Add paddle/memory/README.md

paddle/README.md

@@ -0,0 +1,141 @@
+In my mind, the memory package works like the following:
+
+## Design
+
+### Usage
+
+To allocate 4KB CPU memory:
+
+```cpp
+p = memory::Alloc(platform::CPUPlace(), 4*1024);
+```
+
+To allocate 4KB memory on the 3rd GPU:
+
+```cpp
+p = memory::Alloc(platform::GPUPlace(2), 4*1024);
+```
+
+To free memory and check the so-far used amount of memory on a place:
+
+```cpp
+auto pl = platform::GPUPlace(0);
+p = memory::Alloc(pl, 4*1024);
+cout << memory::Used(pl);
+memory::Free(pl, p);
+```
+
+### The API
+
+In `paddle/memory/memory.h` we have:
+
+```cpp
+template <typeanme Place> void* Alloc(Place, size_t);
+template <typeanme Place> void Free(Place, void*);
+}
+```
+
+These function templates have specializations on either `platform::CPUPlace` or `platform::GPUPlace`:
+
+```cpp
+template<>
+void Alloc<CPUPlace>(CPUPlace p, size_t size) {
+  return GetCPUBuddyAllocator()->Alloc(size);
+}
+```
+
+and 
+
+```cpp
+template<>
+void Alloc(GPUPlace)(GPUPlace p, size_t size) {
+  return GetGPUBuddyAllocator(p.id)->Alloc(size);
+}
+```
+
+### The Implementation
+
+`GetCPUBuddyAllocator` and `GetGPUBuddyAllocator` are singletions.
+
+```cpp
+BuddyAllocator* GetCPUBuddyAllocator() {
+  static BuddyAllocator* a = NULL;
+  if (a == NULL) {
+    a = new BuddyAllocator(new CPUAllocator /*backup allocator*/, ...);
+  }
+  return a;
+}
+
+BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
+  static BuddyAllocator* as = NULL;
+  if (as == NULL) {
+    as = new BuddyAllocator*[platform::NumGPUs()];
+    for (int gpu = 0; gpu < platform::NumGPUs(); gpu++) {
+      as[gpu] = new BuddyAllocator(new GPUAllocator(gpu) /* backup allocator */, ...);
+    }
+  }
+  return as[gpu_id);
+```
+
+#### `BuddyAllocator`
+
+`BuddyAllocator` implements the buddy allocation algorithm.  Its constructor takes parameters only related with the algorithm:
+
+```cpp
+BuddyAllocator::BuddyAllocator(initial_pool_size, max_pool_size) {
+  ...
+}
+```
+
+Please be aware that **`BuddyAllocator` always allocate aligned memory**, aligned on 32-bytes, which can hold a `BuddyAllocator::Block` object:
+
+```cpp
+class BuddyAllocator {
+ private:
+  struct Block {
+    size_t size;
+    Blobk* left, right;
+  };
+  ...
+};
+```
+
+#### System Allocators
+
+The `GPUAllocator` and `CPUAllocator` are calls *system allocators*.  They hold information about the device, including the amount of memory has been allocated.  So that we can call
+
+- `GPUAllocator::Used` and
+- `CPUAllocator::Used`
+
+to get the amount of memory that has been allocated so far.
+
+
+## Why Such a Design
+
+I got inspiration from Majel and Caffe2, though above design look different from both.
+
+### Caffe2
+
+In Caffe2, `Tensor<Context>::mutable_data()` allocates the memroy.  In particular, [`Tensor<Context>::mutable_data`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L523) calls [`Tensor<Context>::raw_mutable_data`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L459), which in turn calls [`Context::New`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L479).
+
+There are two implementations of `Context`:
+
+1. [`CPUContext`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.h#L105), whose [`New` method](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.h#L131) calls [`g_cpu_allocator.get()->New(size_t)`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.cc#L15) to allocate the memory.
+
+1. [`CUDAContext`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.h#L99), which has a data member [`int gpu_id_`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.h#L202).  This looks very similar to class `majel::GPUPlace`, who also has an `int id_` data member.   `CUDAContext::New(size_t)` calls [`g_cub_allocator->DeviceAllocate(&ptr, nbytes)`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.cu#L355) to allocate the memory.
+
+### Majel
+
+In Majel, there are basically two allocator types:
+
+1. `cpu::SystemAllocator`, which has similar functionality to `caffe2::CPUContext::New/Delete`.
+1. `gpu::SystemAllocator`, which has similar functionality to `caffe2::CUDAContext::New/Delete`.
+
+However, memory allocation is not via these two allocators.  Instead, these two allocators are defined in hidden namespaces.
+
+In Majel there are hidden global variables like:
+
+1. `cpu::SystemAllocator g_cpu_allocator`, and
+1. `vector<gpu::SystemAllocator*> g_gpu_allocators(NUM_GPUS)`.
+
+Programs allocate memory via a BuddyAllocator, which can take the `g_cpu_allocator` or a `g_gpu_allocators[gpu_id]` as its *fallback allocator*, so that if BuddyAllocator cannot find a block in its memory pool, it extends its memory pool by calling the fallback allocator's `New(size_t)`.

python/paddle/trainer_config_helpers/networks.py

@@ -1381,7 +1381,7 @@ def inputs(layers, *args):
     if len(args) != 0:
         layers.extend(args)
 
-    Inputs(*[l.name for l in layers])
+    Inputs(* [l.name for l in layers])
 
 
 def outputs(layers, *args):
@@ -1424,7 +1424,7 @@ def __dfs_travel__(layer,
     assert len(layers) > 0
 
     if HasInputsSet():  # input already set
-        Outputs(*[l.name for l in layers])
+        Outputs(* [l.name for l in layers])
         return  # just return outputs.
 
     if len(layers) != 1: