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develop/doc/_sources/design/parallel_do.md.txt

+# Design Doc: Parallel_Do in PaddlePaddle
+
+In PaddlePaddle, we use parallel_do primitive to represent multithread data parallel processing.
+
+## Design overview
+
+The definition of a parallel_do op looks like the following
+
+```c++
+AddInput(kInputs, "Inputs needed to be split onto different devices").AsDuplicable();
+AddInput(kParameters, "Parameters are duplicated over different devices")
+    .AsDuplicable();
+AddInput(kPlaces, "Devices used for parallel processing");
+AddOutput(kOutputs, "Outputs needed to be merged from different devices").AsDuplicable();
+AddOutput(kParallelScopes,
+          "Scopes for all local variables in forward pass. One scope for each device");
+AddAttr<framework::BlockDesc *>(kParallelBlock,
+                                "List of operaters to be executed in parallel");
+```
+
+A vanilla implementation of parallel_do can be shown as the following (`|` means single thread and
+`||||` means multiple threads)
+
+```
+In the forward pass
+  |      Split input onto different devices
+  |      Copy parameter to onto different devices
+  ||||   Compute forward pass in parallel
+  |      Merge output from different devices
+
+In the backward pass
+  |      Split output@grad onto different devices
+  ||||   Compute backward pass in parallel
+  |      accumulate param@grad from different devices to the first device
+  |      Merge input@grad from different devices
+  |      Copy param@grad to the place of parallel_do_op
+```
+
+This implementation allows to write mixed device program like this
+
+```python
+# get embedding feature on CPU
+feature = some_cpu_only_op(data)
+
+gpu_places = get_place(use_gpu=True)
+# parallel processing on multiple GPUs
+pd = ParallelDo(gpu_places)
+with pd.do():
+    read_input(feature)
+    prediction = my_net(feature)
+    write_output(prediction)
+prediction = pd()
+loss = cross_entropy(prediction, label)
+```
+
+And the programDesc are like the following
+
+```
+# start_program will be run by executor(CPUPlace), all w1, w2 will be allocated on CPU
+start_program
+{
+  vars: w1, w2
+  ops: init(w1), init(w2)
+}
+
+main_program
+{
+block0 {
+  vars: data, places, w1, w2
+  ops: data, get_place, parallel_do(block1),
+       parallel_do_grad(block2),
+       sgd(w2, w2_grad),
+       sgd(w1, w1_grad)
+}
+block1 {
+  parent_block: 0
+  vars: data, h1, h2, loss
+  ops: fc, fc, softmax
+}
+block2 {
+  parent_block: 1
+  vars: data_grad, h1_grad, h2_grad, loss_gard, w1_grad, w2_grad
+  ops: softmax_grad,
+       fc_grad
+       fc_grad
+}
+}
+```
+
+## Proformance Imporvement
+
+There are serial places we can make this parallel_do faster.
+
+### forward: split input onto different devices
+
+If the input of the parallel_do is independent from any prior opeartors, we can avoid this step by 
+prefetching the input onto different devices in a seperate background thread. And the python code
+looks like this.
+```python
+pd = ParallelDo(gpu_places)
+with pd.do():
+    feature = get_data_from_prefetch_queue(gpu_places)
+    prediction = my_net(feature)
+    write_output(activation)
+```
+
+### forward: Copy parameter to onto different devices
+
+We can avoid this step by making each device have a copy of the parameter. This requires:
+
+1. `fluid.default_start_up_program()` to be run on all devices
+1. In the backward, allreduce param@grad at different devices, this requires
+    1. `backward.py` add `allreduce` operators at parallel_do_grad
+    1. `allreduce` operators need to be called in async mode to achieve maximum throughput
+1. apply gradients related op(i.e. cliping, normalization, decay, sgd) on different devices in parallel
+
+By doing so, we also avoided "backward: accumulate param@grad from different devices to the first device".
+And the ProgramDesc looks like the following
+
+```
+# w1, w2 will be allocated on all GPUs
+start_program
+{
+block0 {
+  parallel_do(block1)
+}
+block1 {
+  parent_block: 0
+  vars: w1, w2
+  ops: init(w1), init(w2)
+}
+}
+
+main_program
+{
+block0 {
+  vars: data, places, w1, w2
+  ops: data, get_place, parallel_do(block1),
+       parallel_do_grad(block2),      # append_backward
+       parallel_do(block3)            # append_optimization
+       
+}
+block1 {
+  parent_block: 0
+  vars: data, h1, h2, loss
+  ops: fc, fc, softmax
+}
+block2 {
+  parent_block: 1
+  vars: data_grad, h1_grad, h2_grad, loss_gard, w1_grad, w2_grad
+  ops: softmax_grad,
+       fc_grad, allreduce(places, scopes, w1_grad),
+       fc_grad, allreduce(places, scopes, w2_grad)
+}
+block3 {
+  parent_block: 0
+  vars: lr
+  ops: sgd(w2, w2_grad),
+       sgd(w1, w1_grad)
+}
+}
+```

