piiswrong commented on a change in pull request #10311: [MXNET-107]Fused GRU
implementation for CPU
URL: https://github.com/apache/incubator-mxnet/pull/10311#discussion_r192892126
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File path: src/operator/rnn_impl.h
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@@ -454,4 +458,796 @@ void LstmBackward(DType* ws,
dy_ptr = dx.dptr_;
}
}
+
+template<typename DType>
+void GruForwardInferenceSingleLayer(DType* ws,
+ DType* tmp_buf,
+ bool state_outputs,
+ const int D,
+ const int T,
+ const int N,
+ const int I,
+ const int H,
+ const Tensor<cpu, 2, DType> &x,
+ const Tensor<cpu, 2, DType> &hx,
+ DType* wx_ptr,
+ DType* wh_ptr,
+ DType* bx_ptr,
+ DType* bh_ptr,
+ DType* y_ptr,
+ DType* hy_ptr) {
+ DType* ht = y_ptr;
+ DType* ht_1 = y_ptr;
+ DType* back_ht_1 = y_ptr + (T-1) * N * H * D + H;
+ DType* back_ht = back_ht_1;
+ DType* gemmC1 = ws; // [D, T, N, 3 * H]
+ DType* gemmC2 = gemmC1 + D * T * N * 3 * H; // N * 3 * H
+ DType* rt = gemmC2 + N * 3 * H;
+ DType* zt = rt + N * H;
+ DType* nt = zt + N * H;
+ DType* back_wx_ptr = wx_ptr + I * 3 * H + H * 3 * H;
+ DType* back_wh_ptr = wh_ptr + I * 3 * H + H * 3 * H;
+ DType* back_bx_ptr = (bx_ptr != NULL)? bx_ptr + 3 * H * 2 : NULL;
+ DType* back_bh_ptr = (bh_ptr != NULL)? bh_ptr + 3 * H * 2: NULL;
+ DType* back_gemmC1 = gemmC1 + T * N * 3 * H;
+ DType* gemmC1_t = gemmC1;
+
+ const Tensor<cpu, 2, DType> wx(wx_ptr, Shape2(H * 3, I));
+ const Tensor<cpu, 2, DType> wh(wh_ptr, Shape2(H * 3, H));
+ const Tensor<cpu, 2, DType> bx(bx_ptr, Shape2(3, H));
+ const Tensor<cpu, 2, DType> bh(bh_ptr, Shape2(3, H));
+ const Tensor<cpu, 2, DType> back_wx(back_wx_ptr, Shape2(H * 3, I));
+ const Tensor<cpu, 2, DType> back_wh(back_wh_ptr, Shape2(H * 3, H));
+ const Tensor<cpu, 2, DType> back_bx(back_bx_ptr, Shape2(3, H));
+ const Tensor<cpu, 2, DType> back_bh(back_bh_ptr, Shape2(3, H));
+ const int omp_threads =
mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+ if (D == 1) {
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ y_ptr[i * H + j] = hx[i][j];
+ }
+ } else {
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ y_ptr[i * D * H + j] = hx[i][j];
+ back_ht_1[i * D * H + j] = hx[N + i][j];
+ }
+ }
+ Tensor<cpu, 2, DType> dgemmC1(ws, Shape2(T * N, 3 * H));
+ Tensor<cpu, 2, DType> dgemmC2(gemmC2, Shape2(N, 3 * H));
+ Tensor<cpu, 2, DType> dback_gemmC1(back_gemmC1, Shape2(T * N, 3 * H));
+
+ // x * wx.T : [T * N, I] * [I, 3 * H]
+ DType alpha = 1.0;
+ DType beta = 0.0;
+ linalg_gemm(x, wx, dgemmC1, alpha, beta, false, true);
+ if (D == 2) {
+ linalg_gemm(x, back_wx, dback_gemmC1, alpha, beta, false, true);
+ }
+
