// Runs forward propagation of activations on the input line.
// See NetworkCpp for a detailed discussion of the arguments.
void Maxpool::Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output) {
output->ResizeScaled(input, x_scale_, y_scale_, no_);
maxes_.ResizeNoInit(output->Width(), ni_);
back_map_ = input.stride_map();
StrideMap::Index dest_index(output->stride_map());
do {
int out_t = dest_index.t();
StrideMap::Index src_index(input.stride_map(), dest_index.index(FD_BATCH),
dest_index.index(FD_HEIGHT) * y_scale_,
dest_index.index(FD_WIDTH) * x_scale_);
// Find the max input out of x_scale_ groups of y_scale_ inputs.
// Do it independently for each input dimension.
int* max_line = maxes_[out_t];
int in_t = src_index.t();
output->CopyTimeStepFrom(out_t, input, in_t);
for (int i = 0; i < ni_; ++i) {
max_line[i] = in_t;
}
for (int x = 0; x < x_scale_; ++x) {
for (int y = 0; y < y_scale_; ++y) {
StrideMap::Index src_xy(src_index);
if (src_xy.AddOffset(x, FD_WIDTH) && src_xy.AddOffset(y, FD_HEIGHT)) {
output->MaxpoolTimeStep(out_t, input, src_xy.t(), max_line);
}
}
}
} while (dest_index.Increment());
}
// Runs forward propagation of activations on the input line.
// See NetworkCpp for a detailed discussion of the arguments.
void Reconfig::Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output) {
output->ResizeScaled(input, x_scale_, y_scale_, no_);
back_map_ = input.stride_map();
StrideMap::Index dest_index(output->stride_map());
do {
int out_t = dest_index.t();
StrideMap::Index src_index(input.stride_map(), dest_index.index(FD_BATCH),
dest_index.index(FD_HEIGHT) * y_scale_,
dest_index.index(FD_WIDTH) * x_scale_);
// Stack x_scale_ groups of y_scale_ inputs together.
for (int x = 0; x < x_scale_; ++x) {
for (int y = 0; y < y_scale_; ++y) {
StrideMap::Index src_xy(src_index);
if (src_xy.AddOffset(x, FD_WIDTH) && src_xy.AddOffset(y, FD_HEIGHT)) {
output->CopyTimeStepGeneral(out_t, (x * y_scale_ + y) * ni_, ni_,
input, src_xy.t(), 0);
}
}
}
} while (dest_index.Increment());
}
// Runs backward propagation of errors on the deltas line.
// See NetworkCpp for a detailed discussion of the arguments.
bool Reconfig::Backward(bool debug, const NetworkIO& fwd_deltas,
NetworkScratch* scratch,
NetworkIO* back_deltas) {
back_deltas->ResizeToMap(fwd_deltas.int_mode(), back_map_, ni_);
StrideMap::Index src_index(fwd_deltas.stride_map());
do {
int in_t = src_index.t();
StrideMap::Index dest_index(back_deltas->stride_map(),
src_index.index(FD_BATCH),
src_index.index(FD_HEIGHT) * y_scale_,
src_index.index(FD_WIDTH) * x_scale_);
// Unstack x_scale_ groups of y_scale_ inputs that are together.
for (int x = 0; x < x_scale_; ++x) {
for (int y = 0; y < y_scale_; ++y) {
StrideMap::Index dest_xy(dest_index);
if (dest_xy.AddOffset(x, FD_WIDTH) && dest_xy.AddOffset(y, FD_HEIGHT)) {
back_deltas->CopyTimeStepGeneral(dest_xy.t(), 0, ni_, fwd_deltas,
in_t, (x * y_scale_ + y) * ni_);
}
}
}
} while (src_index.Increment());
return needs_to_backprop_;
}
// Runs backward propagation of errors on the deltas line.
