more naming cleanup

megatron/optimizer_param_scheduler.py

@@ -13,18 +13,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Learning rate decay functions."""
+"""Learning rate decay and weight decay incr functions."""
 
 import math
 
 from megatron import print_rank_0
 
 class OptimizerParamScheduler(object):
-    """Anneals the learning rate."""
+    """Anneals learning rate and weight decay"""
 
     def __init__(self, optimizer, max_lr, min_lr,
-                 warmup_steps, decay_steps, decay_style,
-                 start_wd, end_wd, wd_incr_style,
+                 lr_warmup_steps, lr_decay_steps, lr_decay_style,
+                 start_wd, end_wd, wd_incr_steps, wd_incr_style,
                  use_checkpoint_opt_param_scheduler=True,
                  override_opt_param_scheduler=False):
 
@@ -36,19 +36,19 @@ class OptimizerParamScheduler(object):
         assert self.min_lr >= 0.0
         assert self.max_lr >= self.min_lr
 
-        self.warmup_steps = warmup_steps
+        self.lr_warmup_steps = lr_warmup_steps
         self.num_steps = 0
-        self.decay_steps = decay_steps
-        assert self.decay_steps > 0
-        assert self.warmup_steps < self.decay_steps
+        self.lr_decay_steps = lr_decay_steps
+        assert self.lr_decay_steps > 0
+        assert self.lr_warmup_steps < self.lr_decay_steps
 
-        self.decay_style = decay_style
+        self.lr_decay_style = lr_decay_style
 
         self.start_wd = start_wd
         self.end_wd = end_wd
         assert self.start_wd >= 0.0
         assert self.end_wd >= self.start_wd
-        
+        self.wd_incr_steps = wd_incr_steps
         self.wd_incr_style = wd_incr_style
 
         self.override_opt_param_scheduler = override_opt_param_scheduler
@@ -59,26 +59,27 @@ class OptimizerParamScheduler(object):
 
         # Set the learning rate
         self.step(0)
-        print_rank_0('> learning rate decay style: {}'.format(self.decay_style))
+        print_rank_0('> learning rate decay style: {}'.format(self.lr_decay_style))
 
 
     def get_wd(self):
-        if self.num_steps > self.decay_steps:
+        """ Weight decay incr functions"""
+        if self.num_steps > self.wd_incr_steps:
             return self.end_wd
 
         if self.wd_incr_style == 'constant':
             assert self.start_wd == self.end_wd
             return self.end_wd
 
-        decay_ratio = float(self.num_steps) / float(self.decay_steps)
-        assert decay_ratio >= 0.0
-        assert decay_ratio <= 1.0
+        incr_ratio = float(self.num_steps) / float(self.wd_incr_steps)
+        assert incr_ratio >= 0.0
+        assert incr_ratio <= 1.0
         delta_wd = self.end_wd - self.start_wd
 
         if self.wd_incr_style == 'linear':
-            coeff = decay_ratio
+            coeff = incr_ratio
         elif self.wd_incr_style == 'cosine':
-            coeff = 0.5 * (math.cos(math.pi * (1 - decay_ratio)) + 1.0)
+            coeff = 0.5 * (math.cos(math.pi * (1 - incr_ratio)) + 1.0)
         else:
             raise Exception('{} weight decay increment style is not supported.'.format(
                 self.wd_incr_style))
@@ -91,33 +92,33 @@ class OptimizerParamScheduler(object):
               https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
 
         # Use linear warmup for the initial part.
-        if self.warmup_steps > 0 and self.num_steps <= self.warmup_steps:
+        if self.lr_warmup_steps > 0 and self.num_steps <= self.lr_warmup_steps:
             return self.max_lr * float(self.num_steps) / \
-                float(self.warmup_steps)
+                float(self.lr_warmup_steps)
 
         # If the learning rate is constant, just return the initial value.
-        if self.decay_style == 'constant':
+        if self.lr_decay_style == 'constant':
             return self.max_lr
 
-        # For any steps larger than `self.decay_steps`, use `self.min_lr`.
-        if self.num_steps > self.decay_steps:
+        # For any steps larger than `self.lr_decay_steps`, use `self.min_lr`.
+        if self.num_steps > self.lr_decay_steps:
             return self.min_lr
         
         # If we are done with the warmup period, use the decay style.
-        num_steps_ = self.num_steps - self.warmup_steps
-        decay_steps_ = self.decay_steps - self.warmup_steps
+        num_steps_ = self.num_steps - self.lr_warmup_steps
+        decay_steps_ = self.lr_decay_steps - self.lr_warmup_steps
         decay_ratio = float(num_steps_) / float(decay_steps_)
         assert decay_ratio >= 0.0
         assert decay_ratio <= 1.0
         delta_lr = self.max_lr - self.min_lr
 
-        if self.decay_style == 'linear':
+        if self.lr_decay_style == 'linear':
             coeff = (1.0 - decay_ratio)
-        elif self.decay_style == 'cosine':
+        elif self.lr_decay_style == 'cosine':
             coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
         else:
             raise Exception('{} decay style is not supported.'.format(
-                self.decay_style))
+                self.lr_decay_style))
 
         return self.min_lr + coeff * delta_lr
 
@@ -135,11 +136,15 @@ class OptimizerParamScheduler(object):
     def state_dict(self):
         state_dict = {
             'max_lr': self.max_lr,
-            'warmup_steps': self.warmup_steps,
+            'lr_warmup_steps': self.lr_warmup_steps,
             'num_steps': self.num_steps,
-            'decay_style': self.decay_style,
-            'decay_steps': self.decay_steps,
-            'min_lr': self.min_lr
+            'lr_decay_style': self.lr_decay_style,
+            'lr_decay_steps': self.lr_decay_steps,
+            'min_lr': self.min_lr,
+            'start_wd': self.start_wd,
+            'end_wd': self.end_wd,
+            'wd_incr_style': self.wd_incr_style,
+            'wd_incr_steps': self.wd_incr_steps
         }
         return state_dict
 