+ for (int t = 0; t < T; t++) {
+ // perform the first direction, X * wx and H * wh for each step
+ // ht-1 * wh, ht-1:[N, H] wh:[3 * H, H]
+ Tensor<cpu, 2, DType> dht_1(ht_1, Shape2(N, D * H));
+ if (D == 1) {
+ linalg_gemm(dht_1, wh, dgemmC2, alpha, beta, false, true);
+ } else {
+ Tensor<cpu, 3, DType> dht_1_tmp = Tensor<cpu, 3,
DType>(reinterpret_cast<DType*>(tmp_buf),
+ Shape3(D, H, N));
+ dht_1_tmp = reshape(dht_1.T(), Shape3(D, H, N));
+ linalg_gemm(dht_1_tmp[0], wh, dgemmC2, alpha, beta, true, true);
+ }
+ gemmC1_t = gemmC1 + t * N * 3 * H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; ++i) {
+ for (int j = 0; j < H; ++j) {
+ int rtb = i * 3 * H;
+ int ztb = i * 3 * H + H;
+ int ntb = i * 3 * H + 2 * H;
+ rt[i * H + j] = sigmoid(gemmC1_t[rtb + j] + gemmC2[rtb + j]
+ + bx[0][j] + bh[0][j]);
+ zt[i * H + j] = sigmoid(gemmC1_t[ztb + j] + gemmC2[ztb + j]
+ + bx[1][j] + bh[1][j]);
+ nt[i * H + j] = tanh(gemmC1_t[ntb + j] + bx[2][j] +
+ rt[i * H + j] * (gemmC2[ntb + j] + bh[2][j]));
+ ht[i * D * H + j] = (1-zt[i * H + j]) * nt[i * H + j] +
+ zt[i * H + j] * ht_1[i * D * H + j];
+ }
+ }
+ ht_1 = ht;
+ ht = ht + D * H * N;
+ // perform the second direction
+ if (D == 2) {
+ gemmC1_t = back_gemmC1 + (T - 1 - t) * N * 3 * H;
+ Tensor<cpu, 2, DType> dback_ht_1(back_ht_1, Shape2(N, D * H));
+ Tensor<cpu, 3, DType> dback_ht_1_tmp = Tensor<cpu, 3, DType>
+ (reinterpret_cast<DType*>(tmp_buf), Shape3(D, H, N));
+ dback_ht_1_tmp = reshape(dback_ht_1.T(), Shape3(D, H, N));
+ linalg_gemm(dback_ht_1_tmp[0], back_wh, dgemmC2, alpha, beta, true,
true);
+
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; ++i) {
+ for (int j = 0; j < H; ++j) {
+ int rtb = i * 3 * H;
+ int ztb = i * 3 * H + H;
+ int ntb = i * 3 * H + 2 * H;
+ rt[i * H + j] = sigmoid(gemmC1_t[rtb + j] +
+ gemmC2[rtb + j] + back_bx[0][j] + back_bh[0][j]);
+ zt[i * H + j] = sigmoid(gemmC1_t[ztb + j] +
+ gemmC2[ztb + j] + back_bx[1][j]+ back_bh[1][j]);
+ nt[i * H + j] = tanh(gemmC1_t[ntb + j] + back_bx[2][j]
+ + rt[i * H + j] * (gemmC2[ntb + j] + back_bh[2][j]));
+ back_ht[i * D * H + j] = (1 - zt[i * H + j]) * nt[i * H + j]
+ + zt[i * H + j] * back_ht_1[i * D * H + j];
+ }
+ }
+ back_ht_1 = back_ht;
+ back_ht = back_ht - D * H * N;
+ }
+ }
+ // copy last state to hy, from(N, H * D) to (D, N, H)
+ if (state_outputs) {
+ if (D == 1) {
+ DType* y_start = y_ptr + (T - 1) * N * H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ hy_ptr[i * H + j] = y_start[i * H + j];
+ }
+ } else {
+ DType* y_start = y_ptr + (T - 1) * N * H * D;
+ DType* y_back_start = y_ptr + H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ hy_ptr[i * H + j] = y_start[i * D * H + j];