// See NetworkCpp for a detailed discussion of the arguments.
bool LSTM::Backward(bool debug, const NetworkIO& fwd_deltas,
NetworkScratch* scratch,
NetworkIO* back_deltas) {
if (debug) DisplayBackward(fwd_deltas);
back_deltas->ResizeToMap(fwd_deltas.int_mode(), input_map_, ni_);
// ======Scratch space.======
// Output errors from deltas with recurrence from sourceerr.
NetworkScratch::FloatVec outputerr;
outputerr.Init(ns_, scratch);
// Recurrent error in the state/source.
NetworkScratch::FloatVec curr_stateerr, curr_sourceerr;
curr_stateerr.Init(ns_, scratch);
curr_sourceerr.Init(na_, scratch);
ZeroVector<double>(ns_, curr_stateerr);
ZeroVector<double>(na_, curr_sourceerr);
// Errors in the gates.
NetworkScratch::FloatVec gate_errors[WT_COUNT];
for (int g = 0; g < WT_COUNT; ++g) gate_errors[g].Init(ns_, scratch);
// Rotating buffers of width buf_width allow storage of the recurrent time-
// steps used only for true 2-D. Stores one full strip of the major direction.
int buf_width = Is2D() ? input_map_.Size(FD_WIDTH) : 1;
GenericVector<NetworkScratch::FloatVec> stateerr, sourceerr;
if (Is2D()) {
stateerr.init_to_size(buf_width, NetworkScratch::FloatVec());
sourceerr.init_to_size(buf_width, NetworkScratch::FloatVec());
for (int t = 0; t < buf_width; ++t) {
stateerr[t].Init(ns_, scratch);
sourceerr[t].Init(na_, scratch);
ZeroVector<double>(ns_, stateerr[t]);
ZeroVector<double>(na_, sourceerr[t]);
}
}
// Parallel-generated sourceerr from each of the gates.
NetworkScratch::FloatVec sourceerr_temps[WT_COUNT];
for (int w = 0; w < WT_COUNT; ++w)
sourceerr_temps[w].Init(na_, scratch);
int width = input_width_;
// Transposed gate errors stored over all timesteps for sum outer.
NetworkScratch::GradientStore gate_errors_t[WT_COUNT];
for (int w = 0; w < WT_COUNT; ++w) {
gate_errors_t[w].Init(ns_, width, scratch);
}
// Used only if softmax_ != NULL.
NetworkScratch::FloatVec softmax_errors;
NetworkScratch::GradientStore softmax_errors_t;
if (softmax_ != NULL) {
softmax_errors.Init(no_, scratch);
softmax_errors_t.Init(no_, width, scratch);
}
double state_clip = Is2D() ? 9.0 : 4.0;
#if DEBUG_DETAIL > 1
tprintf("fwd_deltas:%s\n", name_.string());
fwd_deltas.Print(10);
#endif
StrideMap::Index dest_index(input_map_);
dest_index.InitToLast();
// Used only by NT_LSTM_SUMMARY.
StrideMap::Index src_index(fwd_deltas.stride_map());
src_index.InitToLast();
do {
int t = dest_index.t();
bool at_last_x = dest_index.IsLast(FD_WIDTH);
// up_pos is the 2-D back step, down_pos is the 2-D fwd step, and are only
// valid if >= 0, which is true if 2d and not on the top/bottom.
int up_pos = -1;
int down_pos = -1;
if (Is2D()) {
if (dest_index.index(FD_HEIGHT) > 0) {
StrideMap::Index up_index(dest_index);
if (up_index.AddOffset(-1, FD_HEIGHT)) up_pos = up_index.t();
}
if (!dest_index.IsLast(FD_HEIGHT)) {
StrideMap::Index down_index(dest_index);
if (down_index.AddOffset(1, FD_HEIGHT)) down_pos = down_index.t();
}
}
// Index of the 2-D revolving buffers (sourceerr, stateerr).
int mod_t = Modulo(t, buf_width); // Current timestep.
