319 lines
13 KiB
MQL5
319 lines
13 KiB
MQL5
//+------------------------------------------------------------------+
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//| KronosTransformerCore.mqh |
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//| MMQ — Muhammad Minhas Qamar |
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//| www.mql5.com/en/articles/23304 |
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//+------------------------------------------------------------------+
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#property copyright "MMQ — Muhammad Minhas Qamar"
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#property link "https://www.mql5.com/en/articles/23304"
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#property version "1.00"
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#ifndef KRONOS_TRANSFORMER_CORE_MQH
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#define KRONOS_TRANSFORMER_CORE_MQH
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//--- transformer core constants
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#define KR_ROPE_BASE 10000.0
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#define KR_NORM_EPS 1e-5
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//+------------------------------------------------------------------+
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//| Linear (PyTorch convention): y = x @ W^T. The weight is loaded |
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//| pre-transposed (KronosLoadMatrixT stores W as [in,out] == W^T), |
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//| so this is a plain MatMul with NO per-call transpose. Profiling |
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//| showed the old per-call W.Transpose() was ~60% of the forward |
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//| pass; the transpose now happens once at load. Math is identical. |
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//+------------------------------------------------------------------+
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matrix LinearT(const matrix &X, const matrix &W) { return X.MatMul(W); }
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//+------------------------------------------------------------------+
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//| Add a per-output-column bias to every row of Y. |
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//+------------------------------------------------------------------+
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void AddRowBias(matrix &Y, const vector &b)
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{
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ulong T = Y.Rows(), O = Y.Cols();
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for(ulong i = 0; i < T; i++)
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for(ulong j = 0; j < O; j++)
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Y[i][j] += b[j];
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}
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//+------------------------------------------------------------------+
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//| RMSNorm (row-wise): out = x / sqrt(mean(x^2) + eps) * weight. |
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//+------------------------------------------------------------------+
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matrix RMSNorm(const matrix &X, const vector &w, double eps = KR_NORM_EPS)
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{
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ulong T = X.Rows(), D = X.Cols();
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matrix out = matrix::Zeros(T, D);
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for(ulong i = 0; i < T; i++)
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{
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double ms = 0.0;
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for(ulong j = 0; j < D; j++)
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ms += X[i][j] * X[i][j];
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ms /= (double)D;
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double scale = 1.0 / MathSqrt(ms + eps);
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for(ulong j = 0; j < D; j++)
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out[i][j] = X[i][j] * scale * w[j];
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}
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return out;
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}
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//+------------------------------------------------------------------+
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//| SiLU activation: x * sigmoid(x). |
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//+------------------------------------------------------------------+
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double SiLU(double x) { return x / (1.0 + MathExp(-x)); }
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//+------------------------------------------------------------------+
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//| SwiGLU feed-forward: w2( SiLU(w1 x) * w3 x ), all bias-free. |
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//+------------------------------------------------------------------+
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matrix SwiGLU(const matrix &X, const matrix &W1, const matrix &W3, const matrix &W2)
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{
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matrix H = LinearT(X, W1);
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matrix G = LinearT(X, W3);
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ulong T = H.Rows(), FF = H.Cols();
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matrix gated = matrix::Zeros(T, FF);
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for(ulong i = 0; i < T; i++)
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for(ulong j = 0; j < FF; j++)
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gated[i][j] = SiLU(H[i][j]) * G[i][j];
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return LinearT(gated, W2);
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}
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//+------------------------------------------------------------------+
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//| Build the RoPE cos/sin tables (T, hd). emb = cat(freqs, freqs), |
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//| so the two halves of each row are duplicated. |
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//+------------------------------------------------------------------+
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void RoPETables(ulong T, ulong hd, matrix &cosT, matrix &sinT)
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{
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ulong half = hd / 2;
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cosT = matrix::Zeros(T, hd);
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sinT = matrix::Zeros(T, hd);
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for(ulong t = 0; t < T; t++)
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for(ulong k = 0; k < half; k++)
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{
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double inv_freq = 1.0 / MathPow(KR_ROPE_BASE, (2.0 * (double)k) / (double)hd);
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double ang = (double)t * inv_freq;
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double c = MathCos(ang), s = MathSin(ang);
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cosT[t][k] = c;
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cosT[t][k + half] = c;
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sinT[t][k] = s;
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sinT[t][k + half] = s;
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}
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}
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//+------------------------------------------------------------------+
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//| Apply RoPE: out = x*cos + rotate_half(x)*sin, where |
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//| rotate_half(x) = cat(-x2, x1). |
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//+------------------------------------------------------------------+
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matrix ApplyRoPE(const matrix &X, const matrix &cosT, const matrix &sinT)
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{
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ulong T = X.Rows(), hd = X.Cols(), half = hd / 2;
