188 lines
6.1 KiB
Plaintext
188 lines
6.1 KiB
Plaintext
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#include <stdio.h>
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#include <assert.h>
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#define MIN_VALUE (-1e38)
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template <typename F>
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__global__ void kernel_forward(
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const int B, const int T, const int C, const F *__restrict__ const _w, const F *__restrict__ const _u,
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const F *__restrict__ const _k, const F *__restrict__ const _v, F *__restrict__ const _y
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) {
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const int idx = blockIdx.x * blockDim.x + threadIdx.x;
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const int _b = idx / C;
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const int _c = idx % C;
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const int _offset = _b * T * C + _c;
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F u = _u[_c];
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F w = _w[_c];
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const F *__restrict__ const k = _k + _offset;
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const F *__restrict__ const v = _v + _offset;
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F *__restrict__ const y = _y + _offset;
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// aa and bb are running sums divided by exp(pp) (to avoid overflow)
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F aa = 0, bb = 0, pp = MIN_VALUE;
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for (int i = 0; i < T; i++) {
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const int ii = i * C;
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const F kk = k[ii];
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const F vv = v[ii];
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F ww = u + kk;
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F p = max(pp, ww);
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F e1 = exp(pp - p);
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F e2 = exp(ww - p);
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y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2);
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ww = w + pp;
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p = max(ww, kk);
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e1 = exp(ww - p);
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e2 = exp(kk - p);
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aa = e1 * aa + e2 * vv;
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bb = e1 * bb + e2;
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pp = p;
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}
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}
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template <typename F>
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__global__ void kernel_forward_with_state(
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const int B, const int T, const int C, const F *__restrict__ const _w, const F *__restrict__ const _u,
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const F *__restrict__ const _k, const F *__restrict__ const _v, F *__restrict__ const _y, F *__restrict__ const _s
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) {
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const int idx = blockIdx.x * blockDim.x + threadIdx.x;
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const int _b = idx / C;
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const int _c = idx % C;
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const int _offset_s = _b * C * 3 + _c * 3;
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const int _offset = _b * T * C + _c;
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F u = _u[_c];
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F w = _w[_c];
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const F *__restrict__ const k = _k + _offset;
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const F *__restrict__ const v = _v + _offset;
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F *__restrict__ const y = _y + _offset;
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F *__restrict__ const s = _s + _offset_s;
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// aa and bb are running sums divided by exp(pp) (to avoid overflow)
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F aa = s[0], bb = s[1], pp = s[2];
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for (int i = 0; i < T; i++) {
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const int ii = i * C;
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const F kk = k[ii];
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const F vv = v[ii];
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F ww = u + kk;
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F p = max(pp, ww);
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F e1 = exp(pp - p);
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F e2 = exp(ww - p);
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y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2);
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ww = w + pp;
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p = max(ww, kk);
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e1 = exp(ww - p);
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e2 = exp(kk - p);
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aa = e1 * aa + e2 * vv;
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bb = e1 * bb + e2;
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pp = p;
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}
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s[0] = aa;
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s[1] = bb;
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s[2] = pp;
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}
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template <typename F>
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__global__ void kernel_backward(
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const int B, const int T, const int C, const F *__restrict__ const _w, const F *__restrict__ const _u,
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const F *__restrict__ const _k, const F *__restrict__ const _v, const F *__restrict__ const _y,
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const F *__restrict__ const _gy, F *__restrict__ const _gw, F *__restrict__ const _gu, F *__restrict__ const _gk,
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F *__restrict__ const _gv
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) {
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const int idx = blockIdx.x * blockDim.x + threadIdx.x;
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const int _b = idx / C;
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const int _c = idx % C;
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const int _offset = _b * T * C + _c;
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F u = _u[_c];
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F w = _w[_c];
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const F *__restrict__ const k = _k + _offset;
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const F *__restrict__ const v = _v + _offset;
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const F *__restrict__ const y = _y + _offset;
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const F *__restrict__ const gy = _gy + _offset;
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F *__restrict__ const gk = _gk + _offset;
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F *__restrict__ const gv = _gv + _offset;
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F q[Tmax], r[Tmax];
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F gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE;
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for (int i = 0; i < T; i++) {
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const int ii = i * C;
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const F kk = k[ii];
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const F vv = v[ii];
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const F yy = y[ii];
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F ww = u + kk;
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F p = max(pp, ww);
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F e1 = exp(pp - p);
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F e2 = exp(ww - p);
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const F qq = gy[ii] / (e1 * bb + e2);
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gw += (ga - gb * yy) * e1 * qq;
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gu += (vv - yy) * e2 * qq;
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q[i] = qq;
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r[i] = ww - p;
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ww = w + pp;
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p = max(ww, kk);
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e1 = exp(ww - p);
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e2 = exp(kk - p);
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ga = e1 * (aa + ga);
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gb = e1 * (bb + gb);
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aa = e1 * aa + e2 * vv;
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bb = e1 * bb + e2;
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pp = p;
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}
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const int _offsetBC = _b * C + _c;
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_gw[_offsetBC] = gw * _w[_c]; // multiply by w because of w -> -exp(w) in python forward()
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_gu[_offsetBC] = gu;
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aa = 0, bb = 0, pp = MIN_VALUE;
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for (int i = T - 1; i >= 0; i--) {
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const int ii = i * C;
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const F kk = k[ii];
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const F vv = v[ii];
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const F yy = y[ii];
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const F qq = q[i];
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const F rr = r[i];
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F e1 = qq * exp(rr);
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F e2 = exp(kk + pp);
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gk[ii] = e1 * (vv - yy) + e2 * (aa * vv + bb);
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gv[ii] = e1 + e2 * aa;
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const F ww = w + pp;
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const F www = rr - u - kk;
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const F p = max(ww, www);
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e1 = exp(ww - p);
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e2 = qq * exp(www - p);
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aa = e1 * aa + e2;
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bb = e1 * bb - e2 * yy;
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pp = p;
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}
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}
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void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y) {
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dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
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assert(B * C % threadsPerBlock.x == 0);
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dim3 numBlocks(B * C / threadsPerBlock.x);
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kernel_forward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
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}
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void cuda_forward_with_state(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *s) {
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dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
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assert(B * C % threadsPerBlock.x == 0);
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dim3 numBlocks(B * C / threadsPerBlock.x);
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kernel_forward_with_state<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, s);
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}
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void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv) {
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dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
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assert(B * C % threadsPerBlock.x == 0);
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dim3 numBlocks(B * C / threadsPerBlock.x);
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kernel_backward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv);
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}
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