public void Dispose()
{
m_Process = null;
m_Allocator = null;
GC.SuppressFinalize(this);
}
public Tensor(IAllocator allocator, DType elementType, long[] sizes, long[] strides)
{
this.sizes = sizes;
this.strides = strides;
this.storageOffset = 0;
this.storage = allocator.Allocate(elementType, TensorDimensionHelpers.GetStorageSize(sizes, strides));
}
public WeightTensor(int rows, int columns, int deviceId, bool normal = false)
{
DeviceId = deviceId;
allocator = TensorAllocator.Allocator(DeviceId);
Rows = rows;
Columns = columns;
var n = rows * columns;
float[] weight = new float[n];
var scale = (float)Math.Sqrt(1.0 / (rows * columns));
if (normal)
{
scale = 0.08f;
}
for (int i = 0; i < n; i++)
{
weight[i] = RandomGenerator.NormalRandom(0.0f, scale);
}
TGradient = new Tensor(allocator, DType.Float32, Rows, Columns);
Ops.Fill(TGradient, 0.0f);
TWeight = Tensor.FromArray(allocator, weight).View(Rows, Columns);
}
public Conv2Layer(IAllocator allocator, SeedSource seedSource, DType elementType, int batchSize, int inputWidth, int inputHeight, int nInputPlane, int nOutputPlane, ConvolutionDesc2d cd)
{
this.cd = cd;
this.weight = new NDArray(allocator, elementType, nOutputPlane, nInputPlane * cd.kW * cd.kH);
this.bias = new NDArray(allocator, elementType, nOutputPlane, 1);
this.gradWeight = new NDArray(allocator, elementType, this.weight.Shape);
this.gradBias = new NDArray(allocator, elementType, this.bias.Shape);
inputSizes = new long[] { batchSize, nInputPlane, inputHeight, inputWidth };
this.gradInput = new NDArray(allocator, elementType, inputSizes);
outputSizes = SpatialConvolutionMM.OutputSize(inputSizes, weight.Shape, cd);
this.activation = new NDArray(allocator, elementType, outputSizes);
this.OutputSizes = outputSizes;
var stdv = 1.0f / (float)Math.Sqrt(cd.kW * cd.kH * nInputPlane);
Ops.RandomUniform(weight, seedSource, -stdv, stdv);
Ops.RandomUniform(bias, seedSource, -stdv, stdv);
}
public ClassNLLCriterion(IAllocator allocator, int batchSize, int nClasses)
{
this.allocator = allocator;
this.output = new NDArray(allocator, DType.Float32, 1);
this.gradInput = new NDArray(allocator, DType.Float32, batchSize, nClasses);
}
public static Tensor Constant(float value, IAllocator allocator, DType dtype, params long[] sizes)
{
Tensor tensor = new Tensor(allocator, dtype, sizes);
TOps.Fill(tensor, value);
return(tensor);
}
public WeightTensor(long[] sizes, int deviceId, string name = "", bool isTrainable = false, bool normal = false)
{
Name = name;
DeviceId = deviceId;
IsTrainable = isTrainable;
allocator = TensorAllocator.Allocator(DeviceId);
Sizes = sizes;
if (normal)
{
var n = Rows * Columns;
float[] weight = new float[n];
var scale = (float)Math.Sqrt(1.0 / (Rows * Columns));
if (normal)
{
scale = 0.08f;
}
for (int i = 0; i < n; i++)
{
weight[i] = RandomGenerator.NormalRandom(0.0f, scale);
}
TGradient = new Tensor(allocator, DType.Float32, Sizes);
Ops.Fill(TGradient, 0.0f);
TWeight = Tensor.FromArray(allocator, weight).View(Sizes);
}
}
private void Init(PythonContext inPython, string stubPath, IAllocator inAllocator)
{
this.GIL = new Lock();
this.python = inPython;
this.allocator = inAllocator;
this.importNames.Push("");
this.importFiles.Push(null);
this.CreateScratchModule();
this.kindaDictProxy = this.CreateFromSnippet(CodeSnippets.KINDA_DICT_PROXY_CODE, "KindaDictProxy");
if (stubPath != null)
{
this.stub = new StubReference(stubPath);
this.stub.Init(new dgt_getfuncptr(this.GetFuncPtr), new dgt_registerdata(this.RegisterData));
string path = Environment.GetEnvironmentVariable("PATH");
string newpath = path + ";" + Path.Combine(Path.GetDirectoryName(stubPath), "support");
Environment.SetEnvironmentVariable("PATH", newpath);
this.ReadyBuiltinTypes();
this.importer = new PydImporter();
this.removeSysHacks = this.CreateFromSnippet(CodeSnippets.INSTALL_IMPORT_HOOK_CODE, "remove_sys_hacks");
// TODO: load builtin modules only on demand?
this.stub.LoadBuiltinModule("posix");
this.stub.LoadBuiltinModule("mmap");
this.stub.LoadBuiltinModule("_csv");
}
this.alive = true;
}
public Conv2Cudnn(IAllocator allocator, SeedSource seedSource, DType elementType, int batchSize, int inputWidth, int inputHeight, int nInputPlane, int nOutputPlane, ConvolutionDesc2d cd)
: base(allocator, seedSource, elementType, batchSize, inputWidth, inputHeight, nInputPlane, nOutputPlane, cd)
{
// Reshape weight and bias - CuDNN expects the dimensions to be structured slightly differently
this.weight = ViewReplace(this.weight, nOutputPlane, nInputPlane, cd.kH, cd.kW);
this.bias = ViewReplace(this.bias, 1, nOutputPlane, 1, 1);
this.gradWeight = ViewReplace(this.gradWeight, this.weight.Shape);
this.gradBias = ViewReplace(this.gradBias, this.bias.Shape);
var fwdWorkspace = DNN.GetConvolutionForwardWorkspaceSize(allocator, fwdAlgo, cd,
new TensorShape(elementType, new long[] { batchSize, nInputPlane, inputHeight, inputWidth }),
new TensorShape(weight),
new TensorShape(activation));
var bwdFilterWorkspace = DNN.GetConvolutionBackwardFilterWorkspaceSize(allocator, bwdFilterAlgo, cd,
new TensorShape(elementType, new long[] { batchSize, nInputPlane, inputHeight, inputWidth }),
new TensorShape(activation),
new TensorShape(weight));
var bwdFilterInputWorkspace = DNN.GetConvolutionBackwardDataWorkspaceSize(allocator, bwdDataAlgo, cd,
new TensorShape(weight),
new TensorShape(activation),
new TensorShape(elementType, new long[] { batchSize, nInputPlane, inputHeight, inputWidth }));
var workspaceSize = Math.Max(Math.Max(fwdWorkspace, bwdFilterWorkspace), bwdFilterInputWorkspace);
this.workspace = (CudaStorage)allocator.Allocate(DType.UInt8, workspaceSize);
}
public static DataSet BuildSet(IAllocator allocator, DigitImage[] images)
{
var inputs = new NDArray(allocator, DType.Float32, images.Length, MnistParser.ImageSize, MnistParser.ImageSize);
var outputs = new NDArray(allocator, DType.Float32, images.Length, MnistParser.LabelCount);
var cpuAllocator = new TensorSharp.Cpu.CpuAllocator();
for (int i = 0; i < images.Length; ++i)
{
var target = inputs.TVar().Select(0, i);
Variable.FromArray(images[i].pixels, cpuAllocator)
.AsType(DType.Float32)
.ToDevice(allocator)
.Evaluate(target);
target.Div(255)
.Evaluate(target);
}
Ops.FillOneHot(outputs, MnistParser.LabelCount, images.Select(x => (int)x.label).ToArray());
var targetValues = NDArray.FromArray(allocator, images.Select(x => (float)x.label).ToArray());
return(new DataSet()
{
inputs = inputs, targets = outputs, targetValues = targetValues
});
}
public static NDArray Constant(float value, IAllocator allocator, DType dtype, params long[] sizes)
{
NDArray tensor = new NDArray(allocator, dtype, sizes);
TOps.Fill(tensor, value);
return(tensor);
}
public static long GetConvolutionBackwardDataWorkspaceSize(IAllocator allocator, DNNConvolutionBwdDataAlgo algo, Cpu.ConvolutionDesc2d cd, TensorShape w, TensorShape dy, TensorShape dx)
{
if (!(allocator is CudaAllocator))
{
throw new InvalidOperationException("allocator must be a CUDA allocator");
}
var cudaAllocator = (CudaAllocator)allocator;
using (var dnn = cudaAllocator.Context.DNNForDevice(cudaAllocator.DeviceId))
{
var convDesc = GetConvDescriptor(cd, w.ElementType);
using (var wDesc = GetFilterDescriptor(w))
using (var dyDesc = GetDescriptor(dy))
using (var dxDesc = GetDescriptor(dx))
