public LSTMAttentionDecoderCell(string name, int batchSize, int hdim, int dim, int contextSize, int deviceId)
{
m_name = name;
m_hdim = hdim;
m_dim = dim;
m_deviceId = deviceId;
m_batchSize = batchSize;
m_Wxhc = new WeightTensor(new long[2] {
dim + hdim + contextSize, hdim * 4
}, deviceId, normal: true, name: $"{name}.{nameof(m_Wxhc)}", isTrainable: true);
m_b = new WeightTensor(new long[2] {
1, hdim * 4
}, 0, deviceId, name: $"{name}.{nameof(m_b)}", isTrainable: true);
Hidden = new WeightTensor(new long[2] {
batchSize, hdim
}, 0, deviceId, name: $"{name}.{nameof(Hidden)}", isTrainable: true);
Cell = new WeightTensor(new long[2] {
batchSize, hdim
}, 0, deviceId, name: $"{name}.{nameof(Cell)}", isTrainable: true);
layerNorm1 = new LayerNormalization($"{name}.{nameof(layerNorm1)}", hdim * 4, deviceId);
layerNorm2 = new LayerNormalization($"{name}.{nameof(layerNorm2)}", hdim, deviceId);
}
public TransformerEncoder(string name, int multiHeadNum, int hiddenDim, int inputDim, int depth, float dropoutRatio, int deviceId, bool isTrainable)
{
Logger.WriteLine($"Creating transformer encoder at device '{deviceId}'. HiddenDim = '{hiddenDim}', InputDim = '{inputDim}', Depth = '{depth}', MultiHeadNum = '{multiHeadNum}'");
m_name = name;
m_multiHeadNum = multiHeadNum;
m_hiddenDim = hiddenDim;
m_inputDim = inputDim;
m_depth = depth;
m_dropoutRatio = dropoutRatio;
m_deviceId = deviceId;
m_isTrainable = isTrainable;
if (hiddenDim != inputDim)
{
throw new ArgumentException($"hiddenDim is not equal to inputDim in TransformerEncoder.");
}
m_encoders.Add(new MultiHeadAttention($"{name}.SelfAttn_0", multiHeadNum, hiddenDim, inputDim, m_dropoutRatio, deviceId, isTrainable: isTrainable));
for (int i = 1; i < depth; i++)
{
m_encoders.Add(new MultiHeadAttention($"{name}.SelfAttn_{i}", multiHeadNum, hiddenDim, hiddenDim, m_dropoutRatio, deviceId, isTrainable: isTrainable));
}
for (int i = 0; i < depth; i++)
{
m_posFFNs.Add(new PositionwiseFeedForward($"{name}.PosFFN_{i}", hiddenDim, m_dropoutRatio, deviceId, isTrainable));
}
layerNorm = new LayerNormalization($"{name}.{nameof(layerNorm)}", hiddenDim, deviceId, isTrainable);
}
public LSTMAttentionDecoderCell(int batchSize, int hdim, int dim, ArchTypeEnums archType, int deviceId)
{
int contextSize = hdim * 2;
this.hdim = hdim;
this.dim = dim;
m_deviceId = deviceId;
m_batchSize = batchSize;
if (archType == ArchTypeEnums.GPU_CUDA)
{
Wxhc = new WeightTensor(dim + hdim + contextSize, hdim * 4, deviceId, true);
b = new WeightTensor(1, hdim * 4, 0, deviceId);
this.ht = new WeightTensor(batchSize, hdim, 0, deviceId);
this.ct = new WeightTensor(batchSize, hdim, 0, deviceId);
}
else
{
Wxhc = new WeightMatrix(dim + hdim + contextSize, hdim * 4, true);
b = new WeightMatrix(1, hdim * 4, 0);
this.ht = new WeightMatrix(batchSize, hdim, 0);
this.ct = new WeightMatrix(batchSize, hdim, 0);
}
layerNorm1 = new LayerNormalization(hdim * 4, archType, deviceId);
layerNorm2 = new LayerNormalization(hdim, archType, deviceId);
}
public PositionwiseFeedForward(string name, int hiddenDim, float dropoutRatio, int deviceId, bool isTrainable, float learningRateFactor = 1.0f)
{
m_name = name;
m_dropoutRatio = dropoutRatio;
layerNorm2 = new LayerNormalization($"{name}.{nameof(layerNorm2)}", hiddenDim, deviceId, isTrainable, learningRateFactor: learningRateFactor);
feedForwardLayer1 = new FeedForwardLayer($"{name}.{nameof(feedForwardLayer1)}", hiddenDim, hiddenDim * 4, m_dropoutRatio, deviceId, isTrainable, learningRateFactor: learningRateFactor);
