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Merge pull request stakira#1093 from yqzhishen/ds-tensor-cache
[DiffSinger] Add tensor caching system
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,311 @@ | ||
| using System; | ||
| using System.Collections.Generic; | ||
| using System.IO; | ||
| using System.Linq; | ||
| using K4os.Hash.xxHash; | ||
| using Microsoft.ML.OnnxRuntime; | ||
| using Microsoft.ML.OnnxRuntime.Tensors; | ||
| using Serilog; | ||
|
|
||
| namespace OpenUtau.Core.DiffSinger { | ||
|
|
||
| public class DiffSingerCache { | ||
| private const string FormatHeader = "TENSORCACHE"; | ||
|
|
||
| private readonly ulong hash; | ||
| private readonly string filename; | ||
|
|
||
| public ulong Hash => hash; | ||
|
|
||
| public DiffSingerCache(ulong identifier, ICollection<NamedOnnxValue> inputs) { | ||
| using var stream = new MemoryStream(); | ||
| using (var writer = new BinaryWriter(stream)) { | ||
| writer.Write(identifier); | ||
| foreach (var onnxValue in inputs.OrderBy(v => v.Name, StringComparer.InvariantCulture)) { | ||
| SerializeNamedOnnxValue(writer, onnxValue); | ||
| } | ||
| } | ||
|
|
||
| hash = XXH64.DigestOf(stream.ToArray()); | ||
| filename = $"ds-{hash:x16}.tensorcache"; | ||
| } | ||
|
|
||
| public ICollection<NamedOnnxValue>? Load() { | ||
| var cachePath = Path.Join(PathManager.Inst.CachePath, filename); | ||
| if (!File.Exists(cachePath)) return null; | ||
|
|
||
| var result = new List<NamedOnnxValue>(); | ||
| using var stream = new FileStream(cachePath, FileMode.Open, FileAccess.Read); | ||
| using var reader = new BinaryReader(stream); | ||
| // header | ||
| if (reader.ReadString() != FormatHeader) { | ||
| throw new InvalidDataException($"[TensorCache] Unexpected file header in {filename}."); | ||
| } | ||
| try { | ||
| // count | ||
| var count = reader.ReadInt32(); | ||
| for (var i = 0; i < count; ++i) { | ||
| // data | ||
| result.Add(DeserializeNamedOnnxValue(reader)); | ||
| } | ||
| } catch (Exception e) { | ||
| Log.Error(e, | ||
| "[TensorCache] Exception encountered when deserializing cache file. Root exception message: {msg}", e.Message); | ||
| Delete(); | ||
| return null; | ||
| } | ||
|
|
||
| return result; | ||
| } | ||
|
|
||
| public void Delete() { | ||
| var cachePath = Path.Join(PathManager.Inst.CachePath, filename); | ||
| if (File.Exists(cachePath)) { | ||
| File.Delete(cachePath); | ||
| } | ||
| } | ||
|
|
||
| public void Save(ICollection<NamedOnnxValue> outputs) { | ||
| var cachePath = Path.Join(PathManager.Inst.CachePath, filename); | ||
| using var stream = new FileStream(cachePath, FileMode.Create, FileAccess.Write); | ||
| using var writer = new BinaryWriter(stream); | ||
| // header | ||
| writer.Write(FormatHeader); | ||
| // count | ||
| writer.Write(outputs.Count); | ||
| foreach (var onnxValue in outputs) { | ||
| // data | ||
| SerializeNamedOnnxValue(writer, onnxValue); | ||
| } | ||
| } | ||
|
|
||
| private static void SerializeNamedOnnxValue(BinaryWriter writer, NamedOnnxValue namedOnnxValue) { | ||
| if (namedOnnxValue.ValueType != OnnxValueType.ONNX_TYPE_TENSOR) { | ||
| throw new NotSupportedException( | ||
| $"[TensorCache] The only supported ONNX value type is {OnnxValueType.ONNX_TYPE_TENSOR}. Got {namedOnnxValue.ValueType} instead." | ||
| ); | ||
| } | ||
| // name | ||
| writer.Write(namedOnnxValue.Name); | ||
| var tensorBase = (TensorBase) namedOnnxValue.Value; | ||
| var elementType = tensorBase.GetTypeInfo().ElementType; | ||
| // dtype | ||
| writer.Write((int)elementType); | ||
| switch (elementType) { | ||
| case TensorElementType.Float: { | ||
| var tensor = namedOnnxValue.AsTensor<float>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt8: { | ||
| var tensor = namedOnnxValue.AsTensor<byte>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int8: { | ||
| var tensor = namedOnnxValue.AsTensor<sbyte>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt16: { | ||
| var tensor = namedOnnxValue.AsTensor<ushort>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int16: { | ||
| var tensor = namedOnnxValue.AsTensor<short>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int32: { | ||
| var tensor = namedOnnxValue.AsTensor<int>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int64: { | ||
| var tensor = namedOnnxValue.AsTensor<long>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.String: { | ||
| var tensor = namedOnnxValue.AsTensor<string>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Bool: { | ||
| var tensor = namedOnnxValue.AsTensor<bool>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Float16: { | ||
| var tensor = namedOnnxValue.AsTensor<Float16>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Double: { | ||
| var tensor = namedOnnxValue.AsTensor<double>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt32: { | ||
