diff --git a/develop/doc/_sources/design/cluster_train/large_model_dist_train.md.txt b/develop/doc/_sources/design/cluster_train/large_model_dist_train.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c4b5bc24c854b7062d509249bea9c50d42bd5f1 --- /dev/null +++ b/develop/doc/_sources/design/cluster_train/large_model_dist_train.md.txt @@ -0,0 +1,101 @@ +# Alalysis of large model distributed training in Paddle + +***NOTE: This is only some note for how we implemeted this scheme in V1, not a new design.*** + +## What is it + +We often encounter cases that the embedding layer parameters(sparse) are so large that we can not store it in the trainer's memory when training. So we need to put them to several servers, and fetch them row by row instead of fetch all of the parameters. + +## How to use + +Specify command-line argument like `--loadsave_parameters_in_pserver=true --ports_num_for_sparse=1 --use_old_updater=1` when starting the paddle trainer. And also add something like `--ports_num_for_sparse=1 --pserver_num_threads=5` when starting pserver processes. + +Accrodingly, configure your embedding layers like: + +```python +SPARSE_REMOTE=True + +w1 = data_layer(name="w1", size=dict_size) +emb1 = embedding_layer(input=w1, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE)) +w2 = data_layer(name="w2", size=dict_size) +emb2 = embedding_layer(input=w2, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE)) +... +``` + +## Implementation details + +```c++ +enum MatType { + MAT_NORMAL, + MAT_NORMAL_SHARED, + MAT_VALUE_SHARED, + MAT_SPARSE_ROW_IDS, + MAT_SPARSE_ROW_AUTO_GROW, + MAT_CACHE_ROW, + MAT_SPARSE_ROW, + MAT_SPARSE_ROW_PREFETCH, + MAT_SPARSE_ROW_PREFETCH_FULL_SIZE, +}; +``` + +`MAT_SPARSE_ROW_PREFETCH` is what we use when configured to fetch only row of matrix when training. + +In `trainer_internal.cpp:L93 trainOneBatch`: + +```c++ + if (config_->getOptConfig().use_sparse_remote_updater()) { + REGISTER_TIMER("prefetch"); + gradientMachine_->prefetch(inArgs); + parameterUpdater_->getParametersRemote(); + } +``` + +When doing actual network forward and backward, at the beginning of each batch, the trainer will try to download one row of data from pserver. + +In `trainer/RemoteParameterUpdater.cpp`: `parameterUpdater_->getParametersRemote();`: + +```c++ +if (fullSize) { + ... +} else { +getParams = [&] { + parameterClient_->getParameterSparse( + /* recvParameterType= */ PARAMETER_VALUE, sendBackParameterType); +}; +applyL1 = [](Parameter& para, real decayRate) { + para.getMat(PARAMETER_VALUE)->applyL1(/*lr=*/1.0f, decayRate); +}; +} +``` + +Calling `parameterClient_->getParameterSparse` will do remote call to pserver's `getParameterSparse`: + +```c++ +void ParameterServer2::getParameterSparse(const SendParameterRequest& request, + std::vector& inputBuffers, + SendParameterResponse* response, + std::vector* outputBuffers) { + (void)inputBuffers; + auto& buffer = *readWriteBuffer_; + size_t numReals = 0; + for (const auto& block : request.blocks()) { + numReals += getParameterConfig(block).dims(1); + } + buffer.resize(numReals); + + VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals; + + ReadLockGuard guard(parameterMutex_); + size_t offset = 0; + for (const auto& block : request.blocks()) { + size_t width = getParameterConfig(block).dims(1); + Buffer buf = {buffer.data() + offset, width}; + int type = request.send_back_parameter_type(); + sendBackParameterSparse(block, type, response, &buf, width, outputBuffers); + offset += width; + } +} +``` + +`getParameterConfig(block).dims(1)` returns the width of the current "parameter block"(a shard of parameter object), +then `getParameterSparse` remote call returns only one row of data to the client. diff --git a/develop/doc/design/cluster_train/large_model_dist_train.html b/develop/doc/design/cluster_train/large_model_dist_train.html new file mode 100644 index 0000000000000000000000000000000000000000..7c597eacc718792a2cd7be31c1b6499c5d18b1aa --- /dev/null +++ b/develop/doc/design/cluster_train/large_model_dist_train.html @@ -0,0 +1,330 @@ + + + + + + + + + + + Alalysis of large model distributed training in Paddle — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Alalysis of large model distributed training in Paddle
    • +
+
+ +
+
+
+
+ +
+

Alalysis of large model distributed training in Paddle

+

NOTE: This is only some note for how we implemeted this scheme in V1, not a new design.

+
+

What is it

+

We often encounter cases that the embedding layer parameters(sparse) are so large that we can not store it in the trainer’s memory when training. So we need to put them to several servers, and fetch them row by row instead of fetch all of the parameters.

+
+
+

How to use

+

Specify command-line argument like --loadsave_parameters_in_pserver=true --ports_num_for_sparse=1 --use_old_updater=1 when starting the paddle trainer. And also add something like --ports_num_for_sparse=1 --pserver_num_threads=5 when starting pserver processes.

+

Accrodingly, configure your embedding layers like:

+
SPARSE_REMOTE=True
+
+w1 = data_layer(name="w1", size=dict_size)
+emb1 = embedding_layer(input=w1, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
+w2 = data_layer(name="w2", size=dict_size)
+emb2 = embedding_layer(input=w2, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
+...
+
+
+
+
+

Implementation details

+
enum MatType {
+  MAT_NORMAL,
+  MAT_NORMAL_SHARED,
+  MAT_VALUE_SHARED,
+  MAT_SPARSE_ROW_IDS,
+  MAT_SPARSE_ROW_AUTO_GROW,
+  MAT_CACHE_ROW,
+  MAT_SPARSE_ROW,
+  MAT_SPARSE_ROW_PREFETCH,
+  MAT_SPARSE_ROW_PREFETCH_FULL_SIZE,
+};
+
+
+

MAT_SPARSE_ROW_PREFETCH is what we use when configured to fetch only row of matrix when training.

+

In trainer_internal.cpp:L93 trainOneBatch:

+
  if (config_->getOptConfig().use_sparse_remote_updater()) {
+    REGISTER_TIMER("prefetch");
+    gradientMachine_->prefetch(inArgs);
+    parameterUpdater_->getParametersRemote();
+  }
+
+
+

When doing actual network forward and backward, at the beginning of each batch, the trainer will try to download one row of data from pserver.

+

In trainer/RemoteParameterUpdater.cpp: parameterUpdater_->getParametersRemote();:

+
if (fullSize) {
+    ...
+} else {
+getParams = [&] {
+    parameterClient_->getParameterSparse(
+        /* recvParameterType= */ PARAMETER_VALUE, sendBackParameterType);
+};
+applyL1 = [](Parameter& para, real decayRate) {
+    para.getMat(PARAMETER_VALUE)->applyL1(/*lr=*/1.0f, decayRate);
+};
+}
+
+
+

Calling parameterClient_->getParameterSparse will do remote call to pserver’s getParameterSparse:

+
void ParameterServer2::getParameterSparse(const SendParameterRequest& request,
+                                          std::vector<Buffer>& inputBuffers,
+                                          SendParameterResponse* response,
+                                          std::vector<Buffer>* outputBuffers) {
+  (void)inputBuffers;
+  auto& buffer = *readWriteBuffer_;
+  size_t numReals = 0;
+  for (const auto& block : request.blocks()) {
+    numReals += getParameterConfig(block).dims(1);
+  }
+  buffer.resize(numReals);
+
+  VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals;
+
+  ReadLockGuard guard(parameterMutex_);
+  size_t offset = 0;
+  for (const auto& block : request.blocks()) {
+    size_t width = getParameterConfig(block).dims(1);
+    Buffer buf = {buffer.data() + offset, width};
+    int type = request.send_back_parameter_type();
+    sendBackParameterSparse(block, type, response, &buf, width, outputBuffers);
+    offset += width;
+  }
+}
+
+
+

getParameterConfig(block).dims(1) returns the width of the current “parameter block”(a shard of parameter object), +then getParameterSparse remote call returns only one row of data to the client.