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matrix out = matrix::Zeros(T, hd);
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for(ulong t = 0; t < T; t++)
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for(ulong k = 0; k < hd; k++)
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{
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double rh = (k < half) ? -X[t][k + half] : X[t][k - half];
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out[t][k] = X[t][k] * cosT[t][k] + rh * sinT[t][k];
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}
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return out;
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}
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//+------------------------------------------------------------------+
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//| Extract one head's columns [c0, c0+hd) into a (T, hd) matrix. |
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//+------------------------------------------------------------------+
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matrix SliceCols(const matrix &M, ulong c0, ulong hd)
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{
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ulong T = M.Rows();
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matrix s = matrix::Zeros(T, hd);
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for(ulong i = 0; i < T; i++)
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for(ulong k = 0; k < hd; k++)
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s[i][k] = M[i][c0 + k];
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return s;
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}
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//+------------------------------------------------------------------+
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//| Write one head's (T, hd) output back into columns [c0, c0+hd). |
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//+------------------------------------------------------------------+
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void WriteCols(matrix &M, const matrix &S, ulong c0)
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{
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ulong T = S.Rows(), hd = S.Cols();
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for(ulong i = 0; i < T; i++)
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for(ulong k = 0; k < hd; k++)
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M[i][c0 + k] = S[i][k];
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}
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//+------------------------------------------------------------------+
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//| Scaled dot-product attention for one head. |
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//| Qh(Tq,hd), Kh(Tk,hd), Vh(Tk,hd). causal=true masks j>i and so |
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//| needs Tq==Tk. scores = Q.Kt * inv_sqrt -> stable row-softmax |
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//| -> weights . V. Returns Oh(Tq,hd). |
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//+------------------------------------------------------------------+
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matrix SDPA(const matrix &Qh, const matrix &Kh, const matrix &Vh, bool causal)
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{
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ulong Tq = Qh.Rows(), Tk = Kh.Rows(), hd = Qh.Cols();
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double inv_sqrt = 1.0 / MathSqrt((double)hd);
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//--- scaled scores
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matrix scores = Qh.MatMul(Kh.Transpose()); // (Tq,Tk)
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scores *= inv_sqrt;
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//--- row-wise softmax with optional causal mask
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for(ulong i = 0; i < Tq; i++)
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{
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//--- causal mask: query i attends to keys 0..i only
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ulong jmax = causal ? i : (Tk - 1);
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double smax = -DBL_MAX;
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for(ulong j = 0; j <= jmax; j++)
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if(scores[i][j] > smax)
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smax = scores[i][j];
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double denom = 0.0;
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for(ulong j = 0; j < Tk; j++)
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{
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if(causal && j > i)
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{
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scores[i][j] = 0.0;
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continue;
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}
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double e = MathExp(scores[i][j] - smax);
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scores[i][j] = e;
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denom += e;
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}
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double inv = 1.0 / denom;
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for(ulong j = 0; j < Tk; j++)
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scores[i][j] *= inv; // masked entries already 0
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}
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return scores.MatMul(Vh); // (Tq,hd)
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}
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//+------------------------------------------------------------------+
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//| Causal multi-head self-attention with RoPE. |
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//| q/k/v/out projections carry a bias. |
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//+------------------------------------------------------------------+
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matrix MHA(const matrix &X,
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const matrix &Wq, const vector &bq,
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const matrix &Wk, const vector &bk,
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const matrix &Wv, const vector &bv,
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const matrix &Wo, const vector &bo,
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int n_heads)
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{
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ulong T = X.Rows();
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ulong d_model = X.Cols();
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ulong hd = d_model / (ulong)n_heads;
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//--- q/k/v projections (with bias)
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matrix Q = LinearT(X, Wq);
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AddRowBias(Q, bq);
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matrix K = LinearT(X, Wk);
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AddRowBias(K, bk);
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matrix V = LinearT(X, Wv);
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AddRowBias(V, bv);
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//--- shared RoPE tables for all heads
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matrix cosT, sinT;
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RoPETables(T, hd, cosT, sinT);
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matrix ctx = matrix::Zeros(T, d_model);
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//--- per-head causal attention
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for(int h = 0; h < n_heads; h++)
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{
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ulong c0 = (ulong)h * hd;
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matrix Qs = SliceCols(Q, c0, hd); // named locals: MQL5 passes matrices by reference only
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matrix Ks = SliceCols(K, c0, hd);
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matrix Qh = ApplyRoPE(Qs, cosT, sinT);
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matrix Kh = ApplyRoPE(Ks, cosT, sinT);
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matrix Vh = SliceCols(V, c0, hd);
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matrix Oh = SDPA(Qh, Kh, Vh, true); // causal
