): + time.sleep(10) + podlist = getPodList() + idMap = getIdMap(podlist) + startPaddle(idMap, train_args_dict) -- GitLab From 0cc7d6eaf6be674ba1da4f197eca422ea2394891 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Thu, 17 Nov 2016 15:18:03 -0800 Subject: [PATCH 028/347] Update install_deps.rst --- doc_cn/build_and_install/cmake/install_deps.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/build_and_install/cmake/install_deps.rst b/doc_cn/build_and_install/cmake/install_deps.rst index 7fa4665a954..6d8727e329a 100644 --- a/doc_cn/build_and_install/cmake/install_deps.rst +++ b/doc_cn/build_and_install/cmake/install_deps.rst @@ -1,4 +1,4 @@ 安装编译PaddlePaddle需要的依赖 ============================== -参见 `安装编译依赖 <../../../doc/build/build_from_source.html#install-dependencies>`_ +参见 `安装编译依赖 `_ -- GitLab From d980c2642fa8283563be012ab15df6b32f049981 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Thu, 17 Nov 2016 15:51:03 -0800 Subject: [PATCH 029/347] Update make_and_install.rst --- doc_cn/build_and_install/cmake/make_and_install.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/build_and_install/cmake/make_and_install.rst b/doc_cn/build_and_install/cmake/make_and_install.rst index 212b9c9352b..8a390ef5818 100644 --- a/doc_cn/build_and_install/cmake/make_and_install.rst +++ b/doc_cn/build_and_install/cmake/make_and_install.rst @@ -1,4 +1,4 @@ make和make install ================== -参见 `make和make install <../../../doc/build/build_from_source.html#build-and-install>`_ +参见 `make和make install `_ -- GitLab From af335da056e931085037ff2631df4bad7a34bd03 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Thu, 17 Nov 2016 16:34:19 -0800 Subject: [PATCH 030/347] Update index.md --- doc_cn/introduction/index.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/doc_cn/introduction/index.md b/doc_cn/introduction/index.md index 164cb7d4943..d6efab0cf1f 100644 --- a/doc_cn/introduction/index.md +++ b/doc_cn/introduction/index.md @@ -1,17 +1,17 @@ # 简介 -PaddlePaddle 是起源于百度的开源深度学习平台。它是简单易用的：你可以通过简单的十数行配置搭建经典的神经网络模型；它也是高效强大的：PaddlePaddle可以支撑复杂集群环境下超大模型的训练，令你受益于深度学习的前沿成果。在百度内部，已经有大量产品线使用了基于PaddlePaddle的深度学习技术。 +PaddlePaddle 源于百度的开源深度学习平台，有如下几个特点。首先，简单易用的：用户可以通过简单的十几行配置脚本搭建经典的神经网络模型。其次，高效强大的：PaddlePaddle可以支撑复杂集群环境下超大模型的训练，令你受益于深度学习的前沿成果。最后，在百度内部，已经有大量产品线使用了基于PaddlePaddle的深度学习技术。 -这份简短的介绍将像你展示如何利用PaddlePaddle解决一个经典的学习问题。 +这份简短的介绍将像你展示如何利用PaddlePaddle来解决一个经典的机器学习问题。 ## 1. 一个经典的任务 -让我们从一个基础问题开始：    单变量的线性回归。问题假定观测到了一批二维空间上的点`(x, y) `，并且已知 `x` 和 `y` 之间存在着某种线性关系，我们的目标是通过观测数据还原这个线性关系。作为一个简单基础的模型，线性回归却有着广泛的应用场景。比如可以想象一个资产定价的简化场景，其中 `x` 对应于房屋的大小，`y` 对应于房屋价格。我们可以通过观察市场上房屋的情况获得二者之间的关系，从而为新房屋的定价提供参考。 +让我们从一个基础问题开始：单变量的线性回归。问题假定观测到了一批二维空间上的点`(x, y) `，并且已知 `x` 和 `y` 之间存在着某种线性关系，我们的目标是通过观测数据来学习这个线性关系。作为一个简单基础的模型，线性回归有着广泛的应用场景。以一个资产定价的问题为例，`x` 对应于房屋的大小，`y` 对应于房屋价格。我们可以通过观察市场上房屋销售的情况拟合 `x` 和 `y` 之间的关系，从而为新房屋的定价提供预测和参考。 ## 2. 准备数据 -假设变量 `X` 和 `Y` 的真实关系为： `Y = 2X + 0.3`，这里展示如何使用观测数据还原这一线性关系。如下Python代码将随机产生2000个观测点，它们将被用作PaddlePaddle的输入。产生PaddlePaddle的输入数据和写一段普通的Python脚本几乎一样，你唯一需要增加的就是定义输入数据的类型。 +假设变量 `X` 和 `Y` 的真实关系为： `Y = 2X + 0.3`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为PaddlePaddle的输入。产生PaddlePaddle的输入数据和写一段普通的Python脚本几乎一样，你唯一需要增加的就是定义输入数据的类型。 ```python # -*- coding:utf-8 -*- @@ -29,7 +29,7 @@ def process(settings, input_file): ## 3. 训练模型 -为了还原 `Y = 2X + 0.3`，我们先从一条随机的直线 `Y' = wX + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `Y'` 和 `Y` 的差距不断减小，最终趋于相同。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。 +为了还原 `Y = 2X + 0.3`，我们先从一条随机的直线 `Y' = wX + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `Y'` 和 `Y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。 在PaddlePaddle里，该模型的网络配置如下。 @@ -50,33 +50,33 @@ settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) # 3. 神经网络配置 x = data_layer(name='x', size=1) y = data_layer(name='y', size=1) -# 线性计算单元: y_predict = wx + b +# 线性计算网络层: y_predict = wx + b y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) -# 损失计算，度量 y_predict 和真实 y 之间的差距 +# 计算误差函数，即 y_predict 和真实 y 之间的距离 cost = regression_cost(input=y_predict, label=y) outputs(cost) ``` 这段简短的配置展示了PaddlePaddle的基本用法： -- 首先，第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的`process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。 +- 第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的`process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。 -- 第二部分主要是选择学习算法，它定义了模型参数如何改变。PaddlePaddle提供了很多优秀的学习算法，但这里使用一个简单的基于momentum的算法就足够了，它每次读取12个数据进行计算和模型更新。 +- 第二部分主要是选择学习算法，它定义了模型参数改变的规则。PaddlePaddle提供了很多优秀的学习算法，这里使用一个基于momentum的随机梯度下降(SGD)算法，该算法每批量(batch)读取12个采样数据进行随机梯度计算来更新更新。 -- 最后一部分是神经网络的配置。由于PaddlePaddle已经实现了丰富的网络单元（Layer），所以很多时候你需要做的只是声明正确的网络单元并把它们拼接起来。这里使用了三种网络单元： - - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到下游的其它单元。这里数据层有两个，分别对应于变量 `X` 和 `Y`。 - - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以挖掘复杂的数据关系。 - - **回归损失层**：回归损失层 `regression_cost`是众多损失函数层的一种，它们在训练过程作为网络的出口，用来计算模型的表现，并指导模型参数的改变。 +- 最后一部分是神经网络的配置。由于PaddlePaddle已经实现了丰富的网络层，所以很多时候你需要做的只是定义正确的网络层并把它们连接起来。这里使用了三种网络单元： + - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `X` 和 `Y`。 + - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。 + - **回归误差代价层**：回归误差代价层 `regression_cost`是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。 -这样定义了网络结构并保存为`trainer_config.py`之后，运行训练命令即可： +定义了网络结构并保存为`trainer_config.py`之后，运行以下训练命令： ``` paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 ``` -PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加损失函数的输出在不断的减小，这意味着模型在不断的改进，直到逼近真实解：` Y = 2X + 0.3 ` +PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` Y = 2X + 0.3 ` ## 4. 模型检验 -训练完成后，我们希望能够检验模型的好坏。一种常用的做法是用模型对另外一组数据进行预测，然后评价预测的效果。但在这个例子中，由于已经知道了真实答案，我们可以直接观察模型的参数是否符合预期来进行检验。 +训练完成后，我们希望能够检验模型的好坏。一种常用的做法是用学习的模型对另外一组测试数据进行预测，评价预测的效果。在这个例子中，由于已经知道了真实答案，我们可以直接观察模型的参数是否符合预期来进行检验。 PaddlePaddle将每个模型参数作为一个numpy数组单独存为一个文件，所以可以利用如下方法读取模型的参数。 @@ -94,9 +94,9 @@ print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b ```

    ![](./parameters.png)
    -从图中可以看到，虽然 `w` 和 `b` 都使用随机值初始化，但在起初的几轮训练中它们都在快速逼近真实值，并且后续仍在不断改进，使得最终得到的模型几乎与真实模型重合。 +从图中可以看到，虽然 `w` 和 `b` 都使用随机值初始化，但在起初的几轮训练中它们都在快速逼近真实值，并且后续仍在不断改进，使得最终得到的模型几乎与真实模型一致。 -这样，我们就完成了对单变量线性回归问题的解决：将数据输入PaddlePaddle，训练模型，最后验证结果。 +这样，我们用PaddlePaddle解决了单变量线性回归问题， 包括数据输入，模型训练和最后的结果验证。 ## 5. 推荐后续阅读 -- GitLab From b3dd2d10b74b495aa525ec1052e5ed1b761aa935 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 18 Nov 2016 17:07:06 +0800 Subject: [PATCH 031/347] Add glossary for Paddle --- doc_cn/algorithm/rnn/hrnn_demo.rst | 7 + doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 183 ++++++++++++++++++ doc_cn/concepts/glossary.rst | 59 ++++++ doc_cn/concepts/glossary_rnn.dot | 42 ++++ doc_cn/concepts/glossary_rnn_with_memory.dot | 48 +++++ doc_cn/index.rst | 3 +- 6 files changed, 341 insertions(+), 1 deletion(-) create mode 100644 doc_cn/algorithm/rnn/hrnn_demo.rst create mode 100644 doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst create mode 100644 doc_cn/concepts/glossary.rst create mode 100644 doc_cn/concepts/glossary_rnn.dot create mode 100644 doc_cn/concepts/glossary_rnn_with_memory.dot diff --git a/doc_cn/algorithm/rnn/hrnn_demo.rst b/doc_cn/algorithm/rnn/hrnn_demo.rst new file mode 100644 index 00000000000..cf38e416c06 --- /dev/null +++ b/doc_cn/algorithm/rnn/hrnn_demo.rst @@ -0,0 +1,7 @@ +.. algo_hrnn_demo: + +################# +双层RNN的使用示例 +################# + +TBD \ No newline at end of file diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst new file mode 100644 index 00000000000..cf181080199 --- /dev/null +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -0,0 +1,183 @@ +.. _algo_hrnn_rnn_api_compare: + +##################### +单双层RNN API对比介绍 +##################### + +这篇教程主要介绍了 :ref:`glossary_双层RNN` 的API接口。本文中的以 :ref:`glossary_paddle` 的 :ref:`glossary_双层RNN` 单元测试为示例，用多对效果完全相同的、分别使用单、双层RNN作为网络配置的模型，来讲解如何使用 :ref:`glossary_双层RNN` 。本文中所有的例子，都只是介绍 :ref:`glossary_双层RNN` 的API接口，并不是使用 :ref:`glossary_双层RNN` 解决实际的问题。如果想要了解 :ref:`glossary_双层RNN` 在具体问题中的使用，请参考 :ref:`algo_hrnn_demo` 。文章中示例所使用的单元测试文件是 `test_RecurrentGradientMachine.cpp `_ 。 + +示例1：双层RNN，子序列间无Memory +================================ + + + +配置：单层RNN（:code:`sequence_layer_group`）和双层RNN（:code:`sequence_nest_layer_group`），语义完全相同。 + +读取双层序列的方法 +------------------ + +首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）\: + +- 单层序列的数据（ :code:`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。 + +.. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg + :language: text + + +- 双层序列的数据（ :code:`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。 + +.. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest + :language: text + +其次，我们看一下单双层序列的不同dataprovider（见 :code:`sequenceGen.py` ）： + +- 单层序列的dataprovider如下： + + - word_slot是integer_value_sequence类型，代表单层序列。 + - label是integer_value类型，代表一个向量。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py + :language: python + :lines: 21-39 + +- 双层序列的dataprovider如下： + + - word_slot是integer_value_sub_sequence类型，代表双层序列。 + - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。 + - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py + :language: python + :lines: 42-71 + +模型中的配置 +------------ + +首先，我们看一下单层序列的配置（见 :code:`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequence_layer_group.conf + :language: python + :lines: 38-63 + + +其次，我们看一下语义相同的双层序列配置（见 :code:`sequence_nest_layer_group.conf` ），并对其详细分析： + +- batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。 +- data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。 +- lstmemory\: + + - 单层序列过了一个mixed_layer和lstmemory_group。 + - 双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。 + +- last_seq\: + + - 单层序列直接取了最后一个元素 + - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。 + - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_layer_group.conf + :language: python + :lines: 38-84 + +示例2：双进双出，subseq间有memory +================================= + +配置：单层RNN（ :code:`sequence_rnn.conf` ），双层RNN（ :code:`sequence_nest_rnn.conf` 和 :code:`sequence_nest_rnn_readonly_memory.conf` ），语义完全相同。 + +读取双层序列的方法 +------------------ + +我们看一下单双层序列的不同数据组织形式和dataprovider（见 :code:`rnn_data_provider.py`） + +.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py + :language: python + :lines: 20-32 + +- 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。 +- 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。 +- 单双层序列的label都分别是0和1 + +模型中的配置 +------------ + +我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 + +- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn.conf + :language: python + :lines: 36-48 + +- 双层序列，外层memory是一个元素： + + - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 + - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf + :language: python + :lines: 39-66 + +- 双层序列，外层memory是单层序列： + + - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 + - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 + +示例3：双进双出，输入不等长 +=========================== + +.. role:: red + +.. raw:: html + + + +**输入不等长** 是指recurrent_group的多个输入在各时刻的长度可以不相等, 但需要指定一个和输出长度一致的input，用 :red:`targetInlink` 表示。参考配置：单层RNN（:code:`sequence_rnn_multi_unequalength_inputs.conf`），双层RNN（:code:`sequence_nest_rnn_multi_unequalength_inputs.conf`） + +读取双层序列的方法 +------------------ + +我们看一下单双层序列的数据组织形式和dataprovider（见 :code:`rnn_data_provider.py` ） + +.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py + :language: python + :lines: 69-97 + +data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 + +- 单层序列：两个样本分别为[[1, 2, 4, 5, 2], [5, 4, 1, 3, 1]] 和 [[0, 2, 2, 5, 0, 1, 2], [1, 5, 4, 2, 3, 6, 1]] +- 双层序列：两个样本分别为 + + - **样本1**\：[[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]]]。fea1和fea2都分别有2个子句，fea1=[[1, 2], [4, 5, 2]], fea2=[[5, 4, 1], [3, 1]] + - **样本2**\：[[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]]]。fea1和fea2都分别有3个子句， fea1=[[0, 2], [2, 5], [0, 1, 2]], fea2=[[1, 5], [4], [2, 3, 6, 1]]。
    + - **注意**\：每个样本中，各特征的子句数目需要相等。这里说的“双进双出，输入不等长”是指fea1在i时刻的输入的长度可以不等于fea2在i时刻的输入的长度。如对于第1个样本，时刻i=2, fea1[2]=[4, 5, 2]，fea2[2]=[3, 1]，3≠2。 + +- 单双层序列中，两个样本的label都分别是0和1 + +模型中的配置 +------------ + +单层RNN（ :code:`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（ :code:`v.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 + +- 单层序列\： + + - 过了一个简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全连接，功能与示例2中`sequence_rnn.conf`的`step`函数完全相同。这里，两个输入x1,x2分别通过calrnn返回最后时刻的状态。结果得到的encoder1_rep和encoder2_rep分别是单层序列，最后取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 + - 注意到这里recurrent_group输入的每个样本中，fea1和fea2的长度都分别相等，这并非偶然，而是因为recurrent_group要求输入为单层序列时，所有输入的长度都必须相等。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf + :language: python + :lines: 41-58 + +- 双层序列\： + + - 双层RNN中，对输入的两个特征分别求时序上的连续全连接(`inner_step1`和`inner_step2`分别处理fea1和fea2)，其功能与示例2中`sequence_nest_rnn.conf`的`outer_step`函数完全相同。不同之处是，此时输入`[SubsequenceInput(emb1), SubsequenceInput(emb2)]`在各时刻并不等长。 + - 函数`outer_step`中可以分别处理这两个特征，但我们需要用targetInlink指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 + - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 + +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf + :language: python + :lines: 41-89 + +示例4：beam_search的生成 +======================== + +TBD diff --git a/doc_cn/concepts/glossary.rst b/doc_cn/concepts/glossary.rst new file mode 100644 index 00000000000..a94aa736753 --- /dev/null +++ b/doc_cn/concepts/glossary.rst @@ -0,0 +1,59 @@ +.. _glossary: + +######################## +Paddle文档中使用的词汇表 +######################## + +.. _glossary_paddle: + +PaddlePaddle +------------ + +TBD + + +.. _glossary_memory: + +Memory +------ + +Memory是 :ref:`glossary_paddle` 实现 :ref:`glossary_RNN` 时候使用的一个概念。 :ref:`glossary_RNN` 即时间递归神经网络，通常要求时间步之间具有一些依赖性，即当前时间步下的神经网络依赖前一个时间步神经网络中某一个神经元输出。如下图所示。 + +.. graphviz:: glossary_rnn.dot + +上图中虚线的连接，即是跨越时间步的网络连接。:ref:`glossary_paddle` 在实现 :ref:`glossary_RNN` 的时候，将这种跨越时间步的连接用一个特殊的神经网络单元实现。这个神经网络单元就叫Memory。Memory可以缓存上一个时刻某一个神经元的输出，然后在下一个时间步输入给另一个神经元。使用Memory的 :ref:`glossary_RNN` 实现便如下图所示。 + +.. graphviz:: glossary_rnn_with_memory.dot + +使用这种方式，:ref:`glossary_paddle` 可以比较简单的判断哪些输出是应该跨越时间步的，哪些不是。 + +.. _glossary_Sequence: + +时间序列 +-------- + +时间序列(time series)是指一系列的特征数据。这些特征数据之间的顺序是有意义的。即特征的数组，而不是特征的集合。而这每一个数组元素，或者每一个系列里的特征数据，即为一个时间步(time step)。值得注意的是，时间序列、时间步的概念，并不真正的和『时间』有关。只要一系列特征数据中的『顺序』是有意义的，即为时间序列的输入。 + +举例说明，例如文本分类中，我们通常将一句话理解成一个时间序列。比如一句话中的每一个单词，会变成词表中的位置。而这一句话就可以表示成这些位置的数组。例如 :code:`[9, 2, 3, 5, 3]` 。 + +关于时间序列(time series)的更详细准确的定义，可以参考 `维基百科页面 Time series `_ 或者 `维基百科中文页面 时间序列 `_ 。 + +另外，Paddle中经常会将时间序列成为 :code:`Sequence` 。他们在Paddle的文档和API中是一个概念。 + +.. _glossary_RNN: + +RNN +--- + +RNN 在 :ref:`glossary_paddle` 的文档中，一般表示 :code:`Recurrent neural network`，即时间递归神经网络。详细介绍可以参考 `维基百科页面 Recurrent neural network `_ 或者 `中文维基百科页面 `_ 中关于时间递归神经网络的介绍。 + +RNN 一般在 :ref:`glossary_paddle` 中，指对于一个 :ref:`glossary_Sequence` 输入数据，每一个时间步之间的神经网络具有一定的相关性。例如，某一个神经元的一个输入为上一个时间步网络中某一个神经元的输出。或者，从每一个时间步来看，神经网络的网络结构中具有有向环结构。 + +.. _glossary_双层RNN: + +双层RNN +------- + +双层RNN顾名思义，即 :ref:`glossary_RNN` 之间有一次嵌套关系。输入数据整体上是一个时间序列，而对于每一个内层特征数据而言，也是一个时间序列。即二维数组，或者数组的数组这个概念。 而双层RNN是可以处理这种输入数据的网络结构。 + +例如，对于段落的文本分类，即将一段话进行分类。我们将一段话看成句子的数组，每个句子又是单词的数组。这便是一种双层RNN的输入数据。而将这个段落的每一句话用lstm编码成一个向量，再对每一句话的编码向量用lstm编码成一个段落的向量。再对这个段落向量进行分类，即为这个双层RNN的网络结构。 diff --git a/doc_cn/concepts/glossary_rnn.dot b/doc_cn/concepts/glossary_rnn.dot new file mode 100644 index 00000000000..2cd0fb1820c --- /dev/null +++ b/doc_cn/concepts/glossary_rnn.dot @@ -0,0 +1,42 @@ +digraph G{ + subgraph cluster_timestep0 { + label="recurrent timestep i-1" + bgcolor=lightgray + node [style=filled,color=white] + fc0_0 [label="fc 0"] + fc0_1 [label="fc 1"] + fc0_2 [label="fc 2"] + + fc0_0 -> fc0_1 + fc0_1 -> fc0_2 + } + + subgraph cluster_timestep1 { + label="recurrent timestep i" + node [style=filled]; + fc1_0 [label="fc 0"] + fc1_1 [label="fc 1"] + fc1_2 [label="fc 2"] + color=blue + + fc1_0 -> fc1_1 + fc1_1 -> fc1_2 + } + + subgraph cluster_timestep2 { + label="recurrent timestep i+1" + bgcolor=lightgray + node [style=filled,color=white] + fc2_0 [label="fc 0"] + fc2_1 [label="fc 1"] + fc2_2 [label="fc 2"] + + fc2_0 -> fc2_1 + fc2_1 -> fc2_2 + } + + + fc0_1 -> fc1_1 [style="dotted" constraint=false] + fc1_1 -> fc2_1 [style="dotted" constraint=false] + +} \ No newline at end of file diff --git a/doc_cn/concepts/glossary_rnn_with_memory.dot b/doc_cn/concepts/glossary_rnn_with_memory.dot new file mode 100644 index 00000000000..0f101ec2d8f --- /dev/null +++ b/doc_cn/concepts/glossary_rnn_with_memory.dot @@ -0,0 +1,48 @@ +digraph G{ + subgraph cluster_timestep0 { + label="recurrent timestep i-1" + bgcolor=lightgray + node [style=filled,color=white] + fc0_0 [label="fc 0"] + fc0_1 [label="fc 1"] + fc0_2 [label="fc 2"] + m0 [label="memory"] + fc0_0 -> fc0_1 + fc0_1 -> fc0_2 + fc0_1 -> m0 + m0 -> fc0_1 + } + + subgraph cluster_timestep1 { + label="recurrent timestep i" + node [style=filled]; + fc1_0 [label="fc 0"] + fc1_1 [label="fc 1"] + fc1_2 [label="fc 2"] + m1 [label="memory"] + color=blue + fc1_0 -> fc1_1 + fc1_1 -> fc1_2 + fc1_1 -> m1 + m1 -> fc1_1 + } + + subgraph cluster_timestep2 { + label="recurrent timestep i+1" + bgcolor=lightgray + node [style=filled,color=white] + fc2_0 [label="fc 0"] + fc2_1 [label="fc 1"] + fc2_2 [label="fc 2"] + m2 [label="memory"] + fc2_0 -> fc2_1 + fc2_1 -> fc2_2 + fc2_1 -> m2 + m2 -> fc2_1 + } + + + m0 -> m1 [style="dotted" constraint=false] + m1 -> m2 [style="dotted" constraint=false] + +} \ No newline at end of file diff --git a/doc_cn/index.rst b/doc_cn/index.rst index f1398206fdd..fef39aa527a 100644 --- a/doc_cn/index.rst +++ b/doc_cn/index.rst @@ -11,6 +11,7 @@ PaddlePaddle文档 * `使用示例 `_ * `模型配置 <../doc/ui/api/trainer_config_helpers/index.html>`_ * `集群训练 `_ +* :ref:`glossary` 开发指南 -------- @@ -22,7 +23,7 @@ PaddlePaddle文档 * `Recurrent Group教程 `_ * `单层RNN示例 <../doc/algorithm/rnn/rnn.html>`_ -* `双层RNN示例 `_ +* :ref:`algo_hrnn_rnn_api_compare` * `支持双层序列作为输入的Layer `_ 常见问题 -- GitLab From d8cca855e1f1b69fcd9ca53ae8bd9bc6260dcfe1 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 18 Nov 2016 18:52:42 +0800 Subject: [PATCH 032/347] Refine use_concepts.rst --- doc_cn/concepts/use_concepts.rst | 97 ++++++++++++++------------------ 1 file changed, 42 insertions(+), 55 deletions(-) diff --git a/doc_cn/concepts/use_concepts.rst b/doc_cn/concepts/use_concepts.rst index 67e98edabc0..d3da9cc16b0 100644 --- a/doc_cn/concepts/use_concepts.rst +++ b/doc_cn/concepts/use_concepts.rst @@ -2,16 +2,16 @@ PaddlePaddle 基本使用概念 ######################### -PaddlePaddle是一个神经网络学习框架。其单机进程为 :code:`paddle train`。 单机的所有设备使用，均在单机进程内调度完成。 而多机辅助进程 :code:`paddle pserver` 负责联合多个单机进程进行通信，进而充分利用集群的计算资源。 PaddlePaddle同时以 :code:`swig api` 的形式，提供训练结果模型预测的方法和自定义训练流程。 +PaddlePaddle是一个深度学习框架，同时支持单机和多机模式的系统。命令 ``paddle train`` 可启动单机模式的进程，我们称之为 ``trainer`` 进程。单机所有设备使用均在单机进程内调度完成。多机模式除了需要启动trainer进程外，还需要通过命令 ``paddle pserver`` 启动多机参数服务器进程, 我们称之为   ``pserver`` 进程。该进程负责多个单机进程间的通信，进而充分利用集群的计算资源。 PaddlePaddle同时以 ``swig api`` 的形式，提供训练结果模型预测的方法和自定义训练流程。 -下面我们会分别介绍主要进程 :code:`paddle train` 中的一些概念。这些概念会对如何使用PaddlePaddle有一定的帮助。 了解这些概念的前提是，读者已经了解 `基本的神经网络/机器学习原理和概念 `_ 。同时，如果想要了解PaddlePaddle实现中的一些概念，请参考 `PaddlePaddle 编程中的基本概念 `_ 。 +下面我们会介绍trainer进程中的一些概念，这些概念会对如何使用PaddlePaddle有一定的帮助。 了解这些概念的前提是，读者已经了解 `基本的神经网络/机器学习原理和概念 `_ 。同时，如果想要了解PaddlePaddle实现中的一些概念，请参考 `PaddlePaddle 编程中的基本概念 `_ 。 .. contents:: -PaddlePaddle 的进程模型 -======================= +系统模块 +======== -PaddlePaddle进程内嵌了一个 :code:`python` 解释器。 这个 :code:`python` 解释器负责解析用户定义的神经网络配置，和解析用户数据，并将用户数据传入给 PaddlePaddle。 +``trainer`` 进程内嵌了一个 ``python`` 解释器， 这个 ``python`` 解释器负责解析用户定义的神经网络配置；解析输入数据流，并将数据传入给 ``trainer`` 系统。 .. graphviz:: @@ -30,95 +30,84 @@ PaddlePaddle进程内嵌了一个 :code:`python` 解释器。 这个 :code:`pyth py -> data_provider [dir="back"]; } -所以，PaddlePaddle单机训练进程，:code:`paddle train` , 对于用户的主要接口语言为 python。 主要需要用户配置的两个文件为 :code:`DataProvider` 和训练文件 :code:`TrainerConfig` 。 +所以，单机训练 ``trainer`` 进程对用户的主要接口语言为Python。用户需要配置文件主要有两个：数据流提供器 ``DataProvider`` 和模型配置 ``TrainerConfig`` 。 DataProvider ============ -DataProvider是 :code:`paddle train` 的数据提供器。 它负责将用户的原始数据转换成 PaddlePaddle 可以识别的数据类型。每当 PaddlePaddle 需要新的数据训练时，都会调用 DataProvider 返回数据。 当所有数据读取完一轮后，DataProvider 便返回空数据通知 PaddlePaddle。PaddlePaddle负责在下一轮训练开始前，将DataProvider重置。 +DataProvider是 ``trainer`` 进程的数据提供器。主要负责将用户的原始数据转换成 ``trainer`` 系统可以识别的数据类型。当系统需要新的数据训练时，会调用DataProvider获取数据接口。当所有数据读取完一轮后，DataProvider返回空数据通知系统一轮数据读取结束。 ``trainer`` 在每一轮训练开始时会重置DataProvider。 -需要注意的是，DataProvider在PaddlePaddle中是被训练逻辑调用的关系， 而不是新的数据驱动训练。并且所有的 :code:`shuffle` , 和一些随机化的噪声添加，都应该在 DataProvider 阶段完成。 +需要注意的是，DataProvider是被 ``trainer`` 系统调用，而不是新数据驱动系统；数据 ``shuffle`` 和一些随机化噪声添加都应该在DataProvider中完成。 -为了方便用户使用自己的数据格式， PaddlePaddle 提供了 `PyDataProvider`_ 来处理数据。 并且在这个Provider中，PaddlePaddle的 C++ 部分接管了如何shuffle，处理 batch，GPU/CPU通信，双缓冲，异步读取等问题。 用户可以参考 `PyDataProvider`_ 的相关文档，继续深入了解 DataProvider 的使用。 +为了用户能够灵活的处理数据，PaddlePaddle提供了处理数据的Python接口（称为 `PyDataProvider`_ ）。 在 ``PyDataProvider`` 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，需要说明的是，一些情况下需要Python接口里处理shuffle，可以参考 `PyDataProvider`_ 的相关文档继续深入了解。 -训练文件 -======== +TrainerConfig +============= -训练文件是PaddlePaddle中配置神经网络结构、学习优化算法、数据传入方式的地方。 训练文件是一个python文件，使用命令行参数 :code:`--config` 传给 paddle 的主程序。 例如\: +模型配置是一个Python文件，主要包括神经网络结构、优化算法、数据传入方式，使用命令行参数 ``--config`` 传给``trainer``主程序。 例如\: .. code-block:: bash paddle train --config=trainer_config.py -一个典型简单的训练文件可能为 +一个简单的模型配置文件为： .. literalinclude:: trainer_config.py :linenos: -下面我们详细的介绍一下训练文件中各个模块的概念。 +下面我们详细的介绍一下模型配置中各个模块的概念。 trainer_config_helpers ---------------------- -PaddlePaddle的配置文件与PaddlePaddle C++端通信的最基础协议是 :code:`protobuf` 。而为了避免用户直接写比较难写的 protobuf string，我们书写了一个helpers来生成这个protobuf包。所以在文件的开始，import这些helpers函数。 +PaddlePaddle配置文件与C++模块通信的最基础协议是 ``protobuf`` 。为了避免用户直接写比较难写的protobuf string，我们通过Python代码来生成protobuf包，这就是helpers的作用。所以在文件的开始，需要import这些helpers函数。 -需要注意的是，这个 :code:`paddle.trainer_config_helpers` 包是标准的python包，这意味着用户可以选择自己喜欢的 :code:`ide` 或者编辑器来编写Paddle的配置文件，这个python包注释文档比较完善，并且考虑了IDE的代码提示与类型注释。 +需要注意的是，这个 ``paddle.trainer_config_helpers`` 包是标准的python包，这意味着用户可以选择自己喜欢的 ``IDE`` 或者编辑器来编写Paddle的配置文件，这个Python包注释文档比较完善，并提供了IDE的代码提示与类型注释。 data_sources ------------ -data_sources是配置神经网络的数据源。这里使用的函数是 :code:`define_py_data_sources2` ，这个函数是定义了使用 `PyDataProvider`_ 作为数据源。 而后缀 :code:`2` 是Paddle历史遗留问题，因为Paddle之前使用的 PyDataProvider 性能较差，所以完全重构了一个新的 `PyDataProvider`_ 。 - -data_sources里面的 train_list 和 test_list 指定的是训练文件列表和测试文件列表。 如果传入一个字符串的话，是指一个训练列表文件。这个训练列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个 list 文件，再传入给 train.list 或者 test.list 。 +data_sources配置神经网络的数据源，使用的函数是 ``define_py_data_sources2`` ，这个函数是定义了使用 `PyDataProvider`_ 提供数据源。后缀 ``2`` 是Paddle历史遗留问题，因为Paddle之前使用的PyDataProvider性能问题，重构了一个新的 `PyDataProvider`_ 。 -而 :code:`module` 和 :code:`obj` 指定了 DataProvider 的模块名和函数名。 +data_sources里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。 -更具体的使用，请参考 `PyDataProvider`_ 。 +其中``module`` 和``obj``指定了DataProvider的文件名和返回数据的函数名。更详细的使用，请参考 `PyDataProvider`_ 。 settings -------- -`settings`_ 是神经网络训练算法相关的设置项。包括学习率，batch_size，优化算法，正则方法等等。具体的使用方法请参考 `settings`_ 文档。 +`settings`_ 设置训练神经网络所使用的算法。包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 `settings`_ 文档。 网络配置 -------- -上述网络配置中余下的部分均是神经网络配置。第一行是定义一个名字叫 "pixel" 的 :code:`data_layer` 。每一个layer返回的都是一个 :code:`LayerOutput` 对象。 这里第一层的输出对象是 :code:`img` 。然后这个对象传输给了另一个 layer 函数， -:code:`simple_img_conv_pool` 。:code:`simple_img_conv_pool` 是一个组合层， -包括了图像的卷积 (convolution) 和池化(pooling)， -并继续接了一个全连接层( :code:`fc_layer` )，然后再接了一个Softmax的全连接层。 +上述配置中余下的部分是神经网络配置，主要包括网络连接、 ``cost`` 层、评估器。 -最终，网络配置输出了 :code:`classification_cost` 。标记网络输出的函数为 -:code:`outputs` 。网络的输出是神经网络的优化目标，神经网络训练的时候，实际上就是 -要最小化这个输出。 +- 首先，定义了一个名字叫"pixel"的 ``data_layer`` ，每个layer返回的都是一个 ``LayerOutput`` 对象，比如第一层的输出对象称作 ``img`` 。 +- 然后，这个对象作为另一个layer（ ``simple_img_conv_pool`` ）的输入， ``simple_img_conv_pool`` 是一个组合层，包括了图像的卷积 (convolution) 和池化(pooling)， +- 其次，连接到全连接层(``fc_layer``)，再连接到一个含Softmax激活的全连接层。 +- 最终，连接到cost层（ ``classification_cost`` ）， ``classification_cost`` 默认使用多类交叉熵损失函数和分类错误率统计评估器。标记网络输出的函数为 ``outputs`` ，网络的输出是神经网络的优化目标，神经网络训练的时候，实际上就是要最小化这个输出。 -在神经网络进行预测的时候，实际上网络的输出也是通过 :code:`outputs` 标记。 +用该模型配置进行预测时，网络的输出也是通过 ``outputs`` 标记。 Layer、Projection、Operator =========================== -PaddlePaddle的网络基本上是基于Layer来配置的。所谓的Layer即是神经网络的某一层， -而神经网络的某一层，一般是封装了许多复杂操作的操作集合。比如最简单的 -:code:`fc_layer` ，也包括矩阵乘法，多输入的求和，和activation。 +PaddlePaddle的网络是基于Layer来配置的。所谓的Layer即是神经网络的某一层，一般是封装了许多复杂操作的操作集合。比如最简单的 ``fc_layer`` ，包括矩阵乘法、多输入的求和、加Bias操作、激活( ``activation`` )函数操作。 .. code-block:: python data = data_layer(name='data', size=200) out = fc_layer(input=data, size=200, act=TanhActivation()) -而对于更灵活配置需求，可能这样基于Layer的配置是不灵活的。于是 PaddlePaddle 提供 -了基于 Projection 或者 Operator 的配置。使用Projection和Operator需要与 -:code:`mixed_layer` 配合使用。 :code:`mixed_layer` 是将layer中的元素累加求和， -并且做一个 :code:`activation` ， 而这个layer具体如何计算，是交由内部的Projection -和 Operator 定义。Projection是指含有可学习参数的操作，而Operator不含有可学习的 -参数，输入全是其他Layer的输出。 +对于更灵活配置需求，PaddlePaddle提供了基于 ``Projection`` 或者 ``Operator`` 的配置，这些需要与 ``mixed_layer`` 配合使用。 ``mixed_layer`` 是将多个输入累加求和，然后加Bias和 ``activation`` 操作。 ``mixed_layer`` 具体计算是通过内部的Projection和Operator完成。Projection含有可学习参数；而Operator不含可学习的参数，输入全是其他Layer的输出。 -例如，和 :code:`fc_layer` 同样功能的 :code:`mixed_layer` 。 +例如，和 ``fc_layer`` 同样功能的 ``mixed_layer`` 是: .. code-block:: python @@ -126,14 +115,12 @@ PaddlePaddle的网络基本上是基于Layer来配置的。所谓的Layer即是 with mixed_layer(size=200) as out: out += full_matrix_projection(input=data) -PaddlePaddle可以使用的mixed layer 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。 -用户可以参考 `mixed_layer`_ 的相关文档进行配置。 +PaddlePaddle可以使用 ``mixed layer`` 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 `mixed_layer`_ 的相关文档进行配置。 如何利用单机的所有GPU或所有CPU核心 -================================== +=============================== -PaddlePaddle的单机进程 :code:`paddle train` 可以充分利用一台计算机上所有的GPU资 -源或者CPU。 +PaddlePaddle的单机 ``trainer`` 进程可以充分利用一台计算机上所有的GPU资源或者CPU。 如果要使用机器上多块GPU，使用如下命令即可\: @@ -145,41 +132,41 @@ PaddlePaddle的单机进程 :code:`paddle train` 可以充分利用一台计算 .. code-block:: bash - paddle train --trainer_config=4 # use 4 cpu cores. + paddle train --trainer_count=4 # use 4 cpu cores. -对于其他设置GPU的选择情况，例如选择第0、2号GPU显卡，则可以使用 :code:`CUDA_VISIBLE_DEVICES` 环境变量来选择部分的显卡。 具体可以参考连接`masking-gpus`_ 。 可以使用的命令为 +如果要指定GPU编号，例如选择第0、2号GPU，则可以设置 ``CUDA_VISIBLE_DEVICES`` 环境变量来指定特定的GPU。具体可以参考连接`masking-gpu`_ ，命令为： .. code-block:: bash - env CUDA_VISIBLE_DEVICES=0,2 paddle train --use_gpu=true --trainer_config=2 + env CUDA_VISIBLE_DEVICES=0,2 paddle train --use_gpu=true --trainer_count=2 如何利用多台机器的计算资源训练神经网络 -====================================== +=================================== -PaddlePaddle多机使用的经典方法是通过 :code:`Parameter Server` 来对多机的 :code:`paddle train` 进行同步。 而多机训练神经网络，首先要讲数据切分到不同的机器上。 切分数据文件的方式在PaddlePaddle的开源实现中并没有提供工具包。 但是切分数据并不是一件非常复杂的事情，也不是神经网络实现的重点。 +PaddlePaddle多机采用经典的 ``Parameter Server`` 架构对多个节点的 ``trainer`` 进行同步。多机训练神经网络，要讲数据切分到不同的机器上，切分数据相对简单，所以在PaddlePaddle的开源实现中并没有提供相关工具包。 -多机训练过程中，经典的拓扑结构如下\: +多机训练的经典拓扑结构如下\: .. graphviz:: pserver_topology.dot -图中每个灰色方块是一台机器，在每个机器中，先去启动一个 :code:`paddle pserver` 进程，并确定整体的端口号。可能的参数是\: +图中每个灰色方块是一台机器，在每个机器中，先启动一个 ``paddle pserver`` 进程，并指定端口号，可能的参数是\: .. code-block:: bash paddle pserver --port=5000 --num_gradient_servers=4 --nics='eth0' -这里说明系统的 :code:`paddle pserver` 的起始端口是 :code:`5000` ，并且有四个训练进程(:code:`gradient_servers`，Paddle同时将 :code:`paddle train` 进程称作 :code:`GradientServer` 。因为其为负责提供Gradient的进程)。 而对于训练进程的话，则需要在 :code:`paddle pserver` 启动之后，再在各个节点上运行如下命令\: +这里说明系统的 ``pserver`` 进程端口是 ``5000`` ，有四个训练进程(即 ``--gradient_servers=4`` ，PaddlePaddle同时将 ``trainer`` 称作 ``GradientServer`` 。因为其为负责提供Gradient)。 启动之后 ``pserver`` 进程之后，需要 ``trainer`` 训练进程，再在各个机器上运行如下命令\: .. code-block:: bash paddle train --port=5000 --pservers=192.168.100.101,192.168.100.102,192.168.100.103,192.168.100.104 --config=... -对于简单的多机协同使用上述方式即可。同时，pserver/train 通常在高级情况下，还有两个参数需要设置，他们是 +对于简单的多机协同训练使用上述方式即可。另外，pserver/train 通常在高级情况下，还需要设置下面两个参数\： * --ports_num\: 一个 pserver进程共绑定多少个端口用来做稠密更新。默认是1 * --ports_num_for_sparse\: 一个pserver进程共绑定多少端口用来做稀疏更新，默认是0 -使用手工指定端口数量，是因为Paddle的网络通信中，使用了 :code:`int32` 作为消息长度，比较容易在大模型下溢出。所以，在 :code:`paddle pserver` 进程中可以启动多个子线程去接受 trainer 的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，还是对性能，尤其是内存占用有一定的开销的，另外稀疏更新的端口如果太大的话，很容易某一个参数服务器没有分配到任何参数。 +使用手工指定端口数量，是因为Paddle的网络通信中，使用了 ``int32`` 作为消息长度，比较容易在大模型下溢出。所以，在 ``pserver`` 进程中可以启动多个子线程去接受trainer的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，对性能尤其是内存占用有一定的开销，另外稀疏更新的端口如果太大的话，很容易导致某一个参数服务器没有分配到任何参数。 详细的说明可以参考，使用 `集群训练Paddle`_ 。 -- GitLab From 07dcf7bfc69166fcf889a82533cd9eca15596246 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Fri, 18 Nov 2016 13:32:56 -0800 Subject: [PATCH 033/347] Update docker_install.rst --- doc_cn/build_and_install/install/docker_install.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/doc_cn/build_and_install/install/docker_install.rst b/doc_cn/build_and_install/install/docker_install.rst index a5f5fb117e1..21a2d8bb941 100644 --- a/doc_cn/build_and_install/install/docker_install.rst +++ b/doc_cn/build_and_install/install/docker_install.rst @@ -1,8 +1,7 @@ 安装PaddlePaddle的Docker镜像 ============================ -PaddlePaddle提供了Docker的使用镜像。PaddlePaddle推荐使用Docker进行PaddlePaddle的部署和 -运行。Docker是一个基于容器的轻量级虚拟环境。具有和宿主机相近的运行效率，并提供 +PaddlePaddle提供了Docker的使用镜像。PaddlePaddle推荐使用Docker进行PaddlePaddle的部署和运行。Docker是一个基于容器的轻量级虚拟环境。具有和宿主机相近的运行效率，并提供 了非常方便的二进制分发手段。 下述内容将分为如下几个类别描述。 @@ -41,7 +40,7 @@ PaddlePaddle提供的Docker镜像版本 * CPU WITHOUT AVX: CPU版本，不支持AVX指令集的CPU也可以运行 * GPU WITHOUT AVX: GPU版本，不需要AVX指令集的CPU也可以运行。 -用户可以选择对应版本的docker image。使用如下脚本可以确定本机的CPU知否支持 :code:`AVX` 指令集\: +用户可以选择对应版本的docker image。使用如下脚本可以确定本机的CPU是否支持 :code:`AVX` 指令集\: .. code-block:: bash -- GitLab From faa32c34182ab2c2383166bcd6bb5dee1b830a10 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Fri, 18 Nov 2016 17:51:50 -0800 Subject: [PATCH 034/347] Update docker_install.rst --- doc_cn/build_and_install/install/docker_install.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc_cn/build_and_install/install/docker_install.rst b/doc_cn/build_and_install/install/docker_install.rst index 21a2d8bb941..0872fd0b7a1 100644 --- a/doc_cn/build_and_install/install/docker_install.rst +++ b/doc_cn/build_and_install/install/docker_install.rst @@ -66,7 +66,7 @@ mac osx或者是windows机器，请参考 .. code-block:: bash - $ docker run -it paddledev/paddlepaddle:cpu-latest + $ docker run -it paddledev/paddle:cpu-latest 即可启动和进入PaddlePaddle的container。如果运行GPU版本的PaddlePaddle，则需要先将 cuda相关的Driver和设备映射进container中，脚本类似于 @@ -75,7 +75,7 @@ cuda相关的Driver和设备映射进container中，脚本类似于 $ export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') - $ docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddlepaddle:latest-gpu + $ docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest 进入Docker container后，运行 :code:`paddle version` 即可打印出PaddlePaddle的版本和构建 信息。安装完成的PaddlePaddle主体包括三个部分， :code:`paddle` 脚本， python的 -- GitLab From eeca7be7ad0ca24d909b2139acb021f08f75dfb2 Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Sun, 20 Nov 2016 10:04:43 +0800 Subject: [PATCH 035/347] fix some md syntax error. --- .../k8s/distributed_training_on_kubernetes.md | 157 ++++++++++++------ doc_cn/cluster/k8s/k8s-paddle-arch.png | Bin 0 -> 22084 bytes 2 files changed, 105 insertions(+), 52 deletions(-) create mode 100644 doc_cn/cluster/k8s/k8s-paddle-arch.png diff --git a/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md b/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md index 8e947f8d56a..e07cf6182f2 100644 --- a/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md +++ b/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md @@ -1,84 +1,75 @@ # Paddle on Kubernetes：分布式训练 -前一篇文章介绍了如何在Kubernetes集群上启动一个单机Paddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式Paddle训练作业。关于Paddle的分布式训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md), 本文利用Kubernetes的调度功能与容器编排能力，快速构建Paddle容器集群，进行分布式训练任务。 +前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)，本文利用Kubernetes的调度功能与容器编排能力，快速构建PaddlePaddle容器集群，进行分布式训练任务。 ## Kubernetes 基本概念 -在介绍分布式训练之前，需要对Kubernetes(k8s)有一个基本的认识，下面先简要介绍一下本文用到的几个k8s概念。 +[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、 扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。在介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 -### Node +- [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点，这个节点可以是物理机或者虚拟机，Kubernetes集群就是由node节点与master节点组成的。 -[`Node`](http://kubernetes.io/docs/admin/node/) 表示一个k8s集群中的一个工作节点，这个节点可以是物理机或者虚拟机，k8s集群就是由`node`节点与`master`节点组成的。每个node都安装有Docker,在本文的例子中，`Paadle`容器就在node上运行。 +- [*Pod*](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器，pod是Kubernetes的最小调度单元，一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET，PID，IPC，UTS等Linux namespace。由于容器之间共享NET namespace，所以它们使用同一个IP地址，可以通过*localhost*互相通信。不同pod之间可以通过IP地址访问。 -### Pod +- [*Job*](http://kubernetes.io/docs/user-guide/jobs/) 是Kubernetes上运行的作业，一次作业称为一个job，通常每个job包括一个或者多个pods。 -一个[`Pod`](http://kubernetes.io/docs/user-guide/pods/) 是一组(一个或多个)容器，pod是k8s的最小调度单元，一个pod中的所有容器会被调度到同一个node上。Pod中的容器共享NET,PID,IPC,UTS等Linux namespace,它们使用同一个IP地址，可以通过`localhost`互相通信。不同pod之间可以通过IP地址访问。 +- [*Volume*](http://kubernetes.io/docs/user-guide/volumes/) 存储卷，是pod内的容器都可以访问的共享目录，也是容器与node之间共享文件的方式，因为容器内的文件都是暂时存在的，当容器因为各种原因被销毁时，其内部的文件也会随之消失。通过volume，就可以将这些文件持久化存储。Kubernetes支持多种volume，例如hostPath(宿主机目录)，gcePersistentDisk，awsElasticBlockStore等。 -### Job - -[`Job`](http://kubernetes.io/docs/user-guide/jobs/) 可以翻译为作业，每个job可以设定pod成功完成的次数,一次作业会创建多个pod，当成功完成的pod个数达到预设值时，就表示job成功结束了。 - -### Volume - -[`Volume`](http://kubernetes.io/docs/user-guide/volumes/) 存储卷，是pod内的容器都可以访问的共享目录，也是容器与node之间共享文件的方式，因为容器内的文件都是暂时存在的，当容器因为各种原因被销毁时，其内部的文件也会随之消失。通过volume,就可以将这些文件持久化存储。k8s支持多种volume,例如`hostPath(宿主机目录)`，`gcePersistentDisk`，`awsElasticBlockStore`等。 - -### Namespace - -[`Namespaces`](http://kubernetes.io/docs/user-guide/volumes/) 命名空间，在k8s中创建的所有资源对象(例如上文的pod,job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要用来为不同的用户提供相对隔离的环境。本文只使用了`default`默认命名空间，读者可以不关心此概念。 +- [*Namespaces*](http://kubernetes.io/docs/user-guide/volumes/) 命名空间，在kubernetes中创建的所有资源对象(例如上文的pod，job)等都属于一个命名空间，在同一个命名空间中，资源对象的名字是唯一的，不同空间的资源名可以重复，命名空间主要为了对象进行逻辑上的分组便于管理。本文只使用了默认命名空间。 ## 整体方案 -### 前提条件 +### 部署Kubernetes集群 + +首先，我们需要拥有一个Kubernetes集群，在这个集群中所有node与pod都可以互相通信。关于Kubernetes集群搭建，可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/)，在以后的文章中我们也会介绍AWS上搭建的方案。本文假设大家能找到几台物理机，并且可以按照官方文档在上面部署Kubernetes。在本文的环境中，Kubernetes集群中所有node都挂载了一个*mfs*(分布式文件系统)共享目录，我们通过这个目录来存放训练文件与最终输出的模型。在训练之前，用户将配置与训练数据切分好放在mfs目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结果图如下： -首先，我们需要拥有一个k8s集群，在这个集群中所有node与pod都可以互相通信。关于k8s集群搭建，可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/)，在以后的文章中我们也会介绍AWS上搭建的方案。在本文的环境中，k8s集群中所有node都挂载了一个`mfs`(分布式文件系统)共享目录，我们通过这个目录来存放训练文件与最终输出的模型。在训练之前，用户将配置与训练数据切分好放在mfs目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。 +![paddle on kubernetes结构图](k8s-paddle-arch.png) -### 使用 `Job` +### 使用 Job -我们使用k8s中的job这个概念来代表一次分布式训练。`Job`表示一次性作业，在作业完成后，k8s会销毁job产生的容器并且释放相关资源。 +我们使用Kubernetes中的job这个概念来代表一次分布式训练。Job表示一次性作业，在作业完成后，Kubernetes会销毁job产生的容器并且释放相关资源。 -在k8s中，可以通过编写一个 `yaml` 文件，来描述这个job，在这个文件中，主要包含了一些配置信息，例如Paddle节点的个数，`paddle pserver`开放的端口个数与端口号，`paddle`使用的网卡设备等，这些信息通过环境变量的形式传递给容器内的程序使用。 +在Kubernetes中，可以通过编写一个YAML文件，来描述这个job，在这个文件中，主要包含了一些配置信息，例如PaddlePaddle的节点个数，`paddle pserver`开放的端口个数与端口号，使用的网卡设备等，这些信息通过环境变量的形式传递给容器内的程序使用。 -在一次分布式训练中，用户确定好本次训练需要的Paddle节点个数，将切分好的训练数据与配置文件上传到`mfs`共享目录中。然后编写这次训练的`job yaml`文件,提交给k8s集群创建并开始作业。 +在一次分布式训练中，用户确定好本次训练需要的PaddlePaddle节点个数，将切分好的训练数据与配置文件上传到mfs共享目录中。然后编写这次训练的job YAML文件，提交给Kubernetes集群创建并开始作业。 -### 创建`Paddle`节点 +### 创建PaddlePaddle节点 -当k8s master收到`job yaml`文件后，会解析相关字段，创建出多个pod(个数为Paddle节点数)，k8s会把这些pod调度到集群的node上运行。一个`pod`就代表一个`Paddle`节点，当pod被成功分配到一台物理/虚拟机上后，k8s会启动pod内的容器，这个容器会根据`job yaml`文件中的环境变量，启动`paddle pserver`与`paddle train`进程。 +当Kubernetes master收到请求，解析完YAML文件后，会创建出多个pod(个数为PaddlePaddle节点数)，Kubernetes会把这些pod调度到集群的node上运行。一个pod就代表一个PaddlePaddle节点，当pod被成功分配到一台物理/虚拟机上后，Kubernetes会启动pod内的容器，这个容器会根据YAML文件中的环境变量，启动`paddle pserver`与`paddle train`进程。 ### 启动训练 -在容器启动后，会通过脚本来启动这次分布式训练，我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的`trainer_id`，由于`Paddle`本身不提供类似服务发现的功能，所以在本文的启动脚本中，每个节点会根据`job name`向`k8s apiserver`查询这个`job`对应的所有`pod`信息(k8s默认会在每个容器的环境变量中写入`apiserver`的地址)。 +在容器启动后，会通过脚本来启动这次分布式训练，我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的trainer_id，由于Paddle本身不提供类似服务发现的功能，所以在本文的启动脚本中，每个节点会根据job name向Kubernetes apiserver查询这个job对应的所有pod信息(Kubernetes默认会在每个容器的环境变量中写入apiserver的地址)。 -根据这些pod信息，就可以通过某种方式，为每个pod分配一个唯一的`trainer_id`。本文把所有pod的IP地址进行排序，将顺序作为每个`Paddle`节点的`trainer_id`。启动脚本的工作流程大致如下： +根据这些pod信息，就可以通过某种方式，为每个pod分配一个唯一的trainer_id。本文把所有pod的IP地址进行排序，将顺序作为每个PaddlePaddle节点的trainer_id。启动脚本的工作流程大致如下： - 1. 查询`k8s apiserver`获取pod信息，根据IP分配`trainer_id` - 1. 从`mfs`共享目录中拷贝训练文件到容器内 + 1. 查询Kubernetes apiserver获取pod信息，根据IP分配trainer_id + 1. 从mfs共享目录中拷贝训练文件到容器内 1. 根据环境变量，解析出`paddle pserver`与`paddle train`的启动参数，启动进程 - 1. 训练时，`Paddle`会自动将结果保存在`trainer_id`为0的节点上，将输出路径设置为`mfs`目录，保存输出的文件 + 1. 训练时，PaddlePaddle会自动将结果保存在trainer_id为0的节点上，将输出路径设置为mfs目录，保存输出的文件 ## 搭建过程 -根据前文的描述，要在已有的k8s集群上进行`Paddle`的分布式训练，主要分为以下几个步骤： +根据前文的描述，要在已有的Kubernetes集群上进行PaddlePaddle的分布式训练，主要分为以下几个步骤： -1. 制作`Paddle`镜像 +1. 制作PaddlePaddle镜像 1. 将训练文件与切分好的数据上传到共享存储 -1. 编写本次训练的`job yaml`文件，创建`k8s job` +1. 编写本次训练的YAML文件，创建一个Kubernetes job 1. 训练结束后查看输出结果 下面就根据这几个步骤分别介绍。 - ### 制作镜像 -`Paddle`镜像需要提供`paddle pserver`与`paddle train`进程的运行环境，用这个镜像创建的容器需要有以下两个功能： +PaddlePaddle镜像需要提供`paddle pserver`与`paddle train`进程的运行环境，用这个镜像创建的容器需要有以下两个功能： - 拷贝训练文件到容器内 - 生成`paddle pserver`与`paddle train`进程的启动参数，并且启动训练 -因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含`Paddle`的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。镜像的`Dockerfile`如下： +因为官方镜像 `paddledev/paddle:cpu-latest` 内已经包含PaddlePaddle的执行程序但是还没上述功能，所以我们可以在这个基础上，添加启动脚本，制作新镜像来完成以上的工作。镜像的*Dockerfile*如下： ```Dockerfile FROM paddledev/paddle:cpu-latest @@ -92,25 +83,87 @@ CMD ["bash"," -c","/root/start.sh"] [`start.sh`](start.sh)文件拷贝训练文件到容器内，然后执行[`start_paddle.py`](start_paddle.py)脚本启动训练，前文提到的获取其他节点IP地址，分配`trainer_id`等都在`start_paddle.py`脚本中完成。 +`start_paddle.py`脚本开始时，会先进行参数的初始化与解析。 + +```python +parser = argparse.ArgumentParser(prog="start_paddle.py", + description='simple tool for k8s') + args, train_args_list = parser.parse_known_args() + train_args = refine_unknown_args(train_args_list) + train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2])) + podlist = getPodList() +``` + +然后通过函数`getPodList()`访问Kubernetes的接口来查询此job对应的所有pod信息。当所有pod都处于running状态（容器运行都运行）时，再通过函数`getIdMap(podlist)`获取trainer_id。 + +```python + podlist = getPodList() + # need to wait until all pods are running + while not isPodAllRunning(podlist): + time.sleep(10) + podlist = getPodList() + idMap = getIdMap(podlist) +``` + +在函数`getIdMap(podlist)`内部，我们通过读取`podlist`中每个pod的IP地址，将IP排序生成的序号作为trainer_id。 + +```python +def getIdMap(podlist): + ''' + generate tainer_id by ip + ''' + ips = [] + for pod in podlist["items"]: + ips.append(pod["status"]["podIP"]) + ips.sort() + idMap = {} + for i in range(len(ips)): + idMap[ips[i]] = i + return idMap +``` + +在得到`idMap`后，通过函数`startPaddle(idMap, train_args_dict)`构造`paddle pserver`与`paddle train`的启动参数并执行进程。 + +在函数`startPaddle`中，最主要的工作就是解析出`paddle pserver`与`paddle train`的启动参数。例如`paddle train`参数的解析，解析环境变量得到`PADDLE_NIC`，`PADDLE_PORT`，`PADDLE_PORTS_NUM`等参数，然后通过自身的IP地址在`idMap`中获取`trainerId`。 + +```python + program = 'paddle train' + args = " --nics=" + PADDLE_NIC + args += " --port=" + str(PADDLE_PORT) + args += " --ports_num=" + str(PADDLE_PORTS_NUM) + args += " --comment=" + "paddle_process_by_paddle" + ip_string = "" + for ip in idMap.keys(): + ip_string += (ip + ",") + ip_string = ip_string.rstrip(",") + args += " --pservers=" + ip_string + args_ext = "" + for key, value in train_args_dict.items(): + args_ext += (' --' + key + '=' + value) + localIP = socket.gethostbyname(socket.gethostname()) + trainerId = idMap[localIP] + args += " " + args_ext + " --trainer_id=" + \ + str(trainerId) + " --save_dir=" + JOB_PATH_OUTPUT +``` 使用 `docker build` 构建镜像： ```bash -docker build -t registry.baidu.com/public/paddle:mypaddle . +docker build -t your_repo/paddle:mypaddle . ``` -然后将构建成功的镜像上传到镜像仓库，注意本文中使用的`registry.baidu.com`是一个私有仓库，读者可以根据自己的情况部署私有仓库或者使用`Docker hub`。 +然后将构建成功的镜像上传到镜像仓库。 ```bash -docker push registry.baidu.com/public/paddle:mypaddle +docker push your_repo/paddle:mypaddle ``` ### 上传训练文件 -本文使用`Paddle`官方的`recommendation demo`作为这次训练的内容，我们将训练文件与数据放在一个`job name`命名的目录中，上传到`mfs`共享存储。完成后`mfs`上的文件内容大致如下： +本文使用Paddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到mfs共享存储。完成后mfs上的文件内容大致如下： ```bash -[root@paddle-k8s-node0 mfs]# tree -d +[root@paddle-kubernetes-node0 mfs]# tree -d . └── paddle-cluster-job ├── data @@ -123,13 +176,13 @@ docker push registry.baidu.com/public/paddle:mypaddle └── recommendation ``` -目录中`paddle-cluster-job`是本次训练对应的`job name`，本次训练要求有3个`Paddle`节点，在`paddle-cluster-job/data`目录中存放切分好的数据，文件夹`0,1,2`分别代表3个节点的`trainer_id`。`recommendation`文件夹内存放训练文件，`output`文件夹存放训练结果与日志。 +目录中paddle-cluster-job是本次训练对应的job name，本次训练要求有3个Paddle节点，在paddle-cluster-job/data目录中存放切分好的数据，文件夹0，1，2分别代表3个节点的trainer_id。recommendation文件夹内存放训练文件，output文件夹存放训练结果与日志。 -### 创建`job` +### 创建Job -`k8s`可以通过`yaml`文件来创建相关对象，然后可以使用命令行工具创建`job`。 +Kubernetes可以通过YAML文件来创建相关对象，然后可以使用命令行工具创建job。 -`job yaml`文件描述了这次训练使用的Docker镜像,需要启动的节点个数以及 `paddle pserver`与 `paddle train`进程启动的必要参数，也描述了容器需要使用的存储卷挂载的情况。`yaml`文件中各个字段的具体含义，可以查看[`k8s官方文档`](http://kubernetes.io/docs/api-reference/batch/v1/definitions/#_v1_job)。例如，本次训练的`yaml`文件可以写成： +Job YAML文件描述了这次训练使用的Docker镜像，需要启动的节点个数以及 `paddle pserver`与 `paddle train`进程启动的必要参数，也描述了容器需要使用的存储卷挂载的情况。YAML文件中各个字段的具体含义，可以查看[Kubernetes Job API](http://kubernetes.io/docs/api-reference/batch/v1/definitions/#_v1_job)。例如，本次训练的YAML文件可以写成： ```yaml apiVersion: batch/v1 @@ -149,7 +202,7 @@ spec: path: /home/work/mfs containers: - name: trainer - image: registry.baidu.com/public/paddle:mypaddle + image: your_repo/paddle:mypaddle command: ["bin/bash", "-c", "/root/start.sh"] env: - name: JOB_NAME @@ -176,7 +229,7 @@ spec: restartPolicy: Never ``` -文件中，`metadata`下的`name`表示这个`job`的名字。`parallelism,completions`字段表示这个`job`会同时开启3个`Paddle`节点，成功训练且退出的`pod`数目为3时，这个`job`才算成功结束。然后申明一个存储卷`jobpath`，代表宿主机目录`/home/work/mfs`，在对容器的描述`containers`字段中，将此目录挂载为容器的`/home/jobpath`目录，这样容器的`/home/jobpath`目录就成为了共享存储，放在这个目录里的文件其实是保存到了`mfs`上。 +文件中，`metadata`下的`name`表示这个job的名字。`parallelism，completions`字段表示这个job会同时开启3个Paddle节点，成功训练且退出的pod数目为3时，这个job才算成功结束。然后申明一个存储卷`jobpath`，代表宿主机目录`/home/work/mfs`，在对容器的描述`containers`字段中，将此目录挂载为容器的`/home/jobpath`目录，这样容器的`/home/jobpath`目录就成为了共享存储，放在这个目录里的文件其实是保存到了mfs上。 `env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内。 @@ -190,21 +243,21 @@ spec: `CONF_PADDLE_GRADIENT_NUM`表示训练节点数量，即`--num_gradient_servers`参数 -编写完`yaml`文件后，可以使用k8s的命令行工具创建`job`. +编写完YAML文件后，可以使用Kubernetes的命令行工具创建job。 ```bash kubectl create -f job.yaml ``` -创建成功后，k8s就会创建3个`pod`作为`Paddle`节点然后拉取镜像，启动容器开始训练。 +创建成功后，Kubernetes就会创建3个pod作为PaddlePaddle节点然后拉取镜像，启动容器开始训练。 ### 查看输出 -在训练过程中，可以在共享存储上查看输出的日志和模型，例如`output`目录下就存放了输出结果。注意`node_0`，`node_1`，`node_2`这几个目录表示`Paddle`节点与`trainer_id`,并不是k8s中的`node`概念。 +在训练过程中，可以在共享存储上查看输出的日志和模型，例如output目录下就存放了输出结果。注意node_0，node_1，node_2这几个目录表示Paddle节点与trainer_id，并不是Kubernetes中的node概念。 ```bash -[root@paddle-k8s-node0 output]# tree -d +[root@paddle-kubernetes-node0 output]# tree -d . ├── node_0 │   ├── server.log @@ -224,7 +277,7 @@ kubectl create -f job.yaml 我们可以通过日志查看容器训练的情况，例如： ```bash -[root@paddle-k8s-node0 node_0]# cat train.log +[root@paddle-kubernetes-node0 node_0]# cat train.log I1116 09:10:17.123121 50 Util.cpp:155] commandline: /usr/local/bin/../opt/paddle/bin/paddle_trainer --nics=eth0 --port=7164 diff --git a/doc_cn/cluster/k8s/k8s-paddle-arch.png b/doc_cn/cluster/k8s/k8s-paddle-arch.png 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--- a/doc_cn/demo/quick_start/index.md +++ /dev/null @@ -1,545 +0,0 @@ -# PaddlePaddle快速入门教程 - -我们以文本分类问题作为背景，介绍PaddlePaddle使用流程和常用的网络基础单元的配置方法。 - -## 安装(Install) - -首先请参考安装教程安装PaddlePaddle。 - -## 使用概述(Overview) - -**文本分类问题**：对于给定的一条文本， 我们从提前给定的类别集合中选择其所属类 -别。比如通过用户对电子商务网站评论，评估产品的质量： - -- 这个显示器很棒！ （好评） -- 用了两个月之后这个显示器屏幕碎了。（差评） - -每一个任务流程都可以分为如下5个基础部分。 -
    ![](./Pipeline.jpg)
    - -1. 数据格式准备 - - 每行保存一条样本，类别Id 和文本信息用Tab间隔， 文本中的单词用空格分隔（如果不切词，则字与字之间用空格分隔），例如：```类别Id ‘\t’ 这 个 显 示 器 很 棒 ！``` -2. 数据向模型传送 - - PaddlePaddle可以读取Python写的传输数据脚本，所有字符都将转换为连续整数表示的Id传给模型 -3. 网络结构（由易到难展示4种不同的网络配置） - - 逻辑回归模型 - - 词向量模型 - - 卷积模型 - - 时序模型 - - 优化算法 -4. 训练模型 -5. 预测 - -## 数据格式准备(Data Preparation) -在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)， -将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了数据下载脚本 -和预处理脚本。 - -```bash -cd demo/quick_start -./data/get_data.sh -./preprocess.sh -``` - -## 数据向模型传送(Transfer Data to Model) - -### Python数据加载脚本(Data Provider Script) - -下面dataprovider_bow.py文件给出了完整例子，主要包括两部分： - -* initalizer： 定义文本信息、类别Id的数据类型。 -* process： yield文本信息和类别Id，和initalizer里定义顺序一致。 - -```python -from paddle.trainer.PyDataProvider2 import * - -# id of the word not in dictionary -UNK_IDX = 0 - -# initializer is called by the framework during initialization. -# It allows the user to describe the data types and setup the -# necessary data structure for later use. -# `settings` is an object. initializer need to properly fill settings.input_types. -# initializer can also store other data structures needed to be used at process(). -# In this example, dictionary is stored in settings. -# `dictionay` and `kwargs` are arguments passed from trainer_config.lr.py -def initializer(settings, dictionary, **kwargs): - # Put the word dictionary into settings - settings.word_dict = dictionary - - # setting.input_types specifies what the data types the data provider - # generates. - settings.input_types = [ - # The first input is a sparse_binary_vector, - # which means each dimension of the vector is either 0 or 1. It is the - # bag-of-words (BOW) representation of the texts. - sparse_binary_vector(len(dictionary)), - # The second input is an integer. It represents the category id of the - # sample. 2 means there are two labels in the dataset. - # (1 for positive and 0 for negative) - integer_value(2)] - -# Delaring a data provider. It has an initializer 'data_initialzer'. -# It will cache the generated data of the first pass in memory, so that -# during later pass, no on-the-fly data generation will be needed. -# `setting` is the same object used by initializer() -# `file_name` is the name of a file listed train_list or test_list file given -# to define_py_data_sources2(). See trainer_config.lr.py. -@provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM) -def process(settings, file_name): - # Open the input data file. - with open(file_name, 'r') as f: - # Read each line. - for line in f: - # Each line contains the label and text of the comment, separated by \t. - label, comment = line.strip().split('\t') - - # Split the words into a list. - words = comment.split() - - # convert the words into a list of ids by looking them up in word_dict. - word_vector = [settings.word_dict.get(w, UNK_IDX) for w in words] - - # Return the features for the current comment. The first is a list - # of ids representing a 0-1 binary sparse vector of the text, - # the second is the integer id of the label. - yield word_vector, int(label) -``` - -### 配置中的数据加载定义(Data Provider in Configure) - -在模型配置中利用`define_py_data_sources2`加载数据： - -```python -from paddle.trainer_config_helpers import * - -file = "data/dict.txt" -word_dict = dict() -with open(dict_file, 'r') as f: - for i, line in enumerate(f): - w = line.strip().split()[0] - word_dict[w] = i -# define the data sources for the model. -# We need to use different process for training and prediction. -# For training, the input data includes both word IDs and labels. -# For prediction, the input data only includs word Ids. -define_py_data_sources2(train_list='data/train.list', - test_list='data/test.list', - module="dataprovider_bow", - obj="process", - args={"dictionary": word_dict}) -``` -* data/train.list,data/test.list: 指定训练、测试数据 -* module="dataprovider": 数据处理Python文件名 -* obj="process": 指定生成数据的函数 -* args={"dictionary": word_dict}: 额外的参数，这里指定词典 - -更详细数据格式和用例请参考 -PyDataProvider2。 - -## 网络结构(Network Architecture) -本节我们将专注于网络结构的介绍。 -
    ![](./PipelineNetwork.jpg)
    - -我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置 -连接请参考Layer文档。 -所有配置在[源码](https://github.com/baidu/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。 - -### 逻辑回归模型(Logistic Regression) - -流程如下： -
    ![](./NetLR.jpg)
    - -- 获取利用one-hot vector表示的每个单词，维度是词典大小 - -```python -word = data_layer(name="word", size=word_dim) -``` - -- 获取该条样本类别Id，维度是类别个数。 - -```python -label = data_layer(name="label", size=label_dim) -``` - -- 利用逻辑回归模型对该向量进行分类，同时会计算分类准确率 - -```python -# Define a fully connected layer with logistic activation (also called softmax activation). -output = fc_layer(input=word, - size=label_dim, - act_type=SoftmaxActivation()) -# Define cross-entropy classification loss and error. -classification_cost(input=output, label=label) -``` - - - input: 除过data层，每个层都有一个或多个input,多个input以list方式输入 - - size: 该层神经元个数 - - act_type: 激活函数类型 - -效果总结：我们将在后面介绍训练和预测的流程的脚本。在此为方便对比不同网络结构， -我们随时总结了各个网络的复杂度和效果。 - - -
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    网络名称参数数量错误率
    逻辑回归252 KB8.652%
    - -
    - -### 词向量模型(Word Vector) - -embedding模型需要稍微改变数据提供的脚本，即`dataprovider_emb.py`，词向量模型、 -卷积模型、时序模型均使用该脚本。其中文本输入类型定义为整数时序类型integer_value_sequence。 - -``` -def initializer(settings, dictionary, **kwargs): - settings.word_dict = dictionary - settings.input_types = [ - # Define the type of the first input as sequence of integer. - # The value of the integers range from 0 to len(dictrionary)-1 - integer_value_sequence(len(dictionary)), - # Define the second input for label id - integer_value(2)] - -@provider(init_hook=initializer) -def process(settings, file_name): - ... - # omitted, it is same as the data provider for LR model -``` - -该模型依然是使用逻辑回归分类网络的框架， 只是将句子利用连续向量表示替换稀疏 -向量表示， 即对第3步进行替换。句子表示的计算更新为2步： -
    ![](./NetContinuous.jpg)
    - -- 利用单词Id查找对应的该单词的连续表示向量(维度为word_dim)， 输入N个单词，输出为N个word_dim维度向量 - -```python -emb = embedding_layer(input=word, size=word_dim) -``` - -- 将该句话包含的所有单词向量求平均得到句子的表示 - -```python -avg = pooling_layer(input=emb, pooling_type=AvgPooling()) -``` - -其它部分和逻辑回归网络结构一致。 -效果总结： - - -
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    词向量模型15 MB8.484%
    -
    -
    - -### 卷积模型(Convolution) -卷积网络是一种特殊的从词向量表示到句子表示的方法， 也就是将词向量模型额步 -骤3-2进行进一步演化， 变为3个新的子步骤。 -
    ![](./NetConv.jpg)
    - -文本卷积分为三个步骤： -1. 获取每个单词左右各k个近邻， 拼接成一个新的向量表示； -2. 对该表示进行非线性变换 （例如Sigmoid变换）, 成为维度为hidden_dim的新的向量； -3. 在每个维度上取出在该句话新的向量集合上该维度的最大值作为最后的句子表示向量。 这3个子步骤可配置为: - -```python -text_conv = sequence_conv_pool(input=emb, - context_start=k, - context_len=2 * k + 1) -``` - -效果总结： - - -
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    卷积模型16 MB5.628%
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    - -### 时序模型(Time Sequence) -
    ![](./NetRNN.jpg)
    - -时序模型即为RNN模型, 包括简单的RNN模型、GRU模型、LSTM模型等。 - -- GRU模型配置： - -```python -gru = simple_gru(input=emb, size=gru_size) -``` - -- LSTM模型配置： - -```python -lstm = simple_lstm(input=emb, size=lstm_size) -``` - -针对本问题，我们采用单层LSTM模型，并使用了Dropout，效果总结： - - -
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    时序模型16 MB4.812%
    - -
    - -## 优化算法(Optimization Algorithm) -优化算法包括 -Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优化方法，加了L2正则和梯度截断。 - -```python -settings(batch_size=128, - learning_rate=2e-3, - learning_method=AdamOptimizer(), - regularization=L2Regularization(8e-4), - gradient_clipping_threshold=25) -``` - -## 训练模型(Training Model) -在完成了数据和网络结构搭建之后， 我们进入到训练部分。 -
    ![](./PipelineTrain.jpg)
    - -训练脚本：我们将训练的命令行保存在了 `train.sh`文件中。训练时所需设置的主要参数如下： - -```bash -paddle train \ ---config=trainer_config.py \ ---log_period=20 \ ---save_dir=./output \ ---num_passes=15 \ ---use_gpu=false -``` -这里没有介绍多机分布式训练，可以参考分布式训练的demo学习如何进行多机训练。 - -## 预测(Prediction) -可以使用训练好的模型评估带有label的验证集，也可以预测没有label的测试集。 -
    ![](./PipelineTest.jpg)
    - -测试脚本如下，将会测试配置文件中test.list指定的数据。 - -```bash -paddle train \ ---use_gpu=false \ ---job=test \ ---init_model_path=./output/pass-0000x -``` - -可以参考Python API预测 -教程，或其他demo的Python预测过程。也可以通过如下方式预测。 - -预测脚本(`predict.sh`)： - -```bash -model="output/pass-00003" -paddle train \ - --config=trainer_config.lstm.py \ - --use_gpu=false \ - --job=test \ - --init_model_path=$model \ - --config_args=is_predict=1 \ - --predict_output_dir=. \ - -mv rank-00000 result.txt -``` -这里以`output/pass-00003`为例进行预测，用户可以根据训练log选择test结果最好的模型来预测。与训练网络配置不同的是：无需label相关的层，指定outputs输出概率层(softmax输出)， -指定batch_size=1，数据传输无需label数据，预测数据指定test_list的位置。 - -预测结果以文本的形式保存在`result.txt`中，一行为一个样本，格式如下： - -``` -预测ID;ID为0的概率 ID为1的概率 -预测ID;ID为0的概率 ID为1的概率 -``` - -``` -is_predict = get_config_arg('is_predict', bool, False) -trn = 'data/train.list' if not is_predict else None -tst = 'data/test.list' if not is_predict else 'data/pred.list' -obj = 'process' if not is_predict else 'process_pre' -batch_size = 128 if not is_predict else 1 -if is_predict: - maxid = maxid_layer(output) - outputs([maxid,output]) -else: - label = data_layer(name="label", size=2) - cls = classification_cost(input=output, label=label) - outputs(cls) -``` - -## 总体效果总结(Summary) -这些流程中的数据下载、网络配置、训练脚本在`/demo/quick_start`目录，我们在此总 -结上述网络结构在Amazon-Elec测试集(25k)上的效果: - -
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    网络名称参数数量错误率配置文件
    逻辑回归模型 252KB 8.652%trainer_config.lr.py
    词向量模型 15MB 8.484%trainer_config.emb.py
    卷积模型 16MB 5.628%trainer_config.cnn.py
    时序模型 16MB 4.812%trainer_config.lstm.py
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    - -## 附录(Appendix) -### 命令行参数(Command Line Argument) - -* \--config：网络配置 -* \--save_dir：模型存储路径 -* \--log_period：每隔多少batch打印一次日志 -* \--num_passes：训练轮次，一个pass表示过一遍所有训练样本 -* \--config_args：命令指定的参数会传入网络配置中。 -* \--init_model_path：指定初始化模型路径，可用在测试或训练时指定初始化模型。 - -默认一个pass保存一次模型，也可以通过saving_period_by_batches设置每隔多少batch保存一次模型。 -可以通过show_parameter_stats_period设置打印参数信息等。 -其他参数请参考令行参数文档。 - -### 输出日志(Log) - -``` -TrainerInternal.cpp:160] Batch=20 samples=2560 AvgCost=0.628761 CurrentCost=0.628761 Eval: classification_error_evaluator=0.304297 CurrentEval: classification_error_evaluator=0.304297 -``` -模型训练会看到这样的日志，详细的参数解释如下面表格： -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    名称解释
    Batch=20 表示过了20个batch
    samples=2560 表示过了2560个样本
    AvgCost 每个pass的第0个batch到当前batch所有样本的平均cost
    CurrentCost 当前log_period个batch所有样本的平均cost
    Eval: classification_error_evaluator 每个pass的第0个batch到当前batch所有样本的平均分类错误率
    CurrentEval: classification_error_evaluator 当前log_period个batch所有样本的平均分类错误率
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    diff --git a/doc_cn/demo/quick_start/index.rst b/doc_cn/demo/quick_start/index.rst new file mode 100644 index 00000000000..9dabf1f661b --- /dev/null +++ b/doc_cn/demo/quick_start/index.rst @@ -0,0 +1,474 @@ +PaddlePaddle快速入门教程 +======================== + +我们将以 `文本分类问题 `_ 为例, +介绍PaddlePaddle的基本使用方法。 + +安装 +==== + +请参考 `安装教程 <../../build_and_install/index.html>`_ 安装PaddlePaddle。 + +使用概述 +======== + +**文本分类问题**：对于给定的一条文本，我们从提前给定的类别集合中选择其所属类别。 + +比如, 在购物网站上，通过查看买家对某个产品的评价反馈, 评估该产品的质量。 + +- 这个显示器很棒！ （好评） +- 用了两个月之后这个显示器屏幕碎了。（差评） + +使用PaddlePaddle, 每一个任务流程都可以被划分为如下五个步骤。 + + .. image:: Pipeline.jpg + :align: center + :scale: 80% + +1. 数据格式准备 + - 本例每行保存一条样本，类别Id和文本信息用 ``Tab`` 间隔，文本中的单词用空格分隔（如果不切词，则字与字之间用空格分隔），例如：``类别Id '\t' 这 个 显 示 器 很 棒 ！`` +2. 向系统传送数据 + - PaddlePaddle可以执行用户的python脚本程序来读取各种格式的数据文件。 + - 本例的所有字符都将转换为连续整数表示的Id传给模型。 +3. 描述网络结构和优化算法 + - 本例由易到难展示4种不同的文本分类网络配置：逻辑回归模型，词向量模型，卷积模型，时序模型。 + - 常用优化算法包括Momentum, RMSProp，AdaDelta，AdaGrad，Adam，Adamax等，本例采用Adam优化方法，加了L2正则和梯度截断。 +4. 训练模型 +5. 应用模型 + +数据格式准备 +------------ + +接下来我们将展示如何用PaddlePaddle训练一个文本分类模型，将 `Amazon电子产品评论数据 `_ 分为好评(正样本)和差评(负样本)两种类别。 +`源代码 `_ 的 ``demo/quick_start`` 目录里提供了该数据的下载脚本和预处理脚本。 + +.. code-block:: bash + + cd demo/quick_start + ./data/get_data.sh + ./preprocess.sh + +向系统传送数据 +============== + +Python数据读取脚本 +------------------ + +`DataProvider <../../ui/data_provider/index.html>`_ 是PaddlePaddle负责提供数据的模块。``DataProvider`` 主要职责在于将训练数据传入内存或者显存，让模型能够得到训练更新，其包括两个函数： + +* initializer：PaddlePaddle会在调用读取数据的Python脚本之前，先调用initializer函数。在下面例子里，我们在initialzier函数里初始化词表，并且在随后的读取数据过程中填充词表。 +* process：PaddlePaddle调用process函数来读取数据。每次读取一条数据后，process函数会用yield语句输出这条数据，从而能够被PaddlePaddle 捕获 (harvest)。 + +``dataprovider_bow.py`` 文件给出了完整例子： + +.. code-block:: python + + from paddle.trainer.PyDataProvider2 import * + + # id of the word not in dictionary + UNK_IDX = 0 + + # initializer is called by the framework during initialization. + # It allows the user to describe the data types and setup the + # necessary data structure for later use. + # `settings` is an object. initializer need to properly fill settings.input_types. + # initializer can also store other data structures needed to be used at process(). + # In this example, dictionary is stored in settings. + # `dictionay` and `kwargs` are arguments passed from trainer_config.lr.py + def initializer(settings, dictionary, **kwargs): + # Put the word dictionary into settings + settings.word_dict = dictionary + + # setting.input_types specifies what the data types the data provider + # generates. + settings.input_types = [ + # The first input is a sparse_binary_vector, + # which means each dimension of the vector is either 0 or 1. It is the + # bag-of-words (BOW) representation of the texts. + sparse_binary_vector(len(dictionary)), + # The second input is an integer. It represents the category id of the + # sample. 2 means there are two labels in the dataset. + # (1 for positive and 0 for negative) + integer_value(2)] + + # Delaring a data provider. It has an initializer 'data_initialzer'. + # It will cache the generated data of the first pass in memory, so that + # during later pass, no on-the-fly data generation will be needed. + # `setting` is the same object used by initializer() + # `file_name` is the name of a file listed train_list or test_list file given + # to define_py_data_sources2(). See trainer_config.lr.py. + @provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM) + def process(settings, file_name): + # Open the input data file. + with open(file_name, 'r') as f: + # Read each line. + for line in f: + # Each line contains the label and text of the comment, separated by \t. + label, comment = line.strip().split('\t') + + # Split the words into a list. + words = comment.split() + + # convert the words into a list of ids by looking them up in word_dict. + word_vector = [settings.word_dict.get(w, UNK_IDX) for w in words] + + # Return the features for the current comment. The first is a list + # of ids representing a 0-1 binary sparse vector of the text, + # the second is the integer id of the label. + yield word_vector, int(label) + +配置中的数据加载定义 +-------------------- + +在模型配置中通过 ``define_py_data_sources2`` 接口来加载数据： + +.. code-block:: python + + from paddle.trainer_config_helpers import * + + file = "data/dict.txt" + word_dict = dict() + with open(dict_file, 'r') as f: + for i, line in enumerate(f): + w = line.strip().split()[0] + word_dict[w] = i + # define the data sources for the model. + # We need to use different process for training and prediction. + # For training, the input data includes both word IDs and labels. + # For prediction, the input data only includs word Ids. + define_py_data_sources2(train_list='data/train.list', + test_list='data/test.list', + module="dataprovider_bow", + obj="process", + args={"dictionary": word_dict}) + + +以下是对上述数据加载的解释： + +- data/train.list,data/test.list: 指定训练数据和测试数据 +- module="dataprovider_bow": 数据处理的Python脚本文件名 +- obj="process": 指定生成数据的函数 +- args={"dictionary": word_dict}: 额外的参数，这里指定词典 + +更详细数据格式和用例请参考 `PyDataProvider2 <../../ui/data_provider/pydataprovider2.html>`_ 。 + +模型网络结构 +============ + +本小节我们将介绍模型网络结构。 + + .. image:: PipelineNetwork.jpg + :align: center + :scale: 80% + + +我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置连接请参考 `Layer文档 <../../../doc/layer.html>`_ 。 +所有配置都在 `源代码 `_ 的 ``demo/quick_start`` 目录下。 + +逻辑回归模型 +------------ + +具体流程如下: + + .. image:: NetLR.jpg + :align: center + :scale: 80% + +- 获取利用one-hot vector表示的每个单词，维度是词典大小 + + .. code-block:: python + + word = data_layer(name="word", size=word_dim) + +- 获取该条样本类别Id，维度是类别个数。 + + .. code-block:: python + + label = data_layer(name="label", size=label_dim) + +- 利用逻辑回归模型对该向量进行分类，同时会计算分类准确率 + + .. code-block:: python + + # Define a fully connected layer with logistic activation (also called softmax activation). + output = fc_layer(input=word, + size=label_dim, + act_type=SoftmaxActivation()) + # Define cross-entropy classification loss and error. + classification_cost(input=output, label=label) + + + - input: 除过data层，每个层都有一个或多个input,多个input以list方式输入 + - size: 该层神经元个数 + - act_type: 激活函数类型 + +**效果总结**：我们将在后面介绍训练和预测流程的脚本。在此为方便对比不同网络结构，我们总结了各个网络的复杂度和效果。 + + ===================== =============================== ================= + 网络名称 参数数量 错误率 + ===================== =============================== ================= + 逻辑回归 252 KB 8.652 % + ===================== =============================== ================= + +词向量模型 +---------- + +embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb.py``，词向量模型、 +卷积模型、时序模型均使用该脚本。其中文本输入类型定义为整数时序类型integer_value_sequence。 + +.. code-block:: python + + def initializer(settings, dictionary, **kwargs): + settings.word_dict = dictionary + settings.input_types = [ + # Define the type of the first input as sequence of integer. + # The value of the integers range from 0 to len(dictrionary)-1 + integer_value_sequence(len(dictionary)), + # Define the second input for label id + integer_value(2)] + + @provider(init_hook=initializer) + def process(settings, file_name): + ... + # omitted, it is same as the data provider for LR model + +该模型依然是使用逻辑回归分类网络的框架， 只是将句子利用连续向量表示替换稀疏 +向量表示， 即对第3步进行替换。句子表示的计算更新为2步： + +.. image:: NetContinuous.jpg + :align: center + :scale: 80% + +- 利用单词Id查找对应的该单词的连续表示向量(维度为word_dim)， 输入N个单词，输出为N个word_dim维度向量 + + .. code-block:: python + + emb = embedding_layer(input=word, size=word_dim) + +- 将该句话包含的所有单词向量求平均得到句子的表示 + + .. code-block:: python + + avg = pooling_layer(input=emb, pooling_type=AvgPooling()) + +其它部分和逻辑回归网络结构一致。 + +**效果总结：** + + ===================== =============================== ================== + 网络名称 参数数量 错误率 + ===================== =============================== ================== + 词向量模型 15 MB 8.484 % + ===================== =============================== ================== + +卷积模型 +----------- + +卷积网络是一种特殊的从词向量表示到句子表示的方法， 也就是将词向量模型额步 +骤3-2进行进一步演化， 变为3个新的子步骤。 + +.. image:: NetConv.jpg + :align: center + :scale: 80% + +文本卷积分为三个步骤: + +1. 获取每个单词左右各k个近邻， 拼接成一个新的向量表示； + +2. 对该表示进行非线性变换 （例如Sigmoid变换）, 成为维度为hidden_dim的新的向量； + +3. 在每个维度上取出在该句话新的向量集合上该维度的最大值作为最后的句子表示向量。 这3个子步骤可配置为: + +.. code-block:: python + + text_conv = sequence_conv_pool(input=emb, + context_start=k, + context_len=2 * k + 1) + +**效果总结：** + + ===================== =============================== ======================== + 网络名称 参数数量 错误率 + ===================== =============================== ======================== + 卷积模型 16 MB 5.628 % + ===================== =============================== ======================== + +时序模型 +---------- + +.. image:: NetRNN.jpg + :align: center + :scale: 80% + +时序模型即为RNN模型, 包括简单的RNN模型、GRU模型、LSTM模型等。 + +- GRU模型配置： + + .. code-block:: python + + gru = simple_gru(input=emb, size=gru_size) + + +- LSTM模型配置： + + .. code-block:: python + + lstm = simple_lstm(input=emb, size=lstm_size) + +针对本问题，我们采用单层LSTM模型，并使用了Dropout，**效果总结：** + + ===================== =============================== ========================= + 网络名称 参数数量 错误率 + ===================== =============================== ========================= + 时序模型 16 MB 4.812 % + ===================== =============================== ========================= + +优化算法 +========= + +`优化算法 <../../../doc/ui/trainer_config_helpers_api.html#module-paddle.trainer_config_helpers.optimizers>`_ 包括 +Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优化方法，加了L2正则和梯度截断。 + +.. code-block:: python + + settings(batch_size=128, + learning_rate=2e-3, + learning_method=AdamOptimizer(), + regularization=L2Regularization(8e-4), + gradient_clipping_threshold=25) + +训练模型 +========= + +在完成了数据和网络结构搭建之后， 我们进入到训练部分。 + +.. image:: PipelineTrain.jpg + :align: center + :scale: 80% + +训练脚本：我们将训练的命令行保存在了 ``train.sh`` 文件中。训练时所需设置的主要参数如下： + + .. code-block:: bash + + paddle train \ + --config=trainer_config.py \ + --log_period=20 \ + --save_dir=./output \ + --num_passes=15 \ + --use_gpu=false + +这里没有介绍多机分布式训练，可以参考 `分布式训练 <../../cluster/index.html>`_ 的demo学习如何进行多机训练。 + +预测 +===== + +可以使用训练好的模型评估带有label的验证集，也可以预测没有label的测试集。 + +.. image:: PipelineTest.jpg + :align: center + :scale: 80% + +测试脚本如下，将会测试配置文件中test.list指定的数据。 + + .. code-block:: bash + + paddle train \ + --use_gpu=false \ + --job=test \ + --init_model_path=./output/pass-0000x + +可以参考 `Python API预测 <../../ui/predict/swig_py_paddle.html>`_ +教程，或其他 `demo <../../demo/index.html>`_ 的Python预测过程。也可以通过如下方式预测。 + +预测脚本(``predict.sh``)： + + .. code-block:: bash + + model="output/pass-00003" + paddle train \ + --config=trainer_config.lstm.py \ + --use_gpu=false \ + --job=test \ + --init_model_path=$model \ + --config_args=is_predict=1 \ + --predict_output_dir=. \ + + mv rank-00000 result.txt + +这里以 ``output/pass-00003`` 为例进行预测，用户可以根据训练log选择test结果最好的模型来预测。与训练网络配置不同的是：无需label相关的层，指定outputs输出概率层(softmax输出)， +指定batch_size=1，数据传输无需label数据，预测数据指定test_list的位置。 + +预测结果以文本的形式保存在 ``result.txt`` 中，一行为一个样本，格式如下： + + .. code-block:: bash + + 预测ID;ID为0的概率 ID为1的概率 + 预测ID;ID为0的概率 ID为1的概率 + + .. code-block:: python + + is_predict = get_config_arg('is_predict', bool, False) + trn = 'data/train.list' if not is_predict else None + tst = 'data/test.list' if not is_predict else 'data/pred.list' + obj = 'process' if not is_predict else 'process_pre' + batch_size = 128 if not is_predict else 1 + if is_predict: + maxid = maxid_layer(output) + outputs([maxid,output]) + else: + label = data_layer(name="label", size=2) + cls = classification_cost(input=output, label=label) + outputs(cls) + +总体效果总结 +============== + +这些流程中的数据下载、网络配置、训练脚本在 ``/demo/quick_start`` 目录，我们在此总 +结上述网络结构在Amazon-Elec测试集(25k)上的效果: + + ===================== =============================== ============= ================================== + 网络名称 参数数量 错误率 配置文件 + ===================== =============================== ============= ================================== + 逻辑回归模型 252 KB 8.652% trainer_config.lr.py + 词向量模型 15 MB 8.484% trainer_config.emb.py + 卷积模型 16 MB 5.628% trainer_config.cnn.py + 时序模型 16 MB 4.812% trainer_config.lstm.py + ===================== =============================== ============= ================================== + + +附录 +===== + +命令行参数 +---------- + +* \--config：网络配置 +* \--save_dir：模型存储路径 +* \--log_period：每隔多少batch打印一次日志 +* \--num_passes：训练轮次，一个pass表示过一遍所有训练样本 +* \--config_args：命令指定的参数会传入网络配置中。 +* \--init_model_path：指定初始化模型路径，可用在测试或训练时指定初始化模型。 + +默认一个pass保存一次模型，也可以通过saving_period_by_batches设置每隔多少batch保存一次模型。 +可以通过show_parameter_stats_period设置打印参数信息等。 +其他参数请参考 `命令行参数文档 <../../ui/index.html#command-line-argument>`_ 。 + +输出日志 +--------- + +.. code-block:: bash + + TrainerInternal.cpp:160] Batch=20 samples=2560 AvgCost=0.628761 CurrentCost=0.628761 Eval: classification_error_evaluator=0.304297 CurrentEval: classification_error_evaluator=0.304297 + +模型训练会看到这样的日志，详细的参数解释如下面表格： + + =========================================== ========================================================== + 名称 解释 + =========================================== ========================================================== + Batch=20 表示过了20个batch + samples=2560 表示过了2560个样本 + AvgCost 每个pass的第0个batch到当前batch所有样本的平均cost + CurrentCost 当前log_period个batch所有样本的平均cost + Eval: classification_error_evaluator 每个pass的第0个batch到当前batch所有样本的平均分类错误率 + CurrentEval: classification_error_evaluator 当前log_period个batch所有样本的平均分类错误率 + =========================================== ========================================================== -- GitLab From a49d1d9529e14638f84c22289b3eff8caf45da80 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sat, 19 Nov 2016 21:37:58 +0800 Subject: [PATCH 037/347] Refine the original hrnn documentation. --- doc_cn/algorithm/rnn/hierarchical-rnn.rst | 179 ------------------ doc_cn/algorithm/rnn/hrnn_demo.rst | 2 +- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 98 +++++----- doc_cn/concepts/glossary.rst | 34 ++++ doc_cn/concepts/use_concepts.rst | 2 + doc_cn/conf.py.in | 2 +- paddle/gserver/tests/sequenceGen.py | 20 +- paddle/gserver/tests/sequence_nest_rnn.conf | 5 +- 8 files changed, 98 insertions(+), 244 deletions(-) delete mode 100644 doc_cn/algorithm/rnn/hierarchical-rnn.rst diff --git a/doc_cn/algorithm/rnn/hierarchical-rnn.rst b/doc_cn/algorithm/rnn/hierarchical-rnn.rst deleted file mode 100644 index 7c81ce8c673..00000000000 --- a/doc_cn/algorithm/rnn/hierarchical-rnn.rst +++ /dev/null @@ -1,179 +0,0 @@ -################# -双层RNN配置与示例 -################# - -我们在 :code:`paddle/gserver/tests/test_RecurrentGradientMachine` 单测中，通过多组语义相同的单双层RNN配置，讲解如何使用双层RNN。 - -示例1：双进双出，subseq间无memory -================================= - -配置：单层RNN（:code:`sequence_layer_group`）和双层RNN（:code:`sequence_nest_layer_group`），语义完全相同。 - -读取双层序列的方法 ------------------- - -首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）\: - -- 单层序列的数据（ :code:`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。 - -.. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg - :language: text - - -- 双层序列的数据（ :code:`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。 - -.. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest - :language: text - -其次，我们看一下单双层序列的不同dataprovider（见 :code:`sequenceGen.py` ）： - -- 单层序列的dataprovider如下： - - - word_slot是integer_value_sequence类型，代表单层序列。 - - label是integer_value类型，代表一个向量。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py - :language: python - :lines: 21-39 - -- 双层序列的dataprovider如下： - - - word_slot是integer_value_sub_sequence类型，代表双层序列。 - - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。 - - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py - :language: python - :lines: 42-71 - -模型中的配置 ------------- - -首先，我们看一下单层序列的配置（见 :code:`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequence_layer_group.conf - :language: python - :lines: 38-63 - - -其次，我们看一下语义相同的双层序列配置（见 :code:`sequence_nest_layer_group.conf` ），并对其详细分析： - -- batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。 -- data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。 -- lstmemory\: - - - 单层序列过了一个mixed_layer和lstmemory_group。 - - 双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。 - -- last_seq\: - - - 单层序列直接取了最后一个元素 - - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。 - - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_layer_group.conf - :language: python - :lines: 38-84 - -示例2：双进双出，subseq间有memory -================================= - -配置：单层RNN（ :code:`sequence_rnn.conf` ），双层RNN（ :code:`sequence_nest_rnn.conf` 和 :code:`sequence_nest_rnn_readonly_memory.conf` ），语义完全相同。 - -读取双层序列的方法 ------------------- - -我们看一下单双层序列的不同数据组织形式和dataprovider（见 :code:`rnn_data_provider.py`） - -.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py - :language: python - :lines: 20-32 - -- 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。 -- 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。 -- 单双层序列的label都分别是0和1 - -模型中的配置 ------------- - -我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 - -- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn.conf - :language: python - :lines: 36-48 - -- 双层序列，外层memory是一个元素： - - - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 - - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf - :language: python - :lines: 39-66 - -- 双层序列，外层memory是单层序列： - - - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 - - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 - -示例3：双进双出，输入不等长 -=========================== - -.. role:: red - -.. raw:: html - - - -**输入不等长** 是指recurrent_group的多个输入在各时刻的长度可以不相等, 但需要指定一个和输出长度一致的input，用 :red:`targetInlink` 表示。参考配置：单层RNN（:code:`sequence_rnn_multi_unequalength_inputs.conf`），双层RNN（:code:`sequence_nest_rnn_multi_unequalength_inputs.conf`） - -读取双层序列的方法 ------------------- - -我们看一下单双层序列的数据组织形式和dataprovider（见 :code:`rnn_data_provider.py` ） - -.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py - :language: python - :lines: 69-97 - -data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 - -- 单层序列：两个样本分别为[[1, 2, 4, 5, 2], [5, 4, 1, 3, 1]] 和 [[0, 2, 2, 5, 0, 1, 2], [1, 5, 4, 2, 3, 6, 1]] -- 双层序列：两个样本分别为 - - - **样本1**\：[[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]]]。fea1和fea2都分别有2个子句，fea1=[[1, 2], [4, 5, 2]], fea2=[[5, 4, 1], [3, 1]] - - **样本2**\：[[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]]]。fea1和fea2都分别有3个子句， fea1=[[0, 2], [2, 5], [0, 1, 2]], fea2=[[1, 5], [4], [2, 3, 6, 1]]。