{
return(dnn.Value.GetConvolutionBackwardDataWorkspaceSize(
wDesc,
dyDesc,
convDesc,
dxDesc,
(cudnnConvolutionBwdDataAlgo)algo));
}
}
}
public PagedObjectPool(IAllocator allocator)
{
_memory = allocator;
_objectMap = new List <NativeArray <T> >();
_freeIds = new NativeStack <uint>(_memory, OBJECTS_PER_POOL);
AddPage();
}
public static AllocationHandle Take <T>(this IAllocator it, int count)
where T : unmanaged
{
var size = SizeOf <T> .Size;
return(it.Take(size * count));
}
public FillExpression(IAllocator allocator, DType elementType, long[] sizes, Action <Tensor> fillAction)
{
this.allocator = allocator;
this.elementType = elementType;
this.sizes = sizes;
this.fillAction = fillAction;
}
// Constructs a convolutional network with two convolutional layers,
// two fully-connected layers, ReLU units and a softmax on the output.
public static void BuildCnn(IAllocator allocator, SeedSource seedSource, int batchSize, bool useCudnn, out Sequential model, out ICriterion criterion, out bool outputIsClassIndices)
{
var inputWidth = MnistParser.ImageSize;
var inputHeight = MnistParser.ImageSize;
var elementType = DType.Float32;
var inputDims = new long[] { batchSize, 1, inputHeight, inputWidth };
model = new Sequential();
model.Add(new ViewLayer(inputDims));
var outSize = AddCnnLayer(allocator, seedSource, elementType, model, inputDims, 20, useCudnn);
outSize = AddCnnLayer(allocator, seedSource, elementType, model, outSize, 40, useCudnn);
var convOutSize = outSize[1] * outSize[2] * outSize[3];
model.Add(new ViewLayer(batchSize, convOutSize));
var hiddenSize = 1000;
var outputSize = 10;
model.Add(new DropoutLayer(allocator, seedSource, elementType, 0.5f, batchSize, convOutSize));
model.Add(new LinearLayer(allocator, seedSource, elementType, (int)convOutSize, hiddenSize, batchSize));
model.Add(new ReLULayer(allocator, elementType, batchSize, hiddenSize));
model.Add(new DropoutLayer(allocator, seedSource, elementType, 0.5f, batchSize, hiddenSize));
model.Add(new LinearLayer(allocator, seedSource, elementType, hiddenSize, outputSize, batchSize));
model.Add(LayerBuilder.BuildLogSoftMax(allocator, elementType, batchSize, outputSize, useCudnn));
criterion = new ClassNLLCriterion(allocator, batchSize, outputSize);
outputIsClassIndices = true; // output of criterion is class indices
}
public Allocation(IAllocator allocator, int offset, int count)
{
_allocator = allocator;
Offset = offset;
Count = count;
}
public EntityChunkList(ILoggerFactory logFactory, IAllocator allocator, EntitySpec specification, int specIndex)
{
_logFactory = logFactory;
_logger = logFactory.CreateLogger <EntityChunkList>();
_allocator = allocator;
Specification = specification;
SpecIndex = specIndex;
}
protected static IColumnStream CreateColumnStream(ICodecFullStream codec, IAllocator allocator, int bufLen)
{
return(new ColumnStreamFullStream <ColumnMemoryStream, ICodecFullStream>(
new ColumnMemoryStream(),
codec,
allocator,
bufLen));
}
public void Dispose()
{
if (Old != null)
{
Allocator.SetAllocator(Old);
}
Old = null;
}
public AllocatorBuffer(IAllocator <A, P, U> allocator, int blockLength, int index, int initialCounts = 100)
{
this.initialCounts = initialCounts;
this.blockLength = blockLength;
this.totalBlocks = 0;
this.Index = index;
this.allocator = allocator;
}
//TODO: Should we really clear to zero by default?
//the EntityChunk should be managing out of bounds indexing and moving of data
public StackAllocator(IAllocator allocator, int size, bool thrash = false)
{
_allocator = allocator;
_handle = allocator.Take(size);
_free = (uint)size;
_dataPtr = _handle.Address;
_thrash = thrash;
}
public Conv2Cuda(IAllocator allocator, SeedSource seedSource, DType elementType, int batchSize, int inputWidth, int inputHeight, int nInputPlane, int nOutputPlane, ConvolutionDesc2d cd)
: base(allocator, seedSource, elementType, batchSize, inputWidth, inputHeight, nInputPlane, nOutputPlane, cd)
{
var finputSizes = TensorSharp.CUDA.SpatialConvolution.FInputSize(inputSizes, outputSizes, cd);
this.finput = new NDArray(allocator, elementType, finputSizes);
this.fgradInput = new NDArray(allocator, elementType, finputSizes);
}
/// <summary>
/// Converts to device.
/// </summary>
/// <param name="device">The device.</param>
/// <returns>TVar.</returns>
public NDArray ToDevice(IAllocator device)
{
if (Storage.Allocator.GetType().Name == device.GetType().Name)
{
return(this);
}
return(new Variable(new ToDeviceExpression(this.TVar().Expression, device)).Evaluate());
}
public CudaStorage(IAllocator allocator, TSCudaContext tsContext, CudaContext context, DType ElementType, long elementCount)
: base(allocator, ElementType, elementCount)
{
TSContext = tsContext;
this.context = context;
bufferHandle = tsContext.AllocatorForDevice(DeviceId).Allocate(ByteLength);
deviceBuffer = bufferHandle.Pointer;
}
public RenderCommandBuffer(IAllocator allocator, GraphicsDevice device)
{
_buffer = new NativeBuffer(allocator);
_device = device;
_defaultEffect = new BasicEffect(_device);
_defaultEffect.TextureEnabled = true;
_defaultEffect.VertexColorEnabled = true;
}
public static Compiler Create(Block romData, IAllocator allocator, IEnumerable <IModule> modules)
{
Compiler compiler = new Compiler();
compiler._romData = romData;
compiler._allocator = allocator;
compiler._modules = modules;
return(compiler);
}
public void Setup()
{
_logFactory = LoggerFactory.Create(builder =>
{
builder.AddConsole();
});
_memory = new HeapAllocator(_logFactory);
}
public SystemManager(ILoggerFactory logFactory, EntityManager em, IAllocator allocator)
{
_logFactory = logFactory;
_logger = logFactory.CreateLogger <SystemManager>();
_entityManager = em;
_allocator = allocator;
DefaultStage = "Default";
_variables = new NativeBuffer(allocator);
}
public EntityPool(ILoggerFactory logFactory, IAllocator allocator)
{
_logFactory = logFactory;
_logger = _logFactory.CreateLogger <EntityPool>();
_memory = allocator;
_freeIds = new NativeStack <uint>(_memory, ENTITIES_PER_POOL);
_entityMap = new List <NativeArray <Entity> >();
//AddPage();
Take();
}
public TimeSeriesRecorder(
string symbol,
ITimeSeriesDbUpdater dbUpdater,
IAllocator allocator)
{
_symbol = symbol;
_dbUpdater = dbUpdater;
_allocator = allocator;
_buffer = allocator.Allocate(Natives.MAX_ENTRY_SIZE);
}
public BodyCollisionFunctor(PhysicsManager manager)
{
_manager = manager;
_taskManager = TaskManager.Current;
int threads = TaskManager.ThreadCount;
_alloc = new IAllocator<ContactConstraint>[threads];