feedForwardLayer2 = new FeedForwardLayer($"{name}.{nameof(feedForwardLayer2)}", hiddenDim * 4, hiddenDim, m_dropoutRatio, deviceId, isTrainable, learningRateFactor: learningRateFactor);
}
public PositionwiseFeedForward(string name, int hiddenDim, float dropoutRatio, int deviceId, bool isTrainable)
{
this.m_name = name;
this.m_hiddenDim = hiddenDim;
this.m_dropoutRatio = dropoutRatio;
this.layerNorm2 = new LayerNormalization($"{name}.{nameof(this.layerNorm2)}", hiddenDim, deviceId, isTrainable);
this.feedForwardLayer1 = new FeedForwardLayer($"{name}.{nameof(this.feedForwardLayer1)}", hiddenDim, hiddenDim * 4, this.m_dropoutRatio, deviceId, isTrainable);
this.feedForwardLayer2 = new FeedForwardLayer($"{name}.{nameof(this.feedForwardLayer2)}", hiddenDim * 4, hiddenDim, this.m_dropoutRatio, deviceId, isTrainable);
}
public MultiHeadAttention(string name, int multiHeadNum, int hiddenDim, int inputDim, float dropoutRatio, int deviceId, bool isTrainable, bool sharedQKV = false, float learningRateFactor = 1.0f)
{
m_name = name;
m_hiddenDim = hiddenDim;
m_multiHeadNum = multiHeadNum;
m_d = m_hiddenDim / m_multiHeadNum;
m_dropoutRatio = dropoutRatio;
m_sharedQKV = sharedQKV;
W0 = new WeightTensor(new long[2] {
hiddenDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(W0)}", isTrainable: isTrainable, normType: NormType.Uniform, learningRateFactor: learningRateFactor);
b0 = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(b0)}", isTrainable: isTrainable);
if (m_sharedQKV == false)
{
Q = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(Q)}", isTrainable: isTrainable, normType: NormType.Uniform, learningRateFactor: learningRateFactor);
Qb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Qb)}", isTrainable: isTrainable, learningRateFactor: learningRateFactor);
K = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(K)}", isTrainable: isTrainable, normType: NormType.Uniform, learningRateFactor: learningRateFactor);
Kb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Kb)}", isTrainable: isTrainable, learningRateFactor: learningRateFactor);
V = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(V)}", isTrainable: isTrainable, normType: NormType.Uniform, learningRateFactor: learningRateFactor);
Vb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Vb)}", isTrainable: isTrainable, learningRateFactor: learningRateFactor);
}
else
{
QKV = new WeightTensor(new long[2] {
inputDim, hiddenDim * 3
}, deviceId, name: $"{name}.{nameof(Q)}", isTrainable: isTrainable, normType: NormType.Uniform, learningRateFactor: learningRateFactor);
QKVb = new WeightTensor(new long[2] {
1, hiddenDim * 3
}, 0, deviceId, name: $"{name}.{nameof(Qb)}", isTrainable: isTrainable, learningRateFactor: learningRateFactor);
}
layerNormQ = new LayerNormalization($"{name}.{nameof(layerNormQ)}", m_hiddenDim, deviceId, isTrainable, learningRateFactor: learningRateFactor);
}
// private readonly bool m_sharedQKV = false;
public MultiHeadAttention(string name, int multiHeadNum, int hiddenDim, int inputDim, float dropoutRatio, int deviceId, bool isTrainable)
{
m_name = name;
m_hiddenDim = hiddenDim;
m_inputDim = inputDim;
m_multiHeadNum = multiHeadNum;
m_d = m_hiddenDim / m_multiHeadNum;
m_dropoutRatio = dropoutRatio;
// m_sharedQKV = sharedQKV;
W0 = new WeightTensor(new long[2] {
hiddenDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(W0)}", isTrainable: isTrainable, normal: NormType.Uniform);
b0 = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(b0)}", isTrainable: isTrainable);
//if (m_sharedQKV == false)
//{
Q = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(Q)}", isTrainable: isTrainable, normal: NormType.Uniform);
Qb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Qb)}", isTrainable: isTrainable);
K = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(K)}", isTrainable: isTrainable, normal: NormType.Uniform);
Kb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Kb)}", isTrainable: isTrainable);
V = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(V)}", isTrainable: isTrainable, normal: NormType.Uniform);
Vb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Vb)}", isTrainable: isTrainable);
//}
//else
//{
// QKV = new WeightTensor(new long[] { 3, inputDim, hiddenDim }, deviceId, name: $"{name}.{nameof(QKV)}", isTrainable: isTrainable, normal: NormType.Uniform);
//}
layerNormQ = new LayerNormalization($"{name}.{nameof(layerNormQ)}", m_hiddenDim, deviceId, isTrainable);
}
public MultiHeadAttention(string name, int multiHeadNum, int hiddenDim, int inputDim, float dropoutRatio, int deviceId, bool isTrainable, bool sharedQKV = false)
{
this.m_name = name;
this.m_hiddenDim = hiddenDim;
this.m_inputDim = inputDim;
this.m_multiHeadNum = multiHeadNum;
this.m_d = this.m_hiddenDim / this.m_multiHeadNum;
this.m_dropoutRatio = dropoutRatio;
this.m_sharedQKV = sharedQKV;
this.W0 = new WeightTensor(new long[2] {
hiddenDim, hiddenDim
}, deviceId, $"{name}.{nameof(this.W0)}", isTrainable, NormType.Uniform);
this.b0 = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, $"{name}.{nameof(this.b0)}", isTrainable);
if (this.m_sharedQKV == false)
{
this.Q = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, $"{name}.{nameof(this.Q)}", isTrainable, NormType.Uniform);
this.Qb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, $"{name}.{nameof(this.Qb)}", isTrainable);
this.K = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, $"{name}.{nameof(this.K)}", isTrainable, NormType.Uniform);
this.Kb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, $"{name}.{nameof(this.Kb)}", isTrainable);
this.V = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, $"{name}.{nameof(this.V)}", isTrainable, NormType.Uniform);
this.Vb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, $"{name}.{nameof(this.Vb)}", isTrainable);
}
else
{
this.QKV = new WeightTensor(new long[] { 3, inputDim, hiddenDim }, deviceId, $"{name}.{nameof(this.QKV)}", isTrainable, NormType.Uniform);
}
this.layerNormQ = new LayerNormalization($"{name}.{nameof(this.layerNormQ)}", this.m_hiddenDim, deviceId, isTrainable);
}
public LSTMCell(string name, int hdim, int dim, int deviceId, bool isTrainable)
{
m_name = name;
m_Wxh = new WeightTensor(new long[2] {
dim + hdim, hdim * 4
}, deviceId, normType: NormType.Uniform, name: $"{name}.{nameof(m_Wxh)}", isTrainable: isTrainable);
m_b = new WeightTensor(new long[2] {
1, hdim * 4
}, 0, deviceId, name: $"{name}.{nameof(m_b)}", isTrainable: isTrainable);
m_hdim = hdim;
m_dim = dim;
m_deviceId = deviceId;
m_layerNorm1 = new LayerNormalization($"{name}.{nameof(m_layerNorm1)}", hdim * 4, deviceId, isTrainable: isTrainable);
m_layerNorm2 = new LayerNormalization($"{name}.{nameof(m_layerNorm2)}", hdim, deviceId, isTrainable: isTrainable);
}
