private static void PackConstants(Model model, Dictionary <string, Layer> constantLayers)
{
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (!LinearLayerFusing.IsLayerLinearMathOp(layer))
{
continue;
}
var constInputs = layer.inputs.Count(x => constantLayers.ContainsKey(x));
// @TODO fuse multi const inputs here
if (!(layer.inputs.Length == 2 && constInputs == 1))
{
continue;
}
var constInput = layer.inputs.ToList().Find(x => constantLayers.ContainsKey(x));
layer.datasets = new Layer.DataSet[constantLayers[constInput].datasets.Length];
Array.Copy(constantLayers[constInput].datasets, layer.datasets, constantLayers[constInput].datasets.Length);
layer.weights = new BarracudaArray(constantLayers[constInput].weights.Length);
BarracudaArray.Copy(constantLayers[constInput].weights, layer.weights, constantLayers[constInput].weights.Length);
model.layers[l].inputs = layer.inputs.Where(x => x != constInput).ToArray();
}
}
/// <summary>
/// Converts Tensor to DataSet
/// </summary>
/// <param name="X">input `Tensor`</param>
/// <param name="index">dataset index</param>
public void ApplyTensorToDataSet(Tensor X, int index)
{
Assert.IsTrue(index < datasets.Length);
var ds = datasets[index];
ds.shape = X.shape;
BarracudaArray.Copy(X.ToReadOnlyArray(), 0, weights, ds.offset, ds.shape.length);
datasets[index] = ds;
}
/// <summary>
/// Convert in place all model weights to given data type
/// </summary>
/// <param name="type">target type for moodel weights</param>
internal void ConvertWeights(DataType type)
{
foreach (var layer in layers)
{
if (layer.weights != null && layer.weights.Type != type)
{
var sourceWeights = layer.weights;
var targetWeights = new BarracudaArray(layer.weights.Length, type);
BarracudaArray.Copy(sourceWeights, targetWeights);
layer.weights = targetWeights;
}
}
}
private static void UnpackConstants(Model model)
{
List <Layer> newConstants = new List <Layer>();
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (!LinearLayerFusing.IsLayerLinearMathOp(layer))
{
continue;
}
if (layer.datasets == null || layer.datasets.Length != 1)
{
continue;
}
var name = "c" + layer.name;
Layer constInput = new Layer(name, Layer.Type.Load);
constInput.datasets = new Layer.DataSet[layer.datasets.Length];
Array.Copy(layer.datasets, constInput.datasets, layer.datasets.Length);
for (int d = 0; d < constInput.datasets.Length; ++d)
{
constInput.datasets[d].name = name;
}
constInput.weights = new BarracudaArray(layer.weights.Length);
BarracudaArray.Copy(layer.weights, constInput.weights, layer.weights.Length);
Array.Resize(ref layer.inputs, layer.inputs.Length + 1);
layer.inputs[layer.inputs.Length - 1] = constInput.name;
newConstants.Add(constInput);
layer.datasets = new Layer.DataSet[0];
layer.weights = new BarracudaArray(0);//TODO fp16
}
newConstants.AddRange(model.layers);
model.layers = newConstants;
}