+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/objects.inv b/develop/doc/objects.inv index 0c4c0b68d1d6c92235ef218fa03d6cca3397b638..2630f5d5e4d120fdf67b9d044b294a14d8108cf2 100644 Binary files a/develop/doc/objects.inv and b/develop/doc/objects.inv differ diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index df44f5fb52a1191a70f694986a99f8fb3322608a..eee9f4b52436eecfa235b8a206f5bbe144529fe8 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/cluster_train/large_model_dist_train.md.txt b/develop/doc_cn/_sources/design/cluster_train/large_model_dist_train.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c4b5bc24c854b7062d509249bea9c50d42bd5f1 --- /dev/null +++ b/develop/doc_cn/_sources/design/cluster_train/large_model_dist_train.md.txt @@ -0,0 +1,101 @@ +# Alalysis of large model distributed training in Paddle + +***NOTE: This is only some note for how we implemeted this scheme in V1, not a new design.*** + +## What is it + +We often encounter cases that the embedding layer parameters(sparse) are so large that we can not store it in the trainer's memory when training. So we need to put them to several servers, and fetch them row by row instead of fetch all of the parameters. + +## How to use + +Specify command-line argument like `--loadsave_parameters_in_pserver=true --ports_num_for_sparse=1 --use_old_updater=1` when starting the paddle trainer. And also add something like `--ports_num_for_sparse=1 --pserver_num_threads=5` when starting pserver processes. + +Accrodingly, configure your embedding layers like: + +```python +SPARSE_REMOTE=True + +w1 = data_layer(name="w1", size=dict_size) +emb1 = embedding_layer(input=w1, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE)) +w2 = data_layer(name="w2", size=dict_size) +emb2 = embedding_layer(input=w2, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE)) +... +``` + +## Implementation details + +```c++ +enum MatType { + MAT_NORMAL, + MAT_NORMAL_SHARED, + MAT_VALUE_SHARED, + MAT_SPARSE_ROW_IDS, + MAT_SPARSE_ROW_AUTO_GROW, + MAT_CACHE_ROW, + MAT_SPARSE_ROW, + MAT_SPARSE_ROW_PREFETCH, + MAT_SPARSE_ROW_PREFETCH_FULL_SIZE, +}; +``` + +`MAT_SPARSE_ROW_PREFETCH` is what we use when configured to fetch only row of matrix when training. + +In `trainer_internal.cpp:L93 trainOneBatch`: + +```c++ + if (config_->getOptConfig().use_sparse_remote_updater()) { + REGISTER_TIMER("prefetch"); + gradientMachine_->prefetch(inArgs); + parameterUpdater_->getParametersRemote(); + } +``` + +When doing actual network forward and backward, at the beginning of each batch, the trainer will try to download one row of data from pserver. + +In `trainer/RemoteParameterUpdater.cpp`: `parameterUpdater_->getParametersRemote();`: + +```c++ +if (fullSize) { + ... +} else { +getParams = [&] { + parameterClient_->getParameterSparse( + /* recvParameterType= */ PARAMETER_VALUE, sendBackParameterType); +}; +applyL1 = [](Parameter& para, real decayRate) { + para.getMat(PARAMETER_VALUE)->applyL1(/*lr=*/1.0f, decayRate); +}; +} +``` + +Calling `parameterClient_->getParameterSparse` will do remote call to pserver's `getParameterSparse`: + +```c++ +void ParameterServer2::getParameterSparse(const SendParameterRequest& request, + std::vector& inputBuffers, + SendParameterResponse* response, + std::vector* outputBuffers) { + (void)inputBuffers; + auto& buffer = *readWriteBuffer_; + size_t numReals = 0; + for (const auto& block : request.blocks()) { + numReals += getParameterConfig(block).dims(1); + } + buffer.resize(numReals); + + VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals; + + ReadLockGuard guard(parameterMutex_); + size_t offset = 0; + for (const auto& block : request.blocks()) { + size_t width = getParameterConfig(block).dims(1); + Buffer buf = {buffer.data() + offset, width}; + int type = request.send_back_parameter_type(); + sendBackParameterSparse(block, type, response, &buf, width, outputBuffers); + offset += width; + } +} +``` + +`getParameterConfig(block).dims(1)` returns the width of the current "parameter block"(a shard of parameter object), +then `getParameterSparse` remote call returns only one row of data to the client. diff --git a/develop/doc_cn/design/cluster_train/large_model_dist_train.html b/develop/doc_cn/design/cluster_train/large_model_dist_train.html new file mode 100644 index 0000000000000000000000000000000000000000..e618928eb3f44d4de6a7795593116dcbd1b30204 --- /dev/null +++ b/develop/doc_cn/design/cluster_train/large_model_dist_train.html @@ -0,0 +1,336 @@ + + + + + + + + + + + Alalysis of large model distributed training in Paddle — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
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+ + + + +
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  • Alalysis of large model distributed training in Paddle
    • +
+
+ +
+
+
+
+ +
+

Alalysis of large model distributed training in Paddle

+

NOTE: This is only some note for how we implemeted this scheme in V1, not a new design.