// Zero the state in the major direction only at the end of every row.
if (at_last_x) {
ZeroVector<double>(na_, curr_sourceerr);
ZeroVector<double>(ns_, curr_stateerr);
}
// Setup the outputerr.
if (type_ == NT_LSTM_SUMMARY) {
if (dest_index.IsLast(FD_WIDTH)) {
fwd_deltas.ReadTimeStep(src_index.t(), outputerr);
src_index.Decrement();
} else {
ZeroVector<double>(ns_, outputerr);
}
} else if (softmax_ == NULL) {
fwd_deltas.ReadTimeStep(t, outputerr);
} else {
softmax_->BackwardTimeStep(fwd_deltas, t, softmax_errors,
softmax_errors_t.get(), outputerr);
}
if (!at_last_x)
AccumulateVector(ns_, curr_sourceerr + ni_ + nf_, outputerr);
if (down_pos >= 0)
AccumulateVector(ns_, sourceerr[mod_t] + ni_ + nf_ + ns_, outputerr);
// Apply the 1-d forget gates.
if (!at_last_x) {
const float* next_node_gf1 = node_values_[GF1].f(t + 1);
for (int i = 0; i < ns_; ++i) {
curr_stateerr[i] *= next_node_gf1[i];
}
}
if (Is2D() && t + 1 < width) {
for (int i = 0; i < ns_; ++i) {
if (which_fg_[t + 1][i] != 1) curr_stateerr[i] = 0.0;
}
if (down_pos >= 0) {
const float* right_node_gfs = node_values_[GFS].f(down_pos);
const double* right_stateerr = stateerr[mod_t];
for (int i = 0; i < ns_; ++i) {
if (which_fg_[down_pos][i] == 2) {
curr_stateerr[i] += right_stateerr[i] * right_node_gfs[i];
}
}
}
}
state_.FuncMultiply3Add<HPrime>(node_values_[GO], t, outputerr,
curr_stateerr);
// Clip stateerr_ to a sane range.
ClipVector<double>(ns_, -state_clip, state_clip, curr_stateerr);
#if DEBUG_DETAIL > 1
if (t + 10 > width) {
tprintf("t=%d, stateerr=", t);
for (int i = 0; i < ns_; ++i)
tprintf(" %g,%g,%g", curr_stateerr[i], outputerr[i],
curr_sourceerr[ni_ + nf_ + i]);
tprintf("\n");
}
#endif
// Matrix multiply to get the source errors.
PARALLEL_IF_OPENMP(GFS)
// Cell inputs.
node_values_[CI].FuncMultiply3<GPrime>(t, node_values_[GI], t,
curr_stateerr, gate_errors[CI]);
ClipVector(ns_, -kErrClip, kErrClip, gate_errors[CI].get());
gate_weights_[CI].VectorDotMatrix(gate_errors[CI], sourceerr_temps[CI]);
gate_errors_t[CI].get()->WriteStrided(t, gate_errors[CI]);
SECTION_IF_OPENMP
// Input Gates.
node_values_[GI].FuncMultiply3<FPrime>(t, node_values_[CI], t,
curr_stateerr, gate_errors[GI]);
ClipVector(ns_, -kErrClip, kErrClip, gate_errors[GI].get());
gate_weights_[GI].VectorDotMatrix(gate_errors[GI], sourceerr_temps[GI]);
gate_errors_t[GI].get()->WriteStrided(t, gate_errors[GI]);
SECTION_IF_OPENMP
// 1-D forget Gates.
if (t > 0) {
node_values_[GF1].FuncMultiply3<FPrime>(t, state_, t - 1, curr_stateerr,
gate_errors[GF1]);
ClipVector(ns_, -kErrClip, kErrClip, gate_errors[GF1].get());
gate_weights_[GF1].VectorDotMatrix(gate_errors[GF1],
sourceerr_temps[GF1]);
} else {
memset(gate_errors[GF1], 0, ns_ * sizeof(gate_errors[GF1][0]));
memset(sourceerr_temps[GF1], 0, na_ * sizeof(*sourceerr_temps[GF1]));
}
gate_errors_t[GF1].get()->WriteStrided(t, gate_errors[GF1]);
// 2-D forget Gates.
