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implementing mlp_sw;
public struct MLVec<int N> : IDifferentiable
{
public NFloat data[N];
[Differentiable]
public NFloat[N] toArray()
{
return data;
}
[Differentiable]
public static MLVec<N> fromArray(NFloat[N] values)
{
MLVec<N> result;
[ForceUnroll]
for (int i = 0; i < N; i++)
result.data[i] = values[i];
return result;
}
}
MLVec<OutputSize> matMulAdd<int OutputSize, int InputSize>(MLVec<InputSize> input, NFloat* matrix, NFloat* bias)
{
let getMatElem = (int row, int col) => matrix[row*InputSize + col];
let getBias = (int idx) => bias[idx];
MLVec<OutputSize> result = {};
for (int i = 0; i < OutputSize; i++)
{
NFloat r = getBias(i);
for (int j = 0; j < InputSize; j++)
r += getMatElem(i, j) * input.data[j];
result.data[i] = r;
}
return result;
}
MLVec<OutputSize> matMulTransposed<int OutputSize, int InputSize>(MLVec<InputSize> input, NFloat* matrix)
{
let getMatElem = (int row, int col) => matrix[col*OutputSize + row];
MLVec<OutputSize> result = {};
for (int i = 0; i < OutputSize; i++)
{
NFloat r = {};
for (int j = 0; j < InputSize; j++)
r += getMatElem(i, j) * input.data[j];
result.data[i] = r;
}
return result;
}
void outerProductAccumulate<int M, int N>(MLVec<M> v0, MLVec<N> v1, NFloat* matrix)
{
for (int i = 0; i < M; i++)
{
for (int j = 0; j < N; j++)
{
let elem = v0.data[i] * v1.data[j];
half original;
InterlockedAddF16Emulated(matrix + (i*N + j), elem, original);
}
}
}
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