develop/doc_cn/_sources/design/parallel_do.md.txt

+# Design Doc: Parallel_Do in PaddlePaddle
+
+In PaddlePaddle, we use parallel_do primitive to represent multithread data parallel processing.
+
+## Design overview
+
+The definition of a parallel_do op looks like the following
+
+```c++
+AddInput(kInputs, "Inputs needed to be split onto different devices").AsDuplicable();
+AddInput(kParameters, "Parameters are duplicated over different devices")
+    .AsDuplicable();
+AddInput(kPlaces, "Devices used for parallel processing");
+AddOutput(kOutputs, "Outputs needed to be merged from different devices").AsDuplicable();
+AddOutput(kParallelScopes,
+          "Scopes for all local variables in forward pass. One scope for each device");
+AddAttr<framework::BlockDesc *>(kParallelBlock,
+                                "List of operaters to be executed in parallel");
+```
+
+A vanilla implementation of parallel_do can be shown as the following (`|` means single thread and
+`||||` means multiple threads)
+
+```
+In the forward pass
+  |      Split input onto different devices
+  |      Copy parameter to onto different devices
+  ||||   Compute forward pass in parallel
+  |      Merge output from different devices
+
+In the backward pass
+  |      Split output@grad onto different devices
+  ||||   Compute backward pass in parallel
+  |      accumulate param@grad from different devices to the first device
+  |      Merge input@grad from different devices
+  |      Copy param@grad to the place of parallel_do_op
+```
+
+This implementation allows to write mixed device program like this
+
+```python
+# get embedding feature on CPU
+feature = some_cpu_only_op(data)
+
+gpu_places = get_place(use_gpu=True)
+# parallel processing on multiple GPUs
+pd = ParallelDo(gpu_places)
+with pd.do():
+    read_input(feature)
+    prediction = my_net(feature)
+    write_output(prediction)
+prediction = pd()
+loss = cross_entropy(prediction, label)
+```
+
+And the programDesc are like the following
+
+```
+# start_program will be run by executor(CPUPlace), all w1, w2 will be allocated on CPU
+start_program
+{
+  vars: w1, w2
+  ops: init(w1), init(w2)
+}
+
+main_program
+{
+block0 {
+  vars: data, places, w1, w2
+  ops: data, get_place, parallel_do(block1),
+       parallel_do_grad(block2),
+       sgd(w2, w2_grad),
+       sgd(w1, w1_grad)
+}
+block1 {
+  parent_block: 0
+  vars: data, h1, h2, loss
+  ops: fc, fc, softmax
+}
+block2 {
+  parent_block: 1
+  vars: data_grad, h1_grad, h2_grad, loss_gard, w1_grad, w2_grad
+  ops: softmax_grad,
+       fc_grad
+       fc_grad
+}
+}
+```
+
+## Proformance Imporvement
+
+There are serial places we can make this parallel_do faster.
+
+### forward: split input onto different devices
+
+If the input of the parallel_do is independent from any prior opeartors, we can avoid this step by 
+prefetching the input onto different devices in a seperate background thread. And the python code
+looks like this.
+```python
+pd = ParallelDo(gpu_places)
+with pd.do():
+    feature = get_data_from_prefetch_queue(gpu_places)
+    prediction = my_net(feature)
+    write_output(activation)
+```
+
+### forward: Copy parameter to onto different devices
+
+We can avoid this step by making each device have a copy of the parameter. This requires:
+
+1. `fluid.default_start_up_program()` to be run on all devices
+1. In the backward, allreduce param@grad at different devices, this requires
+    1. `backward.py` add `allreduce` operators at parallel_do_grad
+    1. `allreduce` operators need to be called in async mode to achieve maximum throughput
+1. apply gradients related op(i.e. cliping, normalization, decay, sgd) on different devices in parallel
+
+By doing so, we also avoided "backward: accumulate param@grad from different devices to the first device".
+And the ProgramDesc looks like the following
+
+```
+# w1, w2 will be allocated on all GPUs
+start_program
+{
+block0 {
+  parallel_do(block1)
+}
+block1 {
+  parent_block: 0
+  vars: w1, w2
+  ops: init(w1), init(w2)
+}
+}
+
+main_program
+{
+block0 {
+  vars: data, places, w1, w2
+  ops: data, get_place, parallel_do(block1),
+       parallel_do_grad(block2),      # append_backward
+       parallel_do(block3)            # append_optimization
+       
+}
+block1 {
+  parent_block: 0
+  vars: data, h1, h2, loss
+  ops: fc, fc, softmax
+}
+block2 {
+  parent_block: 1
+  vars: data_grad, h1_grad, h2_grad, loss_gard, w1_grad, w2_grad
+  ops: softmax_grad,
+       fc_grad, allreduce(places, scopes, w1_grad),
+       fc_grad, allreduce(places, scopes, w2_grad)
+}
+block3 {
+  parent_block: 0
+  vars: lr
+  ops: sgd(w2, w2_grad),
+       sgd(w1, w1_grad)
+}
+}
+```