if (up_pos >= 0) {
node_values_[GFS].FuncMultiply3<FPrime>(t, state_, up_pos, curr_stateerr,
gate_errors[GFS]);
ClipVector(ns_, -kErrClip, kErrClip, gate_errors[GFS].get());
gate_weights_[GFS].VectorDotMatrix(gate_errors[GFS],
sourceerr_temps[GFS]);
} else {
memset(gate_errors[GFS], 0, ns_ * sizeof(gate_errors[GFS][0]));
memset(sourceerr_temps[GFS], 0, na_ * sizeof(*sourceerr_temps[GFS]));
}
if (Is2D()) gate_errors_t[GFS].get()->WriteStrided(t, gate_errors[GFS]);
SECTION_IF_OPENMP
// Output gates.
state_.Func2Multiply3<HFunc, FPrime>(node_values_[GO], t, outputerr,
gate_errors[GO]);
ClipVector(ns_, -kErrClip, kErrClip, gate_errors[GO].get());
gate_weights_[GO].VectorDotMatrix(gate_errors[GO], sourceerr_temps[GO]);
gate_errors_t[GO].get()->WriteStrided(t, gate_errors[GO]);
END_PARALLEL_IF_OPENMP
SumVectors(na_, sourceerr_temps[CI], sourceerr_temps[GI],
sourceerr_temps[GF1], sourceerr_temps[GO], sourceerr_temps[GFS],
curr_sourceerr);
back_deltas->WriteTimeStep(t, curr_sourceerr);
// Save states for use by the 2nd dimension only if needed.
if (Is2D()) {
CopyVector(ns_, curr_stateerr, stateerr[mod_t]);
CopyVector(na_, curr_sourceerr, sourceerr[mod_t]);
}
} while (dest_index.Decrement());
#if DEBUG_DETAIL > 2
for (int w = 0; w < WT_COUNT; ++w) {
tprintf("%s gate errors[%d]\n", name_.string(), w);
gate_errors_t[w].get()->PrintUnTransposed(10);
}
#endif
// Transposed source_ used to speed-up SumOuter.
NetworkScratch::GradientStore source_t, state_t;
source_t.Init(na_, width, scratch);
source_.Transpose(source_t.get());
state_t.Init(ns_, width, scratch);
state_.Transpose(state_t.get());
#ifdef _OPENMP
#pragma omp parallel for num_threads(GFS) if (!Is2D())
#endif
for (int w = 0; w < WT_COUNT; ++w) {
if (w == GFS && !Is2D()) continue;
gate_weights_[w].SumOuterTransposed(*gate_errors_t[w], *source_t, false);
}
if (softmax_ != NULL) {
softmax_->FinishBackward(*softmax_errors_t);
}
return needs_to_backprop_;
}
// Runs forward propagation of activations on the input line.
// See NetworkCpp for a detailed discussion of the arguments.
void LSTM::Forward(bool debug, const NetworkIO& input,
const TransposedArray* input_transpose,
NetworkScratch* scratch, NetworkIO* output) {
input_map_ = input.stride_map();
input_width_ = input.Width();
if (softmax_ != NULL)
output->ResizeFloat(input, no_);
else if (type_ == NT_LSTM_SUMMARY)
output->ResizeXTo1(input, no_);
else
output->Resize(input, no_);
ResizeForward(input);
// Temporary storage of forward computation for each gate.
NetworkScratch::FloatVec temp_lines[WT_COUNT];
for (int i = 0; i < WT_COUNT; ++i) temp_lines[i].Init(ns_, scratch);
// Single timestep buffers for the current/recurrent output and state.