+ hy_ptr[N * H + i * H + j] = y_back_start[i * D * H + j];
+ }
+ }
+ }
+}
+
+template <typename DType>
+void GruForwardInference(DType* ws,
+ bool state_outputs,
+ const int L,
+ const int D,
+ const int T,
+ const int N,
+ int I,
+ const int H,
+ DType* x_ptr,
+ DType* hx_ptr,
+ DType* w_ptr,
+ DType* y_ptr,
+ DType* hy_ptr) {
+ DType* wx = w_ptr;
+ DType* wh = wx + I * H * 3;
+ DType* bx = wh + H * H * 3 + (D - 1) * (H * H * 3 + I * H * 3)
+ + (L - 1) * ((D + 1) * H) * H * 3 * D;
+ DType* bh = bx + H * 3;
+
+ DType* y_tmp = ws;
+ DType* y_l = x_ptr;
+ DType* tmp_buf = y_tmp + D * T * N * H;
+ DType* ws2 = y_tmp + D * T * N * H + D * H * N;
+
+ DType* wx_l = wx;
+ DType* wh_l = wh;
+ DType* bx_l = bx;
+ DType* bh_l = bh;
+ Tensor<cpu, 3, DType> hx(hx_ptr, Shape3(D * L, N, H));
+ DType* hy_l = hy_ptr;
+ for (int l = 0; l < L; l++) {
+ Tensor<cpu, 2, DType> x_l(y_l, Shape2(T * N, I));
+ if ((L + l) % 2) {
+ y_l = y_ptr;
+ } else {
+ y_l = y_tmp;
+ }
+ Tensor<cpu, 2, DType> hx_l = hx[D * l];
+ GruForwardInferenceSingleLayer<DType>(ws2, tmp_buf, state_outputs, D, T,
N, I, H,
+ x_l, hx_l, wx_l, wh_l, bx_l, bh_l,
y_l, hy_l);
+ hy_l = hy_l + D * N * H;
+ bx_l = bx_l + 3 * H * D * 2;
+ bh_l = bh_l + 3 * H * D * 2;
+ wx_l = wx_l + I * H * 3 * D + H * H * 3 * D;
+ if (l == 0) {
+ I = D * H;
+ }
+ wh_l = wx_l + I * 3 * H;
+ }
+}
+
+
+template<typename DType>
+void GruForwardTrainingSingleLayer(DType* ws,
+ DType* tmp_buf,
+ bool state_outputs,
+ const int D,
+ const int T,
+ const int N,
+ const int I,
+ const int H,
+ const Tensor<cpu, 2, DType> &x,
+ const Tensor<cpu, 2, DType> &hx,
+ DType* wx_ptr,
+ DType* wh_ptr,
+ DType* bx_ptr,
+ DType* bh_ptr,
+ DType* gateR,
+ DType* gateZ,
+ DType* gateN,
+ DType* Mnh,
+ DType* y_ptr,
+ DType* hy_ptr) {
+ DType* ht = y_ptr;
+ DType* ht_1 = y_ptr;
+ DType* back_ht_1 = y_ptr + (T - 1)* N * H * D + H;
+ DType* back_ht = back_ht_1;
+
+ DType* gemmC1 = ws; // [D, T, N, 3 * H]
+ DType* gemmC2 = gemmC1 + D * T * N * 3 * H; // N * 3 * H
+ DType* rt = gateR;
+ DType* zt = gateZ;
+ DType* nt = gateN;
+ DType* back_wx_ptr = wx_ptr + I * 3 * H + H * 3 * H;
+ DType* back_wh_ptr = wh_ptr + I * 3 * H + H * 3 * H;
+ DType* back_bx_ptr = (bx_ptr != NULL)? bx_ptr + 3 * H * 2 : NULL;
+ DType* back_bh_ptr = (bh_ptr != NULL)? bh_ptr + 3 * H * 2 : NULL;
+ DType* back_gateR = gateR + T * N * H;
+ DType* back_gateZ = gateZ + T * N * H;
+ DType* back_gateN = gateN + T * N * H;
+ DType* back_Mnh = Mnh + T * N * H;
+ DType* back_gemmC1 = gemmC1 + T * N * 3 * H;
+ DType* gemmC1_t = gemmC1;
+
+ const Tensor<cpu, 2, DType> wx(wx_ptr, Shape2(H * 3, I));
+ const Tensor<cpu, 2, DType> wh(wh_ptr, Shape2(H * 3, H));
+ const Tensor<cpu, 2, DType> bx(bx_ptr, Shape2(3, H));
+ const Tensor<cpu, 2, DType> bh(bh_ptr, Shape2(3, H));
+ const Tensor<cpu, 2, DType> back_wx(back_wx_ptr, Shape2(H * 3, I));
+ const Tensor<cpu, 2, DType> back_wh(back_wh_ptr, Shape2(H * 3, H));
+ const Tensor<cpu, 2, DType> back_bx(back_bx_ptr, Shape2(3, H));
+ const Tensor<cpu, 2, DType> back_bh(back_bh_ptr, Shape2(3, H));
+ const int omp_threads =
mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+ if (D == 1) {
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ y_ptr[i * H + j] = hx[i][j];
+ }
+ } else {
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ y_ptr[i * D * H + j] = hx[i][j];
+ back_ht_1[i * D * H + j] = hx[N + i][j];
+ }
+ }
+
+ Tensor<cpu, 2, DType> dgemmC1(ws, Shape2(T * N, 3 * H));
+ Tensor<cpu, 2, DType> dgemmC2(gemmC2, Shape2(N, 3 * H));
+ Tensor<cpu, 2, DType> dback_gemmC1(back_gemmC1, Shape2(T * N, 3 * H));
+
+ // x * wx.T : [T * N, I] * [I, 3 * H]
+ DType alpha = 1.0;
+ DType beta = 0.0;
+ linalg_gemm(x, wx, dgemmC1, alpha, beta, false, true);
+ if (D == 2) {
+ linalg_gemm(x, back_wx, dback_gemmC1, alpha, beta, false, true);
+ }
+
+ for (int t = 0; t < T; t++) {
+ // perform the first direction, X * wx and H * wh for each step
+ // ht-1 * wh, ht-1:[N, H] wh:[3 * H, H]
+ Tensor<cpu, 2, DType> dht_1(ht_1, Shape2(N, D * H));
+ if (D == 1) {
+ linalg_gemm(dht_1, wh, dgemmC2, alpha, beta, false, true);
+ } else {
+ Tensor<cpu, 3, DType> dht_1_tmp = Tensor<cpu, 3,
DType>(reinterpret_cast<DType*>(tmp_buf),
+ Shape3(D, H, N));
+ dht_1_tmp = reshape(dht_1.T(), Shape3(D, H, N));
+ linalg_gemm(dht_1_tmp[0], wh, dgemmC2, alpha, beta, true, true);
+ }
+ gemmC1_t = gemmC1 + t * N * 3 * H;
+
+ rt = gateR + t * N * H;
+ zt = gateZ + t * N * H;
+ nt = gateN + t * N * H;
+ gemmC1_t = gemmC1 + t * N * 3 * H;
+ DType* Mnht = Mnh + t * N * H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; ++i) {
+ for (int j = 0; j < H; ++j) {
+ int rtb = i * 3 * H;
+ int ztb = i * 3 * H + H;
+ int ntb = i * 3 * H + 2 * H;
+ Mnht[i * H + j] = gemmC2[ntb + j] + bh[2][j];
+ rt[i * H + j] = sigmoid(gemmC1_t[rtb + j] + gemmC2[rtb + j]
+ + bx[0][j] + bh[0][j]);
+ zt[i * H + j] = sigmoid(gemmC1_t[ztb + j] + gemmC2[ztb + j]
+ + bx[1][j] + bh[1][j]);
+ nt[i * H + j] = tanh(gemmC1_t[ntb + j] + bx[2][j] +
+ rt[i * H + j] * (gemmC2[ntb + j] + bh[2][j]));
+ ht[i * D * H + j] = (1-zt[i * H + j]) * nt[i * H + j] +
+ zt[i * H + j] * ht_1[i * D * H + j];
+ }
+ }
+ ht_1 = ht;
+ ht = ht + D * H * N;
+ // perform the second direction
+ if (D == 2) {
+ rt = back_gateR + (T - 1 - t) * N * H;
+ zt = back_gateZ + (T - 1 - t) * N * H;