    - - **注意**\：每个样本中，各特征的子句数目需要相等。这里说的“双进双出，输入不等长”是指fea1在i时刻的输入的长度可以不等于fea2在i时刻的输入的长度。如对于第1个样本，时刻i=2, fea1[2]=[4, 5, 2]，fea2[2]=[3, 1]，3≠2。 - -- 单双层序列中，两个样本的label都分别是0和1 - -模型中的配置 ------------- - -单层RNN（ :code:`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（ :code:`v.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 - -- 单层序列\： - - - 过了一个简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全连接，功能与示例2中`sequence_rnn.conf`的`step`函数完全相同。这里，两个输入x1,x2分别通过calrnn返回最后时刻的状态。结果得到的encoder1_rep和encoder2_rep分别是单层序列，最后取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 - - 注意到这里recurrent_group输入的每个样本中，fea1和fea2的长度都分别相等，这并非偶然，而是因为recurrent_group要求输入为单层序列时，所有输入的长度都必须相等。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf - :language: python - :lines: 41-58 - -- 双层序列\： - - - 双层RNN中，对输入的两个特征分别求时序上的连续全连接(`inner_step1`和`inner_step2`分别处理fea1和fea2)，其功能与示例2中`sequence_nest_rnn.conf`的`outer_step`函数完全相同。不同之处是，此时输入`[SubsequenceInput(emb1), SubsequenceInput(emb2)]`在各时刻并不等长。 - - 函数`outer_step`中可以分别处理这两个特征，但我们需要用targetInlink指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 - - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 - -.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf - :language: python - :lines: 41-89 - -示例4：beam_search的生成 -======================== - -TBD diff --git a/doc_cn/algorithm/rnn/hrnn_demo.rst b/doc_cn/algorithm/rnn/hrnn_demo.rst index cf38e416c06..96396ff105d 100644 --- a/doc_cn/algorithm/rnn/hrnn_demo.rst +++ b/doc_cn/algorithm/rnn/hrnn_demo.rst @@ -1,4 +1,4 @@ -.. algo_hrnn_demo: +.. _algo_hrnn_demo: ################# 双层RNN的使用示例 diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index cf181080199..8ae0f85b295 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -4,101 +4,99 @@ 单双层RNN API对比介绍 ##################### -这篇教程主要介绍了 :ref:`glossary_双层RNN` 的API接口。本文中的以 :ref:`glossary_paddle` 的 :ref:`glossary_双层RNN` 单元测试为示例，用多对效果完全相同的、分别使用单、双层RNN作为网络配置的模型，来讲解如何使用 :ref:`glossary_双层RNN` 。本文中所有的例子，都只是介绍 :ref:`glossary_双层RNN` 的API接口，并不是使用 :ref:`glossary_双层RNN` 解决实际的问题。如果想要了解 :ref:`glossary_双层RNN` 在具体问题中的使用，请参考 :ref:`algo_hrnn_demo` 。文章中示例所使用的单元测试文件是 `test_RecurrentGradientMachine.cpp `_ 。 +这篇教程主要介绍了\ :ref:`glossary_双层RNN`\ 的API接口。本文中的以\ :ref:`glossary_paddle`\ 的\ :ref:`glossary_双层RNN`\ 单元测试为示例，用多对效果完全相同的、分别使用单、双层RNN作为网络配置的模型，来讲解如何使用\ :ref:`glossary_双层RNN`\ 。本文中所有的例子，都只是介绍\ :ref:`glossary_双层RNN`\ 的API接口，并不是使用\ :ref:`glossary_双层RNN`\ 解决实际的问题。如果想要了解\ :ref:`glossary_双层RNN`\ 在具体问题中的使用，请参考\ :ref:`algo_hrnn_demo`\ 。文章中示例所使用的单元测试文件是\ `test_RecurrentGradientMachine.cpp `_\ 。 示例1：双层RNN，子序列间无Memory ================================ +在\ :ref:`glossary_双层RNN`\ 中的经典情况是将内层的每一个\ :ref:`glossary_Sequence`\ 数据，分别进行序列操作。并且内层的序列操作之间是独立没有依赖的，即不需要使用\ :ref:`glossary_Memory`\ 的。 +在本问题中，单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 的网络配置，都是将每一句分好词后的句子，使用\ :ref:`glossary_lstm`\ 作为\ :ref:`glossary_encoder`\ ，压缩成一个向量。区别是\ :ref:`glossary_RNN`\ 使用两层序列模型，将多句话看成一个整体，同时使用\ :ref:`glossary_encoder`\ 压缩，二者语意上完全一致。这组语意相同的示例配置如下 -配置：单层RNN（:code:`sequence_layer_group`）和双层RNN（:code:`sequence_nest_layer_group`），语义完全相同。 +* 单层 \:ref:`glossary_RNN`\: `sequence_layer_group.conf `_ +* :ref:`glossary_双层RNN`\: `sequence_nest_layer_group.conf `_ -读取双层序列的方法 ------------------- -首先，我们看一下单双层序列的不同数据组织形式（您也可以采用别的组织形式）\: +读取双层序列数据 +---------------- + +首先，本示例中使用的原始数据如下\: -- 单层序列的数据（ :code:`Sequence/tour_train_wdseg`）如下，一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。 +- 本里中的原始数据一共有10个\ :ref:`glossary_sample`\ 。每个\ :ref:`glossary_sample`\ 由两部分组成，一个label（此处都为2）和一个已经分词后的句子。这个数据也被单层\ :ref:`glossary_RNN`\ 网络直接使用。 .. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg :language: text -- 双层序列的数据（ :code:`Sequence/tour_train_wdseg.nest`）如下，一共有4个样本。样本间用空行分开，代表不同的双层序列，序列数据和上面的完全一样。每个样本的子句数分别为2,3,2,3。 +- 双层序列数据一共有4个\ :ref:`glossary_sample`\ 。 每个样本间用空行分开，整体数据和原始数据完全一样。而对于双层序列的\ :ref:`glossary_lstm`\ 来说，第一条数据同时\ :ref:`glossary_encode` 两条数据成两个向量。这四条数据同时处理的句子为\ :code:`[2, 3, 2, 3]`\ 。 .. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest :language: text -其次，我们看一下单双层序列的不同dataprovider（见 :code:`sequenceGen.py` ）： - -- 单层序列的dataprovider如下： - - - word_slot是integer_value_sequence类型，代表单层序列。 - - label是integer_value类型，代表一个向量。 +其次，对于两种不同的输入数据类型，不同\ :ref:`glossary_DataProvider`\ 对比如下(`sequenceGen.py `_)\： .. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py :language: python :lines: 21-39 + :linenos: -- 双层序列的dataprovider如下： - - - word_slot是integer_value_sub_sequence类型，代表双层序列。 - - label是integer_value_sequence类型，代表单层序列，即一个子句一个label。注意：也可以为integer_value类型，代表一个向量，即一个句子一个label。通常根据任务需求进行不同设置。 - - 关于dataprovider中input_types的详细用法，参见PyDataProvider2。 +- 这是普通的单层\ :ref:`glossary_Sequence`\ 的\ :ref:`glossary_DataProvider`\ 代码，其说明如下： + + * :ref:`glossary_DataProvider`\ 共返回两个数据，分别是words和label。即上述代码中的第19行。 + - words是原始数据中的每一句话，所对应的词表index数组。它是integer_value_sequence类型的，即整数数组。words即为这个数据中的单层\ :ref:`glossary_Sequence`\ 。 + - label是原始数据中对于每一句话的分类标签，它是integer_value类型的。 .. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py :language: python :lines: 42-71 + :linenos: -模型中的配置 ------------- +- 这是对于同样的数据，本示例中双层\ :ref:`glossary_Sequence`\ 的\ :ref:`glossary_DataProvider`\ 代码，其说明如下： + + - :ref:`glossary_DataProvider`\ 共返回两组数据，分别是sentences和labels。即在双层序列的原始数据中，每一组内的所有句子和labels + - sentences是双层\ :ref:`glossary_Sequence`\ 的数据。他内部包括了每组数据中的所有句子，又使用句子中每一个单词的词表index表示每一个句子，故为双层\ :ref:`glossary_Sequence`\ 。类型为 integer_value_sub_sequence 。 + - labels是每组内每一个句子的标签，故而是一个单层\ :ref:`glossary_Sequence`\ 。 + + +:ref:`glossary_trainer_config`\ 的模型配置 +------------------------------------------ -首先，我们看一下单层序列的配置（见 :code:`sequence_layer_group.conf`）。注意：batchsize=5表示一次过5句单层序列，因此2个batch就可以完成1个pass。 +首先，我们看一下单层\ :ref:`glossary_RNN`\ 的配置。代码中9-15行即为单层RNN序列的使用代码。这里使用了\ :ref:`glossary_paddle`\ 预定义好的\ :ref:`glossary_RNN`\ 处理函数。在这个函数中，\ :ref:`glossary_RNN`\ 对于每一个\ :ref:`glossary_timestep`\ 通过了一个\ :ref:`glossary_lstm`\ 网络。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_layer_group.conf :language: python :lines: 38-63 + :linenos: + :emphasize-lines: 9-15 -其次，我们看一下语义相同的双层序列配置（见 :code:`sequence_nest_layer_group.conf` ），并对其详细分析： +其次，我们看一下语义相同的\ :ref:`glossary_双层RNN`\ 的网络配置。 -- batchsize=2表示一次过2句双层序列。但从上面的数据格式可知，2句双层序列和5句单层序列的数据完全一样。 -- data_layer和embedding_layer不关心数据是否是序列格式，因此两个配置在这两层上的输出是一样的。 -- lstmemory\: +* :ref:`glossary_paddle`\ 中的许多layer并不在意输入是否是\ :ref:`glossary_Sequence`\ ，例如\ :code:`embedding_layer`\ 。在这些layer中，所有的操作都是针对每一个\ :ref:`glossary_timestep`\ 来进行的。 - - 单层序列过了一个mixed_layer和lstmemory_group。 - - 双层序列在同样的mixed_layer和lstmemory_group外，直接加了一层group。由于这个外层group里面没有memory，表示subseq间不存在联系，即起到的作用仅仅是把双层seq拆成单层，因此双层序列过完lstmemory的输出和单层的一样。 +* 在该配置中，7-26行将双层\ :ref:`glossary_Sequence`\ 数据，先变换成单层\ :ref:`glossary_Sequence`\ 数据，在对每一个单层\ :ref:`glossary_Sequence`\ 进行处理。 -- last_seq\: + * 使用\ :code:`recurrent_group`\ 这个函数进行变换，在变换时需要将输入序列传入。由于我们想要的变换是双层\ :ref:`glossary_Sequence`\ => 单层\ :ref:`glossary_Sequence`\ ，所以我们需要将输入数据标记成\ :code:`SubsequenceInput`\ 。 + + * 在本例中，我们将原始数据的每一组，通过\ :code:`recurrent_group`\ 进行拆解，拆解成的每一句话再通过一个\ :ref:`glossary_lstm`\ 网络。这和单层\ :ref:`glossary_RNN`\ 的配置是等价的。 + +* 与单层\ :ref:`glossary_RNN`\ 的配置类似，我们只需要知道使用\ :ref:`glossary_lstm` :ref:`glossary_encode`\ 成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但是，和单层\ :ref:`glossary_RNN`\ 有区别的地方是，我们是对每一个子序列取最后一个元素。于是我们设置\ :code:`agg_level=AggregateLevel.EACH_SEQUENCE`\ 。 - - 单层序列直接取了最后一个元素 - - 双层序列首先（last_seq层）取了每个subseq的最后一个元素，将其拼接成一个新的单层序列；接着（expand_layer层）将其扩展成一个新的双层序列，其中第i个subseq中的所有向量均为输入的单层序列中的第i个向量；最后（average_layer层）取了每个subseq的平均值。 - - 分析得出：第一个last_seq后，每个subseq的最后一个元素就等于单层序列的最后一个元素，而expand_layer和average_layer后，依然保持每个subseq最后一个元素的值不变（这两层仅是为了展示它们的用法，实际中并不需要）。因此单双层序列的输出是一样旳。 +* 至此，\ :code:`lstm_last`\ 便和单层\ :ref:`glossary_RNN`\ 的配置中的\ :code:`lstm_last`\ 具有相同的结果了。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_layer_group.conf :language: python - :lines: 38-84 - -示例2：双进双出，subseq间有memory -================================= + :lines: 38-64 + :linenos: + :emphasize-lines: 7-26 -配置：单层RNN（ :code:`sequence_rnn.conf` ），双层RNN（ :code:`sequence_nest_rnn.conf` 和 :code:`sequence_nest_rnn_readonly_memory.conf` ），语义完全相同。 - -读取双层序列的方法 ------------------- +示例2：:ref:`glossary_双层RNN`，子序列间有\ :ref:`glossary_Memory` +================================================================== -我们看一下单双层序列的不同数据组织形式和dataprovider（见 :code:`rnn_data_provider.py`） +本示例中，意图使用单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 同时实现一个完全等价的全连接\ :ref:`glossary_RNN`\ 。对于单层\ :ref:`glossary_RNN`\ ，输入数据为一个完整的\ :ref:`glossary_Sequence`\ ，例如\ :code:`[4, 5, 2, 0, 9, 8, 1, 4]`\ 。而对于\ :ref:`glossary_双层RNN`\ ，输入数据为在单层\ :ref:`glossary_RNN`\ 数据里面，任意将一些数据组合成双层\ :ref:`glossary_Sequence`\ ，例如\ :code:`[ [4, 5, 2], [0, 9], [8, 1, 4]]`。 -.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py - :language: python - :lines: 20-32 - -- 单层序列：有两句，分别为[1,3,2,4,5,2]和[0,2,2,5,0,1,2]。 -- 双层序列：有两句，分别为[[1,3,2],[4,5,2]]（2个子句）和[[0,2],[2,5],[0,1,2]]（3个子句）。 -- 单双层序列的label都分别是0和1 - -模型中的配置 ------------- +:ref:`glossary_trainer_config`\ 的模型配置 +------------------------------------------ 我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 diff --git a/doc_cn/concepts/glossary.rst b/doc_cn/concepts/glossary.rst index a94aa736753..518712d1fe8 100644 --- a/doc_cn/concepts/glossary.rst +++ b/doc_cn/concepts/glossary.rst @@ -11,6 +11,33 @@ PaddlePaddle TBD +.. _glossary_encode: + +encode +------ + +参考\ :ref:`glossary_encoder`\ 。 + +.. _glossary_encoder: + +encoder +------- + +TBD + +.. _glossary_sample: + +样本 +---- + +TBD Sample的概念 + +.. _glossary_lstm: + +LSTM +---- + +TBD .. _glossary_memory: @@ -27,6 +54,13 @@ Memory是 :ref:`glossary_paddle` 实现 :ref:`glossary_RNN` 时候使用的一 使用这种方式，:ref:`glossary_paddle` 可以比较简单的判断哪些输出是应该跨越时间步的，哪些不是。 +.. _glossary_timestep: + +时间步 +------ + +参考 :ref:`_glossary_Sequence` 。 + .. _glossary_Sequence: 时间序列 diff --git a/doc_cn/concepts/use_concepts.rst b/doc_cn/concepts/use_concepts.rst index 67e98edabc0..73fa78455f5 100644 --- a/doc_cn/concepts/use_concepts.rst +++ b/doc_cn/concepts/use_concepts.rst @@ -32,6 +32,7 @@ PaddlePaddle进程内嵌了一个 :code:`python` 解释器。 这个 :code:`pyth 所以，PaddlePaddle单机训练进程，:code:`paddle train` , 对于用户的主要接口语言为 python。 主要需要用户配置的两个文件为 :code:`DataProvider` 和训练文件 :code:`TrainerConfig` 。 +.. _glossary_DataProvider: DataProvider ============ @@ -42,6 +43,7 @@ DataProvider是 :code:`paddle train` 的数据提供器。 它负责将用户的 为了方便用户使用自己的数据格式， PaddlePaddle 提供了 `PyDataProvider`_ 来处理数据。 并且在这个Provider中，PaddlePaddle的 C++ 部分接管了如何shuffle，处理 batch，GPU/CPU通信，双缓冲，异步读取等问题。 用户可以参考 `PyDataProvider`_ 的相关文档，继续深入了解 DataProvider 的使用。 +.. _glossary_trainer_config: 训练文件 ======== diff --git a/doc_cn/conf.py.in b/doc_cn/conf.py.in index 93242ace406..80e5291815b 100644 --- a/doc_cn/conf.py.in +++ b/doc_cn/conf.py.in @@ -69,7 +69,7 @@ master_doc = 'index' # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'zh_CN' # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: diff --git a/paddle/gserver/tests/sequenceGen.py b/paddle/gserver/tests/sequenceGen.py index fab876fd30d..99440ada535 100644 --- a/paddle/gserver/tests/sequenceGen.py +++ b/paddle/gserver/tests/sequenceGen.py @@ -33,10 +33,10 @@ def process(settings, file_name): label, comment = line.strip().split('\t') label = int(''.join(label.split())) words = comment.split() - word_slot = [ + words = [ settings.word_dict[w] for w in words if w in settings.word_dict ] - yield word_slot, label + yield words, label ## for hierarchical sequence network @@ -52,20 +52,20 @@ def hook2(settings, dict_file, **kwargs): @provider(init_hook=hook2, should_shuffle=False) def process2(settings, file_name): with open(file_name) as fdata: - label_list = [] - word_slot_list = [] + labels = [] + sentences = [] for line in fdata: if (len(line)) > 1: label, comment = line.strip().split('\t') label = int(''.join(label.split())) words = comment.split() - word_slot = [ + words = [ settings.word_dict[w] for w in words if w in settings.word_dict ] - label_list.append(label) - word_slot_list.append(word_slot) + labels.append(label) + sentences.append(words) else: - yield word_slot_list, label_list - label_list = [] - word_slot_list = [] + yield sentences, labels + labels = [] + sentences = [] diff --git a/paddle/gserver/tests/sequence_nest_rnn.conf b/paddle/gserver/tests/sequence_nest_rnn.conf index 93b08eb2f87..524760be765 100644 --- a/paddle/gserver/tests/sequence_nest_rnn.conf +++ b/paddle/gserver/tests/sequence_nest_rnn.conf @@ -55,9 +55,8 @@ def outer_step(x): input=x) last = last_seq(input=inner_rnn_output, name="outer_rnn_state") - # "return last" should also work. But currently RecurrentGradientMachine - # does not handle it, and will report error: In hierachical RNN, all out - # links should be from sequences now. + # "return last" won't work, because recurrent_group only support the input + # sequence type is same as return sequence type. return inner_rnn_output out = recurrent_group( -- GitLab From a146fcf8ad37c111cd0fc44378dd6ca6804b3dfe Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 20 Nov 2016 21:25:25 +0800 Subject: [PATCH 038/347] stash --- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index 8ae0f85b295..eea220c0439 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -98,23 +98,28 @@ :ref:`glossary_trainer_config`\ 的模型配置 ------------------------------------------ -我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 - -- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 +本例配置了两个完全等价的全连接\ :ref:`glossary_RNN`\ 。对于单层序列模型的配置如下: .. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn.conf :language: python :lines: 36-48 + :linenos: -- 双层序列，外层memory是一个元素： +在该配置中，名称为\ :code:`rnn_state`\ 的全连接层暂存到了\ :ref:`glossary_Memory`\ 中。这个\ :ref:`glossary_Memory`\ 变量\ :code:`mem`\ 中可以保存到上一个\ :ref:`glossary_timestep`\ 中的全连接层的输出。从而实现一个全连接的\ :ref:`glossary_RNN`\ 。 - - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 - - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 +而对于\ :ref:`glossary_双层RNN`\ 来说，等价的网络配置如下\: .. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf :language: python :lines: 39-66 +- 双层序列，外层memory是一个元素： + + - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 + - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 + + + - 双层序列，外层memory是单层序列： - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 -- GitLab From 39af2ba2fc8ca961071c3c95cc205db5f25c95f5 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 21 Nov 2016 12:51:20 +0800 Subject: [PATCH 039/347] Fix Debian package name in ubuntu install docs. * Fix #486 --- doc_cn/build_and_install/install/ubuntu_install.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc_cn/build_and_install/install/ubuntu_install.rst b/doc_cn/build_and_install/install/ubuntu_install.rst index 70ac5225bd8..0fb59e25f69 100644 --- a/doc_cn/build_and_install/install/ubuntu_install.rst +++ b/doc_cn/build_and_install/install/ubuntu_install.rst @@ -11,7 +11,7 @@ PaddlePaddle的ubuntu安装包分为四个版本，他们是 cpu、gpu、cpu-noa .. code-block:: shell - gdebi paddle-*-cpu.deb + gdebi paddle-*-cpu*.deb 如果 :code:`gdebi` 没有安装,则需要使用 :code:`sudo apt-get install gdebi`, 来安装 :code:`gdebi` 。 @@ -20,7 +20,7 @@ PaddlePaddle的ubuntu安装包分为四个版本，他们是 cpu、gpu、cpu-noa .. code-block:: shell - dpkg -i paddle-*-cpu.deb + dpkg -i paddle-*-cpu*.deb apt-get install -f 在 :code:`dpkg -i` 的时候如果报一些依赖未找到的错误是正常的， -- GitLab From c9c60a158849863ab830910b950c260d7f69d5f3 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 21 Nov 2016 13:10:18 +0800 Subject: [PATCH 040/347] Change demo datafile location by using CDN in baidu. --- demo/model_zoo/embedding/pre_DictAndModel.sh | 4 +--- demo/model_zoo/resnet/get_model.sh | 4 +--- demo/seqToseq/data/paraphrase_data.sh | 4 +--- demo/seqToseq/data/wmt14_model.sh | 4 +--- 4 files changed, 4 insertions(+), 12 deletions(-) diff --git a/demo/model_zoo/embedding/pre_DictAndModel.sh b/demo/model_zoo/embedding/pre_DictAndModel.sh index 7821850fb25..6d647f5dd93 100755 --- a/demo/model_zoo/embedding/pre_DictAndModel.sh +++ b/demo/model_zoo/embedding/pre_DictAndModel.sh @@ -18,7 +18,5 @@ set -x # download the dictionary and pretrained model for file in baidu.dict model_32.emb model_64.emb model_128.emb model_256.emb do - # following is the google drive address - # you can also directly download from https://pan.baidu.com/s/1o8q577s - wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/embedding/$file --no-check-certificate + wget http://paddlepaddle.bj.bcebos.com/model_zoo/embedding/$file done diff --git a/demo/model_zoo/resnet/get_model.sh b/demo/model_zoo/resnet/get_model.sh index 89312d43edf..133d08fca43 100755 --- a/demo/model_zoo/resnet/get_model.sh +++ b/demo/model_zoo/resnet/get_model.sh @@ -24,9 +24,7 @@ echo "Downloading ResNet models..." for file in resnet_50.tar.gz resnet_101.tar.gz resnet_152.tar.gz mean_meta_224.tar.gz do - # following is the google drive address - # you can also directly download from https://pan.baidu.com/s/1o8q577s - wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/imagenet/$file --no-check-certificate + wget http://paddlepaddle.bj.bcebos.com/model_zoo/imagenet/$file tar -xvf $file rm $file done diff --git a/demo/seqToseq/data/paraphrase_data.sh b/demo/seqToseq/data/paraphrase_data.sh index ea1f8dbcfad..1b3f1d45e11 100755 --- a/demo/seqToseq/data/paraphrase_data.sh +++ b/demo/seqToseq/data/paraphrase_data.sh @@ -16,9 +16,7 @@ set -e set -x # download the in-house paraphrase dataset -# following is the google drive address -# you can also directly download from https://pan.baidu.com/s/1o8q577s -wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/embedding/paraphrase.tar.gz --no-check-certificate +wget http://paddlepaddle.bj.bcebos.com/model_zoo/embedding/paraphrase.tar.gz # untar the dataset tar -zxvf paraphrase.tar.gz diff --git a/demo/seqToseq/data/wmt14_model.sh b/demo/seqToseq/data/wmt14_model.sh index 2cec30688d2..d6e7a732644 100755 --- a/demo/seqToseq/data/wmt14_model.sh +++ b/demo/seqToseq/data/wmt14_model.sh @@ -16,9 +16,7 @@ set -e set -x # download the pretrained model -# following is the google drive address -# you can also directly download from https://pan.baidu.com/s/1o8q577s -wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/wmt14_model.tar.gz --no-check-certificate +wget http://paddlepaddle.bj.bcebos.com/model_zoo/wmt14_model.tar.gz # untar the model tar -zxvf wmt14_model.tar.gz -- GitLab From 8393c19ccf19559493a38f14842d187855b0d0e9 Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 21 Nov 2016 13:44:41 +0800 Subject: [PATCH 041/347] Add recursive mutex and counter for gpu profiler --- doc/optimization/gpu_profiling.rst | 3 ++- doc/optimization/nvprof.png | Bin 487751 -> 0 bytes paddle/math/tests/test_GpuProfiler.cpp | 24 ++++++++++++++++-------- paddle/utils/Stat.cpp | 18 ++++++++++++++++++ paddle/utils/Stat.h | 12 ++++++------ 5 files changed, 42 insertions(+), 15 deletions(-) delete mode 100644 doc/optimization/nvprof.png diff --git a/doc/optimization/gpu_profiling.rst b/doc/optimization/gpu_profiling.rst index 013edb396e0..44ecb348858 100644 --- a/doc/optimization/gpu_profiling.rst +++ b/doc/optimization/gpu_profiling.rst @@ -24,7 +24,7 @@ Why we need profiling? ====================== Since training deep neural network typically take a very long time to get over, performance is gradually becoming the most important thing in deep learning field. The first step to improve performance is to understand what parts -are slow. No point in improving performance of a region which doesn’t take much time! +are slow. There is no point in improving performance of a region which doesn’t take much time! How to do profiling? @@ -59,6 +59,7 @@ above profilers. 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z#8mUa3{S0peD=V$G#x2cGV!=nU1~o53z`9LYrk9MYN$XWa%ogm@U#!6sv9ff%IxcX$|RQ*$gj|k5|*b?EZZVc z8)haVcK(lZS}561Om~4!+qM1HzaVR)%Qy?d#$V7S;$u*V-V*(2l zH+3W^9RwPSSI}n^Bfx8rbQ#{5k4-uwSwMC|j&B*|@uIJEa5{sT6A07$QhDcMkI+u9 ztkwdSl5CVd|LiSs4T}2UVP4SClEEe!-fQQR_You)Hpc$jX}9*YF{vDR^9XC>oWopQkvA6pX%-p|eBMLQzQvkLF z(H`^x`Z7%qdgJ^5@Ou99n*Qr`%>xo-xvwwz2}5`mC>0?GyFEQ3A=<|1h%ai#+5;NV zWV;RJGv5QKy6GIW21(4APdX<9mhdlclf)IcXE2SW&4Oj0o>|c=^50z_>3&#z|AAZ+ z1H|RlMZjW8aGD@%B=}<5!JLGd2e_r8Lml@JA)y9giBNLKGkpqv9WPfW>~i=qngrI` zKrdik(#)hIwKZ#gxVD-8gll!863+A&lv;sV{randomizeUniform(); inputGpu->copyFrom(*input); - target->bilinearForward(*input, imgSizeH, imgSizeW, - 2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW); - targetGpu->bilinearForward(*inputGpu, imgSizeH, imgSizeW, - 2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW); + { + // nvprof: GPU Proflier + REGISTER_GPU_PROFILER("testBilinearFwdBwd"); + target->bilinearForward(*input, imgSizeH, imgSizeW, + 2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW); + targetGpu->bilinearForward(*inputGpu, imgSizeH, imgSizeW, + 2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW); + } // check targetCheck->copyFrom(*targetGpu); @@ -104,25 +108,29 @@ void testBilinearFwdBwd(int numSamples, int imgSizeH, int imgSizeW, MatrixCheckErr(*inputGrad, *targetCheckGrad); } -TEST(Profiler, BilinearFwdBwd) { +TEST(Profiler, testBilinearFwdBwd) { auto numSamples = 10; auto channels = 16; auto imgSize = 64; { // nvprof: GPU Proflier - REGISTER_GPU_PROFILER("testBilinearFwdBwd", - "numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64"); + REGISTER_GPU_PROFILER("testBilinearFwdBwd"); // Paddle built-in timer REGISTER_TIMER_INFO("testBilinearFwdBwd", "numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64"); testBilinearFwdBwd(numSamples, imgSize, imgSize, channels); } - globalStat.printStatus("testBilinearFwdBwd"); + globalStat.printAllStatus(); } int main(int argc, char** argv) { testing::InitGoogleTest(&argc, argv); initMain(argc, argv); + + // nvprof: GPU Proflier + REGISTER_GPU_PROFILER("RecursiveProfilingTest", + "numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64"); + return RUN_ALL_TESTS(); } diff --git a/paddle/utils/Stat.cpp b/paddle/utils/Stat.cpp index 733fc7f1a70..ab140c33502 100644 --- a/paddle/utils/Stat.cpp +++ b/paddle/utils/Stat.cpp @@ -203,4 +203,22 @@ StatInfo::~StatInfo() { } } +static unsigned g_profileCount = 0; +static std::recursive_mutex g_profileMutex; + +GpuProfiler::GpuProfiler(std::string statName, std::string info) + : guard_(g_profileMutex) { + if (++g_profileCount == 1) { + LOG(INFO) << "Enable GPU Profiler Stat: [" + << statName << "] " << info; + hl_profiler_start(); + } +} + +GpuProfiler::~GpuProfiler() { + if (--g_profileCount == 0) { + hl_profiler_end(); + } +} + } // namespace paddle diff --git a/paddle/utils/Stat.h b/paddle/utils/Stat.h index 8f65abb2d05..8bfe42a6948 100644 --- a/paddle/utils/Stat.h +++ b/paddle/utils/Stat.h @@ -283,8 +283,10 @@ inline StatSet& registerTimerArg2(uint64_t threshold = -1, class GpuProfiler final { public: - GpuProfiler() { hl_profiler_start(); } - ~GpuProfiler() { hl_profiler_end(); } + GpuProfiler(std::string statName, std::string info); + ~GpuProfiler(); +private: + std::lock_guard guard_; }; #ifdef PADDLE_DISABLE_PROFILER @@ -293,10 +295,8 @@ public: #else -#define REGISTER_GPU_PROFILER(statName, ...) \ - LOG(INFO) << "Enable GPU Profiler Stat: [" \ - << statName << "] " << #__VA_ARGS__; \ - GpuProfiler __gpuProfiler; +#define REGISTER_GPU_PROFILER(statName, ...) \ + GpuProfiler __gpuProfiler(statName, #__VA_ARGS__); #endif // DISABLE_PROFILER -- GitLab From 4fcf01a849743965b8fb49db9173217e4aed2dfc Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 21 Nov 2016 16:36:07 +0800 Subject: [PATCH 042/347] Refine code, found a bad design --- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 20 ++++++++----- .../simple_full_hierarchical_recurrent.dot | 30 +++++++++++++++++++ .../algorithm/rnn/simple_full_recurrent.dot | 19 ++++++++++++ 3 files changed, 62 insertions(+), 7 deletions(-) create mode 100644 doc_cn/algorithm/rnn/simple_full_hierarchical_recurrent.dot create mode 100644 doc_cn/algorithm/rnn/simple_full_recurrent.dot diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index eea220c0439..09172c53f78 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -107,23 +107,29 @@ 在该配置中，名称为\ :code:`rnn_state`\ 的全连接层暂存到了\ :ref:`glossary_Memory`\ 中。这个\ :ref:`glossary_Memory`\ 变量\ :code:`mem`\ 中可以保存到上一个\ :ref:`glossary_timestep`\ 中的全连接层的输出。从而实现一个全连接的\ :ref:`glossary_RNN`\ 。 +以数据\ :code:`[4, 5, 2, 0, 9, 8, 1, 4]`\ 举例，单层\ :ref:`glossary_RNN`\ 的网络图如下\: + +.. graphviz:: simple_full_recurrent.dot + 而对于\ :ref:`glossary_双层RNN`\ 来说，等价的网络配置如下\: .. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf :language: python :lines: 39-66 + :linenos: + :emphasize-lines: 4-6 -- 双层序列，外层memory是一个元素： - - - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 - - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 +- 在该配置中，外层的\ :code:`outer_mem`\ 和内层的\ :code:`inner_mem`\ 两个变量配合，实现了和单层\ :ref:`glossary_RNN`\ 等价的全连接\ :ref:`glossary_RNN`\ 。 + - 外层\ :code:`outer_step`\ 中的\ :code:`outer_mem`\ 会将神经网络中每个子序列的最后一个结果记录下来。即将第18行的\ :code:`last`\ 变量记录下来。 + - 内层\ :code:`inner_step`\ 中的\ :code:`inner_mem`\ 会将神经网络中子序列中的每一个元素的结果记录下来。即将第7行的\ :code:`out`\ 变量记录下来。 + - 内层的\ :code:`inner_mem`\ 初始值是\ :code:`outer_mem`(:code:`boot_layer`)。于是前一个子序列的最后结果，是新的子序列的初试结果。即完成了简单的全连接\ :code:`glossary_RNN`\ 。 +本例中的\ :ref:`glossary_双层RNN`\ ,以数据\ :code:`[ [4, 5, 2], [0, 9], [8, 1, 4]]`\ 举例，配置图如下\: -- 双层序列，外层memory是单层序列： +.. graphviz:: simple_full_hierarchical_recurrent.dot - - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 - - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 +这里有一点注意事项，Paddle目前实现的\ :ref:`glossary_双层RNN`\ 不完全支持内层\ :ref:`glossary_RNN`\ 的\ :ref:`glossary_Memory`\ 引用外层\ :ref:`glossary_RNN`\ 的某一层序列输入。即\ :code:`inner_mem`的\ :code:`boot_layer`\ 需要是非序列类型的，或者可以是序列类型，但是每个时间步下，序列长度是一致的。从序列类型转换为非序列类型，可以使用\ :code:`pooling_layer`, :code:`last_seq`, :code:`first_seq`\ 等操作进行转换。 示例3：双进双出，输入不等长 =========================== diff --git a/doc_cn/algorithm/rnn/simple_full_hierarchical_recurrent.dot b/doc_cn/algorithm/rnn/simple_full_hierarchical_recurrent.dot new file mode 100644 index 00000000000..ff278a0323b --- /dev/null +++ b/doc_cn/algorithm/rnn/simple_full_hierarchical_recurrent.dot @@ -0,0 +1,30 @@ +digraph G { + rankdir=LR; + + subgraph cluster_t0 { + a [label="4"] + b [label="5"] + c [label="2"] + } + + subgraph cluster_t1 { + d [label="0"] + e [label="9"] + } + + subgraph cluster_t2 { + f [label="8"] + g [label="1"] + h [label="4"] + } + + a -> b; + b -> c; + c -> d [constraint=false]; + + d -> e; + e -> f [constraint=false]; + + f -> g; + g -> h; +} \ No newline at end of file diff --git a/doc_cn/algorithm/rnn/simple_full_recurrent.dot b/doc_cn/algorithm/rnn/simple_full_recurrent.dot new file mode 100644 index 00000000000..cee281fbac9 --- /dev/null +++ b/doc_cn/algorithm/rnn/simple_full_recurrent.dot @@ -0,0 +1,19 @@ +digraph G { + rankdir=LR; + a [label="4"] + b [label="5"] + c [label="2"] + d [label="0"] + e [label="9"] + f [label="8"] + g [label="1"] + h [label="4"] + + a -> b; + b -> c; + c -> d; + d -> e; + e -> f; + f -> g; + g -> h; +} \ No newline at end of file -- GitLab From 8c2e5b2cadf450de6f5b44ffffa42da93651c3c4 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 21 Nov 2016 20:56:00 +0800 Subject: [PATCH 043/347] Update use_concepts --- doc_cn/concepts/use_concepts.rst | 156 ++++++++++++++----------------- doc_cn/faq/index.rst | 19 +++- 2 files changed, 88 insertions(+), 87 deletions(-) diff --git a/doc_cn/concepts/use_concepts.rst b/doc_cn/concepts/use_concepts.rst index d3da9cc16b0..49c45ff7794 100644 --- a/doc_cn/concepts/use_concepts.rst +++ b/doc_cn/concepts/use_concepts.rst @@ -2,16 +2,20 @@ PaddlePaddle 基本使用概念 ######################### -PaddlePaddle是一个深度学习框架，同时支持单机和多机模式的系统。命令 ``paddle train`` 可启动单机模式的进程，我们称之为 ``trainer`` 进程。单机所有设备使用均在单机进程内调度完成。多机模式除了需要启动trainer进程外，还需要通过命令 ``paddle pserver`` 启动多机参数服务器进程, 我们称之为   ``pserver`` 进程。该进程负责多个单机进程间的通信，进而充分利用集群的计算资源。 PaddlePaddle同时以 ``swig api`` 的形式，提供训练结果模型预测的方法和自定义训练流程。 +PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。 -下面我们会介绍trainer进程中的一些概念，这些概念会对如何使用PaddlePaddle有一定的帮助。 了解这些概念的前提是，读者已经了解 `基本的神经网络/机器学习原理和概念 `_ 。同时，如果想要了解PaddlePaddle实现中的一些概念，请参考 `PaddlePaddle 编程中的基本概念 `_ 。 +单节模式用命令 ``paddle train`` 可以启动一个trainer进程，一个单机训练作业只包括一个trainer进程，单机的所有设备使用，均在单机进程内调度完成。 + +如果数据规模比较大，希望加速训练，可以启动分布式作业。一个分布式作业里包括若干trainer进程和若干Parameter Server（或称pserver）进程。用命令 ``paddle pserver`` 可以启动 pserver 进程，pserver进程用于协调多个trainer进程之间的通信。 + +本文首先介绍trainer进程中的一些使用概念，然后介绍pserver进程中概念。 .. contents:: -系统模块 +系统框图 ======== -``trainer`` 进程内嵌了一个 ``python`` 解释器， 这个 ``python`` 解释器负责解析用户定义的神经网络配置；解析输入数据流，并将数据传入给 ``trainer`` 系统。 +下图描述了用户使用框图，PaddlePaddle里链接了Python解释器，trainer进程可以利用这个解释器执行Python脚本，Python脚本里定义了模型配置、训练算法、以及数据读取函数。其中，数据读取程序往往定义在一个单独Python脚本文件里，被称为DataProvider，通常是一个Python函数。模型配置、训练算法通常定义在另一单独Python文件中。下面将分别介绍这两部分。 .. graphviz:: @@ -30,132 +34,105 @@ PaddlePaddle是一个深度学习框架，同时支持单机和多机模式的 py -> data_provider [dir="back"]; } -所以，单机训练 ``trainer`` 进程对用户的主要接口语言为Python。用户需要配置文件主要有两个：数据流提供器 ``DataProvider`` 和模型配置 ``TrainerConfig`` 。 - - DataProvider ============ -DataProvider是 ``trainer`` 进程的数据提供器。主要负责将用户的原始数据转换成 ``trainer`` 系统可以识别的数据类型。当系统需要新的数据训练时，会调用DataProvider获取数据接口。当所有数据读取完一轮后，DataProvider返回空数据通知系统一轮数据读取结束。 ``trainer`` 在每一轮训练开始时会重置DataProvider。 +在不同的应用里，训练数据的格式往往各不相同。因此，为了用户能够灵活的处理数据，我们提供了Python处理数据的接口，称为 `PyDataProvider`_ 。 -需要注意的是，DataProvider是被 ``trainer`` 系统调用，而不是新数据驱动系统；数据 ``shuffle`` 和一些随机化噪声添加都应该在DataProvider中完成。 +trainer进程会调用DataProvider函数，将用户的原始数据转换成系统可以识别的数据类型。当所有数据读取完一轮后，DataProvider返回空数据，通知系统一轮数据读取结束，系统每一轮训练开始时会重置DataProvider。需要注意的是，DataProvider是被系统调用，而不是新数据驱动系统，一些随机化噪声添加都应该在DataProvider中完成。 -为了用户能够灵活的处理数据，PaddlePaddle提供了处理数据的Python接口（称为 `PyDataProvider`_ ）。 在 ``PyDataProvider`` 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，需要说明的是，一些情况下需要Python接口里处理shuffle，可以参考 `PyDataProvider`_ 的相关文档继续深入了解。 +在 ``PyDataProvider`` 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，一些情况下(如：``min_pool_size=0``)需要Python接口里处理shuffle，可以参考 `PyDataProvider`_ 的相关文档继续深入了解。 -TrainerConfig -============= - -模型配置是一个Python文件，主要包括神经网络结构、优化算法、数据传入方式，使用命令行参数 ``--config`` 传给``trainer``主程序。 例如\: - -.. code-block:: bash +模型配置文件 +============ - paddle train --config=trainer_config.py +模型配置主要包括数据传入接口定义(DataConfig)、优化算法(OptimizationConfig)、网络结构(ModelConfig)。 其中数据传入接口定义与DataProvider的关系是：DataProvider里定义数据读取函数，配置文件的DataConfig里指定DataProvider文件名字、生成数据函数接口，请不要混淆。 一个简单的模型配置文件为： .. literalinclude:: trainer_config.py :linenos: -下面我们详细的介绍一下模型配置中各个模块的概念。 - +文件开头 ``from paddle.trainer_config_helpers import *`` ，是因为PaddlePaddle配置文件与C++模块通信的最基础协议是protobuf。为了避免用户直接写复杂的protobuf string，我们为用户定以Python接口来配置网络，该Python代码可以生成protobuf包，这就是的作用`trainer_config_helpers`_的作用。因此，在文件的开始，需要import这些函数。 这个包里面包含了模型配置需要的各个模块。 -trainer_config_helpers ----------------------- +下面分别介绍DataConfig、OptimizationConfig、ModelConfig这三部分该概念。 -PaddlePaddle配置文件与C++模块通信的最基础协议是 ``protobuf`` 。为了避免用户直接写比较难写的protobuf string，我们通过Python代码来生成protobuf包，这就是helpers的作用。所以在文件的开始，需要import这些helpers函数。 +DataConfig +---------- -需要注意的是，这个 ``paddle.trainer_config_helpers`` 包是标准的python包，这意味着用户可以选择自己喜欢的 ``IDE`` 或者编辑器来编写Paddle的配置文件，这个Python包注释文档比较完善，并提供了IDE的代码提示与类型注释。 +使用函数 ``define_py_data_sources2`` 配置数据源，后缀 2 是Paddle历史遗留问题，因为Paddle之前使用的PyDataProvider性能问题，重构了一个新的 `PyDataProvider`_ 。 -data_sources ------------- +``define_py_data_sources2`` 里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。 -data_sources配置神经网络的数据源，使用的函数是 ``define_py_data_sources2`` ，这个函数是定义了使用 `PyDataProvider`_ 提供数据源。后缀 ``2`` 是Paddle历史遗留问题，因为Paddle之前使用的PyDataProvider性能问题，重构了一个新的 `PyDataProvider`_ 。 +``module`` 和 ``obj`` 指定了DataProvider的文件名和返回数据的函数名。更详细的使用，请参考 `PyDataProvider`_ 。 -data_sources里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。 +OptimizationConfig +------------------ -其中``module`` 和``obj``指定了DataProvider的文件名和返回数据的函数名。更详细的使用，请参考 `PyDataProvider`_ 。 +通过`settings`_ 接口设置神经网络所使用的训练参数和优化算法，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 `settings`_ 文档。 -settings --------- +ModelConfig +----------- -`settings`_ 设置训练神经网络所使用的算法。包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 `settings`_ 文档。 +神经网络配置主要包括网络连接、激活函数、损失函数、评估器。 -网络配置 --------- +- 网络连接： 主要由Layer组成，每个Layer返回的都是一个 ``LayerOutput`` 对象，Layer里面可以定义参数属性、激活类型等。 -上述配置中余下的部分是神经网络配置，主要包括网络连接、 ``cost`` 层、评估器。 + 为了更灵活的配置，PaddlePaddle提供了基于 Projection 或者 Operator 的配置，这两个需要与 ``mixed_layer`` 配合使用。这里简单介绍Layer、Projection、Operator的概念: -- 首先，定义了一个名字叫"pixel"的 ``data_layer`` ，每个layer返回的都是一个 ``LayerOutput`` 对象，比如第一层的输出对象称作 ``img`` 。 -- 然后，这个对象作为另一个layer（ ``simple_img_conv_pool`` ）的输入， ``simple_img_conv_pool`` 是一个组合层，包括了图像的卷积 (convolution) 和池化(pooling)， -- 其次，连接到全连接层(``fc_layer``)，再连接到一个含Softmax激活的全连接层。 -- 最终，连接到cost层（ ``classification_cost`` ）， ``classification_cost`` 默认使用多类交叉熵损失函数和分类错误率统计评估器。标记网络输出的函数为 ``outputs`` ，网络的输出是神经网络的优化目标，神经网络训练的时候，实际上就是要最小化这个输出。 + - Layer: 神经网络的某一层，可以有可学习的参数，一般是封装了许多复杂操作的集合。 + - Projection：需要与 ``mixed_layer`` 配合使用，含可学习参数。 + - Operator： 需要与 ``mixed_layer`` 配合使用，不含可学习参数，输入全是其他Layer的输出。 -用该模型配置进行预测时，网络的输出也是通过 ``outputs`` 标记。 + + 这个配置文件网络由 ``data_layer`` 、 ``simple_img_conv_pool`` 、 ``fc_layer`` 组成。 + - `data_layer`_ ： 通常每个配置文件都会包括 ``data_layer`` ，定义输入数据大小。 + - `simple_img_conv_pool`_ ：是一个组合层，包括了图像的卷积 (convolution)和池化(pooling)。 + - `fc_layer`_ ：全连接层，激活函数为Softmax，这里也可叫分类层。 -Layer、Projection、Operator -=========================== + +- 损失函数和评估器：损失函数即为网络的优化目标，评估器可以评价模型结果。 -PaddlePaddle的网络是基于Layer来配置的。所谓的Layer即是神经网络的某一层，一般是封装了许多复杂操作的操作集合。比如最简单的 ``fc_layer`` ，包括矩阵乘法、多输入的求和、加Bias操作、激活( ``activation`` )函数操作。 - -.. code-block:: python + PaddlePaddle包括很多损失函数和评估起，详细可以参考 `损失函数层`_ 和 `评估器`_ 。这里 ``classification_cost`` 默认使用多类交叉熵损失函数和分类错误率统计评估器。 + +- ``outputs``: 标记网络输出的函数为 ``outputs`` 。 - data = data_layer(name='data', size=200) - out = fc_layer(input=data, size=200, act=TanhActivation()) + 训练阶段，网络的输出为神经网络的优化目标；预测阶段，网络的输出也可通过 ``outputs`` 标记。 -对于更灵活配置需求，PaddlePaddle提供了基于 ``Projection`` 或者 ``Operator`` 的配置，这些需要与 ``mixed_layer`` 配合使用。 ``mixed_layer`` 是将多个输入累加求和，然后加Bias和 ``activation`` 操作。 ``mixed_layer`` 具体计算是通过内部的Projection和Operator完成。Projection含有可学习参数；而Operator不含可学习的参数，输入全是其他Layer的输出。 +这里对 ``mixed_layer`` 稍做详细说明， 该Layer将多个输入(Projection 或 Operator)累加求和，具体计算是通过内部的 Projection 和 Operator 完成，然后加 Bias 和 activation 操作， 例如，和 ``fc_layer`` 同样功能的 ``mixed_layer`` 是: .. code-block:: python + + data = data_layer(name='data', size=200) + with mixed_layer(size=200) as out: + out += full_matrix_projection(input=data) - data = data_layer(name='data', size=200) - with mixed_layer(size=200) as out: - out += full_matrix_projection(input=data) - -PaddlePaddle可以使用 ``mixed layer`` 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 `mixed_layer`_ 的相关文档进行配置。 - -如何利用单机的所有GPU或所有CPU核心 -=============================== - -PaddlePaddle的单机 ``trainer`` 进程可以充分利用一台计算机上所有的GPU资源或者CPU。 +PaddlePaddle 可以使用 ``mixed layer`` 配置出非常复杂的网络，甚至可以直接配置一个完整的LSTM。用户可以参考 `mixed_layer`_ 的相关文档进行配置。 -如果要使用机器上多块GPU，使用如下命令即可\: -.. code-block:: bash - - paddle train --use_gpu=true --trainer_count=4 # use 4 gpu card, 0, 1, 2, 3 - -如果要使用机器上多块CPU, 使用如下命令即可\: - -.. code-block:: bash - - paddle train --trainer_count=4 # use 4 cpu cores. - -如果要指定GPU编号，例如选择第0、2号GPU，则可以设置 ``CUDA_VISIBLE_DEVICES`` 环境变量来指定特定的GPU。具体可以参考连接`masking-gpu`_ ，命令为： +分布式训练 +========== -.. code-block:: bash - - env CUDA_VISIBLE_DEVICES=0,2 paddle train --use_gpu=true --trainer_count=2 - -如何利用多台机器的计算资源训练神经网络 -=================================== - -PaddlePaddle多机采用经典的 ``Parameter Server`` 架构对多个节点的 ``trainer`` 进行同步。多机训练神经网络，要讲数据切分到不同的机器上，切分数据相对简单，所以在PaddlePaddle的开源实现中并没有提供相关工具包。 - -多机训练的经典拓扑结构如下\: +PaddlePaddle多机采用经典的 Parameter Server 架构对多个节点的 trainer 进行同步。多机训练的经典拓扑结构如下\: .. graphviz:: pserver_topology.dot -图中每个灰色方块是一台机器，在每个机器中，先启动一个 ``paddle pserver`` 进程，并指定端口号，可能的参数是\: +图中每个灰色方块是一台机器，在每个机器中，先使用命令 ``paddle pserver`` 启动一个pserver进程，并指定端口号，可能的参数是\: .. code-block:: bash paddle pserver --port=5000 --num_gradient_servers=4 --nics='eth0' -这里说明系统的 ``pserver`` 进程端口是 ``5000`` ，有四个训练进程(即 ``--gradient_servers=4`` ，PaddlePaddle同时将 ``trainer`` 称作 ``GradientServer`` 。因为其为负责提供Gradient)。 启动之后 ``pserver`` 进程之后，需要 ``trainer`` 训练进程，再在各个机器上运行如下命令\: +* 指定 pserver 进程端口是 5000 。 +* 有四个训练进程(即 ``--gradient_servers=4`` ，PaddlePaddle同时将 trainer 称作 GradientServer 。因为其为负责提供Gradient) 。 +* 指定以太网类型为TCP网络。 + +启动之后 pserver 进程之后，需要启动 trainer 训练进程，在各个机器上运行如下命令\: .. code-block:: bash @@ -163,16 +140,23 @@ PaddlePaddle多机采用经典的 ``Parameter Server`` 架构对多个节点的 对于简单的多机协同训练使用上述方式即可。另外，pserver/train 通常在高级情况下，还需要设置下面两个参数\： -* --ports_num\: 一个 pserver进程共绑定多少个端口用来做稠密更新。默认是1 +* --ports_num\: 一个 pserver 进程共绑定多少个端口用来做稠密更新。默认是1 * --ports_num_for_sparse\: 一个pserver进程共绑定多少端口用来做稀疏更新，默认是0 -使用手工指定端口数量，是因为Paddle的网络通信中，使用了 ``int32`` 作为消息长度，比较容易在大模型下溢出。所以，在 ``pserver`` 进程中可以启动多个子线程去接受trainer的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，对性能尤其是内存占用有一定的开销，另外稀疏更新的端口如果太大的话，很容易导致某一个参数服务器没有分配到任何参数。 +使用手工指定端口数量，是因为Paddle的网络通信中，使用了 int32 作为消息长度，比较容易在大模型下溢出。所以，在 pserver 进程中可以启动多个子线程去接受 trainer 的数据，这样单个子线程的长度就不会溢出了。但是这个值不可以调的过大，因为增加这个值，对性能尤其是内存占用有一定的开销，另外稀疏更新的端口如果太大的话，很容易导致某一个参数服务器没有分配到任何参数。 详细的说明可以参考，使用 `集群训练Paddle`_ 。 .. _PyDataProvider: ../ui/data_provider/pydataprovider2.html -.. _settings: ../../doc/ui/api/trainer_config_helpers/optimizers.html#settings -.. _mixed_layer: ../../doc/ui/api/trainer_config_helpers/layers.html#mixed-layer -.. _masking-gpu: http://www.acceleware.com/blog/cudavisibledevices-masking-gpus +.. _settings: ../../doc/ui/api/trainer_config_helpers/optimizers.html#settings +.. _trainer_config_helper: ../../doc/ui/api/trainer_config_helpers/index.html +.. _data_layer: ../../doc/ui/api/trainer_config_helpers/layers.html#data-layer +.. _simple_img_conv_pool: ../../doc/ui/api/trainer_config_helpers/networks.html#simple-img-conv-pool +.. _fc_layer: ../../doc/ui/api/trainer_config_helpers/layers.html#fc-layer +.. _损失函数层: ../../doc/ui/api/trainer_config_helpers/layers.html#cost-layers +.. _评估器: ../../doc/ui/api/trainer_config_helpers/evaluators.html +.. _mixed_layer: ../../doc/ui/api/trainer_config_helpers/layers.html#mixed-layer +.. _masking-gpu: http://www.acceleware.com/blog/cudavisibledevices-masking-gpus + .. _集群训练Paddle: ../cluster/index.html diff --git a/doc_cn/faq/index.rst b/doc_cn/faq/index.rst index 3eb0e10ae22..8da21e5b8bd 100644 --- a/doc_cn/faq/index.rst +++ b/doc_cn/faq/index.rst @@ -213,4 +213,21 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字 原因是：单元测试使用了一个旧版本的python包，而没有测试到代码中实际修改的python包。即单元测试需要一个干净的环境： * 如果paddle包已经在python的site-packages里面了，那么单元测试时使用的paddle包，就是site-packages里面的python包，而不是源码目录里 :code:`/python` 目录下的python包。 -* 即便设置了 :code:`PYTHONPATH` 到 :code:`/python` 也没用，因为python的搜索路径是优先已经安装的python包。 \ No newline at end of file +* 即便设置了 :code:`PYTHONPATH` 到 :code:`/python` 也没用，因为python的搜索路径是优先已经安装的python包。 + +9. 如何指定GPU设备 +----------------- + +例如机器上有4块GPU，编号从0开始，指定使用2、3号GPU： + +* 方式1：通过 ``CUDA_VISIBLE_DEVICES`` 环境变量来指定特定的GPU。 + +.. code-block:: bash + + env CUDA_VISIBLE_DEVICES=2,3 paddle train --use_gpu=true --trainer_count=2 + +* 方式2：通过命令行参数 ``--gpu_id`` 指定。 + +.. code-block:: bash + + paddle train --use_gpu=true --trainer_count=2 --gpu_id=2 -- GitLab From b34ab48b5c397245972335925e2095dd60cd330d Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Mon, 21 Nov 2016 14:20:25 -0800 Subject: [PATCH 044/347] Update docker_install.rst address @wangkuiyi comments. --- doc_cn/build_and_install/install/docker_install.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/doc_cn/build_and_install/install/docker_install.rst b/doc_cn/build_and_install/install/docker_install.rst index 0872fd0b7a1..40339659be4 100644 --- a/doc_cn/build_and_install/install/docker_install.rst +++ b/doc_cn/build_and_install/install/docker_install.rst @@ -1,8 +1,7 @@ 安装PaddlePaddle的Docker镜像 ============================ -PaddlePaddle提供了Docker的使用镜像。PaddlePaddle推荐使用Docker进行PaddlePaddle的部署和运行。Docker是一个基于容器的轻量级虚拟环境。具有和宿主机相近的运行效率，并提供 -了非常方便的二进制分发手段。 +PaddlePaddle项目提供官方 `Docker `_ 镜像。Docker镜像是我们目前唯一官方支持的部署和运行方式。 下述内容将分为如下几个类别描述。 -- GitLab From 926f10b8d1beb24c3b8ce2f2890e0c95cc13dff8 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Mon, 21 Nov 2016 15:12:19 -0800 Subject: [PATCH 045/347] Update index.md Thanks for @wangkuiyi comments. --- doc_cn/introduction/index.md | 25 +++++++++++-------------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/doc_cn/introduction/index.md b/doc_cn/introduction/index.md index d6efab0cf1f..f6eb5456c00 100644 --- a/doc_cn/introduction/index.md +++ b/doc_cn/introduction/index.md @@ -1,20 +1,18 @@ # 简介 -PaddlePaddle 源于百度的开源深度学习平台，有如下几个特点。首先，简单易用的：用户可以通过简单的十几行配置脚本搭建经典的神经网络模型。其次，高效强大的：PaddlePaddle可以支撑复杂集群环境下超大模型的训练，令你受益于深度学习的前沿成果。最后，在百度内部，已经有大量产品线使用了基于PaddlePaddle的深度学习技术。 - -这份简短的介绍将像你展示如何利用PaddlePaddle来解决一个经典的机器学习问题。 +PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍将向你展示如何利用PaddlePaddle来解决一个经典的线性回归问题。 ## 1. 一个经典的任务 -让我们从一个基础问题开始：单变量的线性回归。问题假定观测到了一批二维空间上的点`(x, y) `，并且已知 `x` 和 `y` 之间存在着某种线性关系，我们的目标是通过观测数据来学习这个线性关系。作为一个简单基础的模型，线性回归有着广泛的应用场景。以一个资产定价的问题为例，`x` 对应于房屋的大小，`y` 对应于房屋价格。我们可以通过观察市场上房屋销售的情况拟合 `x` 和 `y` 之间的关系，从而为新房屋的定价提供预测和参考。 +我们展示如何用PaddlePaddle解决单变量的线性回归问题。线性回归的输入是一批点`(x, y) `，其中 `y = wx + b + ε`， 而 ε 是一个符合高斯分布的随机变量。线性回归的输出是从这批点估计出来的参数 w 和 b。 +一个例子是房产估值。我们假设房产的价格（y）是其大小（x）的一个线性函数，那么我们可以通过收集市场上房子的大小和价格，用来估计线性函数的参数w 和 b。 ## 2. 准备数据 -假设变量 `X` 和 `Y` 的真实关系为： `Y = 2X + 0.3`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为PaddlePaddle的输入。产生PaddlePaddle的输入数据和写一段普通的Python脚本几乎一样，你唯一需要增加的就是定义输入数据的类型。 +假设变量 `x` 和 `y` 的真实关系为： `y = 2x + 0.3 + ε`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为线性回归的输入。下面脚本符合PaddlePaddle期待的读取数据的Python程序的模式。 ```python -# -*- coding:utf-8 -*- # dataprovider.py from paddle.trainer.PyDataProvider2 import * import random @@ -29,12 +27,11 @@ def process(settings, input_file): ## 3. 训练模型 -为了还原 `Y = 2X + 0.3`，我们先从一条随机的直线 `Y' = wX + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `Y'` 和 `Y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。 +为了还原 `y = 2x + 0.3`，我们先从一条随机的直线 `y' = wx + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `y'` 和 `y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。 在PaddlePaddle里，该模型的网络配置如下。 ```python -# -*- coding:utf-8 -*- # trainer_config.py from paddle.trainer_config_helpers import * @@ -50,10 +47,10 @@ settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) # 3. 神经网络配置 x = data_layer(name='x', size=1) y = data_layer(name='y', size=1) -# 线性计算网络层: y_predict = wx + b -y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) -# 计算误差函数，即 y_predict 和真实 y 之间的距离 -cost = regression_cost(input=y_predict, label=y) +# 线性计算网络层: ȳ = wx + b +ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) +# 计算误差函数，即 ȳ 和真实 y 之间的距离 +cost = regression_cost(input= ȳ, label=y) outputs(cost) ``` 这段简短的配置展示了PaddlePaddle的基本用法： @@ -63,7 +60,7 @@ outputs(cost) - 第二部分主要是选择学习算法，它定义了模型参数改变的规则。PaddlePaddle提供了很多优秀的学习算法，这里使用一个基于momentum的随机梯度下降(SGD)算法，该算法每批量(batch)读取12个采样数据进行随机梯度计算来更新更新。 - 最后一部分是神经网络的配置。由于PaddlePaddle已经实现了丰富的网络层，所以很多时候你需要做的只是定义正确的网络层并把它们连接起来。这里使用了三种网络单元： - - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `X` 和 `Y`。 + - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。 - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。 - **回归误差代价层**：回归误差代价层 `regression_cost`是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。 @@ -72,7 +69,7 @@ outputs(cost) paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 ``` -PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` Y = 2X + 0.3 ` +PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` y = 2x + 0.3 ` ## 4. 模型检验 -- GitLab From 102dfc217e6f64896abb4e1d80496799448e07cf Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Mon, 21 Nov 2016 19:13:45 -0800 Subject: [PATCH 046/347] Update make_and_install.rst Per @luotao1's comments. Thanks. --- doc_cn/build_and_install/cmake/make_and_install.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/build_and_install/cmake/make_and_install.rst b/doc_cn/build_and_install/cmake/make_and_install.rst index 8a390ef5818..212b9c9352b 100644 --- a/doc_cn/build_and_install/cmake/make_and_install.rst +++ b/doc_cn/build_and_install/cmake/make_and_install.rst @@ -1,4 +1,4 @@ make和make install ================== -参见 `make和make install `_ +参见 `make和make install <../../../doc/build/build_from_source.html#build-and-install>`_ -- GitLab From 2eb79c197d4893632ee0db5d28dc213b13d259ef Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Mon, 21 Nov 2016 19:15:39 -0800 Subject: [PATCH 047/347] Update install_deps.rst Thanks for @luotao1's comments. --- doc_cn/build_and_install/cmake/install_deps.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/build_and_install/cmake/install_deps.rst b/doc_cn/build_and_install/cmake/install_deps.rst index 6d8727e329a..7fa4665a954 100644 --- a/doc_cn/build_and_install/cmake/install_deps.rst +++ b/doc_cn/build_and_install/cmake/install_deps.rst @@ -1,4 +1,4 @@ 安装编译PaddlePaddle需要的依赖 ============================== -参见 `安装编译依赖 `_ +参见 `安装编译依赖 <../../../doc/build/build_from_source.html#install-dependencies>`_ -- GitLab From 0de93a437ae0220fe9548302fbf4a41990119806 Mon Sep 17 00:00:00 2001 From: tianbingsz Date: Mon, 21 Nov 2016 19:21:14 -0800 Subject: [PATCH 048/347] Rename index.md to index.rst Thanks for @luotao's comments. --- doc_cn/introduction/{index.md => index.rst} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc_cn/introduction/{index.md => index.rst} (100%) diff --git a/doc_cn/introduction/index.md b/doc_cn/introduction/index.rst similarity index 100% rename from doc_cn/introduction/index.md rename to doc_cn/introduction/index.rst -- GitLab From a61bf5a65f62b2ef2aec153dc7fd01d22543df87 Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 22 Nov 2016 20:51:59 +0800 Subject: [PATCH 049/347] Refine quick start index.rst --- doc_cn/demo/quick_start/index.rst | 105 ++++++++++++------------------ 1 file changed, 42 insertions(+), 63 deletions(-) diff --git a/doc_cn/demo/quick_start/index.rst b/doc_cn/demo/quick_start/index.rst index 9dabf1f661b..08c1c8413b3 100644 --- a/doc_cn/demo/quick_start/index.rst +++ b/doc_cn/demo/quick_start/index.rst @@ -40,7 +40,7 @@ PaddlePaddle快速入门教程 ------------ 接下来我们将展示如何用PaddlePaddle训练一个文本分类模型，将 `Amazon电子产品评论数据 `_ 分为好评(正样本)和差评(负样本)两种类别。 -`源代码 `_ 的 ``demo/quick_start`` 目录里提供了该数据的下载脚本和预处理脚本。 +`源代码 `_ 的 ``demo/quick_start`` 目录里提供了该数据的下载脚本和预处理脚本，你只需要在命令行输入以下命令，就能够很方便的完成数据下载和相应的预处理工作。 .. code-block:: bash @@ -51,7 +51,7 @@ PaddlePaddle快速入门教程 向系统传送数据 ============== -Python数据读取脚本 +Python脚本读取数据 ------------------ `DataProvider <../../ui/data_provider/index.html>`_ 是PaddlePaddle负责提供数据的模块。``DataProvider`` 主要职责在于将训练数据传入内存或者显存，让模型能够得到训练更新，其包括两个函数： @@ -146,7 +146,7 @@ Python数据读取脚本 以下是对上述数据加载的解释： - data/train.list,data/test.list: 指定训练数据和测试数据 -- module="dataprovider_bow": 数据处理的Python脚本文件名 +- module="dataprovider_bow": 处理数据的Python脚本文件 - obj="process": 指定生成数据的函数 - args={"dictionary": word_dict}: 额外的参数，这里指定词典 @@ -162,8 +162,8 @@ Python数据读取脚本 :scale: 80% -我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置连接请参考 `Layer文档 <../../../doc/layer.html>`_ 。 -所有配置都在 `源代码 `_ 的 ``demo/quick_start`` 目录下。 +我们将以最基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置连接请参考 `Layer文档 <../../../doc/layer.html>`_ 。 +所有配置都能在 `源代码 `_ 的 ``demo/quick_start`` 目录下找到。 逻辑回归模型 ------------ @@ -174,7 +174,7 @@ Python数据读取脚本 :align: center :scale: 80% -- 获取利用one-hot vector表示的每个单词，维度是词典大小 +- 获取利用 `one-hot vector `_ 表示的每个单词，维度是词典大小 .. code-block:: python @@ -198,7 +198,7 @@ Python数据读取脚本 classification_cost(input=output, label=label) - - input: 除过data层，每个层都有一个或多个input,多个input以list方式输入 + - input: 除去data层，每个层都有一个或多个input,多个input以list方式输入 - size: 该层神经元个数 - act_type: 激活函数类型 @@ -213,7 +213,7 @@ Python数据读取脚本 词向量模型 ---------- -embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb.py``，词向量模型、 +embedding模型需要稍微改变提供数据的Python脚本，即 ``dataprovider_emb.py``，词向量模型、 卷积模型、时序模型均使用该脚本。其中文本输入类型定义为整数时序类型integer_value_sequence。 .. code-block:: python @@ -232,20 +232,19 @@ embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb. ... # omitted, it is same as the data provider for LR model -该模型依然是使用逻辑回归分类网络的框架， 只是将句子利用连续向量表示替换稀疏 -向量表示， 即对第3步进行替换。句子表示的计算更新为2步： +该模型依然使用逻辑回归分类网络的框架， 只是将句子用连续向量表示替换为用稀疏向量表示， 即对第三步进行替换。句子表示的计算更新为两步： .. image:: NetContinuous.jpg :align: center :scale: 80% -- 利用单词Id查找对应的该单词的连续表示向量(维度为word_dim)， 输入N个单词，输出为N个word_dim维度向量 +- 利用单词Id查找该单词对应的连续向量(维度为word_dim)， 输入N个单词，输出为N个word_dim维度向量 .. code-block:: python emb = embedding_layer(input=word, size=word_dim) -- 将该句话包含的所有单词向量求平均得到句子的表示 +- 将该句话包含的所有单词向量求平均, 得到句子的表示 .. code-block:: python @@ -264,20 +263,21 @@ embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb. 卷积模型 ----------- -卷积网络是一种特殊的从词向量表示到句子表示的方法， 也就是将词向量模型额步 -骤3-2进行进一步演化， 变为3个新的子步骤。 +卷积网络是一种特殊的从词向量表示到句子表示的方法， 也就是将词向量模型进一步演化为三个新步骤。 .. image:: NetConv.jpg :align: center :scale: 80% -文本卷积分为三个步骤: +文本卷积分可为三个步骤: -1. 获取每个单词左右各k个近邻， 拼接成一个新的向量表示； +1. 首先，从每个单词左右两端分别获取k个相邻的单词, 拼接成一个新的向量； -2. 对该表示进行非线性变换 （例如Sigmoid变换）, 成为维度为hidden_dim的新的向量； +2. 其次，对该向量进行非线性变换(例如Sigmoid变换), 使其转变为维度为hidden_dim的新向量； -3. 在每个维度上取出在该句话新的向量集合上该维度的最大值作为最后的句子表示向量。 这3个子步骤可配置为: +3. 最后，对整个新向量集合的每一个维度取最大值来表示最后的句子。 + +这三个步骤可配置为: .. code-block:: python @@ -300,7 +300,7 @@ embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb. :align: center :scale: 80% -时序模型即为RNN模型, 包括简单的RNN模型、GRU模型、LSTM模型等。 +时序模型，也称为RNN模型, 包括简单的RNN模型, GRU模型和LSTM模型等等。 - GRU模型配置： @@ -315,7 +315,7 @@ embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb. lstm = simple_lstm(input=emb, size=lstm_size) -针对本问题，我们采用单层LSTM模型，并使用了Dropout，**效果总结：** +本次试验，我们采用单层LSTM模型，并使用了Dropout，**效果总结：** ===================== =============================== ========================= 网络名称 参数数量 错误率 @@ -326,8 +326,8 @@ embedding模型需要稍微改变数据提供的脚本，即 ``dataprovider_emb. 优化算法 ========= -`优化算法 <../../../doc/ui/trainer_config_helpers_api.html#module-paddle.trainer_config_helpers.optimizers>`_ 包括 -Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优化方法，加了L2正则和梯度截断。 +`优化算法 `_ 包括 +Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优化方法，同时使用了L2正则和梯度截断。 .. code-block:: python @@ -340,13 +340,19 @@ Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优 训练模型 ========= -在完成了数据和网络结构搭建之后， 我们进入到训练部分。 +在数据加载和网络配置完成之后， 我们就可以训练模型了。 .. image:: PipelineTrain.jpg :align: center :scale: 80% -训练脚本：我们将训练的命令行保存在了 ``train.sh`` 文件中。训练时所需设置的主要参数如下： +训练模型，我们只需要运行 ``train.sh`` 训练脚本： + + .. code-block:: bash + + ./train.sh + +``train.sh``中包含了训练模型的基本命令。训练时所需设置的主要参数如下： .. code-block:: bash @@ -357,30 +363,19 @@ Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优 --num_passes=15 \ --use_gpu=false -这里没有介绍多机分布式训练，可以参考 `分布式训练 <../../cluster/index.html>`_ 的demo学习如何进行多机训练。 +这里只简单介绍了单机训练，如何进行分布式训练，可以参考教程 `分布式训练 <../../cluster/index.html>`_ 。 预测 ===== -可以使用训练好的模型评估带有label的验证集，也可以预测没有label的测试集。 +当模型训练好了之后，我们就可以进行预测了。 .. image:: PipelineTest.jpg :align: center :scale: 80% -测试脚本如下，将会测试配置文件中test.list指定的数据。 - - .. code-block:: bash - - paddle train \ - --use_gpu=false \ - --job=test \ - --init_model_path=./output/pass-0000x - -可以参考 `Python API预测 <../../ui/predict/swig_py_paddle.html>`_ -教程，或其他 `demo <../../demo/index.html>`_ 的Python预测过程。也可以通过如下方式预测。 - -预测脚本(``predict.sh``)： +之前配置文件中 ``test.list`` 指定的数据将会被测试，这里直接通过预测脚本 ``predict.sh`` 进行预测, +更详细的说明，可以参考 `Python API预测 <../../ui/predict/swig_py_paddle.html>`_ 教程。 .. code-block:: bash @@ -395,8 +390,7 @@ Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优 mv rank-00000 result.txt -这里以 ``output/pass-00003`` 为例进行预测，用户可以根据训练log选择test结果最好的模型来预测。与训练网络配置不同的是：无需label相关的层，指定outputs输出概率层(softmax输出)， -指定batch_size=1，数据传输无需label数据，预测数据指定test_list的位置。 +这里以 ``output/pass-00003`` 为例进行预测，用户可以根据训练日志，选择测试结果最好的模型来预测。 预测结果以文本的形式保存在 ``result.txt`` 中，一行为一个样本，格式如下： @@ -405,29 +399,14 @@ Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优 预测ID;ID为0的概率 ID为1的概率 预测ID;ID为0的概率 ID为1的概率 - .. code-block:: python - - is_predict = get_config_arg('is_predict', bool, False) - trn = 'data/train.list' if not is_predict else None - tst = 'data/test.list' if not is_predict else 'data/pred.list' - obj = 'process' if not is_predict else 'process_pre' - batch_size = 128 if not is_predict else 1 - if is_predict: - maxid = maxid_layer(output) - outputs([maxid,output]) - else: - label = data_layer(name="label", size=2) - cls = classification_cost(input=output, label=label) - outputs(cls) - 总体效果总结 ============== -这些流程中的数据下载、网络配置、训练脚本在 ``/demo/quick_start`` 目录，我们在此总 -结上述网络结构在Amazon-Elec测试集(25k)上的效果: +在 ``/demo/quick_start`` 目录下，能够找到这里使用的所有数据, 网络配置, 训练脚本等等。 +对于Amazon-Elec测试集(25k), 如下表格，展示了上述网络模型的训练效果: ===================== =============================== ============= ================================== - 网络名称 参数数量 错误率 配置文件 + 网络名称 参数数量 错误率 配置文件 ===================== =============================== ============= ================================== 逻辑回归模型 252 KB 8.652% trainer_config.lr.py 词向量模型 15 MB 8.484% trainer_config.emb.py @@ -460,15 +439,15 @@ Momentum, RMSProp，AdaDelta，AdaGrad，ADAM，Adamax等，这里采用Adam优 TrainerInternal.cpp:160] Batch=20 samples=2560 AvgCost=0.628761 CurrentCost=0.628761 Eval: classification_error_evaluator=0.304297 CurrentEval: classification_error_evaluator=0.304297 -模型训练会看到这样的日志，详细的参数解释如下面表格： +模型训练会看到类似上面这样的日志信息，详细的参数解释，请参考如下表格： - =========================================== ========================================================== + =========================================== ============================================================== 名称 解释 - =========================================== ========================================================== + =========================================== ============================================================== Batch=20 表示过了20个batch samples=2560 表示过了2560个样本 AvgCost 每个pass的第0个batch到当前batch所有样本的平均cost CurrentCost 当前log_period个batch所有样本的平均cost Eval: classification_error_evaluator 每个pass的第0个batch到当前batch所有样本的平均分类错误率 CurrentEval: classification_error_evaluator 当前log_period个batch所有样本的平均分类错误率 - =========================================== ========================================================== + =========================================== ============================================================== -- GitLab From f56643dd902168cf07c64339c3b7d8c74868a5da Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 23 Nov 2016 15:49:06 +0800 Subject: [PATCH 050/347] Remove glossary --- .../rnn}/glossary_rnn.dot | 0 .../rnn}/glossary_rnn_with_memory.dot | 0 doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 124 ++++++++++++------ doc_cn/concepts/glossary.rst | 93 ------------- doc_cn/index.rst | 1 - 5 files changed, 86 insertions(+), 132 deletions(-) rename doc_cn/{concepts => algorithm/rnn}/glossary_rnn.dot (100%) rename doc_cn/{concepts => algorithm/rnn}/glossary_rnn_with_memory.dot (100%) delete mode 100644 doc_cn/concepts/glossary.rst diff --git a/doc_cn/concepts/glossary_rnn.dot b/doc_cn/algorithm/rnn/glossary_rnn.dot similarity index 100% rename from doc_cn/concepts/glossary_rnn.dot rename to doc_cn/algorithm/rnn/glossary_rnn.dot diff --git a/doc_cn/concepts/glossary_rnn_with_memory.dot b/doc_cn/algorithm/rnn/glossary_rnn_with_memory.dot similarity index 100% rename from doc_cn/concepts/glossary_rnn_with_memory.dot rename to doc_cn/algorithm/rnn/glossary_rnn_with_memory.dot diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index 09172c53f78..896dd7ada9c 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -4,16 +4,16 @@ 单双层RNN API对比介绍 ##################### -这篇教程主要介绍了\ :ref:`glossary_双层RNN`\ 的API接口。本文中的以\ :ref:`glossary_paddle`\ 的\ :ref:`glossary_双层RNN`\ 单元测试为示例，用多对效果完全相同的、分别使用单、双层RNN作为网络配置的模型，来讲解如何使用\ :ref:`glossary_双层RNN`\ 。本文中所有的例子，都只是介绍\ :ref:`glossary_双层RNN`\ 的API接口，并不是使用\ :ref:`glossary_双层RNN`\ 解决实际的问题。如果想要了解\ :ref:`glossary_双层RNN`\ 在具体问题中的使用，请参考\ :ref:`algo_hrnn_demo`\ 。文章中示例所使用的单元测试文件是\ `test_RecurrentGradientMachine.cpp `_\ 。 +这篇教程主要介绍了\ :ref:`glossary_双层RNN`\ 的API接口。本文中的以PaddlePaddle的\ :ref:`glossary_双层RNN`\ 单元测试为示例，用多对效果完全相同的、分别使用单、双层RNN作为网络配置的模型，来讲解如何使用\ :ref:`glossary_双层RNN`\ 。本文中所有的例子，都只是介绍\ :ref:`glossary_双层RNN`\ 的API接口，并不是使用\ :ref:`glossary_双层RNN`\ 解决实际的问题。如果想要了解\ :ref:`glossary_双层RNN`\ 在具体问题中的使用，请参考\ :ref:`algo_hrnn_demo`\ 。文章中示例所使用的单元测试文件是\ `test_RecurrentGradientMachine.cpp `_\ 。 示例1：双层RNN，子序列间无Memory ================================ -在\ :ref:`glossary_双层RNN`\ 中的经典情况是将内层的每一个\ :ref:`glossary_Sequence`\ 数据，分别进行序列操作。并且内层的序列操作之间是独立没有依赖的，即不需要使用\ :ref:`glossary_Memory`\ 的。 +在\ :ref:`glossary_双层RNN`\ 中的经典情况是将内层的每一个\ :ref:`glossary_sequence`\ 数据，分别进行序列操作。并且内层的序列操作之间是独立没有依赖的，即不需要使用\ :ref:`glossary_Memory`\ 的。 -在本问题中，单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 的网络配置，都是将每一句分好词后的句子，使用\ :ref:`glossary_lstm`\ 作为\ :ref:`glossary_encoder`\ ，压缩成一个向量。区别是\ :ref:`glossary_RNN`\ 使用两层序列模型，将多句话看成一个整体，同时使用\ :ref:`glossary_encoder`\ 压缩，二者语意上完全一致。这组语意相同的示例配置如下 +在本问题中，单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 的网络配置，都是将每一句分好词后的句子，使用LSTM作为encoder，压缩成一个向量。区别是\ :ref:`glossary_RNN`\ 使用两层序列模型，将多句话看成一个整体，同时使用encoder压缩，二者语意上完全一致。这组语意相同的示例配置如下 -* 单层 \:ref:`glossary_RNN`\: `sequence_layer_group.conf `_ +* 单层\ :ref:`glossary_RNN`\: `sequence_layer_group.conf `_ * :ref:`glossary_双层RNN`\: `sequence_nest_layer_group.conf `_ @@ -22,13 +22,13 @@ 首先，本示例中使用的原始数据如下\: -- 本里中的原始数据一共有10个\ :ref:`glossary_sample`\ 。每个\ :ref:`glossary_sample`\ 由两部分组成，一个label（此处都为2）和一个已经分词后的句子。这个数据也被单层\ :ref:`glossary_RNN`\ 网络直接使用。 +- 本里中的原始数据一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。这个数据也被单层\ :ref:`glossary_RNN`\ 网络直接使用。 .. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg :language: text -- 双层序列数据一共有4个\ :ref:`glossary_sample`\ 。 每个样本间用空行分开，整体数据和原始数据完全一样。而对于双层序列的\ :ref:`glossary_lstm`\ 来说，第一条数据同时\ :ref:`glossary_encode` 两条数据成两个向量。这四条数据同时处理的句子为\ :code:`[2, 3, 2, 3]`\ 。 +- 双层序列数据一共有4个样本。 每个样本间用空行分开，整体数据和原始数据完全一样。而对于双层序列的LSTM来说，第一条数据同时encode两条数据成两个向量。这四条数据同时处理的句子为\ :code:`[2, 3, 2, 3]`\ 。 .. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg.nest :language: text @@ -40,10 +40,10 @@ :lines: 21-39 :linenos: -- 这是普通的单层\ :ref:`glossary_Sequence`\ 的\ :ref:`glossary_DataProvider`\ 代码，其说明如下： +- 这是普通的单层\ :ref:`glossary_sequence`\ 的\ :ref:`glossary_DataProvider`\ 代码，其说明如下： * :ref:`glossary_DataProvider`\ 共返回两个数据，分别是words和label。即上述代码中的第19行。 - - words是原始数据中的每一句话，所对应的词表index数组。它是integer_value_sequence类型的，即整数数组。words即为这个数据中的单层\ :ref:`glossary_Sequence`\ 。 + - words是原始数据中的每一句话，所对应的词表index数组。它是integer_value_sequence类型的，即整数数组。words即为这个数据中的单层\ :ref:`glossary_sequence`\ 。 - label是原始数据中对于每一句话的分类标签，它是integer_value类型的。 .. literalinclude:: ../../../paddle/gserver/tests/sequenceGen.py @@ -51,17 +51,17 @@ :lines: 42-71 :linenos: -- 这是对于同样的数据，本示例中双层\ :ref:`glossary_Sequence`\ 的\ :ref:`glossary_DataProvider`\ 代码，其说明如下： +- 这是对于同样的数据，本示例中双层\ :ref:`glossary_sequence`\ 的\ :ref:`glossary_DataProvider`\ 代码，其说明如下： - :ref:`glossary_DataProvider`\ 共返回两组数据，分别是sentences和labels。即在双层序列的原始数据中，每一组内的所有句子和labels - - sentences是双层\ :ref:`glossary_Sequence`\ 的数据。他内部包括了每组数据中的所有句子，又使用句子中每一个单词的词表index表示每一个句子，故为双层\ :ref:`glossary_Sequence`\ 。类型为 integer_value_sub_sequence 。 - - labels是每组内每一个句子的标签，故而是一个单层\ :ref:`glossary_Sequence`\ 。 + - sentences是双层\ :ref:`glossary_sequence`\ 的数据。他内部包括了每组数据中的所有句子，又使用句子中每一个单词的词表index表示每一个句子，故为双层\ :ref:`glossary_sequence`\ 。类型为 integer_value_sub_sequence 。 + - labels是每组内每一个句子的标签，故而是一个单层\ :ref:`glossary_sequence`\ 。 :ref:`glossary_trainer_config`\ 的模型配置 ------------------------------------------ -首先，我们看一下单层\ :ref:`glossary_RNN`\ 的配置。代码中9-15行即为单层RNN序列的使用代码。这里使用了\ :ref:`glossary_paddle`\ 预定义好的\ :ref:`glossary_RNN`\ 处理函数。在这个函数中，\ :ref:`glossary_RNN`\ 对于每一个\ :ref:`glossary_timestep`\ 通过了一个\ :ref:`glossary_lstm`\ 网络。 +首先，我们看一下单层\ :ref:`glossary_RNN`\ 的配置。代码中9-15行即为单层RNN序列的使用代码。这里使用了PaddlePaddle预定义好的\ :ref:`glossary_RNN`\ 处理函数。在这个函数中，\ :ref:`glossary_RNN`\ 对于每一个\ :ref:`glossary_timestep`\ 通过了一个LSTM网络。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_layer_group.conf :language: python @@ -72,15 +72,15 @@ 其次，我们看一下语义相同的\ :ref:`glossary_双层RNN`\ 的网络配置。 -* :ref:`glossary_paddle`\ 中的许多layer并不在意输入是否是\ :ref:`glossary_Sequence`\ ，例如\ :code:`embedding_layer`\ 。在这些layer中，所有的操作都是针对每一个\ :ref:`glossary_timestep`\ 来进行的。 +* PaddlePaddle中的许多layer并不在意输入是否是\ :ref:`glossary_sequence`\ ，例如\ :code:`embedding_layer`\ 。在这些layer中，所有的操作都是针对每一个\ :ref:`glossary_timestep`\ 来进行的。 -* 在该配置中，7-26行将双层\ :ref:`glossary_Sequence`\ 数据，先变换成单层\ :ref:`glossary_Sequence`\ 数据，在对每一个单层\ :ref:`glossary_Sequence`\ 进行处理。 +* 在该配置中，7-26行将双层\ :ref:`glossary_sequence`\ 数据，先变换成单层\ :ref:`glossary_sequence`\ 数据，在对每一个单层\ :ref:`glossary_sequence`\ 进行处理。 - * 使用\ :code:`recurrent_group`\ 这个函数进行变换，在变换时需要将输入序列传入。由于我们想要的变换是双层\ :ref:`glossary_Sequence`\ => 单层\ :ref:`glossary_Sequence`\ ，所以我们需要将输入数据标记成\ :code:`SubsequenceInput`\ 。 + * 使用\ :code:`recurrent_group`\ 这个函数进行变换，在变换时需要将输入序列传入。由于我们想要的变换是双层\ :ref:`glossary_sequence`\ => 单层\ :ref:`glossary_sequence`\ ，所以我们需要将输入数据标记成\ :code:`SubsequenceInput`\ 。 - * 在本例中，我们将原始数据的每一组，通过\ :code:`recurrent_group`\ 进行拆解，拆解成的每一句话再通过一个\ :ref:`glossary_lstm`\ 网络。这和单层\ :ref:`glossary_RNN`\ 的配置是等价的。 + * 在本例中，我们将原始数据的每一组，通过\ :code:`recurrent_group`\ 进行拆解，拆解成的每一句话再通过一个LSTM网络。这和单层\ :ref:`glossary_RNN`\ 的配置是等价的。 -* 与单层\ :ref:`glossary_RNN`\ 的配置类似，我们只需要知道使用\ :ref:`glossary_lstm` :ref:`glossary_encode`\ 成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但是，和单层\ :ref:`glossary_RNN`\ 有区别的地方是，我们是对每一个子序列取最后一个元素。于是我们设置\ :code:`agg_level=AggregateLevel.EACH_SEQUENCE`\ 。 +* 与单层\ :ref:`glossary_RNN`\ 的配置类似，我们只需要知道使用LSTM encode成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但是，和单层\ :ref:`glossary_RNN`\ 有区别的地方是，我们是对每一个子序列取最后一个元素。于是我们设置\ :code:`agg_level=AggregateLevel.EACH_SEQUENCE`\ 。 * 至此，\ :code:`lstm_last`\ 便和单层\ :ref:`glossary_RNN`\ 的配置中的\ :code:`lstm_last`\ 具有相同的结果了。 @@ -93,43 +93,32 @@ 示例2：:ref:`glossary_双层RNN`，子序列间有\ :ref:`glossary_Memory` ================================================================== -本示例中，意图使用单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 同时实现一个完全等价的全连接\ :ref:`glossary_RNN`\ 。对于单层\ :ref:`glossary_RNN`\ ，输入数据为一个完整的\ :ref:`glossary_Sequence`\ ，例如\ :code:`[4, 5, 2, 0, 9, 8, 1, 4]`\ 。而对于\ :ref:`glossary_双层RNN`\ ，输入数据为在单层\ :ref:`glossary_RNN`\ 数据里面，任意将一些数据组合成双层\ :ref:`glossary_Sequence`\ ，例如\ :code:`[ [4, 5, 2], [0, 9], [8, 1, 4]]`。 +本示例中，意图使用单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 同时实现一个完全等价的全连接\ :ref:`glossary_RNN`\ 。对于单层\ :ref:`glossary_RNN`\ ，输入数据为一个完整的\ :ref:`glossary_sequence`\ ，例如\ :code:`[4, 5, 2, 0, 9, 8, 1, 4]`\ 。而对于\ :ref:`glossary_双层RNN`\ ，输入数据为在单层\ :ref:`glossary_RNN`\ 数据里面，任意将一些数据组合成双层\ :ref:`glossary_sequence`\ ，例如\ :code:`[ [4, 5, 2], [0, 9], [8, 1, 4]]`。 :ref:`glossary_trainer_config`\ 的模型配置 ------------------------------------------ -本例配置了两个完全等价的全连接\ :ref:`glossary_RNN`\ 。对于单层序列模型的配置如下: +我们选取单双层序列配置中的不同部分，来对比分析两者语义相同的原因。 + +- 单层序列：过了一个很简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全链接。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn.conf :language: python :lines: 36-48 - :linenos: - -在该配置中，名称为\ :code:`rnn_state`\ 的全连接层暂存到了\ :ref:`glossary_Memory`\ 中。这个\ :ref:`glossary_Memory`\ 变量\ :code:`mem`\ 中可以保存到上一个\ :ref:`glossary_timestep`\ 中的全连接层的输出。从而实现一个全连接的\ :ref:`glossary_RNN`\ 。 -以数据\ :code:`[4, 5, 2, 0, 9, 8, 1, 4]`\ 举例，单层\ :ref:`glossary_RNN`\ 的网络图如下\: +- 双层序列，外层memory是一个元素： -.. graphviz:: simple_full_recurrent.dot - -而对于\ :ref:`glossary_双层RNN`\ 来说，等价的网络配置如下\: + - 内层inner_step的recurrent_group和单层序列的几乎一样。除了boot_layer=outer_mem，表示将外层的outer_mem作为内层memory的初始状态。外层outer_step中，outer_mem是一个子句的最后一个向量，即整个双层group是将前一个子句的最后一个向量，作为下一个子句memory的初始状态。 + - 从输入数据上看，单双层序列的句子是一样的，只是双层序列将其又做了子序列划分。因此双层序列的配置中，必须将前一个子句的最后一个元素，作为boot_layer传给下一个子句的memory，才能保证和单层序列的配置中“每一个时间步都用了上一个时间步的输出结果”一致。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn.conf :language: python :lines: 39-66 - :linenos: - :emphasize-lines: 4-6 -- 在该配置中，外层的\ :code:`outer_mem`\ 和内层的\ :code:`inner_mem`\ 两个变量配合，实现了和单层\ :ref:`glossary_RNN`\ 等价的全连接\ :ref:`glossary_RNN`\ 。 +- 双层序列，外层memory是单层序列： - - 外层\ :code:`outer_step`\ 中的\ :code:`outer_mem`\ 会将神经网络中每个子序列的最后一个结果记录下来。即将第18行的\ :code:`last`\ 变量记录下来。 - - 内层\ :code:`inner_step`\ 中的\ :code:`inner_mem`\ 会将神经网络中子序列中的每一个元素的结果记录下来。即将第7行的\ :code:`out`\ 变量记录下来。 - - 内层的\ :code:`inner_mem`\ 初始值是\ :code:`outer_mem`(:code:`boot_layer`)。于是前一个子序列的最后结果，是新的子序列的初试结果。即完成了简单的全连接\ :code:`glossary_RNN`\ 。 - -本例中的\ :ref:`glossary_双层RNN`\ ,以数据\ :code:`[ [4, 5, 2], [0, 9], [8, 1, 4]]`\ 举例，配置图如下\: - -.. graphviz:: simple_full_hierarchical_recurrent.dot - -这里有一点注意事项，Paddle目前实现的\ :ref:`glossary_双层RNN`\ 不完全支持内层\ :ref:`glossary_RNN`\ 的\ :ref:`glossary_Memory`\ 引用外层\ :ref:`glossary_RNN`\ 的某一层序列输入。即\ :code:`inner_mem`的\ :code:`boot_layer`\ 需要是非序列类型的，或者可以是序列类型，但是每个时间步下，序列长度是一致的。从序列类型转换为非序列类型，可以使用\ :code:`pooling_layer`, :code:`last_seq`, :code:`first_seq`\ 等操作进行转换。 + - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 + - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 示例3：双进双出，输入不等长 =========================== @@ -190,3 +179,62 @@ data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 ======================== TBD + + +词汇表 +====== + +.. _glossary_memory: + +Memory +------ + +Memory是PaddlePaddle实现 :ref:`glossary_RNN` 时候使用的一个概念。 :ref:`glossary_RNN` 即时间递归神经网络，通常要求时间步之间具有一些依赖性，即当前时间步下的神经网络依赖前一个时间步神经网络中某一个神经元输出。如下图所示。 + +.. graphviz:: glossary_rnn.dot + +上图中虚线的连接，即是跨越时间步的网络连接。PaddlePaddle在实现 :ref:`glossary_RNN` 的时候，将这种跨越时间步的连接用一个特殊的神经网络单元实现。这个神经网络单元就叫Memory。Memory可以缓存上一个时刻某一个神经元的输出，然后在下一个时间步输入给另一个神经元。使用Memory的 :ref:`glossary_RNN` 实现便如下图所示。 + +.. graphviz:: glossary_rnn_with_memory.dot + +使用这种方式，PaddlePaddle可以比较简单的判断哪些输出是应该跨越时间步的，哪些不是。 + +.. _glossary_timestep: + +时间步 +------ + +参考 :ref:`glossary_sequence` 。 + + +.. _glossary_sequence: + +时间序列 +-------- + +时间序列(time series)是指一系列的特征数据。这些特征数据之间的顺序是有意义的。即特征的数组，而不是特征的集合。而这每一个数组元素，或者每一个系列里的特征数据，即为一个时间步(time step)。值得注意的是，时间序列、时间步的概念，并不真正的和『时间』有关。只要一系列特征数据中的『顺序』是有意义的，即为时间序列的输入。 + +举例说明，例如文本分类中，我们通常将一句话理解成一个时间序列。比如一句话中的每一个单词，会变成词表中的位置。而这一句话就可以表示成这些位置的数组。例如 :code:`[9, 2, 3, 5, 3]` 。 + +关于时间序列(time series)的更详细准确的定义，可以参考 `维基百科页面 Time series `_ 或者 `维基百科中文页面 时间序列 `_ 。 + +另外，Paddle中经常会将时间序列成为 :code:`Sequence` 。他们在Paddle的文档和API中是一个概念。 + +.. _glossary_RNN: + +RNN +--- + +RNN 在PaddlePaddle的文档中，一般表示 :code:`Recurrent neural network`，即时间递归神经网络。详细介绍可以参考 `维基百科页面 Recurrent neural network `_ 或者 `中文维基百科页面 `_ 中关于时间递归神经网络的介绍。 + +RNN 一般在PaddlePaddle中，指对于一个 :ref:`glossary_sequence` 输入数据，每一个时间步之间的神经网络具有一定的相关性。例如，某一个神经元的一个输入为上一个时间步网络中某一个神经元的输出。或者，从每一个时间步来看，神经网络的网络结构中具有有向环结构。 + +.. _glossary_双层RNN: + +双层RNN +------- + +双层RNN顾名思义，即 :ref:`glossary_RNN` 之间有一次嵌套关系。输入数据整体上是一个时间序列，而对于每一个内层特征数据而言，也是一个时间序列。即二维数组，或者数组的数组这个概念。 而双层RNN是可以处理这种输入数据的网络结构。 + +例如，对于段落的文本分类，即将一段话进行分类。我们将一段话看成句子的数组，每个句子又是单词的数组。这便是一种双层RNN的输入数据。而将这个段落的每一句话用lstm编码成一个向量，再对每一句话的编码向量用lstm编码成一个段落的向量。再对这个段落向量进行分类，即为这个双层RNN的网络结构。 + diff --git a/doc_cn/concepts/glossary.rst b/doc_cn/concepts/glossary.rst deleted file mode 100644 index 518712d1fe8..00000000000 --- a/doc_cn/concepts/glossary.rst +++ /dev/null @@ -1,93 +0,0 @@ -.. _glossary: - -######################## -Paddle文档中使用的词汇表 -######################## - -.. _glossary_paddle: - -PaddlePaddle ------------- - -TBD - -.. _glossary_encode: - -encode ------- - -参考\ :ref:`glossary_encoder`\ 。 - -.. _glossary_encoder: - -encoder -------- - -TBD - -.. _glossary_sample: - -样本 ----- - -TBD Sample的概念 - -.. _glossary_lstm: - -LSTM ----- - -TBD - -.. _glossary_memory: - -Memory ------- - -Memory是 :ref:`glossary_paddle` 实现 :ref:`glossary_RNN` 时候使用的一个概念。 :ref:`glossary_RNN` 即时间递归神经网络，通常要求时间步之间具有一些依赖性，即当前时间步下的神经网络依赖前一个时间步神经网络中某一个神经元输出。如下图所示。 - -.. graphviz:: glossary_rnn.dot - -上图中虚线的连接，即是跨越时间步的网络连接。:ref:`glossary_paddle` 在实现 :ref:`glossary_RNN` 的时候，将这种跨越时间步的连接用一个特殊的神经网络单元实现。这个神经网络单元就叫Memory。Memory可以缓存上一个时刻某一个神经元的输出，然后在下一个时间步输入给另一个神经元。使用Memory的 :ref:`glossary_RNN` 实现便如下图所示。 - -.. graphviz:: glossary_rnn_with_memory.dot - -使用这种方式，:ref:`glossary_paddle` 可以比较简单的判断哪些输出是应该跨越时间步的，哪些不是。 - -.. _glossary_timestep: - -时间步 ------- - -参考 :ref:`_glossary_Sequence` 。 - -.. _glossary_Sequence: - -时间序列 --------- - -时间序列(time series)是指一系列的特征数据。这些特征数据之间的顺序是有意义的。即特征的数组，而不是特征的集合。而这每一个数组元素，或者每一个系列里的特征数据，即为一个时间步(time step)。值得注意的是，时间序列、时间步的概念，并不真正的和『时间』有关。只要一系列特征数据中的『顺序』是有意义的，即为时间序列的输入。 - -举例说明，例如文本分类中，我们通常将一句话理解成一个时间序列。比如一句话中的每一个单词，会变成词表中的位置。而这一句话就可以表示成这些位置的数组。例如 :code:`[9, 2, 3, 5, 3]` 。 - -关于时间序列(time series)的更详细准确的定义，可以参考 `维基百科页面 Time series `_ 或者 `维基百科中文页面 时间序列 `_ 。 - -另外，Paddle中经常会将时间序列成为 :code:`Sequence` 。他们在Paddle的文档和API中是一个概念。 - -.. _glossary_RNN: - -RNN ---- - -RNN 在 :ref:`glossary_paddle` 的文档中，一般表示 :code:`Recurrent neural network`，即时间递归神经网络。详细介绍可以参考 `维基百科页面 Recurrent neural network `_ 或者 `中文维基百科页面 `_ 中关于时间递归神经网络的介绍。 - -RNN 一般在 :ref:`glossary_paddle` 中，指对于一个 :ref:`glossary_Sequence` 输入数据，每一个时间步之间的神经网络具有一定的相关性。例如，某一个神经元的一个输入为上一个时间步网络中某一个神经元的输出。或者，从每一个时间步来看，神经网络的网络结构中具有有向环结构。 - -.. _glossary_双层RNN: - -双层RNN -------- - -双层RNN顾名思义，即 :ref:`glossary_RNN` 之间有一次嵌套关系。输入数据整体上是一个时间序列，而对于每一个内层特征数据而言，也是一个时间序列。即二维数组，或者数组的数组这个概念。 而双层RNN是可以处理这种输入数据的网络结构。 - -例如，对于段落的文本分类，即将一段话进行分类。我们将一段话看成句子的数组，每个句子又是单词的数组。这便是一种双层RNN的输入数据。而将这个段落的每一句话用lstm编码成一个向量，再对每一句话的编码向量用lstm编码成一个段落的向量。再对这个段落向量进行分类，即为这个双层RNN的网络结构。 diff --git a/doc_cn/index.rst b/doc_cn/index.rst index fef39aa527a..68128a74f87 100644 --- a/doc_cn/index.rst +++ b/doc_cn/index.rst @@ -11,7 +11,6 @@ PaddlePaddle文档 * `使用示例 `_ * `模型配置 <../doc/ui/api/trainer_config_helpers/index.html>`_ * `集群训练 `_ -* :ref:`glossary` 开发指南 -------- -- GitLab From ebd725265a0af43a563742eb58901e622a237daa Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Nov 2016 16:14:27 +0800 Subject: [PATCH 051/347] Update the data of quick start. --- demo/quick_start/data/README.md | 9 ++++++ demo/quick_start/data/get_data.sh | 15 ++++----- demo/quick_start/data/pred.list | 1 - demo/quick_start/data/pred.txt | 2 -- .../proc_from_raw_data/get_data.sh} | 32 +++++++++++++------ .../proc_from_raw_data}/preprocess.py | 10 ++++-- doc/demo/quick_start/index_en.md | 3 +- doc_cn/demo/quick_start/index.md | 4 +-- 8 files changed, 47 insertions(+), 29 deletions(-) create mode 100644 demo/quick_start/data/README.md delete mode 100644 demo/quick_start/data/pred.list delete mode 100644 demo/quick_start/data/pred.txt rename demo/quick_start/{preprocess.sh => data/proc_from_raw_data/get_data.sh} (68%) rename demo/quick_start/{ => data/proc_from_raw_data}/preprocess.py (95%) diff --git a/demo/quick_start/data/README.md b/demo/quick_start/data/README.md new file mode 100644 index 00000000000..63abcf7ebf3 --- /dev/null +++ b/demo/quick_start/data/README.md @@ -0,0 +1,9 @@ +This dataset consists of electronics product reviews associated with +binary labels (positive/negative) for sentiment classification. + +The preprocessed data can be downloaded by script `get_data.sh`. +The data was derived from reviews_Electronics_5.json.gz at + +http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz + +If you want to process the raw data, you can use the script `proc_from_raw_data/get_data.sh`. diff --git a/demo/quick_start/data/get_data.sh b/demo/quick_start/data/get_data.sh index f355d63225b..952de3f3c8f 100755 --- a/demo/quick_start/data/get_data.sh +++ b/demo/quick_start/data/get_data.sh @@ -17,14 +17,11 @@ set -e DIR="$( cd "$(dirname "$0")" ; pwd -P )" cd $DIR -echo "Downloading Amazon Electronics reviews data..." -# http://jmcauley.ucsd.edu/data/amazon/ -wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz +# Download the preprocessed data +wget http://paddlepaddle.bj.bcebos.com/demo/quick_start_preprocessed_data/preprocessed_data.tar.gz -echo "Downloading mosesdecoder..." -#https://github.com/moses-smt/mosesdecoder -wget https://github.com/moses-smt/mosesdecoder/archive/master.zip +# Extract package +tar zxvf preprocessed_data.tar.gz -unzip master.zip -rm master.zip -echo "Done." +# Remove compressed package +rm preprocessed_data.tar.gz diff --git a/demo/quick_start/data/pred.list b/demo/quick_start/data/pred.list deleted file mode 100644 index d88b2b63851..00000000000 --- a/demo/quick_start/data/pred.list +++ /dev/null @@ -1 +0,0 @@ -./data/pred.txt diff --git a/demo/quick_start/data/pred.txt b/demo/quick_start/data/pred.txt deleted file mode 100644 index 6ed5f738dda..00000000000 --- a/demo/quick_start/data/pred.txt +++ /dev/null @@ -1,2 +0,0 @@ -the device is cute , but that 's just about all that 's good. the specs are what you 'd expect : it 's a wifi mic , with some noise filter options. the app has the option to upload your baby 's name and photo , which is a cutesy touch. but the app is otherwise unstable and useless unless you upgrade for $ 60 / year.set up involves downloading the app , turning on the mic , switching your phone to the wifi network of the mic , telling the app your wifi settings , switching your wifi back to your home router. the app is then directly connected to your mic.the app is adware ! the main screen says " cry notifications on / off : upgrade to evoz premium and receive a text message of email when your baby is crying " .but the adware points out an important limitation , this monitor is only intended to be used from your home network. if you want to access it remotely , get a webcam. this app would make a lot more sense of the premium features were included with the hardware . -don 't be fooled by my one star rating. if there was a zero , i would have selected it. this product was a waste of my money.it has never worked like the company said it supposed to. i only have one device , an iphone 4gs. after charging the the iphone mid way , the i.sound portable power max 16,000 mah is completely drained. the led light no longer lit up. when plugging the isound portable power max into a wall outlet to charge , it would charge for about 20-30 minutes and then all four battery led indicator lit up showing a full charge. i would leave it on to charge for the full 8 hours or more but each time with the same result upon using. don 't buy this thing. put your money to good use elsewhere . diff --git a/demo/quick_start/preprocess.sh b/demo/quick_start/data/proc_from_raw_data/get_data.sh similarity index 68% rename from demo/quick_start/preprocess.sh rename to demo/quick_start/data/proc_from_raw_data/get_data.sh index c9190e2dd2e..9c3e9db248a 100755 --- a/demo/quick_start/preprocess.sh +++ b/demo/quick_start/data/proc_from_raw_data/get_data.sh @@ -16,10 +16,23 @@ # 1. size of pos : neg = 1:1. # 2. size of testing set = min(25k, len(all_data) * 0.1), others is traning set. # 3. distinct train set and test set. -# 4. build dict set -e +DIR="$( cd "$(dirname "$0")" ; pwd -P )" +cd $DIR + +# Download data +echo "Downloading Amazon Electronics reviews data..." +# http://jmcauley.ucsd.edu/data/amazon/ +#wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz +echo "Downloading mosesdecoder..." +#https://github.com/moses-smt/mosesdecoder +#wget https://github.com/moses-smt/mosesdecoder/archive/master.zip +#unzip master.zip +#rm master.zip +echo "Done." + export LC_ALL=C UNAME_STR=`uname` @@ -29,10 +42,11 @@ else SHUF_PROG='gshuf' fi -mkdir -p data/tmp -python preprocess.py -i data/reviews_Electronics_5.json.gz +# Start preprocess +mkdir -p tmp +python preprocess.py -i reviews_Electronics_5.json.gz # uniq and shuffle -cd data/tmp +cd tmp echo 'uniq and shuffle...' cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed @@ -53,11 +67,11 @@ cat train.pos train.neg | ${SHUF_PROG} >../train.txt cat test.pos test.neg | ${SHUF_PROG} >../test.txt cd - -echo 'data/train.txt' > data/train.list -echo 'data/test.txt' > data/test.list +echo 'train.txt' > train.list +echo 'test.txt' > test.list # use 30k dict -rm -rf data/tmp -mv data/dict.txt data/dict_all.txt -cat data/dict_all.txt | head -n 30001 > data/dict.txt +rm -rf tmp +mv dict.txt dict_all.txt +cat dict_all.txt | head -n 30001 > dict.txt echo 'preprocess finished' diff --git a/demo/quick_start/preprocess.py b/demo/quick_start/data/proc_from_raw_data/preprocess.py similarity index 95% rename from demo/quick_start/preprocess.py rename to demo/quick_start/data/proc_from_raw_data/preprocess.py index d87fad632a7..56c2c5f16ce 100755 --- a/demo/quick_start/preprocess.py +++ b/demo/quick_start/data/proc_from_raw_data/preprocess.py @@ -14,7 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -1. (remove HTML before or not)tokensizing +1. Tokenize the words and punctuation 2. pos sample : rating score 5; neg sample: rating score 1-2. Usage: @@ -76,7 +76,11 @@ def tokenize(sentences): sentences : a list of input sentences. return: a list of processed text. """ - dir = './data/mosesdecoder-master/scripts/tokenizer/tokenizer.perl' + dir = './mosesdecoder-master/scripts/tokenizer/tokenizer.perl' + if not os.path.exists(dir): + sys.exit( + "The ./mosesdecoder-master/scripts/tokenizer/tokenizer.perl does not exists." + ) tokenizer_cmd = [dir, '-l', 'en', '-q', '-'] assert isinstance(sentences, list) text = "\n".join(sentences) @@ -104,7 +108,7 @@ def tokenize_batch(id): num_batch, instance, pre_fix = parse_queue.get() if num_batch == -1: ### parse_queue finished tokenize_queue.put((-1, None, None)) - sys.stderr.write("tokenize theread %s finish\n" % (id)) + sys.stderr.write("Thread %s finish\n" % (id)) break tokenize_instance = tokenize(instance) tokenize_queue.put((num_batch, tokenize_instance, pre_fix)) diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md index 80d816a768a..01f6f8ef544 100644 --- a/doc/demo/quick_start/index_en.md +++ b/doc/demo/quick_start/index_en.md @@ -59,12 +59,11 @@ To build your text classification system, your code will need to perform five st ## Preprocess data into standardized format In this example, you are going to use [Amazon electronic product review dataset](http://jmcauley.ucsd.edu/data/amazon/) to build a bunch of deep neural network models for text classification. Each text in this dataset is a product review. This dataset has two categories: “positive” and “negative”. Positive means the reviewer likes the product, while negative means the reviewer does not like the product. -`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides scripts for downloading data and preprocessing data as shown below. The data process takes several minutes (about 3 minutes in our machine). +`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides script for downloading the preprocessed data as shown below. (If you want to process the raw data, you can use the script `demo/quick_start/data/proc_from_raw_data/get_data.sh`). ```bash cd demo/quick_start ./data/get_data.sh -./preprocess.sh ``` ## Transfer Data to Model diff --git a/doc_cn/demo/quick_start/index.md b/doc_cn/demo/quick_start/index.md index 4d9b24ba851..514b45a4879 100644 --- a/doc_cn/demo/quick_start/index.md +++ b/doc_cn/demo/quick_start/index.md @@ -32,13 +32,11 @@ ## 数据格式准备(Data Preparation) 在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)， -将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了数据下载脚本 -和预处理脚本。 +将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了下载已经预处理数据的脚本（如果想从最原始的数据处理，可以使用脚本 `./demo/quick_start/data/proc_from_raw_data/get_data.sh`）。 ```bash cd demo/quick_start ./data/get_data.sh -./preprocess.sh ``` ## 数据向模型传送(Transfer Data to Model) -- GitLab From 3bfb898fd812430c205e81f6dcffa05c01d4abdd Mon Sep 17 00:00:00 2001 From: chenguoyan01 Date: Wed, 23 Nov 2016 16:49:03 +0800 Subject: [PATCH 052/347] add mfs fullname --- .../k8s/distributed_training_on_kubernetes.md | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md b/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md index e07cf6182f2..d9ed431ec05 100644 --- a/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md +++ b/doc_cn/cluster/k8s/distributed_training_on_kubernetes.md @@ -1,11 +1,11 @@ -# Paddle on Kubernetes：分布式训练 +# PaddlePaddle on Kubernetes：分布式训练 -前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，可以参考 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)，本文利用Kubernetes的调度功能与容器编排能力，快速构建PaddlePaddle容器集群，进行分布式训练任务。 +前一篇文章介绍了如何在Kubernetes集群上启动一个单机PaddlePaddle训练作业 (Job)。在这篇文章里，我们介绍如何在Kubernetes集群上进行分布式PaddlePaddle训练作业。关于PaddlePaddle的分布式训练，文章 [Cluster Training](https://github.com/baidu/Paddle/blob/develop/doc/cluster/opensource/cluster_train.md)介绍了一种通过SSH远程分发任务，进行分布式训练的方法，与此不同的是，本文将介绍在Kubernetes容器管理平台上快速构建PaddlePaddle容器集群，进行分布式训练的方案。 ## Kubernetes 基本概念 -[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、 扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。在介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 +[*Kubernetes*](http://kubernetes.io/)是Google开源的容器集群管理系统，其提供应用部署、维护、 扩展机制等功能，利用Kubernetes能方便地管理跨机器运行容器化的应用。Kubernetes可以在物理机或虚拟机上运行，且支持部署到[AWS](http://kubernetes.io/docs/getting-started-guides/aws)，[Azure](http://kubernetes.io/docs/getting-started-guides/azure/)，[GCE](http://kubernetes.io/docs/getting-started-guides/gce)等多种公有云环境。介绍分布式训练之前，需要对[Kubernetes](http://kubernetes.io/)有一个基本的认识，下面先简要介绍一下本文用到的几个Kubernetes概念。 - [*Node*](http://kubernetes.io/docs/admin/node/) 表示一个Kubernetes集群中的一个工作节点，这个节点可以是物理机或者虚拟机，Kubernetes集群就是由node节点与master节点组成的。 @@ -21,17 +21,19 @@ ### 部署Kubernetes集群 -首先，我们需要拥有一个Kubernetes集群，在这个集群中所有node与pod都可以互相通信。关于Kubernetes集群搭建，可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/)，在以后的文章中我们也会介绍AWS上搭建的方案。本文假设大家能找到几台物理机，并且可以按照官方文档在上面部署Kubernetes。在本文的环境中，Kubernetes集群中所有node都挂载了一个*mfs*(分布式文件系统)共享目录，我们通过这个目录来存放训练文件与最终输出的模型。在训练之前，用户将配置与训练数据切分好放在mfs目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结果图如下： +首先，我们需要拥有一个Kubernetes集群，在这个集群中所有node与pod都可以互相通信。关于Kubernetes集群搭建，可以参考[官方文档](http://kubernetes.io/docs/getting-started-guides/kubeadm/)，在以后的文章中我们也会介绍AWS上搭建的方案。本文假设大家能找到几台物理机，并且可以按照官方文档在上面部署Kubernetes。在本文的环境中，Kubernetes集群中所有node都挂载了一个[MFS](http://moosefs.org/)（Moose filesystem，一种分布式文件系统）共享目录，我们通过这个目录来存放训练文件与最终输出的模型。关于MFS的安装部署，可以参考[MooseFS documentation](https://moosefs.com/documentation.html)。在训练之前，用户将配置与训练数据切分好放在MFS目录中，训练时，程序从此目录拷贝文件到容器内进行训练，将结果保存到此目录里。整体的结构图如下： ![paddle on kubernetes结构图](k8s-paddle-arch.png) +上图描述了一个3节点的分布式训练场景，Kubernetes集群的每个node上都挂载了一个MFS目录，这个目录可以通过volume的形式挂载到容器中。Kubernetes为这次训练创建了3个pod并且调度到了3个node上运行，每个pod包含一个PaddlePaddle容器。在容器创建后，会启动pserver与trainer进程，读取volume中的数据进行这次分布式训练。 + ### 使用 Job 我们使用Kubernetes中的job这个概念来代表一次分布式训练。Job表示一次性作业，在作业完成后，Kubernetes会销毁job产生的容器并且释放相关资源。 在Kubernetes中，可以通过编写一个YAML文件，来描述这个job，在这个文件中，主要包含了一些配置信息，例如PaddlePaddle的节点个数，`paddle pserver`开放的端口个数与端口号，使用的网卡设备等，这些信息通过环境变量的形式传递给容器内的程序使用。 -在一次分布式训练中，用户确定好本次训练需要的PaddlePaddle节点个数，将切分好的训练数据与配置文件上传到mfs共享目录中。然后编写这次训练的job YAML文件，提交给Kubernetes集群创建并开始作业。 +在一次分布式训练中，用户确定好本次训练需要的PaddlePaddle节点个数，将切分好的训练数据与配置文件上传到MFS共享目录中。然后编写这次训练的job YAML文件，提交给Kubernetes集群创建并开始作业。 ### 创建PaddlePaddle节点 @@ -39,14 +41,14 @@ ### 启动训练 -在容器启动后，会通过脚本来启动这次分布式训练，我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的trainer_id，由于Paddle本身不提供类似服务发现的功能，所以在本文的启动脚本中，每个节点会根据job name向Kubernetes apiserver查询这个job对应的所有pod信息(Kubernetes默认会在每个容器的环境变量中写入apiserver的地址)。 +在容器启动后，会通过脚本来启动这次分布式训练，我们知道`paddle train`进程启动时需要知道其他节点的IP地址以及本节点的trainer_id，由于PaddlePaddle本身不提供类似服务发现的功能，所以在本文的启动脚本中，每个节点会根据job name向Kubernetes apiserver查询这个job对应的所有pod信息(Kubernetes默认会在每个容器的环境变量中写入apiserver的地址)。 根据这些pod信息，就可以通过某种方式，为每个pod分配一个唯一的trainer_id。本文把所有pod的IP地址进行排序，将顺序作为每个PaddlePaddle节点的trainer_id。启动脚本的工作流程大致如下： 1. 查询Kubernetes apiserver获取pod信息，根据IP分配trainer_id - 1. 从mfs共享目录中拷贝训练文件到容器内 + 1. 从MFS共享目录中拷贝训练文件到容器内 1. 根据环境变量，解析出`paddle pserver`与`paddle train`的启动参数，启动进程 - 1. 训练时，PaddlePaddle会自动将结果保存在trainer_id为0的节点上，将输出路径设置为mfs目录，保存输出的文件 + 1. 训练时，PaddlePaddle会自动将结果保存在trainer_id为0的节点上，将输出路径设置为MFS目录，保存输出的文件 ## 搭建过程 @@ -160,7 +162,7 @@ docker push your_repo/paddle:mypaddle ### 上传训练文件 -本文使用Paddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到mfs共享存储。完成后mfs上的文件内容大致如下： +本文使用PaddlePaddle官方的[recommendation demo](http://www.paddlepaddle.org/doc/demo/index.html#recommendation)作为这次训练的内容，我们将训练文件与数据放在一个job name命名的目录中，上传到MFS共享存储。完成后MFS上的文件内容大致如下： ```bash [root@paddle-kubernetes-node0 mfs]# tree -d @@ -176,7 +178,7 @@ docker push your_repo/paddle:mypaddle └── recommendation ``` -目录中paddle-cluster-job是本次训练对应的job name，本次训练要求有3个Paddle节点，在paddle-cluster-job/data目录中存放切分好的数据，文件夹0，1，2分别代表3个节点的trainer_id。recommendation文件夹内存放训练文件，output文件夹存放训练结果与日志。 +目录中paddle-cluster-job是本次训练对应的job name，本次训练要求有3个PaddlePaddle节点，在paddle-cluster-job/data目录中存放切分好的数据，文件夹0，1，2分别代表3个节点的trainer_id。recommendation文件夹内存放训练文件，output文件夹存放训练结果与日志。 ### 创建Job @@ -229,7 +231,7 @@ spec: restartPolicy: Never ``` -文件中，`metadata`下的`name`表示这个job的名字。`parallelism，completions`字段表示这个job会同时开启3个Paddle节点，成功训练且退出的pod数目为3时，这个job才算成功结束。然后申明一个存储卷`jobpath`，代表宿主机目录`/home/work/mfs`，在对容器的描述`containers`字段中，将此目录挂载为容器的`/home/jobpath`目录，这样容器的`/home/jobpath`目录就成为了共享存储，放在这个目录里的文件其实是保存到了mfs上。 +文件中，`metadata`下的`name`表示这个job的名字。`parallelism，completions`字段表示这个job会同时开启3个PaddlePaddle节点，成功训练且退出的pod数目为3时，这个job才算成功结束。然后申明一个存储卷`jobpath`，代表宿主机目录`/home/work/mfs`，在对容器的描述`containers`字段中，将此目录挂载为容器的`/home/jobpath`目录，这样容器的`/home/jobpath`目录就成为了共享存储，放在这个目录里的文件其实是保存到了MFS上。 `env`字段表示容器的环境变量，我们将`paddle`运行的一些参数通过这种方式传递到容器内。 @@ -254,7 +256,7 @@ kubectl create -f job.yaml ### 查看输出 -在训练过程中，可以在共享存储上查看输出的日志和模型，例如output目录下就存放了输出结果。注意node_0，node_1，node_2这几个目录表示Paddle节点与trainer_id，并不是Kubernetes中的node概念。 +在训练过程中，可以在共享存储上查看输出的日志和模型，例如output目录下就存放了输出结果。注意node_0，node_1，node_2这几个目录表示PaddlePaddle节点与trainer_id，并不是Kubernetes中的node概念。 ```bash [root@paddle-kubernetes-node0 output]# tree -d -- GitLab From ce2a2733ca6ae780a87750f1e8666291b547a6b7 Mon Sep 17 00:00:00 2001 From: gangliao Date: Wed, 23 Nov 2016 17:05:16 +0800 Subject: [PATCH 053/347] Fix a wrong link in build doc --- doc/build/build_from_source.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/build/build_from_source.md b/doc/build/build_from_source.md index b8f26f431eb..e5bb7944ca3 100644 --- a/doc/build/build_from_source.md +++ b/doc/build/build_from_source.md @@ -6,7 +6,7 @@ Installing from Sources * [3. Build on Ubuntu](#ubuntu) ## Download and Setup -You can download PaddlePaddle from the [github source](https://github.com/gangliao/Paddle). +You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle). ```bash git clone https://github.com/baidu/Paddle paddle -- GitLab From d8161e384058cd9413993143e105410de986f936 Mon Sep 17 00:00:00 2001 From: gangliao Date: Wed, 23 Nov 2016 17:08:20 +0800 Subject: [PATCH 054/347] Update build docs --- doc/build/build_from_source.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/build/build_from_source.md b/doc/build/build_from_source.md index e5bb7944ca3..e44fa0d38e9 100644 --- a/doc/build/build_from_source.md +++ b/doc/build/build_from_source.md @@ -9,7 +9,7 @@ Installing from Sources You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle). ```bash -git clone https://github.com/baidu/Paddle paddle +git clone https://github.com/PaddlePaddle/Paddle paddle cd paddle ``` -- GitLab From 0cac5391a62cbb20a97067b1563df6213a38e1de Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 23 Nov 2016 17:10:27 +0800 Subject: [PATCH 055/347] fix swig_py_paddle.rst --- doc_cn/ui/predict/swig_py_paddle.rst | 87 ++++++++++++++++------------ 1 file changed, 51 insertions(+), 36 deletions(-) diff --git a/doc_cn/ui/predict/swig_py_paddle.rst b/doc_cn/ui/predict/swig_py_paddle.rst index 012ac4ff6e6..f9750d80c92 100644 --- a/doc_cn/ui/predict/swig_py_paddle.rst +++ b/doc_cn/ui/predict/swig_py_paddle.rst @@ -1,38 +1,53 @@ -PaddlePaddle的Python预测接口 -================================== - -PaddlePaddle目前使用Swig对其常用的预测接口进行了封装，使在Python环境下的预测接口更加简单。 -在Python环境下预测结果，主要分为以下几个步骤。 - -* 读入解析训练配置 -* 构造GradientMachine -* 准备数据 -* 预测 - -典型的预测代码如下，使用mnist手写识别作为样例, 完整代码见 -:code:`src_root/doc/ui/predict/predict_sample.py` 。 - -.. literalinclude:: ../../../doc/ui/predict/predict_sample.py - :language: python - :lines: 15-18,90-100,101-104 - -主要的软件包为py_paddle.swig_paddle，这个软件包文档相对完善。可以使用python的 -:code:`help()` 函数查询文档。主要步骤为: - -* 在程序开始阶段，使用 :code:`swig_paddle.initPaddle()` 传入命令行参数初始化 - PaddlePaddle。详细的命令行参数请参考 - `命令行参数 <../cmd_argument/detail_introduction.html>`_ 。 -* 接下来使用 :code:`parse_config()` 解析训练时的配置文件。这里要注意预测数据通常 - 不包含label, 而且预测网络通常直接输出最后一层的结果而不是像训练时一样以cost - layer作为输出，所以用于预测的配置文件要做相应的修改。 -* 使用 :code:`swig_paddle.GradientMachine.createFromConfigproto()` 根据上一步解 - 析好的配置创建神经网络。 -* 创建一个 :code:`DataProviderConverter` 对象converter。 - - swig_paddle接受的原始数据是C++的Matrix，也就是直接写内存的float数组。 - 这个接口并不用户友好。所以，我们提供了一个工具类DataProviderConverter。 - 这个工具类接收和PyDataProvider2一样的输入数据，详情请参考 - `PyDataProvider2文档 <../../../doc/ui/data_provider/pydataprovider2.html>`_ 。 -* 最后使用 :code:`forwardTest()` 直接提取出神经网络Output层的输出结果。典型的输出结果为\: +基于Python的预测 +================ + +Python预测接口 +-------------- + +PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会生成py_paddle软件包，安装该软件包就可以在python环境下实现模型预测。可以使用python的 ``help()`` 函数查询软件包相关API说明。 + +基于Python的模型预测，主要包括以下五个步骤。 + +1. 初始化PaddlePaddle环境 + 在程序开始阶段，通过调用 ``swig_paddle.initPaddle()`` 并传入相应的命令行参数初始化PaddlePaddle。 +2. 解析模型配置文件 + 初始化之后，可以通过调用 ``parse_config()`` 解析训练模型时用的配置文件。注意预测数据通常不包含label, 同时预测网络通常直接输出最后一层的结果而不是像训练网络一样再接一层cost layer，所以一般需要对训练用的模型配置文件稍作相应修改才能在预测时使用。 +3. 构造paddle.GradientMachine + 通过调用 ``swig_paddle.GradientMachine.createFromConfigproto()`` 传入上一步解析出来的模型配置就可以创建一个 ``GradientMachine``。 +4. 准备预测数据 + swig_paddle中的预测接口的参数是自定义的C++数据类型，py_paddle里面提供了一个工具类 ``DataProviderConverter`` 可以用于接收和PyDataProvider2一样的输入数据并转换成预测接口所需的数据类型。 +5. 模型预测 + 通过调用 ``forwardTest()`` 传入预测数据，直接返回计算结果。 + + +基于Python的预测Demo +-------------------- + +如下是一段使用mnist model来实现手写识别的预测代码。完整的代码见 ``src_root/doc/ui/predict/predict_sample.py`` 。mnist model可以通过 ``src_root\demo\mnist`` 目录下的demo训练出来。 + +.. code-block:: python + + from py_paddle import swig_paddle, DataProviderConverter + from paddle.trainer.PyDataProvider2 import dense_vector + from paddle.trainer.config_parser import parse_config + + TEST_DATA = [...] + + def main(): + conf = parse_config("./mnist_model/trainer_config.py", "") + network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config) + assert isinstance(network, swig_paddle.GradientMachine) # For code hint. + network.loadParameters("./mnist_model/") + converter = DataProviderConverter([dense_vector(784)]) + inArg = converter(TEST_DATA) + print network.forwardTest(inArg) + + if __name__ == '__main__': + swig_paddle.initPaddle("--use_gpu=0") + main() + + +Demo预测输出如下，其中value即为softmax层的输出。由于TEST_DATA包含两条预测数据，所以输出的value包含两个向量 。 .. code-block:: text @@ -45,4 +60,4 @@ PaddlePaddle目前使用Swig对其常用的预测接口进行了封装，使在P 2.70634608e-08, 3.48565123e-08, 5.25639710e-09, 4.48684503e-08]], dtype=float32)}] -其中，value即为softmax层的输出。由于数据是两条，所以输出的value包含两个向量 。 + -- GitLab From 0bcacea5815b710f3d798307d8906cc49347465b Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Wed, 23 Nov 2016 17:35:35 +0800 Subject: [PATCH 056/347] refine ubuntu installation and FAQ doc --- .../install/ubuntu_install.rst | 81 ++++++++----------- doc_cn/faq/index.rst | 58 ++++++------- 2 files changed, 55 insertions(+), 84 deletions(-) diff --git a/doc_cn/build_and_install/install/ubuntu_install.rst b/doc_cn/build_and_install/install/ubuntu_install.rst index 0fb59e25f69..08d55f98d9d 100644 --- a/doc_cn/build_and_install/install/ubuntu_install.rst +++ b/doc_cn/build_and_install/install/ubuntu_install.rst @@ -1,83 +1,66 @@ -使用deb包在Ubuntu上安装PaddlePaddle +Ubuntu部署PaddlePaddle =================================== -PaddlePaddle目前支持使用deb包安装。Paddle的 :code:`deb` 安装包在ubuntu 14.04中正确，但理论上支持其他的 debian 发行版。 +PaddlePaddle提供了deb安装包，并在ubuntu 14.04做了完备测试，理论上也支持其他的debian发行版。 +安装 +------ -PaddlePaddle的ubuntu安装包分为四个版本，他们是 cpu、gpu、cpu-noavx、gpu-noavx 四个版本。其中 noavx 用于不支持AVX指令集的cpu。安装包的下载地址是\: https://github.com/baidu/Paddle/releases/ +安装包的下载地址是\: https://github.com/PaddlePaddle/Paddle/releases +它包含四个版本\: -用户需要先将PaddlePaddle安装包下载到本地，然后执行如下 :code:`gdebi` 命令即可完成安装。 +* cpu版本: 支持主流intel x86处理器平台, 支持avx指令集。 -.. code-block:: shell +* cpu-noavx版本：支持主流intel x86处理器平台，不支持avx指令集。 + +* gpu版本：支持主流intel x86处理器平台，支持nvidia cuda平台，支持avx指令集。 + +* gpu-noavx版本：支持主流intel x86处理器平台，支持nvidia cuda平台，不支持avx指令级。 - gdebi paddle-*-cpu*.deb +下载完相关安装包后，执行: -如果 :code:`gdebi` 没有安装,则需要使用 :code:`sudo apt-get install gdebi`, 来安装 :code:`gdebi` 。 +.. code-block:: shell + sudo apt-get install gdebi + gdebi paddle-*-cpu.deb -或者使用下面一条命令安装. +或者: .. code-block:: shell - dpkg -i paddle-*-cpu*.deb + dpkg -i paddle-*-cpu.deb apt-get install -f + 在 :code:`dpkg -i` 的时候如果报一些依赖未找到的错误是正常的， 在 :code:`apt-get install -f` 里会继续安装 PaddlePaddle。 -需要注意的是，如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中， -并设置好对应的环境变量(LD_LIBRARY_PATH等等)。 - -安装完成后,可以使用命令 :code:`paddle version` 查看安装后的paddle 版本。可能的输出为 +安装完成后，可以使用命令 :code:`paddle version` 查看安装后的paddle 版本: .. literalinclude:: paddle_version.txt 可能遇到的问题 -------------- -libcudart.so/libcudnn.so找不到 -++++++++++++++++++++++++++++++ - -安装完成PaddlePaddle后，运行 :code:`paddle train` 报错\: - -.. code-block:: shell - - 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. - -PaddlePaddle使用运行时动态连接CUDA的so，如果在 LD_LIBRARY_PATH里面找不到这些动态 -库的话，会报寻找不到这些动态库。 - -解决方法很简单，就是将这些动态库加到环境变量里面。比较可能的命令如下。 - -.. code-block:: text - - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH - -CUDA Driver找不到 -+++++++++++++++++ +如何设置gpu版本运行时cuda环境运行GPU版本 +++++++++++++++++++++++++++++++++++++++++ -运行 :code:`paddle train` 报错\: +如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置： -.. code-block:: text +.. code-block:: shell + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH + export PATH=/usr/local/cuda/bin:$PATH - F0831 12:39:16.699000 1090 hl_cuda_device.cc:530] Check failed: cudaSuccess == cudaStat (0 vs. 35) Cuda Error: CUDA driver version is insufficient for CUDA runtime version -PaddlePaddle运行时如果没有寻找到cuda的driver，变会报这个错误。解决办法是将cuda -driver添加到LD_LIBRARY_PATH中。比较可能的命令如下。 - -.. code-block:: text - - export LD_LIBRARY_PATH=/usr/lib64:$LD_LIBRARY_PATH +libcudart.so/libcudnn.so找不到 +++++++++++++++++++++++++++++++ -config文件找不到 -++++++++++++++++ +安装完成后，运行 :code:`paddle train` 报错\: -运行 :code:`paddle train` 得到结果\: +.. code-block:: shell -.. code-block:: text + 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. - F0831 20:53:07.525789 1302 TrainerMain.cpp:94] Check failed: config != nullptr no valid config +原因是未设置cuda运行时环境变量，请参考** 设置gpu版本运行时cuda环境** 解决方案。 -PaddlePaddle在运行时找不到对应的config文件，说明命令行参数 :code:`config` 没有设置。 -而这个一般说明PaddlePaddle已经安装完毕了。 \ No newline at end of file diff --git a/doc_cn/faq/index.rst b/doc_cn/faq/index.rst index 3eb0e10ae22..6e1102e5528 100644 --- a/doc_cn/faq/index.rst +++ b/doc_cn/faq/index.rst @@ -4,22 +4,18 @@ PaddlePaddle常见问题 .. contents:: -1. 如何减少PaddlePaddle的内存占用 +1. 如何减少内存占用 --------------------------------- -神经网络的训练本身是一个非常消耗内存和显存的工作。经常会消耗数十G的内存和数G的显存。 +神经网络的训练本身是一个非常消耗内存和显存的工作，经常会消耗数十G的内存和数G的显存。 PaddlePaddle的内存占用主要分为如下几个方面\: -* DataProvider缓冲池内存 (只针对内存) -* 神经元激活内存 （针对内存和显存） -* 参数内存 (针对内存和显存) +* DataProvider缓冲池内存（只针对内存） +* 神经元激活内存（针对内存和显存） +* 参数内存 （针对内存和显存） * 其他内存杂项 -这其中，其他内存杂项是指PaddlePaddle本身所用的一些内存，包括字符串分配，临时变量等等， -这些内存就不考虑如何缩减了。 - -其他的内存的减少方法依次为 - +其中，其他内存杂项是指PaddlePaddle本身所用的一些内存，包括字符串分配，临时变量等等，暂不考虑在内。 减少DataProvider缓冲池内存 ++++++++++++++++++++++++++ @@ -39,28 +35,28 @@ PyDataProvider使用的是异步加载，同时在内存里直接随即选取数 .. literalinclude:: reduce_min_pool_size.py -这样做可以极大的减少内存占用，并且可能会加速训练过程。 详细文档参考 `这里 +这样做可以极大的减少内存占用，并且可能会加速训练过程，详细文档参考 `这里 <../ui/data_provider/pydataprovider2.html#provider>`_ 。 神经元激活内存 ++++++++++++++ -神经网络在训练的时候，会对每一个激活暂存一些数据，包括激活，參差等等。 +神经网络在训练的时候，会对每一个激活暂存一些数据，如神经元激活值等。 在反向传递的时候，这些数据会被用来更新参数。这些数据使用的内存主要和两个参数有关系， 一是batch size，另一个是每条序列(Sequence)长度。所以，其实也是和每个mini-batch中包含 的时间步信息成正比。 -所以，做法可以有两种。他们是 +所以做法可以有两种： * 减小batch size。 即在网络配置中 :code:`settings(batch_size=1000)` 设置成一个小一些的值。但是batch size本身是神经网络的超参数，减小batch size可能会对训练结果产生影响。 * 减小序列的长度，或者直接扔掉非常长的序列。比如，一个数据集大部分序列长度是100-200, - 但是突然有一个10000长的序列，就很容易导致内存超限。特别是在LSTM等RNN中。 + 但是突然有一个10000长的序列，就很容易导致内存超限，特别是在LSTM等RNN中。 参数内存 ++++++++ PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需要使用不同大小的内存。 -例如如果使用 :code:`adadelta` 算法，则需要使用参数规模大约5倍的内存。 如果参数保存下来的 +例如使用 :code:`adadelta` 算法，则需要使用等于权重参数规模大约5倍的内存。举例，如果参数保存下来的模型目录 文件为 :code:`100M`， 那么该优化算法至少需要 :code:`500M` 的内存。 可以考虑使用一些优化算法，例如 :code:`momentum`。 @@ -68,11 +64,11 @@ PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需 2. 如何加速PaddlePaddle的训练速度 --------------------------------- -PaddlePaddle是神经网络训练平台，加速PaddlePaddle训练有如下几个方面\： +加速PaddlePaddle训练可以考虑从以下几个方面\： * 减少数据载入的耗时 * 加速训练速度 -* 利用更多的计算资源 +* 利用分布式训练驾驭更多的计算资源 减少数据载入的耗时 ++++++++++++++++++ @@ -108,25 +104,20 @@ PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 :code:`spa 利用更多的计算资源可以分为一下几个方式来进行\: * 单机CPU训练 - * 使用多线程训练。设置命令行参数 :code:`trainer_count`，即可以设置参与训练的线程数量。使用方法为 :code:`paddle train --trainer_count=4` + * 使用多线程训练。设置命令行参数 :code:`trainer_count`。 + * 单机GPU训练 - * 使用显卡训练。设置命令行参数 :code:`use_gpu`。 使用方法为 :code:`paddle train --use_gpu=true` - * 使用多块显卡训练。设置命令行参数 :code:`use_gpu` 和 :code:`trainer_count`。使用 :code:`--use_gpu=True` 开启GPU训练，使用 :code:`trainer_count` 指定显卡数量。使用方法为 :code:`paddle train --use_gpu=true --trainer_count=4` + * 使用显卡训练。设置命令行参数 :code:`use_gpu`。 + * 使用多块显卡训练。设置命令行参数 :code:`use_gpu` 和 :code:`trainer_count` 。 + * 多机训练 - * 使用多机训练的方法也比较简单，需要先在每个节点启动 :code:`paddle pserver`，在使用 :code:`paddle train --pservers=192.168.100.1,192.168.100.2` 来指定每个pserver的ip地址 - * 具体的多机训练方法参考 `多机训练 `_ 文档。 + * 具体的多机训练方法参考 `多机训练文档 <../ui/data_provider/pydataprovider2.html#provider>`_ 。 3. 遇到“非法指令”或者是“illegal instruction” -------------------------------------------- -paddle在进行计算的时候为了提升计算性能，使用了avx指令。部分老的cpu型号无法支持这样的指令。通常来说执行下grep avx /proc/cpuinfo看看是否有输出即可知道是否支持。（另：用此方法部分虚拟机可能检测到支持avx指令但是实际运行会挂掉，请当成是不支持，看下面的解决方案） - -解决办法是\: - -* 使用 NO_AVX的 `安装包 <../build_and_install/index.html>`_ 或者 `Docker image <../build_and_install/install/docker_install.html>`_ -* 或者，使用 :code:`-DWITH_AVX=OFF` 重新编译PaddlePaddle。 - +PaddlePaddle使用avx SIMD指令提高cpu执行效率，因此错误的使用二进制发行版可能会导致这种错误，请选择正确的版本。 4. 如何选择SGD算法的学习率 -------------------------- @@ -158,7 +149,7 @@ paddle在进行计算的时候为了提升计算性能，使用了avx指令。 6. 如何共享参数 --------------- -PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字的参数，会共享参数。设置参数的名字，可以使用 :code:`ParamAttr(name="YOUR_PARAM_NAME")` 来设置。更方便的设置方式，是想要共享的参数使用同样的 :code:`ParamAttr` 对象。 +PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字的参数，会共享参数。设置参数的名字，可以使用 :code:`ParamAttr(name="YOUR_PARAM_NAME")` 来设置。更方便的设置方式，是使得要共享的参数使用同样的 :code:`ParamAttr` 对象。 简单的全连接网络，参数共享的配置示例为\: @@ -208,9 +199,6 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字 paddle package is already in your PYTHONPATH. But unittest need a clean environment. Please uninstall paddle package before start unittest. Try to 'pip uninstall paddle'. -解决办法是：卸载paddle包 :code:`pip uninstall paddle`。 - -原因是：单元测试使用了一个旧版本的python包，而没有测试到代码中实际修改的python包。即单元测试需要一个干净的环境： +解决办法是： -* 如果paddle包已经在python的site-packages里面了，那么单元测试时使用的paddle包，就是site-packages里面的python包，而不是源码目录里 :code:`/python` 目录下的python包。 -* 即便设置了 :code:`PYTHONPATH` 到 :code:`/python` 也没用，因为python的搜索路径是优先已经安装的python包。 \ No newline at end of file +* 卸载PaddlePaddle包 :code:`pip uninstall paddle`, 清理掉老旧的PaddlePaddle安装包，使得单元测试有一个干净的环境。如果PaddlePaddle包已经在python的site-packages里面，单元测试会引用site-packages里面的python包，而不是源码目录里 :code:`/python` 目录下的python包。同时，即便设置 :code:`PYTHONPATH` 到 :code:`/python` 也没用，因为python的搜索路径是优先已经安装的python包。 -- GitLab From 0c981164902ea322f5573d645d3bd090b0ce5421 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 23 Nov 2016 17:38:20 +0800 Subject: [PATCH 057/347] Refining documentation --- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 43 +++++-------------- 1 file changed, 11 insertions(+), 32 deletions(-) diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index 896dd7ada9c..e1a847fc9ca 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -115,13 +115,11 @@ :language: python :lines: 39-66 -- 双层序列，外层memory是单层序列： +.. warning:: + PaddlePaddle目前只支持在每一个时间步中，Memory的sequence长度一致的情况。 - - 由于外层每个时间步返回的是一个子句，这些子句的长度往往不等长。因此当外层有is_seq=True的memory时，内层是**无法直接使用**它的，即内层memory的boot_layer不能链接外层的这个memory。 - - 如果内层memory想**间接使用**这个外层memory，只能通过`pooling_layer`、`last_seq`或`first_seq`这三个layer将它先变成一个元素。但这种情况下，外层memory必须有boot_layer，否则在第0个时间步时，由于外层memory没有任何seq信息，因此上述三个layer的前向会报出“**Check failed: input.sequenceStartPositions**”的错误。 - -示例3：双进双出，输入不等长 -=========================== +示例3：双层RNN，输入不等长 +========================== .. role:: red @@ -129,41 +127,22 @@ -**输入不等长** 是指recurrent_group的多个输入在各时刻的长度可以不相等, 但需要指定一个和输出长度一致的input，用 :red:`targetInlink` 表示。参考配置：单层RNN（:code:`sequence_rnn_multi_unequalength_inputs.conf`），双层RNN（:code:`sequence_nest_rnn_multi_unequalength_inputs.conf`） - -读取双层序列的方法 ------------------- - -我们看一下单双层序列的数据组织形式和dataprovider（见 :code:`rnn_data_provider.py` ） - -.. literalinclude:: ../../../paddle/gserver/tests/rnn_data_provider.py - :language: python - :lines: 69-97 - -data2 中有两个样本，每个样本有两个特征, 记fea1, fea2。 +**输入不等长** 是指recurrent_group的多个输入序列，在每个\ :ref:`glossary_timestep`\ 的子序列长度可以不相等。但\ :ref:`glossary_双层RNN`\ 目前需要整体的输出，与某一个输入的序列信息是一致的。使用\ :red:`targetInlink`\ 可以指定和输出序列信息一致。 -- 单层序列：两个样本分别为[[1, 2, 4, 5, 2], [5, 4, 1, 3, 1]] 和 [[0, 2, 2, 5, 0, 1, 2], [1, 5, 4, 2, 3, 6, 1]] -- 双层序列：两个样本分别为 +本例参考配置分别为\ `单层不等长RNN `_\ 和\ `双层不等长RNN `_\ 。 - - **样本1**\：[[[1, 2], [4, 5, 2]], [[5, 4, 1], [3, 1]]]。fea1和fea2都分别有2个子句，fea1=[[1, 2], [4, 5, 2]], fea2=[[5, 4, 1], [3, 1]] - - **样本2**\：[[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]]]。fea1和fea2都分别有3个子句， fea1=[[0, 2], [2, 5], [0, 1, 2]], fea2=[[1, 5], [4], [2, 3, 6, 1]]。
    - - **注意**\：每个样本中，各特征的子句数目需要相等。这里说的“双进双出，输入不等长”是指fea1在i时刻的输入的长度可以不等于fea2在i时刻的输入的长度。如对于第1个样本，时刻i=2, fea1[2]=[4, 5, 2]，fea2[2]=[3, 1]，3≠2。 +本例中对于单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 数据完全相同，对于单层\ :ref:`glossary_RNN`\ 的数据一共有两个样本，他们分别是\ :code:`[1, 2, 4, 5, 2], [5, 4, 1, 3, 1]`\ 和\ :code:`[0, 2, 2, 5, 0, 1, 2], [1, 5, 4, 2, 3, 6, 1]`\ 。对于每一个单层\ :ref:`glossary_RNN`\ 的数据，均有两组特征。在单层数据的基础上，\ :ref:`glossary_双层RNN`\ 数据随意加了一些隔断，例如将第一条数据转化为\ :code:`[[0, 2], [2, 5], [0, 1, 2]],[[1, 5], [4], [2, 3, 6, 1]]`\ 。但是需要注意的是Paddle目前只支持序列数目一样的多输入\ :ref:`glossary_双层RNN`\ 。即两个特征，均有三个子序列。每个子序列长度可以不一致，但是子序列的数目必须一样。 -- 单双层序列中，两个样本的label都分别是0和1 -模型中的配置 ------------- - -单层RNN（ :code:`sequence_rnn_multi_unequalength_inputs.conf`）和双层RNN（ :code:`v.conf`）两个模型配置达到的效果完全一样，区别只在于输入为单层还是双层序列，现在我们来看它们内部分别是如何实现的。 - -- 单层序列\： +:ref:`glossary_trainer_config`\ 的模型配置 +------------------------------------------ - - 过了一个简单的recurrent_group。每一个时间步，当前的输入y和上一个时间步的输出rnn_state做了一个全连接，功能与示例2中`sequence_rnn.conf`的`step`函数完全相同。这里，两个输入x1,x2分别通过calrnn返回最后时刻的状态。结果得到的encoder1_rep和encoder2_rep分别是单层序列，最后取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 - - 注意到这里recurrent_group输入的每个样本中，fea1和fea2的长度都分别相等，这并非偶然，而是因为recurrent_group要求输入为单层序列时，所有输入的长度都必须相等。 +本例中的配置，使用了单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 使用一个\ :code:`recurrent_group`\ 将两个序列同时过完全连接的\ :ref:`glossary_RNN`\ 。对于单层\ :ref:`glossary_RNN`\ 的code如下。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf :language: python :lines: 41-58 + :linenos: - 双层序列\： -- GitLab From da7c0f1326677ec7c22b6cdbd6595e2f4a1d59a2 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 23 Nov 2016 17:41:20 +0800 Subject: [PATCH 058/347] Format sequence_nest_rnn_multi_unequalength*.conf --- ...ce_nest_rnn_multi_unequalength_inputs.conf | 106 ----------------- ...ence_nest_rnn_multi_unequalength_inputs.py | 107 ++++++++++++++++++ ...sequence_rnn_multi_unequalength_inputs.py} | 68 +++++------ .../tests/test_RecurrentGradientMachine.cpp | 19 ++-- 4 files changed, 151 insertions(+), 149 deletions(-) delete mode 100644 paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf create mode 100644 paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py rename paddle/gserver/tests/{sequence_rnn_multi_unequalength_inputs.conf => sequence_rnn_multi_unequalength_inputs.py} (52%) diff --git a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf deleted file mode 100644 index d0b9450f4b9..00000000000 --- a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf +++ /dev/null @@ -1,106 +0,0 @@ -#edit-mode: -*- python -*- -# Copyright (c) 2016 Baidu, Inc. 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 * - -######################## data source ################################ -define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list', - test_list=None, - module='rnn_data_provider', - obj='process_unequalength_subseq') - - -settings(batch_size=2, learning_rate=0.01) -######################## network configure ################################ -dict_dim = 10 -word_dim = 8 -hidden_dim = 8 -label_dim = 2 - -speaker1 = data_layer(name="word1", size=dict_dim) -speaker2 = data_layer(name="word2", size=dict_dim) - -emb1 = embedding_layer(input=speaker1, size=word_dim) -emb2 = embedding_layer(input=speaker2, size=word_dim) - -# This hierachical RNN is designed to be equivalent to the simple RNN in -# sequence_rnn_multi_unequalength_inputs.conf - -def outer_step(x1, x2): - outer_mem1 = memory(name = "outer_rnn_state1", size = hidden_dim) - outer_mem2 = memory(name = "outer_rnn_state2", size = hidden_dim) - def inner_step1(y): - inner_mem = memory(name = 'inner_rnn_state_' + y.name, - size = hidden_dim, - boot_layer = outer_mem1) - out = fc_layer(input = [y, inner_mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'inner_rnn_state_' + y.name) - return out - - def inner_step2(y): - inner_mem = memory(name = 'inner_rnn_state_' + y.name, - size = hidden_dim, - boot_layer = outer_mem2) - out = fc_layer(input = [y, inner_mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'inner_rnn_state_' + y.name) - return out - - encoder1 = recurrent_group( - step = inner_step1, - name = 'inner1', - input = x1) - - encoder2 = recurrent_group( - step = inner_step2, - name = 'inner2', - input = x2) - - sentence_last_state1 = last_seq(input = encoder1, name = 'outer_rnn_state1') - sentence_last_state2_ = last_seq(input = encoder2, name = 'outer_rnn_state2') - - encoder1_expand = expand_layer(input = sentence_last_state1, - expand_as = encoder2) - - return [encoder1_expand, encoder2] - - -encoder1_rep, encoder2_rep = recurrent_group( - name="outer", - step=outer_step, - input=[SubsequenceInput(emb1), SubsequenceInput(emb2)], - targetInlink=emb2) - -encoder1_last = last_seq(input = encoder1_rep) -encoder1_expandlast = expand_layer(input = encoder1_last, - expand_as = encoder2_rep) -context = mixed_layer(input = [identity_projection(encoder1_expandlast), - identity_projection(encoder2_rep)], - size = hidden_dim) - -rep = last_seq(input=context) -prob = fc_layer(size=label_dim, - input=rep, - act=SoftmaxActivation(), - bias_attr=True) - -outputs(classification_cost(input=prob, - label=data_layer(name="label", size=label_dim))) - diff --git a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py new file mode 100644 index 00000000000..1b709a39c4b --- /dev/null +++ b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py @@ -0,0 +1,107 @@ +#edit-mode: -*- python -*- +# Copyright (c) 2016 Baidu, Inc. 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 * + +######################## data source ################################ +define_py_data_sources2( + train_list='gserver/tests/Sequence/dummy.list', + test_list=None, + module='rnn_data_provider', + obj='process_unequalength_subseq') + +settings(batch_size=2, learning_rate=0.01) +######################## network configure ################################ +dict_dim = 10 +word_dim = 8 +hidden_dim = 8 +label_dim = 2 + +speaker1 = data_layer(name="word1", size=dict_dim) +speaker2 = data_layer(name="word2", size=dict_dim) + +emb1 = embedding_layer(input=speaker1, size=word_dim) +emb2 = embedding_layer(input=speaker2, size=word_dim) + +# This hierachical RNN is designed to be equivalent to the simple RNN in +# sequence_rnn_multi_unequalength_inputs.conf + + +def outer_step(x1, x2): + outer_mem1 = memory(name="outer_rnn_state1", size=hidden_dim) + outer_mem2 = memory(name="outer_rnn_state2", size=hidden_dim) + + def inner_step1(y): + inner_mem = memory( + name='inner_rnn_state_' + y.name, + size=hidden_dim, + boot_layer=outer_mem1) + out = fc_layer( + input=[y, inner_mem], + size=hidden_dim, + act=TanhActivation(), + bias_attr=True, + name='inner_rnn_state_' + y.name) + return out + + def inner_step2(y): + inner_mem = memory( + name='inner_rnn_state_' + y.name, + size=hidden_dim, + boot_layer=outer_mem2) + out = fc_layer( + input=[y, inner_mem], + size=hidden_dim, + act=TanhActivation(), + bias_attr=True, + name='inner_rnn_state_' + y.name) + return out + + encoder1 = recurrent_group(step=inner_step1, name='inner1', input=x1) + + encoder2 = recurrent_group(step=inner_step2, name='inner2', input=x2) + + sentence_last_state1 = last_seq(input=encoder1, name='outer_rnn_state1') + sentence_last_state2_ = last_seq(input=encoder2, name='outer_rnn_state2') + + encoder1_expand = expand_layer( + input=sentence_last_state1, expand_as=encoder2) + + return [encoder1_expand, encoder2] + + +encoder1_rep, encoder2_rep = recurrent_group( + name="outer", + step=outer_step, + input=[SubsequenceInput(emb1), SubsequenceInput(emb2)], + targetInlink=emb2) + +encoder1_last = last_seq(input=encoder1_rep) +encoder1_expandlast = expand_layer(input=encoder1_last, expand_as=encoder2_rep) +context = mixed_layer( + input=[ + identity_projection(encoder1_expandlast), + identity_projection(encoder2_rep) + ], + size=hidden_dim) + +rep = last_seq(input=context) +prob = fc_layer( + size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True) + +outputs( + classification_cost( + input=prob, label=data_layer( + name="label", size=label_dim))) diff --git a/paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf b/paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py similarity index 52% rename from paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf rename to paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py index 28b1cb98cf1..4cf7035477c 100644 --- a/paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf +++ b/paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py @@ -16,11 +16,11 @@ from paddle.trainer_config_helpers import * ######################## data source ################################ -define_py_data_sources2(train_list='gserver/tests/Sequence/dummy.list', - test_list=None, - module='rnn_data_provider', - obj='process_unequalength_seq') - +define_py_data_sources2( + train_list='gserver/tests/Sequence/dummy.list', + test_list=None, + module='rnn_data_provider', + obj='process_unequalength_seq') settings(batch_size=2, learning_rate=0.01) ######################## network configure ################################ @@ -38,38 +38,40 @@ emb2 = embedding_layer(input=speaker2, size=word_dim) # This hierachical RNN is designed to be equivalent to the RNN in # sequence_nest_rnn_multi_unequalength_inputs.conf + def step(x1, x2): - def calrnn(y): - mem = memory(name = 'rnn_state_' + y.name, size = hidden_dim) - out = fc_layer(input = [y, mem], - size = hidden_dim, - act = TanhActivation(), - bias_attr = True, - name = 'rnn_state_' + y.name) - return out - - encoder1 = calrnn(x1) - encoder2 = calrnn(x2) - return [encoder1, encoder2] + def calrnn(y): + mem = memory(name='rnn_state_' + y.name, size=hidden_dim) + out = fc_layer( + input=[y, mem], + size=hidden_dim, + act=TanhActivation(), + bias_attr=True, + name='rnn_state_' + y.name) + return out + + encoder1 = calrnn(x1) + encoder2 = calrnn(x2) + return [encoder1, encoder2] + encoder1_rep, encoder2_rep = recurrent_group( - name="stepout", - step=step, - input=[emb1, emb2]) + name="stepout", step=step, input=[emb1, emb2]) -encoder1_last = last_seq(input = encoder1_rep) -encoder1_expandlast = expand_layer(input = encoder1_last, - expand_as = encoder2_rep) -context = mixed_layer(input = [identity_projection(encoder1_expandlast), - identity_projection(encoder2_rep)], - size = hidden_dim) +encoder1_last = last_seq(input=encoder1_rep) +encoder1_expandlast = expand_layer(input=encoder1_last, expand_as=encoder2_rep) +context = mixed_layer( + input=[ + identity_projection(encoder1_expandlast), + identity_projection(encoder2_rep) + ], + size=hidden_dim) rep = last_seq(input=context) -prob = fc_layer(size=label_dim, - input=rep, - act=SoftmaxActivation(), - bias_attr=True) - -outputs(classification_cost(input=prob, - label=data_layer(name="label", size=label_dim))) +prob = fc_layer( + size=label_dim, input=rep, act=SoftmaxActivation(), bias_attr=True) +outputs( + classification_cost( + input=prob, label=data_layer( + name="label", size=label_dim))) diff --git a/paddle/gserver/tests/test_RecurrentGradientMachine.cpp b/paddle/gserver/tests/test_RecurrentGradientMachine.cpp index 80d713dac03..9d86067fb5a 100644 --- a/paddle/gserver/tests/test_RecurrentGradientMachine.cpp +++ b/paddle/gserver/tests/test_RecurrentGradientMachine.cpp @@ -13,12 +13,12 @@ See the License for the specific language governing permissions and limitations under the License. */ #include -#include -#include -#include +#include #include #include -#include +#include +#include +#include P_DECLARE_int32(seed); @@ -45,10 +45,9 @@ public: auto p = const_cast(this); auto& params = p->getGradientMachine()->getParameters(); return std::accumulate( - params.begin(), - params.end(), - 0UL, - [](size_t a, const ParameterPtr& p) { return a + p->getSize(); }); + params.begin(), params.end(), 0UL, [](size_t a, const ParameterPtr& p) { + return a + p->getSize(); + }); } }; @@ -148,8 +147,8 @@ TEST(RecurrentGradientMachine, rnn_multi_input) { TEST(RecurrentGradientMachine, rnn_multi_unequalength_input) { for (bool useGpu : {false, true}) { - test("gserver/tests/sequence_rnn_multi_unequalength_inputs.conf", - "gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf", + test("gserver/tests/sequence_rnn_multi_unequalength_inputs.py", + "gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py", 1e-6, useGpu); } -- GitLab From 6aece5060b600f9c86580c5958b6039973a424fa Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 23 Nov 2016 18:06:54 +0800 Subject: [PATCH 059/347] Stash --- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 11 +-- ...ence_nest_rnn_multi_unequalength_inputs.py | 69 ++++++++----------- 2 files changed, 36 insertions(+), 44 deletions(-) diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index e1a847fc9ca..4d29507ca3c 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -139,20 +139,21 @@ 本例中的配置，使用了单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 使用一个\ :code:`recurrent_group`\ 将两个序列同时过完全连接的\ :ref:`glossary_RNN`\ 。对于单层\ :ref:`glossary_RNN`\ 的code如下。 -.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.conf +.. literalinclude:: ../../../paddle/gserver/tests/sequence_rnn_multi_unequalength_inputs.py :language: python - :lines: 41-58 + :lines: 42-59 :linenos: - 双层序列\： - 双层RNN中，对输入的两个特征分别求时序上的连续全连接(`inner_step1`和`inner_step2`分别处理fea1和fea2)，其功能与示例2中`sequence_nest_rnn.conf`的`outer_step`函数完全相同。不同之处是，此时输入`[SubsequenceInput(emb1), SubsequenceInput(emb2)]`在各时刻并不等长。 - - 函数`outer_step`中可以分别处理这两个特征，但我们需要用targetInlink指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 + - 函数`outer_step`中可以分别处理这两个特征，但我们需要用\ :red:`targetInlink`\ 指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 -.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.conf +.. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py :language: python - :lines: 41-89 + :lines: 42-75, 82-89 + :linenos: 示例4：beam_search的生成 ======================== diff --git a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py index 1b709a39c4b..bf88d00f2df 100644 --- a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py +++ b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py @@ -1,4 +1,4 @@ -#edit-mode: -*- python -*- +# edit-mode: -*- python -*- # Copyright (c) 2016 Baidu, Inc. All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -35,46 +35,37 @@ speaker2 = data_layer(name="word2", size=dict_dim) emb1 = embedding_layer(input=speaker1, size=word_dim) emb2 = embedding_layer(input=speaker2, size=word_dim) -# This hierachical RNN is designed to be equivalent to the simple RNN in -# sequence_rnn_multi_unequalength_inputs.conf - +# This hierarchical RNN is designed to be equivalent to the simple RNN in +# sequence_rnn_multi_unequalength_inputs.conf def outer_step(x1, x2): - outer_mem1 = memory(name="outer_rnn_state1", size=hidden_dim) - outer_mem2 = memory(name="outer_rnn_state2", size=hidden_dim) - - def inner_step1(y): - inner_mem = memory( - name='inner_rnn_state_' + y.name, - size=hidden_dim, - boot_layer=outer_mem1) - out = fc_layer( - input=[y, inner_mem], - size=hidden_dim, - act=TanhActivation(), - bias_attr=True, - name='inner_rnn_state_' + y.name) - return out - - def inner_step2(y): - inner_mem = memory( - name='inner_rnn_state_' + y.name, - size=hidden_dim, - boot_layer=outer_mem2) - out = fc_layer( - input=[y, inner_mem], - size=hidden_dim, - act=TanhActivation(), - bias_attr=True, - name='inner_rnn_state_' + y.name) - return out - - encoder1 = recurrent_group(step=inner_step1, name='inner1', input=x1) - - encoder2 = recurrent_group(step=inner_step2, name='inner2', input=x2) - - sentence_last_state1 = last_seq(input=encoder1, name='outer_rnn_state1') - sentence_last_state2_ = last_seq(input=encoder2, name='outer_rnn_state2') + index = [0] + + def inner_step(ipt): + index[0] += 1 + i = index[0] + outer_mem = memory(name="outer_rnn_state_%d" % i, size=hidden_dim) + + def inner_step_impl(y): + inner_mem = memory( + name="inner_rnn_state_" + y.name, + size=hidden_dim, + boot_layer=outer_mem) + out = fc_layer( + input=[y, inner_mem], + size=hidden_dim, + act=TanhActivation(), + bias_attr=True, + name='inner_rnn_state_' + y.name) + return out + + encoder = recurrent_group( + step=inner_step_impl, name='inner_%d' % i, input=ipt) + last = last_seq(name="outer_rnn_state_%d" % i, input=encoder) + return encoder, last + + _, sentence_last_state1 = inner_step(ipt=x1) + encoder2, _ = inner_step(ipt=x2) encoder1_expand = expand_layer( input=sentence_last_state1, expand_as=encoder2) -- GitLab From 9d377f0996e671f649d9e1fdd40ce327712a5e46 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Nov 2016 19:18:31 +0800 Subject: [PATCH 060/347] Update doc and merge run_multi.sh into run.sh for PaddlePaddle. --- benchmark/README.md | 32 +++++++------- benchmark/caffe/image/run_multi.sh | 2 +- benchmark/paddle/image/run.sh | 13 +++++- benchmark/paddle/image/run_multi.sh | 42 ------------------- benchmark/paddle/rnn/run.sh | 12 ++++++ benchmark/paddle/rnn/run_multi.sh | 34 --------------- .../tensorflow/image/alexnet_multi_gpu.py | 1 - .../tensorflow/image/smallnet_mnist_cifar.py | 1 - 8 files changed, 41 insertions(+), 96 deletions(-) delete mode 100755 benchmark/paddle/image/run_multi.sh delete mode 100755 benchmark/paddle/rnn/run_multi.sh diff --git a/benchmark/README.md b/benchmark/README.md index 8d2cf5737dd..8b453a7b59e 100644 --- a/benchmark/README.md +++ b/benchmark/README.md @@ -7,19 +7,19 @@ Machine: - cuDNN: v5.1 - system: Docker 1.12.1, all platform are tested in docker environment. -Platform: +Platforms: - PaddlePaddle: - Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu -- Caffe: +- Caffe: kaixhin/cuda-caffe -Several convolutional neural networks and recurrent neural network are used to test. +Several convolutional neural networks and recurrent neural networks are used to test. ## Image ### Benchmark Model -AlexNet, GooleNet and a small network which refer the config of cifar10 in Caffe are used. +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. @@ -38,9 +38,9 @@ AlexNet, GooleNet and a small network which refer the config of cifar10 in Caffe | TensorFlow | 223 | 364 | 645 | 1235 | | Caffe | 324 | 627 | 1232 | 2513 | -##### Notation +**Notation** -All platforms use cuDnn-v5.1. You might see that caffe is slower, because the workspace limit size is 8 * 1024 * 1024 in Caffe's cuDnn-conv interface. This size is larger in PaddlePaddle and TensorFlow. Caffe will be faster if increasing the workspace limit size. +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 @@ -59,9 +59,9 @@ All platforms use cuDnn-v5.1. You might see that caffe is slower, because the wo | TensorFlow | 9 | 15 | 28 | 59 | | Caffe | 9.373 | 16.6606 | 31.4797 | 59.719 | -##### Notation +**Notation** -All the tests in caffe use `caffe time` to execute, which is not including the parameter updating process. But the time in PaddlePaddle and TensorFlow contains it. +All the experiments in caffe use `caffe time` to execute, which does not include the time of parameter updating. The time in PaddlePaddle and TensorFlow contains it. But, compared with the total time, the time of parameter updating is relatively little. In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN. @@ -69,13 +69,13 @@ In Tensorflow, they implement algorithm searching method instead of using the al - AlexNet, ms / batch -| totoal-BatchSize | 128 * 4 | 256 * 4 | +| total-BatchSize | 128 * 4 | 256 * 4 | |------------------|----------| -----------| | PaddlePaddle | 347 | 622 | | TensorFlow | 377 | 675 | | Caffe | 1229 | 2435 | -For example, if `totoal-BatchSize = 128 * 4`, the speed is calculated by +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 @@ -86,9 +86,9 @@ For example, if `totoal-BatchSize = 128 * 4`, the speed is calculated by -- GooleNet, ms / batch +- GoogleNet, ms / batch -| totoal-BatchSize | 128 * 4 | 256 * 4 | +| total-BatchSize | 128 * 4 | 256 * 4 | |-------------------|--------------| ----------- | | PaddlePaddle | 1178 | 2367 | | TensorFlow | 1210 | 2292 | @@ -102,7 +102,7 @@ We use lstm network for text classfication to test benchmark. ### Dataset - [IMDB](http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl) -- Sequence legth=100, in fact, PaddlePaddle support training with variable-length sequence. But TensorFlow need to pad, in order to compare, we also pad sequence length to 100 in PaddlePaddle. +- Sequence legth is 100. In fact, PaddlePaddle supports training with variable-length sequence, but TensorFlow needs to pad, 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. @@ -110,7 +110,7 @@ We use lstm network for text classfication to test benchmark. #### LSTM in Text Classification -Testing network for different hidden size, batch size with `2 lstm layer + fc` network. +Testing `2 lstm layer + fc` network with different hidden size and batch size. - Batch size = 64, ms / batch @@ -138,7 +138,7 @@ Testing network for different hidden size, batch size with `2 lstm layer + fc` n #### Seq2Seq -The benchmark of sequence-to-sequence network will be add later. +The benchmark of sequence-to-sequence network will be added later. ### Multi GPU: 4 GPUs @@ -165,4 +165,4 @@ The benchmark of sequence-to-sequence network will be add later. #### Seq2Seq -The benchmark of sequence-to-sequence network will be add later. +The benchmark of sequence-to-sequence network will be added later. diff --git a/benchmark/caffe/image/run_multi.sh b/benchmark/caffe/image/run_multi.sh index f72b062c11c..9a0a71bc185 100755 --- a/benchmark/caffe/image/run_multi.sh +++ b/benchmark/caffe/image/run_multi.sh @@ -9,7 +9,7 @@ function test() { sed -i "/input: \"data\"/{n;s/^input_dim.*/input_dim: ${batch_per_gpu}/g}" $cfg sed -i "/input: \"label\"/{n;s/^input_dim.*/input_dim: ${batch_per_gpu}/g}" $cfg sed -i "1c\net : \"${cfg}\"" solver.prototxt - caffe train --solver=solver.prototxt -gpu all > logs/${prefix}-4gpu-batch${batch}.log 2>&1 + caffe train --solver=solver.prototxt -gpu 0,1,2,3 > logs/${prefix}-4gpu-batch${batch}.log 2>&1 } if [ ! -d "logs" ]; then diff --git a/benchmark/paddle/image/run.sh b/benchmark/paddle/image/run.sh index 6fccf7854c6..a2169288351 100755 --- a/benchmark/paddle/image/run.sh +++ b/benchmark/paddle/image/run.sh @@ -1,5 +1,8 @@ set -e +# If use `paddle train` to run, it must use DataProvider to +# pass the data type to PaddlePaddle system. +# And PaddlePaddle requires training set list (train.list), function gen_file() { if [ ! -d "train.txt" ]; then for ((i=1;i<=1024;i++)) @@ -26,7 +29,6 @@ function train() { --log_period=10 \ --test_period=100 \ --config_args=$args \ - --cudnn_dir=/home/dangqingqing/tools/cudnn-5.1/lib64 \ > logs/$prefix-${thread}gpu-$bz.log 2>&1 } @@ -52,3 +54,12 @@ 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 diff --git a/benchmark/paddle/image/run_multi.sh b/benchmark/paddle/image/run_multi.sh deleted file mode 100755 index c506668fe0b..00000000000 --- a/benchmark/paddle/image/run_multi.sh +++ /dev/null @@ -1,42 +0,0 @@ -set -e - -function gen_file() { - if [ ! -d "train.txt" ]; then - for ((i=1;i<=1024;i++)) - do - echo "train/n09246464/n09246464_38735.jpeg 972" >> train.txt - done - fi - - if [ ! -d "train.list" ]; then - echo "train.txt" > train.list - fi -} - -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 -} - -gen_file -if [ ! -d "logs" ]; then - mkdir logs -fi - -#========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 diff --git a/benchmark/paddle/rnn/run.sh b/benchmark/paddle/rnn/run.sh index 92c6e0b4b42..e9dfeb2e525 100755 --- a/benchmark/paddle/rnn/run.sh +++ b/benchmark/paddle/rnn/run.sh @@ -36,3 +36,15 @@ train rnn.py 1 2 1 1280 128 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 diff --git a/benchmark/paddle/rnn/run_multi.sh b/benchmark/paddle/rnn/run_multi.sh deleted file mode 100755 index 50ee469bcd9..00000000000 --- a/benchmark/paddle/rnn/run_multi.sh +++ /dev/null @@ -1,34 +0,0 @@ -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${3}-hid${5}-batch${6}.log 2>&1 -} - - -if [ ! -d "logs" ]; then - mkdir logs -fi - -#-----config--gpu--lstm_num--padding--hidden_size--batch_size -#==================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 diff --git a/benchmark/tensorflow/image/alexnet_multi_gpu.py b/benchmark/tensorflow/image/alexnet_multi_gpu.py index 949ad77f3b8..f006fb56af7 100644 --- a/benchmark/tensorflow/image/alexnet_multi_gpu.py +++ b/benchmark/tensorflow/image/alexnet_multi_gpu.py @@ -279,7 +279,6 @@ def run_benchmark(): staircase=True) # Create an optimizer that performs gradient descent. - # opt = tf.train.GradientDescentOptimizer(lr) opt = tf.train.MomentumOptimizer(lr, 0.9) # Calculate the gradients for each model tower. diff --git a/benchmark/tensorflow/image/smallnet_mnist_cifar.py b/benchmark/tensorflow/image/smallnet_mnist_cifar.py index b539d1bed06..679dd1ab322 100644 --- a/benchmark/tensorflow/image/smallnet_mnist_cifar.py +++ b/benchmark/tensorflow/image/smallnet_mnist_cifar.py @@ -222,7 +222,6 @@ def run_benchmark(): objective = loss(last_layer, labels) # Compute gradients. - # opt = tf.train.GradientDescentOptimizer(0.001) opt = tf.train.MomentumOptimizer(0.001, 0.9) grads = opt.compute_gradients(objective) global_step = tf.get_variable('global_step', [], -- GitLab From ec0214392152dc88f04b7440b11edbfe0e022d67 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 23 Nov 2016 20:47:04 +0800 Subject: [PATCH 061/347] use literalinclude --- doc_cn/ui/predict/swig_py_paddle.rst | 23 +++-------------------- 1 file changed, 3 insertions(+), 20 deletions(-) diff --git a/doc_cn/ui/predict/swig_py_paddle.rst b/doc_cn/ui/predict/swig_py_paddle.rst index f9750d80c92..4c0a0de820b 100644 --- a/doc_cn/ui/predict/swig_py_paddle.rst +++ b/doc_cn/ui/predict/swig_py_paddle.rst @@ -25,26 +25,9 @@ PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会 如下是一段使用mnist model来实现手写识别的预测代码。完整的代码见 ``src_root/doc/ui/predict/predict_sample.py`` 。mnist model可以通过 ``src_root\demo\mnist`` 目录下的demo训练出来。 -.. code-block:: python - - from py_paddle import swig_paddle, DataProviderConverter - from paddle.trainer.PyDataProvider2 import dense_vector - from paddle.trainer.config_parser import parse_config - - TEST_DATA = [...] - - def main(): - conf = parse_config("./mnist_model/trainer_config.py", "") - network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config) - assert isinstance(network, swig_paddle.GradientMachine) # For code hint. - network.loadParameters("./mnist_model/") - converter = DataProviderConverter([dense_vector(784)]) - inArg = converter(TEST_DATA) - print network.forwardTest(inArg) - - if __name__ == '__main__': - swig_paddle.initPaddle("--use_gpu=0") - main() +.. literalinclude:: ../../../doc/ui/predict/predict_sample.py + :language: python + :lines: 15-18,121-136 Demo预测输出如下，其中value即为softmax层的输出。由于TEST_DATA包含两条预测数据，所以输出的value包含两个向量 。 -- GitLab From 88a7cbd6392540dcef4d84fef1324c0b33c62b0c Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Nov 2016 21:02:00 +0800 Subject: [PATCH 062/347] Use empty file list for paddle/image. --- benchmark/.gitignore | 9 +++++++++ benchmark/paddle/image/provider.py | 9 ++++----- benchmark/paddle/image/run.sh | 20 +++----------------- 3 files changed, 16 insertions(+), 22 deletions(-) create mode 100644 benchmark/.gitignore diff --git a/benchmark/.gitignore b/benchmark/.gitignore new file mode 100644 index 00000000000..7b66e8a5b50 --- /dev/null +++ b/benchmark/.gitignore @@ -0,0 +1,9 @@ +paddle/image/logs +paddle/image/*.pyc +paddle/image/train.list +paddle/rnn/logs +paddle/rnn/*.pyc +paddle/rnn/imdb.pkl +caffe/image/logs +tensorflow/image/logs +tensorflow/rnn/logs diff --git a/benchmark/paddle/image/provider.py b/benchmark/paddle/image/provider.py index b6bc0e9aa21..1ac47212b5a 100644 --- a/benchmark/paddle/image/provider.py +++ b/benchmark/paddle/image/provider.py @@ -20,8 +20,7 @@ def initHook(settings, height, width, color, num_class, **kwargs): @provider( init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM) def process(settings, file_list): - with open(file_list, 'r') as fdata: - for line in fdata: - img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten() - lab = random.randint(0, settings.num_class) - yield img.tolist(), int(lab) + for i in xrange(1024): + img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten() + lab = random.randint(0, settings.num_class) + yield img.astype('float32'), int(lab) diff --git a/benchmark/paddle/image/run.sh b/benchmark/paddle/image/run.sh index a2169288351..717ed487ba7 100755 --- a/benchmark/paddle/image/run.sh +++ b/benchmark/paddle/image/run.sh @@ -1,21 +1,5 @@ set -e -# If use `paddle train` to run, it must use DataProvider to -# pass the data type to PaddlePaddle system. -# And PaddlePaddle requires training set list (train.list), -function gen_file() { - if [ ! -d "train.txt" ]; then - for ((i=1;i<=1024;i++)) - do - echo "train/n09246464/n09246464_38735.jpeg 972" >> train.txt - done - fi - - if [ ! -d "train.list" ]; then - echo "train.txt" > train.list - fi -} - function train() { cfg=$1 thread=$2 @@ -32,7 +16,9 @@ function train() { > logs/$prefix-${thread}gpu-$bz.log 2>&1 } -gen_file +if [ ! -d "train.list" ]; then + echo " " > train.list +fi if [ ! -d "logs" ]; then mkdir logs fi -- GitLab From 0710e67d78746d034fa105f6b59a78a79efad062 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 23 Nov 2016 21:11:44 +0800 Subject: [PATCH 063/347] refine ui index.rst --- doc_cn/ui/index.rst | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/doc_cn/ui/index.rst b/doc_cn/ui/index.rst index 5aba272c627..dd2c845eb73 100644 --- a/doc_cn/ui/index.rst +++ b/doc_cn/ui/index.rst @@ -1,8 +1,9 @@ +######## 用户接口 -======== +######## 数据提供 -'''''''' +======== .. toctree:: :maxdepth: 1 @@ -11,14 +12,19 @@ 命令行参数 -'''''''''' +========== + +.. toctree:: + + cmd/index.rst + * `Use Case <../../doc/ui/cmd_argument/use_case.html>`_ * `Argument Outline <../../doc/ui/cmd_argument/argument_outline.html>`_ * `Detail Description <../../doc/ui/cmd_argument/detail_introduction.html>`_ 预测 -'''' +==== .. toctree:: -- GitLab From 41623561ca39dfd9cabde7f304385d801b635622 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 23 Nov 2016 21:22:35 +0800 Subject: [PATCH 064/347] refine paddle_version.rst --- doc_cn/ui/cmd/paddle_version.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/doc_cn/ui/cmd/paddle_version.rst b/doc_cn/ui/cmd/paddle_version.rst index 0a4f8dd472a..a65239b9ef6 100644 --- a/doc_cn/ui/cmd/paddle_version.rst +++ b/doc_cn/ui/cmd/paddle_version.rst @@ -1,9 +1,7 @@ paddle version的命令行参数 ========================== -paddle version可以打印出paddle的版本信息和编译的选项。常见的输出格式为 +paddle version用于打印当前的版本信息和相关编译选项。常见的输出格式如下。第一行说明了paddle的版本信息，后面跟着一些主要的编译选项。编译选项的具体意义可以参考 +`编译参数选项文件 <../../build_and_install/cmake/compile_options.html>`_ .. literalinclude:: paddle_version.txt - -其第一行说明了paddle的版本，后面跟着一系列编译参数。这里可以参考paddle的 -`编译参数选项文件 <../../build/cmake/compile_options.html>`_ -- GitLab From 24cfc5ab3cb5ee50ff0efe7c5baf5f7d6436ba7a Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Nov 2016 21:35:27 +0800 Subject: [PATCH 065/347] Minor changes for use_concepts.rst. --- doc_cn/concepts/use_concepts.rst | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/doc_cn/concepts/use_concepts.rst b/doc_cn/concepts/use_concepts.rst index c86429f323a..13f6410b987 100644 --- a/doc_cn/concepts/use_concepts.rst +++ b/doc_cn/concepts/use_concepts.rst @@ -15,7 +15,7 @@ PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。 系统框图 ======== -下图描述了用户使用框图，PaddlePaddle里链接了Python解释器，trainer进程可以利用这个解释器执行Python脚本，Python脚本里定义了模型配置、训练算法、以及数据读取函数。其中，数据读取程序往往定义在一个单独Python脚本文件里，被称为DataProvider，通常是一个Python函数。模型配置、训练算法通常定义在另一单独Python文件中。下面将分别介绍这两部分。 +下图描述了用户使用框图，PaddlePaddle的trainer进程里内嵌了Python解释器，trainer进程可以利用这个解释器执行Python脚本，Python脚本里定义了模型配置、训练算法、以及数据读取函数。其中，数据读取程序往往定义在一个单独Python脚本文件里，被称为DataProvider，通常是一个Python函数。模型配置、训练算法通常定义在另一单独Python文件中。下面将分别介绍这两部分。 .. graphviz:: @@ -37,17 +37,15 @@ PaddlePaddle是一个深度学习框架，支持单机模式和多机模式。 DataProvider ============ -在不同的应用里，训练数据的格式往往各不相同。因此，为了用户能够灵活的处理数据，我们提供了Python处理数据的接口，称为 `PyDataProvider`_ 。 +DataProvider是PaddlePaddle系统的数据提供器，trainer进程会调用DataProvider函数，将用户的原始数据转换成系统可以识别的数据类型。当所有数据读取完一轮后，DataProvider返回空数据，通知系统一轮数据读取结束，系统每一轮训练开始时会重置DataProvider。需要注意的是，DataProvider是被系统调用，而不是新数据驱动系统，一些随机化噪声添加都应该在DataProvider中完成。 -trainer进程会调用DataProvider函数，将用户的原始数据转换成系统可以识别的数据类型。当所有数据读取完一轮后，DataProvider返回空数据，通知系统一轮数据读取结束，系统每一轮训练开始时会重置DataProvider。需要注意的是，DataProvider是被系统调用，而不是新数据驱动系统，一些随机化噪声添加都应该在DataProvider中完成。 - -在 ``PyDataProvider`` 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，一些情况下(如：``min_pool_size=0``)需要Python接口里处理shuffle，可以参考 `PyDataProvider`_ 的相关文档继续深入了解。 +在不同的应用里，训练数据的格式往往各不相同。因此，为了用户能够灵活的处理数据，我们提供了Python处理数据的接口，称为 `PyDataProvider`_ 。在 ``PyDataProvider`` 中，系统C++模块接管了shuffle、处理batch、GPU和CPU通信、双缓冲、异步读取等问题，一些情况下(如：``min_pool_size=0``)需要Python接口里处理shuffle，可以参考 `PyDataProvider`_ 的相关文档继续深入了解。 模型配置文件 ============ -模型配置主要包括数据传入接口定义(DataConfig)、优化算法(OptimizationConfig)、网络结构(ModelConfig)。 其中数据传入接口定义与DataProvider的关系是：DataProvider里定义数据读取函数，配置文件的DataConfig里指定DataProvider文件名字、生成数据函数接口，请不要混淆。 +模型配置文件主要包括数据传入接口定义(DataConfig)、优化算法(OptimizationConfig)、网络结构(ModelConfig)。 其中数据传入接口定义与DataProvider的关系是：DataProvider里定义数据读取函数，配置文件的DataConfig里指定DataProvider文件名字、生成数据函数接口，请不要混淆。 一个简单的模型配置文件为： @@ -61,7 +59,7 @@ trainer进程会调用DataProvider函数，将用户的原始数据转换成系 DataConfig ---------- -使用函数 ``define_py_data_sources2`` 配置数据源，后缀 2 是Paddle历史遗留问题，因为Paddle之前使用的PyDataProvider性能问题，重构了一个新的 `PyDataProvider`_ 。 +使用 `PyDataProvider`_ 的函数 ``define_py_data_sources2`` 配置数据源，后缀 2 是Paddle历史遗留问题，因为Paddle之前使用的PyDataProvider性能问题，重构了一个新的 `PyDataProvider`_ 。 ``define_py_data_sources2`` 里通过train_list和test_list指定是训练文件列表和测试文件列表。 如果传入字符串的话，是指一个数据列表文件。这个数据列表文件中包含的是每一个训练或者测试文件的路径。如果传入一个list的话，则会默认生成一个list文件，再传入给train.list或者test.list。 @@ -70,7 +68,7 @@ DataConfig OptimizationConfig ------------------ -通过`settings`_ 接口设置神经网络所使用的训练参数和优化算法，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 `settings`_ 文档。 +通过`settings`_ 接口设置神经网络所使用的训练参数和 `优化算法`_ ，包括学习率、batch_size、优化算法、正则方法等，具体的使用方法请参考 `settings`_ 文档。 ModelConfig ----------- @@ -150,6 +148,7 @@ PaddlePaddle多机采用经典的 Parameter Server 架构对多个节点的 trai .. _PyDataProvider: ../ui/data_provider/pydataprovider2.html .. _settings: ../../doc/ui/api/trainer_config_helpers/optimizers.html#settings +.. _优化算法: ../../doc/ui/api/trainer_config_helpers/optimizers.html#optimizers .. _trainer_config_helper: ../../doc/ui/api/trainer_config_helpers/index.html .. _data_layer: ../../doc/ui/api/trainer_config_helpers/layers.html#data-layer .. _simple_img_conv_pool: ../../doc/ui/api/trainer_config_helpers/networks.html#simple-img-conv-pool -- GitLab From e9d825c1751d36b56f2a9099cbcf5e6816233447 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 23 Nov 2016 22:52:44 +0800 Subject: [PATCH 066/347] refine cmd doc --- doc_cn/ui/cmd/index.rst | 27 ++++++++++++++++----------- doc_cn/ui/cmd/paddle_pserver.rst | 2 -- doc_cn/ui/cmd/paddle_train.rst | 2 -- 3 files changed, 16 insertions(+), 15 deletions(-) diff --git a/doc_cn/ui/cmd/index.rst b/doc_cn/ui/cmd/index.rst index 6d62180a6a5..f975d432c07 100644 --- a/doc_cn/ui/cmd/index.rst +++ b/doc_cn/ui/cmd/index.rst @@ -1,17 +1,22 @@ -命令行参数 -========== +PaddlePaddle的命令行参数 +======================== -安装好的PaddlePaddle脚本包括多条命令，他们是 +安装好PaddlePaddle后，在命令行直接敲击 ``paddle`` 或 ``paddle --help`` 会显示如下一些命令行参数。 -* paddle train即为PaddlePaddle的训练进程。可以使用paddle train完成单机多显卡多线程的训 - 练。也可以和paddle pserver组合使用，完成多机训练。 -* paddle pserver为PaddlePaddle的parameter server进程。负责多机训练中的参数聚合工作。 -* paddle version可以打印出PaddlePaddle的版本和编译时信息。 -* merge_model 可以将PaddlePaddle的模型和配置打包成一个文件。方便部署分发。 -* dump_config 可以将PaddlePaddle的训练模型以proto string的格式打印出来 -* make_diagram 可以使用graphviz对PaddlePaddle的网络模型进行绘制，方便调试使用。 +* ``train`` Start a paddle_trainer + 启动一个PaddlePaddle训练进程。 ``paddle train`` 可以通过命令行参数 ``-local=true`` 启动一个单机的训练进程；也可以和 ``paddle pserver`` 一起使用启动多机的分布式训练进程。 +* ``pserver`` Start a paddle_pserver_main + 在多机分布式训练下启动PaddlePaddle的parameter server进程。 +* ``version`` Print paddle version + 用于打印当前PaddlePaddle的版本和编译选项相关信息。 +* ``merge_model`` Start a paddle_merge_model + 用于将PaddlePaddle的模型参数文件和模型配置文件打包成一个文件，方便做部署分发。 +* ``dump_config`` Dump the trainer config as proto string + 用于将PaddlePaddle的模型配置文件以proto string的格式打印出来。 +* ``make_diagram`` + 使用graphviz对PaddlePaddle的模型配置文件进行绘制。 -更详细的介绍请参考各个命令的命令行参数文档。 +更详细的介绍请参考各命令行参数文档。 .. toctree:: :glob: diff --git a/doc_cn/ui/cmd/paddle_pserver.rst b/doc_cn/ui/cmd/paddle_pserver.rst index 891975c34af..e69de29bb2d 100644 --- a/doc_cn/ui/cmd/paddle_pserver.rst +++ b/doc_cn/ui/cmd/paddle_pserver.rst @@ -1,2 +0,0 @@ -paddle pserver的命令行参数 -========================== diff --git a/doc_cn/ui/cmd/paddle_train.rst b/doc_cn/ui/cmd/paddle_train.rst index 87b84f5cbdb..e69de29bb2d 100644 --- a/doc_cn/ui/cmd/paddle_train.rst +++ b/doc_cn/ui/cmd/paddle_train.rst @@ -1,2 +0,0 @@ -paddle train的命令行参数 -======================== -- GitLab From ed7334d694aa32325697af8a85250fed5d642387 Mon Sep 17 00:00:00 2001 From: Haonan Date: Wed, 23 Nov 2016 11:01:45 -0800 Subject: [PATCH 067/347] fix the has_LayerOutput boolean for StaticInput as the input of recurrent_group --- python/paddle/trainer_config_helpers/layers.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index d984e843204..9a45a515898 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2853,11 +2853,11 @@ def recurrent_group(step, :type targetInlink: LayerOutput|SubsequenceInput :param is_generating: If is generating, none of input type should be LayerOutput; - else, for training or testing, one of the input type must + else, for training or testing, one of the input type must be LayerOutput. : type is_generating: bool - + :return: LayerOutput object. :rtype: LayerOutput """ @@ -2905,15 +2905,16 @@ def recurrent_group(step, seq_reversed=reverse, target_inlinkname=targetInlinkName) in_args = [] - has_LayerOutput = True + has_LayerOutput = False for each_input in input: assert is_single_input(each_input) if isinstance(each_input, LayerOutput): in_args.append(each_input) + has_LayerOutput = True elif isinstance(each_input, SubsequenceInput): in_args.append(each_input.input) + has_LayerOutput = True else: - has_LayerOutput = False mem_name = "__%s_memory__" % each_input.input.name mem = memory( name=mem_name, -- GitLab From 0561dd01b874c94525b42bd596bafb8020b547bc Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 24 Nov 2016 10:13:16 +0800 Subject: [PATCH 068/347] Update doc and proc_from_raw_data/get_data.sh --- .../data/proc_from_raw_data/get_data.sh | 20 ++++++++++--------- doc/demo/quick_start/index_en.md | 2 +- doc_cn/demo/quick_start/index.md | 4 ++-- 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/demo/quick_start/data/proc_from_raw_data/get_data.sh b/demo/quick_start/data/proc_from_raw_data/get_data.sh index 9c3e9db248a..cd85e26842d 100755 --- a/demo/quick_start/data/proc_from_raw_data/get_data.sh +++ b/demo/quick_start/data/proc_from_raw_data/get_data.sh @@ -25,14 +25,17 @@ cd $DIR # Download data echo "Downloading Amazon Electronics reviews data..." # http://jmcauley.ucsd.edu/data/amazon/ -#wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz +wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz echo "Downloading mosesdecoder..." -#https://github.com/moses-smt/mosesdecoder -#wget https://github.com/moses-smt/mosesdecoder/archive/master.zip -#unzip master.zip -#rm master.zip -echo "Done." +# https://github.com/moses-smt/mosesdecoder +wget https://github.com/moses-smt/mosesdecoder/archive/master.zip +unzip master.zip +rm master.zip + +################## +# Preprocess data +echo "Preprocess data..." export LC_ALL=C UNAME_STR=`uname` @@ -42,12 +45,11 @@ else SHUF_PROG='gshuf' fi -# Start preprocess mkdir -p tmp python preprocess.py -i reviews_Electronics_5.json.gz # uniq and shuffle cd tmp -echo 'uniq and shuffle...' +echo 'Uniq and shuffle...' cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed @@ -74,4 +76,4 @@ echo 'test.txt' > test.list rm -rf tmp mv dict.txt dict_all.txt cat dict_all.txt | head -n 30001 > dict.txt -echo 'preprocess finished' +echo 'Done.' diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md index 01f6f8ef544..11d568b8f94 100644 --- a/doc/demo/quick_start/index_en.md +++ b/doc/demo/quick_start/index_en.md @@ -59,7 +59,7 @@ To build your text classification system, your code will need to perform five st ## Preprocess data into standardized format In this example, you are going to use [Amazon electronic product review dataset](http://jmcauley.ucsd.edu/data/amazon/) to build a bunch of deep neural network models for text classification. Each text in this dataset is a product review. This dataset has two categories: “positive” and “negative”. Positive means the reviewer likes the product, while negative means the reviewer does not like the product. -`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides script for downloading the preprocessed data as shown below. (If you want to process the raw data, you can use the script `demo/quick_start/data/proc_from_raw_data/get_data.sh`). +`demo/quick_start` in the [source code](https://github.com/PaddlePaddle/Paddle) provides script for downloading the preprocessed data as shown below. (If you want to process the raw data, you can use the script `demo/quick_start/data/proc_from_raw_data/get_data.sh`). ```bash cd demo/quick_start diff --git a/doc_cn/demo/quick_start/index.md b/doc_cn/demo/quick_start/index.md index 514b45a4879..4a6e07ee1ff 100644 --- a/doc_cn/demo/quick_start/index.md +++ b/doc_cn/demo/quick_start/index.md @@ -32,7 +32,7 @@ ## 数据格式准备(Data Preparation) 在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)， -将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了下载已经预处理数据的脚本（如果想从最原始的数据处理，可以使用脚本 `./demo/quick_start/data/proc_from_raw_data/get_data.sh`）。 +将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/PaddlePaddle/Paddle)的`demo/quick_start`里提供了下载已经预处理数据的脚本（如果想从最原始的数据处理，可以使用脚本 `./demo/quick_start/data/proc_from_raw_data/get_data.sh`）。 ```bash cd demo/quick_start @@ -141,7 +141,7 @@ PyDataProvider2。 我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置 连接请参考Layer文档。 -所有配置在[源码](https://github.com/baidu/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。 +所有配置在[源码](https://github.com/PaddlePaddle/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。 ### 逻辑回归模型(Logistic Regression) -- GitLab From 94ea8aa6657a3ec936eb4d95b996ddc69d21db3e Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 24 Nov 2016 11:29:58 +0800 Subject: [PATCH 069/347] follow comments --- doc_cn/ui/cmd/paddle_version.rst | 2 +- doc_cn/ui/index.rst | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc_cn/ui/cmd/paddle_version.rst b/doc_cn/ui/cmd/paddle_version.rst index a65239b9ef6..537c23df75e 100644 --- a/doc_cn/ui/cmd/paddle_version.rst +++ b/doc_cn/ui/cmd/paddle_version.rst @@ -1,7 +1,7 @@ paddle version的命令行参数 ========================== -paddle version用于打印当前的版本信息和相关编译选项。常见的输出格式如下。第一行说明了paddle的版本信息，后面跟着一些主要的编译选项。编译选项的具体意义可以参考 +paddle version用于打印当前的版本信息和相关编译选项。常见的输出格式如下。第一行说明了PaddlePaddle的版本信息，后面跟着一些主要的编译选项。编译选项的具体意义可以参考 `编译参数选项文件 <../../build_and_install/cmake/compile_options.html>`_ .. literalinclude:: paddle_version.txt diff --git a/doc_cn/ui/index.rst b/doc_cn/ui/index.rst index dd2c845eb73..8079bd9180c 100644 --- a/doc_cn/ui/index.rst +++ b/doc_cn/ui/index.rst @@ -18,9 +18,9 @@ cmd/index.rst -* `Use Case <../../doc/ui/cmd_argument/use_case.html>`_ -* `Argument Outline <../../doc/ui/cmd_argument/argument_outline.html>`_ -* `Detail Description <../../doc/ui/cmd_argument/detail_introduction.html>`_ +* `参数分类 <../../doc/ui/cmd_argument/argument_outline.html>`_ +* `参数描述 <../../doc/ui/cmd_argument/detail_introduction.html>`_ +* `参数用例 <../../doc/ui/cmd_argument/use_case.html>`_ 预测 -- GitLab From 634576128ce3c8075c38dbd7fdf6451ca7885a7f Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 24 Nov 2016 12:42:22 +0800 Subject: [PATCH 070/347] Done for reviewing docs. --- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index 4d29507ca3c..a13d4728a9c 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -144,17 +144,15 @@ :lines: 42-59 :linenos: -- 双层序列\： - - - 双层RNN中，对输入的两个特征分别求时序上的连续全连接(`inner_step1`和`inner_step2`分别处理fea1和fea2)，其功能与示例2中`sequence_nest_rnn.conf`的`outer_step`函数完全相同。不同之处是，此时输入`[SubsequenceInput(emb1), SubsequenceInput(emb2)]`在各时刻并不等长。 - - 函数`outer_step`中可以分别处理这两个特征，但我们需要用\ :red:`targetInlink`\ 指定recurrent_group的输出的格式（各子句长度）只能和其中一个保持一致，如这里选择了和emb2的长度一致。 - - 最后，依然是取encoder1_rep的最后一个时刻和encoder2_rep的所有时刻分别相加得到context。 +而双层序列的代码如下。 .. literalinclude:: ../../../paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py :language: python - :lines: 42-75, 82-89 + :lines: 41-80 :linenos: +在上面代码中，单层和双层序列的使用和示例2中的示例类似，区别是同时处理了两个输入。而对于双层序列，两个输入的子序列长度也并不相同。但是，我们使用了\ :code:`targetInlink`\ 参数设置了外层\ :code:`recurrent_group`\ 的输出格式。所以外层输出的序列形状，和\ :code:`emb2`的序列形状一致。 + 示例4：beam_search的生成 ======================== -- GitLab From dc0dcdac0060a300f74c132d2df6fee90c5d99bd Mon Sep 17 00:00:00 2001 From: zhangjinchao01 Date: Thu, 24 Nov 2016 14:42:43 +0800 Subject: [PATCH 071/347] reveise input order and sort bug --- demo/semantic_role_labeling/dataprovider.py | 9 +++++---- demo/semantic_role_labeling/predict.py | 10 +++------- demo/semantic_role_labeling/predict.sh | 2 +- demo/semantic_role_labeling/test.sh | 2 +- 4 files changed, 10 insertions(+), 13 deletions(-) diff --git a/demo/semantic_role_labeling/dataprovider.py b/demo/semantic_role_labeling/dataprovider.py index d4c137ef42c..2c8e1346273 100644 --- a/demo/semantic_role_labeling/dataprovider.py +++ b/demo/semantic_role_labeling/dataprovider.py @@ -25,12 +25,13 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs): #all inputs are integral and sequential type settings.slots = [ integer_value_sequence(len(word_dict)), - integer_value_sequence(len(predicate_dict)), integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)), integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), integer_value_sequence(2), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(predicate_dict)), + integer_value_sequence(2), integer_value_sequence(len(label_dict)) ] @@ -63,5 +64,5 @@ def process(settings, file_name): label_list = label.split() label_slot = [settings.label_dict.get(w) for w in label_list] - yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ - ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot + yield word_slot, ctx_n2_slot, ctx_n1_slot, \ + ctx_0_slot, ctx_p1_slot, ctx_p2_slot, predicate_slot, mark_slot, label_slot diff --git a/demo/semantic_role_labeling/predict.py b/demo/semantic_role_labeling/predict.py index 2761814e181..a7f1e8f81f5 100644 --- a/demo/semantic_role_labeling/predict.py +++ b/demo/semantic_role_labeling/predict.py @@ -55,18 +55,14 @@ class Prediction(): slots = [ integer_value_sequence(len_dict), - integer_value_sequence(len_pred), integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict), integer_value_sequence(len_dict), + integer_value_sequence(len_pred), integer_value_sequence(2) ] - integer_value_sequence(len_dict), integer_value_sequence(len_dict), - integer_value_sequence(len_dict), integer_value_sequence(len_dict), - integer_value_sequence(len_dict), integer_value_sequence(2) - ] self.converter = DataProviderConverter(slots) def load_dict_label(self, dict_file, label_file, predicate_dict_file): @@ -104,8 +100,8 @@ class Prediction(): marks = mark.split() mark_slot = [int(w) for w in marks] - yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ - ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot + yield word_slot, ctx_n2_slot, ctx_n1_slot, \ + ctx_0_slot, ctx_p1_slot, ctx_p2_slot, predicate_slot, mark_slot def predict(self, data_file, output_file): """ diff --git a/demo/semantic_role_labeling/predict.sh b/demo/semantic_role_labeling/predict.sh index d0acdb0bd09..88ab5898f7d 100644 --- a/demo/semantic_role_labeling/predict.sh +++ b/demo/semantic_role_labeling/predict.sh @@ -18,7 +18,7 @@ set -e function get_best_pass() { cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | \ - sort | head -n 1 + sort -n | head -n 1 } log=train.log diff --git a/demo/semantic_role_labeling/test.sh b/demo/semantic_role_labeling/test.sh index c4ab44f5ca0..f9e1bdcd4c7 100644 --- a/demo/semantic_role_labeling/test.sh +++ b/demo/semantic_role_labeling/test.sh @@ -18,7 +18,7 @@ set -e function get_best_pass() { cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' |\ - sort | head -n 1 + sort -n | head -n 1 } log=train.log -- GitLab From e08c4b8ec1d579bf08a644a9f984b5fa1d67b58b Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 24 Nov 2016 16:24:17 +0800 Subject: [PATCH 072/347] Revise doc and some scripts for demo. --- demo/image_classification/train.sh | 2 +- demo/sentiment/test.sh | 2 +- doc/demo/quick_start/index_en.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/demo/image_classification/train.sh b/demo/image_classification/train.sh index ed9b5220fff..db0a057bf35 100755 --- a/demo/image_classification/train.sh +++ b/demo/image_classification/train.sh @@ -24,7 +24,7 @@ paddle train \ --test_all_data_in_one_period=1 \ --use_gpu=1 \ --trainer_count=1 \ ---num_passes=200 \ +--num_passes=300 \ --save_dir=$output \ 2>&1 | tee $log diff --git a/demo/sentiment/test.sh b/demo/sentiment/test.sh index 098fbb91389..c8b12a0e89d 100755 --- a/demo/sentiment/test.sh +++ b/demo/sentiment/test.sh @@ -17,7 +17,7 @@ set -e function get_best_pass() { cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ sed -r 'N;s/Test.* classification_error_evaluator=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' |\ - sort | head -n 1 + sort -n | head -n 1 } log=train.log diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md index 80d816a768a..659485d9be1 100644 --- a/doc/demo/quick_start/index_en.md +++ b/doc/demo/quick_start/index_en.md @@ -477,7 +477,7 @@ The scripts of data downloading, network configurations, and training scrips are Word embedding 15MB 8.484% -trainer_config.bow.py +trainer_config.emb.py -- GitLab From 5590b4a2a8817e8ab3b2f4516e6b6527008b1da7 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Fri, 25 Nov 2016 10:34:13 +0800 Subject: [PATCH 073/347] Refine hierarchical-layer.rst --- ...chical-layer.md => hierarchical-layer.rst} | 97 ++++++++++++------- 1 file changed, 63 insertions(+), 34 deletions(-) rename doc_cn/algorithm/rnn/{hierarchical-layer.md => hierarchical-layer.rst} (50%) diff --git a/doc_cn/algorithm/rnn/hierarchical-layer.md b/doc_cn/algorithm/rnn/hierarchical-layer.rst similarity index 50% rename from doc_cn/algorithm/rnn/hierarchical-layer.md rename to doc_cn/algorithm/rnn/hierarchical-layer.rst index 519653df081..a9906b8b9c2 100644 --- a/doc_cn/algorithm/rnn/hierarchical-layer.md +++ b/doc_cn/algorithm/rnn/hierarchical-layer.rst @@ -1,6 +1,11 @@ -# 支持双层序列作为输入的Layer +########################### +支持双层序列作为输入的Layer +########################### -## 概述 +.. contents:: + +概述 +==== 在自然语言处理任务中，序列是一种常见的数据类型。一个独立的词语，可以看作是一个非序列输入，或者，我们称之为一个0层的序列；由词语构成的句子，是一个单层序列；若干个句子构成一个段落，是一个双层的序列。 @@ -12,55 +17,79 @@ + 单层序列：排成一列的多个元素，每个元素是一个0层序列，元素之间的顺序是重要的输入信息 + 双层序列：排成一列的多个元素，每个元素是一个单层序列，称之为双层序列的一个子序列（subseq），subseq的每个元素是一个0层序列 - 在 PaddlePaddle中，下面这些Layer能够接受双层序列作为输入，完成相应的计算。 -## pooling_layer - -pooling_layer的使用示例如下，详细见配置API。 -```python -seq_pool = pooling_layer(input=layer, - pooling_type=AvgPooling(), - agg_level=AggregateLevel.EACH_SEQUENCE) -``` + +pooling_layer +============== + +pooling_layer 的使用示例如下，详细见 `pooling_layer`_ 配置API。 + +.. code-block:: bash + + seq_pool = pooling_layer(input=layer, + pooling_type=AvgPooling(), + agg_level=AggregateLevel.EACH_SEQUENCE) + - `pooling_type` 目前支持两种，分别是：MaxPooling()和AvgPooling()。 -- `agg_level=AggregateLevel.TIMESTEP`时（默认值）： + +- `agg_level=AggregateLevel.TIMESTEP` 时（默认值）： + - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列 - 输入：一个双层序列，或一个单层序列 - 输出：一个0层序列，即整个输入序列（单层或双层）的平均值（或最大值） -- `agg_level=AggregateLevel.EACH_SEQUENCE`时： + +- `agg_level=AggregateLevel.EACH_SEQUENCE` 时： + - 作用：一个双层序列经过运算变成一个单层序列 - 输入：必须是一个双层序列 - 输出：一个单层序列，序列的每个元素是原来双层序列每个subseq元素的平均值（或最大值） -## last_seq 和 first_seq +last_seq 和 first_seq +===================== + +last_seq 的使用示例如下（ `first_seq`_ 类似），详细见 `last_seq`_ 配置API。 + +.. code-block:: bash + + last = last_seq(input=layer, + agg_level=AggregateLevel.EACH_SEQUENCE) + +- `agg_level=AggregateLevel.TIMESTEP` 时（默认值）： -last_seq的使用示例如下（first_seq类似），详细见配置API。 -```python -last = last_seq(input=layer, - agg_level=AggregateLevel.EACH_SEQUENCE) -``` -- `agg_level=AggregateLevel.TIMESTEP`时（默认值）： - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列 - 输入：一个双层序列或一个单层序列 - 输出：一个0层序列，即整个输入序列（双层或者单层）最后一个，或第一个元素。 -- `agg_level=AggregateLevel.EACH_SEQUENCE`时： + +- `agg_level=AggregateLevel.EACH_SEQUENCE` 时： - 作用：一个双层序列经过运算变成一个单层序列 - 输入：必须是一个双层序列 - 输出：一个单层序列，其中每个元素是双层序列中每个subseq最后一个（或第一个）元素。 -## expand_layer +expand_layer +============ + +expand_layer 的使用示例如下，详细见 `expand_layer`_ 配置API。 + +.. code-block:: bash + + expand = expand_layer(input=layer1, + expand_as=layer2, + expand_level=ExpandLevel.FROM_TIMESTEP) + +- `expand_level=ExpandLevel.FROM_TIMESTEP` 时（默认值）： -expand_layer的使用示例如下，详细见配置API。 -```python -expand = expand_layer(input=layer1, - expand_as=layer2, - expand_level=ExpandLevel.FROM_TIMESTEP) -``` -- `expand_level=ExpandLevel.FROM_TIMESTEP`时（默认值）： - 作用：一个0层序列经过运算扩展成一个单层序列，或者一个双层序列 - - 输入：layer1必须是一个0层序列，是待扩展的数据；layer2可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息 - - 输出：一个单层序列，或一个双层序列，输出序列的类型（双层序列，或单层序列）和序列中含有元素的数目同 layer2一致。若输出是单层序列，单层序列的每个元素（0层序列），都是对layer1元素的拷贝；若输出是双层序列，双层序列每个subseq中每个元素（0层序列），都是对layer1元素的拷贝 -- `expand_level=ExpandLevel.FROM_SEQUENCE`时： + - 输入：layer1必须是一个0层序列，是待扩展的数据；layer2 可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息 + - 输出：一个单层序列或一个双层序列，输出序列的类型（双层序列或单层序列）和序列中含有元素的数目同 layer2 一致。若输出是单层序列，单层序列的每个元素（0层序列），都是对layer1元素的拷贝；若输出是双层序列，双层序列每个subseq中每个元素（0层序列），都是对layer1元素的拷贝 + +- `expand_level=ExpandLevel.FROM_SEQUENCE` 时： + - 作用：一个单层序列经过运算扩展成一个双层序列 - - 输入：layer1必须是一个单层序列，是待扩展的数据；layer2必须是一个双层序列，提供扩展的长度信息 - - 输出：一个双层序列，序列中含有元素的数目同layer2一致。要求单层序列含有元素的数目（0层序列），和双层序列含有subseq 的数目一致。单层序列第i个元素（0层序列），被扩展为一个单层序列，构成了输出双层序列的第i个subseq。 + - 输入：layer1必须是一个单层序列，是待扩展的数据；layer2 必须是一个双层序列，提供扩展的长度信息 + - 输出：一个双层序列，序列中含有元素的数目同 layer2 一致。要求单层序列含有元素的数目（0层序列）和双层序列含有subseq 的数目一致。