NetworkScratch::FloatVec curr_state, curr_output;
curr_state.Init(ns_, scratch);
ZeroVector<double>(ns_, curr_state);
curr_output.Init(ns_, scratch);
ZeroVector<double>(ns_, curr_output);
// Rotating buffers of width buf_width allow storage of the state and output
// for the other dimension, used only when working in true 2D mode. The width
// is enough to hold an entire strip of the major direction.
int buf_width = Is2D() ? input_map_.Size(FD_WIDTH) : 1;
GenericVector<NetworkScratch::FloatVec> states, outputs;
if (Is2D()) {
states.init_to_size(buf_width, NetworkScratch::FloatVec());
outputs.init_to_size(buf_width, NetworkScratch::FloatVec());
for (int i = 0; i < buf_width; ++i) {
states[i].Init(ns_, scratch);
ZeroVector<double>(ns_, states[i]);
outputs[i].Init(ns_, scratch);
ZeroVector<double>(ns_, outputs[i]);
}
}
// Used only if a softmax LSTM.
NetworkScratch::FloatVec softmax_output;
NetworkScratch::IO int_output;
if (softmax_ != NULL) {
softmax_output.Init(no_, scratch);
ZeroVector<double>(no_, softmax_output);
int rounded_softmax_inputs = gate_weights_[CI].RoundInputs(ns_);
if (input.int_mode())
int_output.Resize2d(true, 1, rounded_softmax_inputs, scratch);
softmax_->SetupForward(input, NULL);
}
NetworkScratch::FloatVec curr_input;
curr_input.Init(na_, scratch);
StrideMap::Index src_index(input_map_);
// Used only by NT_LSTM_SUMMARY.
StrideMap::Index dest_index(output->stride_map());
do {
int t = src_index.t();
// True if there is a valid old state for the 2nd dimension.
bool valid_2d = Is2D();
if (valid_2d) {
StrideMap::Index dim_index(src_index);
if (!dim_index.AddOffset(-1, FD_HEIGHT)) valid_2d = false;
}
// Index of the 2-D revolving buffers (outputs, states).
int mod_t = Modulo(t, buf_width); // Current timestep.
// Setup the padded input in source.
source_.CopyTimeStepGeneral(t, 0, ni_, input, t, 0);
if (softmax_ != NULL) {
source_.WriteTimeStepPart(t, ni_, nf_, softmax_output);
}
source_.WriteTimeStepPart(t, ni_ + nf_, ns_, curr_output);
if (Is2D())
source_.WriteTimeStepPart(t, ni_ + nf_ + ns_, ns_, outputs[mod_t]);
if (!source_.int_mode()) source_.ReadTimeStep(t, curr_input);
// Matrix multiply the inputs with the source.
PARALLEL_IF_OPENMP(GFS)
// It looks inefficient to create the threads on each t iteration, but the
// alternative of putting the parallel outside the t loop, a single around
// the t-loop and then tasks in place of the sections is a *lot* slower.
// Cell inputs.
if (source_.int_mode())
gate_weights_[CI].MatrixDotVector(source_.i(t), temp_lines[CI]);
else
gate_weights_[CI].MatrixDotVector(curr_input, temp_lines[CI]);
FuncInplace<GFunc>(ns_, temp_lines[CI]);
SECTION_IF_OPENMP
// Input Gates.
if (source_.int_mode())
gate_weights_[GI].MatrixDotVector(source_.i(t), temp_lines[GI]);
else
gate_weights_[GI].MatrixDotVector(curr_input, temp_lines[GI]);
FuncInplace<FFunc>(ns_, temp_lines[GI]);
SECTION_IF_OPENMP
// 1-D forget gates.
if (source_.int_mode())
gate_weights_[GF1].MatrixDotVector(source_.i(t), temp_lines[GF1]);
else
gate_weights_[GF1].MatrixDotVector(curr_input, temp_lines[GF1]);
FuncInplace<FFunc>(ns_, temp_lines[GF1]);
// 2-D forget gates.
