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5032722 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 11 20:45:05 2025
"""
# general python
import os
import shutil
import glob
from tqdm import tqdm
from time import process_time
from time import time as wall_clock_time
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None # this is annoying
import pickle
from functools import partial
import platform
import argparse
import json
# jax/flax stuff
import jax
import jax.numpy as jnp
import flax
from flax import linen as nn
import optax
# pytorch imports
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
# custom function/classes imports
from utils.build_optimizer import build_optimizer
from utils.write_config import write_config
from utils.edit_argparse import enforce_valid_defaults
from utils.setup_training_dir import setup_training_dir
from train_eval_fns.general_training_wrapper.training_wrapper_helpers import (timers,
record_postproc_time_table)
from utils.end_training import write_final_eval_results
# specific to training this model
from neural_models.neural_shared.neural_initializer import create_all_tstates
from utils.edit_argparse import feedforward_fill_with_default_values as fill_with_default_values
from utils.edit_argparse import feedforward_share_top_level_args as share_top_level_args
from train_eval_fns.feedforward_predict_train_eval_one_batch import ( train_one_batch,
eval_one_batch )
from train_eval_fns.neural_final_eval_wrapper import final_eval_wrapper
from train_eval_fns.general_training_wrapper.TrainingWrapper import FeedforwardTrainingWrapper as TrainingWrapper
def cont_training_feedforward(args,
dataloader_dict,
new_training_wkdir,
prev_model_ckpts_dir,
tstate_to_load):
###########################################################################
### 0: CHECK CONFIG; IMPORT APPROPRIATE MODULES #########################
###########################################################################
err = (f"{args.pred_model_type} is not feedforward; "+
f"using the wrong training script")
assert args.pred_model_type == 'feedforward', err
del err
# vvv___DIFFERENT FROM ORIGINAL TRAINING CODE___vvv
prev_encoder_savemodel_filename = prev_model_ckpts_dir + '/'+ f'ANC_ENC_{tstate_to_load}.pkl'
prev_decoder_savemodel_filename = prev_model_ckpts_dir + '/'+ f'DESC_DEC_{tstate_to_load}.pkl'
prev_finalpred_savemodel_filename = prev_model_ckpts_dir + '/'+ f'FINAL_PRED_{tstate_to_load}.pkl'
all_prev_savemodel_filenames = [prev_encoder_savemodel_filename,
prev_decoder_savemodel_filename,
prev_finalpred_savemodel_filename]
# ^^^___DIFFERENT FROM ORIGINAL TRAINING CODE___^^^
### edit the argparse object in-place
fill_with_default_values(args)
enforce_valid_defaults(args)
share_top_level_args(args)
if not args.update_grads:
print('DEBUG MODE: DISABLING GRAD UPDATES')
###########################################################################
### 1: SETUP ############################################################
###########################################################################
# vvv___DIFFERENT FROM ORIGINAL TRAINING CODE___vvv
assert args.training_wkdir != new_training_wkdir, 'pick a new training directory'
args.training_wkdir = new_training_wkdir
# ^^^___DIFFERENT FROM ORIGINAL TRAINING CODE___^^^
### initial setup of misc things
# setup the working directory (if not done yet) and this run's sub-directory
setup_training_dir(args)
# initial random key, to carry through execution
rngkey = jax.random.key(args.rng_seednum)
# setup tensorboard writer
writer = SummaryWriter(args.tboard_dir)
# create a new logfile
with open(args.logfile_name,'a') as g:
g.write( f'Feedforward network to predict alignment-augmented descendant\n' )
g.write( f'Ancestor sequence embedder (FULL-CONTEXT): {args.anc_model_type}\n' )
g.write( f'Descendant sequence embedder (CAUSAL): {args.desc_model_type}\n' )
g.write( f'Combine embeddings with: {args.pred_config["postproc_model_type"]}\n' )
g.write( f'when reporting, normalizing losses by: descendant length \n\n' )
### save updated config, provide filename for saving model parameters
encoder_save_model_filename = args.model_ckpts_dir + '/'+ f'ANC_ENC.pkl'
decoder_save_model_filename = args.model_ckpts_dir + '/'+ f'DESC_DEC.pkl'
finalpred_save_model_filename = args.model_ckpts_dir + '/'+ f'FINAL_PRED.pkl'
all_save_model_filenames = [encoder_save_model_filename,
decoder_save_model_filename,
finalpred_save_model_filename]
write_config(args = args, out_dir = args.model_ckpts_dir)
### extract data from dataloader_dict
# use this to update model parameters
training_dset = dataloader_dict['training_dset']
training_dl = dataloader_dict['training_dl']
# use this to decide early stopping
dev_dset = dataloader_dict['dev_dset']
dev_dl = dataloader_dict['dev_dl']
