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gd.cc
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gd.cc
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// Copyright (c) by respective owners including Yahoo!, Microsoft, and
// individual contributors. All rights reserved. Released under a BSD (revised)
// license as described in the file LICENSE.
#include "crossplat_compat.h"
#include <cfloat>
#if !defined(VW_NO_INLINE_SIMD)
#if !defined(__SSE2__) && (defined(_M_AMD64) || defined(_M_X64))
#define __SSE2__
#endif
#if defined(__ARM_NEON__)
#include <arm_neon.h>
#elif defined(__SSE2__)
#include <xmmintrin.h>
#endif
#endif
#include "gd.h"
#include "accumulate.h"
#include "reductions.h"
#include "vw.h"
#define VERSION_SAVE_RESUME_FIX "7.10.1"
#define VERSION_PASS_UINT64 "8.3.3"
using namespace VW::LEARNER;
using namespace VW::config;
// todo:
// 4. Factor various state out of vw&
namespace GD
{
struct gd
{
// double normalized_sum_norm_x;
double total_weight;
size_t no_win_counter;
size_t early_stop_thres;
float initial_constant;
float neg_norm_power;
float neg_power_t;
float sparse_l2;
float update_multiplier;
void (*predict)(gd&, base_learner&, example&);
void (*learn)(gd&, base_learner&, example&);
void (*update)(gd&, base_learner&, example&);
float (*sensitivity)(gd&, base_learner&, example&);
void (*multipredict)(gd&, base_learner&, example&, size_t, size_t, polyprediction*, bool);
bool adaptive_input;
bool normalized_input;
bool adax;
vw* all; // parallel, features, parameters
};
void sync_weights(vw& all);
inline float quake_InvSqrt(float x)
{
// Carmack/Quake/SGI fast method:
float xhalf = 0.5f * x;
static_assert(sizeof(int) == sizeof(float), "Floats and ints are converted between, they must be the same size.");
int i = reinterpret_cast<int&>(x); // store floating-point bits in integer
i = 0x5f3759d5 - (i >> 1); // initial guess for Newton's method
x = reinterpret_cast<float&>(i); // convert new bits into float
x = x * (1.5f - xhalf * x * x); // One round of Newton's method
return x;
}
static inline float InvSqrt(float x)
{
#if !defined(VW_NO_INLINE_SIMD)
#if defined(__ARM_NEON__)
// Propagate into vector
float32x2_t v1 = vdup_n_f32(x);
// Estimate
float32x2_t e1 = vrsqrte_f32(v1);
// N-R iteration 1
float32x2_t e2 = vmul_f32(e1, vrsqrts_f32(v1, vmul_f32(e1, e1)));
// N-R iteration 2
float32x2_t e3 = vmul_f32(e2, vrsqrts_f32(v1, vmul_f32(e2, e2)));
// Extract result
return vget_lane_f32(e3, 0);
#elif defined(__SSE2__)
__m128 eta = _mm_load_ss(&x);
eta = _mm_rsqrt_ss(eta);
_mm_store_ss(&x, eta);
#else
x = quake_InvSqrt(x);
#endif
#else
x = quake_InvSqrt(x);
#endif
return x;
}
template <bool sqrt_rate, bool feature_mask_off, size_t adaptive, size_t normalized, size_t spare>
inline void update_feature(float& update, float x, float& fw)
{
weight* w = &fw;
if (feature_mask_off || fw != 0.)
{
if (spare != 0)
x *= w[spare];
w[0] += update * x;
}
}
// this deals with few nonzero features vs. all nonzero features issues.
template <bool sqrt_rate, size_t adaptive, size_t normalized>
float average_update(float total_weight, float normalized_sum_norm_x, float neg_norm_power)
{
if (normalized)
{
if (sqrt_rate)
{
float avg_norm = (float)(total_weight / normalized_sum_norm_x);
if (adaptive)
return std::sqrt(avg_norm);
else
return avg_norm;
}
else
return powf((float)(normalized_sum_norm_x / total_weight), neg_norm_power);
}
return 1.f;
}
template <bool sqrt_rate, bool feature_mask_off, size_t adaptive, size_t normalized, size_t spare>
void train(gd& g, example& ec, float update)
{
if (normalized)
update *= g.update_multiplier;
foreach_feature<float, update_feature<sqrt_rate, feature_mask_off, adaptive, normalized, spare> >(*g.all, ec, update);
}
void end_pass(gd& g)
{
vw& all = *g.all;
if (all.save_resume)
{
// TODO work out a better system to update state that will be saved in the model.
if (all.sd->gravity != 0.)