public LSTMAttentionDecoderCell(string name, int hiddenDim, int inputDim, int contextDim, int deviceId, bool isTrainable)
{
m_name = name;
m_hiddenDim = hiddenDim;
m_inputDim = inputDim;
m_deviceId = deviceId;
Logger.WriteLine($"Create LSTM attention decoder cell '{name}' HiddemDim = '{hiddenDim}', InputDim = '{inputDim}', ContextDim = '{contextDim}', DeviceId = '{deviceId}'");
m_Wxhc = new WeightTensor(new long[2] {
inputDim + hiddenDim + contextDim, hiddenDim * 4
}, deviceId, normType: NormType.Uniform, name: $"{name}.{nameof(m_Wxhc)}", isTrainable: isTrainable);
m_b = new WeightTensor(new long[2] {
1, hiddenDim * 4
}, 0, deviceId, name: $"{name}.{nameof(m_b)}", isTrainable: isTrainable);
m_layerNorm1 = new LayerNormalization($"{name}.{nameof(m_layerNorm1)}", hiddenDim * 4, deviceId, isTrainable);
m_layerNorm2 = new LayerNormalization($"{name}.{nameof(m_layerNorm2)}", hiddenDim, deviceId, isTrainable);
}
public LSTMCell(string name, int batchSize, int hdim, int dim, int deviceId)
{
m_name = name;
m_Wxh = new WeightTensor(new long[2] {
dim + hdim, hdim * 4
}, deviceId, normal: true, name: $"{name}.{nameof(m_Wxh)}", isTrainable: true);
m_b = new WeightTensor(new long[2] {
1, hdim * 4
}, 0, deviceId, name: $"{name}.{nameof(m_b)}", isTrainable: true);
m_hdim = hdim;
m_dim = dim;
m_batchSize = batchSize;
m_deviceId = deviceId;
m_layerNorm1 = new LayerNormalization($"{name}.{nameof(m_layerNorm1)}", hdim * 4, deviceId);
m_layerNorm2 = new LayerNormalization($"{name}.{nameof(m_layerNorm2)}", hdim, deviceId);
}
public SelfAttention(string name, int multiHeadNum, int hiddenDim, int inputDim, float dropoutRatio, int deviceId)
{
m_name = name;
m_hiddenDim = hiddenDim;
m_multiHeadNum = multiHeadNum;
m_d = m_hiddenDim / m_multiHeadNum;
m_dropoutRatio = dropoutRatio;
W0 = new WeightTensor(new long[2] {
hiddenDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(W0)}", isTrainable: true);
b0 = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(b0)}", isTrainable: true);
Q = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(Q)}", isTrainable: true);
Qb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Qb)}", isTrainable: true);
K = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(K)}", isTrainable: true);
Kb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Kb)}", isTrainable: true);
V = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(V)}", isTrainable: true);
Vb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Vb)}", isTrainable: true);
layerNorm1 = new LayerNormalization($"{name}.{nameof(layerNorm1)}", hiddenDim, deviceId);
layerNorm2 = new LayerNormalization($"{name}.{nameof(layerNorm2)}", hiddenDim, deviceId);
feedForwardLayer1 = new FeedForwardLayer($"{name}.{nameof(feedForwardLayer1)}", hiddenDim, hiddenDim * 4, m_dropoutRatio, deviceId);
feedForwardLayer2 = new FeedForwardLayer($"{name}.{nameof(feedForwardLayer2)}", hiddenDim * 4, hiddenDim, m_dropoutRatio, deviceId);
}
public TransformerDecoder(string name, int multiHeadNum, int hiddenDim, int inputDim, int depth, float dropoutRatio, int deviceId, bool isTrainable, float learningRateFactor = 1.0f)
{
Logger.WriteLine($"Creating transformer decoder at device '{deviceId}'. HiddenDim = '{hiddenDim}', InputDim = '{inputDim}', Depth = '{depth}', MultiHeadNum = '{multiHeadNum}'");
m_name = name;
m_multiHeadNum = multiHeadNum;
m_hiddenDim = hiddenDim;
m_inputDim = inputDim;
// m_outputDim = outputDim;