| var tensor = namedOnnxValue.AsTensor<uint>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt64: { | ||
| var tensor = namedOnnxValue.AsTensor<ulong>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.BFloat16: { | ||
| var tensor = namedOnnxValue.AsTensor<BFloat16>(); | ||
| SerializeTensor(writer, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Complex64: | ||
| case TensorElementType.Complex128: | ||
| case TensorElementType.DataTypeMax: | ||
| default: | ||
| throw new NotSupportedException($"[TensorCache] Unsupported tensor element type: {elementType}."); | ||
| } | ||
| } | ||
|
|
||
| private static void SerializeTensor<T>(BinaryWriter writer, Tensor<T> tensor) { | ||
| if (tensor.IsReversedStride) { | ||
| throw new NotSupportedException("[TensorCache] Tensors in reversed strides are not supported."); | ||
| } | ||
| // rank | ||
| writer.Write(tensor.Rank); | ||
| // shape | ||
| foreach (var dim in tensor.Dimensions) { | ||
| writer.Write(dim); | ||
| } | ||
| // size | ||
| var size = (int)tensor.Length; | ||
| writer.Write(size); | ||
| if (typeof(T) == typeof(string)) { | ||
| // string tensor | ||
| // data | ||
| foreach (var element in tensor.ToArray()) { | ||
| writer.Write(element!.ToString()); | ||
| } | ||
| } else { | ||
| // numeric tensor | ||
| // data | ||
| var data = new byte[size * tensor.GetTypeInfo().TypeSize]; | ||
| Buffer.BlockCopy(tensor.ToArray(), 0, data, 0, data.Length); | ||
| writer.Write(data); | ||
| } | ||
| } | ||
|
|
||
| private static NamedOnnxValue DeserializeNamedOnnxValue(BinaryReader reader) { | ||
| // name | ||
| var name = reader.ReadString(); | ||
| // dtype | ||
| var dtype = (TensorElementType)reader.ReadInt32(); | ||
| // rank | ||
| var rank = reader.ReadInt32(); | ||
| // shape | ||
| int[] shape = new int[rank]; | ||
| for (var i = 0; i < rank; ++i) { | ||
| shape[i] = reader.ReadInt32(); | ||
| } | ||
| // size | ||
| var size = reader.ReadInt32(); | ||
| NamedOnnxValue namedOnnxValue; | ||
| switch (dtype) { | ||
| case TensorElementType.Float: { | ||
| var tensor = DeserializeTensor<float>(reader, size, sizeof(float), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt8: { | ||
| var tensor = DeserializeTensor<byte>(reader, size, sizeof(byte), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int8: { | ||
| var tensor = DeserializeTensor<sbyte>(reader, size, sizeof(sbyte), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt16: { | ||
| var tensor = DeserializeTensor<ushort>(reader, size, sizeof(ushort), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int16: { | ||
| var tensor = DeserializeTensor<short>(reader, size, sizeof(short), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int32: { | ||
| var tensor = DeserializeTensor<int>(reader, size, sizeof(int), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Int64: { | ||
| var tensor = DeserializeTensor<long>(reader, size, sizeof(long), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.String: { | ||
| // string tensor | ||
| Tensor<string> tensor = new DenseTensor<string>(size); | ||
| for (var i = 0; i < size; ++i) { | ||
| tensor[i] = reader.ReadString(); | ||
| } | ||
| tensor = tensor.Reshape(shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Bool: { | ||
| var tensor = DeserializeTensor<bool>(reader, size, sizeof(bool), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Float16: { | ||
| var tensor = DeserializeTensor<Float16>(reader, size, sizeof(ushort), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Double: { | ||
| var tensor = DeserializeTensor<double>(reader, size, sizeof(double), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt32: { | ||
| var tensor = DeserializeTensor<uint>(reader, size, sizeof(uint), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.UInt64: { | ||
| var tensor = DeserializeTensor<ulong>(reader, size, sizeof(ulong), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.BFloat16: { | ||
| var tensor = DeserializeTensor<BFloat16>(reader, size, sizeof(ushort), shape); | ||
| namedOnnxValue = NamedOnnxValue.CreateFromTensor(name, tensor); | ||
| break; | ||
| } | ||
| case TensorElementType.Complex64: | ||
| case TensorElementType.Complex128: | ||
| case TensorElementType.DataTypeMax: | ||
| default: | ||
| throw new NotSupportedException($"[TensorCache] Unsupported tensor element type: {dtype}."); | ||
| } | ||
|
|
||
| return namedOnnxValue; | ||
| } | ||
|
|
||
| private static Tensor<T> DeserializeTensor<T>(BinaryReader reader, int size, int typeSize, ReadOnlySpan<int> shape) | ||
| { | ||
| var bytes = reader.ReadBytes(size * typeSize); | ||
| var data = new T[size]; | ||
| Buffer.BlockCopy(bytes, 0, data, 0, bytes.Length); | ||
| Tensor<T> tensor = new DenseTensor<T>(data, shape); | ||
| return tensor; | ||
| } | ||
| } | ||
| } |
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