{
g.all->options->replace("l1_state", std::to_string(all.sd->gravity));
g.all->options->get_typed_option<double>("l1_state").value(all.sd->gravity);
}
if (all.sd->contraction != 1.)
{
g.all->options->replace("l2_state", std::to_string(all.sd->contraction));
g.all->options->get_typed_option<double>("l2_state").value(all.sd->contraction);
}
}
else
sync_weights(all);
if (all.all_reduce != nullptr)
{
if (all.weights.adaptive)
accumulate_weighted_avg(all, all.weights);
else
accumulate_avg(all, all.weights, 0);
}
all.eta *= all.eta_decay_rate;
if (all.save_per_pass)
save_predictor(all, all.final_regressor_name, all.current_pass);
if (!all.holdout_set_off)
{
if (summarize_holdout_set(all, g.no_win_counter))
finalize_regressor(all, all.final_regressor_name);
if ((g.early_stop_thres == g.no_win_counter) &&
((all.check_holdout_every_n_passes <= 1) || ((all.current_pass % all.check_holdout_every_n_passes) == 0)))
set_done(all);
}
}
#include <algorithm>
struct string_value
{
float v;
std::string s;
friend bool operator<(const string_value& first, const string_value& second);
};
bool operator<(const string_value& first, const string_value& second) { return fabsf(first.v) > fabsf(second.v); }
struct audit_results
{
vw& all;
const uint64_t offset;
std::vector<std::string> ns_pre;
std::vector<string_value> results;
audit_results(vw& p_all, const size_t p_offset) : all(p_all), offset(p_offset) {}
};
inline void audit_interaction(audit_results& dat, const audit_strings* f)
{
if (f == nullptr)
{
if (!dat.ns_pre.empty())
{
dat.ns_pre.pop_back();
}
return;
}
std::string ns_pre;
if (!dat.ns_pre.empty())
ns_pre += '*';
if (f->first != "" && ((f->first) != " "))
{
ns_pre.append(f->first);
ns_pre += '^';
}
if (f->second != "")
{
ns_pre.append(f->second);
}
if (!ns_pre.empty())
{
dat.ns_pre.push_back(ns_pre);
}
}
inline void audit_feature(audit_results& dat, const float ft_weight, const uint64_t ft_idx)
{
parameters& weights = dat.all.weights;
uint64_t index = ft_idx & weights.mask();
size_t stride_shift = weights.stride_shift();
std::string ns_pre;
for (std::string& s : dat.ns_pre) ns_pre += s;
if (dat.all.audit)
{
std::ostringstream tempstream;
tempstream << ':' << (index >> stride_shift) << ':' << ft_weight << ':'
<< trunc_weight(weights[index], (float)dat.all.sd->gravity) * (float)dat.all.sd->contraction;
if (weights.adaptive) // adaptive
tempstream << '@' << (&weights[index])[1];
string_value sv = {weights[index] * ft_weight, ns_pre + tempstream.str()};
dat.results.push_back(sv);
}
if ((dat.all.current_pass == 0 || dat.all.training == false) && dat.all.hash_inv)
{
// for invert_hash
if (dat.offset != 0)
{
// otherwise --oaa output no features for class > 0.