if (Is2D()) {
if (source_.int_mode())
gate_weights_[GFS].MatrixDotVector(source_.i(t), temp_lines[GFS]);
else
gate_weights_[GFS].MatrixDotVector(curr_input, temp_lines[GFS]);
FuncInplace<FFunc>(ns_, temp_lines[GFS]);
}
SECTION_IF_OPENMP
// Output gates.
if (source_.int_mode())
gate_weights_[GO].MatrixDotVector(source_.i(t), temp_lines[GO]);
else
gate_weights_[GO].MatrixDotVector(curr_input, temp_lines[GO]);
FuncInplace<FFunc>(ns_, temp_lines[GO]);
END_PARALLEL_IF_OPENMP
// Apply forget gate to state.
MultiplyVectorsInPlace(ns_, temp_lines[GF1], curr_state);
if (Is2D()) {
// Max-pool the forget gates (in 2-d) instead of blindly adding.
inT8* which_fg_col = which_fg_[t];
memset(which_fg_col, 1, ns_ * sizeof(which_fg_col[0]));
if (valid_2d) {
const double* stepped_state = states[mod_t];
for (int i = 0; i < ns_; ++i) {
if (temp_lines[GF1][i] < temp_lines[GFS][i]) {
curr_state[i] = temp_lines[GFS][i] * stepped_state[i];
which_fg_col[i] = 2;
}
}
}
}
MultiplyAccumulate(ns_, temp_lines[CI], temp_lines[GI], curr_state);
// Clip curr_state to a sane range.
ClipVector<double>(ns_, -kStateClip, kStateClip, curr_state);
if (IsTraining()) {
// Save the gate node values.
node_values_[CI].WriteTimeStep(t, temp_lines[CI]);
node_values_[GI].WriteTimeStep(t, temp_lines[GI]);
node_values_[GF1].WriteTimeStep(t, temp_lines[GF1]);
node_values_[GO].WriteTimeStep(t, temp_lines[GO]);
if (Is2D()) node_values_[GFS].WriteTimeStep(t, temp_lines[GFS]);
}
FuncMultiply<HFunc>(curr_state, temp_lines[GO], ns_, curr_output);
if (IsTraining()) state_.WriteTimeStep(t, curr_state);
if (softmax_ != NULL) {
if (input.int_mode()) {
int_output->WriteTimeStepPart(0, 0, ns_, curr_output);
softmax_->ForwardTimeStep(NULL, int_output->i(0), t, softmax_output);
} else {
softmax_->ForwardTimeStep(curr_output, NULL, t, softmax_output);
}
output->WriteTimeStep(t, softmax_output);
if (type_ == NT_LSTM_SOFTMAX_ENCODED) {
CodeInBinary(no_, nf_, softmax_output);
}
} else if (type_ == NT_LSTM_SUMMARY) {
// Output only at the end of a row.
if (src_index.IsLast(FD_WIDTH)) {
output->WriteTimeStep(dest_index.t(), curr_output);
dest_index.Increment();
}
} else {
output->WriteTimeStep(t, curr_output);
}
// Save states for use by the 2nd dimension only if needed.
if (Is2D()) {
CopyVector(ns_, curr_state, states[mod_t]);
CopyVector(ns_, curr_output, outputs[mod_t]);
}
// Always zero the states at the end of every row, but only for the major
// direction. The 2-D state remains intact.
if (src_index.IsLast(FD_WIDTH)) {
ZeroVector<double>(ns_, curr_state);
ZeroVector<double>(ns_, curr_output);
}
} while (src_index.Increment());
#if DEBUG_DETAIL > 0
tprintf("Source:%s\n", name_.string());
source_.Print(10);
tprintf("State:%s\n", name_.string());
state_.Print(10);
tprintf("Output:%s\n", name_.string());
output->Print(10);
#endif
if (debug) DisplayForward(*output);
}