+
+

What is it

+

We often encounter cases that the embedding layer parameters(sparse) are so large that we can not store it in the trainer’s memory when training. So we need to put them to several servers, and fetch them row by row instead of fetch all of the parameters.

+
+
+

How to use

+

Specify command-line argument like --loadsave_parameters_in_pserver=true --ports_num_for_sparse=1 --use_old_updater=1 when starting the paddle trainer. And also add something like --ports_num_for_sparse=1 --pserver_num_threads=5 when starting pserver processes.

+

Accrodingly, configure your embedding layers like:

+
SPARSE_REMOTE=True
+
+w1 = data_layer(name="w1", size=dict_size)
+emb1 = embedding_layer(input=w1, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
+w2 = data_layer(name="w2", size=dict_size)
+emb2 = embedding_layer(input=w2, size=32, param_attr=ParameterAttribute(sparse_update=SPARSE_REMOTE))
+...
+
+
+
+
+

Implementation details

+
enum MatType {
+  MAT_NORMAL,
+  MAT_NORMAL_SHARED,
+  MAT_VALUE_SHARED,
+  MAT_SPARSE_ROW_IDS,
+  MAT_SPARSE_ROW_AUTO_GROW,
+  MAT_CACHE_ROW,
+  MAT_SPARSE_ROW,
+  MAT_SPARSE_ROW_PREFETCH,
+  MAT_SPARSE_ROW_PREFETCH_FULL_SIZE,
+};
+
+
+

MAT_SPARSE_ROW_PREFETCH is what we use when configured to fetch only row of matrix when training.

+

In trainer_internal.cpp:L93 trainOneBatch:

+
  if (config_->getOptConfig().use_sparse_remote_updater()) {
+    REGISTER_TIMER("prefetch");
+    gradientMachine_->prefetch(inArgs);
+    parameterUpdater_->getParametersRemote();
+  }
+
+
+

When doing actual network forward and backward, at the beginning of each batch, the trainer will try to download one row of data from pserver.

+

In trainer/RemoteParameterUpdater.cpp: parameterUpdater_->getParametersRemote();:

+
if (fullSize) {
+    ...
+} else {
+getParams = [&] {
+    parameterClient_->getParameterSparse(
+        /* recvParameterType= */ PARAMETER_VALUE, sendBackParameterType);
+};
+applyL1 = [](Parameter& para, real decayRate) {
+    para.getMat(PARAMETER_VALUE)->applyL1(/*lr=*/1.0f, decayRate);
+};
+}
+
+
+

Calling parameterClient_->getParameterSparse will do remote call to pserver’s getParameterSparse:

+
void ParameterServer2::getParameterSparse(const SendParameterRequest& request,
+                                          std::vector<Buffer>& inputBuffers,
+                                          SendParameterResponse* response,
+                                          std::vector<Buffer>* outputBuffers) {
+  (void)inputBuffers;
+  auto& buffer = *readWriteBuffer_;
+  size_t numReals = 0;
+  for (const auto& block : request.blocks()) {
+    numReals += getParameterConfig(block).dims(1);
+  }
+  buffer.resize(numReals);
+
+  VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals;
+
+  ReadLockGuard guard(parameterMutex_);
+  size_t offset = 0;
+  for (const auto& block : request.blocks()) {
+    size_t width = getParameterConfig(block).dims(1);
+    Buffer buf = {buffer.data() + offset, width};
+    int type = request.send_back_parameter_type();
+    sendBackParameterSparse(block, type, response, &buf, width, outputBuffers);
+    offset += width;
+  }
+}
+
+
+

getParameterConfig(block).dims(1) returns the width of the current “parameter block”(a shard of parameter object), +then getParameterSparse remote call returns only one row of data to the client.

+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc_cn/objects.inv b/develop/doc_cn/objects.inv index ab3607852502f2a0aff2d77e6f2d082fe1ef7fbf..d0d69fc1878c2e6db8ba38e73eee4b56781da534 100644 Binary files a/develop/doc_cn/objects.inv and b/develop/doc_cn/objects.inv differ diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js index 685e50746fc7ec2ec7b86951c150172d4567463f..95767bb9038dbf717a95b33459c673b5f19e3764 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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