std::ostringstream tempstream;
tempstream << '[' << (dat.offset >> stride_shift) << ']';
ns_pre += tempstream.str();
}
const auto strided_index = index >> stride_shift;
if (!dat.all.index_name_map.count(strided_index))
dat.all.index_name_map.insert(std::make_pair(strided_index, ns_pre));
}
}
void print_lda_features(vw& all, example& ec)
{
parameters& weights = all.weights;
uint32_t stride_shift = weights.stride_shift();
size_t count = 0;
for (features& fs : ec) count += fs.size();
for (features& fs : ec)
{
for (features::iterator_all& f : fs.values_indices_audit())
{
std::cout << '\t' << f.audit()->get()->first << '^' << f.audit()->get()->second << ':'
<< ((f.index() >> stride_shift) & all.parse_mask) << ':' << f.value();
for (size_t k = 0; k < all.lda; k++) std::cout << ':' << (&weights[f.index()])[k];
}
}
std::cout << " total of " << count << " features." << std::endl;
}
void print_features(vw& all, example& ec)
{
if (all.lda > 0)
print_lda_features(all, ec);
else
{
audit_results dat(all, ec.ft_offset);
for (features& fs : ec)
{
if (fs.space_names.size() > 0)
for (features::iterator_all& f : fs.values_indices_audit())
{
audit_interaction(dat, f.audit()->get());
audit_feature(dat, f.value(), f.index() + ec.ft_offset);
audit_interaction(dat, NULL);
}
else
for (features::iterator& f : fs) audit_feature(dat, f.value(), f.index() + ec.ft_offset);
}
INTERACTIONS::generate_interactions<audit_results, const uint64_t, audit_feature, true, audit_interaction>(
all, ec, dat);
stable_sort(dat.results.begin(), dat.results.end());
if (all.audit)
{
for (string_value& sv : dat.results) std::cout << '\t' << sv.s;
std::cout << std::endl;
}
}
}
void print_audit_features(vw& all, example& ec)
{
if (all.audit)
print_result_by_ref(all.stdout_adapter.get(), ec.pred.scalar, -1, ec.tag);
fflush(stdout);
print_features(all, ec);
}
float finalize_prediction(shared_data* sd, vw_logger& logger, float ret)
{
if (std::isnan(ret))
{
ret = 0.;
if (!logger.quiet) {
std::cerr << "NAN prediction in example " << sd->example_number + 1 << ", forcing " << ret << std::endl;
}
return ret;
}
if (ret > sd->max_label)
return (float)sd->max_label;
if (ret < sd->min_label)
return (float)sd->min_label;
return ret;
}
struct trunc_data
{
float prediction;
float gravity;
};
inline void vec_add_trunc(trunc_data& p, const float fx, float& fw)
{
p.prediction += trunc_weight(fw, p.gravity) * fx;
}
inline float trunc_predict(vw& all, example& ec, double gravity)
{
trunc_data temp = {ec.l.simple.initial, (float)gravity};
foreach_feature<trunc_data, vec_add_trunc>(all, ec, temp);
return temp.prediction;
}
inline void vec_add_print(float& p, const float fx, float& fw)
{
p += fw * fx;
std::cerr << " + " << fw << "*" << fx;
}
template <bool l1, bool audit>
void predict(gd& g, base_learner&, example& ec)
{
vw& all = *g.all;
if (l1)
ec.partial_prediction = trunc_predict(all, ec, all.sd->gravity);
else
ec.partial_prediction = inline_predict(all, ec);
ec.partial_prediction *= (float)all.sd->contraction;
ec.pred.scalar = finalize_prediction(all.sd, all.logger, ec.partial_prediction);
if (audit)
print_audit_features(all, ec);
}
template <class T>
inline void vec_add_trunc_multipredict(multipredict_info<T>& mp, const float fx, uint64_t fi)
{
size_t index = fi;
for (size_t c = 0; c < mp.count; c++, index += mp.step)
mp.pred[c].scalar += fx * trunc_weight(mp.weights[index], mp.gravity);
}
template <bool l1, bool audit>
void multipredict(
gd& g, base_learner&, example& ec, size_t count, size_t step, polyprediction* pred, bool finalize_predictions)
{
vw& all = *g.all;
for (size_t c = 0; c < count; c++) pred[c].scalar = ec.l.simple.initial;
if (g.all->weights.sparse)
{
multipredict_info<sparse_parameters> mp = {
count, step, pred, g.all->weights.sparse_weights, (float)all.sd->gravity};
if (l1)
foreach_feature<multipredict_info<sparse_parameters>, uint64_t, vec_add_trunc_multipredict>(all, ec, mp);
else
foreach_feature<multipredict_info<sparse_parameters>, uint64_t, vec_add_multipredict>(all, ec, mp);
}
else
{
multipredict_info<dense_parameters> mp = {count, step, pred, g.all->weights.dense_weights, (float)all.sd->gravity};
if (l1)
foreach_feature<multipredict_info<dense_parameters>, uint64_t, vec_add_trunc_multipredict>(all, ec, mp);
else
foreach_feature<multipredict_info<dense_parameters>, uint64_t, vec_add_multipredict>(all, ec, mp);
}
if (all.sd->contraction != 1.)