单层序列第i个元素（0层序列），被扩展为一个单层序列，构成了输出双层序列的第i个 subseq 。 + + +.. _pooling_layer: ../../../doc/ui/api/trainer_config_helpers/layers.html#pooling-layer +.. _last_seq: ../../../doc/ui/api/trainer_config_helpers/layers.html#last-seq +.. _first_seq: ../../../doc/ui/api/trainer_config_helpers/layers.html#first-seq +.. _expand_layer: ../../../doc/ui/api/trainer_config_helpers/layers.html#expand-layer -- GitLab From 68c5997e035dabb7509ae85190fbcab3089b5e4a Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 25 Nov 2016 11:57:12 +0800 Subject: [PATCH 074/347] merge bugfix #593 and # 597 from develop branch --- demo/image_classification/train.sh | 2 +- demo/semantic_role_labeling/predict.sh | 3 +-- demo/semantic_role_labeling/test.sh | 3 ++- demo/sentiment/test.sh | 2 +- doc/demo/quick_start/index_en.md | 4 ++-- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/demo/image_classification/train.sh b/demo/image_classification/train.sh index ed9b5220fff..db0a057bf35 100755 --- a/demo/image_classification/train.sh +++ b/demo/image_classification/train.sh @@ -24,7 +24,7 @@ paddle train \ --test_all_data_in_one_period=1 \ --use_gpu=1 \ --trainer_count=1 \ ---num_passes=200 \ +--num_passes=300 \ --save_dir=$output \ 2>&1 | tee $log diff --git a/demo/semantic_role_labeling/predict.sh b/demo/semantic_role_labeling/predict.sh index a545b9a5d59..111eea95a22 100644 --- a/demo/semantic_role_labeling/predict.sh +++ b/demo/semantic_role_labeling/predict.sh @@ -18,7 +18,7 @@ set -e function get_best_pass() { cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | \ - sort | head -n 1 + sort -n | head -n 1 } log=train.log @@ -26,7 +26,6 @@ LOG=`get_best_pass $log` LOG=(${LOG}) best_model_path="output/pass-${LOG[1]}" - config_file=db_lstm.py dict_file=./data/src.dict label_file=./data/tgt.dict diff --git a/demo/semantic_role_labeling/test.sh b/demo/semantic_role_labeling/test.sh index 844649e8c0f..f9e1bdcd4c7 100644 --- a/demo/semantic_role_labeling/test.sh +++ b/demo/semantic_role_labeling/test.sh @@ -18,7 +18,7 @@ set -e function get_best_pass() { cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' |\ - sort | head -n 1 + sort -n | head -n 1 } log=train.log @@ -36,4 +36,5 @@ paddle train \ --job=test \ --use_gpu=false \ --config_args=is_test=1 \ + --test_all_data_in_one_period=1 \ 2>&1 | tee 'test.log' diff --git a/demo/sentiment/test.sh b/demo/sentiment/test.sh index 098fbb91389..c8b12a0e89d 100755 --- a/demo/sentiment/test.sh +++ b/demo/sentiment/test.sh @@ -17,7 +17,7 @@ set -e function get_best_pass() { cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \ sed -r 'N;s/Test.* classification_error_evaluator=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' |\ - sort | head -n 1 + sort -n | head -n 1 } log=train.log diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md index e7d74512292..659485d9be1 100644 --- a/doc/demo/quick_start/index_en.md +++ b/doc/demo/quick_start/index_en.md @@ -1,4 +1,4 @@ -# Quick Start Tutorial +# Quick Start This tutorial will teach the basics of deep learning (DL), including how to implement many different models in PaddlePaddle. You will learn how to: - Prepare data into the standardized format that PaddlePaddle accepts. @@ -477,7 +477,7 @@ The scripts of data downloading, network configurations, and training scrips are Word embedding 15MB 8.484% -trainer_config.bow.py +trainer_config.emb.py -- GitLab From b3f4e535398c0e9eeaca6846c5d890525f2ed60e Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 25 Nov 2016 15:45:24 +0800 Subject: [PATCH 075/347] fix LogActivation is not defined --- python/paddle/trainer_config_helpers/activations.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/python/paddle/trainer_config_helpers/activations.py b/python/paddle/trainer_config_helpers/activations.py index 6261934e1bc..eeed18a98a2 100644 --- a/python/paddle/trainer_config_helpers/activations.py +++ b/python/paddle/trainer_config_helpers/activations.py @@ -16,7 +16,8 @@ __all__ = [ "TanhActivation", "SigmoidActivation", "SoftmaxActivation", "IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation', 'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation", - "STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation" + "STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation", + "LogActivation" ] -- GitLab From 341688b583a01d90609e2da362f4ecabdf0ff489 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Fri, 25 Nov 2016 16:56:18 +0800 Subject: [PATCH 076/347] Bumping up version number --- CMakeLists.txt | 2 +- paddle/scripts/docker/Dockerfile.m4 | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 090ac9e1884..0e642799766 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -3,7 +3,7 @@ cmake_minimum_required(VERSION 2.8) project(paddle CXX C) set(PADDLE_MAJOR_VERSION 0) set(PADDLE_MINOR_VERSION 9) -set(PADDLE_PATCH_VERSION 0a0) +set(PADDLE_PATCH_VERSION 0) set(PADDLE_VERSION ${PADDLE_MAJOR_VERSION}.${PADDLE_MINOR_VERSION}.${PADDLE_PATCH_VERSION}) set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake") diff --git a/paddle/scripts/docker/Dockerfile.m4 b/paddle/scripts/docker/Dockerfile.m4 index e14493ed9e8..761aa975d69 100644 --- a/paddle/scripts/docker/Dockerfile.m4 +++ b/paddle/scripts/docker/Dockerfile.m4 @@ -1,7 +1,7 @@ FROM PADDLE_BASE_IMAGE MAINTAINER PaddlePaddle Dev Team COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 +ENV GIT_CHECKOUT=v0.9.0 ENV WITH_GPU=PADDLE_WITH_GPU ENV IS_DEVEL=PADDLE_IS_DEVEL ENV WITH_DEMO=PADDLE_WITH_DEMO -- GitLab From 64f4e547ae7ee332aae6496edb4eafc051979597 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 25 Nov 2016 17:12:18 +0800 Subject: [PATCH 077/347] Refine eng docs structure --- doc/about/index.rst | 10 + doc/algorithm/index.rst | 7 - doc/algorithm/rnn/bi_lstm.jpg | 1 - .../rnn/encoder-decoder-attention-model.png | 1 - doc/{ui => api}/data_provider/index.rst | 0 .../data_provider/pydataprovider2.rst | 0 doc/api/index.md | 14 + doc/{ui => api}/predict/predict_sample.py | 0 doc/{ui => api}/predict/swig_py_paddle_en.rst | 0 .../trainer_config_helpers/activations.rst | 0 .../api/trainer_config_helpers/attrs.rst | 0 .../trainer_config_helpers/data_sources.rst | 0 .../api/trainer_config_helpers/evaluators.rst | 0 .../api/trainer_config_helpers/index.rst | 0 .../api/trainer_config_helpers/layers.rst | 0 .../api/trainer_config_helpers/networks.rst | 0 .../api/trainer_config_helpers/optimizers.rst | 0 .../api/trainer_config_helpers/poolings.rst | 0 doc/cluster/index.rst | 8 - doc/dev/layer.md | 4 - doc/howto/algorithm/index.rst | 7 + doc/{ => howto}/algorithm/rnn/rnn.rst | 0 .../cluster}/cluster_train.md | 2 +- .../cmd_argument/argument_outline.md | 0 .../cmd_argument/detail_introduction.md | 0 doc/howto/cmd_argument/index.md | 5 + doc/{ui => howto}/cmd_argument/use_case.md | 0 doc/{build => howto}/contribute_to_paddle.md | 0 doc/{ => howto}/dev/index.rst | 4 +- doc/howto/dev/layer.md | 5 + .../dev/new_layer/FullyConnected.jpg | Bin doc/{ => 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doc/{demo => tutorials}/semantic_role_labeling/network_arch.png (100%) rename doc/{demo => tutorials}/semantic_role_labeling/semantic_role_labeling.md (97%) rename doc/{demo => tutorials}/sentiment_analysis/bi_lstm.jpg (100%) rename doc/{demo => tutorials}/sentiment_analysis/index.rst (100%) rename doc/{demo => tutorials}/sentiment_analysis/lstm.png (100%) rename doc/{demo => tutorials}/sentiment_analysis/sentiment_analysis.md (100%) rename doc/{demo => tutorials}/sentiment_analysis/stacked_lstm.jpg (100%) rename doc/{demo => tutorials}/text_generation/encoder-decoder-attention-model.png (100%) rename doc/{demo => tutorials}/text_generation/index.rst (100%) rename doc/{demo => tutorials}/text_generation/text_generation.md (100%) delete mode 100644 doc/ui/index.md delete mode 100644 doc/user_guide.rst diff --git a/doc/about/index.rst b/doc/about/index.rst new file mode 100644 index 00000000000..c70940ca852 --- /dev/null +++ b/doc/about/index.rst @@ -0,0 +1,10 @@ +Credits +======== + +PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform, +which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu. + +PaddlePaddle is now open source but far from complete, which is intended to be built upon, improved, scaled, and extended. +We hope to build an active open source community both by providing feedback and by actively contributing to the source code. + +We owe many thanks to `all contributors and developers `_ of PaddlePaddle! diff --git a/doc/algorithm/index.rst b/doc/algorithm/index.rst deleted file mode 100644 index 6073add3c0c..00000000000 --- a/doc/algorithm/index.rst +++ /dev/null @@ -1,7 +0,0 @@ -Algorithm Tutorial -================== - -.. toctree:: - :maxdepth: 1 - - rnn/rnn.rst diff --git a/doc/algorithm/rnn/bi_lstm.jpg b/doc/algorithm/rnn/bi_lstm.jpg deleted file mode 120000 index a53296cf806..00000000000 --- a/doc/algorithm/rnn/bi_lstm.jpg +++ /dev/null @@ -1 +0,0 @@ -../../demo/sentiment_analysis/bi_lstm.jpg \ No newline at end of file diff --git a/doc/algorithm/rnn/encoder-decoder-attention-model.png b/doc/algorithm/rnn/encoder-decoder-attention-model.png deleted file mode 120000 index db71321a43a..00000000000 --- a/doc/algorithm/rnn/encoder-decoder-attention-model.png +++ /dev/null @@ -1 +0,0 @@ -../../demo/text_generation/encoder-decoder-attention-model.png \ No newline at end of file diff --git a/doc/ui/data_provider/index.rst b/doc/api/data_provider/index.rst similarity index 100% rename from doc/ui/data_provider/index.rst rename to doc/api/data_provider/index.rst diff --git a/doc/ui/data_provider/pydataprovider2.rst b/doc/api/data_provider/pydataprovider2.rst similarity index 100% rename from doc/ui/data_provider/pydataprovider2.rst rename to doc/api/data_provider/pydataprovider2.rst diff --git a/doc/api/index.md b/doc/api/index.md new file mode 100644 index 00000000000..8c4a65e0d5c --- /dev/null +++ b/doc/api/index.md @@ -0,0 +1,14 @@ +# API + +## Data Provider + +* [Introduction](data_provider/index.rst) +* [PyDataProvider2](data_provider/pydataprovider2.rst) + +## Trainer Configuration + +* [Model Config Interface](trainer_config_helpers/index.rst) + +## Predict + +* [Python Prediction API](predict/swig_py_paddle_en.rst) diff --git a/doc/ui/predict/predict_sample.py b/doc/api/predict/predict_sample.py similarity index 100% rename from doc/ui/predict/predict_sample.py rename to doc/api/predict/predict_sample.py diff --git a/doc/ui/predict/swig_py_paddle_en.rst b/doc/api/predict/swig_py_paddle_en.rst similarity index 100% rename from doc/ui/predict/swig_py_paddle_en.rst rename to doc/api/predict/swig_py_paddle_en.rst diff --git a/doc/ui/api/trainer_config_helpers/activations.rst b/doc/api/trainer_config_helpers/activations.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/activations.rst rename to doc/api/trainer_config_helpers/activations.rst diff --git a/doc/ui/api/trainer_config_helpers/attrs.rst b/doc/api/trainer_config_helpers/attrs.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/attrs.rst rename to doc/api/trainer_config_helpers/attrs.rst diff --git a/doc/ui/api/trainer_config_helpers/data_sources.rst b/doc/api/trainer_config_helpers/data_sources.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/data_sources.rst rename to doc/api/trainer_config_helpers/data_sources.rst diff --git a/doc/ui/api/trainer_config_helpers/evaluators.rst b/doc/api/trainer_config_helpers/evaluators.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/evaluators.rst rename to doc/api/trainer_config_helpers/evaluators.rst diff --git a/doc/ui/api/trainer_config_helpers/index.rst b/doc/api/trainer_config_helpers/index.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/index.rst rename to doc/api/trainer_config_helpers/index.rst diff --git a/doc/ui/api/trainer_config_helpers/layers.rst b/doc/api/trainer_config_helpers/layers.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/layers.rst rename to doc/api/trainer_config_helpers/layers.rst diff --git a/doc/ui/api/trainer_config_helpers/networks.rst b/doc/api/trainer_config_helpers/networks.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/networks.rst rename to doc/api/trainer_config_helpers/networks.rst diff --git a/doc/ui/api/trainer_config_helpers/optimizers.rst b/doc/api/trainer_config_helpers/optimizers.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/optimizers.rst rename to doc/api/trainer_config_helpers/optimizers.rst diff --git a/doc/ui/api/trainer_config_helpers/poolings.rst b/doc/api/trainer_config_helpers/poolings.rst similarity index 100% rename from doc/ui/api/trainer_config_helpers/poolings.rst rename to doc/api/trainer_config_helpers/poolings.rst diff --git a/doc/cluster/index.rst b/doc/cluster/index.rst deleted file mode 100644 index 9062f85f98d..00000000000 --- a/doc/cluster/index.rst +++ /dev/null @@ -1,8 +0,0 @@ -Cluster Train -==================== - -.. toctree:: - :glob: - - opensource/cluster_train.md - internal/index.md diff --git a/doc/dev/layer.md b/doc/dev/layer.md deleted file mode 100644 index 930fb0de1ac..00000000000 --- a/doc/dev/layer.md +++ /dev/null @@ -1,4 +0,0 @@ -# Layer Documents - -* [Layer Source Code Document](../source/gserver/layers/index.rst) -* [Layer Python API Document](../ui/api/trainer_config_helpers/index.rst) diff --git a/doc/howto/algorithm/index.rst b/doc/howto/algorithm/index.rst new file mode 100644 index 00000000000..b4ecbc48470 --- /dev/null +++ b/doc/howto/algorithm/index.rst @@ -0,0 +1,7 @@ +Algorithm Configuration +======================= + +.. toctree:: + :maxdepth: 1 + + rnn/rnn.rst diff --git a/doc/algorithm/rnn/rnn.rst b/doc/howto/algorithm/rnn/rnn.rst similarity index 100% rename from doc/algorithm/rnn/rnn.rst rename to doc/howto/algorithm/rnn/rnn.rst diff --git a/doc/cluster/opensource/cluster_train.md b/doc/howto/cluster/cluster_train.md similarity index 99% rename from doc/cluster/opensource/cluster_train.md rename to doc/howto/cluster/cluster_train.md index cb493a88f03..6b68596dc12 100644 --- a/doc/cluster/opensource/cluster_train.md +++ b/doc/howto/cluster/cluster_train.md @@ -9,7 +9,7 @@ In this article, we explain how to run distributed Paddle training jobs on clust 1. Aforementioned scripts use a Python library [fabric](http://www.fabfile.org/) to run SSH commands. We can use `pip` to install fabric: ```bash -pip install fabric + pip install fabric ``` 1. We need to install PaddlePaddle on all nodes in the cluster. To enable GPUs, we need to install CUDA in `/usr/local/cuda`; otherwise Paddle would report errors at runtime. diff --git a/doc/ui/cmd_argument/argument_outline.md b/doc/howto/cmd_argument/argument_outline.md similarity index 100% rename from doc/ui/cmd_argument/argument_outline.md rename to doc/howto/cmd_argument/argument_outline.md diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/howto/cmd_argument/detail_introduction.md similarity index 100% rename from doc/ui/cmd_argument/detail_introduction.md rename to doc/howto/cmd_argument/detail_introduction.md diff --git a/doc/howto/cmd_argument/index.md b/doc/howto/cmd_argument/index.md new file mode 100644 index 00000000000..90472c44cb9 --- /dev/null +++ b/doc/howto/cmd_argument/index.md @@ -0,0 +1,5 @@ +# Command Line Argument + +* [Use Case](use_case.md) +* [Argument Outline](argument_outline.md) +* [Detailed Descriptions](detail_introduction.md) diff --git a/doc/ui/cmd_argument/use_case.md b/doc/howto/cmd_argument/use_case.md similarity index 100% rename from doc/ui/cmd_argument/use_case.md rename to doc/howto/cmd_argument/use_case.md diff --git a/doc/build/contribute_to_paddle.md b/doc/howto/contribute_to_paddle.md similarity index 100% rename from doc/build/contribute_to_paddle.md rename to doc/howto/contribute_to_paddle.md diff --git a/doc/dev/index.rst b/doc/howto/dev/index.rst similarity index 71% rename from doc/dev/index.rst rename to doc/howto/dev/index.rst index 0468dd492b6..876c42e9dbb 100644 --- a/doc/dev/index.rst +++ b/doc/howto/dev/index.rst @@ -2,8 +2,8 @@ Development Guide ================= .. toctree:: - :maxdepth: 1 + :maxdepth: 2 layer.md new_layer/new_layer.rst - ../source/index.md + source/index.rst diff --git a/doc/howto/dev/layer.md b/doc/howto/dev/layer.md new file mode 100644 index 00000000000..1ce0cc5829d --- /dev/null +++ b/doc/howto/dev/layer.md @@ -0,0 +1,5 @@ +# Layer Documents + +* [Layer Python API](../../api/trainer_config_helpers/index.rst) +* [Layer Source Code](source/gserver/layers.rst) +* [Writing New Layers](new_layer/new_layer.rst) diff --git a/doc/dev/new_layer/FullyConnected.jpg b/doc/howto/dev/new_layer/FullyConnected.jpg similarity index 100% rename from doc/dev/new_layer/FullyConnected.jpg rename to doc/howto/dev/new_layer/FullyConnected.jpg diff --git a/doc/dev/new_layer/new_layer.rst b/doc/howto/dev/new_layer/new_layer.rst similarity index 100% rename from doc/dev/new_layer/new_layer.rst rename to doc/howto/dev/new_layer/new_layer.rst diff --git a/doc/source/api.rst b/doc/howto/dev/source/api.rst similarity index 100% rename from doc/source/api.rst rename to doc/howto/dev/source/api.rst diff --git a/doc/source/cuda/index.rst b/doc/howto/dev/source/cuda/index.rst similarity index 100% rename from doc/source/cuda/index.rst rename to doc/howto/dev/source/cuda/index.rst diff --git a/doc/source/cuda/matrix.rst b/doc/howto/dev/source/cuda/matrix.rst similarity index 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similarity index 100% rename from doc/source/utils/thread.rst rename to doc/howto/dev/source/utils/thread.rst diff --git a/doc/howto/index.rst b/doc/howto/index.rst new file mode 100644 index 00000000000..e2d688e186c --- /dev/null +++ b/doc/howto/index.rst @@ -0,0 +1,11 @@ +How to +======= + +.. toctree:: + :maxdepth: 1 + + cmd_argument/index.md + cluster/cluster_train.md + algorithm/index.rst + dev/index.rst + contribute_to_paddle.md \ No newline at end of file diff --git a/doc/index.rst b/doc/index.rst index 668ad75a902..a7feed52395 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -4,7 +4,9 @@ PaddlePaddle Documentation .. toctree:: :maxdepth: 1 - introduction/index.md - user_guide.rst - dev/index.rst - algorithm/index.rst + introduction/index.rst + tutorials/index.md + howto/index.rst + api/index.rst + about/index.rst + diff --git a/doc/introduction/basic_usage/basic_usage.rst b/doc/introduction/basic_usage/basic_usage.rst new file mode 100644 index 00000000000..dca7a6b1f4f --- /dev/null +++ b/doc/introduction/basic_usage/basic_usage.rst @@ -0,0 +1,109 @@ +Basic Usage +============= + +PaddlePaddle is a deep learning platform open-sourced by Baidu. With PaddlePaddle, you can easily train a classic neural network within a couple lines of configuration, or you can build sophisticated models that provide state-of-the-art performance on difficult learning tasks like sentiment analysis, machine translation, image caption and so on. + +1. A Classic Problem +--------------------- + +Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression `_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets. + +2. Prepare the Data +-------------------- + +Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types. + + .. code-block:: python + + # dataprovider.py + from paddle.trainer.PyDataProvider2 import * + import random + + # define data types of input: 2 real numbers + @provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False) + def process(settings, input_file): + for i in xrange(2000): + x = random.random() + yield [x], [2*x+0.3] + +3. Train a NeuralNetwork +------------------------- + +To recover this relationship between ``X`` and ``Y``, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line ``Y' = wX + b`` , then we gradually adapt ``w`` and ``b`` to minimize the difference between ``Y'`` and ``Y``. Here is what it looks like in PaddlePaddle: + + .. code-block:: python + + # trainer_config.py + from paddle.trainer_config_helpers import * + + # 1. read data. Suppose you saved above python code as dataprovider.py + data_file = 'empty.list' + with open(data_file, 'w') as f: f.writelines(' ') + define_py_data_sources2(train_list=data_file, test_list=None, + module='dataprovider', obj='process',args={}) + + # 2. learning algorithm + settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) + + # 3. Network configuration + x = data_layer(name='x', size=1) + y = data_layer(name='y', size=1) + y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) + cost = regression_cost(input=y_predict, label=y) + outputs(cost) + +Some of the most fundamental usages of PaddlePaddle are demonstrated: + +- The first part shows how to feed data into PaddlePaddle. In general cases, PaddlePaddle reads raw data from a list of files, and then do some user-defined process to get real input. In this case, we only need to create a placeholder file since we are generating synthetic data on the fly. + +- The second part describes learning algorithm. It defines in what ways adjustments are made to model parameters. PaddlePaddle provides a rich set of optimizers, but a simple momentum based optimizer will suffice here, and it processes 12 data points each time. + +- Finally, the network configuration. It usually is as simple as "stacking" layers. Three kinds of layers are used in this configuration: + - **Data Layer**: a network always starts with one or more data layers. They provide input data to the rest of the network. In this problem, two data layers are used respectively for ``X`` and ``Y``. + - **FC Layer**: FC layer is short for Fully Connected Layer, which connects all the input units to current layer and does the actual computation specified as activation function. Computation layers like this are the fundamental building blocks of a deeper model. + - **Cost Layer**: in training phase, cost layers are usually the last layers of the network. They measure the performance of current model, and provide guidence to adjust parameters. + +Now that everything is ready, you can train the network with a simple command line call: + + .. code-block:: bash + + paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 + + +This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path ``./output``. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess. + + +4. Evaluate the Model +----------------------- + +Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: ``w=2, b=0.3``, thus a better option is to check out model parameters directly. + +In PaddlePaddle, training is just to get a collection of model parameters, which are ``w`` and ``b`` in this case. Each parameter is saved in an individual file in the popular ``numpy`` array format. Here is the code that reads parameters from last pass. + + .. code-block:: python + + import numpy as np + import os + + def load(file_name): + with open(file_name, 'rb') as f: + f.read(16) # skip header for float type. + return np.fromfile(f, dtype=np.float32) + + print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b')) + # w=1.999743, b=0.300137 + + .. image:: parameters.png + :align: center + +Although starts from a random guess, you can see that value of ``w`` changes quickly towards 2 and ``b`` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer. + +There, you have recovered the underlying pattern between ``X`` and ``Y`` only from observed data. + + +5. 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zlQ#;936%aFTq+O^(Sl@NEcw7c4MA+FLl9Qtmi!wY@f-IF*aT*P3IDH#AmRh6k1<{O j{~Ea`=**simple linear regression** : you have observed a set of two-dimensional data points of `X` and `Y`, where `X` is an explanatory variable and `Y` is corresponding dependent variable, and you want to recover the underlying correlation between `X` and `Y`. Linear regression can be used in many practical scenarios. For example, `X` can be a variable about house size, and `Y` a variable about house price. You can build a model that captures relationship between them by observing real estate markets. - -## 2. Prepare the Data - -Suppose the true relationship can be characterized as `Y = 2X + 0.3`, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types. - -```python -# dataprovider.py -from paddle.trainer.PyDataProvider2 import * -import random - -# define data types of input: 2 real numbers -@provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False) -def process(settings, input_file): - for i in xrange(2000): - x = random.random() - yield [x], [2*x+0.3] -``` - -## 3. Train a NeuralNetwork in PaddlePaddle - -To recover this relationship between `X` and `Y`, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line `Y' = wX + b` , then we gradually adapt `w` and `b` to minimize the difference between `Y'` and `Y`. Here is what it looks like in PaddlePaddle: - -```python -# trainer_config.py -from paddle.trainer_config_helpers import * - -# 1. read data. Suppose you saved above python code as dataprovider.py -data_file = 'empty.list' -with open(data_file, 'w') as f: f.writelines(' ') -define_py_data_sources2(train_list=data_file, test_list=None, - module='dataprovider', obj='process',args={}) - -# 2. learning algorithm -settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) - -# 3. Network configuration -x = data_layer(name='x', size=1) -y = data_layer(name='y', size=1) -y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) -cost = regression_cost(input=y_predict, label=y) -outputs(cost) -``` - -Some of the most fundamental usages of PaddlePaddle are demonstrated: - -- The first part shows how to feed data into PaddlePaddle. In general cases, PaddlePaddle reads raw data from a list of files, and then do some user-defined process to get real input. In this case, we only need to create a placeholder file since we are generating synthetic data on the fly. - -- The second part describes learning algorithm. It defines in what ways adjustments are made to model parameters. PaddlePaddle provides a rich set of optimizers, but a simple momentum based optimizer will suffice here, and it processes 12 data points each time. - -- Finally, the network configuration. It usually is as simple as "stacking" layers. Three kinds of layers are used in this configuration: - - **Data Layer**: a network always starts with one or more data layers. They provide input data to the rest of the network. In this problem, two data layers are used respectively for `X` and `Y`. - - **FC Layer**: FC layer is short for Fully Connected Layer, which connects all the input units to current layer and does the actual computation specified as activation function. Computation layers like this are the fundamental building blocks of a deeper model. - - **Cost Layer**: in training phase, cost layers are usually the last layers of the network. They measure the performance of current model, and provide guidence to adjust parameters. - -Now that everything is ready, you can train the network with a simple command line call: - ``` - paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 - ``` - -This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path `./output`. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess. - - -## 4. Evaluate the Model - -Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: `w=2, b=0.3`, thus a better option is to check out model parameters directly. - -In PaddlePaddle, training is just to get a collection of model parameters, which are `w` and `b` in this case. Each parameter is saved in an individual file in the popular `numpy` array format. Here is the code that reads parameters from last pass. - -```python -import numpy as np -import os - -def load(file_name): - with open(file_name, 'rb') as f: - f.read(16) # skip header for float type. - return np.fromfile(f, dtype=np.float32) - -print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b')) -# w=1.999743, b=0.300137 -``` - -
    ![](./parameters.png)
    - -Although starts from a random guess, you can see that value of `w` changes quickly towards 2 and `b` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer. - -There, you have recovered the underlying pattern between `X` and `Y` only from observed data. - - -## 5. Where to Go from Here - -- Build and Installation -- Quick Start -- Example and Demo diff --git a/doc/introduction/index.rst b/doc/introduction/index.rst new file mode 100644 index 00000000000..ff22f05a1b6 --- /dev/null +++ b/doc/introduction/index.rst @@ -0,0 +1,8 @@ +Introduction +============ + +.. toctree:: + :maxdepth: 2 + + build_and_install/index.rst + basic_usage/basic_usage.rst diff --git a/doc/introduction/parameters.png b/doc/introduction/parameters.png deleted file mode 120000 index f47e74c94ff..00000000000 --- a/doc/introduction/parameters.png +++ /dev/null @@ -1 +0,0 @@ -../../doc_cn/introduction/parameters.png \ No newline at end of file diff --git a/doc/demo/embedding_model/index.md b/doc/tutorials/embedding_model/index.md similarity index 100% rename from doc/demo/embedding_model/index.md rename to doc/tutorials/embedding_model/index.md diff --git a/doc/demo/embedding_model/neural-n-gram-model.png b/doc/tutorials/embedding_model/neural-n-gram-model.png similarity index 100% rename from doc/demo/embedding_model/neural-n-gram-model.png rename to doc/tutorials/embedding_model/neural-n-gram-model.png diff --git a/doc/demo/image_classification/cifar.png b/doc/tutorials/image_classification/cifar.png similarity index 100% rename from doc/demo/image_classification/cifar.png rename to doc/tutorials/image_classification/cifar.png diff --git a/doc/demo/image_classification/image_classification.md b/doc/tutorials/image_classification/image_classification.md similarity index 100% rename from doc/demo/image_classification/image_classification.md rename to doc/tutorials/image_classification/image_classification.md diff --git a/doc/demo/image_classification/image_classification.png b/doc/tutorials/image_classification/image_classification.png similarity index 100% rename from doc/demo/image_classification/image_classification.png rename to doc/tutorials/image_classification/image_classification.png diff --git a/doc/demo/image_classification/index.rst b/doc/tutorials/image_classification/index.rst similarity index 100% rename from doc/demo/image_classification/index.rst rename to doc/tutorials/image_classification/index.rst diff --git a/doc/demo/image_classification/lenet.png b/doc/tutorials/image_classification/lenet.png similarity index 100% rename from doc/demo/image_classification/lenet.png rename to doc/tutorials/image_classification/lenet.png diff --git a/doc/demo/image_classification/plot.png b/doc/tutorials/image_classification/plot.png similarity index 100% rename from doc/demo/image_classification/plot.png rename to doc/tutorials/image_classification/plot.png diff --git a/doc/demo/imagenet_model/resnet_block.jpg b/doc/tutorials/imagenet_model/resnet_block.jpg similarity index 100% rename from doc/demo/imagenet_model/resnet_block.jpg rename to doc/tutorials/imagenet_model/resnet_block.jpg diff --git a/doc/demo/imagenet_model/resnet_model.md b/doc/tutorials/imagenet_model/resnet_model.md similarity index 100% rename from doc/demo/imagenet_model/resnet_model.md rename to doc/tutorials/imagenet_model/resnet_model.md diff --git a/doc/demo/index.md b/doc/tutorials/index.md similarity index 96% rename from doc/demo/index.md rename to doc/tutorials/index.md index 289199d496e..c845ca229cb 100644 --- a/doc/demo/index.md +++ b/doc/tutorials/index.md @@ -1,4 +1,4 @@ -# Examples and demos +# Tutorials There are serveral examples and demos here. ## Image diff --git a/doc/demo/quick_start/NetContinuous_en.png b/doc/tutorials/quick_start/NetContinuous_en.png similarity index 100% rename from doc/demo/quick_start/NetContinuous_en.png rename to doc/tutorials/quick_start/NetContinuous_en.png diff --git a/doc/demo/quick_start/NetConv_en.png b/doc/tutorials/quick_start/NetConv_en.png similarity index 100% rename from doc/demo/quick_start/NetConv_en.png rename to doc/tutorials/quick_start/NetConv_en.png diff --git a/doc/demo/quick_start/NetLR_en.png b/doc/tutorials/quick_start/NetLR_en.png similarity index 100% rename from doc/demo/quick_start/NetLR_en.png rename to doc/tutorials/quick_start/NetLR_en.png diff --git a/doc/demo/quick_start/NetRNN_en.png b/doc/tutorials/quick_start/NetRNN_en.png similarity index 100% rename from doc/demo/quick_start/NetRNN_en.png rename to doc/tutorials/quick_start/NetRNN_en.png diff --git a/doc/demo/quick_start/PipelineNetwork_en.jpg b/doc/tutorials/quick_start/PipelineNetwork_en.jpg similarity index 100% rename from doc/demo/quick_start/PipelineNetwork_en.jpg rename to doc/tutorials/quick_start/PipelineNetwork_en.jpg diff --git a/doc/demo/quick_start/PipelineTest_en.png b/doc/tutorials/quick_start/PipelineTest_en.png similarity index 100% rename from doc/demo/quick_start/PipelineTest_en.png rename to doc/tutorials/quick_start/PipelineTest_en.png diff --git a/doc/demo/quick_start/PipelineTrain_en.png b/doc/tutorials/quick_start/PipelineTrain_en.png similarity index 100% rename from doc/demo/quick_start/PipelineTrain_en.png rename to doc/tutorials/quick_start/PipelineTrain_en.png diff --git a/doc/demo/quick_start/Pipeline_en.jpg b/doc/tutorials/quick_start/Pipeline_en.jpg similarity index 100% rename from doc/demo/quick_start/Pipeline_en.jpg rename to doc/tutorials/quick_start/Pipeline_en.jpg diff --git a/doc/demo/quick_start/index_en.md b/doc/tutorials/quick_start/index_en.md similarity index 100% rename from doc/demo/quick_start/index_en.md rename to doc/tutorials/quick_start/index_en.md diff --git a/doc/demo/rec/ml_dataset.md b/doc/tutorials/rec/ml_dataset.md similarity index 100% rename from doc/demo/rec/ml_dataset.md rename to doc/tutorials/rec/ml_dataset.md diff --git a/doc/demo/rec/ml_regression.rst b/doc/tutorials/rec/ml_regression.rst similarity index 100% rename from doc/demo/rec/ml_regression.rst rename to doc/tutorials/rec/ml_regression.rst diff --git a/doc/demo/rec/rec_regression_network.png b/doc/tutorials/rec/rec_regression_network.png similarity index 100% rename from doc/demo/rec/rec_regression_network.png rename to doc/tutorials/rec/rec_regression_network.png diff --git a/doc/demo/semantic_role_labeling/curve.jpg b/doc/tutorials/semantic_role_labeling/curve.jpg similarity index 100% rename from doc/demo/semantic_role_labeling/curve.jpg rename to doc/tutorials/semantic_role_labeling/curve.jpg diff --git a/doc/demo/semantic_role_labeling/feature.jpg b/doc/tutorials/semantic_role_labeling/feature.jpg similarity index 100% rename from doc/demo/semantic_role_labeling/feature.jpg rename to doc/tutorials/semantic_role_labeling/feature.jpg diff --git a/doc/demo/semantic_role_labeling/index.rst b/doc/tutorials/semantic_role_labeling/index.rst similarity index 100% rename from doc/demo/semantic_role_labeling/index.rst rename to doc/tutorials/semantic_role_labeling/index.rst diff --git a/doc/demo/semantic_role_labeling/network_arch.png b/doc/tutorials/semantic_role_labeling/network_arch.png similarity index 100% rename from doc/demo/semantic_role_labeling/network_arch.png rename to doc/tutorials/semantic_role_labeling/network_arch.png diff --git a/doc/demo/semantic_role_labeling/semantic_role_labeling.md b/doc/tutorials/semantic_role_labeling/semantic_role_labeling.md similarity index 97% rename from doc/demo/semantic_role_labeling/semantic_role_labeling.md rename to doc/tutorials/semantic_role_labeling/semantic_role_labeling.md index e2793b2b349..f5bdf64487a 100644 --- a/doc/demo/semantic_role_labeling/semantic_role_labeling.md +++ b/doc/tutorials/semantic_role_labeling/semantic_role_labeling.md @@ -1,200 +1,200 @@ -# Semantic Role labeling Tutorial # - -Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering. An instance is as following [1]: - - [ A0 He ] [ AM-MOD would ][ AM-NEG n’t ] [ V accept] [ A1 anything of value ] from [A2 those he was writing about ]. - -- V: verb -- A0: acceptor -- A1: thing accepted -- A2: accepted-from -- A3: Attribute -- AM-MOD: modal -- AM-NEG: negation - -Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. - -To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. - -## Data Description -The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license, the demo adopts the test data set of CoNLL-2005, which can be reached on website. - -To download and process the original data, user just need to execute the following command: - -```bash -cd data -./get_data.sh -``` -Several new files appear in the `data `directory as follows. -```bash -conll05st-release：the test data set of CoNll-2005 shared task -test.wsj.words：the Wall Street Journal data sentences -test.wsj.props: the propositional arguments -feature: the extracted features from data set -``` - -## Training -### DB-LSTM -Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. - -Unlike Bidirectional-LSTM that used in Sentiment Analysis demo, the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. - -The following figure shows a temporal expanded 2-layer DB-LSTM network. -
    -![pic](./network_arch.png) -
    - -### Features -Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark mr = 1 to denote the argument position if it locates in the predicate context region, or mr = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]: -
    -![pic](./feature.jpg) -
    - -In this sample, the coresponding labelled sentence is: - -[ A1 A record date ] has [ AM-NEG n't ] been [ V set ] . - -In the demo, we adopt the feature template as above, consists of : `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`. - -### Data Provider - -`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The Six features and label are all IndexSlots. -``` -def hook(settings, word_dict, label_dict, **kwargs): - settings.word_dict = word_dict - settings.label_dict = label_dict - #all inputs are integral and sequential type - settings.slots = [ - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(predicate_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(len(word_dict)), - integer_value_sequence(2), - integer_value_sequence(len(label_dict))] -``` -The corresponding data iterator is as following: -``` -@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, - can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM) -def process(settings, file_name): - with open(file_name, 'r') as fdata: - for line in fdata: - sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \ - line.strip().split('\t') - - words = sentence.split() - sen_len = len(words) - word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words] - - predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len - ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len - ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len - ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len - ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len - ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len - - marks = mark.split() - mark_slot = [int(w) for w in marks] - - label_list = label.split() - label_slot = [settings.label_dict.get(w) for w in label_list] - yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ - ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot -``` -The `process`function yield 9 lists which are 8 features and label. - -### Neural Network Config -`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure. - -Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels. - -### Run Training -The script for training is `train.sh`, user just need to execute: -```bash - ./train.sh -``` -The content in `train.sh`: -``` -paddle train \ - --config=./db_lstm.py \ - --use_gpu=0 \ - --log_period=5000 \ - --trainer_count=1 \ - --show_parameter_stats_period=5000 \ - --save_dir=./output \ - --num_passes=10000 \ - --average_test_period=10000000 \ - --init_model_path=./data \ - --load_missing_parameter_strategy=rand \ - --test_all_data_in_one_period=1 \ -2>&1 | tee 'train.log' -``` - -- \--config=./db_lstm.py : network config file. -- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU -- \--log_period=500: print log every 20 batches. -- \--trainer_count=1: set thread number (or GPU count). -- \--show_parameter_stats_period=5000: show parameter statistic every 100 batches. -- \--save_dir=./output: output path to save models. -- \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time. -- \--average_test_period=10000000: do test on average parameter every average_test_period batches -- \--init_model_path=./data: parameter initialization path -- \--load_missing_parameter_strategy=rand: random initialization unexisted parameters -- \--test_all_data_in_one_period=1: test all data in one period - - -After training, the models will be saved in directory `output`. Our training curve is as following: -
    -![pic](./curve.jpg) -
    - -### Run testing -The script for testing is `test.sh`, user just need to execute: -```bash - ./test.sh -``` -The main part in `tesh.sh` -``` -paddle train \ - --config=./db_lstm.py \ - --model_list=$model_list \ - --job=test \ - --config_args=is_test=1 \ -``` - - - \--config=./db_lstm.py: network config file - - \--model_list=$model_list.list: model list file - - \--job=test: indicate the test job - - \--config_args=is_test=1: flag to indicate test - - \--test_all_data_in_one_period=1: test all data in 1 period - - -### Run prediction -The script for prediction is `predict.sh`, user just need to execute: -```bash - ./predict.sh - -``` -In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file -``` -python predict.py - -c $config_file \ - -w $best_model_path \ - -l $label_file \ - -p $predicate_dict_file \ - -d $dict_file \ - -i $input_file \ - -o $output_file -``` - -`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix. - -After prediction, the result is saved in `predict.res`. - -## Reference -[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. - -[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015. +# Semantic Role labeling Tutorial # + +Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering. An instance is as following [1]: + + [ A0 He ] [ AM-MOD would ][ AM-NEG n’t ] [ V accept] [ A1 anything of value ] from [A2 those he was writing about ]. + +- V: verb +- A0: acceptor +- A1: thing accepted +- A2: accepted-from +- A3: Attribute +- AM-MOD: modal +- AM-NEG: negation + +Given the verb "accept", the chunks in sentence would play certain semantic roles. Here, the label scheme is from Penn Proposition Bank. + +To this date, most of the successful SRL systems are built on top of some form of parsing results where pre-defined feature templates over the syntactic structure are used. This tutorial will present an end-to-end system using deep bidirectional long short-term memory (DB-LSTM)[2] for solving the SRL task, which largely outperforms the previous state-of-the-art systems. The system regards SRL task as the sequence labelling problem. + +## Data Description +The relevant paper[2] takes the data set in CoNLL-2005&2012 Shared Task for training and testing. Accordingto data license, the demo adopts the test data set of CoNLL-2005, which can be reached on website. + +To download and process the original data, user just need to execute the following command: + +```bash +cd data +./get_data.sh +``` +Several new files appear in the `data `directory as follows. +```bash +conll05st-release：the test data set of CoNll-2005 shared task +test.wsj.words：the Wall Street Journal data sentences +test.wsj.props: the propositional arguments +feature: the extracted features from data set +``` + +## Training +### DB-LSTM +Please refer to the Sentiment Analysis demo to learn more about the long short-term memory unit. + +Unlike Bidirectional-LSTM that used in Sentiment Analysis demo, the DB-LSTM adopts another way to stack LSTM layer. First a standard LSTM processes the sequence in forward direction. The input and output of this LSTM layer are taken by the next LSTM layer as input, processed in reversed direction. These two standard LSTM layers compose a pair of LSTM. Then we stack LSTM layers pair after pair to obtain the deep LSTM model. + +The following figure shows a temporal expanded 2-layer DB-LSTM network. +
    +![pic](./network_arch.png) +
    + +### Features +Two input features play an essential role in this pipeline: predicate (pred) and argument (argu). Two other features: predicate context (ctx-p) and region mark (mr) are also adopted. Because a single predicate word can not exactly describe the predicate information, especially when the same words appear more than one times in a sentence. With the predicate context, the ambiguity can be largely eliminated. Similarly, we use region mark mr = 1 to denote the argument position if it locates in the predicate context region, or mr = 0 if does not. These four simple features are all we need for our SRL system. Features of one sample with context size set to 1 is showed as following[2]: +
    +![pic](./feature.jpg) +
    + +In this sample, the coresponding labelled sentence is: + +[ A1 A record date ] has [ AM-NEG n't ] been [ V set ] . + +In the demo, we adopt the feature template as above, consists of : `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` and use `B/I/O` scheme to label each argument. These features and labels are stored in `feature` file, and separated by `\t`. + +### Data Provider + +`dataprovider.py` is the python file to wrap data. `hook()` function is to define the data slots for network. The Six features and label are all IndexSlots. +``` +def hook(settings, word_dict, label_dict, **kwargs): + settings.word_dict = word_dict + settings.label_dict = label_dict + #all inputs are integral and sequential type + settings.slots = [ + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(predicate_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(len(word_dict)), + integer_value_sequence(2), + integer_value_sequence(len(label_dict))] +``` +The corresponding data iterator is as following: +``` +@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, + can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM) +def process(settings, file_name): + with open(file_name, 'r') as fdata: + for line in fdata: + sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \ + line.strip().split('\t') + + words = sentence.split() + sen_len = len(words) + word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words] + + predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len + ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len + ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len + ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len + ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len + ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len + + marks = mark.split() + mark_slot = [int(w) for w in marks] + + label_list = label.split() + label_slot = [settings.label_dict.get(w) for w in label_list] + yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \ + ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot +``` +The `process`function yield 9 lists which are 8 features and label. + +### Neural Network Config +`db_lstm.py` is the neural network config file to load the dictionaries and define the data provider module and network architecture during the training procedure. + +Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` . Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels. + +### Run Training +The script for training is `train.sh`, user just need to execute: +```bash + ./train.sh +``` +The content in `train.sh`: +``` +paddle train \ + --config=./db_lstm.py \ + --use_gpu=0 \ + --log_period=5000 \ + --trainer_count=1 \ + --show_parameter_stats_period=5000 \ + --save_dir=./output \ + --num_passes=10000 \ + --average_test_period=10000000 \ + --init_model_path=./data \ + --load_missing_parameter_strategy=rand \ + --test_all_data_in_one_period=1 \ +2>&1 | tee 'train.log' +``` + +- \--config=./db_lstm.py : network config file. +- \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU +- \--log_period=500: print log every 20 batches. +- \--trainer_count=1: set thread number (or GPU count). +- \--show_parameter_stats_period=5000: show parameter statistic every 100 batches. +- \--save_dir=./output: output path to save models. +- \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time. +- \--average_test_period=10000000: do test on average parameter every average_test_period batches +- \--init_model_path=./data: parameter initialization path +- \--load_missing_parameter_strategy=rand: random initialization unexisted parameters +- \--test_all_data_in_one_period=1: test all data in one period + + +After training, the models will be saved in directory `output`. Our training curve is as following: +
    +![pic](./curve.jpg) +
    + +### Run testing +The script for testing is `test.sh`, user just need to execute: +```bash + ./test.sh +``` +The main part in `tesh.sh` +``` +paddle train \ + --config=./db_lstm.py \ + --model_list=$model_list \ + --job=test \ + --config_args=is_test=1 \ +``` + + - \--config=./db_lstm.py: network config file + - \--model_list=$model_list.list: model list file + - \--job=test: indicate the test job + - \--config_args=is_test=1: flag to indicate test + - \--test_all_data_in_one_period=1: test all data in 1 period + + +### Run prediction +The script for prediction is `predict.sh`, user just need to execute: +```bash + ./predict.sh + +``` +In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file +``` +python predict.py + -c $config_file \ + -w $best_model_path \ + -l $label_file \ + -p $predicate_dict_file \ + -d $dict_file \ + -i $input_file \ + -o $output_file +``` + +`predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix. + +After prediction, the result is saved in `predict.res`. + +## Reference +[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. + +[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015. diff --git a/doc/demo/sentiment_analysis/bi_lstm.jpg b/doc/tutorials/sentiment_analysis/bi_lstm.jpg similarity index 100% rename from doc/demo/sentiment_analysis/bi_lstm.jpg rename to doc/tutorials/sentiment_analysis/bi_lstm.jpg diff --git a/doc/demo/sentiment_analysis/index.rst b/doc/tutorials/sentiment_analysis/index.rst similarity index 100% rename from doc/demo/sentiment_analysis/index.rst rename to doc/tutorials/sentiment_analysis/index.rst diff --git a/doc/demo/sentiment_analysis/lstm.png b/doc/tutorials/sentiment_analysis/lstm.png similarity index 100% rename from doc/demo/sentiment_analysis/lstm.png rename to doc/tutorials/sentiment_analysis/lstm.png diff --git a/doc/demo/sentiment_analysis/sentiment_analysis.md b/doc/tutorials/sentiment_analysis/sentiment_analysis.md similarity index 100% rename from doc/demo/sentiment_analysis/sentiment_analysis.md rename to doc/tutorials/sentiment_analysis/sentiment_analysis.md diff --git a/doc/demo/sentiment_analysis/stacked_lstm.jpg b/doc/tutorials/sentiment_analysis/stacked_lstm.jpg similarity index 100% rename from doc/demo/sentiment_analysis/stacked_lstm.jpg rename to doc/tutorials/sentiment_analysis/stacked_lstm.jpg diff --git a/doc/demo/text_generation/encoder-decoder-attention-model.png b/doc/tutorials/text_generation/encoder-decoder-attention-model.png similarity index 100% rename from doc/demo/text_generation/encoder-decoder-attention-model.png rename to doc/tutorials/text_generation/encoder-decoder-attention-model.png diff --git a/doc/demo/text_generation/index.rst b/doc/tutorials/text_generation/index.rst similarity index 100% rename from doc/demo/text_generation/index.rst rename to doc/tutorials/text_generation/index.rst diff --git a/doc/demo/text_generation/text_generation.md b/doc/tutorials/text_generation/text_generation.md similarity index 100% rename from doc/demo/text_generation/text_generation.md rename to doc/tutorials/text_generation/text_generation.md diff --git a/doc/ui/index.md b/doc/ui/index.md deleted file mode 100644 index 9c1ba27bdc1..00000000000 --- a/doc/ui/index.md +++ /dev/null @@ -1,20 +0,0 @@ -# User Interface - -## Data Provider - -* [Introduction](data_provider/index.rst) -* [PyDataProvider2](data_provider/pydataprovider2.rst) - -## API Reference - -* [Model Config Interface](api/trainer_config_helpers/index.md) - -## Command Line Argument - -* [Use Case](cmd_argument/use_case.md) -* [Argument Outline](cmd_argument/argument_outline.md) -* [Detailed Descriptions](cmd_argument/detail_introduction.md) - -## Predict - -* [Python Prediction API](predict/swig_py_paddle_en.rst) diff --git a/doc/user_guide.rst b/doc/user_guide.rst deleted file mode 100644 index d4deb3ca5a4..00000000000 --- a/doc/user_guide.rst +++ /dev/null @@ -1,13 +0,0 @@ -User Guide -========== - -.. toctree:: - :maxdepth: 1 - - demo/quick_start/index_en.md - build/index.rst - build/contribute_to_paddle.md - ui/index.md - ui/api/trainer_config_helpers/index.rst - demo/index.md - cluster/index.md -- GitLab From a48f19cf453fae85f192598e813c1ad80b41bc86 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 25 Nov 2016 17:20:00 +0800 Subject: [PATCH 078/347] Add symbol link --- doc/howto/algorithm/rnn/bi_lstm.jpg | 1 + doc/howto/algorithm/rnn/encoder-decoder-attention-model.png | 1 + 2 files changed, 2 insertions(+) create mode 120000 doc/howto/algorithm/rnn/bi_lstm.jpg create mode 120000 doc/howto/algorithm/rnn/encoder-decoder-attention-model.png diff --git a/doc/howto/algorithm/rnn/bi_lstm.jpg b/doc/howto/algorithm/rnn/bi_lstm.jpg new file mode 120000 index 00000000000..f8f3b17691c --- /dev/null +++ b/doc/howto/algorithm/rnn/bi_lstm.jpg @@ -0,0 +1 @@ +../../../tutorials/sentiment_analysis/bi_lstm.jpg \ No newline at end of file diff --git a/doc/howto/algorithm/rnn/encoder-decoder-attention-model.png b/doc/howto/algorithm/rnn/encoder-decoder-attention-model.png new file mode 120000 index 00000000000..88a1d3e5ac4 --- /dev/null +++ b/doc/howto/algorithm/rnn/encoder-decoder-attention-model.png @@ -0,0 +1 @@ +../../../tutorials/text_generation/encoder-decoder-attention-model.png \ No newline at end of file -- GitLab From 80fb9116d1141951d5a9b60801a3f0378a073e96 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 25 Nov 2016 17:24:20 +0800 Subject: [PATCH 079/347] Change gpu profiling docs position --- doc/howto/index.rst | 1 + doc/{ => howto}/optimization/gpu_profiling.rst | 0 doc/{ => howto}/optimization/index.rst | 0 doc/{ => howto}/optimization/nvvp1.png | Bin doc/{ => howto}/optimization/nvvp2.png | Bin doc/{ => howto}/optimization/nvvp3.png | Bin doc/{ => howto}/optimization/nvvp4.png | Bin 7 files changed, 1 insertion(+) rename doc/{ => howto}/optimization/gpu_profiling.rst (100%) rename doc/{ => howto}/optimization/index.rst (100%) rename doc/{ => howto}/optimization/nvvp1.png (100%) rename doc/{ => howto}/optimization/nvvp2.png (100%) rename doc/{ => howto}/optimization/nvvp3.png (100%) rename doc/{ => howto}/optimization/nvvp4.png (100%) diff --git a/doc/howto/index.rst b/doc/howto/index.rst index e2d688e186c..ed8294b3c15 100644 --- a/doc/howto/index.rst +++ b/doc/howto/index.rst @@ -7,5 +7,6 @@ How to cmd_argument/index.md cluster/cluster_train.md algorithm/index.rst + optimization/index.rst dev/index.rst contribute_to_paddle.md \ No newline at end of file diff --git a/doc/optimization/gpu_profiling.rst b/doc/howto/optimization/gpu_profiling.rst similarity index 100% rename from doc/optimization/gpu_profiling.rst rename to doc/howto/optimization/gpu_profiling.rst diff --git a/doc/optimization/index.rst b/doc/howto/optimization/index.rst similarity index 100% rename from doc/optimization/index.rst rename to doc/howto/optimization/index.rst diff --git a/doc/optimization/nvvp1.png b/doc/howto/optimization/nvvp1.png similarity index 100% rename from doc/optimization/nvvp1.png rename to doc/howto/optimization/nvvp1.png diff --git a/doc/optimization/nvvp2.png b/doc/howto/optimization/nvvp2.png similarity index 100% rename from doc/optimization/nvvp2.png rename to doc/howto/optimization/nvvp2.png diff --git a/doc/optimization/nvvp3.png b/doc/howto/optimization/nvvp3.png similarity index 100% rename from doc/optimization/nvvp3.png rename to doc/howto/optimization/nvvp3.png diff --git a/doc/optimization/nvvp4.png b/doc/howto/optimization/nvvp4.png similarity index 100% rename from doc/optimization/nvvp4.png rename to doc/howto/optimization/nvvp4.png -- GitLab From 5e7e5ccb68205ef0a5ea97389598a0c3ae9e479c Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 25 Nov 2016 17:30:09 +0800 Subject: [PATCH 080/347] Change head to About --- doc/about/index.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/doc/about/index.rst b/doc/about/index.rst index c70940ca852..511c1546413 100644 --- a/doc/about/index.rst +++ b/doc/about/index.rst @@ -1,5 +1,9 @@ +About +======= + + Credits -======== +-------- PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu. -- GitLab From a7671dee4745cf5f65228148504043d817476c6c Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 25 Nov 2016 17:30:15 +0800 Subject: [PATCH 081/347] refine build_docker_image.rst --- doc_cn/build/docker/build_docker_image.rst | 38 ---------------------- doc_cn/build_and_install/index.rst | 5 --- doc_cn/howto/build_docker_image.rst | 35 ++++++++++++++++++++ doc_cn/index.rst | 1 + paddle/scripts/docker/build.sh | 2 +- 5 files changed, 37 insertions(+), 44 deletions(-) delete mode 100644 doc_cn/build/docker/build_docker_image.rst create mode 100644 doc_cn/howto/build_docker_image.rst diff --git a/doc_cn/build/docker/build_docker_image.rst b/doc_cn/build/docker/build_docker_image.rst deleted file mode 100644 index 73409ceaff4..00000000000 --- a/doc_cn/build/docker/build_docker_image.rst +++ /dev/null @@ -1,38 +0,0 @@ -构建PaddlePaddle Docker Image -=========================== - -PaddlePaddle的Docker Image构建源码放置在 :code:`${源码根目录}/paddle/scripts/docker/`目录下。 -该Image基于ubuntu 14.04。该目录下有两个文件，Dockerfile和build.sh。其中: - -* Dockerfile是docker image的主要描述文件。描述了Docker image的构建步骤、各种参数和维护 - 人员等等。 -* build.sh是docker image的主要构建步骤。 - -该image的构建在docker 1.12版本测试通过, 低于docker 1.12版本的情况下并没有测试。主要由于旧版本 -的docker可能缺乏 :code:`--build-arg` 参数，从而不能在运行编译命令的时候接受参数。 - -同时，该构建脚本充分考虑了网络不稳定的情况，对于cuda的Toolkit有断点续传和传输速度过小重启下载的 -简单优化。 - -使用脚本构建PaddlePaddle Docker Image -------------------------------------------- - -该脚本的使用方法是，进入该源码目录，执行 :code:`docker build .` 命令。可以使用 - :code:`--build-arg` 传入的配置参数包括: - -* LOWEST\_DL\_SPEED\: 多线程下载过程中，最低线程的下载速度(默认单位是Bytes，可以传入10K, - 10M，或者10G这样的单位)。如果小于这个下载速度，那么这个下载线程将会关闭。所有的下载线程关闭时， - 下载进程会重启。 -* WITH\_GPU\: ON or OFF。是否开启GPU功能。注意，编译PaddlePaddle的GPU版本并不需要一定在具有GPU - 的机器上进行。但是，运行PaddlePaddle的GPU版本一定要在具有CUDA的机器上运行。 - -简单的使用样例为\: - -.. code-block:: bash - - cd ${源码根目录}/paddle/scripts/docker/ - docker build --build-arg LOWEST_DL_SPEED=50K\ - --build-arg WITH_GPU=ON \ - --tag paddle_gpu:latest . - -即可在本地编译出PaddlePaddle的镜像。 diff --git a/doc_cn/build_and_install/index.rst b/doc_cn/build_and_install/index.rst index 2205e282248..48163fb36e5 100644 --- a/doc_cn/build_and_install/index.rst +++ b/doc_cn/build_and_install/index.rst @@ -8,9 +8,7 @@ PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜 .. toctree:: :maxdepth: 1 - :glob: - 使用Jumbo安装(对内) <../build/internal/install_from_jumbo.rst> install/docker_install.rst install/ubuntu_install.rst @@ -25,8 +23,5 @@ PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜 .. toctree:: :maxdepth: 1 - :glob: - 源码下载(对内) <../build/internal/download_paddle_source_zh_cn.rst> - 从源码编译安装(对内) <../build/internal/build_from_source_zh_cn.rst> cmake/index.rst diff --git a/doc_cn/howto/build_docker_image.rst b/doc_cn/howto/build_docker_image.rst new file mode 100644 index 00000000000..16887672f7a --- /dev/null +++ b/doc_cn/howto/build_docker_image.rst @@ -0,0 +1,35 @@ +构建PaddlePaddle的Docker Image +============================== +PaddlePaddle的Docker Image构建源码放置在 ``${源码根目录}/paddle/scripts/docker/`` 目录下。该目录有三类文件： + +- Dockerfile：Docker Image的描述文件，包括构建步骤、各种参数和维护人员等。 + + - 一共维护了12个Dockerfile，Dockerfile.m4是它们的模板。 + - PaddlePaddle中所有的Image都基于ubuntu 14.04。 + +- build.sh：Docker Image的构建脚本，使用方式见下一小节。 +- generate.sh：通过Dockerfile.m4模板生成不同的Dockerfile。 + +使用脚本构建Docker Image +------------------------ + +进入源码目录，执行 ``docker build`` 命令，即可在本地编译出PaddlePaddle的镜像。简单的使用样例为 + +.. code-block:: bash + + cd ${源码根目录}/paddle/scripts/docker/ + docker build --build-arg LOWEST_DL_SPEED=50K\ + --build-arg WITH_GPU=ON \ + --tag paddle_gpu:latest . + +其中，``--build-arg`` 传入的配置参数包括: + +- LOWEST\_DL\_SPEED\: 在多线程下载过程中，设置下线线程的最低速度。 + + - 默认单位是Bytes，但可以传入10K、10M、或10G等这样的单位。 + - 如果小于这个速度，那么这个线程将会关闭。当所有的线程都关闭了，那么下载进程将会重启。 +- WITH\_GPU\: ON or OFF，是否开启GPU功能。注意， + - **编译** PaddlePaddle的GPU版本 **不一定** 要在具有GPU的机器上进行。 + - **运行** PaddlePaddle的GPU版本 **一定** 要在具有GPU的机器上运行。 + +注意：所有Image的构建在Docker 1.12版本测试通过, 低于1.12的版本并没有测试。原因是旧版本可能缺乏 ``--build-arg`` 参数，从而不能在运行编译命令的时候接受参数。 diff --git a/doc_cn/index.rst b/doc_cn/index.rst index f1398206fdd..c9f2126c3e3 100644 --- a/doc_cn/index.rst +++ b/doc_cn/index.rst @@ -16,6 +16,7 @@ PaddlePaddle文档 -------- * `新写Layer <../doc/dev/new_layer/index.html>`_ * `如何贡献文档 `_ +* `如何构建Docker Image `_ 算法教程 -------- diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index ec5f3bd967d..f8322316a56 100644 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -21,7 +21,7 @@ if [ ${WITH_GPU} == 'ON' ]; then fi cd ~ -git clone https://github.com/baidu/Paddle.git paddle +git clone https://github.com/PaddlePaddle/Paddle.git paddle cd paddle git checkout ${GIT_CHECKOUT} mkdir build -- GitLab From 2664286d6adfdd5cc80560bd22371083a3d64bdd Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 25 Nov 2016 18:32:24 +0800 Subject: [PATCH 082/347] refine dataprovider related rst --- doc_cn/ui/data_provider/dataprovider.rst | 15 + doc_cn/ui/data_provider/index.rst | 17 - doc_cn/ui/data_provider/mnist_config.py | 1 + doc_cn/ui/data_provider/mnist_provider.py | 22 - doc_cn/ui/data_provider/pydataprovider2.rst | 476 ++++++++---------- .../ui/data_provider/sentimental_provider.py | 15 +- .../data_provider/write_new_dataprovider.rst | 4 - doc_cn/ui/index.rst | 7 +- 8 files changed, 244 insertions(+), 313 deletions(-) create mode 100644 doc_cn/ui/data_provider/dataprovider.rst delete mode 100644 doc_cn/ui/data_provider/index.rst delete mode 100644 doc_cn/ui/data_provider/mnist_provider.py delete mode 100644 doc_cn/ui/data_provider/write_new_dataprovider.rst diff --git a/doc_cn/ui/data_provider/dataprovider.rst b/doc_cn/ui/data_provider/dataprovider.rst new file mode 100644 index 00000000000..e1ad330c29a --- /dev/null +++ b/doc_cn/ui/data_provider/dataprovider.rst @@ -0,0 +1,15 @@ +DataProvider的介绍 +================== + +DataProvider是PaddlePaddle负责提供数据的模块。其作用是将数据传入内存或显存，让神经网络可以进行训练或预测。有两种使用方式： + +- 简单使用：使用Python接口 `PyDataProvider2 `_ 来自定义传数据的过程。 +- 高级使用：如果用户有更复杂的使用，或者需要更高的效率，可以在C++端自定义一个 ``DataProvider`` 。 + +PaddlePaddle需要用户在网络配置（trainer_config.py）中定义使用哪种DataProvider，并且在DataProvider中实现如何访问训练文件列表（train.list）或测试文件列表（test.list）。 + +- train.list和test.list存放在本地（推荐直接存放到训练目录，以相对路径引用)。一般情况下，两者均为纯文本文件，其中每一行对应一个数据文件地址： + + - 如果数据文件存于本地磁盘，则将这些文件的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)写在train.list和test.list中。 + - 地址也可以为hdfs文件路径，或者数据库连接地址等。 +- 如果没有设置test.list，或设置为None，那么在训练过程中不会执行测试操作；否则，会根据命令行参数指定的测试方式，在训练过程中进行测试，从而防止过拟合。 diff --git a/doc_cn/ui/data_provider/index.rst b/doc_cn/ui/data_provider/index.rst deleted file mode 100644 index ec8f8e5dc5b..00000000000 --- a/doc_cn/ui/data_provider/index.rst +++ /dev/null @@ -1,17 +0,0 @@ -PaddlePaddle的数据提供(DataProvider)介绍 -======================================== - -数据提供(DataProvider)是PaddlePaddle负责提供数据的模块。其作用是将训练数据传入内存或者显存，让神经网络可以进行训练。简单的使用，用户可以使用Python的 :code:`PyDataProvider` 来自定义传数据的过程。如果有更复杂的使用，或者需要更高的效率，用户也可以在C++端自定义一个 :code:`DataProvider` 。 - -PaddlePaddle需要用户在网络配置(trainer_config.py)中定义使用哪种DataProvider及其参数，训练文件列表(train.list)和测试文件列表(test.list)。 - -其中，train.list和test.list均为本地的两个文件(推荐直接放置到训练目录，以相对路径引用)。如果test.list不设置，或者设置为None，那么在训练过程中，不会执行测试操作。否则，会根据命令行参数指定的测试方式，在训练过程中进行测试，从而防止过拟合。 - -一般情况下，train.list和test.list为纯文本文件，一行对应一个数据文件，数据文件存放在本地磁盘中。将文件的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)写在train.list和test.list中。当然，train.list和test.list也可以放置hdfs文件路径，或者数据库连接地址等等。 - -用户在DataProvider中需要实现如何访问其中每一个文件。DataProvider的具体用法和如何实现一个新的DataProvider，请参考下述文章: - -.. toctree:: - - pydataprovider2.rst - write_new_dataprovider.rst diff --git a/doc_cn/ui/data_provider/mnist_config.py b/doc_cn/ui/data_provider/mnist_config.py index 39becff03b0..429338c57f8 100644 --- a/doc_cn/ui/data_provider/mnist_config.py +++ b/doc_cn/ui/data_provider/mnist_config.py @@ -5,5 +5,6 @@ define_py_data_sources2( test_list=None, module='mnist_provider', obj='process') + img = data_layer(name='pixel', size=784) label = data_layer(name='label', size=10) diff --git a/doc_cn/ui/data_provider/mnist_provider.py b/doc_cn/ui/data_provider/mnist_provider.py deleted file mode 100644 index 8b828641d55..00000000000 --- a/doc_cn/ui/data_provider/mnist_provider.py +++ /dev/null @@ -1,22 +0,0 @@ -from paddle.trainer.PyDataProvider2 import * - - -# Define a py data provider -@provider(input_types=[dense_vector(28 * 28), integer_value(10)]) -def process(settings, filename): # settings is not used currently. - f = open(filename, 'r') # open one of training file - - for line in f: # read each line - label, pixel = line.split(';') - - # get features and label - pixels_str = pixel.split(' ') - - pixels_float = [] - for each_pixel_str in pixels_str: - pixels_float.append(float(each_pixel_str)) - - # give data to paddle. - yield pixels_float, int(label) - - f.close() # close file diff --git a/doc_cn/ui/data_provider/pydataprovider2.rst b/doc_cn/ui/data_provider/pydataprovider2.rst index 80b40084d8f..3455e331da0 100644 --- a/doc_cn/ui/data_provider/pydataprovider2.rst +++ b/doc_cn/ui/data_provider/pydataprovider2.rst @@ -1,257 +1,219 @@ -PyDataProvider2的使用 -===================== - -PyDataProvider是PaddlePaddle使用Python提供数据的推荐接口。使用该接口用户可以只关注如何 -从文件中读取每一条数据，而不用关心数据如何传输给PaddlePaddle，数据如何存储等等。该数据 -接口使用多线程读取数据，并提供了简单的Cache功能。 - - -简单的使用场景 --------------- - -这里以MNIST手写识别为例，来说明简单的PyDataProvider如何使用。MNIST是一个包含有 -70,000张灰度图片的数字分类数据集。对于MNIST而言，标签是0-9的数字，而特征即为 -28*28的像素灰度值。这里我们使用简单的文本文件表示MNIST图片，样例数据如下。 - -.. literalinclude:: mnist_train.txt - -其数据使用;间隔，第一段数据为这张图片的label，第二段数据为这个图片的像素值。 -首先我们将这个数据文件(例如文件名是'mnist_train.txt')写入train.list。那么 -train.list即为 - -.. literalinclude:: train.list - -那么对应的dataprovider既为 - -.. literalinclude:: mnist_provider.py - :linenos: - -其中第一行是引入PaddlePaddle的PyDataProvider2包。主要函数是process函数。process函数 -具有两个参数，第一个参数是 settings 。这个参数在这个样例里没有使用，具 -体可以参考 settings 。第二个参数是filename，这个参数被PaddlePaddle进程传入，为 -train.list中的一行(即train.list若干数据文件路径的某一个路径)。 - -:code:`@provider` 是一个Python的 `Decorator `_ -。这行的作用是设置DataProvider的一些属性，并且标记process函数是一个DataProvider。 -如果不了解 `Decorator `_ 是什么也没关系， -只需要知道这只是一个标记属性的方法就可以了。 - -属性 `input_types`_ 是设置这个DataProvider返回什么样的数据。这里设置的是返回一个 -28*28的稠密向量和一个[0-9]，10维的整数值。 `input_types`_ 具体可以设置成什么其他格 -式，请参考 `input_types`_ 的文档。 - -process函数是实现数据输入的主函数，在这个函数中，实现了打开文本文件，从文本文件中读取 -每一行，并将每行转换成和 `input_types`_ 一致的特征，并在23行返回给PaddlePaddle进程。需要注意 -的是， 返回的顺序需要和 `input_types`_ 中定义的顺序一致。 - -同时，返回数据在PaddlePaddle中是仅仅返回一条完整的训练样本，并且使用关键词 :code:`yield` 。 -在PyDataProvider中，可以为一个数据文件返回多条训练样本(就像这个样例一样)，只需要在 -process函数调用多次 :code:`yield` 即可。 :code:`yield` 是Python的一个关键词，相关的概 -念是 :code:`generator` 。使用这个关键词，可以在一个函数里，多次返回变量。 - -在训练配置里，只需要使用一行代码即可以设置训练引用这个DataProvider。这个设置为 - -.. literalinclude:: mnist_config.py - -这里说明了训练数据是 'train.list'，而没有测试数据。引用的DataProvider是 'mnist_provider' -这个模块中的 'process' 函数。 - -同时，根据模型配置文件中 :code:`data_layer` 的名字，用户也可以显式指定返回的数据对应关系。例如: - -.. literalinclude:: mnist_provider.dict.py - :linenos: - -如果用户不指定返回数据的对应关系，那么PaddlePaddle会粗略的根据layer的声明顺序， -来确定对应关系。这个对应关系可能不正确。所以推荐使用显式指定返回值和数据对应关系。 - -至此，简单的PyDataProvider样例就说明完毕了。对于用户来说，讲数据发送给PaddlePaddle，仅仅需要 -知道如何从 **一个文件** 里面读取 **一条** 样本。而PaddlePaddle进程帮助用户做了 - -* 将数据组合成Batch训练 -* Shuffle训练数据 -* 多线程数据读取 -* 缓存训练数据到内存(可选) -* CPU->GPU双缓存 - -是不是很简单呢？ - -序列模型数据提供 ----------------- - -序列模型是指数据的某一维度是一个序列形式，即包含时间步信息。所谓时间步信息， -不一定和时间有关系，只是说明数据的顺序是重要的。例如，文本信息就是一个序列 -数据。 - -这里举例的数据是英文情感分类的数据。数据是给一段英文文本，分类成正面情绪和 -负面情绪两类(用0和1表示)。样例数据为 - -.. literalinclude:: sentimental_train.txt - -这里，DataProvider可以是 - -.. literalinclude:: sentimental_provider.py - -这个序列模型比较复杂。主要是增加了初始化机制。其中 :code:`on_init` 函数是使用 -`@provider`_ 中的 `init_hook`_ 配置参数配置给DataProvider的。这个函数会在 -DataProvider创建的时候执行。这个初始化函数具有如下参数: - -* 第一个参数是 settings 对象。 -* 其他参数均使用key word argument形式传入。有部分参数是Paddle自动生成的， - 参考 `init_hook`_ 。这里的 :code:`dictionary` 是从训练配置传入的dict对象。 - 即从单词字符串到单词id的字典。 - -传入这个变量的方式为 - -.. literalinclude:: sentimental_config.py - -这个声明基本上和mnist的样例一致。除了 - -* 在配置中读取了字典 -* 在声明DataProvider的时候传入了dictionary作为参数。 - -在 :code:`on_init` 函数中，配置了 `input_types` 。这个和在 `@provider`_ 中配置 -`input_types` 效果一致，但是在 `on_init` 中配置 `input_types` 是在运行时执行的，所以 -可以根据不同的数据配置不同的输入类型。这里的输入特征是词id的序列，所以将 :code:`seq_type` -设置成了序列(同时，也可以使用 :code:`integer_sequence` 类型来设置)。 - -同时，将字典存入了settings 对象。这个字典可以在 :code:`process` 函数中使用。 :code:`process` -函数中的 settings 和 :code:`on_init` 中的settings 是同一个对象。 - -而在 :code:`process` 函数中，基本的处理逻辑也和mnist逻辑一致。依次返回了文件中的每条数据。 - -至此，基本的PyDataProvider使用介绍完毕了。具体DataProvider还具有什么功能，请参考下节reference。 - -参考(Reference) ---------------- - -@provider -+++++++++ - -:code:`@provider` 是一个Python的 `Decorator`_ ，他可以将某一个函数标记成一个PyDataProvider。它包含的参数有: - -* `input_types`_ 是数据输入格式。具体有哪些格式，参考 `input_types`_ 。 -* should_shuffle 是个DataProvider是不是要做shuffle，如果不设置的话，训练的时候默认shuffle， - 测试的时候默认不shuffle。 -* min_pool_size 是设置DataProvider在内存中最小暂存的数据条数。这个也是PaddlePaddle所能够保证的shuffle粒度。 - 设置成-1的话，会预先读取全部数据到内存中。 -* pool_size 是设置DataProvider在内存中暂存的数据条数。设置成-1的话，即不在乎内存暂存多少条数据。 -* can_over_batch_size 表示是否允许Paddle暂存略微多余pool_size的数据。这样做可以避免很多死锁问题。 - 一般推荐设置成True -* calc_batch_size 传入的是一个函数，这个函数以一条数据为参数，返回batch_size的大小。默认情况下一条数据 - 是一个batch size，但是有时为了计算均衡性，可以将一条数据设置成多个batch size -* cache 是数据缓存的策略，参考 `cache`_ -* init_hook 是初始化时调用的函数，参考 `init_hook`_ -* check 设置成true的话，会根据input_types检查数据的合法性。 -* check_fail_continue 如果设置成true的话，即使在check中数据不合法，也会扔到这条数据，继续训练。 如果 - check是false的话，没有作用。 - -input_types -+++++++++++ - -PaddlePaddle的数据包括四种主要类型，和三种序列模式。其中，四种数据类型是 - -* dense_vector 表示稠密的浮点数向量。 -* sparse_binary_vector 表示稀疏的零一向量，即大部分值为0，有值的位置只能取1 -* sparse_float_vector 表示稀疏的向量，即大部分值为0，有值的部分可以是任何浮点数 -* integer 表示整数标签。 - -而三种序列模式为 - -* SequenceType.NO_SEQUENCE 即不是一条序列 -* SequenceType.SEQUENCE 即是一条时间序列 -* SequenceType.SUB_SEQUENCE 即是一条时间序列，且序列的每一个元素还是一个时间序列。 - -不同的数据类型和序列模式返回的格式不同，列表如下 - -+----------------------+---------------------+-----------------------------------+------------------------------------------------+ -| | NO_SEQUENCE | SEQUENCE | SUB_SEQUENCE | -+======================+=====================+===================================+================================================+ -| dense_vector | [f, f, ...] | [[f, ...], [f, ...], ...] | [[[f, ...], ...], [[f, ...], ...],...] | -+----------------------+---------------------+-----------------------------------+------------------------------------------------+ -| sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] | -+----------------------+---------------------+-----------------------------------+------------------------------------------------+ -| sparse_float_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] | -+----------------------+---------------------+-----------------------------------+------------------------------------------------+ -| integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] | -+----------------------+---------------------+-----------------------------------+------------------------------------------------+ - -其中，f代表一个浮点数，i代表一个整数。 - -init_hook -+++++++++ - -init_hook可以传入一个函数。这个函数在初始化的时候会被调用。这个函数的参数是: - -* 第一个参数是 settings 对象。这个对象和process的第一个参数一致。具有的属性有 - * settings.input_types 设置输入类型。参考 `input_types`_ - * settings.logger 一个logging对象 -* 其他参数都使用key word argument传入。这些参数包括paddle定义的参数，和用户传入的参数。 - * Paddle定义的参数包括: - * is_train bool参数，表示这个DataProvider是训练用的DataProvider或者测试用的 - DataProvider - * file_list 所有文件列表。 - * 用户定义的参数使用args在训练配置中设置。 - -注意，PaddlePaddle保留添加参数的权力，所以init_hook尽量使用 :code:`**kwargs` , 来接受不使用的 -函数来保证兼容性。 - -cache -+++++ - -DataProvider提供了两种简单的Cache策略。他们是 - -* CacheType.NO_CACHE 不缓存任何数据，每次都会从python端读取数据 -* CacheType.CACHE_PASS_IN_MEM 第一个pass会从python端读取数据，剩下的pass会直接从内存里 - 读取数据。 - - -注意事项 --------- - -可能的内存泄露问题 -++++++++++++++++++ - -PaddlePaddle将train.list中的每一行，都传递给process函数，从而生成多个generator。 -即如果train.list中，有100个训练文件，即会生成100个generator。这个本身不是一个很 -严重的问题。 - -但是，如果在训练时，每一条训练数据都是一个文件，并且，训练数据非常多的情况下，就 -会生成多个generator。每个generator在没有调用的时候，是几乎不占内存的。但是，当调 -用过一次的时候，generator便会存下当前的上下文(Context)。而这个Context可能会非常 -大。并且，generator至少调用两次才会知道是否停止。所以，即使在process里面只会有一 -个yield，也需要两次随机选择到同样的generator的时候，才会释放该段内存。 - -.. code-block:: python - - def func(): - yield 0 - - f = func() # 创建generator - tmp = next(f) # 调用一次，返回0 - tmp = next(f) # 调用第二次的时候，才会Stop Iteration - -而如果按顺序调用这些generator就不会出现这个问题。 - -所以最佳实践推荐不要将每一个样本都放入train.list。而是将样本的地址放入另一个文本 -文件，train.list写入那个文本文件的地址。 或者在python generator的上下文中尽量留 -下非常少的变量引用。例如 - -.. code-block:: python - - def real_process(fn): - # ... read from fn - return result # 当函数返回的时候，python可以解除掉内部变量的引用。 - - def process(fn): - yield real_process(fn) - -这个问题是PyDataProvider读数据时候的逻辑问题，基本上不能整体修正。 - - -内存不够用的情况 -++++++++++++++++ - -PyDataProvider2会尽量使用内存。所以如果对于内存比较小的机器，推荐设置 -:code:`pool_size` 变量，而这个变量推荐大于训练的batch size，并且在内存足够 -的情况下越大越好。 - +PyDataProvider2的使用 +===================== + +PyDataProvider2是PaddlePaddle使用Python提供数据的接口。该接口使用多线程读取数据，并提供了简单的Cache功能；同时可以使用户只关注如何从文件中读取每一条数据，而不用关心数据如何传输，如何存储等等。 + +.. contents:: + +MNIST的使用场景 +--------------- + +我们以MNIST手写识别为例，来说明如何使用最简单的PyDataProvider2。 + +样例数据 +++++++++ + +MNIST是一个包含有70,000张灰度图片的数字分类数据集。样例数据 ``mnist_train.txt`` 如下： + +.. literalinclude:: mnist_train.txt + +其中每行数据代表一张图片，行内使用 ``;`` 分成两部分。第一部分是图片的标签，为0-9中的一个数字；第二部分是28*28的图片像素灰度值。 对应的 ``train.list`` 为： + +.. literalinclude:: train.list + +dataprovider的使用 +++++++++++++++++++ + +.. literalinclude:: mnist_provider.dict.py + +- 首先，引入PaddlePaddle的PyDataProvider2包。 +- 其次，定义一个Python的 `Decorator `_ `@provider`_ 。用于将下一行的数据输入函数标记成一个PyDataProvider2，同时设置它的input_types属性。 + + - `input_types`_：设置这个PyDataProvider2返回什么样的数据。本例根据网络配置中 ``data_layer`` 的名字，显式指定返回的是一个28*28维的稠密浮点数向量和一个[0-9]的10维整数标签。 + + .. literalinclude:: mnist_config.py + :lines: 9-10 + + - 注意：如果用户不显示指定返回数据的对应关系，那么PaddlePaddle会根据layer的声明顺序，来确定对应关系。但这个关系可能不正确，所以推荐使用显式指定的方式来设置input_types。 +- 最后，实现数据输入函数（如本例的 ``process`` 函数）。 + + - 该函数的功能是：打开文本文件，读取每一行，将行中的数据转换成与input_types一致的格式，然后返回给PaddlePaddle进程。注意， + + - 返回的顺序需要和input_types中定义的顺序一致。 + - 返回时，必须使用关键词 ``yield`` 。一次yield调用，即返回一条完整的样本。如果想为一个数据文件返回多条样本，只需要在函数中调用多次yield即可（本例中使用for循环进行多次调用）。 + + - 该函数具有两个参数： + + - settings：在本例中没有使用，具体可以参考 `init_hook`_ 中的说明。 + - filename：为 ``train.list`` 或 ``test.list`` 中的一行，即若干数据文件路径的某一个。 + +网络配置中的调用 +++++++++++++++++ + +在网络配置里，只需要一行代码就可以调用这个PyDataProvider2，如， + +.. literalinclude:: mnist_config.py + :lines: 1-7 + +训练数据是 ``train.list`` ，测试数据没有，调用的PyDataProvider2是 ``mnist_provider`` 模块中的 ``process`` 函数。 + +时序模型的使用场景 +------------------ +样例数据 +++++++++ + +时序模型是指数据的某一维度是一个序列形式，即包含时间步信息。所谓时间步信息，不一定和时间有关系，只是说明数据的顺序是重要的。例如，文本信息就是一个序列数据。 + +本例采用英文情感分类的数据，即将一段英文文本数据，分类成正面情绪和负面情绪两类(用0和1表示)。样例数据 ``sentimental_train.txt`` 如下： + +.. literalinclude:: sentimental_train.txt + +dataprovider的使用 +++++++++++++++++++ + +相对MNIST而言，这个dataprovider较复杂，主要原因是增加了初始化机制 `init_hook`_。本例的 ``on_init`` 函数就是根据该机制配置的，它会在dataprovider创建的时候执行。 + +- 其中 ``input_types`` 和在 `@provider`_ 中配置的效果一致。本例中的输入特征是词ID的序列，因此使用 ``integer_value_sequence`` 类型来设置。 +- 将 ``dictionary`` 存入settings对象，在 ``process`` 函数中使用。 dictionary是从网络配置中传入的dict对象，即一个将单词字符串映射到单词ID的字典。 + +.. literalinclude:: sentimental_provider.py + +网络配置中的调用 +++++++++++++++++ + +调用这个PyDataProvider2的方法，基本上和MNIST样例一致，除了 + +* 在配置中需要读取外部字典。 +* 在声明DataProvider的时候传入dictionary作为参数。 + +.. literalinclude:: sentimental_config.py + :emphasize-lines: 12-14 + +小结 +----- + +至此，两个PyDataProvider2的样例就说明完毕了。对用户来说，仅需要知道如何从 **一个文件** 中读取 **一条样本** ，就可以将数据传送给PaddlePaddle了。而PaddlePaddle则会帮用户做以下工作： + +* 将数据组合成Batch进行训练 +* 对训练数据进行Shuffle +* 多线程的数据读取 +* 缓存训练数据到内存(可选) +* CPU->GPU双缓存 + +是不是很简单呢？ + +参考(Reference) +--------------- + +@provider ++++++++++ + +``@provider`` 是一个Python的 `Decorator`_ ，可以将某一个函数标记成一个PyDataProvider2。如果不了解 `Decorator`_ 是什么也没关系，只需知道这是一个标记属性的方法就可以了。它包含的属性参数如下: + +* input_types：数据输入格式。具体的格式说明，请参考 `input_types`_ 。 +* should_shuffle：是不是要对数据做Shuffle。训练时默认shuffle，测试时默认不shuffle。 +* min_pool_size：设置内存中最小暂存的数据条数，也是PaddlePaddle所能够保证的shuffle粒度。如果为-1，则会预先读取全部数据到内存中。 +* pool_size： 设置内存中暂存的数据条数。如果为-1（默认），则不在乎内存暂存多少条数据。如果设置，则推荐大于训练时batch size的值，并且在内存足够的情况下越大越好。 +* can_over_batch_size：是否允许暂存略微多余pool_size的数据。由于这样做可以避免很多死锁问题，一般推荐设置成True。 +* calc_batch_size：可以传入一个函数，用于自定义每条数据的batch size（默认为1）。 +* cache： 数据缓存的策略，具体请参考 `cache`_ 。 +* init_hook：初始化时调用的函数，具体请参考 `init_hook`_ 。 +* check：如果为true，会根据input_types检查数据的合法性。 +* check_fail_continue：如果为true，那么当check出数据不合法时，会扔到这条数据，继续训练或预测。（对check=false的情况，没有作用） + +input_types ++++++++++++ + +PaddlePaddle的数据包括四种主要类型，和三种序列模式。 + +四种数据类型： + +* dense_vector：稠密的浮点数向量。 +* sparse_binary_vector：稀疏的01向量，即大部分值为0，但有值的地方必须为1。 +* sparse_float_vector：稀疏的向量，即大部分值为0，但有值的部分可以是任何浮点数。 +* integer：整数标签。 + +三种序列模式： + +* SequenceType.NO_SEQUENCE：不是一条序列 +* SequenceType.SEQUENCE：是一条时间序列 +* SequenceType.SUB_SEQUENCE： 是一条时间序列，且序列的每一个元素还是一个时间序列。 + +不同的数据类型和序列模式返回的格式不同，列表如下： + ++----------------------+---------------------+-----------------------------------+------------------------------------------------+ +| | NO_SEQUENCE | SEQUENCE | SUB_SEQUENCE | ++======================+=====================+===================================+================================================+ +| dense_vector | [f, f, ...] | [[f, ...], [f, ...], ...] | [[[f, ...], ...], [[f, ...], ...],...] | ++----------------------+---------------------+-----------------------------------+------------------------------------------------+ +| sparse_binary_vector | [i, i, ...] | [[i, ...], [i, ...], ...] | [[[i, ...], ...], [[i, ...], ...],...] | ++----------------------+---------------------+-----------------------------------+------------------------------------------------+ +| sparse_float_vector | [(i,f), (i,f), ...] | [[(i,f), ...], [(i,f), ...], ...] | [[[(i,f), ...], ...], [[(i,f), ...], ...],...] | ++----------------------+---------------------+-----------------------------------+------------------------------------------------+ +| integer_value | i | [i, i, ...] | [[i, ...], [i, ...], ...] | ++----------------------+---------------------+-----------------------------------+------------------------------------------------+ + +其中，f代表一个浮点数，i代表一个整数。 + +init_hook ++++++++++ + +init_hook可以传入一个函数。该函数在初始化的时候会被调用，其参数如下: + +* 第一个参数是settings对象，它和数据传入函数的第一个参数（如本例中 ``process`` 函数的 ``settings`` 参数）必须一致。该对象具有以下两个属性： + * settings.input_types：数据输入格式，具体请参考 `input_types`_ 。 + * settings.logger：一个logging对象。 +* 其他参数使用 ``kwargs`` （key word arguments）传入，包括以下两种： + * PaddlePaddle定义的参数: 1）is_train：bool型参数，表示用于训练或预测；2）file_list：所有文件列表。 + * 用户定义的参数：使用args在网络配置中设置。 + +cache ++++++ + +PyDataProvider2提供了两种简单的Cache策略： + +* CacheType.NO_CACHE：不缓存任何数据，每次都会从python端读取数据 +* CacheType.CACHE_PASS_IN_MEM：第一个pass会从python端读取数据，剩下的pass会直接从内存里 + 读取数据。 + + +注意事项 +-------- + +可能的内存泄露问题 +++++++++++++++++++ + +PaddlePaddle将train.list中的每一行都传递给process函数，从而生成多个generator。当训练数据非常多时，就会生成非常多的generator。 + +虽然每个generator在没有调用的时候，是几乎不占内存的；但当调用过一次后，generator便会存下当前的上下文(Context)，而这个Context可能会非常大。并且，generator至少需要调用两次才会知道是否停止。所以，即使process函数里面只有一个yield，也需要两次随机选择到相同generator的时候，才会释放该段内存。 + +.. code-block:: python + + def func(): + yield 0 + + f = func() # 创建generator + tmp = next(f) # 调用一次，返回0 + tmp = next(f) # 调用第二次的时候，才会Stop Iteration + +由于顺序调用这些generator不会出现上述问题，因此有两种解决方案： + +1. **最佳推荐**：将样本的地址放入另一个文本文件，train.list写入那个文本文件的地址。即不要将每一个样本都放入train.list。 +2. 在generator的上下文中尽量留下非常少的变量引用，例如 + +.. code-block:: python + + def real_process(fn): + # ... read from fn + return result # 当函数返回的时候，python可以解除掉内部变量的引用。 + + def process(fn): + yield real_process(fn) + +注意：这个问题是PyDataProvider读数据时候的逻辑问题，很难整体修正。 + +内存不够用的情况 +++++++++++++++++ + +PyDataProvider2会尽可能多的使用内存。因此，对于内存较小的机器，推荐使用 ``pool_size`` 变量来设置内存中暂存的数据条。具体请参考 `@provider`_ 中的说明。 + diff --git a/doc_cn/ui/data_provider/sentimental_provider.py b/doc_cn/ui/data_provider/sentimental_provider.py index 0fb0bb88e95..14bd0e05a92 100644 --- a/doc_cn/ui/data_provider/sentimental_provider.py +++ b/doc_cn/ui/data_provider/sentimental_provider.py @@ -8,19 +8,16 @@ def on_init(settings, dictionary, **kwargs): # set input types in runtime. It will do the same thing as # @provider(input_types) will do, but it is set dynamically during runtime. - settings.input_types = [ + settings.input_types = { # The text is a sequence of integer values, and each value is a word id. # The whole sequence is the sentences that we want to predict its # sentimental. - integer_value( - len(dictionary), seq_type=SequenceType), # text input + 'data': integer_value_sequence(len(dictionary)), # text input + 'label': integer_value(2) # label positive/negative + } - # label positive/negative - integer_value(2) - ] - - # save dictionary as settings.dictionary. It will be used in process - # method. + # save dictionary as settings.dictionary. + # It will be used in process method. settings.dictionary = dictionary diff --git a/doc_cn/ui/data_provider/write_new_dataprovider.rst b/doc_cn/ui/data_provider/write_new_dataprovider.rst deleted file mode 100644 index a2495fe6637..00000000000 --- a/doc_cn/ui/data_provider/write_new_dataprovider.rst +++ /dev/null @@ -1,4 +0,0 @@ -自定义一个DataProvider -==================== - -TBD \ No newline at end of file diff --git a/doc_cn/ui/index.rst b/doc_cn/ui/index.rst index 8079bd9180c..c53ebeefe1d 100644 --- a/doc_cn/ui/index.rst +++ b/doc_cn/ui/index.rst @@ -8,8 +8,8 @@ .. toctree:: :maxdepth: 1 - data_provider/index.rst - + data_provider/dataprovider.rst + data_provider/pydataprovider2.rst 命令行参数 ========== @@ -22,9 +22,8 @@ * `参数描述 <../../doc/ui/cmd_argument/detail_introduction.html>`_ * `参数用例 <../../doc/ui/cmd_argument/use_case.html>`_ - 预测 -==== +======= .. toctree:: -- GitLab From aa6c6dc07d65de97e65e050224e4b07d4f5b0f98 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 25 Nov 2016 22:00:20 +0800 Subject: [PATCH 083/347] Fix pythonlib can not be found --- CMakeLists.txt | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index af193c27ae7..38daa354835 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -12,8 +12,12 @@ include(package) find_package(SWIG 2.0) find_package(CUDA QUIET) find_package(Protobuf REQUIRED) -find_package(PythonLibs 2.7 REQUIRED) + +# Set up the versions we know about, in the order we will search. +# Always add the user supplied additional versions to the front. +set(Python_ADDITIONAL_VERSIONS 2.7) find_package(PythonInterp 2.7 REQUIRED) +find_package(PythonLibs 2.7 REQUIRED) find_package(ZLIB REQUIRED) find_package(NumPy REQUIRED) find_package(Threads REQUIRED) -- GitLab From 74c48d14d937df5a457746a6f71eae85d5b792c8 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 25 Nov 2016 22:54:23 +0800 Subject: [PATCH 084/347] Revert cmake modification in this pr. --- CMakeLists.txt | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 38daa354835..af193c27ae7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -12,12 +12,8 @@ include(package) find_package(SWIG 2.0) find_package(CUDA QUIET) find_package(Protobuf REQUIRED) - -# Set up the versions we know about, in the order we will search. -# Always add the user supplied additional versions to the front. -set(Python_ADDITIONAL_VERSIONS 2.7) -find_package(PythonInterp 2.7 REQUIRED) find_package(PythonLibs 2.7 REQUIRED) +find_package(PythonInterp 2.7 REQUIRED) find_package(ZLIB REQUIRED) find_package(NumPy REQUIRED) find_package(Threads REQUIRED) -- GitLab From aca08255d2c2a9c7ca06ce4c53765b366f66a227 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Nov 2016 22:05:45 -0800 Subject: [PATCH 085/347] Allow docker build to build from local Git commit. --- paddle/scripts/docker/Dockerfile.m4 | 40 +++++++++++++++---- paddle/scripts/docker/build.sh | 40 +++++-------------- paddle/scripts/docker/generate.sh | 60 ++++++----------------------- 3 files changed, 53 insertions(+), 87 deletions(-) mode change 100644 => 100755 paddle/scripts/docker/build.sh mode change 100644 => 100755 paddle/scripts/docker/generate.sh diff --git a/paddle/scripts/docker/Dockerfile.m4 b/paddle/scripts/docker/Dockerfile.m4 index e14493ed9e8..7f08e66653d 100644 --- a/paddle/scripts/docker/Dockerfile.m4 +++ b/paddle/scripts/docker/Dockerfile.m4 @@ -1,12 +1,36 @@ FROM PADDLE_BASE_IMAGE MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 + +# It is good to run apt-get install with Dockerfile RUN directive, +# because if the following invocation to /root/build.sh fails, `docker +# build` wouldn't have to re-install packages after we fix +# /root/build.sh. +RUN apt-get update && \ + apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ + libgoogle-glog-dev libgflags-dev libatlas-dev libatlas3-base g++ m4 python-pip \ + python-protobuf python-numpy python-dev swig openssh-server \ + wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ + sed grep graphviz libjpeg-dev zlib1g-dev doxygen && \ + apt-get clean -y +RUN pip install BeautifulSoup docopt PyYAML pillow \ + 'sphinx>=1.4.0' sphinx_rtd_theme breathe recommonmark + ENV WITH_GPU=PADDLE_WITH_GPU -ENV IS_DEVEL=PADDLE_IS_DEVEL -ENV WITH_DEMO=PADDLE_WITH_DEMO -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF ENV WITH_AVX=PADDLE_WITH_AVX -RUN cd /root/ && bash build.sh + +RUN mkdir /paddle +COPY . /paddle/ +COPY paddle/scripts/docker/build.sh /root/ +RUN /root/build.sh + +RUN echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile +RUN pip install /usr/local/opt/paddle/share/wheels/*.whl +RUN paddle version # print version after build + +# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service +RUN mkdir /var/run/sshd +RUN echo 'root:root' | chpasswd +RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config +RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config +EXPOSE 22 +CMD ["/usr/sbin/sshd", "-D"] diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh old mode 100644 new mode 100755 index ec5f3bd967d..8e2e26b6ba6 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -7,43 +7,21 @@ function abort(){ trap 'abort' 0 set -e -if [ ${USE_UBUNTU_MIRROR} == "ON" ]; then - sed -i 's#http://archive\.ubuntu\.com/ubuntu/#mirror://mirrors\.ubuntu\.com/mirrors\.txt#g'\ - /etc/apt/sources.list -fi -apt-get update -apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ - libgoogle-glog-dev libgflags-dev libatlas-dev libatlas3-base g++ m4 python-pip\ - python-protobuf python-numpy python-dev swig if [ ${WITH_GPU} == 'ON' ]; then ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so fi -cd ~ -git clone https://github.com/baidu/Paddle.git paddle -cd paddle -git checkout ${GIT_CHECKOUT} -mkdir build -cd build -cmake .. -DWITH_DOC=OFF -DWITH_GPU=${WITH_GPU} -DWITH_SWIG_PY=ON\ - -DCUDNN_ROOT=/usr/ -DWITH_STYLE_CHECK=OFF -DWITH_AVX=${WITH_AVX} +mkdir -p /paddle/build # -p means no error if exists +cd /paddle/build +cmake .. \ + -DWITH_DOC=ON \ + -DWITH_GPU=${WITH_GPU} \ + -DWITH_AVX=${WITH_AVX} \ + -DWITH_SWIG_PY=ON \ + -DCUDNN_ROOT=/usr/ \ + -DWITH_STYLE_CHECK=OFF make -j `nproc` -# because durning make install, there are several warning, so set +e, do not cause abort make install -echo 'export LD_LIBRARY_PATH=/usr/lib64:${LD_LIBRARY_PATH}' >> /etc/profile -pip ${PIP_GENERAL_ARGS} install ${PIP_INSTALL_ARGS} /usr/local/opt/paddle/share/wheels/*.whl -paddle version # print version after build -if [ ${WITH_DEMO} == "ON" ]; then - apt-get install -y wget unzip perl python-matplotlib tar xz-utils bzip2 gzip coreutils\ - sed grep graphviz libjpeg-dev zlib1g-dev - pip ${PIP_GENERAL_ARGS} install ${PIP_INSTALL_ARGS} BeautifulSoup docopt \ - PyYAML pillow -fi -if [ ${IS_DEVEL} == "OFF" ]; then # clean build packages. - cd ~ - rm -rf paddle -fi -apt-get clean -y trap : 0 diff --git a/paddle/scripts/docker/generate.sh b/paddle/scripts/docker/generate.sh old mode 100644 new mode 100755 index 2ad7527db12..b808a62ec29 --- a/paddle/scripts/docker/generate.sh +++ b/paddle/scripts/docker/generate.sh @@ -1,60 +1,24 @@ #!/bin/bash + set -e cd `dirname $0` -m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=ON\ + +m4 -DPADDLE_WITH_GPU=OFF \ + -DPADDLE_WITH_AVX=ON \ + -DPADDLE_BASE_IMAGE=ubuntu:14.04 \ Dockerfile.m4 > Dockerfile.cpu -m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=OFF\ +m4 -DPADDLE_WITH_GPU=OFF \ + -DPADDLE_WITH_AVX=OFF \ + -DPADDLE_BASE_IMAGE=ubuntu:14.04 \ Dockerfile.m4 > Dockerfile.cpu-noavx -m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=OFF\ - Dockerfile.m4 > Dockerfile.cpu-noavx-devel - -m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=ON\ - Dockerfile.m4 > Dockerfile.cpu-devel - - -m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \ - -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=ON\ - Dockerfile.m4 > Dockerfile.cpu-demo - -m4 -DPADDLE_WITH_GPU=OFF -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \ - -DPADDLE_BASE_IMAGE=ubuntu:14.04 -DPADDLE_WITH_AVX=OFF\ - Dockerfile.m4 > Dockerfile.cpu-noavx-demo - - -m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \ +m4 -DPADDLE_WITH_GPU=ON \ -DPADDLE_WITH_AVX=ON \ - Dockerfile.m4 > Dockerfile.gpu - -m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=OFF -DPADDLE_WITH_DEMO=OFF \ -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \ - -DPADDLE_WITH_AVX=OFF \ - Dockerfile.m4 > Dockerfile.gpu-noavx - - -m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \ - -DPADDLE_WITH_AVX=ON \ - Dockerfile.m4 > Dockerfile.gpu-devel + Dockerfile.m4 > Dockerfile.gpu -m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=OFF \ - -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \ +m4 -DPADDLE_WITH_GPU=ON \ -DPADDLE_WITH_AVX=OFF \ - Dockerfile.m4 > Dockerfile.gpu-noavx-devel - -m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \ -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \ - -DPADDLE_WITH_AVX=ON \ - Dockerfile.m4 > Dockerfile.gpu-demo - - -m4 -DPADDLE_WITH_GPU=ON -DPADDLE_IS_DEVEL=ON -DPADDLE_WITH_DEMO=ON \ - -DPADDLE_BASE_IMAGE=nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 \ - -DPADDLE_WITH_AVX=OFF \ - Dockerfile.m4 > Dockerfile.gpu-noavx-demo + Dockerfile.m4 > Dockerfile.gpu-noavx -- GitLab From 4e2071f47e488f9f7fd74fbe4af077ffd0edd9da Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Nov 2016 22:06:16 -0800 Subject: [PATCH 086/347] Make cmake derive Paddle version from Git tag or Git commit ID --- CMakeLists.txt | 50 +++++++++++++++++++++++--------------------------- 1 file changed, 23 insertions(+), 27 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index af193c27ae7..3ad86635782 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,10 +1,6 @@ cmake_minimum_required(VERSION 2.8) project(paddle CXX C) -set(PADDLE_MAJOR_VERSION 0) -set(PADDLE_MINOR_VERSION 9) -set(PADDLE_PATCH_VERSION 0a0) -set(PADDLE_VERSION ${PADDLE_MAJOR_VERSION}.${PADDLE_MINOR_VERSION}.${PADDLE_PATCH_VERSION}) set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake") set(PROJ_ROOT ${CMAKE_SOURCE_DIR}) @@ -64,31 +60,31 @@ include(check_packages) include(swig) include(coveralls) -# add PaddlePaddle version -if(DEFINED ENV{PADDLE_VERSION}) - add_definitions(-DPADDLE_VERSION=\"$ENV{PADDLE_VERSION}\") +# Set PaddlePaddle version to Git tag name or Git commit ID. +find_package(Git REQUIRED) +execute_process( + COMMAND ${GIT_EXECUTABLE} describe --tags --exact-match --abbrev=0 + WORKING_DIRECTORY ${PROJ_ROOT} + OUTPUT_VARIABLE GIT_TAG_NAME + RESULT_VARIABLE GIT_RESULT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) +if(NOT ${GIT_RESULT}) + set(PADDLE_VERSION ${GIT_TAG_NAME}) else() - if(EXISTS ${PROJ_ROOT}/.svn/) - find_package(Subversion REQUIRED) - if(SUBVERSION_FOUND) - Subversion_WC_INFO(${PROJ_ROOT} Project) - add_definitions(-DPADDLE_VERSION=${Project_WC_REVISION}) - endif() - elseif(EXISTS ${PROJ_ROOT}/.git/) - find_package(Git REQUIRED) - execute_process( - COMMAND ${GIT_EXECUTABLE} log -1 --format=%H - WORKING_DIRECTORY ${PROJ_ROOT} - OUTPUT_VARIABLE GIT_SHA1 - RESULT_VARIABLE GIT_RESULT - ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) - if(NOT ${GIT_RESULT}) - add_definitions(-DPADDLE_VERSION=\"${GIT_SHA1}\") - else() - message(WARNING "Cannot add paddle version from git tag") - endif() - endif() + execute_process( + COMMAND ${GIT_EXECUTABLE} log -1 --format=%H + WORKING_DIRECTORY ${PROJ_ROOT} + OUTPUT_VARIABLE GIT_SHA1 + RESULT_VARIABLE GIT_RESULT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + if(NOT ${GIT_RESULT}) + set(PADDLE_VERSION ${GIT_SHA1}) + else() + set(PADDLE_VERSION "unknown") + message(WARNING "Cannot add paddle version from git tag") + endif() endif() +add_definitions(-DPADDLE_VERSION=\"${PADDLE_VERSION}\") if(NOT WITH_GPU) -- GitLab From 88802d15cd2a5953efba978040efca86242ba1a2 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Nov 2016 22:10:38 -0800 Subject: [PATCH 087/347] Remove template execution results from Dockerfile.m4 --- paddle/scripts/docker/Dockerfile.cpu | 12 ------------ paddle/scripts/docker/Dockerfile.cpu-demo | 12 ------------ paddle/scripts/docker/Dockerfile.cpu-devel | 12 ------------ paddle/scripts/docker/Dockerfile.cpu-noavx | 12 ------------ paddle/scripts/docker/Dockerfile.cpu-noavx-demo | 12 ------------ paddle/scripts/docker/Dockerfile.cpu-noavx-devel | 12 ------------ paddle/scripts/docker/Dockerfile.gpu | 12 ------------ paddle/scripts/docker/Dockerfile.gpu-demo | 12 ------------ paddle/scripts/docker/Dockerfile.gpu-devel | 12 ------------ paddle/scripts/docker/Dockerfile.gpu-noavx | 12 ------------ paddle/scripts/docker/Dockerfile.gpu-noavx-demo | 12 ------------ paddle/scripts/docker/Dockerfile.gpu-noavx-devel | 12 ------------ 12 files changed, 144 deletions(-) delete mode 100644 paddle/scripts/docker/Dockerfile.cpu delete mode 100644 paddle/scripts/docker/Dockerfile.cpu-demo delete mode 100644 paddle/scripts/docker/Dockerfile.cpu-devel delete mode 100644 paddle/scripts/docker/Dockerfile.cpu-noavx delete mode 100644 paddle/scripts/docker/Dockerfile.cpu-noavx-demo delete mode 100644 paddle/scripts/docker/Dockerfile.cpu-noavx-devel delete mode 100644 paddle/scripts/docker/Dockerfile.gpu delete mode 100644 paddle/scripts/docker/Dockerfile.gpu-demo delete mode 100644 paddle/scripts/docker/Dockerfile.gpu-devel delete mode 100644 paddle/scripts/docker/Dockerfile.gpu-noavx delete mode 100644 paddle/scripts/docker/Dockerfile.gpu-noavx-demo delete mode 100644 paddle/scripts/docker/Dockerfile.gpu-noavx-devel diff --git a/paddle/scripts/docker/Dockerfile.cpu b/paddle/scripts/docker/Dockerfile.cpu deleted file mode 100644 index 69b8363b7ac..00000000000 --- a/paddle/scripts/docker/Dockerfile.cpu +++ /dev/null @@ -1,12 +0,0 @@ -FROM ubuntu:14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=OFF -ENV IS_DEVEL=OFF -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=ON -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.cpu-demo b/paddle/scripts/docker/Dockerfile.cpu-demo deleted file mode 100644 index ccbd183ee3c..00000000000 --- a/paddle/scripts/docker/Dockerfile.cpu-demo +++ /dev/null @@ -1,12 +0,0 @@ -FROM ubuntu:14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=OFF -ENV IS_DEVEL=ON -ENV WITH_DEMO=ON -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=ON -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.cpu-devel b/paddle/scripts/docker/Dockerfile.cpu-devel deleted file mode 100644 index 36460384f38..00000000000 --- a/paddle/scripts/docker/Dockerfile.cpu-devel +++ /dev/null @@ -1,12 +0,0 @@ -FROM ubuntu:14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=OFF -ENV IS_DEVEL=ON -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=ON -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.cpu-noavx b/paddle/scripts/docker/Dockerfile.cpu-noavx deleted file mode 100644 index fa3b7427b0a..00000000000 --- a/paddle/scripts/docker/Dockerfile.cpu-noavx +++ /dev/null @@ -1,12 +0,0 @@ -FROM ubuntu:14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=OFF -ENV IS_DEVEL=OFF -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=OFF -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.cpu-noavx-demo b/paddle/scripts/docker/Dockerfile.cpu-noavx-demo deleted file mode 100644 index 61315f762de..00000000000 --- a/paddle/scripts/docker/Dockerfile.cpu-noavx-demo +++ /dev/null @@ -1,12 +0,0 @@ -FROM ubuntu:14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=OFF -ENV IS_DEVEL=ON -ENV WITH_DEMO=ON -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=OFF -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.cpu-noavx-devel b/paddle/scripts/docker/Dockerfile.cpu-noavx-devel deleted file mode 100644 index 76365311990..00000000000 --- a/paddle/scripts/docker/Dockerfile.cpu-noavx-devel +++ /dev/null @@ -1,12 +0,0 @@ -FROM ubuntu:14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=OFF -ENV IS_DEVEL=ON -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=OFF -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu deleted file mode 100644 index 1e023ae2818..00000000000 --- a/paddle/scripts/docker/Dockerfile.gpu +++ /dev/null @@ -1,12 +0,0 @@ -FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=ON -ENV IS_DEVEL=OFF -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=ON -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.gpu-demo b/paddle/scripts/docker/Dockerfile.gpu-demo deleted file mode 100644 index 92b0dca4026..00000000000 --- a/paddle/scripts/docker/Dockerfile.gpu-demo +++ /dev/null @@ -1,12 +0,0 @@ -FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=ON -ENV IS_DEVEL=ON -ENV WITH_DEMO=ON -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=ON -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.gpu-devel b/paddle/scripts/docker/Dockerfile.gpu-devel deleted file mode 100644 index fb6f351fd2f..00000000000 --- a/paddle/scripts/docker/Dockerfile.gpu-devel +++ /dev/null @@ -1,12 +0,0 @@ -FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=ON -ENV IS_DEVEL=ON -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=ON -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.gpu-noavx b/paddle/scripts/docker/Dockerfile.gpu-noavx deleted file mode 100644 index 7567e620255..00000000000 --- a/paddle/scripts/docker/Dockerfile.gpu-noavx +++ /dev/null @@ -1,12 +0,0 @@ -FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=ON -ENV IS_DEVEL=OFF -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=OFF -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.gpu-noavx-demo b/paddle/scripts/docker/Dockerfile.gpu-noavx-demo deleted file mode 100644 index ac52484c5cb..00000000000 --- a/paddle/scripts/docker/Dockerfile.gpu-noavx-demo +++ /dev/null @@ -1,12 +0,0 @@ -FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=ON -ENV IS_DEVEL=ON -ENV WITH_DEMO=ON -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=OFF -RUN cd /root/ && bash build.sh diff --git a/paddle/scripts/docker/Dockerfile.gpu-noavx-devel b/paddle/scripts/docker/Dockerfile.gpu-noavx-devel deleted file mode 100644 index 19202f306b8..00000000000 --- a/paddle/scripts/docker/Dockerfile.gpu-noavx-devel +++ /dev/null @@ -1,12 +0,0 @@ -FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04 -MAINTAINER PaddlePaddle Dev Team -COPY build.sh /root/ -ENV GIT_CHECKOUT=v0.9.0a0 -ENV WITH_GPU=ON -ENV IS_DEVEL=ON -ENV WITH_DEMO=OFF -ENV PIP_INSTALL_ARGS "" -ENV PIP_GENERAL_ARGS "" -ENV USE_UBUNTU_MIRROR OFF -ENV WITH_AVX=OFF -RUN cd /root/ && bash build.sh -- GitLab From 74bcf34f2db8e594731bbd613fb50ce2d69e3bed Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Nov 2016 23:10:08 -0800 Subject: [PATCH 088/347] Update document --- doc/build/docker_install.rst | 129 +++++++++++------------------------ 1 file changed, 41 insertions(+), 88 deletions(-) diff --git a/doc/build/docker_install.rst b/doc/build/docker_install.rst index e95de35f4da..93e6f6dd7ee 100644 --- a/doc/build/docker_install.rst +++ b/doc/build/docker_install.rst @@ -1,71 +1,39 @@ -Docker installation guide -========================== +Using and Building Docker Images +================================ -PaddlePaddle provide the `Docker `_ image. `Docker`_ is a lightweight container utilities. The performance of PaddlePaddle in `Docker`_ container is basically as same as run it in a normal linux. The `Docker`_ is a very convenient way to deliver the binary release for linux programs. +We release PaddlePaddle in the form of `Docker `_ images on `dockerhub.com `_. Running as Docker containers is currently the only officially-supported way to running PaddlePaddle. -.. note:: +Run Docker images +----------------- - The `Docker`_ image is the recommended way to run PaddlePaddle +For each version of PaddlePaddle, we release 4 variants of Docker images: -PaddlePaddle Docker images --------------------------- ++-----------------+-------------+-------+ +| | CPU AVX | GPU | ++=================+=============+=======+ +| cpu | yes | no | ++-----------------+-------------+-------+ +| cpu-noavx | no | no | ++-----------------+-------------+-------+ +| gpu | yes | yes | ++-----------------+-------------+-------+ +| gpu-noavx | no | yes | ++-----------------+-------------+-------+ -There are 12 `images `_ for PaddlePaddle, and the name is :code:`paddle-dev/paddle`, tags are\: - - -+-----------------+------------------+------------------------+-----------------------+ -| | normal | devel | demo | -+=================+==================+========================+=======================+ -| CPU | cpu-latest | cpu-devel-latest | cpu-demo-latest | -+-----------------+------------------+------------------------+-----------------------+ -| GPU | gpu-latest | gpu-devel-latest | gpu-demo-latest | -+-----------------+------------------+------------------------+-----------------------+ -| CPU WITHOUT AVX | cpu-noavx-latest | cpu-devel-noavx-latest | cpu-demo-noavx-latest | -+-----------------+------------------+------------------------+-----------------------+ -| GPU WITHOUT AVX | gpu-noavx-latest | gpu-devel-noavx-latest | gpu-demo-noavx-latest | -+-----------------+------------------+------------------------+-----------------------+ - -And the three columns are: - -* normal\: The docker image only contains binary of PaddlePaddle. -* devel\: The docker image contains PaddlePaddle binary, source code and essential build environment. -* demo\: The docker image contains the dependencies to run PaddlePaddle demo. - -And the four rows are: - -* CPU\: CPU Version. Support CPU which has :code:`AVX` instructions. -* GPU\: GPU Version. Support GPU, and cpu has :code:`AVX` instructions. -* CPU WITHOUT AVX\: CPU Version, which support most CPU even doesn't have :code:`AVX` instructions. -* GPU WITHOUT AVX\: GPU Version, which support most CPU even doesn't have :code:`AVX` instructions. - -User can choose any version depends on machine. The following script can help you to detect your CPU support :code:`AVX` or not. +The following command line detects if your CPU supports :code:`AVX`. .. code-block:: bash - + if cat /proc/cpuinfo | grep -q avx ; then echo "Support AVX"; else echo "Not support AVX"; fi -If the output is :code:`Support AVX`, then you can choose the AVX version of PaddlePaddle, otherwise, you need select :code:`noavx` version of PaddlePaddle. For example, the CPU develop version of PaddlePaddle is :code:`paddle-dev/paddle:cpu-devel-latest`. -The PaddlePaddle images don't contain any entry command. You need to write your entry command to use this image. See :code:`Remote Access` part or just use following command to run a :code:`bash` +Once we determine the proper variant, we can cope with the Docker image tag name by appending the version number. For example, the following command runs the AVX-enabled image of the most recent version: .. code-block:: bash - docker run -it paddledev/paddle:cpu-latest /bin/bash + docker run -it --rm paddledev/paddle:cpu-latest /bin/bash - -Download and Run Docker images ------------------------------- - -You have to install Docker in your machine which has linux kernel version 3.10+ first. You can refer to the official guide https://docs.docker.com/engine/installation/ for further information. - -You can use :code:`docker pull ` to download images first, or just launch a container with :code:`docker run` \: - -.. code-block:: bash - - docker run -it paddledev/paddle:cpu-latest - - -If you want to launch container with GPU support, you need to set some environment variables at the same time: +To run a GPU-enabled image, you need to install CUDA and let Docker knows about it: .. code-block:: bash @@ -73,50 +41,35 @@ If you want to launch container with GPU support, you need to set some environme export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest +The default entry point of all our Docker images starts the OpenSSH server. To run PaddlePaddle and to expose OpenSSH port to 2202 on the host computer: -Some notes for docker ---------------------- - -Performance -+++++++++++ - -Since Docker is based on the lightweight virtual containers, the CPU computing performance maintains well. And GPU driver and equipments are all mapped to the container, so the GPU computing performance would not be seriously affected. - -If you use high performance nic, such as RDMA(RoCE 40GbE or IB 56GbE), Ethernet(10GbE), it is recommended to use config "-net = host". - - - +.. code-block:: bash -Remote access -+++++++++++++ + docker run -d -p 2202:22 paddledev/paddle:cpu-latest +Then we can login to the container using username :code:`root` and password :code:`root`: -If you want to enable ssh access background, you need to build an image by yourself. Please refer to official guide https://docs.docker.com/engine/reference/builder/ for further information. +.. code-block:: bash -Following is a simple Dockerfile with ssh: + ssh -p 2202 root@localhost -.. literalinclude:: ../../doc_cn/build_and_install/install/paddle_ssh.Dockerfile -Then you can build an image with Dockerfile and launch a container: +Build Docker images +------------------- -.. code-block:: bash +Developers might want to build Docker images from their local commit or from a tagged version. Suppose that your local repo is at :code:`~/work/Paddle`, the following steps builds a cpu variant from your current work: - # cd into Dockerfile directory - docker build . -t paddle_ssh - # run container, and map host machine port 8022 to container port 22 - docker run -d -p 8022:22 --name paddle_ssh_machine paddle_ssh +.. code-block:: bash -Now, you can ssh on port 8022 to access the container, username is root, password is also root: + cd ~/Paddle + ./paddle/scripts/docker/generates.sh # Use m4 to generate Dockerfiles for each variant. + docker build -t paddle:latest -f ./paddle/scripts/docker/Dockerfile.cpu -.. code-block:: bash +As a release engineer, you might want to build Docker images for a certain version and publish them to dockerhub.com. You can do this by switching to the right Git tag, or create a new tag, before running `docker build`. For example, the following commands build Docker images for v0.9.0: - ssh -p 8022 root@YOUR_HOST_MACHINE - -You can stop and delete the container as following: - -.. code-block:: bash +.. code-block:: bash - # stop - docker stop paddle_ssh_machine - # delete - docker rm paddle_ssh_machine + cd ~/Paddle + git checkout tags/v0.9.0 + ./paddle/scripts/docker/generates.sh # Use m4 to generate Dockerfiles for each variant. + docker build -t paddle:cpu-v0.9.0 -f ./paddle/scripts/docker/Dockerfile.cpu -- GitLab From a3a7e76c7fdce9a75d6e6b5a57c3f189b05bd3b7 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Sat, 26 Nov 2016 20:38:56 -0800 Subject: [PATCH 089/347] Respond to Helin's comments --- doc/build/docker_install.rst | 20 +++++++++++++------- paddle/scripts/docker/Dockerfile.m4 | 3 ++- 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/doc/build/docker_install.rst b/doc/build/docker_install.rst index 93e6f6dd7ee..5f272aabd7c 100644 --- a/doc/build/docker_install.rst +++ b/doc/build/docker_install.rst @@ -20,22 +20,28 @@ For each version of PaddlePaddle, we release 4 variants of Docker images: | gpu-noavx | no | yes | +-----------------+-------------+-------+ -The following command line detects if your CPU supports :code:`AVX`. +We run the following command on Linux to check if the CPU supports :code:`AVX`. -.. code-block:: bash +.. code-block:: bash + + if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi + +On Mac OS X, we need to run + +.. code-block:: bash - if cat /proc/cpuinfo | grep -q avx ; then echo "Support AVX"; else echo "Not support AVX"; fi + sysctl -a | grep machdep.cpu.leaf7_features Once we determine the proper variant, we can cope with the Docker image tag name by appending the version number. For example, the following command runs the AVX-enabled image of the most recent version: -.. code-block:: bash +.. code-block:: bash docker run -it --rm paddledev/paddle:cpu-latest /bin/bash To run a GPU-enabled image, you need to install CUDA and let Docker knows about it: -.. code-block:: bash +.. code-block:: bash export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') @@ -43,13 +49,13 @@ To run a GPU-enabled image, you need to install CUDA and let Docker knows about The default entry point of all our Docker images starts the OpenSSH server. To run PaddlePaddle and to expose OpenSSH port to 2202 on the host computer: -.. code-block:: bash +.. code-block:: bash docker run -d -p 2202:22 paddledev/paddle:cpu-latest Then we can login to the container using username :code:`root` and password :code:`root`: -.. code-block:: bash +.. code-block:: bash ssh -p 2202 root@localhost diff --git a/paddle/scripts/docker/Dockerfile.m4 b/paddle/scripts/docker/Dockerfile.m4 index 7f08e66653d..f2822acdde7 100644 --- a/paddle/scripts/docker/Dockerfile.m4 +++ b/paddle/scripts/docker/Dockerfile.m4 @@ -4,7 +4,8 @@ MAINTAINER PaddlePaddle Dev Team # It is good to run apt-get install with Dockerfile RUN directive, # because if the following invocation to /root/build.sh fails, `docker # build` wouldn't have to re-install packages after we fix -# /root/build.sh. +# /root/build.sh. For more about Docker build cache, please refer to +# https://docs.docker.com/engine/userguide/eng-image/dockerfile_best-practices/#/build-cache. RUN apt-get update && \ apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ libgoogle-glog-dev libgflags-dev libatlas-dev libatlas3-base g++ m4 python-pip \ -- GitLab From 7573205c686477a5251d646aa905c177803366de Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 28 Nov 2016 12:46:24 +0800 Subject: [PATCH 090/347] follow comments on config_parser --- proto/ModelConfig.proto.m4 | 8 ++++---- python/paddle/trainer/config_parser.py | 16 ++++++++-------- .../configs/protostr/img_trans_layers.protostr | 2 +- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/proto/ModelConfig.proto.m4 b/proto/ModelConfig.proto.m4 index 2270679e905..ac32c9c5fb8 100644 --- a/proto/ModelConfig.proto.m4 +++ b/proto/ModelConfig.proto.m4 @@ -78,10 +78,10 @@ message ConvConfig { required uint32 stride_y = 12; // if not set, use output_x - optional uint32 output_y = 13 [default = 0]; + optional uint32 output_y = 13; // if not set, use img_size - optional uint32 img_size_y = 14 [default = 0]; + optional uint32 img_size_y = 14; } message PoolConfig { @@ -161,10 +161,10 @@ message NormConfig { optional bool blocked = 8; // if not set, use output_x - optional uint32 output_y = 9 [default = 0]; + optional uint32 output_y = 9; // if not set, use img_size - optional uint32 img_size_y = 10 [default = 0]; + optional uint32 img_size_y = 10; } message BlockExpandConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index f8c916068d1..f917ead680a 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1066,7 +1066,7 @@ def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode): return 1 + int(math.ceil(output)) -#calcualte image_size based on output_size for convolution. +#calcualte image_size based on output_size for de-convolution (ConvTransLayer). #It is the reverse function of cnn_output_size def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode): img_size = (output_size - 1) * stride + filter_size - 2 * padding @@ -1075,7 +1075,7 @@ def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode): return img_size -def set_img_size(input_layer_name, channels): +def get_img_size(input_layer_name, channels): input = g_layer_map[input_layer_name] img_pixels = input.size / channels img_size = input.width if input.width > 0 else int(img_pixels**0.5) @@ -1110,7 +1110,7 @@ def parse_pool(pool, input_layer_name, pool_conf): pool_conf.stride_y = default(pool.stride_y, pool_conf.stride) pool_conf.img_size, pool_conf.img_size_y = \ - set_img_size(input_layer_name, pool.channels) + get_img_size(input_layer_name, pool.channels) config_assert(not pool.start, "start is deprecated in pooling.") @@ -1137,7 +1137,7 @@ def parse_spp(spp, input_layer_name, spp_conf): def parse_image(image, input_layer_name, image_conf): image_conf.channels = image.channels image_conf.img_size, image_conf.img_size_y = \ - set_img_size(input_layer_name, image_conf.channels) + get_img_size(input_layer_name, image_conf.channels) def parse_norm(norm, input_layer_name, norm_conf): @@ -1152,7 +1152,7 @@ def parse_norm(norm, input_layer_name, norm_conf): norm_conf.blocked = norm.blocked norm_conf.img_size, norm_conf.img_size_y = \ - set_img_size(input_layer_name, norm.channels) + get_img_size(input_layer_name, norm.channels) norm_conf.output_x = norm_conf.img_size norm_conf.output_y = norm_conf.img_size_y if norm.norm_type in ['cmrnorm-projection']: @@ -1177,7 +1177,7 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False): if not trans: conv_conf.filter_channels = conv.channels / conv.groups conv_conf.img_size, conv_conf.img_size_y = \ - set_img_size(input_layer_name, conv.channels) + get_img_size(input_layer_name, conv.channels) conv_conf.output_x = cnn_output_size( conv_conf.img_size, conv_conf.filter_size, conv_conf.padding, conv_conf.stride, conv_conf.caffe_mode) @@ -1187,11 +1187,11 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False): else: conv_conf.filter_channels = num_filters / conv.groups conv_conf.output_x, conv_conf.output_y = \ - set_img_size(input_layer_name, conv.channels) + get_img_size(input_layer_name, conv.channels) conv_conf.img_size = cnn_image_size( conv_conf.output_x, conv_conf.filter_size, conv_conf.padding, conv_conf.stride, conv_conf.caffe_mode) - conv_conf.img_size_y = cnn_output_size( + conv_conf.img_size_y = cnn_image_size( conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y, conv_conf.stride_y, conv_conf.caffe_mode) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr index ac1e2adff5d..cd310bd13b3 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr @@ -27,7 +27,7 @@ layers { padding_y: 1 stride_y: 1 output_y: 227 - img_size_y: 198 + img_size_y: 256 } } bias_parameter_name: "___conv_0__.wbias" -- GitLab From a1143460c602fcde55357f7d65b48f7c31fe1be1 Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Mon, 28 Nov 2016 12:53:35 +0800 Subject: [PATCH 091/347] follow comments: refine ubuntu install doc --- .../build_and_install/install/ubuntu_install.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/doc_cn/build_and_install/install/ubuntu_install.rst b/doc_cn/build_and_install/install/ubuntu_install.rst index 08d55f98d9d..e48e4932ac7 100644 --- a/doc_cn/build_and_install/install/ubuntu_install.rst +++ b/doc_cn/build_and_install/install/ubuntu_install.rst @@ -1,7 +1,7 @@ Ubuntu部署PaddlePaddle =================================== -PaddlePaddle提供了deb安装包，并在ubuntu 14.04做了完备测试，理论上也支持其他的debian发行版。 +PaddlePaddle提供了ubuntu 14.04 deb安装包。 安装 ------ @@ -10,13 +10,13 @@ PaddlePaddle提供了deb安装包，并在ubuntu 14.04做了完备测试，理 它包含四个版本\: -* cpu版本: 支持主流intel x86处理器平台, 支持avx指令集。 +* cpu版本: 支持主流x86处理器平台, 使用了avx指令集。 -* cpu-noavx版本：支持主流intel x86处理器平台，不支持avx指令集。 +* cpu-noavx版本：支持主流x86处理器平台，没有使用avx指令集。 -* gpu版本：支持主流intel x86处理器平台，支持nvidia cuda平台，支持avx指令集。 +* gpu版本：支持主流x86处理器平台，支持nvidia cuda平台，使用了avx指令集。 -* gpu-noavx版本：支持主流intel x86处理器平台，支持nvidia cuda平台，不支持avx指令级。 +* gpu-noavx版本：支持主流x86处理器平台，支持nvidia cuda平台，没有使用avx指令集。 下载完相关安装包后，执行: @@ -43,7 +43,7 @@ PaddlePaddle提供了deb安装包，并在ubuntu 14.04做了完备测试，理 可能遇到的问题 -------------- -如何设置gpu版本运行时cuda环境运行GPU版本 +如何设置CUDA环境运行GPU版本 ++++++++++++++++++++++++++++++++++++++++ 如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置： @@ -62,5 +62,5 @@ libcudart.so/libcudnn.so找不到 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. -原因是未设置cuda运行时环境变量，请参考** 设置gpu版本运行时cuda环境** 解决方案。 +原因是未设置cuda运行时环境变量，请参考**如何设置CUDA环境运行GPU版本** 。 -- GitLab From bb97b47ee7057e4dfbb0dbc8ffa90a6704f28202 Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Mon, 28 Nov 2016 12:56:30 +0800 Subject: [PATCH 092/347] add blank char to fix tiny rst doc --- doc_cn/build_and_install/install/ubuntu_install.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc_cn/build_and_install/install/ubuntu_install.rst b/doc_cn/build_and_install/install/ubuntu_install.rst index e48e4932ac7..372ad6a9444 100644 --- a/doc_cn/build_and_install/install/ubuntu_install.rst +++ b/doc_cn/build_and_install/install/ubuntu_install.rst @@ -62,5 +62,5 @@ libcudart.so/libcudnn.so找不到 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. -原因是未设置cuda运行时环境变量，请参考**如何设置CUDA环境运行GPU版本** 。 +原因是未设置cuda运行时环境变量，请参考 **如何设置CUDA环境运行GPU版本** 。 -- GitLab From f63bdc80a1f781abafd9b4d922050b3a2412dcf1 Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Mon, 28 Nov 2016 13:18:42 +0800 Subject: [PATCH 093/347] follow comments: more clean doc --- .../install/ubuntu_install.rst | 21 +++++++------------ doc_cn/faq/index.rst | 2 +- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/doc_cn/build_and_install/install/ubuntu_install.rst b/doc_cn/build_and_install/install/ubuntu_install.rst index 372ad6a9444..4500d6e0b03 100644 --- a/doc_cn/build_and_install/install/ubuntu_install.rst +++ b/doc_cn/build_and_install/install/ubuntu_install.rst @@ -43,24 +43,19 @@ PaddlePaddle提供了ubuntu 14.04 deb安装包。 可能遇到的问题 -------------- -如何设置CUDA环境运行GPU版本 -++++++++++++++++++++++++++++++++++++++++ - -如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置： - -.. code-block:: shell - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH - export PATH=/usr/local/cuda/bin:$PATH - - libcudart.so/libcudnn.so找不到 ++++++++++++++++++++++++++++++ 安装完成后，运行 :code:`paddle train` 报错\: -.. code-block:: shell +.. code-block:: shell - 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. + 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. + +原因是未设置cuda运行时环境变量。 如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置： + +.. code-block:: shell -原因是未设置cuda运行时环境变量，请参考 **如何设置CUDA环境运行GPU版本** 。 + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH + export PATH=/usr/local/cuda/bin:$PATH diff --git a/doc_cn/faq/index.rst b/doc_cn/faq/index.rst index 6e1102e5528..551430eb417 100644 --- a/doc_cn/faq/index.rst +++ b/doc_cn/faq/index.rst @@ -7,7 +7,7 @@ PaddlePaddle常见问题 1. 如何减少内存占用 --------------------------------- -神经网络的训练本身是一个非常消耗内存和显存的工作，经常会消耗数十G的内存和数G的显存。 +神经网络的训练本身是一个非常消耗内存和显存的工作，经常会消耗数10GB的内存和数GB的显存。 PaddlePaddle的内存占用主要分为如下几个方面\: * DataProvider缓冲池内存（只针对内存） -- GitLab From 9935d7d61cb729877493bdfb2d1323168fa120c5 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 28 Nov 2016 14:26:30 +0800 Subject: [PATCH 094/347] Add Release notes --- RELEASE.md | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 RELEASE.md diff --git a/RELEASE.md b/RELEASE.md new file mode 100644 index 00000000000..b2684f5f5de --- /dev/null +++ b/RELEASE.md @@ -0,0 +1,70 @@ +# Release v0.9.0 + +## New Features: + +* Add some layers to Paddle + * bilinear interpolation layer. + * spatial pyramid-pool layer. + * de-convolution layer. + * maxout layer. +* Support rectangle padding, stride, window and input for Pooling Operation. +* Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON. +* Support Mac OS X Sierra by source code. +* Expose cost_weight/nce_layer in `trainer_config_helpers` +* Add FAQ, concepts, h-rnn docs. +* Add Bidi-LSTM and DB-LSTM to quick start demo @alvations +* Add usage track scripts. + +## Improvements + +* Add travis-ci for macos. Enable swig unittest in travis. Skip travis-ci when only docs are changed. +* Add code coverage tools. +* Refine convolution layer to speedup and reduce GPU memory. +* Speed up PyDataProvider2 +* Add ubuntu deb package build scripts. +* Make Paddle use git-flow branching model. +* PServer support no parameter blocks. + +## Bug Fixes + +* add zlib link to py_paddle +* add input sparse data check for sparse layer at runtime +* Bug fix for sparse matrix multiplication +* Fix floating-point overflow problem of tanh +* Fix some nvcc compile options +* Fix a bug in yield dictionary in DataProvider +* Fix SRL hang when exit. + +# Release v0.8.0beta.1 +New features: + +* Mac OSX is supported by source code. #138 + * Both GPU and CPU versions of PaddlePaddle are supported. + +* Support CUDA 8.0 + +* Enhance `PyDataProvider2` + * Add dictionary yield format. `PyDataProvider2` can yield a dictionary with key is data_layer's name, value is features. + * Add `min_pool_size` to control memory pool in provider. + +* Add `deb` install package & docker image for no_avx machines. + * Especially for cloud computing and virtual machines + +* Automatically disable `avx` instructions in cmake when machine's CPU don't support `avx` instructions. + +* Add Parallel NN api in trainer_config_helpers. + +* Add `travis ci` for Github + +Bug fixes: + +* Several bugs in trainer_config_helpers. Also complete the unittest for trainer_config_helpers +* Check if PaddlePaddle is installed when unittest. +* Fix bugs in GTX series GPU +* Fix bug in MultinomialSampler + +Also more documentation was written since last release. + +# Release v0.8.0beta.0 + +PaddlePaddle v0.8.0beta.0 release. The install package is not stable yet and it's a pre-release version. -- GitLab From d6b0b5cb103c83c592bed8ce642250b33b09a8c3 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 28 Nov 2016 15:33:01 +0800 Subject: [PATCH 095/347] note on difference between sparse_vector and dense_vector --- doc_cn/ui/data_provider/pydataprovider2.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc_cn/ui/data_provider/pydataprovider2.rst b/doc_cn/ui/data_provider/pydataprovider2.rst index 3455e331da0..001e0884be5 100644 --- a/doc_cn/ui/data_provider/pydataprovider2.rst +++ b/doc_cn/ui/data_provider/pydataprovider2.rst @@ -155,6 +155,8 @@ PaddlePaddle的数据包括四种主要类型，和三种序列模式。 其中，f代表一个浮点数，i代表一个整数。 +注意：sparse_binary_vector和sparse_float_vector中的元素，可以有None；但dense_vector和integer中的元素，不能出现None。 + init_hook +++++++++ -- GitLab From ddb948a1131466348dc9d30deea42853ebbaae48 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 28 Nov 2016 16:26:42 +0800 Subject: [PATCH 096/347] refine doc_cn/ui/index.rst --- doc_cn/ui/cmd/dump_config.rst | 0 doc_cn/ui/cmd/index.rst | 25 ++++++++----------------- doc_cn/ui/cmd/make_diagram.rst | 0 doc_cn/ui/cmd/merge_model.rst | 0 doc_cn/ui/cmd/paddle_pserver.rst | 0 doc_cn/ui/cmd/paddle_train.rst | 0 doc_cn/ui/cmd/paddle_version.rst | 7 ------- doc_cn/ui/index.rst | 8 +++++--- doc_cn/ui/predict/swig_py_paddle.rst | 8 ++++---- 9 files changed, 17 insertions(+), 31 deletions(-) delete mode 100644 doc_cn/ui/cmd/dump_config.rst delete mode 100644 doc_cn/ui/cmd/make_diagram.rst delete mode 100644 doc_cn/ui/cmd/merge_model.rst delete mode 100644 doc_cn/ui/cmd/paddle_pserver.rst delete mode 100644 doc_cn/ui/cmd/paddle_train.rst delete mode 100644 doc_cn/ui/cmd/paddle_version.rst diff --git a/doc_cn/ui/cmd/dump_config.rst b/doc_cn/ui/cmd/dump_config.rst deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/doc_cn/ui/cmd/index.rst b/doc_cn/ui/cmd/index.rst index f975d432c07..31a8b8a79f4 100644 --- a/doc_cn/ui/cmd/index.rst +++ b/doc_cn/ui/cmd/index.rst @@ -1,29 +1,20 @@ -PaddlePaddle的命令行参数 -======================== +命令 +==== -安装好PaddlePaddle后，在命令行直接敲击 ``paddle`` 或 ``paddle --help`` 会显示如下一些命令行参数。 +安装好PaddlePaddle后，在命令行直接敲击 ``paddle`` 或 ``paddle --help`` 会显示如下一些命令。 * ``train`` Start a paddle_trainer 启动一个PaddlePaddle训练进程。 ``paddle train`` 可以通过命令行参数 ``-local=true`` 启动一个单机的训练进程；也可以和 ``paddle pserver`` 一起使用启动多机的分布式训练进程。 * ``pserver`` Start a paddle_pserver_main 在多机分布式训练下启动PaddlePaddle的parameter server进程。 * ``version`` Print paddle version - 用于打印当前PaddlePaddle的版本和编译选项相关信息。 + 用于打印当前PaddlePaddle的版本和编译选项相关信息。常见的输出格式如下：1）第一行说明了PaddlePaddle的版本信息；2）第二行开始说明了一些主要的编译选项，具体意义可以参考 `编译参数选项文件 <../../build_and_install/cmake/compile_options.html>`_ 。 + + .. literalinclude:: paddle_version.txt + * ``merge_model`` Start a paddle_merge_model 用于将PaddlePaddle的模型参数文件和模型配置文件打包成一个文件，方便做部署分发。 * ``dump_config`` Dump the trainer config as proto string 用于将PaddlePaddle的模型配置文件以proto string的格式打印出来。 * ``make_diagram`` - 使用graphviz对PaddlePaddle的模型配置文件进行绘制。 - -更详细的介绍请参考各命令行参数文档。 - -.. toctree:: - :glob: - - paddle_train.rst - paddle_pserver.rst - paddle_version.rst - merge_model.rst - dump_config.rst - make_diagram.rst + 使用graphviz对PaddlePaddle的模型配置文件进行绘制。 \ No newline at end of file diff --git a/doc_cn/ui/cmd/make_diagram.rst b/doc_cn/ui/cmd/make_diagram.rst deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/doc_cn/ui/cmd/merge_model.rst b/doc_cn/ui/cmd/merge_model.rst deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/doc_cn/ui/cmd/paddle_pserver.rst b/doc_cn/ui/cmd/paddle_pserver.rst deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/doc_cn/ui/cmd/paddle_train.rst b/doc_cn/ui/cmd/paddle_train.rst deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/doc_cn/ui/cmd/paddle_version.rst b/doc_cn/ui/cmd/paddle_version.rst deleted file mode 100644 index 537c23df75e..00000000000 --- a/doc_cn/ui/cmd/paddle_version.rst +++ /dev/null @@ -1,7 +0,0 @@ -paddle version的命令行参数 -========================== - -paddle version用于打印当前的版本信息和相关编译选项。常见的输出格式如下。第一行说明了PaddlePaddle的版本信息，后面跟着一些主要的编译选项。编译选项的具体意义可以参考 -`编译参数选项文件 <../../build_and_install/cmake/compile_options.html>`_ - -.. literalinclude:: paddle_version.txt diff --git a/doc_cn/ui/index.rst b/doc_cn/ui/index.rst index 8079bd9180c..d871ad805ff 100644 --- a/doc_cn/ui/index.rst +++ b/doc_cn/ui/index.rst @@ -11,21 +11,23 @@ data_provider/index.rst -命令行参数 -========== +命令及命令行参数 +================ .. toctree:: + :maxdepth: 1 cmd/index.rst +* `参数用例 <../../doc/ui/cmd_argument/use_case.html>`_ * `参数分类 <../../doc/ui/cmd_argument/argument_outline.html>`_ * `参数描述 <../../doc/ui/cmd_argument/detail_introduction.html>`_ -* `参数用例 <../../doc/ui/cmd_argument/use_case.html>`_ 预测 ==== .. toctree:: + :maxdepth: 1 predict/swig_py_paddle.rst diff --git a/doc_cn/ui/predict/swig_py_paddle.rst b/doc_cn/ui/predict/swig_py_paddle.rst index 4c0a0de820b..89031dd72f5 100644 --- a/doc_cn/ui/predict/swig_py_paddle.rst +++ b/doc_cn/ui/predict/swig_py_paddle.rst @@ -1,8 +1,8 @@ 基于Python的预测 ================ -Python预测接口 --------------- +预测流程 +-------- PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会生成py_paddle软件包，安装该软件包就可以在python环境下实现模型预测。可以使用python的 ``help()`` 函数查询软件包相关API说明。 @@ -20,8 +20,8 @@ PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会 通过调用 ``forwardTest()`` 传入预测数据，直接返回计算结果。 -基于Python的预测Demo --------------------- +预测Demo +-------- 如下是一段使用mnist model来实现手写识别的预测代码。完整的代码见 ``src_root/doc/ui/predict/predict_sample.py`` 。mnist model可以通过 ``src_root\demo\mnist`` 目录下的demo训练出来。 -- GitLab From d853a43f67cc91c3b50dbbe04cc0a7853c3fabd6 Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 28 Nov 2016 16:41:33 +0800 Subject: [PATCH 097/347] Refine quick start index.rst --- doc_cn/demo/quick_start/index.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/doc_cn/demo/quick_start/index.rst b/doc_cn/demo/quick_start/index.rst index 08c1c8413b3..b38b8ca9995 100644 --- a/doc_cn/demo/quick_start/index.rst +++ b/doc_cn/demo/quick_start/index.rst @@ -48,6 +48,11 @@ PaddlePaddle快速入门教程 ./data/get_data.sh ./preprocess.sh +数据预处理完成之后，通过配置类似于 ``dataprovider_*.py`` 的数据读取脚本和类似于 ``trainer_config.*.py`` 的训练模型脚本，PaddlePaddle将以设置参数的方式来设置 +相应的数据读取脚本和训练模型脚本。接下来，我们将对这两个步骤给出了详细的解释，你也可以先跳过本文的解释环节，直接进入训练环节, 使用 ``sh train.sh`` 开始训练模型， +查看`train.sh`内容，通过 **自底向上法** (bottom-up approach)来帮助你理解PaddlePaddle的内部运行机制。 + + 向系统传送数据 ============== -- GitLab From b5e36970e29bf2f52cad934466a88b7cf7672499 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 28 Nov 2016 17:04:28 +0800 Subject: [PATCH 098/347] all input_types should has None element --- doc_cn/ui/data_provider/pydataprovider2.rst | 2 -- 1 file changed, 2 deletions(-) diff --git a/doc_cn/ui/data_provider/pydataprovider2.rst b/doc_cn/ui/data_provider/pydataprovider2.rst index 001e0884be5..3455e331da0 100644 --- a/doc_cn/ui/data_provider/pydataprovider2.rst +++ b/doc_cn/ui/data_provider/pydataprovider2.rst @@ -155,8 +155,6 @@ PaddlePaddle的数据包括四种主要类型，和三种序列模式。 其中，f代表一个浮点数，i代表一个整数。 -注意：sparse_binary_vector和sparse_float_vector中的元素，可以有None；但dense_vector和integer中的元素，不能出现None。 - init_hook +++++++++ -- GitLab From 5b4bec43a623e96e725414c8c3166601361b9174 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 28 Nov 2016 17:36:35 +0800 Subject: [PATCH 099/347] Add syntax='proto2' when using protobuf 3 --- proto/CMakeLists.txt | 15 ++++++++++++++- proto/DataConfig.proto.m4 | 1 + proto/DataFormat.proto.m4 | 1 + proto/ModelConfig.proto.m4 | 1 + proto/ParameterConfig.proto.m4 | 1 + proto/ParameterService.proto.m4 | 2 +- proto/TrainerConfig.proto.m4 | 1 + 7 files changed, 20 insertions(+), 2 deletions(-) diff --git a/proto/CMakeLists.txt b/proto/CMakeLists.txt index 461c73f14c2..ec68b53d440 100644 --- a/proto/CMakeLists.txt +++ b/proto/CMakeLists.txt @@ -1,3 +1,12 @@ +execute_process(COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --version + OUTPUT_VARIABLE PROTOBUF_VERSION) +string(REPLACE "libprotoc " "" PROTOBUF_VERSION ${PROTOBUF_VERSION}) + +set(PROTOBUF_3 OFF) +if (${PROTOBUF_VERSION} VERSION_GREATER "3.0.0" OR ${PROTOBUF_VERSION} VERSION_EQUAL "3.0.0") + set(PROTOBUF_3 ON) +endif() + set(proto_filenames DataConfig.proto DataFormat.proto @@ -11,8 +20,12 @@ set(real_proto_files) # TODO(yuyang18): Some internal proto will also be depended on. # Find a way to automatically calculate all depends. foreach(filename ${proto_filenames}) + set(PROTOBUF_3_FLAGS "") + if (PROTOBUF_3) + set(PROTOBUF_3_FLAGS "-Dproto3") + endif() add_custom_command(OUTPUT ${filename} - COMMAND ${M4_EXECUTABLE} -Dreal=${ACCURACY} -I '${INTERNAL_PROTO_PATH}' + COMMAND ${M4_EXECUTABLE} -Dreal=${ACCURACY} ${PROTOBUF_3_FLAGS} -I '${INTERNAL_PROTO_PATH}' ${PROJ_ROOT}/proto/${filename}.m4 > ${filename} DEPENDS ${PROJ_ROOT}/proto/${filename}.m4 COMMENT "Generate ${filename}") diff --git a/proto/DataConfig.proto.m4 b/proto/DataConfig.proto.m4 index 9862e4e7ef2..01d451ff7d5 100644 --- a/proto/DataConfig.proto.m4 +++ b/proto/DataConfig.proto.m4 @@ -11,6 +11,7 @@ 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. */ +ifdef(`proto3', `syntax = "proto2";') package paddle; diff --git a/proto/DataFormat.proto.m4 b/proto/DataFormat.proto.m4 index 556eace5e19..8a4a0be1b31 100644 --- a/proto/DataFormat.proto.m4 +++ b/proto/DataFormat.proto.m4 @@ -11,6 +11,7 @@ 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. */ +ifdef(`proto3', `syntax = "proto2";') package paddle; diff --git a/proto/ModelConfig.proto.m4 b/proto/ModelConfig.proto.m4 index c835cfd5221..68a5eb9dd22 100644 --- a/proto/ModelConfig.proto.m4 +++ b/proto/ModelConfig.proto.m4 @@ -11,6 +11,7 @@ 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. */ +ifdef(`proto3', `syntax = "proto2";') import "ParameterConfig.proto"; diff --git a/proto/ParameterConfig.proto.m4 b/proto/ParameterConfig.proto.m4 index e8d512445e5..26e7c3ef77b 100644 --- a/proto/ParameterConfig.proto.m4 +++ b/proto/ParameterConfig.proto.m4 @@ -11,6 +11,7 @@ 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. */ +ifdef(`proto3', `syntax = "proto2";') package paddle; diff --git a/proto/ParameterService.proto.m4 b/proto/ParameterService.proto.m4 index 189dc1c9700..0b3f14a2ee5 100644 --- a/proto/ParameterService.proto.m4 +++ b/proto/ParameterService.proto.m4 @@ -11,6 +11,7 @@ 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. */ +ifdef(`proto3', `syntax = "proto2";') import "ParameterConfig.proto"; import "TrainerConfig.proto"; @@ -20,7 +21,6 @@ package paddle; /** * Various structs for communicating with parameter server */ - enum ParameterUpdateMode { // Set parameter PSERVER_UPDATE_MODE_SET_PARAM = 0;//use local param diff --git a/proto/TrainerConfig.proto.m4 b/proto/TrainerConfig.proto.m4 index 3b0e24f90be..965c9cd3935 100644 --- a/proto/TrainerConfig.proto.m4 +++ b/proto/TrainerConfig.proto.m4 @@ -11,6 +11,7 @@ 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. */ +ifdef(`proto3', `syntax = "proto2";') import "DataConfig.proto"; import "ModelConfig.proto"; -- GitLab From 141e2e9856a12e5194ad409798ef0fbddb911dbf Mon Sep 17 00:00:00 2001 From: zhangjinchao01 Date: Mon, 28 Nov 2016 17:44:43 +0800 Subject: [PATCH 100/347] revise data download path of srl demo --- demo/semantic_role_labeling/data/get_data.sh | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/demo/semantic_role_labeling/data/get_data.sh b/demo/semantic_role_labeling/data/get_data.sh index 55e33f46856..84aecdd9a91 100644 --- a/demo/semantic_role_labeling/data/get_data.sh +++ b/demo/semantic_role_labeling/data/get_data.sh @@ -13,11 +13,11 @@ # See the License for the specific language governing permissions and # limitations under the License. set -e -wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz -wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/verbDict.txt --no-check-certificate -wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/targetDict.txt --no-check-certificate -wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/wordDict.txt --no-check-certificate -wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/emb --no-check-certificate +#wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz +wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt +wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt +wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt +wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/emb tar -xzvf conll05st-tests.tar.gz rm conll05st-tests.tar.gz cp ./conll05st-release/test.wsj/words/test.wsj.words.gz . -- GitLab From 2f8947f751d38118be61941588e5141fb5173ec6 Mon Sep 17 00:00:00 2001 From: zhangjinchao01 Date: Mon, 28 Nov 2016 17:47:18 +0800 Subject: [PATCH 101/347] del comments --- demo/semantic_role_labeling/data/get_data.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/semantic_role_labeling/data/get_data.sh b/demo/semantic_role_labeling/data/get_data.sh index 84aecdd9a91..99487e0d9a8 100644 --- a/demo/semantic_role_labeling/data/get_data.sh +++ b/demo/semantic_role_labeling/data/get_data.sh @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. set -e -#wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz +wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt -- GitLab From ff7b4284f41f31b8ee11db67c06c14e3fb51ae12 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Nov 2016 14:56:25 +0800 Subject: [PATCH 102/347] Add a auto compare for BaseMatrix --- paddle/math/tests/CMakeLists.txt | 1 + paddle/math/tests/TensorCheck.h | 113 +++++++++++++++++++ paddle/math/tests/TestUtils.h | 149 ++++++++++++++++++++++++++ paddle/math/tests/test_BaseMatrix.cpp | 56 ++++++++++ 4 files changed, 319 insertions(+) create mode 100644 paddle/math/tests/TensorCheck.h create mode 100644 paddle/math/tests/TestUtils.h create mode 100644 paddle/math/tests/test_BaseMatrix.cpp diff --git a/paddle/math/tests/CMakeLists.txt b/paddle/math/tests/CMakeLists.txt index 247be983ba3..5ac7888748e 100644 --- a/paddle/math/tests/CMakeLists.txt +++ b/paddle/math/tests/CMakeLists.txt @@ -14,3 +14,4 @@ add_simple_unittest(test_perturbation) add_simple_unittest(test_CpuGpuVector) add_simple_unittest(test_Allocator) add_simple_unittest(test_FPException) +add_simple_unittest(test_BaseMatrix) diff --git a/paddle/math/tests/TensorCheck.h b/paddle/math/tests/TensorCheck.h new file mode 100644 index 00000000000..6ca303cc7c5 --- /dev/null +++ b/paddle/math/tests/TensorCheck.h @@ -0,0 +1,113 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 +#include +#include "paddle/math/Matrix.h" + +using namespace paddle; // NOLINT +using namespace std; // NOLINT + +namespace autotest { + +class CheckEqual { +public: + inline int operator()(real a, real b) { + if (a != b) { + return 1; + } + + return 0; + } +}; + +class CheckWithErr { +public: + CheckWithErr() { +#ifndef PADDLE_TYPE_DOUBLE + err_ = 1e-5; +#else + err_ = 1e-10; +#endif + } + + inline int operator()(real a, real b) { + if (std::fabs(a - b) > err_) { + if ((std::fabs(a - b) / std::fabs(a)) > (err_ / 10.0f)) { + return 1; + } + } + return 0; + } + +private: + real err_; +}; + +template +void TensorCheck(Check op, const CpuMatrix& matrix1, const CpuMatrix& matrix2) { + CHECK(matrix1.getHeight() == matrix2.getHeight()); + CHECK(matrix1.getWidth() == matrix2.getWidth()); + + int height = matrix1.getHeight(); + int width = matrix1.getWidth(); + const real* data1 = matrix1.getData(); + const real* data2 = matrix2.getData(); + int count = 0; + for (int i = 0; i < height; i++) { + for (int j = 0; j < width; j++) { + real a = data1[i * width + j]; + real b = data2[i * width + j]; + count += op(a, b); + } + } + EXPECT_EQ(count, 0) << "There are " << count << " different element."; +} + +template +class CopyToCpu; + +template <> +class CopyToCpu { +public: + explicit CopyToCpu(const CpuMatrix& arg) : arg_(arg) {} + const CpuMatrix& copiedArg() const { return arg_; } + +private: + const CpuMatrix& arg_; +}; + +template <> +class CopyToCpu { +public: + explicit CopyToCpu(const GpuMatrix& arg) + : arg_(arg.getHeight(), arg.getWidth()) { + arg_.copyFrom(arg); + } + CpuMatrix& copiedArg() { return arg_; } + +private: + CpuMatrix arg_; +}; + +template +extern void TensorCheckErr(const Tensor1& tensor1, const Tensor2& tensor2) { + TensorCheck( + CheckWithErr(), + CopyToCpu(tensor1).copiedArg(), + CopyToCpu(tensor2).copiedArg()); +} + +} // namespace autotest + diff --git a/paddle/math/tests/TestUtils.h b/paddle/math/tests/TestUtils.h new file mode 100644 index 00000000000..5c9049cacc2 --- /dev/null +++ b/paddle/math/tests/TestUtils.h @@ -0,0 +1,149 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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. */ + + +/** + * TestUtils.h is used to automatically compare CPU and GPU code is consistent. + * + * Auto compare BaseMatrix member function: + * Use case: + * a. void BaseMatrix::tanh(BaseMatrixT& b); + * Compare method: BaseMatrixCompare<0>(&BaseMatrix::tanh); + * + * b. + * +*/ + +#include +#include "paddle/math/Matrix.h" +#include "TensorCheck.h" + +using namespace paddle; // NOLINT + +namespace autotest { + +template +class ReplaceType { +public: + typedef T1 type; +}; + +template<> +class ReplaceType { +public: + typedef CpuMatrix type; +}; + +template<> +class ReplaceType { +public: + typedef GpuMatrix type; +}; + +// construct a argument +template T construct(int height, int width); +template<> float construct(int height, int width) { return 0.0; } +template<> CpuMatrix construct(int height, int width) { + CpuMatrix a(height, width); + return a; +} +template<> GpuMatrix construct(int height, int width) { + GpuMatrix a(height, width); + return a; +} + +// init a argument +template void init(T& v); +template<> void init(float& v) { v = 0.5; } +template<> void init(CpuMatrix& v) { v.randomizeUniform(); } +template<> void init(GpuMatrix& v) { v.randomizeUniform(); } + +// init a tuple which contains a set of arguments. +template +inline typename std::enable_if::type +initTuple(std::tuple& t){} + +template +inline typename std::enable_if::type +initTuple(std::tuple& t) { + init(std::get(t)); + initTuple(t); +} + +// copy a argument, copy src to dest +template void copy(T1& dest, T2& src); +template<> void copy(float& dest, float& src) { dest = src; } +template<> void copy(GpuMatrix& dest, CpuMatrix& src) { + dest.copyFrom(src); +} + +// copy a tuple, copy src to dest +template +inline typename std::enable_if::type +copyTuple(std::tuple& dest, std::tuple& src) {} + +template +inline typename std::enable_if::type +copyTuple(std::tuple& dest, std::tuple& src) { + copy(std::get(dest), std::get(src)); + copyTuple(dest, src); +} + +// call member function +template +R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) { + return (obj.*f)(args...); +} + +template +void BaseMatrixCompare(R (C::*f)(Args...)) { + for (auto height : {1, 11, 73, 128, 200, 330}) { + for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) { + CpuMatrix obj1(height, width); + GpuMatrix obj2(height, width); + init(obj1); + copy(obj2, obj1); + + auto tuple1 = std::make_tuple( + construct>::type>::type, + CpuMatrix>::type>(height, width)...); + + auto tuple2 = std::make_tuple( + construct>::type>::type, + GpuMatrix>::type>(height, width)...); + + initTuple(tuple1); + copyTuple(tuple2, tuple1); + + call(obj1, f, std::get(tuple1)...); + call(obj2, f, std::get(tuple2)...); + + TensorCheckErr(obj1, obj2); + } + } +} + +} // namespace autotest + +template +void BaseMatrixCompare(R (C::*f)(Args...)) { + static_assert(sizeof...(I) == sizeof...(Args), + "size of parameter packs are not equal"); + + autotest::BaseMatrixCompare(f); +} + diff --git a/paddle/math/tests/test_BaseMatrix.cpp b/paddle/math/tests/test_BaseMatrix.cpp new file mode 100644 index 00000000000..c06580ca82e --- /dev/null +++ b/paddle/math/tests/test_BaseMatrix.cpp @@ -0,0 +1,56 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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. */ + +#ifndef PADDLE_ONLY_CPU +/** + * This test file compares the implementation of CPU and GPU function + * in BaseMatrix.cpp. + */ + +#include +#include "paddle/utils/Util.h" +#include "paddle/math/BaseMatrix.h" +#include "TestUtils.h" + +using namespace paddle; // NOLINT +using namespace std; // NOLINT + +TEST(BaseMatrix, apply) { + // member function with no argument + BaseMatrixCompare(&BaseMatrix::neg); + + // If the member function are overloaded, use static_cast to specify which + // member function need be test. + BaseMatrixCompare( + static_cast(&BaseMatrix::exp)); + BaseMatrixCompare( + static_cast(&BaseMatrix::sqrt)); + + // member function with one argument + + BaseMatrixCompare<0>(&BaseMatrix::tanh); + + BaseMatrixCompare<0>( + static_cast(&BaseMatrix::assign)); + BaseMatrixCompare<0>( + static_cast(&BaseMatrix::pow)); +} + +int main(int argc, char** argv) { + testing::InitGoogleTest(&argc, argv); + initMain(argc, argv); + return RUN_ALL_TESTS(); +} + +#endif -- GitLab From 11f97c93b2a0ffa4a6eb23e7af3439e5d5b8c224 Mon Sep 17 00:00:00 2001 From: liaogang Date: Mon, 28 Nov 2016 19:45:54 +0800 Subject: [PATCH 103/347] Change explicit code into literalinclude syntax --- doc_cn/demo/quick_start/index.rst | 85 ++++--------------------------- 1 file changed, 11 insertions(+), 74 deletions(-) diff --git a/doc_cn/demo/quick_start/index.rst b/doc_cn/demo/quick_start/index.rst index b38b8ca9995..db73cb3f34d 100644 --- a/doc_cn/demo/quick_start/index.rst +++ b/doc_cn/demo/quick_start/index.rst @@ -49,7 +49,7 @@ PaddlePaddle快速入门教程 ./preprocess.sh 数据预处理完成之后，通过配置类似于 ``dataprovider_*.py`` 的数据读取脚本和类似于 ``trainer_config.*.py`` 的训练模型脚本，PaddlePaddle将以设置参数的方式来设置 -相应的数据读取脚本和训练模型脚本。接下来，我们将对这两个步骤给出了详细的解释，你也可以先跳过本文的解释环节，直接进入训练环节, 使用 ``sh train.sh`` 开始训练模型， +相应的数据读取脚本和训练模型脚本。接下来，我们将对这两个步骤给出了详细的解释，你也可以先跳过本文的解释环节，直接进入训练模型章节, 使用 ``sh train.sh`` 开始训练模型， 查看`train.sh`内容，通过 **自底向上法** (bottom-up approach)来帮助你理解PaddlePaddle的内部运行机制。 @@ -66,86 +66,23 @@ Python脚本读取数据 ``dataprovider_bow.py`` 文件给出了完整例子： -.. code-block:: python - - from paddle.trainer.PyDataProvider2 import * - - # id of the word not in dictionary - UNK_IDX = 0 - - # initializer is called by the framework during initialization. - # It allows the user to describe the data types and setup the - # necessary data structure for later use. - # `settings` is an object. initializer need to properly fill settings.input_types. - # initializer can also store other data structures needed to be used at process(). - # In this example, dictionary is stored in settings. - # `dictionay` and `kwargs` are arguments passed from trainer_config.lr.py - def initializer(settings, dictionary, **kwargs): - # Put the word dictionary into settings - settings.word_dict = dictionary +.. literalinclude:: ../../../demo/quick_start/dataprovider_bow.py + :language: python + :lines: 21-70 + :linenos: + :emphasize-lines: 8,33 - # setting.input_types specifies what the data types the data provider - # generates. - settings.input_types = [ - # The first input is a sparse_binary_vector, - # which means each dimension of the vector is either 0 or 1. It is the - # bag-of-words (BOW) representation of the texts. - sparse_binary_vector(len(dictionary)), - # The second input is an integer. It represents the category id of the - # sample. 