public DataParallelExecutorManager(Symbol symbol, Context[] ctx, DataIter train_data, string[] arg_names,
string[] param_names,
string[] aux_names, int[] work_load_list = null, Logger logger = null, Func <int, Symbol> sym_gen = null)
{
num_device = ctx.Length;
Logger.Info(string.Format("Start training with {0}", num_device));
if (work_load_list == null)
{
work_load_list = new int[num_device];
for (var i = 0; i < num_device; i++)
{
work_load_list[i] = 1;
}
}
else if (work_load_list.Length != num_device)
{
throw new MXNetException("Invalid setting for work load");
}
slices = ExecuterManager.SplitInputSlice(train_data.BatchSize, work_load_list);
this.arg_names = arg_names;
this.param_names = param_names;
this.aux_names = aux_names;
contexts = ctx;
execgrp = new DataParallelExecutorGroup(symbol, arg_names, param_names, ctx, slices, train_data);
this.symbol = symbol;
this.sym_gen = sym_gen;
if (sym_gen != null)
{
execgrp_bucket.Add(train_data.DefaultBucketKey, execgrp);
}
}
public DataParallelExecutorGroup(Symbol sym, string[] arg_names, string[] param_names, Context[] ctxlist,
Slice[] slices, DataIter train_data, DataParallelExecutorGroup shared_group = null)
{
ExecuterManager.CheckArguments(sym);
if (shared_group == null)
{
foreach (var item in ctxlist)
{
shared_data_arrays.Add(new NDArrayDict());
}
}
else
{
shared_data_arrays = shared_group.shared_data_arrays;
}
foreach (var item in train_data.ProvideData)
{
data_names.Add(item.Name);
}
foreach (var item in train_data.ProvideLabel)
{
label_names.Add(item.Name);
}
aux_names = sym.ListAuxiliaryStates().ToList();
for (var i = 0; i < arg_names.Length; i++)
{
if (param_names.Contains(arg_names[i]))
{
param_idx.Add(i);
this.param_names.Add(arg_names[i]);
}
}
for (var i = 0; i < ctxlist.Length; i++)
{
var data_shapes = new Dictionary <string, Shape>();
var data_types = new Dictionary <string, DType>();
var shapeData = new List <int>();
foreach (var item in train_data.ProvideData)
{
shapeData = item.Shape.Data.ToList();
shapeData.RemoveAt(0);
shapeData.Insert(0, slices[i].End.Value - slices[i].Begin);
data_shapes[item.Name] = new Shape(shapeData);
data_types[item.Name] = item.DataType;
}
foreach (var item in train_data.ProvideLabel)
{
shapeData = item.Shape.Data.ToList();
shapeData.RemoveAt(0);
shapeData.Insert(0, slices[i].End.Value - slices[i].Begin);
data_shapes[item.Name] = new Shape(shapeData);
data_types[item.Name] = item.DataType;
}
var shared_exec = shared_group == null ? null : shared_group.train_execs[i];
var train_exec = ExecuterManager.BindExec(sym, ctxlist[i], data_shapes, param_names, true,
shared_exec, shared_data_arrays[i], data_types);
train_execs.Add(train_exec);
}
foreach (var name in data_names)
{
for (var i = 0; i < train_execs.Count; i++)
{
data_arrays.Add(train_execs[i].ArgmentDictionary()[name]);
}
}
foreach (var name in label_names)
{
for (var i = 0; i < train_execs.Count; i++)
{
label_arrays.Add(train_execs[i].ArgmentDictionary()[name]);
}
}
foreach (var idx in param_idx)
{
for (var i = 0; i < train_execs.Count; i++)
{
param_arrays.Add(train_execs[i].ArgmentArrays[idx]);
}
}
foreach (var idx in param_idx)
{
for (var i = 0; i < train_execs.Count; i++)
{
grad_arrays.Add(train_execs[i].GradientArrays[idx]);
}
}
for (var idx = 0; idx < aux_names.Count; idx++)
{
for (var i = 0; i < train_execs.Count; i++)
{
aux_arrays.Add(train_execs[i].AuxiliaryArrays[i]);
}
}
this.slices = slices;
}
public void LoadDataBatch(DataBatch data_batch)
{
ExecuterManager.LoadData(data_batch, data_arrays.ToArray());
ExecuterManager.LoadData(data_batch, label_arrays.ToArray());
}