for (size_t c = 0; c < count; c++) pred[c].scalar *= (float)all.sd->contraction;
if (finalize_predictions)
for (size_t c = 0; c < count; c++) pred[c].scalar = finalize_prediction(all.sd, all.logger, pred[c].scalar);
if (audit)
{
for (size_t c = 0; c < count; c++)
{
ec.pred.scalar = pred[c].scalar;
print_audit_features(all, ec);
ec.ft_offset += (uint64_t)step;
}
ec.ft_offset -= (uint64_t)(step * count);
}
}
struct power_data
{
float minus_power_t;
float neg_norm_power;
};
template <bool sqrt_rate, size_t adaptive, size_t normalized>
inline float compute_rate_decay(power_data& s, float& fw)
{
weight* w = &fw;
float rate_decay = 1.f;
if (adaptive)
{
if (sqrt_rate)
rate_decay = InvSqrt(w[adaptive]);
else
rate_decay = powf(w[adaptive], s.minus_power_t);
}
if (normalized)
{
if (sqrt_rate)
{
float inv_norm = 1.f / w[normalized];
if (adaptive)
rate_decay *= inv_norm;
else
rate_decay *= inv_norm * inv_norm;
}
else
rate_decay *= powf(w[normalized] * w[normalized], s.neg_norm_power);
}
return rate_decay;
}
struct norm_data
{
float grad_squared;
float pred_per_update;
float norm_x;
power_data pd;
float extra_state[4];
};
constexpr float x_min = 1.084202e-19f;
constexpr float x2_min = x_min * x_min;
constexpr float x2_max = FLT_MAX;
template <bool sqrt_rate, bool feature_mask_off, size_t adaptive, size_t normalized, size_t spare, bool stateless>
inline void pred_per_update_feature(norm_data& nd, float x, float& fw)
{
if (feature_mask_off || fw != 0.)
{
weight* w = &fw;
float x2 = x * x;
if (x2 < x2_min)
{
x = (x > 0) ? x_min : -x_min;
x2 = x2_min;
}
if (x2 > x2_max)
THROW("your features have too much magnitude");
if (stateless) // we must not modify the parameter state so introduce a shadow version.
{
nd.extra_state[0] = w[0];
nd.extra_state[adaptive] = w[adaptive];
nd.extra_state[normalized] = w[normalized];
w = nd.extra_state;
}
if (adaptive)
w[adaptive] += nd.grad_squared * x2;
if (normalized)
{
float x_abs = fabsf(x);
if (x_abs > w[normalized]) // new scale discovered
{
if (w[normalized] >
0.) // If the normalizer is > 0 then rescale the weight so it's as if the new scale was the old scale.
{
if (sqrt_rate)
{
float rescale = w[normalized] / x_abs;
w[0] *= (adaptive ? rescale : rescale * rescale);
}
else
{
float rescale = x_abs / w[normalized];
w[0] *= powf(rescale * rescale, nd.pd.neg_norm_power);
}
}
w[normalized] = x_abs;
}
nd.norm_x += x2 / (w[normalized] * w[normalized]);
}
w[spare] = compute_rate_decay<sqrt_rate, adaptive, normalized>(nd.pd, w[0]);
nd.pred_per_update += x2 * w[spare];
}
}
bool global_print_features = false;
template <bool sqrt_rate, bool feature_mask_off, bool adax, size_t adaptive, size_t normalized, size_t spare,
bool stateless>
float get_pred_per_update(gd& g, example& ec)
{
// We must traverse the features in _precisely_ the same order as during training.