2 means there are two labels in the dataset. - # (1 for positive and 0 for negative) - integer_value(2)] - - # Delaring a data provider. It has an initializer 'data_initialzer'. - # It will cache the generated data of the first pass in memory, so that - # during later pass, no on-the-fly data generation will be needed. - # `setting` is the same object used by initializer() - # `file_name` is the name of a file listed train_list or test_list file given - # to define_py_data_sources2(). See trainer_config.lr.py. - @provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM) - def process(settings, file_name): - # Open the input data file. - with open(file_name, 'r') as f: - # Read each line. - for line in f: - # Each line contains the label and text of the comment, separated by \t. - label, comment = line.strip().split('\t') - - # Split the words into a list. - words = comment.split() - - # convert the words into a list of ids by looking them up in word_dict. - word_vector = [settings.word_dict.get(w, UNK_IDX) for w in words] - - # Return the features for the current comment. The first is a list - # of ids representing a 0-1 binary sparse vector of the text, - # the second is the integer id of the label. - yield word_vector, int(label) 配置中的数据加载定义 -------------------- 在模型配置中通过 ``define_py_data_sources2`` 接口来加载数据： -.. code-block:: python - - from paddle.trainer_config_helpers import * - - file = "data/dict.txt" - word_dict = dict() - with open(dict_file, 'r') as f: - for i, line in enumerate(f): - w = line.strip().split()[0] - word_dict[w] = i - # define the data sources for the model. - # We need to use different process for training and prediction. - # For training, the input data includes both word IDs and labels. - # For prediction, the input data only includs word Ids. - define_py_data_sources2(train_list='data/train.list', - test_list='data/test.list', - module="dataprovider_bow", - obj="process", - args={"dictionary": word_dict}) +.. literalinclude:: ../../../demo/quick_start/trainer_config.emb.py + :language: python + :lines: 19-35 + :linenos: + :emphasize-lines: 12 以下是对上述数据加载的解释： -- GitLab From 1f743d381cfb5dc80c25d4787ea1a703eb3ba07d Mon Sep 17 00:00:00 2001 From: wangyanfei01 Date: Mon, 28 Nov 2016 19:55:34 +0800 Subject: [PATCH 104/347] Redesign test_period meaning: * always do test on all test data * do test at the end of each pass if test_period=0, otherwise do test if test_period batches passed --- doc/ui/cmd_argument/argument_outline.md | 11 ++---- doc/ui/cmd_argument/detail_introduction.md | 12 +----- doc/ui/cmd_argument/use_case.md | 4 +- paddle/trainer/Tester.cpp | 20 ++-------- paddle/trainer/Tester.h | 2 +- paddle/trainer/TesterConfig.h | 12 +----- paddle/trainer/Trainer.cpp | 46 +++++++++------------- 7 files changed, 29 insertions(+), 78 deletions(-) diff --git a/doc/ui/cmd_argument/argument_outline.md b/doc/ui/cmd_argument/argument_outline.md index bafa5dfef2c..013edbc9047 100644 --- a/doc/ui/cmd_argument/argument_outline.md +++ b/doc/ui/cmd_argument/argument_outline.md @@ -68,7 +68,7 @@ It looks like there are a lot of arguments. However, most of them are for develo -test_period_while_training +test_period √√ @@ -143,13 +143,8 @@ It looks like there are a lot of arguments. However, most of them are for develo -testing during trainingtest_batches_while_training -√√√< - - - -testing during trainingtest_batches_while_end -√√√< +testing during trainingtest_period +√√ diff --git a/doc/ui/cmd_argument/detail_introduction.md b/doc/ui/cmd_argument/detail_introduction.md index 1f7e406a53e..823a2266191 100644 --- a/doc/ui/cmd_argument/detail_introduction.md +++ b/doc/ui/cmd_argument/detail_introduction.md @@ -109,8 +109,8 @@ - Load parameter from this pass to test. - type: int32 (default: -1). -* `--test_period_while_training` - - Run test every test_period_while_training batches while doing training. If not 0, test test_batches_while_training batches, if 0, test nothing. +* `--test_period` + - if equal 0, do test on all test data at the end of each pass while if equal non-zero, do test on all test data once each test_period batches passed while training is going on. - type: int32 (default: 0). * `--test_wait` @@ -121,14 +121,6 @@ - File that saves the model list when testing. It was set automatically when using cluster submitting environment after setting model_path. - type: string (default: "", null). -* `--test_batches_while_training` - - Test test_batches_while_training batches if test_batches_while_training != 0 while doing training. If 0, test on all test data. - - type: bool (default: 1000). - -* `--test_batches_while_end` - - Test test_batches_while_end batches if test_batches_while_end != 0 at pass end. If 0, test on all test data. - - type: bool (default: 0). - * `--predict_output_dir` - Directory that saves the layer output. It is configured in Outputs() in network config. Default, this argument is null, meaning save nothing. Specify this directory if you want to save feature map of some layers in testing mode. Note that, layer outputs are values after activation function. - type: string (default: "", null). diff --git a/doc/ui/cmd_argument/use_case.md b/doc/ui/cmd_argument/use_case.md index b243560106d..4d7bb33f36f 100644 --- a/doc/ui/cmd_argument/use_case.md +++ b/doc/ui/cmd_argument/use_case.md @@ -10,9 +10,7 @@ paddle train \ --config=network_config \ --save_dir=output \ --trainer_count=COUNT \ #(default:1) - --test_period_while_training=M \ #(default:0) - --test_batches_while_training=BATCHES \#(default:1000) - --test_batches_while_end=BATCHES \ #(default:0) + --test_period=M \ #(default:0) --num_passes=N \ #(defalut:100) --log_period=K \ #(default:100) --dot_period=1000 \ #(default:1) diff --git a/paddle/trainer/Tester.cpp b/paddle/trainer/Tester.cpp index f57e09d40af..217b8c60be5 100644 --- a/paddle/trainer/Tester.cpp +++ b/paddle/trainer/Tester.cpp @@ -90,20 +90,11 @@ void Tester::testOneDataBatch( testContext_.numSamples += dataBatch.getSize(); } -void Tester::testOnePeriod(bool finishPass) { +void Tester::testOnePeriod() { DataBatch dataBatch; int64_t batchSize = config_->getOptConfig().batch_size(); - bool testAllData = - (!finishPass && !intconfig_->testBatchesWhileTraining) || - (finishPass && !intconfig_->testBatchesWhileEnd); - int batches; - if (testAllData) { - batches = std::numeric_limits::max(); - } else { - batches = finishPass ? - intconfig_->testBatchesWhileEnd : intconfig_->testBatchesWhileTraining; - } + int batches = std::numeric_limits::max(); std::vector outArgs; @@ -115,12 +106,7 @@ void Tester::testOnePeriod(bool finishPass) { if (intconfig_->prevBatchState) { gradientMachine_->resetState(); } - if ((!finishPass && !intconfig_->testBatchesWhileTraining) || - (finishPass && !intconfig_->testBatchesWhileEnd)) { - break; - } else { - num = testDataProvider_->getNextBatch(batchSize, &dataBatch); - } + break; } testOneDataBatch(dataBatch, &outArgs); } diff --git a/paddle/trainer/Tester.h b/paddle/trainer/Tester.h index 21e11422aa4..671ffc5220e 100644 --- a/paddle/trainer/Tester.h +++ b/paddle/trainer/Tester.h @@ -67,7 +67,7 @@ public: * It is convenience to test small set of data when test data set is large and * is training at same time. */ - void testOnePeriod(bool finishPass = true); + void testOnePeriod(); void startTestPeriod(); void finishTestPeriod(); void testOneDataBatch(const DataBatch& dataBatch, diff --git a/paddle/trainer/TesterConfig.h b/paddle/trainer/TesterConfig.h index b7b550dec7c..8392bbcda51 100644 --- a/paddle/trainer/TesterConfig.h +++ b/paddle/trainer/TesterConfig.h @@ -38,17 +38,7 @@ struct TesterConfig { /** * indicate test period */ - int testPeriodWhileTraining; - - /** - * indicate how many batches are used for testing under training - */ - bool testBatchesWhileTraining; - - /** - * indicate how many batches are used for testing at pass end - */ - bool testBatchesWhileEnd; + int testPeriod; /** * indicate whether to save previous batch state diff --git a/paddle/trainer/Trainer.cpp b/paddle/trainer/Trainer.cpp index 507a080cc4f..aca896770ae 100644 --- a/paddle/trainer/Trainer.cpp +++ b/paddle/trainer/Trainer.cpp @@ -42,24 +42,13 @@ limitations under the License. */ P_DEFINE_string(config, "", "Trainer config file"); P_DEFINE_int32(test_period, 0, - "This option was deprecated, use test_period_while_training " - " instead. "); -P_DEFINE_int32(test_period_while_training, 0, - "Run test every test_period_while_training batches." - " If not 0, test test_batches_while_training batches." - " If 0, test nothing."); -P_DEFINE_int32(test_batches_while_training, 1000, - "test test_batches_while_training batches if " - "test_batches_while_training != 0." - " If 0, test on all test data"); -P_DEFINE_int32(test_batches_while_end, 0, - "test test_batches_while_end batches at pass end." - " Always run test at pass end." - " If not 0, test test_batches_while_end batches." - " If 0, test on all test data."); + "if equal 0, do test on all test data at the end of " + "each pass while if equal non-zero, do test on all test " + "data once each test_period batches passed while " + "training is going on"); P_DEFINE_bool(test_all_data_in_one_period, false, - "This option was deprecated, use test_batches_while_training " - "and test_batches_while_end instead"); + "This option was deprecated, since we will always do " + "test on all test set "); P_DEFINE_bool(local, true, "Train in local mode or not"); @@ -467,9 +456,9 @@ void Trainer::trainOneDataBatch(DataBatch& dataBatch) { FOR_TIMING(globalStat.reset()); } - if (testDataProvider_ && FLAGS_test_period_while_training > 0 && - trainPassContext_.batchId % FLAGS_test_period_while_training == 0) { - tester_->testOnePeriod(false); + if (testDataProvider_ && FLAGS_test_period > 0 && + trainPassContext_.batchId % FLAGS_test_period == 0) { + tester_->testOnePeriod(); } if (FLAGS_saving_period_by_batches > 0 && @@ -478,7 +467,7 @@ void Trainer::trainOneDataBatch(DataBatch& dataBatch) { 0 == FLAGS_trainer_id) { trainerInternal_.getParameterUpdater()->catchUpWith(); if (testDataProvider_) { - tester_->testOnePeriod(false); + tester_->testOnePeriod(); } paramUtil_->saveParametersOnePass( trainPassContext_.passId, trainPassContext_.passInnerId); @@ -636,17 +625,18 @@ std::unique_ptr Trainer::createTesterConfig() { TesterConfig* conf = new TesterConfig; if (FLAGS_test_period) { LOG(WARNING) - << "--test_period was deprecated, use --test_period_while_training" - << "--test_batches_while_training --test_batches_while_end instead."; + << "The meaning of --test_period is changed: " + << "if equal 0, do test on all test data at the end of " + << "each pass while if equal non-zero, do test on all test " + << "data once each test_period batches passed while " + << "training is going on"; } if (FLAGS_test_all_data_in_one_period) { LOG(WARNING) - << "--test_all_data_in_one_period was deprecated, use" - << " --test_batches_while_training and --test_batches_while_end instead"; + << "--test_all_data_in_one_period was deprecated, since " + << "we will always do test on all test set "; } - conf->testPeriodWhileTraining = FLAGS_test_period_while_training; - conf->testBatchesWhileTraining = FLAGS_test_batches_while_training; - conf->testBatchesWhileEnd = FLAGS_test_batches_while_end; + conf->testPeriod = FLAGS_test_period; conf->prevBatchState = FLAGS_prev_batch_state; conf->logPeriod = FLAGS_log_period; conf->loadsaveParametersInPserver = FLAGS_loadsave_parameters_in_pserver; -- GitLab From 85e0cd709c1a9efea413975d66af1df62baa9b45 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Nov 2016 20:46:15 +0800 Subject: [PATCH 105/347] move some BaseMatrix test from test_matrixCompare.cpp to test_BaseMatrix.cpp --- paddle/math/tests/TensorCheck.h | 89 ++++--- paddle/math/tests/TestUtils.h | 51 +++- paddle/math/tests/test_BaseMatrix.cpp | 229 +++++++++++++++-- paddle/math/tests/test_matrixCompare.cpp | 298 ----------------------- 4 files changed, 300 insertions(+), 367 deletions(-) diff --git a/paddle/math/tests/TensorCheck.h b/paddle/math/tests/TensorCheck.h index 6ca303cc7c5..f19933d1f6b 100644 --- a/paddle/math/tests/TensorCheck.h +++ b/paddle/math/tests/TensorCheck.h @@ -21,60 +21,29 @@ using namespace std; // NOLINT namespace autotest { -class CheckEqual { +class AssertEqual { public: - inline int operator()(real a, real b) { - if (a != b) { - return 1; - } - - return 0; - } -}; + AssertEqual(real err = 0) : err_(err) {} -class CheckWithErr { -public: - CheckWithErr() { -#ifndef PADDLE_TYPE_DOUBLE - err_ = 1e-5; -#else - err_ = 1e-10; -#endif - } - - inline int operator()(real a, real b) { - if (std::fabs(a - b) > err_) { - if ((std::fabs(a - b) / std::fabs(a)) > (err_ / 10.0f)) { - return 1; + inline bool operator()(real a, real b) { + if (err_ == 0) { + if (a != b) { + return false; + } + } else { + if (std::fabs(a - b) > err_) { + if ((std::fabs(a - b) / std::fabs(a)) > (err_ / 10.0f)) { + return false; + } } } - return 0; + return true; } private: real err_; }; -template -void TensorCheck(Check op, const CpuMatrix& matrix1, const CpuMatrix& matrix2) { - CHECK(matrix1.getHeight() == matrix2.getHeight()); - CHECK(matrix1.getWidth() == matrix2.getWidth()); - - int height = matrix1.getHeight(); - int width = matrix1.getWidth(); - const real* data1 = matrix1.getData(); - const real* data2 = matrix2.getData(); - int count = 0; - for (int i = 0; i < height; i++) { - for (int j = 0; j < width; j++) { - real a = data1[i * width + j]; - real b = data2[i * width + j]; - count += op(a, b); - } - } - EXPECT_EQ(count, 0) << "There are " << count << " different element."; -} - template class CopyToCpu; @@ -101,10 +70,36 @@ private: CpuMatrix arg_; }; -template -extern void TensorCheckErr(const Tensor1& tensor1, const Tensor2& tensor2) { +template +void TensorCheck(AssertEq compare, + const CpuMatrix& matrix1, + const CpuMatrix& matrix2) { + CHECK(matrix1.getHeight() == matrix2.getHeight()); + CHECK(matrix1.getWidth() == matrix2.getWidth()); + + int height = matrix1.getHeight(); + int width = matrix1.getWidth(); + const real* data1 = matrix1.getData(); + const real* data2 = matrix2.getData(); + int count = 0; + for (int i = 0; i < height; i++) { + for (int j = 0; j < width; j++) { + real a = data1[i * width + j]; + real b = data2[i * width + j]; + if (!compare(a, b)) { + count++; + } + } + } + EXPECT_EQ(count, 0) << "There are " << count << " different element."; +} + +template +extern void TensorCheck(AssertEq compare, + const Tensor1& tensor1, + const Tensor2& tensor2) { TensorCheck( - CheckWithErr(), + compare, CopyToCpu(tensor1).copiedArg(), CopyToCpu(tensor2).copiedArg()); } diff --git a/paddle/math/tests/TestUtils.h b/paddle/math/tests/TestUtils.h index 5c9049cacc2..ffd11345427 100644 --- a/paddle/math/tests/TestUtils.h +++ b/paddle/math/tests/TestUtils.h @@ -107,12 +107,16 @@ R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) { return (obj.*f)(args...); } -template -void BaseMatrixCompare(R (C::*f)(Args...)) { +template +void BaseMatrixCompare(R (C::*f)(Args...), AssertEq compare) { for (auto height : {1, 11, 73, 128, 200, 330}) { - for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) { - CpuMatrix obj1(height, width); - GpuMatrix obj2(height, width); + for (auto width : {1, 3, 32, 100, 512, 1000}) { + CpuMatrix obj1(ApplyCol ? 1 : height, + ApplyRow ? 1 : width); + GpuMatrix obj2(ApplyCol ? 1 : height, + ApplyRow ? 1 : width); init(obj1); copy(obj2, obj1); @@ -132,7 +136,7 @@ void BaseMatrixCompare(R (C::*f)(Args...)) { call(obj1, f, std::get(tuple1)...); call(obj2, f, std::get(tuple2)...); - TensorCheckErr(obj1, obj2); + TensorCheck(compare, obj1, obj2); } } } @@ -144,6 +148,39 @@ void BaseMatrixCompare(R (C::*f)(Args...)) { static_assert(sizeof...(I) == sizeof...(Args), "size of parameter packs are not equal"); - autotest::BaseMatrixCompare(f); +#ifndef PADDLE_TYPE_DOUBLE + autotest::AssertEqual compare(1e-5); +#else + autotest::AssertEqual compare(1e-10); +#endif + + autotest::BaseMatrixCompare(f, compare); +} + +template +void BaseMatrixApplyRow(R (C::*f)(Args...)) { + static_assert(sizeof...(I) == sizeof...(Args), + "size of parameter packs are not equal"); + +#ifndef PADDLE_TYPE_DOUBLE + autotest::AssertEqual compare(1e-3); +#else + autotest::AssertEqual compare(1e-8); +#endif + + autotest::BaseMatrixCompare(f, compare); +} + +template +void BaseMatrixApplyCol(R (C::*f)(Args...)) { + static_assert(sizeof...(I) == sizeof...(Args), + "size of parameter packs are not equal"); + +#ifndef PADDLE_TYPE_DOUBLE + autotest::AssertEqual compare(1e-3); +#else + autotest::AssertEqual compare(1e-8); +#endif + autotest::BaseMatrixCompare(f, compare); } diff --git a/paddle/math/tests/test_BaseMatrix.cpp b/paddle/math/tests/test_BaseMatrix.cpp index c06580ca82e..99ab640f48f 100644 --- a/paddle/math/tests/test_BaseMatrix.cpp +++ b/paddle/math/tests/test_BaseMatrix.cpp @@ -26,25 +26,224 @@ limitations under the License. */ using namespace paddle; // NOLINT using namespace std; // NOLINT -TEST(BaseMatrix, apply) { - // member function with no argument - BaseMatrixCompare(&BaseMatrix::neg); +/** + * Test member functions which prototype is + * void (BaseMatrix::*)(). + */ +TEST(BaseMatrix, void) { + typedef void (BaseMatrix::*FunctionProto)(); + #define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare(static_cast(&BaseMatrix::function)); + + BASEMATRIXCOMPARE(neg); + BASEMATRIXCOMPARE(exp); + BASEMATRIXCOMPARE(log); + BASEMATRIXCOMPARE(sqrt); + BASEMATRIXCOMPARE(square); + BASEMATRIXCOMPARE(reciprocal); + BASEMATRIXCOMPARE(abs); + BASEMATRIXCOMPARE(sign); + BASEMATRIXCOMPARE(zero); + BASEMATRIXCOMPARE(one); + + #undef BASEMATRIXCOMPARE +} + +/** + * Test member functions which prototype is + * void (BaseMatrix::*)(real). + */ +TEST(BaseMatrix, real) { + typedef void (BaseMatrix::*FunctionProto)(real); + #define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0>(static_cast(&BaseMatrix::function)); + + BASEMATRIXCOMPARE(pow); + BASEMATRIXCOMPARE(subScalar); + BASEMATRIXCOMPARE(mulScalar); + BASEMATRIXCOMPARE(divScalar); + BASEMATRIXCOMPARE(assign); + BASEMATRIXCOMPARE(add); + BASEMATRIXCOMPARE(biggerThanScalar); + BASEMATRIXCOMPARE(downClip); + + #undef BASEMATRIXCOMPARE +} + +/** + * Test member functions which prototype is + * void (BaseMatrix::*)(real, real). + */ +TEST(BaseMatrix, real_real) { + typedef void (BaseMatrix::*FunctionProto)(real, real); + #define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); + + BASEMATRIXCOMPARE(add); + BASEMATRIXCOMPARE(clip); + + #undef BASEMATRIXCOMPARE +} + +/** + * Test member functions which prototype is + * void (BaseMatrix::*)(BaseMatrix&). + */ +TEST(BaseMatrix, BaseMatrix) { + typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&); + #define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0>(static_cast(&BaseMatrix::function)); + + BASEMATRIXCOMPARE(assign); + BASEMATRIXCOMPARE(add); + BASEMATRIXCOMPARE(relu); + BASEMATRIXCOMPARE(reluDerivative); + BASEMATRIXCOMPARE(softrelu); + BASEMATRIXCOMPARE(softreluDerivative); + BASEMATRIXCOMPARE(brelu); + BASEMATRIXCOMPARE(breluDerivative); + BASEMATRIXCOMPARE(square); + BASEMATRIXCOMPARE(squareDerivative); + BASEMATRIXCOMPARE(tanh); + BASEMATRIXCOMPARE(tanhDerivative); + + BASEMATRIXCOMPARE(reciprocal); + BASEMATRIXCOMPARE(reciprocalDerivative); + BASEMATRIXCOMPARE(abs); + BASEMATRIXCOMPARE(absDerivative); + BASEMATRIXCOMPARE(sigmoid); + BASEMATRIXCOMPARE(sigmoidDerivative); + BASEMATRIXCOMPARE(expDerivative); + BASEMATRIXCOMPARE(sign); + BASEMATRIXCOMPARE(exp); + BASEMATRIXCOMPARE(log); + BASEMATRIXCOMPARE(sqrt); + BASEMATRIXCOMPARE(dotMul); + BASEMATRIXCOMPARE(dotMulSquare); + BASEMATRIXCOMPARE(dotSquareMul); + + BASEMATRIXCOMPARE(addColVector); + BASEMATRIXCOMPARE(addRowVector); + BASEMATRIXCOMPARE(mulRowVector); + BASEMATRIXCOMPARE(divRowVector); + BASEMATRIXCOMPARE(addP2P); + BASEMATRIXCOMPARE(invSqrt); + + #undef BASEMATRIXCOMPARE +} + +/** + * Test member functions which prototype is + * void (BaseMatrix::*)(BaseMatrix&, real). + */ +TEST(BaseMatrix, BaseMatrix_real) { + typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, real); + #define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); - // If the member function are overloaded, use static_cast to specify which - // member function need be test. - BaseMatrixCompare( - static_cast(&BaseMatrix::exp)); - BaseMatrixCompare( - static_cast(&BaseMatrix::sqrt)); + BASEMATRIXCOMPARE(addBias); + BASEMATRIXCOMPARE(add); + BASEMATRIXCOMPARE(sub); + BASEMATRIXCOMPARE(pow); + BASEMATRIXCOMPARE(addScalar); + BASEMATRIXCOMPARE(subScalar); + BASEMATRIXCOMPARE(mulScalar); + BASEMATRIXCOMPARE(divScalar); + BASEMATRIXCOMPARE(scalarDiv); + BASEMATRIXCOMPARE(addSquare); + + BASEMATRIXCOMPARE(isEqualTo); + + #undef BASEMATRIXCOMPARE +} + +/** + * Test member functions which prototype is + * void (BaseMatrix::*)(BaseMatrix&, BaseMatrix&). + */ +TEST(BaseMatrix, BaseMatrix_BaseMatrix) { + typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, BaseMatrix&); + #define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); + + BASEMATRIXCOMPARE(softCrossEntropy); + BASEMATRIXCOMPARE(softCrossEntropyBp); + BASEMATRIXCOMPARE(binaryLabelCrossEntropy); + BASEMATRIXCOMPARE(binaryLabelCrossEntropyBp); + BASEMATRIXCOMPARE(sub); + BASEMATRIXCOMPARE(add2); + BASEMATRIXCOMPARE(dotMul); + BASEMATRIXCOMPARE(dotDiv); + BASEMATRIXCOMPARE(logisticRegressionLoss); + BASEMATRIXCOMPARE(logisticRegressionLossBp); + BASEMATRIXCOMPARE(biggerThan); + BASEMATRIXCOMPARE(max); + BASEMATRIXCOMPARE(dotMulSquare); + BASEMATRIXCOMPARE(dotSquareSquare); + + #undef BASEMATRIXCOMPARE +} + +/** + * Test aggregate member functions which prototype is + * void (BaseMatrix::*)(BaseMatrix&). + */ +TEST(Aggregate, BaseMatrix) { + typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&); + #define BASEMATRIXAPPLYROW(function) \ + BaseMatrixApplyRow<0>(static_cast(&BaseMatrix::function)); + + #define BASEMATRIXAPPLYCOL(function) \ + BaseMatrixApplyCol<0>(static_cast(&BaseMatrix::function)); + + BASEMATRIXAPPLYROW(maxRows); + BASEMATRIXAPPLYROW(minRows); + + BASEMATRIXAPPLYCOL(sumCols); + BASEMATRIXAPPLYCOL(maxCols); + BASEMATRIXAPPLYCOL(minCols); + + #undef BASEMATRIXAPPLYROW + #undef BASEMATRIXAPPLYCOL +} + +/** + * Test aggregate member functions which prototype is + * void (BaseMatrix::*)(BaseMatrix&, BaseMatrix&). + */ +TEST(Aggregate, BaseMatrix_BaseMatrix) { + typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, BaseMatrix&); + #define BASEMATRIXAPPLYROW(function) \ + BaseMatrixApplyRow<0, 1>(static_cast(&BaseMatrix::function)); + + #define BASEMATRIXAPPLYCOL(function) \ + BaseMatrixApplyCol<0, 1>(static_cast(&BaseMatrix::function)); + + BASEMATRIXAPPLYCOL(addDotMulVMM); + + #undef BASEMATRIXAPPLYROW + #undef BASEMATRIXAPPLYCOL +} + +/** + * Test aggregate member functions which prototype is + * void (BaseMatrix::*)(BaseMatrix&, real, real). + */ +TEST(Aggregate, BaseMatrix_real_real) { + typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, real, real); + #define BASEMATRIXAPPLYROW(function) \ + BaseMatrixApplyRow<0, 1, 2>(\ + static_cast(&BaseMatrix::function)); - // member function with one argument + #define BASEMATRIXAPPLYCOL(function) \ + BaseMatrixApplyCol<0, 1, 2>(\ + static_cast(&BaseMatrix::function)); - BaseMatrixCompare<0>(&BaseMatrix::tanh); + BASEMATRIXAPPLYROW(sumRows); + BASEMATRIXAPPLYCOL(sumCols); - BaseMatrixCompare<0>( - static_cast(&BaseMatrix::assign)); - BaseMatrixCompare<0>( - static_cast(&BaseMatrix::pow)); + #undef BASEMATRIXAPPLYROW + #undef BASEMATRIXAPPLYCOL } int main(int argc, char** argv) { diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index ae5bc5a86a1..8f96672b88e 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -448,125 +448,6 @@ void testMatrixZeroAtOffset(int height, int width) { MatrixCheckEqual(*cpuA, *cpuTest); } -void testMatrixBinaryAdd(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - cpuA->add(*cpuB); - gpuA->add(*gpuB); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckEqual(*cpuA, *outputCheck); -} - -void testMatrixAssign(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - - cpuA->randomizeUniform(); - gpuA->copyFrom(*cpuA); - cpuA->assign(2.5); - gpuA->assign(2.5); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckEqual(*cpuA, *outputCheck); -} - -void testMatrixAdd(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - - cpuA->randomizeUniform(); - gpuA->copyFrom(*cpuA); - cpuA->add(2.5); - gpuA->add(2.5); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckEqual(*cpuA, *outputCheck); -} - -void testMatrixSqrt(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - - cpuA->randomizeUniform(); - gpuA->copyFrom(*cpuA); - cpuA->sqrt(); - gpuA->sqrt(); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixTanhDerivative(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - cpuA->tanhDerivative(*cpuB); - gpuA->tanhDerivative(*gpuB); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixTanh(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - cpuA->tanh(*cpuB); - gpuA->tanh(*gpuB); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixTernarySub(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - - cpuA->sub(*cpuB, *cpuC); - gpuA->sub(*gpuB, *gpuC); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckEqual(*cpuA, *outputCheck); -} - void testMatrixSumOfSquaresBp(int height, int width) { MatrixPtr cpuA = std::make_shared(height, width); MatrixPtr cpuB = std::make_shared(height, width); @@ -789,18 +670,7 @@ TEST(Matrix, unary) { for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) { VLOG(3) << " height=" << height << " width=" << width; - // applyUnary - testMatrixAssign(height, width); - testMatrixAdd(height, width); - testMatrixSqrt(height, width); - - // applyBinary - testMatrixBinaryAdd(height, width); - testMatrixTanh(height, width); - testMatrixTanhDerivative(height, width); - // applyTernary - testMatrixTernarySub(height, width); testMatrixSumOfSquaresBp(height, width); // asRowVector @@ -931,165 +801,6 @@ TEST(Matrix, softmax) { } } -void testMatrixAddDotMulVMM(int height, int width, int endCol = 0) { - MatrixPtr cpuA = std::make_shared(1, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(1, width); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - MatrixPtr cpuA1 = std::make_shared(1, width); - MatrixPtr cpuB1 = std::make_shared(height, width); - MatrixPtr cpuC1 = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - cpuA1->copyFrom(*cpuA); - cpuB1->copyFrom(*cpuB); - cpuC1->copyFrom(*cpuC); - - if (!endCol) { - cpuA->addDotMulVMM(*cpuB, *cpuC); - gpuA->addDotMulVMM(*gpuB, *gpuC); - cpuA1->addDotMulVMM2(*cpuB1, *cpuC1); - - MatrixCheckErr(*cpuA, *cpuA1); - } else { - MatrixPtr subCpuA = cpuA->subColMatrix(0, endCol); - MatrixPtr subCpuB = cpuB->subColMatrix(0, endCol); - MatrixPtr subCpuC = cpuC->subColMatrix(0, endCol); - MatrixPtr subGpuA = gpuA->subColMatrix(0, endCol); - MatrixPtr subGpuB = gpuB->subColMatrix(0, endCol); - MatrixPtr subGpuC = gpuC->subColMatrix(0, endCol); - subCpuA->addDotMulVMM(*subCpuB, *subCpuC); - subGpuA->addDotMulVMM(*subGpuB, *subGpuC); - } - - MatrixPtr outputCheck = std::make_shared(1, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixRowSum(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, 1); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, 1); - MatrixPtr gpuB = std::make_shared(height, width); - - MatrixPtr cpuA1 = std::make_shared(height, 1); - MatrixPtr cpuB1 = std::make_shared(height, width); - MatrixPtr gpuA1 = std::make_shared(height, 1); - MatrixPtr gpuB1 = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - cpuA1->copyFrom(*cpuA); - cpuB1->copyFrom(*cpuB); - gpuA1->copyFrom(*cpuA); - gpuB1->copyFrom(*cpuB); - - cpuA->colMerge(*cpuB); - gpuA->colMerge(*gpuB); - - cpuB1->rowSum(*cpuA1); - gpuB1->rowSum(*gpuA1); - - MatrixPtr outputCheck = std::make_shared(height, 1); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); - outputCheck->copyFrom(*gpuA1); - MatrixCheckErr(*cpuA1, *outputCheck); -} - -void testMatrixRowMax(int height, int width, int endCol = 0) { - MatrixPtr cpuA = std::make_shared(height, 1); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, 1); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - - if (!endCol) { - cpuB->rowMax(*cpuA); - gpuB->rowMax(*gpuA); - } else { - MatrixPtr subCpuB = cpuB->subColMatrix(0, endCol); - MatrixPtr subGpuB = gpuB->subColMatrix(0, endCol); - subCpuB->rowMax(*cpuA); - subGpuB->rowMax(*gpuA); - } - - MatrixPtr outputCheck = std::make_shared(height, 1); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixColSum(int height, int width, int endCol = 0) { - MatrixPtr cpuA = std::make_shared(1, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(1, width); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - - if (!endCol) { - cpuA->accumulateColSum(*cpuB); - gpuA->accumulateColSum(*gpuB); - } else { - MatrixPtr subCpuA = cpuA->subColMatrix(0, endCol); - MatrixPtr subGpuA = gpuA->subColMatrix(0, endCol); - MatrixPtr subCpuB = cpuB->subColMatrix(0, endCol); - MatrixPtr subGpuB = gpuB->subColMatrix(0, endCol); - subCpuA->accumulateColSum(*subCpuB); - subGpuA->accumulateColSum(*subGpuB); - } - - MatrixPtr outputCheck = std::make_shared(1, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixColMax(int height, int width, int endCol = 0) { - MatrixPtr cpuA = std::make_shared(1, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(1, width); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - - if (!endCol) { - cpuB->colMax(*cpuA); - gpuB->colMax(*gpuA); - } else { - MatrixPtr subCpuA = cpuA->subColMatrix(0, endCol); - MatrixPtr subGpuA = gpuA->subColMatrix(0, endCol); - MatrixPtr subCpuB = cpuB->subColMatrix(0, endCol); - MatrixPtr subGpuB = gpuB->subColMatrix(0, endCol); - subCpuB->colMax(*subCpuA); - subGpuB->colMax(*subGpuA); - } - - MatrixPtr outputCheck = std::make_shared(1, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - void testMatrixCollectBias(int height, int width) { MatrixPtr cpuA = std::make_shared(1, width); MatrixPtr cpuB = std::make_shared(height, width); @@ -1182,13 +893,8 @@ TEST(Matrix, aggregate) { for (auto height : {1, 11, 16, 32, 64, 73, 128, 200, 1024, 2345}) { for (auto width : {1, 9, 16, 32, 64, 100, 512, 1000, 1024, 2453}) { VLOG(3) << " height=" << height << " width=" << width; - testMatrixRowSum(height, width); - testMatrixRowMax(height, width); - testMatrixColSum(height, width); - testMatrixColMax(height, width); testMatrixCollectBias(height, width); testMatrixTernaryRowDotMul(height, width); - testMatrixAddDotMulVMM(height, width); testMatrixSumOfSquares(height, width); testMatrixBinaryClassificationError(height, width); @@ -1203,11 +909,7 @@ TEST(Matrix, aggregate2) { VLOG(3) << " height=" << height << " width=" << width; int endCol = rand() % width; // NOLINT - testMatrixRowMax(height, width, endCol); testMatrixSumOfSquares(height, width, endCol); - testMatrixColSum(height, width, endCol); - testMatrixColMax(height, width, endCol); - testMatrixAddDotMulVMM(height, width, endCol); } } } -- GitLab From 1bac8e60c3dd10f6a7de031d49ef9b9c6f4d1d15 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Nov 2016 21:27:57 +0800 Subject: [PATCH 106/347] modify the file name test_matrix.cpp to test_SparseMatrix.cpp --- paddle/math/tests/{test_matrix.cpp => test_SparseMatrix.cpp} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename paddle/math/tests/{test_matrix.cpp => test_SparseMatrix.cpp} (100%) diff --git a/paddle/math/tests/test_matrix.cpp b/paddle/math/tests/test_SparseMatrix.cpp similarity index 100% rename from paddle/math/tests/test_matrix.cpp rename to paddle/math/tests/test_SparseMatrix.cpp -- GitLab From 1873945dc7fdf33345dccf986c2f1eb02ad2fb24 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Tue, 29 Nov 2016 00:53:16 +0800 Subject: [PATCH 107/347] Add test_Matrix.cpp for auto compare member functions of class Matrix --- paddle/math/tests/CMakeLists.txt | 5 +- paddle/math/tests/TensorCheck.h | 21 +-- paddle/math/tests/TestUtils.h | 172 ++++++++++++++++------- paddle/math/tests/test_Matrix.cpp | 49 +++++++ paddle/math/tests/test_matrixCompare.cpp | 19 --- 5 files changed, 185 insertions(+), 81 deletions(-) create mode 100644 paddle/math/tests/test_Matrix.cpp diff --git a/paddle/math/tests/CMakeLists.txt b/paddle/math/tests/CMakeLists.txt index d593fe0fa39..893597b1587 100644 --- a/paddle/math/tests/CMakeLists.txt +++ b/paddle/math/tests/CMakeLists.txt @@ -2,7 +2,7 @@ add_simple_unittest(test_ExecViaCpu) add_simple_unittest(test_SIMDFunctions) -add_simple_unittest(test_matrix) +add_simple_unittest(test_SparseMatrix) # TODO(yuyang18): Refactor TestUtil.cpp. Remove this cross module reference. add_unittest(test_matrixCompare @@ -14,5 +14,6 @@ add_simple_unittest(test_perturbation) add_simple_unittest(test_CpuGpuVector) add_simple_unittest(test_Allocator) add_simple_unittest(test_FPException) -add_simple_unittest(test_BaseMatrix) add_simple_unittest(test_GpuProfiler) +add_simple_unittest(test_BaseMatrix) +add_simple_unittest(test_Matrix) diff --git a/paddle/math/tests/TensorCheck.h b/paddle/math/tests/TensorCheck.h index f19933d1f6b..d4821314f3b 100644 --- a/paddle/math/tests/TensorCheck.h +++ b/paddle/math/tests/TensorCheck.h @@ -37,6 +37,7 @@ public: } } } + return true; } @@ -61,7 +62,7 @@ template <> class CopyToCpu { public: explicit CopyToCpu(const GpuMatrix& arg) - : arg_(arg.getHeight(), arg.getWidth()) { + : arg_(arg.getHeight(), arg.getWidth()) { arg_.copyFrom(arg); } CpuMatrix& copiedArg() { return arg_; } @@ -70,7 +71,7 @@ private: CpuMatrix arg_; }; -template +template void TensorCheck(AssertEq compare, const CpuMatrix& matrix1, const CpuMatrix& matrix2) { @@ -94,15 +95,19 @@ void TensorCheck(AssertEq compare, EXPECT_EQ(count, 0) << "There are " << count << " different element."; } -template +template extern void TensorCheck(AssertEq compare, const Tensor1& tensor1, const Tensor2& tensor2) { - TensorCheck( - compare, - CopyToCpu(tensor1).copiedArg(), - CopyToCpu(tensor2).copiedArg()); + TensorCheck(compare, + CopyToCpu(tensor1).copiedArg(), + CopyToCpu(tensor2).copiedArg()); } -} // namespace autotest +template +void TensorCheck(AssertEq compare, real args1, real args2) { + EXPECT_EQ(compare(args1, args2), true) << "[Test error] args1 = " << args1 + << ", args2 = " << args2; +} +} // namespace autotest diff --git a/paddle/math/tests/TestUtils.h b/paddle/math/tests/TestUtils.h index ffd11345427..96ba2c38c8d 100644 --- a/paddle/math/tests/TestUtils.h +++ b/paddle/math/tests/TestUtils.h @@ -12,7 +12,6 @@ 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. */ - /** * TestUtils.h is used to automatically compare CPU and GPU code is consistent. * @@ -21,7 +20,7 @@ limitations under the License. */ * a. void BaseMatrix::tanh(BaseMatrixT& b); * Compare method: BaseMatrixCompare<0>(&BaseMatrix::tanh); * - * b. + * b. * */ @@ -33,102 +32,169 @@ using namespace paddle; // NOLINT namespace autotest { -template +template class ReplaceType { public: typedef T1 type; }; -template<> +template <> class ReplaceType { public: typedef CpuMatrix type; }; -template<> +template <> class ReplaceType { public: typedef GpuMatrix type; }; +template <> +class ReplaceType { +public: + typedef CpuMatrix type; +}; + +template <> +class ReplaceType { +public: + typedef GpuMatrix type; +}; + // construct a argument -template T construct(int height, int width); -template<> float construct(int height, int width) { return 0.0; } -template<> CpuMatrix construct(int height, int width) { +template +T construct(int height, int width); +template <> +float construct(int height, int width) { + return 0.0; +} +template <> +CpuMatrix construct(int height, int width) { CpuMatrix a(height, width); return a; } -template<> GpuMatrix construct(int height, int width) { +template <> +GpuMatrix construct(int height, int width) { GpuMatrix a(height, width); return a; } // init a argument -template void init(T& v); -template<> void init(float& v) { v = 0.5; } -template<> void init(CpuMatrix& v) { v.randomizeUniform(); } -template<> void init(GpuMatrix& v) { v.randomizeUniform(); } +template +void init(T& v); +template <> +void init(float& v) { + v = 0.5; +} +template <> +void init(CpuMatrix& v) { + v.randomizeUniform(); +} +template <> +void init(GpuMatrix& v) { + v.randomizeUniform(); +} // init a tuple which contains a set of arguments. -template -inline typename std::enable_if::type -initTuple(std::tuple& t){} +template +inline typename std::enable_if::type initTuple( + std::tuple& t) {} -template -inline typename std::enable_if::type -initTuple(std::tuple& t) { +template + inline typename std::enable_if < + I::type initTuple(std::tuple& t) { init(std::get(t)); initTuple(t); } // copy a argument, copy src to dest -template void copy(T1& dest, T2& src); -template<> void copy(float& dest, float& src) { dest = src; } -template<> void copy(GpuMatrix& dest, CpuMatrix& src) { +template +void copy(T1& dest, T2& src); +template <> +void copy(float& dest, float& src) { + dest = src; +} +template <> +void copy(GpuMatrix& dest, CpuMatrix& src) { dest.copyFrom(src); } // copy a tuple, copy src to dest -template -inline typename std::enable_if::type -copyTuple(std::tuple& dest, std::tuple& src) {} - -template -inline typename std::enable_if::type -copyTuple(std::tuple& dest, std::tuple& src) { +template +inline typename std::enable_if::type copyTuple( + std::tuple& dest, std::tuple& src) {} + +template + inline typename std::enable_if < + I::type copyTuple(std::tuple& dest, + std::tuple& src) { copy(std::get(dest), std::get(src)); copyTuple(dest, src); } +// Compare output +template +inline typename std::enable_if::type checkTuple( + std::tuple& args1, + std::tuple& args2, + AssertEq compare) {} + +template + inline typename std::enable_if < + I::type checkTuple(std::tuple& args1, + std::tuple& args2, + AssertEq compare) { + TensorCheck(compare, std::get(args1), std::get(args2)); + checkTuple(args1, args2, compare); +} + // call member function template + typename FC, + typename R, + typename... FArgs, + typename... Args> R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) { return (obj.*f)(args...); } -template -void BaseMatrixCompare(R (C::*f)(Args...), AssertEq compare) { +void BaseMatrixCompare(R (C::*f)(Args...), + AssertEq compare, + bool checkArgs = false) { for (auto height : {1, 11, 73, 128, 200, 330}) { for (auto width : {1, 3, 32, 100, 512, 1000}) { - CpuMatrix obj1(ApplyCol ? 1 : height, - ApplyRow ? 1 : width); - GpuMatrix obj2(ApplyCol ? 1 : height, - ApplyRow ? 1 : width); + CpuMatrix obj1(ApplyCol ? 1 : height, ApplyRow ? 1 : width); + GpuMatrix obj2(ApplyCol ? 1 : height, ApplyRow ? 1 : width); init(obj1); copy(obj2, obj1); auto tuple1 = std::make_tuple( - construct>::type>::type, - CpuMatrix>::type>(height, width)...); + construct>::type>::type, + CpuMatrix>::type>(height, width)...); auto tuple2 = std::make_tuple( - construct>::type>::type, - GpuMatrix>::type>(height, width)...); + construct>::type>::type, + GpuMatrix>::type>(height, width)...); initTuple(tuple1); copyTuple(tuple2, tuple1); @@ -137,16 +203,19 @@ void BaseMatrixCompare(R (C::*f)(Args...), AssertEq compare) { call(obj2, f, std::get(tuple2)...); TensorCheck(compare, obj1, obj2); + if (checkArgs) { + checkTuple(tuple1, tuple2, compare); + } } } } } // namespace autotest -template -void BaseMatrixCompare(R (C::*f)(Args...)) { +template +void BaseMatrixCompare(R (C::*f)(Args...), bool checkArgs = false) { static_assert(sizeof...(I) == sizeof...(Args), - "size of parameter packs are not equal"); + "size of parameter packs are not equal"); #ifndef PADDLE_TYPE_DOUBLE autotest::AssertEqual compare(1e-5); @@ -154,13 +223,13 @@ void BaseMatrixCompare(R (C::*f)(Args...)) { autotest::AssertEqual compare(1e-10); #endif - autotest::BaseMatrixCompare(f, compare); + autotest::BaseMatrixCompare(f, compare, checkArgs); } -template +template void BaseMatrixApplyRow(R (C::*f)(Args...)) { static_assert(sizeof...(I) == sizeof...(Args), - "size of parameter packs are not equal"); + "size of parameter packs are not equal"); #ifndef PADDLE_TYPE_DOUBLE autotest::AssertEqual compare(1e-3); @@ -171,10 +240,10 @@ void BaseMatrixApplyRow(R (C::*f)(Args...)) { autotest::BaseMatrixCompare(f, compare); } -template +template void BaseMatrixApplyCol(R (C::*f)(Args...)) { static_assert(sizeof...(I) == sizeof...(Args), - "size of parameter packs are not equal"); + "size of parameter packs are not equal"); #ifndef PADDLE_TYPE_DOUBLE autotest::AssertEqual compare(1e-3); @@ -183,4 +252,3 @@ void BaseMatrixApplyCol(R (C::*f)(Args...)) { #endif autotest::BaseMatrixCompare(f, compare); } - diff --git a/paddle/math/tests/test_Matrix.cpp b/paddle/math/tests/test_Matrix.cpp new file mode 100644 index 00000000000..a4d9d34976b --- /dev/null +++ b/paddle/math/tests/test_Matrix.cpp @@ -0,0 +1,49 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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. */ + +#ifndef PADDLE_ONLY_CPU +/** + * This test file compares the implementation of CPU and GPU function + * in Matrix.cpp. + */ + +#include +#include "paddle/utils/Util.h" +#include "paddle/math/BaseMatrix.h" +#include "TestUtils.h" + +using namespace paddle; // NOLINT +using namespace std; // NOLINT + +/** + * Test member functions which prototype is + * void (Matrix::*)(Matrix&). + */ +TEST(BaseMatrix, real) { + typedef void (Matrix::*FunctionProto)(Matrix&); +#define MATRIXCOMPARE(function) \ + BaseMatrixCompare<0>(static_cast(&Matrix::function), true); + + MATRIXCOMPARE(softmax); + +#undef MATRIXCOMPARE +} + +int main(int argc, char** argv) { + testing::InitGoogleTest(&argc, argv); + initMain(argc, argv); + return RUN_ALL_TESTS(); +} + +#endif diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 8f96672b88e..80596abe821 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -693,24 +693,6 @@ TEST(Matrix, unary) { } } -void testMatrixSoftmax(int height, int width) { - MatrixPtr cpuInput = std::make_shared(height, width); - MatrixPtr cpuOutput = std::make_shared(height, width); - MatrixPtr gpuInput = std::make_shared(height, width); - MatrixPtr gpuOutput = std::make_shared(height, width); - - cpuInput->randomizeUniform(); - gpuInput->copyFrom(*cpuInput); - cpuOutput->zero(); - gpuOutput->zero(); - cpuInput->softmax(*cpuOutput); - gpuInput->softmax(*gpuOutput); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuOutput); - MatrixCheckErr(*cpuOutput, *outputCheck); -} - void testSequenceSoftmax(int batchSize) { // forward int inputDim = 1; @@ -793,7 +775,6 @@ TEST(Matrix, softmax) { for (auto width : {1, 32, 100, 512, 1000}) { VLOG(3) << " height=" << height << " width=" << width; - testMatrixSoftmax(height, width); testMatrixSoftmaxBp(height, width); testMatrixSoftmaxThreshold(height, width); } -- GitLab From cf205d0d43a82ef46cd9374218555be339dab5eb Mon Sep 17 00:00:00 2001 From: xutianbing Date: Tue, 22 Nov 2016 14:24:30 -0800 Subject: [PATCH 108/347] deepSwap --- paddle/math/BaseMatrix.cu | 6 ++++++ paddle/math/BaseMatrix.h | 7 +++++++ 2 files changed, 13 insertions(+) diff --git a/paddle/math/BaseMatrix.cu b/paddle/math/BaseMatrix.cu index 2f32b3fdd1a..a723ef7bc83 100644 --- a/paddle/math/BaseMatrix.cu +++ b/paddle/math/BaseMatrix.cu @@ -1240,6 +1240,12 @@ void BaseMatrixT::assignAtOffset(BaseMatrixT& b, int64_t columnOffset) { } } +DEFINE_MATRIX_BINARY_OP(DeepSwap, T tmp = a; a = b; b = tmp); +template +void BaseMatrixT::deepSwap(BaseMatrixT& b) { + applyBinary(binary::DeepSwap(), b); +} + template<> void BaseMatrixT::rowDotMul(size_t destCol, BaseMatrixT& b, diff --git a/paddle/math/BaseMatrix.h b/paddle/math/BaseMatrix.h index d41dcee682c..dbc217c30f2 100644 --- a/paddle/math/BaseMatrix.h +++ b/paddle/math/BaseMatrix.h @@ -455,6 +455,13 @@ public: */ void assign(T p); + /** + * @code + * swap(this, b) + * @endcode + */ + void deepSwap(BaseMatrixT& b); + /** * @code * this = this + p -- GitLab From c7f96de12e9ce3f32cf8059a583eb9e5ff2b39e3 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Mon, 28 Nov 2016 15:47:16 -0800 Subject: [PATCH 109/347] add unit test for deepSwap --- paddle/math/tests/test_matrixCompare.cpp | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index ae5bc5a86a1..de540dad4c8 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -448,6 +448,24 @@ void testMatrixZeroAtOffset(int height, int width) { MatrixCheckEqual(*cpuA, *cpuTest); } +void testMatrixDeepSwap(int height, int width) { + MatrixPtr cpuA = std::make_shared(height, width); + MatrixPtr cpuB = std::make_shared(height, width); + MatrixPtr cpuCopyA = std::make_shared(height, width); + MatrixPtr cpuCopyB = std::make_shared(height, width); + + cpuA->randomizeUniform(); + cpuB->randomizeUniform(); + cpuCopyA->copyFrom(*cpuA); + cpuCopyB->copyFrom(*cpuB); + + // swap matrix cpuA and cpuB + cpuA->deepSwap(*cpuB); + + MatrixCheckEqual(*cpuA, *cpuCopyB); + MatrixCheckEqual(*cpuB, *cpuCopyA); +} + void testMatrixBinaryAdd(int height, int width) { MatrixPtr cpuA = std::make_shared(height, width); MatrixPtr cpuB = std::make_shared(height, width); @@ -480,6 +498,7 @@ void testMatrixAssign(int height, int width) { MatrixCheckEqual(*cpuA, *outputCheck); } + void testMatrixAdd(int height, int width) { MatrixPtr cpuA = std::make_shared(height, width); MatrixPtr gpuA = std::make_shared(height, width); @@ -798,6 +817,7 @@ TEST(Matrix, unary) { testMatrixBinaryAdd(height, width); testMatrixTanh(height, width); testMatrixTanhDerivative(height, width); + testMatrixDeepSwap(height, width); // applyTernary testMatrixTernarySub(height, width); -- GitLab From 5de5453d15eddc4a3eb2e57f1aaf83e571ab55e0 Mon Sep 17 00:00:00 2001 From: xutianbing Date: Mon, 28 Nov 2016 16:25:56 -0800 Subject: [PATCH 110/347] add code comments for deepSwap --- paddle/math/BaseMatrix.h | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/paddle/math/BaseMatrix.h b/paddle/math/BaseMatrix.h index dbc217c30f2..ea58c861a3d 100644 --- a/paddle/math/BaseMatrix.h +++ b/paddle/math/BaseMatrix.h @@ -458,6 +458,10 @@ public: /** * @code * swap(this, b) + * example: swap two Matrices + * MatrixPtr cpuA = std::make_shared(height, width); + * MatrixPtr cpuB = std::make_shared(height, width); + * cpuA->deepSwap(*cpuB); * @endcode */ void deepSwap(BaseMatrixT& b); -- GitLab From 1d8d9573225f44eb39a6af187b590b06d45dfb95 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 29 Nov 2016 11:32:43 +0800 Subject: [PATCH 111/347] Add `set -e` for paddle boot up script. * error when paddle has a wrong version number. --- paddle/scripts/submit_local.sh.in | 2 ++ 1 file changed, 2 insertions(+) diff --git a/paddle/scripts/submit_local.sh.in b/paddle/scripts/submit_local.sh.in index 20ea2fedc4d..ace2c0dee97 100644 --- a/paddle/scripts/submit_local.sh.in +++ b/paddle/scripts/submit_local.sh.in @@ -29,6 +29,7 @@ function version(){ } function ver2num() { + set -e # convert version to number. if [ -z "$1" ]; then # empty argument printf "%03d%03d%03d%03d%03d" 0 @@ -41,6 +42,7 @@ function ver2num() { printf "%03d%03d%03d%03d%03d" $VERN fi fi + set +e } PADDLE_CONF_HOME="$HOME/.config/paddle" -- GitLab From 7c25d9b8a1f422cdaadf26ac2c23f4b9fac4bc23 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 29 Nov 2016 11:46:49 +0800 Subject: [PATCH 112/347] fix style problem in doc_cn/introduction.rst --- doc_cn/build_and_install/index.rst | 5 - doc_cn/introduction/index.rst | 134 +++++++++++++++------------ doc_cn/ui/predict/swig_py_paddle.rst | 22 +++-- 3 files changed, 89 insertions(+), 72 deletions(-) diff --git a/doc_cn/build_and_install/index.rst b/doc_cn/build_and_install/index.rst index 2205e282248..48163fb36e5 100644 --- a/doc_cn/build_and_install/index.rst +++ b/doc_cn/build_and_install/index.rst @@ -8,9 +8,7 @@ PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜 .. toctree:: :maxdepth: 1 - :glob: - 使用Jumbo安装(对内) <../build/internal/install_from_jumbo.rst> install/docker_install.rst install/ubuntu_install.rst @@ -25,8 +23,5 @@ PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜 .. toctree:: :maxdepth: 1 - :glob: - 源码下载(对内) <../build/internal/download_paddle_source_zh_cn.rst> - 从源码编译安装(对内) <../build/internal/build_from_source_zh_cn.rst> cmake/index.rst diff --git a/doc_cn/introduction/index.rst b/doc_cn/introduction/index.rst index f6eb5456c00..c996f5f4acd 100644 --- a/doc_cn/introduction/index.rst +++ b/doc_cn/introduction/index.rst @@ -1,102 +1,114 @@ -# 简介 +简介 +==== PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍将向你展示如何利用PaddlePaddle来解决一个经典的线性回归问题。 -## 1. 一个经典的任务 +1. 一个经典的任务 +----------------- -我们展示如何用PaddlePaddle解决单变量的线性回归问题。线性回归的输入是一批点`(x, y) `，其中 `y = wx + b + ε`， 而 ε 是一个符合高斯分布的随机变量。线性回归的输出是从这批点估计出来的参数 w 和 b。 +我们展示如何用PaddlePaddle解决 `单变量的线性回归 `_ 问题。线性回归的输入是一批点 `(x, y)` ，其中 `y = wx + b + ε`， 而 ε 是一个符合高斯分布的随机变量。线性回归的输出是从这批点估计出来的参数 `w` 和 `b` 。 一个例子是房产估值。我们假设房产的价格（y）是其大小（x）的一个线性函数，那么我们可以通过收集市场上房子的大小和价格，用来估计线性函数的参数w 和 b。 -## 2. 准备数据 +2. 准备数据 +----------- 假设变量 `x` 和 `y` 的真实关系为： `y = 2x + 0.3 + ε`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为线性回归的输入。下面脚本符合PaddlePaddle期待的读取数据的Python程序的模式。 -```python -# dataprovider.py -from paddle.trainer.PyDataProvider2 import * -import random +.. code-block:: python -# 定义输入数据的类型: 2个浮点数 -@provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False) -def process(settings, input_file): - for i in xrange(2000): - x = random.random() - yield [x], [2*x+0.3] -``` + # dataprovider.py + from paddle.trainer.PyDataProvider2 import * + import random -## 3. 训练模型 + # 定义输入数据的类型: 2个浮点数 + @provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False) + def process(settings, input_file): + for i in xrange(2000): + x = random.random() + yield [x], [2*x+0.3] + +3. 训练模型 +----------- 为了还原 `y = 2x + 0.3`，我们先从一条随机的直线 `y' = wx + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `y'` 和 `y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。 在PaddlePaddle里，该模型的网络配置如下。 -```python -# trainer_config.py -from paddle.trainer_config_helpers import * - -# 1. 定义数据来源，调用上面的process函数获得观测数据 -data_file = 'empty.list' -with open(data_file, 'w') as f: f.writelines(' ') -define_py_data_sources2(train_list=data_file, test_list=None, - module='dataprovider', obj='process',args={}) - -# 2. 学习算法。控制如何改变模型参数 w 和 b -settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) - -# 3. 神经网络配置 -x = data_layer(name='x', size=1) -y = data_layer(name='y', size=1) -# 线性计算网络层: ȳ = wx + b -ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) -# 计算误差函数，即 ȳ 和真实 y 之间的距离 -cost = regression_cost(input= ȳ, label=y) -outputs(cost) -``` +.. code-block:: python + + # trainer_config.py + from paddle.trainer_config_helpers import * + + # 1. 定义数据来源，调用上面的process函数获得观测数据 + data_file = 'empty.list' + with open(data_file, 'w') as f: f.writelines(' ') + define_py_data_sources2(train_list=data_file, test_list=None, + module='dataprovider', obj='process',args={}) + + # 2. 学习算法。控制如何改变模型参数 w 和 b + settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) + + # 3. 神经网络配置 + x = data_layer(name='x', size=1) + y = data_layer(name='y', size=1) + # 线性计算网络层: ȳ = wx + b + ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) + # 计算误差函数，即 ȳ 和真实 y 之间的距离 + cost = regression_cost(input= ȳ, label=y) + outputs(cost) + 这段简短的配置展示了PaddlePaddle的基本用法： -- 第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的`process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。 +- 第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的 `process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。 - 第二部分主要是选择学习算法，它定义了模型参数改变的规则。PaddlePaddle提供了很多优秀的学习算法，这里使用一个基于momentum的随机梯度下降(SGD)算法，该算法每批量(batch)读取12个采样数据进行随机梯度计算来更新更新。 - 最后一部分是神经网络的配置。由于PaddlePaddle已经实现了丰富的网络层，所以很多时候你需要做的只是定义正确的网络层并把它们连接起来。这里使用了三种网络单元： + - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。 - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。 - - **回归误差代价层**：回归误差代价层 `regression_cost`是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。 + - **回归误差代价层**：回归误差代价层 `regression_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。 + +定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令： + +.. code-block:: bash -定义了网络结构并保存为`trainer_config.py`之后，运行以下训练命令： - ``` - paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 - ``` + paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` y = 2x + 0.3 ` -## 4. 模型检验 +4. 模型检验 +----------- 训练完成后，我们希望能够检验模型的好坏。一种常用的做法是用学习的模型对另外一组测试数据进行预测，评价预测的效果。在这个例子中，由于已经知道了真实答案，我们可以直接观察模型的参数是否符合预期来进行检验。 PaddlePaddle将每个模型参数作为一个numpy数组单独存为一个文件，所以可以利用如下方法读取模型的参数。 -```python -import numpy as np -import os +.. code-block:: python -def load(file_name): - with open(file_name, 'rb') as f: - f.read(16) # skip header for float type. - return np.fromfile(f, dtype=np.float32) + import numpy as np + import os + + def load(file_name): + with open(file_name, 'rb') as f: + f.read(16) # skip header for float type. + return np.fromfile(f, dtype=np.float32) -print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b')) -# w=1.999743, b=0.300137 -``` -
    ![](./parameters.png)
    + print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b')) + # w=1.999743, b=0.300137 + +.. image:: ./parameters.png + :align: center + :scale: 80 % 从图中可以看到，虽然 `w` 和 `b` 都使用随机值初始化，但在起初的几轮训练中它们都在快速逼近真实值，并且后续仍在不断改进，使得最终得到的模型几乎与真实模型一致。 -这样，我们用PaddlePaddle解决了单变量线性回归问题， 包括数据输入，模型训练和最后的结果验证。 +这样，我们用PaddlePaddle解决了单变量线性回归问题， 包括数据输入、模型训练和最后的结果验证。 -## 5. 推荐后续阅读 +5. 推荐后续阅读 +--------------- -- 安装/编译：PaddlePaddle的安装与编译文档。 -- 快速入门 ：使用商品评论分类任务，系统性的介绍如何一步步改进，最终得到产品级的深度模型。 -- 示例：各种实用案例，涵盖图像、文本、推荐等多个领域。 +- `安装/编译 <../build_and_install/index.html>`_ ：PaddlePaddle的安装与编译文档。 +- `快速入门 <../demo/quick_start/index.html>`_ ：使用商品评论分类任务，系统性的介绍如何一步步改进，最终得到产品级的深度模型。 +- `示例 <../demo/index.html>`_ ：各种实用案例，涵盖图像、文本、推荐等多个领域。 \ No newline at end of file diff --git a/doc_cn/ui/predict/swig_py_paddle.rst b/doc_cn/ui/predict/swig_py_paddle.rst index 89031dd72f5..05f25345c52 100644 --- a/doc_cn/ui/predict/swig_py_paddle.rst +++ b/doc_cn/ui/predict/swig_py_paddle.rst @@ -9,15 +9,24 @@ PaddlePaddle使用swig对常用的预测接口进行了封装，通过编译会 基于Python的模型预测，主要包括以下五个步骤。 1. 初始化PaddlePaddle环境 - 在程序开始阶段，通过调用 ``swig_paddle.initPaddle()`` 并传入相应的命令行参数初始化PaddlePaddle。 + + 在程序开始阶段，通过调用 ``swig_paddle.initPaddle()`` 并传入相应的命令行参数初始化PaddlePaddle。 + 2. 解析模型配置文件 - 初始化之后，可以通过调用 ``parse_config()`` 解析训练模型时用的配置文件。注意预测数据通常不包含label, 同时预测网络通常直接输出最后一层的结果而不是像训练网络一样再接一层cost layer，所以一般需要对训练用的模型配置文件稍作相应修改才能在预测时使用。 + + 初始化之后，可以通过调用 ``parse_config()`` 解析训练模型时用的配置文件。注意预测数据通常不包含label, 同时预测网络通常直接输出最后一层的结果而不是像训练网络一样再接一层cost layer，所以一般需要对训练用的模型配置文件稍作相应修改才能在预测时使用。 + 3. 构造paddle.GradientMachine - 通过调用 ``swig_paddle.GradientMachine.createFromConfigproto()`` 传入上一步解析出来的模型配置就可以创建一个 ``GradientMachine``。 + + 通过调用 ``swig_paddle.GradientMachine.createFromConfigproto()`` 传入上一步解析出来的模型配置就可以创建一个 ``GradientMachine``。 + 4. 准备预测数据 - swig_paddle中的预测接口的参数是自定义的C++数据类型，py_paddle里面提供了一个工具类 ``DataProviderConverter`` 可以用于接收和PyDataProvider2一样的输入数据并转换成预测接口所需的数据类型。 + + swig_paddle中的预测接口的参数是自定义的C++数据类型，py_paddle里面提供了一个工具类 ``DataProviderConverter`` 可以用于接收和PyDataProvider2一样的输入数据并转换成预测接口所需的数据类型。 + 5. 模型预测 - 通过调用 ``forwardTest()`` 传入预测数据，直接返回计算结果。 + + 通过调用 ``forwardTest()`` 传入预测数据，直接返回计算结果。 预测Demo @@ -34,7 +43,8 @@ Demo预测输出如下，其中value即为softmax层的输出。由于TEST_DATA .. code-block:: text - [{'id': None, 'value': array([[ 5.53018653e-09, 1.12194102e-05, 1.96644767e-09, + [{'id': None, 'value': array( + [[ 5.53018653e-09, 1.12194102e-05, 1.96644767e-09, 1.43630644e-02, 1.51111044e-13, 9.85625684e-01, 2.08823112e-10, 2.32777140e-08, 2.00186201e-09, 1.15501715e-08], -- GitLab From a36df60a1ce7532aa15fec336512a98bbb79a5d5 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 29 Nov 2016 13:59:21 +0800 Subject: [PATCH 113/347] refine dataprovider rst based on comments --- doc_cn/ui/data_provider/dataprovider.rst | 14 ++++---- doc_cn/ui/data_provider/pydataprovider2.rst | 39 +++++++++++---------- 2 files changed, 27 insertions(+), 26 deletions(-) diff --git a/doc_cn/ui/data_provider/dataprovider.rst b/doc_cn/ui/data_provider/dataprovider.rst index e1ad330c29a..e6796429a78 100644 --- a/doc_cn/ui/data_provider/dataprovider.rst +++ b/doc_cn/ui/data_provider/dataprovider.rst @@ -1,15 +1,13 @@ DataProvider的介绍 ================== -DataProvider是PaddlePaddle负责提供数据的模块。其作用是将数据传入内存或显存，让神经网络可以进行训练或预测。有两种使用方式： - -- 简单使用：使用Python接口 `PyDataProvider2 `_ 来自定义传数据的过程。 -- 高级使用：如果用户有更复杂的使用，或者需要更高的效率，可以在C++端自定义一个 ``DataProvider`` 。 +DataProvider是PaddlePaddle负责提供数据的模块。其作用是将数据传入内存或显存，让神经网络可以进行训练或预测。用户可以通过简单使用Python接口 `PyDataProvider2 `_ ，来自定义传数据的过程。如果有更复杂的使用，或者需要更高的效率，用户也可以在C++端自定义一个 ``DataProvider`` 。 PaddlePaddle需要用户在网络配置（trainer_config.py）中定义使用哪种DataProvider，并且在DataProvider中实现如何访问训练文件列表（train.list）或测试文件列表（test.list）。 -- train.list和test.list存放在本地（推荐直接存放到训练目录，以相对路径引用)。一般情况下，两者均为纯文本文件，其中每一行对应一个数据文件地址： - - - 如果数据文件存于本地磁盘，则将这些文件的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)写在train.list和test.list中。 - - 地址也可以为hdfs文件路径，或者数据库连接地址等。 +- train.list和test.list存放在本地（推荐直接存放到训练目录，以相对路径引用)。一般情况下，两者均为纯文本文件，其中每一行对应一个数据文件地址： + + - 如果数据文件存于本地磁盘，这个地址则为它的绝对路径或相对路径(相对于PaddlePaddle程序运行时的路径)。 + - 地址也可以为hdfs文件路径，或者数据库连接路径等。 + - 由于这个地址会被DataProvider使用，因此，如何解析该地址也是用户自定义DataProvider时需要考虑的地方。 - 如果没有设置test.list，或设置为None，那么在训练过程中不会执行测试操作；否则，会根据命令行参数指定的测试方式，在训练过程中进行测试，从而防止过拟合。 diff --git a/doc_cn/ui/data_provider/pydataprovider2.rst b/doc_cn/ui/data_provider/pydataprovider2.rst index 3455e331da0..c0b3286ad52 100644 --- a/doc_cn/ui/data_provider/pydataprovider2.rst +++ b/doc_cn/ui/data_provider/pydataprovider2.rst @@ -1,14 +1,14 @@ PyDataProvider2的使用 ===================== -PyDataProvider2是PaddlePaddle使用Python提供数据的接口。该接口使用多线程读取数据，并提供了简单的Cache功能；同时可以使用户只关注如何从文件中读取每一条数据，而不用关心数据如何传输，如何存储等等。 +PyDataProvider2是PaddlePaddle使用Python提供数据的推荐接口。该接口使用多线程读取数据，并提供了简单的Cache功能；同时可以使用户只关注如何从文件中读取每一条数据，而不用关心数据如何传输，如何存储等等。 .. contents:: MNIST的使用场景 --------------- -我们以MNIST手写识别为例，来说明如何使用最简单的PyDataProvider2。 +我们以MNIST手写识别为例，来说明PyDataProvider2的简单使用场景。 样例数据 ++++++++ @@ -17,7 +17,7 @@ MNIST是一个包含有70,000张灰度图片的数字分类数据集。样例数 .. literalinclude:: mnist_train.txt -其中每行数据代表一张图片，行内使用 ``;`` 分成两部分。第一部分是图片的标签，为0-9中的一个数字；第二部分是28*28的图片像素灰度值。 对应的 ``train.list`` 为： +其中每行数据代表一张图片，行内使用 ``;`` 分成两部分。第一部分是图片的标签，为0-9中的一个数字；第二部分是28*28的图片像素灰度值。 对应的 ``train.list`` 即为这个数据文件的名字： .. literalinclude:: train.list @@ -40,7 +40,8 @@ dataprovider的使用 - 该函数的功能是：打开文本文件，读取每一行，将行中的数据转换成与input_types一致的格式，然后返回给PaddlePaddle进程。注意， - 返回的顺序需要和input_types中定义的顺序一致。 - - 返回时，必须使用关键词 ``yield`` 。一次yield调用，即返回一条完整的样本。如果想为一个数据文件返回多条样本，只需要在函数中调用多次yield即可（本例中使用for循环进行多次调用）。 + - 返回时，必须使用Python关键词 ``yield`` ，相关概念是 ``generator`` 。 + - 一次yield调用，返回一条完整的样本。如果想为一个数据文件返回多条样本，只需要在函数中调用多次yield即可（本例中使用for循环进行多次调用）。 - 该函数具有两个参数： @@ -55,7 +56,20 @@ dataprovider的使用 .. literalinclude:: mnist_config.py :lines: 1-7 -训练数据是 ``train.list`` ，测试数据没有，调用的PyDataProvider2是 ``mnist_provider`` 模块中的 ``process`` 函数。 +训练数据是 ``train.list`` ，没有测试数据，调用的PyDataProvider2是 ``mnist_provider`` 模块中的 ``process`` 函数。 + +小结 ++++++ + +至此，简单的PyDataProvider2样例就说明完毕了。对用户来说，仅需要知道如何从 **一个文件** 中读取 **一条样本** ，就可以将数据传送给PaddlePaddle了。而PaddlePaddle则会帮用户做以下工作： + +* 将数据组合成Batch进行训练 +* 对训练数据进行Shuffle +* 多线程的数据读取 +* 缓存训练数据到内存(可选) +* CPU->GPU双缓存 + +是不是很简单呢？ 时序模型的使用场景 ------------------ @@ -89,19 +103,6 @@ dataprovider的使用 .. literalinclude:: sentimental_config.py :emphasize-lines: 12-14 -小结 ------ - -至此，两个PyDataProvider2的样例就说明完毕了。对用户来说，仅需要知道如何从 **一个文件** 中读取 **一条样本** ，就可以将数据传送给PaddlePaddle了。而PaddlePaddle则会帮用户做以下工作： - -* 将数据组合成Batch进行训练 -* 对训练数据进行Shuffle -* 多线程的数据读取 -* 缓存训练数据到内存(可选) -* CPU->GPU双缓存 - -是不是很简单呢？ - 参考(Reference) --------------- @@ -167,6 +168,8 @@ init_hook可以传入一个函数。该函数在初始化的时候会被调用 * PaddlePaddle定义的参数: 1）is_train：bool型参数，表示用于训练或预测；2）file_list：所有文件列表。 * 用户定义的参数：使用args在网络配置中设置。 +注意：PaddlePaddle保留添加参数的权力，因此init_hook尽量使用 ``**kwargs`` 来接受不使用的函数以保证兼容性。 + cache +++++ -- GitLab From 5c551f90558955fd8d05a3bee55af6c302ddc0aa Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 29 Nov 2016 15:34:52 +0800 Subject: [PATCH 114/347] refine doc structure and revise some words --- doc/about/index.rst | 10 ++--- doc/api/data_provider/index.rst | 4 +- doc/api/data_provider/pydataprovider2.rst | 4 +- doc/api/index.md | 14 ------- doc/api/index.rst | 36 ++++++++++++++++++ doc/api/predict/swig_py_paddle_en.rst | 4 +- doc/api/trainer_config_helpers/attrs.rst | 4 +- doc/api/trainer_config_helpers/index.rst | 14 ------- .../basic_usage/basic_usage.rst | 0 .../basic_usage/parameters.png | Bin .../build_and_install/build_from_source.md | 0 .../build_and_install/cmake.png | Bin .../build_and_install/docker_install.rst | 0 .../build_and_install/index.rst | 0 .../build_and_install/ubuntu_install.rst | 0 doc/{introduction => getstarted}/index.rst | 2 +- doc/howto/algorithm/index.rst | 7 ---- doc/howto/algorithm/rnn/bi_lstm.jpg | 1 - .../rnn/encoder-decoder-attention-model.png | 1 - doc/howto/cluster/cluster_train.md | 2 +- .../arguments.md} | 0 .../detail_introduction.md | 0 .../{cmd_argument => cmd_parameter}/index.md | 4 +- .../use_case.md | 0 doc/howto/contribute_to_paddle.md | 2 +- doc/howto/deep_model/index.rst | 7 ++++ .../{algorithm => deep_model}/rnn/rnn.rst | 4 +- doc/howto/dev/index.rst | 9 ----- doc/howto/dev/layer.md | 5 --- doc/howto/index.rst | 27 ++++++++++--- .../{dev => }/new_layer/FullyConnected.jpg | Bin .../new_layer.rst => new_layer/index.rst} | 6 +-- doc/howto/optimization/index.rst | 4 +- doc/howto/{dev => }/source/api.rst | 0 doc/howto/{dev => }/source/cuda/index.rst | 0 doc/howto/{dev => }/source/cuda/matrix.rst | 0 doc/howto/{dev => }/source/cuda/nn.rst | 0 doc/howto/{dev => }/source/cuda/utils.rst | 0 .../{dev => }/source/gserver/activations.rst | 0 .../source/gserver/dataproviders.rst | 0 .../{dev => }/source/gserver/evaluators.rst | 0 .../source/gserver/gradientmachines.rst | 0 doc/howto/{dev => }/source/gserver/index.rst | 0 doc/howto/{dev => }/source/gserver/layers.rst | 0 .../{dev => }/source/gserver/neworks.rst | 0 doc/howto/{dev => }/source/index.rst | 0 doc/howto/{dev => }/source/math/functions.rst | 0 doc/howto/{dev => 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doc/api/index.md create mode 100644 doc/api/index.rst delete mode 100644 doc/api/trainer_config_helpers/index.rst rename doc/{introduction => getstarted}/basic_usage/basic_usage.rst (100%) rename doc/{introduction => getstarted}/basic_usage/parameters.png (100%) rename doc/{introduction => getstarted}/build_and_install/build_from_source.md (100%) rename doc/{introduction => getstarted}/build_and_install/cmake.png (100%) rename doc/{introduction => getstarted}/build_and_install/docker_install.rst (100%) rename doc/{introduction => getstarted}/build_and_install/index.rst (100%) rename doc/{introduction => getstarted}/build_and_install/ubuntu_install.rst (100%) rename doc/{introduction => getstarted}/index.rst (88%) delete mode 100644 doc/howto/algorithm/index.rst delete mode 120000 doc/howto/algorithm/rnn/bi_lstm.jpg delete mode 120000 doc/howto/algorithm/rnn/encoder-decoder-attention-model.png rename doc/howto/{cmd_argument/argument_outline.md => cmd_parameter/arguments.md} (100%) rename doc/howto/{cmd_argument => cmd_parameter}/detail_introduction.md (100%) rename doc/howto/{cmd_argument => cmd_parameter}/index.md (53%) rename doc/howto/{cmd_argument => cmd_parameter}/use_case.md (100%) create mode 100644 doc/howto/deep_model/index.rst rename doc/howto/{algorithm => deep_model}/rnn/rnn.rst (99%) delete mode 100644 doc/howto/dev/index.rst delete mode 100644 doc/howto/dev/layer.md rename doc/howto/{dev => }/new_layer/FullyConnected.jpg (100%) rename doc/howto/{dev/new_layer/new_layer.rst => new_layer/index.rst} (99%) rename doc/howto/{dev => }/source/api.rst (100%) rename doc/howto/{dev => }/source/cuda/index.rst (100%) rename doc/howto/{dev => }/source/cuda/matrix.rst (100%) rename doc/howto/{dev => }/source/cuda/nn.rst (100%) rename doc/howto/{dev => }/source/cuda/utils.rst (100%) rename doc/howto/{dev => }/source/gserver/activations.rst (100%) rename doc/howto/{dev => }/source/gserver/dataproviders.rst (100%) rename doc/howto/{dev => 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rename doc/howto/{dev => }/source/pserver/network.rst (100%) rename doc/howto/{dev => }/source/pserver/server.rst (100%) rename doc/howto/{dev => }/source/trainer.rst (100%) rename doc/howto/{dev => }/source/utils/customStackTrace.rst (100%) rename doc/howto/{dev => }/source/utils/enum.rst (100%) rename doc/howto/{dev => }/source/utils/index.rst (100%) rename doc/howto/{dev => }/source/utils/lock.rst (100%) rename doc/howto/{dev => }/source/utils/queue.rst (100%) rename doc/howto/{dev => }/source/utils/thread.rst (100%) diff --git a/doc/about/index.rst b/doc/about/index.rst index 511c1546413..8a372d2bc2b 100644 --- a/doc/about/index.rst +++ b/doc/about/index.rst @@ -1,14 +1,14 @@ -About +ABOUT ======= - -Credits --------- - PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu. PaddlePaddle is now open source but far from complete, which is intended to be built upon, improved, scaled, and extended. We hope to build an active open source community both by providing feedback and by actively contributing to the source code. + +Credits +-------- + We owe many thanks to `all contributors and developers `_ of PaddlePaddle! diff --git a/doc/api/data_provider/index.rst b/doc/api/data_provider/index.rst index 3db5b573762..5e7a49d6323 100644 --- a/doc/api/data_provider/index.rst +++ b/doc/api/data_provider/index.rst @@ -1,5 +1,5 @@ -DataProvider Introduction -========================= +Introduction +============== DataProvider is a module that loads training or testing data into cpu or gpu memory for the following triaining or testing process. diff --git a/doc/api/data_provider/pydataprovider2.rst b/doc/api/data_provider/pydataprovider2.rst index e105d3be308..b42cbca576e 100644 --- a/doc/api/data_provider/pydataprovider2.rst +++ b/doc/api/data_provider/pydataprovider2.rst @@ -1,5 +1,5 @@ -How to use PyDataProvider2 -========================== +PyDataProvider2 +================= We highly recommand users to use PyDataProvider2 to provide training or testing data to PaddlePaddle. The user only needs to focus on how to read a single diff --git a/doc/api/index.md b/doc/api/index.md deleted file mode 100644 index 8c4a65e0d5c..00000000000 --- a/doc/api/index.md +++ /dev/null @@ -1,14 +0,0 @@ -# API - -## Data Provider - -* [Introduction](data_provider/index.rst) -* [PyDataProvider2](data_provider/pydataprovider2.rst) - -## Trainer Configuration - -* [Model Config Interface](trainer_config_helpers/index.rst) - -## Predict - -* [Python Prediction API](predict/swig_py_paddle_en.rst) diff --git a/doc/api/index.rst b/doc/api/index.rst new file mode 100644 index 00000000000..ccee7a0f1f3 --- /dev/null +++ b/doc/api/index.rst @@ -0,0 +1,36 @@ +API +==== + +DataProvider API +---------------- + +.. toctree:: + :maxdepth: 1 + + data_provider/index.rst + data_provider/pydataprovider2.rst + +Model Config API +---------------- + +.. toctree:: + :maxdepth: 1 + + trainer_config_helpers/index.rst + trainer_config_helpers/optimizers.rst + trainer_config_helpers/data_sources.rst + trainer_config_helpers/layers.rst + trainer_config_helpers/activations.rst + trainer_config_helpers/poolings.rst + trainer_config_helpers/networks.rst + trainer_config_helpers/evaluators.rst + trainer_config_helpers/attrs.rst + + +Applications API +---------------- + +.. toctree:: + :maxdepth: 1 + + predict/swig_py_paddle_en.rst \ No newline at end of file diff --git a/doc/api/predict/swig_py_paddle_en.rst b/doc/api/predict/swig_py_paddle_en.rst index b743fc45691..9845cd1607b 100644 --- a/doc/api/predict/swig_py_paddle_en.rst +++ b/doc/api/predict/swig_py_paddle_en.rst @@ -1,5 +1,5 @@ -Python Prediction API -===================== +Python Prediction +================== PaddlePaddle offers a set of clean prediction interfaces for python with the help of SWIG. The main steps of predict values in python are: diff --git a/doc/api/trainer_config_helpers/attrs.rst b/doc/api/trainer_config_helpers/attrs.rst index 44919aba90d..ac63127bf7d 100644 --- a/doc/api/trainer_config_helpers/attrs.rst +++ b/doc/api/trainer_config_helpers/attrs.rst @@ -1,5 +1,5 @@ -Parameter and Extra Layer Attribute -=================================== +Parameter Attributes +======================= .. automodule:: paddle.trainer_config_helpers.attrs :members: diff --git a/doc/api/trainer_config_helpers/index.rst b/doc/api/trainer_config_helpers/index.rst deleted file mode 100644 index 8395eb75710..00000000000 --- a/doc/api/trainer_config_helpers/index.rst +++ /dev/null @@ -1,14 +0,0 @@ -Model Config Interface -====================== - -.. toctree:: - :maxdepth: 1 - - optimizers.rst - data_sources.rst - layers.rst - activations.rst - poolings.rst - networks.rst - evaluators.rst - attrs.rst diff --git a/doc/introduction/basic_usage/basic_usage.rst b/doc/getstarted/basic_usage/basic_usage.rst similarity index 100% rename from doc/introduction/basic_usage/basic_usage.rst rename to doc/getstarted/basic_usage/basic_usage.rst diff --git a/doc/introduction/basic_usage/parameters.png b/doc/getstarted/basic_usage/parameters.png similarity index 100% rename from doc/introduction/basic_usage/parameters.png rename to doc/getstarted/basic_usage/parameters.png diff --git a/doc/introduction/build_and_install/build_from_source.md b/doc/getstarted/build_and_install/build_from_source.md similarity index 100% rename from doc/introduction/build_and_install/build_from_source.md rename to doc/getstarted/build_and_install/build_from_source.md diff --git a/doc/introduction/build_and_install/cmake.png b/doc/getstarted/build_and_install/cmake.png similarity index 100% rename from doc/introduction/build_and_install/cmake.png rename to doc/getstarted/build_and_install/cmake.png diff --git a/doc/introduction/build_and_install/docker_install.rst b/doc/getstarted/build_and_install/docker_install.rst similarity index 100% rename from doc/introduction/build_and_install/docker_install.rst rename to doc/getstarted/build_and_install/docker_install.rst diff --git a/doc/introduction/build_and_install/index.rst b/doc/getstarted/build_and_install/index.rst similarity index 100% rename from doc/introduction/build_and_install/index.rst rename to doc/getstarted/build_and_install/index.rst diff --git a/doc/introduction/build_and_install/ubuntu_install.rst b/doc/getstarted/build_and_install/ubuntu_install.rst similarity index 100% rename from doc/introduction/build_and_install/ubuntu_install.rst rename to doc/getstarted/build_and_install/ubuntu_install.rst diff --git a/doc/introduction/index.rst b/doc/getstarted/index.rst similarity index 88% rename from doc/introduction/index.rst rename to doc/getstarted/index.rst index ff22f05a1b6..5f2787066ea 100644 --- a/doc/introduction/index.rst +++ b/doc/getstarted/index.rst @@ -1,4 +1,4 @@ -Introduction +GET STARTED ============ .. toctree:: diff --git a/doc/howto/algorithm/index.rst b/doc/howto/algorithm/index.rst deleted file mode 100644 index b4ecbc48470..00000000000 --- a/doc/howto/algorithm/index.rst +++ /dev/null @@ -1,7 +0,0 @@ -Algorithm Configuration -======================= - -.. toctree:: - :maxdepth: 1 - - rnn/rnn.rst diff --git a/doc/howto/algorithm/rnn/bi_lstm.jpg b/doc/howto/algorithm/rnn/bi_lstm.jpg deleted file mode 120000 index f8f3b17691c..00000000000 --- a/doc/howto/algorithm/rnn/bi_lstm.jpg +++ /dev/null @@ -1 +0,0 @@ -../../../tutorials/sentiment_analysis/bi_lstm.jpg \ No newline at end of file diff --git a/doc/howto/algorithm/rnn/encoder-decoder-attention-model.png b/doc/howto/algorithm/rnn/encoder-decoder-attention-model.png deleted file mode 120000 index 88a1d3e5ac4..00000000000 --- a/doc/howto/algorithm/rnn/encoder-decoder-attention-model.png +++ /dev/null @@ -1 +0,0 @@ -../../../tutorials/text_generation/encoder-decoder-attention-model.png \ No newline at end of file diff --git a/doc/howto/cluster/cluster_train.md b/doc/howto/cluster/cluster_train.md index 6b68596dc12..1de34a6a994 100644 --- a/doc/howto/cluster/cluster_train.md +++ b/doc/howto/cluster/cluster_train.md @@ -1,4 +1,4 @@ -# Distributed Training +# How to Run Distributed Training In this article, we explain how to run distributed Paddle training jobs on clusters. We will create the distributed version of the single-process training example, [recommendation](https://github.com/baidu/Paddle/tree/develop/demo/recommendation). diff --git a/doc/howto/cmd_argument/argument_outline.md b/doc/howto/cmd_parameter/arguments.md similarity index 100% rename from doc/howto/cmd_argument/argument_outline.md rename to doc/howto/cmd_parameter/arguments.md diff --git a/doc/howto/cmd_argument/detail_introduction.md b/doc/howto/cmd_parameter/detail_introduction.md similarity index 100% rename from doc/howto/cmd_argument/detail_introduction.md rename to doc/howto/cmd_parameter/detail_introduction.md diff --git a/doc/howto/cmd_argument/index.md b/doc/howto/cmd_parameter/index.md similarity index 53% rename from doc/howto/cmd_argument/index.md rename to doc/howto/cmd_parameter/index.md index 90472c44cb9..48cf835de14 100644 --- a/doc/howto/cmd_argument/index.md +++ b/doc/howto/cmd_parameter/index.md @@ -1,5 +1,5 @@ -# Command Line Argument +# How to Set Command-line Parameters * [Use Case](use_case.md) -* [Argument Outline](argument_outline.md) +* [Arguments](arguments.md) * [Detailed Descriptions](detail_introduction.md) diff --git a/doc/howto/cmd_argument/use_case.md b/doc/howto/cmd_parameter/use_case.md similarity index 100% rename from doc/howto/cmd_argument/use_case.md rename to doc/howto/cmd_parameter/use_case.md diff --git a/doc/howto/contribute_to_paddle.md b/doc/howto/contribute_to_paddle.md index 1d03eb7362b..d1f12c6ab2f 100644 --- a/doc/howto/contribute_to_paddle.md +++ b/doc/howto/contribute_to_paddle.md @@ -1,4 +1,4 @@ -# Contribute Code +# How to Contribute Code We sincerely appreciate your contributions. You can use fork and pull request workflow to merge your code. diff --git a/doc/howto/deep_model/index.rst b/doc/howto/deep_model/index.rst new file mode 100644 index 00000000000..06ef443f62f --- /dev/null +++ b/doc/howto/deep_model/index.rst @@ -0,0 +1,7 @@ +How to Configure Deep Models +============================ + +.. toctree:: + :maxdepth: 1 + + rnn/rnn.rst diff --git a/doc/howto/algorithm/rnn/rnn.rst b/doc/howto/deep_model/rnn/rnn.rst similarity index 99% rename from doc/howto/algorithm/rnn/rnn.rst rename to doc/howto/deep_model/rnn/rnn.rst index 01d2caefb5c..da29b8efadd 100644 --- a/doc/howto/algorithm/rnn/rnn.rst +++ b/doc/howto/deep_model/rnn/rnn.rst @@ -42,7 +42,7 @@ Simple Gated Recurrent Neural Network Recurrent neural network process a sequence at each time step sequentially. An example of the architecture of LSTM is listed below. -.. image:: ./bi_lstm.jpg +.. image:: ../../../tutorials/sentiment_analysis/bi_lstm.jpg :align: center Generally speaking, a recurrent network perform the following operations from :math:`t=1` to :math:`t=T`, or reversely from :math:`t=T` to :math:`t=1`. @@ -101,7 +101,7 @@ Sequence to Sequence Model with Attention ----------------------------------------- We will use the sequence to sequence model with attention as an example to demonstrate how you can configure complex recurrent neural network models. An illustration of the sequence to sequence model with attention is shown in the following figure. -.. image:: ./encoder-decoder-attention-model.png +.. image:: ../../../tutorials/text_generation/encoder-decoder-attention-model.png :align: center In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`. diff --git a/doc/howto/dev/index.rst b/doc/howto/dev/index.rst deleted file mode 100644 index 876c42e9dbb..00000000000 --- a/doc/howto/dev/index.rst +++ /dev/null @@ -1,9 +0,0 @@ -Development Guide -================= - -.. toctree:: - :maxdepth: 2 - - layer.md - new_layer/new_layer.rst - source/index.rst diff --git a/doc/howto/dev/layer.md b/doc/howto/dev/layer.md deleted file mode 100644 index 1ce0cc5829d..00000000000 --- a/doc/howto/dev/layer.md +++ /dev/null @@ -1,5 +0,0 @@ -# Layer Documents - -* [Layer Python API](../../api/trainer_config_helpers/index.rst) -* [Layer Source Code](source/gserver/layers.rst) -* [Writing New Layers](new_layer/new_layer.rst) diff --git a/doc/howto/index.rst b/doc/howto/index.rst index ed8294b3c15..41877a64a56 100644 --- a/doc/howto/index.rst +++ b/doc/howto/index.rst @@ -1,12 +1,29 @@ -How to +HOW TO ======= +Usage +------- + .. toctree:: :maxdepth: 1 - cmd_argument/index.md + cmd_parameter/index.md + deep_model/index.rst cluster/cluster_train.md - algorithm/index.rst + +Development +------------ + +.. toctree:: + :maxdepth: 1 + + new_layer/index.rst + contribute_to_paddle.md + +Optimization +------------- + +.. toctree:: + :maxdepth: 1 + optimization/index.rst - dev/index.rst - contribute_to_paddle.md \ No newline at end of file diff --git a/doc/howto/dev/new_layer/FullyConnected.jpg b/doc/howto/new_layer/FullyConnected.jpg similarity index 100% rename from doc/howto/dev/new_layer/FullyConnected.jpg rename to doc/howto/new_layer/FullyConnected.jpg diff --git a/doc/howto/dev/new_layer/new_layer.rst b/doc/howto/new_layer/index.rst similarity index 99% rename from doc/howto/dev/new_layer/new_layer.rst rename to doc/howto/new_layer/index.rst index af8b76a3075..922bda5b0d8 100644 --- a/doc/howto/dev/new_layer/new_layer.rst +++ b/doc/howto/new_layer/index.rst @@ -1,6 +1,6 @@ -================== -Writing New Layers -================== +======================= +How to Write New Layers +======================= This tutorial will guide you to write customized layers in PaddlePaddle. We will utilize fully connected layer as an example to guide you through the following steps for writing a new layer. diff --git a/doc/howto/optimization/index.rst b/doc/howto/optimization/index.rst index c9e87e0778d..e2822a00982 100644 --- a/doc/howto/optimization/index.rst +++ b/doc/howto/optimization/index.rst @@ -1,5 +1,5 @@ -Performance Tuning -================== +How to Tune GPU Performance +=========================== .. toctree:: :maxdepth: 3 diff --git a/doc/howto/dev/source/api.rst b/doc/howto/source/api.rst similarity index 100% rename from doc/howto/dev/source/api.rst rename to doc/howto/source/api.rst diff --git a/doc/howto/dev/source/cuda/index.rst b/doc/howto/source/cuda/index.rst similarity index 100% rename from doc/howto/dev/source/cuda/index.rst rename to doc/howto/source/cuda/index.rst diff --git a/doc/howto/dev/source/cuda/matrix.rst b/doc/howto/source/cuda/matrix.rst similarity index 100% rename from doc/howto/dev/source/cuda/matrix.rst rename to 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b/doc/howto/source/trainer.rst similarity index 100% rename from doc/howto/dev/source/trainer.rst rename to doc/howto/source/trainer.rst diff --git a/doc/howto/dev/source/utils/customStackTrace.rst b/doc/howto/source/utils/customStackTrace.rst similarity index 100% rename from doc/howto/dev/source/utils/customStackTrace.rst rename to doc/howto/source/utils/customStackTrace.rst diff --git a/doc/howto/dev/source/utils/enum.rst b/doc/howto/source/utils/enum.rst similarity index 100% rename from doc/howto/dev/source/utils/enum.rst rename to doc/howto/source/utils/enum.rst diff --git a/doc/howto/dev/source/utils/index.rst b/doc/howto/source/utils/index.rst similarity index 100% rename from doc/howto/dev/source/utils/index.rst rename to doc/howto/source/utils/index.rst diff --git a/doc/howto/dev/source/utils/lock.rst b/doc/howto/source/utils/lock.rst similarity index 100% rename from doc/howto/dev/source/utils/lock.rst rename to doc/howto/source/utils/lock.rst diff --git a/doc/howto/dev/source/utils/queue.rst b/doc/howto/source/utils/queue.rst similarity index 100% rename from doc/howto/dev/source/utils/queue.rst rename to doc/howto/source/utils/queue.rst diff --git a/doc/howto/dev/source/utils/thread.rst b/doc/howto/source/utils/thread.rst similarity index 100% rename from doc/howto/dev/source/utils/thread.rst rename to doc/howto/source/utils/thread.rst diff --git a/doc/index.rst b/doc/index.rst index 36a410881ad..3555da1dfc8 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -4,7 +4,7 @@ PaddlePaddle Documentation .. toctree:: :maxdepth: 1 - introduction/index.rst + getstarted/index.rst tutorials/index.md howto/index.rst api/index.rst diff --git a/doc/tutorials/index.md b/doc/tutorials/index.md index c845ca229cb..ebf5397391e 100644 --- a/doc/tutorials/index.md +++ b/doc/tutorials/index.md @@ -1,4 +1,4 @@ -# Tutorials +# TUTORIALS There are serveral examples and demos here. ## Image -- GitLab From 4d487c6f350b168f5e24094adeacb3c193d5d888 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Tue, 29 Nov 2016 07:40:17 +0000 Subject: [PATCH 115/347] Integrate warp-ctc as WarpCTCLayer, including unitest and layer interface. --- .gitmodules | 0 CMakeLists.txt | 5 + cmake/util.cmake | 5 + paddle/cuda/CMakeLists.txt | 34 ++- paddle/cuda/include/hl_dso_loader.h | 12 +- paddle/cuda/include/hl_gpu.h | 1 + paddle/cuda/include/hl_sequence.h | 33 +++ paddle/cuda/include/hl_warpctc_wrap.h | 94 +++++++ paddle/cuda/include/stub/hl_sequence_stub.h | 9 + paddle/cuda/src/hl_cuda_sequence.cu | 118 +++++++++ paddle/cuda/src/hl_cudart_wrap.cc | 1 + paddle/cuda/src/hl_dso_loader.cc | 25 +- paddle/cuda/src/hl_warpctc_wrap.cc | 157 +++++++++++ paddle/gserver/layers/WarpCTCLayer.cpp | 229 ++++++++++++++++ paddle/gserver/layers/WarpCTCLayer.h | 65 +++++ paddle/gserver/tests/CMakeLists.txt | 7 + paddle/gserver/tests/test_WarpCTCLayer.cpp | 247 ++++++++++++++++++ proto/ModelConfig.proto.m4 | 2 + python/paddle/trainer/config_parser.py | 21 ++ .../paddle/trainer_config_helpers/layers.py | 79 ++++++ .../protostr/test_cost_layers.protostr | 17 ++ .../tests/configs/test_cost_layers.py | 2 + 22 files changed, 1140 insertions(+), 23 deletions(-) create mode 100644 .gitmodules create mode 100644 paddle/cuda/include/hl_warpctc_wrap.h create mode 100644 paddle/cuda/src/hl_warpctc_wrap.cc create mode 100644 paddle/gserver/layers/WarpCTCLayer.cpp create mode 100644 paddle/gserver/layers/WarpCTCLayer.h create mode 100644 paddle/gserver/tests/test_WarpCTCLayer.cpp diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 00000000000..e69de29bb2d diff --git a/CMakeLists.txt b/CMakeLists.txt index af193c27ae7..e5e54cc8cfc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -94,6 +94,11 @@ endif() if(NOT WITH_GPU) add_definitions(-DPADDLE_ONLY_CPU) add_definitions(-DHPPL_STUB_FUNC) + + if(WITH_DSO) + add_definitions(-DPADDLE_USE_DSO) + endif(WITH_DSO) + list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu) else() if(${CUDA_VERSION_MAJOR} GREATER 6) diff --git a/cmake/util.cmake b/cmake/util.cmake index a8282f07184..11641f6064b 100644 --- a/cmake/util.cmake +++ b/cmake/util.cmake @@ -148,6 +148,11 @@ function(link_paddle_exe TARGET_NAME) target_link_libraries(${TARGET_NAME} rt) endif() endif() + + if(NOT WITH_DSO) + target_link_libraries(${TARGET_NAME} + ${WARPCTC_LIBRARY}) + endif() endfunction() # link_paddle_test diff --git a/paddle/cuda/CMakeLists.txt b/paddle/cuda/CMakeLists.txt index 11dbfb54b26..7e45d3d5789 100755 --- a/paddle/cuda/CMakeLists.txt +++ b/paddle/cuda/CMakeLists.txt @@ -15,20 +15,29 @@ else() endif() set(CUDA_CXX_WITH_GPU_SOURCES + src/hl_cudart_wrap.cc src/hl_cuda_cublas.cc src/hl_cuda_cudnn.cc - src/hl_cuda_device.cc) + src/hl_cuda_device.cc + ) -set_source_files_properties(${CUDA_CXX_WITH_GPU_SOURCES} - PROPERTIES COMPILE_FLAGS "-D__NVCC__") +if(WITH_GPU) + set(CUDA_CXX_SOURCES + src/hl_dso_loader.cc + src/hl_warpctc_wrap.cc + ${CUDA_CXX_WITH_GPU_SOURCES}) + + set_source_files_properties(${CUDA_CXX_SOURCES} + PROPERTIES COMPILE_FLAGS "-D__NVCC__") +else() + set(CUDA_CXX_SOURCES + src/hl_dso_loader.cc + src/hl_warpctc_wrap.cc) +endif() set_source_files_properties(${AVX_SOURCES} PROPERTIES COMPILE_FLAGS "-mavx") -set(CUDA_DSO_SOURCES - src/hl_dso_loader.cc - src/hl_cudart_wrap.cc) - set(CUDA_CU_SOURCES src/hl_perturbation_util.cu src/hl_cuda_aggregate.cu @@ -44,6 +53,7 @@ set(CUDA_CU_SOURCES set(CUDA_HEADERS include/hl_time.h include/hl_dso_loader.h + include/hl_warpctc_wrap.h include/hl_sequence.h include/hl_cuda_cublas.h include/hl_batch_transpose.h @@ -75,14 +85,14 @@ if(WITH_GPU) cuda_add_library(paddle_cuda ${CUDA_SOURCES} ${CUDA_CU_SOURCES} - ${CUDA_DSO_SOURCES} - ${CUDA_CXX_WITH_GPU_SOURCES}) + ${CUDA_CXX_SOURCES}) else() - add_library(paddle_cuda ${CUDA_SOURCES}) + add_library(paddle_cuda + ${CUDA_SOURCES} + ${CUDA_CXX_SOURCES}) endif() add_style_check_target(paddle_cuda ${CUDA_SOURCES} ${CUDA_HEADERS} - ${CUDA_DSO_SOURCES} - ${CUDA_CXX_WITH_GPU_SOURCES}) + ${CUDA_CXX_SOURCES}) diff --git a/paddle/cuda/include/hl_dso_loader.h b/paddle/cuda/include/hl_dso_loader.h index 1eb9f9ca888..c52066e3d7e 100644 --- a/paddle/cuda/include/hl_dso_loader.h +++ b/paddle/cuda/include/hl_dso_loader.h @@ -18,10 +18,6 @@ limitations under the License. */ #include #include #include -#include -#include -#include -#include #include "hl_base.h" /** @@ -56,4 +52,12 @@ void GetCudartDsoHandle(void** dso_handle); */ void GetCurandDsoHandle(void** dso_handle); +/** + * @brief load the DSO of warp-ctc + * + * @param **dso_handle dso handler + * + */ +void GetWarpctcDsoHandle(void** dso_handle); + #endif // HL_DSO_LOADER_H_ diff --git a/paddle/cuda/include/hl_gpu.h b/paddle/cuda/include/hl_gpu.h index 3be0df3b93b..6dd6d132127 100644 --- a/paddle/cuda/include/hl_gpu.h +++ b/paddle/cuda/include/hl_gpu.h @@ -25,6 +25,7 @@ limitations under the License. */ #include "hl_sparse.h" #include "hl_lstm.h" #include "hl_sequence.h" +#include "hl_warpctc_wrap.h" #ifdef HPPL_STUB_FUNC #include "stub/hl_cuda_stub.h" diff --git a/paddle/cuda/include/hl_sequence.h b/paddle/cuda/include/hl_sequence.h index bb5124df44b..b98d7bdeafe 100644 --- a/paddle/cuda/include/hl_sequence.h +++ b/paddle/cuda/include/hl_sequence.h @@ -172,6 +172,39 @@ extern void hl_sequence2batch_add(real* batch, int batchCount, bool seq2batch); +/** + * @brief Memory copy from sequence to batch, + * while padding all sequences to the same length. + * + * if seq2batch == true + * + * copy from sequence to batch: + * batch[i] = sequence[sequenceStartPositions[i]] + * + * if seq2batch == false + * + * copy from batch to sequence: + * sequence[sequenceStartPositions[i]] = batch[i] + * + * @param[in,out] batch batch matrix. + * @param[in,out] sequence sequence matrix. + * @param[in] sequenceStartPositions index vector. + * @param[in] sequenceWidth width of sequence. + * @param[in] maxSequenceLength maximum length of sequences. + * @param[in] numSequences number of sequences. + * @param[in] normByTimes whether dividing sequence's length. + * @param[in] seq2batch copy direction. + * + */ +extern void hl_sequence2batch_copy_padding(real* batch, + real* sequence, + const int* sequenceStartPositions, + const size_t sequenceWidth, + const size_t maxSequenceLength, + const size_t numSequences, + bool normByTimes, + bool seq2batch); + /** * @brief dst = Op(src), src is sequence. * diff --git a/paddle/cuda/include/hl_warpctc_wrap.h b/paddle/cuda/include/hl_warpctc_wrap.h new file mode 100644 index 00000000000..9d2379a024f --- /dev/null +++ b/paddle/cuda/include/hl_warpctc_wrap.h @@ -0,0 +1,94 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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. */ + +#ifndef HL_WARPCTC_WRAP_H_ +#define HL_WARPCTC_WRAP_H_ + +#include "hl_base.h" +/// #include "hl_cuda.h" +#include "warp-ctc/include/ctc.h" + +typedef ctcStatus_t hl_warpctc_status_t; +typedef ctcOptions hl_warpctc_options_t; + +/** + * @brief Init ctc options. + * + * @param[in] blank blank label used in ctc loss function. + * @param[in] useGpu whether use gpu. + * @param[out] options handle to store cpu or gpu informations. + * + */ +extern void hl_warpctc_init(const size_t blank, + bool useGpu, + hl_warpctc_options_t* options); + +/** + * @brief Compute the connectionist temporal classification loss, + * and optionally compute the gradient with respect to the inputs. + * + * if batchGrad == nullptr + * + * only compute the ctc loss. + * + * if batchGrad != nullptr + * + * compute both ctc loss and gradient. + * + * @param[in] batchInput batch matrix of input probabilities, + * in maxSequenceLength x numSequence x numClasses + * (row-major) format. + * @param[out] batchGrad batch matrix of gradient. + * @param[in] cpuLabels labels always in CPU memory. + * @param[in] cpuLabelLengths length of all labels in CPU memory. + * @param[in] cpuInputLengths length of all sequences in CPU memory. + * @param[in] numClasses number of possible output symbols. + * @param[in] numSequences number of sequence. + * @param[out] cpuCosts cost of each sequence in CPU memory. + * @param[out] workspace workspace to store some temporary results. + * @param[in] options handle to store cpu or gpu informations. + * + */ +extern void hl_warpctc_compute_loss(const real* batchInput, + real* batchGrad, + const int* cpuLabels, + const int* cpuLabelLengths, + const int* cpuInputLengths, + const size_t numClasses, + const size_t numSequences, + real* cpuCosts, + void* workspace, + hl_warpctc_options_t* options); + +/** + * @brief Compute the required workspace size. + * There is no memory allocated operations within warp-ctc. + * + * @param[in] cpuLabelLengths length of all labels in CPU memory. + * @param[in] cpuInputLengths length of all sequences in CPU memory. + * @param[in] numClasses number of possible output symbols. + * @param[in] numSequences number of sequence. + * @param[in] options handle to store cpu or gpu informations. + * @param[out] bytes pointer to a scalar where the memory + * requirement in bytes will be placed. + * + */ +extern void hl_warpctc_get_workspace_size(const int* cpuLabelLengths, + const int* cpuInputLengths, + const size_t numClasses, + const size_t numSequences, + hl_warpctc_options_t* options, + size_t* bytes); + +#endif // HL_WARPCTC_WRAP_H_ diff --git a/paddle/cuda/include/stub/hl_sequence_stub.h b/paddle/cuda/include/stub/hl_sequence_stub.h index 381f0a6f26c..3343463a8d5 100644 --- a/paddle/cuda/include/stub/hl_sequence_stub.h +++ b/paddle/cuda/include/stub/hl_sequence_stub.h @@ -70,6 +70,15 @@ inline void hl_sequence2batch_add(real* batch, int batchCount, bool seq2batch) {} +inline void hl_sequence2batch_copy_padding(real* batch, + real* sequence, + const int* sequenceStartPositions, + const size_t sequenceWidth, + const size_t maxSequenceLength, + const size_t numSequences, + bool normByTimes, + bool seq2batch) {} + inline void hl_sequence_avg_forward(real* dst, real* src, const int* starts, diff --git a/paddle/cuda/src/hl_cuda_sequence.cu b/paddle/cuda/src/hl_cuda_sequence.cu index 63824eaa4c2..0f1d7204393 100644 --- a/paddle/cuda/src/hl_cuda_sequence.cu +++ b/paddle/cuda/src/hl_cuda_sequence.cu @@ -447,6 +447,124 @@ void hl_sequence2batch_add(real *batch, CHECK_SYNC("hl_sequence2batch_add failed"); } +template +__global__ +void KeSequence2BatchPadding(real* batch, + real* sequence, + const int* sequenceStartPositions, + const size_t sequenceWidth, + const size_t maxSequenceLength, + const size_t numSequences) { + int batchIdx = blockIdx.y; + int sequenceStart = sequenceStartPositions[batchIdx]; + int sequenceLength = sequenceStartPositions[batchIdx + 1] - sequenceStart; + + int sequenceIdx = blockIdx.x * blockDim.y + threadIdx.y; + int batchBaseIdx = (sequenceIdx * numSequences + batchIdx) * sequenceWidth; + int sequenceBaseIdx = (sequenceStart + sequenceIdx) * sequenceWidth; + + if (sequenceIdx < sequenceLength) { + if (seq2batch) { + /* sequence -> batch */ + if (normByTimes) { + real scale = 1.0f / (real)sequenceLength; + for (int i = threadIdx.x; i < sequenceWidth; i += blockDim.x) { + batch[batchBaseIdx + i] = scale * sequence[sequenceBaseIdx + i]; + } + } else { + for (int i = threadIdx.x; i < sequenceWidth; i += blockDim.x) { + batch[batchBaseIdx + i] = sequence[sequenceBaseIdx + i]; + } + } + } else { + /* batch -> sequence */ + if (normByTimes) { + real scale = 1.0f / (real)sequenceLength; + for (int i = threadIdx.x; i < sequenceWidth; i += blockDim.x) { + sequence[sequenceBaseIdx + i] = scale * batch[batchBaseIdx + i]; + } + } else { + for (int i = threadIdx.x; i < sequenceWidth; i += blockDim.x) { + sequence[sequenceBaseIdx + i] = batch[batchBaseIdx + i]; + } + } + } + } else if (sequenceIdx < maxSequenceLength) { + if (seq2batch) { + /* sequence -> batch */ + for (int i = threadIdx.x; i < sequenceWidth; i += blockDim.x) { + batch[batchBaseIdx + i] = 0; + } + } + } +} + +void hl_sequence2batch_copy_padding(real* batch, + real* sequence, + const int* sequenceStartPositions, + const size_t sequenceWidth, + const size_t maxSequenceLength, + const size_t numSequences, + bool normByTimes, + bool seq2batch) { + CHECK_NOTNULL(batch); + CHECK_NOTNULL(sequence); + CHECK_NOTNULL(sequenceStartPositions); + + if (!normByTimes && numSequences == 1) { + size_t elementCount = maxSequenceLength * sequenceWidth; + if (seq2batch) { + /* sequence -> batch */ + hl_memcpy_device2device(batch, sequence, sizeof(real) * elementCount); + } else { + /* batch -> sequence */ + hl_memcpy_device2device(sequence, batch, sizeof(real) * elementCount); + } + return; + } + + const int CUDA_BLOCK_SIZE = 512; + + /* At least use 32 threads to copy sequenceWidth elements, + and at least 8 elements for each thread. */ + int blockDimX = ((((sequenceWidth + 7) >> 3) + 31) >> 5) << 5; + blockDimX = (blockDimX < CUDA_BLOCK_SIZE) ? blockDimX : CUDA_BLOCK_SIZE; + + int blockDimY = CUDA_BLOCK_SIZE / blockDimX; + dim3 threads(blockDimX, blockDimY); + + int gridDimX = (maxSequenceLength * blockDimX + CUDA_BLOCK_SIZE - 1) / + CUDA_BLOCK_SIZE; + int gridDimY = numSequences; + dim3 grid(gridDimX, gridDimY); + + if (seq2batch) { + /* sequence -> batch */ + if (normByTimes) { + KeSequence2BatchPadding<1, 1><<< grid, threads, 0, STREAM_DEFAULT >>>( + batch, sequence, sequenceStartPositions, + sequenceWidth, maxSequenceLength, numSequences); + } else { + KeSequence2BatchPadding<0, 1><<< grid, threads, 0, STREAM_DEFAULT >>>( + batch, sequence, sequenceStartPositions, + sequenceWidth, maxSequenceLength, numSequences); + } + } else { + /* batch -> sequence */ + if (normByTimes) { + KeSequence2BatchPadding<1, 0><<< grid, threads, 0, STREAM_DEFAULT >>>( + batch, sequence, sequenceStartPositions, + sequenceWidth, maxSequenceLength, numSequences); + } else { + KeSequence2BatchPadding<0, 0><<< grid, threads, 0, STREAM_DEFAULT >>>( + batch, sequence, sequenceStartPositions, + sequenceWidth, maxSequenceLength, numSequences); + } + } + + CHECK_SYNC("hl_sequence2batch_copy_padding failed"); +} + __device__ inline float my_rsqrt(float x) { return rsqrtf(x); } diff --git a/paddle/cuda/src/hl_cudart_wrap.cc b/paddle/cuda/src/hl_cudart_wrap.cc index ff6b830b7ad..a95f5557afb 100644 --- a/paddle/cuda/src/hl_cudart_wrap.cc +++ b/paddle/cuda/src/hl_cudart_wrap.cc @@ -15,6 +15,7 @@ limitations under the License. */ #ifdef PADDLE_USE_DSO #include +#include #include "hl_dso_loader.h" /** diff --git a/paddle/cuda/src/hl_dso_loader.cc b/paddle/cuda/src/hl_dso_loader.cc index 1a3ce08619f..a6ea2a3b9f4 100644 --- a/paddle/cuda/src/hl_dso_loader.cc +++ b/paddle/cuda/src/hl_dso_loader.cc @@ -30,6 +30,8 @@ P_DEFINE_string(cuda_dir, "build-in function in cudart already ran before main entry). " "If default, dlopen will search cuda from LD_LIBRARY_PATH"); +P_DEFINE_string(warpctc_dir, "", "Specify path for loading libwarpctc.so."); + static inline std::string join(const std::string& part1, const std::string& part2) { // directory separator @@ -92,27 +94,28 @@ static inline void GetDsoHandleFromSearchPath(const std::string& search_root, *dso_handle = dlopen(dlPath.c_str(), dynload_flags); // if not found, search from default path if (nullptr == *dso_handle) { - LOG(WARNING) << "Failed to find cuda library: " << dlPath; + LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " (" + << dlerror() << ")"; dlPath = dso_name; GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags); } } - CHECK(nullptr != *dso_handle) << "Failed to find cuda library: " << dlPath - << std::endl + CHECK(nullptr != *dso_handle) << "Failed to find dynamic library: " << dlPath + << " (" << dlerror() << ") \n" << "Please specify its path correctly using " - "one of the following ways: \n" // NOLINT + "one of the following ways: \n" << "Method 1. set cuda and cudnn lib path at " "runtime. " << "http://www.paddlepaddle.org/doc/ui/" "cmd_argument/" - "argument_outline.html \n" // NOLINT + "argument_outline.html \n" << "For instance, issue command: paddle train " "--use_gpu=1 " << "--cuda_dir=/usr/local/cuda/lib64 " "--cudnn_dir=/usr/local/cudnn/lib " - "...\n" // NOLINT + "...\n" << "Method 2. set environment variable " "LD_LIBRARY_PATH on Linux or " @@ -124,7 +127,7 @@ static inline void GetDsoHandleFromSearchPath(const std::string& search_root, "DYLD_LIBRARY_PATH is impossible " << "unless System Integrity Protection (SIP) " "is disabled. However, " - "method 1 " // NOLINT + "method 1 " << "always work well."; } @@ -159,3 +162,11 @@ void GetCurandDsoHandle(void** dso_handle) { GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.so", dso_handle); #endif } + +void GetWarpctcDsoHandle(void** dso_handle) { +#if defined(__APPLE__) || defined(__OSX__) + GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.dylib", dso_handle); +#else + GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.so", dso_handle); +#endif +} diff --git a/paddle/cuda/src/hl_warpctc_wrap.cc b/paddle/cuda/src/hl_warpctc_wrap.cc new file mode 100644 index 00000000000..99db0f242df --- /dev/null +++ b/paddle/cuda/src/hl_warpctc_wrap.cc @@ -0,0 +1,157 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 +#include "hl_warpctc_wrap.h" +#include "hl_dso_loader.h" +#include "paddle/utils/Logging.h" + +namespace dynload { + +std::once_flag warpctc_dso_flag; +void* warpctc_dso_handle = nullptr; + +/** + * The following macro definition can generate structs + * (for each function) to dynamic load warpctc routine + * via operator overloading. When PADDLE_USE_DSO is + * false, you need to add the path of libwarp-ctc.so to + * the linked-libs of paddle or to LD_PRELOAD. + */ +#ifdef PADDLE_USE_DSO +#define DYNAMIC_LOAD_WARPCTC_WRAP(__name, __type) \ + struct DynLoad__##__name { \ + template \ + __type operator()(Args... args) { \ + typedef __type (*warpctcFunc)(Args...); \ + std::call_once( \ + warpctc_dso_flag, GetWarpctcDsoHandle, &warpctc_dso_handle); \ + void* p_##_name = dlsym(warpctc_dso_handle, #__name); \ + return reinterpret_cast(p_##_name)(args...); \ + } \ + } __name; // struct DynLoad__##__name +#else +#define DYNAMIC_LOAD_WARPCTC_WRAP(__name, __type) \ + struct DynLoad__##__name { \ + template \ + __type operator()(Args... args) { \ + return __name(args...); \ + } \ + } __name; // struct DynLoad__##__name +#endif + +// include all needed warp-ctc functions +DYNAMIC_LOAD_WARPCTC_WRAP(get_warpctc_version, int) +DYNAMIC_LOAD_WARPCTC_WRAP(ctcGetStatusString, const char*) +DYNAMIC_LOAD_WARPCTC_WRAP(compute_ctc_loss, hl_warpctc_status_t) +DYNAMIC_LOAD_WARPCTC_WRAP(get_workspace_size, hl_warpctc_status_t) + +#undef DYNAMIC_LOAD_WARPCTC_WRAP + +} /* namespace dynload */ + +#define WARPCTC_GET_VERSION dynload::get_warpctc_version +#define WARPCTC_GET_STATUS_STRING dynload::ctcGetStatusString + +#ifndef PADDLE_TYPE_DOUBLE +#define WARPCTC_COMPUTE_LOSS dynload::compute_ctc_loss +#define WARPCTC_GET_WORKSPACE_SIZE dynload::get_workspace_size +#else +#define WARPCTC_LOG_FATAL \ + LOG(FATAL) << "warp-ctc [version " << g_warpctcVersion \ + << "] Error: not support double precision." +#define WARPCTC_COMPUTE_LOSS(...) WARPCTC_LOG_FATAL(__VA_ARGS__) +#define WARPCTC_GET_WORKSPACE_SIZE(...) WARPCTC_LOG_FATAL(__VA_ARGS__) +#endif + +/** + * Check build-in warp-ctc function using glog and it also + * support << operator for more details error info. + */ +static int g_warpctcVersion = -1; +#define CHECK_WARPCTC(warpctcStat) \ + CHECK_EQ(CTC_STATUS_SUCCESS, warpctcStat) \ + << "warp-ctc [version " << g_warpctcVersion \ + << "] Error: " << WARPCTC_GET_STATUS_STRING(warpctcStat) << " " + +void hl_warpctc_init(const size_t blank, + bool useGpu, + hl_warpctc_options_t* options) { + CHECK_NOTNULL(options); + + g_warpctcVersion = WARPCTC_GET_VERSION(); + + if (useGpu) { +#ifdef __NVCC__ + options->loc = CTC_GPU; + options->stream = STREAM_DEFAULT; +#else + LOG(FATAL) << "[warpctc init] GPU is not enabled."; +#endif + } else { + options->loc = CTC_CPU; + options->num_threads = 1; + } + + options->blank_label = blank; +} + +void hl_warpctc_compute_loss(const real* batchInput, + real* batchGrad, + const int* cpuLabels, + const int* cpuLabelLengths, + const int* cpuInputLengths, + const size_t numClasses, + const size_t numSequences, + real* cpuCosts, + void* workspace, + hl_warpctc_options_t* options) { + CHECK_NOTNULL(batchInput); + CHECK_NOTNULL(cpuLabels); + CHECK_NOTNULL(cpuLabelLengths); + CHECK_NOTNULL(cpuInputLengths); + CHECK_NOTNULL(cpuCosts); + CHECK_NOTNULL(workspace); + CHECK_NOTNULL(options); + + CHECK_WARPCTC(WARPCTC_COMPUTE_LOSS(batchInput, + batchGrad, + cpuLabels, + cpuLabelLengths, + cpuInputLengths, + numClasses, + numSequences, + cpuCosts, + workspace, + *options)); +} + +void hl_warpctc_get_workspace_size(const int* cpuLabelLengths, + const int* cpuInputLengths, + const size_t numClasses, + const size_t numSequences, + hl_warpctc_options_t* options, + size_t* bytes) { + CHECK_NOTNULL(cpuLabelLengths); + CHECK_NOTNULL(cpuInputLengths); + CHECK_NOTNULL(options); + CHECK_NOTNULL(bytes); + + CHECK_WARPCTC(WARPCTC_GET_WORKSPACE_SIZE(cpuLabelLengths, + cpuInputLengths, + numClasses, + numSequences, + *options, + bytes)); +} diff --git a/paddle/gserver/layers/WarpCTCLayer.cpp b/paddle/gserver/layers/WarpCTCLayer.cpp new file mode 100644 index 00000000000..b99e9b9c7a6 --- /dev/null +++ b/paddle/gserver/layers/WarpCTCLayer.cpp @@ -0,0 +1,229 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 "WarpCTCLayer.h" + +namespace paddle { + +REGISTER_LAYER(warp_ctc, WarpCTCLayer); + +bool WarpCTCLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parament class */ + Layer::init(layerMap, parameterMap); + + CHECK_EQ(inputLayers_.size(), 2UL); + + /* The inputLayers_[0] must be sequence output without softmax */ + numClasses_ = config_.size(); + CHECK_GE(numClasses_, 2UL); + CHECK_EQ(numClasses_, inputLayers_[0]->getSize()); + + blank_ = config_.blank(); + CHECK_GE(blank_, 0UL); + CHECK_LT(blank_, numClasses_); + + normByTimes_ = config_.norm_by_times(); + + // We don't need sequenceStartPositions because each sample of output_ is + // for the cost of one sequence. + setNeedSequenceInfo(false); + + return true; +} + +void WarpCTCLayer::forward(PassType passType) { + Layer::forward(passType); + + const Argument& output = getInput(0); + const Argument& labels = getInput(1); + + CHECK(output.sequenceStartPositions); + CHECK(labels.sequenceStartPositions); + CHECK(labels.ids); + + size_t numSequences = labels.sequenceStartPositions->getSize() - 1; + CHECK_EQ(numSequences, output.sequenceStartPositions->getSize() - 1); + + resizeOutput(numSequences, 1); + + const int* cpuLabelStartPositions = + labels.sequenceStartPositions->getData(false); + const int* cpuOutputStartPositions = + output.sequenceStartPositions->getData(false); + + std::vector cpuLabelLengths(numSequences); + std::vector cpuOutputLengths(numSequences); + for (size_t i = 0; i < numSequences; i++) { + cpuLabelLengths[i] = + cpuLabelStartPositions[i + 1] - cpuLabelStartPositions[i]; + cpuOutputLengths[i] = + cpuOutputStartPositions[i + 1] - cpuOutputStartPositions[i]; + } + + /* Get the maximum sequence length */ + maxSequenceLength_ = 0; + maxSequenceLength_ = *std::max_element( + cpuOutputLengths.data(), cpuOutputLengths.data() + numSequences); + + Matrix::resizeOrCreate(batchValue_, + /* height */ numSequences * maxSequenceLength_, + /* width */ numClasses_, + /* trans */ false, + /* useGpu */ useGpu_); + + Matrix::resizeOrCreate(batchGrad_, + /* height */ numSequences * maxSequenceLength_, + /* width */ numClasses_, + /* trans */ false, + /* useGpu */ useGpu_); + batchGrad_->zeroMem(); + + seq2batchPadding(output.value, batchValue_, output.sequenceStartPositions); + + /* labels always in CPU memory */ + IVector::resizeOrCreate(cpuLabels_, + /* size */ (labels.ids)->getSize(), + /* useGpu */ false); + cpuLabels_->copyFrom(*(labels.ids)); + + /* labels always in CPU memory */ + Matrix::resizeOrCreate(cpuCosts_, + /* width */ numSequences, + /* height */ 1, + /* trans */ false, + /* useGpu */ false); + + /* Init warp-ctc options */ + hl_warpctc_options_t options; + hl_warpctc_init(blank_, useGpu_, &options); + + /* Get the needed workspace size */ + size_t workspaceBytes = 0; + hl_warpctc_get_workspace_size(cpuLabelLengths.data(), + cpuOutputLengths.data(), + numClasses_, + numSequences, + &options, + &workspaceBytes); + CHECK_GT(workspaceBytes, 0UL); + + size_t workspaceLength = workspaceBytes / sizeof(real) + 1; + Vector::resizeOrCreate(workspace_, + /* size */ workspaceLength, + /* useGpu */ useGpu_); + + hl_warpctc_compute_loss(batchValue_->getData(), + batchGrad_->getData(), + cpuLabels_->getData(), + cpuLabelLengths.data(), + cpuOutputLengths.data(), + numClasses_, + numSequences, + cpuCosts_->getData(), + workspace_->getData(), + &options); + + /* Copy the costs */ + output_.value->copyFrom(*cpuCosts_); +} + +void WarpCTCLayer::backward(const UpdateCallback& callback) { + (void)callback; + + const Argument& output = getInput(0); + CHECK(batchGrad_); + + batch2seqPadding( + output.grad, batchGrad_, output.sequenceStartPositions, normByTimes_); +} + +void WarpCTCLayer::seq2batchPadding(const MatrixPtr& seqValue, + MatrixPtr& batchValue, + const ICpuGpuVectorPtr& seqStartPositions) { + size_t numSequences = seqStartPositions->getSize() - 1; + const int* seqStartPositionsData = seqStartPositions->getData(useGpu_); + + real* seqData = seqValue->getData(); + real* batchData = batchValue->getData(); + if (useGpu_) { + hl_sequence2batch_copy_padding(batchData, + seqData, + seqStartPositionsData, + numClasses_, + maxSequenceLength_, + numSequences, + false, + true); + } else { + for (size_t i = 0; i < maxSequenceLength_; i++) { + for (size_t j = 0; j < numSequences; j++) { + size_t sequenceStart = seqStartPositionsData[j]; + size_t sequenceLength = + seqStartPositionsData[j + 1] - seqStartPositionsData[j]; + if (i < sequenceLength) { + memcpy(batchData + (i * numSequences + j) * numClasses_, + seqData + (sequenceStart + i) * numClasses_, + numClasses_ * sizeof(real)); + } else { + memset(batchData + (i * numSequences + j) * numClasses_, + 0, + numClasses_ * sizeof(real)); + } + } + } + } +} + +void WarpCTCLayer::batch2seqPadding(const MatrixPtr& seqValue, + MatrixPtr& batchValue, + const ICpuGpuVectorPtr& seqStartPositions, + bool normByTimes) { + size_t numSequences = seqStartPositions->getSize() - 1; + const int* seqStartPositionsData = seqStartPositions->getData(useGpu_); + + real* seqData = seqValue->getData(); + real* batchData = batchValue->getData(); + if (useGpu_) { + hl_sequence2batch_copy_padding(batchData, + seqData, + seqStartPositionsData, + numClasses_, + maxSequenceLength_, + numSequences, + normByTimes, + false); + } else { + for (size_t i = 0; i < numSequences; i++) { + int sequenceStart = seqStartPositionsData[i]; + int sequenceLength = + seqStartPositionsData[i + 1] - seqStartPositionsData[i]; + for (int j = 0; j < sequenceLength; j++) { + if (normByTimes) { + for (size_t k = 0; k < numClasses_; k++) { + seqData[(sequenceStart + j) * numClasses_ + k] = + batchData[(j * numSequences + i) * numClasses_ + k] / + sequenceLength; + } + } else { + memcpy(seqData + (sequenceStart + j) * numClasses_, + batchData + (j * numSequences + i) * numClasses_, + numClasses_ * sizeof(real)); + } + } + } + } +} + +} // namespace paddle diff --git a/paddle/gserver/layers/WarpCTCLayer.h b/paddle/gserver/layers/WarpCTCLayer.h new file mode 100644 index 00000000000..1b0f5ba267a --- /dev/null +++ b/paddle/gserver/layers/WarpCTCLayer.h @@ -0,0 +1,65 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 "Layer.h" + +namespace paddle { + +/** + * @brief A layer integrating the open-source warp-ctc library + * to compute connectionist + * temporal classification cost. + * + * The config file api is warp_ctc_layer. + */ +class WarpCTCLayer : public Layer { +public: + explicit WarpCTCLayer(const LayerConfig& config) : Layer(config) {} + ~WarpCTCLayer() {} + + virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + virtual void forward(PassType passType); + virtual void backward(const UpdateCallback& callback); + +protected: + /** + * sequence matrix and batch matrix copy: + * sequence (s0, s0, s0, s0; s1, s1; s2, s2, s2; s3) + * batch (s0, s1, s2, s3; s0, s1, s2, 0; s0, 0, s2, 0; s0, 0, 0, 0) + */ + void seq2batchPadding(const MatrixPtr& seqValue, + MatrixPtr& batchValue, + const ICpuGpuVectorPtr& seqStartPositions); + void batch2seqPadding(const MatrixPtr& seqValue, + MatrixPtr& batchValue, + const ICpuGpuVectorPtr& seqStartPositions, + bool normByTimes); + +protected: + size_t numClasses_; + size_t blank_; + size_t maxSequenceLength_; + bool normByTimes_; + + MatrixPtr batchValue_; + MatrixPtr batchGrad_; + VectorPtr workspace_; + + IVectorPtr cpuLabels_; + MatrixPtr cpuCosts_; +}; + +} // namespace paddle diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index 0651d0b4733..5427dc062d5 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -62,6 +62,13 @@ add_unittest(test_RecurrentLayer test_RecurrentLayer.cpp TestUtil.cpp) +############### test_WarpCTCLayer ####################### +if(NOT WITH_DOUBLE) + add_unittest(test_WarpCTCLayer + test_WarpCTCLayer.cpp + TestUtil.cpp) +endif() + ############### test_RecurrentGradientMachine ############### # TODO(yuyang18): There is some bug in test_RecurrentGradientMachine # I will fix it. diff --git a/paddle/gserver/tests/test_WarpCTCLayer.cpp b/paddle/gserver/tests/test_WarpCTCLayer.cpp new file mode 100644 index 00000000000..5289c9892ce --- /dev/null +++ b/paddle/gserver/tests/test_WarpCTCLayer.cpp @@ -0,0 +1,247 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 +#include +#include "paddle/gserver/layers/Layer.h" +#include "paddle/gserver/layers/DataLayer.h" +#include "paddle/gserver/layers/CTCLayer.h" +#include "paddle/gserver/layers/WarpCTCLayer.h" +#include "ModelConfig.pb.h" + +#include "TestUtil.h" + +using namespace paddle; // NOLINT +using namespace std; // NOLINT + +P_DECLARE_bool(use_gpu); + +const real* getData(const Matrix& matrix) { + if (matrix.useGpu()) { + MatrixPtr cpuMatrix = Matrix::create( + matrix.getWidth(), matrix.getHeight(), matrix.isTransposed(), false); + cpuMatrix->copyFrom(matrix); + return cpuMatrix->getData(); + } else { + return matrix.getData(); + } +} + +void checkError(const Matrix& matrix1, const Matrix& matrix2) { + CHECK_EQ(matrix1.getHeight(), matrix2.getHeight()); + CHECK_EQ(matrix1.getWidth(), matrix2.getWidth()); + CHECK_EQ(matrix1.isTransposed(), matrix2.isTransposed()); +#ifndef PADDLE_TYPE_DOUBLE + real err = 1e-3; +#else + real err = 1e-10; +#endif + + int height = matrix1.getHeight(); + int width = matrix1.getWidth(); + + const real* data1 = getData(matrix1); + const real* data2 = getData(matrix2); + int count = 0; + for (int i = 0; i < height; i++) { + for (int j = 0; j < width; j++) { + if (fabs(data1[i * width + j] - data2[i * width + j]) > err) { + count++; + } + } + } + EXPECT_EQ(count, 0) << "There are " << count << " different element."; +} + +void initArgument(size_t batchSize, + int layerSize, + bool useGpu, + Argument& data) { + data.value = Matrix::create(batchSize, layerSize, false, useGpu); + data.grad = Matrix::create(batchSize, layerSize, false, useGpu); + data.value->randomizeUniform(); + data.value->add(-0.5); + /// data.value->sigmoid(*data.value); + data.grad->zeroMem(); + + generateSequenceStartPositions(batchSize, data.sequenceStartPositions); +} + +LayerPtr createDataLayer( + string name, size_t batchSize, int layerSize, bool useGpu, Argument& data) { + LayerConfig layerConfig; + layerConfig.set_name(name); + layerConfig.set_type("data"); + layerConfig.set_size(layerSize); + LayerPtr layer = LayerPtr(new DataLayer(layerConfig)); + + DataLayerPtr dataLayer = std::dynamic_pointer_cast(layer); + dataLayer->setData(data); + dataLayer->forward(PASS_GC); + + /// std::cout << "dataLayer: " << std::endl; + /// (dataLayer->getOutput().value)->print(std::cout); + + return layer; +} + +LayerPtr createLabelLayer(string name, + size_t batchSize, + size_t numClasses, + bool useGpu) { + LayerConfig layerConfig; + layerConfig.set_name(name); + layerConfig.set_type("data"); + layerConfig.set_size(1); + LayerPtr layer = LayerPtr(new DataLayer(layerConfig)); + + Argument data; + data.ids = IVector::create(batchSize, useGpu); + data.ids->rand(numClasses - 1); + + generateSequenceStartPositions(batchSize, data.sequenceStartPositions); + + DataLayerPtr labelLayer = std::dynamic_pointer_cast(layer); + labelLayer->setData(data); + labelLayer->forward(PASS_GC); + + return layer; +} + +LayerPtr createCTCLayer(string name, + size_t numClasses, + bool useGpu, + bool normByTimes, + LayerPtr dataLayer, + LayerPtr labelLayer) { + LayerMap layerMap; + layerMap[dataLayer->getName()] = dataLayer; + layerMap[labelLayer->getName()] = labelLayer; + + ParameterMap parameterMap; + + LayerConfig layerConfig; + layerConfig.set_name(name); + layerConfig.set_type("ctc"); + layerConfig.set_size(numClasses); + layerConfig.set_norm_by_times(normByTimes); + + layerConfig.add_inputs(); + LayerInputConfig& input0 = *(layerConfig.mutable_inputs(0)); + input0.set_input_layer_name(dataLayer->getName()); + + layerConfig.add_inputs(); + LayerInputConfig& input1 = *(layerConfig.mutable_inputs(1)); + input1.set_input_layer_name(labelLayer->getName()); + + LayerPtr layer = LayerPtr(new CTCLayer(layerConfig)); + layerMap[layer->getName()] = layer; + layer->init(layerMap, parameterMap); + + ActivationFunction* softmaxActivation = ActivationFunction::create("softmax"); + + softmaxActivation->forward(dataLayer->getOutput()); + layer->forward(PASS_GC); + + layer->backward(); + softmaxActivation->backward(dataLayer->getOutput()); + + return layer; +} + +LayerPtr createWarpCTCLayer(string name, + size_t numClasses, + bool useGpu, + bool normByTimes, + LayerPtr dataLayer, + LayerPtr labelLayer) { + LayerMap layerMap; + layerMap[dataLayer->getName()] = dataLayer; + layerMap[labelLayer->getName()] = labelLayer; + + ParameterMap parameterMap; + + LayerConfig layerConfig; + layerConfig.set_name(name); + layerConfig.set_type("warp_ctc"); + layerConfig.set_size(numClasses); + layerConfig.set_blank(numClasses - 1); + layerConfig.set_norm_by_times(normByTimes); + + layerConfig.add_inputs(); + LayerInputConfig& input0 = *(layerConfig.mutable_inputs(0)); + input0.set_input_layer_name(dataLayer->getName()); + + layerConfig.add_inputs(); + LayerInputConfig& input1 = *(layerConfig.mutable_inputs(1)); + input1.set_input_layer_name(labelLayer->getName()); + + LayerPtr layer = LayerPtr(new WarpCTCLayer(layerConfig)); + layerMap[layer->getName()] = layer; + layer->init(layerMap, parameterMap); + + layer->forward(PASS_GC); + layer->backward(); + + return layer; +} + +TEST(Layer, WarpCTCLayer) { + for (auto layerSize : {10, 64, 128}) { + for (auto batchSize : {1, 10, 20, 64}) { + for (auto useGpu : {false, true}) { +#ifdef PADDLE_ONLY_CPU + if (useGpu) continue; +#endif + LOG(INFO) << " layerSize=" << layerSize << " batchSize=" << batchSize + << " useGpu=" << useGpu; + + FLAGS_use_gpu = useGpu; + + Argument data0; + initArgument(batchSize, layerSize, useGpu, data0); + + Argument data1; + data1.resizeAndCopyFrom(data0); + + LayerPtr dataLayer0 = + createDataLayer("data", batchSize, layerSize, useGpu, data0); + LayerPtr dataLayer1 = + createDataLayer("data", batchSize, layerSize, useGpu, data1); + + LayerPtr labelLayer = + createLabelLayer("label", batchSize, layerSize, useGpu); + + LayerPtr warpctcLayer = createWarpCTCLayer( + "cost", layerSize, useGpu, false, dataLayer0, labelLayer); + LayerPtr ctcLayer = createCTCLayer( + "cost", layerSize, useGpu, false, dataLayer1, labelLayer); + + /// Check loss + checkError(*(warpctcLayer->getOutput().value), + *(ctcLayer->getOutput().value)); + + /// Check gradients + checkError(*(dataLayer0->getOutput().grad), + *(dataLayer1->getOutput().grad)); + } + } + } +} + +int main(int argc, char** argv) { + testing::InitGoogleTest(&argc, argv); + initMain(argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/proto/ModelConfig.proto.m4 b/proto/ModelConfig.proto.m4 index 68a5eb9dd22..08108a46661 100644 --- a/proto/ModelConfig.proto.m4 +++ b/proto/ModelConfig.proto.m4 @@ -414,6 +414,8 @@ sinclude(`ModelConfigLayer.proto.m4') // to string and reinterpreted in the user's own layer implementation. optional string user_arg = 49; + // For WarpCTCLayer + optional uint32 blank = 50 [default = 0]; } message EvaluatorConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 9db42bf172a..e987ad17d65 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2993,6 +2993,27 @@ class CTCLayer(LayerBase): config_assert(len(self.inputs) == 2, 'CTCLayer must have 2 inputs') +@config_layer('warp_ctc') +class WarpCTCLayer(LayerBase): + def __init__(self, + name, + size, + inputs, + blank=0, + norm_by_times=False, + device=None): + super(WarpCTCLayer, self).__init__( + name, 'warp_ctc', size=size, inputs=inputs, device=device) + self.config.blank = blank + self.config.norm_by_times = norm_by_times + config_assert(len(self.inputs) == 2, 'WarpCTCLayer must have 2 inputs') + input_layer = self.get_input_layer(0) + config_assert( + (input_layer.active_type == '' or + input_layer.active_type == 'linear'), + "Expecting the active_type of input layer to be linear or null") + + @config_layer('recurrent_layer_group') class RecurrentLayerGroup(LayerBase): def __init__(self, name, device=None): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 9a45a515898..888d48722ab 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -91,6 +91,7 @@ __all__ = [ 'linear_comb_layer', 'convex_comb_layer', 'ctc_layer', + 'warp_ctc_layer', 'crf_layer', 'crf_decoding_layer', 'nce_layer', @@ -169,6 +170,7 @@ class LayerType(object): PRINT_LAYER = "print" CTC_LAYER = "ctc" + WARP_CTC_LAYER = "warp_ctc" CRF_LAYER = "crf" CRF_DECODING_LAYER = "crf_decoding" NCE_LAYER = 'nce' @@ -4085,6 +4087,83 @@ def ctc_layer(input, return LayerOutput(name, LayerType.CTC_LAYER, [input, label], size=size) +@wrap_name_default() +@layer_support() +def warp_ctc_layer(input, + label, + size=None, + name=None, + blank=0, + norm_by_times=False, + layer_attr=None): + """ + A layer intergrating the open-source `warp-ctc + ` library, which is used in + `Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin + `, to compute Connectionist Temporal + Classification (CTC) loss. + + More details of CTC can be found by referring to `Connectionist Temporal + Classification: Labelling Unsegmented Sequence Data with Recurrent + Neural Networks `_ + + Note: + - Let num_classes represent the category number. Considering the 'blank' + label needed by CTC, you need to use (num_classes + 1) as the input size. + Thus, the size of both warp_ctc_layer and 'input' layer should be set to + num_classes + 1. + - You can set 'blank' to [0, num_classes - 1], which should be consistent + as that used in your labels. + - As a native 'softmax' activation is interated to the warp-ctc library, + 'linear' activation is expected instead in the 'input' layer. + + The simple usage: + + .. code-block:: python + + ctc = warp_ctc_layer(input=input, + label=label, + size=1001, + blank=1000, + norm_by_times=False) + + :param input: The input layer. + :type input: LayerOutput + :param label: The data layer of label with variable length. + :type label: LayerOutput + :param size: category numbers + 1. + :type size: int + :param name: The name of this layer, which can not specify. + :type name: basestring|None + :param blank: the 'blank' label used in ctc + :type blank: int + :param norm_by_times: Whether to normalization by times. False by default. + :type norm_by_times: bool + :param layer_attr: Extra Layer config. + :type layer_attr: ExtraLayerAttribute|None + :return: LayerOutput object. + :rtype: LayerOutput + """ + assert isinstance(input, LayerOutput) + assert isinstance(label, LayerOutput) + if label.size is not None: + if size is not None: + assert size == label.size + 1 + else: + size = label.size + 1 + Layer( + name=name, + type=LayerType.WARP_CTC_LAYER, + size=size, + blank=blank, + norm_by_times=norm_by_times, + inputs=[input.name, label.name], + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name, LayerType.WARP_CTC_LAYER, parents=[input, label], size=size) + + @wrap_name_default() @wrap_param_attr_default() @layer_support() diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr index f6045fe1f68..10e59e21bc7 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr @@ -47,6 +47,20 @@ layers { } norm_by_times: false } +layers { + name: "__warp_ctc_layer_0__" + type: "warp_ctc" + size: 5001 + active_type: "" + inputs { + input_layer_name: "input" + } + inputs { + input_layer_name: "labels" + } + norm_by_times: false + blank: 0 +} layers { name: "crf_label" type: "data" @@ -244,6 +258,7 @@ input_layer_names: "xe-label" input_layer_names: "huber_probs" input_layer_names: "huber_label" output_layer_names: "__ctc_layer_0__" +output_layer_names: "__warp_ctc_layer_0__" output_layer_names: "__crf_layer_0__" output_layer_names: "__rank_cost_0__" output_layer_names: "__lambda_cost_0__" @@ -260,6 +275,7 @@ sub_models { layer_names: "xe-label" layer_names: "__fc_layer_0__" layer_names: "__ctc_layer_0__" + layer_names: "__warp_ctc_layer_0__" layer_names: "crf_label" layer_names: "__crf_layer_0__" layer_names: "left" @@ -289,6 +305,7 @@ sub_models { input_layer_names: "huber_probs" input_layer_names: "huber_label" output_layer_names: "__ctc_layer_0__" + output_layer_names: "__warp_ctc_layer_0__" output_layer_names: "__crf_layer_0__" output_layer_names: "__rank_cost_0__" output_layer_names: "__lambda_cost_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py index fd979a1e9f4..18ff6b48c49 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py @@ -12,6 +12,8 @@ hidden = fc_layer(input=seq_in, size=4) outputs( ctc_layer( input=seq_in, label=labels), + warp_ctc_layer( + input=seq_in, label=labels, blank=0), crf_layer( input=hidden, label=data_layer( name='crf_label', size=4)), -- GitLab From 18645134bd07d7a1658c08102d51af8a5d0195cd Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 29 Nov 2016 16:16:28 +0800 Subject: [PATCH 116/347] Follow comments --- doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst | 6 +++--- .../tests/sequence_nest_rnn_multi_unequalength_inputs.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst index a13d4728a9c..7ae9f5ef8e6 100644 --- a/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst +++ b/doc_cn/algorithm/rnn/hrnn_rnn_api_compare.rst @@ -4,14 +4,14 @@ 单双层RNN API对比介绍 ##################### -这篇教程主要介绍了\ :ref:`glossary_双层RNN`\ 的API接口。本文中的以PaddlePaddle的\ :ref:`glossary_双层RNN`\ 单元测试为示例，用多对效果完全相同的、分别使用单、双层RNN作为网络配置的模型，来讲解如何使用\ :ref:`glossary_双层RNN`\ 。本文中所有的例子，都只是介绍\ :ref:`glossary_双层RNN`\ 的API接口，并不是使用\ :ref:`glossary_双层RNN`\ 解决实际的问题。如果想要了解\ :ref:`glossary_双层RNN`\ 在具体问题中的使用，请参考\ :ref:`algo_hrnn_demo`\ 。文章中示例所使用的单元测试文件是\ `test_RecurrentGradientMachine.cpp `_\ 。 +这篇教程主要介绍了\ :ref:`glossary_双层RNN`\ 的API接口。本文以PaddlePaddle的\ :ref:`glossary_双层RNN`\ 单元测试为示例，用多对效果完全相同的、分别使用单双层RNN作为网络配置的模型，来讲解如何使用\ :ref:`glossary_双层RNN`\ 。本文中所有的例子，都只是介绍\ :ref:`glossary_双层RNN`\ 的API接口，并不是使用\ :ref:`glossary_双层RNN`\ 解决实际的问题。如果想要了解\ :ref:`glossary_双层RNN`\ 在具体问题中的使用，请参考\ :ref:`algo_hrnn_demo`\ 。本文中示例所使用的单元测试文件是\ `test_RecurrentGradientMachine.cpp `_\ 。 示例1：双层RNN，子序列间无Memory ================================ 在\ :ref:`glossary_双层RNN`\ 中的经典情况是将内层的每一个\ :ref:`glossary_sequence`\ 数据，分别进行序列操作。并且内层的序列操作之间是独立没有依赖的，即不需要使用\ :ref:`glossary_Memory`\ 的。 -在本问题中，单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 的网络配置，都是将每一句分好词后的句子，使用LSTM作为encoder，压缩成一个向量。区别是\ :ref:`glossary_RNN`\ 使用两层序列模型，将多句话看成一个整体，同时使用encoder压缩，二者语意上完全一致。这组语意相同的示例配置如下 +在本示例中，单层\ :ref:`glossary_RNN`\ 和\ :ref:`glossary_双层RNN`\ 的网络配置，都是将每一句分好词后的句子，使用LSTM作为encoder，压缩成一个向量。区别是\ :ref:`glossary_RNN`\ 使用两层序列模型，将多句话看成一个整体，同时使用encoder压缩，二者语意上完全一致。这组语意相同的示例配置如下 * 单层\ :ref:`glossary_RNN`\: `sequence_layer_group.conf `_ * :ref:`glossary_双层RNN`\: `sequence_nest_layer_group.conf `_ @@ -22,7 +22,7 @@ 首先，本示例中使用的原始数据如下\: -- 本里中的原始数据一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。这个数据也被单层\ :ref:`glossary_RNN`\ 网络直接使用。 +- 本例中的原始数据一共有10个样本。每个样本由两部分组成，一个label（此处都为2）和一个已经分词后的句子。这个数据也被单层\ :ref:`glossary_RNN`\ 网络直接使用。 .. literalinclude:: ../../../paddle/gserver/tests/Sequence/tour_train_wdseg :language: text diff --git a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py index bf88d00f2df..163fce956e9 100644 --- a/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py +++ b/paddle/gserver/tests/sequence_nest_rnn_multi_unequalength_inputs.py @@ -64,8 +64,8 @@ def outer_step(x1, x2): last = last_seq(name="outer_rnn_state_%d" % i, input=encoder) return encoder, last - _, sentence_last_state1 = inner_step(ipt=x1) - encoder2, _ = inner_step(ipt=x2) + encoder1, sentence_last_state1 = inner_step(ipt=x1) + encoder2, sentence_last_state2 = inner_step(ipt=x2) encoder1_expand = expand_layer( input=sentence_last_state1, expand_as=encoder2) -- GitLab From d118609582c1bef6b90a973ee9a0e1b94c2801cf Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 29 Nov 2016 16:26:54 +0800 Subject: [PATCH 117/347] Refine documentation in RELEASE.md --- RELEASE.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index b2684f5f5de..a8a245ab442 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -2,14 +2,13 @@ ## New Features: -* Add some layers to Paddle +* New Layers * bilinear interpolation layer. * spatial pyramid-pool layer. * de-convolution layer. * maxout layer. * Support rectangle padding, stride, window and input for Pooling Operation. * Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON. -* Support Mac OS X Sierra by source code. * Expose cost_weight/nce_layer in `trainer_config_helpers` * Add FAQ, concepts, h-rnn docs. * Add Bidi-LSTM and DB-LSTM to quick start demo @alvations @@ -17,7 +16,7 @@ ## Improvements -* Add travis-ci for macos. Enable swig unittest in travis. Skip travis-ci when only docs are changed. +* Add Travis-CI for Mac OS X. Enable swig unittest in Travis-CI. Skip Travis-CI when only docs are changed. * Add code coverage tools. * Refine convolution layer to speedup and reduce GPU memory. * Speed up PyDataProvider2 -- GitLab From f70fc4a4396e327cd469ab1144916339d9df2601 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Tue, 29 Nov 2016 16:49:46 +0800 Subject: [PATCH 118/347] move some test from test_matrixCompare.cpp to test_BaseMatrix.cpp and test_Matrix.cpp --- paddle/math/BaseMatrix.cu | 5 - paddle/math/BaseMatrix.h | 2 - paddle/math/tests/TensorCheck.h | 6 + paddle/math/tests/TestUtils.h | 40 +++- paddle/math/tests/test_BaseMatrix.cpp | 107 +++------- paddle/math/tests/test_Matrix.cpp | 21 +- paddle/math/tests/test_matrixCompare.cpp | 244 ----------------------- 7 files changed, 77 insertions(+), 348 deletions(-) diff --git a/paddle/math/BaseMatrix.cu b/paddle/math/BaseMatrix.cu index 2f32b3fdd1a..75d76fbc6ee 100644 --- a/paddle/math/BaseMatrix.cu +++ b/paddle/math/BaseMatrix.cu @@ -1578,11 +1578,6 @@ void BaseMatrixT::minRows(BaseMatrixT& b) { applyRow(aggregate::min(), b); } -template<> -void BaseMatrixT::sumCols(BaseMatrixT& b) { - applyCol(aggregate::sum(), b); -} - template<> void BaseMatrixT::maxCols(BaseMatrixT& b) { applyCol(aggregate::max(), b); diff --git a/paddle/math/BaseMatrix.h b/paddle/math/BaseMatrix.h index d41dcee682c..ce8113a47c4 100644 --- a/paddle/math/BaseMatrix.h +++ b/paddle/math/BaseMatrix.h @@ -1007,8 +1007,6 @@ public: /// calculate the minimum value of each row of the matrix b. void minRows(BaseMatrixT& b); - /// calculate the sum of each column of the matrix b. - void sumCols(BaseMatrixT& b); /// calculate the maximum value of each column of the matrix b. void maxCols(BaseMatrixT& b); /// calculate the minimum value of each column of the matrix b. diff --git a/paddle/math/tests/TensorCheck.h b/paddle/math/tests/TensorCheck.h index d4821314f3b..beee9e7c0fb 100644 --- a/paddle/math/tests/TensorCheck.h +++ b/paddle/math/tests/TensorCheck.h @@ -110,4 +110,10 @@ void TensorCheck(AssertEq compare, real args1, real args2) { << ", args2 = " << args2; } +template +void TensorCheck(AssertEq compare, size_t args1, size_t args2) { + EXPECT_EQ(args1, args2) << "[Test error] args1 = " << args1 + << ", args2 = " << args2; +} + } // namespace autotest diff --git a/paddle/math/tests/TestUtils.h b/paddle/math/tests/TestUtils.h index 96ba2c38c8d..324ecf80178 100644 --- a/paddle/math/tests/TestUtils.h +++ b/paddle/math/tests/TestUtils.h @@ -65,15 +65,24 @@ public: // construct a argument template T construct(int height, int width); + template <> float construct(int height, int width) { return 0.0; } + +template <> +size_t construct(int height, int width) { + size_t offset = std::rand() % (height < width ? height : width); + return offset; +} + template <> CpuMatrix construct(int height, int width) { CpuMatrix a(height, width); return a; } + template <> GpuMatrix construct(int height, int width) { GpuMatrix a(height, width); @@ -83,14 +92,22 @@ GpuMatrix construct(int height, int width) { // init a argument template void init(T& v); + template <> void init(float& v) { v = 0.5; } + +template <> +void init(size_t& v) { + return; +} + template <> void init(CpuMatrix& v) { v.randomizeUniform(); } + template <> void init(GpuMatrix& v) { v.randomizeUniform(); @@ -111,10 +128,17 @@ template // copy a argument, copy src to dest template void copy(T1& dest, T2& src); + template <> void copy(float& dest, float& src) { dest = src; } + +template <> +void copy(size_t& dest, size_t& src) { + dest = src; +} + template <> void copy(GpuMatrix& dest, CpuMatrix& src) { dest.copyFrom(src); @@ -165,8 +189,8 @@ R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) { return (obj.*f)(args...); } -template -void BaseMatrixApplyRow(R (C::*f)(Args...)) { +void BaseMatrixAsColVector(R (C::*f)(Args...)) { static_assert(sizeof...(I) == sizeof...(Args), "size of parameter packs are not equal"); @@ -237,11 +261,11 @@ void BaseMatrixApplyRow(R (C::*f)(Args...)) { autotest::AssertEqual compare(1e-8); #endif - autotest::BaseMatrixCompare(f, compare); + autotest::BaseMatrixCompare(f, compare); } template -void BaseMatrixApplyCol(R (C::*f)(Args...)) { +void BaseMatrixAsRowVector(R (C::*f)(Args...)) { static_assert(sizeof...(I) == sizeof...(Args), "size of parameter packs are not equal"); @@ -250,5 +274,5 @@ void BaseMatrixApplyCol(R (C::*f)(Args...)) { #else autotest::AssertEqual compare(1e-8); #endif - autotest::BaseMatrixCompare(f, compare); + autotest::BaseMatrixCompare(f, compare); } diff --git a/paddle/math/tests/test_BaseMatrix.cpp b/paddle/math/tests/test_BaseMatrix.cpp index 99ab640f48f..1d334135a0c 100644 --- a/paddle/math/tests/test_BaseMatrix.cpp +++ b/paddle/math/tests/test_BaseMatrix.cpp @@ -24,7 +24,6 @@ limitations under the License. */ #include "TestUtils.h" using namespace paddle; // NOLINT -using namespace std; // NOLINT /** * Test member functions which prototype is @@ -32,8 +31,8 @@ using namespace std; // NOLINT */ TEST(BaseMatrix, void) { typedef void (BaseMatrix::*FunctionProto)(); - #define BASEMATRIXCOMPARE(function) \ - BaseMatrixCompare(static_cast(&BaseMatrix::function)); +#define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare(static_cast(&BaseMatrix::function)); BASEMATRIXCOMPARE(neg); BASEMATRIXCOMPARE(exp); @@ -46,7 +45,7 @@ TEST(BaseMatrix, void) { BASEMATRIXCOMPARE(zero); BASEMATRIXCOMPARE(one); - #undef BASEMATRIXCOMPARE +#undef BASEMATRIXCOMPARE } /** @@ -55,8 +54,8 @@ TEST(BaseMatrix, void) { */ TEST(BaseMatrix, real) { typedef void (BaseMatrix::*FunctionProto)(real); - #define BASEMATRIXCOMPARE(function) \ - BaseMatrixCompare<0>(static_cast(&BaseMatrix::function)); +#define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0>(static_cast(&BaseMatrix::function)); BASEMATRIXCOMPARE(pow); BASEMATRIXCOMPARE(subScalar); @@ -67,7 +66,7 @@ TEST(BaseMatrix, real) { BASEMATRIXCOMPARE(biggerThanScalar); BASEMATRIXCOMPARE(downClip); - #undef BASEMATRIXCOMPARE +#undef BASEMATRIXCOMPARE } /** @@ -76,13 +75,13 @@ TEST(BaseMatrix, real) { */ TEST(BaseMatrix, real_real) { typedef void (BaseMatrix::*FunctionProto)(real, real); - #define BASEMATRIXCOMPARE(function) \ - BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); +#define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); BASEMATRIXCOMPARE(add); BASEMATRIXCOMPARE(clip); - #undef BASEMATRIXCOMPARE +#undef BASEMATRIXCOMPARE } /** @@ -91,8 +90,8 @@ TEST(BaseMatrix, real_real) { */ TEST(BaseMatrix, BaseMatrix) { typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&); - #define BASEMATRIXCOMPARE(function) \ - BaseMatrixCompare<0>(static_cast(&BaseMatrix::function)); +#define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0>(static_cast(&BaseMatrix::function)); BASEMATRIXCOMPARE(assign); BASEMATRIXCOMPARE(add); @@ -129,7 +128,7 @@ TEST(BaseMatrix, BaseMatrix) { BASEMATRIXCOMPARE(addP2P); BASEMATRIXCOMPARE(invSqrt); - #undef BASEMATRIXCOMPARE +#undef BASEMATRIXCOMPARE } /** @@ -138,8 +137,8 @@ TEST(BaseMatrix, BaseMatrix) { */ TEST(BaseMatrix, BaseMatrix_real) { typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, real); - #define BASEMATRIXCOMPARE(function) \ - BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); +#define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); BASEMATRIXCOMPARE(addBias); BASEMATRIXCOMPARE(add); @@ -154,7 +153,7 @@ TEST(BaseMatrix, BaseMatrix_real) { BASEMATRIXCOMPARE(isEqualTo); - #undef BASEMATRIXCOMPARE +#undef BASEMATRIXCOMPARE } /** @@ -163,8 +162,8 @@ TEST(BaseMatrix, BaseMatrix_real) { */ TEST(BaseMatrix, BaseMatrix_BaseMatrix) { typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, BaseMatrix&); - #define BASEMATRIXCOMPARE(function) \ - BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); +#define BASEMATRIXCOMPARE(function) \ + BaseMatrixCompare<0, 1>(static_cast(&BaseMatrix::function)); BASEMATRIXCOMPARE(softCrossEntropy); BASEMATRIXCOMPARE(softCrossEntropyBp); @@ -181,69 +180,25 @@ TEST(BaseMatrix, BaseMatrix_BaseMatrix) { BASEMATRIXCOMPARE(dotMulSquare); BASEMATRIXCOMPARE(dotSquareSquare); - #undef BASEMATRIXCOMPARE +#undef BASEMATRIXCOMPARE } -/** - * Test aggregate member functions which prototype is - * void (BaseMatrix::*)(BaseMatrix&). - */ -TEST(Aggregate, BaseMatrix) { - typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&); - #define BASEMATRIXAPPLYROW(function) \ - BaseMatrixApplyRow<0>(static_cast(&BaseMatrix::function)); - - #define BASEMATRIXAPPLYCOL(function) \ - BaseMatrixApplyCol<0>(static_cast(&BaseMatrix::function)); - - BASEMATRIXAPPLYROW(maxRows); - BASEMATRIXAPPLYROW(minRows); - - BASEMATRIXAPPLYCOL(sumCols); - BASEMATRIXAPPLYCOL(maxCols); - BASEMATRIXAPPLYCOL(minCols); - - #undef BASEMATRIXAPPLYROW - #undef BASEMATRIXAPPLYCOL +// member function without overloaded +TEST(BaseMatrix, Other) { + BaseMatrixCompare<0, 1, 2>(&BaseMatrix::rowScale); + BaseMatrixCompare<0, 1, 2>(&BaseMatrix::rowDotMul); + BaseMatrixCompare<0, 1, 2, 3>(&BaseMatrix::binaryClassificationError); } -/** - * Test aggregate member functions which prototype is - * void (BaseMatrix::*)(BaseMatrix&, BaseMatrix&). - */ -TEST(Aggregate, BaseMatrix_BaseMatrix) { - typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, BaseMatrix&); - #define BASEMATRIXAPPLYROW(function) \ - BaseMatrixApplyRow<0, 1>(static_cast(&BaseMatrix::function)); - - #define BASEMATRIXAPPLYCOL(function) \ - BaseMatrixApplyCol<0, 1>(static_cast(&BaseMatrix::function)); - - BASEMATRIXAPPLYCOL(addDotMulVMM); - - #undef BASEMATRIXAPPLYROW - #undef BASEMATRIXAPPLYCOL -} - -/** - * Test aggregate member functions which prototype is - * void (BaseMatrix::*)(BaseMatrix&, real, real). - */ -TEST(Aggregate, BaseMatrix_real_real) { - typedef void (BaseMatrix::*FunctionProto)(BaseMatrix&, real, real); - #define BASEMATRIXAPPLYROW(function) \ - BaseMatrixApplyRow<0, 1, 2>(\ - static_cast(&BaseMatrix::function)); - - #define BASEMATRIXAPPLYCOL(function) \ - BaseMatrixApplyCol<0, 1, 2>(\ - static_cast(&BaseMatrix::function)); - - BASEMATRIXAPPLYROW(sumRows); - BASEMATRIXAPPLYCOL(sumCols); +TEST(BaseMatrix, Aggregate) { + BaseMatrixAsColVector<0>(&BaseMatrix::maxRows); + BaseMatrixAsColVector<0>(&BaseMatrix::minRows); + BaseMatrixAsColVector<0, 1, 2>(&BaseMatrix::sumRows); - #undef BASEMATRIXAPPLYROW - #undef BASEMATRIXAPPLYCOL + BaseMatrixAsRowVector<0>(&BaseMatrix::maxCols); + BaseMatrixAsRowVector<0>(&BaseMatrix::minCols); + BaseMatrixAsRowVector<0, 1>(&BaseMatrix::addDotMulVMM); + BaseMatrixAsRowVector<0, 1, 2>(&BaseMatrix::sumCols); } int main(int argc, char** argv) { diff --git a/paddle/math/tests/test_Matrix.cpp b/paddle/math/tests/test_Matrix.cpp index a4d9d34976b..485e702a66f 100644 --- a/paddle/math/tests/test_Matrix.cpp +++ b/paddle/math/tests/test_Matrix.cpp @@ -19,25 +19,20 @@ limitations under the License. */ */ #include -#include "paddle/utils/Util.h" -#include "paddle/math/BaseMatrix.h" #include "TestUtils.h" using namespace paddle; // NOLINT -using namespace std; // NOLINT -/** - * Test member functions which prototype is - * void (Matrix::*)(Matrix&). - */ -TEST(BaseMatrix, real) { - typedef void (Matrix::*FunctionProto)(Matrix&); -#define MATRIXCOMPARE(function) \ - BaseMatrixCompare<0>(static_cast(&Matrix::function), true); +TEST(Matrix, Matrix) { + BaseMatrixCompare<0>(&Matrix::softmax, true); + BaseMatrixCompare<0, 1>(&Matrix::sumOfSquaresBp); +} - MATRIXCOMPARE(softmax); +TEST(Matrix, Aggregate) { + BaseMatrixAsRowVector<0, 1>( + static_cast(&Matrix::collectBias)); -#undef MATRIXCOMPARE + BaseMatrixAsColVector<0, 1>(&Matrix::sumOfSquares); } int main(int argc, char** argv) { diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 80596abe821..86a4a0e5ec5 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -448,60 +448,6 @@ void testMatrixZeroAtOffset(int height, int width) { MatrixCheckEqual(*cpuA, *cpuTest); } -void testMatrixSumOfSquaresBp(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - - cpuA->sumOfSquaresBp(*cpuB, *cpuC); - gpuA->sumOfSquaresBp(*gpuB, *gpuC); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixBinaryRowScale(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, 1); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, 1); - - MatrixPtr cpuA1 = std::make_shared(height, width); - MatrixPtr cpuB1 = std::make_shared(height, 1); - MatrixPtr gpuA1 = std::make_shared(height, width); - MatrixPtr gpuB1 = std::make_shared(height, 1); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - cpuA1->copyFrom(*cpuA); - cpuB1->copyFrom(*cpuB); - gpuA1->copyFrom(*cpuA); - gpuB1->copyFrom(*cpuB); - - cpuA->addColVector(*cpuB); - gpuA->addColVector(*gpuB); - cpuA1->addColumnVector(*cpuB1); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckEqual(*cpuA, *outputCheck); - - MatrixCheckEqual(*cpuA, *cpuA1); -} - void testMatrixAddBias(int height, int width, real scale) { MatrixPtr cpuA = std::make_shared(height, width); MatrixPtr cpuB = std::make_shared(1, width); @@ -521,76 +467,6 @@ void testMatrixAddBias(int height, int width, real scale) { MatrixCheckErr(*cpuA, *outputCheck); } -void testMatrixTernaryRowScale(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - MatrixPtr cpuA1 = std::make_shared(height, width); - MatrixPtr cpuB1 = std::make_shared(height, width); - MatrixPtr cpuC1 = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - cpuA1->copyFrom(*cpuA); - cpuB1->copyFrom(*cpuB); - cpuC1->copyFrom(*cpuC); - - int columnOffset = rand() % width; // NOLINT - - cpuA->rowScale(columnOffset, *cpuB, *cpuC); - gpuA->rowScale(columnOffset, *gpuB, *gpuC); - cpuA1->rowScale2(columnOffset, *cpuB1, *cpuC1); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckEqual(*cpuA, *outputCheck); - - MatrixCheckEqual(*cpuA, *cpuA1); -} - -void testMatrixTernaryRowDotMul(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - - MatrixPtr cpuA1 = std::make_shared(height, width); - MatrixPtr cpuB1 = std::make_shared(height, width); - MatrixPtr cpuC1 = std::make_shared(height, width); - - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - cpuA1->copyFrom(*cpuA); - cpuB1->copyFrom(*cpuB); - cpuC1->copyFrom(*cpuC); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - - int columnOffset = rand() % width; // NOLINT - - cpuA->rowDotMul(columnOffset, *cpuB, *cpuC); - gpuA->rowDotMul(columnOffset, *gpuB, *gpuC); - cpuA1->rowDotMul2(columnOffset, *cpuB1, *cpuC1); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *cpuA1); - MatrixCheckErr(*cpuA, *outputCheck); -} - void testMatrixAddDotMulMMV(int height, int width) { MatrixPtr cpuA = std::make_shared(height, width); MatrixPtr cpuB = std::make_shared(height, width); @@ -670,18 +546,11 @@ TEST(Matrix, unary) { for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) { VLOG(3) << " height=" << height << " width=" << width; - // applyTernary - testMatrixSumOfSquaresBp(height, width); - // asRowVector testMatrixAddBias(height, width, 1.0); testMatrixAddBias(height, width, 3.5); testMatrixAddDotMulMMV(height, width); - // asColVector - testMatrixTernaryRowScale(height, width); - testMatrixBinaryRowScale(height, width); - // sum testMatrixGetSum(height, width); @@ -782,119 +651,6 @@ TEST(Matrix, softmax) { } } -void testMatrixCollectBias(int height, int width) { - MatrixPtr cpuA = std::make_shared(1, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(1, width); - MatrixPtr gpuB = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - - real scale = 1.0f / (rand() % 10); // NOLINT - - cpuA->collectBias(*cpuB, scale); - gpuA->collectBias(*gpuB, scale); - - MatrixPtr outputCheck = std::make_shared(1, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixSumOfSquares(int height, int width, int endCol = 0) { - MatrixPtr cpuA = std::make_shared(height, 1); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, 1); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - - if (!endCol) { - cpuA->sumOfSquares(*cpuB, *cpuC); - gpuA->sumOfSquares(*gpuB, *gpuC); - } else { - MatrixPtr subCpuB = cpuB->subColMatrix(0, endCol); - MatrixPtr subCpuC = cpuC->subColMatrix(0, endCol); - MatrixPtr subGpuB = gpuB->subColMatrix(0, endCol); - MatrixPtr subGpuC = gpuC->subColMatrix(0, endCol); - cpuA->sumOfSquares(*subCpuB, *subCpuC); - gpuA->sumOfSquares(*subGpuB, *subGpuC); - } - - MatrixPtr outputCheck = std::make_shared(height, 1); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); -} - -void testMatrixBinaryClassificationError(int height, int width) { - MatrixPtr cpuA = std::make_shared(height, width); - MatrixPtr cpuB = std::make_shared(height, width); - MatrixPtr cpuC = std::make_shared(height, width); - MatrixPtr gpuA = std::make_shared(height, width); - MatrixPtr gpuB = std::make_shared(height, width); - MatrixPtr gpuC = std::make_shared(height, width); - - MatrixPtr cpuA2 = std::make_shared(height, width); - MatrixPtr cpuB2 = std::make_shared(height, width); - MatrixPtr cpuC2 = std::make_shared(height, width); - - cpuA->randomizeUniform(); - cpuB->randomizeUniform(); - cpuC->randomizeUniform(); - gpuA->copyFrom(*cpuA); - gpuB->copyFrom(*cpuB); - gpuC->copyFrom(*cpuC); - cpuA2->copyFrom(*cpuA); - cpuB2->copyFrom(*cpuB); - cpuC2->copyFrom(*cpuC); - - real scale = 0.5; - int columnOffset = rand() % width; // NOLINT - - cpuA->binaryClassificationError(columnOffset, *cpuB, *cpuC, scale); - gpuA->binaryClassificationError(columnOffset, *gpuB, *gpuC, scale); - cpuA2->binaryClassificationError2(columnOffset, *cpuB2, *cpuC2, scale); - - MatrixPtr outputCheck = std::make_shared(height, width); - outputCheck->copyFrom(*gpuA); - MatrixCheckErr(*cpuA, *outputCheck); - MatrixCheckErr(*cpuA, *cpuA2); -} - -TEST(Matrix, aggregate) { - for (auto height : {1, 11, 16, 32, 64, 73, 128, 200, 1024, 2345}) { - for (auto width : {1, 9, 16, 32, 64, 100, 512, 1000, 1024, 2453}) { - VLOG(3) << " height=" << height << " width=" << width; - testMatrixCollectBias(height, width); - testMatrixTernaryRowDotMul(height, width); - - testMatrixSumOfSquares(height, width); - testMatrixBinaryClassificationError(height, width); - } - } -} - -TEST(Matrix, aggregate2) { - for (auto height : {16, 32, 128, 512, 1024}) { - for (auto width : - {16, 32, 64, 128, 256, 512, 768, 1024, 2048, 3072, 4096}) { - VLOG(3) << " height=" << height << " width=" << width; - - int endCol = rand() % width; // NOLINT - testMatrixSumOfSquares(height, width, endCol); - } - } -} - void testMatrixAddAtOffset(int height, int width1, int width2) { MatrixPtr cpuInput = std::make_shared(height, width1); MatrixPtr cpuOutput = std::make_shared(height, width2); -- GitLab From 1733a777fe4bbf9c8491455c78f06845cc2bc9e5 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 29 Nov 2016 19:36:06 +0800 Subject: [PATCH 119/347] Using ApproximatelyEquals for trainer config unittest. * Make float equal works well. * compare 0.01 and 0.009999999999 will return true. --- .../tests/CMakeLists.txt | 4 ++ .../tests/ProtobufEqualMain.cpp | 59 +++++++++++++++++++ .../tests/configs/.gitignore | 2 +- .../tests/configs/file_list.sh | 9 +++ .../tests/configs/generate_protostr.sh | 14 +---- .../tests/configs/run_tests.sh | 30 ++++++++-- 6 files changed, 100 insertions(+), 18 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/ProtobufEqualMain.cpp create mode 100755 python/paddle/trainer_config_helpers/tests/configs/file_list.sh diff --git a/python/paddle/trainer_config_helpers/tests/CMakeLists.txt b/python/paddle/trainer_config_helpers/tests/CMakeLists.txt index cf52b06bfea..78505c85e1b 100644 --- a/python/paddle/trainer_config_helpers/tests/CMakeLists.txt +++ b/python/paddle/trainer_config_helpers/tests/CMakeLists.txt @@ -4,7 +4,11 @@ add_test(NAME layers_test python ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/layers_test.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) +add_paddle_exe(protobuf_equal + ProtobufEqualMain.cpp) + add_test(NAME test_layerHelpers COMMAND ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh + ${CMAKE_CURRENT_BINARY_DIR}/protobuf_equal ) diff --git a/python/paddle/trainer_config_helpers/tests/ProtobufEqualMain.cpp b/python/paddle/trainer_config_helpers/tests/ProtobufEqualMain.cpp new file mode 100644 index 00000000000..06f7de93063 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/ProtobufEqualMain.cpp @@ -0,0 +1,59 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 +#include +#include +#include +#include "TrainerConfig.pb.h" + +bool loadPb(google::protobuf::Message* conf, const std::string& filename) { + std::ifstream fin; + fin.open(filename.c_str()); + if (fin.is_open()) { + std::string str((std::istreambuf_iterator(fin)), + std::istreambuf_iterator()); + bool ok = google::protobuf::TextFormat::ParseFromString(str, conf); + fin.close(); + return ok; + } else { + return false; + } +} + +int main(int argc, char** argv) { + std::unique_ptr config1; + std::unique_ptr config2; + if (argc == 3) { + config1.reset(new paddle::ModelConfig()); + config2.reset(new paddle::ModelConfig()); + } else if (argc == 4) { + config1.reset(new paddle::TrainerConfig()); + config2.reset(new paddle::TrainerConfig()); + } + if (!config1 || !config2) { + return 1; + } else if (!loadPb(config1.get(), argv[1])) { + return 2; + } else if (!loadPb(config2.get(), argv[2])) { + return 3; + } else { + if (google::protobuf::util::MessageDifferencer::ApproximatelyEquals( + *config1, *config2)) { + return 0; + } else { + return 4; + } + } +} diff --git a/python/paddle/trainer_config_helpers/tests/configs/.gitignore b/python/paddle/trainer_config_helpers/tests/configs/.gitignore index eb646b4a71e..c654bd41b0b 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/.gitignore +++ b/python/paddle/trainer_config_helpers/tests/configs/.gitignore @@ -1 +1 @@ -protostr/*.unitest +protostr/*.unittest diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh new file mode 100755 index 00000000000..3f1d99701af --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -0,0 +1,9 @@ +#!/bin/bash +export configs=(test_fc layer_activations projections test_print_layer +test_sequence_pooling test_lstmemory_layer test_grumemory_layer +last_first_seq test_expand_layer test_ntm_layers test_hsigmoid +img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers +test_rnn_group shared_fc shared_lstm test_cost_layers_with_weight +test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops) + +export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh b/python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh index bb594ac2c24..e55f9bd3884 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh @@ -5,24 +5,16 @@ cd `dirname $0` export PYTHONPATH=$PWD/../../../../ protostr=$PWD/protostr - -configs=(test_fc layer_activations projections test_print_layer -test_sequence_pooling test_lstmemory_layer test_grumemory_layer -last_first_seq test_expand_layer test_ntm_layers test_hsigmoid -img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers -test_rnn_group shared_fc shared_lstm test_cost_layers_with_weight -test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops) - -whole_configs=(test_split_datasource) +. file_list.sh for conf in ${configs[*]} do echo "Generating " $conf - python -m paddle.utils.dump_config $conf.py > $protostr/$conf.protostr.unitest + python -m paddle.utils.dump_config $conf.py > $protostr/$conf.protostr.unittest done for conf in ${whole_configs[*]} do echo "Generating " $conf - python -m paddle.utils.dump_config $conf.py "" --whole > $protostr/$conf.protostr.unitest + python -m paddle.utils.dump_config $conf.py "" --whole > $protostr/$conf.protostr.unittest done diff --git a/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh b/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh index 96832883584..5f6ed91030b 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh @@ -9,9 +9,27 @@ files=`ls $protostr | grep -v "unitest"` ./generate_protostr.sh -for file in $files -do - base_protostr=$protostr/$file - new_protostr=$protostr/$file.unitest - diff $base_protostr $new_protostr -u -done +. ./file_list.sh + +if [ -z $1 ]; then + for file in $files + do + base_protostr=$protostr/$file + new_protostr=$protostr/$file.unittest + diff $base_protostr $new_protostr -u + done +else + for file in ${configs[*]} + do + if ! $1 $protostr/$file.protostr $protostr/$file.protostr.unittest; then + diff $protostr/$file.protostr $protostr/$file.protostr.unittest -u + fi + done + + for file in ${whole_configs[*]} + do + if ! $1 $protostr/$file.protostr $protostr/$file.protostr.unittest --whole; then + diff $protostr/$file.protostr $protostr/$file.protostr.unittest -u + fi + done +fi -- GitLab From 1eb9c36fd2a13ff5d267b0280a7f3448290b2c04 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 30 Nov 2016 12:50:14 +0800 Subject: [PATCH 120/347] Make ApproximatelyEquals fit for protobuf2 --- CMakeLists.txt | 11 ++++++++++ proto/CMakeLists.txt | 9 -------- .../tests/CMakeLists.txt | 22 ++++++++++++------- 3 files changed, 25 insertions(+), 17 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7d685587a7a..7ef9368ef5a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -12,6 +12,17 @@ include(package) find_package(SWIG 2.0) find_package(CUDA QUIET) find_package(Protobuf REQUIRED) + +# Check protobuf library version. +execute_process(COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --version + OUTPUT_VARIABLE PROTOBUF_VERSION) +string(REPLACE "libprotoc " "" PROTOBUF_VERSION ${PROTOBUF_VERSION}) + +set(PROTOBUF_3 OFF) +if (${PROTOBUF_VERSION} VERSION_GREATER "3.0.0" OR ${PROTOBUF_VERSION} VERSION_EQUAL "3.0.0") + set(PROTOBUF_3 ON) +endif() + find_package(PythonLibs 2.7 REQUIRED) find_package(PythonInterp 2.7 REQUIRED) find_package(ZLIB REQUIRED) diff --git a/proto/CMakeLists.txt b/proto/CMakeLists.txt index ec68b53d440..d7f523bc8d9 100644 --- a/proto/CMakeLists.txt +++ b/proto/CMakeLists.txt @@ -1,12 +1,3 @@ -execute_process(COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --version - OUTPUT_VARIABLE PROTOBUF_VERSION) -string(REPLACE "libprotoc " "" PROTOBUF_VERSION ${PROTOBUF_VERSION}) - -set(PROTOBUF_3 OFF) -if (${PROTOBUF_VERSION} VERSION_GREATER "3.0.0" OR ${PROTOBUF_VERSION} VERSION_EQUAL "3.0.0") - set(PROTOBUF_3 ON) -endif() - set(proto_filenames DataConfig.proto DataFormat.proto diff --git a/python/paddle/trainer_config_helpers/tests/CMakeLists.txt b/python/paddle/trainer_config_helpers/tests/CMakeLists.txt index 78505c85e1b..6180b2efbca 100644 --- a/python/paddle/trainer_config_helpers/tests/CMakeLists.txt +++ b/python/paddle/trainer_config_helpers/tests/CMakeLists.txt @@ -4,11 +4,17 @@ add_test(NAME layers_test python ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/layers_test.py WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) -add_paddle_exe(protobuf_equal - ProtobufEqualMain.cpp) - -add_test(NAME test_layerHelpers - COMMAND - ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh - ${CMAKE_CURRENT_BINARY_DIR}/protobuf_equal -) +if (PROTOBUF_3) + add_paddle_exe(protobuf_equal + ProtobufEqualMain.cpp) + add_test(NAME test_layerHelpers + COMMAND + ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh + ${CMAKE_CURRENT_BINARY_DIR}/protobuf_equal + ) +else() + add_test(NAME test_layerHelpers + COMMAND + ${PROJ_ROOT}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh + ) +endif() -- GitLab From 63df7ae65d5f003f0166d5a403991b73081db83f Mon Sep 17 00:00:00 2001 From: liaogang Date: Wed, 30 Nov 2016 12:51:13 +0800 Subject: [PATCH 121/347] Refine docker install doc and FAQ for gpu driver --- .../build_and_install/docker_install.rst | 33 +++++++++++++------ .../install/docker_install.rst | 33 ++++++++++++++----- doc_cn/faq/index.rst | 15 +++++++++ 3 files changed, 63 insertions(+), 18 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install.rst b/doc/getstarted/build_and_install/docker_install.rst index e95de35f4da..88d83573044 100644 --- a/doc/getstarted/build_and_install/docker_install.rst +++ b/doc/getstarted/build_and_install/docker_install.rst @@ -56,25 +56,38 @@ The PaddlePaddle images don't contain any entry command. You need to write your Download and Run Docker images ------------------------------ -You have to install Docker in your machine which has linux kernel version 3.10+ first. You can refer to the official guide https://docs.docker.com/engine/installation/ for further information. +Currently, Docker is supported on macOS, Windows and Linux distributions. Please check out `Install Docker Engine `_ to find out much more details. -You can use :code:`docker pull ` to download images first, or just launch a container with :code:`docker run` \: +PaddlePaddle on CPU +..................... -.. code-block:: bash +You can use :code:`docker pull ` to download images, or directly launch a container with :code:`docker run` \: - docker run -it paddledev/paddle:cpu-latest + .. code-block:: bash + docker run -it paddledev/paddle:cpu-latest -If you want to launch container with GPU support, you need to set some environment variables at the same time: +PaddlePaddle on GPU +..................... -.. code-block:: bash +To build GPU version, you will need the following installed: + + .. code-block:: bash + + 1. a CUDA-capable GPU + 2. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) + + +Then, you will need to mount related CUDA driver and library into container. + + .. code-block:: bash - export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" - export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') - docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest + export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" + export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') + docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest -Some notes for docker +Some notes for Docker --------------------- Performance diff --git a/doc_cn/build_and_install/install/docker_install.rst b/doc_cn/build_and_install/install/docker_install.rst index 40339659be4..90a5c937094 100644 --- a/doc_cn/build_and_install/install/docker_install.rst +++ b/doc_cn/build_and_install/install/docker_install.rst @@ -60,21 +60,38 @@ mac osx或者是windows机器，请参考 `mac osx的安装文档 `_ 和 `windows 的安装文档 `_ 。 + +启动CPU版Docker镜像 +................... + 您可以使用 :code:`docker pull` 命令预先下载镜像，也可以直接执行 :code:`docker run` 命令运行镜像。执行方法如下: -.. code-block:: bash + .. code-block:: bash + + $ docker run -it paddledev/paddlepaddle:cpu-latest + +即可启动和进入PaddlePaddle的container。 + +启动GPU版Docker镜像 +................... + +首先, 请参考以下链接，在机器上安装CUDA Toolkit。 + + .. code-block:: bash - $ docker run -it paddledev/paddle:cpu-latest + NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) -即可启动和进入PaddlePaddle的container。如果运行GPU版本的PaddlePaddle，则需要先将 -cuda相关的Driver和设备映射进container中，脚本类似于 +其次，需要将cuda相关的驱动和设备映射进container中，脚本类似于 -.. code-block:: bash + .. code-block:: bash + + $ export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" + $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') + $ docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddlepaddle:latest-gpu - $ export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" - $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') - $ docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:gpu-latest +使用PaddlePaddle +.................. 进入Docker container后，运行 :code:`paddle version` 即可打印出PaddlePaddle的版本和构建 信息。