From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/lstm_layer.c | 1292 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 646 insertions(+), 646 deletions(-)
diff --git a/lib/detecter_tools/darknet/lstm_layer.c b/lib/detecter_tools/darknet/lstm_layer.c
index d4dc4b4..a794556 100644
--- a/lib/detecter_tools/darknet/lstm_layer.c
+++ b/lib/detecter_tools/darknet/lstm_layer.c
@@ -1,646 +1,646 @@
-#include "lstm_layer.h"
-#include "connected_layer.h"
-#include "utils.h"
-#include "dark_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)
-{
- fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
- batch = batch / steps;
- layer l = { (LAYER_TYPE)0 };
- l.batch = batch;
- l.type = LSTM;
- l.steps = steps;
- l.inputs = inputs;
- l.out_w = 1;
- l.out_h = 1;
- l.out_c = outputs;
-
- l.uf = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
- l.uf->batch = batch;
- if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size;
-
- l.ui = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
- l.ui->batch = batch;
- if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size;
-
- l.ug = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
- l.ug->batch = batch;
- if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size;
-
- l.uo = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
- l.uo->batch = batch;
- if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size;
-
- l.wf = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
- l.wf->batch = batch;
- if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size;
-
- l.wi = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
- l.wi->batch = batch;
- if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size;
-
- l.wg = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
- l.wg->batch = batch;
- if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size;
-
- l.wo = (layer*)xcalloc(1, sizeof(layer));
- fprintf(stderr, "\t\t");
- *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
- l.wo->batch = batch;
- if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size;
-
- l.batch_normalize = batch_normalize;
- l.outputs = outputs;
-
- l.output = (float*)xcalloc(outputs * batch * steps, sizeof(float));
- l.state = (float*)xcalloc(outputs * batch, sizeof(float));
-
- l.forward = forward_lstm_layer;
- l.update = update_lstm_layer;
- l.backward = backward_lstm_layer;
-
- l.prev_state_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.prev_cell_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.cell_cpu = (float*)xcalloc(batch*outputs*steps, sizeof(float));
-
- l.f_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.i_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.g_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.o_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.c_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.h_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.temp_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.temp2_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.temp3_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.dc_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
- l.dh_cpu = (float*)xcalloc(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.state_gpu = cuda_make_array(l.state, batch*l.outputs);
-
- 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, int batch, float learning_rate, float momentum, float decay)
-{
- update_connected_layer(*(l.wf), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.wi), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.wg), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.wo), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.uf), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.ui), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.ug), batch, learning_rate, momentum, decay);
- update_connected_layer(*(l.uo), batch, learning_rate, momentum, decay);
-}
-
-void forward_lstm_layer(layer l, network_state state)
-{
- network_state s = { 0 };
- s.train = state.train;
- s.workspace = state.workspace;
- 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 state)
-{
- network_state s = { 0 };
- s.train = state.train;
- s.workspace = state.workspace;
- 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, int batch, float learning_rate, float momentum, float decay, float loss_scale)
-{
- update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay, loss_scale);
- update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay, loss_scale);
-}
-
-void forward_lstm_layer_gpu(layer l, network_state state)
-{
- network_state s = { 0 };
- s.train = state.train;
- s.workspace = state.workspace;
- 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_ongpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1);
- fill_ongpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1);
- fill_ongpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1);
- fill_ongpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1);
-
- fill_ongpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1);
- fill_ongpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1);
- fill_ongpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1);
- fill_ongpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1);
- if (state.train) {
- fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
- }
-
- for (i = 0; i < l.steps; ++i) {
- s.input = 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 = state.input;
- 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_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
-
- activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
- activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
- activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
- activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
-
- copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1);
- activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
- mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1);
- copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1);
-
- state.input += 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 state)
-{
- network_state s = { 0 };
- s.train = state.train;
- s.workspace = state.workspace;
- 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_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_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1);
- copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1);
- if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
- copy_ongpu(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_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
- axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
-
- activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
- activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
- activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
- activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
-
- copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
- activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
-
- copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1);
-
- gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu);
- axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1);
-
- copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
- activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
- mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);
- gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1);
- s.input = l.prev_state_gpu;
- s.delta = l.dh_gpu;
- backward_connected_layer_gpu(wo, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1);
- s.input = state.input;
- s.delta = state.delta;
- backward_connected_layer_gpu(uo, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
- gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu);
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1);
- s.input = l.prev_state_gpu;
- s.delta = l.dh_gpu;
- backward_connected_layer_gpu(wg, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1);
- s.input = state.input;
- s.delta = state.delta;
- backward_connected_layer_gpu(ug, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
- gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1);
- s.input = l.prev_state_gpu;
- s.delta = l.dh_gpu;
- backward_connected_layer_gpu(wi, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1);
- s.input = state.input;
- s.delta = state.delta;
- backward_connected_layer_gpu(ui, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1);
- gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1);
- s.input = l.prev_state_gpu;
- s.delta = l.dh_gpu;
- backward_connected_layer_gpu(wf, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1);
- s.input = state.input;
- s.delta = state.delta;
- backward_connected_layer_gpu(uf, s);
-
- copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
- mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1);
- copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1);
-
- state.input -= l.inputs*l.batch;
- if (state.delta) state.delta -= 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
+#include "lstm_layer.h"
+#include "connected_layer.h"
+#include "utils.h"
+#include "dark_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)
+{
+ fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs);
+ batch = batch / steps;
+ layer l = { (LAYER_TYPE)0 };
+ l.batch = batch;
+ l.type = LSTM;
+ l.steps = steps;
+ l.inputs = inputs;
+ l.out_w = 1;
+ l.out_h = 1;
+ l.out_c = outputs;
+
+ l.uf = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
+ l.uf->batch = batch;
+ if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size;
+
+ l.ui = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
+ l.ui->batch = batch;
+ if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size;
+
+ l.ug = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
+ l.ug->batch = batch;
+ if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size;
+
+ l.uo = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize);
+ l.uo->batch = batch;
+ if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size;
+
+ l.wf = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
+ l.wf->batch = batch;
+ if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size;
+
+ l.wi = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
+ l.wi->batch = batch;
+ if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size;
+
+ l.wg = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
+ l.wg->batch = batch;
+ if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size;
+
+ l.wo = (layer*)xcalloc(1, sizeof(layer));
+ fprintf(stderr, "\t\t");
+ *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize);
+ l.wo->batch = batch;
+ if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size;
+
+ l.batch_normalize = batch_normalize;
+ l.outputs = outputs;
+
+ l.output = (float*)xcalloc(outputs * batch * steps, sizeof(float));
+ l.state = (float*)xcalloc(outputs * batch, sizeof(float));
+
+ l.forward = forward_lstm_layer;
+ l.update = update_lstm_layer;
+ l.backward = backward_lstm_layer;
+
+ l.prev_state_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.prev_cell_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.cell_cpu = (float*)xcalloc(batch*outputs*steps, sizeof(float));
+
+ l.f_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.i_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.g_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.o_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.c_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.h_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.temp_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.temp2_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.temp3_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.dc_cpu = (float*)xcalloc(batch*outputs, sizeof(float));
+ l.dh_cpu = (float*)xcalloc(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.state_gpu = cuda_make_array(l.state, batch*l.outputs);
+
+ 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, int batch, float learning_rate, float momentum, float decay)
+{
+ update_connected_layer(*(l.wf), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.wi), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.wg), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.wo), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.uf), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.ui), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.ug), batch, learning_rate, momentum, decay);
+ update_connected_layer(*(l.uo), batch, learning_rate, momentum, decay);
+}
+
+void forward_lstm_layer(layer l, network_state state)
+{
+ network_state s = { 0 };
+ s.train = state.train;
+ s.workspace = state.workspace;
+ 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 state)
+{
+ network_state s = { 0 };
+ s.train = state.train;
+ s.workspace = state.workspace;
+ 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, int batch, float learning_rate, float momentum, float decay, float loss_scale)
+{
+ update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay, loss_scale);
+ update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay, loss_scale);
+}
+
+void forward_lstm_layer_gpu(layer l, network_state state)
+{
+ network_state s = { 0 };
+ s.train = state.train;
+ s.workspace = state.workspace;
+ 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_ongpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1);
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1);
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1);
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1);
+
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1);
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1);
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1);
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1);
+ if (state.train) {
+ fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1);
+ }
+
+ for (i = 0; i < l.steps; ++i) {
+ s.input = 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 = state.input;
+ 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_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
+
+ activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
+ activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
+ activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
+ activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
+
+ copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1);
+ activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH);
+ mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1);
+ copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1);
+
+ state.input += 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 state)
+{
+ network_state s = { 0 };
+ s.train = state.train;
+ s.workspace = state.workspace;
+ 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_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_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1);
+ copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1);
+ if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1);
+ copy_ongpu(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_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1);
+ axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1);
+
+ activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC);
+ activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC);
+ activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH);
+ activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC);
+
+ copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
+ activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
+
+ copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1);
+
+ gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu);
+ axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1);
+
+ copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1);
+ activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH);
+ mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1);
+ gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1);
+ s.input = l.prev_state_gpu;
+ s.delta = l.dh_gpu;
+ backward_connected_layer_gpu(wo, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1);
+ s.input = state.input;
+ s.delta = state.delta;
+ backward_connected_layer_gpu(uo, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1);
+ gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu);
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1);
+ s.input = l.prev_state_gpu;
+ s.delta = l.dh_gpu;
+ backward_connected_layer_gpu(wg, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1);
+ s.input = state.input;
+ s.delta = state.delta;
+ backward_connected_layer_gpu(ug, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1);
+ gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1);
+ s.input = l.prev_state_gpu;
+ s.delta = l.dh_gpu;
+ backward_connected_layer_gpu(wi, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1);
+ s.input = state.input;
+ s.delta = state.delta;
+ backward_connected_layer_gpu(ui, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1);
+ gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu);
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1);
+ s.input = l.prev_state_gpu;
+ s.delta = l.dh_gpu;
+ backward_connected_layer_gpu(wf, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1);
+ s.input = state.input;
+ s.delta = state.delta;
+ backward_connected_layer_gpu(uf, s);
+
+ copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1);
+ mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1);
+ copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1);
+
+ state.input -= l.inputs*l.batch;
+ if (state.delta) state.delta -= 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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