label_data& ld = ec.l.simple;
vw& all = *g.all;
float grad_squared = ec.weight;
if (!adax)
grad_squared *= all.loss->getSquareGrad(ec.pred.scalar, ld.label);
if (grad_squared == 0 && !stateless)
return 1.;
norm_data nd = {grad_squared, 0., 0., {g.neg_power_t, g.neg_norm_power}, {0}};
foreach_feature<norm_data,
pred_per_update_feature<sqrt_rate, feature_mask_off, adaptive, normalized, spare, stateless> >(all, ec, nd);
if (normalized)
{
if (!stateless)
{
g.all->normalized_sum_norm_x += ((double)ec.weight) * nd.norm_x;
g.total_weight += ec.weight;
g.update_multiplier = average_update<sqrt_rate, adaptive, normalized>(
(float)g.total_weight, (float)g.all->normalized_sum_norm_x, g.neg_norm_power);
}
else
{
float nsnx = ((float)g.all->normalized_sum_norm_x) + ec.weight * nd.norm_x;
float tw = (float)g.total_weight + ec.weight;
g.update_multiplier = average_update<sqrt_rate, adaptive, normalized>(tw, nsnx, g.neg_norm_power);
}
nd.pred_per_update *= g.update_multiplier;
}
return nd.pred_per_update;
}
template <bool sqrt_rate, bool feature_mask_off, bool adax, size_t adaptive, size_t normalized, size_t spare,
bool stateless>
float sensitivity(gd& g, example& ec)
{
if (adaptive || normalized)
return get_pred_per_update<sqrt_rate, feature_mask_off, adax, adaptive, normalized, spare, stateless>(g, ec);
else
return ec.total_sum_feat_sq;
}
template <size_t adaptive>
float get_scale(gd& g, example& /* ec */, float weight)
{
float update_scale = g.all->eta * weight;
if (!adaptive)
{
float t =
(float)(g.all->sd->t + weight - g.all->sd->weighted_holdout_examples - g.all->sd->weighted_unlabeled_examples);
update_scale *= powf(t, g.neg_power_t);
}
return update_scale;
}
template <bool sqrt_rate, bool feature_mask_off, bool adax, size_t adaptive, size_t normalized, size_t spare>
float sensitivity(gd& g, base_learner& /* base */, example& ec)
{
return get_scale<adaptive>(g, ec, 1.) *
sensitivity<sqrt_rate, feature_mask_off, adax, adaptive, normalized, spare, true>(g, ec);
}
template <bool sparse_l2, bool invariant, bool sqrt_rate, bool feature_mask_off, bool adax, size_t adaptive,
size_t normalized, size_t spare>
float compute_update(gd& g, example& ec)
{
// invariant: not a test label, importance weight > 0
label_data& ld = ec.l.simple;
vw& all = *g.all;
float update = 0.;
ec.updated_prediction = ec.pred.scalar;
if (all.loss->getLoss(all.sd, ec.pred.scalar, ld.label) > 0.)
{
float pred_per_update = sensitivity<sqrt_rate, feature_mask_off, adax, adaptive, normalized, spare, false>(g, ec);
float update_scale = get_scale<adaptive>(g, ec, ec.weight);
if (invariant)
update = all.loss->getUpdate(ec.pred.scalar, ld.label, update_scale, pred_per_update);
else
update = all.loss->getUnsafeUpdate(ec.pred.scalar, ld.label, update_scale);
// changed from ec.partial_prediction to ld.prediction
ec.updated_prediction += pred_per_update * update;
if (all.reg_mode && fabs(update) > 1e-8)
{
double dev1 = all.loss->first_derivative(all.sd, ec.pred.scalar, ld.label);
double eta_bar = (fabs(dev1) > 1e-8) ? (-update / dev1) : 0.0;
if (fabs(dev1) > 1e-8)
all.sd->contraction *= (1. - all.l2_lambda * eta_bar);
update /= (float)all.sd->contraction;
all.sd->gravity += eta_bar * all.l1_lambda;
}
}
if (sparse_l2)
update -= g.sparse_l2 * ec.pred.scalar;
return update;
}
template <bool sparse_l2, bool invariant, bool sqrt_rate, bool feature_mask_off, bool adax, size_t adaptive,
size_t normalized, size_t spare>
void update(gd& g, base_learner&, example& ec)
{
// invariant: not a test label, importance weight > 0
float update;
if ((update = compute_update<sparse_l2, invariant, sqrt_rate, feature_mask_off, adax, adaptive, normalized, spare>(
g, ec)) != 0.)
