使用 torch.serialize 两次时 Torch 内存不足

Question

我正在尝试向 torch-dataframe in order to add torchnet compatibility. I've used the tnt.ParallelDatasetIterator and changed it 添加并行数据加载器，以便：

基本批处理在线程外加载
批次被序列化并发送到线程
在线程中批处理被反序列化并将批处理数据转换为张量
张量在具有 input 和 target 键的 table 中返回，以匹配 tnt.Engine 设置。

问题第二次调用 enque 时出现错误：.../torch_distro/install/bin/luajit: not enough memory。我目前只使用 mnist with an adapted mnist-example。 enque 循环现在看起来像这样（带有调试内存输出）：

-- `samplePlaceholder` stands in for samples which have been
-- filtered out by the `filter` function
local samplePlaceholder = {}

-- The enque does the main loop
local idx = 1
local function enqueue()
  while idx <= size and threads:acceptsjob() do
    local batch, reset = self.dataset:get_batch(batch_size)

    if (reset) then
      idx = size + 1
    else
      idx = idx + 1
    end

    if (batch) then
      local serialized_batch = torch.serialize(batch)

      -- In the parallel section only the to_tensor is run in parallel
      --  this should though be the computationally expensive operation
      threads:addjob(
        function(argList)
          io.stderr:write("\n Start");
          io.stderr:write("\n 1: " ..tostring(collectgarbage("count")))
          local origIdx, serialized_batch, samplePlaceholder = unpack(argList)

          io.stderr:write("\n 2: " ..tostring(collectgarbage("count")))
          local batch = torch.deserialize(serialized_batch)
          serialized_batch = nil

          collectgarbage()
          collectgarbage()

          io.stderr:write("\n 3: " .. tostring(collectgarbage("count")))
          batch = transform(batch)

          io.stderr:write("\n 4: " .. tostring(collectgarbage("count")))
          local sample = samplePlaceholder
          if (filter(batch)) then
            sample = {}
            sample.input, sample.target = batch:to_tensor()
          end
          io.stderr:write("\n 5: " ..tostring(collectgarbage("count")))

          collectgarbage()
          collectgarbage()
          io.stderr:write("\n 6: " ..tostring(collectgarbage("count")))

          io.stderr:write("\n End \n");
          return {
            sample,
            origIdx
          }
        end,
        function(argList)
          sample, sampleOrigIdx = unpack(argList)
        end,
        {idx, serialized_batch, samplePlaceholder}
      )
    end
  end
end

我已经洒了 collectgarbage 并且还尝试删除任何不需要的对象。内存输出相当简单：

 Start
 1: 374840.87695312
 2: 374840.94433594
 3: 372023.79101562
 4: 372023.85839844
 5: 372075.41308594
 6: 372023.73632812
 End

循环 enque 的函数是无序函数，它很简单（内存错误在第二个 enque 和）中抛出）：

iterFunction = function()
  while threads:hasjob() do
    enqueue()
    threads:dojob()
    if threads:haserror() then
      threads:synchronize()
    end
    enqueue()

    if table.exact_length(sample) > 0 then
      return sample
    end
  end
end

Answer 1

所以问题是 torch.serialize 设置中的函数将整个数据集耦合到该函数。添加时：

serialized_batch = nil
collectgarbage()
collectgarbage()

问题已解决。我进一步想知道是什么占用了这么多 space，结果证明是我在一个具有与函数交织在一起的大型数据集的环境中定义了函数，从而大大增加了大小。这里原始定义数据local

mnist = require 'mnist'
local dataset = mnist[mode .. 'dataset']()

-- PROBLEMATIC LINE BELOW --
local ext_resource = dataset.data:reshape(dataset.data:size(1),
  dataset.data:size(2) * dataset.data:size(3)):double()

-- Create a Dataframe with the label. The actual images will be loaded
--  as an external resource
local df = Dataframe(
  Df_Dict{
    label = dataset.label:totable(),
    row_id = torch.range(1, dataset.data:size(1)):totable()
  })

-- Since the mnist package already has taken care of the data
--  splitting we create a single subsetter
df:create_subsets{
  subsets = Df_Dict{core = 1},
  class_args = Df_Tbl({
    batch_args = Df_Tbl({
      label = Df_Array("label"),
      data = function(row)
        return ext_resource[row.row_id]
      end
    })
  })
}

事实证明，删除我突出显示的行可将内存使用量从 358 Mb 降低到 0.0008 Mb！我用于测试性能的代码是：

local mem = {}
table.insert(mem, collectgarbage("count"))

local ser_data = torch.serialize(batch.dataset)
table.insert(mem, collectgarbage("count"))

local ser_retriever = torch.serialize(batch.batchframe_defaults.data)
table.insert(mem, collectgarbage("count"))

local ser_raw_retriever = torch.serialize(function(row)
  return ext_resource[row.row_id]
end)
table.insert(mem, collectgarbage("count"))

local serialized_batch = torch.serialize(batch)
table.insert(mem, collectgarbage("count"))

for i=2,#mem do
  print(i-1, (mem[i] - mem[i-1])/1024)
end

最初产生的输出：

1   0.0082607269287109  
2   358.23344707489 
3   0.0017471313476562  
4   358.90182781219

修复后：

1   0.0094480514526367  
2   0.00080204010009766 
3   0.00090408325195312 
4   0.010146141052246

我尝试使用 setfenv 作为函数，但没有解决问题。将序列化数据发送到线程仍然存在性能损失，但主要问题已解决，并且在没有昂贵的数据检索器的情况下，功能要小得多。

使用 torch.serialize 两次时 Torch 内存不足

Torch out of memory in thread when using torch.serialize twice

lua

multithreading

luajit

torch