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WriteCols(ctx, Oh, c0);
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}
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//--- output projection (with bias)
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matrix out = LinearT(ctx, Wo);
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AddRowBias(out, bo);
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return out;
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}
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//+------------------------------------------------------------------+
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//| Non-causal multi-head CROSS-attention with RoPE (inference). |
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//| q from Xq (sibling embed), k/v from Xkv (context); each query |
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//| attends to ALL key positions. q/k/v/out carry a bias. The |
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//| predictor's dep_layer uses n_heads=4 (head_dim=128), NOT 8. |
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//| |
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//| RoPE quirk (matches RotaryPositionalEmbedding.forward): the |
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//| cos/sin cache is sized to the QUERY length and the same cache is |
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//| applied to the keys. So when Tq==1 (a single broadcast s1 pick) |
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//| every key is rotated at position 0; when Tq==Tk keys rotate by |
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//| their own positions. We index the key rotation by (Tq==1 ? 0:j), |
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//| reusing the query's table. |
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//+------------------------------------------------------------------+
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matrix CrossMHA(const matrix &Xq, const matrix &Xkv,
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const matrix &Wq, const vector &bq,
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const matrix &Wk, const vector &bk,
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const matrix &Wv, const vector &bv,
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const matrix &Wo, const vector &bo,
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int n_heads)
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{
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ulong Tq = Xq.Rows();
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ulong Tk = Xkv.Rows();
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ulong d_model = Xq.Cols();
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ulong hd = d_model / (ulong)n_heads; // scaling handled inside SDPA
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//--- q from Xq, k/v from Xkv (all with bias)
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matrix Q = LinearT(Xq, Wq);
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AddRowBias(Q, bq);
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matrix K = LinearT(Xkv, Wk);
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AddRowBias(K, bk);
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matrix V = LinearT(Xkv, Wv);
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AddRowBias(V, bv);
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//--- single cache sized to the query length, reused for keys (PyTorch quirk).
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//--- valid only when Tq==1 (broadcast) or Tq==Tk (position-matched); any other
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//--- mix would have failed PyTorch's broadcast, so reject it loudly.
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if(!(Tq == 1 || Tq == Tk))
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{ PrintFormat("CrossMHA: unsupported Tq=%I64u, Tk=%I64u (need Tq==1 or Tq==Tk)", Tq, Tk); }
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matrix cosT, sinT;
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RoPETables(Tq, hd, cosT, sinT);
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//--- per-key rotation table: row j uses position (Tq==1 ? 0 : j)
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matrix cosK = matrix::Zeros(Tk, hd), sinK = matrix::Zeros(Tk, hd);
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for(ulong j = 0; j < Tk; j++)
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{
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ulong p = (Tq == 1) ? 0 : j; // broadcast when single query
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for(ulong k = 0; k < hd; k++)
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{
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cosK[j][k] = cosT[p][k];
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sinK[j][k] = sinT[p][k];
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}
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}
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matrix ctx = matrix::Zeros(Tq, d_model);
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//--- per-head non-causal cross-attention
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for(int h = 0; h < n_heads; h++)
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{
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ulong c0 = (ulong)h * hd;
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matrix Qs = SliceCols(Q, c0, hd);
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matrix Ks = SliceCols(K, c0, hd);
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matrix Qh = ApplyRoPE(Qs, cosT, sinT);
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matrix Kh = ApplyRoPE(Ks, cosK, sinK);
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matrix Vh = SliceCols(V, c0, hd);
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matrix Oh = SDPA(Qh, Kh, Vh, false); // non-causal: all keys
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WriteCols(ctx, Oh, c0);
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}
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//--- output projection (with bias)
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matrix out = LinearT(ctx, Wo);
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AddRowBias(out, bo);
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return out;
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}
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//+------------------------------------------------------------------+
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//| Pre-norm block: |
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//| x += MHA(RMSNorm1(x)); x += SwiGLU(RMSNorm2(x)). |
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//+------------------------------------------------------------------+
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matrix TransformerBlock(const matrix &X,
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const vector &norm1_w,
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const matrix &Wq, const vector &bq,
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const matrix &Wk, const vector &bk,
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const matrix &Wv, const vector &bv,
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const matrix &Wo, const vector &bo,
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int n_heads,
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const vector &norm2_w,
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const matrix &W1, const matrix &W3, const matrix &W2)
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{
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//--- attention sub-block: x1 = x + MHA(RMSNorm1(x))
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matrix n1 = RMSNorm(X, norm1_w); // named locals: no temporaries by reference
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matrix a = MHA(n1, Wq, bq, Wk, bk, Wv, bv, Wo, bo, n_heads);
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matrix x1 = X + a;
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//--- feed-forward sub-block: out = x1 + SwiGLU(RMSNorm2(x1))
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matrix n2 = RMSNorm(x1, norm2_w);
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matrix f = SwiGLU(n2, W1, W3, W2);
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return x1 + f;
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}
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#endif // KRONOS_TRANSFORMER_CORE_MQH
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//+------------------------------------------------------------------+
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