train<sqrt_rate, feature_mask_off, adaptive, normalized, spare>(g, ec, update);
if (g.all->sd->contraction < 1e-9 || g.all->sd->gravity > 1e3) // updating weights now to avoid numerical instability
sync_weights(*g.all);
}
template <bool sparse_l2, bool invariant, bool sqrt_rate, bool feature_mask_off, bool adax, size_t adaptive,
size_t normalized, size_t spare>
void learn(gd& g, base_learner& base, example& ec)
{
// invariant: not a test label, importance weight > 0
assert(ec.l.simple.label != FLT_MAX);
assert(ec.weight > 0.);
g.predict(g, base, ec);
update<sparse_l2, invariant, sqrt_rate, feature_mask_off, adax, adaptive, normalized, spare>(g, base, ec);
}
void sync_weights(vw& all)
{
// todo, fix length dependence
if (all.sd->gravity == 0. && all.sd->contraction == 1.) // to avoid unnecessary weight synchronization
return;
if (all.weights.sparse)
for (weight& w : all.weights.sparse_weights)
w = trunc_weight(w, (float)all.sd->gravity) * (float)all.sd->contraction;
else
for (weight& w : all.weights.dense_weights)
w = trunc_weight(w, (float)all.sd->gravity) * (float)all.sd->contraction;
all.sd->gravity = 0.;
all.sd->contraction = 1.;
}
size_t write_index(io_buf& model_file, std::stringstream& msg, bool text, uint32_t num_bits, uint64_t i)
{
size_t brw;
uint32_t old_i = 0;
msg << i;
if (num_bits < 31)
{
old_i = (uint32_t)i;
brw = bin_text_write_fixed(model_file, (char*)&old_i, sizeof(old_i), msg, text);
}
else
brw = bin_text_write_fixed(model_file, (char*)&i, sizeof(i), msg, text);
return brw;
}
template <class T>
void save_load_regressor(vw& all, io_buf& model_file, bool read, bool text, T& weights)
{
size_t brw = 1;
if (all.print_invert) // write readable model with feature names
{
std::stringstream msg;
for (auto it = weights.begin(); it != weights.end(); ++it)
{
const auto weight_value = *it;
if (*it != 0.f)
{
const auto weight_index = it.index() >> weights.stride_shift();
const auto map_it = all.index_name_map.find(weight_index);
if (map_it != all.index_name_map.end())
{
msg << map_it->second;
bin_text_write_fixed(model_file, 0 /*unused*/, 0 /*unused*/, msg, true);
}
msg << ":" << weight_index << ":" << weight_value << "\n";
bin_text_write_fixed(model_file, 0 /*unused*/, 0 /*unused*/, msg, true);
}
}
return;
}
uint64_t i = 0;
uint32_t old_i = 0;
uint64_t length = (uint64_t)1 << all.num_bits;
if (read)
do
{
brw = 1;
if (all.num_bits < 31) // backwards compatible
{
brw = model_file.bin_read_fixed((char*)&old_i, sizeof(old_i), "");
i = old_i;
}
else
brw = model_file.bin_read_fixed((char*)&i, sizeof(i), "");
if (brw > 0)
{
if (i >= length)
THROW("Model content is corrupted, weight vector index " << i << " must be less than total vector length "
<< length);
weight* v = &weights.strided_index(i);
brw += model_file.bin_read_fixed((char*)&(*v), sizeof(*v), "");
}
} while (brw > 0);
else // write
for (typename T::iterator v = weights.begin(); v != weights.end(); ++v)
if (*v != 0.)