_items = new List<ContactConstraint>[threads];
_contact = new ContactConstraint[threads];
_a = new Part[threads];
_b = new Part[threads];
for (int i = 0; i < threads; i++)
{
_alloc[i] = new ContactConstraint.Allocator(manager.Game, manager.ContactPoolCapacity, 2, manager.MaxPointsPerContact);
_items[i] = new List<ContactConstraint>();
}
_fastSpeedSquared = manager.SweepThresholdSquared;
}
private static ManagedPointer Insert(Bitmap toInsert, Color[] palette, int blockAdded, bool compressed,
IROM rom, IAllocator<ManagedPointer> allocator)
{
var bytes = GBAGraphics.ToGBARaw(toInsert, palette, GraphicsMode.Tile4bit);
int insertedLength;
int bytesAdded = blockAdded * 32;
if (compressed)
{
bytes = LZ77.Compress(bytes, 0, bytes.Length - bytesAdded);
insertedLength = bytes.Length;
}
else
{
insertedLength = bytes.Length - bytesAdded;
}
var ptr = allocator.Allocate(insertedLength, 4);
if (!ptr.IsNull)
{
rom.WriteData(ptr, bytes, 0, insertedLength);
}
return ptr;
}
public bool CanAllocate(IAllocator allocator, Specification spec)
{
return spec.ResourceType == "Folder" &&
Directory.Exists("C:\\Workspace");
}
public async Task<IResource> Allocate(IAllocator allocator, Specification spec)
{
var directory = new DirectoryInfo("C:\\Workspace");
return new FolderResource(directory.CreateSubdirectory("FolderAllocation_" + RandomString()));
}
public bool CanLease(IAllocator allocator, Specification spec)
{
return spec.ResourceType == "Folder" && !LeasedOut;
}
protected BaseAllocatorTest(IAllocator allocator)
{
_allocator = allocator;
}
/// <summary>
/// Initialize the physics implementation.
/// </summary>
public override void Initialize()
{
base.Initialize();
if (_broadPhase == null)
_broadPhase = new SweepAndPrune();
_islandAlloc = new Pool<Island>(_islandPoolCapacity, 2);
_contacts = new BodyCollisionFunctor(this);
_contactCache = new ContactCache(new ContactCache.Allocator(_contactPoolCapacity, 2, _maxPointsPerContact));
_taskManager = TaskManager.Current;
_generators.Add(_gravityForce);
}
public async Task<ILease> Lease(IAllocator allocator, Specification spec)
{
LeasedOut = true;
_folderLease = new FolderLease(this, Directory);
return _folderLease;
}
private void Init(PythonContext inPython, string stubPath, IAllocator inAllocator)
{
this.GIL = new Lock();
this.python = inPython;
this.allocator = inAllocator;
this.importNames.Push("");
this.importFiles.Push(null);
this.CreateScratchModule();
this.kindaDictProxy = this.CreateFromSnippet(CodeSnippets.KINDA_DICT_PROXY_CODE, "KindaDictProxy");
if (stubPath != null)
{
this.stub = new StubReference(stubPath);
this.stub.Init(new dgt_getfuncptr(this.GetFuncPtr), new dgt_registerdata(this.RegisterData));
string path = Environment.GetEnvironmentVariable("PATH");
string newpath = path + ";" + Path.Combine(Path.GetDirectoryName(stubPath), "support");
Environment.SetEnvironmentVariable("PATH", newpath);
this.ReadyBuiltinTypes();
this.importer = new PydImporter();
this.removeSysHacks = this.CreateFromSnippet(CodeSnippets.INSTALL_IMPORT_HOOK_CODE, "remove_sys_hacks");
// TODO: load builtin modules only on demand?
