private static void PackConstantsForMathOps(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);
layer.axis = constantLayers[constInput].axis; // rank TODO name correctly
model.layers[l].inputs = layer.inputs.Where(x => x != constInput).ToArray();
}
}
private static void UnpackConstantsForMathOps(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);
constInput.axis = layer.axis; // rank TODO rename
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;
}
private void FuseShapesIntoConstants(ref Model model, IDictionary <string, TensorShape?> shapesByName, IDictionary <string, int?> ranksByName, ref List <Model.ImporterWarning> warnings)
{
var toRunnableNCHW = new IntermediateToRunnableNCHWPass();
var knownLayersValue = new Dictionary <string, Tensor>();
var newKnownLayers = new HashSet <string>();
var keepLayers = new HashSet <string>();
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (layer.flags == Layer.Flags.Preserve)
{
keepLayers.Add(layer.name);
}
// NN is a directed graph, if we just fused constants + shapes, update following nodes
// re-evaluate shapes
FuseInputsIntoLayer(ref layer, knownLayersValue, ranksByName, warnings);
// TODO optimization, pass in index, or add shape
IRShapeInferenceHelper.RankInference.UpdateKnownTensorRanks(model, ranksByName);
IRShapeInferenceHelper.ShapeInference.UpdateKnownTensorShapesNCHW(model, ranksByName, ref shapesByName);
if (ModelOptimizer.IsLayerConstant(layer))
{
knownLayersValue[layer.name] = new Tensor(layer.datasets[0].shape, layer.weights);
}
else if (layer.type == Layer.Type.Shape)
{
// assert inputs.Lenght == 1
var input = layer.inputs[0];
if (shapesByName.ContainsKey(input) && shapesByName[input] != null &&
ranksByName.ContainsKey(input) && ranksByName[input] != null
)
{
var shape = shapesByName[input].Value;
var rank = ranksByName[input].Value;
knownLayersValue[layer.name] = ShapeToNCHWTensor(shape, rank);
newKnownLayers.Add(layer.name);
continue;
}
}
bool allInputsAreKnown = layer.inputs.Length > 0 ? knownLayersValue.ContainsKey(layer.inputs[0]) : false;
for (int i = 1; i < layer.inputs.Length; i++)
{
allInputsAreKnown &= knownLayersValue.ContainsKey(layer.inputs[i]);
}
// if all inputs are known, execute layer
if (!allInputsAreKnown)
{
continue;
}
var layerInputs = new Dictionary <string, Tensor>();
var opsModel = new Model();
opsModel.layout = "iNCHW";
for (int i = 0; i < layer.inputs.Length; i++)
{
Model.Input input;
input.name = layer.inputs[i];
input.shape = shapesByName[input.name].Value.ToArray();
input.rank = ranksByName[input.name].Value;
opsModel.inputs.Add(input);
layerInputs[input.name] = knownLayersValue[input.name];
}
Layer newLayer = new Layer(layer.name.ToString(), layer.activation);
newLayer.type = layer.type;
newLayer.activation = layer.activation;
newLayer.pad = layer.pad.ToArray();
newLayer.stride = layer.stride.ToArray();
newLayer.pool = layer.pool.ToArray();
newLayer.axis = layer.axis;
newLayer.alpha = layer.alpha;
newLayer.beta = layer.beta;
newLayer.inputs = layer.inputs.ToArray();
newLayer.datasets = layer.datasets;
newLayer.weights = layer.weights;
if (layer.outputs != null)
{
newLayer.outputs = layer.outputs.ToArray();
}
if (layer.axes != null)
{
newLayer.axes = layer.axes.ToArray();
}
opsModel.layers.Add(newLayer);
opsModel.outputs.Add(newLayer.name);
toRunnableNCHW.Run(ref opsModel);
// bake
var useCPUforBaking = WorkerFactory.Device.CPU;
using (var worker = WorkerFactory.CreateWorker(opsModel, useCPUforBaking))
{
var bakedConstant = worker.Execute(layerInputs).CopyOutput();
knownLayersValue[layer.name] = bakedConstant;
newKnownLayers.Add(layer.name);
}
}
// remove new baked layers since we will insert constants for those
model.layers.RemoveAll(x => newKnownLayers.Contains(x.name) && !keepLayers.Contains(x.name));
// TODO use ModelBuilder?
