Put the global_step and dev score history into the model files so tha…

…t when a checkpoint gets loaded, the training continues from the position it was formerly at rather than restarting from 0

Report some details of the model being loaded after loading it

stanza/models/depparse/trainer.py

@@ -30,16 +30,26 @@ def unpack_batch(batch, device):
 class Trainer(BaseTrainer):
     """ A trainer for training models. """
     def __init__(self, args=None, vocab=None, pretrain=None, model_file=None,
-                 device=None, foundation_cache=None, ignore_model_config=False):
+                 device=None, foundation_cache=None, ignore_model_config=False, reset_history=False):
+        self.global_step = 0
+        self.last_best_step = 0
+        self.dev_score_history = []
+
         orig_args = copy.deepcopy(args)
         # whether the training is in primary or secondary stage
         # during FT (loading weights), etc., the training is considered to be in "secondary stage"
         # during this time, we (optionally) use a different set of optimizers than that during "primary stage".
         #
         # Regardless, we use TWO SETS of optimizers; once primary converges, we switch to secondary
+
         if model_file is not None:
             # load everything from file
             self.load(model_file, pretrain, args, foundation_cache, device)
+
+            if reset_history:
+                self.global_step = 0
+                self.last_best_step = 0
+                self.dev_score_history = []
         else:
             # build model from scratch
             self.args = args
@@ -112,7 +122,10 @@ def save(self, filename, skip_modules=True, save_optimizer=False):
         params = {
                 'model': model_state,
                 'vocab': self.vocab.state_dict(),
-                'config': self.args
+                'config': self.args,
+                'global_step': self.global_step,
+                'last_best_step': self.last_best_step,
+                'dev_score_history': self.dev_score_history,
                 }
 
         if save_optimizer and self.optimizer is not None:
@@ -157,3 +170,6 @@ def load(self, filename, pretrain, args=None, foundation_cache=None, device=None
         if optim_state_dict:
             self.optimizer.load_state_dict(optim_state_dict)
 
+        self.global_step = checkpoint.get("global_step", 0)
+        self.last_best_step = checkpoint.get("last_best_step", 0)
+        self.dev_score_history = checkpoint.get("dev_score_history", list())

stanza/models/parser.py

@@ -198,45 +198,42 @@ def train(args):
         wandb.run.define_metric('dev_score', summary='max')
 
     logger.info("Training parser...")
-    # calculate checkpoint file name and the sav
-    model_to_load = None # used for general loading and reloading
-    checkpoint_file = None # used explicitly as the *PATH TO THE CHECKPOINT* could be None if we don't want to save chkpt
     if args.get("checkpoint"):
-        model_to_load = utils.checkpoint_name(args.get("save_dir"), model_file, args.get("checkpoint_save_name"))
-        checkpoint_file = model_to_load
+        # calculate checkpoint file name from the save filename
+        checkpoint_file = utils.checkpoint_name(args.get("save_dir"), model_file, args.get("checkpoint_save_name"))
         args["checkpoint_save_name"] = checkpoint_file
-    if args["continue_from"]:
-        model_to_load = args["continue_from"]
 
-    if model_to_load is not None and os.path.exists(model_to_load):
-        trainer = Trainer(args=args, pretrain=pretrain, vocab=vocab, model_file=model_to_load, device=args['device'], ignore_model_config=True)
+    if args.get("checkpoint") and os.path.exists(args["checkpoint_save_name"]):
+        trainer = Trainer(args=args, pretrain=pretrain, vocab=vocab, model_file=args["checkpoint_save_name"], device=args['device'], ignore_model_config=True)
+        if len(trainer.dev_score_history) > 0:
+            logger.info("Continuing from checkpoint %s  Model was previously trained for %d steps, with a best dev score of %.4f", args["checkpoint_save_name"], trainer.global_step, max(trainer.dev_score_history))
+    elif args["continue_from"]:
+        if not os.path.exists(args["continue_from"]):
+            raise FileNotFoundError("--continue_from specified, but the file %s does not exist" % args["continue_from"])
+        trainer = Trainer(args=args, pretrain=pretrain, vocab=vocab, model_file=args["continue_from"], device=args['device'], ignore_model_config=True, reset_history=True)
     else:
         trainer = Trainer(args=args, vocab=vocab, pretrain=pretrain, device=args['device'])
 