this.stub.LoadBuiltinModule("posix");
this.stub.LoadBuiltinModule("mmap");
this.stub.LoadBuiltinModule("_csv");
}
this.alive = true;
}
public PythonMapper(PythonContext python, string stubPath, IAllocator allocator)
{
this.Init(python, stubPath, allocator);
}
public PythonMapper(CodeContext context, string stubPath, IAllocator allocator)
: this(context.LanguageContext, stubPath, allocator)
{
}
public PythonMapper(CodeContext context, IAllocator allocator)
: this(context, null, allocator)
{
}
/// <summary>
///
/// </summary>
/// <param name="picture"></param>
/// <param name="allocator"></param>
/// <param name="rom"></param>
/// <returns>
/// first is main portrait offset,
/// second is miniportrait,
/// third is palette and
/// fourth is mouth frames if separate
/// </returns>
public static CanCauseError<ManagedPointer[]> WriteData(Bitmap picture,
IAllocator<ManagedPointer> allocator, IROM rom)
{
var format = GetFormat(rom.GameCode);
return WriteData(format, picture, allocator, rom);
}
/// <summary>
///
/// </summary>
/// <param name="picture"></param>
/// <param name="format"></param>
/// <param name="allocator"></param>
/// <param name="rom"></param>
/// <returns>
/// first is main portrait offset,
/// second is miniportrait,
/// third is palette and
/// fourth is mouth frames if separate
/// </returns>
private static CanCauseError<ManagedPointer[]> WriteData(PortraitFormat format, Bitmap picture,
IAllocator<ManagedPointer> allocator, IROM rom)
{
List<ManagedPointer> pointers = new List<ManagedPointer>(4);
//Get palette
Color[] palette;
bool palettedBitmap = picture.PixelFormat.HasFlag(PixelFormat.Indexed);
if (!palettedBitmap)
{
var colors = picture.GetColors();
colors = GBAPalette.GetGBAColors(colors);
if (colors.Count > 16)
{
return CanCauseError<ManagedPointer[]>.Error("Over 16 colours.");
}
palette = colors.ToArray();
}
else
{
palette = picture.Palette.Entries;
}
//Split to separate portraits
Bitmap mainPortrait = new Bitmap(
format.PortraitSize.Width,
format.PortraitSize.Height, picture.PixelFormat);
Move(picture, mainPortrait, format.PictureMapping);
Bitmap mouthbm;
if (format.SeparateMouthFrames)
{
mouthbm = new Bitmap(format.MouthSize.Width, format.MouthSize.Height, picture.PixelFormat);
}
else
{
mouthbm = mainPortrait;
}
Move(picture, mouthbm, format.MouthMapping);
Bitmap mini = new Bitmap(format.MiniSize.Width, format.MiniSize.Height, picture.PixelFormat);
Move(picture, mouthbm, format.MiniMapping);
//Write data
pointers.Add(Insert(mainPortrait, palette, format.BlockAdd, format.CompressedPortrait, rom, allocator));
pointers.Add(Insert(mini, palette, 0, format.CompressedMini, rom, allocator));
byte[] rawPalette = GBAPalette.toRawGBAPalette(palette);
var ptr = allocator.Allocate(rawPalette.Length, 4);
if (!ptr.IsNull)
{
rom.WriteData(ptr, rawPalette, 0, rawPalette.Length);
}
pointers.Add(ptr);
if (mouthbm != mainPortrait)
{
pointers.Add(Insert(mouthbm, palette, 0, true, rom, allocator));
}
return pointers.ToArray();
}