{
i = v.index() >> weights.stride_shift();
std::stringstream msg;
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), sizeof(*v), msg, text);
}
}
void save_load_regressor(vw& all, io_buf& model_file, bool read, bool text)
{
if (all.weights.sparse)
save_load_regressor(all, model_file, read, text, all.weights.sparse_weights);
else
save_load_regressor(all, model_file, read, text, all.weights.dense_weights);
}
template <class T>
void save_load_online_state(
vw& all, io_buf& model_file, bool read, bool text, gd* g, std::stringstream& msg, uint32_t ftrl_size, T& weights)
{
uint64_t length = (uint64_t)1 << all.num_bits;
uint64_t i = 0;
uint32_t old_i = 0;
size_t brw = 1;
if (read)
do
{
brw = 1;
if (all.num_bits < 31) // backwards compatible
{
brw = model_file.bin_read_fixed((char*)&old_i, sizeof(old_i), "");
i = old_i;
}
else
brw = model_file.bin_read_fixed((char*)&i, sizeof(i), "");
if (brw > 0)
{
if (i >= length)
THROW("Model content is corrupted, weight vector index " << i << " must be less than total vector length "
<< length);
weight buff[8] = {0, 0, 0, 0, 0, 0, 0, 0};
if (ftrl_size > 0)
brw += model_file.bin_read_fixed((char*)buff, sizeof(buff[0]) * ftrl_size, "");
else if (g == NULL || (!g->adaptive_input && !g->normalized_input))
brw += model_file.bin_read_fixed((char*)buff, sizeof(buff[0]), "");
else if ((g->adaptive_input && !g->normalized_input) || (!g->adaptive_input && g->normalized_input))
brw += model_file.bin_read_fixed((char*)buff, sizeof(buff[0]) * 2, "");
else // adaptive and normalized
brw += model_file.bin_read_fixed((char*)buff, sizeof(buff[0]) * 3, "");
uint32_t stride = 1 << weights.stride_shift();
weight* v = &weights.strided_index(i);
for (size_t i = 0; i < stride; i++) v[i] = buff[i];
}
} while (brw > 0);
else // write binary or text
for (typename T::iterator v = weights.begin(); v != weights.end(); ++v)
{
i = v.index() >> weights.stride_shift();
if (ftrl_size == 3)
{
if (*v != 0. || (&(*v))[1] != 0. || (&(*v))[2] != 0.)
{
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << " " << (&(*v))[1] << " " << (&(*v))[2] << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), 3 * sizeof(*v), msg, text);
}
}
else if (ftrl_size == 4)
{
if (*v != 0. || (&(*v))[1] != 0. || (&(*v))[2] != 0. || (&(*v))[3] != 0.)
{
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << " " << (&(*v))[1] << " " << (&(*v))[2] << " " << (&(*v))[3] << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), 4 * sizeof(*v), msg, text);
}
}
else if (ftrl_size == 6)
{
if (*v != 0. || (&(*v))[1] != 0. || (&(*v))[2] != 0. || (&(*v))[3] != 0. || (&(*v))[4] != 0. ||
(&(*v))[5] != 0.)
{
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << " " << (&(*v))[1] << " " << (&(*v))[2] << " " << (&(*v))[3] << " " << (&(*v))[4] << " "
<< (&(*v))[5] << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), 6 * sizeof(*v), msg, text);
}
}
else if (g == nullptr || (!all.weights.adaptive && !all.weights.normalized))
{
if (*v != 0.)
{
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), sizeof(*v), msg, text);
}
}
else if ((all.weights.adaptive && !all.weights.normalized) || (!all.weights.adaptive && all.weights.normalized))
{
// either adaptive or normalized
if (*v != 0. || (&(*v))[1] != 0.)
{
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << " " << (&(*v))[1] << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), 2 * sizeof(*v), msg, text);
}
}
else
{
// adaptive and normalized
if (*v != 0. || (&(*v))[1] != 0. || (&(*v))[2] != 0.)
