Question

我尝试向torch-dataframe添加并行数据加载器以添加torchnet compatibility。我已使用tnt.ParallelDatasetIterator和changed it，因此：

基本批次在线程外部加载
批次序列化并发送到主题
在线程中，批处理被反序列化并将批处理数据转换为张量
张贴在包含input和target键的表格中返回，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。我目前只与mnist合作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

我撒了collectgarbage并尝试删除不需要的任何物品。内存输出相当直接：

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

循环enque的函数是非有序的函数，它是微不足道的（第二个enque抛出了内存错误）：

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，其中设置中的函数将整个数据集耦合到函数中。添加时：

serialized_batch = nil
collectgarbage()
collectgarbage()

问题解决了。我进一步想知道是什么占据了这么多的空间，罪魁祸首竟然是我在一个环境中定义了这个函数，这个环境中有一个与函数交织在一起的大数据集，大大增加了它的大小。这里是本地数据的原始定义

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会将内存使用量从 358 Mb 降低到 0.0008 Mb ！我用来测试性能的代码是：

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

最初产生了输出：

1   0.0082607269287109  
2   358.23344707489 
3   0.0017471313476562  
4   358.90182781219

并在修复之后：

1   0.0094480514526367  
2   0.00080204010009766 
3   0.00090408325195312 
4   0.010146141052246

我尝试使用setfenv功能，但它没有解决问题。将序列化数据发送到线程仍然存在性能损失，但主要问题已得到解决，如果没有昂贵的数据检索器，则功能要小得多。

使用torch.serialize两次时，在线程中火炬内存不足

1 个答案: