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darknet/src/lstm_layer.c

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#include "lstm_layer.h"
#include "connected_layer.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
#include "gemm.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
static void increment_layer(layer *l, int steps)
{
int num = l->outputs*l->batch*steps;
l->output += num;
l->delta += num;
l->x += num;
l->x_norm += num;
#ifdef GPU
l->output_gpu += num;
l->delta_gpu += num;
l->x_gpu += num;
l->x_norm_gpu += num;
#endif
}
layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam)
{
fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
batch = batch / steps;
layer l = { 0 };
l.batch = batch;
l.type = LSTM;
l.steps = steps;
l.inputs = inputs;
l.uf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
l.uf->batch = batch;
l.ui = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
l.ui->batch = batch;
l.ug = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
l.ug->batch = batch;
l.uo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam);
l.uo->batch = batch;
l.wf = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
l.wf->batch = batch;
l.wi = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
l.wi->batch = batch;
l.wg = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
l.wg->batch = batch;
l.wo = malloc(sizeof(layer));
fprintf(stderr, "\t\t");
*(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam);
l.wo->batch = batch;
l.batch_normalize = batch_normalize;
l.outputs = outputs;
l.output = calloc(outputs*batch*steps, sizeof(float));
l.state = calloc(outputs*batch, sizeof(float));
l.forward = forward_lstm_layer;
l.update = update_lstm_layer;
l.prev_state_cpu = calloc(batch*outputs, sizeof(float));
l.prev_cell_cpu = calloc(batch*outputs, sizeof(float));
l.cell_cpu = calloc(batch*outputs*steps, sizeof(float));
l.f_cpu = calloc(batch*outputs, sizeof(float));
l.i_cpu = calloc(batch*outputs, sizeof(float));
l.g_cpu = calloc(batch*outputs, sizeof(float));
l.o_cpu = calloc(batch*outputs, sizeof(float));
l.c_cpu = calloc(batch*outputs, sizeof(float));
l.h_cpu = calloc(batch*outputs, sizeof(float));
l.temp_cpu = calloc(batch*outputs, sizeof(float));
l.temp2_cpu = calloc(batch*outputs, sizeof(float));
l.temp3_cpu = calloc(batch*outputs, sizeof(float));
l.dc_cpu = calloc(batch*outputs, sizeof(float));
l.dh_cpu = calloc(batch*outputs, sizeof(float));
#ifdef GPU
l.forward_gpu = forward_lstm_layer_gpu;
l.backward_gpu = backward_lstm_layer_gpu;
l.update_gpu = update_lstm_layer_gpu;
l.output_gpu = cuda_make_array(0, batch*outputs*steps);
l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps);
l.prev_state_gpu = cuda_make_array(0, batch*outputs);
l.prev_cell_gpu = cuda_make_array(0, batch*outputs);
l.cell_gpu = cuda_make_array(0, batch*outputs*steps);
l.f_gpu = cuda_make_array(0, batch*outputs);
l.i_gpu = cuda_make_array(0, batch*outputs);
l.g_gpu = cuda_make_array(0, batch*outputs);
l.o_gpu = cuda_make_array(0, batch*outputs);
l.c_gpu = cuda_make_array(0, batch*outputs);
l.h_gpu = cuda_make_array(0, batch*outputs);
l.temp_gpu = cuda_make_array(0, batch*outputs);
l.temp2_gpu = cuda_make_array(0, batch*outputs);
l.temp3_gpu = cuda_make_array(0, batch*outputs);
l.dc_gpu = cuda_make_array(0, batch*outputs);
l.dh_gpu = cuda_make_array(0, batch*outputs);
#ifdef CUDNN
cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w);
cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w);
cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w);
cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w);
cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w);
cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w);
cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w);
cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w);
#endif
#endif
return l;
}
void update_lstm_layer(layer l, update_args a)
{
update_connected_layer(*(l.wf), a);
update_connected_layer(*(l.wi), a);
update_connected_layer(*(l.wg), a);
update_connected_layer(*(l.wo), a);
update_connected_layer(*(l.uf), a);
update_connected_layer(*(l.ui), a);
update_connected_layer(*(l.ug), a);
update_connected_layer(*(l.uo), a);
}
void forward_lstm_layer(layer l, network state)
{
network s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1);
fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1);
if (state.train) {
fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1);
}
for (i = 0; i < l.steps; ++i) {
s.input = l.h_cpu;
forward_connected_layer(wf, s);
forward_connected_layer(wi, s);
forward_connected_layer(wg, s);
forward_connected_layer(wo, s);
s.input = state.input;
forward_connected_layer(uf, s);
forward_connected_layer(ui, s);
forward_connected_layer(ug, s);
forward_connected_layer(uo, s);
copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1);
copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1);
copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1);
copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1);
activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.g_cpu, l.outputs*l.batch, TANH);
activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC);
copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1);
activate_array(l.h_cpu, l.outputs*l.batch, TANH);
mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1);
copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1);
state.input += l.inputs*l.batch;
l.output += l.outputs*l.batch;
l.cell_cpu += l.outputs*l.batch;
increment_layer(&wf, 1);
increment_layer(&wi, 1);
increment_layer(&wg, 1);
increment_layer(&wo, 1);
increment_layer(&uf, 1);
increment_layer(&ui, 1);
increment_layer(&ug, 1);
increment_layer(&uo, 1);
}
}
void backward_lstm_layer(layer l, network state)
{
network s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
increment_layer(&wf, l.steps - 1);
increment_layer(&wi, l.steps - 1);
increment_layer(&wg, l.steps - 1);
increment_layer(&wo, l.steps - 1);
increment_layer(&uf, l.steps - 1);
increment_layer(&ui, l.steps - 1);
increment_layer(&ug, l.steps - 1);
increment_layer(&uo, l.steps - 1);
state.input += l.inputs*l.batch*(l.steps - 1);
if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1);
l.output += l.outputs*l.batch*(l.steps - 1);
l.cell_cpu += l.outputs*l.batch*(l.steps - 1);
l.delta += l.outputs*l.batch*(l.steps - 1);
for (i = l.steps - 1; i >= 0; --i) {
if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1);
copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1);
if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1);
copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1);
l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch;
copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1);
copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1);
copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1);
copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1);
axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1);
activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC);
activate_array(l.g_cpu, l.outputs*l.batch, TANH);
activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC);
copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1);
activate_array(l.temp_cpu, l.outputs*l.batch, TANH);
copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1);
mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1);
gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu);
axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1);
copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1);
activate_array(l.temp_cpu, l.outputs*l.batch, TANH);
mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1);
gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wo, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(uo, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1);
gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wg, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(ug, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1);
gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wi, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(ui, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1);
gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1);
s.input = l.prev_state_cpu;
s.delta = l.dh_cpu;
backward_connected_layer(wf, s);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1);
s.input = state.input;
s.delta = state.delta;
backward_connected_layer(uf, s);
copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1);
mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1);
copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1);
state.input -= l.inputs*l.batch;
if (state.delta) state.delta -= l.inputs*l.batch;
l.output -= l.outputs*l.batch;
l.cell_cpu -= l.outputs*l.batch;
l.delta -= l.outputs*l.batch;
increment_layer(&wf, -1);
increment_layer(&wi, -1);
increment_layer(&wg, -1);
increment_layer(&wo, -1);
increment_layer(&uf, -1);
increment_layer(&ui, -1);
increment_layer(&ug, -1);
increment_layer(&uo, -1);
}
}
#ifdef GPU
void update_lstm_layer_gpu(layer l, update_args a)
{
update_connected_layer_gpu(*(l.wf), a);
update_connected_layer_gpu(*(l.wi), a);
update_connected_layer_gpu(*(l.wg), a);
update_connected_layer_gpu(*(l.wo), a);
update_connected_layer_gpu(*(l.uf), a);
update_connected_layer_gpu(*(l.ui), a);
update_connected_layer_gpu(*(l.ug), a);
update_connected_layer_gpu(*(l.uo), a);
}
void forward_lstm_layer_gpu(layer l, network state)
{
network s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
fill_gpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1);
fill_gpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1);
if (state.train) {
fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
}
for (i = 0; i < l.steps; ++i) {
s.input_gpu = l.h_gpu;
forward_connected_layer_gpu(wf, s);
forward_connected_layer_gpu(wi, s);
forward_connected_layer_gpu(wg, s);
forward_connected_layer_gpu(wo, s);
s.input_gpu = state.input_gpu;
forward_connected_layer_gpu(uf, s);
forward_connected_layer_gpu(ui, s);
forward_connected_layer_gpu(ug, s);
forward_connected_layer_gpu(uo, s);
copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH);
activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
copy_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1);
copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1);
activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH);
mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1);
copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1);
copy_gpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1);
state.input_gpu += l.inputs*l.batch;
l.output_gpu += l.outputs*l.batch;
l.cell_gpu += l.outputs*l.batch;
increment_layer(&wf, 1);
increment_layer(&wi, 1);
increment_layer(&wg, 1);
increment_layer(&wo, 1);
increment_layer(&uf, 1);
increment_layer(&ui, 1);
increment_layer(&ug, 1);
increment_layer(&uo, 1);
}
}
void backward_lstm_layer_gpu(layer l, network state)
{
network s = { 0 };
s.train = state.train;
int i;
layer wf = *(l.wf);
layer wi = *(l.wi);
layer wg = *(l.wg);
layer wo = *(l.wo);
layer uf = *(l.uf);
layer ui = *(l.ui);
layer ug = *(l.ug);
layer uo = *(l.uo);
increment_layer(&wf, l.steps - 1);
increment_layer(&wi, l.steps - 1);
increment_layer(&wg, l.steps - 1);
increment_layer(&wo, l.steps - 1);
increment_layer(&uf, l.steps - 1);
increment_layer(&ui, l.steps - 1);
increment_layer(&ug, l.steps - 1);
increment_layer(&uo, l.steps - 1);
state.input_gpu += l.inputs*l.batch*(l.steps - 1);
if (state.delta_gpu) state.delta_gpu += l.inputs*l.batch*(l.steps - 1);
l.output_gpu += l.outputs*l.batch*(l.steps - 1);
l.cell_gpu += l.outputs*l.batch*(l.steps - 1);
l.delta_gpu += l.outputs*l.batch*(l.steps - 1);
for (i = l.steps - 1; i >= 0; --i) {
if (i != 0) copy_gpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1);
copy_gpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1);
if (i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1);
l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch;
copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH);
activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);
copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH);
copy_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1);
gradient_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu);
axpy_gpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1);
copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH);
mul_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);
gradient_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1);
s.input_gpu = l.prev_state_gpu;
s.delta_gpu = l.dh_gpu;
backward_connected_layer_gpu(wo, s);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1);
s.input_gpu = state.input_gpu;
s.delta_gpu = state.delta_gpu;
backward_connected_layer_gpu(uo, s);
copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
gradient_array_gpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1);
s.input_gpu = l.prev_state_gpu;
s.delta_gpu = l.dh_gpu;
backward_connected_layer_gpu(wg, s);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1);
s.input_gpu = state.input_gpu;
s.delta_gpu = state.delta_gpu;
backward_connected_layer_gpu(ug, s);
copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
gradient_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1);
s.input_gpu = l.prev_state_gpu;
s.delta_gpu = l.dh_gpu;
backward_connected_layer_gpu(wi, s);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1);
s.input_gpu = state.input_gpu;
s.delta_gpu = state.delta_gpu;
backward_connected_layer_gpu(ui, s);
copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_gpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1);
gradient_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1);
s.input_gpu = l.prev_state_gpu;
s.delta_gpu = l.dh_gpu;
backward_connected_layer_gpu(wf, s);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1);
s.input_gpu = state.input_gpu;
s.delta_gpu = state.delta_gpu;
backward_connected_layer_gpu(uf, s);
copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1);
copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1);
state.input_gpu -= l.inputs*l.batch;
if (state.delta_gpu) state.delta_gpu -= l.inputs*l.batch;
l.output_gpu -= l.outputs*l.batch;
l.cell_gpu -= l.outputs*l.batch;
l.delta_gpu -= l.outputs*l.batch;
increment_layer(&wf, -1);
increment_layer(&wi, -1);
increment_layer(&wg, -1);
increment_layer(&wo, -1);
increment_layer(&uf, -1);
increment_layer(&ui, -1);
increment_layer(&ug, -1);
increment_layer(&uo, -1);
}
}
#endif
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