foreach (var l in newKnownLayers)
{
if (keepLayers.Contains(l))
{
continue;
}
var name = l;
var tensor = knownLayersValue[name];
Layer c = new Layer(name, Layer.Type.Load);
c.datasets = new Layer.DataSet[1];
c.datasets[0].name = name;
c.datasets[0].shape = tensor.shape;
c.datasets[0].itemSizeInBytes = 4;
c.datasets[0].length = tensor.shape.length;
c.datasets[0].offset = 0;
c.axis = ranksByName[c.name].Value;
c.weights = new BarracudaArray(tensor.length);
BarracudaArray.Copy(tensor.ToReadOnlyArray(), c.weights, tensor.length);
model.layers.Insert(0, c);
}
foreach (var l in knownLayersValue)
{
l.Value.Dispose();
}
// TODO remove?
// remove unused constants
var removeUnusedLayersPass = new Cleanup.RemoveUnusedLayersPass();
removeUnusedLayersPass.Run(ref model);
}
public static void FuseConstants(ref Model model)
{
var knownLayersValue = new Dictionary <string, Tensor>();
var newKnownLayers = new HashSet <string>();
var keepLayers = new HashSet <string>();
for (int l = 0; l < model.layers.Count; ++l)
{
var layer = model.layers[l];
if (layer.flags == Layer.Flags.Preserve)
{
keepLayers.Add(layer.name);
}
// NN is a directed graph, if we just fused constants + shapes, update following nodes
// TODO optimization, pass in index, or add shape
if (ModelOptimizer.IsLayerConstant(layer))
{
knownLayersValue[layer.name] = new Tensor(layer.datasets[0].shape, layer.weights);
}
bool allInputsAreKnown = layer.inputs.Length > 0 ? knownLayersValue.ContainsKey(layer.inputs[0]) : false;
for (int i = 1; i < layer.inputs.Length; i++)
{
allInputsAreKnown &= knownLayersValue.ContainsKey(layer.inputs[i]);
}
// if all inputs are known, execute layer
if (!allInputsAreKnown)
{
continue;
}
var layerInputs = new Dictionary <string, Tensor>();
var opsModel = new Model();
for (int i = 0; i < layer.inputs.Length; i++)
{
Model.Input input;
input.name = layer.inputs[i];
input.shape = knownLayersValue[input.name].shape.ToArray();
input.rank = knownLayersValue[input.name].shape.dimensions;
opsModel.inputs.Add(input);
layerInputs[input.name] = knownLayersValue[input.name];
}
opsModel.layers.Add(layer);
opsModel.outputs.Add(layer.name);
// bake
var useCPUforBaking = WorkerFactory.Device.CPU;
using (var worker = WorkerFactory.CreateWorker(opsModel, useCPUforBaking))
{
// TODO use ModelIR2RunnableNCHWPass
var bakedConstant = worker.Execute(layerInputs).PeekOutput();
bakedConstant.TakeOwnership();
knownLayersValue[layer.name] = bakedConstant;
newKnownLayers.Add(layer.name);
}
}
// remove new baked layers since we will insert constants for those
model.layers.RemoveAll(x => newKnownLayers.Contains(x.name) && !keepLayers.Contains(x.name));
// TODO use ModelBuilder?
foreach (var l in newKnownLayers)
{
if (keepLayers.Contains(l))
{
continue;
}
var name = l;
var tensor = knownLayersValue[name];
Layer c = new Layer(name, Layer.Type.Load);
c.datasets = new Layer.DataSet[1];
c.datasets[0].name = name;
c.datasets[0].shape = tensor.shape;
c.datasets[0].itemSizeInBytes = 4;
c.datasets[0].length = tensor.shape.length;
c.datasets[0].offset = 0;
c.axis = tensor.shape.dimensions;
c.weights = new BarracudaArray(tensor.length);
BarracudaArray.Copy(tensor.ToReadOnlyArray(), c.weights, tensor.length);
model.layers.Insert(0, c);
}
// clear allocated tensors
foreach (var l in knownLayersValue)
{
l.Value.Dispose();
}
// remove unused constants
var removeUnusedLayersPass = new Cleanup.RemoveUnusedLayersPass();
removeUnusedLayersPass.Run(ref model);
}
public static void CopyToBarracudaArray(this float[] sourceArray, BarracudaArray destinationArray, long destinationIndex)
{
BarracudaArray.Copy(sourceArray, 0, destinationArray, (int)destinationIndex, sourceArray.Length);
}