@@ -153,7 +158,7 @@ class OptimizerParamScheduler(object):
 
         if not self.use_checkpoint_opt_param_scheduler:
             assert cls_value == sd_value, \
-                f'AnnealingLR: class input value {cls_value} and checkpoint' \
+                f'OptimizerParamScheduler: class input value {cls_value} and checkpoint' \
                 f'value {sd_value} for {name} do not match'
         print_rank_0(' > using checkpoint value {} for {}'.format(sd_value,
                                                                   name))
@@ -174,24 +179,56 @@ class OptimizerParamScheduler(object):
 
         if 'warmup_iter' in sd:
             warmup_steps_ = sd['warmup_iter']
-        else:
+        elif 'warmup_steps' in sd:
             warmup_steps_ = sd['warmup_steps']
-        self.warmup_steps = self._check_and_set(self.warmup_steps,
-                                                warmup_steps_,
+        else:
+            lr_warmup_steps_ = sd['lr_warmup_steps']
+        self.lr_warmup_steps = self._check_and_set(self.lr_warmup_steps,
+                                                lr_warmup_steps_,
                                                 'warmup iterations')
 
         if 'end_iter' in sd:
-            decay_steps_ = sd['end_iter']
+            lr_decay_steps_ = sd['end_iter']
+        elif 'decay_steps' in sd:
+            lr_decay_steps_  = sd['decay_steps']
         else:
-            decay_steps_ = sd['decay_steps']
-        self.decay_steps = self._check_and_set(self.decay_steps, decay_steps_,
+            lr_decay_steps_ = sd['lr_decay_steps']
+        self.lr_decay_steps = self._check_and_set(self.lr_decay_steps, lr_decay_steps_,
                                                'total number of iterations')
-        self.decay_style = self._check_and_set(self.decay_style,
-                                               sd['decay_style'],
-                                               'decay style')
+
+        if 'decay_style' in sd:
+            lr_decay_style_ = sd['decay_style']
+        else:
+            lr_decay_style_ = sd['lr_decay_style']
+        self.lr_decay_style = self._check_and_set(self.lr_decay_style,
+                                               lr_decay_style_,
+                                               'learning rate decay style')
 
         if 'num_iters' in sd:
             num_steps = sd['num_iters']
         else:
             num_steps = sd['num_steps']
         self.step(increment=num_steps)
+
+
+        if 'start_wd' in sd:
+            self.start_wd = self._check_and_set(self.start_wd,
+                                                sd['start_wd'],
+                                                "start weight decay")
+            self.end_wd = self._check_and_set(self.end_wd,
+                                                sd['end_wd'],
+                                                "end weight decay")
+            self.wd_incr_steps = self._check_and_set(self.wd_incr_steps,
+                                                sd['wd_incr_steps'],
+                                                "total number of weight decay iterations")
+            self.wd_incr_style = self._check_and_set(self.wd_incr_style,
+                                                sd['wd_incr_style'],
+                                                "weight decay incr style")
+            
+
+
+
+
+
+
+

megatron/training.py

@@ -312,11 +312,12 @@ def get_optimizer_param_scheduler(optimizer):
     if args.train_iters:
         if args.lr_decay_iters is None:
             args.lr_decay_iters = args.train_iters
-        decay_steps = args.lr_decay_iters * args.global_batch_size
+        lr_decay_steps = args.lr_decay_iters * args.global_batch_size
+        wd_incr_steps = args.train_iters * args.global_batch_size
         if args.lr_warmup_fraction is not None:
-            warmup_steps = args.lr_warmup_fraction * decay_steps
+            lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
         else:
-            warmup_steps = args.lr_warmup_iters * args.global_batch_size
+            lr_warmup_steps = args.lr_warmup_iters * args.global_batch_size
     # Sample-based training.
     elif args.train_samples:
         # We need to set training iters for later use. Technically
@@ -325,11 +326,12 @@ def get_optimizer_param_scheduler(optimizer):
         update_train_iters(args)
         if args.lr_decay_samples is None:
             args.lr_decay_samples = args.train_samples
-        decay_steps = args.lr_decay_samples
+        lr_decay_steps = args.lr_decay_samples
+        wd_incr_steps = args.train_samples
         if args.lr_warmup_fraction is not None:
-            warmup_steps = args.lr_warmup_fraction * decay_steps
+            lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
         else:
-            warmup_steps = args.lr_warmup_samples
+            lr_warmup_steps = args.lr_warmup_samples
     else:
         raise Exception(
             'either train-iters or train-samples should be provided.')
@@ -338,11 +340,12 @@ def get_optimizer_param_scheduler(optimizer):
         optimizer,
         max_lr=args.lr,
         min_lr=args.min_lr,
-        warmup_steps=warmup_steps,
-        decay_steps=decay_steps,
-        decay_style=args.lr_decay_style,
+        lr_warmup_steps=lr_warmup_steps,
+        lr_decay_steps=lr_decay_steps,
+        lr_decay_style=args.lr_decay_style,
         start_wd=args.start_weight_decay,
         end_wd=args.end_weight_decay,
+        wd_incr_steps=wd_incr_steps,
         wd_incr_style=args.weight_decay_incr_style,
         use_checkpoint_opt_param_scheduler=args.use_checkpoint_opt_param_scheduler,
         override_opt_param_scheduler=args.override_opt_param_scheduler)