安装完成的PaddlePaddle主体包括三个部分， :code:`paddle` 脚本， python的 diff --git a/doc_cn/faq/index.rst b/doc_cn/faq/index.rst index 551430eb417..838fa651d85 100644 --- a/doc_cn/faq/index.rst +++ b/doc_cn/faq/index.rst @@ -202,3 +202,18 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字 解决办法是： * 卸载PaddlePaddle包 :code:`pip uninstall paddle`, 清理掉老旧的PaddlePaddle安装包，使得单元测试有一个干净的环境。如果PaddlePaddle包已经在python的site-packages里面，单元测试会引用site-packages里面的python包，而不是源码目录里 :code:`/python` 目录下的python包。同时，即便设置 :code:`PYTHONPATH` 到 :code:`/python` 也没用，因为python的搜索路径是优先已经安装的python包。 + + +9. 运行Docker GPU镜像出现 "CUDA driver version is insufficient" +---------------------------------------------------------------- + +用户在使用PaddlePaddle GPU的Docker镜像的时候，常常出现 `Cuda Error: CUDA driver version is insufficient for CUDA runtime version`, 原因在于没有把机器上CUDA相关的驱动和库映射到容器内部。 +具体的解决方法是： + +.. code-block:: bash + + $ export CUDA_SO="$(\ls usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" + $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') + $ docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddlepaddle:latest-gpu + +更多关于Docker的安装与使用, 请参考 `PaddlePaddle Docker 文档 `_ 。 -- GitLab From 9d72cab0a4323a6d96bdc443f9cbac5c5658edbc Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 30 Nov 2016 11:55:00 +0800 Subject: [PATCH 122/347] Accelerating image processing for CNN --- CMakeLists.txt | 4 + plugin/opencv/CMakeLists.txt | 39 ++++++ plugin/opencv/DataTransformer.cpp | 179 +++++++++++++++++++++++++ plugin/opencv/DataTransformer.h | 123 +++++++++++++++++ plugin/opencv/PyDecodejpeg.cpp | 173 ++++++++++++++++++++++++ python/paddle/utils/image_multiproc.py | 170 +++++++++++++++++++++++ python/paddle/utils/image_util.py | 31 +++-- 7 files changed, 705 insertions(+), 14 deletions(-) create mode 100644 plugin/opencv/CMakeLists.txt create mode 100644 plugin/opencv/DataTransformer.cpp create mode 100644 plugin/opencv/DataTransformer.h create mode 100644 plugin/opencv/PyDecodejpeg.cpp create mode 100644 python/paddle/utils/image_multiproc.py diff --git a/CMakeLists.txt b/CMakeLists.txt index af193c27ae7..40f18f15508 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -195,3 +195,7 @@ if(WITH_DOC) add_subdirectory(doc) add_subdirectory(doc_cn) endif() + +if(USE_OPENCV) + add_subdirectory(plugin/opencv) +endif() diff --git a/plugin/opencv/CMakeLists.txt b/plugin/opencv/CMakeLists.txt new file mode 100644 index 00000000000..4a253f346a0 --- /dev/null +++ b/plugin/opencv/CMakeLists.txt @@ -0,0 +1,39 @@ +# use opencv plugin + +project(DeJpeg CXX C) +set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake") +set(PROJ_ROOT ${CMAKE_SOURCE_DIR}) +list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules) +set(DEJPEG_LINKER_LIBS "") + +# opencv +find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc) +include_directories(${OpenCV_INCLUDE_DIRS}) +list(APPEND DEJPEG_LINKER_LIBS ${OpenCV_LIBS}) +message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})") +add_definitions(-DUSE_OPENCV) + +# boost-python +set(Boost_NO_SYSTEM_PATHS ON) +if (Boost_NO_SYSTEM_PATHS) + set(BOOST_ROOT $ENV{BOOST_ROOT}) + set(Boost_DIR ${BOOST_ROOT}) + set(Boost_INCLUDE_DIR "${BOOST_ROOT}/include") + set(Boost_LIBRARIES "${BOOST_ROOT}/lib/") +endif (Boost_NO_SYSTEM_PATHS) +find_package(Boost 1.46 COMPONENTS python) +include_directories(SYSTEM ${Boost_INCLUDE_DIR}) +link_directories(${Boost_INCLUDE_DIR}) +message(STATUS "Boost found (${Boost_INCLUDE_DIR})") +message(STATUS "Boost found (${Boost_LIBRARIES})") +list(APPEND DEJPEG_LINKER_LIBS ${Boost_LIBRARIES}) + + +file(GLOB DEJPEG_HEADER "${CMAKE_CURRENT_SOURCE_DIR}" "*.h") +file(GLOB DEJPEG_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}" "*.cpp") + +set(CMAKE_CXX_FLAGS "-std=c++11 -O3 -fPIC -Wno-unused-parameter") + +add_library(DeJpeg SHARED ${DEJPEG_SOURCES}) +target_link_libraries(DeJpeg ${DEJPEG_LINKER_LIBS}) +set_target_properties(DeJpeg PROPERTIES PREFIX "") diff --git a/plugin/opencv/DataTransformer.cpp b/plugin/opencv/DataTransformer.cpp new file mode 100644 index 00000000000..f4e21db8861 --- /dev/null +++ b/plugin/opencv/DataTransformer.cpp @@ -0,0 +1,179 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 "DataTransformer.h" +#include +#include + +DataTransformer::DataTransformer(int threadNum, + int capacity, + bool isTest, + bool isColor, + int cropHeight, + int cropWidth, + int imgSize, + bool isEltMean, + bool isChannelMean, + float* meanValues) + : isTest_(isTest), + isColor_(isColor), + cropHeight_(cropHeight), + cropWidth_(cropWidth), + imgSize_(imgSize), + capacity_(capacity), + prefetchFree_(capacity), + prefetchFull_(capacity) { + fetchCount_ = -1; + scale_ = 1.0; + isChannelMean_ = isChannelMean; + isEltMean_ = isEltMean; + loadMean(meanValues); + + imgPixels_ = cropHeight * cropWidth * (isColor_ ? 3 : 1); + + prefetch_.reserve(capacity); + for (int i = 0; i < capacity; i++) { + auto d = std::make_shared(new float[imgPixels_ * 3], 0); + prefetch_.push_back(d); + memset(prefetch_[i]->first, 0, imgPixels_ * sizeof(float)); + prefetchFree_.enqueue(prefetch_[i]); + } + + numThreads_ = 12; + syncThreadPool_.reset(new SyncThreadPool(numThreads_, false)); +} + +void DataTransformer::loadMean(float* values) { + if (values) { + int c = isColor_ ? 3 : 1; + int sz = isChannelMean_ ? c : cropHeight_ * cropWidth_ * c; + meanValues_ = new float[sz]; + memcpy(meanValues_, values, sz * sizeof(float)); + } +} + +void DataTransformer::startFetching(const char* src, + const int size, + float* trg) { + vector imbuf(src, src + size); + int cvFlag = (isColor_ ? CV_LOAD_IMAGE_COLOR : CV_LOAD_IMAGE_GRAYSCALE); + cv::Mat im = cv::imdecode(cv::Mat(imbuf), cvFlag); + if (!im.data) { + LOG(ERROR) << "Could not decode image"; + LOG(ERROR) << im.channels() << " " << im.rows << " " << im.cols; + } + this->transform(im, trg); +} + +int DataTransformer::Rand(int min, int max) { + std::random_device source; + std::mt19937 rng(source()); + std::uniform_int_distribution dist(min, max); + return dist(rng); +} + +void DataTransformer::transform(Mat& cvImgOri, float* target) { + const int imgChannels = cvImgOri.channels(); + const int imgHeight = cvImgOri.rows; + const int imgWidth = cvImgOri.cols; + const bool doMirror = (!isTest_) && Rand(0, 1); + int h_off = 0; + int w_off = 0; + int th = imgHeight; + int tw = imgWidth; + cv::Mat img; + if (imgSize_ > 0) { + if (imgHeight > imgWidth) { + tw = imgSize_; + th = int(double(imgHeight) / imgWidth * tw); + th = th > imgSize_ ? th : imgSize_; + } else { + th = imgSize_; + tw = int(double(imgWidth) / imgHeight * th); + tw = tw > imgSize_ ? tw : imgSize_; + } + cv::resize(cvImgOri, img, cv::Size(tw, th)); + } else { + cv::Mat img = cvImgOri; + } + + cv::Mat cv_cropped_img = img; + if (cropHeight_ && cropWidth_) { + if (!isTest_) { + h_off = Rand(0, th - cropHeight_); + w_off = Rand(0, tw - cropWidth_); + } else { + h_off = (th - cropHeight_) / 2; + w_off = (tw - cropWidth_) / 2; + } + cv::Rect roi(w_off, h_off, cropWidth_, cropHeight_); + cv_cropped_img = img(roi); + } else { + CHECK_EQ(cropHeight_, imgHeight); + CHECK_EQ(cropWidth_, imgWidth); + } + int height = cropHeight_; + int width = cropWidth_; + int top_index; + for (int h = 0; h < height; ++h) { + const uchar* ptr = cv_cropped_img.ptr(h); + int img_index = 0; + for (int w = 0; w < width; ++w) { + for (int c = 0; c < imgChannels; ++c) { + if (doMirror) { + top_index = (c * height + h) * width + width - 1 - w; + } else { + top_index = (c * height + h) * width + w; + } + float pixel = static_cast(ptr[img_index++]); + if (isEltMean_) { + int mean_index = (c * imgHeight + h) * imgWidth + w; + target[top_index] = (pixel - meanValues_[mean_index]) * scale_; + } else { + if (isChannelMean_) { + target[top_index] = (pixel - meanValues_[c]) * scale_; + } else { + target[top_index] = pixel * scale_; + } + } + } + } + } // target: BGR +} + +void DataTransformer::start(vector& data, int* datalen, int* labels) { + auto job = [&](int tid, int numThreads) { + for (int i = tid; i < data.size(); i += numThreads) { + DataTypePtr ret = prefetchFree_.dequeue(); + char* buf = data[i]; + int size = datalen[i]; + ret->second = labels[i]; + this->startFetching(buf, size, ret->first); + prefetchFull_.enqueue(ret); + } + }; + syncThreadPool_->exec(job); + fetchCount_ = data.size(); +} + +void DataTransformer::obtain(float* data, int* label) { + fetchCount_--; + if (fetchCount_ < 0) { + LOG(FATAL) << "Empty data"; + } + DataTypePtr ret = prefetchFull_.dequeue(); + *label = ret->second; + memcpy(data, ret->first, sizeof(float) * imgPixels_); + prefetchFree_.enqueue(ret); +} diff --git a/plugin/opencv/DataTransformer.h b/plugin/opencv/DataTransformer.h new file mode 100644 index 00000000000..c4f04a58785 --- /dev/null +++ b/plugin/opencv/DataTransformer.h @@ -0,0 +1,123 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 +#include +// #define OPENCV_CAN_BREAK_BINARY_COMPATIBILITY +#include +#include +#include +#include + +#include "paddle/utils/Thread.h" + +using namespace std; +using namespace cv; +using namespace paddle; + +/** + * This is an image processing module with OpenCV, such as + * resizing, scaling, mirroring, substracting the image mean... + * + * This class has a double BlockQueue and they shared the same memory. + * It is used to avoid create memory each time. And it also can + * return the data even if the data are processing in multi-threads. + */ +class DataTransformer { +public: + DataTransformer(int threadNum, + int capacity, + bool isTest, + bool isColor, + int cropHeight, + int cropWidth, + int imgSize, + bool isEltMean, + bool isChannelMean, + float* meanValues); + virtual ~DataTransformer() { + if (meanValues_) { + free(meanValues_); + } + } + + /** + * @brief Start multi-threads to transform a list of input data. + * The processed data will be saved in Queue of prefetchFull_. + * + * @param data Data containing the image string to be transformed. + * @param label The label of input image. + */ + void start(vector& data, int* datalen, int* labels); + + /** + * @brief Applies the transformation on one image Mat. + * + * @param img The input img to be transformed. + * @param target target is used to save the transformed data. + */ + void transform(Mat& img, float* target); + + /** + * @brief Decode the image string, then calls transform() function. + * + * @param src The input image string. + * @param size The length of string. + * @param trg trg is used to save the transformed data. + */ + void startFetching(const char* src, const int size, float* trg); + + /** + * @brief Return the transformed data and its label. + */ + void obtain(float* data, int* label); + +private: + int isTest_; + int isColor_; + int cropHeight_; + int cropWidth_; + int imgSize_; + int capacity_; + int fetchCount_; + bool isEltMean_; + bool isChannelMean_; + int numThreads_; + float scale_; + int imgPixels_; + float* meanValues_; + + /** + * Initialize the mean values. + */ + void loadMean(float* values); + + /** + * @brief Generates a random integer from Uniform({min, min + 1, ..., max}). + * @param min The lower bound (inclusive) value of the random number. + * @param max The upper bound (inclusive) value of the random number. + * + * @return + * A uniformly random integer value from ({min, min + 1, ..., max}). + */ + int Rand(int min, int max); + + typedef pair DataType; + typedef std::shared_ptr DataTypePtr; + std::vector prefetch_; + std::unique_ptr syncThreadPool_; + BlockingQueue prefetchFree_; + BlockingQueue prefetchFull_; + +}; // class DataTransformer diff --git a/plugin/opencv/PyDecodejpeg.cpp b/plugin/opencv/PyDecodejpeg.cpp new file mode 100644 index 00000000000..b004d7cad80 --- /dev/null +++ b/plugin/opencv/PyDecodejpeg.cpp @@ -0,0 +1,173 @@ +/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. + +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 +#include +#include +#include +#include +#include +#include + +#include + +#include "DataTransformer.h" + +using namespace boost::python; +using namespace std; + +/** + * DecodeJpeg is an image processing API for interfacing Python and C++ + * code DataTransformer, which used OpenCV and multi-threads to accelerate + * image processing. + * The Boost Python Library is used to wrap C++ interfaces. + */ + +class DecodeJpeg { +public: + /** + * The constructor will create and nitialize an object of DataTransformer. + */ + DecodeJpeg(int threadNum, + int capacity, + bool isTest, + bool isColor, + int resize_min_size, + int cropSizeH, + int cropSizeW, + PyObject* meanValues) { + int channel = isColor ? 3 : 1; + bool isEltMean = false; + bool isChannelMean = false; + float* mean = NULL; + if (meanValues || meanValues != Py_None) { + if (!PyArray_Check(meanValues)) { + LOG(FATAL) << "Object is not a numpy array"; + } + pyTypeCheck(meanValues); + int size = PyArray_SIZE(meanValues); + isChannelMean = (size == channel) ? true : false; + isEltMean = (size == channel * cropSizeH * cropSizeW) ? true : false; + CHECK(isChannelMean != isEltMean); + mean = (float*)PyArray_DATA(meanValues); + } + tfhandlerPtr_ = std::make_shared(threadNum, + capacity, + isTest, + isColor, + cropSizeH, + cropSizeW, + resize_min_size, + isEltMean, + isChannelMean, + mean); + } + + ~DecodeJpeg() {} + + /** + * @brief This function is used to parse the Python object and convert + * the data to C++ format. Then it called the function of + * DataTransformer to start image processing. + * @param pysrc The input image list with string type. + * @param pylabel The input label of image. + * It's type is numpy.array with int32. + */ + void start(boost::python::list& pysrc, PyObject* pydlen, PyObject* pylabel) { + vector data; + int num = len(pysrc); + for (int t = 0; t < num; ++t) { + char* src = boost::python::extract(pysrc[t]); + data.push_back(src); + } + int* dlen = (int*)PyArray_DATA(pydlen); + int* dlabels = (int*)PyArray_DATA(pylabel); + tfhandlerPtr_->start(data, dlen, dlabels); + } + + /** + * @brief Return one processed data. + * @param pytrg The processed image. + * @param pylabel The label of processed image. + */ + void get(PyObject* pytrg, PyObject* pylab) { + pyWritableCheck(pytrg); + pyWritableCheck(pylab); + pyContinuousCheck(pytrg); + pyContinuousCheck(pylab); + float* data = (float*)PyArray_DATA(pytrg); + int* label = (int*)PyArray_DATA(pylab); + tfhandlerPtr_->obtain(data, label); + } + + /** + * @brief An object of DataTransformer, which is used to call + * the image processing funtions. + */ + std::shared_ptr tfhandlerPtr_; + +private: + /** + * @brief Check whether the type of PyObject is valid or not. + */ + void pyTypeCheck(const PyObject* o) { + int typenum = PyArray_TYPE(o); + + // clang-format off + int type = + typenum == NPY_UBYTE ? CV_8U : + typenum == NPY_BYTE ? CV_8S : + typenum == NPY_USHORT ? CV_16U : + typenum == NPY_SHORT ? CV_16S : + typenum == NPY_INT || typenum == NPY_LONG ? CV_32S : + typenum == NPY_FLOAT ? CV_32F : + typenum == NPY_DOUBLE ? CV_64F : -1; + // clang-format on + + if (type < 0) { + LOG(FATAL) << "toMat: Data type = " << type << " is not supported"; + } + } + + /** + * @brief Check whether the PyObject is writable or not. + */ + void pyWritableCheck(PyObject* o) { CHECK(PyArray_ISWRITEABLE(o)); } + + /** + * @brief Check whether the PyObject is c-contiguous or not. + */ + void pyContinuousCheck(PyObject* o) { CHECK(PyArray_IS_C_CONTIGUOUS(o)); } +}; + +/** + * @brief Initialize the Python interpreter and numpy. + */ +static void initPython() { + Py_Initialize(); + PyOS_sighandler_t sighandler = PyOS_getsig(SIGINT); + import_array(); + PyOS_setsig(SIGINT, sighandler); +} + +/** + * Use Boost.Python to expose C++ interface to Python. + */ +BOOST_PYTHON_MODULE(DeJpeg) { + initPython(); + class_("DecodeJpeg", + init()) + .def("start", &DecodeJpeg::start) + .def("get", &DecodeJpeg::get); +}; diff --git a/python/paddle/utils/image_multiproc.py b/python/paddle/utils/image_multiproc.py new file mode 100644 index 00000000000..ccc0a531a78 --- /dev/null +++ b/python/paddle/utils/image_multiproc.py @@ -0,0 +1,170 @@ +import os, psutil +import cv2 +from paddle.utils.image_util import * +import multiprocessing +import subprocess, signal, sys + + +class CvImageTransfomer(ImageTransformer): + """ + CvImageTransfomer used python-opencv to process image. + """ + + def __init__(self, + min_size=None, + crop_size=None, + transpose=None, + 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 cv_resize_fixed_short_side(self, im, min_size): + row, col = im.shape[:2] + scale = min_size / float(min(row, col)) + if row < col: + row = min_size + col = int(round(col * scale)) + col = col if col > min_size else min_size + else: + col = min_size + row = int(round(row * scale)) + row = row if row > min_size else min_size + resized_size = row, col + im = cv2.resize(im, resized_size, interpolation=cv2.INTER_CUBIC) + return im + + def crop_img(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.cv_resize_fixed_short_side(im, self.min_size) + im = self.crop_img(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) + + +class MultiProcessImageTransfomer(): + def __init__(self, + procnum=10, + capacity=10240, + min_size=None, + crop_size=None, + transpose=None, + channel_swap=None, + mean=None, + is_train=True, + is_color=True): + self.procnum = procnum + self.capacity = capacity + self.size = 0 + self.count = 0 + signal.signal(signal.SIGTERM, self.kill_child_processes) + self.fetch_queue = multiprocessing.Queue(maxsize=capacity) + self.cv_transformer = CvImageTransfomer(min_size, crop_size, transpose, + channel_swap, mean, is_train, + is_color) + + def __del__(self): + try: + for p in self.procs: + p.join() + except Exception as e: + print str(e) + + def reset(self, size): + self.size = size + self.count = 0 + self.procs = [] + + def run_proc(self, data, label): + dlen = len(label) + self.reset(dlen) + for i in xrange(self.procnum): + start = dlen * i / self.procnum + end = dlen * (i + 1) / self.procnum + proc = multiprocessing.Process( + target=self.batch_transfomer, + args=(data[start:end], label[start:end])) + proc.daemon = True + self.procs.append(proc) + for p in self.procs: + p.start() + + def get(self): + """ + Return one processed image. + """ + # block if necessary until an item is available + data, lab = self.fetch_queue.get(block=True) + self.count += 1 + if self.count == self.size: + try: + for p in self.procs: + p.join() + except Exception as e: + print str(e) + return data, lab + + def batch_transfomer(self, data, label): + """ + param data: input data in format of image string + type data: a list of string + label: the label of image + """ + for i in xrange(len(label)): + res = self.cv_transformer.transform_from_string(data[i]) + self.fetch_queue.put((res, int(label[i]))) + + def kill_child_processes(self, signum, frame): + """ + Kill a process's child processes in python. + """ + parent_id = os.getpid() + ps_command = subprocess.Popen( + "ps -o pid --ppid %d --noheaders" % parent_id, + shell=True, + stdout=subprocess.PIPE) + ps_output = ps_command.stdout.read() + retcode = ps_command.wait() + for pid_str in ps_output.strip().split("\n")[:-1]: + os.kill(int(pid_str), signal.SIGTERM) + sys.exit() diff --git a/python/paddle/utils/image_util.py b/python/paddle/utils/image_util.py index b5c6431c06f..e6c6b04de0a 100644 --- a/python/paddle/utils/image_util.py +++ b/python/paddle/utils/image_util.py @@ -186,29 +186,32 @@ class ImageTransformer: channel_swap=None, mean=None, is_color=True): - self.transpose = transpose - self.channel_swap = None - self.mean = None self.is_color = is_color + self.set_transpose(transpose) + self.set_channel_swap(channel_swap) + self.set_mean(mean) def set_transpose(self, order): - if self.is_color: - assert 3 == len(order) + if order is not None: + if self.is_color: + assert 3 == len(order) self.transpose = order def set_channel_swap(self, order): - if self.is_color: - assert 3 == len(order) + if order is not None: + if self.is_color: + assert 3 == len(order) self.channel_swap = order def set_mean(self, mean): - # mean value, may be one value per channel - if mean.ndim == 1: - mean = mean[:, np.newaxis, np.newaxis] - else: - # elementwise mean - if self.is_color: - assert len(mean.shape) == 3 + if mean is not None: + # mean value, may be one value per channel + if mean.ndim == 1: + mean = mean[:, np.newaxis, np.newaxis] + else: + # elementwise mean + if self.is_color: + assert len(mean.shape) == 3 self.mean = mean def transformer(self, data): -- GitLab From 1d1a04c033e9a63690b39be2573de4747c51f156 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 30 Nov 2016 13:52:48 +0800 Subject: [PATCH 123/347] follow comments on rectangle CNN --- paddle/gserver/layers/DataLayer.cpp | 9 +++++++-- python/paddle/trainer/config_parser.py | 7 +++---- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/paddle/gserver/layers/DataLayer.cpp b/paddle/gserver/layers/DataLayer.cpp index f6f4bf1d9cb..67c49230367 100644 --- a/paddle/gserver/layers/DataLayer.cpp +++ b/paddle/gserver/layers/DataLayer.cpp @@ -49,8 +49,13 @@ void DataLayer::copyDataToOutput(Argument& output) { output.ids->copyFrom(*data_.ids); } } - output.setFrameHeight(config_.height()); - output.setFrameWidth(config_.width()); + if (config_.height() && config_.width()) { + output.setFrameHeight(config_.height()); + output.setFrameWidth(config_.width()); + } else { + output.setFrameHeight(data_.getFrameHeight()); + output.setFrameHeight(data_.getFrameHeight()); + } output.cpuSequenceDims = data_.cpuSequenceDims; output.sequenceStartPositions = data_.sequenceStartPositions; output.subSequenceStartPositions = data_.subSequenceStartPositions; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index f917ead680a..0f4839e8315 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1931,10 +1931,9 @@ class MaxOutLayer(LayerBase): input_layer = self.get_input_layer(0) maxout_conf = self.config.inputs[0].maxout_conf parse_maxout(self.inputs[0].maxout, input_layer.name, maxout_conf) - self.set_layer_size(g_layer_map[input_layer.name].size / - maxout_conf.groups) - self.set_layer_height_width(g_layer_map[input_layer.name].height, - g_layer_map[input_layer.name].width) + out_channels = maxout_conf.image_conf.channels / maxout_conf.groups + self.set_cnn_layer(name, g_layer_map[input_layer.name].height, + g_layer_map[input_layer.name].width, out_channels) # key: cost type -- GitLab From a342d130cc2f4cc6ad0ee8e1c330e2f4689fcc4f Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 30 Nov 2016 15:27:23 +0800 Subject: [PATCH 124/347] remove build of doxyfile --- doc/CMakeLists.txt | 19 +- doc/Doxyfile.in | 2384 -------------------------------------------- doc/conf.py.in | 15 - 3 files changed, 2 insertions(+), 2416 deletions(-) delete mode 100644 doc/Doxyfile.in diff --git a/doc/CMakeLists.txt b/doc/CMakeLists.txt index ef4e9d102d3..efcf8b0ad3d 100644 --- a/doc/CMakeLists.txt +++ b/doc/CMakeLists.txt @@ -15,25 +15,11 @@ set(SPHINX_CACHE_DIR "${CMAKE_CURRENT_BINARY_DIR}/_doctrees") # HTML output directory set(SPHINX_HTML_DIR "${CMAKE_CURRENT_BINARY_DIR}/html") - -set(PADDLE_DOXYGEN_OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/doxygen_xml") - configure_file( "${CMAKE_CURRENT_SOURCE_DIR}/conf.py.in" "${BINARY_BUILD_DIR}/conf.py" @ONLY) -configure_file( - "${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in" - "${CMAKE_CURRENT_BINARY_DIR}/Doxyfile" - @ONLY - ) - -add_custom_target(paddle_doxygen_docs ALL - ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} -) - sphinx_add_target(paddle_docs html ${BINARY_BUILD_DIR} @@ -41,6 +27,5 @@ sphinx_add_target(paddle_docs ${CMAKE_CURRENT_SOURCE_DIR} ${SPHINX_HTML_DIR}) -add_dependencies(paddle_docs - gen_proto_py - paddle_doxygen_docs) +add_dependencies(paddle_docs + gen_proto_py) diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in deleted file mode 100644 index a1fc3801925..00000000000 --- a/doc/Doxyfile.in +++ /dev/null @@ -1,2384 +0,0 @@ -# Doxyfile 1.8.10 - -# This file describes the settings to be used by the documentation system -# doxygen (www.doxygen.org) for a project. -# -# All text after a double hash (##) is considered a comment and is placed in -# front of the TAG it is preceding. -# -# All text after a single hash (#) is considered a comment and will be ignored. -# The format is: -# TAG = value [value, ...] -# For lists, items can also be appended using: -# TAG += value [value, ...] -# Values that contain spaces should be placed between quotes (\" \"). - 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By default this option -# is disabled and one has to add nested compounds explicitly via \ingroup. -# The default value is: NO. - -GROUP_NESTED_COMPOUNDS = NO - -# Set the SUBGROUPING tag to YES to allow class member groups of the same type -# (for instance a group of public functions) to be put as a subgroup of that -# type (e.g. under the Public Functions section). Set it to NO to prevent -# subgrouping. Alternatively, this can be done per class using the -# \nosubgrouping command. -# The default value is: YES. - -SUBGROUPING = YES - -# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions -# are shown inside the group in which they are included (e.g. using \ingroup) -# instead of on a separate page (for HTML and Man pages) or section (for LaTeX -# and RTF). -# -# Note that this feature does not work in combination with -# SEPARATE_MEMBER_PAGES. -# The default value is: NO. - -INLINE_GROUPED_CLASSES = NO - -# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions -# with only public data fields or simple typedef fields will be shown inline in -# the documentation of the scope in which they are defined (i.e. file, -# namespace, or group documentation), provided this scope is documented. If set -# to NO, structs, classes, and unions are shown on a separate page (for HTML and -# Man pages) or section (for LaTeX and RTF). -# The default value is: NO. - -INLINE_SIMPLE_STRUCTS = NO - -# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or -# enum is documented as struct, union, or enum with the name of the typedef. So -# typedef struct TypeS {} TypeT, will appear in the documentation as a struct -# with name TypeT. When disabled the typedef will appear as a member of a file, -# namespace, or class. And the struct will be named TypeS. This can typically be -# useful for C code in case the coding convention dictates that all compound -# types are typedef'ed and only the typedef is referenced, never the tag name. -# The default value is: NO. - -TYPEDEF_HIDES_STRUCT = NO - -# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This -# cache is used to resolve symbols given their name and scope. Since this can be -# an expensive process and often the same symbol appears multiple times in the -# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small -# doxygen will become slower. If the cache is too large, memory is wasted. The -# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range -# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536 -# symbols. At the end of a run doxygen will report the cache usage and suggest -# the optimal cache size from a speed point of view. -# Minimum value: 0, maximum value: 9, default value: 0. - -LOOKUP_CACHE_SIZE = 0 - -#--------------------------------------------------------------------------- -# Build related configuration options -#--------------------------------------------------------------------------- - -# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in -# documentation are documented, even if no documentation was available. Private -# class members and static file members will be hidden unless the -# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES. -# Note: This will also disable the warnings about undocumented members that are -# normally produced when WARNINGS is set to YES. -# The default value is: NO. - -EXTRACT_ALL = NO - -# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will -# be included in the documentation. -# The default value is: NO. - -EXTRACT_PRIVATE = NO - -# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal -# scope will be included in the documentation. -# The default value is: NO. - -EXTRACT_PACKAGE = NO - -# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be -# included in the documentation. -# The default value is: NO. - -EXTRACT_STATIC = NO - -# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined -# locally in source files will be included in the documentation. If set to NO, -# only classes defined in header files are included. Does not have any effect -# for Java sources. -# The default value is: YES. - -EXTRACT_LOCAL_CLASSES = YES - -# This flag is only useful for Objective-C code. If set to YES, local methods, -# which are defined in the implementation section but not in the interface are -# included in the documentation. If set to NO, only methods in the interface are -# included. -# The default value is: NO. - -EXTRACT_LOCAL_METHODS = NO - -# If this flag is set to YES, the members of anonymous namespaces will be -# extracted and appear in the documentation as a namespace called -# 'anonymous_namespace{file}', where file will be replaced with the base name of -# the file that contains the anonymous namespace. By default anonymous namespace -# are hidden. -# The default value is: NO. - -EXTRACT_ANON_NSPACES = NO - -# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all -# undocumented members inside documented classes or files. If set to NO these -# members will be included in the various overviews, but no documentation -# section is generated. This option has no effect if EXTRACT_ALL is enabled. -# The default value is: NO. - -HIDE_UNDOC_MEMBERS = NO - -# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all -# undocumented classes that are normally visible in the class hierarchy. If set -# to NO, these classes will be included in the various overviews. This option -# has no effect if EXTRACT_ALL is enabled. -# The default value is: NO. - -HIDE_UNDOC_CLASSES = NO - -# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend -# (class|struct|union) declarations. If set to NO, these declarations will be -# included in the documentation. -# The default value is: NO. - -HIDE_FRIEND_COMPOUNDS = NO - -# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any -# documentation blocks found inside the body of a function. If set to NO, these -# blocks will be appended to the function's detailed documentation block. -# The default value is: NO. - -HIDE_IN_BODY_DOCS = NO - -# The INTERNAL_DOCS tag determines if documentation that is typed after a -# \internal command is included. If the tag is set to NO then the documentation -# will be excluded. Set it to YES to include the internal documentation. -# The default value is: NO. - -INTERNAL_DOCS = NO - -# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file -# names in lower-case letters. If set to YES, upper-case letters are also -# allowed. This is useful if you have classes or files whose names only differ -# in case and if your file system supports case sensitive file names. Windows -# and Mac users are advised to set this option to NO. -# The default value is: system dependent. - -CASE_SENSE_NAMES = YES - -# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with -# their full class and namespace scopes in the documentation. If set to YES, the -# scope will be hidden. -# The default value is: NO. - -HIDE_SCOPE_NAMES = NO - -# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will -# append additional text to a page's title, such as Class Reference. If set to -# YES the compound reference will be hidden. -# The default value is: NO. - -HIDE_COMPOUND_REFERENCE= NO - -# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of -# the files that are included by a file in the documentation of that file. -# The default value is: YES. - -SHOW_INCLUDE_FILES = NO - -# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each -# grouped member an include statement to the documentation, telling the reader -# which file to include in order to use the member. -# The default value is: NO. - -SHOW_GROUPED_MEMB_INC = NO - -# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include -# files with double quotes in the documentation rather than with sharp brackets. -# The default value is: NO. - -FORCE_LOCAL_INCLUDES = NO - -# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the -# documentation for inline members. -# The default value is: YES. - -INLINE_INFO = YES - -# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the -# (detailed) documentation of file and class members alphabetically by member -# name. If set to NO, the members will appear in declaration order. -# The default value is: YES. - -SORT_MEMBER_DOCS = YES - -# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief -# descriptions of file, namespace and class members alphabetically by member -# name. If set to NO, the members will appear in declaration order. Note that -# this will also influence the order of the classes in the class list. -# The default value is: NO. - -SORT_BRIEF_DOCS = NO - -# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the -# (brief and detailed) documentation of class members so that constructors and -# destructors are listed first. If set to NO the constructors will appear in the -# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS. -# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief -# member documentation. -# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting -# detailed member documentation. -# The default value is: NO. - -SORT_MEMBERS_CTORS_1ST = NO - -# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy -# of group names into alphabetical order. If set to NO the group names will -# appear in their defined order. -# The default value is: NO. - -SORT_GROUP_NAMES = NO - -# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by -# fully-qualified names, including namespaces. If set to NO, the class list will -# be sorted only by class name, not including the namespace part. -# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. -# Note: This option applies only to the class list, not to the alphabetical -# list. -# The default value is: NO. - -SORT_BY_SCOPE_NAME = NO - -# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper -# type resolution of all parameters of a function it will reject a match between -# the prototype and the implementation of a member function even if there is -# only one candidate or it is obvious which candidate to choose by doing a -# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still -# accept a match between prototype and implementation in such cases. -# The default value is: NO. - -STRICT_PROTO_MATCHING = NO - -# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo -# list. This list is created by putting \todo commands in the documentation. -# The default value is: YES. - -GENERATE_TODOLIST = YES - -# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test -# list. This list is created by putting \test commands in the documentation. -# The default value is: YES. - -GENERATE_TESTLIST = YES - -# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug -# list. This list is created by putting \bug commands in the documentation. -# The default value is: YES. - -GENERATE_BUGLIST = YES - -# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO) -# the deprecated list. This list is created by putting \deprecated commands in -# the documentation. -# The default value is: YES. - -GENERATE_DEPRECATEDLIST= YES - -# The ENABLED_SECTIONS tag can be used to enable conditional documentation -# sections, marked by \if ... \endif and \cond -# ... \endcond blocks. - -ENABLED_SECTIONS = - -# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the -# initial value of a variable or macro / define can have for it to appear in the -# documentation. If the initializer consists of more lines than specified here -# it will be hidden. Use a value of 0 to hide initializers completely. The -# appearance of the value of individual variables and macros / defines can be -# controlled using \showinitializer or \hideinitializer command in the -# documentation regardless of this setting. -# Minimum value: 0, maximum value: 10000, default value: 30. - -MAX_INITIALIZER_LINES = 30 - -# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at -# the bottom of the documentation of classes and structs. If set to YES, the -# list will mention the files that were used to generate the documentation. -# The default value is: YES. - -SHOW_USED_FILES = YES - -# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This -# will remove the Files entry from the Quick Index and from the Folder Tree View -# (if specified). -# The default value is: YES. - -SHOW_FILES = YES - -# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces -# page. This will remove the Namespaces entry from the Quick Index and from the -# Folder Tree View (if specified). -# The default value is: YES. - -SHOW_NAMESPACES = YES - -# The FILE_VERSION_FILTER tag can be used to specify a program or script that -# doxygen should invoke to get the current version for each file (typically from -# the version control system). Doxygen will invoke the program by executing (via -# popen()) the command command input-file, where command is the value of the -# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided -# by doxygen. Whatever the program writes to standard output is used as the file -# version. For an example see the documentation. - -FILE_VERSION_FILTER = - -# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed -# by doxygen. The layout file controls the global structure of the generated -# output files in an output format independent way. To create the layout file -# that represents doxygen's defaults, run doxygen with the -l option. You can -# optionally specify a file name after the option, if omitted DoxygenLayout.xml -# will be used as the name of the layout file. -# -# Note that if you run doxygen from a directory containing a file called -# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE -# tag is left empty. - -LAYOUT_FILE = - -# The CITE_BIB_FILES tag can be used to specify one or more bib files containing -# the reference definitions. This must be a list of .bib files. The .bib -# extension is automatically appended if omitted. This requires the bibtex tool -# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info. -# For LaTeX the style of the bibliography can be controlled using -# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the -# search path. See also \cite for info how to create references. - -CITE_BIB_FILES = - -#--------------------------------------------------------------------------- -# Configuration options related to warning and progress messages -#--------------------------------------------------------------------------- - -# The QUIET tag can be used to turn on/off the messages that are generated to -# standard output by doxygen. If QUIET is set to YES this implies that the -# messages are off. -# The default value is: NO. - -QUIET = NO - -# The WARNINGS tag can be used to turn on/off the warning messages that are -# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES -# this implies that the warnings are on. -# -# Tip: Turn warnings on while writing the documentation. -# The default value is: YES. - -WARNINGS = YES - -# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate -# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag -# will automatically be disabled. -# The default value is: YES. - -WARN_IF_UNDOCUMENTED = NO - -# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for -# potential errors in the documentation, such as not documenting some parameters -# in a documented function, or documenting parameters that don't exist or using -# markup commands wrongly. -# The default value is: YES. - -WARN_IF_DOC_ERROR = YES - -# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that -# are documented, but have no documentation for their parameters or return -# value. If set to NO, doxygen will only warn about wrong or incomplete -# parameter documentation, but not about the absence of documentation. -# The default value is: NO. - -WARN_NO_PARAMDOC = NO - -# The WARN_FORMAT tag determines the format of the warning messages that doxygen -# can produce. The string should contain the $file, $line, and $text tags, which -# will be replaced by the file and line number from which the warning originated -# and the warning text. Optionally the format may contain $version, which will -# be replaced by the version of the file (if it could be obtained via -# FILE_VERSION_FILTER) -# The default value is: $file:$line: $text. - -WARN_FORMAT = "$file:$line: $text" - -# The WARN_LOGFILE tag can be used to specify a file to which warning and error -# messages should be written. If left blank the output is written to standard -# error (stderr). - -WARN_LOGFILE = - -#--------------------------------------------------------------------------- -# Configuration options related to the input files -#--------------------------------------------------------------------------- - -# The INPUT tag is used to specify the files and/or directories that contain -# documented source files. You may enter file names like myfile.cpp or -# directories like /usr/src/myproject. Separate the files or directories with -# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING -# Note: If this tag is empty the current directory is searched. - -INPUT = @PROJ_ROOT@/paddle - -# This tag can be used to specify the character encoding of the source files -# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses -# libiconv (or the iconv built into libc) for the transcoding. See the libiconv -# documentation (see: http://www.gnu.org/software/libiconv) for the list of -# possible encodings. -# The default value is: UTF-8. - -INPUT_ENCODING = UTF-8 - -# If the value of the INPUT tag contains directories, you can use the -# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and -# *.h) to filter out the source-files in the directories. -# -# Note that for custom extensions or not directly supported extensions you also -# need to set EXTENSION_MAPPING for the extension otherwise the files are not -# read by doxygen. -# -# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp, -# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, -# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, -# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, -# *.vhdl, *.ucf, *.qsf, *.as and *.js. - -FILE_PATTERNS = *.c *.cc *.cpp *.cu *.h *.hpp *.cuh *.ph - -# The RECURSIVE tag can be used to specify whether or not subdirectories should -# be searched for input files as well. -# The default value is: NO. - -RECURSIVE = YES - -# The EXCLUDE tag can be used to specify files and/or directories that should be -# excluded from the INPUT source files. This way you can easily exclude a -# subdirectory from a directory tree whose root is specified with the INPUT tag. -# -# Note that relative paths are relative to the directory from which doxygen is -# run. - -EXCLUDE = - -# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or -# directories that are symbolic links (a Unix file system feature) are excluded -# from the input. -# The default value is: NO. - -EXCLUDE_SYMLINKS = NO - -# If the value of the INPUT tag contains directories, you can use the -# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude -# certain files from those directories. -# -# Note that the wildcards are matched against the file with absolute path, so to -# exclude all test directories for example use the pattern */test/* - -EXCLUDE_PATTERNS = */x86_64-scm-linux-gnu/* */internals/* */mkl/* */test/* */tests/* */platform/* - -# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names -# (namespaces, classes, functions, etc.) that should be excluded from the -# output. The symbol name can be a fully qualified name, a word, or if the -# wildcard * is used, a substring. Examples: ANamespace, AClass, -# AClass::ANamespace, ANamespace::*Test -# -# Note that the wildcards are matched against the file with absolute path, so to -# exclude all test directories use the pattern */test/* - -EXCLUDE_SYMBOLS = - -# The EXAMPLE_PATH tag can be used to specify one or more files or directories -# that contain example code fragments that are included (see the \include -# command). - -EXAMPLE_PATH = - -# If the value of the EXAMPLE_PATH tag contains directories, you can use the -# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and -# *.h) to filter out the source-files in the directories. If left blank all -# files are included. - -EXAMPLE_PATTERNS = - -# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be -# searched for input files to be used with the \include or \dontinclude commands -# irrespective of the value of the RECURSIVE tag. -# The default value is: NO. - -EXAMPLE_RECURSIVE = NO - -# The IMAGE_PATH tag can be used to specify one or more files or directories -# that contain images that are to be included in the documentation (see the -# \image command). - -IMAGE_PATH = - -# The INPUT_FILTER tag can be used to specify a program that doxygen should -# invoke to filter for each input file. Doxygen will invoke the filter program -# by executing (via popen()) the command: -# -# -# -# where is the value of the INPUT_FILTER tag, and is the -# name of an input file. Doxygen will then use the output that the filter -# program writes to standard output. If FILTER_PATTERNS is specified, this tag -# will be ignored. -# -# Note that the filter must not add or remove lines; it is applied before the -# code is scanned, but not when the output code is generated. If lines are added -# or removed, the anchors will not be placed correctly. - -INPUT_FILTER = - -# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern -# basis. Doxygen will compare the file name with each pattern and apply the -# filter if there is a match. The filters are a list of the form: pattern=filter -# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how -# filters are used. If the FILTER_PATTERNS tag is empty or if none of the -# patterns match the file name, INPUT_FILTER is applied. - -FILTER_PATTERNS = - -# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using -# INPUT_FILTER) will also be used to filter the input files that are used for -# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES). -# The default value is: NO. - -FILTER_SOURCE_FILES = NO - -# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file -# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and -# it is also possible to disable source filtering for a specific pattern using -# *.ext= (so without naming a filter). -# This tag requires that the tag FILTER_SOURCE_FILES is set to YES. - -FILTER_SOURCE_PATTERNS = - -# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that -# is part of the input, its contents will be placed on the main page -# (index.html). This can be useful if you have a project on for instance GitHub -# and want to reuse the introduction page also for the doxygen output. - -USE_MDFILE_AS_MAINPAGE = - -#--------------------------------------------------------------------------- -# Configuration options related to source browsing -#--------------------------------------------------------------------------- - -# If the SOURCE_BROWSER tag is set to YES then a list of source files will be -# generated. Documented entities will be cross-referenced with these sources. -# -# Note: To get rid of all source code in the generated output, make sure that -# also VERBATIM_HEADERS is set to NO. -# The default value is: NO. - -SOURCE_BROWSER = NO - -# Setting the INLINE_SOURCES tag to YES will include the body of functions, -# classes and enums directly into the documentation. -# The default value is: NO. - -INLINE_SOURCES = NO - -# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any -# special comment blocks from generated source code fragments. Normal C, C++ and -# Fortran comments will always remain visible. -# The default value is: YES. - -STRIP_CODE_COMMENTS = YES - -# If the REFERENCED_BY_RELATION tag is set to YES then for each documented -# function all documented functions referencing it will be listed. -# The default value is: NO. - -REFERENCED_BY_RELATION = NO - -# If the REFERENCES_RELATION tag is set to YES then for each documented function -# all documented entities called/used by that function will be listed. -# The default value is: NO. - -REFERENCES_RELATION = NO - -# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set -# to YES then the hyperlinks from functions in REFERENCES_RELATION and -# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will -# link to the documentation. -# The default value is: YES. - -REFERENCES_LINK_SOURCE = YES - -# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the -# source code will show a tooltip with additional information such as prototype, -# brief description and links to the definition and documentation. Since this -# will make the HTML file larger and loading of large files a bit slower, you -# can opt to disable this feature. -# The default value is: YES. -# This tag requires that the tag SOURCE_BROWSER is set to YES. - -SOURCE_TOOLTIPS = YES - -# If the USE_HTAGS tag is set to YES then the references to source code will -# point to the HTML generated by the htags(1) tool instead of doxygen built-in -# source browser. The htags tool is part of GNU's global source tagging system -# (see http://www.gnu.org/software/global/global.html). You will need version -# 4.8.6 or higher. -# -# To use it do the following: -# - Install the latest version of global -# - Enable SOURCE_BROWSER and USE_HTAGS in the config file -# - Make sure the INPUT points to the root of the source tree -# - Run doxygen as normal -# -# Doxygen will invoke htags (and that will in turn invoke gtags), so these -# tools must be available from the command line (i.e. in the search path). -# -# The result: instead of the source browser generated by doxygen, the links to -# source code will now point to the output of htags. -# The default value is: NO. -# This tag requires that the tag SOURCE_BROWSER is set to YES. - -USE_HTAGS = NO - -# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a -# verbatim copy of the header file for each class for which an include is -# specified. Set to NO to disable this. -# See also: Section \class. -# The default value is: YES. - -VERBATIM_HEADERS = YES - -#--------------------------------------------------------------------------- -# Configuration options related to the alphabetical class index -#--------------------------------------------------------------------------- - -# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all -# compounds will be generated. Enable this if the project contains a lot of -# classes, structs, unions or interfaces. -# The default value is: YES. - -ALPHABETICAL_INDEX = YES - -# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in -# which the alphabetical index list will be split. -# Minimum value: 1, maximum value: 20, default value: 5. -# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. - -COLS_IN_ALPHA_INDEX = 5 - -# In case all classes in a project start with a common prefix, all classes will -# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag -# can be used to specify a prefix (or a list of prefixes) that should be ignored -# while generating the index headers. -# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. - -IGNORE_PREFIX = - -#--------------------------------------------------------------------------- -# Configuration options related to the HTML output -#--------------------------------------------------------------------------- - -# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output -# The default value is: YES. - -GENERATE_HTML = NO - -# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a -# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of -# it. -# The default directory is: html. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_OUTPUT = html - -# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each -# generated HTML page (for example: .htm, .php, .asp). -# The default value is: .html. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_FILE_EXTENSION = .html - -# The HTML_HEADER tag can be used to specify a user-defined HTML header file for -# each generated HTML page. If the tag is left blank doxygen will generate a -# standard header. -# -# To get valid HTML the header file that includes any scripts and style sheets -# that doxygen needs, which is dependent on the configuration options used (e.g. -# the setting GENERATE_TREEVIEW). It is highly recommended to start with a -# default header using -# doxygen -w html new_header.html new_footer.html new_stylesheet.css -# YourConfigFile -# and then modify the file new_header.html. See also section "Doxygen usage" -# for information on how to generate the default header that doxygen normally -# uses. -# Note: The header is subject to change so you typically have to regenerate the -# default header when upgrading to a newer version of doxygen. For a description -# of the possible markers and block names see the documentation. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_HEADER = - -# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each -# generated HTML page. If the tag is left blank doxygen will generate a standard -# footer. See HTML_HEADER for more information on how to generate a default -# footer and what special commands can be used inside the footer. See also -# section "Doxygen usage" for information on how to generate the default footer -# that doxygen normally uses. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_FOOTER = - -# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style -# sheet that is used by each HTML page. It can be used to fine-tune the look of -# the HTML output. If left blank doxygen will generate a default style sheet. -# See also section "Doxygen usage" for information on how to generate the style -# sheet that doxygen normally uses. -# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as -# it is more robust and this tag (HTML_STYLESHEET) will in the future become -# obsolete. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_STYLESHEET = - -# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined -# cascading style sheets that are included after the standard style sheets -# created by doxygen. Using this option one can overrule certain style aspects. -# This is preferred over using HTML_STYLESHEET since it does not replace the -# standard style sheet and is therefore more robust against future updates. -# Doxygen will copy the style sheet files to the output directory. -# Note: The order of the extra style sheet files is of importance (e.g. the last -# style sheet in the list overrules the setting of the previous ones in the -# list). For an example see the documentation. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_EXTRA_STYLESHEET = - -# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or -# other source files which should be copied to the HTML output directory. Note -# that these files will be copied to the base HTML output directory. Use the -# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these -# files. In the HTML_STYLESHEET file, use the file name only. Also note that the -# files will be copied as-is; there are no commands or markers available. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_EXTRA_FILES = - -# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen -# will adjust the colors in the style sheet and background images according to -# this color. Hue is specified as an angle on a colorwheel, see -# http://en.wikipedia.org/wiki/Hue for more information. For instance the value -# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300 -# purple, and 360 is red again. -# Minimum value: 0, maximum value: 359, default value: 220. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_COLORSTYLE_HUE = 220 - -# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors -# in the HTML output. For a value of 0 the output will use grayscales only. A -# value of 255 will produce the most vivid colors. -# Minimum value: 0, maximum value: 255, default value: 100. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_COLORSTYLE_SAT = 100 - -# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the -# luminance component of the colors in the HTML output. Values below 100 -# gradually make the output lighter, whereas values above 100 make the output -# darker. The value divided by 100 is the actual gamma applied, so 80 represents -# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not -# change the gamma. -# Minimum value: 40, maximum value: 240, default value: 80. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_COLORSTYLE_GAMMA = 80 - -# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML -# page will contain the date and time when the page was generated. Setting this -# to YES can help to show when doxygen was last run and thus if the -# documentation is up to date. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_TIMESTAMP = NO - -# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML -# documentation will contain sections that can be hidden and shown after the -# page has loaded. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_DYNAMIC_SECTIONS = NO - -# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries -# shown in the various tree structured indices initially; the user can expand -# and collapse entries dynamically later on. Doxygen will expand the tree to -# such a level that at most the specified number of entries are visible (unless -# a fully collapsed tree already exceeds this amount). So setting the number of -# entries 1 will produce a full collapsed tree by default. 0 is a special value -# representing an infinite number of entries and will result in a full expanded -# tree by default. -# Minimum value: 0, maximum value: 9999, default value: 100. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_INDEX_NUM_ENTRIES = 100 - -# If the GENERATE_DOCSET tag is set to YES, additional index files will be -# generated that can be used as input for Apple's Xcode 3 integrated development -# environment (see: http://developer.apple.com/tools/xcode/), introduced with -# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a -# Makefile in the HTML output directory. Running make will produce the docset in -# that directory and running make install will install the docset in -# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at -# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html -# for more information. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_DOCSET = NO - -# This tag determines the name of the docset feed. A documentation feed provides -# an umbrella under which multiple documentation sets from a single provider -# (such as a company or product suite) can be grouped. -# The default value is: Doxygen generated docs. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_FEEDNAME = "Doxygen generated docs" - -# This tag specifies a string that should uniquely identify the documentation -# set bundle. This should be a reverse domain-name style string, e.g. -# com.mycompany.MyDocSet. Doxygen will append .docset to the name. -# The default value is: org.doxygen.Project. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_BUNDLE_ID = org.doxygen.Project - -# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify -# the documentation publisher. This should be a reverse domain-name style -# string, e.g. com.mycompany.MyDocSet.documentation. -# The default value is: org.doxygen.Publisher. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_PUBLISHER_ID = org.doxygen.Publisher - -# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher. -# The default value is: Publisher. -# This tag requires that the tag GENERATE_DOCSET is set to YES. - -DOCSET_PUBLISHER_NAME = Publisher - -# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three -# additional HTML index files: index.hhp, index.hhc, and index.hhk. The -# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop -# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on -# Windows. -# -# The HTML Help Workshop contains a compiler that can convert all HTML output -# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML -# files are now used as the Windows 98 help format, and will replace the old -# Windows help format (.hlp) on all Windows platforms in the future. Compressed -# HTML files also contain an index, a table of contents, and you can search for -# words in the documentation. The HTML workshop also contains a viewer for -# compressed HTML files. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_HTMLHELP = NO - -# The CHM_FILE tag can be used to specify the file name of the resulting .chm -# file. You can add a path in front of the file if the result should not be -# written to the html output directory. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -CHM_FILE = - -# The HHC_LOCATION tag can be used to specify the location (absolute path -# including file name) of the HTML help compiler (hhc.exe). If non-empty, -# doxygen will try to run the HTML help compiler on the generated index.hhp. -# The file has to be specified with full path. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -HHC_LOCATION = - -# The GENERATE_CHI flag controls if a separate .chi index file is generated -# (YES) or that it should be included in the master .chm file (NO). -# The default value is: NO. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -GENERATE_CHI = NO - -# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc) -# and project file content. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -CHM_INDEX_ENCODING = - -# The BINARY_TOC flag controls whether a binary table of contents is generated -# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it -# enables the Previous and Next buttons. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -BINARY_TOC = NO - -# The TOC_EXPAND flag can be set to YES to add extra items for group members to -# the table of contents of the HTML help documentation and to the tree view. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTMLHELP is set to YES. - -TOC_EXPAND = NO - -# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and -# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that -# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help -# (.qch) of the generated HTML documentation. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_QHP = NO - -# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify -# the file name of the resulting .qch file. The path specified is relative to -# the HTML output folder. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QCH_FILE = - -# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help -# Project output. For more information please see Qt Help Project / Namespace -# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace). -# The default value is: org.doxygen.Project. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_NAMESPACE = org.doxygen.Project - -# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt -# Help Project output. For more information please see Qt Help Project / Virtual -# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual- -# folders). -# The default value is: doc. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_VIRTUAL_FOLDER = doc - -# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom -# filter to add. For more information please see Qt Help Project / Custom -# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom- -# filters). -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_CUST_FILTER_NAME = - -# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the -# custom filter to add. For more information please see Qt Help Project / Custom -# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom- -# filters). -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_CUST_FILTER_ATTRS = - -# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this -# project's filter section matches. Qt Help Project / Filter Attributes (see: -# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes). -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHP_SECT_FILTER_ATTRS = - -# The QHG_LOCATION tag can be used to specify the location of Qt's -# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the -# generated .qhp file. -# This tag requires that the tag GENERATE_QHP is set to YES. - -QHG_LOCATION = - -# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be -# generated, together with the HTML files, they form an Eclipse help plugin. To -# install this plugin and make it available under the help contents menu in -# Eclipse, the contents of the directory containing the HTML and XML files needs -# to be copied into the plugins directory of eclipse. The name of the directory -# within the plugins directory should be the same as the ECLIPSE_DOC_ID value. -# After copying Eclipse needs to be restarted before the help appears. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_ECLIPSEHELP = NO - -# A unique identifier for the Eclipse help plugin. When installing the plugin -# the directory name containing the HTML and XML files should also have this -# name. Each documentation set should have its own identifier. -# The default value is: org.doxygen.Project. -# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES. - -ECLIPSE_DOC_ID = org.doxygen.Project - -# If you want full control over the layout of the generated HTML pages it might -# be necessary to disable the index and replace it with your own. The -# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top -# of each HTML page. A value of NO enables the index and the value YES disables -# it. Since the tabs in the index contain the same information as the navigation -# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -DISABLE_INDEX = NO - -# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index -# structure should be generated to display hierarchical information. If the tag -# value is set to YES, a side panel will be generated containing a tree-like -# index structure (just like the one that is generated for HTML Help). For this -# to work a browser that supports JavaScript, DHTML, CSS and frames is required -# (i.e. any modern browser). Windows users are probably better off using the -# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can -# further fine-tune the look of the index. As an example, the default style -# sheet generated by doxygen has an example that shows how to put an image at -# the root of the tree instead of the PROJECT_NAME. Since the tree basically has -# the same information as the tab index, you could consider setting -# DISABLE_INDEX to YES when enabling this option. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -GENERATE_TREEVIEW = NO - -# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that -# doxygen will group on one line in the generated HTML documentation. -# -# Note that a value of 0 will completely suppress the enum values from appearing -# in the overview section. -# Minimum value: 0, maximum value: 20, default value: 4. -# This tag requires that the tag GENERATE_HTML is set to YES. - -ENUM_VALUES_PER_LINE = 4 - -# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used -# to set the initial width (in pixels) of the frame in which the tree is shown. -# Minimum value: 0, maximum value: 1500, default value: 250. -# This tag requires that the tag GENERATE_HTML is set to YES. - -TREEVIEW_WIDTH = 250 - -# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to -# external symbols imported via tag files in a separate window. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -EXT_LINKS_IN_WINDOW = NO - -# Use this tag to change the font size of LaTeX formulas included as images in -# the HTML documentation. When you change the font size after a successful -# doxygen run you need to manually remove any form_*.png images from the HTML -# output directory to force them to be regenerated. -# Minimum value: 8, maximum value: 50, default value: 10. -# This tag requires that the tag GENERATE_HTML is set to YES. - -FORMULA_FONTSIZE = 10 - -# Use the FORMULA_TRANPARENT tag to determine whether or not the images -# generated for formulas are transparent PNGs. Transparent PNGs are not -# supported properly for IE 6.0, but are supported on all modern browsers. -# -# Note that when changing this option you need to delete any form_*.png files in -# the HTML output directory before the changes have effect. -# The default value is: YES. -# This tag requires that the tag GENERATE_HTML is set to YES. - -FORMULA_TRANSPARENT = YES - -# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see -# http://www.mathjax.org) which uses client side Javascript for the rendering -# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX -# installed or if you want to formulas look prettier in the HTML output. When -# enabled you may also need to install MathJax separately and configure the path -# to it using the MATHJAX_RELPATH option. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -USE_MATHJAX = NO - -# When MathJax is enabled you can set the default output format to be used for -# the MathJax output. See the MathJax site (see: -# http://docs.mathjax.org/en/latest/output.html) for more details. -# Possible values are: HTML-CSS (which is slower, but has the best -# compatibility), NativeMML (i.e. MathML) and SVG. -# The default value is: HTML-CSS. -# This tag requires that the tag USE_MATHJAX is set to YES. - -MATHJAX_FORMAT = HTML-CSS - -# When MathJax is enabled you need to specify the location relative to the HTML -# output directory using the MATHJAX_RELPATH option. The destination directory -# should contain the MathJax.js script. For instance, if the mathjax directory -# is located at the same level as the HTML output directory, then -# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax -# Content Delivery Network so you can quickly see the result without installing -# MathJax. However, it is strongly recommended to install a local copy of -# MathJax from http://www.mathjax.org before deployment. -# The default value is: http://cdn.mathjax.org/mathjax/latest. -# This tag requires that the tag USE_MATHJAX is set to YES. - -MATHJAX_RELPATH = http://cdn.mathjax.org/mathjax/latest - -# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax -# extension names that should be enabled during MathJax rendering. For example -# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols -# This tag requires that the tag USE_MATHJAX is set to YES. - -MATHJAX_EXTENSIONS = - -# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces -# of code that will be used on startup of the MathJax code. See the MathJax site -# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an -# example see the documentation. -# This tag requires that the tag USE_MATHJAX is set to YES. - -MATHJAX_CODEFILE = - -# When the SEARCHENGINE tag is enabled doxygen will generate a search box for -# the HTML output. The underlying search engine uses javascript and DHTML and -# should work on any modern browser. Note that when using HTML help -# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET) -# there is already a search function so this one should typically be disabled. -# For large projects the javascript based search engine can be slow, then -# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to -# search using the keyboard; to jump to the search box use + S -# (what the is depends on the OS and browser, but it is typically -# , /

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Bin 0 -> 253700 bytes doc/optimization/nvvp4.png | Bin 0 -> 283198 bytes 4 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 doc/optimization/nvvp2.png create mode 100644 doc/optimization/nvvp3.png create mode 100644 doc/optimization/nvvp4.png diff --git a/doc/optimization/gpu_profiling.rst b/doc/optimization/gpu_profiling.rst index 583c2d6caee..efdf5552c35 100644 --- a/doc/optimization/gpu_profiling.rst +++ b/doc/optimization/gpu_profiling.rst @@ -8,6 +8,7 @@ This tutorial will guide you step-by-step through how to conduct profiling and p - How to do profiling? - Profile tools - Hands-on Tutorial +- Profiling tips What's profiling? ================= @@ -68,10 +69,59 @@ respectively to avoid program crashes when CPU version of PaddlePaddle invokes t Hands-on Approach ================= +To use this command line profiler :code:`nvprof`, you can simply issue the command: + +.. code-block:: bash + + nvprof ./paddle/math/tests/test_GpuProfiler + +Then, you can get the following profiling result: + .. image:: nvprof.png :align: center :scale: 30% +For visual profiler :code:`nvvp`, you can either import the output of :code:`nvprof –o ...` or +run application through GUI. + .. image:: nvvp1.png :align: center - :scale: 30% \ No newline at end of file + :scale: 30% + +From the perspective of kernel functions, :code:`nvvp` can even illustrate why does an operation take a long time? +As shown in the following figure, kernel's block usage, register usage and shared memory usage from :code:`nvvp` +allow us to fully utilize all warps on the GPU. + +.. image:: nvvp2.png + :align: center + :scale: 30% + +From the perspective of application, :code:`nvvp` can give you some suggestions to address performance bottleneck. +For instance, some advice in data movement and compute utilization from the below figure can guide you to tune performance. + +.. image:: nvvp3.png + :align: center + :scale: 30% + +.. image:: nvvp4.png + :align: center + :scale: 30% + +Profiling tips +============== + +- The :code:`nvprof` and :code:`nvvp` output is a very good place to start +- The timeline is a good place to go next +- Only dig deep into a kernel if it’s taking a significant amount of your time. +- Where possible, try to match profiler output with theory. + 1) For example, if I know I’m moving 1GB, and my kernel takes 10ms, I expect the profiler to report 100GB/s. + 2) Discrepancies are likely to mean your application isn’t doing what you thought it was. +- Know your hardware: If your GPU can do 6 TFLOPs, and you’re already doing 5.5 TFLOPs, you won’t go much faster! + + +Profiling is a key step in optimisation. Sometimes quite simple changes can lead to big improvements in performance. +Your mileage may vary! + +Reference +========= +Jeremy Appleyard, `GPU Profiling for Deep Learning `_, 2015 diff --git a/doc/optimization/nvvp2.png b/doc/optimization/nvvp2.png new file mode 100644 index 0000000000000000000000000000000000000000..177c9db708da6863d1075f3e615f5962dbe18b29 GIT binary patch literal 495117 zcmeFYcUTn9*Du)Q93>;55+#TtNl6NlL_{S@lq7K&$$3UaB!grH0TmEXaU@F|$pRvh zWr#zRJOeTeOkf+o;dh_+xzFC+ecs)F_7-&ahtsD{pHqFR>eT7t=;zTKz z0Dx!U4{(GA0(3(>?gN0aF(3^906joWApp>T2xI{?DFpw;dK6Ls)gL+~0K|9z)PM8b z1h2;hl=i32UsuXkl>czhzkEgY52kr@%zCs5sNQmi20;DYp$|@7Ja-;Y)iyMyK9&fU zKUn+^WFHYqoJavGsaGE|Jm0`;!;a?Jt{Cd*INZ8pqGxzR_m3fL=N%tB@TO!30Ekb3 z-yMDJQ&xAaPtnZ*i~uFT1jqsx9G(3iT)lPc#<9$QuE+R41vmCbcVJlVSk`~&|CbPl zi>tpgX#6dZ+sVb>83N*J0HEe`e&81X05sn~x@d5~gJX;VF_#~xAOO%F+sozl7v?+0 zPJdz9KRjl4bU>af;45Kvbo6rr0M=vuPX#%6XU0@ef{e9goEfwyDmpFjRPPY~}wu)Y42 zGvJxo-Ou0-c;y1yt<(!@bc~rn+~nhQ(*(qvApYtBTJ$$Yxd$4UgZP+#!`WX4Y)gO# 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