private torch.Tensor ForwardEmbedding(torch.Tensor tokens, torch.Tensor segmentLabels, torch.Tensor positions)
{
using var disposeScope = torch.NewDisposeScope();
var x = TokenEmbedding.forward(tokens);
if (EmbedScale != null)
{
x.mul_(EmbedScale);
}
if (PositionalEmbedding != null)
{
var positionalEmbedding = PositionalEmbedding.forward(tokens,
new Dictionary <string, object> {
{ PositionalEmbedding.PositionKey, positions }
});
x.add_(positionalEmbedding);
}
if (SegmentEmbedding != null && segmentLabels.IsNotNull())
{
var segmentEmbedding = SegmentEmbedding.forward(segmentLabels);
x.add_(segmentEmbedding);
}
if (EmbeddingLayerNorm != null)
{
x = EmbeddingLayerNorm.forward(x);
}
x = EmbedTransfer.forward(x, (int)x.size()[x.size().Length - 1]);
x = DropoutLayer.forward(x);
return(x.MoveToOuterDisposeScope());
}
public torch.Tensor forward(
torch.Tensor query,
torch.Tensor key,
torch.Tensor value,
out torch.Tensor outAttentionWeights,
torch.Tensor keyPaddingMask = null,
Dictionary <string, Dictionary <string, torch.Tensor> > incrementalState = null,
bool needWeights = true,
bool staticKv = false,
torch.Tensor attentionMask = null)
{
outAttentionWeights = null;
if (query.IsNull() || query.size().Length != 3 || query.size(2) != _embeddingDim)
{
throw new ArgumentException("query must NOT be null and must be 3D in multi-head attention;" +
"the last dimension should be the same as embedding dimension.");
}
using var disposeScope = torch.NewDisposeScope();
var qSize = query.size();
var tgtLen = qSize[0];
var batchSize = qSize[1];
var embedDim = qSize[2];
// Get saved state from incrementalState
Dictionary <string, torch.Tensor> savedState = null;
if (incrementalState != null)
{
savedState = GetInputBuffer(incrementalState);
// previous time steps are cached - no need to recompute key and value if they are static.
if (savedState.ContainsKey(PrevKeyKey) && savedState.ContainsKey(PrevValueKey) && staticKv)
{
if (_selfAttention || !_encoderDecoderAttention)
{
throw new ArgumentException(
"prevKey and prevValue are only valid in encoder-decoder attention.");
}
key = value = null;
}
}
// Calculate current qkv projection
var(q, k, v) = QkvProjection(query, key, value);
// Simulate using-statement by try-finally
torch.Tensor attentionMaskPad = attentionMask?.alias();
torch.Tensor keyPaddingMaskPad = keyPaddingMask?.alias();
q.mul_(_scaling);
if (_addBiasKv)
{
var kRepeat = KBias.repeat(1, batchSize, 1);
var vRepeat = VBias.repeat(1, batchSize, 1);
k = torch.cat(new List <torch.Tensor> {
k, kRepeat
}, dimension: 0);
v = torch.cat(new List <torch.Tensor> {
v, vRepeat
}, dimension: 0);
attentionMaskPad = PadMask(attentionMaskPad);
keyPaddingMaskPad = PadMask(keyPaddingMaskPad);
}
q = q.view(tgtLen, batchSize * _numHeads, _headDim).transpose_(0, 1);
k = k?.view(-1, batchSize * _numHeads, _headDim).transpose_(0, 1);
v = v?.view(-1, batchSize * _numHeads, _headDim).transpose_(0, 1);
if (savedState != null)
{
// saved states are stored with shape (batchSize, NumHeads, seqLen, HeadDim)
if (savedState.ContainsKey(PrevKeyKey))
{
var prevKey = savedState[PrevKeyKey].view(batchSize * _numHeads, -1, _headDim);
k = staticKv
? prevKey
: torch.cat(new List <torch.Tensor> {
prevKey, k
}, dimension: 1);
}
if (savedState.ContainsKey(PrevValueKey))
{
var prevValue = savedState[PrevValueKey].view(batchSize * _numHeads, -1, _headDim);
v = staticKv
? prevValue
: torch.cat(new List <torch.Tensor> {
prevValue, v
}, dimension: 1);
}
savedState[PrevKeyKey].Dispose();
savedState[PrevKeyKey] = k?.view(batchSize, _numHeads, -1, _headDim);
savedState[PrevValueKey].Dispose();
savedState[PrevValueKey] = v?.view(batchSize, _numHeads, -1, _headDim);
SetInputBuffer(incrementalState, savedState);
}
Debug.Assert(k.IsNotNull() && v.IsNotNull());
var srcLen = k !.size(1);
// This is part of a workaround to get around fork/join parallelism not supporting Optional types.
if (keyPaddingMaskPad?.shape.Length == 0)
{
keyPaddingMaskPad = null;
}
Debug.Assert(keyPaddingMaskPad.IsNull() ||
(keyPaddingMaskPad.size(0) == batchSize && keyPaddingMaskPad.size(1) == srcLen));
if (_addZeroAttention)
{
srcLen += 1;
var zeroPadSize = k.size();
zeroPadSize[1] = 1;
var kZeros = k.new_zeros(zeroPadSize);
var vZeros = v !.new_zeros(zeroPadSize);
k = torch.cat(new List <torch.Tensor> {
k, kZeros
}, dimension: 1);
v = torch.cat(new List <torch.Tensor> {
v, vZeros
}, dimension: 1);
attentionMaskPad = PadMask(attentionMaskPad);
keyPaddingMaskPad = PadMask(keyPaddingMaskPad);
}
var attentionWeights = torch.matmul(q, k.transpose(1, 2));
Debug.Assert(attentionWeights.size().SequenceEqual(new[] { batchSize *_numHeads, tgtLen, srcLen }));
if (attentionMaskPad.IsNotNull())
{
attentionWeights.add_(attentionMaskPad.unsqueeze(0));
}
if (keyPaddingMaskPad.IsNotNull())
{
// Don't attend to pad symbols
keyPaddingMaskPad = keyPaddingMaskPad.unsqueeze(1).unsqueeze(2);
attentionWeights = attentionWeights
.view(batchSize, _numHeads, tgtLen, srcLen)
.masked_fill(keyPaddingMaskPad, float.NegativeInfinity)
.view(batchSize * _numHeads, tgtLen, srcLen);
}
attentionWeights = torch.nn.functional.softmax(attentionWeights, dim: -1);
attentionWeights = DropoutLayer.forward(attentionWeights);
if (needWeights)
{
// Average attention weights over heads
var weightsView = attentionWeights.view(batchSize, _numHeads, tgtLen, srcLen);
outAttentionWeights = weightsView.sum(dim: 1).div_(_numHeads);
}
var attention = torch.matmul(attentionWeights, v);
Debug.Assert(attention.size().SequenceEqual(new[] { batchSize *_numHeads, tgtLen, _headDim }));
attention = attention.transpose(0, 1).contiguous().view(tgtLen, batchSize, embedDim);
var attentionOutput = OutProjLinear.forward(attention);
outAttentionWeights?.MoveToOuterDisposeScope();
return(attentionOutput.MoveToOuterDisposeScope());
}