# use this as final held-out test set
final_test_dset = dataloader_dict['test_dset']
final_test_dl = dataloader_dict['test_dl']
###########################################################################
### 2: MODEL INIT, TRAINING ##############################################
###########################################################################
print('2: model init')
with open(args.logfile_name,'a') as g:
g.write('\n')
g.write(f'2: model init\n')
# init the optimizer
tx = build_optimizer(args)
rngkey, model_init_rngkey = jax.random.split(rngkey, num=2)
### determine shapes for init
# unaligned sequences sizes
global_seq_max_length = max([training_dset.global_seq_max_length,
dev_dset.global_seq_max_length,
final_test_dset.global_seq_max_length])
largest_seqs = (args.batch_size, global_seq_max_length)
# aligned datasets sizes
if args.use_scan_fns:
max_dim1 = args.chunk_length
elif not args.use_scan_fns:
max_dim1 = max([training_dset.global_align_max_length,
dev_dset.global_align_max_length,
final_test_dset.global_align_max_length]) - 1
largest_aligns = (args.batch_size, max_dim1)
del max_dim1
seq_shapes = [largest_seqs, largest_aligns]
# time
t_per_sample = args.pred_config['t_per_sample']
if t_per_sample:
dummy_t_for_each_sample = jnp.empty( (args.batch_size,) )
elif not t_per_sample:
dummy_t_for_each_sample = None
# batch provided to train/eval functions consist of:
# 1.) unaligned sequences (B, L_seq, 2)
# 2.) aligned data matrices (B, L_align, 4)
# 3.) time per sample; (B,) if present, None otherwise
# 4, not used.) sample index (B,)
seq_shapes = [largest_seqs, largest_aligns, dummy_t_for_each_sample]
### initialize functions, determine concat_fn
out = create_all_tstates( seq_shapes = seq_shapes,
tx = tx,
model_init_rngkey = model_init_rngkey,
tabulate_file_loc = args.model_ckpts_dir,
anc_model_type = args.anc_model_type,
desc_model_type = args.desc_model_type,
pred_model_type = args.pred_model_type,
anc_enc_config = args.anc_enc_config,
desc_dec_config = args.desc_dec_config,
pred_config = args.pred_config,
t_array_for_all_samples = None )
blank_tstate_lst, all_model_instances, concat_fn = out
del out
# vvv___DIFFERENT FROM ORIGINAL TRAINING CODE___vvv
# load values
all_trainstates = []
for i in range(3):
with open(all_prev_savemodel_filenames[i], 'rb') as f:
state_dict = pickle.load(f)
tstate = flax.serialization.from_state_dict( blank_tstate_lst[i],
state_dict )
all_trainstates.append(tstate)
del blank_tstate_lst, i, state_dict, all_prev_savemodel_filenames
# ^^^___DIFFERENT FROM ORIGINAL TRAINING CODE___^^^
### jit-compilations
# training function
parted_train_fn = partial( train_one_batch,
all_model_instances = all_model_instances,
interms_for_tboard = args.interms_for_tboard,
concat_fn = concat_fn,
norm_loss_by_for_reporting = 'desc_len',
update_grads = args.update_grads )
train_fn_jitted = jax.jit(parted_train_fn,
static_argnames = ['max_seq_len',
'max_align_len',
'record_interms_this_batch'])
del parted_train_fn
### eval_fn used in training loop (to monitor progress)
# pass arguments into eval_one_batch; make a parted_eval_fn that doesn't
# return any intermediates
no_returns = {k: False for k in args.interms_for_tboard.keys()}
extra_args_for_eval = {'output_attn_weights': False}
# if this is a transformer model, will have extra arguments for eval funciton
if (args.anc_model_type == 'Transformer' or args.desc_model_type == 'Transformer'):
extra_args_for_eval['output_attn_weights'] = False
parted_eval_fn = partial( eval_one_batch,
all_model_instances = all_model_instances,
interms_for_tboard = no_returns,
concat_fn = concat_fn,
norm_loss_by_for_reporting = 'desc_len',
extra_args_for_eval = extra_args_for_eval )
# jit compile this eval function
eval_fn_jitted = jax.jit(parted_eval_fn,
static_argnames = ['max_seq_len', 'max_align_len'])
del parted_eval_fn
### initialize training wrapper
training_wrapper = TrainingWrapper( args = args,
initial_training_rngkey = rngkey,
dataloader_dict = dataloader_dict,
train_fn_jitted = train_fn_jitted,
eval_fn_jitted = eval_fn_jitted,
all_save_model_filenames = all_save_model_filenames,
writer = writer )
### train
print(f'3: main training loop')
with open(args.logfile_name,'a') as g:
g.write('\n')
g.write(f'3: main training loop\n')
out = training_wrapper.run_train_loop( all_trainstates = all_trainstates )
early_stop, best_epoch, best_trainstates = out
del out
###########################################################################
### FINAL EVAL ##########################################################
###########################################################################
print(f'4: post-training actions')
# write to logfile
with open(args.logfile_name,'a') as g:
g.write('\n')
g.write(f'4: post-training actions\n')
# don't accidentally use old trainstates or eval fn
del all_trainstates, eval_fn_jitted
# new timer
postproc_timer_class = timers( num_epochs = 1 )
postproc_timer_class.start_timer()
### write to output logfile
with open(args.logfile_name,'a') as g:
# if early stopping was never triggered, record results at last epoch
if not early_stop:
g.write(f'Regular stopping after {args.num_epochs} full epochs:\n\n')
# finish up logfile, regardless of early stopping or not
g.write(f'Epoch with lowest average dev set loss ("best epoch"): {best_epoch}\n')
g.write(f'RE-EVALUATING ALL DATA WITH BEST PARAMS\n\n')
### save the argparse object by itself
args.epoch_idx = best_epoch
with open(f'{args.model_ckpts_dir}/TRAINING_ARGPARSE.pkl', 'wb') as g:
pickle.dump(args, g)
### jit compile new eval function
# if this is a transformer model, will have extra arguments for eval function
extra_args_for_eval = {'output_attn_weights': args.interms_for_tboard.get('attn_weights', False)}
parted_eval_fn = partial( eval_one_batch,
all_model_instances = all_model_instances,
interms_for_tboard = args.interms_for_tboard,
concat_fn = concat_fn,
norm_loss_by_for_reporting = 'desc_len',
extra_args_for_eval = extra_args_for_eval )
del extra_args_for_eval
# jit compile this eval function
eval_fn_jitted = jax.jit( parted_eval_fn,
static_argnames = ['max_seq_len', 'max_align_len'])
del parted_eval_fn
###########################################
### loop through training dataloader and #
### score with best params #
###########################################
with open(args.logfile_name,'a') as g:
g.write(f'SCORING ALL TRAIN SEQS\n')
# DON'T save arrays yet; takes up too much memory
train_summary_stats = final_eval_wrapper( dataloader = training_dl,
dataset = training_dset,
best_trainstates = best_trainstates,
jitted_determine_seqlen_bin = training_wrapper.seqlen_bin_fn,
jitted_determine_alignlen_bin = training_wrapper.alignlen_bin_fn,
eval_fn_jitted = eval_fn_jitted,
out_alph_size = args.out_alph_size,
save_arrs = False,
save_per_sample_losses = args.save_per_sample_losses,
interms_for_tboard = args.interms_for_tboard,
logfile_dir = args.logfile_dir,
out_arrs_dir = args.out_arrs_dir,
outfile_prefix = f'train-set')
##########################################
### loop through dev set dataloader and #
### score with best params #
##########################################
with open(args.logfile_name,'a') as g:
g.write(f'SCORING ALL DEV SEQS\n')
# DON'T save arrays yet; takes up too much memory
dev_summary_stats = final_eval_wrapper( dataloader = dev_dl,
dataset = dev_dset,
best_trainstates = best_trainstates,
jitted_determine_seqlen_bin = training_wrapper.seqlen_bin_fn,
jitted_determine_alignlen_bin = training_wrapper.alignlen_bin_fn,
eval_fn_jitted = eval_fn_jitted,
out_alph_size = args.out_alph_size,
save_arrs = False,
save_per_sample_losses = args.save_per_sample_losses,
interms_for_tboard = args.interms_for_tboard,
logfile_dir = args.logfile_dir,
out_arrs_dir = args.out_arrs_dir,
outfile_prefix = f'dev-set')
###########################################
### loop through test dataloader and #
### score with best params #
###########################################
with open(args.logfile_name,'a') as g:
g.write(f'SCORING ALL HELD-OUT TEST SEQS\n')
# output_attn_weights also controlled by cond1 and cond2
final_test_summary_stats = final_eval_wrapper( dataloader = final_test_dl,
dataset = final_test_dset,
best_trainstates = best_trainstates,
jitted_determine_seqlen_bin = training_wrapper.seqlen_bin_fn,
jitted_determine_alignlen_bin = training_wrapper.alignlen_bin_fn,
eval_fn_jitted = eval_fn_jitted,
out_alph_size = args.out_alph_size,
save_arrs = args.save_arrs,
save_per_sample_losses = args.save_per_sample_losses,
interms_for_tboard = args.interms_for_tboard,
logfile_dir = args.logfile_dir,
out_arrs_dir = args.out_arrs_dir,
outfile_prefix = f'final-test-set')
###########################################
### update the logfile with final losses #
###########################################
write_final_eval_results(args = args,
summary_stats = train_summary_stats,
filename = 'TRAIN_AVE-LOSSES.tsv')
write_final_eval_results(args = args,
summary_stats = dev_summary_stats,
filename = 'DEV_AVE-LOSSES.tsv')
write_final_eval_results(args = args,
summary_stats = final_test_summary_stats,
filename = 'FINAL-TEST_AVE-LOSSES.tsv')
# record total time spent on post-training actions; write this to a table
# instead of a scalar collection
record_postproc_time_table( already_started_timer_class = postproc_timer_class,
writer = writer )
writer.close()
# clean up intermediates
for file_path in glob.glob(f"{args.model_ckpts_dir}/*_INPROGRESS.pkl"):
try:
os.remove(file_path)
except FileNotFoundError:
pass # File might have been deleted already
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