{
brw = write_index(model_file, msg, text, all.num_bits, i);
msg << ":" << *v << " " << (&(*v))[1] << " " << (&(*v))[2] << "\n";
brw += bin_text_write_fixed(model_file, (char*)&(*v), 3 * sizeof(*v), msg, text);
}
}
}
}
void save_load_online_state(
vw& all, io_buf& model_file, bool read, bool text, double& total_weight, gd* g, uint32_t ftrl_size)
{
// vw& all = *g.all;
std::stringstream msg;
msg << "initial_t " << all.initial_t << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.initial_t, sizeof(all.initial_t), "", read, msg, text);
msg << "norm normalizer " << all.normalized_sum_norm_x << "\n";
bin_text_read_write_fixed(
model_file, (char*)&all.normalized_sum_norm_x, sizeof(all.normalized_sum_norm_x), "", read, msg, text);
msg << "t " << all.sd->t << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->t, sizeof(all.sd->t), "", read, msg, text);
msg << "sum_loss " << all.sd->sum_loss << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->sum_loss, sizeof(all.sd->sum_loss), "", read, msg, text);
msg << "sum_loss_since_last_dump " << all.sd->sum_loss_since_last_dump << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->sum_loss_since_last_dump,
sizeof(all.sd->sum_loss_since_last_dump), "", read, msg, text);
float dump_interval = all.sd->dump_interval;
msg << "dump_interval " << dump_interval << "\n";
bin_text_read_write_fixed(model_file, (char*)&dump_interval, sizeof(dump_interval), "", read, msg, text);
if (!read || (all.training && all.preserve_performance_counters)) // update dump_interval from input model
all.sd->dump_interval = dump_interval;
msg << "min_label " << all.sd->min_label << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->min_label, sizeof(all.sd->min_label), "", read, msg, text);
msg << "max_label " << all.sd->max_label << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->max_label, sizeof(all.sd->max_label), "", read, msg, text);
msg << "weighted_labeled_examples " << all.sd->weighted_labeled_examples << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->weighted_labeled_examples,
sizeof(all.sd->weighted_labeled_examples), "", read, msg, text);
msg << "weighted_labels " << all.sd->weighted_labels << "\n";
bin_text_read_write_fixed(
model_file, (char*)&all.sd->weighted_labels, sizeof(all.sd->weighted_labels), "", read, msg, text);
msg << "weighted_unlabeled_examples " << all.sd->weighted_unlabeled_examples << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->weighted_unlabeled_examples,
sizeof(all.sd->weighted_unlabeled_examples), "", read, msg, text);
msg << "example_number " << all.sd->example_number << "\n";
bin_text_read_write_fixed(
model_file, (char*)&all.sd->example_number, sizeof(all.sd->example_number), "", read, msg, text);
msg << "total_features " << all.sd->total_features << "\n";
bin_text_read_write_fixed(
model_file, (char*)&all.sd->total_features, sizeof(all.sd->total_features), "", read, msg, text);
if (!read || all.model_file_ver >= VERSION_SAVE_RESUME_FIX)
{
// restore some data to allow --save_resume work more accurate
// fix average loss
msg << "total_weight " << total_weight << "\n";
bin_text_read_write_fixed(model_file, (char*)&total_weight, sizeof(total_weight), "", read, msg, text);
// fix "loss since last" for first printed out example details
msg << "sd::oec.weighted_labeled_examples " << all.sd->old_weighted_labeled_examples << "\n";
bin_text_read_write_fixed(model_file, (char*)&all.sd->old_weighted_labeled_examples,
sizeof(all.sd->old_weighted_labeled_examples), "", read, msg, text);
// fix "number of examples per pass"
msg << "current_pass " << all.current_pass << "\n";
if (all.model_file_ver >= VERSION_PASS_UINT64)
bin_text_read_write_fixed(model_file, (char*)&all.current_pass, sizeof(all.current_pass), "", read, msg, text);
else // backwards compatiblity.
{
size_t temp_pass = (size_t)all.current_pass;
bin_text_read_write_fixed(model_file, (char*)&temp_pass, sizeof(temp_pass), "", read, msg, text);
all.current_pass = temp_pass;
}
}
if (read &&
(!all.training ||
!all.preserve_performance_counters)) // reset various things so that we report test set performance properly
{
all.sd->sum_loss = 0;
all.sd->sum_loss_since_last_dump = 0;
all.sd->weighted_labeled_examples = 0.;
all.sd->weighted_labels = 0.;
all.sd->weighted_unlabeled_examples = 0.;
all.sd->old_weighted_labeled_examples = 0.;
all.sd->example_number = 0;
all.sd->total_features = 0;
all.current_pass = 0;
}
if (all.weights.sparse)
save_load_online_state(all, model_file, read, text, g, msg, ftrl_size, all.weights.sparse_weights);
else
save_load_online_state(all, model_file, read, text, g, msg, ftrl_size, all.weights.dense_weights);
}
template <class T>
class set_initial_gd_wrapper
{
public:
static void func(weight& w, std::pair<float, float>& initial, uint64_t /* index */)
{
w = initial.first;
(&w)[1] = initial.second;
}
};
void save_load(gd& g, io_buf& model_file, bool read, bool text)
{
vw& all = *g.all;
if (read)
{
initialize_regressor(all);
if (all.weights.adaptive && all.initial_t > 0)
{
float init_weight = all.initial_weight;
std::pair<float, float> p = std::make_pair(init_weight, all.initial_t);