-    global_step = 0
     max_steps = args['max_steps']
-    dev_score_history = []
-    best_dev_preds = []
     current_lr = args['lr']
     global_start_time = time.time()
     format_str = 'Finished STEP {}/{}, loss = {:.6f} ({:.3f} sec/batch), lr: {:.6f}'
 
     is_second_stage = False
-    last_best_step = 0
     # start training
     train_loss = 0
     while True:
         do_break = False
         for i, batch in enumerate(train_batch):
             start_time = time.time()
-            global_step += 1
+            trainer.global_step += 1
             loss = trainer.update(batch, eval=False) # update step
             train_loss += loss
-            if global_step % args['log_step'] == 0:
+            if trainer.global_step % args['log_step'] == 0:
                 duration = time.time() - start_time
-                logger.info(format_str.format(global_step, max_steps, loss, duration, current_lr))
+                logger.info(format_str.format(trainer.global_step, max_steps, loss, duration, current_lr))
 
-            if global_step % args['eval_interval'] == 0:
+            if trainer.global_step % args['eval_interval'] == 0:
                 # eval on dev
                 logger.info("Evaluating on dev set...")
                 dev_preds = []
@@ -250,60 +247,62 @@ def train(args):
                 _, _, dev_score = scorer.score(system_pred_file, gold_file)
 
                 train_loss = train_loss / args['eval_interval'] # avg loss per batch
-                logger.info("step {}: train_loss = {:.6f}, dev_score = {:.4f}".format(global_step, train_loss, dev_score))
+                logger.info("step {}: train_loss = {:.6f}, dev_score = {:.4f}".format(trainer.global_step, train_loss, dev_score))
 
                 if args['wandb']:
                     wandb.log({'train_loss': train_loss, 'dev_score': dev_score})
 
                 train_loss = 0
 
                 # save best model
-                if len(dev_score_history) == 0 or dev_score > max(dev_score_history):
-                    last_best_step = global_step
+                if len(trainer.dev_score_history) == 0 or dev_score > max(trainer.dev_score_history):
+                    trainer.last_best_step = trainer.global_step
                     trainer.save(model_file)
                     logger.info("new best model saved.")
-                    best_dev_preds = dev_preds
 
-                dev_score_history += [dev_score]
+                trainer.dev_score_history += [dev_score]
 
             if not is_second_stage and args.get('second_optim', None) is not None:
-                if global_step - last_best_step >= args['max_steps_before_stop'] or (args['second_optim_start_step'] is not None and global_step >= args['second_optim_start_step']):
+                if trainer.global_step - trainer.last_best_step >= args['max_steps_before_stop'] or (args['second_optim_start_step'] is not None and trainer.global_step >= args['second_optim_start_step']):
                     logger.info("Switching to second optimizer: {}".format(args.get('second_optim', None)))
                     args["second_stage"] = True
                     # if the loader gets a model file, it uses secondary optimizer
                     trainer = Trainer(args=args, vocab=trainer.vocab, pretrain=pretrain,
                                       model_file=model_file, device=args['device'])
                     logger.info('Reloading best model to continue from current local optimum')
                     is_second_stage = True
-                    last_best_step = global_step
+                    trainer.last_best_step = trainer.global_step
             else:
-                if global_step - last_best_step >= args['max_steps_before_stop']:
+                if trainer.global_step - trainer.last_best_step >= args['max_steps_before_stop']:
                     do_break = True
                     break
 
-            if global_step % args['eval_interval'] == 0:
+            if trainer.global_step % args['eval_interval'] == 0:
                 # if we need to save checkpoint, do so
                 # (save after switching the optimizer, if applicable, so that
                 # the new optimizer is the optimizer used if a restart happens)
                 if checkpoint_file is not None:
                     trainer.save(checkpoint_file, save_optimizer=True)
                     logger.info("new model checkpoint saved.")
 
-            if global_step >= args['max_steps']:
+            if trainer.global_step >= args['max_steps']:
                 do_break = True
                 break
 
         if do_break: break
 
         train_batch.reshuffle()
 
-    logger.info("Training ended with {} steps.".format(global_step))
+    logger.info("Training ended with {} steps.".format(trainer.global_step))
 
     if args['wandb']:
         wandb.finish()
 
-    if len(dev_score_history) > 0:
-        best_f, best_eval = max(dev_score_history)*100, np.argmax(dev_score_history)+1
+    if len(trainer.dev_score_history) > 0:
+        # TODO: technically the iteration position will be wrong if
+        # the eval_interval changed when running from a checkpoint
+        # could fix this by saving step & score instead of just score
+        best_f, best_eval = max(trainer.dev_score_history)*100, np.argmax(trainer.dev_score_history)+1
         logger.info("Best dev F1 = {:.2f}, at iteration = {}".format(best_f, best_eval * args['eval_interval']))
     else:
         logger.info("Dev set never evaluated.  Saving final model.")