m_depth = depth;
m_dropoutRatio = dropoutRatio;
m_deviceId = deviceId;
m_isTrainable = isTrainable;
m_learningRateFactor = learningRateFactor;
if (hiddenDim != inputDim)
{
throw new ArgumentException($"hiddenDim is not equal to inputDim in TransformerDecoder.");
}
m_selfAttns.Add(new MultiHeadAttention($"{name}.SelfAttn_0", multiHeadNum, hiddenDim, inputDim, m_dropoutRatio, deviceId, isTrainable: isTrainable, sharedQKV: true, learningRateFactor: learningRateFactor));
for (int i = 1; i < depth; i++)
{
m_selfAttns.Add(new MultiHeadAttention($"{name}.SelfAttn_{i}", multiHeadNum, hiddenDim, hiddenDim, m_dropoutRatio, deviceId, isTrainable: isTrainable, sharedQKV: true, learningRateFactor: learningRateFactor));
}
m_encAttns.Add(new MultiHeadAttention($"{name}.EncAttn_0", multiHeadNum, hiddenDim, inputDim, m_dropoutRatio, deviceId, isTrainable: isTrainable, learningRateFactor: learningRateFactor));
for (int i = 1; i < depth; i++)
{
m_encAttns.Add(new MultiHeadAttention($"{name}.EncAttn_{i}", multiHeadNum, hiddenDim, hiddenDim, m_dropoutRatio, deviceId, isTrainable: isTrainable, learningRateFactor: learningRateFactor));
}
for (int i = 0; i < depth; i++)
{
m_posFFNs.Add(new PositionwiseFeedForward($"{name}.PosFFN_{i}", hiddenDim, m_dropoutRatio, deviceId, isTrainable, learningRateFactor: learningRateFactor));
}
layerNorm = new LayerNormalization($"{name}.{nameof(layerNorm)}", hiddenDim, deviceId, isTrainable, learningRateFactor: learningRateFactor);
// m_decoderFFLayer = new FeedForwardLayer($"{name}.FeedForward", hiddenDim, outputDim, 0.0f, deviceId: deviceId, isTrainable: isTrainable);
}
public LSTMCell(int batchSize, int hdim, int dim, ArchTypeEnums archType, int deviceId)
{
if (archType == ArchTypeEnums.GPU_CUDA)
{
Wxh = new WeightTensor(dim + hdim, hdim * 4, deviceId, true);
b = new WeightTensor(1, hdim * 4, 0, deviceId);
}
else
{
Wxh = new WeightMatrix(dim + hdim, hdim * 4, true);
b = new WeightMatrix(1, hdim * 4, 0);
}
this.hdim = hdim;
this.dim = dim;
this.batchSize = batchSize;
this.deviceId = deviceId;
layerNorm1 = new LayerNormalization(hdim * 4, archType, deviceId);
layerNorm2 = new LayerNormalization(hdim, archType, deviceId);
}
public MultiHeadAttention(string name, int multiHeadNum, int hiddenDim, int inputDim, float dropoutRatio, int deviceId, bool isTrainable)
{
m_name = name;
m_hiddenDim = hiddenDim;
m_multiHeadNum = multiHeadNum;
m_d = m_hiddenDim / m_multiHeadNum;
m_dropoutRatio = dropoutRatio;
W0 = new WeightTensor(new long[2] {
hiddenDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(W0)}", isTrainable: isTrainable, normal: NormType.Uniform);
b0 = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(b0)}", isTrainable: isTrainable);
Q = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(Q)}", isTrainable: isTrainable);
Qb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Qb)}", isTrainable: isTrainable);
K = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(K)}", isTrainable: isTrainable);
Kb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Kb)}", isTrainable: isTrainable);
V = new WeightTensor(new long[2] {
inputDim, hiddenDim
}, deviceId, name: $"{name}.{nameof(V)}", isTrainable: isTrainable);
Vb = new WeightTensor(new long[2] {
1, hiddenDim
}, 0, deviceId, name: $"{name}.{nameof(Vb)}", isTrainable: isTrainable);
layerNorm1 = new LayerNormalization($"{name}.{nameof(layerNorm1)}", hiddenDim, deviceId, isTrainable);
}