+ nt = back_gateN + (T - 1 - t) * N * H;
+ gemmC1_t = back_gemmC1 + (T - 1 - t) * N * 3 * H;
+ Tensor<cpu, 2, DType> dback_ht_1(back_ht_1, Shape2(N, D * H));
+ Tensor<cpu, 3, DType> dback_ht_1_tmp = Tensor<cpu, 3, DType>
+ (reinterpret_cast<DType*>(tmp_buf), Shape3(D, H, N));
+ dback_ht_1_tmp = reshape(dback_ht_1.T(), Shape3(D, H, N));
+ linalg_gemm(dback_ht_1_tmp[0], back_wh, dgemmC2, alpha, beta, true,
true);
+
+ DType* back_Mnht = back_Mnh + (T - 1 - t) * N * H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; ++i) {
+ for (int j = 0; j < H; ++j) {
+ int rtb = i * 3 * H;
+ int ztb = i * 3 * H + H;
+ int ntb = i * 3 * H + 2 * H;
+ back_Mnht[i * H + j] = gemmC2[ntb + j] + back_bh[2][j];
+ rt[i * H + j] = sigmoid(gemmC1_t[rtb + j] +
+ gemmC2[rtb + j] + back_bx[0][j] + back_bh[0][j]);
+ zt[i * H + j] = sigmoid(gemmC1_t[ztb + j] +
+ gemmC2[ztb + j] + back_bx[1][j] + back_bh[1][j]);
+ nt[i * H + j] = tanh(gemmC1_t[ntb + j] + back_bx[2][j]
+ + rt[i * H + j] * (gemmC2[ntb + j] + back_bh[2][j]));
+ back_ht[i * D * H + j] = (1 - zt[i * H + j]) * nt[i * H + j]
+ + zt[i * H + j] * back_ht_1[i * D * H + j];
+ }
+ }
+ back_ht_1 = back_ht;
+ back_ht = back_ht - D * H * N;
+ }
+ }
+
+ // copy last state to hy, from(N, H * D) to (D, N, H)
+ if (state_outputs) {
+ if (D == 1) {
+ DType* y_start = y_ptr + (T - 1) * N * H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ hy_ptr[i * H + j] = y_start[i * H + j];
+ }
+ } else {
+ DType* y_start = y_ptr + (T - 1) * N * H * D;
+ DType* y_back_start = y_ptr + H;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < N; i++)
+ for (int j = 0; j < H; j++) {
+ hy_ptr[i * H + j] = y_start[i * D * H + j];
+ hy_ptr[N * H + i * H + j] = y_back_start[i * D * H + j];
+ }
+ }
+ }
+}
+
+template <typename DType>
+void GruForwardTraining(DType* ws,
+ DType* rs,
+ bool state_outputs,
+ const int L,
+ const int D,
+ const int T,
+ const int N,
+ int I,
+ const int H,
+ DType* x_ptr,
+ DType* hx_ptr,
+ DType* w_ptr,
+ DType* y_ptr,
+ DType* hy_ptr) {
+ DType* wx = w_ptr;
+ DType* wh = wx + I * H * 3;
+ DType* bx = wh + H * H * 3 + (D - 1) * (H * H * 3 + I * H * 3)
+ + (L - 1) * ((D + 1) * H) * H * 3 * D;
+ DType* bh = bx + H * 3;
+ Tensor<cpu, 3, DType> hx(hx_ptr, Shape3(D * L, N, H));
+ DType* hy_l = hy_ptr;
+ DType* gateR_l = rs;
+ DType* gateZ_l = gateR_l + L * T * D * N * H;
+ DType* gateN_l = gateZ_l + L * T * D * N * H;
+ DType* y_l = gateN_l + L * T * D * N * H;
+ DType* Mnh_l = y_l + L * T * N * H * D;
+ DType* tmp_buf = Mnh_l + L * D * T * N * H;
+ DType* ws2 = Mnh_l + L * D * T * N * H + D * H * N;
+ DType* wx_l = wx;
+ DType* wh_l = wh;
+ DType* bx_l = bx;
+ DType* bh_l = bh;
+ DType* y_tmp = x_ptr;
+
+ for (int l = 0; l < L; l++) {
+ if (l != 0) {
+ y_tmp = y_l;
+ y_l = y_l + T * N * H * D;
+ }
+ Tensor<cpu, 2, DType> x_l(y_tmp, Shape2(T * N, I));
+ Tensor<cpu, 2, DType> hx_l = hx[D * l];
+ GruForwardTrainingSingleLayer<DType>(ws2, tmp_buf, state_outputs, D, T, N,
I, H,
+ x_l, hx_l, wx_l, wh_l, bx_l, bh_l,
+ gateR_l, gateZ_l, gateN_l, Mnh_l,
y_l, hy_l);
+ gateR_l = gateR_l + T * D * N * H;
+ gateZ_l = gateZ_l + T * D * N * H;
+ gateN_l = gateN_l + T * D * N * H;
+ Mnh_l = Mnh_l + T * D * N * H;
+ hy_l = hy_l + D * N * H;
+ bx_l = bx_l + 3 * H * D * 2;
+ bh_l = bh_l + 3 * H * D * 2;
+
+ wx_l = wx_l + I * H * 3 * D + H * H * 3 * D;
+ if (l == 0) {
+ I = D * H;
+ }
+ wh_l = wx_l + I * 3 * H;
+ }
+ memcpy(y_ptr, y_l, T * N * H * D * sizeof(DType));
+}
+
+template <typename DType>
+void GruBackwardSingleLayer(DType* ws,
+ DType* tmp_buf,
+ const int D,
+ const int T,
+ const int N,
+ const int I,
+ const int H,
+ const Tensor<cpu, 2, DType> &x,
+ const Tensor<cpu, 2, DType> &hx,
+ DType* wx_ptr,
+ DType* wh_ptr,
+ DType* y_ptr,
+ DType* dy_ptr,
+ DType* dhy_ptr,
+ DType* gateR,
+ DType* gateZ,
+ DType* gateN,
+ DType* Mnh,
+ DType* dx,
+ DType* dhx,
+ DType* dwx,
+ DType* dwh,
+ DType* dbx,
+ DType* dbh) {
+ DType* dyt;
+ DType* ht1; // [N, D, H]
+ DType* rt;
+ DType* zt;
+ DType* nt;
+ DType* dat;
+ DType* dart;
+ DType* dar = ws; // [T, N, 3 * H]
+ DType* da = dar + T * N * 3 * H; // [T, N, 3 * H]
+ DType* dht1 = da + T * N * 3 * H; // [D, N, H]
+ DType* hx_ = dht1 + D * N * H; // [N, D, H]
+ DType* Mnht = Mnh;
+
+ DType* back_ht1;
+ DType* back_dht1 = dht1 + N * H; // [N, H]
+ DType* back_Mnht = Mnh + T * N * H;
+ DType* back_gateR = gateR + T * N * H;
+ DType* back_gateZ = gateZ + T * N * H;
+ DType* back_gateN = gateN + T * N * H;
+ DType* back_wx_ptr = wx_ptr + I * 3 * H + H * 3 * H;
+ DType* back_wh_ptr = wh_ptr + I * 3 * H + H * 3 * H;
+ DType* back_dwx = dwx + I * 3 * H + H * 3 * H;
+ DType* back_dwh = dwh + I * 3 * H + H * 3 * H;
+ DType* back_dbx = dbx + 3 * H * 2;
+ DType* back_dbh = dbh + 3 * H * 2;
+
+ DType alpha = 1.0;
+ DType beta = 0.0;
+ const Tensor<cpu, 2, DType> wx(wx_ptr, Shape2(H * 3, I));
+ const Tensor<cpu, 2, DType> wh(wh_ptr, Shape2(H * 3, H));
+ const Tensor<cpu, 2, DType> back_wx(back_wx_ptr, Shape2(H * 3, I));
+ const Tensor<cpu, 2, DType> back_wh(back_wh_ptr, Shape2(H * 3, H));
+ const int omp_threads =
mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int i = 0; i < D * H * 3 * H; ++i) {
+ dwh[i] = 0;
Review comment:
This is still overwriting gradient even when req[kParams] = kAddTo ?
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